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})$')
bovy_plot.bovy_end_print(plotfilename)
return None
def determine_kxky_error():
#Load fiducial bar model
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015%s.sav' % _HIVRESSTR)[1::2,1::2]
#spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-14%s.sav' % _HIVRESSTR)[1::2,1::2]*0.85
#spvlos= galpy_simulations.vlos('../sim/spiral_rect_omegas0.33_alpha-7%s.sav' % _HIVRESSTR)[1::2,1::2]*0.25
psd2d= bovy_psd.psd2d(spvlos)
kmax= 1./_RCDX
#Calculate maximum
psd2d[psd2d.shape[0]/2-1:psd2d.shape[0]/2+2,psd2d.shape[1]/2-1:psd2d.shape[1]/2+2]= 0.
tmax= numpy.unravel_index(numpy.argmax(psd2d),psd2d.shape)
tmax0= float(psd2d.shape[0]/2-tmax[0])/psd2d.shape[0]*2
tmax1= float(tmax[1]-psd2d.shape[1]/2)/psd2d.shape[1]*2
print tmax0*kmax, tmax1*kmax
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(psd2d.T,origin='lower',cmap='jet',
interpolation='nearest',
xrange=[-kmax,kmax],
yrange=[-kmax,kmax],
xlabel=r'$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 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 determine_vsolar_mock(data,dfc,trueVsolar,spiral=False):
#At the position of each real data point, generate a mock velocity
data= create_mock_sample(data,dfc,trueVsolar,spiral=spiral)
#Get velocity field
pix= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=_RCDX,dy=_RCDX)
vsolars= numpy.linspace(0.,40.,51)
lpower= large_scale_power(pix,vsolars,vc=220.,dx=_RCDX,beta=0.)
p= numpy.polyfit(vsolars,lpower,2)
minvsolar= -0.5*p[1]/p[0]
return minvsolar
def plot_rcresidualkinematics(plotfilename,sbd10=False,vc=220.):
#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
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=_RCDX,dy=_RCDX)
bovy_plot.bovy_print()
vmin, vmax= -16., 16.
if sbd10:
pixrc.plot(lambda x: dvlosgal(x,vc=vc,vtsun=vc+12.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
pyplot.annotate(r'$V_c = %i\,\mathrm{km\,s}^{-1}, V_{\odot-c}= 12\,\mathrm{km\,s}^{-1}$' % vc,
(0.5,1.1),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=18.)
else:
resv= pixrc.plot(lambda x: dvlosgal(x,vc=vc,vtsun=vc+24.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
notNan= True-numpy.isnan(resv)
print numpy.sum(notNan)
print numpy.median(resv[notNan])
print 1.4826*numpy.median(numpy.fabs(resv[notNan]-numpy.median(resv[notNan])))
print numpy.mean(resv[notNan])
print numpy.std(resv[notNan])
pyplot.annotate(r'$V_c = %i\,\mathrm{km\,s}^{-1}, V_{\odot-c}= 24\,\mathrm{km\,s}^{-1}$' % vc,
(0.5,1.1),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=18.)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_psd2d(plotfilename):
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx)
data= data[indx]
#Get residuals
dx= _RCDX
binsize= .8#.765
pix= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x,vtsun=220.+22.6),
returnz=True,justcalc=True)
resvunc= pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
returnz=True,justcalc=True)
psd2d= bovy_psd.psd2d(resv)
tmax= numpy.unravel_index(numpy.argmax(psd2d),psd2d.shape)
tmax0= float(psd2d.shape[0]/2-tmax[0])/psd2d.shape[0]*2
tmax1= float(tmax[1]-psd2d.shape[1]/2)/psd2d.shape[1]*2
kmax= 1./_RCDX
print tmax0*kmax, tmax1*kmax
#kmax= numpy.amax(numpy.fft.fftfreq(resv.shape[0]*2,_RCDX))
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(psd2d.T,origin='lower',cmap='jet',
interpolation='nearest',
xrange=[-kmax,kmax],
yrange=[-kmax,kmax],
xlabel=r'$k_x\,(\mathrm{kpc}^{-1})$',
ylabel=r'$k_y\,(\mathrm{kpc}^{-1})$')
bovy_plot.bovy_end_print(plotfilename)
if True:
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_hivres.sav')[1::2,1::2]
potscale= 1.
def plot_2dkinematics(basesavefilename,datafilename=None):
#Plot 2D los field
#First read the sample
if not datafilename is None:
data= fitsio.read(datafilename)
else:
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx)
data= data[indx]
pix= pixelize_sample.pixelXY(data)
vmin, vmax= -75., 75.
