def calc_age_pdf(self,fehdist='casagrande'):
"""
NAME:
calc_age_pdf
PURPOSE:
calculate the age PDF for a uniform SFH for a given metallicity
distribution
INPUT:
fehdist= either:
1) a single metallicity
2) 'casagrande': local metallicity distribution following Casagrande et al. (2011)
3) a function giving
OUTPUT:
a function between 800 Myr and 10 Gyr giving the age distribution
HISTORY:
2014-02-27 - Written in this form - Bovy (IAS)
"""
if isinstance(fehdist,(int,float,numpy.float32,numpy.float64)):
pz= numpy.zeros(len(self._zs))
pz[numpy.argmin(numpy.fabs(self._zs-isodist.FEH2Z(fehdist,zsolar=zsolar())))]= 1.
elif isinstance(fehdist,str) and fehdist.lower() == 'casagrande':
pz= numpy.array([localfehdist(isodist.Z2FEH(z,zsolar=zsolar()))/z for z in self._zs])
else:
pz= numpy.array([fehdist(isodist.Z2FEH(z,zsolar=zsolar()))/z for z in self._zs])
pz/= numpy.sum(pz)
agezdist= self._omega/self._coarsemass
pz= numpy.tile(pz,(len(self._coarselages),1)).T
postage= numpy.nansum(pz*agezdist,axis=0)/numpy.nansum(pz,axis=0)/10.**self._coarselages
postage= postage[self._coarselages > numpy.log10(0.8)]
postage/= numpy.nanmax(postage)
lages= self._coarselages[self._coarselages > numpy.log10(0.8)]
postage_spline= interpolate.InterpolatedUnivariateSpline(lages,
numpy.log(postage),
k=3)
return lambda x: dummy_page(x,copy.copy(postage_spline))
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 popmass(self, feh, lage):
"""
NAME:
calc_popmass
PURPOSE:
calculate the mass represented by each RC star
INPUT:
feh - metallicity
lage - log10 age
OUTPUT:
stellar-pop mass for each RC star
HISTORY:
2014-02-28 - Written in this form - Bovy (IAS)
"""
if lage < numpy.log10(0.8):
return numpy.nan
z = isodist.FEH2Z(feh, zsolar=zsolar())
zindx = numpy.argmin(numpy.fabs(z - self._zs))
aindx = numpy.argmin((numpy.fabs(lage - self._coarselages)))
return self._coarsemass[zindx, aindx] / self._omega[zindx, aindx]
def avgmass(self, feh, lage):
"""
NAME:
calc_avgmass
PURPOSE:
calculate the average mass
INPUT:
feh - metallicity
lage - log10 age
OUTPUT:
average mass in solar masses
HISTORY:
2014-02-28 - Written in this form - Bovy (IAS)
"""
if lage < numpy.log10(0.8):
return numpy.nan
z = isodist.FEH2Z(feh, zsolar=zsolar())
zindx = numpy.argmin(numpy.fabs(z - self._zs))
aindx = numpy.argmin((numpy.fabs(lage - self._finelages)))
return self._finemass[zindx, aindx]
print(
"%i / %i = %i%% APOKASC CLUMP stars are in the RC catalog (COMPLETENESS)"
% (numpy.sum(rcclumpseismo), numpy.sum(clumpseismo),
float(numpy.sum(rcclumpseismo)) / numpy.sum(clumpseismo) * 100.))
print(
"%i / %i = %i%% APOKASC non-CLUMP stars out of all stars in the RC catalog with evolutionary measurements are in the RC catalog (CONTAMINATION)"
% (numpy.sum(rcnoclumpseismo), numpy.sum(rcnoclumpseismo) +
numpy.sum(rcclumpseismo), float(numpy.sum(rcnoclumpseismo)) /
(numpy.sum(rcnoclumpseismo) + numpy.sum(rcclumpseismo)) * 100.))
rcindx = data['RC'] == 1
#Statistics using simple seismo logg cut
kascLoggTag = 'KASC_RG_LOGG_SCALE_2'
try:
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
except ValueError:
kascLoggTag = 'LOGGRG'
clumplogg= (data[kascLoggTag] > 1.8)\
*(data[kascLoggTag] < rcmodel.loggteffcut(data['TEFF'],
isodist.FEH2Z(data['METALS'],zsolar=0.017),upper=True))#2.8)
if False:
rcclumplogg = clumplogg * (data['RC'] == 1)
print("%i / %i = %i%% APOKASC logg clump stars are in the RC catalog" %
(numpy.sum(rcclumplogg), numpy.sum(clumplogg),
float(numpy.sum(rcclumplogg)) / numpy.sum(clumplogg) * 100))
rcnoclumplogg = (True ^ clumplogg) * (data['RC'] == 1)
print(
"%i / %i = %i%% APOKASC logg non-clump stars are in the RC catalog"
% (numpy.sum(rcnoclumplogg), numpy.sum(True ^ clumplogg),
float(numpy.sum(rcnoclumplogg)) / numpy.sum(True ^ clumplogg) *
def plot_nonaxi(parser):
(options, args) = parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
cutmultiples=options.cutmultiples,
jkmax=options.jkmax)
#data= data[0:20]
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
indx = (data['GLON'] <= 30.)
data['GLON'][
indx] = 30.05 #Hack because the non-axi models don't go below Ro/2
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
elif options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso = [
iso,
isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
dwarf=True,
expsfh=options.expsfh)
]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, 'wb')
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpisodwarf = None
""" #Does not matter anyway
#clean logpiso
dataindx= []
for ii in range(len(data)):
thislogpiso= logpiso[ii,:]-logsumexp(logpiso[ii,:])
if numpy.all(thislogpiso == 0.): dataindx.append(False)
else: dataindx.append(True)
dataindx= numpy.array(dataindx,dtype='bool')
data= data[dataindx]
logpiso= logpiso[dataindx,:]
logpisodwarf= logpisodwarf[dataindx,:]
"""
#Calculate data means etc.
#Calculate means
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
avg_plate = numpy.zeros(nlocs)
sig_plate = numpy.zeros(nlocs)
siga_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
avg_plate[ii] = numpy.mean(data['VHELIO'][indx])
sig_plate[ii] = numpy.std(data['VHELIO'][indx])
siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt(
numpy.sum(indx))
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
savefile.close()
#First calculate fiducial model
if not options.dwarf:
logpisodwarf = None
avg_plate_model_fid = calc_model(params, options, data, logpiso,
logpisodwarf, df, nlocs, locations, iso)
print l_plate, avg_plate_model_fid
#Plot everything
bovy_plot.bovy_print(fig_height=6., fig_width=7.)
dx = 0.8 / 5.
left, bottom, width, height = 0.1, 0.9 - dx, 0.8, dx
axTop = pyplot.axes([left, bottom, width, height])
allaxes = [axTop]
fig = pyplot.gcf()
fig.sca(axTop)
bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{fiducial}$', top_right=True, size=14.)
thisax = pyplot.gca()
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Second is bar
print "Reading bar file ..."
barfile = '/work/bovy/data/bovy/nonaximw/bar/bar_rect_vr_tform-150_tsteady-4pi_51_101.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 2. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{bar}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Third is spiral
print "Reading spiral file ..."
