def sample(C_si, C_rel):
# Imports
import sir_ddft
import numpy as np
from numpy import pi, sqrt
from dispy.dispynode import dispynode_logger
# Constants
NUM_THREADS = 2
DT = 0.1
MIN_TIME = 10
TIMEOUT = 400
THRESHOLD = 1e-4
# Use model parameters from original publication
C_sd = C_si / C_rel
L = 10
N = 512 # Powers of 2 are favorable for the internal FFT
dx = L / N
params = sir_ddft.SIRParameters(1.0, 0.1)
diff_params = sir_ddft.SIRDiffusionParameters(0.01, 0.01, 0.01)
ddft_params = sir_ddft.SIRDDFTParameters(1.0, 1.0, 1.0, C_sd, 100, C_si,
100)
x = np.linspace(0, L, N, endpoint=False) + dx / 2
x, y = np.meshgrid(x, x)
S = lambda x, y: np.exp(-1 / (L * 2.0 * L / 50.0) * ((x - L / 2)**2 +
(y - L / 2)**2))
norm_fac = N**2 / np.sum(S(x, y)) * sqrt(pi) / 5
S = lambda x, y, S=S: S(x, y) * norm_fac
I = lambda x, y: 0.001 * S(x, y)
total_SIR = np.sum(S(x, y) + I(x, y)) * dx * dx # Total population
# Create initial state and solver
grid = sir_ddft.Grid2D.new_equidistant(0, L, 0, L, N, N)
state = sir_ddft.SIRStateSpatial2D(
grid, lambda x, y: [S(x, y), I(x, y), 0.0])
solver = sir_ddft.SIRDDFT2DSolver(params, diff_params, ddft_params, state,
NUM_THREADS)
t = []
S = []
I = []
R = []
def store_result(result):
t.append(result["time"])
S.append(np.sum(result["S"]) * dx * dx)
I.append(np.sum(result["I"]) * dx * dx)
R.append(np.sum(result["R"]) * dx * dx)
store_result(solver.get_result())
while t[-1] < MIN_TIME or (I[-1] / total_SIR > THRESHOLD
and t[-1] < TIMEOUT):
solver.add_time(DT)
solver.integrate()
store_result(solver.get_result())
dispynode_logger.info(t[-1])
return (t, S, I, R)
import numpy as np from numpy import pi, sqrt import os, os.path import sys sys.path.append(os.path.abspath(os.path.join(__file__, "../../../target/release"))) import sir_ddft from common_2d import run_sim L=10 N=512 params = sir_ddft.SIRParameters(1.0,0.1) diff_params = sir_ddft.SIRDiffusionParameters(0.01,0.01,0.01) ddft_params = sir_ddft.SIRDDFTParameters(1.0,1.0,1.0,-10,100,-30,100) grid = sir_ddft.Grid2D.new_equidistant(0,L,0,L,N,N) S = lambda x,y: np.exp(-1/(L*2.0*L/50.0)*((x-L/2)**2 + (y-L/2)**2)) * 2.832 I = lambda x,y: 0.001*S(x,y) state = sir_ddft.SIRStateSpatial2D(grid, lambda x,y: [S(x,y),I(x,y),0.0]) solver = sir_ddft.SIRDDFT2DSolver(params, diff_params, ddft_params, state, 6) run_sim(solver, 0.1, 200, "SIR DDFT model")
import numpy as np
import os, os.path
import sys
sys.path.append(
os.path.abspath(os.path.join(__file__, "../../../target/release")))
import sir_ddft as sir_ddft
from common_1d import run_sim
params = sir_ddft.SIRParameters(0.5, 0.1)
diff_params = sir_ddft.SIRDiffusionParameters(0.01, 0.01, 0.01)
ddft_params = sir_ddft.SIRDDFTParameters(1.0, 1.0, 1.0, -5, 100, -10, 100)
grid = sir_ddft.Grid1D.new_equidistant(0, 1, 256)
def initfunc(x):
variance = 50**(-2)
S = np.exp(-(x - 0.5)**2 / (2 * variance)) / np.sqrt(2 * np.pi * variance)
return [S, S * 0.001, 0.0]
state = sir_ddft.SIRStateSpatial1D(grid, initfunc)
solver = sir_ddft.SIRDDFT1DSolver(params, diff_params, ddft_params, state, 4)
run_sim(solver, 0.25, 400, "SIR DDFT model")