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setuptools-scm and one-liner setup.py (#3714)
* Replace versioneer with setuptools-scm. Replace setup.py with setup.cfg. Remove pytest-runner. * Python 3.8 eggs * Drop minimum pandas version * twine check
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@@ -5,4 +5,3 @@ omit = | |
| xarray/core/npcompat.py | ||
| xarray/core/pdcompat.py | ||
| xarray/core/pycompat.py | ||
| xarray/_version.py | ||
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| # reduce the number of merge conflicts | ||
| doc/whats-new.rst merge=union | ||
| xarray/_version.py export-subst |
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| @@ -1,110 +1,4 @@ | ||
| #!/usr/bin/env python | ||
| import sys | ||
| from setuptools import setup | ||
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| import versioneer | ||
| from setuptools import find_packages, setup | ||
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| DISTNAME = "xarray" | ||
| LICENSE = "Apache" | ||
| AUTHOR = "xarray Developers" | ||
| AUTHOR_EMAIL = "xarray@googlegroups.com" | ||
| URL = "https://github.com/pydata/xarray" | ||
| CLASSIFIERS = [ | ||
| "Development Status :: 5 - Production/Stable", | ||
| "License :: OSI Approved :: Apache Software License", | ||
| "Operating System :: OS Independent", | ||
| "Intended Audience :: Science/Research", | ||
| "Programming Language :: Python", | ||
| "Programming Language :: Python :: 3", | ||
| "Programming Language :: Python :: 3.6", | ||
| "Programming Language :: Python :: 3.7", | ||
| "Topic :: Scientific/Engineering", | ||
| ] | ||
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| PYTHON_REQUIRES = ">=3.6" | ||
| INSTALL_REQUIRES = ["numpy >= 1.14", "pandas >= 0.24"] | ||
| needs_pytest = {"pytest", "test", "ptr"}.intersection(sys.argv) | ||
| SETUP_REQUIRES = ["pytest-runner >= 4.2"] if needs_pytest else [] | ||
| TESTS_REQUIRE = ["pytest >= 2.7.1"] | ||
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| DESCRIPTION = "N-D labeled arrays and datasets in Python" | ||
| LONG_DESCRIPTION = """ | ||
| **xarray** (formerly **xray**) is an open source project and Python package | ||
| that makes working with labelled multi-dimensional arrays simple, | ||
| efficient, and fun! | ||
| Xarray introduces labels in the form of dimensions, coordinates and | ||
| attributes on top of raw NumPy_-like arrays, which allows for a more | ||
| intuitive, more concise, and less error-prone developer experience. | ||
| The package includes a large and growing library of domain-agnostic functions | ||
| for advanced analytics and visualization with these data structures. | ||
| Xarray was inspired by and borrows heavily from pandas_, the popular data | ||
| analysis package focused on labelled tabular data. | ||
| It is particularly tailored to working with netCDF_ files, which were the | ||
| source of xarray's data model, and integrates tightly with dask_ for parallel | ||
| computing. | ||
| .. _NumPy: https://www.numpy.org | ||
| .. _pandas: https://pandas.pydata.org | ||
| .. _dask: https://dask.org | ||
| .. _netCDF: https://www.unidata.ucar.edu/software/netcdf | ||
| Why xarray? | ||
| ----------- | ||
| Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called | ||
| "tensors") are an essential part of computational science. | ||
| They are encountered in a wide range of fields, including physics, astronomy, | ||
| geoscience, bioinformatics, engineering, finance, and deep learning. | ||
| In Python, NumPy_ provides the fundamental data structure and API for | ||
| working with raw ND arrays. | ||
| However, real-world datasets are usually more than just raw numbers; | ||
| they have labels which encode information about how the array values map | ||
| to locations in space, time, etc. | ||
| Xarray doesn't just keep track of labels on arrays -- it uses them to provide a | ||
| powerful and concise interface. For example: | ||
| - Apply operations over dimensions by name: ``x.sum('time')``. | ||
| - Select values by label instead of integer location: | ||
| ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. | ||
| - Mathematical operations (e.g., ``x - y``) vectorize across multiple | ||
| dimensions (array broadcasting) based on dimension names, not shape. | ||
| - Flexible split-apply-combine operations with groupby: | ||
| ``x.groupby('time.dayofyear').mean()``. | ||
| - Database like alignment based on coordinate labels that smoothly | ||
| handles missing values: ``x, y = xr.align(x, y, join='outer')``. | ||
| - Keep track of arbitrary metadata in the form of a Python dictionary: | ||
| ``x.attrs``. | ||
| Learn more | ||
| ---------- | ||
| - Documentation: http://xarray.pydata.org | ||
| - Issue tracker: http://github.com/pydata/xarray/issues | ||
| - Source code: http://github.com/pydata/xarray | ||
| - SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk | ||
| """ | ||
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| setup( | ||
| name=DISTNAME, | ||
| version=versioneer.get_version(), | ||
| cmdclass=versioneer.get_cmdclass(), | ||
| license=LICENSE, | ||
| author=AUTHOR, | ||
| author_email=AUTHOR_EMAIL, | ||
| classifiers=CLASSIFIERS, | ||
| description=DESCRIPTION, | ||
| long_description=LONG_DESCRIPTION, | ||
| python_requires=PYTHON_REQUIRES, | ||
| install_requires=INSTALL_REQUIRES, | ||
| setup_requires=SETUP_REQUIRES, | ||
| tests_require=TESTS_REQUIRE, | ||
| url=URL, | ||
| packages=find_packages(), | ||
| package_data={ | ||
| "xarray": ["py.typed", "tests/data/*", "static/css/*", "static/html/*"] | ||
| }, | ||
| ) | ||
| setup(use_scm_version=True) |
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