FEAT-8b: spreadsheet import tooling (stages 1-2) + review report
tools/import/ (Python, zero-dep migration tooling, not product code): - xlsx_util.py: dependency-free .xlsx reader (shared strings, cells, drawing anchors) - genotype.py: notation -> frozen 8-locus mapping + verbatim rawGenotype + unmappedTokens; '-' -> '?' - extract.py: 10 Stammbaum charts + Wurfchronik -> animals.json/litters.json + anchor-mapped photos; dedup on normalise(name)+DOB -> German review-report.md (no DB load) Run: 889 raw -> 587 unique animals, 24 conflicts, 310 ambiguous, 123 photos, 752 litters. Output gitignored except review-report.md. Re-runnable per file (Wurfchronik Teil2+). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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tools/import/.gitignore
vendored
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tools/import/.gitignore
vendored
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# Generated extraction artifacts — large/binary, regenerated by extract.py.
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# Per FEAT-8b: ignore the output dir EXCEPT the human-review report.
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output/*
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!output/review-report.md
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__pycache__/
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*.pyc
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58
tools/import/README.md
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tools/import/README.md
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# GerbilManager import tooling (FEAT-8b)
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One-off **migration tooling** (Python, no third-party deps) that turns Julian's
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wife's hand-built spreadsheets into normalised JSON for review and, later, import.
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This is *not* product code — it lives outside the app and is run manually.
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See the format analysis in `FEAT-8a-format-spec.md` (Pam's hive workspace).
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## What it does
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`extract.py` runs **stages 1–2** of the pipeline:
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1. **Extract (stage 1)**
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- 10 *Stammbaum* pedigree charts → animals (name, DOB, death, Farbschlag,
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genotype, breeder, positionally-reconstructed parent links, photos).
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- *Wurfchronik* litter chronicle → litters (date, dam, sire, Wurfstärke,
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sex breakdown, Zuchtnummer, notes). Columns are read **by header row** because
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the two sheets use different schemas.
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- Embedded photos (`xl/media`) → `output/photos/<animal-slug>/`, mapped to the
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animal by drawing anchor position.
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2. **Dedup + review (stage 2)**
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- Merge animals on `normalise(name) + DOB` (corroborated by DOB+genotype).
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- Emit a German-language `output/review-report.md` for the breeder to verify
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(merges, **conflicts**, ambiguous/incomplete entries, unmapped genotype tokens).
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- **Nothing is loaded into the database** — stage 3 (API load) is separate and
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waits on DATA-2 + FEAT-1b phase 2.
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Genotypes are mapped to the frozen 8-locus contract (A C D E G P Sp Re) while
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preserving everything: `genotype.mapped8locus`, `genotype.rawGenotype` (verbatim),
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`genotype.unmappedTokens` (e.g. the `Uw` locus, markers `WFNZ/WP/DP`). A `-`
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(unknown second allele) maps to `?`.
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## Run
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```sh
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cd tools/import
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python extract.py # uses the default source paths
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python extract.py --stammbaeume "<dir>" --wurfchronik "<file.xlsx>"
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```
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Requires Python 3. **Re-runnable / idempotent** — re-run when more files arrive
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(Wurfchronik `Teil2+`, or new charts).
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## Output (`tools/import/output/`, git-ignored except the report)
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| File | Contents |
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|---|---|
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| `animals.json` | deduped animals with genotype, parentRefs, photos, sourceFiles |
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| `litters.json` | litters from the Wurfchronik |
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| `photos/<slug>/…` | extracted, anchor-mapped images |
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| `review-report.md` | **human review deliverable** (committed) |
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## Files
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- `xlsx_util.py` — dependency-free `.xlsx` reader (zip + XML): shared strings,
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cells by reference, image/drawing anchors.
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- `genotype.py` — genotype notation parser → 8-locus mapping + raw + unmapped.
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- `extract.py` — the pipeline (stages 1–2).
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tools/import/extract.py
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tools/import/extract.py
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#!/usr/bin/env python3
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"""FEAT-8b stages 1-2 — extract + dedup the GerbilManager source spreadsheets.
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Stage 1: parse the 10 Stammbaum pedigree charts and the Wurfchronik litter
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chronicle into normalised animals.json + litters.json, and extract
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embedded photos (anchor-mapped to animals).
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Stage 2: dedup animals (key = normalise(name)+DOB, corroborated by DOB+genotype)
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and emit a German-friendly review-report.md for Julian's wife. No DB load.
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Re-runnable per file (later Wurfchronik "Teil2+" / more charts just re-run).
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Migration tooling — Python, not product code. Zero third-party deps.
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See FEAT-8a-format-spec.md (in Pam's hive workspace) for the format analysis.
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"""
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import os
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import re
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import sys
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import json
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import glob
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import shutil
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import argparse
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import unicodedata
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import xlsx_util as xu
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import genotype as gt
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HERE = os.path.dirname(os.path.abspath(__file__))
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DEFAULT_STAMMBAEUME = r"C:\Users\gulum\dev\Sttammbäume"
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DEFAULT_WURFCHRONIK = r"C:\Users\gulum\dev\Wurfchronik der Kleine Chaoten Teil1.xlsx"
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OUT = os.path.join(HERE, "output")
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DOB = re.compile(r"\*\s?(\d{1,2}\.\d{1,2}\.(?:\d{4}|\d{2}))")
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DEATH = re.compile(r"\+\s?(\d{1,2}\.\d{1,2}\.(?:\d{4}|\d{2})|\d{4})")
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# ---------------------------------------------------------------- helpers ----
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def gen_of(colnum):
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"""Map a column number to a generation band (0=proband ... 5=deepest)."""
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if colnum <= 6:
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return 0 # E band (proband / "Kids")
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if colnum <= 9:
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return 1 # H band (parents)
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if colnum <= 12:
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return 2 # K band (grandparents)
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if colnum <= 15:
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return 3 # N band (great-grandparents)
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if colnum <= 17:
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return 4 # Q band (gg-grandparents)
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return 5 # R/S band (name-pairs)
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def norm_name(name):
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if not name:
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return ""
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n = name.lower()
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n = re.sub(r"\[.*?\]", " ", n) # drop [line] tags (Wurfchronik)
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n = re.sub(r"\bgen\.\b", " ", n) # "gen." nickname marker
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n = re.sub(r"\bv\.\s?d\.\b", " von den ", n)
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n = re.sub(r"\b(von der|von den|von|of)\b", " ", n) # cattery/line connectors
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n = unicodedata.normalize("NFKD", n)
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n = re.sub(r"[^a-z0-9äöüß]", "", n)
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return n
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def norm_dob(d):
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if not d:
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return ""
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p = d.split(".")
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if len(p) == 3 and len(p[2]) == 2:
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p[2] = "20" + p[2]
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return ".".join(x.zfill(2) if i < 2 else x for i, x in enumerate(p))
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def slug(name, dob):
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base = norm_name(name) or "unbekannt"
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d = norm_dob(dob).replace(".", "")
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return (base[:40] + ("-" + d if d else "")) or "unbekannt"
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def clean_name(raw):
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"""Strip detail/markers from a name cell, keep the human name + [line]."""
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n = raw.strip().strip(",").strip()
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return n
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# --------------------------------------------------- Stammbaum extraction ----
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def parse_detail(text):
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"""From a string that contains *DOB and/or genotype, pull (dob, death, geno_str).
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For compact lines ("Name,*DOB[/+death], genotype") the genotype is everything
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after the date — we must NOT scan from the first locus-looking letter, or stray
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name words ("den", "of") get swallowed as genotype tokens.
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"""
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dob = DOB.search(text)
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death = DEATH.search(text)
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geno = ""
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if dob:
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tail = text[dob.end():]
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tail = re.sub(r"^\s*/?\+?\s?\d[\d.]*", "", tail) # drop any /+death remnant
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tail = tail.lstrip(" ,").strip()
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if gt.looks_like_genotype(tail):
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geno = tail
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return (dob.group(1) if dob else "",
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death.group(1) if death else "",
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geno)
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def extract_stammbaum(path):
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"""Return list of animal dicts for one chart file."""
