Updates

bom_extract.py

@@ -20,7 +20,8 @@ import openpyxl
 import pandas as pd
 
 BOM_DIR     = Path("BoM")
-OUTPUT_FILE = Path("bom_parts.xlsx")
+OUTPUT_DIR  = Path("OUTPUT")
+CHUNK_SIZE  = 500
 
 SKIP_MPNS = {
     "", "tbd", "n/a", "na", "-", "--", "---", "?", "none",
@@ -41,20 +42,36 @@ def _cell(value) -> str:
 
 
 def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[tuple[str, str]]:
-    """Return all (manufacturer, mpn) pairs found across every table in the row list."""
+    """
+    Return all (manufacturer, mpn) pairs found across every table in the row list.
+    Handles multiple tables side-by-side on the same header row.
+    """
     parts: list[tuple[str, str]] = []
     i = 0
     while i < len(indexed_rows):
         _, row = indexed_rows[i]
         row_str = [_cell(v) for v in row]
 
-        mfr_col = next((c for c, v in enumerate(row_str) if v.lower() == "manufacturer"), None)
-        mpn_col = next((c for c, v in enumerate(row_str) if v.lower() == "mpn"), None)
+        mfr_cols = [c for c, v in enumerate(row_str) if v.lower() == "manufacturer"]
+        mpn_cols = [c for c, v in enumerate(row_str) if v.lower() == "mpn"]
 
-        if mfr_col is None or mpn_col is None:
+        if not mfr_cols or not mpn_cols:
             i += 1
             continue
 
+        # Pair each mfr_col with its nearest unpaired mpn_col
+        pairs: list[tuple[int, int]] = []
+        used_mpn: set[int] = set()
+        for mfr_col in mfr_cols:
+            available = [c for c in mpn_cols if c not in used_mpn]
+            if not available:
+                break
+            best_mpn = min(available, key=lambda c: abs(c - mfr_col))
+            pairs.append((mfr_col, best_mpn))
+            used_mpn.add(best_mpn)
+
+        max_j = i + 1
+        for mfr_col, mpn_col in pairs:
             j = i + 1
             empty_streak = 0
             while j < len(indexed_rows):
@@ -77,7 +94,9 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[tuple[str, str]]
                     parts.append((mfr, mpn))
                 j += 1
 
-        i = j
+            max_j = max(max_j, j)
+
+        i = max_j
     return parts
 
 
@@ -117,18 +136,29 @@ def extract(bom_dir: Path) -> list[tuple[str, str]]:
     return parts
 
 
-def write(parts: list[tuple[str, str]], output: Path) -> None:
+def write_chunks(parts: list[tuple[str, str]], output_dir: Path) -> None:
+    output_dir.mkdir(exist_ok=True)
+
     df = pd.DataFrame(parts, columns=["Manufacturer", "MPN"])
     df.sort_values(["Manufacturer", "MPN"], inplace=True, ignore_index=True)
 
-    with pd.ExcelWriter(output, engine="openpyxl") as writer:
-        df.to_excel(writer, index=False, sheet_name="Parts")
+    total   = len(df)
+    n_files = (total + CHUNK_SIZE - 1) // CHUNK_SIZE
+
+    for idx in range(n_files):
+        chunk = df.iloc[idx * CHUNK_SIZE : (idx + 1) * CHUNK_SIZE]
+        out   = output_dir / f"bom_parts_{idx + 1}_of_{n_files}.xlsx"
+
+        with pd.ExcelWriter(out, engine="openpyxl") as writer:
+            chunk.to_excel(writer, index=False, sheet_name="Parts")
             ws = writer.sheets["Parts"]
             for col in ws.columns:
                 width = max(len(str(cell.value or "")) for cell in col)
                 ws.column_dimensions[col[0].column_letter].width = min(width + 3, 60)
 
-    log.info(f"Written → {output}  ({len(parts)} unique parts)")
+        log.info(f"  Written → {out}  ({len(chunk)} parts)")
+
+    log.info(f"Done – {total} unique parts across {n_files} file(s) in {output_dir}/")
 
 
 if __name__ == "__main__":
@@ -137,4 +167,4 @@ if __name__ == "__main__":
         sys.exit(1)
 
     parts = extract(BOM_DIR)
-    write(parts, OUTPUT_FILE)
+    write_chunks(parts, OUTPUT_DIR)

bom_price_checker.py

@@ -168,6 +168,7 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[TableLocation]:
     """
     Scan a list of (1-based-row-num, row-values-tuple) pairs for sub-tables
     that have both a 'Manufacturer' and 'MPN' header column.
+    Handles multiple tables side-by-side on the same header row.
     """
     tables: list[TableLocation] = []
     i = 0
@@ -175,14 +176,26 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[TableLocation]:
         row_num, row = indexed_rows[i]
         row_str = [_cell(v) for v in row]
 
