"""Here is defined the Leaf class.""" from __future__ import annotations import json import math import warnings from typing import Any, Literal, NamedTuple, Union, TYPE_CHECKING from pathlib import Path from functools import lru_cache import numpy as np from .node import Node from .utils import byteorders, lazyattr, SizeType from .flavor import check_flavor, internal_flavor, toarray from .flavor import alias_map as flavor_alias_map from .filters import Filters from .exceptions import ( NoSuchChunkError, NotChunkAlignedError, NotChunkedError, PerformanceWarning, ) if TYPE_CHECKING: from .group import Group # These should be declared as type aliases, # but ``TypeAlias`` requires Python >= 3.10 # and ``type`` statements require Python >= 3.12. # ``np.typing.NDArray[np.uint8]`` requires NumPy >= 1.21. NPByteArray = np.ndarray[tuple[int], np.dtype[np.uint8]] # ``Buffer`` requires Python >= 3.12. BufferLike = Union[bytes, bytearray, memoryview, NPByteArray] def read_cached_cpu_info() -> dict[str, Any]: """Load the CPU information from a cache file.""" try: with open(Path.home() / ".pytables-cpuinfo.json") as f: return json.load(f) except FileNotFoundError: return {} def write_cached_cpu_info(cpu_info_dict: dict[str, Any]) -> None: """Write CPU information to a cache file.""" with open(Path.home() / ".pytables-cpuinfo.json", "w") as f: json.dump(cpu_info_dict, f, indent=4) @lru_cache(maxsize=1) def get_cpu_info() -> dict[str, Any]: """Return a dictionary containing CPU information.""" cached_info = read_cached_cpu_info() if cached_info: return cached_info try: import cpuinfo except ImportError: return {} cpu_info_dict = cpuinfo.get_cpu_info() try: write_cached_cpu_info(cpu_info_dict) except OSError: # cpu info cannot be stored. # will need to be recomputed in the next process pass return cpu_info_dict def csformula(expected_mb: int) -> int: """Return the fitted chunksize for expected_mb.""" # For a basesize of 8 KB, this will return: # 8 KB for datasets <= 1 MB # 1 MB for datasets >= 10 TB basesize = 8 * 1024 # 8 KB is a good minimum return basesize * int(2 ** math.log10(expected_mb)) def limit_es(expected_mb: int) -> int: """Protection against creating too small or too large chunks.""" if expected_mb < 1: # < 1 MB expected_mb = 1 elif expected_mb > 10**7: # > 10 TB expected_mb = 10**7 return expected_mb def calc_chunksize(expected_mb: int) -> int: """Compute the optimum HDF5 chunksize for I/O purposes. Rational: HDF5 takes the data in bunches of chunksize length to write the on disk. A BTree in memory is used to map structures on disk. The more chunks that are allocated for a dataset the larger the B-tree. Large B-trees take memory and causes file storage overhead as well as more disk I/O and higher contention for the meta data cache. You have to balance between memory and I/O overhead (small B-trees) and time to access to data (big B-trees). The tuning of the chunksize parameter affects the performance and the memory consumed. This is based on my own experiments and, as always, your mileage may vary. """ expected_mb = limit_es(expected_mb) zone = int(math.log10(expected_mb)) expected_mb = 10**zone chunksize = csformula(expected_mb) # XXX: Multiply by 8 seems optimal for sequential access return chunksize * 8 class ChunkInfo(NamedTuple): """Information about storage for a given chunk. It may also refer to a chunk which is within the dataset's shape but that does not exist in storage, i.e. a missing chunk. An instance of this named tuple class contains the following information, in field order: .. attribute:: start The coordinates in dataset items where the chunk starts, a tuple of integers with the same rank as the dataset. These coordinates are always aligned with chunk boundaries. Also present for missing chunks. .. attribute:: filter_mask An integer where each active bit signals that the filter in its position in the pipeline was disabled when storing the chunk. For instance, ``0b10`` disables shuffling, ``0b100`` disables szip, and so on. ``None`` for missing chunks. .. attribute:: offset An integer which indicates the offset in bytes of chunk data as it exists in storage. ``None`` for missing chunks. .. attribute:: size An integer which indicates the size in bytes of chunk data as it exists in storage. ``None`` for missing chunks. """ start: tuple[int, ...] | None filter_mask: int | None offset: int | None size: int | None class Leaf(Node): """Abstract base class for all PyTables leaves. A leaf