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
zlabel=r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
bovy_plot.bovy_text(r'$\mathrm{typical\ uncertainty\!:}\ 3\,\mathrm{km\,s}^{-1}$',
bottom_left=True,size=18.)
bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',top_right=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_XY.'+_EXT)
#R,phi
pix= pixelize_sample.pixelXY(data,rphi=True,
ymin=-22.5,ymax=37.5,
# dx=0.3,dy=2.)
# dx=3.,dy=20.)
dx=1.,dy=5.)
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
zlabel=r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI.'+_EXT)
#Plot the dispersion / sqrt(n)
pix= pixelize_sample.pixelXY(data,
dx=1.,dy=1.)
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
zlabel=r'$\delta\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=10.)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI_vlosunc.'+_EXT)
#Plot the dispersion
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x))),
zlabel=r'$\mathrm{MAD}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=40.)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI_vlosdisp.'+_EXT)
#Now plot the los velocity corrected for the Solar motion
#XY
vmin, vmax= -250., 250.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: vlosgal(x),
zlabel=r'$\mathrm{median}\ V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
bovy_plot.bovy_end_print(basesavefilename+'_Vrot_XY.'+_EXT)
pix= pixelize_sample.pixelXY(data,rphi=True,
ymin=-22.5,ymax=37.5,
dx=1.,dy=5.)
#R,phi
vmin, vmax= -250., 250.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: vlosgal(x),
zlabel=r'$\mathrm{median}\ V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
#Plot tangent point
rs= numpy.linspace(6.,8.,101)
bovy_plot.bovy_plot(rs,numpy.arccos(rs/8.)*180./numpy.pi,'k--',lw=2.,
overplot=True)
bovy_plot.bovy_plot(rs,-numpy.arccos(rs/8.)*180./numpy.pi,'k--',lw=2.,
overplot=True)
bovy_plot.bovy_end_print(basesavefilename+'_Vrot_RPHI.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=220.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
meanresv= numpy.mean(resv[medindx])
sigresv= numpy.std(resv[medindx])
bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (meanresv,sigresv,round(numpy.sqrt(numpy.sum(medindx)))),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_RPHI.'+_EXT)
#XY
pixXY= pixelize_sample.pixelXY(data,
xmin=5.,xmax=13,
ymin=-3.,ymax=4.,
dx=1.,dy=1.)
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
pixXY.plot(lambda x: dvlosgal(x,beta=0.,vc=220.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=False)
medindx= True-numpy.isnan(resv)
meanresv= numpy.mean(resv[medindx])
sigresv= numpy.std(resv[medindx])
bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (meanresv,sigresv,round(numpy.sqrt(numpy.sum(medindx)))),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_XY.'+_EXT)
#Plot a histogram of the residuals
bovy_plot.bovy_print()
bovy_plot.bovy_hist(resv[medindx],color='k',bins=11,xrange=[-25.,25.],
yrange=[0.,15.],
histtype='step',normed=False,
xlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$')
xs= numpy.linspace(-25.,25.,1001)
bovy_plot.bovy_plot(xs,
50./11.*numpy.sum(medindx)/numpy.sqrt(2.*numpy.pi)/sigresv\
*numpy.exp(-0.5*(xs-meanresv)**2./sigresv**2.),
'k-',lw=2,overplot=True)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_hist.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model w/ Vc-240 and standard solar motion
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=240.,vtsun=252.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
#bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (numpy.median(resv[medindx]),numpy.median(numpy.fabs(resv[medindx]-numpy.median(resv[medindx])))*1.4826,round(numpy.sqrt(numpy.sum(medindx)))),
# top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12Vc240VsSBD_RPHI.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model w/ Vc-220 and standard solar motion
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=220.,vtsun=232.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
# bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (numpy.median(resv[medindx]),numpy.median(numpy.fabs(resv[medindx]-numpy.median(resv[medindx])))*1.4826,round(numpy.sqrt(numpy.sum(medindx)))),
# top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12Vc220VsSBD_RPHI.'+_EXT)
#FFT
dx= 1.
pix= pixelize_sample.pixelXY(data,
xmin=5.,xmax=13,
ymin=-3.,ymax=4.,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
resvunc= pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
zlabel=r'$\delta\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=10.,returnz=True)
import psds
binsize= 1.5
psd2d= psds.power_spectrum(resv,
binsize=binsize,wavenumber=True)
#Simulate the noise
newresv= numpy.random.normal(size=resv.shape)*resvunc
newresvuni= numpy.random.normal(size=resv.shape)*5.
psd2dnoise= psds.power_spectrum(newresv,
binsize=binsize,wavenumber=True)
psd2duni= psds.power_spectrum(newresvuni,
binsize=binsize,wavenumber=True)
bovy_plot.bovy_print()
ks= psd2d[0][1:]/dx
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2d[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],'ko-',
xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1}\,\mathrm{kpc}^{1/2})$',
xrange=[0.,1./dx/2.],
yrange=[0.,10.],
semilogy=False)
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2dnoise[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],
'-',color='0.5',overplot=True,lw=2.)
