pyspi: Statistics for Pairwise Interactions
pyspi GitHub
  • Welcome to pyspi
    • Citing pyspi
  • Installing and using pyspi
    • Installation
      • Alternative Installation Options
      • Troubleshooting
    • Usage
      • Walkthrough Tutorials
        • Getting Started: A Simple Demonstration
        • Neuroimaging: fMRI Time Series
        • Finance: Stock Price Time Series
        • Distributing Calculations
      • Advanced Usage
        • Creating a reduced SPI set
        • Distributing calculations on a cluster
      • FAQ
  • Information about pyspi
    • SPIs
      • Glossary of Terms
      • Table of SPIs
      • SPI Descriptions
        • Basic Statistics
        • Distance Similarity
        • Causal Inference
        • Information Theory
        • Spectral
        • Miscellaneous
      • SPI Subsets
    • API Reference
      • pyspi.calculator.CorrelationFrame
      • pyspi.calculator.Calculator
      • pyspi.data.Data
      • pyspi.calculator.CalculatorFrame
      • pyspi.utils.filter_spis
    • Publications using pyspi
    • Related Packages
  • Development
    • Development
      • Incorporating new SPIs
      • Contributing to pyspi
      • Code of Conduct
    • License
Powered by GitBook

All page cover images on this wiki are created with the help of DALL-E, an AI program developed by OpenAI, or stock images from Unsplash.

On this page
  1. Information about pyspi
  2. SPIs

SPI Subsets

Description of all pre-defined subsets in pyspi.

PreviousMiscellaneousNextAPI Reference

Last updated 1 year ago

As standard, we include several SPI subsets in pyspi that can be used directly when instantiating a . Below we provide details about each subset including its intended purpose.

Subset
# of SPIs
Description

fabfour

4

Four basic pairwise measures that are commonly used in the literature.

sonnet

14

A minimally redundant set obtained by grouping SPIs according to their empirical behaviour on over 1000 MTS. Further details are provided in this . We also provide for each of the features in this subset.

fast

216

A subset of SPIs that are fastest to compute. Provides an ideal balance between comprehensiveness of pairwise measures and computational demand.

all

284

Default option. Contains the entire library of SPIs.

Subset Usage

To specify a subset, simply initialise the using the subset name as a parameter:

from pyspi.calculator import Calculator
import numpy as np 

dat = np.random.randn(2, 100)
calc = Calculator(dataset=dat, subset='fast')
calc.compute()
Page cover image
Calculator
Calculator
paper
short names