moo-viewer

Streamlit dashboard for visualising multi-objective optimisation results.

Installation

pip install -e .

Or without installing:

pip install streamlit plotly pandas
streamlit run app.py

Usage

# After installing
moo-viewer

# Or directly
streamlit run app.py

Then in the sidebar:

1. Enter the results path (e.g. /path/to/results/)
2. Enter the case prefix (e.g. case_social)
3. Pick a plot from the dropdown

Expected folder structure

results/
  case_social_50_0.0/
    total_cost.csv
    cap_inst.csv
    curtailment.csv
    flow_out.csv
  case_social_50_0.1/
    ...

Folder names must follow the pattern <case>_<cap>_<rho>.

Package layout

moo_viewer/
  __init__.py       # version
  __main__.py       # CLI entry point
  constants.py      # tech colours, plot list
  data.py           # file discovery + cached loaders
  sidebar.py        # Streamlit sidebar widget
  views.py          # one render_*() per plot type
  plots/
    pareto.py       # Pareto front scatter
    bars.py         # capacity & curtailment stacked bars
    generation.py   # hourly area grid + summed bars
app.py              # thin Streamlit entry point
pyproject.toml

Adding a new plot

1. Add a builder function in moo_viewer/plots/.
2. Add a loader in moo_viewer/data.py if needed.
3. Add a render_*() function in moo_viewer/views.py.
4. Add the plot name to PLOT_OPTIONS in moo_viewer/constants.py.
5. Wire it up in app.py.