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This commit is contained in:
David Rice
2026-05-05 15:02:49 +01:00
parent 82171742d2
commit 1ebd78f538
4 changed files with 181 additions and 105 deletions
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BACKUP/Axio4_Nexio_Conduent Subsystem Cost Analysis.xlsx
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BoM/Axio4_Nexio_Conduent Subsystem Cost Analysis.xlsx
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28
inspect_seout.py Normal file
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@@ -0,0 +1,28 @@
#!/usr/bin/env python3
"""Quick diagnostic – prints every sheet name, its headers, and 3 sample rows."""
from pathlib import Path
import openpyxl
path = Path("OCTO") / "seout.xlsx"
if not path.exists():
print(f"NOT FOUND: {path}")
raise SystemExit(1)
wb = openpyxl.load_workbook(path, data_only=True, read_only=True)
for sheet in wb.sheetnames:
ws = wb[sheet]
rows = list(ws.iter_rows(values_only=True))
print(f"\n=== Sheet: {sheet!r} ({len(rows)} rows) ===")
# Print first 15 rows so we can see past the metadata
for i, row in enumerate(rows[:15]):
vals = [v for v in row if v is not None]
if vals:
print(f" row {i+1}: {list(row)}")
# Also find any row that looks like a header (contains text with "part" or "mfg" or "price")
print("\n --- Searching for header row ---")
for i, row in enumerate(rows):
row_str = [str(v).lower() for v in row if v is not None]
if any("part" in v or "mfg" in v or "price" in v or "avg" in v for v in row_str):
print(f" row {i+1}: {list(row)}")
break
wb.close()

210
octo_fill.py
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@@ -1,19 +1,21 @@
#!/usr/bin/env python3
"""
Octo Fill
=========
Reads the Octopart export (OCTO/octo.xlsx) and fills the
Octo Fill – Silicon Expert
==========================
Reads the Silicon Expert export (OCTO/seout.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.
Silicon Expert column mapping:
Manufacturer → UPLOADED MFG
MPN → UPLOADED PART
Price → BUDGETARY PRICES column, parses "Min X & Avg Y" → uses Avg (EUR)
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.
1. Exact match on both Uploaded Mfg + Uploaded Part (preferred)
2. Fallback: match on Uploaded Part alone
Where a part appears more than once, the lowest price is used.
Cells that already contain a value are left untouched.
Usage:
@@ -27,6 +29,7 @@ import logging
from pathlib import Path
from typing import Optional
import re
import openpyxl
from openpyxl.cell.cell import MergedCell
@@ -41,6 +44,7 @@ _SFP.__init__ = _sfp_patched
BOM_DIR = Path("BoM")
OCTO_DIR = Path("OCTO")
SEOUT_FILE = OCTO_DIR / "seout.xlsx"
COST_HEADER = "Unit Cost EUR @1000"
SKIP_MPNS = {
@@ -57,12 +61,28 @@ logging.basicConfig(
log = logging.getLogger(__name__)
# ── Load Octopart data ─────────────────────────────────────────────────────────
# ── Load Silicon Expert data ───────────────────────────────────────────────────
def _find_col(headers: dict[str, int], substring: str) -> Optional[int]:
"""Return the index of the first header whose name contains substring (case-insensitive)."""
for name, idx in headers.items():
if substring.lower() in name.lower():
return idx
return None
def load_seout(path: Path) -> tuple[dict[tuple[str, str], float], dict[str, float]]:
"""
Load seout.xlsx into lookup maps.
exact_map – (mfg_lower, part_lower) → lowest unit price (EUR)
mpn_map – part_lower → lowest unit price (EUR) [fallback]
"""
exact_map: dict[tuple[str, str], float] = {}
mpn_map: dict[str, float] = {}
def _load_single(path: Path, exact_map: dict, mpn_map: dict) -> int:
"""Load one Octopart xlsx into the shared maps. Returns number of entries added."""
wb = openpyxl.load_workbook(path, data_only=True, read_only=True)
added = 0
avg_col_name = None
for sheet_name in wb.sheetnames:
ws = wb[sheet_name]
@@ -72,30 +92,54 @@ def _load_single(path: Path, exact_map: dict, mpn_map: dict) -> int:
row = list(row)
if headers is None:
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}
has_part = any("uploaded part" in v for v in row_lower)
has_mfg = any("uploaded mfg" in v for v in row_lower)
if has_part and has_mfg:
headers = {
str(row[i]).strip(): i
for i in range(len(row))
if row[i] is not None
}
log.info(f" Sheet '{sheet_name}' headers: {list(headers.keys())}")
for h in headers:
if "budgetary" in h.lower() or "price" in h.lower():
avg_col_name = h
break
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")
mpn_col = _find_col(headers, "uploaded part")
mfr_col = _find_col(headers, "uploaded mfg")
# Silicon Expert stores prices as "Min X & Avg Y" in a BUDGETARY PRICES column
price_col = _find_col(headers, "budgetary") or _find_col(headers, "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
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 = str(row[price_col]).strip() if price_col < len(row) and row[price_col] is not None else ""
if not mpn or mpn.lower() in SKIP_MPNS:
continue
# Parse "Min 0.818 & Avg 1.3225562077" → extract the Avg value
avg_match = re.search(r'Avg\s+([\d.]+)', price_raw, re.IGNORECASE)
if not avg_match:
continue
try:
price = float(price_raw)
except (TypeError, ValueError):
price = float(avg_match.group(1))
except ValueError:
continue
if price <= 0:
@@ -111,39 +155,19 @@ def _load_single(path: Path, exact_map: dict, mpn_map: dict) -> int:
mpn_map[mpn_k] = price
wb.close()
return added
def load_octo(octo_dir: Path) -> tuple[dict[tuple[str, str], float], dict[str, float]]:
"""
Reads every .xlsx file in octo_dir into shared lookup maps.
exact_map – (manufacturer_lower, mpn_lower) → lowest unit price
mpn_map – mpn_lower → lowest unit price (fallback)
"""
files = sorted(octo_dir.glob("*.xlsx"))
if not files:
log.error(f"No .xlsx files found in {octo_dir}/")
sys.exit(1)
exact_map: dict[tuple[str, str], float] = {}
mpn_map: dict[str, float] = {}
for f in files:
added = _load_single(f, exact_map, mpn_map)
log.info(f" {f.name}: {added} entries loaded")
log.info(f"Octopart total: {len(exact_map)} unique (manufacturer, part) entries")
if not exact_map:
log.warning(
f"No entries loaded from {path.name}. "
"Check that the file has columns containing 'Uploaded Part', 'Uploaded Mfg', "
"Expected a 'BUDGETARY PRICES' column with values like 'Min X & Avg Y'."
)
log.info(
f"Silicon Expert ({path.name}): {len(exact_map)} unique (mfg, part) entries "
f"— avg price column: '{avg_col_name}'"
)
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:
@@ -153,8 +177,7 @@ def _cell(value) -> str:
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.
Handles multiple tables side-by-side on the same row.
Includes 'start_col' so the cost-column search stays within each table.
"""
i = 0
@@ -162,7 +185,6 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]):
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"]
@@ -170,7 +192,6 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]):
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:
@@ -182,6 +203,8 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]):
used_mpn.add(best_mpn)
max_j = i + 1
new_header_j = None # earliest row where a same-column header reappeared
for mfr_col, mpn_col in pairs:
data: list[tuple[int, str, str]] = []
j = i + 1
@@ -199,10 +222,11 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]):
continue
empty_streak = 0
# Detect a new table header anywhere in the row (handles sub-tables
# at different column positions than the current table)
dr_str_lower = [_cell(v).lower() for v in dr]
if "manufacturer" in dr_str_lower and "mpn" in dr_str_lower:
# Same-column header detected — record it but let other pairs
# continue reading past it so their data isn't truncated.
if mfr.lower() == "manufacturer" and mpn.lower() == "mpn":
if new_header_j is None or j < new_header_j:
new_header_j = j
break
if mpn and mpn.lower() not in SKIP_MPNS:
@@ -212,13 +236,16 @@ def _find_tables(indexed_rows: list[tuple[int, tuple]]):
max_j = max(max_j, j)
