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 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 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)