reno.components.Distribution

class reno.components.Distribution(*operands, per_timestep=False)

Bases: EquationPart

Represents a probability distribution or set that is drawn from for each sample (n) in a simulation.

Probably shouldn’t directly be using this, although you can in theory likely pass in ``partial()``s with numpy functions (just minus the size variable.)

Parameters:

per_timestep (bool) – Whether to draw a different value from the distribution for each timestep.

Methods

__init__(*operands[, per_timestep])
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.)

__annotations__ = {}

__module__ = 'reno.components'

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

Get the vector of samples pulled from the probability distribution.

Note that this expects populate() to already have been called.

Parameters:

• t (int)

• save (bool)

• force (bool)

Return type:

ndarray

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.

Return type:

int

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.