def plot_masserr(self, filename):
import pcolors
data=eu.io.read(filename)
nr200 = data.size
nb = data['B'][0].size
plt = FramedPlot()
colors = pcolors.rainbow(nr200, 'hex')
clist = []
for ri in xrange(nr200):
r200 = data['r200'][ri]
m200 = data['m200'][ri]
p = Points(data['B'][ri], (m200-data['m200meas'][ri])/m200,
type='filled circle', color=colors[ri])
crv = Curve(data['B'][ri], (m200-data['m200meas'][ri])/m200,
color=colors[ri])
crv.label = 'r200: %0.2f' % r200
clist.append(crv)
plt.add(p,crv)
key = PlotKey(0.1,0.4,clist)
plt.add(key)
plt.xlabel=r'$\Omega_m \sigma_8^2 D(z)^2 b(M,z)$'
plt.ylabel = r'$(M_{200}^{true}-M)/M_{200}^{true}$'
epsfile = filename.replace('.rec','.eps')
print 'writing eps:',epsfile
plt.write_eps(eu.ostools.expand_path(epsfile))
def doplot(self):
tab = Table(2, 1)
tab.title = self.title
xfit, yfit, gprior = self.get_prior_vals()
nrand = 100000
binsize = self.binsize
h = self.h
h1 = self.h1
h2 = self.h2
g1rand, g2rand = gprior.sample2d(nrand)
grand = gprior.sample1d(nrand)
hrand = histogram(grand, binsize=binsize, min=0.0, max=1.0, more=True)
h1rand = histogram(g1rand, binsize=binsize, min=-1.0, max=1.0, more=True)
fbinsize = xfit[1] - xfit[0]
hrand["hist"] = hrand["hist"] * float(yfit.sum()) / hrand["hist"].sum() * fbinsize / binsize
h1rand["hist"] = h1rand["hist"] * float(h1["hist"].sum()) / h1rand["hist"].sum()
pltboth = FramedPlot()
pltboth.xlabel = r"$g$"
hplt1 = Histogram(h1["hist"], x0=h1["low"][0], binsize=binsize, color="red")
hplt2 = Histogram(h2["hist"], x0=h2["low"][0], binsize=binsize, color="blue")
hpltrand = Histogram(hrand["hist"], x0=hrand["low"][0], binsize=binsize, color="magenta")
hplt1rand = Histogram(h1rand["hist"], x0=h1rand["low"][0], binsize=binsize, color="magenta")
hplt1.label = r"$g_1$"
hplt2.label = r"$g_2$"
hplt1rand.label = "rand"
hpltrand.label = "rand"
keyboth = PlotKey(0.9, 0.9, [hplt1, hplt2, hplt1rand], halign="right")
pltboth.add(hplt1, hplt2, hplt1rand, keyboth)
tab[0, 0] = pltboth
plt = FramedPlot()
plt.xlabel = r"$|g|$"
hplt = Histogram(h["hist"], x0=h["low"][0], binsize=binsize)
hplt.label = "|g|"
line = Curve(xfit, yfit, color="blue")
line.label = "model"
key = PlotKey(0.9, 0.9, [hplt, line, hpltrand], halign="right")
plt.add(line, hplt, hpltrand, key)
tab[1, 0] = plt
if self.show:
tab.show()
return tab
def plot_ellip_vs_input(self, show=False):
'''
Plot the measured ellip as a function of the input for sigma_index=0
which is a reasonably large object
'''
import biggles
from biggles import PlotLabel,FramedPlot,Table,Curve,PlotKey,Points
from pcolors import rainbow
import pprint
data = self.read()
w=where1(data['sigma_index'] == 10)
data = data[w]
e1_input, e2_input, Tinput = util.mom2ellip(data['Irr_input'],
data['Irc_input'],
data['Icc_input'])
e1_meas, e2_meas, Tinput = util.mom2ellip(data['Irr_meas'],
data['Irc_meas'],
data['Icc_meas'])
einput = sqrt(e1_input**2 + e2_input**2)
emeas = sqrt(e1_meas**2 + e2_meas**2)
plt=FramedPlot()
p = Points(einput,emeas, type='filled circle')
plt.add(p)
plt.xlabel=r'$\epsilon_{in}$'
plt.ylabel=r'$\epsilon_{meas}$'
sig=sqrt((data['Irr_meas'][0]+data['Icc_meas'][0])/2)
lab1=PlotLabel(0.1,0.9,self.model, halign='left')
lab2=PlotLabel(0.1,0.8,r'$\sigma: %0.2f$' % sig, halign='left')
plt.add(lab1,lab2)
einput.sort()
c = Curve(einput, einput, color='red')
c.label = r'$\epsilon_{input} = \epsilon_{meas}$'
key=PlotKey(0.95,0.07,[c], halign='right')
plt.add(c)
plt.add(key)
if show:
plt.show()
epsfile=self.epsfile('ellip-vs-input')
print("Writing eps file:",epsfile)
plt.write_eps(epsfile)
def test_xi_converge_nplk(epsfile=None):
"""
Test how xi converges with the number of k points per log10(k)
Note we should test other convergence factors too!
