<!DOCTYPE html><html><head><title></title><style type="text/css">p.MsoNormal,p.MsoNoSpacing{margin:0}</style></head><body><div>Hi there,<br></div><div><br></div><div>Thanks for reaching out.</div><div><br></div><div>On Wed, Jan 27, 2021, at 04:24, Swan BOSC wrote:<br></div><blockquote type="cite" id="qt" style=""><div>Namely, the expfit function, as presented <a href="https://octave.sourceforge.io/optim/function/expfit.html" target="_blank" title="https://octave.sourceforge.io/optim/function/expfit.html" rel="nofollow">here</a> (<a href="https://octave.sourceforge.io/optim/function/expfit.html">https://octave.sourceforge.io/optim/function/expfit.html</a>) 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.<br></div><div><br></div><div>AFAIK, the preferred way to fit a Polynomial nowadays is to call <code class="qt-sig-prename qt-descclassname">numpy.polynomial.polynomial.</code><code class="qt-sig-name qt-descname">Polynomial.</code> <br></div><div>Do you think that a class for `Exponential` based on that one would be an interesting addition to NumPy ?<br></div></blockquote><div><br></div><div>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.<br></div><div><br></div><div>Best regards,<br></div><div>Stéfan</div></body></html>