def get_images(model,
sample,
return_raw_data=False,
calc_chi2=False,
gen_more=None,
batch_size=128):
X, Y = sample
assert X.ndim == 2
assert X.shape[1] == 4
if gen_more is None:
gen_features = X
else:
gen_features = np.tile(X, [gen_more] + [1] * (X.ndim - 1))
gen_scaled = np.concatenate(
[
model.make_fake(gen_features[i:i + batch_size]).numpy()
for i in range(0, len(gen_features), batch_size)
],
axis=0,
)
real = model.scaler.unscale(Y)
gen = model.scaler.unscale(gen_scaled)
gen[gen < 0] = 0
gen1 = np.where(gen < 1.0, 0, gen)
features = {
'crossing_angle': (X[:, 0], gen_features[:, 0]),
'dip_angle': (X[:, 1], gen_features[:, 1]),
'drift_length': (X[:, 2], gen_features[:, 2]),
'time_bin_fraction': (X[:, 2] % 1, gen_features[:, 2] % 1),
'pad_coord_fraction': (X[:, 3] % 1, gen_features[:, 3] % 1),
}
images = make_metric_plots(real,
gen,
features=features,
calc_chi2=calc_chi2)
if calc_chi2:
images, chi2 = images
images1 = make_metric_plots(real, gen1, features=features)
img_amplitude = make_histograms(Y.flatten(),
gen_scaled.flatten(),
'log10(amplitude + 1)',
logy=True)
result = [images, images1, img_amplitude]
if return_raw_data:
result += [(gen_features, gen)]
if calc_chi2:
result += [chi2]
return result
def get_images(return_raw_data=False,
calc_chi2=False,
gen_more=None,
sample=(X_test, Y_test),
batch_size=128):
X, Y = sample
if gen_more is None:
gen_features = X
else:
gen_features = np.tile(X, [gen_more] + [1] * (X.ndim - 1))
gen_scaled = np.concatenate(
[
model.make_fake(gen_features[i:i + batch_size]).numpy()
for i in range(0, len(gen_features), batch_size)
],
axis=0,
)
real = unscale(Y)
gen = unscale(gen_scaled)
gen[gen < 0] = 0
gen1 = np.where(gen < 1.0, 0, gen)
images = make_metric_plots(real,
gen,
features={'angle': (X, gen_features)},
calc_chi2=calc_chi2)
if calc_chi2:
images, chi2 = images
images1 = make_metric_plots(real,
gen1,
features={'angle': (X, gen_features)})
img_amplitude = make_histograms(Y_test.flatten(),
gen_scaled.flatten(),
'log10(amplitude + 1)',
logy=True)
result = [images, images1, img_amplitude]
if return_raw_data:
result += [(gen_features, gen)]
if calc_chi2:
result += [chi2]
return result
def write_hist_summary(step):
if step % args.save_every == 0:
gen_scaled = model.make_fake(len(X_test)).numpy()
real = unscale(X_test)
gen = unscale(gen_scaled)
gen[gen < 0] = 0
gen1 = np.where(gen < 1.0, 0, gen)
images = make_metric_plots(real, gen)
images1 = make_metric_plots(real, gen1)
img_amplitude = make_histograms(X_test.flatten(),
gen_scaled.flatten(),
'log10(amplitude + 1)',
logy=True)
with writer_val.as_default():
for k, img in images.items():
tf.summary.image(k, img, step)
for k, img in images1.items():
tf.summary.image("{} (amp > 1)".format(k), img, step)
tf.summary.image("log10(amplitude + 1)", img_amplitude, step)
def write_hist_summary(step):
if step % 50 == 0:
gen_scaled = baseline_10x15.make_fake(len(X_test)).numpy()
real = unscale(X_test)
gen = unscale(gen_scaled)
gen[gen < 0] = 0
gen30 = np.where(gen < 30.0, 0, gen)
images = make_metric_plots(real, gen)
images30 = make_metric_plots(real, gen30)
img_amplitude = make_histograms(X_test.flatten(),
gen_scaled.flatten(),
'log10(amplitude + 1)',
logy=True)
with writer_val.as_default():
for k, img in images.items():
tf.summary.image(k, img, step)
for k, img in images30.items():
tf.summary.image("{} (amp > 30)".format(k), img, step)
tf.summary.image("log10(amplitude + 1)", img_amplitude, step)