"""Atom classes for describing dataset contents.""" from __future__ import annotations import re import pickle import inspect import warnings from typing import Any, NoReturn, dataclass_transform from collections.abc import Callable import numpy as np from numpy.typing import DTypeLike from .utils import SizeType from .misc.enum import Enum from .exceptions import FlavorWarning Shape = tuple[SizeType, ...] __docformat__ = "reStructuredText" """The format of documentation strings in this module.""" all_types = set() # filled as atom classes are created """Set of all PyTables types.""" atom_map: dict[str, Any] = {} # filled as atom classes are created """Maps atom kinds to item sizes and atom classes. If there is a fixed set of possible item sizes for a given kind, the kind maps to another mapping from item size in bytes to atom class. Otherwise, the kind maps directly to the atom class. """ deftype_from_kind = {} # filled as atom classes are created """Maps atom kinds to their default atom type (if any).""" _type_re = re.compile(r"^([a-z]+)([0-9]*)$") def split_type(type_: str) -> tuple[str, int | None]: """Split a PyTables type into a PyTables kind and an item size. Returns a tuple of (kind, itemsize). If no item size is present in the type (in the form of a precision), the returned item size is None:: >>> split_type('int32') ('int', 4) >>> split_type('string') ('string', None) >>> split_type('int20') Traceback (most recent call last): ... ValueError: precision must be a multiple of 8: 20 >>> split_type('foo bar') Traceback (most recent call last): ... ValueError: malformed type: 'foo bar' """ match = _type_re.match(type_) if not match: raise ValueError("malformed type: %r" % type_) kind, precision = match.groups() itemsize = None if precision: precision = int(precision) itemsize, remainder = divmod(precision, 8) if remainder: # 0 could be a valid item size raise ValueError( "precision must be a multiple of 8: %d" % precision ) return (kind, itemsize) def _invalid_itemsize_error( kind: str, itemsize: int, itemsizes: list[int] ) -> ValueError: isizes = sorted(itemsizes) return ValueError( "invalid item size for kind ``%s``: %r; " "it must be one of ``%r``" % (kind, itemsize, isizes) ) def _normalize_shape(shape: Shape | np.integer | int) -> Shape: """Check that the `shape` is safe to be used and return it as a tuple.""" if isinstance(shape, (np.integer, int)): if shape < 1: raise ValueError("shape value must be greater than 0: %d" % shape) shape = (shape,) # N is a shorthand for (N,) try: shape = tuple(shape) except TypeError: raise TypeError(f"shape must be an integer or sequence: {shape!r}") # XXX Get from HDF5 library if possible. # HDF5 does not support ranks greater than 32 if len(shape) > 32: raise ValueError(f"shapes with rank > 32 are not supported: {shape!r}") return tuple(SizeType(s) for s in shape) def _normalize_default(value: Any, dtype: DTypeLike) -> np.ndarray: """Return `value` as a valid default of NumPy type `dtype`.""" # Create NumPy objects as defaults # This is better in order to serialize them as attributes if value is None: value = 0 basedtype = dtype.base try: default = np.array(value, dtype=basedtype) except ValueError: array = np.array(value) if array.shape != basedtype.shape: raise # Maybe nested dtype with "scalar" value. default = np.array(value, dtype=basedtype.base) # 0-dim arrays will be representented as NumPy scalars # (PyTables attribute convention) if default.shape == (): default = default[()] return default def _cmp_dispatcher(other_method_name: str) -> Callable[[Any, Any], bool]: """Dispatch comparisons to a method of the *other* object. Returns a new *rich comparison* method which dispatches calls to the method `other_method_name` of the *other* object. If there is no such method in the object, ``False`` is returned. This is part of the implementation of a double dispatch pattern. """ def dispatched_cmp(self, other) -> bool: try: other_method: Callable[[Any], bool] = getattr( other, other_method_name ) except AttributeError: return False return other_method(self) return dispatched_cmp @dataclass_transform() class MetaAtom(type): """Atom metaclass. This metaclass ensures that data about atom classes gets inserted into the suitable registries. """ kind: str def __init__(cls, name: str, bases: tuple, dict_: dict[str, Any]) -> None: super().