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- Miscellaneous: HDF5, YAML, ASDF, Parquet, pickle (`astropy.io.misc`)

# Miscellaneous: HDF5, YAML, ASDF, Parquet, pickle (`astropy.io.misc`)

The `astropy.io.misc` module contains miscellaneous input/output routines that do not fit elsewhere, and are often used by other `astropy` sub-packages. For example, `astropy.io.misc.hdf5` contains functions to read/write `Table` objects from/to HDF5 files, but these should not be imported directly by users. Instead, users can access this functionality via the `Table` class itself (see Unified File Read/Write Interface). Routines that are intended to be used directly by users are listed in the `astropy.io.misc` section.

## astropy.io.misc Package

This package contains miscellaneous utility functions for data input/output with astropy.

### Functions

`fnpickle`(object, fileorname[, protocol, append]) | Pickle an object to a specified file.
`fnunpickle`(fileorname[, number]) | Unpickle pickled objects from a specified file and return the contents.

## astropy.io.misc.hdf5 Module

This package contains functions for reading and writing HDF5 tables that are not meant to be used directly, but instead are available as readers/writers in `astropy.table`. See Unified File Read/Write Interface for more details.

### Functions

`read_table_hdf5`(input[, path, ...]) | Read a Table object from an HDF5 file
`write_table_hdf5`(table, output[, path, ...]) | Write a Table object to an HDF5 file

## astropy.io.misc.yaml Module

This module contains functions for serializing core astropy objects via the YAML protocol. It provides functions `dump`, `load`, and `load_all` which call the corresponding functions in PyYaml but use the `AstropyDumper` and `AstropyLoader` classes to define custom YAML tags for the following astropy classes: - `astropy.units.Unit` - `astropy.units.Quantity` - `astropy.time.Time` - `astropy.time.TimeDelta` - `astropy.coordinates.SkyCoord` - `astropy.coordinates.Angle` - `astropy.coordinates.Latitude` - `astropy.coordinates.Longitude` - `astropy.coordinates.EarthLocation` - `astropy.table.SerializedColumn`

### Example

::

      >>> from astropy.io.misc import yaml
      >>> import astropy.units as u
      >>> from astropy.time import Time
      >>> from astropy.coordinates import EarthLocation
      >>> t = Time(2457389.0, format='mjd',
      ...          location=EarthLocation(1000, 2000, 3000, unit=u.km))
      >>> td = yaml.dump(t)
      >>> print(td)
      !astropy.time.Time
      format: mjd
      in_subfmt: '*'
      jd1: 4857390.0
      jd2: -0.5
      location: !astropy.coordinates.earth.EarthLocation
        ellipsoid: WGS84
        x: !astropy.units.Quantity
          unit: &id001 !astropy.units.Unit {unit: km}
          value: 1000.0
        y: !astropy.units.Quantity
          unit: *id001
          value: 2000.0
        z: !astropy.units.Quantity
          unit: *id001
          value: 3000.0
      out_subfmt: '*'
      precision: 3
      scale: utc
      >>> ty = yaml.load(td)
      >>> ty
      <Time object: scale='utc' format='mjd' value=2457389.0>
      >>> ty.location
      <EarthLocation (1000., 2000., 3000.) km>

### Functions

`load`(stream) | Parse the first YAML document in a stream using the AstropyLoader and produce the corresponding Python object.
`load_all`(stream) | Parse the all YAML documents in a stream using the AstropyLoader class and produce the corresponding Python object.
`dump`(data[, stream]) | Serialize a Python object into a YAML stream using the AstropyDumper class.

### Classes

`AstropyLoader`(stream) | Custom SafeLoader that constructs astropy core objects as well as Python tuple and unicode objects.
`AstropyDumper`(stream[, default_style, ...]) | Custom SafeDumper that represents astropy core objects as well as Python tuple and unicode objects.

### Class Inheritance Diagram

## astropy.io.misc.parquet Module

This package contains functions for reading and writing Parquet tables that are not meant to be used directly, but instead are available as readers/writers in `astropy.table`. See Unified File Read/Write Interface for more details.

