DateTime

SUMMARY

An absolute, timezone-aware date and time.

from datetime import datetime, timezone

from telekinesis import datatypes
value = datatypes.DateTime(datetime.now(timezone.utc))

API Reference

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

View Reference →

Parameters

Parameter	Type	Default	Description
data	datetime.datetime	Required	A timezone-aware datetime, normalized to UTC on storage. Intended for metadata such as created_at/updated_at/deleted_at.

Raises

Exception	Condition
TypeError	data is not a datetime.datetime
ValueError	data is naive (tzinfo is None, or utcoffset() is None)

Attributes

Attribute	Type	Description
data	datetime.datetime	The wrapped value, converted to UTC (tzinfo=timezone.utc) via astimezone. Since datetime.datetime is immutable, no copying is ever required — reading data returns the normalized value directly. Assigning a new value re-validates and re-normalizes it the same way as construction.

Methods

Method	Type	Description
DateTime.coerce(value, name="value")	DateTime	Converts a timezone-aware datetime.datetime into a DateTime. If value is already a DateTime, it is returned unchanged. The optional name is only used to make validation error messages more descriptive. Naive datetimes are not accepted.

Operators

Operation	Returns
a == b	bool
a != b	bool
a < b	bool
a > b	bool

==/< accept a DateTime or a datetime.datetime on the right-hand side; comparing against a naive datetime.datetime raises ValueError rather than returning False/NotImplemented, since normalization is attempted before comparing. > works only through Python's automatic reflected fallback to __lt__ — there is no explicit __gt__. <= and >= are intentionally not implemented and raise TypeError. Because .data is a real datetime.datetime, arithmetic like value.data - other_datetime works directly through Python's datetime API and returns a timedelta.

Visualization

import rerun as rr

# Your code block
# ....

rr.init("datetime_example", spawn=True)
datatypes.visualize(value, entity_path="/value", label="DateTime")

Example

"""Demonstrates the Telekinesis DateTime datatype."""

import time
from datetime import datetime, timedelta, timezone

import rerun as rr
from loguru import logger

from telekinesis import datatypes

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

    # ======================= Create ============================================
    created_at = datatypes.DateTime(datetime.now(timezone.utc))
    logger.info(f"Created DateTime: {created_at}")

    coerced = datatypes.DateTime.coerce(datetime.now(timezone.utc))
    logger.info(f"DateTime coerced from datetime: {coerced}")

    # ======================= Inspect ===========================================
    logger.info(f"data={created_at.data}")

    # ======================= Operations ========================================
    created_at.data = created_at.data + timedelta(minutes=5)
    logger.info(f"Updated DateTime: {created_at}")

    # Any timezone-aware datetime is accepted; it is normalized to UTC on storage.
    pst = datatypes.DateTime(datetime.now(timezone(timedelta(hours=-8))))
    logger.info(f"DateTime from PST input, normalized to UTC: {pst}")

    earlier = datatypes.DateTime(created_at.data - timedelta(minutes=1))

    logger.info(f"EQ: {created_at} == {created_at} = {created_at == created_at}")
    logger.info(f"LT: {earlier} < {created_at} = {earlier < created_at}")
    logger.info(f"GT (via reflected __lt__): {created_at} > {earlier} = {created_at > earlier}")

    # ======================= Visualize =========================================
    rr.init("datetime_example", spawn=True)
    datatypes.visualize(created_at, entity_path="/datetime/updated")
    datatypes.visualize(pst, entity_path="/datetime/pst")

    # ======================= Serialize / Deserialize ===========================
    start = time.perf_counter()
    serialized = datatypes.serialize(created_at)
    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 DateTime: {deserialized}")
    logger.info(f"Round-trip successful: {created_at == deserialized}")
    logger.info(f"Serialization time: {serialization_ms:.3f} ms")
    logger.info(f"Deserialization time: {deserialization_ms:.3f} ms")


if __name__ == "__main__":
    datetime_example()