e_ops positional arguement resolutions

NiallCarbery · NiallCarbery · commit 04e4377a755f · 2025-09-07T14:13:48.000+01:00
diff --git a/tutorials-v5/lectures/Lecture-0-Introduction-to-QuTiP.md b/tutorials-v5/lectures/Lecture-0-Introduction-to-QuTiP.md
@@ -410,7 +410,7 @@ psi0 = basis(2, 0)
 # list of times for which the solver should store the state vector
 tlist = np.linspace(0, 10, 100)
 
-result = mesolve(H, psi0, tlist, [], [])
+result = mesolve(H, psi0, tlist, [])
 ```
 
 ```python
@@ -458,12 +458,12 @@ axes.set_xlabel(r"$t$", fontsize=20)
 axes.set_ylabel(r"$\left<\sigma_z\right>$", fontsize=20);
 ```
 
-If we are only interested in expectation values, we could pass a list of operators to the `mesolve` function that we want expectation values for, and have the solver compute then and store the results in the `Odedata` class instance that it returns.
+If we are only interested in expectation values, we could pass a list of operators to the `mesolve` function that we want expectation values for, and have the solver compute then and store the results in the `Odedata` class instance that it returns.\
 
 For example, to request that the solver calculates the expectation values for the operators $\sigma_x, \sigma_y, \sigma_z$:
 
 ```python
-result = mesolve(H, psi0, tlist, [], [sigmax(), sigmay(), sigmaz()])
+result = mesolve(H, psi0, tlist, [], e_ops=[sigmax(), sigmay(), sigmaz()])
 ```
 
 Now the expectation values are available in `result.expect[0]`, `result.expect[1]`, and `result.expect[2]`:
diff --git a/tutorials-v5/lectures/Lecture-1-Jaynes-Cumming-model.md b/tutorials-v5/lectures/Lecture-1-Jaynes-Cumming-model.md
@@ -111,7 +111,7 @@ if rate > 0.0:
 Here we evolve the system with the Lindblad master equation solver, and we request that the expectation values of the operators $a^\dagger a$ and $\sigma_+\sigma_-$ are returned by the solver by passing the list `[a.dag()*a, sm.dag()*sm]` as the fifth argument to the solver.
 
 ```python
-output = mesolve(H, psi0, tlist, c_ops, [a.dag() * a, sm.dag() * sm])
+output = mesolve(H, psi0, tlist, c_ops, e_ops=[a.dag() * a, sm.dag() * sm])
 ```
 
 ## Visualize the results
@@ -139,7 +139,7 @@ In addition to the cavity's and atom's excitation probabilities, we may also be
 To calculate the Wigner function in QuTiP, we first recalculte the evolution without specifying any expectation value operators, which will result in that the solver returns a list of density matrices for the system for the given time coordinates.
 
 ```python
-output = mesolve(H, psi0, tlist, c_ops, [])
+output = mesolve(H, psi0, tlist, c_ops)
 ```
 
 Now, `output.states` contains a list of density matrices for the system for the time points specified in the list `tlist`:
diff --git a/tutorials-v5/lectures/Lecture-10-cQED-dispersive-regime.md b/tutorials-v5/lectures/Lecture-10-cQED-dispersive-regime.md
@@ -115,7 +115,7 @@ tlist = np.linspace(0, 250, 1000)
 ```
 
 ```python
-res = mesolve(H, psi0, tlist, [], [], options=SolverOptions(nsteps=5000))
+res = mesolve(H, psi0, tlist, [], options={'nsteps': 5000})
 ```
 
 ### Excitation numbers
diff --git a/tutorials-v5/lectures/Lecture-11-Charge-Qubits.md b/tutorials-v5/lectures/Lecture-11-Charge-Qubits.md
@@ -304,7 +304,7 @@ psi0 = psi_g
 
 ```python
 tlist = np.linspace(0.0, 100.0, 500)
-result = mesolve(Heff, psi0, tlist, [], [ket2dm(psi_e)], args=args)
+result = mesolve(Heff, psi0, tlist, [], e_ops=[ket2dm(psi_e)], args=args)
 ```
 
 ```python
@@ -362,7 +362,7 @@ psi_e = Qobj(psi_e.full()[keep_states, :])
 
