def plot_vr(plotfilename):
#Read the APOGEE-RC data and pixelate it
#APOGEE-RC
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
xmin, xmax= 5.5, 13.5
dx= 1.
pix= pixelize_sample.pixelXY(data,
xmin=xmin,xmax=xmax,
ymin=-dx/2.,ymax=dx/2.,
dx=dx,dy=dx)
# dx=_RCDX,dy=_RCDX)
vr= pix.plot(lambda x: dvlosgal(x),
returnz=True,justcalc=True)
vrunc= pix.plot(lambda x: dvlosgal(x),
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
returnz=True,justcalc=True)
sr= pix.plot(lambda x: dvlosgal(x),
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x))),
returnz=True,justcalc=True)
srunc= pix.plot(lambda x: dvlosgal(x),
func=disperror,
returnz=True,justcalc=True)
#print numpy.median(vr.flatten()[numpy.array([True,True,False,True,True,True,True,True,True],dtype='bool')])
print vr.flatten()
print vrunc.flatten()
print sr.flatten()
print srunc.flatten()
rs= numpy.arange(xmin+dx/2.,xmax-dx/2.+0.00001,dx)
print rs
bovy_plot.bovy_print()
srAxes= pyplot.axes([0.1,0.5,0.8,0.4])
vrAxes= pyplot.axes([0.1,0.1,0.8,0.4])
pyplot.sca(srAxes)
pyplot.errorbar(rs,sr.flatten(),yerr=srunc.flatten(),
marker='o',ls='none',ms=6.,color='k')
pyplot.xlim(0.,15.)
pyplot.ylim(9.5,49.)
#srAxes.set_yscale('log')
bovy_plot._add_ticks(yticks=False)
bovy_plot._add_axislabels(r'$ $',
r'$\sigma_R\,(\mathrm{km\,s}^{-1})$')
nullfmt = NullFormatter() # no labels
srAxes.xaxis.set_major_formatter(nullfmt)
pyplot.sca(vrAxes)
pyplot.errorbar(rs,vr.flatten(),yerr=vrunc.flatten(),
marker='o',ls='none',ms=6.,color='k')
pyplot.plot([0.,20.],numpy.median(vr.flatten())*numpy.ones(2),'k--')
bovy_plot._add_ticks()
pyplot.xlim(0.,15.)
pyplot.ylim(-14.,14.)
bovy_plot._add_axislabels(r'$R\,(\mathrm{kpc})$',
r'$\langle V_R\rangle\,(\mathrm{km\,s}^{-1})$')
bovy_plot.bovy_end_print(plotfilename)
return None
def veldist_1d_rolr(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_rolr
PURPOSE:
make a plot showing the influence of the bar R_OLR
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-09-11 - Written - Bovy (NYU)
"""
rolrs= [0.85,0.9,0.95]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'rolr_')
for rolr in rolrs:
thissavefilename= basesavefilename+'%.3f.sav' % rolr
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at R_OLR %.2f" % rolr
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at R_OLR %.2f" % rolr
potparams= (rolr,0.01,25.*_degtorad,.8,None)
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[1],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.amax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[0],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.5',
overplot=True,zorder=2,lw=1.5)
plot.bovy_text(r'$\mathrm{bar}\ R_{\mathrm{OLR}}$',title=True)
plot.bovy_text(0.36,.75,r'$R_{\mathrm{OLR}} = 0.95\ R_0$'+'\n'+r'$R_{\mathrm{OLR}} = 0.90\ R_0$'+ '\n'+r'$R_{\mathrm{OLR}} = 0.85\ R_0$')
plot.bovy_end_print(plotfilename)
def veldist_1d_vrconvolve(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_vrconvolve
PURPOSE:
make a plot showing the influence of the los velocity uncertainties
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-09-11 - Written - Bovy (NYU)
"""
convolves= [0.02,0.04,0.08]#0, 5, 10, 20 km/s
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
thissavefilename= os.path.join(saveDir,'convolve_')+'%.1f.sav' % 0.
print "Restoring los-velocity distribution at distance uncertainties %.1f" % 0.
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
vlosds.append(vlosd)
basesavefilename= os.path.join(saveDir,'vrconvolve_')
for distsig in convolves:
thissavefilename= os.path.join(saveDir,'convolve_')+'%.1f.sav' % 0.
print "Restoring los-velocity distribution at distance uncertainties %.1f" % 0.
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
#Create Gaussian
gauss= sc.exp(-0.5*vloss**2./distsig**2.)
#gauss= gauss/sc.sum(gauss)/(vloss[1]-vloss[0])
vlosd= signal.convolve(vlosd,gauss,mode='same')
vlosd= vlosd/sc.sum(vlosd)/(vloss[1]-vloss[0])
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[0],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.6',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
overplot=True,zorder=2,lw=2.)
kms= r' \mathrm{km\ s}^{-1}$'
plot.bovy_text(r'$\mathrm{line-of-sight\ velocity\ uncertainties}$',title=True)
plot.bovy_text(0.4,.65,r'$\sigma_v = 0\ '+kms+'\n'+r'$\sigma_v = 5\ '+kms+'\n'+r'$\sigma_v = 10\ '+kms+'\n'+r'$\sigma_v = 20\ '+kms)
plot.bovy_end_print(plotfilename)
def plotJuricIvezicDiff(dir):
#Plot difference for a few metallicities
fehs= [0.,-0.1,-0.2,-0.3,-0.5,-1.,-1.5]
colors= ['b','c','g','y','orange','m','r']
#Set up plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot([-100.,-100.],[-100.,-100],'k,',
xrange=[0.47,0.58],
yrange=[-1.,1.],
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$\mathrm{DM}_{\mathrm{Juri\acute{c}}}-\mathrm{DM}_{\mathrm{Ivezi\acute{c}}}\ [\mathrm{mag}]$')
xlegend, ylegend, dy= 0.55, 0.8,-0.12
grs= numpy.linspace(0.48,0.55,1001)
for ii in range(len(fehs)):
ybright= -1.*(_mr_ri_bright(_ri_gr(grs))-_mr_gi(_gi_gr(grs),fehs[ii]))
yfaint= -1.*(_mr_ri_faint(_ri_gr(grs))-_mr_gi(_gi_gr(grs),fehs[ii]))
bovy_plot.bovy_plot(grs,
ybright,
'-',color=colors[ii],
overplot=True)
bovy_plot.bovy_plot(grs,
yfaint,
'--',color=colors[ii],
overplot=True)
bovy_plot.bovy_text(xlegend,ylegend+ii*dy,
r'$[\mathrm{Fe/H]=%+4.1f}$' % fehs[ii],
color=colors[ii])
bovy_plot.bovy_end_print(os.path.join(dir,'dm_juric_ivezic.'+_EXT))
def plotprops(options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
npops= 62
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
npops= 30
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
ndatas= pickle.load(savefile)
savefile.close()
for ii in range(npops):
bovy_plot.bovy_print()
bovy_plot.bovy_text(r'$[\mathrm{Fe/H}] = %.2f$' % (fehs[ii])
+'\n'
r'$[\alpha/\mathrm{Fe}] = %.3f$' % (afes[ii])
+'\n'
r'$N_{\mathrm{data}} = %i$' % (ndatas[ii])
+'\n'
r'$\ln h_R / 8\,\mathrm{kpc} = %.1f$' % (numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii])/8.))
+'\n'
+r'$\ln \sigma_R / 220\,\mathrm{km\,s}^{-1} = %.1f$' % (numpy.log(monoAbundanceMW.sigmar(fehs[ii],afes[ii])/220.))
+'\n'
+r'$\ln \sigma_Z / 220\,\mathrm{km\,s}^{-1} = %.1f$' % (numpy.log(monoAbundanceMW.sigmaz(fehs[ii],afes[ii])/220.)),
top_left=True,size=16.)
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
def plotKz(pot,plotfilename,surfrs,kzs,kzerrs):
krs= numpy.linspace(4./_REFR0,10./_REFR0,1001)
modelkz= numpy.array([-potential.evaluatezforces(kr,1.1/_REFR0,pot)\
*bovy_conversion.force_in_2piGmsolpc2(_REFV0,_REFR0) for kr in krs])
bovy_plot.bovy_print(fig_height=3.5)
bovy_plot.bovy_plot(krs*_REFR0,modelkz,'-',color='0.6',lw=2.,
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$F_{Z}(R,|Z| = 1.1\,\mathrm{kpc})\ (2\pi G\,M_\odot\,\mathrm{pc}^{-2})$',
semilogy=True,
yrange=[10.,1000.],
xrange=[4.,10.],
zorder=0)
pyplot.errorbar(surfrs,
kzs,
yerr=kzerrs,
marker='o',
elinewidth=1.,capsize=3,zorder=1,
color='k',linestyle='none')
pyplot.errorbar([_REFR0],[69.],yerr=[6.],marker='d',ms=10.,
elinewidth=1.,capsize=3,zorder=10,
color='0.4',linestyle='none')
bovy_plot.bovy_end_print(plotfilename)
#Do an exponential fit to the model Kz and return the scale length
indx= krs < 9./_REFR0
p= numpy.polyfit(krs[indx],numpy.log(modelkz[indx]),1)
return -1./p[0]
def plot_rcdistancecomparison(plotfilename):
# Get the sample
rcdata = define_rcsample.get_rcsample()
# Now plot the differece
bovy_plot.bovy_print()
levels = special.erf(numpy.arange(1, 3) / numpy.sqrt(2.))
bovy_plot.scatterplot(
rcdata['RC_DIST'],
(rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
conditional=True,
levels=levels,
linestyle='none',
color='k',
marker=',',
xrange=[0., 7.49],
yrange=[-0.075, 0.075],
xlabel=r'$M_{K_s}\!-\!\mathrm{based\ distance\,(kpc)}$',
ylabel=
r'$\mathrm{Fractional\ difference\ of}\ M_H\ \mathrm{vs.}\ M_{K_s}$',
onedhistx=True,
bins=31)
bovy_plot.bovy_plot([0., 10.], [0., 0.],
'--',
lw=2.,
color='0.75',
overplot=True)
# Plot lowess
lowess = sm.nonparametric.lowess
z = lowess((rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
rcdata['RC_DIST'],
frac=.3)
bovy_plot.bovy_plot(z[:, 0], z[:, 1], 'w--', lw=2., overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_dustwapogee(plotname):
# Load the dust map
green15map= dust.load_green15(5.,nest=True,nside_out=_NSIDE)
green15map[green15map == healpy.UNSEEN]= -1.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,min=0.,max=.8,
format=r'$%g$',
title='',
cmap='gist_yarg',
unit='$A_H\,(\mathrm{mag})$')
# Load the RC data to get the fields
data= define_rcsample.get_rcsample()
loc_ids= numpy.array(list(set(data['LOCATION_ID'])))
# Load the selection function, just to get the field centers
apo= apogee.select.apogeeSelect(_justprocessobslog=True)
theta= numpy.empty(len(loc_ids))
phi= numpy.empty(len(loc_ids))
for ii,loc_id in enumerate(loc_ids):
tl, tb= apo.glonGlat(loc_id)
theta[ii]= (90.-tb)/180.*numpy.pi
phi[ii]= tl/180.*numpy.pi
hib= numpy.fabs((numpy.pi/2.-theta)) > (8./180.*numpy.pi)
healpy.visufunc.projplot(theta[hib],phi[hib],'o',ms=5.,mfc='none',mew=0.8,
mec='k')
lowb= True-hib
healpy.visufunc.projplot(theta[lowb],phi[lowb],'o',ms=5.,mfc='none',
mec='w',mew=0.8)
bovy_plot.bovy_end_print(plotname)
def plotMetallicityRZ(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,
sample=options.sample)
R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**0.5
XYZ[:,2]+= _ZSUN
#Set up ranges
#Set up ranges
if options.sample.lower() == 'g':
xrange=[0.46,0.57]
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
xrange=[0.51,0.79]
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55,0.75
colorrange=[0.55,0.75]
if options.metal.lower() == 'rich':
yrange=[-0.55,0.5]
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
yrange=[-1.6,0.3]
fehrange= _ARICHFEHRANGE
Zrange= [0.25,3.25]
Rrange= [5.,12.]
if options.plottype.lower() == 'z':
_NBINS= 7
Zranges= [[Zrange[0]+ii*(Zrange[1]-Zrange[0])/(_NBINS-1),
Zrange[0]+(ii+1)*(Zrange[1]-Zrange[0])/(_NBINS-1)] \
for ii in range(_NBINS-1)]
Zranges.append([Zranges[-1][1],4.])
Rranges= [Rrange for ii in range(_NBINS)]
elif options.plottype.lower() == 'r':
_NBINS= 7
Zranges= [Zrange for ii in range(_NBINS)]
Rranges= [[5.,7.]]
Rranges.extend([[Rrange[0]+ii*(Rrange[1]-Rrange[0])/_NBINS,
Rrange[0]+(ii+1)*(Rrange[1]-Rrange[0])/_NBINS] \
for ii in range(1,_NBINS)])
#First plot all data
bovy_plot.bovy_print()
_plotMRZ_single(XYZ,R,data,options,args,all=True,overplot=False,
xrange=xrange,yrange=yrange,colorrange=colorrange,
fehrange=fehrange,
Zrange=Zrange,Rrange=Rrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_'+options.plottype+'_'+options.sample+'_'+options.metal+'.'+ext))
#Then plot them in ranges
for ii in range(_NBINS):
_plotMRZ_single(XYZ,R,data,options,args,all=False,overplot=False,
xrange=xrange,Zrange=Zranges[ii],Rrange=Rranges[ii],
yrange=yrange,colorrange=colorrange,fehrange=fehrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_'+options.plottype+'_'+options.sample+'_'+options.metal+'_%i.' % ii + ext))
return None
def triangleMAPs(savefilename,basename):
with open(savefilename,'rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples= numpy.array(pickle.load(savefile))
bf_g15= numpy.array(pickle.load(savefile))
samples_g15= numpy.array(pickle.load(savefile))
bf_zero= numpy.array(pickle.load(savefile))
samples_zero= numpy.array(pickle.load(savefile))
labels= []
for jj in range(samples.shape[2]):
labels.append(r"$\mathrm{param}\ %i$" % jj)
maps= define_rcsample.MAPs()
for ii, map in enumerate(maps.map()):
if ii >= len(bf): break
tfeh= numpy.nanmedian(map['FE_H'])
tafe= numpy.nanmedian(map[define_rcsample._AFETAG])
for tbf,tsamples,ext in zip([bf,bf_g15,bf_zero],
[samples,samples_g15,samples_zero],
['fid','g15','zero']):
try:
triangle.corner(tsamples[ii,].T,quantiles=[0.16, 0.5, 0.84],
labels=labels,
show_titles=True,title_args={"fontsize": 12},
bins=21)
except ValueError: pass
else:
bovy_plot.bovy_text(r'$[\mathrm{{Fe/H}}] = {feh:.1f},$'\
.format(feh=tfeh)+'\n'
+r'$[\alpha/\mathrm{{Fe}}] = {afe:.2f}$'\
.format(afe=tafe),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basename+"_%i_%s.png" % (ii,ext))
return None
def plotAnIvezicDiff(dir):
#Load An isochrones
a= isodist.AnIsochrone()
#Plot difference for a few metallicities
fehs= [0.,-0.1,-0.2,-0.3,-0.5,-1.,-1.5]
colors= ['b','c','g','y','orange','m','r']
#Set up plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot([-100.,-100.],[-100.,-100],'k,',
#xrange=[0.47,0.58],
xrange=[0.53,0.78],
yrange=[-0.25,.25],
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$\mathrm{DM}_{\mathrm{An}}-\mathrm{DM}_{\mathrm{Ivezi\acute{c}}}\ [\mathrm{mag}]$')
xlegend, ylegend, dy= 0.545, 0.2,-0.03
for ii in range(len(fehs)):
iso= a(numpy.log10(10.),feh=fehs[ii])
#Get G dwarfs
indx= (iso['g']-iso['r'] <= 0.75)*(iso['g']-iso['r'] >= 0.55)\
*(iso['logg'] > 4.1)
y= -1.*(iso['r'][indx]-_mr_gi(_gi_gr(iso['g'][indx]
-iso['r'][indx]),fehs[ii]))
bovy_plot.bovy_plot(iso['g'][indx]-iso['r'][indx],
y,
'-',color=colors[ii],
overplot=True)
bovy_plot.bovy_text(xlegend,ylegend+ii*dy,
r'$[\mathrm{Fe/H]=%+4.1f}$' % fehs[ii],
color=colors[ii])
bovy_plot.bovy_end_print(os.path.join(dir,'dm_an_ivezic.'+_EXT))
def plotIllustrative(plotfilename):
#Make an illustrative plot of the effect of uncertainties on sigmaz
bovy_plot.bovy_print()
feh, afe= -0.15,0.125
nzs= 1001
zs= numpy.linspace(500.,2000.,nzs)
sigs= numpy.zeros(nzs)+sigmaz(feh,afe)
bovy_plot.bovy_plot(zs,sigs,'k-',
xlabel=r'$|Z|\ [\mathrm{pc}]$',
ylabel=r'$\sigma_z(Z)\ [\mathrm{km\ s}^{-1}]$',
xrange=[0.,2700.],
yrange=[0.,60.])
