### Correlation integral C2

`c2 -d# -M# -t# -n# [-N# -## -r# -R# -o` outfile`-l# -x# -c# -V# -h]` file(s)

> `-d` delay
> `-M` maximal embedding dimension
> `-t` minimal time separation
> `-n` minimal number of center points
> `-N` maximal number of pairs (1000)
> `-#` resolution, values per octave (2)
> `-r` minimal length to be probed (as long as pairs found)
> `-R` maximal length to be probed (xmax-xmin)
> `-l` number of values to be read (all)
> `-x` number of values to be skipped (0)
> `-c` column to be read (1 or file,#)
> `-o` output file name, just `-o` means file`_c2`
> `-V` verbosity level (0 = only fatal errors)
> `-h` show this message

Determines the correlation sums of scalar time series in file(s). For each file, a file named file`_c2` is produced, containing the distance and the correlation sum as its two columns. This file is re-written every time a new length scale is finished. Thus you may view intermediate results while th eprogram is still running. Fast neighbour search is applied.

Note: You will probably use one of the auxilliary programs c2d, c2t, or c2g to process the output further. See also example below.

Note: If all length scales and all centre points are asked for, the program is slower than the naive approach, c2naive.

Note: If the data is coarsely discretised, the program may be confused by many identical pairs. Since discretisation causes artifacts in the correlation sum anyway, add uniform noise of the size of a discretisation step to the data first, e.g. using addnoise.

See also d2

### Usage example

Try also just running: `gnuplot c2.gnu` in the `examples` directory.

    > henon -l10000 > data
    > c2 -M10 -d1 -t50 -n500 data

    gnuplot> set logscale x
    gnuplot> set yrange [0:3]
    gnuplot> plot '< c2d -a2 data_c2', 1.2

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