Cosmology For Developers¶
Cosmologies in Functions¶
It is often useful to assume a default cosmology so that the exact cosmology does not have to be specified every time a function or method is called. In this case, it is possible to specify a “default” cosmology.
You can set the default cosmology to a predefined value by using the
“default_cosmology” option in the
[cosmology.core] section of the
configuration file (see Configuration System (astropy.config)). Alternatively, you can use the
set() function of
default_cosmology to set a cosmology for the current Python session. If you
have not set a default cosmology using one of the methods described above, then
the cosmology module will default to using the
You can override the default cosmology through the
state object, using something like the following:
from astropy.cosmology import default_cosmology def myfunc(..., cosmo=None): if cosmo is None: cosmo = default_cosmology.get() ... function code here ...
This ensures that all code consistently uses the default cosmology unless explicitly overridden.
If you are preparing a paper and thus need to ensure your code provides
reproducible results, it is better to use an explicit cosmology (for
WMAP9.H(0) instead of
Use of the default cosmology should generally be reserved for code that
allows for the global cosmology state to be changed; e.g. code included in
astropy core or an affiliated package.
astropy.cosmology cosmologies are classes, so custom cosmologies may
be implemented by subclassing
Cosmology (or more likely
FLRW) and adding
details specific to that cosmology. Here we will review some of those details
and tips and tricks to building a performant cosmology class.
from astropy.cosmology import FLRW class CustomCosmology(FLRW): ... # [details discussed below]
Cosmology is characterized by 1) its class, which encodes the
physics, and 2) its free parameter(s), which specify a cosmological realization.
When defining the former, all parameters must be declared using
should have values assigned at instantiation.
Parameter is a descriptor.
When accessed from a class it transparently stores information, like the units
and accepted equivalencies, that might be opaquely contained in the constructor
signature or more deeply in the code. On a cosmology instance, the descriptor
will return the parameter value.
There are a number of best practices. For a reference, this is excerpted from
the definition of
class FLRW(Cosmology): H0 = Parameter(doc="Hubble constant as an `~astropy.units.Quantity` at z=0", unit="km/(s Mpc)", fvalidate="scalar") Om0 = Parameter(doc="Omega matter; matter density/critical density at z=0", fvalidate="non-negative") Ode0 = Parameter(doc="Omega dark energy; dark energy density/critical density at z=0.", fvalidate="float") Tcmb0 = Parameter(doc="Temperature of the CMB as `~astropy.units.Quantity` at z=0.", unit="Kelvin", fmt="0.4g", fvalidate="scalar") Neff = Parameter(doc="Number of effective neutrino species.", fvalidate="non-negative") m_nu = Parameter(doc="Mass of neutrino species.", unit="eV", equivalencies=u.mass_energy(), fmt="") Ob0 = Parameter(doc="Omega baryon; baryonic matter density/critical density at z=0.") def __init__(self, H0, Om0, Ode0, Tcmb0=0.0*u.K, Neff=3.04, m_nu=0.0*u.eV, Ob0=None, *, name=None, meta=None): self.H0 = H0 ... # for each Parameter in turn @Ob0.validator def Ob0(self, param, value): """Validate baryon density to None or positive float > matter density.""" if value is None: return value value = _validate_non_negative(self, param, value) if value > self.Om0: raise ValueError("baryonic density can not be larger than total matter density.") return value
First note that all the parameters are also arguments in
__init__. This is
not strictly necessary, but is good practice. If the parameter has units (and
related equivalencies) these must be specified on the Parameter, as seen in
The next important thing to note is how the parameter value is set, in
Parameter allows for a value to be set once (before
self.H0 = H0 will use this setter and put the value on
“._H0”. The advantage of this method over direct assignment to the private
attribute is the use of validators.
Parameter allows for custom value
validators, using the method-decorator
validator, that can check a value’s
validity and modify the value, e.g to assign units. If no custom
is specified the default is to check if the
Parameter has defined units and
if so, return the value as a
Quantity with those units, using all enabled and
the parameter’s unit equivalencies.