barfile = '/work/bovy/data/bovy/nonaximw/spiral/spiral_rect_vr_51_101.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 3. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{spiral}$', top_right=True, size=14.)
pyplot.ylabel(
r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}} \ [\mathrm{km\ s}^{-1}]$'
)
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
#Fourth is first elliptical
print "Reading elliptical file ..."
barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, 0.)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 4. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Fourth is first elliptical
print "Reading elliptical file ..."
barfile = '/work/bovy/data/bovy/nonaximw/elliptical/el_rect_so_0.2_res_51_grid_101_tform_-150._tsteady_125._cp_0.05_nsigma_4.sav'
avg_plate_bar = calc_nonaxi(params, barfile, options, data, logpiso, nlocs,
locations, -45. * _DEGTORAD)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 5. * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0., 360.], [0., 0.],
'-',
color='0.5',
overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid,
'o',
overplot=True,
color='0.6')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid,
yerr=siga_plate,
marker='o',
color='0.6',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
'o',
overplot=True,
color='k')
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model_fid - avg_plate_bar,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{elliptical}$', top_right=True, size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#thisax.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
def make_rcsample(parser):
options, args = parser.parse_args()
savefilename = options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename = os.path.join(
appath._APOGEE_DATA, 'rcsample_' + appath._APOGEE_REDUX + '.fits')
print("Saving to %s ..." % savefilename)
#Read the base-sample
data = apread.allStar(adddist=_ADDHAYDENDIST, rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data = esutil.numpy_util.remove_fields(data, [
'TARGET_ID', 'FILE', 'AK_WISE', 'SFD_EBV', 'SYNTHVHELIO_AVG',
'SYNTHVSCATTER', 'SYNTHVERR', 'SYNTHVERR_MED', 'RV_TEFF', 'RV_LOGG',
'RV_FEH', 'RV_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM',
'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2',
'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION',
'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2',
'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR',
'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0',
'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID',
'SRC_H', 'PM_SRC'
])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l30' in appath._APOGEE_REDUX \
and int(appath._APOGEE_REDUX[1:]) < 500:
data = esutil.numpy_util.remove_fields(data, ['ELEM'])
#Select red-clump stars
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
if 'l30' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
from apogee.tools import paramIndx
if False:
#Use my custom logg calibration that's correct for the RC
logg = (1. - 0.042) * data['FPARAM'][:, paramIndx('logg')] - 0.213
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.255
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.3726
else:
#Use my custom logg calibration that's correct on average
logg = (1. + 0.03) * data['FPARAM'][:, paramIndx('logg')] - 0.37
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.34
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.256
else:
logg = data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg <= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data = data[indx]
#Add more aggressive flag cut
data = esutil.numpy_util.add_fields(data, [('ADDL_LOGG_CUT', numpy.int32)])
data['ADDL_LOGG_CUT'] = (
(data['TEFF'] - 4800.) / 1000. + 2.75) > data['LOGG']
if options.loggcut:
data = data[data['ADDL_LOGG_CUT'] == 1]
print("Making catalog of %i objects ..." % len(data))
#Add distances
data = esutil.numpy_util.add_fields(data, [('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd = rcmodel.rcdist()
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
data['RC_DIST'] = rcd(jk, z, appmag=data['K0']) * options.distfac
data['RC_DM'] = 5. * numpy.log10(data['RC_DIST']) + 10.
XYZ = bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
R, phi, Z = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.,
Zsun=0.025)
data['RC_GALR'] = R
data['RC_GALPHI'] = phi
data['RC_GALZ'] = Z
#Save
fitsio.write(savefilename, data, clobber=True)
# Add Tycho-2 matches
if options.tyc2:
data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32),
('TYC1', numpy.int32),
('TYC2', numpy.int32),
('TYC3', numpy.int32)])
data['TYC2MATCH'] = 0
data['TYC1'] = -1
data['TYC2'] = -1
data['TYC3'] = -1
# Write positions
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
usecols=(1, 2, 7, 8, 9))
iis = numpy.arange(len(data))
mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai] = 1
data['TYC1'][mai] = ma[:, 2]
data['TYC2'][mai] = ma[:, 3]
data['TYC3'][mai] = ma[:, 4]
os.remove(posfilename)
os.remove(resultfilename)
if not options.nostat:
#Determine statistical sample and add flag
apo = apogee.select.apogeeSelect()
statIndx = apo.determine_statistical(data)
mainIndx = apread.mainIndx(data)
data = esutil.numpy_util.add_fields(data, [('STAT', numpy.int32),
('INVSF', float)])
data['STAT'] = 0
data['STAT'][statIndx * mainIndx] = 1
for ii in range(len(data)):
if (statIndx * mainIndx)[ii]:
data['INVSF'][ii] = 1. / apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii] = -1.
if options.nopm:
fitsio.write(savefilename, data, clobber=True)
return None
#Get proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms.fits'
if os.path.exists(pmfile):
pmdata = fitsio.read(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:UCAC4',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 17, 18))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions
try: #These already exist currently, but may not always exist
data = esutil.numpy_util.remove_fields(data, ['PMRA', 'PMDEC'])
except ValueError:
pass
data = esutil.numpy_util.add_fields(data, [('PMRA', numpy.float),
('PMDEC', numpy.float),
('PMRA_ERR', numpy.float),
('PMDEC_ERR', numpy.float),
('PMMATCH', numpy.int32)])
data['PMMATCH'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA'][m2] = pmdata['PMRA'][m1]
data['PMDEC'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH'] == 1
data['PMRA'][True - pmindx] = -9999.99
data['PMDEC'][True - pmindx] = -9999.99
data['PMRA_ERR'][True - pmindx] = -9999.99
data['PMDEC_ERR'][True - pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR', numpy.float),
('GALVT', numpy.float),
('GALVZ', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA'],
data['PMDEC'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vR, vT, vZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR'] = vR
data['GALVT'] = vT
data['GALVZ'] = vZ
data['GALVR'][True - pmindx] = -9999.99
data['GALVT'][True - pmindx] = -9999.99
data['GALVZ'][True - pmindx] = -9999.99
#Get PPMXL proper motions, in a somewhat roundabout way
pmfile = savefilename.split('.')[0] + '_pms_ppmxl.fits'
if os.path.exists(pmfile):
pmdata = fitsio.read(pmfile, 1)
else:
pmdata = numpy.recarray(
len(data),
formats=['f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4'],
names=[
'RA', 'DEC', 'PMRA', 'PMDEC', 'PMRA_ERR', 'PMDEC_ERR',
'PMMATCH'
])
# Write positions, again ...
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=4', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:PPMXL',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Match back and only keep the closest one
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
converters={
15: lambda s: float(s.strip() or -9999),
16: lambda s: float(s.strip() or -9999),
17: lambda s: float(s.strip() or -9999),
18: lambda s: float(s.strip() or -9999)
},
usecols=(4, 5, 15, 16, 19, 20))
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
ma[:, 0],
ma[:, 1],
4. / 3600.,
maxmatch=1)
pmdata['PMMATCH'] = 0
pmdata['RA'] = data['RA']
pmdata['DEC'] = data['DEC']
pmdata['PMMATCH'][m1] = 1
pmdata['PMRA'][m1] = ma[m2, 2]
pmdata['PMDEC'][m1] = ma[m2, 3]
pmdata['PMRA_ERR'][m1] = ma[m2, 4]
pmdata['PMDEC_ERR'][m1] = ma[m2, 5]
pmdata['PMMATCH'][(pmdata['PMRA'] == -9999) \
+(pmdata['PMDEC'] == -9999) \
+(pmdata['PMRA_ERR'] == -9999) \
+(pmdata['PMDEC_ERR'] == -9999)]= 0
fitsio.write(pmfile, pmdata, clobber=True)
#To make sure we're using the same format below
pmdata = fitsio.read(pmfile, 1)
os.remove(posfilename)
os.remove(resultfilename)
#Match proper motions to ppmxl
data = esutil.numpy_util.add_fields(data,
[('PMRA_PPMXL', numpy.float),
('PMDEC_PPMXL', numpy.float),
('PMRA_ERR_PPMXL', numpy.float),
('PMDEC_ERR_PPMXL', numpy.float),
('PMMATCH_PPMXL', numpy.int32)])
data['PMMATCH_PPMXL'] = 0
h = esutil.htm.HTM()
m1, m2, d12 = h.match(pmdata['RA'],
pmdata['DEC'],
data['RA'],
data['DEC'],
2. / 3600.,
maxmatch=1)
data['PMRA_PPMXL'][m2] = pmdata['PMRA'][m1]
data['PMDEC_PPMXL'][m2] = pmdata['PMDEC'][m1]
data['PMRA_ERR_PPMXL'][m2] = pmdata['PMRA_ERR'][m1]
data['PMDEC_ERR_PPMXL'][m2] = pmdata['PMDEC_ERR'][m1]
data['PMMATCH_PPMXL'][m2] = pmdata['PMMATCH'][m1].astype(numpy.int32)
pmindx = data['PMMATCH_PPMXL'] == 1
data['PMRA_PPMXL'][True - pmindx] = -9999.99
data['PMDEC_PPMXL'][True - pmindx] = -9999.99
data['PMRA_ERR_PPMXL'][True - pmindx] = -9999.99
data['PMDEC_ERR_PPMXL'][True - pmindx] = -9999.99
#Calculate Galactocentric velocities
data = esutil.numpy_util.add_fields(data, [('GALVR_PPMXL', numpy.float),
('GALVT_PPMXL', numpy.float),
('GALVZ_PPMXL', numpy.float)])
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
XYZ = bovy_coords.lbd_to_XYZ(lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(data['PMRA_PPMXL'],
data['PMDEC_PPMXL'],
data['RA'],
data['DEC'],
degree=True)
vxvyvz = bovy_coords.vrpmllpmbb_to_vxvyvz(data['VHELIO_AVG'],
pmllpmbb[:, 0],
pmllpmbb[:, 1],
lb[:, 0],
lb[:, 1],
data['RC_DIST'],
degree=True)
vR, vT, vZ = bovy_coords.vxvyvz_to_galcencyl(
vxvyvz[:, 0],
vxvyvz[:, 1],
vxvyvz[:, 2],
8. - XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2] + 0.025,
vsun=[-11.1, 30.24 * 8.,
7.25]) #Assumes proper motion of Sgr A* and R0=8 kpc, zo= 25 pc
data['GALVR_PPMXL'] = vR
data['GALVT_PPMXL'] = vT
data['GALVZ_PPMXL'] = vZ
data['GALVR_PPMXL'][True - pmindx] = -9999.99
data['GALVT_PPMXL'][True - pmindx] = -9999.99
data['GALVZ_PPMXL'][True - pmindx] = -9999.99
#Save
fitsio.write(savefilename, data, clobber=True)
return None
def plot_distanceprior(parser):
(options, args) = parser.parse_args()
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
l = data['GLON'] * _DEGTORAD
b = data['GLAT'] * _DEGTORAD
sinl = numpy.sin(l)
cosl = numpy.cos(l)
sinb = numpy.sin(b)
cosb = numpy.cos(b)
jk = data['J0MAG'] - data['K0MAG']
jk[(jk < 0.5)] = 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
h = data['H0MAG']
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
elif options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
#Set up polar grid
res = 51
xgrid = numpy.linspace(0., 2. * math.pi * (1. - 1. / res / 2.), 2 * res)
ygrid = numpy.linspace(0.5, 2.8, res)
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)
plotthis = numpy.zeros((2 * res, res, len(data))) - numpy.finfo(
numpy.dtype(numpy.float64)).max
#_BINTEGRATENBINS= 11 #For quick testing
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
dm = _dm(ds)
for ii in range(len(data)):
mh = h[ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
jk[ii], mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
jk[ii], mh)
else:
logpiso[ii, :] = iso(numpy.zeros(_BINTEGRATENBINS) + jk[ii], mh)
for jj in range(_BINTEGRATENBINS):
d = ds[jj] / _REFR0
R = numpy.sqrt(1. + d**2. - 2. * d * cosl)
indx = (R == 0.)
R[indx] += 0.0001
theta = numpy.arcsin(d / R * sinl)
indx = (1. / cosl < d) * (cosl > 0.)
theta[indx] = numpy.pi - theta[indx]
indx = (theta < 0.)
theta[indx] += 2. * math.pi
thisout = _logpd([0., 1.], d, None, None, None, None, None, options, R,
theta, 1., 0., logpiso[:, jj])
#Find bin to which these contribute
thetabin = numpy.floor((theta - xgrid[0]) / (xgrid[1] - xgrid[0]) +
0.5)
Rbin = numpy.floor((R - plotygrid[0]) / (plotygrid[1] - plotygrid[0]))
indx = (thetabin < 0)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (thetabin >= 2 * res)
thetabin[indx] = 0. #Has to be
#Rbin[indx]= 0
#thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (Rbin < 0)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (Rbin >= res)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
thetabin = thetabin.astype('int')
Rbin = Rbin.astype('int')
for ii in range(len(data)):
plotthis[thetabin, Rbin, ii] = thisout[ii]
#Normalize
for ii in range(2 * res):
for jj in range(res):
plotthis[ii, jj, 0] = logsumexp(plotthis[ii, jj, :])
plotthis = plotthis[:, :, 0]
plotthis -= numpy.amax(plotthis)
plotthis = numpy.exp(plotthis)
plotthis[(plotthis == 0.)] = numpy.nan
#Get los
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
bovy_plot.bovy_print()
ax = pyplot.subplot(111, projection='galpolar') #galpolar is in bovy_plot
vmin, vmax = 0., 1.
out = ax.pcolor(plotxgrid,
plotygrid,
plotthis.T,
cmap='gist_yarg',
vmin=vmin,
vmax=vmax,
zorder=2)
#Overlay los
for ii in range(nlocs):
lds = numpy.linspace(0., 2.95, 501)
lt = numpy.zeros(len(lds))
lr = numpy.zeros(len(lds))
lr = numpy.sqrt(1. + lds**2. -
2. * lds * numpy.cos(l_plate[ii] * _DEGTORAD))
lt = numpy.arcsin(lds / lr * numpy.sin(l_plate[ii] * _DEGTORAD))
indx = (1. / numpy.cos(l_plate[ii] * _DEGTORAD) < lds) * (numpy.cos(
l_plate[ii] * _DEGTORAD) > 0.)
