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"""
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Utilities for inspecting encoded music data.
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"""
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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import anticipation.ops as ops
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from anticipation.config import *
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from anticipation.vocab import *
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def visualize(tokens, output, selected=None):
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colors = ['white', '#426aa0', '#b26789', '#de9283', '#eac29f', 'silver', 'red', 'sienna', 'darkorange', 'gold', 'yellow', 'palegreen', 'seagreen', 'cyan', 'dodgerblue', 'slategray', 'navy']
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plt.rcParams['figure.dpi'] = 300
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plt.rcParams['savefig.dpi'] = 300
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max_time = ops.max_time(tokens, seconds=False)
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grid = np.zeros([max_time, MAX_PITCH])
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instruments = list(sorted(list(ops.get_instruments(tokens).keys())))
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if 128 in instruments:
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instruments.remove(128)
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for j, (tm, dur, note) in enumerate(zip(tokens[0::3],tokens[1::3],tokens[2::3])):
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if note == SEPARATOR:
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assert tm == SEPARATOR and dur == SEPARATOR
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print(j, 'SEPARATOR')
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continue
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if note == REST:
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continue
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assert note < CONTROL_OFFSET
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tm = tm - TIME_OFFSET
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dur = dur - DUR_OFFSET
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note = note - NOTE_OFFSET
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instr = note//2**7
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pitch = note - (2**7)*instr
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if instr == 128:
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continue
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if selected and instr not in selected:
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continue
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grid[tm:tm+dur, pitch] = 1+instruments.index(instr)
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plt.clf()
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plt.axis('off')
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cmap = matplotlib.colors.ListedColormap(colors)
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bounds = list(range(MAX_TRACK_INSTR)) + [16]
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norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
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plt.imshow(np.flipud(grid.T), aspect=16, cmap=cmap, norm=norm, interpolation='none')
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patches = [matplotlib.patches.Patch(color=colors[i+1], label=f"{instruments[i]}")
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for i in range(len(instruments))]
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plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0. )
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plt.tight_layout()
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plt.savefig(output)
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