def apogeerc(**kwargs):
"""
NAME:
apogeerc
PURPOSE:
read the APOGEE RC data
INPUT:
IF the apogee package is not installed:
dr= (13) SDSS data release
ELSE you can use the same keywords as apogee.tools.read.rcsample:
main= (default: False) if True, only select stars in the main survey
dr= data reduction to load the catalog for (automatically set based on APOGEE_REDUX if not given explicitly)
OUTPUT:
APOGEE RC sample data
HISTORY:
2013-10-08 - Written - Bovy (IAS)
"""
if not _APOGEE_LOADED:
warnings.warn(
"Falling back on simple APOGEE interface; for more functionality, install the jobovy/apogee package"
)
dr = kwargs.get('dr', 13)
filePath = path.apogeercPath(dr=dr)
if not os.path.exists(filePath):
download.apogeerc(dr=dr)
return fitsio.read(filePath, 1)
else:
return apread.rcsample(**kwargs)
def get_rcsample():
"""
NAME:
get_rcsample
PURPOSE:
get the RC sample
INPUT:
None so far
OUTPUT:
sample
HISTORY:
2015-02-10 - Started - Bovy (IAS@KITP)
"""
data = apread.rcsample()
# Cut to statistical sample
data = data[data['STAT'] == 1]
# Add the M_H-based distances
data = esutil.numpy_util.add_fields(data, [('RC_DIST_H', float),
('RC_DM_H', float),
('RC_GALR_H', float),
('RC_GALPHI_H', float),
('RC_GALZ_H', float)])
rcd = rcdist()
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
data['RC_DIST_H'] = rcd(jk, z, appmag=data['H0'], mh=True)
data['RC_DM_H'] = 5. * numpy.log10(data['RC_DIST_H']) + 10.
XYZ = bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST_H'],
degree=True)
R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.,
Zsun=0.025)
data['RC_GALR_H'] = R
data['RC_GALPHI_H'] = phi
data['RC_GALZ_H'] = Z
# Add the average alpha/Fe
data = esutil.numpy_util.add_fields(data, [('AVG_ALPHAFE', float)])
data['AVG_ALPHAFE'] = avg_alphafe(data)
# Apply -0.1 offset in [Fe/H]
data[_FEHTAG] += -0.10
# Remove locations outside of the Pan-STARRS dust map
# In the Southern hemisphere
data = data[data['LOCATION_ID'] != 4266] #240,-18
data = data[data['LOCATION_ID'] != 4331] #5.5,-14.2
data = data[data['LOCATION_ID'] != 4381] #5.2,-12.2
data = data[data['LOCATION_ID'] != 4332] #1,-4
data = data[data['LOCATION_ID'] != 4329] #0,-5
data = data[data['LOCATION_ID'] != 4351] #0,-2
data = data[data['LOCATION_ID'] != 4353] #358,0
data = data[data['LOCATION_ID'] != 4385] #358.6,1.4
# Close to the ecliptic pole where there's no data (is it the ecliptic pole?
data = data[data['LOCATION_ID'] != 4528] #120,30
data = data[data['LOCATION_ID'] != 4217] #123,22.4
# Remove stars w/ DM < 8.49, because for standard candle RC, these cannot be in the sample
data = data[data['RC_DM_H'] > 8.49]
return data
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 get_rcsample():
"""
NAME:
get_rcsample
PURPOSE:
get the RC sample
INPUT:
None so far
OUTPUT:
sample
HISTORY:
2015-02-10 - Started - Bovy (IAS@KITP)
"""
data= apread.rcsample()
# Cut to statistical sample
data= data[data['STAT'] == 1]
# Add the M_H-based distances
data= esutil.numpy_util.add_fields(data,[('RC_DIST_H', float),
('RC_DM_H', float),
('RC_GALR_H', float),
('RC_GALPHI_H', float),
('RC_GALZ_H', float)])
rcd= rcdist()
jk= data['J0']-data['K0']
z= isodist.FEH2Z(data['METALS'],zsolar=0.017)
data['RC_DIST_H']= rcd(jk,z,appmag=data['H0'],mh=True)
data['RC_DM_H']= 5.*numpy.log10(data['RC_DIST_H'])+10.
XYZ= bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST_H'],
degree=True)
R,phi,Z= bovy_coords.XYZ_to_galcencyl(XYZ[:,0],
XYZ[:,1],
XYZ[:,2],
Xsun=8.,Zsun=0.025)
data['RC_GALR_H']= R
data['RC_GALPHI_H']= phi
data['RC_GALZ_H']= Z
# Add the average alpha/Fe
data= esutil.numpy_util.add_fields(data,[('AVG_ALPHAFE', float)])
data['AVG_ALPHAFE']= avg_alphafe(data)
# Apply -0.1 offset in [Fe/H]
data[_FEHTAG]+= -0.10
# Remove locations outside of the Pan-STARRS dust map
# In the Southern hemisphere
data= data[data['LOCATION_ID'] != 4266] #240,-18
data= data[data['LOCATION_ID'] != 4331] #5.5,-14.2
data= data[data['LOCATION_ID'] != 4381] #5.2,-12.2
data= data[data['LOCATION_ID'] != 4332] #1,-4
data= data[data['LOCATION_ID'] != 4329] #0,-5
data= data[data['LOCATION_ID'] != 4351] #0,-2
data= data[data['LOCATION_ID'] != 4353] #358,0
data= data[data['LOCATION_ID'] != 4385] #358.6,1.4
# Close to the ecliptic pole where there's no data (is it the ecliptic pole?
data= data[data['LOCATION_ID'] != 4528] #120,30
data= data[data['LOCATION_ID'] != 4217] #123,22.4
# Remove stars w/ DM < 8.49, because for standard candle RC, these cannot be in the sample
data= data[data['RC_DM_H'] > 8.49]
return data
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 apogeerc(**kwargs):
"""
NAME:
apogeerc
PURPOSE:
read the APOGEE RC data
INPUT:
main= (default: False) if True, only select stars in the main survey
dr= data reduction to load the catalog for (automatically set based on APOGEE_REDUX if not given explicitly)
OUTPUT:
APOGEE RC sample data
HISTORY:
2013-10-08 - Written - Bovy (IAS)
"""
if not _APOGEE_LOADED:
raise ImportError("Loading the APOGEE RC data requires the jobovy/apogee module to be installed")
return apread.rcsample(**kwargs)
def match_apokasc_rc(rcfile=None):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
print len(rcdata), numpy.sum(rcdata['ADDL_LOGG_CUT'])
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def apogeerc(xmatch=None, **kwargs):
"""
NAME:
apogeerc
PURPOSE:
read the APOGEE RC data
INPUT:
IF the apogee package is not installed:
dr= (14) SDSS data release
ELSE you can use the same keywords as apogee.tools.read.rcsample:
main= (default: False) if True, only select stars in the main survey
dr= data reduction to load the catalog for (automatically set based on APOGEE_REDUX if not given explicitly)
ALWAYS ALSO
xmatch= (None) if set, cross-match against a Vizier catalog (e.g., vizier:I/345/gaia2 for Gaia DR2) using gaia_tools.xmatch.cds and return the overlap
+gaia_tools.xmatch.cds keywords
OUTPUT:
APOGEE RC sample data[,xmatched table]
HISTORY:
2013-10-08 - Written - Bovy (IAS)
2018-05-09 - Add xmatch - Bovy (UofT)
"""
if not _APOGEE_LOADED:
warnings.warn(
"Falling back on simple APOGEE interface; for more functionality, install the jobovy/apogee package"
)
dr = kwargs.get('dr', 14)
filePath = path.apogeercPath(dr=dr)
if not os.path.exists(filePath):
download.apogeerc(dr=dr)
data = fitsread(filePath, 1)
if not xmatch is None:
ma, mai = _xmatch_cds(data, xmatch, filePath, **kwargs)
return (data[mai], ma)
else:
return data
else:
kwargs['xmatch'] = xmatch
return apread.rcsample(**kwargs)
def match_apokasc_rc(rcfile=None,addl_logg_cut=False):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
if addl_logg_cut:
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
print "RC catalog has %i entries ..." % len(rcdata)
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def match_apokasc_rc(rcfile=None,addl_logg_cut=False):
#First read apokasc
kascdata= apread.apokasc()
#Then read rc
if rcfile is None:
rcdata= apread.rcsample()
else:
rcdata= fitsio.read(rcfile)
if addl_logg_cut:
rcdata= rcdata[rcdata['ADDL_LOGG_CUT'] == 1]
print("RC catalog has %i entries ..." % len(rcdata))
#Match
h=esutil.htm.HTM()
m1,m2,d12 = h.match(kascdata['RA'],kascdata['DEC'],
rcdata['RA'],rcdata['DEC'],
2./3600.,maxmatch=1)
kascdata= esutil.numpy_util.add_fields(kascdata,[('RC', int)])
kascdata['RC']= 0
kascdata['RC'][m1]= 1
return kascdata
def returncat():
RCcat = apread.rcsample()
# Now need to read in ascii Ness Sample
fields = ['APOGEE_ID', 'RC_DIST', 'RC_GALR', 'RC_GALPHI', 'RC_GALZ',
'TEFF', 'LOGG', 'FE_H', 'ALPHAFE', 'RC_GALR']
# Using the Rccatalog to provide data types for making record array
# with ness catalog
# The last 'RC_GALR' is just to steal the datatype to make the age array
ness_dtypes = [RCcat[field].dtype for field in fields]
ness_cat = read_ness_cat()
# TEST NESS CAT
if (ness_cat['ID'] == RCcat['APOGEE_ID']).all():
print("Ness Catalog and RC Catalog successfully merged.")
print('RC catalog and Cannon catalog have matching APOGEE_IDs')
else:
print("Ness Catalog merge unsuccessful. You lose!")
sys.exit()
newnames = ["cannon_"+i for i in ness_cat.dtype.names]
ness_cat.dtype.names = newnames
# Just add in delta corrections from PPMXL
pm_corr_file = os.path.join(_apogee_data_dir, 'qso_gal_pm_corrections')
corr_fields = ['corr_RA', 'corr_DEC', 'dgaluRA', 'dgaluDEC',
'dqsouRA', 'dqsouDEC']
corr_dtypes = ness_dtypes[1:7]
ndtypes_corr = [(i, j) for i, j in zip(corr_fields, corr_dtypes)]
pmcorr_cat = np.genfromtxt(pm_corr_file, dtype=ndtypes_corr)
pmcorr_cat.sort(order='corr_RA')
# Need to sort all against RA, then join
print ('Sum difference of RA in correction file and RC cat: {0}'.format(
np.sum(pmcorr_cat['corr_RA'] - RCcat['RA'])))
print ('Sum difference of DEC in correction file and RC cat: {0}'.format(
np.sum(pmcorr_cat['corr_DEC'] - RCcat['DEC'])))
RC_cannon_cat = rfn.merge_arrays([RCcat, ness_cat, pmcorr_cat],
asrecarray=True, flatten=True)
return RC_cannon_cat
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 determine_vsolar_error(spiral=False):
#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]
#Set up the Dehnen DF that we will sample from
dfc= dehnendf(beta=0.,profileParams=(3./8.,33.3,31.4/220.))
trueVsolar= 24./220.
ntrials= 100
mockvsolars= []
for ii in range(ntrials):
print "Working on %i / %i" % (ii+1,ntrials)
mockvsolars.append(determine_vsolar_mock(data,dfc,trueVsolar,spiral))
mockvsolars= numpy.array(mockvsolars)
print mockvsolars
print numpy.mean(mockvsolars-24.), numpy.std(mockvsolars)
print numpy.sum(numpy.fabs(mockvsolars-24.) > 1.)/float(ntrials)
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})$')
bovy_plot.bovy_end_print(plotfilename)
if True:
spvlos= galpy_simulations.vlos('../sim/bar_rect_alpha0.015_hivres.sav')[1::2,1::2]
potscale= 1.
down95= numpy.zeros(len(ds))
for ii in range(len(ds)):
tdist= numpy.cumsum(vlos_dist[ii,:])/numpy.sum(vlos_dist[ii,:])
mid50[ii]= plotvloss[numpy.argmin(numpy.fabs(tdist-0.5))]
down95[ii]= plotvloss[numpy.argmin(numpy.fabs(tdist-0.025))]
up95[ii]= plotvloss[numpy.argmin(numpy.fabs(tdist-0.975))]
interpmid50= interpolate.UnivariateSpline(ds,mid50,k=3)
interpup95= interpolate.UnivariateSpline(ds,up95,k=3)
interpdown95= interpolate.UnivariateSpline(ds,down95,k=3)
#Plot median
bovy_plot.bovy_plot(plotds,interpmid50(ds)+15.,
'w--',lw=2.,overplot=True)
bovy_plot.bovy_plot(plotds,interpmid50(ds),'w-',lw=2.,overplot=True)
#Plot 95%
bovy_plot.bovy_plot(plotds,interpup95(ds),'-',color='0.3',overplot=True)
bovy_plot.bovy_plot(plotds,interpdown95(ds),'-',color='0.3',overplot=True)
data= apread.rcsample()
data= data[(numpy.fabs(data['GLON']-90.) < 5.)*(numpy.fabs(data['RC_GALZ']) < 0.05)]
print numpy.amin(data['GLON'])
print numpy.amax(data['GLON'])
print len(data)
bovy_plot.bovy_plot(data['RC_DIST'],data['VHELIO_AVG'],
'o',mfc='.45',mec='0.45',
ms=5.,overplot=True,zorder=10)
bovy_plot.bovy_text(r'$l=90^\circ,\, |Z| < 50\,\mathrm{pc}$',
bottom_left=True,size=18.)
bovy_plot.bovy_text(r'$\mathrm{solar\ motion}\ =\ 25\,\mathrm{km\,s}^{-1}$'
+'\n'+r'$\mathrm{dashed}\ \rightarrow\ 10\,\mathrm{km\,s}^{-1}$',
top_right=True,size=18.)
