mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-14 02:23:32 +01:00
459 lines
16 KiB
Python
Executable file
459 lines
16 KiB
Python
Executable file
#!/usr/bin/env python3
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from lib import util
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from scipy import signal, interpolate, stats
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import matplotlib.pyplot as plt
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import numpy as np
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from itertools import zip_longest
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try:
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from tqdm import tqdm
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except:
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tqdm = lambda x: x
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rng = np.random.default_rng()
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class Waveform:
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name = None
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signal = None
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dt = None
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_t = None
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def __init__(self,signal=None, dt=None, t=None, name=None):
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self.signal = signal
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self.name = name
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if t is not None:
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assert len(t) == len(signal)
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self._t = t
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self.dt = t[1] - t[0]
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elif dt is not None:
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self.dt = dt
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# Lazy evaluation of time
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@property
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def t(self):
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if self._t is None:
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return self.dt * np.arange(0, len(self.signal))
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return self._t
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@t.setter
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def t(self, value):
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self._t = value
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@t.deleter
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def t(self):
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del self._t
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def __len__():
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return len(self.signal)
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def white_noise_realisation(N_samples, noise_sigma=1, rng=rng):
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return rng.normal(0, noise_sigma or 0, size=N_samples)
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def antenna_bp(trace, low_bp, high_bp, dt, order=3):
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fs = 1/dt
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bp_filter = signal.butter(order, [low_bp, high_bp], 'band', fs=fs, output='sos')
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bandpassed = signal.sosfilt(bp_filter, trace)
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return bandpassed
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def my_correlation(in1, template, lags=None):
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template_length = len(template)
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in1_length = len(in1)
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if lags is None:
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lags = np.arange(-template_length+1, in1_length + 1)
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# do the correlation jig
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corrs = np.zeros_like(lags, dtype=float)
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for i, l in enumerate(lags):
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if l <= 0: # shorten template at the front
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in1_start = 0
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template_end = template_length
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template_start = -template_length - l
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in1_end = max(0, min(in1_length, -template_start)) # 0 =< l + template_length =< in1_lengt
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elif l > in1_length - template_length:
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# shorten template from the back
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in1_end = in1_length
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template_start = 0
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in1_start = min(l, in1_length)
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template_end = max(0, in1_length - l)
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else:
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in1_start = min(l, in1_length)
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in1_end = min(in1_start + template_length, in1_length)
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# full template
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template_start = 0
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template_end = template_length
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# Slice in1 and template
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in1_slice = in1[in1_start:in1_end]
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template_slice = template[template_start:template_end]
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corrs[i] = np.dot(in1_slice, template_slice)
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return corrs, (in1, template, lags)
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def trace_upsampler(trace, template_t, trace_t):
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template_dt = template.t[1] - template.t[0]
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trace_dt = trace_t[1] - trace_t[0]
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upsample_factor = trace_dt/template_dt
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upsampled_trace_N = np.ceil(len(trace) * upsample_factor)
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upsample_factor = int(upsample_factor)
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upsampled_trace_N = int(upsampled_trace_N)
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# upsample trace
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upsampled_trace = np.zeros(upsampled_trace_N)
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upsampled_trace[::upsample_factor] = trace
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#upsampled_t = np.arange(trace_t[0], trace_t[-1], template_dt)
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upsampled_t = template_dt * np.arange(len(upsampled_trace)) + trace_t[0]
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return upsampled_trace, upsampled_t
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def trace_downsampler(trace, template_t, trace_t, offset):
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pass
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if __name__ == "__main__":
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import os
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import matplotlib
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import sys
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if os.name == 'posix' and "DISPLAY" not in os.environ:
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matplotlib.use('Agg')
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bp_freq = (30e-3, 80e-3) # GHz
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template_dt = 5e-2 # ns
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template_length = 500 # ns
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N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
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noise_factors = [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 5e-1, 7e-1] # amplitude factor
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antenna_dt = 2 # ns
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antenna_timelength = 2048 # ns
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#
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# Create the template
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#
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template = Waveform(None, dt=template_dt, name='Template')
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_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/template.dt, offset=0)
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template.signal = antenna_bp(_deltapeak[0], *bp_freq, template_dt)
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template.peak_sample = _deltapeak[1]
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template.peak_time = template.dt * template.peak_sample
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interp1d_template = interpolate.interp1d(template.t, template.signal, assume_sorted=True, fill_value=0, bounds_error=False)
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if True: # show template
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fig, ax = plt.subplots()
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ax.set_title("Deltapeak and Bandpassed Template")
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude")
