sampo.pipeline.base#
Overview#
Base class to build different pipeline, that help to use the framework |
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The part of pipeline, that manipulates with the whole entire schedule. |
Classes#
- class sampo.pipeline.base.InputPipeline#
Bases:
abc.ABCBase class to build different pipeline, that help to use the framework
- abstract wg(wg: sampo.schemas.graph.WorkGraph | pandas.DataFrame | str, full_connection: bool = False, change_base_on_history: bool = False) InputPipeline#
- abstract contractors(contractors: list[sampo.schemas.contractor.Contractor] | pandas.DataFrame | str) InputPipeline#
- abstract name_mapper(name_mapper: sampo.utilities.task_name.NameMapper) InputPipeline#
- abstract history(history: pandas.DataFrame | str) InputPipeline#
- abstract landscape(landscape_config: sampo.schemas.landscape.LandscapeConfiguration) InputPipeline#
- abstract spec(spec: sampo.schemas.schedule_spec.ScheduleSpec) InputPipeline#
- abstract time_shift(time: sampo.schemas.time.Time) InputPipeline#
- abstract lag_optimize(lag_optimize: sampo.pipeline.lag_optimization.LagOptimizationStrategy) InputPipeline#
Mandatory argument. Shows should graph be lag-optimized or not. If not defined, pipeline should search the best variant of this argument in result.
- Parameters:
lag_optimize –
- Returns:
the pipeline object
- abstract work_estimator(work_estimator: sampo.schemas.time_estimator.WorkTimeEstimator) InputPipeline#
- abstract node_order(node_order: list[sampo.schemas.graph.GraphNode]) InputPipeline#
- abstract optimize_local(optimizer: sampo.scheduler.utils.local_optimization.OrderLocalOptimizer, area: range) InputPipeline#
- abstract schedule(scheduler: sampo.scheduler.base.Scheduler) SchedulePipeline#
- class sampo.pipeline.base.SchedulePipeline#
Bases:
abc.ABCThe part of pipeline, that manipulates with the whole entire schedule.
- abstract optimize_local(optimizer: sampo.scheduler.utils.local_optimization.ScheduleLocalOptimizer, area: range) SchedulePipeline#
- abstract finish() sampo.schemas.schedule.Schedule#