Source code for tables.table

########################################################################
#
# License: BSD
# Created: September 4, 2002
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
########################################################################

"""Here is defined the Table class."""

import math
import operator
import os.path
import sys
import warnings

from functools import reduce as _reduce
from time import time

import numpy
import numexpr

from . import tableextension
from .lrucacheextension import ObjectCache, NumCache
from .atom import Atom
from .conditions import compile_condition
from numexpr.necompiler import getType as numexpr_getType, double
from numexpr.expressions import functions as numexpr_functions
from .flavor import flavor_of, array_as_internal, internal_to_flavor
from .utils import is_idx, lazyattr, SizeType, NailedDict as CacheDict
from .leaf import Leaf
from .description import (IsDescription, Description, Col, descr_from_dtype)
from .exceptions import (
    NodeError, HDF5ExtError, PerformanceWarning, OldIndexWarning,
    NoSuchNodeError)
from .utilsextension import get_nested_field

from .path import join_path, split_path
from .index import (
    OldIndex, default_index_filters, default_auto_index, Index, IndexesDescG,
    IndexesTableG)


profile = False
# profile = True  # Uncomment for profiling
if profile:
    from .utils import show_stats


# 2.2: Added support for complex types. Introduced in version 0.9.
# 2.2.1: Added suport for time types.
# 2.3: Changed the indexes naming schema.
# 2.4: Changed indexes naming schema (again).
# 2.5: Added the FIELD_%d_FILL attributes.
# 2.6: Added the FLAVOR attribute (optional).
# 2.7: Numeric and numarray flavors are gone.
obversion = "2.7"  # The Table VERSION number


try:
    # int_, long_ are only available in numexpr >= 2.1
    from numexpr.necompiler import int_, long_
except ImportError:
    int_ = int
    long_ = int

# Maps NumPy types to the types used by Numexpr.
_nxtype_from_nptype = {
    numpy.bool_: bool,
    numpy.int8: int_,
    numpy.int16: int_,
    numpy.int32: int_,
    numpy.int64: long_,
    numpy.uint8: int_,
    numpy.uint16: int_,
    numpy.uint32: long_,
    numpy.uint64: long_,
    numpy.float32: float,
    numpy.float64: double,
    numpy.complex64: complex,
    numpy.complex128: complex,
    numpy.bytes_: bytes,
}

_nxtype_from_nptype[numpy.str_] = str

if hasattr(numpy, 'float16'):
    _nxtype_from_nptype[numpy.float16] = float    # XXX: check
if hasattr(numpy, 'float96'):
    _nxtype_from_nptype[numpy.float96] = double   # XXX: check
if hasattr(numpy, 'float128'):
    _nxtype_from_nptype[numpy.float128] = double  # XXX: check
if hasattr(numpy, 'complec192'):
    _nxtype_from_nptype[numpy.complex192] = complex  # XXX: check
if hasattr(numpy, 'complex256'):
    _nxtype_from_nptype[numpy.complex256] = complex  # XXX: check


# The NumPy scalar type corresponding to `SizeType`.
_npsizetype = numpy.array(SizeType(0)).dtype.type


def _index_name_of(node):
    return '_i_%s' % node._v_name


def _index_pathname_of(node):
    nodeParentPath = split_path(node._v_pathname)[0]
    return join_path(nodeParentPath, _index_name_of(node))


def _index_pathname_of_column(table, colpathname):
    return join_path(_index_pathname_of(table), colpathname)


# The next are versions that work with just paths (i.e. we don't need
# a node instance for using them, which can be critical in certain
# situations)


def _index_name_of_(nodeName):
    return '_i_%s' % nodeName


def _index_pathname_of_(nodePath):
    nodeParentPath, nodeName = split_path(nodePath)
    return join_path(nodeParentPath, _index_name_of_(nodeName))


def _index_pathname_of_column_(tablePath, colpathname):
    return join_path(_index_pathname_of_(tablePath), colpathname)


def restorecache(self):
    # Define a cache for sparse table reads
    params = self._v_file.params
    chunksize = self._v_chunkshape[0]
    nslots = params['TABLE_MAX_SIZE'] / (chunksize * self._v_dtype.itemsize)
    self._chunkcache = NumCache((nslots, chunksize), self._v_dtype,
                                'table chunk cache')
    self._seqcache = ObjectCache(params['ITERSEQ_MAX_SLOTS'],
                                 params['ITERSEQ_MAX_SIZE'],
                                 'Iter sequence cache')
    self._dirtycache = False


def _table__where_indexed(self, compiled, condition, condvars,
                          start, stop, step):
    if profile:
        tref = time()
    if profile:
        show_stats("Entering table_whereIndexed", tref)
    self._use_index = True
    # Clean the table caches for indexed queries if needed
    if self._dirtycache:
        restorecache(self)

    # Get the values in expression that are not columns
    values = []
    for key, value in condvars.items():
        if isinstance(value, numpy.ndarray):
            values.append((key, value.item()))
    # Build a key for the sequence cache
    seqkey = (condition, tuple(values), (start, stop, step))
    # Do a lookup in sequential cache for this query
    nslot = self._seqcache.getslot(seqkey)
    if nslot >= 0:
        # Get the row sequence from the cache
        seq = self._seqcache.getitem(nslot)
        if len(seq) == 0:
            return iter([])
        # seq is a list.
        seq = numpy.array(seq, dtype='int64')
        # Correct the ranges in cached sequence
        if (start, stop, step) != (0, self.nrows, 1):
            seq = seq[(seq >= start) & (
                seq < stop) & ((seq - start) % step == 0)]
        return self.itersequence(seq)
    else:
        # No luck.  self._seqcache will be populated
        # in the iterator if possible. (Row._finish_riterator)
        self._seqcache_key = seqkey

    # Compute the chunkmap for every index in indexed expression
    idxexprs = compiled.index_expressions
    strexpr = compiled.string_expression
    cmvars = {}
    tcoords = 0
    for i, idxexpr in enumerate(idxexprs):
        var, ops, lims = idxexpr
        col = condvars[var]
        index = col.index
        assert index is not None, "the chosen column is not indexed"
        assert not index.dirty, "the chosen column has a dirty index"

        # Get the number of rows that the indexed condition yields.
        range_ = index.get_lookup_range(ops, lims)
        ncoords = index.search(range_)
        tcoords += ncoords
        if index.reduction == 1 and ncoords == 0:
            # No values from index condition, thus the chunkmap should be empty
            nrowsinchunk = self.chunkshape[0]
            nchunks = int(math.ceil(float(self.nrows) / nrowsinchunk))
            chunkmap = numpy.zeros(shape=nchunks, dtype="bool")
        else:
            # Get the chunkmap from the index
            chunkmap = index.get_chunkmap()
        # Assign the chunkmap to the cmvars dictionary
        cmvars["e%d" % i] = chunkmap

    if index.reduction == 1 and tcoords == 0:
        # No candidates found in any indexed expression component, so leave now
        self._seqcache.setitem(seqkey, [], 1)
        return iter([])

    # Compute the final chunkmap
    chunkmap = numexpr.evaluate(strexpr, cmvars)
    if not chunkmap.any():
        # The chunkmap is all False, so the result is empty
        self._seqcache.setitem(seqkey, [], 1)
        return iter([])

    if profile:
        show_stats("Exiting table_whereIndexed", tref)
    return chunkmap


def create_indexes_table(table):
    itgroup = IndexesTableG(
        table._v_parent, _index_name_of(table),
        "Indexes container for table " + table._v_pathname, new=True)
    return itgroup


def create_indexes_descr(igroup, dname, iname, filters):
    idgroup = IndexesDescG(
        igroup, iname,
        "Indexes container for sub-description " + dname,
        filters=filters, new=True)
    return idgroup


def _column__create_index(self, optlevel, kind, filters, tmp_dir,
                          blocksizes, verbose):
    name = self.name
    table = self.table
    dtype = self.dtype
    descr = self.descr
    index = self.index
    get_node = table._v_file._get_node

    # Warn if the index already exists
    if index:
        raise ValueError("%s for column '%s' already exists. If you want to "
                         "re-create it, please, try with reindex() method "
                         "better" % (str(index), str(self.pathname)))

    # Check that the datatype is indexable.
    if dtype.str[1:] == 'u8':
        raise NotImplementedError(
            "indexing 64-bit unsigned integer columns "
            "is not supported yet, sorry")
    if dtype.kind == 'c':
        raise TypeError("complex columns can not be indexed")
    if dtype.shape != ():
        raise TypeError("multidimensional columns can not be indexed")

    # Get the indexes group for table, and if not exists, create it
    try:
        itgroup = get_node(_index_pathname_of(table))
    except NoSuchNodeError:
        itgroup = create_indexes_table(table)

    # Create the necessary intermediate groups for descriptors
    idgroup = itgroup
    dname = ""
    pathname = descr._v_pathname
    if pathname != '':
        inames = pathname.split('/')
        for iname in inames:
            if dname == '':
                dname = iname
            else:
                dname += '/' + iname
            try:
                idgroup = get_node('{}/{}'.format(itgroup._v_pathname, dname))
            except NoSuchNodeError:
                idgroup = create_indexes_descr(idgroup, dname, iname, filters)

    # Create the atom
    assert dtype.shape == ()
    atom = Atom.from_dtype(numpy.dtype((dtype, (0,))))

    # Protection on tables larger than the expected rows (perhaps the
    # user forgot to pass this parameter to the Table constructor?)
    expectedrows = table._v_expectedrows
    if table.nrows > expectedrows:
        expectedrows = table.nrows

    # Create the index itself
    index = Index(
        idgroup, name, atom=atom,
        title="Index for %s column" % name,
        kind=kind,
        optlevel=optlevel,
        filters=filters,
        tmp_dir=tmp_dir,
        expectedrows=expectedrows,
        byteorder=table.byteorder,
        blocksizes=blocksizes)

    table._set_column_indexing(self.pathname, True)

    # Feed the index with values

    # Add rows to the index if necessary
    if table.nrows > 0:
        indexedrows = table._add_rows_to_index(
            self.pathname, 0, table.nrows, lastrow=True, update=False)
    else:
        indexedrows = 0
    index.dirty = False
    table._indexedrows = indexedrows
    table._unsaved_indexedrows = table.nrows - indexedrows

    # Optimize the index that has been already filled-up
    index.optimize(verbose=verbose)

    # We cannot do a flush here because when reindexing during a
    # flush, the indexes are created anew, and that creates a nested
    # call to flush().
    # table.flush()

    return indexedrows


class _ColIndexes(dict):
    """Provides a nice representation of column indexes."""

    def __repr__(self):
        """Gives a detailed Description column representation."""

        rep = ['  \"{}\": {}'.format(k, v) for k, v in self.items()]
        return '{\n  %s}' % (',\n  '.join(rep))


