def evolvePDF(bestfitloc='posteriorpdf.fits', stepsize=50000):
"""
Plot the evolution of the PDF of each parameter of the model.
"""
import setuputil
# Get upper and lower limits on the parameters to set the plot limits
paramData = setuputil.loadParams(config)
p_u = paramData['p_u']
p_l = paramData['p_l']
limits = [p_l, p_u]
# read posterior PDF
fitresults = fits.getdata(bestfitloc)
nresults = len(fitresults)
print("Output from emcee has = " + str(nresults) + " iterations.")
start = 0
for iresult in range(0, nresults, stepsize):
strstep = str(stepsize)
nchar = len(str(nresults))
striresult = str(iresult).zfill(nchar)
tag = 'evolution' + strstep + '.' + striresult + '.'
trimresults = fitresults[start:start + stepsize]
start += stepsize
visualutil.plotPDF(trimresults,
tag,
limits=limits,
Ngood=1000,
axes='initial')
def evolvePDF(bestfitloc='posteriorpdf.fits', stepsize=50000):
"""
Plot the evolution of the PDF of each parameter of the model.
"""
import setuputil
# Get upper and lower limits on the parameters to set the plot limits
paramData = setuputil.loadParams(config)
p_u = paramData['p_u']
p_l = paramData['p_l']
limits = [p_l, p_u]
# read posterior PDF
fitresults = fits.getdata(bestfitloc)
nresults = len(fitresults)
print("Output from emcee has = " + str(nresults) + " iterations.")
start = 0
for iresult in range(0, nresults, stepsize):
strstep = str(stepsize)
nchar = len(str(nresults))
striresult = str(iresult).zfill(nchar)
tag = 'evolution' + strstep + '.' + striresult + '.'
trimresults = fitresults[start:start + stepsize]
start += stepsize
visualutil.plotPDF(trimresults, tag, limits=limits, Ngood=1000,
axes='initial')
def plot(cleanup=True, configloc='sandbox.yaml', interactive=True, threshold=1.2, plot=True, tag='sandbox'):
'''
Parameters
----------
threshold: float
in mJy, cleaning threshold
'''
# read the input parameters
configfile = open(configloc, 'r')
config = yaml.load(configfile)
paramSetup = setuputil.loadParams(config)
fixindx = setuputil.fixParams(paramSetup)
testfit = paramSetup['p_l']
visfile = config['UVData']
uuu, vvv, www = uvutil.uvload(visfile)
pcd = uvutil.pcdload(visfile)
vis_complex, wgt = uvutil.visload(visfile)
# remove the data points with zero or negative weight
positive_definite = wgt > 0
vis_complex = vis_complex[positive_definite]
wgt = wgt[positive_definite]
uuu = uuu[positive_definite]
vvv = vvv[positive_definite]
testlnprob, testmu = lnprob(testfit, vis_complex, wgt, uuu, vvv, pcd,
fixindx, paramSetup, computeamp=True)
# prepend 1 dummy value to represent lnprob
testfit = numpy.append(testlnprob, testfit)
# append nmu dummy values that represent the magnification factors
nlensedsource = paramSetup['nlensedsource']
nlensedregions = paramSetup['nlensedregions']
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
for i in range(nmu):
testfit = numpy.append(testfit, 0)
print("lnprob: %f" %testlnprob)
print("Using the following model parameters:")
for k, v in zip(paramSetup['pname'], testfit[1:-4]):
print("%s : %.4f" %(k,v))
if plot:
visualutil.plotFit(config, testfit, threshold,
tag=tag, cleanup=cleanup,
interactive=interactive)
return testlnprob
def plot(cleanup=True, configloc='sandbox.yaml', interactive=True):
# read the input parameters
configfile = open(configloc, 'r')
config = yaml.load(configfile)
paramSetup = setuputil.loadParams(config)
fixindx = setuputil.fixParams(paramSetup)
testfit = paramSetup['p_l']
visfile = config['UVData']
uuu, vvv, www = uvutil.uvload(visfile)
pcd = uvutil.pcdload(visfile)
vis_complex, wgt = uvutil.visload(visfile)
# remove the data points with zero or negative weight
positive_definite = wgt > 0
vis_complex = vis_complex[positive_definite]
wgt = wgt[positive_definite]
uuu = uuu[positive_definite]
vvv = vvv[positive_definite]
testlnprob, testmu = lnprob(testfit,
vis_complex,
wgt,
uuu,
vvv,
pcd,
fixindx,
paramSetup,
computeamp=True)
# prepend 1 dummy value to represent lnprob
testfit = numpy.append(testlnprob, testfit)
# append nmu dummy values that represent the magnification factors
nlensedsource = paramSetup['nlensedsource']
nlensedregions = paramSetup['nlensedregions']
