def plot_rcdistancecomparison(plotfilename):
# Get the sample
rcdata = define_rcsample.get_rcsample()
# Now plot the differece
bovy_plot.bovy_print()
levels = special.erf(numpy.arange(1, 3) / numpy.sqrt(2.))
bovy_plot.scatterplot(
rcdata['RC_DIST'],
(rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
conditional=True,
levels=levels,
linestyle='none',
color='k',
marker=',',
xrange=[0., 7.49],
yrange=[-0.075, 0.075],
xlabel=r'$M_{K_s}\!-\!\mathrm{based\ distance\,(kpc)}$',
ylabel=
r'$\mathrm{Fractional\ difference\ of}\ M_H\ \mathrm{vs.}\ M_{K_s}$',
onedhistx=True,
bins=31)
bovy_plot.bovy_plot([0., 10.], [0., 0.],
'--',
lw=2.,
color='0.75',
overplot=True)
# Plot lowess
lowess = sm.nonparametric.lowess
z = lowess((rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
rcdata['RC_DIST'],
frac=.3)
bovy_plot.bovy_plot(z[:, 0], z[:, 1], 'w--', lw=2., overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_apogee_jkz(plotfilename,sample):
if sample == 'disk':
data= readData()
elif sample == 'halo':
data= readData(halo=True)
elif sample == 'bulge':
data= readData(bulge=True)
ntotal= len(data)
#logg cut
data= data[(data['LOGG_AVG'] < 2.8)*(data['LOGG_AVG'] > 1.8)]
npass= len(data)
#Deredden
aj= data['AK_WISE']*2.5
ah= data['AK_WISE']*1.55
j0= data['JMAG']-aj
h0= data['HMAG']-ah
k0= data['KMAG']-data['AK_WISE']
#calculate Zs
zsolar= 0.019
z= isodist.FEH2Z(data['METALS_AVG'])/zsolar
#Plot
bovy_plot.bovy_print()
if sample == 'bulge':
bovy_plot.bovy_plot(j0-k0,z,'ko',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',ms=5.)
else:
bovy_plot.scatterplot(j0-k0,z,'k,',
xrange=[0.5,0.75],
yrange=[0.,0.03/zsolar],
xlabel=r'$(J-K_s)_0$',
ylabel=r'$Z/Z_\odot$',
bins=21)
#Overplot cuts
jks= numpy.linspace(0.5,0.75,1001)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks)/zsolar,
'k--',lw=2.,overplot=True)
bovy_plot.bovy_plot(jks,rcmodel.jkzcut(jks,upper=True)/zsolar,
'k--',lw=2.,overplot=True)
#Data between the cuts
indx= (j0-k0 < 0.75)*(j0-k0 > 0.5)\
*(z <= rcmodel.jkzcut(j0-k0,upper=True)/zsolar)\
*(z >= rcmodel.jkzcut(j0-k0)/zsolar)
# j0= j0[indx]
# h0= h0[indx]
# k0= k0[indx]
# z= z[indx]
# bovy_plot.bovy_plot(j0-k0,z,'w.',
# overplot=True,
# mec='w',
# ms=.5)
bovy_plot.bovy_text(r'$\mathrm{APOGEE\ ' + sample + '\ sample}$',
title=True)
bovy_plot.bovy_text(r'$%i/%i/%i$' % (numpy.sum(indx),npass,ntotal),
bottom_right=True,size=14.)
bovy_plot.bovy_end_print(plotfilename)
return None
def _plotMRZ_single(XYZ,R,data,options,args,all=True,overplot=True,xrange=None,
Zrange=None,Rrange=None,yrange=None,colorrange=None,
fehrange=None):
if all:
bovy_plot.scatterplot(data.dered_g-data.dered_r,
data.feh,
'k,',
bins=21,
xrange=xrange,
yrange=yrange,
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
onedhists=True)
platestr= '\mathrm{all\ plates}'
bovy_plot.bovy_text(r'$'+platestr+'$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data.feh),top_right=True,size=16)
else:
#Cut to range
indx= (numpy.fabs(XYZ[:,2]) > Zrange[0])\
*(numpy.fabs(XYZ[:,2]) <= Zrange[1])\
*(R > Rrange[0])\
*(R <= Rrange[1])
thisdata= data[indx]
if len(thisdata) > 1500:
bovy_plot.scatterplot(thisdata.dered_g-thisdata.dered_r,
thisdata.feh,
'k,',
bins=21,
xrange=xrange,
yrange=yrange,
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
onedhists=True)
else:
bovy_plot.bovy_plot(thisdata.dered_g-thisdata.dered_r,
thisdata.feh,
'k,',
bins=21,
xrange=xrange,
yrange=yrange,
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
onedhists=True)
if options.plottype.lower() == 'r':
lbstr= r'$%i < R / \mathrm{kpc} \leq %i$' % (int(Rrange[0]),int(Rrange[1]))
else:
lbstr= r'$%i < |Z| / \mathrm{pc} \leq %i$' % (int(1000*Zrange[0]),int(1000*Zrange[1]))
bovy_plot.bovy_text(r'$%i \ \ \mathrm{stars}$' %
len(thisdata.feh)
+'\n'+
lbstr,top_right=True,size=16)
return None
def plotXD(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
savefile = open(args[0], 'rb')
xamp = pickle.load(savefile)
xmean = pickle.load(savefile)
xcovar = pickle.load(savefile)
savefile.close()
else:
print args[0] + " does not exist ..."
print "Returning ..."
return
#Load XD object in xdtarget
xdt = xdtarget.xdtarget(amp=xamp, mean=xmean, covar=xcovar)
out = xdt.sample(nsample=options.nsamples)
#Prepare for plotting
if options.expd1: xs = nu.exp(out[:, options.d1])
elif not options.divided1 is None:
xs = out[:, options.d1] / options.divided1
else:
xs = out[:, options.d1]
if options.expd2: ys = nu.exp(out[:, options.d2])
elif not options.divided2 is None:
ys = out[:, options.d2] / options.divided2
else:
ys = out[:, options.d2]
if options.type == 'DRW':
#plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
#Convert to logA
xs = (nu.log(2.) + xs + nu.log(1. - nu.exp(-1. / nu.exp(ys)))) / 2.
elif options.d1 == 1 and options.d2 == 0:
#Convert to logA
ys = (nu.log(2.) + ys + nu.log(1. - nu.exp(-1. / nu.exp(xs)))) / 2.
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
xrange=[options.xmin, options.xmax],
yrange=[options.ymin, options.ymax],
xlabel=options.xlabel,
ylabel=options.ylabel)
bovy_plot.bovy_end_print(options.plotfilename)
def afeh(options,args):
"""Plot the [alpha/Fe] vs. [Fe/H] distribution of the sample"""
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal='allall',
sample=options.sample)
bovy_plot.bovy_print()
bovy_plot.scatterplot(data.feh,data.afe,'k,',
xrange=[-2.,0.5],
yrange=[-0.1,0.6],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
onedhists=True)
#Overplot cuts, metal-rich
lw=1.3
bovy_plot.bovy_plot(_APOORFEHRANGE,[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot(_APOORFEHRANGE,[_APOORAFERANGE[1],_APOORAFERANGE[1]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_APOORFEHRANGE[0],_APOORFEHRANGE[0]],_APOORAFERANGE,
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_APOORFEHRANGE[1],_APOORFEHRANGE[1]],_APOORAFERANGE,
'k--',overplot=True,lw=lw)
#metal-poor
bovy_plot.bovy_plot(_ARICHFEHRANGE,[_ARICHAFERANGE[0],_ARICHAFERANGE[0]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot(_ARICHFEHRANGE,[_ARICHAFERANGE[1],_ARICHAFERANGE[1]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_ARICHFEHRANGE[0],_ARICHFEHRANGE[0]],_ARICHAFERANGE,
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_ARICHFEHRANGE[1],_ARICHFEHRANGE[1]],_ARICHAFERANGE,
'k--',overplot=True,lw=lw)
#metal-richrich
bovy_plot.bovy_plot([-0.6,-0.6],_APOORAFERANGE,'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.3,-0.3],_APOORAFERANGE,'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[1],_APOORAFERANGE[1]],
'k:',overplot=True,lw=lw)
#metal-poorpoor
bovy_plot.bovy_plot([-0.7,-0.7],_ARICHAFERANGE,
'k:',overplot=True,lw=lw)
bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_'
+options.sample+'_'+
options.metal+'.'+ext))
def _plotMC_single(data,options,args,all=False,overplot=False,xrange=None,
yrange=None,platels=None,platebs=None,
rmin=None,rmax=None,grmin=None,grmax=None,
rx=None,ry=None,fehrange=None,colorrange=None):
if all:
bovy_plot.scatterplot(data.dered_g-data.dered_r,
data.feh,
'k,',
bins=21,
xrange=xrange,
yrange=yrange,
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
onedhists=True)
platestr= '\mathrm{all\ plates}'
bovy_plot.bovy_text(r'$'+platestr+'$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data.feh),top_right=True,size=16)
else:
bovy_plot.bovy_plot(data.dered_g-data.dered_r,
data.feh,
'k,',
bins=21,
xrange=xrange,
yrange=yrange,
xlabel=r'$g-r\ [\mathrm{mag}]$',
ylabel=r'$[\mathrm{Fe/H}]$',
onedhists=True)
platestr= '%i\ \mathrm{plates}' % len(set(list(data.plate)))
lbstr= '$l = %i^\circ \pm %i^\circ$' % (
int(numpy.mean(platels)),int(numpy.std(platels)))+'\n'\
+'$b = %i^\circ\pm%i^\circ$' % (int(numpy.mean(platebs)),
int(numpy.std(platebs)))
bovy_plot.bovy_text(r'$'+platestr+'$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data.feh)
+'\n'+
lbstr,top_right=True,size=16)
_add_coordinset(rx=rx,ry=ry,platels=platels,platebs=platebs,
feh=numpy.mean(data.feh),
rmin=rmin,rmax=rmax,
grmin=grmin,grmax=grmin)
def scatterplot(self,d1,d2,*args,**kwargs):
"""
NAME:
scatterplot
PURPOSE:
make a scatterplot of the data
INPUT:
d1, d2 - x and y dimension to plot
hoggscatter - if True, hogg_scatterplot
+bovy_plot.plot or bovy_plot.scatterplot args and kwargs
OUTPUT:
plot to output device
HISTORY:
2010-08-09 - Written - Bovy (NYU)
"""
if kwargs.has_key('hoggscatter'):
hoggscatter= kwargs['hoggscatter']
kwargs.pop('hoggscatter')
else:
hoggscatter= False
if not kwargs.has_key('xlabel'):
kwargs['xlabel']= str(d1)
if not kwargs.has_key('ylabel'):
kwargs['ylabel']= str(d2)
if hoggscatter:
if hasattr(self,'weight'):
kwargs['weights']= self.weight
bovy_plot.scatterplot(self.a[:,d1],self.a[:,d2],
*args,**kwargs)
else:
bovy_plot.bovy_plot(self.a[:,d1],self.a[:,d2],
*args,**kwargs)
def plot_rcdistancecomparison(plotfilename):
# Get the sample
rcdata= define_rcsample.get_rcsample()
# Now plot the differece
bovy_plot.bovy_print()
levels= special.erf(numpy.arange(1,3)/numpy.sqrt(2.))
bovy_plot.scatterplot(rcdata['RC_DIST'],
(rcdata['RC_DIST_H']-rcdata['RC_DIST'])/rcdata['RC_DIST'],
conditional=True,
levels=levels,
linestyle='none',color='k',marker=',',
xrange=[0.,7.49],yrange=[-0.075,0.075],
xlabel=r'$M_{K_s}\!-\!\mathrm{based\ distance\,(kpc)}$',
ylabel=r'$\mathrm{Fractional\ difference\ of}\ M_H\ \mathrm{vs.}\ M_{K_s}$',
onedhistx=True,bins=31)
bovy_plot.bovy_plot([0.,10.],[0.,0.],'--',lw=2.,color='0.75',overplot=True)
# Plot lowess
lowess= sm.nonparametric.lowess
z= lowess((rcdata['RC_DIST_H']-rcdata['RC_DIST'])/rcdata['RC_DIST'],
rcdata['RC_DIST'],frac=.3)
bovy_plot.bovy_plot(z[:,0],z[:,1],'w--',lw=2.,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit Combination Pal5 and GD1 to MW Potential Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# -----------------------
# Adding in the force measurements from Pal 5 *and* GD-1; also fitting
# $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_b15_pal5gd1_voro = mw_pot.fit(
fitc=True,
c=None,
addpal5=True,
addgd1=True,
fitvoro=True,
mc16=True,
plots=fpath + "fit.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-fitvoro-pal5gd1-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_b15_pal5gd1_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-fitvoro-pal5gd1-samples.pdf",
mc16=True,
addpal5=True,
addgd1=True,
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
addpal5=True,
addgd1=True,
savefig=fpath + "mwpot14varyc-fitvoro-pal5gd1-samples-corner.pdf",
)
# -----------------------
bf_savefilename = opath + "mwpot14varyc-bf.pkl" # should already exist
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(
fitc=False,
c=c,
plots=fpath + "mwpot14varyc-bf-fit.pdf",
)
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "mwpot14varyc-bf-combo_pal5_gd1-dependence.pdf",
)
if save_figures:
plt.savefig(
os.path.join(
os.getenv("PAPERSDIR"),
"2016-mwhalo-shape",
"mwpot14-varyc-wp5g1.pdf",
),
bbox_inches="tight",
)
# -----------------------
plt.figure(figsize=(4, 4))
cindx = 9
dum = bovy_plot.bovy_hist(
s[cindx],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.5, 1.5],
normed=True,
)
plt.savefig(fpath + "mwpot14varyc-bf-combo_pal5_gd1-shape_hist.pdf")
with open(opath + "fit_potential_combo-pal5-gd1.txt", "w") as file:
sortedc = np.array(sorted(s[cindx][-50000:]))
file.write("2.5%% and 0.5%% lower limits: %.3f, %.3f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
file.write("2.5%% and 0.5%% upper limits: %.3f, %.3f" % (
sortedc[int(np.floor(0.975 * len(sortedc)))],
sortedc[int(np.floor(0.995 * len(sortedc)))],
))
file.write("Median, 68%% confidence: %.3f, %.3f, %.3f" % (
np.median(sortedc),
sortedc[int(np.floor(0.16 * len(sortedc)))],
sortedc[int(np.floor(0.84 * len(sortedc)))],
))
file.write("Mean, std. dev.: %.2f,%.2f" % (
np.mean(sortedc),
np.std(sortedc),
))
# -----------------------
# What is the constraint on the mass of the halo?
tR = 20.0 / REFR0
skip = 1
hmass = []
for sa in tqdm(s.T[::skip]):
pot = mw_pot.setup_potential(
sa,
sa[-1],
True,
False,
REFR0 * sa[8],
REFV0 * sa[7],
fitvoro=True,
)
hmass.append(-integrate.quad(
lambda x: tR**2.0 * potential.evaluaterforces(
pot[2], tR * x, tR * np.sqrt(1.0 - x**2.0), phi=0.0),
0.0,
1.0,
)[0] * bovy_conversion.mass_in_1010msol(REFV0, REFR0) / 10.0)
hmass = np.array(hmass)
with open(opath + "fit_potential_combo-pal5-gd1.txt",
"a") as file: # append
file.write("\nMass Constraints:")
sortedhm = np.array(sorted(hmass))
file.write("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedhm[int(np.floor(0.025 * len(sortedhm)))],
sortedhm[int(np.floor(0.005 * len(sortedhm)))],
))
file.write("2.5%% and 0.5%% upper limits: %.2f, %.2f" % (
sortedhm[int(np.floor(0.975 * len(sortedhm)))],
sortedhm[int(np.floor(0.995 * len(sortedhm)))],
))
file.write("Median, 68%% confidence: %.2f, %.2f, %.2f" % (
np.median(sortedhm),
sortedhm[int(np.floor(0.16 * len(sortedhm)))],
sortedhm[int(np.floor(0.84 * len(sortedhm)))],
))
# -----------------------
bovy_plot.scatterplot(
hmass,
s[-1, ::skip],
"k,",
onedhists=True,
bins=31,
xrange=[0.5, 1.5],
yrange=[0.5, 1.5],
xlabel=r"$M_{\mathrm{halo}} (r<20\,\mathrm{kpc})\,(M_\odot)$",
ylabel=r"$c/a$",
)
plt.savefig(fpath + "scatterplot.pdf")
def plot_afefeh(plotfilename):
# Load the data
data = define_rcsample.get_rcsample()
# Plot the data
bovy_plot.bovy_print()
bovy_plot.scatterplot(data[define_rcsample._FEHTAG],
data[define_rcsample._AFETAG],
'k.',
ms=.8,
levels=special.erf(
numpy.arange(1, 2) / numpy.sqrt(2.)),
xrange=[-1., 0.4],
yrange=[-0.15, 0.35],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=define_rcsample._AFELABEL)
# Overplot sub-samples
# low alpha, low feh
lowfeh = define_rcsample._lowlow_lowfeh(0.)
highfeh = define_rcsample._lowlow_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._lowlow_lowafe(highfeh),
define_rcsample._lowlow_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._lowlow_highafe(lowfeh),
define_rcsample._lowlow_highafe(highfeh)
],
'k--',
lw=2.)
# high alpha
lowfeh = define_rcsample._highalpha_lowfeh(0.)
highfeh = define_rcsample._highalpha_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._highalpha_lowafe(highfeh),
define_rcsample._highalpha_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highalpha_highafe(lowfeh),
define_rcsample._highalpha_highafe(highfeh)
],
'k--',
lw=2.)
# solar
lowfeh = define_rcsample._solar_lowfeh(0.)
highfeh = define_rcsample._solar_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._solar_lowafe(highfeh),
define_rcsample._solar_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._solar_highafe(lowfeh),
define_rcsample._solar_highafe(highfeh)
],
'k--',
lw=2.)
# high [Fe/H]
lowfeh = define_rcsample._highfeh_lowfeh(0.)
highfeh = define_rcsample._highfeh_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._highfeh_lowafe(highfeh),
define_rcsample._highfeh_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highfeh_highafe(lowfeh),
define_rcsample._highfeh_highafe(highfeh)
],
'k--',
lw=2.)
# Label them
bovy_plot.bovy_text(-0.4,
0.265,
r'$\mathrm{high}\ [\alpha/\mathrm{Fe}]$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(-0.975,
0.05,
r'$\mathrm{low\ [Fe/H]}$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(0.,
-0.125,
r'$\mathrm{high\ [Fe/H]}$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(-0.225,
-0.125,
r'$\mathrm{solar}$',
size=15.,
backgroundcolor='w')
# Loci
if False:
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'k-',
lw=2.,
overplot=True)
haloc = define_rcsample.lowalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'k-',
lw=2.,
overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plotActionData(options,args):
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Setup error mc integration
options.nmcerr= 1#in case this isn't set correctly
raw, errstuff= setup_err_mc(raw,options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
#print numpy.argmin((fehs+0.25)**2.+(afes-0.175)**2.)