"""
tab=Table(2,1)
pltbig = FramedPlot()
pltzoom = FramedPlot()
pltbig.xlabel='r'
pltbig.ylabel='xi(r)'
pltbig.xlog=True
pltbig.ylog=True
pltzoom.xlabel='r'
pltzoom.ylabel='xi(r)'
l=Linear()
r=10.0**linspace(0.0,2.3,1000)
nplk_vals = [20,60,100,140,160]
color_vals = ['blue','skyblue','green','orange','magenta','red']
plist=[]
lw=2.4
for nplk,color in zip(nplk_vals,color_vals):
print 'nplk:',nplk
xi = l.xi(r, nplk=nplk)
limxi = where(xi < 1.e-5, 1.e-5, xi)
climxi = Curve(r,limxi,color=color,linewidth=lw)
climxi.label = 'nplk: %i' % nplk
pltbig.add(climxi)
plist.append(climxi)
w=where1(r > 50.0)
cxi = Curve(r[w], xi[w], color=color, linewidth=lw)
pltzoom.add(cxi)
key = PlotKey(0.7,0.8, plist)
pltzoom.add(key)
tab[0,0] = pltbig
tab[1,0] = pltzoom
if epsfile is not None:
tab.write_eps(epsfile)
else:
tab.show()
def plot_size_vs_input(self, show=False):
'''
Plot recovered size vs input for ellip=0, which is ellip_index=0
'''
import biggles
from biggles import PlotLabel,FramedPlot,Table,Curve,PlotKey,Points
from pcolors import rainbow
import pprint
data = self.read()
w=where1(data['ellip_index'] == 0)
data = data[w]
siginput = sqrt(data['Irr_input'])
sigmeas = sqrt(data['Irr_meas'])
pars=numpy.polyfit(siginput, sigmeas, 1)
print("offset:",pars[1])
print("slope: ",pars[0])
print("IGNORING OFFSET")
plt=FramedPlot()
p = Points(siginput,sigmeas, type='filled circle')
plt.add(p)
plt.xlabel=r'$\sigma_{in}$'
plt.ylabel=r'$\sigma_{meas}$'
lab=PlotLabel(0.1,0.9,self.model)
plt.add(lab)
yfit2=pars[0]*siginput
cfit2=Curve(siginput, yfit2, color='steel blue')
cfit2.label = r'$%0.2f \sigma_{in}$' % pars[0]
plt.add( cfit2 )
key=PlotKey(0.95,0.07,[cfit2], halign='right')
plt.add(key)
if show:
plt.show()
epsfile=self.epsfile('size-vs-input')
print("Writing eps file:",epsfile)
plt.write_eps(epsfile)
def _draw_background(self, context):
pc = _PlotComposite()
nl = self.ribs_l
b = _series(-90 // 2, 90 // 2, 2 * math.pi / 180.)
for l0 in _series(-nl, nl, math.pi / nl):
c = Curve([l0] * len(b), b, **self.ribs_style)
#c = apply( Curve, ([l0]*len(b), b), self.ribs_style )
pc.add(c)
nb = self.ribs_b
l = _series(-180 // 2, 180 // 2, 2 * math.pi / 180.)
for b0 in _series(-nb, nb, 0.5 * math.pi / (nb + 1)):
c = Curve(l, [b0] * len(l), **self.ribs_style)
#c = apply( Curve, (l, [b0]*len(l)), self.ribs_style )
pc.add(c)
pc.render(context)
def plot_T(self, k, T, Tc=None, Tb=None):
from biggles import FramedPlot, Curve, PlotKey
plt=FramedPlot()
c=Curve(k, T**2)
c.label = '$T^2$'
plt.add(c)
plist = [c]
if Tc is not None:
cc=Curve(k,Tc**2,color='blue')
cc.label = '$Tc^2$'
plt.add(cc)
plist.append(cc)
if Tb is not None:
tmp = where(Tb < 1.e-5, 1.e-5, Tb)
cb=Curve(k,tmp**2,color='red')
cb.label = '$Tb^2$'
plt.add(cb)
plist.append(cb)
plt.xlog=True
plt.ylog=True
plt.ylabel = '$T^2'
plt.xlabel = 'k'
plt.yrange = [1.e-8,1.0]
plt.aspect_ratio=1
if Tc is not None or Tb is not None:
key=PlotKey(0.1,0.9,plist)
plt.add(key)
plt.show()
def test(self, relerr=0.2,dowrite=False, epsfile=None):
omega_m=0.25
z = 0.3
nfw = lensing.nfw.NFW(omega_m, z)
lin = lensing.linear.Linear(omega_m=omega_m)
r200=0.5
c=5.0
B=1.0
print 'generating fake data with %i%% errors' % (relerr*100,)
nr = 21
logrmin=-2.0
logrmax=1.5
r = logspace(logrmin,logrmax,nr)
ds = nfw.dsig(r,r200,c) + B*lin.dsig(r)
dserr = ds*relerr
ds += dserr*numpy.random.standard_normal(nr)
if dowrite:
f='~/tmp/fake-dsig.dat'
print 'Writing fake data to:',f
colprint(r,ds,dserr,format='%0.8f', file=f)
print 'generating fine grid truth values'
rtrue = logspace(logrmin,logrmax,nr*5)
dstrue = nfw.dsig(rtrue,r200,c) + B*lin.dsig(rtrue)
drhotrue = nfw.rho(rtrue,r200,c) + B*lin.drho(rtrue)
# add linear term
mnfw = nfw.m(rtrue, r200,c)
mlin = lin.m(rtrue, B)
mtrue = mnfw + mlin
print 'Doing inversion'
odict = self.invert(r,ds,dserr=dserr)
tab = Table(2,2)
od = lensing.plotting.plot_dsig(odict, noshow=True)
# show some log-interpolated points of dsig
nper = 10
for i in xrange(nr-1):
amp = odict['ds_amp'][i]
slope = odict['ds_slope'][i]
off = odict['ds_offset'][i]
x = logspace(log10(r[i]),log10(r[i+1]),nper)
y = amp*x**(-slope) - off
od['plt'].add(Curve(x,y,color='blue'))
od['plt'].add(Curve(rtrue,dstrue,color='red'))
tab[0,0] = od['plt']
# plot uncorrected with no error bar
rod = lensing.plotting.plot_drho(r=odict['rdrho'],
