def plot_data_h_jk(location=None,
plotfilename=os.path.join(OUTDIR, 'data_h_jk.' + OUTEXT)):
data = readVclosData()
if not location is None:
if location == 0:
locs = set(data['LOCATION'])
for l in locs:
plot_data_h_jk(location=l,
plotfilename=os.path.join(
OUTDIR, 'data_h_jk_%i.' % l + OUTEXT))
return None
data = data[(data['LOCATION'] == location)]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['J0MAG'] - data['K0MAG'],
data['H0MAG'],
'k,',
xlabel=r'$(J-K_s)_0\ [\mathrm{mag}]$',
ylabel=r'$H_0\ [\mathrm{mag}]$',
xrange=[0.4, 1.4],
yrange=[5., 14.],
onedhists=True,
bins=31)
#Overplot distance if wanted
if _PLOTDISTANCE:
iso = isomodel(imfmodel='lognormalChabrier2001', Z=0.019, expsfh=True)
nds = 101
ds = numpy.zeros((nds, nds))
jks = numpy.linspace(0.5, 1.2, nds)
hs = numpy.linspace(14., 5., nds)
for ii in range(nds):
for jj in range(nds):
ds[ii, jj] = iso.peak(jks[ii], hs[jj])
#Now contour this
levels = [1., 3., 10., 30.]
colors = '0.6' #['0.5','0.5','0.5','k','k','k']
CS = pyplot.contour(jks,
hs,
ds.T,
levels=levels,
colors=colors,
zorder=10.,
linewidths=2.)
ys = [5.3, 6.7, 9.22, 11.7]
for ii in range(len(levels)):
bovy_plot.bovy_text(1.21,
ys[ii],
r'$%.0f\ \mathrm{kpc}$' % levels[ii],
fontsize=14.,
color='0.3')
if False:
pyplot.clabel(CS,
levels,
inline=1,
fmt='%.0f',
fontsize=14,
colors=colors,
zorder=10.)
bovy_plot.bovy_end_print(plotfilename)
return None
def simplePlot(location=4242,
plotfile=None,
predict=False,
nv=11,
dmax=10. / 8.,
**kwargs):
"""
NAME:
simplePlot
PURPOSE:
make a simple histogram for a given location
INPUT:
location - location ID
+readVclosData inputs
OPTIONAL INPUT:
plotfile= if set, save plot to this file
OUTPUT:
plot to display or file
HISTORY:
2012-01-25 - Written - Bovy (IAS)
"""
#Read data
data = readVclosData(**kwargs)
data = data[(data['LOCATION'] == location)]
if not plotfile is None:
bovy_plot.bovy_print()
range = [-200., 200.]
hist, xvec, p = bovy_plot.bovy_hist(
data['VHELIO'],
range=range,
bins=31,
histtype='step',
color='k',
xlabel=
r'$\mathrm{heliocentric}\ v_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Prediction
if predict:
pred_vs = numpy.linspace(range[0], range[1], nv)
pred_dist = _calc_pred(pred_vs, location, numpy.mean(data['GLON']),
dmax)
data_int = numpy.sum(hist) * (xvec[1] - xvec[0])
pred_dist *= data_int / numpy.sum(pred_dist) / (pred_vs[1] -
pred_vs[0])
bovy_plot.bovy_plot(pred_vs,
pred_dist,
'-',
color='0.65',
overplot=True)
#Add text
bovy_plot.bovy_text(r'$\mathrm{location}\ =\ %i$' % location + '\n' +
r'$l\ \approx\ %.0f^\circ$' % numpy.mean(data['GLON']),
top_right=True,
size=14.)
if not plotfile is None:
bovy_plot.bovy_end_print(plotfile)
def fieldsTable(parser):
(options, args) = parser.parse_args()
#Read 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,
jkmax=options.jkmax)
#Parse data
locs = numpy.array(sorted(list(set(data['LOCATION']))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locs[ii])
platel[ii] = numpy.mean(data['GLON'][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
outfile = open(options.plotfilename, 'w')
for ii in range(nlocs):
indx = (data['LOCATION'] == locs[ii])
#l
printline = '$%i^\circ$ ' % int(round(numpy.mean(data['GLON'][indx])))
# # of data
printline += '& %i' % numpy.sum(indx)
# # of data H < 12.2
printline += '& %i' % numpy.sum(
(data['LOCATION'] == locs[ii]) * (data['HMAG'] < 12.2))
# # of data 12.2 <= H < 12.8
nn = numpy.sum((data['LOCATION'] == locs[ii]) *
(data['HMAG'] >= 12.2) * (data['HMAG'] < 12.8))
if nn > 0:
printline += '& %i' % nn
else:
printline += '& 0'
# # of data 12.8 <= H < 13.8
nn = numpy.sum((data['LOCATION'] == locs[ii]) *
(data['HMAG'] >= 12.8) * (data['HMAG'] < 13.8))
if nn > 0: printline += '& %i' % nn
else: printline += '& 0 '
#median ak
printline += '& %.1f ' % numpy.median(data['AK'][indx])
#median visits
#printline+= '& %i ' % numpy.median(data['NVISITS'][indx])
#Write the line
if not ii == (nlocs - 1): printline += '\\\\\n'
else: printline += '\n'
outfile.write(printline)
outfile.close()
def fieldsTable(parser):
(options,args)= parser.parse_args()
#Read 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,
jkmax=options.jkmax)
#Parse data
locs= numpy.array(sorted(list(set(data['LOCATION']))))
nlocs= len(locs)
platel= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locs[ii])
platel[ii]= numpy.mean(data['GLON'][indx])
sortindx= numpy.argsort(platel)
locs= locs[sortindx]
outfile= open(options.plotfilename,'w')
for ii in range(nlocs):
indx= (data['LOCATION'] == locs[ii])
#l
printline= '$%i^\circ$ ' % int(round(numpy.mean(data['GLON'][indx])))
# # of data
printline+= '& %i' % numpy.sum(indx)
# # of data H < 12.2
printline+= '& %i' % numpy.sum((data['LOCATION'] == locs[ii])*(data['HMAG'] < 12.2))
# # of data 12.2 <= H < 12.8
nn= numpy.sum((data['LOCATION'] == locs[ii])*(data['HMAG'] >= 12.2)*(data['HMAG'] < 12.8))
if nn > 0:
printline+= '& %i' % nn
else:
printline+= '& 0'
# # of data 12.8 <= H < 13.8
nn= numpy.sum((data['LOCATION'] == locs[ii])*(data['HMAG'] >= 12.8)*(data['HMAG'] < 13.8))
if nn > 0: printline+= '& %i' % nn
else: printline+= '& 0 '
#median ak
printline+= '& %.1f ' % numpy.median(data['AK'][indx])
#median visits
#printline+= '& %i ' % numpy.median(data['NVISITS'][indx])
#Write the line
if not ii == (nlocs-1): printline+= '\\\\\n'
else: printline+= '\n'
outfile.write(printline)
outfile.close()
def plot_data_h_jk(location=None,
plotfilename=os.path.join(OUTDIR,'data_h_jk.'+OUTEXT)):
data= readVclosData()
if not location is None:
if location == 0:
locs= set(data['LOCATION'])
for l in locs:
plot_data_h_jk(location=l,
plotfilename=os.path.join(OUTDIR,'data_h_jk_%i.' % l +OUTEXT))
return None
data= data[(data['LOCATION'] == location)]
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['J0MAG']-data['K0MAG'],
data['H0MAG'],'k,',
xlabel=r'$(J-K_s)_0\ [\mathrm{mag}]$',
ylabel=r'$H_0\ [\mathrm{mag}]$',
xrange=[0.4,1.4],
yrange=[5.,14.],
onedhists=True,bins=31)
#Overplot distance if wanted
if _PLOTDISTANCE:
iso= isomodel(imfmodel='lognormalChabrier2001',
Z=0.019,
expsfh=True)
nds= 101
ds= numpy.zeros((nds,nds))
jks= numpy.linspace(0.5,1.2,nds)
hs= numpy.linspace(14.,5.,nds)
for ii in range(nds):
for jj in range(nds):
ds[ii,jj]= iso.peak(jks[ii],hs[jj])
#Now contour this
levels=[1.,3.,10.,30.]
colors='0.6'#['0.5','0.5','0.5','k','k','k']
CS=pyplot.contour(jks,hs,ds.T,levels=levels,
colors=colors,zorder=10.,linewidths=2.)
ys= [5.3,6.7,9.22,11.7]
for ii in range(len(levels)):
bovy_plot.bovy_text(1.21,ys[ii],r'$%.0f\ \mathrm{kpc}$' % levels[ii],
fontsize=14.,color='0.3')
if False:
pyplot.clabel(CS, levels,
inline=1,
fmt='%.0f',
fontsize=14,
colors=colors,zorder=10.)
bovy_plot.bovy_end_print(plotfilename)
return None
def simplePlot(location=4242,plotfile=None,predict=False,nv=11,dmax=10./8.,
**kwargs):
"""
NAME:
simplePlot
PURPOSE:
make a simple histogram for a given location
INPUT:
location - location ID
+readVclosData inputs
OPTIONAL INPUT:
plotfile= if set, save plot to this file
OUTPUT:
plot to display or file
HISTORY:
2012-01-25 - Written - Bovy (IAS)
"""
#Read data
data= readVclosData(**kwargs)
data= data[(data['LOCATION'] == location)]
if not plotfile is None:
bovy_plot.bovy_print()
range= [-200.,200.]
hist, xvec, p= bovy_plot.bovy_hist(data['VHELIO'],range=range,bins=31,
histtype='step',color='k',
xlabel=r'$\mathrm{heliocentric}\ v_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Prediction
if predict:
pred_vs= numpy.linspace(range[0],range[1],nv)
pred_dist= _calc_pred(pred_vs,location,numpy.mean(data['GLON']),dmax)
data_int= numpy.sum(hist)*(xvec[1]-xvec[0])
pred_dist*= data_int/numpy.sum(pred_dist)/(pred_vs[1]-pred_vs[0])
bovy_plot.bovy_plot(pred_vs,pred_dist,'-',color='0.65',overplot=True)
#Add text
bovy_plot.bovy_text(r'$\mathrm{location}\ =\ %i$' % location
+'\n'
+r'$l\ \approx\ %.0f^\circ$' % numpy.mean(data['GLON']),
top_right=True,size=14.)
if not plotfile is None:
bovy_plot.bovy_end_print(plotfile)
def plot_data_feh(location=0,
plotfilename=os.path.join(OUTDIR,'data_h_jk.'+OUTEXT)):
data= readVclosData()
#Good feh
data= data[(data['FEH']!= -9999.00)]
if not location is None:
if location == 0:
locs= set(data['LOCATION'])
for l in locs:
plot_data_feh(location=l,
plotfilename=os.path.join(OUTDIR,'data_feh_%i.' % l +OUTEXT))
return None
data= data[(data['LOCATION'] == location)]
meanfeh= numpy.mean(data['FEH'])
sigfeh= numpy.std(data['FEH'])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data['FEH'],
xlabel=r'$[\mathrm{Fe/H}]$',
xrange=[meanfeh-1.,meanfeh+1],
bins=16)
bovy_plot.bovy_text(r'$\sigma = %.2f$' % sigfeh,top_right=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_data_feh(location=0,
plotfilename=os.path.join(OUTDIR, 'data_h_jk.' + OUTEXT)):
data = readVclosData()
#Good feh
data = data[(data['FEH'] != -9999.00)]
if not location is None:
if location == 0:
locs = set(data['LOCATION'])
for l in locs:
plot_data_feh(location=l,
plotfilename=os.path.join(
OUTDIR, 'data_feh_%i.' % l + OUTEXT))
return None
data = data[(data['LOCATION'] == location)]
meanfeh = numpy.mean(data['FEH'])
sigfeh = numpy.std(data['FEH'])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data['FEH'],
xlabel=r'$[\mathrm{Fe/H}]$',
xrange=[meanfeh - 1., meanfeh + 1],
bins=16)
bovy_plot.bovy_text(r'$\sigma = %.2f$' % sigfeh, top_right=True)
bovy_plot.bovy_end_print(plotfilename)
return None
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"] <= 30.0
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=6.0, fig_width=7.0)
dx = 0.8 / 5.0
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([0.0, 360.0], [0.0, 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=14.0)
thisax = pyplot.gca()
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
# pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.0, 360.0)
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, 0.0)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 2.0 * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 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=14.0)
# 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(0.0, 360.0)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
# pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.0, 360.0)
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, 0.0)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 3.0 * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 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=14.0)
pyplot.ylabel(r"$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}} \ [\mathrm{km\ s}^{-1}]$")
thisax.set_ylim(-14.5, 14.5)
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
# pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.0, 360.0)
# 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, 0.0)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 4.0 * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 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=14.0)
# 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(0.0, 360.0)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
# pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.0, 360.0)
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, -45.0 * _DEGTORAD)
print avg_plate_bar
left, bottom, width, height = 0.1, 0.9 - 5.0 * dx, 0.8, dx
thisax = pyplot.axes([left, bottom, width, height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 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=14.0)
# 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(0.0, 360.0)
bovy_plot._add_ticks()
# thisax.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$")
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
# Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
vR = -thisvs[ii] * math.cos(
theta + thisl) #vperp=0 for marginalization
vT = thisvs[ii] * math.sin(theta + thisl)
o = Orbit([R, vR, vT, theta])
surfmass = dfc.surfacemassLOS(d, thisl, deg=False)
out += surfmass * dfc(
o, marginalizeVperp=True) / dfc.targetSurfacemass(R)
norm += surfmass
pred_dist[ii] = out / norm
ii += 1
if _PREDICTCACHE:
savefile = open(savefilename, 'wb')
try:
pickle.dump(pred_dist, savefile)
finally:
savefile.close()
return pred_dist
if __name__ == '__main__':
data = readVclosData()
locations = list(set(data['LOCATION']))
for l in locations:
print l
simplePlot(location=l,
plotfile=os.path.join(
os.getenv('HOME'),
'Desktop/locationPlots/vlosdist_%i.png' % l),
predict=True,
nv=201)
def mockTable(parser):
(options, args) = parser.parse_args()
cmdline = '%python mockTable.py ' + args[0]
if len(args) == 0:
parser.print_help()
return
#Open savefile for flat fit and samples
savefile = open(_FLATFIT, 'rb')
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, 'rb')
flatsamples = pickle.load(savefile)
savefile.close()
#Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], 'rb')
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], 'rb')
mocksamples.append(pickle.load(savefile))
savefile.close()
#Set up sections
names = [
'$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0\ [\mathrm{kpc}]$',
'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
'$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
'$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0/h_\sigma$',
'$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$',
'$\Delta\chi^2/\mathrm{dof}$'
]
scale = [_REFV0, _REFR0, _VRSUN, 1., _REFV0, 1., 1., 1.]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
#V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
#R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
#VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
#Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
#\sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
#R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
#X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
#chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = '../fits/isos.dat'
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data['LOCATION']))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locs[ii])
platel[ii] = numpy.mean(data['GLON'][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append(
'$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$' %
int(round(platel[jj])))
scale.append(1.)
indx = (data['LOCATION'] != locs[jj])
flatbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
#V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
#R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
#VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
#Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
#\sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
#R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
#X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
#chi2
#Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = (data['LOCATION'] != locs[jj])
thisbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(mockparams)))
#Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
#Make table
quantile = 0.68
outfile = open(args[0], 'w')
for ii in range(len(names)):
#Set up line
printline = names[ii]
#Flat
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
printline += ' \ldots & '
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
#Mocks
for jj in range(_NMOCKS):
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[
ii] * scale[ii]
printline += '$%.2f$ & ' % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += '\\\\'
outfile.write(printline + '\n')
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
if not ii == (len(names) - 1):
printline += '\\\\'
if ii == (len(names) - (len(locs) + 2)):
printline += '\\\\'
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + '\n')
outfile.write('\\enddata\n')
outfile.write(cmdline + '\n')
outfile.close()
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 mockTable(parser):
(options, args) = parser.parse_args()
cmdline = "%python mockTable.py " + args[0]
if len(args) == 0:
parser.print_help()
return
# Open savefile for flat fit and samples
savefile = open(_FLATFIT, "rb")
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, "rb")
flatsamples = pickle.load(savefile)
savefile.close()
# Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], "rb")
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], "rb")
mocksamples.append(pickle.load(savefile))
savefile.close()
# Set up sections
names = [
"$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0\ [\mathrm{kpc}]$",
"$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$",
"$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$",
"$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0/h_\sigma$",
"$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$",
"$\Delta\chi^2/\mathrm{dof}$",
]
scale = [_REFV0, _REFR0, _VRSUN, 1.0, _REFV0, 1.0, 1.0, 1.0]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
# V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
# R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
# VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
# Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
# \sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
# R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
# X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
# chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = "../fits/isos.dat"
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data["LOCATION"]))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = data["LOCATION"] == locs[ii]
platel[ii] = numpy.mean(data["GLON"][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append("$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$" % int(round(platel[jj])))
scale.append(1.0)
indx = data["LOCATION"] != locs[jj]
flatbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
# V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
# R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
# VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
# Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
# \sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
# R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
# X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
# chi2
# Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = data["LOCATION"] != locs[jj]
thisbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(mockparams)))
# Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
# Make table
quantile = 0.68
outfile = open(args[0], "w")
for ii in range(len(names)):
# Set up line
printline = names[ii]
# Flat
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
printline += " \ldots & "
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
# Mocks
for jj in range(_NMOCKS):
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[ii] * scale[ii]
printline += "$%.2f$ & " % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += "\\\\"
outfile.write(printline + "\n")
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
if not ii == (len(names) - 1):
printline += "\\\\"
if ii == (len(names) - (len(locs) + 2)):
printline += "\\\\"
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + "\n")
outfile.write("\\enddata\n")
outfile.write(cmdline + "\n")
outfile.close()
def plot_chi2(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((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)
if not options.index is None:
params= params[options.index]
savefile.close()
#params[0]= 245./235.
#params[1]= 8.5/8.
#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)
fidlogl= logl.logl(init=params,data=data,options=options)
logl_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])
#Logl
logl_plate[ii]= -2.*(numpy.sum(fidlogl[indx])-numpy.sum(fidlogl)/len(indx)*numpy.sum(indx))
#Calculate plate means and variances from the model
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, 0.3
axTop= pyplot.axes([left,bottom,width,height])
left, bottom, width, height= 0.1, 0.1, 0.8, 0.3
axChi2= 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()
#Plot the difference
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,
'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'$\langle v_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle v_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
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()
#Plot the chi2
fig.sca(axChi2)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot(l_plate,
logl_plate,
'ko',overplot=True)
pyplot.ylabel(r'$\Delta \chi^2$')
#pyplot.ylim(numpy.amin(logl_plate),numpy.amax(logl_plate))
pyplot.ylim(-150.,150.)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
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
#set up; kind of plot
justData= False
dataMean=False
noSolar= False
justSolar= False
betas, vcbetas= True, 250.
hrs= False
dmaxs= False
addNonAxi= False
#Data selection
nodups= True
fehcut= False
postshutdown= True
cohort= None
ext= 'png'
data= readVclosData(postshutdown=postshutdown,fehcut=fehcut,cohort=cohort)
#Calculate means
plates= list(set(data['PLATE']))
nplates= len(plates)
l_plate= numpy.zeros(nplates)
avg_plate= numpy.zeros(nplates)
sig_plate= numpy.zeros(nplates)
siga_plate= numpy.zeros(nplates)
for ii in range(nplates):
indx= (data['PLATE'] == plates[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))
#print sig_plate
#print l_plate
if __name__ == '__main__':
#Get options
parser= get_options()
options,args= parser.parse_args()
#Read data
#AK quantiles?
if not options.aklow is None:
akquantiles= [options.aklow,options.akhigh]
else:
akquantiles= None
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
jkmax=options.jkmax,
datafilename=options.fakedata,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
akquantiles=akquantiles)
if options.location == 0:
locations= list(set(data['LOCATION']))
else:
locations= [options.location]
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Fit parameters
if options.dwarf:
params= [270./_REFV0,8./_REFR0,numpy.log(35./_REFV0),0.025,0.5,1.]
else:
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(-200.0, 200.0, 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 ** 2.0 * pvlos) - avg_plate_model[ii] ** 2.0)
# 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(0.0, 360.0)
pyplot.ylim(-200.0, 200.0)
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_plot(data["GLON"], data["VHELIO"], "k,", yrange=[-200.0, 200.0], xrange=[0.0, 360.0], overplot=True)
ndata_t = int(math.floor(len(data) / 1000.0))
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=10.0, mew=1.5, color="0.7")
# Legend
bovy_plot.bovy_plot([260.0], [150.0], "k,", overplot=True)
bovy_plot.bovy_plot([260.0], [120.0], "o", mfc="0.5", mec="none", overplot=True)
bovy_plot.bovy_plot([260.0], [90.0], "x", ms=10.0, mew=1.5, color="0.7", overplot=True)
bovy_plot.bovy_text(270.0, 145.0, r"$\mathrm{data}$")
bovy_plot.bovy_text(270.0, 115.0, r"$\mathrm{data\ mean}$")
bovy_plot.bovy_text(270.0, 85.0, r"$\mathrm{model\ mean}$")
bovy_plot._add_ticks()
# Now plot the difference
fig.sca(axMean)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 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(0.0, 360.0)
bovy_plot._add_ticks()
# axMean.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$")
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
# Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
# Sigma
fig.sca(axSig)
pyplot.plot([0.0, 360.0], [1.0, 1.0], "-", 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 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_lb():
#Read basic data
data = readVclosData()
ndata = len(data)
if _PLOTHIGHB:
yrange = [-6., 6.]
else:
yrange = [-2.5, 2.5]
#Plot
if OUTEXT == 'ps':
bovy_plot.bovy_print(fig_width=8.5, fig_height=6. / 16. * 8.5)
else:
bovy_plot.bovy_print(fig_width=16., fig_height=6.)
