def plot(self):
super(ShearCorrelationMinusPlot, self).plot()
nbin = 0
for i in range(1, 100):
filename = self.file_path("shear_xi_minus",
"bin_{0}_{0}".format(i))
if os.path.exists(filename):
nbin += 1
else:
break
if nbin == 0:
IOError("No data for plot: %s" % self.__class__.__name__[:-4])
theta = self.load_file("shear_xi_minus", "theta")
sz = 1.0 / (nbin + 2)
for i in range(1, nbin + 1):
for j in range(1, i + 1):
rect = (i * sz, j * sz, sz, sz)
self.figure.add_axes(rect)
#pylab.ploy()
#pylab.subplot(nbin, nbin, (nbin*nbin)-nbin*(j-1)+i)
xi = self.load_file("shear_xi_minus",
"bin_{0}_{1}".format(i, j))
pylab.loglog(theta, xi)
pylab.xlim(1e-4, 1e-1)
pylab.ylim(2e-7, 1e-3)
if i == 1 and j == 1:
pylab.xlabel("$\\theta$")
pylab.ylabel("$\\xi_+(\\theta)$")
else:
pylab.gca().xaxis.set_ticklabels([])
pylab.gca().yaxis.set_ticklabels([])
pylab.gca().tick_params(length=0.0, which='minor')
pylab.gca().tick_params(length=3.0, which='major')
pylab.gca().tick_params(labelsize=10)
pylab.text(1.5e-3,
1.8e-4,
"(%d,%d)" % (i, j),
fontsize=8,
color='red')
pylab.grid()
def plot_section(self, section):
nbin = 0
for i in range(1, 100):
filename = self.file_path(section, "bin_{0}_{0}".format(i))
if os.path.exists(filename):
nbin += 1
else:
break
if nbin == 0:
IOError("No data for plot: %s" % self.__class__.__name__[:-4])
ell = self.load_file(section, "ell")
sz = 1.0 / (nbin + 2)
for i in range(1, nbin + 1):
for j in range(1, i + 1):
rect = (i * sz, j * sz, sz, sz)
self.figure.add_axes(rect)
#pylab.ploy()
#pylab.subplot(nbin, nbin, (nbin*nbin)-nbin*(j-1)+i)
cl = self.load_file(section, "bin_{0}_{1}".format(i, j))
if all(cl <= 0):
cl *= -1
pylab.loglog(ell, ell * (ell + 1.) * cl / 2 / np.pi)
pylab.ylim(*self.ylim)
if i == 1 and j == 1:
pylab.xlabel("$\\ell$")
pylab.ylabel("$\\ell (\\ell+1) C_\\ell / 2 \\pi$")
else:
pylab.gca().xaxis.set_ticklabels([])
pylab.gca().yaxis.set_ticklabels([])
pylab.gca().tick_params(length=0.0, which='minor')
pylab.gca().tick_params(length=3.0, which='major')
pylab.gca().tick_params(labelsize=10)
if section == "shear_cl":
pylab.text(1.5 * ell.min(),
1.8e-4,
"(%d,%d)" % (i, j),
fontsize=8,
color='red')
pylab.grid()
def run(self):
#Get the columns we need, in reduced form
#since this is not MCMC
weight = self.weight_col()
weight = weight / weight.max()
#Sort the final 500 samples, since they
#are otherwise un-ordered
weight[-500:] = np.sort(weight[-500:])
#x-axis is just the iteration number
x = np.arange(weight.size)
#Choose a filename and make a figure for your plot.
figure, filename = self.figure("nest_weight")
#Do the plotting, and set the limits and labels
pylab.plot(x, weight / weight.max())
#pylab.xlim(-353, -341.5)
pylab.xlim(0, 8000)
pylab.xlabel("Iteration number $i$")
pylab.ylabel(r"$p_i$")
#Add some helpful text, on the title and the plot itself
pylab.title("Normalised posterior weight $p_i$", size='medium')
pylab.text(500,
0.4,
"Increasing likelihood,\n volume still large",
size="small")
pylab.text(5500, 0.65, "Volume\ndecreasing", size='small')
pylab.text(6500, 0.12, "Final $n_\mathrm{live}$\npoints", size='small')
#Return the list of filenames we made
return [filename]