agentlib_mpc.utils package

Subpackages

Submodules

agentlib_mpc.utils.analysis module

agentlib_mpc.utils.analysis.admm_at_time_step(data: DataFrame | Series, time_step: float = None, variable=None, iteration: float = -1, index_offset: float | Literal['auto'] | bool = True, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] = 'seconds') → DataFrame[source]

Gets the results of an optimization at a time step. :param index_offset: Determines how the index will be updated when loading the data. :param The offset will be subtracted from the time-index. This is useful for results: :param of realtime systems: to cut the number down to something understandable. For example, if the time

index (level 0 of the input Dataframe) is [105, 115, 125] and we give an index_offset of 100, the data will be handled as if the index was [5, 15, 25].

If “auto” or True is provided as an argument, the index will be modified to

start at 0. If 0 or False are provided, no modifications will be made.

Parameters:

  • want (where the time value with be a unix time stamp and we) –

    to cut the number down to something understandable. For example, if the time index (level 0 of the input Dataframe) is [105, 115, 125] and we give an index_offset of 100, the data will be handled as if the index was [5, 15, 25].

    If “auto” or True is provided as an argument, the index will be modified to

    start at 0. If 0 or False are provided, no modifications will be made.

  • data – The multi-indexed results data frame from the mpc

  • time_step – The time step from which results should be shown. If no exact match, shows closest.

  • variable – If specified, only returns results with regard to a certain variable.

  • iteration – Specifies, from which inner ADMM iteration data should be from. If negative, counts from last iteration. Default -1.

  • convert_to – Whether the data should be converted to datetime, minutes etc.

Returns:

A single-indexed Dataframe of the optimization results at the specified time step. If variable is not specified, returns all variables with a double column index, if it is specified returns only values and/or bounds with single indexed columns.

agentlib_mpc.utils.analysis.convert_index(convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'], index: Index)[source]

Converts an index from seconds to datetime or another unit :param convert_to: unit, e.g. minutes, hours, datetime :param index: pandas index object

Returns:

agentlib_mpc.utils.analysis.convert_multi_index(data: DataFrame, convert_to: Literal['seconds', 'minutes', 'hours', 'days'] | Literal['datetime'])[source]

Converts an index of an MPC or ADMM results Dataframe to a different unit, assuming it is passed in seconds.

agentlib_mpc.utils.analysis.first_vals_at_trajectory_index(data: DataFrame | Series)[source]

Gets the first values at each time step of a results trajectory.

agentlib_mpc.utils.analysis.get_number_of_iterations(data: DataFrame) → dict[agentlib_mpc.utils.analysis.SimulationTime, int][source]

Returns the number of iterations at each time instance of the ADMM simulation.

agentlib_mpc.utils.analysis.get_time_steps(data: DataFrame) → Iterable[float][source]

Returns the time steps at which an MPC step was performed.

agentlib_mpc.utils.analysis.last_vals_at_trajectory_index(data: DataFrame | Series)[source]

Gets the last values at each time step of a results trajectory.

agentlib_mpc.utils.analysis.load_admm(file: Path | str) → DataFrame[source]
agentlib_mpc.utils.analysis.load_mpc(file: Path | str) → DataFrame[source]
agentlib_mpc.utils.analysis.load_mpc_stats(results_file: str | Path) → DataFrame | None[source]
agentlib_mpc.utils.analysis.load_sim(file: Path, causality=None) → DataFrame[source]
agentlib_mpc.utils.analysis.mpc_at_time_step(data: DataFrame, time_step: float, variable=None, variable_type='variable', index_offset: float | Literal['auto'] | bool = True) → DataFrame[source]

Gets the results of an optimization at a time step.

Parameters:

  • data – The multi-indexed results data frame from the mpc

  • time_step – The time step from which results should be shown. If no exact match, shows closest.

  • variable – If specified, only returns results with regard to a certain variable.

  • variable_type – The type of the variable provided (parameter, variable, lower, …)

  • index_offset – Determines how the index will be updated when loading the data.

  • results (The offset will be subtracted from the time-index. This is useful for) –

  • systems (of realtime) –

    to cut the number down to something understandable. For example, if the time index (level 0 of the input Dataframe) is [105, 115, 125] and we give an index_offset of 100, the data will be handled as if the index was [5, 15, 25].

    If “auto” or True is provided as an argument, the index will be modified to

    start at 0. If 0 or False are provided, no modifications will be made.

  • want (where the time value with be a unix time stamp and we) –

    to cut the number down to something understandable. For example, if the time index (level 0 of the input Dataframe) is [105, 115, 125] and we give an index_offset of 100, the data will be handled as if the index was [5, 15, 25].

    If “auto” or True is provided as an argument, the index will be modified to

    start at 0. If 0 or False are provided, no modifications will be made.

Returns:

A single-indexed Dataframe of the optimization results

at the specified time step. If variable is not specified, returns all variables with a double column index, if it is specified returns only values and/or bounds with single indexed columns.

Return type:

pd.DataFrame

agentlib_mpc.utils.analysis.perform_index_update(data: DataFrame, offset: float | Literal['auto'] | bool, admm: bool = False) → DataFrame[source]

Updates the index of a raw mpc/admm result dataframe, to be offset by a desired time value.

agentlib_mpc.utils.debug module

Module contents

Package containing utils for agentlib_mpc.