Merge pull request #101 from neuroscout/kwargs_collections

Add kwargs to get_uploads

pyns/models/analysis.py

@@ -385,15 +385,40 @@ def _ts_first(paths):
 
         return req
 
-    def get_uploads(self, id):
+    def get_uploads(self, id, select='latest', **kwargs):
         """ Get NeuroVault uploads associated with this analysis
         :param str id: Analysis hash_id.
+        :param str select: How to select from multiple collections.
+        Options: "latest", "oldest" or None. If None, returns all results.
+        :param dict kwargs: Attributes to filter collections on.
+         If any attributes are not found, they are ignored.
         :return: client response object
         """
-        return self.get(id=id, sub_route='upload')
+        uploads = self.get(id=id, sub_route='upload')
+
+        # Sort by date
+        uploads = sorted(uploads, key=lambda x: datetime.datetime.strptime(
+                x['uploaded_at'], '%Y-%m-%dT%H:%M'),
+                         reverse=(select == 'latest'))
+
+        # Select collections based on filters
+        uploads = [
+            u for u in uploads
+            if all([u.get(k) == v for k, v in kwargs.items() if k in u])
+            ]
+
+        if not uploads:
+            return None
+
+        # Select first item unless all are requested
+        if select is not None:
+            uploads = [uploads[0]]
+
+        return uploads
 
     def load_uploads(self, id, select='latest',
-                     download_dir=None, **kwargs):
+                     download_dir=None, collection_filters={}, 
+                     image_filters={}):
         """ Load collection upload as NiBabel images and associated meta-data
         You can filter which images are loaded based on either collection
         level attributes or statmap image level attributes. These correspond
@@ -405,7 +430,8 @@ def load_uploads(self, id, select='latest',
         :param str select: How to select from multiple collections.
         Options: "latest", "oldest" or None. If None, returns all results.
         :param str download_dir: Path to download images. If None, tempdir.
-        :param dict kwargs: Attributes to filter images on.
+        :param dict collection_filters: Attributes to filter collections on.
+        :param dict image_filters: Attributes to filter images on.
         If any attributes are not found, they are ignored.
         :return list list of tuples of format (Nifti1Image, kwargs).
         """
@@ -415,30 +441,15 @@ def load_uploads(self, id, select='latest',
             download_dir = Path(download_dir)
 
         # Sort uploads for upload date
-        uploads = self.get_uploads(id)
-        for u in uploads:
-            u['uploaded_at'] = datetime.datetime.strptime(
-                u['uploaded_at'], '%Y-%m-%dT%H:%M')
-        uploads = sorted(uploads, key=lambda x: x['uploaded_at'],
-                         reverse=(select == 'latest'))
-
-        # Select collections based on filters
-        uploads = [
-            u for u in uploads
-            if all([u.get(k) == v for k, v in kwargs.items() if k in u])
-            ]
-
+        uploads = self.get_uploads(id, **collection_filters)
+
         if not uploads:
             return None
 
-        # Select first item unless all are requested
-        if select is not None:
-            uploads = [uploads[0]]
-
         # Extract entities from file path
         def _get_entities(path):
             di = {}
-            for t in ['task', 'contrast', 'stat']:
+            for t in ['task', 'contrast', 'stat', 'space']:
                 matches = re.findall(f"{t}-(.*?)_", path)
                 if matches:
                     di[t] = matches[0]
@@ -452,7 +463,7 @@ def _get_entities(path):
 
                 # If file matches kwargs and is in NV
                 if f.pop('status') == 'OK' and all(
-                  [f.get(k, None) == v for k, v in kwargs.items() if k in f]):
+                  [f.get(k, None) == v  if k in f else False for k, v in image_filters.items()]):
                     # Download and open
                     img_url = "https://neurovault.org/media/images/" \
                         f"{u['collection_id']}/{f['basename']}"
@@ -482,7 +493,7 @@ def plot_uploads(self, id, plot_args={}, **kwargs):
             plots = []
             for niimg, _ in images:
                 plots.append(
-                    nilearn.plotting.plot_stat_map(niimg, **plot_args))
+                    niplt.plot_stat_map(niimg, **plot_args))
 
             return plots
         else:

pyns/models/utils.py

@@ -79,3 +79,8 @@ def build_model(name, variables, tasks, subjects, runs=None, session=None,
         model['Input']['Session'] = session
 
     return model
+
+
+def snake_to_camel(string):
+    words = string.split('_')
+    return words[0] + ''.join(word.title() for word in words[1:])

tests/test_analysis.py

@@ -254,7 +254,8 @@ def test_load_analysis(recorder, neuroscout):
       m = neuroscout.analyses.get_analysis('gbp6i')
       assert len(m.load_uploads()) == 3
       assert len(m.load_uploads(select=None)) == 3
-      nistats = m.load_uploads(estimator='nistats')
+      nistats = m.load_uploads(
+        collection_filters=dict(estimator='nistats'))
       latest_up = nistats[0][1]['uploaded_at']
       assert len(nistats) == 3
       assert nistats[0][1]['estimator'] == 'nistats'