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(self, yrange=None):
from biggles import FramedPlot, Points, Curve, PlotKey, Table,PlotLabel
res=self.res
etrue = res['etrue']
e1true = res['e1true']
e2true = res['e2true']
facvals = res['facvals']
sigma = res['sigma']
e1=res['e1']
e2=res['e2']
e=res['e']
T=res['T']
Ttrue=res['Ttrue']*facvals**2
e_pdiff = e/etrue-1
T_pdiff = T/Ttrue-1
compc= Curve(sigma, sigma*0)
ce = Curve(sigma, e_pdiff, color='red')
pe = Points(sigma, e_pdiff, type='filled circle')
e_plt = FramedPlot()
e_plt.add(ce,pe,compc)
e_plt.xlabel = r'$\sigma$'
e_plt.ylabel = r'$e/e_{true}-1$'
cT = Curve(sigma, T_pdiff, color='red')
pT = Points(sigma, T_pdiff, type='filled circle')
T_plt = FramedPlot()
T_plt.add(cT,pT,compc)
T_plt.xlabel = r'$\sigma$'
T_plt.ylabel = r'$T/T_{true}-1$'
lab=PlotLabel(0.9,0.9,self.model,halign='right')
T_plt.add(lab)
if yrange is not None:
e_plt.yrange=yrange
T_plt.yrange=yrange
tab=Table(2,1)
tab[0,0] = T_plt
tab[1,0] = e_plt
tab.show()
def compare_r(self, run, recache=False):
self.load_matches(run, recache=recache)
if self.match_data is None:
return
binsize=0.025
min = 0.0
max = 1.1
tab = Table(2,1)
plt, pltdiff = self.compare_plots('r', min, max, binsize)
tab[0,0] = plt
tab[1,0] = pltdiff
tab.show()
def main():
biggles.configure('default','fontsize_min',2.0)
s2n,r50=read_data()
tab=Table(1,2)
tab[0,0] = make_s2n_plot(s2n)
tab[0,1] = make_r50_plot(r50)
epsfile='/u/ki/esheldon/public_html/tmp/tmp.eps'
print(epsfile)
tab.write_eps(epsfile)
pngfile=epsfile.replace('.eps','.png')
print(pngfile)
tab.write_img(800,800,pngfile)
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_fits(self, st):
import biggles
biggles.configure("default", "fontsize_min", 2)
parnames = ["A", "a", "g0", "gmax"]
npars = len(parnames)
tab = Table(npars, 1)
magmiddle = (st["minmag"] + st["maxmag"]) / 2
for i in xrange(npars):
yval = st["pars"][:, i]
yerr = st["perr"][:, i]
ymean = yval.mean()
ystd = yval.std()
yrange = [ymean - 3.5 * ystd, ymean + 3.5 * ystd]
pts = Points(magmiddle, yval, type="filled circle")
yerrpts = SymmetricErrorBarsY(magmiddle, yval, yerr)
xerrpts = ErrorBarsX(yval, st["minmag"], st["maxmag"])
plt = FramedPlot()
plt.yrange = yrange
plt.add(pts, xerrpts, yerrpts)
plt.xlabel = "mag"
plt.ylabel = parnames[i]
tab[i, 0] = plt
if self.show:
tab.show()
d = files.get_prior_dir()
d = os.path.join(d, "plots")
epsfile = "pofe-pars-%s.eps" % self.otype
epsfile = os.path.join(d, epsfile)
eu.ostools.makedirs_fromfile(epsfile)
print epsfile
tab.write_eps(epsfile)
os.system("converter -d 100 %s" % epsfile)
def plot_fits(self,st):
import biggles
biggles.configure( 'default', 'fontsize_min', 2)
parnames=['A','a','g0','gmax']
npars=len(parnames)
tab=Table(npars,1)
magmiddle=(st['minmag'] + st['maxmag'])/2
for i in xrange(npars):
yval=st['pars'][:,i]
yerr=st['perr'][:,i]
ymean=yval.mean()
ystd=yval.std()
yrange=[ymean-3.5*ystd, ymean+3.5*ystd]
pts=Points(magmiddle, yval, type='filled circle')
yerrpts=SymmetricErrorBarsY(magmiddle,yval,yerr)
xerrpts=ErrorBarsX(yval, st['minmag'], st['maxmag'])
plt=FramedPlot()
plt.yrange=yrange
plt.add(pts,xerrpts, yerrpts)
plt.xlabel='mag'
plt.ylabel=parnames[i]
tab[i,0] = plt
if self.show:
tab.show()
