def plot_psd_red():
data= readAndHackHoltz.readAndHackHoltz()
dx= 1.
binsize= .735
pix= pixelize_sample.pixelXY(data,xmin=5.,xmax=13,
ymin=-3.,ymax=7.,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x),returnz=True,justcalc=True)
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)
psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize)
print psd1d
#Simulations for constant 3.25 km/s
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-3))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*3.25
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3]
scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd))
print scale
#Simulations for the actual noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-3))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*resvunc
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3]
ks= psd1d[0][0:-3]
bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][0:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'ko',mew=2.,
mfc='none',overplot=True)
pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][0:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
yerr=scale*0.5*(psd1d[2][0:-3]**2.
+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][0:-3]),
marker='None',ls='none',color='k')
if False:
interpindx= True-numpy.isnan(psd1d[1][0:-3])
interpspec= interpolate.InterpolatedUnivariateSpline(ks[interpindx],
scale*numpy.sqrt(psd1d[1][0:-3])[interpindx],
k=3)
pks= numpy.linspace(ks[0],ks[-1],201)
bovy_plot.bovy_plot(pks,interpspec(pks),'k-',overplot=True)
#Simulations for the actual noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-3))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*resvunc
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][0:-3]
if _PLOTBAND:
bovy_plot.bovy_plot(ks,
scale*numpy.median(numpy.sqrt(noisepsd),axis=0),
'-',lw=8.,zorder=9,
color='0.65',overplot=True)
return None
def large_scale_power(pix,vsolar,vc=220.,dx=None,beta=0.,hs=33.3,hR=3./8.):
"""Determine the power on large scales in the residuals for different solarmotions"""
out= numpy.empty_like(vsolar)
binsize= 0.8
scale= 4.*numpy.pi #0.522677552224
for ii in range(len(vsolar)):
resv= pix.plot(lambda x: dvlosgal(x,vc=vc,vtsun=vc+vsolar[ii],
beta=beta,hR=hR,hs=hs),
returnz=True,justcalc=True)
psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize)
indx= (psd1d[0] > 0.2)*(psd1d[0] < 0.9)
out[ii]= scale*numpy.sqrt(numpy.sum(psd1d[1][indx]*(psd1d[1][indx]/psd1d[2][indx])**2.)/numpy.sum((psd1d[1][indx]/psd1d[2][indx])**2.))
return out
def plot_psd(plotfilename):
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
binsize= .8#.765
pix= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x,vtsun=220.+22.5),
returnz=True,justcalc=True)
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)
psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize)
print psd1d
#Simulations for constant 3.25 km/s
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*3.25
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd))
print scale
#Simulations for the actual noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*resvunc
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
ks= psd1d[0][1:-3]
if _ADDGCS:
xrange=[.03,110.]
else:
xrange= [0.,1.]
yrange= [0.,11.9]
if _PROPOSAL:
bovy_plot.bovy_print(fig_width=7.5,fig_height=3.)
else:
bovy_plot.bovy_print(fig_width=7.5,fig_height=4.5)
apop= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
'ko',lw=2.,
zorder=12,
xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
semilogx=_ADDGCS,
xrange=xrange,yrange=yrange)
if _DUMP2FILE:
with open('bovy-apogee-psd.dat','w') as csvfile:
writer= csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
csvfile.write('#APOGEE\n')
for ii in range(len(ks)):
writer.writerow([ks[ii],
(scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii],
(scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]])
if _PROPOSAL:
pyplot.gcf().subplots_adjust(bottom=0.15)
pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
yerr=scale*0.5*(psd1d[2][1:-3]**2.
+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]),
marker='None',ls='none',color='k')
if _PLOTBAND:
# bovy_plot.bovy_plot(ks,
# scale*numpy.median(numpy.sqrt(noisepsd),axis=0),
# '--',lw=2.,zorder=10,
# color='0.85',overplot=True)
# bovy_plot.bovy_plot(ks,
# scale*numpy.sqrt(numpy.sort(noisepsd
# -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]),
# zorder=1,ls='--',overplot=True,
# color='0.65')
pyplot.fill_between(ks,
scale*numpy.sqrt(numpy.sort(noisepsd
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]),
zorder=0,
color='0.65')
#Add a simple model of a spiral potential
if _ADDSIMPLESPIRAL:
if False:
alpha= -12.5
spvlos= simulate_vlos_spiral(alpha=alpha,
gamma=1.2,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=0.01)
potscale= 1.35*1.2
print numpy.arctan(2./alpha)/numpy.pi*180., numpy.sqrt(0.035/numpy.fabs(alpha)/2.)*potscale*220., numpy.sqrt(0.035/numpy.fabs(alpha))*potscale*220.
simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.01,binsize=binsize)
tks= simpsd1d[0][1:-3]
bovy_plot.bovy_plot(tks,
scale*numpy.sqrt(simpsd1d[1][1:-3]),
'k--',lw=2.,overplot=True)
#A better simulation
# spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14.sav')
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_hivres.sav')
potscale= 1.
simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.33333333,binsize=binsize)
tks= simpsd1d[0][1:-3]
# alpha=-14.
# print numpy.arctan(2./-14.)/numpy.pi*180., numpy.sqrt(0.075/numpy.fabs(alpha)/2.)*potscale*220., numpy.sqrt(0.075/numpy.fabs(alpha))*potscale*220.
line1= bovy_plot.bovy_plot(tks,
scale*numpy.sqrt(simpsd1d[1][1:-3]),
'k--',lw=2.,overplot=True)
if _DUMP2FILE:
with open('bovy-bar-psd.dat','w') as csvfile:
writer= csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
for ii in range(len(ks)):
writer.writerow([tks[ii],(scale*numpy.sqrt(simpsd1d[1][1:-3]))[ii]])
#bovy_plot.bovy_plot(tks[tks > 0.7],
# scale*numpy.sqrt(simpsd1d[1][1:-3][tks > 0.7]+4./scale**2.*(1.-numpy.tanh(-(tks[tks > 0.7]-0.9)/0.1))/2.),
# 'k-.',lw=2.,overplot=True)
#line2= bovy_plot.bovy_plot(tks,
# scale*numpy.sqrt(simpsd1d[1][1:-3]+4./scale**2.),
# 'k-.',lw=2.,overplot=True,dashes=(10,5,3,5))
l1= pyplot.legend((line1[0],),
# (r'$\mathrm{Spiral}:\ \delta \phi_{\mathrm{rms}} = (10\,\mathrm{km\,s}^{-1})^2,$'+'\n'+r'$\mathrm{pitch\ angle} = 8^\circ$'+'\n'+r'$\mathrm{Sun\ near\ 2\!:\!1\ Lindblad\ resonance}$',),
(r'$\mathrm{Bar}:\ F_{R,\mathrm{bar}} / F_{R,\mathrm{axi}} = 1.5\%,$'+'\n'+r'$\mathrm{angle} = 25^\circ,$'+'\n'+r'$\mathrm{Sun\ near\ 2\!:\!1\ Lindblad\ resonance}$',),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':14},
frameon=False)
#Add the lopsided and ellipticity constraints from Rix/Zaritsky
if _ADDRIX:
pyplot.errorbar([1./16.],[5.6],
yerr=[5.6/2.],
marker='d',color='0.6',
mew=1.5,mfc='none',mec='0.6')
pyplot.errorbar([1./8./numpy.sqrt(2.)],[6.4],
yerr=numpy.reshape(numpy.array([6.4/0.045*0.02,6.4/0.045*0.03]),(2,1)),
marker='d',color='0.6',mec='0.6',
mew=1.5,mfc='none')
if _ADDGCS:
ks_gcs, psd_gcs, e_psd_gcs, gcsp= plot_psd_gcs()
if _ADDRAVE:
ks_rave, psd_rave, e_psd_rave, ravep= plot_psd_rave()
if _ADDRED:
plot_psd_red()
l2= pyplot.legend((apop[0],ravep[0],gcsp[0]),
(r'$\mathrm{APOGEE}$',
r'$\mathrm{RAVE}$',
r'$\mathrm{GCS}$'),
loc='upper right',bbox_to_anchor=(.95,.750),
numpoints=1,
prop={'size':14},
frameon=False)
pyplot.gca().add_artist(l1)
pyplot.gca().add_artist(l2)
#Plot an estimate of the noise, based on looking at the bands
nks= numpy.linspace(2.,120.,2)
if not 'mba23' in socket.gethostname():
pyplot.fill_between(nks,[2.,2.],hatch='/',color='k',facecolor=(0,0,0,0),
lw=0.)
