reno.components.Metric

class reno.components.Metric(eq=None, label=None)

Bases: Reference

Metrics run in a separate after-simulation analysis. Generally intended to be a series aggregate type equation (e.g. using series_min/series_max/sum on slices of values across time from references computed during the simulation etc.

Methods

__init__([eq, label])
clip(min, max)
debug_equation(t[, sample])
equal(obj)
equation(**kwargs)	Get the representation of the full equation for the metric as a latex string.
eval([t, save, force])	Execute the compute graph for this equation, this needs to be implemented in every subclass.
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.
from_dict(data, refs)	Deserialize reference and parse data from dictionary previously saved from to_dict()
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)	String representation suitable for a latex display.
not_equal(obj)
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.
qual_name([dot])	Get a string with both the model and the reference name if this model is a submodel of something else.
seek_refs()	Immediate refs only, depth=1.
series_max()
series_min()
sum([axis])
to_dict()	Serialize class into a dictionary for saving to file.

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.)
model	Keep a reference to container model, makes it easier to compare refs across multiple models.
label	Label is what's used in any visual representation (e.g. allows spaces where name does not.).
doc	A docstring to explain/describe the reference.

Parameters:

• eq (EquationPart)

• label (str)

__annotations__ = {}

__module__ = 'reno.components'

__repr__()

Return repr(self).

__setattr__(name, value)

Implement setattr(self, name, value).

debug_equation(t, sample=0, **kwargs)

equation(**kwargs)

Get the representation of the full equation for the metric as a latex string.

Return type:

str

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

Execute the compute graph for this equation, this needs to be implemented in every subclass.

Note that throughout a compute tree, this should effectively recurse through .eval() calls to all subparts as well.

Parameters:

• t (int) – Timestep along simulation at which to evaluate.

• save (bool) – Whether to store/track/cache the output in a tracked matrix. This is really only applicable to ``TrackedReference``s, but given recursive nature of this function, needs to always be passed down through all subsequent calls.

• force (bool) – Whether to ignore a previously cached value and compute regardless.

from_dict(data, refs)

Deserialize reference and parse data from dictionary previously saved from to_dict()

Parameters:

• data (dict)

• refs (dict[str, TrackedReference])

model

Keep a reference to container model, makes it easier to compare refs across multiple models.

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

qual_name(dot=False)

Get a string with both the model and the reference name if this model is a submodel of something else.

This is primarily used for helping distinguish things in a multimodel setup.

Parameters:

dot (bool)

to_dict()

Serialize class into a dictionary for saving to file.

Return type:

dict