spexwavepy

Version:

1.0.1

Authors:

Lingfei Hu (USTC), Hongchang Wang (DLS)

Dependencies:

• Numpy

• Scipy

• cv2(opencv-python): This is OpenCV support for python.

• natsort: This package is used for natural sorting.

E-mail:

hulf88@ustc.edu.cn, hongchang.wang@diamond.ac.uk

What is spexwavepy

Spexwavepy is an open-source Python package dedicated to the speckle-based wavefront sensing techniques for X-ray optics. Its name is the abbreviation of the speckle-based x-ray wavefront sensing Python package. It covers a variety of X-ray speckle-based techniques and provides examples with real experiment data. This documentation will show users how to get the spexwavepy package, where to download the shared experiment data, how to run the examples, how to build your own Python code using this package, etc. We hope this Python package can help users get familiar with the data processing for X-ray wavefront sensing using speckle-based techniques.

The principle of speckle-based wavefront sensing techniques

• The speckle-based wavefront sensing techniques

  • Conventional X-ray Speckle Tracking (XST) technique with reference beam

  • Self-reference X-ray Speckle Tracking (XST) technique

  • X-ray Speckle Scanning (XSS) technique with reference beam

  • Self-reference X-ray Speckle Scanning (XSS) technique

  • X-ray Speckle Vector Tracking (XSVT) technique

  • Other X-ray speckle-based techniques

Getting started

• Getting started

  • Installation

  • Getting raw data

  • Computational consumption

  • Tutorial

    • 1. Read the image stack

    • 2. Determine the detector pixel size

    • 3. Stability check

    • 4. Single CRL measurement

Examples

• Examples

  • Plane mirror measurement with reference beam

  • Measurement of the wavefront local curvature after a plane mirror

  • Mirror slope error curve (1D) reconstructed from the dowmstream setup

  • Comparison between self-reference XSS technique and self-reference XST technique

  • KB mirror alignment using self-reference XST technique

  • Hartmann-like data processing scheme

User guide

• User guide

  • Fundamental algorithm

    • Cross-correlation

    • Sub-pixel registration

      • Default method

      • Gaussian peak finding method

      • Parabola peak finding method

    • Image match

    • Image normalization

    • Auxiliary functions

  • Image stack and its functions

    • Image stack

    • Data reading

    • Preprocessing of the images

      • Normalization

      • Smoothing

      • Fliping the images

      • Rotating the images

      • Detector pixel size determination

  • The speckle-based techniques included in Tracking class

    • Stability checking using speckle patterns

    • Reference and sample image stacks collimating before tracking

    • XSS technique with reference beam

    • Self-reference XSS technique

    • Self-reference XST technique

    • Conventional XST technique with reference beam

    • XSVT technique

    • Hartmann-like data processing scheme

  • Post processing of the tracked speckle pattern shifts

    • Slope reconstruction

    • Local curvature reconstruction

    • 2D integration for post processing

    • Auxiliary functions

API reference

• API reference

Indices and tables

• Index

• Module Index

• Search Page