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 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 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 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 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 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 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 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}$' % fehs[ii],
color=colors[ii])
bovy_plot.bovy_end_print(os.path.join(dir,'dm_an_ivezic.'+_EXT))
def plotprops(options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
npops= 62
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
npops= 30
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
ndatas= pickle.load(savefile)
savefile.close()
for ii in range(npops):
bovy_plot.bovy_print()
bovy_plot.bovy_text(r'$[\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$' % (numpy.log(monoAbundanceMW.sigmaz(fehs[ii],afes[ii])/220.)),
top_left=True,size=16.)
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
def plotIllustrative(plotfilename):
#Make an illustrative plot of the effect of uncertainties on sigmaz
bovy_plot.bovy_print()
feh, afe= -0.15,0.125
nzs= 1001
zs= numpy.linspace(500.,2000.,nzs)
sigs= numpy.zeros(nzs)+sigmaz(feh,afe)
bovy_plot.bovy_plot(zs,sigs,'k-',
xlabel=r'$|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_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 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_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 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_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 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_dJ(self,indx=-1):
bovy_plot.bovy_print(axes_labelsize=17.,text_fontsize=12.,xtick_labelsize=15.,ytick_labelsize=15.)
plt.figure(figsize=(7,6))
plt.xlabel(r'$z\,\,(kpc)$')
plt.ylabel(r'$v_z\,\,(km/s)$')
plt.imshow(self.deltaJ[:,:,indx].T,aspect='auto',extent=[-self.zmax,self.zmax,-self.vmax,self.vmax])
plt.colorbar()
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 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_orbit(self,mint=0,maxt=None):
if maxt==None:
maxt=len(self.tt)
bovy_plot.bovy_print(axes_labelsize=17.,text_fontsize=12.,xtick_labelsize=15.,ytick_labelsize=15.)
plt.figure(figsize=(14,7))
plt.xlabel(r'$time\,\,(Gyr)$')
plt.ylabel(r'$z\,\,(kpc)$')
plt.plot(self.tt[mint:maxt],np.array(self.discOrb.x(self.tt[mint:maxt])).T)
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_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 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 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 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 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 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 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 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 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(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 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 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 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_maphz(plotname):
# Load the three fits
with open('../mapfits/tribrokenexpflare.sav','rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples= numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexp.sav','rb') as savefile:
bfnf= numpy.array(pickle.load(savefile))
samplesnf= numpy.array(pickle.load(savefile))
with open('../mapfits/tribrokenexpfixedflare.sav','rb') as savefile:
bfff= numpy.array(pickle.load(savefile))
samplesff= numpy.array(pickle.load(savefile))
maps= define_rcsample.MAPs()
plotthisz= numpy.zeros(len(bf))+numpy.nan
plotthisze= numpy.zeros(len(bf))+numpy.nan
for ii, map in enumerate(maps.map()):
if numpy.median(numpy.exp(samples[ii,3])[True-numpy.isnan(numpy.exp(samples[ii,3]))]) < 5.:
tmed= numpy.median((1./samplesnf[ii,1])[True-numpy.isnan(1./samplesnf[ii,1])])
terr= numpy.std((1./samplesnf[ii,1])[True-numpy.isnan(1./samplesnf[ii,1])])
else:
tmed= numpy.median((1./samplesff[ii,1])[True-numpy.isnan(1./samplesff[ii,1])])
terr= numpy.std((1./samplesff[ii,1])[True-numpy.isnan(1./samplesff[ii,1])])
plotthisz[ii]= tmed
plotthisze[ii]= terr
plotthisz[plotthisze/plotthisz > 0.2]= numpy.nan
bovy_plot.bovy_print()
maps.plot(plotthisz*1000.,
vmin=200.,vmax=1000.,
minnstar=15,
zlabel=r'$h_Z\,(\mathrm{pc})$',
