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    "info": {
        "author": "Lo\u00efc Dutrieux",
        "author_email": "loic.dutrieux@gmail.com",
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        "description": "geextract\n=========\n\n*Google Earth Engine data extraction tool. Quickly obtain Landsat multispectral time-series for exploratory analysis and algorithm testing*\n\nOnline documentation available at https://loicdtx.github.io/landsat-extract-gee\n\n.. image:: https://coveralls.io/repos/github/loicdtx/landsat-extract-gee/badge.svg?branch=master\n    :target: https://coveralls.io/github/loicdtx/landsat-extract-gee?branch=master\n\n.. image:: https://travis-ci.org/loicdtx/landsat-extract-gee.svg?branch=master\n    :target: https://travis-ci.org/loicdtx/landsat-extract-gee\n\n.. image:: https://badge.fury.io/py/geextract.svg\n    :target: https://badge.fury.io/py/geextract\n\n\n\nIntroduction\n------------\n\n\nA python library (API + command lines) to extract Landsat time-series from the Google Earth Engine platform. Can query single pixels or spatially aggregated values over polygons. When used via the command line, extracted time-series are written to a sqlite database.\n\nThe idea is to provide quick access to Landsat time-series for exploratory analysis or algorithm testing. Instead of downloading the whole stack of Landsat scenes, preparing the data locally and extracting the time-series of interest, which may take several days, ``geextract`` allows to get time-series in a few seconds.\n\nCompatible with python 2.7 and 3.\n\nUsage\n-----\n\nAPI\n^^^\n\nThe principal function of the API is ``ts_extract``\n\n.. code-block:: python\n\n    from geextract import ts_extract\n    from datetime import datetime\n\n    # Extract a Landsat 7 time-series for a 500m radius circular buffer around\n    # a location in Yucatan\n    lon = -89.8107197\n    lat = 20.4159611\n    LE7_dict_list = ts_extract(lon=lon, lat=lat, sensor='LE7',\n                               start=datetime(1999, 1, 1), radius=500)\n\n\nCommand line\n^^^^^^^^^^^^\n\n``geextract`` comes with two command lines, for extracting Landsat time-series directly from the command line.\n\n- ``gee_extract.py``: Extract a Landsat multispectral time-series for a single site. Extracted data are automatically added to a sqlite database.\n- ``gee_extract_batch.py``: Batch order Landsat multispectral time-series for multiple locations.\n  \n.. code-block:: bash\n    \n    gee_extract.py --help\n\n    # Extract all the LT5 bands for a location in Yucatan for the entire Landsat period, with a 500m radius\n    gee_extract.py -s LT5 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1\n    gee_extract.py -s LE7 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1\n    gee_extract.py -s LC8 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1\n\n.. code-block:: bash\n\n    gee_extract_batch.py --help\n\n    # Extract all the LC8 bands in a 500 meters for two locations between 2012 and now\n    echo \"4.7174,44.7814,rompon\\n-149.4260,-17.6509,tahiti\" > site_list.txt\n    gee_extract_batch.py site_list.txt -b 1984-01-01 -s LT5 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts\n    gee_extract_batch.py site_list.txt -b 1984-01-01 -s LE7 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts\n    gee_extract_batch.py site_list.txt -b 1984-01-01 -s LC8 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts\n\n\n.. image:: https://github.com/loicdtx/landsat-extract-gee/raw/master/docs/figs/multispectral_uxmal.png\n\n\n\n\nInstallation\n------------\n\nYou must have a `Google Earth Engine <http://signup.earthengine.google.com/#!/>`__ account to use the package.\n\nThen, in a vitual environment run:\n\n.. code-block:: bash\n\n    pip install geextract\n    earthengine authenticate\n\n\nThis will open a google authentication page in your browser, and will give you an authentication token to paste back in the terminal.\n\nYou can check that the authentication process was successful by running.\n\n.. code-block:: bash\n\n    python -c \"import ee; ee.Initialize()\"\n\n\nIf nothing happens... it's working.\n\n\nBenchmark\n---------\n\nA quick benchmark of the extraction speed, using a 500 m buffer.\n\n.. code-block:: python\n\n    import time\n    from datetime import datetime\n    from pprint import pprint\n    import geextract\n\n    lon = -89.8107197\n    lat = 20.4159611\n\n    for sensor in ['LT5', 'LE7', 'LT4', 'LC8']:\n        start = time.time()\n        out = geextract.ts_extract(lon=lon, lat=lat, sensor=sensor, start=datetime(1980, 1, 1, 0, 0),\n                                   end=datetime.today(), radius=500)\n        end = time.time()\n\n        pprint('%s. Extracted %d records in %.1f seconds' % (sensor, len(out), end - start))\n\n.. code-block:: pycon\n\n    # 'LT5. Extracted 142 records in 1.9 seconds'\n    # 'LE7. Extracted 249 records in 5.8 seconds'\n    # 'LT4. Extracted 7 records in 1.0 seconds'\n    # 'LC8. Extracted 72 records in 2.4 seconds'",
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