__init__(name, bases, dict_) kind = dict_.get("kind") itemsize = dict_.get("itemsize") type_ = dict_.get("type") deftype = dict_.get("_deftype") if kind and deftype: deftype_from_kind[kind] = deftype if type_: all_types.add(type_) if kind and itemsize and not hasattr(itemsize, "__int__"): # Atom classes with a non-fixed item size do have an # ``itemsize``, but it's not a number (e.g. property). atom_map[kind] = cls return if kind: # first definition of kind, make new entry atom_map[kind] = {} if itemsize and hasattr(itemsize, "__int__"): # fixed kind = cls.kind # maybe from superclasses atom_map[kind][int(itemsize)] = cls class Atom(metaclass=MetaAtom): """Defines the type of atomic cells stored in a dataset. The meaning of *atomic* is that individual elements of a cell can not be extracted directly by indexing (i.e. __getitem__()) the dataset; e.g. if a dataset has shape (2, 2) and its atoms have shape (3,), to get the third element of the cell at (1, 0) one should use dataset[1,0][2] instead of dataset[1,0,2]. The Atom class is meant to declare the different properties of the *base element* (also known as *atom*) of CArray, EArray and VLArray datasets, although they are also used to describe the base elements of Array datasets. Atoms have the property that their length is always the same. However, you can grow datasets along the extensible dimension in the case of EArray or put a variable number of them on a VLArray row. Moreover, they are not restricted to scalar values, and they can be *fully multidimensional objects*. Parameters ---------- nptype : str or np.dtype Sets the Numpy data type of the atom. shape : tuple Sets the shape of the atom. An integer shape of N is equivalent to the tuple (N,). dflt : Any Sets the default value for the atom. The following are the public methods and attributes of the Atom class. Notes ----- A series of descendant classes are offered in order to make the use of these element descriptions easier. You should use a particular Atom descendant class whenever you know the exact type you will need when writing your code. Otherwise, you may use one of the Atom.from_*() factory Methods. .. rubric:: Atom attributes .. attribute:: dflt The default value of the atom. If the user does not supply a value for an element while filling a dataset, this default value will be written to disk. If the user supplies a scalar value for a multidimensional atom, this value is automatically *broadcast* to all the items in the atom cell. If dflt is not supplied, an appropriate zero value (or *null* string) will be chosen by default. Please note that default values are kept internally as NumPy objects. .. attribute:: dtype The NumPy dtype that most closely matches this atom. .. attribute:: itemsize Size in bytes of a single item in the atom. Specially useful for atoms of the string kind. .. attribute:: kind The PyTables kind of the atom (a string). .. attribute:: shape The shape of the atom (a tuple for scalar atoms). .. attribute:: type The PyTables type of the atom (a string). Atoms can be compared with atoms and other objects for strict (in)equality without having to compare individual attributes:: >>> atom1 = StringAtom(itemsize=10) # same as ``atom2`` >>> atom2 = Atom.from_kind('string', 10) # same as ``atom1`` >>> atom3 = IntAtom() >>> bool(atom1 == 'foo') False >>> bool(atom1 == atom2) True >>> bool(atom2 != atom1) False >>> bool(atom1 == atom3) False >>> bool(atom3 != atom2) True """ dflt: Any dtype: np.dtype itemsize: int kind: str shape: Shape type: str # noqa: A003 @classmethod def prefix(cls) -> str: """Return the atom class prefix.""" cname = cls.__name__ return cname[: cname.rfind("Atom")] @classmethod def from_sctype( cls, sctype: str | np.dtype, shape: Shape = (), dflt: Any = None ) -> Atom: """Create an Atom from a NumPy scalar type sctype. Optional shape and default value may be specified as the shape and dflt arguments, respectively. Information in the sctype not represented in an Atom is ignored:: >>> import numpy as np >>> Atom.from_sctype(np.int16, shape=(2, 2)) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_sctype('S5', dflt='hello') Traceback (most recent call last): ... ValueError: unknown NumPy scalar type: 'S5' >>> Atom.from_sctype('float64') Float64Atom(shape=(), dflt=0.0) """ if not isinstance(sctype, type) or not issubclass(sctype, np.generic): assert isinstance(sctype, str) if "," in sctype: raise ValueError(f"unknown NumPy scalar type: {sctype!r}") try: dtype = np.dtype(sctype) except TypeError: raise ValueError( f"unknown NumPy scalar type: {sctype!r}" ) from None if issubclass(dtype.type, np.flexible) and dtype.itemsize > 0: raise ValueError(f"unknown NumPy scalar type: {sctype!r}") sctype_resolved = dtype.type else: sctype_resolved = sctype return cls.from_dtype(np.dtype((sctype_resolved, shape)), dflt) @classmethod def from_dtype(cls, dtype: np.dtype, dflt: Any = None) -> Atom: """Create an Atom from a NumPy dtype. An optional default value may be specified as the dflt argument. Information in the dtype not represented in an Atom is ignored:: >>> import numpy as np >>> Atom.from_dtype(np.dtype((np.int16, (2, 2)))) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_dtype(np.dtype('float64')) Float64Atom(shape=(), dflt=0.0) Note: for easier use in Python 3, where all strings lead to the Unicode dtype, this