Lars
7226e04e9c
- Added `resolve_effective_csv_delimiter` function to determine the correct delimiter based on the uploaded file and template. - Updated CSV import logic to utilize the new delimiter resolution method, ensuring accurate parsing of CSV files with varying delimiters. - Enhanced documentation to reflect changes in delimiter handling. - Added unit tests for the new delimiter resolution functionality.
261 lines
8.2 KiB
Python
261 lines
8.2 KiB
Python
"""
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CSV bytes → text, delimiter sniffing, strukturierte Erstzeilen für Analyse (Issue #21).
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"""
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from __future__ import annotations
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import csv
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import io
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import re
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from typing import Any, Dict, Iterator, List, Sequence, Tuple
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_DEFAULT_DELIMS = [",", ";", "\t"]
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def decode_raw_bytes(raw: bytes) -> str:
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"""UTF-8 bevorzugt, Fallback Latin-1; BOM entfernen."""
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if not raw:
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return ""
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for enc in ("utf-8-sig", "utf-8", "latin-1"):
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try:
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text = raw.decode(enc)
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break
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except UnicodeDecodeError:
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text = ""
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continue
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else:
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text = raw.decode("utf-8", errors="replace")
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if text.startswith("\ufeff"):
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text = text[1:]
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return text
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def sniff_delimiter(sample_line: str) -> str:
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"""
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Heuristik: Zähle Vorkommen der Kandidaten in der ersten Datenzeile.
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Kein csv.Sniffer (robuster gegen kurze Zeilen).
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"""
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if not sample_line or not sample_line.strip():
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return ","
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best = ","
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best_count = -1
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for d in _DEFAULT_DELIMS:
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c = sample_line.count(d)
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if c > best_count:
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best_count = c
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best = d
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return best
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def _csv_field_count(line: str, delimiter: str) -> int:
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"""Anzahl Felder in einer Zeile (csv.reader, berücksichtigt Anführungszeichen)."""
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if not line or not line.strip():
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return 0
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try:
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row = next(csv.reader(io.StringIO(line), delimiter=delimiter))
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except StopIteration:
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return 0
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return len(row)
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def resolve_effective_csv_delimiter(text: str, template_delimiter: str | None = None) -> str:
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"""
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Trennzeichen für die hochgeladene Datei wählen. Gespeicherte Vorlagen haben oft «,»
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(Apple EN), tatsächliche Exporte je nach Region «;» (Apple DE / Excel) — mit falschem
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Zeichen wird die Kopfzeile zu **einer** Spalte und das Mapping bricht vollständig.
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"""
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tpl = (template_delimiter or "").strip()
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if tpl not in _DEFAULT_DELIMS:
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tpl = None
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lines = _split_first_lines(text, max_lines=5)
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if not lines:
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return tpl or ","
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header = lines[0]
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scores: list[tuple[int, str]] = []
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for d in _DEFAULT_DELIMS:
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scores.append((_csv_field_count(header, d), d))
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max_n = max(n for n, _ in scores)
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if max_n <= 1:
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return tpl or sniff_delimiter(header)
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at_max = [d for n, d in scores if n == max_n]
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if tpl and tpl in at_max:
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return tpl
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return at_max[0]
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def _split_first_lines(text: str, max_lines: int = 5) -> List[str]:
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lines: List[str] = []
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for line in text.splitlines():
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if line.strip():
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lines.append(line)
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if len(lines) >= max_lines:
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break
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return lines
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def canonical_csv_header_label(name: str | None) -> str:
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"""
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Einheitlicher Spalten-Key für Analyse (Vorlage/Dialog), Import und Signatur.
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BOM und NBSP (häufig in Excel/Apple-Exporten) werden vereinheitlicht, damit
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field_mappings exakt zu DictReader-Zeilen passt.
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"""
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if name is None:
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return ""
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s = str(name).replace("\ufeff", "").replace("\u00a0", " ").strip()
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return s
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def parse_csv_sample(
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text: str,
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delimiter: str | None = None,
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has_header: bool = True,
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max_data_rows: int = 5,
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) -> Tuple[List[str], List[dict[str, str]], str]:
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"""
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Gibt (headers, rows_as_dicts, verwendetes_delimiter) zurück.
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rows sind Rohstrings pro Zelle.
