| Name | asdf JSON |
| Version |
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| home_page | None |
| Summary | Python implementation of the ASDF Standard |
| upload_time | 2025-01-31 19:39:31 |
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| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2021 Association of Universities for Research in Astronomy. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
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ASDF - Advanced Scientific Data Format
======================================
.. _begin-badges:
.. image:: https://github.com/asdf-format/asdf/workflows/CI/badge.svg
:target: https://github.com/asdf-format/asdf/actions
:alt: CI Status
.. image:: https://github.com/asdf-format/asdf/workflows/Downstream/badge.svg
:target: https://github.com/asdf-format/asdf/actions
:alt: Downstream CI Status
.. image:: https://readthedocs.org/projects/asdf/badge/?version=latest
:target: https://asdf.readthedocs.io/en/latest/
.. image:: https://codecov.io/gh/asdf-format/asdf/branch/main/graphs/badge.svg
:target: https://codecov.io/gh/asdf-format/asdf
.. _begin-zenodo:
.. image:: https://zenodo.org/badge/18112754.svg
:target: https://zenodo.org/badge/latestdoi/18112754
.. _end-zenodo:
.. image:: https://img.shields.io/pypi/l/asdf.svg
:target: https://img.shields.io/pypi/l/asdf.svg
.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white
:target: https://github.com/pre-commit/pre-commit
:alt: pre-commit
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
.. _end-badges:
.. _begin-summary-text:
The **A**\ dvanced **S**\ cientific **D**\ ata **F**\ ormat (ASDF) is a
next-generation interchange format for scientific data. This package
contains the Python implementation of the ASDF Standard. More
information on the ASDF Standard itself can be found
`here <https://asdf-standard.readthedocs.io>`__.
The ASDF format has the following features:
* A hierarchical, human-readable metadata format (implemented using `YAML
<http://yaml.org>`__)
* Numerical arrays are stored as binary data blocks which can be memory
mapped. Data blocks can optionally be compressed.
* The structure of the data can be automatically validated using schemas
(implemented using `JSON Schema <http://json-schema.org>`__)
* Native Python data types (numerical types, strings, dicts, lists) are
serialized automatically
* ASDF can be extended to serialize custom data types
.. _end-summary-text:
ASDF is under active development `on github
<https://github.com/asdf-format/asdf>`__. More information on contributing
can be found `below <#contributing>`__.
Overview
--------
This section outlines basic use cases of the ASDF package for creating
and reading ASDF files.
Creating a file
~~~~~~~~~~~~~~~
.. _begin-create-file-text:
We're going to store several `numpy` arrays and other data to an ASDF file. We
do this by creating a "tree", which is simply a `dict`, and we provide it as
input to the constructor of `AsdfFile`:
.. code:: python
import asdf
import numpy as np
# Create some data
sequence = np.arange(100)
squares = sequence**2
random = np.random.random(100)
# Store the data in an arbitrarily nested dictionary
tree = {
"foo": 42,
"name": "Monty",
"sequence": sequence,
"powers": {"squares": squares},
"random": random,
}
# Create the ASDF file object from our data tree
af = asdf.AsdfFile(tree)
# Write the data to a new file
af.write_to("example.asdf")
If we open the newly created file's metadata section, we can see some of the key features
of ASDF on display:
.. _begin-example-asdf-metadata:
.. code:: yaml
#ASDF 1.0.0
#ASDF_STANDARD 1.2.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 2.0.0}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension.BuiltinExtension
software: {name: asdf, version: 2.0.0}
foo: 42
name: Monty
powers:
squares: !core/ndarray-1.0.0
source: 1
datatype: int64
byteorder: little
shape: [100]
random: !core/ndarray-1.0.0
source: 2
datatype: float64
byteorder: little
shape: [100]
sequence: !core/ndarray-1.0.0
source: 0
datatype: int64
byteorder: little
shape: [100]
...
.. _end-example-asdf-metadata:
The metadata in the file mirrors the structure of the tree that was stored. It
is hierarchical and human-readable. Notice that metadata has been added to the
tree that was not explicitly given by the user. Notice also that the numerical
array data is not stored in the metadata tree itself. Instead, it is stored as
binary data blocks below the metadata section (not shown above).
