prospect.models#

prospect.models#

prospect.models.priors#

priors.py – This module contains various objects to be used as priors. When called these return the ln-prior-probability, and they can also be used to construct prior transforms (for nested sampling) and can be sampled from.

class prospect.models.priors.Prior(parnames=[], name='', **kwargs)#

Encapsulate the priors in an object. Each prior should have a distribution name and optional parameters specifying scale and location (e.g. min/max or mean/sigma). These can be aliased at instantiation using the parnames keyword. When called, the argument should be a variable and the object should return the ln-prior-probability of that value.

ln_prior_prob = Prior(param=par)(value)

Should be able to sample from the prior, and to get the gradient of the prior at any variable value. Methods should also be avilable to give a useful plotting range and, if there are bounds, to return them.

Parameters

parnames (sequence of strings) – A list of names of the parameters, used to alias the intrinsic parameter names. This way different instances of the same Prior can have different parameter names, in case they are being fit for….

params#

The values of the parameters describing the prior distribution.

Type

dictionary

update(**kwargs)#

Update self.params values using alias.

sample(nsample=None, **kwargs)#

Draw a sample from the prior distribution.

Parameters

nsample – (optional) Unused

unit_transform(x, **kwargs)#

Go from a value of the CDF (between 0 and 1) to the corresponding parameter value.

Parameters

x – A scalar or vector of same length as the Prior with values between zero and one corresponding to the value of the CDF.

Returns theta

The parameter value corresponding to the value of the CDF given by x.

inverse_unit_transform(x, **kwargs)#

Go from the parameter value to the unit coordinate using the cdf.

property loc#

This should be overridden.

property scale#

This should be overridden.

prospect.models.transforms#

transforms.py – This module contains parameter transformations that may be useful to transform from parameters that are easier to _sample_ in to the parameters required for building SED models.

They can be used as "depends_on" entries in parameter specifications.

prospect.models.transforms.stellar_logzsol(logzsol=0.0, **extras)#

Simple function that takes an argument list and returns the value of the logzsol argument (i.e. the stellar metallicity)

Parameters

logzsol (float) – FSPS stellar metaliicity parameter.

Returns

logzsol – The same.

Return type

float

prospect.models.transforms.delogify_mass(logmass=0.0, **extras)#

Simple function that takes an argument list including a logmass parameter and returns the corresponding linear mass.

Parameters

logmass (float) – The log10(mass)

Returns

mass – The mass in linear units

Return type

float

prospect.models.transforms.tburst_from_fage(tage=0.0, fage_burst=0.0, **extras)#

This function transfroms from a fractional age of a burst to an absolute age. With this transformation one can sample in fage_burst without worry about the case tburst > tage.

Parameters

  • tage (float, Gyr) – The age of the host galaxy.

  • fage_burst (float between 0 and 1) – The fraction of the host age at which the burst occurred.

Returns

tburst – The age of the host when the burst occurred (i.e. the FSPS tburst parameter)

Return type

float, Gyr

prospect.models.transforms.tage_from_tuniv(zred=0.0, tage_tuniv=1.0, **extras)#

This function calculates a galaxy age from the age of the universe at zred and the age given as a fraction of the age of the universe. This allows for both zred and tage parameters without tage exceeding the age of the universe.

Parameters

  • zred (float) – Cosmological redshift.

  • tage_tuniv (float between 0 and 1) – The ratio of tage to the age of the universe at zred.

Returns

tage – The stellar population age, in Gyr

Return type

float

prospect.models.transforms.zred_to_agebins(zred=0.0, agebins=[], **extras)#

Set the nonparameteric SFH age bins depending on the age of the universe at zred. The first bin is not altered and the last bin is always 15% of the upper edge of the oldest bin, but the intervening bins are evenly spaced in log(age).

Parameters

  • zred (float) – Cosmological redshift. This sets the age of the universe.

  • agebins (ndarray of shape (nbin, 2)) – The SFH bin edges in log10(years).

Returns

agebins – The new SFH bin edges.

Return type

ndarray of shape (nbin, 2)

prospect.models.transforms.dustratio_to_dust1(dust2=0.0, dust_ratio=0.0, **extras)#

Set the value of dust1 from the value of dust2 and dust_ratio

Parameters

  • dust2 (float) – The diffuse dust V-band optical depth (the FSPS dust2 parameter.)

  • dust_ratio (float) – The ratio of the extra optical depth towards young stars to the diffuse optical depth affecting all stars.

Returns

dust1 – The extra optical depth towards young stars (the FSPS dust1 parameter.)

Return type

float

prospect.models.transforms.logsfr_ratios_to_masses(logmass=None, logsfr_ratios=None, agebins=None, **extras)#

This converts from an array of log_10(SFR_j / SFR_{j+1}) and a value of log10(Sum_i M_i) to values of M_i. j=0 is the most recent bin in lookback time.

prospect.models.transforms.logsfr_ratios_to_sfrs(logmass=None, logsfr_ratios=None, agebins=None, **extras)#

Convenience function

prospect.models.transforms.logsfr_ratios_to_agebins([NBINS, 2])#
use equation:

delta(t1) = tuniv / (1 + SUM(n=1 to n=nbins-1) PROD(j=1 to j=n) Sn) where Sn = SFR(n) / SFR(n+1) and delta(t1) is width of youngest bin

prospect.models.transforms.zfrac_to_masses(total_mass=None, z_fraction=None, agebins=None, **extras)#

This transforms from independent dimensionless z variables to sfr fractions and then to bin mass fractions. The transformation is such that sfr fractions are drawn from a Dirichlet prior. See Betancourt et al. 2010 and Leja et al. 2017

Parameters

  • total_mass (float) – The total mass formed over all bins in the SFH.

  • z_fraction (ndarray of shape (Nbins-1,)) – latent variables drawn from a specific set of Beta distributions. (see Betancourt 2010)

Returns

masses – The stellar mass formed in each age bin.

Return type

ndarray of shape (Nbins,)

prospect.models.transforms.zfrac_to_sfrac(z_fraction=None, **extras)#

This transforms from independent dimensionless z variables to sfr fractions. The transformation is such that sfr fractions are drawn from a Dirichlet prior. See Betancourt et al. 2010 and Leja et al. 2017

Parameters

z_fraction (ndarray of shape (Nbins-1,)) – latent variables drawn from a specific set of Beta distributions. (see Betancourt 2010)

Returns

sfrac – The star formation fractions (See Leja et al. 2017 for definition).

Return type

ndarray of shape (Nbins,)

prospect.models.transforms.zfrac_to_sfr(total_mass=None, z_fraction=None, agebins=None, **extras)#

This transforms from independent dimensionless z variables to SFRs.

Returns sfrs

The SFR in each age bin (msun/yr).

prospect.models.transforms.masses_to_zfrac(mass=None, agebins=None, **extras)#

The inverse of zfrac_to_masses(), for setting mock parameters based on mock bin masses.

Returns

  • total_mass (float) – The total mass

  • zfrac (ndarray of shape (Nbins-1,)) – latent variables drawn from a specific set of Beta distributions. (see Betancourt 2010) related to the fraction of mass formed in each bin.