mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-14 02:23:32 +01:00
351 lines
11 KiB
Python
Executable file
351 lines
11 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
|
|
|
|
from lib import util
|
|
from scipy import signal, interpolate
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
try:
|
|
from tqdm import tqdm
|
|
except:
|
|
tqdm = lambda x: x
|
|
|
|
rng = np.random.default_rng()
|
|
|
|
class Waveform:
|
|
name = None
|
|
signal = None
|
|
dt = None
|
|
|
|
_t = None
|
|
|
|
def __init__(self,signal=None, dt=None, t=None, name=None):
|
|
self.signal = signal
|
|
self.name = name
|
|
|
|
if t is not None:
|
|
assert len(t) == len(signal)
|
|
|
|
self._t = t
|
|
self.dt = t[1] - t[0]
|
|
elif dt is not None:
|
|
self.dt = dt
|
|
|
|
# Lazy evaluation of time
|
|
@property
|
|
def t(self):
|
|
if self._t is None:
|
|
return self.dt * np.arange(0, len(self.signal))
|
|
return self._t
|
|
|
|
@t.setter
|
|
def t(self, value):
|
|
self._t = value
|
|
|
|
@t.deleter
|
|
def t(self):
|
|
del self._t
|
|
|
|
def __len__():
|
|
return len(self.signal)
|
|
|
|
def white_noise_realisation(N_samples, noise_sigma=1, rng=rng):
|
|
return rng.normal(0, noise_sigma or 0, size=N_samples)
|
|
|
|
def antenna_bp(trace, low_bp, high_bp, dt, order=3):
|
|
# order < 6 for stability
|
|
fs = 1/dt
|
|
nyq = 1* fs
|
|
low_bp = low_bp / nyq
|
|
high_bp = high_bp / nyq
|
|
|
|
bp_filter = signal.butter(order, [low_bp, high_bp], 'band', fs=fs)
|
|
bandpassed = signal.lfilter(*bp_filter, trace)
|
|
|
|
return bandpassed
|
|
|
|
def my_correlation(in1, template, lags=None):
|
|
template_length = len(template)
|
|
in1_length = len(in1)
|
|
|
|
if lags is None:
|
|
lags = np.arange(-template_length+1, in1_length + 1)
|
|
|
|
# do the correlation jig
|
|
corrs = np.zeros_like(lags, dtype=float)
|
|
for i, l in enumerate(lags):
|
|
if l <= 0: # shorten template at the front
|
|
in1_start = 0
|
|
template_end = template_length
|
|
|
|
template_start = -template_length - l
|
|
in1_end = max(0, min(in1_length, -template_start)) # 0 =< l + template_length =< in1_lengt
|
|
|
|
elif l > in1_length - template_length:
|
|
# shorten template from the back
|
|
in1_end = in1_length
|
|
template_start = 0
|
|
|
|
in1_start = min(l, in1_length)
|
|
template_end = max(0, in1_length - l)
|
|
|
|
else:
|
|
in1_start = min(l, in1_length)
|
|
in1_end = min(in1_start + template_length, in1_length)
|
|
|
|
# full template
|
|
template_start = 0
|
|
template_end = template_length
|
|
|
|
# Slice in1 and template
|
|
in1_slice = in1[in1_start:in1_end]
|
|
template_slice = template[template_start:template_end]
|
|
|
|
corrs[i] = np.dot(in1_slice, template_slice)
|
|
|
|
return corrs, (in1, template, lags)
|
|
|
|
def trace_upsampler(trace, template_t, trace_t):
|
|
template_dt = template.t[1] - template.t[0]
|
|
trace_dt = trace_t[1] - trace_t[0]
|
|
|
|
upsample_factor = trace_dt/template_dt
|
|
upsampled_trace_N = np.ceil(len(trace) * upsample_factor)
|
|
|
|
upsample_factor = int(upsample_factor)
|
|
upsampled_trace_N = int(upsampled_trace_N)
|
|
|
|
# upsample trace
|
|
upsampled_trace = np.zeros(upsampled_trace_N)
|
|
upsampled_trace[::upsample_factor] = trace
|
|
|
|
#upsampled_t = np.arange(trace_t[0], trace_t[-1], template_dt)
|
|
upsampled_t = template_dt * np.arange(len(upsampled_trace)) + trace_t[0]
|
|
|
|
return upsampled_trace, upsampled_t
|
|
|
|
def trace_downsampler(trace, template_t, trace_t, offset):
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import os
|
|
import matplotlib
|
|
import sys
|
|
