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bfit 4.21.2
bfit
bfit is a Python application 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 instruments and research program are governed through TRIUMF's CMMS,
with more information given at https://bnmr.triumf.ca.
An overview of instrumentation details and scientific applications of the
β-NMR/β-NQR techniques can be found in several recent journal articles:
W. A. MacFarlane.
Implanted-ion βNMR: a new probe for nanoscience.
Solid State Nucl. Magn. Reson. 68-69, 1-12 (2015).
G. D. Morris.
β-NMR.
Hyperfine Interact. 225, 173-182 (2014).
The intended user of bfit is anyone performing experiments with or analyzing
data taken from TRIUMF's β-NMR or β-NQR spectrometers - independent of whether
they are a new student, visiting scientist, or someone with decades of experience.
(e.g., someone from the "local" TRIUMF/CMMS/UBC group).
A key goal of the project is to alleviate much of the technical tedium that is
often encountered during any analysis.
More generally, bfit has been written to fulfill the following needs:
Provide the means for quick on-line analyses during beam time.
Provide a useful and flexible API for refined analyses in Python,
to be used in conjunction with bdata and the SciPy ecosystem.
Provide an intuitive, user-friendly interface for non-programmers.
Be easily maintainable and distributable.
Citing
If you use bfit in your work, please cite:
D. Fujimoto, "bfit: A Python Application For Beta-Detected NMR," J. Open Source Softw. 6, 3598 (2021).
Useful Links
bfit
Wiki
API Reference
API Tutorial
GUI Tutorial
mudpy
bdata
Community Guidelines
Contributing:
Please submit your contribution to bfit through the list of
Pull Requests!
Reporting issues and/or seeking support:
Please file a new ticket in bfit's list of Issues - I will get an email
notification of your problem and try to fix it ASAP!
Installation and Use
Dependencies
The following packages/applications are needed prior to bfit installation:
Python 3.9 or higher: a dynamically typed programming language. [install]
Tkinter : Python's de facto standard GUI package. [install]
Install Instructions
Command
From the PyPI as user (recommended)
pip install --user bfit
From the PyPI as root
pip install bfit
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 Setup
For convenience,
you may want to tell bfit 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 bfit cannot find the data, it will attempt to download the relavent MUD
(i.e., .msr) files from https://cmms.triumf.ca/mud/runSel.html.
This is the default behaviour for bfit installed from PyPI.
First Startup
To launch the GUI from a terminal simply call bfit, if this fails, one can also use the alternative syntax python3 -m bfit, where python3 may be replaced with any (version 3) Python executable.
Testing
Testing your installation of bfit is accomplished by running pytest within the installation folder. Note that some tests, particularly those involving drawing, fail when run as group in this environment, but they should pass on a subsequent attempts: pytest --lf. Further testing information can be found here.
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