Yuan (Cyrus) Chiang commited on
Commit
d937c80
1 Parent(s): f9d3b3b

Add COM drift for combustion (#10)

Browse files

* add chgnet checkpoint info

* add com drifts + animation; add streamlit test

* remove test interdependency

mlip_arena/models/registry.yaml CHANGED
@@ -28,7 +28,7 @@ CHGNet:
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  class: CHGNet
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  family: chgnet
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  package: chgnet==0.3.8
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- checkpoint:
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  username: cyrusyc
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  last-update: 2024-07-08T00:00:00
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  datetime: 2024-07-08T00:00:00
 
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  class: CHGNet
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  family: chgnet
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  package: chgnet==0.3.8
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+ checkpoint: v0.3.0
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  username: cyrusyc
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  last-update: 2024-07-08T00:00:00
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  datetime: 2024-07-08T00:00:00
mlip_arena/tasks/combustion/chgnet/hydrogen.json CHANGED
@@ -1 +1 @@
1
- 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mlip_arena/tasks/combustion/m3gnet/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/mace-mp/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/orb/hydrogen.json CHANGED
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mlip_arena/tasks/combustion/sevennet/hydrogen.json CHANGED
The diff for this file is too large to render. See raw diff
 
mlip_arena/tasks/combustion/water.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -99,7 +99,7 @@
99
  },
100
  {
101
  "cell_type": "code",
102
- "execution_count": 3,
103
  "metadata": {
104
  "tags": []
105
  },
@@ -119,7 +119,7 @@
119
  },
120
  {
121
  "cell_type": "code",
122
- "execution_count": 4,
123
  "metadata": {},
124
  "outputs": [
125
  {
@@ -130,7 +130,7 @@
130
  "\n",
131
  "#SBATCH -A matgen\n",
132
  "#SBATCH --mem=0\n",
133
- "#SBATCH -t 01:45:00\n",
134
  "#SBATCH -J combustion-water\n",
135
  "#SBATCH -q regular\n",
136
  "#SBATCH -N 1\n",
@@ -140,18 +140,9 @@
140
  "source ~/.bashrc\n",
141
  "module load python\n",
142
  "source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\n",
143
- "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/bin/python -m distributed.cli.dask_worker tcp://128.55.64.22:43419 --name dummy-name --nthreads 1 --memory-limit 59.60GiB --nanny --death-timeout 86400\n",
144
  "\n"
145
  ]
146
- },
147
- {
148
- "name": "stderr",
149
- "output_type": "stream",
150
- "text": [
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- "2024-09-21 02:07:15,149 - distributed.scheduler - ERROR - Task md-8-ae8c6f68afcf406fae6680a18eca9577-1 marked as failed because 4 workers died while trying to run it\n",
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- "2024-09-21 02:07:15,181 - distributed.scheduler - ERROR - Task md-6-04c53616e6ed4c76a754de000182bcad-1 marked as failed because 4 workers died while trying to run it\n",
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- "2024-09-21 03:49:24,062 - distributed.scheduler - ERROR - Task md-9-8d9936131878409688579c984283ffaf-1 marked as failed because 4 workers died while trying to run it\n"
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  "source": [
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  " memory=\"64 GB\",\n",
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  " shebang=\"#!/bin/bash\",\n",
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  " account=\"matgen\",\n",
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- " walltime=\"01:45:00\",\n",
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  " job_mem=\"0\",\n",
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- "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">18:15:31.005 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | prefect.task_runner.dask - Connecting to an existing Dask cluster at tcp://128.55.64.22:43419\n",
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- "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">18:15:31.511 | <span style=\"color: #008080; text-decoration-color: #008080\">INFO</span> | Flow run<span style=\"color: #800080; text-decoration-color: #800080\"> 'sepia-buffalo'</span> - Created task run 'md-2' for task 'md'\n",
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  "cells": [
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  "cell_type": "code",
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+ "execution_count": 1,
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  "#SBATCH -A matgen\n",
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  "#SBATCH --mem=0\n",
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+ "#SBATCH -t 02:00:00\n",
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  "#SBATCH -J combustion-water\n",
135
  "#SBATCH -q regular\n",
136
  "#SBATCH -N 1\n",
 
