replce np.all to alltrue

chunkflow/chunk/base.py

@@ -73,7 +73,7 @@ def __init__(self, array: np.ndarray,
         self.voxel_size = voxel_size
         if voxel_size is not None:
             assert len(voxel_size) == 3
-            assert np.alltrue([vs > 0 for vs in voxel_size])
+            assert np.all([vs > 0 for vs in voxel_size])
 
         if layer_type is not None:
             self.layer_type = layer_type 

chunkflow/flow/downsample_upload.py

@@ -56,7 +56,7 @@ def __init__(self,
         self.start_mip = start_mip
         self.stop_mip = stop_mip
         assert len(factor) == 3
-        assert np.alltrue([f>=1 for f in factor])
+        assert np.all([f>=1 for f in factor])
         assert stop_mip > start_mip
         assert start_mip > chunk_mip
 

chunkflow/flow/load_precomputed.py

@@ -179,4 +179,4 @@ def _validate_chunk(self, chunk):
             validate_input = np.squeeze(validate_input, axis=3)
 
         # use the validate input to check the downloaded input
-        assert np.alltrue(validate_input == clamped_input)
+        assert np.all(validate_input == clamped_input)

chunkflow/flow/mask.py

@@ -59,7 +59,7 @@ def __call__(self, chunks: list):
 
         mask_in_high_mip = self._read_mask_in_high_mip(chunks[0].bbox, factor)
 
-        if np.alltrue(mask_in_high_mip == 0):
+        if np.all(mask_in_high_mip == 0):
             print('the mask is all black, mask all the voxels directly')
             for chunk in chunks:
                 np.multiply(chunk, 0, out=chunk)
@@ -100,7 +100,7 @@ def _read_mask_in_high_mip(self, chunk_bbox, factor):
         # the slices did not contain the channel dimension
         mask = self.mask_vol[mask_slices[::-1]]
         # this is a cloudvolume VolumeCutout rather than a normal numpy array
-        # which will make np.alltrue(mask_in_high_mip == 0) to be
+        # which will make np.all(mask_in_high_mip == 0) to be
         # VolumeCutout(False) rather than False, so we need to transform it 
         # to numpy
         mask = mask.astype(bool)

tests/flow/divid_conquer/test_inferencer.py

@@ -29,7 +29,7 @@ def test_test_time_augmentation():
     output = output * 255
     output.astype(np.uint8)
 
-    assert np.alltrue(np.isclose(image, output, atol=1))
+    assert np.all(np.isclose(image, output, atol=1))
 
 def test_aligned_input_size():
     print('\ntest block inference with aligned input size...')
@@ -55,7 +55,7 @@ def test_aligned_input_size():
     output = output * 255
     output.astype(np.uint8)
 
-    assert np.alltrue(np.isclose(image, output, atol=1))
+    assert np.all(np.isclose(image, output, atol=1))
 
 def test_aligned_patch_num():
     print('\ntest block inference with patch number...')