def test_under_fraserMode(self):
'Test ``bgFinder._fraserMode``'
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._fraserMode()
self.assertAlmostEqual(4.5, r, places=1)
def test_stats(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._stats()
self.assertEqual(8, r[0])
self.assertAlmostEqual(85.1, r[1], places=1)
def test_stats(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._stats()
self.assertEqual(8, r[0])
self.assertAlmostEqual(85.1, r[1], places=1)
def test_under_fraserMode(self):
'Test ``bgFinder._fraserMode``'
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._fraserMode()
self.assertAlmostEqual(4.5, r, places=1)
def test_gaussFit(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._gaussFit()
self.assertAlmostEqual(127.0, r, places=1)
self.assertAlmostEqual(r, b.gauss[0], places=1)
self.assertAlmostEqual(73.61159329, b.gauss[1], places=1)
def test_gaussFit(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._gaussFit()
self.assertAlmostEqual(127.0, r, places=1)
self.assertAlmostEqual(r, b.gauss[0], places=1)
self.assertAlmostEqual(73.61159329, b.gauss[1], places=1)
def fitWithModelPSF(self,
x_in,
y_in,
m_in=-1.,
fitWidth=7,
bg=None,
ftol=1.e-8,
useLinePSF=False,
verbose=False):
"""
This is still experimental and hasn't been fully vetted yet.
Use at your own risk.
"""
self.useLinePSF = useLinePSF
if fitWidth > self.psf.boxSize:
raise NotImplementedError('Need to keep the fitWidth <= boxSize.')
(A, B) = self.imageData.shape
#ai = max(0,int(y_in)-fitWidth)
#bi = min(A,int(y_in)+fitWidth+1)
#ci = max(0,int(x_in)-fitWidth)
#di = min(B,int(x_in)+fitWidth+1)
dat = np.copy(self.imageData)
if bg == None:
bgf = bgFinder.bgFinder(self.imageData)
bg = bgf.smartBackground()
dmbg = dat - bg
print('Subtracting background {}'.format(bg))
else:
dmbg = dat - bg
if m_in == -1.:
if useLinePSF:
m_in = self.psf.repFact * self.psf.repFact * np.sum(
dat) / np.sum(self.psf.longPSF)
else:
m_in = self.psf.repFact * self.psf.repFact * np.sum(
dat) / np.sum(self.psf.fullPSF)
lsqf = opti.leastsq(resid, (x_in, y_in, m_in),
args=(dmbg, self.psf, fitWidth, useLinePSF,
verbose),
maxfev=1000,
ftol=ftol)
fitPars = lsqf[0]
l = likelihood_for_LS(fitPars,
dat,
bg,
self.psf,
boxWidth=fitWidth,
useLinePSF=useLinePSF)
fitPars = np.concatenate([fitPars, np.array([l])])
return fitPars
def __call__(self,moffatWidth,moffatSNR,initAlpha=5.,initBeta=2.,repFact=5,xWidth=51,yWidth=51,
includeCheesySaturationCut=True,autoTrim=False,noVisualSelection=False,verbose=False):
self.moffatWidth=moffatWidth
self.moffatSNR=moffatSNR
self.initAlpha=initAlpha
self.initBeta=initBeta
self.repFact=repFact
self.fwhms=[]
self.points=[]
self.moffs=[]
self.moffr=num.linspace(0,30,80)
self.starsFlatR=[]
self.starsFlatF=[]
self.subsecs=[]
self.goodStars=[]
self.starsScat=None
print 'Fitting stars with moffat profiles...'
for j in range(len(self.XWIN_IMAGE)):
if self.FLUX_AUTO[j]/self.FLUXERR_AUTO[j]>self.moffatSNR:
if self.XWIN_IMAGE[j]-1<0 or self.XWIN_IMAGE[j]-1>=self.data.shape[1] or self.YWIN_IMAGE[j]-1<0 or self.YWIN_IMAGE[j]-1>=self.data.shape[0]:
continue
mpsf=psf.modelPSF(num.arange(xWidth),num.arange(yWidth),alpha=self.initAlpha,beta=self.initBeta,repFact=self.repFact)
mpsf.fitMoffat(self.data,self.XWIN_IMAGE[j],self.YWIN_IMAGE[j],boxSize=self.moffatWidth,verbose=verbose)
fwhm=mpsf.FWHM(fromMoffatProfile=True)
#if includeCheesySaturationCut:
# if (mpsf.fDist[0]-mpsf.bg)/(mpsf.moffat(0)*mpsf.A)<0.9: #cheesy saturation cut
# #print 'Saturated'
# continue
#norm=Im.normalise(mpsf.subsec,[self.z1,self.z2])
norm=self.normer(mpsf.subSec)
if fwhm<>None and not (num.isnan(mpsf.beta) or num.isnan(mpsf.alpha)):
#print self.XWIN_IMAGE[j],self.YWIN_IMAGE[j],mpsf.alpha,mpsf.beta,fwhm
print '{: 8.2f} {: 8.2f} {: 5.2f} {: 5.2f} {: 5.2f}'.format(self.XWIN_IMAGE[j],self.YWIN_IMAGE[j],mpsf.alpha,mpsf.beta,fwhm)
self.subsecs.append(norm*1.)
self.goodStars.append(True)
self.moffs.append(mpsf.moffat(self.moffr)*1.)
self.starsFlatR.append(psf.downSample2d(mpsf.repRads,mpsf.repFact))
self.starsFlatF.append((mpsf.subSec-mpsf.bg)/(mpsf.moffat(0)*mpsf.A))
self.moffs[len(self.moffs)-1]/=num.max(self.moffs[len(self.moffs)-1])
self.points.append([fwhm,mpsf.chi,mpsf.alpha,mpsf.beta,self.XWIN_IMAGE[j],self.YWIN_IMAGE[j],mpsf.bg])
self.points=num.array(self.points)
self.goodStars=num.array(self.goodStars)
if autoTrim:
bg=bgFinder.bgFinder(self.points[:,0])
mode=bg('fraserMode')
w=num.where(num.abs(self.points[:,0]-mode)>0.5)
self.goodStars[w]=False
self.figPSF=pyl.figure('Point Source Selector')
self.sp1=pyl.subplot2grid((4,4),(0,1),colspan=3,rowspan=2)
pyl.scatter(self.points[:,0],self.points[:,1],picker=True)
pyl.title('Select PSF range with zoom and then close the plot window.')
self.sp2=pyl.subplot2grid((4,4),(2,1),colspan=3,sharex=self.sp1,rowspan=1)
bins=num.arange(num.min(self.points[:,0]),num.max(self.points[:,0])+0.5,0.5)
pyl.hist(self.points[:,0],bins=bins)
pyl.xlabel('FWHM (pix)')
self.sp3=pyl.subplot2grid((4,4),(0,0),rowspan=2,sharey=self.sp1)
pyl.hist(self.points[:,1],bins=30,orientation='horizontal')
pyl.ylabel('RMS')
self.sp4=pyl.subplot2grid((4,4),(2,0),rowspan=2)
self.moffPatchList=[]
self.showing=[]
for j in range(len(self.moffs)):
self.moffPatchList.append(self.sp4.plot(self.moffr,self.moffs[j]))
self.showing.append(1)
self.sp4.set_xlim(0,30)
self.sp4.set_ylim(0,1.02)
self.sp5=pyl.subplot2grid((4,4),(3,1))
self.sp5.set_aspect('equal')
self.psfPlotLimits=[self.sp1.get_xlim(),self.sp1.get_ylim()]
self.conn1=self.sp1.callbacks.connect('ylim_changed',self.PSFrange)
self.conn2=pyl.connect('pick_event',self.ScatterPSF)
if not noVisualSelection: pyl.show()
fwhm_lim=self.sp1.get_xlim()
chi_lim=self.sp1.get_ylim()
w=num.where((self.points[:,0]>fwhm_lim[0])&(self.points[:,0]<fwhm_lim[1])&(self.points[:,1]>chi_lim[0])&(self.points[:,1]<chi_lim[1])&(self.goodStars==True))
pyl.close()
goodFits=self.points[w]
goodMeds=num.median(goodFits[:4],axis=0)
goodSTDs=num.std(goodFits[:4],axis=0)
return (goodFits,goodMeds,goodSTDs)
def fitDoubleWithModelPSF(self,
x_in,
y_in,
X_in,
Y_in,
bRat_in,
m_in=-1.,
bg=None,
fitWidth=20,
nWalkers=30,
nBurn=50,
nStep=100,
useErrorMap=False,
useLinePSF=False,
verbose=False):
"""
Using emcee (It's hammer time!) two sources are fit using
the provided psf to find the best x,y and amplitude, and confidence
range on the fitted parameters.
x_in, y_in, m_in, X_in, Y_in, bRat - initial guesses on the true centroids, the amplitude and brightness ratio
of the two sources
fitWidth - the width +- of x_in/y_in of the data used in the fit
nWalkers, nBurn, nStep - emcee fitting paramters. If you don't know what these are RTFM
bg - the background of the image. Needed for the uncertainty table.
