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import matplotlib.pyplot as plt
import numpy as np
def plot_self_attention_map(spectra, att_map, offset=400):
if att_map is not None:
fig, ax1 = plt.subplots(figsize=(12, 12))
ax2 = plt.twinx().twiny()
ax2.set_xlim(offset, offset+len(spectra))
ax1.set_xlabel('Patch index', fontsize=18)
ax1.set_ylabel('Patch index', fontsize=18)
ax1.imshow(att_map[1:, 1:], cmap='inferno', interpolation='nearest', aspect="auto")
# tem_x = np.zeros_like(spectra)
x = np.linspace(offset, offset + len(spectra), len(spectra))
# for i, s in enumerate(spectra):
# tem_x[i] = i + 400
ax2.set_xlabel('Wavelength', fontsize=18)
ax2.set_ylabel('Intensity (a.u.)', fontsize=18)
ax2.plot(x, spectra)
return fig
def plot_spectra(spectra, offset=400):
fig = plt.figure()
x = np.linspace(offset, offset + len(spectra), len(spectra))
# fg.title("Spectra Signal")
plt.plot(x, spectra)
return fig
def plot_result(name, score):
fig, ax = plt.subplots(figsize=(10, 10))
y_pos = np.arange(len(name))
ax.barh(y_pos, score, align='center')
ax.set_yticks(y_pos, labels=name)
for value, pos in zip(score, y_pos):
ax.text(value-0.2, pos, value, ha='right')
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Confidence')
ax.set_title('Prediction')
return fig
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