140
  "source ~/.bashrc\n",
141
  "module load python\n",
142
  "source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\n",
143
+ "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/bin/python -m distributed.cli.dask_worker tcp://128.55.64.18:39213 --name dummy-name --nthreads 1 --memory-limit 59.60GiB --nanny --death-timeout 86400\n",
144
  "\n"
145
  ]
 
 
 
 
 
 
 
 
 
146
  }
147
  ],
148
  "source": [
 
155
  " memory=\"64 GB\",\n",
156
  " shebang=\"#!/bin/bash\",\n",
157
  " account=\"matgen\",\n",
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+ " walltime=\"02:00:00\",\n",
159
  " job_mem=\"0\",\n",
160
  " job_script_prologue=[\n",
161
  " \"source ~/.bashrc\",\n",
 
188
  },
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  {
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  "cell_type": "code",
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+ "execution_count": 4,
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  "</pre>\n"
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  "text/plain": [
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+ "17:24:17.427 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-6' for task 'md'\n"
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  "</pre>\n"
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+ "17:24:17.545 | \u001b[36mINFO\u001b[0m | Flow run\u001b[35m 'prompt-newt'\u001b[0m - Created task run 'md-9' for task 'md'\n"
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537
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  "metadata": {},
mlip_arena/tasks/diatomics/run.ipynb CHANGED
The diff for this file is too large to render. See raw diff
 
mlip_arena/tasks/registry.yaml CHANGED
@@ -13,7 +13,7 @@ Combustion:
13
  category: Molecular dynamics
14
  task-page: combustion
15
  task-layout: centered
16
- rank-page:
17
  # nacl:
18
  # last-update: 2024-03-25T14:30:00 # TODO: Fake datetime
19
  # alexandria:
 
13
  category: Molecular dynamics
14
  task-page: combustion
15
  task-layout: centered
16
+ rank-page: combustion
17
  # nacl:
18
  # last-update: 2024-03-25T14:30:00 # TODO: Fake datetime
19
  # alexandria:
pyproject.toml CHANGED
@@ -50,7 +50,7 @@ run = [
50
  "alignn==2024.5.27"
51
  ]
52
  app = [
53
- "streamlit",
54
  "plotly",
55
  "bokeh==2.4.3",
56
  "statsmodels"
 
50
  "alignn==2024.5.27"
51
  ]
52
  app = [
53
+ "streamlit==1.38.0",
54
  "plotly",
55
  "bokeh==2.4.3",
56
  "statsmodels"
requirements.txt CHANGED
@@ -1,4 +1,4 @@
1
- streamlit
2
  plotly
3
  numpy
4
  scipy
 
1
+ streamlit==1.38.0
2
  plotly
3
  numpy
4
  scipy
serve/leaderboard.py CHANGED
@@ -98,16 +98,18 @@ for task in TASKS:
98
  continue
99
 
100
  st.header(task, divider=True)
101
- st.page_link(
102
- f"tasks/{TASKS[task]['task-page']}.py",
103
- label="Link to task page",
104
- icon=":material/link:",
105
- )
106
 
107
  task_module = importlib.import_module(f"ranks.{TASKS[task]['rank-page']}")
108
 
109
  # Call the function from the imported module
110
  if hasattr(task_module, "render"):
111
  task_module.render()
 
 
 
 
 
 
 
112
  else:
113
- st.write("Results for the task are not available yet.")
 