dtype will also generate a StringAtom. Since this is only viable for strings that are castable as ascii, a warning is issued. >>> Atom.from_dtype(np.dtype('U20')) # doctest: +SKIP Atom.py:392: FlavorWarning: support for unicode type is very limited, and only works for strings that can be cast as ascii StringAtom(itemsize=20, shape=(), dflt=b'') """ basedtype = dtype.base shape = tuple(SizeType(i) for i in dtype.shape) if basedtype.names: raise ValueError( "compound data types are not supported: %r" % dtype ) if basedtype.shape != (): raise ValueError("nested data types are not supported: %r" % dtype) if basedtype.kind == "S": # can not reuse something like 'string80' itemsize = basedtype.itemsize return cls.from_kind("string", itemsize, shape, dflt) elif basedtype.kind == "U": # workaround for unicode type (standard string type in Python 3) warnings.warn( "support for unicode type is very limited, and " "only works for strings that can be cast as ascii", FlavorWarning, ) itemsize = basedtype.itemsize // 4 assert ( str(itemsize) in basedtype.str ), "something went wrong in handling unicode." return cls.from_kind("string", itemsize, shape, dflt) # Most NumPy types have direct correspondence with PyTables types. return cls.from_type(basedtype.name, shape, dflt) @classmethod def from_type( cls, type_: str, shape: Shape = (), dflt: Any = None ) -> Atom: """Create an Atom from a PyTables type. Optional shape and default value may be specified as the shape and dflt arguments, respectively:: >>> Atom.from_type('bool') BoolAtom(shape=(), dflt=False) >>> Atom.from_type('int16', shape=(2, 2)) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_type('string40', dflt='hello') Traceback (most recent call last): ... ValueError: unknown type: 'string40' >>> Atom.from_type('Float64') Traceback (most recent call last): ... ValueError: unknown type: 'Float64' """ if type_ not in all_types: raise ValueError(f"unknown type: {type_!r}") kind, itemsize = split_type(type_) return cls.from_kind(kind, itemsize, shape, dflt) @classmethod def from_kind( cls, kind: str, itemsize: int | None = None, shape: Shape = (), dflt: Any = None, ) -> Atom: """Create an Atom from a PyTables kind. Optional item size, shape and default value may be specified as the itemsize, shape and dflt arguments, respectively. Bear in mind that not all atoms support a default item size:: >>> Atom.from_kind('int', itemsize=2, shape=(2, 2)) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_kind('int', shape=(2, 2)) Int32Atom(shape=(2, 2), dflt=0) >>> Atom.from_kind('int', shape=1) Int32Atom(shape=(1,), dflt=0) >>> Atom.from_kind('string', dflt=b'hello') Traceback (most recent call last): ... ValueError: no default item size for kind ``string`` >>> Atom.from_kind('Float') Traceback (most recent call last): ... ValueError: unknown kind: 'Float' Moreover, some kinds with atypical constructor signatures are not supported; you need to use the proper constructor:: >>> Atom.from_kind('enum') #doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: the ``enum`` kind is not supported... """ kwargs: dict[str, Any] = {"shape": shape} if kind not in atom_map: raise ValueError(f"unknown kind: {kind!r}") # This incompatibility detection may get out-of-date and is # too hard-wired, but I couldn't come up with something # smarter. -- Ivan (2007-02-08) if kind in ["enum"]: raise ValueError( "the ``%s`` kind is not supported; " "please use the appropriate constructor" % kind ) # If no `itemsize` is given, try to get the default type of the # kind (which has a fixed item size). if itemsize is None: if kind not in deftype_from_kind: raise ValueError("no default item size for kind ``%s``" % kind) type_ = deftype_from_kind[kind] kind, itemsize = split_type(type_) kdata = atom_map[kind] # Look up the class and set a possible item size. if hasattr(kdata, "kind"): # atom class: non-fixed item size atomclass = kdata kwargs["itemsize"] = itemsize else: # dictionary: fixed item size if itemsize not in kdata: raise _invalid_itemsize_error(kind, itemsize, kdata) atomclass = kdata[itemsize] # Only set a `dflt` argument if given (`None` may not be understood). if dflt is not None: kwargs["dflt"] = dflt return atomclass(**kwargs) @property def size(self) -> int: """Total size in bytes of the atom.""" return self.dtype.itemsize @property def recarrtype(self) -> str: """Return the string type to be used in `numpy.rec.array()`.""" return str(self.dtype.shape) + self.dtype.base.str[1:] @property def ndim(self) -> int: """Return the number of dimensions of the atom. .. versionadded:: 2.4 """ return len(self.shape) def __init__( self, nptype: str | np.dtype, shape: Shape, dflt: Any ) -> None: if not hasattr(self, "type"): raise NotImplementedError( f"``{self.__class__.