silvio is an environment for Simulation of Virtual Organisms. silvio contains several linked microbial models.
Project description
Silvio
silvio is an environment to combine microbiological models to simulate virtual cells.
Authors: Ulf Liebal, Lars Blank
Contact: ulf.liebal@rwth-aachen.de
Licence: MIT Licence
Free software: MIT license
Documentation: https://silvio.readthedocs.io
Features
Simulate different components of a biological host using event-driven messaging.
Produce data on procedures performed on the host at the given state.
Writing a Module
Prefer the use of [standardized code style](https://pep8.org/).
Make use of [python type hints](https://docs.python.org/3/library/typing.html) whenever possible. When specifying types for variables and methods, your IDE will help you with organizing the inputs, outputs and arguments that you may use.
# Initial definition of a variable to store a probability
some_probability: float = 0
some_probability = 0.4 # Will work. The variable may receive fractional numbers.
some_probability = 0 # Will work. Integers are also numbers.
some_probability = "a lot" # Error! The IDE will notify us about this bad assignment.
some_probability = "0.3" # Error! This is still a string. No more conversion problems.
some_probability = -1.4 # Unfortunately this still works. Typing only defines simple types.
When writing classes, keep all properties (variables inside a class) at the top of the class definition, outside of the constructor. The constructor should only perform the initial assignment.
class BayesianNetworkNode :
"""
Each class should document what it does. Ideally, it should have a single purpose.
"""
# Probability that this node is true.
true_prob: float
# Probability that the node is false. Should be inverse of true probability.
false_prob: float
def __init__ ( self, true_prob: float ) :
# Notice that constructor arguments may have the same name as properties.
self.true_prob = true_prob
# The constructor only uses necessary arguments to initialize all properties.
self.false_prob = 1 - true_prob
How to name things is a very debated topic in many languages. When in doubt, follow the conventions that have been laid by the [python standard](https://www.python.org/dev/peps/pep-0008/#naming-conventions). Some common examples are.
# Use lower_case with underscores. Prefer distinct names to single letters.
num_strands = 2
# Constants are values embedded into the code. Use UPPER_CASE with underscores.
GOLDEN_RATIO = 1.6180
# Module names use lower_case and avoids underscore when possible.
import biolabsim.sequencing.evaluation
# Custom types use PascalCase.
from typing import Tuple, Literal
GeneBase = Literal['A','T','C','G']
# Functions use lower_case and typically start with a verb.
def complement_base ( base:GeneBase ) -> GeneBase : # (input) -> output
# Include most initilization on top of the method.
orig_bases: List[GeneBase] = ['A','T','C','G'] # Common words may be shortened. orig = original
comp_bases: List[GeneBase] = ['T','A','G','C'] # But spell it out in comments. comp = complementary
# Split your code into blocks of related operations. Provide a small summary of each block.
# Comments should help outsiders to skim through the code and to explain programming decisions.
found_orig_index = orig_bases.index(base) # Avoid one-liners. Variable names provide context.
return comp_bases[found_orig_index]
# Use simple types to construct more complex ones.
Codon = Tuple[ GeneBase, GeneBase, GeneBase ]
# Classes use PascalCase as well.
class AminoAcid :
# Class properties use lower_case as well.
gene_triplet : Codon
# Constructors initialize the properties.
def __init__ ( self, base1:GeneBase, base2:GeneBase, base3:GeneBase ) :
self.gene_triplet = ( base1, base2, base3 )
# Leave enough space between method definitions.
def complement_triplet (self) -> Codon :
return ( # Use multiple lines and more spacing if the
complement_base( self.gene_triplet[0] ), # code becomes too bulky.
complement_base( self.gene_triplet[1] ),
complement_base( self.gene_triplet[2] ),
)
Generate Sphinx documentation.
Sphinx is not very automatic on how documentation is extracted from the code. We use [sphinx-apidoc](https://www.sphinx-doc.org/en/master/man/sphinx-apidoc.html) to periodically generate the documentation .rst files.
# Assuming you start at the project root directory.
# Enter the documentation directory.
cd docs
# Remove the old API documentation.
rm -ri ./api
# Generate the new reStructuredText files for the API documentation.
sphinx-apidoc --module-first -d 4 -o api ../biolabsim
# Generate the HTML from all documentation files.
make html
Credits
Extensive credits can be found in the author notes.
History
0.1.0 (2021-10-17)
First release on PyPI.
0.1.4 (2022-04-07)
add catalog with RecExpSim functions in src
0.1.5 (2022-04-07)
add __init__.py to catalog folder
0.1.6 (2022-04-07)
in RecExperiment: round print failure rate to two decimals
in RecExperiment.simulate_growth: separate argument progress bar waiting
0.1.7 (2022-05-03)
remove requirement cobra
0.1.8 (2022-05-03)
remove cobra code dependencies
0.1.8 (2022-05-03)
add cobra code dependencies
remove undelete_gene
0.2.0 (2023-03-29)
add GroExpSim, a class to simulate growth experiments
0.2.1 (2023-08-20)
add storage of simulated data to Data folder
0.2.2 (2023-09-02)
- GroExpSim with:
measure_DryWeight: measure the OD to DW conversion factor
measure_TemperatureGrowth: measure the growth curve at different temperatures
measure_BiomassSubstrateExp: measure the growth curve and substrate concentrations
check_Results: check the results of the parameters
0.2.2 (2023-09-02)
GroExpSim, nightshift must be within 15h of experiment
0.2.5 (2024-02-22)
GroExpSim, export single growth experiments to existing reference excel sheet
0.2.6 (2024-04-23)
RecExpSim, add umax argument to ‘make’ in ‘RecHost’ for new argument demands of function ‘Make_TempGrowthExp’ in ‘extesions/modules/growth_behaviour.py’
Project details
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