Tuto test

>>> import matplotlib.pyplot as plt
>>> import networkx as nx
>>> import numpy as np
>>> import pandas as pd
>>>
>>> import phasik as pk

>>> # generate static network
>>> edges = [('a', 'b'), ('a', 'c'), ('b', 'c'), ('a', 'd')]
>>>
>>> static_network = nx.Graph(edges)
>>>
>>> nodes = list(static_network.nodes)
>>> N = static_network.number_of_nodes()
>>>
>>> nx.draw_networkx(static_network)
>>> plt.show()

(png, hires.png, pdf)

>>> # generate node time series
>>> T = 10 # number of timepoints
>>> node_series_arr = np.random.random((N, T))
>>>
>>> node_series = pd.DataFrame(node_series_arr, index=nodes) #

>>> node_series

	0	1	2	3	4	5	6	7	8	9
a	0.401917	0.881092	0.127991	0.912639	0.661475	0.555443	0.256126	0.727078	0.248045	0.015757
b	0.852820	0.936582	0.988778	0.107239	0.126230	0.250864	0.463638	0.491446	0.369278	0.982625
c	0.012937	0.909779	0.865591	0.049600	0.566255	0.119082	0.581458	0.970764	0.809731	0.236107
d	0.655355	0.173176	0.325639	0.097256	0.452466	0.646218	0.821179	0.192487	0.783225	0.798528

>>> node_series.T.plot()
>>> plt.xlabel("Time")
>>> plt.ylabel("Edge time series")
>>>
>>> plt.show()

(png, hires.png, pdf)

>>> # create the temporal network by combining
>>> # the static network with the node timeseries
>>> temporal_network = pk.TemporalNetwork.from_static_network_and_node_timeseries(
...     static_network,
...     node_series,
...     static_edge_default_weight=1,
...     normalise='minmax', # method to normalise the edge weights
... )

>>> print(temporal_network)
<class 'phasik.classes.TemporalNetwork.TemporalNetwork'> with 4 nodes and 10 times