[docs]class Table(tableextension.Table, Leaf): """This class represents heterogeneous datasets in an HDF5 file. Tables are leaves (see the Leaf class in :ref:`LeafClassDescr`) whose data consists of a unidimensional sequence of *rows*, where each row contains one or more *fields*. Fields have an associated unique *name* and *position*, with the first field having position 0. All rows have the same fields, which are arranged in *columns*. Fields can have any type supported by the Col class (see :ref:`ColClassDescr`) and its descendants, which support multidimensional data. Moreover, a field can be *nested* (to an arbitrary depth), meaning that it includes further fields inside. A field named x inside a nested field a in a table can be accessed as the field a/x (its *path name*) from the table. The structure of a table is declared by its description, which is made available in the Table.description attribute (see :class:`Table`). This class provides new methods to read, write and search table data efficiently. It also provides special Python methods to allow accessing the table as a normal sequence or array (with extended slicing supported). PyTables supports *in-kernel* searches working simultaneously on several columns using complex conditions. These are faster than selections using Python expressions. See the :meth:`Table.where` method for more information on in-kernel searches. Non-nested columns can be *indexed*. Searching an indexed column can be several times faster than searching a non-nested one. Search methods automatically take advantage of indexing where available. When iterating a table, an object from the Row (see :ref:`RowClassDescr`) class is used. This object allows to read and write data one row at a time, as well as to perform queries which are not supported by in-kernel syntax (at a much lower speed, of course). Objects of this class support access to individual columns via *natural naming* through the :attr:`Table.cols` accessor. Nested columns are mapped to Cols instances, and non-nested ones to Column instances. See the Column class in :ref:`ColumnClassDescr` for examples of this feature. Parameters ---------- parentnode The parent :class:`Group` object. .. versionchanged:: 3.0 Renamed from *parentNode* to *parentnode*. name : str The name of this node in its parent group. description An IsDescription subclass or a dictionary where the keys are the field names, and the values the type definitions. In addition, a pure NumPy dtype is accepted. If None, the table metadata is read from disk, else, it's taken from previous parameters. title Sets a TITLE attribute on the HDF5 table entity. filters : Filters An instance of the Filters class that provides information about the desired I/O filters to be applied during the life of this object. expectedrows A user estimate about the number of rows that will be on table. If not provided, the default value is ``EXPECTED_ROWS_TABLE`` (see ``tables/parameters.py``). If you plan to save bigger tables, try providing a guess; this will optimize the HDF5 B-Tree creation and management process time and memory used. chunkshape The shape of the data chunk to be read or written as a single HDF5 I/O operation. The filters are applied to those chunks of data. Its rank for tables has to be 1. If ``None``, a sensible value is calculated based on the `expectedrows` parameter (which is recommended). byteorder The byteorder of the data *on-disk*, specified as 'little' or 'big'. If this is not specified, the byteorder is that of the platform, unless you passed a recarray as the `description`, in which case the recarray byteorder will be chosen. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 Notes ----- The instance variables below are provided in addition to those in Leaf (see :ref:`LeafClassDescr`). Please note that there are several col* dictionaries to ease retrieving information about a column directly by its path name, avoiding the need to walk through Table.description or Table.cols. .. rubric:: Table attributes .. attribute:: coldescrs Maps the name of a column to its Col description (see :ref:`ColClassDescr`). .. attribute:: coldflts Maps the name of a column to its default value. .. attribute:: coldtypes Maps the name of a column to its NumPy data type. .. attribute:: colindexed Is the column which name is used as a key indexed? .. attribute:: colinstances Maps the name of a column to its Column (see :ref:`ColumnClassDescr`) or Cols (see :ref:`ColsClassDescr`) instance. .. attribute:: colnames A list containing the names of *top-level* columns in the table. .. attribute:: colpathnames A list containing the pathnames of *bottom-level* columns in the table. These are the leaf columns obtained when walking the table description left-to-right, bottom-first. Columns inside a nested column have slashes (/) separating name components in their pathname. .. attribute:: cols A Cols instance that provides *natural naming* access to non-nested (Column, see :ref:`ColumnClassDescr`) and nested (Cols, see :ref:`ColsClassDescr`) columns. .. attribute:: coltypes Maps the name of a column to its PyTables data type. .. attribute:: description A Description instance (see :ref:`DescriptionClassDescr`) reflecting the structure of the table. .. attribute:: extdim The index of the enlargeable dimension (always 0 for tables). .. attribute:: indexed Does this table have any indexed columns? .. attribute:: nrows The current number of rows in the table. """ # Class identifier. _c_classid = 'TABLE' # Properties # ~~~~~~~~~~ @lazyattr def row(self): """The associated Row instance (see :ref:`RowClassDescr`).""" return tableextension.Row(self) @lazyattr def dtype(self): """The NumPy ``dtype`` that most closely matches this table.""" return self.description._v_dtype # Read-only shorthands # ```````````````````` @property def shape(self): """The shape of this table.""" return (self.nrows,) @property def rowsize(self): """The size in bytes of each row in the table.""" return self.description._v_dtype.itemsize @property def size_in_memory(self): """The size of this table's data in bytes when it is fully loaded into memory. This may be used in combination with size_on_disk to calculate the compression ratio of the data.""" return self.nrows * self.rowsize # Lazy attributes # ``````````````` @lazyattr def _v_iobuf(self): """A buffer for doing I/O.""" return self._get_container(self.nrowsinbuf) @lazyattr def _v_wdflts(self): """The defaults for writing in recarray format.""" # First, do a check to see whether we need to set default values # different from 0 or not. for coldflt in self.coldflts.values(): if isinstance(coldflt, numpy.ndarray) or coldflt: break else: # No default different from 0 found. Returning None. return None wdflts = self._get_container(1) for colname, coldflt in self.coldflts.items(): ra = get_nested_field(wdflts, colname) ra[:] = coldflt return wdflts @lazyattr def _colunaligned(self): """The pathnames of unaligned, *unidimensional* columns.""" colunaligned, rarr = [], self._get_container(0) for colpathname in self.colpathnames: carr = get_nested_field(rarr, colpathname) if not carr.flags.aligned and carr.ndim == 1: colunaligned.append(colpathname) return frozenset(colunaligned) # Index-related properties # ```````````````````````` # **************** WARNING! *********************** # This function can be called during the destruction time of a table # so measures have been taken so that it doesn't have to revive # another node (which can fool the LRU cache). The solution devised # has been to add a cache for autoindex (Table._autoindex), populate # it in creation time of the cache (which is a safe period) and then # update the cache whenever it changes. # This solves the error when running test_indexes.py ManyNodesTestCase. # F. Alted 2007-04-20 # ************************************************** @property def autoindex(self): """Automatically keep column indexes up to date? Setting this value states whether existing indexes should be automatically updated after an append operation or recomputed after an index-invalidating operation (i.e. removal and modification of rows). The default is true. This value gets into effect whenever a column is altered. If you don't have automatic indexing activated and you want to do an an immediate update use `Table.flush_rows_to_index()`; for an immediate reindexing of invalidated indexes, use `Table.reindex_dirty()`. This value is persistent. .. versionchanged:: 3.0 The *autoIndex* property has been renamed into *autoindex*. """ if self._autoindex is None: try: indexgroup = self._v_file._get_node(_index_pathname_of(self)) except NoSuchNodeError: self._autoindex = default_auto_index # update cache return self._autoindex else: self._autoindex = indexgroup.auto # update cache return self._autoindex else: # The value is in cache, return it return self._autoindex @autoindex.setter def autoindex(self, auto): auto = bool(auto) try: indexgroup = self._v_file._get_node(_index_pathname_of(self)) except NoSuchNodeError: indexgroup = create_indexes_table(self) indexgroup.auto = auto # Update the cache in table instance as well self._autoindex = auto @property def indexedcolpathnames(self): """List of pathnames of indexed columns in the table.""" return [_colpname for _colpname in self.colpathnames if self.colindexed[_colpname]] @property def colindexes(self): """A dictionary with the indexes of the indexed columns.""" return _ColIndexes( (_colpname, self.cols._f_col(_colpname).index) for _colpname in self.colpathnames if self.colindexed[_colpname]) @property def _dirtyindexes(self): """Whether some index in table is dirty.""" return self._condition_cache._nailcount > 0 # Other methods # ~~~~~~~~~~~~~ def __init__(self, parentnode, name, description=None, title="", filters=None, expectedrows=None, chunkshape=None, byteorder=None, _log=True, track_times=True): self._v_new = new = description is not None """Is this the first time the node has been created?""" self._v_new_title = title """New title for this node.""" self._v_new_filters = filters """New filter properties for this node.""" self.extdim = 0 # Tables only have one dimension currently """The index of the enlargeable dimension (always 0 for tables).""" self._v_recarray = None """A structured array to be stored in the table.""" self._rabyteorder = None """The computed byteorder of the self._v_recarray.""" if expectedrows is None: expectedrows = parentnode._v_file.params['EXPECTED_ROWS_TABLE'] self._v_expectedrows = expectedrows """The expected number of rows to be stored in the table.""" self.nrows = SizeType(0) """The current number of rows in the table.""" self.description = None """A Description instance (see :ref:`DescriptionClassDescr`) reflecting the structure of the table.""" self._time64colnames = [] """The names of ``Time64`` columns.""" self._strcolnames = [] """The names of ``String`` columns.""" self._colenums = {} """Maps the name of an enumerated column to its ``Enum`` instance.""" self._v_chunkshape = None """Private storage for the `chunkshape` property of the leaf.""" self.indexed = False """Does this table have any indexed columns?""" self._indexedrows = 0 """Number of rows indexed in disk.""" self._unsaved_indexedrows = 0 """Number of rows indexed in memory but still not in disk.""" self._listoldindexes = [] """The list of columns with old indexes.""" self._autoindex = None """Private variable that caches the value for autoindex.""" self.colnames = [] """A list containing the names of *top-level* columns in the table.""" self.colpathnames = [] """A list containing the pathnames of *bottom-level* columns in the table. These are the leaf columns obtained when walking the table description left-to-right, bottom-first. Columns inside a nested column have slashes (/) separating name components in their pathname. """ self.colinstances = {} """Maps the name of a column to its Column (see :ref:`ColumnClassDescr`) or Cols (see :ref:`ColsClassDescr`) instance.""" self.coldescrs = {} """Maps the name of a column to its Col description (see :ref:`ColClassDescr`).""" self.coltypes = {} """Maps the name of a column to its PyTables data type.""" self.coldtypes = {} """Maps the name of a column to its NumPy data type.""" self.coldflts = {} """Maps the name of a column to its default value.""" self.colindexed = {} """Is the column which name is used as a key indexed?""" self._use_index = False """Whether an index can be used or not in a search. Boolean.""" self._where_condition = None """Condition function and argument list for selection of values.""" self._seqcache_key = None """The key under which to save a query's results (list of row indexes) or None to not save.""" max_slots = parentnode._v_file.params['COND_CACHE_SLOTS'] self._condition_cache = CacheDict(max_slots) """Cache of already compiled conditions.""" self._exprvars_cache = {} """Cache of variables participating in numexpr expressions.""" self._enabled_indexing_in_queries = True """Is indexing enabled in queries? *Use only for testing.*""" self._empty_array_cache = {} """Cache of empty arrays.""" self._v_dtype = None """The NumPy datatype fopr this table.""" self.cols = None """ A Cols instance that provides *natural naming* access to non-nested (Column, see :ref:`ColumnClassDescr`) and nested (Cols, see :ref:`ColsClassDescr`) columns. """ self._dirtycache = True """Whether the data caches are dirty or not. Initially set to yes.""" self._descflavor = None """Temporarily keeps the flavor of a description with data.""" # Initialize this object in case is a new Table # Try purely descriptive description objects. if new and isinstance(description, dict): # Dictionary case self.description = Description(description, ptparams=parentnode._v_file.params) elif new and (type(description) == type(IsDescription) and issubclass(description, IsDescription)): # IsDescription subclass case descr = description() self.description = Description(descr.columns, ptparams=parentnode._v_file.params) elif new and isinstance(description, Description): # It is a Description instance already self.description = description # No description yet? if new and self.description is None: # Try NumPy dtype instances if isinstance(description, numpy.dtype): self.description, self._rabyteorder = \ descr_from_dtype(description, ptparams=parentnode._v_file.params) # No description yet? if new and self.description is None: # Try structured array description objects. try: self._descflavor = flavor = flavor_of(description) except TypeError: # probably not an array pass else: if flavor == 'python': nparray = numpy.rec.array(description) else: nparray = array_as_internal(description, flavor) self.nrows = nrows = SizeType(nparray.size) # If `self._v_recarray` is set, it will be used as the # initial buffer. if nrows > 0: self._v_recarray = nparray self.description, self._rabyteorder = \ descr_from_dtype(nparray.dtype, ptparams=parentnode._v_file.params) # No description yet? if new and self.description is None: raise TypeError( "the ``description`` argument is not of a supported type: " "``IsDescription`` subclass, ``Description`` instance, " "dictionary, or structured array") # Check the chunkshape parameter if new and chunkshape is not None: if isinstance(chunkshape, (int, numpy.integer)): chunkshape = (chunkshape,) try: chunkshape = tuple(chunkshape) except TypeError: raise TypeError( "`chunkshape` parameter must be an integer or sequence " "and you passed a %s" % type(chunkshape)) if len(chunkshape) != 1: raise ValueError("`chunkshape` rank (length) must be 1: %r" % (chunkshape,)) self._v_chunkshape = tuple(SizeType(s) for s in chunkshape) super().