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Code: Alles auswählen

import numpy as np
import matplotlib.pyplot as plt
 
# BW 20Hz cutoff at 100Hz samplerate
# 2.376⋅yi = (1⋅xi + 1⋅xi-1) + 0.376⋅yi-1

class BW:
    
    def __init__(self, a, b):
        self._a = a
        self._b = b
        self._xn_1 = None
        self._yn_1 = 0
        
    def __call__(self, x):
        if self._xn_1 is None:
            self._xn_1 = x
            
        y = self._yn_1 = ((x + self._xn_1) + self._a * self._yn_1) / self._b
        self._xn_1 = x
        return y
            
# 5Hz
filter = BW(5.376, 7.376)
# 1Hz
filter = BW(30.821, 32.821)
filter = np.vectorize(filter)

sensor_data = []
with open('samplesdata.csv') as inf:
    next(inf) # skip header
    for i, line in enumerate(inf, start=2):
        if line.strip():
            v = float(line.strip().split(';')[-1].replace(',', '.'))
            sensor_data.append(v)
            
sensor_data=np.array(sensor_data)
  

filtered_data = filter(sensor_data)

plt.figure()
plt.subplot(211)
plt.plot(sensor_data, color='green')
plt.grid(color='green', linestyle='--', linewidth=0.5)
plt.title('original signal')
 
plt.subplot(212)
plt.plot(filtered_data, color='red')
plt.title('Filtered')
plt.grid(color='green', linestyle='--', linewidth=0.5)
plt.show()

Code: Alles auswählen

class BW:
    
    def __init__(self, a, b):
        self._a = a
        self._b = b
        self._xn_1 = None
        self._yn_1 = 0
        
    def __call__(self, x):
        if self._xn_1 is None:
            self._xn_1 = x
            self._yn_1 = x
        y = self._yn_1 = ((x + self._xn_1) + self._a * self._yn_1) / self._b
        self._xn_1 = x
        return y