98
  continue
99
 
100
  st.header(task, divider=True)
 
 
 
 
 
101
 
102
  task_module = importlib.import_module(f"ranks.{TASKS[task]['rank-page']}")
103
 
104
  # Call the function from the imported module
105
  if hasattr(task_module, "render"):
106
  task_module.render()
107
+ st.page_link(
108
+ f"tasks/{TASKS[task]['task-page']}.py",
109
+ label="Task page",
110
+ icon=":material/link:",
111
+ )
112
+ # if st.button(f"Go to task page"):
113
+ # st.switch_page(f"tasks/{TASKS[task]['task-page']}.py")
114
  else:
115
+ st.write("Results for the task are not available yet.")
serve/ranks/homonuclear-diatomics.py CHANGED
@@ -2,12 +2,7 @@ from pathlib import Path
2
 
3
  import numpy as np
4
  import pandas as pd
5
- import plotly.colors as pcolors
6
- import plotly.graph_objects as go
7
  import streamlit as st
8
- from ase.data import chemical_symbols
9
- from plotly.subplots import make_subplots
10
- from scipy.interpolate import CubicSpline
11
 
12
  from mlip_arena.models import REGISTRY as MODELS
13
 
@@ -131,4 +126,4 @@ def render():
131
  s,
132
  use_container_width=True,
133
  )
134
- # return table
 
2
 
3
  import numpy as np
4
  import pandas as pd
 
 
5
  import streamlit as st
 
 
 
6
 
7
  from mlip_arena.models import REGISTRY as MODELS
8
 
 
126
  s,
127
  use_container_width=True,
128
  )
129
+ # return table
serve/tasks/combustion.py CHANGED
@@ -3,10 +3,11 @@ from pathlib import Path
3
  import numpy as np
4
  import pandas as pd
5
  import plotly.colors as pcolors
 
6
  import plotly.graph_objects as go
7
  import streamlit as st
8
 
9
- from mlip_arena.models import REGISTRY
10
 
11
  DATA_DIR = Path("mlip_arena/tasks/combustion")
12
 
@@ -17,12 +18,24 @@ st.markdown("""
17
 
18
  st.markdown("### Methods")
19
  container = st.container(border=True)
20
- valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
 
 
 
 
21
 
22
  models = container.multiselect(
23
- "MLIPs",
24
- valid_models,
25
- ["MACE-MP(M)", "CHGNet", "M3GNet", "SevenNet", "ORB", "EquiformerV2(OC22)", "eSCN(OC20)"]
 
 
 
 
 
 
 
 
26
  )
27
 
28
  st.markdown("### Settings")
@@ -38,7 +51,6 @@ color_palettes.pop("__all__", None)
38
 
39
  palette_names = list(color_palettes.keys())
40
  palette_colors = list(color_palettes.values())
41
-
42
  palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
43
 
44
  color_sequence = color_palettes[palette_name]
@@ -46,14 +58,25 @@ color_sequence = color_palettes[palette_name]
46
  if not models:
47
  st.stop()
48
 
49
- families = [REGISTRY[str(model)]["family"] for model in models]
50
 
51
- dfs = [
52
- pd.read_json(DATA_DIR / family.lower() / "hydrogen.json")
53
- for family in families
54
- ]
55
- df = pd.concat(dfs, ignore_index=True)
56
- df.drop_duplicates(inplace=True, subset=["formula", "method"])
 
 
 
 
 
 
 
 
 
 
 
 
57
 
58
  method_color_mapping = {
59
  method: color_sequence[i % len(color_sequence)]
@@ -98,7 +121,11 @@ for method in df["method"].unique():
98
  y=row["temperatures"],
99
  mode="markers",
100
  name=method,
101
- line=dict(color=method_color_mapping[method]),
 
 
 
 
102
  showlegend=True,
103
  ),
104
  )
@@ -106,13 +133,11 @@ for method in df["method"].unique():
106
  target_steps = df["target_steps"].iloc[0]
107
  fig.add_trace(
108
  go.Line(
109
- x=[0, target_steps/3, target_steps/3*2, target_steps],
110
  y=[300, 3000, 3000, 300],
111
  mode="lines",
112
  name="Target",
113
- line=dict(
114
- dash="dash",
115
- ),
116
  showlegend=True,
117
  ),
118
  )
@@ -126,8 +151,8 @@ fig.update_layout(
126
  overlaying="y",
127
  side="right",
128
  range=[0, 100],
129
- tickmode="sync"
130
- )
131
  # template="plotly_dark",
132
  )
133
 