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fname = os.path.basename(path)
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z = __import__("zipfile").ZipFile(path)
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ss = xu.shared_strings(z)
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sheets = xu.sheet_paths(z)
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cells = xu.read_cells(z, sheets[0], ss)
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# group cells by column for block reconstruction
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by_col = {}
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for (c, r), t in cells.items():
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by_col.setdefault(c, []).append((r, t))
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for c in by_col:
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by_col[c].sort()
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animals = []
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used = set()
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for (c, r), t in sorted(cells.items()):
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if (c, r) in used:
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continue
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compact = re.match(r"^(.+?),\s*\*", t) # "Name,*DOB, genotype"
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is_block_dob = bool(re.match(r"^\*\s?\d", t)) # standalone "*DOB"
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if not compact and not is_block_dob:
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continue
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if compact:
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name = clean_name(compact.group(1))
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dob, death, geno = parse_detail(t)
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farbschlag = ""
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breeder = ""
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used.add((c, r))
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else:
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# full block: name above, farbschlag/genotype/breeder below
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dob, death, geno0 = parse_detail(t)
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name = ""
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for rr in range(r - 1, r - 4, -1):
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if (c, rr) in cells and not re.match(r"^\*?\s?\d", cells[(c, rr)]) \
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and not gt.looks_like_genotype(cells[(c, rr)]):
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name = clean_name(cells[(c, rr)])
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used.add((c, rr))
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break
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farbschlag = ""
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geno = geno0
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breeder = ""
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for rr in range(r + 1, r + 4):
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cell = cells.get((c, rr))
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if not cell:
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continue
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if gt.looks_like_genotype(cell):
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geno = cell
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used.add((c, rr))
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elif re.search(r"\b(Zucht|Privatzucht)\b", cell) or cell.startswith("("):
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breeder = cell
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used.add((c, rr))
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elif not farbschlag and not re.match(r"^\*?\s?\d", cell):
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farbschlag = cell
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used.add((c, rr))
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used.add((c, r))
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g = parse_detail(t) if compact else (dob, death, geno)
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genodict = gt.parse(geno)
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animals.append({
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"id": None, # assigned in dedup
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"name": name,
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"nameVariants": [],
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"dob": dob,
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"death": death,
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"gender": None,
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"farbschlag": farbschlag,
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"genotype": genodict,
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"breeder": breeder,
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"parentRefs": [],
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"photos": [],
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"sourceFiles": [fname],
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"_gen": gen_of(c),
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"_col": c,
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"_row": r,
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"_file": fname,
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})
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# name-pair cells "X & Y" (deepest generation, names only)
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for (c, r), t in cells.items():
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if (c, r) in used:
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continue
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if " & " in t and not DOB.search(t) and len(t) < 90 and gen_of(c) >= 4:
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for part in t.split(" & "):
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part = clean_name(part)
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if part:
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animals.append({
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"id": None, "name": part, "nameVariants": [],
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"dob": "", "death": "", "gender": None, "farbschlag": "",
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"genotype": gt.parse(""), "breeder": "", "parentRefs": [],
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"photos": [], "sourceFiles": [fname],
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"_gen": gen_of(c), "_col": c, "_row": r, "_file": fname,
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})
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_reconstruct_parents(animals)
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_attach_photos(z, sheets, animals, fname)
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return animals
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def _reconstruct_parents(animals):
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"""Positional: an animal's parents are the bracketing blocks one generation
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deeper (father = nearest block above, mother = nearest below). Role guess is
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by vertical position (German charts: Vater oben) — flagged for review; the
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Wurfchronik is authoritative for matched animals (Stage 3)."""
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by_gen = {}
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for a in animals:
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by_gen.setdefault(a["_gen"], []).append(a)
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for g, group in by_gen.items():
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nxt = sorted(by_gen.get(g + 1, []), key=lambda a: a["_row"])
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if not nxt:
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continue
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for a in group:
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r = a["_row"]
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above = [x for x in nxt if x["_row"] <= r]
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below = [x for x in nxt if x["_row"] > r]
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father = above[-1] if above else None
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mother = below[0] if below else None
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for parent, role in ((father, "father"), (mother, "mother")):
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if parent and parent["name"]:
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a["parentRefs"].append({
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"name": parent["name"],
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"dob": parent["dob"],
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"roleGuess": role,
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"method": "chart-position",
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"confidence": "medium",
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})
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def _attach_photos(z, sheets, animals, fname):
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anchors = [a for a in xu.image_anchors(z)]
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if not anchors:
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return
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by_gen = {}
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for a in animals:
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by_gen.setdefault(a["_gen"], []).append(a)
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media_dir = os.path.join(OUT, "photos")
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for i, (sp, col, row, media) in enumerate(anchors):
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g = gen_of(col)
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cands = by_gen.get(g, [])
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if not cands:
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# fall back to nearest animal by row across all gens
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cands = animals
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target = min(cands, key=lambda a: abs(a["_row"] - row)) if cands else None
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if not target:
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continue
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ext = os.path.splitext(media)[1] or ".img"
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sl = slug(target["name"], target["dob"])
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dest_dir = os.path.join(media_dir, sl)
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os.makedirs(dest_dir, exist_ok=True)
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rel = f"photos/{sl}/{os.path.basename(media)}"
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try:
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with z.open(media) as src, open(os.path.join(OUT, rel), "wb") as dst:
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shutil.copyfileobj(src, dst)
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target["photos"].append(rel)
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except KeyError:
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pass
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# -------------------------------------------------- Wurfchronik extraction ---
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def extract_wurfchronik(path):
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"""Return list of litter dicts. Parses columns BY HEADER (sheets differ)."""
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fname = os.path.basename(path)
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z = __import__("zipfile").ZipFile(path)
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ss = xu.shared_strings(z)
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litters = []
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for sp in xu.sheet_paths(z):
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cells = xu.read_cells(z, sp, ss)
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if not cells:
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continue
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# build row -> {colnum: text}
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rows = {}
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for (c, r), t in cells.items():
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rows.setdefault(r, {})[c] = t
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hdr = xu.header_row(cells) # colnum -> header label
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hdr_row = min(r for (_, r) in cells) # the header row number, to skip it
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# map header label -> colnum (fuzzy by keyword)
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def find(*keys):
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for c, lbl in hdr.items():
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low = lbl.lower()
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if any(k in low for k in keys):
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return c
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return None
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col_id = find("wurfbuchstabe", "buchstabe")
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col_date = find("geburtsdatum", "datum")
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col_dam = find("mutter")
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col_sire = find("vater")
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col_ws = find("ws", "wurfstärke", "wurfstaerke")
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col_breakdown = find("männchen", "maennchen", "weibchen")
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col_zn = find("zuchtnummer")
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col_note = find("bemerkung")
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sheet_name = os.path.basename(sp)
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for r in sorted(rows):
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if r == hdr_row: # skip the header row itself
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continue
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row = rows[r]
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# skip empty-id + "Jahr YYYY" section rows
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txt_b = row.get(col_date, "") if col_date else ""
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if not row.get(col_id):
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continue
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if "jahr" in " ".join(row.values()).lower() and not DOB.search(txt_b):
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continue
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dob = DOB.search(txt_b)
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bd = row.get(col_breakdown, "") if col_breakdown else ""
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m = re.findall(r"\d+", bd)
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breakdown = {}
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if len(m) >= 1:
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keys = ["maennchen", "weibchen", "totgeburt", "s"]
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for k, val in zip(keys, m):
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breakdown[k] = int(val)
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lid = row.get(col_id, "")
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datestr = dob.group(1) if dob else ""
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litters.append({
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"id": f"{sheet_name.replace('.xml','')}-{lid}-{norm_dob(datestr)}",
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"litterId": lid,
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"date": datestr,
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"damName": row.get(col_dam, "") if col_dam else "",
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"sireName": row.get(col_sire, "") if col_sire else "",
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"wurfstaerke": _to_int(row.get(col_ws)) if col_ws else None,
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"sexBreakdown": breakdown,
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"zuchtnummer": row.get(col_zn, "") if col_zn else "",
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"note": row.get(col_note, "") if col_note else "",
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"sourceFile": fname,
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"sheet": sheet_name,
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"row": r,
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})
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return litters
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|
||||
def _to_int(s):
|
||||
if not s:
|
||||
return None
|
||||
m = re.search(r"\d+", s)
|
||||
return int(m.group()) if m else None
|
||||
|
||||
|
||||
# ------------------------------------------------------------- stage 2: dedup
|
||||
def dedup(animals):
|
||||
"""Merge by normalise(name)+DOB. Returns (merged, conflicts, orphans)."""