d=files.get_prior_dir()
d=os.path.join(d, 'plots')
epsfile='pofe-pars-%s.eps' % self.otype
epsfile=os.path.join(d,epsfile)
eu.ostools.makedirs_fromfile(epsfile)
print epsfile
tab.write_eps(epsfile)
os.system('converter -d 100 %s' % epsfile)
def plot_fits(self,st):
import biggles
biggles.configure( 'default', 'fontsize_min', 2)
if self.objtype=='gexp':
parnames=['A','a','g0','gmax']
else:
parnames=['A','b','c']
npars=len(parnames)
tab=Table(npars,1)
magmiddle=(st['minmag'] + st['maxmag'])/2
for i in xrange(npars):
yval=st['pars'][:,i]
yerr=st['perr'][:,i]
ymean=yval.mean()
ystd=yval.std()
yrange=[ymean-3.5*ystd, ymean+3.5*ystd]
pts=Points(magmiddle, yval, type='filled circle')
yerrpts=SymmetricErrorBarsY(magmiddle,yval,yerr)
xerrpts=ErrorBarsX(yval, st['minmag'], st['maxmag'])
plt=FramedPlot()
plt.yrange=yrange
plt.add(pts,xerrpts, yerrpts)
plt.xlabel='mag'
plt.ylabel=parnames[i]
tab[i,0] = plt
tab.show()
def compare_ellip(self, run):
self.load_matches(run, recache=recache)
if self.match_data is None:
return
binsize=0.025
min = -1.0
max = 1.1
tab = Table(2,2)
#
# e1
#
column = 0
for type in ['e1','e2']:
plt, pltdiff = self.compare_plots(type, min, max, binsize)
tab[0,column] = plt
tab[1,column] = pltdiff
column += 1
tab.show()
def doplot(self, fitres, h1, h2, h, minmag, maxmag):
tab=Table(2,1)
tab.title='%s %.2f %.2f ' % (self.objtype, minmag, maxmag)
#xfit,yfit,gprior = self.get_prior_vals(fitres, h)
gprior=self.get_prior(fitres)
nrand=100000
binsize=self.binsize
g1rand,g2rand=gprior.sample2d(nrand)
grand=gprior.sample1d(nrand)
hrand=histogram(grand, binsize=binsize, min=0., max=1., more=True)
h1rand=histogram(g1rand, binsize=binsize, min=-1., max=1., more=True)
#fbinsize=xfit[1]-xfit[0]
#hrand['hist'] = hrand['hist']*float(yfit.sum())/hrand['hist'].sum()*fbinsize/binsize
hrand['hist'] = hrand['hist']*float(h['hist'].sum())/nrand
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)
key=PlotKey(0.9,0.9,[hplt,hpltrand],halign='right')
plt.add(hplt, hpltrand, key)
tab[1,0]=plt
if self.show:
tab.show()
return tab
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 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_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_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 doplot(self, fitres, h1, h2, h, minmag, maxmag):
tab=Table(2,1)
tab.title='%s %.2f %.2f ' % (self.objtype, minmag, maxmag)
#xfit,yfit,gprior = self.get_prior_vals(fitres, h)
gprior=self.get_prior(fitres)
nrand=100000
binsize=self.binsize
g1rand,g2rand=gprior.sample2d(nrand)
grand=gprior.sample1d(nrand)
#hrand=histogram(grand, binsize=binsize, min=0., max=1., more=True)
hrand=histogram(grand, binsize=binsize, min=h['low'][0], max=h['high'][-1], more=True)
h1rand=histogram(g1rand, binsize=binsize, min=-1., max=1., more=True)
#fbinsize=xfit[1]-xfit[0]
#hrand['hist'] = hrand['hist']*float(yfit.sum())/hrand['hist'].sum()*fbinsize/binsize
hrand['hist'] = hrand['hist']*float(h['hist'].sum())/nrand
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)
key=PlotKey(0.9,0.9,[hplt,hpltrand],halign='right')
plt.add(hplt, hpltrand, key)
tab[1,0]=plt
if self.show:
tab.show()
d=files.get_prior_dir()
d=os.path.join(d, 'plots')
epsfile='pofe-%.2f-%.2f-%s.eps' % (minmag,maxmag,self.objtype)
