离散颜色条，中间有刻度线标签

Question

我正在尝试使用离散的颜色条来绘制一些数据。我正在按照给出的示例（https://gist.github.com/jakevdp/91077b0cae40f8f8244a）进行操作，但问题是此示例无法以不同的间距在1-1中工作。例如，链接中示例中的间距仅增加了1，而我的数据却增加了0.5。您可以从我的代码中看到输出。。任何帮助，将不胜感激。我知道我在这里缺少一些关键，但无法弄清楚。

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()

Answer 1

不确定什么版本的matplotlib / pyplot引入了此功能，但plt.get_cmap现在支持一个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。顺便说一下，对色条pandas有更好的处理。

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 colorba
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(ticks_locs)

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