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 doplot(self, fitres, h, minmag, maxmag):
tab=biggles.Table(2,1)
plt=FramedPlot()
plt.title='%s %.2f %.2f ' % (self.objtype, minmag, maxmag)
plt.xlabel=r'$\sigma$'
sprior=fitres.get_model()
nrand=100000
binsize=self.binsize
hplt=Histogram(h['hist'], x0=h['low'][0], binsize=binsize)
hplt.label='data'
srand=sprior.sample(nrand)
hrand=histogram(srand, binsize=binsize, min=h['low'][0], max=h['high'][-1], more=True)
hrand['hist'] = hrand['hist']*float(h['hist'].sum())/nrand
hpltrand=Histogram(hrand['hist'], x0=hrand['low'][0], binsize=binsize,
color='blue')
hpltrand.label='rand'
key=PlotKey(0.9,0.9,[hplt,hpltrand],halign='right')
plt.add(hplt, hpltrand, key)
tplt=fitres.plot_trials(show=False,fontsize_min=0.88888888)
tab[0,0] = plt
tab[1,0] = tplt
if self.show:
tab.show()
d=files.get_prior_dir()
d=os.path.join(d, 'plots')
epsfile='pofs-%.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, 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(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 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 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 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
