python mk趋势检验的实现

网上查了很久MK大部分突变检验代码都是基于matlab实现。因为我不熟悉。matlab，于是将matlab代码转换成python最终调试正确可操作的代码。

代码

import numpy as np

import pandas as pd

from matplotlib import pyplot as plt

plt.rcParams['font.family'] = ['MicroSoft YaHei']

plt.rcParams['axes.unicode_minus'] = False

df = pd.read_excel(r'D:\py\data.xls')

# 获取数据

x = df['year']

y = df['data']

n = len(y)

# 正序计算

# 定义累计量序列Sk，长度n，初始值为0

Sk = np.zeros(n)

UFk = np.zeros(n)

# 定义Sk序列元素s

s = 0

for i in range(1, n):

for j in range(0,i):

if y.iloc[i] > y.iloc[j]:

s = 1

Sk[i] = s

E = (i 1)*(i/4)

Var = (i 1)*i*(2*(i 1) 5)/72

UFk[i] = (Sk[i] - E)/np.sqrt(Var)

# 逆序计算

# 逆累计量序列的定义Sk2

# 逆统计量序列的定义Sk2

y2 = np.zeros(n)

Sk2 = np.zeros(n)

UBk = np.zeros(n)

s = 0

y2 = y[::-1]

for i in range(1, n):

for j in range(0,i):

if y2.iloc[i] > y2.iloc[j]:

s = 1

Sk2[i] = s

E = (i 1)*(i/4)

Var = (i 1)*i*(2*(i 1) 5)/72

UBk[i] = -(Sk2[i] - E)/np.sqrt(Var)

UBk2 = UBk[::-1]

# 画图

plt.figure(figsize=(7, 6), dpi=350)

plt.plot(range(18),UFk, label='UF', color='black',marker='s')

plt.plot(range(18), UBk2, label='UB',color='black', linestyle='--', marker='o')

plt.ylabel('Mann-Kendall检验值')

plt.xlabel('年份 Year')

# 添加辅助线

x_lim = plt.xlim()

# 添加显著的水平线和y=0

plt.plot(x_lim,[-1.96,-1.96],':',color='black',label=5%明显水平

plt.plot(x_lim, color='black')

plt.plot(x_lim,[1.96,1.color='black')

plt.xticks(range(18), x.tolist(), rotation=45)

# plt.legend(loc='upper right', bbox_to_anchor=(0.9,0.95),ncol=3,fancybox=True)

# 设置图例位置，调整左右位置的第一个参数，调整上下位置的第二个参数

plt.legend(bbox_to_anchor=(0.75,0.07), facecolor='w',frameon=False)

# 添加文本注释

plt.text(0,-1.六、检验突变点)

plt.savefig("../IMG/MK检验.png")

plt.show()

结果

标签：plt,Sk2,python,检验,color,range,black,mk,np

来源： https://blog.csdn.net/qq_41898946/article/details/113823174