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ax.plot(template.t, max(template.signal)*_deltapeak[0], label='Impulse Template')
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ax.plot(template.t, template.signal, label='Filtered Template')
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fig.savefig('figures/11_template_deltapeak.pdf')
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if True: # show filtering equivalence samplerates
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_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/antenna_dt, offset=0)
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_time = util.sampled_time(end=template_length, sample_rate=1/antenna_dt)
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_bandpassed = antenna_bp(_deltapeak[0], *bp_freq, antenna_dt)
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ax.plot(_time, max(_bandpassed)*_deltapeak[0], label='Impulse Antenna')
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ax.plot(_time, _bandpassed, label='Filtered Antenna')
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ax.legend()
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fig.savefig('figures/11_template_deltapeak+antenna.pdf')
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if True:
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plt.close(fig)
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time_accuracies = np.zeros(len(noise_factors))
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for k, noise_sigma_factor in tqdm(enumerate(noise_factors)):
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print() #separating tqdm
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#
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# Find difference between true and templated times
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#
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time_residuals = np.zeros(N_residuals)
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for j in tqdm(range(N_residuals)):
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do_plots = j==0
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# receive at antenna
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## place the deltapeak signal at a random location
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antenna = Waveform(None, dt=antenna_dt, name='Signal')
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if not True: # Create antenna trace without template
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antenna_true_signal, antenna_peak_sample = util.deltapeak(timelength=antenna_timelength, samplerate=1/antenna.dt, offset=[0.2, 0.8], rng=rng)
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antenna.peak_sample = antenna_peak_sample
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antenna.peak_time = antenna.dt * antenna.peak_sample
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antenna.signal = antenna_bp(antenna.signal, *bp_freq, antenna.dt)
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print(f"Antenna Peak Time: {antenna.peak_time}")
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print(f"Antenna Peak Sample: {antenna.peak_sample}")
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else: # Sample the template at some offset
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antenna.peak_time = antenna_timelength * ((0.8 - 0.2) *rng.random(1) + 0.2)
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sampling_offset = rng.random(1)*antenna.dt
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antenna.t = util.sampled_time(1/antenna.dt, start=0, end=antenna_timelength)
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antenna.signal = interp1d_template(antenna.t - antenna.peak_time)
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antenna.peak_sample = antenna.peak_time/antenna.dt
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antenna_true_signal = antenna.signal
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true_time_offset = antenna.peak_time - template.peak_time
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if False: # flip polarisation
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antenna.signal *= -1
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## Add noise
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noise_amplitude = max(template.signal) * noise_sigma_factor
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noise_realisation = noise_amplitude * white_noise_realisation(len(antenna.signal))
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filtered_noise = antenna_bp(noise_realisation, *bp_freq, antenna.dt)
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antenna.signal += filtered_noise
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if do_plots: # show signals
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fig, axs = plt.subplots(2, sharex=True)
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axs[0].set_title("Antenna Waveform")
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axs[-1].set_xlabel("Time [ns]")
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axs[0].set_ylabel("Amplitude")
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axs[0].plot(antenna.t, antenna.signal, label='bandpassed w/ noise', alpha=0.9)
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axs[0].plot(antenna.t, antenna.signal - filtered_noise, label='bandpassed w/o noise', alpha=0.9)
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axs[0].legend()
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axs[1].set_title("Template")
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axs[1].set_ylabel("Amplitude")
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axs[1].plot(template.t, template.signal, label='orig')
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axs[1].plot(template.t + true_time_offset, template.signal, label='true moved orig')
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axs[1].legend()
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axs[0].grid()
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axs[1].grid()
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fig.savefig('figures/11_antenna_signals.pdf')
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if True: # zoom
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wx = 100
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x0 = true_time_offset
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old_xlims = axs[0].get_xlim()
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axs[0].set_xlim( x0-wx, x0+wx)
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fig.savefig('figures/11_antenna_signals_zoom.pdf')
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# restore
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axs[0].set_xlim(*old_xlims)
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if True:
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plt.close(fig)
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axs2 = None
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if True: # upsampled trace
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upsampled_trace, upsampled_t = trace_upsampler(antenna.signal, template.t, antenna.t)
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if do_plots: # Show upsampled traces
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fig2, axs2 = plt.subplots(1, sharex=True)
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if not hasattr(axs2, '__len__'):
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axs2 = [axs2]
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axs2[-1].set_xlabel("Time [ns]")
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axs2[0].set_ylabel("Amplitude")
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axs2[0].plot(antenna.t, antenna.signal, marker='o', label='orig')
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axs2[0].plot(upsampled_t, upsampled_trace, label='upsampled')
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axs2[0].legend(loc='upper right')
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fig2.savefig('figures/11_upsampled.pdf')
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wx = 1e2
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x0 = upsampled_t[0] + wx - 5
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axs2[0].set_xlim(x0-wx, x0+wx)
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fig2.savefig('figures/11_upsampled_zoom.pdf')
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if True:
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plt.close(fig2)
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# determine correlations with arguments
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lag_dt = upsampled_t[1] - upsampled_t[0]
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corrs, (out1_signal, out2_template, lags) = my_correlation(upsampled_trace, template.signal)
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else: # downsampled template
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raise NotImplementedError
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corrs, (out1_signal, out2_template, lags) = my_downsampling_correlation(template.signal, antenna.signal, template.t, antenna.t)
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lag_dt = upsampled_t[1] - upsampled_t[0]
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# Determine best correlation time
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idx = np.argmax(abs(corrs))
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best_sample_lag = lags[idx]
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best_time_lag = best_sample_lag * lag_dt
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time_residuals[j] = best_time_lag - true_time_offset
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if not do_plots:
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continue
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if do_plots and axs2:
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axs2[-1].axvline(best_time_lag, color='r', alpha=0.5, linewidth=2)
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axs2[-1].axvline(true_time_offset, color='g', alpha=0.5, linewidth=2)
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# Show the final signals correlated
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if do_plots:
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# amplitude scaling required for single axis plotting
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template_amp_scaler = max(abs(template.signal)) / max(abs(antenna.signal))
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# start the figure
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fig, axs = plt.subplots(2, sharex=True)
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ylabel_kwargs = dict(
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#rotation=0,
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ha='right',
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va='center'
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)
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axs[-1].set_xlabel("Time [ns]")
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offset_list = [
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[best_time_lag, dict(label=template.name, color='orange')],
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[true_time_offset, dict(label='True offset', color='green')],
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]
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# Signal
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i=0
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axs[i].set_ylabel("Amplitude", **ylabel_kwargs)
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axs[i].plot(antenna.t, antenna.signal, label=antenna.name)
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# Plot the template
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for offset_args in offset_list:
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this_kwargs = offset_args[1]
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offset = offset_args[0]
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l = axs[i].plot(offset + template.t, template_amp_scaler * template.signal, **this_kwargs)
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axs[i].legend()
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# Correlation
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i=1
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axs[i].set_ylabel("Correlation", **ylabel_kwargs)
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axs[i].plot(lags * lag_dt, corrs)
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# Lines across both axes
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for offset_args in offset_list:
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this_kwargs = offset_args[1]
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offset = offset_args[0]
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for i in [0,1]:
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axs[i].axvline(offset, ls='--', color=this_kwargs['color'], alpha=0.7)
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axs[0].axvline(offset + len(template.signal) * (template.t[1] - template.t[0]), color=this_kwargs['color'], alpha=0.7)
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if True: # zoom
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wx = len(template.signal) * (template.dt)/2
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t0 = best_time_lag
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old_xlims = axs[0].get_xlim()
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axs[i].set_xlim( x0-wx, x0+3*wx)
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fig.savefig('figures/11_corrs_zoom.pdf')
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# restore
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axs[i].set_xlim(*old_xlims)
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fig.tight_layout()
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fig.savefig('figures/11_corrs.pdf')
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if True:
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plt.close(fig)
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print()# separating tqdm
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# Make a plot of the time residuals
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if len(time_residuals) > 1:
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time_accuracies[k] = np.std(time_residuals)
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hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
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fig, ax = plt.subplots()
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ax.set_title(
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"Template Correlation Lag finding"
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+ f"\n template dt: {template.dt*1e3: .1e}ps"
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+ f"; antenna dt: {antenna.dt: .1e}ns"
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+ f"; noise_factor: {noise_sigma_factor: .1e}"
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)
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ax.set_xlabel("Time Residual [ns]")
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ax.set_ylabel("#")
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counts, bins, _patches = ax.hist(time_residuals, **hist_kwargs)
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if True: # fit gaussian to histogram
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min_x = min(time_residuals)
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max_x = max(time_residuals)
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dx = bins[1] - bins[0]
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scale = len(time_residuals) * dx
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xs = np.linspace(min_x, max_x)
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# do the fit
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name = "Norm"
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param_names = [ "$\\mu$", "$\\sigma$" ]
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distr_func = stats.norm
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label = name +"(" + ','.join(param_names) + ')'
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# plot
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fit_params = distr_func.fit(time_residuals)
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fit_ys = scale * distr_func.pdf(xs, *fit_params)
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ax.plot(xs, fit_ys, label=label)
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# chisq
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ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
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if True:
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ct *= np.sum(counts)/np.sum(ct)
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c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
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chisq_strs = [
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f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
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]
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# text on plot
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text_str = "\n".join(
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[label]
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+
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[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, fit_params, fillvalue='?') ]
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+
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chisq_strs
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)
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ax.text( *(0.02, 0.95), text_str, fontsize=12, ha='left', va='top', transform=ax.transAxes)
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fig.savefig("figures/11_time_residual_hist_{noise_sigma_factor: .1e}.pdf")
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if True:
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plt.close(fig)
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# SNR time accuracy plot
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if True:
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fig, ax = plt.subplots()
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ax.set_title("Template matching SNR vs time accuracy")
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ax.set_xlabel("Signal to Noise Factor")
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ax.set_ylabel("Time Accuracy [ns]")
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if True:
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ax.set_xscale('log')
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ax.set_yscale('log')
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# plot the values
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ax.plot(1/np.asarray(noise_factors), time_accuracies, ls='none', marker='o')
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# Set horizontal line at 1 ns
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if True:
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ax.axhline(1, ls='--', alpha=0.8, color='g')
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ax.grid()
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fig.tight_layout()
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fig.savefig("figures/11_time_res_vs_snr.pdf")
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plt.show()
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