is a node (see the Node class in :class:`Node`) which hangs from a group (see the Group class in :class:`Group`) but, unlike a group, it can not have any further children below it (i.e. it is an end node). This definition includes all nodes which contain actual data (datasets handled by the Table - see :ref:`TableClassDescr`, Array - see :ref:`ArrayClassDescr`, CArray - see :ref:`CArrayClassDescr`, EArray - see :ref:`EArrayClassDescr`, and VLArray - see :ref:`VLArrayClassDescr` classes) and unsupported nodes (the UnImplemented class - :ref:`UnImplementedClassDescr`) these classes do in fact inherit from Leaf. .. rubric:: Leaf attributes These instance variables are provided in addition to those in Node (see :ref:`NodeClassDescr`): .. attribute:: byteorder The byte ordering of the leaf data *on disk*. It will be either ``little`` or ``big``. .. attribute:: dtype The NumPy dtype that most closely matches this leaf type. .. attribute:: extdim The index of the enlargeable dimension (-1 if none). .. attribute:: nrows The length of the main dimension of the leaf data. .. attribute:: nrowsinbuf The number of rows that fit in internal input buffers. You can change this to fine-tune the speed or memory requirements of your application. .. attribute:: shape The shape of data in the leaf. """ # These are a little hard to override, but so are properties. attrs = Node._v_attrs """The associated AttributeSet instance - see :ref:`AttributeSetClassDescr` (This is an easier-to-write alias of :attr:`Node._v_attrs`.""" title = Node._v_title """A description for this node (This is an easier-to-write alias of :attr:`Node._v_title`).""" @property def name(self) -> str: """Name of the node. The name of this node in its parent group (This is an easier-to-write alias of :attr:`Node._v_name`). """ return self._v_name @property def chunkshape(self) -> tuple[int, ...]: """HDF5 chunk size for chunked leaves (a tuple). This is read-only because you cannot change the chunk size of a leaf once it has been created. """ return getattr(self, "_v_chunkshape", None) @property def object_id(self) -> int: """Node identifier, which may change from run to run. (This is an easier-to-write alias of :attr:`Node._v_objectid`). .. versionchanged:: 3.0 The *objectID* property has been renamed into *object_id*. """ return self._v_objectid @property def ndim(self) -> int: """Return the number of dimensions of the leaf data. .. versionadded: 2.4 """ return len(self.shape) @lazyattr def filters(self) -> Filters: """Filter properties for this leaf. See Also -------- Filters """ return Filters._from_leaf(self) @property def track_times(self) -> bool: """Return True if the timestamps for the leaf are recorded. If the leaf is not a dataset, this will fail with HDF5ExtError. The track times dataset creation property does not seem to survive closing and reopening as of HDF5 1.8.17. Currently, it may be more accurate to test whether the ctime for the dataset is 0: track_times = (leaf._get_obj_timestamps().ctime == 0) """ return self._get_obj_track_times() @property def maindim(self) -> int: """Dimension along which iterators work. Its value is 0 (i.e. the first dimension) when the dataset is not extendable, and self.extdim (where available) for extendable ones. """ if self.extdim < 0: return 0 # choose the first dimension return self.extdim @property def flavor(self) -> Literal["numpy", "python"]: """Type of the data object read from this leaf. It can be any of 'numpy' or 'python'. You can (and are encouraged to) use this property to get, set and delete the FLAVOR HDF5 attribute of the leaf. When the leaf has no such attribute, the default flavor is used. """ return self._flavor @flavor.setter def flavor(self, flavor: Literal["numpy", "python"]) -> None: self._v_file._check_writable() check_flavor(flavor) self._v_attrs.FLAVOR = self._flavor = flavor # logs the change @flavor.deleter def flavor(self) -> None: del self._v_attrs.FLAVOR self._flavor = internal_flavor @property def size_on_disk(self) -> int: """Size on disk of the object. The size of this leaf's data in bytes as it is stored on disk. If the data is compressed, this shows the compressed size. In the case of uncompressed, chunked data, this may be slightly larger than the amount of data, due to partially filled chunks. """ return self._get_storage_size() def __init__( self, parentnode: Group, name: str, new: bool = False, filters: Filters | None = None, byteorder: Literal["little", "big", None] = None, _log: bool = True, track_times: bool = True, ) -> None: self._v_new = new """Is this the first time the node has been created?""" self.nrowsinbuf: int | None = None """ The number of rows that fits in internal input buffers. You can change this to fine-tune the speed or memory requirements of your application. """ self._flavor: Literal["numpy", "python", None] = None """Private storage for the `flavor` property.""" if new: # Get filter properties from parent group if not given. if filters is None: filters = parentnode._v_filters self.