@@ -137,8 +162,6 @@ st.plotly_chart(fig)
137
 
138
  fig = go.Figure()
139
 
140
- # df["yield"] = np.array(df["nproducts"]) / 128 * 100
141
-
142
  df = df.sort_values("yield", ascending=True)
143
 
144
  fig.add_trace(
@@ -173,7 +196,7 @@ fig.add_trace(
173
  opacity=0.75,
174
  orientation="h",
175
  marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
176
- text=df["steps_per_second"].round(1)
177
  )
178
  )
179
 
@@ -184,3 +207,137 @@ fig.update_layout(
184
  )
185
 
186
  st.plotly_chart(fig)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  import numpy as np
4
  import pandas as pd
5
  import plotly.colors as pcolors
6
+ import plotly.express as px
7
  import plotly.graph_objects as go
8
  import streamlit as st
9
 
10
+ from mlip_arena.models import REGISTRY as MODELS
11
 
12
  DATA_DIR = Path("mlip_arena/tasks/combustion")
13
 
 
18
 
19
  st.markdown("### Methods")
20
  container = st.container(border=True)
21
+ valid_models = [
22
+ model
23
+ for model, metadata in MODELS.items()
24
+ if Path(__file__).stem in metadata.get("gpu-tasks", [])
25
+ ]
26
 
27
  models = container.multiselect(
28
+ "MLIPs",
29
+ valid_models,
30
+ [
31
+ "MACE-MP(M)",
32
+ "CHGNet",
33
+ "M3GNet",
34
+ "SevenNet",
35
+ "ORB",
36
+ "EquiformerV2(OC22)",
37
+ "eSCN(OC20)",
38
+ ],
39
  )
40
 
41
  st.markdown("### Settings")
 
51
 
52
  palette_names = list(color_palettes.keys())
53
  palette_colors = list(color_palettes.values())
 
54
  palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
55
 
56
  color_sequence = color_palettes[palette_name]
 
58
  if not models:
59
  st.stop()
60
 
 
61
 
62
+ @st.cache_data
63
+ def get_data(models):
64
+ families = [MODELS[str(model)]["family"] for model in models]
65
+ dfs = [
66
+ pd.read_json(DATA_DIR / family.lower() / "hydrogen.json") for family in families
67
+ ]
68
+ df = pd.concat(dfs, ignore_index=True)
69
+ df.drop_duplicates(inplace=True, subset=["formula", "method"])
70
+ return df
71
+
72
+
73
+ df = get_data(models)
74
+
75
+ # families = [MODELS[str(model)]["family"] for model in models]
76
+
77
+ # dfs = [pd.read_json(DATA_DIR / family.lower() / "hydrogen.json") for family in families]
78
+ # df = pd.concat(dfs, ignore_index=True)
79
+ # df.drop_duplicates(inplace=True, subset=["formula", "method"])
80
 
81
  method_color_mapping = {
82
  method: color_sequence[i % len(color_sequence)]
 
121
  y=row["temperatures"],
122
  mode="markers",
123
  name=method,
124
+ line=dict(
125
+ color=method_color_mapping[method],
126
+ # width=1
127
+ ),
128
+ marker=dict(color=method_color_mapping[method], size=3),
129
  showlegend=True,
130
  ),
131
  )
 
133
  target_steps = df["target_steps"].iloc[0]
134
  fig.add_trace(
135
  go.Line(
136
+ x=[0, target_steps / 3, target_steps / 3 * 2, target_steps],
137
  y=[300, 3000, 3000, 300],
138
  mode="lines",
139
  name="Target",
140
+ line=dict(dash="dash", color="white"),
 
 
141
  showlegend=True,
142
  ),
143
  )
 
151
  overlaying="y",
152
  side="right",
153
  range=[0, 100],
154
+ tickmode="sync",
155
+ ),
156
  # template="plotly_dark",
157
  )
158
 
 
162
 
163
  fig = go.Figure()
164
 
 
 