#Setup potential
params= numpy.array([-1.33663190049,0.998420232634,-3.49031638164,0.31949840593,-1.63965169376])
try:
pot= setup_potential(params,options,0)#Assume that the potential parameters come from a file with a single set of df parameters first
except RuntimeError: #if this set of parameters gives a nonsense potential
raise
ro= 1.
vo= params[1]
aA= setup_aA(pot,options)
jr, lz, jz= [], [], []
#Get data ready
params= [None,None,None,None,None,None,params[0],params[1],params[2],params[3],params[4]]
for indx in range(len(fehs)):
if numpy.log(monoAbundanceMW.hr(fehs[indx],afes[indx],k=(options.sample.lower() == 'k'))/8.) > -0.5 :
continue
R,vR,vT,z,vz,e,ee,eee= prepare_coordinates(params,indx,fehs,afes,binned,
errstuff,
options,1)
tjr, tlz, tjz= aA(R.flatten(),vR.flatten(),vT.flatten(),z.flatten(),vz.flatten())
jr.extend(list(tjr))
lz.extend(list(tlz))
jz.extend(list(tjz))
#Now plot
jr= numpy.array(jr).flatten()*ro*vo*_REFR0*_REFV0
lz= numpy.array(lz).flatten()*ro*vo*_REFR0*_REFV0
jz= numpy.array(jz).flatten()*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_print()
levels= special.erf(numpy.sqrt(0.5)*numpy.arange(1,3))
levels= list(levels)
levels.append(1.01)
print len(jr)
if options.type.lower() == 'lzjr':
axScatter, axHistx,axHisty= bovy_plot.scatterplot(lz/220.,jr/220.,',',
xlabel=r'$L_z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
ylabel=r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
xrange=[0.,3600./220.],
yrange=[0.,500./220.],
onedhists=True,
bins=41,
levels=levels,retAxes=True)
axScatter.set_xlim(0.,3600./220.)
axScatter.set_ylim(0.,500./220.)
axScatter.set_xlabel(r'$L_z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axScatter.set_ylabel(r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
#Calculate locus of 6 kpc pericenter
nlzs= 1001
plzs= numpy.linspace(0.,6./8.,nlzs)
pjrs= numpy.zeros(nlzs)
for ii in range(nlzs):
pjrs[ii]= aA(6./8.,0.,plzs[ii]/6.*8.,0.,0.)[0]*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_plot(plzs*ro*vo*_REFR0*_REFV0/220.,
pjrs/220.,'k--',overplot=True)
plzs= numpy.linspace(11./8.,2.,nlzs)
pjrs= numpy.zeros(nlzs)
for ii in range(nlzs):
pjrs[ii]= aA(11./8.,0.,plzs[ii]/11.*8.,0.,0.)[0]*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_plot(plzs*ro*vo*_REFR0*_REFV0/220.,
pjrs/220.,'k--',overplot=True)
elif options.type.lower() == 'jrjz':
axScatter, axHistx, axHisty= bovy_plot.scatterplot(jr/220.,jz/220.,color='k',marker=',',ls='none',
xlabel=r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
ylabel=r'$J_Z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
xrange=[0.,500./220.],
yrange=[0.,250./220.],
bins=41,
onedhists=True,
levels=levels,
retAxes=True)
axScatter.set_xlim(0.,500./220.)
axScatter.set_ylim(0.,250./220.)
axScatter.set_ylabel(r'$J_Z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axScatter.set_xlabel(r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
#if options.sample == 'g':
# bovy_plot.bovy_text(r'$\mathrm{G\!-\!type\ dwarfs}$',top_right=True,size=16.)
#elif options.sample == 'k':
# bovy_plot.bovy_text(r'$\mathrm{K\!-\!type\ dwarfs}$',top_right=True,size=16.)
bovy_plot.bovy_end_print(options.outfilename)
return None
def plotVelPDFs(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=True)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Savefile1
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
velfits= pickle.load(savefile)
savefile.close()
if os.path.exists(args[1]):#Load savefile
savefile= open(args[1],'rb')
densfits= pickle.load(savefile)
savefile.close()
#Uncertainties are in savefile3 and 4
if len(args) > 3 and os.path.exists(args[3]):
savefile= open(args[3],'rb')
denssamples= pickle.load(savefile)
savefile.close()
denserrors= True
else:
denssamples= None
denserrors= False
if len(args) > 2 and os.path.exists(args[2]):
savefile= open(args[2],'rb')
velsamples= pickle.load(savefile)
savefile.close()
velerrors= True
else:
velsamples= None
velerrors= False
#Now plot
#Run through the pixels and gather
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
plotthis= []
errors= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
if options.kde: allsamples= []
sausageFehAfe= [options.feh,options.afe]#-0.15,0.075]#[[-0.85,0.425],[-0.45,0.275],[-0.15,0.075]]
if options.subtype.lower() == 'sausage':
sausageSamples= []
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if not (numpy.fabs(tightbinned.feh(ii)-sausageFehAfe[0])< 0.01 and numpy.fabs(tightbinned.afe(jj) - sausageFehAfe[1]) < 0.01):
continue
thisdensfit= densfits[afeindx+fehindx*binned.npixafe()]
thisvelfit= velfits[afeindx+fehindx*binned.npixafe()]
if options.velmodel.lower() == 'hwr':
thisplot=[tightbinned.feh(ii),
tightbinned.afe(jj),
numpy.exp(thisvelfit[1]),
numpy.exp(thisvelfit[4]),
len(data),
thisvelfit[2],
thisvelfit[3]]
#Als find min and max z for this data bin, and median
zsorted= sorted(numpy.fabs(data.zc+_ZSUN))
zmin= zsorted[int(numpy.ceil(0.16*len(zsorted)))]
zmax= zsorted[int(numpy.floor(0.84*len(zsorted)))]
thisplot.extend([zmin,zmax,numpy.mean(numpy.fabs(data.zc+_ZSUN))])
#Errors
if velerrors:
thesesamples= velsamples[afeindx+fehindx*binned.npixafe()]
break
#Now plot
if options.type.lower() == 'slopequad':
plotx= numpy.array([thesesamples[ii][3] for ii in range(len(thesesamples))])
ploty= numpy.array([thesesamples[ii][2] for ii in range(len(thesesamples))])
xrange= [-10.,10.]
yrange= [-20.,20.]
xlabel=r'$\frac{\mathrm{d}^2 \sigma_z}{\mathrm{d} z^2}(z_{1/2})\ [\mathrm{km}\ \mathrm{s}^{-1}\ \mathrm{kpc}^{-2}]$'
ylabel=r'$\frac{\mathrm{d} \sigma_z}{\mathrm{d} z}(z_{1/2})\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif options.type.lower() == 'slopehsm':
ploty= numpy.array([thesesamples[ii][2] for ii in range(len(thesesamples))])
plotx= numpy.exp(-numpy.array([thesesamples[ii][4] for ii in range(len(thesesamples))]))
xrange= [0.,0.3]
yrange= [-20.,20.]
xlabel=r'$h^{-1}_\sigma\ [\mathrm{kpc}^{-1}]$'
ylabel=r'$\frac{\mathrm{d} \sigma_z}{\mathrm{d} z}(z_{1/2})\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif options.type.lower() == 'slopesz':
ploty= numpy.array([thesesamples[ii][2] for ii in range(len(thesesamples))])
plotx= numpy.exp(numpy.array([thesesamples[ii][1] for ii in range(len(thesesamples))]))
xrange= [0.,60.]
yrange= [-20.,20.]
xlabel=r'$\sigma_z(z_{1/2}) [\mathrm{km\ s}^{-1}$'
ylabel=r'$\frac{\mathrm{d} \sigma_z}{\mathrm{d} z}(z_{1/2})\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif options.type.lower() == 'szhsm':
plotx= numpy.exp(-numpy.array([thesesamples[ii][4] for ii in range(len(thesesamples))]))
ploty= numpy.exp(numpy.array([thesesamples[ii][1] for ii in range(len(thesesamples))]))
yrange= [0.,60.]
xrange= [0.,0.3]
xlabel=r'$h^{-1}_\sigma\ [\mathrm{kpc}^{-1}]$'
ylabel=r'$\sigma_z(z_{1/2}) [\mathrm{km\ s}^{-1}$'
bovy_plot.bovy_print()
axScatter, axHistx, axHisty= bovy_plot.scatterplot(plotx,ploty,'k,',
onedhists=True,
bins=31,
xlabel=xlabel,ylabel=ylabel,
xrange=xrange,
onedhistynormed=True,
yrange=yrange,
retAxes=True)
if options.type.lower() == 'slopequad':
#Also add `quadratic term = 0' to vertical histogram
ploty= ploty[(numpy.fabs(plotx) < 0.5)]
histy, edges, patches= axHisty.hist(ploty,
bins=51,
orientation='horizontal',
weights=numpy.ones(len(ploty))/float(len(ploty))/2.,
histtype='step',
range=sorted(yrange),
color='0.6',
lw=2.)
#Label
bovy_plot.bovy_text(r'$[\mathrm{Fe/H}]\ =\ %.2f$' % options.feh
+'\n'
+r'$[\alpha/\mathrm{Fe}]\ =\ %.3f$' % options.afe,
top_right=True,
size=18)
bovy_plot.bovy_end_print(options.plotfile)
def plotFits(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params= []
for filename in args:
if os.path.exists(filename):
savefile= open(filename,'rb')
if options.kmeansOut:
thisparams= pickle.load(savefile)
thisDict= {}
for kk in range(len(thisparams)):
thisDict[str(kk)]= thisparams[kk]
params.append(thisDict)
else:
params.append(pickle.load(savefile))
savefile.close()
else:
print filename+" does not exist ..."
print "Returning ..."
return
if options.plottype == 'Ag':
ys= nu.array([p['gamma'] for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs= nu.array([p['logAr'] for p in params[0].values()]).reshape(len(params[0]))/2.
else:
xs= nu.array([p['logA'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[-9.21/2.,0.]
yrange=[0.,1.25]
xlabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'As':
ys= nu.array([p['s']-1. for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs= nu.array([p['logAr'] for p in params[0].values()]).reshape(len(params[0]))/2.
else:
xs= nu.array([p['logA'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[-9.21/2.,0.]
yrange=[-0.9,0.4]
xlabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$s_{'+options.band+r'}$'
if options.plottype == 'gs':
ys= nu.array([p['s']-1. for p in params[0].values()]).reshape(len(params[0]))
xs= nu.array([p['gamma'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[0.,1.2]
yrange=[-0.9,0.4]
xlabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power-law\ index)}$'
ylabel= r'$s_{'+options.band+r'}$'
elif options.plottype == 'AA':
ys= []
xs= []
for key in params[0].keys():
try:
ys.append(params[1][key]['logA']/2.)
xs.append(params[0][key]['logA']/2.)
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
xrange=[-9.21/2.,0.]
yrange=xrange
xlabel= r'$\log A_'+options.band[0]+r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log A_'+options.band[1]+r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits= r'$\log A_'+options.band[0]+r'$'
ylabelnounits= r'$\log A_'+options.band[1]+r'$'
elif options.plottype == 'AAz':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
try:
xtmp= qsos[qsoDict[key]].z
except KeyError:
continue
try:
ytmp=params[1][key]['logA']/2.
except KeyError:
continue
ys.append(params[0][key]['logA']/2-ytmp)
xs.append(xtmp)
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
yrange=[-1.,1.]
xrange=[0.,5.5]
ylabel= r'$\log A_r/A_g\ \mathrm{(amplitudes\ at\ 1\ yr)}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gg':
ys= []
xs= []
for key in params[0].keys():
try:
ys.append(params[1][key]['gamma'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
xrange=[0.,1.15]
yrange=xrange
xlabel= r'$\gamma_'+options.band[0]+r'\ \mathrm{(power\ law\ exponent)}$'
ylabel= r'$\gamma_'+options.band[1]+r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits= r'$\gamma_'+options.band[0]+r'$'
ylabelnounits= r'$\gamma_'+options.band[1]+r'$'
elif options.plottype == 'Acgc':
ys= nu.array([p['gammagr'] for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs= nu.array([p['logAr'] for p in params[0].values()]).reshape(len(params[0]))/2.
else:
xs= nu.array([p['logAgr'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[-9.21/2.,0.]
yrange=[0.,1.25]
xlabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\gamma^c_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Acg':
ys= nu.array([p['gamma'] for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs= nu.array([p['logAr'] for p in params[0].values()]).reshape(len(params[0]))/2.
else:
xs= nu.array([p['logAgr'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[-9.21/2.,0.]
yrange=[0.,1.25]
xlabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'AcAc':
ys= []
xs= []
for key in params[0].keys():
try:
ys.append(params[1][key]['logAgr']/2.)
xs.append(params[0][key]['logAgr']/2.)
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
xrange=[-9.21/2.,0.]
yrange=xrange
xlabel= r'$\log A^c_'+options.band[0]+r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log A^c_'+options.band[1]+r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits= r'$\log A^c_'+options.band[0]+r'$'
ylabelnounits= r'$\log A^c_'+options.band[1]+r'$'
elif options.plottype == 'gcgc':
ys= []
xs= []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gammagr'])
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
xrange=[0.,1.15]
yrange=xrange
xlabel= r'$\gamma^c_'+options.band[0]+r'\ \mathrm{(power\ law\ exponent)}$'
ylabel= r'$\gamma^c_'+options.band[1]+r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits= r'$\gamma^c_'+options.band[0]+r'$'
ylabelnounits= r'$\gamma^c_'+options.band[1]+r'$'
elif options.plottype == 'AAc':
if options.type == 'powerlawSFratios':
xs= nu.array([p['logAr'] for p in params[0].values()]).reshape(len(params[0]))/2.
ys= nu.array([p['logAri'] for p in params[0].values()]).reshape(len(params[0]))/2.
else:
xs= nu.array([p['logA'] for p in params[0].values()]).reshape(len(params[0]))/2.
ys= nu.array([p['logAgr'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[-9.21/2.,0.]
yrange=[-9.21/2.,0.]
xlabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'ggc':
if len(params) == 2:
xs, ys= [], []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(ys))
else:
xs= nu.array([p['gamma'] for p in params[0].values()]).reshape(len(params[0]))
ys= nu.array([p['gammagr'] for p in params[0].values()]).reshape(len(params[0]))/2.
xrange=[0.,1.25]
yrange=xrange
xlabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
ylabel= r'$\gamma^c_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Ai':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].mags[3])
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys= nu.array(ys)/2.
xs= nu.array(xs)
yrange=[-9.21/2.,0.]
xrange=[15.0,21.3]
ylabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel= r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gi':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].mags[3])
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[0.,1.25]
xrange=[15.0,21.3]
ylabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel= r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'Az':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys= nu.array(ys)/2.
xs= nu.array(xs)
yrange=[-9.21/2.,0.]
xrange=[0.,5.5]
ylabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'sz': #for KS11
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if len(params) == 2:
try:
ys.append(params[0][key]['s']/params[1][key]['s'])
except KeyError:
xs.pop()
continue
else:
ys.append(params[0][key]['s'])
ys= nu.array(ys)-1.
xs= nu.array(xs)
yrange=[-1.1,0.4]
xrange=[0.,5.5]
ylabel= r'$s_{'+options.band+r'}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'scatterz': #for KS11
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
ys.append(params[0][key]['logsigma'])
ys= nu.array(ys)-1.
xs= nu.array(xs)
yrange=[-10,0.]
xrange=[0.,5.5]
ylabel= r'$\sigma^2_{'+options.band+r'}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'ss':
ys, xs= [], []
if options.band2 is None:
for key in params[0].keys():
try:
ys.append(params[1][key]['s'][0]-1.)
except KeyError:
continue
xs.append(params[0][key]['s'][0]-1.)
else:
for key in params[0].keys():
try:
ys.append(params[1][key]['s'+options.band2][0]-1.)
except KeyError:
continue
xs.append(params[0][key]['s'+options.band][0]-1.)
xs= nu.array(xs)
ys= nu.array(ys)
xrange=[-0.9,0.4]
yrange=[-0.9,0.4]
if options.band2 is None:
xlabel= r'$s$'
ylabel= r'$s$'
else:
xlabel= r'$s_{'+options.band+r'}$'
ylabel= r'$s_{'+options.band2+r'}$'
ylabelnounits= ylabel
xlabelnounits= xlabel
elif options.plottype == 'gz':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].z)
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[0.,1.25]
xrange=[0.,5.5]
ylabel= r'$\gamma_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Aci':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys= nu.array(ys)/2.
xs= nu.array(xs)
yrange=[-9.21/2.,0.]
xrange=[15.0,21.3]
ylabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel= r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gci':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[0.,1.25]
xrange=[15.0,21.3]
ylabel= r'$\gamma^c_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel= r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'loglike2':
ys= []
xs= []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike'])
xs.append(params[0][key]['loglike'])
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[0.,200.]
xrange=[0.,200.]
ylabel= r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ '+options.band[0]+r'$'
xlabel= r'$\log L_{\mathrm{PL}}\ \mathrm{in}\ '+options.band[0]+r'$'
elif options.plottype == 'loglike3z':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike']
-params[0][key]['loglike'])
xs.append(qsos[qsoDict[key]].z)
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[-15.,15.]
xrange=[0.,5.5]
ylabel= r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ '+options.band[0]+r' - \log L_{\mathrm{PL}}\ \mathrm{in}\ '+options.band[0]+r'$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Acz':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].z)
ys= nu.array(ys)/2.
xs= nu.array(xs)
yrange=[-9.21/2.,0.]
xrange=[0.,5.5]
ylabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gcz':
qsos= open_qsos()
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
ys= []
xs= []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].z)
ys= nu.array(ys)
xs= nu.array(xs)
yrange=[0.,1.25]
xrange=[0.,5.5]
ylabel= r'$\gamma^c_{'+options.band+r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel= r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'AsAc': #files: g, Ac, KS11
xs= []
ys= []
if not options.imax is None:
qsos= open_qsos()
qsos= qsos[(qsos.mags[:,3] < options.imax)]
qsoDict= {}
ii=0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '')+'.fit']= ii
ii+= 1
for key in params[0].keys():
if not key in qsoDict.keys(): continue
try:
if options.type == 'powerlawSFratios':
Ac= params[1][key]['logAri']/2.+0.14
A= params[0][key]['logA']/2.
else:
Ac= params[1][key]['logAgr']/2.