__dict__["filters"] = filters # bypass the property if byteorder not in (None, "little", "big"): raise ValueError( "the byteorder can only take 'little' or 'big' values " "and you passed: %s" % byteorder ) self.byteorder = byteorder """The byte ordering of the leaf data *on disk*.""" self._want_track_times = track_times # Existing filters need not be read since `filters` # is a lazy property that automatically handles their loading. super().__init__(parentnode, name, _log) def __len__(self) -> int: """Return the length of the main dimension of the leaf data. Please note that this may raise an OverflowError on 32-bit platforms for datasets having more than 2**31-1 rows. This is a limitation of Python that you can work around by using the nrows or shape attributes. """ return self.nrows def __str__(self) -> str: """Return the string representation of the object. The string representation for this object is its pathname in the HDF5 object tree plus some additional metainfo. """ filters = [] if self.filters.fletcher32: filters.append("fletcher32") if self.filters.complevel: if self.filters.shuffle: filters.append("shuffle") if self.filters.bitshuffle: filters.append("bitshuffle") filters.append(f"{self.filters.complib}({self.filters.complevel})") return ( f"{self._v_pathname} ({self.__class__.__name__}" f"{self.shape}{', '.join(filters)}) {self._v_title!r}" ) def _g_post_init_hook(self) -> None: """Code to be run after node creation and before creation logging. This method gets or sets the flavor of the leaf. """ super()._g_post_init_hook() if self._v_new: # set flavor of new node if self._flavor is None: self._flavor = internal_flavor else: # flavor set at creation time, do not log if self._v_file.params["PYTABLES_SYS_ATTRS"]: self._v_attrs._g__setattr("FLAVOR", self._flavor) else: # get flavor of existing node (if any) if self._v_file.params["PYTABLES_SYS_ATTRS"]: flavor = getattr(self._v_attrs, "FLAVOR", internal_flavor) self._flavor = flavor_alias_map.get(flavor, flavor) else: self._flavor = internal_flavor def _calc_chunkshape( self, expectedrows: int, rowsize: int, itemsize: int ) -> tuple[int, ...]: """Calculate the shape for the HDF5 chunk.""" # In case of a scalar shape, return the unit chunksize if self.shape == (): return (SizeType(1),) # Compute the chunksize MB = 1024 * 1024 # noqa: N806 expected_mb = (expectedrows * rowsize) // MB chunksize = calc_chunksize(expected_mb) complib = self.filters.complib if ( complib is not None and complib.startswith("blosc2") and self._c_classid in ("TABLE", "CARRAY", "EARRAY") ): # Blosc2 can introspect into blocks, so we can increase the # chunksize for improving HDF5 perf for its internal btree. # For the time being, this has been implemented efficiently # just for tables, but in the future *Array objects could also # be included. # Use a decent default value for chunksize chunksize *= 16 # Now, go explore the L3 size and try to find a smarter chunksize cpu_info = get_cpu_info() if "l3_cache_size" in cpu_info: # In general, is a good idea to set the chunksize equal to L3 l3_cache_size = cpu_info["l3_cache_size"] # cpuinfo sometimes returns cache sizes as strings (like, # "4096 KB"), so refuse the temptation to guess and use the # value only when it is an actual int. # Also, sometimes cpuinfo does not return a correct L3 size; # so in general, enforcing L3 > L2 is a good sanity check. l2_cache_size = cpu_info.get("l2_cache_size", "Not found") if ( type(l3_cache_size) is int and type(l2_cache_size) is int and l3_cache_size > l2_cache_size ): chunksize = l3_cache_size # In Blosc2, the chunksize cannot be larger than 2 GB # BLOSC2_MAX_BUFFERSIZE if chunksize > 2**31 - 32: chunksize = 2**31 - 32 maindim = self.maindim # Compute the chunknitems chunknitems = chunksize // itemsize # Safeguard against itemsizes being extremely large if chunknitems == 0: chunknitems = 1 chunkshape = list(self.shape) # Check whether