dfeh, dafe= 0.2, 0.1
sigs= sigmazObs(zs,feh,afe,dfeh,dafe)
bovy_plot.bovy_plot(zs,sigs,'k--',overplot=True)
dfeh, dafe= .4,.2
sigs= sigmazObs(zs,feh,afe,dfeh,dafe)
bovy_plot.bovy_plot(zs,sigs,'k-.',overplot=True)
feh, afe= -0.65,0.375
nzs= 1001
zs= numpy.linspace(500.,2000.,nzs)
sigs= numpy.zeros(nzs)+sigmaz(feh,afe)
bovy_plot.bovy_plot(zs,sigs,'k-',overplot=True)
dfeh, dafe= 0.2, 0.1
sigs= sigmazObs(zs,feh,afe,dfeh,dafe)
bovy_plot.bovy_plot(zs,sigs,'k--',overplot=True)
dfeh, dafe= 0.4,0.2
sigs= sigmazObs(zs,feh,afe,dfeh,dafe)
bovy_plot.bovy_plot(zs,sigs,'k-.',overplot=True)
bovy_plot.bovy_end_print(plotfilename)
def plot_maphz(plotname):
# Load the two fit
with open('../mapfits/tribrokentwoexp.sav','rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples= numpy.array(pickle.load(savefile))
maps= define_rcsample.MAPs()
plotthisz1= numpy.zeros(len(bf))+numpy.nan
plotthisz1e= numpy.zeros(len(bf))+numpy.nan
plotthisz2= numpy.zeros(len(bf))+numpy.nan
plotthisz2e= numpy.zeros(len(bf))+numpy.nan
for ii, map in enumerate(maps.map()):
hzindx= (True-numpy.isnan(samples[ii,4]))\
*(True-numpy.isnan(samples[ii,5]))\
*(densprofiles.ilogit(samples[ii,4]) > 0.15)
tmed= numpy.median(1./samples[ii,1,hzindx])
terr= numpy.std(1./samples[ii,1,hzindx])
plotthisz1[ii]= tmed
plotthisz1e[ii]= terr
tmed= numpy.median(1./samples[ii,5,hzindx])
terr= numpy.std(1./samples[ii,5,hzindx])
plotthisz2[ii]= tmed
plotthisz2e[ii]= terr
plotthisz1[plotthisz1e/plotthisz1 > 0.5]= numpy.nan
bovy_plot.bovy_print()
bovy_plot.bovy_plot(plotthisz2*1000.,plotthisz1*1000.,'ko',
xrange=[0.,1200.],yrange=[0.,1200.],
xlabel=r'$2^\mathrm{nd}\ \mathrm{scale\ height\,(pc)}$',
ylabel=r'$1^\mathrm{st}\ \mathrm{scale\ height\,(pc)}$',zorder=2)
bovy_plot.bovy_plot([0,1200],[0,1200],'k--',overplot=True,lw=2.)
pyplot.errorbar(plotthisz2*1000.,plotthisz1*1000.,
yerr=plotthisz1e*1000.,
marker='o',color='k',ls='none',zorder=1)
bovy_plot.bovy_end_print(plotname)
return None
def checkAbundanceScatterClusters():
# First read the cluster data
cldata= read_clusterdata.read_caldata()
# Read the allStar data to match
# For each of the calibration open clusters, calculate the offset from the
# mean in our FEHTAG and AFETAG
clusters= ['M71','N2158','N2420','N188','M67','N7789','N6819',
'N6791']
fehoffset= []
afeoffset= []
for cluster in clusters:
tdata= cldata[cldata['CLUSTER'] == cluster.upper()]
tdata= tdata[(tdata['TEFF'] < _TEFFMAX)\
*(tdata['TEFF'] > _TEFFMIN)\
*(tdata['LOGG'] < 3.5)]
# Compute the average feh and afe and save the offsets
medianfeh= numpy.median(tdata['FE_H'])
medianafe= numpy.median(tdata[define_rcsample._AFETAG])
fehoffset.extend(tdata['FE_H']-medianfeh)
afeoffset.extend(tdata[define_rcsample._AFETAG]-medianafe)
if cluster == 'M67': print medianfeh, medianafe, len(tdata)
fehoffset= numpy.array(fehoffset)
afeoffset= numpy.array(afeoffset)
print 'FE_H scatter %g' % (numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3]))
print 'A_FE scatter %g' % (numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
gindx= (numpy.fabs(fehoffset) < 0.3)*(numpy.fabs(afeoffset) < 0.3)
print 'FE_H/A_FE correlation %g' % (numpy.mean(afeoffset[gindx]*fehoffset[gindx])/numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3])/numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
print 'FE_H robust scatter %g' % (1.4826*numpy.median(numpy.fabs(fehoffset)))
print 'A_FE robust scatter %g' % (1.4826*numpy.median(numpy.fabs(afeoffset)))
bovy_plot.bovy_print()
bovy_plot.bovy_hist(fehoffset,range=[-0.3,0.3],bins=31,histtype='step')
bovy_plot.bovy_hist(afeoffset,range=[-0.3,0.3],bins=31,histtype='step',
overplot=True)
bovy_plot.bovy_end_print('test.png')
return None
def plot_data_h_jk(location=None,
plotfilename=os.path.join(OUTDIR, 'data_h_jk.' + OUTEXT)):
data = readVclosData()
if not location is None:
if location == 0:
locs = set(data['LOCATION'])
for l in locs:
plot_data_h_jk(location=l,
plotfilename=os.path.join(
OUTDIR, 'data_h_jk_%i.' % l + OUTEXT))
return None
data = data[(data['LOCATION'] == location)]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['J0MAG'] - data['K0MAG'],
data['H0MAG'],
'k,',
xlabel=r'$(J-K_s)_0\ [\mathrm{mag}]$',
ylabel=r'$H_0\ [\mathrm{mag}]$',
xrange=[0.4, 1.4],
yrange=[5., 14.],
onedhists=True,
bins=31)
#Overplot distance if wanted
if _PLOTDISTANCE:
iso = isomodel(imfmodel='lognormalChabrier2001', Z=0.019, expsfh=True)
nds = 101
ds = numpy.zeros((nds, nds))
jks = numpy.linspace(0.5, 1.2, nds)
hs = numpy.linspace(14., 5., nds)
for ii in range(nds):
for jj in range(nds):
ds[ii, jj] = iso.peak(jks[ii], hs[jj])
#Now contour this
levels = [1., 3., 10., 30.]
colors = '0.6' #['0.5','0.5','0.5','k','k','k']
CS = pyplot.contour(jks,
hs,
ds.T,
levels=levels,
colors=colors,
zorder=10.,
linewidths=2.)
ys = [5.3, 6.7, 9.22, 11.7]
for ii in range(len(levels)):
bovy_plot.bovy_text(1.21,
ys[ii],
r'$%.0f\ \mathrm{kpc}$' % levels[ii],
fontsize=14.,
color='0.3')
if False:
pyplot.clabel(CS,
levels,
inline=1,
fmt='%.0f',
fontsize=14,
colors=colors,
zorder=10.)
bovy_plot.bovy_end_print(plotfilename)
return None
def plotDMMetallicityColor(options,args):
"""Make a density plot of DM vs FeH and g-r"""
if options.png: ext= 'png'
else: ext= 'ps'
if options.metal.lower() == 'rich':
yrange=[-0.55,0.5]
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
yrange=[-1.6,0.3]
fehrange= _ARICHFEHRANGE
xrange=[0.46,0.57]
grmin, grmax= 0.48, 0.55
colorrange=[0.48,0.55]
#Set up arrays
nfehs, ngrs= 201,201
grs= numpy.linspace(xrange[0],xrange[1],nfehs)
fehs= numpy.linspace(yrange[0],yrange[1],ngrs)
plotthis= numpy.zeros((ngrs,nfehs))
for ii in range(ngrs):
for jj in range(nfehs):
if grs[ii] < colorrange[0] \
or grs[ii] > colorrange[1] \
or fehs[jj] < fehrange[0] \
or fehs[jj] > fehrange[1]:
plotthis[ii,jj]= numpy.nan
continue
plotthis[ii,jj]= segueSelect._mr_gi(segueSelect._gi_gr(grs[ii]),fehs[jj])
if options.sample.lower() == 'g':
if options.metal.lower() == 'rich':
levels= [4.5,4.75,5.,5.25,5.5]
else:
levels= [5.25,5.5,5.75,6.,6.25]
cntrlabelcolors= ['w' for ii in range(3)]
cntrlabelcolors.extend(['k' for ii in range(2)])
#nlevels= 6
#levelsstart= int(20.*numpy.nanmin(plotthis))/20.
#levelsd= int(20.*(numpy.nanmax(plotthis)-numpy.nanmin(plotthis)))/20.
#levels= [levelsstart+ii/float(nlevels)*levelsd for ii in range(nlevels)]
#Plot it
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
zlabel=r'$M_r\ [\mathrm{mag}]$',
colorbar=True,
cmap=pyplot.cm.gist_gray,
contours=True,
levels=levels,
cntrcolors=cntrlabelcolors,
cntrlabel=True,
cntrlabelcolors=cntrlabelcolors,
cntrinline=True,
interpolation='nearest',
extent=[xrange[0],xrange[1],
yrange[0],yrange[1]],
aspect=(xrange[1]-xrange[0])/\
(yrange[1]-yrange[0]),
shrink=.78)
bovy_plot.bovy_end_print(os.path.join(args[0],'dm_FeH_gr_'+options.sample+'_'+options.metal+'.'+ext))
def plot_apogee_jkz(plotfilename,sample):
if sample == 'disk':
data= readData()
elif sample == 'halo':
data= readData(halo=True)
elif sample == 'bulge':
data= readData(bulge=True)
ntotal= len(data)
#logg cut
data= data[(data['LOGG_AVG'] < 2.8)*(data['LOGG_AVG'] > 1.8)]
npass= len(data)
#Deredden
aj= data['AK_WISE']*2.5
ah= data['AK_WISE']*1.55
j0= data['JMAG']-aj
h0= data['HMAG']-ah
k0= data['KMAG']-data['AK_WISE']
#calculate Zs
zsolar= 0.019
z= isodist.FEH2Z(data['METALS_AVG'])/zsolar
#Plot
bovy_plot.bovy_print()
if sample == 'bulge':
bovy_plot.bovy_plot(j0-k0,z,'ko',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',ms=5.)
else:
bovy_plot.scatterplot(j0-k0,z,'k,',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',
bins=21)
#Overplot cuts
jks= numpy.linspace(0.5,0.75,1001)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks)/zsolar,
'k--',lw=2.,overplot=True)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks,upper=True)/zsolar,
'k--',lw=2.,overplot=True)
#Data between the cuts
indx= (j0-k0 < 0.75)*(j0-k0 > 0.5)\
*(z <= rcmodel.jkzcut(j0-k0,upper=True)/zsolar)\
*(z >= rcmodel.jkzcut(j0-k0)/zsolar)
# j0= j0[indx]
# h0= h0[indx]
# k0= k0[indx]
# z= z[indx]
# bovy_plot.bovy_plot(j0-k0,z,'w.',
# overplot=True,
# mec='w',
# ms=.5)
bovy_plot.bovy_text(r'$\mathrm{APOGEE\ ' + sample + '\ sample}$',
title=True)
bovy_plot.bovy_text(r'$%i/%i/%i$' % (numpy.sum(indx),npass,ntotal),
bottom_right=True,size=14.)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_age(plotfilename,massfile,omegafile):
if os.path.exists(massfile):
savefile= open(massfile,'rb')
mass= pickle.load(savefile)
zs= pickle.load(savefile)
lages= pickle.load(savefile)
savefile.close()
else:
raise IOError("file %s has to exist ..." % massfile)
if os.path.exists(omegafile):
savefile= open(omegafile,'rb')
omega= pickle.load(savefile)
savefile.close()
else:
raise IOError("file %s has to exist ..." % omegafile)
agezdist= omega/mass
pz= numpy.exp(numpy.array([localzdist(z,zsolar=0.017) for z in zs]))
pz/= numpy.sum(pz)
page= 1.
#Tile
pz= numpy.tile(pz,(len(lages),1)).T
#Build age pdfs
postage= page*numpy.nansum(pz*agezdist,axis=0)/numpy.nansum(pz,axis=0)/10.**lages
postage= postage[lages > numpy.log10(0.8)]
postage/= numpy.nanmax(postage)
#Interpolate
lages= lages[lages > numpy.log10(0.8)]
postage_spline= interpolate.InterpolatedUnivariateSpline(lages,
numpy.log(postage),
k=3)
plages= numpy.linspace(0.8,10.,1001)
plpostage= numpy.exp(postage_spline(numpy.log10(plages)))
plpostage/= numpy.nansum(plpostage)*(plages[1]-plages[0])
bovy_plot.bovy_print(fig_width=7.)
yrange=[0.,0.4]
# yrange=[0.,0.7]
bovy_plot.bovy_plot(plages,plpostage,
'k-',lw=2.,
xlabel=r'$\mathrm{Age}\,(\mathrm{Gyr})$',
ylabel=r'$p(\mathrm{RC | population\ Age})$',
xrange=[0.,10.],
yrange=yrange,
zorder=10,loglog=False)
#Baseline
bovy_plot.bovy_plot(plages,1./9.2*numpy.ones(len(plages)),
'-',color='0.4',overplot=True,zorder=3,lw=2.)
#Fit the dependence
if _FIT:
opt= optimize.fmin_powell(chi2,[-1.,0.,0.,0.,-1.,0.,0.,0.],#[-4.,0.,0.25,0.,0.,1.],
args=(numpy.log10(plages),
numpy.log(plpostage)))
print opt
bovy_plot.bovy_plot(plages,numpy.exp(_fit_func(opt,numpy.log10(plages))),
'r-',overplot=True,zorder=11)
a= numpy.log10(plages)
bovy_plot.bovy_plot(plages[a <= 0.23],numpy.exp(polyfit1(a[a <= 0.23])),'b-',overplot=True,zorder=12)
bovy_plot.bovy_plot(plages[a > 0.23],numpy.exp(polyfit2(a[a > 0.23])),'b-',overplot=True,zorder=12)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_jr(plotfilename):
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp,tintJ=200,ntintJ=20000)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,0.88719443,
-0.47713334,0.12019596])
bovy_plot.bovy_print(fig_width=6.)
aAI.plot(*obs,type='jr',downsample=True)
bovy_plot.bovy_end_print(plotfilename)
def plot_maprmax(savefilename, plotname):
with open(savefilename, 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
bf_g15 = numpy.array(pickle.load(savefile))
samples_g15 = numpy.array(pickle.load(savefile))
bf_zero = numpy.array(pickle.load(savefile))
samples_zero = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthis = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
tmed = numpy.median(
numpy.exp(samples[ii, 3])[True -
numpy.isnan(numpy.exp(samples[ii, 3]))])
if tmed < 5.:
tmed = 0.
plotthis[ii] = tmed
bovy_plot.bovy_print()
maps.plot(plotthis,
vmin=5.,
vmax=13.,
minnstar=15,
zlabel=r'$R_{\mathrm{peak}}\,(\mathrm{kpc})$',
shrink=0.68,
cmap='coolwarm_r')
# Sequences
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
haloc = define_rcsample.lowalphalocus()
haloc = haloc[(haloc[:, 0] > -0.55) * (haloc[:, 0] < 0.225)]
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
# Label
#t= pyplot.text(-0.51,0.235,r'$\mathrm{single}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
#t= pyplot.text(-0.475,0.195,r'$\mathrm{exponential}$',
# size=16.,color='w')
t = pyplot.text(-0.625,
0.195,
r'$R_{\mathrm{peak}} < 5\,\mathrm{kpc}$',
size=16.,
color='w')
t.set_bbox(dict(alpha=0.5, color=cm.coolwarm_r(0.), edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname, dpi=300)
return None
def plotGPSamples(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
#Restore fit
if os.path.exists(args[0]):
fitsfile = open(args[0], 'rb')
params = pickle.load(fitsfile)
type = pickle.load(fitsfile)
band = pickle.load(fitsfile)
try:
mean = pickle.load(fitsfile)
except EOFError:
mean = 'zero'
fitsfile.close()
else:
raise IOError("You need to specify the file that holds the fits")
if options.star:
dir = '../data/star/'
elif options.nuvx:
dir = '../data/nuvx/'
elif options.nuvxall:
dir = '../data/nuvx_all/'
elif options.uvx:
dir = '../data/uvx/'
elif options.rrlyrae:
dir = '../data/rrlyrae/'
else:
dir = '../data/s82qsos/'
#Load object
v = varqso.VarQso(os.path.join(dir, options.key))
v.LCparams = params[options.key]
v.LC = varqso.LCmodel(trainSet=v._build_trainset(band),
type=type,
mean=mean)
v.LCtype = type
v.LCmean = mean
v.fitband = band
if os.path.exists(options.savefilename):
print "Reusing samples from previous sampling"
savefile = open(options.savefilename, 'rb')
samples = pickle.load(savefile)
savefile.close()
v.set_sampleGP(samples)
else:
v.sampleGP(nsamples=options.nsamples)
samples = v.get_sampleGP()
#Save
savefile = open(options.savefilename, 'wb')
pickle.dump(samples, savefile)
savefile.close()
#Now plot
bovy_plot.bovy_print()
v.plot_sampleGP(d1=options.d1, d2=options.d2, whitemax=options.whitemax)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_pdfs_l(plotfilename):
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Calculate the density as a function of l, p(l)
#Sample from sdf
llbd= sdf.sample(n=40000,lb=True)
tlbd= sdft.sample(n=50000,lb=True)
b,e= numpy.histogram(llbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
lspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=1.)
lls= numpy.linspace(t[0],t[-1],_NLS)
lps= numpy.exp(lspl(lls))
lps/= numpy.sum(lps)*(lls[1]-lls[0])*2.
b,e= numpy.histogram(tlbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
tspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=0.5)
tls= numpy.linspace(t[0],t[-1],_NLS)
tps= numpy.exp(tspl(tls))
tps/= numpy.sum(tps)*(tls[1]-tls[0])*2.
