... | ... | @@ -22,17 +22,28 @@ This is where you can store all the data files for all stages of the pipeline. T |
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* **4_study:** The pipeline does not automatically save any files into this folder, but it is useful to store files that are included in your study.
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NOTE: If desired, a subject-wise folder structure is also possible to set up with a few minor changes:
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**NOTE:** If desired, a subject-wise folder structure is also possible to set up with a few minor changes:
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1. Remove all the default folders from the Data directory.
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2. Copy your subject folders into this directory (assumes that each subject's files for all steps will be stored within its own subject folder).
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3. When running a job through the File->Batch->Run History Template Batch menu, replace the values for [in_path] (and [out_path], if exists),
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located in the "replace_string" field of each batch config file, with analysis/data (or [batch_dfp]) if this is what you enter into the "path:" field).
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Example: [in_path],analysis/data/1_init ----- change to -----> [in_path],analysis/data
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*Example:* [in_path],analysis/data/1_init ----- change to -----> [in_path],analysis/data
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[out_path],analysis/data/2_preproc ----- change to -----> [out_path],analysis/data
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4. For now, the path to the folder containing the subject folders must be manually typed/pasted into the "path:" field. This will usually be analysis/data if that's
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the folder you'd like to use. Likewise, the _init.set file name(s) must be manually typed/pasted into the "file:" field. In this case they must include the name
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of the subject folder in the name.
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Example: subj001/subj001_init.set (this should work regardless of how many folders deep the init file is stored)
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*Example:* subj001/subj001_init.set (this should work regardless of how many folders deep the init file is stored)
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5. If you are running your jobs remotely, remember to also change the folder structure on the remote end in the same way as in steps 1-4 above.
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# Log
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