drho=odict['drho_noendc'],
drhoerr=odict['drhoerr']*0,
noshow=True)
rod2 = lensing.plotting.add_to_log_plot(rod['plt'],
odict['rdrho'],
odict['drho'],
odict['drhoerr'],
color='blue')
pnocorr = rod['p']
p = rod2['p']
pnocorr.label = 'no end correction'
p.label = 'end correction'
plist = [pnocorr,p]
drhotrue_curve = Curve(rtrue,drhotrue,color='red')
drhotrue_curve.label = 'Truth'
rod['plt'].add(drhotrue_curve)
plist.append(drhotrue_curve)
rod['plt'].add(PlotKey(0.1,0.2,plist))
tab[0,1] = rod['plt']
# for the mass stuff
# first show log-interpolated points of drho to see how
# it looks
rod_interp = lensing.plotting.plot_drho(odict, noshow=True)
nper = 10
for i in xrange(odict['drho'].size-1):
amp = odict['drho_amp'][i]
slope = odict['drho_slope'][i]
off = odict['drho_offset'][i]
x = logspace(log10(r[i]),log10(r[i+1]),nper)
y = amp*x**(-slope) - off
y = where(y < 1.e-5, 1.e-5, y)
rod_interp['plt'].add(Curve(x,y,color='blue'))
tab[1,0] = rod_interp['plt']
mod = lensing.plotting.plot_mass(odict, noshow=True)
mp = mod['p']
mp.label = 'Recovered Mass'
nfw_curve = Curve(rtrue,mnfw,color='green')
nfw_curve.label = 'NFW'
mod['plt'].add(nfw_curve)
lin_curve = Curve(rtrue,mlin,color='orange')
lin_curve.label = 'linear'
mod['plt'].add(lin_curve)
true_curve = Curve(rtrue,mtrue,color='magenta')
true_curve.label = 'True'
mod['plt'].add(true_curve)
#m200 = nfw.m200(r200)
#m200p = Points([r200],[m200],type='cross',color='blue')
#m200p.label = r'$M_{200} true'
#mod['plt'].add(m200p)
# now find r200 from the mass profile
#mvf = MvirFinder(omega_m, z, 200)
#rvir,rvir_err,mvir,mvir_err = mvf.find_mvir(odict['rmass'],
# odict['mass'],
# odict['masserr'])
#mvp = Points([rvir],[mvir],type='filled square',color='red')
#mvexp = SymErrX([rvir],[mvir],[rvir_err],color='red')
#mveyp = SymErrY([rvir],[mvir],[mvir_err],color='red')
#mvp.label = r'$M_{200}'
#mod['plt'].add(mvp,mvexp,mveyp)
#mod['plt'].add(PlotKey(0.1,0.9,[mp,true_curve,nfw_curve,lin_curve,
# m200p,mvp]))
mod['plt'].add(PlotKey(0.1,0.9,[mp,true_curve,nfw_curve,lin_curve]))
tab[1,1] = mod['plt']
if dowrite:
f='~/tmp/fake-dsig-invert.dat'
print 'Writing inverted fake data to:',f
colprint(odict['rdrho'], odict['drho'],odict['mass'],odict['masserr'],
format='%20.8e',file=f)
if epsfile is not None:
print 'Writing eps file:',epsfile
tab.write_eps(epsfile)
else:
tab.show()
def plot(self, odict, epsfile=None, show=True, overdrho=True):
"""
odict is the output of invert()
"""
tab = Table(2,2)
od = lensing.plotting.plot_dsig(odict, noshow=True)
# show some log-interpolated points of dsig
nper = 10
r = odict['r']
for i in xrange(r.size-1):
amp = odict['ds_amp'][i]
slope = odict['ds_slope'][i]
off = odict['ds_offset'][i]
x = logspace(log10(r[i]),log10(r[i+1]),nper)
y = amp*x**(-slope) - off
od['plt'].add(Curve(x,y,color='blue'))
tab[0,0] = od['plt']
# plot uncorrected with no error bar
rod = lensing.plotting.plot_drho(r=odict['rdrho'],
drho=odict['drho_noendc'],
drhoerr=odict['drhoerr']*0,
noshow=True)
rod2 = lensing.plotting.add_to_log_plot(rod['plt'],
odict['rdrho'],
odict['drho'],
odict['drhoerr'],
color='blue')
pnocorr = rod['p']
p = rod2['p']
pnocorr.label = 'no end correction'
p.label = 'end correction'
rod['plt'].add(PlotKey(0.1,0.2,[pnocorr,p]))
tab[0,1] = rod['plt']
# for the mass stuff
# first show log-interpolated points of drho to see how
# it looks
rod_interp = lensing.plotting.plot_drho(odict, noshow=True)
nper = 10
for i in xrange(odict['drho'].size-1):
amp = odict['drho_amp'][i]
slope = odict['drho_slope'][i]
off = odict['drho_offset'][i]
x = logspace(log10(r[i]),log10(r[i+1]),nper)
y = amp*x**(-slope) - off
y = where(y < 1.e-5, 1.e-5, y)
rod_interp['plt'].add(Curve(x,y,color='blue'))
tab[1,0] = rod_interp['plt']
mod = lensing.plotting.plot_mass(odict, noshow=True)
mp = mod['p']
mp.label = 'Mass Avg'
odin=lensing.plotting.add_to_log_plot(mod['plt'],
odict['rmass'],
odict['massin'],
odict['massinerr'],
color='green', type='filled diamond')
pin = odin['p']
pin.label = 'Mass In'
mod['plt'].add(pin)
odout=lensing.plotting.add_to_log_plot(mod['plt'],
odict['rmass'],
odict['massout'],
odict['massouterr'],
color='magenta', type='filled diamond')
pout = odout['p']
pout.label = 'Mass Out'
rhoc0 = 0.27752
mdel = (4./3.)*PI*200*rhoc0*1.e12*odict['rmass']**3
