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')