fig = pyplot.figure()
#Set up axes
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.7
bottom, height = 0.1, 0.85
bottom_h = left_h = left + width
rect_scatter = [left, bottom, width, height]
rect_histy = [left_h, bottom, 0.15, height]
axScatter = pyplot.axes(rect_scatter)
axHisty = pyplot.axes(rect_histy)
# no labels
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
bovy_plot.bovy_plot(data['GLON'],
data['GLAT'],
'k.',
ms=1.5,
overplot=True)
pyplot.xlim(0., 250.)
pyplot.ylim(yrange[0], yrange[1])
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ l\ \mathrm{[deg]}$')
pyplot.ylabel(r'$\mathrm{Galactic\ latitude}\ b\ \mathrm{[deg]}$')
bovy_plot._add_ticks()
#bovy_plot.bovy_end_print(os.path.join(OUTDIR,'data_lb.'+OUTEXT))
if _PLOTHIGHB:
#Read other data
otherdata = readVclosData(meanb=-4., bmax=10.)
nmdata = len(otherdata)
otherl = list(otherdata['GLON'])
otherb = list(otherdata['GLAT'])
otherdata2 = readVclosData(meanb=4., bmax=10.)
npdata = len(otherdata2)
otherl.extend(list(otherdata2['GLON']))
otherb.extend(list(otherdata2['GLAT']))
bovy_plot.bovy_plot(otherl,
otherb,
'.',
mec='0.5',
mfc='0.5',
ms=1.5,
overplot=True)
#Add histogram of bs
otherl.extend(list(data['GLON']))
otherb.extend(list(data['GLAT']))
else:
#Add histogram of bs
otherl = list(data['GLON'])
otherb = list(data['GLAT'])
nmdata, npdata = 0, 0
histy, edges, patches = axHisty.hist(otherb,
bins=49,
orientation='horizontal',
normed=True,
histtype='step',
range=yrange,
color='k',
zorder=2)
#Overlay predictions
#Center, constant
ys = numpy.linspace(-1.5, 1.5, 1001)
pdf = numpy.sqrt(1.5**2. - ys**2.)
pdf *= float(ndata) / (ndata + nmdata + npdata) / numpy.sum(pdf) / (ys[1] -
ys[0])
axHisty.plot(pdf, ys, 'k-', zorder=-2)
constpdf = pdf
#Center, exponential disk
"""
dfc= dehnendf(beta=0.,correct=True,niter=20)
pdf= numpy.zeros(len(ys))
plates= list(set(data['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= data[(data['PLATE'] == plates[ii])]
thisl= numpy.mean(data['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(ys[jj],thisl,0.,dmax/ro,0.,dfc,hz/ro)
pdf+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdf*= float(ndata)/(ndata+nmdata+npdata)/numpy.sum(pdf)/(ys[1]-ys[0])
axHisty.plot(pdf,ys,'-',color='0.',lw=1.,zorder=-1.)
#axHisty.plot(.8*pdf+.2*constpdf,ys,'-',color='0.',lw=1.,zorder=-1.)
"""
if _PLOTHIGHB:
#positive b, constant
constpdf *= float(npdata) / ndata
axHisty.plot(constpdf, ys + 4., 'k-', zorder=-2)
"""
#Positive, exponential disk
pdfp= numpy.zeros(len(ys))
plates= list(set(otherdata2['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= otherdata2[(otherdata2['PLATE'] == plates[ii])]
thisl= numpy.mean(otherdata2['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(4.+ys[jj],
thisl,0.,dmax/ro,4.,dfc,hz/ro)
pdfp+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdfp*= float(npdata)/(ndata+nmdata+npdata)/numpy.sum(pdfp)/(ys[1]-ys[0])
axHisty.plot(pdfp,4.+ys,'-',color='0.',lw=1.,zorder=-1)
#axHisty.plot(.8*pdfp+.2*constpdf,4.+ys,'-',color='0.',lw=1.,zorder=-1)
"""
#negative b, constant
constpdf *= nmdata / float(npdata)
axHisty.plot(constpdf, ys - 4., 'k-', zorder=-2.)