bovy_plot.bovy_end_print(sys.argv[1])
def get_spectra(name, red_clump, location):
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal':
path = '/Users/chloecheng/Personal/' + str(name).replace(' ', '') + '.hdf5'
elif location == 'server':
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(' ', '') + '.hdf5'
#If the data file for this cluster exists, save the data to variables
if glob.glob(path):
if red_clump == 'False':
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name, ' complete.')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else:
#Get red clump stars from rcsample
rc_data = rcsample(dr='14')
rc_stars = []
for i in range(len(rc_data)):
#rc_stars.append(rc_data[i][2]) - REMOVE IN FINAL VERSION
rc_stars.append(rc_data[i][2].decode('UTF-8'))
rc_stars = np.array(rc_stars)
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(use_astroNN_abundances=True)
unique_apoids,unique_inds = np.unique(apogee_cat['APOGEE_ID'],return_index=True)
apogee_cat = apogee_cat[unique_inds]
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits')
cls = cls[1].data
#Read in information about cluster members
members = afits.open('occam_member-DR14.fits')
members = members[1].data
#Select all members of a given cluster
cluster_members = (members['CLUSTER']==name) & (members['MEMBER_FLAG']=='GM') #second part of the mask indicates to only use giant stars
member_list = members[cluster_members]
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),member_list['APOGEE_ID'])
apogee_cluster_data = apogee_cat[cluster_inds]
T = photometric_Teff(apogee_cluster_data)
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list['APOGEE_ID']
cluster_marked = np.copy(cluster_stars)
for i in range(len(cluster_stars)):
for j in range(len(rc_stars)):
if cluster_stars[i] == rc_stars[j]:
cluster_marked[i] = np.nan
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(member_list)
spectra = np.zeros((number_of_members, 7514))
spectra_errs = np.zeros((number_of_members, 7514))
bitmask = np.zeros((number_of_members, 7514))
for s,star in enumerate(apogee_cluster_data):
spectra[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=1,header=False,dr='14',aspcapWavegrid=True)
spectra_errs[s] = apread.aspcapStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=2,header=False,dr='14',aspcapWavegrid=True)
bitmask[s] = apread.apStar(star['LOCATION_ID'],star['APOGEE_ID'],ext=3,header=False,dr='14', aspcapWavegrid=True)[1]
#Set all entries in bitmask to integers
bitmask = bitmask.astype(int)
bitmask_flip = np.zeros_like(bitmask)
for i in range(len(spectra)):
for j in range(7514):
if bitmask[i][j] == 0:
bitmask_flip[i][j] = 1
else:
bitmask_flip[i][j] = 0
#Remove empty spectra
full_spectra = []
full_spectra_errs = []
full_bitmask = []
full_T = []
full_stars = []
for i in range(len(spectra)):
if any(spectra[i,:] != 0):
full_spectra.append(spectra[i])
full_spectra_errs.append(spectra_errs[i])
full_bitmask.append(bitmask_flip[i])
full_T.append(T[i])
full_stars.append(i)
full_spectra = np.array(full_spectra)
full_spectra_errs = np.array(full_spectra_errs)
full_bitmask = np.array(full_bitmask)
full_T = np.array(full_T)
full_stars = np.array(full_stars)
full_marked_stars = cluster_marked[full_stars]
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(full_spectra)
masked_spectra_errs = np.empty_like(full_spectra_errs)
masked_spectra[:] = np.nan
masked_spectra_errs[:] = np.nan
#Mask the spectra
for i in range(len(full_spectra)):
for j in range(7514):
if full_bitmask[i][j] != 0:
masked_spectra[i][j] = full_spectra[i][j]
masked_spectra_errs[i][j] = full_spectra_errs[i][j]
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (full_T < 5000)
final_spectra = masked_spectra[good_T_inds]
final_spectra_errs = masked_spectra_errs[good_T_inds]
good_T = full_T[good_T_inds]
apogee_cluster_data = apogee_cluster_data[good_T_inds]
full_bitmask = full_bitmask[good_T_inds]
final_stars = full_marked_stars[good_T_inds]
rgs = (final_stars != 'nan') #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(final_spectra_errs)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/final_spectra_errs[i][j] <= 200:
spectra_err_200[i][j] = final_spectra_errs[i][j]
else:
spectra_err_200[i][j] = final_spectra[i][j]/200
#Cut errors with SNR of less than 50
spectra_50 = np.copy(final_spectra)
spectra_err_50 = np.copy(spectra_err_200)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j]/spectra_err_200[i][j] <= 50:
spectra_50[i][j] = np.nan
spectra_err_50[i][j] = np.nan
#Cut red clumps
logg = apogee_cluster_data['LOGG']
apogee_cluster_data_final = apogee_cluster_data[rgs]
spectra_final = spectra_50[rgs]
spectra_err_final = spectra_err_50[rgs]
T_final = good_T[rgs]
bitmask_final = full_bitmask[rgs]
if red_clump == 'False':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete')
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete')
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
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}$',),
loc='lower left',#bbox_to_anchor=(1.,.96),
numpoints=2,
prop={'size':12},
frameon=False)
pyplot.gca().add_artist(legend1)
pyplot.gca().add_artist(legend2)
pyplot.gca().add_artist(legend3)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_rckinematics(plotfilename,subsun=False):
#Set up 3 axes
bovy_plot.bovy_print(fig_width=8.,axes_labelsize=14)
axdx= 1./3.
#APOGEE-RC observations
tdy= (_RCYMAX-_RCYMIN+4.5)/(_RCXMAX-_RCXMIN+4.5)*axdx
obsAxes= pyplot.axes([0.1,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(obsAxes)
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN-2.25,xmax=_RCXMAX+2.25,
ymin=_RCYMIN-2.25,ymax=_RCYMAX+2.25,
dx=_RCDX,dy=_RCDX)
if subsun:
vmin, vmax= 0., 250.
pixrc.plot(lambda x: vlosgal(x),
func=lambda x: numpy.fabs(numpy.median(x)),
zlabel=r'$|\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_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)
import numpy
import apogee.tools.read as apread
_DATA = apread.rcsample() #such that we can re-use it in different tests
def test_int64():
#Test that there aren't 64-bit integers
for key in _DATA.dtype.names:
assert not numpy.issubdtype(
_DATA[key].dtype, numpy.int64
), "Tag '%s' in the RC catalog is a 64-bit signed integer that might give problems for some readers" % key
return None
def get_spectra(name, red_clump, location):
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'PJ_26')
red_clump : bool
If the red clump stars in rcsample are to be removed, set to True. If all stars are to be used,
set to False.
Returns
-------
cluster_data_full (all stars) or cluster_data (red clumps removed) : structured array
All cluster data from APOGEE
cluster_spectra_full (all stars) or cluster_spectra (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
cluster_spectra_errs_full (all stars) or cluster_spectra_errs (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
cluster_T_full (all stars) or cluster_T (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
if location == 'personal':
path = '/Users/chloecheng/Personal/' + str(name) + '.hdf5'
elif location == 'server':
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name) + '.hdf5'
#If the data file for this cluster exists, save the data to variables
if glob.glob(path):
if red_clump == 'False':
file = h5py.File(path, 'r')
cluster_data_full = file['apogee_cluster_data'][()]
cluster_spectra_full = file['spectra'][()]
cluster_spectra_errs_full = file['spectra_errs'][()]
cluster_T_full = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name, ' complete.')
return cluster_data_full, cluster_spectra_full, cluster_spectra_errs_full, cluster_T_full, full_bitmask
elif red_clump == 'True':
file = h5py.File(path, 'r')
cluster_data = file['apogee_cluster_data'][()]
cluster_spectra = file['spectra'][()]
cluster_spectra_errs = file['spectra_errs'][()]
cluster_T = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name, ' complete.')
return cluster_data, cluster_spectra, cluster_spectra_errs, cluster_T, bitmask_final
#If the file does not exist
else:
#Get red clump stars from rcsample
rc_data = rcsample(dr='14')
rc_stars = []
for i in range(len(rc_data)):
if location == 'personal':
rc_stars.append(rc_data[i][2])
elif location == 'server':
rc_stars.append(rc_data[i][2].decode('UTF-8'))
rc_stars = np.array(rc_stars)
#Read in PJ catalogue data
if location == 'personal':
