# fig.tight_layout()
# fig.savefig(os.path.join(data_dir, 'bias_corner_{}.eps'.format(freq)),
# dpi=1200, format='eps')
# Estimate bias using bootstrap
boot_dir = '/home/ilya/Dropbox/papers/boot/bias/new/stationary/boot'
for i in range(1, 101, 1):
art_fits_fname = 'art_{}_{}.fits'.format(freq, i)
art_fits_path = os.path.join(data_dir, art_fits_fname)
dfm_model_path = os.path.join(data_dir,
'out_{}_{}.mdl'.format(freq, i))
bootstrap_uvfits_with_difmap_model(
art_fits_path,
dfm_model_path,
boot_dir=boot_dir,
out_txt_file=os.path.join(boot_dir,
'bias_{}_{}.txt'.format(freq, i)),
out_plot_file=None,
n_boot=100,
niter=100)
originals = dict()
corrected = dict()
for freq in ('x', 'j', 'u'):
originals[freq] = list()
corrected[freq] = list()
biases = list()
r1 = list()
for i in range(1, 101, 1):
bias_file = os.path.join(boot_dir, 'bias_{}_{}.txt'.format(freq, i))
with open(bias_file, 'r') as fo:
# download_mojave_uv_fits(source, epochs=[epoch], bands=['u'],
# download_dir=data_dir)
uv_fits_fname = mojave_uv_fits_fname(source, 'u', epoch)
uv_fits_path = os.path.join(data_dir, uv_fits_fname)
original_model_path = os.path.join(data_dir, 'initial.mdl')
cg1 = CGComponent(2., 0., 0., 0.2)
cg2 = CGComponent(0.25, 0., 0.2, 0.2)
mdl = Model(stokes='I')
mdl.add_components(cg1, cg2)
uvdata = UVData(uv_fits_path)
noise = uvdata.noise()
for i in range(1, 2):
uvdata = UVData(uv_fits_path)
uvdata.substitute([mdl])
uvdata.noise_add(noise)
art_fits_fname = 'art_{}.fits'.format(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(i),
niter=100, path=data_dir, mdl_path=data_dir,
out_path=data_dir)
from bootstrap import bootstrap_uvfits_with_difmap_model
i=1
art_fits_fname = 'art_{}.fits'.format(i)
art_fits_path = os.path.join(data_dir, art_fits_fname)
bootstrap_uvfits_with_difmap_model(art_fits_path, os.path.join(data_dir, 'out_{}.mdl'.format(i)),
boot_dir=os.path.join(data_dir, 'boot'),
pairs=False)
uvdata = UVData(uv_fits_path)
try:
del uvdata.hdu.header['HISTORY']
uvdata.save(rewrite=True, downscale_by_freq=False)
except KeyError:
pass
out_txt_file = os.path.join(txt_file_dir, 'errors_{}.mod'.format(epoch))
out_png_file = os.path.join(txt_file_dir, 'errors_{}.png'.format(epoch))
try:
bootstrap_uvfits_with_difmap_model(uv_fits_path,
original_model_path,
n_boot=100,
boot_dir=boot_dir,
out_txt_file=out_txt_file,
out_plot_file=out_png_file,
clean_after=True,
niter=200,
out_rchisq_file=os.path.join(
txt_file_dir,
"{}_rchisq.dat".format(epoch)))
except IOError:
with open(os.path.join(txt_file_dir, '{}_io_error.txt'.format(epoch)),
'w'):
print("IO Error")
continue
except VerifyError:
with open(
os.path.join(txt_file_dir,
'{}_verify_error.txt'.format(epoch)), 'w'):
print("Verify Error")
refitted_mdl_fname = 'new2.mdl'
refitted_mdl_path = os.path.join(source_dir, refitted_mdl_fname)
# modelfit_difmap(uv_fits_fname, dfm_model_fname, refitted_mdl_fname,
# niter=300, path=source_dir, mdl_path=source_dir,
# out_path=source_dir)
# comps = import_difmap_model(refitted_mdl_fname, source_dir)
# model = Model(stokes='I')
# model.add_components(*comps)
# uvdata = UVData(uv_fits_path)
# fig = uvdata.uvplot()
# uvdata.substitute([model])
# uvdata.uvplot(fig=fig, color='r')
# bootstrapped_uv_fits = sorted(glob.glob(os.path.join(source_dir,
# 'bootstrapped_data*.fits')))
fig = bootstrap_uvfits_with_difmap_model(uv_fits_path, refitted_mdl_path,
boot_dir=source_dir, n_boot=100,
clean_after=False,
use_kde=True,
use_v=False,
out_plot_file=os.path.join(source_dir, 'plot.pdf'),
niter=150,
bootstrapped_uv_fits=None)
# from bootstrap import analyze_bootstrap_samples
# booted_mdl_paths = glob.glob(os.path.join(source_dir, 'mdl_booted*'))
# fig = analyze_bootstrap_samples(refitted_mdl_fname, booted_mdl_paths,
# dfm_model_dir=source_dir, plot_comps=[11, 10],
# plot_file=os.path.join(source_dir, '0_1_boot.eps'),
# txt_file=os.path.join(source_dir, 'txt.txt'))
# DOESNT WORK - ERRORS STILL SMALL
import os
from mojave import mojave_uv_fits_fname
from uv_data import UVData
from bootstrap import bootstrap_uvfits_with_difmap_model
data_dir = '/home/ilya/silke'
epoch = '2017_01_28'
original_model_fname = '2017_01_28us'
original_model_path = os.path.join(data_dir, original_model_fname)
uv_fits_fname = mojave_uv_fits_fname('0851+202', 'u', epoch)
uv_fits_path = os.path.join(data_dir, uv_fits_fname)
uvdata = UVData(uv_fits_path)
uvdata.noise_add({baseline: [0.137] for baseline in uvdata.baselines})
new_fits_path = os.path.join(data_dir, 'added_noise.fits')
uvdata.save(new_fits_path)
bootstrap_uvfits_with_difmap_model(new_fits_path,
original_model_path,
boot_dir=os.path.join(data_dir, 'boot'))
uv_fits_fname = mojave_uv_fits_fname('0851+202', 'u', epoch)
uv_fits_path = os.path.join(data_dir, uv_fits_fname)
uvdata = UVData(uv_fits_path)
try:
del uvdata.hdu.header['HISTORY']
uvdata.save(rewrite=True, downscale_by_freq=False)
except KeyError:
pass
out_txt_file = os.path.join(txt_file_dir, 'errors_{}.dml'.format(epoch))
out_png_file = os.path.join(txt_file_dir, 'errors_{}.png'.format(epoch))
try:
bootstrap_uvfits_with_difmap_model(uv_fits_path, original_model_path,
n_boot=300, boot_dir=boot_dir,
out_txt_file=out_txt_file,
out_plot_file=out_png_file,
clean_after=True, niter=200)
# except IOError:
# with open(os.path.join(txt_file_dir, '{}_io_error.txt'.format(epoch)), 'w'):
# print "IO Error"
# continue
except VerifyError:
with open(os.path.join(txt_file_dir, '{}_verify_error.txt'.format(epoch)), 'w'):
print "Verify Error"
bootstrapped_uv_fits = sorted(glob.glob(os.path.join(boot_dir,
'bootstrapped_data*.fits')))
for file_ in bootstrapped_uv_fits:
os.unlink(file_)
continue