Seaborn热图图中的离散图例 [英] Discrete legend in seaborn heatmap plot

问题描述

我正在使用此处提供的数据，通过seaborn和pandas构建此热图.

I am using the data present here to construct this heat map using seaborn and pandas.

代码:

    import pandas
    import seaborn.apionly as sns

    # Read in csv file
    df_trans = pandas.read_csv('LUH2_trans_matrix.csv')

    sns.set(font_scale=0.8)
    cmap = sns.cubehelix_palette(start=2.8, rot=.1, light=0.9, as_cmap=True)
    cmap.set_under('gray')  # 0 values in activity matrix are shown in gray (inactive transitions)
    df_trans = df_trans.set_index(['Unnamed: 0'])
    ax = sns.heatmap(df_trans, cmap=cmap, linewidths=.5, linecolor='lightgray')

    # X - Y axis labels
    ax.set_ylabel('FROM')
    ax.set_xlabel('TO')

    # Rotate tick labels
    locs, labels = plt.xticks()
    plt.setp(labels, rotation=0)
    locs, labels = plt.yticks()
    plt.setp(labels, rotation=0)

    # revert matplotlib params
    sns.reset_orig()

从csv文件中可以看到，它包含3个离散值:0，-1和1.我想要离散图例而不是颜色条.分别将0标记为A，将-1标记为B，将1标记为C.

As you can see from csv file, it contains 3 discrete values: 0, -1 and 1. I want a discrete legend instead of the colorbar. Labeling 0 as A, -1 as B and 1 as C. How can I do that?

推荐答案

当然，有多种方法可以实现此目的.在这种情况下，只需要三种颜色，我自己创建一个LinearSegmentedColormap而不是用cubehelix_palette生成它们来自己选择颜色.如果有足够的颜色需要使用cubehelix_palette进行担保，则可以使用cbar_kws参数的boundaries选项在色图上定义线段.无论哪种方式，都可以使用set_ticks和set_ticklabels手动指定刻度.

Well, there's definitely more than one way to accomplish this. In this case, with only three colors needed, I would pick the colors myself by creating a LinearSegmentedColormap instead of generating them with cubehelix_palette. If there were enough colors to warrant using cubehelix_palette, I would define the segments on colormap using the boundaries option of the cbar_kws parameter. Either way, the ticks can be manually specified using set_ticks and set_ticklabels.

下面的代码示例演示了LinearSegmentedColormap的手动创建，并包含有关如何使用cubehelix_palette来指定边界的注释.

The following code sample demonstrates the manual creation of LinearSegmentedColormap, and includes comments on how to specify boundaries if using a cubehelix_palette instead.

import matplotlib.pyplot as plt
import pandas
import seaborn.apionly as sns
from matplotlib.colors import LinearSegmentedColormap

sns.set(font_scale=0.8)
dataFrame = pandas.read_csv('LUH2_trans_matrix.csv').set_index(['Unnamed: 0'])

# For only three colors, it's easier to choose them yourself.
# If you still really want to generate a colormap with cubehelix_palette instead,
# add a cbar_kws={"boundaries": linspace(-1, 1, 4)} to the heatmap invocation
# to have it generate a discrete colorbar instead of a continous one.
myColors = ((0.8, 0.0, 0.0, 1.0), (0.0, 0.8, 0.0, 1.0), (0.0, 0.0, 0.8, 1.0))
cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))

ax = sns.heatmap(dataFrame, cmap=cmap, linewidths=.5, linecolor='lightgray')

# Manually specify colorbar labelling after it's been generated
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([-0.667, 0, 0.667])
colorbar.set_ticklabels(['B', 'A', 'C'])

# X - Y axis labels
ax.set_ylabel('FROM')
ax.set_xlabel('TO')

# Only y-axis labels need their rotation set, x-axis labels already have a rotation of 0
_, labels = plt.yticks()
plt.setp(labels, rotation=0)

plt.show()

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