Neuro-Optical Signal Analysis

NOSA is an analytical toolbox designed specifically for the analysis and interpretation of voltage imaging data. NOSA features baseline fitting and filtering algorithms to extract electrical patterns from high speed recordings with low signal-to-noise ratios. NOSA also includes features for spike- and burst detection, movement artefact compensation, and the ability to analyze simultaneously performed optical and electrical recordings. Moreover, NOSA provides analytical tools to identify temporal relations in multicellular electrical patterns via cross correlation analysis. NOSA is stand-alone software that requires no installation and comes with an intuitive user-interface that allows to precisely control and comprehend each analytical step. For more information, see the documentation.

To run NOSA from source code, the following prerequisites are needed:

python>=3.7.1
numpy>=1.16.2
scipy>=1.2.1
pyqt5==5.11.3
pyqtgraph>=0.10.0
matplotlib>=3.0.3
pystackreg>=0.2.1
dipy>=0.15.0
qdarkstyle==2.5.4
pyabf>=2.2.8
quantities>=0.12.3
xlsxwriter>=1.1.5
tifffile>=2019.3.8

Please note the exact version of PyQt5 and qdarkstyle. If a newer version of these packages is used, NOSA may be displayed messy. However, all functionalities should work.

When the prerequisites are met, run NOSA with python main.py.

To create executable files, PyInstaller is used. A configuration file for PyInstaller is given. To create the executable, run pyinstaller main.spec.

There are some unittests for NOSA, located in tests directory. Run these with python -m unittest tests.FeatureTests.

The documentation is written in Markdown. The source file is source/doc.md in the documentation directory. The distributable file is doc.html. Please note that the assets, images, and package_license directories are necessary for doc/doc.html.

For converting the .md file to a .html file, the markdown-styles package is used. To create the distributable .html file from the .md file, run generate-md --layout github --input source --output . in the documentation directory.