def test_lattice(self):
# define links and network layout
N_side = 20
N = N_side**2
links = get_2D_lattice_links(N_side,
periodic=True,
diagonal_links=True)
network = get_grid_layout(N)
# define model
R0 = 3
recovery_rate = 1 / 8
model = StochasticSIRModel(N,
R0,
recovery_rate,
edge_weight_tuples=links)
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 2
visualize(model,
network,
sampling_dt,
config={
'draw_nodes_as_rectangles': True,
'draw_links': False,
'show_legend': False,
'show_curves': False,
})
def test_network(self):
N = 1000
k = 4
links = []
for i in range(N):
neighs = np.random.randint(0, N - 1, size=(k, ), dtype=int)
neighs[neighs >= i] += 1
for neigh in neighs:
links.append((i, int(neigh), 1.0))
network = get_random_layout(N, links, windowwidth=500)
model = StochasticEpiModel(
list("SIRXTQ"),
N=len(network['nodes']),
directed=True,
edge_weight_tuples=links,
)
k0 = model.out_degree.mean()
R0 = 10
recovery_rate = 1 / 8
quarantine_rate = 1 / 16
tracing_rate = 1 / 2
waning_immunity_rate = 1 / 14
infection_rate = R0 * (recovery_rate) / k0
model.set_node_transition_processes([
("I", recovery_rate, "R"),
("I", quarantine_rate, "T"),
("T", tracing_rate, "X"),
("Q", waning_immunity_rate, "S"),
("X", recovery_rate, "R"),
])
model.set_link_transmission_processes([("I", "S", infection_rate, "I",
"I")])
model.set_conditional_link_transmission_processes({
("T", "->", "X"): [
("X", "I", 0.5, "X", "T"),
#("X","S",0.5,"X","Q"),
],
})
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 0.08
visualize(model,
network,
sampling_dt,
ignore_plot_compartments=['S'],
quarantine_compartments=['X', 'T', 'Q'])
def MHRN_network_vis():
import epipack as epk
from epipack.vis import visualize
import netwulf as nw
network, _, __ = nw.load('../cookbook/readme_vis/MHRN.json')
N = len(network['nodes'])
links = [(l['source'], l['target'], 1.0) for l in network['links']]
model = epk.StochasticEpiModel(["S","I","R"],N,links)\
.set_link_transmission_processes([ ("I", "S", 1.0, "I", "I") ])\
.set_node_transition_processes([ ("I", 1.0, "R") ])\
.set_random_initial_conditions({ "S": N-5, "I": 5 })
visualize(model, network, sampling_dt=0.1)
def test_grid_well_mixed(self):
N = 20 * 20
network = get_grid_layout(N)
R0 = 3
recovery_rate = 1 / 8
model = StochasticSIRModel(N, R0, recovery_rate)
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 2
visualize(model,
network,
sampling_dt,
ignore_plot_compartments=['S'],
config={
'draw_nodes_as_rectangles': True,
'show_legend': False,
})
def network_simulation_visualization():
from epipack import StochasticEpiModel
from epipack.plottools import plot
import matplotlib.pyplot as pl
import networkx as nx
k0 = 50
R0 = 2.5
rho = 1
eta = R0 * rho / k0
omega = 1 / 14
N = int(1e4)
edges = [ (e[0], e[1], 1.0) for e in \
nx.fast_gnp_random_graph(N,k0/(N-1)).edges() ]
SIRS = StochasticEpiModel(
compartments=list('SIR'),
N=N,
edge_weight_tuples=edges
)\
.set_link_transmission_processes([
('I', 'S', eta, 'I', 'I'),
])\
.set_node_transition_processes([
('I', rho, 'R'),
('R', omega, 'S'),
])\
.set_random_initial_conditions({
'S': N-100,
'I': 100
})
from epipack.vis import visualize
from epipack.networks import get_random_layout
layouted_network = get_random_layout(N, edges)
visualize(SIRS,
layouted_network,
sampling_dt=0.1,
config={'draw_links': False})
model.set_node_transition_processes([
("I", recovery_rate, "R"),
("I", quarantine_rate, "T"),
("T", tracing_rate, "X"),
("Q", waning_immunity_rate, "S"),
])
model.set_conditional_link_transmission_processes({
("T", "->", "X"): [
("X", "I", 0.03, "X", "T"),
("X", "S", 0.03, "X", "Q"),
],
})
#model.set_random_initial_conditions({'I':20,'S':N-20})
# set initial conditions with a small number of infected
model.set_random_initial_conditions({'I': 10, 'S': N - 10})
# in every step of the simulation/visualization, let a time of `sampling_dt` pass
sampling_dt = 0.1
# simulate and visualize, do not plot the "S" count,
# and remove links from nodes that transition to "X"
visualize(
model,
network,
sampling_dt,
ignore_plot_compartments=[],
quarantine_compartments=['X'],
)
# usual infection process
model.set_link_transmission_processes([("I", "S", infection_rate, "I", "I")])
