README for smooth3d

Contact: garrethmartin@arizona.edu

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Purpose:

Adaptively smooths sparsely sampled particles according to their local density. Similar to the approach described in Merritt+2020, Section 3.1

The basic method is as follows:

1. Calculate the k-NN local number density in units of image element (i.e. pixel length$^{3}$) for every particle
2. For every particle below the density threshold (3 particles per element by default), calculate the k-NN distance, d
3. Split each of these particles into N sub-particles with a 3D Gaussian distribution with the mean equal to the original particle and sigma=d
4. Bin the new particles

Currently options for smoothing the sum and mean values

Prerequisites:

• numpy
• scipy
• sklearn
• fast_histogram
• tqdm

Usage

inputs:

X: particle co-ordinates: array shape=(3, Nparticles)

quantity_sum: particle quantities to be summed: array shape=(Nquantities, Nparticles); ignore if [False]

quantity_average: particle quantities to be averaged: array shape=(Nquantities, Nparticles); ignore if [False]

res: desired size of resolution unit in same units as X

extent: desired size of the image in the same units as X

upper_threshold: number density of particles above which they will no longer be smoothed

lower_threshold: number density of particles below which they will no longer be smoothed

njobs: number of workers to assign

nsteps: total number of density bins to do smoothing over, keep this fairly large or the result will lose accuracy

k: k nearest neighbour density estimate

n_resample: number of sub-particles to split each particle into for smoothing

projection: axis of projection used to produce the images

antialias: specifies whether the final image is antialiased

outputs:

img: smoothed image for each summed quantity: shape = (Nquantities, Npixels, Npixels)

average_img: smoothed image for each averaged quantity: shape = (Nquantities, Npixels, Npixels)

Example script

python example.py