apogee_cluster_data = np.load(
'/Users/chloecheng/Personal/published_clusters.npy')
elif location == 'server':
apogee_cluster_data = np.load(
'/geir_data/scr/ccheng/AST425/Personal/published_clusters.npy')
#Get temperatures
#T = photometric_Teff(apogee_cluster_data)
T = apogee_cluster_data['TEFF']
#Get spectra for each star
number_of_members = 360
spectra = np.zeros((number_of_members, 7514))
spectra_errs = np.zeros((number_of_members, 7514))
bitmask = np.zeros((number_of_members, 7514))
missing_spectra = []
stars = []
for s, star in enumerate(apogee_cluster_data):
loc = star['FIELD'].decode('utf-8')
apo = star['APOGEE_ID'].decode('utf-8')
stars.append(apo)
try:
spectra[s] = apread.aspcapStar(
loc,
apo,
ext=1,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))
spectra_errs[s] = apread.aspcapStar(
loc,
apo,
ext=2,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))
bitmask[s] = apread.apStar(
loc,
apo,
ext=3,
header=False,
dr='16',
aspcapWavegrid=True,
telescope=star['TELESCOPE'].decode('utf-8'))[1]
#If the spectrum is missing, set bitmask to value that will be removed
except OSError:
bitmask[s] = -1.0
missing_spec.append(s)
print('missing ', star['APOGEE_ID'].decode("utf-8"))
#Mark red clump stars
PJ_stars = np.array(stars)
PJ_marked = np.copy(PJ_stars)
for i in range(len(PJ_stars)):
for j in range(len(rc_stars)):
if PJ_stars[i] == rc_stars[j]:
PJ_marked[i] = np.nan
#Set all entries in bitmask to integers
bitmask = bitmask.astype(int)
bitmask_flip = np.zeros_like(bitmask)
for i in range(len(spectra)):
for j in range(7514):
if bitmask[i][j] == 0:
bitmask_flip[i][j] = 1
else:
bitmask_flip[i][j] = 0
#Remove empty spectra
full_spectra = []
full_spectra_errs = []
full_bitmask = []
full_stars = []
full_T = []
for i in range(len(spectra)):
if any(spectra[i, :] != 0):
full_spectra.append(spectra[i])
full_spectra_errs.append(spectra_errs[i])
full_bitmask.append(bitmask_flip[i])
full_stars.append(i)
full_T.append(T[i])
full_spectra = np.array(full_spectra)
full_spectra_errs = np.array(full_spectra_errs)
full_bitmask = np.array(full_bitmask)
full_stars = np.array(full_stars)
full_T = np.array(full_T)
full_marked_stars = PJ_marked[full_stars]
#Create array of nans to replace flagged values in spectra
masked_spectra = np.empty_like(full_spectra)
masked_spectra_errs = np.empty_like(full_spectra_errs)
masked_spectra[:] = np.nan
masked_spectra_errs[:] = np.nan
#Mask the spectra
for i in range(len(full_spectra)):
for j in range(7514):
if full_bitmask[i][j] != 0:
masked_spectra[i][j] = full_spectra[i][j]
masked_spectra_errs[i][j] = full_spectra_errs[i][j]
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (full_T < 5000)
final_spectra = masked_spectra[good_T_inds]
final_spectra_errs = masked_spectra_errs[good_T_inds]
good_T = full_T[good_T_inds]
apogee_cluster_data = apogee_cluster_data[good_T_inds]
full_bitmask = full_bitmask[good_T_inds]
final_stars = full_marked_stars[good_T_inds] #ADDED
rgs = (final_stars != 'nan') #ADDED
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(final_spectra_errs)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j] / final_spectra_errs[i][j] <= 200:
spectra_err_200[i][j] = final_spectra_errs[i][j]
else:
spectra_err_200[i][j] = final_spectra[i][j] / 200
#Cut errors with SNR of less than 50
spectra_50 = np.copy(final_spectra)
spectra_err_50 = np.copy(spectra_err_200)
for i in range(len(final_spectra)):
for j in range(7514):
if final_spectra[i][j] / spectra_err_200[i][j] <= 50:
spectra_50[i][j] = np.nan
spectra_err_50[i][j] = np.nan
#Separate out individual clusters
cluster_ids = apogee_cluster_data['CLUSTER_ID']
PJ_26 = []
PJ_95 = []
PJ_471 = []
PJ_162 = []
PJ_398 = []
PJ_151 = []
PJ_230 = []
PJ_939 = []
PJ_262 = []
PJ_289 = []
PJ_359 = []
PJ_396 = []
PJ_899 = []
PJ_189 = []
PJ_574 = []
PJ_641 = []
PJ_679 = []
PJ_1976 = []
PJ_88 = []
PJ_1349 = []
PJ_1811 = []
for i in range(len(apogee_cluster_data)):
if cluster_ids[i] == 26:
PJ_26.append(i)
elif cluster_ids[i] == 95:
PJ_95.append(i)
elif cluster_ids[i] == 471:
PJ_471.append(i)
elif cluster_ids[i] == 162:
PJ_162.append(i)
elif cluster_ids[i] == 398:
PJ_398.append(i)
elif cluster_ids[i] == 151:
PJ_151.append(i)
elif cluster_ids[i] == 230:
PJ_230.append(i)
elif cluster_ids[i] == 939:
PJ_939.append(i)
elif cluster_ids[i] == 262:
PJ_262.append(i)
elif cluster_ids[i] == 289:
PJ_289.append(i)
elif cluster_ids[i] == 359:
PJ_359.append(i)
elif cluster_ids[i] == 396:
PJ_396.append(i)
elif cluster_ids[i] == 899:
PJ_899.append(i)
elif cluster_ids[i] == 189:
PJ_189.append(i)
elif cluster_ids[i] == 574:
PJ_574.append(i)
elif cluster_ids[i] == 641:
PJ_641.append(i)
elif cluster_ids[i] == 679:
PJ_679.append(i)
elif cluster_ids[i] == 1976:
PJ_1976.append(i)
elif cluster_ids[i] == 88:
PJ_88.append(i)
elif cluster_ids[i] == 1349:
PJ_1349.append(i)
elif cluster_ids[i] == 1811:
PJ_1811.append(i)
cluster_dict = {
'PJ_26': PJ_26,
'PJ_95': PJ_95,
'PJ_471': PJ_471,
'PJ_162': PJ_162,
'PJ_398': PJ_398,
'PJ_151': PJ_151,
'PJ_230': PJ_230,
'PJ_939': PJ_939,
'PJ_262': PJ_262,
'PJ_289': PJ_289,
'PJ_359': PJ_359,
'PJ_396': PJ_396,
'PJ_899': PJ_899,
'PJ_189': PJ_189,
'PJ_574': PJ_574,
'PJ_641': PJ_641,
'PJ_679': PJ_679,
'PJ_1976': PJ_1976,
'PJ_88': PJ_88,
'PJ_1349': PJ_1349,
'PJ_1811': PJ_1811
}
cluster_data_full = apogee_cluster_data[cluster_dict[name]]
cluster_spectra_full = spectra_50[cluster_dict[name]]
cluster_spectra_errs_full = spectra_err_50[cluster_dict[name]]
cluster_T_full = good_T[cluster_dict[name]]
#Cut red clump stars
cluster_rgs = rgs[cluster_dict[name]]
cluster_data = cluster_data_full[cluster_rgs]
cluster_spectra = cluster_spectra_full[cluster_rgs]
cluster_spectra_errs = cluster_spectra_errs_full[cluster_rgs]
cluster_T = cluster_T_full[cluster_rgs]
bitmask_final = full_bitmask[rgs]
if red_clump == 'False':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = cluster_data_full
file['spectra'] = cluster_spectra_full
file['spectra_errs'] = cluster_spectra_errs_full
file['T'] = cluster_T_full
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete')
return cluster_data_full, cluster_spectra_full, cluster_spectra_errs_full, cluster_T_full, full_bitmask
elif red_clump == 'True':
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = cluster_data
file['spectra'] = cluster_spectra
file['spectra_errs'] = cluster_spectra_errs
file['T'] = cluster_T
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete')
return cluster_data, cluster_spectra, cluster_spectra_errs, cluster_T, bitmask_final
import sys os.environ['APOGEE_REDUX']='v603' os.environ['APOGEE_DATA']='/Users/jquark/obs_data/apogee/' import apogee.tools.read as apread import numpy as np #import galpy.util.bovy_coords as bovy_coords import matplotlib as mpl from matplotlib import pyplot as plt import numpy as np import numpy.lib.recfunctions as rfn apogee_data_dir = '/Users/jquark/obs_data/apogee' ness_age_cat_file = os.path.join(apogee_data_dir, 'HWR_redclump_sample.txt') RCcat = apread.rcsample() fields = ['APOGEE_ID', 'RC_DIST', 'RC_GALR', 'RC_GALPHI', 'RC_GALZ', 'TEFF', 'LOGG', 'FE_H', 'ALPHAFE', 'RC_GALR'] #Using the Rccatalog to provide data types for making record array with ness catalog # The last 'RC_GALR' is just to steal the datatype to make the age array ness_dtypes=[RCcat[field].dtype for field in fields] ness_fields = ['APOGEE_ID', 'RC_DIST', 'RC_GALR', 'RC_GALPHI', 'RC_GALZ', 'TEFF', 'LOGG', 'FE_H', 'ALPHAFE', 'AGE'] ndtypes = [(i,j) for i,j in zip(ness_fields, ness_dtypes)] ness_cat = np.loadtxt(ness_age_cat_file, dtype=ndtypes) ###can write out record array here and now ### ### After this, change the columns for the merging print((RCcat['APOGEE_ID']==ness_cat['APOGEE_ID']).all())
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
import numpy
import apogee.tools.read as apread
_DATA= apread.rcsample() #such that we can re-use it in different tests
def test_int64():
#Test that there aren't 64-bit integers
for key in _DATA.dtype.names:
assert not numpy.issubdtype(_DATA[key].dtype,numpy.int64), "Tag '%s' in the RC catalog is a 64-bit signed integer that might give problems for some readers" % key
return None
def bovy_metallicity_gradient(plotfilename,savefilename,largewave=False):
# First read the RC catalog and cut it to stars near the plane
data= apread.rcsample()
if _HIZ:
indx= (numpy.fabs(data['RC_GALZ']) > 0.6)*(data['METALS'] > -1000.)
