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 or macOS
      • On Windows
    • Python
      • Python3 Users
    • IDE
    • Pip
    • Pycotools
  • The Model
    • The Zi2012 Model
    • 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
    • Build a new model: The Build Context Manager
    • Build With Antimony
  • Build Models With Antimony
    • A Negative Feedback Motif
      • Simulate from Negative Feedback Model
    • Positive Plus Negative Feedbck Loop
      • Simulate
  • 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
      • Stochastic MM
    • Run a Time Course Using Direct Method
    • Plot stochastic time course
  • Parameter Scan
    • Get Model String
    • Parse Model
    • Parameter Scan
    • Two Way Parameter Scan
    • Repeat Scan Items
  • Parameter Estimation Tutorial
    • Build a Model
    • Experimental Data Files
    • Simulate Experimental Data
    • Format the Simulated Data
    • Setup and run single parameter estimation
      • Use Particle Swarm
    • Use Genetic Algorithm
    • Write Parameter Estimation Configuration File
    • Configure Parameter Estimation
    • Set up the Parameter Estimation
    • Run Parameter Estimation
      • Check Parameter Estimation Report Exists
    • Visualize Parameter Estimaton Data
      • Save to File
      • Choose Results Directory
      • Specify Which Variables to Plot
    • Multiple Data Files
      • Generate Multiple Synthetic Data Files
      • Setup and Run Parameter Estimation
      • Plot the Results
    • Steady State Experiments
      • Setup and Run Parameter Estimation
  • 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
    • Distributions of parameters
      • Boxplots
      • Histograms
    • Correlations
      • Pearsons Correlations
    • Scatters
    • Time course Ensemble
    • Profile Likelihoods
      • Run Local Chaser Estimation
      • Run Profile Likelihoods
      • Plot Profile Likelihoods
        • Interpretation
    • Summary
      • 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
  • Model Selection Workflow
    • Simulate the Models
    • Simulate Synthetic Data
    • Perform Model Selection
    • Calculate and Plot Model Selection Criteria
    • Best Parameters
    • Interpretation
    • Subsequent Analysis
      • Ensemble Time Courses
      • Likelihood ranks
      • Boxplots
      • Correlations
      • Scatter Graphs
    • Profile Likelihoods
      • Calculation
    • Visualization
  • Examples
    • The Lorenz attractor system
    • Visualization
      • Time on x axis
      • Phase Space Plots
    • The Lotka Volterra System
    • Visualization
    • Repressilator
      • Define the Model
      • Simulate
  • API Documentation
    • model
    • tasks
    • Viz
      • kwargs
        • Kwargs for plotting
        • savefig kwargs
        • truncate-kwargs
  • Caveats
    • Non-Ascii Characters
    • Parameter Estimation
  • Duplicate Names
  • Known Bugs
Pycotools
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© Copyright 2017, Ciaran Welsh.

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