Metadata-Version: 1.2
Name: ldcpy
Version: 0.14.3
Summary: A library for lossy compression of netCDF files using xarray
Home-page: https://ldcpy.readthedocs.io
Maintainer: Alex Pinard
Maintainer-email: apinard@mines.edu
License: Apache 2.0
Project-URL: Documentation, https://ldcpy.readthedocs.io
Project-URL: Source, https://github.com/NCAR/ldcpy
Project-URL: Tracker, https://github.com/NCAR/ldcpy/issues
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            :target: https://anaconda.org/conda-forge/ldcpy
            :alt: Conda Version
        
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           :target: https://zenodo.org/badge/latestdoi/215409079
        
        Large Data Comparison for Python
        =================================
        
        ldcpy is a utility for gathering and plotting metrics from NetCDF or Zarr files using the Pangeo stack.
        It also contains a number of statistical and visual tools for gathering metrics and comparing Earth System Model data files.
        
        
        :AUTHORS: Alex Pinard, Allison Baker, Anderson Banihirwe, Dorit Hammerling
        :COPYRIGHT: 2020 University Corporation for Atmospheric Research
        :LICENSE: Apache 2.0
        
        Documentation and usage examples are available `here <http://ldcpy.readthedocs.io>`_.
        
        
        Installation using Conda (recommended)
        ______________________________________
        
        Ensure conda is up to date and create a clean Python (3.6+) environment:
        
        .. code-block:: bash
        
            conda update conda
            conda create --name ldcpy python=3.8
            conda activate ldcpy
        
        Now install ldcpy:
        
        .. code-block:: bash
        
            conda install -c conda-forge ldcpy
        
        Alternative Installation
        ________________________
        
        Ensure pip is up to date, and your version of python is at least 3.6:
        
        .. code-block:: bash
        
            pip install --upgrade pip
            python --version
        
        Install cartopy using the instructions provided at https://scitools.org.uk/cartopy/docs/latest/installing.html.
        
        Then install ldcpy:
        
        .. code-block:: bash
        
            pip install ldcpy
        
        Accessing the tutorial
        ______________________
        
        If you want access to the tutorial notebook, clone the repository (this will create a local repository in the current directory):
        
        .. code-block:: bash
        
            git clone https://github.com/NCAR/ldcpy.git
        
        Start by enabling Hinterland for code completion and code hinting in Jupyter Notebook and then opening the tutorial notebook:
        
        .. code-block:: bash
        
            jupyter nbextension enable hinterland/hinterland
            jupyter notebook
        
        
        The tutorial notebook can be found in
        docs/source/notebooks/TutorialNotebook.ipynb, feel free to gather your
        own metrics or create your own plots in this notebook!
        
        Other example notebooks that use the sample data in this repository include
        PopData.ipynb and MetricsNotebook.ipynb.
        
        The AWSDataNotebook grabs data from AWS, so can be run on a laptop
        with the caveat that the files are large.
        
        The following notebooks asume that you are using NCAR's JupyterHub
        (https://jupyterhub.ucar.edu):
        LargeDataGladenotebook.ipynb, CompressionSamples.ipynb, and error_bias.ipynb
        
        
        Re-create notebooks with Pangeo Binder
        ____________________________________________
        Try the notebooks hosted in this repo on Pangeo Binder. Note that the session is ephemeral.
        Your home directory will not persist, so remember to download your notebooks if you
        make changes that you need to use at a later time!
        
        Note: All example notebooks are in docs/source/notebooks (the easiest
        ones to use in binder first are TutorialNotebook.ipynb and PopData.ipynb)
        
        
        .. image:: https://img.shields.io/static/v1.svg?logo=Jupyter&label=Pangeo+Binder&message=GCP+us-central1&color=blue&style=for-the-badge
            :target: https://binder.pangeo.io/v2/gh/NCAR/ldcpy/main?urlpath=lab
            :alt: Binder
        
Keywords: compression,xarray
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
