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Metadata-Version: 2.1
Name: asdf
Version: 2.7.2
Summary: Python tools to handle ASDF files
Home-page: http://github.com/asdf-format/asdf
Author: The ASDF Developers
Author-email: help@stsci.edu
License: BSD-3-Clause
Project-URL: Bug Tracker, https://github.com/asdf-format/asdf/issues
Project-URL: Documentation, https://asdf.readthedocs.io/en/stable
Project-URL: Source Code, https://github.com/asdf-format/asdf
Description: ASDF - Advanced Scientific Data Format
        ======================================
        
        .. _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/spacetelescope/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, we can see some of the key features
        of ASDF on display:
        
        ::
        
            #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: Space Telescope Science Institute, homepage: 'http://github.com/spacetelescope/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]
            ...
        
        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 here).
        
        It is possible to compress the array data when writing the file:
        
        .. code:: python
        
            af.write_to('compressed.asdf', all_array_compression='zlib')
        
        Available compression algorithms are ``'zlib'``, ``'bzp2'``, and
        ``'lz4'``.
        
        .. _end-create-file-text:
        
        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:
                ...
        
        To access the data stored in the file, use the top-level `AsdfFile.tree`
        attribute:
        
        .. code:: python
        
            >>> import asdf
            >>> af = asdf.open('example.asdf')
            >>> af.tree
            {'asdf_library': {'author': 'Space Telescope Science Institute',
              'homepage': 'http://github.com/spacetelescope/asdf',
              'name': 'asdf',
              'version': '1.3.1'},
             'foo': 42,
             'name': 'Monty',
             'powers': {'squares': <array (unloaded) shape: [100] dtype: int64>},
             'random': <array (unloaded) shape: [100] dtype: float64>,
             'sequence': <array (unloaded) shape: [100] dtype: int64>}
        
        The tree is simply a Python `dict`, and nodes are accessed like any other
        dictionary entry:
        
        .. code:: python
        
            >>> af.tree['name']
            'Monty'
            >>> af.tree['powers']
            {'squares': <array (unloaded) shape: [100] dtype: int64>}
        
        Array data remains unloaded until it is explicitly accessed:
        
        .. code:: python
        
            >>> af.tree['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.tree['powers']['squares'], expected).all()
            True
        
        By default, uncompressed data blocks are memory mapped for efficient
        access. Memory mapping can be disabled by using the ``copy_arrays``
        option of `open` when reading:
        
        .. code:: python
        
            af = asdf.open('example.asdf', copy_arrays=True)
        
        .. _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 tag types that correspond to custom user types. More
        information on extending ASDF can be found in the `official
        documentation <http://asdf.readthedocs.io/en/latest/asdf/extensions.html>`__.
        
        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 ``master`` branch
        `on github <https://github.com/spacetelescope/asdf>`__. To clone the project:
        
        ::
        
            $ git clone https://github.com/spacetelescope/asdf
        
        To install:
        
        ::
        
            $ cd asdf
            $ git submodule update --init
            $ pip install .
        
        To install in `development
        mode <https://packaging.python.org/tutorials/distributing-packages/#working-in-development-mode>`__::
        
            $ pip install -e .
        
        .. note::
        
            The source repository makes use of a git submodule for referencing the
            schemas provided by the ASDF standard. While this submodule is
            automatically initialized when installing the package (including in
            development mode), it may be necessary for developers to manually update
            the submodule if changes are made upstream. See the `documentation on git
            submodules <https://git-scm.com/docs/git-submodule>`__ for more
            information.
        
        .. _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. In a Python interpreter:
        
        .. code:: python
        
            import asdf
            asdf.test()
        
        Please note that the `astropy <https://github.com/astropy/astropy>`__
        package must be installed to run the tests.
        
        It is also possible to run the tests using `tox
        <https://tox.readthedocs.io/en/latest/>`__. It is first necessary to install
        ``tox`` and `tox-conda <https://github.com/tox-dev/tox-conda>`__:
        
        ::
        
           $ pip install tox tox-conda
        
        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/>`__.
        
        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.md>`__ 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>`__.
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
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: Development Status :: 5 - Production/Stable
Requires-Python: >=3.5
Description-Content-Type: text/x-rst
Provides-Extra: docs
Provides-Extra: all
Provides-Extra: tests