## 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,:])