predict_pulse_script_3.py

import numpy as np
import Functions as fn
import matplotlib.pyplot as plt
import h5py
import importlib

importlib.reload(fn)

transfer = 'TF_train_waveform_convert.hdf5'

h5_reformed = h5py.File(transfer, 'r')

if 'Pulse_truth' not in h5_reformed:
    raise Exception('No "Pulse_truth" in file.')
else:
    Pulse_truth = h5_reformed['Pulse_truth']

for key in list(h5_reformed.keys()):
    print('shape of {} is {}'.format(key, h5_reformed[key].shape))

cut_bot = 0000
cut_top = 100000

cut = np.unique(np.random.random_integers(low=cut_bot, high=cut_top, size=1))
print(cut)
ground_truther = Pulse_truth[cut, ...]
mag_truth = ground_truther[:, 0, :][0]
phase_truth = ground_truther[:, 1, :][0]

h5_reformed.close()

predictions = classifier.predict(
    input_fn=lambda: fn.predict_hdf5_functor(transfer=transfer,
                                             select=cut,
                                             batch_size=1))

fig, ax = plt.subplots(nrows=int(2), ncols=int(1),
                       figsize=(22, 44))
grid = np.indices(dimensions=(2, 1))
for predict in predictions:
    mag_pred = predict['output'][0:100]
    phase_pred = predict['output'][100:200]
ax[0].plot(mag_truth, 'b', mag_pred, 'r')
ax[1].plot(phase_truth, 'b', phase_pred, 'r')
print(np.sum(((mag_pred - mag_truth) ** 2 + (phase_pred - phase_truth) ** 2)) / 200.)
# display(fig)

# fig.savefig('Images/sampleWaveforms4.png', dpi= 700)