Reference

Core functions

class taskpacker.taskpacker.Resource(name, full_name=None, capacity=1)

Resources are machines/humans that can perform tasks.

Parameters

name

Name of the resource.

full_name

Full name of the resource.

capacity

How many jobs a resource can do at the same time.

Methods

hash
to_dict

class taskpacker.taskpacker.Task(name, resources, duration, follows=(), max_wait=None, scheduled_start=None, scheduled_resources=None, priority=1, due_time=None, color='blue')

Tasks are the steps of a work unit, performed using specific resources.

Parameters

name

Name of the task (appears when printed and plotted).

resources

list of Resource objects specifying the resources occupied by the task.

duration

Duration of the task (the choice of the unit is left to the user.

scheduled_resource

Either None or a dict {resource: slot_used}

Attributes

scheduled_end

Methods

to_dict

taskpacker.taskpacker.numberjack_scheduler(tasks, upper_bound=500, lower_bound=None, optimize=True, time_limit=5, solver_method='Mistral', randomization=False, verbose_solver=False)

Makes an optimized schedule for the processes.

Parameters

tasks

A list of tasks to be sceduled.

upper_bound

Upper bound for the time. The unit depends on the unit chosen for the duration of the work unit’s tasks.

optimize

If false, any solution satisfying the constraints (including deadlines) will be returned. But sometimes it is not possible to respect all deadlines. If True, the function will try to return a schedule which minimizes the days over the deadlines. The function minimized is the sum of (wu.priority*wu.delay) for all work units with a due time.

time_limit

Time in seconds after which the optimizer stops. If the optimizer stops because of this time limit the solution may not be optimal.

solver_method

The solver used by NumberJack (see NumberJack docs).

Input/Output methods

taskpacker.io.plot_schedule(tasks, legend=False, ax=None, edgewidth=1.0)

Plot the work units schedule in a Gantt chart-like way.

This is quite basic and arbitrary and really meant for R&D purposes.

taskpacker.io.plot_tasks_dependency_graph(tasks, ax=None)

Plot the graph of all inter-dependencies in the provided tasks list.

taskpacker.io.plot_tree_graph(levels, edges, draw_node, elements_positions=None, ax=None, width_factor=2.5, height_factor=2, scale=1.0, edge_left_space=0.015, edge_right_space=0.015, interlevel_shift=0, **txt_kw)

General function for plotting tree graphs.

Parameters

levels

A list of lists of nodes grouped by “level”, i.e distance to the in the graph to the level 0. levels will be displayed on a same column.

edges

List of nodes pairs (source node, target node).

draw_node

A function f(x , y , node, ax, **kw) which draws something related to the node at the position x,y on the given Matplotlib ax.

ax

The matplotlib ax to use. If none is provided, a new ax is generated.