pyns 0.3.1

Neuroscout API wrapper

pyNS 🌲

The Neuroscout API wrapper for Python

Overview

pyNS is a python package to easily interact with the Neuroscout API.

For more API documentation, check out the Swagger API Docs: http://neuroscout.org/swagger-ui/

Installation

pyNS is supported in Python 3.4+ Use pip to install it:

pip install pyns

Quickstart

For a tutorial on how to build an analysis, see this Jupyter Notebook: https://github.com/neuroscout/pyNS/blob/master/examples/Tutorial.ipynb

We are assuming you already have valid Neuroscout API credentials (and if you dont, sign up at: alpha.neuroscout.org)

First, instantiate a Neuroscout API Client object:

from pyns import Neuroscout
neuroscout = Neuroscout(username='USERNAME', password='PASSWORD')

With the neuroscout instance, you can interact with the API. All of the major routes are linked to the main neuroscout object, and return requests Response objects.

For example we can retrieve our user profile:

>>> neuroscout.user.get()
{'email': '[e-mail]',
 'analyses': [ {'description': 'Does the brain care about language?',
  'hash_id': 'RZd',
  'modified_at': '2018-08-09T23:3',
  'name': 'My new analysis',
  'status': 'PASSED'}]]}

Or query various endpoints, such as datasets:

>>> neuroscout.datasets.get()
[{'description': {'Acknowledgements': '',
   'Authors': ['Tomoyasu Horikawa', 'Yukiyasu Kamitani'],
   'DatasetDOI': '',
   'Funding': '',
   'HowToAcknowledge': '',
   'License': '',
   'Name': 'Generic Object Decoding (fMRI on ImageNet)',
   'ReferencesAndLinks': ['Horikawa & Kamitani (2017) Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications volume 8:15037. doi:10.1038/ncomms15037']},
  'id': 1,
  'name': 'generic_object_decoding',
...
  'tasks': [{'id': 8, 'name': 'life'}]}]

For example, we could use this to get the first predictor associated with a dataset:

>>> first = neuroscout.predictors.get(dataset_id=5)[0]
{'description': 'Bounding polygon around face. y coordinate for vertex 1',
 'extracted_feature': {'created_at': '2018-04-12 00:44:14.868349',
  'description': 'Bounding polygon around face. y coordinate for vertex 1',
  'extractor_name': 'GoogleVisionAPIFaceExtractor',
  'id': 102,
  'modality': None},
 'id': 197,
 'name': 'boundingPoly_vertex1_y',
 'source': 'extracted'}

And get the predictor-events associated with that predictor:

>>> neuroscout.predictor_events.get(predictor_id=first['id'])[0:2]
[{'duration': 9.0,
  'id': '1050781',
  'onset': 114.0,
  'predictor_id': 197,
  'run_id': 2,
  'value': '13'},
 {'duration': 9.0,
  'id': '1050782',
  'onset': 114.0,
  'predictor_id': 197,
  'run_id': 26,
  'value': '13'}]

Testing

We use pytest for testing, and betamax to record HTTP requests used in test into cassettes.

To re-run tests locally set theUSER_TEST_EMAIL and USER_TEST_PWD environment variables with valid API credentials.