.. _end-create-file-text:
.. _begin-compress-file:
It is possible to compress the array data when writing the file:
.. code:: python
af.write_to("compressed.asdf", all_array_compression="zlib")
The built-in compression algorithms are ``'zlib'``, and ``'bzp2'``. The
``'lz4'`` algorithm becomes available when the `lz4 <https://python-lz4.readthedocs.io/>`__ package
is installed. Other compression algorithms may be available via extensions.
.. _end-compress-file:
Reading a file
~~~~~~~~~~~~~~
.. _begin-read-file-text:
To read an existing ASDF file, we simply use the top-level `open` function of
the `asdf` package:
.. code:: python
import asdf
af = asdf.open("example.asdf")
The `open` function also works as a context handler:
.. code:: python
with asdf.open("example.asdf") as af:
...
.. warning::
The ``memmap`` argument replaces ``copy_arrays`` as of ASDF 4.0
(``memmap == not copy_arrays``).
To get a quick overview of the data stored in the file, use the top-level
`AsdfFile.info()` method:
.. code:: pycon
>>> import asdf
>>> af = asdf.open("example.asdf")
>>> af.info()
root (AsdfObject)
├─asdf_library (Software)
│ ├─author (str): The ASDF Developers
│ ├─homepage (str): http://github.com/asdf-format/asdf
│ ├─name (str): asdf
│ └─version (str): 2.8.0
├─history (dict)
│ └─extensions (list)
│ └─[0] (ExtensionMetadata)
│ ├─extension_class (str): asdf.extension.BuiltinExtension
│ └─software (Software)
│ ├─name (str): asdf
│ └─version (str): 2.8.0
├─foo (int): 42
├─name (str): Monty
├─powers (dict)
│ └─squares (NDArrayType): shape=(100,), dtype=int64
├─random (NDArrayType): shape=(100,), dtype=float64
└─sequence (NDArrayType): shape=(100,), dtype=int64
The `AsdfFile` behaves like a Python `dict`, and nodes are accessed like
any other dictionary entry:
.. code:: pycon
>>> af["name"]
'Monty'
>>> af["powers"]
{'squares': <array (unloaded) shape: [100] dtype: int64>}
Array data remains unloaded until it is explicitly accessed:
.. code:: pycon
>>> af["powers"]["squares"]
array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,
121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441,
484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024,
1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,
1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,
3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,
4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,
5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,
7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,
9801])
>>> import numpy as np
>>> expected = [x**2 for x in range(100)]
>>> np.equal(af["powers"]["squares"], expected).all()
True
By default, uncompressed data blocks are memory mapped for efficient
access. Memory mapping can be disabled by using the ``memmap``
option of `open` when reading:
.. code:: python
af = asdf.open("example.asdf", memmap=False)
.. _end-read-file-text:
For more information and for advanced usage examples, see the
`documentation <#documentation>`__.
Extending ASDF
~~~~~~~~~~~~~~
Out of the box, the ``asdf`` package automatically serializes and
deserializes native Python types. It is possible to extend ``asdf`` by
implementing custom tags that correspond to custom user types. More
information on extending ASDF can be found in the `official
documentation <http://asdf.readthedocs.io/en/latest/#extending-asdf>`__.
Installation
------------
.. _begin-pip-install-text:
Stable releases of the ASDF Python package are registered `at
PyPi <https://pypi.python.org/pypi/asdf>`__. The latest stable version
can be installed using ``pip``:
::
$ pip install asdf
.. _begin-source-install-text:
The latest development version of ASDF is available from the ``main`` branch
`on github <https://github.com/asdf-format/asdf>`__. To clone the project:
::
$ git clone https://github.com/asdf-format/asdf
To install:
::
$ cd asdf
$ pip install .
To install in `development
mode <https://packaging.python.org/tutorials/distributing-packages/#working-in-development-mode>`__::
$ pip install -e .
.. _end-source-install-text:
Testing
-------
.. _begin-testing-text:
To install the test dependencies from a source checkout of the repository:
::
$ pip install -e ".[tests]"
To run the unit tests from a source checkout of the repository:
::
$ pytest
It is also possible to run the test suite from an installed version of
the package.
::
$ pip install "asdf[tests]"
$ pytest --pyargs asdf
It is also possible to run the tests using `tox
<https://tox.readthedocs.io/en/latest/>`__.
::
$ pip install tox
To list all available environments:
::
$ tox -va
To run a specific environment:
::
$ tox -e <envname>
.. _end-testing-text:
Documentation
-------------
More detailed documentation on this software package can be found
`here <https://asdf.readthedocs.io>`__.
