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 plot_boss_geometry(color=None, colorwheel=None, plt=None, width=1, show=True,
region=None):
"""
Plot the boundaries in the boss_survey.par file
"""
import esutil as eu
import biggles
from biggles import FramedPlot, Curve
bg = read_boss_geometry()
if plt is None:
plt = FramedPlot()
plt.xlabel=r'$\lambda$'
plt.ylabel=r'$\eta$'
if color is not None:
colors = [color]*len(bg)
elif colorwheel is not None:
colors = colorwheel
else:
colors = ['red','blue','green','magenta','navyblue','seagreen',
'firebrick','cadetblue','green4']
for i in xrange(len(bg)):
b = bg[i]
color = colors[i % len(colors)]
c = eu.plotting.bbox( b['clambdaMin'], b['clambdaMax'], b['cetaMin'], b['cetaMax'],
color=color, width=width)
plt.add(c)
if region == 'ngc':
plt.yrange = [-40.,50.]
plt.xrange = [-80.,80.]
elif region == 'sgc':
plt.yrange = [105.,165.]
plt.xrange = [-60.,60.]
else:
plt.yrange = [-40.,165.]
plt.xrange = [-80.,80.]
plt.aspect_ratio = (plt.yrange[1]-plt.yrange[0])/(plt.xrange[1]-plt.xrange[0])
if show:
plt.show()
return plt
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 = '$T^2'
plt.xlabel = 'k'
plt.yrange = [1.e-8,1.0]
plt.aspect_ratio=1
if Tc is not None or Tb is not None:
key=PlotKey(0.1,0.9,plist)
plt.add(key)
plt.show()
def test_fit_nfw_dsig(rmin=0.01):
from biggles import FramedPlot,Points,SymmetricErrorBarsY,Curve
omega_m=0.25
z=0.25
n = lensing.nfw.NFW(omega_m, z)
r200 = 1.0
c = 5.0
rmax = 5.0
log_rmin = log10(rmin)
log_rmax = log10(rmax)
npts = 25
logr = numpy.linspace(log_rmin,log_rmax,npts)
r = 10.0**logr
ds = n.dsig(r, r200, c)
# 10% errors
dserr = 0.1*ds
ds += dserr*numpy.random.standard_normal(ds.size)
guess = numpy.array([r200,c],dtype='f8')
# add 10% error to the guess
guess += 0.1*guess*numpy.random.standard_normal(guess.size)
res = fit_nfw_dsig(omega_m, z, r, ds, dserr, guess)
r200_fit = res['r200']
r200_err = res['r200_err']
c_fit = res['c']
c_err = res['c_err']
print 'Truth:'
print ' r200: %f' % r200
print ' c: %f' % c
print 'r200_fit: %f +/- %f' % (r200_fit,r200_err)
print ' c_fit: %f +/- %f' % (c_fit,c_err)
print 'Cov:'
print res['cov']
logr = numpy.linspace(log_rmin,log_rmax,1000)
rlots = 10.0**logr
yfit = n.dsig(rlots,r200_fit,c_fit)
plt=FramedPlot()
plt.add(Points(r,ds,type='filled circle'))
plt.add(SymmetricErrorBarsY(r,ds,dserr))
plt.add(Curve(rlots,yfit,color='blue'))
plt.xlabel = r'$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 make_plot(self):
bindata=self.bindata
bin_field=self.bin_field
plt=FramedPlot()
plt.uniform_limits=1
plt.xlog=True
#plt.xrange=[0.5*bindata[bin_field].min(), 1.5*bindata[bin_field].max()]
plt.xrange=self.s2n_range
plt.xlabel=bin_field
plt.ylabel = r'$\gamma$'
if self.yrange is not None:
plt.yrange=self.yrange
xdata=bindata[bin_field]
xerr=bindata[bin_field+'_err']
if self.shnum in sh1exp:
g1exp=zeros(xdata.size)+sh1exp[self.shnum]
g2exp=zeros(xdata.size)+sh2exp[self.shnum]
g1exp_plt=Curve(xdata, g1exp)
g2exp_plt=Curve(xdata, g2exp)