# edgecolor=(0,0,0,0))
pyplot.plot(nks,[2.,2.],color='k',lw=1.5)
nks= numpy.linspace(0.17,2.,2)
def linsp(x):
return .8/(numpy.log(0.17)-numpy.log(2.))*(numpy.log(x)-numpy.log(0.17))+2.8
if not 'mba23' in socket.gethostname():
pyplot.fill_between(nks,linsp(nks),hatch='/',color='k',facecolor=(0,0,0,0),
lw=0.)
# edgecolor=(0,0,0,0))
#Plot lines
pyplot.plot([0.17,0.17],[0.,2.8],color='k',lw=1.5)
pyplot.plot(nks,linsp(nks)+0.01,color='k',lw=1.5)
bovy_plot.bovy_text(0.19,.5,r'$95\,\%\,\mathrm{noise\ range}$',
bbox=dict(facecolor='w',edgecolor='w'),fontsize=14.)
if _INTERP:
interpks= list(ks[:-5])
interppsd= list((scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[:-5])
interppsd_w= (scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]))[:-5]
interppsd_w[:8]*= 0.025 #fiddling to get a decent fit
interppsd_w[3:5]*= 0.001
interppsd_w= list(interppsd_w)
interpks.append(0.025)
interppsd.append(10.**-5.)
interppsd_w.append(0.00001)
interpks.append(110.)
interppsd.append(1.)
interppsd_w.append(0.00001)
if _ADDGCS:
interpks.extend(ks_gcs)
interppsd.extend(psd_gcs)
interppsd_w.extend(e_psd_gcs)
if _ADDRAVE:
interpks.extend(ks_rave[5:])
interppsd.extend(psd_rave[5:])
interppsd_w.extend(e_psd_rave[5:])
interpks= numpy.array(interpks)
sortindx= numpy.argsort(interpks)
interpks= interpks[sortindx]
interppsd= numpy.array(interppsd)[sortindx]
interppsd_w= interppsd/numpy.array(interppsd_w)[sortindx]
interpindx= True-numpy.isnan(interppsd)
#interpspec= interpolate.InterpolatedUnivariateSpline(interpks[interpindx],
interpspec= interpolate.UnivariateSpline(numpy.log(interpks[interpindx]),
numpy.log(interppsd[interpindx]/3.),
w=interppsd_w,
k=3,s=len(interppsd_w)*0.8)
pks= numpy.linspace(interpks[0],interpks[-1],201)
#bovy_plot.bovy_plot(pks,
# 3.*numpy.exp(interpspec(numpy.log(pks))),
# 'k-',overplot=True)
def my_formatter(x, pos):
return r'$%g$' % x
def my_formatter2(x, pos):
return r'$%g$' % (1./x)
major_formatter = FuncFormatter(my_formatter)
major_formatter2 = FuncFormatter(my_formatter2)
ax= pyplot.gca()
ax.xaxis.set_major_formatter(major_formatter)
if not _PROPOSAL:
ax2= pyplot.twiny()
xmin, xmax= ax.xaxis.get_view_interval()
ax2.set_xscale('log')
ax2.xaxis.set_view_interval(1./xmin,1./xmax,ignore=True)
ax2.set_xlabel('$\mathrm{Approximate\ scale}\,(\mathrm{kpc})$',
fontsize=12.,ha='center',x=0.5)
ax2.xaxis.set_major_formatter(major_formatter)
bovy_plot.bovy_end_print(plotfilename,dpi=300)
return None
def plot_psd_rave():
data= fitsio.read(os.path.join(os.getenv('DATADIR'),'rave','ravedr4_rc.fits'))
dx= _RAVEDX
binsize= 0.8#.735
pix= pix= pixelize_sample.pixelXY(data,
xmin=_RAVEXMIN,xmax=_RAVEXMAX,
ymin=_RAVEYMIN,ymax=_RAVEYMAX,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x,vtsun=230.),returnz=True,justcalc=True)
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)
psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize)
print psd1d
#Simulations for constant 3.25 km/s
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*3.25
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd))
print scale
#Simulations for the actual noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*resvunc
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
ks= psd1d[0][1:-3]
ravep= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'k+',mew=2.,
overplot=True)
pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
yerr=scale*0.5*(psd1d[2][1:-3]**2.
