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plot.py
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plot.py
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"""
plot.py
"""
from pylab import *
from PyGalKin import *
#import PyGalKin.db as DB
import pylab as P # to make P not self-reference to plot.py
import matplotlib
import matplotlib.mlab as mlab
from matplotlib import rcParams, colors
from mpl_toolkits.axes_grid import AxesGrid
#from DjCigale.table import models as M
#matplotlib.use('Agg')
#matplotlib.rc('text', usetex = True)
######### SAURON COLORMAP ##############
#
# Original format:
#x = [1.0, 43.5, 86.0, 86.0+20, 128.5-10, 128.5, 128.5+10, 171.0-20, 171.0, 213.5, 256.0]
#red = [0.01, 0.0, 0.4, 0.5, 0.3, 0.0, 0.7, 1.0, 1.0, 1.0, 0.9]
#green = [0.01, 0.0, 0.85, 1.0, 1.0, 0.9, 1.0, 1.0, 0.85, 0.0, 0.9]
#blue = [0.01, 1.0, 1.0, 1.0, 0.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9]
_sauron_data = {
'red': ((0.0,0.01,0.01),
(0.169921875,0.0,0.0),
(0.3359375,0.4,0.4),
(0.4140625,0.5,0.5),
(0.462890625,0.3,0.3),
(0.501953125,0.0,0.0),
(0.541015625,0.7,0.7),
(0.58984375,1.0,1.0),
(0.66796875,1.0,1.0),
(0.833984375,1.0,1.0),
(1.0,0.9,0.9)),
'green':((0.0,0.01,0.01),
(0.169921875,0.0,0.0),
(0.3359375,0.85,0.85),
(0.4140625,1.0,1.0),
(0.462890625,1.0,1.0),
(0.501953125,0.9,0.9),
(0.541015625,1.0,1.0),
(0.58984375,1.0,1.0),
(0.66796875,0.85,0.85),
(0.833984375,0.0,0.0),
(1.0,0.9,0.9)),
'blue':((0.0,0.01,0.01),
(0.169921875,1.0,1.0),
(0.3359375,1.0,1.0),
(0.4140625,1.0,1.0),
(0.462890625,0.7,0.7),
(0.501953125,0.0,0.0),
(0.541015625,0.0,0.0),
(0.58984375,0.0,0.0),
(0.66796875,0.0,0.0),
(0.833984375,0.0,0.0),
(1.0,0.9,0.9))
}
_sauron_inv_data = {
'red': ((0.0,1.0,1.0),
(0.169921875,1.0,1.0),
(0.3359375,1.0,1.0),
(0.4140625,0.4,0.4),
(0.462890625,0.7,0.7),
(0.501953125,0.0,0.0),
(0.541015625,0.3,0.3),
(0.58984375,0.5,0.5),
(0.66796875,0.0,0.0),
(0.833984375,1.0,1.0),
(1.0,0.01,0.01)),
'green':((0.0,1.0,1.0),
(0.169921875,0.0,0.0),
(0.3359375,0.85,0.85),
(0.4140625,1.0,1.0),
(0.462890625,1.0,1.0),
(0.501953125,0.9,0.9),
(0.541015625,1.0,1.0),
(0.58984375,1.0,1.0),
(0.66796875,0.85,0.85),
(0.833984375,0.0,0.0),
(1.0,0.01,0.01)),
'blue':((0.0,1.0,1.0),
(0.169921875,0.0,0.0),
(0.3359375,0.0,0.0),
(0.4140625,0.0,0.0),
(0.462890625,0.0,0.0),
(0.501953125,0.0,0.0),
(0.541015625,0.7,0.7),
(0.58984375,1.0,1.0),
(0.66796875,1.0,1.0),
(0.833984375,1.0,1.0),
(1.0,0.01,0.01))
}
sauron=colors.LinearSegmentedColormap('sauron', _sauron_data)
sauron_inv=colors.LinearSegmentedColormap('sauron_inv', _sauron_inv_data)
register_cmap(cmap=sauron)
register_cmap(cmap=sauron_inv)
rcParams['image.cmap']='sauron'
def imshow(*args, **kwargs):
kwargs.setdefault('interpolation','nearest')
kwargs.setdefault('origin','lower')
kwargs.setdefault('cmap','sauron')
args = list(args)
args[0] = N.transpose(args[0])
P.imshow(*args, **kwargs)
def plot(*args, **kwargs):
