sigma['gaussian_ps']['ero'] = {'': 2.3/2.35*arcsec, 'z1': 2.4*arcsec/2.35}
sigma['gaussian_ps']['drg'] = {'': 0.7/2.35*arcsec, 'z1': 0.7*arcsec/2.35}
datapath = '/data2/lindroos/ecdfs/aless/stack'
# vis = {}
model='gaussian_ps'
# for sample in samples:
# for subsample in subsamples:
for sample in ['drg']:
for subsample in ['z1']:
vis = os.path.join(datapath, sample+subsample, 'stack.uv.ms')
parameters, chi2 = \
vesta.run(vis=vis, model=model,
flux_ext=flux_ext[model][sample][subsample], dflux_ext = 0.9e-3,
flux_ps=flux_ps[model][sample][subsample], dflux_ps = 0.9e-3,
sigma=sigma[model][sample][subsample], dsigma=0.40*arcsec,
x=0., y=0., nscan = 20)
np.save('results/{}_{}_{}_parameters.npy'.format(sample, subsample, model),
parameters)
np.save('results/{}_{}_{}_chi2.npy'.format(sample, subsample, model),
chi2)
sigmas = np.linspace(sigma-dsigma, sigma+dsigma, nscan)
fluxes_ext, fluxes_ps, sigmas = meshgrid(fluxes_ext, fluxes_ps, sigmas, indexing='xy')
parameters = np.zeros(shape+[5])
parameters[:,:,:,0] = fluxes_ext
parameters[:,:,:,1] = xs
parameters[:,:,:,2] = ys
parameters[:,:,:,3] = sigmas
parameters[:,:,:,4] = fluxes_ps
# fluxes,sigmas = np.meshgrid(fluxes, sigmas)
chi2_scan = libchi2.c_chi2_scan
c_chi2 = c_ndarray(chi2, dtype=chi2.dtype, ndim=chi2.ndim)
c_parameters = c_ndarray(parameters, dtype=parameters.dtype, ndim=parameters.ndim)
chi2_scan(c_chi2, c_int(chi2.ndim), c_char_p(vis), c_model[model], c_parameters)
return parameters, chi2
if __name__ == '__main__':
import vesta
parameters, chi2 = \
vesta.run(vis = '/data2/lindroos/ecdfs/aless/stack/sbzk/stack.uv.ms',
flux = 2.3349e-3, dflux = 0.6e-3,
sigma=0.30536*arcsec, dsigma=0.16*arcsec,
x=1.489274040147295e-07, y=9.887614358626681e-09,
nscan = 20)
np.save('results/sbzk_parameters.npy', parameters)
np.save('results/sbzk_chi2.npy', chi2)