nmu = 2 * (numpy.array(nlensedsource).sum() + nlensedregions)
for i in range(nmu):
testfit = numpy.append(testfit, 0)
tag = 'sandbox'
print("lnprob: %f" % testlnprob)
print("Using the following model parameters:")
for k, v in zip(paramSetup['pname'], testfit[1:-4]):
print("%s : %.4f" % (k, v))
visualutil.plotFit(config,
testfit,
tag=tag,
cleanup=cleanup,
interactive=interactive)
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 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')
fitdir = anloc + dataname[itarg] + '/' + goodfit + '/'
os.chdir(fitdir)
# ------------------------------------------------------------------------
# Read posterior probability distributions
fitloc = 'posteriorpdf.fits'
fitresults = Table.read(fitloc)
fitresults = fitresults[-5000:]
fitresultsgood = modifypdf.cleanColumns(fitresults)
fitresultsgood = modifypdf.bigScoop(fitresultsgood)
fitresultsgood = modifypdf.prune(fitresultsgood, quiet=True)
nfits = len(fitresultsgood)
configfile = open('config.yaml')
config = yaml.load(configfile)
paramData = setuputil.loadParams(config)
if objname != objname_previous:
radeg_centroid = config['Region0']['RACentroid']
decdeg_centroid = config['Region0']['DecCentroid']
c = SkyCoord(ra=radeg_centroid*u.degree,
dec=decdeg_centroid*u.degree)
fullRA = c.ra.to_string(unit='hour', pad=True, sep=':', precision=3)
fullDec = c.dec.to_string('deg', pad=True, alwayssign=True, sep=':', precision=2)
msg = '${0:9}$ ${1:12}$ ${2:12}$'
msgfmt = msg.format(objname, fullRA, fullDec)
#print(msgfmt)
objname_previous = objname
nregions = paramData['nregions']
def all(goodfit, update=False, mu_estimate=False):
# Check to see whether we've already loaded the data
if not update:
ffile = 'flux_total_observed.dat'
if os.path.exists(ffile):
ffile = 'flux_total_observed.dat'
flux_total_observed = Table.read(ffile, format='ascii')
ffile = 'flux_indiv_observed.dat'
flux_indiv_observed = Table.read(ffile, format='ascii')
ffile = 'flux_indiv_intrinsic.dat'
flux_indiv_intrinsic = Table.read(ffile, format='ascii')
else:
update = True
if update:
cwd = os.getcwd()
# read in the sample targets
anloc = '../../ModelFits/'
dataphotloc = 'targetlist.dat'
dataphot = Table.read(dataphotloc, format='ascii')
dataname = dataphot['dataname']
ntarg = dataname.size
dataflag = numpy.zeros(ntarg)
for i in range(ntarg):
datanamei = dataname[i]
dataloc = anloc + datanamei + '/' + goodfit
if os.path.exists(dataloc):
dataflag[i] = 1
yesmodel = dataflag == 1
dataphot = dataphot[yesmodel]
dataname = dataphot['dataname']
ntarg = len(dataphot)
dataname_indiv = []
flux_total = []
e_flux_total = []
flux_indiv = []
e_flux_indiv = []
flux_indiv_int = []
e_flux_indiv_int = []
for itarg in range(0, ntarg):
# extract object name from directory
objname = dataname[itarg]
iauname = dataphot['iauname'][itarg]
fitdir = anloc + dataname[itarg] + '/' + goodfit + '/'
os.chdir(fitdir)
# ------------------------------------------------------------------------
# Read posterior probability distributions
fitloc = 'posteriorpdf.fits'
fitresults = Table.read(fitloc)
fitresults = fitresults[-5000:]
fitresultsgood = modifypdf.cleanColumns(fitresults)
fitresultsgood = modifypdf.bigScoop(fitresultsgood)
fitresultsgood = modifypdf.prune(fitresultsgood, quiet=True)
configfile = open('config.yaml')
config = yaml.load(configfile)
paramData = setuputil.loadParams(config)
nregions = paramData['nregions']
flux_target = 0
os.chdir(cwd)
for iregion in range(nregions):
sr = str(iregion)
nsource = paramData['nsource_regions'][iregion]
for isource in range(nsource):
ss = str(isource)
datanamesource = dataname[itarg] + '.Source' + ss
tag1 = 'IntrinsicFlux_Source' + ss + '_Region' + sr
if tag1 not in fitresults.keys():
tag1 = 'Region' + sr + ' Source' + ss + ' IntrinsicFlux'
fluxdist = fitresultsgood[tag1]
tag2 = 'mu_tot.Source' + ss + '.Region' + sr
# get the by-eye magnification estimates
if mu_estimate:
datamu = Table.read('mu_estimate.dat', format='ascii')
muindx = datamu['target'] == datanamesource
muerr = datamu['muerr'][muindx][0]
if muerr == 0.25:
mu_by_eye = datamu['mu'][muindx][0]
else:
mu_by_eye = 1.