165
  df = df.sort_values("yield", ascending=True)
166
 
167
  fig.add_trace(
 
196
  opacity=0.75,
197
  orientation="h",
198
  marker=dict(color=[method_color_mapping[method] for method in df["method"]]),
199
+ text=df["steps_per_second"].round(1),
200
  )
201
  )
202
 
 
207
  )
208
 
209
  st.plotly_chart(fig)
210
+
211
+ # COM drift
212
+
213
+ st.markdown("""### Center of mass drift
214
+
215
+ The center of mass (COM) drift is a measure of the stability of the simulation. A well-behaved simulation should have a COM drift close to zero. The COM drift is calculated as the displacement of the COM of the system from the initial position.
216
+ """)
217
+
218
+
219
+ @st.cache_data
220
+ def get_com_drifts(df):
221
+ df_exploded = df.explode(["timestep", "com_drifts"]).reset_index(drop=True)
222
+
223
+ # Convert the 'com_drifts' column (which are arrays) into separate columns for x, y, and z components
224
+ df_exploded[["com_drift_x", "com_drift_y", "com_drift_z"]] = pd.DataFrame(
225
+ df_exploded["com_drifts"].tolist(), index=df_exploded.index
226
+ )
227
+
228
+ # Drop the original 'com_drifts' column
229
+ df_flat = df_exploded.drop(columns=["com_drifts"])
230
+
231
+ df_flat["total_com_drift"] = np.sqrt(
232
+ df_flat["com_drift_x"] ** 2 + df_flat["com_drift_y"] ** 2 + df_flat["com_drift_z"] ** 2
233
+ )
234
+
235
+ return df_flat
236
+
237
+
238
+ df_exploded = get_com_drifts(df)
239
+
240
+ if "play" not in st.session_state:
241
+ st.session_state.play = False
242
+
243
+
244
+ def toggle_playing():
245
+ st.session_state.play = not st.session_state.play
246
+
247
+
248
+ st.button(
249
+ "Play" if not st.session_state.play else "Pause",
250
+ type="primary" if not st.session_state.play else "secondary",
251
+ on_click=toggle_playing,
252
+ )
253
+
254
+ increment = df["target_steps"].max() // 200
255
+
256
+ if "time_range" not in st.session_state:
257
+ st.session_state.time_range = (0, increment)
258
+
259
+
260
+ @st.fragment(run_every=1e-3 if st.session_state.play else None)
261
+ def draw_com_drifts_plot():
262
+ if st.session_state.play:
263
+ start, end = st.session_state.time_range
264
+
265
+ end += increment
266
+
267
+ if end > df["target_steps"].max():
268
+ start = 0
269
+ end = 0
270
+
271
+ st.session_state.time_range = (start, end)
272
+
273
+ start_timestep, end_timestep = st.slider(
274
+ "Timestep",
275
+ min_value=0,
276
+ max_value=df["target_steps"].max(),
277
+ value=st.session_state.time_range,
278
+ key="time_range",
279
+ # on_change=check_range,
280
+ )
281
+
282
+ mask = (df_exploded["timestep"] >= start_timestep) & (
283
+ df_exploded["timestep"] <= end_timestep
284
+ )
285
+ df_filtered = df_exploded[mask]
286
+ df_filtered.sort_values(["method", "timestep"], inplace=True)
287
+
288
+ fig = px.line_3d(
289
+ data_frame=df_filtered,
290
+ x="com_drift_x",
291
+ y="com_drift_y",
292
+ z="com_drift_z",
293
+ labels={"com_drift_x": "Δx (Å)", "com_drift_y": "Δy (Å)", "com_drift_z": "Δz (Å)"},
294
+ category_orders={"method": df_exploded["method"].unique()},
295
+ color_discrete_sequence=[
296
+ method_color_mapping[method] for method in df_exploded["method"].unique()
297
+ ],
298
+ color="method",
299
+ width=800,
300
+ height=800,
301
+ )
302
+
303
+ fig.update_layout(
304
+ scene=dict(
305
+ aspectmode="cube",
306
+ ),
307
+ legend=dict(
308
+ orientation="v",
309
+ x=0.95,
310
+ xanchor="right",
311
+ y=1,
312
+ yanchor="top",
313
+ bgcolor="rgba(0, 0, 0, 0)",
314
+ ),
315
+ )
316
+ fig.add_traces([
317
+ go.Scatter3d(
318
+ x=[0],
319
+ y=[0],
320
+ z=[0],
321
+ mode="markers",
322
+ marker=dict(size=2, color="white"),
323
+ name="Origin",
324
+ ),
325
+ # add last point of each method and annotate the total drift
326
+ go.Scatter3d(
327
+ # df_filtered.groupby("method")["com_drift_x"].last(),
328
+ x=df_filtered.groupby("method")["com_drift_x"].last(),
329
+ y=df_filtered.groupby("method")["com_drift_y"].last(),
330
+ z=df_filtered.groupby("method")["com_drift_z"].last(),
331
+ mode="markers+text",
332
+ marker=dict(size=2, color="rgba(0, 0, 0, 128)"),
333
+ text=df_filtered.groupby("method")["total_com_drift"].last().round(3),
334
+ # size=5,
335
+ name="total drifts",
336
+ textposition="top center",
337
+ ),
338
+ ])
339
+
340
+ st.plotly_chart(fig)
341
+
342
+
343
+ draw_com_drifts_plot()
serve/tasks/homonuclear-diatomics.py CHANGED
@@ -60,18 +60,23 @@ DATA_DIR = Path("mlip_arena/tasks/diatomics")
60
  if not mlip_methods and not dft_methods:
61
  st.stop()
62
 
