使用Python将高程(XYZ)数据转换为坡度/坡度贴图
问题描述
我有一个文本文件，其中包含东距(X)、北距(Y)和高程数据(Z)，如下所示：
x y z
241736.69 3841916.11 132.05
241736.69 3841877.89 138.76
241736.69 3841839.67 142.89
241736.69 3841801.45 148.24
241736.69 3841763.23 157.92
241736.69 3841725.02 165.01
241736.69 3841686.80 171.86
241736.69 3841648.58 178.80
241736.69 3841610.36 185.26
241736.69 3841572.14 189.06
241736.69 3841533.92 191.28
241736.69 3841495.71 193.27
241736.69 3841457.49 193.15
241736.69 3841419.27 194.85
241736.69 3841381.05 192.31
241736.69 3841342.83 188.73
241736.69 3841304.61 183.68
241736.69 3841266.39 176.97
241736.69 3841228.18 160.83
241736.69 3841189.96 145.69
241736.69 3841151.74 129.09
241736.69 3841113.52 120.03
241736.69 3841075.30 111.84
241736.69 3841037.08 104.82
241736.69 3840998.86 101.63
241736.69 3840960.65 97.66
241736.69 3840922.43 93.38
241736.69 3840884.21 88.84
...
我可以使用plt.contour和plt.contourf轻松地从上面的数据中获取高程图，如下所示：
但是，我正在尝试获取我拥有的数据的斜率图，如下所示：
我尝试做的是使用GDAL将我的XYZ数据转换为DEM(如here所述)，并使用richdem加载DEM(如here所述)，但我得到的坡度值是错误的。
转换为.tif得到的结果：
这是我用richdem尝试过的代码：
import richdem as rd
dem_path = 'convertedXYZ.tif'
dem = rd.LoadGDAL(dem_path, no_data=-9999)
slope = rd.TerrainAttribute(dem, attrib='slope_riserun')
rd.rdShow(slope, axes=True, cmap='gist_yarg', figsize=(16, 9))
我得到的情节：
颜色栏上的值太高，不正确，绘图必须反转才能与上面的绘图匹配(目前不是我的主要问题)。
我在将Python用于GIS目的时并不是专家(我主要使用Python进行数据分析)，我希望这不像我想象的那么复杂。
解决方案
ei能够正确编写函数，但首先我需要表扬answer，因为它为我节省了编写自己的移动窗口函数的时间(效果非常好！)：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
def window3x3(arr, shape=(3, 3)):
r_win = np.floor(shape[0] / 2).astype(int)
c_win = np.floor(shape[1] / 2).astype(int)
x, y = arr.shape
for i in range(x):
xmin = max(0, i - r_win)
xmax = min(x, i + r_win + 1)
for j in range(y):
ymin = max(0, j - c_win)
ymax = min(y, j + c_win + 1)
yield arr[xmin:xmax, ymin:ymax]
def gradient(XYZ_file, min=0, max=15, figsize=(15, 10), **kwargs):
"""
:param XYZ_file: XYZ file in the following format: x,y,z (inlcuding headers)
:param min: color bar minimum range.
:param max: color bar maximum range.
:param figsize: figure size.
:param kwargs:
plot: to plot a gradient map. Default is True.
:return: returns an array with the shape of the grid with the computed slopes
The algorithm calculates the gradient using a first-order forward or backward difference on the corner points, first
order central differences at the boarder points, and a 3x3 moving window for every cell with 8 surrounding cells (in
the middle of the grid) using a third-order finite difference weighted by reciprocal of squared distance
Assumed 3x3 window:
-------------------------
| a | b | c |
-------------------------
| d | e | f |
-------------------------
| g | h | i |
-------------------------
"""
kwargs.setdefault('plot', True)
grid = XYZ_file.to_numpy()
nx = XYZ_file['x'].unique().size
ny = XYZ_file['y'].unique().size
xs = grid[:, 0].reshape(ny, nx, order='F')
ys = grid[:, 1].reshape(ny, nx, order='F')
zs = grid[:, 2].reshape(ny, nx, order='F')
dx = abs((xs[:, 1:] - xs[:, :-1]).mean())
dy = abs((ys[1:, :] - ys[:-1, :]).mean())
gen = window3x3(zs)
windows_3x3 = np.asarray(list(gen))
windows_3x3 = windows_3x3.reshape(ny, nx)
dzdx = np.empty((ny, nx))
dzdy = np.empty((ny, nx))
loc_string = np.empty((ny, nx), dtype="S25")
for ax_y in trange(ny):
for ax_x in range(nx):
# corner points
if ax_x == 0 and ax_y == 0: # top left corner
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][0][1] - windows_3x3[ax_y, ax_x][0][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][0] - windows_3x3[ax_y, ax_x][0][0]) / dy
loc_string[ax_y, ax_x] = 'top left corner'
elif ax_x == nx - 1 and ax_y == 0: # top right corner
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][0][1] - windows_3x3[ax_y, ax_x][0][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][0][1]) / dy
loc_string[ax_y, ax_x] = 'top right corner'
elif ax_x == 0 and ax_y == ny - 1: # bottom left corner
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][1][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][0] - windows_3x3[ax_y, ax_x][0][0]) / dy
loc_string[ax_y, ax_x] = 'bottom left corner'
elif ax_x == nx - 1 and ax_y == ny - 1: # bottom right corner
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][1][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][0][1]) / dy
loc_string[ax_y, ax_x] = 'bottom right corner'
# top boarder
elif (ax_y == 0) and (ax_x != 0 and ax_x != nx - 1):
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][0][-1] - windows_3x3[ax_y, ax_x][0][0]) / (2 * dx)
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][0][1]) / dy
loc_string[ax_y, ax_x] = 'top boarder'
# bottom boarder
elif ax_y == ny - 1 and (ax_x != 0 and ax_x != nx - 1):
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][-1] - windows_3x3[ax_y, ax_x][1][0]) / (2 * dx)
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][0][1]) / dy
loc_string[ax_y, ax_x] = 'bottom boarder'
# left boarder
elif ax_x == 0 and (ax_y != 0 and ax_y != ny - 1):
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][1][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][-1][0] - windows_3x3[ax_y, ax_x][0][0]) / (2 * dy)
loc_string[ax_y, ax_x] = 'left boarder'
# right boarder
elif ax_x == nx - 1 and (ax_y != 0 and ax_y != ny - 1):
dzdx[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][1][1] - windows_3x3[ax_y, ax_x][1][0]) / dx
dzdy[ax_y, ax_x] = (windows_3x3[ax_y, ax_x][-1][-1] - windows_3x3[ax_y, ax_x][0][-1]) / (2 * dy)
loc_string[ax_y, ax_x] = 'right boarder'
# middle grid
else:
a = windows_3x3[ax_y, ax_x][0][0]
b = windows_3x3[ax_y, ax_x][0][1]
c = windows_3x3[ax_y, ax_x][0][-1]
d = windows_3x3[ax_y, ax_x][1][0]
f = windows_3x3[ax_y, ax_x][1][-1]
g = windows_3x3[ax_y, ax_x][-1][0]
h = windows_3x3[ax_y, ax_x][-1][1]
i = windows_3x3[ax_y, ax_x][-1][-1]
dzdx[ax_y, ax_x] = ((c + 2 * f + i) - (a + 2 * d + g)) / (8 * dx)
dzdy[ax_y, ax_x] = ((g + 2 * h + i) - (a + 2 * b + c)) / (8 * dy)
loc_string[ax_y, ax_x] = 'middle grid'
hpot = np.hypot(abs(dzdy), abs(dzdx))
slopes_angle = np.degrees(np.arctan(hpot))
if kwargs['plot']:
slopes_angle[(slopes_angle < min) | (slopes_angle > max)]
plt.figure(figsize=figsize)
plt.pcolormesh(xs, ys, slopes_angle, cmap=plt.cm.gist_yarg, vmax=max, vmin=min)
plt.colorbar()
plt.tight_layout()
plt.show()
return slopes_angle
if __name__ == '__main__':
XYZ = pd.read_csv('xyz_file')
slopes = gradient(XYZ)
和最终情节：
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