Source code for tables.index

#######################################################################
#
# License: BSD
# Created: June 08, 2004
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
########################################################################

"""Here is defined the Index class."""


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

from time import time, perf_counter

import numpy

from .idxutils import (calc_chunksize, calcoptlevels,
                             get_reduction_level, nextafter, inftype)

from . import indexesextension
from .node import NotLoggedMixin
from .atom import UIntAtom, Atom
from .earray import EArray
from .carray import CArray
from .leaf import Filters
from .indexes import CacheArray, LastRowArray, IndexArray
from .group import Group
from .path import join_path
from .exceptions import PerformanceWarning
from .utils import is_idx, idx2long, lazyattr
from .utilsextension import (nan_aware_gt, nan_aware_ge,
                                   nan_aware_lt, nan_aware_le,
                                   bisect_left, bisect_right)
from .lrucacheextension import ObjectCache



# default version for INDEX objects
# obversion = "1.0"    # Version of indexes in PyTables 1.x series
# obversion = "2.0"    # Version of indexes in PyTables Pro 2.0 series
obversion = "2.1"     # Version of indexes in PyTables Pro 2.1 and up series,
                      # including the join 2.3 Std + Pro version


debug = False
# debug = True  # Uncomment this for printing sizes purposes
profile = False
# profile = True  # Uncomment for profiling
if profile:
    from .utils import show_stats


# The default method for sorting
# defsort = "quicksort"
# Changing to mergesort to fix #441
defsort = "mergesort"

# Default policy for automatically updating indexes after a table
# append operation, or automatically reindexing after an
# index-invalidating operation like removing or modifying table rows.
default_auto_index = True
# Keep in sync with ``Table.autoindex`` docstring.

# Default filters used to compress indexes.  This is quite fast and
# compression is pretty good.
# Remember to keep these defaults in sync with the docstrings and UG.
default_index_filters = Filters(complevel=1, complib='zlib',
                                shuffle=True, fletcher32=False)

# Deprecated API
defaultAutoIndex = default_auto_index
defaultIndexFilters = default_index_filters

# The list of types for which an optimised search in cython and C has
# been implemented. Always add here the name of a new optimised type.
opt_search_types = ("int8", "int16", "int32", "int64",
                    "uint8", "uint16", "uint32", "uint64",
                    "float32", "float64")

# The upper limit for uint32 ints
max32 = 2**32


def _table_column_pathname_of_index(indexpathname):
    names = indexpathname.split("/")
    for i, name in enumerate(names):
        if name.startswith('_i_'):
            break
    tablepathname = "/".join(names[:i]) + "/" + name[3:]
    colpathname = "/".join(names[i + 1:])
    return (tablepathname, colpathname)