plt.add(g1exp_plt)
plt.add(g2exp_plt)
xerrpts1 = SymmetricErrorBarsX(xdata, bindata['g1'], xerr)
xerrpts2 = SymmetricErrorBarsX(xdata, bindata['g2'], xerr)
type='filled circle'
g1color='blue'
g2color='red'
g1pts = Points(xdata, bindata['g1'], type=type, color=g1color)
g1errpts = SymmetricErrorBarsY(xdata, bindata['g1'], bindata['g1_err'], color=g1color)
g2pts = Points(xdata, bindata['g2'], type=type, color=g2color)
g2errpts = SymmetricErrorBarsY(xdata, bindata['g2'], bindata['g2_err'], color=g2color)
g1pts.label=r'$\gamma_1$'
g2pts.label=r'$\gamma_2$'
key=biggles.PlotKey(0.9,0.5,[g1pts,g2pts],halign='right')
plt.add( xerrpts1, g1pts, g1errpts )
plt.add( xerrpts2, g2pts, g2errpts )
plt.add(key)
labels=self.get_labels()
plt.add(*labels)
plt.aspect_ratio=1
self.plt=plt
def plot_sub_pixel(ellip,theta, show=False):
import biggles
from biggles import PlotLabel,FramedPlot,Table,Curve,PlotKey,Points
from pcolors import rainbow
f=subpixel_file(ellip,theta,'fits')
data = eu.io.read(f)
colors = rainbow(data.size,'hex')
pltSigma = FramedPlot()
pltSigma.ylog=1
pltSigma.xlog=1
curves=[]
for j in xrange(data.size):
sigest2 = (data['Irr'][j,:] + data['Icc'][j,:])/2
pdiff = sigest2/data['sigma'][j]**2 -1
nsub=numpy.array(data['nsub'][j,:])
#pc = biggles.Curve(nsub, pdiff, color=colors[j])
pp = Points(data['nsub'][j,:], pdiff, type='filled circle',color=colors[j])
pp.label = r'$\sigma: %0.2f$' % data['sigma'][j]
curves.append(pp)
pltSigma.add(pp)
#pltSigma.add(pc)
#pltSigma.yrange=[0.8,1.8]
#pltSigma.add(pp)
c5 = Curve(linspace(1,8, 20), .005+zeros(20))
pltSigma.add(c5)
key=PlotKey(0.95,0.95,curves,halign='right',fontsize=1.7)
key.key_vsep=1
pltSigma.add(key)
pltSigma.xlabel='N_{sub}'
pltSigma.ylabel=r'$\sigma_{est}^2 /\sigma_{True}^2 - 1$'
lab=PlotLabel(0.05,0.07,r'$\epsilon: %0.2f \theta: %0.2f$' % (ellip,theta),halign='left')
pltSigma.add(lab)
pltSigma.yrange = [1.e-5,0.1]
pltSigma.xrange = [0.8,20]
if show:
pltSigma.show()
epsfile=subpixel_file(ellip,theta,'eps')
print("Writing eps file:",epsfile)
pltSigma.write_eps(epsfile)
def plot_dsig_one(r, dsig, dsigerr, **kw):
"""
plot delta sigma
useful if adding to an existing plot
parameters
----------
r: array
radius
dsig: array
delta sigma
dsigerr: array
error on delta sigma
"""
from biggles import FramedPlot
nbin=1
_set_biggles_defs(nbin)
visible=kw.get('visible',True)
xlog=kw.get('xlog',True)
ylog=kw.get('ylog',True)
aspect_ratio=kw.get('aspect_ratio',1)
is_ortho=kw.get('is_ortho',False)
plt=kw.get('plt',None)
if plt is None:
plt=FramedPlot()
plt.aspect_ratio=aspect_ratio
plt.xlog=xlog
plt.ylog=ylog
plt.xlabel = LABELS['rproj']
if is_ortho:
plt.ylabel = LABELS['osig']
else:
plt.ylabel = LABELS['dsig']
xrng, yrng = _add_dsig_to_plot(plt, r, dsig, dsigerr, **kw)
plt.xrange=xrng
plt.yrange=yrng
if visible:
plt.show()
return plt
def plot_drho(comb=None, r=None, drho=None, drhoerr=None,
color='black',type='filled circle',
nolabel=False, noshow=False, minval=1.e-5,
aspect_ratio=1):
"""
This one stands alone.
