sampo.pipeline.default

Overview

Classes

DefaultInputPipeline	Default pipeline, that help to use the framework
DefaultSchedulePipeline	The part of pipeline, that manipulates with the whole entire schedule.

Classes

class sampo.pipeline.default.DefaultInputPipeline

Bases: sampo.pipeline.base.InputPipeline

Default pipeline, that help to use the framework

wg(wg: sampo.schemas.graph.WorkGraph | pandas.DataFrame | str, is_wg_has_full_info_about_connections: bool = False, change_base_on_history: bool = False) → sampo.pipeline.base.InputPipeline

Mandatory argument.

Parameters:

• change_base_on_history – whether it is necessary to change project information based on connection history data

• is_wg_has_full_info_about_connections – does the project information contain full details of the works

• wg – the WorkGraph object for scheduling task

Returns:

the pipeline object

contractors(contractors: list[sampo.schemas.contractor.Contractor] | pandas.DataFrame | str) → sampo.pipeline.base.InputPipeline

Mandatory argument.

Parameters:

contractors – the contractors list for scheduling task

Returns:

the pipeline object

landscape(landscape_config: sampo.schemas.landscape.LandscapeConfiguration) → sampo.pipeline.base.InputPipeline

Set landscape configuration

Parameters:

landscape_config –

Returns:

name_mapper(name_mapper: sampo.utilities.task_name.NameMapper) → sampo.pipeline.base.InputPipeline

Set works’ name mapper :param name_mapper: :return:

history(history: pandas.DataFrame | str) → sampo.pipeline.base.InputPipeline

Set historical data. Mandatory method, if work graph hasn’t info about links :param history: :return:

spec(spec: sampo.schemas.schedule_spec.ScheduleSpec) → sampo.pipeline.base.InputPipeline

Set specification of schedule

Parameters:

spec –

Returns:

time_shift(time: sampo.schemas.time.Time) → sampo.pipeline.base.InputPipeline

If the schedule should start at a certain time

Parameters:

time –

Returns:

lag_optimize(lag_optimize: sampo.pipeline.lag_optimization.LagOptimizationStrategy) → sampo.pipeline.base.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

work_estimator(work_estimator: sampo.schemas.time_estimator.WorkTimeEstimator) → sampo.pipeline.base.InputPipeline

node_order(node_order: list[sampo.schemas.graph.GraphNode]) → sampo.pipeline.base.InputPipeline

optimize_local(optimizer: sampo.scheduler.utils.local_optimization.OrderLocalOptimizer, area: range) → sampo.pipeline.base.InputPipeline

schedule(scheduler: sampo.scheduler.base.Scheduler) → sampo.pipeline.base.SchedulePipeline

static _check_is_contractors_can_perform_work_graph(contractors: list[sampo.schemas.contractor.Contractor], wg: sampo.schemas.graph.WorkGraph) → bool

class sampo.pipeline.default.DefaultSchedulePipeline(s_input: DefaultInputPipeline, wg: sampo.schemas.graph.WorkGraph, schedule: sampo.schemas.schedule.Schedule)

Bases: sampo.pipeline.base.SchedulePipeline

The part of pipeline, that manipulates with the whole entire schedule.

optimize_local(optimizer: sampo.scheduler.utils.local_optimization.ScheduleLocalOptimizer, area: range) → sampo.pipeline.base.SchedulePipeline

finish() → sampo.schemas.schedule.Schedule