else:
mu_by_eye = 1.
flux_indiv_int.append(fluxdist.mean() / mu_by_eye)
e_flux_indiv_int.append(fluxdist.std() / mu_by_eye)
if tag2 in fitresults.keys():
if fitresultsgood[tag2].mean() == 100:
continue
observedflux = fluxdist * fitresultsgood[tag2]
print(datanamesource,
fitresultsgood[tag2].mean(),
fitresultsgood[tag2].std())
else:
observedflux = fluxdist
print(datanamesource, 1.0)
flux_target += observedflux
flux_indiv.append(observedflux.mean())
e_flux_indiv.append(observedflux.std())
dataname_indiv.append(datanamesource)
flux_measure = flux_target.mean()
flux_total.append(flux_measure)
flux_error = flux_target.std()
e_flux_total.append(flux_error)
#import matplotlib.pyplot as plt
#plt.hist(flux_target)
#plt.show()
#import pdb; pdb.set_trace()
#print(objname, flux_measure, ' +/- ', flux_error)
flux_total_observed = {'targets': dataname, 'fnu': flux_total,
'e_fnu': e_flux_total}
t1 = Table(flux_total_observed)
t1.write('flux_total_observed.dat', format='ascii')
flux_indiv_observed = {'targets': dataname_indiv, 'fnu': flux_indiv,
'e_fnu': e_flux_indiv}
t1 = Table(flux_indiv_observed)
t1.write('flux_indiv_observed.dat', format='ascii')
flux_indiv_intrinsic = {'targets': dataname_indiv, 'fnu': flux_indiv_int,
'e_fnu': e_flux_indiv_int}
t1 = Table(flux_indiv_intrinsic)
t1.write('flux_indiv_intrinsic.dat', format='ascii')
#import matplotlib.pyplot as plt
#m1 = 0
#m2 = 1 + int(numpy.array(flux_total + flux_indiv + flux_indiv_int).max())
#binwidth = 2
#bins = numpy.arange(m1, m2 + binwidth, binwidth)
#plt.hist(flux_total, bins = bins, histtype='stepfilled')
#binwidth = 1.8
#bins = numpy.arange(m1, m2 + binwidth, binwidth)
#plt.hist(flux_indiv, bins = bins, alpha=0.5, histtype='stepfilled')
#binwidth = 1.6
#bins = numpy.arange(m1, m2 + binwidth, binwidth)
#plt.hist(flux_indiv_int, bins = bins, alpha=0.2, histtype='stepfilled')
#plt.show()
return flux_total_observed, flux_indiv_observed, flux_indiv_intrinsic
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 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')
raise TypeError
# remove the data points with zero or negative weight
positive_definite = wgt > 0
vis_complex = vis_complex[positive_definite]
wgt = wgt[positive_definite]
uuu = uuu[positive_definite]
vvv = vvv[positive_definite]
#www = www[positive_definite]
npos = wgt.size
#----------------------------------------------------------------------------
# Load input parameters
paramSetup = setuputil.loadParams(config)
nwalkers = paramSetup['nwalkers']
nregions = paramSetup['nregions']
nparams = paramSetup['nparams']
pname = paramSetup['pname']
nsource_regions = paramSetup['nsource_regions']
# Use an intermediate posterior PDF to initialize the walkers if it exists
posteriorloc = 'posteriorpdf.fits'
if os.path.exists(posteriorloc):
# read the latest posterior PDFs
print("Found existing posterior PDF file: {:s}".format(posteriorloc))
posteriordat = Table.read(posteriorloc)
if len(posteriordat) > 1:
