def plotWavAnalysisDeep():
"""
Read .WAV files, then plot a full analysis of the signal as in:
https://matplotlib.org/gallery/lines_bars_and_markers/spectrum_demo.html#sphx-glr-gallery-lines-bars-and-markers-spectrum-demo-py
Plot 1:
"""
# Cut to the last ~30 seconds of the song since it had the most interesting
# spectrogram.
rate, data = wavfile.read('./Give_Love_A_Try.wav')
f, t, Sxx = signal.spectrogram(data,rate)
myfft = sf.sfft2(f)
Interval = t[t.size-1] - t[0]
Fs = len(myfft) # no. of samples
s = np.abs(myfft)[:len(myfft)//2] # Real signal symmetry only requires half the samples
# plot time signal:
plt.subplot(3,2,1)
plt.title("Signal")
plt.plot(s , color='C0')
plt.xlabel("Time")
plt.ylabel("Amplitude")
# plot different spectrum types:
plt.subplot(3,2,3)
plt.title("Magnitude Spectrum")
plt.magnitude_spectrum(s, Fs=Fs, color='C1')
plt.subplot(3,2,4)
plt.title("Logarithmic Magnitude Spectrum")
plt.magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1')
plt.subplot(3,2,5)
plt.title("Phase Spectrum")
plt.phase_spectrum(s, Fs=Fs, color='C2')
plt.subplot(3,2,6)
plt.title("Anlge Spectrum")
plt.angle_spectrum(s, Fs=Fs, color='C2')
plt.tight_layout()
plt.show()
import numpy as np np.random.seed(0) dt = 0.01 Fs = 1 / dt t = np.arange(0, 10, dt) nse = np.random.randn(len(t)) r = np.exp(-t / 0.05) cnse = np.convolve(nse, r) * dt cnse = cnse[:len(t)] s = 0.1 * np.sin(2 * np.pi * t) + cnse plt.subplot(3, 2, 1) plt.plot(t, s) plt.subplot(3, 2, 3) plt.magnitude_spectrum(s, Fs=Fs) plt.subplot(3, 2, 4) plt.magnitude_spectrum(s, Fs=Fs, scale='dB') plt.subplot(3, 2, 5) plt.angle_spectrum(s, Fs=Fs) plt.subplot(3, 2, 6) plt.phase_spectrum(s, Fs=Fs) plt.show()
def angle_spectrum(*args, **kwargs):
r"""starkplot wrapper for angle_spectrum"""
return _pyplot.angle_spectrum(*args, **kwargs)
cnse = np.convolve(nse, r) * dt
cnse = cnse[:len(t)]
s = 0.1 * np.sin(2 * np.pi * t) + cnse
plt.subplot(3, 2, 1)
plt.plot(t, s)
plt.subplot(3, 2, 3)
plt.magnitude_spectrum(s, Fs=Fs)
plt.subplot(3, 2, 4)
plt.magnitude_spectrum(s, Fs=Fs, scale='dB')
plt.subplot(3, 2, 5)
plt.angle_spectrum(s, Fs=Fs)
plt.subplot(3, 2, 6)
plt.phase_spectrum(s, Fs=Fs)
plt.show()
print("Test04-----------Start------------------")
x = np.array([0, 1, 2, 3, 4])
y = np.array([-1, 0.2, 0.9, 2.1, 1.2])
A = np.vstack([x, np.ones(len(x))]).T
m, c = np.linalg.lstsq(A, y)[0]
plt.plot(x, y, 'o', label='Original data', markersize=10)
plt.plot(x, m * x + c, 'r', label='Fitted line')
plt.legend()
#plt.grid(True)
plt.show()
#exemplo_8()
'''
mode : [ 'default' | 'psd' | 'magnitude' | 'angle' | 'phase' ]
What sort of spectrum to use. Default is 'psd', which takes
the power spectral density. 'complex' returns the complex-valued
frequency spectrum. 'magnitude' returns the magnitude spectrum.
'angle' returns the phase spectrum without unwrapping. 'phase'
returns the phase spectrum with unwrapping.
'''
'''
# Parametros de entrada (pode alterar)
Fs = 250.0 # Hz
Ts = 1/Fs
t0 = 0.0
t1 = 6.0
time = np.arange(t0, t1+Ts, Ts)
print time.shape
f1 = 40
print 'Curva cosseno com frequencia:', f1, 'Hz'
x = np.cos(time * 2 * np.pi * f1)
print '\n'
#plt.specgram(x, NFFT=128, noverlap=64, Fs=250, cmap='rainbow', mode='angle')# mode='phase')