File size: 3,319 Bytes
f5dbfe6 | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | import numpy as np
import matplotlib.pyplot as plt
encoderCountReal = 0.0
encoderCountInteger = 0
ENCODER_COUNT = 2**32
filterData = [0, 0]
PRESCALER = 1 * 2**32
INPUT_ROUNDING = 32
OUTPUT_ROUNDING = 32
A1 = int(-0.04515176221313341 * 2**32)
A2 = int(-0.9404614587008322 * 2**32)
B0 = int(0.003596694771508549 * 2**32)
B1 = int(0.007193389543017098 * 2**32)
B2 = int(0.003596694771508549 * 2**32)
times = []
filteredPos = []
realEncoder = []
integerEncoder = []
for i in range(4000):
# update encoder
encoderCountReal += .001
encoderCountInteger = round(encoderCountReal, 0)
if(encoderCountInteger > ENCODER_COUNT-1):
encoderCountInteger -= ENCODER_COUNT
encoderCountReal -= ENCODER_COUNT
elif(encoderCountInteger < 0):
encoderCountInteger += ENCODER_COUNT
encoderCountReal += ENCODER_COUNT
if(encoderCountInteger > 0):
print("true")
if(i == 200):
encoderCountReal += ENCODER_COUNT -50
temp = int(encoderCountInteger * PRESCALER)
temp -= int(filterData[0] * A1)
temp -= int(filterData[1] * A2)
dn = round(temp / pow(2, INPUT_ROUNDING), 0)
temp2 = int(dn * B0)
temp2 += int(filterData[0] * B1)
temp2 += int(filterData[1] * B2)
yn = round(temp2 / pow(2, OUTPUT_ROUNDING), 0)
filterData.insert(0, dn)
filterData.pop(-1)
print(filterData)
times.append(i)
filteredPos.append(yn)
realEncoder.append(encoderCountReal)
integerEncoder.append(encoderCountInteger)
plt.plot(times, filteredPos)
plt.plot(times, realEncoder)
plt.plot(times, integerEncoder)
plt.show()
# import numpy as np
# class BiquadFilter:
# def __init__(self, b0, b1, b2, a1, a2):
# # Initialize coefficients
# self.b0, self.b1, self.b2 = b0, b1, b2
# self.a1, self.a2 = a1, a2
# # Initialize state variables
# self.x1, self.x2 = 0.0, 0.0
# self.y1, self.y2 = 0.0, 0.0
# def process(self, x):
# # Direct Form II filter implementation
# y = self.b0 * x + self.b1 * self.x1 + self.b2 * self.x2 - self.a1 * self.y1 - self.a2 * self.y2
# # Update state
# self.x2 = self.x1
# self.x1 = x
# self.y2 = self.y1
# self.y1 = y
# return y
# # Example usage
# if __name__ == "__main__":
# # Define filter coefficients (example values)
# b0, b1, b2 = 5.074331248486237e-9, 1.0148662496972475e-8, 5.074331248486237e-9
# a1, a2 = -1.999798506779029, 0.9997985270763541
# # Create a BiquadFilter object
# filter = BiquadFilter(b0, b1, b2, a1, a2)
# # Example input signal (simple sinusoidal signal)
# fs = 44100 # Sampling frequency
# t = np.linspace(0, 1, fs, endpoint=False) # Time vector
# input_signal = np.sin(2 * np.pi * 50 * t) # 5 Hz sine wave
# # Process the signal through the filter
# output_signal = np.array([filter.process(x) for x in input_signal])
# # You can use matplotlib to visualize the input and output signals
# import matplotlib.pyplot as plt
# plt.figure()
# plt.subplot(2, 1, 1)
# plt.title('Input Signal')
# plt.plot(t, input_signal)
# plt.subplot(2, 1, 2)
# plt.title('Output Signal')
# plt.plot(t, output_signal)
# plt.show()
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