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Editing: cd.py
from __future__ import annotations import importlib from codecs import IncrementalDecoder from collections import Counter from functools import lru_cache from typing import Counter as TypeCounter from .constant import ( FREQUENCIES, KO_NAMES, LANGUAGE_SUPPORTED_COUNT, TOO_SMALL_SEQUENCE, ZH_NAMES, ) from .md import is_suspiciously_successive_range from .models import CoherenceMatches from .utils import ( is_accentuated, is_latin, is_multi_byte_encoding, is_unicode_range_secondary, unicode_range, ) def encoding_unicode_range(iana_name: str) -> list[str]: """ Return associated unicode ranges in a single byte code page. """ if is_multi_byte_encoding(iana_name): raise OSError("Function not supported on multi-byte code page") decoder = importlib.import_module(f"encodings.{iana_name}").IncrementalDecoder p: IncrementalDecoder = decoder(errors="ignore") seen_ranges: dict[str, int] = {} character_count: int = 0 for i in range(0x40, 0xFF): chunk: str = p.decode(bytes([i])) if chunk: character_range: str | None = unicode_range(chunk) if character_range is None: continue if is_unicode_range_secondary(character_range) is False: if character_range not in seen_ranges: seen_ranges[character_range] = 0 seen_ranges[character_range] += 1 character_count += 1 return sorted( [ character_range for character_range in seen_ranges if seen_ranges[character_range] / character_count >= 0.15 ] ) def unicode_range_languages(primary_range: str) -> list[str]: """ Return inferred languages used with a unicode range. """ languages: list[str] = [] for language, characters in FREQUENCIES.items(): for character in characters: if unicode_range(character) == primary_range: languages.append(language) break return languages @lru_cache() def encoding_languages(iana_name: str) -> list[str]: """ Single-byte encoding language association. Some code page are heavily linked to particular language(s). This function does the correspondence. """ unicode_ranges: list[str] = encoding_unicode_range(iana_name) primary_range: str | None = None for specified_range in unicode_ranges: if "Latin" not in specified_range: primary_range = specified_range break if primary_range is None: return ["Latin Based"] return unicode_range_languages(primary_range) @lru_cache() def mb_encoding_languages(iana_name: str) -> list[str]: """ Multi-byte encoding language association. Some code page are heavily linked to particular language(s). This function does the correspondence. """ if ( iana_name.startswith("shift_") or iana_name.startswith("iso2022_jp") or iana_name.startswith("euc_j") or iana_name == "cp932" ): return ["Japanese"] if iana_name.startswith("gb") or iana_name in ZH_NAMES: return ["Chinese"] if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES: return ["Korean"] return [] @lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT) def get_target_features(language: str) -> tuple[bool, bool]: """ Determine main aspects from a supported language if it contains accents and if is pure Latin. """ target_have_accents: bool = False target_pure_latin: bool = True for character in FREQUENCIES[language]: if not target_have_accents and is_accentuated(character): target_have_accents = True if target_pure_latin and is_latin(character) is False: target_pure_latin = False return target_have_accents, target_pure_latin def alphabet_languages( characters: list[str], ignore_non_latin: bool = False ) -> list[str]: """ Return associated languages associated to given characters. """ languages: list[tuple[str, float]] = [] source_have_accents = any(is_accentuated(character) for character in characters) for language, language_characters in FREQUENCIES.items(): target_have_accents, target_pure_latin = get_target_features(language) if ignore_non_latin and target_pure_latin is False: continue if target_have_accents is False and source_have_accents: continue character_count: int = len(language_characters) character_match_count: int = len( [c for c in language_characters if c in characters] ) ratio: float = character_match_count / character_count if ratio >= 0.2: languages.append((language, ratio)) languages = sorted(languages, key=lambda x: x[1], reverse=True) return [compatible_language[0] for compatible_language in languages] def characters_popularity_compare( language: str, ordered_characters: list[str] ) -> float: """ Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language. The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit). Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.) """ if language not in FREQUENCIES: raise ValueError(f"{language} not available") character_approved_count: int = 0 FREQUENCIES_language_set = set(FREQUENCIES[language]) ordered_characters_count: int = len(ordered_characters) target_language_characters_count: int = len(FREQUENCIES[language]) large_alphabet: bool = target_language_characters_count > 26 for character, character_rank in zip( ordered_characters, range(0, ordered_characters_count) ): if character not in FREQUENCIES_language_set: continue character_rank_in_language: int = FREQUENCIES[language].index(character) expected_projection_ratio: float = ( target_language_characters_count / ordered_characters_count ) character_rank_projection: int = int(character_rank * expected_projection_ratio) if ( large_alphabet is False and abs(character_rank_projection - character_rank_in_language) > 4 ): continue if ( large_alphabet is True and abs(character_rank_projection - character_rank_in_language) < target_language_characters_count / 3 ): character_approved_count += 1 continue characters_before_source: list[str] = FREQUENCIES[language][ 0:character_rank_in_language ] characters_after_source: list[str] = FREQUENCIES[language][ character_rank_in_language: ] characters_before: list[str] = ordered_characters[0:character_rank] characters_after: list[str] = ordered_characters[character_rank:] before_match_count: int = len( set(characters_before) & set(characters_before_source) ) after_match_count: int = len( set(characters_after) & set(characters_after_source) ) if len(characters_before_source) == 0 and before_match_count <= 4: character_approved_count += 1 continue if len(characters_after_source) == 0 and after_match_count <= 4: character_approved_count += 1 continue if ( before_match_count / len(characters_before_source) >= 0.4 or after_match_count / len(characters_after_source) >= 0.4 ): character_approved_count += 1 continue return character_approved_count / len(ordered_characters) def alpha_unicode_split(decoded_sequence: str) -> list[str]: """ Given a decoded text sequence, return a list of str. Unicode range / alphabet separation. Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list; One containing the latin letters and the other hebrew. """ layers: dict[str, str] = {} for character in decoded_sequence: if character.isalpha() is False: continue character_range: str | None = unicode_range(character) if character_range is None: continue layer_target_range: str | None = None for discovered_range in layers: if ( is_suspiciously_successive_range(discovered_range, character_range) is False ): layer_target_range = discovered_range break if layer_target_range is None: layer_target_range = character_range if layer_target_range not in layers: layers[layer_target_range] = character.lower() continue layers[layer_target_range] += character.lower() return list(layers.values()) def merge_coherence_ratios(results: list[CoherenceMatches]) -> CoherenceMatches: """ This function merge results previously given by the function coherence_ratio. The return type is the same as coherence_ratio. """ per_language_ratios: dict[str, list[float]] = {} for result in results: for sub_result in result: language, ratio = sub_result if language not in per_language_ratios: per_language_ratios[language] = [ratio] continue per_language_ratios[language].append(ratio) merge = [ ( language, round( sum(per_language_ratios[language]) / len(per_language_ratios[language]), 4, ), ) for language in per_language_ratios ] return sorted(merge, key=lambda x: x[1], reverse=True) def filter_alt_coherence_matches(results: CoherenceMatches) -> CoherenceMatches: """ We shall NOT return "English—" in CoherenceMatches because it is an alternative of "English". This function only keeps the best match and remove the em-dash in it. """ index_results: dict[str, list[float]] = dict() for result in results: language, ratio = result no_em_name: str = language.replace("—", "") if no_em_name not in index_results: index_results[no_em_name] = [] index_results[no_em_name].append(ratio) if any(len(index_results[e]) > 1 for e in index_results): filtered_results: CoherenceMatches = [] for language in index_results: filtered_results.append((language, max(index_results[language]))) return filtered_results return results @lru_cache(maxsize=2048) def coherence_ratio( decoded_sequence: str, threshold: float = 0.1, lg_inclusion: str | None = None ) -> CoherenceMatches: """ Detect ANY language that can be identified in given sequence. The sequence will be analysed by layers. A layer = Character extraction by alphabets/ranges. """ results: list[tuple[str, float]] = [] ignore_non_latin: bool = False sufficient_match_count: int = 0 lg_inclusion_list = lg_inclusion.split(",") if lg_inclusion is not None else [] if "Latin Based" in lg_inclusion_list: ignore_non_latin = True lg_inclusion_list.remove("Latin Based") for layer in alpha_unicode_split(decoded_sequence): sequence_frequencies: TypeCounter[str] = Counter(layer) most_common = sequence_frequencies.most_common() character_count: int = sum(o for c, o in most_common) if character_count <= TOO_SMALL_SEQUENCE: continue popular_character_ordered: list[str] = [c for c, o in most_common] for language in lg_inclusion_list or alphabet_languages( popular_character_ordered, ignore_non_latin ): ratio: float = characters_popularity_compare( language, popular_character_ordered ) if ratio < threshold: continue elif ratio >= 0.8: sufficient_match_count += 1 results.append((language, round(ratio, 4))) if sufficient_match_count >= 3: break return sorted( filter_alt_coherence_matches(results), key=lambda x: x[1], reverse=True )
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