__init__(parentnode, name, new, filters, byteorder, _log, track_times) def _g_post_init_hook(self): # We are putting here the index-related issues # as well as filling general info for table # This is needed because we need first the index objects created # First, get back the flavor of input data (if any) for # `Leaf._g_post_init_hook()`. self._flavor, self._descflavor = self._descflavor, None super()._g_post_init_hook() # Create a cols accessor. self.cols = Cols(self, self.description) # Place the `Cols` and `Column` objects into `self.colinstances`. colinstances, cols = self.colinstances, self.cols for colpathname in self.description._v_pathnames: colinstances[colpathname] = cols._g_col(colpathname) if self._v_new: # Columns are never indexed on creation. self.colindexed = {cpn: False for cpn in self.colpathnames} return # The following code is only for opened tables. # Do the indexes group exist? indexesgrouppath = _index_pathname_of(self) igroup = indexesgrouppath in self._v_file oldindexes = False for colobj in self.description._f_walk(type="Col"): colname = colobj._v_pathname # Is this column indexed? if igroup: indexname = _index_pathname_of_column(self, colname) indexed = indexname in self._v_file self.colindexed[colname] = indexed if indexed: column = self.cols._g_col(colname) indexobj = column.index if isinstance(indexobj, OldIndex): indexed = False # Not a vaild index oldindexes = True self._listoldindexes.append(colname) else: # Tell the condition cache about columns with dirty # indexes. if indexobj.dirty: self._condition_cache.nail() else: indexed = False self.colindexed[colname] = False if indexed: self.indexed = True if oldindexes: # this should only appear under 2.x Pro warnings.warn( "table ``%s`` has column indexes with PyTables 1.x format. " "Unfortunately, this format is not supported in " "PyTables 2.x series. Note that you can use the " "``ptrepack`` utility in order to recreate the indexes. " "The 1.x indexed columns found are: %s" % (self._v_pathname, self._listoldindexes), OldIndexWarning) # It does not matter to which column 'indexobj' belongs, # since their respective index objects share # the same number of elements. if self.indexed: self._indexedrows = indexobj.nelements self._unsaved_indexedrows = self.nrows - self._indexedrows # Put the autoindex value in a cache variable self._autoindex = self.autoindex def _calc_nrowsinbuf(self): """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 # The number of rows in buffer needs to be an exact multiple of # chunkshape[0] for queries using indexed columns. # Fixes #319 and probably #409 too. nrowsinbuf -= nrowsinbuf % self.chunkshape[0] else: nrowsinbuf = 1 # tableextension.pyx performs an assertion # to make sure nrowsinbuf is greater than or # equal to the chunksize. # See gh-206 and gh-238 if self.chunkshape is not None: if nrowsinbuf < self.chunkshape[0]: nrowsinbuf = self.chunkshape[0] # 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("""\ The Table ``%s`` is exceeding the maximum recommended rowsize (%d bytes); be ready to see PyTables asking for *lots* of memory and possibly slow I/O. You may want to reduce the rowsize by trimming the value of dimensions that are orthogonal (and preferably close) to the *main* dimension of this leave. Alternatively, in case you have specified a very small/large chunksize, you may want to increase/decrease it.""" % (self._v_pathname, maxrowsize), PerformanceWarning) return nrowsinbuf def _getemptyarray(self, dtype): # Acts as a cache for empty arrays key = dtype if key in self._empty_array_cache: return self._empty_array_cache[key] else: self._empty_array_cache[ key] = arr = numpy.empty(shape=0, dtype=key) return arr def _get_container(self, shape): """Get the appropriate buffer for data depending on table nestedness.""" # This is *much* faster than the numpy.rec.array counterpart return numpy.empty(shape=shape, dtype=self._v_dtype) def _get_type_col_names(self, type_): """Returns a list containing 'type_' column names.""" return [colobj._v_pathname for colobj in self.description._f_walk('Col') if colobj.type == type_] def _get_enum_map(self): """Return mapping from enumerated column names to `Enum` instances.""" enumMap = {} for colobj in self.description._f_walk('Col'): if colobj.kind == 'enum': enumMap[colobj._v_pathname] = colobj.enum return enumMap def _g_create(self): """Create a new table on disk.""" # Warning against assigning too much columns... # F. Alted 2005-06-05 maxColumns = self._v_file.params['MAX_COLUMNS'] if (len(self.description._v_names) > maxColumns): warnings.warn( "table ``%s`` is exceeding the recommended " "maximum number of columns (%d); " "be ready to see PyTables asking for *lots* of memory " "and possibly slow I/O" % (self._v_pathname, maxColumns), PerformanceWarning) # 1. Create the HDF5 table (some parameters need to be computed). # Fix the byteorder of the recarray and update the number of # expected rows if necessary if self._v_recarray is not None: self._v_recarray = self._g_fix_byteorder_data(self._v_recarray, self._rabyteorder) if len(self._v_recarray) > self._v_expectedrows: self._v_expectedrows = len(self._v_recarray) # Compute a sensible chunkshape if self._v_chunkshape is None: self._v_chunkshape = self._calc_chunkshape( self._v_expectedrows, self.rowsize, self.rowsize) # Correct the byteorder, if still needed if self.byteorder is None: self.byteorder = sys.byteorder # Cache some data which is already in the description. # This is necessary to happen before creation time in order # to be able to populate the self._v_wdflts self._cache_description_data() # After creating the table, ``self._v_objectid`` needs to be # set because it is needed for setting attributes afterwards. self._v_objectid = self._create_table( self._v_new_title, self.filters.complib or '', obversion) self._v_recarray = None # not useful anymore self._rabyteorder = None # not useful anymore # 2. Compute or get chunk shape and buffer size parameters. self.nrowsinbuf = self._calc_nrowsinbuf() # 3. Get field fill attributes from the table description and # set them on disk. if self._v_file.params['PYTABLES_SYS_ATTRS']: set_attr = self._v_attrs._g__setattr for i, colobj in enumerate(self.description._f_walk(type="Col")): fieldname = "FIELD_%d_FILL" % i set_attr(fieldname, colobj.dflt) return self._v_objectid def _g_open(self): """Opens a table from disk and read the metadata on it. Creates an user description on the flight to easy the access to the actual data. """ # 1. Open the HDF5 table and get some data from it. self._v_objectid, description, chunksize = self._get_info() self._v_expectedrows = self.nrows # the actual number of rows # 2. Create an instance description to host the record fields. validate = not self._v_file._isPTFile # only for non-PyTables files self.description = Description(description, validate=validate, ptparams=self._v_file.params) # 3. Compute or get chunk shape and buffer size parameters. if chunksize == 0: self._v_chunkshape = self._calc_chunkshape( self._v_expectedrows, self.rowsize, self.rowsize) else: self._v_chunkshape = (chunksize,) self.nrowsinbuf = self._calc_nrowsinbuf() # 4. If there are field fill attributes, get them from disk and # set them in the table description. if self._v_file.params['PYTABLES_SYS_ATTRS']: if "FIELD_0_FILL" in self._v_attrs._f_list("sys"): i = 0 get_attr = self._v_attrs.__getattr__ for objcol in self.description._f_walk(type="Col"): colname = objcol._v_pathname # Get the default values for each column fieldname = "FIELD_%s_FILL" % i defval = get_attr(fieldname) if defval is not None: objcol.dflt = defval else: warnings.warn("could not load default value " "for the ``%s`` column of table ``%s``; " "using ``%r`` instead" % (colname, self._v_pathname, objcol.dflt)) defval = objcol.dflt i += 1 # Set also the correct value in the desc._v_dflts dictionary for descr in self.description._f_walk(type="Description"): for name in descr._v_names: objcol = descr._v_colobjects[name] if isinstance(objcol, Col): descr._v_dflts[objcol._v_name] = objcol.dflt # 5. Cache some data which is already in the description. self._cache_description_data() return self._v_objectid def _cache_description_data(self): """Cache some data which is already in the description. Some information is extracted from `self.description` to build some useful (but redundant) structures: * `self.colnames` * `self.colpathnames` * `self.coldescrs` * `self.coltypes` * `self.coldtypes` * `self.coldflts` * `self._v_dtype` * `self._time64colnames` * `self._strcolnames` * `self._colenums` """ self.colnames = list(self.description._v_names) self.colpathnames = [ col._v_pathname for col in self.description._f_walk() if not hasattr(col, '_v_names')] # bottom-level # Find ``time64`` column names. self._time64colnames = self._get_type_col_names('time64') # Find ``string`` column names. self._strcolnames = self._get_type_col_names('string') # Get a mapping of enumerated columns to their `Enum` instances. self._colenums = self._get_enum_map() # Get info about columns for colobj in self.description._f_walk(type="Col"): colname = colobj._v_pathname # Get the column types, types and defaults self.coldescrs[colname] = colobj self.coltypes[colname] = colobj.type self.coldtypes[colname] = colobj.dtype self.coldflts[colname] = colobj.dflt # Assign _v_dtype for this table self._v_dtype = self.description._v_dtype def _get_column_instance(self, colpathname): """Get the instance of the column with the given `colpathname`. If the column does not exist in the table, a `KeyError` is raised. """ try: return _reduce(getattr, colpathname.split('/'), self.description) except AttributeError: raise KeyError("table ``%s`` does not have a column named ``%s``" % (self._v_pathname, colpathname)) _check_column = _get_column_instance def _disable_indexing_in_queries(self): """Force queries not to use indexing. *Use only for testing.* """ if not self._enabled_indexing_in_queries: return # already disabled # The nail avoids setting/getting compiled conditions in/from # the cache where indexing is used. self._condition_cache.nail() self._enabled_indexing_in_queries = False def _enable_indexing_in_queries(self): """Allow queries to use indexing. *Use only for testing.* """ if self._enabled_indexing_in_queries: return # already enabled self._condition_cache.unnail() self._enabled_indexing_in_queries = True def _required_expr_vars(self, expression, uservars, depth=1): """Get the variables required by the `expression`. A new dictionary defining the variables used in the `expression` is returned. Required variables are first looked up in the `uservars` mapping, then in the set of top-level columns of the table. Unknown variables cause a `NameError` to be raised. When `uservars` is `None`, the local and global namespace where the API callable which uses this method is called is sought instead. This mechanism will not work as expected if this method is not used *directly* from an API callable. To disable this mechanism, just specify a mapping as `uservars`. Nested columns and columns from other tables are not allowed (`TypeError` and `ValueError` are raised, respectively). Also, non-column variable values are converted to NumPy arrays. `depth` specifies the depth of the frame in order to reach local or global variables. """ # Get the names of variables used in the expression. exprvarscache = self._exprvars_cache if expression not in exprvarscache: # Protection against growing the cache too much if len(exprvarscache) > 256: # Remove 10 (arbitrary) elements from the cache for k in list(exprvarscache)[:10]: del exprvarscache[k] cexpr = compile(expression, '<string>', 'eval') exprvars = [var for var in cexpr.co_names if var not in ['None', 'False', 'True'] and var not in numexpr_functions] exprvarscache[expression] = exprvars else: exprvars = exprvarscache[expression] # Get the local and global variable mappings of the user frame # if no mapping has been explicitly given for user variables. user_locals, user_globals = {}, {} if uservars is None: # We use specified depth to get the frame where the API # callable using this method is called. For instance: # # * ``table._required_expr_vars()`` (depth 0) is called by # * ``table._where()`` (depth 1) is called by # * ``table.where()`` (depth 2) is called by # * user-space functions (depth 3) user_frame = sys._getframe(depth) user_locals = user_frame.f_locals user_globals = user_frame.f_globals colinstances = self.colinstances tblfile, tblpath = self._v_file, self._v_pathname # Look for the required variables first among the ones # explicitly provided by the user, then among implicit columns, # then among external variables (only if no explicit variables). reqvars = {} for var in exprvars: # Get the value. if uservars is not None and var in uservars: val = uservars[var] elif var in colinstances: val = colinstances[var] elif uservars is None and var in user_locals: val = user_locals[var] elif uservars is None and var in user_globals: val = user_globals[var] else: raise NameError("name ``%s`` is not defined" % var) # Check the value. if hasattr(val, 'pathname'): # non-nested column if val.shape[1:] != (): raise NotImplementedError( "variable ``%s`` refers to " "a multidimensional column, " "not yet supported in conditions, sorry" % var) if (val._table_file is not tblfile or val._table_path != tblpath): raise ValueError("variable ``%s`` refers to a column " "which is not part of table ``%s``" % (var, tblpath)) if val.dtype.str[1:] == 'u8': raise NotImplementedError( "variable ``%s`` refers to " "a 64-bit unsigned integer column, " "not yet supported in conditions, sorry; " "please use