"""
#Negative, exponential disk
pdfm= numpy.zeros(len(ys))
plates= list(set(otherdata['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= otherdata[(otherdata['PLATE'] == plates[ii])]
thisl= numpy.mean(otherdata['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(-4.+ys[jj],
thisl,0.,dmax/ro,-4.,dfc,hz/ro)
pdfm+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdfm*= float(nmdata)/(ndata+nmdata+npdata)/numpy.sum(pdfm)/(ys[1]-ys[0])
axHisty.plot(pdfm,-4.+ys,'-',color='0.',lw=1.,zorder=-1)
#axHisty.plot(.8*pdfm+.2*constpdf,-4.+ys,'-',color='0.',lw=1.,zorder=-1)
"""
axHisty.set_ylim(yrange[0], yrange[1])
axHisty.set_xlim(0, 1.2 * numpy.amax(histy))
pyplot.sca(axHisty)
bovy_plot._add_ticks()
bovy_plot.bovy_end_print(os.path.join(OUTDIR, 'data_lb.' + OUTEXT))
return None
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_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)
def plot_lb():
#Read basic data
data= readVclosData()
ndata= len(data)
if _PLOTHIGHB:
yrange= [-6.,6.]
else:
yrange= [-2.5,2.5]
#Plot
if OUTEXT == 'ps':
bovy_plot.bovy_print(fig_width=8.5,fig_height=6./16.*8.5)
else:
bovy_plot.bovy_print(fig_width=16.,fig_height=6.)
fig= pyplot.figure()
#Set up axes
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.7
bottom, height = 0.1, 0.85
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histy = [left_h, bottom, 0.15, height]
axScatter = pyplot.axes(rect_scatter)
axHisty = pyplot.axes(rect_histy)
# no labels
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
bovy_plot.bovy_plot(data['GLON'],data['GLAT'],'k.',ms=1.5,
overplot=True)
pyplot.xlim(0.,250.)
pyplot.ylim(yrange[0],yrange[1])
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ l\ \mathrm{[deg]}$')
pyplot.ylabel(r'$\mathrm{Galactic\ latitude}\ b\ \mathrm{[deg]}$')
bovy_plot._add_ticks()
#bovy_plot.bovy_end_print(os.path.join(OUTDIR,'data_lb.'+OUTEXT))
if _PLOTHIGHB:
#Read other data
otherdata= readVclosData(meanb=-4.,bmax=10.)
nmdata= len(otherdata)
otherl= list(otherdata['GLON'])
otherb= list(otherdata['GLAT'])
otherdata2= readVclosData(meanb=4.,bmax=10.)
npdata= len(otherdata2)
otherl.extend(list(otherdata2['GLON']))
otherb.extend(list(otherdata2['GLAT']))
bovy_plot.bovy_plot(otherl,otherb,'.',mec='0.5',mfc='0.5',ms=1.5,
overplot=True)
#Add histogram of bs
otherl.extend(list(data['GLON']))
otherb.extend(list(data['GLAT']))
else:
#Add histogram of bs
otherl= list(data['GLON'])
otherb= list(data['GLAT'])
nmdata, npdata= 0,0
histy, edges, patches= axHisty.hist(otherb,
bins=49,
orientation='horizontal',
normed=True,
histtype='step',
range=yrange,
color='k',zorder=2)
#Overlay predictions
#Center, constant
ys= numpy.linspace(-1.5,1.5,1001)
pdf= numpy.sqrt(1.5**2.-ys**2.)
pdf*= float(ndata)/(ndata+nmdata+npdata)/numpy.sum(pdf)/(ys[1]-ys[0])
axHisty.plot(pdf,ys,'k-',zorder=-2)
constpdf= pdf
#Center, exponential disk
"""
dfc= dehnendf(beta=0.,correct=True,niter=20)
pdf= numpy.zeros(len(ys))
plates= list(set(data['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= data[(data['PLATE'] == plates[ii])]
thisl= numpy.mean(data['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(ys[jj],thisl,0.,dmax/ro,0.,dfc,hz/ro)
pdf+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdf*= float(ndata)/(ndata+nmdata+npdata)/numpy.sum(pdf)/(ys[1]-ys[0])
axHisty.plot(pdf,ys,'-',color='0.',lw=1.,zorder=-1.)
#axHisty.plot(.8*pdf+.2*constpdf,ys,'-',color='0.',lw=1.,zorder=-1.)
"""
if _PLOTHIGHB:
#positive b, constant
constpdf*= float(npdata)/ndata
axHisty.plot(constpdf,ys+4.,'k-',zorder=-2)
"""
#Positive, exponential disk
pdfp= numpy.zeros(len(ys))
plates= list(set(otherdata2['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= otherdata2[(otherdata2['PLATE'] == plates[ii])]
thisl= numpy.mean(otherdata2['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(4.+ys[jj],
thisl,0.,dmax/ro,4.,dfc,hz/ro)
pdfp+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdfp*= float(npdata)/(ndata+nmdata+npdata)/numpy.sum(pdfp)/(ys[1]-ys[0])
axHisty.plot(pdfp,4.+ys,'-',color='0.',lw=1.,zorder=-1)
#axHisty.plot(.8*pdfp+.2*constpdf,4.+ys,'-',color='0.',lw=1.,zorder=-1)
"""
#negative b, constant
constpdf*= nmdata/float(npdata)
axHisty.plot(constpdf,ys-4.,'k-',zorder=-2.)
"""
#Negative, exponential disk
pdfm= numpy.zeros(len(ys))
plates= list(set(otherdata['PLATE']))
for ii in range(len(plates)):
print ii
#For each plate, calculate pdf and add with data weights
thisdata= otherdata[(otherdata['PLATE'] == plates[ii])]
thisl= numpy.mean(otherdata['GLON'])
thisndata= len(thisdata)
thispdf= numpy.zeros(len(ys))
for jj in range(len(ys)):
thispdf[jj]= surfacemassLOSd(-4.+ys[jj],
thisl,0.,dmax/ro,-4.,dfc,hz/ro)
pdfm+= thispdf/numpy.sum(thispdf)/(ys[1]-ys[0])*thisndata
pdfm*= float(nmdata)/(ndata+nmdata+npdata)/numpy.sum(pdfm)/(ys[1]-ys[0])
axHisty.plot(pdfm,-4.+ys,'-',color='0.',lw=1.,zorder=-1)
#axHisty.plot(.8*pdfm+.2*constpdf,-4.+ys,'-',color='0.',lw=1.,zorder=-1)
"""
axHisty.set_ylim(yrange[0],yrange[1])
axHisty.set_xlim( 0, 1.2*numpy.amax(histy))
pyplot.sca(axHisty)
bovy_plot._add_ticks()
bovy_plot.bovy_end_print(os.path.join(OUTDIR,'data_lb.'+OUTEXT))
return None
R= math.sqrt(1.+d**2.-2.*d*math.cos(thisl))
if R == 0.:
R+= 0.0001
d+= 0.0001
if 1./math.cos(thisl) < d and math.cos(thisl) > 0.:
theta= math.pi-math.asin(d/R*math.sin(thisl))
else:
theta= math.asin(d/R*math.sin(thisl))
vR= -thisvs[ii]*math.cos(theta+thisl) #vperp=0 for marginalization
vT= thisvs[ii]*math.sin(theta+thisl)
o= Orbit([R,vR,vT,theta])
surfmass= dfc.surfacemassLOS(d,thisl,deg=False)
out+= surfmass*dfc(o,marginalizeVperp=True)/dfc.targetSurfacemass(R)
norm+= surfmass
pred_dist[ii]= out/norm
ii+= 1
if _PREDICTCACHE:
savefile= open(savefilename,'wb')
try:
pickle.dump(pred_dist,savefile)
finally:
savefile.close()
return pred_dist
if __name__ == '__main__':
data= readVclosData()
locations= list(set(data['LOCATION']))
for l in locations:
print l
simplePlot(location=l,plotfile=os.path.join(os.getenv('HOME'),'Desktop/locationPlots/vlosdist_%i.png' % l),predict=True,nv=201)
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_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)
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 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
#set up; kind of plot
justData = False
dataMean = False
noSolar = False
justSolar = False
betas, vcbetas = True, 250.
hrs = False
dmaxs = False
addNonAxi = False
#Data selection
nodups = True
fehcut = False
postshutdown = True
cohort = None
ext = 'png'
data = readVclosData(postshutdown=postshutdown, fehcut=fehcut, cohort=cohort)
#Calculate means
plates = list(set(data['PLATE']))
nplates = len(plates)
l_plate = numpy.zeros(nplates)
avg_plate = numpy.zeros(nplates)
sig_plate = numpy.zeros(nplates)
siga_plate = numpy.zeros(nplates)
for ii in range(nplates):
indx = (data['PLATE'] == plates[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))
#print sig_plate
if __name__ == '__main__':
#Get options
parser = get_options()
options, args = parser.parse_args()
#Read data
#AK quantiles?
if not options.aklow is None:
akquantiles = [options.aklow, options.akhigh]
else:
akquantiles = None
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
jkmax=options.jkmax,
datafilename=options.fakedata,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
akquantiles=akquantiles)
if options.location == 0:
locations = list(set(data['LOCATION']))
else:
locations = [options.location]
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
#Fit parameters
if options.dwarf:
params = [
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