__name__}`` is an abstract class; " f"please use one of its subclasses" ) self.shape = shape = _normalize_shape(shape) """The shape of the atom (a tuple for scalar atoms).""" # Curiously enough, NumPy isn't generally able to accept NumPy # integers in a shape. ;( npshape = tuple(int(s) for s in shape) self.dtype = dtype = np.dtype((nptype, npshape)) """The NumPy dtype that most closely matches this atom.""" self.dflt = _normalize_default(dflt, dtype) """The default value of the atom. If the user does not supply a value for an element while filling a dataset, this default value will be written to disk. If the user supplies a scalar value for a multidimensional atom, this value is automatically *broadcast* to all the items in the atom cell. If dflt is not supplied, an appropriate zero value (or *null* string) will be chosen by default. Please note that default values are kept internally as NumPy objects.""" def __repr__(self) -> str: args = f"shape={self.shape}, dflt={self.dflt!r}" if not hasattr(self.__class__.itemsize, "__int__"): # non-fixed args = f"itemsize={self.itemsize}, {args}" return f"{self.__class__.__name__}({args})" __eq__ = _cmp_dispatcher("_is_equal_to_atom") def __ne__(self, other: Atom) -> bool: return not self.__eq__(other) # XXX: API incompatible change for PyTables 3 line # Overriding __eq__ blocks inheritance of __hash__ in 3.x # def __hash__(self): # return hash((self.__class__, self.type, self.shape, self.itemsize, # self.dflt)) def copy(self, **override) -> Atom: """Get a copy of the atom, possibly overriding some arguments. Constructor arguments to be overridden must be passed as keyword arguments:: >>> atom1 = Int32Atom(shape=12) >>> atom2 = atom1.copy() >>> print(atom1) Int32Atom(shape=(12,), dflt=0) >>> print(atom2) Int32Atom(shape=(12,), dflt=0) >>> atom1 is atom2 False >>> atom3 = atom1.copy(shape=(2, 2)) >>> print(atom3) Int32Atom(shape=(2, 2), dflt=0) >>> atom1.copy(foobar=42) #doctest: +ELLIPSIS Traceback (most recent call last): ... TypeError: ...__init__() got an unexpected keyword argument ... """ newargs = self._get_init_args() newargs.update(override) return self.__class__(**newargs) def _get_init_args(self) -> dict[str, Any]: """Get a dictionary of instance constructor arguments. This implementation works on classes which use the same names for both constructor arguments and instance attributes. """ signature = inspect.signature(self.__init__) parameters = signature.parameters args = [ arg for arg, p in parameters.items() if p.kind is p.POSITIONAL_OR_KEYWORD ] return {arg: getattr(self, arg) for arg in args if arg != "self"} def _is_equal_to_atom(self, atom: Atom) -> bool: """Return True if the object is equal to the given `atom`.""" return ( self.type == atom.type and self.shape == atom.shape and self.itemsize == atom.itemsize and np.all(self.dflt == atom.dflt) ) def _abstract_atom_init( deftype: str, defvalue: Any ) -> Callable[[Atom, int | None, Shape, Any], None]: """Return a constructor for an abstract `Atom` class.""" defitemsize = split_type(deftype)[1] def __init__( # noqa: N807 self: Atom, itemsize: int | None = defitemsize, shape: Shape = (), dflt: Any = defvalue, ) -> None: assert self.kind in atom_map try: atomclass = atom_map[self.kind][itemsize] except KeyError: raise _invalid_itemsize_error( self.kind, itemsize, atom_map[self.kind] ) self.__class__ = atomclass atomclass.__init__(self, shape, dflt) return __init__ class StringAtom(Atom): # type: ignore[misc] """Defines an atom of type string. The item size is the *maximum* length in characters of strings. """ kind: str = "string" type: str = "string" # noqa: A003 _defvalue: bytes = b"" @property # type: ignore[misc] def itemsize(self) -> int: # type: ignore[override] """Size in bytes of a sigle item in the atom.""" return self.dtype.base.itemsize def __init__( self, itemsize: int, shape: Shape = (), dflt: str | bytes = _defvalue ) -> None: if not hasattr(itemsize, "__int__") or int(itemsize) < 0: raise ValueError( f"invalid item size for kind ``string``: {itemsize!r}; " f"it must be a positive integer" ) Atom.__init__(self, f"S{itemsize}", shape, dflt) class BoolAtom(Atom): # type: ignore[misc] """Defines an atom of type bool.""" kind: str = "bool" itemsize: int = 1 type: str = "bool" # noqa: A003 _deftype = "bool8" _defvalue = False def __init__(self, shape: Shape = (), dflt: bool = _defvalue) -> None: Atom.