More information on the ASDF Standard itself can be found
`here <https://asdf-standard.readthedocs.io>`__.
There are two mailing lists for ASDF:
* `asdf-users <https://groups.google.com/forum/#!forum/asdf-users>`_
* `asdf-developers <https://groups.google.com/forum/#!forum/asdf-developers>`_
If you are looking for the **A**\ daptable **S**\ eismic **D**\ ata
**F**\ ormat, information can be found
`here <https://seismic-data.org/>`__.
License
-------
ASDF is licensed under a BSD 3-clause style license. See `LICENSE.rst <LICENSE.rst>`_
for the `licenses folder <licenses>`_ for
licenses for any included software.
Contributing
------------
We welcome feedback and contributions to the project. Contributions of
code, documentation, or general feedback are all appreciated. Please
follow the `contributing guidelines <CONTRIBUTING.rst>`__ to submit an
issue or a pull request.
We strive to provide a welcoming community to all of our users by
abiding to the `Code of Conduct <CODE_OF_CONDUCT.md>`__.
Raw data
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"description": "ASDF - Advanced Scientific Data Format\n======================================\n\n.. _begin-badges:\n\n.. image:: https://github.com/asdf-format/asdf/workflows/CI/badge.svg\n :target: https://github.com/asdf-format/asdf/actions\n :alt: CI Status\n\n.. image:: https://github.com/asdf-format/asdf/workflows/Downstream/badge.svg\n :target: https://github.com/asdf-format/asdf/actions\n :alt: Downstream CI Status\n\n.. image:: https://readthedocs.org/projects/asdf/badge/?version=latest\n :target: https://asdf.readthedocs.io/en/latest/\n\n.. image:: https://codecov.io/gh/asdf-format/asdf/branch/main/graphs/badge.svg\n :target: https://codecov.io/gh/asdf-format/asdf\n\n.. _begin-zenodo:\n\n.. image:: https://zenodo.org/badge/18112754.svg\n :target: https://zenodo.org/badge/latestdoi/18112754\n\n.. _end-zenodo:\n\n.. image:: https://img.shields.io/pypi/l/asdf.svg\n :target: https://img.shields.io/pypi/l/asdf.svg\n\n.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white\n :target: https://github.com/pre-commit/pre-commit\n :alt: pre-commit\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n :target: https://github.com/psf/black\n\n.. _end-badges:\n\n.. _begin-summary-text:\n\nThe **A**\\ dvanced **S**\\ cientific **D**\\ ata **F**\\ ormat (ASDF) is a\nnext-generation interchange format for scientific data. This package\ncontains the Python implementation of the ASDF Standard. More\ninformation on the ASDF Standard itself can be found\n`here <https://asdf-standard.readthedocs.io>`__.\n\nThe ASDF format has the following features:\n\n* A hierarchical, human-readable metadata format (implemented using `YAML\n <http://yaml.org>`__)\n* Numerical arrays are stored as binary data blocks which can be memory\n mapped. Data blocks can optionally be compressed.\n* The structure of the data can be automatically validated using schemas\n (implemented using `JSON Schema <http://json-schema.org>`__)\n* Native Python data types (numerical types, strings, dicts, lists) are\n serialized automatically\n* ASDF can be extended to serialize custom data types\n\n.. _end-summary-text:\n\nASDF is under active development `on github\n<https://github.com/asdf-format/asdf>`__. More information on contributing\ncan be found `below <#contributing>`__.\n\nOverview\n--------\n\nThis section outlines basic use cases of the ASDF package for creating\nand reading ASDF files.