Skip to main content

GitHub  •  LinkedIn  •  X  •  Discord

Telekinesis Datatypes

Telekinesis Datatypes is a core library providing canonical, strongly typed data structures for robotics and computer vision applications within the Telekinesis ecosystem.

It includes:

  • 3D data types: point clouds, meshes, transforms, and geometric primitives
  • 2D data types: images, bounding boxes, masks, and pixel-space geometry
  • Standardized representations for common perception and geometry formats
  • Efficient serialization and deserialization for reliable data exchange

Release Model

Telekinesis Datatypes is currently in active development (pre-1.0). Modules are introduced incrementally, and the API may evolve between minor releases. To ensure compatibility and access to the latest capabilities, always install or upgrade to the most recent version of the package.

Installation

  1. Create an isolated environment so that there is no dependency conflicts. We recommend installing Miniconda environment by following instructions from here.

  2. Create a new conda environment called telekinesis-datatypes:

    We currently support Python versions - 3.11, 3.12. Ensure your environment is in the specified Python version.

    conda create -n telekinesis-datatypes python=3.11
    
  3. Activate the environment:

    conda activate telekinesis-datatypes
    
  4. Install the core package using pip:

    pip install telekinesis-datatypes
    

    Note: The Python module is called datatypes, while the package published on PyPI is telekinesis-datatypes.

Example

Run a sample python code to quickly test your installation.

import rerun as rr
from telekinesis import datatypes

url = "https://assets.telekinesis.ai/examples/v1/images/screws_standing.jpg"
image_from_url = datatypes.Image.from_url(url)

rr.init("datatypes_examples", spawn=True)
datatypes.visualize(image_from_url, entity_path="/ImageFromURL")

You are now set up with Telekinesis Datatypes.

Resources

Support

For issues and questions:

Metadata

Release files for telekinesis-datatypes 0.4.4

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

Source distribution (sdist)

Source distribution for telekinesis-datatypes 0.4.4
File Size Uploaded Provenance Actions
telekinesis_datatypes-0.4.4.tar.gz 232.1 kB Without provenance Details for telekinesis_datatypes-0.4.4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for telekinesis-datatypes 0.4.4
File Interpreter ABI Platform Actions
telekinesis_datatypes-0.4.4-py3-none-any.whl Python 3 none any Details for telekinesis_datatypes-0.4.4-py3-none-any.whl

Total release size: 473.1 kB

Release files / telekinesis_datatypes-0.4.4.tar.gz

Download URL telekinesis_datatypes-0.4.4.tar.gz
Size 232.1 kB
Tags Source
SHA-256 checksum
How to use checksums
a0efb80cb71bcb0919ac2440dc32243ae6b257b72dcf8e3ed85bcf0f8c00b320
BLAKE2b-256 checksum
How to use checksums
824fa1cba802dfb0b481a41ad2634bf89c800f0eee2f2303a27c72d562be9b29
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.11.15

Release files / telekinesis_datatypes-0.4.4-py3-none-any.whl

Download URL telekinesis_datatypes-0.4.4-py3-none-any.whl
Size 241.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
f41578e3e52051b278586dc30c896a65598630397a09014d70bfcc98a75616a8
BLAKE2b-256 checksum
How to use checksums
a0c4b163c5bc950d2a562837579c0d6ce4d3c2bf0d8eb8ca4d654a63cf911aee
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.11.15

Security for telekinesis-datatypes 0.4.4

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.

Release history Release notifications | RSS feed

This release

0.4.4 This release

2 release files No provenance

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