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 __init__(self,u,v,k,f,Du,Dv,periodic=True,diagonal_links=False,animate='u'):
assert(u.shape == v.shape)
assert(len(u.shape)==2)
assert(u.shape[0] == u.shape[1])
self.N = u.shape[0]
self.u = u.flatten()
self.v = v.flatten()
self.k = k
self.f = f
self.Du = Du
self.Dv = Dv
self.animate = animate
links = get_2D_lattice_links(self.N,periodic=periodic,diagonal_links=diagonal_links)
row = []
col = []
data = []
for u,v,w in links:
row.extend([v,u,u,v])
col.extend([u,v,u,v])
data.extend([w, w, -w, -w])
self.L = sprs.coo_matrix((data,(row,col)),shape=(self.N**2, self.N**2),dtype=float).tocsr()
if animate == 'u':
self.y0 = self.u
else:
self.y0 = self.v
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
})
from epipack import MatrixEpiModel, get_2D_lattice_links
from epipack.vis import visualize_reaction_diffusion, get_grid_layout
import numpy as np
from time import time
start = time()
N_side = 30
N = N_side**2
links = get_2D_lattice_links(N_side, periodic=False, diagonal_links=False)
network = get_grid_layout(N)
base_compartments = "SIR"
compartments = [(node, C) for node in range(N) for C in base_compartments]
model = MatrixEpiModel(compartments)
infection_rate = 2
recovery_rate = 1
mobility_rate = 0.1
quadratic_processes = []
linear_processes = []
print("defining processes")
for node in range(N):
quadratic_processes.append(
((node, "S"), (node, "I"), infection_rate, (node, "I"), (node, "I")), )
from epipack.vis import visualize, get_grid_layout
from epipack import get_2D_lattice_links
if __name__ == "__main__":
import netwulf as nw
from epipack import StochasticEpiModel
import networkx as nx
N_side = 200
N = N_side * N_side
links = get_2D_lattice_links(N_side, periodic=True, diagonal_links=True)
#links = get_2D_lattice_links(N_side,periodic=True,diagonal_links=False)
network = get_grid_layout(N, links, windowwidth=400)
k0 = 2 * len(links) / N
model = StochasticEpiModel(
list("SIRXTQ"),
N=N,
edge_weight_tuples=links,
)
Reff = 3
R0 = 10
recovery_rate = 1 / 8
quarantine_rate = 1 / 32
tracing_rate = 1 / 2
waning_immunity_rate = 1 / 14
infection_rate = Reff * (recovery_rate + quarantine_rate) / k0
infection_rate = R0 * (recovery_rate) / k0