Pycotools
0.0
Quick Start
Create Michaelis-Menten Model
Simulate Michaelis-Menten model
Plot results
Inspect the model
Prepare Time Course Results for Parameter Estimation
Do parameter estimations
Visualize data
Profile Likelihoods
Best Parameters
Installation, dependencies, versions, environment variables
COPASI
COPASI Environment Variables
On Linux
On Windows
Python
Python3 Users
IDE
Pip
Pycotools
Get Zi Model for this Example
Parse a Model into Pycotools
Open and Save
Get Model Information
Model attributes
Access Model Attributes
Get, Set, Add and Remove
How to get model objects
Get the Smad3c metabolite
Get any global quantity with a fixed simulation_type attribute
Get a function by its expression
Get all local parameters in the R17_LRC_formation reaction
How to change existing model attributes
Change the name of a metabolite
Change initial_value of a global_quantity
How to add a model component
Add a metabolite
Add a global quantity to the model
Add a reaction
Remove model components
TimeCourse
Imports and Getting the Test Model
Get Model Object
Deterministic Time Course
Run a deterministic time course
Save time course configured model
Being selective about which output variables to select
Visualization
Plot the results
Plot on the same axis
Choose Y variables
Plot in Phase Space
Save to file
Alternative Solvers
Parameter Scan
Get Model String
Parse Model
Parameter Scan
2 Way Parameter Scan
Repeat Scan Items
Parameter Estimation
Imports and Getting Test Model
Simulate Synthetic Data for Demonstration
Parameter Estimation Data Files
Setup and run single parameter estimation
Use HookeJeeves algorithm
Use particle swarm algorithm
Use genetic algorithm
Write and configure item template
Configure manually
Configure with API
Setup and run parameter estimation
Run with CopasiSE
Visualization
Plot all estimated parameters
Save to file (default options)
Save to file (user specified options)
Select specific variables to plot
Multiple Data Files
Pre-requisites
Change a parameter value
Simulate second time course
Format the second synthetic time course data
Add independent variable
Setup parameter estimation with two data files
Run parameter estimation with two data files
Visualization
Steady State data
Setup parameter estimation with steady state
Multiple Parameter Estimations
Pre-requisites
Get the Demonstration model
Generate Synthetic Data
Format synthetic data
The MultipleParameterEstimation Class
Custom Results Directory
Visualization
Time course ensembles
Boxplots
Histograms
Table of Contents
Vizualization
Imports and Get Model
Simulate parameter estimation data to work with
Format PE Data
RSS Versus Iteration
Visualize distributions with boxplots and histograms
Time Course Ensemble
PCA
Identify similar parameter sets
Identify parameters with similar variance with PCA
InsertParameters
From Pandas
From File
From Folder of Parameter Estimation Files
Examples
The Lorenz attractor system
Visualization
Time on x axis
Phase Space Plots
The Lotka Volterra System
Visualization
API Documentation
model
tasks
Viz
kwargs
Kwargs for plotting
savefig kwargs
truncate-kwargs
Caveats
Non-Ascii Characters
Parameter Estimation
Units
Assignments
Duplicate Names
Pycotools
Docs
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