import emcee
import triangle
import scipy as sp
import numpy as np
from from_fits import create_uvdata_from_fits_file
from components import CGComponent
from model import Model, CCModel
from stats import LnPost
if __name__ == '__main__':
uv_fname = '1633+382.l22.2010_05_21.uvf'
map_fname = '1633+382.l22.2010_05_21.icn.fits'
uvdata = create_uvdata_from_fits_file(uv_fname)
# Create several components
cg1 = CGComponent(1.0, 0.0, 0.0, 1.)
cg1.add_prior(flux=(
sp.stats.uniform.logpdf,
[0., 3.],
dict(),
),
bmaj=(
sp.stats.uniform.logpdf,
[0, 10.],
dict(),
))
# Create model
mdl1 = Model(stokes='I')
# Add components to model
mdl1.add_component(cg1)
# Create posterior for data & model
lnpost = LnPost(uvdata, mdl1)
import emcee
import triangle
import scipy as sp
from uv_data import UVData
from components import CGComponent
from model import Model
from stats import LnPost
if __name__ == '__main__':
uvdata = UVData('J0006-0623_X_2008_07_09_pus_vis.fits')
uv = uvdata.uvw[:, :2]
# Create several components
cg1 = CGComponent(2.0, 0.0, 0.0, 0.1)
# cg2 = CGComponent(1.0, 0.1, -1.05, 1.18)
cg1.add_prior(flux=(sp.stats.uniform.logpdf, [0., 3.], dict(),),
bmaj=(sp.stats.uniform.logpdf, [0, 2.], dict(),))
# cg2.add_prior(flux=(sp.stats.uniform.logpdf, [0., 0.1], dict(),),
# bmaj=(sp.stats.uniform.logpdf, [0, 3.], dict(),))
# Create model
mdl1 = Model(stokes='I')
# Add components to model
mdl1.add_component(cg1)
# mdl1.add_component(cg2)
# Create posterior for data & model
lnpost = LnPost(uvdata, mdl1)
# lnpr = LnPrior(mdl1)
# lnlik = LnLikelihood(uvdata, mdl1)
# model.uvplot(uv = uv)
# model.ft(uv=uv)
ndim = mdl1.size
from mojave import mojave_uv_fits_fname, download_mojave_uv_fits
from spydiff import modelfit_difmap, import_difmap_model
from bootstrap import bootstrap_uvfits_with_difmap_model
source = '1514-241'
epoch = '2006_04_28'
data_dir = '/home/ilya/Dropbox/papers/boot/bias/new/stationary'
# download_mojave_uv_fits(source, epochs=[epoch], bands=['u'],
# download_dir=data_dir)
uv_fits_fnames = {freq: mojave_uv_fits_fname(source, freq, epoch) for freq in
('x', 'j', 'u')}
for freq, uv_fits_fname in uv_fits_fnames.items():
uv_fits_path = os.path.join(data_dir, uv_fits_fname)
cg1 = CGComponent(2.0, 0., 0., 0.2)
cg2 = CGComponent(1.0, 0., 0.3, 0.3)
cg3 = CGComponent(0.5, 0., 1.5, 0.4)
mdl = Model(stokes='I')
mdl.add_components(cg1, cg2, cg3)
uvdata = UVData(uv_fits_path)
noise = uvdata.noise()
for i in range(1, 101):
uvdata = UVData(uv_fits_path)
uvdata.substitute([mdl])
uvdata.noise_add(noise)
art_fits_fname = 'art_{}_{}.fits'.format(freq, i)
art_fits_path = os.path.join(data_dir, art_fits_fname)
uvdata.save(art_fits_path)
modelfit_difmap(art_fits_fname, 'initial.mdl', 'out_{}_{}.mdl'.format(freq, i),
niter=100, path=data_dir, mdl_path=data_dir,
bmaj=(
sp.stats.uniform.logpdf,
[0, 20.],
dict(),
),
e=(
sp.stats.uniform.logpdf,
[0, 1.],
dict(),
),
bpa=(
sp.stats.uniform.logpdf,
[0, np.pi],
dict(),
))
cg2 = CGComponent(0.5, 0., -2., 0.55)
cg2.add_prior(flux=(
sp.stats.uniform.logpdf,
[0., 5.],
dict(),
),
bmaj=(
sp.stats.uniform.logpdf,
[0, 20.],
dict(),
))
model = Model(stokes='I')
model.add_components(cg1, cg2)
uvdata.substitute([model])
uvdata.noise_add(noise)
uvdata.save(os.path.join(data_dir, 'fake.uvf'))
import emcee
import triangle
import scipy as sp
import numpy as np
from from_fits import create_uvdata_from_fits_file
from components import CGComponent
from model import Model
from stats import LnPost
if __name__ == '__main__':
uv_fname = 'J0005+3820_X_1998_06_24_fey_vis.fits'
map_fname = 'J0005+3820_X_1998_06_24_fey_map.fits'
uvdata = create_uvdata_from_fits_file(uv_fname)
# Estimate noise
# noise = uvdata.noise(average_freq=True, use_V=False)
# Create some model of couple components 10 mas away
cg1 = CGComponent(1.0, 0.0, 0.0, 1.5)
cg2 = CGComponent(0.3, 3.0, 10.0, 3.)
# Create model
mdl = Model(stokes='RR')
# Add components to model
mdl.add_components(cg1, cg2)
#uvdata.substitute([mdl])
#uvdata.noise_add(noise)
# radplot
#uvdata.uvplot(stokes='RR')
# plot image
mdl_image = mdl.make_image(map_fname)
# FIXME: doesn't plot component with nonzero coordinates?
mdl_image.plot(min_rel_level=1.)