ciaranwelsh / pycotools Goto Github PK

A Python Toolbox for COPASI

Python 26.25% Jupyter Notebook 37.93% Component Pascal 34.89% Makefile 0.01% CSS 0.18% JavaScript 0.67% HTML 0.07% Batchfile 0.01%

copasi ode modelling-biological-systems systems-biology parameter-estimation

pycotools's Introduction

The code in this repo supports Python 2.7. For PyCoTools that works on Python 3 please see this repo

PyCoTools

A set of tools for interacting with COPASI simulation software from Python.

Version Notice

The code in this repo supports Python 2.7. For PyCoTools that works on Python 3 please see this repo. Note that while the code in this repository is stable, it is no longer maintained and users are recommended to use the Python 3 version.

Tutorials and Documentation

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python3pkg altingia bilgan thompsondd

pycotools's Issues

No tasks.MultiModelFit PycoTools in version pycotools3-2.1.7 or pycotools3-2.1.10

Dear Ciaran,

I tried to run example 1.3.3 from the supplementary material, I got the AttributeError: module 'pycotools3.tasks' has no attribute 'MultiModelFit'
All examples up to that point as well as running time series simulations with some models of my own worked fine. Any idea what might be the problem? Was this function removed or not available in pycotools3-2.1.7 or pycotools3-2.1.10 or maybe renamed?
I also noted tasks.MultiModelFit functions is not described or mentioned in the documentation at htps://pycotools3.readthedocs.io

Regards,

Niels Zondervan

Support for ODE based models

Hi there,

I'm currently trying to run a time-course simulation with a COPASI model based on ODEs (not reactions). However, I'm always getting an error
"InputError: type should be either fixed or assignment. ODE not supported as Reactions can be used."

Is there any possibility to do this with the current PyCoTools release? Or is an implementation of time course simulations with ODE models planned for the future?

Thanks.

Profile likelihoods colouring

Colour profile likelihoods by RSS

Tasks as model.Model methods

It would be better if there were methods from the model.Model class to access tasks such as timecourse, parameter estimation and profile likelihoods.

viz PlotTimeCourse

I am running the "Quick start" model in my jupyter lab notebook. I can simulate the model. When I visualized the results by viz.PlotTimeCourse(TC, separate=False), I had an "AttributeError" as "'PlotTimeCourse' object has no attribute 'plot_kwargs'".
How could I fix this to visualize the results?

How to format Steady State data for parameter estimation using PycoTools

How to format and load steady state data and use it for (multiple) parameter estimations using PycoTools?
I would ask the question on Stack Overflow but do not have enough reputation to create the tag 'pycotools'.
In Copasi when I load such a file data each column is a metabolite and after loading I can specify what model object it refers to, e.g. dependent concentration, independent concentration, reaction flux or ignored. See example below. How to do this for PycoTools?

Implement more useful means of controlling parameter estimation setup without manually configuring config file

Implement dictionary argument for parameter estimation for specifying boundaries of individual parameters and affected experiments etc.

Tutorials and Documentation

Link for "Tutorials and Documentation" on ReadMe.md is broken.

Python 3.7 or 3.8 support?

Hi,

I was intending to use PyCoTools for a couple of simulations. However, the last supported Python version seems to be 3.6...
So, I was wondering: Are there big changes to be made to have PyCoTools running in 3.7, or where's the problem exactly?
Actually, I just need a wrapper around CopasiSE, whihc adapts some of the Copasi tasks (just time courses)... What would be the best qay to achieve this?

Best,
Paul

Pycotools using deprecated sklearn rather than successor scikit-learn

Using Python 2.10.13 on Windows.

PS> pip install pycotools
...
Collecting sklearn (from pycotools)
  Downloading sklearn-0.0.post11.tar.gz (3.6 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [18 lines of output]
      The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
      rather than 'sklearn' for pip commands.

      Here is how to fix this error in the main use cases:
      - use 'pip install scikit-learn' rather than 'pip install sklearn'
      - replace 'sklearn' by 'scikit-learn' in your pip requirements files
        (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
      - if the 'sklearn' package is used by one of your dependencies,
        it would be great if you take some time to track which package uses
        'sklearn' instead of 'scikit-learn' and report it to their issue tracker
      - as a last resort, set the environment variable
        SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error

      More information is available at
      https://github.com/scikit-learn/sklearn-pypi-package

      If the previous advice does not cover your use case, feel free to report it at
      https://github.com/scikit-learn/sklearn-pypi-package/issues/new
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

Feature Requests

Add a new InsertParameters class that works on the Copasi version 19 release. This is the only difference between the new and old version - that I know of.

Ensure other features of PyCoTools work with Copasi 19

Documentation should be updated. Make sure defaults are available for every kwarg. Include tables for kwargs that do the same thing.

Modify camel case to lower case where necessary. Be consistent with python conventions.

Change PEAnalysis to support seaborn, rather than just using bare matplotlib.

Create a PCA class in PEAnalysis

Need interface to the plotting features of PEAnalysis from multi-model and model selection stuff

Support for bifurcation analysis