def plot_detrend(self, data, e1dt, e2dt, w, camcol, rmag_min, rmag_max,
coeffs, show=False):
"""
Make a table of plots.
"""
band=self.band
d=self.plotdir()
f = 'rg-%(run)s%(band)s-meane-vs-R-%(rmag_max)0.2f-%(camcol)s-detrend.eps'
f = f % {'run':self.procrun,'band':self.band,
'rmag_max':rmag_max,'camcol':camcol}
epsfile=path_join(d,'bycamcol',f)
# one column for each type (R,e1psf,e2psf)
tab=Table(1,3)
gratio=1.61803399
#tab.aspect_ratio = 1/gratio
tab.aspect_ratio = 1./2.
weights = 1.0/(0.32**2 + data['uncer_rg_'+band][w]**2)
nperbin = 200000
# trends vs R
print("\n* R")
xmin=0.29
xmax=1.01
if band=='r':
ymin = -0.03
ymax = 0.03
else:
ymin = -0.03
ymax = 0.06
pe1,pe1err,pe2,pe2err,ph,t1,t2 = \
self.make_plot_components(data['R_rg_'+band][w],
data['e1_rg_'+band][w],
data['e2_rg_'+band][w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax,
coeffs=coeffs)
pe1dt,pe1dterr,pe2dt,pe2dterr,phdt = \
self.make_plot_components(data['R_rg_'+band][w],
e1dt[w],
e2dt[w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax)
# one row for before and after detrend
Ra=FramedArray(2,1)
Ra.xrange = [xmin,xmax]
Ra.yrange = [ymin,ymax]
Ra.xlabel = 'R'
Ra.ylabel = '<e>'
rmag_lab = \
PlotLabel(0.1,0.07,'%0.2f < rmag < %0.2f' % (rmag_min,rmag_max),
halign='left')
cflab = PlotLabel(0.1,0.15,
'camcol: %s filter: %s' % (camcol, self.band),
halign='left')
key = PlotKey(0.1,0.9, [pe1,pe2])
keydt = PlotKey(0.1,0.9, [pe1dt,pe2dt])
Rz=Curve([xmin,xmax],[0,0])
Ra[0,0].add( Rz,pe1,pe1err,pe2,pe2err,ph,t1,t2,key)
Ra[1,0].add( Rz,pe1dt,pe1dterr,pe2dt,pe2dterr,phdt,keydt,rmag_lab,cflab)
if show:
Ra.show()
tab[0,0] = Ra
# trends vs e1 psf
print("\n* e1_psf")
xmin = -0.275
xmax = 0.275
#ymin = -0.015
#ymax = 0.015
pe1psf_e1,pe1psf_e1err,pe1psf_e2,pe1psf_e2err,pe1psf_ph = \
self.make_plot_components(data['e1_psf_'+band][w],
data['e1_rg_'+band][w],
data['e2_rg_'+band][w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax)
pe1psf_e1_dt,pe1psf_e1_dterr,pe1psf_e2_dt,pe1psf_e2_dterr,pe1psf_ph_dt = \
self.make_plot_components(data['e1_psf_'+band][w],
e1dt[w],
e2dt[w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax)
# one row for before and after detrend
e1psf_a=FramedArray(2,1)
e1psf_a.xrange = [xmin,xmax]
e1psf_a.yrange = [ymin,ymax]
e1psf_a.xlabel = r'$e1_{PSF}$'
#e1psf_a.ylabel = '<e>'
e1psf_key = PlotKey(0.1,0.9, [pe1psf_e1,pe1psf_e2])
e1psf_keydt = PlotKey(0.1,0.9, [pe1psf_e1_dt,pe1psf_e2_dt])
e1psf_z=Curve([xmin,xmax],[0,0])
e1psf_a[0,0].add(e1psf_z,
pe1psf_e1,pe1psf_e1err,
pe1psf_e2, pe1psf_e2err,
pe1psf_ph, e1psf_key)
e1psf_a[1,0].add(e1psf_z,
pe1psf_e1_dt, pe1psf_e1_dterr,
pe1psf_e2_dt, pe1psf_e2_dterr,
pe1psf_ph_dt,
e1psf_keydt)
if show:
e1psf_a.show()
tab[0,1] = e1psf_a
# trends vs e2 psf
print("\n* e2_psf")
pe2psf_e1,pe2psf_e1err,pe2psf_e2,pe2psf_e2err,pe2psf_ph = \
self.make_plot_components(data['e2_psf_'+band][w],
data['e1_rg_'+band][w],
data['e2_rg_'+band][w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax)
pe2psf_e1_dt,pe2psf_e1_dterr,pe2psf_e2_dt,pe2psf_e2_dterr,pe2psf_ph_dt = \
self.make_plot_components(data['e2_psf_'+band][w],
e1dt[w],
e2dt[w],
weights, nperbin, ymin, ymax,
fmin=xmin,fmax=xmax)
