[Numpy-discussion] Re: Enhancement: np.convolve(..., mode="normalized")

I wonder whether you are looking for the solution in the right direction. Is there theory for the shape of the curve? In that case it might be better to see the problem as a fitting problem. Other than that I think option 2 is too ad hoc for scientific work. I would opt for simply not showing t

[Numpy-discussion] NEP 55 Updates and call for testing

Hi all, This week I updated NEP 55 to reflect the changes I made to the prototype since I initially sent out the NEP. The updated NEP is available on the NumPy website: https://numpy.org/neps/nep-0055-string_dtype.html. Updates to the NEP ++ The changes since the original version

[Numpy-discussion] Enhancement: np.convolve(..., mode="normalized")

Convolution is often used for smoothing noisy data; a typical use will keep the 'same' length of data and may look like this: >convol = 2**-np.linspace(-2,2,100)**2; >y2 = np.convolve(y,convol/np.sum(convol), mode='same') ## simple smoothing >ax.plot(x, y2, label="simple smoothing",