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    "info": {
        "author": "Johannes Hansen",
        "author_email": "johannes.hansen@ed.ac.uk",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "[![Build Status](https://travis-ci.com/jnhansen/nd.svg?branch=master)](https://travis-ci.com/jnhansen/nd)\n[![codecov](https://codecov.io/gh/jnhansen/nd/branch/master/graph/badge.svg)](https://codecov.io/gh/jnhansen/nd)\n[![Documentation](https://readthedocs.org/projects/nd/badge/?version=latest)](https://nd.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/nd.svg)](https://badge.fury.io/py/nd)\n\n\n# nd\n\nThis package contains a selection of tools to handle and analyze satellite data.\n``nd`` is making heavy use of the ``xarray`` and ``rasterio`` libraries.\nThe GDAL library is only used via ``rasterio`` as a compatibility layer to enable reading supported file formats.\nInternally, all data is passed around as ``xarray`` Datasets and all provided methods expect this format as inputs.\n`nd.open_dataset` may be used to read any NetCDF file or any GDAL-readable file into an ``xarray.Dataset``.\n\nAn ``xarray.Dataset`` is essentially a Python representation of the NetCDF file format and as such easily reads/writes NetCDF files.\n\n\n## What does this library add?\n\n``xarray`` provides all data structures required for dealing with `n`-dimensional data in Python. ``nd`` explicitly does not aim to add additional data structures or file formats.\nRather, the aim is to bring the various corners of the scientific ecosystem in Python closer together.\n\nAs such, ``nd`` adds functionality to more seamlessly integrate libraries like ``xarray``, ``rasterio``, ``scikit-learn``, etc.\n\nFor example:\n\n * ``nd`` allows to reproject an entire multivariate and multi-temporal dataset between different coordinate systems by wrapping ``rasterio`` methods.\n\n * ``nd`` provides a wrapper for ``scikit-learn`` estimators to easily apply classification algorithms to raster data [in progress].\n\nAdditionally, ``nd`` contains a growing library of algorithms that are especially useful for spatio-temporal datacubes, for example:\n\n * change detection algorithms\n\n * spatio-temporal filters\n\nSince ``xarray`` is our library of choice for representing geospatial raster data, this is also an attempt to promote the use of ``xarray`` and the NetCDF file format in the Earth Observation community.\n\n\n## Why NetCDF?\n\nNetCDF (specifically NetCDF-4) is a highly efficient file format that was built on top of HDF5. It is capable of random access which ties in with indexing and slicing in ``numpy``.\nBecause slices of a large dataset can be accessed independently, it becomes feasible to handle larger-than-memory file sizes. NetCDF-4 also supports data compression using ``zlib``. Random access capability for compressed data is maintained through data chunking.\nFurthermore, NetCDF is designed to be fully self-descriptive. Crucially, it has a concept of named dimensions and coordinates, can store units and arbitrary metadata.",
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        "license": "MIT",
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