Simple detector¶
Like most other diffraction processing packages, pyFAI allows the definition of 2D detectors with a constant pixel size and recoded in S.I.. Typical pixel size are 50e-6 m (50 microns) and will be used as example in the numerical application.
Pixels of the detector are indexed from the origin located at the lower left corner when looking from the sample. The pixel’s center is located at half integer index:
pixel 0 goes from position 0 m to 50e-6m and is centered at 25e-6 m.
pixel 1 goes from position 50e-6 m to 100e-6 m and is centered at 75e-6 m
Nota: Most of the time you will need to pass the optional argument origin=”lower” to matplotlib’s imshow function when displaying the image to avoid confusion.
Complex detectors¶
The simple detector approach reaches its limits with several detector types, such as multi-module and fiber optic taper coupled detectors (most CCDs). Large area pixel detectors are often composed of smaller modules (i.e. Pilatus from Dectris, Maxipix from ESRF, …).
By construction, such detectors exhibit gaps between modules along with pixels of various sizes within a single module, hence they require specific data masks. Optically coupled detectors need also to be corrected for small spatial displacements, often called geometric distortion. This is why detectors need more complex definitions than just that of a pixel size. To avoid complicated and error-prone sets of parameters, two tools have been introduced: either detector classes define programmatically detector or Nexus saved detector setup.
Detectors classes¶
They are used to define families of detectors. In order to take the specificities of each detector into account, pyFAI contains about 58 detector class definitions (and 168 with aliases) which contain a mask (invalid pixels, gaps, …) and a method to calculate the pixel positions in Cartesian coordinates. Available detectors can be printed using:
>>> import pyFAI
>>> print(pyFAI.detectors.ALL_DETECTORS)
For optically coupled CCD detectors, the geometric distortion is often described by a bi-dimensional cubic spline which can be imported into the detector instance and be used to calculate the actual pixel position in space.
Nexus Detectors¶
Any detector object in pyFAI, can be saved into a HDF5 file following the NeXus convention (http://nexusformat.org). Detector objects can subsequently be restored from the disk, making complex detector definitions less error-prone. Pixels of an area detector are saved as a 4D dataset: i.e. a 2D array of vertices pointing to every corner of each pixel, generating an array of shape: (Ny, Nx, Nc, 3) where Nx and Ny are the dimensions of the detector, Nc is the number of corners of each pixel, usually 4, and the last entry contains the coordinates of the vertex itself (z,y,x). This kind of definitions, while relying on large description files, can address some of the most complex detector layouts:
hexagonal pixels (i.e. Pixirad detectors, still under development)
curved/bent imaging plates (i.e. Rigaku, Aarhus detector)
pixel detectors with tiled modular (i.e. Xpad detectors from ImXpad)
semi-cylindrical pixel detectors (i.e. Pilatus12M from Dectris or CirPad from Soleil).
The detector instance can be saved as HDF5, either programmatically, either on the command line.
from pyFAI import detectors
frelon = detectors.FReLoN("halfccd.spline")
print(frelon)
frelon.save("halfccd.h5")
Using the detector2nexus script to convert a complex detector definition (multiple modules, possibly in 3D) into a single NeXus detector definition together with the mask:
detector2nexus -s halfccd.spline -o halfccd.h5
Conclusion¶
Detector definition in pyFAI is very versatile. Fortunately, most detectors are already described, making the usage transparent for most users. There are a couple of tutorial on detector definition which will help you understanding the underlying mechanism:
Distortion which explains how to correct images for geometric distortion
CCD-calibration which explains how to calibrate such detectors for geometric distortion.