def test_nice_colors():
for i in range(10):
# check that the entrances are 3:
assert len(col.nice_colors(i)) == 3
# check that they are positive and smaller than 1, i.e. it is legal RGB:
for j in col.nice_colors(i):
assert j >= 0.0
assert j <= 1.0
def test_nice_colors():
for i in xrange(10):
# check that the entrances are 3:
assert len(col.nice_colors(i))==3
# check that they are positive and smaller than 1, i.e. it is legal RGB:
for j in col.nice_colors(i):
assert j>=0.0
assert j<=1.0
# print to screen number of SN:
if not args.quiet:
for k in xrange(0, number_SN_mock):
print 'Total number of SN in '+names[k]+' :', len(data[k][:,1])
print
# plot SN redshift distribution and luminosity distance:
ax_1 = plt.subplot(plot_grid[1,0]) # distribution
ax_2 = plt.subplot(plot_grid[1,1]) # d_L
# cycle over files:
for k in xrange(0, number_SN_mock):
# grab the x values:
x_values = data[k][:,1]
y_values = data[k][:,2]
# do the instogram
ax_1.hist(x_values, 50, histtype='step', color=fc.nice_colors(k) )
# do the luminosity plot:
y_err = np.sqrt( data[k][:,5] )
# do the plot:
ax_2.errorbar(x_values, y_values, yerr=y_err,
fmt='o', color=fc.nice_colors(k),
markersize=1, markeredgecolor = 'none',
markerfacecolor=fc.nice_colors(k), errorevery=errorevery, capsize=0)
# finalise the plot:
ax_1.set_xlabel( '$z$' )
ax_1.set_ylabel( '$n_{\\rm SN}(z)$' )
ax_2.set_xlabel( '$z$' )
ax_2.set_ylabel( '$\mu_{\\rm obs}(z)$' )
# plot SN mock color:
np.place(y_values, y_values == 0.0, [cutoff])
# get the plot bounds:
try:
lower_y = 0.9 * np.amin(
y_values[np.abs(y_values) > cutoff])
except:
lower_y = cutoff
try:
upper_y = 1.1 * np.amax(
y_values[np.abs(y_values) > cutoff])
except:
upper_y = cutoff
plotrange_x.append([np.amin(x_values), np.amax(x_values)])
plotrange_y.append([lower_y, upper_y])
# do the plot:
ax.plot(x_values, y_values, color=fc.nice_colors(k))
# now set the appearence of the plot:
plotrange_x = np.array(plotrange_x)
plotrange_y = np.array(plotrange_y)
# log scale:
if not do_lin:
ax.set_xscale('log')
ax.set_yscale('log')
# ranges:
x_min = fu.v_nice_number(np.amin(plotrange_x[:, 0]), 1)
x_max = fu.v_nice_number(np.amax(plotrange_x[:, 1]), 1)
if (x_min != x_max):
ax.set_xlim([x_min, x_max])
y_min = fu.v_nice_number(np.amin(plotrange_y[:, 0]), 2)
y_max = fu.v_nice_number(np.amax(plotrange_y[:, 1]), 0)
if (y_min != y_max):
if not args.quiet:
for k in xrange(0, number_SN_mock):
print 'Total number of SN in ' + names[k] + ' :', len(data[k][:,
1])
print
# plot SN redshift distribution and luminosity distance:
ax_1 = plt.subplot(plot_grid[1, 0]) # distribution
ax_2 = plt.subplot(plot_grid[1, 1]) # d_L
# cycle over files:
for k in xrange(0, number_SN_mock):
# grab the x values:
x_values = data[k][:, 1]
y_values = data[k][:, 2]
# do the instogram
ax_1.hist(x_values, 50, histtype='step', color=fc.nice_colors(k))
# do the luminosity plot:
y_err = np.sqrt(data[k][:, 5])
# do the plot:
ax_2.errorbar(x_values,
y_values,
yerr=y_err,
fmt='o',
color=fc.nice_colors(k),
markersize=1,
markeredgecolor='none',
markerfacecolor=fc.nice_colors(k),
errorevery=errorevery,
capsize=0)
# finalise the plot:
# get the Cls values:
if do_abs:
y_values = np.abs( factor*data[k][:,col] )
else:
y_values = factor*data[k][:,col]
# protect against zeroes:
np.place(y_values, y_values==0.0, [cutoff])
# get the plot bounds:
try: lower_y = 0.9*np.amin(y_values[np.abs(y_values)>cutoff])
except: lower_y = cutoff
try: upper_y = 1.1*np.amax(y_values[np.abs(y_values)>cutoff])
except: upper_y = cutoff
plotrange_x.append( [ np.amin(x_values), np.amax(x_values) ] )
plotrange_y.append( [ lower_y,upper_y ] )
# do the plot:
ax.plot( x_values, y_values, color=fc.nice_colors(k) )
# now set the appearence of the plot:
plotrange_x = np.array( plotrange_x )
plotrange_y = np.array( plotrange_y )
# log scale:
if not do_lin:
ax.set_xscale('log')
ax.set_yscale('log')
# ranges:
x_min = fu.v_nice_number( np.amin(plotrange_x[:,0]),1)
x_max = fu.v_nice_number( np.amax(plotrange_x[:,1]),1)
if ( x_min != x_max ):
ax.set_xlim( [ x_min,x_max ] )
y_min = fu.v_nice_number( np.amin(plotrange_y[:,0]),2)
y_max = fu.v_nice_number( np.amax(plotrange_y[:,1]),0)
if ( y_min != y_max ):
