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setuputil.py
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setuputil.py
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import numpy
from astropy.io import fits
import re
def loadParams(config):
headim = fits.getheader(config['ImageName'])
# get resolution in ALMA image
celldata = numpy.abs(headim['CDELT1'] * 3600)
#--------------------------------------------------------------------------
# Define the number of walkers
nwalkers = config['Nwalkers']
# Determine method of computing lnlike
if config.keys().count('LogLike') > 0:
lnlikemethod = config['LogLike']
else:
lnlikemethod = 'MLE'
# determine the number of regions for which we need surface brightness maps
configkeys = config.keys()
configkeystring = " ".join(configkeys)
regionlist = re.findall('Region.', configkeystring)
nregions = len(regionlist)
# instantiate lists that must be carried through to lnprob function
x = []
y = []
modelheader = []
nlens_regions = []
nsource_regions = []
p_u = []
p_l = []
prior_shape = []
poff = []
pname = []
pzero = []
model_types = []
nparams_total = 0
nlensedsource = 0
nlensedregions = 0
nregion = len(regionlist)
for iregion in range(nregion):
ri = str(iregion)
region = 'Region' + ri
cfdr = config[region]
ra_centroid = cfdr['RACentroid']
dec_centroid = cfdr['DecCentroid']
extent = cfdr['RadialExtent']
if cfdr.keys().count('Oversample') > 0:
oversample = cfdr['Oversample']
else:
oversample = 1
# count the number of lenses
configkeys = cfdr.keys()
configkeystring = " ".join(configkeys)
lenslist = re.findall('Lens.', configkeystring)
nlens = len(lenslist)
# count the number of sources
sourcelist = re.findall('Source.', configkeystring)
nsource = len(sourcelist)
# Append the number of lenses and sources for this region
nlens_regions.append(nlens)
nsource_regions.append(nsource)
# define number of pixels in lensed surface brightness map
dx = 2 * extent
nxmod = oversample * int(round(dx / celldata))
dy = 2 * extent
nymod = oversample * int(round(dy / celldata))
# make x and y coordinate images for lens model
onex = numpy.ones(nxmod)
oney = numpy.ones(nymod)
linspacex = numpy.linspace(0, 1, nxmod)
linspacey = numpy.linspace(0, 1, nymod)
x.append(dx * numpy.outer(oney, linspacex) - extent)
y.append(dy * numpy.outer(linspacey, onex) - extent)
# Provide world-coordinate system transformation data in the header of
# the lensed surface brightness map
headmod = headim.copy()
crpix1 = nxmod / 2 + 1
crpix2 = nymod / 2 + 1
cdelt1 = -1 * celldata / 3600 / oversample
cdelt2 = celldata / 3600 / oversample
headmod['naxis1'] = nxmod
headmod['cdelt1'] = cdelt1
headmod['crpix1'] = crpix1
headmod['crval1'] = ra_centroid
headmod['ctype1'] = 'RA---SIN'
headmod['naxis2'] = nymod
headmod['cdelt2'] = cdelt2
headmod['crpix2'] = crpix2
headmod['crval2'] = dec_centroid
headmod['ctype2'] = 'DEC--SIN'
modelheader.append(headmod)
# the parameter initialization vectors
p1 = []
p2 = []
for ilens in range(nlens):
li = str(ilens)
lens = 'Lens' + li
cfdrl = cfdr[lens]
# constraints on the lenses
lensparams = ['EinsteinRadius',
'DeltaRA',
'DeltaDec',
'AxialRatio',
'PositionAngle']
for param in lensparams:
cfdrlp = cfdrl[param]
limits = cfdrlp['Limits']
# upper limits
p_u.append(limits[3])
# lower limits
p_l.append(limits[0])
# upper bound on walker initialization
p2.append(limits[2])
# lower bound on walker initialization
p1.append(limits[1])
# store the shape of the priors
configkeys = cfdrlp.keys()
configkeystring = " ".join(configkeys)
priorshapelist = re.findall('PriorShape', configkeystring)
npriorshape = len(priorshapelist)
if npriorshape == 1:
prior_shape.append(cfdrlp[priorshapelist[0]])
else:
prior_shape.append('Uniform')
# store the parameter to which this parameter is fixed
fixedtolist = re.findall('FixedTo', configkeystring)
nfixedto = len(fixedtolist)
if nfixedto == 1:
poff.append(cfdrlp[fixedtolist[0]])
else:
poff.append('Free')
nametag = region + ' ' + lens + ' ' + param
pname.append(nametag)
model_types_source = []
if nlens > 0:
nlensedsource += nsource
