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    "info": {
        "author": "Michael Delgado",
        "author_email": "delgado.michaelt@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "======\nxshape\n======\n\n\n.. image:: https://img.shields.io/pypi/v/xshape.svg\n        :target: https://pypi.python.org/pypi/xshape\n\n.. image:: https://img.shields.io/travis/ClimateImpactLab/xshape.svg\n        :target: https://travis-ci.org/ClimateImpactLab/xshape\n\n.. image:: https://readthedocs.org/projects/xshape/badge/?version=latest\n        :target: https://xshape.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://pyup.io/repos/github/ClimateImpactLab/xshape/shield.svg\n     :target: https://pyup.io/repos/github/ClimateImpactLab/xshape/\n     :alt: Updates\n\n\nTools for working with shapefiles, topographies, and polygons in xarray\n\n\n* Free software: MIT license\n* Documentation: https://xshape.readthedocs.io.\n\n\nFeatures\n--------\n\n* Read a shapefile and obtain an xarray DataArray of field records\n* Draw shapefile boundaries on gridded data\n* Plot xarray DataArray data indexed by shapefile records as a choropleth\n\nUsage\n-----\n\nGetting records for fields in a shapefile\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    In [1]: import xshape\n\n    In [2]: fields, polygons = xshape.parse_shapefile(\n       ...:     'tests/data/shapefiles/CA_counties/CA_counties',\n       ...:     encoding='latin1')\n\n    In [3]: fields\n    Out[3]: \n    <xarray.Dataset>\n    Dimensions:   (shape: 58)\n    Coordinates:\n      * shape     (shape) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n    Data variables:\n        STATEFP   (shape) <U22 '06' '06' '06' '06' '06' '06' '06' '06' '06' '06' ...\n        COUNTYFP  (shape) <U22 '107' '009' '047' '079' '097' '041' '023' '051' ...\n        COUNTYNS  (shape) <U22 '00277318' '01675885' '00277288' '00277304' ...\n        GEOID     (shape) <U22 '06107' '06009' '06047' '06079' '06097' '06041' ...\n        NAME      (shape) <U22 'Tulare' 'Calaveras' 'Merced' 'San Luis Obispo' ...\n        NAMELSAD  (shape) <U22 'Tulare County' 'Calaveras County' ...\n        LSAD      (shape) <U22 '06' '06' '06' '06' '06' '06' '06' '06' '06' '06' ...\n        CLASSFP   (shape) <U22 'H1' 'H1' 'H1' 'H1' 'H1' 'H1' 'H1' 'H1' 'H1' 'H1' ...\n        MTFCC     (shape) <U22 'G4020' 'G4020' 'G4020' 'G4020' 'G4020' 'G4020' ...\n        CSAFP     (shape) <U22 '' '' '' '' '488' '488' '' '' '' '' '488' '472' ...\n        CBSAFP    (shape) <U22 '47300' '' '32900' '42020' '42220' '41860' ...\n        METDIVFP  (shape) <U22 '' '' '' '' '' '41884' '' '' '' '' '36084' '' '' ...\n        FUNCSTAT  (shape) <U22 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' 'A' ...\n        ALAND     (shape) <U22 '12494707314' '2641820029' '5011554680' ...\n        AWATER    (shape) <U22 '37391604' '43810423' '112760479' '820974619' ...\n        INTPTLAT  (shape) <U22 '+36.2288317' '+38.1846184' '+37.1948063' ...\n        INTPTLON  (shape) <U22 '-118.7810618' '-120.5593996' '-120.7228019' ...\n\nDrawing shape boundaries on gridded data\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    In [4]: import xarray as xr, numpy as np, xshape\n\n    # generate sample data\n    In [5]: da = xr.DataArray(\n       ...:    np.cos((\n       ...:         np.arange(41*45).reshape((41, 45)) * np.arange(41*45).reshape((45, 41)\n       ...:         ).T)/4e5),\n       ...:    dims=('lat', 'lon'),\n       ...:    coords={\n       ...:        'lon': np.linspace(-125, -114, 45),\n       ...:        'lat': np.linspace(32, 42, 41)})\n       ...:\n\n    In [6]: da.xshape.overlay(\n       ...:     'tests/data/shapefiles/CA_counties/CA_counties',\n       ...:     encoding='latin1',\n       ...:     cmap='YlGnBu');\n       ...:\n\n.. figure:: docs/images/california_map.png\n\n\nPlotting regional data in a choropleth\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nUsing the xarray extension, we can plot ``DataArray`` data directly:\n\n.. code-block:: python\n\n    In [7]: import xshape, xarray as xr, pandas as pd\n\n    In [8]: df = pd.read_csv('tests/data/datasets/co-est2016.csv', encoding='latin1')\n       ...: ca = df[(df['STATE'] == 6) & (df['COUNTY'] > 0)].copy()\n       ...: ca['fips'] = df['STATE'] * 1000 + df['COUNTY']\n       ...: da = ca.set_index(['fips'])['POPESTIMATE2016'].to_xarray()\n       ...: da.coords['GEOID'] = ('fips', ), list(map('{:05}'.format, da.fips.values))\n       ...: da = da.swap_dims({'fips': 'GEOID'})\n\n    In [9]: da.xshape.plot(\n       ...:     'tests/data/shapefiles/CA_counties/CA_counties',\n       ...:     encoding='latin1',\n       ...:     cmap='YlGnBu');\n       ...:\n\n.. figure:: docs/images/california_map_pop.png\n\nWe can also combine the information from the fields with the data contained in the DataArray:\n\n.. code-block:: python\n\n    In [10]: land_area = (\n       ....:     fields\n       ....:     .set_coords('GEOID')\n       ....:     .swap_dims({'shape': 'GEOID'})\n       ....:     .ALAND.astype(float))\n\n    In [11]: np.log(da / land_area).xshape.plot(\n       ....:     'tests/data/shapefiles/CA_counties/CA_counties',\n       ....:     encoding='latin1',\n       ....:     cmap='YlGnBu');\n       ....:\n\n.. figure:: docs/images/california_map_pop_per_m2.png\n\nTODO\n----\n\n* Use shapefiles to reshape gridded/pixel data\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-01-13)\n------------------\n\n* First release on PyPI.\n\n\n",
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