### Fixed mass estimation of C1 (information dimension)

`c1 -d# -m# -M# -t# -n# [-## -K# -o` outfile`-l# -x# -c# -V# -h]` file

> `-d` delay
> `-m` minimal embedding dimension
> `-M` maximal embedding dimension (at least 2)
> `-t` minimal time separation
> `-n` minimal number of center points
> `-#` resolution, values per octave (2)
> `-K` maximal number of neighbours (100)
> `-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`_c1`
> `-V` verbosity level (0 = only fatal errors)
> `-h` show this message

Computes curves for the fixed mass computation of the information dimension. The output is written to a file named file`_c1`, containing as two columns the necessary radius and the `mass'. Although the `mass' is the independent quantity here, this is to conform with the output of c2naive and d2.

Note: You will probably use the auxiliary programs c2d or c2t to process the output further. The formula used for the Gaussian kernel correlation sum does not apply to the information dimension. See also the example below.

### Usage example

Try also just running: `gnuplot c1.gnu` in the `examples` directory.

    > henon -l10000 > data
    > c1 -m2 -M6 -d1 -t50 -n500 data

    gnuplot> set logscale x
    gnuplot> set yrange [0:3]
    gnuplot> plot '< c2d -a2 data_c1', 1.2

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