kwargs.setdefault('drawstyle','steps-mid')
P.plot(*args, **kwargs)
def noticks():
P.setp(P.gca(),'xticks',[])
P.setp(P.gca(),'yticks',[])
def plotscale(p,left=0.65,right=0.9,frombottom=0.1,kpc=False):
axi=N.array(P.gca().axis())
y=axi[2]+frombottom*(axi[3]-axi[2])
x1=axi[0]+left*(axi[1]-axi[0])
x2=axi[0]+right*(axi[1]-axi[0])
P.plot([x1,x2],[y,y],'k-')
arcs=(x2-x1)*p['echelle']
if not kpc:
kpc=arcsec2kpc(arcs,p['vr0'])
P.text(x1-0.01,y+0.1,r'$%.1f^{\prime\prime}\approx %.1fkpc$'%(arcs,kpc),fontsize=9)
def setYaxis_pc(inarr):
""" set the plotting-axes to parsec """
nticks=inarr.ny()*inarr.scale()/1000
nticks=nticks.sum()
factor=1.0
while nticks > 10:
nticks/=2
factor/=2
P.setp(P.gca(),'yticks',str(N.arange(nticks)/inarr.scale()*1000/factor))
P.setp(P.gca(),'yticklabels',str(N.arange(nticks)/factor))
P.ylabel('[kpc]',font)
def setXaxis_pc(inarr):
""" set the plotting-axes to parsec """
nticks=inarr.nx()*inarr.scale()/1000
nticks=nticks[0]
factor=1.
while nticks > 10:
nticks/=2
factor/=2
P.setp(P.gca(),'xticks',N.arange(nticks)/inarr.scale()*1000/factor)
P.setp(P.gca(),'xticklabels',N.arange(nticks)/factor)
P.xlabel('[kpc]',font)
def showsum(data,vmin=1E5,vmax=2E6,range='cat',Z=1.002912,typ='sum'):
if typ=='sum': dat=selsum(data,range=range,axis=2)
elif typ=='aver': dat=selav(data,range=range,axis=2)
P.imshow(N.transpose(dat),origin='lower',interpolation='nearest',vmin=vmin,vmax=vmax)
def plotspec(data,region=None,plotlines=False,Z=1.0206,style=False,linestyle='steps',vminmax=None):
from PyArgus import SpecLen,Lamb0,Step
if style:
P.plot((N.arange(SpecLen)*Step)+Lamb0,data,style,linestyle=linestyle)
else:
P.plot((N.arange(SpecLen)*Step)+Lamb0,data,linestyle=linestyle)
if plotlines == True:
CaTz= CaT * Z
Paschenz=Paschen * Z
EmissionLinesz=EmissionLines * Z
for i in N.arange(len(CaTz)): P.plot([CaTz[i],CaTz[i]],P.axis()[2:],'r')
for i in N.arange(len(Paschenz)): P.plot([Paschenz[i],Paschenz[i]],P.axis()[2:],'k')
for i in N.arange(len(EmissionLinesz)): P.plot([EmissionLinesz[i],EmissionLinesz[i]],P.axis()[2:],'b')
if region == 'cat': # legacy
region=[8470,8700]
if region != None:
relevant=data[lamb2pix(region[0]*Z,Lamb0,Step):lamb2pix(region[1]*Z,Lamb0,Step)]
if vminmax=='sigbased':
vmax=relevant.mean() + 4*relevant.std()
vmin=relevant.mean() - 4*relevant.std()
else: vmin,vmax=relevant.min(),relevant.max()
P.axis([float(region[0]*Z),float(region[1]*Z),vmin,vmax])
else: pass
def fillederrorplot(x,y,e1,e2=None,f='r--',c='r',alpha=0.5,label=None):
if e2==None: e2=e1/2.0; e1=e2
P.plot(x,y,f,linewidth=2,label=label)
P.plot(x,y-e1,c+'-',linewidth=0.5)
P.plot(x,y+e2,c+'-',linewidth=0.5)
P.fill(N.concatenate((x,x[::-1])),N.concatenate((y+e2,(y-e1)[::-1])),c,alpha=alpha)
##################################
def velSigmaPlot():
gs1=M.Galax.objects.filter(sample=1,maxvel__isnull=False)
gs2=M.Galax.objects.filter(sample=2,maxvel__isnull=False)
P.plot([9.,103.],[9.,103.],'k--')
