def convert_static_network(N, edge_list, tmax=1.0):
"""Get a single frame which consists of the static network.
Parameters
----------
N : int
Number of nodes.
edge_list : :obj:`list` of :obj:`tuple` of int
The edges of the static graph
tmax : double, default : 1.0
The maximum time until the network is looped.
Returns
-------
:class:`_tacoma.edge_lists`
An instance of `tacoma.edge_lists` with t = [0.0], tmax = ``tmax``.
"""
new_edge_list = []
for e in edge_list:
if e[0] > e[1]:
new_edge_list.append((e[1], e[0]))
elif e[1] > e[0]:
new_edge_list.append((e[0], e[1]))
this = tc.edge_lists()
this.t = [0.]
this.tmax = tmax
this.edges = [new_edge_list]
this.N = N
return this
def load_json_taco(fp):
"""
Loads a temporal network from a .taco-file (which is actually in json format).
Parameters
----------
fp : file-like or :obj:`str`
read from this file
Returns
-------
temporal network
type as given in the .taco-file
"""
file_is_string = isinstance(fp, str)
if file_is_string:
fp = os.path.abspath(os.path.expanduser(fp))
with open(fp, 'r') as f:
this_data = json.load(f)
else:
this_data = json.load(fp)
if this_data['type'] == 'edge_changes':
temporal_network = tc.edge_changes()
temporal_network.t = this_data['t']
temporal_network.t0 = this_data['t0']
temporal_network.tmax = this_data['tmax']
temporal_network.N = this_data['N']
temporal_network.edges_initial = this_data['edges_initial']
temporal_network.edges_in = this_data['edges_in']
temporal_network.edges_out = this_data['edges_out']
temporal_network.int_to_node = {
int(i): s
for i, s in this_data['int_to_node'].items()
}
temporal_network.notes = this_data['notes']
temporal_network.time_unit = this_data['time_unit']
elif this_data['type'] == 'edge_lists':
temporal_network = tc.edge_lists()
temporal_network.t = this_data['t']
temporal_network.tmax = this_data['tmax']
temporal_network.N = this_data['N']
temporal_network.edges = this_data['edges']
temporal_network.int_to_node = {
int(i): s
for i, s in this_data['int_to_node'].items()
}
temporal_network.notes = this_data['notes']
temporal_network.time_unit = this_data['time_unit']
else:
raise ValueError(
'file is corrupted, unknown temporal network format: ' +
this_data['type'])
return temporal_network
def complete_graph(N, tmax=1.0):
"""Get a single frame which consists of a complete network.
Parameters
----------
N : int
Number of nodes.
tmax : float, default : 1.0
Maximum time of the static network.
Returns
-------
:class:`_tacoma.edge_lists`
An instance of `tacoma.edge_lists` with t = [0.0], tmax = ``tmax``.
"""
edge_list = []
for i in range(N - 1):
for j in range(i + 1, N):
edge_list.append((i, j))
this = tc.edge_lists()
this.t = [0.]
this.tmax = tmax
this.edges = [edge_list]
this.N = N
return this
def continuous_time_simulation(n_nodes,
t0,
t_max,
shape,
scale,
edge_contact_time,
alpha,
beta,
gamma,
delta_in,
delta_out,
mean_degree=1.5):
# # static structure parameters
# p = mean_degree / (n_nodes - 1.0)
# temporal parameters
edge_lists = []
t = []
this_time = t0
edge_lists, t = scale_free_graph(n_nodes,
t0=t0,
t_max=t_max,
shape=shape,
scale=scale,
edge_contact_time=edge_contact_time,
alpha=alpha,
beta=beta,
gamma=gamma,
delta_in=delta_in,
delta_out=delta_out)
# while this_time < tmax:
# G = nx.fast_gnp_random_graph(n_nodes, p) # Generate a new random network
# # G = nx.gnm_random_graph(N, M) # Generate a Erdos Renyi network
# # G = nx.barabasi_albert_graph(N, M) # Generate a Erdos Renyi network
# these_edges = list(G.edges())
# t.append(this_time)
# edge_lists.append(these_edges)
# this_time += np.random.exponential(scale=scale)
# save to _tacoma-object
el = tc.edge_lists()
el.N = n_nodes
el.t = t
el.edges = edge_lists
el.tmax = t_max
res_sim = []
for k in range(len(el.t)):
t = el.t[k]
# print('el.t[k]', el.t[k])
# print('el.edges[k]', el.edges[k])
for i, j in el.edges[k]:
res_sim.append([i, j, t])
return res_sim, el
import tacoma as tc
import numpy as np
print "===== edge_lists => edge_lists ====="
L = tc.edge_lists()
L.N = 3
L.t = [0.0,1.0,2.0]
L.tmax = 3.0
L.edges = [
[
(0,1)
],
[
(1,2), (0,2)
],
[
(0,1)
],
]
result = tc.measure_group_sizes_and_durations_for_edge_lists(L)
print "N_m =", result.aggregated_size_histogram
print "sum(m*N_m) =", np.dot(np.arange(0,L.N+1),result.aggregated_size_histogram), "should be N =", L.N
print "===== edge_changes => edge_lists ====="
C = tc.edge_changes()
import tacoma as tc
temporal_network = tc.edge_lists()
temporal_network.N = 8
temporal_network.t = [0., 1., 1.5, 3., 4., 7., 7.31]
temporal_network.tmax = 8.1
temporal_network.edges = [[(0, 1), (1, 7)], [(0, 1)], [(0, 1), (1, 7)],
[(2, 5), (1, 7)], [(2, 5)], [(0, 1), (2, 5)],
[(0, 1)]]
temporal_network.time_unit = 's'
temporal_network.notes = 'This experiment was conducted as a test.'
