Remove libdvid references in tests

tests/data_io/config.py

@@ -1,14 +1,12 @@
 from __future__ import print_function
 
 import os
-from libdvid.voxels import VoxelsAccessor
 import h5py
 
 DEBUG = True
 
 using_in_memory = False
 
-# dataset_source_type = VoxelsAccessor
 # dataset_source_type = 'hdf5 file paths'
 dataset_source_type = h5py.File
 

tests/data_io/load_datasets.py

@@ -6,8 +6,7 @@
 import malis
 import numpy as np
 
-from libdvid.voxels import VoxelsAccessor
-
+from dvision import DVIDDataInstance
 from .config import using_in_memory, path_to_training_datasets, training_dataset_names, dataset_source_type, dvid_uuid
 
 
@@ -25,14 +24,15 @@ def get_train_dataset(dataset_source_type_, using_in_memory=False):
             data='grayscale',
             components='labels',
         )
-        dvid_hostname = 'emdata2.int.janelia.org:7000'
+        dvid_hostname = 'emdata2.int.janelia.org'
+        dvid_port = 7000
         dataset['name'] = dname
         dataset['nhood'] = malis.mknhood3d()
         for key in ['data', 'components']:
-            if dataset_source_type_ == VoxelsAccessor:
+            if dataset_source_type_ == DVIDDataInstance:
                 if key in dvid_data_names:
                     data_name = dvid_data_names[key]
-                    dataset[key] = VoxelsAccessor(hostname=dvid_hostname, uuid=dvid_uuid, data_name=data_name)
+                    dataset[key] = DVIDDataInstance(dvid_hostname, dvid_port, dvid_uuid, data_name)
             elif dataset_source_type_ == h5py.File:
                 dataset[key] = h5py.File(h5_filenames[key], 'r')['main']
                 if using_in_memory:

tests/data_io/test_data_loader.py

@@ -3,18 +3,18 @@
 import unittest
 
 import h5py
-from libdvid.voxels import VoxelsAccessor
 import numpy as np
 
 from data_io import DataLoader
 from data_io.dataset_reading import get_numpy_dataset
+from dvision import DVIDDataInstance
 from .load_datasets import get_train_dataset
 
 
 class TestDataLoader(unittest.TestCase):
     # @mock.patch('data_io.get_numpy_dataset', side_effect=mock_get_numpy_dataset)
     def test_loads_data_chunks_from_dvid(self):
-        train_dataset = get_train_dataset(VoxelsAccessor)
+        train_dataset = get_train_dataset(DVIDDataInstance)
         dataset_to_test = train_dataset[0]
         dataset, numpy_dataset = self.get_datasets_for(dataset_to_test, offset=(3000, 3000, 3000))
         for key in ['data', 'components', 'label', 'mask']:

tests/data_io/test_get_numpy_dataset.py

@@ -3,14 +3,15 @@
 import unittest
 
 import numpy as np
-from libdvid.voxels import VoxelsAccessor
 import h5py
 import malis
 
 from data_io.dataset_reading import get_numpy_dataset
-from .load_datasets import get_train_dataset
-from data_io.dvid_connectivity import get_good_components
 from data_io.util import replace_array_except_whitelist
+from dvision import DVIDDataInstance
+from dvision.component_filtering import get_good_components
+from tests.data_io.load_datasets import get_train_dataset
+
 
 class TestGetNumpyDataset(unittest.TestCase):
     def test_works_with_hdf5_fibsem(self):
@@ -27,7 +28,7 @@ def test_works_with_hdf5_fibsem(self):
             )
 
     def test_works_with_dvid(self):
-        train_dataset = get_train_dataset(VoxelsAccessor)
+        train_dataset = get_train_dataset(DVIDDataInstance)
         dataset = train_dataset[0]
         dataset['body_names_to_exclude'] = ['out']
         origin = (3000, 3000, 3000)
@@ -61,8 +62,8 @@ def get_datasets(dataset, origin):
         components_slices[-3:] = output_slices
         components_slices = tuple(components_slices)
         expected_components_array = np.array(dataset['components'][components_slices]).reshape((1,) + output_shape)
-        if type(dataset['components']) is VoxelsAccessor:
-            print("Is VoxelsAccessor...")
+        if type(dataset['components']) is DVIDDataInstance:
+            print("Is DVIDDataInstance...")
             print("uniques before:", np.unique(expected_components_array))
             dvid_uuid = dataset['components'].uuid
             body_names_to_exclude = dataset.get('body_names_to_exclude')
@@ -74,7 +75,7 @@ def get_datasets(dataset, origin):
         components_for_affinity_generation = expected_components_array.reshape(output_shape)
         expected_label = malis.seg_to_affgraph(components_for_affinity_generation, malis.mknhood3d())
         expected_dataset['label'] = expected_label
-        if type(dataset['components']) is VoxelsAccessor:
+        if type(dataset['components']) is DVIDDataInstance:
             expected_mask = np.array(expected_components_array > 0).astype(np.uint8)
         else:
             expected_mask = np.ones(shape=(1,) + output_shape, dtype=np.uint8)