lt[indx] = numpy.pi - lt[indx]
ax.plot(lt, lr, ls='--', color='w', zorder=3)
from matplotlib.patches import Arrow, FancyArrowPatch
arr = FancyArrowPatch(posA=(-math.pi / 2., 1.8),
posB=(-math.pi / 4., 1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (-math.pi / 16.),
shrinkA=2.0,
shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,
fc='k')
ax.add_patch(arr)
bovy_plot.bovy_text(-math.pi / 2.,
1.97,
r'$\mathrm{Galactic\ rotation}$',
rotation=-22.5)
radii = numpy.array([0.5, 1., 1.5, 2., 2.5])
labels = []
for r in radii:
ax.plot(numpy.linspace(
0.,
2. * math.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=-32.5)
bovy_plot.bovy_text(5.785, 2.82, r'$\mathrm{kpc}$')
azs = numpy.array([0., 45., 90., 135., 180., 225., 270., 315.]) * _DEGTORAD
for az in azs:
ax.plot(numpy.zeros(501) + az,
numpy.linspace(0., 2.8, 501),
'-',
color='0.6',
lw=0.5,
zorder=1)
#Sun
bovy_plot.bovy_text(0.065, .9075, r'$\odot$')
pyplot.ylim(0., 2.8)
bovy_plot.bovy_end_print(options.plotfile)
options.fitsratio + 2 * options.fitsratioinnerouter]
if options.fitahinnerouter and data[ii]['GLON'] < 35.:
ah = params[5 - options.nooutliermean +
(options.rotcurve.lower() == 'linear') +
(options.rotcurve.lower() == 'powerlaw') + 2 *
(options.rotcurve.lower() == 'quadratic') + 3 *
(options.rotcurve.lower() == 'cubic') +
2 * options.fitvpec + options.dwarf +
options.fitsratio +
2 * options.fitsratioinnerouter + options.fiths +
options.fitsrinnerouter + options.dwarfinnerouter +
options.fitah + options.fitdm]
mh = data['H0MAG'][ii] - dm + ah
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](
numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](
numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii] +
1.5 / 1.55 * ah, mh)
elif options.fitdm:
def logl(init=None,data=None,options=None):
if options is None:
parser= get_options()
options, args= parser.parse_args([])
if data is None:
#Read data
data= readVclosData(lmin=25.,
bmax=2.,
ak=True,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
correctak=options.correctak,
jkmax=1.1)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
elif options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(Z=0.019)
if options.dwarf:
iso= [iso,
isomodel.isomodel(Z=0.019,
dwarf=True)]
else:
iso= [iso]
if not options.isofile is None:
isofile= open(options.isofile,'wb')
pickle.dump(iso,isofile)
if options.indivfeh:
pickle.dump(zs,isofile)
elif options.varfeh:
pickle.dump(locl,isofile)
isofile.close()
df= None
#Pre-calculate distance prior
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
if isinstance(init,str): #FILE
#Load initial parameters from file
savefile= open(init,'rb')
params= pickle.load(savefile)
savefile.close()
else: #Array
params= init
#Prep data
l= data['GLON']*_DEGTORAD
b= data['GLAT']*_DEGTORAD
sinl= numpy.sin(l)
cosl= numpy.cos(l)
sinb= numpy.sin(b)
cosb= numpy.cos(b)
jk= data['J0MAG']-data['K0MAG']
try:
jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
except TypeError:
pass #HACK
h= data['H0MAG']
options.multi= 1
out= -mloglike(params,data['VHELIO'],
l,
b,
jk,
h,
df,options,
sinl,
cosl,
cosb,
sinb,
logpiso,
logpisodwarf,True,None,iso,data['FEH']) #None iso for now
return out
def imf_h_jk(plotfile,Z=None,dwarf=False,log=False,h=12.,basti=False,
dartmouth=False,kroupa=False):
#Read isochrones
if basti:
zs= numpy.array([0.0001,0.0003,0.0006,0.001,0.002,0.004,0.008,
0.01,0.0198,0.03,0.04])
elif dartmouth:
zs= isodist.FEH2Z(numpy.array([-2.5,-2.,-1.5,-1.,-0.5,0.,0.2,0.3,0.5]))
else:
zs= numpy.arange(0.0005,0.03005,0.0005)
if Z is None:
Zs= zs
elif not basti and not dartmouth:
if Z < 0.01:
Zs= [Z-0.001,Z-0.0005,Z,Z+0.0005,Z+0.001] #build up statistics
else:
Zs= [Z-0.0005,Z,Z+0.0005] #build up statistics
else:
Zs= [Z]
if basti:
p= isodist.BastiIsochrone(Z=Zs)
elif dartmouth:
p= isodist.DartmouthIsochrone(feh=isodist.Z2FEH(Zs),onlyold=True)
else:
p= isodist.PadovaIsochrone(Z=Zs)
#Get relevant data
sample= []
weights= []
for logage in p.logages():
for z in Zs:
thisiso= p(logage,z)
if basti: mini= thisiso['M_ini']
elif dartmouth: mini= thisiso['M']
else: mini= thisiso['M_ini']
if basti:
int_IMF= isodist.imf.lognormalChabrier2001(thisiso['M_ini'],
int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
elif dartmouth:
int_IMF= isodist.imf.lognormalChabrier2001(thisiso['M'],
int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
else:
if kroupa:
int_IMF= isodist.imf.kroupa2003(thisiso['M_ini'],int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
else:
dmpm= numpy.roll(thisiso['int_IMF'],-1)-thisiso['int_IMF']
for ii in range(1,len(mini)-1):
if basti:
JK= thisiso['J'][ii]-thisiso['K'][ii]
else:
JK= thisiso['J'][ii]-thisiso['Ks'][ii]
H= thisiso['H'][ii]
if JK < 0.: # or thisiso['logg'][ii] > 3.5:
continue
if dmpm[ii] > 0.:
if basti:
sample.append([thisiso['J'][ii]-thisiso['K'][ii],
thisiso['H'][ii]])
else:
sample.append([thisiso['J'][ii]-thisiso['Ks'][ii],
thisiso['H'][ii]])
if dartmouth:
if logage > numpy.log10(5.)+9.:
weights.append(2.*dmpm[ii]) #Dartmouth are linearly spaced, but spacing is bigger at > 5 Gyr
else:
weights.append(dmpm[ii]) #Dartmouth are linearly spaced?
else:
weights.append(dmpm[ii]*10**(logage-7.))