**Defaults to None.** When set to default, it will invoke
the background measurement and apply that. Otherwise, it assumes you are dealing with
background subtracted data already.
useErrorMap - if true, a simple pixel uncertainty map is used in the fit. This is adopted as
ue_ij=(imageData_ij+bg)**0.5, that is, the poisson noise estimate. Note the fit confidence range
is only honest if useErrorMap=True.
useLinePSF - use the TSF? If not, use the PSF
verbose - if set to true, lots of information printed to screen
"""
self.useLinePSF = useLinePSF
(A, B) = self.imageData.shape
ai = max(0, int((y_in + Y_in) / 2) - fitWidth)
bi = min(A, int((y_in + Y_in) / 2) + fitWidth + 1)
ci = max(0, int((x_in + X_in) / 2) - fitWidth)
di = min(B, int((x_in + X_in) / 2) + fitWidth + 1)
dat = np.copy(self.imageData)
if bg == None:
bgf = bgFinder.bgFinder(self.imageData)
bg = bgf.smartBackground()
dat -= bg
if not useErrorMap:
ue = dat * 0.0 + 1.
else:
ue = (dat + bg)**0.5
if m_in == -1.:
if useLinePSF:
m_in = np.sum(dat) / np.sum(self.psf.longPSF)
else:
m_in = np.sum(dat) / np.sum(self.psf.fullPSF)
nDim = 6
r0 = []
for ii in range(nWalkers):
r0.append(
np.array([x_in, y_in, m_in, X_in, Y_in, m_in * bRat_in]) +
sci.randn(6) *
np.array([1., 1., m_in * 0.4, 1., 1., m_in * 0.4 * bRat_in]))
r0 = np.array(r0)
sampler = emcee.EnsembleSampler(nWalkers,
nDim,
lnprobDouble,
args=[
dat, (ai, bi, ci, di), self.psf,
ue, self.useLinePSF, verbose
])
pos, prob, state = sampler.run_mcmc(r0, nBurn)
sampler.reset()
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = np.copy(dat)
self.fitted = True
def fitWithModelPSF(self,
x_in,
y_in,
m_in=-1.,
fitWidth=20,
nWalkers=20,
nBurn=10,
nStep=20,
bg=None,
useErrorMap=False,
useLinePSF=False,
fitRateAngle=False,
rate_in=None,
angle_in=None,
exptime=None,
pixScale=None,
verbose=False,
rand_pos=0.1):
"""
Using emcee (It's hammer time!) the provided image is fit using
the provided psf to find the best x,y and amplitude, and confidence
range on the fitted parameters.
x_in, y_in, m_in - initial guesses on the true centroid and amplitude of the object
fitWidth - the width +- of x_in/y_in of the data used in the fit
nWalkers, nBurn, nStep - emcee fitting paramters. If you don't know what these are RTFM
bg - the background of the image. Needed for the uncertainty table.
**Defaults to None.** When set to default, it will invoke
the background measurement and apply that. Otherwise, it assumes you are dealing with
background subtracted data already.
useErrorMap - if true, a simple pixel uncertainty map is used in the fit. This is adopted as
ue_ij=(imageData_ij+bg)**0.5, that is, the poisson noise estimate. Note the fit confidence range
is only honest if useErrorMap=True.
useLinePSF - use the TSF? If not, use the PSF
verbose - if set to true, lots of information printed to screen
"""
print "Initializing sampler"
self.nForFitting = 0
self.useLinePSF = useLinePSF
if fitWidth > self.psf.boxSize:
raise NotImplementedError('Need to keep the fitWidth <= boxSize.')
(A, B) = self.imageData.shape
ai = max(0, int(y_in) - fitWidth)
bi = min(A, int(y_in) + fitWidth + 1)
ci = max(0, int(x_in) - fitWidth)
di = min(B, int(x_in) + fitWidth + 1)
dat = np.copy(self.imageData)
if bg == None:
bgf = bgFinder.bgFinder(self.imageData)
bg = bgf.smartBackground()
dat -= bg
print 'Subtracting background {}'.format(bg)
if not useErrorMap:
ue = dat * 0.0 + 1.
else:
ue = (dat + bg)**0.5
self.fitted = True
if m_in == -1.:
if useLinePSF:
m_in = self.psf.repFact * self.psf.repFact * np.sum(
dat) / np.sum(self.psf.longPSF)
else:
m_in = self.psf.repFact * self.psf.repFact * np.sum(
dat) / np.sum(self.psf.fullPSF)
if not fitRateAngle:
nDim = 2
r0 = []
for ii in range(nWalkers):
r0.append(
np.array([x_in, y_in]) +
sci.randn(2) * np.array([rand_pos, rand_pos]))
r0 = np.array(r0)
#fit first using input best guess amplitude
sampler = emcee.EnsembleSampler(nWalkers,
nDim,
lnprob,
args=[
dat, (ai, bi, ci, di),
self.psf, ue, useLinePSF,
verbose, (-1, -1, m_in)
])
print "Executing xy burn-in... this may take a while."
pos, prob, state = sampler.run_mcmc(r0, nBurn) #, 10)
sampler.reset()
print "Executing xy production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = np.copy(dat)
out = self.fitResults()
(x, y, junk) = out[0]
dx = (out[1][0][1] - out[1][0][0]) / 2.0
dy = (out[1][1][1] - out[1][1][0]) / 2.0
nDim = 1 # need to put two here rather than one because the fitresults code does a residual subtraction
r0 = []
for ii in range(nWalkers):
r0.append(
np.array([m_in]) + sci.randn(1) * np.array([m_in * 0.25]))
r0 = np.array(r0)
#now fit the amplitude using the best-fit x,y from above
#could probably cut the nBurn and nStep numbers down by a factor of 2
sampler = emcee.EnsembleSampler(nWalkers,
nDim,
lnprob,
args=[
dat, (ai, bi, ci, di),
self.psf, ue, useLinePSF,
verbose, (x, y, -1)
])
print "Executing amplitude burn-in... this may take a while."
pos, prob, state = sampler.run_mcmc(r0, max(nBurn / 2, 10))
sampler.reset()
print "Executing amplitude production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos,
max(nStep / 2, 10),
rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = np.copy(dat)
out = self.fitResults()
amp = out[0][0]
damp = (out[1][0][1] - out[1][0][0]) / 2.0
#now do a full 3D fit using a small number of burn and steps.
nDim = 3
r0 = []
for ii in range(nWalkers):
r0.append(
np.array([x, y, amp]) +
sci.randn(3) * np.array([dx, dy, damp]))
r0 = np.array(r0)
sampler = emcee.EnsembleSampler(nWalkers,
nDim,
lnprob,
args=[
dat, (ai, bi, ci, di),
self.psf, ue, useLinePSF,
verbose
])
print "Executing xy-amp burn-in... this may take a while."
pos, prob, state = sampler.run_mcmc(r0, nBurn)
sampler.reset()
print "Executing xy-amp production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
else:
nDim = 5
r0 = []
for ii in range(nWalkers):
r0.append(
np.array([x_in, y_in, m_in, rate_in, angle_in]) +
sci.randn(nDim) *
np.array([0.1, 0.1, m_in * 0.1, rate_in * 0.1, 4.0]))
r0 = np.array(r0)
#fit first using input best guess amplitude
sampler = emcee.EnsembleSampler(nWalkers,
nDim,
lnprob_varRateAngle_LSSTHACK,
args=[
dat, (ai, bi, ci, di),
self.psf, ue, useLinePSF,
exptime, pixScale, verbose
])
#dat,lims,psf,ue,useLinePSF, exptime, pixScale, verbose=False):
print "Executing burn-in... this may take a while."
pos, prob, state = sampler.run_mcmc(r0, nBurn, 10)
sampler.reset()
print "Executing production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = np.copy(dat)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = np.copy(dat)
def fitDoubleWithModelPSF(self,x_in,y_in,X_in,Y_in,bRat_in,m_in=-1.,bg=None,
fitWidth=20,nWalkers=30,nBurn=50,nStep=100,
useErrorMap=False,
useLinePSF=False,verbose=False):
"""
Using emcee (It's hammer time!) two sources are fit using
the provided psf to find the best x,y and amplitude, and confidence
range on the fitted parameters.
x_in, y_in, m_in, X_in, Y_in, bRat - initial guesses on the true centroids, the amplitude and brightness ratio
of the two sources
fitWidth - the width +- of x_in/y_in of the data used in the fit
nWalkers, nBurn, nStep - emcee fitting paramters. If you don't know what these are RTFM
bg - the background of the image. Needed for the uncertainty table.
**Defaults to None.** When set to default, it will invoke
the background measurement and apply that. Otherwise, it assumes you are dealing with
background subtracted data already.
useErrorMap - if true, a simple pixel uncertainty map is used in the fit. This is adopted as
ue_ij=(imageData_ij+bg)**0.5, that is, the poisson noise estimate. Note the fit confidence range
is only honest if useErrorMap=True.
useLinePSF - use the TSF? If not, use the PSF
verbose - if set to true, lots of information printed to screen
"""
self.useLinePSF=useLinePSF
(A,B)=self.imageData.shape
ai=max(0,int((y_in+Y_in)/2)-fitWidth)
bi=min(A,int((y_in+Y_in)/2)+fitWidth+1)
ci=max(0,int((x_in+X_in)/2)-fitWidth)
di=min(B,int((x_in+X_in)/2)+fitWidth+1)
dat=num.copy(self.imageData)
if bg==None:
bgf=bgFinder.bgFinder(self.imageData)
bg=bgf.smartBackground()
dat-=bg
if not useErrorMap:
ue=dat*0.0+1.
else:
ue=(dat+bg)**0.5
if m_in==-1.:
if useLinePSF:
m_in=num.sum(dat)/num.sum(self.psf.longPSF)
else:
m_in=num.sum(dat)/num.sum(self.psf.fullPSF)
nDim=6
r0=[]
for ii in range(nWalkers):
r0.append(num.array([x_in,y_in,m_in,X_in,Y_in,m_in*bRat_in])+sci.randn(6)*num.array([1.,1.,
m_in*0.4,
1.,1.,
m_in*0.4*bRat_in]))
r0=num.array(r0)
sampler=emcee.EnsembleSampler(nWalkers,nDim,lnprobDouble,args=[dat,(ai,bi,ci,di),self.psf,ue,self.useLinePSF,verbose])
pos, prob, state=sampler.run_mcmc(r0,nBurn)
sampler.reset()
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps=sampler.chain
self.probs=sampler.lnprobability
self.dat=num.copy(dat)
self.fitted=True
def test_gaussLike(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._gaussLike([1.0, 2.0])
self.assertAlmostEqual(679178.581857, r, places=1)
def create_finder():
retval = bgFinder(MagicMock())
retval.data = MagicMock()
return retval
def runSex(file,
fn,
chip,
mask_file,
svsPath,
showProgress=False,
verbose=False,
includeImageMask=False,
kron_cut=-0.5,
runSextractor=True):
badflags = 1 + 512 + 8 + 64 + 16 + 4 + 256 + 2 + 128 + 4096
if path.isfile(mask_file):
mask = mask_file + '[0]'
if includeImageMask:
with fits.open(mask_file) as han:
mask_data = han[0].data
#print('Using image mask')
else:
mask_data = None
else:
mask = None
mask_data = None
with fits.open(file) as han:
header = han[0].header
header1 = han[1].header
if includeImageMask:
han2 = han[2].data
if mask_data is None:
mask_data = np.zeros(han2.shape).astype(han2.dtype)
#good pixels in sextractor have mask value == 1, bad have mask value==0
"""
w = np.where((han2>2080)|(han2==12)|(han2==5)|(han2==1024)) #12 is cosmic rays, everything above 32 seems to be saturation or bad columns
try:
mask_data[w] = 0.0
"""
w = np.where(((han2 & badflags) == 0)) #good pixels
try:
mask_data[w] = 1
except:
print(np.max(w[0]), np.max(w[1]))
exit()
if runSextractor:
fits.writeto(file + '.mask', mask_data, overwrite=True)
mask = file + '.mask'
if showProgress:
data = han[0].data
seeing = header['SEEING_MODE']
try:
apNum = apertures[round(seeing)]
except:
#seeing variable is -9999. This apNum variable will be reset based on sextractor output
apNum = apertures[2]
catObject = catObj()
catObject.seeing = header['SEEING_MODE']
catObject.ellip = header['ELL_MED']
catObject.astrms = header['WCS_RMS']
if showProgress:
pyl.imshow(data, interpolation='nearest', cmap='gray', origin='lower')
pyl.colorbar()
pyl.show()
if runSextractor:
if mask is not None:
scamp.runSex('subaru_LDAC.sex',
file + '[0]',
options={
'CATALOG_NAME':
svsPath + fn.replace('.fits', '.cat'),
'WEIGHT_IMAGE': mask,
'WEIGHT_TYPE': 'map_weight'
},
verbose=verbose)
else:
scamp.runSex('subaru_LDAC.sex',
file + '[0]',
options={
'CATALOG_NAME':
svsPath + fn.replace('.fits', '.cat')
},
verbose=verbose)
catalog = scamp.getCatalog(svsPath + fn.replace('.fits', '.cat'),
paramFile='sextract.param')
#get rid of the flux=0 sources
w = np.where((catalog['FLUX_APER(9)'][:,0]>0) & (catalog['FLUX_APER(9)'][:,1]>0) & (catalog['FLUX_APER(9)'][:,2]>0) & (catalog['FLUX_APER(9)'][:,3]>0) & (catalog['FLUX_APER(9)'][:,4]>0)\
& (catalog['FLUX_APER(9)'][:,5]>0) & (catalog['FLUX_APER(9)'][:,6]>0)\
& (catalog['FLUX_AUTO']>0))
for key in catalog:
catalog[key] = catalog[key][w]
if chip < 100:
x_high = 2045
y_high = 4173
else:
print('Chip>100')
x_high = 4173
y_high = 2045
if catObject.seeing <= 0:
#need to estimate seeing because header value is non-sense
#use all snr>40 sources, and take the median FWHM_IMAGE value
FWHM_IMAGE = np.sort(catalog['FWHM_IMAGE'][np.where(
(catalog['X_IMAGE'] > 50) & (catalog['X_IMAGE'] < x_high - 50)
& (catalog['Y_IMAGE'] > 50) & (catalog['Y_IMAGE'] < y_high - 50)
& (catalog['FLUX_APER(9)'][:, apNum] /
catalog['FLUXERR_APER(9)'][:, apNum] > 40))])
#fwhm_mode = FWHM_IMAGE[len(FWHM_IMAGE)/2]
fwhm_median = np.median(FWHM_IMAGE)
catObject.seeing = fwhm_median
apNum = apertures[int(round(catObject.seeing))]
#setup cut on Kron magnitude, by getting the median difference between kron and aperture magnitude for star-like objects.
#avoid the edges
w = np.where((catalog['X_IMAGE'] > 50) & (catalog['X_IMAGE'] < x_high - 50)
& (catalog['Y_IMAGE'] > 50)
& (catalog['Y_IMAGE'] < y_high - 50)
& ((catalog['FLUX_APER(9)'][:, apNum] /
catalog['FLUXERR_APER(9)'][:, apNum]) > 40)
& (catalog['FWHM_IMAGE'] > 1.5))
mag_aper = -2.5 * np.log10(catalog['FLUX_APER(9)'][:, apNum][w] /
header['EXPTIME']) + header['MAGZERO']
mag_auto = -2.5 * np.log10(
catalog['FLUX_AUTO'][w] / header['EXPTIME']) + header['MAGZERO']
mag_diff = mag_auto - mag_aper
bgf = bgFinder.bgFinder(mag_diff)
med_mag_diff = bgf.fraserMode(0.4)
#med_mag_diff = getMeanMagDiff(mag_aper,mag_diff)
#if np.isnan(med_mag_diff):
# med_mag_diff = getMeanMagDiff(mag_aper,mag_diff,returnMax = True)
#cut on position, SNR, and FWHM
snr = catalog['FLUX_APER(9)'][:, apNum] / catalog['FLUXERR_APER(9)'][:,
apNum]
if catObject.seeing > 0:
w = np.where((catalog['X_IMAGE'] > 3) & (catalog['X_IMAGE'] < x_high)
& (catalog['Y_IMAGE'] > 3) & (catalog['Y_IMAGE'] < y_high)
& (catalog['FWHM_IMAGE'] < catObject.seeing * 5.0)
& (snr > 3) & (catalog['FWHM_IMAGE'] > 1.5)
& (catalog['A_IMAGE'] > 1.0) & (catalog['B_IMAGE'] > 1.0))
else:
w = np.where(
(catalog['X_IMAGE'] > 3) & (catalog['X_IMAGE'] < x_high) &
(catalog['Y_IMAGE'] > 3) & (catalog['Y_IMAGE'] < y_high) &
(catalog['FWHM_IMAGE'] < 10.0) & (snr > 3) &
(catalog['FWHM_IMAGE'] > 1.5) & (catalog['A_IMAGE'] > 1.5) &
(catalog['B_IMAGE'] >
1.0)) #now measured in arcseconds rather than in units of FWHM
#now cut on difference between kron and aperture magnitudes, assuming there were enough sources for the cut
if not np.isnan(med_mag_diff):
mag_aper = -2.5 * np.log10(catalog['FLUX_APER(9)'][:, apNum][w] /
header['EXPTIME']) + header['MAGZERO']
mag_auto = -2.5 * np.log10(
catalog['FLUX_AUTO'][w] / header['EXPTIME']) + header['MAGZERO']
mag_diff = mag_auto - mag_aper - med_mag_diff
w = [w[0][np.where(mag_diff > kron_cut)]]
#pyl.scatter(mag_aper,mag_diff)
#print(len(w[0]))
#pyl.scatter(mag_aper[np.where(np.abs(mag_diff)<kron_cut)],mag_diff[np.where(np.abs(mag_diff)<kron_cut)])
#pyl.show()
#exit()
catObject.fwhm_image = catalog['FWHM_IMAGE'][w]
catObject.x = catalog['X_IMAGE'][w] - 1.0
catObject.y = catalog['Y_IMAGE'][w] - 1.0
catObject.flux = catalog['FLUX_APER(9)'][:, apNum][w]
catObject.snr = snr[w]
catObject.jd = 2400000.5 + header['MJD'] + header['EXPTIME'] / (24.0 *
3600.0)
WCS = wcs.WCS(header1)
(ra, dec) = WCS.all_pix2world(catObject.x, catObject.y, 0)
catObject.ra = ra
catObject.dec = dec
catObject.mag = 2.5 * np.log10(
catObject.flux / header['EXPTIME']) + header['MAGZERO']
pickle.dump(catObject,
open(svsPath + fn.replace('.fits', '.sex_save'), 'wb'))
if includeImageMask and runSextractor:
os.remove(file + '.mask')
print(file, len(catObject.ra))
return catObject
def make_trippy_profile(psf_image, stamp):
with fits.open(psf_image) as seepsf: # Get pixel values and Gaussian fit value par1 from seepsf image
for ext in range(len(seepsf)):
try:
header = seepsf[ext].header
psfdata = seepsf[ext].data # PSF image data
par1 = header['PAR1']
except:
continue
with fits.open(stamp) as stp: # Postage stamp image
header = stp[0].header
ra_rate = header['RARATE'] # Values placed in header ext 0 previously
dec_rate = header['DECRATE']
for ext in range(len(stp)): # Look in each header section for these values
try:
header = stp[ext].header
stampdata = stp[ext].data # Stamp image data
exptime = header['EXPTIME']
zpt = header['PHOTZP']
pixscale = header['PIXSCAL1']
except:
continue
imgdim = 501 # Should be odd number so that we have a central pixel
halfdim = (imgdim-1)/2
emptyimg = (np.random.poisson(1000, (imgdim, imgdim)) - 1000).astype('float64') # Create empty array with "noise", place psf in center of larger "image" for giving to trippy
leftsize = (psfdata.shape[0]-1)/2 # Pixels left of center
rightsize = (psfdata.shape[0]+1)/2 # Pixels right of center
emptyimg[halfdim-leftsize : halfdim+rightsize, halfdim-leftsize : halfdim+rightsize] += psfdata # Plant PSF from seepsf in center of empty image
fwhm = 2*math.sqrt(2*math.log(2))*par1 # Get full width/half max using par1 of Gaussian PSF
rate = math.sqrt(ra_rate**2+dec_rate**2)
angle = math.degrees(math.atan2(dec_rate, ra_rate)) # Angle w.r.t. horizontal, between +/-90
if angle>90: # Since angle can only be in range +/-90
angle -= 180
if angle <-90:
angle += 180
if dec_rate<0:
angle=-1*angle
# the center of the source is the center of the image
seecen = np.array([emptyimg.shape[1]/2.+0.5])
# Generate model of size 61x61, alpha and beta are moffat fit values, 1.75 determined experimentally when matching moffat to Gaussian shape
goodPSF=psf.modelPSF(np.arange(61), np.arange(61), alpha=1.75*par1, beta=2)
goodPSF.genLookupTable(emptyimg, seecen, seecen) # Generate lookup table for model PSF, centered at the center of the psf image
'''z2 = goodPSF.lookupTable.max()
z1 = goodPSF.lookupTable.min()
normer=interval.ManualInterval(z1,z2)
pyl.imshow(normer(goodPSF.lookupTable))
pyl.show()'''
goodPSF.line(rate, angle, exptime/3600., pixScale=pixscale, useLookupTable=True) # Generate trailed/line PSF
# Use if you want to save image of the trippy psf model
#modelimg = psf_image.replace('seepsf','model')
#goodPSF.psfStore(modelimg)
stampdata = stampdata.astype('float64') # Stamp data is originally ints
bgf=bgFinder.bgFinder(stampdata) # Calculate and subtract background
bg=bgf.smartBackground()
stampdata-=bg
centroid = sep_phot(stampdata, 0.002, 3) # Use Kristi's code to get centroid info using SEP
r_close = 1000 # Arbitrary number that will intentionally be too large
for i in range(len(centroid['x'])): # For all the objects identified in the stamp by SEP, find the one closest to the center of the image
dist = (centroid['x'][i]-stampdata.shape[0]/2)**2 + (centroid['y'][i]-stampdata.shape[1]/2)**2
if dist < r_close:
r_close = dist
closest = i
xcen, ycen = centroid['x'][closest], centroid['y'][closest] # Use the centroid that was closest to the center, since it will correspond to the asteroid
print 'Using centroid {} {} (in numpy coordinates)'.format(xcen,ycen)
phot=pill.pillPhot(stampdata) # Perform trailed source photometry, calculate SNR, total flux
phot(xcen, ycen, radius=1.1*fwhm, l=(exptime/3600.)*rate/pixscale, a=angle, skyRadius=4*fwhm, width=6*fwhm, zpt=zpt, exptime=exptime)
phot.SNR(verbose=True)
fitter = MCMCfit.MCMCfitter(goodPSF, stampdata) # Fit trailed model to background subtracted stamp image
fitter.fitWithModelPSF(xcen, ycen, m_in=-1, bg=bg, useLinePSF=True,fitWidth=15,nWalkers=20,nStep=20,nBurn=20,useErrorMap=True)
(fitPars,fitRange)=fitter.fitResults(0.67) # 0.67 = fit to 1 sigma
print '1-sigma range on Best Point', fitRange
modelImage=goodPSF.plant(fitPars[0],fitPars[1],fitPars[2],stampdata,addNoise=False,useLinePSF=True,returnModel=True) # Model the trailed source
pyl.imshow(modelImage)
pyl.show()
removed=goodPSF.remove(fitPars[0],fitPars[1],fitPars[2],stampdata,useLinePSF=True) # Subtract the model from the stamp
HDU=fits.PrimaryHDU(removed)
List=fits.HDUList([HDU])
psf_removed = psf_image.replace('seepsf','removed')
os.unlink(psf_image)
List.writeto(psf_removed,clobber=True)
print 'Asteroid PSF source flux: {}'.format(np.sum(modelImage))
z1 = stampdata[stampdata.shape[0]/2-30:stampdata.shape[0]/2 + 30,stampdata.shape[1]/2-30:stampdata.shape[1]/2 + 30].min()
z2 = stampdata[stampdata.shape[0]/2-30:stampdata.shape[0]/2 + 30,stampdata.shape[1]/2-30:stampdata.shape[1]/2 + 30].max()
normer=interval.ManualInterval(z1,z2)
pyl.imshow(normer(stampdata))
pyl.show()
pyl.imshow(normer(removed))
pyl.show()
def create_finder():
retval = bgFinder(MagicMock())
retval.data = MagicMock()
return retval
def __call__(self,
moffatWidth,
moffatSNR,
initAlpha=5.,
initBeta=2.,
repFact=5,
xWidth=51,
yWidth=51,
includeCheesySaturationCut=False,
autoTrim=False,
noVisualSelection=False,
verbose=False):
self.moffatWidth = moffatWidth
self.moffatSNR = moffatSNR
self.initAlpha = initAlpha
self.initBeta = initBeta
self.repFact = repFact
self.fwhms = []
self.points = []
self.moffs = []
self.moffr = np.linspace(0, self.moffatWidth, self.moffatWidth * 3)
self.starsFlatR = []
self.starsFlatF = []
self.subsecs = []
self.goodStars = []
self.starsScat = None
print 'Fitting stars with moffat profiles...'
