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
The Model
Get Zi2012 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
Two 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
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
Setup parameter estimation with two data files
Run parameter estimation with two data files
Visualization
Steady State data
Create fake steady state data
Setup parameter estimation with steady state
Parameter Estimation Workflow
Build Example Model
Simulate Time Course
Generate Synthetic Data
Format synthetic data
Run parameter estimations
Exploratory data analysis on parameter estimation data
Evaluate the performance of the optimization algorithm
Likelihood-Ranks Plot
PCA
Distributions of parameters
Boxplots
Histograms
Correlations
Pearsons Correlations
Scatters
Time course Ensemble
Profile Likelihoods
Run Local Chaser Estimation
Run Profile Likelihoods
Plot Profile Likelihoods
Summary for Round 1
Optimization performance
Trajectories
Distributions
Correlations
Profile Likelihoods
Modifications for Fit2
Best parameters Versus True Parameters
Insert Parameters
Build Example Model
Insert Parameters from Python Dictionary
Insert Parameters from Pandas DataFrame
Insert Parameters from Parameter Estimation Output
Insert Parameters using the
model.Model().insert_parameters
method
Change parameters using
model.Model().set
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
Known Bugs
Pycotools
Docs
»
Pycotools’s Documentation
View page source
Pycotools’s Documentation
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QuickStart
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Quick Start
Tutorials
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Tutorial 0: Installation and Configuration
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Installation, dependencies, versions, environment variables
Tutorial 1: The Model
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The Model
Tutorial 2: Time Course
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TimeCourse
Tutorial 3: Scan
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Parameter Scan
Tutorial 4: Single Parameter Estimations
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Parameter Estimation
Tutorial 5: Parameter Estimation Workflow
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Parameter Estimation Workflow
Tutorial 6
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Insert Parameters
Examples
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Examples
API
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API Documentation
Caveats
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Caveats
Units
Assignments
Duplicate Names
Known Bugs
Indices and tables
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Index
Module Index
Search Page