t_run_total = 500.0
tmax = 1000.0
seed = 7925
infection_rate = 1.0
recovery_rate = 0.1
E = flockwork_P_equilibrium_configuration(N, P[0])
fwP_ec = tc.flockwork_P_varying_rates(E,
N,
P,
t_run_total,
rewiring_rate,
tmax,
seed=seed)
fwP_el = tc.convert(fwP_ec)
start = time.time()
SIS = tc.node_based_SIS(N,
t_run_total * 2,
infection_rate,
recovery_rate,
N // 2,
seed=seed,
verbose=True)
tc.gillespie_node_based_SIS(fwP_ec, SIS, verbose=True)
end = time.time()
print("node-based simulation on edge_changes took", end - start, "seconds")
pl.plot(SIS.time, SIS.I)
import tacoma as tc
ec = tc.convert(tc.dynamic_RGG(3, 3, mean_link_duration=1.))
C = tc.edge_changes()
C.N = 3
C.edges_initial = [(0, 1)]
C.t0 = 0.0
C.tmax = 3.0
C.t = [1.0, 2.0]
C.edges_in = [
[(1, 2), (0, 2)],
[
(0, 1),
],
]
C.edges_out = [
[(0, 1)],
[(1, 2), (0, 2)],
]
fw_args = tc.get_flockwork_P_args(C, dt=1.)
print fw_args.rewiring_rate
print fw_args.P
print fw_args.__dict__
[(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)
C.int_to_node[0] = 'sdasd'
pp.pprint(C.N)
pp.pprint(C.t0)
pp.pprint(C.t)
pp.pprint(C.tmax)
pp.pprint(C.edges_initial)
pp.pprint(C.edges_in)
import matplotlib.pyplot as pl
import tacoma as tc
from tacoma.drawing import draw_edges
from tacoma.drawing import get_edge_order
import numpy as np
#tn = tc.load_sociopatterns_hypertext_2009()
tn = tc.load_json_taco('~/.tacoma/dtu_1_weeks.taco')
tn = tc.convert(tn)
edge_traj = tc.get_edge_trajectories(tn, return_edge_similarities=True)
print(edge_traj.edge_similarities[:10])
edge_order = get_edge_order(edge_traj, threshold=3600.)
print(edge_order)
print(np.all(edge_order == np.sort(edge_order)))
draw_edges(edge_traj.trajectories, edge_order=edge_order)
draw_edges(edge_traj.trajectories)
pl.show()
import tacoma as tc
orig = tc.convert(tc.load_json_taco('~/.tacoma/ht09.taco'))
#orig = tc.convert( tc.load_json_taco('~/.tacoma/hs13.taco') )
#orig = tc.load_json_taco('~/.tacoma/dtu_1_weeks.taco')
dt_for_inference = 120.
dt_binning = 20.
fetch_k_scaling = True
if fetch_k_scaling:
k_scaling = tc.estimate_k_scaling_gradient_descent(
orig,
dt_for_inference=dt_for_inference,
dt_for_binning=dt_binning,
measurements_per_configuration=20,
learning_rate=0.5,
relative_error=1e-2,
)
else:
k_scaling = 5
from tacoma.model_conversions import estimate_flockwork_P_args
import matplotlib.pyplot as pl
import numpy as np
t_orig, k_orig = tc.mean_degree(tc.bin(orig, dt_binning))
fig, ax = pl.subplots(1, 2, sharex=True, sharey=True)
ax[0].plot(t_orig, k_orig, label='original')
ax[1].plot(t_orig, k_orig, label='original')
L = tc.edge_lists()
L.N = 5
L.t = [0., 1., 2., 3. ]
L.tmax = 4.
L.edges = [
[],
[
(0, 1), (2,3), (3, 4), (2, 4),
],
[
(2,3), (3, 4), (2, 4),
],
[],
]
C = tc.convert(L)
L_result = tc.measure_group_sizes_and_durations(L)
C_result = tc.measure_group_sizes_and_durations(C)
for res in [L_result, C_result]:
contact_durations = res.contact_durations
pair_durations = res.group_durations[2]
print("contact durations =", contact_durations)
print("(should be [ 1.0, 2.0, 2.0, 2.0])")
print("pair durations =", pair_durations)
print("(should be [ 1.0 ])")
print()
import tacoma as tc
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)],
]
new = tc.convert(L)
print "N =", new.N
print "t0 =", new.t0
print "t =", new.t
print "tmax =", new.tmax
print "edges_in =", new.edges_in
print "edges_out =", new.edges_out
print "edges_initial =", new.edges_initial
new = tc.convert(new)
print
print "N =", new.N
print "t =", new.t
print "tmax =", new.tmax
import tacoma as tc
import epipack as epk
# load DTU data as edge_changes
dtu = tc.load_json_taco('~/.tacoma/dtu_1_weeks.taco')
# convert to edge_lists
dtu = tc.convert(dtu)
k = tc.time_average(*tc.mean_degree(dtu), tmax=dtu.tmax)
R0 = 3
recovery_rate = 1 / (24 * 3600)
infection_rate = R0 / k * recovery_rate
tmax = 7 * 24 * 3600
SIS = tc.SIS(dtu.N, dtu.tmax, infection_rate, recovery_rate)