This page does not contain any Ocean content - it is used for testing aspects of the theme.

# Inline Markup¶

Paragraphs contain text and may contain inline markup: emphasis, strong emphasis, inline literals, standalone hyperlinks (http://www.python.org), external hyperlinks (Python), internal cross-references (Title), external hyperlinks with embedded URIs (Python web site), footnote references (manually numbered 1, anonymous auto-numbered 3, labeled auto-numbered 2, or symbolic *), and citation references (12). Character-level inline markup is also possible (although exceedingly ugly!) in reStructuredText. Problems are indicated by |problematic| text (generated by processing errors; this one is intentional).

# Footnotes¶

1(1,2)

A footnote contains body elements, consistently indented by at least 3 spaces.

This is the footnote’s second paragraph.

2(1,2)

Footnotes may be numbered, either manually (as in 1) or automatically using a “#”-prefixed label. This footnote has a label so it can be referred to from multiple places, both as a footnote reference (2) and as a hyperlink reference (label).

3

This footnote is numbered automatically and anonymously using a label of “#” only.

*

Footnotes may also use symbols, specified with a “*” label. Here’s a reference to the next footnote: .

This footnote shows the next symbol in the sequence.

4

Here’s an unreferenced footnote, with a reference to a nonexistent footnote: _.

# Math¶

This is a test. Here is an equation: $$X_{0:5} = (X_0, X_1, X_2, X_3, X_4)$$. Here is another:

(1)$\nabla^2 f = \frac{1}{r^2} \frac{\partial}{\partial r} \left( r^2 \frac{\partial f}{\partial r} \right) + \frac{1}{r^2 \sin \theta} \frac{\partial f}{\partial \theta} \left( \sin \theta \, \frac{\partial f}{\partial \theta} \right) + \frac{1}{r^2 \sin^2\theta} \frac{\partial^2 f}{\partial \phi^2}$

You can add a link to equations like the one above (1) by using :eq:.

# Python¶

Code block

print('Hello world')


Example

>>> print('Hello world')
Hello world


Attention

Directives at large.

Caution

Don’t take any wooden nickels.

Danger

Error

Does not compute.

Hint

It’s bigger than a bread box.

Important

• Including the closet.

• The bathroom too.

• Take the trash out of the bathroom.

• Clean the sink.

Note

This is a note. Equations within a note: $$G_{\mu\nu} = 8 \pi G (T_{\mu\nu} + \rho_\Lambda g_{\mu\nu})$$.

Tip

15% if the service is good.

Example

Thing1

Thing2

Thing3

Warning

Strong prose may provoke extreme mental exertion. Reader discretion is strongly advised.

And, by the way…

# Deprecations¶

New in version v0.1: This is a version added message.

Changed in version v0.2: This is a version changed message.

Deprecated since version v0.3: This is a deprecation message.

# Autosphinx¶

class BinaryQuadraticModel(*args, offset: = None, vartype: = None, dtype: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType[numpy.dtype], str, Tuple[Any, int], Tuple[Any, Union[typing_extensions.SupportsIndex, Sequence[typing_extensions.SupportsIndex]]], List[Any], numpy.typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None)[source]

Binary quadratic models (BQMs) are problems of the form:

$E(\bf{v}) = \sum_{i=1} a_i v_i + \sum_{i<j} b_{i,j} v_i v_j + c \qquad\qquad v_i \in\{-1,+1\} \text{ or } \{0,1\}$

where $$a_{i}, b_{ij}, c$$ are real values.

This class encodes Ising and quadratic unconstrained binary optimization (QUBO) models used by samplers such as the D-Wave system.

With one or more of the following parameters,

• vartype: The valid variable types for binary quadratic models, is one of:

• bqm: An existing BQM.

• n: Required number of variables.

• quadratic: Quadratic biases, as a dictionary of form {(u, v): b, ...} or a square array_like.

• linear: Linear biases, as a dictionary of the form {v: b, ...} or a one-dimensional array_like.

• offset: Offset as a number.

you can create BQMs in several ways:

• BinaryQuadraticModel(vartype) with no variables or interactions.

• BinaryQuadraticModel(bqm) from an existing BQM. The resulting BQM has the same variables, linear biases, quadratic biases and offset as bqm.

• BinaryQuadraticModel(bqm, vartype) from an existing BQM, changing to the specified vartype if necessary.

• BinaryQuadraticModel(n, vartype) with n variables, indexed linearly from zero, setting all biases to zero.

• BinaryQuadraticModel(quadratic, vartype) from quadratic biases. When formed with SPIN-variables, biases on the diagonal are added to the offset.

• BinaryQuadraticModel(linear, quadratic, vartype) from linear and quadratic biases.

• BinaryQuadraticModel(linear, quadratic, offset, vartype) from linear and quadratic biases and an offset.

Parameters
• *args – See above.

• offset – Offset (see above) may be supplied as a keyword argument.

• vartype – Variable type (see above) may be supplied as a keyword argument.

• dtype – Data type. numpy.float32 and numpy.float64 are supported. Defaults to numpy.float64.

## Methods/Attributes¶

 num_variables Number of variables in the model. get_linear(v) Get the linear bias of a variable.