python中griddata_python – 来自xyz数据的Matplotlib轮廓：griddata无效索引[通俗易懂]

我正在尝试使用具有以下格式的文件的matplotlib来绘制等高线图：

x1 y1 z1

x2 y2 z2

等等

我可以用numpy.loadtxt加载它来获取向量.到目前为止,没有麻烦.

我读这个是为了学习如何绘图,并且可以通过复制粘贴来重现它,所以我确定我的安装没有错：

我知道我必须输入x和y作为矢量,z作为数组输入,这可以用griddata完成.这也是我在这个网站上找到的.

文件说：

zi = griddata(x,y,z,xi,yi) fits a surface of the form z = f*(*x, y) to the data in the (usually) nonuniformly spaced vectors (x, y, z). griddata() interpolates this surface at the points specified by (xi, yi) to produce zi. xi and yi must describe a regular grid, can be either 1D or 2D, but must be monotonically increasing.

为了这个例子,我写了这段代码：

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.mlab as ml

x=np.linspace(1.,10.,20)

y=np.linspace(1.,10.,20)

z=np.linspace(1.,2.,20)

xi=np.linspace(1.,10.,10)

yi=np.linspace(1.,10.,10)

zi = ml.griddata(x,y,z,xi,yi)

但是,当涉及到griddata时,我收到以下错误：

IndexError：索引无效

所以,我试着修改一下doc的例子,如下所示：

from matplotlib.mlab import griddata

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(-2.1,2.1,300)

y = np.linspace(-2.1,2.1,300)

z = x*np.exp(-x**2-y**2)

# define grid.

xi = np.linspace(-2.1,2.1,100)

yi = np.linspace(-2.1,2.1,200)

# grid the data.

zi = griddata(x,y,z,xi,yi,interp=’linear’)

我得到了同样的错误.我不明白出了什么问题.

谢谢你的帮助.

解决方法:

考虑：

x = np.linspace(1., 10., 20)

y = np.linspace(1., 10., 20)

z = np.linspace(1., 2., 20)

这意味着我们知道沿x = y线的某些点的z值.

从那里,

zi = ml.griddata(x,y,z,xi,yi)

要求mlab.griddata推断矩形网格中所有点的z值.

我们已经提供了很多关于z如何沿着这条线变化的信息,但没有关于z如何在垂直方向上变化的信息(远离x = y线).由于mlab.griddata拒绝猜测,因此引发了错误.

如果您的初始x,y数据更随机分布,您将获得更好的结果：

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.mlab as ml

ndata = 10

ny, nx = 100, 200

xmin, xmax = 1, 10

ymin, ymax = 1, 10

# x = np.linspace(1, 10, ndata)

# y = np.linspace(1, 10, ndata)

x = np.random.randint(xmin, xmax, ndata)

y = np.random.randint(ymin, ymax, ndata)

z = np.random.random(ndata)

xi = np.linspace(xmin, xmax, nx)

yi = np.linspace(ymin, ymax, ny)

zi = ml.griddata(x, y, z, xi, yi)

plt.contour(xi, yi, zi, 15, linewidths = 0.5, colors = ‘k’)

plt.pcolormesh(xi, yi, zi, cmap = plt.get_cmap(‘rainbow’))

plt.colorbar()

plt.scatter(x, y, marker = ‘o’, c = ‘b’, s = 5, zorder = 10)

plt.xlim(xmin, xmax)

plt.ylim(ymin, ymax)

plt.show()

如果你想让mlab.griddata以任意方式沿着x = y线将数据外推到整个网格,你可以添加两个额外的边界点(xmin,ymax,z [0])和(xmax,ymin,z [ – 1])：

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.mlab as ml

np.random.seed(8)

ndata = 10

ny, nx = 100, 200

xmin, xmax = 1, 10

ymin, ymax = 1, 10

x = np.linspace(1, 10, ndata)

y = np.linspace(1, 10, ndata)

z = np.random.random(ndata)

x = np.r_[x,xmin,xmax]

y = np.r_[y,ymax,ymin]

z = np.r_[z,z[0],z[-1]]

xi = np.linspace(xmin, xmax, nx)

yi = np.linspace(ymin, ymax, ny)

# Requires installation of natgrid

# http://sourceforge.net/projects/matplotlib/files/matplotlib-toolkits/

zi = ml.griddata(x, y, z, xi, yi, interp=’nn’)

# Or, without natgrid:

# zi = ml.griddata(x, y, z, xi, yi, interp=’linear’)

plt.contour(xi, yi, zi, 15, linewidths = 0.5, colors = ‘k’)

plt.pcolormesh(xi, yi, zi, cmap = plt.get_cmap(‘rainbow’))

plt.colorbar()

plt.scatter(x, y, marker = ‘o’, c = ‘b’, s = 10, zorder = 10)

plt.xlim(xmin, xmax)

plt.ylim(ymin, ymax)

plt.show()

标签：python,matplotlib,interpolation

来源： https://codeday.me/bug/20191004/1851562.html