fix: remove jupyter-execute blocks

The Jupyter Sphinx integration is currently unable to properly suppress warnings resulting in prints to stderr [1]. This causes the docs to fail building properly. Qiskit Terra already removed the usage of `jupyter-execute` statements a while ago [2], so we are following suit in this regard, too. [1]: jupyter/jupyter-sphinx#178 [2]: Qiskit/qiskit#9346
mrossinek · Feb 6, 2023 · a900578 · a900578
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Showing 5 changed files with 35 additions and 35 deletions.
diff --git a/qiskit_nature/second_q/hamiltonians/lattices/hyper_cubic_lattice.py b/qiskit_nature/second_q/hamiltonians/lattices/hyper_cubic_lattice.py
@@ -30,7 +30,7 @@ class HyperCubicLattice(Lattice):
     tuples of `size`, `edge_parameters`, and `boundary_conditions`.
     For example,
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.hamiltonians.lattices import (
             BoundaryCondition,

diff --git a/qiskit_nature/second_q/operators/fermionic_op.py b/qiskit_nature/second_q/operators/fermionic_op.py
@@ -46,7 +46,7 @@ class FermionicOp(SparseLabelOp):
     A ``FermionicOp`` is initialized with a dictionary, mapping terms to their respective
     coefficients:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import FermionicOp
 
@@ -62,7 +62,7 @@ class FermionicOp(SparseLabelOp):
     If you have very restricted memory resources available, or would like to avoid the additional
     copy, the dictionary will be stored by reference if you disable ``copy`` like so:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         some_big_data = {
             "+_0 -_0": 1.0,
@@ -91,40 +91,40 @@ class FermionicOp(SparseLabelOp):
 
     Addition
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       FermionicOp({"+_1": 1}, num_spin_orbitals=2) + FermionicOp({"+_0": 1}, num_spin_orbitals=2)
 
     Sum
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       sum(FermionicOp({label: 1}, num_spin_orbitals=3) for label in ["+_0", "-_1", "+_2 -_2"])
 
     Scalar multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       0.5 * FermionicOp({"+_1": 1}, num_spin_orbitals=2)
 
     Operator multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op1 = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
       op2 = FermionicOp({"-_0 +_0 +_1": 1}, num_spin_orbitals=2)
       print(op1 @ op2)
 
     Tensor multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
       print(op ^ op)
 
     Adjoint
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       FermionicOp({"+_0 -_1": 1j}, num_spin_orbitals=2).adjoint()
 

diff --git a/qiskit_nature/second_q/operators/polynomial_tensor.py b/qiskit_nature/second_q/operators/polynomial_tensor.py
@@ -79,7 +79,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
     made about the *contents* of the keys. However, the length of each key determines the dimension
     of the matrix which it maps, too. For example:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         import numpy as np
 
@@ -96,7 +96,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
     axis of the matrix, when an operator gets built from the tensor. This means, that the previous
     example would expand for example like so:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import FermionicOp, PolynomialTensor
 
@@ -113,28 +113,28 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
 
     Addition
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       matrix = np.array([[0, 1], [2, 3]], dtype=float)
       0.5 * PolynomialTensor({"+-": matrix}) + PolynomialTensor({"+-": matrix})
 
     Operator multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       tensor = PolynomialTensor({"+-": matrix})
       print(tensor @ tensor)
 
     Tensor multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       print(tensor ^ tensor)
 
     You can also implement more advanced arithmetic via the :meth:`apply` and :meth:`einsum`
     methods.
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       print(PolynomialTensor.apply(np.transpose, tensor))
       print(PolynomialTensor.apply(np.conjugate, 1j * tensor))
@@ -148,7 +148,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
     it needs to support more than 2-dimensional arrays, we rely on the
     `sparse <https://sparse.pydata.org/en/stable/index.html>`_ library.
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         import sparse as sp
 

diff --git a/qiskit_nature/second_q/operators/spin_op.py b/qiskit_nature/second_q/operators/spin_op.py
@@ -61,7 +61,7 @@ class SpinOp(SparseLabelOp):
     A ``SpinOp`` is initialized with a dictionary, mapping terms to their respective
     coefficients. For example:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import SpinOp
 