shrink=0.655)
# 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.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname,dpi=300)
return None
def plot_seguem10(plotfilename):
savefile= open('../potential-fits/fitall_10ksamples_derived_whalomass.sav','rb')
data= pickle.load(savefile)
savefile.close()
mh= numpy.array([x[8] for x in data])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(mh,range=[0.,10.],
bins=71,histtype='step',color='k',
xlabel=r'$M_{\mathrm{halo}}(<10\,\mathrm{kpc})\,(10^{10}\,M_\odot)$',
normed=True)
xs= numpy.linspace(0.,10.,1001)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi*numpy.var(mh[True-numpy.isnan(mh)]))*numpy.exp(-0.5*(xs-numpy.mean(mh[True-numpy.isnan(mh)]))**2./numpy.var(mh[True-numpy.isnan(mh)])),
'k-',lw=2,overplot=True)
bovy_plot.bovy_end_print(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 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 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_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_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 main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit MWPotential with Double-Exponential Disk Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# -------------------------------------------------------------------------
# Using an exponential disk instead of a Miyamoto-Nagai disk
# -----------------------
# $c=1$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=1.0,
dblexp=True,
plots=fpath + "Clemens-c_1.pdf",
)
# -----------------------
# $c=0.5$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=0.5,
dblexp=True,
plots=fpath + "Clemens-c_0p5.pdf",
)
# -----------------------
# $c=1.5$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=1.5,
dblexp=True,
plots=fpath + "Clemens-c_1p5.pdf",
)
# -----------------------
# leave c free
plt.figure(figsize=(16, 5))
p_exp_cfree = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
plots=fpath + "Clemens-c_free.pdf",
)
# -----------------------
bf_savefilename = opath + "mwpot14varyc-dblexp-bf.pkl"
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(
fitc=False,
c=c,
dblexp=True,
plots=fpath + "mwpot14varyc-dblexp-bf-fit.pdf",
)
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-dblexp-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_exp_cfree[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-samples.pdf",
dblexp=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
False,
savefig=fpath + "varyc-dblexp-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
False,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[7],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[7]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-samples-shape_hist.pdf")
# -----------------------
# Also fitting $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_exp_cfree_voro = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
fitvoro=True,
plots=fpath + "fitvoro-samples.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-dblexp-fitvoro-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_exp_cfree_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-fitvoro-samples.pdf",
dblexp=True,
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
savefig=fpath + "varyc-dblexp-fitvoro-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-fitvoro-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[9],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[9]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-fitvoro-samples-samples-shape_hist.pdf")
# -----------------------
# Also adding in a gas disk (and still also fitting $R_0$ and $V_c(R_0)$)
plt.figure(figsize=(16, 5))
p_exp_cfree_voro_wgas = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
fitvoro=True,
addgas=True,
plots=fpath + "varyc-dblexp-fitvoro-addgas.pdf",
)
# -----------------------
samples_savefilename = "mwpot14varyc-dblexp-fitvoro-addgas-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample_multi(
nsamples=100000,
params=p_exp_cfree_voro_wgas[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-fitvoro-addgas-samples.pdf",
dblexp=True,
fitvoro=True,
addgas=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
savefig=fpath + "varyc-dblexp-fitvoro-addgas-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-fitvoro-addgas-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[9],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[9]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-fitvoro-addgas-samples-shape_hist.pdf")
