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signalProcessing.py
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signalProcessing.py
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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.fftpack import fft
def get_fft(y_val, T, N, f):
f_val=np.linspace(0.0, 1.0/(2.0*T), N//2)
fft_val=fft(y_val)
fft_val=2.0/N * np.abs(fft_val[:N//2])
return f_val, fft_val
t=10
N=1000
T=t/N
f=1/T
amplitudes=[4, 6, 8, 10, 14]
frequencies=[6.5, 5, 3, 1.5, 1]
x_val=np.linspace(0, t, num=N)
y_val=[amplitudes[i]*np.sin(2*np.pi*frequencies[i]*x_val) for i in range(len(amplitudes))]
composite_y=np.sum(y_val, axis=0)
'''
y_val=[]
for i in range(len(amplitudes)):
rate=2*np.pi*frequencies[i]
y_val.append(amplitudes*np.sin(rate)*x_val)
'''
f_val, fft_val=get_fft(composite_y, T, N, f)
'''
plt.plot(f_val, fft_val, linestyle='-', color='blue')
plt.xlabel('Frequency [Hz]', fontsize=16)
plt.ylabel('Amplitude', fontsize=16)
plt.title('Frequency domain of the Signal', fontsize=16)
plt.show()
'''
colors=['k', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b']
fig=plt.figure(figsize=(8, 8))
ax=fig.add_subplot(111, projection='3d')
ax.set_xlabel('Time [s]', fontsize=16)
ax.set_ylabel('Frequency [Hz]', fontsize=16)
ax.set_zlabel('Amplitude', fontsize=16)
new_y=[composite_y]+list(reversed(y_val))
frequencies.sort()
for i in range(0, len(new_y)):
signal=new_y[i]
color=colors[i]
length=signal.shape[0]
x=np.linspace(0, 10, 1000)
y=np.array([frequencies[i]]*length)
z=signal
if i==0:
linewidth=4
else:
linewidth=2
ax.plot(list(x), list(y), zs=list(z), linewidth=linewidth, color=color)
x=[10]*75
y=f_val[:75]
z=fft_val[:75]*3
ax.plot(list(x), list(y), zs=list(z), linewidth=2, color='red')
plt.tight_layout()
plt.show()