If a new cosmology modifies an existing Parameter, then the
clone() method is useful to deep-copy the
parameter and change any constructor argument. For example, see
astropy.cosmology.flrw (also shown below).
class FlatFLRWMixin(FlatCosmologyMixin): ... Ode0 = FLRW.Ode0.clone(derived=True) # now a derived param.
Mixins are used in
cosmology to reuse code across multiple classes in different
inheritance lines. We use the term loosely as mixins are meant to be strictly
orthogonal, but may not be, particularly in
Currently the only mixin is
FlatCosmologyMixin and its
FlatFLRWMixin. “Flat” cosmologies should use this mixin.
FlatFLRWMixin must precede the base class in the multiple-inheritance so that
__init__ proceeds the base class’.
Speeding up Integrals in Custom Cosmologies¶
The supplied cosmology classes use a few tricks to speed up distance and time
integrals. It is not necessary for anyone subclassing
FLRW to use these
tricks – but if they do, such calculations may be a lot faster.
The first, more basic, idea is that, in many cases, it’s a big deal to provide
explicit formulae for
inv_efunc() rather than
simply setting up
de_energy_scale – assuming there is a nice expression.
As noted above, almost all of the provided classes do this, and that template
can pretty much be followed directly with the appropriate formula changes.
The second, and more advanced, option is to also explicitly provide a scalar
only version of
inv_efunc(). This results in a
fairly large speedup (>10x in most cases) in the distance and age integrals,
even if only done in python, because testing whether the inputs are iterable or
pure scalars turns out to be rather expensive. To take advantage of this, the
key thing is to explicitly set the instance variables
self._inv_efunc_scalar_args in the
constructor for the subclass, where the latter are all the arguments except
_inv_efunc_scalar. The provided classes do use this optimization,
and in fact go even further and provide optimizations for no radiation, and for
radiation with massless neutrinos coded in cython. Consult the
scalar_inv_efuncs for the details.
However, the important point is that it is not necessary to do this.
Astropy Interoperability: I/O and your Cosmology Package¶
If you are developing a package and want to be able to interoperate with
Cosmology, you’re in the right place! Here we will discuss how to enable
Astropy to read and write your file formats, and convert your cosmology objects
to and from Astropy’s
The following presumes knowledge of how Astropy structures I/O functions. For a quick tutorial see Read, Write, and Convert Cosmology Objects.
Consider a package – since this is mine, it’s cleverly named
with the following file structure: a module for cosmology codes and a module
for defining related input/output functions. In the cosmology module are
defined cosmology classes and a file format –
myformat – and everything
should interoperate with astropy. The tests are done with
pytest and are
integrated within the code structure.
mypackage/ __init__.py cosmology/ __init__.py ... io/ __init__.py astropy_convert.py astropy_io.py ... tests/ __init__.py test_astropy_convert.py test_astropy_io.py ...
Converting Objects Between Packages¶
We want to enable conversion between cosmology objects from
Cosmology. All the Astropy interface code is defined in
mypackage/io/astropy_convert.py. The following is a rough outline of the
necessary functions and how to register them with astropy’s unified I/O to be
automatically available to
Reading and Writing¶
Everything Astropy read/write related is defined in
mypackage/io/astropy_io.py. The following is a rough outline of the read,
write, and identify functions and how to register them with astropy’s unified
IO to be automatically available to
If Astropy is an optional dependency¶
astropy_convert modules are written assuming Astropy
is installed. If in
mypackage it is an optional dependency then it is
important to detect if Astropy is installed (and the correct version) before
We do this in
Astropy Interoperability Tests¶
Lastly, it’s important to test that everything works. In this example package
all such tests are contained in
These tests require Astropy and will be skipped if it is not installed (and
not the correct version), so at least one test in the test matrix should
astropy >= 5.0.