temporal_network.int_to_node = {
0: 'Alice',
1: 'Bob',
2: 'Clara',
4: 'Darren',
5: 'Elle',
5: 'Felicitas',
6: 'George',
7: 'Harriett',
}
C = tc.convert(temporal_network)
traj = tc.convert_to_edge_trajectories(C)
C2 = tc.convert_edge_trajectories(traj)
import pprint
pp = pprint.PrettyPrinter(indent=4)
from __future__ import print_function import tacoma as tc dyn_RGG = tc.dynamic_RGG(N=3, t_run_total=10, mean_link_duration=2.0) test_list = tc.edge_lists(dyn_RGG) #test_list.copy_from(dyn_RGG) print(test_list.t, dyn_RGG.t) print(test_list.tmax, dyn_RGG.tmax) print(test_list.edges) print(dyn_RGG.edges) print(test_list.N, dyn_RGG.N) zsbb = tc.ZSBB_model([], 3, 0.6, 0.6, 0.6, t_run_total=100) test_list = tc.edge_changes(zsbb) print(test_list.t, zsbb.t) print(test_list.t0, zsbb.t0) print(test_list.tmax, zsbb.tmax) print(test_list.edges_in) print(zsbb.edges_in) print(test_list.edges_out) print(zsbb.edges_out) print(test_list.N, zsbb.N)
import matplotlib.pyplot as pl
N = 10
t_run = 10000
result = tc.dynamic_RGG(
N=N,
t_run_total=t_run,
mean_link_duration=10.,
periodic_boundary_conditions_for_link_building=False,
record_sizes_and_durations=True,
critical_density=1.0,
#verbose = True)
seed=2335)
this = tc.edge_lists(result)
second_result = tc.measure_group_sizes_and_durations_for_edge_lists(
this,
#verbose=True,
)
#print result.durations
#print second_result.contact_durations
#print result.size_histograms
#print second_result.size_histograms
print(("all size histograms are the same:",
all([
hist1 == hist2 for hist1, hist2 in zip(
result.size_histograms, second_result.size_histograms)
import tacoma as tc
print("===== edge_lists => edge_lists =====")
L = tc.edge_lists()
L.N = 4
L.t = [0.0, 1.0, 2.0]
L.tmax = 3.0
L.edges = [
[(0, 1)],
[(1, 2), (0, 2)],
[(0, 1)],
]
L2 = tc.edge_lists()
L2.N = 4
L2.t = [0.0, 1.0, 2.0]
L2.tmax = 3.0
L2.edges = [
[(3, 1)],
[(3, 2), (0, 2)],
]
new = tc.concatenate([L, L2, L])
print(new.N)
print(new.t)
print(new.tmax)
print(new.edges)
def download_and_convert_sociopatterns_high_school_2013(
url="http://www.sociopatterns.org/wp-content/uploads/2015/07/High-School_data_2013.csv.gz",
filename="~/.tacoma/hs13.taco",
):
"""Download the SocioPatterns 'High school 2013 dynamic contact network' data,
extract it and save it as taco. This data is actually binned in intervals
of `[t-20s, t]`.
Parameters
----------
url : :obj:`str`, optional
The url from which the tsv-data should be retrieved
filename : :obj:`str`, optional
this is the path where the taco will be saved to. default : "~/.tacoma/hs13.taco"
Returns
-------
edge_lists : :mod:`edge_lists`
The temporal network of the 'High school 2013 dynamic contact network'.
Notes
-----
If you use this data, please cite
:: [HS13]
R. Mastrandrea, J. Fournet, A. Barrat,
Contact patterns in a high school: a comparison between data collected
using wearable sensors, contact diaries and friendship surveys.