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2duni[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],
'-',color='b',overplot=True,lw=2.)
bovy_plot.bovy_end_print(basesavefilename+'_FFTPSD.'+_EXT)
return None
def plot_rckinematics(plotfilename,subsun=False):
#Set up 3 axes
bovy_plot.bovy_print(fig_width=8.,axes_labelsize=14)
axdx= 1./3.
#APOGEE-RC observations
tdy= (_RCYMAX-_RCYMIN+4.5)/(_RCXMAX-_RCXMIN+4.5)*axdx
obsAxes= pyplot.axes([0.1,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(obsAxes)
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN-2.25,xmax=_RCXMAX+2.25,
ymin=_RCYMIN-2.25,ymax=_RCYMAX+2.25,
dx=_RCDX,dy=_RCDX)
if subsun:
vmin, vmax= 0., 250.
pixrc.plot(lambda x: vlosgal(x),
func=lambda x: numpy.fabs(numpy.median(x)),
zlabel=r'$|\mathrm{median}\ V^{\mathrm{GC}}_{\mathrm{los}}|\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
else:
vmin, vmax= -75., 75.
img= pixrc.plot('VHELIO_AVG',
vmin=vmin,vmax=vmax,overplot=True,
colorbar=False)
resv= pixrc.plot('VHELIO_AVG',
justcalc=True,returnz=True) #for later
bovy_plot.bovy_text(r'$\mathrm{typical\ uncertainty\!:}\ 3\,\mathrm{km\,s}^{-1}$',
bottom_left=True,size=8.25)
bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',top_right=True,size=10.)
pyplot.annotate(r'$\mathrm{APOGEE\!-\!RC\ data}$',
(0.5,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.)
pyplot.axis([pixrc.xmin,pixrc.xmax,pixrc.ymin,pixrc.ymax])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$')
#Colorbar
cbaxes = pyplot.axes([0.1625,(1.-tdy)/2.+tdy+0.065,2.*axdx-0.195,0.02])
CB1= pyplot.colorbar(img,orientation='horizontal',
cax=cbaxes)#,ticks=[-16.,-8.,0.,8.,16.])
CB1.set_label(r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
#Now calculate the expected field
xgrid= numpy.arange(_RCXMIN-2.25+_RCDX/2.,_RCXMAX+2.25+_RCDX/2.,_RCDX)
ygrid= numpy.arange(_RCYMIN-2.25+_RCDX/2.,_RCYMAX+2.25+_RCDX/2.,_RCDX)
xv,yv= numpy.meshgrid(xgrid,ygrid,indexing='ij')
rs= numpy.sqrt(xv**2.+yv**2.)
phis= numpy.arctan2(yv,xv)
d,l= bovy_coords.rphi_to_dl_2d(rs/8.,phis)
expec_vlos= numpy.empty((len(xgrid),len(ygrid)))
for ii in range(len(xgrid)):
for jj in range(len(ygrid)):
expec_vlos[ii,jj]= modelvlosgal(rs[ii,jj],phis[ii,jj],l[ii,jj],
vc=218.,vtsun=242.)
modelAxes= pyplot.axes([0.03+axdx,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(modelAxes)
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$'
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$'
indx= True-numpy.isnan(resv)
plotthis= copy.copy(expec_vlos)
plotthis[numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=xlabel,ylabel=ylabel,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,zorder=3)
if True:
#Now plot the pixels outside the APOGEE data set
plotthis= copy.copy(expec_vlos)
plotthis[True-numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
alpha=0.3,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,
zorder=0)
pyplot.annotate(r'$\mathrm{Bovy\ et.\ al\ (2012)\ model}$',
(1.02,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.,zorder=3)
pyplot.axis([_RCXMIN-2.25,_RCXMAX+2.25,_RCYMIN-2.25,_RCYMAX+2.25])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(xlabel,r'$ $')
#Finally, add a polar plot of the whole disk
res= 51
rmin, rmax= 0.2, 2.4
xgrid= numpy.linspace(0.,2.*numpy.pi*(1.-1./res/2.),
2.*res)
ygrid= numpy.linspace(rmin,rmax,res)
nx= len(xgrid)
ny= len(ygrid)
savefile= 'expec_vlos.sav'
if os.path.exists(savefile):
savefile= open(savefile,'rb')
expec_vlos= pickle.load(savefile)
savefile.close()
else:
expec_vlos= numpy.zeros((nx,ny))
for ii in range(nx):
for jj in range(ny):
R, phi= ygrid[jj], xgrid[ii]
d,l= bovy_coords.rphi_to_dl_2d(R,phi)
expec_vlos[ii,jj]= modelvlosgal(R*8.,phi,l,
vc=218.,vtsun=242.)
