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    "info": {
        "author": "John Volk and Chris Pearson",
        "author_email": "jmvolk@unr.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "gridwxcomp\n==========\n\n|Build| |Coverage| |Documentation Status| |Downloads per month| |PyPI version|\n\n-----------\n\nA package for comparing climate station time series data to `gridMET <http://www.climatologylab.org/gridmet.html>`_ (or other gridded) data. Major functionality includes tools to: \n\n* pair station locations with overlapping grid cells \n* download gridMET data from OpenDap server \n* calculate mean bias ratios between station and gridded data and other statistics \n* spatial interpolation of bias ratios with multiple interpolation options \n* build geo-referenced files of point data and interpolated rasters\n* extract zonal means for a subset of gridcells  \n* graphics, e.g. time series comparisons and interpolated residuals at stations \n\nBias ratios calculated by ``gridwxcomp`` can be used to correct bias of grid to station data based on the properties of the stations. For example, monthly humidity ratios between station and grid for stations within agricultural settings can be used to estimate grid bias relative to agricultural locations. ``gridwxcomp`` includes an intuitive command line interface and a Python API.\n\nDocumentation\n-------------\n`Online documentation <https://wswup.github.io/gridwxcomp/>`_\n\nInstallation\n------------\n\nCurrently we recommend using the provided conda environment file to install ``gridwxcomp`` and its dependencies in a virtual environment. Download the `environment.yml <https://raw.githubusercontent.com/WSWUP/gridwxcomp/master/gridwxcomp/env/environment.yml>`_ file and then install and activate it. If you don't have conda `get it here <https://conda.io/projects/conda/en/latest/user-guide/install/index.html>`_. To install dependencies in a virtual environment run \n\n.. code-block:: bash\n\n    $ conda env create -f environment.yml\n\nTo activate the environment before using ``gridwxcomp`` run\n\n.. code-block:: bash\n\n    $ conda activate gridwxcomp\n\nOptionally, install using `pip <https://pip.pypa.io/en/stable/installing/>`_,\n\n.. code-block:: bash\n\n    $ pip install gridwxcomp\n\nDue to dependency conflicts you may have issues directly installing with pip before activating the conda environment.\n\nAlternatively, or if there are installation issues, you can manually install. First activate the ``gridwxcomp`` conda environment (above). Next, clone or download the package from `GitHub <https://github.com/WSWUP/gridwxcomp>`_ or `PyPI <https://pypi.org/project/gridwxcomp/>`_ and then install locally with pip in \"editable\" mode. For example with cloning,\n\n.. code-block:: bash\n\n    $ git clone https://github.com/WSWUP/gridwxcomp.git\n    $ cd gridwxcomp\n    $ pip install -e .\n\nIf you downloaded the source distribution then run ``pip install -e .`` in the root directory where the setup.py file is located. This installation method is ideal if you want to be able to modify the source code.\n\n\nQuick start from command line\n-----------------------------\n\nThis example uses data provided with ``gridwxcomp`` including climate variable time series data for four climate stations, it uses the gridMET as the gridded dataset however any uniform gridded data product can be used with ``gridwxcomp`` if extra information including a vector grid file is provided. \n\nAfter installation you can find the location of the data needed for the example by typing the following at the command line,\n\n.. code-block:: bash\n\n    $ python -c \"import pkg_resources; print(pkg_resources.resource_filename('gridwxcomp', 'example_data/Station_Data.txt'))\"\n\nOnce complete, this example will calculate bias ratios between station and gridMET ETr (reference evapotranspiration), spatially interpolate GeoTIFF rasters of bias ratios at 400 meter resolution, and calculate zonal statistics of mean bias ratios for each gridMET cell in the region of the stations, similar to what is shown in the figure below.