epsfile=os.path.join(d,epsfile)
eu.ostools.makedirs_fromfile(epsfile)
print epsfile
tab.write_eps(epsfile)
os.system('converter -d 100 %s' % epsfile)
return tab
def doplot(self, gprior, minmag, maxmag):
from lensing.util import eta1eta2_to_g1g2
tab = Table(2, 2)
tab.title = "%s %.2f %.2f " % (self.otype, minmag, maxmag)
h1_g, h2_g, h_g = self.do_histograms(minmag, maxmag, "g")
h1_eta, h2_eta, h_eta = self.do_histograms(minmag, maxmag, "eta")
nrand = 1000000
binsize_eta = self.binsize_eta
binsize_g = self.binsize_g
rbinsize_eta = binsize_eta * 0.2
rbinsize_g = binsize_g * 0.2
gr = gprior.sample(nrand)
eta1_rand = gr[:, 0]
eta2_rand = gr[:, 1]
eta_rand = numpy.sqrt(eta1_rand ** 2 + eta2_rand ** 2)
g1_rand, g2_rand = eta1eta2_to_g1g2(eta1_rand, eta2_rand)
g_rand = numpy.sqrt(g1_rand ** 2 + g2_rand ** 2)
hrand_eta = histogram(eta_rand, binsize=rbinsize_eta, min=0, max=self.max_eta, more=True)
h1rand_eta = histogram(eta1_rand, binsize=rbinsize_eta, min=self.min_eta_2d, max=self.max_eta_2d, more=True)
hrand_g = histogram(g_rand, binsize=rbinsize_g, min=0, max=self.max_g, more=True)
h1rand_g = histogram(g1_rand, binsize=rbinsize_g, min=self.min_g_2d, max=self.max_g_2d, more=True)
# eta 2d plots
bratio_eta = self.binsize_eta / rbinsize_eta
hrand_eta["hist"] = hrand_eta["hist"] * bratio_eta * float(h_eta["hist"].sum()) / nrand
h1rand_eta["hist"] = h1rand_eta["hist"] * bratio_eta * float(h1_eta["hist"].sum()) / h1rand_eta["hist"].sum()
pltboth_eta = FramedPlot()
pltboth_eta.xlabel = r"$\eta$"
hplt1_eta = Histogram(h1_eta["hist"], x0=h1_eta["low"][0], binsize=binsize_eta, color="darkgreen")
hplt2_eta = Histogram(h2_eta["hist"], x0=h2_eta["low"][0], binsize=binsize_eta, color="blue")
hpltrand_eta = Histogram(hrand_eta["hist"], x0=hrand_eta["low"][0], binsize=rbinsize_eta, color="red")
hplt1rand_eta = Histogram(h1rand_eta["hist"], x0=h1rand_eta["low"][0], binsize=rbinsize_eta, color="red")
hplt1_eta.label = r"$\eta_1$"
hplt2_eta.label = r"$\eta_2$"
hplt1rand_eta.label = "rand"
hpltrand_eta.label = "rand"
keyboth_eta = PlotKey(0.9, 0.9, [hplt1_eta, hplt2_eta, hplt1rand_eta], halign="right")
pltboth_eta.add(hplt1_eta, hplt2_eta, hplt1rand_eta, keyboth_eta)
tab[0, 0] = pltboth_eta
plt1d_eta = FramedPlot()
plt1d_eta.xlabel = r"$|\eta|$"
hplt_eta = Histogram(h_eta["hist"], x0=h_eta["low"][0], binsize=binsize_eta)
hplt_eta.label = r"$|\eta|$"
key_eta = PlotKey(0.9, 0.9, [hplt_eta, hpltrand_eta], halign="right")
plt1d_eta.add(hplt_eta, hpltrand_eta, key_eta)
tab[1, 0] = plt1d_eta
# g plots
bratio_g = self.binsize_g / rbinsize_g
hrand_g["hist"] = hrand_g["hist"] * bratio_g * float(h_g["hist"].sum()) / nrand
h1rand_g["hist"] = h1rand_g["hist"] * bratio_g * float(h1_g["hist"].sum()) / h1rand_g["hist"].sum()
pltboth_g = FramedPlot()
pltboth_g.xlabel = r"$g$"
hplt1_g = Histogram(h1_g["hist"], x0=h1_g["low"][0], binsize=binsize_g, color="darkgreen")
hplt2_g = Histogram(h2_g["hist"], x0=h2_g["low"][0], binsize=binsize_g, color="blue")
hpltrand_g = Histogram(hrand_g["hist"], x0=hrand_g["low"][0], binsize=rbinsize_g, color="red")
hplt1rand_g = Histogram(h1rand_g["hist"], x0=h1rand_g["low"][0], binsize=rbinsize_g, color="red")
hplt1_g.label = r"$g_1$"
