What’s New in Astropy 5.1?¶
Overview¶
Astropy 5.1 is a major release that adds significant new functionality since the 5.0 LTS release.
In particular, this release includes:
In addition to these major changes, Astropy v5.1 includes a large number of smaller improvements and bug fixes, which are described in the Full Changelog. By the numbers:
1077 commits have been added since 5.0
155 issues have been closed since 5.0
289 pull requests have been merged since 5.0
58 people have contributed since 5.0
13 of which are new contributors
Updates to cosmology¶
Cosmology is now an abstract base class, and subclasses must override the
abstract property is_flat. For FLRW,
is_flat checks that Ok0 == 0 and Otot0 ==
1.
Astropy v5.0 introduced cosmological equivalency – with method
is_equivalent() – where two cosmologies may
be equivalent even if not of the same class. For example, an instance of
LambdaCDM might have \(\Omega_0=1\) and \(\Omega_k=0\) and therefore
be flat, like FlatLambdaCDM. Now the keyword argument format is added to
extend the notion of equivalence to any Python object that can be converted to
a Cosmology:
>>> from astropy.cosmology import Planck18
>>> tbl = Planck18.to_format("astropy.table")
>>> Planck18.is_equivalent(tbl, format=True)
True
The list of valid formats, e.g. the Table in this example, may be
checked with Cosmology.from_format.list_formats().
A new property nonflat has been
added to flat cosmologies (FlatCosmologyMixin subclasses) to get an
equivalent Cosmology, but of the corresponding non-flat class:
>>> Planck18.nonflat
LambdaCDM(name="Planck18", H0=67.66 km / (Mpc s), ...
astropy.cosmology.FlatCosmologyMixin.clone() has been enhanced to allow
for a flat Cosmology to clone on the equivalent non-flat Cosmology. For
example:
>>> Planck18.clone(to_nonflat=True, Ode0=1)
LambdaCDM(name="Planck18 (modified)", H0=67.66 km / (Mpc s), Om0=0.30966, Ode0=1.0, ...
doppler_redshift() equivalency¶
New astropy.units.equivalencies.doppler_redshift() is added to
provide conversion between Doppler redshift and radial velocity.
Specifying data types when reading ASCII tables¶
The syntax for specifying the data type of columns when reading a table using
astropy.io.ascii.read() has been simplified considerably. For instance,
to force every column in a table to be read as a float you can now do:
>>> from astropy.table import Table
>>> t = Table.read('table.dat', format='ascii', converters={'*': float})
Previously, doing the same data type specification required using the
convert_numpy() function and providing the dict
value as a list even for only one element:
>>> from astropy.io.ascii import convert_numpy
>>> t = Table.read('table.dat', format='ascii',
... converters={'*': [convert_numpy(float)]})
Note that the previous syntax is still supported for backwards compatibility and there is no intent to remove this. See Converters for Specifying Dtype for details.
Structured Columns¶
Columns which are numpy structured arrays are now fully supported,
effectively allowing tables within tables. This applies to Column,
MaskedColumn, and Quantity columns, and includes possible structured
units. These structured data columns can be stored in ECSV, FITS, and HDF5
formats. A new way to specify the formatting of a structured column is now
available using parameter substitutions corresponding to the structure field
names. See Structured array columns for details and an example.
As part of this update, there is an API change in the behavior when a structured
numpy.ndarray is added as a column to a Table. Previously this was
converted to an NdarrayMixin subclass of numpy.ndarray and added as a mixin
column. This was because saving as a file (e.g. HDF5, FITS, ECSV) was not
supported for structured array columns. Now a structured numpy.ndarray is
added to a Table as a native Column and saving to file is supported.
New model fitters have been added¶
New fitters have been added to fitting based around
the available algorithms provided by scipy.optimize.least_squares(),
which is now the recommended least-squares optimization algorithm from
scipy. These new fitters are:
TRFLSQFitter, which uses the Trust Region Reflective (TRF) algorithm.LMLSQFitter, which uses the Levenberg-Marquardt (LM) algorithm, via thescipy.optimize.least_squares()function.DogBoxLSQFitter, which uses the dogleg algorithm.
Allow time conversions without predictive Earth rotation data (IERS-A)¶
Some time conversions like UTC -> UT1 require additional Earth rotation data for
full accuracy. These data are provided by the online IERS service as the IERS-A
tables and are downloaded as required. In some use cases this download is not
desired or possible. Examples include an application where full accuracy is not
required, running on a cluster node without internet access, or the rare
instances when the IERS server and mirror are not available. For these cases
there is a new config item iers.conf.iers_degraded_accuracy that specifies the behavior
when times are outside the range of available IERS data. The options are
'error' (default to raise an IERSRangeError),
'warn' (issue an IERSDegradedAccuracyWarning) or
'ignore' (ignore the warning).
In addition, the logic for auto-downloads was changed to guarantee that no matter what happens with the IERS download operations, only warnings will be issued. These warnings can be ignored if desired.
Uncertainty classes can be transformed into each other¶
Subclasses of astropy.nddata.NDUncertainty can now be
converted between each other. This is done via transforming the original
uncertainty values into a variance (if possible), and then transforming the
variance into the desired uncertainty. A simple example of this is:
>>> import numpy as np
>>> from astropy.nddata import InverseVariance, StdDevUncertainty
>>> StdDevUncertainty(np.arange(1, 10)).represent_as(InverseVariance)
InverseVariance([1. , 0.25 , 0.11111111, 0.0625 ,
0.04 , 0.02777778, 0.02040816, 0.015625 ,
0.01234568])
Schechter1D Model¶
A new astropy.modeling.powerlaws.Schechter1D model,
parameterized in terms of magnitudes, for luminosity functions was
added.
Full change log¶
To see a detailed list of all changes in version v5.1, including changes in API, please see the Full Changelog.
Contributors to the v5.0 release¶
The people who have contributed to the code for this release are:
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Where a * indicates that this release contains their first contribution to astropy.