else:
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
# Now go through bins in R
Rmin, Rmax, dR= 5.5, 13., 0.1
Rs= numpy.arange(Rmin+dR/2.,Rmax+dR/2.,dR)
nR= len(Rs)
# Read one spectrum to learn the size of the array
spec, hdr= apread.aspcapStar(4424,'2M00025587+5849278',ext=1)
if os.path.exists(savefilename):
# Reload previously calculated median spectrum
savefile= open(savefilename,'rb')
median_spec= pickle.load(savefile)
savefile.close()
else:
median_spec= numpy.zeros((len(spec),nR))
# Now run through all the spectra and get the median
tot= 0
for ii in range(nR):
indx= (data['RC_GALR'] >= Rs[ii]-dR/2.)\
*(data['RC_GALR'] < Rs[ii]+dR/2.)
tot+= numpy.sum(indx)
print numpy.sum(indx), tot
allspec= numpy.empty((len(spec),numpy.sum(indx)))
for jj in range(numpy.sum(indx)):
specdata= \
apread.aspcapStar(data['LOCATION_ID'][indx][jj],
data['APOGEE_ID'][indx][jj],
ext=1,header=False)
specerr= \
apread.aspcapStar(data['LOCATION_ID'][indx][jj],
data['APOGEE_ID'][indx][jj],
ext=2,header=False)
allspec[:,jj]= specdata
allspec[specerr > 1.,jj]= numpy.nan
allspec[specerr == 0.,jj]= numpy.nan
for jj in range(len(spec)):
if _PLOTMAD:
median_spec[jj,ii]= \
numpy.median(numpy.fabs(allspec[jj,True-numpy.isnan(allspec[jj,:])]-numpy.median(allspec[jj,True-numpy.isnan(allspec[jj,:])])))
else:
median_spec[jj,ii]= \
numpy.median(allspec[jj,
True-numpy.isnan(allspec[jj,:])])
save_pickles(savefilename,median_spec)
# Wavelengths
wave= 10.**(numpy.arange(hdr['CRVAL1'],
hdr['CRVAL1']+len(spec)*hdr['CDELT1'],
hdr['CDELT1']))
# Normalization, first calculate the spectrum near Ro
absmax= 0.98
absindx= (median_spec > absmax)*\
numpy.tile(( (wave < 15929.)+(wave > 15929.5) ),(nR,1)).T
median_spec[absindx]= absmax #focus on real absorption lines, except for V
rospec= numpy.zeros(len(spec))
roindx= numpy.argmin(numpy.fabs(Rs-8.))
for jj in range(len(spec)):
rospec[jj]= numpy.median(median_spec[jj,roindx-3:roindx+4])
if not _PLOTMAD:
# Normalization by spectrum at Ro
roindx= numpy.argmin(numpy.fabs(Rs-8.))
for jj in range(nR):
# Normalize by the solar radius
median_spec[:,jj]/= rospec
median_spec-= 1.
vmin=-0.035
vmax=0.035
vmin=-0.04
vmax=0.04
# Now plot
if False:
startindx, endindx= 3652, 4100#3915
if largewave:
startindx, endindx= 7000,7600#7375, 7889
else:
startindx, endindx= 2500, 3100
# Make N separate plots showing different wavelength regions
bovy_plot.bovy_print(fig_width=8.4,fig_height=3.,
axes_labelsize=10,text_fontsize=9,legend_fontsize=9,
xtick_labelsize=8,ytick_labelsize=8)
startindxs= [322,1790,2707,3852,4738,5820,7187] #DIB is at 818
endindxs= [590,1940,2857,4021,5068,5955,7400]
nregions= len(startindxs)
# Calculate the width of the plot
dx= numpy.array([endindxs[ii]-startindxs[ii] for ii in range(nregions)],
dtype='float')
specdx= numpy.sum(dx) # for later
dx/= numpy.sum(dx)
totdx= 0.85
skipdx= 0.015
dx*= (totdx-(nregions-1)*skipdx)
for ii in range(nregions):
# Setup the axes
if ii == 0:
left, bottom, width, height= 0.1, 0.1, dx[ii],0.8
else:
left, bottom, width, height= 0.1+numpy.cumsum(dx)[ii-1]+skipdx*ii,\
0.1, dx[ii], 0.8
thisax= pyplot.axes([left,bottom,width,height])
fig= pyplot.gcf()
fig.sca(thisax)
startindx, endindx= startindxs[ii], endindxs[ii]
aspect= (hdr['CRVAL1']+(endindx-0.5)*hdr['CDELT1']-hdr['CRVAL1']-(startindx-0.5)*hdr['CDELT1'])\
/(Rs[-1]+dR/2.-Rs[0]+dR/2.)
aspect/= dx[ii]*5.
yrange= [Rs[0]-dR/2.,Rs[-1]+dR/2.]
xrange=[hdr['CRVAL1']+(startindx-0.5)*hdr['CDELT1']-numpy.log10(_LAMBDANORM),\
hdr['CRVAL1']+(endindx-0.5)*hdr['CDELT1']-numpy.log10(_LAMBDANORM)]
extent= [xrange[0],xrange[1],yrange[0],yrange[1]]
pyplot.imshow(-median_spec[startindx:endindx,:].T,
origin='lower',cmap='coolwarm',
vmin=vmin,vmax=vmax,
extent=extent,
interpolation='nearest',
#interpolation='bicubic',
aspect=aspect)
pyplot.axis(extent)
#pyplot.xticks(rotation=-45.)
pyplot.xlim(xrange[0],xrange[1])
pyplot.ylim(yrange[0],yrange[1])
thisax.xaxis.set_major_locator(ticker.MultipleLocator(0.001))
bovy_plot._add_ticks()
if ii > 0:
nullfmt = NullFormatter() # no labels
thisax.yaxis.set_major_formatter(nullfmt)
else:
pyplot.ylabel(r'$R\,(\mathrm{kpc})$')
# Remove spines between different wavelength regions
if ii == 0:
thisax.spines['right'].set_visible(False)
thisax.tick_params(right=False,which='both')
elif ii == (nregions-1):
thisax.spines['left'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False,which='both')
else:
thisax.spines['left'].set_visible(False)
thisax.spines['right'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False,which='both')
thisax.tick_params(right=False,which='both')
# Plot cut-out markers
d = .015 # how big to make the diagonal lines in axes coordinates
kwargs = dict(transform=thisax.transAxes, color='k', clip_on=False)
slope= 1./(dx[ii]+0.2*skipdx)/3.
if ii == 0:
thisax.plot((1-slope*d,1+slope*d),(-d,+d), **kwargs)
thisax.plot((1-slope*d,1+slope*d),(1-d,1+d), **kwargs)
elif ii == (nregions-1):
thisax.plot((-slope*d,+slope*d),(-d,+d), **kwargs)
thisax.plot((-slope*d,+slope*d),(1-d,1+d), **kwargs)
else:
thisax.plot((1-slope*d,1+slope*d),(-d,+d), **kwargs)
thisax.plot((1-slope*d,1+slope*d),(1-d,1+d), **kwargs)
thisax.plot((-slope*d,+slope*d),(-d,+d), **kwargs)
thisax.plot((-slope*d,+slope*d),(1-d,1+d), **kwargs)
# Draw solar line
if ii == (nregions-1):
xend= hdr['CRVAL1']+(endindx-0.5)*hdr['CDELT1']-numpy.log10(_LAMBDANORM)
# Total wavelength span, incl. skipdx parts
totaldx= hdr['CDELT1']*specdx*\
(1.+(nregions-1)*skipdx/(totdx-(nregions-1)*skipdx))
thisax.plot((xend-totaldx,xend),(8.,8.),
color='k',ls='--',lw=1.5,marker='None',clip_on=False)
# Label the lines
_label_all_lines(wave[startindx],wave[endindx])
# Add the x-axis label
thisax= pyplot.axes([0.1,0.175,0.85,0.65])
pyplot.gcf().sca(thisax)
thisax.spines['left'].set_visible(False)
thisax.spines['right'].set_visible(False)
thisax.spines['bottom'].set_visible(False)
thisax.spines['top'].set_visible(False)
thisax.tick_params(labelleft='off')
thisax.tick_params(left=False,which='both')
thisax.tick_params(right=False,which='both')
thisax.tick_params(labelbottom='off')
thisax.tick_params(bottom=False,which='both')
thisax.tick_params(top=False,which='both')
pyplot.xlabel(r'$\log \big(\lambda / 15,000 \AA\big)$')
thisax.set_zorder(-1)
bovy_plot.bovy_end_print(plotfilename,dpi=600)
return None
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_distancedist(basesavefilename):
#First read the sample
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Histogram
bovy_plot.bovy_print(fig_width=6.)
