def plot(self,var='kappa',output=None):
plt.clf()
# Panel 1: Galaxy positions:
ax1 = plt.subplot(3,3,(1,4), aspect ='equal')
self.plotFieldOfView('light',ax1)
# Panel 2: Halo mass distributions:
ax2 = plt.subplot(3,3,(2,5), aspect ='equal')
self.plotFieldOfView('mass',ax2)
# Panel 3: Kappa contributions:
ax3 = plt.subplot(3,3,(3,6), aspect ='equal')
self.plotFieldOfView(var,ax3)
# Lower panel: View along redshift axis
ax4 = plt.subplot(3,3,(7,9))
self.plotLineOfSight(var,ax4)
if output != None:
pangloss.rm(output)
plt.savefig(output,dpi=300)
return None
def plot(self, output=None):
plt.clf()
# Panel 1: Galaxy positions:
ax1 = plt.subplot(3, 3, (1, 4), aspect="equal")
self.plotFieldOfView("light", ax1)
# Panel 2: Halo mass distributions:
ax2 = plt.subplot(3, 3, (2, 5), aspect="equal")
self.plotFieldOfView("mass", ax2)
# Panel 3: Kappa contributions:
ax3 = plt.subplot(3, 3, (3, 6), aspect="equal")
self.plotFieldOfView("kappa", ax3)
# Lower panel: View along redshift axis
ax4 = plt.subplot(3, 3, (7, 9))
self.plotLineOfSight("kappa", ax4)
if output != None:
pangloss.rm(output)
plt.savefig(output, dpi=300)
return None
def plotContributions(self,quantity,output):
plt.clf()
# Point positions:
zmax = self.zs+0.1
z = numpy.linspace(0.0,zmax,100)
# Plot the points:
if quantity == 'mass':
contr = numpy.zeros(len(z))
for i in range(len(z)):
galaxies = self.galaxies.where(self.galaxies.z <= z[i])
size = galaxies.Mhalo_obs
contr[i] = numpy.sum(size)
plt.plot(z, contr)
plt.title('Cumulative Sum of Halo Mass')
elif quantity == 'kappa':
contr = numpy.zeros(len(z))
for i in range(len(z)):
galaxies = self.galaxies.where(self.galaxies.z <= z[i])
size = galaxies.kappa
contr[i] = numpy.sum(size)
plt.plot(z, contr)
plt.title(r'Cumulative Sum of $\kappa_h$')
elif quantity == 'mu':
contr = numpy.zeros(len(z))
for i in range(len(z)):
galaxies = self.galaxies.where(self.galaxies.z <= z[i])
size = galaxies.mu
contr[i] = numpy.sum(size)
plt.plot(z, contr)
plt.title(r'Cumulative Sum of $\mu_h$')
elif quantity == 'stellarmass':
contr = numpy.zeros(len(z))
for i in range(len(z)):
galaxies = self.galaxies.where(self.galaxies.z <= z[i])
size = galaxies.Mstar_obs
contr[i] = numpy.sum(size)
plt.plot(z, contr)
plt.title('Cumulative Sum of Stellar Mass')
else:
raise "Lightcone plotting error: unknown quantity "+quantity
# Axis limits:
zmax = max(self.galaxies.z.max(),self.zs+0.1)
# Labels:
plt.xlabel('redshift z')
if output != None:
pangloss.rm(output)
plt.savefig(output,dpi=300)
return
def plot(self,key1,key2=None,weight="weight",output=None,bins=None,title=None):
import pylab as plt
assert key1 in self.parameters, self.errmsg()
if key2!=None:
assert key2 in self.parameters, self.errmsg()
if key2=="weight":
print "This code automatically uses the weights to form a histogram, you can state this explicitly using weightkey=weight if you have several weight columns"
key2==None
reformatnames={}
reformatnames["mu"]="$\mu$"
reformatnames["mu_cone"]="$\mu_{lc}$"
reformatnames["kappa_cone"]="$\kappa_{lc}$"
reformatnames["mu_tot"]="$\mu_{\mathrm{tot}}$"
reformatnames["kappa_tot"]="$\kappa_{\mathrm{tot}}$"
reformatnames["kappa_ext"]="$\kappa_{\mathrm{ext}}$"
reformatnames["Kappah_median"]="$\widetilde{\kappa}_{\mathrm{halos}}$"
key1name=key1[:]
if key1name in reformatnames.keys():
key1name = reformatnames[key1name]
if key2 != None:
key2name=key2[:]
if key2name in reformatnames.keys():
key2name = reformatnames[key2name]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 1D histogram:
if key2 == None:
par1 = self.getParameter(key1)
# Check to see if samples are weighted:
if weight!=None and weight in self.parameters:
weights = self.getParameter(weight)