trimming the main dimension is enough chunkshape[maindim] = 1 newchunknitems = np.prod(chunkshape, dtype=SizeType) if newchunknitems <= chunknitems: chunkshape[maindim] = chunknitems // newchunknitems else: # No, so start trimming other dimensions as well for j in range(len(chunkshape)): # Check whether trimming this dimension is enough chunkshape[j] = 1 newchunknitems = np.prod(chunkshape, dtype=SizeType) if newchunknitems <= chunknitems: chunkshape[j] = chunknitems // newchunknitems break else: # Ops, we ran out of the loop without a break # Set the last dimension to chunknitems chunkshape[-1] = chunknitems return tuple(SizeType(s) for s in chunkshape) def _calc_nrowsinbuf(self) -> int: """Calculate the number of rows that fits on a PyTables buffer.""" params = self._v_file.params # Compute the nrowsinbuf rowsize = self.rowsize buffersize = params["IO_BUFFER_SIZE"] if rowsize != 0: nrowsinbuf = buffersize // rowsize else: nrowsinbuf = 1 # Safeguard against row sizes being extremely large if nrowsinbuf == 0: nrowsinbuf = 1 # If rowsize is too large, issue a Performance warning maxrowsize = params["BUFFER_TIMES"] * buffersize if rowsize > maxrowsize: warnings.warn( f"The Leaf ``{self._v_pathname}`` is exceeding the " f"maximum recommended rowsize ({maxrowsize} bytes); " f"be ready to see PyTables asking for *lots* " f"of memory and possibly slow I/O. " f"You may want to reduce the rowsize by trimming the " f"value of dimensions that are orthogonal (and preferably " f"close) to the *main* dimension of this leave. " f"Alternatively, in case you have specified a very " f"small/large chunksize, you may want to " f"increase/decrease it.", PerformanceWarning, ) return nrowsinbuf # This method is appropriate for calls to __getitem__ methods def _process_range( self, start: int, stop: int, step: int, dim: int | None = None, warn_negstep: bool = True, ) -> tuple[int, int, int]: if dim is None: nrows = self.nrows # self.shape[self.maindim] else: nrows = self.shape[dim] if warn_negstep and step and step < 0: raise ValueError("slice step cannot be negative") # if start is not None: start = long(start) # if stop is not None: stop = long(stop) # if step is not None: step = long(step) return slice(start, stop, step).indices(int(nrows)) # This method is appropriate for calls to read() methods def _process_range_read( self, start: int, stop: int, step: int, warn_negstep: bool = True ) -> tuple[int, int, int]: nrows = self.nrows if start is not None and stop is None and step is None: # Protection against start greater than available records # nrows == 0 is a special case for empty objects if 0 < nrows <= start: raise IndexError( "start of range (%s) is greater than " "number of rows (%s)" % (start, nrows) ) step = 1 if start == -1: # corner case stop = nrows else: stop = start + 1 # Finally, get the correct values (over the main dimension) start, stop, step = self._process_range( start, stop, step, warn_negstep=warn_negstep ) return (start, stop, step) def _g_copy( self, newparent: Group, newname: str, recursive: bool, _log: bool = True, **kwargs, ) -> Leaf: # Compute default arguments. start = kwargs.pop("start", None) stop = kwargs.pop("stop", None) step = kwargs.pop("step", None) title = kwargs.pop("title", self._v_title) filters = kwargs.pop("filters", self.filters) chunkshape = kwargs.pop("chunkshape", self.chunkshape) copyuserattrs = kwargs.pop("copyuserattrs", True) stats = kwargs.pop("stats", None) if chunkshape == "keep": chunkshape = self.chunkshape # Keep the original chunkshape elif chunkshape == "auto": chunkshape = None # Will recompute chunkshape # Fix arguments with explicit None values for backwards compatibility. if title is None: title = self._v_title if filters is None: filters = self.filters # Create a copy of the object. (new_node, bytes_) = self._g_copy_with_stats( newparent, newname, start, stop, step, title, filters, chunkshape, _log, **kwargs, ) # Copy user attributes if requested (or the flavor at least). if copyuserattrs: self._v_attrs._g_copy(new_node._v_attrs, copyclass=True) elif "FLAVOR" in self._v_attrs: if self._v_file.params["PYTABLES_SYS_ATTRS"]: new_node._v_attrs._g__setattr("FLAVOR", self._flavor) new_node._flavor = self._flavor # update cached value # Update statistics if needed. if stats is not None: stats["leaves"] += 1 stats["bytes"] += bytes_ return new_node def _g_fix_byteorder_data( self, data: np.ndarray, dbyteorder: str ) -> np.ndarray: """Fix the byteorder of data