N = 100
P = [0.5]
rewiring_rate = [(0.0, 1.0)]
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()
import time
import numpy as np
# ======== get original network =============
socio = tc.load_json_taco("~/.tacoma/dtu_1_weeks.taco")
socio_binned = tc.bin(socio, dt=300)
socio_result = tc.measure_group_sizes_and_durations(socio)
# ============== generate surrogate network from flockwork_P model ==============
fwP_params = estimate_flockwork_P_args(
socio_binned, dt=3600., aggregated_network=socio_result.aggregated_network)
fwP = tc.flockwork_P_varying_rates_neighbor_affinity(**fwP_params)
fwP_params = estimate_flockwork_P_args(socio_binned, dt=3600.)
fwP = tc.flockwork_P_varying_rates(**fwP_params)
fwP_binned = tc.bin(fwP, dt=300)
N = fwP.N
R0 = 2.0
rho = 1.0 / (3 * 24 * 3600.)
dt = 3600.
t_simulation = 4 * fwP.tmax
t_sample = np.arange(int(t_simulation / dt) + 1, dtype=float) * dt
N_meas = 30
fig, ax = pl.subplots(1, 3, figsize=(12, 4))
import scipy.sparse as sprs
import time
dtu_week = tc.dtu_week()
E = []
P = dtu_week.P
gamma = dtu_week.gamma
N = dtu_week.N
tmax = dtu_week.tmax
t_run_total = tmax
seed = 45
edge_changes = tc.flockwork_P_varying_rates(E,
N,
P,
t_run_total,
gamma,
tmax,
seed=seed)
print("measuring groups and durations, ignore histograms differences =", True)
start = time.time()
result = tc.measure_group_sizes_and_durations_for_edge_changes(
edge_changes, ignore_size_histogram_differences=True)
end = time.time()
print("took", end - start, "seconds")
new_fig, axes = pl.subplots(2, 2)
axes = axes.flatten()
for group_size in range(1, 10):
# plot properties
result = tc.measure_group_sizes_and_durations(zsbb_binned)
#print result.group_durations[4]
fig, ax, data = temporal_network_group_analysis(result, max_group=5)
fig.tight_layout()
traj = tc.get_edge_trajectories(zsbb_binned)
draw_edges(traj.trajectories, ax=ax[3])
# ============== generate surrogate network from flockwork_P model ==============
#fwP_params = estimate_flockwork_P_args(socio_binned,dt=1800.,aggregated_network=socio_result.aggregated_network)
fwP_params = estimate_flockwork_P_args(socio_binned,
dt=3600.,
k_over_k_real_scaling=1.2)
#tc.write_fwP_args(fwP_params,"./fwP_args_dtu_1_weeks.json")
#fwP = tc.flockwork_P_varying_rates_neighbor_affinity(**fwP_params)
fwP = tc.flockwork_P_varying_rates(seed=45, **fwP_params)
fwP_binned = tc.bin(fwP, dt=300)
result = tc.measure_group_sizes_and_durations(fwP_binned)
fig, ax, data = temporal_network_group_analysis(result,
time_unit='h',
time_normalization_factor=1 /
3600.)
fig.tight_layout()
traj = tc.get_edge_trajectories(fwP_binned)
draw_edges(traj.trajectories,
ax=ax[3],
time_unit='h',
time_normalization_factor=1 / 3600.)
# ============== generate surrogate network from dynamic RGG model ==============
import matplotlib.pyplot as pl from itertools import izip N = 100 dtu_week = tc.dtu_week() E = [] P = dtu_week.P gamma = dtu_week.gamma tmax = dtu_week.tmax t_run_total = tmax seed = 45 plot_size = False print "simulating" result = tc.flockwork_P_varying_rates(E,N,P,t_run_total,gamma,tmax,seed=seed) print "done" this = tc.edge_changes() this.t = result.t this.N = result.N this.edges_initial = result.edges_initial this.t0 = result.t0 this.tmax = result.tmax this.edges_in = result.edges_in this.edges_out = result.edges_out print len(this.t), len(this.edges_in) print "first time point: ", this.t[0] second_result = tc.measure_group_sizes_and_durations_for_edge_changes(
n_k_meas = 6
for iscl, scaling in enumerate([k_scaling, 1.0]):
these_ks = []
for meas in range(n_k_meas):
args = estimate_flockwork_P_args(
orig,
dt=dt_for_inference,
k_over_k_real_scaling=scaling,
ensure_empty_network=True,
adjust_last_bin_if_dt_does_not_fit=True)
fwP = tc.flockwork_P_varying_rates(**args)
fwP_binned = tc.bin(fwP, dt_binning)
t_fw, k_fw = tc.mean_degree(fwP_binned)
these_ks.append(k_fw)
print("scaling =", scaling, " ; measurement =", meas)
ndx = np.where(np.logical_and(k_orig > 0.0, k_fw > 0.0))
print("k_fw / k_orig = ", np.mean(k_fw[ndx] / k_orig[ndx]))
these_ks = np.array(these_ks).mean(axis=0)
ax[iscl].plot(t_fw,
these_ks,
'-',
c=tc.color_sequence[iscl + 1],
import tacoma as tc
ec = tc.flockwork_P_varying_rates([],
10, [0.4, 0.8],
600, [(0, 1.), (300, 2.)],
600,
seed=25456)
print(ec.edges_out[:5])
print(ec.edges_in[:5])
import numpy as np
ec = tc.flockwork_P_varying_rates_for_each_node(
[],
10, [np.random.random(10).tolist(),
np.random.random(10).tolist()],
600, [(0, np.random.random(10).tolist()),
(300, np.random.random(10).tolist())],
600,
seed=25456)
print(ec.edges_out[:5])
print(ec.edges_in[:5])
return kwargs
if __name__ == "__main__":
import tacoma as tc
import matplotlib.pyplot as pl
from tacoma.analysis import temporal_network_group_analysis
# THESE TESTS ARE DEPRECATED
test = tc.dtu_week()
rewiring_rate = test.gamma
P = test.P
fw = tc.flockwork_P_varying_rates([],100,P,24*3600,rewiring_rate,tmax=24*3600*7)
fw_binned = tc.sample(fw,dt=300)
fw_binned_result = tc.measure_group_sizes_and_durations(fw_binned)
kwargs = get_ZSBB_parameters(fw_binned,fw_binned_result,fit_discrete=True,dt=300.)
print("lambda =", kwargs['lambda'])
print("b0 =", kwargs['b0'])
print("b1 =", kwargs['b1'])
kwargs['t_run_total'] = (len(fw_binned.t) + 1)*kwargs['N']
zsbb = tc.ZSBB_model(**kwargs)
zsbb_binned = tc.sample(zsbb,dt=kwargs['N'])
zsbb_binned_result = tc.measure_group_sizes_and_durations(zsbb_binned)
fig, ax, data = temporal_network_group_analysis(fw_binned_result)
temporal_network_group_analysis(zsbb_binned_result,