"""Declare exceptions and warnings that are specific to PyTables.""" from __future__ import annotations import os import warnings import traceback from collections.abc import Callable __all__ = [ "ChunkError", "ClosedFileError", "ClosedNodeError", "DataTypeWarning", "ExperimentalFeatureWarning", "FileModeError", "FiltersWarning", "FlavorError", "FlavorWarning", "HDF5ExtError", "NaturalNameWarning", "NoSuchChunkError", "NoSuchNodeError", "NodeError", "NotChunkedError", "NotChunkAlignedError", "OldIndexWarning", "PerformanceWarning", "UnclosedFileWarning", "UndoRedoError", "UndoRedoWarning", ] __docformat__ = "reStructuredText" """The format of documentation strings in this module.""" class HDF5ExtError(RuntimeError): """A low level HDF5 operation failed. This exception is raised the low level PyTables components used for accessing HDF5 files. It usually signals that something is not going well in the HDF5 library or even at the Input/Output level. Errors in the HDF5 C library may be accompanied by an extensive HDF5 back trace on standard error (see also :func:`tables.silence_hdf5_messages`). .. versionchanged:: 2.4 Parameters ---------- message error message h5bt This parameter (keyword only) controls the HDF5 back trace handling. Any keyword arguments other than h5bt is ignored. * if set to False the HDF5 back trace is ignored and the :attr:`HDF5ExtError.h5backtrace` attribute is set to None * if set to True the back trace is retrieved from the HDF5 library and stored in the :attr:`HDF5ExtError.h5backtrace` attribute as a list of tuples * if set to "VERBOSE" (default) the HDF5 back trace is stored in the :attr:`HDF5ExtError.h5backtrace` attribute and also included in the string representation of the exception * if not set (or set to None) the default policy is used (see :attr:`HDF5ExtError.DEFAULT_H5_BACKTRACE_POLICY`) """ # NOTE: in order to avoid circular dependencies between modules the # _dump_h5_backtrace method is set at initialization time in # the utilsextension.pyx. _dump_h5_backtrace: ( Callable[[], list[tuple[str, int, str, str]]] | None ) = None DEFAULT_H5_BACKTRACE_POLICY = "VERBOSE" """Default policy for HDF5 backtrace handling * if set to False the HDF5 back trace is ignored and the :attr:`HDF5ExtError.h5backtrace` attribute is set to None * if set to True the back trace is retrieved from the HDF5 library and stored in the :attr:`HDF5ExtError.h5backtrace` attribute as a list of tuples * if set to "VERBOSE" (default) the HDF5 back trace is stored in the :attr:`HDF5ExtError.h5backtrace` attribute and also included in the string representation of the exception This parameter can be set using the :envvar:`PT_DEFAULT_H5_BACKTRACE_POLICY` environment variable. Allowed values are "IGNORE" (or "FALSE"), "SAVE" (or "TRUE") and "VERBOSE" to set the policy to False, True and "VERBOSE" respectively. The special value "DEFAULT" can be used to reset the policy to the default value .. versionadded:: 2.4 """ @classmethod def set_policy_from_env(cls) -> str: """Set the policy from environment variables.""" envmap = { "IGNORE": False, "FALSE": False, "SAVE": True, "TRUE": True, "VERBOSE": "VERBOSE", "DEFAULT": "VERBOSE", } oldvalue = cls.DEFAULT_H5_BACKTRACE_POLICY envvalue = os.environ.get("PT_DEFAULT_H5_BACKTRACE_POLICY", "DEFAULT") try: newvalue = envmap[envvalue.upper()] except KeyError: warnings.warn( "Invalid value for the environment variable " "'PT_DEFAULT_H5_BACKTRACE_POLICY'. The default " "policy for HDF5 back trace management in PyTables " "will be: '%s'" % oldvalue ) else: cls.DEFAULT_H5_BACKTRACE_POLICY = newvalue return oldvalue def __init__(self, *args, **kargs) -> None: super().__init__(*args) self._h5bt_policy = kargs.get("h5bt", self.DEFAULT_H5_BACKTRACE_POLICY) if self._h5bt_policy and self._dump_h5_backtrace is not None: self.h5backtrace = self._dump_h5_backtrace() """HDF5 back trace. Contains the HDF5 back trace as a (possibly empty) list of tuples. Each tuple has the following format:: (filename, line number, function name, text) Depending on the value of the *h5bt* parameter passed to the initializer the h5backtrace attribute can be set to None. This means that the HDF5 back trace has been simply ignored (not retrieved from the HDF5 C library error stack) or that there has been an error (silently ignored) during the HDF5 back trace retrieval. .. versionadded:: 2.4 See Also -------- traceback.format_list : :func:`traceback.format_list` """ # XXX: check _dump_h5_backtrace failures else: self.h5backtrace = None def __str__(self) -> str: """Return a sting representation of the exception. The actual result depends on policy set in the initializer :meth:`HDF5ExtError.__init__`. .. versionadded:: 2.4 """ verbose = bool(self._h5bt_policy in ("VERBOSE", "verbose")) if verbose and self.h5backtrace: bt = "\n".join( [ "HDF5 error back trace\n", self.format_h5_backtrace(), "End of HDF5 error back trace", ] ) if len(self.args) == 1 and isinstance(self.args[0], str): msg = super().__str__() msg = f"{bt}\n\n{msg}" elif self.h5backtrace[-1][-1]: msg = f"{bt}\n\n{self.h5backtrace[-1][-1]}" else: msg = bt else: msg = super().