download_dir=data_dir)
# Fetch model file
get_mojave_mdl_file(tsv_table, source, epoch, outdir=data_dir)
# Clean uv-fits
clean_difmap(uv_fits,
'cc.fits',
'I', [1024, 0.1],
path=data_dir,
path_to_script=path_to_script,
outpath=data_dir)
# Create clean image instance
cc_image = create_clean_image_from_fits_file(os.path.join(data_dir, 'cc.fits'))
comps = import_difmap_model(mdl_fname, data_dir)
model = Model(stokes='I')
model.add_components(*comps)
# Check that model fits UV-data well
uv_data = UVData(os.path.join(data_dir, uv_fits))
uv_data.uvplot()
mdl_data = copy.deepcopy(uv_data)
mdl_data.substitute([model])
mdl_data.uvplot(sym='.r')
cc_image_ = copy.deepcopy(cc_image)
cc_image_._image = np.zeros(cc_image._image.shape, dtype=float)
cc_image_.add_model(model)
plt.figure()
plt.matshow(cc_image_.cc_image - cc_image.cc_image)
plt.colorbar()
components_priors.append({'flux': (sp.stats.uniform.ppf, [0, 1], {}),
'x': (sp.stats.uniform.ppf, [-2, 4], {}),
'y': (sp.stats.uniform.ppf, [-2, 4], {}),
'bmaj': (sp.stats.uniform.ppf, [0, 1], {})})
components_priors.append({'flux': (sp.stats.uniform.ppf, [0, 1], {}),
'x': (sp.stats.uniform.ppf, [-5, 10], {}),
'y': (sp.stats.uniform.ppf, [-5, 10], {}),
'bmaj': (sp.stats.uniform.ppf, [0, 2], {})})
components_priors.append({'flux': (sp.stats.uniform.ppf, [0, 1], {}),
'x': (sp.stats.uniform.ppf, [-6, 12], {}),
'y': (sp.stats.uniform.ppf, [-6, 12], {}),
'bmaj': (sp.stats.uniform.ppf, [0, 3], {})})
results = fit_model_with_nestle(uv_fits, mdl_file, components_priors,
outdir=outdir)
data_dir = '/home/ilya/code/vlbi_errors/silke'
# uv_fits = '0851+202.u.2012_11_11.uvf'
uv_fits = '0851+202.u.2004_11_05.uvf'
# mdl_fname = '2.mod.2012_11_11'
mdl_fname = '1.mod.2004_11_05'
uv_data = UVData(os.path.join(data_dir, uv_fits))
comps = import_difmap_model(mdl_fname, data_dir)
model = Model(stokes='I')
model.add_components(*comps)
fig = uv_data.uvplot(style='a&p')
uv_data.substitute([model])
uv_data.uvplot(color='r', fig=fig, phase_range=[-0.2, 0.2])
sys.path.insert(0, '/home/ilya/github/vlbi_errors/vlbi_errors') import numpy as np from skimage.transform import rotate import matplotlib.pyplot as plt from uv_data import UVData from components import ImageComponent from model import Model mas_to_rad = 4.8481368 * 1E-09 # uv_file = '/home/ilya/github/bck/jetshow/uvf/0716+714_raks01xg_C_LL_0060s_uva.fits' uv_file = '/home/ilya/github/bck/jetshow/uvf/2200+420_K_SVLBI.uvf' uvdata = UVData(uv_file) # fig = uvdata.uvplot(stokes=["LL"]) fig = uvdata.uvplot() images = list() angles = range(0, 180, 30) # image = '/home/ilya/github/bck/jetshow/uvf/map_i_09_C.txt' image = '/home/ilya/github/bck/jetshow/cmake-build-debug/map_i.txt' image = np.loadtxt(image) images.append(image) # imsize = 1096 imsize = 1734 imsize = (imsize, imsize) # mas_in_pix = 0.005 mas_in_pix = 0.00253 y, z = np.meshgrid(np.arange(imsize[0]), np.arange(imsize[1])) y = y - imsize[0] / 2. + 0.5
uv_fname_uv = '0945+408.u.2007_04_18.uvf'
dfm_model_fname = 'dfmp_original_model.mdl'
comps = import_difmap_model(dfm_model_fname, base_path)
model_uv = Model(stokes='I')
model_uv.add_components(*comps)
uvdata = UVData(os.path.join(base_path, uv_fname_uv))
uvdata_m = UVData(os.path.join(base_path, uv_fname_uv))
uvdata_m.substitute([model_uv])
uvdata_r = uvdata - uvdata_m
# Plot uv-data
label_size = 12
matplotlib.rcParams['xtick.labelsize'] = label_size
matplotlib.rcParams['ytick.labelsize'] = label_size
uvdata.uvplot(style='re&im', freq_average=True)
matplotlib.pyplot.show()
matplotlib.pyplot.savefig('/home/ilya/sandbox/heteroboot/uvdata_original.png',
bbox_inches='tight', dpi=400)
matplotlib.pyplot.close()
# # Plot residuals in radplot
# label_size = 12
# matplotlib.rcParams['xtick.labelsize'] = label_size
# matplotlib.rcParams['ytick.labelsize'] = label_size
# for i, color in enumerate(colors):
# uvdata_r.uvplot(baselines=uvdata_r.baselines[i], freq_average=True,
# color=color, style='re&im')
# matplotlib.pyplot.show()
# matplotlib.pyplot.savefig('/home/ilya/sandbox/heteroboot/radplot_res_gauss.png',
# bbox_inches='tight', dpi=400)
trc=trc,
beam=image.beam,
show_beam=True)
# Create posterior for data & model
lnpost = LnPost(uvdata, model)
ndim = model.size
nwalkers = 50
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost)
p_std1 = [0.1, 0.1, 0.1, 0.1]
p_std2 = [0.1, 0.1, 0.1, 0.1]
p0 = emcee.utils.sample_ball(model.p, p_std1 + p_std2, size=nwalkers)
pos, prob, state = sampler.run_mcmc(p0, 100)
sampler.reset()
sampler.run_mcmc(pos, 300)
# Overplot data and model
p_map = list()
for i in range(ndim):
counts, bin_values = np.histogram(sampler.flatchain[::10, i], bins=30)
p_map.append(bin_values[counts == counts.max()][0])
mdl = Model(stokes='I')
cg1 = CGComponent(*(p_map[:4]))
cg2 = CGComponent(*(p_map[4:]))
mdl.add_components(cg1, cg2)
uvdata.uvplot(stokes='I')
mdl.uvplot(uv=uvdata.uv)
fig = corner.corner(
sampler.flatchain[::10, :],
labels=["$flux$", "$y$", "$x$", "$maj$", "$flux$", "$y$", "$x$", "$maj$"])