|
||||
groups = {}
|
||||
orphans = []
|
||||
for a in animals:
|
||||
key = (norm_name(a["name"]), norm_dob(a["dob"]))
|
||||
if not key[0] or not key[1]:
|
||||
orphans.append(a)
|
||||
# orphans still get a stable id but are not merged
|
||||
key = ("__orphan__", id(a))
|
||||
groups.setdefault(key, []).append(a)
|
||||
|
||||
merged = []
|
||||
conflicts = []
|
||||
for key, grp in groups.items():
|
||||
base = dict(grp[0])
|
||||
variants = set([base["name"]])
|
||||
files = set(base["sourceFiles"])
|
||||
photos = list(base["photos"])
|
||||
parent_refs = list(base["parentRefs"])
|
||||
genos = set()
|
||||
farb = set()
|
||||
deaths = set()
|
||||
for a in grp:
|
||||
variants.add(a["name"])
|
||||
files.update(a["sourceFiles"])
|
||||
photos.extend(a["photos"])
|
||||
parent_refs.extend(a["parentRefs"])
|
||||
if a["genotype"]["rawGenotype"]:
|
||||
genos.add(a["genotype"]["rawGenotype"])
|
||||
if a["farbschlag"]:
|
||||
farb.add(a["farbschlag"])
|
||||
if a["death"]:
|
||||
deaths.add(norm_dob(a["death"]))
|
||||
# pick the richest genotype (most mapped loci, then longest raw)
|
||||
best = max((a["genotype"] for a in grp),
|
||||
key=lambda gd: (len(gd["mapped8locus"]), len(gd["rawGenotype"])))
|
||||
out = {
|
||||
"id": slug(base["name"], base["dob"]),
|
||||
"name": base["name"],
|
||||
"nameVariants": sorted(v for v in variants if v),
|
||||
"dob": norm_dob(base["dob"]),
|
||||
"death": sorted(deaths)[0] if deaths else "",
|
||||
"gender": None,
|
||||
"farbschlag": sorted(farb)[0] if farb else "",
|
||||
"farbschlagVariants": sorted(farb),
|
||||
"genotype": best,
|
||||
"breeder": next((a["breeder"] for a in grp if a["breeder"]), ""),
|
||||
"parentRefs": _dedup_parentrefs(parent_refs),
|
||||
"photos": sorted(set(photos)),
|
||||
"sourceFiles": sorted(files),
|
||||
"mentions": len(grp),
|
||||
}
|
||||
merged.append(out)
|
||||
# conflict: same animal, disagreeing genotype or farbschlag or death
|
||||
if len(genos) > 1 or len(farb) > 1 or len(deaths) > 1:
|
||||
conflicts.append({
|
||||
"id": out["id"], "name": base["name"], "dob": out["dob"],
|
||||
"genotypes": sorted(genos), "farbschlaege": sorted(farb),
|
||||
"deaths": sorted(deaths), "files": sorted(files),
|
||||
})
|
||||
merged.sort(key=lambda a: (a["dob"], a["name"]))
|
||||
return merged, conflicts, orphans
|
||||
|
||||
|
||||
def _dedup_parentrefs(refs):
|
||||
seen = {}
|
||||
for r in refs:
|
||||
k = (norm_name(r["name"]), r["roleGuess"])
|
||||
if k not in seen:
|
||||
seen[k] = r
|
||||
return list(seen.values())
|
||||
|
||||
|
||||
# ------------------------------------------------------------------ reporting
|
||||
def write_report(merged, conflicts, orphans, raw_count, litters, photo_count):
|
||||
keyset = set((a["dob"], norm_name(a["name"])) for a in merged)
|
||||
multi = [a for a in merged if a["mentions"] > 1]
|
||||
with_dob = [a for a in merged if a["dob"]]
|
||||
lit_dates = set(norm_dob(l["date"]) for l in litters if l["date"])
|
||||
joinable = [a for a in merged if a["dob"] and norm_dob(a["dob"]) in lit_dates]
|
||||
|
||||
L = []
|
||||
L.append("# FEAT-8b — Import-Vorschau & Prüfbericht (Stammbäume + Wurfchronik)\n")
|
||||
L.append("_Automatisch erzeugt von `tools/import/extract.py` — **noch nichts in die Datenbank geladen.** "
|
||||
"Bitte prüfen, bevor importiert wird._\n")
|
||||
L.append("## Überblick\n")
|
||||
L.append(f"- Rohe Tier-Einträge aus den Stammbäumen: **{raw_count}**")
|
||||
L.append(f"- Nach Zusammenführung (eindeutige Tiere): **{len(merged)}**")
|
||||
L.append(f" - davon mit Geburtsdatum: {len(with_dob)}")
|
||||
L.append(f" - in mehreren Dateien gefunden (Dubletten zusammengeführt): {len(multi)}")
|
||||
L.append(f"- Konflikte zur Klärung: **{len(conflicts)}**")
|
||||
L.append(f"- Mehrdeutige / unvollständige Einträge (ohne Name+Datum): **{len(orphans)}**")
|
||||
L.append(f"- Fotos zugeordnet: **{photo_count}**")
|
||||
L.append(f"- Würfe aus der Wurfchronik: **{len(litters)}**")
|
||||
L.append(f" - Tiere, deren Geburtsdatum zu einem Wurf passt (verknüpfbar): {len(joinable)}\n")
|
||||
|
||||
L.append("## Zusammenführungs-Schlüssel\n")
|
||||
L.append("Tiere wurden zusammengeführt über **normalisierter Name + Geburtsdatum**. "
|
||||
"Namensvarianten (z. B. `v.d.` ↔ `von den`, `gen.`-Spitznamen, Zuchtsuffixe) "
|
||||
"werden als `nameVariants` erhalten.\n")
|
||||
|
||||
L.append("## ⚠️ Konflikte (bitte prüfen)\n")
|
||||
if conflicts:
|
||||
L.append("Gleiches Tier (Name+Datum), aber widersprüchliche Angaben in verschiedenen Dateien:\n")
|
||||
L.append("| Tier | Geburtsdatum | abweichende Genotypen | abweichende Farbschläge | Sterbedaten | Dateien |")
|
||||
L.append("|---|---|---|---|---|---|")
|
||||
for c in conflicts[:200]:
|
||||
L.append("| {} | {} | {} | {} | {} | {} |".format(
|
||||
c["name"], c["dob"],
|
||||
" // ".join(c["genotypes"]) or "—",
|
||||
" // ".join(c["farbschlaege"]) or "—",
|
||||
" // ".join(c["deaths"]) or "—",
|
||||
", ".join(os.path.splitext(f)[0] for f in c["files"])))
|
||||
else:
|
||||
L.append("_Keine._\n")
|
||||
|
||||
L.append("\n## Mehrdeutige / unvollständige Einträge\n")
|
||||
L.append(f"{len(orphans)} Einträge ohne sichere Name+Datum-Kombination "
|
||||
"(z. B. `Name1 & Name2`-Paarzellen der tiefsten Generation, oder Zellen ohne Datum). "
|
||||
"Diese werden NICHT automatisch zusammengeführt.\n")
|
||||
sample = [o for o in orphans if o["name"]][:40]
|
||||
for o in sample:
|
||||
L.append(f"- {o['name']} · {o.get('_file','')}")
