Python坐标线性插值应用实现

这篇文章主要介绍了Python坐标线性插值应用实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

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-600',dpi = 600) plt.show()