v,s,inc=map(N.array,zip(*gs1.values_list('maxvel','sigma_cent','incl')))
P.plot(s*sq3,v,'sk',label=r'$v_{max,1}$',mfc='w')
P.plot(s*sq3,v/N.sin(N.radians(inc)),'ks',label='$v_{max,i,1}$',mfc='#ffd74e')
v,s,inc=map(N.array,zip(*gs2.values_list('maxvel','sigma_cent','incl')))
P.plot(s*sq3,v,'Dk',label=r'$v_{max,2}$',mfc='b')
P.plot(s*sq3,v/N.sin(N.radians(inc)),'kD',label='$v_{max,i,2}$',mfc='g')
P.ylabel(r'$v_{max}\quad (km\,s^{-1})$')
P.xlabel(r'$\sigma^\ast_{c}\quad (km\,s^{-1})$')
P.grid()
P.axis((11,108,0,189))
#P.legend(loc='upper left')
def rotMassRandPlot():
gs1=M.Galax.objects.filter(sample=1,mass_p2p__isnull=False)
gs2=M.Galax.objects.filter(sample=2,mass_p2p__isnull=False)
roterr1=N.array([g.p['velmasserr'] for g in gs1])
roterr2=N.array([g.p['velmasserr'] for g in gs2])
sigerr1=N.array([g.p['sigmasserr'] for g in gs1])
sigerr2=N.array([g.p['sigmasserr'] for g in gs2])
P.plot([7.4,10.3],[7.4,10.3],'k--',label='$\mathrm{unity}$')
ms,mv=zip(*gs1.values_list('mass_sig','mass_p2p'))
mse=sigerr1/ms * 0.434/2
mve=roterr1/mv * 0.434
P.errorbar(N.log10(ms),N.log10(mv),mve,mse, 'ok',mfc='w',label='$sample\, 1$')
ms,mv=zip(*gs2.values_list('mass_sig','mass_p2p'))
mse=sigerr2/ms * 0.434/2
mve=roterr2/mv * 0.434
P.errorbar(N.log10(ms),N.log10(mv),mve,mse, 'ok',mfc='k',label='$sample\, 2$')
P.ylabel(r'$\log M_{rot}\quad (M_\odot)$')
P.xlabel(r'$\log M_{disp}\quad (M_\odot)$')
P.axis((7.2,10.5,6.3,10.5))
#P.grid(True)
P.legend(loc='lower right')
def masshistPlot():
mfr1=array([g.mass_p2p/g.mass_sig for g in M.Galax.objects.filter(sample=1) ])
mfr2=array([g.mass_p2p/g.mass_sig for g in M.Galax.objects.filter(sample=2) ])
P.hist(log10(mfr1),bins=arange(-2.5,2.5,0.5),color='w',alpha=0.5,lw=2,hatch='--')
P.hist(log10(mfr2),bins=arange(-2.5,2.5,0.5),color='w',alpha=0.5,lw=2,hatch='//')
P.ylabel('$N$')
P.xlabel('$\log (M_{rot}/M_{disp})$')
def photMassRandPlot():
gs=M.Galax.objects.all()
fus = [dynMassDisk, dynMassSphere]
rs = ['reff','rholm','h25','h27']
fac = [1,1,1.68,1.68]
fmts = ['o','D']
cols = ['b','r','g','y']
lab1 = ['disk + ','sphere + ']
for i,r in enumerate(rs):
for j,fu in enumerate(fus):
for g in gs:
rad=getattr(g,r) or 0
rad*=fac[i]
mass = fu(arcsec2kpc(rad,g.vsys),g.sigma_cent or 0) or None
P.loglog(g.mass_phot,mass,cols[i]+fmts[j])
P.xlabel(r'$M_{phot}\quad (M_\odot)$')
P.ylabel(r'$M_{dispersion}\quad (M_\odot)$')
P.grid()
def sigmasPlot():
gs1=M.Galax.objects.filter(sample=1)
gs2=M.Galax.objects.filter(sample=2)
P.plot([7,65],[7,65],'k--')#,label='$\mathrm{unity}$')
s1,s2,s3=zip(*gs1.values_list('sigma_cent','sigma_weighmean','sigma_collap'))
P.plot(s1,s2,'s',label=r'$\sigma_{i,1}$',mfc='w')
P.plot(s1,s3,'s',label=r'$\langle\sigma\rangle_{i,1}$',mfc='#ffd74e')
s1,s2,s3=zip(*gs2.values_list('sigma_cent','sigma_weighmean','sigma_collap'))
P.plot(s1,s2,'D',label=r'$\sigma_{i,2}$',mfc='g')
P.plot(s1,s3,'D',label=r'$\langle\sigma\rangle_{i,2}$',mfc='b')