## astropy.io.misc.asdf Package

The asdf sub-package contains code that is used to serialize `astropy` types so that they can be represented and stored using the Advanced Scientific Data Format (ASDF).

If both asdf and astropy are installed, no further configuration is required in order to process ASDF files that contain astropy types. The asdf package has been designed to automatically detect the presence of the tags defined by astropy.

For convenience, users can write `Table` objects to ASDF files using the Unified File Read/Write Interface. See Using ASDF With Table I/O below.

Documentation on the ASDF Standard can be found here. Documentation on the ASDF Python module can be found here. Additional details for Astropy developers can be found in Details.

Note

`astropy.io.misc.asdf` is being replaced by the asdf-astropy package. It is recommended that you install this package if you wish to use ASDF with `astropy`. The documentation for asdf-astropy can be found asdf-astropy:asdf-astropy.

### Using ASDF With Table I/O

ASDF provides readers and writers for `Table` using the Unified File Read/Write Interface. This makes it convenient to read and write ASDF files with `Table` data.

#### Basic Usage

Given a table, it is possible to write it out to an ASDF file:

    from astropy.table import Table

    # Create a simple table
    t = Table(dtype=[('a', 'f4'), ('b', 'i4'), ('c', 'S2')])
    # Write the table to an ASDF file
    t.write('table.asdf')

The I/O registry automatically selects the appropriate writer function to use based on the `.asdf` extension of the output file.

Reading a file generated in this way is also possible using `read`:

    t2 = Table.read('table.asdf')

The I/O registry automatically selects the appropriate reader function based on the extension of the input file.

In the case of both reading and writing, if the file extension is not `.asdf` it is possible to explicitly specify the reader/writer function to be used:

    t3 = Table.read('table.zxcv', format='asdf')

#### Advanced Usage

The fundamental ASDF data structure is the tree, which is a nested combination of basic data structures (see this for a more detailed description). At the top level, the tree is a `dict`.

The consequence of this is that a `Table` object (or any object, for that matter) can be stored at any arbitrary location within an ASDF tree. The basic writer use case described above stores the given `Table` at the top of the tree using a default key. The basic reader case assumes that a `Table` is stored in the same place.

However, it may sometimes be useful for users to specify a different top-level key to be used for storage and retrieval of a `Table` from an ASDF file. For this reason, the ASDF I/O interface provides `data_key` as an optional keyword when writing and reading:

    from astropy.table import Table

    t = Table(dtype=[('a', 'f4'), ('b', 'i4'), ('c', 'S2')])
    # Write the table to an ASDF file using a non-default key
    t.write('foo.asdf', data_key='foo')

A `Table` stored using a custom data key can be retrieved by passing the same argument to `read`:

    foo = Table.read('foo.asdf', data_key='foo')

The `data_key` option only applies to `Table` objects that are stored at the top of the ASDF tree. For full generality, users may pass a callback when writing or reading ASDF files to define precisely where the `Table` object should be placed in the tree. The option for the write case is `make_tree`. The function callback should accept exactly one argument, which is the `Table` object, and should return a `dict` representing the tree to be stored:

    def make_custom_tree(table):
        # Return a nested tree where the table is stored at the second level
        return dict(foo=dict(bar=table))

    t = Table(dtype=[('a', 'f4'), ('b', 'i4'), ('c', 'S2')])
    # Write the table to an ASDF file using a non-default key
    t.write('foobar.asdf', make_tree=make_custom_tree)

Similarly, when reading an ASDF file, the user can pass a custom callback to locate the table within the ASDF tree. The option in this case is `find_table`. The callback should accept exactly one argument, which is an `dict` representing the ASDF tree, and it should return a `Table` object:

    def find_table(tree):
        # This returns the Table that was stored by the example above
        return tree['foo']['bar']

    foo = Table.read('foobar.asdf', find_table=find_table)

### Details

The asdf sub-package defines classes, referred to as tags, that implement the logic for serialization and deserialization of `astropy` types. Users should never need to refer to tag implementations directly. Their presence should be entirely transparent when processing ASDF files.

