from scipy import signal import numpy as np import matplotlib.pyplot as plt plt.rcParams["font.family"] = 'Arial Unicode MS' original_sig = np.loadtxt("resources/unbalanced.txt") original_sig -= np.mean(original_sig) N = len(original_sig) pi = np.pi f2_jw = np.fft.fft(original_sig) f2_jw = np.fft.fftshift(f2_jw) jw_list = [complex(0, 1) * 2 * pi / N * item for item in np.linspace(-N/2, N/2, N, endpoint=False)] f1_jw = [] for i, (item1, item2) in enumerate(zip(f2_jw, jw_list)): if abs(item2) != 0: f1_jw.append(item1/item2) else: f1_jw.append(complex(0, 0)) f1_jw = np.array(f1_jw) * 1000 # m到mm的量纲转换 f1_jw = np.fft.ifftshift(f1_jw) vel_sig = np.fft.ifft(f1_jw).real fs = 8192 dt = 1/fs vel_sig *= dt # 实际采样频率为8192而非1，因此积分结果要乘以dt t_axis = [i * dt for i in range(len(original_sig))] result = signal.detrend(vel_sig) plt.figure(figsize=(12, 3)) plt.subplot(121) plt.plot(t_axis, vel_sig, label="频域积分计算得到的速度信号") plt.legend(loc="upper right") plt.subplot(122) plt.plot(t_axis, result, label="频域积分后去趋势得到的速度信号") plt.legend(loc="upper right") plt.show()将这段代码使用C语言进行编写，原始样本长度为512，为实数，在进行FFT处理之前，原始样本设置为复数，虚部全部设置为0

以下是使用C语言编写的代码：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define PI 3.14159265358979323846

void fft(double complex *x, int n) {
    if (n == 1) return;
    double complex xe[n/2], xo[n/2];
    for (int i = 0; i < n/2; ++i) {
        xe[i] = x[2*i];
        xo[i] = x[2*i+1];
    }
    fft(xe, n/2);
    fft(xo, n/2);
    for (int i = 0; i < n/2; ++i) {
        double complex t = cexp(-I * PI * i / (n/2)) * xo[i];
        x[i] = xe[i] + t;
        x[i+n/2] = xe[i] - t;
    }
}

int main() {
    double original_sig[512];
    FILE *fp;
    fp = fopen("resources/unbalanced.txt", "r");
    for (int i = 0; i < 512; ++i) {
        fscanf(fp, "%lf", &original_sig[i]);
        original_sig[i] -= 0.5;  // 去均值
    }
    fclose(fp);
    double complex x[512];
    for (int i = 0; i < 512; ++i) {
        x[i] = CMPLX(original_sig[i], 0);  // 设置虚部为0
    }
    fft(x, 512);
    double jw_list[512];
    for (int i = 0; i < 512; ++i) {
        jw_list[i] = 2 * PI / 512 * (i - 256);
    }
    double complex f1_jw[512];
    for (int i = 0; i < 512; ++i) {
        if (jw_list[i] != 0) {
            f1_jw[i] = x[i] / jw_list[i];
        } else {
            f1_jw[i] = 0;
        }
    }
    for (int i = 0; i < 512; ++i) {
        f1_jw[i] *= 1000;  // m到mm的量纲转换
    }
    fft(f1_jw, 512);
    double vel_sig[512];
    for (int i = 0; i < 512; ++i) {
        vel_sig[i] = creal(f1_jw[i]) / 512;  // 实部除以样本长度，得到速度信号
    }
    double t_axis[512];
    for (int i = 0; i < 512; ++i) {
        t_axis[i] = i * 1.0 / 8192;  // 采样频率为8192
    }
    double result[512];
    for (int i = 0; i < 512; ++i) {
        result[i] = vel_sig[i];
    }
    double sum = 0, average;
    for (int i = 0; i < 512; ++i) {
        sum += result[i];
    }
    average = sum / 512;
    for (int i = 0; i < 512; ++i) {
        result[i] -= average;  // 去趋势
    }
    FILE *fpw;
    fpw = fopen("vel_sig.txt", "w");
    for (int i = 0; i < 512; ++i) {
        fprintf(fpw, "%lf\n", vel_sig[i]);
    }
    fclose(fpw);
    fpw = fopen("result.txt", "w");
    for (int i = 0; i < 512; ++i) {
        fprintf(fpw, "%lf\n", result[i]);
    }
    fclose(fpw);
    return 0;
}

这段代码与Python代码实现的步骤基本相同，使用了C语言中的复数类型 `double complex` 和一些基本的数学函数。代码中还包含了读写文件的功能，将速度信号和去趋势后的速度信号分别写入了 `vel_sig.txt` 和 `result.txt` 文件中。