__init__(self, self.type, shape, dflt) class IntAtom(Atom): # type: ignore[misc] """Defines an atom of a signed integral type (int kind).""" kind: str = "int" signed: bool = True _deftype = "int32" _defvalue = 0 __init__ = _abstract_atom_init( _deftype, _defvalue ) # type: ignore[assignment] class UIntAtom(Atom): # type: ignore[misc] """Defines an atom of an unsigned integral type (uint kind).""" kind: str = "uint" signed: bool = False _deftype = "uint32" _defvalue = 0 __init__ = _abstract_atom_init( _deftype, _defvalue ) # type: ignore[assignment] class FloatAtom(Atom): # type: ignore[misc] """Defines an atom of a floating point type (float kind).""" kind: str = "float" _deftype = "float64" _defvalue = 0.0 __init__ = _abstract_atom_init( _deftype, _defvalue ) # type: ignore[assignment] class Int8Atom(IntAtom): # type: ignore[misc] """Atom for 8 bit integers.""" itemsize: int = 1 type: str = "int8" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "int8", shape, dflt) class Int16Atom(IntAtom): # type: ignore[misc] """Atom for 12 bit integers.""" itemsize: int = 2 type: str = "int16" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "int16", shape, dflt) class Int32Atom(IntAtom): # type: ignore[misc] """Atom for 32 bit integers.""" itemsize: int = 4 type: str = "int32" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "int32", shape, dflt) class Int64Atom(IntAtom): # type: ignore[misc] """Atom for 64 bit integers.""" itemsize: int = 8 type: str = "int64" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "int64", shape, dflt) class UInt8Atom(UIntAtom): # type: ignore[misc] """Atom for 8 bit unsoged integers.""" itemsize: int = 1 type: str = "uint8" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "uint8", shape, dflt) class UInt16Atom(UIntAtom): # type: ignore[misc] """Atom for 16 bit unsigned integers.""" itemsize: int = 2 type: str = "uint16" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "uint16", shape, dflt) class UInt32Atom(UIntAtom): # type: ignore[misc] """Atom for 32 bit unsigned integers.""" itemsize: int = 4 type: str = "uint32" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "uint32", shape, dflt) class UInt64Atom(UIntAtom): # type: ignore[misc] """Atom for 16 bit unsigned integers.""" itemsize: int = 8 type: str = "uint64" # noqa: A003 def __init__(self, shape: Shape = (), dflt: int = 0) -> None: Atom.__init__(self, "uint64", shape, dflt) if hasattr(np, "float16"): class Float16Atom(FloatAtom): # type: ignore[misc] """FLoat 16 atom.""" itemsize: int = 2 type: str = "float16" # noqa: A003 def __init__(self, shape: Shape = (), dflt: float = 0.0) -> None: Atom.__init__(self, "float16", shape, dflt) class Float32Atom(FloatAtom): # type: ignore[misc] """Float 32 atom.""" itemsize: int = 4 type: str = "float32" # noqa: A003 def __init__(self, shape: Shape = (), dflt: float = 0.0) -> None: Atom.__init__(self, "float32", shape, dflt) class Float64Atom(FloatAtom): # type: ignore[misc] """Float 64 atom.""" itemsize: int = 8 type: str = "float64" # noqa: A003 def __init__(self, shape: Shape = (), dflt: float = 0.0) -> None: Atom.__init__(self, "float64", shape, dflt) if hasattr(np, "float96"): class Float96Atom(FloatAtom): # type: ignore[misc] """Float 96 atom.""" itemsize: int = 12 type: str = "float96" # noqa: A003 def __init__(self, shape: Shape = (), dflt: float = 0.0) -> None: Atom.__init__(self, "float96", shape, dflt) if hasattr(np, "float128"): class Float128Atom(FloatAtom): # type: ignore[misc] """Float 128 atom.""" itemsize: int = 16 type: str = "float128" # noqa: A003 def __init__(self, shape: Shape = (), dflt: float = 0.0) -> None: Atom.__init__(self, "float128", shape, dflt) class ComplexAtom(Atom): """Defines an atom of kind complex. Allowed item sizes are 8 (single precision) and 16 (double precision). This class must be used instead of more concrete ones to avoid confusions with numarray-like precision specifications used in PyTables 1.X. """ # This definition is a little more complex (no pun intended) # because, although the complex kind is a normal numerical one, # the usage of bottom-level classes is artificially forbidden. # Everything will be back to normality when people has stopped # using the old bottom-level complex classes. kind = "complex" _deftype = "complex128" _defvalue = 0j _isizes = [8, 16] @property # type: ignore[misc] def itemsize(self) -> int: # type: ignore[override] """Size in bytes of a sigle item in the atom.""" return self.dtype.base.itemsize # Only instances have a `type` attribute, so complex types must be # registered by hand. all_types.add("complex64") all_types.add("complex128") if hasattr(np, "complex192"): all_types.add("complex192") _isizes.append(24) if hasattr(np, "complex256"): all_types.add("complex256") _isizes.append(32) def __init__( self, itemsize: int, shape: Shape = (), dflt: Any = _defvalue ) -> None: if itemsize not in self._isizes: raise _invalid_itemsize_error("complex", itemsize, self._isizes) self.type = "%s%d" % (self.kind, itemsize * 8) Atom.