pcolormesh 缺少值？

发布于 2024-12-09 18:20:55 字数 584 浏览 0 评论 0原文

我有 3 个 1-D ndarrays：x、y、z

和以下代码：

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as spinterp

## define data
npoints = 50
xreg = np.linspace(x.min(),x.max(),npoints)
yreg = np.linspace(y.min(),y.max(),npoints)
X,Y = np.meshgrid(xreg,yreg)
Z = spinterp.griddata(np.vstack((x,y)).T,z,(X,Y),
                      method='linear').reshape(X.shape)

## plot
plt.close()
ax = plt.axes()
col = ax.pcolormesh(X,Y,Z.T)
plt.draw()

我的图显示为空白，我怀疑这是因为 method='线性' 插值与 nan 一起出现。我尝试转换为屏蔽数组，但无济于事 - 绘图仍然是空白。你能告诉我我做错了什么吗？谢谢。

原文

I have 3 1-D ndarrays: x, y, z

and the following code:

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as spinterp

## define data
npoints = 50
xreg = np.linspace(x.min(),x.max(),npoints)
yreg = np.linspace(y.min(),y.max(),npoints)
X,Y = np.meshgrid(xreg,yreg)
Z = spinterp.griddata(np.vstack((x,y)).T,z,(X,Y),
                      method='linear').reshape(X.shape)

## plot
plt.close()
ax = plt.axes()
col = ax.pcolormesh(X,Y,Z.T)
plt.draw()

My plot comes out blank, and I suspect it is because the method='linear' interpolation comes out with nans. I've tried converting to a masked array, but to no avail - plot is still blank. Can you tell me what I am doing wrong? Thanks.

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一笑百媚生 2024-12-16 18:20:55

知道了。这似乎是迂回的，但这就是解决方案：

import numpy.ma as ma

Zm = ma.masked_where(np.isnan(Z),Z)
plt.pcolormesh(X,Y,Zm.T)

如果 Z 矩阵包含 nan，它必须是 pcolormesh 的掩码数组，必须创建该数组与ma.masked_where，或者，

Zm = ma.array(Z,mask=np.isnan(Z))

Got it. This seems round-about, but this was the solution:

import numpy.ma as ma

Zm = ma.masked_where(np.isnan(Z),Z)
plt.pcolormesh(X,Y,Zm.T)

If the Z matrix contains nan's, it has to be a masked array for pcolormesh, which has to be created with ma.masked_where, or, alternatively,

Zm = ma.array(Z,mask=np.isnan(Z))

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很快妥协 2024-12-16 18:20:55

对所选答案的轻微改进

import numpy.ma as ma
Zm = ma.masked_invalid(Z)
plt.pcolormesh(X, Y, Zm.T)

masked_invalid 屏蔽所有 NaN 值，从而无需指定

mask = np.isnan(Z)

A slight improvement on the chosen answer

import numpy.ma as ma
Zm = ma.masked_invalid(Z)
plt.pcolormesh(X, Y, Zm.T)

masked_invalid masks all NaN values, thereby saving the need to specify

mask = np.isnan(Z)

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永言不败 2024-12-16 18:20:55

请注意，在 matplotlib master 中不再需要显式屏蔽，因为数组现在在内部自动屏蔽。将合并到 matplotlib >2.1 中。查看我合并的拉取请求 https://github.com/matplotlib/matplotlib/pull/5451

所以现在就这么简单

plt.pcolormesh(X,Y,Z.T)

Note that the explicit masking is no longer necessary in matplotlib master as arrays are now masked automatically internally. Will be incorporated into matplotlib >2.1. See my merged pull request https://github.com/matplotlib/matplotlib/pull/5451

So now it's as simple as