Welcome to folie’s documentation!

folie (Finding Optimal Langevin Inferred Equations) intends to provide a simple to use module for inference of Langevin equations from trajectories of collectives variables. Please refer to the documentation for a more complete description.

Installation

Installing the library is as simple as running

pip install git+https://github.com/langevinmodel/folie.git

If you also want to install optionnal dependencies, run for the torch dependency

pip install git+https://github.com/langevinmodel/folie.git[deep-learning]

or

pip install git+https://github.com/langevinmodel/folie.git[finite-element]

to install dependencies for the finite element part of the library.

Getting Started

The general pattern of using folie is defining a model, loading trajectories data, fit the model and analyse the resulting model.

Table of contents

Documentation

• Welcome to folie’s documentation!
• How-to guides
  • Estimation
  • Analysis
• For developper
  • Input of trajectories data
  • Building an estimator of Langevin dynamics
  • Model of Langevin Dynamics
  • Documenting the code

API docs

• Data loading
  • Loading trajectories
• Models of Langevin dynamics
  • Available models of Langevin equation
• Spatial dependence
  • Parameteric Functions
  • Non Parametric Functions
• Estimation
  • Direct estimation
  • Likelihood estimation
• Analysis
  • Overdamped 1D models

Indices and tables

• Index

• Module Index

• Search Page