我正在color_palette
或matplotlib
中展开我的seaborn
,以便在scipy
的树状图中使用< / strong>因此它以不同的方式为每个群集着色。
目前,color_palette
只有几种颜色,因此多个群集会映射到相同的颜色。我知道有1600万RGB
种颜色,所以......
如何在这种类型的图中使用这个巨大调色板中的更多颜色?
#!/usr/bin/python
from __future__ import print_function
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import colorsys
from scipy.cluster.hierarchy import dendrogram,linkage,fcluster
from scipy.spatial import distance
np.random.seed(0) #43984
#Dims
n,m = 10,1000
#DataFrame: rows = Samples, cols = Attributes
attributes = ["a" + str(j) for j in range(m)]
DF_data = pd.DataFrame(np.random.randn(n, m),#
columns = attributes)
A_dist = distance.cdist(DF_data.as_matrix().T, DF_data.as_matrix().T)
DF_dist = pd.DataFrame(A_dist, index = attributes, columns = attributes)
#Linkage Matrix
Z = linkage(squareform(DF_dist.as_matrix()),method="average") #metric="euclidead" necessary since the input is a dissimilarity measure?
#Create dendrogram
D_dendro = dendrogram(
Z,
labels=DF_dist.index,
no_plot=True,
color_threshold=3.5,
count_sort = "ascending",
#link_color_func=lambda k: colors[k]
)
#Display dendrogram
def plotTree(D_dendro):
fig,ax = plt.subplots(figsize=(25, 10))
icoord = np.array( D_dendro['icoord'] )
dcoord = np.array( D_dendro['dcoord'] )
color_list = np.array( D_dendro['color_list'] )
x_min, x_max = icoord.min(), icoord.max()
y_min, y_max = dcoord.min(), dcoord.max()
for xs, ys, color in zip(icoord, dcoord, color_list):
plt.plot(xs, ys, color)
plt.xlim( x_min-10, x_max + 0.1*abs(x_max) )
plt.ylim( y_min, y_max + 0.1*abs(y_max) )
plt.title("Dendrogram", fontsize=30)
plt.xlabel("Clusters", fontsize=25)
plt.ylabel("Distance", fontsize=25)
plt.yticks(fontsize = 20)
plt.show()
return(fig,ax)
fig,ax = plotTree(D_dendro) #wrapper I made
#Dims
print(
len(set(D_dendro["color_list"])), "^ # of colors from dendrogram",
len(D_dendro["ivl"]), "^ # of labels",sep="\n")
# 7
# ^ # of colors from dendrogram
# 1000
# ^ # of labels
答案 0 :(得分:1)
大多数matplotlib色彩图都会给出一个给定介于0和1之间的值的值。例如,
import matplotlib.pyplot as plt
import numpy as np
print [plt.cm.Greens(i) for i in np.linspace(0, 1, 5)]
将打印
[(0.9686274528503418, 0.98823529481887817, 0.96078431606292725, 1.0),
(0.77922338878407194, 0.91323337695177864, 0.75180316742728737, 1.0),
(0.45176470875740049, 0.76708959481295413, 0.46120723030146432, 1.0),
(0.13402538141783546, 0.54232989970375511, 0.26828144368003398, 1.0),
(0.0, 0.26666668057441711, 0.10588235408067703, 1.0)]
因此,您不再需要限制提供给您的值。只需选择一个色图,然后根据一些分数从该色图中获取颜色。例如,在您的代码中,您可以考虑,
for xs, ys in zip(icoord, dcoord):
color = plt.cm.Spectral( ys/6.0 )
plt.plot(xs, ys, color)
或其他相似之处。我不确定你想要如何显示你的颜色,但我相信你可以很容易地修改你的代码,以实现你想要的任何颜色组合......
你可以尝试的另一件事是
N = D_dendro["color_list"]
colorList = [ plt.cm.Spectral( float(i)/(N-1) ) for i in range(N)]
并传递colorList
。
玩一下......