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
bovy_plot.bovy_plot(lls,lps,'k-',lw=1.5,
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=r'$p(l)$',
xrange=[65.,250.],
yrange=[0.,1.2*numpy.nanmax(numpy.hstack((lps,tps)))])
bovy_plot.bovy_plot(tls,tps,'k-',lw=1.5,overplot=True)
#Also plot the stream histogram
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
#Transform to (l,b)
XYZ= bovy_coords.galcenrect_to_XYZ(data[:,1],data[:,3],data[:,2],Xsun=8.)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
lbd= lbd[indx,:]
bovy_plot.bovy_hist(lbd[:,0],bins=40,range=[65.,250.],
histtype='step',normed=True,
overplot=True,
lw=1.5,color='k')
bovy_plot.bovy_end_print(plotfilename)
def plot_dust_gaia(dist, plotname):
# Load the dust map
green15map = dust.load_combined(dist, nest=True, nside_out=_NSIDE)
dm = dust.dist2distmod(dist)
green15map[green15map == healpy.UNSEEN] = -1.
print "%i are NaN" % (numpy.sum(numpy.isnan(green15map)))
#theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),nest=True)
#print (numpy.pi/2.-theta)[numpy.isnan(green15map)]/numpy.pi*180.
#print phi[numpy.isnan(green15map)]/numpy.pi*180.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,
min=0.,
max=round(10. * (20. - dm - 0.68)) / 10.,
format=r'$%g$',
cmap='gist_yarg',
title="",
unit='$A_G\,(\mathrm{mag})$')
# Plot outline of Marshall et al.
ls = numpy.linspace(-100.125, 100.125, 1001)
healpy.visufunc.projplot(ls, ls * 0. - 10.125, 'k--', lw=1.2, lonlat=True)
healpy.visufunc.projplot(ls, ls * 0. + 10.125, 'k--', lw=1.2, lonlat=True)
bs = numpy.linspace(-10.125, 10.125, 1001)
healpy.visufunc.projplot(bs * 0. - 100.125, bs, 'k--', lw=1.2, lonlat=True)
healpy.visufunc.projplot(bs * 0. + 100.125, bs, 'k--', lw=1.2, lonlat=True)
# Plot boundary of Green et al. map, dec > -30.
ras = numpy.linspace(0., 360., 1001)
decs = ras * 0. - 30.
lbs = bovy_coords.radec_to_lb(ras, decs, degree=True)
ls = lbs[:, 0]
bs = lbs[:, 1]
# rm those within Marshall et al.
keepIndx = True - ((ls > 259.875) * (numpy.fabs(bs) < 10.125) +
(ls < 100.125) * (numpy.fabs(bs) < 10.125))
ls = ls[keepIndx]
bs = bs[keepIndx]
healpy.visufunc.projplot(ls, bs, 'k-.', lw=1.2, lonlat=True)
# Labels
healpy.visufunc.projtext(350.,
-8.,
r'$\mathrm{Marshall\ et\ al.\ (2006)}$',
lonlat=True,
size=13.)
healpy.visufunc.projtext(10.,
-60.,
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$',
lonlat=True,
size=13.)
healpy.visufunc.projtext(160.,
40.,
r'$\mathrm{Green\ et\ al.\ (2015)}$',
lonlat=True,
size=13.)
bovy_plot.bovy_end_print(plotname)
def determine_kxky_error():
#Load fiducial bar model
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR)[1::2,1::2]
#spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR)[1::2,1::2]*0.85
#spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-7%s.sav' % _HIVRESSTR)[1::2,1::2]*0.25
psd2d= bovy_psd.psd2d(spvlos)
kmax= 1./_RCDX
#Calculate maximum
psd2d[psd2d.shape[0]/2-1:psd2d.shape[0]/2+2,psd2d.shape[1]/2-1:psd2d.shape[1]/2+2]= 0.
tmax= numpy.unravel_index(numpy.argmax(psd2d),psd2d.shape)
tmax0= float(psd2d.shape[0]/2-tmax[0])/psd2d.shape[0]*2
tmax1= float(tmax[1]-psd2d.shape[1]/2)/psd2d.shape[1]*2
print tmax0*kmax, tmax1*kmax
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(psd2d.T,origin='lower',cmap='jet',
interpolation='nearest',
xrange=[-kmax,kmax],
yrange=[-kmax,kmax],
xlabel=r'$k_x\,(\mathrm{kpc}^{-1})$',
ylabel=r'$k_y\,(\mathrm{kpc}^{-1})$')
bovy_plot.bovy_end_print('/Users/bovy/Desktop/test.png')
#Read the data for the noise
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx)
data= data[indx]
#Get residuals
dx= _RCDX
pix= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=dx,dy=dx)
resvunc= pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
returnz=True,justcalc=True)
#Now do 1000 MC simulations to determine the error on kmax
nmc= 1000
kxmax= numpy.zeros(nmc)
kymax= numpy.zeros(nmc)
for ii in range(nmc):
newresv= spvlos+numpy.random.normal(size=spvlos.shape)*resvunc/220.
simpsd2d= bovy_psd.psd2d(newresv*220.)
simpsd2d[simpsd2d.shape[0]/2-1:simpsd2d.shape[0]/2+2,simpsd2d.shape[1]/2-1:simpsd2d.shape[1]/2+2]= 0.
tmax= numpy.unravel_index(numpy.argmax(simpsd2d),psd2d.shape)
tmax0= float(psd2d.shape[0]/2-tmax[0])/psd2d.shape[0]*2
tmax1= float(tmax[1]-psd2d.shape[1]/2)/psd2d.shape[1]*2
kmax= 1./_RCDX
kxmax[ii]= tmax0*kmax
kymax[ii]= tmax1*kmax
print numpy.mean(kxmax), numpy.std(kxmax)
print numpy.mean(kymax), numpy.std(kymax)
return None
def simplePlot(location=4242,
plotfile=None,
predict=False,
nv=11,
dmax=10. / 8.,
**kwargs):
"""
NAME:
simplePlot
PURPOSE:
make a simple histogram for a given location
INPUT:
location - location ID
+readVclosData inputs
OPTIONAL INPUT:
plotfile= if set, save plot to this file
OUTPUT:
plot to display or file
HISTORY:
2012-01-25 - Written - Bovy (IAS)
"""
#Read data
data = readVclosData(**kwargs)
data = data[(data['LOCATION'] == location)]
if not plotfile is None:
bovy_plot.bovy_print()
range = [-200., 200.]
hist, xvec, p = bovy_plot.bovy_hist(
data['VHELIO'],
range=range,
bins=31,
histtype='step',
color='k',
xlabel=
r'$\mathrm{heliocentric}\ v_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Prediction
if predict:
pred_vs = numpy.linspace(range[0], range[1], nv)
pred_dist = _calc_pred(pred_vs, location, numpy.mean(data['GLON']),
dmax)
data_int = numpy.sum(hist) * (xvec[1] - xvec[0])
pred_dist *= data_int / numpy.sum(pred_dist) / (pred_vs[1] -
pred_vs[0])
bovy_plot.bovy_plot(pred_vs,
pred_dist,
'-',
color='0.65',
overplot=True)
#Add text
bovy_plot.bovy_text(r'$\mathrm{location}\ =\ %i$' % location + '\n' +
r'$l\ \approx\ %.0f^\circ$' % numpy.mean(data['GLON']),
top_right=True,
size=14.)
if not plotfile is None:
bovy_plot.bovy_end_print(plotfile)
def plotGPSamples(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
#Restore fit
if os.path.exists(args[0]):
fitsfile= open(args[0],'rb')
params= pickle.load(fitsfile)
type= pickle.load(fitsfile)
band= pickle.load(fitsfile)
try:
mean= pickle.load(fitsfile)
except EOFError:
mean= 'zero'
fitsfile.close()
else:
raise IOError("You need to specify the file that holds the fits")
if options.star:
dir= '../data/star/'
elif options.nuvx:
dir= '../data/nuvx/'
elif options.nuvxall:
dir= '../data/nuvx_all/'
elif options.uvx:
dir= '../data/uvx/'
elif options.rrlyrae:
dir= '../data/rrlyrae/'
else:
dir= '../data/s82qsos/'
#Load object
v= varqso.VarQso(os.path.join(dir,options.key))
v.LCparams= params[options.key]
v.LC= varqso.LCmodel(trainSet=v._build_trainset(band),type=type,mean=mean)
v.LCtype= type
v.LCmean= mean
v.fitband= band
if os.path.exists(options.savefilename):
print "Reusing samples from previous sampling"
savefile= open(options.savefilename,'rb')
samples= pickle.load(savefile)
savefile.close()
v.set_sampleGP(samples)
else:
v.sampleGP(nsamples=options.nsamples)
samples= v.get_sampleGP()
#Save
savefile= open(options.savefilename,'wb')
pickle.dump(samples,savefile)
savefile.close()
#Now plot
bovy_plot.bovy_print()
v.plot_sampleGP(d1=options.d1,d2=options.d2,whitemax=options.whitemax)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_dfcorrections(plotfilename):
niters= [1,2,3,4,5,10,15,20,25]
bovy_plot.bovy_print(fig_height=7.,fig_width=8.)
ii= 0
# Load DF
pyplot.subplot(2,1,1)
dfc= dehnendf(beta=0.,correct=True,niter=niters[ii])
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfc._corr._corrections[:,0]),
'-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,
xrange=[0.,5.],
yrange=[-0.25,0.25],
ylabel=r'$\ln \Sigma_{\mathrm{out}}(R)-\ln\Sigma_{\mathrm{DF}}(R)$')
linthresh= 0.0001
pyplot.yscale('symlog',linthreshy=linthresh)
for ii,niter in enumerate(niters[1:]):
dfcn= dehnendf(beta=0.,correct=True,niter=niter)
dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfcn._corr._corrections[:,0])-numpy.log(dfcp._corr._corrections[:,0]),
'-',overplot=True,
color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,
zorder=ii+2)
pyplot.fill_between(numpy.linspace(0.,5.,2.),
-linthresh*numpy.ones(2),
linthresh*numpy.ones(2),color='0.9',
zorder=0)
bovy_plot.bovy_text(4.,-0.00008,r'$\mathrm{linear\ scale}$',
backgroundcolor='w',size=16.)
pyplot.subplot(2,1,2)
bovy_plot.bovy_plot(dfc._corr._rs,
0.5*numpy.log(dfc._corr._corrections[:,1]),
'-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,
xrange=[0.,5.],
yrange=[-0.25,0.25],
xlabel=r'$R/R_0$',
ylabel=r'$\ln \sigma_{R,\mathrm{out}}(R)-\ln\sigma_{R,\mathrm{DF}}(R)$')
pyplot.yscale('symlog',linthreshy=linthresh)
for ii,niter in enumerate(niters[1:]):
dfcn= dehnendf(beta=0.,correct=True,niter=niter)
dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfcn._corr._corrections[:,1])-numpy.log(dfcp._corr._corrections[:,1]),
'-',overplot=True,
color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,
zorder=ii+2)
pyplot.fill_between(numpy.linspace(0.,5.,2.),
-linthresh*numpy.ones(2),
linthresh*numpy.ones(2),color='0.9',
zorder=0)
bovy_plot.bovy_text(4.,-0.00008,r'$\mathrm{linear\ scale}$',
backgroundcolor='w',size=16.)
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotfilename)
return None
def plotCombinedPDF(options,args):
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.group is None:
gafes, gfehs, legend= getMultiComparisonBins(options)
else:
legend= None
if not options.multi is None:
PDFs= multi.parallel_map((lambda x: calcAllPDFs(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
PDFs= []
for ii in range(npops):
PDFs.append(calcAllPDFs(ii,options,args))
#Go through and combine
combined_lnpdf= numpy.zeros((options.nrds,options.nfhs))
for ii in range(npops):
if not options.group is None:
if numpy.amin((gfehs-fehs[ii])**2./0.1+(gafes-afes[ii])**2./0.0025) > 0.001:
continue
combined_lnpdf+= PDFs[ii]
alogl= combined_lnpdf-numpy.nanmax(combined_lnpdf)
#Now plot
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(numpy.exp(alogl).T,
origin='lower',cmap='gist_yarg',
interpolation='nearest',
xrange=[1.9,3.5],yrange=[-1./32.,1.+1./32.],
xlabel=r'$R_d\ (\mathrm{kpc})$',ylabel=r'$f_h$')
if not legend is None:
bovy_plot.bovy_text(legend,top_left=True,
size=14.)
bovy_plot.bovy_end_print(options.outfilename)
#Calculate and print derived properties
derivProps= rawDerived(alogl,options,
vo=options.fixvc/_REFV0,zh=options.fixzh,
dlnvcdlnr=options.dlnvcdlnr)
for key in derivProps.keys():
if not '_err' in key:
print key, derivProps[key], derivProps[key+'_err'], \
derivProps[key]/derivProps[key+'_err']
return None
def plotRbestvsRmean(savefilename,plotfilename):
#Read surface densities
#First read the surface densities
if os.path.exists(savefilename):
surffile= open(savefilename,'rb')
surfrs= pickle.load(surffile)
surfs= pickle.load(surffile)
surferrs= pickle.load(surffile)
surffile.close()
else:
raise IOError("savefilename with surface-densities has to exist")
if True:#options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif False:#options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
#Load g orbits
orbitsfile= 'gOrbitsNew.sav'
savefile= open(orbitsfile,'rb')
orbits= pickle.load(savefile)
savefile.close()
#Cut to S/N, logg, and EBV
indx= (orbits.sna > 15.)*(orbits.logga > 4.2)*(orbits.ebv < 0.3)
orbits= orbits[indx]
#Load the orbits into the pixel structure
pix= pixelAfeFeh(orbits,dfeh=0.1,dafe=0.05)
#Now calculate meanr
rmean= numpy.zeros_like(surfrs)
for ii in range(len(surfrs)):
data= pix(fehs[ii],afes[ii])
vals= data.densrmean*8.
if False:#True:
rmean[ii]= numpy.mean(vals)
else:
rmean[ii]= numpy.median(vals)
#Plot
indx= numpy.isnan(surfrs)
indx[50]= True
indx[57]= True
indx= True - indx
surfrs= surfrs[indx]
rmean= rmean[indx]
fehs= fehs[indx]
afes= afes[indx]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(rmean,surfrs,c=afes,marker='o',scatter=True,
xlabel=r'$\mathrm{mean\ orbital\ radius\ of\ MAP}\,(\mathrm{kpc})$',
ylabel=r'$\mathrm{radius\ at\ which\ MAP\ measures}\ \Sigma_{1.1}\,(\mathrm{kpc})$',
xrange=[4.,10.],
yrange=[4.,10.],
edgecolor='none',zorder=10,s=50.)
bovy_plot.bovy_plot([4.5,9.],[4.5,9.],'-',color='0.50',overplot=True,lw=1.)
bovy_plot.bovy_end_print(plotfilename)
def plot_aagrid(plotfilename1,plotfilename2):
#Setup orbit
E, Lz= -1.25, 0.6
o= Orbit([0.8,0.3,Lz/0.8,0.,numpy.sqrt(2.*(E-evalPot(0.8,0.,MWPotential2014)-(Lz/0.8)**2./2.-0.3**2./2.)),0.])
delta= 0.434
#Integrate the orbit, setup Staeckel already to calculate the period
aAS= actionAngleStaeckel(pot=MWPotential2014,delta=delta,c=True)
orbt= 2.*numpy.pi/aAS.actionsFreqs(o)[4]
norb= 5.
