def plot_fields(self, fig=None):
'''
Plots the fields at 7 round trips distributed through
all the round trips.
'''
if not self.fields_are_calculated():
self.logger.error('Fields are not calculated')
message = 'Calculate fields before ploting'
raise NotReady(message)
if fig is None:
fig, (axI, axP) = EField.create_figure()
step = round_to_odd(self.fields.shape[0] / 7)
for i in range(0, self.fields.shape[0], step):
efield = EField(self.input_efield.x, self.fields[i, :])
fig, (axI, axP) = efield.plot(fig,
label='# {}'.format(i),
normalize=True)
axI.legend()
return fig, (axI, axP)
for dL in tqdm(dLs, desc='Spectrum calculation')
]
t_spectrum = time() - t
E_cav_1 = EField(x, compute_total_field(fields, lda, res.x))
E_cav_2 = EField(x, compute_total_field(fields, lda, res.x + lda / 4))
# Timings
print('Time for fields calculation : {:.1f} s'.format(t_fields))
print('Time for maxima search : {:.1f} ms'.format(t_minimize * 1e3))
print('Time for {} points spectrum : {:.1f} s'.format(n, t_spectrum))
print('Maxima found at dL = {:.3f} lda'.format(res.x / lda))
# Plot few round-trips
fig, (axI, axP) = E_ref.plot(label='Input', normalize=True)
for i in range(0, N_rt, round_to_odd(N_rt / 7)):
EField(x, fields[i, :]).plot(fig,
label='# {}'.format(i),
normalize=False)
axI.legend(loc='upper right')
# Plot spectrum
plt.figure()
plt.semilogy(dLs / lda, power)
plt.xlabel(r'$\Delta L$ ($\lambda$)')
plt.semilogy([res.x / lda, res.x / lda], plt.ylim(), '--')
plt.semilogy([res.x / lda + 0.25, res.x / lda + 0.25], plt.ylim(), '--')
plt.ylabel('Power')
# Plot modes
E = HG_efield(n=0, x=x, w=win,)
E.E = E.E / np.sqrt(E.I.max())
angle = 1e-2
print('Electric Field E : {}'.format(E))
print('Power : {:.5f}'.format(E.P))
print(len(E))
dist = 2.0
E_prop = E.tilt(angle).propagate(dist=dist)
zr = np.pi * win**2 / E.lda
w_th = win*np.sqrt(1+(dist/zr)**2)
E_theo = EField(x, gaussian(x, amplitude=np.sqrt(win/w_th),
center=angle * dist, waist=w_th, offset=0))
E_theo = HG_efield(n=0, x=x, amplitude=np.sqrt(win/w_th),
w=w_th, x0=angle*dist)
# plt.figure()
# plt.plot(x, abs(E_theo.I/E_theo.I.max()-E_prop.I/E_prop.I.max()))
s = 'Result agrees with theory : {}'
print(s.format(np.allclose(E_theo.I, E_prop.I)))
fig, (axI, _) = E.plot(label='Reference')
E_prop.plot(fig, label='Propagated')
E_theo.plot(fig, linestyle='--', label='Theoretic')
axI.legend()
plt.show()
method='bounded',
options={'xatol': lda / 10000})
n = 200
DdL = lda / 2 / 20
dLs = np.linspace(0, 1.2 * lda / 2, n)
dLs = np.hstack([
np.linspace(res.x - DdL, res.x + DdL, n),
np.linspace(res.x + DdL, res.x + lda / 2 - DdL, 15)
])
power = [compute_power(x, fields, lda, dL) for dL in dLs]
E_cav = EField(x, compute_total_field(fields, lda, res.x))
print('Maxima found at dL = {:.3f} lda'.format(res.x / lda))
fig, (axI, axP) = E_ref.plot(label='Input', normalize=True)
for i in range(0, N_rt, round_to_odd(N_rt / 5)):
EField(x, fields[i, :]).plot(fig, label='# {}'.format(i), normalize=True)
axI.legend(loc='upper right')
plt.figure()
plt.semilogy(dLs / lda, power)
plt.xlabel(r'$\Delta L$ ($\lambda$)')
plt.plot([res.x / lda, res.x / lda], plt.ylim(), '--')
plt.ylabel('Power')
fig, (axI, axP) = E_ref.plot(label='Input', normalize=True)
E_cav.plot(fig, label='Mode', normalize=True)
plt.show()