From e63f066263ffd8462ddce2256bf0523d6d2cedf9 Mon Sep 17 00:00:00 2001
From: jlefortbesnard <jlefortbesnard@tuta.io>
Date: Tue, 13 Feb 2024 16:18:24 +0100
Subject: [PATCH] add first level 27SS

---
 narps_open/pipelines/team_27SS.firstlevel |  52 +----------
 narps_open/pipelines/team_6VV2.firstlevel | 107 ++++++++++++++++++++++
 2 files changed, 112 insertions(+), 47 deletions(-)
 create mode 100644 narps_open/pipelines/team_6VV2.firstlevel

diff --git a/narps_open/pipelines/team_27SS.firstlevel b/narps_open/pipelines/team_27SS.firstlevel
index 9d1ba399..0531f48d 100755
--- a/narps_open/pipelines/team_27SS.firstlevel
+++ b/narps_open/pipelines/team_27SS.firstlevel
@@ -5,17 +5,10 @@
 # version afni used by the team: AFNI 18.3.12
 # version afni used by the reproducibility team: AFNI Version 23.0.02 Commodus
 
-# Store arguments (directory where to store results, subjects list, directory where data are stored)
-set expdir="$1" # /home/jlefortb/narps_open_pipelines/data/results/team_27SS_afni/firstlevel/
-set subject="$2" # sub-001
-set datadir="$3" # /home/jlefortb/narps_open_pipelines/data/original/ds001734/
-
 # In a terminal, path /home/jlefortb/narps_open_pipelines, run:
 # /home/jlefortb/narps_open_pipelines/narps_open/pipelines/team_27SS.firstlevel /home/jlefortb/narps_open_pipelines/data/results/team_27SS_afni/firstlevel/, sub-001, /home/jlefortb/narps_open_pipelines/data/original/ds001734/
 
 
-
-
 # Store arguments (directory where to store results, subjects list, directory where data are stored)
 set expdir="$1"
 set subject="$2"
@@ -23,84 +16,49 @@ set datadir="$3"
 
 
 afni_proc.py \
-  # Spécification du script de sortie
   -script ${expdir}/proc.${subject}.block \
-  # Autoriser l'écrasement du script existant
   -scr_overwrite \
-  # Spécification de l'identifiant du sujet
   -subj_id ${subject} \
-  # Spécification du répertoire de sortie
   -out_dir ${expdir}/${subject}.results.block \
-  # Spécification des ensembles de données EPI
   -dsets ${datadir}/${subject}/func/${subject}_task-MGT_run-01_bold.nii.gz \
     ${datadir}/${subject}/func/${subject}_task-MGT_run-02_bold.nii.gz \
     ${datadir}/${subject}/func/${subject}_task-MGT_run-03_bold.nii.gz \
     ${datadir}/${subject}/func/${subject}_task-MGT_run-04_bold.nii.gz \
-  # Copie de l'image anatomique
   -copy_anat ${datadir}/${subject}/anat/${subject}_T1w.nii.gz \
-  # Spécification de la présence du crâne dans l'image anatomique
   -anat_has_skull yes \
-  # Blocs de prétraitement inclus dans le pipeline
   -blocks despike tshift align tlrc volreg blur mask scale regress \
-  # Option de déspike
   -despike_new yes \
-  # Spécification de la base TLRC
   -tlrc_base MNI152_T1_2009c+tlrc \
-  #  Specify blur size
   -blur_size  6.0 \
-  # Options d'alignement
   -align_opts_aea \
-  # Déplacement géant
   -giant_move \
-  # Coût de l'alignement
   -cost lpc+ZZ \
-  # Options for temporal shift
   -tshift_opts_ts -quintic \
-  # Alignement des volumes EPI sur l'outlier minimal
   -volreg_align_to MIN_OUTLIER \
-  # Interpolation method for volume registration
   -volreg_interp cubic \
-  # Alignement EPI vers anatomique
   -volreg_align_e2a \
-  # Flou dans le masque automatique
   -blur_in_automask \
-  # Model motion per run
   -regress_motion_per_run \
-  # Censor motion outliers with framewise displacement greater than  1.5 mm
   -regress_censor_motion  1.5 \
-  # Specify the regressors and their corresponding labels
   -regress_stim_times \
     ${datadir}/${subject}/func/times+gain.1D \
     ${datadir}/${subject}/func/times+loss.1D \
-  -regress_stim_labels task nuisance \
-  # Modélisation des régresseurs de stimulus
+  -regress_stim_types AM2 \
+  -regress_stim_labels \
+    GAIN \
+    LOSS \
   -regress_basis 'BLOCK(4,1)' \
-  # Application du masque anatomique
   -mask_apply anat \
-  # Options supplémentaires pour 3dDeconvolve
   -regress_opts_3dD \
     -GOFORIT 8 \
     -jobs 6 \
     -ortvec ${datadir}/${subject}/func/motion_parameters.1D motion_params \
     -allzero_OK \
-  # Application des types de mouvement pour la régression
   -regress_apply_mot_types demean \
-  # Censure des premiers TRs
   -regress_censor_first_trs 3 \
-  # Estimation des erreurs de flou
-  # Estimate the blur epochs
   -regress_est_blur_epits \
-  # Estimate the blur error times
   -regress_est_blur_errts \
-  # Exécution du pipeline directement après création du script TCSH
-
-  # No gradient distortion correction specified
-  # No distortion correction specified
-  # No intensity correction specified
-  # No intensity normalization specified
-  # No spatial smoothing specified
-  # Execute the pipeline script
-#   -execute
+   # -execute
 