PLoS ONE 10(9): e0136497 (2015)
"""
# get directory name for download
directory, _ = os.path.split(os.path.abspath(os.path.expanduser(filename)))
mkdirp_customdir(directory)
# download
wget.download(url, out=directory)
# open gzipped file
gzip_file = os.path.join(directory, 'High-School_data_2013.csv.gz')
with gzip.open(gzip_file, mode='rt') as f:
reader = csv.reader(f, delimiter=' ')
# mappings of nodes to integers
node_to_int = {}
int_to_node = {}
# get an initial t_old
# (this is done to detect changes in the tsv
t_old = None
# list of edge lists
edges = []
# time points
time = []
count = 0
for row in reader:
if count == 0:
t0 = int(row[0]) - 20
# this is to account for the interval choice [t-20s, t]
t = float(int(row[0]) - 20 - t0)
# if the time changed, we save the new time and
# prepare to save new edges
if t_old != t:
if (t_old is not None) and (t - t_old > 20):
edges.append([])
time.append(t_old + 20)
edges.append([])
time.append(t)
# get the edge
i = int(row[1])
j = int(row[2])
# map the edge to integers
if i not in node_to_int:
this_int = len(node_to_int)
node_to_int[i] = len(node_to_int)
int_to_node[this_int] = str(i)
if j not in node_to_int:
this_int = len(node_to_int)
node_to_int[j] = len(node_to_int)
int_to_node[this_int] = str(j)
# save the edge
edges[-1].append(tuple(sorted([node_to_int[i], node_to_int[j]])))
t_old = t
count += 1
N = len(node_to_int)
tmax = time[-1] + 20.0
# get a new `edge_lists` instance
el = tc.edge_lists()
el.N = N
el.tmax = tmax
el.edges = edges
el.t = time
el.time_unit = 's'
el.notes = """
This data is binned.
In this data, t0 = 0.0 corresponds to UNIX time """ + str(t0) + """.
For more info, please visit
http://www.sociopatterns.org/datasets/high-school-contact-and-friendship-networks/ .
If you use this data, please cite
R. Mastrandrea, J. Fournet, A. Barrat,
Contact patterns in a high school: a comparison between data collected
using wearable sensors, contact diaries and friendship surveys.
PLoS ONE 10(9): e0136497 (2015)
"""
el.int_to_node = int_to_node
# verifying that this is a valid temporal network
tc.verify(el)
# save this edge_lists instance
with open(os.path.abspath(os.path.expanduser(filename)), 'w') as f:
write_json_taco(el, f)
# remove the downloaded gzipped file
os.remove(gzip_file)
return el
def download_and_convert_sociopatterns_hypertext_2009(
url="http://www.sociopatterns.org/files/datasets/003/ht09_contact_list.dat.gz",
filename="~/.tacoma/ht09.taco",
):
"""Download the SocioPatterns 'Hypertext 2009 dynamic contact network' data,
extract it and save it as taco. This data is actually binned in intervals
of `[t-20s, t]`.
Parameters
----------
url : :obj:`str`, optional
The url from which the tsv-data should be retrieved
filename : :obj:`str`, optional
this is the path where the taco will be saved to. default : "~/.tacoma/ht09.taco"
Returns
-------
edge_lists : :mod:`edge_lists`
The temporal network of the 'Hypertext 2009 dynamic contact network'.
Notes
-----
If you use this data, please cite
::
L. Isella et al., What's in a crowd? Analysis of face-to-face behavioral networks,
Journal of Theoretical Biology 271, 166 (2011).
"""
# get directory name for download
directory, _ = os.path.split(os.path.abspath(os.path.expanduser(filename)))
mkdirp_customdir(directory)
# download
wget.download(url, out=directory)
# open gzipped file
gzip_file = os.path.join(directory, 'ht09_contact_list.dat.gz')
with gzip.open(gzip_file, mode='rt') as f:
reader = csv.reader(f, delimiter='\t')
# mappings of nodes to integers
node_to_int = {}
int_to_node = {}
# get an initial t_old
# (this is done to detect changes in the tsv
t_old = None
# list of edge lists
edges = []
# time points
time = []
for row in reader:
# this is to account for the interval choice [t-20s, t]
t = float(int(row[0]) - 20)
# if the time changed, we save the new time and
# prepare to save new edges
if t_old != t:
if (t_old is not None) and (t - t_old > 20):
edges.append([])
time.append(t_old + 20)
edges.append([])
time.append(t)
# get the edge
i = int(row[1])
j = int(row[2])
# map the edge to integers
if i not in node_to_int:
this_int = len(node_to_int)
node_to_int[i] = len(node_to_int)
int_to_node[this_int] = str(i)
if j not in node_to_int:
this_int = len(node_to_int)
node_to_int[j] = len(node_to_int)
int_to_node[this_int] = str(j)
# save the edge
edges[-1].append(tuple(sorted([node_to_int[i], node_to_int[j]])))
t_old = t
N = len(node_to_int)
tmax = time[-1] + 20.0
# get a new `edge_lists` instance
el = tc.edge_lists()
el.N = N
el.tmax = tmax
el.edges = edges
el.t = time
el.time_unit = 's'
el.notes = """
This data is binned.
In this data, t0 = 0.0 corresponds to 8am on Jun 29th 2009 (UNIX time 1246255200).
For more info, please visit http://www.sociopatterns.org/datasets/hypertext-2009-dynamic-contact-network/ .
If you use this data, please cite
L. Isella et al., What's in a crowd? Analysis of face-to-face behavioral networks,
Journal of Theoretical Biology 271, 166 (2011).
"""
el.int_to_node = int_to_node
# verifying that this is a valid temporal network
tc.verify(el)
# save this edge_lists instance
with open(os.path.abspath(os.path.expanduser(filename)), 'w') as f:
write_json_taco(el, f)
# remove the downloaded gzipped file
os.remove(gzip_file)
return el
