338 lines
11 KiB
Python
338 lines
11 KiB
Python
|
from encodings.aliases import aliases
|
||
|
from hashlib import sha256
|
||
|
from json import dumps
|
||
|
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
|
||
|
|
||
|
from .constant import TOO_BIG_SEQUENCE
|
||
|
from .utils import iana_name, is_multi_byte_encoding, unicode_range
|
||
|
|
||
|
|
||
|
class CharsetMatch:
|
||
|
def __init__(
|
||
|
self,
|
||
|
payload: bytes,
|
||
|
guessed_encoding: str,
|
||
|
mean_mess_ratio: float,
|
||
|
has_sig_or_bom: bool,
|
||
|
languages: "CoherenceMatches",
|
||
|
decoded_payload: Optional[str] = None,
|
||
|
):
|
||
|
self._payload: bytes = payload
|
||
|
|
||
|
self._encoding: str = guessed_encoding
|
||
|
self._mean_mess_ratio: float = mean_mess_ratio
|
||
|
self._languages: CoherenceMatches = languages
|
||
|
self._has_sig_or_bom: bool = has_sig_or_bom
|
||
|
self._unicode_ranges: Optional[List[str]] = None
|
||
|
|
||
|
self._leaves: List[CharsetMatch] = []
|
||
|
self._mean_coherence_ratio: float = 0.0
|
||
|
|
||
|
self._output_payload: Optional[bytes] = None
|
||
|
self._output_encoding: Optional[str] = None
|
||
|
|
||
|
self._string: Optional[str] = decoded_payload
|
||
|
|
||
|
def __eq__(self, other: object) -> bool:
|
||
|
if not isinstance(other, CharsetMatch):
|
||
|
raise TypeError(
|
||
|
"__eq__ cannot be invoked on {} and {}.".format(
|
||
|
str(other.__class__), str(self.__class__)
|
||
|
)
|
||
|
)
|
||
|
return self.encoding == other.encoding and self.fingerprint == other.fingerprint
|
||
|
|
||
|
def __lt__(self, other: object) -> bool:
|
||
|
"""
|
||
|
Implemented to make sorted available upon CharsetMatches items.
|
||
|
"""
|
||
|
if not isinstance(other, CharsetMatch):
|
||
|
raise ValueError
|
||
|
|
||
|
chaos_difference: float = abs(self.chaos - other.chaos)
|
||
|
coherence_difference: float = abs(self.coherence - other.coherence)
|
||
|
|
||
|
# Below 1% difference --> Use Coherence
|
||
|
if chaos_difference < 0.01 and coherence_difference > 0.02:
|
||
|
return self.coherence > other.coherence
|
||
|
elif chaos_difference < 0.01 and coherence_difference <= 0.02:
|
||
|
# When having a difficult decision, use the result that decoded as many multi-byte as possible.
|
||
|
return self.multi_byte_usage > other.multi_byte_usage
|
||
|
|
||
|
return self.chaos < other.chaos
|
||
|
|
||
|
@property
|
||
|
def multi_byte_usage(self) -> float:
|
||
|
return 1.0 - (len(str(self)) / len(self.raw))
|
||
|
|
||
|
def __str__(self) -> str:
|
||
|
# Lazy Str Loading
|
||
|
if self._string is None:
|
||
|
self._string = str(self._payload, self._encoding, "strict")
|
||
|
return self._string
|
||
|
|
||
|
def __repr__(self) -> str:
|
||
|
return "<CharsetMatch '{}' bytes({})>".format(self.encoding, self.fingerprint)
|
||
|
|
||
|
def add_submatch(self, other: "CharsetMatch") -> None:
|
||
|
if not isinstance(other, CharsetMatch) or other == self:
|
||
|
raise ValueError(
|
||
|
"Unable to add instance <{}> as a submatch of a CharsetMatch".format(
|
||
|
other.__class__
|
||
|
)
|
||
|
)
|
||
|
|
||
|
other._string = None # Unload RAM usage; dirty trick.
|
||
|
self._leaves.append(other)
|
||
|
|
||
|
@property
|
||
|
def encoding(self) -> str:
|
||
|
return self._encoding
|
||
|
|
||
|
@property
|
||
|
def encoding_aliases(self) -> List[str]:
|
||
|
"""
|
||
|
Encoding name are known by many name, using this could help when searching for IBM855 when it's listed as CP855.
|
||
|
"""
|
||
|
also_known_as: List[str] = []
|
||
|
for u, p in aliases.items():
|
||
|
if self.encoding == u:
|
||
|
also_known_as.append(p)
|
||
|
elif self.encoding == p:
|
||
|
also_known_as.append(u)
|
||
|
return also_known_as
|
||
|
|
||
|
@property
|
||
|
def bom(self) -> bool:
|
||
|
return self._has_sig_or_bom
|
||
|
|
||
|
@property
|
||
|
def byte_order_mark(self) -> bool:
|
||
|
return self._has_sig_or_bom
|
||
|
|
||
|
@property
|
||
|
def languages(self) -> List[str]:
|
||
|
"""
|
||
|
Return the complete list of possible languages found in decoded sequence.