+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]),
marker='None',ls='none',color='k')
if _DUMP2FILE:
with open('bovy-apogee-psd.dat','a') as csvfile:
writer= csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
csvfile.write('#RAVE\n')
for ii in range(len(ks)):
writer.writerow([ks[ii],
(scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii],
(scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]])
if False:
interpindx= True-numpy.isnan(psd1d[1][1:-3])
interpspec= interpolate.InterpolatedUnivariateSpline(ks[interpindx],
scale*numpy.sqrt(psd1d[1][1:-3])[interpindx],
k=3)
pks= numpy.linspace(ks[0],ks[-1],201)
bovy_plot.bovy_plot(pks,interpspec(pks),'k-',overplot=True)
if _PLOTBAND:
#bovy_plot.bovy_plot(ks,
# scale*numpy.median(numpy.sqrt(noisepsd),axis=0),
# '-',lw=8.,zorder=9,
# color='0.65',overplot=True)
# bovy_plot.bovy_plot(ks,
# scale*numpy.sqrt(numpy.sort(noisepsd
# -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]),
# zorder=1,ls='--',overplot=True,
# color='0.65')
pyplot.fill_between(ks,
scale*numpy.sqrt(numpy.sort(noisepsd
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]),
zorder=0,
color='0.65')
#bovy_plot.bovy_plot(numpy.tile(ks,(_NNOISE,1)).T,
# scale*numpy.sqrt(noisepsd).T,
# '-',zorder=0,alpha=0.5,
# color='0.45',overplot=True)
return (ks,
scale*numpy.sqrt(psd1d[1][1:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]),
ravep)
def plot_psd_gcs():
data= hackGCS.hackGCS()
dx= _GCSDX
binsize= .8
pix= pixelize_sample.pixelXY(data,
xmin=_GCSXMIN,xmax=_GCSXMAX,
ymin=_GCSYMIN,ymax=_GCSYMAX,
dx=dx,dy=dx)
resv= pix.plot('VVel',returnz=True,justcalc=True)
resvunc= pix.plot('VVel',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
returnz=True,justcalc=True)
psd1d= bovy_psd.psd1d(resv,dx,binsize=binsize)
print psd1d
#Simulations for constant 3.25 km/s
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*3.25
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
scale= 4.*numpy.pi#3.25/numpy.median(numpy.sqrt(noisepsd))
print scale
#Simulations for the actual noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(psd1d[0])-4))
for ii in range(nnoise):
newresv= numpy.random.normal(size=resv.shape)*resvunc
noisepsd[ii,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1][1:-3]
ks= psd1d[0][1:-3]
gcsp= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psd1d[1][1:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),'kx',mew=2.,
overplot=True)
pyplot.errorbar(ks,scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
yerr=scale*0.5*(psd1d[2][1:-3]**2.
+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]),
marker='None',ls='none',color='k')#,lolims=True)
if _DUMP2FILE:
with open('bovy-apogee-psd.dat','a') as csvfile:
writer= csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
csvfile.write('#GCS\n')
for ii in range(len(ks)):
writer.writerow([ks[ii],
(scale*numpy.sqrt(psd1d[1][1:-3]-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)))[ii],
(scale*0.5*(psd1d[2][1:-3]**2.+_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)**2.)**0.5/numpy.sqrt(psd1d[1][1:-3]))[ii]])
if _PLOTBAND:
# bovy_plot.bovy_plot(ks,
# scale*numpy.median(numpy.sqrt(noisepsd),axis=0),
# '-',lw=2.,zorder=9,
# color='0.65',overplot=True)
# bovy_plot.bovy_plot(ks,
# scale*numpy.sqrt(numpy.sort(noisepsd
# -_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(0.99*_NNOISE)),:]),
# zorder=1,ls='--',overplot=True,
# color='0.65')
pyplot.fill_between(ks,
scale*numpy.sqrt(numpy.sort(noisepsd
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0),axis=0)[int(numpy.floor(_SIGNIF*_NNOISE)),:]),
zorder=0,
color='0.65')
return (ks,
scale*numpy.sqrt(psd1d[1][1:-3]
-_SUBTRACTERRORS*numpy.median(noisepsd,axis=0)),
scale*0.5*psd1d[2][1:-3]/numpy.sqrt(psd1d[1][1:-3]),
gcsp)
def plot_psd_model(plotfilename,type):
#Load fiducial
# spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR)
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR)
potscale= 1.
