离散颜色条，中间有刻度线标签 [英] Discrete Color Bar with Tick labels in between colors

问题描述

我正在尝试使用不连续的颜色条来绘制一些数据.我正在按照给出的示例进行操作(.任何帮助，将不胜感激.我知道我在这里错过了一些关键，但无法弄清楚.

I am trying to plot some data with a discrete color bar. I was following the example given (. Any help with this would be appreciated. I know I am missing something key here but cant figure it out.

import matplotlib.pylab as plt
import numpy as np

def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""

    # Note that if base_cmap is a string or None, you can simply do
    #    return plt.cm.get_cmap(base_cmap, N)
    # The following works for string, None, or a colormap instance:

    base = plt.cm.get_cmap(base_cmap)
    color_list = base(np.linspace(0, 1, N))
    cmap_name = base.name + str(N)
    return base.from_list(cmap_name, color_list, N)
num=11

x = np.random.randn(40)
y = np.random.randn(40)
c = np.random.randint(num, size=40)


plt.figure(figsize=(10,7.5))

plt.scatter(x, y, c=c, s=50, cmap=discrete_cmap(num, 'jet'))
plt.colorbar(ticks=np.arange(0,5.5,0.5))
plt.clim(-0.5, num - 0.5)
plt.show()

推荐答案

不确定是什么版本的matplotlib/pyplot引入了此功能，但是 plt.get_cmap 现在支持 int 参数，用于指定要获取的离散色图的颜色数.这自动导致颜色条是离散的.顺便说一句， pandas 对颜色条的处理甚至更好.

Not sure what version of matplotlib/pyplot introduced this, but plt.get_cmap now supports an int argument specifying the number of colors you want to get, for discrete colormaps. This automatically results in the colorbar being discrete. By the way, pandas has an even better handling of the colorbar.

import numpy as np
from matplotlib import pyplot as plt
plt.style.use('ggplot')
# remove if not using Jupyter/IPython
%matplotlib inline

# choose number of clusters and number of points in each cluster
n_clusters = 5
n_samples = 20

# there are fancier ways to do this
clusters = np.array([k for k in range(n_clusters) for i in range(n_samples)])

# generate the coordinates of the center 
# of each cluster by shuffling a range of values
clusters_x = np.arange(n_clusters)
clusters_y = np.arange(n_clusters)

np.random.shuffle(clusters_x)
np.random.shuffle(clusters_y)

# get dicts like cluster -> center coordinate
x_dict = dict(enumerate(clusters_x))
y_dict = dict(enumerate(clusters_y))

# get coordinates of cluster center for each point
x = np.array(list(x_dict[k] for k in clusters)).astype(float)
y = np.array(list(y_dict[k] for k in clusters)).astype(float)

# add noise
x += np.random.normal(scale=0.5, size=n_clusters*n_samples)
y += np.random.normal(scale=0.5, size=n_clusters*n_samples)

### Finally, plot
fig, ax = plt.subplots(figsize=(12,8))

# get discrete colormap
cmap = plt.get_cmap('viridis', n_clusters)

# scatter points
scatter = ax.scatter(x, y, c=clusters, cmap=cmap)

# scatter cluster centers
ax.scatter(clusters_x, clusters_y, c='red')

# add colorbar
cbar = plt.colorbar(scatter)

# set ticks locations (not very elegant, but it works):
# - shift by 0.5
# - scale so that the last value is at the center of the last color
tick_locs = (np.arange(n_clusters) + 0.5)*(n_clusters-1)/n_clusters
cbar.set_ticks(tick_locs)

# set tick labels (as before)
cbar.set_ticklabels(np.arange(n_clusters))

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