#weights.append(dmpm[ii]*10**(logage-7.)*numpy.exp((10.**(logage-7.))/800.))
else:
continue #no use in continuing here
#Form array
sample= numpy.array(sample)
weights= numpy.array(weights)
#Histogram
if dwarf:
hist, edges= numpy.histogramdd(sample,weights=weights,bins=51,
range=[[0.,1.6],[2.,9.]])
else:
hist, edges= numpy.histogramdd(sample,weights=weights,bins=49,
range=[[0.3,1.6],[-11.,2]])
#Normalize each J-K
for ii in range(len(hist[:,0])):
hist[ii,:]/= numpy.nanmax(hist[ii,:])/numpy.nanmax(hist)
rev= copy.copy(hist[ii,::-1]) #reverse, but in one go does not always work
hist[ii,:]= rev
#Plot
bovy_plot.bovy_print()
if log:
hist= numpy.log(hist)
bovy_plot.bovy_dens2d(hist.T,origin='lower',cmap='gist_yarg',
xrange=[edges[0][0],edges[0][-1]],
yrange=[edges[1][-1],edges[1][0]],
aspect=(edges[0][-1]-edges[0][0])/float(edges[1][-1]-edges[1][0]),
xlabel=r'$(J-K_s)_0\ [\mathrm{mag}]$',
ylabel=r'$M_H\ [\mathrm{mag}]$',
interpolation='nearest')
#Add extinction arrow
djk= 0.45
dh= 1.55/1.5*djk
from matplotlib.patches import FancyArrowPatch
ax=pyplot.gca()
ax.add_patch(FancyArrowPatch((1.,-2.),(1+djk,-2+dh),
arrowstyle='->',mutation_scale=20,fill=True,
lw=1.25))
bovy_plot.bovy_text(1.05,-2.05,r'$\mathrm{extinction}$',
rotation=-math.atan(1.5/1.55*1.3/13.)/math.pi*180.,
size=14.)
#Add color cut
bovy_plot.bovy_plot([0.5,0.5],[-20.,20.],'--',color='0.6',overplot=True)
ax.add_patch(FancyArrowPatch((0.5,-6.),(0.7,-6.),
arrowstyle='->',mutation_scale=20,fill=True,
lw=1.25,ls='dashed',color='0.6'))
bovy_plot.bovy_text(0.43,-8.,r'$\mathrm{APOGEE\ color\ cut}$',rotation=90.,
size=14.)
#Add twin y axis
ax= pyplot.gca()
def my_formatter(x, pos):
"""distance in kpc for m=h"""
xs= 10.**((h-x)/5.-2.)
return r'$%.0f$' % xs
def my_formatter2(x, pos):
"""distance in kpc for m=h"""
xs= 10.**((h-x)/5.+1.)
return r'$%.0f$' % xs
ax2= pyplot.twinx()
if dwarf:
major_formatter = FuncFormatter(my_formatter2)
else:
major_formatter = FuncFormatter(my_formatter)
ax2.yaxis.set_major_formatter(major_formatter)
ystep= ax.yaxis.get_majorticklocs()
ystep= ystep[1]-ystep[0]
ax2.yaxis.set_minor_locator(MultipleLocator(ystep/5.))
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
ymin, ymax= ax.yaxis.get_view_interval()
ax2.yaxis.set_view_interval(ymin,ymax,ignore=True)
if dwarf:
ax2.set_ylabel('$\mathrm{distance\ for}\ H_0\ =\ %.1f\ [\mathrm{pc}]$' % h)
else:
ax2.set_ylabel('$\mathrm{distance\ for}\ H_0\ =\ %.1f\ [\mathrm{kpc}]$' % h)
xstep= ax.xaxis.get_majorticklocs()
xstep= xstep[1]-xstep[0]
ax2.xaxis.set_minor_locator(MultipleLocator(xstep/5.))
if Z is None:
bovy_plot.bovy_end_print(plotfile)
else:
bovy_plot.bovy_text(r'$Z\ =\ %.3f$' % Z,top_right=True,size=14.)
bovy_plot.bovy_end_print(plotfile)
return None
def createFakeData(parser):
options, args = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(options.plotfile):
print "Outfile " + options.plotfile + " exists ..."
print "Returning ..."
return None
#Read the data
numpy.random.seed(options.seed)
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
ak=True,
cutmultiples=options.cutmultiples,
jkmax=options.jkmax)
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
#Set up the isochrone
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
elif options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso = [
iso,
isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
dwarf=True,
expsfh=options.expsfh)
]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, 'wb')
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin((thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpisodwarf = None
#Load initial parameters from file
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
savefile.close()
#Prep data
l = data['GLON'] * _DEGTORAD
b = data['GLAT'] * _DEGTORAD
sinl = numpy.sin(l)
cosl = numpy.cos(l)
sinb = numpy.sin(b)
cosb = numpy.cos(b)
jk = data['J0MAG'] - data['K0MAG']
jk[(jk < 0.5)] = 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
h = data['H0MAG']
#Re-sample
vlos = numpy.linspace(-200., 200., options.nvlos)
pvlos = numpy.zeros((len(data), options.nvlos))
if options.dwarf:
thislogpisodwarf = logpisodwarf
else:
thislogpisodwarf = None
if not options.multi is None and options.multi > 1:
thismulti = options.multi
options.multi = 1 #To avoid conflict
thispvlos = multi.parallel_map(
(lambda x: -mloglike(params,
numpy.zeros(len(data)) + vlos[x], l, b, jk, h,
df, options, sinl, cosl, cosb, sinb, logpiso,
thislogpisodwarf, True, None, None, None)),
range(options.nvlos),
numcores=numpy.amin(
[len(vlos), multiprocessing.cpu_count(), thismulti]))
for jj in range(options.nvlos):
pvlos[:, jj] = thispvlos[jj]
else:
for jj in range(options.nvlos):
pvlos[:, jj] = -mloglike(
params,
numpy.zeros(len(data)) + vlos[jj], l, b, jk, h, df, options,
sinl, cosl, cosb, sinb, logpiso, thislogpisodwarf, True, None,
None, None)
"""
for jj in range(options.nvlos):
pvlos[:,jj]= -mloglike(params,numpy.zeros(len(data))+vlos[jj],
l,
b,
jk,
h,
df,options,
sinl,
cosl,
cosb,
sinb,
logpiso,
thislogpisodwarf,True,None,None,None)
"""
for ii in range(len(data)):
pvlos[ii, :] -= logsumexp(pvlos[ii, :])
pvlos[ii, :] = numpy.exp(pvlos[ii, :])
pvlos[ii, :] = numpy.cumsum(pvlos[ii, :])
pvlos[ii, :] /= pvlos[ii, -1]
#Draw
randindx = numpy.random.uniform()
kk = 0
while pvlos[ii, kk] < randindx:
kk += 1
data['VHELIO'][ii] = vlos[kk]
#Dump raw
fitsio.write(options.plotfile, data, clobber=True)
def plot_bestfit(parser):
(options, args) = parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
#Cut inner disk locations
#data= data[(data['GLON'] > 75.)]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
print "Using %i data points ..." % len(data)
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
if options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso = [
iso,
isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
dwarf=True,
expsfh=options.expsfh)
]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, 'wb')
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpisodwarf = None
#Calculate data means etc.