__author__ = "Jeremy J Webb"
__all__ = [
"starplot",
"skyplot",
]
import matplotlib.pyplot as plt
import numpy as np
from galpy.util import bovy_plot
import seaborn as sns
import os
from scipy.ndimage import gaussian_filter
import matplotlib.colors as colors
bovy_plot.bovy_print(axes_labelsize=18.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0)
current_palette = sns.color_palette()
def _scatter(x,
y,
z=None,
xlabel="",
ylabel="",
legend=False,
title="",
xlim=None,
ylim=None,
scale=True,
filename=None,
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 waveregions(*args, **kwargs):
"""
NAME:
waveregions
PURPOSE:
plot selected regions of the spectrum in one row
INPUT:
Either:
(a) wavelength, spectrum (\AA,spectrum units)
(b) spectrum (assumed on standard APOGEE re-sampled wavelength grid)
(c) location ID, APOGEE ID (default loads aspcapStar, loads extension ext(=1); apStar=True loads apStar spectrum)
KEYWORDS:
File loading:
ext= (1) extension to load
apStar= (False) if True, load the apStar spectrum
apStarIndx= (1) index in the apStar spectrum to load
Chunks position:
startlams, endlams= start and end wavelength in \AA of the various chunks (takes precedence over startindxs, endindxs)
startindxs, endindxs= star and end index in the wavelength array of the various chunks
Plotting-specific keywords
labelLines= (True) label some lines
noMolecLines= (False) don't label the molecules
cleanZero= (True) replace <= zero entries with NaN
labelID= A string ID that will be placed in the top-left corner
labelTeff, labellogg, labelmetals, labelafe= parameter labels that will be placed in the top-right corner
noxlabel= (False) if True, don't label the x axis
pyplot.plot args and kwargs
OUTPUT:
plot to output
The final axes allow one to put additional labels on the plot, e.g., for adding the APOGEE ID:
bovy_plot.bovy_text(r'$\mathrm{%s}$' % '2M02420597+0837017',top_left=True)
Note that an ID (e.g., the apogee ID) and Teff, logg, metallicity, and alpha-enhancement labels can be added using the keywords label* above
HISTORY:
2015-01-18 - Written (based on older code) - Bovy (IAS)
"""
# Grab non-pyplot.plot kwargs
apStar = kwargs.pop('apStar', False)
labelLines = kwargs.pop('labelLines', not 'overplot' in kwargs)
noMolecLines = kwargs.pop('noMolecLines', False)
cleanZero = kwargs.pop('cleanZero', True)
noxticks = kwargs.pop('_noxticks', False)
noxlabel = kwargs.pop('noxlabel', False)
noskipdiags = kwargs.pop('_noskipdiags', False)
labelwav = kwargs.pop('_labelwav', False)
plotw = kwargs.pop('_plotw', None)
markLines = kwargs.pop('markLines', False)
markwav = kwargs.pop('_markwav', None)
# Labels
labelID = kwargs.pop('labelID', None)
labelTeff = kwargs.pop('labelTeff', None)
labellogg = kwargs.pop('labellogg', None)
labelmetals = kwargs.pop('labelmetals', None)
labelafe = kwargs.pop('labelafe', None)
# Clean bad lines
if cleanZero:
args[1][args[1] <= 0.] = numpy.nan
# Chunk parameters
if 'startlams' in kwargs:
# Turn startlams into a startindxs and similar for endlams
startlams = kwargs.pop('startlams')
endlams = kwargs.pop('endlams')
startindxs = []
endindxs = []
for ii in range(len(startlams)):
startindxs.append(numpy.argmin(numpy.fabs(startlams[ii] -
args[0])))
endindxs.append(numpy.argmin(numpy.fabs(endlams[ii] - args[0])))
else:
startindxs = kwargs.pop('startindxs',
[322, 1794, 2707, 3850, 4740, 5820, 7185])
endindxs = kwargs.pop('endindxs',
[590, 1940, 2857, 4025, 5070, 5955, 7400])
nregions = len(startindxs)
# Calculate the width of the plot
dx= numpy.array([args[0][numpy.amin([len(args[0])-1,endindxs[ii]])]\
-args[0][numpy.amax([0,startindxs[ii]-1])] \
for ii in range(nregions)],
dtype='float')
# Adjust 0 (and -1) to start (end) a little further
startendskip = kwargs.pop('_startendskip', _STARTENDSKIP)
dx[0]= args[0][numpy.amin([len(args[0])-1,endindxs[0]])]\
-args[0][numpy.amax([0,startindxs[0]-startendskip])]
dx[-1]= args[0][numpy.amin([len(args[0])-1,endindxs[-1]+startendskip])]\
-args[0][numpy.amax([0,startindxs[-1]-1])]
if nregions == 1: #special case
dx= [args[0][numpy.amin([len(args[0])-1,endindxs[0]+startendskip])]\
-args[0][numpy.amax([0,startindxs[0]-startendskip])]]
# Determine a good step for the tickmarks
tickStepTmp = numpy.log10(numpy.sum(dx) / 10.) % 1
if tickStepTmp > numpy.log10(1.5) and tickStepTmp < numpy.log10(3.5):
tickStep = 2. * 10.**int(numpy.log10(numpy.sum(dx) / 10.))