regular Python selections" % var) elif hasattr(val, '_v_colpathnames'): # nested column raise TypeError( "variable ``%s`` refers to a nested column, " "not allowed in conditions" % var) else: # only non-column values are converted to arrays # XXX: not 100% sure about this if isinstance(val, str): val = numpy.asarray(val.encode('ascii')) else: val = numpy.asarray(val) reqvars[var] = val return reqvars def _get_condition_key(self, condition, condvars): """Get the condition cache key for `condition` with `condvars`. Currently, the key is a tuple of `condition`, column variables names, normal variables names, column paths and variable paths (all are tuples). """ # Variable names for column and normal variables. colnames, varnames = [], [] # Column paths and types for each of the previous variable. colpaths, vartypes = [], [] for (var, val) in condvars.items(): if hasattr(val, 'pathname'): # column colnames.append(var) colpaths.append(val.pathname) else: # array try: varnames.append(var) vartypes.append(numexpr_getType(val)) # expensive except ValueError: # This is more clear than the error given by Numexpr. raise TypeError("variable ``%s`` has data type ``%s``, " "not allowed in conditions" % (var, val.dtype.name)) colnames, varnames = tuple(colnames), tuple(varnames) colpaths, vartypes = tuple(colpaths), tuple(vartypes) condkey = (condition, colnames, varnames, colpaths, vartypes) return condkey def _compile_condition(self, condition, condvars): """Compile the `condition` and extract usable index conditions. This method returns an instance of ``CompiledCondition``. See the ``compile_condition()`` function in the ``conditions`` module for more information about the compilation process. This method makes use of the condition cache when possible. """ # Look up the condition in the condition cache. condcache = self._condition_cache condkey = self._get_condition_key(condition, condvars) compiled = condcache.get(condkey) if compiled: return compiled.with_replaced_vars(condvars) # bingo! # Bad luck, the condition must be parsed and compiled. # Fortunately, the key provides some valuable information. ;) (condition, colnames, varnames, colpaths, vartypes) = condkey # Extract more information from referenced columns. # start with normal variables typemap = dict(list(zip(varnames, vartypes))) indexedcols = [] for colname in colnames: col = condvars[colname] # Extract types from *all* the given variables. coltype = col.dtype.type typemap[colname] = _nxtype_from_nptype[coltype] # Get the set of columns with usable indexes. if (self._enabled_indexing_in_queries # no in-kernel searches and self.colindexed[col.pathname] and not col.index.dirty): indexedcols.append(colname) indexedcols = frozenset(indexedcols) # Now let ``compile_condition()`` do the Numexpr-related job. compiled = compile_condition(condition, typemap, indexedcols) # Check that there actually are columns in the condition. if not set(compiled.parameters).intersection(set(colnames)): raise ValueError("there are no columns taking part " "in condition ``%s``" % (condition,)) # Store the compiled condition in the cache and return it. condcache[condkey] = compiled return compiled.with_replaced_vars(condvars)
[docs] def will_query_use_indexing(self, condition, condvars=None): """Will a query for the condition use indexing? The meaning of the condition and *condvars* arguments is the same as in the :meth:`Table.where` method. If condition can use indexing, this method returns a frozenset with the path names of the columns whose index is usable. Otherwise, it returns an empty list. This method is mainly intended for testing. Keep in mind that changing the set of indexed columns or their dirtiness may make this method return different values for the same arguments at different times. """ # Compile the condition and extract usable index conditions. condvars = self._required_expr_vars(condition, condvars, depth=2) compiled = self._compile_condition(condition, condvars) # Return the columns in indexed expressions idxcols = [condvars[var].pathname for var in compiled.index_variables] return frozenset(idxcols)
[docs] def where(self, condition, condvars=None, start=None, stop=None, step=None): r"""Iterate over values fulfilling a condition. This method returns a Row iterator (see :ref:`RowClassDescr`) which only selects rows in the table that satisfy the given condition (an expression-like string). The condvars mapping may be used to define the variable names appearing in the condition. condvars should consist of identifier-like strings pointing to Column (see :ref:`ColumnClassDescr`) instances *of this table*, or to other values (which will be converted to arrays). A default set of condition variables is provided where each top-level, non-nested column with an identifier-like name appears. Variables in condvars override the default ones. When condvars is not provided or None, the current local and global namespace is sought instead of condvars. The previous mechanism is mostly intended for interactive usage. To disable it, just specify a (maybe empty) mapping as condvars. If a range is supplied (by setting some of the start, stop or step parameters), only the rows in that range and fulfilling the condition are used. The meaning of the start, stop and step parameters is the same as for Python slices. When possible, indexed columns participating in the condition will be used to speed up the search. It is recommended that you place the indexed columns as left and out in the condition as possible. Anyway, this method has always better performance than regular Python selections on the table. You can mix this method with regular Python selections in order to support even more complex queries. It is strongly recommended that you pass the most restrictive condition as the parameter to this method if you want to achieve maximum performance. .. warning:: When in the middle of a table row iterator, you should not use methods that can change the number of rows in the table (like :meth:`Table.append` or :meth:`Table.remove_rows`) or unexpected errors will happen. Examples -------- :: >>> passvalues = [ row['col3'] for row in ... table.where('(col1 > 0) & (col2 <= 20)', step=5) ... if your_function(row['col2']) ] >>> print("Values that pass the cuts:", passvalues) .. note:: A special care should be taken when the query condition includes string literals. Indeed Python 2 string literals are string of bytes while Python 3 strings are unicode objects. Let's assume that the table ``table`` has the following structure:: class Record(IsDescription): col1 = StringCol(4) # 4-character String of bytes col2 = IntCol() col3 = FloatCol() The type of "col1" do not change depending on the Python version used (of course) and it always corresponds to strings of bytes. Any condition involving "col1" should be written using the appropriate type for string literals in order to avoid :exc:`TypeError`\ s. The code below will work fine in Python 2 but will fail with a :exc:`TypeError` in Python 3:: condition = 'col1 == "AAAA"' for record in table.where(condition): # TypeError in Python3 # do something with "record" The reason is that in Python 3 "condition" implies a comparison between a string of bytes ("col1" contents) and an unicode literal ("AAAA"). The correct way to write the condition is:: condition = 'col1 == b"AAAA"' .. versionchanged:: 3.0 The start, stop and step parameters now behave like in slice. """ return self._where(condition, condvars, start, stop, step)
def _where(self, condition, condvars, start=None, stop=None, step=None): """Low-level counterpart of `self.where()`.""" if profile: tref = time() if profile: show_stats("Entering table._where", tref) # Adjust the slice to be used. (start, stop, step) = self._process_range_read(start, stop, step) if start >= stop: # empty range, reset conditions self._use_index = False self._where_condition = None return iter([]) # Compile the condition and extract usable index conditions. condvars = self._required_expr_vars(condition, condvars, depth=3) compiled = self._compile_condition(condition, condvars) # Can we use indexes? if compiled.index_expressions: chunkmap = _table__where_indexed( self, compiled, condition, condvars, start, stop, step) if not isinstance(chunkmap, numpy.ndarray): # If it is not a NumPy array it should be an iterator # Reset conditions self._use_index = False self._where_condition = None # ...and return the iterator return chunkmap else: chunkmap = None # default to an in-kernel query args = [condvars[param] for param in compiled.parameters] self._where_condition = (compiled.function, args, compiled.kwargs) row = tableextension.Row(self) if profile: show_stats("Exiting table._where", tref) return row._iter(start, stop, step, chunkmap=chunkmap)
[docs] def read_where(self, condition, condvars=None, field=None, start=None, stop=None, step=None): """Read table data fulfilling the given *condition*. This method is similar to :meth:`Table.read`, having their common arguments and return values the same meanings. However, only the rows fulfilling the *condition* are included in the result. The meaning of the other arguments is the same as in the :meth:`Table.where` method. """ self._g_check_open() coords = [p.nrow for p in self._where(condition, condvars, start, stop, step)] self._where_condition = None # reset the conditions if len(coords) > 1: cstart, cstop = coords[0], coords[-1] + 1 if cstop - cstart == len(coords): # Chances for monotonically increasing row values. Refine. inc_seq = numpy.alltrue( numpy.arange(cstart, cstop) == numpy.array(coords)) if inc_seq: return self.read(cstart, cstop, field=field) return self.read_coordinates(coords, field)
[docs] def append_where(self, dstTable, condition=None, condvars=None, start=None, stop=None, step=None): """Append rows fulfilling the condition to the dstTable table. dstTable must be capable of taking the rows resulting from the query, i.e. it must have columns with the expected names and compatible types. The meaning of the other arguments is the same as in the :meth:`Table.where` method. The number of rows appended to dstTable is returned as a result. .. versionchanged:: 3.0 The *whereAppend* method has been renamed into *append_where*. """ self._g_check_open() # Check that the destination file is not in read-only mode. dstTable._v_file._check_writable() # Row objects do not support nested columns, so we must iterate # over the flat column paths. When rows support nesting, # ``self.colnames`` can be directly iterated upon. colNames = [colName for colName in self.colpathnames] dstRow = dstTable.row nrows = 0 if condition is not None: srcRows = self._where(condition, condvars, start, stop, step) else: srcRows = self.iterrows(start, stop, step) for srcRow in srcRows: for colName in colNames: dstRow[colName] = srcRow[colName] dstRow.append() nrows += 1 dstTable.flush() return nrows
[docs] def get_where_list(self, condition, condvars=None, sort=False, start=None, stop=None, step=None): """Get the row coordinates fulfilling the given condition. The coordinates are returned as a list of the current flavor. sort means that you want to retrieve the coordinates ordered. The default is to not sort them. The meaning of the other arguments is the same as in the :meth:`Table.where` method. """ self._g_check_open() coords = [p.nrow for p in self._where(condition, condvars, start, stop, step)] coords = numpy.array(coords, dtype=SizeType) # Reset the conditions self._where_condition = None if sort: coords = numpy.sort(coords) return internal_to_flavor(coords, self.flavor)
[docs] def itersequence(self, sequence): """Iterate over a sequence of row coordinates.""" if not hasattr(sequence, '__getitem__'): raise TypeError("Wrong 'sequence' parameter type. Only sequences " "are suported.") # start, stop and step are necessary for the new iterator for # coordinates, and perhaps it would be useful to add them as # parameters in the future (not now, because I've just removed # the `sort` argument for 2.1). # # *Important note*: Negative values for step are not supported # for the general case, but only for the itersorted() and # read_sorted() purposes! The self._process_range_read will raise # an appropiate error. # F. Alted 2008-09-18 # A.V. 20130513: _process_range_read --> _process_range (start, stop, step) = self._process_range(None, None, None) if (start > stop) or (len(sequence) == 0): return iter([]) row = tableextension.Row(self) return row._iter(start, stop, step, coords=sequence)
def _check_sortby_csi(self, sortby, checkCSI): if isinstance(sortby, Column): icol = sortby elif isinstance(sortby, str): icol = self.cols._f_col(sortby) else: raise TypeError( "`sortby` can only be a `Column` or string object, " "but you passed an object of type: %s" % type(sortby)) if icol.is_indexed and icol.index.kind == "full": if checkCSI and not icol.index.is_csi: # The index exists, but it is not a CSI one. raise ValueError( "Field `%s` must have associated a CSI index " "in table `%s`, but the existing one is not. " % (sortby, self)) return icol.index else: raise ValueError( "Field `%s` must have associated a 'full' index " "in table `%s`." % (sortby, self))
[docs] def itersorted(self, sortby, checkCSI=False, start=None, stop=None, step=None): """Iterate table data following the order of the index of sortby column. The sortby column must have associated a full index. If you want to ensure a fully sorted order, the index must be a CSI one. You may want to use the checkCSI argument in order to explicitly check for the existence of a CSI index. The meaning of the start, stop and step arguments is the same as in :meth:`Table.read`. .. versionchanged:: 3.0 If the *start* parameter is provided and *stop* is None then the table is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned. """ index = self._check_sortby_csi(sortby, checkCSI) # Adjust the slice to be used. (start, stop, step) = self._process_range(start, stop, step, warn_negstep=False) if (start > stop and 0 < step) or (start < stop and 0 > step): # Fall-back action is to return an empty iterator return iter([]) row = tableextension.Row(self) return row._iter(start, stop, step, coords=index)
