return self.u.flatten()
else:
return self.v.flatten()
if __name__=="__main__":
NSide = 200
N = NSide**2
Du = 0.16
Dv = 0.08
f = 0.060
k = 0.062
u, v = get_initial_configuration(NSide,random_influence=0.2)
dt = 20
GS = GrayScott(u,v,k,f,Du,Dv,periodic=True,diagonal_links=True)
network = get_grid_layout(N)
visualize_reaction_diffusion(GS,
network,
dt,
node_compartments=np.arange(N),
n_integrator_midpoints=20,
value_extent=[0,1],
config={'draw_nodes_as_rectangles':True}
)
)
linear_processes.append(
( (node, "I"), recovery_rate, (node, "R") )
)
for u, v, w in links:
for C in base_compartments:
linear_processes.extend([
( (u, C), w*mobility_rate, (v, C) ),
( (v, C), w*mobility_rate, (u, C) ),
])
print("set processes")
model.set_processes(quadratic_processes+linear_processes,allow_nonzero_column_sums=True)
initial_conditions = { ( node, "S" ): 1.0 for node in range(N) }
initial_conditions[(0, "S")] = 0.8
initial_conditions[(0, "I")] = 0.2
model.set_initial_conditions(initial_conditions,allow_nonzero_column_sums=True)
t = np.linspace(0,20,100)
node_compartments = [ (node, "I" ) for node in range(N) ]
dt = 0.04
visualize_reaction_diffusion(model, network, dt, node_compartments, value_extent=[0,0.3])
((node, "S"), (node, "I"), infection_rate, (node, "I"), (node, "I")), )
linear_processes.append(((node, "I"), recovery_rate, (node, "R")))
for u, v, w in links:
for C in base_compartments:
linear_processes.extend([
((u, C), w * mobility_rate, (v, C)),
((v, C), w * mobility_rate, (u, C)),
])
print("set processes")
model.set_processes(quadratic_processes + linear_processes,
allow_nonzero_column_sums=False)
initial_conditions = {(node, "S"): 1.0 for node in range(N)}
initial_conditions[(0, "S")] = 0.99
initial_conditions[(0, "I")] = 0.01
model.set_initial_conditions(initial_conditions,
allow_nonzero_column_sums=True)
t = np.linspace(0, 20, 100)
plot_node_indices = [(node, "R") for node in range(N)]
dt = 0.02
visualize_reaction_diffusion(model, network, dt, plot_node_indices)
NSide = 200
N = NSide**2
u, v = get_initial_configuration(NSide,random_influence=0.2)
Du = 0.16
Dv = 0.08
f = 0.060
k = 0.062
#DA, DB, f, k = 0.14, 0.06, 0.035, 0.065 # bacteria
dt = 1
updates_per_frame = 40
sampling_dt = updates_per_frame * dt
GS = GrayScott(u,v,k,f,Du,Dv,periodic=True,diagonal_links=False)
network = get_grid_layout(N,windowwidth=NSide)
visualize_reaction_diffusion(GS,
network,
sampling_dt,
node_compartments=np.arange(N),
n_integrator_midpoints=updates_per_frame-1,
#value_extent=[0.35,0.48],
config={'draw_nodes_as_rectangles':True}
)
#from bfmplot import pl
#pl.hist(GS.u)
#pl.show()
((u, C), w * mobility_rate, (v, C)),
((v, C), w * mobility_rate, (u, C)),
])
print("set processes")
model.set_processes(quadratic_processes + linear_processes,
allow_nonzero_column_sums=False)
initial_conditions = {(node, "S"): 1.0 for node in range(N)}
initial_conditions[(0, "S")] = 0.99
initial_conditions[(0, "I")] = 0.01
model.set_initial_conditions(initial_conditions,
allow_nonzero_column_sums=True)
t = np.linspace(0, 20, 100)
plot_node_indices = [(node, "I") for node in range(N)]
dt = 0.3
visualize_reaction_diffusion(model,
network,
dt,
plot_node_indices,
value_extent=[0, 0.2],
config={
'draw_nodes_as_rectangles': True,
'draw_links': False
})