import tacoma as tc
L = tc.edge_lists()
L.N = 2
L.t = [0.,0.2,0.1]
L.tmax = 0.11
L.edges = [
[ (0,1), (0,0), (1,0) ],
[ (1,2) ],
]
tc.verify(L)
print
C = tc.edge_changes()
C.N = 2
C.t0 = 0
C.t = [0.2,0.1]
C.tmax = 0.05
C.edges_initial = [
(5,4), (0,1), (0,1),
]
C.edges_in = [ [(2,3), (0,0), (2,3)],[(1,0),(0,1)] ]
C.edges_out = [ [(0,0), (1,0), (1,0)],[(1,0),(0,1) ] ]
tc.verify(C)
t0 = 0.0
tmax = 100.0
t = []
this_time = t0
# Generate a new random network
while this_time < tmax:
G = nx.fast_gnp_random_graph(N, p)
these_edges = list(G.edges())
t.append(this_time)
edge_lists.append(these_edges)
this_time += np.random.exponential(scale=1 / mean_tau)
# save to _tacoma-object
el = tc.edge_lists()
el.N = N
el.t = t
el.edges = edge_lists
el.tmax = tmax
print("Number of mistakes:", tc.verify(el))
from tacoma.drawing import edge_activity_plot
from bfmplot import pl
edge_activity_plot(el, fit=True)
pl.show()
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('ht09.taco', 'w') as f:
tc.write_json_taco(el, f)
from tacoma.flockwork import flockwork_P
# Define structural properties
N = 100
k = 2.0
P = k / (k+1)
# The disconnection rate per node is set
# to gamma = 1, so the total run time is
# in units of 1/gamma = 1
t_run_total = 100
# In order to start with any initial state,
# pass an edge list to this function.
# If you don't, the function assumes
# you want to start with an equilibrium
# configuration and generates one.
fw = flockwork_P(N, P, t_run_total)
# verify that the Flockwork is in a proper
# format
import tacoma as tc
print("Number of rule violations in generated temporal network:",
tc.verify(fw))
print(type(fw))
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)
],
]
print("errors in temporal network:", tc.verify(L))
P_k = tc.degree_distribution_from_edge_lists(L)
for k, P in enumerate(P_k):
print(k, P)
P_k = tc.degree_distribution(L)
for k, P in enumerate(P_k):
print(k, P)
print("===== edge_changes => edge_changes =====")
C = tc.edge_changes()
C.N = 3
C.edges_initial = [ (0,1) ]
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
import tacoma as tc
L = tc.edge_lists()
L.N = 2
L.t = [0.,0.2,0.1]
L.tmax = 0.11
L.edges = [
[ (0,1), (0,0), (1,0) ],
[ (1,2) ],
]
L.int_to_node = { 0: '1', 1: '1' }
print("number of errors =", tc.verify(L))
print()
C = tc.edge_changes()
C.N = 3
C.t0 = 0
C.t = [0.2,0.1]
C.tmax = 0.05
C.edges_initial = [
(5,4), (0,1), (0,1),
]
C.edges_in = [ [(2,3), (0,0), (2,3)],[(1,0),(0,1)] ]
C.edges_out = [ [(0,0), (1,0), (1,0)],[(1,0),(0,1) ] ]
C.int_to_node = { 0: '1', 1: '1' }