Merge pull request #544 from FZJ-INM1-BDA/feat_ngInfoLocalCache

fix: info uses siibra.retrieval module for local file cache

Author: AhmetNSimsek

siibra/volumes/providers/neuroglancer.py

@@ -20,13 +20,14 @@
 from ...locations import boundingbox as _boundingbox
 
 from neuroglancer_scripts.precomputed_io import get_IO_for_existing_dataset
-from neuroglancer_scripts.accessor import get_accessor_for_url
+from neuroglancer_scripts.http_accessor import HttpAccessor
 from neuroglancer_scripts.mesh import read_precomputed_mesh, affine_transform_mesh
 from io import BytesIO
 import nibabel as nib
 import os
 import numpy as np
 from typing import Union, Dict, Tuple
+import json
 
 
 class NeuroglancerProvider(_provider.VolumeProvider, srctype="neuroglancer/precomputed"):
@@ -210,8 +211,9 @@ def __init__(self, url: str):
         assert isinstance(url, str)
         self.url = url
         self._scales_cached = None
-        self._io = None
+        self._info = None
         self._transform_nm = None
+        self._io = None
 
     @property
     def transform_nm(self):
@@ -234,34 +236,40 @@ def transform_nm(self, val):
         self._transform_nm = val
 
     @property
-    def map_type(self):
+    def io(self):
         if self._io is None:
+            accessor = HttpAccessor(self.url)
+            self._io = get_IO_for_existing_dataset(accessor)
+        return self._io
+
+    @property
+    def map_type(self):
+        if self._info is None:
             self._bootstrap()
         return (
             MapType.LABELLED
-            if self._io.info.get("type") == "segmentation"
+            if self._info.get("type") == "segmentation"
             else MapType.STATISTICAL
         )
 
     @map_type.setter
     def map_type(self, val):
         if val is not None:
             logger.debug(
-                "NeuroglancerVolume can determine its own maptype from self._io.info.get('type')"
+                "NeuroglancerVolume can determine its own maptype from self._info.get('type')"
             )
 
     def _bootstrap(self):
-        accessor = get_accessor_for_url(self.url)
-        self._io = get_IO_for_existing_dataset(accessor)
+        self._info = requests.HttpRequest(f"{self.url}/info", func=lambda b: json.loads(b.decode())).get()
         self._scales_cached = sorted(
-            [NeuroglancerScale(self, i) for i in self._io.info["scales"]]
+            [NeuroglancerScale(self, i) for i in self._info["scales"]]
         )
 
     @property
     def dtype(self):
-        if self._io is None:
+        if self._info is None:
             self._bootstrap()
-        return np.dtype(self._io.info["data_type"])
+        return np.dtype(self._info["data_type"])
 
     @property
     def scales(self):
@@ -422,7 +430,7 @@ def _read_chunk(self, gx, gy, gz):
         y0 = gy * self.chunk_sizes[1]
         z0 = gz * self.chunk_sizes[2]
         x1, y1, z1 = np.minimum(self.chunk_sizes + [x0, y0, z0], self.size)
-        chunk_czyx = self.volume._io.read_chunk(self.key, (x0, x1, y0, y1, z0, z1))
+        chunk_czyx = self.volume.io.read_chunk(self.key, (x0, x1, y0, y1, z0, z1))
         if not chunk_czyx.shape[0] == 1 and not self.color_warning_issued:
             logger.warning(
                 "Color channel data is not yet supported. Returning first channel only."

test/volumes/providers/test_neuroglancer.py

@@ -0,0 +1,14 @@
+from unittest.mock import patch, MagicMock
+
+from siibra.volumes.providers.neuroglancer import NeuroglancerVolume
+from siibra.retrieval import requests
+
+
+@patch.object(requests, 'HttpRequest')
+def test_ngvol_info_uses_cached(MockedHttpReq):
+    MockedHttpReq.return_value = MagicMock()
+    MockedHttpReq.return_value.get.return_value = {"scales": []}
+    vol = NeuroglancerVolume("foo/bar")
+    vol._bootstrap()
+    assert MockedHttpReq.called
+    MockedHttpReq.return_value.get.assert_called_once()