Jscatter’s documentation

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

Jscatter Logo
  • Reading and analyzing experimental data with associated attributes as temperature, wavevector, comment, ….
  • Multidimensional fitting taking the attributes (as fixed parameters) 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 (preferred in xmgrace).
  • Easy model building for non programmers.
  • Python scripts to document data evaluation and modelling.
https://zenodo.org/badge/DOI/10.5281/zenodo.1470307.svg

Main concept

  • Link data from experiment, analytical model or simulation with attributes as .temperature, .wavevector, .pressure,…
  • Methods for fitting, filter, merging,… using the attributes by name.
  • Provide an extensible library with common theories for fitting of physical models.
  1. Data organisation

Multiple measurements are stored in a dataList (subclass of list) containing dataArray ´s (subclass of numpy ndarray). Both allow attributes to contain additional information of the measurement.

Thus dataList represents e.g. a temperature series (as dataList) with measurements (dataArray) as list elements.

Special attributes are .X,.Y,.eY…- for convenience and easy reading. Full numpy ndarray functionality is preserved.

  1. Read/Write data

The intention is to read everything from a file to use it later if needed. Multiple measurement files can be read at once and then filtered according to attributes to get subsets.

A file may consist of multiple sets of data with optional attributes or comments in between. Data are a matrix like values in a file. Attribute lines have a name in front. Everything else is a comment and might be used later. Thus the first two words (separated by whitespace) decide about assignment of a line:

  • string + value -> attribute with attribute name + list of values
  • value + value -> data line as sequence of numbers
  • string + string -> comment
  • single words -> comment
  • string+@unique_name-> link to other dataArray with a unique_name

Even complex ASCII files can be read with a few changes given as options. The ASCII file is still human readable and can be edited. New attributes can be generated from content of the comments if not detected automatically (see Reading ASCII files).

  1. Fitting

Multidimensional attribute dependent fitting (least square Levenberg-Marquardt, differential evolution, …from scipy.optimize).

Attributes are used automatically as fixed fit parameters.

Simulation with changed parameters (e.g. to observe change within error limits).

See fit() for detailed description.

  1. Plotting

We use an adaption of Xmgrace for 2D plots (a wrapper; see GracePlot) as it allows interactive publication ready output in high quality for 2D plots and is much faster than matplotlib.

The figure is stored as ASCII file (.agr) including data points and not as non-editable image as jpg/pdf… This allows a later change of the plot layout without recalculation, because data are stored as data and not as image. Imagine the boss/reviewer asking for a change of colors/symbol size.

Nevertheless a small matplotlib interface is there and matplotlib can be used as it is (e.g. for 3D plots).

  1. Models

By intention the user should write own models or modify existing ones to combine different contributions (to include e.g. a background, instrument resolution, …)

Models can be defined as lambda function or normal functions within a script or in interactive session of (I)python. See How to build simple models and How to build a more complex model .

A set of models/theories is included, see e.g. formel, formfactor (ff) and structurefactor (sf). Models contain model parameters as attributes for later access.

Contribution by new models is welcome. Please give a documentation, reference to relevant publication and authorship as in the provided models.

Some special functions:

How to use Jscatter or see Examples and Beginners Guide / Help

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

# read the data (16 sets) 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,elastic,wavevector=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
# single valued start parameters are the same for all dataArrays
# list start parameters indicate independent fitting for datasets
# the command line shows progress and the final result, which is found in .lastfit
i5.showlastErrPlot(yscale='l') # opens plot with residuals

# open a plot with fixed size and plot
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])

# 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)

if 0: # optional; save in different formats
    p.save('DiffusionFit.agr')
    p.save('DiffusionFit.jpg')
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
p.plot(exampledL)             # plot the read dataList

If not otherwise stated in the files:

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

Installation

  • Dependencies
  • numpy, scipy -> Mandatory, automatically installed by pip
  • Pillow -> Mandatory, for reading of SAXS images, automatic install by pip
  • matplotlib -> Mandatory, for 3D plots and on Windows
  • Ipython -> Optional, for convenience as a powerfull python shell
  • gfortran -> Optional, without some functions dont work or use a slower python version
  • xmgrace -> Optional, prefered plotting on Unix like (use matplotlib on Windows)

Installation may need root privileges. Use “sudo” on Linux and MacOS if needed.

