Initial upload of files

Butterworth bandpass filter function

Author: yahrMason

butterworth_bandpass.py

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+from scipy import signal
+from matplotlib import pyplot as plt
+from matplotlib import style
+import numpy as np
+
+
+# import sampling rate and time stamp from noise_signal module
+from noise_signal import t, my_signal
+
+# Critcal Frequencies
+lowcut = 200
+highcut = 700
+
+sig = my_signal
+time = t
+
+def band_butter(sig, time, crit_low, crit_high, order):
+    
+    # sampling frequency
+    Fs = len(time)/max(time)
+    
+    # nyquist frequency 
+    Fn = 0.5*Fs 
+    
+    # normalize critical frequencies
+    low = lowcut/Fn 
+    high = highcut/Fn
+    
+    # filter coefficients
+    b,a = signal.butter(order,[low,high],btype='band')
+
+    # filtered signal
+    sig_filt = signal.lfilter(b,a,sig)
+    
+    # frequency response
+    w,h = signal.freqz(b,a,worN=2000)
+    
+    # Plots
+    style.use('ggplot')
+    style.use('dark_background')
+    
+    plt.title('Filter Frequency Response')
+    plt.plot((Fs*0.5/np.pi)*w, abs(h),label = f'order={order}', color = 'orange')
+    plt.xlabel('Frequency (Hz)')
+    plt.ylabel('Gain')
+    plt.grid()
+    plt.legend(loc='best')
+
+    f,plt_arr= plt.subplots(2,sharex=True)
+    
+    plt_arr[0].plot(time, sig, color ='red')
+    plt_arr[0].set_title('Input Signal', color ='red')
+    plt_arr[0].set_ylabel('Amplitude')
+     
+    plt_arr[1].plot(time, sig_filt,color='cyan')
+    plt_arr[1].set_title('Filtered Signal', color ='cyan')
+    plt_arr[1].set_ylabel('Amplitude')
+    plt_arr[1].set_xlabel('time (seconds)')
+   
+    plt.show()
+    
+band_butter(my_signal, t, lowcut, highcut, 6)

noise_signal.py

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+import numpy as np
+
+# generate signal
+fs =5000
+T = 0.1
+t = np.linspace(0.0,T,T*fs)
+
+x1 = 0.11*np.sin(2*np.pi*0.89*np.sqrt(t)+0.65) # amp = 0.1 freq = 2.2
+x2 = 0.03*np.sin(2*np.pi*150*t+0.2) # amp = 0.03 freq = 300
+x3 = 0.015*np.sin(2*np.pi*600*t) # amp = 0.2 freq = 900
+x4 = 0.015*np.sin(2*np.pi*2000*t) # amp = 0.2 freq = 900
+
+my_signal = x1+x2+x3+x4