"""
if comb is not None:
r=comb['rdrho']
drho=comb['drho']
drhoerr=comb['drhoerr']
else:
if r is None or drho is None or drhoerr is None:
raise ValueError("Send a combined struct or r,drho,drhoerr")
plt=FramedPlot()
plt.aspect_ratio=aspect_ratio
plt.xlog=True
plt.ylog=True
if not nolabel:
plt.xlabel = r'$r$ [$h^{-1}$ Mpc]'
plt.ylabel = r'$\delta\rho ~ [M_{\odot} pc^{-3}]$'
od=add_to_log_plot(plt, r, drho, drhoerr,
color=color,
type=type,
minval=minval)
# for drho we need even broader yrange
plt.xrange = od['xrange']
yr=od['yrange']
plt.yrange = [0.5*yr[0], 3*yr[1]]
if not noshow:
plt.show()
od['plt'] = plt
return od
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_mass(comb=None, r=None, mass=None, masserr=None,
color='black',type='filled circle',
nolabel=False, noshow=False, minval=1.e11,
aspect_ratio=1):
if comb is not None:
r=comb['rmass']
mass=comb['mass']
masserr=comb['masserr']
else:
if r is None or mass is None or masserr is None:
raise ValueError("Send a combined struct or r,mass,masserr")
plt=FramedPlot()
plt.aspect_ratio=aspect_ratio
plt.xlog=True
plt.ylog=True
if not nolabel:
plt.xlabel = r'$r$ [$h^{-1}$ Mpc]'
plt.ylabel = r'$M(<r) ~ [h^{-1} M_{\odot}]$'
od=add_to_log_plot(plt, r, mass, masserr,
color=color,
type=type,
minval=minval)
plt.xrange = od['xrange']
plt.yrange = od['yrange']
if not noshow:
plt.show()
od['plt'] = plt
return od
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 plot_dsig(**keys):
"""
This one stands alone.
"""
comb=keys.get('comb',None)
r=keys.get('r',None)
dsig=keys.get('dsig',None)
dsigerr=keys.get('dsigerr',None)
color=keys.get('color','black')
type=keys.get('type','filled circle')
nolabel=keys.get('nolabel',False)
show=keys.get('show',True)
minval=keys.get('minval',1.e-3)
xlog=keys.get('xlog',True)
ylog=keys.get('ylog',True)
aspect_ratio=keys.get('aspect_ratio',1)
plt=keys.get('plt',None)
label=keys.get('label',None)
if comb is not None:
r=comb['r']
dsig=comb['dsig']
dsigerr=comb['dsigerr']
else:
if r is None or dsig is None or dsigerr is None:
raise ValueError("Send a combined struct or r,dsig,dsigerr")
if plt is None:
plt=FramedPlot()
plt.aspect_ratio=aspect_ratio
plt.xlog=xlog
plt.ylog=ylog
if not nolabel:
plt.xlabel = labels['rproj']
plt.ylabel = labels['dsig']
if ylog:
od=add_to_log_plot(plt, r, dsig, dsigerr,
color=color,
type=type,
minval=minval)
plt.xrange = od['xrange']
plt.yrange = od['yrange']
if label:
od['p'].label=label
else:
zpts=Curve(r, dsig*0)
plt.add(zpts)
pts=Points(r, dsig, type=type, color=color)
if label:
pts.label=label
plt.add(pts)
if dsigerr is not None:
epts=SymErrY(r, dsig, dsigerr, color=color)
plt.add(epts)
yrng=keys.get('yrange',None)
xrng=keys.get('xrange',None)
if yrng:
plt.yrange=yrng
if xrng:
plt.xrange=xrng
else:
if xlog:
plt.xrange=eu.plotting.get_log_plot_range(r)
if show:
plt.show()
if ylog:
od['plt'] = plt
return od
else:
return plt
def plot2dsig_over(r1,dsig1,dsig1err,r2,dsig2, dsig2err, **keys):
ptype1=keys.get('ptype1','filled circle')
ptype2=keys.get('ptype2','filled circle')
size1=keys.get('size1',1)
size2=keys.get('size2',1)
color1=keys.get('color1','red')
color2=keys.get('color2','blue')
label = keys.get('label',None)
label1 = keys.get('label1',None)
label2 = keys.get('label2',None)
xrng = keys.get('xrange',None)
yrng = keys.get('yrange',None)
show = keys.get('show',True)
ylog = keys.get('ylog',True)
plt=keys.get('plt',None)
yall=numpy.concatenate((dsig1, dsig2))
yerrall=numpy.concatenate((dsig1err, dsig2err))
if yrng is None:
if ylog:
yrng = eu.plotting.get_log_plot_range(yall, err=yerrall,
input_range=yrng)
rr=numpy.concatenate((r1,r2))
if xrng is None:
xrng = eu.plotting.get_log_plot_range(rr)
if plt is None:
plt=FramedPlot()
plt.xlog=True
plt.ylog=ylog
plt.xrange=xrng
plt.yrange=yrng
plt.xlabel = labels['rproj']
plt.ylabel = labels['dsig']
dsig1_p = Points(r1, dsig1, color=color1, type=ptype1, size=size1)
dsig1err_p = SymErrY(r1, dsig1, dsig1err, color=color2)
dsig1_p.label=label1
dsig2_p = Points(r2, dsig2, color=color2, type=ptype2, size=size2)
dsig2err_p = SymErrY(r2, dsig2, dsig2err, color=color2)
dsig2_p.label=label2
plt.add(dsig1_p, dsig2_p)
if ylog:
# biggles chokes if you give it negative data for a log plot
eu.plotting.add_log_error_bars(plt,'y',r1,dsig1,dsig1err,yrng,
color=color1)
eu.plotting.add_log_error_bars(plt,'y',r2,dsig2,dsig2err,yrng,
color=color2)
else:
err1 = biggles.SymmetricErrorBarsY(r1,dsig1,dsig1err,color=color1)
err2 = biggles.SymmetricErrorBarsY(r2,dsig2,dsig2err,color=color2)
plt.add(err1,err2)
zerop = biggles.Curve(xrng, [0,0])
plt.add(zerop)
if label is not None:
plt.add(PlotLabel(0.9,0.9,label,halign='right'))
if label1 is not None or label2 is not None:
key = PlotKey(0.9,0.15, [dsig1_p,dsig2_p], halign='right')
plt.add(key)
if show:
plt.show()
return plt
def plot_coverage(self, region='both', show=True, dops=True):
import biggles
from biggles import FramedPlot, Points, PlotKey
l = self.read()
w,=numpy.where( (l['ra'] >= 0.0) & (l['ra'] <= 360.0) )
if w.size != l.size:
print("threw out",l.size-w.size,"with bad ra")
l=l[w]
llam,leta = eu.coords.eq2sdss(l['ra'],l['dec'])
maskflags = l['maskflags']
lammin,lammax = (-70.,70.)
