def plot_scalCovCls(self):
"""
Plots and saves all the scalar covariance Cls in a unique image
"""
# number of Cls:
num = int(math.sqrt(self.scalCovCls.shape[1] - 1))
# l values:
xvalues = self.scalCovCls[:, 0]
# set up the plots:
plots = CMB_plots()
plots.color = self.color
fig = plt.gcf()
# setup a dictionary with the names of the Cls
dict = {1: 'T', 2: 'E', 3: '$\phi$'}
for i in xrange(4, num + 1):
dict[i] = 'W' + str(i)
for ind in xrange(1, num + 1):
for ind2 in xrange(1, ind + 1):
temp = plt.subplot2grid((num, num), (ind - 1, ind2 - 1))
col = ind + num * (ind2 - 1)
yvalues = self.scalCovCls[:, col]
temp.text(0.15,
0.15,
dict[ind] + dict[ind2],
ha='center',
va='center',
transform=temp.transAxes)
plots.Generic_Cl(temp, xvalues, yvalues)
if ind == num:
temp.set_xlabel(r'$l$')
# global title of the plot
plt.suptitle(self.name_h + ' scalar Cov Cls', fontsize=16)
# define the image size and layout. We need a bigger image for the covariance.
fig.set_size_inches(1.61803398875 * self.x_size_inc, self.x_size_inc)
fig.tight_layout()
fig.subplots_adjust(hspace=0)
fig.subplots_adjust(
top=0.90
) # adjust for the fact that tight_layout does not consider suptitle
# save the figure and close
plt.savefig(self.outpath + self.name + '_scalCovCls.png')
plt.clf()
def plot_scalCls(self):
"""
Plots and saves all the scalar Cls in a unique image
"""
# number of Cls:
num = self.scalCls.shape[1] - 1
if self.transfer:
num += 1
# l values:
xvalues = self.scalCls[:, 0]
# set up the plots:
plots = CMB_plots()
plots.color = self.color
index = 0
fig = plt.gcf()
for ind in xrange(1, num + 1):
index2 = int((ind - 1) / 2)
temp = plt.subplot2grid((int(math.ceil(num / 2.0)), 2),
(index2, index))
if ind == 1:
yvalues = self.scalCls[:, ind]
plots.TT_plot(temp, xvalues, yvalues)
temp.set_title('TT power spectrum')
elif ind == 2:
yvalues = self.scalCls[:, ind]
plots.EE_plot(temp, xvalues, yvalues)
temp.set_title('EE power spectrum')
elif ind == 3:
yvalues = self.scalCls[:, ind]
plots.TE_plot(temp, xvalues, yvalues)
temp.set_title('TE power spectrum')
elif ind == 4 and self.lensing:
yvalues = self.scalCls[:, ind]
plots.Phi_plot(temp, xvalues, yvalues)
temp.set_title('$\phi$ power spectrum')
elif ind == 5 and self.lensing:
yvalues = self.scalCls[:, ind]
plots.PhiT_plot(temp, xvalues, yvalues)
temp.set_title('$\phi$T power spectrum')
elif ind == num and self.transfer:
xvalues = self.matterpower[:, 0]
yvalues = self.matterpower[:, 1]
plots.Matter_plot(temp, xvalues, yvalues)
temp.set_title('Matter power spectrum')
else:
yvalues = self.scalCls[:, ind]
plots.Generic_Cl(temp, xvalues, yvalues)
if index == 0: index = 1
elif index == 1: index = 0
# global title of the plot
plt.suptitle(self.name_h + ' scalar Cls', fontsize=16)
# define the image size and layout
fig.set_size_inches(self.x_size_inc, self.y_size_inc)
fig.tight_layout()
fig.subplots_adjust(
top=0.90
) # adjust for the fact that tight_layout does not consider suptitle
# save the figure and close
plt.savefig(self.outpath + self.name + '_scalCls.png')
plt.clf()
def plot_compare_scalCls(self):
"""
Plots and saves the comparison of all the scalar Cls in a unique image
"""
# number of Cls in the two files:
num1 = self.scalCls_1.shape[1]-1
num2 = self.scalCls_2.shape[1]-1
# protection against different runs
if num1!=num2:
print 'wrong number of Cls'
return
# add the matter power spectrum if required:
if self.transfer: num1 += 1
# values of l:
xvalues_1 = self.scalCls_1[:,0]
xvalues_2 = self.scalCls_2[:,0]
# get minimuma and maximum l values:
l_min = np.amax( [np.amin(xvalues_1), np.amin(xvalues_2)] )
l_max = np.amin( [np.amax(xvalues_1), np.amax(xvalues_2)] )