#Calculate means
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
avg_plate = numpy.zeros(nlocs)
sig_plate = numpy.zeros(nlocs)
siga_plate = numpy.zeros(nlocs)
sigerr_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
avg_plate[ii] = numpy.mean(data['VHELIO'][indx])
sig_plate[ii] = numpy.std(data['VHELIO'][indx])
siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt(
numpy.sum(indx))
sigerr_plate[ii] = bootstrap_sigerr(data['VHELIO'][indx])
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
if not options.index is None:
params = params[options.index]
savefile.close()
#params[0]= 245./235.
#params[1]= 8.5/8.
avg_plate_model = numpy.zeros(nlocs)
sig_plate_model = numpy.zeros(nlocs)
for ii in range(nlocs):
#Calculate vlos | los
indx = (data['LOCATION'] == locations[ii])
thesedata = data[indx]
thislogpiso = logpiso[indx, :]
if options.dwarf:
thislogpisodwarf = logpisodwarf[indx, :]
else:
thislogpisodwarf = None
vlos = numpy.linspace(-200., 200., options.nvlos)
pvlos = numpy.zeros(options.nvlos)
if not options.multi is None:
pvlos = multi.parallel_map(
(lambda x: pvlosplate(params, vlos[x], thesedata, df, options,
thislogpiso, thislogpisodwarf, iso)),
range(options.nvlos),
numcores=numpy.amin(
[len(vlos),
multiprocessing.cpu_count(), options.multi]))
else:
for jj in range(options.nvlos):
print jj
pvlos[jj] = pvlosplate(params, vlos[jj], thesedata, df,
options, thislogpiso, thislogpisodwarf,
iso)
pvlos -= logsumexp(pvlos)
pvlos = numpy.exp(pvlos)
#Calculate mean and velocity dispersion
avg_plate_model[ii] = numpy.sum(vlos * pvlos)
sig_plate_model[ii]= numpy.sqrt(numpy.sum(vlos**2.*pvlos)\
-avg_plate_model[ii]**2.)
#Plot everything
left, bottom, width, height = 0.1, 0.4, 0.8, 0.5
axTop = pyplot.axes([left, bottom, width, height])
left, bottom, width, height = 0.1, 0.1, 0.8, 0.3
axMean = pyplot.axes([left, bottom, width, height])
#left, bottom, width, height= 0.1, 0.1, 0.8, 0.2
#axSig= pyplot.axes([left,bottom,width,height])
fig = pyplot.gcf()
fig.sca(axTop)
pyplot.ylabel(r'$\mathrm{Heliocentric\ velocity}\ [\mathrm{km\ s}^{-1}]$')
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
pyplot.ylim(-200., 200.)
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_plot(data['GLON'],
data['VHELIO'],
'k,',
yrange=[-200., 200.],
xrange=[0., 360.],
overplot=True)
ndata_t = int(math.floor(len(data) / 1000.))
ndata_h = len(data) - ndata_t * 1000
bovy_plot.bovy_plot(l_plate,
avg_plate,
'o',
overplot=True,
mfc='0.5',
mec='none')
bovy_plot.bovy_plot(l_plate,
avg_plate_model,
'x',
overplot=True,
ms=10.,
mew=1.5,
color='0.7')
#Legend
bovy_plot.bovy_plot([260.], [150.], 'k,', overplot=True)
bovy_plot.bovy_plot([260.], [120.],
'o',
mfc='0.5',
mec='none',
overplot=True)
bovy_plot.bovy_plot([260.], [90.],
'x',
ms=10.,
mew=1.5,
color='0.7',
overplot=True)
bovy_plot.bovy_text(270., 145., r'$\mathrm{data}$')
bovy_plot.bovy_text(270., 115., r'$\mathrm{data\ mean}$')
bovy_plot.bovy_text(270., 85., r'$\mathrm{model\ mean}$')
bovy_plot._add_ticks()
#Now plot the difference
fig.sca(axMean)
bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$')
pyplot.ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#axMean.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
#Sigma
fig.sca(axSig)
pyplot.plot([0., 360.], [1., 1.], '-', color='0.5')
bovy_plot.bovy_plot(l_plate,
sig_plate / sig_plate_model,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
sig_plate / sig_plate_model,
yerr=sigerr_plate / sig_plate_model,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
pyplot.ylabel(
r'$\sigma_{\mathrm{los}}^{\mathrm{data}}/ \sigma_{\mathrm{los}}^{\mathrm{model}}$'
)
pyplot.ylim(0.5, 1.5)
def plot_internalcomparison(parser):
(options,args)= parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
#data= data[0:20]
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
print "Using %i data points ..." % len(data)
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
if options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso= [iso,
isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
dwarf=True,expsfh=options.expsfh)]
else:
iso= [iso]
if not options.isofile is None:
isofile= open(options.isofile,'wb')
pickle.dump(iso,isofile)
if options.indivfeh:
pickle.dump(zs,isofile)
elif options.varfeh:
pickle.dump(locl,isofile)
isofile.close()
df= None
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
#Calculate data means etc.
#Calculate means
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile= open(args[0],'rb')
params= pickle.load(savefile)
if not options.index is None:
params= params[options.index]
savefile.close()
#First calculate fiducial model
if not options.dwarf:
logpisodwarf= None
avg_plate_model_fid= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#Plot everything
bovy_plot.bovy_print(fig_height=10.,fig_width=7.)
dx= 0.8/9.
left, bottom, width, height= 0.1, 0.9, 0.8, dx
axTop= pyplot.axes([left,bottom,width,height])
allaxes= [axTop]
fig= pyplot.gcf()
fig.sca(axTop)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'ko',overplot=True)
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{fiducial}$',top_right=True,size=14.)
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Second is Dehnen
fid_dfmodel= options.dfmodel
options.dfmodel= 'dehnen'
avg_plate_model= avg_plate_model_fid
#avg_plate_model= calc_model(params,options,data,
# logpiso,logpisodwarf,
# df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{Dehnen\ DF}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Third = hR= 2. kpc
fid_hr= options.hr
options.dfmodel= fid_dfmodel
options.hr= 2.
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-2.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$h_R = 2\ \mathrm{kpc}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Fourth = hR= 4. kpc
options.dfmodel= fid_dfmodel
options.hr= 4.
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-3.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$h_R = 4\ \mathrm{kpc}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Fifth = hs= 5. kpc
fid_hs= options.hs
options.hr= fid_hr
options.hs= 5.
