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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

Create an isolated environment so that there is no dependency conflicts. We recommend installing Miniconda environment by following instructions from here.
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
```
Activate the environment:
```
conda activate telekinesis-datatypes
```
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

All module examples are available in Telekinesis Examples
Full SDK documentation and usage details: Telekinesis Documentation

Support

For issues and questions:

Create an issue in the GitHub repository.
Contact the Telekinesis development team at support@telekinesis.ai or on Discord.

Metadata

Release files for telekinesis-datatypes 0.4.3

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 0.4.3
File	Size	Uploaded
telekinesis_datatypes-0.4.3.tar.gz	237.5 kB	Sep 26, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for telekinesis-datatypes 0.4.3
File	Interpreter	ABI	Platform	Reset
telekinesis_datatypes-0.4.3-py3-none-any.whl 247.0 kB Sep 26, 2026	Python 3	none	any	Details

Total release size: 484.5 kB

Release files / telekinesis_datatypes-0.4.3.tar.gz

Download URL	telekinesis_datatypes-0.4.3.tar.gz
Size	237.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`33d51bee20e116dcc94f8c7185679f942e2cdfbe95f6fd1f78358f1577ddc061`
BLAKE2b-256 checksum How to use checksums	`4e44dae5432316f4806d9fb428b4be5f476b969d198f8433551c1a818d07786a`
Upload date	Sep 26, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.11.15`

Release files / telekinesis_datatypes-0.4.3-py3-none-any.whl

Download URL	telekinesis_datatypes-0.4.3-py3-none-any.whl
Size	247.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c8d3e92b703ba7d2e64e0abe01818eb559ca1428d5d2e1c05bda2d1e08304929`
BLAKE2b-256 checksum How to use checksums	`284580d08fad7dcad7f424a411c075a26e63b48c624f64627ec35cf8ca69a63f`
Upload date	Sep 26, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.11.15`