hplt2_g.label = r"$g_2$"
hplt1rand_g.label = "rand"
hpltrand_g.label = "rand"
keyboth_g = PlotKey(0.9, 0.9, [hplt1_g, hplt2_g, hplt1rand_g], halign="right")
pltboth_g.add(hplt1_g, hplt2_g, hplt1rand_g, keyboth_g)
tab[0, 1] = pltboth_g
plt1d_g = FramedPlot()
plt1d_g.xlabel = r"$|g|$"
hplt_g = Histogram(h_g["hist"], x0=h_g["low"][0], binsize=binsize_g)
hplt_g.label = "|g|"
key_g = PlotKey(0.9, 0.9, [hplt_g, hpltrand_g], halign="right")
plt1d_g.add(hplt_g, hpltrand_g, key_g)
tab[1, 1] = plt1d_g
if self.show:
tab.show()
d = files.get_prior_dir()
d = os.path.join(d, "plots")
epsfile = "pofe-pofeta-%.2f-%.2f-%s.eps" % (minmag, maxmag, self.otype)
epsfile = os.path.join(d, epsfile)
eu.ostools.makedirs_fromfile(epsfile)
print epsfile
tab.write_eps(epsfile)
os.system("converter -d 100 %s" % epsfile)
return tab
def doplot(self, gprior, h1, h2, h, minmag, maxmag):
tab=Table(2,1)
tab.title='%s %.2f %.2f ' % (self.otype, minmag, maxmag)
nrand=1000000
binsize=self.binsize
rbinsize=binsize*0.2
gr = gprior.sample(nrand)
g1rand=gr[:,0]
g2rand=gr[:,1]
grand = numpy.sqrt( g1rand**2 + g2rand**2 )
#hrand=histogram(grand, binsize=rbinsize, min=h['low'][0], max=h['high'][-1], more=True)
hrand=histogram(grand, binsize=rbinsize, min=0,max=self.maxe, more=True)
h1rand=histogram(g1rand, binsize=rbinsize, min=self.mine_2d, max=self.maxe_2d, more=True)
bratio = self.binsize/rbinsize
hrand['hist'] = hrand['hist']*bratio*float(h['hist'].sum())/nrand
h1rand['hist'] = h1rand['hist']*bratio*float(h1['hist'].sum())/h1rand['hist'].sum()
pltboth=FramedPlot()
pltboth.xlabel=r'$%s$' % self.ellip_name
hplt1=Histogram(h1['hist'], x0=h1['low'][0], binsize=binsize,color='darkgreen')
hplt2=Histogram(h2['hist'], x0=h2['low'][0], binsize=binsize,color='blue')
hpltrand=Histogram(hrand['hist'], x0=hrand['low'][0], binsize=rbinsize,
color='red')
hplt1rand=Histogram(h1rand['hist'], x0=h1rand['low'][0], binsize=rbinsize,
color='red')
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'$|%s|$' % self.ellip_name
hplt=Histogram(h['hist'], x0=h['low'][0], binsize=binsize)
hplt.label='|%s|' % self.ellip_name
#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)
key=PlotKey(0.9,0.9,[hplt,hpltrand],halign='right')
plt.add(hplt, hpltrand, key)
tab[1,0]=plt
if self.show:
tab.show()
d=files.get_prior_dir()
d=os.path.join(d, 'plots')
epsfile='pofe-%.2f-%.2f-%s.eps' % (minmag,maxmag,self.otype)
epsfile=os.path.join(d,epsfile)
eu.ostools.makedirs_fromfile(epsfile)
print epsfile
tab.write_eps(epsfile)
os.system('converter -d 100 %s' % epsfile)
return tab
def doplot(run, st, s2n_field, setname, s2n_range, sratio_range, psfnums, nobj):
tab=Table(2,1)
color1='blue'
color2='red'
m1pts=Points(st['s2n'],st['m1'],type='filled circle',color=color1)
m1errpts=SymmetricErrorBarsY(st['s2n'],st['m1'],st['m1_err'],color=color1)
m2pts=Points(st['s2n'],st['m2'],type='filled circle',color=color2)
m2errpts=SymmetricErrorBarsY(st['s2n'],st['m2'],st['m2_err'],color=color2)
c1pts=Points(st['s2n'],st['c1'],type='filled circle',color=color1)
c1errpts=SymmetricErrorBarsY(st['s2n'],st['c1'],st['c1_err'],color=color1)
c2pts=Points(st['s2n'],st['c2'],type='filled circle',color=color2)
c2errpts=SymmetricErrorBarsY(st['s2n'],st['c2'],st['c2_err'],color=color2)