nt= 501
ts= numpy.linspace(0.,norb*orbt,nt)
o.integrate(ts,MWPotential2014,method='symplec4_c')
#First do adiabatic
aAA= actionAngleAdiabatic(pot=MWPotential2014,gamma=1.,c=True)
aAAG= actionAngleAdiabaticGrid(pot=MWPotential2014,gamma=1.,c=True,
nR=31,nEz=31,nEr=51,nLz=51)
jfa= aAA(o.R(ts),o.vR(ts),o.vT(ts),o.z(ts),o.vz(ts),o.phi(ts))
jfag= aAAG(o.R(ts),o.vR(ts),o.vT(ts),o.z(ts),o.vz(ts),o.phi(ts))
#First do adiabatic
#aAS already setup
aASG= actionAngleStaeckelGrid(pot=MWPotential2014,delta=delta,c=True,
nE=51,npsi=51,nLz=51)
jfs= aAS(o.R(ts),o.vR(ts),o.vT(ts),o.z(ts),o.vz(ts),o.phi(ts))
jfsg= aASG(o.R(ts),o.vR(ts),o.vT(ts),o.z(ts),o.vz(ts),o.phi(ts))
bovy_plot.bovy_print()
line1= bovy_plot.bovy_plot(jfa[0],jfa[2],'r.',
xrange=[0.045,0.055],
yrange=[0.0075,0.011],
xlabel=r'$J_R$',ylabel=r'$J_z$',zorder=2)
line2= bovy_plot.bovy_plot(jfag[0],jfag[2],'rx',overplot=True,zorder=1)
bovy_plot.bovy_plot(jfs[0],jfs[2],'k,',overplot=True)
pyplot.legend((line1[0],line2[0]),
(r'$\mathrm{\texttt{actionAngleAdiabatic}}$',
r'$\mathrm{\texttt{actionAngleAdiabaticGrid}}$',),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=1,
prop={'size':14},
frameon=False)
bovy_plot.bovy_end_print(plotfilename1)
#Zoom of Staeckel
line1= bovy_plot.bovy_plot(jfs[0],jfs[2],'k.',
xrange=[0.05025,0.05145],
yrange=[0.0086,0.00933],
xlabel=r'$J_R$',ylabel=r'$J_z$')
line2= bovy_plot.bovy_plot(jfsg[0],jfsg[2],'kx',overplot=True)
pyplot.legend((line1[0],line2[0]),
(r'$\mathrm{\texttt{actionAngleStaeckel}}$',
r'$\mathrm{\texttt{actionAngleStaeckelGrid}}$',),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=1,
prop={'size':14},
frameon=False)
bovy_plot.bovy_end_print(plotfilename2)
return None
def plotPriorSurf(plotfilename):
#Calculate the surface density profile for each trial potential, then plot the range
if '.png' in plotfilename:
savefilename= plotfilename.replace('.png','.sav')
elif '.ps' in plotfilename:
savefilename= plotfilename.replace('.ps','.sav')
if not os.path.exists(savefilename):
options= setup_options(None)
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
options.fitdvt= False
rs= numpy.linspace(4.2,9.8,101)
rds= numpy.linspace(2.,3.4,8)
fhs= numpy.linspace(0.,1.,16)
surfz= numpy.zeros((len(rs),len(rds)*len(fhs)))+numpy.nan
ro= 1.
vo= 230./220.
dlnvcdlnr= 0.
zh= 400.
for jj in range(len(rds)):
for kk in range(len(fhs)):
#Setup potential to calculate stuff
potparams= numpy.array([numpy.log(rds[jj]/8.),vo,numpy.log(zh/8000.),fhs[kk],dlnvcdlnr])
try:
pot= setup_potential(potparams,options,0,returnrawpot=True)
except RuntimeError:
continue
for ii in range(len(rs)):
surfz[ii,jj*len(fhs)+kk]= 2.*integrate.quad((lambda zz: potential.evaluateDensities(rs[ii]/8.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
#Find minimum and maximum curves
minsurfz= numpy.nanmin(surfz,axis=1)
maxsurfz= numpy.nanmax(surfz,axis=1)
#Save
save_pickles(savefilename,rs,minsurfz,maxsurfz)
else:
savefile= open(savefilename,'rb')
rs= pickle.load(savefile)
minsurfz= pickle.load(savefile)
maxsurfz= pickle.load(savefile)
savefile.close()
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot([numpy.nan],[numpy.nan],'ko',
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\Sigma(R,|Z| \leq 1.1\,\mathrm{kpc})\ (M_\odot\,\mathrm{pc}^{-2})$',
xrange=[4.,10.],
yrange=[10,1050.],
semilogy=True)
pyplot.fill_between(rs,minsurfz,maxsurfz,
color='0.50')
bovy_plot.bovy_text(8.,68.,r'$\odot$',size=16.,verticalalignment='center',
horizontalalignment='center')
bovy_plot.bovy_end_print(plotfilename)
return None
def plotSkew(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
savefilename= args[0]
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
skews= pickle.load(savefile)
gaussskews= pickle.load(savefile)
type= pickle.load(savefile)
band= pickle.load(savefile)
mean= pickle.load(savefile)
taus= pickle.load(savefile)
savefile.close()
else:
parser.print_help()
return
#Accumulate
keys= skews.keys()
allskews= numpy.zeros((len(keys),len(taus)))
allgaussskews= numpy.zeros((len(keys),gaussskews[keys[0]].shape[0],
len(taus)))
for ii, key in enumerate(keys):
allskews[ii,:]= -skews[key] #go to regular definition
allgaussskews[ii,:]= -gaussskews[key]
#Statistic
q= 0.99
statistic= numpy.median
if not options.indx is None:
print "indx option not allowed"
return None
indx= numpy.all(numpy.isnan(allskews),axis=1)
allskews= allskews[True-indx,:]
allgaussskews= allgaussskews[True-indx,:,:]
#Median
medianskew= statistic(allskews,axis=0)
mediangaussskew= statistic(allgaussskews,axis=0)
#Determine 1-sigma
sigma= quantile(mediangaussskew,q=q)
#Plot
tauindx= 5
print "Showing tau %f" % (taus[tauindx]*365.25)
bovy_plot.bovy_print(fig_width=7.)
bovy_plot.bovy_hist(allskews[:,tauindx],bins=31,
color='k',normed=True,histtype='step',
xlabel=r'$\mathrm{skew}(\tau = %i\ \mathrm{days})$' % (int(taus[tauindx]*365.25)))
bovy_plot.bovy_plot([numpy.median(allskews[:,tauindx]),numpy.median(allskews[:,tauindx])],
[0.,10.],'k-',overplot=True)
bovy_plot.bovy_plot([numpy.mean(allskews[:,tauindx]),numpy.mean(allskews[:,tauindx])],
[0.,10.],'k--',overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_distanceintegral(savename,plotname,rmcenter=False,
onlygreen=False):
if os.path.exists(savename):
with open(savename,'rb') as savefile:
area= pickle.load(savefile)
else:
# For samping over the absolute magnitude distribution
iso= gaia_rc.load_iso()
Gsamples= gaia_rc.sample_Gdist(iso,n=_NGSAMPLES)
# l and b of the pixels
theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,
numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
nest=False)
cosb= numpy.sin(theta)
area= multi.parallel_map(lambda x: distanceIntegrand(\
dust._GREEN15DISTS[x],cosb,Gsamples,rmcenter,onlygreen),
range(len(dust._GREEN15DISTS)),
numcores=numpy.amin([16,
len(dust._GREEN15DISTS),
multiprocessing.cpu_count()]))
save_pickles(savename,area)
# Plot the power spectrum
if True:
psdx, psd= signal.periodogram(area*dust._GREEN15DISTS**3./numpy.sum(area*dust._GREEN15DISTS**3.),
fs=1./(dust._GREEN15DISTMODS[1]-dust._GREEN15DISTMODS[0]),
detrend=lambda x: x,scaling='spectrum')
bovy_plot.bovy_print(fig_height=3.)
matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{yfonts}"]
bovy_plot.bovy_plot(psdx[1:],psd[1:],
'k-',loglog=True,
xlabel=r'$2\pi\,k_\mu\,(\mathrm{mag}^{-1})$',
ylabel=r'$P_k$',
xrange=[0.04,4.])
bovy_plot.bovy_text(r'$\mathrm{normalized}\ D^3\,\nu_*(\mu|\theta)\,\textswab{S}(\mu)$',
bottom_left=True,size=16.)
bovy_plot.bovy_end_print(plotname)
else:
bovy_plot.bovy_print(fig_height=3.)
matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{yfonts}"]
bovy_plot.bovy_plot(dust._GREEN15DISTMODS,
area*dust._GREEN15DISTS**3.,
'k-',
xlabel=r'$\mu\,(\mathrm{mag}^{-1})$',
ylabel=r'$D^3\,\nu_*(\mu|\theta)\,\textswab{S}(\mu)$')
bovy_plot.bovy_end_print(plotname)
spl= interpolate.InterpolatedUnivariateSpline(dust._GREEN15DISTMODS,
area*dust._GREEN15DISTS**3.,
k=5)
fthder= [spl.derivatives(dm)[4] for dm in dust._GREEN15DISTMODS]
print "Simpson error= %g, volume= %g" % (0.5**4./180.*numpy.mean(numpy.fabs(fthder))/integrate.simps(area*dust._GREEN15DISTS**3.,dx=0.5),numpy.sum(area*dust._GREEN15DISTS**3.))
return None
def plot_dustnearplane(plotname,green=False):
if green: savefile= _SAVEFILE_GREEN
else: savefile= _SAVEFILE_MARSHALL
# Grid
ls= numpy.linspace(15.,70.,_NL)
bs= numpy.linspace(-2.,2.,_NB)
if not os.path.exists(savefile):
# Setup dust map
if green:
dmap= mwdust.Green15(filter='2MASS H')
else:
dmap= mwdust.Marshall06(filter='2MASS H')
plotthis= numpy.empty((_NL,_NB))
rad= 0.5 # deg
for jj in range(_NB):
print jj
for ii in range(_NL):
pa, ah= dmap.dust_vals_disk(ls[ii],bs[jj],7.,rad)
plotthis[ii,jj]= numpy.sum(pa*ah)/numpy.sum(pa)
save_pickles(savefile,plotthis)
else:
with open(savefile,'rb') as f:
plotthis= pickle.load(f)
# Now plot
bovy_plot.bovy_print(fig_width=8.4,fig_height=4.)
bovy_plot.bovy_dens2d(plotthis[::-1].T,origin='lower',cmap=cm.coolwarm,
# interpolation='nearest',
colorbar=True,shrink=0.45,
vmin=0.,vmax=2.-0.75*green,aspect=3.,
xrange=[ls[-1]+(ls[1]-ls[0])/2.,
ls[0]-(ls[1]-ls[0])/2.],
yrange=[bs[0]-(bs[1]-bs[0])/2.,
bs[-1]+(bs[1]-bs[0])/2.],
xlabel=r'$l\,\mathrm{(deg)}$',
ylabel=r'$b\,\mathrm{(deg)}$',
zlabel=r'$A_H\,(\mathrm{mag})$',
zorder=0)
bovy_plot.bovy_text(r'$D = 7\,\mathrm{kpc}$',top_left=True,
color='w',size=16.)
# Overplot fields
glons= [34.,64.,27.]
glats= [0.,0.,0.]
colors= ['w','w','y']
xs= numpy.linspace(-1.5,1.5,201)
ys= numpy.sqrt(1.5**2.-xs**2.)
for glon,glat,c in zip(glons,glats,colors):
bovy_plot.bovy_plot(xs+glon,ys+glat,'-',overplot=True,zorder=1,lw=2.,
color=c)
bovy_plot.bovy_plot(xs+glon,-ys+glat,'-',overplot=True,zorder=1,lw=2.,
color=c)
bovy_plot.bovy_end_print(plotname)
return None
def plotRdsz_single(ii,options,args):
if True:
if _NOTDONEYET:
spl= options.restart.split('.')
else:
spl= args[0].split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
savefile= open(newname,'rb')
try:
if not _NOTDONEYET:
params= pickle.load(savefile)
mlogl= pickle.load(savefile)
logl= pickle.load(savefile)
except:
return None
finally:
savefile.close()
if _NOTDONEYET:
logl[(logl == 0.)]= -numpy.finfo(numpy.dtype(numpy.float64)).max
marglogl= numpy.zeros((logl.shape[0],logl.shape[6]))
for jj in range(marglogl.shape[0]):
for kk in range(marglogl.shape[1]):
indx= True-numpy.isnan(logl[jj,0,0,:,:,:,kk,:,:,:,:].flatten())
if numpy.sum(indx) > 0:
marglogl[jj,kk]= misc.logsumexp(logl[jj,0,0,:,:,:,kk,:,:,:,:].flatten()[indx])
else:
marglogl[jj,kk]= -numpy.finfo(numpy.dtype(numpy.float64)).max
#Normalize
alogl= marglogl-numpy.amax(marglogl)
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(numpy.exp(alogl).T,
origin='lower',cmap='gist_yarg',
interpolation='nearest',
xrange=[1.5,4.5],
yrange=[numpy.log(15./220.),numpy.log(60./220.)],
xlabel=r'$R_d$',
ylabel=r'$\ln \sigma_Z / 220\ \mathrm{km\,s}^{-1}$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
return None
def plot_distanceintegral_final(plotname):
# Reload full area
with open('../savs/distInt.sav', 'rb') as savefile:
area_full = pickle.load(savefile)
# Reload full area w/o center
with open('../savs/distIntRmcenter.sav', 'rb') as savefile:
area_rmcenter = pickle.load(savefile)
# Calculate PSD of each
psdx_full, psd_full= \
signal.periodogram(area_full*dust._GREEN15DISTS**3./numpy.sum(area_full*dust._GREEN15DISTS**3.),
fs=1./(dust._GREEN15DISTMODS[1]-dust._GREEN15DISTMODS[0]),
detrend=lambda x: x,scaling='spectrum')
psdx_rmcenter, psd_rmcenter= \
signal.periodogram(area_rmcenter*dust._GREEN15DISTS**3./numpy.sum(area_rmcenter*dust._GREEN15DISTS**3.),
fs=1./(dust._GREEN15DISTMODS[1]-dust._GREEN15DISTMODS[0]),
detrend=lambda x: x,scaling='spectrum')
bovy_plot.bovy_print(fig_height=5.5)
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{yfonts}"]
line1 = bovy_plot.bovy_plot(
psdx_full[1:],
numpy.sqrt(psd_full[1:]),
'k-',
loglog=True,
xlabel=r'$\mathrm{distance\ resolution}\ k_\mu\,(\mathrm{mag}^{-1})$',
ylabel=r'$\mathrm{effective\ volume\ error}\ \sqrt{P_k}$',
xrange=[0.04, 20.],
yrange=[10**-11., 5.])
line2 = bovy_plot.bovy_plot(psdx_rmcenter[1:],
numpy.sqrt(psd_rmcenter[1:]),
'r-',
overplot=True)
bovy_plot.bovy_plot([1., 10.], [6. * 10.**-4., 6. * 10.**-7.],
'k--',
overplot=True)
bovy_plot.bovy_plot([1., 10.], [2. * 10.**-5., 2. * 10.**-10.],
'r--',
overplot=True)
pyplot.legend(
(line1[0], line2[0]),
(r'$\mathrm{full\ sky}$',
r'$\mathrm{excluding}\ |180^\circ-l| > 155^\circ, |b| < 25^\circ$'),
loc='upper right', #bbox_to_anchor=(.02,.02),
numpoints=8,
prop={'size': 14},
frameon=False)
bovy_plot.bovy_text(
r'$\mathrm{normalized}\ D^3\,\nu_*(\mu|\theta)\,\textswab{S}(\mu)$',
bottom_left=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def testErrs(options,args):
ndfehs, ndafes= 201,201
dfehs= numpy.linspace(0.01,0.4,ndfehs)
dafes= numpy.linspace(0.01,0.3,ndafes)
if os.path.exists(args[0]):
savefile= open(args[0],'rb')
loglike= pickle.load(savefile)
ii= pickle.load(savefile)
jj= pickle.load(savefile)
savefile.close()
else:
loglike= numpy.zeros((ndfehs,ndafes))
ii, jj= 0, 0
while ii < ndfehs:
while jj < ndafes:
sys.stdout.write('\r'+"Working on %i / %i" %(ii*ndafes+jj+1,ndafes*ndfehs))
sys.stdout.flush()
loglike[ii,jj]= errsLogLike(dfehs[ii],dafes[jj],options)
jj+= 1
ii+= 1
jj= 0
save_pickles(args[0],loglike,ii,jj)
save_pickles(args[0],loglike,ii,jj)
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
if options.prior:
prior= numpy.zeros((ndfehs,ndafes))
for ii in range(ndfehs):
prior[ii,:]= -0.5*(dafes-0.1)**2./0.1**2.-0.5*(dfehs[ii]-0.2)**2./0.1**2.
loglike+= prior
loglike-= maxentropy.logsumexp(loglike)
loglike= numpy.exp(loglike)
loglike/= numpy.sum(loglike)*(dfehs[1]-dfehs[0])*(dafes[1]-dafes[0])
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(loglike.T,origin='lower',
cmap='gist_yarg',
xlabel=r'\delta_{[\mathrm{Fe/H}]}',
ylabel=r'\delta_{[\alpha/\mathrm{Fe}]}',
xrange=[dfehs[0],dfehs[-1]],
yrange=[dafes[0],dafes[-1]],
contours=True,
cntrmass=True,
onedhists=True,
levels= special.erf(0.5*numpy.arange(1,4)))
if options.prior:
bovy_plot.bovy_text(r'$\mathrm{with\ Gaussian\ prior:}$'+
'\n'+r'$\delta_{[\mathrm{Fe/H}]}= 0.2 \pm 0.1$'
+'\n'+r'$\delta_{[\alpha/\mathrm{Fe}]}= 0.1 \pm 0.1$',
top_right=True)
bovy_plot.bovy_end_print(options.plotfile)
def plotXD(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
savefile = open(args[0], 'rb')
xamp = pickle.load(savefile)
xmean = pickle.load(savefile)
xcovar = pickle.load(savefile)
savefile.close()
else:
print args[0] + " does not exist ..."
print "Returning ..."
return
#Load XD object in xdtarget
xdt = xdtarget.xdtarget(amp=xamp, mean=xmean, covar=xcovar)
out = xdt.sample(nsample=options.nsamples)
#Prepare for plotting
if options.expd1: xs = nu.exp(out[:, options.d1])
elif not options.divided1 is None:
xs = out[:, options.d1] / options.divided1
else:
xs = out[:, options.d1]
if options.expd2: ys = nu.exp(out[:, options.d2])
elif not options.divided2 is None:
ys = out[:, options.d2] / options.divided2
else:
ys = out[:, options.d2]
if options.type == 'DRW':
#plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
#Convert to logA
xs = (nu.log(2.) + xs + nu.log(1. - nu.exp(-1. / nu.exp(ys)))) / 2.
elif options.d1 == 1 and options.d2 == 0:
#Convert to logA
ys = (nu.log(2.) + ys + nu.log(1. - nu.exp(-1. / nu.exp(xs)))) / 2.