 # # extract beta values
 # 3dbucket -prefix GAIN ${expdir}/${subject}.results.block/stats.sub-001+tlrc.BRIK[3]
diff --git a/narps_open/pipelines/team_6VV2.firstlevel b/narps_open/pipelines/team_6VV2.firstlevel
new file mode 100644
index 00000000..e7d89ce7
--- /dev/null
+++ b/narps_open/pipelines/team_6VV2.firstlevel
@@ -0,0 +1,107 @@
+#!/bin/tcsh
+
+# created by team 6VV2, reproduced by Narps reproducibility team
+# creation date: 22 June 2023
+# read and ran by team_6VV2.py script
+# version afni used by the team : AFNI Version 19.0.01 Tiberius
+# version afni used by the reproducibility team :AFNI Version 23.0.02 Commodus
+# Last update: June 2023
+
+
+# exemple run:
+# In a terminal, path /home/jlefortb/narps_open_pipelines, run:
+# tcsh /home/jlefortb/narps_open_pipelines/narps_open/pipelines/team_6VV2.firstlevel /home/jlefortb/narps_open_pipelines/data/results/team_6VV2_afni/firstlevel/, sub-001, /home/jlefortb/narps_open_pipelines/data/original/ds001734/
+
+
+
+
+# Store arguments (directory where to store results, subjects list, directory where data are stored)
+set expdir="$1"
+set subject="$2"
+set datadir="$3"
+
+
+afni_proc.py \
+  -script ${expdir}/proc.${subject}.block \
+  -scr_overwrite \
+  -subj_id ${subject} \
+  -out_dir ${expdir}/${subject}.results.block \
+  -dsets ${datadir}/${subject}/func/${subject}_task-MGT_run-01_bold.nii.gz \
+    ${datadir}/${subject}/func/${subject}_task-MGT_run-02_bold.nii.gz \
+    ${datadir}/${subject}/func/${subject}_task-MGT_run-03_bold.nii.gz \
+    ${datadir}/${subject}/func/${subject}_task-MGT_run-04_bold.nii.gz \
+  -copy_anat ${datadir}/${subject}/anat/${subject}_T1w.nii.gz \
+  -anat_has_skull yes \
+  -blocks despike tshift align tlrc volreg blur mask scale regress \
+  -despike_new yes \
+  -tlrc_base MNI152_T1_2009c+tlrc \
+  -tlrc_NL_warp \
+  -align_opts_aea \
+  -giant_move \
+  -cost lpc+ZZ \
+  -volreg_align_to MIN_OUTLIER \
+  -volreg_tlrc_warp \
+  -volreg_align_e2a \
+  -blur_in_automask \
+  -regress_stim_times \
+    ${datadir}/${subject}/func/times+gain.1D \
+    ${datadir}/${subject}/func/times+loss.1D \
+  -regress_stim_types AM2 \
+  -regress_stim_labels \
+    GAIN \
+    LOSS \
+  -regress_basis \
+    'BLOCK(4,1)' \
+  -mask_apply anat \
+  -regress_motion_per_run \
+  -test_stim_files no \
+  -regress_opts_3dD \
+    -GOFORIT 8 \
+    -jobs 6 \
+  -regress_censor_motion 0.2 \
+  -regress_apply_mot_types demean deriv \
+  -regress_censor_first_trs 3 \
