用python实现 电子学 electronics 课程设计

# https://en.wikipedia.org/wiki/Circular_convolution """ Circular convolution, also known as cyclic convolution, is a special case of periodic convolution, which is the convolution of two periodic functions that have the same period. Periodic convolution arises, for example, in the context of the discrete-time Fourier transform (DTFT). In particular, the DTFT of the product of two discrete sequences is the periodic convolution of the DTFTs of the individual sequences. And each DTFT is a periodic summation of a continuous Fourier transform function. Source: https://en.wikipedia.org/wiki/Circular_convolution """ import doctest from collections import deque import numpy as np class CircularConvolution: """ This class stores the first and second signal and performs the circular convolution """ def __init__(self) -> None: """ First signal and second signal are stored as 1-D array """ self.first_signal = [2, 1, 2, -1] self.second_signal = [1, 2, 3, 4] def circular_convolution(self) -> list[float]: """ This function performs the circular convolution of the first and second signal using matrix method Usage: >>> import circular_convolution as cc >>> convolution = cc.CircularConvolution() >>> convolution.circular_convolution() [10, 10, 6, 14] >>> convolution.first_signal = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6] >>> convolution.second_signal = [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5] >>> convolution.circular_convolution() [5.2, 6.0, 6.48, 6.64, 6.48, 6.0, 5.2, 4.08] >>> convolution.first_signal = [-1, 1, 2, -2] >>> convolution.second_signal = [0.5, 1, -1, 2, 0.75] >>> convolution.circular_convolution() [6.25, -3.0, 1.5, -2.0, -2.75] >>> convolution.first_signal = [1, -1, 2, 3, -1] >>> convolution.second_signal = [1, 2, 3] >>> convolution.circular_convolution() [8, -2, 3, 4, 11] """ length_first_signal = len(self.first_signal) length_second_signal = len(self.second_signal) max_length = max(length_first_signal, length_second_signal) # create a zero matrix of max_length x max_length matrix = [[0] * max_length for i in range(max_length)] # fills the smaller signal with zeros to make both signals of same length if length_first_signal < length_second_signal: self.first_signal += [0] * (max_length - length_first_signal) elif length_first_signal > length_second_signal: self.second_signal += [0] * (max_length - length_second_signal) """ Fills the matrix in the following way assuming 'x' is the signal of length 4 [ [x[0], x[3], x[2], x[1]], [x[1], x[0], x[3], x[2]], [x[2], x[1], x[0], x[3]], [x[3], x[2], x[1], x[0]] ] """ for i in range(max_length): rotated_signal = deque(self.second_signal) rotated_signal.rotate(i) for j, item in enumerate(rotated_signal): matrix[i][j] += item # multiply the matrix with the first signal final_signal = np.matmul(np.transpose(matrix), np.transpose(self.first_signal)) # rounding-off to two decimal places return [round(i, 2) for i in final_signal] if __name__ == "__main__": doctest.testmod()