|
||||
|
||||
# orphan -> likely same-named full record (soft hint, not auto-merged)
|
||||
from collections import Counter
|
||||
name_index = {}
|
||||
for a in merged:
|
||||
if a["dob"]:
|
||||
name_index.setdefault(norm_name(a["name"]), []).append(a)
|
||||
matchable = []
|
||||
for o in orphans:
|
||||
if not o["name"]:
|
||||
continue
|
||||
cands = name_index.get(norm_name(o["name"]))
|
||||
if cands:
|
||||
matchable.append((o, cands))
|
||||
L.append("\n## Wahrscheinliche Zuordnungen unvollständiger Einträge\n")
|
||||
L.append(f"{len(matchable)} namenlose/datenlose Einträge tragen denselben Namen wie ein "
|
||||
"vollständiges Tier — vermutlich dasselbe Tier (zur Bestätigung):\n")
|
||||
for o, cands in matchable[:60]:
|
||||
opts = "; ".join(f"{c['name']} (*{c['dob']})" for c in cands[:3])
|
||||
L.append(f"- „{o['name']}“ → {opts}")
|
||||
|
||||
# unmapped-token summary (for Kevin / GEN-2 + the wife)
|
||||
tok = Counter()
|
||||
for a in merged:
|
||||
for t in a["genotype"]["unmappedTokens"]:
|
||||
tok[t] += 1
|
||||
L.append("\n## Nicht ins 8-Loci-Modell abgebildete Tokens (verbatim erhalten)\n")
|
||||
L.append("Diese Tokens stehen weiter in `rawGenotype`/`unmappedTokens` — Entscheidung "
|
||||
"(Modell erweitern vs. als Notiz) liegt bei Julian/Kevin:\n")
|
||||
L.append("| Token | Vorkommen | Bedeutung (Vermutung) |")
|
||||
L.append("|---|---|---|")
|
||||
hint = {"Uwuw[d]": "9. Locus Uw (nicht im Modell)", "UwUw": "9. Locus Uw",
|
||||
"[WFNZ]": "Marker", "[DP]": "Marker (Dunkelpigment?)", "DP": "Marker",
|
||||
"WP": "Marker", "[WP]": "Marker", "C(C)": "Schreibweise (C trägt c)",
|
||||
"chmchm": "Schreibweise (c[chm]c[chm])"}
|
||||
for t, n in tok.most_common(25):
|
||||
L.append(f"| `{t}` | {n} | {hint.get(t, '?')} |")
|
||||
|
||||
L.append("\n## Hinweise für den Import (Stufe 3, später)\n")
|
||||
L.append("- **Wurfchronik = Quelle der Würfe** (Datum, Wurfstärke, Eltern, Zuchtnummer); "
|
||||
"**Stammbäume = Abstammung + Genotyp + Fotos**. Verknüpfung über Geburtsdatum + Elternnamen.")
|
||||
L.append("- Eltern-Verknüpfungen (`parentRefs`) stammen aus der **Position im Stammbaum** "
|
||||
"(Vater oben / Mutter unten, mittlere Konfidenz) — die Wurfchronik korrigiert dies maßgeblich.")
|
||||
L.append("- Genotyp: `mapped8locus` (A C D E G P Sp Re), `rawGenotype` (wortgetreu), "
|
||||
"`unmappedTokens` (z. B. `Uw`, `Sls`, `Dea`, Marker wie `WFNZ/WP/DP`) — **nichts geht verloren**.")
|
||||
L.append("- `-` (unbekanntes zweites Allel) → `?` (Platzhalter; Annahme, bitte bestätigen).")
|
||||
with open(os.path.join(OUT, "review-report.md"), "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(L) + "\n")
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------ main
|
||||
def main():
|
||||
try:
|
||||
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
|
||||
except Exception:
|
||||
pass
|
||||
ap = argparse.ArgumentParser(description="FEAT-8b extractor (stages 1-2)")
|
||||
ap.add_argument("--stammbaeume", default=DEFAULT_STAMMBAEUME)
|
||||
ap.add_argument("--wurfchronik", default=DEFAULT_WURFCHRONIK)
|
||||
args = ap.parse_args()
|
||||
|
||||
if os.path.isdir(OUT):
|
||||
# keep the report stable across reruns but refresh data/photos
|
||||
for sub in ("photos",):
|
||||
p = os.path.join(OUT, sub)
|
||||
if os.path.isdir(p):
|
||||
shutil.rmtree(p)
|
||||
os.makedirs(OUT, exist_ok=True)
|
||||
|
||||
raw_animals = []
|
||||
files = sorted(glob.glob(os.path.join(args.stammbaeume, "*.xlsx")))
|
||||
print(f"Stammbaum-Dateien: {len(files)}")
|
||||
for path in files:
|
||||
got = extract_stammbaum(path)
|
||||
print(f" {len(got):4d} {os.path.basename(path)}")
|
||||
raw_animals.extend(got)
|
||||
|
||||
litters = []
|
||||
if os.path.isfile(args.wurfchronik):
|
||||
litters = extract_wurfchronik(args.wurfchronik)
|
||||
print(f"Wurfchronik: {len(litters)} Würfe")
|
||||
|
||||
merged, conflicts, orphans = dedup(raw_animals)
|
||||
photo_count = sum(len(a["photos"]) for a in merged)
|
||||
|
||||
# strip private (_) fields from the JSON output
|
||||
def clean(a):
|
||||
return {k: v for k, v in a.items() if not k.startswith("_")}
|
||||
|
||||
with open(os.path.join(OUT, "animals.json"), "w", encoding="utf-8") as f:
|
||||
json.dump([clean(a) for a in merged], f, ensure_ascii=False, indent=2)
|
||||
with open(os.path.join(OUT, "litters.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(litters, f, ensure_ascii=False, indent=2)
|
||||
|
||||
write_report(merged, conflicts, orphans, len(raw_animals), litters, photo_count)
|
||||
|
||||
print(f"\nRoh: {len(raw_animals)} → eindeutig: {len(merged)} "
|
||||
f"| Konflikte: {len(conflicts)} | Orphans: {len(orphans)} | Fotos: {photo_count}")
|
||||
print(f"Ausgabe in {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
109
tools/import/genotype.py
Normal file
109
tools/import/genotype.py
Normal file
@@ -0,0 +1,109 @@
|
||||
"""Parse the breeder's free-text genotype notation into our frozen 8-locus
|
||||
contract while losing nothing (FEAT-8b ruling from god):
|
||||
|
||||
- mapped8locus : {locus: [allele1, allele2]} for A C D E G P Sp Re
|
||||
- rawGenotype : the verbatim source string
|
||||
- unmappedTokens: tokens we couldn't map (Uw/Sls/Dea, markers like WFNZ/WP/DP, …)
|
||||
|
||||
Conventions in the source data:
|
||||
- allele superscripts are bracketed: c[chm] -> c^chm, c[h] -> c^h, e[f] -> e^f
|
||||
- a single '-' for the second allele means "unknown" -> mapped to '?'
