dankmemer.py 0.1.0a2

A lightweight async wrapper for the DankAlert API.

Alpha Release Notice:

dankmemer.py is currently in alpha. At this stage, only the items route is implemented. Future releases will include additional routes and enhanced features.

dankmemer.py is a simple and powerful asynchronous Python wrapper for the DankAlert API — giving you easy access to Dank Memer-related data like items, NPCs, skills, tools, and more.

🚀 Features

• Built-in caching with configurable TTL

• Powerful filtering with support for exact, fuzzy, and numeric range queries

📦 Installation

You can install the project using any of the following aliases:

pip install dankmemer
pip install dankmemer.py

Items Route

The Items Route provides access to the /items endpoint from the DankAlert API. It automatically caches responses for a configurable period, converts raw JSON into Item objects, and allows easy filtering via the ItemsFilter class and the Fuzzy helper.

Basic Example

import asyncio
from dankmemer import DankMemerClient, ItemsFilter, Fuzzy

async def main():
    async with DankMemerClient() as client:
        # Retrieve all items without filtering.
        all_items = await client.items.query()
        print("All items:", all_items)

        # Example: Filtering items using various criteria:
        # - Fuzzy matching on the 'name' field (e.g. items with names similar to "trash")
        # - Boolean filtering for 'hasUse' flag
        # - Numeric range filtering for 'marketValue'
        filter_obj = ItemsFilter(
            name=Fuzzy("trash", cutoff=80),  # Fuzzy match on name with a cutoff of 80%
            hasUse=False,                    # Only items that are not usable
            marketValue=(5000, 10000000)      # Market value between 5,000 and 10,000,000
        )

        filtered_items = await client.items.query(filter_obj)
        print("Filtered items:", filtered_items)

asyncio.run(main())

This basic example demonstrates: - Exact vs. Fuzzy Matching: Use a plain string for exact matches (e.g. name="Trash") or wrap your string with the Fuzzy helper (e.g. name=Fuzzy("trash", cutoff=80)) for fuzzy matching. - Numeric Range Filtering: Supply a tuple (min, max) to filter numeric fields such as marketValue. - Boolean Filtering: Directly pass boolean values (e.g. hasUse=True).

Documentation

Full documentation for dankmemer.py can be found at:

https://dankmemer-py.readthedocs.io

Feel free to test, report issues, and contribute to this alpha release!