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_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