yield {
"header_row": row_num,
"mfr_col": mfr_col + 1, # 1-based
"mfr_col": mfr_col + 1,
"mpn_col": mpn_col + 1,
"start_col": min(mfr_col, mpn_col) + 1, # leftmost col of this table
"start_col": min(mfr_col, mpn_col) + 1,
"data": data,
}
i = max_j
# Rewind to the earliest sub-table header so the outer loop can pick it
# up, while still allowing wider tables (other columns) to have yielded
# their full data above.
i = new_header_j if new_header_j is not None else max_j
# ── Write back to BoM files ────────────────────────────────────────────────────
@@ -240,8 +267,8 @@ def fill_boms(
for f in files:
log.info(f"Processing {f.name}")
try:
# data_only gives us resolved cell values (not formula strings) for
# table/part detection; the writable wb is used for reading/writing prices.
# data_only resolves formula cells (e.g. =UPPER("Mfr")) to their values
# for detection; the writable wb is used for writing prices.
wb_ro = openpyxl.load_workbook(f, data_only=True, read_only=True)
wb = openpyxl.load_workbook(f)
except Exception as exc:
@@ -255,6 +282,16 @@ def fill_boms(
for i, row in enumerate(wb_ro[sheet_name].iter_rows(values_only=True), start=1)
]
# Reuse the same cost column for all stacked tables at the same
# start_col on this sheet, so a second sub-table doesn't create a
# new column one position to the right.
sheet_cost_cols: dict[int, int] = {}
KNOWN_COST_HEADERS = {
COST_HEADER.lower(),
"unit cost 1000x data",
}
for table in _find_tables(indexed):
header_row = table["header_row"]
data_rows = [r for r, _, _ in table["data"]]
@@ -267,24 +304,18 @@ def fill_boms(
f"table at col {table['start_col']}, {len(table['data'])} parts{row_range}"
)
# 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",
}
if table["start_col"] in sheet_cost_cols:
# Stacked table — reuse the cost column found/created by the
# first table at this column position on this sheet.
cost_col = sheet_cost_cols[table["start_col"]]
else:
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:
val_str = str(val).strip()
# Don't count formula placeholders as "used" columns
if not val_str.startswith("="):
last_used = c
if val_str.lower() in KNOWN_COST_HEADERS:
@@ -297,6 +328,11 @@ def fill_boms(
cost_col += 1
ws.cell(header_row, cost_col).value = COST_HEADER
sheet_cost_cols[table["start_col"]] = cost_col
log.info(f" Cost column: {cost_col} ('{ws.cell(header_row, cost_col).value}')")
tbl_filled = tbl_skipped = tbl_missing = 0
for row_num, mfr, mpn in table["data"]:
cell = ws.cell(row_num, cost_col)
if isinstance(cell, MergedCell):
@@ -309,11 +345,11 @@ def fill_boms(
or (isinstance(existing, (int, float)) and existing == 0)
)
if not is_empty and not is_formula:
log.info(f" Skip row {row_num} [{mpn}]: cell already has {repr(existing)}")
log.debug(f" Skip row {row_num} [{mpn}]: cell already has {repr(existing)}")
total_skipped += 1
tbl_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())
@@ -322,10 +358,18 @@ def fill_boms(
if price is not None:
cell.value = price
cell.number_format = "0.000000"
total_filled += 1
tbl_filled += 1
else:
total_missing += 1
log.info(f" No match in Octopart: [{mfr}] [{mpn}]")
tbl_missing += 1
log.debug(f" No match: [{mfr}] [{mpn}]")
log.info(
f" → filled {tbl_filled}, skipped {tbl_skipped}, "
f"no match {tbl_missing}"
)
wb_ro.close()
try:
@@ -339,7 +383,7 @@ def fill_boms(
log.info(
f"Done – filled: {total_filled}, "
f"already populated (skipped): {total_skipped}, "
f"no match in Octopart: {total_missing}"
f"no match in Silicon Expert: {total_missing}"
)
@@ -351,5 +395,9 @@ if __name__ == "__main__":
log.error(f"Not found: {p}")
sys.exit(1)
exact_map, mpn_map = load_octo(OCTO_DIR)
if not SEOUT_FILE.exists():
log.error(f"Silicon Expert export not found: {SEOUT_FILE}")
sys.exit(1)
exact_map, mpn_map = load_seout(SEOUT_FILE)
fill_boms(BOM_DIR, exact_map, mpn_map)