if region=='ngc':
# a bit high to make room for the legend
emin,emax=(-40,60)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
elif region=='sgc':
emin,emax=(100,165)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
else:
emin,emax=(-40,165)
biggles.configure('screen','width', 1140)
biggles.configure('screen','height', 1140)
wl=where1((leta > emin) & (leta < emax))
llam=llam[wl]
leta=leta[wl]
maskflags=maskflags[wl]
plt=FramedPlot()
plt.xlabel=r'$\lambda$'
plt.ylabel=r'$\eta$'
print("adding all lenses")
type = 'filled circle'
symsize=0.2
allp = Points(llam,leta,type=type,size=symsize)
allp.label='all'
plt.add(allp)
wquad = es_sdsspy.stomp_maps.quad_check(maskflags)
print("adding quad pass")
quadp = Points(llam[wquad],leta[wquad],type=type,color='red',size=symsize)
quadp.label = 'quad good'
plt.add(quadp)
fakepoints = eu.plotting.fake_filled_circles(['all','quad good'],['black','red'])
key=PlotKey(0.95,0.95,fakepoints,halign='right')
plt.add(key)
es_sdsspy.stomp_maps.plot_boss_geometry(color='blue',plt=plt,show=False)
xrng = (lammin, lammax)
yrng = (emin, emax)
plt.xrange = xrng
plt.yrange = yrng
plt.aspect_ratio = (yrng[1]-yrng[0])/float(xrng[1]-xrng[0])
if show:
plt.show()
if dops:
d = lensing.files.sample_dir(type='lcat',sample=self['sample'])
d = os.path.join(d,'plots')
if not os.path.exists(d):
os.makedirs(d)
epsfile = os.path.join(d, 'lcat-%s-%s-coverage.eps' % (self['sample'],region) )
print("Writing to eps file:",epsfile)
plt.write_eps(epsfile)
return plt
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 plot_vs_field(self, field, plot_type,
rmag_min=None,
rmag_max=None,
fmin=None, fmax=None,
nbin=20, nperbin=50000,
xrng=None, yrng=None, show=True):
allowed=['meane','residual']
if plot_type not in allowed:
raise ValueError("plot_type should be in [%s]" % ','.join(allowed))
if plot_type == 'residual' and self.sweeptype != 'star':
raise ValueError("residuals only supported for stars")
if rmag_min is None:
if self.sweeptype == 'gal':
rmag_min=18.0
else:
rmag_min=15.0
if rmag_max is None:
if self.sweeptype == 'gal':
rmag_max=21.8
else:
rmag_max=19.0
# this will only load the main data once.
self.load_data(field)
print("Using rmag range: [%0.2f,%0.2f]" % (rmag_min,rmag_max))
# notes
# - amflags here is really corrflags_rg for gals
# - e1 is really e1_rg for gals
logic = ((self['amflags'] == 0)
& (self['e1'] < 4)
& (self['e1'] > -4)
& (self['rmag'] > rmag_min)
& (self['rmag'] < rmag_max) )
if self.sweeptype == 'gal':
logic = logic & (self['R'] > 1.0/3.0) & (self['R'] < 1.0)
w=where1(logic)
print("Number passing cuts:",w.size)
minnum=31000
if w.size < minnum:
print("want %d good objects, found %d" % (minnum,w.size))
return
weights = 1.0/(0.32**2 + self['uncer'][w]**2)
# we can try to get nice labels for some fields
if field == 'fwhm_psf':
field_data = self['fwhm_psf'][w]
fstr = 'PSF FWHM (arcsec)'
elif field == 'sigma_psf':
field_data = self['sigma_psf'][w]
fstr = r'\sigma_{PSF}'
elif field == 'sigma':
field_data = self['sigma'][w]
fstr = r'\sigma_{obj+PSF}'
else:
field_data = self[field][w]
fstr=field
fstr = fstr.replace('_','\_')
print("Plotting for field:",field)
if plot_type == 'residual':
print(' doing: residual')
be1 = eu.stat.Binner(field_data, self['e1'][w]-self['e1_psf'][w],
weights=weights)
be2 = eu.stat.Binner(field_data, self['e2'][w]-self['e2_psf'][w],
weights=weights)
ylabel = r'$<e_{star}-e_{PSF}>$'
else:
print(' doing meane')
be1 = eu.stat.Binner(field_data, self['e1'][w], weights=weights)
be2 = eu.stat.Binner(field_data, self['e2'][w], weights=weights)
ylabel = r'$<e>$'
# regular hist for display
print(" regular fixed binsize hist")
xm,xe,xstd=eu.stat.wmom(field_data, weights, sdev=True)
#hxmin = xm-4.0*xstd
#hxmax = xm+4.0*xstd
bsize = xstd/5.
