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        "author": "Alejandro de la Vega",
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        "description": "# pyNS \ud83c\udf32\n[![Build Status](https://travis-ci.org/neuroscout/pyNS.svg?branch=master)](https://travis-ci.org/neuroscout/pyns)\n[![codecov](https://codecov.io/gh/neuroscout/pyns/branch/master/graph/badge.svg)](https://codecov.io/gh/neuroscout/pyns)\n\nThe Neuroscout API wrapper for Python\n\n### Overview\npyNS is a python package to easily interact with the Neuroscout API.\n\nFor more API documentation, check out the Swagger API Docs: http://neuroscout.org/swagger-ui/\n\n### Installation\npyNS is supported in Python 3.4+\nUse `pip` to install it:\n\n    pip install pyns\n\n### Quickstart\nFor a tutorial on how to build an analysis, see this Jupyter Notebook: https://github.com/neuroscout/pyNS/blob/master/examples/Tutorial.ipynb\n\nWe are assuming you already have valid Neuroscout API credentials (and if you dont, sign up at: `neuroscout.org`)\n\nFirst, instantiate a Neuroscout API Client object:\n\n    from pyns import Neuroscout\n    neuroscout = Neuroscout(username='USERNAME', password='PASSWORD')\n\nWith the `neuroscout` instance, you can interact with the API. All of the major routes are linked to the main `neuroscout` object,\nand return `requests` `Response` objects.\n\nFor example we can retrieve our user profile:\n\n    >>> neuroscout.user.get()\n    {'email': 'user@example.com',\n     'analyses': [ {'description': 'Does the brain care about language?',\n      'hash_id': 'RZd',\n      'modified_at': '2018-08-09T23:3',\n      'name': 'My new analysis',\n      'status': 'PASSED'}]]}\n\nOr query various endpoints, such as `datasets`:\n\n    >>> neuroscout.datasets.get()\n    [{'description': {'Acknowledgements': '',\n       'Authors': ['Tomoyasu Horikawa', 'Yukiyasu Kamitani'],\n       'DatasetDOI': '',\n       'Funding': '',\n       'HowToAcknowledge': '',\n       'License': '',\n       'Name': 'Generic Object Decoding (fMRI on ImageNet)',\n       'ReferencesAndLinks': ['Horikawa & Kamitani (2017) Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications volume 8:15037. doi:10.1038/ncomms15037']},\n      'id': 1,\n      'name': 'generic_object_decoding',\n    ...\n      'tasks': [{'id': 8, 'name': 'life'}]}]\n\nFor example, we could use this to get the first predictor associated with a dataset:\n\n    >>> first = neuroscout.predictors.get(dataset_id=5)[0]\n    {'description': 'Bounding polygon around face. y coordinate for vertex 1',\n     'extracted_feature': {'created_at': '2018-04-12 00:44:14.868349',\n      'description': 'Bounding polygon around face. y coordinate for vertex 1',\n      'extractor_name': 'GoogleVisionAPIFaceExtractor',\n      'id': 102,\n      'modality': None},\n     'id': 197,\n     'name': 'boundingPoly_vertex1_y',\n     'source': 'extracted'}\n\n\nAnd get the predictor-events associated with that predictor:\n\n    >>> neuroscout.predictor_events.get(predictor_id=first['id'])[0:2]\n    [{'duration': 9.0,\n      'id': '1050781',\n      'onset': 114.0,\n      'predictor_id': 197,\n      'run_id': 2,\n      'value': '13'},\n     {'duration': 9.0,\n      'id': '1050782',\n      'onset': 114.0,\n      'predictor_id': 197,\n      'run_id': 26,\n      'value': '13'}]\n\n\n\n\n### Testing\nWe use pytest for testing, and betamax to record HTTP requests used in test into cassettes.\n\nTo re-run tests locally set the`USER_TEST_EMAIL` and `USER_TEST_PWD` environment variables with valid API credentials.\n\n\n",
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