AshKelly

import time
import numba
import numpy as np


# Compare numba-accelerated for loop convolution to Numpy's function
@numba.jit(
    "float64[::1](float64[::1], float64[::1])",
    locals={
        "n_sig": numba.int64,

AshKelly

import numpy as np
import pyquad


def test_integrand_func(x, alpha, beta, j):
    return x * alpha * beta + j


x = np.array([1., 2.])
y = np.array([3., 4.])

AshKelly

GSOC 2018 Work product submission.

Summary of GSoC project with yt-project (NumFocus)

yt is a package for analyzing and visualizing volumetric data. The current infrastructure of yt is designed to deal with adaptive mesh refinement (AMR) data. However, a wide range of modern N-body hydrodynamics simulations now use smoothed particle hydrodynamics (SPH) which produces data consisting of volume filling fields (see Springel 2015 and Schaller 2013).

The project aimed to continue the development and introduction of a yt API for SPH data that ensures all SPH fields return data defined at the locations of the particles - the initial development is discussed here. The summer project focussed on adding "scatter" and "gather" pixelization routines to a variety of yt data containers.

Below I will list the main features implemented along with links to the code, more detials can be found below about each of these features such as useage, examples and