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public:de_bruijn_software [2018/08/07 17:47]
aguirreg [Basic Examples]
public:de_bruijn_software [2018/08/07 17:48] (current)
aguirreg [Basic Examples]
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 |<​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.|
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