/
visualutil.py
1046 lines (879 loc) · 36.3 KB
/
visualutil.py
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def plotPDF(fitresults, tag, limits='', Ngood=5000, axes='auto'):
"""
Plot the PDF of each parameter of the model.
"""
import numpy
import matplotlib.pyplot as plt
from pylab import savefig
from matplotlib import rc
import modifypdf
# plotting parameters
rc('font',**{'family':'sans-serif', 'sans-serif':['Arial Narrow'],
'size':'12'})
# grab the last Ngood fits
fitresults = fitresults[-Ngood:]
#lnprobstring = "prior to pruning <Ln Prob>: {:f}"
#print(lnprobstring.format(fitresults['lnprob'].mean()))
# identify the good fits
fitresultsgood = modifypdf.prune(fitresults)
# determine dimensions of PDF plots
nparams = len(fitresultsgood[0])
ncol = 4
nrow = nparams / ncol + 1
j = 1
plt.figure(figsize=(12.0, 1.5 * nrow))
# set up the plotting window
plt.subplots_adjust(left=0.08, bottom=0.15, right=0.95, top=0.95,
wspace=0.4, hspace=0.65)
pnames = fitresultsgood.names
counter = 0
for pname in pnames:
# Position of primary lens
frg = fitresultsgood[pname]
rmsval = numpy.std(frg)
if rmsval > 1e-6:
avgval = numpy.mean(frg)
print(pname + ' = ' + str(avgval) + ' +/- ' + str(rmsval))
totalwidth = frg.max() - frg.min()
nbins = totalwidth / rmsval * 5
ax = plt.subplot(nrow, ncol, j)
j += 1
plt.hist(frg, nbins, edgecolor='blue')
plt.ylabel('N')
plt.xlabel(pname)
if axes == 'auto':
start, end = ax.get_xlim()
nticks = 5
stepsize = (end - start) / nticks
ax.xaxis.set_ticks(numpy.arange(start, end + 0.99*stepsize,
stepsize))
elif axes == 'initial':
oldaxis = plt.axis()
if pname[0:6] == 'lnprob':
xmin = frg.min()
xmax = frg.max()
elif pname[0:2] == 'mu':
xmin = 0
xmax = 30
else:
p_l = limits[0]
p_u = limits[1]
xmin = p_l[counter]
xmax = p_u[counter]
counter += 1
ymin = oldaxis[2]
ymax = oldaxis[3]
plt.axis([xmin, xmax, ymin, ymax])
savefile = tag + 'PDFs.png'
savefig(savefile)
def makeSBmap(config, fitresult):
"""
Make a surface brightness map of the lensed image for a given set of model
parameters.
"""
import lensutil
from astropy.io import fits
import os
import setuputil
import re
import numpy
# Loop over each region
# read the input parameters
paramData = setuputil.loadParams(config)
nlensedsource = paramData['nlensedsource']
nlensedregions = paramData['nlensedregions']
npar_previous = 0
configkeys = config.keys()
configkeystring = " ".join(configkeys)
regionlist = re.findall('Region.', configkeystring)
SBmap_all = 0
LensedSBmap_all = 0
nregion = len(regionlist)
for regioni in range(nregion):
regstring = 'Region' + str(regioni)
#indx = paramData['regionlist'].index(regstring)
cr = config[regstring]
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
if nmu > 0:
allparameters0 = list(fitresult)[1:-nmu]
else:
allparameters0 = list(fitresult)[1:]
# search poff_models for parameters fixed relative to other parameters
fixindx = setuputil.fixParams(paramData)
poff = paramData['poff']
ndim_total = len(poff)
fixed = (numpy.where(fixindx >= 0))[0]
nfixed = fixindx[fixed].size
parameters_offset = numpy.zeros(ndim_total)
for ifix in range(nfixed):
ifixed = fixed[ifix]
subindx = fixindx[ifixed]
par0 = 0
if fixindx[subindx] > 0:
par0 = fitresult[fixindx[subindx] + 1]
parameters_offset[ifixed] = fitresult[subindx + 1] + par0
