Skip to main content

Caterpillar Diagram

Project description

Caterpillar Diagram

A generic innovative visualization technique for univariate time-series data capable of forecasting the next-state transition using Markov chains.


Python GitHub made-with-sphinx-doc PyPI

GitHub Workflow Status (with branch) Documentation Status PyPI - Downloads GitHub repo size


This is a software implementation of the proposed Caterpillar Diagram in the research article titled "An innovative color-coding scheme for terrorism threat advisory system".


What is a Caterpillar Diagram?

A Caterpillar Diagram is a visualization technique used for analyzing univariate time-series data. It consists of a series of colored circles with varying radii. The circle's color represents the direction of change in the time-series data, and the circle's size shows its variation.

It implements the innovative and intuitive Difference of Differences (DoD) approach to create a color schema. As proposed, it segregates the time-series data under analysis into a cohort of three consecutive time units. Further, it utilizes the unsigned differences between observations to assign a size to each cohort. This novel visualization technique can segregate the time-series data using seven colors or five stages of Aggressive, Ascent, Descent, Controlled, and Status Quo.

Further, the proposed mechanism utilizes the accumulated color information regarding each cohort to forecast the next step transition using a stationary matrix of Markov Chains.

Authors

  1. ORCID logo Prabal Pratap Singh - https://orcid.org/0000-0002-0738-7629
  2. ORCID logo Prof. Deepu Philip - https://orcid.org/0000-0002-4607-9020

Installation instructions

To install caterpillard package from PyPI:

(env) $ pip install caterpillard

Documentation

The documentation for the package is available here

Compatibility

This package has been tested on Python 3.9 and Python 3.10 across all major operating systems like Linux, MacOs and Windows.

License

GitHub

This package is licensed under GNU Affero General Public License v3.0

Contributions

Please read CONTRIBUTING.md for more details.

Cite

This package has been developed as a part of the doctoral research titled "Modeling & Analysis of Terrorism" by Prabal Pratap Singh under the supervision of Prof. Deepu Philip at Indian Institute of Technology Kanpur.

If you utilize this package then please use the bibliography in IEEE format to cite this package and the associated Journal article in your work:

[1] Prabal Pratap Singh and Deepu Philip, “Caterpillar Diagram.” Jan. 12, 2023. Accessed: Jan. 12, 2023. [Online]. Available: https://github.com/mechaprabal/caterpillard

[2] P. P. Singh and D. Philip, “An innovative color-coding scheme for terrorism threat advisory system,” Methodological Innovations, p. 20597991221144576, Dec. 2022, doi: 10.1177/20597991221144577.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

caterpillard-0.0.4.tar.gz (306.0 kB view details)

Uploaded Source

Built Distribution

caterpillard-0.0.4-py3-none-any.whl (43.2 kB view details)

Uploaded Python 3

File details

Details for the file caterpillard-0.0.4.tar.gz.

File metadata

  • Download URL: caterpillard-0.0.4.tar.gz
  • Upload date:
  • Size: 306.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.2

File hashes

Hashes for caterpillard-0.0.4.tar.gz
Algorithm Hash digest
SHA256 c2bf053be5e24c11cb5d78197a55851b27b2d51af7e26c1450f9909b99619eb4
MD5 251bc2a4b99310b5d11d08cb7feaf389
BLAKE2b-256 82ab7ade50d2a4f15f756e16e3d1f1bce6ecf41c20d6eeaf28df0a863c3cad89

See more details on using hashes here.

File details

Details for the file caterpillard-0.0.4-py3-none-any.whl.

File metadata

File hashes

Hashes for caterpillard-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 7141ad2e0e761de7f010a266181d58a9d458ba7e4236a91517e24c600b7f8f24
MD5 babfe09dcc92d9edb09299b5da3327c6
BLAKE2b-256 a1a2bd5fdc33b5a8d98643ecaa5774cab7702d19caaab1fe9c17f6fc7c773802

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page