@@ -72,7 +72,7 @@ class SpinOp(SparseLabelOp):
     are :math:`S^x, S^y, S^z` for spin 3/2 system.
     The two qutrit Heisenberg model with transverse magnetic field is
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         SpinOp({
                 "X_0 X_1": -1,
@@ -88,7 +88,7 @@ class SpinOp(SparseLabelOp):
 
     An example using labels with powers would be:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import SpinOp
 
@@ -99,7 +99,7 @@ class SpinOp(SparseLabelOp):
     If you have very restricted memory resources available, or would like to avoid the additional
     copy, the dictionary will be stored by reference if you disable ``copy`` like so:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         some_big_data = {
             "X_0 Y_0": 1.0,
@@ -133,40 +133,40 @@ class SpinOp(SparseLabelOp):
 
     Addition
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         SpinOp({"X_1": 1}, num_spins=2) + SpinOp({"X_0": 1}, num_spins=2)
 
     Sum
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         sum(SpinOp({label: 1}, num_spins=3) for label in ["X_0", "Z_1", "X_2 Z_2"])
 
     Scalar multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         0.5 * SpinOp({"X_1": 1}, num_spins=2)
 
     Operator multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         op1 = SpinOp({"X_0 Z_1": 1}, num_spins=2)
         op2 = SpinOp({"Z_0 X_0 X_1": 1}, num_spins=2)
         print(op1 @ op2)
 
     Tensor multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         op = SpinOp({"X_0 Z_1": 1}, num_spins=2)
         print(op ^ op)
 
     Adjoint
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         SpinOp({"X_0 Z_1": 1j}, num_spins=2).adjoint()
 

diff --git a/qiskit_nature/second_q/operators/vibrational_op.py b/qiskit_nature/second_q/operators/vibrational_op.py
@@ -49,7 +49,7 @@ class VibrationalOp(SparseLabelOp):
     A ``VibrationalOp`` is initialized with a dictionary, mapping terms to their respective
     coefficients:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import VibrationalOp
 
@@ -67,7 +67,7 @@ class VibrationalOp(SparseLabelOp):
     If you have very restricted memory resources available, or would like to avoid the additional
     copy, the dictionary will be stored by reference if you disable ``copy`` like so:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         some_big_data = {
             "+_0_0 -_0_0": 1.0,
@@ -90,7 +90,7 @@ class VibrationalOp(SparseLabelOp):
     If :code:`num_modals` is not provided then the maximum :code:`modal_index` per
     mode will determine the :code:`num_modals` for that mode.
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import VibrationalOp
 
@@ -112,40 +112,40 @@ class VibrationalOp(SparseLabelOp):
 
     Addition
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       VibrationalOp({"+_1_0": 1}, num_modals=[2, 2]) + VibrationalOp({"+_0_0": 1}, num_modals=[2, 2])
 
     Sum
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       sum(VibrationalOp({label: 1}, num_modals=[1, 1, 1]) for label in ["+_0_0", "-_1_0", "+_2_0 -_2_0"])
 
     Scalar multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       0.5 * VibrationalOp({"+_1_0": 1}, num_modals=[1, 1])
 
     Operator multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op1 = VibrationalOp({"+_0_0 -_1_0": 1}, num_modals=[1, 1])
       op2 = VibrationalOp({"-_0_0 +_0_0 +_1_0": 1}, num_modals=[1, 1])
       print(op1 @ op2)
 
     Tensor multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op = VibrationalOp({"+_0_0 -_1_0": 1}, num_modals=[1, 1])
       print(op ^ op)
 
     Adjoint
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       VibrationalOp({"+_0_0 -_1_0": 1j}, num_modals=[1, 1]).adjoint()