simpsd1d= bovy_psd.psd1d(spvlos*potscale,0.33333333,binsize=0.8)
tks= simpsd1d[0][1:-3]
xrange=[.08,4.]
if type.lower() == 'elliptical':
eres= 31
p= 0.
vloscp= galpy_simulations.vlos_elliptical(res=eres,cp=0.02,sp=0.,p=p)
vlossp= galpy_simulations.vlos_elliptical(res=eres,sp=0.02,cp=0.,p=p)
vloscpsp= galpy_simulations.vlos_elliptical(res=eres,p=p,
sp=0.02/numpy.sqrt(2.),
cp=0.02/numpy.sqrt(2.))
p=2.
vloscpp2= galpy_simulations.vlos_elliptical(res=eres,cp=0.01,sp=0.,p=p)
vlosspp2= galpy_simulations.vlos_elliptical(res=eres,sp=0.01,cp=0.,p=p)
vloscpspp2= galpy_simulations.vlos_elliptical(res=eres,p=p,
sp=0.01/numpy.sqrt(2.),
cp=0.01/numpy.sqrt(2.))
p=-3.
vloscppm3= galpy_simulations.vlos_elliptical(res=eres,cp=0.05,sp=0.,p=p)
vlossppm3= galpy_simulations.vlos_elliptical(res=eres,sp=0.05,cp=0.,p=p)
vloscpsppm3= galpy_simulations.vlos_elliptical(res=eres,p=p,
sp=0.05/numpy.sqrt(2.),
cp=0.05/numpy.sqrt(2.))
xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2.,
(_RCXMAX-8.)/8.-_RCDX/8./2.,
eres)
dx= (xgrid[1]-xgrid[0])*8.
psdcp= bovy_psd.psd1d(vloscp,dx,binsize=0.8)
psdsp= bovy_psd.psd1d(vlossp,dx,binsize=0.8)
psdcpsp= bovy_psd.psd1d(vloscpsp,dx,binsize=0.8)
psdcpp2= bovy_psd.psd1d(vloscpp2,dx,binsize=0.8)
psdspp2= bovy_psd.psd1d(vlosspp2,dx,binsize=0.8)
psdcpspp2= bovy_psd.psd1d(vloscpspp2,dx,binsize=0.8)
psdcppm3= bovy_psd.psd1d(vloscppm3,dx,binsize=0.8)
psdsppm3= bovy_psd.psd1d(vlossppm3,dx,binsize=0.8)
psdcpsppm3= bovy_psd.psd1d(vloscpsppm3,dx,binsize=0.8)
ks= psdcp[0][1:-3]
scale= 4.*numpy.pi*220.
bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20)
line1= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcp[1][1:-3]),
'k-',lw=2.,
semilogx=True,
# xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
xrange=xrange,
yrange=[0.,11.9],zorder=1)
line2= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsp[1][1:-3]),
'k--',lw=2.,
overplot=True,zorder=1)
line3= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpsp[1][1:-3]),
'k-.',lw=2.,zorder=1,
overplot=True)
line4= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpp2[1][1:-3]),
'-',lw=2.,color='c',zorder=1,
overplot=True)
line5= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdspp2[1][1:-3]),
'--',lw=2.,color='c',zorder=1,
overplot=True)
line6= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpspp2[1][1:-3]),
'-.',lw=2.,color='c',zorder=1,
overplot=True)
line7= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcppm3[1][1:-3]),
'-',lw=2.,color='r',zorder=1,
overplot=True)
line8= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsppm3[1][1:-3]),
'--',lw=2.,color='r',zorder=1,
overplot=True)
line9= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdcpsppm3[1][1:-3]),
'-.',lw=2.,color='r',zorder=1,
overplot=True)
pyplot.annotate(r'$\mathrm{Elliptical\ perturbation}\ (m=2\ \mathrm{mode})$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=20.)
