multiple updates to allow script (i) plot multiple conditions on a single graph via for loop, (ii) read a dictionary python script to use condition names, (iii) read a text file to use channels for averaging, (iv) account for python indexing at 0 while mne's indices begin at 1, and (v) set zero baseline for plotting
added optional root-mean-square
added legend to plot
added option to bold baseline condition's plot line
added pickle module for importing experiment dictionary, and added comment headings to organize the script
Removed pickle module because it requires the researcher to know how to make a pickled file and it adds more work. Replaced the use of pickling with the sys module and __import__()
changes to instructions
added colormap to give each plot line a unique color in the 'cool' colormap order for better visual decoding
removed unnecessary commands, added more detailed help guide and instructions
analysis_steps.txt change
removed args.expCondDict and related content because setno in mne.fiff.Evoked() can now be the name of the condition (before was only dataset ID number (int)).
added new AUDI paradigms
added a feature to extract coreg info from coregistration text file saved from MEG160. Note that it is ideal not to modify the text file from MEG160 before running this script.
removed the python command to run makeAveFiles.py so it is a separate step
added experiment date argument, which is used in .elp and .hsp filenames
mne_cov-fwd-inv.csh executes three mne commands that are used for source-space analysis. It creates the noise covariance matrix, the forward solution, and finally the inverse operator. This allows the researcher to localize the source of activation on the brain (via mne_analyze).
new AUDI paradigms have been added
updated analysis_steps.txt
commented out everything, and added four command lines at the top of script to make any scripts that runs this condCodes.py to open and execute a python file in the researcher's specified experiment folder for the experiment's specific condCodes information.
updated example & ipython command comment