pdel = Curve(odict['rmass'],mdel,color='blue')
pdel.label = r'$200 \rho_{c} V$'
mod['plt'].add(pdel)
mod['plt'].add(PlotKey(0.1,0.9,[mp,pin,pout,pdel]))
tab[1,1] = mod['plt']
if epsfile is not None:
print 'Writing to epsfile:',epsfile
tab.write_eps(epsfile)
if show:
tab.show()
def test_fit_nfw_lin_dsig(rmin=0.01):
from biggles import FramedPlot,Points,SymmetricErrorBarsY,Curve,PlotKey
omega_m=0.25
z=0.25
r200 = 1.0
c = 5.0
B=10.0
rmax = 50.0
log_rmin = log10(rmin)
log_rmax = log10(rmax)
npts = 30
r = 10.0**linspace(log_rmin,log_rmax,npts)
fitter = NFWBiasFitter(omega_m,z,r)
ds = fitter.dsig(r, r200, c, B)
# 10% errors
dserr = 0.1*ds
ds += dserr*numpy.random.standard_normal(ds.size)
guess = numpy.array([r200,c,B],dtype='f8')
# add 10% error to the guess
guess += 0.1*guess*numpy.random.standard_normal(guess.size)
res = fitter.fit(ds,dserr,guess, more=True)
r200_fit=res['r200']
r200_err = res['r200_err']
c_fit=res['c']
c_err = res['c_err']
B_fit=res['B']
B_err = res['B_err']
print 'Truth:'
print ' r200: %f' % r200
print ' c: %f' % c
print ' B: %f' % B
print 'r200_fit: %f +/- %f' % (r200_fit,r200_err)
print ' c_fit: %f +/- %f' % (c_fit,c_err)
print ' B_fit: %f +/- %f' % (B_fit,B_err)
print 'Cov:'
print res['cov']
rfine = 10.0**linspace(log_rmin,log_rmax,100)
fitter2 = NFWBiasFitter(omega_m,z,rfine)
yfit = fitter2.dsig(rfine, r200_fit, c_fit, B_fit)
yfit_nfw = fitter2.nfw.dsig(rfine, r200_fit, c_fit)
yfit_lin = fitter2.lin_dsig(rfine,B_fit)
plt=FramedPlot()
plt.add(Points(r,ds,type='filled circle'))
plt.add(SymmetricErrorBarsY(r,ds,dserr))
cyfit = Curve(rfine,yfit,color='blue')
cyfit_nfw = Curve(rfine,yfit_nfw,color='red')
cyfit_lin = Curve(rfine,yfit_lin,color='orange')
cyfit.label = 'Best Fit'
cyfit_nfw.label = 'NFW'
cyfit_lin.label = 'linear'
key=PlotKey(0.1,0.3,[cyfit,cyfit_nfw,cyfit_lin])
plt.add(cyfit,cyfit_nfw,cyfit_lin,key)
plt.xlabel = r'$r$ [$h^{-1}$ Mpc]'
plt.ylabel = r'$\Delta\Sigma ~ [M_{sun} pc^{-2}]$'
plt.xrange = [0.5*rmin, 1.5*rmax]
plt.yrange = [0.5*(ds-dserr).min(), 1.5*(ds+dserr).max()]
plt.xlog=True
plt.ylog=True
plt.show()
def test_scinv_npts(self, nplot, show=False, reload=False, type="png"):
"""
Test accuracy as a function of the numer of points used in the
integration.
"""
dzl = 0.015
zlmin = 0.02
zlmax = 0.6
from biggles import Points, FramedPlot, PlotKey, Table, Histogram, Curve
from time import time
import lensing
import pcolors
if self.data is None or reload:
self.load_example_data()
# this is old ScinvCalculator, need to make work
# with new one
scalc1000 = ScinvCalculator(self.zs, dzl, zlmin, zlmax, npts=1000)
nptsvals = [100, 200, 300, 400, 500, 600, 700, 800, 900]
numcheck = len(nptsvals)
# colors=['black','magenta','blue','green','orange','red']
colors = pcolors.rainbow(len(nptsvals), "hex")
scalc = []
for npts in nptsvals:
scalc.append(ScinvCalculator(self.zs, dzl, zlmin, zlmax, npts=npts))
times = numpy.zeros(numcheck, dtype="f8")
time1000 = 0.0
# we'll fill this in
scinv_all = numpy.zeros((numcheck, scalc1000.zlvals.size))
xlim = [0, scalc1000.zsvals.max()]
for i in xrange(nplot):
pz = self.data["pofz"][i]
print("Doing 1000...", end="")
tm0 = time()
scinv1000 = scalc1000.calc_mean_scinv(pz)
time1000 += time() - tm0
print("done")
for j in xrange(numcheck):
npts = nptsvals[j]
print("%d " % npts, end="")
tm0 = time()
scinv_all[j, :] = scalc[j].calc_mean_scinv(pz)
times[j] += time() - tm0
print("\nplotting")
# plot the p(z)
tab = Table(3, 1)
binsize = scalc1000.zsvals[1] - scalc1000.zsvals[0]
pzh = Histogram(pz, x0=scalc1000.zsvals[0], binsize=binsize)
plt_pzh = FramedPlot()
plt_pzh.xrange = xlim
plt_pzh.xtitle = r"$z_s$"
plt_pzh.ytitle = r"$P(z_s)$"
plt_pzh.add(pzh)
tab[0, 0] = plt_pzh
# plot scinv for each npts value
plt_scinv = FramedPlot()
plt_scinv.xrange = xlim
scinv_plots = []
for j in xrange(numcheck):
npts = nptsvals[j]
p = Curve(scalc[j].zlvals, scinv_all[j, :], type="solid", color=colors[j])
p.label = "npts: %d" % npts
plt_scinv.add(p)
scinv_plots.append(p)
scinv_key = PlotKey(0.95, 0.9, scinv_plots, halign="right")
plt_scinv.add(scinv_key)
plt_scinv.ylabel = r"$\langle \Sigma_{crit}^{-1}(z_{lens}) \rangle$"
plt_scinv.xlabel = r"$z_{lens}$"
plt_scinv.yrange = [0, 2.1e-4]
tab[1, 0] = plt_scinv
# ratio to 1000 points
plt_rat = FramedPlot()
plt_rat.xrange = xlim
