@@ -931,7 +931,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True):
931931 # See <w,v> for [[1,2,0], [9,1,0.01], [1,2,10*np.pi]] before/after balance
932932 b , (sca , perm ) = matrix_balance (sys_ss .A , separate = True )
933933 p , l , r = eig (b , left = True , right = True )
934- # Reciprocal of inner product <w,v> for each λ , (bound the ~infs by 1e12)
934+ # Reciprocal of inner product <w,v> for each eigval , (bound the ~infs by 1e12)
935935 # G = Transfer([1], [1,0,1]) gives zero sensitivity (bound by 1e-12)
936936 eig_sens = np .reciprocal (maximum (1e-12 , einsum ('ij,ij->j' , l , r ).real ))
937937 eig_sens = minimum (1e12 , eig_sens )
@@ -951,7 +951,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True):
951951 dc = np .zeros_like (p , dtype = float )
952952 # well-conditioned nonzero poles, np.abs just in case
953953 ok = np .abs (eig_sens ) <= 1 / sqrt_eps
954- # the averaged t→∞ response of each simple λ on each i/o channel
954+ # the averaged t->inf response of each simple eigval on each i/o channel
955955 # See, A = [[-1, k], [0, -2]], response sizes are k-dependent (that is
956956 # R/L eigenvector dependent)
957957 dc [ok ] = norm (v [ok , :], axis = 1 )* norm (w [:, ok ], axis = 0 )* eig_sens [ok ]
@@ -973,7 +973,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True):
973973 if np .any (iw ):
974974 tfinal += (total_cycles * 2 * np .pi / wnsub [iw ]).tolist ()
975975 dt += (2 * np .pi / pts_per_cycle / wnsub [iw ]).tolist ()
976- # The rest ~ts = log(%ss value) / exp(Re(λ )t)
976+ # The rest ~ts = log(%ss value) / exp(Re(eigval )t)
977977 texp_mode = log_decay_percent / np .abs (psub [~ iw & ~ ints ].real )
978978 tfinal += texp_mode .tolist ()
979979 dt += minimum (texp_mode / 50 ,
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