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
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© Copyright 2017, Ciaran Welsh.

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