reno.ops.Observation#

class reno.ops.Observation(ref, sigma=1.0, data=None)#

Represents a Normal distribution around an observed value.

Should only be used for supplying observational data with likelihoods to bayesian models constructed with model.pymc()

Parameters:

ref (reno.components.Reference) – The equation to supply an observed value for.
sigma (float) – The std dev to use for the likelihood Normal distribution.
data (list) – The actual observed data to apply.

Methods

`__init__`(ref[, sigma, data])
`add_tensors`(pymc_model)
`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.)

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.