elif tickStepTmp > numpy.log10(3.5) and tickStepTmp < numpy.log10(7.5):
tickStep = 5. * 10.**int(numpy.log10(numpy.sum(dx) / 10.))
else:
tickStep = 10.**int(numpy.log10(numpy.sum(dx) / 10.))
dx /= numpy.sum(dx)
if noxticks:
totdx = 0.825
else:
totdx = 0.85
skipdx = kwargs.pop('skipdx', 0.015)
dx *= (totdx - (nregions - 1) * skipdx)
# Setup plot
overplot = kwargs.pop('overplot', False)
if not overplot:
bovy_plot.bovy_print(fig_width=kwargs.pop('fig_width', 8.4),
fig_height=kwargs.pop('fig_height', 2.5),
axes_labelsize=16,
text_fontsize=14,
legend_fontsize=12,
xtick_labelsize=12,
ytick_labelsize=12)
pyplot.figure()
if overplot:
yrange = numpy.array(pyplot.gca().get_ylim())
kwargs.pop('yrange', None) # pop if there
elif apStar:
yrange = kwargs.pop('yrange', [0., 1.1 * numpy.nanmax(args[1])])
else:
yrange = kwargs.pop('yrange', [0.2, 1.2])
# Deal with the label
if apStar:
ylabel = kwargs.pop(
'ylabel',
r'$f_\lambda(\lambda)\,(10^{-17}\,\mathrm{erg\, s}^{-1}\,\mathrm{cm}^{-2}\,\AA^{-1})$'
)
else:
ylabel = kwargs.pop('ylabel', r'$f/f_c(\lambda)$')
kwargs['zorder'] = kwargs.get('zorder', 10)
for ii in range(nregions):
# Setup the axes
if ii == 0:
left, bottom, width, height = 0.1 + (
0.85 - totdx) * 2., 0.125, dx[ii], 0.8
else:
left, bottom, width, height= 0.1+(0.85-totdx)*2.+numpy.cumsum(dx)[ii-1]+skipdx*ii,\
0.125, dx[ii], 0.8
thisax = pyplot.axes([left, bottom, width, height])
fig = pyplot.gcf()
fig.sca(thisax)
startindx, endindx = startindxs[ii], endindxs[ii]
if ii == 0 and nregions == 1:
xrange = [
args[0][numpy.amax([0, startindx - startendskip])] -
_LAMBDASUB, args[0][numpy.amin(
[len(args[0]) - 1, endindx + startendskip])] - _LAMBDASUB
]
elif ii == 0:
xrange = [
args[0][numpy.amax([0, startindx - startendskip])] -
_LAMBDASUB,
args[0][numpy.amin([len(args[0]) - 1, endindx])] - _LAMBDASUB
]
elif ii == (nregions - 1):
xrange = [
args[0][numpy.amax([0, startindx - 1])] - _LAMBDASUB,
args[0][numpy.amin([len(args[0]) - 1, endindx + startendskip
])] - _LAMBDASUB
]
else:
xrange = [
args[0][numpy.amax([0, startindx - 1])] - _LAMBDASUB,
args[0][numpy.amin([len(args[0]) - 1, endindx])] - _LAMBDASUB
]
thisax.plot(args[0][startindx:endindx] - _LAMBDASUB,
args[1][startindx:endindx], *args[2:], **kwargs)
if not plotw is None:
thisax.plot(args[0][startindx:endindx] - _LAMBDASUB,
plotw[startindx:endindx],
'-',
lw=2.,
color='0.65',
zorder=1)
thisax.set_xlim(xrange[0], xrange[1])
thisax.set_ylim(yrange[0], yrange[1])
if noxticks:
nullfmtx = NullFormatter() # no labels, assume 1\AA
thisax.xaxis.set_major_formatter(nullfmtx)
thisax.xaxis.set_major_locator(ticker.MultipleLocator(2.))