allparameters = allparameters0 + parameters_offset
# count the number of lenses
configkeys = cr.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)
nparperlens = 5
nparpersource = 6
nparlens = nparperlens * nlens
nparsource = nparpersource * nsource
npar = nparlens + nparsource + npar_previous
parameters = allparameters[npar_previous:npar]
npar_previous = npar
#nlens = paramData['nlens_regions'][indx]
#nsource = paramData['nsource_regions'][indx]
x = paramData['x'][regioni]
y = paramData['y'][regioni]
modelheader = paramData['modelheader'][regioni]
model_types = paramData['model_types'][regioni]
SBmap, LensedSBmap, Aperture, LensedAperture, mu_tot, mu_mask = \
lensutil.sbmap(x, y, nlens, nsource, parameters, model_types, \
computeamp=True)
caustics = False
if caustics:
deltapar = parameters[0:nparlens + nparpersource]
refine = 2
nx = x[:, 0].size * refine
ny = y[:, 0].size * refine
x1 = x[0, :].min()
x2 = x[0, :].max()
linspacex = numpy.linspace(x1, x2, nx)
y1 = y[:, 0].min()
y2 = y[:, 0].max()
linspacey = numpy.linspace(y1, y2, ny)
onex = numpy.ones(nx)
oney = numpy.ones(ny)
finex = numpy.outer(oney, linspacex)
finey = numpy.outer(linspacey, onex)
mumap = numpy.zeros([ny, nx])
for ix in range(nx):
for iy in range(ny):
deltapar[-nparpersource + 0] = finex[ix, iy]
deltapar[-nparpersource + 1] = finey[ix, iy]
xcell = paramData['celldata']
deltapar[-nparpersource + 2] = xcell
deltaunlensed, deltalensed, A1, A2, mu_xy, mu_xymask = \
lensutil.sbmap(finex, finey, nlens, 1, deltapar, ['Delta'])
mumap[ix, iy] = mu_xy[0]
import matplotlib.pyplot as plt
plt.imshow(mumap, origin='lower')
plt.contour(mumap, levels=[mumap.max()/1.1])
import pdb; pdb.set_trace()
SBmap_all += SBmap
LensedSBmap_all += LensedSBmap
LensedSBmapLoc = 'LensedSBmap.fits'
SBmapLoc = 'SBmap_Region.fits'
cmd = 'rm -rf ' + LensedSBmapLoc + ' ' + SBmapLoc
os.system(cmd)
fits.writeto(LensedSBmapLoc, LensedSBmap_all, modelheader)
fits.writeto(SBmapLoc, SBmap_all, modelheader)
return
def makeVis(config, miriad=False, idtag=''):
"""
Make simulated visibilities given a model image and observed visibilities.
Writes the visibilities to uvfits files.
"""
import uvmodel
import os
# get the uvfits files
visfile = config['UVData']
#----------------------------------------------------------------------
# Python version of UVMODEL
# "Observe" the lensed emission with the SMA and write to a new file
#----------------------------------------------------------------------
# Python version of UVMODEL's "replace" subroutine:
# is visfile a list of visibilities files?
if not type(visfile) is list:
visname, visext = os.path.splitext(visfile)
if miriad:
# We use miriad to do the imaging
tag = '.miriad'
DataMiriad = visname + tag
if not os.path.exists(DataMiriad):
print("Creating new miriad data file: " + DataMiriad)
os.system('rm -rf ' + DataMiriad)
command = 'fits op=uvin in=' + visfile + ' out=' + DataMiriad
os.system(command)
else:
# We use CASA to do the imaging
tag = '.ms'
# check to see if the CASA ms exists
try:
from taskinit import tb
tb.open(visname + tag)
tb.close()
print "Found an existing CASA ms file."
except RuntimeError:
print "No CASA ms file found, creating one from " + visname \
+ ".uvfits file."