nlensedregions += 1
for isource in range(nsource):
si = str(isource)
source = 'Source' + si
cfdrs = cfdr[source]
sourceparams = ['IntrinsicFlux',
'EffectiveRadius',
'DeltaRA',
'DeltaDec',
'AxialRatio',
'PositionAngle']
#tag = '_Source' + si + '_Region' + ri
for param in sourceparams:
cfdrsp = cfdrs[param]
limits = cfdrsp['Limits']
# upper limits
p_u.append(limits[3])
# lower limits
p_l.append(limits[0])
# upper bound on walker initialization
p2.append(limits[2])
# lower bound on walker initialization
p1.append(limits[1])
# store the shape of the priors
configkeys = cfdrsp.keys()
configkeystring = " ".join(configkeys)
priorshapelist = re.findall('PriorShape', configkeystring)
npriorshape = len(priorshapelist)
if npriorshape == 1:
prior_shape.append(cfdrsp[priorshapelist[0]])
else:
prior_shape.append('Uniform')
# store the parameter to which this parameter is fixed
fixedtolist = re.findall('FixedTo', configkeystring)
nfixedto = len(fixedtolist)
if nfixedto == 1:
poff.append(cfdrsp[fixedtolist[0]])
else:
poff.append('Free')
nametag = region + ' ' + source + ' ' + param
pname.append(nametag)
# get the model type
if cfdrs.keys().count('LightProfile') > 0:
model_types_source.append(cfdrs['LightProfile'])
else:
model_types_source.append('Gaussian')
# append the set of model types for this region
model_types.append(model_types_source)
# determine the number of free parameters in the model
nparams = len(p1)
# add that number to the total number of free parameters considering
# all regions so far
nparams_total += nparams
# Otherwise, choose an initial set of positions for the walkers.
pzero_model = numpy.zeros((nwalkers, nparams))
for j in range(nparams):
#if p3[j] == 'uniform':
pzero_model[:, j] = numpy.random.uniform(p1[j], p2[j], nwalkers)
#if p3[j] == 'normal':
# pzero_model[:,j] = (numpy.random.normal(loc=p1[j],
# scale=p2[j], size=nwalkers))
#if p4[j] == 'pos':
# pzero[:, j] = numpy.abs(pzero[:, j])
if pzero == []:
pzero = pzero_model
else:
pzero = numpy.append(pzero, pzero_model, axis=1)
paramSetup = {'x': x,
'y': y,
'modelheader': modelheader,
'regionlist': regionlist,
'nlens_regions': nlens_regions,
'nsource_regions': nsource_regions,
'nlensedsource': nlensedsource,
'nlensedregions': nlensedregions,
'p_u': numpy.array(p_u),
'p_l': numpy.array(p_l),
'PriorShape': numpy.array(prior_shape),
'poff': poff,
'pname': pname,
'pzero': pzero,
'model_types': model_types,
'nwalkers': nwalkers,
'nparams': nparams_total,
'celldata': celldata,
'lnlikemethod': lnlikemethod,
'nregions': nregions}
return paramSetup
def fixParams(paramSetup):
"""
Determine the indices for fixed parameters.
"""
nparams = paramSetup['nparams']
poff = paramSetup['poff']
pname = paramSetup['pname']
fixindx = numpy.zeros(nparams) - 1
for ifix in range(nparams):
if pname.count(poff[ifix]) > 0:
fixindx[ifix] = pname.index(poff[ifix])
return fixindx
def getCell(headim):
celldata = numpy.abs(headim['CDELT1'] * 3600)
return celldata
def makeMask(config):
from astropy import wcs
imloc = config['ImageName']
headim = fits.getheader(imloc)
im = fits.getdata(imloc)
im = im[0, 0, :, :]
celldata = getCell(headim)
datawcs = wcs.WCS(headim, naxis=2)
nx = im[0,:].size
ny = im[:,0].size
# compute rms within central 3/4 of the image
mask = im.copy()
mask[:] = 1
yr0 = ny / 4
yr1 = 3 * ny / 4
xr0 = nx / 4
xr1 = 3 * nx / 4
mask[yr0:yr1, xr0:xr1] = 0
# determine the number of regions for which we need surface brightness maps
configkeys = config.keys()
configkeystring = " ".join(configkeys)
regionlist = re.findall('Region.', configkeystring)
for region in regionlist:
ra_centroid = config[region]['RACentroid']
dec_centroid = config[region]['DecCentroid']
extent = config[region]['RadialExtent']
# mask regions containing significant emission
skyxy = datawcs.wcs_world2pix(ra_centroid, dec_centroid, 1)
x_center = skyxy[0]
y_center = skyxy[1]
pixextent = extent / celldata
xm0 = x_center - pixextent / 2
xm1 = x_center + pixextent / 2
ym0 = y_center - pixextent / 2
ym1 = y_center + pixextent / 2
mask[ym0:ym1, xm0:xm1] = 2
return mask