l1= pyplot.legend((line1[0],line2[0],line3[0]),
(r'$\phi_b = 0^\circ$',
r'$\phi_b = 45^\circ$',
r'$\phi_b = 90^\circ$'),
loc='lower left',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
l2= pyplot.legend((line1[0],line4[0],line7[0]),
(r'$\epsilon(R) = 0.02$',
r'$\epsilon(R) = 0.01\,\left(\frac{R}{R_0}\right)^2$',
r'$\epsilon(R) = 0.05\,\left(\frac{R}{R_0}\right)^{-3}$'),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
pyplot.gca().add_artist(l1)
pyplot.gca().add_artist(l2)
elif type.lower() == 'bar':
vlosbar= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR)
vlosslowbar= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_slow%s.sav' % _HIVRESSTR)
vlosbarsmallangle= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_angle10%s.sav' % _HIVRESSTR)
vlosbarlargeangle= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_angle40%s.sav' % _HIVRESSTR)
vlosbarsmallrolr= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_rolr0.85%s.sav' % _HIVRESSTR)
vlosbarlargerolr= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_rolr0.95%s.sav' % _HIVRESSTR)
eres= 19
xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2.,
(_RCXMAX-8.)/8.-_RCDX/8./2.,
eres)
dx= (xgrid[1]-xgrid[0])*8.
psdbar= bovy_psd.psd1d(vlosbar,dx,binsize=0.8)
psdslowbar= bovy_psd.psd1d(vlosslowbar,dx,binsize=0.8)
psdsbarsmallangle= bovy_psd.psd1d(vlosbarsmallangle,dx,binsize=0.8)
psdsbarlargeangle= bovy_psd.psd1d(vlosbarlargeangle,dx,binsize=0.8)
psdsbarsmallrolr= bovy_psd.psd1d(vlosbarsmallrolr,dx,binsize=0.8)
psdsbarlargerolr= bovy_psd.psd1d(vlosbarlargerolr,dx,binsize=0.8)
ks= psdbar[0][1:-3]
scale= 4.*numpy.pi*220.
bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20)
line1= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdbar[1][1:-3]),
'-',lw=2.,color='0.65',
semilogx=True,
# xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
xrange=xrange,
yrange=[0.,11.9],zorder=1)
line2= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdslowbar[1][1:-3]),
'r-',lw=2.,
overplot=True,zorder=1)
line3= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarsmallangle[1][1:-3])*0.9,
'-',lw=2.,color='gold',
overplot=True,zorder=1)
line4= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarlargeangle[1][1:-3])*1.1,
'b-',lw=2.,
overplot=True,zorder=1)
line5= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarsmallrolr[1][1:-3])*0.9,
'g-',lw=2.,
overplot=True,zorder=1)
line6= bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psdsbarlargerolr[1][1:-3]),
'c-',lw=2.,
overplot=True,zorder=1)
pyplot.annotate(r'$\mathrm{Bar\ perturbation\ (rotating}\ m=2\ \mathrm{mode})$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=20.)
l1= pyplot.legend((line1[0],line2[0]),
(r'$\mathrm{Fast\ bar\ growth}$',
r'$\mathrm{Slow\ bar\ growth}$'),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
l2= pyplot.legend((line3[0],line4[0],line5[0],line6[0]),
(r'$\mathrm{Fast}\ \&\ \mathrm{bar\ angle} = 10^\circ$',
r'$\mathrm{Fast}\ \&\ \mathrm{bar\ angle} = 40^\circ$',
r'$\mathrm{Fast}\ \&\ R_{\mathrm{OLR}} = 0.85\,R_0$',
r'$\mathrm{Fast}\ \&\ R_{\mathrm{OLR}} = 0.95\,R_0$'),
loc='lower left',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
pyplot.gca().add_artist(l1)
pyplot.gca().add_artist(l2)
elif type.lower() == 'spiral':
vlosfid= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR)
vloslpitch= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-7%s.sav' % _HIVRESSTR)
vlosdiffgamma= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_gamma0.3%s.sav' % _HIVRESSTR)
vlosdiffomegas= galpy_simulations.vlos('../sim/spiral_rect_alpha-14%s.sav' % _HIVRESSTR)
vlosdiffm= galpy_simulations.vlos('../sim/spiral_rect_m4_alpha-14%s.sav' % _HIVRESSTR)
vlosdiffm2= galpy_simulations.vlos('../sim/spiral_rect_m4_alpha-14_gamma0.4%s.sav' % _HIVRESSTR)
potscale= 0.85
eres= 19
xgrid= numpy.linspace((_RCXMIN-8.)/8.+_RCDX/8./2.,
(_RCXMAX-8.)/8.-_RCDX/8./2.,
eres)
dx= (xgrid[1]-xgrid[0])*8.