if os.name == 'posix' and "DISPLAY" not in os.environ:
|
|
matplotlib.use('Agg')
|
|
|
|
bp_freq = (30e-3, 80e-3) # GHz
|
|
template_dt = 5e-2 # ns
|
|
template_length = 500 # ns
|
|
noise_sigma_factor = 1e0 # keep between 10 and 0.1
|
|
|
|
N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
|
|
|
|
antenna_dt = 2 # ns
|
|
antenna_timelength = 2048 # ns
|
|
|
|
#
|
|
# Create the template
|
|
#
|
|
template = Waveform(None, dt=template_dt, name='Template')
|
|
_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/template.dt, offset=10/template.dt)
|
|
template.signal = antenna_bp(_deltapeak[0], *bp_freq, (np.sqrt(antenna_dt/template_dt))*template_dt) # TODO: fix sqrt constant
|
|
template.peak_sample = _deltapeak[1]
|
|
template.peak_time = template.dt * template.peak_sample
|
|
|
|
|
|
if True: # show template
|
|
fig, ax = plt.subplots()
|
|
ax.set_title("Deltapeak and Bandpassed Template")
|
|
ax.set_xlabel("Time [ns]")
|
|
ax.set_ylabel("Amplitude")
|
|
ax.plot(template.t, max(template.signal)*_deltapeak[0])
|
|
ax.plot(template.t, template.signal)
|
|
fig.savefig('figures/11_template_deltapeak.pdf')
|
|
|
|
if True:
|
|
plt.close(fig)
|
|
|
|
#
|
|
# Find difference between true and templated times
|
|
#
|
|
time_residuals = np.zeros(N_residuals)
|
|
for j in tqdm(range(N_residuals)):
|
|
do_plots = j==0
|
|
|
|
# receive at antenna
|
|
## place the deltapeak signal at a random location
|
|
antenna = Waveform(None, dt=antenna_dt, name='Signal')
|
|
antenna_true_signal, antenna_peak_sample = util.deltapeak(timelength=antenna_timelength, samplerate=1/antenna.dt, offset=[0.2, 0.8], rng=rng)
|
|
antenna.signal = antenna_true_signal
|
|
antenna.peak_sample = antenna_peak_sample
|
|
antenna.peak_time = antenna.dt * antenna.peak_sample
|
|
|
|
if do_plots:
|
|
print(f"Antenna Peak Time: {antenna.peak_time}")
|
|
print(f"Antenna Peak Sample: {antenna.peak_sample}")
|
|
|
|
if False: # bandpass when emitting the signal
|
|
antenna.signal = antenna_bp(antenna.signal, *bp_freq, antenna.dt)
|
|
|
|
if False: # flip polarisation
|
|
antenna.signal *= -1
|
|
|
|
## Add noise
|
|
noise_amplitude = max(template.signal) * noise_sigma_factor
|
|
noise_realisation = noise_amplitude * white_noise_realisation(len(antenna.signal))
|
|
|
|
antenna_unfiltered_signal = antenna.signal + noise_realisation
|
|
antenna.signal = antenna_bp(antenna_unfiltered_signal, *bp_freq, antenna.dt)
|
|
|
|
true_time_offset = antenna.peak_time - template.peak_time
|
|
|
|
if do_plots: # show signals
|
|
fig, axs = plt.subplots(2, sharex=True)
|
|
axs[0].set_title("Antenna Waveform")
|
|
axs[-1].set_xlabel("Time [ns]")
|
|
axs[0].set_ylabel("Amplitude")
|
|
axs[0].plot(antenna.t, antenna.signal, label='bandpassed w/ noise', alpha=0.9)
|
|
axs[0].plot(antenna.t, antenna_unfiltered_signal, label='true signal w/ noise', alpha=0.9)
|
|
axs[0].plot(antenna.t, antenna_true_signal, label='true signal w/o noise', alpha=0.9)
|
|
axs[0].legend()
|
|
|
|
axs[1].set_title("Template")
|
|
axs[1].set_ylabel("Amplitude")
|
|
axs[1].plot(template.t, template.signal, label='orig')
|
|
axs[1].plot(template.t + true_time_offset, template.signal, label='true moved orig')
|
|
axs[1].legend()
|
|
|
|
fig.savefig('figures/11_antenna_signals.pdf')
|
|
|
|
if True: # zoom
|
|
wx = 100
|
|
x0 = true_time_offset
|
|
|
|
old_xlims = axs[0].get_xlim()
|
|
axs[0].set_xlim( x0-wx, x0+wx)
|
|
fig.savefig('figures/11_antenna_signals_zoom.pdf')
|
|
|
|
# restore
|
|
axs[0].set_xlim(*old_xlims)
|
|
|
|
if False:
|
|
plt.close(fig)
|
|
|
|
axs2 = None
|
|
if True: # upsampled trace