63
- dfs = [
64
- pd.read_json(DATA_DIR / REGISTRY[method]["family"] / "homonuclear-diatomics.json")
65
- for method in mlip_methods
66
- ]
67
- dfs.extend(
68
- [
69
- pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json")
70
- for method in dft_methods
71
  ]
72
- )
73
- df = pd.concat(dfs, ignore_index=True)
74
- df.drop_duplicates(inplace=True, subset=["name", "method"])
 
 
 
 
 
 
 
 
75
 
76
  method_color_mapping = {
77
  method: color_sequence[i % len(color_sequence)]
@@ -81,133 +86,152 @@ method_color_mapping = {
81
  # img_dir = Path('./images')
82
  # img_dir.mkdir(exist_ok=True)
83
 
84
- for i, symbol in enumerate(chemical_symbols[1:]):
85
- if i % ncols == 0:
86
- cols = st.columns(ncols)
87
 
88
- rows = df[df["name"] == symbol + symbol]
 
89
 
90
- if rows.empty:
91
- continue
92
 
93
- fig = make_subplots(specs=[[{"secondary_y": True}]])
 
94
 
95
- elo, flo = float("inf"), float("inf")
96
 
97
- for j, method in enumerate(rows["method"].unique()):
98
- if method not in mlip_methods and method not in dft_methods:
99
  continue
100
- row = rows[rows["method"] == method].iloc[0]
101
 
102
- rs = np.array(row["R"])
103
- es = np.array(row["E"])
104
- fs = np.array(row["F"])
105
 
106
- rs = np.array(rs)
107
- ind = np.argsort(rs)
108
- es = np.array(es)
109
- fs = np.array(fs)
110
 
111
- rs = rs[ind]
112
- es = es[ind]
113
- if "GPAW" not in method:
114
- es = es - es[-1]
115
- else:
116
- pass
117
 
118
- if "GPAW" not in method:
119
- fs = fs[ind]
 
120
 
121
- if "GPAW" in method:
122
- xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
123
- else:
124
- xs = rs
 
 
 
 
 
 
 
 
 
 
125
 
126
- if energy_plot:
127
  if "GPAW" in method:
128
- cs = CubicSpline(rs, es)
129
- ys = cs(xs)
130
  else:
131
- ys = es
132
-
133
- elo = min(elo, max(ys.min() * 1.2, -15), -1)
134
-
135
- fig.add_trace(
136
- go.Scatter(
137
- x=xs,
138
- y=ys,
139
- mode="lines",
140
- line=dict(
141
- color=method_color_mapping[method],
142
- width=3,
 
