New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
ENH: multi-taper PSD estimation #89
Conversation
|
||
# define frequencies of interest | ||
fmin, fmax = 0., 70. | ||
bw = 4. # bandwidth of the windows in Hz |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
can you use bandwidth rather than bw?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Ok. Any objections if I change the parameter name to "bandwidth" as well?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
+1 for bandwidth everywhere
besides this looks good to me ! |
can you rebase so the PR can be merged nicely with the green button? |
Rebased. The NW issue was confusing. Apparently dpss_windows() expected NW to be the standardized half-bandwidth but then "Kmax" is wrong. I fixed this and opened a PR in nitime nipy/nitime#105 One problem is that the tests will fail unless you use nitime with my PR included.. |
merging ! I guess we'll have a failing test for a few days... |
ENH: multi-taper PSD estimation
Multi-taper PSD estimation, direct and for MNE-type inverse methods in source space.
The functions for the DPSS window computation were copied from nitime with some minor modifications. The PSD computation was optimized s.t. it requires less memory and is faster than the nitime version: When adaptive weights are used, the tapered spectra are combined directly instead of first returning the weights (~50% memory saving), in addition, parallel processing can be used to speed up the computation.