a8k_app/lib-algo/opt_algo.hpp

#pragma once
#include <fstream>
#include <functional>
#include <iostream>
#include <list>
#include <map>
#include <memory>
#include <set>
#include <sstream>
#include <string>
#include <vector>
namespace opt_algo {
using namespace std;


typedef enum {
  k250,
  k1000,
} ProcessPointNumType_t;

typedef enum {
  k_ecode_ok                      = 0,
  k_ecode_can_not_find_peak       = 1,
  k_ecode_can_not_find_peak_start = 2,
  k_ecode_can_not_find_peak_end   = 3,
} Error_t;

class LineContext {
 public:
  vector<float> raw;                 //
  vector<float> avg;                 //
  vector<float> diff;                //
  float         agvline;             //
  float         baseline_slope;      //
  float         baseline_intercept;  //

  vector<float> raw250;   //
  vector<float> avg250;   //
  vector<float> diff250;  //
};

class PeakInfo {
 public:
  bool  find_peak;
  float peak_full_area;
  float peak_base_line_area;
  float area;
  int   peak_pos;
  int   peak_start_pos;
  int   peak_end_pos;
};

class AlgoResult {
 public:
  /**
   * @brief
   * 原始数据处理逻辑:
   *  1) 1200点中值滤波
   *  2) 1200点线性填充到6000点
   *  3) 6000点窗口平滑滤波
   *  4) 6000点均值压缩到1000点
   *
   *  5) 1000点 13点滑动均值滤波
   *  6) 1000点 20点最小二乘法求斜率
   *
   *  7) 找峰
   *  8）找峰起始位
   *  9) 找峰结束位
   *
   */
  AlgoResult() {
    error_code  = k_ecode_ok;
    lineContext = make_shared<LineContext>();
    for (int i = 0; i < 5; i++) {
      peakInfo[i] = make_shared<PeakInfo>();
    }
  }
  vector<float> ogigin_val;             // 1200
  vector<float> supper_val;             // 原始数据，线性填充，1200*5=6000
  vector<float> supper_median_val;      // supper_val 窗口平滑滤波，6000
  vector<float> supper_smooth_sub_val;  // supper_smooth_val 均值压缩，6000/6=1000

  shared_ptr<LineContext> lineContext;  // supper_smooth_sub_val 13点滑动均值滤波，1000

  Error_t error_code;  // 错误码

  // result
  shared_ptr<PeakInfo> peakInfo[5];
  int                  peakNum;
};

class OptAlgo {
 public:
  /**
   * @brief
   *
   * @param context
   * @param pconfig
   * @param ogigin_val expect 1200
   */
  static shared_ptr<AlgoResult> calculate(vector<float> ogigin_val, int peaknum);
  static int                    calculate_peak_num(vector<float>& ogigin_val);

  static int getAlgoVersion();

 private:
  static Error_t findpeak(shared_ptr<LineContext> lineContext, int32_t search_start, int32_t peakwindth, shared_ptr<PeakInfo> retpeak);
  static int     sub_find_peak(shared_ptr<LineContext> lineContext, int start_off, int windos_size, int judge_win_size);
  static int     find_peak_endpoint(shared_ptr<LineContext> lineContext, int peakpos, int search_direction, int search_windows);

 private:
  /*******************************************************************************
   *                                  基础方法                                   *
   *******************************************************************************/

  /**
   * @brief 过采样原始数据
   *
   * @param inputRaw
   * @param nInputLength
   * @param nUpSampleRate
   * @return vector<float>
   */
  static vector<float> super_sampling(vector<float>& inputRaw, int32_t nInputLength, int32_t nUpSampleRate);
  /**
   * @brief 均值压缩数据
   *
   * @param inputRaw
   * @param nSubSampleRate
   * @return vector<float>
   */
  static vector<float> sub_sampling(vector<float>& inputRaw, int nSubSampleRate);
  /**
   * @brief 移动窗口平滑数据
   *
   * @param inputRaw
   * @param windows_size
   * @return vector<float>
   */
  static vector<float> smooth_windows(vector<float>& inputRaw, int windows_size);

  /**
   * @brief 移动窗口平滑数据
   *
   * @param inputRaw
   * @param windows_size
   * @return vector<float>
   */
  static vector<float> median_filtering(vector<float>& inputRaw, int windows_size);
  /**
   * @brief 计算均值线
   *
   * @param inputRaw
   * @return float
   */
  static float find_avg_line(vector<float>& inputRaw);
  /**
   * @brief 简单曲线求导
   *
   * @param inputRaw 原始数据
   * @return vector<float> 和原始数据等长导数曲线
   */
  static vector<float> differentiate(vector<float>& inputRaw);
  /**
   * @brief 最小二乘法求导
   *
   * @param inputRaw 原始数据
   * @param windows_size 窗口大小
   * @return vector<float> 和原始数据等长导数曲线
   */
  static vector<float> least_square_method_differentiate(vector<float>& inputRaw, int windows_size);

  /**
   * @brief 当前数值是否是窗口内最大值（往前windsize/2，往后windowsize/2）
   *
   * @param val 当前数值指针
   * @param windows_size 窗口大小，必须是奇数
   * @return true
   * @return false
   */
  static bool is_maxval_in_windows(float* val, int windows_size);
  /**
   * @brief 比较两个浮点数是否相等
   *
   * @param a
   * @param b
   * @param epsilon
   * @return true
   * @return false
   */
  static bool feq(float a, float b, float epsilon = 0.00001);
  /**
   * @brief 最小二乘法 求一次函数斜率
   *
   * @param val
   * @param size
   * @return float
   */
  static void linear_least_squares(vector<float>& x, vector<float>& y, float& slope, float& intercept);
  static void linear_least_squares(float* y, int size, float& slope, float& intercept);
  static void linear_least_squares_muti_windos(float* y, int size, vector<int> startx, int windowssize, float& slope, float& intercept);

  static float get_avg_in_windows(vector<float>& src, int off, int windows);

  static void          sort_vector(vector<float>& src);
  static vector<float> getwindowspoint(vector<float>& src, int off, int windows);
};

}  // namespace opt_algo