 ```python
 tlist = np.linspace(0.0, 100.0, 500)
-result = mesolve(Heff, psi0, tlist, [], [ket2dm(psi_e)], args=args)
+result = mesolve(Heff, psi0, tlist, [], e_ops=[ket2dm(psi_e)], args=args)
 ```
 
 ```python
diff --git a/tutorials-v5/lectures/Lecture-12-Decay-into-a-squeezed-vacuum-field.md b/tutorials-v5/lectures/Lecture-12-Decay-into-a-squeezed-vacuum-field.md
@@ -161,7 +161,7 @@ e_ops = [sigmax(), sigmay(), sigmaz()]
 ```
 
 ```python
-result1 = mesolve(L, psi0, tlist, [], e_ops)
+result1 = mesolve(L, psi0, tlist, [], e_ops=e_ops)
 ```
 
 ```python
@@ -205,7 +205,7 @@ c_ops = [np.sqrt(gamma0) *
 ```
 
 ```python
-result2 = mesolve(H, psi0, tlist, c_ops, e_ops)
+result2 = mesolve(H, psi0, tlist, c_ops, e_ops=e_ops)
 ```
 
 And we can verify that it indeed gives the same results:
@@ -292,7 +292,7 @@ c_ops = [np.sqrt(gamma0) *
 ```
 
 ```python
-result1 = mesolve(H, psi0, tlist, c_ops, e_ops)
+result1 = mesolve(H, psi0, tlist, c_ops, e_ops=e_ops)
 ```
 
 ```python
@@ -304,7 +304,7 @@ c_ops = [np.sqrt(gamma0) *
 ```
 
 ```python
-result2 = mesolve(H, psi0, tlist, c_ops, e_ops)
+result2 = mesolve(H, psi0, tlist, c_ops, e_ops=e_ops)
 ```
 
 ```python
diff --git a/tutorials-v5/lectures/Lecture-13-Resonance-flourescence.md b/tutorials-v5/lectures/Lecture-13-Resonance-flourescence.md
@@ -85,7 +85,7 @@ psi0 = basis(2, 0)
 
 ```python
 tlist = np.linspace(0, 20 / (2 * np.pi), 200)
-result = mesolve(HL, psi0, tlist, c_ops, e_ops)
+result = mesolve(HL, psi0, tlist, c_ops, e_ops=e_ops)
 ```
 
 ```python
@@ -118,7 +118,7 @@ fig, ax = plt.subplots(1, 1, figsize=(12, 6), sharex=True)
 for idx, gamma0 in enumerate([0.1 * Omega, 0.5 * Omega, 1.0 * Omega]):
 
     HL, c_ops = system_spec(Omega, gamma0, N)
-    result = mesolve(HL, psi0, tlist, c_ops, e_ops)
+    result = mesolve(HL, psi0, tlist, c_ops, e_ops=e_ops)
 
     ax.plot(result.times, result.expect[5], "b",
             label=fr"$P_e$ ($\gamma_0={gamma0:.2f}$)")
@@ -136,7 +136,7 @@ fig, ax = plt.subplots(1, 1, figsize=(12, 6), sharex=True)
 for idx, gamma0 in enumerate([0.1 * Omega, 0.5 * Omega, 1.0 * Omega]):
 
