quadpy | : triangular_ruler : Numerical integration | Math library

I have found the solution. I fixed the issue by creating a wrapper function taking in *args, converting args to a numpy array, and integrating the wrapper function.

Here's an example:

Generally it is much, much faster to do a summation via matrix operations than to use scipy.integrate.quad (or dblquad). You could rewrite your f(q, z, t) to take in a q, z and t vector and return a 3D-array of f-values using np.tensordot, then multiply your area element (dtdz) with the function values and sum them using np.sum. If your area element is not constant, you have to make an array of area-elements and use np.einsum To take your integration limits into account you can use a masked array to mask the function values outside your integration limits before summarizing. Take note that np.einsum overlooks the masks, so if you use einsum you can use np.where to set function values outside your integration limits to zero. Example (with constant area element and simple integration limits):

After some more experimenting I have found that quadpy's quad method takes the same argument as scipys quad method, which can be found here:

• https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html#scipy.integrate.quad

Using the epsabs, epsrel, limit optional arguments I can thus prevent the error:

According to the release notes, scipy.integrate.quad_vec was released with scipy version 1.4.0. Thus you need to upgrade your scipy.

Inserting into the middle of a list a bunch of times is going to be very slow for a large dataset. It seems like you can just build a new list like you're doing: