def voigtfunc_and_jac(vwave, vpars, convolve_jac=True):
### Check to see if cfg variables are set
if isinstance(cfg.fitidx, int) | isinstance(cfg.wave, int):
cfg.fitidx = fitpix(vwave, vpars)
cfg.wave = vwave
if len(cfg.lsfs) == 0:
makevoigt.get_lsfs()
g, dg_dcol, dg_dv, dg_db = makevoigt.voigt_and_jac(vwave[cfg.fitidx],
vpars[0], vpars[1],
vpars[2], vpars[3],
vpars[4])
g_full = np.ones(len(vwave))
g_full[cfg.fitidx] = g
convfactor = makevoigt.convolvecos(vwave, g_full, vpars[0], vpars[3])
jac = np.vstack([dg_dcol, dg_db, dg_dv])
jac_full = np.zeros([jac.shape[0], len(vwave)])
jac_full[:, cfg.fitidx] = jac
# might be better to wait to do the convolution until after things are agglomerated
if convolve_jac:
for e, row in enumerate(jac_full):
# minus 1 plus 1 because convolvecos pads with 1s
jac_full[e] = makevoigt.convolvecos(vwave, 1 + row, vpars[0],
vpars[3]) - 1
return convfactor, jac_full
def stevevoigterrfunc_w_jac(x, xall0, notfixed, indices, wavelength, flux,
sig):
xall = xall0.copy()
xall[notfixed] = x[indices]
folded_xall = np.asarray(foldpars(xall))
model, jac = voigtfunc_and_jac(wavelength, folded_xall, convolve_jac=False)
status = 0
residuals = (flux[cfg.fitidx] - model[cfg.fitidx]) / sig[cfg.fitidx]
jac = jac[:, cfg.fitidx].reshape([3, -1, len(residuals)])
jac_x = np.zeros([len(x), jac.shape[2]])
line_inds, var_inds = np.unravel_index(notfixed, folded_xall.T.shape)
var_ind_mapping_dict = {1: 0, 2: 1, 4: 2}
for x_ind, line_ind, var_ind in zip(indices, line_inds, var_inds):
jac_x[x_ind] += jac[var_ind_mapping_dict[var_ind], line_ind]
jac_full = np.zeros([len(x), len(wavelength)])
jac_full[:, cfg.fitidx] = jac_x
for row_e, row in enumerate(jac_full):
jac_full[row_e] = makevoigt.convolvecos(wavelength, row + 1, None,
None) - 1
jac_x = jac_full[:, cfg.fitidx]
jac_x /= sig[cfg.fitidx]
jac_x *= -1
try:
residuals = residuals.value
except AttributeError:
pass
return residuals, jac_x
def voigtfunc(vwave,vpars): ### Check to see if cfg variables are set if isinstance(cfg.fitidx, int)|isinstance(cfg.wave, int): cfg.fitidx = fitpix(vwave, vpars) cfg.wave = vwave if len(cfg.lsfs) == 0: makevoigt.get_lsfs() vflux=np.zeros(len(vwave))+1. factor=makevoigt.voigt(vwave,vpars[0],vpars[1],vpars[2],vpars[3],vpars[4]) convfactor=makevoigt.convolvecos(vwave,factor,vpars[0],vpars[3]) vflux*=convfactor return vflux