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