fusedio / udfs

Public Fused UDFs. Build any scale workflows with the Fused Python SDK and Workbench webapp, and integrate them into your stack with the Fused Hosted API.

Fused Python UDFs

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This repo is a public collection of Fused User Defined Functions (UDFs).

Fused is the glue layer that interfaces data platforms and data tools via a managed serverless API. With Fused, you can write, share, or discover UDFs which are the building blocks of serverless geospatial operations. UDFs are Python functions that turn into live HTTP endpoints that load their output into any tools that can call an API.

Quickstart

1. Install Fused Python SDK

The Fused Python SDK is available at PyPI. Use the standard Python installation tools. UDFs this repo expect the most recent version.

python3 -m venv .venv
source .venv/bin/activate
pip install fused

It's possible that to run UDFs locally the local environment might require additional packages not found locally. If that is the case, this command installs all required dependencies.

!pip install fused odc-stac duckdb numba xarray-spatial planetary-computer 'odc-stac[botocore]' py3dep stackstac pynhd boto3

2. Load a UDF into a workflow

This snippet shows how to import a UDF from this repo into a Python environment. The URL is of the directory that contains a UDF generated with Fused.

import fused

udf = fused.load("https://github.com/fusedio/udfs/tree/main/public/DuckDB_NYC_Example")
gdf = fused.run(udf=udf)
gdf

Similarly, as a bash oneliner.

python -c "import fused; udf = fused.load('https://github.com/fusedio/udfs/tree/main/public/DuckDB_NYC_Example'); print(fused.run(udf=udf));"

Walkthrough

Repo structure

This repository is structured to facilitate easy access of UDFs and their supporting files. Each UDF, like Sample_UDF, is contained within its own subdirectory within the public directory - along with its documentation, code, metadata, and utility function code.

Each UDF can be thought of as a standalone Python package.

├── README.md
└── public
    └── Sample_UDF
        ├── README.MD
        ├── Sample_UDF.py
        ├── meta.json
        └── utils.py

Files relevant to each UDF are:

• README.md Provides details of the UDF's purpose and how it works.
• Sample_UDF.py This eponymous Python file contains the UDF's business logic as a Python function decorated with @fused.udf.
• meta.json This file contains metadata needed to render the UDF in the Fused explorer and for the UDF to run correctly.
• utils.py This Python file contains helper functions the UDF (optionally) imports and references.

Contribute a UDF

1. Save UDF

import fused

@fused.udf
def my_udf(bbox=None):
    import pandas as pd
    return pd.DataFrame({'Hello': ['from Fused']})

# Run locally
print(fused.run(my_udf))

# Save locally
my_udf.to_directory('my_udf')
# or for zip file: my_udf.to_file('my_udf.zip')

# Save remotely to Fused
my_udf.to_fused('my_udf')

"Save locally" generates the UDF folder on your local system, which you'll use in the following step.

2. Open a PR

Clone this repo to your local system and add the UDF folder under public or community. Create a PR on this repo.

Ecosystem

Build any scale workflows with the Fused Python SDK and Workbench webapp, and integrate them into your stack with the Fused Hosted API.

Documentation

Fused documentation is in docs.fused.io.

Contribution guidelines

All UDF contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

Pre commit

Please run pre-commit hooks on your UDF prior to submitting.

pre-commit install
pre-commit run --files public/PC_Sentinel2/*

License

MIT License