[docs]class Index(NotLoggedMixin, Group, indexesextension.Index): """Represents the index of a column in a table. This class is used to keep the indexing information for columns in a Table dataset (see :ref:`TableClassDescr`). It is actually a descendant of the Group class (see :ref:`GroupClassDescr`), with some added functionality. An Index is always associated with one and only one column in the table. .. note:: This class is mainly intended for internal use, but some of its documented attributes and methods may be interesting for the programmer. 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. atom : Atom An Atom object representing the shape and type of the atomic objects to be saved. Only scalar atoms are supported. title Sets a TITLE attribute of the Index entity. kind The desired kind for this index. The 'full' kind specifies a complete track of the row position (64-bit), while the 'medium', 'light' or 'ultralight' kinds only specify in which chunk the row is (using 32-bit, 16-bit and 8-bit respectively). optlevel The desired optimization level for this index. 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. tmp_dir The directory for the temporary files. expectedrows Represents an user estimate about the number of row slices that will be added to the growable dimension in the IndexArray object. byteorder The byteorder of the index datasets *on-disk*. blocksizes The four main sizes of the compound blocks in index datasets (a low level parameter). """ _c_classid = 'INDEX' @property def kind(self): """The kind of this index.""" return {1: 'ultralight', 2: 'light', 4: 'medium', 8: 'full'}[self.indsize] @property def filters(self): """Filter properties for this index - see Filters in :ref:`FiltersClassDescr`.""" return self._v_filters @property def dirty(self): """Whether the index is dirty or not. Dirty indexes are out of sync with column data, so they exist but they are not usable. """ # If there is no ``DIRTY`` attribute, index should be clean. return getattr(self._v_attrs, 'DIRTY', False) @dirty.setter def dirty(self, dirty): wasdirty, isdirty = self.dirty, bool(dirty) self._v_attrs.DIRTY = dirty # If an *actual* change in dirtiness happens, # notify the condition cache by setting or removing a nail. conditioncache = self.table._condition_cache if not wasdirty and isdirty: conditioncache.nail() if wasdirty and not isdirty: conditioncache.unnail() @property def column(self): """The Column (see :ref:`ColumnClassDescr`) instance for the indexed column.""" tablepath, columnpath = _table_column_pathname_of_index( self._v_pathname) table = self._v_file._get_node(tablepath) column = table.cols._g_col(columnpath) return column @property def table(self): """Accessor for the `Table` object of this index.""" tablepath, columnpath = _table_column_pathname_of_index(self._v_pathname) table = self._v_file._get_node(tablepath) return table @property def nblockssuperblock(self): """The number of blocks in a superblock.""" return self.superblocksize // self.blocksize @property def nslicesblock(self): """The number of slices in a block.""" return self.blocksize // self.slicesize @property def nchunkslice(self): """The number of chunks in a slice.""" return self.slicesize // self.chunksize @property def nsuperblocks(self): """The total number of superblocks in index.""" # Last row should not be considered as a superblock nelements = self.nelements - self.nelementsILR nblocks = nelements // self.superblocksize if nelements % self.blocksize > 0: nblocks += 1 return nblocks @property def nblocks(self): """The total number of blocks in index.""" # Last row should not be considered as a block nelements = self.nelements - self.nelementsILR nblocks = nelements // self.blocksize if nelements % self.blocksize > 0: nblocks += 1 return nblocks @property def nslices(self): """The number of complete slices in index.""" return self.nelements // self.slicesize @property def nchunks(self): """The number of complete chunks in index.""" return self.nelements // self.chunksize @property def shape(self): """The shape of this index (in slices and elements).""" return (self.nrows, self.slicesize) @property def temp_required(self): """Whether a temporary file for indexes is required or not.""" return self.indsize > 1 and self.optlevel > 0 and self.table.nrows > self.slicesize @property def want_complete_sort(self): """Whether we should try to build a completely sorted index or not.""" return self.indsize == 8 and self.optlevel == 9 @property def is_csi(self): """Whether the index is completely sorted or not. .. versionchanged:: 3.0 The *is_CSI* property has been renamed into *is_csi*. """ if self.nelements == 0: # An index with 0 indexed elements is not a CSI one (by definition) return False if self.indsize < 8: # An index that is not full cannot be completely sorted return False # Try with the 'is_csi' attribute if 'is_csi' in self._v_attrs: return self._v_attrs.is_csi # If not, then compute the overlaps manually # (the attribute 'is_csi' will be set there) self.compute_overlaps(self, None, False) return self.noverlaps == 0 @lazyattr def nrowsinchunk(self): """The number of rows that fits in a *table* chunk.""" return self.table.chunkshape[0] @lazyattr def lbucket(self): """Return the length of a bucket based index type.""" # Avoid to set a too large lbucket size (mainly useful for tests) lbucket = min(self.nrowsinchunk, self.chunksize) if self.indsize == 1: # For ultra-light, we will never have to keep track of a # bucket outside of a slice. maxnb = 2**8 if self.slicesize > maxnb * lbucket: lbucket = int(math.ceil(float(self.slicesize) / maxnb)) elif self.indsize == 2: # For light, we will never have to keep track of a # bucket outside of a block. maxnb = 2**16 if self.blocksize > maxnb * lbucket: lbucket = int(math.ceil(float(self.blocksize) / maxnb)) else: # For medium and full indexes there should not be a need to # increase lbucket pass return lbucket def __init__(self, parentnode, name, atom=None, title="", kind=None, optlevel=None, filters=None, tmp_dir=None, expectedrows=0, byteorder=None, blocksizes=None, new=True): self._v_version = None """The object version of this index.""" self.optlevel = optlevel """The optimization level for this index.""" self.tmp_dir = tmp_dir """The directory for the temporary files.""" self.expectedrows = expectedrows """The expected number of items of index arrays.""" if byteorder in ["little", "big"]: self.byteorder = byteorder else: self.byteorder = sys.byteorder """The byteorder of the index datasets.""" if atom is not None: self.dtype = atom.dtype.base self.type = atom.type """The datatypes to be stored by the sorted index array.""" ############### Important note ########################### # The datatypes saved as index values are NumPy native # types, so we get rid of type metainfo like Time* or Enum* # that belongs to HDF5 types (actually, this metainfo is # not needed for sorting and looking-up purposes). ########################################################## indsize = { 'ultralight': 1, 'light': 2, 'medium': 4, 'full': 8}[kind] assert indsize in (1, 2, 4, 8), "indsize should be 1, 2, 4 or 8!" self.indsize = indsize """The itemsize for the indices part of the index.""" self.nrows = None """The total number of slices in the index.""" self.nelements = None """The number of currently indexed rows for this column.""" self.blocksizes = blocksizes """The four main sizes of the compound blocks (if specified).""" self.dirtycache = True """Dirty cache (for ranges, bounds & sorted) flag.""" self.superblocksize = None """Size of the superblock for this index.""" self.blocksize = None """Size of the block for this index.""" self.slicesize = None """Size of the slice for this index.""" self.chunksize = None """Size of the chunk for this index.""" self.tmpfilename = None """Filename for temporary bounds.""" self.opt_search_types = opt_search_types """The types for which and optimized search has been implemented.""" self.noverlaps = -1 """The number of overlaps in an index. 0 means a completely sorted index. -1 means that this number is not computed yet.""" self.tprof = 0 """Time counter for benchmarking purposes.""" from .file import open_file self._openFile = open_file """The `open_file()` function, to avoid a circular import.""" super().