bdata

bdata is a lightwieght Python package aimed to aid in the analysis of β-detected nuclear magnetic/quadrupole resonance (β-NMR and β-NQR) data taken at TRIUMF. These techniques are similar to muon spin rotation (μSR) and "conventional" nuclear magnetic resonance (NMR), but use radioactive nuclei as their NMR probe in place of the muon or a stable isotope.

The intended user of bdata is anyone analyzing data taken from TRIUMF's β-NMR or β-NQR spectrometers. A key goal of the project is to alleviate much of the technical tedium that is often encountered during any analysis.

Used with bfit and the SciPy ecosystem, bdata forms part of a flexible API in the analysis of β-NMR and β-NQR data. bdata has been written to fullfill the following needs:

• Provide an intuitive means of interfacing with MUD files in Python.
• Fetch missing local data from the archive.
• Support analyses by providing common data manipulations, such as calculating asymmetries or combining scans.

Contents

• bdata [object]: access β-NMR and β-NQR MUD files
• bjoined [object]: append bdata objects
• bmerged [object]: combine bdata objects
• life [mdict object]: dictionary of probe lifetimes.
• containers [module]: specially defined mdict objects with set function.
• exceptions [module]: custom exceptions and warnings for common issues
• asym_fns [module]: functions for calculating asymmetries and errors

Citing

If you use bdata in your work, please cite:

• D. Fujimoto. Digging Into MUD With Python: mudpy, bdata, and bfit. arXiv:2004.10395 [physics.data-an].

Community Guidelines

• Please submit contributions to bdata via a pull request
• To report issues or get support, please file a new issue

Installation and Use

Dependencies

The following packages/applications are needed prior to bdata installation:

• Python 3.6 or higher: a dynamically typed programming language. [install]

and the following are handelled automatically when retrieving bdata from the PyPI:

• iminuit : a Jupyter-friendly Python interface for the MINUIT2 library.
• mudpy : data structures for parsing TRIUMF MUD files.
• pandas : a fast, powerful, flexible and easy to use data analysis/manipulation tool.
• requests : an elegant and simple HTTP library for Python.
• SciPy : fundamental algorithms for scientific computing in Python.

Install Instructions

	Command
From the PyPI as user (recommended)	pip install --user bdata
From the PyPI as root	pip install bdata
From source	python3 setup.py install

Note that pip should point to a (version 3) Python executable (e.g., python3, python3.8, etc.). If the above does not work, try using pip3 or python3 -m pip instead.

Optional Configuration

For convenience, you may want to tell bdata where the data is stored on your machine. This is done by defining two environment variables: BNMR_ARCHIVE and BNQR_ARCHIVE. This can be done, for example, in your .bashrc script. Both variables expect the data to be stored in directories with a particular heirarchy:

/path/
    bnmr/
    bnqr/
        2017/
        2018/
            045123.msr

Here, the folders /path/bnmr/ and /path/bnqr/ both contain runs (i.e., .msr files) organized into subdirectories by year of aquasition. In this case, you would set (in your .bashrc):

export BNMR_ARCHIVE=/path/bnmr/
export BNQR_ARCHIVE=/path/bnqr/

If bdata cannot find the data, it will attempt to download the relavent MUD files from the archive and store them in $HOME/.bdata. This is the default behaviour for bdata installed from PyPI