xaspy

🔬xray absorption spectroscopy module 💫 suitable for XMCD sum rule analysis

View the Project on GitHub gnzng/xaspy

🔬 xaspy - framework for X-ray absorption spectroscopy

This small python module xaspy wants to help with X-ray absorption spectroscopy analysis and pre-evaluation during and after beam times. Similar software and strongly inspired by (but mostly for EXAFS): larch. Strong focus on X-ray magnetic circular dichroism XMCD.

  1. installation
  2. telegram bot
  3. XAS
  4. import data
  5. despiking of data
  6. next steps

installation

install via pip

xaspy can be installed via PyPi or downloaded here from github as the source code.

The necessary requirements can be found in requirement.txt file. Install via pip:

pip install xaspy

Use cell magic in jupyter notebook. You may need to restart the kernel, after updating.

%pip install xaspy

update via pip

Update to the newest version with:

pip install -U xaspy

XMCD

The function XMCD merges and interpolates the spectra for same spin angular momentum of the photon. Correlates the curves on top of each other and builds the XMCD and XAS signal. After that subtraction of different backgrounds is possible (linear, stepfunctions, …). Also different normalization factors are possible.

hysteresis loops

For multiple hysteresis curves in one file use class mHYST with included functions. Use plot_separated() to plot multiple hysteresis loops separated. Function average_loops() takes the loop numbers and averages them.

backgrounds

the xaspy.xas.backgrounds module provides multiple possible backgrounds to correct the measured data.

import data

beam lines

VEKMAG/PM3 at BESSY

Different read in functions for special beamlines: e.g. VEKMAG/PM3 at BESSY II in Berlin.

6.3.1 at ALS

SPECS files

Large SPECS files usually contain multiple runs in one large ASCII file. A very useful SPECS file splitter (split.py) can be found in folder utils. For usage see the docstring in the file header.

Mössbauer

First functions for the implementation of the Pi program for Mössbauer analysis. readin.PiMoss('path/filename') can no be used to import plotting data from the .dat-files generated from Pi. Different implemented plotting functions can be called from the class.

I would recommend using the following saving procedures:

extension content
filename.mos raw data from measurement
filename.mos.rtf fitting information important for Pi
filename.dat exported ascii data table

import of theoretical calculations and simulations

Reading functions for output files for programs like FEFF, multiX, xraylarch, quanty …

despiking of data

This function is to remove spikes from data while loading the data into the RAM. It does not change the raw data. This function creates ‘.spike’ file with list of columns to avoid, which will automatically be dropped while reading data in via a pandas df. Please use a basic read in function as follows:

#function for read in a is number of scan
def rd(a,raw=False):
    path = '../path/to/'
    file = path + 'file'
    a = a
    dff = pd.read_csv(path+'file_{0:03}'.format(a), delim_whitespace=True,skiprows=[1]) # example readin
    if raw==False:
        try:
            with open(file+'.spike','rb') as f:
                b = pickle.load(f)
                if a in b:
                    todrop = b[a]
                    dff = dff.drop(todrop) #returns cleaned pandas file if .spike is existent and has an entry for scan number
        except:
            pass
    else:
        pass
    return dff #returns pandas file

Alternatively you can also load the raw or spiked data with:

rd(nr, raw=True)

next steps (maybe/hopefully)