Frequenz CS Reporting Library

Overview

Streamlit library that ships a ready-to-use client reporting UI. It fetches data from the Frequenz reporting API, applies the energy reporting utilities from frequenz-lib-notebooks, and renders dashboards, tables, and plots with reusable Streamlit components.

Features

• Pre-built Streamlit app with navigation, landing page, and reporting view.
• Connects to the Frequenz reporting API to fetch microgrid measurements.
• Ready-made dashboards (metrics, plots, and tables) powered by frequenz-lib-notebooks.
• Reusable components (sidebar filters, charts, tables) for your own pages.

Quick start

1. Install the library (Python 3.12):

   pip install "frequenz-cs-reporting"
   

2. Provide environment variables (see below). A .env file works with Streamlit:

   REPORTING_API_URL=https://your-reporting-endpoint
   API_KEY=your-api-key
   API_SECRET=your-api-secret
   MICROGRID_CONFIG_DIR=toml_directory/
   

3. Add .toml files to the toml_directory.
4. Run the bundled UI from the repo root:

   streamlit run app.py
   

   Use the sidebar to pick a microgrid, date range, timezone, and resolution.

Configuration

Environment

• REPORTING_API_URL (required): Base URL for the Frequenz reporting API.
• API_KEY and API_SECRET (required): Credentials used by the data client.
• MICROGRID_CONFIG_DIR (optional): Directory containing TOML microgrid configs. Defaults to toml_directory/.

Microgrid configs

Microgrid definitions are loaded from TOML files in MICROGRID_CONFIG_DIR.

Running the Streamlit app

The app entry point is app.py. When you run streamlit run app.py, it:

• Discovers pages from frequenz.cs_reporting.app_pages (the default build ships Home and Reporting pages).
• Loads microgrid configs from MICROGRID_CONFIG_DIR and lists available IDs.
• Fetches data via the reporting API.

Running in Deepnote

• Running in Deepnote is supported; required environment variables can be injected via the Deepnote integration.
• Add this library as a requirement in requirements.txt
• Add the docker image from dockerhub (currently named: CS-Reporting in deepnote).
• Copy the app.py to the folder structure in Deepnote.
• Click on create_streamlit_application in Deepnote UI to create the app.

Library usage

Fetch microgrid data programmatically (sync wrapper shown):

from datetime import datetime, timedelta
from frequenz.cs_reporting.services.data_service import get_microgrid_data

df = get_microgrid_data(
    microgrid_id=241,
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 1, 2),
    resolution=timedelta(minutes=15),
)

Build your own Streamlit page and add it to the navigation by defining a PageSpec in frequenz.cs_reporting.app_pages:

# app_pages/custom.py
from frequenz.cs_reporting.rep_cs_core.page_spec import PageSpec
import streamlit as st

def render() -> None:
    st.title("Custom view")
    st.write("Add your own charts or tables here.")

PAGE = PageSpec(key="custom", title="Custom", icon="🛠️", order=10, render=render)

Development

• Install dev tools: pip install -e ".[dev]".
• Run tests: nox -l to see sessions, e.g. nox -s tests.
• Build docs with MkDocs (README.md is the landing page). After installing the mkdocs extra you can use the docs nox session (if available) or run mkdocs serve.

Supported Platforms

The following platforms are officially supported (tested):

• Python: 3.12
• Operating System: Ubuntu Linux 20.04
• Architectures: amd64, arm64

Contributing

If you want to know how to build this project and contribute to it, please check out the Contributing Guide.