reno.components.Variable#

class reno.components.Variable(eq=None, label=None, doc=None, min=None, max=None, dim=1, user=False, group='', cgroup='', dtype=None)#

Bases: TrackedReference

A variable is a static value(s) or function that can be used as part of other equations, e.g. flow definitons.

https://insightmaker.com/docs/variables

Methods

__init__([eq, label, doc, min, max, dim, ...])

Create a variable reference.

astype(dtype)

Returns a symbolic operation to convert the output of this equation to the specified type.

clip(min, max)

Returns a symbolic operation for enforcing the output is between the passed min and max.

debug_equation(t[, sample])

Get a latex string output with the debug version of this equation.

equal(obj)

Returns a symbolic operation for checking equality with passed object.

equation(**kwargs)

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

eval([t, save, force])

Compute the equation for the given timestep.

find_parts_of_type(search_type[, ...])

Recursively search for all EquationParts in the tree of the specified type.

from_dict(data, refs)

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

get_shape()

Get the size of the additional "data" dimension.

get_type()

Get the type of the target output of this equation expression.

history(index_eq)

Get a reference to a previous value of this reference.

initial_vals()

Assign the starting values for t=0, the first step of the simulation.

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.

max_refs()

Get any references found in the max constraint equation.

mean([axis])

Returns a symbolic operation to find the series-wise mean of the array.

min_refs()

Get any references found in the min constraint equation.

not_equal(obj)

Returns a symbolic operation for checking inequality with passed object.

plot([ax])

Plot (or add to passed axes) this reference's values.

populate(n, steps)

Initialize the matrix of values with size n x steps.

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.

resolve_init_array(obj_or_eq)

Convert a number or scalar/distribution into correct starting array.

seek_refs([include_ref_types])

Recursively find a list of all References immediately underneath this part.

series_max()

Returns a symbolic operation to find the series-wise maximum of the array.

series_min()

Returns a symbolic operation to find the series-wise minimum of the array.

sum([axis])

Returns a symbolic operation to find the series-wise sum of the array.

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

Returns symbolic operation for getting a timeseries view of the data.

user

If True, use visual interface to allow changing it via widgets.

model

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

init

Initial value/equation for initial value for stock/flow/var at t=0

dim

Size of an optional extra dimension, allowing a given reference to describe a vector of values at every timestep.

implicit

Implicit components (normally created as subcomponents of more advanced operations) don't show up in diagrams, latex, or output JSON, since they are created by operations.

computed_mask

Follows same shape as value (up through first two dimensions), set of booleans indicating which values have already been evaluated and saved.

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:
__annotations__ = {}#
__module__ = 'reno.components'#
__setattr__(name, value)#

Implement setattr(self, name, value).

Parameters:

  • name (str)

  • value (Any)

Return type:

None

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

Get a latex string output with the debug version of this equation.

Parameters:

  • t (int)

  • sample (int)

  • kwargs (dict)

Return type:

str

equation(**kwargs)#

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

Parameters:

kwargs (dict)

Return type:

str

initial_vals()#

Assign the starting values for t=0, the first step of the simulation.

Variables can be set to distributions, so the inital vals in that case will be a population samled from the eq distribution.

Return type:

None

user#

If True, use visual interface to allow changing it via widgets.