plt_rat.yrange = [1 - 1.0e-2, 1 + 1.0e-2]
rat_plots = []
for j in xrange(numcheck):
npts = nptsvals[j]
w = where1(scinv1000 > 0)
ratio = scinv_all[j, w] / scinv1000[w]
# ratio=scinv_all[j,:]/scinv1000[:]
p = Curve(scalc[j].zlvals[w], ratio, type="solid", color=colors[j])
p.label = "npts: %d" % npts
plt_rat.add(p)
rat_plots.append(p)
key = PlotKey(0.95, 0.9, rat_plots, halign="right")
plt_rat.add(key)
plt_rat.ylabel = r"$\langle \Sigma_{crit}^{-1} \rangle / \langle \Sigma_{crit}^{-1} \rangle_{1000}$"
plt_rat.xlabel = r"$z_{lens}$"
tab[2, 0] = plt_rat
if show:
tab.show()
plotfile = self.npts_plot_file(i, type)
print("writing to file:", plotfile)
if type == "png":
tab.write_img(1000, 1000, plotfile)
else:
tab.write_eps(plotfile)
print("time npts=1000:", time1000)
for j in xrange(numcheck):
npts = nptsvals[j]
print("time npts=%s: %s" % (npts, times[j]))
def test_sigmacritinv_npts(epsfile=None):
"""
Test accuracy of sigmacrit inv as a function of number
of points
"""
from biggles import FramedPlot, PlotKey, PlotLabel, Curve, FramedArray, Table
c1000 = Cosmo(npts=1000)
c5 = Cosmo(npts=5)
c4 = Cosmo(npts=4)
c3 = Cosmo(npts=3)
c2 = Cosmo(npts=2)
tab = Table(2, 2)
tab.uniform_limits = 0
tab.cellspacing = 1
p5 = FramedPlot()
p5.xlabel = 'zsource'
p5.ylabel = '% diff'
p4 = FramedPlot()
p4.xlabel = 'zsource'
p4.ylabel = '% diff'
p3 = FramedPlot()
p3.xlabel = 'zsource'
p3.ylabel = '% diff'
p2 = FramedPlot()
p2.xlabel = 'zsource'
p2.ylabel = '% diff'
l5 = PlotLabel(0.2, 0.7, 'npts = 5')
p5.add(l5)
l4 = PlotLabel(0.2, 0.1, 'npts = 4')
p4.add(l4)
l3 = PlotLabel(0.2, 0.9, 'npts = 3')
p3.add(l3)
l2 = PlotLabel(0.2, 0.9, 'npts = 2')
p2.add(l2)
colors = [
'black', 'violet', 'blue', 'green', 'orange', 'magenta', 'firebrick',
'red'
]
zlvals = numpy.arange(0.1, 0.9, 0.1)
if zlvals.size != len(colors):
raise ValueError("mismatch colors and zlvals")
i = 0
allc5 = []
for zl in zlvals:
zs = numpy.arange(zl + 0.01, 2.0, 0.01)
scinv = c1000.sigmacritinv(zl, zs)
scinv5 = c5.sigmacritinv(zl, zs)
scinv4 = c4.sigmacritinv(zl, zs)
scinv3 = c3.sigmacritinv(zl, zs)
scinv2 = c2.sigmacritinv(zl, zs)
curve5 = Curve(zs, 100 * (scinv - scinv5) / scinv, color=colors[i])
curve5.label = 'zlens: %0.2f' % zl
allc5.append(curve5)
p5.add(curve5)
curve4 = Curve(zs, 100 * (scinv - scinv4) / scinv, color=colors[i])
p4.add(curve4)
curve3 = Curve(zs, 100 * (scinv - scinv3) / scinv, color=colors[i])
p3.add(curve3)
curve2 = Curve(zs, 100 * (scinv - scinv2) / scinv, color=colors[i])
p2.add(curve2)
i += 1
key = PlotKey(0.15, 0.5, allc5)
p5.add(key)
tab[0, 0] = p5
tab[0, 1] = p4
tab[1, 0] = p3
tab[1, 1] = p2
tab.show()
if epsfile is not None:
tab.write_eps(epsfile)
def test_scinv_dz(self, beg, end, yrange=[0, 2.1e-4], show=False, reload=False, type="png"):
"""
Test accuracy of interpolating scinv as a function of dzl, the
lens redshift spacing.
"""
import biggles
from biggles import Points, FramedPlot, PlotKey, Table, Histogram, Curve
from time import time
import lensing
import pcolors
biggles.configure("default", "fontface", "HersheySans")
biggles.configure("default", "fontsize_min", 1.3)
zsmin = self.zs[0]
zsmax = self.zs[-1]
zlmin = 0.00
zlmax = 1.0
# dzl_vals = numpy.linspace(0.001,0.015,10)
dzl_vals = numpy.linspace(0.001, 0.015, 4)
nzl_vals = ((zlmax - zlmin) / dzl_vals).astype("i8")
numcheck = len(dzl_vals)
colors = pcolors.rainbow(numcheck, "hex")
scalc = []
for nzl in nzl_vals:
s = ScinvCalculator(zlmin, zlmax, nzl, zsmin, zsmax, npts=100)
scalc.append(s)
times = numpy.zeros(numcheck, dtype="f8")
# we'll fill this in
# scinv_all = numpy.zeros( (numcheck, scalc[0].zlvals.size) )
xlim = [0, zsmax]
for i in xrange(beg, end):
scinv_all = []
pz = self.data["pofz"][i]
# print(pz)
for j in xrange(numcheck):
dzl = dzl_vals[j]
nzl = nzl_vals[j]
print(" nzl: %s dzl: %g" % (nzl, dzl))
tm0 = time()
# scinv_all[j,:] = scalc[j].calc_mean_scinv(pz)
sc = scalc[j].calc_mean_scinv(self.zs, pz)
# sc=sc.clip(min=0.0)
# print("sc",j,sc)
scinv_all.append(sc)
times[j] += time() - tm0
print("\nplotting")
# plot the p(z)
tab = Table(3, 1)
binsize = self.zs[1] - self.zs[0]
pzh = Histogram(pz, x0=self.zs[0], binsize=binsize)
plt_pzh = FramedPlot()
plt_pzh.xrange = xlim
plt_pzh.xtitle = r"$z_s$"
plt_pzh.ytitle = r"$P(z_s)$"
plt_pzh.add(pzh)
tab[0, 0] = plt_pzh
