如何从Matplotlib中的简单数组生成颜色图数组 [英] How can I generate a colormap array from a simple array in matplotlib

问题描述

在matplotlib的某些函数中，我们必须传递color参数而不是cmap参数，例如bar3d.

In some functions of matplotlib, we have to pass an color argument instead of a cmap argument, like bar3d.

因此，我们必须手动生成Colormap.如果我有这样的dz数组:

So we have to generate a Colormap manually. If I have a dz array like this:

dz = [1,2,3,4,5]

我想要的是:

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=cm.jet(dz), zsort='average')

但是，它不起作用，看来Colormap实例只能转换规范化的数组.

However, It does not work, it seems Colormap instances can only convert normalized arrays.

>>> dz = [1,2,3,4,5]
>>> cm.jet(dz)
array([[ 0.        ,  0.        ,  0.51782531,  1.        ],
       [ 0.        ,  0.        ,  0.53565062,  1.        ],
       [ 0.        ,  0.        ,  0.55347594,  1.        ],
       [ 0.        ,  0.        ,  0.57130125,  1.        ],
       [ 0.        ,  0.        ,  0.58912656,  1.        ]])

当然，这不是我想要的.

Of course, this is not what I want.

我必须做这样的事情:

>>> cm.jet(plt.Normalize(min(dz),max(dz))(dz))
array([[ 0.        ,  0.        ,  0.5       ,  1.        ],
       [ 0.        ,  0.50392157,  1.        ,  1.        ],
       [ 0.49019608,  1.        ,  0.47754586,  1.        ],
       [ 1.        ,  0.58169935,  0.        ,  1.        ],
       [ 0.5       ,  0.        ,  0.        ,  1.        ]])

代码多么丑陋！

在 matplotlib的文档中说:

通常，Colormap实例用于转换数据值(浮点数) 从间隔[0，1]到相应的RGBA颜色 色彩图代表.有关将数据缩放到[0，1]间隔的信息，请参见 matplotlib.colors.Normalize.值得一提的是 matplotlib.cm.ScalarMappable子类大量使用了此方法 data-> normalize-> map-to-color处理链.

Typically Colormap instances are used to convert data values (floats) from the interval [0, 1] to the RGBA color that the respective Colormap represents. For scaling of data into the [0, 1] interval see matplotlib.colors.Normalize. It is worth noting that matplotlib.cm.ScalarMappable subclasses make heavy use of this data->normalize->map-to-color processing chain.

那为什么我不能只使用cm.jet(dz)?

So why I can't use just cm.jet(dz)?

这是我正在使用的导入

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm

推荐答案

问题的答案在您复制到问题中的文档的摘要中给出:

The answer to your question is given in the snipplet of the documentation that you copied into your question:

...从间隔[0，1]到RGBA颜色...

...from the interval [0, 1] to the RGBA color...

但是，如果您发现代码丑陋，则可以尝试使其更好:

But if you find your code ugly you could try to make it nicer:

1. 您不必手动指定标准化的限制(如果您打算使用最小/最大):

1. You don't have to specify the limits to the normalization manually (iff you intent to use min/max):

norm = plt.Normalize()
colors = plt.cm.jet(norm(dz))

如果您发现丑陋(不过，我不明白为什么)，您可以继续手动进行操作:

If you find that ugly (I don't understand why, though), you could go on and do it manually):

colors = plt.cm.jet(np.linspace(0,1,len(dz)))

但是，这种解决方案仅限于等距的颜色(这是您在示例中给出的dz想要的颜色.)

However this is solution is limited to equally spaced colors (which is what you want given the dz in your example.

然后您还可以复制Normalize的功能(因为您似乎不喜欢它):

And then you can also replicate the functionality of Normalize (since you seem to not like it):

lower = dz.min()
upper = dz.max()
colors = plt.cm.jet((dz-lower)/(upper-lower))

使用助手功能:

Use a helper function:

def get_colors(inp, colormap, vmin=None, vmax=None):
    norm = plt.Normalize(vmin, vmax)
    return colormap(norm(inp))

现在您可以像这样使用它:

Now you can use it like this:

colors = get_colors(dz, plt.cm.jet)

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