save_pickles(savefile,expec_vlos)
plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2.,
xgrid[-1]+(xgrid[1]-xgrid[0])/2.,
len(xgrid)+1)
plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2.,
ygrid[-1]+(ygrid[1]-ygrid[0])/2.,
len(ygrid)+1)
fullmodelAxes= pyplot.axes([-0.05+2.*axdx,(1.-tdy)/2.,axdx,tdy],polar=True)
ax= fullmodelAxes
pyplot.sca(fullmodelAxes)
vmin, vmax= -150., 150.
zlabel= r'$\mathrm{line\!-\!of\!-\!sight\ velocity}\ (\mathrm{km\,s}^{-1})$'
out= ax.pcolor(plotxgrid,plotygrid,expec_vlos.T,cmap='jet',
vmin=vmin,vmax=vmax,clip_on=False)
shrink= 0.8
if False:
CB1= pyplot.colorbar(out,shrink=shrink)
bbox = CB1.ax.get_position().get_points()
CB1.ax.set_position(transforms.Bbox.from_extents(bbox[0,0]+0.025,
bbox[0,1],
bbox[1,0],
bbox[1,1]))
CB1.set_label(zlabel)
from matplotlib.patches import FancyArrowPatch
arr= FancyArrowPatch(posA=(numpy.pi+0.1,1.8),
posB=(3*numpy.pi/2.+0.1,1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (numpy.pi/8.-0.05),
shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax.add_patch(arr)
bovy_plot.bovy_text(numpy.pi+0.17,1.7,r'$\mathrm{Galactic\ rotation}$',
rotation=-30.,size=9.)
radii= numpy.array([0.5,1.,1.5,2.,2.5])
labels= []
for r in radii:
ax.plot(numpy.linspace(0.,2.*numpy.pi,501,),
numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5)
labels.append(r'$%i$' % int(r*8.))
pyplot.rgrids(radii,labels=labels,angle=147.5)
thetaticks = numpy.arange(0,360,45)
# set ticklabels location at x times the axes' radius
ax.set_thetagrids(thetaticks,frac=1.16,backgroundcolor='w',zorder=3)
bovy_plot.bovy_text(3.*numpy.pi/4.+0.06,2.095,r'$\mathrm{kpc}$',size=10.)
pyplot.ylim(0.,2.8)
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the connectors on the modelAxes
xlow=-4.*8.
ylow= 2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMAX+2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
xlow=-4.*8.
ylow= -2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMIN-2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
#Colorbar
cbaxes = pyplot.axes([0.01+2.*axdx,(1.-tdy)/2.+tdy+0.065,axdx-0.125,0.02])
CB1= pyplot.colorbar(out,orientation='horizontal',
cax=cbaxes,ticks=[-150.,-75.,0.,75.,150.])
#CB1.set_label(r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
bovy_plot.bovy_end_print(plotfilename,dpi=300)
return None
def plot_rckinematics(plotfilename,subsun=False):
#Set up 3 axes
bovy_plot.bovy_print(fig_width=8.,axes_labelsize=14)
axdx= 1./3.
#APOGEE-RC observations
tdy= (_RCYMAX-_RCYMIN+4.5)/(_RCXMAX-_RCXMIN+4.5)*axdx
obsAxes= pyplot.axes([0.1,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(obsAxes)
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN-2.25,xmax=_RCXMAX+2.25,
ymin=_RCYMIN-2.25,ymax=_RCYMAX+2.25,
dx=_RCDX,dy=_RCDX)
vmin, vmax= -16.,16.
img= pixrc.plot(lambda x: dvlosgal(x,vtsun=220.+24.),
vmin=vmin,vmax=vmax,overplot=True,
colorbar=False)
resv= pixrc.plot(lambda x: dvlosgal(x,vtsun=220.+24.),
justcalc=True,returnz=True) #for later
pyplot.annotate(r'$\mathrm{APOGEE\!-\!RC\ data}$',
(0.5,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.)
pyplot.axis([pixrc.xmin,pixrc.xmax,pixrc.ymin,pixrc.ymax])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$')
#Colorbar
cbaxes = pyplot.axes([0.1+axdx/2.,(1.-tdy)/2.+tdy+0.065,2.*axdx-0.195,0.02])
CB1= pyplot.colorbar(img,orientation='horizontal',
cax=cbaxes)#,ticks=[-16.,-8.,0.,8.,16.])
