def test_itoSRI2_R74(self, exact_solution_R74):
(dW, I, J, f, f_strat, G, G_separate, y0, tspan,
y) = exact_solution_R74
ySRI2 = sdeint.itoSRI2(f, G_separate, y0, tspan, dW=dW, I=I)
_assert_close(ySRI2, y, 1e-2, 1e-2)
return ySRI2
tdex = 2.
n = open(nome, 'w')
n.write('%f %f %f\n' % (X[0, 0], X[0, 1], X[0, 2]))
for j in range(1, np.shape(X)[0]):
idex += 1
if ((idex * dt) % tau == 0):
n.write('%f %f %f\n' % (X[j, 0], X[j, 1], X[j, 2]))
tdex += 1
n.close()
def f(X, t):
dydt = np.array(
[-X[s] * (np.sum(np.dot(A, X)[0, s])) for s in range(0, len(X))])
return dydt
def u(X, t):
dydt = np.array([
1. / np.sqrt(SS) * np.sqrt(X[s] * (np.sum(np.dot(abs(A), X)[0, s])))
for s in range(0, len(X))
])
return np.diag(dydt)
x0 = np.array([0.4, 0.3, 0.3])
ts = sdeint.itoSRI2(f, u, x0, t, dW=d_Wiener)
tau = 2
StampaSerieTemporale(ts, 'RPS.txt', tau)
StampaSerieTemporale(d_Wiener, 'rumore_rps.txt', tau)
def test_itoSRI2_KPS445(self, exact_solution_KPS445):
(dW, I, J, f, f_strat, G, y0, tspan, y) = exact_solution_KPS445
ySRI2 = sdeint.itoSRI2(f, G, y0, tspan, dW=dW, I=I)[:, 0]
_assert_close(ySRI2, y, 1e-2, 1e-2)
return ySRI2
np.sqrt(X[s] *
(r[s] + np.sum(np.dot(A, X)[0, s])) * HeavisideTheta(X[s]))
for s in range(0, len(X))
])
return np.diag(dydt)
################
printing = True
ini_cond = integrate.odeint(f, X_f1, t)
X_f1 = np.array([
ini_cond[len(t) - 1, 0], ini_cond[len(t) - 1, 1], ini_cond[len(t) - 1, 2],
ini_cond[len(t) - 1, 3]
])
######### Now that you are on the attractor run the true simulation
stochastic_dynamics = sdeint.itoSRI2(f, u, X_f1, t, dW=d_Wiener)
if printing == True:
tau = 2.
StampaSerieTemporale(stochastic_dynamics, 'Chaotic_LV.txt', tau)
StampaSerieTemporale(d_Wiener, 'rumore_chaotic.txt', tau)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.plot(stochastic_dynamics[:, 0],
stochastic_dynamics[:, 2],
stochastic_dynamics[:, 3],
color='b')
plt.show()
with open(folder + fn + '_r_1.csv', newline='') as csvfile:
rl = list(
csv.reader(csvfile, delimiter=' ',
quoting=csv.QUOTE_NONNUMERIC)) #growth rate
r = np.array(rl).flatten()
with open(folder + fn + '_x0_1.csv', newline='') as csvfile:
x0l = list(
csv.reader(csvfile, delimiter=' ',
quoting=csv.QUOTE_NONNUMERIC)) #initial conditions
x0 = np.array(x0l).flatten()
print(fn)
##%% solve stochastic system
Muts1 = sdeint.itoSRI2(Mut_eq, mutN_eq, x0, tarray)
print("100done")
# Ac=np.zeros(A.shape) #remove competition
# Muts0 = sdeint.itoSRI2(Mut_eq, addN_eq, x0, tarray)
# print("0done")
##%% plot to check simulation results
plt.figure()
plt.plot(Muts1)
plt.ylim(0, 1)
plt.savefig(fn + "_ts1_" + num_test + ".png", dpi=300)
# plt.close()
# plt.figure()
# plt.plot(Muts0)
# plt.ylim(0,100)
with open(folder + fn + '_r_1.csv', newline='') as csvfile:
rl = list(
csv.reader(csvfile, delimiter=' ',
quoting=csv.QUOTE_NONNUMERIC)) #growth rate
r = np.array(rl).flatten()
with open(folder + fn + '_x0_1.csv', newline='') as csvfile:
x0l = list(
csv.reader(csvfile, delimiter=' ',
quoting=csv.QUOTE_NONNUMERIC)) #initial conditions
x0 = np.array(x0l).flatten()
print(fn)
##%% solve stochastic system
Muts1 = sdeint.itoSRI2(Mut_eq, addN_eq, x0, tarray)
print("sim done")
# Ac=np.zeros(A.shape) #remove competition
# Muts0 = sdeint.itoSRI2(Mut_eq, addN_eq, x0, tarray)
# print("0done")
##%% plot to check simulation results
plt.figure()
plt.plot(Muts1)
plt.ylim(0, 100)
plt.savefig(fn + "_ts1_" + num_test + ".png", dpi=300)
# plt.close()
# plt.figure()
# plt.plot(Muts0)
# plt.ylim(0,100)
def test_itoSRI2_R74(self, exact_solution_R74):
(dW, I, J, f, f_strat, G, G_separate, y0, tspan,y) = exact_solution_R74
ySRI2 = sdeint.itoSRI2(f, G_separate, y0, tspan, dW=dW, I=I)
_assert_close(ySRI2, y, 1e-2, 1e-2)
return ySRI2
def test_itoSRI2_KPS445(self, exact_solution_KPS445):
(dW, I, J, f, f_strat, G, y0, tspan, y) = exact_solution_KPS445
ySRI2 = sdeint.itoSRI2(f, G, y0, tspan, dW=dW, I=I)[:,0]
_assert_close(ySRI2, y, 1e-2, 1e-2)
return ySRI2
