def get_prepared_network(tn, dt):
tn_b = tc.bin(tn, dt=dt) # rebin the network
tn_b.t = [t / 3600. for t in tn_b.t] # rescale the network's time
tn_b.tmax /= 3600.
tn_b.time_unit = 'h' # set time unit
return tn_b
def _get_prepared_network(tn, dt, time_unit, time_normalization_factor):
"""Prepare the provided network (i.e. bin it in discrete time)"""
tn_b = tc.bin(tn, dt=dt) # rebin the network
# rescale the network's time
tn_b.t = [t * time_normalization_factor for t in tn_b.t]
tn_b.tmax *= time_normalization_factor
if time_unit is None:
time_unit = ""
tn_b.time_unit = time_unit # set time unit
return tn_b
import tacoma as tc
from tacoma.drawing import draw_edges
from tacoma.analysis import temporal_network_group_analysis
import matplotlib.pyplot as pl
from tacoma.model_conversions import estimate_ZSBB_args
from tacoma.model_conversions import estimate_flockwork_P_args
from tacoma.model_conversions import estimate_dynamic_RGG_args
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
import tacoma as tc
from tacoma.drawing import draw_edges
from tacoma.analysis import temporal_network_group_analysis
import matplotlib.pyplot as pl
from tacoma.model_conversions import estimate_ZSBB_args
from tacoma.model_conversions import estimate_flockwork_P_args
from tacoma.model_conversions import estimate_dynamic_RGG_args
# ======== get original network =============
socio = tc.load_sociopatterns_hypertext_2009()
socio_binned = tc.bin(socio, dt=20.)
# ========= plot properties ==============
socio_result = tc.measure_group_sizes_and_durations(socio)
fig, ax, data = temporal_network_group_analysis(socio_result,
time_unit=socio.time_unit)
fig.tight_layout()
traj = tc.get_edge_trajectories(socio)
draw_edges(traj.trajectories, ax=ax[3])
# ========== generate surrogate from ZSBB model ======
ZSBB_params = estimate_ZSBB_args(socio, group_sizes_and_durations=socio_result)
ZSBB_params['b0'] = 0.51
ZSBB_params['b1'] = 1.0
ZSBB_params['lambda'] = 0.9
zsbb = tc.ZSBB_model(**ZSBB_params)
this_t0 = zsbb.t0
zsbb.t0 = 0.
zsbb.tmax -= this_t0
import tacoma as tc
from tacoma.drawing import draw_edges
from tacoma.analysis import temporal_network_group_analysis
import matplotlib.pyplot as pl
from tacoma.model_conversions import estimate_ZSBB_args
from tacoma.model_conversions import estimate_flockwork_P_args
from tacoma.model_conversions import estimate_dynamic_RGG_args
# ======== get original network =============
socio = tc.load_json_taco("~/.tacoma/dtu_1_weeks.taco")
socio_binned = tc.bin(socio, dt=300)
# ========= plot properties ==============
socio_result = tc.measure_group_sizes_and_durations(socio)
fig, ax, data = temporal_network_group_analysis(socio_result,
time_normalization_factor=1 /
3600.,
time_unit='h')
fig.tight_layout()
traj = tc.get_edge_trajectories(socio)
draw_edges(
traj.trajectories,
ax=ax[3],
time_unit='h',
time_normalization_factor=1 / 3600.,
)
# ========== generate surrogate from ZSBB model ======
ZSBB_params = estimate_ZSBB_args(socio, group_sizes_and_durations=socio_result)
print(ZSBB_params)
# print('\n',alpha,'\n')
x_groups.append(x)
y_groups.append(y)
xs = [x_k, [], x_contact ] + x_groups
ys = [y_k, grp_sizes, y_contact ] + y_groups
return xs, ys
if __name__ == "__main__":
import matplotlib.pyplot as pl
orig = tc.load_json_taco('~/.tacoma/ht09.taco')
orig_binned = tc.bin(orig,20.)
result = tc.measure_group_sizes_and_durations(orig_binned)
n_bins = 100
durations = np.array(result.group_durations[1]) / 3600.
bins = np.append([0.],np.logspace(log10(durations.min())-1,log10(durations.max()),n_bins) )
x, y = get_ccdf(durations)
y_sampled = tc.sample_a_function(x,y,bins)
print("====== HEAD ======")
print("original", x[:4], y[:4])
print("sampled", bins[:4], y_sampled[:4])
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')
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,
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)],
]
L = _tacoma.dynamic_RGG(100, 100, mean_link_duration=10)
#F = tc.flockwork_P_varying_rates([],100,[0.5],100,[(0.0,1.0)],tmax=100)
F = L
FBIN = tc.bin(F, dt=1)
# draw_rows(FBIN)
start = time.time()
traj, similarities = tc.get_edge_trajectories(
FBIN, return_edge_similarities=True)
end = time.time()
print(similarities)
print("needed ", end - start, "seconds")
draw_edges(traj, fit=True)
start = time.time()
result = tc.get_edge_trajectories(F)
end = time.time()
print("needed ", end - start, "seconds")