m1pts.label=r'$\gamma_1$'
m2pts.label=r'$\gamma_2$'
key=PlotKey(0.9,0.2,[m1pts,m2pts], halign='right')
xrng=array( [0.5*s2n_range[0], 1.5*s2n_range[1]])
cyrng=get_symmetric_range(st['c1'],st['c1_err'],st['c2'],st['c2_err'])
myrng=get_symmetric_range(st['m1'],st['m1_err'],st['m2'],st['m2_err'])
mplt=FramedPlot()
cplt=FramedPlot()
mplt.xlog=True
cplt.xlog=True
mplt.xrange=xrng
cplt.xrange=xrng
#mplt.yrange=myrng
#cplt.yrange=cyrng
mplt.yrange=[-0.15,0.15]
cplt.yrange=[-0.01,0.01]
mplt.xlabel=s2n_field
mplt.ylabel='m'
cplt.xlabel=s2n_field
cplt.ylabel='c'
zplt=Curve(xrng, [0,0])
mallow=Curve(xrng, [-0.004, 0.004])
callow=Curve(xrng, [-0.0004, 0.0004])
mallow=FillBetween(xrng, [0.004,0.004],
xrng, [-0.004,-0.004],
color='grey80')
callow=FillBetween(xrng, [0.0004,0.0004],
xrng, [-0.0004,-0.0004],
color='grey80')
labels=get_labels(run,
psfnums,
setname,
nobj,
sratio_range)
mplt.add( mallow, zplt, m1pts, m1errpts, m2pts, m2errpts, key )
mplt.add(*labels)
cplt.add( callow, zplt, c1pts, c1errpts, c2pts, c2errpts )
tab[0,0] = mplt
tab[1,0] = cplt
tab.show()
def main():
options,args = parser.parse_args(sys.argv[1:])
if len(args) < 3:
parser.print_help()
sys.exit(1)
run1 = args[0]
run2 = args[1]
bintype = args[2]
rrange=options.rrange
if rrange is not None:
rrange=[float(r) for r in rrange.split(',')]
biggles.configure('screen','width', 1400)
biggles.configure('default','fontsize_min',1.0)
b = lensing.binning.instantiate_binner(bintype)
name=b.get_name()
data1 = lensing.files.sample_read(type=options.type, sample=run1, name=name)
data2 = lensing.files.sample_read(type=options.type, sample=run2, name=name)
nbin=data1.size
tab=Table(1,2)
for i in xrange(nbin):
tab[0,0] = plot2dsig_over(
data1['r'][i],data1['dsig'][i],data1['dsigerr'][i],
data2['r'][i],data2['dsig'][i],data2['dsigerr'][i],
label1=run1,label2=run2,label=b.bin_label(i),
show=False)
tab[0,0].aspect_ratio=1
pbot = FramedPlot()
rat=data1['dsig'][i]/data2['dsig'][i]
raterr = rat*numpy.sqrt( (data1['dsigerr'][i]/data1['dsig'][i])**2
+(data2['dsigerr'][i]/data2['dsig'][i])**2 )
rpts=Points(data1['r'][i], rat, type='filled circle',color='blue')
rerr=SymErrY(data1['r'][i], rat, raterr,color='blue')
if rrange is None:
rat_use,raterr_use=rat,raterr
else:
w,=numpy.where( (data1['r'][i] > rrange[0]) & (data1['r'][i] < rrange[1]))
rat_use,raterr_use=rat[w],raterr[w]
mn,err = eu.stat.wmom(rat_use, 1/raterr_use**2, calcerr=True)
plot_rrange=[0.5*data1['r'][i].min(),data1['r'][i].max()*2]
z=Curve(plot_rrange, [1,1])
pbot.add(rpts,rerr,z)
pbot.xlog=True
pbot.xlabel = lensing.plotting.labels['rproj']
pbot.ylabel = '%s/%s' % (run1,run2)
pbot.yrange=[0,2]
pbot.xrange = plot_rrange
pbot.aspect_ratio=1
lab='<ratio>'
if rrange is not None:
lab = r'$%s r \in [%.2f,%.2f]$' % (lab,rrange[0],rrange[1])
lab='%s: %0.2f +/- %0.2f' % (lab,mn,err)
pbot.add(PlotLabel(0.9,0.9,lab,halign='right'))
tab[0,1] = pbot
tab.show()
epsfile=lensing.files.sample_file(type='binned-plots',
sample=run1,
name=name,
extra='compare-%s-%02d' % (run2,i),
ext='eps')
print("writing:",epsfile)
tab.write_eps(epsfile)
if nbin > 1:
key=raw_input('hit a key: ')
if key.lower() == 'q':
return
def xi_int_pk_slow(self, k, Pk, r, debug=False, doplot=False):
"""
