Miscellaneous: HDF5, YAML, ASDF, 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¶
|
Pickle an object to a specified file. |
|
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 a Table object from an HDF5 file |
|
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
Note
This module requires PyYaml version 3.12 or later.
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¶
|
Parse the first YAML document in a stream using the AstropyLoader and produce the corresponding Python object. |
|
Parse the all YAML documents in a stream using the AstropyLoader class and produce the corresponding Python object. |
|
Serialize a Python object into a YAML stream using the AstropyDumper class. |
Classes¶
|
Custom SafeLoader that constructs astropy core objects as well as Python tuple and unicode objects. |
|
Custom SafeDumper that represents astropy core objects as well as Python tuple and unicode objects. |
Class Inheritance Diagram¶
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.
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 verison
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.