|
|
upsampled_trace, upsampled_t = trace_upsampler(antenna.signal, template.t, antenna.t)
|
|
|
|
if do_plots: # Show upsampled traces
|
|
fig2, axs2 = plt.subplots(1, sharex=True)
|
|
if not hasattr(axs2, '__len__'):
|
|
axs2 = [axs2]
|
|
|
|
axs2[-1].set_xlabel("Time [ns]")
|
|
axs2[0].set_ylabel("Amplitude")
|
|
axs2[0].plot(antenna.t, antenna.signal, marker='o', label='orig')
|
|
axs2[0].plot(upsampled_t, upsampled_trace, label='upsampled')
|
|
axs2[0].legend(loc='upper right')
|
|
|
|
fig2.savefig('figures/11_upsampled.pdf')
|
|
|
|
wx = 1e2
|
|
x0 = upsampled_t[0] + wx - 5
|
|
axs2[0].set_xlim(x0-wx, x0+wx)
|
|
fig2.savefig('figures/11_upsampled_zoom.pdf')
|
|
|
|
if True:
|
|
plt.close(fig2)
|
|
|
|
# determine correlations with arguments
|
|
lag_dt = upsampled_t[1] - upsampled_t[0]
|
|
corrs, (out1_signal, out2_template, lags) = my_correlation(upsampled_trace, template.signal)
|
|
|
|
else: # downsampled template
|
|
raise NotImplementedError
|
|
|
|
corrs, (out1_signal, out2_template, lags) = my_downsampling_correlation(template.signal, antenna.signal, template.t, antenna.t)
|
|
lag_dt = upsampled_t[1] - upsampled_t[0]
|
|
|
|
# Determine best correlation time
|
|
idx = np.argmax(abs(corrs))
|
|
best_sample_lag = lags[idx]
|
|
best_time_lag = best_sample_lag * lag_dt
|
|
time_residuals[j] = best_time_lag - true_time_offset
|
|
|
|
if do_plots and axs2:
|
|
axs2[-1].axvline(best_time_lag, color='r', alpha=0.5, linewidth=2)
|
|
|
|
# Show the final signals correlated
|
|
if do_plots:
|
|
fig, axs = plt.subplots(2, sharex=True)
|
|
ylabel_kwargs = dict(
|
|
#rotation=0,
|
|
ha='right',
|
|
va='center'
|
|
)
|
|
axs[-1].set_xlabel("Time [ns]")
|
|
|
|
offset_list = [
|
|
[best_time_lag, dict(label=template.name, color='orange')],
|
|
[true_time_offset, dict(label='True offset', color='green')],
|
|
]
|
|
|
|
# Signal
|
|
i=0
|
|
axs[i].set_ylabel("Amplitude", **ylabel_kwargs)
|
|
axs[i].plot(antenna.t, antenna.signal, label=antenna.name)
|
|
|
|
# Put template on an twinned axis (magnitudes are different)
|
|
ax2 = axs[i].twinx()
|
|
for i, offset_args in enumerate(offset_list):
|
|
this_kwargs = offset_args[1]
|
|
offset = offset_args[0]
|
|
|
|
ax2.axvline(offset + len(template.signal) * (template.t[1] - template.t[0]), color=this_kwargs['color'], alpha=0.7)
|
|
ax2.axvline(offset, color=this_kwargs['color'], alpha=0.7)
|
|
ax2.plot(offset + template.t, template.signal, **this_kwargs)
|
|
|
|
# Correlation
|
|
i=1
|
|
axs[i].set_ylabel("Correlation", **ylabel_kwargs)
|
|
axs[i].plot(lags * lag_dt, corrs)
|
|
for i, offset_args in enumerate(offset_list):
|
|
this_kwargs = offset_args[1]
|
|
offset = offset_args[0]
|
|
|
|
axs[i].axvline(offset, ls='--', **this_kwargs)
|
|
|
|
if True: # zoom
|
|
wx = len(template.signal) * (template.dt)/2
|
|
t0 = best_time_lag
|
|
|
|
old_xlims = axs[0].get_xlim()
|
|
axs[i].set_xlim( x0-wx, x0+3*wx)
|
|
fig.savefig('figures/11_corrs_zoom.pdf')
|
|
|
|
# restore
|
|
axs[i].set_xlim(*old_xlims)
|
|
|
|
fig.tight_layout()
|
|
fig.legend()
|
|
fig.savefig('figures/11_corrs.pdf')
|
|
|
|
if False:
|
|
plt.close(fig)
|
|
|
|
# Make a plot of the time residuals
|
|
if len(time_residuals) > 1:
|
|
fig, ax = plt.subplots()
|
|
ax.set_title("Template Correlation Lag finding")
|
|
ax.set_xlabel("Time Residual [ns]")
|
|
ax.set_ylabel("#")
|
|
ax.hist(time_residuals, bins='sqrt', density=False)
|
|
|
|
ax.legend(title=f"template dt: {template.dt: .1e}ns\nantenna dt: {antenna.dt: .1e}ns")
|
|
|
|
fig.savefig("figures/11_time_residual_hist.pdf")
|
|
|
|
plt.show()
|