pySPFM.cli.auc_to_estimates.auc_to_estimates

pySPFM.cli.auc_to_estimates.auc_to_estimates#

auc_to_estimates(data_fn, auc_fn, mask_fn, output_filename, tr, thr=95.0, thr_strategy='static', out_dir='.', te=[0], hrf_model='spm', block_model=False, n_jobs=4, use_bids=False, group=False, group_distance=3, block_dist=2, debug=False, quiet=False, command_str=None)[source]#

Estimate the activity-inducing signal from AUC data.

Parameters:

  • data_fn (str) – The name of the file containing fMRI data.

  • auc_fn (str) – The name of the file containing AUC data.

  • mask_fn (list) – The name of the files containing the mask for the fMRI data and the AUC thresholding mask.

  • output_filename (str) – The name of the output file with no extension.

  • tr (float) – TR of the fMRI data acquisition.

  • thr (float, optional) – Percentile to threshold the AUC data with or the threshold value itself. The percentile is applied to the second mask provided with the ‘-m’ flag if the second mask is a binary mask. If the second mask is not binary, the values on the second mask are used as the threshold. When the threshold value is given, the second mask is ignored. Percentiles are given in the range [1, 100], while threshold values are given in the range [0, 1). Default is 95.0.

  • thr_strategy (str, optional) – Strategy to threshold the AUC data with. If the second mask is a binary mask, the can be applied with a static threshold (‘static’) or a time-dependet threshold (‘time’). Default is ‘static’.

  • out_dir (str, optional) – Output directory. Default is current.

  • te (list, optional) – List with TE of the fMRI data acquisition. Default = [0].

  • hrf_model (str, optional) – HRF model to use. Default is ‘spm’. Options are ‘spm’, ‘glover’, or a custom HRF file with the ‘.1D’ or ‘.txt’ extension.

  • block_model (bool, optional) – Estimate innovation signals. Default = False.

  • n_jobs (int, optional) – Number of jobs to parallelize for loops (default = 4)., by default 4.

  • use_bids (bool, optional) – Use BIDS-style suffix on the given output (default = False). pySPFM assumes that output follows the BIDS convention. Not using this option will default to using AFNI to update the header of the output.

  • group (bool, optional) – Consider consecutive coefficients as belonging to the same block activation. Default = False.

  • group_distance (int, optional) – Maximum distance between coefficients to be considered part of the same block activation. Default = 3.

  • block_dist (int, optional) – Minimum number of TRs in between of the peaks found. Default = 2.

  • debug (bool, optional) – Logs in the terminal will have increased verbosity, and will also be written into a .tsv file in the output directory, by default False

  • quiet (bool, optional) – Log only warnings and errors, by default False.

  • command_str (_type_, optional) – String with the command-line call to this function, by default None.

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