add hydrogen combution rank

serve/app.py

@@ -4,21 +4,6 @@ import streamlit as st
 
 from mlip_arena.tasks import REGISTRY as TASKS
 
-# if "logged_in" not in st.session_state:
-#     st.session_state.logged_in = False
-
-# def login():
-#     if st.button("Log in"):
-#         st.session_state.logged_in = True
-#         st.rerun()
-
-# def logout():
-#     if st.button("Log out"):
-#         st.session_state.logged_in = False
-#         st.rerun()
-
-# login_page = st.Page(login, title="Log in", icon=":material/login:")
-# logout_page = st.Page(logout, title="Log out", icon=":material/logout:")
 
 leaderboard = st.Page(
     "leaderboard.py", title="Leaderboard", icon=":material/trophy:", default=True

serve/ranks/combustion.py

@@ -28,7 +28,7 @@ df = get_data(valid_models)
 
 @st.cache_data
 def get_com_drifts(df):
-    df_exploded = df.explode(["timestep", "com_drifts"]).reset_index(drop=True)
+    df_exploded = df.explode(["timestep", "energies", "com_drifts"]).reset_index(drop=True)
 
     # Convert the 'com_drifts' column (which are arrays) into separate columns for x, y, and z components
     df_exploded[["com_drift_x", "com_drift_y", "com_drift_z"]] = pd.DataFrame(
@@ -42,14 +42,109 @@ def get_com_drifts(df):
         df_flat["com_drift_x"] ** 2 + df_flat["com_drift_y"] ** 2 + df_flat["com_drift_z"] ** 2
     )
 
+    df_flat = df_flat.drop(columns=["com_drift_x", "com_drift_y", "com_drift_z"])
+
     return df_flat
 
 df_exploded = get_com_drifts(df)
 
+for method, row in df_exploded.groupby("method"):
+#     # row = df[df["method"] == method].iloc[0]
+    energies = np.array(row["energies"])
+    df_exploded.loc[df_exploded["method"] == method,"reaction_enthlapy_diff"] = (energies[-1] - energies[0]) / 128 * 23.
+    df_exploded.loc[df_exploded["method"] == method, "final_com_drift"] = np.array(row["total_com_drift"])[-1]
+
+exp_ref = -68.3078 # kcal/mol
+
+
+df_exploded.drop(columns=["temperatures", "pressures", "total_steps", "energies", "kinetic_energies", "timestep", "nproducts", "total_com_drift", "target_steps", "reaction", "formula", "natoms", "seconds_per_step", "seconds_per_step_per_atom", "final_step", "total_time_seconds"], axis=1, inplace=True)
+
+df_exploded.drop_duplicates(inplace=True, subset=["method"])
+
+print(df_exploded.columns)
+
+df_exploded.set_index("method", inplace=True)
+
+df_exploded.rename(columns={
+    "method": "Model"
+}, inplace=True)
+
+
 table = pd.DataFrame()
 
-# def render():
-#     st.dataframe(
-#         table,
-#         use_container_width=True,
-#     )
+for index, row in df_exploded.iterrows():
+
+    new_row = {
+        "Model": index,
+        "Reaction enthalpy error [kcal/mol]": row["reaction_enthlapy_diff"],
+        "Final COM drift [Å]": row["final_com_drift"],
+        "Steps per second": row["steps_per_second"],
+        "Yield [%]": row["yield"] * 100,
+    }
+
+    table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
+
+table.set_index("Model", inplace=True)
+
+table.sort_values("Reaction enthalpy error [kcal/mol]", ascending=True, inplace=True)
+table["Rank"] = np.argsort(np.abs(table["Reaction enthalpy error [kcal/mol]"].to_numpy()))
+
+table.sort_values("Final COM drift [Å]", ascending=True, inplace=True)
+table["Rank"] += np.argsort(table["Final COM drift [Å]"].to_numpy())
+
+table.sort_values("Steps per second", ascending=False, inplace=True)
+table["Rank"] += np.argsort(-table["Steps per second"].to_numpy())
+
+table.sort_values("Yield [%]", ascending=False, inplace=True)
+table["Rank"] += np.argsort(-table["Yield [%]"].to_numpy())
+
+table["Rank"] += 1
+
+table.sort_values(["Rank"], ascending=True, inplace=True)
+
+table["Rank aggr."] = table["Rank"]
+table["Rank"] = table["Rank aggr."].rank(method='min').astype(int)
+
+
+table = table.reindex(
+    columns=[
+        "Rank",
+        "Rank aggr.",
+        "Reaction enthalpy error [kcal/mol]",
+        "Final COM drift [Å]",
+        "Steps per second",
+        "Yield [%]",
+    ]
+)
+
+s = (
+    table.style.background_gradient(
+        cmap="Oranges",
+        subset=["Reaction enthalpy error [kcal/mol]"],
+    )
+    .background_gradient(
+        cmap="Oranges",
+        subset=["Final COM drift [Å]"],
+        gmap=np.log10(table["Final COM drift [Å]"].to_numpy() + 1e-10),
+    )
+    .background_gradient(
+        cmap="Oranges_r",
+        subset=["Steps per second", "Yield [%]"]
+    )
+    .background_gradient(
+        cmap="Blues",
+        subset=["Rank", "Rank aggr."],
+    )
+    .format(
+        "{:.3e}",
+        subset=["Final COM drift [Å]"],
+    )
+)
+
+
+def render():
+
+    st.dataframe(
+        s,
+        use_container_width=True,
+    )

serve/tasks/combustion.py

@@ -250,36 +250,6 @@ fig.update_layout(
 
 st.plotly_chart(fig)
 
-# Total Energy
-
-# fig = go.Figure()
-
-# for method in df["method"].unique():
-#     row = df[df["method"] == method].iloc[0]
-#     fig.add_trace(
-#         go.Scatter(
-#             x=row["timestep"],
-#             y=np.array(row["energies"]) - row["energies"][0] + np.array(row["kinetic_energies"]),
-#             mode="lines",
-#             name=method,
-#             line=dict(
-#                 color=method_color_mapping[method],
-#                 # width=1
-#             ),
-#             marker=dict(color=method_color_mapping[method], size=3),
-#             showlegend=True,
-#         ),
-#     )
-
-# fig.update_layout(
-#     # title="Hydrogen Combustion (2H2 + O2 -> 2H2O, 64 units)",
-#     xaxis_title="Timestep",
-#     yaxis_title="Total Energy 𝚫 (eV)",
-#     # template="plotly_dark",
-# )
-
-# st.plotly_chart(fig)
-
 # Reaction energy
 
 fig = go.Figure()
@@ -334,16 +304,8 @@ fig.add_annotation(
 )
 
 fig.update_layout(
-    # title="Reaction energy 𝚫H (kcal/mol)",
     xaxis_title="Method <br> <span style='font-size: 10px;'>[1] Lide, D. R. (Ed.). (2004). CRC handbook of chemistry and physics (Vol. 85). CRC press.</span>",
     yaxis_title="Reaction energy 𝚫H (kcal/mol)",
-    # annotations = [
-    #     dict(
-    #         x=0.5, xref="paper", xanchor="center",
-    #         y=-0.5, yref="paper", yanchor="bottom",
-    #         text="Caption",
-    #     )
-    # ]
 )
 
 st.plotly_chart(fig)