# standard SIR dynamic with additional quarantine of symptomatic infecteds
model.set_node_transition_processes([
("I", recovery_rate, "R"),
("I", quarantine_rate, "X"),
])
# set initial conditions with a small number of infected
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
# in every step of the simulation/visualization, let a time of `sampling_dt` pass
sampling_dt = 0.12
# simulate and visualize, do not plot the "S" count,
# and remove links from nodes that transition to "X"
visualize(
model,
network,
sampling_dt,
ignore_plot_compartments=['S'],
quarantine_compartments=['X'],
config={
#'link_color': '#A1A1A1',
'compartment_colors':
[colors[name] for name in palettes['french79 pastel']],
'bgcolor':
hex_bg_colors['french79 pastel'],
})
from epipack.vis import visualize, get_grid_layout
from epipack import StochasticSIRModel
if __name__ == "__main__":
N = 100 * 100
network = get_grid_layout(N)
edge_list = [(link['source'], link['target'], 1.0)
for link in network['links']]
R0 = 3
recovery_rate = 1 / 8
model = StochasticSIRModel(N, R0, recovery_rate)
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 0.5
visualize(model,
network,
sampling_dt,
ignore_plot_compartments=['S'],
config={'draw_nodes_as_rectangles': True})
("Q", waning_immunity_rate, "S"),
])
model.set_conditional_link_transmission_processes({
("T", "->", "X"): [
("X", "I", 0.03, "X", "T"),
("X", "S", 0.03, "X", "Q"),
],
})
#model.set_random_initial_conditions({'I':20,'S':N-20})
# set initial conditions with a small number of infected
model.set_random_initial_conditions({'I': 10, 'S': N - 10})
# in every step of the simulation/visualization, let a time of `sampling_dt` pass
sampling_dt = 1
# simulate and visualize, do not plot the "S" count,
# and remove links from nodes that transition to "X"
visualize(model,
network,
sampling_dt,
ignore_plot_compartments=['S'],
quarantine_compartments=['X'],
config={
'compartment_colors':
[colors[name] for name in palettes['brewer dark']],
'bgcolor':
hex_bg_colors['brewer dark'],
})
import epipack as epk
import epipack.vis as vis
import netwulf as nw
network, cfg, g = nw.load('MHRN.json')
N = len(network['nodes'])
links = [ (l['source'], l['target'], 1.0) for l in network['links'] ]
model = epk.StochasticEpiModel(["S","I","R"],N,links)\
.set_link_transmission_processes([ ("I", "S", 1.0, "I", "I") ])\
.set_node_transition_processes([ ("I", 1.0, "R") ])\
.set_random_initial_conditions({ "S": N-5, "I": 5 })
vis.visualize(model, network, sampling_dt=0.1)
from epipack.vis import visualize, get_grid_layout
from epipack import StochasticSISModel, get_2D_lattice_links
if __name__ == "__main__":
N_side = 100
N = N_side**2
network = get_grid_layout(N)
edge_list = get_2D_lattice_links(N_side,
periodic=True,
diagonal_links=False)
R0 = 3
recovery_rate = 1 / 8
model = StochasticSISModel(N,
R0,
recovery_rate,
edge_weight_tuples=edge_list)
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 1
visualize(model,
network,
sampling_dt,
config={
'draw_nodes_as_rectangles': True,
'show_legend': False
})
#("I",quarantine_rate,"T"),
("T", tracing_rate, "X"),
("Q", waning_immunity_rate, "S"),
("X", recovery_rate, "R"),
])
model.set_link_transmission_processes([("I", "S", infection_rate, "I", "I")
])
model.set_conditional_link_transmission_processes({
("T", "->", "X"): [
("X", "I", 1, "X", "T"),
("X", "S", 1, "X", "Q"),
],
})
model.set_random_initial_conditions({'I': 20, 'S': N - 20})
sampling_dt = 0.1
visualize(
model,
network,
sampling_dt,
ignore_plot_compartments=['S', 'R'],
quarantine_compartments=['X', 'T', 'Q'],
config={
'draw_nodes_as_rectangles': True,
'draw_links': False,
#'draw_nodes':False
'plot_sampled_curve': True,
'show_curves': False,
})
from epipack.vis import visualize, get_grid_layout
from epipack import StochasticSIRModel, get_2D_lattice_links
# define links and network layout
N_side = 100
N = N_side**2
links = get_2D_lattice_links(N_side, periodic=True, diagonal_links=True)
network = get_grid_layout(N)
# define model
R0 = 3; recovery_rate = 1/8
model = StochasticSIRModel(N,R0,recovery_rate,
edge_weight_tuples=links)
model.set_random_initial_conditions({'I':20,'S':N-20})
sampling_dt = 1
visualize(model,network,sampling_dt,
config={
'draw_nodes_as_rectangles':True,
'draw_links':False,
}
)