from casa import importuvfits
infile = visname + '.uvfits'
outfile = visname + '.ms'
importuvfits(fitsfile=infile, vis=outfile)
visfile = visname + tag
SBmapLoc = 'LensedSBmap.fits'
modelvisfile = visname + '_model_' + idtag + tag
os.system('rm -rf ' + modelvisfile)
if miriad:
SBmapMiriad = 'LensedSBmap' + tag
os.system('rm -rf ' + SBmapMiriad)
command = 'fits op=xyin in=' + SBmapLoc + ' out=' \
+ SBmapMiriad
os.system(command)
command = 'uvmodel options=replace vis=' + visfile + \
' model=' + SBmapMiriad + ' out=' + modelvisfile
os.system(command + ' > uvmodeloutput.txt')
#command = 'cp ' + visfile + '/wflags ' + modelvisfile
#os.system(command)
else:
#print(visfile, modelvisfile)
uvmodel.replace(SBmapLoc, visfile, modelvisfile,
miriad=miriad)
#print(visfile, modelvisfile)
# Python version of UVMODEL's "subtract" subroutine:
modelvisfile = visname + '_residual_' + idtag + tag
os.system('rm -rf ' + modelvisfile)
if miriad:
SBmapMiriad = 'LensedSBmap' + tag
os.system('rm -rf ' + SBmapMiriad)
command = 'fits op=xyin in=' + SBmapLoc + ' out=' \
+ SBmapMiriad
os.system(command)
os.system('rm -rf ' + modelvisfile)
command = 'uvmodel options=subtract vis=' + visfile + \
' model=' + SBmapMiriad + ' out=' + modelvisfile
os.system(command)
#command = 'cp ' + visfile + '/wflags ' + modelvisfile
#os.system(command)
else:
#print(visfile, modelvisfile)
uvmodel.subtract(SBmapLoc, visfile, modelvisfile,
miriad=miriad)
else:
for i, ivisfile in enumerate(visfile):
visname, visext = os.path.splitext(ivisfile)
print(visname)
if miriad:
tag = '.uvfits'
else:
tag = '.ms'
# check to see if the CASA ms exists
try:
from taskinit import tb
tb.open(visname + tag)
tb.close()
print "Found an existing CASA ms file."
except RuntimeError:
print "No CASA ms file found, creating one from " + visname \
+ ".uvfits file."
from casa import importuvfits
infile = visname + '.uvfits'
outfile = visname + '.ms'
importuvfits(fitsfile=infile, vis=outfile)
SBmapLoc = 'LensedSBmap.fits'
if miriad:
SBmapMiriad = 'LensedSBmap.miriad'
os.system('rm -rf ' + SBmapMiriad)
command = 'fits op=xyin in=' + SBmapLoc + ' out=' \
+ SBmapMiriad
os.system(command)
ivisfile = visname + '.miriad'
modelivisfile = visname + '_model_' + idtag + '.miriad'
os.system('rm -rf ' + modelivisfile)
command = 'uvmodel options=replace vis=' + ivisfile + \
' model=' + SBmapMiriad + ' out=' + modelivisfile
os.system(command)
#command = 'cp ' + ivisfile + '/wflags ' + modelivisfile
#os.system(command)
else:
ivisfile = visname + tag
modelivisfile = visname + '_model_' + idtag + tag
os.system('rm -rf ' + modelivisfile)
uvmodel.replace(SBmapLoc, ivisfile, modelivisfile,
miriad=miriad)
# Python version of UVMODEL's "subtract" subroutine:
if miriad:
SBmapMiriad = 'LensedSBmap.miriad'
os.system('rm -rf ' + SBmapMiriad)
command = 'fits op=xyin in=' + SBmapLoc + ' out=' \
+ SBmapMiriad
os.system(command)
modelivisfile = visname + '_residual_' + idtag + '.miriad'
os.system('rm -rf ' + modelivisfile)
command = 'uvmodel options=subtract vis=' + ivisfile + \
' model=' + SBmapMiriad + ' out=' + modelivisfile
os.system(command)
#command = 'cp ' + ivisfile + '/wflags ' + modelivisfile
#os.system(command)
else:
modelivisfile = visname + '_residual_' + idtag + tag
os.system('rm -rf ' + modelivisfile)
uvmodel.subtract(SBmapLoc, ivisfile, modelivisfile,
miriad=miriad)
#except:
# msg = "Visibility datasets must be specified as either a string " \
# + "or a list of strings."
# print(msg)
# raise TypeError
def makeImage(config, interactive=True, miriad=False, idtag=''):
"""
Make an image of the model and the residual from simulated model
visibilities. Requires CASA or miriad.