     HL, c_ops = system_spec(Omega, gamma0, N)
-    result = mesolve(HL, psi0, tlist, c_ops, e_ops)
+    result = mesolve(HL, psi0, tlist, c_ops, e_ops=e_ops)
 
     ax.plot(
         result.times, np.imag(result.expect[4]),
diff --git a/tutorials-v5/lectures/Lecture-14-Kerr-nonlinearities.md b/tutorials-v5/lectures/Lecture-14-Kerr-nonlinearities.md
@@ -184,7 +184,7 @@ psi0 = coherent(N, 2.0)
 # and evolve the state under the influence of the hamiltonian.
 # by passing an empty list as expecation value operators argument,
 # we get the full state of the system in result.states
-result = mesolve(H, psi0, tlist, [], [])
+result = mesolve(H, psi0, tlist, [])
 ```
 
 First, let's look at how the expecation values and variances of the photon number operator $n$ and the $x$ and $p$ quadratures evolve in time:
diff --git a/tutorials-v5/lectures/Lecture-2A-Cavity-Qubit-Gates.md b/tutorials-v5/lectures/Lecture-2A-Cavity-Qubit-Gates.md
@@ -139,7 +139,7 @@ H_t = [[Hc, wc_t], [H1, w1_t], [H2, w2_t], Hc1 + Hc2]
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, [], [])
+res = mesolve(H_t, psi0, tlist, [])
 ```
 
 ### Plot the results
@@ -239,7 +239,7 @@ c_ops = [np.sqrt(kappa) * a, np.sqrt(gamma1) * sm1, np.sqrt(gamma2) * sm2]
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, c_ops, [])
+res = mesolve(H_t, psi0, tlist, c_ops)
 ```
 
 ### Plot the results
@@ -323,7 +323,7 @@ fig.tight_layout()
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, [], [])
+res = mesolve(H_t, psi0, tlist, [])
 ```
 
 ### Plot the results
@@ -408,7 +408,7 @@ fig.tight_layout()
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, [], [])
+res = mesolve(H_t, psi0, tlist, [])
 ```
 
 ### Plot the results
@@ -488,7 +488,7 @@ c_ops = [np.sqrt(kappa) * a, np.sqrt(gamma1) * sm1, np.sqrt(gamma2) * sm2]
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, c_ops, [])
+res = mesolve(H_t, psi0, tlist, c_ops)
 ```
 
 ### Plot results
@@ -574,7 +574,7 @@ H_t = [[Hc, wc_t], H1 * w1 + H2 * w2 + Hc1 + Hc2]
 ### Evolve the system
 
 ```python
-res = mesolve(H_t, psi0, tlist, c_ops, [])
+res = mesolve(H_t, psi0, tlist, c_ops)
 ```
 
 ### Plot the results
diff --git a/tutorials-v5/lectures/Lecture-2B-Single-Atom-Lasing.md b/tutorials-v5/lectures/Lecture-2B-Single-Atom-Lasing.md
@@ -130,8 +130,8 @@ if rate > 0.0:
 Here we evolve the system with the Lindblad master equation solver, and we request that the expectation values of the operators $a^\dagger a$ and $\sigma_+\sigma_-$ are returned by the solver by passing the list `[a.dag()*a, sm.dag()*sm]` as the fifth argument to the solver.
 
 ```python
-opt = SolverOptions(nsteps=2000)  # allow extra time-steps
-output = mesolve(H, psi0, tlist, c_ops, [a.dag() * a, sm.dag() * sm],
+opt = {'nsteps': 2000} # allow extra time-steps
+output = mesolve(H, psi0, tlist, c_ops, e_ops=[a.dag() * a, sm.dag() * sm],
                  options=opt)
 ```
 
@@ -191,8 +191,8 @@ axes[0].set_ylabel("Occupation probability", fontsize=18);
 
 ```python
 tlist = np.linspace(0, 25, 5)
-output = mesolve(H, psi0, tlist, c_ops, [],
-                 options=SolverOptions(nsteps=5000))
+output = mesolve(H, psi0, tlist, c_ops,
+                 options={'nsteps': 5000})
 ```
 
 ```python
diff --git a/tutorials-v5/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md b/tutorials-v5/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md
@@ -206,7 +206,7 @@ psi0 = tensor(basis(N, 1), basis(2, 0))
 
 ```python
 tlist = np.linspace(0, 20, 1000)
-output = mesolve(H, psi0, tlist, [], [a.dag() * a, sm.dag() * sm])
+output = mesolve(H, psi0, tlist, [], e_ops=[a.dag() * a, sm.dag() * sm])
 ```
 
