Metadata-Version: 1.1
Name: sspals
Version: 0.1.2
Summary: process single-shot positron annihlation lifetime spectra
Home-page: https://github.com/PositroniumSpectroscopy/sspals
Author: Adam Deller
Author-email: a.deller@ucl.ac.uk
License: BSD
Description: sspals
        ======
        
        python tools for analysing single-shot positron annihilation lifetime
        spectra
        
        .. image:: https://zenodo.org/badge/49681355.svg
           :target: https://zenodo.org/badge/latestdoi/49681355
        
        Prerequisites
        -------------
        
        Tested using Anaconda (Continuum Analytics) with Python 2.7 and 3.5.
        
        Package dependencies:
        
        -  scipy, numpy, pandas
        
        Installation
        ------------
        
        via pip (recommended):
        
        ::
        
            pip install sspals
        
        alternatively, try the development version
        
        ::
        
            git clone https://github.com/PositroniumSpectroscopy/sspals
            cd sspals
        
        and then run
        
        ::
        
            python setup.py install
            pytest
        
        About
        -----
        
        Single-shot positron annihilation lifetime spectroscopy (SSPALS) [Ref.
        1] essentially consists of studying the number of annihilation
        gamma-rays measured as a function of time following implantation of a
        time-focused (~5 ns) positron bunch into a solid target material.
        
        For certain materials a significant fraction of the positrons (~ 30%)
        will bind to electrons to form positronium (Ps), which can then be
        re-emitted to vacuum. Ps has a characteristic mean lifetime of 142 ns in
        vacuum, which makes it relatively easy to identify in SSPALS spectra.
        
        This package includes a handful of useful tools for working with SSPALS
        data. The two main functions are used to: (i) combine data split across
        hi/ low gain channels of a digital oscilloscope, and (ii) to estimate
        the amount of Ps formed using the so-called delayed fraction.
        
        *sspals.chmx(hi, low)* > Remove zero offset from hi and low gain data,
        invert and splice together by swapping saturated values from the hi-gain
        channel for those from the low-gain channel. Apply along rows of a 2D
        array.
        
        *sspals.sspals(arr, dt, limits=[A, B, C])* > Calculate the trigger time
        t0 (using a cfd) and the delayed fraction (DF) (integral B->C / integral
        A->C) for each row of a 2D array. Return a pandas DataFrame [(t0, AC,
        BC, DF)].
        
        Raw data (hi, low) is expected to be 2D arrays of repeat measurements,
        where each row contains a single SSPALS waveform.
        
        For examples see the IPython/ Jupter notebooks,
        
        https://github.com/PositroniumSpectroscopy/sspals/tree/master/examples
        
        **Refs**.
        
        1. D. B. Cassidy et al. (2006), Appl. Phys. Lett., 88, 194105.
           http://dx.doi.org/10.1063/1.2203336
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Physics