# plt_pzh.show()
# plot scinv for each dzl
plt_scinv = FramedPlot()
plt_scinv.xrange = xlim
scinv_plots = []
for j in xrange(numcheck):
dzl = dzl_vals[j]
nzl = nzl_vals[j]
p = Curve(scalc[j].zlvals, scinv_all[j], type="solid", color=colors[j])
p.label = r"$nz_{lens}: %s dz_{lens}: %0.3f$" % (nzl, dzl)
plt_scinv.add(p)
scinv_plots.append(p)
scinv_key = PlotKey(0.95, 0.9, scinv_plots, halign="right")
plt_scinv.add(scinv_key)
plt_scinv.ylabel = r"$\Sigma_{crit}^{-1}(z_{lens})$"
plt_scinv.xlabel = r"$z_{lens}$"
plt_scinv.yrange = yrange
# plt_scinv.show()
tab[1, 0] = plt_scinv
# %diff to best dz
plt_pdiff = FramedPlot()
plt_pdiff.xrange = xlim
plt_pdiff.yrange = [-0.05, 0.05]
pdiff_plots = []
zl_interp = numpy.linspace(zlmin, zlmax, 1000)
scinv_interp_best = esutil.stat.interplin(scinv_all[0], scalc[0].zlvals, zl_interp)
w = where1(scinv_interp_best > 0)
for j in xrange(numcheck):
dzl = dzl_vals[j]
nzl = nzl_vals[j]
scinv_interp = esutil.stat.interplin(scinv_all[j], scalc[j].zlvals, zl_interp)
if w.size > 0:
pdiff = scinv_interp[w] / scinv_interp_best[w] - 1.0
p = Curve(zl_interp[w], pdiff, type="solid", color=colors[j])
else:
pdiff = numpy.ones(scinv_interp.size)
p = Curve(zl_interp, pdiff, type="solid", color=colors[j])
p.label = r"$nz_{lens}: %s dz_{lens}: %0.3f$" % (nzl, dzl)
plt_pdiff.add(p)
pdiff_plots.append(p)
key = PlotKey(0.95, 0.9, pdiff_plots, halign="right")
plt_pdiff.add(key)
plt_pdiff.ylabel = r"$\Sigma_{crit}^{-1} / \Sigma_{crit}^{-1}_{best} - 1$"
plt_pdiff.xlabel = r"$z_{lens}$"
tab[2, 0] = plt_pdiff
if show:
tab.show()
plotfile = self.dzl_plot_file(i, type)
print("writing to file:", plotfile)
if type == "png":
tab.write_img(1000, 1000, plotfile)
else:
tab.write_eps(plotfile)
for j in xrange(numcheck):
dzl = dzl_vals[j]
print("time dzl=%s: %s" % (dzl, times[j]))
def plot_nfw_lin_fits_byrun(run, name, npts=100, prompt=False,
withlin=True,
ymin=0.01, ymax=2000.0):
"""
This should be made not specific for the m-z splits we
used on the sims
"""
conf = lensing.files.cascade_config(run)
if withlin:
ex='lin'
nex='lin'
else:
nex=''
ex=''
d = lensing.sample_read(type='fit',sample=run, name=name, extra=ex)
omega_m = conf['cosmo_config']['omega_m']
rravel = d['r'].ravel()
xrange = [0.5*rravel.min(), 1.5*rravel.max()]
#for i in xrange(d.size):
i=0
for dd in d:
zrange = dd['z_range']
mrange = dd['m200_range']
if dd['rrange'][0] > 0:
log_rmin = log10(dd['rrange'][0])
log_rmax = log10(dd['rrange'][1])
else:
log_rmin = log10(dd['r'][0])
log_rmax = log10(dd['r'][-1])
rvals = 10.0**linspace(log_rmin,log_rmax,npts)
plt = FramedPlot()
lensing.plotting.add_to_log_plot(plt, dd['r'],dd['dsig'],dd['dsigerr'])
z = dd['z_mean']
fitter = lensing.fit.NFWBiasFitter(omega_m,z,rvals,withlin=withlin)
if withlin:
yfit = fitter.nfw_lin_dsig(rvals, dd['r200_fit'],dd['c_fit'],dd['B_fit'])
yfit_nfw = fitter.nfw.dsig(rvals,dd['r200_fit'],dd['c_fit'])
yfit_lin = fitter.lin_dsig(rvals,dd['B_fit'])
yfit = where(yfit < 1.e-5, 1.e-5, yfit)
yfit_lin = where(yfit_lin < 1.e-5, 1.e-5, yfit_lin)
cyfit = Curve(rvals,yfit,color='blue')
cyfit_nfw = Curve(rvals,yfit_nfw,color='red')
cyfit_lin = Curve(rvals,yfit_lin,color='orange')
cyfit.label = 'Best Fit'
cyfit_nfw.label = 'NFW'
cyfit_lin.label = 'linear'
key=PlotKey(0.1,0.3,[cyfit,cyfit_nfw,cyfit_lin])
plt.add(cyfit,cyfit_nfw,cyfit_lin,key)
else:
yfit_nfw = fitter.nfw.dsig(rvals,dd['r200_fit'],dd['c_fit'])
cyfit_nfw = Curve(rvals,yfit_nfw,color='blue')
plt.add(cyfit_nfw)
zlab='%0.2f < z < %0.2f' % (zrange[0],zrange[1])
plt.add(PlotLabel(0.7,0.8,zlab))
ll = (log10(mrange[0]),log10(mrange[1]))
mlab = r'$%0.2f < logM_{200} < %0.2f$' % ll
plt.add(PlotLabel(0.7,0.9,mlab))
#yrange = [ymin,(dd['dsig']+dd['dsigerr']).max()*1.5]
yrange = [ymin,ymax]
plt.xrange = xrange
plt.yrange = yrange
plt.xlog=True
plt.ylog=True
plt.xlabel = r'$r$ [$h^{-1}$ Mpc]'
plt.ylabel = r'$\Delta\Sigma ~ [M_{sun} pc^{-2}]$'
plt.aspect_ratio=1
if prompt:
plt.show()
rinput = raw_input('hit a key: ')
if rinput == 'q':
return
else:
d = lensing.files.lensbin_plot_dir(run,name)
if not os.path.exists(d):
os.makedirs(d)
epsfile=path_join(d,'desmocks-nfw%s-fit-%02i.eps' % (nex,i))
print 'Writing epsfile:',epsfile
plt.write_eps(epsfile)
i += 1
def compare_idl(ratios=False, epsfile=None):
from biggles import FramedPlot,Points, Table
omega_m=0.25
