Welcome to pythonLab’s documentation!¶
Project Version¶
0.2.3
This is the specification and development repository of pythonLab, a universal, extendable and safe language for laboratory processes.
Reproduciblity and reliability is in the core of science. Describing lab operations in an precise, easy and standardised form will help to transfer knowledge in every lab around the world. A laboratory process description language is a long desired goal in laboratory process standardisation and lab automation. It will enable automated execution of steps and better machine learning and AI predictions, since these process descriptions can be packed as metadata to the data resulting form this process.
Since this process language needs many charcateristics of a programming language, like conditions (if …), loops (for/while), variables, etc. we do not want to re-invent the weel twice but rather use the python syntax, which is very popular in science.
Key (desired) Features¶
easy and simple to learn and write (close to simple English)
clear, human readable syntax
universal - applicable for most laboratory operations
transferable from one lab to another
easy mapping between abstract resource representation and actual lab resource
*Turing-complete*, including conditions and loops
easy extendible - prepared for the constant development of science
close to real laboratory work
vendor independant
safe to execute
converter from other lab description languages to pythonLab easy to implement
Applications of pythonLab¶
general lab processes, common in any natural sciences lab (very braod application)
description of lab automation workflows
workflows on the lab devices (e.g. HPLC processes - sometimes also called ‘methods’, plate reader processes etc.)
data evalution workflows
Architecture of pythonLab¶
pythonLab processes are denoted in a python like syntax, but they are not directly executed by a python interpreter. They are rather parsed into a workflow graph, which can be used by a Scheduler to calculate an optimal schedule (=order of execution). This order of execution might be different from the initial notation. An Orchestrator executes then the schedule and supervises the device communitation, e.g. to SiLA servers/devices.
pythonLab Architectore¶
Specification¶
Please find a draft of the pythonLab specification in docs/specification (very early stage !).
Very briefly, the generic lab description language should have many features a common programming language has and following the desired Turning-completeness, like:
variables (x = value)
conditions (if, else, …)
loops (for … while ….)
functions / methods and subroutines
modules
namespaces and versions for unique addressing of a process step
(at a later stage of language development: object orientation)
!! This is a proposal - we would like to discuss it with a wide range of scientist to find the best common ground
Documentation¶
The pythonLab Documentation can be found in docs
Language Core extensions¶
extensible Modules for e.g. - liquid handling - cultivation - (bio-chemical) assays - molecular biology - chemical synthesis - data evaluation
are in preparation
Examples¶
A simple description of liquid transfer step
# using settings: volume unit: uL, liquid class: water
# these are set in a settings module
# specifying resources
from pythonlab.resource import LabwareResource, DeviceResource
from pythonlab.liquid_handling import aspirate, dispense
cont1 = LabwareResource()
cont2 = LabwareResource()
liquid_handler = DeviceResource()
# process steps
liquid_handler.aspirate(cont1, row=1, col=3, vol=4.0)
liquid_handler.dispense(cont2, row=2, col=3 , vol=7.2)
...
A bit more complex example
# default units (SI) are specified in the standard unit module
# additional unit definitions can be added in the code
# specifying resources
from pythonlab.resource.labware import LabwareResource
from pythonlab.resource.services import MoverServiceResource, IncubationServiceResource
cont1 = LabwareResource()
mover = MoverServiceResource()
incubator = IncubationServiceResource()
start_pos = cont1.set_start_position(pos=1)
incubation_duration = 6 # hours
# initialise the process
incubator.init()
# process steps
mover.move(cont1, start_pos, incubator.nest1)
incubator.incubate(cont1, incubation_duration, unit="h")
mover.move(cont1, incubator.nest1, start_pos)
...
And finally a higher level example
# default units (SI) are specified in the standard unit module
# additional unit definitions can be added in the code
# specifying resources
from pythonlab.resource.labware import LabwareResource
from pythonlab.resource.services import MoverServiceResource, DispensionServiceResource, IncubationServiceResource
from pythonlab.processes.base import incubate, centrifugate
from pythonlab.bioprocess import inoculate
Labware_set = [LabwareResource(name=f"growth_plate_{cont}")
for cont in range(8)]
dispenser = DispensionServiceResource()
incubator = IncubationServiceResource()
inoculate([dispenser, Labware_set], source="starting_culture")
incubate([incubator, Labware_set], temp=310.0, shaking=(700,2) ) # temp in K
centrifugate([incubator, Labware_set], duration=600, force=4500)
...
Why python ?¶
Python is a programming language that is very common in modern scientific laboratories and covers all the desired characteristics we expect of a user-friendly lab process programming language.
The syntax is very simple, and intuitive to learn. Syntax validation comes for free: the python interpreter already does it.
Standardisation of a minimal set of functionalty will be achieved by standardised packages provided by this site (or any publically available site). Defined namespaces and versioning allow unique addressing of a process step.
Implementation¶
As a proof-of-concept we are planning to provide a pypy-sandbox implementation in the future. pypy-sandbox offeres a safe execution environment to execute insecure code. A new version is currently developed to by the pypy community. Alternatively WASM will be a possible safe execution environment.
