reno.components.Scalar

class reno.components.Scalar(value)

Bases: EquationPart

A static, single value equation part, representing some simple value that doesn’t need to be computed.

Methods

__init__(value)	Create a static value node holding the provided value.
astype(dtype)	Returns a symbolic operation to convert the output of this equation to the specified type.
clip(min, max)	Returns a symbolic operation for enforcing the output is between the passed min and max.
equal(obj)	Returns a symbolic operation for checking equality with passed object.
eval([t, save, force])	No compute necessary, just retrieve statically defined value.
find_parts_of_type(search_type[, ...])	Recursively search for all EquationParts in the tree of the specified type.
get_shape()	Get the size of the additional "data" dimension.
get_type()	Get the type of the target output of this equation expression.
is_static()	Convenience shortcut for reno.utils.is_static() - True if this equation doesn't rely on any dynamic values (thus constant), False if it does.
latex(**kwargs)	Construct a string representation of this portion of the equation for use in a latex display.
mean([axis])	Returns a symbolic operation to find the series-wise mean of the array.
not_equal(obj)	Returns a symbolic operation for checking inequality with passed object.
pt(**refs)	Get a pytensor graph representing this piece of an equation.
pt_str(**refs)	Construct a string containing relevant pytensor code for this piece of the equation.
seek_refs([include_ref_types])	Recursively find a list of all References immediately underneath this part.
series_max()	Returns a symbolic operation to find the series-wise maximum of the array.
series_min()	Returns a symbolic operation to find the series-wise minimum of the array.
sum([axis])	Returns a symbolic operation to find the series-wise sum of the array.

Attributes

dtype	The type of each underlying value.
shape	The size of the data dimension, 1 by default.
timeseries	Returns symbolic operation for getting a timeseries view of the data.

Parameters:

value (int | float | list | np.ndarray)

__annotations__ = {}

__module__ = 'reno.components'

__repr__()

Return repr(self).

Return type:

str

eval(t=0, save=False, force=False, **kwargs)

No compute necessary, just retrieve statically defined value.

Parameters:

• t (int)

• save (bool)

• force (bool)

• kwargs (dict)

Return type:

int | float | ndarray

get_shape()

Get the size of the additional “data” dimension.

Prefer using shape property over directly calling this function.

Return type:

int

get_type()

Get the type of the target output of this equation expression.

Prefer using dtype property over directly calling this function.

Return type:

type

pt(**refs)

Get a pytensor graph representing this piece of an equation.

Parameters:

refs (dict[str, TensorVariable])

Return type:

TensorVariable

pt_str(**refs)

Construct a string containing relevant pytensor code for this piece of the equation. This is useful for “compiling” into pymc code.

Parameters:

refs (dict[str, str])

Return type:

str