在上次代码的基础上做了一点儿修改，将定义的函数单独放在一个模块里面，主函数去单独调用该模块。

DEMslopeAspect模块

from osgeo import gdal,ogr,osr
import numpy as np
import math
import datetime

# Python matplotlib模块代码示例 https://vimsky.com/examples/detail/python-module-matplotlib.html
# Axes3D是mpl_toolkits.mplot3d中的一个绘图函数，mpl_toolkits.mplot3d;是Matplotlib里面专门用来画三维图的工具包。
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cbook, cm
from matplotlib.colors import LightSource
import matplotlib.pyplot as plt
# 正则表达式(regular expression)描述了一种字符串匹配的模式，可以用来检查一个串是否含有某种子串、将匹配的子串做替换或者从某个串中取出符合某个条件的子串等。
import re


# 读取TIFF遥感影像
def read_img(filename):

    # dataset = gdal.Open(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\test.tif')  # 打开文件
    # dataset = gdal.Open(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\proUTM50python.tif')
    dataset = gdal.Open(filename)
    im_width = dataset.RasterXSize  # 栅格矩阵的列数
    im_height = dataset.RasterYSize  # 栅格矩阵的行数
    im_bands = dataset.RasterCount  # 波段数
    im_geotrans = dataset.GetGeoTransform()  # 仿射矩阵，左上角像素的大地坐标和像素分辨率
    im_proj = dataset.GetProjection()  # 地图投影信息，字符串表示
    im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
    datatype = im_data.dtype
    del dataset  # 关闭对象dataset，释放内存

    return   im_data, im_proj, im_geotrans, im_height,im_width, im_bands, datatype


# 为便于后续坡度计算，需要在原图像的周围添加一圈数值
def AddRound(npgrid):

    nx, ny = npgrid.shape[0], npgrid.shape[1]   # ny:行数，nx:列数;此处注意顺序
    # np.zeros()返回来一个给定形状和类型的用0填充的数组；
    zbc=np.zeros((nx+2,ny+2))
    # 填充原数据数组
    zbc[1:-1,1:-1]=npgrid

    #四边填充数据
    zbc[0,1:-1]=npgrid[0,:]  #上边；0行，所有列；
    zbc[-1,1:-1]=npgrid[-1,:] #下边；最后一行，所有列；
    zbc[1:-1,0]=npgrid[:,0]  #左边；所有行，0列。
    zbc[1:-1,-1]=npgrid[:,-1] #右边；所有行，最后一列

    #填充剩下四个角点值
    zbc[0,0]=npgrid[0,0]
    zbc[0,-1]=npgrid[0,-1]
    zbc[-1,0]=npgrid[-1,0]
    zbc[-1,-1]=npgrid[-1,0]

    return zbc


#####计算xy方向的梯度
def Cacdxdy(npgrid,sizex,sizey):

    nx, ny = npgrid.shape
    s_dx = np.zeros((nx,ny))
    s_dy = np.zeros((nx,ny))
    a_dx = np.zeros((nx, ny))
    a_dy = np.zeros((nx, ny))
    # 忘记加range报错：object is not iterable
    # 坡度、坡向变化率的计算：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.html#/na/009z000000vz000000/
    for i in range(1,nx-1):
        for j in range(1,ny-1):
            s_dx[i,j] = ((npgrid[i-1,j+1]+2*npgrid[i,j+1]+npgrid[i+1,j+1])-(npgrid[i-1,j-1]+2*npgrid[i,j-1]+npgrid[i+1,j-1])) / (8 * sizex)
            s_dy[i, j] = ((npgrid[i+1, j-1] + 2 * npgrid[i+1, j] + npgrid[i+1,j+1])-(npgrid[i-1,j-1]+2 * npgrid[i-1,j] + npgrid[i-1,j+1])) / (8 * sizey)

    a_dx=s_dx*sizex
    a_dy=s_dy*sizey
    # 保留原数据区域的梯度值
    s_dx = s_dx[1:-1,1:-1]
    s_dy = s_dy[1:-1,1:-1]
    a_dx = a_dx[1:-1, 1:-1]
    a_dy = a_dy[1:-1, 1:-1]
    # np.savetxt(r"D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\1dxdy.csv",dx,delimiter=",")

    return s_dx,s_dy,a_dx,a_dy


####计算坡度/坡向
def CacSlopAsp(s_dx,s_dy,a_dx,a_dy):

    # 坡度
    slope=(np.arctan(np.sqrt(s_dx*s_dx+s_dy*s_dy)))*180/math.pi    #转换成°

    #坡向
    # #出错：TypeError: only size-1 arrays can be converted to Python scalars
    # a2 = math.atan2(a_dy,-a_dx)*180/math.pi
    a=np.zeros((a_dy.shape[0],a_dy.shape[1]))
    for i in range(0,a_dx.shape[0]):
        for j in range(0,a_dx.shape[1]):
            a[i,j] = math.atan2(a_dy[i,j], -a_dx[i,j]) * 180 / math.pi

    # 输出
    aspect = a
    # 坡向值将根据以下规则转换为罗盘方向值（0 到 360 度）：
    # https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.html#/na/009z000000vp000000/
    x, y = a.shape[0],a.shape[1]
    for m in range(0,x):
        for n in range(0,y):
            if a[m,n] < 0:
                aspect[m,n] = 90-a[m,n]
            elif a[m,n] > 90:
                aspect[m,n] = 360.0 - a[m,n] + 90.0
            else:
                aspect[m,n] =  90.0 - a[m,n]

    return slope,aspect

# 遥感影像的存储，写GeoTiff文件
def write_img(filename, tar_proj, im_geotrans, im_data, datatype):

    # 判断栅格数据的数据类型
    if 'int8' in im_data.dtype.name:
        datatype = gdal.GDT_Byte
    elif 'int16' in im_data.dtype.name:
        datatype = gdal.GDT_UInt16
    else:
        datatype = gdal.GDT_Float32

    # 判读数组维数
    if len(im_data.shape) == 3:
        # 注意数据的存储波段顺序：im_bands, im_height, im_width
        im_bands, im_height, im_width = im_data.shape
    else:
        im_bands, (im_height, im_width) = 1, im_data.shape

    # 创建文件时 driver = gdal.GetDriverByName("GTiff")，数据类型必须要指定，因为要计算需要多大内存空间。
    driver = gdal.GetDriverByName("GTiff")
    dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)

    dataset.SetGeoTransform(im_geotrans)  # 写入仿射变换参数
    dataset.SetProjection(tar_proj)  # 写入投影

    if im_bands == 1:
        dataset.GetRasterBand(1).WriteArray(im_data)  # 写入数组数据
    else:
        for i in range(im_bands):
            dataset.GetRasterBand(i + 1).WriteArray(im_data[i])

    del dataset

# 定义投影函数（此次运行没有用到）
def SetPro(filename,tar_proj,outputfilename):

    ds = gdal.Open(filename)
    im_geotrans = ds.GetGeoTransform()  # 仿射矩阵信息
    im_proj = ds.GetProjection()  # 地图投影信息
    im_width = ds.RasterXSize  # 栅格矩阵的列数
    im_height = ds.RasterYSize  # 栅格矩阵的行数
    im_bands = ds.RasterCount
    ds_array = ds.ReadAsArray(0, 0, im_width, im_height)  # 获取原数据信息，包括数据类型int16，维度，数组等信息

    # 设置数据类型(原图像有负值)
    datatype = gdal.GDT_Float32
    # 目标投影
    img_proj = tar_proj
    # 输出影像路径及名称
    name = outputfilename
    driver = gdal.GetDriverByName("GTiff")  # 创建文件驱动
    dataset = driver.Create(name, im_width, im_height, im_bands, datatype)
    dataset.SetGeoTransform(im_geotrans)  # 写入原图像的仿射变换参数
    dataset.SetProjection(img_proj)  # 写入目标投影

    # 写入影像数据
    dataset.GetRasterBand(1).WriteArray(ds_array)

    del dataset


####绘制平面栅格图
def Drawgrid(judge,pre=[],A=[],strs=""):
    if judge==0:
        if strs == "":
            plt.imshow(A, interpolation='nearest', cmap=plt.cm.hot, origin='lower')  # cmap='bone'  cmap=plt.cm.hot
            # plt.imshow(A, interpolation='nearest', cmap=plt.cm.hot, origin='lower')  # cmap='bone'  cmap=plt.cm.hot
            plt.colorbar(shrink=0.8)
            plt.xticks(())
            plt.yticks(())
            plt.show()
        else:
            plt.imshow(A, interpolation='nearest', cmap=strs, origin='lower')  # cmap='bone'  cmap=plt.cm.hot
            plt.colorbar(shrink=0.8)
            # 影像范围（原始图像的im_geotrans六参数有）
            X = np.arange(113.99986111111112, 6113.999861111111, 30)
            Y = np.arange(35.00013888888889, 3035.000138888889, 30)
            plt.xticks(())
            plt.yticks(())
            plt.show()

    # judge==1绘制三维DEM
    elif judge==1:
        fig = plt.figure()
        ax = Axes3D(fig)
        X = np.arange(113.99986111111112,6113.999861111111, 30)
        Y = np.arange(35.00013888888889, 3035.000138888889, 30)
        # xt=range(114.79763889,853584.79763889 , 30)
        # yt=range(38.21347222, 413348.21347222, 30)
        X, Y = np.meshgrid(X, Y)
        Z = pre
        ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))  # cmap=plt.get_cmap('rainbow')
        ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
        ax.set_zlim(0, 200000)
        plt.show()

主函数

import D1_DEMslopeAspect as dem
from D1_DEMslopeAspect import Drawgrid
import datetime

# 程序入口
if __name__ == "__main__":

    startime = datetime.datetime.now() # 程序开始时间
    # 读取ASTER GDEM遥感影像
    demgrid, proj, geotrans, row, column, band, type =dem.read_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\test2.tif')
    # geotrans = (114.79763889, 0.00027777777778, 0.0, 38.21347222, 0.0, -0.00027777777778)
    # row = 13777
    # col = 28449
    demgridata = demgrid
    # 为计算梯度给影像添加周围一圈数据
    demgrid = dem.AddRound(demgrid)
    # 梯度计算
    dx1,dy1,dx2,dy2 = dem.Cacdxdy(demgrid,30,30)
    # 坡度、坡向计算
    slope,aspect =dem.CacSlopAsp(dx1,dy1,dx2,dy2)
    # 设置要投影的投影信息，此处是WGS84-UTM-50N
    tar_proj = '''PROJCS["WGS 84 / UTM zone 50N",
      GEOGCS["WGS 84",
          DATUM["WGS_1984",
              SPHEROID["WGS 84",6378137,298.257223563,
                  AUTHORITY["EPSG","7030"]],
              AUTHORITY["EPSG","6326"]],
          PRIMEM["Greenwich",0,
              AUTHORITY["EPSG","8901"]],
          UNIT["degree",0.01745329251994328,
              AUTHORITY["EPSG","9122"]],
          AUTHORITY["EPSG","4326"]],
      UNIT["metre",1,
          AUTHORITY["EPSG","9001"]],
      PROJECTION["Transverse_Mercator"],
      PARAMETER["latitude_of_origin",0],
      PARAMETER["central_meridian",117],
      PARAMETER["scale_factor",0.9996],
      PARAMETER["false_easting",500000],
      PARAMETER["false_northing",0],
      AUTHORITY["EPSG","32650"],
      AXIS["Easting",EAST],
      AXIS["Northing",NORTH]]'''
    # 输出TIFF格式遥感影像,并设置投影坐标
    slopeT = dem.write_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\slopetest2.tif', tar_proj, geotrans, slope, type)
    aspectT = dem.write_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\aspecttest2.tif', tar_proj, geotrans, aspect, type)

    endtime = datetime.datetime.now()  # 程序结束时间
    runtime = endtime - startime  # 程序运行时间
    print('运行时间为： %d 秒' % (runtime.seconds))

    # 绘制三维DEM
    Drawgrid(judge=1, pre=demgridata)
    # 绘制二维DEM
    Drawgrid(judge=0, A=demgridata, strs="bone")
    # 绘制坡度图
    Drawgrid(judge=0, A=slope, strs="rainbow")
    # 绘制坡向图
    Drawgrid(judge=0, A=aspect)

效果图

由于整个山东省的面积太大，故而选择了一小片区域（100*200）测试。

                                                      原始DEM图

                                                       TIF坡度图

                                                             TIF坡向图

                                                             三维DEM图

                                                                   二维DEM图

                                                                     坡度图

                                                                坡向图

参考

[1]坡度：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.html#/na/009z000000vz000000/

[2]坡向：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.html#/na/009z000000vp000000/

[3]博主锃光瓦亮的枕小路：https://blog.csdn.net/weixin_45561357/article/details/106677574

[4]https://blog.csdn.net/weixin_40501429/article/details/114894497

[5]博主箜_Kong：https://blog.csdn.net/liminlu0314/article/details/8498985?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522161657597316780266219174%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=161657597316780266219174&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v1~rank_blog_v1-1-8498985.pc_v1_rank_blog_v1&utm_term=%E5%9D%A1%E5%BA%A6%E5%9D%A1%E5%90%91