scipy-cluster 定制生成的树状图
这是scipy-cluster生成的树状图不显示的后续内容。
from matplotlib.pyplot import show
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import linkage, dendrogram
from numpy.random import rand
X = rand( 5, 3 )
X[0:5, :] *= 2
Y = pdist( X )
Z = linkage( Y )
dendrogram( Z )
show()
当dendrogram()返回一个带有ivl、leaves、color_list、icoord键的字典时，pyplot正在拾取。在将标签和叶子长度传递给 pyplot 之前，如何修改它们？
做类似的事情：
d=dendrogram( Z )
d['leaves']=['label1','label2','label3','label4','label5']
似乎不影响它。
叶子长度应该是这样的：
This is a follow up to Dendrogram generated by scipy-cluster does not show.
from matplotlib.pyplot import show
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import linkage, dendrogram
from numpy.random import rand
X = rand( 5, 3 )
X[0:5, :] *= 2
Y = pdist( X )
Z = linkage( Y )
dendrogram( Z )
show()
when dendrogram() returns a dictionary with keys ivl, leaves, color_list, icoord that pyplot is picking up. How can I modify the labels and the leaf length before they are passed to pyplot?
Doing something like:
d=dendrogram( Z )
d['leaves']=['label1','label2','label3','label4','label5']
does not seem to affect it.
The leaf length should be something like this:
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根据 树形图文档，您应该能够在调用时定义标签（通过labels或leaf_label_func参数）。因此无需事后尝试篡改标签。
According the dendrogram documentation, you should be able to define labels when you are calling it (either via labels or leaf_label_func args). So there is no need to try to tamper afterwards with labels.