def _log_likelihood_transit(theta, params, model, t, flux, err_flux,<EOL>priorbounds):
u = []<EOL>for ix, key in enumerate(sorted(priorbounds.keys())):<EOL><INDENT>if key == '<STR_LIT>':<EOL><INDENT>params.rp = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.t0 = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.a = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.inc = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.per = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.per = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>params.w = theta[ix]<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>u.append(theta[ix])<EOL><DEDENT>elif key == '<STR_LIT>':<EOL><INDENT>u.append(theta[ix])<EOL>params.u = u<EOL><DEDENT><DEDENT>lc = model.light_curve(params)<EOL>residuals = flux - lc<EOL>log_likelihood = -<NUM_LIT:0.5>*(<EOL>np.sum((residuals/err_flux)**<NUM_LIT:2> + np.log(<NUM_LIT:2>*np.pi*(err_flux)**<NUM_LIT:2>))<EOL>)<EOL>return log_likelihood<EOL>
Given a batman TransitModel and its proposed parameters (theta), update the
batman params object with the proposed parameters and evaluate the gaussian
likelihood.

Note: the priorbounds are only needed to parse theta.