py21cmmc.likelihood.Likelihood1DPowerCoeval#

class py21cmmc.likelihood.Likelihood1DPowerCoeval(n_psbins=None, min_k=0.1, max_k=1.0, logk=True, model_uncertainty=0.15, error_on_model=True, ignore_kperp_zero=True, ignore_kpar_zero=False, ignore_k_zero=False, name='', *args, **kwargs)[source]#

A Gaussian likelihood for the 1D power spectrum of a coeval cube.

Requires the CoreCoevalModule to be loaded to work, and inherently deals with the multiple-redshift cubes which that module produces.

Parameters:

datafile (str, optional) – Path to file containing data. See LikelihoodBaseFile for details.
noisefile (str, optional) – Path to file containing noise data. See LikelihoodBaseFile for details.
n_psbins (int, optional) – The number of bins for the spherically averaged power spectrum. By default automatically calculated from the number of cells.
min_k (float, optional) – The minimum k value at which to compare model and data (units 1/Mpc).
max_k (float, optional) – The maximum k value at which to compare model and data (units 1/Mpc).
logk (bool, optional) – Whether the power spectrum bins should be regular in logspace or linear space.
model_uncertainty (float, optional) – The amount of uncertainty in the modelling, per power spectral bin (as fraction of the amplitude).
error_on_model (bool, optional) – Whether the model_uncertainty is applied to the model, or the data.
ignore_kperp_zero (bool, optional) – Whether to ignore the kperp=0 when generating the power spectrum.
ignore_kpar_zero (bool, optional) – Whether to ignore the kpar=0 when generating the power spectrum.
ignore_k_zero (bool, optional) – Whether to ignore the |k| = 0 mode when generating the power spectrum.

Other Parameters:

**kwargs – All other parameters sent to LikelihoodBaseFile. In particular, datafile, noisefile, simulate and use_data.

Notes

The datafile and noisefile have specific formatting required. Both should be .npz files. The datafile should have ‘k’ and ‘delta’ arrays in it (k-modes in 1/Mpc and power spectrum respectively) and the noisefile should have ‘k’ and ‘errs’ arrays in it (k-modes and their standard deviations respectively). Note that the latter is almost the default output of 21cmSense, except that 21cmSense has k in units of h/Mpc, whereas 21cmFAST/21CMMC use units of 1/Mpc.

Warning

Please ensure that the data/noise is in the correct units for 21CMMC, as this class does not automatically convert units!

To make this more flexible, simply subclass this class, and overwrite the _read_data() or _read_noise() methods, then use that likelihood instead of this in your likelihood chain. Both of these functions should return dictionaries in which the above entries exist. For example:

>>> class MyCoevalLikelihood(Likelihood1DPowerCoeval):
>>>    def _read_data(self):
>>>        data = np.genfromtxt(self.datafile)
>>>        return {"k": data[:, 0], "p": data[:, 1]}

Also note that an extra method, define_noise may be used to define the noise properties dynamically (i.e. without reading it). This method will be called if available and simulate=True. It should have the signature define_noise(self, ctx, model), where ctx is the context with all cores having added their data, and model is the output of the simulate() method.

Methods

`__init__`([n_psbins, min_k, max_k, logk, ...])
`computeLikelihood`(model)	Compute the likelihood given a model.
`compute_power`(brightness_temp, L, n_psbins)	Compute power spectrum from coeval box.
`get_fiducial_model`()	Compute and return a model dictionary at the fiducial set of parameters.
`reduce_data`(ctx)	Reduce the data in the context to a list of models (one for each redshift).
`setup`()	Perform post-init setup.
`store`(model, storage)	Save elements of the model to the storage dict.

Attributes

`chain`	Reference to the `LikelihoodComputationChain` containing this core.
`core_primary`	The first core that appears in the requirements.
`data_spline`	Splines of data power spectra.
`noise_spline`	Splines of noise power spectra.
`paired_core`	The PS core that is paired with this likelihood.
`parameter_names`	Names of the parameters of the full chain.
`redshift`	The redshifts of coeval simulations.
`required_cores`