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m-thesis-introduction
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ZH: findks: u rewrite function from samples to periods 

This makes sure to actually use interpolation to find the best period offset instead of rolling the samples.
The latter won't work when the period is not dividable by the sampling period.
This commit is contained in:
Eric Teunis de Boone 2023-04-28 17:08:53 +02:00
parent 9f610ab2e7
commit 3bc5504c2b
1 changed files with 16 additions and 25 deletions
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41
airshower_beacon_simulation/ca_period_from_shower.py
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@ -41,9 +41,6 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
else:
plot_iteration_with_shifted_trace = []
allowed_sample_shifts = np.rint(allowed_ks * period /dt).astype(int)
sample_shift_first_trace = np.rint(period_shift_first_trace * period/dt).astype(int)
# propagate to test location
for i, ant in enumerate(antennas):
aloc = [ant.x, ant.y, ant.z]
@ -72,14 +69,13 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
t_sum = np.arange(t_min+max_neg_shift, t_max+max_pos_shift, dt)
a_sum = np.zeros(len(t_sum))
best_sample_shifts = np.zeros( (len(antennas)) ,dtype=int)
best_period_shifts = np.zeros( (len(antennas)) ,dtype=int)
for i, (t_r, E_) in enumerate(zip(t_, a_)):
f = interp1d(t_r, E_, assume_sorted=True, bounds_error=False, fill_value=0)
a_int = f(t_sum)
if i == 0:
a_sum += a_int
best_sample_shifts[i] = sample_shift_first_trace
a_sum += f(t_sum)
best_period_shifts[i] = period_shift_first_trace
continue
# init figure
@ -90,26 +86,27 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
ax.set_ylabel("Amplitude")
ax.plot(t_sum, a_sum)
shift_maxima = np.zeros( len(allowed_sample_shifts) )
for j, shift in enumerate(allowed_sample_shifts):
augmented_a = np.roll(a_int, shift)
# find the maxima for each period shift k
shift_maxima = np.zeros( len(allowed_ks) )
for j, k in enumerate(allowed_ks):
augmented_a = f(t_sum + k*period)
shift_maxima[j] = np.max(augmented_a + a_sum)
if i in plot_iteration_with_shifted_trace:
ax.plot(t_sum, augmented_a, alpha=0.7, ls='dashed', label=f'{shift}')
if i in plot_iteration_with_shifted_trace and abs(k) <= 3:
ax.plot(t_sum, augmented_a, alpha=0.7, ls='dashed', label=f'{k:g}')
# transform maximum into best_sample_shift
best_idx = np.argmax(shift_maxima)
best_sample_shifts[i] = allowed_sample_shifts[best_idx]
best_augmented_a = np.roll(a_int, best_sample_shifts[i])
best_period_shifts[i] = allowed_ks[best_idx]
best_augmented_a = f(t_sum + k*period)
a_sum += best_augmented_a
# cleanup figure
if i in plot_iteration_with_shifted_trace:
if True: # plot best k again
ax.plot(t_sum, augmented_a, alpha=0.8, label=f'best k={best_sample_shifts[i]}', lw=2)
ax.plot(t_sum, best_augmented_a, alpha=0.8, label=f'best $k$={best_period_shifts[i]:g}', lw=2)
ax.legend( ncol=5 )
if fig_dir:
@ -126,7 +123,7 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
if True: # zoomed on peak of this trace
x = t_r[np.argmax(E_)]
wx = 50 + (max(best_sample_shifts) - min(best_sample_shifts))*dt
wx = 50 + (max(best_period_shifts) - min(best_period_shifts))*dt
ax.set_xlim(x-wx, x+wx)
fig.savefig(fname + ".zoomed.peak.pdf")
@ -138,10 +135,10 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
# sort by antenna (undo sorting by maximum)
undo_sort_idx = np.argsort(sort_idx)
best_sample_shifts = best_sample_shifts[undo_sort_idx]
best_period_shifts = best_period_shifts[undo_sort_idx]
# Return ks
return best_sample_shifts, np.max(a_sum)
return best_period_shifts, np.max(a_sum)
if __name__ == "__main__":
import sys
@ -413,16 +410,10 @@ if __name__ == "__main__":
yy.append(y_+yoff)
# Find best k for each antenna
shifts, maximum = find_best_period_shifts_summing_at_location(test_loc, ev.antennas, allowed_ks, period=1/f_beacon, dt=dt,
ks_per_loc[i], maxima_per_loc[i] = find_best_period_shifts_summing_at_location(test_loc, ev.antennas, allowed_ks, period=1/f_beacon, dt=dt,
plot_iteration_with_shifted_trace=[ 5, len(ev.antennas)-1],
fig_dir=tmp_fig_subdir, fig_distinguish=f"run{r}.")
# Translate sample shifts back into period multiple k
ks = np.rint(shifts*f_beacon*dt)
ks_per_loc[i] = ks
maxima_per_loc[i] = maximum
xx = np.array(xx)
yy = np.array(yy)
locs = list(zip(xx, yy))