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-4.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$h_\sigma = 5\ \mathrm{kpc}$',top_right=True,size=14.)
pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}\ [\mathrm{km\ s}^{-1}]$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Sixth: multiple pops
options.hs= fid_hs
options.dfmodel= 'multiplepops'
fid_params=copy.copy(params)
params[2]= -1.8
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-5.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{Multiple\ populations,\ SFR} = \exp\left( -t/8\ \mathrm{Gyr}\right)$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Seventh: cut multiples
options.dfmodel= fid_dfmodel
params= fid_params
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=True,
jkmax=options.jkmax)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
l_plate_fid= l_plate
avg_plate_fid= avg_plate
siga_plate_fid= siga_plate
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-6.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate_fid,
avg_plate_fid-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid,
yerr=siga_plate_fid,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{Multiple\ visit\ dispersion} < 1\ \mathrm{km\ s}^{-1}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Eight b=2
options.dfmodel= fid_dfmodel
params= fid_params
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=10.,
meanb=4.,
ak=True,
cutmultiples=options.cutmultiples,
jkmax=options.jkmax)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-7.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate_fid,
avg_plate_fid-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid,
yerr=siga_plate_fid,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$b = 4^\circ\ \mathrm{fields}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Nintht b=-2
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=10.,
meanb=-4.,
ak=True,
cutmultiples=options.cutmultiples,
jkmax=options.jkmax)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-8.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate_fid,
avg_plate_fid-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate_fid,avg_plate_fid-avg_plate_model_fid,
yerr=siga_plate_fid,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$b = -4^\circ\ \mathrm{fields}$',top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#thisax.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
def make_rcsample(parser):
options, args = parser.parse_args()
savefilename = options.savefilename
if savefilename is None:
#Create savefilename if not given
savefilename = os.path.join(
appath._APOGEE_DATA, 'rcsample_' + appath._APOGEE_REDUX + '.fits')
print("Saving to %s ..." % savefilename)
#Read the base-sample
data = apread.allStar(adddist=_ADDHAYDENDIST, rmdups=options.rmdups)
#Remove a bunch of fields that we do not want to keep
data = esutil.numpy_util.remove_fields(data, [
'TARGET_ID', 'FILE', 'AK_WISE', 'SFD_EBV', 'SYNTHVHELIO_AVG',
'SYNTHVSCATTER', 'SYNTHVERR', 'SYNTHVERR_MED', 'RV_TEFF', 'RV_LOGG',
'RV_FEH', 'RV_ALPHA', 'RV_CARB', 'RV_CCFWHM', 'RV_AUTOFWHM',
'SYNTHSCATTER', 'STABLERV_CHI2', 'STABLERV_RCHI2',
'STABLERV_CHI2_PROB', 'CHI2_THRESHOLD', 'APSTAR_VERSION',
'ASPCAP_VERSION', 'RESULTS_VERSION', 'WASH_M', 'WASH_M_ERR', 'WASH_T2',
'WASH_T2_ERR', 'DDO51', 'DDO51_ERR', 'IRAC_3_6', 'IRAC_3_6_ERR',
'IRAC_4_5', 'IRAC_4_5_ERR', 'IRAC_5_8', 'IRAC_5_8_ERR', 'IRAC_8_0',
'IRAC_8_0_ERR', 'WISE_4_5', 'WISE_4_5_ERR', 'TARG_4_5', 'TARG_4_5_ERR',
'WASH_DDO51_GIANT_FLAG', 'WASH_DDO51_STAR_FLAG', 'REDUCTION_ID',
'SRC_H', 'PM_SRC'
])
# More
if appath._APOGEE_REDUX.lower() == 'l33':
data = esutil.numpy_util.remove_fields(data, [
'GAIA_SOURCE_ID', 'GAIA_PARALLAX', 'GAIA_PARALLAX_ERROR',
'GAIA_PMRA', 'GAIA_PMRA_ERROR', 'GAIA_PMDEC', 'GAIA_PMDEC_ERROR',
'GAIA_PHOT_G_MEAN_MAG', 'GAIA_PHOT_BP_MEAN_MAG',
'GAIA_PHOT_RP_MEAN_MAG', 'GAIA_RADIAL_VELOCITY',
'GAIA_RADIAL_VELOCITY_ERROR', 'GAIA_R_EST', 'GAIA_R_LO',
'GAIA_R_HI', 'TEFF_SPEC', 'LOGG_SPEC'
])
if not appath._APOGEE_REDUX.lower() == 'current' \
and not 'l3' in appath._APOGEE_REDUX \
and int(appath._APOGEE_REDUX[1:]) < 500:
data = esutil.numpy_util.remove_fields(data, ['ELEM'])
#Select red-clump stars
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
if 'l31' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif 'l30' in appath._APOGEE_REDUX:
logg = data['LOGG']
elif appath._APOGEE_REDUX.lower() == 'current' \
or int(appath._APOGEE_REDUX[1:]) > 600:
if False:
#Use my custom logg calibration that's correct for the RC
logg = (1. - 0.042) * data['FPARAM'][:, paramIndx('logg')] - 0.213
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.255
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.3726
else:
#Use my custom logg calibration that's correct on average
logg = (1. + 0.03) * data['FPARAM'][:, paramIndx('logg')] - 0.37
lowloggindx = data['FPARAM'][:, paramIndx('logg')] < 1.
logg[lowloggindx] = data['FPARAM'][lowloggindx,
paramIndx('logg')] - 0.34
hiloggindx = data['FPARAM'][:, paramIndx('logg')] > 3.8
logg[hiloggindx] = data['FPARAM'][hiloggindx,
paramIndx('logg')] - 0.256
else:
logg = data['LOGG']
indx= (jk < 0.8)*(jk >= 0.5)\
*(z <= 0.06)\
*(z <= rcmodel.jkzcut(jk,upper=True))\
*(z >= rcmodel.jkzcut(jk))\
*(logg >= rcmodel.loggteffcut(data['TEFF'],z,upper=False))\
*(logg+0.1*('l31' in appath._APOGEE_REDUX
or 'l33' in appath._APOGEE_REDUX) \
<= rcmodel.loggteffcut(data['TEFF'],z,upper=True))
data = data[indx]
#Add more aggressive flag cut
data = esutil.numpy_util.add_fields(data, [('ADDL_LOGG_CUT', numpy.int32)])
data['ADDL_LOGG_CUT'] = (
(data['TEFF'] - 4800.) / 1000. + 2.75) > data['LOGG']
if options.loggcut:
data = data[data['ADDL_LOGG_CUT'] == 1]
print("Making catalog of %i objects ..." % len(data))
#Add distances
data = esutil.numpy_util.add_fields(data, [('RC_DIST', float),
('RC_DM', float),
('RC_GALR', float),
('RC_GALPHI', float),
('RC_GALZ', float)])
rcd = rcmodel.rcdist()
jk = data['J0'] - data['K0']
z = isodist.FEH2Z(data['METALS'], zsolar=0.017)
data['RC_DIST'] = rcd(jk, z, appmag=data['K0']) * options.distfac
data['RC_DM'] = 5. * numpy.log10(data['RC_DIST']) + 10.
