将Matplotlib轮廓限制/屏蔽到数据区域 [英] Limit/mask matplotlib contour to data area

问题描述

我有一个熊猫DataFrame，其中的数据点由x，y和z列给定，它们之间的空间不均匀，其中x和y是变量对，而z是因变量.例如:

I have a pandas DataFrame with non-uniformly spaced data points given by an x, y and z column, where x and y are pairs of variables and z is the dependent variable. For example:

import matplotlib.pyplot as plt
from matploblib.mlab import griddata
import numpy as np
import pandas as pd

df = pd.DataFrame({'x':[0, 0, 1, 1, 3, 3, 3, 4, 4, 4], 
                   'y':[0, 1, 0, 1, 0.2, 0.7, 1.4, 0.2, 1.4, 2], 
                   'z':[50, 40, 40, 30, 30, 30, 20, 20, 20, 10]})

x = df['x']
y = df['y']
z = df['z']

我想在x和y上绘制因变量z的等高线图.为此，我创建了一个新的网格以使用matplotlib.mlab的griddata函数对数据进行插值.

I want to do a contour plot of the dependent variable z over x and y. For this, I create a new grid to interpolate the data on using matplotlib.mlab's griddata function.

xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
z_grid = griddata(x, y, z, xi, yi, interp='linear')
plt.contourf(xi, yi, z_grid, 15)
plt.scatter(x, y, color='k') # The original data points
plt.show()

虽然可行，但输出不是我想要的.我不希望griddata在x和y数据的最小值和最大值所给定的边界之外进行插值.以下图表是调用plt.show()之后显示的内容，然后以紫色突出显示我要插入和绘制轮廓的数据的哪些区域.紫色线外的轮廓应该为空白.我该如何掩盖外围数据?

While this works, the output is not what I want. I do not want griddata to interpolate outside of the boundaries given by the min and max values of the x and y data. The following plots are what shows up after calling plt.show(), and then highlighted in purple what area of the data I want to have interpolated and contoured. The contour outside the purple line is supposed to be blank. How could I go about masking the outlying data?

链接的问题不幸的是没有回答我的问题，因为我没有明确的数学方法来定义进行三角剖分的条件.以上述数据框为例，是否可以仅基于数据定义屏蔽数据的条件?

The linked question does unfortunately not answer my question, as I don't have a clear mathematical way to define the conditions on which to do a triangulation. Is it possible to define a condition to mask the data based on the data alone, taking the above Dataframe as an example?

推荐答案

如

As seen in the answer to this question one may introduce a condition to mask the values.

问题中的句子 我不希望griddata在x和y数据的最小值和最大值所给定的边界之外进行插值." 暗示存在一些可以使用的最小/最大条件.

The sentence from the question "I do not want griddata to interpolate outside of the boundaries given by the min and max values of the x and y data." implies that there is some min/max condition present, which can be used.

如果不是这种情况，可以使用路径剪切轮廓.需要指定此路径的点，因为没有通用的方法可以知道哪些点应为边.下面的代码针对三种可能的路径执行此操作.

Should that not be the case, one may clip the contour using a path. The points of this path need to be specified as there is no generic way of knowing which points should be the edges. The code below does this for three different possible paths.

import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
from matplotlib.mlab import griddata
import numpy as np
import pandas as pd

df = pd.DataFrame({'x':[0, 0, 1, 1, 3, 3, 3, 4, 4, 4], 
                   'y':[0, 1, 0, 1, 0.2, 0.7, 1.4, 0.2, 1.4, 2], 
                   'z':[50, 40, 40, 30, 30, 30, 20, 20, 20, 10]})

x = df['x']
y = df['y']
z = df['z']

xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
z_grid = griddata(x, y, z, xi, yi, interp='linear')

clipindex = [ [0,2,4,7,8,9,6,3,1,0],
              [0,2,4,7,5,8,9,6,3,1,0],
              [0,2,4,7,8,9,6,5,3,1,0]]

fig, axes = plt.subplots(ncols=3, sharey=True)
for i, ax in enumerate(axes):
    cont = ax.contourf(xi, yi, z_grid, 15)
    ax.scatter(x, y, color='k') # The original data points
    ax.plot(x[clipindex[i]], y[clipindex[i]], color="crimson")

    clippath = Path(np.c_[x[clipindex[i]], y[clipindex[i]]])
    patch = PathPatch(clippath, facecolor='none')
    ax.add_patch(patch)
    for c in cont.collections:
        c.set_clip_path(patch)

plt.show()

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