omega_b=0.055
sigma_8=1.0
h=1.0
ns=0.98
r=eu.recfile.Open('/home/esheldon/tmp/linear-compare/pkidl.dat',
delim=' ',dtype=[('k','f8'),('pk','f8')])
pkidlst = r.read()
r.close()
k,pkidl = pkidlst['k'],pkidlst['pk']
r=eu.recfile.Open('/home/esheldon/tmp/linear-compare/xiidl.dat',
delim=' ',dtype=[('r','f8'),('xi','f8')])
xiidlst = r.read()
r.close()
r,xiidl = xiidlst['r'],xiidlst['xi']
l = Linear(omega_m=omega_m,omega_b=omega_b,sigma_8=sigma_8,h=h,ns=ns)
pk = l.pk(k)
xi = l.xi(r, nplk=1000)
#xi = l.xi(r)
pkrat = pk/pkidl
xirat = xi/xiidl
tab=Table(2,1)
symsize=1
if ratios:
pkplt = FramedPlot()
pkplt.xlabel='k'
pkplt.xlog=True
pkplt.ylabel='P(k)/P(k) dave'
pkplt.add( Points(k,pkrat,type='filled circle',size=symsize) )
pkplt.add( Curve(k,pkrat,color='blue') )
tab[0,0] = pkplt
if ratios:
xiplt = FramedPlot()
xiplt.xlabel='r'
xiplt.xlog=True
xiplt.ylabel='xi(r)/xi(r) dave'
xiplt.add( Points(r,xirat,type='filled circle',size=symsize) )
xiplt.add( Curve(r,xirat,color='blue') )
tab[1,0] = xiplt
else:
# big log-log plot
limxi = where(xi < 1.e-5, 1.e-5, xi)
#pxi = Points(r,limxi,type='filled circle',size=symsize)
cxi = Curve(r,limxi,color='blue')
limxiidl = where(xiidl < 1.e-5, 1.e-5, xiidl)
#pxiidl = Points(r,limxiidl,type='filled circle',size=symsize)
cxiidl = Curve(r,limxiidl,color='red')
xipltall = FramedPlot()
xipltall.xlabel='r'
xipltall.xlog=True
xipltall.ylog=True
xipltall.ylabel='xi(r)'
#xipltall.add(pxi,cxi,pxiidl,cxiidl)
xipltall.add(cxi,cxiidl)
tab[0,0] = xipltall
# zoomed in plot
xiplt = FramedPlot()
xiplt.xlabel='r'
xiplt.xlog=True
xiplt.ylabel='xi(r)'
pxi = Points(r,xi,type='filled circle',size=symsize)
cxi = Curve(r,xi,color='blue')
cxi.label = 'Mine'
pxiidl = Points(r,xiidl,type='filled circle',size=symsize)
cxiidl = Curve(r,xiidl,color='red')
cxiidl.label = 'Dave'
key=PlotKey(0.8,0.9, [cxi,cxiidl])
xiplt.add(pxi,cxi,pxiidl,cxiidl)
xiplt.xrange = [50.0,r.max()]
xiplt.yrange=[-2.e-3,1.e-2]
xiplt.add(key)
tab[1,0] = xiplt
if epsfile is not None:
tab.write_eps(epsfile)
else:
tab.show()
def plot_sheardiff_bys2n(self, nperbin, nperbin_sub, show=False):
import biggles
from biggles import FramedPlot,PlotLabel, Curve, Points, Table, PlotKey
data=self.get_sheardiff_data()
epsfile=get_shear_compare_plot_url(self['serun'],self['merun'])
print >>stderr,'Will write summary plot:',epsfile
print >>stderr,'histogramming shear_s2n',nperbin
hdict=eu.stat.histogram(data['shear_s2n'],nperbin=nperbin,
rev=True,more=True)
rev=hdict['rev']
cdlist=[]
for i in xrange(hdict['hist'].size):
if rev[i] != rev[i+1]:
w=rev[ rev[i]:rev[i+1] ]
label=r'$%0.3g < S/N < %0.3g$' \
% (hdict['low'][i],hdict['high'][i])
print >>stderr,label,'mean:',hdict['mean'][i],'num:',w.size
cd=self.plot_sheardiff(nperbin_sub,indices=w,label=label,
num=i,
show=show)
cdlist.append(cd)
slopes1=[cd['coeff1'][0] for cd in cdlist]
offsets1=[cd['coeff1'][1] for cd in cdlist]
slopes2=[cd['coeff2'][0] for cd in cdlist]
offsets2=[cd['coeff2'][1] for cd in cdlist]
tab=Table(2,1)
plt_slope=FramedPlot()
cslope1 = Curve(hdict['mean'],slopes1,color='red')
cslope2 = Curve(hdict['mean'],slopes2,color='blue')
pslope1 = Points(hdict['mean'],slopes1,color='red',
type='filled circle')
pslope2 = Points(hdict['mean'],slopes2,color='blue',
type='filled circle')
cslope1.label = r'$\gamma_1$'
cslope2.label = r'$\gamma_2$'
key=PlotKey(0.1,0.2,[cslope1,cslope2],halign='left')
plt_slope.add(cslope1,cslope2,pslope1,pslope2,key)
plt_slope.xlabel = 'Shear S/N'
plt_slope.ylabel = 'Slope'
plt_slope.xlog=True
plt_slope.xrange = eu.plotting.get_log_plot_range(hdict['mean'])
plt_offset=FramedPlot()
coffset1 = Curve(hdict['mean'],offsets1,color='red')
coffset2 = Curve(hdict['mean'],offsets2,color='blue')
poffset1 = Points(hdict['mean'],offsets1,color='red',
type='filled circle')
poffset2 = Points(hdict['mean'],offsets2,color='blue',
type='filled circle')
plt_offset.add(coffset1,coffset2,poffset1,poffset2)
plt_offset.xlabel = 'Shear S/N'
plt_offset.ylabel = 'Offset'
plt_offset.xlog=True
plt_offset.xrange = eu.plotting.get_log_plot_range(hdict['mean'])
tab[0,0] = plt_slope
tab[1,0] = plt_offset
if show:
tab.show()
print >>stderr,'Writing summary plot:',epsfile
tab.write_eps(epsfile)
converter.convert(epsfile,dpi=90,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
def test_sigmacritinv_npts(epsfile=None):
