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61 lines
1.7 KiB
61 lines
1.7 KiB
import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pylab as plt
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import numpy as np
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def save_figure_to_numpy(fig):
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# save it to a numpy array.
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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return data
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def plot_alignment_to_numpy(alignment, info=None):
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(alignment, aspect='auto', origin='lower',
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interpolation='none')
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fig.colorbar(im, ax=ax)
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xlabel = 'Decoder timestep'
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if info is not None:
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xlabel += '\n\n' + info
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plt.xlabel(xlabel)
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plt.ylabel('Encoder timestep')
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plt.tight_layout()
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fig.canvas.draw()
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data = save_figure_to_numpy(fig)
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plt.close()
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return data
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def plot_spectrogram_to_numpy(spectrogram):
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fig, ax = plt.subplots(figsize=(12, 3))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower",
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interpolation='none')
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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fig.canvas.draw()
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data = save_figure_to_numpy(fig)
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plt.close()
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return data
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def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
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fig, ax = plt.subplots(figsize=(12, 3))
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ax.scatter(range(len(gate_targets)), gate_targets, alpha=0.5,
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color='green', marker='+', s=1, label='target')
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ax.scatter(range(len(gate_outputs)), gate_outputs, alpha=0.5,
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color='red', marker='.', s=1, label='predicted')
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plt.xlabel("Frames (Green target, Red predicted)")
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plt.ylabel("Gate State")
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plt.tight_layout()
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fig.canvas.draw()
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data = save_figure_to_numpy(fig)
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plt.close()
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return data
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