CB1.set_label(r'$\mathrm{median}\ \Delta V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
#Now calculate the expected field
expec_vlos= galpy_simulations.vlos_altrect('../sim/bar_altrect_alpha0.015_hivres.sav')*220.
modelAxes= pyplot.axes([0.03+axdx,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(modelAxes)
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$'
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$'
indx= True-numpy.isnan(resv)
plotthis= copy.copy(expec_vlos)
plotthis[numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=xlabel,ylabel=ylabel,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,zorder=3)
if True:
#Now plot the pixels outside the APOGEE data set
plotthis= copy.copy(expec_vlos)
plotthis[True-numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
alpha=0.3,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,
zorder=0)
pyplot.annotate(r'$\mathrm{Favored\ bar\ model}$',
(1.02,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.,zorder=3)
pyplot.axis([_RCXMIN-2.25,_RCXMAX+2.25,_RCYMIN-2.25,_RCYMAX+2.25])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(xlabel,r'$ $')
#Finally, add a polar plot of the whole disk
res= 51
rmin, rmax= 0.2, 2.4
xgrid= numpy.linspace(0.,2.*numpy.pi*(1.-1./res/2.),
2.*res)
ygrid= numpy.linspace(rmin,rmax,res)
expec_vlos= galpy_simulations.vlos_polar('../sim/bar_polar_alpha0.015_hivres.sav')*220.
plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2.,
xgrid[-1]+(xgrid[1]-xgrid[0])/2.,
len(xgrid)+1)
plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2.,
ygrid[-1]+(ygrid[1]-ygrid[0])/2.,
len(ygrid)+1)
fullmodelAxes= pyplot.axes([-0.05+2.*axdx,(1.-tdy)/2.,axdx,tdy],polar=True)
ax= fullmodelAxes
pyplot.sca(fullmodelAxes)
out= ax.pcolor(plotxgrid,plotygrid,expec_vlos.T,cmap='jet',
vmin=vmin,vmax=vmax,clip_on=False)
from matplotlib.patches import FancyArrowPatch
arr= FancyArrowPatch(posA=(numpy.pi+0.1,1.8),
posB=(3*numpy.pi/2.+0.1,1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (numpy.pi/8.-0.05),
shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0,
mutation_aspect=None,fc='k',zorder=10)
ax.add_patch(arr)
bovy_plot.bovy_text(numpy.pi+0.17,1.7,r'$\mathrm{Galactic\ rotation}$',
rotation=-30.,size=9.)
radii= numpy.array([0.5,1.,1.5,2.,2.5])
labels= []
for r in radii:
ax.plot(numpy.linspace(0.,2.*numpy.pi,501,),
numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5)
labels.append(r'$%i$' % int(r*8.))
pyplot.rgrids(radii,labels=labels,angle=147.5)
thetaticks = numpy.arange(0,360,45)
# set ticklabels location at x times the axes' radius
ax.set_thetagrids(thetaticks,frac=1.16,backgroundcolor='w',zorder=3)
bovy_plot.bovy_text(3.*numpy.pi/4.+0.06,2.095,r'$\mathrm{kpc}$',size=10.)
pyplot.ylim(0.,2.8)
# Plot the bar position
ets= numpy.linspace(0.,2.*numpy.pi,501,)
a= 0.421766
b= a*0.4
dtr= numpy.pi/180.
ax.plot(ets,
a*b/numpy.sqrt((b*numpy.cos(ets-25.*dtr))**2.
+(a*numpy.sin(ets-25.*dtr))**2.),
zorder=1,lw=1.5,color='w')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the connectors on the modelAxes
xlow=-4.*8.
ylow= 2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMAX+2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
xlow=-4.*8.
