PAN Blend grazing-incidence packages

BINoculars is a tool for data reduction and analysis of large sets of surface diffraction data that have been acquired with a two-dimensional X-ray detector. The intensity of each pixel of a two-dimensional detector is projected onto a three-dimensional grid in reciprocal-lattice coordinates using a binning algorithm. This allows for fast acquisition and processing of high-resolution data sets and results in a significant reduction of the size of the data set. The subsequent analysis then proceeds in reciprocal space. It has evolved from the specific needs of the ID03 beamline at the ESRF, but it has a modular design and can be easily adjusted and extended to work with data from other beamlines or from other measurement techniques.

BornAgain은 방목 입사각에서 소각 산란을 시뮬레이션하고 피팅하는 소프트웨어 패키지입니다. X 선 (GISAXS) 및 중성자 (GISANS) 데이터 분석 모두를 지원합니다. 계산은 일그러진 파동 보른 근사 (distorted wave Born approximation/DWBA) 프레임워크에서 수행됩니다. BornAgain은 대화형 사용을 위한 그래픽 사용자 인터페이스뿐만 아니라 정밀 또는 대략적인 인터페이스와 다양한 유형의 내장 나노 입자가 있는 다층 샘플을 모델링 하기 위한 Python 및 C++ 프레임워크를 제공합니다

BornAgain은 아래 사항을 지원합니다:

레이어:

• 레이어 수에 제한이 없는 멀티레이어
• 인터페이스 강성 상관
• 자성 재료

입자:

• 다양한 모양의 입자 중에서 선택 (폼 팩터)
• 내부 구조를 갖는 입자
• 입자의 집합체
• 입자의 크기 분포(다분산도)

입자의 위치

• 수직 위치와 수평 위치 간의 분리된 구현
• 수직 분포: 레이어 또는 상단의 특정 깊이에 있는 입자
• 평면 분포:
• 완전히 무질서한 시스템
• 단거리 질서 분포 (paracrystals)
• 2 차원 및 1차원 겨자

입력 빔:

• 극성 및 비극성 중성자
• X 선
• 다양한 분포에 따른 입력빔 (파장, 입사각)의 발산
• 입력 강도의 정규화 기능

탐지기:

• 정반사 산란 해제
• 2 차원 강도 매트릭스, 출력 각도의 함수

BornAgain 사용:

• 생성된 샘플에서 GISAXS 및 GISANS 시뮬레이션
• 참조 데이터에 피팅 (실험 또는 수치)
• Python 스크립트 또는 그래픽 사용자 인터페이스를 통한 상호 작용

BornAgain을 업무에 사용하는 경우 아래 사항을 인용해야 합니다.

  C. Durniak, M. Ganeva, G. Pospelov, W. Van Herck, J. Wuttke(2015), BornAgain — 방목 입사각에서 X선 및 중성자 소각 산란을 시뮬레이션하고 피팅하기 위한 소프트웨어, 버전 <사용한 버전> , http://www.bornagainproject.org

BornAgain is a software package to simulate and fit small-angle scattering at grazing incidence. It supports analysis of both X-ray (GISAXS) and neutron (GISANS) data. Calculations are carried out in the framework of the distorted wave Born approximation (DWBA). BornAgain provides a graphical user interface for interactive use as well as a generic Python and C++ framework for modeling multilayer samples with smooth or rough interfaces and with various types of embedded nanoparticles.

BornAgain supports:

Layers:

• Multilayers without any restrictions on the number of layers
• Interface roughness correlation
• Magnetic materials

Particles:

• Choice between different shapes of particles (form factors)
• Particles with inner structures
• Assemblies of particles
• Size distribution of the particles (polydispersity)

Positions of Particles:

• Decoupled implementations between vertical and planar positions
• Vertical distributions: particles at specific depth in layers or on top.
• Planar distributions:
• fully disordered systems
• short-range order distribution (paracrystals)
• two- and one-dimensional lattices

Input Beam:

• Polarized or unpolarized neutrons
• X-ray
• Divergence of the input beam (wavelength, incident angles) following different distributions
• Possible normalization of the input intensity

Detector:

• Off specular scattering
• Two-dimensional intensity matrix, function of the output angles

Use of BornAgain:

• Simulation of GISAXS and GISANS from the generated sample
• Fitting to reference data (experimental or numerical)
• Interactions via Python scripts or Graphical User Interface

If you use BornAgain in your work, please cite

 C. Durniak, M. Ganeva, G. Pospelov, W. Van Herck, J. Wuttke (2015), BornAgain
 — Software for simulating and fitting X-ray and neutron small-angle
 scattering at grazing incidence, version <version you used>,
 http://www.bornagainproject.org
 This package contains the BornAgain documentation.

xrayutilities is a collection of scripts used to analyze x-ray diffraction data. It consists of a Python package and several routines coded in C. It especially useful for the reciprocal space conversion of diffraction data taken with linear and area detectors.

