for j, (vov, flen) in enumerate(zip(afterpulse_tile21.AfterPulseTile21.defaultparams, length)):
ap21 = afterpulse_tile21.AfterPulseTile21(vov)
fig = plt.figure(num=f'figlaserpos2-{j}0', clear=True, figsize=[4.5, 3.5])
ap21.sim.hist('mainpos-offset', 'mainnpe==1', fig=fig, selection=False)
ax, = fig.get_axes()
textbox.textbox(ax, f'{vov} VoV', fontsize='medium', loc='lower center')
ax.set_xlabel('Laser peak position [ns]')
row = [fig]
fig = plt.figure(num=f'figlaserpos2-{j}1', clear=True, figsize=[4.5, 3.5])
ap21.sim.hist2d('mainpos-offset', 'mainampl', f'(mainnpe==1)&(length=={flen})', fig=fig, cmap=colormap.uniform(), selection=False)
ax, _ = fig.get_axes()
textbox.textbox(ax, f'{vov} VoV', fontsize='medium', loc='lower center')
textbox.textbox(ax, f'{flen} ns filter', fontsize='medium', loc='upper left')
ax.set_xlabel('Laser peak position [ns]')
ax.set_ylabel('Peak height')
row.append(fig)
figs.append(row)
for row in figs:
for fig in row:
fig.tight_layout()
fig.show()
import figlatex
import hist2d
import colormap
commands = [
'-m 100000 -L 860 -v 860 -l 8900 darksidehd/LF_TILE15_77K_73V_9VoV_1.wav',
'-m 100000 -L 750 -v 750 -l 8900 darksidehd/nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav',
'-m 100000 -L 750 -v 750 -l 8900 darksidehd/nuvhd_lf_3x_tile59_77K_64V_6VoV_1.wav',
]
###########################
figs = []
cmap = colormap.uniform()
for ifile, cmd in enumerate(commands):
figname = f'fighist2dtile155759-{ifile}'
fig = plt.figure(num=figname, clear=True, figsize=[9, 4])
save = f'figthesis/{figname}.npz'
if not os.path.exists(save):
hist = hist2d.Hist2D(cmd.split())
print(f'save {save}...')
hist.save(save, compress=True)
print(f'load {save}...')
hist = hist2d.Hist2D.load(save)
hist.hist2d(fig, cmap=cmap)
command = '-m 100000 -L 1 -t -l 500 darksidehd/nuvhd_lf_3x_tile57_77K_64V_6VoV_1.wav'
###########################
def naivelinear(colors=['black', '#f55', 'white'], N=256, return_pos=False):
rgb0 = np.array([_colors.to_rgb(color) for color in colors])
t0 = np.linspace(0, 1, len(rgb0))
t = np.linspace(0, 1, N)
rgb = interpolate.interp1d(t0, rgb0, axis=0)(t)
rt = _colors.ListedColormap(rgb)
if return_pos:
rt = (rt, t0)
return rt
cmap1 = naivelinear()
cmap2 = colormap.uniform()
figs = []
for ifile, cmap in enumerate([cmap1, cmap2]):
figname = f'fighist2dtile57-cmap-{ifile}'
fig = plt.figure(num=figname, clear=True, figsize=[9, 4])
save = f'figthesis/{figname}.npz'
if not os.path.exists(save):
hist = hist2d.Hist2D(command.split())
print(f'save {save}...')
hist.save(save, compress=True)
print(f'load {save}...')
hist = hist2d.Hist2D.load(save)
prefix = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1'
###########################
templ = _template.Template.load('templates/' + prefix + '-template.npz')
signal_loc = np.linspace(0, 1, 3 * 8 + 1)
event_length = templ.template_length // 8 + int(np.max(np.ceil(signal_loc))) + 1
simulated_signal = templ.generate(event_length, signal_loc, randampl=False, aligned='trigger')
fig, ax = plt.subplots(num='figinterptempl', clear=True, figsize=[6.4, 4])
ax.set_xlabel('Sample number @ 125 MSa/s')
colors = colormap.uniform(['black', 'red'], len(signal_loc), (0, 60)).colors
for i in range(len(signal_loc)):
kw = dict(
label=f'{signal_loc[i]:.2f}',
color=colors[i],
)
ax.plot(simulated_signal[i], **kw)
ax.legend(loc='upper right', title_fontsize='large', title='Signal start [sample]', ncol=4)
ax.set_xlim(7, 13)
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
def snrplot(tau,
delta_ma,
delta_exp,
delta_mf,
waveform,
snr,
fig1=None,
fig2=None,
plottemplate=True):
"""
Plot SNR as a function of tau and delta. Called by snrseries().
Parameters
----------
tau, delta_ma, delta_exp, delta_mf, waveform, snr : arrays
The output from snrseries().
fig1, fig2 : matplotlib figure, optional
The figures where the plot is drawn.
plottemplate : bool
If True (default), plot the matched filter template.
Returns
-------
fig1, fig2 : matplotlib figure
The figures with the plots.
"""
if fig1 is None:
fig = plt.figure('fingersnr-snrplot', figsize=[10.2, 7.1])
fig.clf()
else:
fig = fig1
grid = gridspec.GridSpec(2, 2)
ax0 = fig.add_subplot(grid[0, 0])
ax1 = fig.add_subplot(grid[0, 1], sharex=ax0, sharey=ax0)
ax2 = fig.add_subplot(grid[1, :], sharex=ax0, sharey=ax0)
axs = [ax0, ax1, ax2]
axs[0].set_title('Moving average')
axs[1].set_title('Exponential moving average')
axs[2].set_title('Cross correlation')
colors = colormap.uniform(['black', 'black'], len(tau),
(0, 100 * (1 - 1 / len(tau)))).colors
for i, (ax, d) in enumerate(zip(axs, [delta_ma, delta_exp, delta_mf])):
for j in range(len(tau)):
label = f'{tau[j]}'
ax.plot(d[j],
snr[i, j],
color=colors[j],
label=label,
zorder=2.5 - 0.01 * j)
if ax.is_first_col():
ax.set_ylabel('SNR')
if ax.is_last_row():
ax.set_xlabel('Offset from trigger [ns]')
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
axs[2].legend(loc='best', title='Filter length [ns]', ncol=2)
fig.tight_layout()
fig.show()
fig1 = fig
if plottemplate and fig2 is None:
fig = plt.figure('fingersnr-snrplot2')
fig.clf()
else:
fig = fig2
if plottemplate:
ax = fig.subplots(1, 1)
ax.set_title('Matched filter template')
ax.set_xlabel('Sample number [ns]')
ax.plot(waveform)
ax.grid()
fig.tight_layout()
fig.show()
fig2 = fig
return fig1, fig2