显著性检验 python p值_python相关性分析与p值检验

## 最近两天的成果

'''

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# #

# 不忘初心 砥砺前行. #

# 418__yj #

##########################################

'''

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import pearsonr

import datetime

import os

#求原始图像各波段相关系数与P值

def corr_p(data,spad):

print('[INFO]处理原始光谱曲线')

l1=[]

l2=[]

col=data.columns

num=len(data.index)

index=np.linspace(0,num-1,num)

data.index=index

spad.index=index

for i in col:

#pearsonr函数返回两个值，分别为相关系数以及P值(显著性水平)

#l1:相关系数列表，l2:p值列表

value=pearsonr(spad[spad.columns[0]],data[i])

l1.append(value[0])

l2.append(round(value[1],3))

corr_se=pd.Series(l1,index=col)

p_se=pd.Series(l2,index=col)

#因为不可避免的存在0.01,0.05水平线不存在，因此依次在附近寻找了+-0.002范围的值

index_001_list=[0.010,0.011,0.009,0.012,0.008]

index_005_list=[0.050,0.051,0.049,0.052,0.048]

index_001=[]

index_001_01=[]

index_005=[]

index_005_01=[]

for i in index_001_list:

index_001.append(list(p_se[p_se==i].index.values))

index_001_01.append(list(p_se[p_se==-i].index.values))

for i in index_005_list:

index_005.append(list(p_se[p_se==i].index.values))

index_005_01.append(list(p_se[p_se==-i].index.values))

#数据清洗

index_001=[list(i) for i in index_001 if len(list(i))!=0]

index_001_01=[list(i) for i in index_001_01 if len(list(i))!=0]

index_005=[list(i) for i in index_005 if len(list(i))!=0]

index_005_01=[list(i) for i in index_005_01 if len(list(i))!=0]

print(index_001,index_005)

#p=0.01,p=0.05所对应波段的相关系数值

p_001=corr_se[index_001[0][0]]

p_005=corr_se[index_005[0][0]]

#对单个值的横向填充为PD.SERIES

p_001_data=get_p_value(p_001,corr_se,name='p_001')

p_005_data=get_p_value(p_005,corr_se,name='p_005')

corr=pd.concat([corr_se,p_001_data,p_005_data],axis=1)

idmax=corr_se.idxmax()

idmin=corr_se.idxmin()

print('p_001,p_005')

print(p_001,p_005)

print('*')

print('[INFO]idmax:%s,idmin:%s'%(idmax,idmin))

#绘图

s='diff.png'

xticks=np.arange(339,2539,200)

draw(corr,s,xticks)

#写入txt文档，0.01,0.05交点用于分析

with open('./output/corr_original.txt','w') as f:

f.writelines(u'最大相关系数所对应波段:'+str(idmax)+'\n')

f.writelines('相关系数最大值:'+str(corr_se[idmax])+'\n')

f.writelines('负相关最大所对应波段:'+str(idmin)+'\n')

f.writelines('负相关最大值:'+str(corr_se[idmin])+'\n')

f.writelines('0.05水平线与负相关系数曲线交点:'+str(index_005)+'\n')

f.writelines('0.05水平线与正相关系数曲线交点:'+str(index_005_01)+'\n')

f.writelines('0.01水平线与负相关系数曲线交点:'+str(index_001)+'\n')

f.writelines('0.01水平线与正相关系数曲线交点:'+str(index_001_01)+'\n')

def get_p_value(p_value,corr_se,name):

#empty=np.zeros_like(corr_se)

min_corr=corr_se.min()

max_corr=corr_se.max()

if min_corr*max_corr>0:

se=pd.Series(p_value,index=corr_se.index)

se.name=name

return se

else:

#empty_1=empty.copy()

#empty_1[:]=p_se

se_1=pd.Series(p_value,index=corr_se.index)

se_1.name=name

#empty_2=empty.copy()

#empty_2[:]=-p_se

se_2=pd.Series(-p_value,index=corr_se.index)

se_2.name=name+'_01'

return pd.concat([se_1,se_2],axis=1)

def draw(corr,s,xticks):

'''

if corr.columns[0]==338:

xticks=np.arange(338,2538,200)

xlim=(338,2538)

elif corr.columns[0]==339:

xticks=np.arange(339,2539,200)

xlim=(339,2539)

UnboundLocalError:

'''

print('corrr.columns：%s'%corr.columns)

style={0:'k','p_001':'k','p_001_01':'k','p_005':'k--','p_005_01':'k--'}

corr.plot(style=style,xticks=xticks,xlim=(xticks.min(),xticks.max()),figsize=(12,9))

ax=plt.gca()

ax.spines['top'].set_color('none')

ax.spines['right'].set_color('none')

plt.savefig('./output/'+s)

def diff_corr_p(data,spad):

print('[INFO]处理一阶导数曲线')

l1=[]

l2=[]

#相比原始图像，一阶导数要先转为np.array做差分处理，再转为dataframe

array=np.array(data)

diff_array=np.diff(array,axis=1)

num=len(data.index)

index=np.linspace(0,num-1,num)

spad.index=index

data=pd.DataFrame(diff_array,columns=data.columns[:-1],index=index)

col=data.columns

#输出Excel

#data.to_excel('./output/diff.xlsx')

for i in col:

#pearsonr函数返回两个值，分别为相关系数以及P值(显著性水平)

