**-c**- Try to find optimal parameters for filtering
**-u**- Fit the result curve by upper boundary
**--overwrite**- Allow output files to overwrite existing files
**--help**- Print usage summary
**--verbose**- Verbose module output
**--quiet**- Quiet module output
**--ui**- Force launching GUI dialog

**input**=*string[,**string*,...]**[required]**- Raster names of equally spaced time series
**result_prefix**=*string***[required]**- Prefix for raster names of filtered X(t)
**method**=*string*- Used method
- Default:
*savgol* **winsize**=*integer*- Length of running window for the filter
- Default:
*9* **order**=*integer*- Order of the Savitzky-Golay filter
- Default:
*2* **opt_points**=*integer*- Count of random points used for parameter optimization
- Default:
*50* **diff_penalty**=*float*- Penalty for difference between original and filtered signals
- Default:
*1.0* **deriv_penalty**=*float*- Penalty for big derivates of the filtered signal
- Default:
*1.0* **iterations**=*integer*- Number of iterations
- Default:
*1*

*-c*: Find optimal parameters of used filter. The function to
optimize depends on difference between original and filtered signals and
on derivates of the filtered signal.

*-u*: Filter using upper boundary of the signal values (Useful for
vegetation indexes filtering).

*input*: Raster names of equally spaced time series *X*.

*result_prefix*: Prefix for raster names of filterd *X*.

*method*: Filtering method. Implemented filters are Savitzky-Golay
filter *savgol* and median filter *median*.

*winsize*: The length of the filter window. *winsize* must
be a positive odd integer.

*order*: The order of the polynomial used to fit the samples. The
*order* must be less than *winsize* (Savitzky-Golay only).

*iterations*: Number of filtering iterations.

*opt_points*: If *-c* is specifed, then random sample
*opt_points* and use them in parameter optimization.

*diff_penalty*: Penalty for difference between original and
filtered signals (see Notes).

*deriv_penalty*: Penalty for derivates of filtered signal
(see Notes).

There is a procedure for searching for good filtering parameters:
it uses *opt_points* random points and perfoms filtering in that
points. The result of the filtering can be tested for quality. The
quality function is a trade of two features: accuracy and smoothing.
Accuracy can be estimated as the (abs) difference between original and
filtered data, quality of smoothing can be estimated as absalute values
of the derivates. So there are two parameters *diff_penalty* and
*deriv_penalty* that can ajust the trade-of.

So the optimizing procedure performs loop over filtering parameters and calculates the next penalty function:

penalty = diff_penalty * sum(abs(Xi-Fi)) + sum(abs(dFi))

The optimal parameters are used for signal filtering in the whole region.

If *-u* flag is specifed, then filter uses Chen's algorithm (see
link bellow). The algorithm is usefull for vegetation indexes filtering.
It creates a curve that flows on upper boundary of the signal.

for T in $(seq -w 0 10 360) do name="test_raster"$T r.mapcalc -s "$name = sin($T) + rand(-0.3, 0.3)" done

Create smooth raster series using Savitzky-Golay method:

maps=$(g.list rast patt="test_*" sep=,) r.series.filter input=$maps result_prefix="flt." method=savgol winsize=9 order=2 --o

Look at the result (plot the curves for a pixel):

maps=$(g.list rast patt="test_*" sep=,) fmaps=$(g.list rast patt="flt.*" sep=,) eval $(g.region -cg) i.spectral -g raster=$maps coor=$center_easting,$center_northing out=signal.png i.spectral -g raster=$fmaps coor=$center_easting,$center_northing out=flt.png

*Last changed: $Date: 2018-12-27 22:20:04 +0100 (Thu, 27 Dec 2018) $*

Available at: r.series.filter source code (history)

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