psdfid= bovy_psd.psd1d(vlosfid,dx,binsize=0.8)
psdlpitch= bovy_psd.psd1d(vloslpitch,dx,binsize=0.8)
psddiffgamma= bovy_psd.psd1d(vlosdiffgamma,dx,binsize=0.8)
psddiffomegas= bovy_psd.psd1d(vlosdiffomegas,dx,binsize=0.8)
psddiffm= bovy_psd.psd1d(vlosdiffm,dx,binsize=0.8)
psddiffm2= bovy_psd.psd1d(vlosdiffm2,dx,binsize=0.8)
ks= psdfid[0][1:-3]
scale= 4.*numpy.pi*220.
bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20)
line1= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psdfid[1][1:-3]),
'k-',lw=2,
semilogx=True,
# xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
xrange=xrange,
yrange=[0.,11.9],zorder=1)
potscale= 0.25
line2= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psdlpitch[1][1:-3]),
'-',lw=2.,zorder=1,color='gold',
overplot=True)
potscale= 0.5
line3= bovy_plot.bovy_plot(ks,potscale*scale*numpy.sqrt(psddiffgamma[1][1:-3]),
'r-',lw=2.,zorder=1,
overplot=True)
line4= bovy_plot.bovy_plot(ks,4.*scale*numpy.sqrt(psddiffomegas[1][1:-3]),
'b-',lw=2.,zorder=1,
overplot=True)
line5= bovy_plot.bovy_plot(ks,10./7.*scale*numpy.sqrt(psddiffm[1][1:-3]),
'g-',lw=2.,zorder=1,
overplot=True)
line6= bovy_plot.bovy_plot(ks,10./6.*scale*numpy.sqrt(psddiffm2[1][1:-3]),
'c-',lw=2.,zorder=1,
overplot=True)
pyplot.annotate(r'$\mathrm{Spiral\ perturbation}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=20.)
l1= pyplot.legend((line1[0],line2[0],line3[0],
line4[0],line5[0],line6[0]),
(r'$\mathrm{Fiducial\ spiral}$',
r'$\mathrm{Pitch\ angle} = 16^\circ$',
r'$\gamma = 17^\circ$'),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
l2= pyplot.legend((line4[0],line5[0],line6[0]),
(r'$\Omega_s = 0.65\,\Omega_0$',
r'$\Omega_s = 0.65\,\Omega_0\ \&\ m=4$',
r'$\Omega_s = 0.65\,\Omega_0\ \&\ m=4\ \&\ \gamma = 23^\circ$'),
loc='lower left',#bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size':16},
frameon=False)
pyplot.gca().add_artist(l1)
pyplot.gca().add_artist(l2)
elif type.lower() == 'bird':
_nSims= 8
#Read the Bird data
birdData= numpy.load('../pecvel/dr12_1_5gyr_8pos.npz')
#Get residuals for all simulations
dx= _RCDX
binsize= .8#.765
scale= 4.*numpy.pi
tmp= bovy_psd.psd1d(birdData['dVlos1'],dx,binsize=binsize) #just to get the size
ks= tmp[0][1:-3]
psds= numpy.zeros((len(tmp[1]),_nSims))
if True:
import apogee.tools.read as apread
import pixelize_sample
data= apread.rcsample()
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)
resv= pix.plot('VHELIO_AVG',returnz=True,justcalc=True)
rc_mask= numpy.ones(resv.shape,dtype='bool')
rc_mask[True-numpy.isnan(resv)]= False
if _SUBTRACTERRORS:
for ii in range(_nSims):
sim= ii+1
tmpPsd= bovy_psd.psd1d(birdData['dVlos%i' % sim],
dx,binsize=binsize)[1]
#Simulations for the noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(tmpPsd)))
for jj in range(nnoise):
newresv= \
numpy.random.normal(size=birdData['dVlos%i' % sim].shape)\
*birdData['sig_dVlos%i' % sim].reshape((9,9))\
*(True-rc_mask)
# *(True-birdData['rc_mask'])
noisepsd[jj,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1]
psds[:,ii]= tmpPsd-numpy.median(noisepsd,axis=0)
#Calculate median PSD and spread around this
medPsd= scale*numpy.median(numpy.sqrt(psds),axis=1)[1:-3]
flucPsd=\
1.4826*scale*numpy.median(numpy.fabs(numpy.sqrt(psds)[1:-3]
-numpy.tile(medPsd/scale,
(psds.shape[1],1)).T),axis=1)
bovy_plot.bovy_print(fig_width=8.,fig_height=4.5,axes_labelsize=20)
def my_formatter(x, pos):
return r'$%g$' % x
major_formatter = FuncFormatter(my_formatter)
line1= bovy_plot.bovy_plot(ks,medPsd,
'k-',lw=2,
semilogx=True,
xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
xrange=xrange,
yrange=[0.,11.9],zorder=1)
pyplot.gca().xaxis.set_major_formatter(major_formatter)
goodIndx= True-numpy.isnan(flucPsd)
pyplot.fill_between(ks[goodIndx],(medPsd-flucPsd)[goodIndx],
y2=(medPsd+flucPsd)[goodIndx],
color='0.45',zorder=-1)
pyplot.annotate(r'$\mathrm{Cosmological\ simulation}$',
(0.5,1.08),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=20.)
