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 9, 2023 · 273714b · 273714b
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commit 273714b
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Showing 5 changed files with 36 additions and 36 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
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -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()