\n\nCreating a file\n~~~~~~~~~~~~~~~\n\n.. _begin-create-file-text:\n\nWe're going to store several `numpy` arrays and other data to an ASDF file. We\ndo this by creating a \"tree\", which is simply a `dict`, and we provide it as\ninput to the constructor of `AsdfFile`:\n\n.. code:: python\n\n import asdf\n import numpy as np\n\n # Create some data\n sequence = np.arange(100)\n squares = sequence**2\n random = np.random.random(100)\n\n # Store the data in an arbitrarily nested dictionary\n tree = {\n \"foo\": 42,\n \"name\": \"Monty\",\n \"sequence\": sequence,\n \"powers\": {\"squares\": squares},\n \"random\": random,\n }\n\n # Create the ASDF file object from our data tree\n af = asdf.AsdfFile(tree)\n\n # Write the data to a new file\n af.write_to(\"example.asdf\")\n\nIf we open the newly created file's metadata section, we can see some of the key features\nof ASDF on display:\n\n.. _begin-example-asdf-metadata:\n\n.. code:: yaml\n\n #ASDF 1.0.0\n #ASDF_STANDARD 1.2.0\n %YAML 1.1\n %TAG ! tag:stsci.edu:asdf/\n --- !core/asdf-1.1.0\n asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',\n name: asdf, version: 2.0.0}\n history:\n extensions:\n - !core/extension_metadata-1.0.0\n extension_class: asdf.extension.BuiltinExtension\n software: {name: asdf, version: 2.0.0}\n foo: 42\n name: Monty\n powers:\n squares: !core/ndarray-1.0.0\n source: 1\n datatype: int64\n byteorder: little\n shape: [100]\n random: !core/ndarray-1.0.0\n source: 2\n datatype: float64\n byteorder: little\n shape: [100]\n sequence: !core/ndarray-1.0.0\n source: 0\n datatype: int64\n byteorder: little\n shape: [100]\n ...\n\n.. _end-example-asdf-metadata:\n\nThe metadata in the file mirrors the structure of the tree that was stored. It\nis hierarchical and human-readable. Notice that metadata has been added to the\ntree that was not explicitly given by the user. Notice also that the numerical\narray data is not stored in the metadata tree itself. Instead, it is stored as\nbinary data blocks below the metadata section (not shown above).\n\n.. _end-create-file-text:\n.. _begin-compress-file:\n\nIt is possible to compress the array data when writing the file:\n\n.. code:: python\n\n af.write_to(\"compressed.asdf\", all_array_compression=\"zlib\")\n\nThe built-in compression algorithms are ``'zlib'``, and ``'bzp2'``. The\n``'lz4'`` algorithm becomes available when the `lz4 <https://python-lz4.readthedocs.io/>`__ package\nis installed. Other compression algorithms may be available via extensions.\n\n.. _end-compress-file:\n\nReading a file\n~~~~~~~~~~~~~~\n\n.. _begin-read-file-text:\n\nTo read an existing ASDF file, we simply use the top-level `open` function of\nthe `asdf` package:\n\n.. code:: python\n\n import asdf\n\n af = asdf.open(\"example.asdf\")\n\nThe `open` function also works as a context handler:\n\n.. code:: python\n\n with asdf.open(\"example.asdf\") as af:\n ...\n\n.. warning::\n The ``memmap`` argument replaces ``copy_arrays`` as of ASDF 4.0\n (``memmap == not copy_arrays``).\n\nTo get a quick overview of the data stored in the file, use the top-level\n`AsdfFile.info()` method:\n\n.. code:: pycon\n\n >>> import asdf\n >>> af = asdf.open(\"example.asdf\")\n >>> af.info()\n root (AsdfObject)\n \u251c\u2500asdf_library (Software)\n \u2502 \u251c\u2500author (str): The ASDF Developers\n \u2502 \u251c\u2500homepage (str): http://github.com/asdf-format/asdf\n \u2502 \u251c\u2500name (str): asdf\n \u2502 \u2514\u2500version (str): 2.8.0\n \u251c\u2500history (dict)\n \u2502 \u2514\u2500extensions (list)\n \u2502 \u2514\u2500[0] (ExtensionMetadata)\n \u2502 \u251c\u2500extension_class (str): asdf.extension.BuiltinExtension\n \u2502 \u2514\u2500software (Software)\n \u2502 \u251c\u2500name (str): asdf\n \u2502 \u2514\u2500version (str): 2.8.0\n \u251c\u2500foo (int): 42\n \u251c\u2500name (str): Monty\n \u251c\u2500powers (dict)\n \u2502 \u2514\u2500squares (NDArrayType): shape=(100,), dtype=int64\n \u251c\u2500random (NDArrayType): shape=(100,), dtype=float64\n \u2514\u2500sequence (NDArrayType): shape=(100,), dtype=int64\n\nThe `AsdfFile` behaves like a Python `dict`, and nodes are accessed like\nany other dictionary entry:\n\n.. code:: pycon\n\n >>> af[\"name\"]\n 'Monty'\n >>> af[\"powers\"]\n {'squares': <array (unloaded) shape: [100] dtype: int64>}\n\nArray data remains unloaded until it is explicitly accessed:\n\n.. code:: pycon\n\n >>> af[\"powers\"][\"squares\"]\n array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100,\n 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441,\n 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024,\n 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,\n 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,\n 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,\n 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,\n 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,\n 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,\n 9801])\n\n >>> import numpy as np\n >>> expected = [x**2 for x in range(100)]\n >>> np.equal(af[\"powers\"][\"squares\"], expected).all()\n True\n\nBy default, uncompressed data blocks are memory mapped for efficient\naccess. Memory mapping can be disabled by using the ``memmap``\noption of `open` when reading:\n\n.. code:: python\n\n af = asdf.open(\"example.asdf\", memmap=False)\n\n.. _end-read-file-text:\n\nFor more information and for advanced usage examples, see the\n`documentation <#documentation>`__.\n\nExtending ASDF\n~~~~~~~~~~~~~~\n\nOut of the box, the ``asdf`` package automatically serializes and\ndeserializes native Python types. It is possible to extend ``asdf`` by\nimplementing custom tags that correspond to custom user types. More\ninformation on extending ASDF can be found in the `official\ndocumentation <http://asdf.readthedocs.io/en/latest/#extending-asdf>`__.\n\nInstallation\n------------\n\n.. _begin-pip-install-text:\n\nStable releases of the ASDF Python package are registered `at\nPyPi <https://pypi.python.org/pypi/asdf>`__. The latest stable version\ncan be installed using ``pip``:\n\n::\n\n $ pip install asdf\n\n.. _begin-source-install-text:\n\nThe latest development version of ASDF is available from the ``main`` branch\n`on github <https://github.com/asdf-format/asdf>`__. To clone the project:\n\n::\n\n $ git clone https://github.com/asdf-format/asdf\n\nTo install:\n\n::\n\n $ cd asdf\n $ pip install .\n\nTo install in `development\nmode <https://packaging.python.org/tutorials/distributing-packages/#working-in-development-mode>`__::\n\n $ pip install -e .\n\n.. _end-source-install-text:\n\nTesting\n-------\n\n.. _begin-testing-text:\n\nTo install the test dependencies from a source checkout of the repository:\n\n::\n\n $ pip install -e \".[tests]\"\n\nTo run the unit tests from a source checkout of the repository:\n\n::\n\n $ pytest\n\nIt is also possible to run the test suite from an installed version of\nthe package.\n\n::\n\n $ pip install \"asdf[tests]\"\n $ pytest --pyargs asdf\n\nIt is also possible to run the tests using `tox\n<https://tox.readthedocs.io/en/latest/>`__.\n\n::\n\n $ pip install tox\n\nTo list all available environments:\n\n::\n\n $ tox -va\n\nTo run a specific environment:\n\n::\n\n $ tox -e <envname>\n\n\n.. _end-testing-text:\n\nDocumentation\n-------------\n\nMore detailed documentation on this software package can be found\n`here <https://asdf.readthedocs.io>`__.\n\nMore information on the ASDF Standard itself can be found\n`here <https://asdf-standard.readthedocs.io>`__.\n\nThere are two mailing lists for ASDF:\n\n* `asdf-users <https://groups.google.com/forum/#!forum/asdf-users>`_\n* `asdf-developers <https://groups.google.com/forum/#!forum/asdf-developers>`_\n\n If you are looking for the **A**\\ daptable **S**\\ eismic **D**\\ ata\n **F**\\ ormat, information can be found\n `here <https://seismic-data.org/>`__.\n\nLicense\n-------\n\nASDF is licensed under a BSD 3-clause style license. See `LICENSE.rst <LICENSE.rst>`_\nfor the `licenses folder <licenses>`_ for\nlicenses for any included software.\n\nContributing\n------------\n\nWe welcome feedback and contributions to the project. Contributions of\ncode, documentation, or general feedback are all appreciated. Please\nfollow the `contributing guidelines <CONTRIBUTING.rst>`__ to submit an\nissue or a pull request.\n\nWe strive to provide a welcoming community to all of our users by\nabiding to the `Code of Conduct <CODE_OF_CONDUCT.md>`__.\n",
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"license": "BSD 3-Clause License Copyright (c) 2021 Association of Universities for Research in Astronomy. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
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