# print "pdf: plotting weighted histogram..."
else:
weights = numpy.ones(len(par1))*1.0
# print "pdf: plotting unweighted histogram..."
weights /= numpy.sum(weights)
if bins==None:
nb=len(par1)*0.05
if nb<20: nb=20
bins=numpy.linspace(par1.min(),par1.max(),20)
plt.figure()
plt.hist(par1,weights=weights,bins=bins)
plt.xlabel(key1name)
plt.ylabel("P(%s|$\mathcal{D}$)"%key1name)
if title != None: plt.title(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 2D scatter plot:
elif key2 != None:
assert weight==None, "I don't know about weighted 2D plots yet."
# print "pdf: plotting a 2D scatter plot ..."
plt.figure()
plt.scatter(self.getParameter(key1),self.getParameter(key2),\
c='k',s=2, edgecolors=None)
plt.xlabel(key1name)
plt.ylabel(key2name)
if title != None: plt.title(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if output == None:
plt.show(block=True)
else:
pangloss.rm(output)
plt.savefig(output,dpi=300)
return None
def plot(self,
key1,
key2=None,
weight="weight",
output=None,
bins=None,
title=None):
import pylab as plt
assert key1 in self.parameters, self.errmsg()
if key2 != None:
assert key2 in self.parameters, self.errmsg()
if key2 == "weight":
print "This code automatically uses the weights to form a histogram, you can state this explicitly using weightkey=weight if you have several weight columns"
key2 == None
reformatnames = {}
reformatnames["mu"] = "$\mu$"
reformatnames["mu_cone"] = "$\mu_{lc}$"
reformatnames["kappa_cone"] = "$\kappa_{lc}$"
reformatnames["mu_tot"] = "$\mu_{\mathrm{tot}}$"
reformatnames["kappa_tot"] = "$\kappa_{\mathrm{tot}}$"
reformatnames["kappa_ext"] = "$\kappa_{\mathrm{ext}}$"
reformatnames[
"Kappah_median"] = "$\widetilde{\kappa}_{\mathrm{halos}}$"
key1name = key1[:]
if key1name in reformatnames.keys():
key1name = reformatnames[key1name]
if key2 != None:
key2name = key2[:]
if key2name in reformatnames.keys():
key2name = reformatnames[key2name]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 1D histogram:
if key2 == None:
par1 = self.getParameter(key1)
# Check to see if samples are weighted:
if weight != None and weight in self.parameters:
weights = self.getParameter(weight)
# print "pdf: plotting weighted histogram..."
else:
weights = numpy.ones(len(par1)) * 1.0
# print "pdf: plotting unweighted histogram..."
weights /= numpy.sum(weights)
if bins == None:
nb = len(par1) * 0.05
if nb < 20: nb = 20
bins = numpy.linspace(par1.min(), par1.max(), 20)
plt.figure()
plt.hist(par1, weights=weights, bins=bins)
plt.xlabel(key1name)
plt.ylabel("P(%s|$\mathcal{D}$)" % key1name)
if title != None: plt.title(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 2D scatter plot:
elif key2 != None:
assert weight == None, "I don't know about weighted 2D plots yet."
# print "pdf: plotting a 2D scatter plot ..."
plt.figure()
plt.scatter(self.getParameter(key1),self.getParameter(key2),\
c='k',s=2, edgecolors=None)
plt.xlabel(key1name)
plt.ylabel(key2name)
if title != None: plt.title(title)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if output == None:
plt.show(block=True)
else:
pangloss.rm(output)
plt.savefig(output, dpi=300)
return None
