一种算法

#!usr/bin/env python
# -*- coding:utf-8 -*-
"""
@author: Suyue
@file: speedeeinsert.py
@time: 2024/04/16
@desc:
"""
# import numpy as np
import pandas as pd
df1 = pd.read_excel('G:/尺度速度.xls')

file_path = 'G:/NM004-20230627224400-20230627224859-0.txt'

# 读整个txt文件读取到单个字符串
with open(file_path, 'r', errors='ignore') as file:
    file_content = file.read()

# 按时间戳拆分内容以查找单独的部分
# 时间戳的格式为 YYYY-MM-DD HH：MM：SS，因此我们将使用正则表达式根据此模式进行拆分
import re
sections = re.split(r'\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\n', file_content)
# print(sections)

# 如果txt第一个元素为空值（由于拆分），则将其删除
if not sections[0]:
    sections.pop(0)

# 将每个部分放入列表
list = []
for section in sections[1:]:
    # 将字符串拆分为几行，然后按空格拆分每行并转换为 DataFrame
    lines = section.strip().split('\n')
    matrix = [line.split() for line in lines]
    df = pd.DataFrame(matrix)

    # df.columns = df1['直径']
    # df.index = df1['速度']

    # 进行数浓度计算
    n = df.iloc[0:33,2].values.astype(int)
    v = df1.iloc[0:33,1].values.astype(int)
    A = float(0.0054)
    t = float(60)

    # 循环读取ΔD
    d = df1['变化直径']
    for i in d:
        ND = n / A * t * v * i
        ND1 = ND.sum()
        # print(ND1)
    print(ND1)


# 显示每个dataframe形状以确认
# df_shapes = [df.shape for df in list]
# print(df_shapes)