frb.dm.mcmc

Methods for MCMC analysis of the Macquart relation

frb.dm.mcmc.grab_parmdict(tight_ObH=False)[source]

Generate the parameter dict for the MCMC run

Args:

tight_ObH (bool, optional) – [description]. Defaults to False.

Raises:

IOError – [description]

Returns:

[description]

Return type:

dict

frb.dm.mcmc.one_prob(Obh70, F, DM_FRBp, z_FRB, mu=150.0, lognorm_s=1.0, lognorm_floor=0.0, orig=False, beta=4.0)[source]

Calculate the probability for a single FRB

Args:

  • Obh70 (float) – Value of Omega_b * H_0

  • F (float) – Feedback parameter

  • DM_FRBp (np.ndarray) – Values of DM_FRBp for analysis

  • z_FRB (np.ndarray) – z values for evaluation

  • mu (float, optional) – Mean of log-normal PDF

  • lognorm_s (float, optional) – Sigma of log-normal PDF

  • lognorm_floor (float, optional) – Floor to the log-normal PDF

  • orig (bool, optional) – if True (not recommended!), use the original approach to calculating sigma

  • beta (float, optional) – Parameter for DM PDF

Returns:

Likelihood probability

Return type:

float

frb.dm.mcmc.mcquinn_DM_PDF_grid(Delta_values, C0, sigma, alpha=3.0, beta=3.0)[source]

PDF(Delta) for the McQuinn formalism describing the DM_cosmic PDF

Args:

  • Delta (2D ndarray) – DM / averageDM values

  • C0 (np.ndarray) – C0 values

  • sigma (np.ndarray) – sigma values

  • alpha (float, optional)

  • beta (float, optional)

Returns:

frb.dm.mcmc.all_prob(Obh70, F, in_DM_FRBp, z_FRB, mu=150.0, lognorm_s=1.0, lognorm_floor=0.0, beta=3.0)[source]

Calculate the probability for a set of FRBs

Args:

  • Obh70 (float) – Value of Omega_b * H_0

  • F (float) – Feedback parameter

  • in_DM_FRBp (np.ndarray) – Values of DM_FRBp for analysis Not used?!

  • z_FRB (np.ndarray) – z values for evaluation

  • mu (float, optional) – Mean of log-normal PDF

  • lognorm_s (float, optional) – Sigma of log-normal PDF

  • lognorm_floor (float, optional) – Floor to the log-normal PDF

  • beta (float, optional) – Parameter for DM PDF

Returns:

Log like-lihood

Return type:

float

frb.dm.mcmc.calc_likelihood_four_beta3(Obh70, F, mu, lognorm_s)

Calculate likelihood for the real data

Args:

  • Obh70 (float) – Value of Omega_b * H_0

  • F (float) – Feedback parameter

  • mu (float) – Mean of log-normal PDF

  • lognorm_s (float) – Sigma of log-normal PDF

Returns:

Array of log likelihood values, one per FRB

in the global variable frbs

Return type:

np.ndarray

frb.dm.mcmc.pm_four_parameter_model(parm_dict: dict, tight_ObH=False, beta=3.0)[source]

Builds a pymc3 model for the 4-parameter MCMC

Args:

  • parm_dict (dict) – dict with the pymc3 parameters

  • tight_ObH (bool, optional) – If True, restrict the ObH0 value based on CMB. Defaults to False.

  • beta (float, optional) – PDF parameter. Defaults to 3..

Raises:

IOError – [description]

Returns:

pymc3 model

Return type:

pm.Model

frb.dm.mcmc.tt_spl_sigma(value)[source]
frb.dm.mcmc.tt_spl_C0_3(value)[source]