-        mfr_col_0 = next((c for c, v in enumerate(row_str) if v.lower() == "manufacturer"), None)
-        mpn_col_0 = next((c for c, v in enumerate(row_str) if v.lower() == "mpn"), None)
+        mfr_cols = [c for c, v in enumerate(row_str) if v.lower() == "manufacturer"]
+        mpn_cols = [c for c, v in enumerate(row_str) if v.lower() == "mpn"]
 
-        if mfr_col_0 is None or mpn_col_0 is None:
+        if not mfr_cols or not mpn_cols:
             i += 1
             continue
 
-        # Found a header row – consume data rows below it
+        # Pair each mfr_col with its nearest unpaired mpn_col
+        pairs: list[tuple[int, int]] = []
+        used_mpn: set[int] = set()
+        for mfr_col_0 in mfr_cols:
+            available = [c for c in mpn_cols if c not in used_mpn]
+            if not available:
+                break
+            best_mpn = min(available, key=lambda c: abs(c - mfr_col_0))
+            pairs.append((mfr_col_0, best_mpn))
+            used_mpn.add(best_mpn)
+
+        max_j = i + 1
+        for mfr_col_0, mpn_col_0 in pairs:
             data: list[tuple[int, str, str]] = []
             j = i + 1
             empty_streak = 0
@@ -199,7 +212,6 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[TableLocation]:
                     continue
                 empty_streak = 0
 
-            # Another header row signals the end of this table
                 if mfr.lower() == "manufacturer" and mpn.lower() == "mpn":
                     break
 
@@ -207,14 +219,16 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]) -> list[TableLocation]:
                     data.append((dr_num, mfr, mpn))
                 j += 1
 
+            max_j = max(max_j, j)
             tables.append(TableLocation(
-            sheet_name="",          # filled by caller
+                sheet_name="",
                 header_row=row_num,
                 mfr_col=mfr_col_0 + 1,  # convert to 1-based
                 mpn_col=mpn_col_0 + 1,
                 data=data,
             ))
-        i = j  # jump past the table we just consumed
+
+        i = max_j
     return tables
 
 