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
xrange=[options.xmin, options.xmax],
yrange=[options.ymin, options.ymax],
xlabel=options.xlabel,
ylabel=options.ylabel)
bovy_plot.bovy_end_print(options.plotfilename)
def plot_distanceareaintegral(savename,
plotname,
rmcenter=False,
onlygreen=False):
if os.path.exists(savename):
with open(savename, 'rb') as savefile:
area = pickle.load(savefile)
else:
# For samping over the absolute magnitude distribution
iso = gaia_rc.load_iso()
Gsamples = gaia_rc.sample_Gdist(iso, n=_NGSAMPLES)
# l and b of the pixels
theta, phi = healpy.pixelfunc.pix2ang(
_NSIDE,
numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
nest=False)
cosb = numpy.sin(theta)
area= multi.parallel_map(lambda x: distanceAreaIntegrand(\
dust._GREEN15DISTS[x],cosb,Gsamples,rmcenter,onlygreen),
range(len(dust._GREEN15DISTS)),
numcores=numpy.amin([16,
len(dust._GREEN15DISTS),
multiprocessing.cpu_count()]))
save_pickles(savename, area)
# Plot the power spectrum
area = numpy.array(area)
if True:
psdthis = ((area.T * dust._GREEN15DISTS**3.).T / numpy.sum(
(area.T * dust._GREEN15DISTS**3.), axis=1))
psdx, psd = signal.periodogram(
psdthis,
fs=1. / (dust._GREEN15DISTMODS[1] - dust._GREEN15DISTMODS[0]),
detrend=lambda x: x,
scaling='spectrum',
axis=0)
bovy_plot.bovy_print(fig_height=3.)
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{yfonts}"]
healpy.visufunc.mollview(numpy.log10(psd[-2]),
nest=False,
xsize=4000,
min=-10.,
max=-3.,
cmap='gist_yarg',
title="")
bovy_plot.bovy_end_print(plotname)
return None
def checkAbundanceScatterClusters():
# First read the cluster data
cldata = read_clusterdata.read_caldata()
# Read the allStar data to match
# For each of the calibration open clusters, calculate the offset from the
# mean in our FEHTAG and AFETAG
clusters = [
'M71', 'N2158', 'N2420', 'N188', 'M67', 'N7789', 'N6819', 'N6791'
]
fehoffset = []
afeoffset = []
for cluster in clusters:
tdata = cldata[cldata['CLUSTER'] == cluster.upper()]
tdata= tdata[(tdata['TEFF'] < _TEFFMAX)\
*(tdata['TEFF'] > _TEFFMIN)\
*(tdata['LOGG'] < 3.5)]
# Compute the average feh and afe and save the offsets
medianfeh = numpy.median(tdata['FE_H'])
medianafe = numpy.median(tdata[define_rcsample._AFETAG])
fehoffset.extend(tdata['FE_H'] - medianfeh)
afeoffset.extend(tdata[define_rcsample._AFETAG] - medianafe)
if cluster == 'M67': print medianfeh, medianafe, len(tdata)
fehoffset = numpy.array(fehoffset)
afeoffset = numpy.array(afeoffset)
print 'FE_H scatter %g' % (numpy.nanstd(
fehoffset[numpy.fabs(fehoffset) < 0.3]))
print 'A_FE scatter %g' % (numpy.nanstd(
afeoffset[numpy.fabs(afeoffset) < 0.3]))
gindx = (numpy.fabs(fehoffset) < 0.3) * (numpy.fabs(afeoffset) < 0.3)
print 'FE_H/A_FE correlation %g' % (
numpy.mean(afeoffset[gindx] * fehoffset[gindx]) /
numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3]) /
numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
print 'FE_H robust scatter %g' % (1.4826 *
numpy.median(numpy.fabs(fehoffset)))
print 'A_FE robust scatter %g' % (1.4826 *
numpy.median(numpy.fabs(afeoffset)))
bovy_plot.bovy_print()
bovy_plot.bovy_hist(fehoffset, range=[-0.3, 0.3], bins=31, histtype='step')
bovy_plot.bovy_hist(afeoffset,
range=[-0.3, 0.3],
bins=31,
histtype='step',
overplot=True)
bovy_plot.bovy_end_print('test.png')
return None
def plot_dustinfield(plotname, field, threshold=False):
savefile = _SAVEFILE_MARSHALL
savefile = savefile.replace('FIELD', '%i' % field)
# Grid
ls = numpy.linspace(-1.75, 1.75, _NL) + field
bs = numpy.linspace(-1.75, 1.75, _NB)
if not os.path.exists(savefile):
# Setup dust map
dmap = mwdust.Marshall06(filter='2MASS H')
plotthis = numpy.empty((_NL, _NB))
for jj in range(_NB):
print jj
for ii in range(_NL):
plotthis[ii, jj] = dmap(ls[ii], bs[jj], 7.)
save_pickles(savefile, plotthis)
else:
with open(savefile, 'rb') as f:
plotthis = pickle.load(f)
# Remove outside the field
tls = numpy.tile(ls, (_NB, 1)).T
tbs = numpy.tile(bs, (_NL, 1))
plotthis[(tls - field)**2. + tbs**2. > 1.5**2.] = numpy.nan
if threshold:
plotthis[plotthis > 1.4] = numpy.nan
# Now plot
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(
plotthis[::-1].T,
origin='lower',
cmap=cm.coolwarm,
interpolation='nearest',
vmin=0.,
vmax=2.,
aspect=1.,
xrange=[ls[-1] + (ls[1] - ls[0]) / 2., ls[0] - (ls[1] - ls[0]) / 2.],
yrange=[bs[0] - (bs[1] - bs[0]) / 2., bs[-1] + (bs[1] - bs[0]) / 2.],
xlabel=r'$l\,\mathrm{(deg)}$',
ylabel=r'$b\,\mathrm{(deg)}$',
zlabel=r'$A_H\,(\mathrm{mag})$',
zorder=0)
bovy_plot.bovy_text(r'$(l,b) = (%i,0)$' % field,
top_left=True,
color='k',
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def plot_maphz(plotname):
# Load the two fit
with open('../mapfits/tribrokentwoexp.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthisz1 = numpy.zeros(len(bf)) + numpy.nan
plotthisz1e = numpy.zeros(len(bf)) + numpy.nan
plotthisz2 = numpy.zeros(len(bf)) + numpy.nan
plotthisz2e = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
hzindx= (True-numpy.isnan(samples[ii,4]))\
*(True-numpy.isnan(samples[ii,5]))\
*(densprofiles.ilogit(samples[ii,4]) > 0.15)
tmed = numpy.median(1. / samples[ii, 1, hzindx])
terr = numpy.std(1. / samples[ii, 1, hzindx])
plotthisz1[ii] = tmed
plotthisz1e[ii] = terr
tmed = numpy.median(1. / samples[ii, 5, hzindx])
terr = numpy.std(1. / samples[ii, 5, hzindx])
plotthisz2[ii] = tmed
plotthisz2e[ii] = terr
plotthisz1[plotthisz1e / plotthisz1 > 0.5] = numpy.nan
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
plotthisz2 * 1000.,
plotthisz1 * 1000.,
'ko',
xrange=[0., 1200.],
yrange=[0., 1200.],
xlabel=r'$2^\mathrm{nd}\ \mathrm{scale\ height\,(pc)}$',
ylabel=r'$1^\mathrm{st}\ \mathrm{scale\ height\,(pc)}$',
zorder=2)
bovy_plot.bovy_plot([0, 1200], [0, 1200], 'k--', overplot=True, lw=2.)
pyplot.errorbar(plotthisz2 * 1000.,
plotthisz1 * 1000.,
yerr=plotthisz1e * 1000.,
marker='o',
color='k',
ls='none',
zorder=1)
bovy_plot.bovy_end_print(plotname)
return None
def plot_dustwapogee(plotname):
# Load the dust map
green15map = dust.load_green15(5., nest=True, nside_out=_NSIDE)
green15map[green15map == healpy.UNSEEN] = -1.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,
min=0.,
max=.8,
format=r'$%g$',
title='',
cmap='gist_yarg',
unit='$A_H\,(\mathrm{mag})$')
# Load the RC data to get the fields
data = define_rcsample.get_rcsample()
loc_ids = numpy.array(list(set(data['LOCATION_ID'])))
# Load the selection function, just to get the field centers
apo = apogee.select.apogeeSelect(_justprocessobslog=True)
theta = numpy.empty(len(loc_ids))
phi = numpy.empty(len(loc_ids))
for ii, loc_id in enumerate(loc_ids):
tl, tb = apo.glonGlat(loc_id)
theta[ii] = (90. - tb) / 180. * numpy.pi
phi[ii] = tl / 180. * numpy.pi
hib = numpy.fabs((numpy.pi / 2. - theta)) > (8. / 180. * numpy.pi)
healpy.visufunc.projplot(theta[hib],
phi[hib],
'o',
ms=5.,
mfc='none',
mew=0.8,
mec='k')
lowb = True - hib
healpy.visufunc.projplot(theta[lowb],
phi[lowb],
'o',
ms=5.,
mfc='none',
mec='w',
mew=0.8)
bovy_plot.bovy_end_print(plotname)
def plot_rotcurves(plotfilename):
rs= numpy.linspace(3.5,16.,1001)
#1: flat
vo,ro= 0.92772766, 1.00939298
vflat= rs**0.*vo*_REFV0
#2: power-law
vo, ro, beta= 0.92881531, 1.0009845, 0.00750639
vpl= vo*(rs/_REFR0/ro)**beta*_REFV0
#3: linear
vo, ro, dvdr= 0.93050113, 1.00377579, 0.01625223
vlinear= (vo+dvdr*(rs/_REFR0-1.))*_REFV0
#4: quadratic
vo, ro, dvdr, d2vdr2= 0.92954738, 1.00440273, 0.02909439, 0.25129582
vquadratic= (vo+dvdr*(rs/_REFR0-1.)+d2vdr2*(rs/_REFR0-1.)**2.)*_REFV0
#5: cubic
vo, ro, dvdr, d2vdr2, d3vdr3= 0.9416574, 1.01691786, -0.10708624, -0.07733835, 0.42241186
vcubic= (vo+dvdr*(rs/_REFR0-1.)+d2vdr2*(rs/_REFR0-1.)**2.\
+d3vdr3*(rs/_REFR0-1.)**3.)*_REFV0
#Plot all
bovy_plot.bovy_print(fig_width=8.)
bovy_plot.bovy_plot(rs,vflat,'k-',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\ [\mathrm{km\ s}^{-1}]$',
xrange=[0.,20.],
yrange=[150.,300.])
bovy_plot.bovy_plot(rs,vpl,'k--',overplot=True)
bovy_plot.bovy_plot(rs,vlinear,'k-.',overplot=True)
bovy_plot.bovy_plot(rs,vquadratic,'k:',overplot=True)
bovy_plot.bovy_plot(rs,vcubic,'k-.',overplot=True,color='red')
if _PLOTM31:
#Read file
m31data= numpy.loadtxt('../data/m31.dat',comments='#')
rm31= m31data[:,0]
vcm31= m31data[:,1]
bovy_plot.bovy_plot(rm31,vcm31,'ks',overplot=True,mfc='none',mew=2.)
bovy_plot.bovy_text(17.,260.,r'$\mathrm{M31}$',size=14.)
indx= (rm31 > 15.2)*(rm31 <= 16.8)
bovy_plot.bovy_plot([17.,rm31[indx]],[260.,vcm31[indx]],'k-',
overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_spatial_broad(plotname):
# Load subsamples, plot
load_funcs = [
define_rcsample.get_lowlowsample, define_rcsample.get_highalphasample,
define_rcsample.get_solarsample, define_rcsample.get_highfehsample
]
names = ['lowlow', 'highalpha', 'solar', 'highfeh']
labels = [
r'$\mathrm{low\ [Fe/H]}$', r'$\mathrm{high}\ [\alpha/\mathrm{Fe}]$',
r'$\mathrm{solar}$', r'$\mathrm{high\ [Fe/H]}$'
]
for ii in range(4):
data = load_funcs[ii]()
if ii == 1:
ylabel = r'$Z\,(\mathrm{kpc})$'
else:
ylabel = ' '
bovy_plot.bovy_print(axes_labelsize=24,
text_fontsize=24,
xtick_labelsize=24,
ytick_labelsize=24)
bovy_plot.bovy_plot(data['RC_GALR_H'],
data['RC_GALZ_H'],
'k.',
ms=2.,
xrange=[0., 16.],
yrange=[-3., 3.],
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=ylabel,
onedhists=True,
bins=31)
if ii != 1:
bovy_plot.bovy_text(labels[ii], top_left=True, size=24)
else:
bovy_plot.bovy_text(labels[ii], bottom_left=True, size=24)
bovy_plot.bovy_end_print(plotname.replace('SAMPLE', names[ii]))
return None
def plot_data_feh(location=0,
plotfilename=os.path.join(OUTDIR, 'data_h_jk.' + OUTEXT)):
data = readVclosData()
#Good feh
data = data[(data['FEH'] != -9999.00)]
if not location is None:
if location == 0:
locs = set(data['LOCATION'])
for l in locs:
plot_data_feh(location=l,
plotfilename=os.path.join(
OUTDIR, 'data_feh_%i.' % l + OUTEXT))
return None
data = data[(data['LOCATION'] == location)]
meanfeh = numpy.mean(data['FEH'])
sigfeh = numpy.std(data['FEH'])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data['FEH'],
xlabel=r'$[\mathrm{Fe/H}]$',
xrange=[meanfeh - 1., meanfeh + 1],
bins=16)
bovy_plot.bovy_text(r'$\sigma = %.2f$' % sigfeh, top_right=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def triangleMAPs(savefilename, basename):
with open(savefilename, 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
bf_g15 = numpy.array(pickle.load(savefile))
samples_g15 = numpy.array(pickle.load(savefile))
bf_zero = numpy.array(pickle.load(savefile))
samples_zero = numpy.array(pickle.load(savefile))
labels = []
for jj in range(samples.shape[2]):
labels.append(r"$\mathrm{param}\ %i$" % jj)
maps = define_rcsample.MAPs()
for ii, map in enumerate(maps.map()):
if ii >= len(bf): break
tfeh = numpy.nanmedian(map['FE_H'])
tafe = numpy.nanmedian(map[define_rcsample._AFETAG])
for tbf, tsamples, ext in zip([bf, bf_g15, bf_zero],
[samples, samples_g15, samples_zero],
['fid', 'g15', 'zero']):
try:
triangle.corner(tsamples[ii, ].T,
quantiles=[0.16, 0.5, 0.84],
labels=labels,
show_titles=True,
title_args={"fontsize": 12},
bins=21)
except ValueError:
pass
else:
bovy_plot.bovy_text(r'$[\mathrm{{Fe/H}}] = {feh:.1f},$'\
.format(feh=tfeh)+'\n'
+r'$[\alpha/\mathrm{{Fe}}] = {afe:.2f}$'\
.format(afe=tafe),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basename + "_%i_%s.png" % (ii, ext))
return None
def plot_mapflare(plotname):
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexpinvlinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_invlin = numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexplinflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples_brexp_lin = numpy.array(pickle.load(savefile))
plotmaps = [19, 26, 32, 39, 45]
bovy_plot.bovy_print(fig_width=8., fig_height=9. * 3.99 / 8.98)
maps = define_rcsample.MAPs()
cmap = cm.coolwarm
overplot = False
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all flaring profiles
#Rmin= numpy.sort(map['RC_GALR_H'])[int(round(0.005*len(map)))]
#Rmax= numpy.sort(map['RC_GALR_H'])[numpy.amin([len(map)-1,int(round(0.995*len(map)))])]
Rs = numpy.linspace(4., 14., 1001)
samples = samples_brexp[ii, :, ::_SKIP]
samples_invlin = samples_brexp_invlin[ii, :, ::_SKIP]
samples_lin = samples_brexp_lin[ii, :, ::_SKIP]
nsamples = len(samples[0])
tRs = numpy.tile(Rs, (nsamples, 1)).T
ldp = numpy.empty((len(Rs), nsamples))
ldp = samples[1] * numpy.exp(samples[4] * (tRs - densprofiles._R0))
ldp_invlin= samples_invlin[1]\
*(1.+samples_invlin[4]*(tRs-densprofiles._R0)*(numpy.exp(1.)-1.))
