py21cmmc.core.CoreForest

class py21cmmc.core.CoreForest(name='', observation='bosman_optimistic', n_realization=150, mean_flux=None, **kwargs)

A Core Module that produces model effective optical depth at a range of redshifts.

namestr

The name used to match the likelihood

observationstr

The observation that is used to construct the tau_eff statisctic. Currently, only bosman_optimistic and bosman_pessimistic are provided.

n_realizationint

The number of realizations to evaluate the error covariance matrix, default is 150.

mean_fluxfloat

The mean flux (usually from observation) used to rescale the modelling results. If not provided, the modelled mean flux will be rescaled according to input parameters log10_f_rescale and f_rescale_slope.

Other Parameters:

**kwargs – All other parameters are the same as CoreCoevalModule.

Methods

__init__([name, observation, n_realization, ...])
build_model_data(ctx)	Compute all data defined by this core and add it to the context.
convert_model_to_mock(ctx)	Generate random mock data.
find_n_rescale(tau, mean_fluxave_target)	Find the rescaling factor so that the mean transmission equal to observations.
prepare_storage(ctx, storage)	Add variables to dict which cosmoHammer will automatically store with the chain.
setup()	Run post-init setup.
simulate_mock(ctx)	Generate all mock data and add it to the context.
tau_GP(gamma_bg, delta, temp, redshifts)	Calculating the lyman-alpha optical depth in each pixel using the fluctuating GP approximation.

Attributes

chain	Reference to the LikelihoodComputationChain containing this core.
core_primary	The first core that appears in the requirements.
parameter_names	Names of the parameters of the full chain.
required_cores