"""
import os
from astropy.io import fits
import miriadutil
visfile = config['UVData']
target = config['ObjectName']
fitsim = config['ImageName']
fitshead = fits.getheader(fitsim)
imsize = [fitshead['NAXIS1'], fitshead['NAXIS2']]
cell = str(fitshead['CDELT2'] * 3600) + 'arcsec'
# search for an existing mask
index = fitsim.find('.fits')
maskname = fitsim[0:index] + '.mask'
try:
maskcheck = os.path.exists(maskname)
except:
maskcheck = False
if maskcheck:
mask = maskname
else:
mask = ''
# invert and clean the simulated model visibilities
if miriad:
try:
# use miriad for imaging
imsize = str(fitshead['NAXIS1'])
index = visfile.find('.uvfits')
name = visfile[0:index]
uvfitsloc = name + '_model_' + idtag + '.uvfits'
uvmirloc = name + '_model_' + idtag + '.miriad'
command = 'rm -rf ' + uvmirloc
#os.system(command)
command = 'fits op=uvin options=varwt in=' + uvfitsloc \
+ ' out=' + uvmirloc
#os.system(command + ' > fitsoutput.txt')
imloc = target + '_clean_model'
niter = '10000'
cell = str(fitshead['CDELT2'] * 3600)
cutoff = '2e-3'
cutoff2 = '4e-3'
robust = '+0.5'
region = 'quarter'
region2 = 'quarter'
gain = '0.1'
fwhm = '0'
sup = '0'
parameters = [uvmirloc, imloc, imsize, cell, niter, cutoff, \
cutoff2, robust, region, region2, gain, fwhm, sup]
miriadutil.makeScript(parameters)
command = 'csh image.csh'
os.system(command + ' > imageoutput.txt')
# the simulated residual visibilities
uvfitsloc = name + '_residual_' + idtag + '.uvfits'
uvmirloc = name + '_residual_' + idtag + '.miriad'
command = 'rm -rf ' + uvmirloc
#os.system(command)
command = 'fits op=uvin options=varwt in=' + uvfitsloc \
+ ' out=' + uvmirloc
#os.system(command + ' >> fitsoutput.txt')
imloc = target + '_clean_residual'
parameters = [uvmirloc, imloc, imsize, cell, niter, cutoff, \
cutoff2, robust, region, region2, gain, fwhm, sup]
miriadutil.makeScript(parameters)
command = 'csh image.csh'
os.system(command)# + ' >> imageoutput.txt')
except:
try:
modellist = []
residlist = []
for ivisfile in visfile:
# use miriad for imaging
imsize = str(fitshead['NAXIS1'])
index = ivisfile.find('.uvfits')
name = ivisfile[0:index]
uvfitsloc = name + '_model_' + idtag + '.uvfits'
uvmirloc = name + '_model_' + idtag + '.miriad'
modellist.append(uvmirloc)
command = 'rm -rf ' + uvmirloc
#os.system(command)
command = 'fits op=uvin in=' + uvfitsloc + ' out=' \
+ uvmirloc
#os.system(command + ' > fitsoutput.txt')
# the simulated residual visibilities
uvfitsloc = name + '_residual_' + idtag + '.uvfits'
uvmirloc = name + '_residual_' + idtag + '.miriad'
residlist.append(uvmirloc)
command = 'rm -rf ' + uvmirloc
#os.system(command)
command = 'fits op=uvin in=' + uvfitsloc + ' out=' \
+ uvmirloc
#os.system(command + ' >> fitsoutput.txt')
invisloc = ','.join(modellist)
imloc = target + '_model'
parameters = [invisloc, imloc, imsize, cell, niter, cutoff, \
cutoff2, robust, region, region2, gain, fwhm, sup]
miriadutil.makeScript(parameters)
command = 'csh image.csh'
os.system(command + ' > imageoutput.txt')
invisloc = ','.join(residlist)
imloc = target + '_residual'
parameters = [invisloc, imloc, imsize, cell, niter, cutoff, \
cutoff2, robust, region, region2, gain, fwhm, sup]
miriadutil.makeScript(parameters)
command = 'csh image.csh'
os.system(command)# + ' >> imageoutput.txt')
except:
msg = "Visibility datasets must be specified as either a "\
"string or a list of strings."