octo_fill.py

@@ -0,0 +1,320 @@
+#!/usr/bin/env python3
+"""
+Octo Fill
+=========
+Reads the Octopart export (OCTO/octo.xlsx) and fills the
+"Unit Cost EUR @1000" column in every component table across every
+sheet/tab of every BoM file in the BoM/ folder.
+
+Matching strategy:
+  1. Exact match on both Original Manufacturer + Original Part  (preferred)
+  2. Fallback: match on Original Part alone (handles slight manufacturer
+     name differences between BoM and Octopart)
+
+Where a part appears more than once in octo.xlsx (multiple distributor
+offers), the lowest price is used.
+
+Cells that already contain a value are left untouched.
+
+Usage:
+    python octo_fill.py
+"""
+
+from __future__ import annotations
+
+import sys
+import logging
+from pathlib import Path
+from typing import Optional
+
+import openpyxl
+from openpyxl.cell.cell import MergedCell
+
+# ── Patch openpyxl for newer Excel attribute it doesn't know about ─────────────
+from openpyxl.worksheet.dimensions import SheetFormatProperties as _SFP
+_sfp_orig = _SFP.__init__
+def _sfp_patched(self, **kw):
+    kw.pop("defaultColWidthPt", None)
+    _sfp_orig(self, **kw)
+_SFP.__init__ = _sfp_patched
+# ──────────────────────────────────────────────────────────────────────────────
+
+BOM_DIR    = Path("BoM")
+OCTO_FILE  = Path("OCTO/octo.xlsx")
+COST_HEADER = "Unit Cost EUR @1000"
+
+SKIP_MPNS = {
+    "", "0", "tbd", "n/a", "na", "-", "--", "---", "?", "none",
+    "null", "nan", "xxx", "x", "dnf", "dnp", "do not fit",
+    "do not populate",
+}
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(message)s",
+    datefmt="%H:%M:%S",
+)
+log = logging.getLogger(__name__)
+
+
+# ── Load Octopart data ─────────────────────────────────────────────────────────
+
+def load_octo(path: Path) -> tuple[dict[tuple[str,str], float], dict[str, float]]:
+    """
+    Returns:
+      exact_map  – (manufacturer_lower, mpn_lower) → lowest unit price
+      mpn_map    – mpn_lower → lowest unit price  (fallback)
+    """
+    log.info(f"Reading Octopart data from {path}")
+    wb = openpyxl.load_workbook(path, data_only=True, read_only=True)
+
+    exact_map: dict[tuple[str, str], float] = {}
+    mpn_map:   dict[str, float] = {}
+
+    for sheet_name in wb.sheetnames:
+        ws = wb[sheet_name]
+        headers: Optional[dict[str, int]] = None  # col_name → 0-based index
+
+        for row in ws.iter_rows(values_only=True):
+            row = list(row)
+            if headers is None:
+                # Find header row
+                row_lower = [str(v).strip().lower() if v is not None else "" for v in row]
+                if "original part" in row_lower and "original manufacturer" in row_lower:
+                    headers = {str(row[i]).strip(): i for i in range(len(row)) if row[i] is not None}
+                continue
+
+            if not any(row):
+                continue
+
+            mpn_col  = _find_col(headers, "original part")
+            mfr_col  = _find_col(headers, "original manufacturer")
+            price_col = _find_col(headers, "unit price")
+
+            if mpn_col is None or price_col is None:
+                continue
+
+            mpn   = str(row[mpn_col]).strip()  if mpn_col  < len(row) and row[mpn_col]  is not None else ""
+            mfr   = str(row[mfr_col]).strip()  if mfr_col  is not None and mfr_col < len(row) and row[mfr_col]  is not None else ""
+            price_raw = row[price_col] if price_col < len(row) else None
+
+            if not mpn or mpn.lower() in SKIP_MPNS:
+                continue
+
+            try:
+                price = float(price_raw)
+            except (TypeError, ValueError):
+                continue
+
+            if price <= 0:
+                continue
+
+            key = (mfr.lower(), mpn.lower())
+            if key not in exact_map or price < exact_map[key]:
+                exact_map[key] = price
+
+            mpn_k = mpn.lower()
+            if mpn_k not in mpn_map or price < mpn_map[mpn_k]:
+                mpn_map[mpn_k] = price
+
+    wb.close()
+
+    log.info(f"  Loaded {len(exact_map)} unique (manufacturer, part) entries from Octopart")
+    return exact_map, mpn_map
+
+
+def _find_col(headers: dict[str, int], prefix: str) -> Optional[int]:
+    """Case-insensitive prefix match on header names."""
+    for name, idx in headers.items():
+        if name.lower().startswith(prefix.lower()):
+            return idx
+    return None
+
+
+# ── BoM table finding ──────────────────────────────────────────────────────────
+
+def _cell(value) -> str:
+    return str(value).strip() if value is not None else ""
+
+
+def _find_tables(indexed_rows: list[tuple[int, tuple]]):
+    """
+    Yields TableInfo dicts per component table found.
+    Handles multiple tables side-by-side on the same row by finding ALL
+    Manufacturer+MPN column pairs in a header row, not just the first.
+    Includes 'start_col' so the cost-column search stays within each table.
+    """
+    i = 0
+    while i < len(indexed_rows):
+        row_num, row = indexed_rows[i]
+        row_str = [_cell(v) for v in row]
+
+        # All column positions that are "manufacturer" or "mpn"
+        mfr_cols = [c for c, v in enumerate(row_str) if v.lower() == "manufacturer"]
+        mpn_cols = [c for c, v in enumerate(row_str) if v.lower() == "mpn"]
+
+        if not mfr_cols or not mpn_cols:
+            i += 1
+            continue
+
+        # Pair each mfr_col with its nearest unpaired mpn_col
+        pairs: list[tuple[int, int]] = []
+        used_mpn: set[int] = set()
+        for mfr_col in mfr_cols:
+            available = [c for c in mpn_cols if c not in used_mpn]
+            if not available:
+                break
+            best_mpn = min(available, key=lambda c: abs(c - mfr_col))