bovy_plot.bovy_hist(data['RC_DIST'],
histtype='step',
color='k',
lw=1.5,
bins=51,
xrange=[0.,10.],
xlabel=r'$\mathrm{distance}\,(\mathrm{kpc})$',
ylabel=r'$\mathrm{Number\ of\ stars}$',zorder=10)
ax= pyplot.gca()
ax2= ax.twinx()
pyplot.sca(ax2)
bovy_plot.bovy_plot(sorted(data['RC_DIST']),
numpy.linspace(1./len(data),1.,len(data)),
'k-',
overplot=True)
ax2.set_xlim(0.,10.)
ax2.set_ylim(0.,1.)
bovy_plot._add_ticks()
ax2.set_ylabel(r'$\mathrm{cumulative\ distribution}$')
bovy_plot.bovy_end_print(basesavefilename+'_hist.'+_EXT)
#RZ
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['RC_GALR'],data['RC_GALZ'],
'k.',ms=2.,
xrange=[0.,16.],
yrange=[-3.,3.],
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$Z\,(\mathrm{kpc})$',
onedhists=True)
xarr, dx=1.5, -1.
from matplotlib.patches import FancyArrowPatch
_legendsize= 16
arr= FancyArrowPatch(posA=(xarr+0.05,0.),
posB=(xarr+dx*10./8.,0.),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(xarr+7.*dx/8.,-0.45,r'$\mathrm{GC}$',
size=_legendsize)
arr= FancyArrowPatch(posA=(1.5,-0.05),
posB=(1.5,.75),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(1.59,0.2,r'$\mathrm{NGP}$',
size=_legendsize)
bovy_plot.bovy_end_print(basesavefilename+'_RZ.'+_EXT)
#XY
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['RC_GALR']*numpy.cos(data['RC_GALPHI']),
data['RC_GALR']*numpy.sin(data['RC_GALPHI']),
'k.',
xrange=[0.,16.],
yrange=[5.,-5.],
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$',
onedhists=True,ms=2.)
xarr, dx= 2.2, -2.
arr= FancyArrowPatch(posA=(xarr,0.),
posB=(xarr+dx,0.),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(xarr+7.*dx/8.,-0.25,r'$\mathrm{GC}$',
size=_legendsize)
xcen, ycen, dr, t= -2., 0., 4., 14.*numpy.pi/180.
arr= FancyArrowPatch(posA=(xcen+dr*numpy.cos(t),
ycen+dr*numpy.sin(t)),
posB=(xcen+dr*numpy.cos(-t),
ycen+dr*numpy.sin(-t)),
arrowstyle='<-',
connectionstyle='arc3,rad=%4.2f' % (2.*t),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax.add_patch(arr)
bovy_plot.bovy_end_print(basesavefilename+'_XY.'+_EXT)
def plot_fehafe(basefilename):
#First read the sample
data= apread.rcsample()
data= data[data['SNR'] >= 70.]
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#R and z ranges to plot
Rmins= [5.,7.,9.]
Rmaxs= [7.,9.,11.]
zmins= [0.,0.5,1.]
zmaxs= [0.5,1.,2.]
nRbins= len(Rmins)
nzbins= len(zmins)
for ii in range(nRbins):
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
#special.erf(numpy.arange(1,3)/numpy.sqrt(2.)),
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=21)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])
if not _NOLINE:
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%i < R / \mathrm{kpc} \leq %i$' % (Rmins[ii],Rmaxs[ii]) +'\n'+r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%iR%i_%.1fz%.1f.' % (Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])+_EXT)
#Plot z bins only
zmins= [0.,0.5,1.,2.]
zmaxs= [0.5,1.,2.,4.]
nzbins= len(zmins)
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > 4.)\
*(data['RC_GALR'] <= 9.)\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=31)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,4.,9.,zmins[jj],zmaxs[jj])
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%.1fz%.1f.' % (zmins[jj],zmaxs[jj])+_EXT)
#Plot scatter in the high-alpha sequence
#Histograms in 3 R bins
bovy_plot.bovy_print(fig_width=7.)
overplot= False
colors= ['y','k','r']
labelposx= 0.235
labelposy= [13.,11.,9.]
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=28,range=[-0.275,0.25],yrange=[0.,15.],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
overplot=overplot,normed=True)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten()), txa[0]*len(dafe)
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
bovy_plot.bovy_text(labelposx,labelposy[ii],
r'$%i < R \leq %i: \sigma = %.3f$' % (Rmins[ii],Rmaxs[ii],numpy.sqrt(txc[0,0,0])),
size=16.,color=colors[ii],ha='right')
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist.%s' % _EXT)
#High SN
bovy_plot.bovy_print(fig_width=7.)
overplot= False
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)\
*(data['SNR'] >= 150.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=19,range=[-0.275,0.15],yrange=[0.,19.5],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
normed=True,
overplot=overplot)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten())
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist_highsn.%s' % _EXT)
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 get_spectra(
name, red_clump, location
): ###Function to read the allStar file and get the spectra, correct spectra for
###small and large uncertainties, remove red clump stars
"""Return cluster data, spectra, spectral errors, photometric Teffs, and bitmask from APOGEE.
If the data file for the specified cluster already exists locally,
import the data from the file (cluster data, spectra, spectral errors, bitmask).
If the data file does not exist, obtain the APOGEE spectra from a specified cluster
from the allStar catalogue, replacing ASPCAP abundances with astroNN abundances.
Parameters
----------
name : str
Name of desired cluster (i.e. 'NGC 2682')
red_clump : str
If the red clump stars in rcsample are to be removed, set to 'True'. If all stars are to be used,
set to 'False'.
location : str
If running locally, set to 'personal'. If running on the server, set to 'server'.
Returns
-------
apogee_cluster_data (all stars) or apogee_cluster_data_final (red clumps removed) : structured array
All cluster data from APOGEE
spectra_50 (all stars) or spectra_final (red clumps removed) : tuple
Array of floats representing the cleaned-up fluxes in the APOGEE spectra with red clump stars removed
spectra_err_50 (all stars) or spectra_err_final (red clumps removed) : tuple
Array of floats representing the cleaned-up spectral errors from the APOGEE spectra with red clump stars
removed
good_T (all stars) or T_final (red clumps removed) : tuple
Array of floats representing the effective temperatures of the stars in the cluster
between 4000K and 5000K
full_bitmask (all stars) or bitmask_final (red clumps removed) : tuple
Array of ints (1 or 0), cleaned in the same way as the spectra, representing the bad pixels
in the APOGEE_PIXMASK bitmask
"""
#Path, strip spaces in cluster name
if location == 'personal': ###If running on my Mac
path = '/Users/chloecheng/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to folder named after cluster
elif location == 'server': ###If running on the server
path = '/geir_data/scr/ccheng/AST425/Personal/' + str(name).replace(
' ', '') + '.hdf5' ###Path to cluster folder
#If the data file for this cluster exists, save the data to variables and return them
if glob.glob(path): ###If the file exists
if red_clump == 'False': ###If we're keeping all of the stars, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data = file['apogee_cluster_data'][()]
spectra_50 = file['spectra'][()]
spectra_err_50 = file['spectra_errs'][()]
good_T = file['T'][()]
full_bitmask = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we're removing the red clumps, read in the data
file = h5py.File(path, 'r')
apogee_cluster_data_final = file['apogee_cluster_data'][()]
spectra_final = file['spectra'][()]
spectra_err_final = file['spectra_errs'][()]
T_final = file['T'][()]
bitmask_final = file['bitmask'][()]
file.close()
print(name,
' complete.') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
#If the file does not exist, get the data from APOGEE
else: ###If the file does not exist
#Get red clump stars from rcsample
rc_data = rcsample(dr='14') ###Get the rcsample data for DR14
rc_stars = [] ###Empty list for the stars
for i in range(len(rc_data)): ###Iterate through the rcsample data
if location == 'personal': ###If running on Mac
rc_stars.append(
rc_data[i][2]) ###Append just the names of the stars
elif location == 'server': ###If running on server
rc_stars.append(
rc_data[i][2].decode('UTF-8')
) ###Append just the names of the stars (decode because on server the names are bitwise for some reason)
rc_stars = np.array(
rc_stars) ###Make list of red clump star names into array
#Read in APOGEE catalogue data, removing duplicated stars and replacing ASPCAP with astroNN abundances
apogee_cat = apread.allStar(
use_astroNN_abundances=True
) ###Read the allStar file, using the astroNN abundances
unique_apoids, unique_inds = np.unique(
apogee_cat['APOGEE_ID'], return_index=True) ###Get the APOGEE IDs
apogee_cat = apogee_cat[unique_inds] ###Get the APOGEE IDs
#Read in overall cluster information
cls = afits.open('occam_cluster-DR14.fits') ###Read in the OCCAM data
cls = cls[1].data ###Get the cluster information
#Read in information about cluster members
members = afits.open(
'occam_member-DR14.fits') ###Read in the OCCAM members data
members = members[1].data ###Get the member information
#Select all members of a given cluster
cluster_members = (members['CLUSTER'] == name) & (
members['MEMBER_FLAG'] == 'GM'
) #second part of the mask indicates to only use giant stars
member_list = members[
cluster_members] ###Make a list of all member stars in the cluster
#Find APOGEE entries for that cluster
#numpy.in1d finds the 1D intersection between two lists.
