reno.ops.Normal#

class reno.ops.Normal(mean, std=1.0, per_timestep=False)#

Methods

`__init__`(mean[, std, 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.)

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.

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.

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. This is useful for “compiling” into pymc code.