vcti-datanode 2.0.0

Lightweight data-plus-attributes node containers, with an optional lazily-loaded variant for out-of-core data.

Data Node

The node value type for a hierarchical data model — a single DataNode that composes optional array data, structural identity, and layered metadata.

Overview

vcti-datanode provides DataNode, the payload for a tree whose nodes carry heavy array data, metadata, both, or neither. A node composes three independent, optional parts rather than splitting into a subclass per combination:

• a DataSource — how the node's array is obtained and how long it stays resident. EagerDataSource wraps an array already in hand; LazyDataSource produces one on demand via a callable (read a file, compute a derived array, query a service) and can be released and re-loaded. A node with no data has no data source.
• intrinsic attributes — the node's structural identity (name, shape, dtype, …), kept in their own namespace so they never collide with free-form metadata.
• metadata in two provenance layers — source_attributes (immutable, as received) and enriched_attributes (mutable, added later), merged for reading as attributes.

It is built for CAE-style models read from a file (or built in memory), transformed, and exported — but depends on nothing beyond numpy and works anywhere a data-plus-metadata node is useful. See docs/design.md for the concepts and rationale.

Installation

pip install vcti-datanode

In requirements.txt

vcti-datanode>=2.0.0

In pyproject.toml dependencies

dependencies = [
    "vcti-datanode>=2.0.0",
]

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Quick Start

import numpy as np
from vcti.datanode import DataNode, EagerDataSource, LazyDataSource

# A metadata-only node (no data) — e.g. a grouping node.
group = DataNode(name="results", enriched_attributes={"analysis": "modal"})

# An eager node: the array is already in hand.
stress = DataNode(
    name="stress",
    data_source=EagerDataSource(np.array([1.0, 2.0, 3.0])),
    source_attributes={"units": "MPa"},
)
stress.shape            # (3,) — a derived intrinsic, read without touching data
stress.attributes["units"]   # 'MPa'

# A lazy node: the array is fetched on demand and can be released.
disp = DataNode(
    name="displacement",
    data_source=LazyDataSource(lambda: np.load("disp.npy"), shape=(2, 3), dtype="float64"),
    source_attributes={"units": "mm"},
)
disp.is_loaded          # False — but disp.shape is already (2, 3)
arr = disp.load()       # loader runs once; cached thereafter
disp.unload()           # frees the array; metadata and intrinsics remain

Comparison is explicit (nodes compare by identity, not value):

from vcti.datanode import metadata_equal, data_equal, node_equal

metadata_equal(a, b)    # intrinsic + metadata; never loads data
data_equal(a, b)        # compares arrays, materialising if needed
node_equal(a, b)        # both

Run the example end to end to see the package working:

python examples/basic_usage.py

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Documentation

• Changelog — Release history and versioning policy
• Design — Concepts and the rationale behind the model
• Source Guide — File descriptions and execution flows
• Extension Guide — Writing a custom DataSource
• API Reference — Autodoc for all modules
• examples/basic_usage.py — Runnable tour of the API

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Dependencies

• numpy>=1.26 — array data, and structural facts (shape/dtype).