__init__(self, self.type, shape, dflt) class _ComplexErrorAtom(ComplexAtom, metaclass=type): """Reminds the user to stop using the old complex atom names.""" def __init__( self, shape: Shape = (), dflt=ComplexAtom._defvalue ) -> NoReturn: raise TypeError( "to avoid confusions with PyTables 1.X complex atom names, " "please use ``ComplexAtom(itemsize=N)``, " "where N=8 for single precision complex atoms, " "and N=16 for double precision complex atoms" ) Complex32Atom = Complex64Atom = Complex128Atom = _ComplexErrorAtom if hasattr(np, "complex192"): Complex192Atom = _ComplexErrorAtom if hasattr(np, "complex256"): Complex256Atom = _ComplexErrorAtom class TimeAtom(Atom): # type: ignore[misc] """Defines an atom of time type (time kind). There are two distinct supported types of time: a 32 bit integer value and a 64 bit floating point value. Both of them reflect the number of seconds since the Unix epoch. This atom has the property of being stored using the HDF5 time datatypes. """ kind: str = "time" _deftype = "time32" _defvalue: int | float = 0 __init__ = _abstract_atom_init( _deftype, _defvalue ) # type: ignore[assignment] class Time32Atom(TimeAtom): # type: ignore[misc] """Defines an atom of type time32.""" itemsize: int = 4 type: str = "time32" # noqa: A003 _defvalue = 0 def __init__(self, shape: Shape = (), dflt=_defvalue) -> None: Atom.__init__(self, "int32", shape, dflt) class Time64Atom(TimeAtom): # type: ignore[misc] """Defines an atom of type time64.""" itemsize: int = 8 type: str = "time64" # noqa: A003 _defvalue: float = 0.0 def __init__(self, shape: Shape = (), dflt: float = _defvalue) -> None: Atom.__init__(self, "float64", shape, dflt) class EnumAtom(Atom): """Description of an atom of an enumerated type. Instances of this class describe the atom type used to store enumerated values. Those values belong to an enumerated type, defined by the first argument (enum) in the constructor of the atom, which accepts the same kinds of arguments as the Enum class (see :ref:`EnumClassDescr`). The enumerated type is stored in the enum attribute of the atom. A default value must be specified as the second argument (dflt) in the constructor; it must be the *name* (a string) of one of the enumerated values in the enumerated type. When the atom is created, the corresponding concrete value is broadcast and stored in the dflt attribute (setting different default values for items in a multidimensional atom is not supported yet). If the name does not match any value in the enumerated type, a KeyError is raised. Another atom must be specified as the base argument in order to determine the base type used for storing the values of enumerated values in memory and disk. This *storage atom* is kept in the base attribute of the created atom. As a shorthand, you may specify a PyTables type instead of the storage atom, implying that this has a scalar shape. The storage atom should be able to represent each and every concrete value in the enumeration. If it is not, a TypeError is raised. The default value of the storage atom is ignored. The type attribute of enumerated atoms is always enum. Enumerated atoms also support comparisons with other objects:: >>> enum = ['T0', 'T1', 'T2'] >>> atom1 = EnumAtom(enum, 'T0', 'int8') # same as ``atom2`` >>> atom2 = EnumAtom(enum, 'T0', Int8Atom()) # same as ``atom1`` >>> atom3 = EnumAtom(enum, 'T0', 'int16') >>> atom4 = Int8Atom() >>> atom1 == enum False >>> atom1 == atom2 True >>> atom2 != atom1 False >>> atom1 == atom3 False >>> atom1 == atom4 False >>> atom4 != atom1 True Examples -------- The next C enum construction:: enum myEnum { T0, T1, T2 }; would correspond to the following PyTables declaration:: >>> my_enum_atom = EnumAtom(['T0', 'T1', 'T2'], 'T0', 'int32') Please note the dflt argument with a value of 'T0'. Since the concrete value matching T0 is unknown right now (we have not used explicit concrete values), using the name is the only option left for defining a default value for the atom. The chosen representation of values for this enumerated atom uses unsigned 32-bit integers, which surely wastes quite a lot of memory. Another size could be selected by using the base argument (this time with a full-blown storage atom):: >>> my_enum_atom = EnumAtom(['T0', 'T1', 'T2'], 'T0', UInt8Atom()) You can also define multidimensional arrays for data elements:: >>> my_enum_atom = EnumAtom( ... ['T0', 'T1', 'T2'], 'T0', base='uint32', shape=(3,2)) for 3x2 arrays of uint32. """ # Registering this class in the class map may be a little wrong, # since the ``Atom.from_kind()`` method fails miserably with # enumerations, as they don't support an ``itemsize`` argument. # However, resetting ``__metaclass__`` to ``type`` doesn't seem to # work and I don't feel like creating a subclass of ``MetaAtom``. kind = "enum" type = "enum" # noqa: A003 @property # type: ignore[misc] def itemsize(self) -> int: # type: ignore[override] """Size in bytes of a single item in the atom.""" return self.dtype.base.itemsize def _checkbase(self, base: Atom) -> None: """Check the `base` storage atom.""" if base.kind == "enum": raise TypeError( "can not use an enumerated atom " "as a storage atom: %r" % base ) # Check whether the storage atom can represent