def plot_filter_output_histogram(self, fname=None):
fname = self._fname(fname)
figname = 'temps1.Simulation.plot_filter_output_histogram.'
figname += fname.replace(' ', '_')
fig, ax = plt.subplots(num=figname, clear=True)
axr = ax.twinx()
ax.set_title(fname.capitalize() + ' filter output distribution')
ax.set_xlabel('Filter output value')
ax.set_ylabel('Rate per bin [s$^{-1}$]')
axr.set_ylabel('Fraction of S1 per bin [%]')
x = self.values[fname]['dcr']
counts, bins = np.histogram(x, bins='auto')
counts = counts / (self.nmc * self.T_sim * 1e-9)
linenoise, = plot_histogram(ax, counts, bins, **self.plotkw['dcr'])
x = self.values[fname]['s1'][self.signal[fname]['s1']]
counts, bins = np.histogram(x, bins='auto')
counts = counts * 100 / len(x)
linesigpure, = plot_histogram(axr, counts, bins, **self.plotkw['s1'])
N = len(x)
x = self.values[fname]['all'][self.signal[fname]['all']]
counts, bins = np.histogram(x, bins='auto')
counts = counts * 100 / N
linesig, = plot_histogram(axr, counts, bins, **self.plotkw['all'])
textbox.textbox(axr, self.infotext(), loc='upper right')
axr.legend([
linenoise,
linesigpure,
linesig,
], [
'Fake rate (left scale)',
'Signal % (right scale)',
'Signal within noise (relative)',
],
loc='upper left')
ax.minorticks_on()
axr.minorticks_on()
aligntwin.alignYaxes([ax, axr], [0, 0])
ax.set_ylim(0, ax.get_ylim()[1])
axr.set_ylim(0, axr.get_ylim()[1])
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
fig.tight_layout()
return fig
def plot_filter_output_histogram(self, fname=None):
"""
Plot the histogram of the filter peak value of S1 candidates for noise
and signal.
Parameters
----------
fname : str, optional
The filter. Optional if there's only one filter.
Return
------
fig : matplotlib figure
The figure where the plot is drawn.
"""
fname = self._fname(fname)
figname = 'temps1.Simulation.plot_filter_output_histogram.'
figname += fname.replace(' ', '_')
fig, ax = plt.subplots(num=figname, clear=True)
axr = ax.twinx()
ax.set_title(fname.capitalize() + ' filter output distribution')
ax.set_xlabel('Filter output value')
ax.set_ylabel('Rate per bin [s$^{-1}$]')
axr.set_ylabel('Fraction of S1 per bin [%]')
x = self.values[fname]['dcr']
counts, bins = np.histogram(x, bins='auto')
counts = counts / (self.nmc * self.T * 1e-9)
linenoise, = plot_histogram(ax, counts, bins, **self.plotkw['dcr'])
x = self.values[fname]['all'][self.signal[fname]['all']]
counts, bins = np.histogram(x, bins='auto')
counts = counts * 100 / len(x)
linesig, = plot_histogram(axr, counts, bins, **self.plotkw['all'])
x = self.values[fname]['s1'][self.signal[fname]['s1']]
counts, bins = np.histogram(x, bins='auto')
counts = counts * 100 / len(x)
linesigpure, = plot_histogram(axr, counts, bins, **self.plotkw['s1'])
textbox.textbox(axr,
self.infotext(),
loc='upper right',
fontsize='small')
axr.legend([
linenoise,
linesig,
linesigpure,
], [
'Fake rate (left scale)',
'Signal % (right scale)',
'Signal without noise',
],
loc='upper left')
ax.minorticks_on()
axr.minorticks_on()
aligntwin.alignYaxes([ax, axr], [0, 0])
ax.set_ylim(0, ax.get_ylim()[1])
axr.set_ylim(0, axr.get_ylim()[1])
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
fig.tight_layout()
return fig