 
 
 
 
 
 
 
 
143
  ),
144
- name=method,
145
- ),
146
- secondary_y=False,
147
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
148
 
149
- if force_plot and "GPAW" not in method:
150
- ys = fs
151
 
152
- flo = min(flo, max(ys.min() * 1.2, -50))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
153
 
154
- fig.add_trace(
155
- go.Scatter(
156
- x=xs,
157
- y=ys,
158
- mode="lines",
159
- line=dict(
160
- color=method_color_mapping[method],
161
- width=2,
162
- dash="dashdot",
163
- ),
164
- name=method,
165
- showlegend=not energy_plot,
 
 
 
 
 
 
 
 
 
166
  ),
167
- secondary_y=True,
168
  )
169
 
170
- name = f"{symbol}-{symbol}"
171
-
172
- fig.update_layout(
173
- showlegend=True,
174
- legend=dict(
175
- orientation="v",
176
- x=0.95,
177
- xanchor="right",
178
- y=1,
179
- yanchor="top",
180
- bgcolor="rgba(0, 0, 0, 0)"
181
- # entrywidth=0.3,
182
- # entrywidthmode='fraction',
183
- ),
184
- title_text=f"{name}",
185
- title_x=0.5,
186
- )
187
 
188
- # Set x-axis title
189
- fig.update_xaxes(title_text="Distance [Å]")
190
 
191
- # Set y-axes titles
192
- if energy_plot:
193
- fig.update_layout(
194
- yaxis=dict(
195
- title=dict(text="Energy [eV]"),
196
- side="left",
197
- range=[elo, 1.5 * (abs(elo))],
198
- )
199
- )
200
 
201
- if force_plot:
202
- fig.update_layout(
203
- yaxis2=dict(
204
- title=dict(text="Force [eV/Å]"),
205
- side="right",
206
- range=[flo, 1.0 * abs(flo)],
207
- overlaying="y",
208
- tickmode="sync",
209
- ),
210
- )
211
 
 
 
 
 
 
212
  cols[i % ncols].plotly_chart(fig, use_container_width=True)
213
- # fig.write_image(format='svg', file=img_dir / f"{name}.svg")
 
60
  if not mlip_methods and not dft_methods:
61
  st.stop()
62
 
63
+ @st.cache_data
64
+ def get_data(mlip_methods, dft_methods):
65
+ dfs = [
66
+ pd.read_json(DATA_DIR / REGISTRY[method]["family"] / "homonuclear-diatomics.json")
67
+ for method in mlip_methods
 
 
 
68
  ]
69
+ dfs.extend(
70
+ [
71
+ pd.read_json(DATA_DIR / method.lower() / "homonuclear-diatomics.json")
72
+ for method in dft_methods
73
+ ]
74
+ )
75
+ df = pd.concat(dfs, ignore_index=True)
76
+ df.drop_duplicates(inplace=True, subset=["name", "method"])
77
+ return df
78
+
79
+ df = get_data(mlip_methods, dft_methods)
80
 
81
  method_color_mapping = {
82
  method: color_sequence[i % len(color_sequence)]
 
86
  # img_dir = Path('./images')
87
  # img_dir.mkdir(exist_ok=True)
88
 
 
 
 
89
 
90
+ @st.cache_data
91
+ def get_plots(df, energy_plot, force_plot):
92
 
93
+ figs = []
 
94
 
95
+ for i, symbol in enumerate(chemical_symbols[1:]):
96
+
97
 
98
+ rows = df[df["name"] == symbol + symbol]
99
 
100
+ if rows.empty:
 
101
  continue
 
102
 
103
+ fig = make_subplots(specs=[[{"secondary_y": True}]])
 
 
104
 
105
+ elo, flo = float("inf"), float("inf")
 
 
 
106
 
107
+ for j, method in enumerate(rows["method"].unique()):
108
+ if method not in mlip_methods and method not in dft_methods:
109
+ continue
110
+ row = rows[rows["method"] == method].iloc[0]
 
 
111
 
112
+ rs = np.array(row["R"])
113
+ es = np.array(row["E"])
114
+ fs = np.array(row["F"])
115
 
116
+ rs = np.array(rs)
117
+ ind = np.argsort(rs)
118
+ es = np.array(es)
119
+ fs = np.array(fs)
120
+
121
+ rs = rs[ind]
122
+ es = es[ind]
123
+ if "GPAW" not in method:
124
+ es = es - es[-1]
125
+ else:
126
+ pass
127
+
128
+ if "GPAW" not in method:
129
+ fs = fs[ind]
130
 
 
131
  if "GPAW" in method:
132
+ xs = np.linspace(rs.min() * 0.99, rs.max() * 1.01, int(5e2))
 
133
  else:
134
+ xs = rs
135
+
136
+ if energy_plot:
137
+ if "GPAW" in method:
138
+ cs = CubicSpline(rs, es)
139
+ ys = cs(xs)
140
+ else:
141
+ ys = es
142
+
143
+ elo = min(elo, max(ys.min() * 1.2, -15), -1)
144
+
145
+ fig.add_trace(
146
+ go.Scatter(
147
+ x=xs,
148
+ y=ys,
149
+ mode="lines",
150
+ line=dict(
151
+ color=method_color_mapping[method],
152
+ width=3,
153
+ ),
154
+ name=method,
155
  ),
156
+ secondary_y=False,
157
+ )
158
+
159
+ if force_plot and "GPAW" not in method:
160
+ ys = fs
161
+
162
+ flo = min(flo, max(ys.min() * 1.2, -50))
163
+
164
+ fig.add_trace(
165
+ go.Scatter(
166
+ x=xs,
167
+ y=ys,
168
+ mode="lines",
169
+ line=dict(
170
+ color=method_color_mapping[method],
171
+ width=2,
172
+ dash="dashdot",
173
+ ),
174
+ name=method,
175
+ showlegend=not energy_plot,
176
+ ),
177
+ secondary_y=True,
178
+ )
179
 
180
+ name = f"{symbol}-{symbol}"
 
181
 
182
+ fig.update_layout(
183
+ showlegend=True,
184
+ legend=dict(
185
+ orientation="v",
186
+ x=0.95,
187
+ xanchor="right",
188
+ y=1,
189
+ yanchor="top",
190
+ bgcolor="rgba(0, 0, 0, 0)"
191
+ # entrywidth=0.3,
192
+ # entrywidthmode='fraction',
193
+ ),
194
+ title_text=f"{name}",
195
+ title_x=0.5,
196
+ )
197
 
198
+ # Set x-axis title
199
+ fig.update_xaxes(title_text="Distance [Å]")
200
+
201
+ # Set y-axes titles
202
+ if energy_plot:
203
+ fig.update_layout(
204
+ yaxis=dict(
205
+ title=dict(text="Energy [eV]"),
206
+ side="left",
207
+ range=[elo, 1.5 * (abs(elo))],
208
+ )
209
+ )
210
+
211
+ if force_plot:
212
+ fig.update_layout(
213
+ yaxis2=dict(
214
+ title=dict(text="Force [eV/Å]"),
215
+ side="right",
216
+ range=[flo, 1.0 * abs(flo)],
217
+ overlaying="y",
218
+ tickmode="sync",
219
  ),
 
220
  )
221
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
222
 
223
+ # cols[i % ncols].plotly_chart(fig, use_container_width=True)
 
224
 
225
+ figs.append(fig)
226
+
227
+ return figs
228
+ # fig.write_image(format='svg', file=img_dir / f"{name}.svg")
 
 
 
 
 
229
 
 
 
 
 
 
 
 
 
 
 
230
 
231
+ figs = get_plots(df, energy_plot, force_plot)
232
+
233
+ for i, fig in enumerate(figs):
234
+ if i % ncols == 0:
235
+ cols = st.columns(ncols)
236
  cols[i % ncols].plotly_chart(fig, use_container_width=True)
237
+