一、背景
在野外布设700米的测线，点距为10米，用GPS每隔50米测量一个坐标，再把测线的头和为测量一个坐标。现在需使用线性插值的方法求取每两个坐标之间的其他4个点的值。
二、插值原理
使用等比插值的方法
起始值为 a
终止值为 b
步长值为 (a-b)/5
后面的数分别为 a+n, a+2n, a+3n, a+4n
三、代码实习对 x 插值
interx.py
import numpy as np
f = np.loadtxt('datax.txt')
a = f[:, 0]
b = f[:, 1]
for j in np.arange(len(a)):
aa = a[j]*1000 # np.arrange()会自动去掉小数
bb = b[j]*1000
n = (bb-aa) / 5
x = np.arange(6)
x[0] = aa
print(x[0]/1000)
for i in range(1, 5, 1):
x[i] = x[i-1]+n
print(x[i]/1000)
i = i+1
# print(bb/1000)
# print("\n")datax.txt
514873.536 514883.939
514883.939 514894.358
514894.358 514903.837
514903.837 514903.807
514903.807 514907.179
514907.179 514911.356
514911.356 514913.448
514913.448 514913.315
514913.315 514917.344
514917.344 514923.684
514923.684 514924.801
514924.801 514929.697
514929.697 514916.274对 y 插值
intery.py
import numpy as np
f = np.loadtxt('datay.txt')
a = f[:, 0]
b = f[:, 1]
for j in np.arange(len(a)):
aa = (a[j] - 2820000)*1000 # 数据太长会溢出
bb = (b[j]-2820000)*1000
n = (bb-aa) / 5
x = np.arange(6)
x[0] = aa
print(x[0]/1000+2820000)
for i in range(1, 5, 1):
x[i] = x[i-1]+n
print(x[i]/1000+2820000)
i = i+1
# print(bb/1000)
# print("\n")datay.txt
2820617.820 2820660.225
2820660.225 2820693.988
2820693.988 2820819.199
2820819.199 2820831.510
2820831.510 2820858.666
2820858.666 2820973.487
2820973.487 2821017.243
2821017.243 2821019.518
2821019.518 2821058.223
2821058.223 2821097.575
2821097.575 2821144.436
2821144.436 2821173.356
2821173.356 2821218.889 四、最终成果
手动把两次插值结果复制到dataxy中
dataxy.txt
514873.536 2820617.819
514875.616 2820626.300
514877.696 2820634.781
514879.776 2820643.262
514881.856 2820651.743
514883.939 2820660.225
514886.022 2820666.977
514888.105 2820673.729
514890.188 2820680.481
514892.271 2820687.233
514894.358 2820693.987
514896.253 2820719.029
514898.148 2820744.071
514900.043 2820769.113
514901.938 2820794.155
514903.837 2820819.199
514903.831 2820821.661
514903.825 2820824.123
514903.819 2820826.585
514903.813 2820829.047
514903.807 2820831.509
514904.481 2820836.940
514905.155 2820842.371
514905.829 2820847.802
514906.503 2820853.233
514907.179 2820858.666
514908.014 2820881.630
514908.849 2820904.594
514909.684 2820927.558
514910.519 2820950.522
514911.356 2820973.487
514911.774 2820982.238
514912.192 2820990.989
514912.610 2820999.740
514913.028 2821008.491
514913.448 2821017.242
514913.421 2821017.697
514913.394 2821018.152
514913.367 2821018.607
514913.340 2821019.062
514913.315 2821019.518
514914.120 2821027.259
514914.925 2821035.000
514915.730 2821042.741
514916.535 2821050.482
514917.344 2821058.223
514918.612 2821066.093
514919.880 2821073.963
514921.148 2821081.833
514922.416 2821089.703
514923.684 2821097.575
514923.907 2821106.947
514924.130 2821116.319
514924.353 2821125.691
514924.576 2821135.063
514924.801 2821144.436
514925.780 2821150.219
514926.759 2821156.002
514927.738 2821161.785
514928.717 2821167.568
514929.697 2821173.356
514927.012 2821182.462
514924.327 2821191.568
514921.642 2821200.674
514918.957 2821209.780 
五、画图对比
dataxy.py
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
# 解决中文字体显示不出来
mpl.rcParams["font.sans-serif"]=["SimHei"]
mpl.rcParams["axes.unicode_minus"] = False
a = np.loadtxt("datax.txt")
b = np.loadtxt('datay.txt')
c = np.loadtxt('dataxy.txt')
x = a[: ,0]
y = b[: ,0]
xx = c[:,0]
yy = c[:,1]
plt.plot(x,y,color = 'orange',
label = '插值线段')
plt.scatter(xx,yy,marker='o',
c = 'deepskyblue',
alpha = 0.6,
label = '实测点位')
plt.legend()
plt.title('Python坐标插值')
plt.grid()
# 保存高清图片，dpi表示分辨率
plt.savefig('out.png',dpi = 600)
plt.show()
文件结构
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。