nsim 0.1.10

Simulate systems from ODEs or SDEs, analyze timeseries.

Simulate systems from ODEs or SDEs, analyze timeseries.

N.B. this is a pre-release: still a lot left to be done

Overview

nsim is for systems in physics, biology and finance that are modelled in continuous time with differential equations. nsim makes it easy to define and simulate these (including proper treatment of noise in SDEs) and to analyze the properties of the resulting timeseries.

• Automatic parallel computing / cluster computing: For multiple or repeated simulations, nsim distributes these across a cluster (or across the CPUs of one computer) without needing to do any parallel programming.

  (First start an IPython cluster e.g. by typing ipcluster start)

• Model parameters can optionally be specified as random distributions, instead of fixed values, to create multiple non-identical simulations.

• nsim provides a Timeseries class. This is a numpy array.

  It allows slicing the array by time instead of by array index, and can keep track of channel names (or variable names) of a multivariate time series.
• As well as the usual methods of numpy arrays, the Timeseries objects have extra methods for easy filtering, plotting and analysis. Analyses can be chained together in a pipeline. This can easily be extended with your own analysis functions by calling Timeseries.add_analyses()

  Analyses of multiple time series are distributed on the cluster, without needing to do any parallel programming.

• Besides simulations, arrays of time series data can be loaded from MATLAB .mat files or .EDF files for distributed analysis.

• For best results use numpy 1.11.0 or later (not yet released!) this enables us to support distributed computation when analysing the resulting time series. You can get a development snapshot of numpy here: https://github.com/numpy/numpy/archive/master.zip

TODO

• Write statistical analyses applying to ensembles of repeated SDE simulations

• Directly support SDEs expressed in Ito form. (Currently need to write it in Stratonovich form as an intermediate step before simulating in nsim)

• Add support for models with time delays (DDEs and delay SDEs)

• Support network models of dynamical nodes, auto-generated from models of node dynamics and a network graph structure. (use shared memory and multiple CPU cores on each cluster host for simulation of network models, splitting degrees of freedom evenly across CPUs).

• Auto-generate multiple simulations covering a lattice of points in parameter space, to run in parallel.

• Optionally allow the equations to be specified and integrated in C, for speed

Thanks

Incorporates extra time series analyses from Forrest Sheng Bao’s pyeeg http://fsbao.net

IPython parallel computing, see: http://ipython.org/ipython-doc/dev/parallel/