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public:de_bruijn_software [2018/08/07 21:47]
aguirreg [Basic Examples] |
public:de_bruijn_software [2022/01/28 18:08] (current)
haggerty |
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| <WRAP download round box 200px center>**Download**\\ \\ [[https://github.com/gkaguirrelab/DeBruijn |GitHub repo -- debruijn for OSX]]</WRAP> | **Download**\\ \\ [[https://github.com/gkaguirrelab/DeBruijn |GitHub repo -- debruijn for OSX]] |
| <WRAP info round box 200px center>**Download**\\ \\ [[http://neuro.debian.net/pkgs/debruijn.html|Link to NeuroDebian project]]</WRAP> | \\ \\ [[http://neuro.debian.net/pkgs/debruijn.html|Link to NeuroDebian project]] |
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| **Version History** | **Version History** |
| |<code>./debruijn 17 3</code>|Generates a deBruijn sequence with 17 labels and 3rd-level counterbalancing.| | |<code>./debruijn 17 3</code>|Generates a deBruijn sequence with 17 labels and 3rd-level counterbalancing.| |
| |<code>./debruijn -v 17 3</code>|Generates a deBruijn sequence with 17 labels and 3rd-level counterbalancing, and prints output in verbose mode.| | |<code>./debruijn -v 17 3</code>|Generates a deBruijn sequence with 17 labels and 3rd-level counterbalancing, and prints output in verbose mode.| |
| |<code>./debruijn -t 10 2 5 my_neural_model_matrix.txt [34,55]</code>|Generates a deBruijn sequence with 10 labels and 2nd-level counterbalancing, and prints output in terse mode. The sequence is generated using 5 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function that is a sum of sinusoids with periods varying from 34 to 55 elements.| | |<code>./debruijn -t 10 2 5 [34,55] my_neural_model_matrix.txt</code>|Generates a deBruijn sequence with 10 labels and 2nd-level counterbalancing, and prints output in terse mode. The sequence is generated using 5 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function that is a sum of sinusoids with periods varying from 34 to 55 elements.| |
| |<code>./debruijn -t 10 2 5 HRF my_neural_model_matrix.txt -eval 1500</code>|Generates a deBruijn sequence with 10 labels and 2nd-level counterbalancing, and prints output in terse mode. The sequence is generated using 5 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function informed by the filtering properties of the BOLD hemodynamic response function is used. A stimulus-onset asynchrony of 1000 milliseconds is used in the evaluation of the sequences, and the theoretical detection power is returned.| | |<code>./debruijn -t 10 2 5 HRF my_neural_model_matrix.txt -eval 1500</code>|Generates a deBruijn sequence with 10 labels and 2nd-level counterbalancing, and prints output in terse mode. The sequence is generated using 5 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function informed by the filtering properties of the BOLD hemodynamic response function is used. A stimulus-onset asynchrony of 1000 milliseconds is used in the evaluation of the sequences, and the theoretical detection power is returned.| |
| |<code>./debruijn 17 2 10 my_guide_function.txt my_neural_model_matrix.txt -eval 1000</code>|Generates a deBruijn sequence with 17 labels and 2nd-level counterbalancing, and prints output in normal mode. The sequence is generated using 10 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function specified in the file //my_guide_function.txt//. A stimulus-onset asynchrony of 1000 milliseconds is used in the evaluation of the sequences, and the theoretical detection power is returned.| | |<code>./debruijn 17 2 10 my_guide_function.txt my_neural_model_matrix.txt -eval 1000</code>|Generates a deBruijn sequence with 17 labels and 2nd-level counterbalancing, and prints output in normal mode. The sequence is generated using 10 bins, a neural model matrix specified in the file //my_neural_model_matrix.txt//, and a guide function specified in the file //my_guide_function.txt//. A stimulus-onset asynchrony of 1000 milliseconds is used in the evaluation of the sequences, and the theoretical detection power is returned.| |