|
||||
(frozen-contract wildcard; assumption pending the wife's confirmation)
|
||||
"""
|
||||
import re
|
||||
|
||||
LOCI = ["A", "C", "D", "E", "G", "P", "Sp", "Re"]
|
||||
|
||||
# locus -> regex that matches that locus's token (longest alternatives first)
|
||||
_LOCUS_TOKEN = {
|
||||
"Sp": re.compile(r"^(Sp|sp)(Sp|sp|-)?$"),
|
||||
"Re": re.compile(r"^(Re|re)(Re|re|-)?$"),
|
||||
"A": re.compile(r"^(A|a)(A|a|-)?$"),
|
||||
"C": re.compile(r"^(C|c)(\[(?:chm|chl|ch|h|hm|e|-)\])?(C|c|-)?(\[(?:chm|chl|ch|h|hm|e|-)\])?$"),
|
||||
"D": re.compile(r"^(D|d)(D|d|-)?$"),
|
||||
"E": re.compile(r"^(E|e)(\[(?:f|-)\])?(E|e|-)?(\[(?:f|-)\])?$"),
|
||||
"G": re.compile(r"^(G|g)(G|g|-)?$"),
|
||||
"P": re.compile(r"^(P|p)(P|p|-)?$"),
|
||||
}
|
||||
# loci our model does NOT have but the data uses
|
||||
_KNOWN_UNMAPPED = re.compile(r"^(Uw|uw)(\[d\])?(Uw|uw)?(\[d\])?$|^(Sls|sls|Dea|dea)$", re.I)
|
||||
_MARKER = re.compile(r"^\[?(WFNZ|WP|DP|GV|RV)\]?$|^\((taub|hörend|hoerend|RV|GV|extern[^)]*)\)$", re.I)
|
||||
|
||||
|
||||
# one allele unit per locus (longest-match alternatives first); '-' = unknown
|
||||
_ALLELE_UNIT = {
|
||||
"Sp": re.compile(r"Sp|sp|-"),
|
||||
"Re": re.compile(r"Re|re|-"),
|
||||
"C": re.compile(r"[Cc]\[(?:chm|chl|ch|hm|h|e|-)\]|[Cc]|-"),
|
||||
"E": re.compile(r"[Ee]\[(?:f|-)\]|[Ee]|-"),
|
||||
"A": re.compile(r"[Aa]|-"),
|
||||
"D": re.compile(r"[Dd]|-"),
|
||||
"G": re.compile(r"[Gg]|-"),
|
||||
"P": re.compile(r"[Pp]|-"),
|
||||
}
|
||||
|
||||
|
||||
def _alleles_for(locus, token):
|
||||
"""Extract the (allele1, allele2) pair from a single locus token, handling
|
||||
two-letter alleles (Sp/Re) and bracketed superscripts (c[chm] -> c^chm)."""
|
||||
pat = _ALLELE_UNIT.get(locus)
|
||||
units = pat.findall(token) if pat else re.findall(r"[A-Za-z](?:\[[a-z]+\])?|-", token)
|
||||
alleles = []
|
||||
for u in units:
|
||||
if u == "-":
|
||||
alleles.append("?")
|
||||
else:
|
||||
m = re.match(r"([A-Za-z]+)\[([a-z\-]+)\]", u)
|
||||
if m:
|
||||
# [-] = sub-allele unknown -> keep the base letter only
|
||||
alleles.append(m.group(1) if m.group(2) == "-" else f"{m.group(1)}^{m.group(2)}")
|
||||
else:
|
||||
alleles.append(u)
|
||||
if len(alleles) == 1:
|
||||
alleles.append("?")
|
||||
return alleles[:2]
|
||||
|
||||
|
||||
def parse(raw):
|
||||
"""raw: a genotype string (may include trailing free text/markers).
|
||||
|
||||
Returns dict {mapped8locus, rawGenotype, unmappedTokens}.
|
||||
"""
|
||||
raw = (raw or "").strip()
|
||||
mapped = {}
|
||||
unmapped = []
|
||||
# tokenise on whitespace; keep order
|
||||
for tok in raw.split():
|
||||
t = tok.strip().rstrip(",")
|
||||
if not t:
|
||||
continue
|
||||
matched = False
|
||||
for locus in LOCI:
|
||||
pat = _LOCUS_TOKEN.get(locus)
|
||||
if pat and pat.match(t):
|
||||
if locus not in mapped: # first occurrence wins
|
||||
mapped[locus] = _alleles_for(locus, t)
|
||||
matched = True
|
||||
break
|
||||
if matched:
|
||||
continue
|
||||
if _KNOWN_UNMAPPED.match(t) or _MARKER.match(t):
|
||||
unmapped.append(t)
|
||||
else:
|
||||
# anything else (stray notes, malformed tokens) -> unmapped, nothing lost
|
||||
unmapped.append(t)
|
||||
return {
|
||||
"mapped8locus": mapped,
|
||||
"rawGenotype": raw,
|
||||
"unmappedTokens": unmapped,
|
||||
}
|
||||
|
||||
|
||||
def looks_like_genotype(text):
|
||||
"""Heuristic: does this cell text contain >=3 recognisable locus tokens?"""
|
||||
n = 0
|
||||
for tok in text.split():
|
||||
t = tok.rstrip(",")
|
||||
if any(p.match(t) for p in _LOCUS_TOKEN.values()):
|
||||
n += 1
|
||||
return n >= 3
|
||||
176
tools/import/output/review-report.md
Normal file
176
tools/import/output/review-report.md
Normal file
@@ -0,0 +1,176 @@
|
||||
# FEAT-8b — Import-Vorschau & Prüfbericht (Stammbäume + Wurfchronik)
|
||||
|
||||
_Automatisch erzeugt von `tools/import/extract.py` — **noch nichts in die Datenbank geladen.** Bitte prüfen, bevor importiert wird._
|
||||
|
||||
## Überblick
|
||||
|
||||
- Rohe Tier-Einträge aus den Stammbäumen: **889**
|
||||
- Nach Zusammenführung (eindeutige Tiere): **587**
|
||||
- davon mit Geburtsdatum: 292
|
||||
- in mehreren Dateien gefunden (Dubletten zusammengeführt): 142
|
||||
- Konflikte zur Klärung: **24**
|
||||
- Mehrdeutige / unvollständige Einträge (ohne Name+Datum): **310**
|
||||
- Fotos zugeordnet: **123**
|
||||
- Würfe aus der Wurfchronik: **752**
|
||||
- Tiere, deren Geburtsdatum zu einem Wurf passt (verknüpfbar): 152
|
||||
|
||||
## Zusammenführungs-Schlüssel
|
||||
|
||||
Tiere wurden zusammengeführt über **normalisierter Name + Geburtsdatum**. Namensvarianten (z. B. `v.d.` ↔ `von den`, `gen.`-Spitznamen, Zuchtsuffixe) werden als `nameVariants` erhalten.
