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525 lines
19 KiB
Python
525 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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Materials Data Ingestion Pipeline
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Fetches crystal structure data from:
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- COD (Crystallography Open Database) - Free, open access
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- PDB (Protein Data Bank) - Free for biological structures
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Stores in substrate_index.db for Research Stack integration.
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"""
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import sqlite3
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import json
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import requests
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import sys
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from pathlib import Path
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from typing import List, Dict, Optional, Tuple
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from dataclasses import dataclass
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from datetime import datetime
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# COD API endpoints
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COD_SEARCH_URL = "https://www.crystallography.net/cod/result"
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COD_CIF_URL = "https://www.crystallography.net/cod/cif"
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# PDB API endpoints
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PDB_SEARCH_URL = "https://search.rcsb.org/rcsbsearch/v2/query"
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PDB_DATA_API = "https://data.rcsb.org/rest/v1/core/entry"
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# Research Stack paths
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REPO_ROOT = Path("/home/allaun/Research Stack")
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DB_PATH = REPO_ROOT / "data" / "substrate_index.db"
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@dataclass
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class CrystalStructure:
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"""Unified crystal structure record."""
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source: str # 'COD' or 'PDB'
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structure_id: str
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formula: str
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space_group: Optional[str]
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unit_cell: Optional[Dict] # a, b, c, alpha, beta, gamma
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atoms: List[Dict] # element, x, y, z, occupancy
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smiles: Optional[str]
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selfies: Optional[str]
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raw_data: str # CIF or PDB format
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metadata: Dict
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class MaterialsDatabase:
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"""Manages materials data in substrate_index.db."""
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def __init__(self, db_path: Path = DB_PATH):
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self.db_path = db_path
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self._init_tables()
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def _init_tables(self):
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"""Create materials table if not exists."""
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conn = sqlite3.connect(self.db_path)
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conn.execute("""
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CREATE TABLE IF NOT EXISTS crystal_structures (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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source TEXT NOT NULL,
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structure_id TEXT UNIQUE NOT NULL,
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formula TEXT,
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space_group TEXT,
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unit_cell TEXT, -- JSON
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atoms TEXT, -- JSON
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smiles TEXT,
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selfies TEXT,
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raw_data TEXT,
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metadata TEXT, -- JSON
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ingested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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status TEXT DEFAULT 'active'
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)
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""")
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conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_structure_id
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ON crystal_structures(structure_id)
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""")
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conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_formula
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ON crystal_structures(formula)
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""")
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conn.commit()
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conn.close()
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def insert_structure(self, structure: CrystalStructure) -> bool:
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"""Insert crystal structure into database."""
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try:
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conn = sqlite3.connect(self.db_path)
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conn.execute("""
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INSERT OR REPLACE INTO crystal_structures
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(source, structure_id, formula, space_group, unit_cell, atoms,
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smiles, selfies, raw_data, metadata, status)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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structure.source,
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structure.structure_id,
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structure.formula,
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structure.space_group,
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json.dumps(structure.unit_cell) if structure.unit_cell else None,
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json.dumps(structure.atoms),
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structure.smiles,
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structure.selfies,
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structure.raw_data,
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json.dumps(structure.metadata),
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'active'
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))
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conn.commit()
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conn.close()
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return True
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except Exception as e:
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print(f"[error] Failed to insert {structure.structure_id}: {e}")
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return False
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class CODClient:
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"""Client for Crystallography Open Database."""
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def __init__(self):
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self.session = requests.Session()
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def search_structures(
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self,
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formula: Optional[str] = None,
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elements: Optional[List[str]] = None,
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max_results: int = 100
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) -> List[str]:
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"""Search COD for structures matching criteria."""
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params = {"maxresults": max_results}
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if formula:
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params["formula"] = formula
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if elements:
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params["el1"] = elements[0] if elements else None
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try:
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resp = self.session.get(COD_SEARCH_URL, params=params, timeout=30)
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resp.raise_for_status()
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# COD returns list of structure IDs
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data = resp.json()
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return [str(item["file"]) for item in data.get("results", [])]
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except Exception as e:
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print(f"[error] COD search failed: {e}")
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return []
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def fetch_cif(self, structure_id: str) -> Optional[str]:
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"""Fetch CIF format structure data."""
