Profiles¶

Contents:

Profiles module consists of classes and functions neccessary to reproduce the plots from Bektesevic & Vinkovic et. al. 2017 (arxiv: 1707.07223).

The module provides classes for various different types of object brightness profiles (PointSource, GaussianSource, DiskSource, RabinaSource), seeing (Gaus- Kolmogorov), and defocusing effects (FluxPerAngle).

Sources represent 1D integrated brightness profiles of different source distributions as well as various parameters describing these distributions (such as FWHM, width, distance from instrument, variance etc.) in a common lookalike interface. They usually carry with them their resolution, scale or units (units are more for personal reference than actually useful).

Convolving Sources with Seeing and/or Defocus profile produce a new Source, which brightness profile corresponds to that of an source which is affected by these efects. So a convolution of a PointSource and seeing represents a source in focus observed through an atmoshere and PointSource convolved with Defocus corresponds to a defocused point source in ideal seeing (no seeing effects) etc

The module also contains a more powerful generic sampler that produces profiles and/or specific measurements in given points.

Examples

A simple convolution of a point profile with SDSS seeing and defocus:

from lfd.analysis import profiles

point = profiles.PointSource(100)
seeing = profiles.GausKolmogorov(profiles.SDSSSEEING)
defocus = profiles.FluxPerAngle(100, *profiles.SDSS)

a = profiles.convolve(point, seeing, defocus)

Simplest use of a generic sampler is just creating many point profiles at different heights as seen by SDSS:

from lfd.analysis import profiles
points = profiles.generic_sampler(profiles.PointSource, profiles.HEIGHTS)

A more complicated use case would be creating disk profilse of different radii at many different seeings FWHMs and heights as seen by SDSS:

from lfd.analysis import profiles
disks = profiles.generic_sampler(profiles.DiskSource,
                                 radius = (1, 2, 3),
                                 seeingFWHM=profiles.SEEINGS,
                                 h = profiles.HEIGHTS)

By default the generic sampler would return the actual profiles but it can also return a strucutred array containing all information pertinent (used profiles, instrument, height radius, FWHM measurements etc…) to the created profiles instead:

from lfd.analysis import profiles
disks = profiles.generic_sampler(profiles.DiskSource,
                                 radius = (1, 2, 3),
                                 seeingFWHM=profiles.SEEINGS,
                                 h = profiles.HEIGHTS,
                                 returnType="grid")

As well as convenient functionality to convert the retrieved structured arrays into something easily plottable with matplotlib for example (see meshgrid).