如何在matplotlib中添加2D色彩条或色轮?

问题描述

我正在分析样品的磁化强度图.获取渐变及其方向后，我将它们绘制为HSV(从-π到π的方向映射到Hue从0到1，并且值是归一化的渐变)，通过 img_rgb = mpl.colors转换为RGB.hsv_to_rgb(img_hsv).

我设法使用 vmin 和 vmax 添加了一个 HSV 颜色条，但这并没有显示渐变的大小:

  plt.imshow(img_rgb，cmap ='hsv'，vmin = -180，vmax = 180，范围=(0，100，0,100))plt.xlabel('μm')plt.ylabel('μm')plt.colorbar()

理想情况下，我想添加一个色轮来对方向和幅度进行编码(可能类似于极坐标图?).如果这是不可能的，添加一个 2D 绘图，扩展当前颜色条以包括 x 轴上的梯度幅度.

子情节显然是可能的，但它们似乎是杂七杂八的.有没有更好的办法?

解决方案

首先，如果您要同时显示两个不同的参数，则可以通过为它们分配两个不同的通道(例如红色和绿色)来做到这一点..这可以通过规范化您的两个2d数组并将它们馈送到与

原则上，同样的事情也可以用极地的 Axes 执行，但是根据

I am analyzing the magnetization mapping of a sample. After getting the gradient and its direction, I plotted them as an HSV (the direction from -π to π was mapped to Hue from 0 to 1, and Value was the normalized gradient) converted to RGB by img_rgb = mpl.colors.hsv_to_rgb(img_hsv).

I managed to add an HSV colorbar by using vmin and vmax, but this does not show the magnitude of the gradient:

plt.imshow(img_rgb, cmap='hsv', vmin=-180, vmax=180, extent=(0, 100, 0,100))
plt.xlabel('μm')
plt.ylabel('μm')
plt.colorbar()

My current plot:

Ideally, I would like to add a color wheel which encodes both the direction and the magnitude (maybe as something like a polar plot?). If that is not possible, adding a 2D plot which extends the current colorbar to include the gradient magnitude on the x-axis.

Subplots are obviously possible, but they seem like a kludge. Is there a better way?

解决方案

First off, if you have two different parameters that you want to visualise simultaneously, you can do that by assigning two different channels to them (say red and green). This can be done by normalising your two 2d arrays and feeding them to imshow stacked similarly to this answer.

If you are content with a square-shaped 2d colormap, you can then get this colormap in the same way, by creating a meshgrid that you then again stack and feed to imshow:

from matplotlib import pyplot as plt
import numpy as np

##generating some  data
x,y = np.meshgrid(
    np.linspace(0,1,100),
    np.linspace(0,1,100),
)
directions = (np.sin(2*np.pi*x)*np.cos(2*np.pi*y)+1)*np.pi
magnitude = np.exp(-(x*x+y*y))


##normalize data:
def normalize(M):
    return (M-np.min(M))/(np.max(M)-np.min(M))

d_norm = normalize(directions)
m_norm = normalize(magnitude)

fig,(plot_ax, bar_ax) = plt.subplots(nrows=1,ncols=2,figsize=(8,4))

plot_ax.imshow(
    np.dstack((d_norm,m_norm, np.zeros_like(directions))),
    aspect = 'auto',
    extent = (0,100,0,100),
)

bar_ax.imshow(
    np.dstack((x, y, np.zeros_like(x))),
    extent = (
        np.min(directions),np.max(directions),
        np.min(magnitude),np.max(magnitude),
    ),
    aspect = 'auto',
    origin = 'lower',
)
bar_ax.set_xlabel('direction')
bar_ax.set_ylabel('magnitude')

plt.show()

The result looks like this:

In principle the same thing should also be doable with a polar Axes, but according to a comment in this github ticket, imshow does not support polar axes and I couldn't make imshow fill the entire disc.

EDIT:

Thanks to ImportanceOfBeingErnest and his answer to another question (the color keyword did it), here now a 2d colormap on a polar axis using pcolormesh. There were a few caveats, most notable, the colors dimension needs to be one smaller than the meshgrid in theta direction, otherwise the colormap has a spiral form:

fig= plt.figure(figsize=(8,4))
plot_ax = fig.add_subplot(121)
bar_ax = fig.add_subplot(122, projection = 'polar')

plot_ax.imshow(
    np.dstack((d_norm,m_norm, np.zeros_like(directions))),
    aspect = 'auto',
    extent = (0,100,0,100),
)

theta, R = np.meshgrid(
    np.linspace(0,2*np.pi,100),
    np.linspace(0,1,100),
)

t,r = np.meshgrid(
    np.linspace(0,1,99),
    np.linspace(0,1,100),
)    

image = np.dstack((t, r, np.zeros_like(r)))

color = image.reshape((image.shape[0]*image.shape[1],image.shape[2]))

bar_ax.pcolormesh(
    theta,R,
    np.zeros_like(R),
    color = color,
)

bar_ax.set_xticks(np.linspace(0,2*np.pi,5)[:-1])
bar_ax.set_xticklabels(
    ['{:.2}'.format(i) for i in np.linspace(np.min(directions),np.max(directions),5)[:-1]]
)
bar_ax.set_yticks(np.linspace(0,1,5))
bar_ax.set_yticklabels(
    ['{:.2}'.format(i) for i in np.linspace(np.min(magnitude),np.max(magnitude),5)]
)
bar_ax.grid('off')

plt.show()

This produces this figure:

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