*This was published a while ago on Github.*

Langevin integrator for SDEs with constant drift and diffusion on continuous intervals with circular boundary conditions.

CILES is written in Cython and uses GSL for interpolation of drift & diffusion fields, to be able to simulate continuous variables.

### Description

Given a discretized drift field A(x) and a (position dependent) diffusion coefficient B(x) this tool performs simple time-forward integration of the SDE:

1 |
dx(t)/dt = A(x(t)) + sqrt(B(x(t))) * eta(t) |

where eta(t) is a gaussian white noise term and x is a variable on an interval with circular boundaries (commonly 0 <= x < 2PI).

Both drift field A and diffusion B need to be arrays of the same dimension. They are internally interpolated (using`gsl_interp_cspline_periodic`

) to provide continuous fields, which are then used in the forward integration.

Forward integration is performed with the Euler-Murayama scheme

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x(t+dt) = x(t) + dt * A(x(t)) + r * sqrt(dt * B(x(t))) |

where r is a normally distributed random number with zero mean and unit variance.

### Dependencies

- Numpy
- Cython
- Cython-gsl

### Installation

To install ciles in your Python distribution: – Clone repository – `python setup.py install`

– To test (using `nosetests`

): `nosetests`

You can also use ciles locally without installing: – Clone repository – `python setup.py build_ext --inplace`

### Example use

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from ciles.integrator import LangevinIntegrator as LI import numpy as np drift = np.zeros(100) # no drift field diff = np.ones(100) # constant diffusion with 1 deg^2/s dt = 1e-3 # 1 ms timestep tmax = 1. # simulate until 1s # initialize the integrator li = LI(drift, diff, dt=dt, tmax=tmax) # simulate a single trajectory li.run(1) out = li.out |

### More examples

Below are the plot results of the currently available examples from ciles.examples.

#### Final distributions after 2s diffusion

See the source

#### Trajectories for drift-field with 2 fixed points

See the source

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