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 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()
# ========= start server ============
thread = threading.Thread(None, server.run)
thread.start()
webbrowser.open("http://localhost:" + str(port) + "/?data=" + subdir)
try:
while True:
time.sleep(2)
except KeyboardInterrupt:
# thread.join()
print('stopping server ...')
server.stop_this()
thread.join()
# time.sleep(1)
print('changing directory back to', cwd)
os.chdir(cwd)
if __name__ == "__main__":
# download_d3()
a = tc.load_json_taco("~/.tacoma/ht09.taco")
visualize(a,
frame_dt=20,
titles='HT09',
time_unit='h',
time_normalization_factor=1. / 3600.)
import tacoma as tc
a = tc.load_json_taco("~/.tacoma/ht09.taco")
tc.write_edge_trajectory_coordinates(
a,
"~/Sites/tacoma-interactive/ht09_edge_trajectories.json",
filter_for_duration=0)
a = tc.load_json_taco("~/.tacoma/dtu_1_weeks.taco")
tc.write_edge_trajectory_coordinates(
a,
"~/Sites/tacoma-interactive/dtu_1_weeks_edge_trajectories.json",
filter_for_duration=1200.)
#if group_size == 1:
# 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])
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')
from tacoma.model_conversions import estimate_flockwork_P_args
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
# ============ HT 09 ==============
tn = tc.load_json_taco("~/.tacoma/ht09.taco")
tn_b = get_prepared_network(tn, dt=20)
tc.write_edge_trajectory_coordinates(
tn_b,
"~/Sites/tacoma/data/ht09_edge_trajectories.json",
filter_for_duration=0)
tc.write_json_taco(tn_b, "~/Sites/tacoma/data/ht09_binned.taco")
aggregated_network = tc.measure_group_sizes_and_durations(
tn).aggregated_network
fw_params = estimate_flockwork_P_args(tn,
dt=120,
k_over_k_real_scaling=2.05,
aggregated_network=aggregated_network,
ensure_empty_network=True,