P.xlabel(r'$\sigma_{c} (km\,s^{-1})$')
P.ylabel(r'$\sigma\, (km\,s^{-1})$')
#P.legend(loc='lower right')
P.axis((5,70,5,70))
P.grid()
def LsigmaPlot():
gs1=M.Galax.objects.filter(sample=1)
gs2=M.Galax.objects.filter(sample=2)
#sig,mb=zip(*gs1.values_list('sigma_cent','mb'))
HaLum,sig=zip(*[(g.p.get('HaLum'),g.sigma_cent) for g in gs1])
HaLum=N.log10(N.array(HaLum).astype('Float64'))
sigerr=N.array([g.p.get('sigerr') for g in gs1])
sigerr= 0.433 * sigerr/sig
sig=N.log10(N.array(sig) *sq3)
P.plot([1.25,2.25],[40.1,43.1],'--k')
#P.errorbar(sig,HaLum,xerr=sigerr,fmt='o',ecolor='k',label=r'$\sigma_{1}',mfc='w')
P.plot(sig,HaLum,'o',label=r'$\sigma_{1}',mfc='w')
HaLum,sig=zip(*[(g.p.get('HaLum'),g.sigma_cent) for g in gs2])
HaLum=N.log10(N.array(HaLum).astype('Float64'))
sigerr=N.array([g.p.get('sigerr') for g in gs2])
sigerr= 0.433 * sigerr/sig
sig=N.log10(N.array(sig)*sq3)
#P.errorbar(sig,HaLum,xerr=sigerr,fmt='o',ecolor='k',label=r'$\sigma_{2}',mfc='k')
P.plot(sig,HaLum,'o',label=r'$\sigma_{2}',mfc='k')
# P.grid()
P.axis((1.15,2.1,38.5,42.5))
P.ylabel(r'$\log L(H\alpha)\,\,(erg\,s^{-1})$')
P.xlabel(r'$\log \sigma^\ast_c\, (km\,s^{-1})$')
def MsigmaPlot():
gs1=M.Galax.objects.filter(sample=1)
gs2=M.Galax.objects.filter(sample=2)
sig,mb=zip(*gs1.values_list('sigma_cent','mb'))
sig=N.log10(N.array(sig) *sq3)
P.plot(mb,sig,'o',label=r'$\sigma_{1}',mfc='w')
sig,mb=zip(*gs2.values_list('sigma_cent','mb'))
sig=N.log10(N.array(sig) *sq3)
P.plot(mb,sig,'o',label=r'$\sigma_{2}',mfc='k')
P.grid()
P.axis((-13.2,-20.7,1.21,2.09))
P.xlabel(r'$M_B$')
P.ylabel(r'$\log \sigma_c^\ast\, (km\,s^{-1})$')
def mu_h_mergPlot():
gs1=M.Galax.objects.filter(sample=1)
gs2=M.Galax.objects.filter(sample=2)
h1=N.array([arcsec2kpc(g.h27 or g.h25 or nan,g.vsys)[0] or nan for g in gs1])
mu1=N.array([g.p.get('mu0host') or nan for g in gs1])
merg1=N.array([g.doub+g.irrvf for g in gs1])
P.scatter(log10(h1),array(mu1),s=(merg1+1)*40,c=merg1,cmap=cm.bone_r,alpha=0.8,lw=1.6,marker='D')
h2=N.array([arcsec2kpc(g.h27 or g.h25 or nan,g.vsys)[0] or nan for g in gs2])
mu2=N.array([g.p['mu0host'] or nan for g in gs2])
merg2=N.array([g.doub+g.irrvf for g in gs2])
P.scatter(log10(h2),array(mu2),s=(merg2+1)*40,c=merg2,cmap=cm.bone_r,alpha=0.8,lw=1.6,marker='o')
P.axis((-0.75,0.9,25.8,18.1))
P.xlabel(r'$log_{10}(h_r)$')
P.ylabel(r'$\mu_{0,host}$')
def M_burst_mergPlot():
gs1=M.Galax.objects.filter(sample=1)
gs2=M.Galax.objects.filter(sample=2)
burst1=N.array([g.p.get('bfrac27') or g.p.get('bfrac25') or nan for g in gs1])
mb1=N.array([g.mb or nan for g in gs1])
merg1=N.array([g.doub+g.irrvf for g in gs1])
P.plot([-14,-20],[0.79,.97],'k--')
P.scatter(mb1,burst1,s=(merg1+1)*40,c=merg1,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
burst2=N.array([g.p.get('bfrac27') or g.p.get('bfrac25') or nan for g in gs2])
mb2=N.array([g.mb or nan for g in gs2])
merg2=N.array([g.doub+g.irrvf for g in gs2])
