-
Notifications
You must be signed in to change notification settings - Fork 0
/
read_wav_fft.py
76 lines (67 loc) · 2.84 KB
/
read_wav_fft.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
# author: Ovidiu Mura
# date: May 22, 2019
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
from scipy.fftpack import fft,fftfreq, ifft
# It provides the implementation of the READ_WAV_FFT class which reads the frequencies using FFT (Fast Fourier Transform)
# algorithm from a signal which can be read from file or passed as argument. The frequencies can be visualized using the
# plot_fft and plot methods.
class READ_WAV_FFT:
def __init__(self):
self.samplerate = None
self.data = None
self.fftabs = None
self.freqs = None
self.file_name = None
# It finds the frequencies from a given signal stored in a file.
def read_wav_fft(self, file_name='SineWave_440Hz.wav'):
self.file_name = file_name
self.samplerate, self.data = wavfile.read(file_name)
samples = self.data.shape[0]
datafft = fft(self.data)
samples = self.data.shape[0]
datafft = fft(self.data)
# Get the absolute value of real and complex component
self.fftabs = abs(datafft)
self.freqs = fftfreq(samples,1/self.samplerate)
# It finds the frequenceis of a given signal
def read_fft(self, signal, samplerate):
self.data = signal
self.samplerate = samplerate
datafft = fft(self.data)
samples = len(self.data)
datafft = fft(self.data)
samples = len(self.data)
# Get the absolute value of real and complex component
self.fftabs = abs(datafft)
self.freqs = fftfreq(samples,1/self.samplerate)
# It plots the frequencies for visualization.
def plot_fft(self):
plt.xlim( [10, self.samplerate/2] )
plt.title('FFT - file: ' + str(self.file_name))
plt.xscale( 'log' )
plt.grid( True )
plt.xlabel( 'Frequency (Hz)' )
plt.plot(self.freqs[0:int(self.freqs.size/2)],self.fftabs[0:int(self.freqs.size/2)])
for i in range(int(self.freqs.size/2)):
if(self.fftabs[i] > 1000000):
idx = list(self.fftabs).index(self.fftabs[i])
#print(self.freqs[idx])
plt.show()
# It plots the found frequencies
def plot(self):
plt.xlim( [10, self.samplerate/2] )
plt.title('FFT - file: ' + str(self.file_name))
plt.xscale( 'log' )
plt.grid( True )
plt.ylabel( 'Amplitudes' )
plt.xlabel( 'Frequency (Hz)' )
if('mix' in self.file_name):
plt.plot(self.freqs[0:int(self.freqs.size/2)-10000],self.fftabs[0:int(self.freqs.size/2)-10000], color="blue")
else:
#plt.plot(self.freqs[:int(self.freqs.size/2)],self.fftabs[:int(self.freqs.size/2)], color="blue")
plt.plot(self.freqs,self.fftabs, color="blue")
serie = self.file_name + " Signal"
plt.legend((serie, serie), loc="upper right")
plt.show()