Jscatter’s documentation

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

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  • 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.

Binder citation install license pyversion Read the Docs Beginners Guide

Main concept

Exemplary functions:

Physical equations and useful formulas as quadrature of vector functions

  • scatteringLengthDensityCalc() -> Electron density, coh and inc neutron scattering length, mass.

  • smear() -> Smearing enabling simultaneous fits of differently smeared SANS/SAXS data.

  • desmear() -> Desmearing according to the Lake algorithm for the above.

See formel and smallanglescattering (sas)

Particle Formfactors

See formfactor (ff)

Particle Structurefactors

  • RMSA() -> Rescaled MSA structure factor for dilute charged colloidal dispersions.

  • twoYukawa() -> Structure factor for a two Yukawa potential in mean spherical approximation.

  • hydrodynamicFunct() -> Hydrodynamic function from hydrodynamic pair interaction.

See structurefactor (sf)

Dynamics

See dynamic

biomacromoleules

See Biomacromolecules (bio)

How to use Jscatter

see Examples and Beginners Guide / Help or try Jscatter live at Binder .

# import jscatter and numpy
import numpy as np
import jscatter as js

# read the data (16 dataArrays) with attributes as q, Dtrans .... into dataList
i5 = js.dL(js.examples.datapath + '/iqt_1hho.dat')

# define a model for the fit
diffusion = lambda A, D, t, wavevector, e=0: A*np.exp(-wavevector**2*D*t) + e

# do the fit
# single valued start parameters are the same for all 16 dataArrays
# list start parameters [...] indicate independent fitting for dataArrays
# the command line shows progress and the final result, which is found in i5.lastfit
i5.fit(model=diffusion,                         # the fit function
       freepar={'D': [0.08], 'A': 0.98},        # free start parameters
       fixpar={'e': 0.0},                       # fixed parameters
       mapNames={'t': 'X', 'wavevector': 'q'})  # map names from model to data

# open plot with results and residuals
i5.showlastErrPlot(yscale='l')

# open a plot with fixed size and plot data and fit result
p = js.grace(1.2, 0.8)
# plot the data with Q values in legend as symbols
p.plot(i5, symbol=[-1, 0.4, -1], legend='Q=$q')
# plot fit results in lastfit as lines without symbol or legend
p.plot(i5.lastfit, symbol=0, line=[1, 1, -1])
# extend to longer times by changing a model parameter (points)
p.plot(i5.modelValues(t=np.r_[10:120:5]), symbol=0, line=[2, 1, -1])

# pretty up if needed
p.yaxis(min=0.02, max=1.1, scale='log', charsize=1.5, label='I(Q,t)/I(Q,0)')
p.xaxis(min=0, max=130, charsize=1.5, label='t / ns')
p.legend(x=110, y=0.9, charsize=1)
p.title('I(Q,t) as measured by Neutron Spinecho Spectroscopy', size=1.3)
p.text('for diffusion a single exp. decay', x=60, y=0.35, rot=360 - 20, color=4)
p.text(r'f(t)=A*e\S-Q\S2\N\SDt', x=100, y=0.025, rot=0, charsize=1.5)
Picture about diffusion fit

Shortcuts:

import jscatter as js
js.showDoc()                  # Show html documentation in browser
exampledA=js.dA('test.dat')   # shortcut to create dataArray from file
exampledL=js.dL('test.dat')   # shortcut to create dataList from file
p=js.grace()                  # create plot in XmGrace
p=js.mplot()                  # create plot in matplotlib
p.plot(exampledL)             # plot the read dataList

jscatter.usempl(*args)

Switch between using mpl and grace in dataArray/dataList fitting.

jscatter.headless(*args)

Switch to headless mode (NO-GUI) to work on a cluster.

jscatter.version

Jscatter version is changed in pyproject.toml
If not otherwise stated in the files:

written by Ralf Biehl at the Forschungszentrum Jülich ,
Jülich Center for Neutron Science (JCNS-1)
Jscatter is a program to read, analyse and plot data
Copyright (C) 2015-2026 Ralf Biehl

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.

Jscatter package contents

Indices and tables