Delete plotting_utils.py

plotting_utils.py

@@ -1,83 +0,0 @@
-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()
-    data = data.transpose(2, 0, 1)
-    return data
-
-
-def plot_gst_scores_to_numpy(gst_scores, info=None):
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(gst_scores, aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Validation samples'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Style Tokens')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = save_figure_to_numpy(fig)
-    plt.close()
-    data = data.transpose(2, 0, 1)
-    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()
-    data = data.transpose(2, 0, 1)
-    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()
-    data = data.transpose(2, 0, 1)
-    return data