concrete values # in the enumeration... basedtype = base.dtype pyvalues = [value for (name, value) in self.enum] try: npgenvalues = np.array(pyvalues) except ValueError: raise TypeError("concrete values are not uniformly-shaped") try: npvalues = np.array(npgenvalues, dtype=basedtype.base) except ValueError: raise TypeError( "storage atom type is incompatible with " "concrete values in the enumeration" ) if npvalues.shape[1:] != basedtype.shape: raise TypeError( "storage atom shape does not match that of " "concrete values in the enumeration" ) if npvalues.tolist() != npgenvalues.tolist(): raise TypeError( "storage atom type lacks precision for " "concrete values in the enumeration" ) # ...with some implementation limitations. if npvalues.dtype.kind not in ["i", "u"]: raise NotImplementedError( "only integer concrete values " "are supported for the moment, sorry" ) if len(npvalues.shape) > 1: raise NotImplementedError( "only scalar concrete values " "are supported for the moment, sorry" ) def _get_init_args(self) -> dict[str, Any]: """Get a dictionary of instance constructor arguments.""" return { "enum": self.enum, "dflt": self._defname, "base": self.base, "shape": self.shape, } def _is_equal_to_atom(self, atom) -> bool: """Return True if the object is equal to the given `atom`.""" return False def _is_equal_to_enumatom(self, enumatom: EnumAtom) -> bool: """Return True if the object is equal to the given `enumatom`.""" return ( self.enum == enumatom.enum and self.shape == enumatom.shape and np.all(self.dflt == enumatom.dflt) and self.base == enumatom.base ) def __init__( self, enum: Enum | Any, dflt: Any, base: Atom | str, shape: Shape = () ) -> None: if not isinstance(enum, Enum): enum = Enum(enum) self.enum = enum if isinstance(base, str): base = Atom.from_type(base) self._checkbase(base) self.base = base assert isinstance(self.base, Atom) default = enum[dflt] # check default value self._defname = dflt # kept for representation purposes # These are kept to ease dumping this particular # representation of the enumeration to storage. names, values = [], [] for name, value in enum: names.append(name) values.append(value) basedtype = self.base.dtype self._names = names self._values = np.array(values, dtype=basedtype.base) Atom.__init__(self, basedtype, shape, default) def __repr__(self) -> str: return "EnumAtom(enum={!r}, dflt={!r}, base={!r}, shape={!r})".format( self.enum, self._defname, self.base, self.shape, ) __eq__ = _cmp_dispatcher("_is_equal_to_enumatom") # XXX: API incompatible change for PyTables 3 line # Overriding __eq__ blocks inheritance of __hash__ in 3.x # def __hash__(self): # return hash((self.__class__, self.enum, self.shape, self.dflt, # self.base)) class ReferenceAtom(Atom): """Defines an atom of type object to read references. This atom is read-only. """ kind = "reference" type = "object" # noqa: A003 _deftype = "NoneType" _defvalue = None @property # type: ignore[misc] def itemsize(self) -> int: # type: ignore[override] """Size in bytes of a single item in the atom.""" return self.dtype.base.itemsize def __init__(self, shape: Shape = ()) -> None: Atom.__init__(self, self.type, shape, self._defvalue) def __repr__(self) -> str: return f"ReferenceAtom(shape={self.shape})" # Pseudo-atom classes # =================== # # Now, there come three special classes, `ObjectAtom`, `VLStringAtom` # and `VLUnicodeAtom`, that actually do not descend from `Atom`, but # which goal is so similar that they should be described here. # Pseudo-atoms can only be used with `VLArray` datasets, and they do # not support multidimensional values, nor multiple values per row. # # They can be recognised because they also have ``kind``, ``type`` and # ``shape`` attributes, but no ``size``, ``itemsize`` or ``dflt`` # ones. Instead, they have a ``base`` atom which defines the elements # used for storage. # # See ``examples/vlarray1.py`` and ``examples/vlarray2.py`` for # further examples on `VLArray` datasets, including object # serialization and string management. class PseudoAtom: """Pseudo-atoms can only be used in ``VLArray`` nodes. They can be recognised because they also have `kind`, `type` and `shape` attributes, but no `size`, `itemsize` or `dflt` ones. Instead, they have a `base` atom which defines the elements used for storage. """ base: Atom def __repr__(self) -> str: return "%s()" % self.__class__.