On the use of adding an expfit class in or next to the polynomial module
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Hello people ! First-of-all, thank you for your great service to the Python community. I'm working for a project on porting some Octave code base towards Python. As you can imagine, it is a lot of fun, but I sometimes come across functions that are not (yet ?) available in the mighty NumPy package. Namely, the expfit function, as presented [here](https://octave.sourceforge.io/optim/function/expfit.html) (https://octave.sourceforge.io/optim/function/expfit.html) doesn't seem to exist. I'm currently building a replacement for it in my words (keywords I mean ;) ) but maybe a better solution would be to contribute some code into NumPy. AFAIK, the preferred way to fit a Polynomial nowadays is to call numpy.polynomial.polynomial.Polynomial. Do you think that a class for `Exponential` based on that one would be an interesting addition to NumPy ? Thanks in advance, Swan BOSC.
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Hi there, Thanks for reaching out. On Wed, Jan 27, 2021, at 04:24, Swan BOSC wrote:
I think Prony's method would probably be a better fit for `scipy.signal`. Please be mindful not to translate this from existing GPL code, but to implement it afresh. Best regards, Stéfan
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Hi there, Thanks for reaching out. On Wed, Jan 27, 2021, at 04:24, Swan BOSC wrote:
I think Prony's method would probably be a better fit for `scipy.signal`. Please be mindful not to translate this from existing GPL code, but to implement it afresh. Best regards, Stéfan
participants (2)
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Stefan van der Walt
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Swan BOSC