Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
c460477
commit e63f066
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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) | ||
