FCr = ( 0.5*ρ*S*C_aero*v**2 + m*g*math.sin(α) + m*a )*v / (γ*FED) 在python中这个函数对t积分

这个函数是一个物理公式，表示了一个物体在空气中受到的总力对时间的积分。在Python中，可以使用数值积分方法来对其进行积分。以下是一个示例代码：

import math
from scipy.integrate import quad

def integrand(t):
    # 定义被积函数
    ρ = 1.2  # 空气密度
    S = 1.0  # 物体的参考面积
    C_aero = 0.5  # 空气动力系数
    v = 10.0  # 物体的速度
    m = 1.0  # 物体的质量
    g = 9.8  # 重力加速度
    α = math.pi / 4  # 物体的倾角
    a = 1.0  # 物体的加速度
    γ = 1.0  # 系数
    FED = 1.0  # 力的单位换算系数

    return (0.5 * ρ * S * C_aero * v**2 + m * g * math.sin(α) + m * a) * v / (γ * FED)

result, error = quad(integrand, 0, t)  # 对函数进行积分，积分区间为0到t

print("积分结果：", result)
print("误差：", error)

请注意，上述代码中使用了`scipy`库中的`quad`函数来进行数值积分。你需要先安装`scipy`库，可以使用`pip install scipy`命令进行安装。

相关问题

/* Write flash memory. */

To write data to flash memory, you need to follow these steps:

1. Unlock the flash memory: Before you can write to the flash memory, you need to unlock it. This is done by setting the correct values in the Flash Control Register (FCR). You can refer to your microcontroller's datasheet for the specific register values.

2. Erase the flash memory: Flash memory needs to be erased before it can be written to. This is because flash memory is organized into sectors, and each sector needs to be erased before new data can be written to it. Again, you can refer to your microcontroller's datasheet for the specific erase procedure.

3. Write the data: Once the flash memory has been unlocked and erased, you can write your data to it. This is done by setting the correct values in the Flash Data Register (FDR). You will need to write the data one byte at a time, and make sure to increment the memory address after each write.

4. Lock the flash memory: After you have finished writing your data, you need to lock the flash memory again. This is done by setting the correct values in the FCR.

It's important to note that writing to flash memory is a delicate process, and any errors can result in permanent damage to the memory. Therefore, it's important to follow the correct procedures and to thoroughly test your code before deploying it in a production environment.

优化这段代码df_in_grown_ebv = pd.read_table(open(r"C:\Users\荆晓燕\Desktop\20230515分品种计算育种值\生长性能育种值N72分组 (7).txt"), delim_whitespace=True, encoding="gb18030", header=None) df_in_breed_ebv = pd.read_table(open(r"C:\Users\荆晓燕\Desktop\20230515分品种计算育种值\繁殖性能育种值N72分组 (7).txt"), delim_whitespace=True, encoding="gb18030", header=None) # df_in_grown_Phenotype.columns = ['个体号', '活仔EBV', '21d窝重EBV', '断配EBV'] # df_in_breed_Phenotype.columns = ['个体号', '115EBV', '饲料转化率EBV', '瘦肉率EBV', '眼肌EBV', '背膘EBV'] df_in_breed_ebv.columns = ['个体号', '活仔EBV', '21d窝重EBV', '断配EBV'] df_in_grown_ebv.columns = ['个体号', '115daysEBV', '饲料转化率EBV', '瘦肉率EBV', '眼肌EBV', '背膘EBV'] NBA_mean = np.mean(df_in_breed_ebv['活仔EBV']) NBA_std = np.std(df_in_breed_ebv['活仔EBV']) days_mean = np.mean(df_in_grown_ebv['115daysEBV']) days_std = np.std(df_in_grown_ebv['115daysEBV']) fcr_mean = np.mean(df_in_grown_ebv['饲料转化率EBV']) fcr_std = np.std(df_in_grown_ebv['饲料转化率EBV']) output = pd.merge(df_in_grown_ebv, df_in_breed_ebv, how='inner', left_on='个体号', right_on='个体号') # output['计算长白母系指数'] = 0.3 * (NBA - NBA_mean)/NBA_std - 0.3 * (days - days_mean)/days_std - 0.3 * (fcr-fcr_mean)/fcr_std + 0.1 * (pcl-pcl_mean)/pcl_std output['计算长白母系指数'] = 0.29 * (df_in_breed_ebv['活仔EBV'] - NBA_mean)/NBA_std - 0.58 * (df_in_grown_ebv['115daysEBV']- days_mean)/days_std - 0.13 * (df_in_grown_ebv['饲料转化率EBV']-fcr_mean)/fcr_std MLI_mean = np.mean(output['计算长白母系指数']) MLI_std = np.std(output['计算长白母系指数']) output['校正长白母系指数'] = 25 * ((output['计算长白母系指数'] - MLI_mean)/MLI_std) + 100

可以优化的地方有：

1. 重复的路径可以抽象成一个变量，避免代码重复。

2. 可以使用 with 语句来处理文件读取，避免忘记关闭文件。

3. 可以在读取文件时指定列名，避免后续需要重新指定列名。

4. 可以将计算指数和计算校正指数的代码分成两个函数，提高代码的可读性和可维护性。

优化后的代码如下：

import pandas as pd
import numpy as np

# 定义文件路径
breed_file = r"C:\Users\荆晓燕\Desktop\20230515分品种计算育种值\繁殖性能育种值N72分组 (7).txt"
grown_file = r"C:\Users\荆晓燕\Desktop\20230515分品种计算育种值\生长性能育种值N72分组 (7).txt"

def read_file(file_path, columns):
    with open(file_path, encoding="gb18030") as f:
        df = pd.read_table(f, delim_whitespace=True, header=None)
        df.columns = columns
    return df

# 读取文件并指定列名
df_in_breed_ebv = read_file(breed_file, ['个体号', '活仔EBV', '21d窝重EBV', '断配EBV'])
df_in_grown_ebv = read_file(grown_file, ['个体号', '115daysEBV', '饲料转化率EBV', '瘦肉率EBV', '眼肌EBV', '背膘EBV'])

def calculate_index(df_in_breed_ebv, df_in_grown_ebv):
    # 计算指数
    NBA_mean = np.mean(df_in_breed_ebv['活仔EBV'])
    NBA_std = np.std(df_in_breed_ebv['活仔EBV'])
    days_mean = np.mean(df_in_grown_ebv['115daysEBV'])
    days_std = np.std(df_in_grown_ebv['115daysEBV'])
    fcr_mean = np.mean(df_in_grown_ebv['饲料转化率EBV'])
    fcr_std = np.std(df_in_grown_ebv['饲料转化率EBV'])
    
    df = pd.merge(df_in_grown_ebv, df_in_breed_ebv, how='inner', on='个体号')
    df['计算长白母系指数'] = 0.29 * (df['活仔EBV'] - NBA_mean) / NBA_std - 0.58 * (df['115daysEBV'] - days_mean) / days_std - 0.13 * (df['饲料转化率EBV'] - fcr_mean) / fcr_std
    return df

def calculate_correction_index(df):
    # 计算校正指数
    MLI_mean = np.mean(df['计算长白母系指数'])
    MLI_std = np.std(df['计算长白母系指数'])
    df['校正长白母系指数'] = 25 * ((df['计算长白母系指数'] - MLI_mean) / MLI_std) + 100
    return df

# 计算指数和校正指数
df_index = calculate_index(df_in_breed_ebv, df_in_grown_ebv)
df_correction_index = calculate_correction_index(df_index)