configuration.py

import os

N = 10
SUBJECTS_PREFIX = "sub-"
# Subject's ID such as sub-01 or s-10
SUBJECTS = [
    SUBJECTS_PREFIX + "0" + str(i) if i < 10 else SUBJECTS_PREFIX + str(i)
    for i in range(1, N, 1)
]

# Data structure of the study
BANCO = "/hpc/banco"
PRIMAVOICE = os.path.join(BANCO, "Primavoice_Data_and_Analysis")
# BrainVISA database location
BV_DB = os.path.join(PRIMAVOICE, "DTI")
# BrainVISA field values
CENTER = "cerimed"
SUBJ_DIRS = {subject: os.path.join(BV_DB, CENTER, subject) for subject in SUBJECTS}
MODALITY = "dmri"
ACQUISITION = "default_acquisition"
ACQUISITION_DIRS = {
    subject: os.path.join(SUBJ_DIRS[subject], MODALITY, ACQUISITION)
    for subject in SUBJECTS
}
CORRECTION = "default_analysis"
CORRECTION_DIRS = {
    subject: os.path.join(ACQUISITION_DIRS[subject], CORRECTION) for subject in SUBJECTS
}
# Data generated through Diffuse (to check the three following paths)
CORRECTED_DWI = {
    subject: os.path.join(
        CORRECTION_DIRS[subject], "corrected_dwi" + "_" + subject + ".nii.gz"
    )
    for subject in SUBJECTS
}
BVECS = {
    subject: os.path.join(
        CORRECTION_DIRS[subject], "corrected_bvecs" + "_" + subject + ".txt"
    )
    for subject in SUBJECTS
}
BVALS = {
    subject: os.path.join(
        ACQUISITION_DIRS[subject], "raw_bvals" + "_" + subject + ".txt"
    )
    for subject in SUBJECTS
}
# Added directory that contains the mrtrix generated data (no subdirs)
PROCESSING = "Mrtrix"
PROCESSING_DIRS = {
    subject: os.path.join(CORRECTION_DIRS[subject], PROCESSING) for subject in SUBJECTS
}
MRTRIX_CORRECTED_DWIS = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "dwi" + ".mif")
    for subject in SUBJECTS
}
TENSORS = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "tensor" + ".mif")
    for subject in SUBJECTS
}
FAS = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "fa" + ".mif")
    for subject in SUBJECTS
}
MASKS = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "dwi_mask" + ".mif")
    for subject in SUBJECTS
}
WM_RESPONSES = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "wm" + ".txt")
    for subject in SUBJECTS
}
WM_FODS = {
    subject: os.path.join(PROCESSING_DIRS[subject], subject + "_" + "wm_fod" + ".mif")
    for subject in SUBJECTS
}

TRACKS = {
    subject: os.path.join(
        PROCESSING_DIRS[subject], subject + "_" + "raw_tractogram.tck"
    )
    for subject in SUBJECTS
}
FILTERED_TRACKS = {
    subject: os.path.join(
        PROCESSING_DIRS[subject], subject + "_" + "sift_filtered_tractogram.tck"
    )
    for subject in SUBJECTS
}

# T1 is registered rigidly in the MNI152 space
FUNCTIONAL_ANALYSIS = {
    subject: os.path.join(PRIMAVOICE, "analysis" + "_" + subject)
    for subject in SUBJECTS
}
T1 = {
    os.path.join(
        FUNCTIONAL_ANALYSIS[subject],
        "anat",
        subject + "_" + "ses-01_T1W_denoised_debiased_in_MNI152.nii.gz",
    )
    for subject in SUBJECTS
}
CONTRAST = {
    os.path.join(
        FUNCTIONAL_ANALYSIS[subject],
        "spm_realign",
        "results_8WM_9CSF_0mvt",
        "In-MNI152" + "_" + subject + "_" + "res-8WM_9CSF_0mvt_human_vs_all_t.nii.gz",

    for subject in SUBJECTS
}


pass