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Manuscript Title: Spatial Spectrum Analyzer (SSA): a tool for calculations of spatial distribution of fast Fourier transform spectrum from Object Oriented Micromagnetic Framework output data
Authors: Marek Frankowski, Jakub Chęciński, Maciej Czapkiewicz
Program title: Spatial Spectrum Analyzer (SSA)
Catalogue identifier: AEUU_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 189(2015)207
Programming language: Bourne Again SHell (Bash), MATLAB.
Computer: Any computer with MATLAB and Bourne Again SHell (Bash) installed.
Operating system: Any system with MATLAB and Bourne Again SHell (Bash) installed.
Keywords: Micromagnetics, Fast Fourier transform, Local modes, Spatial distribution, Object Oriented Micromagnetic Framework.
Classification: 9.

External routines: MATLAB Parallel Computing Toolbox

Nature of problem:
Numerous dynamic problems of ferromagnetic structures can be investigated by micromagnetic simulations using The Object Oriented MicroMagnetic Framework (OOMMF). However, large amounts of OOMMF output data (typically magnetization configuration files take up several gigabytes for few nanoseconds simulation, depending on the structure size) are difficult to process because of being stored in separate files for each simulation step. In particular, for the Fourier Transform (FT) purposes data from all time steps in a single point is needed, but instead the standard output provided by OOMMF and other popular micromagnetic software contains data from all points in a single time step. What is more, calculations of FT for each simulation cell are usually a demanding task. The total time for sequential analysis can exceed the simulation time itself up to several times.

Solution method:
The SSA tool reorganizes the simulation data into separate files, which describe a single simulation point each and contain values from all time steps . Afterwards, reorganized files are used to compute Fast Fourier Transform for a chosen frequency. The final result is a spectral density map of a sample at a given frequency. Both parts of the SSA tool make use of parallel computing, greatly decreasing the total time needed to process the data.

Running time:
Strongly depends on sample size and time span. Time for the example described in this paper varies from single hours to few days depending on the number of involved processes.