openelectricity 0.11.0

OpenElectricity Python Client

OpenElectricity Python Client

A Python client for the OpenElectricity API, providing access to electricity and energy network data and metrics for Australia.

To obtain an API key visit platform.openelectricity.org.au

For documentation visit docs.openelectricity.org.au

Features

• Synchronous and asynchronous API clients
• Fully typed with comprehensive type annotations
• Automatic request retries and error handling
• Context manager support
• Modern Python (3.10+) with full type annotations
• Direct conversion to Pandas and Polars DataFrames

Installation

# or with uv (recommended)
uv add openelectricity

# Install with data analysis support (Polars/Pandas)
uv add "openelectricity[analysis]"

# Install base package with pip
pip install openelectricity

Quick Start

First, set your API key in the environment:

# Set your API key
export OPENELECTRICITY_API_KEY=your-api-key

# Optional: Override API server (defaults to production)
export OPENELECTRICITY_API_URL=http://localhost:8000/v4

Quick Test

You can test the client and authentication with the following:

Data Examples

Examples of using the client are in the examples directory. Here are some basic examples:

from datetime import datetime, timedelta
from openelectricity import OEClient
from openelectricity.types import DataMetric, UnitFueltechType, UnitStatusType

# Calculate date range
end_date = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
start_date = end_date - timedelta(days=7)

# Using context manager (recommended)
with OEClient() as client:
    # Get operating solar and wind facilities
    facilities = client.get_facilities(
        network_id=["NEM"],
        status_id=[UnitStatusType.OPERATING],
        fueltech_id=[UnitFueltechType.SOLAR_UTILITY, UnitFueltechType.WIND],
    )

    # Get network data for NEM
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.POWER, DataMetric.ENERGY],
        interval="1d",
        date_start=start_date,
        date_end=end_date,
        secondary_grouping="fueltech_group",
    )

    # Print results
    for series in response.data:
        print(f"\nMetric: {series.metric}")
        print(f"Unit: {series.unit}")

        for result in series.results:
            print(f"\n  {result.name}:")
            print(f"  Fuel Tech Group: {result.columns.fueltech_group}")
            for point in result.data:
                print(f"    {point.timestamp}: {point.value:.2f} {series.unit}")

For async usage:

from openelectricity import AsyncOEClient
import asyncio

async def main():
    async with AsyncOEClient() as client:
        # Get operating solar and wind facilities
        facilities = await client.get_facilities(
            network_id=["NEM"],
            status_id=[UnitStatusType.OPERATING],
            fueltech_id=[UnitFueltechType.SOLAR_UTILITY, UnitFueltechType.WIND],
        )

        # Get network data
        response = await client.get_network_data(
            network_code="NEM",
            metrics=[DataMetric.POWER],
            interval="1d",
            secondary_grouping="fueltech_group",
        )
        # Process response...

asyncio.run(main())

Data Analysis

The client provides built-in support for converting API responses to popular data analysis formats.

Using with Polars

# Make sure you've installed with analysis extras
# uv add "openelectricity[analysis]"

from openelectricity import OEClient
from openelectricity.types import DataMetric

with OEClient() as client:
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.POWER, DataMetric.ENERGY],
        interval="1d",
        secondary_grouping="fueltech_group",
    )

    # Convert to Polars DataFrame
    df = response.to_polars()

    # Get metric units
    units = response.get_metric_units()

    # Analyze data
    energy_by_fueltech = (
        df.group_by("fueltech_group")
        .agg(
            pl.col("energy").sum().alias("total_energy_mwh"),
            pl.col("power").mean().alias("avg_power_mw"),
        )
        .sort("total_energy_mwh", descending=True)
    )

Using with Pandas

# Make sure you've installed with analysis extras
# uv add "openelectricity[analysis]"

from openelectricity import OEClient
from openelectricity.types import DataMetric

with OEClient() as client:
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.POWER, DataMetric.ENERGY],
        interval="1d",
        secondary_grouping="fueltech_group",
    )

    # Convert to Pandas DataFrame
    df = response.to_pandas()

    # Get metric units
    units = response.get_metric_units()

    # Analyze data
    energy_by_fueltech = (
        df.groupby("fueltech_group")
        .agg({
            "energy": "sum",
            "power": "mean",
        })
        .sort_values("energy", ascending=False)
    )

Development

Development is preferred with uv and there are targets in the Makefile for common tasks and managing releases. There are optional dependency groups for development, analysis and testing. You can install all of them with make install (which runs uv sync --all-extras).

1. Clone the repository

2. Install development dependencies:

   make install
   

3. Run tests:

   make test
   

4. Format code:

   make format
   

5. Run linters:

   make lint
   

License

This project is licensed under the MIT License - see the LICENSE file for details.