def plot_matches(self, type, index=None, data=None, nmatchmin=5):
if data is None:
data=self.read_matched(type)
if index is None:
index=numpy.arange(data.size, dtype='i4')
else:
index=numpy.array(index, ndmin=1, dtype='i4')
mspec = data['mspec'][index]
h,rev = eu.stat.histogram(mspec, binsize=1, rev=True)
for i in xrange(h.size):
if rev[i] != rev[i+1]:
w=rev[ rev[i]:rev[i+1] ]
if w.size >= nmatchmin:
cmag = data['cmodelmag_dered_r'][w]
cmagerr = data['cmodelmag_dered_err_r'][w]
flux, ivar = sdsspy.mag2nmgy(cmag, cmagerr)
err=sqrt(1./ivar)
minf = (flux-3.5*err).min()
maxf = (flux+3.5*err).max()
fvals = linspace(minf, maxf, 100)
plt=biggles.FramedPlot()
for wi in xrange(w.size):
if data['survey_primary'][w[wi]] == 1:
color='red'
pmag = cmag[wi]
else:
color='black'
g = exp(-0.5*( (fvals-flux[wi])/err[wi])**2)/sqrt(2.*PI)/err[wi]
gcurve = biggles.Curve(fvals, g, color=color)
plt.add(gcurve)
#std = cmag.std()
#binsize=std*0.4
#plt=eu.plotting.bhist(cmag, binsize=binsize, show=False)
nlab=biggles.PlotLabel(0.05,0.95,'nmatch: %s' % w.size, halign='left')
maglab = biggles.PlotLabel(0.05,0.9,'primary mag: %0.2f' % pmag, halign='left')
plt.add(nlab, maglab)
plt.title=type
plt.xlabel = 'flux'
plt.show()
k=raw_input('hit a key (q to quit): ')
if k.lower() == 'q':
return
else:
print("less than %s matches" % nmatchmin)
def extract_good_matches(self, ra, dec, cmag_r, cmag_err_r, survey_primary, htminfo,
spec_ra, spec_dec,
nsigma=2.5, matchrad=1./3600.):
"""
Match by ra/dec.
Demand one of the matches is survey_primary, and use it as a reference
point to remove outliers at nsigma in flux.
Choosing nsigma=2.5 and 1'' match radius to make sure we get good matches
"""
if survey_primary is None:
maxmatch=1
else:
maxmatch=100
print("Matching to %0.2f arcsec" % (matchrad*3600.,))
h=eu.htm.HTM(10)
mspec,mphot,d12 = h.match(spec_ra,spec_dec, ra, dec,
matchrad,
htmid2=htminfo['htmid'],
minid=htminfo['minid'],
maxid=htminfo['maxid'],
htmrev2=htminfo['htmrev'],
maxmatch=maxmatch)
print("Matched: %s/%s" % (mspec.size,spec_ra.size))
if mspec.size == 0 or survey_primary==None:
return mspec, mphot
print(" unique:",numpy.unique(mspec).size)
print(" getting good flux matches")
h,rev = eu.stat.histogram(mspec, binsize=1, rev=True)
keep = numpy.zeros(mspec.size, dtype='i1')
for i in xrange(h.size):
if rev[i] != rev[i+1]:
# indices of mspec/mphot that have a particular index into
# mspec
w=rev[ rev[i]:rev[i+1] ]
# make sure this makes sense
wbad=where1(mspec[w] != mspec[w[0]])
if wbad != 0:
raise ValueError("expected all indices to have same mspec")
# indices into the photometric sample
wphot=mphot[w]
flux, ivar = sdsspy.mag2nmgy(cmag_r[wphot], cmag_err_r[wphot])
werr=where1(ivar > 0.)
if werr.size > 0:
# reduce the indices into mspec/mphot
w=w[werr]
# trim photometric data
flux=flux[werr]
ivar=ivar[werr]
wphot=wphot[werr]
# demand a survey primary match
wp = where1(survey_primary[wphot] == 1)
if wp.size > 0:
# always keep the primary
keep[w[wp]] = 1
wphot_primary = wphot[wp[0]]
# flux in primary will be reference
pflux, pivar = sdsspy.mag2nmgy(cmag_r[wphot_primary],
cmag_err_r[wphot_primary])
# nsigma in the combined error
err = sqrt(1./ivar)
perr = sqrt(1./pivar)
comb_err = sqrt(err**2 + perr**2)
wgood = where1( abs(flux-pflux) < nsigma*comb_err)
if wgood.size > 0:
w=w[wgood]
keep[w] = 1
wkeep = where1(keep == 1)
print(" kept: %s/%s" % (wkeep.size, keep.size))
if wkeep.size > 0:
mspec = mspec[wkeep]
mphot = mphot[wkeep]
else:
mspec = numpy.array([],dtype='i4')
mphot = numpy.array([],dtype='i4')
return mspec, mphot