# one row for before and after detrend
e2psf_a=FramedArray(2,1)
e2psf_a.xrange = [xmin,xmax]
e2psf_a.yrange = [ymin,ymax]
e2psf_a.xlabel = r'$e2_{PSF}$'
#e2psf_a.ylabel = '<e>'
e2psf_key = PlotKey(0.1,0.9, [pe2psf_e1,pe2psf_e2])
e2psf_keydt = PlotKey(0.1,0.9, [pe2psf_e1_dt,pe2psf_e2_dt])
e2psf_z=Curve([xmin,xmax],[0,0])
e2psf_a[0,0].add(e2psf_z,
pe2psf_e1, pe2psf_e1err,
pe2psf_e2, pe2psf_e2err,
pe2psf_ph, e2psf_key)
e2psf_a[1,0].add(e2psf_z,
pe2psf_e1_dt, pe2psf_e1_dterr,
pe2psf_e2_dt, pe2psf_e2_dterr,
pe2psf_ph_dt,
e2psf_keydt)
if show:
e2psf_a.show()
tab[0,2] = e2psf_a
if show:
tab.show()
tab.write_eps(epsfile)
converter.convert(epsfile, dpi=150, verbose=True)
def plot_lens_s2n(zl, zslow, zshigh, pzs, cumulative=True):
"""
Calculate the cumulative expected usefulness of lenses as a function of
redshift for the given source N(z).
"""
import biggles
from biggles import FramedPlot, Curve, PlotLabel, PlotKey, Table, Histogram
biggles.configure('screen','width',1140)
biggles.configure('screen','height',1140)
nzl=zshigh.size
zlmin = 0.0
zlmax = max([zl.max(), zshigh.max()])
# first get the mean inverse critical density as a function
# of lens redshift
zsmid = (zshigh+zslow)/2.
sc = lensing.sigmacrit.ScinvCalculator(zlmin, zlmax, nzl, zsmid[0], zsmind[-1])
mean_scinv = sc.calc_mean_scinv(pzs)
# histogram the lens redshifts in a binning corresponding
# to the mean_scinv
hdict = eu.stat.histogram(zl, min=zlmin, max=zlmax, binsize=dz, more=True)
# get distances to the bin centers
cosmo=cosmology.Cosmo()
Dlens = cosmo.Da(0.0, hdict['center'])
# interpolate the mean_scinv onto the centers
mean_scinv = eu.stat.interplin(mean_scinv, sc.zlvals, hdict['center'])
# this is the S/N at a given lens redshift
s2n = sqrt(hdict['hist']/Dlens**2)*mean_scinv
hd = hdict['hist']/Dlens**2
hdcum = hd.cumsum()
s2ncum = (mean_scinv*hd).cumsum()/sqrt(hdcum.clip(1.,hdcum.max()))
s2n /= s2n.max()
s2ncum /= s2ncum.max()
tab = Table(2,2)
tab.aspect_ratio=1
# redshift histograms
tab[0,0] = FramedPlot()
zl_histp = Histogram(hdict['hist']/float(hdict['hist'].sum()), x0=hdict['low'][0], binsize=dz, color='blue')
zl_histp.label = 'lenses'
zs_histp = Histogram(pzs/float(pzs.sum()), x0=zslow[0], binsize=dz, color='red')
zs_histp.label = 'sources'
hkey=PlotKey(0.9,0.9,[zl_histp,zs_histp],halign='right')
tab[0,0].add(zl_histp, zs_histp, hkey)
tab[0,0].xlabel = 'z'
# mean inverse critical density
tab[0,1] = FramedPlot()
tab[0,1].xlabel = 'lens redshift'
tab[0,1].ylabel = r'$\langle \Sigma_{crit}^{-1} \rangle$'
mc = Curve(hdict['center'], mean_scinv)
tab[0,1].add(mc)
# cumulative volume
tab[1,0] = FramedPlot()
tab[1,0].xlabel = 'z'
tab[1,0].ylabel = 'cumulative volume'
v=zeros(hdict['center'].size)
for i in xrange(v.size):
v[i] = cosmo.V(0.0, hdict['center'][i])
v /= v.max()
cv=Curve(hdict['center'], v)
tab[1,0].add(cv)
# S/N
tab[1,1] = FramedPlot()
tab[1,1].xlabel = 'lens redshift'
tab[1,1].ylabel = 'S/N'
cs2n = Curve(hdict['center'], s2n, color='blue')
cs2ncum = Curve(hdict['center'], s2ncum, color='red')
cs2n.label = 'S/N'
cs2ncum.label = 'Cumulative S/N'
ckey=PlotKey(0.9,0.9,[cs2n, cs2ncum],halign='right')
tab[1,1].add(cs2n, cs2ncum, ckey)
tab.show()
return tab
