使用python编写基于紫外-可见光谱的水质COD检测代码，读取三份excel文件，分别为10mg/L,15mg/L,20mg/L的标准溶液，excel文件中第一列数据为波长，第二列为透过率，对数据进行一阶导数计算，得到一阶导数谱图，并绘制在同一个折线图上，再计算一阶导数谱的排列熵，通过排列熵值进行特征波长的提取，最后，利用提取的特征波长处的特征值与对应的COD浓度进行PLS建模，得到COD的预测模型

好的，这是一个比较复杂的任务，需要用到多个Python库，我会一步一步地为您讲解代码实现过程。在开始之前，请确保已经安装了以下库：

- pandas
- numpy
- matplotlib
- scipy
- scikit-learn

首先，读取三份excel文件，分别为10mg/L,15mg/L,20mg/L的标准溶液。假设这三份文件分别为"10mg.xlsx"，"15mg.xlsx"和"20mg.xlsx"，并且这三个文件都有两列数据，第一列为波长，第二列为透过率。

import pandas as pd

# 读取10mg/L的标准溶液
df1 = pd.read_excel('10mg.xlsx')
wavelength = df1.iloc[:,0]  # 波长
transmittance1 = df1.iloc[:,1]  # 透过率

# 读取15mg/L的标准溶液
df2 = pd.read_excel('15mg.xlsx')
transmittance2 = df2.iloc[:,1]  # 透过率

# 读取20mg/L的标准溶液
df3 = pd.read_excel('20mg.xlsx')
transmittance3 = df3.iloc[:,1]  # 透过率

接下来，对数据进行一阶导数计算，得到一阶导数谱图，并绘制在同一个折线图上。

import numpy as np
import matplotlib.pyplot as plt

# 计算一阶导数
def derivative(x, y):
    dy = np.gradient(y, x)
    dx = np.gradient(x)
    dydx = dy / dx
    return dydx

# 计算一阶导数谱
def derivative_spectrum(wavelength, transmittance):
    dydx = derivative(wavelength, transmittance)
    dydx_spectrum = np.abs(dydx)
    return dydx_spectrum

# 计算三个标准溶液的一阶导数谱
dydx_spectrum1 = derivative_spectrum(wavelength, transmittance1)
dydx_spectrum2 = derivative_spectrum(wavelength, transmittance2)
dydx_spectrum3 = derivative_spectrum(wavelength, transmittance3)

# 绘制三个标准溶液的一阶导数谱图
plt.plot(wavelength, dydx_spectrum1, label='10mg/L')
plt.plot(wavelength, dydx_spectrum2, label='15mg/L')
plt.plot(wavelength, dydx_spectrum3, label='20mg/L')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Derivative spectrum')
plt.legend()
plt.show()

下一步是计算一阶导数谱的排列熵，通过排列熵值进行特征波长的提取。

from scipy.stats import entropy

# 计算一阶导数谱的排列熵
def permutation_entropy(spectrum):
    p = np.zeros((len(spectrum), len(spectrum)))
    for i in range(len(spectrum)):
        for j in range(len(spectrum)):
            if spectrum[i] <= spectrum[j]:
                p[i,j] = 1
    pe = entropy(p.sum(axis=1))
    return pe

# 计算三个标准溶液的一阶导数谱的排列熵
pe1 = permutation_entropy(dydx_spectrum1)
pe2 = permutation_entropy(dydx_spectrum2)
pe3 = permutation_entropy(dydx_spectrum3)

# 提取特征波长
feature_wavelength = wavelength[np.argmin([pe1, pe2, pe3])]

最后，利用提取的特征波长处的特征值与对应的COD浓度进行PLS建模，得到COD的预测模型。假设COD浓度数据保存在"COD.xlsx"文件中，第一列为COD浓度，第二列为特征波长处的特征值。

from sklearn.cross_decomposition import PLSRegression

# 读取COD浓度数据
df_cod = pd.read_excel('COD.xlsx')
cod_concentration = df_cod.iloc[:,0]  # COD浓度
feature_value = df_cod.iloc[:,1]  # 特征波长处的特征值

# 进行PLS建模
X = np.array(feature_value).reshape(-1, 1)
y = np.array(cod_concentration)
pls = PLSRegression(n_components=1)
pls.fit(X, y)

# 预测COD浓度
predict_value = pls.predict(np.array(feature_wavelength).reshape(1, -1))[0]
print('Predicted COD concentration:', predict_value)

完整代码如下：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import entropy
from sklearn.cross_decomposition import PLSRegression

# 读取10mg/L的标准溶液
df1 = pd.read_excel('10mg.xlsx')
wavelength = df1.iloc[:,0]  # 波长
transmittance1 = df1.iloc[:,1]  # 透过率

# 读取15mg/L的标准溶液
df2 = pd.read_excel('15mg.xlsx')
transmittance2 = df2.iloc[:,1]  # 透过率

# 读取20mg/L的标准溶液
df3 = pd.read_excel('20mg.xlsx')
transmittance3 = df3.iloc[:,1]  # 透过率

# 计算一阶导数
def derivative(x, y):
    dy = np.gradient(y, x)
    dx = np.gradient(x)
    dydx = dy / dx
    return dydx

# 计算一阶导数谱
def derivative_spectrum(wavelength, transmittance):
    dydx = derivative(wavelength, transmittance)
    dydx_spectrum = np.abs(dydx)
    return dydx_spectrum

# 计算三个标准溶液的一阶导数谱
dydx_spectrum1 = derivative_spectrum(wavelength, transmittance1)
dydx_spectrum2 = derivative_spectrum(wavelength, transmittance2)
dydx_spectrum3 = derivative_spectrum(wavelength, transmittance3)

# 绘制三个标准溶液的一阶导数谱图
plt.plot(wavelength, dydx_spectrum1, label='10mg/L')
plt.plot(wavelength, dydx_spectrum2, label='15mg/L')
plt.plot(wavelength, dydx_spectrum3, label='20mg/L')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Derivative spectrum')
plt.legend()
plt.show()

# 计算一阶导数谱的排列熵
def permutation_entropy(spectrum):
    p = np.zeros((len(spectrum), len(spectrum)))
    for i in range(len(spectrum)):
        for j in range(len(spectrum)):
            if spectrum[i] <= spectrum[j]:
                p[i,j] = 1
    pe = entropy(p.sum(axis=1))
    return pe

# 计算三个标准溶液的一阶导数谱的排列熵
pe1 = permutation_entropy(dydx_spectrum1)
pe2 = permutation_entropy(dydx_spectrum2)
pe3 = permutation_entropy(dydx_spectrum3)

# 提取特征波长
feature_wavelength = wavelength[np.argmin([pe1, pe2, pe3])]

# 读取COD浓度数据
df_cod = pd.read_excel('COD.xlsx')
cod_concentration = df_cod.iloc[:,0]  # COD浓度
feature_value = df_cod.iloc[:,1]  # 特征波长处的特征值

# 进行PLS建模
X = np.array(feature_value).reshape(-1, 1)
y = np.array(cod_concentration)
pls = PLSRegression(n_components=1)
pls.fit(X, y)

# 预测COD浓度
predict_value = pls.predict(np.array(feature_wavelength).reshape(1, -1))[0]
print('Predicted COD concentration:', predict_value)