naspy 1.0.0

A lightweight memory protocol inspired by cognitive structure

NASPy: Neural Associative Structure (Python Edition)

NASPy is a minimal, hash-based memory framework designed to mimic how associative memory works, storing and reconstructing structured data using atomic units. Built in Python, NASPy provides the tools to build memory streams, deduplicate data, and serialize complex structures into verifiable, non-redundant records.

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🧠 Core Concepts

🔹 Datom

A "data atom," the smallest unit of memory. Each Datom has:

• A data type (e.g., text, int, symbol)
• A value (e.g., "login", 42)
• A hash computed from the above

🔹 DatomIndex

A lookup table that deduplicates datoms. Prevents storing the same value twice.

🔹 Scanner

Accepts objects (dicts, classes) and flattens them into lists of Datom hashes. Adds new Datoms to the index as needed.

🔹 DataculeStream

An append-only list of memory entries. Each entry is:

(datacule_hash, [datom_hash1, datom_hash2, ...])

Where datacule_hash is the hash of the ordered list of datom hashes.

🔹 .nason files

Serialized memory: stores the DatomIndex and DataculeStream as JSON. You can export and reload entire memory sessions using:

• export_nason(index, stream, "file.nason")
• index, stream = load_nason("file.nason")

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📦 Installation

Clone the repo and import the package locally:

git clone https://github.com/yourname/naspy.git
cd naspy

Then in your Python files:

from naspy.datom import Datom
from naspy.scanner import Scanner

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✅ Usage Example

from naspy.datom_index import DatomIndex
from naspy.dataculestream import DataculeStream
from naspy.scanner import Scanner
from naspy.io import export_nason, load_nason

index = DatomIndex()
stream = DataculeStream()
scanner = Scanner(index)

obj = {"user": "user", "event": "login"}
datacule = scanner.observe(obj)
stream.add_datacule(datacule)

export_nason(index, stream, "memory.nason")

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🧪 Tests

Run the test suite from the root:

python -m unittest discover -s tests

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🧩 Use Cases

• Cognitive memory simulation
• Game state snapshotting
• Transparent logging
• AI input tracking
• Behavioral analytics

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🛠 Future Work

• NASPy CLI tools
• Visual datacule explorer
• Integration with Feeds™
• .nason file versioning and metadata

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📄 License

MIT — free to use, build on, and enhance.