def from_txt(self, fname, style='difmap'):
"""
Function that reads TXT-files with models in difmap or AIPS format.
:param fname:
File with components.
:param style: (optional)
Style of model file. ``difmap`` or ``aips``. (default: ``difmap``)
"""
if style not in ['difmap', 'aips']:
raise Exception
if style == 'aips':
raise NotImplementedError
mdlo = open(fname)
lines = mdlo.readlines()
comps = list()
for line in lines:
if line.startswith('!'):
continue
line = line.strip('\n ')
flux, radius, theta, major, axial, phi, type_, freq, spec =\
line.split()
x = -float(radius[:-1]) * np.sin(np.deg2rad(float(theta[:-1])))
y = -float(radius[:-1]) * np.cos(np.deg2rad(float(theta[:-1])))
flux = float(flux[:-1])
if int(type_) == 0:
comp = DeltaComponent(flux, x, y)
elif int(type_) == 1:
try:
bmaj = float(major)
except ValueError:
bmaj = float(major[:-1])
if float(axial[:-1]) == 1:
comp = CGComponent(flux, x, y, bmaj)
else:
try:
e = float(axial)
except ValueError:
e = float(axial[:-1])
try:
bpa = -np.deg2rad(float(phi)) + np.pi / 2.
except ValueError:
bpa = -np.deg2rad(float(phi[:-1])) + np.pi / 2.
comp = EGComponent(flux, x, y, bmaj, e, bpa)
else:
raise NotImplementedError("Only CC, CG & EG are implemented")
comps.append(comp)
self.add_components(comps)
def sum_components(comp0, comp1, type="eg"):
print("Summing components {} and {}".format(comp0, comp1))
flux = comp0.p[0] + comp1.p[0]
x = (comp0.p[0] * comp0.p[1] + comp1.p[0] * comp1.p[1]) / flux
y = (comp0.p[0] * comp0.p[2] + comp1.p[0] * comp1.p[2]) / flux
bmaj = (comp0.p[0] * comp0.p[3] + comp1.p[0] * comp1.p[3]) / flux
if type == "cg":
component = CGComponent(flux, x, y, bmaj)
elif type == "eg":
dx = comp0.p[1] - comp1.p[1]
dy = comp0.p[2] - comp1.p[2]
bpa = 180 * np.arctan(dx / dy) / np.pi
e = 0.5 * (comp0.p[3] + comp1.p[3]) / np.hypot(comp0.p[1] - comp1.p[1],
comp0.p[2] - comp1.p[2])
component = EGComponent(flux, x, y, bmaj, e, bpa)
else:
raise Exception
print("Sum = {}".format(component))
return component
if __name__ == '__main__':
# Test LS_estimates
import sys
from components import CGComponent, EGComponent
from uv_data import UVData
from model import Model
try:
from scipy.optimize import minimize, fmin
except ImportError:
sys.exit("install scipy for ml estimation")
uv_fname = '/home/ilya/vlbi_errors/examples/L/1633+382/1633+382.l18.2010_05_21.uvf'
uvdata = UVData(uv_fname)
# Create model
cg1 = EGComponent(1.0, -0.8, 0.2, .7, 0.5, 0)
cg2 = CGComponent(0.8, 2.0, -.3, 2.3)
cg3 = CGComponent(0.2, 5.0, .0, 2.)
mdl = Model(stokes='I')
mdl.add_components(cg1, cg2, cg3)
# Create log of likelihood function
lnlik = LnLikelihood(uvdata, mdl, average_freq=True, amp_only=False)
# Nelder-Mead simplex algorithm
p_ml = fmin(lambda p: -lnlik(p), mdl.p)
# Various methods of minimization (some require jacobians)
# TODO: Implement analitical grad of likelihood (it's gaussian)
fit = minimize(lambda p: -lnlik(p),
mdl.p,
method='L-BFGS-B',
options={
'maxiter': 30000,
import os
import copy
from astropy.io import fits as pf
from uv_data import UVData
from model import Model
from components import EGComponent, CGComponent
from spydiff import modelfit_difmap, import_difmap_model
data_dir = '/home/ilya/code/vlbi_errors/tests/ft'
uv_fits = '1308+326.U1.2009_08_28.UV_CAL'
hdus_orig = pf.open(os.path.join(data_dir, uv_fits))
print "orig", hdus_orig[0].data[0][0]
uvdata = UVData(os.path.join(data_dir, uv_fits))
noise = uvdata.noise(use_V=False)
# noise = {bl: 0.1*noise_ for bl, noise_ in noise.items()}
eg1 = EGComponent(5., 0, 0, 0.15, 0.33, 0.2)
eg2 = EGComponent(2.5, 1, 1, 0.5, 0.5, 0.)
