ericteunis/m-thesis-introduction
1
0
Fork 0
mirror of https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git synced 2024-12-22 03:23:34 +01:00
Code Issues Projects Releases Packages Wiki Activity

ZH: do reconstruction for best_k for each grid run iteration

Browse source
This commit is contained in:
Eric Teunis de Boone 2023-01-09 16:09:17 +01:00
parent 72a98d3f09
commit 49d4779180
1 changed files with 56 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

67
simulations/airshower_beacon_simulation/ca_period_from_shower.py
View file

@ -154,15 +154,9 @@ if __name__ == "__main__":
fig_subdir = path.join(fig_dir, 'shifts/')
show_plots = False
allowed_ks = np.arange(-2, 3, 1)
allowed_ks = [ -2, -1, 0, 1, 2]
Xref = 400
x_coarse = np.linspace(-20e3, 20e3, 10)
y_coarse = np.linspace(-20e3, 20e3, 10)
x_fine = np.linspace(-2e3, 2e3, 30)
y_fine = np.linspace(-2e3, 2e3, 30)
N_runs = 3
####
@ -250,16 +244,42 @@ if __name__ == "__main__":
plt.show()
##
## Determine grid positions
##
dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),Xref,0)
scale2d = dXref*np.tan(np.deg2rad(2.))
scale4d = dXref*np.tan(np.deg2rad(4.))
if not True: #quicky
N_runs = 2
x_coarse = np.linspace(-scale2d, scale2d, 4)
y_coarse = np.linspace(-scale2d, scale2d, 4)
x_fine = x_coarse/4
y_fine = y_coarse/4
else: # long
N_runs = 5
x_coarse = np.linspace(-scale4d, scale4d, 40)
y_coarse = np.linspace(-scale4d, scale4d, 40)
x_fine = np.linspace(-scale2d, scale2d, 40)
y_fine = np.linspace(-scale2d, scale2d, 40)
##
## Do calculations on the grid
##
# Setup Plane grid to test
for r in range(N_runs):
# Setup Plane grid to test
xoff, yoff = 0,0
if r == 0:
x = x_coarse
y = y_coarse
else:
# zooming in
old_ks_per_loc = ks_per_loc[best_idx]
xoff = xx[best_idx]
yoff = yy[best_idx]
@ -308,7 +328,7 @@ if __name__ == "__main__":
if True: #plot maximum at test locations
fig, axs = plt.subplots()
axs.set_title(f"Grid Run {r}")
axs.set_title(f"Optimizing signal strength, Grid Run {r}")
axs.set_ylabel("vxvxB [km]")
axs.set_xlabel(" vxB [km]")
axs.set_aspect('equal', 'datalim')
@ -328,15 +348,37 @@ if __name__ == "__main__":
axs.set_ylim(*old_ylims)
fig.tight_layout()
## Save ks to file
##
best_idx = np.argmax(maxima_per_loc)
np.savetxt(fig_dir + __file__+f'.bestk.run{r}.txt', ks_per_loc[best_idx] )
best_k = ks_per_loc[best_idx]
print("Max at location: ", locs[best_idx])
print('Best k:', ks_per_loc[best_idx])
print('Best k:', best_k)
## Save best ks to file
np.savetxt(fig_dir + __file__+f'.bestk.run{r}.txt', best_k )
## Do a small reconstruction of the shower for best ks
if True:
print("Reconstructing for best k")
_, __, p, ___ = rit.shower_plane_slice(ev, X=Xref, Nx=len(x), Ny=len(y), wx=scale2d, wy=scale2d, xoff=xoff, yoff=yoff, zgr=0)
fig, axs = plt.subplots()
axs.set_title(f"Shower reconstruction with best k, Grid Run {r}")
axs.set_ylabel("vxvxB [km]")
axs.set_xlabel(" vxB [km]")
axs.set_aspect('equal', 'datalim')
sc = axs.scatter(xx/1e3, yy/1e3, c=p, cmap='Spectral_r', alpha=0.6)
fig.colorbar(sc, ax=axs)
if fig_dir:
fig.tight_layout()
fig.savefig(path.join(fig_dir, __file__+f'.reconstruction.run{r}.pdf'))
# Abort if no improvement
if ( r!= 0 and (old_ks_per_loc == ks_per_loc[best_idx]).all() ):
print("No changes from previous grid, breaking")
# TODO: notate this case somewhere
break
old_ks_per_loc = ks_per_loc[best_idx]
@ -359,4 +401,5 @@ if __name__ == "__main__":
h5attrs['best_k'] = old_ks_per_loc[i]
h5attrs['best_k_time'] = old_ks_per_loc[i]*dt/f_beacon
if show_plots:
plt.show()