我正在创建一个以节点为图像的图形,
# 图片来自http://matplotlib.sourceforge.net/users/image_tutorial.html
我想创建一个圆形布局,节点zero位于中心。Egdelist 是 [(0,1),(0,2),(0,3),(0,4),(0,5)]
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import networkx as nx
img=mpimg.imread('stinkbug.png')
G=nx.complete_graph(6)
G.node[0]['image']=img
G.node[1]['image']=img
G.node[2]['image']=img
G.node[3]['image']=img
G.node[4]['image']=img
G.node[5]['image']=img
print(G.nodes())
G.add_edge(0,1)
G.add_edge(0,2)
G.add_edge(0,3)
G.add_edge(0,4)
G.add_edge(0,5)
print(G.edges())
nx.draw_circular(G)
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但是,在输出中我找到了额外的边(附有快照)。有没有办法删除这些额外的边?我只想要这些连接 Egdelist 是 [(0,1),(0,2),(0,3),(0,4),(0,5)]。此外,原始图像不显示在节点中.

有什么建议?
我想在带有噪声的时间序列数据中找到达到某个值的时刻。如果数据中没有峰值,我可以在 MATLAB 中执行以下操作。
代码从这里
% create example data
d=1:100;
t=d/100;
ts = timeseries(d,t);
% define threshold
thr = 55;
data = ts.data(:);
time = ts.time(:);
ind = find(data>thr,1,'first');
time(ind) %time where data>threshold
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但是当有噪音时,我不确定必须做什么。
In the time-series data plotted in the above image I want to find the time instant at which the y-axis value 5 is reached. The data actually stabilizes to 5 at t>=100 s. But due to the presence of noise in the data, we see a …
algorithm signal-processing time-series data-analysis convergence
我有以下 Mathematica 代码,用于将图的边长度缩放为等于边权重。\n( ref )
\nedges = {1 <-> 2, 1 <-> 3, 1 <-> 4, 2 <-> 5, 2 <-> 6, 5 <-> 6, \n 3 <-> 4, 3 <-> 7, 6 <-> 7, 7 <-> 8, 2 <-> 9};\n\nvd = {{75., 25., 0}, {115., 45., 0}, {10., 5., 0}, {45., 0, 0}, \n {90., 60., 0}, {45., 55., 0}, {0, 25., 0}, {10., 50., 0}, {115., 25.,0}};\n\nvl = Range[Length@vd];\n\nvcoords = MapIndexed[#2[[1]] -> # &, vd];\new = {1 …Run Code Online (Sandbox Code Playgroud) optimization wolfram-mathematica graph mathematical-optimization python-3.x
我想将数据框中的列移动到最后一列,我尝试使用shift. 但这并没有改变立场。
import pandas a pd
df = #input dataframe
df['x'] = df['x'].shift(axis=1)
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Error:
raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
ValueError: No axis named 1 for object type Series
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还有其他选择吗?有人可以建议吗?
graph ×2
python-3.x ×2
algorithm ×1
convergence ×1
dataframe ×1
networkx ×1
optimization ×1
pandas ×1
shift ×1
time-series ×1