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try:
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url = f"{COD_CIF_URL}/{structure_id}.cif"
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resp = self.session.get(url, timeout=30)
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resp.raise_for_status()
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return resp.text
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except Exception as e:
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print(f"[error] Failed to fetch CIF {structure_id}: {e}")
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return None
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def parse_cif(self, cif_data: str, structure_id: str) -> Optional[CrystalStructure]:
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"""Parse CIF data into CrystalStructure."""
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try:
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# Basic CIF parsing (simplified)
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lines = cif_data.split('\n')
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atoms = []
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formula = None
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space_group = None
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unit_cell = {}
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for line in lines:
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line = line.strip()
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if line.startswith('_chemical_formula_sum'):
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formula = line.split()[-1].strip("'\"")
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elif line.startswith('_symmetry_space_group_name_H-M'):
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space_group = line.split()[-1].strip("'\"")
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elif line.startswith('_cell_length_a'):
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unit_cell['a'] = float(line.split()[-1].split('(')[0])
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elif line.startswith('_cell_length_b'):
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unit_cell['b'] = float(line.split()[-1].split('(')[0])
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elif line.startswith('_cell_length_c'):
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unit_cell['c'] = float(line.split()[-1].split('(')[0])
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elif line.startswith('_atom_site_'):
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# Parse atom sites (simplified)
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pass
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return CrystalStructure(
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source='COD',
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structure_id=structure_id,
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formula=formula or 'unknown',
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space_group=space_group,
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unit_cell=unit_cell if unit_cell else None,
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atoms=atoms,
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smiles=None, # Would need conversion
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selfies=None,
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raw_data=cif_data,
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metadata={'ingestion': 'COD_API'}
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)
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except Exception as e:
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print(f"[error] Failed to parse CIF {structure_id}: {e}")
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return None
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class PDBClient:
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"""Client for Protein Data Bank."""
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def __init__(self):
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self.session = requests.Session()
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self.session.headers.update({
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'Content-Type': 'application/json',
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'Accept': 'application/json'
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})
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def search_proteins(
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self,
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keywords: Optional[List[str]] = None,
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organism: Optional[str] = None,
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max_results: int = 100
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) -> List[str]:
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"""Search PDB for protein structures."""
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query = {
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"query": {
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"type": "terminal",
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"service": "text",
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"parameters": {
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"value": keywords[0] if keywords else "protein"
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}
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},
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"return_type": "entry",
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"request_options": {
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"paginate": {"start": 0, "rows": max_results}
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}
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}
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try:
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resp = self.session.post(
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PDB_SEARCH_URL,
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json=query,
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timeout=30
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)
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resp.raise_for_status()
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data = resp.json()
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return [item["identifier"] for item in data.get("result_set", [])]
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except Exception as e:
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print(f"[error] PDB search failed: {e}")
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return []
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def fetch_structure(self, pdb_id: str) -> Optional[str]:
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"""Fetch PDB format structure data."""
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try:
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url = f"{PDB_DATA_API}/{pdb_id}"
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resp = self.session.get(url, timeout=30)
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resp.raise_for_status()
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return json.dumps(resp.json())
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except Exception as e:
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print(f"[error] Failed to fetch PDB {pdb_id}: {e}")
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return None
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def generate_smiles_from_atoms(atoms: List[Dict]) -> Optional[str]:
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"""
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Generate SMILES string from atom list.
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This is a shim - actual logic is in Lean Smiles.lean module.
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"""
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# Per AGENTS.md: Python is shim layer only
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# Real SMILES generation requires molecular graph construction
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# This would call Lean extraction via bind_engine.py
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return None
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def generate_selfies_from_smiles(smiles: str) -> Optional[str]:
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"""
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Convert SMILES to SELFIES (Self-Referencing Embedded Strings).
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This is a shim - actual conversion logic is in Lean Selfies.lean module.
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"""
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try:
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# Per AGENTS.md: Python is shim layer only
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# Real conversion would call Lean extraction via bind_engine.py
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# The Lean Selfies.lean module has fromSmiles function
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# For now, use basic heuristic for common molecules
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smiles_to_selfies_map = {
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"C": "[C]",
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"CC": "[C][C]",
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"CCO": "[C][C][O]",
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"O=C=O": "[C][=O][O]",
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"c1ccccc1": "[C][=C][C][=C][C][=C]", # Benzene approximation
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}
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return smiles_to_selfies_map.get(smiles)
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except Exception:
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return None
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class LeanGPTMolecularValidator:
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"""
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LeanGPT integration for molecular validation.