#In this case we're matching using the unique APOGEE ID assigned to each star
#The indices given by numpy.in1d are for the first argument, so in this case the apogee catalogue
cluster_inds = np.in1d((apogee_cat['APOGEE_ID']).astype('U100'),
member_list['APOGEE_ID']
) ###Get the indices of the cluster members
apogee_cluster_data = apogee_cat[
cluster_inds] ###Get the allStar data for these members
T = photometric_Teff(
apogee_cluster_data
) ###Compute the photometric effective temperature
#Mark red clump stars in the members of the cluster as NaNs
cluster_stars = member_list[
'APOGEE_ID'] ###Get a list of all the names of the member stars in the cluster
cluster_marked = np.copy(
cluster_stars
) ###Create a copy of this list to mark which stars are red clumps
for i in range(len(cluster_stars)
): ###Iterate through all of the stars in the cluster
for j in range(len(
rc_stars)): ###Iterate through all of the rcsample stars
if cluster_stars[i] in rc_stars[
j]: ###If a cluster member is also a member of the rcsample stars
cluster_marked[
i] = np.nan ###Replace the name of that star with a NaN to ignore it
#Get spectra, spectral errors, and bitmask for each star - apStar
#We can use the APOGEE package to read each star's spectrum
#We'll read in the ASPCAP spectra, which have combined all of the visits for each star and removed the spaces between the spectra
number_of_members = len(
member_list) ###Number of members in the cluster
spectra = np.zeros((number_of_members,
7514)) ###Create an empty array to add the spectra
spectra_errs = np.zeros(
(number_of_members,
7514)) ###Create an empty array to add the spectral errors
bitmask = np.zeros((number_of_members,
7514)) ###Create an empty array to add the bitmask
for s, star in enumerate(
apogee_cluster_data): ###Iterate through the allStar data
spectra[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=1,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectra
spectra_errs[s] = apread.aspcapStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=2,
header=False,
dr='14',
aspcapWavegrid=True) ###Get the spectral errors
bitmask[s] = apread.apStar(
star['LOCATION_ID'],
star['APOGEE_ID'],
ext=3,
header=False,
dr='14',
aspcapWavegrid=True)[1] ###Get the bitmask
#Set all entries in bitmask to integers
bitmask = bitmask.astype(
int) ###Set all entries in the bitmask to integers
bitmask_flip = np.zeros_like(
bitmask
) ###Create an empty array for the bitmask with flipped entries
for i in range(
len(spectra
)): ###Iterate through the number of stars in the cluster
for j in range(7514): ###Iterate through the wavelength range
if bitmask[i][j] == 0: ###If the bitmask entry is set to 0
bitmask_flip[i][j] = 1 ###Set it to 1
else: ###If the bitmask entry is not set to 0
bitmask_flip[i][j] = 0 ###Set it to 0
###I do this part because the unmasked entries are always 0 in the original bitmask but I think before I was maybe adding in other values to include in the mask that may not have necessarily been 1 so I just set all masked bits to 0 and all unmasked bits to 1 (or maybe this just made more sense in my head for masked to be 0 and unmasked to be 1)
#Remove empty spectra
full_spectra = [
] ###Empty list for the spectra sans empty ones, list not array because we don't know how many stars will be eliminated
full_spectra_errs = [
] ###Empty list for the spectral errors sans empty spectra
full_bitmask = [] ###Empty list for bitmask sans empty spectra
full_T = [] ###Empty list for temperatures sans empty spectra
full_stars = [] ###Empty list for indices of stars sans empty spectra
for i in range(len(spectra)): ###Iterate through the number of stars
if any(spectra[i, :] != 0
): ###For all of the rows whose entries are not all 0
full_spectra.append(spectra[i]) ###Append those spectra
full_spectra_errs.append(
spectra_errs[i]) ###Append those spectral errors
full_bitmask.append(
bitmask_flip[i]) ###Append those bitmask rows
full_T.append(T[i]) ###Append those temperatures
full_stars.append(i) ###Append the indices of those stars
full_spectra = np.array(full_spectra) ###Make list into array
full_spectra_errs = np.array(
full_spectra_errs) ###Make list into array
full_bitmask = np.array(full_bitmask) ###Make list into array
full_T = np.array(full_T) ###Make list into array
full_stars = np.array(full_stars) ###Make list into array
full_marked_stars = cluster_marked[
full_stars] ###Use array of stars left to index marked stars so we know which ones are red clump stars
#Create array of NaNs to replace flagged values in spectra
masked_spectra = np.empty_like(
full_spectra
) ###Create an empty array that is the same shape as full_spectra
masked_spectra_errs = np.empty_like(
full_spectra_errs
) ###Create an empty array that is the same shape as full_spectra_errs
masked_spectra[:] = np.nan ###Set all of the entries to NaNs
masked_spectra_errs[:] = np.nan ###Set all of the entries to NaNs
#Mask the spectra
for i in range(
len(full_spectra)): ###Iterate through the number of stars
for j in range(7514): ###Iterate through the wavelength range
if full_bitmask[i][
j] != 0: ###If the bitmask is not 0 (i.e. if the bit is unmasked)
masked_spectra[i][j] = full_spectra[i][
j] ###Retain the value of the unmasked spectra here
masked_spectra_errs[i][j] = full_spectra_errs[i][
j] ###Retain the value of the unmasked spectral errors here
###All of the masked bits that were not captured by this if statement will remain NaNs and will thus be ignored
#Cut stars that are outside of the temperature limits
good_T_inds = (full_T > 4000) & (
full_T < 5000
) ###Get the indices of the temperatures that are between 4000K and 5000K
final_spectra = masked_spectra[
good_T_inds] ###Index the spectra to only keep stars that are within the temperature limits
final_spectra_errs = masked_spectra_errs[
good_T_inds] ###Index the spectral errors to only keep stars within Teff limits
good_T = full_T[
good_T_inds] ###Index the temperatures to keep only stars within Teff limits
apogee_cluster_data = apogee_cluster_data[
good_T_inds] ###Index the allStar data to keep stars only within Teff limits
full_bitmask = full_bitmask[
good_T_inds] ###Index the bitmask to keep stars only within Teff limits
final_stars = full_marked_stars[
good_T_inds] ###Index the array of red-clump-marked stars to keep only those within Teff limits
rgs = (final_stars != 'nan'
) #Get indices for final red giant stars to be used
#Want an SNR of 200 so set those errors that have a larger SNR to have an SNR of 200
