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Build Status PkgEval Coverage License

Stable Dev

Fast, allocation-free point-spread function (PSF) representations


  • gaussian (or normal)
  • airydisk
  • moffat


From the Julia REPL

julia> ]

(@v1.5) pkg> add PSFModels

To import the library

julia> using PSFModels
julia> using PSFModels: gaussian

julia> model = gaussian(x=0, y=0, fwhm=8)

or you can create an alias for PSFModels

# julia version 1.5 or below
using PSFModels
const M = PSFModels
# julia version 1.6 or above
import PSFModels as M

model = M.gaussian(fwhm=10)


For more in-depth usage and examples, please see the documentation.

First, load the package

julia> using PSFModels

Evaluating models

Directly evaluating the functions is the most straightforward way to use this package

julia> gaussian(0, 0; x=0, y=0, fwhm=3)

julia> gaussian(BigFloat, 0, 0; x=0, y=0, fwhm=3, amp=0.1)

We also provide "curried" versions of the functions, which allow you to specify the parameters and evaluate the PSF later

julia> model = gaussian(x=0, y=0, fwhm=3);

julia> model(0, 0)

If we want to collect the model into a dense matrix, simply iterate over indices

julia> inds = CartesianIndices((-2:2, -2:2));

julia> model.(inds) # broadcasting
5×5 Matrix{Float64}:
 0.0850494  0.214311  0.291632  0.214311  0.0850494
 0.214311   0.54003   0.734867  0.54003   0.214311
 0.291632   0.734867  1.0       0.734867  0.291632
 0.214311   0.54003   0.734867  0.54003   0.214311
 0.0850494  0.214311  0.291632  0.214311  0.0850494

This makes it very easy to evaluate the PSF on the same axes as an image (array)

julia> img = randn(5, 5);

julia> model.(CartesianIndices(img))
5×5 Matrix{Float64}:
 0.54003      0.214311     0.0459292    0.00531559   0.000332224
 0.214311     0.0850494    0.018227     0.00210949   0.000131843
 0.0459292    0.018227     0.00390625   0.000452087  2.82555e-5
 0.00531559   0.00210949   0.000452087  5.2322e-5    3.27013e-6
 0.000332224  0.000131843  2.82555e-5   3.27013e-6   2.04383e-7

this is trivially expanded to fit "stamps" in images

julia> big_img = randn(1000, 1000);

julia> stamp_inds = (750:830, 400:485);

julia> stamp = @view big_img[stamp_inds...];

julia> stamp_model = model.(CartesianIndices(stamp_inds));

or we can create a loss function for fitting PSFs without allocating any memory. We are simply iterating over the image array!

julia> using Statistics

julia> mse = mean(I -> (big_img[I] - model(I))^2, CartesianIndices(stamp_inds));

Fitting data

There exists a simple, yet powerful, API for fitting data with these PSF models. See the full documentation for more details and examples.

# `fit` is not exported to avoid namespace clashes
using PSFModels: fit

data = # load data
stamp_inds = # optionally choose indices to "cutout"

# use an isotropic Gaussian
P0 = (x=12, y=13, fwhm=3.2, amp=0.1)
params, synthpsf = fit(gaussian, P0, data, stamp_inds)

# elliptical, rotated Gaussian
P0 = (x=12, y=13, fwhm=(3.2, 3.2), amp=0.1, theta=0)
params, synthpsf = fit(gaussian, P0, data, stamp_inds)

# obscured Airy disk
P0 = (x=12, y=13, fwhm=3.2, amp=0.1, ratio=0.3)
params, synthpsf = fit(airydisk, P0, data, stamp_inds)

# bivariate Moffat with arbitrary alpha
P0 = (x=12, y=13, fwhm=(3.2, 3.2), amp=0.1, alpha=1)
# fixed ("frozen") rotation angle
func_kwargs = (;theta=15)
params, synthpsf = fit(moffat, P0, data, stamp_inds; func_kwargs)

Plotting models

We provide simple user recipes from RecipesBase.jl, which can be called with psfplot/psfplot!

using Plots

inds = (1:30, 1:30)
model = airydisk(x=12, y=13, fwhm=(4.5, 6.7), theta=12, ratio=0.3)
psfplot(model, inds, colorbar_scale=:log10)

Contributing and Support

If you would like to contribute, feel free to open a pull request. If you want to discuss something before contributing, head over to discussions and join or open a new topic. If you're having problems with something, please open an issue.