A= params[0][key]['logA']/2.
s= params[2][key]['s']
ys.append(Ac)
xs.append(A+nu.log(1.-s))
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(ys))
xrange=[-9.21/2.,0.]
yrange=[-9.21/2.,0.]
xlabel= r'$\log [ (1-s ) A_{'+options.band+r'}]\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log A^c_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'st': #DRW
ys= nu.array([p['logl'] for p in params[0].values()]).reshape(len(params[0]))
ys/= nu.log(10.)
ys+= nu.log10(365.)
xs= nu.array([p['loga2'] for p in params[0].values()]).reshape(len(params[0]))
xs/= nu.log(10.)
xs+= nu.log10(2.)-ys
#print nu.amin(ys), nu.amax(ys)
xrange=[-6.,0.]
yrange=[-.5,3.5]
xlabel= r'$\log_{10} \sigma^2_{'+options.band+r'}\ \mathrm{(short-timescale\ variance)}$'
ylabel= r'$\log_{10} \tau_{'+options.band+r'}\ \mathrm{(damping\ timescale\ in\ days)}$'
elif options.plottype == 'a2l': #DRW
ys= nu.array([p['logl'] for p in params[0].values()]).reshape(len(params[0]))
xs= nu.array([p['loga2'] for p in params[0].values()]).reshape(len(params[0]))
xrange=[-2.5,0.]
yrange=[-10.,4.]
xlabel= r'$\log a^2_{'+options.band+r'}\ \mathrm{(variance)}$'
ylabel= r'$\log \tau_{'+options.band+r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
elif options.plottype == 'Al': #DRW
ys= nu.array([p['logl'] for p in params[0].values()]).reshape(len(params[0]))
xs= nu.array([p['loga2'] for p in params[0].values()]).reshape(len(params[0]))
xs= (nu.log(2.)+xs+nu.log(1.-nu.exp(-1./nu.exp(ys))))/2.
xrange=[-9.21/2.,0.]
yrange=[-10.,4.]
xlabel= r'$\log A_{'+options.band+r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log \tau_{'+options.band+r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,ys,'k,',onedhists=True,
yrange=yrange,
xrange=xrange,bins=30,
xlabel=xlabel,
ylabel=ylabel)
if options.plottype == 'AA' or options.plottype == 'gg' \
or options.plottype == 'loglike2' \
or options.plottype == 'AsAc' \
or options.plottype == 'ss' \
or options.plottype == 'AcAc':
bovy_plot.bovy_plot(nu.array(xrange),nu.array(xrange),'0.5',
overplot=True)
#also fit if desired
if options.linearfit:
indx= (xs > xrange[0])*(xs < xrange[1])\
*(ys > yrange[0])*(ys < yrange[1])
b= _fit_linear(xs[indx],ys[indx],removeoutliers=options.removeoutliers)
bovy_plot.bovy_plot(nu.array(xrange),nu.array(xrange)+b,'0.25',
overplot=True)
bovy_plot.bovy_text(ylabelnounits+r'$ = $'+
xlabelnounits+r'$ %4.2f$' % b,
bottom_right=True,color='0.25')
bovy_plot.bovy_end_print(options.plotfilename)
return None
def main(args: Optional[list] = None, opts: Optional[Namespace] = None):
"""Fit MWPotential2014 Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
opts : Namespace, optional
pre-constructed results of parsed args
if not None, used ONLY if args is None
"""
if opts is not None and args is None:
pass
else:
if opts is not None:
warnings.warn("Not using `opts` because `args` are given")
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# plot chains
print("Plotting Chains")
fig = mcmc_util.plot_chains(opath)
fig.savefig(fpath + "chains.pdf")
plt.close(fig)
print("Need to continue chains:", end=" ")
for i in range(32):
ngood = mcmc_util.determine_nburn(
filename=opath + f"mwpot14-fitsigma-{i:02}.dat", return_nsamples=True,
)
if ngood < 4000:
print(f"{i:02} (N={ngood})", end=", ")
###############################################################
# RESULTING PDFS
data, _, weights, _ = read_mcmc(nburn=None, skip=1, evi_func=lambda x: 1.0)
plot_corner(data, weights=weights)
plt.savefig("figures/PDFs/corner.pdf")
plt.close()
# --------------
savefilename = "output/pal5_forces_mcmc.pkl"
if not os.path.exists(savefilename):
data_wf, index_wf, weights_wf, evi_wf = read_mcmc(
nburn=None, addforces=True, skip=1, evi_func=lambda x: 1.0
)
save_pickles(savefilename, data_wf, index_wf, weights_wf, evi_wf)
else:
with open(savefilename, "rb") as savefile:
data_wf = pickle.load(savefile)
index_wf = pickle.load(savefile)
weights_wf = pickle.load(savefile)
evi_wf = pickle.load(savefile)
# --------------
plot_corner(data_wf, weights=weights_wf, addvcprior=False)
plt.savefig("figures/PDFs/corner_wf.pdf")
plt.close()
# --------------
# Which potential is preferred?
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_harmonic)
fig = plt.figure(figsize=(6, 4))
bovy_plot.bovy_plot(
potindx,
np.log(evidences),
"o",
xrange=[-1, 34],
yrange=[-35, -22],
xlabel=r"$\mathrm{Potential\ index}$",
ylabel=r"$\ln\ \mathrm{evidence}$",
)
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_laplace)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 30.0, "d", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(np.amax(x[:, -1]))
)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 8.0, "s", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(-25.0)
if (np.log(evi_harmonic(x)) > -25.0)
else np.exp(-50.0)
)
bovy_plot.bovy_plot(potindx, np.log(evidences), "o", overplot=True)
plt.savefig("figures/PDFs/preferred_pot.pdf")
plt.close()
###############################################################
# Look at the results for individual potentials
# --------------
# The flattening $c$
npot = 32
nwalkers = 12
plt.figure(figsize=(16, 6))
cmap = cm.plasma
maxl = np.zeros((npot, 2))
for en, ii in enumerate(range(npot)):
data_ip, _, weights_ip, evi_ip = read_mcmc(singlepot=ii, evi_func=evi_harmonic)
try:
maxl[en, 0] = np.amax(data_ip[:, -1])
maxl[en, 1] = np.log(evi_ip[0])
except ValueError:
maxl[en] = -10000000.0
plt.subplot(2, 4, en // 4 + 1)
bovy_plot.bovy_hist(
data_ip[:, 0],
range=[0.5, 2.0],
bins=26,
histtype="step",
color=cmap((en % 4) / 3.0),
normed=True,
xlabel=r"$c$",
lw=1.5,
overplot=True,
)
if en % 4 == 0:
bovy_plot.bovy_text(
r"$\mathrm{Potential\ %i\ to\ % i}$" % (en, en + 3),
size=17.0,
top_left=True,
)
plt.tight_layout()
plt.savefig("figures/flattening_c.pdf")
plt.close()
###############################################################
# What is the effective prior in $(F_R,F_Z)$?
frfzprior_savefilename = "frfzprior.pkl"
if not os.path.exists(frfzprior_savefilename):
# Compute for each potential separately
nvoc = 10000
ro = 8.0
npot = 32
fs = np.zeros((2, nvoc, npot))
for en, ii in tqdm(enumerate(range(npot))):
fn = f"output/fitsigma/mwpot14-fitsigma-{i:02}.dat"
# Read the potential parameters
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
for jj in range(nvoc):
c = np.random.uniform() * 1.5 + 0.5
tvo = np.random.uniform() * 50.0 + 200.0
pot = mw_pot.setup_potential(potparams, c, False, False, REFR0, tvo)
fs[:, jj, ii] = np.array(pal5_util.force_pal5(pot, 23.46, REFR0, tvo))[
:2
]
save_pickles(frfzprior_savefilename, fs)
else:
with open(frfzprior_savefilename, "rb") as savefile:
fs = pickle.load(savefile)
# --------------
plt.figure(figsize=(6, 6))
bovy_plot.scatterplot(
fs[0].flatten(),
fs[1].flatten(),
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
xlabel=r"$F_R(\mathrm{Pal\ 5})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})$",
onedhists=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
justcontours=True,
cntrcolors="w",
overplot=True,
onedhists=True,
)
plt.axvline(-0.81, color=sns.color_palette()[0])
plt.axhline(-1.85, color=sns.color_palette()[0])
plt.savefig("figures/effective_force_prior.pdf")
plt.close()
# --------------
# The ratio of the posterior and the prior
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12.5, 4))
def axes_white():
for k, spine in plt.gca().spines.items(): # ax.spines is a dictionary
spine.set_color("w")
plt.gca().tick_params(axis="x", which="both", colors="w")
plt.gca().tick_params(axis="y", which="both", colors="w")
[t.set_color("k") for t in plt.gca().xaxis.get_ticklabels()]
[t.set_color("k") for t in plt.gca().yaxis.get_ticklabels()]
return None
bins = 32
trange = [[-1.75, -0.25], [-2.5, -1.2]]
tw = copy.deepcopy(weights_wf)
tw[index_wf == 14] = 0.0 # Didn't converge properly
H_prior, xedges, yedges = np.histogram2d(
fs[0].flatten(), fs[1].flatten(), bins=bins, range=trange, normed=True
)
H_post, xedges, yedges = np.histogram2d(
data_wf[:, 7], data_wf[:, 8], weights=tw, bins=bins, range=trange, normed=True,
)
H_like = H_post / H_prior
H_like[H_prior == 0.0] = 0.0
plt.subplot(1, 3, 1)
bovy_plot.bovy_dens2d(
H_prior.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Prior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 2)
bovy_plot.bovy_dens2d(
H_post.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Posterior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 3)
bovy_plot.bovy_dens2d(
H_like.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
vmin=0.1,
vmax=4.0,
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Likelihood}$", top_left=True, size=19.0, color="w")
axes_white()
def qline(FR, q=0.95):
return 2.0 * FR / q ** 2.0
q = 0.94
plt.plot([-1.25, -0.2], [qline(-1.25, q=q), qline(-0.2, q=q)], "w--")
bovy_plot.bovy_text(-1.7, -2.2, r"$q_\Phi = 0.94$", size=16.0, color="w")
plt.plot((-1.25, -1.02), (-2.19, qline(-1.02, q=q)), "w-", lw=0.8)
plt.tight_layout()
plt.savefig(
"figures/pal5post.pdf", bbox_inches="tight",
)
plt.close()
# --------------
# Projection onto the direction perpendicular to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\perp$"
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-1.5, 1.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_perp_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Projection onto the direction parallel to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\parallel$"
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-2.5, 2.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_prll_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Thus, there is only a weak constraint on $F_\parallel$.
nrow = int(np.ceil(npot / 4.0))
plt.figure(figsize=(16, nrow * 4))
for en, ii in enumerate(range(npot)):
plt.subplot(nrow, 4, en + 1)
if ii % 4 == 0:
tylabel = r"$F_Z(\mathrm{Pal\ 5})$"
else:
tylabel = None
if ii // 4 == nrow - 1:
txlabel = r"$F_R(\mathrm{Pal\ 5})$"
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, en],
fs[1][:, en],
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors="w",
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
# cntrcolors=sns.color_palette()[2],
overplot=True,
)
plt.axvline(-0.80, color=sns.color_palette()[0])
plt.axhline(-1.83, color=sns.color_palette()[0])
bovy_plot.bovy_text(r"$\mathrm{Potential}\ %i$" % ii, size=17.0, top_left=True)
plt.savefig("figures/PDFs/constain_F_prll.pdf")
plt.close()
# --------------
# Let's plot four representative ones for the paper:
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
nullfmt = NullFormatter()
nrow = 1
plt.figure(figsize=(15, nrow * 4))
for en, ii in enumerate([0, 15, 24, 25]):
plt.subplot(nrow, 4, en + 1)
if en % 4 == 0:
tylabel = (
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
tylabel = None
if en // 4 == nrow - 1:
txlabel = (
r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, ii],
fs[1][:, ii],
"k,",
bins=31,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[2],
cntrls="--",
cntrlw=2.0,
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[0],
cntrlw=2.5,
overplot=True,
)
if en > 0:
plt.gca().yaxis.set_major_formatter(nullfmt)
plt.tight_layout()
plt.savefig(
"figures/pal5post_examples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about $q_\Phi$?
bins = 47
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
histtype="step",
lw=2.0,
bins=bins,
xlabel=r"$q_\mathrm{\Phi}$",
xrange=[0.7, 1.25],
normed=True,
)
dum = bovy_plot.bovy_hist(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
histtype="step",
lw=2.0,
bins=bins,
overplot=True,
xrange=[0.7, 1.25],
normed=True,
)
mq = np.sum(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) * weights_wf
) / np.sum(weights_wf)
sq = np.sqrt(
np.sum(
(np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) - mq) ** 2.0
* weights_wf
)
/ np.sum(weights_wf)
)
print("From posterior samples: q = %.3f +/- %.3f" % (mq, sq))
Hq_post, xedges = np.histogram(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
bins=bins,
range=[0.7, 1.25],
normed=True,
)
Hq_prior, xedges = np.histogram(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
bins=bins,
range=[0.7, 1.25],
normed=True,
)
qs = 0.5 * (xedges[:-1] + xedges[1:])
Hq_like = Hq_post / Hq_prior
Hq_like[Hq_post == 0.0] = 0.0
mq = np.sum(qs * Hq_like) / np.sum(Hq_like)
sq = np.sqrt(np.sum((qs - mq) ** 2.0 * Hq_like) / np.sum(Hq_like))
print("From likelihood of samples: q = %.3f +/- %.3f" % (mq, sq))
plt.savefig("figures/q_phi.pdf")
plt.close()
# It appears that $q_\Phi$ is the quantity that is the most strongly constrained by the Pal 5 data.
###############################################################
# A sampling of tracks from the MCMC
savefilename = "mwpot14-pal5-mcmcTracks.pkl"
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
if os.path.exists(savefilename):
with open(savefilename, "rb") as savefile:
pal5_track_samples = pickle.load(savefile)
forces = pickle.load(savefile)
all_potparams = pickle.load(savefile)
all_params = pickle.load(savefile)
else:
np.random.seed(1)
ntracks = 21
multi = 8
pal5_track_samples = np.zeros((ntracks, 2, 6, pal5varyc[0].shape[1]))
forces = np.zeros((ntracks, 2))
all_potparams = np.zeros((ntracks, 5))
all_params = np.zeros((ntracks, 7))
for ii in range(ntracks):
# Pick a random potential from among the set, but leave 14 out
pindx = 14
while pindx == 14:
pindx = np.random.permutation(32)[0]
# Load this potential
fn = f"output/fitsigma/mwpot14-fitsigma-{pindx:02}.dat"
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
all_potparams[ii] = potparams
# Now pick a random sample from this MCMC chain
tnburn = mcmc_util.determine_nburn(fn)
tdata = np.loadtxt(fn, comments="#", delimiter=",")
tdata = tdata[tnburn::]
tdata = tdata[np.random.permutation(len(tdata))[0]]
all_params[ii] = tdata
tvo = tdata[1] * REFV0
pot = mw_pot.setup_potential(potparams, tdata[0], False, False, REFR0, tvo)
forces[ii, :] = pal5_util.force_pal5(pot, 23.46, REFR0, tvo)[:2]
# Now compute the stream model for this setup
dist = tdata[2] * 22.0
pmra = -2.296 + tdata[3] + tdata[4]
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
pmdec = -2.257 + tdata[3] * pmdecpar + tdata[4] * pmdecperp
vlos = -58.7
sigv = 0.4 * np.exp(tdata[5])
prog = Orbit(
[229.018, -0.124, dist, pmra, pmdec, vlos],
radec=True,
ro=REFR0,
vo=tvo,
solarmotion=[-11.1, 24.0, 7.25],
)
tsdf_trailing, tsdf_leading = pal5_util.setup_sdf(
pot,
prog,
sigv,
10.0,
REFR0,
tvo,
multi=multi,
nTrackChunks=8,
trailing_only=False,
verbose=True,
useTM=False,
)
# Compute the track
for jj, sdf in enumerate([tsdf_trailing, tsdf_leading]):
trackRADec = bovy_coords.lb_to_radec(
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
trackpmRADec = bovy_coords.pmllpmbb_to_pmrapmdec(
sdf._interpolatedObsTrackLB[:, 4],
sdf._interpolatedObsTrackLB[:, 5],
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
# Store the track
pal5_track_samples[ii, jj, 0] = trackRADec[:, 0]
pal5_track_samples[ii, jj, 1] = trackRADec[:, 1]
pal5_track_samples[ii, jj, 2] = sdf._interpolatedObsTrackLB[:, 2]
pal5_track_samples[ii, jj, 3] = sdf._interpolatedObsTrackLB[:, 3]
pal5_track_samples[ii, jj, 4] = trackpmRADec[:, 0]
pal5_track_samples[ii, jj, 5] = trackpmRADec[:, 1]
save_pickles(
savefilename, pal5_track_samples, forces, all_potparams, all_params
)
# --------------
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12, 8))
gs = gridspec.GridSpec(2, 2, wspace=0.225, hspace=0.1, right=0.94)
ntracks = pal5_track_samples.shape[0]
cmap = cm.plasma
alpha = 0.7
for ii in range(ntracks):
tc = cmap((forces[ii, 1] + 2.5) / 1.0)
for jj in range(2):
# RA, Dec
plt.subplot(gs[0])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 1],
"-",
color=tc,
alpha=alpha,
)
# RA, Vlos
plt.subplot(gs[1])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 3],
"-",
color=tc,
alpha=alpha,
)
# RA, Dist
plt.subplot(gs[2])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 2] - all_params[ii, 2] * 22.0,
"-",
color=tc,
alpha=alpha,
)
# RA, pm_parallel
plt.subplot(gs[3])
plt.plot(
pal5_track_samples[ii, jj, 0, : 500 + 500 * (1 - jj)],
np.sqrt(1.0 + (2.257 / 2.296) ** 2.0)
* (
(pal5_track_samples[ii, jj, 4, : 500 + 500 * (1 - jj)] + 2.296)
* pmdecperp
- (pal5_track_samples[ii, jj, 5, : 500 + 500 * (1 - jj)] + 2.257)
)
/ (pmdecpar - pmdecperp),
"-",
color=tc,
alpha=alpha,
)
plot_data_add_labels(
p1=(gs[0],), p2=(gs[1],), noxlabel_dec=True, noxlabel_vlos=True
)
plt.subplot(gs[2])
plt.xlim(250.0, 221.0)
plt.ylim(-3.0, 1.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mathrm{Distance}\,(\mathrm{kpc})$")
plt.subplot(gs[3])
plt.xlim(250.0, 221.0)
plt.ylim(-0.5, 0.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mu_\parallel\,(\mathrm{mas\,yr}^{-1})$")
# Colorbar
gs2 = gridspec.GridSpec(2, 1, wspace=0.0, left=0.95, right=0.975)
plt.subplot(gs2[:, -1])
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=-2.5, vmax=-1.5))
sm._A = []
CB1 = plt.colorbar(sm, orientation="vertical", cax=plt.gca())
CB1.set_label(
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
fontsize=18.0,
)
plt.savefig(
"figures/pal5tracksamples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about Kuepper et al. (2015)?