ldp_lin= samples_lin[1]\
/(1.-(tRs-densprofiles._R0)*samples_lin[4]*(numpy.exp(1.)-1.))
ldp = 1000. / ldp # make it hz instead of its inverse
ldp_invlin = 1000. / ldp_invlin
ldp_lin = 1000. / ldp_lin
# Label and relative normalization
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.0: tfeh = 0.0
offset = 10.**(-6. * (tfeh + 0.1)) - 0.4
if tfeh < -0.1: offset /= 1.5
if tfeh < -0.2: offset /= 1.15
print ii, tfeh, len(map), offset
bovy_plot.bovy_plot(
Rs,
numpy.median(ldp, axis=1) * offset,
'-',
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05),
lw=2.,
overplot=overplot,
ylabel=r'$h_Z\,(\mathrm{pc})\times\mathrm{constant}$',
xrange=[0., 16.],
yrange=[10.**2., 10**5.99],
zorder=10 + ii,
semilogy=True)
#skipped because the median for the other profiles are indistinguishable for that with exponential flaring
"""
pyplot.fill_between(Rs,
numpy.median(ldp_lin,axis=1)*offset,
numpy.median(ldp_invlin,axis=1)*offset,
color='0.65',
lw=0.,zorder=ii-1)
"""
pyplot.fill_between(
Rs,
numpy.sort(ldp, axis=1)[:, int(round(_SIGNIF * nsamples))] *
offset,
numpy.sort(ldp, axis=1)[:,
int(round(
(1. - _SIGNIF) * nsamples))] * offset,
color=cmap((tfeh + 0.5) / 0.4),
lw=0.,
zorder=ii)
line, = pyplot.plot(Rs,
Rs * 0. + 300. * offset,
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05),
ls='--',
lw=2. * 0.8,
zorder=ii + 5)
line.set_dashes([8, 6])
overplot = True
if ii == 19:
bovy_plot.bovy_text(2.,
10.**5.6,
r'$[\mathrm{Fe/H}]$',
size=16.,
color='k')
bovy_plot.bovy_text(2.,
numpy.median(ldp, axis=1)[0] * offset,
r'$%+.1f$' % tfeh,
size=16.,
color=cmap((tfeh + 0.5) * 0.95 / 0.5 + 0.05))
bovy_plot.bovy_text(10.,
10.**5.6,
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
bovy_plot.bovy_end_print(plotname)
plt.subplot(2, 3, 6)
bovy_plot.bovy_plot(numpy.linspace(0., 1., nstep) * nstep * step,
numpy.sum(Q, axis=1),
lw=3.,
loglog=True,
xlabel=r'$t$',
ylabel=r'$\chi^2$',
gcf=True,
yrange=[1., 10**7.0])
#gca().yaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
#gca().xaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
plt.tight_layout()
if Add_perturbation == True:
bovy_plot.bovy_end_print('m2m_results_perturbed_mock.jpg')
else:
bovy_plot.bovy_end_print('m2m_results_stable_mock.jpg')
### Save the results in a file
if Add_perturbation == True:
savefilename = 'm2m_results_perturbed_mock.sav'
else:
savefilename = 'm2m_results_stable_mock.sav'
save_pickles(savefilename,w_out,omega_out,xnm_out,z_m2m,vz_m2m,zsun_true,
vzsun_true,data_dicts,z_mock,vz_mock,v_obs,v_obs_noise, \
w_init,h_m2m,omega_m2m,xnm_m2m,zsun_m2m,\
dens_init,v2_init,v_init,\
h_obs,xnm_true,omegadm_true,totmass_true,zh_true,sigma_true,
nstep,step,tdyn,skipomega,skipxnm,dttdyn,eps,Q,wevol,windx)
def plot_nonaxi(parser):
(options, args) = parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
cutmultiples=options.cutmultiples,
jkmax=options.jkmax)
#data= data[0:20]
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
indx = (data['GLON'] <= 30.)
data['GLON'][
indx] = 30.05 #Hack because the non-axi models don't go below Ro/2
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
elif options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso = [
iso,
isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
dwarf=True,
expsfh=options.expsfh)
]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, 'wb')
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpisodwarf = None
""" #Does not matter anyway
#clean logpiso
dataindx= []
for ii in range(len(data)):
thislogpiso= logpiso[ii,:]-logsumexp(logpiso[ii,:])
if numpy.all(thislogpiso == 0.): dataindx.append(False)
else: dataindx.append(True)
dataindx= numpy.array(dataindx,dtype='bool')
data= data[dataindx]
logpiso= logpiso[dataindx,:]
logpisodwarf= logpisodwarf[dataindx,:]
"""
#Calculate data means etc.
#Calculate means
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
avg_plate = numpy.zeros(nlocs)
sig_plate = numpy.zeros(nlocs)
siga_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
avg_plate[ii] = numpy.mean(data['VHELIO'][indx])
sig_plate[ii] = numpy.std(data['VHELIO'][indx])
siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt(
numpy.sum(indx))
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
savefile.close()
#First calculate fiducial model
if not options.dwarf:
logpisodwarf = None
avg_plate_model_fid = calc_model(params, options, data, logpiso,
logpisodwarf, df, nlocs, locations, iso)
print l_plate, avg_plate_model_fid
#Plot everything
bovy_plot.bovy_print(fig_height=6., fig_width=7.)
dx = 0.8 / 5.
left, bottom, width, height = 0.1, 0.9 - dx, 0.8, dx
axTop = pyplot.axes([left, bottom, width, height])
allaxes = [axTop]
fig = pyplot.gcf()
fig.sca(axTop)
bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{fiducial}$', top_right=True, size=14.)
thisax = pyplot.gca()
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Second is bar
print "Reading bar file ..."
barfile = '/work/bovy/data/bovy/nonaximw/bar/bar_rect_vr_tform-150_tsteady-4pi_51_101.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 2. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{bar}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Third is spiral
print "Reading spiral file ..."
barfile = '/work/bovy/data/bovy/nonaximw/spiral/spiral_rect_vr_51_101.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 3. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{spiral}$', top_right=True, size=14.)
pyplot.ylabel(
r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}} \ [\mathrm{km\ s}^{-1}]$'
)
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
#Fourth is first elliptical
print "Reading elliptical file ..."
barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 4. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Fourth is first elliptical
print "Reading elliptical file ..."
barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, -45. * _DEGTORAD)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 5. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#thisax.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
def snapshotToMovie(snap, filename, *args, **kwargs):
"""
NAME:
snapshotToMovie
PURPOSE:
turn a list of snapshots into a movie
INPUT:
snap - the snapshots (list)
filename - name of the file to save the movie to
framerate= in fps
bitrate= ?
thumbnail=False : create thumbnail image (filename-extension+.jpg)
thumbsize= size of thumbnail
+Snapshot.plot args and kwargs
OUTPUT:
movie is saved to file
DEPENDENCIES:
this procedure uses ffmpeg and convert
BUGS:
matplotlib's 'Agg' backend has a memory leak that prevents it from
creating hundred's of figures. It is recommended to call
import matplotlib
matplotlib.use('PDF')
at the beginning of the movie creating script as the PDF backend does
not have the same memory leak.
HISTORY:
2011-02-06 - Written - Bovy (NYU)
"""
if kwargs.has_key('tmpdir'):
tmpdir = kwargs['tmpdir']
kwargs.pop('tmpdir')
else:
tmpdir = '/tmp'
if kwargs.has_key('framerate'):
framerate = kwargs['framerate']
kwargs.pop('framerate')
else:
framerate = 25
if kwargs.has_key('bitrate'):
bitrate = kwargs['bitrate']
kwargs.pop('bitrate')
else:
bitrate = 1000
if kwargs.has_key('thumbnail') and kwargs['thumbnail']:
thumbnail = True
kwargs.pop('thumbnail')
elif kwargs.has_key('thumbnail'):
kwargs.pop('thumbnail')
thumbnail = False
else:
thumbnail = False
if kwargs.has_key('thumbsize'):
thumbsize = kwargs['thumbsize']
else:
thumbsize = 300
#Create all of the files
tempdir = tempfile.mkdtemp(dir=tmpdir) #Temporary directory
tmpfiles = []
nsnap = len(snap)
file_length = int(m.ceil(m.log10(nsnap)))
#Determine good xrange BOVY TO DO
if not kwargs.has_key('xrange'):
pass
if not kwargs.has_key('yrange'):
pass
for ii in range(nsnap):
tmpfiles.append(os.path.join(tempdir, str(ii).zfill(file_length)))
bovy_plot.bovy_print()
snap[ii].plot(*args, **kwargs)
bovy_plot.bovy_end_print(tmpfiles[ii] + '.pdf')
#Convert to jpeg
try:
subprocess.check_call(
['convert', tmpfiles[ii] + '.pdf', tmpfiles[ii] + '.jpg'])
except subprocess.CalledProcessError:
print("'convert' failed")
raise subprocess.CalledProcessError
#turn them into a movie
try:
subprocess.check_call([
'ffmpeg', '-r',
str(framerate), '-b',
str(bitrate), '-i',
os.path.join(tempdir, '%' + '0%id.jpg' % file_length), '-y',
filename
])
if thumbnail:
thumbnameTemp = re.split(r'\.', filename)
thumbnameTemp = thumbnameTemp[0:len(thumbnameTemp) - 1]
thumbname = ''
for t in thumbnameTemp:
thumbname += t
thumbname += '.jpg'
subprocess.check_call([
'ffmpeg', '-itsoffset', '-4', '-y', '-i', filename, '-vcodec',
'mjpeg', '-vframes', '1', '-an', '-f', 'rawvideo', '-s',
'%ix%i' % (thumbsize, thumbsize), thumbname
])
except subprocess.CalledProcessError:
print("'ffmpeg' failed")
_cleanupMovieTempdir(tempdir)
raise subprocess.CalledProcessError
finally:
_cleanupMovieTempdir(tempdir)
def singleBandSampleExample(
datafilename='../data/SDSSJ203817.37+003029.8.fits',
band='g',
nsamples=1000,
basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
singleBandSampleExample
PURPOSE:
Sample an example of a power-law structure function GP covariance
function to an SDSS quasar
INPUT:
datafilename
band - band to fit
nsamples - number of samples to use
basefilename
OUTPUT:
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file = fu.table_fields(datafilename)
if band == 'u':
mjd_g = nu.array(file.mjd_u) / 365.25
g = nu.array(file.u)
err_g = nu.array(file.err_u)
elif band == 'g':
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
elif band == 'r':
mjd_g = nu.array(file.mjd_r) / 365.25
g = nu.array(file.r)
err_g = nu.array(file.err_r)
elif band == 'i':
mjd_g = nu.array(file.mjd_i) / 365.25
g = nu.array(file.i)
err_g = nu.array(file.err_i)
elif band == 'z':
mjd_g = nu.array(file.mjd_z) / 365.25
g = nu.array(file.z)
err_g = nu.array(file.err_z)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mjd_i = nu.array(file.mjd_i) / 365.25
i = nu.array(file.i)
err_i = nu.array(file.err_i)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7)
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
i = i[mask]
i -= nu.mean(i)
err_i = err_i[mask]
mjd_i = mjd_i[mask]
mjd_i -= nu.amin(mjd_i)
meanErr_i = nu.mean(err_i)
savefilename = basefilename + '.sav'
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
gammas = pickle.load(savefile)
if 'gr' in basefilename:
logGammas = pickle.load(savefile)
gammagrs = pickle.load(savefile)
logGammagrs = pickle.load(savefile)
else:
logAs = pickle.load(savefile)
out = pickle.load(savefile)
savefile.close()
else:
if 'gri' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listx.extend([(t, 'i') for t in mjd_i])
listy = [m for m in g]
listy.extend([m for m in r])
listy.extend([m for m in i])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise.extend([m for m in err_i])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
elif 'gr' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
else:
from gp.powerlawSF import covarFunc
trainSet = trainingSet(listx=mjd_g, listy=g, noise=err_g)
params = [0., 0.]
SF = covarFunc(gamma=.2, A=0.000524) #Power
#SF= covarFunc(a=.0001,l=.001) #OU
#Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
print "Sampling ..."
out = sampleGP(trainSet,
outcovarFunc,
nsamples=nsamples,
step=[0.2 for ii in range(len(params))])
gammas = nu.array([o._dict['gamma'] for o in out]).reshape(len(out))
if 'gr' in basefilename:
gammagrs = nu.array([o._dict['gammagr']
for o in out]).reshape(len(out))
logGammas = nu.array([o._dict['logGamma']
for o in out]).reshape(len(out))
logGammagrs = nu.array([o._dict['logGammagr']
for o in out]).reshape(len(out))
else:
logAs = nu.array([o._dict['logA'] for o in out]).reshape(len(out))
print nu.mean(logAs), nu.sqrt(nu.var(logAs))
print nu.mean(gammas), nu.sqrt(nu.var(gammas))
#Save
savefile = open(savefilename, 'wb')
pickle.dump(gammas, savefile)
if 'gr' in basefilename:
pickle.dump(logGammas, savefile)
pickle.dump(gammagrs, savefile)
pickle.dump(logGammagrs, savefile)
else:
pickle.dump(logAs, savefile)
pickle.dump(out, savefile)
savefile.close()
#if not 'gr' in basefilename:
# logGammas= logAs/gammas
#Plot
bovy_plot.bovy_print()
bovy_plot.scatterplot(logAs / 2.,
gammas,
'k,',
ylabel=r'$\gamma$',
xlabel=r'\log A',
xrange=[-9.21 / 2., 0.],
yrange=[0., 1.25],
bins=50,
onedhists=True)
bovy_plot.bovy_end_print(basefilename + '_sample_2d.png')
def plot_mapsurfdens(plotname):
with open('../mapfits/tribrokenexpflare.sav','rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples_brexp= numpy.array(pickle.load(savefile))
plotmaps= [19,26,32,39,45]
bovy_plot.bovy_print(fig_width=8.,fig_height=3.)
maps= define_rcsample.MAPs()
cmap= cm.coolwarm
overplot= False
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
# Create all density profiles
Rs= numpy.linspace(4.,14.,1001)
samples= samples_brexp[ii,:,::_SKIP]
nsamples= len(samples[0])
tRs= numpy.tile(Rs,(nsamples,1)).T
ldp= numpy.empty((len(Rs),nsamples))
Rb= numpy.tile(numpy.exp(samples[3]),(len(Rs),1))
ihRin= numpy.tile(samples[0],(len(Rs),1))
ihRout= numpy.tile(samples[2],(len(Rs),1))
# Rb >= R0
leRb= (tRs <= Rb)*(Rb >= densprofiles._R0)
ldp[leRb]= ihRin[leRb]*(tRs[leRb]-densprofiles._R0)
gtRb= (tRs > Rb)*(Rb >= densprofiles._R0)
ldp[gtRb]= -ihRout[gtRb]*(tRs[gtRb]-densprofiles._R0)\
+ihRout[gtRb]*(Rb[gtRb]-densprofiles._R0)\
+ihRin[gtRb]*(Rb[gtRb]-densprofiles._R0)
# Rb < R0, normalize outer at R0
leRb= (tRs <= Rb)*(Rb < densprofiles._R0)
ldp[leRb]= ihRin[leRb]*(tRs[leRb]-densprofiles._R0)\
-ihRout[leRb]*(Rb[leRb]-densprofiles._R0)\
-ihRin[leRb]*(Rb[leRb]-densprofiles._R0)
gtRb= (tRs > Rb)*(Rb < densprofiles._R0)
ldp[gtRb]= -ihRout[gtRb]*(tRs[gtRb]-densprofiles._R0)
# Label and relative normalization
tfeh= round(numpy.median(map['FE_H'])*20.)/20.
if tfeh == 0.25: tfeh= 0.3
if tfeh == -0.1: tfeh= -0.1
print ii, tfeh, len(map)
anorm= 10**(-10.*(-tfeh-0.5))
#if (-tfeh-0.4) > 0.3: anorm= 10**(-12.*(-tfeh+0.4))
if (-tfeh-0.5) == -0.2: anorm= 10**(-11.*(-tfeh-0.5))
anorm= 1./anorm # re-order
anorm/= 3.
norm= numpy.exp(numpy.median(ldp,axis=1))[numpy.argmin(numpy.fabs(Rs-densprofiles._R0))]/anorm
bovy_plot.bovy_plot(Rs,numpy.exp(numpy.median(ldp,axis=1))/norm,
'-',
color=cmap((tfeh+0.5)*0.95/0.5+0.05),
lw=2.,overplot=overplot,
ylabel=r'$\Sigma(R)\times\mathrm{constant}$',
xrange=[0.,16.],
yrange=[0.000001,90.],
semilogy=True)
pyplot.fill_between(Rs,
numpy.exp(numpy.sort(ldp,axis=1)[:,int(round(_SIGNIF*nsamples))])/norm,
numpy.exp(numpy.sort(ldp,axis=1)[:,int(round((1.-_SIGNIF)*nsamples))])/norm,
color=cmap((tfeh+0.5)/0.4),
lw=0.)