#l1:相关系数列表，l2:p值列表

value=pearsonr(spad[spad.columns[0]],data[i])

l1.append(value[0])

l2.append(round(value[1],3))

corr_se=pd.Series(l1,index=col)

p_se=pd.Series(l2,index=col)

#因为不可避免的存在0.01,0.05水平线不存在，因此依次在附近寻找了+-0.002范围的值

index_001_list=[0.010,0.011,0.009,0.012,0.008]

index_005_list=[0.050,0.051,0.049,0.052,0.048]

index_001=[]

index_001_01=[]

index_005=[]

index_005_01=[]

for i in index_001_list:

index_001.append(list(p_se[p_se==i].index.values))

index_001_01.append(list(p_se[p_se==-i].index.values))

for i in index_005_list:

index_005.append(list(p_se[p_se==i].index.values))

index_005_01.append(list(p_se[p_se==-i].index.values))

#数据清洗

index_001=[list(i) for i in index_001 if len(list(i))!=0]

index_001_01=[list(i) for i in index_001_01 if len(list(i))!=0]

index_005=[list(i) for i in index_005 if len(list(i))!=0]

index_005_01=[list(i) for i in index_005_01 if len(list(i))!=0]

print(index_001,index_005)

#p=0.01,p=0.05所对应波段的相关系数值

p_001=corr_se[index_001[0][0]]

p_005=corr_se[index_005[0][0]]

#对单个值的横向填充为PD.SERIES

p_001_data=get_p_value(p_001,corr_se,name='p_001')

p_005_data=get_p_value(p_005,corr_se,name='p_005')

corr=pd.concat([corr_se,p_001_data,p_005_data],axis=1)

idmax=corr_se.idxmax()

idmin=corr_se.idxmin()

print('[INFO]idmax:%s,idmin:%s'%(idmax,idmin))

#绘图

s='diff.png'

xticks=np.arange(339,2539,200)

draw(corr,s,xticks)

#写入txt文档，0.01,0.05交点用于分析

with open('./output/corr_diff.txt','w') as f:

f.writelines(u'最大相关系数所对应波段:'+str(idmax)+'\n')

f.writelines('相关系数最大值:'+str(corr_se[idmax])+'\n')

f.writelines('负相关最大所对应波段:'+str(idmin)+'\n')

f.writelines('负相关最大值:'+str(corr_se[idmin])+'\n')

f.writelines('0.05水平线与负相关系数曲线交点:'+str(index_005)+'\n')

f.writelines('0.05水平线与正相关系数曲线交点:'+str(index_005_01)+'\n')

f.writelines('0.01水平线与负相关系数曲线交点:'+str(index_001)+'\n')

f.writelines('0.01水平线与正相关系数曲线交点:'+str(index_001_01)+'\n')

def main():

starttime = datetime.datetime.now()

print(__doc__)

print('''该脚本可能会运行几分钟，最终结果会保存在当前目录的output文件夹下，包括以下内容：

1：经过重采样处理的SVC的.sig文件以EXCEL形式汇总[sig.xlsx]

2: 所有小区一阶导数[diff.xlsx]

3：0.05水平，0.01水平的原始图像相关性检验[original.png]

4：0.05水平，0.01水平的一阶导数光谱相关性检验[diff.png]

5：原始图像相关性最大波段的及相关系数[corr_original.txt]

6：一阶导数相关性最大波段的及相关系数[corr_diff.txt]

说明：本人才疏学浅，对遥感反演原理不甚了解，数据处理中有诸多纰漏，望慎重使用，以免给各位带来不必要的麻烦。

''')

print('[INFO]加载数据集...')

path_sig='../output/sig.xlsx'

sig=pd.read_excel(path_sig,'Sheet3')

path='../spad/spad.xlsx'

spad=pd.read_excel(path)

if not os.path.exists('./output'):

os.mkdir('./output')

corr_p(sig.copy(),spad.copy())

diff_corr_p(sig.copy(),spad.copy())

endtime = datetime.datetime.now()

print('-'*80)

print('程序运行时间:%s s'%((endtime - starttime).seconds))

'''

corr_se[1334]

Out[28]: -0.16722162390032191

EMPTY_SE_001=np.zeros_like(corr_se)

EMPTY_SE_001[:]=-0.16722162390032191

se_01=pd.Series(EMPTY_SE_001,index=index)

corr=pd.concat([corr_se,se_01,se_05],axis=1)

'''

#diff_array=np.diff(sig_array,axis=1)

'''

diff_corr_se[diff_p_se[diff_p_se==0.01].index]

Out[100]:

991 -0.167330

1071 -0.167740

1100 0.166970

1215 0.166174

1232 0.167815

1308 -0.166201

1426 -0.166492

1709 -0.166323

1735 -0.167574

1819 -0.166347

2094 -0.167152

2129 -0.167569

2321 0.167649

2383 0.167009

2478 0.167431

2505 0.166817

dtype: float64

'''

'''

diff_1.idxmax()

Out[139]:

0 759

1 339

2 339

1 339

2 339

dtype: int64

diff_1[0][759]

Out[140]: 0.8388844431717504

diff_1.idxmin()

Out[141]:

0 523

1 339

2 339

1 339

2 339

dtype: int64

diff_1[0][523]

Out[142]: -0.7787709002181252

'''

main()