print ' X Y chi a b FWHM'
for j in range(len(self.XWIN_IMAGE)):
if self.FLUX_AUTO[j] / self.FLUXERR_AUTO[j] > self.moffatSNR:
if self.XWIN_IMAGE[j] - 1 - (
moffatWidth + 1) < 0 or self.XWIN_IMAGE[j] - 1 + (
moffatWidth + 1
) >= self.data.shape[1] or self.YWIN_IMAGE[j] - 1 - (
moffatWidth + 1) < 0 or self.YWIN_IMAGE[j] - 1 + (
moffatWidth + 1) >= self.data.shape[0]:
continue
#this psf object creator has to be here. It's to do with the way PSF objects are initialized. Some time
#in the future I will adjust this for a small speed boost.
mpsf = psf.modelPSF(np.arange(xWidth),
np.arange(yWidth),
alpha=self.initAlpha,
beta=self.initBeta,
repFact=self.repFact)
mpsf.fitMoffat(self.data,
self.XWIN_IMAGE[j],
self.YWIN_IMAGE[j],
boxSize=self.moffatWidth,
verbose=verbose)
fwhm = mpsf.FWHM(fromMoffatProfile=True)
#norm=Im.normalise(mpsf.subsec,[self.z1,self.z2])
norm = self.normer(mpsf.subSec)
if (fwhm is not None) and not (np.isnan(mpsf.beta)
or np.isnan(mpsf.alpha)):
print ' {: 8.2f} {: 8.2f} {:3.2f} {: 5.2f} {: 5.2f} {: 5.2f} '.format(
self.XWIN_IMAGE[j], self.YWIN_IMAGE[j],
mpsf.chiFluxNorm, mpsf.alpha, mpsf.beta, fwhm)
self.subsecs.append(norm * 1.)
self.goodStars.append(True)
self.moffs.append(mpsf.moffat(self.moffr) * 1.)
self.starsFlatR.append(
psf.downSample2d(mpsf.repRads, mpsf.repFact))
self.starsFlatF.append(
(mpsf.subSec - mpsf.bg) / (mpsf.moffat(0) * mpsf.A))
self.moffs[len(self.moffs) - 1] /= np.max(
self.moffs[len(self.moffs) - 1])
self.points.append([
fwhm, mpsf.chi, mpsf.alpha, mpsf.beta,
self.XWIN_IMAGE[j], self.YWIN_IMAGE[j], mpsf.bg
])
self.points = np.array(self.points)
self.goodStars = np.array(self.goodStars)
FWHM_mode_width = 1.0 #could be 0.5 just as well
ab_std_width = 2.0
if autoTrim:
print '\nDoing auto star selection.'
#first use Frasermode on the distribution of FWHM to get a good handle on the true FWHM of stars
#select only those stars with FWHM of +-1 pixel of the mode.
bg = bgFinder.bgFinder(self.points[:, 0])
mode = bg('fraserMode')
w = np.where(np.abs(self.points[:, 0] - mode) > FWHM_mode_width)
self.goodStars[w] = False
#now mean select on both moffat a and b parameters to get only those with common shapes
w = np.where(self.goodStars)
mean_a = np.mean(self.points[w][:, 2])
std_a = np.std(self.points[w][:, 2])
mean_b = np.mean(self.points[w][:, 3])
std_b = np.std(self.points[w][:, 3])
w = np.where(
(np.abs(self.points[:, 0] - mode) > FWHM_mode_width)
| (np.abs(self.points[:, 2] - mean_a) > ab_std_width * std_a)
| (np.abs(self.points[:, 3] - mean_b) > ab_std_width * std_b))
self.goodStars[w] = False
#now eliminate the ones with sources nearby
for ii in range(len(self.goodStars)):
dist = ((self.XWIN_IMAGE - self.points[ii, 4])**2 +
(self.YWIN_IMAGE - self.points[ii, 5])**2)**0.5
args = np.argsort(dist)
dist = dist[args]
#print self.points[ii,4:6],dist
if dist[1] < moffatWidth:
self.goodStars[ii] = False
"""
#now print out the number of pixels that are 3-sigma above the naive expectation of the moffat profile itself
mpsf = psf.modelPSF(np.arange(xWidth), np.arange(yWidth), alpha=mean_a, beta=mean_b,
repFact=self.repFact)
print 'Fitting amplitudes to each good star.'
for ii in range(len(self.moffs)):
if self.goodStars[ii]:
mpsf.fitMoffat(self.data,
self.XWIN_IMAGE[ii], self.YWIN_IMAGE[ii],
boxSize = self.moffatWidth,
fixAB = True)
a,b = int(self.YWIN_IMAGE[ii]-yWidth/2),int(self.YWIN_IMAGE[ii]+yWidth/2)+1
c,d = int(self.XWIN_IMAGE[ii]-xWidth/2),int(self.XWIN_IMAGE[ii]+xWidth/2)+1
x,y = self.XWIN_IMAGE[ii]-int(self.XWIN_IMAGE[ii])+xWidth/2+1.0, self.YWIN_IMAGE[ii]-int(self.YWIN_IMAGE[ii])+yWidth/2+1.0
cut = self.data[a:b,c:d]
removed = mpsf.remove(self.XWIN_IMAGE[ii]-0.5,self.YWIN_IMAGE[ii]-0.5, mpsf.A, self.data)[a:b,c:d]-mpsf.bg
print ii,len(np.where( np.abs(removed/cut**0.5)>3)[0])
exit()
"""
self.figPSF = pyl.figure('Point Source Selector')
self.sp1 = pyl.subplot2grid((4, 4), (0, 1), colspan=3, rowspan=2)
pyl.scatter(self.points[:, 0], self.points[:, 1], picker=True)
pyl.title(
'Select PSF range with zoom,\n' +
'inspect stars (a = next, d = previous, w = toggle on/off,\n' +
'or left/right click to show/toggle),\n' +
'then close the plot window.')
