Hm, let’s see. Are you installing Ubuntu 16.04 as your version of Linux on Windows10? And/or, what is the output of:
Assuming it is 16.04, can you try re-copying+pasting the commands in step “a” here:
Does that solve it? If not, what is the output of:
thanks for your help. it worked.
OK, great. And did you do the rest of the steps on that page? Basically, to run AFNI on windows10, you have do do the windows-specific stuff to get bash and Ubuntu working, and then you have to do the Ubuntu 16.04 stuff to get your Linux environment set up.
The “afni_system_check.py -check_all” should tell you how everything is going.
Year. I have run all the steps in the afni installation in WIN10. For the afni_system_check.py step, it did echo one error to fix; and I post this message in anoter topic.
I have tried some program for fun. I met one trouble until now: Open the AFNI, see TT_N27+tlrc, right click the image, select the “where am i”. In principle, it should echo the coordinates, brain region name, broadman stuff… However, my afni GUI QUIT after 2-4s… It makes me sad. I checked the command line to see whether it reported some errors. But nothing error I can see. Here is the command line report:
Precompiled binary linux_ubuntu_16_64: Jan 11 2018 (Version AFNI_18.0.03)
Thanks go to ZS Saad for useful feedback
Initializing: X11[Colin Harrison v 60900031]… Widgets… Input files:
session # 1 = /home/charujing/abin/ ==> 82 datasets
dataset count = 82
Time series = 0 files read
NLfit & NLerr= Optimizer (AFNI_NLFIM_METHOD) is SIMPLEX
NLfit & NLerr= Found 29 models
++ AFNI is detached from terminal.
Plugins = 52 libraries read
++ NOTE: This version of AFNI was built Jan 11 2018 ++
++ NOTE: ‘Define Markers’ is hidden: right-click ‘DataDir’ to see it
++ NOTE: Use ‘-seehidden’ option to see which plugins are hidden
------------------------- AFNI Startup Tip (34/62)---------------------------
The Define Datamode control panel lets you control how the OLay dataset is
resampled to fit the ULay dataset (that defines the basis for the pixel grid
on which the images are displayed). The options are:
NN = Nearest Neighbor Li = Linear
Cu = Cubic Bk = Blocky (between NN and Li)
When the OverLay is at a coarser resolution than the UnderLay (common in FMRI),
Li will produce ‘nicer’ looking maps, but NN will be more ‘honest’ looking.
reading MNIa_caez_pmaps_18+tlrc(328 million [mega] bytes)…done