hist = eu.stat.histogram(field_data, binsize=bsize,
weights=weights, more=True)
print(" hist e1, nperbin: ",nperbin)
be1.dohist(nperbin=nperbin, min=fmin, max=fmax)
#be1.dohist(nbin=nbin, min=fmin, max=fmax)
print(" stats e1")
be1.calc_stats()
print(" hist e2, nperbin: ",nperbin)
be2.dohist(nperbin=nperbin, min=fmin, max=fmax)
#be2.dohist(nbin=nbin, min=fmin, max=fmax)
print(" stats e2")
be2.calc_stats()
plt = FramedPlot()
if xrng is not None:
plt.xrange=xrng
else:
if field == 'R':
plt.xrange=[0.29,1.01]
if yrng is not None:
plt.yrange=yrng
ymin = yrng[0]
ymax = 0.8*yrng[1]
else:
ymin = min( be1['wymean'].min(),be2['wymean'].min() )
ymax = 0.8*max( be1['wymean'].max(),be2['wymean'].max() )
# this is a histogram-like object
ph = eu.plotting.make_hist_curve(hist['low'], hist['high'], hist['whist'],
ymin=ymin, ymax=ymax, color='grey50')
plt.add(ph)
p1 = Points( be1['wxmean'], be1['wymean'],
type='filled circle', color='blue')
p1err = SymErrY( be1['wxmean'], be1['wymean'], be1['wyerr2'], color='blue')
p1.label = r'$e_1$'
p2 = Points( be2['wxmean'], be2['wymean'],
type='filled circle', color='red')
p2.label = r'$e_2$'
p2err = SymErrY( be2['wxmean'], be2['wymean'], be2['wyerr2'], color='red')
key = PlotKey(0.1,0.9, [p1,p2])
plt.add(p1, p1err, p2, p2err, key)
if self.camcol != 'any' and field == 'R' and plot_type=='meane':
order=3
print(" getting poly order",order)
coeff1 = numpy.polyfit(be1['wxmean'], be1['wymean'], order)
poly1=numpy.poly1d(coeff1)
coeff2 = numpy.polyfit(be2['wxmean'], be2['wymean'], order)
poly2=numpy.poly1d(coeff2)
ps1 = Curve( be1['wxmean'], poly1(be1['wxmean']), color='blue')
ps2 = Curve( be2['wxmean'], poly2(be2['wxmean']), color='red')
plt.add(ps1,ps2)
polyf = self.R_polyfile(self.camcol, rmag_max)
out={'coeff_e1':list([float(c) for c in coeff1]),
'coeff_e2':list([float(c) for c in coeff2])}
print(" -- Writing poly coeffs to:",polyf)
eu.io.write(polyf,out)
if field != 'rmag':
rmag_lab = \
PlotLabel(0.1,0.05,'%0.2f < rmag < %0.2f' % (rmag_min,rmag_max),
halign='left')
plt.add(rmag_lab)
procrun_lab = PlotLabel(0.1,0.1,
'procrun: %s filter: %s' % (self.procrun, self.band),
halign='left')
plt.add(procrun_lab)
cy=0.9
if self.run != 'any':
run_lab = PlotLabel(0.9,0.9, 'run: %06i' % self.run, halign='right')
plt.add(run_lab)
cy=0.8
if self.camcol != 'any':
run_lab = PlotLabel(0.9,cy, 'camcol: %i' % self.camcol, halign='right')
plt.add(run_lab)
plt.xlabel = r'$'+fstr+'$'
plt.ylabel = ylabel
if show:
plt.show()
epsfile = self.plotfile(field, rmag_max, plot_type=plot_type)
eu.ostools.makedirs_fromfile(epsfile, verbose=True)
print(" Writing eps file:",epsfile)
plt.write_eps(epsfile)
converter.convert(epsfile, verbose=True)
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 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 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_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_coverage(self, region='both', show=True, dops=True):
"""
Plot a random subset of the randoms along with the
boss survey geometry area as bounding boxes
"""
import biggles
from biggles import FramedPlot, Points, PlotKey
symsize=0.5
l = self.read()
llam,leta = eu.coords.eq2sdss(l['ra'],l['dec'])
lammin,lammax = (-70.,70.)