|
||||
|
||||
## ⚠️ Konflikte (bitte prüfen)
|
||||
|
||||
Gleiches Tier (Name+Datum), aber widersprüchliche Angaben in verschiedenen Dateien:
|
||||
|
||||
| Tier | Geburtsdatum | abweichende Genotypen | abweichende Farbschläge | Sterbedaten | Dateien |
|
||||
|---|---|---|---|---|---|
|
||||
| Louis von den Kleinen Chaoten | 15.07.2017 | Aa Cc[] D- Ee Gg P- spsp // Aa Cc[chm] D- Ee Uwuw[d] P- spsp | Roswitha von den Kleinen Chaoten | 01.07.2020 | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity |
|
||||
| Roswitha von den Kleinen Chaoten | 10.09.2018 | aa CC D- ee[f] Gg P- spsp // aa CC D- ee[f] Uwuw[d] P- spsp | — | 05.08.2021 | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity |
|
||||
| Firefly von den Kleinen Chaoten | 18.12.2019 | /+, Aa c[chm]c[chm] D- Ee Gg PP Spsp // Aa c[chm]c[chm] DD Ee Gg PP Spsp | — | 2024 | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Fire Kids, Stammbaum von Valentino Firehearts Kids |
|
||||
| Zuleika von den Kleinen Chaoten | 24.10.2015 | aa c[chm]c[h] D- E G P- spsp // aa c[chm]c[h] D- Ee Gg P- spsp // aa c[chm]c[h] DD Ee Gg P- spsp | — | 24.02.2019 | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Valentino Firehearts Kids |
|
||||
| WildFire von den Kleinen Chaoten | 05.10.2017 | aa c[chm]c[chm] D- Ee gg P- spsp // aa c[chm]c[chm] D- Ee gg PP spsp | — | — | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Fire Kids, Stammbaum von Valentino Firehearts Kids |
|
||||
| Flint von den Kleinen Chaoten | 23.12.2017 | aa Cc[chm] D- ee Gg P- spsp | — | 10.05.2021 // 10.05.2022 | Stammbaum von Akio Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity |
|
||||
| Silenos gen. Adonis v.d. Kleinen Chaoten | 11.10.2015 | aa Cc[chm] D- Ee Gg PP spsp // aa Cc[chm] D- Ee Uwuw[d] PP spsp | — | 18.07.2019 | Stammbaum von Akio Kids, Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Watarus Kids |
|
||||
| Milka of LennyLengo | 09.12.2018 | aa C- dd E- Gg P- Spsp // aa Cc[h] dd EE Gg P- Spsp | — | 22.12.2021 | Stammbaum von Alberto Kids, Stammbaum von Stella Kids |
|
||||
| Hedwig of BGB | 30.10.2019 | aa CC DD E- G- P- Spsp WP // aa CC DD E- G- P- Spsp WP DP (hörend) | — | 30.08.2023 | Stammbaum von Alberto Kids, Stammbaum von Fire Kids, Stammbaum von Stella Kids |
|
||||
| Silvain von den Kleinen Chaoten | 27.03.2022 | aa c[chm]c[chm] Dd Ee[-] Gg P- Spsp // aa c[chm]c[chm] Dd ee[-] Gg Pp Spsp | — | 31.12.2024 | Stammbaum von Alberto Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity |
|
||||
| Pitari gen. Piti von den Kleinen Chaoten | 16.05.2021 | Aa CC dd ee Gg P- Spsp DP // Aa CC dd ee Gg P- Spsp [DP] | — | — | Stammbaum von Alberto Kids, Stammbaum von Fire Kids, Stammbaum von Stella Kids |
|
||||
| Brandon Stark von den Kleinen Chaoten | 13.12.2017 | aa Cc[chm] D- Ee Gg P- spsp // aa Cc[chm] D- Ee Uwuw[d] P- spsp | — | — | Stammbaum von Alberto Kids, Stammbaum von Fire Kids, Stammbaum von Kohlief, Goldfuchsef Sp von Chrissi, Stammbaum von Stella Kids |
|
||||
| Enya von den Kleinen Chaoten | 01.11.2017 | Aa c[chm]c[chm] D- ee[-] G- P- spsp // Aa c[chm]c[chm] D- ee[-] Uwuw[d] P- spsp | — | — | Stammbaum von Alberto Kids, Stammbaum von Fire Kids, Stammbaum von Kohlief, Goldfuchsef Sp von Chrissi, Stammbaum von Stella Kids |
|
||||
| Little Hero of Black Forest | 22.02.2018 | AA CC DD EE GG PP [WFNZ] // AA CC DD EE GG PP spsp [WFNZ] | — | 18.06.2021 | Stammbaum von Alberto Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Fire Kids, Stammbaum von Kohlief, Goldfuchsef Sp von Chrissi, Stammbaum von Stella Kids, Stammbaum von Valentino Firehearts Kids |
|
||||
| Molly of Black Forest | 13.09.2021 | /+, Aa Cc[chm] D- Ee gg P- spsp // Aa Cc[chm] Dd Ee gg Pp spsp | — | 03.05.2021 | Stammbaum von Alberto Kids, Stammbaum von CP-Fuchs, CP-Sa Sp von Unity |
|
||||
| Little Runner's Big Ben | 03.02.2020 | Aa Cc[chm] DD Ee Gg PP Spsp // Aa Cc[chm] DD Ee Gg Pp Spsp | Daja of Little Rose | 14.10.2023 | Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Fire Kids, Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Kohlief, Goldfuchsef Sp von Chrissi, Stammbaum von Valentino Firehearts Kids, Stammbaum von Watarus Kids |
|
||||
| Daja of Little Rose | 16.05.2021 | aa chmchm D- EE Gg P- // aa chmchm D- EE Gg P- spsp | — | — | Stammbaum von CP-Fuchs, CP-Sa Sp von Unity, Stammbaum von Fire Kids, Stammbaum von Valentino Firehearts Kids |
|
||||
| Ichika von den Kleinen Chaoten | 19.04.2020 | aa CC D- ee Gg pp spsp // aa CC D- ee[f] Gg pp spsp | — | 27.11.2023 | Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Watarus Kids |
|
||||
| Victoria Welby gen. Welby v.d. Kleinen Chaoten | 16.01.2023 | Aa CC D- Ee[f] Gg pp Spsp [DP] // Aa CC D- ee[f] Gg pp Spsp [DP] | — | 17.02.2026 | Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Watarus Kids |
|
||||
| Zac gen. Action von den Kleinen Chaoten | 25.12.2020 | aa C- D- Ee G- Pp Spsp [DP] // aa CC D- Ee G- Pp Spsp [DP] | Belica gen. Emi von den Kleinen Chaoten | 31.01.2025 | Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Kohlief, Goldfuchsef Sp von Chrissi, Stammbaum von Watarus Kids |
|
||||
| Chelsea von den Kleinen Chaoten | 02.04.2021 | /+, Aa CC Dd ee gg Pp spsp // Aa CC Dd ee gg Pp spsp | — | — | Stammbaum von Goldfuchs Sp (Pikachu) Kids, Stammbaum von Valentino Firehearts Kids |
|
||||
| Ethan von den Kleinen Chaoten | 09.07.2020 | Aa Cc[chm] D- ee[f] Gg Pp Spsp | Ichika von den Kleinen Chaoten // Orangeschimmel, hell Kragenschecke | 30.07.2024 | Stammbaum von Kentucky, Stammbaum von Watarus Kids |
|
||||
| Quied Soldier of Black Forest | 07.06.2018 | /+, Aa C- D- ee[f] GG Pp Spsp [DP] // Aa C- D- ee[f] GG Pp Spsp DP | Hoshi von den Kleinen Chaoten | — | Stammbaum von Kentucky |
|
||||
| Hanami von den Kleinen Chaoten | 10.09.2015 | aa Cc[chm] D- Ee gg P- spsp | — | 12.12.2019 // 14.01.2020 | Stammbaum von Kentucky, Stammbaum von Stella Kids |
|
||||
|
||||
## Mehrdeutige / unvollständige Einträge
|
||||
|
||||
310 Einträge ohne sichere Name+Datum-Kombination (z. B. `Name1 & Name2`-Paarzellen der tiefsten Generation, oder Zellen ohne Datum). Diese werden NICHT automatisch zusammengeführt.