|
||
|
Usually not really useful. Returned list may be empty even if 'language' property return something != 'Unknown'.
|
||
|
"""
|
||
|
return [e[0] for e in self._languages]
|
||
|
|
||
|
@property
|
||
|
def language(self) -> str:
|
||
|
"""
|
||
|
Most probable language found in decoded sequence. If none were detected or inferred, the property will return
|
||
|
"Unknown".
|
||
|
"""
|
||
|
if not self._languages:
|
||
|
# Trying to infer the language based on the given encoding
|
||
|
# Its either English or we should not pronounce ourselves in certain cases.
|
||
|
if "ascii" in self.could_be_from_charset:
|
||
|
return "English"
|
||
|
|
||
|
# doing it there to avoid circular import
|
||
|
from charset_normalizer.cd import encoding_languages, mb_encoding_languages
|
||
|
|
||
|
languages = (
|
||
|
mb_encoding_languages(self.encoding)
|
||
|
if is_multi_byte_encoding(self.encoding)
|
||
|
else encoding_languages(self.encoding)
|
||
|
)
|
||
|
|
||
|
if len(languages) == 0 or "Latin Based" in languages:
|
||
|
return "Unknown"
|
||
|
|
||
|
return languages[0]
|
||
|
|
||
|
return self._languages[0][0]
|
||
|
|
||
|
@property
|
||
|
def chaos(self) -> float:
|
||
|
return self._mean_mess_ratio
|
||
|
|
||
|
@property
|
||
|
def coherence(self) -> float:
|
||
|
if not self._languages:
|
||
|
return 0.0
|
||
|
return self._languages[0][1]
|
||
|
|
||
|
@property
|
||
|
def percent_chaos(self) -> float:
|
||
|
return round(self.chaos * 100, ndigits=3)
|
||
|
|
||
|
@property
|
||
|
def percent_coherence(self) -> float:
|
||
|
return round(self.coherence * 100, ndigits=3)
|
||
|
|
||
|
@property
|
||
|
def raw(self) -> bytes:
|
||
|
"""
|
||
|
Original untouched bytes.
|
||
|
"""
|
||
|
return self._payload
|
||
|
|
||
|
@property
|
||
|
def submatch(self) -> List["CharsetMatch"]:
|
||
|
return self._leaves
|
||
|
|
||
|
@property
|
||
|
def has_submatch(self) -> bool:
|
||
|
return len(self._leaves) > 0
|
||
|
|
||
|
@property
|
||
|
def alphabets(self) -> List[str]:
|
||
|
if self._unicode_ranges is not None:
|
||
|
return self._unicode_ranges
|
||
|
# list detected ranges
|
||
|
detected_ranges: List[Optional[str]] = [
|
||
|
unicode_range(char) for char in str(self)
|
||
|
]
|
||
|
# filter and sort
|
||
|
self._unicode_ranges = sorted(list({r for r in detected_ranges if r}))
|
||
|
return self._unicode_ranges
|
||
|
|
||
|
@property
|
||
|
def could_be_from_charset(self) -> List[str]:
|
||
|
"""
|
||
|
The complete list of encoding that output the exact SAME str result and therefore could be the originating
|
||
|
encoding.
|
||
|
This list does include the encoding available in property 'encoding'.
|
||
|
"""
|
||
|
return [self._encoding] + [m.encoding for m in self._leaves]
|
||
|
|
||
|
def output(self, encoding: str = "utf_8") -> bytes:
|
||
|
"""
|
||
|
Method to get re-encoded bytes payload using given target encoding. Default to UTF-8.
|
||
|
Any errors will be simply ignored by the encoder NOT replaced.
|
||
|
"""
|
||
|
if self._output_encoding is None or self._output_encoding != encoding:
|
||
|
self._output_encoding = encoding
|
||
|
self._output_payload = str(self).encode(encoding, "replace")
|
||
|
|
||
|
return self._output_payload # type: ignore
|
||
|
|
||
|
@property
|
||
|
def fingerprint(self) -> str:
|
||
|
"""
|
||
|
Retrieve the unique SHA256 computed using the transformed (re-encoded) payload. Not the original one.
|
||
|
"""
|
||
|
return sha256(self.output()).hexdigest()
|
||
|
|
||
|
|
||
|
class CharsetMatches:
|
||
|
"""
|
||
|
Container with every CharsetMatch items ordered by default from most probable to the less one.