self.sp2 = pyl.subplot2grid((4, 4), (2, 1),
colspan=3,
sharex=self.sp1,
rowspan=1)
bins = np.arange(np.min(self.points[:, 0]),
np.max(self.points[:, 0]) + 0.5, 0.5)
pyl.hist(self.points[:, 0], bins=bins)
pyl.xlabel('FWHM (pix)')
self.sp3 = pyl.subplot2grid((4, 4), (0, 0), rowspan=2, sharey=self.sp1)
pyl.hist(self.points[:, 1], bins=30, orientation='horizontal')
pyl.ylabel('RMS')
self.sp4 = pyl.subplot2grid((4, 4), (2, 0), rowspan=2)
self.moffPatchList = []
self.showing = []
self.selected_star = -1
for j in range(len(self.moffs)):
self.moffPatchList.append(self.sp4.plot(self.moffr, self.moffs[j]))
self.showing.append(1)
self.sp4.set_xlim(0, 30)
self.sp4.set_ylim(0, 1.02)
self.sp5 = pyl.subplot2grid((4, 4), (3, 1))
self.sp5.set_aspect('equal')
self.psfPlotLimits = [self.sp1.get_xlim(), self.sp1.get_ylim()]
self.conn1 = self.sp1.callbacks.connect('ylim_changed', self.PSFrange)
self.conn2 = pyl.connect('pick_event', self.ScatterPSF)
self.conn3 = pyl.connect('key_press_event', self.ScatterPSF_keys)
self.conn4 = pyl.connect('close_event', self.HandleClose)
self.ScatterPSF(None)
if not noVisualSelection:
pyl.show()
self._fwhm_lim = self.sp1.get_xlim()
self._chi_lim = self.sp1.get_ylim()
w = np.where((self.points[:, 0] > self._fwhm_lim[0])
& (self.points[:, 0] < self._fwhm_lim[1])
& (self.points[:, 1] > self._chi_lim[0])
& (self.points[:, 1] < self._chi_lim[1])
& (self.goodStars == True))
pyl.clf()
pyl.close()
goodFits = self.points[w]
goodMeds = np.median(goodFits[:4], axis=0)
goodSTDs = np.std(goodFits[:4], axis=0)
return (goodFits, goodMeds, goodSTDs)
HDU = fits.PrimaryHDU(data, header)
IHDU = fits.ImageHDU(bg)
RHDU = fits.ImageHDU(rem)
List = fits.HDUList([HDU, IHDU, RHDU])
List.writeto(fitsFile.replace('.', '_bgr.'), overwrite=True)
sys.exit()
div = 50
for i in range(0, B, B / div):
f = np.median(data[:, i:i + B / div], axis=1)
print poly(np.arange(len(f)) + 1.0, f, skip)
continue
sys.exit()
#g = np.std(data[:,i:i+B/20],axis=1)*(B/20)**-0.5
#print g/f
#sys.exit()
bgf = bgFinder.bgFinder(f)
fraser = bgf.fraserMode()
median = np.median(f)
pyl.plot([0, len(f)], [fraser, fraser], 'r-')
pyl.plot([0, len(f)], [median, median], 'k-')
pyl.plot(f)
#pyl.plot(f+g,'k--')
#pyl.plot(f-g,'k--')
pyl.title(i + B / 40)
pyl.show()
def fitWithModelPSF(self, x_in, y_in, m_in=-1., fitWidth=20,
nWalkers=20, nBurn=10, nStep=20,
bg=None, useErrorMap=False,
useLinePSF=False, verbose=False):
"""
Using emcee (It's hammer time!) the provided image is fit using
the provided psf to find the best x,y and amplitude, and confidence
range on the fitted parameters.
:param float x_in: initial guess on the x coordinate of the true centroid of the object
:param float y_in: initial guess on the y coordinate of the true centroid of the object
:param float m_in: initial guess on the true amplitude of the object
:param float fitWidth: the width +- of x_in/y_in of the data used in the fit
:param int nWalkers: The number of Goodman & Weare “walkers”. See :class:`emcee.EnsembleSampler`.
:param int nBurn: The number of iterations to run the chain during burn-in.
See :class:`emcee.EnsembleSampler.run_mcmc`.
:param int nStep: The number of iterations to run the chain in the production run.
See :class:`emcee.EnsembleSampler.run_mcmc`.
:param bg: the background of the image. Needed for the uncertainty table.
**Defaults to None.**
When set to default, it will invoke the background measurement and apply that.
Otherwise, it assumes you are dealing with background subtracted data already.
:param boolean useErrorMap: If true, a simple pixel uncertainty map is used in the fit.
This is adopted as ue_ij=(imageData_ij+bg)**0.5, that is, the poisson noise estimate.
Note the fit confidence range is only honest if useErrorMap=True.
:param boolean useLinePSF: If True, use the TSF. If False, use the PSF
:param boolean verbose: lots of information printed to screen
"""
print("Initializing sampler")
self.nForFitting = 0
self.useLinePSF = useLinePSF
if fitWidth > self.psf.boxSize:
raise NotImplementedError('Need to keep the fitWidth <= boxSize.')
(A, B) = self.imageData.shape
ai = max(0, int(y_in) - fitWidth)
bi = min(A, int(y_in) + fitWidth + 1)
ci = max(0, int(x_in) - fitWidth)
di = min(B, int(x_in) + fitWidth + 1)
dat = num.copy(self.imageData)
if bg == None:
bgf = bgFinder.bgFinder(self.imageData)
bg = bgf.smartBackground()
dat -= bg
if not useErrorMap:
ue = dat * 0.0 + 1.
else:
ue = (dat + bg) ** 0.5
if m_in == -1.:
if useLinePSF:
m_in = self.psf.repFact * self.psf.repFact * num.sum(dat) / num.sum(self.psf.longPSF)
else:
m_in = self.psf.repFact * self.psf.repFact * num.sum(dat) / num.sum(self.psf.fullPSF)
nDim = 3
r0 = []
for ii in range(nWalkers):
r0.append(num.array([x_in, y_in, m_in]) + sci.randn(3) * num.array([0.1, 0.1, m_in * 0.25]))
r0 = num.array(r0)
sampler = emcee.EnsembleSampler(nWalkers, nDim, lnprob,
args=[dat, (ai, bi, ci, di), self.psf, ue, useLinePSF, verbose])
print("Executing burn-in... this may take a while.")
pos, prob, state = sampler.run_mcmc(r0, nBurn)
sampler.reset()
print("Executing production run... this will also take a while.")
