Transfer¶

class hmf.density_field.transfer.Transfer(*args, **kwargs)[source]¶

A transfer function framework.

The purpose of this hmf._frameworks.Framework is to calculate transfer functions, power spectra and several tightly associated quantities given a basic model for the transfer function.

As in all frameworks, to update parameters optimally, use the update() method. All output quantities are calculated only when needed (but stored after first calculation for quick access).

In addition to the parameters directly passed to this class, others are available which are passed on to its superclass. To read a standard documented list of (all) parameters, use Transfer.parameter_info(). If you want to just see the plain list of available parameters, use Transfer.get_all_parameters().To see the actual defaults for each parameter, use Transfer.get_all_parameter_defaults().

By default, the growth_model is GrowthFactor. However, if using a wCDM cosmology and camb is installed, it will default to CambGrowth.

Attributes

cosmo¶

The cosmography object with custom parameters applied.

A astropy.cosmology.FLRW instance with custom cosmology from cosmo_params applied.

cosmo_model¶

The cosmological model instance.

Basis for the cosmology with custom subclasses supported. Defaults to Planck18. See astropy documentation for details.

Type:: instance of astropy.cosmology.FLRW subclass
Type:: Parameter

cosmo_params¶

Parameters deviating from the base cosmology.

Useful for repeated updates of a single parameter, leaving others the same. Default is the empty dict. Parameters passed must match the allowed parameters of cosmo_model. For the basic class, these are:

Tcmb0:: Temperature of the CMB at z=0
Neff:: Number of massless neutrino species
M_nu:: Mass of neutrino species (list)
H0:: The hubble constant at z=0
Om0:: The normalised matter density at z=0
Type:: dict
Type:: Parameter

delta_k¶: Dimensionless power spectrum, \(\Delta_k = \frac{k^3 P(k)}{2\pi^2}\).

dlnk¶

Step-size of log wave-numbers.

Type:: float
Type:: Parameter

growth¶: The instantiated growth model.

growth_factor¶: The growth factor.

growth_model¶

The model to use to calculate the growth function/growth rate.

"ClassGrowth" needs the optional classy package (pip install hmf[class]).

Type:: hmf.growth_factor.BaseGrowthFactor subclass
Type:: Parameter

growth_params¶

Relevant parameters of the growth_model.

Type:: dict
Type:: Parameter

k¶: Wavenumbers, [h/Mpc].

lnk_max¶

Maximum (natural) log wave-number, k [h/Mpc].

Type:: float
Type:: Parameter

lnk_min¶

Minimum (natural) log wave-number, k [h/Mpc].

Type:: float
Type:: Parameter

mean_density0¶: Mean density of universe at z=0, [Msun h^2 / Mpc**3].

n¶

Spectral index of fluctuations.

Must be greater than -3 and less than 4.

Type:: float
Type:: Parameter

nonlinear_delta_k¶

Dimensionless nonlinear power spectrum.

\[\Delta_k = \frac{k^3 P_{\rm nl}(k)}{2\pi^2}\]

Non-linear corrections come from HALOFIT.

If you use this, please cite HALOFIT: Smith et al. [1], and also Takahashi et al. [2] when takahashi is True (the default).

References

[1]

Smith, R. E., et al., 2003. MNRAS 341, 1311. arXiv:astro-ph/0207664

[2]

Takahashi, R., et al., 2012. ApJ 761, 152. arXiv:1208.2701

nonlinear_power¶

Non-linear log power [units \(Mpc^3/h^3\)].

Non-linear corrections come from HALOFIT.

If you use this, please cite HALOFIT: Smith et al. [1], and also Takahashi et al. [2] when takahashi is True (the default).

References

[1]

Smith, R. E., et al., 2003. MNRAS 341, 1311. arXiv:astro-ph/0207664

[2]

Takahashi, R., et al., 2012. ApJ 761, 152. arXiv:1208.2701

parameter_values¶: Dictionary of all parameters and their current values.

power¶: Normalised log power spectrum [units \(Mpc^3/h^3\)].

references: ClassVar[tuple[str, ...]] = ()¶: References for the framework itself (not its component models), each a formatted citation string. References of parent framework classes are included too.

sigma_8¶

RMS linear density fluctuations in spheres of radius 8 Mpc/h.

Type:: float
Type:: Parameter

sigma_8_species¶

"tot", "cb" or None.

The default, "tot", means sigma_8 is the r.m.s. of the total matter field (including massive neutrinos), which is how experiments such as Planck quote it, and how the default sigma_8 is defined. When the transfer model computes the CDM+baryon field (matter_species="cb", the default), its power spectrum is normalised so that the total matter field from the same model has r.m.s. sigma_8. "cb" means sigma_8 is that of the CDM+baryon field, and None means whichever field the transfer model computes.

This has no effect with massless neutrinos (the fields are then identical) or for transfer models that don’t distinguish the two fields (i.e. have no matter_species parameter, like EH or BBKS). Otherwise, normalising a field other than the computed one needs that field’s transfer function too. With CAMB or CLASS this reuses the same run, which gives both fields. See Massive neutrinos: which matter field to use.

Type:: str or None
Type:: Parameter
Type:: Which matter field sigma_8 describes

takahashi¶

Whether to use updated HALOFIT coefficients from Takahashi+12.

If False, use the original coefficients from Smith+2003.

Type:: bool
Type:: Parameter

transfer¶: The instantiated transfer model.

transfer_function¶: Normalised CDM log transfer function.

transfer_model¶

Defines which transfer function model to use.

Built-in available models are found in the hmf.transfer_models module. Default is CAMB if installed, otherwise EH. "CLASS" needs the optional classy package (pip install hmf[class]).

Type:: str or hmf.transfer_models.TransferComponent subclass, optional
Type:: Parameter

transfer_params¶

Relevant parameters of the transfer_model.

Type:: dict
Type:: Parameter

z¶

Redshift.

Must be non-negative.

Type:: float
Type:: Parameter

Methods

clone(**kwargs)¶: Create and return an updated clone of the current object.

get_acknowledgments(flat: bool = False) → dict[str, tuple[str, ...]] | list[str]¶

Get the references to cite for the current setup of the framework.

The references are grouped by where they come from, in this order:

"hmf": the paper describing hmf itself.
The framework’s class name (e.g. "MassFunction"): the framework’s own references, including those of its parent framework classes. Only present if there are any.
Each *_model parameter (e.g. "hmf_model", "transfer_model"): the references of the model currently set on it. A model with nothing to cite gives an empty tuple; a parameter set to None is left out.
Each sub-framework, with its keys prefixed by the sub-framework’s name (e.g. "tracer.transfer_model").

The keys say which model each citation belongs to, not whether that model was used in a quantity you computed. All references are read from the model classes, so this does not compute anything.

Parameters:: flat – If True, return a single list of all the references with duplicates removed (keeping the first occurrence), e.g. for a paper’s bibliography.
Returns:: A dict mapping each source to a tuple of formatted citations, or a flat list of citations if flat is True.
Return type:: dict or list

Examples

>>> from hmf import MassFunction
>>> mf = MassFunction(hmf_model="Tinker08")
>>> by_model = mf.get_acknowledgments()
>>> bibliography = mf.get_acknowledgments(flat=True)

classmethod get_all_parameter_defaults(recursive=True)¶

Dictionary of all parameters and defaults.

Defaults may be set with some logic in __init__, so they are read from an instance. The instance is not validated, so no quantities are computed.

classmethod get_all_parameter_names() → list[str]¶

Return all parameter names in the class.

These are read from the class’s @parameter definitions, so the class is not instantiated.

get_dependencies(*q)¶

Determine all parameter dependencies of the quantities in q.

Parameters:: q (str) – String(s) labelling a quantity
Returns:: deps – A set containing all parameters on which quantities in q are dependent.
Return type:: set

classmethod parameter_info(names=None)¶

Prints information about each parameter in the class.

Optionally, restrict printed parameters to those found in the list of names provided.

classmethod quantities_available() → list[str]¶

Obtain a list of all available (public) output quantities.

These are the class’s @cached_quantity and @subframework definitions, read without instantiating the class.

update(**kwargs)¶

Update parameters of the framework with kwargs.

The update is atomic: if setting any parameter, or the validation of the new set of parameters, fails, all parameters are restored to their values from before the call, and the error is re-raised.

Parameters:: **kwargs – New values of parameters of the framework. A key <name>_params, where <name> is a sub-framework, takes a dict of parameters with which to update that sub-framework.
Raises:: ValueError – If a key is not a parameter of the framework (or a sub-framework’s _params), or if the new parameters fail validation.

validate()[source]¶: Perform validation of the input parameters as they relate to each other.