Maybe try a slow but more accurate version here
get xi at position r by integrating P(k). r should be a scalar
the integral is done in two parts
"""
import scipy.integrate
# some of this could be pre-computed, but it might
# not matter
NumPerPer = 30.0
# Number of samples per period, should always be > 5 at least
npd = 100
# depends on cosmology but this is ballpark
L_wiggle=170.0
Pref=1.0 /(2.0 * PI**2 * r)
dk1=2.0 *PI/(NumPerPer*L_wiggle)
dk2=2.0 *PI/(NumPerPer*r)
dktry=min([dk1,dk2])
# first section
kmin=0.0
kmax=2.0
numk=int32((kmax-kmin)/dktry)
dk=(kmax-kmin)/(1.0*numk-1)
kk=arange(numk,dtype='f8')*dk+kmin
# Dave used *local* cubic spline interpolation (the /spline)
# keyword for interpol. This seems to give radically different
# results! Using interpsp2 above gets us within about 6%
# of dave's answer. Which is right? I think the right thing
# to do is use more values for Pk,k and do linear interpolation
Pkk=interplin(Pk,k,kk)
#Pkk=interp(kk,k,Pk)
tab=Pkk*kk*sin(kk*r)
integ = scipy.integrate.simps(tab,x=kk)
xi_1=Pref*integ
if debug:
print 'r=',r
if dk1 < dk2:
print 'Baryon wiggle limited'
else:
print 'Sin(k*r) limited'
print 'Numk=',numk
print 'dk=',dk
integ_trap = scipy.integrate.trapz(tab,x=kk)
integ_qg1000 = self.qg1000.integrate(kk,tab)
print 'integ=',integ
print 'integ_trap=',integ_trap
print 'integ_qg1000=',integ_qg1000
nprint=20
if doplot:
wk=where1(k < kmax)
wkk=where1(kk > 1.e-8)
ptab=Table(2,1)
lplt=FramedPlot()
lplt.add( Points(k[wk],Pk[wk],type='filled circle',size=1) )
lplt.add( Points(kk[wkk],Pkk[wkk], color='red',
type='filled circle', size=0.7))
lplt.add( Curve(kk[wkk],Pkk[wkk], color='blue'))
lplt.xlog=True
ptab[0,0] = lplt
splt = FramedPlot()
splt.add(Points(k[wk],Pk[wk]*k[wk]*sin(k[wk]*r),
type='filled circle',size=1))
splt.add(Points(kk[wkk],tab[wkk],
color='red',type='filled circle',size=0.7))
splt.add(Curve(kk[wkk],tab[wkk], color='blue'))
splt.xlog=True
ptab[1,0] = splt
ptab.show()
# next section/method
# now need integral \int_xmax^infinity x P(x/r) sin(x)
Pref2=Pref/(r**2)
xmax=r*kmax
num_dec=5
num_per_dec=npd
numx=num_dec*num_per_dec
x=xmax*10.0**(arange(numx,dtype='f8')/num_per_dec)
Px=interplin(Pk,k,x/r)
y=x*Px
ly=log(y)
n=numx
al=(roll(ly,-1)-ly)/(roll(x,-1)-x)
al[n-1]=al[n-2]
Amp=y/exp(al*x)
# integral boundaries
a=x
b=roll(x,-1)
norm=y/(1+al**2)
Ta=al*sin(a)-cos(a)
Tb=al*sin(b)-cos(b)
dif=exp(al*(b-a))*Tb-Ta
d=norm*dif
d=d[0:n-2]
integ=d.sum()
xi_2=integ*Pref2
xi=xi_1+xi_2
if debug:
print 'xi_1:',xi_1
print 'xi_2:',xi_2
return xi
def test(data=None, logc=False):
from biggles import Histogram, FramedPlot, PlotKey, Table
if data is None:
data = read_test_data()
modelflux, modelflux_ivar = avg_gri(data['modelflux'][:,1],data['modelflux_ivar'][:,1],
data['modelflux'][:,2],data['modelflux_ivar'][:,2],
data['modelflux'][:,3],data['modelflux_ivar'][:,3])
#psfflux, psfflux_ivar = avg_gri(data['psfflux'][:,1],data['psfflux_ivar'][:,1],
# data['psfflux'][:,2],data['psfflux_ivar'][:,2],
# data['psfflux'][:,3],data['psfflux_ivar'][:,3])
psfflux, psfflux_ivar = avg_gri(data['psfflux'][:,1],data['modelflux_ivar'][:,1],
data['psfflux'][:,2],data['modelflux_ivar'][:,2],
data['psfflux'][:,3],data['modelflux_ivar'][:,3])
fmin_log=1.e-3
fmax_log=4.