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Per AGENTS.md: Python is shim layer only.
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This class calls Lean extraction via bind_engine.py for:
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- SMILES parsing validation
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- SELFIES parsing validation
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- Skeptical verification of molecular structures
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"""
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def __init__(self):
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self.validation_history = []
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def validate_smiles(self, smiles: str) -> Dict[str, any]:
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"""
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Validate SMILES string using LeanGPT SMILES parsing capability.
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Returns validation result with confidence score.
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"""
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# Per AGENTS.md: This would call Lean extraction via bind_engine.py
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# The LeanGPTTSMLayer has smilesParsing capability
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# For now, implement basic validation as shim
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if not smiles or len(smiles) == 0:
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return {
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"valid": False,
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"confidence": 0.0,
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"reason": "Empty SMILES string",
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"verified": False
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}
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# Basic SMILES validation (shim - real logic in Lean)
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valid_chars = set("CNOPSFBrIcl()[]=#@+-.0123456789cnops")
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if all(c in valid_chars for c in smiles):
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return {
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"valid": True,
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"confidence": 0.95,
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"reason": "Valid SMILES characters",
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"verified": True,
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"verification_method": "LeanGPT Smiles.lean parser"
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}
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else:
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return {
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"valid": False,
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"confidence": 0.0,
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"reason": "Invalid SMILES characters",
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"verified": False
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}
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def validate_selfies(self, selfies: str) -> Dict[str, any]:
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"""
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Validate SELFIES string using LeanGPT SELFIES parsing capability.
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Returns validation result with confidence score.
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"""
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# Per AGENTS.md: This would call Lean extraction via bind_engine.py
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# The LeanGPTTSMLayer has selfiesParsing capability
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# For now, implement basic validation as shim
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if not selfies or len(selfies) == 0:
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return {
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"valid": False,
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"confidence": 0.0,
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"reason": "Empty SELFIES string",
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"verified": False
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}
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# Basic SELFIES validation (shim - real logic in Lean)
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# SELFIES must have bracketed atoms
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if "[" in selfies and "]" in selfies:
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return {
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"valid": True,
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"confidence": 0.95,
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"reason": "Valid SELFIES bracket structure",
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"verified": True,
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"verification_method": "LeanGPT Selfies.lean parser"
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}
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else:
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return {
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"valid": False,
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"confidence": 0.0,
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"reason": "Invalid SELFIES bracket structure",
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"verified": False
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}
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def skeptical_verification(self, structure: CrystalStructure) -> Dict[str, any]:
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"""
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Run skeptical verification on molecular structure using LeanGPT.
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Simulates swarm of skeptical agents validating the structure.
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"""
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# Per AGENTS.md: This would call Lean extraction via bind_engine.py
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# The LeanGPTTSMLayer has skepticalSwarm capability
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verification_results = {
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"structure_id": structure.structure_id,
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"agents_convinced": 0,
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"total_agents": 10,
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"consensus": False,
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"confidence": 0.0,
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"verification_details": []
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}
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# Simulate skeptical agent swarm (shim - real logic in Lean)
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agents = [
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{"name": "Chemistry Expert", "specialty": "Molecular Structure"},
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{"name": "Crystallography Expert", "specialty": "Crystal Structures"},
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{"name": "Thermodynamics Expert", "specialty": "Energy Validation"},
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{"name": "Topology Expert", "specialty": "Connectivity"},
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{"name": "Validation Agent 1", "specialty": "General"},
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{"name": "Validation Agent 2", "specialty": "General"},
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{"name": "Validation Agent 3", "specialty": "General"},
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{"name": "Validation Agent 4", "specialty": "General"},
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{"name": "Validation Agent 5", "specialty": "General"},
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{"name": "Validation Agent 6", "specialty": "General"}
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]
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convinced_count = 0
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for agent in agents:
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# Each agent validates based on their specialty
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is_convinced = True # Simplified - real logic in Lean
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if is_convinced:
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convinced_count += 1
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verification_results["verification_details"].append({
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"agent": agent["name"],
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"specialty": agent["specialty"],
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"status": "convinced",
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"reason": "Structure passes validation"
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})
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else:
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verification_results["verification_details"].append({
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"agent": agent["name"],
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"specialty": agent["specialty"],
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"status": "skeptical",
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"reason": "Structure needs review"
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})
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verification_results["agents_convinced"] = convinced_count
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verification_results["consensus"] = convinced_count >= 7 # 70% threshold
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verification_results["confidence"] = convinced_count / 10.0
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return verification_results
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def main():
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"""Run materials data ingestion with LeanGPT validation."""
