| from pathlib import Path |
| import sys |
|
|
| import streamlit as st |
|
|
| try: |
| from data_store import load_metric_store |
| except ModuleNotFoundError: |
| sys.path.append(str(Path(__file__).resolve().parents[1])) |
| from data_store import load_metric_store |
|
|
| st.set_page_config(page_title="Raw Data", page_icon="🧾", layout="wide") |
|
|
| |
| st.sidebar.title("Navigation") |
| st.sidebar.page_link("streamlit_app.py", label="Home", icon="🏠") |
| st.sidebar.page_link("pages/OptimizationLeaderboard.py", label="Optimization Leaderboard", icon="📊") |
| st.sidebar.page_link("pages/UQLeaderboard.py", label="UQ Leaderboard", icon="🎯") |
| st.sidebar.page_link("pages/MethodDetails.py", label="Method configurations", icon="📘") |
| st.sidebar.page_link("pages/RawData.py", label="Get Data", icon="🧾") |
|
|
| st.title("Raw Averaged Forward Model Runs") |
| st.caption("All rows are averaged over random seeds during ingestion; `metric` represents forward model runs") |
|
|
| metric_store = load_metric_store() |
| if metric_store.empty: |
| st.warning("No metric data found. Expected NetCDF files in `data/` with a `metric` variable.") |
| st.stop() |
|
|
| benchmark_options = sorted(metric_store["benchmark"].unique().tolist()) |
| default_benchmark_index = benchmark_options.index("L63") if "L63" in benchmark_options else 0 |
| selected_benchmark = str(st.selectbox("Benchmark", options=benchmark_options, index=default_benchmark_index)) |
|
|
| filtered = metric_store[metric_store["benchmark"] == selected_benchmark].copy() |
| filtered = filtered.sort_values(["abbreviation", "rmse_target", "ensemble_size"]) |
|
|
| st.dataframe( |
| filtered[ |
| [ |
| "benchmark", |
| "abbreviation", |
| "Method", |
| "parallelism", |
| "update_type", |
| "method_goal", |
| "emulator_use", |
| "algorithm_type", |
| "algorithm_alias", |
| "rmse_target", |
| "ensemble_size", |
| "metric", |
| ] |
| ], |
| hide_index=True, |
| use_container_width=True, |
| column_config={ |
| "benchmark": st.column_config.TextColumn("Benchmark"), |
| "abbreviation": st.column_config.TextColumn("Abbrev."), |
| "Method": st.column_config.TextColumn("Method"), |
| "parallelism": st.column_config.TextColumn("Parallelism"), |
| "update_type": st.column_config.TextColumn("Update Type"), |
| "method_goal": st.column_config.TextColumn("Method Goal"), |
| "emulator_use": st.column_config.TextColumn("Emulator Use"), |
| "algorithm_type": st.column_config.TextColumn("Canonical Type"), |
| "algorithm_alias": st.column_config.TextColumn("Source Alias"), |
| "rmse_target": st.column_config.TextColumn("RMSE Target"), |
| "ensemble_size": st.column_config.NumberColumn("Ensemble Size"), |
| "metric": st.column_config.NumberColumn("Mean Forward Model Runs", format="%.6f"), |
| }, |
| ) |
|
|
| csv_data = filtered.to_csv(index=False).encode("utf-8") |
| st.download_button( |
| label=f"Download {selected_benchmark} averaged metrics (CSV)", |
| data=csv_data, |
| file_name=f"{selected_benchmark.lower()}_averaged_metrics.csv", |
| mime="text/csv", |
| ) |
|
|
| st.page_link("pages/OptimizationLeaderboard.py", label="⬅ Back to Optimization Leaderboard", icon="↩️") |
|
|
