Metadata-Version: 2.1
Name: nanslice
Version: 1.0.0
Summary: Scripts to slice and display neuroimages (probably stored in nifti format)
Home-page: https://github.com/spinicist/nanslice
Author: Tobias Wood
Author-email: tobias@spinicist.org.uk
License: MPL
Description: # Not Another Neuroimaging Slicer - nanslice #
        
        Credit / Blame / Contact - Tobias Wood - tobias.wood@kcl.ac.uk
        
        This Source Code Form is subject to the terms of the Mozilla Public
        License, v. 2.0. If a copy of the MPL was not distributed with this
        file, You can obtain one at http://mozilla.org/MPL/2.0/.
         
        If you find the tools useful the author would love to hear from you.
        
        # Brief Description #
        
        ![Screenshot](doc/dualcode.png)
        
        This is a pure Python module for creating slices through neuro-imaging datasets.
        The main motivation for building this was to implement the 'Dual-Coding'
        visualisation method that can be found in this paper: 
        http://dx.doi.org/10.1016/j.neuron.2012.05.001. However, it then expanded to
        include standard visualisation methods, and an interactive viewer for Jupyter
        notebooks.
        
        Documentation can be found at https://nanslice.readthedocs.io/en/latest/.
        
        A Jupyter Notebook demonstrating the module can be found at https://mybinder.org/v2/gh/spinicist/nanslice/master?filepath=doc%2Fexample.ipynb.
        
        In dual-coding instead of plotting thresholded blobs of T-statistics or p-values
        on top of structural images, transparency (or alpha) is used to convey the 
        p-value of T-statistic, while color can be used to convey the effect size or
        difference in group means etc. Finally, contours can be added at a specific
        p-value, e.g. p < 0.05. In this way, 'dual-coded' overlays contain all the
        information that standard overlays do, but also show much of the data that is
        'hidden' beneath the p-value threshold.
        
        Whether you think this is useful or not will depend on your attitude towards
        p-values and thresholds. Personally, I think that sub-threshold but
        anatomically plausible blobs are at least worth *showing* to readers, who can
        then make their own mind up about significance.
        
        This is a sister project to https://github.com/spinicist/QUIT. I mainly work
        with quantitative T1 & T2 maps, where group mean difference or "percent change"
        is a meaningful, well-defined quantity. If you use these tools to plot "percent
        BOLD signal change", I hope you know what you what you are doing and wish you
        luck with your reviewers.
        
        # Installation #
        
        NaNSlice is available on `PyPI`. Run `pip install nanslice` to install the
        stable version. Alternatively, clone the repository from Github and then run
        `pip install -e .` to use the development version.
        
        # Performance #
        
        These are Python scripts. The core sampling/blending code was written over 3
        evenings while on the Bruker programming course. Most of nanviewer was written
        in literally 4 hours across a Monday and Tuesday. After a refactoring, it is
        surprisingly responsive on my MacBook. The Jupyter viewer, on the other hand,
        is not wildly performant. Patches are welcome!
Keywords: neuroimaging nifti
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3
Description-Content-Type: text/markdown