else:
thisax.xaxis.set_major_locator(ticker.MultipleLocator(tickStep))
bovy_plot._add_ticks(xticks=True - noxticks)
if ii > 0:
nullfmt = NullFormatter() # no labels
thisax.yaxis.set_major_formatter(nullfmt)
elif not overplot:
pyplot.ylabel(ylabel)
# Remove spines between different wavelength regions
if ii == 0 and not nregions == 1:
thisax.spines['right'].set_visible(False)
thisax.tick_params(right=False, which='both')
elif ii == (nregions - 1) and not nregions == 1:
thisax.spines['left'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False, which='both')
elif not nregions == 1:
thisax.spines['left'].set_visible(False)
thisax.spines['right'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False, which='both')
thisax.tick_params(right=False, which='both')
# Plot cut-out markers
cutOutkwargs = dict(transform=thisax.transAxes,
color='k',
clip_on=False)
if not noskipdiags:
d = .015 # how big to make the diagonal lines in axes coordinates
slope = 1. / (dx[ii] + 0.2 * skipdx) / 3.
if ii == 0 and not nregions == 1:
thisax.plot((1 - slope * d, 1 + slope * d), (-d, +d),
**cutOutkwargs)
thisax.plot((1 - slope * d, 1 + slope * d), (1 - d, 1 + d),
**cutOutkwargs)
elif ii == (nregions - 1) and not nregions == 1:
thisax.plot((-slope * d, +slope * d), (-d, +d), **cutOutkwargs)
thisax.plot((-slope * d, +slope * d), (1 - d, 1 + d),
**cutOutkwargs)
elif not nregions == 1:
thisax.plot((1 - slope * d, 1 + slope * d), (-d, +d),
**cutOutkwargs)
thisax.plot((1 - slope * d, 1 + slope * d), (1 - d, 1 + d),
**cutOutkwargs)
thisax.plot((-slope * d, +slope * d), (-d, +d), **cutOutkwargs)
thisax.plot((-slope * d, +slope * d), (1 - d, 1 + d),
**cutOutkwargs)
else: #plot gray bands
cutOutkwargs['color'] = '0.5'
thisax.fill_between((1., 1. + skipdx), (0., 0.), (1., 1.),
**cutOutkwargs)
# Label the lines
if labelLines:
_label_all_lines(args[0][startindx], args[0][endindx], thisax,
args[0], args[1], noMolecLines)
# Mark the lines
if markLines:
_mark_lines(markwav, args[0][startindx], args[0][endindx], thisax,
args[0], args[1])
# Label the largest round wavelength in angstrom for windows
if labelwav:
bovy_plot.bovy_text(
2 * numpy.floor((xrange[1] - (nregions > 15)) / 2.),
yrange[0] + 0.05 * (yrange[1] - yrange[0]),
r'$\lambda\kern 0.1em%i,%03i$' %
(15 + int(numpy.floor(xrange[1] / 1000.)),
int(2. * numpy.floor(
(xrange[1] - (nregions > 15)) / 2.) % 1000.)),
horizontalalignment='center',
verticalalignment='bottom',
rotation='vertical',
fontsize=10.)