 ```python
@@ -223,7 +223,7 @@ fig.tight_layout()
 
 ```python
 tlist = np.linspace(0, 0.35, 8)
-output = mesolve(H, psi0, tlist, [], [])
+output = mesolve(H, psi0, tlist, [])
 ```
 
 ```python
@@ -269,7 +269,7 @@ kappa = 0.25
 ```python
 tlist = np.linspace(0, 20, 1000)
 output = mesolve(H, psi0, tlist, [np.sqrt(kappa) * a],
-                 [a.dag() * a, sm.dag() * sm])
+                 e_ops=[a.dag() * a, sm.dag() * sm])
 ```
 
 ```python
@@ -281,7 +281,7 @@ axes.legend(loc=0);
 
 ```python
 tlist = np.linspace(0, 10, 8)
-output = mesolve(H, psi0, tlist, [np.sqrt(kappa) * a], [])
+output = mesolve(H, psi0, tlist, [np.sqrt(kappa) * a])
 ```
 
 ```python
@@ -323,7 +323,7 @@ tlist = np.linspace(0, 30, 50)
 
 psi0 = H.groundstate()[1]
 
-output = mesolve(H, psi0, tlist, [np.sqrt(kappa) * a], [])
+output = mesolve(H, psi0, tlist, [np.sqrt(kappa) * a])
 ```
 
 ```python
diff --git a/tutorials-v5/lectures/Lecture-4-Correlation-Functions.md b/tutorials-v5/lectures/Lecture-4-Correlation-Functions.md
@@ -61,7 +61,7 @@ c_ops = [np.sqrt(G1 * (1 + n_th)) * a, np.sqrt(G1 * n_th) * a.dag()]
 rho0 = coherent_dm(N, 2.0)
 
 # first calculate the occupation number as a function of time
-n = mesolve(H, rho0, taulist, c_ops, [a.dag() * a]).expect[0]
+n = mesolve(H, rho0, taulist, c_ops, e_ops=[a.dag() * a]).expect[0]
 n = np.array(n)
 
 # calculate the correlation function G1 and normalize with n to obtain g1
@@ -112,7 +112,7 @@ def correlation_ss_gtt(H, tlist, c_ops, a_op, b_op, c_op, d_op, rho0=None):
         rho0 = steadystate(H, c_ops)
 
     return mesolve(H, d_op * rho0 * a_op, tlist, c_ops,
-                   [b_op * c_op]).expect[0]
+                   e_ops=[b_op * c_op]).expect[0]
 ```
 
 ```python
diff --git a/tutorials-v5/lectures/Lecture-5-Parametric-Amplifier.md b/tutorials-v5/lectures/Lecture-5-Parametric-Amplifier.md
@@ -76,7 +76,7 @@ e_ops = []
 ```
 
 ```python
-output = mesolve(H, psi0, tlist, c_ops, e_ops)
+output = mesolve(H, psi0, tlist, c_ops, e_ops=e_ops)
 output
 ```
 
diff --git a/tutorials-v5/lectures/Lecture-9-Squeezed-states-of-harmonic-oscillator.md b/tutorials-v5/lectures/Lecture-9-Squeezed-states-of-harmonic-oscillator.md
@@ -130,7 +130,7 @@ psi0 = coherent(N, 2.0)
 ```
 
 ```python
-result = mesolve(H, psi0, tlist, c_ops, [])
+result = mesolve(H, psi0, tlist, c_ops)
 ```
 
 ```python
@@ -168,7 +168,7 @@ psi0 = squeeze(N, 1.0) * basis(N, 0)
 ```
 
 ```python
-result = mesolve(H, psi0, tlist, c_ops, [])
+result = mesolve(H, psi0, tlist, c_ops)
 ```
 
 ```python
@@ -208,7 +208,7 @@ psi0 = (
 ```
 
 ```python
-result = mesolve(H, psi0, tlist, c_ops, [])
+result = mesolve(H, psi0, tlist, c_ops)
 ```
 
 ```python