# Can we recover the Kuepper et al. (2015) result as prior + $q_\Phi =
# 0.94 \pm 0.05$ + $V_c(R_0)$ between 200 and 280 km/s? For simplicity
# we will not vary $R_0$, which should not have a big impact.
s = sample_kuepper_flattening_post(50000, 0.94, 0.05)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples.pdf")
plt.close()
# The constraint on the potential flattening gets you far, but there
# is more going on (the constraint on the halo mass and scale
# parameter appear to come completely from the $V_c(R_0)$ constraint).
# Let's add the weak constraint on the sum of the forces, scaled to
# Kuepper et al.'s best-fit acceleration (which is higher than ours):
s = sample_kuepper_flatteningforce_post(50000, 0.94, 0.05, -0.83, 0.36)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples_flatF.pdf")
plt.close()
# This gets a tight relation between $M_\mathrm{halo}$ and the scale
# parameter of the halo, but does not lead to a constraint on either
# independently; the halo potential flattening constraint is that of
# Kuepper et al. Based on this, it appears that the information that
# ties down $M_\mathrm{halo}$ comes from the overdensities, which may
# be spurious (Thomas et al. 2016) and whose use in dynamical modeling
# is dubious anyway.
# What is Kuepper et al.'s prior on $a_{\mathrm{Pal\ 5}}$?
apal5s = []
ns = 100000
for ii in range(ns):
Mh = np.random.uniform() * (10.0 - 0.001) + 0.001
a = np.random.uniform() * (100.0 - 0.1) + 0.1
q = np.random.uniform() * (1.8 - 0.2) + 0.2
pot = setup_potential_kuepper(Mh, a)
FR, FZ = force_pal5_kuepper(pot, q)
apal5s.append(np.sqrt(FR ** 2.0 + FZ ** 2.0))
apal5s = np.array(apal5s)
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
apal5s, range=[0.0, 10.0], lw=2.0, bins=51, histtype="step", normed=True,
)
plt.savefig("figures/kuepper_samples_prior.pdf")
plt.close()
def plot_fehafe(basefilename):
#First read the sample
data= apread.rcsample()
data= data[data['SNR'] >= 70.]
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#R and z ranges to plot
Rmins= [5.,7.,9.]
Rmaxs= [7.,9.,11.]
zmins= [0.,0.5,1.]
zmaxs= [0.5,1.,2.]
nRbins= len(Rmins)
nzbins= len(zmins)
for ii in range(nRbins):
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
#special.erf(numpy.arange(1,3)/numpy.sqrt(2.)),
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=21)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])
if not _NOLINE:
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%i < R / \mathrm{kpc} \leq %i$' % (Rmins[ii],Rmaxs[ii]) +'\n'+r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%iR%i_%.1fz%.1f.' % (Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])+_EXT)
#Plot z bins only
zmins= [0.,0.5,1.,2.]
zmaxs= [0.5,1.,2.,4.]
nzbins= len(zmins)
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > 4.)\
*(data['RC_GALR'] <= 9.)\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=31)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,4.,9.,zmins[jj],zmaxs[jj])
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%.1fz%.1f.' % (zmins[jj],zmaxs[jj])+_EXT)
#Plot scatter in the high-alpha sequence
#Histograms in 3 R bins
bovy_plot.bovy_print(fig_width=7.)
overplot= False
colors= ['y','k','r']
labelposx= 0.235
labelposy= [13.,11.,9.]
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=28,range=[-0.275,0.25],yrange=[0.,15.],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
overplot=overplot,normed=True)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten()), txa[0]*len(dafe)
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
bovy_plot.bovy_text(labelposx,labelposy[ii],
r'$%i < R \leq %i: \sigma = %.3f$' % (Rmins[ii],Rmaxs[ii],numpy.sqrt(txc[0,0,0])),
size=16.,color=colors[ii],ha='right')
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist.%s' % _EXT)
#High SN
bovy_plot.bovy_print(fig_width=7.)
overplot= False
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)\
*(data['SNR'] >= 150.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=19,range=[-0.275,0.15],yrange=[0.,19.5],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
normed=True,
overplot=overplot)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten())
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist_highsn.%s' % _EXT)
return None
def plot_vpecvo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
if _ROTCURVE == 'flat':
vpec = numpy.array([s[7] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[6] for s in params]) * _VRSUN #6 w/ dwarf
elif _ROTCURVE == 'powerlaw' or _ROTCURVE == 'linear':
vpec = numpy.array([s[8] for s in params
]) * _PMSGRA * ros - vos #7 w/ dwarf
vpecR = numpy.array([s[7] for s in params]) * _VRSUN #6 w/ dwarf
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
axScatter, axHistx, axHisty = bovy_plot.scatterplot(
vpecR, #vos/ros+vpec/ros,
vpec,
'k,',
levels=levels,
xlabel=r'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
ylabel=r'$V_{\phi,\odot}-V_c\ [\mathrm{km\ s}^{-1}]$',
bins=31,
xrange=[-15., 0.],
yrange=[0., 35.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg',
retAxes=True)
#SBD10 value
bovy_plot.bovy_plot([-20., 40.], [12.24, 12.24],
'--',
color='0.5',
overplot=True)
bovy_plot.bovy_text(-4., 12.7, r'$\mathrm{SBD10}$')
axHisty.plot([0., 100.], [12.24, 12.24], '--', color='0.5')
bovy_plot.bovy_plot([_VRSUN, _VRSUN], [-100., 100.],
'--',
color='0.5',
overplot=True)
bovy_plot.bovy_text(_VRSUN - .75, 7., r'$\mathrm{SBD10}$', rotation=90.)
axHistx.plot([_VRSUN, _VRSUN], [0., 100.], '--', color='0.5')
#Reid / Brunthaler
#bovy_plot.bovy_plot([_PMSGRA,_PMSGRA],[-10.,100.],
# '--',color='0.5',overplot=True)
#axHistx.plot([_PMSGRA,_PMSGRA],[0.,100.],'--',color='0.5')
#bovy_plot.bovy_text(29.4,5.,r'$\mathrm{RB04}$')
#Inset, closed orbit at the Sun
lp = LogarithmicHaloPotential(normalize=1.)
ep = EllipticalDiskPotential(phib=numpy.pi / 2.,
p=0.,
tform=-100.,
tsteady=-100.,
twophio=14. / 220.) #0.072725)
o = Orbit([1., 0., 1. + 14. / 220., 0.])
oc = Orbit([1., 0., 1., 0.])
ts = numpy.linspace(0., 4. * numpy.pi, 1001)
o.integrate(ts, [lp, ep])
print o.e(analytic=True, pot=lp)
oc.integrate(ts, lp)
left, bottom, width, height = 0.45, 0.45, 0.25, 0.25
axInset = pyplot.axes([left, bottom, width, height])
pyplot.sca(axInset)
pyplot.plot(o._orb.orbit[:, 0] * numpy.cos(o._orb.orbit[:, 3]),
o._orb.orbit[:, 0] * numpy.sin(o._orb.orbit[:, 3]), 'k-')
pyplot.plot(oc._orb.orbit[:, 0] * numpy.cos(oc._orb.orbit[:, 3]),
oc._orb.orbit[:, 0] * numpy.sin(oc._orb.orbit[:, 3]),
'--',
color='0.6')
pyplot.xlim(-1.5, 1.5)
pyplot.ylim(-1.5, 1.5)
#pyplot.xlabel(r'$x / R_0$')
#pyplot.ylabel(r'$y / R_0$')
nullfmt = NullFormatter() # no labels
axInset.xaxis.set_major_formatter(nullfmt)
axInset.yaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_end_print(plotfilename)
def compareMagFluxFits(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params = []
for filename in args:
if os.path.exists(filename):
savefile = open(filename, 'rb')
params.append(pickle.load(savefile))
savefile.close()
else:
print filename + " does not exist ..."
print "Returning ..."
return
if options.plottype == 'AA':
ys = []
xs = []
for key in params[1].keys():
try:
ys.append(params[0][key]['logA'] / 2.)
xs.append(params[1][key]['logA'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
ys = xs - ys - nu.log(nu.log(10.) / 2.5)
xrange = [-9.21 / 2., 0.]
yrange = [-.25, .25]
xlabel = r'$\log A^{\mathrm{flux}}_' + options.band + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^{\mathrm{flux}}_' + options.band + r'-\log A^{\mathrm{mag}}_' + options.band + r'- \log\left(\frac{\log 10}{2.5}\right)$'
elif options.plottype == 'gg':
ys = []
xs = []
for key in params[1].keys():
try:
ys.append(params[0][key]['gamma'])
xs.append(params[1][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
print len(xs)
ys = xs - ys
xrange = [0., 1.2]
yrange = [-.25, .25]
xlabel = r'$\gamma^{\mathrm{flux}}_' + options.band + r'\ \mathrm{(power-law\ exponent)}$'
ylabel = r'$\gamma^{\mathrm{flux}}_' + options.band + r'- \gamma^{\mathrm{mag}}_' + options.band + '$'
elif options.plottype == 'loglike2':
ys = []
xs = []
nepochs = []
cnt = 0
for key in params[1].keys():
#cnt+= 1
#print cnt
#if cnt > 10: break
#Get the number of epochs
v = VarQso(os.path.join('../data/s82qsos/', key))
try:
ys.append(params[0][key]['loglike'] -
v.nepochs(options.band) * nu.log(nu.log(10.) / 2.5))
nepochs.append(v.nepochs(options.band))
xs.append(params[1][key]['loglike'])
except KeyError:
continue
xs = -nu.array(xs).reshape(len(xs))
ys = -nu.array(ys).reshape(len(xs))
nepochs = nu.array(nepochs).reshape(len(nepochs))
ys /= nepochs
xs /= nepochs
ys = xs - ys
xrange = [-3., 0.]
yrange = [-.1, .1]
xlabel = r'$\log \mathcal{L}^{\mathrm{flux}}_{' + options.band + r',\mathrm{red}}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log \mathcal{L}^{\mathrm{flux}}_{' + options.band + r',\mathrm{red}}- \log \mathcal{L}^{\mathrm{mag}}_{' + options.band + ',\mathrm{red}}' + r'- \log\left(\frac{\log 10}{2.5}\right)$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
yrange=yrange,
xrange=xrange,
bins=31,
xlabel=xlabel,
ylabel=ylabel)
bovy_plot.bovy_plot(nu.array(xrange), [0., 0.], '0.5', overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_rovo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options = plot_pdfs.set_options(None)
nros = 15
noos = 15
ros = numpy.linspace(7., 13., nros)
oos = numpy.linspace(20., 30., noos)
ll = numpy.zeros((noos, nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss = []
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
theseparamss.append(theseparams)
thisll = multi.parallel_map(
(lambda x: numpy.sum(
logl.logl(init=theseparamss[x], options=options))),
range(nros),
numcores=numpy.amin(
[nros, _MULTI,
multiprocessing.cpu_count()]))
ll[ii, :] = thisll
else:
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
ll[ii, jj] = numpy.sum(
logl.logl(init=theseparams, options=options))
#Normalize
ll -= logsumexp(ll)
ll = numpy.exp(ll)
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
bovy_plot.bovy_dens2d(
ll.T,
origin='lower',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20., 35.],
yrange=[7., 13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(
vos / ros,
ros,
'k,',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200. / 8., 250. / 8.],
yrange=[7., 9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
def plot_mcmc_c(samples,
fitvoro,
cs,
bf_params,
add_families=False,
savefig=None):
"""Plot MCMC C-Parameter.
Parameters
----------
samples : list
fitvoro : bool
add_families : bool, optional
Returns
-------
None
"""
if add_families:
st0 = np.random.get_state()
np.random.seed(1)
with open("output/mwpot14varyc-samples.pkl", "rb") as savefile:
pot_samples = pickle.load(savefile)
rndindices = np.random.permutation(pot_samples.shape[1])
pot_params = np.zeros((8, 32))
for ii in range(32):
pot_params[:, ii] = pot_samples[:, rndindices[ii]]
cindx = 9 if fitvoro else 7
cmap = cm.viridis
levels = list(special.erf(np.arange(1, 3) / np.sqrt(2.0)))
levels.append(1.01)
def axes_white():
for k, spine in plt.gca().spines.items(): # ax.spines is a dictionary
spine.set_color("w")
plt.gca().tick_params(axis="x", which="both", colors="w")
plt.gca().tick_params(axis="y", which="both", colors="w")
[t.set_color("k") for t in plt.gca().xaxis.get_ticklabels()]
[t.set_color("k") for t in plt.gca().yaxis.get_ticklabels()]
return None
# ------------------------------
fig = plt.figure(figsize=(16 + 2 * fitvoro, 4))
gs = gridspec.GridSpec(1, 5 + 2 * fitvoro, wspace=0.015)
plt.subplot(gs[0])
bovy_plot.scatterplot(
samples[0],
samples[cindx],
",",
gcf=True,
bins=31,
cmap=cmap,
cntrcolors="0.6",
xrange=[0.0, 0.99],
yrange=[0.0, 4.0],
levels=levels,
xlabel=r"$f_d$",
ylabel=r"$c/a$",
zorder=1,
)
bovy_plot.bovy_plot([bp[0] for bp in bf_params], cs, "w-", overplot=True)
if add_families:
fm_kw = {
"color": "w",
"marker": "x",
"ls": "none",
"ms": 4.0,
"mew": 1.2,
}
bovy_plot.bovy_plot(pot_params[0],
pot_params[7],
overplot=True,
**fm_kw)
axes_white()
plt.subplot(gs[1])
bovy_plot.scatterplot(
samples[1],
samples[cindx],
",",
gcf=True,
bins=31,
cmap=cmap,
cntrcolors="0.6",
xrange=[0.0, 0.99],
yrange=[0.0, 4.0],
levels=levels,
xlabel=r"$f_h$",
)
bovy_plot.bovy_plot([bp[1] for bp in bf_params], cs, "w-", overplot=True)
if add_families:
bovy_plot.bovy_plot(pot_params[1],
pot_params[7],
overplot=True,
**fm_kw)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
axes_white()
plt.subplot(gs[2])
bovy_plot.scatterplot(
np.exp(samples[2]) * REFR0,
samples[cindx],
",",
gcf=True,
bins=21,
levels=levels,
xrange=[2.0, 4.49],
yrange=[0.0, 4.0],
cmap=cmap,
cntrcolors="0.6",
xlabel=r"$h_R\,(\mathrm{kpc})$",
)
bovy_plot.bovy_plot(
[np.exp(bp[2]) * REFR0 for bp in bf_params],
cs,
"w-",
overplot=True,
)
if add_families:
bovy_plot.bovy_plot(
np.exp(pot_params[2]) * REFR0,
pot_params[7],
overplot=True,
**fm_kw,
)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
axes_white()
plt.subplot(gs[3])
bovy_plot.scatterplot(
np.exp(samples[3]) * REFR0 * 1000.0,
samples[cindx],
",",
gcf=True,
bins=26,
levels=levels,
xrange=[150.0, 445.0],
yrange=[0.0, 4.0],
cmap=cmap,
cntrcolors="0.6",
xlabel=r"$h_z\,(\mathrm{pc})$",
)
bovy_plot.bovy_plot(
[np.exp(bp[3]) * REFR0 * 1000.0 for bp in bf_params],
cs,
"w-",
overplot=True,
)
if add_families:
bovy_plot.bovy_plot(
np.exp(pot_params[3]) * REFR0 * 1000.0,
pot_params[7],
overplot=True,
**fm_kw,
)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
plt.gca().xaxis.set_ticks([200.0, 300.0, 400.0])
axes_white()
plt.subplot(gs[4])
bovy_plot.scatterplot(
np.exp(samples[4]) * REFR0,
samples[cindx],
",",
gcf=True,
bins=26,
levels=levels,
xrange=[0.0, 39.0],
yrange=[0.0, 4.0],
cmap=cmap,
cntrcolors="0.6",
xlabel=r"$r_s\,(\mathrm{kpc})$",
)
bovy_plot.bovy_plot(
[np.exp(bp[4]) * REFR0 for bp in bf_params],
cs,
"w-",
overplot=True,
)
if add_families:
bovy_plot.bovy_plot(
np.exp(pot_params[4]) * REFR0,
pot_params[7],
overplot=True,
**fm_kw,
)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
axes_white()
if fitvoro:
plt.subplot(gs[5])
bovy_plot.scatterplot(
samples[7] * REFR0,
samples[cindx],
",",
gcf=True,
bins=26,
levels=levels,
xrange=[7.1, 8.9],
yrange=[0.0, 4.0],
cmap=cmap,
cntrcolors="0.6",
xlabel=r"$R_0\,(\mathrm{kpc})$",
)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
axes_white()
plt.subplot(gs[6])
bovy_plot.scatterplot(
samples[8] * REFV0,
samples[cindx],
",",
gcf=True,
bins=26,
levels=levels,
xrange=[200.0, 250.0],
yrange=[0.0, 4.0],
cmap=cmap,
cntrcolors="0.6",
xlabel=r"$V_c(R_0)\,(\mathrm{km\,s}^{-1})$",
)
nullfmt = NullFormatter()
plt.gca().yaxis.set_major_formatter(nullfmt)
axes_white()
if savefig is not None:
fig.savefig(savefig)
return None
def plot_afefeh(plotfilename):
# Load the data
data= define_rcsample.get_rcsample()
# Plot the data
bovy_plot.bovy_print()
bovy_plot.scatterplot(data[define_rcsample._FEHTAG],
data[define_rcsample._AFETAG],
'k.',ms=.8,
levels=special.erf(numpy.arange(1,2)/numpy.sqrt(2.)),
xrange=[-1.,0.4],
yrange=[-0.15,0.35],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=define_rcsample._AFELABEL)
# Overplot sub-samples
# low alpha, low feh
lowfeh= define_rcsample._lowlow_lowfeh(0.)
highfeh= define_rcsample._lowlow_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._lowlow_lowafe(highfeh),
define_rcsample._lowlow_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._lowlow_highafe(lowfeh),
define_rcsample._lowlow_highafe(highfeh)],
'k--',lw=2.)
# high alpha
lowfeh= define_rcsample._highalpha_lowfeh(0.)
highfeh= define_rcsample._highalpha_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._highalpha_lowafe(highfeh),
define_rcsample._highalpha_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highalpha_highafe(lowfeh),
define_rcsample._highalpha_highafe(highfeh)],
'k--',lw=2.)
# solar
lowfeh= define_rcsample._solar_lowfeh(0.)
highfeh= define_rcsample._solar_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._solar_lowafe(highfeh),
define_rcsample._solar_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._solar_highafe(lowfeh),
define_rcsample._solar_highafe(highfeh)],
'k--',lw=2.)
