E=compare_E,
title='compare E, index: {}'.format(str(compare_index)),
mse=None,
integrate=integrate)
for frog, E, index1, mse1 in zip(their_frog, similar_E_fields, their_index,
their_mse):
plot_frog_and_E(frog=np.array(frog),
E=E,
title='mse<{}, index: {}'.format(
str(msemax), str(index1)),
mse=mse1,
integrate=integrate)
plt.show()
_, t, w, dt, w0, _ = retrieve_data(plot_frog_bool=False, print_size=False)
# find_nearly_matching_frog_traces(compare_index=4, msemax=0.006, search_size=10000,
# file="frogtrainingdata.hdf5", integrate='real')
find_nearly_matching_frog_traces(compare_index=3,
msemax=0.01,
search_size=14000,
file="frogtrainingdata.hdf5",
integrate='real')
# find_nearly_matching_E_field(compare_index=1, msemax=0.0001, search_size=10000,
# file="frogtrainingdata.hdf5")
from tensorflow.contrib.keras import models
import matplotlib.pyplot as plt
from generate_data import retrieve_data
import numpy as np
import tables
# model = models.load_model('model.hdf5')
model = models.load_model('1000_epochs_600_samples.hdf5')
_, t, _, _, _, _ = retrieve_data(False, False)
def test_sample(index):
index_test = index
E_actual = E_real[index_test] + 1j * E_imag[index_test]
pred = model.predict(frog[index_test].reshape(1, 58, 106, 1))
E_imag_pred = pred[0][128:]
E_real_pred = pred[0][:128]
E_pred = E_real_pred + 1j * E_imag_pred
fig, ax = plt.subplots(2, 2)
# ax[0][0].pcolormesh(frog[index_test].reshape(58, 106), cmap='jet')
ax[1][1].plot(t, np.abs(E_pred), color='blue', linestyle='dashed')
ax[1][1].plot(t, np.real(E_pred), color='blue')
ax[1][1].plot(t, np.imag(E_pred), color='red')
ax[0][0].pcolormesh(frog[index_test].reshape(58, 106), cmap='jet')
def test_data_show():
for j in range(tests):
# generate data
_, t, w, dt, w0, _ = retrieve_data(plot_frog_bool=False, print_size=False)
E_actual = E_real[j] + 1j * E_imag[j]
frogtrace_flat = frog[j]
# generate model prediction
pred = model.predict(frogtrace_flat.reshape(1, 58, 106, 1))
E_imag_pred = pred[0][128:]
E_real_pred = pred[0][:128]
E_pred = E_real_pred + 1j * E_imag_pred
# plot actual E(t) and phi(t)
ax1 = plt.subplot2grid((4,2), (0,0), rowspan=1, colspan=1)
ax1.plot(t, np.real(E_actual), color="blue", alpha=0.5)
ax1.plot(t, np.imag(E_actual), color="red", alpha=0.5)
ax1.plot(t, np.abs(E_actual), color='black')
ax1.set_title("Actual $E(t)$")
ax1.set_xticks([])
ax1.set_yticks([])
axtwin = ax1.twinx()
if j == tests-1:
axtwin.set_ylabel('$\phi (t)$', color='green')
axtwin.plot(t, np.unwrap(np.angle(E_actual)), color='green')
# plot actual E(w) and phi(w)
E_w_actual = np.fft.fftshift(np.fft.fft(np.fft.fftshift(E_actual)))
ax1_2 = plt.subplot2grid((4,2), (0,1), rowspan=1, colspan=1)
ax1_2.plot(w, np.real(E_w_actual), color="blue", alpha=0.5)
ax1_2.plot(w, np.imag(E_w_actual), color="red", alpha=0.5)
ax1_2.plot(w, np.abs(E_w_actual), color='black')
ax1_2.set_title("Actual $E(\omega)$")
ax1_2.set_xticks([])
ax1_2.set_yticks([])
axtwin = ax1_2.twinx()
if j == tests-1:
axtwin.set_ylabel('$\phi (\omega)$', color='green')
axtwin.plot(w, np.unwrap(np.angle(E_w_actual)), color='green')
# plot actual FROG trace
ax2 = plt.subplot2grid((4, 2), (1, 0), rowspan=1, colspan=2)
ax2.pcolormesh(frogtrace_flat.reshape(58, 106), cmap='jet')
ax2.set_title("Network Input FROG trace")
ax2.set_xticks([])
ax2.set_yticks([])
# plot retrieved E(t) and phi(t)
ax3 = plt.subplot2grid((4, 2), (2, 0), rowspan=1, colspan=1)
ax3.plot(t, np.real(E_pred), color="blue", alpha=0.5)
ax3.plot(t, np.imag(E_pred), color="red", alpha=0.5)
ax3.plot(t, np.abs(E_pred), color='black')
ax3.set_title("Retrieved $E(t)$")
ax3.set_xticks([])
ax3.set_yticks([])
axtwin = ax3.twinx()
if j == tests-1:
axtwin.set_ylabel('$\phi (t)$', color='green')
axtwin.plot(t, np.unwrap(np.angle(E_pred)), color='green')
# plot retrieved E(w) and phi(w)
E_w_pred = np.fft.fftshift(np.fft.fft(np.fft.fftshift(E_pred)))
ax3_2 = plt.subplot2grid((4,2), (2, 1), rowspan=1, colspan=1)
ax3_2.plot(w, np.real(E_w_pred), color="blue", alpha=0.5)
ax3_2.plot(w, np.imag(E_w_pred), color="red", alpha=0.5)
ax3_2.plot(w, np.abs(E_w_pred), color="black")
ax3_2.set_title("Retrieved $E(\omega)$")
ax3_2.set_xticks([])
ax3_2.set_yticks([])
axtwin = ax3_2.twinx()
if j == tests-1:
axtwin.set_ylabel('$\phi (\omega)$', color='green')
axtwin.plot(w, np.unwrap(np.angle(E_w_pred)), color='green')
# plot the reconstructed FROG trace
frogtrace, tau_frog, w_frog = plot_frog(E=E_pred, t=t, w=w, dt=dt, w0=w0, plot=False)
ax4 = plt.subplot2grid((4, 2), (3, 0), rowspan=1, colspan=2)
ax4.pcolormesh(frogtrace, cmap="jet")
ax4.set_title("Network Reconstructed FROG trace")
ax4.set_xticks([])
ax4.set_yticks([])
# add y labels on left side of graph only
if j == 0:
ax1.set_ylabel('Actual E(t)')
ax2.set_ylabel('FROG trace')
ax3.set_ylabel('Retrieved E(t)')
ax4.set_ylabel("Reconstructed FROG trace")