Source code for orbitize.lnlike

import numpy as np

"""
This module contains functions for computing log(likelihood).
"""


[docs]def chi2_lnlike(data, errors, model, jitter, seppa_indices): """Compute Log of the chi2 Likelihood Args: data (np.array): Nobsx2 array of data, where data[:,0] = sep/RA/RV for every epoch, and data[:,1] = corresponding pa/DEC/np.nan. errors (np.array): Nobsx2 array of errors for each data point. Same format as ``data``. model (np.array): Nobsx2xM array of model predictions, where M is the \ number of orbits being compared against the data. If M is 1, \ ``model`` can be 2 dimensional. jitter (np.array): Nobsx2xM array of jitter values to add to errors. Elements of array should be 0 for for all data other than stellar \ rvs. seppa_indices (list): list of epoch numbers whose observations are given in sep/PA. This list is located in System.seppa. Returns: np.array: Nobsx2xM array of chi-squared values. .. note:: **Example**: We have 8 epochs of data for a system. OFTI returns an array of 10,000 sets of orbital parameters. The ``model`` input for this function should be an array of dimension 8 x 2 x 10,000. """ if np.ndim(model) == 3: # move M dimension to the primary axis, so that numpy knows to iterate over it model = np.rollaxis(model, 2, 0) # now MxNobsx2 in dimensions jitter = np.rollaxis(jitter, 2, 0) third_dim = True elif np.ndim(model) == 2: model.shape = (1,) + model.shape jitter.shape = (1,) + jitter.shape third_dim = False residual = (data - model) # if there are PA values, we should take the difference modulo angle wrapping if np.size(seppa_indices) > 0: residual[:, seppa_indices, 1] = (residual[:, seppa_indices, 1] + 180.) % 360. - 180. sigma2 = errors**2 + jitter**2 # including the second term of chi2 chi2 = -0.5 * residual**2 / sigma2 - np.log(np.sqrt(2*np.pi*sigma2)) if third_dim: # move M dimension back to the last axis model = np.rollaxis(model, 0, 3) # now MxNobsx2 in dimensions jitter = np.rollaxis(jitter, 0, 3) chi2 = np.rollaxis(chi2, 0, 3) # same with chi2 else: model.shape = model.shape[1:] chi2.shape = chi2.shape[1:] jitter.shape = jitter.shape[1:] return chi2