补丁中的matplotlib颜色渐变？

Question

我想在matplotlib中创建椭圆，填充颜色有一个alpha（不透明度）值，这个值取决于半径

例如，二维高斯分布。

Is there any way to do this?

创建具有颜色渐变的矩形图是很容易的（比如Gradient facecolor matplotlib bar plot和this），但是我不知道如何对圆/椭圆进行同样的操作。

Answer 1

下面是使用Alex文章中的思想的函数示例

import matplotlib.pyplot as plt,numpy as np

def gauplot(centers, radiuses, xr=None, yr=None):
        nx, ny = 1000.,1000.
        xgrid, ygrid = np.mgrid[xr[0]:xr[1]:(xr[1]-xr[0])/nx,yr[0]:yr[1]:(yr[1]-yr[0])/ny]
        im = xgrid*0 + np.nan
        xs = np.array([np.nan])
        ys = np.array([np.nan])
        fis = np.concatenate((np.linspace(-np.pi,np.pi,100), [np.nan]) )
        cmap = plt.cm.gray
        cmap.set_bad('white')
        thresh = 3
        for curcen,currad in zip(centers,radiuses):
                curim=(((xgrid-curcen[0])**2+(ygrid-curcen[1])**2)**.5)/currad*thresh
                im[curim<thresh]=np.exp(-.5*curim**2)[curim<thresh]
                xs = np.append(xs, curcen[0] + currad * np.cos(fis))
                ys = np.append(ys, curcen[1] + currad * np.sin(fis))
        plt.imshow(im.T, cmap=cmap, extent=xr+yr)
        plt.plot(xs, ys, 'r-')

这是你跑步时得到的

    gauplot([(0,0), (2,3), (5,1), (6, 7), (6.1, 6.1)], [.3,. 4, .5, 1, .4], [-1,10], [-1,10])
             #           centers of circles           # radii of circles#

Answer 2

我不认为matplotlib当前支持补丁的渐变填充-请参见this email。

john> Hello, I am trying to set a bar (a patched series of rectangles) with a fill pattern instead of just a solid color. Is there an easy way to do this in matplotlib?
john> I am thinking of something like Qt's QBrush which has cross, vertical, dense, etc. patterns.

There is no support for this currently -- it wouldn't be too hard to add for backends that support this kind of thing. Basically, we need to specify the API for it, and add support to backends. I have been wanting to add gradient fills for patches (eg polygons, rectangles) and it would be good to do both at once.

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与使用面片不同，您可以创建网格，使用函数计算颜色，然后使用imshow插值：

# Taken from http://matplotlib.sourceforge.net/examples/pylab_examples/layer_images.html

def func3(x,y):
    return (1- x/2 + x**5 + y**3)*exp(-x**2-y**2)

# make these smaller to increase the resolution
dx, dy = 0.05, 0.05

x = arange(-3.0, 3.0, dx)
y = arange(-3.0, 3.0, dy)
X,Y = meshgrid(x, y)

xmin, xmax, ymin, ymax = amin(x), amax(x), amin(y), amax(y)
extent = xmin, xmax, ymin, ymax

fig = plt.figure(frameon=False)

Z2 = func3(X, Y)

im2 = imshow(Z2, cmap=cm.jet, alpha=.9, interpolation='bilinear', extent=extent)

show()

这将导致以下结果（忽略方格背景）：