I am pretty sure that the plotting issue is resolved and that you can configure the programs to use the wxt terminal everytime.
They're definitely using wxt by default now. I see some random junk in the plot window (screenshot attached), but that is easily rectified by changing the character encoding per your previous instructions.
If the thing I fixed is the same issue that you are seeing, the fit is not retroactive. That is, I cannot fix the history in an existing project file. But I think your next project file will work as you expect.
I'm still seeing differences between apparently identical fits. I still can't figure out exactly what steps are causing the problem, but the following seems to be a fairly reliable way of doing so (not every step may be necessary, though): 1) Load chi(k) data into Artemis - I've attached a datafile I'm experiencing this issue with. 2) Setup fitting windows, paths and GDS for a QFS fit to two paths. In particular, change the fitting windows from Artemis' automatic value and set S02 to consist of two parameters - eg. ampau*xauau and ampau*xauag; set the common parameter rather than guess it (this gives us something easy to change in the later steps) 3) run a fit 4) In GDS change the set value (ampau) to something else 5) run a fit 6) Save the project, close Artemis and then reopen the project (this step seems to be fairly important) 7) run a fit 8) change the set GDS parameter back to its original value 9) run a fit 10) compare the log from the final fit to the log from the first fit In the attached Artemis file I've attached compare fit 1 to fit 4: In this case the differences are relatively small, but I still don't think they should be there! This file was produced using the 0.9.23 pre-release Artemis. Some observations: 1) I haven't seen this with every data file - it seems to be related to data quality? For instance it seems to be impossible to reproduce this problem with foil data, but the low quality data I've attached is very prone to it. 2) The effect seems most pronounced with poorer models. A model that is basically correct displays only small changes (probably small enough to be lost to the uncertainty), while one that is basically wrong may change wildly. (2b) It may be possible that the some of the smaller changes are the result of the fitting algorithm getting confused by noise and stuck in a different local min/max, rather than an actual bug, if it uses some kind of monte carlo or other random-chance based approach?) Attachments: Screenshot of plot window Example chi(k) datafile which displays this problem: ig060.cho Example fitting project: singleedgetest.fpj Thanks, Ian