# get the l values:
l_values = np.linspace(l_min, l_max, l_max-l_min)
# set up the plots:
plots_1 = CMB_plots()
plots_2 = CMB_plots()
plots_compa = CMB_plots()
plots_1.color = self.color1
plots_2.color = self.color2
plots_compa.color = self.color_compa
plots_compa.comparison = True
plots_compa.axes_label_position = 'right'
fig = plt.gcf()
# do the plots:
for ind in xrange(1,num1+1):
# distribute the plots in the figure:
temp = plt.subplot2grid((num1,2), (ind-1, 0))
temp_comp = plt.subplot2grid((num1,2), (ind-1, 1))
# do the l sampling and comparison:
if not ( ind == num1 and self.transfer ):
# get the y values:
yvalues_1 = self.scalCls_1[:,ind]
yvalues_2 = self.scalCls_2[:,ind]
# interpolate the two spectra:
spline_1 = UnivariateSpline( xvalues_1, yvalues_1, s=0)
spline_2 = UnivariateSpline( xvalues_2, yvalues_2, s=0)
# evaluate on the l grid:
yvalues_1 = spline_1(l_values)
yvalues_2 = spline_2(l_values)
# protect against zeroes:
yvalues_1_temp = np.abs( yvalues_1 )
yvalues_2_temp = np.abs( yvalues_2 )
try:
min2val_1 = np.amin(yvalues_1_temp[np.nonzero(yvalues_1_temp)])
min2val_2 = np.amin(yvalues_2_temp[np.nonzero(yvalues_2_temp)])
except:
min2val_1 = cutoff
min2val_2 = cutoff
np.place(yvalues_1, yvalues_1 == 0.0, min2val_1)
np.place(yvalues_2, yvalues_2 == 0.0, min2val_2)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100
# protection against values too small:
np.place(yvalues_comp, abs(yvalues_comp)<cutoff, [cutoff])
else: # matter power spectrum case
# get the k values:
xvalues_1 = self.matterpower_1[:,0]
xvalues_2 = self.matterpower_2[:,0]
# get the y values:
yvalues_1 = self.matterpower_1[:,1]
yvalues_2 = self.matterpower_2[:,1]
# get the k grid:
k_min = np.amax( [np.amin(xvalues_1), np.amin(xvalues_2)] )
k_max = np.amin( [np.amax(xvalues_1), np.amax(xvalues_2)] )
k_values = np.logspace(np.log10(k_min), np.log10(k_max), 1000)
# interpolate the two spectra:
spline_1 = UnivariateSpline( xvalues_1, yvalues_1, s=0)
spline_2 = UnivariateSpline( xvalues_2, yvalues_2, s=0)
# evaluate on the l grid:
yvalues_1 = spline_1(k_values)
yvalues_2 = spline_2(k_values)
# protect against zeroes:
yvalues_1_temp = np.abs( yvalues_1 )
yvalues_2_temp = np.abs( yvalues_2 )
try:
min2val_1 = np.amin(yvalues_1_temp[np.nonzero(yvalues_1_temp)])
min2val_2 = np.amin(yvalues_2_temp[np.nonzero(yvalues_2_temp)])
except:
min2val_1 = cutoff
min2val_2 = cutoff
np.place(yvalues_1, yvalues_1 == 0.0, min2val_1)
np.place(yvalues_2, yvalues_2 == 0.0, min2val_2)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100
# protection against values too small:
np.place(yvalues_comp, abs(yvalues_comp)<cutoff, [cutoff])
# make the plots:
if ind == 1: # TT power spectrum:
plots_1.TT_plot(temp, l_values, yvalues_1)
plots_2.TT_plot(temp, l_values, yvalues_2)
plots_compa.TT_plot(temp_comp, l_values, yvalues_comp)
temp_comp.set_yscale('Log')
temp.set_title('TT power spectrum')
elif ind == 2: # EE power spectrum:
plots_1.EE_plot(temp, l_values, yvalues_1)
plots_2.EE_plot(temp, l_values, yvalues_2)
plots_compa.EE_plot(temp_comp, l_values, yvalues_comp)
temp.set_title('EE power spectrum')
elif ind == 3: # TE power spectrum:
plots_1.TE_plot(temp, l_values, yvalues_1)
plots_2.TE_plot(temp, l_values, yvalues_2)
plots_compa.TE_plot(temp_comp, l_values, yvalues_comp)
temp.set_title('TE power spectrum')
elif ind == 4 and self.lensing: # CMB lensing power spectrum:
plots_1.Phi_plot(temp, l_values, yvalues_1)
plots_2.Phi_plot(temp, l_values, yvalues_2)
plots_compa.Phi_plot(temp_comp, l_values, yvalues_comp)
temp.set_title('$\phi$ power spectrum')
elif ind == 5 and self.lensing: # CMB lensing - Temperature power spectrum:
plots_1.PhiT_plot(temp, l_values, yvalues_1)
