## a0 | J00 | J01 | a1 | J10 | J11 | err1 | err2
sa_id = 0
for pi in params:
for r in range(REPEATS_PER_PARAMETER_SET):
a = parameters[pi,1]
b = parameters[pi,2]
c = parameters[pi,3]
d = parameters[pi,4]
x00 = parameters[pi,14]
x10 = parameters[pi,15]
T2P = parameters[pi,13]
ls = hollingII_simulator(a, b, c,d, x00, x10, DT, T2P, noise, plot_dynamics)
ls.run()
D = ls.get_dynamics()
E_prey = np.asarray(ls.ext_prey)
E_pred = np.asarray(ls.ext_pred)
## now do inference:
S = sampler(si, D)
S.sample()
calc = timme_calculator(S, NUMBER_OF_BINS)
results, err = calc.calculate()
results = results[0:6]
results = np.append(results, err[0])
results = np.append(results, err[1])
os.chdir("./param_%d" %p)
summary_array = np.zeros((REPEATS_PER_PARAMETER_SET, 10)) # to store and save summary results for each simulation
for r in range(REPEATS_PER_PARAMETER_SET):
a = parameters[p,1]
b = parameters[p,2]
c = parameters[p,3]
d = parameters[p,4]
x00 = parameters[p,14]
x10 = parameters[p,15]
T2P = parameters[p,13]
ls = hollingII_simulator(a, b, c, d, x00, x10, DT, T2P, NOISE_VALUE, True)
ls.run()
D = ls.get_dynamics()
E_prey = np.asarray(ls.ext_prey)
E_pred = np.asarray(ls.ext_pred)
np.savetxt("rep_%d.dynamics" %r, D, delimiter=',')
np.savetxt("rep_%d_prey.extinctions" %r, E_prey, delimiter=',')
np.savetxt("rep_%d_pred.extinctions" %r, E_pred, delimiter=',')
summary_array[r,0] = np.min(D[1,:])
summary_array[r,1] = np.max(D[1,:])
summary_array[r,2] = np.mean(D[1,:])
summary_array[r,3] = np.var(D[1,:])
summary_array[r,4] = np.min(D[2,:])
summary_array[r,5] = np.max(D[2,:])