# Add the x-axis label
if not nregions == 1:
thisax = pyplot.axes([0.1 + (0.85 - totdx) * 2., 0.125, totdx, 0.8])
pyplot.gcf().sca(thisax)
thisax.set_ylim(yrange[0], yrange[1])
thisax.spines['left'].set_visible(False)
thisax.spines['right'].set_visible(False)
thisax.spines['bottom'].set_visible(False)
thisax.spines['top'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False, which='both')
thisax.tick_params(right=False, which='both')
thisax.tick_params(labelbottom='off')
thisax.tick_params(bottom=False, which='both')
thisax.tick_params(top=False, which='both')
if not overplot and not noxticks and not noxlabel:
thisax.set_xlabel(r'$\lambda-%i,000\,(\AA)$' %
(int(_LAMBDASUB / 1000.)),
labelpad=10 - (nregions == 1) * 10)
elif not overplot and noxticks and not noxlabel:
thisax.set_xlabel(r'$\lambda\,(\AA)$',
labelpad=3 - (nregions == 1) * 3)
if not nregions == 1:
thisax.set_zorder(-1)
# Start another axis object for later labeling
thisax = pyplot.axes([0.1 + (0.85 - totdx) * 2., 0.125, totdx, 0.8])
pyplot.gcf().sca(thisax)
thisax.patch.set_facecolor('None')
thisax.set_zorder(10)
# Labels
if not labelID is None:
bovy_plot.bovy_text(r'$\mathrm{%s}$' % labelID,
top_left=True,
fontsize=10)
if not labelTeff is None or not labellogg is None \
or not labelmetals is None or not labelafe is None:
nParamLabels= int(not labelTeff is None)\
+int(not labellogg is None)\
+int(not labelmetals is None)\
+int(not labelafe is None)
# Label parameters
paramStr = ''
if not labelTeff is None:
paramStr += r'T_\mathrm{eff}= %i\,\mathrm{K}' % (int(labelTeff))
nParamLabels -= 1
if nParamLabels > 0:
paramStr += ',\ '
if not labellogg is None:
paramStr += r'\log g= %.1f' % labellogg
nParamLabels -= 1
if nParamLabels > 0:
paramStr += ',\ '
if not labelmetals is None:
paramStr += r'[\mathrm{M/H}]= %.2f' % labelmetals
nParamLabels -= 1
if nParamLabels > 0:
paramStr += ',\ '
if not labelafe is None:
paramStr += r'[\alpha/\mathrm{M}]= %.2f' % labelafe
nParamLabels -= 1
if nParamLabels > 0:
paramStr += ',\ '
bovy_plot.bovy_text(r'$%s$' % paramStr, top_right=True, fontsize=10)
return None
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)
tdyn = zh_init / sigma_init
print(' initial zh, tdyn =', zh_init, tdyn)
# DM density
omega_m2m = omegadm_true * 1.5
print('Initial omega, Xnm and sigma =', omega_m2m, xnm_m2m, sigma_init)
z_m2m, vz_m2m, w_init = wendym2m.sample_sech2(sigma_init,
totmass_init,
n=n_m2m)
z_out = numpy.linspace(zmin, zmax, 101)
dens_init = xnm_m2m * hom2m.compute_dens(
z_m2m, zsun_true, z_out, h_m2m, w=w_init)
v2_init = hom2m.compute_v2(z_m2m, vz_m2m, zsun_true, z_out, h_m2m, w=w_init)
v_init = hom2m.compute_v(z_m2m, vz_m2m, zsun_true, z_out, h_m2m, w=w_init)
bovy_plot.bovy_print(axes_labelsize=17.,
text_fontsize=12.,
xtick_labelsize=15.,
ytick_labelsize=15.)
### adiabatically add omega to the initial model
g = wendypy.nbody(z_m2m, vz_m2m, w_init, dttdyn * tdyn, approx=True, nleap=1)
nt = 4000
print('Addiabatically adding omega, nt=', nt)
zt = numpy.empty((n_m2m, nt + 1))
vzt = numpy.empty((n_m2m, nt + 1))
# Et = numpy.empty((nt+1))
zt[:, 0] = z_m2m
vzt[:, 0] = vz_m2m
# Et[0] = wendy.energy(z_init, vz_init, m_init)
# increasing omega
nstep_omega = 1000
if nstep_omega > nt:
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)
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$' % b,
bottom_right=True,
color='0.25')
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_rotcurve_samples(options=None, args=None):
"""Plot sample rotation curves"""
options = set_options(options)
params = numpy.random.permutation(load_samples(args[0]))
rotcurves = []
rs = numpy.linspace(3.5, 16., 1001)
add = 0
if options.nooutliermean: add -= 1
if not options.dwarf: add -= 1
for ii in range(options.nsamples):
if options.rotcurve.lower() == 'flat':
thisrc = params[ii][0] * rs**0. * _REFV0
elif options.rotcurve.lower() == 'powerlaw':
thisrc = params[ii][0] * (
rs / _REFR0 / params[ii][1])**params[ii][6 + add] * _REFV0
elif options.rotcurve.lower() == 'linear':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.)) * _REFV0
elif options.rotcurve.lower() == 'quadratic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.) +
params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1)**2.) * _REFV0
elif options.rotcurve.lower() == 'cubic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1) + params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**2. +
params[ii][8 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**3.) * _REFV0
rotcurves.append(thisrc)
bovy_plot.bovy_print(fig_width=8.)