# high [Fe/H]
lowfeh= define_rcsample._highfeh_lowfeh(0.)
highfeh= define_rcsample._highfeh_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._highfeh_lowafe(highfeh),
define_rcsample._highfeh_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highfeh_highafe(lowfeh),
define_rcsample._highfeh_highafe(highfeh)],
'k--',lw=2.)
# Label them
bovy_plot.bovy_text(-0.4,0.265,r'$\mathrm{high}\ [\alpha/\mathrm{Fe}]$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(-0.975,0.05,r'$\mathrm{low\ [Fe/H]}$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(0.,-0.125,r'$\mathrm{high\ [Fe/H]}$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(-0.225,-0.125,r'$\mathrm{solar}$',
size=15.,backgroundcolor='w')
# Loci
if False:
haloc= define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'k-',lw=2.,overplot=True)
haloc= define_rcsample.lowalphalocus()
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'k-',lw=2.,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plotXDall(parser):
nu.random.seed(1)
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
savefile = open(args[0], 'rb')
xamp = pickle.load(savefile)
xmean = pickle.load(savefile)
xcovar = pickle.load(savefile)
savefile.close()
else:
print args[0] + " does not exist ..."
print "Returning ..."
return
if os.path.exists(args[1]):
savefile = open(args[1], 'rb')
starxamp = pickle.load(savefile)
starxmean = pickle.load(savefile)
starxcovar = pickle.load(savefile)
savefile.close()
else:
print args[1] + " does not exist ..."
print "Returning ..."
return
if os.path.exists(args[2]):
savefile = open(args[2], 'rb')
rrxamp = pickle.load(savefile)
rrxmean = pickle.load(savefile)
rrxcovar = pickle.load(savefile)
savefile.close()
else:
print args[2] + " does not exist ..."
print "Returning ..."
return
if options.nsamplesstar is None: options.nsamplesstar = options.nsamples
if options.nsamplesrrlyrae is None:
options.nsamplesrrlyrae = options.nsamples
#Load XD object in xdtarget
xdt = xdtarget.xdtarget(amp=xamp, mean=xmean, covar=xcovar)
out = xdt.sample(nsample=options.nsamples)
xdt = xdtarget.xdtarget(amp=starxamp, mean=starxmean, covar=starxcovar)
starout = xdt.sample(nsample=options.nsamplesstar)
xdt = xdtarget.xdtarget(amp=rrxamp, mean=rrxmean, covar=rrxcovar)
rrout = xdt.sample(nsample=options.nsamplesrrlyrae)
#Prepare for plotting
if options.expd1: xs = nu.exp(out[:, options.d1])
elif not options.divided1 is None:
xs = out[:, options.d1] / options.divided1
else:
xs = out[:, options.d1]
if options.expd2: ys = nu.exp(out[:, options.d2])
elif not options.divided2 is None:
ys = out[:, options.d2] / options.divided2
else:
ys = out[:, options.d2]
if options.type == 'DRW':
#plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
#Convert to logA
xs = (nu.log(2.) + xs + nu.log(1. - nu.exp(-1. / nu.exp(ys)))) / 2.
elif options.d1 == 1 and options.d2 == 0:
#Convert to logA
ys = (nu.log(2.) + ys + nu.log(1. - nu.exp(-1. / nu.exp(xs)))) / 2.
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
#stars
if options.expd1: starxs = nu.exp(starout[:, options.d1])
elif not options.divided1 is None:
starxs = starout[:, options.d1] / options.divided1
else:
starxs = starout[:, options.d1]
if options.expd2: starys = nu.exp(starout[:, options.d2])
elif not options.divided2 is None:
starys = starout[:, options.d2] / options.divided2
else:
starys = starout[:, options.d2]
if options.type == 'DRW':
#plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
#Convert to logA
starxs = (nu.log(2.) + starxs +
nu.log(1. - nu.exp(-1. / nu.exp(starys)))) / 2.
elif options.d1 == 1 and options.d2 == 0:
#Convert to logA
starys = (nu.log(2.) + starys +
nu.log(1. - nu.exp(-1. / nu.exp(starxs)))) / 2.
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
#RR Lyrae
if options.expd1: rrxs = nu.exp(rrout[:, options.d1])
elif not options.divided1 is None:
rrxs = rrout[:, options.d1] / options.divided1
else:
rrxs = rrout[:, options.d1]
if options.expd2: rrys = nu.exp(rrout[:, options.d2])
elif not options.divided2 is None:
rrys = rrout[:, options.d2] / options.divided2
else:
rrys = rrout[:, options.d2]
if options.type == 'DRW':
#plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
#Convert to logA
rrxs = (nu.log(2.) + rrxs +
nu.log(1. - nu.exp(-1. / nu.exp(rrys)))) / 2.
elif options.d1 == 1 and options.d2 == 0:
#Convert to logA
rrys = (nu.log(2.) + rrys +
nu.log(1. - nu.exp(-1. / nu.exp(rrxs)))) / 2.
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
#Plot
xrange = [options.xmin, options.xmax]
yrange = [options.ymin, options.ymax]
data = sc.array([xs, ys]).T
bins = int(round(0.3 * sc.sqrt(options.nsamples)))
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
#Censor hist ASSUMES gamma=[0.,1.2], logA=[-9.21/2.,0.] for powerlawSF
x = nu.zeros((bins, bins))
y = nu.zeros((bins, bins))
for bb in range(bins):
x[:, bb] = nu.linspace(options.xmin, options.xmax, bins)
y[bb, :] = nu.linspace(options.ymin, options.ymax, bins)
#mask
if options.type == 'powerlawSF':
hist[(y < 0.1) * (x > -3.) * (x < -1.5)] = nu.nan
hist[(x < -3.)] = nu.nan
hist[(x > -2.) * (y < (0.25 * x + 0.6))] = nu.nan
onedhistyweights = nu.ones(len(ys)) / 100.
elif options.type == 'DRW':
hist[(y < (-4.223 * (x + 2) - 10))] = nu.nan
hist[(y < -4.153) * (y < (58.47 * (x + 2.1) - 10.))] = nu.nan
hist[(y > -4.153) * (y < (2.93 * x + 2.) - 1.)] = nu.nan
onedhistyweights = nu.ones(len(ys)) / 2500.
bovy_plot.bovy_print()
#First just plot contours
cdict = {
'red': ((.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'blue': ((.0, 1.0, 1.0), (1.0, 1.0, 1.0))
}
allwhite = matplotlib.colors.LinearSegmentedColormap(
'allwhite', cdict, 256)
bovy_plot.scatterplot(xs,
ys,
'b,',
onedhists=True,
bins=bins,
cmap=allwhite,
onedhistynormed=False,
onedhistyweights=onedhistyweights,
xrange=xrange,
yrange=yrange,
onedhistec='b',
xlabel=options.xlabel,
ylabel=options.ylabel)
bovy_plot.scatterplot(starxs,
starys,
'k,',
onedhists=True,
bins=bins,
cmap=allwhite,
xrange=xrange,
yrange=yrange,
overplot=True)
bovy_plot.scatterplot(rrxs,
rrys,
'r,',
onedhists=True,
bins=bins,
cmap=allwhite,
onedhistec='r',
xrange=xrange,
yrange=yrange,
overplot=True)
hist /= nu.nansum(hist)
#Custom colormap
cdict = {
'red': ((.0, 1.0, 1.0), (1.0, .0, .0)),
'green': ((0.0, 1.0, 1.0), (1.0, .0, .0)),
'blue': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0))
}
my_cmap = matplotlib.colors.LinearSegmentedColormap(
'my_colormap', cdict, 256)
bovy_plot.scatterplot(xs,
ys,
'b,',
onedhists=False,
contours=False,
levels=[1.01],
bins=bins,
cmap=my_cmap,
hist=hist,
edges=edges,
onedhistynormed=False,
onedhistyweights=onedhistyweights,
xrange=xrange,
yrange=yrange,
overplot=True)
#Stars
data = sc.array([starxs, starys]).T
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
if options.type == 'powerlawSF':
hist[(x > -2.5)] = nu.nan
hist[(x < -2.5) * (y > (-.19 * (x + 2.5)))] = nu.nan
elif options.type == 'DRW':
hist[(y >= (-4.223 * (x + 2) - 10))] = nu.nan
hist /= nu.nansum(hist)
bovy_plot.scatterplot(
starxs,
starys,
'k,',
onedhists=True,
contours=False,
levels=[1.01], #HACK such that outliers aren't plotted
bins=bins,
hist=hist,
edges=edges,
xrange=xrange,
yrange=yrange,
overplot=True)
#RR Lyrae
data = sc.array([rrxs, rrys]).T
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
if options.type == 'powerlawSF':
hist[(x < -2.5)] = nu.nan
hist[(x > -2.5) * (y > ((x + 2.5) / 1.5)**9. * 1.15 + 0.1)] = nu.nan
#hist[(x > -2.5)*(y > (.25*x+.6))]= nu.nan
elif options.type == 'DRW':
hist[(y < -4.153) * (y >=
(58.47 * (x + 2.1) - 10.)) * (y > -7.5)] = nu.nan
hist[(y < -7.5) * (x < -2.1)] = nu.nan
hist[(y > -4.153) * (y >= (2.93 * x + 2. - 1.5))] = nu.nan
#Custom colormap
cdict = {
'red': ((.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0.0, 1.0, 1.0), (1.0, .0, .0)),
'blue': ((0.0, 1.0, 1.0), (1.0, .0, .0))
}
my_cmap = matplotlib.colors.LinearSegmentedColormap(
'my_colormap', cdict, 256)
hist /= nu.nansum(hist)
bovy_plot.scatterplot(
rrxs,
rrys,
'r,',
onedhists=False,
contours=False,
levels=[1.01], #HACK such that outliers aren't plotted
bins=bins,
cmap=my_cmap,
hist=hist,
edges=edges,
xrange=xrange,
yrange=yrange,
overplot=True)
#Label
if options.type == 'powerlawSF':
bovy_plot.bovy_text(-4.4, 1.15, r'$\mathrm{F/G\ stars}$', color='k')
bovy_plot.bovy_text(-4.4, 1.05, r'$\mathrm{QSOs}$', color='b')
bovy_plot.bovy_text(-4.4, 0.95, r'$\mathrm{RR\ Lyrae}$', color='r')
elif options.type == 'DRW':
bovy_plot.bovy_text(-4.4, 2.88, r'$\mathrm{F/G\ stars}$', color='k')
bovy_plot.bovy_text(-4.4, 1.76, r'$\mathrm{QSOs}$', color='b')
bovy_plot.bovy_text(-4.4, .64, r'$\mathrm{RR\ Lyrae}$', color='r')
bovy_plot.bovy_end_print(options.plotfilename)
def singleBandSampleExample(
datafilename="SDSSJ000051.56+001202.5.fit", band="g", nsamples=1000, basefilename="SDSSJ203817.37+003029.8"
):
"""
NAME:
singleBandSampleExample
PURPOSE:
Sample an example of a power-law structure function GP covariance
function to an SDSS quasar
INPUT:
datafilename
band - band to fit
nsamples - number of samples to use
basefilename
OUTPUT:
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
nu.random.seed(1)
# Get data
file = fu.table_fields(datafilename)
if band == "u":
mjd_g = nu.array(file.mjd_u) / 365.25
g = nu.array(file.u)
err_g = nu.array(file.err_u)
elif band == "g":
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
elif band == "r":
mjd_g = nu.array(file.mjd_r) / 365.25
g = nu.array(file.r)
err_g = nu.array(file.err_r)
elif band == "i":
mjd_g = nu.array(file.mjd_i) / 365.25
g = nu.array(file.i)
err_g = nu.array(file.err_i)
elif band == "z":
mjd_g = nu.array(file.mjd_z) / 365.25
g = nu.array(file.z)
err_g = nu.array(file.err_z)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mjd_i = nu.array(file.mjd_i) / 365.25
i = nu.array(file.i)
err_i = nu.array(file.err_i)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7)
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
i = i[mask]
i -= nu.mean(i)
err_i = err_i[mask]
mjd_i = mjd_i[mask]
mjd_i -= nu.amin(mjd_i)
meanErr_i = nu.mean(err_i)
savefilename = basefilename + ".sav"
if os.path.exists(savefilename):
savefile = open(savefilename, "rb")
gammas = pickle.load(savefile)
if "gr" in basefilename:
logGammas = pickle.load(savefile)
gammagrs = pickle.load(savefile)
logGammagrs = pickle.load(savefile)
else:
logAs = pickle.load(savefile)
out = pickle.load(savefile)
savefile.close()
else:
if "gri" in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
"logGamma": array([-7.79009776]),
"logGammagr": array([-28.0487848]),
"gamma": array([0.45918053]),
"gammagr": array([0.21333858]),
}
SF = covarFunc(**params)
listx = [(t, "g") for t in mjd_g]
listx.extend([(t, "r") for t in mjd_r])
listx.extend([(t, "i") for t in mjd_i])
listy = [m for m in g]
listy.extend([m for m in r])
listy.extend([m for m in i])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise.extend([m for m in err_i])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
elif "gr" in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
"logGamma": array([-7.79009776]),
"logGammagr": array([-28.0487848]),
"gamma": array([0.45918053]),
"gammagr": array([0.21333858]),
}
SF = covarFunc(**params)
listx = [(t, "g") for t in mjd_g]
listx.extend([(t, "r") for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
else:
from gp.powerlawSF import covarFunc
trainSet = trainingSet(listx=mjd_g, listy=g, noise=err_g)
params = [0.0, 0.0]
SF = covarFunc(gamma=0.2, A=0.000524) # Power
# SF= covarFunc(a=.0001,l=.001) #OU
# Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
print "Sampling ..."
out = sampleGP(trainSet, outcovarFunc, nsamples=nsamples, step=[0.2 for ii in range(len(params))])
gammas = nu.array([o._dict["gamma"] for o in out]).reshape(len(out))
if "gr" in basefilename:
gammagrs = nu.array([o._dict["gammagr"] for o in out]).reshape(len(out))
logGammas = nu.array([o._dict["logGamma"] for o in out]).reshape(len(out))
logGammagrs = nu.array([o._dict["logGammagr"] for o in out]).reshape(len(out))
else:
logAs = nu.array([o._dict["logA"] for o in out]).reshape(len(out))
print nu.mean(logAs), nu.sqrt(nu.var(logAs))
print nu.mean(gammas), nu.sqrt(nu.var(gammas))
# Save
savefile = open(savefilename, "wb")
pickle.dump(gammas, savefile)
if "gr" in basefilename:
pickle.dump(logGammas, savefile)
pickle.dump(gammagrs, savefile)
pickle.dump(logGammagrs, savefile)
else:
pickle.dump(logAs, savefile)
pickle.dump(out, savefile)
savefile.close()
# if not 'gr' in basefilename:
# logGammas= logAs/gammas
# Plot
bovy_plot.bovy_print()
bovy_plot.scatterplot(
logAs / 2.0,
gammas,
"k,",
ylabel=r"$\gamma$",
xlabel=r"\log A",
xrange=[-9.21 / 2.0, 0.0],
yrange=[0.0, 1.25],
bins=50,
onedhists=True,
)
bovy_plot.bovy_end_print(basefilename + "_sample_2d.png")
def plot_fake_data(parser):
"""
NAME:
plot_fake_data
PURPOSE:
plot the fake data created by create_fake_data
INPUT:
parser - from optparse
OUTPUT:
stuff as specified by the options
HISTORY:
2011-04-20 - Written - Bovy (NYU)
"""
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(-1)
#Set up DF
dfc= dehnendf(beta=options.beta,profileParams=(options.rd,options.rs,options.so),correct=True,niter=20)
#Open pickle
picklefile= open(args[0],'rb')
out= pickle.load(picklefile)
picklefile.close()
if not options.vlosdname is None:
#Plot distribution of vlos vs. d
vloss=nu.array([o.vlos(obs=[1.,0.,0.,0.,1.,0.],ro=1.,vo=1.) \
for o in out]).flatten()
ds= nu.array([o.dist(obs=[1.,0.,0.],ro=1.) \
for o in out]).flatten()
#Also calculate the expected relation
ntheory= 1001
dx= nu.linspace(0.,2.,ntheory)
vtheory= nu.zeros(ntheory)
l= options.los*_DEGTORAD
for ii in range(ntheory):
R= nu.sqrt(1.+dx[ii]**2.-2.*dx[ii]*nu.cos(l))
if 1./nu.cos(l) < dx[ii] and nu.cos(l) > 0.:
phi= nu.pi-m.asin(dx[ii]/R*nu.sin(l))
else:
phi= m.asin(dx[ii]/R*nu.sin(l))
vtheory[ii]= (R**options.beta-dfc.asymmetricdrift(R))*m.sin(phi+l)-m.sin(l) #last term is the LSR
bovy_plot.bovy_print()
bovy_plot.scatterplot(ds,vloss,'.',bins=options.scatterbins,
xlabel=r'$d / R_0$',
ylabel=r'$v_{los} / v_0$')
bovy_plot.bovy_plot(dx,vtheory,overplot=True)
bovy_plot.bovy_text(r'$l = %i^\circ$' % round(options.los),
top_left=True)
bovy_plot.bovy_end_print(options.vlosdname)
if not options.vtname is None:
vts= nu.array([o[1]-1. for o in out]).flatten() #subtract vc
#Calculate expected
ntheory= 1001
vx= nu.linspace(-1.,1.,ntheory)
y= nu.zeros(ntheory)
for ii in range(ntheory):
y[ii]= dfc(Orbit([1.,0.,vx[ii]+1.]))
y/= nu.sum(y)*(vx[1]-vx[0])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(vts,bins=options.histbins,normed=True,
xlabel=r'$v_T / v_0 - 1$')
bovy_plot.bovy_plot(vx,y,overplot=True)
bovy_plot.bovy_end_print(options.vtname)
if not options.vrname is None:
vrs= nu.array([o[0] for o in out]).flatten() #subtract vc
#Calculate expected
ntheory= 1001
vx= nu.linspace(-1.,1.,ntheory)
y= nu.zeros(ntheory)
vT= 1.-dfc.asymmetricdrift(1.)
for ii in range(ntheory):
y[ii]= dfc(Orbit([1.,vx[ii],vT]))
y/= nu.sum(y)*(vx[1]-vx[0])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(vrs,bins=options.histbins,normed=True,
xlabel=r'$v_R / v_0$')
bovy_plot.bovy_plot(vx,y,overplot=True)
bovy_plot.bovy_end_print(options.vrname)
if not options.vrvtname is None:
vts= nu.array([o[1]-1. for o in out]).flatten() #subtract vc
vrs= nu.array([o[0] for o in out]).flatten() #subtract vc
bovy_plot.bovy_print()
bovy_plot.scatterplot(vrs,vts,'.',bins=options.scatterbins,
xlabel=r'$v_R / v_0$',
ylabel=r'$v_T / v_0 - 1$')
bovy_plot.bovy_end_print(options.vrvtname)
def fitSigz(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
params= pickle.load(savefile)
samples= pickle.load(savefile)
savefile.close()
if _DEBUG:
print "Printing mean and std dev of samples ..."
for ii in range(len(params)):
xs= numpy.array([s[ii] for s in samples])
print numpy.mean(xs), numpy.std(xs)
else:
#First read the data
if _VERBOSE:
print "Reading and parsing data ..."
