Package: magni
Primary developers:Christian Schou Oxvig,
Patrick Steffen PedersenAdditional developers:Jan Østergaard,
Thomas Arildsen,
Tobias L. Jensen,
Torben LarsenInstitution:Aalborg University,
Department of Electronic Systems,
Signal and Information ProcessingVersion:1.7.0Travis CI test status:IntroductionMagni is a Python package which provides functionality for increasing the speed
of image acquisition using Atomic Force Microscopy (AFM). The image
acquisition algorithms of Magni are based on the Compressed Sensing (CS) signal
acquisition paradigm and include both sensing and reconstruction. The sensing
part of the acquisition generates sensed data from regular images possibly
acquired using AFM. This is done by AFM hardware simulation. The reconstruction
part of the acquisition reconstructs images from sensed data. This is done by
CS reconstruction using well-known CS reconstruction algorithms modified for
the purpose. The Python implementation of the above functionality uses the
standard library, a number of third-party libraries, and additional utility
functionality designed and implemented specifically for Magni. The
functionality provided by Magni can thus be divided into five groups:Atomic Force Microscopy: AFM specific functionality including AFM image
acquisition, AFM hardware simulation, and AFM data file handling.Compressed Sensing: General CS functionality including signal
reconstruction and phase transition determination.Imaging: General imaging functionality including measurement matrix and
dictionary construction in addition to visualisation and evaluation.Reproducibility: Tools that may aid in increasing the reproducibility of
results obtained using Magni.Utilities: General Python utilities including multiprocessing, tracing,
and validation.DownloadingMagni can be downloaded in a number of ways:All official releases of Magni are available for download at
‘http://dx.doi.org/10.5278/VBN/MISC/Magni’.The source code is hosted on GitHub at ‘https://github.com/SIP-AAU/Magni’
where every release of Magni is available for download and whereknown
issuesare tracked.Furthermore, all official releases of the magni package (withoutexamples
and documentation) are made available through PyPI and binstar. Both of these
are considered unofficial channels and provided solely for your convenience.Using PyPI located at ‘https://pypi.python.org/pypi/magni/’.Using Anaconda.org located at ‘https://anaconda.org/chroxvi/magni/’.InstallationTo use Magni, extract the downloaded archive and include the extracted Magni
folder in your PYTHONPATH.Magni has been designed for use with Python 2 >= 2.7 or Python 3 >= 3.3.The required thid party dependencies and optional third party dependencies for
Magni are listed in the compiled documentation which can be found in the Magni
folder under ‘/doc/build/html/index.html’. You may use the ‘dep_check.py’
script found in the Magni folder under ‘/magni/tests/’ to check for missing
dependencies for Magni. Simply run the script to print a dependency report.DocumentationThe included subpackages, modules, classes and functions are documented through
Python docstrings using the same format as the third-party library, numpy, i.e.
using the numpydoc standard. A description of any entity can thus be found in
the source code of Magni in the docstring of that entity. For readability, the
documentation has been compiled using Sphinx to produce an HTML page which can
be found in the Magni folder under ‘/doc/build/html/index.html’. The entire
documentation is also available as a PDF file in the Magni folder under
‘doc/pdf/index.pdf’. Note, that neither the HTML version nor the PDF version of
the documentation is provided through PyPI and Anaconda.org.TestsA test suite consisting of unittests, doctests, IPython notebook examples, and
several style checks is included in Magni. The tests are organised in python
modules found in the Magni folder under ‘/magni/tests/’. These may be invoked
using any test runner that supports the unittest.TestCase classes. The entire
test suite may be run by executing the convenience script ‘run_tests.py’. The
code is this repository is continuously tested byTravis CI.ExamplesA large number of examples showing the capabilities of Magni are included. Some
of these examples are in the form of doctests whereas the more elaborated
examples are in the form of IPython notebook examples which can be found in the
Magni folder under ‘/examples/’.Citing MagniA Magni software metapaper has been published:C.S. Oxvig, P.S. Pedersen, T. Arildsen, J. Østergaard, and T. Larsen, “Magni: A
Python Package for Compressive Sampling and Reconstruction of Atomic Force
Microscopy Images”,Journal of Open Research Software, 2(1):e29, 2014
DOI:http://dx.doi.org/10.5334/jors.bkFurthermore, themagni.utils.validationsystem is detailed in:P.S. Pedersen, C.S. Oxvig, J. Østergaard, and T. Larsen, “Validating Function
Arguments in Python Signal Processing Applications”, inProceedings of the
15th Python in Science Conference, pp. 106-113, Austin, Texas, USA,
July 11 - 17, 2016
URL:http://conference.scipy.org/proceedings/scipy2016/patrick_pedersen.htmlAnd themagni.reproducibilitysub-package is detailed in:C.S. Oxvig, T. Arildsen, and T. Larsen, “Storing Reproducible Results from
Computational Experiments using Scientific Python Packages”, inProceedings of
the 15th Python in Science Conference, pp. 45-50, Austin, Texas, USA,
July 11 - 17, 2016
URL:http://conference.scipy.org/proceedings/scipy2016/christian_oxvig.htmlPlease consider citing one or more of these papers if you publish any work in
which you have used Magni.