|
||
|
Act like a list(iterable) but does not implements all related methods.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, results: Optional[List[CharsetMatch]] = None):
|
||
|
self._results: List[CharsetMatch] = sorted(results) if results else []
|
||
|
|
||
|
def __iter__(self) -> Iterator[CharsetMatch]:
|
||
|
yield from self._results
|
||
|
|
||
|
def __getitem__(self, item: Union[int, str]) -> CharsetMatch:
|
||
|
"""
|
||
|
Retrieve a single item either by its position or encoding name (alias may be used here).
|
||
|
Raise KeyError upon invalid index or encoding not present in results.
|
||
|
"""
|
||
|
if isinstance(item, int):
|
||
|
return self._results[item]
|
||
|
if isinstance(item, str):
|
||
|
item = iana_name(item, False)
|
||
|
for result in self._results:
|
||
|
if item in result.could_be_from_charset:
|
||
|
return result
|
||
|
raise KeyError
|
||
|
|
||
|
def __len__(self) -> int:
|
||
|
return len(self._results)
|
||
|
|
||
|
def __bool__(self) -> bool:
|
||
|
return len(self._results) > 0
|
||
|
|
||
|
def append(self, item: CharsetMatch) -> None:
|
||
|
"""
|
||
|
Insert a single match. Will be inserted accordingly to preserve sort.
|
||
|
Can be inserted as a submatch.
|
||
|
"""
|
||
|
if not isinstance(item, CharsetMatch):
|
||
|
raise ValueError(
|
||
|
"Cannot append instance '{}' to CharsetMatches".format(
|
||
|
str(item.__class__)
|
||
|
)
|
||
|
)
|
||
|
# We should disable the submatch factoring when the input file is too heavy (conserve RAM usage)
|
||
|
if len(item.raw) <= TOO_BIG_SEQUENCE:
|
||
|
for match in self._results:
|
||
|
if match.fingerprint == item.fingerprint and match.chaos == item.chaos:
|
||
|
match.add_submatch(item)
|
||
|
return
|
||
|
self._results.append(item)
|
||
|
self._results = sorted(self._results)
|
||
|
|
||
|
def best(self) -> Optional["CharsetMatch"]:
|
||
|
"""
|
||
|
Simply return the first match. Strict equivalent to matches[0].
|
||
|
"""
|
||
|
if not self._results:
|
||
|
return None
|
||
|
return self._results[0]
|
||
|
|
||
|
def first(self) -> Optional["CharsetMatch"]:
|
||
|
"""
|
||
|
Redundant method, call the method best(). Kept for BC reasons.
|
||
|
"""
|
||
|
return self.best()
|
||
|
|
||
|
|
||
|
CoherenceMatch = Tuple[str, float]
|
||
|
CoherenceMatches = List[CoherenceMatch]
|
||
|
|
||
|
|
||
|
class CliDetectionResult:
|
||
|
def __init__(
|
||
|
self,
|
||
|
path: str,
|
||
|
encoding: Optional[str],
|
||
|
encoding_aliases: List[str],
|
||
|
alternative_encodings: List[str],
|
||
|
language: str,
|
||
|
alphabets: List[str],
|
||
|
has_sig_or_bom: bool,
|
||
|
chaos: float,
|
||
|
coherence: float,
|
||
|
unicode_path: Optional[str],
|
||
|
is_preferred: bool,
|
||
|
):
|
||
|
self.path: str = path
|
||
|
self.unicode_path: Optional[str] = unicode_path
|
||
|
self.encoding: Optional[str] = encoding
|
||
|
self.encoding_aliases: List[str] = encoding_aliases
|
||
|
self.alternative_encodings: List[str] = alternative_encodings
|
||
|
self.language: str = language
|
||
|
self.alphabets: List[str] = alphabets
|
||
|
self.has_sig_or_bom: bool = has_sig_or_bom
|
||
|
self.chaos: float = chaos
|
||
|
self.coherence: float = coherence
|
||
|
self.is_preferred: bool = is_preferred
|
||
|
|
||
|
@property
|
||
|
def __dict__(self) -> Dict[str, Any]: # type: ignore
|
||
|
return {
|
||
|
"path": self.path,
|
||
|
"encoding": self.encoding,
|
||
|
"encoding_aliases": self.encoding_aliases,
|
||
|
"alternative_encodings": self.alternative_encodings,
|
||
|
"language": self.language,
|
||
|
"alphabets": self.alphabets,
|
||
|
"has_sig_or_bom": self.has_sig_or_bom,
|
||
|
"chaos": self.chaos,
|
||
|
"coherence": self.coherence,
|
||
|
"unicode_path": self.unicode_path,
|
||
|
"is_preferred": self.is_preferred,
|
||
|
}
|
||
|
|
||
|
def to_json(self) -> str:
|
||
|
return dumps(self.__dict__, ensure_ascii=True, indent=4)
|