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Taskpacker Documentation | ||
========================== | ||
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.. image:: _static/images/title.png | ||
:width: 600px | ||
:align: center | ||
.. include:: ../README.rst | ||
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.. toctree:: | ||
:hidden: | ||
:maxdepth: 3 | ||
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Taskpacker is a generic schedule optimization and visualization library for Python. | ||
For instance, below is the optimized schedule of 20 batches of 96 DNA assemblies, | ||
using first-generation cloning technologies: | ||
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.. image:: ../examples/dna_assembly.png | ||
:alt: [logo] | ||
:align: center | ||
:width: 600px | ||
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Such 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 full | ||
with no downtime) and that buying a third oven will increase your factory's | ||
throughput. | ||
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Main features | ||
-------------- | ||
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Taskpacker was built as a toy project to have an easily-extensible scheduling tool in Python. | ||
It is pretty simple and limited (the core code is ~200 lines) but comes with enough features to cover many cases: | ||
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- 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 tasks | ||
can be started) and maximum waiting time (i.e. some tasks must be started at the | ||
latest X minutes after their *parents* are completed) | ||
- Supports pre-scheduled tasks (such as breaks for human operators, scheduled robotic maintenance etc.) | ||
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Work in progress - contribute ! | ||
------------------------------------------ | ||
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Taskpacker is an open-source software originally written by `Zulko <https://github.com/Zulko>`_ to | ||
optimize the robot-operated DNA assembly operations at the Edinburgh Genome Foundry (EGF). at the `Edinburgh Genome Foundry | ||
<http://www.genomefoundry.io>`_. It is released and `released on Github <https://github.com/Edinburgh-Genome-Foundry/Taskpacker>`_ | ||
under the MIT licence (¢ Edinburgh Genome Foundry), with no warranties: this is | ||
an experimental piece of software which we hope will be as useful for you as it was for us. | ||
And everyone is welcome to contribute ! | ||
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Installation | ||
-------------- | ||
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Taskpacker can be installed by unzipping the source code in one directory and using this command: :: | ||
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sudo python setup.py install | ||
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You can also install it directly from the Python Package Index with this command: :: | ||
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sudo pip taskpacker install | ||
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It is probable that you will need some dependencies to build Numberjack. On Ubuntu you can install these with: :: | ||
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sudo apt install libxml2-dev swig | ||
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Basic Example | ||
-------------- | ||
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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, then | ||
assist 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 at | ||
the same time, while Bob can't. | ||
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Here is how you would use Taskpacker to find when they will do each task so as | ||
to finish as early as possible: | ||
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.. code:: python | ||
from taskpacker import Task, Resource, numberjack_scheduler, plot_schedule | ||
alice = 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 spreadsheets | ||
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Assume that you have a process consisting in several tasks, each task depending | ||
on some resources to be available, and possibly on other tasks. Such process can | ||
be summarized in a spreadsheet like this one `this file <https://github.com/Edinburgh-Genome-Foundry/Taskpacker/raw/master/examples/examples_data/dna_assembly.xls>`_, which is loaded in | ||
Taskpacker as follows: | ||
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.. code:: python | ||
from 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: | ||
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.. code:: python | ||
from taskpacker import plot_tasks_dependency_graph | ||
plot_tasks_dependency_graph(process_tasks) | ||
.. image:: _static/images/process_plan.png | ||
:alt: [logo] | ||
:align: center | ||
:width: 600px | ||
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Or simply schedule the tasks: | ||
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.. code:: python | ||
from taskpacker import numberjack_scheduler | ||
scheduled_tasks = numberjack_scheduler(process_tasks) | ||
Throughput estimations | ||
------------------------ | ||
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Given a list of tasks forming a process, you might ask "how many of these processes | ||
can my factory run in a day ?". The following code loads 20 of these processes | ||
and asks Taskpacker to stack them one by one as compactly as possible: | ||
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.. code:: python | ||
from taskpacker import (get_process_from_spreadsheet, | ||
get_resources_from_spreadsheet, | ||
schedule_processes_series, | ||
plot_tasks_dependency_tree, | ||
plot_schedule, Task) | ||
import matplotlib.cm as cm | ||
colors = [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 SCHEDULE | ||
new_processes = schedule_processes_series( | ||
processes, est_process_duration=5000, time_limit=5) | ||
# PLOT THE OPTIMIZED SCHEDULE | ||
all_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:: ../examples/dna_assembly.png | ||
:alt: [dna_assembly.png] | ||
:align: center | ||
:width: 600px | ||
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Note that it is also possible to add scheduled breaks so that your Igor can rest: | ||
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.. code:: python | ||
self | ||
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scheduled_breaks = [ | ||
Task("break_%03d" % i, | ||
resources=[resources["igor"]], | ||
scheduled_resource={resources["igor"]: 1}, | ||
duration=12 * 60, # The break lasts 12H | ||
scheduled_start=24 * 60 * i, # The break happens every 24H | ||
color='white') | ||
for i in range(6) | ||
] | ||
.. toctree:: | ||
:hidden: | ||
:caption: Reference | ||
:maxdepth: 3 | ||
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new_processes = schedule_processes_series( | ||
processes, est_process_duration=5000, time_limit=5, | ||
scheduled_tasks=scheduled_breaks) | ||
ref | ||
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.. image:: ../examples/dna_assembly_with_breaks.png | ||
:alt: [dna_assembly_with_breaks.png] | ||
:align: center | ||
:width: 600px | ||
.. _Zulko: https://github.com/Zulko/ | ||
.. _Github: https://github.com/EdinburghGenomeFoundry/Taskpacker | ||
.. _PyPI: https://pypi.org/project/taskpacker/ | ||
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.. raw:: html | ||
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<a href="https://twitter.com/share" class="twitter-share-button" | ||
data-text="Taskpacker - a schedule optimization library for Python" data-size="large" data-hashtags="Bioprinting">Tweet | ||
data-text="Taskpacker - a schedule optimization library for Python" data-size="large" >Tweet | ||
</a> | ||
<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https'; | ||
if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js'; | ||
fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs'); | ||
</script> | ||
<iframe src="http://ghbtns.com/github-btn.html?user=Edinburgh-Genome-Foundry&repo=Taskpacker&type=watch&count=true&size=large" | ||
allowtransparency="true" frameborder="0" scrolling="0" width="152px" height="30px" margin-bottom="30px"></iframe> | ||
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.. toctree:: | ||
:hidden: | ||
:maxdepth: 3 | ||
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self | ||
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.. toctree:: | ||
:hidden: | ||
:caption: Reference | ||
:maxdepth: 3 | ||
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ref | ||
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.. _Zulko: https://github.com/Zulko/ | ||
.. _Github: https://github.com/EdinburghGenomeFoundry/bandwitch | ||
.. _PYPI: https://pypi.python.org/pypi/bandwitch |
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