如何使用 visibility_graph 生成网络？

Question

然后我将学习使用 visibility_graph 将我的时间序列数据转换为网络地图。但是当我输入我的数据时，我并没有得到一个非常明显的网络，并且大多数只是呈现一个环。 https://github.com/rgarcia-herrera/visibility_graph

以下是我的数据示例

[ 8.34  3.24  9.82  2.09  6.43  2.88  6.51  6.47 12.41  6.52  5.65  6.13
  5.28  6.87 13.22  7.05 13.65  5.7  16.88  3.43 15.81  4.87  9.74  4.43
  18.77  8.24 16.2  10.58 18.31 10.4  12.33  8.21 22.74  5.67 19.18  8.55
  16.9  10.22 21.68  8.61 17.81 11.4  27.51 11.19 25.78  8.31 29.87  6.35
  24.14 10.36 20.13 12.01 25.47  6.66 14.09 10.72 23.52  7.11 24.88  9.75
  22.6   7.24]

下面是我试过的代码（我试过平滑）

from visibility_graph import visibility_graph
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import scipy as sp
import scipy.ndimage

for user, group in data.groupby('cons_no'):
    for i in range(12): # every month
            u = pd.DataFrame(group)
            u.sort_index(inplace=True) # Time sorting

            values = []
            for row in u.itertuples():
                if row.Index.year == 2017 and row.Index.month == i + 1:
                    values.append((round(row.pap_r1, 1), round(row.pap_r2, 1)))

            temp = []
            for v in values:
                temp.append(v[0])
                temp.append(v[1])

            temp = np.array(temp)
            # Min = np.min(temp)
            # Max = np.max(temp)
            # temp = [max_min(x, Max, Min) for x in temp]
            temp = sp.ndimage.gaussian_filter(temp, sigma=1, mode='constant')
            print(temp)
            temp = [round(x, 1) for x in temp]
            temp = np.log10(temp)
            values = temp
            # print(values)
            G = visibility_graph(values)
            plt.subplot(121)
            nx.draw_networkx(G, with_labels=False, node_size=50)
            plt.title(str(user))
            plt.savefig('./user_' + str(user) + '_com.png')
            print('./user_' + str(user) + '_com.png')
            # plt.show()

我希望有人可以帮助我了解如何正确修改我的数据，以便 visibility_graph 出现在网络上。

Answer 1

回答我自己的问题。 我认为这个包中有一个错误。 于是我修改了源代码，让它可以正常工作。

from itertools import combinations
import networkx as nx

def visibility_graph(series):

    g = nx.Graph()

    # convert list of magnitudes into list of tuples that hold the index
    tseries = []
    n = 0
    for magnitude in series:
        tseries.append( (n, magnitude ) )
        n += 1

    # contiguous time points always have visibility
    for n in range(0,len(tseries)-1):
        (ta, ya) = tseries[n]
        (tb, yb) = tseries[n+1]
        g.add_node(ta, mag=ya)
        g.add_node(tb, mag=yb)
        g.add_edge(ta, tb)

    for a,b in combinations(tseries, 2):
        # two points, maybe connect
        (ta, ya) = a
        (tb, yb) = b

        connect = True

        # let's see all other points in the series
        for tc, yc in tseries:
            # other points, not a or b
            if tc != ta and tc != tb and ta < tc < tb: # The condition here is the key.
                # does c obstruct?
                if yc > yb + (ya - yb) * ( (tb - tc) / (tb - ta) ):
                    connect = False

        if connect:
            g.add_edge(ta, tb)


    return g

Modified renderings.