if region=='ngc':
# a bit high to make room for the legend
emin,emax=(-40,60)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
width=1.5
elif region=='sgc':
emin,emax=(125,165)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
width=2
else:
emin,emax=(-40,165)
biggles.configure('screen','width', 1140)
biggles.configure('screen','height', 1140)
width=2
wl=where1((leta > emin) & (leta < emax))
llam=llam[wl]
leta=leta[wl]
plt=FramedPlot()
plt.xlabel=r'$\lambda$'
plt.ylabel=r'$\eta$'
xrng = (lammin, lammax)
yrng = (emin, emax)
plt.xrange = xrng
plt.yrange = yrng
print("adding random subset of randoms")
ii = eu.numpy_util.random_subset(llam.size, 500000)
allp = Points(llam[ii],leta[ii],type='dot',size=symsize)
plt.add(allp)
plt.aspect_ratio = (yrng[1]-yrng[0])/float(xrng[1]-xrng[0])
#es_sdsspy.stomp_maps.plot_boss_geometry(color='blue',plt=plt,show=False)
es_sdsspy.stomp_maps.plot_boss_geometry(plt=plt,show=False,width=width)
if show:
plt.show()
if dops:
d = lensing.files.sample_dir(type='lcat',sample=self['sample'])
d = os.path.join(d,'plots')
if not os.path.exists(d):
os.makedirs(d)
epsfile = os.path.join(d, '%s-%s-coverage.eps' % ('lcat',self['sample']))
print("Writing to eps file:",epsfile)
plt.write_eps(epsfile)
return plt
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 plot_coverage_bybin(self, binner, region='both',
show=True, dops=True, rand=None):
import pcolors
import biggles
import converter
from biggles import FramedPlot, Points, PlotKey
orig = self.read_original()
lcat = self.read()
all_clam,all_ceta = eu.coords.eq2sdss(orig['ra'],orig['dec'])
l = orig[lcat['zindex']]
clam,ceta = eu.coords.eq2sdss(lcat['ra'],lcat['dec'])
clammin,clammax = (-70.,120.)
if region=='ngc':
# a bit high to make room for the legend
emin,emax=(-40,60)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
clammin,clammax = (-70.,120.)
elif region=='sgc':
emin,emax=(105,165)
biggles.configure('screen','width', 1800)
biggles.configure('screen','height', 1140)
clammin,clammax = (-50.,90.)
else:
emin,emax=(-40,165)
biggles.configure('screen','width', 1140)
biggles.configure('screen','height', 1140)
clammin,clammax = (-70.,120.)
wl=where1((all_ceta > emin) & (all_ceta < emax))
all_clam=all_clam[wl]
all_ceta=all_ceta[wl]
wl=where1((ceta > emin) & (ceta < emax))
clam=clam[wl]
ceta=ceta[wl]
l=l[wl]
plt=FramedPlot()
plt.xlabel=r'$\lambda$'
plt.ylabel=r'$\eta$'
xrng = (clammin, clammax)
yrng = (emin, emax)
plt.xrange = xrng
plt.yrange = yrng
print("adding all lenses")
type = 'filled circle'
symsize=0.2
colors = pcolors.rainbow(binner['nbin'],'hex')
if rand is not None:
clam_r,ceta_r = eu.coords.eq2sdss(rand['ra'],rand['dec'])
wl=where1((ceta_r > emin) & (ceta_r < emax))
clam_r=clam_r[wl]
ceta_r=ceta_r[wl]
rp = Points(clam_r, ceta_r, type='dot', size=0.2)
plt.add(rp)
size_min=0.2
size_max=4
sizes=[]
minlambda = l['lambda_zred'].min()
maxlambda = l['lambda_zred'].max()
for i in xrange(binner['nbin']):
w=binner.select_bin(l, i)
mlam=l['lambda_zred'][w].mean()
# scale 0 to 1
sz=(mlam-minlambda)/maxlambda
# now scale size
sz = size_min + sz*(size_max-size_min)
sizes.append(sz)
