|
1 | 1 | #!/usr/bin/env python |
2 | | -import sys |
| 2 | +from setuptools import setup |
3 | 3 |
|
4 | | -import versioneer |
5 | | -from setuptools import find_packages, setup |
6 | | - |
7 | | -DISTNAME = "xarray" |
8 | | -LICENSE = "Apache" |
9 | | -AUTHOR = "xarray Developers" |
10 | | -AUTHOR_EMAIL = "[email protected]" |
11 | | -URL = "https://github.com/pydata/xarray" |
12 | | -CLASSIFIERS = [ |
13 | | - "Development Status :: 5 - Production/Stable", |
14 | | - "License :: OSI Approved :: Apache Software License", |
15 | | - "Operating System :: OS Independent", |
16 | | - "Intended Audience :: Science/Research", |
17 | | - "Programming Language :: Python", |
18 | | - "Programming Language :: Python :: 3", |
19 | | - "Programming Language :: Python :: 3.6", |
20 | | - "Programming Language :: Python :: 3.7", |
21 | | - "Topic :: Scientific/Engineering", |
22 | | -] |
23 | | - |
24 | | -PYTHON_REQUIRES = ">=3.6" |
25 | | -INSTALL_REQUIRES = ["numpy >= 1.15", "pandas >= 0.25"] |
26 | | -needs_pytest = {"pytest", "test", "ptr"}.intersection(sys.argv) |
27 | | -SETUP_REQUIRES = ["pytest-runner >= 4.2"] if needs_pytest else [] |
28 | | -TESTS_REQUIRE = ["pytest >= 2.7.1"] |
29 | | - |
30 | | -DESCRIPTION = "N-D labeled arrays and datasets in Python" |
31 | | -LONG_DESCRIPTION = """ |
32 | | -**xarray** (formerly **xray**) is an open source project and Python package |
33 | | -that makes working with labelled multi-dimensional arrays simple, |
34 | | -efficient, and fun! |
35 | | -
|
36 | | -Xarray introduces labels in the form of dimensions, coordinates and |
37 | | -attributes on top of raw NumPy_-like arrays, which allows for a more |
38 | | -intuitive, more concise, and less error-prone developer experience. |
39 | | -The package includes a large and growing library of domain-agnostic functions |
40 | | -for advanced analytics and visualization with these data structures. |
41 | | -
|
42 | | -Xarray was inspired by and borrows heavily from pandas_, the popular data |
43 | | -analysis package focused on labelled tabular data. |
44 | | -It is particularly tailored to working with netCDF_ files, which were the |
45 | | -source of xarray's data model, and integrates tightly with dask_ for parallel |
46 | | -computing. |
47 | | -
|
48 | | -.. _NumPy: https://www.numpy.org |
49 | | -.. _pandas: https://pandas.pydata.org |
50 | | -.. _dask: https://dask.org |
51 | | -.. _netCDF: https://www.unidata.ucar.edu/software/netcdf |
52 | | -
|
53 | | -Why xarray? |
54 | | ------------ |
55 | | -
|
56 | | -Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called |
57 | | -"tensors") are an essential part of computational science. |
58 | | -They are encountered in a wide range of fields, including physics, astronomy, |
59 | | -geoscience, bioinformatics, engineering, finance, and deep learning. |
60 | | -In Python, NumPy_ provides the fundamental data structure and API for |
61 | | -working with raw ND arrays. |
62 | | -However, real-world datasets are usually more than just raw numbers; |
63 | | -they have labels which encode information about how the array values map |
64 | | -to locations in space, time, etc. |
65 | | -
|
66 | | -Xarray doesn't just keep track of labels on arrays -- it uses them to provide a |
67 | | -powerful and concise interface. For example: |
68 | | -
|
69 | | -- Apply operations over dimensions by name: ``x.sum('time')``. |
70 | | -- Select values by label instead of integer location: |
71 | | - ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. |
72 | | -- Mathematical operations (e.g., ``x - y``) vectorize across multiple |
73 | | - dimensions (array broadcasting) based on dimension names, not shape. |
74 | | -- Flexible split-apply-combine operations with groupby: |
75 | | - ``x.groupby('time.dayofyear').mean()``. |
76 | | -- Database like alignment based on coordinate labels that smoothly |
77 | | - handles missing values: ``x, y = xr.align(x, y, join='outer')``. |
78 | | -- Keep track of arbitrary metadata in the form of a Python dictionary: |
79 | | - ``x.attrs``. |
80 | | -
|
81 | | -Learn more |
82 | | ----------- |
83 | | -
|
84 | | -- Documentation: http://xarray.pydata.org |
85 | | -- Issue tracker: http://github.com/pydata/xarray/issues |
86 | | -- Source code: http://github.com/pydata/xarray |
87 | | -- SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk |
88 | | -""" |
89 | | - |
90 | | - |
91 | | -setup( |
92 | | - name=DISTNAME, |
93 | | - version=versioneer.get_version(), |
94 | | - cmdclass=versioneer.get_cmdclass(), |
95 | | - license=LICENSE, |
96 | | - author=AUTHOR, |
97 | | - author_email=AUTHOR_EMAIL, |
98 | | - classifiers=CLASSIFIERS, |
99 | | - description=DESCRIPTION, |
100 | | - long_description=LONG_DESCRIPTION, |
101 | | - python_requires=PYTHON_REQUIRES, |
102 | | - install_requires=INSTALL_REQUIRES, |
103 | | - setup_requires=SETUP_REQUIRES, |
104 | | - tests_require=TESTS_REQUIRE, |
105 | | - url=URL, |
106 | | - packages=find_packages(), |
107 | | - package_data={ |
108 | | - "xarray": ["py.typed", "tests/data/*", "static/css/*", "static/html/*"] |
109 | | - }, |
110 | | -) |
| 4 | +setup(use_scm_version=True) |
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