ylow= -2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMIN-2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
bovy_plot.bovy_end_print(plotfilename,dpi=300)
return None
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_metallicity(basesavefilename, datafilename=None):
# First read the sample
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (numpy.fabs(data["RC_GALZ"]) < 0.05) * (data["METALS"] > -1000.0)
print "Using %i stars for low-Z metallicity gradient analysis" % numpy.sum(indx)
data = data[indx]
# First do the metallicity gradient
rs = numpy.arange(0.5, 18.5001, 0.85)
fehs = numpy.zeros_like(rs) + numpy.nan
sigfehs = numpy.zeros_like(rs) + numpy.nan
efehs = numpy.zeros_like(rs) + numpy.nan
for ii in range(len(rs) - 1):
tindx = (data["RC_GALR"] > rs[ii]) * (data["RC_GALR"] <= rs[ii + 1])
if numpy.sum(tindx) < 20:
continue
fehs[ii] = numpy.median(data["METALS"][tindx])
sigfehs[ii] = numpy.std(data["METALS"][tindx])
efehs[ii] = numpy.std(data["METALS"][tindx]) / numpy.sqrt(numpy.sum(tindx))
# Plot
bovy_plot.bovy_print(fig_width=6.0)
bovy_plot.bovy_plot(
rs + 0.5 * (rs[1] - rs[0]),
fehs,
"ko",
xlabel=r"$R\,(\mathrm{kpc})$",
ylabel=r"$\mathrm{median\ [Fe/H]}\,(\mathrm{dex})$",
xrange=[0.0, 15.0],
yrange=[-0.6, 0.5],
zorder=10,
)
pyplot.errorbar(rs + 0.5 * (rs[1] - rs[0]), fehs, yerr=efehs, marker="None", ls="none", color="k", zorder=9)
# bovy_plot.bovy_plot(data['RC_GALR'],data['METALS'],'k,',overplot=True)
# FIT
indx = True - numpy.isnan(fehs)
bestfit = optimize.curve_fit(
linfit, (rs + 0.5 * (rs[1] - rs[0]))[indx], fehs[indx], sigma=efehs[indx], p0=(-0.1, 0.0)
)
if _JACKERRS:
fitrs = (rs + 0.5 * (rs[1] - rs[0]))[indx]
fitfehs = fehs[indx]
fitefehs = efehs[indx]
np = len(fitfehs)
fitp0 = []
fitp1 = []
for ii in range(np):
findx = numpy.ones(np, dtype="bool")
findx[ii] = False
tbestfit = optimize.curve_fit(linfit, fitrs[findx], fitfehs[findx], sigma=fitefehs[findx], p0=(-0.1, 0.0))
fitp0.append(tbestfit[0][0])
fitp1.append(tbestfit[0][1])
fitp0 = numpy.array(fitp0)
fitp1 = numpy.array(fitp1)
p0err = numpy.sqrt((np - 1.0) * numpy.var(fitp0))
p1err = numpy.sqrt((np - 1.0) * numpy.var(fitp1))
print "radial metallicity errors"
print bestfit[0][0], p0err
print bestfit[0][1], p1err
bovy_plot.bovy_plot(
rs + 0.5 * (rs[1] - rs[0]),
bestfit[0][0] * ((rs + 0.5 * (rs[1] - rs[0]) - 8.0)) + bestfit[0][1],
"k--",
overplot=True,
)
bovy_plot.bovy_text(r"$|Z| < 50\,\mathrm{pc}$", top_right=True, size=18.0)
bovy_plot.bovy_text(
r"$[\mathrm{Fe/H}] = %.2f\,\frac{\mathrm{dex}}{\mathrm{kpc}}\,(R-8\,\mathrm{kpc}) + %.2f$"
% (bestfit[0][0], bestfit[0][1]),
bottom_left=True,
size=18.0,
)
bovy_plot.bovy_end_print(basesavefilename + "_radialgradient." + _EXT)
# Now plot azimuthal stuff
# First read the sample again
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (numpy.fabs(data["RC_GALZ"]) < 0.25) * (data["METALS"] > -1000.0)
print "Using %i stars for low-Z azimuthal metallicity gradient analysis" % numpy.sum(indx)
data = data[indx]
pix = pixelize_sample.pixelXY(data)
bovy_plot.bovy_print()
pix.plot("METALS", zlabel=r"$\mathrm{median\ [Fe/H]}\,(\mathrm{dex})$", vmin=-0.4, vmax=0.3)
if "l30" in appath._APOGEE_REDUX:
bovy_plot.bovy_text(r"$\mathrm{typical\ uncertainty\!:}\ 0.015\,\mathrm{dex}$", bottom_left=True, size=17.0)