all_plots=[]
labels=[]
#for i in xrange(binner['nbin']):
for i in reversed(xrange(binner['nbin'])):
w=binner.select_bin(l, i)
#points = Points(clam[w], ceta[w],type=type,size=symsize, color=colors[i])
points = Points(clam[w], ceta[w],type=type,size=sizes[i], color=colors[i])
labels.append(binner.bin_label(i))
plt.add(points)
labels.reverse()
fakepoints = eu.plotting.fake_filled_circles(labels, colors)
key=PlotKey(0.95,0.95,fakepoints,halign='right',size=1.5)
plt.add(key)
plt.aspect_ratio = (yrng[1]-yrng[0])/float(xrng[1]-xrng[0])
es_sdsspy.stomp_maps.plot_boss_geometry(color='blue',plt=plt,show=False)
if show:
plt.show()
if dops:
d = lensing.files.sample_dir(type='lcat',sample=self['sample'])
d = os.path.join(d,'plots')
if not os.path.exists(d):
os.makedirs(d)
epsfile = os.path.join(d, 'lcat-%s-coverage-bybin.eps' % self['sample'])
if rand is not None:
epsfile=epsfile.replace('.eps','-withrand.eps')
if region in ['sgc','ngc']:
epsfile=epsfile.replace('.eps','-%s.eps' % region)
print("Writing to eps file:",epsfile)
plt.write_eps(epsfile)
print("converting to png")
converter.convert(epsfile, dpi=300)
return plt
def plot_ellip_vs_field(self, field, rmag_max=21.8, fmin=None, fmax=None, nbin=20, nperbin=50000,
yrange=None, show=True):
self.load_data()
w=where1((self['cmodelmag_dered_r'] > 18.0) & (self['cmodelmag_dered_r'] < rmag_max) )
if w.size == 0:
print("no good objects")
return
weights = 1.0/(0.32**2 + self['uncer_rg'][w]**2)
if field == 'psf_fwhm':
field_data = self['psf_fwhm'][w]
fstr = 'PSF FWHM (arcsec)'
elif field == 'psf_sigma':
field_data = self['psf_sigma'][w]
fstr = r'$\sigma_{PSF}$'
elif field == 'R_rg':
field_data = self['r_rg'][w]
fstr = 'R_rg'
else:
field_data = self[field][w]
fstr=field
print("Plotting mean e for field:",field)
fstr = fstr.replace('_','\_')
be1 = eu.stat.Binner(field_data, self['e1_rg'][w], weights=weights)
be2 = eu.stat.Binner(field_data, self['e2_rg'][w], weights=weights)
print(" hist e1")
be1.dohist(nperbin=nperbin, min=fmin, max=fmax)
#be1.dohist(nbin=nbin, min=fmin, max=fmax)
print(" stats e1")
be1.calc_stats()
print(" hist e2")
be2.dohist(nperbin=nperbin, min=fmin, max=fmax)
#be2.dohist(nbin=nbin, min=fmin, max=fmax)
print(" stats e2")
be2.calc_stats()
plt = FramedPlot()
p1 = Points( be1['wxmean'], be1['wymean'], type='filled circle', color='blue')
p1err = SymErrY( be1['wxmean'], be1['wymean'], be1['wyerr2'], color='blue')
p1.label = r'$e_1$'
p2 = Points( be2['wxmean'], be2['wymean'], type='filled circle', color='red')
p2.label = r'$e_2$'
p2err = SymErrY( be2['wxmean'], be2['wymean'], be2['wyerr2'], color='red')
key = PlotKey(0.8, 0.9, [p1,p2])
plt.add(p1, p1err, p2, p2err, key)
if field != 'cmodelmag_dered_r':
rmag_lab = PlotLabel(0.1,0.05,'rmag < %0.2f' % rmag_max, halign='left')
plt.add(rmag_lab)
plab = PlotLabel(0.1,0.1, 'CH+RM', halign='left')
plt.add(plab)
plt.xlabel = r'$'+fstr+'$'
plt.ylabel = r'$<e>$'
if yrange is not None:
plt.yrange=yrange
if show:
plt.show()
epsfile = self.plotfile(field, rmag_max)
print(" Writing eps file:",epsfile)
plt.write_eps(epsfile)
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