[80GC] Reproduction

Afni_proc_through_nipype/_subject_id_001/afni_proc/_0x40130b82c0418a5dc016fe0334a2c8f6_unfinished.json

@@ -0,0 +1,25 @@
+[
+    [
+        "command",
+        [
+            "/home/jlefortb/narps_open_pipelines/narps_open/pipelines/team_6VV2.firstlevel",
+            "d24bc0377b166d70c1e7e74f97b48e4b"
+        ]
+    ],
+    [
+        "data_dir",
+        "/home/jlefortb/narps_open_pipelines/data/original/ds001734/"
+    ],
+    [
+        "function_str",
+        "def run(command, results_dir, subject, data_dir):\n    import subprocess\n    subject= \"sub-{}\".format(subject)\n    subprocess.run([command, results_dir, subject, data_dir])\n    print(\"Done\")\n"
+    ],
+    [
+        "results_dir",
+        "/home/jlefortb/narps_open_pipelines/data/results/derived/team_6VV2_afni/firstlevel/"
+    ],
+    [
+        "subject",
+        "001"
+    ]
+]

Afni_proc_through_nipype/_subject_id_001/afni_proc/_report/report.rst

@@ -0,0 +1,23 @@
+Node: afni_proc (utility)
+=========================
+
+
+ Hierarchy : Afni_proc_through_nipype.afni_proc
+ Exec ID : afni_proc.a070
+
+
+Original Inputs
+---------------
+
+
+* command : /home/jlefortb/narps_open_pipelines/narps_open/pipelines/team_6VV2.firstlevel
+* data_dir : /home/jlefortb/narps_open_pipelines/data/original/ds001734/
+* function_str : def run(command, results_dir, subject, data_dir):
+    import subprocess
+    subject= "sub-{}".format(subject)
+    subprocess.run([command, results_dir, subject, data_dir])
+    print("Done")
+
+* results_dir : /home/jlefortb/narps_open_pipelines/data/results/derived/team_6VV2_afni/firstlevel/
+* subject : 001
+

Afni_proc_through_nipype/_subject_id_001/create_stimuli/_0x4b88c28a232fe352aaa3991b95b3cbfc.json

@@ -0,0 +1,14 @@
+[
+    [
+        "data_dir",
+        "/home/jlefortb/narps_open_pipelines/data/original/ds001734/"
+    ],
+    [
+        "function_str",
+        "def create_stimuli_file(subject, data_dir):\n    # create 1D stimuli file :\n    import pandas as pd \n    from os.path import join as opj\n    df_run1 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-01_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run1 = df_run1[[\"onset\", \"gain\", \"loss\"]].T\n    df_run2 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-02_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run2 = df_run2[[\"onset\", \"gain\", \"loss\"]].T\n    df_run3 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-03_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run3 = df_run3[[\"onset\", \"gain\", \"loss\"]].T\n    df_run4 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-04_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run4 = df_run4[[\"onset\", \"gain\", \"loss\"]].T\n\n    df_gain = pd.DataFrame(index=range(0,4), columns=range(0,64))\n    df_gain.loc[0] = [\"{}*{}\".format(df_run1[col].loc['onset'], df_run1[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[1] = [\"{}*{}\".format(df_run2[col].loc['onset'], df_run2[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[2] = [\"{}*{}\".format(df_run3[col].loc['onset'], df_run3[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[3] = [\"{}*{}\".format(df_run4[col].loc['onset'], df_run4[col].loc['gain']) for col in range(0, 64)]\n    df_loss = pd.DataFrame(index=range(0,4), columns=range(0,64))\n    df_loss.loc[0] = [\"{}*{}\".format(df_run1[col].loc['onset'], df_run1[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[1] = [\"{}*{}\".format(df_run2[col].loc['onset'], df_run2[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[2] = [\"{}*{}\".format(df_run3[col].loc['onset'], df_run3[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[3] = [\"{}*{}\".format(df_run4[col].loc['onset'], df_run4[col].loc['loss']) for col in range(0, 64)]\n\n    df_gain.to_csv(opj(data_dir, \"sub-{}/func/times+gain.1D\".format(subject)), \n            sep='\\t', index=False, header=False)\n    df_loss.to_csv(opj(data_dir, \"sub-{}/func/times+loss.1D\".format(subject)), \n            sep='\\t', index=False, header=False)\n    print(\"Done\")\n"
+    ],
+    [
+        "subject",
+        "001"
+    ]
+]