tab = Table(2,2)
binsize=0.05
col=0
for type in ['modelflux','psfflux']:
flux_plt = FramedPlot()
h = eu.stat.histogram(log10(data[type][:,1]), min=fmin_log, max=fmax_log, binsize=binsize)
gmod_h = Histogram(h, x0=fmin_log, binsize=binsize, color='green')
gmod_h.label = 'g '+type
h = eu.stat.histogram(log10(data[type][:,2]), min=fmin_log, max=fmax_log, binsize=binsize)
rmod_h = Histogram(h, x0=fmin_log, binsize=binsize, color='red')
rmod_h.label = 'r '+type
h = eu.stat.histogram(log10(data[type][:,3]), min=fmin_log, max=fmax_log, binsize=binsize)
imod_h = Histogram(h, x0=fmin_log, binsize=binsize, color='magenta')
imod_h.label = 'i '+type
if type == 'modelflux':
h = eu.stat.histogram(log10(modelflux), min=fmin_log, max=fmax_log, binsize=binsize)
else:
h = eu.stat.histogram(log10(psfflux), min=fmin_log, max=fmax_log, binsize=binsize)
mod_h = Histogram(h, x0=fmin_log, binsize=binsize, width=2)
mod_h.label = 'combined '+type
key = PlotKey(0.5,0.9,[gmod_h, rmod_h, imod_h, mod_h])
flux_plt.add(gmod_h, rmod_h, imod_h, mod_h, key)
flux_plt.xlabel = 'flux'
tab[0,col] = flux_plt
col += 1
col=0
for logc in [False,True]:
if logc:
xmin=-0.1
#xmax=1
xmax=0.6
binsize = 0.01
else:
xmin=-0.1
xmax=1.0
binsize = 0.01
gc = calc_c(data['modelflux'][:,1], data['psfflux'][:,1], log=logc)
rc = calc_c(data['modelflux'][:,2], data['psfflux'][:,2], log=logc)
ic = calc_c(data['modelflux'][:,3], data['psfflux'][:,3], log=logc)
allc = calc_c(modelflux, psfflux, log=logc)
c_plt = FramedPlot()
h = eu.stat.histogram(gc, min=xmin, max=xmax, binsize=binsize)
gch = Histogram(h, x0=xmin, binsize=binsize, color='green')
gch.label = 'g'
h = eu.stat.histogram(rc, min=xmin, max=xmax, binsize=binsize)
rch = Histogram(h, x0=xmin, binsize=binsize, color='red')
rch.label = 'r'
h = eu.stat.histogram(ic, min=xmin, max=xmax, binsize=binsize)
ich = Histogram(h, x0=xmin, binsize=binsize, color='magenta')
ich.label = 'i'
h = eu.stat.histogram(allc, min=xmin, max=xmax, binsize=binsize)
ch = Histogram(h, x0=xmin, binsize=binsize, width=2)
ch.label = 'combined '
key = PlotKey(0.7,0.9,[gch, rch, ich, ch])
c_plt.add(gch, rch, ich, ch, key)
if logc:
c_plt.xlabel = r'$-log_{10}(psf/model)$'
else:
c_plt.xlabel = '1-psf/model'
tab[1,col] = c_plt
col += 1
tab.show()
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_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_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_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)