# Bumps 0.9.1 documentation

## Simple functions

Bumps allows fits with varying levels of complexity. Simple fits accept a function \(f(x;p)\) and data \(x,y,\sigma_y\), where vector \(y\) is the value measured in conditions \(x\), and \(\sigma_y\) is the \(1-\sigma\) uncertainty in the measurement. Bumps also provides a simple wrapper for poisson data taken from counting statistics, with function \(f(x;p)\) and data \(x,y\). sim.py is a simulation of data from a poisson process, showing maximum likelihood, expected value and variance.

The ode2 example shows how to fit a system of coupled differential equations where multiple values are tracked at each time step.

- Fitting a curve
- Fitting Poisson data
- Poisson simulation
- Fitting an ODE
- Fitting a multi-valued function