def do_visibilities(bestfit_pars, headers, models_filename, uvmaps_filename, **kwargs):
from fit_main import setup
obs, model, prob, fitter, imagers = setup(headers)
ref_coords = prob.get_parameters(bestfit_pars)
analysis.compute_visibilities(obs, imagers, ref_coords, models_filename,
uvmaps_filename, **kwargs)
def do_models(flatchain, headers, models_filename, **kwargs):
from fit_main import setup
# how to select the models to be computed? E.g. solved using NUM_STEPS/10
obs, model, prob, fitter, imagers = setup(headers)
analysis.compute_models(flatchain, headers, model, prob,
models_filename, **kwargs)
def do_bestfit_disk_structure(bestfit_pars, headers,
bestfit_structure_filename):
from fit_main import setup
from py2layer.utils import print_model_structure
obs, model, prob, fitter, imagers = setup(headers)
res = model.compute(*prob.get_parameters(bestfit_pars)['model'],
return_opacity=True)
print_model_structure(res, model, bestfit_structure_filename)
def do_plot_uv_density(bestfit_pars, uvmaps_filename, uvlim, plots_dir, plot_uv_density_basename, **kwargs):
from fit_main import setup
loaded = util.load_models(uvmaps_filename)
uvmaps = loaded['models']
headers = loaded['headers']
obs, model, prob, fitter, imagers, = setup(headers)
ref_coords = prob.get_parameters(bestfit_pars)
util_plot.plot_visibilities(uvmaps, headers, obs,
headers['analysis']['uvbinsize'],
headers['analysis']['uvdist_bin'],
plots_dir, plot_uv_density_basename,
ref_coords, uvlim,
export_ascii=False, **kwargs)
def do_plot_uv_bestfit(bestfit_pars, headers, uvbinsize, uvlim, Jylims,
uvdata_dir, bestfit_dir, plot_filename, **kwargs):
# TODO: generalize for nwle>1
from fit_main import setup
obs, model, prob, fitter, imagers, = setup(headers)
ref_coords = prob.get_parameters(bestfit_pars)
inc = np.array([ref_coords['inc'], ref_coords['inc']])
PA = np.array([ref_coords['PA'], ref_coords['PA']])
uvtables = [os.path.join(uvdata_dir, headers['obs']['data_filenames'][0]),
os.path.join(bestfit_dir, headers['obs']['data_filenames'][0][:-4] + "_mod.txt")]
unit_multiplier = 1.
wavelength = [headers['obs']['wle_mm'], headers['obs']['wle_mm']]
ismodel = [False, True]
colors = ['k', 'b']
linestyle = ['.', '-']
apply_shift = dict(delta_alpha=[ref_coords['delta_alpha'][0],
ref_coords['delta_alpha'][0]],
delta_delta=[ref_coords['delta_delta'][0],
ref_coords['delta_delta'][0]])
from pyvfit.utils.util_plot import plot_uvplot
fig = plot_uvplot(uvtables, [uvbinsize[0], uvbinsize[0]], wavelength, inc,
PA, np.array(Jylims[0]) * unit_multiplier,
np.array(Jylims[0][1]) * unit_multiplier, linestyle,
colors, uvlim=uvlim[0], ismodel=ismodel, fontsize=30,
fontsize_ticklabels=20, unit_multiplier=unit_multiplier,
apply_shift=apply_shift)
ax = fig.get_axes()
plot_label = kwargs.get("inset_caption", "")
ax[0].text(uvlim[0][1] * 0.85, ax[0].get_ylim()[1] * unit_multiplier * 0.85,
plot_label, fontsize=30, fontweight='bold', ha='right')
for tick in ax[1].xaxis.get_major_ticks():
tick.label.set_fontsize(25)
for tick in ax[1].yaxis.get_major_ticks():
tick.label.set_fontsize(25)
for tick in ax[0].yaxis.get_major_ticks():
tick.label.set_fontsize(25)
fig.tight_layout()
fig.savefig(plot_filename)
def do_bestfit_model(bestfit_pars, headers, bestfit_dir,
bestfit_model_filename, bestfit_uvmaps_filename, **kwargs):
from fit_main import setup
print("Bestfit parameters:\n{0} \n".format(repr(bestfit_pars)))
obs, model, prob, fitter, imagers, = setup(headers)
ref_coords = prob.get_parameters(bestfit_pars)
# to compute the bestfit model (and residuals) we must shift the model in the disk center
ref_coords['delta_delta'] = [0.]
ref_coords['delta_alpha'] = [0.]
analysis.compute_models(None, headers, model, prob,
bestfit_model_filename, ncpus=1,
single_model_pars=bestfit_pars)
analysis.compute_visibilities(obs, imagers, ref_coords, bestfit_model_filename,
bestfit_uvmaps_filename, working_dir=bestfit_dir,
ncpus=1, **kwargs)
t = Table(names=('field', 'info', 'bestfit', '16th', '50th', '84th'),
dtype=('S15', 'S15', 'float64', 'float64', 'float64', 'float64'))
t.add_row(('starname', STARNAME, np.nan, np.nan, np.nan, np.nan))
for i in xrange(npars):
t.add_row(('par{0}'.format(i), labels[i], bestfit_pars_real[i],
percentiles[i, 0], percentiles[i, 1], percentiles[i, 2]))
print(t)
t.write(os.path.join(analysis_dir, "results_{0}.txt".format(STARNAME)),
format='ascii.fixed_width_two_line')
if do_bestfit_disk_structure:
from scipy.integrate import cumtrapz
from fit_main import setup
obs, model, prob, fitter, imagers = setup(headers)
res = model.compute(*prob.get_parameters(bestfit_pars)['model'],
return_opacity=True)
# determine location of 0.88, 1.3, 3.0 mm in gridwle
iw_0_88mm = np.where(res['gridwle'] >= 0.880 / 10.)[0][-1]
iw_1_3mm = np.where(res['gridwle'] >= 1.3 / 10.)[0][-1]
iw_3mm = np.where(res['gridwle'] >= 3. / 10.)[0][-1]
cos_inc = np.cos(res['inc'])
dist2 = model.star.dist.cm**2.
mJy = 1.e-26 # erg/s/cm2/Hz/sr
from astropy.table import Table
col_names = ('R', 'Sigma', 'T_mid', 'T_sur', 'H_sur', 'I')
percentiles = util_chain.compute_percentiles(flatchain_real)
from astropy.table import Table
t = Table(names=('field', 'info', 'bestfit', '16th', '50th', '84th'), dtype=('S15', 'S15', 'float64', 'float64', 'float64', 'float64'))
t.add_row(('starname', STARNAME, np.nan, np.nan, np.nan, np.nan))
for i in xrange(npars):
t.add_row(('par{0}'.format(i), labels[i], bestfit_pars_real[i], percentiles[i, 0], percentiles[i, 1], percentiles[i, 2]))
print(t)
t.write(os.path.join(analysis_dir ,"results_{0}.txt".format(STARNAME)), format='ascii.fixed_width_two_line')
if do_bestfit_disk_structure:
from scipy.integrate import cumtrapz
from fit_main import setup
obs, model, prob, fitter, imagers = setup(headers)
res = model.compute(*prob.get_parameters(bestfit_pars)['model'], return_opacity=True)
# determine location of 0.88, 1.3, 3.0 mm in gridwle
iw_0_88mm = np.where(res['gridwle']>=0.880/10.)[0][-1]
iw_1_3mm = np.where(res['gridwle']>=1.3/10.)[0][-1]
iw_3mm = np.where(res['gridwle']>=3./10.)[0][-1]
cos_inc = np.cos(res['inc'])
dist2 = model.star.dist.cm**2.
mJy = 1.e-26 # erg/s/cm2/Hz/sr
from astropy.table import Table
col_names = ('R', 'Sigma', 'T_mid', 'T_sur', 'H_sur', 'I')
# t = Table(names=col_names, dtype=('float64', 'float64', 'float64', 'float64', 'float64', 'float64'))