__name__ def toarray(self, object_: Any) -> NoReturn: """Convert an `object_` into an array of base atoms.""" raise NotImplementedError def fromarray(self, array: Any) -> NoReturn: """Convert an `array` of base atoms into an object.""" raise NotImplementedError class _BufferedAtom(PseudoAtom): """Pseudo-atom which stores data as a buffer (flat array of uints).""" shape = () def toarray(self, object_: Any) -> np.ndarray: buffer_ = self._tobuffer(object_) array = np.ndarray( buffer=buffer_, dtype=self.base.dtype, shape=len(buffer_) ) return array def _tobuffer(self, object_: Any) -> NoReturn: """Convert an `object_` into a buffer.""" raise NotImplementedError class VLStringAtom(_BufferedAtom): """Defines an atom of type ``vlstring``. This class describes a *row* of the VLArray class, rather than an atom. It differs from the StringAtom class in that you can only add *one instance of it to one specific row*, i.e. the :meth:`VLArray.append` method only accepts one object when the base atom is of this type. This class stores bytestrings. It does not make assumptions on the encoding of the string, and raw bytes are stored as is. To store a string you will need to *explicitly* convert it to a bytestring before you can save them:: >>> s = 'A unicode string: hbar = \u210f' >>> bytestring = s.encode('utf-8') >>> VLArray.append(bytestring) # noqa: F821 # doctest: +SKIP For full Unicode support, using VLUnicodeAtom (see :ref:`VLUnicodeAtom`) is recommended. Variable-length string atoms do not accept parameters and they cause the reads of rows to always return Python bytestrings. You can regard vlstring atoms as an easy way to save generic variable length strings. """ kind = "vlstring" type = "vlstring" # noqa: A003 base = UInt8Atom() def _tobuffer(self, object_: bytes) -> np.bytes_: if not isinstance(object_, bytes): raise TypeError(f"object is not bytes: {object_!r}") return np.bytes_(object_) def fromarray(self, array: np.ndarray) -> bytes: """Convert array data into bytes.""" return array.tobytes() class VLUnicodeAtom(_BufferedAtom): """Defines an atom of type vlunicode. This class describes a *row* of the VLArray class, rather than an atom. It is very similar to VLStringAtom (see :ref:`VLStringAtom`), but it stores Unicode strings (using 32-bit characters a la UCS-4, so all strings of the same length also take up the same space). This class does not make assumptions on the encoding of plain input strings. Plain strings are supported as long as no character is out of the ASCII set; otherwise, you will need to *explicitly* convert them to Unicode before you can save them. Variable-length Unicode atoms do not accept parameters and they cause the reads of rows to always return Python Unicode strings. You can regard vlunicode atoms as an easy way to save variable length Unicode strings. """ kind = "vlunicode" type = "vlunicode" # noqa: A003 base = UInt32Atom() # numpy.unicode_ no more implements the buffer interface in Python 3 # # When the Python build is UCS-2, we need to promote the # Unicode string to UCS-4. We *must* use a 0-d array since # NumPy scalars inherit the UCS-2 encoding from Python (see # NumPy ticket #525). Since ``_tobuffer()`` can't return an # array, we must override ``toarray()`` itself. def toarray(self, object_: str) -> np.ndarray: """Convert a string into a numpy array.""" if not isinstance(object_, str): raise TypeError(f"object is not a string: {object_!r}") ustr = str(object_) uarr = np.array(ustr, dtype="U") return np.ndarray(buffer=uarr, dtype=self.base.dtype, shape=len(ustr)) def _tobuffer(self, object_: str) -> np.str_: # This works (and is used) only with UCS-4 builds of Python, # where the width of the internal representation of a # character matches that of the base atoms. if not isinstance(object_, str): raise TypeError(f"object is not a string: {object_!r}") return np.str_(object_) def fromarray(self, array: np.ndarray) -> str: """Convert array data into a string.""" length = len(array) if length == 0: return "" # ``array.view('U0')`` raises a `TypeError` return array.view("U%d" % length).item() class ObjectAtom(_BufferedAtom): """Defines an atom of type object. This class is meant to fit *any* kind of Python object in a row of a VLArray dataset by using pickle behind the scenes. Due to the fact that you can not foresee how long will be the output of the pickle serialization (i.e. the atom already has a *variable* length), you can only fit *one object per row*. However, you can still group several objects in a single tuple or list and pass it to the :meth:`VLArray.append` method. Object atoms do not accept parameters and they cause the reads of rows to always return Python objects. You can regard object atoms as an easy way to save an arbitrary number of generic Python objects in a VLArray dataset. """ kind = "object" type = "object" # noqa: A003 base = UInt8Atom() def _tobuffer(self, object_: object) -> bytes: return pickle.dumps(object_, pickle.HIGHEST_PROTOCOL) def fromarray(self, array: np.ndarray) -> Any | None: """Deserialize data contained in the input array. A Python object is returned. """ # We have to check for an empty array because of a possible # bug in HDF5 which makes it claim that a dataset has one # record when in fact it is empty. if array.size == 0: return None return pickle.loads(array.tobytes())