reno.ops.List

class reno.ops.List(values)

Bases: Distribution

Tile passed list to the sample size so each value is hit roughly equally (dependent on exact sample size) and deterministically

Methods

__init__(values)
clip(min, max)
equal(obj)
eval([t, save, force])	Get the vector of samples pulled from the probability distribution.
find_refs_of_type(search_type[, already_checked])	Actually recursive as opposed to seek_refs, returns a list of all equation parts matching passed type.
get_shape()	For now this is returning an integer because we only allow a single additional dimension.
get_type()	Similar to shape, this gets computed recursively, used to automatically determine if the value needs to be initialized with a certain numpy type.
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.
not_equal(obj)
populate(n[, steps, dim])	Generate n x dim samples based on this probability distribution, assigns as a vector/matrix to self.value.
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()	Immediate refs only, depth=1.
series_max()
series_min()
sum([axis])

Attributes

dtype	The type of each underlying value.
shape	The size of the data dimension, 1 by default.
timeseries	Get a timeseries view of the data (includes all historical data across all timesteps.)

Parameters:

values (list | ndarray | set)

__annotations__ = {}

__module__ = 'reno.ops'

__repr__()

Return repr(self).

get_shape()

For now this is returning an integer because we only allow a single additional dimension. Note that this shape _does not_ incoporate time or batch dimensions, only the “data” dimension if applicable. This should be overridden by subclasses, e.g. operations which would change the shape.

get_type()

Similar to shape, this gets computed recursively, used to automatically determine if the value needs to be initialized with a certain numpy type.

Return type:

type

latex(**kwargs)

Construct a string representation of this portion of the equation for use in a latex display. Should probably be overriden in most subclasses, and often needs to be called recursively on sub_equation_parts.

populate(n, steps=0, dim=1)

Generate n x dim samples based on this probability distribution, assigns as a vector/matrix to self.value.

Parameters:

• n (int) – Number of samples to draw.

• steps (int) – Number of timesteps for which to draw samples for, only relevant if per_timestep is True.

• dim (int) – If > 1, draw samples into a vector of this size for each n.