Examples

We provide a collection of example notebooks to get a better idea of how to use pyPESTO, and illustrate core features.

The notebooks can be run locally with an installation of jupyter (pip install jupyter), or online on Google Colab or nbviewer, following the links at the top of each notebook. At least an installation of pyPESTO is required, which can be performed by

# install if not done yet
!pip install pypesto --quiet

Potentially, further dependencies may be required.

Getting started

• pyPESTO: Getting started
  • 1. Objective Definition
  • 2. Optimizer Choice
  • 3. Fitting of large scale models
  • 4. Uncertainty quantification
  • 5. Storage
  • Software Development Standards:
  • Further topics
• Custom Objective Function
  • 1. Objective + Problem Definition
  • 2. Optimization
  • 3. Profiling

PEtab and AMICI

• AMICI in pyPESTO
  • 1. Create a pyPESTO problem
  • 2. Optimization
  • 3. Optimization visualization
  • 4. Uncertainty quantification
  • 5. Saving results
• Model import using the PEtab format
  • Import
  • Run optimization
  • Visualize

Algorithms and features

• Fixed parameters
  • Define the two problems
  • Optimize
  • Visualize
• Prior definition
  • Example: Rosenbrock Banana
• A sampler study
  • The pipeline
  • 1-dim test problem
  • 2-dim test problem: Rosenbrock banana
• MCMC sampling diagnostics
  • The pipeline
• Result Storage
  • Imports
  • Optimization
  • Profiling
  • Sampling
  • Optimization
  • Profiling
  • Sampling
  • Memory History
  • CSV History
  • HDF5 History
• Optimizer Convergence and Comparison
  • Imports and constants
  • Creating optimization results
  • Optimizer convergence
  • Reloading from History
• Model Selection
  • Example Model
  • Model Space Specifications File
  • Forward Selection, Multiple Searches
  • Backward Selection, Custom Initial Model
  • Additional Options
  • Multistart
• Julia objectives
  • Comments
  • Inference problem
  • Optimization
  • Sampling
• Hierarchical optimization with relative data
  • Note on inclusion of additional data types:
• Parameter estimation using ordinal data
  • Import model from the petab_problem
  • Construct the objective and pypesto problem
  • Run optimization using optimal scaling approach
  • Compare results
• Parameter estimation using censored data
  • Import model from the petab_problem
  • Construct the objective and pypesto problem
  • Run optimization using optimal scaling approach
  • Visualizing the result
• Parameter estimation using non-linear semi-quantitative data
  • Problem specification & importing model from the petab_problem
  • Constructing the objective and pypesto problem
  • Running optimization using spline approximation
  • Caution when using minimal difference
• RoadRunner in pyPESTO
  • Creating pyPESTO problem from PEtab
  • Optimization

Application examples

• Conversion reaction
  • Compile AMICI model
  • Load AMICI model
  • Optimize
  • Visualize
  • Profiles
  • Sampling
  • Predict
  • Analyze parameter ensembles
• Optimization with Synthetic Data
  • Requirements
  • Standard Optimization
  • Synthetic Optimization