def spectrum(signal, Fs, N):
import stft
import windows
F = stft.stft(signal, N, N / 2, win=windows.hann(N))
stft.spectroplot(F.T, N, N / 2, Fs)
def comparePlot(signal1, signal2, Fs, fft_size=512, norm=False, equal=False, title1=None, title2=None):
import matplotlib.pyplot as plt
td_amp = np.maximum(np.abs(signal1).max(), np.abs(signal2).max())
if norm:
if equal:
signal1 /= np.abs(signal1).max()
signal2 /= np.abs(signal2).max()
else:
signal1 /= td_amp
signal2 /= td_amp
td_amp = 1.
plt.subplot(2,2,1)
plt.plot(np.arange(len(signal1))/float(Fs), signal1)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title1 is not None:
plt.title(title1)
plt.subplot(2,2,2)
plt.plot(np.arange(len(signal2))/float(Fs), signal2)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title2 is not None:
plt.title(title2)
import stft
import windows
eps = constants.get('eps')
F1 = stft.stft(signal1, fft_size, fft_size / 2, win=windows.hann(fft_size))
F2 = stft.stft(signal2, fft_size, fft_size / 2, win=windows.hann(fft_size))
# try a fancy way to set the scale to avoid having the spectrum
# dominated by a few outliers
p_min = 1
p_max = 99.5
all_vals = np.concatenate((dB(F1+eps), dB(F2+eps))).flatten()
vmin, vmax = np.percentile(all_vals, [p_min, p_max])
cmap = 'jet'
interpolation='sinc'
plt.subplot(2,2,3)
stft.spectroplot(F1.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
plt.subplot(2,2,4)
stft.spectroplot(F2.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
def comparePlot(signal1, signal2, Fs, fft_size=512, norm=False, equal=False, title1=None, title2=None):
import matplotlib.pyplot as plt
td_amp = np.maximum(np.abs(signal1).max(), np.abs(signal2).max())
if norm:
if equal:
signal1 /= np.abs(signal1).max()
signal2 /= np.abs(signal2).max()
else:
signal1 /= td_amp
signal2 /= td_amp
td_amp = 1.
plt.subplot(2,2,1)
plt.plot(np.arange(len(signal1))/float(Fs), signal1)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title1 is not None:
plt.title(title1)
plt.subplot(2,2,2)
plt.plot(np.arange(len(signal2))/float(Fs), signal2)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title2 is not None:
plt.title(title2)
from constants import eps
import stft
import windows
F1 = stft.stft(signal1, fft_size, fft_size / 2, win=windows.hann(fft_size))
F2 = stft.stft(signal2, fft_size, fft_size / 2, win=windows.hann(fft_size))
# try a fancy way to set the scale to avoid having the spectrum
# dominated by a few outliers
p_min = 1
p_max = 99.5
all_vals = np.concatenate((dB(F1+eps), dB(F2+eps))).flatten()
vmin, vmax = np.percentile(all_vals, [p_min, p_max])
cmap = 'jet'
interpolation='sinc'
plt.subplot(2,2,3)
stft.spectroplot(F1.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
plt.subplot(2,2,4)
stft.spectroplot(F2.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
x3 = np.floor(end*np.array([0.48, 0.64]))
y3 = np.floor(top*np.array([0.44, 0.56]))
box3 = [[x3[0],y3[0]],[x3[0],y3[1]],[x3[1],y3[1]],[x3[1],y3[0]],[x3[0],y3[0]]]
boxes = [Polygon(box1, True, fill=False, facecolor='none'),
Polygon(box2, True, fill=False, facecolor='none'),
Polygon(box3, True, fill=False, facecolor='none'),]
ec=np.array([0,0,0])
lw = 0.5
# Draw first the spectrograms with boxes on top
fig, ax = plt.subplots(figsize=figsize2, nrows=2, ncols=4)
ax = plt.subplot(2,4,1)
spectroplot(F0.T, fft_size+fft_zp, fft_hop, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation, colorbar=False)
ax.add_collection(PatchCollection(boxes, facecolor='none', edgecolor=ec, linewidth=lw))
ax.text(F0.shape[0]-300, F0.shape[1]/2-60, 'A', weight='bold')
ax.set_ylabel('')
ax.set_xlabel('')
aspect = ax.get_aspect()
ax.axis('off')
ax = plt.subplot(2,4,2)
spectroplot(F1.T, fft_size+fft_zp, fft_hop, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation, colorbar=False)
ax.add_collection(PatchCollection(boxes, facecolor='none', edgecolor=ec, linewidth=lw))
ax.text(F0.shape[0]-300, F0.shape[1]/2-60, 'B', weight='bold')
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis('off')
Polygon(box1, True, fill=False, facecolor='none'),
Polygon(box2, True, fill=False, facecolor='none'),
Polygon(box3, True, fill=False, facecolor='none'),
]
ec = np.array([0, 0, 0])
lw = 0.5
# Draw first the spectrograms with boxes on top
fig, ax = plt.subplots(figsize=figsize2, nrows=2, ncols=4)
ax = plt.subplot(2, 4, 1)
spectroplot(F0.T,
fft_size + fft_zp,
fft_hop,
Fs,
vmin=vmin,
vmax=vmax,
cmap=plt.get_cmap(cmap),
interpolation=interpolation,
colorbar=False)
ax.add_collection(
PatchCollection(boxes, facecolor='none', edgecolor=ec, linewidth=lw))
ax.text(F0.shape[0] - 300, F0.shape[1] / 2 - 60, 'A', weight='bold')
ax.set_ylabel('')
ax.set_xlabel('')
aspect = ax.get_aspect()
ax.axis('off')
ax = plt.subplot(2, 4, 2)
spectroplot(F1.T,
fft_size + fft_zp,
