def plot(self, type, Rmin=0.0, show=True, yrange=None, dotitle=False,
reduce_plot_key=True):
"""
Send Rmin to limit the plotted R values
if too many s2 use reduce_plot_key=False
"""
pfile = self.plotfile(self['run'],
self['objmodel'],
self['psfmodel'],
type,
Rmin=Rmin,
yrange=yrange,
dotitle=dotitle)
if 'R_rg' in self.alldata[0].dtype.names:
print("using R_rg")
rname = 'R_rg'
else:
rname = 'R'
#rname='R'
keepdata = []
for st in self.alldata:
if numpy.median(st[rname]) > Rmin:
keepdata.append(st)
#keepdata = self.alldata
ndata = len(keepdata)
colors=pcolors.rainbow(ndata, 'hex')
biggles.configure('PlotKey','key_vsep',1.0)
plt = biggles.FramedPlot()
plt.aspect_ratio=1
plt.xlabel='object ellipticity'
plt.ylabel=r'$\Delta \gamma/\gamma$'
allplots=[]
i=0
for st in reversed(keepdata):
# this s2 is the value we were aiming for, could be pretty
# far off for some models
"""
if 's2noweight' in st.dtype.names:
s2 = st['s2noweight'][0]
else:
s2 = st['s2'][0]
"""
s2 = numpy.median(st['s2admom'])
#s2 = numpy.median(st['s2noweight'])
#s2 = st['s2'][0]
# this "etrue" is adaptive moments of pre-psf image
s = st['etrue'].argsort()
etrue = st['etrue'][s]
if type == 'regauss':
emeas = st['ecorr_rg'][s]
elif type == 'am+':
emeas = st['ecorr'][s]
elif type == 'noweight':
emeas = st['ecorr_uw'][s]
else:
raise ValueError("type should be 'regauss','am+', or 'noweight'")
gamma_frac_rg = shear_fracdiff(etrue, emeas, deriv=1.0)
Rmean = numpy.median( st[rname] )
label = '%0.3f (%0.3f)' % (s2,Rmean)
crg = biggles.Curve(etrue, gamma_frac_rg, color=colors[i])
crg.label=label
plt.add(crg)
allplots.append(crg)
i += 1
if dotitle:
title='obj: %s psf: %s run: %s' \
% (self['objmodel'],self['psfmodel'],self['run'])
if 'forcegauss' in self.config:
if self.config['forcegauss']:
title += ' forcegauss'
plt.title=title
fsize=1.5
if not reduce_plot_key:
key = biggles.PlotKey(0.9,0.9, allplots, halign='right', fontsize=fsize)
else:
# pick a few
nplot=len(allplots)
tplots = [allplots[0],
allplots[nplot*1/4],
allplots[nplot/2],
allplots[nplot*3/4],
allplots[-1]]
key = biggles.PlotKey(0.9,0.9, tplots, halign='right', fontsize=fsize)
plt.add(key)
klabtext=r'$<\sigma^2_{psf}/\sigma^2_{gal}> (<R>)$'
klab = biggles.PlotLabel(0.90,0.95,klabtext,
fontsize=1.5,halign='right')
plt.add(klab)
plab='%s %s %s' % (type,self['objmodel'],self['psfmodel'])
l = biggles.PlotLabel(0.1,0.9, plab, halign='left')
plt.add(l)
siglab=r'$\sigma_{PSF}: %.1f$ pix' % self['psf_sigma']
if 's2n' in self.config:
siglab+=r'$ S/N: %(s2n)d N_{trial}: %(ntrial)d$' % self.config
elif 'ntrial' in self.config:
siglab+=r'$ N_{trial}: %(ntrial)d$' % self.config
sl = biggles.PlotLabel(0.075,0.1, siglab, halign='left',fontsize=2.5)
plt.add(sl)
if not reduce_plot_key:
plt.xrange = [0,1.4]
if yrange is not None:
plt.yrange = yrange
print("Writing plot file:",pfile)
if show:
plt.show()
plt.write_eps(pfile)
converter.convert(pfile,dpi=100,verbose=True)
def plot_with_alt(self, alt, Rmin=0.0, show=True, yrange=None, dotitle=False):
'''
Plot the regauss with another for comparison, e.g. noweight or AM+
'''
pfile = self.plotfile(self['run'],
self['objmodel'],
self['psfmodel'],
'regauss-'+alt,
alt=alt, Rmin=Rmin, yrange=yrange,
dotitle=dotitle)
keepdata = []
for st in self.alldata:
if numpy.median(st['R']) > Rmin:
keepdata.append(st)
#keepdata = self.alldata
ndata = len(keepdata)
colors=pcolors.rainbow(ndata, 'hex')
allplots=[]
i=0
for st in keepdata:
# this s2 is the value we were aiming for, could be pretty
# far off for some models
if 's2noweight' in st.dtype.names:
s2 = st['s2noweight'][0]
else:
s2 = st['s2'][0]
# this "etrue" is adaptive moments of pre-psf image
s = st['etrue'].argsort()
etrue = st['etrue'][s]
# the top plot will be the regular analytic correction or the
# unweighted correction.
if alt == 'noweight':
gamma_frac_alt = shear_fracdiff(etrue, st['ecorr_uw'][s])
elif alt == 'am+':
gamma_frac_alt = shear_fracdiff(etrue, st['ecorr'][s])
else:
raise ValueError("alt should be am+ or noweight")
gamma_frac_rg = shear_fracdiff(etrue, st['ecorr_rg'][s])
Rmean = numpy.median( st['R'] )
if Rmean >= Rmin:
pdict={}
label = 's2: %0.3f R: %0.3f' % (s2,Rmean)
calt = biggles.Curve(etrue, gamma_frac_alt, color=colors[i])
calt.label = label
pdict['calt'] = calt
crg = biggles.Curve(etrue, gamma_frac_rg, color=colors[i])
crg.label=label
pdict['crg'] = crg
allplots.append(pdict)
i += 1
conv=self.config.get('sim_conv', 'analytic')
if dotitle:
title='obj: %s psf: %s run: %s' \
% (self['objmodel'],self['psfmodel'],self['run'])
if 'forcegauss' in self.config:
if self.config['forcegauss']:
title += ' forcegauss'
else:
title=None
arr=biggles.FramedArray(2,1, title=title)
arr.xlabel='object ellipticity'
arr.ylabel=r'$\Delta \gamma/\gamma$'
allc1 = []
allc2 = []
i=0
for p in allplots:
arr[0,0].add( p['calt'] )
arr[1,0].add( p['crg'] )
if i < (ndata//2):
allc1.append(p['calt'])
else:
allc2.append(p['calt'])
i+=1
fsize=2
key1 = biggles.PlotKey(0.95,0.9, allc1, halign='right', fontsize=fsize)
arr[0,0].add(key1)
key2 = biggles.PlotKey(0.95,0.9, allc2, halign='right', fontsize=fsize)
arr[1,0].add(key2)
if alt == 'noweight':
l1 = biggles.PlotLabel(0.1,0.9,'UnWeighted', halign='left')
else:
l1 = biggles.PlotLabel(0.1,0.9,'AM+', halign='left')
l2 = biggles.PlotLabel(0.1,0.9,'ReGauss', halign='left')
arr[0,0].add(l1)
arr[1,0].add(l2)
arr.uniform_limits = 1
arr.xrange = [0,1.2]
if yrange is not None:
arr.yrange = yrange
print("Writing plot file:",pfile)
arr.write_eps(pfile)
if show:
arr.show()
