Merge pull request #83 from neuroscout/multi-task

Allow multi task analyses in creation and querying, record estimator

pyns/models/analysis.py

@@ -160,7 +160,7 @@ def clone(self, id):
         return self.post(id=id, sub_route='clone')
 
     def create_analysis(self, *, name, dataset_name, predictor_names,
-                        task=None, subject=None, run=None, session=None,
+                        tasks=None, subjects=None, runs=None, session=None,
                         hrf_variables=None, contrasts=None,
                         dummy_contrasts=True, transformations=None, **kwargs):
         """ Analysis creation "wizard". Given run selection filters, and name
@@ -177,36 +177,38 @@ def create_analysis(self, *, name, dataset_name, predictor_names,
             dataset = dataset[0]
 
         # Get task name
-        if task is not None:
-            search = [t for t in dataset['tasks'] if t['name'] == task]
-            if len(search) != 1:
-                raise ValueError(
-                    "Task name does not match any tasks in the dataset")
-            task_id = search[0]['id']
+        if tasks is not None:
+            if not isinstance(tasks, list):
+                tasks = [tasks]
+
+            task_ids = []
+            for task in tasks:
+                search = [t for t in dataset['tasks'] if t['name'] == task]
+                if len(search) != 1:
+                    raise ValueError(
+                        "Task name does not match any tasks in the dataset")
+                task_ids.append(search[0]['id'])
         else:
-            if len(dataset['tasks']) > 1:
-                raise ValueError(
-                    "No task specified, but dataset has more than one task")
-            res = dataset['tasks'][0]
-            task = res['name']
-            task_id = res['id']
+            # All tasks
+            tasks = [t['name'] for t in dataset['tasks']]
+            task_ids = [t['id'] for t in dataset['tasks']]
 
         # Get Run IDs
         run_models = self._client.runs.get(
-            dataset_id=dataset['id'], task_id=task_id,
-            subject=subject, number=run, session=session)
+            dataset_id=dataset['id'], task_id=task_ids,
+            subject=subjects, number=runs, session=session)
 
         if len(run_models) < 1:
             raise ValueError("No runs could be found with the given criterion")
 
-        subject = list(set(r['subject'] for r in run_models))
-        run = list(set(r['number'] for r in run_models if r['number']))
-        run = run or None
+        subjects = list(set(r['subject'] for r in run_models))
+        runs = list(set(r['number'] for r in run_models if r['number']))
+        runs = runs or None
 
-        run_id = [r['id'] for r in run_models]
+        run_ids = [r['id'] for r in run_models]
         # Get Predictor IDs
         public_preds = self._client.predictors.get(
-            run_id=run_id, name=predictor_names, active_only=False)
+            run_id=run_ids, name=predictor_names, active_only=False)
 
         predictors = [p['id'] for p in public_preds]
 
@@ -218,7 +220,7 @@ def create_analysis(self, *, name, dataset_name, predictor_names,
             for pred in private_preds:
                 predictors += [p['id']
                                for p in self._client.user.get_predictors(
-                                   run_id=run_id, name=pred)]
+                                   run_id=run_ids, name=pred)]
 
         if len(predictors) != len(predictor_names):
             raise ValueError(
@@ -229,15 +231,15 @@ def create_analysis(self, *, name, dataset_name, predictor_names,
         if transformations:
             transformations = transformations.copy()
         model = build_model(
-            name, predictor_names, task,
-            subject=subject, run=run, session=session,
+            name, predictor_names, tasks,
+            subjects=subjects, runs=runs, session=session,
             hrf_variables=hrf_variables,
             transformations=transformations,
             contrasts=contrasts, dummy_contrasts=dummy_contrasts
             )
 
         analysis = Analysis(analyses=self, dataset_id=dataset['id'],
-                            name=name, model=model, runs=run_id,
+                            name=name, model=model, runs=run_ids,
                             predictors=predictors, **kwargs)
 
         return analysis
@@ -316,7 +318,7 @@ def plot_report(self, id, run_id=None, plot_type='design_matrix_plot',
     def upload_neurovault(self, id, validation_hash, subject_paths=None,
                           group_paths=None, collection_id=None, force=False,
                           cli_version=None, fmriprep_version=None,
-                          n_subjects=None):
+                          estimator=None, n_subjects=None):
         """ Submit analysis for report generation
         :param str id: Analysis hash_id.
         :param str validation_hash: Validation hash string.
@@ -325,6 +327,7 @@ def upload_neurovault(self, id, validation_hash, subject_paths=None,
         :param bool force: Force upload with unique timestamped name.
         :param str: neuroscout-cli version at runtime
         :param str: fmriprep version at runtime
+        :param str: estimator used in fitlins (anfi/nilearn)
         :param int n_subjects: Number of subjects in analysis.
         :return: client response object
         """
@@ -345,8 +348,9 @@ def _ts_first(paths):
                 req = self.post(
                     id=id, sub_route='upload', files=files, level='GROUP',
                     validation_hash=validation_hash, force=force,
-                    fmriprep_version=fmriprep_version, cli_version=cli_version,
-                    n_subjects=n_subjects, collection_id=collection_id)
+                    fmriprep_version=fmriprep_version, estimator=estimator,
+                    cli_version=cli_version, n_subjects=n_subjects,
+                    collection_id=collection_id)
                 if collection_id is None:
                     collection_id = req['collection_id']
 
@@ -357,8 +361,8 @@ def _ts_first(paths):
                 req = self.post(
                     id=id, sub_route='upload', files=files, level='SUBJECT',
                     validation_hash=validation_hash, force=force,
-                    fmriprep_version=fmriprep_version, cli_version=cli_version,
-                    collection_id=collection_id)
+                    fmriprep_version=fmriprep_version, estimator=estimator,
+                    cli_version=cli_version, collection_id=collection_id)
                 if collection_id is None:
                     collection_id = req['collection_id']
 

pyns/models/predictor.py

@@ -28,7 +28,7 @@ def create_collection(self, collection_name, dataset_id,
                          descriptions=descriptions)
 
     def get_collection(self, collection_id):
-        return self.get('collection', collection_id=collection_id)
+        return self.get(f'collection/{collection_id}')
 
 
 class PredictorEvents(Base):

pyns/models/utils.py

@@ -16,7 +16,7 @@ def attempt_to_import(dependency, name=None, fromlist=None):
     return mod
 
 
-def build_model(name, variables, task, subject, run=None, session=None,
+def build_model(name, variables, tasks, subjects, runs=None, session=None,
                 hrf_variables=None, transformations=None,
                 contrasts=None, dummy_contrasts=True):
     """ Builds a basic two level BIDS-Model """
@@ -46,8 +46,8 @@ def build_model(name, variables, task, subject, run=None, session=None,
           }
         ],
         "Input": {
-          "Subject": subject,
-          "Task": task
+          "Subject": subjects,
+          "Task": tasks
         },
         "Name": name,
     }
@@ -72,8 +72,8 @@ def build_model(name, variables, task, subject, run=None, session=None,
     if dummy_contrasts == 'hrf' and hrf_variables:
         model['Steps'][0]['DummyContrasts']['Conditions'] = hrf_variables
 
-    if run is not None:
-        model['Input']['Run'] = run
+    if runs is not None:
+        model['Input']['Run'] = runs
 
     if session is not None:
         model['Input']['Session'] = session

tests/cassettes/analysis.json

@@ -1,7 +1,7 @@
 {
   "http_interactions": [
     {
-      "recorded_at": "2020-05-07T17:42:28",
+      "recorded_at": "2020-12-17T21:53:03",
       "request": {
         "body": {
           "encoding": "utf-8",
@@ -15,7 +15,7 @@
             "gzip, deflate"
           ],
           "Authorization": [
-            "JWT eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE1ODk0NzgxMzAsImlhdCI6MTU4ODg3MzMzMCwibmJmIjoxNTg4ODczMzMwLCJpZGVudGl0eSI6MX0.IMSWGp4eutrnISnNiDRSkqUUUrbKJQvB0wmYn63DSKU"
+            "JWT eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE2MDg4NDY3NTYsImlhdCI6MTYwODI0MTk1NiwibmJmIjoxNjA4MjQxOTU2LCJpZGVudGl0eSI6MX0.IIKlNQgC2YlfKmqMDaNKtyKm2kjpWJZroMebZLXW4Ls"
           ],
           "Connection": [
             "keep-alive"
@@ -27,15 +27,15 @@
             "application/json"
           ],
           "User-Agent": [
-            "python-requests/2.22.0"
+            "python-requests/2.21.0"
           ]
         },
         "method": "POST",
         "uri": "https://neuroscout.org/api/analyses"
       },
       "response": {
         "body": {
-          "base64_string": "H4sIAAAAAAAAA31SwW7CMAy97zN8DlPpBoje0BjSpLED9DAJoSq0rsho0yhxkKqKf5/TwrQdWE/xS/38nl86SDeQxI8TAblFSVhkkiCBOIqjUTQZRbN0PEueQUAhSTqkTBWQzLlEl1tlSDUaEu2rSsBRumN/DYvGRFPuqZr8hAyUsnIooG4KrCDp4E0bT+Gw8dy9G+8FbP3hC3MGd2BVEYVvDIyn0p2YsVIlwkXAh6yRS9MSOsqkllXrlONRW0LjuLuDl0aTlY5CxQRLX9ftL6yDtDWBgwLfO56DpF6IgPVN4GfQ4QxifoQ9/5ZaqV3Z2FoGwz3zRXT3uVev68Vv+pu7f5v+CFoO64ZLGM+LU6W6n42+txUjLeohsyEjY7FQ+dUEwA/Q2GBqHM+jyT6A6sxvARKynnOzvrccP035zpEkH5qXm8UqZQbnD7Wi28sZxrD2U3ZVNUT38A3hP+mkagIAAA==",
+          "base64_string": "H4sIAAAAAAAAA31STWvCQBC992fMOUKSYou5Sa1QqD1oDgWRMCYT3LrZLPshhuB/72xSiz3YnDJv9715b2Z7KA2ho6pABxmkcRpPknSSPOdpkk0hggodWnKFqCCbcUm2NEI70SrIlJcyggPaw3AM6pxO98yRbXkkBmqUliJo2ookZD28Ke1d+Fl7Zm+TXQQbv/+iksEtGFHF4UuA8RztkRWlqAkuEXxgQ1zqzpF1BSqUnRWWW20cacvsHl5a5QxaFyoWWPim6W6wHvJOBw0X9N7pFCwNRiJYXQ1+Bh9WE5UH2PG13KCydWsaDIEH5UvU39devq7mt/LXdP+S/hhajOOGS2jPgxO1uL8bdW8qGg2pcWfjjrShSpQ/IQB+gdaEUEk6i6e7AIoTvwXInPG8N+OHyOnjE59Zh84H8mI9X+asYP2+Ee76csY23pLhK3TGRksqhvLy8A3YCsTBZAIAAA==",
           "encoding": null,
           "string": ""
         },
@@ -53,7 +53,7 @@
             "application/json"
           ],
           "Date": [
-            "Thu, 07 May 2020 17:42:28 GMT"
+            "Thu, 17 Dec 2020 21:53:03 GMT"
           ],
           "Server": [
             "nginx/1.15.6"