ASDF makes use of abstract data type definitions called schemas. The tag classes provided here are specific implementations of particular schemas. Some of the tags in `astropy` (e.g., those related to transforms) implement schemas that are defined by the ASDF Standard. In other cases, both the tags and schemas are defined within `astropy` (e.g., those related to many of the coordinate frames). Documentation of the individual schemas defined by `astropy` can be found below in the Schemas section.

Not all `astropy` types are currently serializable by ASDF. Attempting to write unsupported types to an ASDF file will lead to a `RepresenterError`. In order to support new types, new tags and schemas must be created. See Writing ASDF Extensions for additional details, as well as the following example.

### Example: Adding a New Object to the Astropy ASDF Extension

In this example, we will show how to implement serialization for a new `Model` object, but the basic principles apply to serialization of other `astropy` objects. As mentioned, adding a new object to the `astropy` ASDF extension requires both a tag and a schema.

All schemas for transforms are currently defined within the ASDF standard. Any new serializable transforms must have a corresponding new schema here. Let’s consider a new model called `MyModel`, a new model in `astropy.modeling.functional_models` that has two parameters `amplitude` and `x_0`. We would like to strictly require both of these parameters be set. We would also like to specify that these parameters can either be numeric type, or `astropy.units.quantity` type. A schema describing this model would look like:

    %YAML 1.1
    ---
    $schema: "http://stsci.edu/schemas/yaml-schema/draft-01"
    id: "http://stsci.edu/schemas/asdf/transform/mymodel-1.0.0"
    tag: "tag:stsci.edu:asdf/transform/mymodel-1.0.0"
    title: >
      Example new model.

    description: >
      Example new model, which describes the distribution of ABC.

    allOf:
      - $ref: "transform-1.2.0"
      - type: object
        properties:
          amplitude:
            anyOf:
              - $ref: "../unit/quantity-1.1.0"
              - type: number
            description: Amplitude of distribution.
          x_0:
            anyOf:
              - $ref: "../unit/quantity-1.1.0"
              - type: number
            description: X center position.

        required: ['amplitude', 'x_0]
    ...

All new transform schemas reference the base transform schema of the latest type. This schema describes the other model attributes that are common to all or many models, so that individual schemas only handle the parameters specific to that model. Additionally, this schema references the latest version of the `quantity` schema, so that models can retain information about units and quantities. References allow previously defined objects to be used inside new custom types.

The next component is the tag class. This class must have a `to_tree` method in which the required attributes of the object in question are obtained, and a `from_tree` method which reconstructs the object based on the parameters written to the ASDF file. `astropy` Models inherit from the `TransformType` base class tag, which takes care of attributes (e.g `name`, `bounding_box`, `n_inputs`) that are common to all or many Model classes to limit redundancy in individual tags. Each individual model tag then only has to obtain and set model-specific parameters:

    from .basic import TransformType
    from . import _parameter_to_value

    class MyModelType(TransformType):
    name = 'transform/mymodel'
    version = '1.0.0'
    types = ['astropy.modeling.functional_models.MyModel']

    @classmethod
    def from_tree_transform(cls, node, ctx):
        return functional_models.MyModel(amplitude=node['amplitude'],
                                         x_0=node['x_0'])

    @classmethod
    def to_tree_transform(cls, model, ctx):
        node = {'amplitude': _parameter_to_value(amplitude),
                'x_0': _parameter_to_value(x_0)}
        return node

This tag class contains all the machinery to deconstruct objects to and reconstruct them from ASDF files. The tag class - by convention named by the object name appended with ‘Type’ - references the schema and version, and the object in `astropy.modeling.functional_models`. The basic model parameters are handled in the `to_tree_transform` and `from_tree_transform` of the base `TransformType` class, while model-specific parameters are handled here in `MyModelType`. Since this model can take units and quantities with input parameters, the imported `_parameter_to_value` allows this to flexibly work with both basic numeric values as well as quantities.

### Schemas

Documentation for each of the individual ASDF schemas defined by `astropy` can be found below.

- Schemas
  - Coordinates
  - FITS
  - Table
  - Time
  - Units