XYZ,vxvyvz,cov_vxvyvz,rawdata= readData(metal=options.metal,
sample=options.sample)
vxvyvz= vxvyvz.astype(numpy.float64)
cov_vxvyvz= cov_vxvyvz.astype(numpy.float64)
R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**(0.5)
XYZ[:,2]+= _ZSUN
d= numpy.fabs((XYZ[:,2]-numpy.mean(numpy.fabs(XYZ[:,2]))))
#Optimize likelihood
if _VERBOSE:
print "Optimizing the likelihood ..."
if options.model.lower() == 'hwr':
if options.metal == 'rich':
params= numpy.array([0.02,numpy.log(25.),0.,0.,numpy.log(6.)])
elif options.metal == 'poor':
params= numpy.array([0.02,numpy.log(40.),0.,0.,numpy.log(15.)])
else:
params= numpy.array([0.02,numpy.log(30.),0.,0.,numpy.log(15.)])
like_func= _HWRLikeMinus
pdf_func= _HWRLike
#Slice sampling keywords
step= [0.01,0.05,0.3,0.3,0.3]
create_method=['full','step_out','step_out',
'step_out','step_out']
isDomainFinite=[[True,True],[False,False],
[False,False],[False,False],
[False,False]]
domain=[[0.,1.],[0.,0.],[0.,0.],[0.,0.],
[0.,4.6051701859880918]]
elif options.model.lower() == 'isotherm':
if options.metal == 'rich':
params= numpy.array([0.02,numpy.log(25.),numpy.log(6.)])
elif options.metal == 'poor':
params= numpy.array([0.02,numpy.log(40.),numpy.log(15.)])
else:
params= numpy.array([0.02,numpy.log(30.),numpy.log(15.)])
like_func= _IsothermLikeMinus
pdf_func= _IsothermLike
#Slice sampling keywords
step= [0.01,0.05,0.3]
create_method=['full','step_out','step_out']
isDomainFinite=[[True,True],[False,False],
[False,True]]
domain=[[0.,1.],[0.,0.],[0.,4.6051701859880918]]
params= optimize.fmin_powell(like_func,params,
args=(XYZ,vxvyvz,cov_vxvyvz,R,d))
if _VERBOSE:
print "Optimal likelihood:", params
#Now sample
if _VERBOSE:
print "Sampling the likelihood ..."
samples= bovy_mcmc.slice(params,
step,
pdf_func,
(XYZ,vxvyvz,cov_vxvyvz,R,d),
create_method=create_method,
isDomainFinite=isDomainFinite,
domain=domain,
nsamples=options.nsamples)
if _DEBUG:
print "Printing mean and std dev of samples ..."
for ii in range(len(params)):
xs= numpy.array([s[ii] for s in samples])
print numpy.mean(xs), numpy.std(xs)
if _VERBOSE:
print "Saving ..."
savefile= open(args[0],'wb')
pickle.dump(params,savefile)
pickle.dump(samples,savefile)
savefile.close()
if options.noplots: return None
#Plot
if options.plotfunc:
#First plot the best fit
zs= numpy.linspace(0.3,1.2,1001)
ds= zs-1.
func= zfunc
maxys= math.exp(params[1])+params[2]*ds+params[3]*ds**2.
if options.xmin is None or options.xmax is None:
xrange= [numpy.amin(zs)-0.2,numpy.amax(zs)+0.1]
else:
xrange= [options.xmin,options.xmax]
if options.ymin is None or options.ymax is None:
yrange= [numpy.amin(ys)-1.,numpy.amax(ys)+1.]
else:
yrange= [options.ymin,options.ymax]
#Now plot the mean and std-dev from the posterior
zmean= numpy.zeros(len(zs))
nsigs= 3
zsigs= numpy.zeros((len(zs),2*nsigs))
fs= numpy.zeros((len(zs),len(samples)))
ds= zs-1.
for ii in range(len(samples)):
thisparams= samples[ii]
fs[:,ii]= math.exp(thisparams[1])+thisparams[2]*ds+thisparams[3]*ds**2.
#Record mean and std-devs
zmean[:]= numpy.mean(fs,axis=1)
bovy_plot.bovy_print()
bovy_plot.bovy_plot(zs,zmean,'k-',xrange=xrange,yrange=yrange,
xlabel=options.xlabel,
ylabel=options.ylabel)
for ii in range(nsigs):
for jj in range(len(zs)):
thisf= sorted(fs[jj,:])
thiscut= 0.5*special.erfc((ii+1.)/math.sqrt(2.))
zsigs[jj,2*ii]= thisf[int(math.floor(thiscut*len(samples)))]
thiscut= 1.-thiscut
zsigs[jj,2*ii+1]= thisf[int(math.floor(thiscut*len(samples)))]
colord, cc= (1.-0.75)/nsigs, 1
nsigma= nsigs
pyplot.fill_between(zs,zsigs[:,0],zsigs[:,1],color='0.75')
while nsigma > 1:
pyplot.fill_between(zs,zsigs[:,cc+1],zsigs[:,cc-1],
color='%f' % (.75+colord*cc))
pyplot.fill_between(zs,zsigs[:,cc],zsigs[:,cc+2],
color='%f' % (.75+colord*cc))
cc+= 1.
nsigma-= 1
bovy_plot.bovy_plot(zs,zmean,'k-',overplot=True)
#bovy_plot.bovy_plot(zs,maxys,'w--',overplot=True)
bovy_plot.bovy_end_print(options.plotfile)
else:
xs= numpy.array([s[options.d1] for s in samples])
ys= numpy.array([s[options.d2] for s in samples])
if options.expd1: xs= numpy.exp(xs)
if options.expd2: ys= numpy.exp(ys)
if options.xmin is None or options.xmax is None:
xrange= [numpy.amin(xs),numpy.amax(xs)]
else:
xrange= [options.xmin,options.xmax]
if options.ymin is None or options.ymax is None:
yrange= [numpy.amin(ys),numpy.amax(ys)]
else:
yrange= [options.ymin,options.ymax]
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,ys,'k,',onedhists=True,xrange=xrange,
yrange=yrange,xlabel=options.xlabel,
ylabel=options.ylabel)
maxx, maxy= params[options.d1], params[options.d2]
if options.expd1: maxx= math.exp(maxx)
if options.expd2: maxy= math.exp(maxy)
bovy_plot.bovy_plot([maxx],[maxy],'wx',
overplot=True,ms=10.,mew=2.)
bovy_plot.bovy_end_print(options.plotfile)
cntrThisa= numpy.reshape(cntrThisa,Z.shape)
print "vertex deviation", -0.5*math.atan(2*V[0,1]/(V[0,0]-V[1,1]))/math.pi*180.
print m
print a
print numpy.sqrt([V[0,0],V[1,1]])
print "vertex deviation", -0.5*math.atan(2*Va[0,1]/(Va[0,0]-Va[1,1]))/math.pi*180.
print ma
print aa
print numpy.sqrt([Va[0,0],Va[1,1]])
bovy_plot.bovy_print()
axScatter, axHistx,axHisty= bovy_plot.scatterplot(vs[0,:],vs[1,:],
'k,',zorder=2,
xlabel=r'$v_R / v_0$',
ylabel=r'$v_T / v_0$',
yrange=[.0,1.5],
xrange=[-1.,1.],bins=51,
onedhists=True,
retAxes=True,
cntrls='dashed',
onedhistxnormed=True,
onedhistynormed=True)
#grid.df/= numpy.sum(grid.df)*(grid.vRgrid[1]-grid.vRgrid[0])\
# *(grid.vTgrid[1]-grid.vTgrid[0])
#axScatter, axHistx,axHisty= bovy_plot.bovy_dens2d(grid.df[:,:,0].T,origin='lower',
# cmap='gist_yarg',
# contours=True,
# cntrmass=True,
# levels=special.erf(0.5*numpy.arange(1,4)),
# xlabel=r'$v_R / v_0$',
# ylabel=r'$v_T / v_0$',
# yrange=[grid.vTgrid[0],grid.vTgrid[-1]],
def plotFits(parser):
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params = []
for filename in args:
if os.path.exists(filename):
savefile = open(filename, 'rb')
if options.kmeansOut:
thisparams = pickle.load(savefile)
thisDict = {}
for kk in range(len(thisparams)):
thisDict[str(kk)] = thisparams[kk]
params.append(thisDict)
else:
params.append(pickle.load(savefile))
savefile.close()
else:
print filename + " does not exist ..."
print "Returning ..."
return
if options.plottype == 'Ag':
ys = nu.array([p['gamma']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'As':
ys = nu.array([p['s'] - 1.
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-0.9, 0.4]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$s_{' + options.band + r'}$'
if options.plottype == 'gs':
ys = nu.array([p['s'] - 1.
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['gamma'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [0., 1.2]
yrange = [-0.9, 0.4]
xlabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power-law\ index)}$'
ylabel = r'$s_{' + options.band + r'}$'
elif options.plottype == 'AA':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['logA'] / 2.)
xs.append(params[0][key]['logA'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [-9.21 / 2., 0.]
yrange = xrange
xlabel = r'$\log A_' + options.band[
0] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A_' + options.band[
1] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits = r'$\log A_' + options.band[0] + r'$'
ylabelnounits = r'$\log A_' + options.band[1] + r'$'
elif options.plottype == 'AAz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xtmp = qsos[qsoDict[key]].z
except KeyError:
continue
try:
ytmp = params[1][key]['logA'] / 2.
except KeyError:
continue
ys.append(params[0][key]['logA'] / 2 - ytmp)
xs.append(xtmp)
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
yrange = [-1., 1.]
xrange = [0., 5.5]
ylabel = r'$\log A_r/A_g\ \mathrm{(amplitudes\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gg':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['gamma'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [0., 1.15]
yrange = xrange
xlabel = r'$\gamma_' + options.band[
0] + r'\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma_' + options.band[
1] + r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits = r'$\gamma_' + options.band[0] + r'$'
ylabelnounits = r'$\gamma_' + options.band[1] + r'$'
elif options.plottype == 'Acgc':
ys = nu.array([p['gammagr']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Acg':
ys = nu.array([p['gamma']
for p in params[0].values()]).reshape(len(params[0]))
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [0., 1.25]
xlabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'AcAc':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['logAgr'] / 2.)
xs.append(params[0][key]['logAgr'] / 2.)
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [-9.21 / 2., 0.]
yrange = xrange
xlabel = r'$\log A^c_' + options.band[
0] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_' + options.band[
1] + r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabelnounits = r'$\log A^c_' + options.band[0] + r'$'
ylabelnounits = r'$\log A^c_' + options.band[1] + r'$'
elif options.plottype == 'gcgc':
ys = []
xs = []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gammagr'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(xs))
xrange = [0., 1.15]
yrange = xrange
xlabel = r'$\gamma^c_' + options.band[
0] + r'\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma^c_' + options.band[
1] + r'\ \mathrm{(power\ law\ exponent)}$'
xlabelnounits = r'$\gamma^c_' + options.band[0] + r'$'
ylabelnounits = r'$\gamma^c_' + options.band[1] + r'$'
elif options.plottype == 'AAc':
if options.type == 'powerlawSFratios':
xs = nu.array([p['logAr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
ys = nu.array([p['logAri'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
else:
xs = nu.array([p['logA'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
ys = nu.array([p['logAgr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-9.21 / 2., 0.]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'ggc':
if len(params) == 2:
xs, ys = [], []
for key in params[0].keys():
try:
ys.append(params[1][key]['gammagr'])
xs.append(params[0][key]['gamma'])
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(ys))
else:
xs = nu.array([p['gamma'] for p in params[0].values()
]).reshape(len(params[0]))
ys = nu.array([p['gammagr'] for p in params[0].values()]).reshape(
len(params[0])) / 2.
xrange = [0., 1.25]
yrange = xrange
xlabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
elif options.plottype == 'Ai':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].mags[3])
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [15.0, 21.3]
ylabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gi':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [15.0, 21.3]
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'Az':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if options.type == 'powerlawSFratios':
ys.append(params[0][key]['logAr'])
else:
ys.append(params[0][key]['logA'])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [0., 5.5]
ylabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'sz': #for KS11
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
if len(params) == 2:
try:
ys.append(params[0][key]['s'] / params[1][key]['s'])
except KeyError:
xs.pop()
continue
else:
ys.append(params[0][key]['s'])
ys = nu.array(ys) - 1.
xs = nu.array(xs)
yrange = [-1.1, 0.4]
xrange = [0., 5.5]
ylabel = r'$s_{' + options.band + r'}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'scatterz': #for KS11
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
try:
xs.append(qsos[qsoDict[key]].z)
except KeyError:
continue
ys.append(params[0][key]['logsigma'])
ys = nu.array(ys) - 1.
xs = nu.array(xs)
yrange = [-10, 0.]
xrange = [0., 5.5]
ylabel = r'$\sigma^2_{' + options.band + r'}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'ss':
ys, xs = [], []
if options.band2 is None:
for key in params[0].keys():
try:
ys.append(params[1][key]['s'][0] - 1.)
except KeyError:
continue
xs.append(params[0][key]['s'][0] - 1.)
else:
for key in params[0].keys():
try:
ys.append(params[1][key]['s' + options.band2][0] - 1.)
except KeyError:
continue
xs.append(params[0][key]['s' + options.band][0] - 1.)
xs = nu.array(xs)
ys = nu.array(ys)
xrange = [-0.9, 0.4]
yrange = [-0.9, 0.4]
if options.band2 is None:
xlabel = r'$s$'
ylabel = r'$s$'
else:
xlabel = r'$s_{' + options.band + r'}$'
ylabel = r'$s_{' + options.band2 + r'}$'
ylabelnounits = ylabel
xlabelnounits = xlabel
elif options.plottype == 'gz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gamma'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [0., 5.5]
ylabel = r'$\gamma_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Aci':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [15.0, 21.3]
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'gci':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].mags[3])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [15.0, 21.3]
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$i_0\ [\mathrm{mag}]$'
elif options.plottype == 'loglike2':
ys = []
xs = []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike'])
xs.append(params[0][key]['loglike'])
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 200.]
xrange = [0., 200.]
ylabel = r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
xlabel = r'$\log L_{\mathrm{PL}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
elif options.plottype == 'loglike3z':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
if not params[1].has_key(key): continue
ys.append(params[1][key]['loglike'] - params[0][key]['loglike'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [-15., 15.]
xrange = [0., 5.5]
ylabel = r'$\log L_{\mathrm{DRW}}\ \mathrm{in}\ ' + options.band[
0] + r' - \log L_{\mathrm{PL}}\ \mathrm{in}\ ' + options.band[
0] + r'$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'Acz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['logAgr'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys) / 2.
xs = nu.array(xs)
yrange = [-9.21 / 2., 0.]
xrange = [0., 5.5]
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'gcz':
qsos = open_qsos()
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
ys = []
xs = []
for key in params[0].keys():
ys.append(params[0][key]['gammagr'])
xs.append(qsos[qsoDict[key]].z)
ys = nu.array(ys)
xs = nu.array(xs)
yrange = [0., 1.25]
xrange = [0., 5.5]
ylabel = r'$\gamma^c_{' + options.band + r'}\ \mathrm{(power\ law\ exponent)}$'
xlabel = r'$z\ (\mathrm{redshift})$'
elif options.plottype == 'AsAc': #files: g, Ac, KS11
xs = []
ys = []
if not options.imax is None:
qsos = open_qsos()
qsos = qsos[(qsos.mags[:, 3] < options.imax)]
qsoDict = {}
ii = 0
for qso in qsos:
qsoDict[qso.oname.strip().replace(' ', '') + '.fit'] = ii
ii += 1
for key in params[0].keys():
if not key in qsoDict.keys(): continue
try:
if options.type == 'powerlawSFratios':
Ac = params[1][key]['logAri'] / 2. + 0.14
A = params[0][key]['logA'] / 2.
else:
Ac = params[1][key]['logAgr'] / 2.
A = params[0][key]['logA'] / 2.
s = params[2][key]['s']
ys.append(Ac)
xs.append(A + nu.log(1. - s))
except KeyError:
continue
xs = nu.array(xs).reshape(len(xs))
ys = nu.array(ys).reshape(len(ys))
xrange = [-9.21 / 2., 0.]
yrange = [-9.21 / 2., 0.]
xlabel = r'$\log [ (1-s ) A_{' + options.band + r'}]\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log A^c_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
elif options.plottype == 'st': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
ys /= nu.log(10.)
ys += nu.log10(365.)