[docs] def read_sorted(self, sortby, checkCSI=False, field=None, start=None, stop=None, step=None): """Read table data following the order of the index of sortby column. The sortby column must have associated a full index. If you want to ensure a fully sorted order, the index must be a CSI one. You may want to use the checkCSI argument in order to explicitly check for the existence of a CSI index. If field is supplied only the named column will be selected. If the column is not nested, an *array* of the current flavor will be returned; if it is, a *structured array* will be used instead. If no field is specified, all the columns will be returned in a structured array of the current flavor. The meaning of the start, stop and step arguments is the same as in :meth:`Table.read`. .. versionchanged:: 3.0 The start, stop and step parameters now behave like in slice. """ self._g_check_open() index = self._check_sortby_csi(sortby, checkCSI) coords = index[start:stop:step] return self.read_coordinates(coords, field)
[docs] def iterrows(self, start=None, stop=None, step=None): """Iterate over the table using a Row instance. If a range is not supplied, *all the rows* in the table are iterated upon - you can also use the :meth:`Table.__iter__` special method for that purpose. If you want to iterate over a given *range of rows* in the table, you may use the start, stop and step parameters. .. warning:: When in the middle of a table row iterator, you should not use methods that can change the number of rows in the table (like :meth:`Table.append` or :meth:`Table.remove_rows`) or unexpected errors will happen. See Also -------- tableextension.Row : the table row iterator and field accessor Examples -------- :: result = [ row['var2'] for row in table.iterrows(step=5) if row['var1'] <= 20 ] .. versionchanged:: 3.0 If the *start* parameter is provided and *stop* is None then the table is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned. """ (start, stop, step) = self._process_range(start, stop, step, warn_negstep=False) if (start > stop and 0 < step) or (start < stop and 0 > step): # Fall-back action is to return an empty iterator return iter([]) row = tableextension.Row(self) return row._iter(start, stop, step)
[docs] def __iter__(self): """Iterate over the table using a Row instance. This is equivalent to calling :meth:`Table.iterrows` with default arguments, i.e. it iterates over *all the rows* in the table. See Also -------- tableextension.Row : the table row iterator and field accessor Examples -------- :: result = [ row['var2'] for row in table if row['var1'] <= 20 ] Which is equivalent to:: result = [ row['var2'] for row in table.iterrows() if row['var1'] <= 20 ] """ return self.iterrows()
def _read(self, start, stop, step, field=None, out=None): """Read a range of rows and return an in-memory object.""" select_field = None if field: if field not in self.coldtypes: if field in self.description._v_names: # Remember to select this field select_field = field field = None else: raise KeyError(("Field {} not found in table " "{}").format(field, self)) else: # The column hangs directly from the top dtype_field = self.coldtypes[field] # Return a rank-0 array if start > stop if (start >= stop and 0 < step) or (start <= stop and 0 > step): if field is None: nra = self._get_container(0) return nra return numpy.empty(shape=0, dtype=dtype_field) nrows = len(range(start, stop, step)) if out is None: # Compute the shape of the resulting column object if field: # Create a container for the results result = numpy.empty(shape=nrows, dtype=dtype_field) else: # Recarray case result = self._get_container(nrows) else: # there is no fast way to byteswap, since different columns may # have different byteorders if not out.dtype.isnative: raise ValueError("output array must be in system's byteorder " "or results will be incorrect") if field: bytes_required = dtype_field.itemsize * nrows else: bytes_required = self.rowsize * nrows if bytes_required != out.nbytes: raise ValueError(('output array size invalid, got {} bytes, ' 'need {} bytes').format(out.nbytes, bytes_required)) if not out.flags['C_CONTIGUOUS']: raise ValueError('output array not C contiguous') result = out # Call the routine to fill-up the resulting array if step == 1 and not field: # This optimization works three times faster than # the row._fill_col method (up to 170 MB/s on a pentium IV @ 2GHz) self._read_records(start, stop - start, result) # Warning!: _read_field_name should not be used until # H5TBread_fields_name in tableextension will be finished # F. Alted 2005/05/26 # XYX Ho implementem per a PyTables 2.0?? elif field and step > 15 and 0: # For step>15, this seems to work always faster than row._fill_col. self._read_field_name(result, start, stop, step, field) else: self.row._fill_col(result, start, stop, step, field) if select_field: return result[select_field] else: return result
[docs] def read(self, start=None, stop=None, step=None, field=None, out=None): """Get data in the table as a (record) array. The start, stop and step parameters can be used to select only a *range of rows* in the table. Their meanings are the same as in the built-in Python slices. If field is supplied only the named column will be selected. If the column is not nested, an *array* of the current flavor will be returned; if it is, a *structured array* will be used instead. If no field is specified, all the columns will be returned in a structured array of the current flavor. Columns under a nested column can be specified in the field parameter by using a slash character (/) as a separator (e.g. 'position/x'). The out parameter may be used to specify a NumPy array to receive the output data. Note that the array must have the same size as the data selected with the other parameters. Note that the array's datatype is not checked and no type casting is performed, so if it does not match the datatype on disk, the output will not be correct. When specifying a single nested column with the field parameter, and supplying an output buffer with the out parameter, the output buffer must contain all columns in the table. The data in all columns will be read into the output buffer. However, only the specified nested column will be returned from the method call. When data is read from disk in NumPy format, the output will be in the current system's byteorder, regardless of how it is stored on disk. If the out parameter is specified, the output array also must be in the current system's byteorder. .. versionchanged:: 3.0 Added the *out* parameter. Also the start, stop and step parameters now behave like in slice. Examples -------- Reading the entire table:: t.read() Reading record n. 6:: t.read(6, 7) Reading from record n. 6 to the end of the table:: t.read(6) """ self._g_check_open() if field: self._check_column(field) if out is not None and self.flavor != 'numpy': msg = ("Optional 'out' argument may only be supplied if array " "flavor is 'numpy', currently is {}").format(self.flavor) raise TypeError(msg) start, stop, step = self._process_range(start, stop, step, warn_negstep=False) arr = self._read(start, stop, step, field, out) return internal_to_flavor(arr, self.flavor)
def _read_coordinates(self, coords, field=None): """Private part of `read_coordinates()` with no flavor conversion.""" coords = self._point_selection(coords) ncoords = len(coords) # Create a read buffer only if needed if field is None or ncoords > 0: # Doing a copy is faster when ncoords is small (<1000) if ncoords < min(1000, self.nrowsinbuf): result = self._v_iobuf[:ncoords].copy() else: result = self._get_container(ncoords) # Do the real read if ncoords > 0: # Turn coords into an array of coordinate indexes, if necessary if not (isinstance(coords, numpy.ndarray) and coords.dtype.type is _npsizetype and coords.flags.contiguous and coords.flags.aligned): # Get a contiguous and aligned coordinate array coords = numpy.array(coords, dtype=SizeType) self._read_elements(coords, result) # Do the final conversions, if needed if field: if ncoords > 0: result = get_nested_field(result, field) else: # Get an empty array from the cache result = self._getemptyarray(self.coldtypes[field]) return result
[docs] def read_coordinates(self, coords, field=None): """Get a set of rows given their indexes as a (record) array. This method works much like the :meth:`Table.read` method, but it uses a sequence (coords) of row indexes to select the wanted columns, instead of a column range. The selected rows are returned in an array or structured array of the current flavor. """ self._g_check_open() result = self._read_coordinates(coords, field) return internal_to_flavor(result, self.flavor)
[docs] def get_enum(self, colname): """Get the enumerated type associated with the named column. If the column named colname (a string) exists and is of an enumerated type, the corresponding Enum instance (see :ref:`EnumClassDescr`) is returned. If it is not of an enumerated type, a TypeError is raised. If the column does not exist, a KeyError is raised. """ self._check_column(colname) try: return self._colenums[colname] except KeyError: raise TypeError( "column ``%s`` of table ``%s`` is not of an enumerated type" % (colname, self._v_pathname))
[docs] def col(self, name): """Get a column from the table. If a column called name exists in the table, it is read and returned as a NumPy object. If it does not exist, a KeyError is raised. Examples -------- :: narray = table.col('var2') That statement is equivalent to:: narray = table.read(field='var2') Here you can see how this method can be used as a shorthand for the :meth:`Table.read` method. """ return self.read(field=name)
[docs] def __getitem__(self, key): """Get a row or a range of rows from the table. If key argument is an integer, the corresponding table row is returned as a record of the current flavor. If key is a slice, the range of rows determined by it is returned as a structured array of the current flavor. In addition, NumPy-style point selections are supported. In particular, if key is a list of row coordinates, the set of rows determined by it is returned. Furthermore, if key is an array of boolean values, only the coordinates where key is True are returned. Note that for the latter to work it is necessary that key list would contain exactly as many rows as the table has. Examples -------- :: record = table[4] recarray = table[4:1000:2] recarray = table[[4,1000]] # only retrieves rows 4 and 1000 recarray = table[[True, False, ..., True]] Those statements are equivalent to:: record = table.read(start=4)[0] recarray = table.read(start=4, stop=1000, step=2) recarray = table.read_coordinates([4,1000]) recarray = table.read_coordinates([True, False, ..., True]) Here, you can see how indexing can be used as a shorthand for the :meth:`Table.read` and :meth:`Table.read_coordinates` methods. """ self._g_check_open() if is_idx(key): key = operator.index(key) # Index out of range protection if key >= self.nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += self.nrows (start, stop, step) = self._process_range(key, key + 1, 1) return self.read(start, stop, step)[0] elif isinstance(key, slice): (start, stop, step) = self._process_range( key.start, key.stop, key.step) return self.read(start, stop, step) # Try with a boolean or point selection elif type(key) in (list, tuple) or isinstance(key, numpy.ndarray): return self._read_coordinates(key, None) else: raise IndexError("Invalid index or slice: {!r}".format(key))
[docs] def __setitem__(self, key, value): """Set a row or a range of rows in the table. It takes different actions depending on the type of the *key* parameter: if it is an integer, the corresponding table row is set to *value* (a record or sequence capable of being converted to the table structure). If *key* is a slice, the row slice determined by it is set to *value* (a record array or sequence capable of being converted to the table structure). In addition, NumPy-style point selections are supported. In particular, if key is a list of row coordinates, the set of rows determined by it is set to value. Furthermore, if key is an array of boolean values, only the coordinates where key is True are set to values from value. Note that for the latter to work it is necessary that key list would contain exactly as many rows as the table has. Examples -------- :: # Modify just one existing row table[2] = [456,'db2',1.2] # Modify two existing rows rows = numpy.rec.array([[457,'db1',1.2],[6,'de2',1.3]], formats='i4,a3,f8') table[1:30:2] = rows # modify a table slice table[[1,3]] = rows # only modifies rows 1 and 3 table[[True,False,True]] = rows # only modifies rows 0 and 2 Which is equivalent to:: table.modify_rows(start=2, rows=[456,'db2',1.2]) rows = numpy.rec.array([[457,'db1',1.2],[6,'de2',1.3]], formats='i4,a3,f8') table.modify_rows(start=1, stop=3, step=2, rows=rows) table.modify_coordinates([1,3,2], rows) table.modify_coordinates([True, False, True], rows) Here, you can see how indexing can be used as a shorthand for the :meth:`Table.modify_rows` and :meth:`Table.modify_coordinates` methods. """ self._g_check_open() self._v_file._check_writable() if is_idx(key): key = operator.index(key) # Index out of range protection if key >= self.nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += self.nrows return self.modify_rows(key, key + 1, 1, [value]) elif isinstance(key, slice): (start, stop, step) = self._process_range( key.start, key.stop, key.step) return self.modify_rows(start, stop, step, value) # Try with a boolean or point selection elif type(key) in (list, tuple) or isinstance(key, numpy.ndarray): return self.modify_coordinates(key, value) else: raise IndexError("Invalid index or slice: {!r}".format(key))
def _save_buffered_rows(self, wbufRA, lenrows): """Update the indexes after a flushing of rows.""" self._open_append(wbufRA) self._append_records(lenrows) self._close_append() if self.indexed: self._unsaved_indexedrows += lenrows # The table caches for indexed queries are dirty now self._dirtycache = True if self.autoindex: # Flush the unindexed rows self.flush_rows_to_index(_lastrow=False) else: # All the columns are dirty now self._mark_columns_as_dirty(self.colpathnames)
