### Nonlinear noise reduction in a data stream

`noise -m# -q# -r# -K# [-d# -k# -T# -w# -s# -l# -x# -c# -h]` file

> `-m` embedding dimension
> `-q` dimension of manifold projected on
> `-r` diameter of neighbourhood
> `-K` maximal number of neighbours
> `-d` delay (1)
> `-k` minimal number of neighbours (m)
> `-T` maximal time in the past considered as neighbours (all)
> `-w` range represented by each centre (e/4)
> `-s` stack depth (500)
> `-l` number of points (whole file)
> `-x` number of values to be skipped (0)
> `-c` column to be read (1 or file,#)
> `-h` show this message

Performs one iteration of nonlinear projective noise reduction. This routine can work in a pipeline without having to read in the whole data first. Output file (the cleaned sequence) is file_c. As a second column, the difference between original and cleaned sequence is printed.

Note: This routine does not use all available neighbours and may therefore be faster than the original program project.

This routine is based on

> T. Schreiber and M. Richter
> Nonlinear projective filtering in a data stream
> preprint (1998).

### Usage example

Try also just running: `gnuplot noise.gnu` in the `wuppertal/docs/` directory.

    > henon -l10000 > data
    > addnoise -v0.02 data
    > cat data_noisy | noise -m7 -q2 -r0.3 -K500 \
    >  | noise -m7 -q2 -r0.2 -K500 > data_noisy_c

    gnuplot> plot '< cat data_noisy | embed -d1'
    gnuplot> plot '< cat data_noisy_c | embed -d1'

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