"""__init__.pyi - Type stubs for the netCDF4 Python package""" # Notes: # # - The stubs in this file are manually-generated and must be updated if and when the API is changed. # - The following **ruff** commands may be used to format this file according to # https://typing.readthedocs.io/en/latest/source/stubs.html # # ruff format --line-length 130 src/netCDF4/__init__.pyi # format code # ruff check --line-length 130 --select I --fix src/netCDF4/__init__.pyi # sort imports # # - The Variable class is a generic and may thus be statically typed, but this has limited utility for the following reasons: # - The return type of `Variable.__getitem__()` (and `Variable.getValue()`) depends on a number of factors (e.g. variable # shape, key shape, whether masking is enabled) that cannot be easily determined statically. # - Similarly, the types and shapes of data that `Variable.__setitem__()` may accept varies widely depending on many factors # and is intractable to determine statically. # - Some facility for automatically typing a Variable on creation has been provided, however it is not exhaustive as a variable # may created with a string literal indicating its type and it would require an excessive number of overloads to enumerate # each of these cases. # - It is not possible to statically type a Variable of any user-defined type (CompoundType, EnumType, VLType) as these types # are created dynamically. # Thus it is most often best for the user to implement TypeGuards and/or perform other mixed static/runtime type-checking to # ensure the type and shape of data retrieved from this library. # - The return type of some functions or properties (such as `Dataset.__getitem__()`) may one of a number of types. Where it is # not possible to narrow the type with overloads, the authors of these stubs have generally chosen to use `Any` as the return # type rather than a union of the possible types. # - `MFDataset.dimensions` returns `dict[str, Dimension]` and `MFDataset.variables` returns `dict[str, Variable]` even though the # dict value types may actually be `_Dimension` and `_Variable`, respectively. The original authors of this stubfile have # elected to do this for simplicity's sake, but it may make sense to change this in the future, or just return `dict[str, Any]`. import datetime as dt import os import sys from typing import ( TYPE_CHECKING, Any, Callable, final, Final, Generic, Iterable, Iterator, Literal, Mapping, NoReturn, Sequence, TypedDict, TypeVar, Union, overload, ) import cftime import numpy as np import numpy.typing as npt from typing_extensions import Buffer, Self, TypeAlias, disjoint_base __all__ = [ "Dataset", "Variable", "Dimension", "Group", "MFDataset", "MFTime", "CompoundType", "VLType", "date2num", "num2date", "rc_get", "rc_set", "date2index", "stringtochar", "chartostring", "stringtoarr", "getlibversion", "EnumType", "get_chunk_cache", "set_chunk_cache", "set_alignment", "get_alignment", ] __pdoc__ = {"utils": False} # string type specifiers # fmt: off RealTypeLiteral: TypeAlias = Literal[ "i1", "b", "B", "int8", # NC_BYTE "u1", "uint8", # NC_UBYTE "i2", "h", "s", "int16", # NC_SHORT "u2", "uint16", # NC_USHORT "i4", "i", "l", "int32", # NC_INT "u4", "uint32", # NC_UINT "i8", "int64", "int", # NC_INT64 "u8", "uint64", # NC_UINT64 "f4", "f", "float32", # NC_FLOAT "f8", "d", "float64", "float" # NC_DOUBLE ] # fmt: on ComplexTypeLiteral: TypeAlias = Literal["c8", "c16", "complex64", "complex128"] NumericTypeLiteral: TypeAlias = RealTypeLiteral | ComplexTypeLiteral CharTypeLiteral: TypeAlias = Literal["S1", "c"] # NC_CHAR TypeLiteral: TypeAlias = NumericTypeLiteral | CharTypeLiteral # Numpy types NumPyRealType: TypeAlias = ( np.int8 | np.uint8 | np.int16 | np.uint16 | np.int32 | np.uint32 | np.int64 | np.uint64 | np.float16 | np.float32 | np.float64 ) NumPyComplexType: TypeAlias = np.complex64 | np.complex128 NumPyNumericType: TypeAlias = NumPyRealType | NumPyComplexType # Classes that can create instances of NetCDF user-defined types NetCDFUDTClass: TypeAlias = CompoundType | VLType | EnumType # Possible argument types for the datatype argument used in Variable creation. At this time, it is not possible to allow unknown # strings arguments in the datatype field but exclude and string literals that are not one of `TypeLiteral`, so really # `TypeLiteral` is made irrelevant, except for anyone who looks at this file. DatatypeType: TypeAlias = ( TypeLiteral | str | np.dtype[NumPyNumericType | np.str_] | type[int | float | NumPyNumericType | str | np.str_] | NetCDFUDTClass ) VarT = TypeVar("VarT") RealVarT = TypeVar("RealVarT", bound=NumPyRealType) ComplexVarT = TypeVar("ComplexVarT", bound=NumPyComplexType) NumericVarT = TypeVar("NumericVarT", bound=NumPyNumericType) DimensionsType: TypeAlias = Union[str, bytes, Dimension, Iterable[Union[str, bytes, Dimension]]] CompressionType: TypeAlias = Literal[ "zlib", "szip", "zstd", "bzip2", "blosc_lz", "blosc_lz4", "blosc_lz4hc", "blosc_zlib", "blosc_zstd" ] CompressionLevel: TypeAlias = Literal[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] AccessMode: TypeAlias = Literal["r", "w", "r+", "a", "x", "rs", "ws", "r+s", "as"] Format: TypeAlias = Literal["NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_CLASSIC", "NETCDF3_64BIT_OFFSET", "NETCDF3_64BIT_DATA"] DiskFormat: TypeAlias = Literal["NETCDF3", "HDF5", "HDF4", "PNETCDF", "DAP2", "DAP4", "UNDEFINED"] QuantizeMode: TypeAlias = Literal["BitGroom", "BitRound", "GranularBitRound"] EndianType: TypeAlias = Literal["native", "little", "big"] CalendarType: TypeAlias = Literal[ "standard", "gregorian", "proleptic_gregorian", "noleap", "365_day", "360_day", "julian", "all_leap", "366_day" ] BoolInt: TypeAlias = Literal[0, 1] DateTimeArray: TypeAlias = npt.NDArray[np.object_] """numpy array of datetime.datetime or cftime.datetime""" class BloscInfo(TypedDict): compressor: Literal["blosc_lz", "blosc_lz4", "blosc_lz4hc", "blosc_zlib", "blosc_zstd"] shuffle: Literal[0, 1, 2] class SzipInfo(TypedDict): coding: Literal["nn", "ec"] pixels_per_block: Literal[4, 8, 16, 32] class FiltersDict(TypedDict): """Dict returned from netCDF4.Variable.filters()""" zlib: bool szip: Literal[False] | SzipInfo zstd: bool bzip2: bool blosc: Literal[False] | BloscInfo shuffle: bool complevel: int fletcher32: bool __version__: str __netcdf4libversion__: str __hdf5libversion__: str __has_rename_grp__: BoolInt __has_nc_inq_path__: BoolInt __has_nc_inq_format_extended__: BoolInt __has_nc_open_mem__: BoolInt __has_nc_create_mem__: BoolInt __has_cdf5_format__: BoolInt __has_parallel4_support__: BoolInt __has_pnetcdf_support__: BoolInt __has_parallel_support__: BoolInt __has_quantization_support__: BoolInt __has_zstandard_support__: BoolInt __has_bzip2_support__: BoolInt __has_blosc_support__: BoolInt __has_szip_support__: BoolInt __has_set_alignment__: BoolInt __has_ncfilter__: BoolInt __has_nc_rc_set__: BoolInt is_native_little: bool is_native_big: bool default_encoding: Final = "utf-8" unicode_error: Final = "replace" default_fillvals: dict[str, int | float | str] # date2index, date2num, and num2date are actually provided by cftime and if stubs for # cftime are completed these should be removed. def date2index( dates: dt.datetime | cftime.datetime | Sequence[dt.datetime | cftime.datetime] | DateTimeArray, nctime: Variable, calendar: CalendarType | str | None = None, select: Literal["exact", "before", "after", "nearest"] = "exact", has_year_zero: bool | None = None, ) -> int | npt.NDArray[np.int_]: ... def date2num( dates: dt.datetime | cftime.datetime | Sequence[dt.datetime | cftime.datetime] | DateTimeArray, units: str, calendar: CalendarType | str | None = None, has_year_zero: bool | None = None, longdouble: bool = False, ) -> np.number | npt.NDArray[np.number]: ... def num2date( times: Sequence[int | float | np.number] | npt.NDArray[np.number], units: str, calendar: CalendarType | str = "standard", only_use_cftime_datetimes: bool = True, only_use_python_datetimes: bool = False, has_year_zero: bool | None = None, ) -> dt.datetime | DateTimeArray: ... class NetCDF4MissingFeatureException(Exception): def __init__(self, feature: str, version: str): ... def dtype_is_complex(dtype: str) -> bool: ... @disjoint_base class Dataset: def __init__( self, filename: str | os.PathLike, mode: AccessMode = "r", clobber: bool = True, format: Format = "NETCDF4", diskless: bool = False, persist: bool = False, keepweakref: bool = False, memory: Buffer | int | None = None, encoding: str | None = None, parallel: bool = False, comm: Any = None, info: Any = None, auto_complex: bool = False, **kwargs: Any, ): ... @property def name(self) -> str: ... @property def groups(self) -> dict[str, Group]: ... @property def dimensions(self) -> dict[str, Dimension]: ... @property def variables(self) -> dict[str, Variable[Any]]: ... @property def cmptypes(self) -> dict[str, CompoundType]: ... @property def vltypes(self) -> dict[str, VLType]: ... @property def enumtypes(self) -> dict[str, EnumType]: ... @property def data_model(self) -> Format: ... @property def file_format(self) -> Format: ... @property def disk_format(self) -> DiskFormat: ... @property def parent(self) -> Dataset | None: ... @property def path(self) -> str: ... @property def keepweakref(self) -> bool: ... @property def auto_complex(self) -> bool: ... @property def _ncstring_attrs__(self) -> bool: ... @property def __orthogonal_indexing__(self) -> bool: ... def filepath(self, encoding: str | None = None) -> str: ... def isopen(self) -> bool: ... def close(self) -> memoryview: ... # only if writing and memory != None, but otherwise people ignore the return None anyway def sync(self) -> None: ... def set_fill_on(self) -> None: ... def set_fill_off(self) -> None: ... def createDimension(self, dimname: str, size: int | None = None) -> Dimension: ... def renameDimension(self, oldname: str, newname: str) -> None: ... @overload def createVariable( self, varname: str, datatype: np.dtype[NumericVarT] | type[NumericVarT], dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, ) -> Variable[NumericVarT]: ... @overload def createVariable( self, varname: str, datatype: np.dtype[np.str_] | type[str | np.str_], dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, ) -> Variable[str]: ... @overload def createVariable( self, varname: str, datatype: DatatypeType, dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, ) -> Variable: ... def renameVariable(self, oldname: str, newname: str) -> None: ... def createGroup(self, groupname: str) -> Group: ... def renameGroup(self, oldname: str, newname: str) -> None: ... def renameAttribute(self, oldname: str, newname: str) -> None: ... def createCompoundType( self, datatype: npt.DTypeLike | Sequence[tuple[str, npt.DTypeLike]], datatype_name: str ) -> CompoundType: ... def createVLType(self, datatype: npt.DTypeLike, datatype_name: str) -> VLType: ... def createEnumType( self, datatype: np.dtype[np.integer] | type[np.integer] | type[int] | str, datatype_name: str, enum_dict: Mapping[str, int | np.integer], ) -> EnumType: ... def ncattrs(self) -> list[str]: ... def setncattr_string(self, name: str, value: Any) -> None: ... def setncattr(self, name: str, value: Any) -> None: ... def setncatts(self, attdict: Mapping[str, Any]) -> None: ... def getncattr(self, name: str, encoding: str = "utf-8") -> Any: ... def delncattr(self, name: str) -> None: ... def set_auto_chartostring(self, value: bool) -> None: ... def set_auto_maskandscale(self, value: bool) -> None: ... def set_auto_mask(self, value: bool) -> None: ... def set_auto_scale(self, value: bool) -> None: ... def set_always_mask(self, value: bool) -> None: ... def set_ncstring_attrs(self, value: bool) -> None: ... def get_variables_by_attributes(self, **kwargs: Callable[[Any], bool] | Any) -> list[Variable]: ... @staticmethod def fromcdl( cdlfilename: str | os.PathLike, ncfilename: str | os.PathLike | None = None, mode: AccessMode = "a", format: Format = "NETCDF4" ) -> Dataset: ... @overload def tocdl(self, coordvars: bool = False, data: bool = False, outfile: None = None) -> str: ... @overload def tocdl(self, coordvars: bool = False, data: bool = False, *, outfile: str | os.PathLike) -> None: ... def has_blosc_filter(self) -> bool: ... def has_zstd_filter(self) -> bool: ... def has_bzip2_filter(self) -> bool: ... def has_szip_filter(self) -> bool: ... def __getitem__(self, elem: str) -> Any: ... # should be Group | Variable, but this causes too many problems # __iter__ and __contains__ always error because iteration and membership ops are not allowed def __iter__(self) -> NoReturn: ... def __contains__(self, key) -> NoReturn: ... def __setattr__(self, name: str, value: Any) -> None: ... def __getattr__(self, name: str) -> Any: ... def __delattr__(self, name: str): ... def __reduce__(self) -> NoReturn: ... def __enter__(self) -> Self: ... def __exit__(self, atype, value, traceback) -> None: ... class Group(Dataset): def __init__(self, parent: Dataset, name: str, **kwargs: Any) -> None: ... def close(self) -> NoReturn: ... @final class Dimension: def __init__(self, grp: Dataset, name: str, size: int | None = None, **kwargs: Any) -> None: ... @property def name(self) -> str: ... @property def size(self) -> int: ... def group(self) -> Dataset: ... def isunlimited(self) -> bool: ... def __len__(self) -> int: ... class _VarDatatypeProperty: # A faux descriptor definition of the property to allow overloads @overload def __get__(self, instance: Variable[RealVarT], owner: Any) -> np.dtype[RealVarT]: ... @overload def __get__(self, instance: Variable[ComplexVarT], owner: Any) -> CompoundType: ... @overload def __get__(self, instance: Variable[str], owner: Any) -> VLType: ... @overload def __get__( self, instance: Variable, owner: Any ) -> Any: ... # actual return type np.dtype | CompoundType | VLType | EnumType class _VarDtypeProperty: # A faux descriptor definition of the property to allow overloads @overload def __get__(self, instance: Variable[NumericVarT], owner: Any) -> np.dtype[NumericVarT]: ... @overload def __get__(self, instance: Variable[str], owner: Any) -> type[str]: ... @overload def __get__(self, instance: Variable, owner: Any) -> Any: ... # actual return type np.dtype | Type[str] @final class Variable(Generic[VarT]): # Overloads of __new__ are provided for some cases where the Variable's type may be statically inferred from the datatype arg @overload def __new__( cls, grp: Dataset, name: str, datatype: np.dtype[NumericVarT] | type[NumericVarT], dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, **kwargs: Any, ) -> Variable[NumericVarT]: ... @overload def __new__( cls, grp: Dataset, name: str, datatype: np.dtype[np.str_] | type[str | np.str_], dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, **kwargs: Any, ) -> Variable[str]: ... @overload def __new__( cls, grp: Dataset, name: str, datatype: DatatypeType, dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, **kwargs: Any, ) -> Variable: ... def __init__( self, grp: Dataset, name: str, datatype: DatatypeType, dimensions: DimensionsType = (), compression: CompressionType | None = None, zlib: bool = False, complevel: CompressionLevel | None = 4, shuffle: bool = True, szip_coding: Literal["nn", "ec"] = "nn", szip_pixels_per_block: Literal[4, 8, 16, 32] = 8, blosc_shuffle: Literal[0, 1, 2] = 1, fletcher32: bool = False, contiguous: bool = False, chunksizes: Sequence[int] | None = None, endian: EndianType = "native", least_significant_digit: int | None = None, significant_digits: int | None = None, quantize_mode: QuantizeMode = "BitGroom", fill_value: int | float | np.generic | str | bytes | Literal[False] | np.ndarray | None = None, chunk_cache: int | None = None, **kwargs: Any, ) -> None: ... datatype: _VarDatatypeProperty dtype: _VarDtypeProperty @property def name(self) -> str: ... @property def shape(self) -> tuple[int, ...]: ... @property def size(self) -> int: ... @property def dimensions(self) -> tuple[str, ...]: ... @property def ndim(self) -> int: ... @property def scale(self) -> bool: ... @property def mask(self) -> bool: ... @property def chartostring(self) -> bool: ... @property def always_mask(self) -> bool: ... @property def __orthogonal_indexing__(self) -> bool: ... def group(self) -> Dataset: ... def ncattrs(self) -> list[str]: ... def setncattr(self, name: str, value: Any) -> None: ... def setncattr_string(self, name: str, value: Any) -> None: ... def setncatts(self, attdict: Mapping[str, Any]) -> None: ... def getncattr(self, name: str, encoding="utf-8"): ... def delncattr(self, name: str) -> None: ... def filters(self) -> FiltersDict: ... def quantization(self) -> tuple[int, QuantizeMode] | None: ... def endian(self) -> EndianType: ... def chunking(self) -> Literal["contiguous"] | list[int]: ... def get_var_chunk_cache(self) -> tuple[int, int, float]: ... def set_var_chunk_cache( self, size: int | None = None, nelems: int | None = None, preemption: float | None = None ) -> None: ... def renameAttribute(self, oldname: str, newname: str) -> None: ... def assignValue(self, val: Any) -> None: ... def getValue(self) -> Any: ... def get_fill_value(self) -> Any: ... def set_auto_chartostring(self, chartostring: bool) -> None: ... def use_nc_get_vars(self, use_nc_get_vars: bool) -> None: ... def set_auto_maskandscale(self, maskandscale: bool) -> None: ... def set_auto_scale(self, scale: bool) -> None: ... def set_auto_mask(self, mask: bool) -> None: ... def set_always_mask(self, always_mask: bool) -> None: ... def set_ncstring_attrs(self, ncstring_attrs: bool) -> None: ... def set_collective(self, value: bool) -> None: ... def get_dims(self) -> tuple[Dimension, ...]: ... def __delattr__(self, name: str) -> None: ... def __setattr__(self, name: str, value: Any) -> None: ... def __getattr__(self, name: str) -> Any: ... def __getitem__(self, elem: Any) -> Any: ... def __setitem__(self, elem: Any, data: npt.ArrayLike) -> None: ... def __array__(self) -> np.ndarray: ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[Any]: ... # faux method so mypy believes Variable is iterable @final class CompoundType: dtype: np.dtype dtype_view: np.dtype name: str def __init__( self, grp: Dataset, dt: npt.DTypeLike | Sequence[tuple[str, npt.DTypeLike]], dtype_name: str, **kwargs: Any ) -> None: ... def __reduce__(self) -> NoReturn: ... @final class VLType: dtype: np.dtype name: str | None def __init__(self, grp: Dataset, dt: npt.DTypeLike, dtype_name: str, **kwargs: Any) -> None: ... def __reduce__(self) -> NoReturn: ... @final class EnumType: dtype: np.dtype[np.integer] name: str enum_dict: Mapping[str, int] def __init__( self, grp: Dataset, dt: np.dtype[np.integer] | type[np.integer] | type[int] | str, dtype_name: str, enum_dict: Mapping[str, int | np.integer], **kwargs: Any, ) -> None: ... def __reduce__(self) -> NoReturn: ... class MFDataset(Dataset): def __init__( self, files: str | Sequence[str | os.PathLike], check: bool = False, aggdim: str | None = None, exclude: Sequence[str] = [], master_file: str | os.PathLike | None = None, ) -> None: ... @property def dimensions(self) -> dict[str, Dimension]: ... # this should be: dict[str, Dimension | _Dimension] @property def variables(self) -> dict[str, Variable[Any]]: ... # this should be: dict[str, _Variable[Any] | _Variable] class _Dimension: dimlens: list[int] dimtolen: int def __init__(self, dimname: str, dim: Dimension, dimlens: list[int], dimtotlen: int) -> None: ... def __len__(self) -> int: ... def isunlimited(self) -> Literal[True]: ... class _Variable: dimensions: tuple[str, ...] dtype: np.dtype | type[str] def __init__(self, dset: Dataset, varname: str, var: Variable[Any], recdimname: str) -> None: ... # shape, ndim, and name actually come from __getattr__ @property def shape(self) -> tuple[int, ...]: ... @property def ndim(self) -> int: ... @property def name(self) -> str: ... def typecode(self) -> np.dtype | type[str]: ... def ncattrs(self) -> list[str]: ... def _shape(self) -> tuple[int, ...]: ... def set_auto_chartostring(self, val: bool) -> None: ... def set_auto_maskandscale(self, val: bool) -> None: ... def set_auto_mask(self, val: bool) -> None: ... def set_auto_scale(self, val: bool) -> None: ... def set_always_mask(self, val: bool) -> None: ... def __getattr__(self, name: str) -> Any: ... def __getitem__(self, elem: Any) -> Any: ... def __len__(self) -> int: ... class MFTime(_Variable): calendar: CalendarType | None units: str | None def __init__(self, time: Variable, units: str | None = None, calendar: CalendarType | str | None = None): ... def __getitem__(self, elem: Any) -> np.ndarray: ... @overload def stringtoarr( string: str, NUMCHARS: int, dtype: Literal["S"] | np.dtype[np.bytes_] = "S", ) -> npt.NDArray[np.bytes_]: ... @overload def stringtoarr( string: str, NUMCHARS: int, dtype: Literal["U"] | np.dtype[np.str_], ) -> npt.NDArray[np.str_]: ... @overload def stringtochar( a: npt.NDArray[np.character], encoding: Literal["none", "None", "bytes"], n_strlen: int | None = None, ) -> npt.NDArray[np.bytes_]: ... @overload def stringtochar( a: npt.NDArray[np.character], encoding: str = ..., ) -> npt.NDArray[np.str_] | npt.NDArray[np.bytes_]: ... @overload def chartostring( b: npt.NDArray[np.character], encoding: Literal["none", "None", "bytes"] = ..., ) -> npt.NDArray[np.bytes_]: ... @overload def chartostring( b: npt.NDArray[np.character], encoding: str = ..., ) -> npt.NDArray[np.str_] | npt.NDArray[np.bytes_]: ... def getlibversion() -> str: ... def rc_get(key: str) -> str | None: ... def rc_set(key: str, value: str) -> None: ... def set_alignment(threshold: int, alignment: int): ... def get_alignment() -> tuple[int, int]: ... def set_chunk_cache(size: int | None = None, nelems: int | None = None, preemption: float | None = None) -> None: ... def get_chunk_cache() -> tuple[int, int, float]: ...