P.scatter(mb2,burst2,s=(merg2+1)*40,c=merg2,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.axis((-13,-20.5,0,1))
P.xlabel(r'$M_B$')
P.ylabel(r'$\mathrm{burst\, fraction}$')
def EWsigmaPlot():
gs=M.Galax.objects.filter(sample=1)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
sig=N.array([g.sigma_cent for g in gs]) * sq3
P.scatter(N.log10(sig),N.log10(ew),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
sig=N.array([g.sigma_cent for g in gs]) * sq3
P.scatter(N.log10(sig),N.log10(ew),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.xlabel(r'$\log \sigma_c^\ast\, (km\,s^{-1})$')
P.ylabel(r'$\log \mathrm{EW}(H\alpha)$')
def L_EWPlot():
gs=M.Galax.objects.filter(sample=1)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(N.log10(ew),N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(N.log10(ew),N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.ylabel(r'$\log L(H\alpha)\,\,(erg\,s^{-1})$')
P.xlabel(r'$\log \mathrm{EW}(H\alpha)$')
def EW_burstPlot():
gs=M.Galax.objects.filter(sample=1)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
burst=N.array([g.p.get('bfrac27') or g.p.get('bfrac25') or nan for g in gs])
P.scatter(N.log10(ew),burst,s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
burst=N.array([g.p.get('bfrac27') or g.p.get('bfrac25') or nan for g in gs])
P.scatter(N.log10(ew),burst,s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.ylabel('$\mathrm{burst\, fraction}$')
P.xlabel(r'$\log \mathrm{EW}(H\alpha)$')
def M_vsigPlot():
gs=M.Galax.objects.filter(sample=1,mb__isnull=False)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
Mb=N.array([g.mb for g in gs],dtype='Float64')
P.scatter(vsig,Mb,s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2,mb__isnull=False)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
Mb=N.array([g.mb for g in gs],dtype='Float64')
P.scatter(vsig,Mb,s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.axis((0,2.6,-13,-21))
P.xlabel(r'$v_{max} / \sigma^\ast_c$')
P.ylabel(r'$M_B$')
def L_vsigPlot():
gs=M.Galax.objects.filter(sample=1)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(vsig,N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(vsig,N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.axis((0,2.65,38.51,42.4))
P.xlabel(r'$v_{max} / \sigma^\ast_c$')
P.ylabel(r'$\log L(H\alpha)\,\,(erg\,s^{-1})$')
def L_MMPlot():
gs=M.Galax.objects.filter(sample=1)
mm=N.array([g.mass_p2p/g.mass_sig for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(N.log10(mm),N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
mm=N.array([g.mass_p2p/g.mass_sig for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
LHa=N.array([g.p.get('HaLum') for g in gs],dtype='Float64')
P.scatter(N.log10(mm),N.log10(LHa),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.xlabel(r'$\log\, M_{rot} / M_{disp}$')
P.ylabel(r'$\log\, L(H\alpha)\,\,(erg\,s^{-1})$')
def EW_vsigPlot():