overplot= True
if ii == 19:
bovy_plot.bovy_text(2.,
10.**1.,
r'$[\mathrm{Fe/H}]$',size=16.,color='k')
bovy_plot.bovy_text(2.,(numpy.exp(numpy.median(ldp,axis=1))/norm)[0],
r'$%+.1f$' % tfeh,size=16.,
color=cmap((tfeh+0.5)*0.95/0.5+0.05))
bovy_plot.bovy_text(10.,10.**1.,
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
size=16.)
pyplot.gca().tick_params(axis='y',which='minor',left='off',right='off')
bovy_plot.bovy_end_print(plotname)
def plot_vpecvo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
if _ROTCURVE == 'flat':
vpec = numpy.array([s[7] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[6] for s in params]) * _VRSUN #6 w/ dwarf
elif _ROTCURVE == 'powerlaw' or _ROTCURVE == 'linear':
vpec = numpy.array([s[8] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[7] for s in params]) * _VRSUN #6 w/ dwarf
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
axScatter, axHistx, axHisty = bovy_plot.scatterplot(
vpecR, #vos/ros+vpec/ros,
vpec,
'k,',
levels=levels,
xlabel=r'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
ylabel=r'$V_{\phi,\odot}-V_c\ [\mathrm{km\ s}^{-1}]$',
bins=31,
xrange=[-15., 0.],
yrange=[0., 35.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg',
retAxes=True)
#SBD10 value
bovy_plot.bovy_plot([-20., 40.], [12.24, 12.24],
'--',
color='0.5',
overplot=True)
bovy_plot.bovy_text(-4., 12.7, r'$\mathrm{SBD10}$')
axHisty.plot([0., 100.], [12.24, 12.24], '--', color='0.5')
bovy_plot.bovy_plot([_VRSUN, _VRSUN], [-100., 100.],
'--',
color='0.5',
overplot=True)
bovy_plot.bovy_text(_VRSUN - .75, 7., r'$\mathrm{SBD10}$', rotation=90.)
axHistx.plot([_VRSUN, _VRSUN], [0., 100.], '--', color='0.5')
#Reid / Brunthaler
#bovy_plot.bovy_plot([_PMSGRA,_PMSGRA],[-10.,100.],
# '--',color='0.5',overplot=True)
#axHistx.plot([_PMSGRA,_PMSGRA],[0.,100.],'--',color='0.5')
#bovy_plot.bovy_text(29.4,5.,r'$\mathrm{RB04}$')
#Inset, closed orbit at the Sun
lp = LogarithmicHaloPotential(normalize=1.)
ep = EllipticalDiskPotential(phib=numpy.pi / 2.,
p=0.,
tform=-100.,
tsteady=-100.,
twophio=14. / 220.) #0.072725)
o = Orbit([1., 0., 1. + 14. / 220., 0.])
oc = Orbit([1., 0., 1., 0.])
ts = numpy.linspace(0., 4. * numpy.pi, 1001)
o.integrate(ts, [lp, ep])
print o.e(analytic=True, pot=lp)
oc.integrate(ts, lp)
left, bottom, width, height = 0.45, 0.45, 0.25, 0.25
axInset = pyplot.axes([left, bottom, width, height])
pyplot.sca(axInset)
pyplot.plot(o._orb.orbit[:, 0] * numpy.cos(o._orb.orbit[:, 3]),
o._orb.orbit[:, 0] * numpy.sin(o._orb.orbit[:, 3]), 'k-')
pyplot.plot(oc._orb.orbit[:, 0] * numpy.cos(oc._orb.orbit[:, 3]),
oc._orb.orbit[:, 0] * numpy.sin(oc._orb.orbit[:, 3]),
'--',
color='0.6')
pyplot.xlim(-1.5, 1.5)
pyplot.ylim(-1.5, 1.5)
#pyplot.xlabel(r'$x / R_0$')
#pyplot.ylabel(r'$y / R_0$')
nullfmt = NullFormatter() # no labels
axInset.xaxis.set_major_formatter(nullfmt)
axInset.yaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_end_print(plotfilename)
def test_windows(options):
elems = [
'C', 'N', 'O', 'Na', 'Mg', 'Al', 'Si', 'S', 'K', 'Ca', 'Ti', 'V', 'Mn',
'Fe', 'Ni', 'Ce', 'Co', 'Cr', 'Cu', 'Ge', 'Nd', 'P', 'Rb', 'Y'
]
if options.savefilename is None or \
not os.path.exists(options.savefilename):
# Set default linelist for Turbospectrum or MOOG
if options.linelist is None and options.moog:
linelist = 'moog.201312161124.vac'
elif options.linelist is None:
linelist = 'turbospec.201312161124'
else:
linelist = options.linelist
# set up a model atmosphere for the requested atmospheric parameters
if options.arcturus:
options.teff = 4286.
options.logg = 1.66
options.metals = -0.52
options.am = 0.4
options.cm = 0.09
options.vm = 1.7
atm = atlas9.Atlas9Atmosphere(teff=options.teff,
logg=options.logg,
metals=options.metals,
am=options.am,
cm=options.cm)
# create baseline
if options.moog:
baseline= \
apogee.modelspec.moog.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
else:
baseline= \
apogee.modelspec.turbospec.synth(modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,isotopes='arcturus',
vmicro=options.vm)
# Loop through elements
elem_synspec = {}
# Run through once to simulate all differences
for elem in elems:
# First check that this element has windows
elemPath = apwindow.path(elem, dr=options.dr)
if not os.path.exists(elemPath): continue
# Simulate deltaAbu up and down
print "Working on %s" % (elem.capitalize())
abu = [atomic_number(elem), -options.deltaAbu, options.deltaAbu]
if options.moog:
synspec= \
apogee.modelspec.moog.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
else:
synspec= \
apogee.modelspec.turbospec.synth(abu,
modelatm=atm,
linelist=linelist,
lsf='all',cont='aspcap',
vmacro=6.,
isotopes='arcturus',
vmicro=options.vm)
elem_synspec[elem] = synspec
if not options.savefilename is None:
save_pickles(options.savefilename, baseline, elem_synspec)
else:
with open(options.savefilename, 'rb') as savefile:
baseline = pickle.load(savefile)
elem_synspec = pickle.load(savefile)
# Now run through the different elements again and plot windows for each
# with elements that vary significantly
colors = sns.color_palette("colorblind")
plotelems = [
elem if not elem in ['C', 'N', 'O', 'Fe'] else '%s1' % elem
for elem in elems
]
plotelems.extend(['C2', 'N2', 'O2', 'Fe2'])
for pelem in plotelems:
if '1' in pelem or '2' in pelem: elem = pelem[:-1]
else: elem = pelem
if not elem in elem_synspec: continue
# Figure out which elements have significant variations in these
# windows and always plot the element that should vary
elemIndx = apwindow.tophat(elem, dr=options.dr)
elemWeights = apwindow.read(elem, dr=options.dr)
elemWeights /= numpy.nansum(elemWeights)
# Start with the element in question
splot.windows(1. + options.amplify *
(elem_synspec[elem][0] - baseline[0]),
pelem,
color=colors[0],
yrange=[0., 1.4],
plot_weights=True,
zorder=len(elems))
splot.windows(1. + options.amplify *
(elem_synspec[elem][1] - baseline[0]),
pelem,
color=colors[0],
overplot=True,
zorder=len(elems))
elem_shown = [elem]
# Run through the rest to figure out the order
elemVar = numpy.zeros(len(elems))
for ii, altElem in enumerate(elems):
if altElem == elem: continue
if not altElem in elem_synspec: continue
elemVar[ii] = 0.5 * numpy.nansum(
(elem_synspec[altElem][0] - baseline[0])**2. * elemWeights)
elemVar[ii] += 0.5 * numpy.nansum(
(elem_synspec[altElem][1] - baseline[0])**2. * elemWeights)
jj = 0
sortindx = numpy.argsort(elemVar)[::-1]
for altElem in numpy.array(elems)[sortindx]:
if altElem == elem: continue
if not altElem in elem_synspec: continue
if numpy.fabs(\
numpy.nanmax([(elem_synspec[altElem][0]-baseline[0])[elemIndx],
(elem_synspec[altElem][1]-baseline[0])[elemIndx]]))\
> options.varthreshold:
jj += 1
if jj >= len(colors): jj = len(colors) - 1
elem_shown.append(altElem)
splot.windows(1. + options.amplify *
(elem_synspec[altElem][0] - baseline[0]),
pelem,
color=colors[jj],
overplot=True,
zorder=len(elems) - jj)
splot.windows(1. + options.amplify *
(elem_synspec[altElem][1] - baseline[0]),
pelem,
color=colors[jj],
overplot=True,
zorder=len(elems) - jj)
t = pyplot.gca().transData
fig = pyplot.gcf()
for s, c in zip(elem_shown, colors[:jj + 1]):
xc = 0.05
if elem == 'K' or elem == 'Ce' or elem == 'Ge' or elem == 'Nd' \
or elem == 'Rb':
xc = apwindow.waveregions(elem, dr=options.dr,
pad=3)[0][0] - 15000. + 1.
text = pyplot.text(xc,
1.2,
" " + (r"$\mathrm{%s}$" % s) + " ",
color=c,
transform=t,
size=16.,
backgroundcolor='w')
text.draw(fig.canvas.get_renderer())
ex = text.get_window_extent()
t = transforms.offset_copy(text._transform,
x=1.5 * ex.width,
units='dots')
# Save
bovy_plot.bovy_end_print(options.plotfilename.replace('ELEM', pelem))
return None
def plot_rotcurve_samples(options=None, args=None):
"""Plot sample rotation curves"""
options = set_options(options)
params = numpy.random.permutation(load_samples(args[0]))
rotcurves = []
rs = numpy.linspace(3.5, 16., 1001)
add = 0
if options.nooutliermean: add -= 1
if not options.dwarf: add -= 1
for ii in range(options.nsamples):
if options.rotcurve.lower() == 'flat':
thisrc = params[ii][0] * rs**0. * _REFV0
elif options.rotcurve.lower() == 'powerlaw':
thisrc = params[ii][0] * (
rs / _REFR0 / params[ii][1])**params[ii][6 + add] * _REFV0
elif options.rotcurve.lower() == 'linear':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.)) * _REFV0
elif options.rotcurve.lower() == 'quadratic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.) +
params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1)**2.) * _REFV0
elif options.rotcurve.lower() == 'cubic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1) + params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**2. +
params[ii][8 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**3.) * _REFV0
rotcurves.append(thisrc)
bovy_plot.bovy_print(fig_width=8.)
ii = 0
bovy_plot.bovy_plot(rs,
rotcurves[ii],
'-',
color='0.65',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\ [\mathrm{km\ s}^{-1}]$',
xrange=[0., 20.],
yrange=[150., 300.])
for ii in range(1, options.nsamples):
bovy_plot.bovy_plot(rs,
rotcurves[ii],
'-',
color='0.65',
overplot=True,
alpha=0.1)
if _PLOTM31:
#Read file
m31data = numpy.loadtxt('../data/m31.dat', comments='#')
rm31 = m31data[:, 0]
vcm31 = m31data[:, 1]
bovy_plot.bovy_plot(rm31,
vcm31,
'ks',
overplot=True,
mfc='none',
mew=2.)
bovy_plot.bovy_text(17., 260., r'$\mathrm{M31}$', size=14.)
indx = (rm31 > 15.2) * (rm31 <= 16.8)
bovy_plot.bovy_plot([17., rm31[indx]], [260., vcm31[indx]],
'k-',
overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
plt.subplot(2,3,3)
bovy_plot.bovy_plot(numpy.linspace(0.,1.,nstep)*nstep*step,xnm_out,'-',
color=sns.color_palette()[0],
yrange=[0.,xnm_m2m*2.0],
semilogx=True,xlabel=r'$\mathrm{orbits}$',ylabel=r'$X_{nm}(t)$',gcf=True)
# axhline(xnm_true,ls='--',color='0.65',lw=2.,zorder=0)
#gca().xaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
# for ii in range(len(wevol)):
# bovy_plot.bovy_plot(numpy.linspace(0.,1.,nstep)*nstep*step,wevol[ii,0],'-',
# color=cm.viridis(z_m2m[windx][ii]/0.3),
# yrange=[-0.2/len(z_m2m),numpy.amax(wevol)*1.1],
# semilogx=True,xlabel=r'$t$',ylabel=r'$w(t)$',gcf=True,overplot=ii>0)
#gca().xaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
# Xi^2
plt.subplot(2,3,6)
bovy_plot.bovy_plot(numpy.linspace(0.,1.,nstep)*nstep*step,numpy.sum(Q,axis=1),lw=3.,
loglog=True,xlabel=r'$t$',ylabel=r'$\chi^2$',gcf=True,
yrange=[1.,10**7.0])
#gca().yaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
#gca().xaxis.set_major_formatter(FuncFormatter(
# lambda y,pos: (r'${{:.{:1d}f}}$'.format(int(numpy.maximum(-numpy.log10(y),0)))).format(y)))
plt.tight_layout()
bovy_plot.bovy_end_print('m2m_results.jpg')
def plot_distanceintegral_fewfields(savename, plotname, apogee=False):
#ls and bs of the patches
ls = [280., 30., 12., 37.5, 90., 127.5, 30., 60., 20., -20., -40.]
bs = [45., 4., 2., 0., 0., 0., 0., 0., 0., 0., 0.]
radius = 1.49
locs = [4214, 4244, 4378, 4312, 4242, 4318, 4240, 4241] # for apogee
# If we are doing APOGEE selection, load the APOGEE selection function
if apogee:
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
if os.path.exists(savename):
with open(savename, 'rb') as savefile:
area = pickle.load(savefile)
if apogee:
area_cdens = pickle.load(savefile)
else:
if not apogee:
# For samping over the absolute magnitude distribution
iso = gaia_rc.load_iso()
Gsamples = gaia_rc.sample_Gdist(iso, n=_NGSAMPLES)
# Loop through each distance slice
area = numpy.zeros((len(ls), len(dust._GREEN15DISTS)))
if apogee:
area_cdens = numpy.zeros((len(ls), len(dust._GREEN15DISTS)))
for ii, dist in enumerate(dust._GREEN15DISTS):
print "Working on distance %i: %f" % (ii, dist)
G0 = 0.68 + dust.dist2distmod(dist)
H0 = -1.49 + dust.dist2distmod(dist)
# Load the dust map
combinedmap = dust.load_combined(dist,
nest=False,
nside_out=_NSIDE)
# Loop through a few patches
theta, phi = healpy.pixelfunc.pix2ang(
_NSIDE,
numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
nest=False)
cosb = numpy.sin(theta)
for ff, (ll, bb) in enumerate(zip(ls, bs)):
if ff >= len(ls) - 3 * apogee: break
vec = healpy.pixelfunc.ang2vec((90. - bb) * _DEGTORAD,
ll * _DEGTORAD)
ipixs = healpy.query_disc(_NSIDE,
vec,
radius * _DEGTORAD,
inclusive=False,
nest=False)
ipixIndx = numpy.in1d(numpy.arange(
healpy.pixelfunc.nside2npix(_NSIDE)),
ipixs,
assume_unique=True)
# Calculate the density
densmap = densprofiles.expdisk(phi[ipixIndx],
numpy.pi / 2. - theta[ipixIndx],
dist * numpy.ones(len(ipixs)),
glon=True,
params=[1. / 3., 1. / 0.3])
combinedmask = numpy.zeros(len(ipixs))
tcombinedmap = combinedmap[ipixIndx]
if apogee:
# Loop through the three cohorts
for c in ['short', 'medium', 'long']:
hmin = apo.Hmin(locs[ff], cohort=c)
hmax = apo.Hmax(locs[ff], cohort=c)
combinedmask+= ((tcombinedmap > (hmin-H0))\
*(tcombinedmap < (hmax-H0))).astype('float')*apo(locs[ff],hmin+0.01)
else:
for jj in range(_NGSAMPLES):
combinedmask+= ((tcombinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
*(tcombinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
combinedmask /= _NGSAMPLES
# Compute cross correlation
area[ff,ii]= float(numpy.sum(cosb[ipixIndx]*densmap\
*combinedmask))
if apogee:
# Also store the approximation that the density is constant
cdens = densprofiles.expdisk(ll * _DEGTORAD,
bb * _DEGTORAD,
dist,
glon=True,
params=[1. / 3., 1. / 0.3])
area_cdens[ff,ii]= float(numpy.sum(cosb[ipixIndx]*cdens\
*combinedmask))
if apogee:
save_pickles(savename, area, area_cdens)
else:
save_pickles(savename, area)
# Plot the power spectrum
if True:
bovy_plot.bovy_print(fig_height=3.)
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{yfonts}"]
colors = [
'b', 'g', 'r', 'c', 'gold', 'm', 'k', 'orange', 'y', 'b', 'g'
]
lss = ['-', '-', '-', '-', '-', '-', '-', '-', '-', '--', '--']
for ff in range(len(ls)):
if ff >= len(ls) - 3 * apogee: break
psdx, psd = signal.periodogram(
area[ff] * dust._GREEN15DISTS**3. /
numpy.sum(area[ff] * dust._GREEN15DISTS**3.),
fs=1. / (dust._GREEN15DISTMODS[1] - dust._GREEN15DISTMODS[0]),
detrend=lambda x: x,
scaling='spectrum')
bovy_plot.bovy_plot(psdx[1:],
psd[1:],
'-',
loglog=True,
xlabel=r'$2\pi\,k_\mu\,(\mathrm{mag}^{-1})$',
ylabel=r'$P_k$',
xrange=[0.04, 4.],
color=colors[ff],
ls=lss[ff],
overplot=ff > 0)
if ff == 0:
bovy_plot.bovy_text(
r'$\mathrm{normalized}\ D^3\,\nu_*(\mu|\theta)\,\textswab{S}(\mu)$',
bottom_left=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
else:
bovy_plot.bovy_print(fig_height=3.)