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = num.copy(dat)
self.fitted = True
def test_gaussLike(self):
data = array(range(0, 255), int)
b = bgFinder(data)
r = b._gaussLike([1.0, 2.0])
self.assertAlmostEqual(679178.581857, r, places=1)
def sourceTrim(fn,snr_lim,doBright = True,doBinary = False, aperFWHMmulti = 1.0, medMagDiff = None):
split = fn.split('-')[-1].split('.fits')[0]
chip = int(float(split[:3]))
if doBright :
o = fn.replace('.fits','_bright_planted.coords')
file = fn.replace('.fits','_bright_planted.fits')
elif doBinary :
o = fn.replace('.fits','_binary_planted.coords')
file = fn.replace('.fits','_binary_planted.fits')
else:
o = fn.replace('.fits','_faint_planted.coords')
file = fn.replace('.fits','_faint_planted.fits')
with open(o) as han:
d = han.readlines()
if doBinary:
planted = []
for i in range(len(d)):
s = d[i].split()
(x,y,z,X,Y,Z) = (float(s[0]),float(s[1]),float(s[2]),float(s[3]),float(s[4]),float(s[5]))
xg = (x*z+X*Z)/(z+Z)
yg = (y*z+Y*Z)/(z+Z)
planted.append([xg,yg,z+Z])
planted = np.array(planted)
else:
planted = []
for i in range(len(d)):
s = d[i].split()
planted.append([float(s[0]),float(s[1]),float(s[2])])
planted = np.array(planted)
mask_file = '/home/fraserw/idl_progs/hscp9/sextract/mask'+str(chip).zfill(3)+'.fits'
runSex(file,file,chip,mask_file,svsPath = '',showProgress = False, verbose = False, includeImageMask = True,runSextractor=True)
pcatalog = scamp.getCatalog(file.replace('.fits','.cat'),paramFile='sextract.param')
with fits.open(file) as han:
header = han[0].header
data = han[1].data
try:
FWHM = header['FWHMplant']
except:
FWHM = header['fwhmRobust']
apNum = apertures[round(FWHM*aperFWHMmulti)]
w = np.where((pcatalog['FLUX_APER(5)'][:,0]>0) & (pcatalog['FLUX_APER(5)'][:,1]>0) &\
(pcatalog['FLUX_APER(5)'][:,2]>0) & (pcatalog['FLUX_APER(5)'][:,3]>0) &\
(pcatalog['FLUX_APER(5)'][:,4]>0) & (pcatalog['FLUX_AUTO']>0))
for key in pcatalog:
pcatalog[key] = pcatalog[key][w]
psnr = pcatalog['FLUX_APER(5)'][:,apNum]/pcatalog['FLUXERR_APER(5)'][:,apNum]
w = np.where((pcatalog['X_IMAGE']>50) & (pcatalog['X_IMAGE']<1995) & (pcatalog['Y_IMAGE']>50) & (pcatalog['Y_IMAGE']<4123) \
& (psnr>5) & (pcatalog['FWHM_IMAGE']>1.5))
w1 = np.where((psnr[w]>=snr_lim[0])&(psnr[w]<snr_lim[1]))
if doBinary:
dist_max = 2.5
else:
dist_max = 1.5
p = []
planted_x,planted_y = [],[]
for i in range(len(planted)):
dist = ((planted[i,0]-pcatalog['X_IMAGE'][w][w1]+1)**2 + (planted[i,1]-pcatalog['Y_IMAGE'][w][w1]+1)**2)**0.5
if np.min(dist)<dist_max:
arg = np.argmin(dist)
p.append(arg)
planted_x.append(pcatalog['X_IMAGE'][w][w1][arg])
planted_y.append(pcatalog['Y_IMAGE'][w][w1][arg])
#else:
# print(i,planted[i],np.min(dist))
p = np.array(p)
planted_x = np.array(planted_x)
planted_y = np.array(planted_y)
print('Number of planted sources we are making use of:',len(p))
pmag_aper = -2.5*np.log10(pcatalog['FLUX_APER(5)'][:,apNum][w][w1][p]/header['EXPTIME'])+header['MAGZERO']
pmag_auto = -2.5*np.log10(pcatalog['FLUX_AUTO'][w][w1][p]/header['EXPTIME'])+header['MAGZERO']
pmag_diff = pmag_auto - pmag_aper
if doBright:
bgf = bgFinder.bgFinder(pmag_diff)
pmed_mag_diff = bgf.fraserMode(0.4)
#pmed_mag_diff = getMeanMagDiff(pmag_aper,pmag_diff)
#if np.isnan(pmed_mag_diff):
# pmed_mag_diff = getMeanMagDiff(pmag_aper,pmag_diff,returnMax = True)
else:
#use an input one from the planted single sources
pmed_mag_diff = medMagDiff
if not np.isnan(pmed_mag_diff):
pmag_diff = pmag_auto-pmag_aper-pmed_mag_diff
if doBright:
plantedMedMagDiff = pmed_mag_diff
catalog = scamp.getCatalog(fn.replace('.fits','.cat'),paramFile='sextract.param')
with fits.open(fn) as han:
header = han[0].header
data = han[1].data
(A,B) = data.shape
w = np.where((catalog['FLUX_APER(5)'][:,0]>0) & (catalog['FLUX_APER(5)'][:,1]>0) &\
(catalog['FLUX_APER(5)'][:,2]>0) & (catalog['FLUX_APER(5)'][:,3]>0) &\
(catalog['FLUX_APER(5)'][:,4]>0) & (catalog['FLUX_AUTO']>0)&\
(catalog['X_IMAGE']>FWHM) & (catalog['X_IMAGE']<B-FWHM) & (catalog['Y_IMAGE']>FWHM) & (catalog['Y_IMAGE']<A-FWHM) )
for key in catalog:
catalog[key] = catalog[key][w]
snr = catalog['FLUX_APER(5)'][:,apNum]/catalog['FLUXERR_APER(5)'][:,apNum]
w = np.where((snr>5) & (catalog['FWHM_IMAGE']>1.5))
w1 = np.where((snr[w]>snr_lim[0])&(snr[w]<snr_lim[1]))
mag_aper = -2.5*np.log10(catalog['FLUX_APER(5)'][:,apNum][w][w1]/header['EXPTIME'])+header['MAGZERO']
mag_auto = -2.5*np.log10(catalog['FLUX_AUTO'][w][w1]/header['EXPTIME'])+header['MAGZERO']
if doBright:
pyl.scatter(mag_auto,mag_auto-mag_aper)
pyl.scatter(pmag_auto,pmag_auto-pmag_aper)
pyl.show()
exit()
mag_diff = mag_auto-mag_aper-pmed_mag_diff
if doBright:
pred = np.ones(len(mag_diff))
pred[np.where((mag_diff<snr_lim[2])| (mag_diff>np.max(mag_diff)))] = -1
else:
args = np.argsort(pmag_auto)
x = pmag_auto[args]
y = pmag_diff[args]
bins = np.zeros(len(x))+abs(snr_lim[2])
old_i = 0
for i in range(len(x)):
if bins[i]<y[i]:
bins[old_i:] = y[i]
old_i = i
ww = np.where(bins==np.max(bins))
#generate a quadratic function that encompasses the true sources
b = (bins[ww[0][0]]+0.01+snr_lim_f[2])*((x[ww[0][0]]-np.min(x))**-2)
X = np.linspace(np.min(mag_auto)-10,np.max(mag_auto)+10,200)
Y = -snr_lim[2] + b*(X-np.min(x))**2
ww = np.where(X<np.min(x))
Y[ww] = -snr_lim[2]
#now setup the lower limit function with the cutout to include binaries
Yn=-Y
ww = np.where(Yn>-0.5)
Yn[ww]=-0.5
f = interp.interp1d(X,Y)
fneg = interp.interp1d(X,Yn)
pred = -np.ones(len(mag_diff))
for i in range(len(pred)):
if mag_diff[i]<f(mag_auto[i]) and mag_diff[i]>(fneg(mag_auto[i])):
pred[i]=1
for k in catalog:
catalog[k] = catalog[k][w][w1]
if doBright:
return (catalog,data,pred,mag_auto,mag_diff,pmag_auto,pmag_diff,p,apNum,plantedMedMagDiff)
elif doBinary:
return (catalog,data,pred,mag_auto,mag_diff,pmag_auto,pmag_diff,p,apNum)
else:
return (catalog,data,pred,mag_auto,mag_diff,pmag_auto,pmag_diff,p,f,fneg,apNum)
def fitWithModelPSF(self,x_in,y_in,m_in=-1.,fitWidth=20,
nWalkers=20,nBurn=10,nStep=20,
bg=None,useErrorMap=False,
useLinePSF=False,verbose=False):
"""
Using emcee (It's hammer time!) the provided image is fit using
the provided psf to find the best x,y and amplitude, and confidence
range on the fitted parameters.
x_in, y_in, m_in - initial guesses on the true centroid and amplitude of the object
fitWidth - the width +- of x_in/y_in of the data used in the fit
nWalkers, nBurn, nStep - emcee fitting paramters. If you don't know what these are RTFM
bg - the background of the image. Needed for the uncertainty table.
**Defaults to None.** When set to default, it will invoke
the background measurement and apply that. Otherwise, it assumes you are dealing with
background subtracted data already.
useErrorMap - if true, a simple pixel uncertainty map is used in the fit. This is adopted as
ue_ij=(imageData_ij+bg)**0.5, that is, the poisson noise estimate. Note the fit confidence range
is only honest if useErrorMap=True.
useLinePSF - use the TSF? If not, use the PSF
verbose - if set to true, lots of information printed to screen
"""
print "Initializing sampler"
self.nForFitting=0
self.useLinePSF=useLinePSF
if fitWidth>self.psf.boxSize:
raise NotImplementedError('Need to keep the fitWidth <= boxSize.')