  • Pip installation options (use pip2 or pip3 dependent if you use python2.7 or python3)

    sudo pip install jscatter
    

    As user in home directory (pip default is in ~/.local/). No sudo needed, only user privileges:

    pip install jscatter --user
    

    from a local repository (development versions):

    pip install jscatter --user --upgrade --pre --find-links /where/the/file/is/saved
    
    options
    --user       : Install in user directory (folder defined by PYTHONUSERBASE or the default ~/.local)
    --find-links : look in the given path for package links e.g development releases
    --upgrade    : to install upgrades
    --pre        : to install also development versions
    
  • Ubuntu, all Debian related Linux

    sudo apt-get install gfortran grace python-matplotlib  # or python3-matplotlib
    sudo pip install ipython
    sudo pip install jscatter
    
  • CentOs, Suse, Fedora … do same as above but with yum/zypper…

  • MacOs, install Homebrew first as given on their web page (see Homebrew)

    # install XQuartz from homebrew or from the AppStore
    sudo brew cask install xquartz
    # install xmgrace and gfortran
    sudo brew install grace gfortran matplotlib
    # then use pip
    sudo pip install ipython
    sudo pip install jscatter
    
  • Windows: Anaconda is a python distribution as alternative with numpy, scipy, matplotlib, Ipython preinstalled. Need of sudo depends on how Anaconda was installed (root or user). Maybee the matplotlib backend needs to be configured on Windows to work properly.

    And there was more to adjust, when i stopped waisting my time. In my testcase it was a pain, but test and examples work (no Xmgrace, no fortran).

    I strongly advise to use Linux in a VirtualMachine as it is easier to install and use.

    # install jscatter on working anaconda environment
    pip install jscatter
    
  • Manjaro Linux (yaourt asks for permission as root or prepend sudo as above)

    # install gfortran
    yaourt gcc-fortran
    # install xmgrace (only found in AUR), fonts are needed for the interface, fonts are loaded after restart
    yaourt xorg-fonts-75 xorg-fonts-100 grace-openmotif python-matplotlib
    pip install ipython
    pip install jscatter
    
  • CONTIN in DLS module (Only if needed).

    See DLS module documentation for details how to get and compile the original fortran code.

  • Testing

    You can test basic functionality of jscatter after installation:

    import jscatter as js
    js.test.doTest()
    #basic graphics and fitting
    js.examples.runExample(7)
    
    Example 7 shows :
    • 3 sine fit plots with one sine
    • a fit plot with 5 sine curves fitted simultanous
    • a simple plot with 5 points ( phase against Amplitude of the 5 sines)
    • a 3D Sinusoidal fit in matplotlib.

    During development:

    python setup.py test
    
  • Troubleshooting and tips

    If xmgrace is not found by jscatter the path to the executable may be not on your PATH variable. Check this by calling xmgrace in a shell. Change your PATH in your .bashrc by adding:

    export PATH=/path/to/xmgrace:$(PATH) )
    
    To open .agr files by klicking add a new file association e.g. to KDE.

    In SystemSettings/FileAssociations add a new type xmgrace. Inside this add FilenamePatterns ‘*.agr’ and similar.

    In ‘ApplicationPreferenceOrder’ add xmgrace and edit this new application :

    In General : edit name to “xmgrace” (keep it). In Application : edit name to “xmgracefree” and command to “xmgrace -free”. This will open files in free floating size format.

    In ‘ApplicationPreferenceOrder’ add again application xmgrace (no changes) The second opens files in fixed size format (no ‘-free’).

Indices and tables