# Introduction¶

Conceptually, this simulation code has three layers of abstraction:
• A base package for wavefront propagation through generic optical systems (provided by POPPY)
• Models of the JWST instruments implemented on top of that base system (provided by WebbPSF)
• An optional graphical user interface

It is entirely possible (and indeed recommended for scripting) to just use the WebbPSF interface without the GUI, but the GUI can provide a quicker method for many simple interactive calculations.

## Why WebbPSF?¶

For any space telescope, an ability to predict the properties of point spread functions (PSFs) is needed before launch for a wide range of preparatory science studies and tool development. Tools for producing high quality model PSFs must be easily accessible to the entire astronomical community. WebbPSF provides an easy-to-use tool for PSF simulations of JWST and WFIRST, in the style of the highly successful “Tiny Tim” PSF simulator for Hubble.

WebbPSF simulations are based on a mixture of observatory design parameters and as-built properties. The software provides a highly flexible and scriptable toolkit in Python for simulating a very wide range of observing modes and science scenarios, using efficient computational methods (including optional parallelization and use of GPUs). WebbPSF is a key building block in higher-level observatory simulators, including the JWST Exposure Time Calculator.

## Algorithms Overview¶

Read on if you’re interested in details of how the computations are performed. Otherwise, jump to Quick Start.

The problem at hand is to transform supplied, precomputed OPDs (derived from a detailed optomechanical model of the telescope) into observed PSFs as seen with one or more of JWST’s various detectors. This requires knowledge of the location and orientation of the detector planes, the properties of relevant optics such as bandpass filters and/or coronagraphic image and pupil plane masks, and a model of light propagation between them.

Instrumental properties are taken from project documentation and the published literature as appropriate; see the References for detailed provenance information. Optics may be described either numerically (for instance, a FITS file containing a mask image for a Lyot plane or a FITS bintable giving a spectral bandpass) or analytically (for instance, a coronagraph occulter described as a circle of a given radius or a band-limited mask function with given free parameters).

WebbPSF computes PSFs under the assumption that JWST’s instruments are well described by Fraunhofer diffraction, as implemented using the usual Fourier relationship between optical pupil and image planes (e.g. Goodman et al. 1996). Two specific types of 2D Fourier transform are implemented: a Fast Fourier Transform and a discrete Matrix Fourier Transform.

The familiar Fast Fourier Transform (FFT) algorithm achieves its speed at the cost of imposing a specific fixed relationship between pixel sampling in the pupil and image planes. As a result, obtaining finely sampled PSFs requires transforming very large arrays consisting mostly of zero-padding. A more computationally attractive method is to use a discrete matrix Fourier transform, which provides flexibility to compute PSFs on any desired output sampling without requiring any excess padding of the input arrays. While this algorithm’s computational cost grows as O(N^3) versus O(N log N) for the FFT, the FFT’s apparent advantage is immediately lost due to the need to resample the output onto the real pixel grid, which is an O(N^2) operation. By performing a matrix fourier transform directly to the desired output pixel scale, we can achieve arbitrarily fine sampling without the use of memory-intensive large padded arrays, and with lower overall computation time.

Further optimizations are available in coronagraphic mode using the semi-analytic coronagraphic propagation algorithm of Soummer et al. 2007. In this approach, rather than propagating the entire wavefront from pupil to image and back to pupil in order to account for the coronagraphic masks, we can propagate only the subset of the wavefront that is actually blocked by the image occulter and then subtract it from the rest of the wavefront at the Lyot plane. This relies on Babinet’s principle to achieve the same final PSF with more computational efficiency, particularly for the case of highly oversampled image planes (as is necessary to account for fine structure in image plane occulter masks). See Soummer et al. 2007 for a detailed description of this algorithm.

### Types of Fourier Transform Calculation in WebbPSF¶

• Any direct imaging calculation, any instrument: Matrix DFT
• NIRCam coronagraphy with circular occulters: Semi-Analytic Fast Coronagraphy and Matrix DFT
• NIRCam coronagraphy with wedge occulters: FFT and Matrix DFT
• MIRI Coronagraphy: FFT and Matrix DFT
• NIRISS NRM, GR799XD: Matrix DFT
• NIRSpec and NIRISS slit spectroscopy: FFT and Matrix DFT

See Optimizing Performance and Parallelization in the POPPY documentation for more details on calculation performance.

## Getting WebbPSF¶

The WebbPSF software is installable through pip, but it depends on data files distributed through STScI. Since there is more than one way to install scientific Python software, the possibilities are covered in Requirements & Installation.

The AstroConda distribution includes WebbPSF and its associated data files. If you installed the packages into an environment named astroconda, the command to upgrade all STScI software (including WebbPSF) to the latest version would be conda update --name astroconda stsci.

For those who prefer to use pip (and have NumPy, SciPy, and matplotlib already installed) the command is:

\$ pip install -U webbpsf


This command installs (or upgrades) WebbPSF to the latest version on PyPI. Before WebbPSF will run, you must download the WebbPSF data files and set the WEBBPSF_DATA environment variable to point to the place you extracted them. You may also want to install Pysynphot, an optional dependency, to improve PSF fidelity.

For detailed installation instructions, refer to Requirements & Installation. (This document also explains how to install optional dependencies, install supporting data files, install from GitHub source, etc.)

## Quick Start¶

First, download and install the software. Then just start python and

>>> import webbpsf
>>> webbpsf.gui()


and you should be able to test drive things using the GUI:

Figure 1: The main window of the WebbPSF GUI when first launched.

Most controls should be self-explanatory, so feel free to experiment. The article Using WebbPSF via the Graphical User Interface provides a detailed explanation of the GUI options. (Please note, the GUI only provides basic access to WebbPSF for introductory purposes; the Python programming interface is the recommended best way to use WebbPSF.)

WebbPSF can save a detailed log of its calculations and results. This will by default be shown on screen but can also be saved to disk.

>>> webbpsf.setup_logging(filename='my_log_file.txt')


Log settings are persistent between sessions, so you can just set this once the very first time you start WebbPSF and logging will be enabled thereafter until you explicitly change it.

For further information, consult Using WebbPSF via the Python API or Using WebbPSF via the Graphical User Interface.