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Code (prototyping) for a research project concerning the information capacity of the human motor system at HIIT.
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Scripts for information capacity evaluation

Information capacity evaluation scripts

  • eval.R (the actual functions to call when you want to evaluate data)
  • infocapacity.R (residual calculators, the actual evaluation functions, normalization etc.)
  • data_handling.R (handling of files, aligning data etc.)

The actual functions to call:

The function residual_complexity(fps, pca) calculates throughputs for the whole directory, assuming the original files (named 01.txt, 02.txt etc.) are in the current directory and a subdirectory alignment contains the alignment vectors indicating frame duplications as produced by CTW (named 1_ali_2.txt etc.).

The function pair_residual_complexity(seqnum_a, seqnum_b, fps, pca) calculates throughputs for the given pair of sequences, indicated by the given sequence numbers.

The function subdir_based_residual_complexity(fps, pca) calculates throughputs for the current directory assuming that each subdirectory 01, 02 etc. contains all the repetitions of one type of sequence. The directory structure and file naming is assumed to be like this:

- working directory
  -- 01 -- contains three repetitions of sequence #1
  |  |
  |  * 01.txt
  |  * 02.txt
  |  * 03.txt
  |  -- alignment -- contains the alignment vectors
  |     |
  |     * 1_ali_2.txt
  |     * 1_ali_3.txt
  |     * ...
  -- 02 -- contains two repetitions of sequence #2
  |  |
  |  * 01.txt
  |  * 02.txt
  |  -- alignment
  |     |
  |     * 1_ali_2.txt
 ...    * 2_ali_1.txt

Of the parameters for each of these functions, fps indicates the framerate in frames per second (default is 120) and pca is a logical value indicating that PCA should be used (default is false, PCA is not performed).

Plotting scripts

  • plot_aligned.R (plotting aligned sequences into pdf files)
  • plot_outliers.R (diagnostic functions for "outlier" residuals, very messy prototypes)
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