chippr currently enables estimation of the redshift density function.

The log_z_dens Class

class log_z_dens.log_z_dens(catalog, hyperprior, truth=None, loc='.', prepend='', vb=True)

calculate_mexp(vb=True)

Calculates the marginalized expected value estimator of the redshift density function

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress Returns:log_exp_nz – array of logged redshift density function bin values Return type:ndarray, float

calculate_mmap(vb=True)

Calculates the marginalized maximum a posteriori estimator of the redshift density function

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress Returns:log_map_nz – array of logged redshift density function bin values Return type:ndarray, float

calculate_mmle(start, vb=True, no_data=0, no_prior=0)

Calculates the marginalized maximum likelihood estimator of the redshift density function

Parameters:

• start (numpy.ndarray, float) – array of log redshift density function bin values at which to begin optimization
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress
• no_data (boolean, optional) – True to exclude data contribution to hyperposterior
• no_prior (boolean, optional) – True to exclude prior contribution to hyperposterior

Returns:

log_mle_nz – array of logged redshift density function bin values maximizing hyperposterior

Return type:

numpy.ndarray, float

calculate_samples(ivals, n_accepted=3, n_burned=2, vb=True, n_procs=1, no_data=0, no_prior=0, gr_threshold=1.2)

Calculates samples estimating the redshift density function

Parameters:

• ivals (numpy.ndarray, float) – initial values of log n(z) for each walker
• n_accepted (int, optional) – log10 number of samples to accept per walker
• n_burned (int, optional) – log10 number of samples between tests of burn-in condition
• n_procs (int, optional) – number of processors to use, defaults to single-thread
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress
• no_data (boolean, optional) – True to exclude data contribution to hyperposterior
• no_prior (boolean, optional) – True to exclude prior contribution to hyperposterior

Returns:

log_samples_nz – array of sampled log redshift density function bin values

Return type:

ndarray, float

calculate_stacked(vb=True)

Calculates the stacked estimator of the redshift density function

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress Returns:log_stk_nz – array of logged redshift density function bin values Return type:ndarray, float

compare(vb=True)

Calculates all available goodness of fit measures

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress Returns:out_info – dictionary of all available statistics Return type:dict

evaluate_log_hyper_likelihood(log_nz)

Function to evaluate log hyperlikelihood

Parameters:log_nz (numpy.ndarray, float) – vector of logged redshift density bin values at which to evaluate the hyperlikelihood Returns:log_hyper_likelihood – log likelihood probability associated with parameters in log_nz Return type:float

evaluate_log_hyper_posterior(log_nz)

Function to evaluate log hyperposterior

Parameters:log_nz (numpy.ndarray, float) – vector of logged redshift density bin values at which to evaluate the full posterior Returns:log_hyper_posterior – log hyperposterior probability associated with parameters in log_nz Return type:float

evaluate_log_hyper_prior(log_nz)

Function to evaluate log hyperprior

Parameters:log_nz (numpy.ndarray, float) – vector of logged redshift density bin values at which to evaluate the hyperprior Returns:log_hyper_prior – log prior probability associated with parameters in log_nz Return type:float

optimize(start, no_data, no_prior, vb=True)

Maximizes the hyperposterior of the redshift density

Parameters:

• start (numpy.ndarray, float) – array of log redshift density function bin values at which to begin optimization
• no_data (boolean) – True to exclude data contribution to hyperposterior
• no_prior (boolean) – True to exclude prior contribution to hyperposterior
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

res.x – array of logged redshift density function bin values maximizing hyperposterior

Return type:

numpy.ndarray, float

plot_estimators(log=True, mini=True)

Plots all available estimators of the redshift density function.

read(read_loc, style='pickle', vb=True)

Function to load inferred quantities from files.

Parameters:

• read_loc (string) – filepath where inferred redshift density function is stored
• style (string, optional) – keyword for file format, currently only ‘pickle’ supported
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

self.info – returns the log_z_dens information dictionary object

Return type:

dict

sample(ivals, n_samps, vb=True)

Samples the redshift density hyperposterior

Parameters:

• ivals (numpy.ndarray, float) – initial values of the walkers
• n_samps (int) – number of samples to accept before stopping
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

mcmc_outputs – dictionary containing array of sampled redshift density function bin values as well as posterior probabilities, acceptance fractions, and autocorrelation times

Return type:

dict

write(write_loc, style='pickle', vb=True)

Function to write results of inference to files.

Parameters:

• write_loc (string) – filepath where results of inference should be saved.
• style (string, optional) – keyword for file format, currently only ‘pickle’ supported
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress