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    "info": {
        "author": "Justin Deal, Matt Isnor",
        "author_email": "deal.justin@gmail.com, isnor.matt@gmail.com",
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        "description": "Python CMR\n==========\n\n.. image:: https://travis-ci.org/jddeal/python-cmr.svg?branch=master\n    :target: https://travis-ci.org/jddeal/python-cmr\n\nPython CMR is an easy to use wrapper to the NASA EOSDIS\n`Common Metadata Repository API <https://cmr.earthdata.nasa.gov/search/>`_. This package aims to make\nquerying the API intuitive and less error-prone by providing methods that will preemptively check\nfor invalid input and handle the URL encoding the CMR API expects.\n\nGetting access to NASA's earth science metadata is as simple as this:\n\n::\n\n    >>> from cmr import CollectionQuery, GranuleQuery\n\n    >>> api = CollectionQuery()\n    >>> collections = api.archive_center(\"LP DAAC\").keyword(\"AST_L1*\").get(5)\n\n    >>> for collection in collections:\n    >>>   print(collection[\"short_name\"])\n    AST_L1A\n    AST_L1AE\n    AST_L1T\n\n    >>> api = GranuleQuery()\n    >>> granules = api.short_name(\"AST_L1T\").point(-112.73, 42.5).get(3)\n\n    >>> for granule in granules:\n    >>>   print(granule[\"title\"])\n    SC:AST_L1T.003:2149105822\n    SC:AST_L1T.003:2149105820\n    SC:AST_L1T.003:2149155037\n\n\nInstallation\n============\n\nTo install from pypi:\n\n::\n\n    $ pip install python-cmr\n\n\nTo install from github, perhaps to try out the dev branch:\n\n::\n\n    $ git clone https://github.com/jddeal/python-cmr\n    $ cd python-cmr\n    $ pip install .\n\n\nExamples\n========\n\nThis library is broken into two classes, `CollectionQuery` and `GranuleQuery`. Each of these\nclasses provide a large set of methods used to build a query for CMR. Not all parameters provided\nby the CMR API are covered by this version of python-cmr.\n\nThe following methods are available to both collecton and granule queries:\n\n::\n\n    # search for granules matching a specific product/short_name\n    >>> api.short_name(\"AST_L1T\")\n\n    # search for granules matching a specific version\n    >>> api.version(\"006\")\n\n    # search for granules at a specific longitude and latitude\n    >>> api.point(-112.73, 42.5)\n\n    # search for granules in an area bound by a box (lower left lon/lat, upper right lon/lat)\n    >>> api.bounding_box(-112.70, 42.5, -110, 44.5)\n\n    # search for granules in a polygon (these need to be in counter clockwise order and the\n    # last coordinate must match the first in order to close the polygon)\n    >>> api.polygon([(-100, 40), (-110, 40), (-105, 38), (-100, 40)])\n\n    # search for granules in a line\n    >>> api.line([(-100, 40), (-90, 40), (-95, 38)])\n\n    # search for granules in an open or closed date range\n    >>> api.temporal(\"2016-10-10T01:02:00Z\", \"2016-10-12T00:00:30Z\")\n    >>> api.temporal(\"2016-10-10T01:02:00Z\", None)\n    >>> api.temporal(datetime(2016, 10, 10, 1, 2, 0), datetime.now())\n\n    # only include granules available for download\n    >>> api.downloadable()\n\n    # only include granules that are unavailable for download\n    >>> api.online_only()\n\n    # search for collections/granules associated with or identified by concept IDs\n    # note: often the ECHO collection ID can be used here as well\n    # note: when using CollectionQuery, only collection concept IDs can be passed\n    # note: when uses GranuleQuery, passing a collection's concept ID will filter by granules associated\n    #       with that particular collection.\n    >>> api.concept_id(\"C1299783579-LPDAAC_ECS\")\n    >>> api.concept_id([\"G1327299284-LPDAAC_ECS\", \"G1326330014-LPDAAC_ECS\"])\n\n\nGranule searches support these methods (in addition to the shared methods above):\n\n::\n\n    # search for a granule by its unique ID\n    >>> api.granule_ur(\"SC:AST_L1T.003:2150315169\")\n    # search for granules from a specific orbit\n    >>> api.orbit_number(5000)\n\n    # filter by the day/night flag\n    >>> api.day_night_flag(\"day\")\n\n    # filter by cloud cover percentage range\n    >>> api.cloud_cover(25, 75)\n\n    # filter by specific instrument or platform\n    >>> api.instrument(\"MODIS\")\n    >>> api.platform(\"Terra\")\n\n\nCollection searches support these methods (in addition to the shared methods above):\n\n::\n\n    # search for collections from a specific archive center\n    >>> api.archive_center(\"LP DAAC\")\n\n    # case insensitive, wildcard enabled text search through most collection fields\n    >>> api.keyword(\"M*D09\")\n\n\nAs an alternative to chaining methods together to set the parameters of your query, a\nmethod exists to allow you to pass your parameters as keyword arguments:\n\n::\n\n    # search for AST_L1T version 003 granules at latitude 42, longitude -100\n    >>> api.parameters(\n        short_name=\"AST_L1T\",\n        version=\"003\",\n        point=(-100, 42)\n    )\n\nNote: the kwarg key should match the name of a method from the above examples, and the value\nshould be a tuple if it's a parameter that requires multiple values.\n\n\nTo inspect and retreive results from the API, the following methods are available:\n\n::\n\n    # inspect the number of results the query will return without downloading the results\n    >>> print(api.hits())\n\n    # retrieve 100 granules\n    >>> granules = api.get(100)\n\n    # retrieve 25,000 granules\n    >>> granules = api.get(25000)\n\n    # retrieve all the granules possible for the query\n    >>> granules = api.get_all()  # this is a shortcut for api.get(api.hits())\n\n\nBy default the responses will return as json and be accessible as a list of python dictionaries.\nOther formats can be specified before making the request:\n\n::\n\n    >>> granules = api.format(\"echo10\").get(100)\n\nThe following formats are supported for both granule and collection queries:\n\n* json (default)\n* xml\n* echo10\n* iso\n* iso19115\n* csv\n* atom\n* kml\n* native\n\nCollection queries also support the following formats:\n\n* dif\n* dif10\n* opendata\n* umm_json\n* umm_json_vX_Y (ex: umm_json_v1_9)\n\n\n",
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