i = 0 leading_eig_vals = [] while i < len(rN_val_range): current_rN = rN_val_range[i] curr_eig_vals = [] k = 0 while k < len(KN_val_range): current_KN = KN_val_range[k] jac = hinv.jacobian(H1, PL1, PM1, IL1, IM1, L1, M1, N1, current_KN, current_rN) vals, vects, leadingVal = hinv.eigvals(jac) curr_eig_vals.append(leadingVal) k += 1 leading_eig_vals.append(curr_eig_vals) i += 1 matplotlib.rcParams.update({'font.size': 22}) plt.figure(1) #ax = plt.subplot(111)
i = 0 leading_eig_vals = [] while i < len(rN_val_range): current_rN = rN_val_range[i] curr_eig_vals = [] k = 0 while k < len(KN_val_range): current_KN = KN_val_range[k] jac = hinv.jacobian(H1, PL1, PM1, IL1, IM1, L1, M1, N1, current_KN, current_rN) vals, vects, leadingVal = hinv.eigvals(jac) curr_eig_vals.append(leadingVal) k += 1 leading_eig_vals.append(curr_eig_vals) i += 1 matplotlib.rcParams.update({'font.size': 22}) plt.figure(1) #ax = plt.subplot(111) plt.contourf(KN_val_range,
leading_eigvals = [] while i < len(var_range): curr_var = var_range[i] H1, PL1, PM1, IL1, IM1, L1, M1 = m.run(1000, 0, curr_var) curr_eigvals = [] k = 0 while k < len(rN_range): curr_rN = rN_range[k] jac = hinv.jacobian(H1, PL1, PM1, IL1, IM1, L1, M1, N1, curr_var, curr_rN) vals, vects, leadingVal = hinv.eigvals(jac) curr_eigvals.append(leadingVal) print 'Running simulation ' + str(i) + ":" + str(k) k += 1 leading_eigvals.append(curr_eigvals) i += 1 matplotlib.rcParams.update({'font.size': 22}) plt.figure(1) #ax = plt.subplot(111) plt.contourf(rN_range, var_range, leading_eigvals, cmap=plt.cm.coolwarm) plt.yscale('log')