ii = 0
bovy_plot.bovy_plot(rs,
rotcurves[ii],
'-',
color='0.65',
xlabel=r'$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}$', size=14.)
indx = (rm31 > 15.2) * (rm31 <= 16.8)
bovy_plot.bovy_plot([17., rm31[indx]], [260., vcm31[indx]],
'k-',
overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_vpecvo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
if _ROTCURVE == 'flat':
vpec = numpy.array([s[7] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[6] for s in params]) * _VRSUN #6 w/ dwarf
elif _ROTCURVE == 'powerlaw' or _ROTCURVE == 'linear':
vpec = numpy.array([s[8] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[7] for s in params]) * _VRSUN #6 w/ dwarf
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
axScatter, axHistx, axHisty = bovy_plot.scatterplot(
vpecR, #vos/ros+vpec/ros,
vpec,
'k,',
levels=levels,
xlabel=r'$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$')
nullfmt = NullFormatter() # no labels
axInset.xaxis.set_major_formatter(nullfmt)
axInset.yaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_end_print(plotfilename)
def elements(elem, *args, **kwargs):
"""
NAME:
elements
PURPOSE:
make a plot of measurements of the elemental abundances vs. atomic number
INPUT:
elem - dictionary with elemental abundances relative to H
wrtFe= (True) if True, plot elements wrt Fe on the left Y
inclwrtH= (True) if True, indicate what X/H is on the right Y
bovy_plot.bovy_plot args and kwargs
OUTPUT:
plot to output
HISTORY:
2015-03-10 - Written - Bovy (IAS)
"""
# Process the input dictionary
xs = []
names = []
ys = []
wrtFe = kwargs.pop('wrtFe', True)
for el in elem:
try:
xs.append(atomic_number(el))
except KeyError: # ignore things that aren't known elements
continue
names.append(r'$\mathrm{%s}$' % el.lower().capitalize())
try:
if not wrtFe: raise KeyError
ys.append(elem[el] - elem['Fe'])
except KeyError:
ys.append(elem[el])
wrtFe = False
xs = numpy.array(xs, dtype='int')
ys = numpy.array(ys)
names = numpy.array(names)
# sort
sindx = numpy.argsort(xs)
xs = xs[sindx]
ys = ys[sindx]
names = names[sindx]
# add second y axis?
inclwrtH = kwargs.pop('inclwrtH', True)
if wrtFe:
feh = elem['Fe']
ylabel = kwargs.pop('ylabel', r'$[\mathrm{X/Fe}]$')
else:
ylabel = kwargs.pop('ylabel', r'$[\mathrm{X/H}]$')
if not kwargs.get('overplot', False):
bovy_plot.bovy_print(fig_width=7., fig_height=4.)
basezorder = kwargs.pop('zorder', 0)
yrange = kwargs.pop('yrange', [-0.5, 0.5])
ls = kwargs.pop('ls', '-')
lw = kwargs.pop('lw', 0.25)
bovy_plot.bovy_plot(xs,
ys,
*args,
ylabel=ylabel,
xrange=[4, numpy.amax(xs) + 2],
yrange=yrange,
zorder=2 + basezorder,
ls=ls,
lw=lw,
**kwargs)
pyplot.xticks(list(xs), names)
pyplot.tick_params(axis='x', labelsize=11.)
if wrtFe and inclwrtH:
bovy_plot.bovy_plot([4, numpy.amax(xs) + 2], [0., 0.],
'-',
lw=2.,
color='0.65',
overplot=True,
zorder=basezorder)
ax = pyplot.gca()
ax2 = ax.twinx()
ax2.set_ylim(yrange[0] + feh, yrange[1] + feh)
ax2.set_ylabel(r'$[\mathrm{X/H}]$')
pyplot.sca(ax2)
bovy_plot._add_ticks(yticks=True, xticks=False)
return None
