jscatter 1.9.0

Combines dataArrays with attributes for fitting plotting and analysis including models for Xray and neutron scattering

The aim of Jscatter is treatment of experimental data and models:

• Reading and analyzing experimental data with associated attributes as temperature, wavevector, comment, ….

• Multidimensional fitting taking attributes into account.

• Providing useful models for neutron and X-ray scattering form factors, structure factors and dynamic models (quasi elastic neutron scattering) and other topics.

• Simplified plotting with paper ready quality.

• Easy model building for non programmers.

• Python scripts/Jupyter Notebooks to document data evaluation and modelling.

Main concept

• Data are organised in dataArray/dataList with attributes as .temperature, .wavevector, .pressure and methods for filter, merging and more.

• Multidimensional, attribute dependent fitting (least square Levenberg-Marquardt, Bayesian fit, differential evolution, regularisation, …).

• Provide relative simple plotting commands to allow a fast view on data and possible later pretty up.

• User write models using the existing model library or self created models.

  The model library contains routines e.g. for vectorized quadrature (formel) or specialised models for scattering as formfactor, structurefactor, dynamic and biomacromolecules.

Documentation

Documentation located at http://jscatter.readthedocs.io. A number of examples how to use Jscatter is provided and can be run from Jscatter.

A short example how to use Jscatter

import jscatter as js

i5=js.dL(js.examples.datapath+'/iqt_1hho.dat')     # read the data (16 sets) with attributes
# define a model for the fit
diffusion=lambda A,D,t,wavevector,elastic=0:A*np.exp(-wavevector**2*D*t)+elastic

# do the fit
i5.fit(model=diffusion,                     # the fit function
       freepar={'D':[0.08],'A':0.98},       # start parameters, "[]" -> independent fit
       fixpar={'elastic':0.0},              # fixed parameters
       mapNames={'t':'X','wavevector':'q'}) # map names from the model to names from the data

p=js.grace(1.2,0.8)                         # open a plot
p.plot(i5,symbol=[-1,0.4,-1],legend='Q=$q') # plot with Q values in legend
p.plot(i5.lastfit,symbol=0,line=[1,1,-1])   # plot fit as lines
p.save('test.agr')

** Released under the GPLv3 **