__str__() return msg def format_h5_backtrace( self, backtrace: list[tuple[str, int, str, str]] | None = None ) -> str: """Convert the HDF5 trace back into a string. The HDF5 trace back is represented as a list of tuples. See :attr:`HDF5ExtError.h5backtrace`. .. versionadded:: 2.4 """ if backtrace is None: backtrace = self.h5backtrace if backtrace is None: return "No HDF5 back trace available" else: return "".join(traceback.format_list(backtrace)) # Initialize the policy for HDF5 back trace handling HDF5ExtError.set_policy_from_env() # The following exceptions are concretions of the ``ValueError`` exceptions # raised by ``file`` objects on certain operations. class ClosedNodeError(ValueError): """The operation can not be completed because the node is closed. For instance, listing the children of a closed group is not allowed. """ pass class ClosedFileError(ValueError): """The operation can not be completed because the hosting file is closed. For instance, getting an existing node from a closed file is not allowed. """ pass class FileModeError(ValueError): """FIle mode error. The operation can not be carried out because the mode in which the hosting file is opened is not adequate. For instance, removing an existing leaf from a read-only file is not allowed. """ pass class NodeError(AttributeError, LookupError): """Invalid hierarchy manipulation operation requested. This exception is raised when the user requests an operation on the hierarchy which can not be run because of the current layout of the tree. This includes accessing nonexistent nodes, moving or copying or creating over an existing node, non-recursively removing groups with children, and other similarly invalid operations. A node in a PyTables database cannot be simply overwritten by replacing it. Instead, the old node must be removed explicitly before another one can take its place. This is done to protect interactive users from inadvertently deleting whole trees of data by a single erroneous command. """ pass class NoSuchNodeError(NodeError): """An operation was requested on a node that does not exist. This exception is raised when an operation gets a path name or a ``(where, name)`` pair leading to a nonexistent node. """ pass class UndoRedoError(Exception): """Problems with doing/redoing actions with Undo/Redo feature. This exception indicates a problem related to the Undo/Redo mechanism, such as trying to undo or redo actions with this mechanism disabled, or going to a nonexistent mark. """ pass class UndoRedoWarning(Warning): """Issued when an action not supporting Undo/Redo is run. This warning is only shown when the Undo/Redo mechanism is enabled. """ pass class NaturalNameWarning(Warning): """Issued when a non-pythonic name is given for a node. This is not an error and may even be very useful in certain contexts, but one should be aware that such nodes cannot be accessed using natural naming (instead, ``getattr()`` must be used explicitly). """ pass class PerformanceWarning(Warning): """Warning for operations which may cause a performance drop. This warning is issued when an operation is made on the database which may cause it to slow down on future operations (i.e. making the node tree grow too much). """ pass class FlavorError(ValueError): """Unsupported or unavailable flavor or flavor conversion. This exception is raised when an unsupported or unavailable flavor is given to a dataset, or when a conversion of data between two given flavors is not supported nor available. """ pass class FlavorWarning(Warning): """Unsupported or unavailable flavor conversion. This warning is issued when a conversion of data between two given flavors is not supported nor available, and raising an error would render the data inaccessible (e.g. on a dataset of an unavailable flavor in a read-only file). See the `FlavorError` class for more information. """ pass class FiltersWarning(Warning): """Unavailable filters. This warning is issued when a valid filter is specified but it is not available in the system. It may mean that an available default filter is to be used instead. """ pass class OldIndexWarning(Warning): """Unsupported index format. This warning is issued when an index in an unsupported format is found. The index will be marked as invalid and will behave as if it doesn't exist. """ pass class DataTypeWarning(Warning): """Unsupported data type. This warning is issued when an unsupported HDF5 data type is found (normally in a file created with other tool than PyTables). """ pass class ExperimentalFeatureWarning(Warning): """Generic warning for experimental features. This warning is issued when using a functionality that is still experimental and that users have to use with care. """ pass class UnclosedFileWarning(Warning): """Warning raised when there are still open files at program exit. PyTables will close remaining open files at exit, but raise this warning. """ pass class ChunkError(Exception): """An operation related to direct chunk access failed. This exception may be related with the properties of the dataset or the chunk being accessed, or with how the chunk is being accessed. It is a base for more specific exceptions. """ pass class NotChunkedError(ChunkError): """A direct chunking operation was attempted on a non-chunked dataset. For instance, chunk information was requested for a plain ``Array`` instance. """ pass class NotChunkAlignedError(ChunkError): """Coordinate not aligned to the chunks. A direct chunk read/write operation was given coordinates that do not match the chunk's start. These operations require coordinates that are integer multiples of the dataset's chunksize. """ pass class NoSuchChunkError(ChunkError): """The chunk with the given coordinates does not exist in storage. The coordinates are within the dataset's shape, though. This is only an error when the chunk is to be read. Such a missing chunk can be written, in which case it is created in storage. """ pass