Merge pull request #46 from neuroscout/enh/nv_ss

Upload single subject files

Author: adelavega

pyns/models/analysis.py

@@ -3,7 +3,7 @@
 from pathlib import Path
 from functools import partial
 from .utils import build_model
-
+import tqdm
 
 class Analysis:
     """ Analysis object class. Object representing an analysis that can be
@@ -240,20 +240,42 @@ def get_report(self, id, run_id=None):
         """
         return self.get(id=id, sub_route='report', run_id=run_id)
 
-    def upload_neurovault(self, id, tarball, validation_hash, force=False,
+    def upload_neurovault(self, id, validation_hash, subject_paths=None,
+                          group_paths=None, collection_id=None, force=False,
                           n_subjects=None):
         """ Submit analysis for report generation
         :param str id: Analysis hash_id.
-        :param str tarball: Path to tarball.
         :param str validation_hash: Validation hash string.
+        :param list(str) subject_paths: List of image paths.
+        :param list(str) group_paths: List of image paths.
         :param bool force: Force upload with unique timestamped name.
         :param int n_subjects: Number of subjects in analysis.
         :return: client response object
         """
-        files = {'tarball': open(tarball, 'rb')}
-        return self.post(id=id, sub_route='upload', files=files,
-                         validation_hash=validation_hash, force=force,
-                         n_subjects=n_subjects)
+        # Do group, then subject level
+        if group_paths is not None:
+            print("Uploading group images")
+            for path in tqdm.tqdm(group_paths):
+                files = {'image_file': open(path, 'rb')}
+                req = self.post(
+                    id=id, sub_route='upload', files=files, level='GROUP',
+                    validation_hash=validation_hash, force=force,
+                    n_subjects=n_subjects, collection_id=collection_id)
+                if collection_id is None:
+                    collection_id = req['collection_id']
+
+        if subject_paths is not None:
+            print("Uploading subject images")
+            for path in tqdm.tqdm(subject_paths):
+                files = {'image_file': open(path, 'rb')}
+                req = self.post(
+                    id=id, sub_route='upload', files=files, level='SUBJECT',
+                    validation_hash=validation_hash, force=force,
+                    collection_id=collection_id)
+                if collection_id is None:
+                    collection_id = req['collection_id']
+
+        return req
 
     def get_uploads(self, id):
         """ Get NeuroVault uploads associated with this analysis

requirements.txt

@@ -2,3 +2,4 @@ pytest
 requests
 betamax
 betamax_serializers
+tqdm

tests/cassettes/upload_analysis.json

@@ -216,8 +216,13 @@ def test_upload_analysis(recorder, neuroscout, analysis, get_test_data_path):
     analysis_id = 'qxA'  # This is an existing analysis on the api
 
     with recorder.use_cassette('upload_analysis'):
+        group_paths = [str(p) for p in
+                       (get_test_data_path / 'fitlins').glob('task*')]
+        sub_paths = [str(p) for p in
+                     (get_test_data_path / 'fitlins').glob('sub*')]
         resp = neuroscout.analyses.upload_neurovault(
-            id=analysis_id, tarball=str(get_test_data_path / 'tarball.tar.gz'),
+            id=analysis_id, group_paths=group_paths,
+            subject_paths=sub_paths,
             validation_hash='8Av1Jbo1aO', force=True,
             n_subjects=99)
 
@@ -231,12 +236,13 @@ def test_upload_analysis(recorder, neuroscout, analysis, get_test_data_path):
         newest = [u for u in uploads if u['uploaded_at'] == uploaded_at][0]
 
         # Wait until compiled
-        while(newest['status'] == 'PENDING'):
-            sleep(1)
+        while(
+          any([True for f in newest['files'] if f['status'] == 'PENDING'])):
+            sleep(2)
             uploads = neuroscout.analyses.get_uploads(id=analysis_id)
             newest = [u for u in uploads if u['uploaded_at'] == uploaded_at]
             if len(newest) > 0:
                 newest = newest[0]
 
-        assert newest['status'] == 'OK'
+        assert all([f['status'] == 'OK' for f in newest['files']])
         assert newest['collection_id'] is not None