\n\n.. image:: https://raw.githubusercontent.com/WSWUP/gridwxcomp/master/docs/source/_static/test_case.png\n   :align: center\n\nThe same procedure can be done for climate variables other than ETr, e.g. observed evapotranspiration, temperature, precipitation, wind speed, short wave radiation, etc.\n\nAfter installing with pip the ``gridwxcomp`` command line interface can be used from any directory, the first step pairs climate station data with their nearest gridMET cell and produces a CSV file used in the following steps,\n\n.. code-block:: bash\n\n    $ gridwxcomp prep-input <PATH_TO example_data/Station_Data.txt>  \n\nThis will result in the file \"merged_input.csv\". Next download matching gridMET climate time series with `OpeNDAP <https://www.opendap.org>`_ by running\n\n.. code-block:: bash\n\n    $ gridwxcomp download-gridmet-opendap merged_input.csv -y 2016-2017\n\nThe time series of gridMET data that correpond with the stations in \"merged_input.csv\" will be saved to a new folder called \"gridmet_data\" by default. In this case only the years 2016-2017 are used. \n\nNext, to calculate mean monthly and annual bias ratios for each station/gridMET pair along with other statistics and metadata and save to CSV files, \n\n.. code-block:: bash\n\n    $ gridwxcomp calc-bias-ratios merged_input.csv -o monthly_ratios \n\nLast, to calculate interpolated surfaces of mean bias ratios and extract zonal means to gridMET cells using the default interpolation method (inverse distance weighting):\n\n.. code-block:: bash\n\n    $ gridwxcomp spatial monthly_ratios/etr_mm_summary_comp_all_yrs.csv -b 5\n\nThe ``[-b 5]`` option indicates that we want to expand the rectangular bounding area for interpolation by five gridMET cells (extrapolation in the outer regions).\n\nGeoTIFF rasters of interpolated ratios will be saved to \"monthly_ratios/spatial/etr_mm_invdist_400m/\". Note, the gridMET variable name (etr_mm), the interpolation method (invdist), and the raster resolution (400m) are specified in the output directory. A fishnet grid with gridMET id values and a point shapefile of station ratios should all be created and saved in the \"monthly_ratios/spatial/\" directory.\n\nThe output file \"monthly_ratios/spatial/etr_mm_invdist_400m/gridMET_stats.csv\" contains monthly bias ratios for each gridMET cell in the interpolation region, similar to what is shown below. \n\n    ========== ======== ======== ======== ===\n    GRIDMET_ID Jan_mean Feb_mean Mar_mean ...\n    ========== ======== ======== ======== ===\n    515902     0.66     0.76     0.96     ...\n    514516     0.66     0.77     0.96     ...\n    513130     0.67     0.77     0.97     ...\n    511744     0.67     0.78     0.97     ...\n    510358     0.68     0.79     0.97     ...\n    ...        ...      ...      ...      ...\n    ========== ======== ======== ======== ===\n\nNote ``GRIDMET_ID`` is the index of the master gridMET dataset 4 km fishnet grid starting at 0 in the upper left corner and moving across rows and down columns. This value can be joined with previously created data, e.g. the ID values can be joined to centroid coordinates of gridMET cells. \n\nBar plots that show the residual between station mean ratios and interpolated estimates are saved to \"monthly_ratios/spatial/etr_mm_invdist_400m/residual_plots/\".\n\nTo get abbreviated descriptions for any of the above ``gridwxcomp`` commands use the ``[--help]`` option, e.g.\n\n.. code-block:: bash\n\n    $ gridwxcomp spatial --help\n\n\n\n.. |Coverage| image:: https://coveralls.io/repos/github/WSWUP/gridwxcomp/badge.svg?branch=master\n   :target: https://coveralls.io/github/WSWUP/gridwxcomp?branch=master\n\n.. |Build| image:: https://travis-ci.org/WSWUP/gridwxcomp.svg?branch=master\n   :target: https://travis-ci.org/WSWUP/gridwxcomp\n\n.. |Downloads per month| image:: https://img.shields.io/pypi/dm/gridwxcomp.svg\n   :target: https://pypi.python.org/pypi/gridwxcomp/\n\n.. |Documentation Status| image:: https://img.shields.io/website-up-down-green-red/http/shields.io.svg\n   :target: https://wswup.github.io/gridwxcomp/\n\n.. |PyPI version| image:: https://img.shields.io/pypi/v/gridwxcomp.svg\n   :target: https://pypi.python.org/pypi/gridwxcomp/\n\n\n",
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        "name": "gridwxcomp",
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