Mat3x3

SUMMARY

A 3 × 3 matrix.

from telekinesis import datatypes
mat3x3 = datatypes.Mat3x3([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])

API Reference

Complete API documentation for Mat3x3, including parameters, attributes, and methods.

View Reference →

Parameters

Parameter	Type	Default	Description
data	np.ndarray | list | tuple	Required	Array-like data of shape (3, 3).

Raises

Exception	Condition
TypeError	data can't be converted to a float32 array (e.g. non-numeric elements)
ValueError	data's rank isn't 2, its shape isn't (3, 3), or it contains a non-finite value (NaN/Inf)

Attributes

Attribute	Type	Description
shape_spec	ClassVar[tuple[int, ...]]	Class-level shape spec, (3, 3).
data	np.ndarray	The wrapped matrix. Reading it returns a copy, so mutating the result doesn't affect the Mat3x3; assigning a new value re-validates it the same way as construction.
shape	tuple[int, ...]	Always (3, 3).
ndim	int	Always 2.
dtype	np.dtype	Always float32.
size	int	Always 9.

Methods

Method	Type	Description
Mat3x3.coerce(value)	Mat3x3	Converts array-like data into a Mat3x3. Accepts a np.ndarray, list, or tuple of shape (3, 3). If value is already a Mat3x3, it is returned unchanged.
to_numpy(copy=True)	np.ndarray	Returns the matrix as a plain array. With the default copy=True you get an independent copy; pass copy=False to get a direct reference to the internal array instead, so mutating it also mutates the Mat3x3.
copy()	Mat3x3	Returns a new, independent Mat3x3 with the same data.

Operators

Operation	Behavior
m1 == m2	True only if other is exactly a Mat3x3 (not a subclass, not another datatype) with element-equal data. False for anything else.
len(m)	Always 3 (length of axis 0, i.e. the number of rows).
np.asarray(m)	Returns a copy of data as an np.ndarray; NumPy functions accept a Mat3x3 directly.

Visualization

import rerun as rr

# Your code block
# ....

rr.init("mat3x3_example", spawn=True)
datatypes.visualize(mat3x3, entity_path="/mat3x3", label="Mat3x3")

Example

"""Demonstrates the Telekinesis Mat3x3 datatype."""

import time

import numpy as np
import rerun as rr
from loguru import logger

from telekinesis import datatypes

def mat3x3_example():
    """Demonstrate creation, inspection, operations, visualization, and serialization."""

    # ======================= Create ============================================
    matrix = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
    mat3x3 = datatypes.Mat3x3(matrix)
    logger.info(f"Created Mat3x3: {mat3x3}")

    # ======================= Inspect ===========================================
    logger.info(f"data={mat3x3.data}")
    logger.info(f"shape={mat3x3.shape}")
    logger.info(f"ndim={mat3x3.ndim}")
    logger.info(f"dtype={mat3x3.dtype}")
    logger.info(f"size={mat3x3.size}")

    # ======================= Operations =========================================
    mat3x3.data = [[9.0, 8.0, 7.0], [6.0, 5.0, 4.0], [3.0, 2.0, 1.0]]
    logger.info(f"Updated Mat3x3: {mat3x3}")

    mat3x3_copy = mat3x3.copy()
    logger.info(f"Copied Mat3x3: {mat3x3_copy}")

    mat3x3_numpy = mat3x3.to_numpy(copy=True)
    logger.info(f"NumPy Mat3x3:\n{mat3x3_numpy}")

    numpy_array = np.asarray(mat3x3)
    transposed = np.transpose(mat3x3)
    determinant = np.linalg.det(mat3x3)
    logger.info(f"NumPy array:\n{numpy_array}")
    logger.info(f"Transposed:\n{transposed}")
    logger.info(f"Determinant: {determinant}")

    # ======================= Visualize =========================================
    rr.init("mat3x3_example", spawn=True)
    datatypes.visualize(mat3x3, entity_path="/mat3x3")

    # ======================= Serialize / Deserialize ===========================
    start = time.perf_counter()
    serialized = datatypes.serialize(mat3x3)
    serialization_ms = (time.perf_counter() - start) * 1000

    start = time.perf_counter()
    deserialized = datatypes.deserialize(serialized)["param_0"]
    deserialization_ms = (time.perf_counter() - start) * 1000

    logger.info(f"Deserialized Mat3x3: {deserialized}")
    logger.info(f"Round-trip successful: {mat3x3 == deserialized}")
    logger.info(f"Serialization time: {serialization_ms:.3f} ms")
    logger.info(f"Deserialization time: {deserialization_ms:.3f} ms")


if __name__ == "__main__":
    mat3x3_example()