plots_2.PhiT_plot(temp, l_values, yvalues_2)
plots_compa.PhiT_plot(temp_comp, l_values, yvalues_comp)
temp.set_title('$\phi$T power spectrum')
elif ind == num1 and self.transfer: # matter power spectrum:
plots_1.Matter_plot(temp, k_values, yvalues_1)
plots_2.Matter_plot(temp, k_values, yvalues_2)
plots_compa.Matter_plot(temp_comp, k_values, yvalues_comp)
temp.set_title('Matter power spectrum')
else: # generic Cl comparison:
plots_1.Generic_Cl(temp, l_values, yvalues_1)
plots_2.Generic_Cl(temp, l_values, yvalues_2)
plots_compa.Generic_Cl(temp_comp, l_values, yvalues_comp)
# set the size of the image
fig.set_size_inches( self.x_size_inc, self.y_size_inc)
# set a tight layout
fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3)
# set the global title
plt.suptitle(self.name_h1+' VS '+self.name_h2+
' comparison of scalar Cls', fontsize=16)
# set the global legend
fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p],
labels = [self.name_h1, self.name_h2, 'Cosmic variance'],
loc='lower center', ncol=3 ,fancybox=True)
# adjust the size of the plot
fig.subplots_adjust(top=0.92, bottom=0.08)
# save the result and close
plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCls_comp.pdf')
plt.clf()
plt.close("all")
def plot_compare_scalCovCls(self):
"""
Plots and saves the comparison of all the scalar Cov Cls in a unique image
"""
# number of Cls:
num1 = self.scalCovCls_1.shape[1]-1
num2 = self.scalCovCls_2.shape[1]-1
# protection against different runs
if num1!=num2:
print 'wrong number of Cls'
return
# size of the Cl Cov matrix:
num1 = int(math.sqrt(num1))
num_tot = sum(xrange(1,num1+1))
# set up the plots:
plots_1 = CMB_plots()
plots_2 = CMB_plots()
plots_compa = CMB_plots()
plots_1.color = self.color1
plots_2.color = self.color2
plots_compa.color = self.color_compa
plots_compa.comparison = True
plots_compa.axes_label_position = 'right'
fig = plt.gcf()
# setup a dictionary with the names of the Cls
dict = { 1: 'T', 2: 'E', 3: '$\phi$'}
for i in xrange(4, num1+1):
dict[i] = 'W'+str(i)
# values of the multipoles:
xvalues_1 = self.scalCovCls_1[:,0]
xvalues_2 = self.scalCovCls_2[:,0]
# get the l values:
l_min = np.amax( [np.amin(xvalues_1), np.amin(xvalues_2)] )
l_max = np.amin( [np.amax(xvalues_1), np.amax(xvalues_2)] )
# get the l values:
l_values = np.linspace(l_min, l_max, l_max-l_min)
# other stuff:
ind_tot = 0
# do the plots:
for ind in xrange(1,num1+1):
for ind2 in xrange(1, ind+1):
ind_tot += 1
# place the plots:
temp = plt.subplot2grid((num_tot,2), (ind_tot-1, 0))
temp_comp = plt.subplot2grid((num_tot,2), (ind_tot-1, 1))
# values of the Cls:
col = ind + num1*(ind2-1)
# get the y values:
yvalues_1 = self.scalCovCls_1[:,col]
yvalues_2 = self.scalCovCls_2[:,col]
# interpolate the two spectra:
spline_1 = UnivariateSpline( xvalues_1, yvalues_1, s=0)
spline_2 = UnivariateSpline( xvalues_2, yvalues_2, s=0)
# evaluate on the l grid:
yvalues_1 = spline_1(l_values)
yvalues_2 = spline_2(l_values)
# protect against zeroes:
yvalues_1_temp = np.abs( yvalues_1 )
yvalues_2_temp = np.abs( yvalues_2 )
try:
min2val_1 = np.amin(yvalues_1_temp[np.nonzero(yvalues_1_temp)])
min2val_2 = np.amin(yvalues_2_temp[np.nonzero(yvalues_2_temp)])
except:
min2val_1 = cutoff
min2val_2 = cutoff
np.place(yvalues_1, yvalues_1 == 0.0, min2val_1)
np.place(yvalues_2, yvalues_2 == 0.0, min2val_2)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100
# protection against values too small:
np.place(yvalues_comp, abs(yvalues_comp)<cutoff, [cutoff])
# make the plots:
plots_1.Generic_Cl(temp, l_values, yvalues_1)
plots_2.Generic_Cl(temp, l_values, yvalues_2)
plots_compa.Generic_Cl(temp_comp, l_values, yvalues_comp)
