def plot_lensedCls(self):
"""
Plots and saves all the lensed Cls in a unique image
"""
# protection from direct calls if lensing is not included
if not self.lensing: return
# number of Cls:
num = self.lensedCls.shape[1] - 1
# l values:
xvalues = self.lensedCls[:, 0]
# set up the plots:
plots = CMB_plots()
plots.color = self.color
index = 0
fig = plt.gcf()
for ind in xrange(1, num + 1):
yvalues = self.lensedCls[:, ind]
index2 = int((ind - 1) / 2)
temp = plt.subplot2grid((int(math.ceil(num / 2.0)), 2),
(index2, index))
if ind == 1:
plots.TT_plot(temp, xvalues, yvalues)
temp.set_title('TT power spectrum')
elif ind == 2:
plots.EE_plot(temp, xvalues, yvalues)
temp.set_title('EE power spectrum')
elif ind == 3:
plots.BB_plot(temp, xvalues, yvalues)
temp.set_title('BB power spectrum')
elif ind == 4:
plots.TE_plot(temp, xvalues, yvalues)
temp.set_title('TE power spectrum')
else:
temp.plot(xvalues, yvalues, color=self.color)
if index == 0: index = 1
elif index == 1: index = 0
# global title of the plot
plt.suptitle(self.name_h + ' lensed 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 + '_lensCls.png')
plt.clf()
def plot_compare_totalCls(self):
"""
Plots and saves the comparison of all the total (scalar + tensor) Cls in a unique image
If lensing is included lensed Cls are used.
"""
# protection from direct calls if tensors are not included
if not self.tensor: return
# decide what data to use:
if self.lensing:
data1 = self.lensedtotCls_1
data2 = self.lensedtotCls_2
else:
data1 = self.totCls_1
data2 = self.totCls_2
# number of Cls:
num1 = data1.shape[1]-1
num2 = data2.shape[1]-1
# protection against different runs
if num1!=num2:
print 'wrong number of Cls'
return
# get the x values:
xvalues_1 = data1[:,0]
xvalues_2 = data2[:,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)
# 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):
temp = plt.subplot2grid((num1,2), (ind-1, 0))
temp_comp = plt.subplot2grid((num1,2), (ind-1, 1))
# get the y values:
yvalues_1 = data1[:,ind]
yvalues_2 = data2[:,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])
if ind == 1:
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.set_yscale('Log')
temp.set_title('TT power spectrum')
elif ind == 2:
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:
plots_1.BB_plot(temp, l_values, yvalues_1)
plots_2.BB_plot(temp, l_values, yvalues_2)
plots_compa.BB_plot(temp_comp, l_values, yvalues_comp)
temp.set_title('BB power spectrum')
elif ind == 4:
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')
else:
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)
# 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
if self.lensing:
plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total lensed Cls', fontsize=16)
else:
plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total 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+'_totCls_comp.pdf')
plt.clf()
plt.close("all")
def plot_compare_totalCls(self):
"""
Plots and saves the comparison of all the total (scalar + tensor) Cls in a unique image
If lensing is included lensed Cls are used.
"""
# protection from direct calls if tensors are not included
if not self.tensor: return
# decide what data to use:
if self.lensing:
data1 = self.lensedtotCls_1
data2 = self.lensedtotCls_2
else:
data1 = self.totCls_1
data2 = self.totCls_2
# number of Cls:
num1 = data1.shape[1] - 1
num2 = data2.shape[1] - 1
# protection against different runs
if num1 != num2:
print 'wrong number of Cls'
return
if len(data1[:, 0]) != len(data2[:, 0]):
print 'different lmax'
return
xvalues = data1[:, 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):
temp = plt.subplot2grid((num1, 2), (ind - 1, 0))
temp_comp = plt.subplot2grid((num1, 2), (ind - 1, 1))
yvalues_1 = data1[:, ind]
yvalues_2 = data2[:, ind]
min2val = np.min(yvalues_1[np.nonzero(yvalues_1)])
np.place(yvalues_1, yvalues_1 == 0.0, min2val)
yvalues_comp = (yvalues_1 - yvalues_2) / abs(yvalues_1) * 100
# Protection against all zero: put to machine precision the values that are zero
np.place(yvalues_comp,
abs(yvalues_comp) < 10.0**(-16), [10.0**(-16)])
if ind == 1:
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.set_yscale('Log')
temp.set_title('TT power spectrum')
elif ind == 2:
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:
plots_1.BB_plot(temp, xvalues, yvalues_1)
plots_2.BB_plot(temp, xvalues, yvalues_2)
plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp)
temp.set_title('BB power spectrum')
elif ind == 4:
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')
else:
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)
# 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
if self.lensing:
plt.suptitle(self.name_h1 + ' VS ' + self.name_h2 +
' comparison of total lensed Cls',
fontsize=16)
else:
plt.suptitle(self.name_h1 + ' VS ' + self.name_h2 +
' comparison of total 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 +
'_totCls_comp.pdf')
plt.clf()
plt.close("all")
