false manip cancelled

narps_open/pipelines/team_6VV2.firstlevel

@@ -7,13 +7,11 @@
 # 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:
-/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/
 
+# 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/
 
-"""
 
 
 
@@ -67,10 +65,43 @@ afni_proc.py \
   -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]
+# # 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
+# # 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)