gs=M.Galax.objects.filter(sample=1)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
P.scatter(vsig,N.log10(ew),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='D')
gs=M.Galax.objects.filter(sample=2)
vsig=N.array([g.maxvel/g.sigma_cent/sq3/N.sin(N.radians(g.incl)) for g in gs])
merg=N.array([g.doub+g.irrvf for g in gs])
ew=N.array([g.p.get('HaEW') for g in gs],dtype='Float64')
P.scatter(vsig,N.log10(ew),s=(merg+1)*40,c=merg,cmap=cm.bone_r,alpha=0.8,lw=1.4,marker='o')
P.axis((0,2.4,0.51,3.49))
P.xlabel(r'$v_{max} / \sigma_c^\ast$')
P.ylabel(r'$\log \mathrm{EW}(H\alpha)$')
def classifPlot():
P.axis((-13.1,-21,-0.2,4.4))
P.yticks(range(5),['$%d$'%i for i in range(5)])
gs=M.Galax.objects.filter(sample=1,mb__isnull=False)
for g in gs:
P.text(g.mb,g.doub+g.irrvf+0.1,'$\mathrm{%s}$'%(g.p.get('morphclass') or ''),
ha="center", va="center",
bbox=dict(fc='k', alpha=0.1, ec='white',boxstyle='round'))
gs=M.Galax.objects.filter(sample=2)
for g in gs:
P.text(g.mb,g.doub+g.irrvf-0.1,'$\mathrm{%s}$'%(g.p.get('morphclass') or ''),
color='w',ha="center", va="center",
bbox=dict( fc='k', alpha=0.9, ec='white',boxstyle='round'))
P.xlabel(r'$M_B$')
P.ylabel(r'$P_v\, +\, P_d$')
def KmassPlot():
mr,ms,mb,bv,vk=loadtxt('/home/tom/table2.txt',unpack=True,usecols=(9,10,15,16,17))
mk=mb-bv-vk
sol_K=3.28
klum=mag2flux(-mk+sol_K)
P.plot([7.6,10.4],[7.6,10.4],'k-',label='$M/L = 1$')
P.plot([7.6,10.4],[7.6-0.3,10.4-0.3],'k--',label='$M/L = 0.3$')
P.plot(N.log10(klum),N.log10(mr*1E6),'sk',mew=1.1, label='$M_{rot}$')
P.plot(N.log10(klum),N.log10(ms*1E6),'Dw',mew=1.1,label='$M_{disp}$')
P.axis((7.51,10.6,6.3,11.1))
P.legend(loc='lower right',prop={'size':10})
P.xlabel('$L_K\:(L_\odot)$')
P.ylabel('$M_{dyn}\: (M_\odot)$')
##################################
def plotbadpix(color='w'):
""" for the Argus FOV """
P.plot([0,1],[0,1],color)
P.plot([1,2],[0,1],color)
P.plot([20,21],[0,1],color)
P.plot([21,22],[0,1],color)
P.plot([0,1],[13,14],color)
P.plot([1,2],[13,14],color)
P.plot([20,21],[13,14],color)
P.plot([21,22],[13,14],color)
P.plot([3,4],[4,5],color)
P.plot([20,21],[8,9],color)
P.plot([20,21],[9,10],color)
P.plot([20,21],[10,11],color)
P.plot([0,1],[1,0],color)
P.plot([1,2],[1,0],color)
P.plot([20,21],[1,0],color)
P.plot([21,22],[1,0],color)
P.plot([0,1],[14,13],color)
P.plot([1,2],[14,13],color)
P.plot([20,21],[14,13],color)
P.plot([21,22],[14,13],color)
P.plot([3,4],[5,4],color)
P.plot([20,21],[9,8],color)
P.plot([20,21],[10,9],color)
P.plot([20,21],[11,10],color)
def plotonpix(data3,data2=None,layout='argus',line='k-',range=None):
if not data2: data2=N.sum(data3,axis=2)
if layout=='argus': x=22;y=14
f=P.figure()
P.imshow(N.transpose(data2))
corn=P.gca().figbox.corners()
sx,sy=corn[0,0],corn[0,1]
lx,ly=corn[-1,0]-sx,corn[-1,1]-sy
xl,yl=lx/x,ly/y
xs,ys=sx,sy
for i in N.arange(x):
for j in N.arange(y):
ax=P.axes([xs,ys,xl,yl],frameon=False)
P.setp(ax, xticks=[], yticks=[])
P.plot(data3[j,i,:],line)
ys+=yl
ys=sy
xs+=xl