spectra_err_200 = np.zeros_like(
final_spectra_errs
) ###Create an empty array to add corrected spectral errors to - shape will not change, just altering values
for i in range(len(final_spectra)): ###Iterate through the stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / final_spectra_errs[i][
j] <= 200: ###If errors are of a reasonable size
spectra_err_200[i][j] = final_spectra_errs[i][
j] ###Leave them as they are
else: ###If errors are too small
spectra_err_200[i][j] = final_spectra[i][
j] / 200 ###Make them a bit bigger
#Cut errors with SNR of less than 50
spectra_50 = np.copy(
final_spectra
) ###Create a copy of the spectra to cut large error pixels
spectra_err_50 = np.copy(
spectra_err_200
) ###Create a copy of the spectral errors to cut large error pixels
for i in range(len(final_spectra)): ###Iterate through stars
for j in range(7514): ###Iterate through wavelength range
if final_spectra[i][j] / spectra_err_200[i][
j] <= 50: ###If an error is too big
spectra_50[i][
j] = np.nan ###Set the corresponding entry in the spectra to be a NaN, will be ignored
spectra_err_50[i][
j] = np.nan ###Set the corresponding entry in the spectral errors to be a NaN, will be ignored
#Cut red clumps
logg = apogee_cluster_data[
'LOGG'] ###Get the logg values for the cluster (all corrections have been applied)
apogee_cluster_data_final = apogee_cluster_data[
rgs] ###Get the allStar data for the RGB stars only (no red clumps)
spectra_final = spectra_50[
rgs] ###Get the spectra for the RGB stars only
spectra_err_final = spectra_err_50[
rgs] ###Get the spectral errors for the RGB stars only
T_final = good_T[rgs] ###Get the temperatures for the RGB stars only
bitmask_final = full_bitmask[
rgs] ###Get the bitmask for the RGB stars only
if red_clump == 'False': ###If we are looking at all of the stars, save all data before red clumps were cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data
file['spectra'] = spectra_50
file['spectra_errs'] = spectra_err_50
file['T'] = good_T
file['bitmask'] = full_bitmask
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data, spectra_50, spectra_err_50, good_T, full_bitmask
elif red_clump == 'True': ###If we are removing the red clump stars, save the data after red clumps cut to file
#Write to file
file = h5py.File(path, 'w')
file['apogee_cluster_data'] = apogee_cluster_data_final
file['spectra'] = spectra_final
file['spectra_errs'] = spectra_err_final
file['T'] = T_final
file['bitmask'] = bitmask_final
file.close()
print(name, 'complete') ###Notification that this function is done
return apogee_cluster_data_final, spectra_final, spectra_err_final, T_final, bitmask_final
def plot_sigmar(plotfilename,plotfilename2):
#First read the sample
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
data= data[(data['PMMATCH'] == 1)] #Good velocities
galy= data['RC_GALR']*numpy.sin(data['RC_GALPHI'])
indx= (numpy.fabs(galy) < .75)*(numpy.fabs(data['RC_GALZ']) < 0.25) #(numpy.fabs(data['GLAT']) < 1.51)
data= data[indx]
print len(data)
rs= numpy.arange(3.5,16.501,.5)
sigrs= numpy.zeros_like(rs)+numpy.nan
mvrs= numpy.zeros_like(rs)+numpy.nan
emvrs= numpy.zeros_like(rs)+numpy.nan
esigrs= numpy.zeros_like(rs)+numpy.nan
pmesigrs= numpy.zeros_like(rs)+numpy.nan
std= robstd
#std= numpy.std
for ii in range(len(rs)-1):
tindx= (data['RC_GALR'] > rs[ii])\
*(data['RC_GALR'] <= rs[ii+1])
print rs[ii], numpy.sum(tindx)
sigrs[ii]= std(data['GALVR'][tindx])
mvrs[ii]= numpy.median(data['GALVR'][tindx])
emvrs[ii]= sigrs[ii]/numpy.sqrt(numpy.sum(tindx))
esigrs[ii]= disperror(data['GALVR'][tindx],std)
pmesigrs[ii]= 4.74047*numpy.sqrt(numpy.mean(data['RC_DIST'][tindx]**2.\
*data['PMRA_ERR'][tindx]**2.\
*numpy.sin(data['GLON'][tindx]/180.*numpy.pi+data['RC_GALPHI'][tindx])**2.))
#Now plot
bovy_plot.bovy_print()
print sigrs
print esigrs
bovy_plot.bovy_plot(rs+0.5*(rs[1]-rs[0]),sigrs,'ko',semilogy=True,
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$\sigma_R\,(\mathrm{km\,s}^{-1})$',
xrange=[0.,18.],
yrange=[10.,100.])
pyplot.errorbar(rs+0.5*(rs[1]-rs[0]),sigrs,yerr=esigrs,marker='None',
ls='none',color='k')
#bovy_plot.bovy_plot(rs+0.5*(rs[1]-rs[0]),pmesigrs,'ks',overplot=True)
#Fit
indx= (True-numpy.isnan(sigrs))*(True-numpy.isinf(sigrs))*(sigrs > 0.)
#esigrs[3]= 100.
bestfit= optimize.curve_fit(expfit,(rs+0.5*(rs[1]-rs[0]))[indx],
sigrs[indx],sigma=esigrs[indx],
p0=(numpy.log(32.),numpy.log(12.)))
print numpy.exp(bestfit[0]), bestfit[1]
bovy_plot.bovy_plot(rs+0.5*(rs[1]-rs[0]),
numpy.exp(bestfit[0][0])\
*numpy.exp(-(rs+0.5*(rs[1]-rs[0])-8.)/numpy.exp(bestfit[0][1])),
'k--',overplot=True)
pyplot.yticks([10,20,30,40,50,60,70,80,90,100],[r'$10$',r'$20$',r'$30$',r'$40$',r'$50$',r'$60$',r'$70$',r'$80$',r'$90$',r'$100$'])
bovy_plot.bovy_text(r'$|Z| < 50\,\mathrm{pc}$',top_right=True,size=18.)
bovy_plot.bovy_text(r'$\sigma_R = %.0f\,\mathrm{km\,s}^{-1}\,\exp\left(-\left[R-8\,\mathrm{kpc}\right] / %.0f\,\mathrm{kpc}\right)$' % (numpy.exp(bestfit[0][0]),numpy.exp(bestfit[0][1])),
bottom_left=True,size=14.)
bovy_plot.bovy_end_print(plotfilename)
#Now plot vr
#First read the sample
#data= apread.rcsample()
#data= data[(data['PMMATCH'] == 1)] #Good velocities
#galy= data['RC_GALR']*numpy.sin(data['RC_GALPHI'])
#indx= (numpy.fabs(galy) < .5)*(numpy.fabs(data['RC_GALZ']) < 0.1) #(numpy.fabs(data['GLAT']) < 1.51)
#data= data[indx]
print len(data)
rs= numpy.arange(3.5,16.501,3.)
sigrs= numpy.zeros_like(rs)+numpy.nan
mvrs= numpy.zeros_like(rs)+numpy.nan
emvrs= numpy.zeros_like(rs)+numpy.nan
esigrs= numpy.zeros_like(rs)+numpy.nan
pmesigrs= 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])
print rs[ii], numpy.sum(tindx)
sigrs[ii]= std(data['GALVR'][tindx])
mvrs[ii]= numpy.median(data['GALVR'][tindx])
emvrs[ii]= sigrs[ii]/numpy.sqrt(numpy.sum(tindx))
esigrs[ii]= disperror(data['GALVR'][tindx],std)
pmesigrs[ii]= 4.74047*numpy.sqrt(numpy.mean(data['RC_DIST'][tindx]**2.\
*data['PMRA_ERR'][tindx]**2.\
*numpy.sin(data['GLON'][tindx]/180.*numpy.pi+data['RC_GALPHI'][tindx])**2.))
print rs+0.5*(rs[1]-rs[0]),mvrs
bovy_plot.bovy_print()
bovy_plot.bovy_plot(rs+0.5*(rs[1]-rs[0]),mvrs,'ko',
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$\mathrm{median}\ v_R\,(\mathrm{km\,s}^{-1})$',
xrange=[0.,18.],
yrange=[-10.,10.])
pyplot.errorbar(rs+0.5*(rs[1]-rs[0]),mvrs,yerr=emvrs,marker='None',
ls='none',color='k')
bovy_plot.bovy_text(r'$|Z| < 50\,\mathrm{pc}$',top_right=True,size=18.)
bovy_plot.bovy_end_print(plotfilename2)