+            pairs.append((mfr_col, best_mpn))
+            used_mpn.add(best_mpn)
+
+        max_j = i + 1
+        for mfr_col, mpn_col in pairs:
+            data: list[tuple[int, str, str]] = []
+            j = i + 1
+            empty_streak = 0
+            while j < len(indexed_rows):
+                dr_num, dr = indexed_rows[j]
+                mfr = _cell(dr[mfr_col] if mfr_col < len(dr) else None)
+                mpn = _cell(dr[mpn_col] if mpn_col < len(dr) else None)
+
+                if not mfr and not mpn:
+                    empty_streak += 1
+                    if empty_streak >= 3:
+                        break
+                    j += 1
+                    continue
+                empty_streak = 0
+
+                if mfr.lower() == "manufacturer" and mpn.lower() == "mpn":
+                    break
+
+                if mpn and mpn.lower() not in SKIP_MPNS:
+                    data.append((dr_num, mfr, mpn))
+                j += 1
+
+            max_j = max(max_j, j)
+            yield {
+                "header_row": row_num,
+                "mfr_col":    mfr_col + 1,   # 1-based
+                "mpn_col":    mpn_col + 1,
+                "start_col":  min(mfr_col, mpn_col) + 1,  # leftmost col of this table
+                "data":       data,
+            }
+
+        i = max_j
+
+
+# ── Write back to BoM files ────────────────────────────────────────────────────
+
+def fill_boms(
+    bom_dir: Path,
+    exact_map: dict[tuple[str, str], float],
+    mpn_map:   dict[str, float],
+) -> None:
+    files = sorted(f for f in bom_dir.iterdir() if f.suffix.lower() in {".xlsx", ".xlsm"})
+    if not files:
+        log.error(f"No .xlsx/.xlsm files found in {bom_dir}/")
+        sys.exit(1)
+
+    total_filled  = 0
+    total_skipped = 0
+    total_missing = 0
+
+    for f in files:
+        log.info(f"Processing  {f.name}")
+        try:
+            wb = openpyxl.load_workbook(f)
+        except Exception as exc:
+            log.error(f"  Cannot open {f.name}: {exc}")
+            continue
+
+        for sheet_name in wb.sheetnames:
+            ws = wb[sheet_name]
+            indexed = [
+                (i, tuple(row))
+                for i, row in enumerate(ws.iter_rows(values_only=True), start=1)
+            ]
+
+            for table in _find_tables(indexed):
+                header_row = table["header_row"]
+                log.info(
+                    f"  Sheet '{sheet_name}' row {header_row}: "
+                    f"table at col {table['start_col']}, {len(table['data'])} parts"
+                )
+
+                # Find or create the cost column.
+                # Accept either of the two known column names (the primary
+                # COST_HEADER or the name used by the earlier write-back script).
+                KNOWN_COST_HEADERS = {
+                    COST_HEADER.lower(),
+                    "unit cost 1000x data",
+                }
+                cost_col   = None
+                last_used  = table["start_col"]
+                max_col    = ws.max_column or 1
+                # Search only within this table's column range (from its
+                # leftmost column rightward) so side-by-side tables don't
+                # steal each other's cost column.
+                for c in range(table["start_col"], max_col + 1):
+                    val = ws.cell(header_row, c).value
+                    if val is not None:
+                        last_used = c
+                        if str(val).strip().lower() in KNOWN_COST_HEADERS:
+                            cost_col = c
+                            break
+
+                if cost_col is None:
+                    cost_col = last_used + 1
+                    while isinstance(ws.cell(header_row, cost_col), MergedCell):
+                        cost_col += 1
+                    ws.cell(header_row, cost_col).value = COST_HEADER
+
+                for row_num, mfr, mpn in table["data"]:
+                    cell = ws.cell(row_num, cost_col)
+                    if isinstance(cell, MergedCell):
+                        continue
+                    existing = cell.value
+                    if existing is not None and str(existing).strip() not in ("", "0") and existing != 0:
+                        total_skipped += 1
+                        continue
+
+                    # Look up price: exact match first, then MPN-only fallback
+                    price = exact_map.get((mfr.lower(), mpn.lower()))
+                    if price is None:
+                        price = mpn_map.get(mpn.lower())
+                        if price is not None:
+                            log.debug(f"  MPN-only match: {mpn} (mfr '{mfr}' not matched)")
+
+                    if price is not None:
+                        cell.value = price
+                        total_filled += 1
+                    else:
+                        total_missing += 1
+                        log.info(f"    No match in Octopart: [{mfr}]  [{mpn}]")
+
+        try:
+            wb.save(f)
+            log.info(f"  Saved  {f.name}")
+        except PermissionError:
+            log.error(f"  Cannot save {f.name} – close it in Excel first.")
+        except Exception as exc:
+            log.error(f"  Save failed for {f.name}: {exc}")
+
+    log.info(
+        f"Done – filled: {total_filled}, "
+        f"already populated (skipped): {total_skipped}, "
+        f"no match in Octopart: {total_missing}"
+    )
+
+
+# ── Main ───────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    for p in (BOM_DIR, OCTO_FILE):
+        if not p.exists():
+            log.error(f"Not found: {p}")
+            sys.exit(1)
+
+    exact_map, mpn_map = load_octo(OCTO_FILE)
+    fill_boms(BOM_DIR, exact_map, mpn_map)