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xs /= nu.log(10.)
xs += nu.log10(2.) - ys
#print nu.amin(ys), nu.amax(ys)
xrange = [-6., 0.]
yrange = [-.5, 3.5]
xlabel = r'$\log_{10} \sigma^2_{' + options.band + r'}\ \mathrm{(short-timescale\ variance)}$'
ylabel = r'$\log_{10} \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ days)}$'
elif options.plottype == 'a2l': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xrange = [-2.5, 0.]
yrange = [-10., 4.]
xlabel = r'$\log a^2_{' + options.band + r'}\ \mathrm{(variance)}$'
ylabel = r'$\log \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
elif options.plottype == 'Al': #DRW
ys = nu.array([p['logl']
for p in params[0].values()]).reshape(len(params[0]))
xs = nu.array([p['loga2']
for p in params[0].values()]).reshape(len(params[0]))
xs = (nu.log(2.) + xs + nu.log(1. - nu.exp(-1. / nu.exp(ys)))) / 2.
xrange = [-9.21 / 2., 0.]
yrange = [-10., 4.]
xlabel = r'$\log A_{' + options.band + r'}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel = r'$\log \tau_{' + options.band + r'}\ \mathrm{(damping\ timescale\ in\ yr)}$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,
ys,
'k,',
onedhists=True,
yrange=yrange,
xrange=xrange,
bins=30,
xlabel=xlabel,
ylabel=ylabel)
if options.plottype == 'AA' or options.plottype == 'gg' \
or options.plottype == 'loglike2' \
or options.plottype == 'AsAc' \
or options.plottype == 'ss' \
or options.plottype == 'AcAc':
bovy_plot.bovy_plot(nu.array(xrange),
nu.array(xrange),
'0.5',
overplot=True)
#also fit if desired
if options.linearfit:
indx= (xs > xrange[0])*(xs < xrange[1])\
*(ys > yrange[0])*(ys < yrange[1])
b = _fit_linear(xs[indx],
ys[indx],
removeoutliers=options.removeoutliers)
bovy_plot.bovy_plot(nu.array(xrange),
nu.array(xrange) + b,
'0.25',
overplot=True)
bovy_plot.bovy_text(ylabelnounits + r'$ = $' + xlabelnounits +
r'$ %4.2f$' % b,
bottom_right=True,
color='0.25')
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plotXDall(parser):
nu.random.seed(1)
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if os.path.exists(args[0]):
savefile = open(args[0], "rb")
xamp = pickle.load(savefile)
xmean = pickle.load(savefile)
xcovar = pickle.load(savefile)
savefile.close()
else:
print args[0] + " does not exist ..."
print "Returning ..."
return
if os.path.exists(args[1]):
savefile = open(args[1], "rb")
starxamp = pickle.load(savefile)
starxmean = pickle.load(savefile)
starxcovar = pickle.load(savefile)
savefile.close()
else:
print args[1] + " does not exist ..."
print "Returning ..."
return
if os.path.exists(args[2]):
savefile = open(args[2], "rb")
rrxamp = pickle.load(savefile)
rrxmean = pickle.load(savefile)
rrxcovar = pickle.load(savefile)
savefile.close()
else:
print args[2] + " does not exist ..."
print "Returning ..."
return
if options.nsamplesstar is None:
options.nsamplesstar = options.nsamples
if options.nsamplesrrlyrae is None:
options.nsamplesrrlyrae = options.nsamples
# Load XD object in xdtarget
xdt = xdtarget.xdtarget(amp=xamp, mean=xmean, covar=xcovar)
out = xdt.sample(nsample=options.nsamples)
xdt = xdtarget.xdtarget(amp=starxamp, mean=starxmean, covar=starxcovar)
starout = xdt.sample(nsample=options.nsamplesstar)
xdt = xdtarget.xdtarget(amp=rrxamp, mean=rrxmean, covar=rrxcovar)
rrout = xdt.sample(nsample=options.nsamplesrrlyrae)
# Prepare for plotting
if options.expd1:
xs = nu.exp(out[:, options.d1])
elif not options.divided1 is None:
xs = out[:, options.d1] / options.divided1
else:
xs = out[:, options.d1]
if options.expd2:
ys = nu.exp(out[:, options.d2])
elif not options.divided2 is None:
ys = out[:, options.d2] / options.divided2
else:
ys = out[:, options.d2]
if options.type == "DRW":
# plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
# Convert to logA
xs = (nu.log(2.0) + xs + nu.log(1.0 - nu.exp(-1.0 / nu.exp(ys)))) / 2.0
elif options.d1 == 1 and options.d2 == 0:
# Convert to logA
ys = (nu.log(2.0) + ys + nu.log(1.0 - nu.exp(-1.0 / nu.exp(xs)))) / 2.0
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
# stars
if options.expd1:
starxs = nu.exp(starout[:, options.d1])
elif not options.divided1 is None:
starxs = starout[:, options.d1] / options.divided1
else:
starxs = starout[:, options.d1]
if options.expd2:
starys = nu.exp(starout[:, options.d2])
elif not options.divided2 is None:
starys = starout[:, options.d2] / options.divided2
else:
starys = starout[:, options.d2]
if options.type == "DRW":
# plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
# Convert to logA
starxs = (nu.log(2.0) + starxs + nu.log(1.0 - nu.exp(-1.0 / nu.exp(starys)))) / 2.0
elif options.d1 == 1 and options.d2 == 0:
# Convert to logA
starys = (nu.log(2.0) + starys + nu.log(1.0 - nu.exp(-1.0 / nu.exp(starxs)))) / 2.0
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
# RR Lyrae
if options.expd1:
rrxs = nu.exp(rrout[:, options.d1])
elif not options.divided1 is None:
rrxs = rrout[:, options.d1] / options.divided1
else:
rrxs = rrout[:, options.d1]
if options.expd2:
rrys = nu.exp(rrout[:, options.d2])
elif not options.divided2 is None:
rrys = rrout[:, options.d2] / options.divided2
else:
rrys = rrout[:, options.d2]
if options.type == "DRW":
# plot logA, logA = 0
if options.d1 == 0 and options.d2 == 1:
# Convert to logA
rrxs = (nu.log(2.0) + rrxs + nu.log(1.0 - nu.exp(-1.0 / nu.exp(rrys)))) / 2.0
elif options.d1 == 1 and options.d2 == 0:
# Convert to logA
rrys = (nu.log(2.0) + rrys + nu.log(1.0 - nu.exp(-1.0 / nu.exp(rrxs)))) / 2.0
else:
print "d1 and d2 have to be 0 or 1 (and not the same!) ..."
print "Returning ..."
return
# Plot
xrange = [options.xmin, options.xmax]
yrange = [options.ymin, options.ymax]
data = sc.array([xs, ys]).T
bins = int(round(0.3 * sc.sqrt(options.nsamples)))
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
# Censor hist ASSUMES gamma=[0.,1.2], logA=[-9.21/2.,0.] for powerlawSF
x = nu.zeros((bins, bins))
y = nu.zeros((bins, bins))
for bb in range(bins):
x[:, bb] = nu.linspace(options.xmin, options.xmax, bins)
y[bb, :] = nu.linspace(options.ymin, options.ymax, bins)
# mask
if options.type == "powerlawSF":
hist[(y < 0.1) * (x > -3.0) * (x < -1.5)] = nu.nan
hist[(x < -3.0)] = nu.nan
hist[(x > -2.0) * (y < (0.25 * x + 0.6))] = nu.nan
onedhistyweights = nu.ones(len(ys)) / 100.0
elif options.type == "DRW":
hist[(y < (-4.223 * (x + 2) - 10))] = nu.nan
hist[(y < -4.153) * (y < (58.47 * (x + 2.1) - 10.0))] = nu.nan
hist[(y > -4.153) * (y < (2.93 * x + 2.0) - 1.0)] = nu.nan
onedhistyweights = nu.ones(len(ys)) / 2500.0
bovy_plot.bovy_print()
# First just plot contours
cdict = {
"red": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
}
allwhite = matplotlib.colors.LinearSegmentedColormap("allwhite", cdict, 256)
bovy_plot.scatterplot(
xs,
ys,
"b,",
onedhists=True,
bins=bins,
cmap=allwhite,
onedhistynormed=False,
onedhistyweights=onedhistyweights,
xrange=xrange,
yrange=yrange,
onedhistec="b",
xlabel=options.xlabel,
ylabel=options.ylabel,
)
bovy_plot.scatterplot(
starxs, starys, "k,", onedhists=True, bins=bins, cmap=allwhite, xrange=xrange, yrange=yrange, overplot=True
)
bovy_plot.scatterplot(
rrxs,
rrys,
"r,",
onedhists=True,
bins=bins,
cmap=allwhite,
onedhistec="r",
xrange=xrange,
yrange=yrange,
overplot=True,
)
hist /= nu.nansum(hist)
# Custom colormap
cdict = {
"red": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"green": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
}
my_cmap = matplotlib.colors.LinearSegmentedColormap("my_colormap", cdict, 256)
bovy_plot.scatterplot(
xs,
ys,
"b,",
onedhists=False,
contours=False,
levels=[1.01],
bins=bins,
cmap=my_cmap,
hist=hist,
edges=edges,
onedhistynormed=False,
onedhistyweights=onedhistyweights,
xrange=xrange,
yrange=yrange,
overplot=True,
)
# Stars
data = sc.array([starxs, starys]).T
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
if options.type == "powerlawSF":
hist[(x > -2.5)] = nu.nan
hist[(x < -2.5) * (y > (-0.19 * (x + 2.5)))] = nu.nan
elif options.type == "DRW":
hist[(y >= (-4.223 * (x + 2) - 10))] = nu.nan
hist /= nu.nansum(hist)
bovy_plot.scatterplot(
starxs,
starys,
"k,",
onedhists=True,
contours=False,
levels=[1.01], # HACK such that outliers aren't plotted
bins=bins,
hist=hist,
edges=edges,
xrange=xrange,
yrange=yrange,
overplot=True,
)
# RR Lyrae
data = sc.array([rrxs, rrys]).T
hist, edges = sc.histogramdd(data, bins=bins, range=[xrange, yrange])
if options.type == "powerlawSF":
hist[(x < -2.5)] = nu.nan
hist[(x > -2.5) * (y > ((x + 2.5) / 1.5) ** 9.0 * 1.15 + 0.1)] = nu.nan
# hist[(x > -2.5)*(y > (.25*x+.6))]= nu.nan
elif options.type == "DRW":
hist[(y < -4.153) * (y >= (58.47 * (x + 2.1) - 10.0)) * (y > -7.5)] = nu.nan
hist[(y < -7.5) * (x < -2.1)] = nu.nan
hist[(y > -4.153) * (y >= (2.93 * x + 2.0 - 1.5))] = nu.nan
# Custom colormap
cdict = {
"red": ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
"green": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
"blue": ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)),
}
my_cmap = matplotlib.colors.LinearSegmentedColormap("my_colormap", cdict, 256)
hist /= nu.nansum(hist)
bovy_plot.scatterplot(
rrxs,
rrys,
"r,",
onedhists=False,
contours=False,
levels=[1.01], # HACK such that outliers aren't plotted
bins=bins,
cmap=my_cmap,
hist=hist,
edges=edges,
xrange=xrange,
yrange=yrange,
overplot=True,
)
# Label
if options.type == "powerlawSF":
bovy_plot.bovy_text(-4.4, 1.15, r"$\mathrm{F/G\ stars}$", color="k")
bovy_plot.bovy_text(-4.4, 1.05, r"$\mathrm{QSOs}$", color="b")
bovy_plot.bovy_text(-4.4, 0.95, r"$\mathrm{RR\ Lyrae}$", color="r")
elif options.type == "DRW":
bovy_plot.bovy_text(-4.4, 2.88, r"$\mathrm{F/G\ stars}$", color="k")
bovy_plot.bovy_text(-4.4, 1.76, r"$\mathrm{QSOs}$", color="b")
bovy_plot.bovy_text(-4.4, 0.64, r"$\mathrm{RR\ Lyrae}$", color="r")
bovy_plot.bovy_end_print(options.plotfilename)
def singleBandSampleExample(
datafilename='../data/SDSSJ203817.37+003029.8.fits',
band='g',
nsamples=1000,
basefilename='SDSSJ203817.37+003029.8'):
"""
NAME:
singleBandSampleExample
PURPOSE:
Sample an example of a power-law structure function GP covariance
function to an SDSS quasar
INPUT:
datafilename
band - band to fit
nsamples - number of samples to use
basefilename
OUTPUT:
HISTORY:
2010-08-08 - Written - Bovy (NYU)
"""
nu.random.seed(1)
#Get data
file = fu.table_fields(datafilename)
if band == 'u':
mjd_g = nu.array(file.mjd_u) / 365.25
g = nu.array(file.u)
err_g = nu.array(file.err_u)
elif band == 'g':
mjd_g = nu.array(file.mjd_g) / 365.25
g = nu.array(file.g)
err_g = nu.array(file.err_g)
elif band == 'r':
mjd_g = nu.array(file.mjd_r) / 365.25
g = nu.array(file.r)
err_g = nu.array(file.err_r)
elif band == 'i':
mjd_g = nu.array(file.mjd_i) / 365.25
g = nu.array(file.i)
err_g = nu.array(file.err_i)
elif band == 'z':
mjd_g = nu.array(file.mjd_z) / 365.25
g = nu.array(file.z)
err_g = nu.array(file.err_z)
mjd_r = nu.array(file.mjd_r) / 365.25
r = nu.array(file.r)
err_r = nu.array(file.err_r)
mjd_i = nu.array(file.mjd_i) / 365.25
i = nu.array(file.i)
err_i = nu.array(file.err_i)
mask = (mjd_g != 0) * (g < 20.6) * (g > 19.7)
g = g[mask]
g -= nu.mean(g)
err_g = err_g[mask]
mjd_g = mjd_g[mask]
mjd_g -= nu.amin(mjd_g)
meanErr_g = nu.mean(err_g)
r = r[mask]
r -= nu.mean(r)
err_r = err_r[mask]
mjd_r = mjd_r[mask]
mjd_r -= nu.amin(mjd_r)
meanErr_r = nu.mean(err_r)
i = i[mask]
i -= nu.mean(i)
err_i = err_i[mask]
mjd_i = mjd_i[mask]
mjd_i -= nu.amin(mjd_i)
meanErr_i = nu.mean(err_i)
savefilename = basefilename + '.sav'
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
gammas = pickle.load(savefile)
if 'gr' in basefilename:
logGammas = pickle.load(savefile)
gammagrs = pickle.load(savefile)
logGammagrs = pickle.load(savefile)
else:
logAs = pickle.load(savefile)
out = pickle.load(savefile)
savefile.close()
else:
if 'gri' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listx.extend([(t, 'i') for t in mjd_i])
listy = [m for m in g]
listy.extend([m for m in r])
listy.extend([m for m in i])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise.extend([m for m in err_i])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
elif 'gr' in basefilename:
raise NotImplementedError
from powerlawSFgr import covarFunc
params = {
'logGamma': array([-7.79009776]),
'logGammagr': array([-28.0487848]),
'gamma': array([0.45918053]),
'gammagr': array([0.21333858])
}
SF = covarFunc(**params)
listx = [(t, 'g') for t in mjd_g]
listx.extend([(t, 'r') for t in mjd_r])
listy = [m for m in g]
listy.extend([m for m in r])
listy = nu.array(listy)
noise = [m for m in err_g]
noise.extend([m for m in err_r])
noise = nu.array(noise)
trainSet = trainingSet(listx=listx, listy=listy, noise=noise)
else:
from gp.powerlawSF import covarFunc
trainSet = trainingSet(listx=mjd_g, listy=g, noise=err_g)
params = [0., 0.]
SF = covarFunc(gamma=.2, A=0.000524) #Power
#SF= covarFunc(a=.0001,l=.001) #OU
#Train
print "Training ..."
outcovarFunc = trainGP(trainSet, SF, useDerivs=False)
print "Best-fit:"
print outcovarFunc._dict
print "Sampling ..."
out = sampleGP(trainSet,
outcovarFunc,
nsamples=nsamples,
step=[0.2 for ii in range(len(params))])
gammas = nu.array([o._dict['gamma'] for o in out]).reshape(len(out))
if 'gr' in basefilename:
gammagrs = nu.array([o._dict['gammagr']
for o in out]).reshape(len(out))
logGammas = nu.array([o._dict['logGamma']
for o in out]).reshape(len(out))
logGammagrs = nu.array([o._dict['logGammagr']
for o in out]).reshape(len(out))
else:
logAs = nu.array([o._dict['logA'] for o in out]).reshape(len(out))
print nu.mean(logAs), nu.sqrt(nu.var(logAs))
print nu.mean(gammas), nu.sqrt(nu.var(gammas))
#Save
savefile = open(savefilename, 'wb')
pickle.dump(gammas, savefile)
if 'gr' in basefilename:
pickle.dump(logGammas, savefile)
pickle.dump(gammagrs, savefile)
pickle.dump(logGammagrs, savefile)
else:
pickle.dump(logAs, savefile)
pickle.dump(out, savefile)
savefile.close()
#if not 'gr' in basefilename:
# logGammas= logAs/gammas
#Plot
bovy_plot.bovy_print()
bovy_plot.scatterplot(logAs / 2.,
gammas,
'k,',
ylabel=r'$\gamma$',
xlabel=r'\log A',
xrange=[-9.21 / 2., 0.],
yrange=[0., 1.25],
bins=50,
onedhists=True)
bovy_plot.bovy_end_print(basefilename + '_sample_2d.png')
def plot_rovo(filename,plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile= open(filename,'rb')
params= pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options= plot_pdfs.set_options(None)
nros= 15
noos= 15
ros= numpy.linspace(7.,13.,nros)
oos= numpy.linspace(20.,30.,noos)
ll= numpy.zeros((noos,nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss= []
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
theseparamss.append(theseparams)
thisll= multi.parallel_map((lambda x: numpy.sum(logl.logl(init=theseparamss[x],options=options))),
range(nros),
numcores=numpy.amin([nros,_MULTI,multiprocessing.cpu_count()]))
ll[ii,:]= thisll
else:
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
ll[ii,jj]= numpy.sum(logl.logl(init=theseparams,
options=options))
#Normalize
ll-= logsumexp(ll)
ll= numpy.exp(ll)
levels= list(special.erf(0.5*numpy.arange(1,4)))
bovy_plot.bovy_dens2d(ll.T,origin='lower',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20.,35.],
yrange=[7.,13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos= numpy.array([s[0] for s in params])*_REFV0
ros= numpy.array([s[1] for s in params])*_REFR0
bovy_plot.bovy_print()
levels= list(special.erf(0.5*numpy.arange(1,4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(vos/ros,ros,'k,',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200./8.,250./8.],
yrange=[7.,9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
def plotMass(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=True)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Savefile
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
mass= pickle.load(savefile)
ii= pickle.load(savefile)
jj= pickle.load(savefile)
savefile.close()
else:
mass= []
ii, jj= 0, 0
#parameters
if os.path.exists(args[1]):#Load initial
savefile= open(args[1],'rb')
fits= pickle.load(savefile)
savefile.close()
else:
print "Error: must provide parameters of best fits"
print "Returning ..."
return None
#Mass uncertainties are in savefile3
if len(args) > 2 and os.path.exists(args[2]):
savefile= open(args[2],'rb')
masssamples= pickle.load(savefile)
savefile.close()
masserrors= True
else:
masssamples= None
masserrors= False
if len(args) > 3 and os.path.exists(args[3]): #Load savefile
savefile= open(args[3],'rb')
denssamples= pickle.load(savefile)
savefile.close()
denserrors= True
else:
denssamples= None
denserrors= False
if len(args) > 4 and os.path.exists(args[4]):#Load savefile
savefile= open(args[4],'rb')
velfits= pickle.load(savefile)
savefile.close()
velfitsLoaded= True
else:
velfitsLoaded= False
if len(args) > 5 and os.path.exists(args[5]):
savefile= open(args[5],'rb')
velsamples= pickle.load(savefile)
savefile.close()
velerrors= True
else:
velsamples= None
velerrors= False
#Now plot
#Run through the pixels and gather
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
if denserrors: errors= []
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if afeindx+fehindx*binned.npixafe() >= len(fits):
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
thismass= mass[afeindx+fehindx*binned.npixafe()]
if masserrors:
thismasssamples= masssamples[afeindx+fehindx*binned.npixafe()]
else:
thismasssamples= None
thisfit= fits[afeindx+fehindx*binned.npixafe()]
if denserrors:
thisdenssamples= denssamples[afeindx+fehindx*binned.npixafe()]
else:
thisdenssamples= None
if velfitsLoaded:
thisvelfit= velfits[afeindx+fehindx*binned.npixafe()]
if velerrors:
thisvelsamples= velsamples[afeindx+fehindx*binned.npixafe()]
if thisfit is None:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if len(data) < options.minndata:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if options.type == 'mass':
if not options.distzmin is None or not options.distzmax is None:
if options.distzmin is None and not options.distzmax is None:
thismass*= (1.-numpy.exp(-options.distzmax/numpy.exp(thisfit[0])/1000.))
