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        "author": "Lucas Cardozo",
        "author_email": "lucasecardozo@gmail.com",
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        "description": "# spots [![Build Status](https://travis-ci.org/lecardozo/spots.svg?branch=master)](https://travis-ci.org/lecardozo/spots)\n\nGoogle Location History utilities\n\n## Installation\n```\n$ pip install spots\n```\n\n## Usage\n#### Load location history json as pandas DataFrame\n```python\nfrom spots import LocationHistory\nlocdf = LocationHistory.from_json(\"your-location-history-file.json\")\nlocdf.head()\n\n#   accuracy   activity                                         lat        lon               timestamp\n#           confidence               timestamp     type                                              \n#0       24        100 2014-01-05 09:47:07.808  UNKNOWN -23.340981 -46.579202 2014-01-05 09:47:07.808\n#1       24        100 2014-01-05 09:47:54.558  TILTING -23.123471 -46.631244 2014-01-05 09:48:21.891\n#2       24        100 2014-01-05 09:49:21.461  UNKNOWN -23.456211 -46.640234 2014-01-05 09:49:21.461\n#3       24        100 2014-01-05 09:50:21.470  UNKNOWN -23.464231 -46.604355 2014-01-05 09:50:21.470\n#4       25        100 2014-01-05 09:51:21.623  UNKNOWN -23.490080 -46.709021 2014-01-05 09:51:21.623\n\n```\n\n#### Calculate stay points for your trajectory\nThe `StayPointDetection` class implements the same interface used by `sklearn` clustering\nalgorithms.\n\n```python\nfrom spots import StayPointDetection\nimport numpy as np\n\nspd = StayPointDetection(distance=0.05, time=np.timedelta(15, 'm'))\nstaypoints = spd.fit_predict(X=locdf[['lat', 'lon']].values, timestamp=locdf.timestamp)\nlocdf.loc[:, \"staypoint_id\"] = staypoints\n```\n\n\n",
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