Parameters

Parameters extend Sympy symbols. They can be used in any python expression and result as Sympy Expressions to be used as amounts in Exchanges.

Create parameters

Float parameters

A float parameter represents a decimal value. The user should provide its name, unit and statistical distribution.

lca_algebraic.newFloatParam(name, default, min: float | None = None, max: float | None = None, unit: str | None = None, description: str | None = None, label: str | None = None, group: str | None = None, distrib: DistributionType = 'linear', formula=None, save=True, **kwargs)

Creates a float (decimal) parameter.

Parameters

name:

Name of the parameter

default:

Default value

min:

Minimum value

max:

Maximum value

unit:

Unit of the parameter

description:

Long description (optional)

label:

Extended name (optional)

group:

Name of the group (optional). Used to organize parameters.

distrib:

Type of the distribution (optional) Linear (uniform) by default

formula:

Sympy expression. Optional. If provided the default value of this parameter (if not provided at runtime) will be computed from other parameter values.

kwargs:

Extra parameters required for advanced distribution types.

Examples

The following code defines a float parameter p1 of unit kg, with a triangle distribution.

>>> p1 = newFloatParam("p1", min=1.0, max=3.0, default=2.0, distrib=DistributionType.TRIANGLE, unit="kg")

Returns

The newly created parameter

Distribution types

The following types of float distributions are supported.

class lca_algebraic.DistributionType

Type of statistic distribution of a float parameter. Some type of distribution requires extra parameters, in italic, to be provided in the constructor of **ParamDef**()

BETA = 'beta'

Beta distribution with extra params a and b, using default value as ‘loc’ (0 of beta distribution) and std as ‘scale’ (1 of beta distribution) See [scipy doc](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html#scipy.stats.beta)

FIXED = 'fixed'

Fixed value, not considered as a variable input for monte carlo simulation.

LINEAR = 'linear'

Uniform distribution between min and max

LOGNORMAL = 'lognormal'

Lognormal distribution, centered on default value (mean), with deviation of std, not truncated

NORMAL = 'normal'

Normal distribution, centered on default value (mean), with deviation of std and truncated between min and max

TRIANGLE = 'triangle'

Triangle distribution between min and max (set to zero probability), with highest probability at default value

Boolean parameters

Boolean parameters can take only two values 0 and 1.

lca_algebraic.newBoolParam(name, default, description: str | None = None, label: str | None = None, group: str | None = None, formula=None, save=True, **kwargs)

Creates a boolean parameter.

Parameters

name:

Name of the parameter

default:

Default value

description:

Long description (optional)

label:

Extended name (optional)

group:

Name of the group (optional). Used to organize parameters.

formula:

Sympy expression. Optional. If provided the default value of this parameter (if not provided at runtime) will be computed from other parameter values.

Examples

>>> p1 = newBoolParam("p1", default=0, group="param group")

Returns

The newly created parameter

Enum parameters

Enum parameters represent a set of mutually exclusive choices.

lca_algebraic.newEnumParam(name: str, default: str, values: List[str] | Dict[str, float], description: str | None = None, label: str | None = None, group: str | None = None, save=True, **kwargs)

Creates an enum parameter : a set of mutually exclusive boolean choices. Enum parameters themselves are not Sympy symbols. Each of the choice is represented internally as a boolean sympy symbol, that can be accessed via param.symbol(“choice_name”)

Parameters

name:

Name of the parameter

default:

Default

values:

Possible choices. The values can be provided as a list of strings, in which case every choice is equiprobable. They can also be provided as a python dictionnary of “choice” => value. The proability to be picked is then the pro-rata of the value.

description:

Long description (optional)

label:

Extended name (optional)

group:

Name of the group (optional). Used to organize parameters.

Examples

p1 is an enum param with equiprobable choices “choice_a”, “choice_b” and “choice_c”

>>> p1 = newEnumParam("p1", default="choice_a", values=["choice_a", "choice_b", "choice_c"])

p2 is an anum param with “choice_a” and “choice_b” of probability 25% and “choice_c” of probability 50%.

>>> p2 = newEnumParam("p2", default="choice_a", values={"choice_a":1, "choice_b":1, "choice_c":2})

Utils

List of parameters

lca_algebraic.list_parameters(name_type=NameType.NAME, as_dataframe=False)

Prints a pretty list of all defined parameters

Parameters

as_dataframe:

If true, a pandas Dataframe is returned. Otherwise, an HTML table is generated.

lca_algebraic.all_params() → Dict[str, ParamDef]

Return the dict of all parameters defined in memory

Save / load params

lca_algebraic.resetParams(db_name=None)

Clear parameters in live memory (registry) and on disk. Clear either all params (project and all db params) or db params from a single database (if db_name provided).

This is a good practice in your code to start fresh, cleaning your foreground database and parameters and redefine all programmatically at the start. This ensures the state of the projet / database is always in sync your code and your session / in memory.

lca_algebraic.freezeParams(db_name, **params: Dict[str, float])

Freezes amounts in all exchanges for a given set of parameter values. The formulas are computed and the ‘amount’ attributes are set with the result.

This enables parametric datasets to be used by standard, non-parametric tools of Brightway2 (like Activities browser).

Parameters

db_name :

Name of the database for freeze (your foreground db usually)

params:

All other parameters of this function are threated as the values of lca_algebraic parameters

Examples

>>> freezeParams("USER_DB", p1=0.1, p2=3.0)

lca_algebraic.loadParams(global_variable=True, dbname=None)

Load parameters from Brightway database, as per : https://stats-arrays.readthedocs.io/en/latest/

The recommended way is to delete parameters at the start of your session and to define them programmically each time.

However, it can be useful if your parameters come from an import or where defined in Activity Browser.

Parameters

global_variable:

If true, loaded parameters are made available as global variable.

dbname:

If provided, load only database parameters for the given db

Returns

A dictionary of parameters

lca_algebraic.persistParams()

Persist parameters into Brightway project, as per : https://stats-arrays.readthedocs.io/en/latest/.

This is important only in case you want t o: - Use parameters outside lca_algebraic (Activity Browser for instance) - Use loadParams() to init your parameters instead of deleting them and creating them programmaically each time.

This function is automatically run when creating new parameters. However, it should be called manually after updating manually some properties of a parameter.

Misc

lca_algebraic.switchValue(param: EnumParam, **values: Dict[str, float | Expr])

Helper method defining an expression that returns a different value / formula for each possible choice of an anum param.

Parameters

param: EnumParam

The enum param

values: Dict[str, ValueOrExpression]

Each param should correspond to a valid choice of the num parameter.

Examples

Given the enum parameter p1 :

>>> p1 = newEnumParam("p1", values=["choice1", "choice2", "choice3"])

The following code defines an expression worth 0.1 for choice1, 0.2 for choice2 and 4 x p2 for choice3

>>> amount = switchValue(p1, choice1=0.1, choice2=0.2, choice3=4*p2)