>>> import arcpy
... from arcpy import env
... import numpy
... from arcpy.sa import *
... import os
... 
... #rasterPath = arcpy.GetParameterAsText(0)   #载入栅格数据
... rasterPath_LST = r'C:\Users\Administrator\Desktop\LstDem_essay\data.gdb\JJJ_LST_1000'   #载入温度栅格数据
... rasterPath_DEM = r'C:\Users\Administrator\Desktop\LstDem_essay\data.gdb\JJJ_DEM_1000'   #载入温度栅格数据
... 
... # outraster = arcpy.GetParameterAsText(1)  #定义输出地址
... # Output raster (outraster)                                     #定义输出栅格数据
... #outraster = os.path.join(os.path.dirname(rasterPath)，"LST_Result.tif") #定义输出栅格数据
... 
... #打开栅格数据
... openFile_LST = Raster(rasterPath_LST)
... openFile_DEM = Raster(rasterPath_DEM)
... 
... workpath=r'C:\Users\Administrator\Desktop\LstDem_essay\data.gdb'      #指定工作空间路径
... env.workpath = workpath       #定义工作空间
... env.scratchWorkspace = workpath       #定义临时工作空间    
... env.overwriteOutput = True      #允许对原始文件覆盖
... arcpy.env.extent = arcpy.Extent(4728039.31346127, 3896494.23063293, 5242039.31346127, 4661494.23063293)  #定义分析研究的范围
... #mx_min = rasterPath.extent.XMin 
... #mx_max = rasterPath.extent.XMax
... #my_min = rasterPath.extent.YMin 
... #my_max = rasterPath.extent.YMax
... 
... prjfile = r'C:\Users\Administrator\Desktop\LstDem_essay\data\JJJ_Board.prj'   #输入栅格图像的空间参考
... spatialRef = arcpy.SpatialReference(prjfile)
... myref = spatialRef.name
... print myref
... arcpy.env.outputCoordinateSystem = spatialRef   #设置环境中的投影坐标系
... arcpy.env.cellSize = rasterPath_LST  #设置环境中栅格的分辨率30米
... 
... #获取栅格中的NoData值
... noDataValue = openFile_LST.noDataValue
... noDataValue = openFile_DEM.noDataValue
... 
... #将栅格数据转换为Numpy
... array_LST = arcpy.RasterToNumPyArray(openFile_LST)
... array_DEM = arcpy.RasterToNumPyArray(openFile_DEM)
... my_array = arcpy.RasterToNumPyArray(openFile_LST)
... 
... #波段、行数、列数
... rowNum_LST,colNum_LST = array_LST.shape
... rowNum_DEM,colNum_DEM = array_DEM.shape
... 
... #设置栅格分辨率
... CELL_size = 1000.0
... 
... #计算地表温度平均值以及S值            
... countN_SUM = 0.0
... LST_sum1 = 0.0        #命名地表温度累加和
... LST_sum2 = 0.0        #命名地表温度的平方后累加和
... LST_average = 31.578     #命名区域内的平均地表温度
... S = 0.0               #命名S
... for i in range(1,rowNum_LST-1):
...     for j in range(1,colNum_LST-1):
...         if array_LST[i][j] != 0 :
...             countN_SUM += 1
...             LST_sum1 = LST_sum1 + array_LST[i][j]
...             LST_sum2 = LST_sum2 + array_LST[i][j] * array_LST[i][j] 
... LST_average = LST_sum1 / countN_SUM   #区域内的平均地表温度
... print countN_SUM
... print LST_average 
... print LST_sum1 
... print LST_sum2 
... S = numpy.sqrt((LST_sum2/countN_SUM) - (LST_average * LST_average))   #区域内S值
...    
... #计算G值
... for i in range(1,rowNum_LST-1):
...     for j in range(1,colNum_LST-1):
...         if array_LST[i][j] != 0:
...             
...             Distance_1 = 0.0    #三维距离定义
...             Distance_2 = 0.0    #三维距离定义
...             Distance_3 = 0.0    #三维距离定义
...             Distance_4 = 0.0    #三维距离定义
...             Distance_5 = 0.0    #三维距离定义
...             Distance_6 = 0.0    #三维距离定义
...             Distance_7 = 0.0    #三维距离定义
...             Distance_8 = 0.0    #三维距离定义
...             Distance_sum = 0.0    #单位面积内的三维距离和定义
...             
...             WX_sum = 0.0    #权重与X的乘积和
...             WS_sum = 0.0    #权重平方的乘积和
...             K = 0.0         #指代S旁边根号下的内容
...             K1 = 0.0        #指代K的第一子部分内容
...             G = 0.0         #指代K的第一子部分内容
...             
...             count = 0
...             #计算单位区域内的反距离
...             for m in range(-1,2):
...                 for n in range(-1,2):              
...                     
...                         if m==-1 and n==-1:
...                             H_1 = 0.0    #
...                             d_1 = 0.0    #
...                             D_1 = 0.0    #
...                              
...                             H_1 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_1 = numpy.square(H_1)+numpy.square(1.0)+numpy.square(1.0)
...                             D_1 = numpy.sqrt(d_1)
...                             Distance_1 = 1.0/(D_1*D_1)
...                             Distance_sum = Distance_sum + Distance_1
...                             count += 1  
...                         elif m==-1 and n==0:
...                             H_2 = 0.0    #
...                             d_2 = 0.0    #
...                             D_2 = 0.0    #
...                                
...                             H_2 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_2 = numpy.square(H_2)+numpy.square(1.0)
...                             D_2 = numpy.sqrt(d_2)
...                             Distance_2 = 1.0/(D_2*D_2)
...                             Distance_sum = Distance_sum + Distance_2
...                             count += 1   
...                         elif m==-1 and n==1:
...                             H_3 = 0.0    #
...                             d_3 = 0.0    #
...                             D_3 = 0.0    #
...                                   
...                             H_3 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_3 = numpy.square(H_3)+numpy.square(1.0)+numpy.square(1.0)
...                             D_3 = numpy.sqrt(d_3)
...                             Distance_3 = 1.0/(D_3*D_3)
...                             Distance_sum = Distance_sum + Distance_3
...                             count += 1 
...                         elif m==0 and n==-1: 
...                             H_4 = 0.0    #
...                             d_4 = 0.0    #
...                             D_4 = 0.0    #
...                                 
...                             H_4 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_4 = numpy.square(H_4)+numpy.square(1.0)
...                             D_4 = numpy.sqrt(d_4)
...                             Distance_4 = 1.0/(D_4*D_4)
...                             Distance_sum = Distance_sum + Distance_4
...                             count += 1 
...                         elif m==0 and n==1:
...                             H_5 = 0.0    #
...                             d_5 = 0.0    #
...                             D_5 = 0.0    #
...                                 
...                             H_5 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_5 = numpy.square(H_5)+numpy.square(1.0)
...                             D_5 = numpy.sqrt(d_5)
...                             Distance_5 = 1.0/(D_5*D_5)
...                             Distance_sum = Distance_sum + Distance_5
...                             count += 1 
...                         elif m==1 and n==-1:
...                             H_6 = 0.0    #
...                             d_6 = 0.0    #
...                             D_6 = 0.0    #
...                                 
...                             H_6 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_6 = numpy.square(H_6)+numpy.square(1.0)+numpy.square(1.0)
...                             D_6 = numpy.sqrt(d_6)
...                             Distance_6 = 1.0/(D_6*D_6)
...                             Distance_sum = Distance_sum + Distance_6
...                             count += 1 
...                         elif m==1 and n==0: 
...                             H_7 = 0.0    #
...                             d_7 = 0.0    #
...                             D_7 = 0.0    #
...                             
...                             H_7 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_7 = numpy.square(H_7)+numpy.square(1.0)
...                             D_7 = numpy.sqrt(d_7)
...                             Distance_7 = 1.0/(D_7*D_7)
...                             Distance_sum = Distance_sum + Distance_7
...                             count += 1 
...                         elif m==1 and n==1: 
...                             H_8 = 0.0    #
...                             d_8 = 0.0    #
...                             D_8 = 0.0    #
...                                
...                             H_8 = float(array_DEM[i+m][j+n]-array_DEM[i][j])/CELL_size
...                             d_8 = numpy.square(H_8)+numpy.square(1.0)+numpy.square(1.0)
...                             D_8 = numpy.sqrt(d_8)
...                             Distance_8 = 1.0/(D_8*D_8)
...                             Distance_sum = Distance_sum + Distance_8
...                             count += 1 
...                         elif m==0 and n==0:
...                             count += 1 
...                         
...                         
...             #计算反距离权重                
...             if count != 0: 
...                 for p in range(-1,2):
...                     for q in range(-1,2):
...                         if p==-1 and q==-1: 
...                             Weight_1 = 0.0
...                             Weight_1Square = 0.0 
...                             Weight_1 = Distance_1/Distance_sum
...                             Weight_1Square = Weight_1*Weight_1
...                             WX_sum = WX_sum + Weight_1 * array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_1Square
...                         elif p==-1 and q==0:
...                             Weight_2 = 0.0
...                             Weight_2Square = 0.0   
...                             Weight_2 = Distance_2/Distance_sum
...                             Weight_2Square = Weight_2*Weight_2
...                             WX_sum = WX_sum + Weight_2*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_2Square
...                         elif p==-1 and q==1: 
...                             Weight_3 = 0.0
...                             Weight_3Square = 0.0  
...                             Weight_3 = Distance_3/Distance_sum
...                             Weight_3Square = Weight_3*Weight_3
...                             WX_sum = WX_sum + Weight_3*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_3Square
...                         elif p==0 and q==-1: 
...                             Weight_4 = 0.0
...                             Weight_4Square = 0.0  
...                             Weight_4 = Distance_4/Distance_sum
...                             Weight_4Square = Weight_4*Weight_4
...                             WX_sum = WX_sum + Weight_4*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_4Square
...                         elif p==0 and q==1:
...                             Weight_5 = 0.0
...                             Weight_5Square = 0.0   
...                             Weight_5 = Distance_5/Distance_sum
...                             Weight_5Square = Weight_1*Weight_5
...                             WX_sum = WX_sum + Weight_5*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_5Square
...                         elif p==1 and q==-1:
...                             Weight_6 = 0.0
...                             Weight_6Square = 0.0   
...                             Weight_6 = Distance_6/Distance_sum
...                             Weight_6Square = Weight_6*Weight_6
...                             WX_sum = WX_sum + Weight_6*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_6Square
...                         elif p==1 and q==0: 
...                             Weight_7 = 0.0
...                             Weight_7Square = 0.0  
...                             Weight_7 = Distance_7/Distance_sum
...                             Weight_7Square = Weight_7*Weight_7
...                             WX_sum = WX_sum + Weight_7*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_7Square
...                         elif p==1 and q==1:
...                             Weight_8 = 0.0
...                             Weight_8Square = 0.0   
...                             Weight_8 = Distance_8/Distance_sum
...                             Weight_8Square = Weight_8*Weight_8
...                             WX_sum = WX_sum + Weight_8*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + Weight_8Square
...                         elif p==0 and q==0:
...                             Weight_9 = 1.0
...                             Weight_8Square = 1.0   
...                             WX_sum = WX_sum + 1*array_LST[i+p][j+q]
...                             WS_sum = WS_sum + 1
...             #计算G值    
...             if count >=1:
...                 K1 = (float(countN_SUM) * WS_sum -4.0)/float(countN_SUM-1)
...                 K = numpy.sqrt(K1)
...                 G = (WX_sum - LST_average*2)/(S*K)
...             my_array[i][j] = float(G)  
... outraster = arcpy.NumPyArrayToRaster(my_array,arcpy.Point(4728039.31346127, 3896494.23063293),1000.0,1000.0)
... outraster.save(os.path.dirname(rasterPath_LST) + "_LST_Result.tif")
... openraster = Raster(os.path.dirname(rasterPath_LST) + "_LST_Result.tif")
... env.mask = r'C:\Users\Administrator\Desktop\LstDem_essay\data.gdb\JJJ_Board'  #设置环境中掩膜
... openrasterClip = ApplyEnvironment(openraster)   #环境设置中的各种应用
... openrasterClip.save(os.path.dirname(rasterPath_LST) + "_LST_Result_30.tif")