This package includes the manual in HTML format.

BornAgain is a software package to simulate and fit small-angle scattering at grazing incidence. It supports analysis of both X-ray (GISAXS) and neutron (GISANS) data. Calculations are carried out in the framework of the distorted wave Born approximation (DWBA). BornAgain provides a graphical user interface for interactive use as well as a generic Python and C++ framework for modeling multilayer samples with smooth or rough interfaces and with various types of embedded nanoparticles.

BornAgain supports:

Layers:

• Multilayers without any restrictions on the number of layers
• Interface roughness correlation
• Magnetic materials

Particles:

• Choice between different shapes of particles (form factors)
• Particles with inner structures
• Assemblies of particles
• Size distribution of the particles (polydispersity)

Positions of Particles:

• Decoupled implementations between vertical and planar positions
• Vertical distributions: particles at specific depth in layers or on top.
• Planar distributions:
• fully disordered systems
• short-range order distribution (paracrystals)
• two- and one-dimensional lattices

Input Beam:

• Polarized or unpolarized neutrons
• X-ray
• Divergence of the input beam (wavelength, incident angles) following different distributions
• Possible normalization of the input intensity

Detector:

• Off specular scattering
• Two-dimensional intensity matrix, function of the output angles

Use of BornAgain:

• Simulation of GISAXS and GISANS from the generated sample
• Fitting to reference data (experimental or numerical)
• Interactions via Python scripts or Graphical User Interface

If you use BornAgain in your work, please cite

 C. Durniak, M. Ganeva, G. Pospelov, W. Van Herck, J. Wuttke (2015), BornAgain
 — Software for simulating and fitting X-ray and neutron small-angle
 scattering at grazing incidence, version <version you used>,
 http://www.bornagainproject.org
 This package contains the Python bindings for use in scripts.

DENSS is an algorithm used for calculating ab initio electron density maps directly from solution scattering data. DENSS implements a novel iterative structure factor retrieval algorithm to cycle between real space density and reciprocal space structure factors, applying appropriate restraints in each domain to obtain a set of structure factors whose intensities are consistent with experimental data and whose electron density is consistent with expected real space properties of particles.

DENSS utilizes the NumPy Fast Fourier Transform for moving between real and reciprocal space domains. Each domain is represented by a grid of points (Cartesian), N x N x N. N is determined by the size of the system and the desired resolution. The real space size of the box is determined by the maximum dimension of the particle, D, and the desired sampling ratio. Larger sampling ratio results in a larger real space box and therefore a higher sampling in reciprocal space (i.e. distance between data points in q). Smaller voxel size in real space corresponds to higher spatial resolution and therefore to larger q values in reciprocal space.

GenX is a versatile program using the differential evolution algorithm for fitting, primarily, X-ray and neutron reflectivity data, lately also surface x-ray diffraction data. The differential evolution algorithm is a robust optimization method which avoids local minima but at same is a highly effective. GenX is written in Python and uses the wxpython package for the Graphical User Interface (GUI). A model to fit is defined either through a GUI plug-in or via a Python script. The possibility to script everything makes it easy to develop completely new fitting model. Clearly, GenX is extremely modular, making it possible to extend the program with models and plug-ins for most fitting problems. At the present GenX is shipped with models for x-ray and neutron specular reflectivity, off-specular x-ray reflectivity and surface x-ray diffraction

Flexible, powerful, Python package for generalised curvefitting analysis, specifically neutron and X-ray reflectometry data.

It uses several scipy.optimize algorithms for fitting data, and estimating parameter uncertainties. As well as the scipy algorithms refnx uses the emcee Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler for Bayesian parameter estimation.

xrayutilities is a collection of scripts used to analyze X-rays diffraction data. It consists of a Python package and several routines coded in C. It especially useful for the reciprocal space conversion of diffraction data taken with linear and area detectors.

This is the Python 3 version of the package.

MoviePy is a Python library for video editing: cutting, concatenations, title insertions, video compositing (a.k.a. non-linear editing), video processing, and creation of custom effects.