[docs] def append(self, rows): """Append a sequence of rows to the end of the table. The rows argument may be any object which can be converted to a structured array compliant with the table structure (otherwise, a ValueError is raised). This includes NumPy structured arrays, lists of tuples or array records, and a string or Python buffer. Examples -------- :: import tables as tb class Particle(tb.IsDescription): name = tb.StringCol(16, pos=1) # 16-character String lati = tb.IntCol(pos=2) # integer longi = tb.IntCol(pos=3) # integer pressure = tb.Float32Col(pos=4) # float (single-precision) temperature = tb.FloatCol(pos=5) # double (double-precision) fileh = tb.open_file('test4.h5', mode='w') table = fileh.create_table(fileh.root, 'table', Particle, "A table") # Append several rows in only one call table.append([("Particle: 10", 10, 0, 10 * 10, 10**2), ("Particle: 11", 11, -1, 11 * 11, 11**2), ("Particle: 12", 12, -2, 12 * 12, 12**2)]) fileh.close() """ self._g_check_open() self._v_file._check_writable() if not self._chunked: raise HDF5ExtError( "You cannot append rows to a non-chunked table.", h5bt=False) # Try to convert the object into a recarray compliant with table try: iflavor = flavor_of(rows) if iflavor != 'python': rows = array_as_internal(rows, iflavor) # Works for Python structures and always copies the original, # so the resulting object is safe for in-place conversion. wbufRA = numpy.rec.array(rows, dtype=self._v_dtype) except Exception as exc: # XXX raise ValueError("rows parameter cannot be converted into a " "recarray object compliant with table '%s'. " "The error was: <%s>" % (str(self), exc)) lenrows = wbufRA.shape[0] # If the number of rows to append is zero, don't do anything else if lenrows > 0: # Save write buffer to disk self._save_buffered_rows(wbufRA, lenrows)
def _conv_to_recarr(self, obj): """Try to convert the object into a recarray.""" try: iflavor = flavor_of(obj) if iflavor != 'python': obj = array_as_internal(obj, iflavor) if hasattr(obj, "shape") and obj.shape == (): # To allow conversion of scalars (void type) into arrays. # See http://projects.scipy.org/scipy/numpy/ticket/315 # for discussion on how to pass buffers to constructors # See also http://projects.scipy.org/scipy/numpy/ticket/348 recarr = numpy.array([obj], dtype=self._v_dtype) else: # Works for Python structures and always copies the original, # so the resulting object is safe for in-place conversion. recarr = numpy.rec.array(obj, dtype=self._v_dtype) except Exception as exc: # XXX raise ValueError("Object cannot be converted into a recarray " "object compliant with table format '%s'. " "The error was: <%s>" % (self.description._v_nested_descr, exc)) return recarr
[docs] def modify_coordinates(self, coords, rows): """Modify a series of rows in positions specified in coords. The values in the selected rows will be modified with the data given in rows. This method returns the number of rows modified. The possible values for the rows argument are the same as in :meth:`Table.append`. """ if rows is None: # Nothing to be done return SizeType(0) # Convert the coordinates to something expected by HDF5 coords = self._point_selection(coords) lcoords = len(coords) if len(rows) < lcoords: raise ValueError("The value has not enough elements to fill-in " "the specified range") # Convert rows into a recarray recarr = self._conv_to_recarr(rows) if len(coords) > 0: # Do the actual update of rows self._update_elements(lcoords, coords, recarr) # Redo the index if needed self._reindex(self.colpathnames) return SizeType(lcoords)
[docs] def modify_rows(self, start=None, stop=None, step=None, rows=None): """Modify a series of rows in the slice [start:stop:step]. The values in the selected rows will be modified with the data given in rows. This method returns the number of rows modified. Should the modification exceed the length of the table, an IndexError is raised before changing data. The possible values for the rows argument are the same as in :meth:`Table.append`. """ if step is None: step = 1 if rows is None: # Nothing to be done return SizeType(0) if start is None: start = 0 if start < 0: raise ValueError("'start' must have a positive value.") if step < 1: raise ValueError( "'step' must have a value greater or equal than 1.") if stop is None: # compute the stop value. start + len(rows)*step does not work stop = start + (len(rows) - 1) * step + 1 (start, stop, step) = self._process_range(start, stop, step) if stop > self.nrows: raise IndexError("This modification will exceed the length of " "the table. Giving up.") # Compute the number of rows to read. nrows = len(range(start, stop, step)) if len(rows) != nrows: raise ValueError("The value has different elements than the " "specified range") # Convert rows into a recarray recarr = self._conv_to_recarr(rows) lenrows = len(recarr) if start + lenrows > self.nrows: raise IndexError("This modification will exceed the length of the " "table. Giving up.") # Do the actual update self._update_records(start, stop, step, recarr) # Redo the index if needed self._reindex(self.colpathnames) return SizeType(lenrows)
[docs] def modify_column(self, start=None, stop=None, step=None, column=None, colname=None): """Modify one single column in the row slice [start:stop:step]. The colname argument specifies the name of the column in the table to be modified with the data given in column. This method returns the number of rows modified. Should the modification exceed the length of the table, an IndexError is raised before changing data. The *column* argument may be any object which can be converted to a (record) array compliant with the structure of the column to be modified (otherwise, a ValueError is raised). This includes NumPy (record) arrays, lists of scalars, tuples or array records, and a string or Python buffer. """ if step is None: step = 1 if not isinstance(colname, str): raise TypeError("The 'colname' parameter must be a string.") self._v_file._check_writable() if column is None: # Nothing to be done return SizeType(0) if start is None: start = 0 if start < 0: raise ValueError("'start' must have a positive value.") if step < 1: raise ValueError( "'step' must have a value greater or equal than 1.") # Get the column format to be modified: objcol = self._get_column_instance(colname) descr = [objcol._v_parent._v_nested_descr[objcol._v_pos]] # Try to convert the column object into a NumPy ndarray try: # If the column is a recarray (or kind of), convert into ndarray if hasattr(column, 'dtype') and column.dtype.kind == 'V': column = numpy.rec.array(column, dtype=descr).field(0) else: # Make sure the result is always a *copy* of the original, # so the resulting object is safe for in-place conversion. iflavor = flavor_of(column) column = array_as_internal(column, iflavor) except Exception as exc: # XXX raise ValueError("column parameter cannot be converted into a " "ndarray object compliant with specified column " "'%s'. The error was: <%s>" % (str(column), exc)) # Get rid of single-dimensional dimensions column = column.squeeze() if column.shape == (): # Oops, stripped off to much dimensions column.shape = (1,) if stop is None: # compute the stop value. start + len(rows)*step does not work stop = start + (len(column) - 1) * step + 1 (start, stop, step) = self._process_range(start, stop, step) if stop > self.nrows: raise IndexError("This modification will exceed the length of " "the table. Giving up.") # Compute the number of rows to read. nrows = len(range(start, stop, step)) if len(column) < nrows: raise ValueError("The value has not enough elements to fill-in " "the specified range") # Now, read the original values: mod_recarr = self._read(start, stop, step) # Modify the appropriate column in the original recarray mod_col = get_nested_field(mod_recarr, colname) mod_col[:] = column # save this modified rows in table self._update_records(start, stop, step, mod_recarr) # Redo the index if needed self._reindex([colname]) return SizeType(nrows)
[docs] def modify_columns(self, start=None, stop=None, step=None, columns=None, names=None): """Modify a series of columns in the row slice [start:stop:step]. The names argument specifies the names of the columns in the table to be modified with the data given in columns. This method returns the number of rows modified. Should the modification exceed the length of the table, an IndexError is raised before changing data. The columns argument may be any object which can be converted to a structured array compliant with the structure of the columns to be modified (otherwise, a ValueError is raised). This includes NumPy structured arrays, lists of tuples or array records, and a string or Python buffer. """ if step is None: step = 1 if type(names) not in (list, tuple): raise TypeError("The 'names' parameter must be a list of strings.") if columns is None: # Nothing to be done return SizeType(0) if start is None: start = 0 if start < 0: raise ValueError("'start' must have a positive value.") if step < 1: raise ValueError("'step' must have a value greater or " "equal than 1.") descr = [] for colname in names: objcol = self._get_column_instance(colname) descr.append(objcol._v_parent._v_nested_descr[objcol._v_pos]) # descr.append(objcol._v_parent._v_dtype[objcol._v_pos]) # Try to convert the columns object into a recarray try: # Make sure the result is always a *copy* of the original, # so the resulting object is safe for in-place conversion. iflavor = flavor_of(columns) if iflavor != 'python': columns = array_as_internal(columns, iflavor) recarray = numpy.rec.array(columns, dtype=descr) else: recarray = numpy.rec.fromarrays(columns, dtype=descr) except Exception as exc: # XXX raise ValueError("columns parameter cannot be converted into a " "recarray object compliant with table '%s'. " "The error was: <%s>" % (str(self), exc)) if stop is None: # compute the stop value. start + len(rows)*step does not work stop = start + (len(recarray) - 1) * step + 1 (start, stop, step) = self._process_range(start, stop, step) if stop > self.nrows: raise IndexError("This modification will exceed the length of " "the table. Giving up.") # Compute the number of rows to read. nrows = len(range(start, stop, step)) if len(recarray) < nrows: raise ValueError("The value has not enough elements to fill-in " "the specified range") # Now, read the original values: mod_recarr = self._read(start, stop, step) # Modify the appropriate columns in the original recarray for i, name in enumerate(recarray.dtype.names): mod_col = get_nested_field(mod_recarr, names[i]) mod_col[:] = recarray[name].squeeze() # save this modified rows in table self._update_records(start, stop, step, mod_recarr) # Redo the index if needed self._reindex(names) return SizeType(nrows)
[docs] def flush_rows_to_index(self, _lastrow=True): """Add remaining rows in buffers to non-dirty indexes. This can be useful when you have chosen non-automatic indexing for the table (see the :attr:`Table.autoindex` property in :class:`Table`) and you want to update the indexes on it. """ rowsadded = 0 if self.indexed: # Update the number of unsaved indexed rows start = self._indexedrows nrows = self._unsaved_indexedrows for (colname, colindexed) in self.colindexed.items(): if colindexed: col = self.cols._g_col(colname) if nrows > 0 and not col.index.dirty: rowsadded = self._add_rows_to_index( colname, start, nrows, _lastrow, update=True) self._unsaved_indexedrows -= rowsadded self._indexedrows += rowsadded return rowsadded
def _add_rows_to_index(self, colname, start, nrows, lastrow, update): """Add more elements to the existing index.""" # This method really belongs to Column, but since it makes extensive # use of the table, it gets dangerous when closing the file, since the # column may be accessing a table which is being destroyed. index = self.cols._g_col(colname).index slicesize = index.slicesize # The next loop does not rely on xrange so that it can # deal with long ints (i.e. more than 32-bit integers) # This allows to index columns with more than 2**31 rows # F. Alted 2005-05-09 startLR = index.sorted.nrows * slicesize indexedrows = startLR - start stop = start + nrows - slicesize + 1 while startLR < stop: index.append( [self._read(startLR, startLR + slicesize, 1, colname)], update=update) indexedrows += slicesize startLR += slicesize # index the remaining rows in last row if lastrow and startLR < self.nrows: index.append_last_row( [self._read(startLR, self.nrows, 1, colname)], update=update) indexedrows += self.nrows - startLR return indexedrows
[docs] def remove_rows(self, start=None, stop=None, step=None): """Remove a range of rows in the table. If only start is supplied, that row and all following will be deleted. If a range is supplied, i.e. both the start and stop parameters are passed, all the rows in the range are removed. .. versionchanged:: 3.0 The start, stop and step parameters now behave like in slice. .. seealso:: remove_row() Parameters ---------- start : int Sets the starting row to be removed. It accepts negative values meaning that the count starts from the end. A value of 0 means the first row. stop : int Sets the last row to be removed to stop-1, i.e. the end point is omitted (in the Python range() tradition). Negative values are also accepted. If None all rows after start will be removed. step : int The step size between rows to remove. .. versionadded:: 3.0 Examples -------- Removing rows from 5 to 10 (excluded):: t.remove_rows(5, 10) Removing all rows starting from the 10th:: t.remove_rows(10) Removing the 6th row:: t.remove_rows(6, 7) .. note:: removing a single row can be done using the specific :meth:`remove_row` method. """ (start, stop, step) = self._process_range(start, stop, step) nrows = self._remove_rows(start, stop, step) # remove_rows is a invalidating index operation self._reindex(self.colpathnames) return SizeType(nrows)
[docs] def remove_row(self, n): """Removes a row from the table. Parameters ---------- n : int The index of the row to remove. .. versionadded:: 3.0 Examples -------- Remove row 15:: table.remove_row(15) Which is equivalent to:: table.remove_rows(15, 16) .. warning:: This is not equivalent to:: table.remove_rows(15) """ self.remove_rows(start=n, stop=n + 1)
def _g_update_dependent(self): super()._g_update_dependent() # Update the new path in columns self.cols._g_update_table_location(self) # Update the new path in the Row instance, if cached. Fixes #224. if 'row' in self.__dict__: self.__dict__['row'] = tableextension.Row(self) def _g_move(self, newparent, newname): """Move this node in the hierarchy. This overloads the Node._g_move() method. """ itgpathname = _index_pathname_of(self) # First, move the table to the new location. super()._g_move(newparent, newname) # Then move the associated index group (if any). try: itgroup = self._v_file._get_node(itgpathname) except NoSuchNodeError: pass else: newigroup = self._v_parent newiname = _index_name_of(self) itgroup._g_move(newigroup, newiname) def _g_remove(self, recursive=False, force=False): # Remove the associated index group (if any). itgpathname = _index_pathname_of(self) try: itgroup = self._v_file._get_node(itgpathname) except NoSuchNodeError: pass else: itgroup._f_remove(recursive=True) self.indexed = False # there are indexes no more # Remove the leaf itself from the hierarchy. super()._g_remove(recursive, force) def _set_column_indexing(self, colpathname, indexed): """Mark the referred column as indexed or non-indexed.""" colindexed = self.colindexed isindexed, wasindexed = bool(indexed), colindexed[colpathname] if isindexed == wasindexed: return # indexing state is unchanged # Changing the set of indexed columns invalidates the condition cache self._condition_cache.clear() colindexed[colpathname] = isindexed self.indexed = max(colindexed.values()) # this is an OR :) def _mark_columns_as_dirty(self, colnames): """Mark column indexes in `colnames` as dirty.""" assert len(colnames) > 0 if self.indexed: colindexed, cols = self.colindexed, self.cols # Mark the proper indexes as dirty for colname in colnames: if colindexed[colname]: col = cols._g_col(colname) col.index.dirty = True def _reindex(self, colnames): """Re-index columns in `colnames` if automatic indexing is true.""" if self.indexed: colindexed, cols = self.colindexed, self.cols colstoindex = [] # Mark the proper indexes as dirty for colname in colnames: if colindexed[colname]: col = cols._g_col(colname) col.index.dirty = True colstoindex.append(colname) # Now, re-index the dirty ones if self.autoindex and colstoindex: self._do_reindex(dirty=True) # The table caches for indexed queries are dirty now self._dirtycache = True def _do_reindex(self, dirty): """Common code for `reindex()` and `reindex_dirty()`.""" indexedrows = 0 for (colname, colindexed) in self.colindexed.items(): if colindexed: indexcol = self.cols._g_col(colname) indexedrows = indexcol._do_reindex(dirty) # Update counters in case some column has been updated if indexedrows > 0: self._indexedrows = indexedrows self._unsaved_indexedrows = self.nrows - indexedrows return SizeType(indexedrows)
[docs] def reindex(self): """Recompute all the existing indexes in the table. This can be useful when you suspect that, for any reason, the index information for columns is no longer valid and want to rebuild the indexes on it. """ self._do_reindex(dirty=False)
[docs] def reindex_dirty(self): """Recompute the existing indexes in table, *if* they are dirty. This can be useful when you have set :attr:`Table.autoindex` (see :class:`Table`) to false for the table and you want to update the indexes after a invalidating index operation (:meth:`Table.remove_rows`, for example). """ self._do_reindex(dirty=True)
def _g_copy_rows(self, object, start, stop, step, sortby, checkCSI): """Copy rows from self to object""" if sortby is None: self._g_copy_rows_optim(object, start, stop, step) return lenbuf = self.nrowsinbuf absstep = step if step < 0: absstep = -step start, stop = stop + 1, start + 1 if sortby is not None: index = self._check_sortby_csi(sortby, checkCSI) for start2 in range(start, stop, absstep * lenbuf): stop2 = start2 + absstep * lenbuf if stop2 > stop: stop2 = stop # The next 'if' is not needed, but it doesn't bother either if sortby is None: rows = self[start2:stop2:step] else: coords = index[start2:stop2:step] rows = self.read_coordinates(coords) # Save the records on disk object.append(rows) object.flush() def _g_copy_rows_optim(self, object, start, stop, step): """Copy rows from self to object (optimized version)""" nrowsinbuf = self.nrowsinbuf object._open_append(self._v_iobuf) nrowsdest = object.nrows for start2 in range(start, stop, step * nrowsinbuf): # Save the records on disk stop2 = start2 + step * nrowsinbuf if stop2 > stop: stop2 = stop # Optimized version (it saves some conversions) nrows = ((stop2 - start2 - 1) // step) + 1 self.row._fill_col(self._v_iobuf, start2, stop2, step, None) # The output buffer is created anew, # so the operation is safe to in-place conversion. object._append_records(nrows) nrowsdest += nrows object._close_append() def _g_prop_indexes(self, other): """Generate index in `other` table for every indexed column here.""" oldcols, newcols = self.colinstances, other.colinstances for colname in newcols: if (isinstance(oldcols[colname], Column)): oldcolindexed = oldcols[colname].is_indexed if oldcolindexed: oldcolindex = oldcols[colname].index newcol = newcols[colname] newcol.create_index( kind=oldcolindex.kind, optlevel=oldcolindex.optlevel, filters=oldcolindex.filters, tmp_dir=None) def _g_copy_with_stats(self, group, name, start, stop, step, title, filters, chunkshape, _log, **kwargs): """Private part of Leaf.copy() for each kind of leaf.""" # Get the private args for the Table flavor of copy() sortby = kwargs.pop('sortby', None) propindexes = kwargs.pop('propindexes', False) checkCSI = kwargs.pop('checkCSI', False) # Compute the correct indices. (start, stop, step) = self._process_range_read( start, stop, step, warn_negstep=sortby is None) # And the number of final rows nrows = len(range(start, stop, step)) # Create the new table and copy the selected data. newtable = Table(group, name, self.description, title=title, filters=filters, expectedrows=nrows, chunkshape=chunkshape, _log=_log) self._g_copy_rows(newtable, start, stop, step, sortby, checkCSI) nbytes = newtable.nrows * newtable.rowsize # Generate equivalent indexes in the new table, if required. if propindexes and self.indexed: self._g_prop_indexes(newtable) return (newtable, nbytes) # This overloading of copy is needed here in order to document # the additional keywords for the Table case.
[docs] def copy(self, newparent=None, newname=None, overwrite=False, createparents=False, **kwargs): """Copy this table and return the new one. This method has the behavior and keywords described in :meth:`Leaf.copy`. Moreover, it recognises the following additional keyword arguments. Parameters ---------- sortby If specified, and sortby corresponds to a column with an index, then the copy will be sorted by this index. If you want to ensure a fully sorted order, the index must be a CSI one. A reverse sorted copy can be achieved by specifying a negative value for the step keyword. If sortby is omitted or None, the original table order is used. checkCSI If true and a CSI index does not exist for the sortby column, an error will be raised. If false (the default), it does nothing. You can use this flag in order to explicitly check for the existence of a CSI index. propindexes If true, the existing indexes in the source table are propagated (created) to the new one. If false (the default), the indexes are not propagated. """ return super().copy( newparent, newname, overwrite, createparents, **kwargs)
def flush(self): """Flush the table buffers.""" # Flush rows that remains to be appended if 'row' in self.__dict__: self.row._flush_buffered_rows() if self.indexed and self.autoindex: # Flush any unindexed row rowsadded = self.flush_rows_to_index(_lastrow=True) assert rowsadded <= 0 or self._indexedrows == self.nrows, \ ("internal error: the number of indexed rows (%d) " "and rows in the table (%d) is not equal; " "please report this to the authors." % (self._indexedrows, self.nrows)) if self._dirtyindexes: # Finally, re-index any dirty column self.reindex_dirty() super().flush() def _g_pre_kill_hook(self): """Code to be called before killing the node.""" # Flush the buffers before to clean-up them # self.flush() # It seems that flushing during the __del__ phase is a sure receipt for # bringing all kind of problems: # 1. Illegal Instruction # 2. Malloc(): trying to call free() twice # 3. Bus Error # 4. Segmentation fault # So, the best would be doing *nothing* at all in this __del__ phase. # As a consequence, the I/O will not be cleaned until a call to # Table.flush() would be done. This could lead to a potentially large # memory consumption. # NOTE: The user should make a call to Table.flush() whenever he has # finished working with his table. # I've added a Performance warning in order to compel the user to # call self.flush() before the table is being preempted. # F. Alted 2006-08-03 if (('row' in self.__dict__ and self.row._get_unsaved_nrows() > 0) or (self.indexed and self.autoindex and (self._unsaved_indexedrows > 0 or self._dirtyindexes))): warnings.warn(("table ``%s`` is being preempted from alive nodes " "without its buffers being flushed or with some " "index being dirty. This may lead to very " "ineficient use of resources and even to fatal " "errors in certain situations. Please do a call " "to the .flush() or .reindex_dirty() methods on " "this table before start using other nodes.") % (self._v_pathname), PerformanceWarning) # Get rid of the IO buffers (if they have been created at all) mydict = self.__dict__ if '_v_iobuf' in mydict: del mydict['_v_iobuf'] if '_v_wdflts' in mydict: del mydict['_v_wdflts'] def _f_close(self, flush=True): if not self._v_isopen: return # the node is already closed # .. note:: # # As long as ``Table`` objects access their indices on closing, # ``File.close()`` will need to make *two separate passes* # to first close ``Table`` objects and then ``Index`` hierarchies. # # Flush right now so the row object does not get in the middle. if flush: self.flush() # Some warnings can be issued after calling `self._g_set_location()` # in `self.__init__()`. If warnings are turned into exceptions, # `self._g_post_init_hook` may not be called and `self.cols` not set. # One example of this is # ``test_create.createTestCase.test05_maxFieldsExceeded()``. cols = self.cols if cols is not None: cols._g_close() # Close myself as a leaf. super()._f_close(False) def __repr__(self): """This provides column metainfo in addition to standard __str__""" if self.indexed: format = """\ %s description := %r byteorder := %r chunkshape := %r autoindex := %r colindexes := %r""" return format % (str(self), self.description, self.byteorder, self.chunkshape, self.autoindex, _ColIndexes(self.colindexes)) else: return """\ %s description := %r byteorder := %r chunkshape := %r""" % \ (str(self), self.description, self.byteorder, self.chunkshape)
[docs]class Cols: """Container for columns in a table or nested column. This class is used as an *accessor* to the columns in a table or nested column. It supports the *natural naming* convention, so that you can access the different columns as attributes which lead to Column instances (for non-nested columns) or other Cols instances (for nested columns). For instance, if table.cols is a Cols instance with a column named col1 under it, the later can be accessed as table.cols.col1. If col1 is nested and contains a col2 column, this can be accessed as table.cols.col1.col2 and so on. Because of natural naming, the names of members start with special prefixes, like in the Group class (see :ref:`GroupClassDescr`). Like the Column class (see :ref:`ColumnClassDescr`), Cols supports item access to read and write ranges of values in the table or nested column. .. rubric:: Cols attributes .. attribute:: _v_colnames A list of the names of the columns hanging directly from the associated table or nested column. The order of the names matches the order of their respective columns in the containing table. .. attribute:: _v_colpathnames A list of the pathnames of all the columns under the associated table or nested column (in preorder). If it does not contain nested columns, this is exactly the same as the :attr:`Cols._v_colnames` attribute. .. attribute:: _v_desc The associated Description instance (see :ref:`DescriptionClassDescr`). """ @property def _v_table(self): """The parent Table instance (see :ref:`TableClassDescr`).""" return self._v__tableFile._get_node(self._v__tablePath) def __init__(self, table, desc): myDict = self.__dict__ myDict['_v__tableFile'] = table._v_file myDict['_v__tablePath'] = table._v_pathname myDict['_v_desc'] = desc myDict['_v_colnames'] = desc._v_names myDict['_v_colpathnames'] = table.description._v_pathnames # Put the column in the local dictionary for name in desc._v_names: if name in desc._v_types: myDict[name] = Column(table, name, desc) else: myDict[name] = Cols(table, desc._v_colobjects[name]) def _g_update_table_location(self, table): """Updates the location information about the associated `table`.""" myDict = self.__dict__ myDict['_v__tableFile'] = table._v_file myDict['_v__tablePath'] = table._v_pathname # Update the locations in individual columns. for colname in self._v_colnames: myDict[colname]._g_update_table_location(table)
[docs] def __len__(self): """Get the number of top level columns in table.""" return len(self._v_colnames)
[docs] def _f_col(self, colname): """Get an accessor to the column colname. This method returns a Column instance (see :ref:`ColumnClassDescr`) if the requested column is not nested, and a Cols instance (see :ref:`ColsClassDescr`) if it is. You may use full column pathnames in colname. Calling cols._f_col('col1/col2') is equivalent to using cols.col1.col2. However, the first syntax is more intended for programmatic use. It is also better if you want to access columns with names that are not valid Python identifiers. """ if not isinstance(colname, str): raise TypeError("Parameter can only be an string. You passed " "object: %s" % colname) if ((colname.find('/') > -1 and colname not in self._v_colpathnames) and colname not in self._v_colnames): raise KeyError(("Cols accessor ``%s.cols%s`` does not have a " "column named ``%s``") % (self._v__tablePath, self._v_desc._v_pathname, colname)) return self._g_col(colname)
def _g_col(self, colname): """Like `self._f_col()` but it does not check arguments.""" # Get the Column or Description object inames = colname.split('/') cols = self for iname in inames: cols = cols.__dict__[iname] return cols
[docs] def __getitem__(self, key): """Get a row or a range of rows from a table or nested column. If key argument is an integer, the corresponding nested type row is returned as a record of the current flavor. If key is a slice, the range of rows determined by it is returned as a structured array of the current flavor. Examples -------- :: record = table.cols[4] # equivalent to table[4] recarray = table.cols.Info[4:1000:2] Those statements are equivalent to:: nrecord = table.read(start=4)[0] nrecarray = table.read(start=4, stop=1000, step=2).field('Info') Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.read` method. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step)[0] else: crecord = table.read(start, stop, step)[0] return crecord[colgroup] elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step) else: crecarray = table.read(start, stop, step) if hasattr(crecarray, "field"): return crecarray.field(colgroup) # RecArray case else: return get_nested_field(crecarray, colgroup) # numpy case else: raise TypeError("invalid index or slice: {!r}".format(key))
[docs] def __setitem__(self, key, value): """Set a row or a range of rows in a table or nested column. If key argument is an integer, the corresponding row is set to value. If key is a slice, the range of rows determined by it is set to value. Examples -------- :: table.cols[4] = record table.cols.Info[4:1000:2] = recarray Those statements are equivalent to:: table.modify_rows(4, rows=record) table.modify_column(4, 1000, 2, colname='Info', column=recarray) Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.modify_rows` and :meth:`Table.modify_column` methods. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) else: raise TypeError("invalid index or slice: {!r}".format(key)) # Actually modify the correct columns colgroup = self._v_desc._v_pathname if colgroup == "": # The root group table.modify_rows(start, stop, step, rows=value) else: table.modify_column( start, stop, step, colname=colgroup, column=value)
def _g_close(self): # First, close the columns (ie possible indices open) for col in self._v_colnames: colobj = self._g_col(col) if isinstance(colobj, Column): colobj.close() # Delete the reference to column del self.__dict__[col] else: colobj._g_close() self.__dict__.clear() def __str__(self): """The string representation for this object.""" # The pathname tablepathname = self._v__tablePath descpathname = self._v_desc._v_pathname if descpathname: descpathname = "." + descpathname # Get this class name classname = self.__class__.__name__ # The number of columns ncols = len(self._v_colnames) return "%s.cols%s (%s), %s columns" % \ (tablepathname, descpathname, classname, ncols) def __repr__(self): """A detailed string representation for this object.""" out = str(self) + "\n" for name in self._v_colnames: # Get this class name classname = getattr(self, name).__class__.__name__ # The type if name in self._v_desc._v_dtypes: tcol = self._v_desc._v_dtypes[name] # The shape for this column shape = (self._v_table.nrows,) + \ self._v_desc._v_dtypes[name].shape else: tcol = "Description" # Description doesn't have a shape currently shape = () out += " {} ({}{}, {})".format(name, classname, shape, tcol) + "\n" return out
[docs]class Column: """Accessor for a non-nested column in a table. Each instance of this class is associated with one *non-nested* column of a table. These instances are mainly used to read and write data from the table columns using item access (like the Cols class - see :ref:`ColsClassDescr`), but there are a few other associated methods to deal with indexes. .. rubric:: Column attributes .. attribute:: descr The Description (see :ref:`DescriptionClassDescr`) instance of the parent table or nested column. .. attribute:: name The name of the associated column. .. attribute:: pathname The complete pathname of the associated column (the same as Column.name if the column is not inside a nested column). Parameters ---------- table The parent table instance name The name of the column that is associated with this object descr The parent description object """ # Lazy read-only attributes # ````````````````````````` @lazyattr def dtype(self): """The NumPy dtype that most closely matches this column.""" return self.descr._v_dtypes[self.name].base # Get rid of shape info @lazyattr def type(self): """The PyTables type of the column (a string).""" return self.descr._v_types[self.name] # Properties # ~~~~~~~~~~ @property def table(self): """The parent Table instance (see :ref:`TableClassDescr`).""" return self._table_file._get_node(self._table_path) @property def index(self): """The Index instance (see :ref:`IndexClassDescr`) associated with this column (None if the column is not indexed).""" indexPath = _index_pathname_of_column_(self._table_path, self.pathname) try: index = self._table_file._get_node(indexPath) except NodeError: index = None # The column is not indexed return index @lazyattr def _itemtype(self): return self.descr._v_dtypes[self.name] @property def shape(self): """The shape of this column.""" return (self.table.nrows,) + self.descr._v_dtypes[self.name].shape @property def is_indexed(self): """True if the column is indexed, false otherwise.""" if self.index is None: return False else: return True @property def maindim(self): """"The dimension along which iterators work. Its value is 0 (i.e. the first dimension).""" return 0 def __init__(self, table, name, descr): self._table_file = table._v_file self._table_path = table._v_pathname self.name = name """The name of the associated column.""" self.pathname = descr._v_colobjects[name]._v_pathname """The complete pathname of the associated column (the same as Column.name if the column is not inside a nested column).""" self.descr = descr """The Description (see :ref:`DescriptionClassDescr`) instance of the parent table or nested column.""" def _g_update_table_location(self, table): """Updates the location information about the associated `table`.""" self._table_file = table._v_file self._table_path = table._v_pathname
[docs] def __len__(self): """Get the number of elements in the column. This matches the length in rows of the parent table. """ return self.table.nrows
[docs] def __getitem__(self, key): """Get a row or a range of rows from a column. If key argument is an integer, the corresponding element in the column is returned as an object of the current flavor. If key is a slice, the range of elements determined by it is returned as an array of the current flavor. Examples -------- :: print("Column handlers:") for name in table.colnames: print(table.cols._f_col(name)) print("Select table.cols.name[1]-->", table.cols.name[1]) print("Select table.cols.name[1:2]-->", table.cols.name[1:2]) print("Select table.cols.name[:]-->", table.cols.name[:]) print("Select table.cols._f_col('name')[:]-->", table.cols._f_col('name')[:]) The output of this for a certain arbitrary table is:: Column handlers: /table.cols.name (Column(), string, idx=None) /table.cols.lati (Column(), int32, idx=None) /table.cols.longi (Column(), int32, idx=None) /table.cols.vector (Column(2,), int32, idx=None) /table.cols.matrix2D (Column(2, 2), float64, idx=None) Select table.cols.name[1]--> Particle: 11 Select table.cols.name[1:2]--> ['Particle: 11'] Select table.cols.name[:]--> ['Particle: 10' 'Particle: 11' 'Particle: 12' 'Particle: 13' 'Particle: 14'] Select table.cols._f_col('name')[:]--> ['Particle: 10' 'Particle: 11' 'Particle: 12' 'Particle: 13' 'Particle: 14'] See the :file:`examples/table2.py` file for a more complete example. """ table = self.table # Generalized key support not there yet, but at least allow # for a tuple with one single element (the main dimension). # (key,) --> key if isinstance(key, tuple) and len(key) == 1: key = key[0] if is_idx(key): key = operator.index(key) # Index out of range protection if key >= table.nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += table.nrows (start, stop, step) = table._process_range(key, key + 1, 1) return table.read(start, stop, step, self.pathname)[0] elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) return table.read(start, stop, step, self.pathname) else: raise TypeError( "'%s' key type is not valid in this context" % key)
def __iter__(self): """Iterate through all items in the column.""" table = self.table itemsize = self.dtype.itemsize nrowsinbuf = table._v_file.params['IO_BUFFER_SIZE'] // itemsize buf = numpy.empty((nrowsinbuf, ), self._itemtype) max_row = len(self) for start_row in range(0, len(self), nrowsinbuf): end_row = min(start_row + nrowsinbuf, max_row) buf_slice = buf[0:end_row - start_row] table.read(start_row, end_row, 1, field=self.pathname, out=buf_slice) yield from buf_slice
[docs] def __setitem__(self, key, value): """Set a row or a range of rows in a column. If key argument is an integer, the corresponding element is set to value. If key is a slice, the range of elements determined by it is set to value. Examples -------- :: # Modify row 1 table.cols.col1[1] = -1 # Modify rows 1 and 3 table.cols.col1[1::2] = [2,3] Which is equivalent to:: # Modify row 1 table.modify_columns(start=1, columns=[[-1]], names=['col1']) # Modify rows 1 and 3 columns = numpy.rec.fromarrays([[2,3]], formats='i4') table.modify_columns(start=1, step=2, columns=columns, names=['col1']) """ table = self.table table._v_file._check_writable() # Generalized key support not there yet, but at least allow # for a tuple with one single element (the main dimension). # (key,) --> key if isinstance(key, tuple) and len(key) == 1: key = key[0] if is_idx(key): key = operator.index(key) # Index out of range protection if key >= table.nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += table.nrows return table.modify_column(key, key + 1, 1, [[value]], self.pathname) elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) return table.modify_column(start, stop, step, value, self.pathname) else: raise ValueError("Non-valid index or slice: %s" % key)
[docs] def create_index(self, optlevel=6, kind="medium", filters=None, tmp_dir=None, _blocksizes=None, _testmode=False, _verbose=False): """Create an index for this column. .. warning:: In some situations it is useful to get a completely sorted index (CSI). For those cases, it is best to use the :meth:`Column.create_csindex` method instead. Parameters ---------- optlevel : int The optimization level for building the index. The levels ranges from 0 (no optimization) up to 9 (maximum optimization). Higher levels of optimization mean better chances for reducing the entropy of the index at the price of using more CPU, memory and I/O resources for creating the index. kind : str The kind of the index to be built. It can take the 'ultralight', 'light', 'medium' or 'full' values. Lighter kinds ('ultralight' and 'light') mean that the index takes less space on disk, but will perform queries slower. Heavier kinds ('medium' and 'full') mean better chances for reducing the entropy of the index (increasing the query speed) at the price of using more disk space as well as more CPU, memory and I/O resources for creating the index. Note that selecting a full kind with an optlevel of 9 (the maximum) guarantees the creation of an index with zero entropy, that is, a completely sorted index (CSI) - provided that the number of rows in the table does not exceed the 2**48 figure (that is more than 100 trillions of rows). See :meth:`Column.create_csindex` method for a more direct way to create a CSI index. filters : Filters Specify the Filters instance used to compress the index. If None, default index filters will be used (currently, zlib level 1 with shuffling). tmp_dir When kind is other than 'ultralight', a temporary file is created during the index build process. You can use the tmp_dir argument to specify the directory for this temporary file. The default is to create it in the same directory as the file containing the original table. """ kinds = ['ultralight', 'light', 'medium', 'full'] if kind not in kinds: raise ValueError("Kind must have any of these values: %s" % kinds) if (not isinstance(optlevel, int) or (optlevel < 0 or optlevel > 9)): raise ValueError("Optimization level must be an integer in the " "range 0-9") if filters is None: filters = default_index_filters if tmp_dir is None: tmp_dir = os.path.dirname(self._table_file.filename) else: if not os.path.isdir(tmp_dir): raise ValueError("Temporary directory '%s' does not exist" % tmp_dir) if (_blocksizes is not None and (not isinstance(_blocksizes, tuple) or len(_blocksizes) != 4)): raise ValueError("_blocksizes must be a tuple with exactly 4 " "elements") idxrows = _column__create_index(self, optlevel, kind, filters, tmp_dir, _blocksizes, _verbose) return SizeType(idxrows)
[docs] def create_csindex(self, filters=None, tmp_dir=None, _blocksizes=None, _testmode=False, _verbose=False): """Create a completely sorted index (CSI) for this column. This method guarantees the creation of an index with zero entropy, that is, a completely sorted index (CSI) -- provided that the number of rows in the table does not exceed the 2**48 figure (that is more than 100 trillions of rows). A CSI index is needed for some table methods (like :meth:`Table.itersorted` or :meth:`Table.read_sorted`) in order to ensure completely sorted results. For the meaning of filters and tmp_dir arguments see :meth:`Column.create_index`. Notes ----- This method is equivalent to Column.create_index(optlevel=9, kind='full', ...). """ return self.create_index( kind='full', optlevel=9, filters=filters, tmp_dir=tmp_dir, _blocksizes=_blocksizes, _testmode=_testmode, _verbose=_verbose)
def _do_reindex(self, dirty): """Common code for reindex() and reindex_dirty() codes.""" index = self.index dodirty = True if dirty and not index.dirty: dodirty = False if index is not None and dodirty: self._table_file._check_writable() # Get the old index parameters kind = index.kind optlevel = index.optlevel filters = index.filters # We *need* to tell the index that it is going to be undirty. # This is needed here so as to unnail() the condition cache. index.dirty = False # Delete the existing Index index._f_remove() # Create a new Index with the previous parameters return SizeType(self.create_index( kind=kind, optlevel=optlevel, filters=filters)) else: return SizeType(0) # The column is not intended for indexing
[docs] def reindex(self): """Recompute the index associated with this column. This can be useful when you suspect that, for any reason, the index information is no longer valid and you want to rebuild it. This method does nothing if the column is not indexed. """ self._do_reindex(dirty=False)
[docs] def reindex_dirty(self): """Recompute the associated index only if it is dirty. This can be useful when you have set :attr:`Table.autoindex` to false for the table and you want to update the column's index after an invalidating index operation (like :meth:`Table.remove_rows`). This method does nothing if the column is not indexed. """ self._do_reindex(dirty=True)
[docs] def remove_index(self): """Remove the index associated with this column. This method does nothing if the column is not indexed. The removed index can be created again by calling the :meth:`Column.create_index` method. """ self._table_file._check_writable() # Remove the index if existing. if self.is_indexed: index = self.index index._f_remove() self.table._set_column_indexing(self.pathname, False)
def close(self): """Close this column.""" self.__dict__.clear() def __str__(self): """The string representation for this object.""" # The pathname tablepathname = self._table_path pathname = self.pathname.replace('/', '.') # Get this class name classname = self.__class__.__name__ # The shape for this column shape = self.shape # The type tcol = self.descr._v_types[self.name] return "%s.cols.%s (%s%s, %s, idx=%s)" % \ (tablepathname, pathname, classname, shape, tcol, self.index) def __repr__(self): """A detailed string representation for this object.""" return str(self)
## Local Variables: ## mode: python ## py-indent-offset: 4 ## tab-width: 4 ## fill-column: 72 ## End: