{
    "info": {
        "author": "Andreas Motl",
        "author_email": "andreas@hiveeyes.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Affero General Public License v3",
            "Natural Language :: English",
            "Natural Language :: German",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Topic :: Database",
            "Topic :: Education",
            "Topic :: Internet :: File Transfer Protocol (FTP)",
            "Topic :: Internet :: WWW/HTTP",
            "Topic :: Internet :: WWW/HTTP :: Indexing/Search",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Atmospheric Science",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Software Development :: Libraries",
            "Topic :: System :: Archiving",
            "Topic :: Text Processing :: Filters",
            "Topic :: Text Processing :: Indexing",
            "Topic :: Utilities"
        ],
        "description": ".. image:: https://img.shields.io/badge/Python-2.7-green.svg\n    :target: https://pypi.org/project/phenodata/\n\n.. image:: https://img.shields.io/pypi/v/phenodata.svg\n    :target: https://pypi.org/project/phenodata/\n\n.. image:: https://img.shields.io/github/tag/hiveeyes/phenodata.svg\n    :target: https://github.com/hiveeyes/phenodata\n\n|\n\n#################################################\nphenodata - phenology data acquisition for humans\n#################################################\n\n\n*****\nAbout\n*****\nphenodata is a data acquisition and manipulation toolkit for open access phenology data.\nIt is written in Python.\n\nCurrently, it implements data wrappers for acquiring phenology observation data published\non the DWD Climate Data Center (CDC) FTP server operated by \u00bbDeutscher Wetterdienst\u00ab (DWD).\n\nUnder the hood, it uses the fine Pandas_ data analysis library for data mangling, amongst others.\n\n.. _Pandas: https://pandas.pydata.org/\n\n\n****************\nAcknowledgements\n****************\nThanks to the many observers, \u00bbDeutscher Wetterdienst\u00ab,\nthe \u00bbGlobal Phenological Monitoring programme\u00ab and all people working behind\nthe scenes for their commitment in recording the observations and for making\nthe excellent datasets available to the community. You know who you are.\n\n\n***************\nGetting started\n***************\n\nIntroduction\n============\nFor most acquisition tasks, you must choose from one of two different datasets: `annual-reporters`_ and `immediate-reporters`_.\n\nTo improve data acquisition performance, also consider applying\nthe ``--filename=`` parameter for file name filtering.\n\nExample: When using ``--filename=Hasel,Schneegloeckchen``, only file names containing\n``Hasel`` or ``Schneegloeckchen`` will be retrieved, thus minimizing the required effort\nto acquire all files.\n\n.. _annual-reporters: https://www.dwd.de/DE/klimaumwelt/klimaueberwachung/phaenologie/daten_deutschland/jahresmelder/jahresmelder_node.html\n.. _immediate-reporters: https://www.dwd.de/DE/klimaumwelt/klimaueberwachung/phaenologie/daten_deutschland/sofortmelder/sofortmelder_node.html\n\n\nInstall\n=======\nIf you know your way around Python, installing this software is really easy::\n\n    pip install phenodata --upgrade\n\nPlease refer to the `virtualenv`_ page about further recommendations how to install and use this software.\n\n.. _virtualenv: https://github.com/hiveeyes/phenodata/blob/master/doc/virtualenv.rst\n\n\nUsage\n=====\n::\n\n    $ phenodata --help\n    Usage:\n      phenodata info\n      phenodata list-species --source=dwd [--format=csv]\n      phenodata list-phases --source=dwd [--format=csv]\n      phenodata list-stations --source=dwd --dataset=immediate [--all] [--format=csv]\n      phenodata nearest-station --source=dwd --dataset=immediate --latitude=52.520007 --longitude=13.404954 [--format=csv]\n      phenodata nearest-stations --source=dwd --dataset=immediate [--all] --latitude=52.520007 --longitude=13.404954 [--limit=10] [--format=csv]\n      phenodata list-quality-levels --source=dwd [--format=csv]\n      phenodata list-quality-bytes --source=dwd [--format=csv]\n      phenodata list-filenames --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--year=2017]\n      phenodata list-urls --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--year=2017]\n      phenodata (observations|forecast) --source=dwd --dataset=immediate --partition=recent [--filename=Hasel,Schneegloeckchen] [--station-id=164,717] [--species-id=113,127] [--phase-id=5] [--quality-level=10] [--quality-byte=1,2,3] [--station=berlin,brandenburg] [--species=hazel,snowdrop] [--species-preset=mellifera-primary] [--phase=flowering] [--quality=ROUTKLI] [--year=2017] [--humanize] [--show-ids] [--language=german] [--long-station] [--sort=Datum] [--format=csv]\n      phenodata drop-cache --source=dwd\n      phenodata --version\n      phenodata (-h | --help)\n\n    Data acquisition options:\n      --source=<source>         Data source. Currently \"dwd\" only.\n      --dataset=<dataset>       Data set. Use \"immediate\" or \"annual\" for --source=dwd.\n      --partition=<dataset>     Partition. Use \"recent\" or \"historical\" for --source=dwd.\n      --filename=<file>         Filter by file names (comma-separated list)\n\n    Direct filtering options:\n      --years=<years>           Filter by years (comma-separated list)\n      --station-id=<station-id> Filter by station ids (comma-separated list)\n      --species-id=<species-id> Filter by species ids (comma-separated list)\n      --phase-id=<phase-id>     Filter by phase ids (comma-separated list)\n\n    Humanized filtering options:\n      --station=<station>       Filter by strings from \"stations\" data (comma-separated list)\n      --species=<species>       Filter by strings from \"species\" data (comma-separated list)\n      --phase=<phase>           Filter by strings from \"phases\" data (comma-separated list)\n      --species-preset=<preset> Filter by strings from \"species\" data (comma-separated list) loaded from ``presets.json`` file\n\n    Data output options:\n      --format=<format>         Output data in designated format. Choose one of \"tabular\", \"json\", \"csv\" or \"string\".\n                                With \"tabular\", it is also possible to specify the table format,\n                                see https://bitbucket.org/astanin/python-tabulate. e.g. \"tabular:presto\".\n                                [default: tabular:psql]\n      --sort=<sort>             Sort by given column names (comma-separated list)\n      --humanize                Resolve ID-based columns to real names with \"observations\" and \"forecast\" output.\n      --show-ids                Show IDs alongside resolved text representation when using ``--humanize``.\n      --language=<language>     Use labels in designated language when using ``--humanize`` [default: english].\n      --long-station            Use long station name including \"Naturraumgruppe\" and \"Naturraum\".\n      --limit=<limit>           Limit output of \"nearest-stations\" to designated number of entries.\n                                [default: 10]\n\n----\n\n**************\nOutput example\n**************\n\n==========  ======================  ======================  =====================\nDatum       Spezies                 Phase                   Station\n==========  ======================  ======================  =====================\n2018-02-17  common snowdrop         beginning of flowering  Berlin-Dahlem, Berlin\n2018-02-19  common hazel            beginning of flowering  Berlin-Dahlem, Berlin\n2018-03-30  goat willow             beginning of flowering  Berlin-Dahlem, Berlin\n2018-04-07  dandelion               beginning of flowering  Berlin-Dahlem, Berlin\n2018-04-15  cherry (late ripeness)  beginning of flowering  Berlin-Dahlem, Berlin\n2018-04-21  winter oilseed rape     beginning of flowering  Berlin-Dahlem, Berlin\n2018-04-23  apple (early ripeness)  beginning of flowering  Berlin-Dahlem, Berlin\n2018-05-03  apple (late ripeness)   beginning of flowering  Berlin-Dahlem, Berlin\n2018-05-24  black locust            beginning of flowering  Berlin-Dahlem, Berlin\n2018-08-20  common heather          beginning of flowering  Berlin-Dahlem, Berlin\n==========  ======================  ======================  =====================\n\n----\n\n\n*******************\nInvocation examples\n*******************\n\n\nMetadata\n========\n\nList of species::\n\n    phenodata list-species --source=dwd\n\nList of phases::\n\n    phenodata list-phases --source=dwd\n\nList of all stations::\n\n    phenodata list-stations --source=dwd --dataset=immediate\n\nList of filtered stations::\n\n    phenodata list-stations --source=dwd --dataset=annual --filter=\"Fr\u00e4nkische Alb\"\n\nList of file names of recent observations by the annual reporters::\n\n    phenodata list-filenames --source=dwd --dataset=annual --partition=recent\n\nList of full URLs to observations using filename-based filtering::\n\n    phenodata list-urls --source=dwd --dataset=annual --partition=recent --filename=Hasel,Schneegloeckchen\n\nDisplay nearest station for given position::\n\n    phenodata nearest-station --source=dwd --dataset=immediate --latitude=52.520007 --longitude=13.404954\n\nDisplay 20 nearest stations for given position::\n\n    phenodata nearest-stations \\\n        --source=dwd --dataset=immediate \\\n        --latitude=52.520007 --longitude=13.404954 --limit=20\n\n\nObservations\n============\n\nObservations of hazel and snowdrop, using filename-based filtering at data acquisition time::\n\n    phenodata observations --source=dwd --dataset=annual --partition=recent --filename=Hasel,Schneegloeckchen\n\nObservations of hazel and snowdrop (dito), but for station ids 164 and 717 only::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen --station-id=164,717\n\nAll observations for station ids 164 and 717 in years 2016 and 2017::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station-id=164,717 --year=2016,2017\n\nAll observations for station ids 164 and 717 and species ids 113 and 127::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station-id=164,717 --species-id=113,127\n\nAll invalid observations::\n\n    phenodata list-quality-bytes --source=dwd\n    phenodata observations --source=dwd --dataset=annual --partition=recent --quality-byte=5,6,7,8\n\n\nForecasting\n===========\nAcquire data from observations in Berlin-Dahlem and M\u00fcnchen-Pasing and forecast to current year\nusing grouping and by computing the \"mean\" value of the \"Jultag\" column::\n\n    phenodata forecast \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen,Apfel,Birne \\\n        --station-id=12132,10961 --format=string\n\n\n\n*************************\nHumanized output examples\n*************************\nThe option ``--humanize`` will improve textual output by resolving ID columns\nin the observation data to their appropriate text representions from metadata files.\n\nObservations\n============\nObservations for species \"hazel\", \"snowdrop\", \"apple\" and \"pear\" at station \"Berlin-Dahlem\",\noutput texts in the German language if possible::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen,Apfel,Birne \\\n        --station-id=12132 \\\n        --humanize --language=german\n\n\nForecasting\n===========\n\nSpecific events\n---------------\nForecast of \"beginning of flowering\" events at station \"Berlin-Dahlem\".\nUse all species of the \"primary group\": \"hazel\", \"snowdrop\", \"goat willow\",\n\"dandelion\", \"cherry\", \"apple\", \"winter oilseed rape\", \"black locust\" and \"common heather\".\nSort by date, ascending.\n::\n\n    phenodata forecast \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen,Sal-Weide,Loewenzahn,Suesskirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \\\n        --station-id=12132 --phase-id=5 \\\n        --humanize \\\n        --sort=Datum \\\n        --format=tabular:rst\n\nEvent sequence for each species\n-------------------------------\nForecast of all events at station \"Berlin-Dahlem\".\nUse all species of the \"primary group\" (dito).\nSort by species and date, ascending.\n::\n\n    phenodata forecast \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen,Sal-Weide,Loewenzahn,Suesskirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \\\n        --station-id=12132 \\\n        --humanize --language=german \\\n        --sort=Spezies,Datum\n\n\n*************************\nHumanized search examples\n*************************\n\nObservations\n============\nQuery observations by using textual representation of \"station\" information::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --filename=Hasel,Schneegloeckchen \\\n        --station=berlin,brandenburg \\\n        --humanize --sort=Datum\n\nObservations near Munich for species \"hazel\" or \"snowdrop\" in 2018::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=m\u00fcnchen \\\n        --species=hazel,snowdrop \\\n        --year=2018 \\\n        --humanize --sort=Datum\n\nObservations for any \"flowering\" events in 2017 and 2018 around Munich::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=m\u00fcnchen \\\n        --phase=flowering \\\n        --year=2017,2018 \\\n        --humanize --sort=Datum\n\nSame observations but with \"ROUTKLI\" quality::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=m\u00fcnchen \\\n        --phase=flowering \\\n        --quality=ROUTKLI \\\n        --year=2017 \\\n        --humanize --sort=Datum\n\nInvestigate some \"flowering\" observations near Munich which have seen corrections last year::\n\n    phenodata observations \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=m\u00fcnchen \\\n        --phase=flowering \\\n        --quality=korrigiert \\\n        --year=2017 \\\n        --humanize --sort=Datum\n\n\nForecasting\n===========\nForecast based on \"beginning of flowering\" events of 2015-2017 in Th\u00fcringen and Bayern for the given list of species.\nSort by species and date.\n::\n\n    phenodata forecast \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=th\u00fcringen,bayern \\\n        --species=Hasel,Schneegl\u00f6ckchen,Sal-Weide,L\u00f6wenzahn,S\u00fc\u00dfkirsche,Apfel,Winterraps,Robinie,Winter-Linde,Heidekraut \\\n        --phase-id=5 \\\n        --year=2015,2016,2017 \\\n        --humanize --language=german \\\n        --sort=Spezies,Datum\n\nForecast based on \"beginning of flowering\" events of 2015-2017 in Berlin for the named list of species \"mellifera-de-primary\".\nSort by date.\n::\n\n    phenodata forecast \\\n        --source=dwd --dataset=annual --partition=recent \\\n        --station=k\u00f6ln \\\n        --phase=\"beginning of flowering\" \\\n        --year=2015,2016,2017 \\\n        --humanize --language=german \\\n        --sort=Datum \\\n        --species-preset=mellifera-de-primary\n\n.. note::\n\n    The species presets like ``mellifera-de-primary`` and others are currently stored in\n    `presets.json <https://github.com/hiveeyes/phenodata/blob/master/phenodata/dwd/presets.json>`__.\n\n\n*******************\nProject information\n*******************\n\nAbout\n=====\nThe \"phenodata\" program is released under the AGPL license.\nThe code lives on `GitHub <https://github.com/hiveeyes/phenodata>`_ and\nthe Python package is published to `PyPI <https://pypi.org/project/phenodata/>`_.\nYou might also want to have a look at the `documentation <https://hiveeyes.org/docs/phenodata/>`_.\n\nThe software has been tested on Python 2.7.\n\nIf you'd like to contribute you're most welcome!\nSpend some time taking a look around, locate a bug, design issue or\nspelling mistake and then send us a pull request or create an issue.\n\nThanks in advance for your efforts, we really appreciate any help or feedback.\n\nCode license\n============\nLicensed under the AGPL license. See LICENSE_ file for details.\n\n.. _LICENSE: https://github.com/hiveeyes/phenodata/blob/master/LICENSE\n\nData license\n============\nThe DWD has information about their re-use policy in German and English.\nPlease refer to the respective Disclaimer\n(`de <https://www.dwd.de/DE/service/disclaimer/disclaimer_node.html>`__,\n`en <https://www.dwd.de/EN/service/disclaimer/disclaimer.html>`__)\nand Copyright\n(`de <https://www.dwd.de/DE/service/copyright/copyright_node.html>`__,\n`en <https://www.dwd.de/EN/service/copyright/copyright_artikel.html>`__)\ninformation.\n\nDisclaimer\n==========\nThe project and its authors are not affiliated with DWD, USA-NPN or any\nother data provider in any way. It is a sole project from the community\nfor making data more accessible in the spirit of open data.",
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