cosmoslik_plugins.models package¶
Subpackages¶
Submodules¶
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class
cosmoslik_plugins.models.bbn_consistency.
bbn_consistency
¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
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__call__
(ombh2, Neff=3.046, **kwargs)¶ Calculation code here.
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__init__
()¶ Initialization code here.
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class
cosmoslik_plugins.models.camb.
camb
(**defaults)¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
Compute the CMB power spectrum with CAMB.
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__call__
(ALens=None, As=None, DoLensing=None, H0=None, k_eta_max_scalar=None, lmax=None, massive_neutrinos=None, massless_neutrinos=None, mnu=None, Neff=None, NonLinear=None, ns=None, ombh2=None, omch2=None, omk=None, pivot_scalar=None, tau=None, theta=None, Yp=None, nowarn=False, **kwargs)¶ Parameters: - nowarn (bool) – don’t warn about unrecognized parameters which were passed in
- Returns (dict) – dictionary of {‘TT’:array(), ‘TE’:array(), …} giving the CMB Dl’s in muK^2
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__init__
(**defaults)¶ - defaults : dict
- any of the parameters accepted by __call__. these will be their defaults unless explicitly passed to __call__.
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convert_params
(**params)¶ Convert from CosmoSlik params to pycamb
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class
cosmoslik_plugins.models.classy.
classy
(**defaults)¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
Compute the CMB power spectrum with CLASS.
Based on work by: Brent Follin, Teresa Hamill
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__call__
(As=None, DoLensing=True, H0=None, lmax=None, mnu=None, Neff=None, nrun=None, ns=None, ombh2=None, omch2=None, omk=None, output='tCl, lCl, pCl', pivot_scalar=None, r=None, tau=None, Tcmb=2.7255, theta=None, w=None, Yp=None, nowarn=False, **kwargs)¶ Calculation code here.
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__init__
(**defaults)¶ Initialization code here.
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convert_params
(**params)¶ Convert from CosmoSlik params to CLASS
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class
cosmoslik_plugins.models.clust_poisson_egfs.
clust_poisson_egfs
(*args, **kwargs)¶
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class
cosmoslik_plugins.models.cosmology.
cosmology
(model='', **kwargs)¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
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__init__
(model='', **kwargs)¶ Initialization code here.
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class
cosmoslik_plugins.models.egfs.
egfs
(*args, **kwargs)¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
An
To create your own extra-galactic foreground model, create a subclass of
cosmoslik.plugins.models.egfs.egfs
and override the functionget_egfs
to return a dictionary of extra-galactic foreground components.Also passed to the get_egfs function is information from the dataset, such as
- spectra : e.g. cl_TT or cl_EE
- freq : a dictionary for different effective frequencies, e.g. {‘dust’: 153, ‘radio’: 151, ‘tsz’:150}
- fluxcut : the fluxcut in mJy
- lmax : the necessary maximum l
Here’s an example egfs model:
from cosmoslik.plugins.models.egfs import egfs
class MyEgfs(egfs):
- def get_egfs(self, p, spectra, freq, fluxcut, lmax, **kwargs):
- return {‘single_component’: p[‘amp’] * ones(lmax)}
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__call__
(**kwargs)¶ Calculation code here.
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class
cosmoslik_plugins.models.egfs.
egfs_specs
(*args, **kwargs)¶ Bases:
cosmoslik.cosmoslik.SlikDict
This class stores information needed to calculate the extra-galactic foreground contribution to some particular power spectrum. This information is,
kind : ‘TT’, ‘TE’, … freqs : tuple of dicts
the pair of frequencies being correlated. each entry isnt a number, rathers its a dict with keys ‘dust’, ‘radio’, and ‘tsz’, specfying the band center for each type of component in GHz- fluxcut : the fluxcut
- the fluxcut in mJy
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class
cosmoslik_plugins.models.pico.
pico
(datafile)¶ Bases:
cosmoslik.cosmoslik.SlikPlugin
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__call__
(outputs=[], force=False, onfail=None, **kwargs)¶ Calculation code here.
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__init__
(datafile)¶ Initialization code here.
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