+  -regress_est_blur_errts \
+  # -execute
+
+
+# # extract beta values
+# 3dbucket -prefix GAIN ${expdir}/${subject}.results.block/stats.sub-001+tlrc.BRIK[3]
+# 3dbucket -prefix LOSS ${expdir}/${subject}.results.block/stats.sub-001+tlrc.BRIK[8]
+
+# # convert BRIK to nii
+# 3dAFNItoNIFTI -prefix GAIN ${subject}_GAIN+tlrc
+# 3dAFNItoNIFTI -prefix LOSS ${subject}_LOSS+tlrc
+
+
+
+# run this file with "tcsh 6VV2_afni_proc.simple"
+
+# create 1D stimuli file :
+# import pandas as pd 
+
+# df_run1 = pd.read_csv("/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_events.tsv", sep="\t")
+# df_run1 = df_run1[["onset", "gain", "loss"]].T
+# df_run2 = pd.read_csv("/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-02_events.tsv", sep="\t")
+# df_run2 = df_run2[["onset", "gain", "loss"]].T
+# df_run3 = pd.read_csv("/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-03_events.tsv", sep="\t")
+# df_run3 = df_run3[["onset", "gain", "loss"]].T
+# df_run4 = pd.read_csv("/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-04_events.tsv", sep="\t")
+# df_run4 = df_run4[["onset", "gain", "loss"]].T
+
+# df_gain = pd.DataFrame(index=range(0,4), columns=range(0,64))
+# df_gain.loc[0] = ["{}*{}".format(df_run1[col].loc['onset'], df_run1[col].loc['gain']) for col in range(0, 64)]
+# df_gain.loc[1] = ["{}*{}".format(df_run2[col].loc['onset'], df_run2[col].loc['gain']) for col in range(0, 64)]
+# df_gain.loc[2] = ["{}*{}".format(df_run3[col].loc['onset'], df_run3[col].loc['gain']) for col in range(0, 64)]
+# df_gain.loc[3] = ["{}*{}".format(df_run4[col].loc['onset'], df_run4[col].loc['gain']) for col in range(0, 64)]
+# df_loss = pd.DataFrame(index=range(0,4), columns=range(0,64))
+# df_loss.loc[0] = ["{}*{}".format(df_run1[col].loc['onset'], df_run1[col].loc['loss']) for col in range(0, 64)]
+# df_loss.loc[1] = ["{}*{}".format(df_run2[col].loc['onset'], df_run2[col].loc['loss']) for col in range(0, 64)]
+# df_loss.loc[2] = ["{}*{}".format(df_run3[col].loc['onset'], df_run3[col].loc['loss']) for col in range(0, 64)]
+# df_loss.loc[3] = ["{}*{}".format(df_run4[col].loc['onset'], df_run4[col].loc['loss']) for col in range(0, 64)]
+
+
+# df_gain.to_csv('/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/times+gain.1D', 
+#         sep='\t', index=False, header=False)
+# df_loss.to_csv('/home/jlefortb/narps_open_pipelines/data/original/ds001734/sub-001/func/times+loss.1D', 
+#         sep='\t', index=False, header=False)