temp.set_title(dict[ind2]+dict[ind]+' power spectrum')
# set the size of the image
fig.set_size_inches( self.x_size_inc, self.y_size_inc/5.0*num_tot)
# set a tight layout
fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3)
# set the global legend
fig.legend( handles = [plots_1.Generic_Cl_plot, plots_2.Generic_Cl_plot, plots_compa.CV_p],
labels = [self.name_h1, self.name_h2, 'Cosmic variance'],
loc='lower center', ncol=3 ,fancybox=True)
# set the global title
plt.suptitle(self.name_h1+' VS '+self.name_h2+
' comparison of scalar Cov Cls', fontsize=16)
# adjust the size of the plot
fig.subplots_adjust(top=0.92, bottom=0.08)
# fig.subplots_adjust(top=0.96, bottom=0.01)
# save the result and close
plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCovCls_comp.pdf')
plt.clf()
plt.close("all")
def plot_compare_scalCls(self):
"""
Plots and saves the comparison of all the scalar Cls in a unique image
"""
# number of Cls in the two files:
num1 = self.scalCls_1.shape[1] - 1
num2 = self.scalCls_2.shape[1] - 1
# protection against different runs
if num1 != num2:
print 'wrong number of Cls'
return
if len(self.scalCls_1[:, 0]) != len(self.scalCls_2[:, 0]):
print 'different lmax'
return
if len(self.matterpower_1[:, 0]) != len(self.matterpower_2[:, 0]):
self.transfer = False
# add the matter power spectrum if required:
if self.transfer: num1 += 1
# values of l
xvalues = self.scalCls_1[:, 0]
# set up the plots:
plots_1 = CMB_plots()
plots_2 = CMB_plots()
plots_compa = CMB_plots()
plots_1.color = self.color1
plots_2.color = self.color2
plots_compa.color = self.color_compa
plots_compa.comparison = True
plots_compa.axes_label_position = 'right'
fig = plt.gcf()
# do the plots:
for ind in xrange(1, num1 + 1):
# distribute the plots in the figure:
temp = plt.subplot2grid((num1, 2), (ind - 1, 0))
temp_comp = plt.subplot2grid((num1, 2), (ind - 1, 1))
if ind == 1: # TT power spectrum:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.TT_plot(temp, xvalues, yvalues_1)
plots_2.TT_plot(temp, xvalues, yvalues_2)
plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp)
temp_comp.set_yscale('Log')
temp.set_title('TT power spectrum')
elif ind == 2: # EE power spectrum:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.EE_plot(temp, xvalues, yvalues_1)
plots_2.EE_plot(temp, xvalues, yvalues_2)
plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('EE power spectrum')
elif ind == 3: # TE power spectrum:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.TE_plot(temp, xvalues, yvalues_1)
plots_2.TE_plot(temp, xvalues, yvalues_2)
plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('TE power spectrum')
elif ind == 4 and self.lensing: # CMB lensing power spectrum:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.Phi_plot(temp, xvalues, yvalues_1)
plots_2.Phi_plot(temp, xvalues, yvalues_2)
plots_compa.Phi_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('$\phi$ power spectrum')
elif ind == 5 and self.lensing: # CMB lensing - Temperature power spectrum:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.PhiT_plot(temp, xvalues, yvalues_1)
plots_2.PhiT_plot(temp, xvalues, yvalues_2)
plots_compa.PhiT_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('$\phi$T power spectrum')
elif ind == num1 and self.transfer: # matter power spectrum:
xvalues = self.matterpower_2[:, 0]
yvalues_1 = self.matterpower_1[:, 1]
yvalues_2 = self.matterpower_2[:, 1]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.Matter_plot(temp, xvalues, yvalues_1)
plots_2.Matter_plot(temp, xvalues, yvalues_2)
plots_compa.Matter_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('Matter power spectrum')
