issue_comments: 169008093
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https://github.com/pydata/xarray/issues/706#issuecomment-169008093 | https://api.github.com/repos/pydata/xarray/issues/706 | 169008093 | MDEyOklzc3VlQ29tbWVudDE2OTAwODA5Mw== | 1197350 | 2016-01-05T13:57:34Z | 2016-01-05T13:57:34Z | MEMBER | Hi Rafael, I do lots of multidimensional spectral analysis on geophysical data (mostly ocean satellite fields, this paper http://journals.ametsoc.org/doi/abs/10.1175/JPO-D-14-0160.1, for example), and I have recently started trying passing some of these calculations through xray. An example is in this notebook https://gist.github.com/rabernat/be4526e157eb1fc69f50, where I define a function to compute an isotropic power spectrum over specified dimensions. One huge source of confusion for students starting out with such calculations is the questions, what are the spectral coordinates that come out of fft? (E.g. is it "shifted"?, is there a 2 pi factor in the units?, etc.) Because of xray's data model, these difficulties can be completely bypassed by including verbose descriptions of the dimensions and coordinates. My view is that spectral analysis is out of scope for xray. However, I think there is the need for a domain specific spectral analysis package focused on geophysical data, which would naturally be built on xray. (As a comparison, consider the nitime http://nipy.org/nitime/ package for neuroimaging timeseries analysis.) This is something that I, and probably many others, would be interested in collaborating on. Some features I would like to see are: - wrapping of numpy fft to work on xray dataarrays, including proper handling of coordinates (pretty easy) - support for different windowing / multitaper methods - proper treatment of errors - built-in plotting - parallelization for out-of-core data (this is a hard one with fft but would be very useful) I think such a package would really take off in popularity and would help to displace MATLAB for this very common type of analysis. The question is whether there really is enough common interest among different scientists to justify a new package, as opposed to everyone just "rolling their own" solution. Based on your email, it sounds like you might be interested in such an effort. Cheers, Ryan Abernathey . On Tue, Jan 5, 2016 at 2:55 AM, Rafael Guedes notifications@github.com wrote:
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