|
||||
|
||||
- Oskar v.d. bunten Fellnase · Stammbaum von Akio Kids.xlsx
|
||||
- Raya v.d. bunten Fellnasen · Stammbaum von Akio Kids.xlsx
|
||||
- Oskar v.d. bunten Fellnase · Stammbaum von Akio Kids.xlsx
|
||||
- Raya v.d. bunten Fellnasen · Stammbaum von Akio Kids.xlsx
|
||||
- Tai of Lennylengo · Stammbaum von Akio Kids.xlsx
|
||||
- Trixxy · Stammbaum von Akio Kids.xlsx
|
||||
- Katsu · Stammbaum von Akio Kids.xlsx
|
||||
- Akina (RV) · Stammbaum von Akio Kids.xlsx
|
||||
- Arrow PZ Niederlande · Stammbaum von Akio Kids.xlsx
|
||||
- Isa of Golden Lights · Stammbaum von Akio Kids.xlsx
|
||||
- BlackFire · Stammbaum von Akio Kids.xlsx
|
||||
- Leila v. Jessica Walldorf · Stammbaum von Akio Kids.xlsx
|
||||
- Osamu · Stammbaum von Akio Kids.xlsx
|
||||
- Montana · Stammbaum von Akio Kids.xlsx
|
||||
- Isiri of Golden Lights · Stammbaum von Akio Kids.xlsx
|
||||
- Danjo of Golden Lights · Stammbaum von Akio Kids.xlsx
|
||||
- Sam gen. Shelly v.d. bunten Fellnasen · Stammbaum von Akio Kids.xlsx
|
||||
- Jacky · Stammbaum von Akio Kids.xlsx
|
||||
- BlackFire · Stammbaum von Akio Kids.xlsx
|
||||
- Katara · Stammbaum von Akio Kids.xlsx
|
||||
- Jack Jr. · Stammbaum von Akio Kids.xlsx
|
||||
- Nala · Stammbaum von Akio Kids.xlsx
|
||||
- Brandon Stark · Stammbaum von Akio Kids.xlsx
|
||||
- Enya · Stammbaum von Akio Kids.xlsx
|
||||
- Oscar of Black Forest · Stammbaum von Akio Kids.xlsx
|
||||
- Naho · Stammbaum von Akio Kids.xlsx
|
||||
- Hagrid Rubeus of Black Forest · Stammbaum von Akio Kids.xlsx
|
||||
- Lilo of LennyLengo · Stammbaum von Akio Kids.xlsx
|
||||
- Kazuya · Stammbaum von Akio Kids.xlsx
|
||||
- Rainny · Stammbaum von Akio Kids.xlsx
|
||||
- Eddward · Stammbaum von Akio Kids.xlsx
|
||||
- Harumi · Stammbaum von Akio Kids.xlsx
|
||||
- Marc Sloan · Stammbaum von Akio Kids.xlsx
|
||||
- Iris · Stammbaum von Akio Kids.xlsx
|
||||
- Pan · Stammbaum von Akio Kids.xlsx
|
||||
- Gin · Stammbaum von Akio Kids.xlsx
|
||||
- Jack II · Stammbaum von Akio Kids.xlsx
|
||||
- Isa of Golden Lights · Stammbaum von Akio Kids.xlsx
|
||||
- Mystique of Black Forest · Stammbaum von Alberto Kids.xlsx
|
||||
- Verpaarung von Fleur · Stammbaum von Alberto Kids.xlsx
|
||||
|
||||
## Wahrscheinliche Zuordnungen unvollständiger Einträge
|
||||
|
||||
38 namenlose/datenlose Einträge tragen denselben Namen wie ein vollständiges Tier — vermutlich dasselbe Tier (zur Bestätigung):
|
||||
|
||||
- „Oscar of Black Forest“ → Oscar of Black Forest (*12.06.2019)
|
||||
- „Hagrid Rubeus of Black Forest“ → Hagrid Rubeus of Black Forest (*18.07.2019)
|
||||
- „Lilo of LennyLengo“ → Lilo of LennyLengo (*04.11.2018)
|
||||
- „Mystique of Black Forest“ → Mystique of Black Forest (*12.03.2022)
|
||||
- „Hagrid Rubeus of Black Forest“ → Hagrid Rubeus of Black Forest (*18.07.2019)
|
||||
- „Charly of Golden Lights“ → Charly of Golden Lights (*05.04.2016)
|
||||
- „Ziwa of Golden Lights“ → Ziwa of Golden Lights (*29.04.2016)
|
||||
- „Chelsea von den Kleinen Chaoten“ → Chelsea von den Kleinen Chaoten (*02.04.2021); Chelsea von den Kleinen Chaoten (*15.10.2021)
|
||||
- „Pinto of Fiomi“ → Pinto of Fiomi (*28.08.2016)
|
||||
- „Living Force's Idefix“ → Living Force's Idefix (*05.04.2016)
|
||||
- „Scarlett of Samsimar“ → Scarlett of Samsimar (*05.09.2018)
|
||||
- „Living Force's Idefix“ → Living Force's Idefix (*05.04.2016)
|
||||
- „Rosie of LennyLengo“ → Rosie of LennyLengo (*15.03.2016)
|
||||
- „Stich von Privatzucht Gießen“ → Stich von Privatzucht Gießen (*01.09.2018)
|
||||
- „Lilo of LennyLengo“ → Lilo of LennyLengo (*04.11.2018)
|
||||
- „Living Force's Idefix“ → Living Force's Idefix (*05.04.2016)
|
||||
- „Rosie of LennyLengo“ → Rosie of LennyLengo (*15.03.2016)
|
||||
- „Living Force's Idefix“ → Living Force's Idefix (*05.04.2016)
|
||||
- „Rosie of LennyLengo“ → Rosie of LennyLengo (*15.03.2016)
|
||||
- „Inusch of Black Forest“ → Inusch of Black Forest (*25.10.2017)
|
||||
- „Little Hero of Black Forest“ → Little Hero of Black Forest (*22.02.2018)
|
||||
- „Little Runner's Destiny“ → Little Runner's Destiny (*02.03.2019)
|
||||
- „Chevrolet Camaro of Topolino“ → Chevrolet Camaro of Topolino (*10.04.2018)
|
||||
- „Marlin of Black Forest“ → Marlin of Black Forest (*28.05.2019)
|
||||
- „Nisha of Black Forest“ → Nisha of Black Forest (*28.03.2018)
|
||||
- „Nisha of Black Forest“ → Nisha of Black Forest (*28.03.2018)
|
||||
- „Dorie of Black Forest“ → Dorie of Black Forest (*28.05.2019)
|
||||
- „Zadar from Zeko i ptica, Croatia“ → Zadar from Zeko i ptica, Croatia (*12.04.2019)
|
||||
- „Living Force's Vally“ → Living Force's Vally (*01.11.2014)
|
||||
- „Pinto of Fiomi“ → Pinto of Fiomi (*28.08.2016)
|
||||
- „Oscar of Black Forest“ → Oscar of Black Forest (*12.06.2019)
|
||||
- „Hagrid Rubeus of Black Forest“ → Hagrid Rubeus of Black Forest (*18.07.2019)
|
||||
- „Lilo of LennyLengo“ → Lilo of LennyLengo (*04.11.2018)
|
||||
- „Chevrolet Camaro of Topolino“ → Chevrolet Camaro of Topolino (*10.04.2018)
|
||||
- „Lilo of LennyLengo“ → Lilo of LennyLengo (*04.11.2018)
|
||||
- „Rosie of LennyLengo“ → Rosie of LennyLengo (*15.03.2016)
|
||||
- „Rosie of LennyLengo“ → Rosie of LennyLengo (*15.03.2016)
|
||||
- „Little Runner's Destiny“ → Little Runner's Destiny (*02.03.2019)
|
||||
|
||||
## Nicht ins 8-Loci-Modell abgebildete Tokens (verbatim erhalten)
|
||||
|
||||
Diese Tokens stehen weiter in `rawGenotype`/`unmappedTokens` — Entscheidung (Modell erweitern vs. als Notiz) liegt bei Julian/Kevin:
|
||||
|
||||
| Token | Vorkommen | Bedeutung (Vermutung) |
|
||||
|---|---|---|
|
||||
| `[DP]` | 16 | Marker (Dunkelpigment?) |
|
||||
| `[WFNZ]` | 13 | Marker |
|
||||
| `DP` | 10 | Marker |
|
||||
| `WP` | 8 | Marker |
|
||||
| `/+` | 8 | ? |
|
||||
| `Uwuw[d]` | 4 | 9. Locus Uw (nicht im Modell) |
|
||||
| `[WP]` | 3 | Marker |
|
||||
| `-g` | 2 | ? |
|
||||
| `C(C)` | 2 | Schreibweise (C trägt c) |
|
||||
| `UwUw` | 2 | 9. Locus Uw |
|
||||
| `chmchm` | 2 | Schreibweise (c[chm]c[chm]) |
|
||||
| `Cc[]` | 1 | ? |
|
||||
| `-psp` | 1 | ? |
|
||||
| `G(G)` | 1 | ? |
|
||||
| `uw[d]uw[d]` | 1 | ? |
|
||||
| `[DP` | 1 | ? |
|
||||
| `Dea/dea]` | 1 | ? |
|
||||
| `DD-Tumor` | 1 | ? |
|
||||
| `bei` | 1 | ? |
|
||||
| `Geschwistern` | 1 | ? |
|
||||
| `C-D-` | 1 | ? |
|
||||
| `Sls` | 1 | ? |
|
||||
| `(hörend)` | 1 | ? |
|
||||
| `-DD` | 1 | ? |
|
||||
|
||||
## Hinweise für den Import (Stufe 3, später)
|
||||
|
||||
- **Wurfchronik = Quelle der Würfe** (Datum, Wurfstärke, Eltern, Zuchtnummer); **Stammbäume = Abstammung + Genotyp + Fotos**. Verknüpfung über Geburtsdatum + Elternnamen.
