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Update README.md
README fixes.
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README.md

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@@ -48,51 +48,45 @@ conda activate dsstdeface
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## Usage
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To deface anatomical scans in the dataset, run the `src/dsst_defacing_wf.py` script. From within the `dsst-defacing-pipeline` cloned directory, run the following command to see the help message.
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To deface anatomical scans in the dataset, run the `src/run.py` script. From within the `dsst-defacing-pipeline` cloned directory, run the following command to see the help message.
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```text
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% python src/run.py -h
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usage: dsst_defacing_wf.py [-h] [-n N_CPUS]
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[-p PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
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[-s SESSION_ID [SESSION_ID ...]]
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[--no-clean]
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bids_dir output_dir
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usage: run.py [-h] [-n N_CPUS] [-p PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
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[-s SESSION_ID [SESSION_ID ...]] [--no-clean]
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bids_dir output_dir
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Deface anatomical scans for a given BIDS dataset or a subject
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directory in BIDS format.
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Deface anatomical scans for a given BIDS dataset or a subject directory in
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BIDS format.
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positional arguments:
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bids_dir The directory with the input dataset
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formatted according to the BIDS standard.
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output_dir The directory where the output files should
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be stored.
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bids_dir The directory with the input dataset formatted
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according to the BIDS standard.
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output_dir The directory where the output files should be stored.
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options:
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optional arguments:
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-h, --help show this help message and exit
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-n N_CPUS, --n-cpus N_CPUS
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Number of parallel processes to run when
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there is more than one folder. Defaults to
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1, meaning "serial processing".
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Number of parallel processes to run when there is more
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than one folder. Defaults to 1, meaning "serial
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processing".
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-p PARTICIPANT_LABEL [PARTICIPANT_LABEL ...], --participant-label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
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The label(s) of the participant(s) that
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should be defaced. The label corresponds to
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sub-<participant_label> from the BIDS spec
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(so it does not include "sub-"). If this
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parameter is not provided all subjects
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should be analyzed. Multiple participants
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can be specified with a space separated
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list.
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-s SESSION_ID [SESSION_ID ...], --session-id SESSION_ID [SESSION_ID ...]
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The ID(s) of the session(s) that should be
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The label(s) of the participant(s) that should be
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defaced. The label corresponds to
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ses-<session_id> from the BIDS spec (so it
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does not include "ses-"). If this parameter
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is not provided all subjects should be
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analyzed. Multiple sessions can be specified
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with a space separated list.
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--no-clean If this argument is provided, then AFNI
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intermediate files are preserved.
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sub-<participant_label> from the BIDS spec (so it does
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not include "sub-"). If this parameter is not provided
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all subjects should be analyzed. Multiple participants
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can be specified with a space separated list.
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-s SESSION_ID [SESSION_ID ...], --session-id SESSION_ID [SESSION_ID ...]
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The ID(s) of the session(s) that should be defaced.
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The label corresponds to ses-<session_id> from the
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BIDS spec (so it does not include "ses-"). If this
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parameter is not provided all subjects should be
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analyzed. Multiple sessions can be specified with a
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space separated list.
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--no-clean If this argument is provided, then AFNI intermediate
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files are preserved.
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```
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The script can be run serially on a BIDS dataset or in parallel at subject/session level. Both these methods of running
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If you have a small dataset with less than 10 subjects, then it might be easiest to run the defacing algorithm serially.
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```bash
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python src/dsst_defacing_wf.py ${INPUT_DIR} ${OUTPUT_DIR}
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python src/run.py ${INPUT_DIR} ${OUTPUT_DIR}
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```
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### Option 2: Parallel defacing
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subject/session in the dataset using the `-n/--n-cpus` option. The following example command will run the pipeline occupying 10 processors at a time.
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```bash
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python src/dsst_defacing_wf.py ${INPUT_DIR} ${OUTPUT_DIR} -n 10
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python src/run.py ${INPUT_DIR} ${OUTPUT_DIR} -n 10
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```
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Additionally, the pipeline can be run on a single subject or session using the `-p/--participant-label` and `-s/--session-id`, respectively.

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