(A,B)=self.imageData.shape
ai=max(0,int(y_in)-fitWidth)
bi=min(A,int(y_in)+fitWidth+1)
ci=max(0,int(x_in)-fitWidth)
di=min(B,int(x_in)+fitWidth+1)
dat=num.copy(self.imageData)
if bg==None:
bgf=bgFinder.bgFinder(self.imageData)
bg=bgf.smartBackground()
dat-=bg
if not useErrorMap:
ue=dat*0.0+1.
else:
ue=(dat+bg)**0.5
if m_in==-1.:
if useLinePSF:
m_in=self.psf.repFact*self.psf.repFact*num.sum(dat)/num.sum(self.psf.longPSF)
else:
m_in=self.psf.repFact*self.psf.repFact*num.sum(dat)/num.sum(self.psf.fullPSF)
nDim=2
r0=[]
for ii in range(nWalkers):
r0.append(num.array([x_in,y_in])+sci.randn(2)*num.array([0.1,0.1]))
r0=num.array(r0)
#fit first using input best guess amplitude
sampler=emcee.EnsembleSampler(nWalkers,nDim,lnprob,args=[dat,(ai,bi,ci,di),self.psf,ue,useLinePSF,verbose,(-1,-1,m_in)])
print "Executing xy burn-in... this may take a while."
pos, prob, state=sampler.run_mcmc(r0, nBurn, 10)
sampler.reset()
print "Executing xy production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps=sampler.chain
self.probs=sampler.lnprobability
self.dat=num.copy(dat)
self.fitted=True
out = self.fitResults()
(x,y,junk) = out[0]
dx = (out[1][0][1] - out[1][0][0])/2.0
dy = (out[1][1][1] - out[1][1][0])/2.0
nDim = 1 # need to put two here rather than one because the fitresults code does a residual subtraction
r0 = []
for ii in range(nWalkers):
r0.append(num.array([m_in]) + sci.randn(1) * num.array([m_in*0.25]))
r0 = num.array(r0)
#now fit the amplitude using the best-fit x,y from above
#could probably cut the nBurn and nStep numbers down by a factor of 2
sampler = emcee.EnsembleSampler(nWalkers, nDim, lnprob,
args=[dat, (ai, bi, ci, di), self.psf, ue, useLinePSF, verbose, (x, y, -1)])
print "Executing amplitude burn-in... this may take a while."
pos, prob, state = sampler.run_mcmc(r0, max(nBurn/2,10))
sampler.reset()
print "Executing amplitude production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, max(nStep/2,10), rstate0=state)
self.samps = sampler.chain
self.probs = sampler.lnprobability
self.dat = num.copy(dat)
self.fitted = True
out = self.fitResults()
amp = out[0][0]
damp = (out[1][0][1]-out[1][0][0])/2.0
#now do a full 3D fit using a small number of burn and steps.
nDim=3
r0=[]
for ii in range(nWalkers):
r0.append(num.array([x,y,amp])+sci.randn(3)*num.array([dx,dy,damp]))
r0=num.array(r0)
sampler=emcee.EnsembleSampler(nWalkers,nDim,lnprob,args=[dat,(ai,bi,ci,di),self.psf,ue,useLinePSF,verbose])
print "Executing xy-amp burn-in... this may take a while."
pos, prob, state=sampler.run_mcmc(r0,nBurn)
sampler.reset()
print "Executing xy-amp production run... this will also take a while."
pos, prob, state = sampler.run_mcmc(pos, nStep, rstate0=state)
self.samps=sampler.chain
self.probs=sampler.lnprobability
self.dat=num.copy(dat)
self.fitted=True
def setUpClass(self):
self.rates = [0.4, 1.0, 2.5]
self.angles = [37.0, 0.0, -58.7]
self.EXPTIME = 360.0
#os.system('gunzip test_image.fits.gz')
self.gened_bg = 1000.0
if not (os.path.isfile('test_image.fits')
and os.path.isfile('planted_locations.pickle')):
print('You may not have the necessary fits files for these tests.')
print('Please execute the following two wget commands:')
print(
'wget https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/files/vault/fraserw/test_psf.fits'
)
print(
'wget https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/files/vault/fraserw/test_image.fits'
)
print('')
exit()
with fits.open('test_image.fits') as han:
self.image = han[0].data
self.loadedPSF = psf.modelPSF(restore='test_psf.fits')
with open('planted_locations.pickle', 'rb') as han:
if sys.version_info[0] == 3:
x = pickle.load(han, encoding='latin1')
elif sys.version_info[0] == 2:
x = pickle.load(han)
self.planted_locations = np.array(x)
self.bg = bgFinder.bgFinder(self.image)
scamp.makeParFiles.writeConv()
scamp.makeParFiles.writeParam()
scamp.makeParFiles.writeSex('test.sex', minArea=2, threshold=2)
scamp.runSex('test.sex', 'test_image.fits', verbose=True)
self.catalog = scamp.getCatalog('def.cat', paramFile='def.param')
os.remove('default.conv')
os.remove('def.param')
os.remove('test.sex')
os.remove('def.cat')
#os.system('gzip test_image.fits')
starChooser = psfStarChooser.starChooser(self.image,
self.catalog['XWIN_IMAGE'],
self.catalog['YWIN_IMAGE'],
self.catalog['FLUX_AUTO'],
self.catalog['FLUXERR_AUTO'])
(self.goodFits, self.goodMeds,
self.goodSTDs) = starChooser(30,
25,
noVisualSelection=True,
autoTrim=False,
quickFit=False,
repFact=5)
(self.goodFitsQF, self.goodMedsQF,
self.goodSTDsQF) = starChooser(30,
25,
noVisualSelection=True,
autoTrim=False,
quickFit=True,
repFact=5)
#using manual alpha and beta because the fitting done with the star chooser results in
#different answers with the different versions of scipy
ALPHA = 29.81210639392963
BETA = 19.32497948470224
self.goodPSF = psf.modelPSF(np.arange(51),
np.arange(51),
alpha=ALPHA,
beta=BETA,
repFact=10)
#self.goodPSF = psf.modelPSF(np.arange(51),np.arange(51), alpha=self.goodMeds[2],beta=self.goodMeds[3],repFact=10)
self.goodPSF.genLookupTable(self.image,
self.goodFits[:, 4],
self.goodFits[:, 5],
verbose=False)
self.goodPSF.line(self.rates[0],
self.angles[0],
self.EXPTIME / 3600.,
pixScale=0.185,
useLookupTable=True)
#self.goodPSF.psfStore('test_psf.fits')
self.pill = pill.pillPhot(self.image)
rads = np.arange(5.0, 25.0, 5)
x_test, y_test = 652.4552577047876, 101.62067493726078 #necessray to use custom coordinates to avoid errors due to different sextractor versions
self.pill.computeRoundAperCorrFromSource(x_test,
y_test,
rads,
width=60.0,
skyRadius=50.0,
display=False,
displayAperture=False)
self.pill(x_test,
y_test,
7.0,
l=5.0,
display=False,
enableBGSelection=False)
self.pill.SNR(verbose=True)
self.distances = {}
self.distances[26] = 0.2913793325
self.distances[16] = 0.5179553032
self.distances[7] = 0.1751143336
self.distances[0] = 0.7428739071
self.distances[23] = 0.3347620368
self.distances[18] = 0.7867022753
self.distances[14] = 0.1851933748
self.distances[5] = 0.4436303377
self.distances[28] = 0.3333371580
self.distances[9] = 0.1997155696
self.distances[29] = 0.3528487086
self.distances[22] = 0.4013085365
self.distances[19] = 0.5801378489
self.distances[17] = 0.6873673797
self.distances[1] = 0.1621235311
self.distances[6] = 0.1248580739
self.distances[13] = 2.0296137333
self.distances[24] = 1.3238428831
self.distances[3] = 0.7816261053
self.distances[25] = 0.3067137897
self.distances[6] = 0.8099660873