在图形工具中,有没有办法只绘制最大的连接子图?我目前有一个不大感兴趣的大连接子图和几个小连接子图。我不确定在绘制子图之前如何找到子图,所以如果有graph_tool提供的方法,我将很感兴趣
如果有帮助,则来源为:https://github.com/jvdheyden/DBS/blob/master/projekt/phase3/main.py
答案 0 :(得分:2)
您可以使用def f4(p, op):
def ipf(x):
if op == 'greater':
return (x > p).sum()
elif op == 'less':
return (x < p).sum()
else:
raise ValueError("second argument has to be greater or less only")
ipf.__name__ = 'Frequency'
return ipf
d = {'acceleration':['mean', 'median', 'min'],
'velocity':[f4(3.4, 'less'), 'sum' ,'count', 'median', 'min'],
'velocity_rate':f4(0.2, 'greater'),
'acc_rate':f4(.25, 'greater'),
'bearing':['sum', f4(10, 'greater')],
'bearing_rate':'sum',
'Vincenty_distance':'sum'}
df1 = df.groupby(['userid','trip_id','Transportation_Mode','segmentid'], sort=False).agg(d)
#flatenning MultiIndex in columns
df1.columns = df1.columns.map('_'.join)
#MultiIndex in index to columns
df1 = df1.reset_index(level=2, drop=False).reset_index()
过滤掉最大的组件,然后绘制它。
print (df1.head())
userid trip_id segmentid Transportation_Mode acceleration_mean \
0 141 1.0 1 walk 0.061083
1 141 2.0 1 walk 0.109148
2 141 3.0 1 walk 0.106771
3 141 4.0 1 walk 0.141180
4 141 5.0 1 walk 1.147157
acceleration_median acceleration_min velocity_Frequency velocity_sum \
0 -1.168583e-02 -2.994428 1000.0 1506.679506
1 1.665535e-09 -3.234188 464.0 712.429005
2 -3.055414e-08 -3.131293 996.0 1394.746071
3 9.241707e-09 -3.307262 340.0 513.461259
4 -2.609489e-02 -3.190424 493.0 729.702854
velocity_count velocity_median velocity_min velocity_rate_Frequency \
0 1028 1.294657 0.284747 288.0
1 486 1.189650 0.284725 134.0
2 1020 1.241419 0.284733 301.0
3 352 1.326324 0.339590 93.0
4 504 1.247868 0.284740 168.0
acc_rate_Frequency bearing_sum bearing_Frequency bearing_rate_sum \
0 169.0 81604.187066 884.0 -371.276356
1 89.0 25559.589869 313.0 -357.869944
2 203.0 -71540.141199 57.0 946.382581
3 78.0 9548.920765 167.0 -943.184805
4 93.0 -24021.555784 67.0 535.333624
Vincenty_distance_sum
0 1506.679506
1 712.429005
2 1395.328768
3 513.461259
4 731.823664
这样,您不会丢失整个图形,以防在绘制后需要它。