added openneuro download of t1w

Author: tclose

README.rst

@@ -30,12 +30,29 @@ or include in your package's ``test_requires``.
 Usage
 -----
 
+Create a pytest fixture consisting of a dummy image with field-map metadata in DICOM format
+
 .. code-block:: python
 
     # Import medimages4tests generator functions
-    from medimages4tests.dicom.mri.fmap.ge.discovery_mr888.dv26_0_r05_2008a import sample_image
+    from medimages4tests.dummy.dicom.mri.fmap.ge.discovery_mr888.dv26_0_r05_2008a import get_image
 
     # Return generated images in pytest fixtures (or alternative test framework)
     @pytest.fixture()
     def ge_dicom_fmap():
-        return sample_image()
+        return get_image()
+
+Create a dummy NIfTI image
+
+.. code-block:: python
+
+    import numpy
+    # Import `get_image` function
+    from medimages4tests.dummy.nifti import get_image
+
+    # Create dummy nifti image of 10x10x10 containing all ones
+    @pytest.fixture()
+    def ones_nifti():
+        return get_image(
+            data=numpy.ones((10, 10, 10))
+        )

medimages4tests/dummy/nifti.py

@@ -1,3 +1,4 @@
+import tempfile
 from pathlib import Path
 import gzip
 import shutil
@@ -6,13 +7,16 @@
 
 
 def get_image(
-    out_file: Path,
+    out_file: Path = None,
     data: np.ndarray = None,
     vox_sizes=(1.0, 1.0, 1.0),
     qform=(1, 2, 3, 1),
     compressed=False,
 ) -> Path:
     """Create a random Nifti file to satisfy BIDS parsers"""
+    if out_file is None:
+        out_file = Path(tempfile.mkdtemp()) / "sample.nii"
+
     if data is None:
         data = np.random.randint(0, 1, size=[10, 10, 10])
 

medimages4tests/mri/neuro/base.py

@@ -1,10 +0,0 @@
-from pathlib import Path
-import attrs
-
-
-@attrs.define
-class OpenneuroSpec:
-
-    dataset: str
-    tag: str
-    path: Path

medimages4tests/mri/neuro/t1w.py

@@ -1,9 +1,5 @@
-from tempfile import mkdtemp
-import shutil
-from pathlib import Path
-import openneuro
 from medimages4tests import base_cache_dir
-from ..neuro import OpenneuroSpec
+from medimages4tests.utils import retrieve_from_openneuro, OpenneuroSpec
 
 
 cache_dir = base_cache_dir / "mri" / "neuro" / "t1w"
@@ -18,22 +14,5 @@
 }
 
 
-def get_image(sample_name):
-    sample = SAMPLES[sample_name]
-    if cache_dir.exists():
-        cache_dir.mkdir(parents=True)
-    out_path = (cache_dir / sample_name).with_suffix(".nii.gz")
-    if not out_path.exists():
-        tmpdir = Path(mkdtemp())
-        openneuro.download(
-            dataset=sample.dataset,
-            tag=sample.tag,
-            target_dir=tmpdir,
-            include=[sample.path],
-        )
-        for ext in (".nii.gz", ".json"):
-            shutil.copyfile(
-                (tmpdir / sample.path).with_suffix(ext)
-                (cache_dir / sample_name).with_suffix(ext)
-            )
-    return out_path
+def get_image(sample="ds004130-ON01016"):
+    return retrieve_from_openneuro(SAMPLES[sample], cache_dir / sample)

medimages4tests/utils.py

@@ -0,0 +1,34 @@
+from tempfile import mkdtemp
+import shutil
+from pathlib import Path
+import openneuro
+import attrs
+
+
+@attrs.define
+class OpenneuroSpec:
+
+    dataset: str
+    tag: str
+    path: Path
+
+
+def retrieve_from_openneuro(
+    sample, cache_path, suffixes=(".nii.gz", ".json"), force_download=False
+):
+    if not cache_path.parent.exists():
+        cache_path.parent.mkdir(parents=True)
+    out_path = cache_path.with_suffix(suffixes[0])
+    if not out_path.exists() or force_download:
+        tmpdir = Path(mkdtemp())
+        openneuro.download(
+            dataset=sample.dataset,
+            tag=sample.tag,
+            target_dir=tmpdir,
+            include=[sample.path],
+        )
+        for ext in suffixes:
+            shutil.copyfile(
+                (tmpdir / sample.path).with_suffix(ext), cache_path.with_suffix(ext)
+            )
+    return out_path

tests/test_openneuro.py

@@ -0,0 +1,11 @@
+import nibabel as nb
+from medimages4tests.mri.neuro.t1w import get_image
+
+
+def test_openneuro_retrieve():
+
+    nifti_fpath = get_image()
+
+    nifti = nb.load(nifti_fpath)
+
+    assert nifti.shape == (204, 256, 256)