Afni_proc_through_nipype/_subject_id_001/create_stimuli/_report/report.rst

@@ -0,0 +1,170 @@
+Node: create_stimuli (utility)
+==============================
+
+
+ Hierarchy : Afni_proc_through_nipype.create_stimuli
+ Exec ID : create_stimuli.a070
+
+
+Original Inputs
+---------------
+
+
+* data_dir : /home/jlefortb/narps_open_pipelines/data/original/ds001734/
+* function_str : def create_stimuli_file(subject, data_dir):
+    # create 1D stimuli file :
+    import pandas as pd 
+    from os.path import join as opj
+    df_run1 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-01_events.tsv".format(subject, subject)), sep="\t")
+    df_run1 = df_run1[["onset", "gain", "loss"]].T
+    df_run2 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-02_events.tsv".format(subject, subject)), sep="\t")
+    df_run2 = df_run2[["onset", "gain", "loss"]].T
+    df_run3 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-03_events.tsv".format(subject, subject)), sep="\t")
+    df_run3 = df_run3[["onset", "gain", "loss"]].T
+    df_run4 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-04_events.tsv".format(subject, subject)), 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(opj(data_dir, "sub-{}/func/times+gain.1D".format(subject)), 
+            sep='\t', index=False, header=False)
+    df_loss.to_csv(opj(data_dir, "sub-{}/func/times+loss.1D".format(subject)), 
+            sep='\t', index=False, header=False)
+    print("Done")
+
+* subject : 001
+
+
+Execution Inputs
+----------------
+
+
+* data_dir : /home/jlefortb/narps_open_pipelines/data/original/ds001734/
+* function_str : def create_stimuli_file(subject, data_dir):
+    # create 1D stimuli file :
+    import pandas as pd 
+    from os.path import join as opj
+    df_run1 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-01_events.tsv".format(subject, subject)), sep="\t")
+    df_run1 = df_run1[["onset", "gain", "loss"]].T
+    df_run2 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-02_events.tsv".format(subject, subject)), sep="\t")
+    df_run2 = df_run2[["onset", "gain", "loss"]].T
+    df_run3 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-03_events.tsv".format(subject, subject)), sep="\t")
+    df_run3 = df_run3[["onset", "gain", "loss"]].T
+    df_run4 = pd.read_csv(opj(data_dir, "sub-{}/func/sub-{}_task-MGT_run-04_events.tsv".format(subject, subject)), 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(opj(data_dir, "sub-{}/func/times+gain.1D".format(subject)), 
+            sep='\t', index=False, header=False)
+    df_loss.to_csv(opj(data_dir, "sub-{}/func/times+loss.1D".format(subject)), 
+            sep='\t', index=False, header=False)
+    print("Done")
+
+* subject : 001
+
+
+Execution Outputs
+-----------------
+
+
+* Stimuli : None
+
+
+Runtime info
+------------
+
+
+* duration : 0.012201
+* hostname : ptb-03230001.irisa.fr
+* prev_wd : /home/jlefortb/narps_open_pipelines
+* working_dir : /home/jlefortb/narps_open_pipelines/Afni_proc_through_nipype/_subject_id_001/create_stimuli
+
+
+Environment
+~~~~~~~~~~~
+
+
+* COLORTERM : truecolor
+* DBUS_SESSION_BUS_ADDRESS : unix:path=/run/user/670967/bus
+* DEBUGINFOD_URLS : https://debuginfod.fedoraproject.org/ 
