syris 0.3

X-ray imaging simulation

Syris (synchrotron radiation imaging simulation) is a framework for simulations of X-ray absorption and phase contrast dynamic imaging experiments, like time-resolved radiography, tomography or laminography. It includes X-ray sources, various sample shape creation possibilities, complex refractive index lookup options, motion model and indirect detection model (scintillator combined with a conventional camera). Phase contrast is simulated by the Angular spectrum method, which enables one to include various optical elements in the simulation, e.g. gratings and X-ray lenses.

Compute-intensive algorithms like Fourier transforms, sample shape creation and free-space propagation are implemented by using OpenCL, which enables one to execute the code on graphic cards.

There are numerous examples of how to use syris described below which ship directly with the code. Enjoy!

Usage

The first thing you have to do is to initialize syris by the syris.init() function. After that you only need to do whatever is necessary for your program. A simple white beam propagation example looks like this:

import matplotlib.pyplot as plt
import numpy as np
import quantities as q
import syris
from syris.physics import propagate
from syris.bodies.simple import make_sphere
from syris.materials import make_henke

syris.init()
energies = np.arange(10, 30) * q.keV
n = 1024
pixel_size = 0.4 * q.um
distance = 2 * q.m
material = make_henke('PMMA', energies)

sample = make_sphere(n, n / 4 * pixel_size, pixel_size, material=material)
image = propagate([sample], (n, n), energies, distance, pixel_size).get()
plt.imshow(image)
plt.show()

For more detailed information please see the reference.

Examples

Are stored in the examples directory. Make sure you install their dependencies by pip install -r requirements-examples.txt and then you can run them from the top-level directory of syris repository by e.g. python -m examples.simple.

Citation

If you use this software for publishing your data, we kindly ask to cite the article below.

Faragó, T., Mikulík, P., Ershov, A., Vogelgesang, M., Hänschke, D. & Baumbach, T. (2017). J. Synchrotron Rad. 24, https://doi.org/10.1107/S1600577517012255