smoothn

version 2.3 (9.47 KB) by Damien Garcia

SMOOTHN allows automatized and robust smoothing in arbitrary dimension w/wo missing values

http://www.biomecardio.com

4.9

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Updated 20 Jun 2020

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Editor's Note: This file was selected as MATLAB Central Pick of the Week

SMOOTHN provides a fast, unsupervised and robust discretized spline smoother for data of arbitrary dimension.

SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y can be any N-D multicomponent noisy array (e.g. time series, images, 3D data, 3D vector fields, tensors...).

To smooth a vector field or multi-component data, Y must be a cell array. For example, if you need to smooth a 3-D vectorial flow (Vx,Vy,Vz), use Y = {Vx,Vy,Vz}. The output Z is also a cell array which contains the smoothed components.

SMOOTHN can deal with missing (NaN) values (see screenshot and examples).

SMOOTHN(...,'robust') carries out a robust smoothing that minimizes the influence of outlying data (see screenshot and examples).

SMOOTHN is made unsupervised by the minimization of the generalized cross-validation score.

Enter "help smoothn" in the Matlab command window for complete instructions and 1-D to 3-D examples.

A series of 8 documented examples is available here:
http://www.biomecardio.com/matlab/smoothn_doc.html

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When using this algorithm, please refer to these 2 papers:
1) Garcia D. Robust smoothing of gridded data in one and higher dimensions with missing values.
Comput Statist Data Anal, 2010;54:1167-1178
http://www.biomecardio.com/publis/csda10.pdf
2) Garcia D. A fast all-in-one method for automated post-processing of PIV data.
Exp Fluids, 2011;50:1247-1259.
http://www.biomecardio.com/publis/expfluids11.pdf
-----

Cite As

Damien Garcia (2022). smoothn (https://www.mathworks.com/matlabcentral/fileexchange/25634-smoothn), MATLAB Central File Exchange. Retrieved December 9, 2022.

Garcia, Damien. “Robust Smoothing of Gridded Data in One and Higher Dimensions with Missing Values.” Computational Statistics & Data Analysis, vol. 54, no. 4, Elsevier BV, Apr. 2010, pp. 1167–78, doi:10.1016/j.csda.2009.09.020.

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Garcia, Damien. “A Fast All-in-One Method for Automated Post-Processing of PIV Data.” Experiments in Fluids, vol. 50, no. 5, Springer Science and Business Media LLC, Oct. 2010, pp. 1247–59, doi:10.1007/s00348-010-0985-y.

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MATLAB Release Compatibility

Created with R2017a

Compatible with R2017a to R2020a

Platform Compatibility

Windows macOS Linux

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Acknowledgements

Inspired: PCG_unwrap_2D(ph_nii, ph_file, mask_nii, max_iter, epsi_con, N), Quick & easy smoothing, Robust smoothing for 1-D to N-D data (easy version of SMOOTHN), PIVsuite

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smoothn

Download	20 Jun 2020	2.3	BSXFUN is no longer used (version thus must be >= R2017)
Download	16 Sep 2018	2.2.1	verLessThan('matlab','9.2'); instead of verLessThan('matlab','9.1');
Download	5 Sep 2018	2.2	- fixed bug in isstruct(varargin{end-1}) - no longer use BSXFUN w/ recent Matlab versions
Download	21 Sep 2017	2.1.4.0	updated links
Download	21 Sep 2017	2.1.3.0	updated links
Download	21 Sep 2017	2.1.2.0	updated links
Download	25 Jul 2016	2.1.1.0	.
Download	25 Jul 2016	2.1.0.0	minor updates: 1) use narginchk, 2) two warning messages slightly modified
Download	25 Jul 2016	2.0.0.0	minor update: 1) use narginchk, 2) some warning messages slightly modified
Download	21 Jan 2015	1.37.0.0	I noticed a minor error in one comment
Download	12 Oct 2014	1.36.0.0	.
Download	12 Oct 2014	1.35.0.0	.
Download	12 Oct 2014	1.34.0.0	Some modifications in the help text
Download	10 Feb 2014	1.33.0.0	To simplify the syntax, the termination parameters are now in a structure (OPTIONS).
Download	20 Dec 2013	1.32.0.0	Numerical adjustment in the leverage (for robust smoothing) in the case of unequal spacing increments.
Download	19 Dec 2013	1.31.0.0	SMOOTHN, by default, assumes that the spacing increments are constant and equal in all the directions (i.e. dx = dy = dz = ...). Spacing increments that differ from one direction to the other can now be used (enter "help smoothn" for details).
Download	20 Nov 2013	1.30.0.0	There was a non negligible bug in the new SMOOTHN function: d = ndims(y) => d = ndims(y{1}) Many thanks to Gianni Pedrizzetti.
Download	11 Nov 2013	1.28.0.0	Update of the help text
Download	4 Nov 2013	1.27.0.0	A minor bug in the "Initial Guess" criterion has been removed
Download	11 Sep 2013	1.26.0.0	A bug was present when dealing with complex numbers
Download	27 Aug 2013	1.25.0.0	A small bug was present in the subfunction InitialGuess (Thank you Jonathan!)
Download	27 Jun 2013	1.24.0.0	One bug has been fixed.
Download	31 May 2013	1.23.0.0	SMOOTHN now works with multicomponent data (e.g. 3D vector fields) by using cell arrays. See the help text for details.
Download	30 Apr 2013	1.22.0.0	DCTN and IDCTN are now included in SMOOTHN
Download	11 Apr 2012	1.21.0.0	some typos
Download	25 Nov 2011	1.20.0.0	3 optional weighting functions
Download	23 Nov 2011	1.19.0.0	3 optional weighting functions
Download	8 Nov 2010	1.12.0.0	minor modifications: mlint messages removed
Download	10 Jun 2010	1.11.0.0	Some options have been removed/added
Download	26 Apr 2010	1.10.0.0	fix a minor bug with the warning messages
Download	25 Feb 2010	1.9.0.0	Minor modification in the relaxation factor
Download	27 Jan 2010	1.8.0.0	A minor bug in error(nargchk(...))
Download	19 Jan 2010	1.7.0.0	Gamma was undefined when "isauto" variable was false.
Download	15 Jan 2010	1.6.0.0	The link to the manuscript has been modified.
Download	17 Dec 2009	1.5.0.0	A bug has been reported with a smoothness parameter input. Thank you B.S.
Download	7 Dec 2009	1.3.0.0	Very much faster now with weighted/missing data: 1) SOR 2) Initial guess from nearest-neighbor interpolation 3) GCV score is minimized every 2^k iterations (k = 0...)
Download	17 Nov 2009	1.2.0.0	The "upsample" option has been removed
Download	30 Oct 2009	1.1.0.0	An example has been added
Download	22 Oct 2009	1.0.0.0

MLA	Garcia, Damien. “Robust Smoothing of Gridded Data in One and Higher Dimensions with Missing Values.” Computational Statistics & Data Analysis, vol. 54, no. 4, Elsevier BV, Apr. 2010, pp. 1167–78, doi:10.1016/j.csda.2009.09.020.
APA	Garcia, D. (2010). Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), 1167–1178. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.csda.2009.09.020
BibTeX	@article{Garcia_2010, doi = {10.1016/j.csda.2009.09.020}, url = {https://doi.org/10.1016%2Fj.csda.2009.09.020}, year = 2010, month = {apr}, publisher = {Elsevier {BV}}, volume = {54}, number = {4}, pages = {1167--1178}, author = {Damien Garcia}, title = {Robust smoothing of gridded data in one and higher dimensions with missing values}, journal = {Computational Statistics {\&} Data Analysis} }

MLA	Garcia, Damien. “A Fast All-in-One Method for Automated Post-Processing of PIV Data.” Experiments in Fluids, vol. 50, no. 5, Springer Science and Business Media LLC, Oct. 2010, pp. 1247–59, doi:10.1007/s00348-010-0985-y.
APA	Garcia, D. (2010). A fast all-in-one method for automated post-processing of PIV data. Experiments in Fluids, 50(5), 1247–1259. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs00348-010-0985-y
BibTeX	@article{Garcia_2010, doi = {10.1007/s00348-010-0985-y}, url = {https://doi.org/10.1007%2Fs00348-010-0985-y}, year = 2010, month = {oct}, publisher = {Springer Science and Business Media {LLC}}, volume = {50}, number = {5}, pages = {1247--1259}, author = {Damien Garcia}, title = {A fast all-in-one method for automated post-processing of {PIV} data}, journal = {Experiments in Fluids} }