elif "v6" in appath._APOGEE_REDUX and int(appath._APOGEE_REDUX[1:]) > 600:
bovy_plot.bovy_text(r"$\mathrm{typical\ uncertainty\!:}\ 0.015\,\mathrm{dex}$", bottom_left=True, size=17.0)
else:
bovy_plot.bovy_text(r"$\mathrm{typical\ uncertainty\!:}\ 0.02\,\mathrm{dex}$", bottom_left=True, size=18.0)
bovy_plot.bovy_text(r"$|Z| < 250\,\mathrm{pc}$", top_right=True, size=18.0)
bovy_plot.bovy_end_print(basesavefilename + "_XY." + _EXT)
# R,phi
pix = pixelize_sample.pixelXY(data, rphi=True, ymin=-22.5, ymax=37.5, dy=5.0)
bovy_plot.bovy_print()
metals2d = pix.plot(
"METALS",
# func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
zlabel=r"$\delta\,\mathrm{median\ [Fe/H]}\,(\mathrm{dex})$",
# vmin=0.,vmax=0.05,submediany=False,returnz=True)
vmin=-0.1,
vmax=0.1,
submediany=True,
returnz=True,
)
sigs = []
for ii in range(metals2d.shape[0]):
tindx = True - numpy.isnan(metals2d[ii, :])
if numpy.sum(tindx) > 2:
sigs.append(1.4826 * numpy.median(numpy.fabs(metals2d[ii, tindx])))
sigs = numpy.array(sigs)
print numpy.median(sigs), numpy.amax(sigs), sigs
# bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename + "_RPHI." + _EXT)
# vs. alpha
# First read the sample
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (numpy.fabs(data["RC_GALZ"]) < 0.1) * (data["METALS"] > -1000.0)
data = data[indx]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
data["METALS"],
data["ALPHAFE"],
"k.",
ms=2.0,
xlabel=r"$[\mathrm{Fe/H}]\,(\mathrm{dex})$",
ylabel=r"$[\alpha/\mathrm{Fe}]\,(\mathrm{dex})$",
xrange=[-1.0, 0.5],
yrange=[-0.15, 0.35],
onedhists=True,
bins=31,
)
bovy_plot.bovy_text(r"$|Z| < 100\,\mathrm{pc}$", top_right=True, size=18.0)
bovy_plot.bovy_text(r"$\mathrm{raw\ sample\ counts}$", bottom_left=True, size=18.0)
bovy_plot.bovy_end_print(basesavefilename + "_alpha." + _EXT)
# vs. alpha
# First read the sample
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (numpy.fabs(data["RC_GALZ"]) > 0.5) * (numpy.fabs(data["RC_GALZ"]) < 1.0) * (data["METALS"] > -1000.0)
data = data[indx]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
data["METALS"],
data["ALPHAFE"],
"k.",
ms=2.0,
xlabel=r"$[\mathrm{Fe/H}]\,(\mathrm{dex})$",
ylabel=r"$[\alpha/\mathrm{Fe}]\,(\mathrm{dex})$",
xrange=[-1.0, 0.5],
yrange=[-0.15, 0.35],
onedhists=True,
bins=31,
)
bovy_plot.bovy_text(r"$0.5\,\mathrm{kpc} < |Z| < 1\,\mathrm{kpc}$", top_right=True, size=18.0)
bovy_plot.bovy_text(r"$\mathrm{raw\ sample\ counts}$", bottom_left=True, size=18.0)
bovy_plot.bovy_end_print(basesavefilename + "_alpha_0.5z1." + _EXT)
# vs. alpha
# First read the sample
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (numpy.fabs(data["RC_GALZ"]) > 1.0) * (numpy.fabs(data["RC_GALZ"]) < 2.0) * (data["METALS"] > -1000.0)
data = data[indx]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
data["METALS"],
data["ALPHAFE"],
"k.",
ms=2.0,
xlabel=r"$[\mathrm{Fe/H}]\,(\mathrm{dex})$",
ylabel=r"$[\alpha/\mathrm{Fe}]\,(\mathrm{dex})$",
xrange=[-1.0, 0.5],
yrange=[-0.15, 0.35],
onedhists=True,
bins=21,
)
bovy_plot.bovy_text(r"$1\,\mathrm{kpc} < |Z| < 2\,\mathrm{kpc}$", top_right=True, size=18.0)
bovy_plot.bovy_text(r"$\mathrm{raw\ sample\ counts}$", bottom_left=True, size=18.0)
bovy_plot.bovy_end_print(basesavefilename + "_alpha_1z2." + _EXT)
# vs. alpha
# First read the sample
if not datafilename is None:
data = fitsio.read(datafilename)
else:
data = apread.rcsample()
if _ADDLLOGGCUT:
data = data[data["ADDL_LOGG_CUT"] == 1]
# Cut
indx = (data["STAT"] == 1) * (data["METALS"] > -1000.0)
data = data[indx]
hist, edges = numpy.histogramdd(
numpy.array([data["METALS"], data["ALPHAFE"]]).T, bins=[15, 10], range=[[-1.0, 0.5], [-0.15, 0.35]]
)
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(
hist.T,
contours=False,
shrink=0.78,
cmap="gist_yarg",
origin="lower",
xrange=[-1.0, 0.5],
yrange=[-0.15, 0.35],
vmin=50.0,
vmax=1000.0,
xlabel=r"$[\mathrm{Fe/H}]\,(\mathrm{dex})$",
ylabel=r"$[\alpha/\mathrm{Fe}]\,(\mathrm{dex})$",
interpolation="nearest",
colorbar=True,
)
bovy_plot.bovy_text(r"$\mathrm{main\ sample}$", top_right=True, size=18.0)
bovy_plot.bovy_text(r"$\mathrm{raw\ sample\ counts}$", bottom_left=True, size=18.0)
bovy_plot.bovy_end_print(basesavefilename + "_alpha_main." + _EXT)
def determine_vsolar(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
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN,xmax=_RCXMAX,
ymin=_RCYMIN,ymax=_RCYMAX,
dx=_RCDX,dy=_RCDX)
vsolars= numpy.linspace(0.,40.,51)
lpower= large_scale_power(pixrc,vsolars,vc=220.,dx=_RCDX)
bovy_plot.bovy_print()
line1= bovy_plot.bovy_plot(vsolars,lpower,'k-',lw=2.,
xrange=[vsolars[0],vsolars[-1]],
yrange=[0.,22.9],
xlabel=r'$V_{\odot-c}\,(\mathrm{km\,s}^{-1})$',
ylabel=r'$\sqrt\langle P_k(0.2 < k / (\mathrm{kpc}^{-1}) < 0.9)\rangle\,(\mathrm{km\,s}^{-1})$')
#Find the minimum by fitting a second order polynomial
p= numpy.polyfit(vsolars,lpower,2)
minvsolar= -0.5*p[1]/p[0]
bovy_plot.bovy_plot([minvsolar,minvsolar],[-10.,100.],'k-',lw=0.8,
overplot=True)
bovy_plot.bovy_text(22.65,1.,
r'$V_{\odot-c}=%.1f\,\mathrm{km\,s}^{-1}$' % minvsolar,
size=15.)
lpower240= large_scale_power(pixrc,vsolars,vc=240.,dx=_RCDX)
line2= bovy_plot.bovy_plot(vsolars,lpower240,'k--',lw=2.,overplot=True)
lpower200= large_scale_power(pixrc,vsolars,vc=200.,dx=_RCDX)
line3= bovy_plot.bovy_plot(vsolars,lpower200,'k-.',lw=2.,overplot=True)
lpowerbetam0p2= large_scale_power(pixrc,vsolars,dx=_RCDX,beta=-0.1)
line4= bovy_plot.bovy_plot(vsolars,lpowerbetam0p2,
'--',dashes=(20,10),
color='0.6',lw=3.,overplot=True)
lpowerbetap0p2= large_scale_power(pixrc,vsolars,dx=_RCDX,beta=0.1)
line5= bovy_plot.bovy_plot(vsolars,lpowerbetap0p2,
'--',dashes=(20,10),
color='0.4',lw=3.,overplot=True)
lva= large_scale_power(pixrc,vsolars,vc=220.,dx=_RCDX,hs=6./8.,hR=3./8.)
line6= bovy_plot.bovy_plot(vsolars,lva,
'-',color='0.8',lw=5.,overplot=True,zorder=-1)
#Add legend
legend1= pyplot.legend((line3[0],line1[0],line2[0]),
(r'$V_c = 200\,\mathrm{km\,s}^{-1}$',
r'$V_c = 220\,\mathrm{km\,s}^{-1}$',
r'$V_c = 240\,\mathrm{km\,s}^{-1}$'),
loc='upper left',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':14},
frameon=False)
legend2= pyplot.legend((line4[0],line5[0]),
(r'$\beta = -0.1$',
r'$\beta = \phantom{-}0.1$'),
loc='upper right',bbox_to_anchor=(1.,.96),
numpoints=2,
prop={'size':14},
frameon=False)
legend3= pyplot.legend((line6[0],),
(r'$\Delta V_a(R_0) =5\,\mathrm{km\,s}^{-1}$',),
loc='lower left',#bbox_to_anchor=(1.,.96),
numpoints=2,
prop={'size':12},
frameon=False)
pyplot.gca().add_artist(legend1)
pyplot.gca().add_artist(legend2)
pyplot.gca().add_artist(legend3)
bovy_plot.bovy_end_print(plotfilename)
return None
