def bestFit(bestfitloc='posteriorpdf.fits',
showOptical=False,
cleanup=True,
interactive=True,
plotonly=False):
"""
Read posterior PDF and identify best-fit parameters. Plot the best-fit
model and compare to the data. Also plot the residuals obtained after
subtracting the best-fit model from the data and compare to the data.
Optionally plot the best available optical image and compare to the data.
"""
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# identify best-fit model
minchi2 = fitresults['lnprob'].max()
index = fitresults['lnprob'] == minchi2
bestfit = fitresults[index][0]
tag = 'bestfit'
printFitParam(bestfit, fitKeys)
visualutil.plotFit(config,
bestfit,
tag=tag,
cleanup=cleanup,
showOptical=showOptical,
interactive=interactive,
plotonly=plotonly)
def bestFit(bestfitloc='posteriorpdf.fits', showOptical=False, cleanup=True,
interactive=True, plotonly=False):
"""
Read posterior PDF and identify best-fit parameters. Plot the best-fit
model and compare to the data. Also plot the residuals obtained after
subtracting the best-fit model from the data and compare to the data.
Optionally plot the best available optical image and compare to the data.
"""
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# identify best-fit model
minchi2 = fitresults['lnprob'].max()
index = fitresults['lnprob'] == minchi2
bestfit = fitresults[index][0]
tag = 'bestfit'
printFitParam(bestfit, fitKeys)
visualutil.plotFit(config, bestfit, tag=tag, cleanup=cleanup,
showOptical=showOptical, interactive=interactive,
plotonly=plotonly)
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 goodFits(bestfitloc='posteriorpdf.fits',
Nfits=12,
Ngood=5000,
cleanup=True,
interactive=True,
showOptical=False):
"""
Read posterior PDF and draw Nfits realizations from the final Ngood models
at random. Plot the model from each realization and compare to the data.
Also plot the residuals obtained after subtracting the model from the data
and compare to the data. By default: Nfits = 12, Ngood=5000.
"""
import modifypdf
import numpy
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
fitresults = fitresults[-Ngood:]
fitresults = modifypdf.prune(fitresults)
# get keys
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# select the random realizations model
Nunprune = len(fitresults)
realids = numpy.floor(numpy.random.uniform(0, Nunprune, Nfits))
for ifit in range(Nfits):
realid = numpy.int(realids[ifit])
fitresult = fitresults[realid]
tag = 'goodfit' + str(realid).zfill(4)
printFitParam(fitresult, fitKeys)
visualutil.plotFit(config,
fitresult,
tag=tag,
showOptical=showOptical,
cleanup=cleanup,
interactive=interactive)
def goodFits(bestfitloc='posteriorpdf.fits', Nfits=12, Ngood=5000,
cleanup=True, interactive=True, showOptical=False):
"""
Read posterior PDF and draw Nfits realizations from the final Ngood models
at random. Plot the model from each realization and compare to the data.
Also plot the residuals obtained after subtracting the model from the data
and compare to the data. By default: Nfits = 12, Ngood=5000.
"""
import modifypdf
import numpy
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
fitresults = fitresults[-Ngood:]
fitresults = modifypdf.prune(fitresults)
# get keys
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# select the random realizations model
Nunprune = len(fitresults)
realids = numpy.floor(numpy.random.uniform(0, Nunprune, Nfits))
for ifit in range(Nfits):
realid = numpy.int(realids[ifit])
fitresult = fitresults[realid]
tag = 'goodfit' + str(realid).zfill(4)
printFitParam(fitresult, fitKeys)
visualutil.plotFit(config, fitresult, tag=tag, showOptical=showOptical,
cleanup=cleanup, interactive=interactive)