print(msg)
raise TypeError
else:
# use CASA for imaging
from clean import clean
from casa import exportfits
# ---------------------------------------
# invert and clean the model visibilities
# remove any existing clean products
imloc = target + '_clean_model'
os.system('rm -rf ' + imloc + '*')
# handle lists of visibility files
if type(visfile) is list:
modelvisloc = []
for i, ivisfile in enumerate(visfile):
visname, visext = os.path.splitext(ivisfile)
modelvisloc.append(visname + '_model_' + idtag + '.ms')
# handle single visibility files
else:
visname, visext = os.path.splitext(visfile)
modelvisloc = visname + '_model_' + idtag + '.ms'
# use CASA's clean task to make the images
clean(vis=modelvisloc, imagename=imloc, mode='mfs', niter=10000,
threshold='0.2mJy', interactive=interactive, mask=mask,
imsize=imsize, cell=cell, weighting='briggs', robust=0.5)
# export the cleaned image to a fits file
os.system('rm -rf ' + imloc + '.fits')
exportfits(imagename=imloc + '.image', fitsimage=imloc + '.fits')
# ---------------------------------------
# invert and clean the residual visibilities
# remove any existing clean products
imloc = target + '_clean_residual'
os.system('rm -rf ' + imloc + '*')
# handle lists of visibility files
if type(visfile) is list:
modelvisloc = []
for i, ivisfile in enumerate(visfile):
visname, visext = os.path.splitext(ivisfile)
modelvisloc.append(visname + '_residual_' + idtag + '.ms')
# handle single visibility files
else:
visname, visext = os.path.splitext(visfile)
modelvisloc = visname + '_residual_' + idtag + '.ms'
# use CASA's clean task to make the images
clean(vis=modelvisloc, imagename=imloc, mode='mfs', niter=10000,
threshold='0.2mJy', interactive=interactive, mask=mask,
imsize=imsize, cell=cell, weighting='briggs', robust=0.5)
# export the cleaned image to a fits file
os.system('rm -rf ' + imloc + '.fits')
exportfits(imagename=imloc + '.image', fitsimage=imloc + '.fits')
return
def plotImage(model, data, config, modeltype, fitresult, tag=''):
"""
Make a surface brightness map of a given model image. Overlay with red
contours a surface brightness map of the data image to which the model was
fit.
"""
import numpy
from astropy import wcs
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from pylab import savefig
import setuputil
import re
# set font properties
font = {'family' : 'Arial Narrow',
'weight' : 'bold',
'size' : 10}
matplotlib.rc('font', **font)
matplotlib.rcParams['axes.linewidth'] = 1.5
fig = plt.figure(figsize=(3.0, 3.0))
ax = fig.add_subplot(1, 1, 1)
plt.subplots_adjust(left=0.08, right=0.97, top=0.97,
bottom=0.08, wspace=0.35)
paramData = setuputil.loadParams(config)
nlensedsource = paramData['nlensedsource']
nlensedregions = paramData['nlensedregions']
npar_previous = 0
configkeys = config.keys()
configkeystring = " ".join(configkeys)
regionlist = re.findall('Region.', configkeystring)
nregion = len(regionlist)
for iregion in range(nregion):
region = 'Region' + str(iregion)
cr = config[region]
ra_centroid = cr['RACentroid']
dec_centroid = cr['DecCentroid']
radialextent = cr['RadialExtent']
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
if nmu > 0:
allparameters0 = list(fitresult)[1:-nmu]
else:
allparameters0 = list(fitresult)[1:]
# search poff_models for parameters fixed relative to other parameters
fixindx = setuputil.fixParams(paramData)
poff = paramData['poff']
ndim_total = len(poff)
fixed = (numpy.where(fixindx >= 0))[0]
nfixed = fixindx[fixed].size
parameters_offset = numpy.zeros(ndim_total)
for ifix in range(nfixed):
ifixed = fixed[ifix]
subindx = fixindx[ifixed]
par0 = 0
if fixindx[subindx] > 0:
par0 = fitresult[fixindx[subindx] + 1]
parameters_offset[ifixed] = fitresult[subindx + 1] + par0
allparameters = allparameters0 + parameters_offset
# count the number of lenses
configkeys = cr.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)
nparlens = 5 * nlens
nparsource = 6 * nsource
npar = nparlens + nparsource + npar_previous
parameters = allparameters[npar_previous:npar]
npar_previous = npar
for i in range(nsource):
i6 = i * 6
xxx = parameters[i6 + 2 + nparlens]
yyy = parameters[i6 + 3 + nparlens]
source_pa = 90 - parameters[i6 + 5 + nparlens]
#model_type = model_types[i]
#if model_type == 'gaussian':
norm = 2.35
#if model_type == 'cylinder':
# norm = numpy.sqrt(2)
meansize = norm * parameters[i6 + 1 + nparlens]
source_bmaj = meansize / numpy.sqrt(parameters[i6 + 4 + nparlens])
source_bmin = meansize * numpy.sqrt(parameters[i6 + 4 + nparlens])
e = Ellipse((xxx, yyy), source_bmaj, source_bmin, \
angle=source_pa, ec='white', lw=0.5, fc='magenta', \
zorder=2, fill=True, alpha=0.5)
ax.add_artist(e)
if config.keys().count('xtextoff') > 0:
xytext = (xxx + config['xtextoff'][i],
yyy + config['ytextoff'][i])
if iregion == 0:
if modeltype != 'optical':
plt.annotate(str(i), xy=(xxx, yyy), xytext=xytext,
fontsize='xx-large')
else:
xytext = (xxx + config['xtextoff'][i+2],
yyy + config['ytextoff'][i+2])
if modeltype != 'optical':
plt.annotate(str(i + 2), xy=(xxx, yyy), xytext=xytext,
fontsize='xx-large')
for i in range(nlens):
i5 = i * 5
xxx = numpy.array([parameters[i5 + 1]])
yyy = numpy.array([parameters[i5 + 2]])
plt.plot(xxx, yyy, 'o', ms=5., mfc='black', mec='white', mew=0.5, \
label='Lens Position', zorder=20)
lens_pa = 90 - parameters[i5 + 4]
meansize = 2 * parameters[i5]
lens_bmaj = meansize / numpy.sqrt(parameters[i5 + 3])
lens_bmin = meansize * numpy.sqrt(parameters[i5 + 3])
elens = Ellipse((xxx, yyy), lens_bmaj, lens_bmin, \
angle=lens_pa, ec='orange', lw=1.0, \
zorder=20, fill=False)
ax.add_artist(elens)
# get the image centroid in model pixel coordinates
headim = data[0].header
headmod = model[0].header
im = data[0].data
im = im[0, 0, :, :]
# good region is where mask is zero
mask = setuputil.makeMask(config)
goodregion = mask == 0
# compute sigma image from cutout of SMA flux image
# dv = 1.
# bunit = headim['BUNIT']
# if bunit == 'JY/BEAM.KM/S':
# dv = 500.
rms = im[goodregion].std()# * dv
# Obtain measurements of beamsize and image min/max
bmaj = headim['BMAJ'] * 3600
bmin = headim['BMIN'] * 3600
bpa = headim['BPA']
cdelt1 = headim['CDELT1'] * 3600
cdelt2 = headim['CDELT2'] * 3600
cell = numpy.sqrt( abs(cdelt1) * abs(cdelt2) )
im_model = model[0].data
if im_model.ndim == 4:
im_model = im_model[0, 0, :, :]
#nx_model = im_model[0, :].size
pixextent = radialextent / cell
datawcs = wcs.WCS(headim, naxis=2)
pix = datawcs.wcs_world2pix(ra_centroid, dec_centroid, 1)
x0 = numpy.round(pix[0])
y0 = numpy.round(pix[1])
imrady = numpy.round(radialextent / cell)# nymod / 2.
imradx = numpy.round(radialextent / cell)# nxmod / 2.
# make data cutout
totdx1 = x0 - imradx
totdx2 = x0 + imradx
totdy1 = y0 - imrady
totdy2 = y0 + imrady
datacut = im[totdy1:totdy2,totdx1:totdx2]
# make cleaned model cutout
headerkeys = headmod.keys()
cd1_1 = headerkeys.count('CD1_1')
cd1_2 = headerkeys.count('CD1_2')
if cd1_1 == 0:
cdelt1_model = numpy.abs(headmod['CDELT1'] * 3600)
cdelt2_model = numpy.abs(headmod['CDELT2'] * 3600)
else:
cdelt1_model = numpy.abs(headmod['CD1_1'] * 3600)
cdelt2_model = numpy.abs(headmod['CD2_2'] * 3600)
cd11 = headmod['CD1_1']
if cd1_2 == 0:
cd12 = 0
cd21 = 0
else:
cd12 = headmod['CD1_2']
cd21 = headmod['CD2_1']
cd22 = headmod['CD2_2']
cdelt1_model = numpy.sqrt(cd11 ** 2 + cd12 ** 2) * 3600
cdelt2_model = numpy.sqrt(cd21 ** 2 + cd22 ** 2) * 3600
if cd12 == 0:
cd12 = cd11 / 1e8
cdratio = numpy.abs(cd11 / cd12)
if cdratio < 1:
cdratio = 1 / cdratio
cellmod = numpy.sqrt( abs(cdelt1_model) * abs(cdelt2_model) )
modelwcs = wcs.WCS(headmod, naxis=2)
pix = modelwcs.wcs_world2pix(ra_centroid, dec_centroid, 1)
x0 = numpy.round(pix[0])
y0 = numpy.round(pix[1])
modrady = numpy.round(radialextent / cellmod)
modradx = numpy.round(radialextent / cellmod)
totdx1 = x0 - modradx
totdx2 = x0 + modradx
totdy1 = y0 - modrady
totdy2 = y0 + modrady
modelcut = im_model[totdy1:totdy2,totdx1:totdx2]
#cellp = cell * (2 * pixextent + 1.1) / (2 * pixextent)
xlo = -radialextent
xhi = radialextent
ylo = -radialextent
yhi = radialextent
ncell = modelcut[:, 0].size#(xhi - xlo) / cell
modx = -numpy.linspace(xlo, xhi, ncell)
mody = numpy.linspace(ylo, yhi, ncell)
#modx = -(numpy.arange(2 * pixextent) - pixextent) * cellp - cell/2.
#mody = (numpy.arange(2 * pixextent) - pixextent) * cellp + cell/2.
cornerextent = [modx[0], modx[-1], mody[0], mody[-1] ]
if modeltype == 'residual':
grayscalename = 'Residual'
pcolor = 'white'
ncolor = 'black'
vmax = 5 * rms
vmin = -5 * rms
elif modeltype == 'model':
grayscalename = 'Model'
pcolor = 'red'
ncolor = 'red'
vmax = modelcut.max()
vmin = modelcut.min()
else:
grayscalename = config['OpticalTag']
filtindx = grayscalename.find(' ')
filtname = grayscalename[filtindx + 1:]
if filtname == 'F110W':
modelcut = numpy.log10(modelcut - modelcut.min() + 1)
pcolor = 'red'
ncolor = 'red'
vmax = modelcut.max()
vmin = modelcut.min()
plt.imshow(modelcut, cmap='gray_r', interpolation='nearest', \
extent=cornerextent, origin='lower', vmax=vmax, vmin=vmin)
plevs = 3*rms * 2**(numpy.arange(10))
nlevs = sorted(-3 * rms * 2**(numpy.arange(4)))
pcline = 'solid'
ncline = 'dashed'
#nx_contour = datacut[0, :].size
#ny_contour = datacut[:, 0].size
#cmodx = -(numpy.arange(nx_contour) - pixextent) * cellp - cell/2.
#cmody = (numpy.arange(ny_contour) - pixextent) * cellp + cell/2.