|
||||
- Eltern-Verknüpfungen (`parentRefs`) stammen aus der **Position im Stammbaum** (Vater oben / Mutter unten, mittlere Konfidenz) — die Wurfchronik korrigiert dies maßgeblich.
|
||||
- Genotyp: `mapped8locus` (A C D E G P Sp Re), `rawGenotype` (wortgetreu), `unmappedTokens` (z. B. `Uw`, `Sls`, `Dea`, Marker wie `WFNZ/WP/DP`) — **nichts geht verloren**.
|
||||
- `-` (unbekanntes zweites Allel) → `?` (Platzhalter; Annahme, bitte bestätigen).
|
||||
135
tools/import/xlsx_util.py
Normal file
135
tools/import/xlsx_util.py
Normal file
@@ -0,0 +1,135 @@
|
||||
"""Minimal dependency-free .xlsx reader (xlsx = zip of XML).
|
||||
|
||||
We only need: shared strings, cell text by reference, and image/drawing anchors.
|
||||
Using stdlib zipfile + regex keeps this migration tooling free of openpyxl so it
|
||||
runs anywhere Python 3 is present.
|
||||
"""
|
||||
import zipfile
|
||||
import re
|
||||
import html
|
||||
import os
|
||||
|
||||
_T = re.compile(r"<t[^>]*>(.*?)</t>", re.S)
|
||||
_SI = re.compile(r"<si>(.*?)</si>", re.S)
|
||||
_CELL = re.compile(
|
||||
r'<c\s+([^>]*?)>(?:<f[^>]*>.*?</f>)?(?:<v>(.*?)</v>|<is>(.*?)</is>)?</c>', re.S)
|
||||
_ATTR = re.compile(r'(\w+)="([^"]*)"')
|
||||
|
||||
|
||||
def col_to_num(col):
|
||||
n = 0
|
||||
for ch in col:
|
||||
n = n * 26 + (ord(ch) - 64)
|
||||
return n
|
||||
|
||||
|
||||
def num_to_col(n):
|
||||
s = ""
|
||||
while n > 0:
|
||||
n, r = divmod(n - 1, 26)
|
||||
s = chr(65 + r) + s
|
||||
return s
|
||||
|
||||
|
||||
def shared_strings(z):
|
||||
try:
|
||||
raw = z.read("xl/sharedStrings.xml").decode("utf-8")
|
||||
except KeyError:
|
||||
return []
|
||||
return [html.unescape("".join(_T.findall(si))).strip() for si in _SI.findall(raw)]
|
||||
|
||||
|
||||
def sheet_paths(z):
|
||||
"""Return worksheet xml paths in workbook order (best effort)."""
|
||||
paths = sorted(n for n in z.namelist()
|
||||
if re.match(r"xl/worksheets/sheet\d+\.xml$", n))
|
||||
return paths
|
||||
|
||||
|
||||
def read_cells(z, sheet_path, ss=None):
|
||||
"""Return {(colnum, row): text} for a worksheet."""
|
||||
if ss is None:
|
||||
ss = shared_strings(z)
|
||||
raw = z.read(sheet_path).decode("utf-8")
|
||||
cells = {}
|
||||
for attrs, v, istr in _CELL.findall(raw):
|
||||
a = dict(_ATTR.findall(attrs))
|
||||
ref = a.get("r")
|
||||
if not ref:
|
||||
continue
|
||||
m = re.match(r"([A-Z]+)(\d+)", ref)
|
||||
if not m:
|
||||
continue
|
||||
col, row = m.group(1), int(m.group(2))
|
||||
typ = a.get("t")
|
||||
if typ == "s" and v != "":
|
||||
try:
|
||||
text = ss[int(v)]
|
||||
except (ValueError, IndexError):
|
||||
text = ""
|
||||
elif typ == "inlineStr" and istr:
|
||||
text = html.unescape("".join(_T.findall(istr)))
|
||||
elif v != "":
|
||||
text = html.unescape(v)
|
||||
else:
|
||||
continue
|
||||
if text.strip():
|
||||
cells[(col_to_num(col), row)] = text.strip()
|
||||
return cells
|
||||
|
||||
|
||||
def header_row(cells):
|
||||
"""Return {colnum: header_text} for the topmost row that has text."""
|
||||
if not cells:
|
||||
return {}
|
||||
top = min(r for (_, r) in cells)
|
||||
return {c: t for (c, r), t in cells.items() if r == top}
|
||||
|
||||
|
||||
def image_anchors(z):
|
||||
"""Yield (sheet_path, from_col, from_row, media_zip_path) for every image anchor.
|
||||
|
||||
drawing rels map rId -> media target; the worksheet rels map the drawing to a
|
||||
sheet. We resolve sheet via worksheet _rels where possible, else attribute all
|
||||
anchors to the single worksheet (these files are single-sheet charts).
|
||||
"""
|
||||
out = []
|
||||
# sheet -> drawing
|
||||
sheet_drawing = {}
|
||||
for sp in sheet_paths(z):
|
||||
rels = "xl/worksheets/_rels/" + os.path.basename(sp) + ".rels"
|
||||
try:
|
||||
r = z.read(rels).decode("utf-8")
|
||||
except KeyError:
|
||||
continue
|
||||
for rid, tgt in re.findall(r'Id="([^"]+)"[^>]*Target="([^"]+)"', r):
|
||||
if "drawing" in tgt:
|
||||
dpath = os.path.normpath(os.path.join(
|
||||
"xl/worksheets", tgt)).replace("\\", "/")
|
||||
sheet_drawing[sp] = dpath
|
||||
for sp, dpath in sheet_drawing.items():
|
||||
try:
|
||||
d = z.read(dpath).decode("utf-8")
|
||||
except KeyError:
|
||||
continue
|
||||
drels = os.path.join(os.path.dirname(dpath), "_rels",
|
||||
os.path.basename(dpath) + ".rels").replace("\\", "/")
|
||||
relmap = {}
|
||||
try:
|
||||
rr = z.read(drels).decode("utf-8")
|
||||
for rid, tgt in re.findall(r'Id="([^"]+)"[^>]*Target="([^"]+)"', rr):
|
||||
relmap[rid] = os.path.normpath(os.path.join(
|
||||
os.path.dirname(dpath), tgt)).replace("\\", "/")
|
||||
except KeyError:
|
||||
pass
|
||||
for anc in re.findall(r"<xdr:(?:two|one)CellAnchor.*?</xdr:(?:two|one)CellAnchor>", d, re.S):
|
||||
fm = re.search(
|
||||
r"<xdr:from>.*?<xdr:col>(\d+)</xdr:col>.*?<xdr:row>(\d+)</xdr:row>", anc, re.S)
|
||||
emb = re.search(r'r:embed="([^"]+)"', anc)
|
||||
if fm and emb:
|
||||
media = relmap.get(emb.group(1))
|
||||
if media:
|
||||
# xdr col/row are 0-based; convert to 1-based to match cell refs
|
||||
out.append((sp, int(fm.group(1)) + 1,
|
||||
int(fm.group(2)) + 1, media))
|
||||
return out
|
||||
Reference in New Issue
Block a user