"""
Test accuracy of sigmacrit inv as a function of number
of points
"""
from biggles import FramedPlot, PlotKey, PlotLabel, Curve, FramedArray, Table
c1000 = Cosmo(npts=1000)
c5 = Cosmo(npts=5)
c4 = Cosmo(npts=4)
c3 = Cosmo(npts=3)
c2 = Cosmo(npts=2)
tab = Table( 2, 2 )
tab.uniform_limits = 0
tab.cellspacing = 1
p5 = FramedPlot()
p5.xlabel = 'zsource'
p5.ylabel = '% diff'
p4 = FramedPlot()
p4.xlabel = 'zsource'
p4.ylabel = '% diff'
p3 = FramedPlot()
p3.xlabel = 'zsource'
p3.ylabel = '% diff'
p2 = FramedPlot()
p2.xlabel = 'zsource'
p2.ylabel = '% diff'
l5 = PlotLabel(0.2,0.7,'npts = 5')
p5.add(l5)
l4 = PlotLabel(0.2,0.1,'npts = 4')
p4.add(l4)
l3 = PlotLabel(0.2,0.9,'npts = 3')
p3.add(l3)
l2 = PlotLabel(0.2,0.9,'npts = 2')
p2.add(l2)
colors = ['black','violet','blue','green','orange','magenta','firebrick','red']
zlvals = numpy.arange(0.1,0.9,0.1)
if zlvals.size != len(colors):
raise ValueError("mismatch colors and zlvals")
i=0
allc5 = []
for zl in zlvals:
zs = numpy.arange(zl+0.01, 2.0, 0.01)
scinv = c1000.sigmacritinv(zl, zs)
scinv5 = c5.sigmacritinv(zl, zs)
scinv4 = c4.sigmacritinv(zl, zs)
scinv3 = c3.sigmacritinv(zl, zs)
scinv2 = c2.sigmacritinv(zl, zs)
curve5 = Curve(zs, 100*(scinv-scinv5)/scinv, color=colors[i])
curve5.label = 'zlens: %0.2f' % zl
allc5.append(curve5)
p5.add(curve5)
curve4 = Curve(zs, 100*(scinv-scinv4)/scinv, color=colors[i])
p4.add(curve4)
curve3 = Curve(zs, 100*(scinv-scinv3)/scinv, color=colors[i])
p3.add(curve3)
curve2 = Curve(zs, 100*(scinv-scinv2)/scinv, color=colors[i])
p2.add(curve2)
i+=1
key = PlotKey(0.15,0.5,allc5)
p5.add(key)
tab[0,0] = p5
tab[0,1] = p4
tab[1,0] = p3
tab[1,1] = p2
tab.show()
if epsfile is not None:
tab.write_eps(epsfile)
def plot_meanshear_vs_trueshear_bys2n(self, nperbin, nperbin_sub, show=False):
from biggles import FramedPlot, Curve, Points, Table, PlotKey
type = "vs_shear"
html_file = get_shear_compare_html_url(self["run"], self["mock_catalog"], type)
print "html_file:", html_file
data = self.get_data()
epsfile = get_shear_compare_plot_url(self["run"], self["mock_catalog"], type)
print >> stderr, "Will write summary plot:", epsfile
print >> stderr, "histogramming shear_s2n", nperbin
hdict = eu.stat.histogram(data["shear_s2n"], nperbin=nperbin, rev=True, more=True)
rev = hdict["rev"]
cdlist = []
pngfiles = []
for i in xrange(hdict["hist"].size):
if rev[i] != rev[i + 1]:
w = rev[rev[i] : rev[i + 1]]
label = r"$%0.3g < S/N < %0.3g$" % (hdict["low"][i], hdict["high"][i])
print >> stderr, label, "mean:", hdict["mean"][i], "num:", w.size
cd, png = self.plot_meanshear_vs_trueshear(nperbin_sub, indices=w, label=label, num=i, show=show)
cdlist.append(cd)
pngfiles.append(png)
slopes1 = [cd["coeff1"][0] for cd in cdlist]
offsets1 = [cd["coeff1"][1] for cd in cdlist]
slopes2 = [cd["coeff2"][0] for cd in cdlist]
offsets2 = [cd["coeff2"][1] for cd in cdlist]
tab = Table(2, 1)
plt_slope = FramedPlot()
cslope1 = Curve(hdict["mean"], slopes1, color="red")
cslope2 = Curve(hdict["mean"], slopes2, color="blue")
pslope1 = Points(hdict["mean"], slopes1, color="red", type="filled circle")
pslope2 = Points(hdict["mean"], slopes2, color="blue", type="filled circle")
cslope1.label = r"$\gamma_1$"
cslope2.label = r"$\gamma_2$"
key = PlotKey(0.1, 0.2, [cslope1, cslope2], halign="left")
plt_slope.add(cslope1, cslope2, pslope1, pslope2, key)
plt_slope.xlabel = "Shear S/N"
plt_slope.ylabel = "Slope"
plt_slope.xlog = True
plt_slope.xrange = eu.plotting.get_log_plot_range(hdict["mean"])
plt_offset = FramedPlot()
coffset1 = Curve(hdict["mean"], offsets1, color="red")
coffset2 = Curve(hdict["mean"], offsets2, color="blue")
poffset1 = Points(hdict["mean"], offsets1, color="red", type="filled circle")
poffset2 = Points(hdict["mean"], offsets2, color="blue", type="filled circle")
plt_offset.add(coffset1, coffset2, poffset1, poffset2)
plt_offset.xlabel = "Shear S/N"
plt_offset.ylabel = "Offset"
plt_offset.xlog = True
plt_offset.xrange = eu.plotting.get_log_plot_range(hdict["mean"])
tab[0, 0] = plt_slope
tab[1, 0] = plt_offset
if show:
tab.show()
print >> stderr, "Writing summary plot:", epsfile
tab.write_eps(epsfile)
converter.convert(epsfile, dpi=90, verbose=True)
png = epsfile.replace(".eps", ".png")
pngfiles = [png] + pngfiles
self.write_html(pngfiles, html_file)