model

pywr.core.Model

Yes

agg_func

Scenario aggregation function to use when aggregated_value is called

Yes

name

Name of the recorder

comment

Comment or description of the recorder

ignore_nan

Flag to ignore NaN values when calling aggregated_value

is_objective

Flag to denote the direction, if any, of optimisation undertaken with this recorder

epsilon

Epsilon distance used by some optimisation algorithms

constraint_lower_bounds, constraint_upper_bounds

The value(s) to use for lower and upper bound definitions. These values determine whether the recorder instance is marked as a constraint. Either bound can be None (the default) to disable the respective bound. If both bounds are None then the is_constraint property will return False. The lower bound must be strictly less than the upper bound. An equality constraint can be created by setting both bounds to the same value.

The constraint bounds are not used during model simulation. Instead they are intended for use by optimisation wrappers (or other external tools) to define constrained optimisation problems.

model

pywr.core.Model

Yes

param

The parameter to record

Yes

agg_func

Scenario aggregation function to use when aggregated_value is called

Yes

name

Name of the recorder

Yes

comment

Comment or description of the recorder

Yes

ignore_nan

Flag to ignore NaN values when calling aggregated_value

Yes

is_objective

Flag to denote the direction, if any, of optimisation undertaken with this recorder

Yes

epsilon

Epsilon distance used by some optimisation algorithms

Yes

constraint_lower_bounds, constraint_upper_bounds

The value(s) to use for lower and upper bound definitions. These values determine whether the recorder instance is marked as a constraint. Either bound can be None (the default) to disable the respective bound. If both bounds are None then the is_constraint property will return False. The lower bound must be strictly less than the upper bound. An equality constraint can be created by setting both bounds to the same value.

The constraint bounds are not used during model simulation. Instead they are intended for use by optimisation wrappers (or other external tools) to define constrained optimisation problems.

Yes

model

pywr.core.Model

Yes

param

The parameter to record

Yes

agg_func

Scenario aggregation function to use when aggregated_value is called

Yes

name

Name of the recorder

Yes

comment

Comment or description of the recorder

Yes

ignore_nan

Flag to ignore NaN values when calling aggregated_value

Yes

is_objective

Flag to denote the direction, if any, of optimisation undertaken with this recorder

Yes

epsilon

Epsilon distance used by some optimisation algorithms

Yes

constraint_lower_bounds, constraint_upper_bounds

The value(s) to use for lower and upper bound definitions. These values determine whether the recorder instance is marked as a constraint. Either bound can be None (the default) to disable the respective bound. If both bounds are None then the is_constraint property will return False. The lower bound must be strictly less than the upper bound. An equality constraint can be created by setting both bounds to the same value.

The constraint bounds are not used during model simulation. Instead they are intended for use by optimisation wrappers (or other external tools) to define constrained optimisation problems.

Yes

model

pywr.core.Model

Yes

agg_func

Scenario aggregation function to use when aggregated_value is called

Yes

name

Name of the recorder

comment

Comment or description of the recorder

ignore_nan

Flag to ignore NaN values when calling aggregated_value

is_objective

Flag to denote the direction, if any, of optimisation undertaken with this recorder

epsilon

Epsilon distance used by some optimisation algorithms

constraint_lower_bounds, constraint_upper_bounds

The value(s) to use for lower and upper bound definitions. These values determine whether the recorder instance is marked as a constraint. Either bound can be None (the default) to disable the respective bound. If both bounds are None then the is_constraint property will return False. The lower bound must be strictly less than the upper bound. An equality constraint can be created by setting both bounds to the same value.

The constraint bounds are not used during model simulation. Instead they are intended for use by optimisation wrappers (or other external tools) to define constrained optimisation problems.

func

The aggregation function to use. Can be a string or dict defining aggregation functions, or a callable custom function that performs aggregation.

When a string it can be one of: “sum”, “min”, “max”, “mean”, “median”, “product”, or “count_nonzero”. These strings map to and cause the aggregator to use the corresponding numpy functions.

A dict can be provided containing a “func” key, and optional “args” and “kwargs” keys. The value of “func” should be a string corresponding to the aforementioned numpy function names with the additional options of “percentile” and “percentileofscore”. These latter two functions require additional arguments (the percentile and score) to function and must be provided as the values in either the “args” or “kwargs” keys of the dictionary. Please refer to the corresponding numpy (or scipy) function definitions for documentation on these arguments.

Finally, a callable function can be given. This function must accept either a 1D or 2D numpy array as the first argument, and support the “axis” keyword as integer value that determines which axis over which the function should apply aggregation. The axis keyword is only supplied when a 2D array is given. Therefore,` the callable function should behave in a similar fashion to the numpy functions.

Yes

func_args

func_args: list

Yes

func_kwargs

func_kwargs: dict

Yes

model

pywr.core.Model

Yes

agg_func

Scenario aggregation function to use when aggregated_value is called

Yes

name

Name of the recorder

comment

Comment or description of the recorder

ignore_nan

Flag to ignore NaN values when calling aggregated_value

is_objective

Flag to denote the direction, if any, of optimisation undertaken with this recorder

epsilon

Epsilon distance used by some optimisation algorithms

constraint_lower_bounds, constraint_upper_bounds

The value(s) to use for lower and upper bound definitions. These values determine whether the recorder instance is marked as a constraint. Either bound can be None (the default) to disable the respective bound. If both bounds are None then the is_constraint property will return False. The lower bound must be strictly less than the upper bound. An equality constraint can be created by setting both bounds to the same value.

The constraint bounds are not used during model simulation. Instead they are intended for use by optimisation wrappers (or other external tools) to define constrained optimisation problems.