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"""
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lines = _split_first_lines(text, max_lines=50)
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if not lines:
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return [], [], ","
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delim = delimiter if delimiter is not None else sniff_delimiter(lines[0])
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reader = csv.reader(io.StringIO(text.replace("\r\n", "\n").replace("\r", "\n")), delimiter=delim)
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rows_raw: List[List[str]] = []
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for i, row in enumerate(reader):
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if i >= 1 + max_data_rows + (1 if has_header else 0):
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break
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if not any(c.strip() for c in row):
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continue
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rows_raw.append(row)
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if not rows_raw:
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return [], [], delim
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if has_header:
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headers = [canonical_csv_header_label(h) for h in rows_raw[0]]
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data = rows_raw[1 : 1 + max_data_rows]
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else:
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n = len(rows_raw[0])
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headers = [f"col_{i}" for i in range(n)]
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data = rows_raw[:max_data_rows]
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dict_rows: List[dict[str, str]] = []
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for r in data:
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row_dict: dict[str, str] = {}
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for j, h in enumerate(headers):
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row_dict[h] = r[j].strip() if j < len(r) else ""
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dict_rows.append(row_dict)
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return headers, dict_rows, delim
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def normalize_header_for_signature(name: str) -> str:
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s = canonical_csv_header_label(name).lower()
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s = re.sub(r"\s+", "_", s)
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s = re.sub(r"[^a-z0-9_äöüß().%-]+", "_", s)
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return s.strip("_")
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def column_signature(headers: List[str]) -> List[str]:
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"""Sortierte normalisierte Spaltennamen für Signatur-Vergleich."""
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return sorted(
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{normalize_header_for_signature(h) for h in headers if h is not None and canonical_csv_header_label(str(h))}
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)
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def headers_signature_match_score(sig_csv: List[str], sig_template: List[str]) -> float:
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"""Jaccard-Überlappung 0..1 (|A∩B|/|A∪B|). Fällt stark, wenn die CSV viele Zusatzspalten hat."""
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a, b = set(sig_csv), set(sig_template)
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if not a and not b:
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return 1.0
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if not a or not b:
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return 0.0
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inter = len(a & b)
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union = len(a | b)
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return inter / union if union else 0.0
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def headers_signature_template_recall(sig_csv: Sequence[str], sig_template: Sequence[str]) -> float:
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"""
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Anteil der Template-Spalten (Signatur), die in der CSV vorkommen: |A∩B|/|B|.
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100 %, sobald alle für die Vorlage relevanten Spalten in der Datei sind — unabhängig von
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Zusatzspalten (Gewicht + Ernährung in einer Datei erzeugt keinen „Abzug“ für die jeweilige Vorlage).
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"""
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a = set(sig_csv)
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b = {normalize_header_for_signature(str(x)) for x in sig_template}
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b.discard("")
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if not b:
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return 1.0 if not a else 0.0
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inter = len(a & b)
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return inter / len(b)
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def headers_signature_rank_metrics(sig_csv: List[str], sig_template: List[str]) -> dict[str, Any]:
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"""
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Einheitliche Kennzahlen für Vorlagen-Ranking und UI.
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confidence = template_recall (empfohlen für Anzeige / Sortierung primär).
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"""
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a = set(sig_csv)
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b = {normalize_header_for_signature(str(x)) for x in sig_template}
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b.discard("")
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inter = a & b
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n_inter = len(inter)
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n_b = len(b)
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n_a = len(a)
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union = len(a | b)
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template_recall = n_inter / n_b if n_b else (1.0 if not n_a else 0.0)
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jaccard = n_inter / union if union else 0.0
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return {
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"confidence": round(template_recall, 4),
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"template_recall": round(template_recall, 4),
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"jaccard": round(jaccard, 4),
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"columns_matched": n_inter,
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"columns_in_template": n_b,
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"columns_in_csv": n_a,
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}
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def get_csv_import_limits(conn_row: dict | None) -> dict[str, int]:
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"""Liest Limits aus system_config.csv_import; Fallback bei fehlendem Key."""
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defaults = {"max_rows_per_file": 50_000, "max_file_bytes": 52_428_800}
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if not conn_row or "value" not in conn_row:
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return defaults
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val = conn_row["value"]
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if isinstance(val, dict):
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out = {**defaults, **{k: int(v) for k, v in val.items() if k in defaults}}
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return out
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return defaults
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def iter_csv_dict_rows(
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text: str,
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delimiter: str,
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*,
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has_header: bool = True,
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) -> Iterator[Dict[str, str]]:
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"""
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Vollständige Datei zeilenweise als Dict (Header = Keys).
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Spaltenreihenfolge ist egal; zusätzliche Spalten werden ignoriert, wenn sie nicht
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in field_mappings vorkommen. Keine Obergrenze für die Spaltenanzahl (nur Zeilenlimits
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kommen aus system_config / Import-Router).
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"""
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if not has_header:
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raise ValueError("CSV ohne Kopfzeile wird für Import noch nicht unterstützt")
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normalized = text.replace("\r\n", "\n").replace("\r", "\n")
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reader = csv.DictReader(io.StringIO(normalized), delimiter=delimiter)
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for row in reader:
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if row is None:
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continue
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if not any(v and str(v).strip() for v in row.values()):
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continue
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yield {
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canonical_csv_header_label(k): (v or "").strip()
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for k, v in row.items()
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if canonical_csv_header_label(k)
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}
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