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from pathlib import Path
import pandas as pd
import plotly.colors as pcolors
import plotly.graph_objects as go
import streamlit as st
from mlip_arena.models import REGISTRY
DATA_DIR = Path("mlip_arena/tasks/combustion")
st.markdown("""
# Combustion
""")
st.markdown("### Methods")
container = st.container(border=True)
valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
models = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "CHGNet", "M3GNet", "EquiformerV2(OC22)", "SevenNet"])
st.markdown("### Settings")
vis = st.container(border=True)
# Get all attributes from pcolors.qualitative
all_attributes = dir(pcolors.qualitative)
color_palettes = {
attr: getattr(pcolors.qualitative, attr)
for attr in all_attributes
if isinstance(getattr(pcolors.qualitative, attr), list)
}
color_palettes.pop("__all__", None)
palette_names = list(color_palettes.keys())
palette_colors = list(color_palettes.values())
palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
color_sequence = color_palettes[palette_name]
if not models:
st.stop()
families = [REGISTRY[str(model)]["family"] for model in models]
dfs = [
pd.read_json(DATA_DIR / family.lower() / "hydrogen.json")
for family in families
]
df = pd.concat(dfs, ignore_index=True)
df.drop_duplicates(inplace=True, subset=["formula", "method"])
method_color_mapping = {
method: color_sequence[i % len(color_sequence)]
for i, method in enumerate(df["method"].unique())
}
###
# Number of products
fig = go.Figure()
for method in df["method"].unique():
row = df[df["method"] == method].iloc[0]
fig.add_trace(
go.Scatter(
x=row["timesteps"],
y=row["nproducts"],
mode="lines",
name=method,
line=dict(color=method_color_mapping[method]),
showlegend=True,
),
)
fig.update_layout(
title="Hydrogen Combustion (2H2 + O2 -> 2H2O, 64 units)",
xaxis_title="Timesteps",
yaxis_title="Number of water molecules",
)
st.plotly_chart(fig)
# tempearture
fig = go.Figure()
for method in df["method"].unique():
row = df[df["method"] == method].iloc[0]
fig.add_trace(
go.Scatter(
x=row["timesteps"],
y=row["temperatures"],
mode="markers",
name=method,
line=dict(color=method_color_mapping[method]),
showlegend=True,
),
)
target_steps = df["target_steps"].iloc[0]
fig.add_trace(
go.Line(
x=[0, target_steps/3, target_steps/3*2, target_steps],
y=[300, 3000, 3000, 300],
mode="lines",
name="Target",
line=dict(
dash="dash",
),
showlegend=True,
),
)
fig.update_layout(
title="Hydrogen Combustion (2H2 + O2 -> 2H2O, 64 units)",
xaxis_title="Timesteps",
yaxis_title="Temperatures",
yaxis2=dict(
title="Product Percentage (%)",
overlaying="y",
side="right",
range=[0, 100],
tickmode="sync"
)
# template="plotly_dark",
)
st.plotly_chart(fig)
# MD runtime speed
fig = go.Figure()
df = df.sort_values("steps_per_second", ascending=True)
fig.add_trace(
go.Bar(
x=df["steps_per_second"],
y=df["method"],
opacity=0.75,
orientation="h",
marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
)
)
fig.update_layout(
title="MD runtime speed (on single A100 GPU)",
xaxis_title="Steps per second",
yaxis_title="Method",
)
st.plotly_chart(fig)
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