XYZ = bovy_coords.lbd_to_XYZ(data['GLON'],
data['GLAT'],
data['RC_DIST'],
degree=True)
RphiZ = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=8.15,
Zsun=0.0208)
R = RphiZ[:, 0]
phi = RphiZ[:, 1]
Z = RphiZ[:, 2]
data['RC_GALR'] = R
data['RC_GALPHI'] = phi
data['RC_GALZ'] = Z
#Save
fitswrite(savefilename, data, clobber=True)
# Add Tycho-2 matches
if options.tyc2:
data = esutil.numpy_util.add_fields(data, [('TYC2MATCH', numpy.int32),
('TYC1', numpy.int32),
('TYC2', numpy.int32),
('TYC3', numpy.int32)])
data['TYC2MATCH'] = 0
data['TYC1'] = -1
data['TYC2'] = -1
data['TYC3'] = -1
# Write positions
posfilename = tempfile.mktemp('.csv', dir=os.getcwd())
resultfilename = tempfile.mktemp('.csv', dir=os.getcwd())
with open(posfilename, 'w') as csvfile:
wr = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
wr.writerow(['RA', 'DEC'])
for ii in range(len(data)):
wr.writerow([data[ii]['RA'], data[ii]['DEC']])
# Send to CDS for matching
result = open(resultfilename, 'w')
try:
subprocess.check_call([
'curl', '-X', 'POST', '-F', 'request=xmatch', '-F',
'distMaxArcsec=2', '-F', 'RESPONSEFORMAT=csv', '-F',
'cat1=@%s' % os.path.basename(posfilename), '-F', 'colRA1=RA',
'-F', 'colDec1=DEC', '-F', 'cat2=vizier:Tycho2',
'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'
],
stdout=result)
except subprocess.CalledProcessError:
os.remove(posfilename)
if os.path.exists(resultfilename):
result.close()
os.remove(resultfilename)
result.close()
# Directly match on input RA
ma = numpy.loadtxt(resultfilename,
delimiter=',',
skiprows=1,
usecols=(1, 2, 7, 8, 9))
iis = numpy.arange(len(data))
mai = [iis[data['RA'] == ma[ii, 0]][0] for ii in range(len(ma))]
data['TYC2MATCH'][mai] = 1
data['TYC1'][mai] = ma[:, 2]
data['TYC2'][mai] = ma[:, 3]
data['TYC3'][mai] = ma[:, 4]
os.remove(posfilename)
os.remove(resultfilename)
if not options.nostat:
#Determine statistical sample and add flag
apo = apogee.select.apogeeSelect()
statIndx = apo.determine_statistical(data)
mainIndx = apread.mainIndx(data)
data = esutil.numpy_util.add_fields(data, [('STAT', numpy.int32),
('INVSF', float)])
data['STAT'] = 0
data['STAT'][statIndx * mainIndx] = 1
for ii in range(len(data)):
if (statIndx * mainIndx)[ii]:
data['INVSF'][ii] = 1. / apo(data['LOCATION_ID'][ii],
data['H'][ii])
else:
data['INVSF'][ii] = -1.
if options.nopm:
fitswrite(savefilename, data, clobber=True)
return None
data = _add_proper_motions(data, savefilename)
# Save
fitswrite(savefilename, data, clobber=True)
return None
def plot_externalcomparison(parser):
(options,args)= parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
#data= data[0:20]
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
elif options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso= [iso,
isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
dwarf=True,expsfh=options.expsfh)]
else:
iso= [iso]
if not options.isofile is None:
isofile= open(options.isofile,'wb')
pickle.dump(iso,isofile)
if options.indivfeh:
pickle.dump(zs,isofile)
elif options.varfeh:
pickle.dump(locl,isofile)
isofile.close()
df= None
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
#Calculate data means etc.
#Calculate means
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile= open(args[0],'rb')
params= pickle.load(savefile)
if not options.index is None:
params= params[options.index]
savefile.close()
#First calculate fiducial model
if not options.dwarf:
logpisodwarf= None
fid_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options))) #Copy options, since logl sets multi=1
avg_plate_model_fid= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#Plot everything
bovy_plot.bovy_print(fig_height=6.,fig_width=7.)
dx= 0.8/5.
left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx
axTop= pyplot.axes([left,bottom,width,height])
allaxes= [axTop]
fig= pyplot.gcf()
fig.sca(axTop)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'ko',overplot=True)
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{fiducial}$',top_right=True,size=14.)
thisax= pyplot.gca()
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Second is powerlaw with beta=0.15
#fid_slope= params[5-options.nooutliermean+options.dwarf]
#params[5-options.nooutliermean+options.dwarf]= 0.
fid_params= copy.copy(params)
if options.rotcurve.lower() == 'flat':
params= list(params)
params.insert(6,0.15)
params= numpy.array(params)
fid_rotcurve= options.rotcurve
options.rotcurve= 'powerlaw'
elif options.rotcurve.lower() == 'powerlaw':
params[6]= 0.15
fid_rotcurve= options.rotcurve
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#params[5-options.nooutliermean+options.dwarf]= fid_slope
#Undo changes
params= fid_params
options.rotcurve= fid_rotcurve
left, bottom, width, height= 0.1, 0.9-2.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = 0.15$',
top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Third is powerlaw with beta=-0.15
#fid_slope= params[5-options.nooutliermean+options.dwarf]
#params[5-options.nooutliermean+options.dwarf]= 0.
fid_params= copy.copy(params)
if options.rotcurve.lower() == 'flat':
params= list(params)
params.insert(6,-0.15)
params= numpy.array(params)
fid_rotcurve= options.rotcurve
options.rotcurve= 'powerlaw'
elif options.rotcurve.lower() == 'powerlaw':
params[6]= -0.15
fid_rotcurve= options.rotcurve
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#params[5-options.nooutliermean+options.dwarf]= fid_slope
#Undo changes
params= fid_params
options.rotcurve= fid_rotcurve
left, bottom, width, height= 0.1, 0.9-3.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = -0.15$',
top_right=True,size=14.)
pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Second is vc=250
#Load initial parameters from file
savefile= open(options.vo250file,'rb')
params= pickle.load(savefile)
savefile.close()
options.fixvo= 250.
options.fixro= 8.4
vo250_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options)))
avg_plate_modelr84= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#Load initial parameters from file
savefile= open(options.vo250r8file,'rb')
paramsr8= pickle.load(savefile)
savefile.close()
options.fixvo= 250.
options.fixro= 8.
vo250r8_logl= numpy.sum(logl.logl(init=paramsr8,data=data,options=copy.copy(options)))
avg_plate_modelr8= calc_model(paramsr8,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
options.fixro= None
options.fixvo= None
left, bottom, width, height= 0.1, 0.9-4.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_modelr84,
'x',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr84,
yerr=siga_plate,marker='x',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_modelr8,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr8,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
#top_right=True,size=14.)
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.,2.*(fid_logl-vo250r8_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250r8_logl),10.))*numpy.sqrt(2.)),
top_right=True,size=13.)
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.4,2.*(fid_logl-vo250_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250_logl),10.))*numpy.sqrt(2.)),
bottom_right=True,size=13.)
bovy_plot.bovy_plot([40.],[-15.],'kx',overplot=True)
bovy_plot.bovy_plot([40.],[12.],'ko',overplot=True)
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Fourth = vpec=SBD10
#Load sbd fit
#Load initial parameters from file
savefile= open(options.sbdfile,'rb')
params= pickle.load(savefile)
savefile.close()
options.sbdvpec= True
options.fitvpec= False
sbd_logl= numpy.nansum(logl.logl(init=params,data=data,options=copy.copy(options)))
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-5.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ \vec{V}_\odot = \vec{V}_c(R_0) + \mathrm{SBD10}, \Delta \chi^2 = %.0f$' % (2.*(fid_logl-sbd_logl)),#,special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.)),
top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#thisax.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
print fid_logl, sbd_logl, 2.*(fid_logl-sbd_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.)
return None
