adding some unittests

tests/python/test_read.py

@@ -0,0 +1,133 @@
+import unittest
+import io, pathlib, shutil, logging, sys
+import bfio
+import numpy as np
+import tensorstore as ts
+import argolid
+import ome_types
+
+TEST_DIR = pathlib.Path(__file__).with_name("data")
+
+logging.basicConfig(
+    format="%(asctime)s - %(name)-8s - %(levelname)-8s - %(message)s",
+    datefmt="%d-%b-%y %H:%M:%S",
+)
+logger = logging.getLogger("bfio.test")
+
+if "-v" in sys.argv:
+    logger.setLevel(logging.INFO)
+
+def setUpModule():
+    """Create images for testing"""
+    TEST_DIR.mkdir(exist_ok=True)
+    for ch in range(4):
+        for r in range(3):
+            for c in range(2):
+                image_name = f"{TEST_DIR}/test_image_r{r}_c{c}_ch{ch}.ome.tiff"
+                with bfio.BioWriter(image_name, backend="python", X=1024, Y=1024, C=1, Z=1, T=1) as bw:
+                    test_val = np.ones((1024,1024), dtype=np.uint16)
+                    # setting up test data for mean sampling
+                    if r==0 and c==0:
+                        test_val[0,0] = 8
+                        test_val[0,1] = 9
+                        test_val[1,0] = 7
+                        test_val[1,1] = 14
+                    # setting up test data for mode sampling
+                    if r==0 and c==1 and ch==1:
+                        test_val[0,0] = 8
+                        test_val[0,1] = 8
+                        test_val[1,0] = 9
+                        test_val[1,1] = 9
+
+                    bw[0:1024, 0:1024, 0, 0, 0] = test_val 
+
+
+
+class TestSingleChannelVivPyramidFromImageCollection(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        input_dir = f"{TEST_DIR}"
+        file_pattern = "test_image_r{x:d+}_c{y:d+}_ch0.ome.tiff"
+        output_dir = f"{TEST_DIR}"
+        image_name = "single_channel_image_viv"
+        self._image_name = image_name + ".zarr"
+        pyr_gen = argolid.PyramidGenerartor()
+        pyr_gen.set_log_level(4)
+        pyr_gen.generate_from_image_collection(input_dir, file_pattern, image_name, output_dir, 512, "Viv")
+
+    def test_omexml_metadata_exists(self):
+        ome_xml_path = f"{TEST_DIR}/{self._image_name}/METADATA.ome.xml"
+        assert pathlib.Path(ome_xml_path).is_file() == True
+
+
+    def test_valid_omexml_metadata(self):
+        ome_xml_path = f"{TEST_DIR}/{self._image_name}/METADATA.ome.xml"
+        ome_metadata = ome_types.from_xml(ome_xml_path)
+        assert len(ome_metadata.images) == 1
+        assert len(ome_metadata.images[0].pixels.channels) == 1
+        assert ome_metadata.images[0].pixels.type == ome_types.model.PixelType.UINT8
+        assert ome_metadata.images[0].pixels.size_x == 3072
+        assert ome_metadata.images[0].pixels.size_y == 2048
+        assert ome_metadata.images[0].pixels.size_z == 1
+        assert ome_metadata.images[0].pixels.size_c == 1
+        assert ome_metadata.images[0].pixels.size_t == 1
+        assert ome_metadata.images[0].pixels.dimension_order == ome_types.model.Pixels_DimensionOrder.XYZCT
+
+
+    def test_num_pyramid_levels(self):
+        for i in range(5):
+            assert pathlib.Path(TEST_DIR.joinpath(f"{self._image_name}/data.zarr/0/{i}")).is_dir() == True
+
+
+class TestMultipleChannelVivPyramidFromImageCollection(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        input_dir = f"{TEST_DIR}"
+        file_pattern = "test_image_r{x:d+}_c{y:d+}_ch{c:d}.ome.tiff"
+        output_dir = f"{TEST_DIR}"
+        image_name = "multiple_channel_image_viv"
+        self._image_name = image_name + ".zarr"
+        pyr_gen = argolid.PyramidGenerartor()
+        pyr_gen.set_log_level(4)
+        pyr_gen.generate_from_image_collection(input_dir, file_pattern, image_name, output_dir, 1024, "Viv")
+
+    def test_valid_omexml_metadata(self):
+        ome_xml_path = f"{TEST_DIR}/{self._image_name}/METADATA.ome.xml"
+        ome_metadata = ome_types.from_xml(ome_xml_path)
+        assert len(ome_metadata.images) == 1
+        assert len(ome_metadata.images[0].pixels.channels) == 4
+        assert ome_metadata.images[0].pixels.type == ome_types.model.PixelType.UINT8
+        assert ome_metadata.images[0].pixels.size_x == 3072
+        assert ome_metadata.images[0].pixels.size_y == 2048
+        assert ome_metadata.images[0].pixels.size_z == 1
+        assert ome_metadata.images[0].pixels.size_c == 4
+        assert ome_metadata.images[0].pixels.size_t == 1
+        assert ome_metadata.images[0].pixels.dimension_order == ome_types.model.Pixels_DimensionOrder.XYZCT
+
+    def test_num_pyramid_levels(self):
+        for i in range(4):
+            assert pathlib.Path(TEST_DIR.joinpath(f"{self._image_name}/data.zarr/0/{i}")).is_dir() == True
+
+
+    def test_base_layer_data(self):
+        dataset_future = ts.open({  'driver':'zarr', 
+                                    'kvstore':
+                                        {'driver':'file', 
+                                         'path':f'{TEST_DIR}/{self._image_name}/data.zarr/0/0'
+                                        }
+                                })
+        dataset = dataset_future.result()
+        assert dataset.shape == (1, 4, 1, 2048, 3072)
+# test Viv compatible Zarr is produced
+# test OmeXml metadata
+    # num channels
+# test correct number of pyramid layers are produced
+# test single channel image
+# test multi channel image
+# test proper downsampling is done
+    # mode_max, mode_min, mean
+# test channel specific downsampling
+
+# test Neuroglance compatible Zarr is produced
+
+# test Precomputed Neuroglancer is produced.