Skip to content

Semiempirical-DFT mapping and semiempirical splitting #10

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
craabreu wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Semiempirical-DFT mapping and semiempirical splitting #10

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7e3924e
Notebooks + matching results
craabreu Mar 19, 2026
772817e
Added missing CSV file
craabreu Mar 19, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
335 changes: 335 additions & 0 deletions scripts/semiempirical_split/nonts_analysis.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,335 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"import pathlib\n",
"import re\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from pyarrow import parquet as pq\n",
"\n",
"from MDAnalysis.analysis.rms import rmsd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"quantum_green = pathlib.Path(\"/home/shared/projects/quantum_green\")\n",
"paquets = pathlib.Path(\"./parquets\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def xyz_str_to_array(xyz_str):\n",
" rows = []\n",
" for s in xyz_str.split(\"\\n\"):\n",
" try:\n",
" lst = s.split()\n",
" rows.append([float(lst[i]) for i in [1, 2, 3]])\n",
" except Exception:\n",
" pass\n",
" return np.array(rows)\n",
"\n",
"\n",
"def last_conformer_to_array(xyz):\n",
" if xyz[-1].dtype != np.dtype(\"float32\"):\n",
" xyz = xyz[-1]\n",
" return np.stack(xyz)[:, 3:]\n",
"\n",
"\n",
"SEMIEMPIRICAL_DFT_MAPPING = {\n",
" \".tar\": \".log\",\n",
" \"/data1/\": \"/home/gridsan/\",\n",
" \"semiempirical_opt\": \"DFT_opt_freq\",\n",
"}\n",
"\n",
"\n",
"REGEX = re.compile(r\"/(\\d+)/\\1.log\")\n",
"\n",
"\n",
"def postprocess(source):\n",
" for k, v in SEMIEMPIRICAL_DFT_MAPPING.items():\n",
" source = source.replace(k, v)\n",
" result = REGEX.search(source)\n",
" if result:\n",
" g1 = result.group(1)\n",
" return source.replace(f\"/{g1}/{g1}.\", f\"/{g1}.\")\n",
" else:\n",
" return source"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read and process the consolidated species data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"species_data = pd.read_pickle(\n",
" quantum_green / \"reactants\" / \"quantum_green_species_data_24march12b.pkl\"\n",
")\n",
"species_data[\"source\"] = species_data[\"species_dft_log_path\"].apply(postprocess)\n",
"species_data[\"xyz\"] = species_data[\"xyz_str\"].apply(xyz_str_to_array)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"assert species_data[\"species_id\"].nunique() == len(species_data)\n",
"species_data[\"species_id\"].min(), species_data[\"species_id\"].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read and process the parsed DFT data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dft = pq.read_table(\n",
" paquets / \"dft_nonts_dft_v9.parquet\", columns=[\"source\", \"initial_xyz\", \"xyz\"]\n",
").to_pandas()\n",
"dft[\"source\"] = dft[\"source\"].apply(postprocess)\n",
"dft[\"initial_xyz\"] = dft[\"initial_xyz\"].apply(last_conformer_to_array)\n",
"dft[\"xyz\"] = dft[\"xyz\"].apply(last_conformer_to_array)\n",
"dft[\"dft_index\"] = dft.index"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"filtered_dft = dft[dft[\"source\"].isin(species_data[\"source\"])].copy()\n",
"filtered_dft[\"species_xyz\"] = filtered_dft[\"source\"].map(\n",
" species_data.set_index(\"source\")[\"xyz\"]\n",
")\n",
"filtered_dft[\"rmsd\"] = np.vectorize(rmsd)(\n",
" filtered_dft[\"xyz\"], filtered_dft[\"species_xyz\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"matched_dft = filtered_dft.loc[\n",
" filtered_dft.groupby(\"source\")[\"rmsd\"].idxmin()\n",
"].reset_index(drop=True)\n",
"matched_dft[\"species_id\"] = matched_dft[\"source\"].map(\n",
" species_data.set_index(\"source\")[\"species_id\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"matched_dft[\"rmsd\"].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign the initial xyz to each species data point"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"species_data[\"initial_xyz\"] = species_data[\"source\"].map(\n",
" matched_dft.set_index(\"source\")[\"initial_xyz\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read and process the semiempirical data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"semiempirical = pq.read_table(\n",
" paquets / \"semiempirical_nonts_semiempirical_v9.parquet\",\n",
" columns=[\"source\", \"initial_xyz\", \"std_xyz\"],\n",
").to_pandas().rename(columns={\"std_xyz\": \"final_xyz\"})\n",
"semiempirical[\"source\"] = semiempirical[\"source\"].apply(postprocess)\n",
"semiempirical[\"initial_xyz\"] = semiempirical[\"initial_xyz\"].apply(last_conformer_to_array)\n",
"semiempirical[\"final_xyz\"] = semiempirical[\"final_xyz\"].apply(last_conformer_to_array)\n",
"semiempirical[\"semiempirical_index\"] = semiempirical.index"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"df = filtered_semiempirical = semiempirical[\n",
" semiempirical[\"source\"].isin(species_data[\"source\"])\n",
"].copy()\n",
"df[\"dft_initial_xyz\"] = df[\"source\"].map(matched_dft.set_index(\"source\")[\"initial_xyz\"])\n",
"\n",
"\n",
"def msd_func(x, y):\n",
" msd = np.square(x - y).sum(-1).mean()\n",
" return msd if msd > 1e-8 else 0.0\n",
"\n",
"\n",
"msd = {\n",
" case: np.vectorize(msd_func)(\n",
" df[f\"{case}_xyz\"], df[\"dft_initial_xyz\"]\n",
" )\n",
" for case in [\"initial\", \"final\"]\n",
"}\n",
"\n",
"df[\"rmsd\"] = np.sqrt(np.minimum(msd[\"initial\"], msd[\"final\"]))\n",
"df[\"from_initial\"] = msd[\"initial\"] < msd[\"final\"]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"df = matched_semiempirical = filtered_semiempirical.loc[\n",
" filtered_semiempirical.groupby(\"source\")[\"rmsd\"].idxmin()\n",
"].reset_index(drop=True)\n",
"df[\"species_id\"] = df[\"source\"].map(species_data.set_index(\"source\")[\"species_id\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"matched_semiempirical[\"rmsd\"].describe(percentiles=[0.999])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"matched_semiempirical[\"from_initial\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = matched_semiempirical[matched_semiempirical[\"from_initial\"]]\n",
"total = 0\n",
"for case in [\"aug11b\", \"sep1a_filtered\", \"co2_capture\"]:\n",
" num = sum(case in s for s in df[\"source\"])\n",
" total += num\n",
" print(case, num)\n",
"print(\"total\", total, len(df))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = matched_semiempirical[matched_semiempirical[\"rmsd\"] > 0.0].copy()\n",
"total = 0\n",
"for case in [\"aug11b\", \"sep1a_filtered\", \"co2_capture\"]:\n",
" num = sum(case in s for s in df[\"source\"])\n",
" total += num\n",
" print(case, num)\n",
"print(\"total\", total, len(df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save the results"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"df = matched_semiempirical\n",
"df[\"dft_index\"] = df[\"source\"].map(matched_dft.set_index(\"source\")[\"dft_index\"])\n",
"df[[\"species_id\", \"dft_index\", \"semiempirical_index\", \"rmsd\", \"from_initial\"]].sort_values(\n",
" \"species_id\"\n",
").to_csv(\"quantum_green_species_data_match.csv\", index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Loading