Package: taskpacker
Taskpacker===========.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Taskpacker/master/docs/_static/images/title.png:alt: [logo]:align: center:width: 600pxTaskpacker is a generic schedule optimization and visualization library for Python.For instance, below is the optimized schedule of 20 batches of 96 DNA assemblies,assuming a robotic foundry running 24/7:.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Taskpacker/master/examples/dna_assembly.png:alt: [dna_assembly.png]:align: center:width: 600pxSuch plots enable you to spot the bottlenecks of your factory. In this example,it appears that ovens are the limiting elements (the only machines packed fullwith no downtime) and that buying a third oven will increase your factory'sthroughput.Main features--------------Taskpacker was built as a toy project to have an easily-extensible scheduling tool in Python.Only Python2 is supported right now (sorry for that, there is a complex bug with Numberjack in Python3).It is pretty simple and limited (the core code is ~200 lines) but comes with enough features to cover many cases:- Supports resources (typically, people or robots) and resource capacity(= how much jobs a resource can do at the same time)- Supports tasks dependencies (some tasks must be finished before other taskscan be started) and maximum waiting time (i.e. some tasks must be started at thelatest X minutes after their *parents* are completed)- Supports pre-scheduled tasks (such as breaks for human operators, scheduled robotic maintenance etc.)Work in progress - contribute !------------------------------------------Taskpacker is an open-source software originally written to optimize the robot-operated DNA assembly operations at the `Edinburgh Genome Foundry <http://www.genomefoundry.io>`_. It is `released on Github <https://github.com/Edinburgh-Genome-Foundry/plateo>`_under the MIT licence (¢ Edinburgh Genome Foundry), with no warranties: this isan experimental piece of software which we hope will be as useful for you as it was for us.And everyone is welcome to contribute !Installation--------------Taskpacker can be installed by unzipping the source code in one directory and using this command: ::sudo python setup.py installYou can also install it directly from the Python Package Index with this command: ::sudo pip taskpacker installBasic Example--------------In this example two labbies have been assigned a list of chores.Alice will visit the GMO plants, cook the hamsters, and feed the gremlins.Bob will clean the scalpels, dice the hamsters once they are cooked, thenassist Alice in gremlins feeding (a task that takes two people).Certain tasks can only be done after other tasks have been completed.Alice has a stereotypical predisposition to multitasking: she can do 2 jobs atthe same time, while Bob can't.Here is how you would use Taskpacker to find when they will do each task so asto finish as early as possible:.. code:: pythonfrom taskpacker import Task, Resource, numberjack_scheduler, plot_schedulealice = Resource("Alice", capacity=2)bob = Resource("Bob", capacity=1)clean_scalpels = Task("Clean the scalpels", resources=[bob], duration=20,color="white")visit_plants = Task("Visit the plants", resources=[alice], duration=60,color="yellow")cook_hamsters = Task("Cook the hamsters", resources=[alice], duration=30,color="red")dice_hamsters = Task("Dice the hamsters", resources=[bob], duration=40,color="blue", follows=[cook_hamsters, clean_scalpels])feed_gremlins = Task("Feed the gremlins", resources=[alice, bob], duration=50,color="orange", follows=[dice_hamsters])all_tasks = [clean_scalpels, visit_plants, cook_hamsters, dice_hamsters,feed_gremlins]scheduled_tasks = numberjack_scheduler(all_tasks)fig, ax = plot_schedule(scheduled_tasks)ax.figure.set_size_inches(7, 3)ax.figure.savefig("alice_and_bod.png", bbox_inches="tight")## Modeling tasks and reources with spreadsheetsAssume that you have a process consisting in several tasks, each task dependingon some resources to be available, and possibly on other tasks. Such process canbe summarized in a spreadsheet like this one `this file <>`_, which is loaded inTaskpacker as follows:.. code:: pythonfrom taskpacker import (get_resources_from_spreadsheet,get_process_from_spreadsheet)resources = get_resources_from_spreadsheet(spreadsheet_path="path/to/spreadsheet.xls", sheetname="resources")process_tasks = get_process_from_spreadsheet(spreadsheet_path="path/to/spreadsheet.xls",sheetname="process",resources_dict=resources)Then you can for instance plot the dependency graph of the tasks:.. code:: pythonfrom taskpacker import plot_tasks_dependency_graphplot_tasks_dependency_graph(process_tasks).. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Taskpacker/master/docs/_static/images/process_plan.png:alt: [logo]:align: center:width: 600pxOr simply schedule the tasks:.. code:: pythonfrom taskpacker import numberjack_schedulerscheduled_tasks = numberjack_scheduler(process_tasks)Throughput estimations-----------------------Given a list of tasks forming a process, you might ask "how many of these processescan my factory run in a day ?". The following code loads 20 of these processesand asks Taskpacker to stack them one by one as compactly as possible:.. code:: pythonfrom taskpacker import (get_process_from_spreadsheet,get_resources_from_spreadsheet,schedule_processes_series,plot_tasks_dependency_tree,plot_schedule, Task)import matplotlib.cm as cmcolors = [cm.Paired(0.21 * i % 1.0) for i in range(30)]resources = get_resources_from_spreadsheet(spreadsheet_path="path/to/spreadsheet.xls", sheetname="resources")processes = [get_process_from_spreadsheet(spreadsheet_path="path/to/spreadsheet.xls",sheetname="process",resources_dict=resources,tasks_color=colors[i],task_name_prefix="WU%d_" % (i + 1))for i in range(20)]# OPTIMIZE THE SCHEDULEnew_processes = schedule_processes_series(processes, est_process_duration=5000, time_limit=5)# PLOT THE OPTIMIZED SCHEDULEall_tasks = [t for process in new_processes for t in process]fig, ax = plot_schedule(all_tasks)ax.set_xlabel("time (min)")ax.figure.savefig("dna_assembly_schedule.png", bbox_inches="tight").. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Taskpacker/master/examples/dna_assembly.png:alt: [dna_assembly.png]:align: center:width: 600pxNote that it is also possible to add scheduled breaks so that your Igor can rest:.. code:: pythonscheduled_breaks = [Task("break_%03d" % i,resources=[resources["igor"]],scheduled_resource={resources["igor"]: 1},duration=12 * 60, # The break lasts 12Hscheduled_start=24 * 60 * i, # The break happens every 24Hcolor='white')for i in range(6)]new_processes = schedule_processes_series(processes, est_process_duration=5000, time_limit=5,scheduled_tasks=scheduled_breaks).. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Taskpacker/master/examples/dna_assembly_with_breaks.png:alt: [dna_assembly_with_breaks.png]:align: center:width: 600px