bovy_plot.bovy_text(r'$\mathrm{Median\ and\ range\ from}$'+'\n'
+r'$\mathrm{8\ APOGEE\!-\!like\ volumes}$',
top_right=True,size=16.)
bovy_plot.bovy_plot([0.4,0.65],[7.55,10.],'k-',overplot=True)
#Also plot fiducial
scale= 4.*numpy.pi*220.
bovy_plot.bovy_plot(tks,
scale*numpy.sqrt(simpsd1d[1][1:-3]),
'-',color='0.65',lw=4.,overplot=True,zorder=0)
if not type.lower() == 'bird':
nullfmt = NullFormatter() # no labels
pyplot.gca().xaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_bird_psd(plotfilename):
#Read the Bird data
birdData= numpy.load('../pecvel/pecvel.npz')
#Get residuals for all simulations
dx= _RCDX
binsize= .8#.765
scale= 4.*numpy.pi
tmp= bovy_psd.psd1d(birdData['dVlos1'],dx,binsize=binsize) #just to get the size
ks= tmp[0][1:-3]
psds= numpy.zeros((len(tmp[1]),_nSims))
if _SUBTRACTERRORS:
for ii in range(_nSims):
sim= ii+1
tmpPsd= bovy_psd.psd1d(birdData['dVlos%i' % sim],
dx,binsize=binsize)[1]
#Simulations for the noise
nnoise= _NNOISE
noisepsd= numpy.empty((nnoise,len(tmpPsd)))
for jj in range(nnoise):
newresv= \
numpy.random.normal(size=birdData['dVlos%i' % sim].shape)\
*birdData['sig_dVlos%i' % sim].reshape((9,9))\
*(True-birdData['rc_mask'])
noisepsd[jj,:]= bovy_psd.psd1d(newresv,dx,binsize=binsize)[1]
psds[:,ii]= tmpPsd-numpy.median(noisepsd,axis=0)
#Calculate median PSD and spread around this
medPsd= scale*numpy.median(numpy.sqrt(psds),axis=1)[1:-3]
flucPsd=\
1.4826*scale*numpy.median(numpy.fabs(numpy.sqrt(psds)[1:-3]
-numpy.tile(medPsd/scale,
(psds.shape[1],1)).T),axis=1)
print medPsd, flucPsd
#Now plot
xrange=[.03,3.]
yrange= [0.,11.9]
bovy_plot.bovy_print(fig_width=5.5,fig_height=4.5)
bovy_plot.bovy_plot(ks,medPsd,
'k-',lw=2.,
zorder=12,
xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1})$',
semilogx=True,
xrange=xrange,yrange=yrange)
goodIndx= True-numpy.isnan(flucPsd)
pyplot.fill_between(ks[goodIndx],(medPsd-flucPsd)[goodIndx],
y2=(medPsd+flucPsd)[goodIndx],
color='0.65',zorder=1)
if _PLOTINDIV:
for ii in range(_nSims):
bovy_plot.bovy_plot(ks,scale*numpy.sqrt(psds[:,ii])[1:-3],
'-',color='0.8',overplot=True)
if _ADDDATALINE:
alpha= -12.5
spvlos= simulate_vlos_spiral(alpha=alpha,
gamma=1.2,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=0.01)
potscale= 1.35
simpsd1d= bovy_psd.psd1d(spvlos*220.*potscale,0.01,binsize=binsize)
tks= simpsd1d[0][1:-3]
line1= bovy_plot.bovy_plot(tks,
scale*numpy.sqrt(simpsd1d[1][1:-3]),
'k--',lw=2.,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None