[python]librosa安装后测试代码plot_display

import numpy as np
import matplotlib.pyplot as plt
import librosa
y, sr = librosa.load(librosa.ex('trumpet'))
D = librosa.stft(y)  
S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max)
plt.figure()
librosa.display.specshow(S_db)
plt.colorbar()
fig, ax = plt.subplots()
img = librosa.display.specshow(S_db, ax=ax)
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots()
img = librosa.display.specshow(S_db, x_axis='time', y_axis='linear', ax=ax)
ax.set(title='Now with labeled axes!')
fig.colorbar(img, ax=ax, format="%+2.f dB")
fig, ax = plt.subplots()
img = librosa.display.specshow(S_db, x_axis='time', y_axis='log', ax=ax)
ax.set(title='Using a logarithmic frequency axis')
fig.colorbar(img, ax=ax, format="%+2.f dB")
fig, ax = plt.subplots()
D_highres = librosa.stft(y, hop_length=256, n_fft=4096)
S_db_hr = librosa.amplitude_to_db(np.abs(D_highres), ref=np.max)
img = librosa.display.specshow(S_db_hr, hop_length=256, x_axis='time', y_axis='log',
                               ax=ax)
ax.set(title='Higher time and frequency resolution')
fig.colorbar(img, ax=ax, format="%+2.f dB")
fig, ax = plt.subplots()
M = librosa.feature.melspectrogram(y=y, sr=sr)
M_db = librosa.power_to_db(M, ref=np.max)
img = librosa.display.specshow(M_db, y_axis='mel', x_axis='time', ax=ax)
ax.set(title='Mel spectrogram display')
fig.colorbar(img, ax=ax, format="%+2.f dB")
C = librosa.cqt(y=y, sr=sr)
C_db = librosa.amplitude_to_db(np.abs(C), ref=np.max)
fig, ax = plt.subplots()
librosa.display.specshow(C_db, y_axis='cqt_hz', x_axis='time', ax=ax)
ax.set(title='Frequency (Hz) axis decoration')
fig, ax = plt.subplots()
librosa.display.specshow(C_db, y_axis='cqt_note', x_axis='time', ax=ax)
ax.set(title='Pitch axis decoration')
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
fig, ax = plt.subplots()
img = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time', ax=ax)
ax.set(title='Chromagram demonstration')
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots()
img = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time',
                               key='C:min', ax=ax)
ax.set(title='Chromagram explicitly in F:dorian')
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots()
img = librosa.display.specshow(chroma, y_axis='chroma_h', x_axis='time',
                               Sa=5, thaat='kafi', ax=ax)
ax.set(title='Chromagram with Hindustani notation')
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots()
img = librosa.display.specshow(chroma, y_axis='chroma_c', x_axis='time',
                               Sa=5, mela=22, ax=ax)
ax.set(title='Chromagram with Carnatic notation')
fig.colorbar(img, ax=ax)
Sa = librosa.note_to_hz('F4')
fig, ax = plt.subplots()
librosa.display.specshow(C_db, y_axis='cqt_svara', Sa=Sa, x_axis='time', ax=ax)
ax.set(title='Hindustani decoration',
       ylim=[Sa, 2*Sa])
fig, ax = plt.subplots()
librosa.display.specshow(C_db, y_axis='cqt_svara', Sa=Sa, mela=22, x_axis='time', ax=ax)
ax.set(title='Carnatic decoration',
       ylim=[Sa, 2*Sa])
R = librosa.segment.recurrence_matrix(chroma, mode='affinity')
fig, ax = plt.subplots()
img = librosa.display.specshow(R, y_axis='time', x_axis='time', ax=ax)
ax.set(title='Recurrence / self-similarity')
fig.colorbar(img, ax=ax)
ccov = np.cov(chroma)
fig, ax = plt.subplots()
img = librosa.display.specshow(ccov, y_axis='chroma', x_axis='chroma',
                               key='Eb:maj', ax=ax)
ax.set(title='Chroma covariance')
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots()
img = librosa.display.specshow(S_db, cmap='gray_r', y_axis='log', x_axis='time', ax=ax)
ax.set(title='Inverted grayscale')
fig.colorbar(img, ax=ax, format="%+2.f dB")
max_var = np.max(np.abs(ccov))
fig, ax = plt.subplots()
img = librosa.display.specshow(ccov, vmin=-max_var, vmax=max_var,
                               y_axis='chroma', x_axis='chroma',
                               key='Eb:maj', ax=ax)
ax.set(title='Chroma covariance')
fig.colorbar(img, ax=ax)
fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True)
img1 = librosa.display.specshow(S_db, x_axis='time', y_axis='log', ax=ax[0])
ax[0].set(title='STFT (log scale)')
img2 = librosa.display.specshow(M_db, x_axis='time', y_axis='mel', ax=ax[1])
ax[1].set(title='Mel')
img3 = librosa.display.specshow(chroma, x_axis='time', y_axis='chroma', 
                                key='Eb:maj', ax=ax[2])
ax[2].set(title='Chroma')
for ax_i in ax:
    ax_i.label_outer()
fig.colorbar(img1, ax=[ax[0], ax[1]])
fig.colorbar(img3, ax=[ax[2]])
ax[0].set(xlim=[1, 3])  
y, sr = librosa.load(librosa.ex('nutcracker'))
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
beats = librosa.util.fix_frames(beats, x_min=0)
chroma_sync = librosa.util.sync(chroma, beats, aggregate=np.median)
beat_times = librosa.frames_to_time(beats)
fig, ax = plt.subplots(nrows=2, sharex=True)
img = librosa.display.specshow(chroma, y_axis='chroma', x_axis='time', ax=ax[0],
                               key='Eb:maj')
ax[0].set(title='Uniform time sampling')
ax[0].label_outer()
librosa.display.specshow(chroma_sync, y_axis='chroma', x_axis='time',
                         x_coords=beat_times, ax=ax[1], key='Eb:maj')
ax[1].set(title='Beat-synchronous sampling')
fig.colorbar(img, ax=ax)
ax[1].set(xlim=[10, 25])