elif not options.distzmin is None and options.distzmax is None:
thismass*= numpy.exp(-options.distzmin/numpy.exp(thisfit[0])/1000.)
else:
thismass*= (numpy.exp(-options.distzmin/numpy.exp(thisfit[0])/1000.)-numpy.exp(-options.distzmax/numpy.exp(thisfit[0])/1000.))
if options.logmass:
plotthis[ii,jj]= numpy.log10(thismass/10**6.)
else:
plotthis[ii,jj]= thismass/10**6.
elif options.type == 'nstars':
if options.logmass:
plotthis[ii,jj]= numpy.log10(len(data))
else:
plotthis[ii,jj]= len(data)
elif options.model.lower() == 'hwr':
if options.type == 'hz':
plotthis[ii,jj]= numpy.exp(thisfit[0])*1000.
elif options.type == 'hr':
plotthis[ii,jj]= numpy.exp(thisfit[1])
elif options.type.lower() == 'afe' \
or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis.append([tightbinned.feh(ii),
tightbinned.afe(jj),
numpy.exp(thisfit[0])*1000.,
numpy.exp(thisfit[1]),
len(data),
thismass/10.**6.,
thismasssamples])
if denserrors:
theseerrors= []
thesesamples= denssamples[afeindx+fehindx*binned.npixafe()]
if options.model.lower() == 'hwr':
for kk in [0,1]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(0.5*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
errors.append(theseerrors)
if velfitsLoaded:
if options.velmodel.lower() == 'hwr':
plotthis[-1].extend([numpy.exp(thisvelfit[1]),
numpy.exp(thisvelfit[4]),
thisvelfit[2],
thisvelfit[3]])
if velerrors:
theseerrors= []
thesesamples= velsamples[afeindx+fehindx*binned.npixafe()]
for kk in [1,4]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(0.5*(numpy.exp(numpy.mean(xs))-numpy.exp(numpy.mean(xs)-numpy.std(xs))-numpy.exp(numpy.mean(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
xs= numpy.array([s[4] for s in thesesamples])
theseerrors.append(0.5*(numpy.exp(-numpy.mean(xs))-numpy.exp(numpy.mean(-xs)-numpy.std(-xs))-numpy.exp(numpy.mean(-xs))+numpy.exp(numpy.mean(-xs)+numpy.std(-xs))))
errors[-1].extend(theseerrors)
#Set up plot
#print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
if options.type == 'mass':
if options.logmass:
vmin, vmax= numpy.log10(0.01), numpy.log10(2.)
zlabel=r'$\log_{10} \Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
else:
vmin, vmax= 0.,1.
zlabel=r'$\Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if not options.distzmin is None or not options.distzmax is None:
vmin, vmax= None, None
title=r'$\mathrm{mass\ weighted}$'
elif options.type == 'nstars':
if options.logmass:
vmin, vmax= 2., 3.
zlabel=r'$\log_{10} \mathrm{raw\ number\ of\ G}$-$\mathrm{type\ dwarfs}$'
else:
vmin, vmax= 100.,1000.
zlabel=r'$\mathrm{raw\ number\ of\ G}$-$\mathrm{type\ dwarfs}$'
title= r'$\mathrm{raw\ sample\ counts}$'
elif options.type == 'afe':
vmin, vmax= 0.0,.5
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.5,0.
zlabel=r'$[\mathrm{Fe/H}]$'
elif options.type == 'fehafe':
vmin, vmax= -.7,.7
zlabel=r'$[\mathrm{Fe/H}]-[\mathrm{Fe/H}]_{1/2}([\alpha/\mathrm{Fe}])$'
elif options.type == 'afefeh':
vmin, vmax= -.15,.15
zlabel=r'$[\alpha/\mathrm{Fe}]-[\alpha/\mathrm{Fe}]_{1/2}([\mathrm{Fe/H}])$'
if options.tighten:
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
else:
xrange=[-2.,0.6]
yrange=[-0.1,0.6]
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
#Gather hR and hz
sz_err, sz, hz_err, hr_err, mass_err, mass, hz, hr,afe, feh, ndata= [], [], [], [], [], [], [], [], [], [], []
for ii in range(len(plotthis)):
if denserrors:
hz_err.append(errors[ii][0]*1000.)
hr_err.append(errors[ii][1])
mass.append(plotthis[ii][5])
hz.append(plotthis[ii][2])
hr.append(plotthis[ii][3])
afe.append(plotthis[ii][1])
feh.append(plotthis[ii][0])
ndata.append(plotthis[ii][4])
if velfitsLoaded:
sz.append(plotthis[ii][7])
if velerrors:
sz_err.append(errors[ii][2])
if masserrors:
mass_err.append(numpy.std(numpy.array(plotthis[ii][6])/10.**6.))
"""
if options.logmass:
mass_err.append(numpy.std(numpy.log10(numpy.array(plotthis[ii][6])/10.**6.)))
else:
mass_err.append(numpy.std(numpy.array(plotthis[ii][6])/10.**6.))
"""
if denserrors:
hz_err= numpy.array(hz_err)
hr_err= numpy.array(hr_err)
if velfitsLoaded:
sz= numpy.array(sz)
if velerrors:
sz_err= numpy.array(sz_err)
mass= numpy.array(mass)
if masserrors:
mass_err= numpy.array(mass_err)
hz= numpy.array(hz)
hr= numpy.array(hr)
afe= numpy.array(afe)
feh= numpy.array(feh)
ndata= numpy.array(ndata)
#Process ndata
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
#ndata= numpy.log(ndata)/numpy.log(numpy.median(ndata))
#ndata= (ndata-numpy.amin(ndata))/(numpy.amax(ndata)-numpy.amin(ndata))*25+12.
if options.type.lower() == 'afe':
plotc= afe
elif options.type.lower() == 'feh':
plotc= feh
elif options.type.lower() == 'afefeh':
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(afe))
for ii in range(tightbinned.npixfeh()):
fehbin= ii
data= tightbinned.data[(tightbinned.data.feh > tightbinned.fehedges[fehbin])\
*(tightbinned.data.feh <= tightbinned.fehedges[fehbin+1])]
medianafe= numpy.median(data.afe)
for jj in range(len(afe)):
if feh[jj] == tightbinned.feh(ii):
plotc[jj]= afe[jj]-medianafe
else:
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(feh))
for ii in range(tightbinned.npixafe()):
afebin= ii
data= tightbinned.data[(tightbinned.data.afe > tightbinned.afeedges[afebin])\
*(tightbinned.data.afe <= tightbinned.afeedges[afebin+1])]
medianfeh= numpy.median(data.feh)
for jj in range(len(feh)):
if afe[jj] == tightbinned.afe(ii):
plotc[jj]= feh[jj]-medianfeh
xrange= [150,1200]
if options.cumul:
#Print total surface mass and uncertainty
totmass= numpy.sum(mass)
if options.ploterrors:
toterr= numpy.sqrt(numpy.sum(mass_err**2.))
else:
toterr= 0.
print "Total surface-mass density: %4.1f +/- %4.2f" %(totmass,toterr)
ids= numpy.argsort(hz)
plotc= plotc[ids]
ndata= ndata[ids]
mass= mass[ids]
mass= numpy.cumsum(mass)
hz.sort()
ylabel=r'$\mathrm{cumulative}\ \Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if options.logmass:
yrange= [0.01,30.]
else:
yrange= [-0.1,30.]
else:
if options.logmass:
yrange= [0.005*0.13/.07,10.*.13/.07]
else:
yrange= [-0.1,10.*.13/.07]
ylabel=r'$\Sigma_{R_0}(h_z)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if not options.vstructure and not options.hzhr:
if options.hr:
ploth= hr
plotherr= hr_err
xlabel=r'$\mathrm{radial\ scale\ length\ [kpc]}$'
xrange= [1.2,4.]
bins= 11
elif options.sz:
ploth= sz**2.
plotherr= 2.*sz_err*sz
xlabel=r'$\sigma_z^2\ \mathrm{[km}^2\ \mathrm{s}^{-2}]$'
xrange= [12.**2.,50.**2.]
ylabel=r'$\Sigma_{R_0}(\sigma^2_z)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
bins= 9
else:
ploth= hz
plotherr= hz_err
xlabel=r'$\mathrm{vertical\ scale\ height}\ h_z\ \mathrm{[pc]}$'
xrange= [165,1200]
bins= 12
bovy_plot.bovy_plot(ploth,mass*.13/0.07,
s=ndata,c=plotc,
cmap='jet',
xlabel=xlabel,
ylabel=ylabel,
clabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
scatter=True,edgecolors='none',
colorbar=True,zorder=2,
semilogy=options.logmass)
if not options.cumul and masserrors and options.ploterrors:
colormap = cm.jet
for ii in range(len(hz)):
pyplot.errorbar(ploth[ii],mass[ii]*.13/0.07,
yerr=mass_err[ii]*.13/0.07,
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))),
elinewidth=1.,capsize=3,zorder=0)
if not options.cumul and denserrors and options.ploterrors:
colormap = cm.jet
for ii in range(len(hz)):
pyplot.errorbar(ploth[ii],mass[ii]*.13/0.07,xerr=plotherr[ii],
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))),
elinewidth=1.,capsize=3,zorder=0)
#Add binsize label
bovy_plot.bovy_text(r'$\mathrm{points\ use}\ \Delta [\mathrm{Fe/H}] = 0.1,$'+'\n'+r'$\Delta [\alpha/\mathrm{Fe}] = 0.05\ \mathrm{bins}$',
bottom_left=True)
#Overplot histogram
#ax2 = pyplot.twinx()
pyplot.hist(ploth,range=xrange,weights=mass*0.13/0.07,color='k',histtype='step',
bins=bins,lw=3.,zorder=10)
#Also XD?
if options.xd:
#Set up data
ydata= numpy.zeros((len(hz),1))
if options.sz:
ydata[:,0]= numpy.log(sz)
else:
ydata[:,0]= numpy.log(hz)
ycovar= numpy.zeros((len(hz),1))
if options.sz:
ycovar[:,0]= sz_err**2./sz**2.
else:
ycovar[:,0]= hz_err**2./hz**2.
#Set up initial conditions
xamp= numpy.ones(options.k)/float(options.k)
xmean= numpy.zeros((options.k,1))
for kk in range(options.k):
xmean[kk,:]= numpy.mean(ydata,axis=0)\
+numpy.random.normal()*numpy.std(ydata,axis=0)
xcovar= numpy.zeros((options.k,1,1))
for kk in range(options.k):
xcovar[kk,:,:]= numpy.cov(ydata.T)
#Run XD
print extreme_deconvolution(ydata,ycovar,xamp,xmean,xcovar,
weight=mass)*len(hz)
print xamp, xmean, xcovar
#Plot
xs= numpy.linspace(xrange[0],xrange[1],1001)
xdys= numpy.zeros(len(xs))
for kk in range(options.k):
xdys+= xamp[kk]/numpy.sqrt(2.*numpy.pi*xcovar[kk,0,0])\
*numpy.exp(-0.5*(numpy.log(xs)-xmean[kk,0])**2./xcovar[kk,0,0])
xdys/= xs
bovy_plot.bovy_plot(xs,xdys,'-',color='0.5',overplot=True)
# ax2.set_yscale('log')
# ax2.set_yticklabels('')
# if options.hr:
# pyplot.ylim(10**-2.,10.**0.)
# if options.sz:
# pyplot.ylim(10**-5.,10.**-2.5)
# else:
# pyplot.ylim(10**-5.5,10.**-1.5)
# pyplot.xlim(xrange[0],xrange[1])
ax= pyplot.gca()
if options.sz:
def my_formatter(x, pos):
"""s^2"""
xs= int(round(math.sqrt(x)))
return r'$%i^2$' % xs
major_formatter = FuncFormatter(my_formatter)
ax.xaxis.set_major_formatter(major_formatter)
xstep= ax.xaxis.get_majorticklocs()
xstep= xstep[1]-xstep[0]
ax.xaxis.set_minor_locator(MultipleLocator(xstep/5.))
elif options.hzhr:
#Make density plot in hR and hz
bovy_plot.scatterplot(hr,hz,'k,',
levels=[1.01],#HACK such that outliers aren't plotted
cmap='gist_yarg',
bins=11,
xrange=[1.,7.],
yrange=[150.,1200.],
ylabel=r'$\mathrm{vertical\ scale\ height\ [pc]}$',
xlabel=r'$\mathrm{radial\ scale\ length\ [kpc]}$',
onedhists=False,
weights=mass)
else:
#Make an illustrative plot of the vertical structure
nzs= 1001
zs= numpy.linspace(200.,3000.,nzs)
total= numpy.zeros(nzs)
for ii in range(len(hz)):
total+= mass[ii]/2./hz[ii]*numpy.exp(-zs/hz[ii])
bovy_plot.bovy_plot(zs,total,color='k',ls='-',lw=3.,
semilogy=True,
xrange=[0.,3200.],
yrange=[0.000001,0.02],
xlabel=r'$\mathrm{vertical\ height}\ Z$',
ylabel=r'$\rho_*(R=R_0,Z)\ [\mathrm{M}_\odot\ \mathrm{pc}^{-3}]$',
zorder=10)
if options.vbinned:
#Bin
mhist, edges= numpy.histogram(hz,range=xrange,
weights=mass,bins=10)
stotal= numpy.zeros(nzs)
for ii in range(len(mhist)):
hz= (edges[ii+1]+edges[ii])/2.
if options.vcumul:
if ii == 0.:
pstotal= numpy.zeros(nzs)+0.0000001
else:
pstotal= copy.copy(stotal)
stotal+= mhist[ii]/2./hz*numpy.exp(-zs/hz)
pyplot.fill_between(zs,stotal,pstotal,
color='%.6f' % (0.25+0.5/(len(mhist)-1)*ii))
else:
bovy_plot.bovy_plot([zs[0],zs[-1]],
1.*numpy.array([mhist[ii]/2./hz*numpy.exp(-zs[0]/hz),
mhist[ii]/2./hz*numpy.exp(-zs[-1]/hz)]),
color='0.5',ls='-',overplot=True,
zorder=0)
else:
colormap = cm.jet
for ii in range(len(hz)):
bovy_plot.bovy_plot([zs[0],zs[-1]],
100.*numpy.array([mass[ii]/2./hz[ii]*numpy.exp(-zs[0]/hz[ii]),
mass[ii]/2./hz[ii]*numpy.exp(-zs[-1]/hz[ii])]),
ls='-',overplot=True,alpha=0.5,
zorder=0,
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))))
else:
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='gist_yarg',
interpolation='nearest',
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
onedhists=True,
contours=False)
bovy_plot.bovy_text(title,top_right=True,fontsize=16)
if not options.distzmin is None or not options.distzmax is None:
if options.distzmin is None:
distlabel= r'$|Z| < %i\ \mathrm{pc}$' % int(options.distzmax)
elif options.distzmax is None:
distlabel= r'$|Z| > %i\ \mathrm{pc}$' % int(options.distzmin)
else:
distlabel= r'$%i < |Z| < %i\ \mathrm{pc}$' % (int(options.distzmin),int(options.distzmax))
bovy_plot.bovy_text(distlabel,bottom_left=True,fontsize=16)
bovy_plot.bovy_end_print(options.plotfile)
return None
def compareMagFluxFits(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
params= []
for filename in args:
if os.path.exists(filename):
savefile= open(filename,'rb')
params.append(pickle.load(savefile))
savefile.close()
else:
print filename+" does not exist ..."
print "Returning ..."
return
if options.plottype == 'AA':
ys= []
xs= []
for key in params[1].keys():
try:
ys.append(params[0][key]['logA']/2.)
xs.append(params[1][key]['logA']/2.)
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
ys= xs-ys-nu.log(nu.log(10.)/2.5)
xrange=[-9.21/2.,0.]
yrange=[-.25,.25]
xlabel= r'$\log A^{\mathrm{flux}}_'+options.band+r'\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log A^{\mathrm{flux}}_'+options.band+r'-\log A^{\mathrm{mag}}_'+options.band+r'- \log\left(\frac{\log 10}{2.5}\right)$'
elif options.plottype == 'gg':
ys= []
xs= []
for key in params[1].keys():
try:
ys.append(params[0][key]['gamma'])
xs.append(params[1][key]['gamma'])
except KeyError:
continue
xs= nu.array(xs).reshape(len(xs))
ys= nu.array(ys).reshape(len(xs))
print len(xs)
ys= xs-ys
xrange=[0.,1.2]
yrange=[-.25,.25]
xlabel= r'$\gamma^{\mathrm{flux}}_'+options.band+r'\ \mathrm{(power-law\ exponent)}$'
ylabel= r'$\gamma^{\mathrm{flux}}_'+options.band+r'- \gamma^{\mathrm{mag}}_'+options.band+'$'
elif options.plottype == 'loglike2':
ys= []
xs= []
nepochs= []
cnt= 0
for key in params[1].keys():
#cnt+= 1
#print cnt
#if cnt > 10: break
#Get the number of epochs
v= VarQso(os.path.join('../data/s82qsos/',key))
try:
ys.append(params[0][key]['loglike']
-v.nepochs(options.band)*nu.log(nu.log(10.)/2.5))
nepochs.append(v.nepochs(options.band))
xs.append(params[1][key]['loglike'])
except KeyError:
continue
xs= -nu.array(xs).reshape(len(xs))
ys= -nu.array(ys).reshape(len(xs))
nepochs= nu.array(nepochs).reshape(len(nepochs))
ys/= nepochs
xs/= nepochs
ys= xs-ys
xrange=[-3.,0.]
yrange=[-.1,.1]
xlabel= r'$\log \mathcal{L}^{\mathrm{flux}}_{'+options.band+r',\mathrm{red}}\ \mathrm{(amplitude\ at\ 1\ yr)}$'
ylabel= r'$\log \mathcal{L}^{\mathrm{flux}}_{'+options.band+r',\mathrm{red}}- \log \mathcal{L}^{\mathrm{mag}}_{'+options.band+',\mathrm{red}}'+r'- \log\left(\frac{\log 10}{2.5}\right)$'
bovy_plot.bovy_print()
bovy_plot.scatterplot(xs,ys,'k,',onedhists=True,
yrange=yrange,
xrange=xrange,bins=31,
xlabel=xlabel,
ylabel=ylabel)
bovy_plot.bovy_plot(nu.array(xrange),[0.,0.],'0.5',
overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
