我可以协调 seaborn 和 networkx 之间的颜色吗?

Can I coordinate colors between seaborn and networkx?

我正在尝试协调 networkx 网络图表上的颜色与 seaborn 图表上的颜色。当我使用相同的颜色托盘 (Dark2) 和相同的组 ID 时,这两个图仍然不同。需要明确的是,第 0 组中的节点应该与第 0 组中的条相同。这同样适用于第 1 组和第 2 组。这是我在 运行 下面的代码时得到的图表,显示颜色不一致:

当我 运行 下面的代码时,在网络图中,第 0 组的颜色与它们在计数图中的颜色相同。但是第 1 组和第 2 组在网络图和计数图之间改变颜色。 有谁知道如何协调颜色?

我比较了 plt.cm.Dark2.colorssns.color_palette('Dark2', 3) 中的颜色映射,它们看起来是一样的(除了 sns 只包含前 3 种颜色这一事实。

另外值得注意的是,seaborn是在expected order of colors之后,networkx不是。

import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns

# create dataframe of connections
df = pd.DataFrame({ 'from':['A', 'B', 'C','A'], 'to':['D', 'A', 'E','C']})

# create graph
G = nx.Graph()
for i, r in df.iterrows():
    G.add_edge(r['from'], r['to'])

# create data frame mapping nodes to groups
groups_df = pd.DataFrame()

for i in G.nodes():
    if i in 'AD':
        group = 0
    elif i in 'BC':
        group = 1
    else:
        group = 2

    groups_df.loc[i, 'group'] = group

# make sure it's in same order as nodes of graph
groups_df = groups_df.reindex(G.nodes())

# create node node and count chart where color is group id
fig, ax = plt.subplots(ncols=2)
nx.draw(G, with_labels=True, node_color=groups_df['group'], cmap=plt.cm.Dark2, ax=ax[0])

sns.countplot('index', data=groups_df.reset_index(), palette='Dark2', hue='group', ax=ax[1])

networkx 在颜色图的颜色上平均分配值。由于它显然不能接受 norm(这是解决此问题的常用方法),因此您需要创建一个仅包含您感兴趣的颜色的新颜色图。

cmap = ListedColormap(plt.cm.Dark2(np.arange(3)))

此外,seaborn 会降低要使用的颜色的饱和度,因此要获得与颜色图相同的颜色,您需要在 seaborn 调用中设置 saturation=1

import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns

# create dataframe of connections
df = pd.DataFrame({ 'from':['A', 'B', 'C','A'], 'to':['D', 'A', 'E','C']})
# create graph
G = nx.Graph()
for i, r in df.iterrows():
    G.add_edge(r['from'], r['to'])

# create data frame mapping nodes to groups
groups_df = pd.DataFrame()

for i in G.nodes():
    if i in 'AD':
        group = 0
    elif i in 'BC':
        group = 1
    else:
        group = 2
    groups_df.loc[i, 'group'] = group

# make sure it's in same order as nodes of graph
groups_df = groups_df.reindex(G.nodes())

# create node node and count chart where color is group id
fig, ax = plt.subplots(ncols=2)

# create new colormap with only the first 3 colors from Dark2
cmap = ListedColormap(plt.cm.Dark2(np.arange(3)))
nx.draw(G, with_labels=True, node_color=groups_df['group'], cmap=cmap, ax=ax[0])

sns.countplot('index', data=groups_df.reset_index(), palette=cmap.colors, hue='group', ax=ax[1], saturation=1)

plt.show()