passed in constructors.""" dbyteorder = byteorders[dbyteorder] # If self.byteorder has not been passed as an argument of # the constructor, then set it to the same value of data. if self.byteorder is None: self.byteorder = dbyteorder # Do an additional in-place byteswap of data if the in-memory # byteorder doesn't match that of the on-disk. This is the only # place that we have to do the conversion manually. In all the # other cases, it will be HDF5 the responsible for doing the # byteswap properly. if dbyteorder in ["little", "big"]: if dbyteorder != self.byteorder: # if data is not writeable, do a copy first if not data.flags.writeable: data = data.copy() data.byteswap(True) else: # Fix the byteorder again, no matter which byteorder have # specified the user in the constructor. self.byteorder = "irrelevant" return data def _point_selection(self, key: list | tuple | np.ndarray) -> np.ndarray: """Perform a point-wise selection. `key` can be any of the following items: * A boolean array with the same shape as self. Those positions with True values will signal the coordinates to be returned. * A numpy array (or list or tuple) with the point coordinates. This has to be a two-dimensional array of size len(self.shape) by num_elements containing a list of zero-based values specifying the coordinates in the dataset of the selected elements. The order of the element coordinates in the array specifies the order in which the array elements are iterated through when I/O is performed. Duplicate coordinate locations are not checked for. Return the coordinates array. If this is not possible, raise a `TypeError` so that the next selection method can be tried out. This is useful for whatever `Leaf` instance implementing a point-wise selection. """ input_key = key if type(key) in (list, tuple): if isinstance(key, tuple) and len(key) > len(self.shape): raise IndexError(f"Invalid index or slice: {key!r}") # Try to convert key to a numpy array. If not possible, # a TypeError will be issued (to be catched later on). try: key = toarray(key) except ValueError: raise TypeError(f"Invalid index or slice: {key!r}") elif not isinstance(key, np.ndarray): raise TypeError(f"Invalid index or slice: {key!r}") # Protection against empty keys if len(key) == 0: return np.array([], dtype="i8") if key.dtype.kind == "b": if not key.shape == self.shape: raise IndexError( "Boolean indexing array has incompatible shape" ) # Get the True coordinates (64-bit indices!) coords = np.asarray(key.nonzero(), dtype="i8") coords = np.transpose(coords) elif key.dtype.kind == "i" or key.dtype.kind == "u": if len(key.shape) > 2: raise IndexError( "Coordinate indexing array has incompatible shape" ) elif len(key.shape) == 2: if key.shape[0] != len(self.shape): raise IndexError( "Coordinate indexing array has incompatible shape" ) coords = np.asarray(key, dtype="i8") coords = np.transpose(coords) else: # For 1-dimensional datasets coords = np.asarray(key, dtype="i8") # handle negative indices base = coords if coords.base is None else coords.base if base is input_key: # never modify the original "key" data coords = coords.copy() idx = coords < 0 coords[idx] = (coords + self.shape)[idx] # bounds check if np.any(coords < 0) or np.any(coords >= self.shape): raise IndexError("Index out of bounds") else: raise TypeError("Only integer coordinates allowed.") # We absolutely need a contiguous array if not coords.flags.contiguous: coords = coords.copy() return coords def _check_chunked(self) -> None: if self.chunkshape is None: raise NotChunkedError("The dataset is not chunked") def _check_chunk_within(self, coords: tuple[int, ...]) -> None: if len(coords) != self.ndim: raise ValueError( f"Chunk coordinates do not match dataset shape: " f"{coords} !~ {self.shape}" ) if any(c < 0 or c >= s for (c, s) in zip(coords, self.shape)): raise IndexError( f"Chunk coordinates not within dataset shape: " f"{coords} <> {self.shape}" ) def _check_chunk_coords(self, coords: tuple[int, ...]) -> None: if any(c % cs for (c, cs) in zip(coords, self.chunkshape)): raise NotChunkAlignedError( f"Coordinates are not multiples of chunk shape: " f"{tuple(coords)} !