azel_fit

pytelpoint.fitting.azel_fit(coo_ref, coo_meas, nsamp=2000, ntune=500, target_accept=0.95, random_seed=8675309, cores=None, fit_terms=('ia', 'ie', 'an', 'aw', 'ca', 'npae', 'tf', 'tx'), fixed_terms=None, init_pars=None, prior_sigmas=None)

Fit full az/el pointing model using PyMC. The terms are analogous to those used by TPOINT(tm). This fit includes the eight normal terms used and described in azel with additional terms, az_sigma and el_sigma, that describe the intrinsic/observational scatter.

Parameters:

coo_refSkyCoord instance

Reference coordinates

coo_measSkyCoord instance

Measured coordinates

nsampint (default: 2000)

Number of inference samples per chain

ntuneint (default: 500)

Number of burn-in samples per chain

target_acceptfloat (default: 0.95)

Sets acceptance probability target for determining step size

random_seedint (default: 8675309)

Seed number for random number generator

coresint (default: None)

Number of cores to use for parallel chains. The default of None will use the number of available cores, but no more than 4.

fit_termslist-like (default: AZEL_TERMS)

Model terms to include in the fit.

fixed_termsdict (default: {})

Dict of terms to fix to a specified value.

init_parsdict (default: None -> {‘ia’: 1200.})

Initial guesses for the fit parameters. Keys are the same those provided by best_fit_pars and described in azel: ‘ia’, ‘ie’, ‘an’, ‘aw’, ‘ca’, ‘npae’, ‘tf’, ‘tx’, ‘az_sigma’, ‘el_sigma’. The default for ‘ia’ is appropriate for the MMTO. If not specified, then the initial guess for a parameter is assumed to be 0.

prior_sigmasdict (default: None -> {‘ia’: 100., ‘ie’: 50.})

The priors for the fit parameters are assumed to be Normal distributions. The sigmas for these can be specified here. The index parameters, ‘ia’ and ‘ie’, have default sigma values of 100 and 50, respectively. The rest default to 25 if not specified.

Returns:

idataInferenceData

Inference data from the pointing model