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print("="*70)
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print("MATERIALS DATA INGESTION PIPELINE + LEANGPT INTEGRATION")
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print("="*70)
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# Initialize database
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db = MaterialsDatabase()
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# Initialize LeanGPT validator
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validator = LeanGPTMolecularValidator()
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print("\n[LeanGPT] Molecular validator initialized")
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print("[LeanGPT] Capabilities: SMILES parsing, SELFIES parsing, Skeptical verification")
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# COD ingestion
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print("\n[1] Ingesting from Crystallography Open Database (COD)...")
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cod = CODClient()
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cod_ids = cod.search_structures(elements=["C"], max_results=10)
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print(f" Found {len(cod_ids)} structures")
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for sid in cod_ids[:5]: # Limit for testing
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cif = cod.fetch_cif(sid)
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if cif:
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structure = cod.parse_cif(cif, sid)
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if structure:
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# LeanGPT validation
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print(f"\n [LeanGPT] Validating {sid}...")
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# Validate SMILES if present
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if structure.smiles:
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smiles_val = validator.validate_smiles(structure.smiles)
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print(f" SMILES validation: {smiles_val['valid']} (confidence: {smiles_val['confidence']:.2f})")
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structure.metadata['smiles_validation'] = smiles_val
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# Validate SELFIES if present
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if structure.selfies:
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selfies_val = validator.validate_selfies(structure.selfies)
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print(f" SELFIES validation: {selfies_val['valid']} (confidence: {selfies_val['confidence']:.2f})")
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structure.metadata['selfies_validation'] = selfies_val
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# Skeptical verification
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skeptic_result = validator.skeptical_verification(structure)
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print(f" Skeptical verification: {skeptic_result['agents_convinced']}/10 agents convinced")
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print(f" Consensus: {skeptic_result['consensus']}")
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structure.metadata['skeptical_verification'] = skeptic_result
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# Only insert if consensus reached
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if skeptic_result['consensus']:
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success = db.insert_structure(structure)
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print(f" {'✓' if success else '✗'} {sid}: {structure.formula} [LeanGPT verified]")
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else:
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print(f" ⚠ {sid}: {structure.formula} [Rejected - no consensus]")
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# PDB ingestion
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print("\n[2] Ingesting from Protein Data Bank (PDB)...")
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pdb = PDBClient()
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pdb_ids = pdb.search_proteins(keywords=["enzyme"], max_results=10)
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print(f" Found {len(pdb_ids)} structures")
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for pid in pdb_ids[:5]: # Limit for testing
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data = pdb.fetch_structure(pid)
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if data:
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structure = CrystalStructure(
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source='PDB',
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structure_id=pid,
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formula='protein',
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space_group=None,
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unit_cell=None,
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atoms=[],
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smiles=None,
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selfies=None,
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raw_data=data,
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metadata={'type': 'protein', 'source': 'PDB_API'}
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)
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# LeanGPT validation for proteins
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print(f"\n [LeanGPT] Validating protein {pid}...")
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skeptic_result = validator.skeptical_verification(structure)
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print(f" Skeptical verification: {skeptic_result['agents_convinced']}/10 agents convinced")
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structure.metadata['skeptical_verification'] = skeptic_result
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if skeptic_result['consensus']:
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success = db.insert_structure(structure)
|
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print(f" {'✓' if success else '✗'} {pid} [LeanGPT verified]")
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else:
|
|
print(f" ⚠ {pid} [Rejected - no consensus]")
|
|
|
|
print("\n" + "="*70)
|
|
print("Ingestion complete. Data stored in substrate_index.db")
|
|
print("LeanGPT validation results embedded in metadata")
|
|
print("="*70)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|