Pythonを用いた空間統計分析のための衛星データ（LST, NDVI）の処理2

import h5py
import pandas as pd
import tables
from pyhdf.SD import SD, SDC
import numpy as np
import pandas as pd
from scipy.interpolate import griddata
import seaborn as sns
import matplotlib.pyplot as plt

import os
import glob
import csv

import jismesh.utils as ju



## 6〜8月

path0000 = "./Data/27City_LST_6to9_AVE"
path0001 = "./Data/27City_NDVI_6to9_AVE"

files0 = os.listdir(path0000)
ff0 = files0[0:]

files1 = os.listdir(path0001)
ff1 = files1[0:]


### 年間平均

# 対象期間
annual = [2012,2013,2014,2015]

path10000 = "./Data/27City_LST_annual_AVE"
path10010 = "./Data/27City_NDVI_annual_AVE"


files10 = os.listdir(path10000 + "/" + str(annual[0]))
ff10 = files10[0:]

files11 = os.listdir(path10010 + "/" + str(annual[0]))
ff11 = files11[0:]


# 全部を結合 

####### LST

City0 = pd.read_csv(path0000 + "/" + ff0[0],header=0,engine="python")

for g in range(len(ff0)-1):
    City1 = pd.read_csv(path0000 + "/" + ff0[g+1],header=0,engine="python")
    City0 = pd.concat([City0,City1])

City0 = City0.reset_index(drop=True)
City0 = City0.rename(columns={'1': 'X', '2': 'Y', '3':'LST'})
City0.to_csv("./Data/27City_LST_6to9_AVE/ALL_6to9_AVE.csv",index = False)


####### NDVI
City0 = pd.read_csv(path0001 + "/" + ff1[0],header=0,engine="python")

for g in range(len(ff1)-1):

    City1 = pd.read_csv(path0001 + "/" + ff1[g+1],header=0,engine="python")
    City0 = pd.concat([City0,City1])

City0 = City0.reset_index(drop=True)
City0 = City0.rename(columns={'1': 'X', '2': 'Y', '3':'NDVI'})
City0.to_csv("./Data/27City_NDVI_6to9_AVE/ALL_6to9_AVE.csv",index = False)


### 年間 LST
for j in range(len(annual)):
    City0 = pd.read_csv(path10000 + "/" + str(annual[j]) + "/" 
    + ff10[0],header=0,engine="python")
    
    for g in range(len(ff10)-1):
        City1 = pd.read_csv(path10000 + "/" + str(annual[j]) + "/" 
        + ff10[g+1],header=0,engine="python")
        City0 = pd.concat([City0,City1])
        
    City0 = City0.reset_index(drop=True)
    City0 = City0.rename(columns={'1': 'X', '2': 'Y', '3':'LST'})
    City0.to_csv("./Data/27City_LST_annual_AVE/" + str(annual[j]) + "/" 
    + str(annual[j]) + "_ALLannual_AVE.csv",index = False)


### 年間 NDVI
for j in range(len(annual)):
    City0 = pd.read_csv(path10010 + "/" + str(annual[j]) + "/" 
    + ff11[0],header=0,engine="python")
    
    for g in range(len(ff11)-1):
        City1 = pd.read_csv(path10010 + "/" + str(annual[j]) + "/" 
        + ff11[g+1], header=0,engine="python")
        City0 = pd.concat([City0,City1])
        
    City0 = City0.reset_index(drop=True)
    City0 = City0.rename(columns={'1': 'X', '2': 'Y', '3':'NDVI'})    
    City0.to_csv("./Data/27City_NDVI_annual_AVE/" 
    + str(annual[j]) + "/" + str(annual[j]) 
    + "_ALLannual_AVE.csv",index = False)