ncell = datacut[:, 0].size#(xhi - xlo) / cell
cmodx = -numpy.linspace(xlo, xhi, ncell)
cmody = numpy.linspace(ylo, yhi, ncell)
plt.contour(cmodx, cmody, datacut, colors=pcolor, levels=plevs, \
linestyles=pcline, linewidths=1.5)
plt.contour(cmodx, cmody, datacut, colors=ncolor, levels=nlevs, \
linestyles=ncline, linewidths=1.5)
# plot the critical curve
#plt.contour(cmodx, cmody, dmu, colors='orange', levels=[100])
#axisrange = plt.axis()
axisrange = numpy.array([xhi,xlo,ylo,yhi]).astype(float)
plt.axis(axisrange)
plt.minorticks_on()
plt.tick_params(width=1.5, which='both')
plt.tick_params(length=2, which='minor')
plt.tick_params(length=4, which='major')
#plt.xlabel(r'$\Delta$RA (arcsec)', fontsize='x-large')
#plt.ylabel(r'$\Delta$Dec (arcsec)', fontsize='x-large')
bparad = bpa / 180 * numpy.pi
beamx = numpy.abs(numpy.sin(bparad) * bmaj) + \
numpy.abs(numpy.cos(bparad) * bmin)
beamy = numpy.abs(numpy.cos(bparad) * bmaj) + \
numpy.abs(numpy.sin(bparad) * bmin)
beamxhi = 2 * pixextent / cell
beamxlo = -2 * pixextent / cell
beamyhi = 2 * pixextent / cell
beamylo = -2 * pixextent / cell
beamdx = numpy.float(beamxhi) - numpy.float(beamxlo)
beamdy = numpy.float(beamyhi) - numpy.float(beamylo)
bufferx = 0.03 * beamdx / 6.0
buffery = 0.03 * beamdx / 6.0
xpos = 1 - beamx/beamdx/2 - bufferx
ypos = beamy/beamdy/2 + buffery
#beamx = bmaj * numpy.abs(numpy.cos(bparad))
#beamy = bmaj * numpy.abs(numpy.sin(bparad))
xpos = 0.95 * axisrange[1] + 0.95 * beamx / 2.
ypos = 0.95 * axisrange[2] + 0.95 * beamy / 2.
e = Ellipse((xpos,ypos), bmaj, bmin, angle=90 - bpa, ec='black', \
hatch='//////', lw=1.0, fc='None', zorder=10, fill=True)
ax.add_artist(e)
plt.text(0.92, 0.88, grayscalename, transform=ax.transAxes,
fontsize='xx-large', ha='right')
try:
from astropy.table import Table
tloc = '../../../Papers/Bussmann_2015a/Bussmann2015/Data/targetlist.dat'
hackstep = Table.read(tloc, format='ascii')
objname = config['ObjectName']
match = hackstep['dataname'] == objname
shortname = hackstep['shortname'][match][0]
plt.text(0.08, 0.88, shortname, transform=ax.transAxes,
fontsize='xx-large')
except:
pass
bigtag = '.' + modeltype + '.' + tag
savefig('LensedSBmap' + bigtag + '.pdf')
#plt.clf()
def removeTempFiles():
"""
Remove files created along the way by visualutil routines.
"""
import os
cmd = 'rm -rf *SBmap*fits *_model* *_residual* *output.txt'
os.system(cmd)
def plotFit(config, fitresult, tag='', cleanup=True, showOptical=False,
interactive=True, plotonly=False):
"""
Plot a particular model fit.
"""
from astropy.io import fits
if not plotonly:
# make the lensed image
makeSBmap(config, fitresult)
# are we using miriad to image the best-fit model?
if config.keys().count('UseMiriad') > 0:
miriad = config['UseMiriad']
if miriad == 'Visualize':
miriad = True
interactive = False
else:
miriad = False
# make the simulated visibilities
makeVis(config, miriad=miriad, idtag=tag)
# image the simulated visibilities
makeImage(config, miriad=miriad, interactive=interactive, idtag=tag)
# read in the images of the simulated visibilities
objectname = config['ObjectName']
simimloc = objectname + '_clean_model.fits'
model = fits.open(simimloc)
simimloc = objectname + '_clean_residual.fits'
residual = fits.open(simimloc)
# read in the data
data = fits.open(config['ImageName'])
if showOptical:
# read in the data
optical = fits.open(config['OpticalImage'])
# plot the images
plotImage(optical, data, config, 'optical', fitresult, tag=tag)
# plot the images
plotImage(model, data, config, 'model', fitresult, tag=tag)
# plot the residual
plotImage(residual, residual, config, 'residual', fitresult, tag=tag)
# remove the intermediate files
if cleanup:
removeTempFiles()
def preProcess(config, paramData, fitresult, tag='', cleanup=True,
showOptical=False, interactive=True):
"""
Cycle through each region and run plotFit, selecting parameters
appropriately.
"""
import setuputil
import numpy
import re
# Loop over each region
nlensedsource = paramData['nlensedsource']
nlensedregions = paramData['nlensedregions']
npar_previous = 0
configkeys = config.keys()