Update docs to use the matplotlib plot directive instead of jupyter execute

docs/conf.py

@@ -30,13 +30,13 @@
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
     'sphinx.ext.extlinks',
-    'jupyter_sphinx',
     'sphinx_autodoc_typehints',
     'reno.sphinxext',
     'sphinx.ext.intersphinx',
     'nbsphinx',
     'sphinxcontrib.bibtex',
     "qiskit_sphinx_theme",
+    "matplotlib.sphinxext.plot_directive",
 ]
 templates_path = ["_templates"]
 
@@ -61,6 +61,10 @@
 
 html_context = {"version_list": ["0.4"]}
 
+html_theme_options = {
+    "sidebar_qiskit_ecosystem_member": True,
+}
+
 # autodoc/autosummary options
 autosummary_generate = True
 autosummary_generate_overwrite = False

docs/tutorials/Lindblad_dynamics_simulation.rst

@@ -64,7 +64,9 @@ syntax of ``Pauli`` classes to indicate a qubit number, as below.
 Below, we first set the number of qubits :math:`N` to be simulated, and then prepare and store the
 single-qubit Pauli operators that will be used in the rest of this tutorial.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     import numpy as np
     from qiskit.quantum_info import Operator, Pauli
@@ -100,7 +102,9 @@ two-qubit terms. Since there are no time-dependent terms, and we do not plan to
 derivatives of parameters, we do not use the :class:`Signal` class in this tutorial. See the other
 tutorials for various generalizations of this approach supported with ``qiskit-dynamics``.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     from qiskit_dynamics import Solver, Signal
 
@@ -140,7 +144,9 @@ tutorials for various generalizations of this approach supported with ``qiskit-d
 We now define the initial state for the simulation, the time span to simulate for, and the
 intermediate times for which the solution is requested.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     from qiskit.quantum_info import DensityMatrix
 
@@ -175,7 +181,9 @@ invariant as well. Hence the mean Bloch vector should be equal to any qubit’s
 observing that this equality holds is a simple and useful verification of the numerical solution
 that will be added in the next section.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     n_times = len(sol.y)
     x_data = np.zeros((N, n_times))
@@ -218,11 +226,12 @@ tilt along :math:`+x`, while for :math:`J=3` it will significantly shorten (the
 a mixed state), becoming tilted along :math:`-y`. This complex dependence of the Bloch vector on the
 parameters can be systematically analyzed - we encourage you to try it!
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     from qiskit.visualization import plot_bloch_vector
     import matplotlib.pyplot as plt
-    %matplotlib inline
 
     fontsize = 16
 
@@ -235,8 +244,8 @@ parameters can be systematically analyzed - we encourage you to try it!
     ax.set_xlabel('$t$', fontsize = fontsize)
     ax.set_title('Mean Bloch vector vs. $t$', fontsize = fontsize)
 
-    display(plot_bloch_vector([x_mean[-1], y_mean[-1], z_mean[-1]],
-                      f'Mean Bloch vector at $t = {t_eval[-1]}$'))
+    plot_bloch_vector([x_mean[-1], y_mean[-1], z_mean[-1]],
+                      f'Mean Bloch vector at $t = {t_eval[-1]}$')
 
     if N > 1 and ((abs(x_mean[-1]) > 1e-5 and abs(x_data[0, -1] / x_mean[-1] - 1) > 1e-5 or
                   (abs(z_mean[-1]) > 1e-5 and abs(z_data[1, -1] / z_mean[-1] - 1) > 1e-5))):

docs/tutorials/Rabi_oscillations.rst

@@ -35,7 +35,9 @@ then setup the :class:`.Solver` class instance that stores and manipulates the m
 using matrices and :class:`.Signal` instances. For the time-independent :math:`z` term we set the
 signal to a constant, while for the trasverse driving term we setup a harmonic signal.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     import numpy as np
     from qiskit.quantum_info import Operator
@@ -62,7 +64,9 @@ signal to a constant, while for the trasverse driving term we setup a harmonic s
 We now define the initial state for the simulation, the time span to simulate for, and the
 intermediate times for which the solution is requested, and solve the evolution.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     from qiskit.quantum_info.states import Statevector
     from qiskit.quantum_info import DensityMatrix
@@ -96,11 +100,12 @@ increases and decreases). This mechanism of Rabi oscillations is the basis for t
 gates used to manipulate quantum devices - in particular this is a realization of the :math:`X`
 gate.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     from qiskit.visualization import plot_bloch_vector
     import matplotlib.pyplot as plt
-    %matplotlib inline
 
     fontsize = 16
 
@@ -125,8 +130,8 @@ gate.
         ax.set_title('Bloch vector vs. $t$', fontsize = fontsize)
         plt.show()
 
-        display(plot_bloch_vector([x_data[-1], y_data[-1], z_data[-1]],
-                                  f'Bloch vector at $t = {t_eval[-1]}$'))
+        plot_bloch_vector([x_data[-1], y_data[-1], z_data[-1]],
+                                  f'Bloch vector at $t = {t_eval[-1]}$')
 
     plot_qubit_dynamics(sol, t_eval, X, Y, Z)
 
@@ -161,7 +166,9 @@ since in ``qiskit`` and in ``qiskit-dynamics`` the syntax of many functions is i
 state vectors and density matrices. The shrinking of the qubit’s state within the Bloch sphere due
 to the incoherent evolution can be clearly seen in the plots below.
 
-.. jupyter-execute::
+.. plot::
+    :context: close-figs
+    :include-source:
 
     Gamma_1 = .8
     Gamma_2 = .2