Metadata-Version: 2.1
Name: spatiotemporal
Version: 1.0.0
Summary: Tools for spatial and temporal autocorrelation
Home-page: https://github.com/mwshinn/spatiotemporal
Author: Max Shinn
Author-email: m.shinn@ucl.ac.uk
Maintainer: Max Shinn
Maintainer-email: m.shinn@ucl.ac.uk
License: MIT
Description: # Spatiotemporal modeling tools for Python
        
        This package provides tools for modeling and analyzing spatial and temporal
        autocorrelation in Python.  It is based on the methods from the paper [Spatial
        and temporal autocorrelation weave human brain
        networks](https://www.biorxiv.org/content/10.1101/2021.06.01.446561v1).
        Included are methods to compute the following statistics:
        
        - Compute TA-Δ<sub>1</sub> (i.e. first-order temporal autocorrelation)
        - Compute SA-λ and SA-∞ (i.e. measurements of spatial autocorrelation)
        - Lin's concordance
        - Fingerprinting performance, from [Finn et al (2015)](https://www.nature.com/articles/nn.4135)
        
        It will also generate surrogate timeseries for the following:
        
        - Spatiotemporal model from [Shinn et al (2022)](https://www.biorxiv.org/content/10.1101/2021.06.01.446561v1)
        - Noiseless spatiotemporal model from [Shinn et al (2022)](https://www.biorxiv.org/content/10.1101/2021.06.01.446561v1)
        - Zalesky matching model from [Zalesky et al (2012)](https://www.sciencedirect.com/science/article/abs/pii/S1053811912001784)
        - Eigensurrogate model from [Shinn et al (2022)](https://www.biorxiv.org/content/10.1101/2021.06.01.446561v1)
        - Phase scramble null model
        
        [See complete documentation](https://spatiotemporal.readthedocs.io)
        
        ## Installation
        
        To install:
        
            pip install spatiotemporal
        
        Otherwise, download the package and do:
        
            python setup.py install --user
        
        System requirements are:
        
        - Numpy
        - Scipy
        - Pandas
        
        ## Citation
        
        If you use this package for a paper, please cite: [Shinn et al (2022)](https://www.biorxiv.org/content/10.1101/2021.06.01.446561v1)
        
        ## Contact
        
        Please report bugs to <https://github.com/mwshinn/spatiotemporal/issues>.  This
        includes any problems with the documentation.  Pull Requests for bugs are
        greatly appreciated.
        
        This package is actively maintained.  However, it is feature complete, so no new
        features will not be added.  This is intended to be a supplement for the paper,
        not a general purpose package for all aspects of spatiotemporal data analysis.
        
        For all other questions or comments, contact m.shinn@ucl.ac.uk.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