model = Model(stokes='I')
model.add_components(eg1, eg2)
# model.add_components(eg1, eg2)
uvdata_c = copy.deepcopy(uvdata)
uvdata_c.substitute([model])
uvdata_c.noise_add(noise)
uvdata_c.save(os.path.join(data_dir, 'fake.fits'), rewrite=True)
modelfit_difmap('fake.fits',
'mod_c2_2ee.mdl',
'out_2c.mdl',
path=data_dir,
mdl_path=data_dir,
out_path=data_dir,
niter=100)
# uv_fits_file = '1038+064.l22.2010_05_21.uvf'
# ccmodel = CCModel(stokes='I')
# ccmodel.add_cc_from_fits(cc_fits_file)
# uvdata = create_uvdata_from_fits_file(uv_fits_file)
# uv = uvdata.uvw[:, :2]
# ft = ccmodel.ft(uv)
# ccmodel.uvplot(uv)
# TESTING fitting gaussian components to uv-data
# Load uv-data
from uv_data import UVData
uvdata = UVData('1308+326.U1.2009_08_28.UV_CAL')
uv = uvdata.uvw[:, :2]
#twickle
# Create several components
cg1 = EGComponent(2.44, 0.02, -0.02, 0.10, 0.5, -1.)
cg2 = CGComponent(0.041, 0.71, -1.05, 1.18)
cg3 = CGComponent(0.044, 2.60, -3.20, 0.79)
cg4 = CGComponent(0.021, 1.50, -5.60, 2.08)
cg1.add_prior(flux=(
sp.stats.uniform.logpdf,
[0., 3.],
dict(),
),
bmaj=(
sp.stats.uniform.logpdf,
[0, 1.],
dict(),
),
e=(
sp.stats.uniform.logpdf,
def import_difmap_model(mdl_fname, mdl_dir=None):
"""
Function that reads difmap-format model and returns list of ``Components``
instances.
:param mdl_fname:
File name with difmap model.
:param mdl_dir: (optional)
Directory with difmap model. If ``None`` then use CWD. (default:
``None``)
:return:
List of ``Components`` instances.
"""
if mdl_dir is None:
mdl_dir = os.getcwd()
mdl = os.path.join(mdl_dir, mdl_fname)
mdlo = open(mdl)
lines = mdlo.readlines()
comps = list()
for line in lines:
if line.startswith('!'):
continue
line = line.strip('\n ')
try:
flux, radius, theta, major, axial, phi, type_, freq, spec = line.split(
)
except ValueError:
try:
flux, radius, theta, major, axial, phi, type_ = line.split()
except ValueError:
try:
flux, radius, theta = line.split()
except ValueError:
print "Problem parsing line :\n"
print line
raise ValueError
axial = 1.0
major = 0.0
type_ = 0
list_fixed = list()
if flux[-1] != 'v':
list_fixed.append('flux')
x = -float(radius[:-1]) * np.sin(np.deg2rad(float(theta[:-1])))
y = -float(radius[:-1]) * np.cos(np.deg2rad(float(theta[:-1])))
flux = float(flux[:-1])
if int(type_) == 0:
comp = DeltaComponent(flux, x, y)
elif int(type_) == 1:
try:
bmaj = float(major)
list_fixed.append('bmaj')
except ValueError:
bmaj = float(major[:-1])
if float(axial[:-1]) == 1:
comp = CGComponent(flux, x, y, bmaj, fixed=list_fixed)
else:
try:
e = float(axial)
except ValueError:
e = float(axial[:-1])
try:
bpa = np.deg2rad(float(phi)) + np.pi / 2.
except ValueError:
bpa = np.deg2rad(float(phi[:-1])) + np.pi / 2.
comp = EGComponent(flux, x, y, bmaj, e, bpa, fixed=list_fixed)
else:
raise NotImplementedError("Only CC, CG & EG are implemented")
comps.append(comp)
return comps
import os
from uv_data import UVData
from model import Model
from stats import LnPost
from components import EGComponent
import emcee
import corner
import matplotlib.pyplot as plt
import scipy as sp
import numpy as np
data_dir = '/home/ilya/Dropbox/ACC/BK/simulations/0952'
uv_fits = '0952_15GHz_BK.fits'
eg = EGComponent(10., 0., 0., 0.3, 0.5, 1.)
eg.add_prior(flux=(
sp.stats.uniform.logpdf,
[0., 30.],
dict(),
),
bmaj=(
sp.stats.uniform.logpdf,
[0, 3.],
dict(),
),
e=(
sp.stats.uniform.logpdf,
[0, 1.],
dict(),
),
bpa=(
sp.stats.uniform.logpdf,
except ImportError:
sys.exit("install scipy for ml estimation")
import scipy as sp
from stats import LnLikelihood, LnPost
from image import find_bbox
from image import plot as iplot
from image_ops import rms_image
import emcee
import corner
data_dir = '/home/ilya/Dropbox/Ilya/0235_bk150_uvf_data/'
uv_fname = '1803+784.u.2012_11_28.uvf'
uvdata = UVData(os.path.join(data_dir, uv_fname))
noise = uvdata.noise()
cg1 = EGComponent(2., 0., 0., 0.15, 0.33, 0.3)
cg1.add_prior(flux=(
sp.stats.uniform.logpdf,
[0., 5.],
dict(),
),
bmaj=(
sp.stats.uniform.logpdf,
[0, 20.],
dict(),
),
e=(
sp.stats.uniform.logpdf,
[0, 1.],
dict(),
),