Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

MAT-view: Visualization Tools for Multiple Aspect Trajectory Data Mining [MAT-Tools Framework]


[Publication] [Bibtex] [GitHub] [PyPi]

The present application offers a set of web tools to support the user in the visualization of multiple-aspect trajectories and movelets (the parts of the trajectory that better discriminate a class). Also, present a detailed list of available datasets on a public repository, descriptions of MAT data mining methods, experimental results exploration, environmental and scripting generators. It integrates into a unique platform of the approaches available for multiple aspects trajectories and in general for multidimensional sequence classification into a unique web-based and python library system.

Created on Jun, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

* This package docstrings are limited. But you can refer to the Demonstration Video for more information on how to use it.

Screenshots

Home

Results

Scripting

View

Installation

Install directly from PyPi repository, or, download from github. (python >= 3.7 required)

    pip install mat-view

Usage

This package enables a environment script to run the web application, just run:

    MAT-Web.py

By default Dash will run on http://127.0.0.1:8050/

* In case you have trouble loading any data into the page (or an action is not updating the page), refresh the site and try again.

Citing

If you use mat-view please cite the following paper:

  • Portela, T. T.; Machado, V. L.; Renso, C. Unified Approach to Trajectory Data Mining and Multi-Aspect Trajectory Analysis with MAT-Tools Framework. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC. [Bibtex]

Collaborate with us

Any contribution is welcome. This is an active project and if you would like to include your algorithm in mat-view, feel free to fork the project, open an issue and contact us.

Feel free to contribute in any form, such as scientific publications referencing mat-view, teaching material and workshop videos.

Related packages

This package is part of MAT-Tools Framework for Multiple Aspect Trajectory Data Mining, check the guide project:

  • mat-tools: Reference guide for MAT-Tools Framework repositories

Change Log

This is a package under construction, see CHANGELOG.md

Metadata

Release files for mat-view 0.1rc1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mat-view 0.1rc1
File Size Uploaded Provenance
mat_view-0.1rc1.tar.gz 4.0 MB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for mat-view 0.1rc1
File Interpreter ABI Platform
mat_view-0.1rc1-py3-none-any.whl Python 3 none any Details

Total release size: 4.2 MB

Release files / mat_view-0.1rc1.tar.gz

Download URL mat_view-0.1rc1.tar.gz
Size 4.0 MB
Tags Source
SHA-256 checksum
How to use checksums
ef34f296a21e165078a51ee00c215224b55946e5c1306803a984ade03aa68792
BLAKE2b-256 checksum
How to use checksums
a6242e62624aabb647d93cfe6fd36f01ceb75003d1994d7cc5d6ac488c9205c6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.9.13

Release files / mat_view-0.1rc1-py3-none-any.whl

Download URL mat_view-0.1rc1-py3-none-any.whl
Size 232.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
240a7a6a6f90c7f873e1d3ac42afd163ec9ae90953de2fac382978f8596d217e
BLAKE2b-256 checksum
How to use checksums
f40d6096ed3f340264eaf6585d482aeeaa3ebd8aaccd8e77dfe3efd08b34bcd6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.9.13

Security for mat-view 0.1rc1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page