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 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__
import tacoma as tc
from rocsNWL import draw
import matplotlib.pyplot as pl
import networkx as nx
from collections import Counter
import numpy as np
import time
N = 400
start = time.time()
edge_lists = tc.dynamic_RGG(
N=N,
t_run_total=1000,
mean_link_duration=5.,
periodic_boundary_conditions_for_link_building=True,
record_sizes_and_durations=True,
#verbose = True)
seed=2335)
end = time.time()
print("needed", end - start, "seconds")
G = nx.Graph()
G.add_edges_from(edge_lists.edges[-1])
G.add_nodes_from(list(range(N)))
#draw(G)
#pl.show()
socio_binned, dt=(socio_binned.tmax - socio_binned.t[0]) / 120.)
fwP = tc.flockwork_P_varying_rates(**fwP_params)
fwP_binned = tc.bin(fwP, dt=20.)
#fwP_binned = fwP
result = tc.measure_group_sizes_and_durations(fwP_binned)
fig, ax, data = temporal_network_group_analysis(result,
time_unit=socio.time_unit)
fig.tight_layout()
traj = tc.get_edge_trajectories(fwP_binned)
draw_edges(traj.trajectories, ax=ax[3], time_unit=socio.time_unit)
# ============== generate surrogate network from dynamic RGG model ==============
dRGG_params = estimate_dynamic_RGG_args(socio_binned,
group_sizes_and_durations=socio_result)
dRGG = tc.dynamic_RGG(**dRGG_params)
result = tc.measure_group_sizes_and_durations(dRGG)
fig, ax, data = temporal_network_group_analysis(result,
time_normalization_factor=20.,
time_unit=socio.time_unit)
fig.tight_layout()
traj = tc.get_edge_trajectories(dRGG)
draw_edges(traj.trajectories,
ax=ax[3],
time_normalization_factor=20.,
time_unit=socio.time_unit)
fig.savefig('talk/dyn_RGG.pdf')
pl.show()
import matplotlib.pyplot as pl
import DynGillEpi as gill
import numpy as np
N = 50
t_run_total = 10
complete_graph = []
for i in range(N - 1):
for j in range(i + 1, N):
complete_graph.append((i, j))
new_list = [complete_graph for g in range(t_run_total)]
N_meas = 10
L_ = tc.dynamic_RGG(50, t_run_total=t_run_total, mean_link_duration=2)
L = tc.edge_lists()
L.copy_from(L_)
L.t = [0., 0.1, 4.0, 4.1, 5.2, 6.9, 7.5, 8.1, 8.5, 9.0]
#print len(L.edges)
L.edges = new_list
etas = np.logspace(-0.1, 0.5, 10)
R0 = np.logspace(-0.1, 1, 10)
rho = 1.0
etas = R0 * rho / (N - 1.)
#R0 = etas * (N-1.)/rho
tc_vals = np.zeros((len(etas), N_meas))
dyn_vals = np.zeros((len(etas), N_meas))
import tacoma as tc
import numpy as np
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([