* {self.chunkshape}" ) # Tree manipulation def remove(self) -> None: """Remove this node from the hierarchy. This method has the behavior described in :meth:`Node._f_remove`. Please note that there is no recursive flag since leaves do not have child nodes. """ self._f_remove(False) def rename(self, newname: str) -> None: """Rename this node in place. This method has the behavior described in :meth:`Node._f_rename()`. """ self._f_rename(newname) def move( self, newparent: Group | None = None, newname: str | None = None, overwrite: bool = False, createparents: bool = False, ) -> None: """Move or rename this node. This method has the behavior described in :meth:`Node._f_move` """ self._f_move(newparent, newname, overwrite, createparents) def copy( self, newparent: Group | None = None, newname: str | None = None, overwrite: bool = False, createparents: bool = False, **kwargs, ) -> Leaf: """Copy this node and return the new one. This method has the behavior described in :meth:`Node._f_copy`. Please note that there is no recursive flag since leaves do not have child nodes. .. warning:: Note that unknown parameters passed to this method will be ignored, so may want to double check the spelling of these (i.e. if you write them incorrectly, they will most probably be ignored). Parameters ---------- title The new title for the destination. If omitted or None, the original title is used. filters : Filters Specifying this parameter overrides the original filter properties in the source node. If specified, it must be an instance of the Filters class (see :ref:`FiltersClassDescr`). The default is to copy the filter properties from the source node. copyuserattrs You can prevent the user attributes from being copied by setting this parameter to False. The default is to copy them. start, stop, step : int Specify the range of rows to be copied; the default is to copy all the rows. stats This argument may be used to collect statistics on the copy process. When used, it should be a dictionary with keys 'groups', 'leaves' and 'bytes' having a numeric value. Their values will be incremented to reflect the number of groups, leaves and bytes, respectively, that have been copied during the operation. chunkshape The chunkshape of the new leaf. It supports a couple of special values. A value of keep means that the chunkshape will be the same as original leaf (this is the default). A value of auto means that a new shape will be computed automatically in order to ensure the best performance when accessing the dataset through the main dimension. Any other value should be an integer or a tuple matching the dimensions of the leaf. """ return self._f_copy( newparent, newname, overwrite, createparents=createparents, **kwargs, ) def truncate(self, size: int) -> None: """Truncate the main dimension to be size rows. If the main dimension previously was larger than this size, the extra data is lost. If the main dimension previously was shorter, it is extended, and the extended part is filled with the default values. The truncation operation can only be applied to *enlargeable* datasets, else a TypeError will be raised. """ # A non-enlargeable arrays (Array, CArray) cannot be truncated if self.extdim < 0: raise TypeError("non-enlargeable datasets cannot be truncated") self._g_truncate(size) def isvisible(self) -> bool: """Return True if this node is visible. This method has the behavior described in :meth:`Node._f_isvisible()`. """ return self._f_isvisible() # Attribute handling def get_attr(self, name: str) -> Any: """Get a PyTables attribute from this node. This method has the behavior described in :meth:`Node._f_getattr`. """ return self._f_getattr(name) def set_attr(self, name: str, value: Any) -> None: """Set a PyTables attribute for this node. This method has the behavior described in :meth:`Node._f_setattr()`. """ self._f_setattr(name, value) def del_attr(self, name: str) -> None: """Delete a PyTables attribute from this node. This method has the behavior described in :meth:`Node_f_delAttr`. """ self._f_delattr(name) # Data handling def flush(self) -> None: """Flush pending data to disk. Saves whatever remaining buffered data to disk. It also releases I/O buffers, so if you are filling many datasets in the same PyTables session, please call flush() extensively so as to help PyTables to keep memory requirements low. """ self._g_flush() def chunk_info(self, coords: tuple[int, ...]) -> ChunkInfo: """Get storage information about the chunk containing the `coords`. The coordinates `coords` are a tuple of integers with the same rank as the dataset. Return a :class:`ChunkInfo` instance with the information. The coordinates need not be aligined with chunk boundaries. This means that this method may be used to get the start coordinates of the chunk that contains the item at the given coordinates, for use with other direct chunking operations (see :attr:`ChunkInfo.start`). If the coordinates are within the dataset's shape but there is no such chunk in storage (missing chunk), a :class:`ChunkInfo` with a valid ``start`` and ``filter_mask = offset = size = None`` is returned. If the coordinates are beyond the shape, :exc:`IndexError` is raised (even if the start of the chunk would fall within the shape). Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. """ self._check_chunked() self._check_chunk_within(coords) coords = np.array(coords, dtype=SizeType) filter_mask, offset, size = self._g_chunk_info(coords) # Align coordinates to chunk boundary. chunkshape = self.chunkshape coords //= chunkshape coords *= chunkshape return ChunkInfo(tuple(coords.tolist()), filter_mask, offset, size) def read_chunk( self, coords: tuple[int, ...], out: bytearray | NPByteArray | None = None, ) -> bytes | memoryview: """Get the raw chunk that starts at the given `coords` from storage. The coordinates `coords` are a tuple of integers with the same rank as the dataset. If they are not multiples of its chunkshape, :exc:`NotChunkAlignedError` is raised. If a buffer-like `out` argument is given, it receives chunk data. If it has insufficient storage for the chunk, :exc:`ValueError` is raised (use :meth:`chunk_info()` to get the required capacity). The obtained data is supposed to have gone at storage time through dataset filters, minus those in the chunk's filter mask (use :meth:`chunk_info()` to get it). Return the chunk's raw content, either as a `bytes` instance (if `out` is ``None``) or as a `memoryview` over the object given as `out`. Reading a chunk within the dataset's shape, but not in storage (missing chunk) raises a :exc:`NoSuchChunkError`. If the chunk is beyond the shape, :exc:`IndexError` is raised. Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. """ self._check_chunked() self._check_chunk_within(coords) self._check_chunk_coords(coords) if out is not None: out = np.ndarray((len(out),), dtype="u1", buffer=out) coords = np.array(coords, dtype=SizeType) chunk = self._g_read_chunk(coords, out) if chunk is None: raise NoSuchChunkError( f"Can't read missing chunk at coordinates " f"{tuple(coords)}" ) return chunk.tobytes() if out is None else memoryview(out) def write_chunk( self, coords: tuple[int, ...], data: BufferLike, filter_mask: int = 0 ) -> None: """Write `data` to storage for the chunk starting at the given `coords`. The coordinates `coords` are a tuple of integers with the same rank as the dataset. If they are not multiples of its chunkshape, :exc:`NotChunkAlignedError` is raised. The content of the buffer-like `data` must already have gone through dataset filters, minus those in the given `filter_mask` (which is to be saved along data; see :attr:`ChunkInfo.filter_mask`). Writing a chunk which is already in storage replaces it, otherwise it is added to storage as long as it is within the dataset's shape (missing chunk). This means that you may use :meth:`truncate()` to grow an enlargeable dataset cheaply (as no chunk data is written), then sparsely write selected chunks in arbitrary order. If the chunk is beyond the dataset's shape, :exc:`IndexError` is raised. Calling this method on a non-chunked dataset raises a :exc:`NotChunkedError`. """ self._check_chunked() self._check_chunk_within(coords) self._check_chunk_coords(coords) coords = np.array(coords, dtype=SizeType) data = np.ndarray((len(data),), dtype="u1", buffer=data) self._g_write_chunk(coords, data, filter_mask) def _f_close(self, flush: bool = True) -> None: """Close this node in the tree. This method has the behavior described in :meth:`Node._f_close`. Besides that, the optional argument flush tells whether to flush pending data to disk or not before closing. """ if not self._v_isopen: return # the node is already closed or not initialized # Only do a flush in case the leaf has an IO buffer. The # internal buffers of HDF5 will be flushed afterwards during the # self._g_close() call. Avoiding an unnecessary flush() # operation accelerates the closing for the unbuffered leaves. if flush and hasattr(self, "_v_iobuf"): self.flush() # Close the dataset and release resources self._g_close() # Close myself as a node. super()._f_close() def close(self, flush: bool = True) -> None: """Close this node in the tree. This method is completely equivalent to :meth:`Leaf._f_close`. """ self._f_close(flush)