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{yfonts}"]
for ff in range(len(ls)):
if ff >= len(ls) - 3 * apogee: break
bovy_plot.bovy_plot(
dust._GREEN15DISTMODS,
area[ff] * dust._GREEN15DISTS**3.,
'k-',
xlabel=r'$\mu\,(\mathrm{mag}^{-1})$',
ylabel=r'$D^3\,\nu_*(\mu|\theta)\,\textswab{S}(\mu)$')
bovy_plot.bovy_end_print(plotname)
for ff in range(len(ls)):
if ff >= len(ls) - 3 * apogee: break
spl = interpolate.InterpolatedUnivariateSpline(dust._GREEN15DISTMODS,
area[ff] *
dust._GREEN15DISTS**3.,
k=5)
fthder = [spl.derivatives(dm)[4] for dm in dust._GREEN15DISTMODS]
print "Simpson error (%f,%f)= %g, volume = %g" % (
ls[ff], bs[ff], 0.5**4. / 180. * numpy.mean(numpy.fabs(fthder)) /
integrate.simps(area[ff] * dust._GREEN15DISTS**3., dx=0.5),
numpy.sum(area[ff] * dust._GREEN15DISTS**3.))
return None
def plotFits(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params = []
for filename in args:
if os.path.exists(filename):
savefile = open(filename, 'rb')
if options.kmeansOut:
thisparams = pickle.load(savefile)
thisDict = {}
for kk in range(len(thisparams)):
thisDict[str(kk)] = thisparams[kk]
params.append(thisDict)
else:
params.append(pickle.load(savefile))
savefile.close()
else:
print filename + " does not exist ..."
print "Returning ..."
return
if options.plottype == 'Ag':
ys = nu.array([p['gamma']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'As':
ys = nu.array([p['s'] - 1.
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-0.9, 0.4]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$s_{' + options.band + r'}$'
if options.plottype == 'gs':
ys = nu.array([p['s'] - 1.
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['gamma'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [0., 1.2]
yrange = [-0.9, 0.4]
xlabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power-law\ index)}$'
ylabel = r'$s_{' + options.band + r'}$'
elif options.plottype == 'AA':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['logA'] / 2.)
xs.append(params[0][key]['logA'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [-9.21 / 2., 0.]
yrange = xrange
xlabel = r'$\log A_' + options.band[
0] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A_' + options.band[
1] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits = r'$\log A_' + options.band[0] + r'$'
ylabelnounits = r'$\log A_' + options.band[1] + r'$'
elif options.plottype == 'AAz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xtmp = qsos[qsoDict[key]].z
except KeyError:
continue
try:
ytmp = params[1][key]['logA'] / 2.
except KeyError:
continue
ys.append(params[0][key]['logA'] / 2 - ytmp)
xs.append(xtmp)
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
yrange = [-1., 1.]
xrange = [0., 5.5]
ylabel = r'$\log A_r/A_g\ \mathrm{(amplitudes\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gg':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['gamma'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [0., 1.15]
yrange = xrange
xlabel = r'$\gamma_' + options.band[
0] + r'\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma_' + options.band[
1] + r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits = r'$\gamma_' + options.band[0] + r'$'
ylabelnounits = r'$\gamma_' + options.band[1] + r'$'
elif options.plottype == 'Acgc':
ys = nu.array([p['gammagr']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Acg':
ys = nu.array([p['gamma']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'AcAc':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['logAgr'] / 2.)
xs.append(params[0][key]['logAgr'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [-9.21 / 2., 0.]
yrange = xrange
xlabel = r'$\log A^c_' + options.band[
0] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_' + options.band[
1] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits = r'$\log A^c_' + options.band[0] + r'$'
ylabelnounits = r'$\log A^c_' + options.band[1] + r'$'
elif options.plottype == 'gcgc':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gammagr'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [0., 1.15]
yrange = xrange
xlabel = r'$\gamma^c_' + options.band[
0] + r'\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma^c_' + options.band[
1] + r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits = r'$\gamma^c_' + options.band[0] + r'$'
ylabelnounits = r'$\gamma^c_' + options.band[1] + r'$'
elif options.plottype == 'AAc':
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
ys = nu.array([p['logAri'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
ys = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-9.21 / 2., 0.]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'ggc':
if len(params) == 2:
xs, ys = [], []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(ys))
else:
xs = nu.array([p['gamma'] for p in params[0].values()
]).reshape(len(params[0]))
ys = nu.array([p['gammagr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [0., 1.25]
yrange = xrange
xlabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Ai':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].mags[3])
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [15.0, 21.3]
ylabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gi':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [15.0, 21.3]
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'Az':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [0., 5.5]
ylabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'sz': #for KS11
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if len(params) == 2:
try:
ys.append(params[0][key]['s'] / params[1][key]['s'])
except KeyError:
xs.pop()
continue
else:
ys.append(params[0][key]['s'])
ys = nu.array(ys) - 1.
xs = nu.array(xs)
yrange = [-1.1, 0.4]
xrange = [0., 5.5]
ylabel = r'$s_{' + options.band + r'}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'scatterz': #for KS11
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
ys.append(params[0][key]['logsigma'])
ys = nu.array(ys) - 1.
xs = nu.array(xs)
yrange = [-10, 0.]
xrange = [0., 5.5]
ylabel = r'$\sigma^2_{' + options.band + r'}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'ss':
ys, xs = [], []
if options.band2 is None:
for key in params[0].keys():
try:
ys.append(params[1][key]['s'][0] - 1.)
except KeyError:
continue
xs.append(params[0][key]['s'][0] - 1.)
else:
for key in params[0].keys():
try:
ys.append(params[1][key]['s' + options.band2][0] - 1.)
except KeyError:
continue
xs.append(params[0][key]['s' + options.band][0] - 1.)
xs = nu.array(xs)
ys = nu.array(ys)
xrange = [-0.9, 0.4]
yrange = [-0.9, 0.4]
if options.band2 is None:
xlabel = r'$s$'
ylabel = r'$s$'
else:
xlabel = r'$s_{' + options.band + r'}$'
ylabel = r'$s_{' + options.band2 + r'}$'
ylabelnounits = ylabel
xlabelnounits = xlabel
elif options.plottype == 'gz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [0., 5.5]
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Aci':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [15.0, 21.3]
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gci':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [15.0, 21.3]
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'loglike2':
ys = []
xs = []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike'])
xs.append(params[0][key]['loglike'])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 200.]
xrange = [0., 200.]
ylabel = r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
xlabel = r'$\log L_{\mathrm{PL}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
elif options.plottype == 'loglike3z':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike'] - params[0][key]['loglike'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [-15., 15.]
xrange = [0., 5.5]
ylabel = r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ ' + options.band[
0] + r' - \log L_{\mathrm{PL}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Acz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [0., 5.5]
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gcz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [0., 5.5]
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'AsAc': #files: g, Ac, KS11
xs = []
ys = []
if not options.imax is None:
qsos = open_qsos()
qsos = qsos[(qsos.mags[:, 3] < options.imax)]
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
for key in params[0].keys():
if not key in qsoDict.keys(): continue
try:
if options.type == 'powerlawSFratios':
Ac = params[1][key]['logAri'] / 2. + 0.14
A = params[0][key]['logA'] / 2.
else:
Ac = params[1][key]['logAgr'] / 2.
A = params[0][key]['logA'] / 2.
s = params[2][key]['s']
ys.append(Ac)
xs.append(A + nu.log(1. - s))
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(ys))
xrange = [-9.21 / 2., 0.]
yrange = [-9.21 / 2., 0.]
xlabel = r'$\log [ (1-s ) A_{' + options.band + r'}]\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'st': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
ys /= nu.log(10.)
ys += nu.log10(365.)
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xs /= nu.log(10.)
xs += nu.log10(2.) - ys
#print nu.amin(ys), nu.amax(ys)
xrange = [-6., 0.]
yrange = [-.5, 3.5]
xlabel = r'$\log_{10} \sigma^2_{' + options.band + r'}\ \mathrm{(short-timescale\ variance)}$'
ylabel = r'$\log_{10} \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ days)}$'
elif options.plottype == 'a2l': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xrange = [-2.5, 0.]
yrange = [-10., 4.]
xlabel = r'$\log a^2_{' + options.band + r'}\ \mathrm{(variance)}$'
ylabel = r'$\log \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
elif options.plottype == 'Al': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xs = (nu.log(2.) + xs + nu.log(1. - nu.exp(-1. / nu.exp(ys)))) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-10., 4.]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
yrange=yrange,
xrange=xrange,
bins=30,
xlabel=xlabel,
ylabel=ylabel)
if options.plottype == 'AA' or options.plottype == 'gg' \
or options.plottype == 'loglike2' \
or options.plottype == 'AsAc' \
or options.plottype == 'ss' \
or options.plottype == 'AcAc':
bovy_plot.bovy_plot(nu.array(xrange),
nu.array(xrange),
'0.5',
overplot=True)
#also fit if desired
if options.linearfit:
indx= (xs > xrange[0])*(xs < xrange[1])\
*(ys > yrange[0])*(ys < yrange[1])
b = _fit_linear(xs[indx],
ys[indx],
removeoutliers=options.removeoutliers)
bovy_plot.bovy_plot(nu.array(xrange),
nu.array(xrange) + b,
'0.25',
overplot=True)
bovy_plot.bovy_text(ylabelnounits + r'$ = $' + xlabelnounits +
r'$ %4.2f$' % b,
bottom_right=True,
color='0.25')
bovy_plot.bovy_end_print(options.plotfilename)
return None
def showExamplegr(filename='../data/SDSSJ203817.37+003029.8.fits',
constraints=None,basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
showExamplegr
PURPOSE:
show an example of a power-law structure function GP covariance function
fit to an SDSS quasar for g and r
INPUT:
filename - filename with the data
constraints - if None, use all constraints, if [] use no constraints!
basefilename - basefilename for plots
OUTPUT:
writes several plots
HISTORY:
2010-08-11 - Written - Bovy (NYU)
"""
file= fu.table_fields(filename)
mjd_g= nu.array(file.mjd_g)/365.25
g= nu.array(file.g)
err_g= nu.array(file.err_g)
mjd_r= nu.array(file.mjd_r)/365.25
r= nu.array(file.r)
err_r= nu.array(file.err_r)
mask= (mjd_g != 0)*(g < 20.6)*(g > 19.7)#Adjust for non-g
g= g[mask]
g-= nu.mean(g)
err_g= err_g[mask]
mjd_g= mjd_g[mask]
mjd_g-= nu.amin(mjd_g)
meanErr_g= nu.mean(err_g)
r= r[mask]
r-= nu.mean(r)
err_r= err_r[mask]
mjd_r= mjd_r[mask]
mjd_r-= nu.amin(mjd_r)
meanErr_r= nu.mean(err_r)
meanErr_gr= nu.mean(nu.sqrt(err_r**2.+err_g**2.))
nu.random.seed(4)
nGP=5
nx=201
params_mean= ()
from powerlawSFgr import covarFunc
params= {'logGamma': array([-9.27821954]), 'logGammagr': array([-19.85172122]), 'gamma': array([ 0.45932629]), 'gammagr': array([ 0.29784021])}
cf= covarFunc(**params)
params_covar= (cf)
ndata= len(g)
if constraints is None:
listx= [(t,'g') for t in mjd_g]
listx.extend([(t,'r') for t in mjd_r])
listy= [m for m in g]
listy.extend([m for m in r])
listy= nu.array(listy)
noise= [m for m in err_g]
noise.extend([m for m in err_r])
noise= nu.array(noise)
trainSet= trainingSet(listx=listx,listy=listy,noise=noise)
constraints= trainSet
else:
constraints= nu.array([])
useconstraints= constraints
txs= nu.linspace(-0.1,6.5,nx)
xs= [(txs[ii],'g') for ii in range(nx)]
xs.extend([(txs[ii],'r') for ii in range(nx)])
GPsamples= eval_gp(xs,mean,covar,(),params_covar,nGP=nGP,constraints=useconstraints,tiny_cholesky=.000001)
thismean= calc_constrained_mean(xs,mean,params_mean,covar,params_covar,useconstraints)
thiscovar= calc_constrained_covar(xs,covar,params_covar,useconstraints)
plot.bovy_print()
pyplot.plot(txs,GPsamples[0,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_g,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_full.ps')
plot.bovy_print()
pyplot.figure()
pyplot.plot(txs,GPsamples[0,nx-1:-1],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(mjd_r,r,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(txs,GPsamples[ii,nx-1:-1],'-',color=str(0.25+ii*.5/(nGP-1)))
pyplot.plot(txs,thismean[nx-1:-1],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.25,yerr=meanErr_r,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$r-\langle r\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_fullr.ps')
plot.bovy_print()
pyplot.figure()
ii= 0
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color='0.25')
if isinstance(constraints,trainingSet):
plot.bovy_plot(mjd_g,g-r,'k.',zorder=5,ms=10,overplot=True)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
colors= colors-nu.mean(colors)
pyplot.plot(txs,colors,'-',color=str(0.25+ii*.5/(nGP-1)))
plotthismean= nu.zeros(nx)
for ii in range(nx):
plotthismean[ii]= thismean[ii]-thismean[ii+nx]
pyplot.plot(txs,plotthismean,'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(6.15,-0.18,yerr=meanErr_gr,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$g-r- \langle g - r \rangle\ [\mathrm{mag}]$')
pyplot.xlim(-0.1,6.5)
if isinstance(constraints,trainingSet):
pyplot.ylim(-0.25,.25)
else:
pass #pyplot.ylim(-10.,10.)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_color.ps')
plot.bovy_print()
pyplot.figure()
ii= 2
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,:nx],'-',color='0.25')
if isinstance(constraints,trainingSet):
pyplot.plot(g-r,g,'k.',zorder=5,ms=10)
plot.bovy_text(r'$\mathrm{'+basefilename+r'}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
colors= nu.array([GPsamples[ii,jj]-GPsamples[ii,jj+nx] for jj in range(nx)])
pyplot.plot(colors,GPsamples[ii,0:nx],'-',color=str(0.25+ii*.5/(nGP-1)))
colors= nu.array([thismean[jj]-thismean[jj+nx] for jj in range(nx)])
pyplot.plot(colors,thismean[:nx],'k-',linewidth=2)
if isinstance(constraints,trainingSet):
pyplot.errorbar(.13,-0.3,yerr=meanErr_g,xerr=meanErr_gr,color='k')
pyplot.xlabel(r'$g-r-\langle g-r\rangle\ [\mathrm{mag}]$')
pyplot.ylabel(r'$g-\langle g\rangle\ [\mathrm{mag}]$')
pyplot.xlim(-.2,.2)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_ggr.ps')
return
plot.bovy_print()
pyplot.plot(xs,GPsamples[0,:],'-',color='0.25')
if not constraints == []:
plot.bovy_plot(mjd_g,g,'k.',zorder=5,ms=10,overplot=True)
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
#pyplot.fill_between(xs,thismean-sc.sqrt(sc.diagonal(thiscovar)),thismean+sc.sqrt(sc.diagonal(thiscovar)),color='.75')
for ii in range(1,nGP):
pyplot.plot(xs,GPsamples[ii,:],'-',color=str(0.25+ii*.5/(nGP-1)))
#pyplot.plot(xs,thismean,'k-',linewidth=2)
if not constraints == []:
pyplot.errorbar(6.15,-0.1,yerr=meanErr_g,color='k')
pyplot.xlabel(r'$\mathrm{MJD-constant}\ [\mathrm{yr}]$')
pyplot.ylabel(r'$m-\langle m\rangle\ [\mathrm{mag}]$')
pyplot.xlim(3,6.5)
plot._add_ticks()
plot.bovy_end_print(basefilename+'_zoom.ps')
plot.bovy_print()
pyplot.figure()
for ii in range(nGP):
pyplot.loglog(sc.arange(1.,len(GPsamples[ii,:])/2)*(xs[1]-xs[0]),
2.*sc.var(GPsamples[ii,:])-2.*sc.correlate(GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),GPsamples[ii,:]-sc.mean(GPsamples[ii,:]),"same")[1:len(GPsamples[ii,:])/2][::-1]/len(GPsamples[ii,:]),
color=str(0.25+ii*.5/(nGP-1)))
xline= [(xs[1]-xs[0]),xs[len(xs)/2]]
pyplot.loglog(xline,nu.exp(-3.02045715)*nu.array(xline)**(0.5868293),'k--')
pyplot.xlabel(r'$\Delta t\ [\mathrm{yr}]$')
pyplot.ylabel(r'$\mathrm{structure\ function}$')
#plot.bovy_text(r'$\mathrm{SDSSJ203817.37+003029.8}$',title=True)
plot.bovy_end_print(basefilename+'_structfunc.ps')
