{ "info": { "author": "Adam Deller", "author_email": "a.deller@ucl.ac.uk", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Physics" ], "description": "sspals\n======\n\npython tools for analysing single-shot positron annihilation lifetime\nspectra\n\n.. image:: https://zenodo.org/badge/49681355.svg\n :target: https://zenodo.org/badge/latestdoi/49681355\n\nPrerequisites\n-------------\n\nTested using Anaconda (Continuum Analytics) with Python 2.7 and 3.5.\n\nPackage dependencies:\n\n- scipy, numpy, pandas\n\nInstallation\n------------\n\nvia pip (recommended):\n\n::\n\n pip install sspals\n\nalternatively, try the development version\n\n::\n\n git clone https://github.com/PositroniumSpectroscopy/sspals\n cd sspals\n\nand then run\n\n::\n\n python setup.py install\n pytest\n\nAbout\n-----\n\nSingle-shot positron annihilation lifetime spectroscopy (SSPALS) [Ref.\n1] essentially consists of studying the number of annihilation\ngamma-rays measured as a function of time following implantation of a\ntime-focused (~5 ns) positron bunch into a solid target material.\n\nFor certain materials a significant fraction of the positrons (~ 30%)\nwill bind to electrons to form positronium (Ps), which can then be\nre-emitted to vacuum. Ps has a characteristic mean lifetime of 142 ns in\nvacuum, which makes it relatively easy to identify in SSPALS spectra.\n\nThis package includes a handful of useful tools for working with SSPALS\ndata. The two main functions are used to: (i) combine data split across\nhi/ low gain channels of a digital oscilloscope, and (ii) to estimate\nthe amount of Ps formed using the so-called delayed fraction.\n\n*sspals.chmx(hi, low)* > Remove zero offset from hi and low gain data,\ninvert and splice together by swapping saturated values from the hi-gain\nchannel for those from the low-gain channel. Apply along rows of a 2D\narray.\n\n*sspals.sspals(arr, dt, limits=[A, B, C])* > Calculate the trigger time\nt0 (using a cfd) and the delayed fraction (DF) (integral B->C / integral\nA->C) for each row of a 2D array. Return a pandas DataFrame [(t0, AC,\nBC, DF)].\n\nRaw data (hi, low) is expected to be 2D arrays of repeat measurements,\nwhere each row contains a single SSPALS waveform.\n\nFor examples see the IPython/ Jupter notebooks,\n\nhttps://github.com/PositroniumSpectroscopy/sspals/tree/master/examples\n\n**Refs**.\n\n1. D. B. Cassidy et al. (2006), Appl. Phys. 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