main_local_single_subject.py

""" 
@author: Joao Periquito 
iBEAt Analysis MAIN LOCAL Scrpit
2022
Download XNAT dataset -> Name Standardization ->    Execute MDR   ->  Apply UNETR to kidney segmentation  ->  Custom Moddeling (T1, T2...) ->     Biomarker extraction   -> Google Drive Upload
  pipelines.xnat.py   -> pipelines.rename.py  -> pipelines.mdr.py ->    piplines.apply_AI_segmentation    ->    pipelines.mapping.py       -> pipelines.export_ROI_stats -> scripts.upload.py
"""

from scripts.single_subject_internal_analysis import single_subject
import utilities.XNAT_credentials as XNAT_cred
import utilities.select_folder_to_save as select_save_folder

if __name__ == '__main__':

    #SELECT ONE OPTION TO CHOOSE A DATASET

    dataset = [6,1,7]    #entry in xnat
    #dataset = '2128_007' #specific dataset
    #dataset = 'load'      #load a previously downloaded dataset

    path = select_save_folder.internal()
    #XNAT Credentials
    username, password = XNAT_cred.main()

    #SELECT YOUR DATASET
    #dataset = [site, study, dataset] see below "EXAMPLE DATASET SELECTION"

    ################################################# EXAMPLE DATASET SELECTION #############################################################
    #DATASET CODE FOR LEEDS
    #  (FIRST NUMBER)                 (SECOND NUMBER)                                 (THIRD NUMBER - INNPUT from --num when you run the main script: python main_cluster.py --num n)
    #  2: BEAt-DKD-WP4-Bordeaux    (selected) BEAt-DKD-WP4-Leeds                 (selected) BEAt-DKD-WP4-Leeds -> (selected) Leeds_Patients
    #  3: BEAt-DKD-WP4-Exeter       ->0: Leeds_Patients                            0: Leeds_Patient_4128001
    #  4: BEAt-DKD-WP4-Turku          1: Leeds_volunteer_repeatability_study       1: Leeds_Patient_4128002
    #  5: BEAt-DKD-WP4-Bari           2: Leeds_Phantom_scans                       2: Leeds_Patient_4128004
    #->6: BEAt-DKD-WP4-Leeds          3: Leeds_RAVE_Reconstructions                       ..........
    #  7: BEAt-DKD-WP4-Sheffield      4: Leeds_setup_scans                      ->14: Leeds_Patient_4128015
    #########################################################################################################################################

    #function responsable for ibeat analysis of a single subject (processed images, logs, and csv results are exported to Google Drive)
    single_subject(username, password, path, dataset)






    ####### FOR REPEATABILITY STUDY USING BESSEMER #######
    # Change: pipeline -> fetch.py: def find_mask_in_local_rep(database)         use: mask_folder = '//mnt//fastdata//md1jdsp//Leeds_Vol_Masks'       (use cluster path)
    #                               def kidney_masks(folder)                     use: find_mask_in_local_rep(folder)                                  (to use pre made masks, not AI, no DCE)
    # Change: pipeline -> export.py: def kidney_masks_as_png                     use: backgroud_series = 'Dixon_out_phase'                            (use pre contrast images as background for exporting masks)