+* DESKTOP_SESSION : gnome
+* DISPLAY : :0
+* EDITOR : /usr/bin/nano
+* FSLDIR : /usr/local/fsl
+* FSLGECUDAQ : cuda.q
+* FSLMULTIFILEQUIT : TRUE
+* FSLOUTPUTTYPE : NIFTI_GZ
+* FSLTCLSH : /usr/local/fsl/bin/fsltclsh
+* FSLWISH : /usr/local/fsl/bin/fslwish
+* FSL_LOAD_NIFTI_EXTENSIONS : 0
+* FSL_SKIP_GLOBAL : 0
+* GDMSESSION : gnome
+* GDM_LANG : en_US.UTF-8
+* GNOME_SETUP_DISPLAY : :1
+* GNOME_TERMINAL_SCREEN : /org/gnome/Terminal/screen/3627c221_9f8a_4ca7_a192_68617ea35d25
+* GNOME_TERMINAL_SERVICE : :1.180
+* GUESTFISH_INIT : \e[1;34m
+* GUESTFISH_OUTPUT : \e[0m
+* GUESTFISH_PS1 : \[\e[1;32m\]><fs>\[\e[0;31m\] 
+* GUESTFISH_RESTORE : \e[0m
+* HISTCONTROL : ignoredups
+* HISTSIZE : 1000
+* HOME : /home/jlefortb
+* HOSTNAME : ptb-03230001.irisa.fr
+* KDEDIRS : /usr
+* LANG : en_US.UTF-8
+* LESSOPEN : ||/usr/bin/lesspipe.sh %s
+* LOGNAME : jlefortb
+* LS_COLORS : rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=01;37;41:su=37;41:sg=30;43:ca=00:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.avif=01;35:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.webp=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=01;36:*.au=01;36:*.flac=01;36:*.m4a=01;36:*.mid=01;36:*.midi=01;36:*.mka=01;36:*.mp3=01;36:*.mpc=01;36:*.ogg=01;36:*.ra=01;36:*.wav=01;36:*.oga=01;36:*.opus=01;36:*.spx=01;36:*.xspf=01;36:*~=00;90:*#=00;90:*.bak=00;90:*.old=00;90:*.orig=00;90:*.part=00;90:*.rej=00;90:*.swp=00;90:*.tmp=00;90:*.dpkg-dist=00;90:*.dpkg-old=00;90:*.ucf-dist=00;90:*.ucf-new=00;90:*.ucf-old=00;90:*.rpmnew=00;90:*.rpmorig=00;90:*.rpmsave=00;90:
+* MAIL : /var/spool/mail/jlefortb
+* MOZ_GMP_PATH : /usr/lib64/mozilla/plugins/gmp-gmpopenh264/system-installed
+* PATH : /home/jlefortb/reproduction/bin:/usr/local/fsl/share/fsl/bin:/usr/local/fsl/share/fsl/bin:/usr/local/fsl/share/fsl/bin:/usr/local/fsl/share/fsl/bin:/home/jlefortb/reproduction/bin:/usr/local/fsl/share/fsl/bin:/usr/local/fsl/share/fsl/bin:/home/jlefortb/.local/bin:/home/jlefortb/bin:/usr/lib64/qt-3.3/bin:/usr/lib64/ccache:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/home/jlefortb/abin:/home/jlefortb/abin
+* PS1 : (reproduction) [\u@\h (Fedora 37) \W]$ 
+* PWD : /home/jlefortb
+* QTDIR : /usr/lib64/qt-3.3
+* QTINC : /usr/lib64/qt-3.3/include
+* QTLIB : /usr/lib64/qt-3.3/lib
+* QT_IM_MODULE : ibus
+* R_LIBS : /home/jlefortb/R
+* SESSION_MANAGER : local/unix:@/tmp/.ICE-unix/12471,unix/unix:/tmp/.ICE-unix/12471
+* SHELL : /bin/bash
+* SHLVL : 1
+* SSH_AUTH_SOCK : /run/user/670967/keyring/ssh
+* SYSTEMD_EXEC_PID : 12526
+* TERM : xterm-256color
+* USER : jlefortb
+* USERNAME : jlefortb
+* VIRTUAL_ENV : /home/jlefortb/reproduction
+* VIRTUAL_ENV_PROMPT : (reproduction) 
+* VTE_VERSION : 7006
+* WAYLAND_DISPLAY : wayland-0
+* XAUTHORITY : /run/user/670967/.mutter-Xwaylandauth.LIAOF2
+* XDG_CURRENT_DESKTOP : GNOME
+* XDG_DATA_DIRS : /home/jlefortb/.local/share/flatpak/exports/share:/var/lib/flatpak/exports/share:/usr/local/share/:/usr/share/
+* XDG_MENU_PREFIX : gnome-
+* XDG_RUNTIME_DIR : /run/user/670967
+* XDG_SESSION_CLASS : user
+* XDG_SESSION_DESKTOP : gnome
+* XDG_SESSION_TYPE : wayland
+* XMODIFIERS : @im=ibus
+* _ : /home/jlefortb/reproduction/bin/ipython
+

Afni_proc_through_nipype/_subject_id_002/afni_proc/_report/report.rst

@@ -0,0 +1,23 @@
+Node: afni_proc (utility)
+=========================
+
+
+ Hierarchy : Afni_proc_through_nipype.afni_proc
+ Exec ID : afni_proc.a005
+
+
+Original Inputs
+---------------
+
+
+* command : /home/jlefortb/narps_open_pipelines/narps_open/pipelines/team_6VV2.firstlevel
+* data_dir : /home/jlefortb/narps_open_pipelines/data/original/ds001734/
+* function_str : def run(command, results_dir, subject, data_dir):
+    import subprocess
+    subject= "sub-{}".format(subject)
+    subprocess.run([command, results_dir, subject, data_dir])
+    print("Done")
+
+* results_dir : /home/jlefortb/narps_open_pipelines/data/results/derived/team_6VV2_afni/firstlevel/
+* subject : 002
+

Afni_proc_through_nipype/_subject_id_002/create_stimuli/_0x828dba5362000ae4a98c5e5894950bc2.json

@@ -0,0 +1,14 @@
+[
+    [
+        "data_dir",
+        "/home/jlefortb/narps_open_pipelines/data/original/ds001734/"
+    ],
+    [
+        "function_str",
+        "def create_stimuli_file(subject, data_dir):\n    # create 1D stimuli file :\n    import pandas as pd \n    from os.path import join as opj\n    df_run1 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-01_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run1 = df_run1[[\"onset\", \"gain\", \"loss\"]].T\n    df_run2 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-02_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run2 = df_run2[[\"onset\", \"gain\", \"loss\"]].T\n    df_run3 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-03_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run3 = df_run3[[\"onset\", \"gain\", \"loss\"]].T\n    df_run4 = pd.read_csv(opj(data_dir, \"sub-{}/func/sub-{}_task-MGT_run-04_events.tsv\".format(subject, subject)), sep=\"\\t\")\n    df_run4 = df_run4[[\"onset\", \"gain\", \"loss\"]].T\n\n    df_gain = pd.DataFrame(index=range(0,4), columns=range(0,64))\n    df_gain.loc[0] = [\"{}*{}\".format(df_run1[col].loc['onset'], df_run1[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[1] = [\"{}*{}\".format(df_run2[col].loc['onset'], df_run2[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[2] = [\"{}*{}\".format(df_run3[col].loc['onset'], df_run3[col].loc['gain']) for col in range(0, 64)]\n    df_gain.loc[3] = [\"{}*{}\".format(df_run4[col].loc['onset'], df_run4[col].loc['gain']) for col in range(0, 64)]\n    df_loss = pd.DataFrame(index=range(0,4), columns=range(0,64))\n    df_loss.loc[0] = [\"{}*{}\".format(df_run1[col].loc['onset'], df_run1[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[1] = [\"{}*{}\".format(df_run2[col].loc['onset'], df_run2[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[2] = [\"{}*{}\".format(df_run3[col].loc['onset'], df_run3[col].loc['loss']) for col in range(0, 64)]\n    df_loss.loc[3] = [\"{}*{}\".format(df_run4[col].loc['onset'], df_run4[col].loc['loss']) for col in range(0, 64)]\n\n    df_gain.to_csv(opj(data_dir, \"sub-{}/func/times+gain.1D\".format(subject)), \n            sep='\\t', index=False, header=False)\n    df_loss.to_csv(opj(data_dir, \"sub-{}/func/times+loss.1D\".format(subject)), \n            sep='\\t', index=False, header=False)\n    print(\"Done\")\n"
+    ],
+    [
+        "subject",
+        "002"
+    ]
+]