else: # generic Cl comparison:
yvalues_1 = self.scalCls_1[:, ind]
yvalues_2 = self.scalCls_2[:, ind]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.Generic_Cl(temp, xvalues, yvalues_1)
plots_2.Generic_Cl(temp, xvalues, yvalues_2)
plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp)
# set the size of the image
fig.set_size_inches(self.x_size_inc, self.y_size_inc)
# set a tight layout
fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3)
# set the global title
plt.suptitle(self.name_h1 + ' VS ' + self.name_h2 +
' comparison of scalar Cls',
fontsize=16)
# set the global legend
fig.legend(handles=[plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p],
labels=[self.name_h1, self.name_h2, 'Cosmic variance'],
loc='lower center',
ncol=3,
fancybox=True)
# adjust the size of the plot
fig.subplots_adjust(top=0.92, bottom=0.08)
# save the result and close
plt.savefig(self.outpath + self.name1 + '_' + self.name2 +
'_scalCls_comp.pdf')
plt.clf()
plt.close("all")
def plot_compare_scalCovCls(self):
"""
Plots and saves the comparison of all the scalar Cov Cls in a unique image
"""
# number of Cls:
num1 = self.scalCovCls_1.shape[1] - 1
num2 = self.scalCovCls_2.shape[1] - 1
# protection against different runs
if num1 != num2:
print 'wrong number of Cls'
return
if len(self.scalCovCls_1[:, 0]) != len(self.scalCovCls_2[:, 0]):
print 'different lmax'
return
# size of the Cl Cov matrix:
num1 = int(math.sqrt(num1))
num_tot = sum(xrange(1, num1 + 1))
# set up the plots:
plots_1 = CMB_plots()
plots_2 = CMB_plots()
plots_compa = CMB_plots()
plots_1.color = self.color1
plots_2.color = self.color2
plots_compa.color = self.color_compa
plots_compa.comparison = True
plots_compa.axes_label_position = 'right'
fig = plt.gcf()
# setup a dictionary with the names of the Cls
dict = {1: 'T', 2: 'E', 3: '$\phi$'}
for i in xrange(4, num1 + 1):
dict[i] = 'W' + str(i)
# values of the multipoles:
xvalues = self.scalCovCls_1[:, 0]
# other stuff:
ind_tot = 0
# do the plots:
for ind in xrange(1, num1 + 1):
for ind2 in xrange(1, ind + 1):
ind_tot += 1
# place the plots:
temp = plt.subplot2grid((num_tot, 2), (ind_tot - 1, 0))
temp_comp = plt.subplot2grid((num_tot, 2), (ind_tot - 1, 1))
# values of the Cls:
col = ind + num1 * (ind2 - 1)
yvalues_1 = self.scalCovCls_1[:, col]
yvalues_2 = self.scalCovCls_2[:, col]
# protection against values equal to zero:
yvalues_temp = np.array(map(abs, yvalues_1))
min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
# computation of the percentual comparison:
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# protection against values too small:
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
# make the plots:
plots_1.Generic_Cl(temp, xvalues, yvalues_1)
plots_2.Generic_Cl(temp, xvalues, yvalues_2)
plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp)
temp.set_title(dict[ind2] + dict[ind] + ' power spectrum')
# set the size of the image
fig.set_size_inches(self.x_size_inc, self.y_size_inc / 5.0 * num_tot)
# set a tight layout
fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3)
# set the global legend
fig.legend(handles=[
plots_1.Generic_Cl_plot, plots_2.Generic_Cl_plot, plots_compa.CV_p
],
labels=[self.name_h1, self.name_h2, 'Cosmic variance'],
loc='lower center',
ncol=3,
fancybox=True)
# set the global title
plt.suptitle(self.name_h1 + ' VS ' + self.name_h2 +
' comparison of scalar Cov Cls',
fontsize=16)
# adjust the size of the plot
fig.subplots_adjust(top=0.92, bottom=0.08)
# fig.subplots_adjust(top=0.96, bottom=0.01)
# save the result and close
plt.savefig(self.outpath + self.name1 + '_' + self.name2 +
'_scalCovCls_comp.pdf')
plt.clf()
plt.close("all")