__init__(parentnode, name, title, new, filters) def _g_post_init_hook(self): if self._v_new: # The version for newly created indexes self._v_version = obversion super()._g_post_init_hook() # Index arrays must only be created for new indexes if not self._v_new: idxversion = self._v_version # Set-up some variables from info on disk and return attrs = self._v_attrs # Coerce NumPy scalars to Python scalars in order # to avoid undesired upcasting operations. self.superblocksize = int(attrs.superblocksize) self.blocksize = int(attrs.blocksize) self.slicesize = int(attrs.slicesize) self.chunksize = int(attrs.chunksize) self.blocksizes = (self.superblocksize, self.blocksize, self.slicesize, self.chunksize) self.optlevel = int(attrs.optlevel) sorted = self.sorted indices = self.indices self.dtype = sorted.atom.dtype self.type = sorted.atom.type self.indsize = indices.atom.itemsize # Some sanity checks for slicesize, chunksize and indsize assert self.slicesize == indices.shape[1], "Wrong slicesize" assert self.chunksize == indices._v_chunkshape[ 1], "Wrong chunksize" assert self.indsize in (1, 2, 4, 8), "Wrong indices itemsize" if idxversion > "2.0": self.reduction = int(attrs.reduction) nelementsSLR = int(self.sortedLR.attrs.nelements) nelementsILR = int(self.indicesLR.attrs.nelements) else: self.reduction = 1 nelementsILR = self.indicesLR[-1] nelementsSLR = nelementsILR self.nrows = sorted.nrows self.nelements = self.nrows * self.slicesize + nelementsILR self.nelementsSLR = nelementsSLR self.nelementsILR = nelementsILR if nelementsILR > 0: self.nrows += 1 # Get the bounds as a cache (this has to remain here!) rchunksize = self.chunksize // self.reduction nboundsLR = (nelementsSLR - 1) // rchunksize if nboundsLR < 0: nboundsLR = 0 # correction for -1 bounds nboundsLR += 2 # bounds + begin + end # All bounds values (+begin + end) are at the end of sortedLR self.bebounds = self.sortedLR[ nelementsSLR:nelementsSLR + nboundsLR] return # The index is new. Initialize the values self.nrows = 0 self.nelements = 0 self.nelementsSLR = 0 self.nelementsILR = 0 # The atom atom = Atom.from_dtype(self.dtype) # The filters filters = self.filters # Compute the superblocksize, blocksize, slicesize and chunksize values # (in case these parameters haven't been passed to the constructor) if self.blocksizes is None: self.blocksizes = calc_chunksize( self.expectedrows, self.optlevel, self.indsize) (self.superblocksize, self.blocksize, self.slicesize, self.chunksize) = self.blocksizes if debug: print("blocksizes:", self.blocksizes) # Compute the reduction level self.reduction = get_reduction_level( self.indsize, self.optlevel, self.slicesize, self.chunksize) rchunksize = self.chunksize // self.reduction rslicesize = self.slicesize // self.reduction # Save them on disk as attributes self._v_attrs.superblocksize = numpy.uint64(self.superblocksize) self._v_attrs.blocksize = numpy.uint64(self.blocksize) self._v_attrs.slicesize = numpy.uint32(self.slicesize) self._v_attrs.chunksize = numpy.uint32(self.chunksize) # Save the optlevel as well self._v_attrs.optlevel = self.optlevel # Save the reduction level self._v_attrs.reduction = self.reduction # Create the IndexArray for sorted values sorted = IndexArray(self, 'sorted', atom, "Sorted Values", filters, self.byteorder) # Create the IndexArray for index values IndexArray(self, 'indices', UIntAtom(itemsize=self.indsize), "Number of chunk in table", filters, self.byteorder) # Create the cache for range values (1st order cache) CacheArray(self, 'ranges', atom, (0, 2), "Range Values", filters, self.expectedrows // self.slicesize, byteorder=self.byteorder) # median ranges EArray(self, 'mranges', atom, (0,), "Median ranges", filters, byteorder=self.byteorder, _log=False) # Create the cache for boundary values (2nd order cache) nbounds_inslice = (rslicesize - 1) // rchunksize CacheArray(self, 'bounds', atom, (0, nbounds_inslice), "Boundary Values", filters, self.nchunks, (1, nbounds_inslice), byteorder=self.byteorder) # begin, end & median bounds (only for numerical types) EArray(self, 'abounds', atom, (0,), "Start bounds", filters, byteorder=self.byteorder, _log=False) EArray(self, 'zbounds', atom, (0,), "End bounds", filters, byteorder=self.byteorder, _log=False) EArray(self, 'mbounds', atom, (0,), "Median bounds", filters, byteorder=self.byteorder, _log=False) # Create the Array for last (sorted) row values + bounds shape = (rslicesize + 2 + nbounds_inslice,) sortedLR = LastRowArray(self, 'sortedLR', atom, shape, "Last Row sorted values + bounds", filters, (rchunksize,), byteorder=self.byteorder) # Create the Array for the number of chunk in last row shape = (self.slicesize,) # enough for indexes and length indicesLR = LastRowArray(self, 'indicesLR', UIntAtom(itemsize=self.indsize), shape, "Last Row indices", filters, (self.chunksize,), byteorder=self.byteorder) # The number of elements in LR will be initialized here sortedLR.attrs.nelements = 0 indicesLR.attrs.nelements = 0 # All bounds values (+begin + end) are uninitialized in creation time self.bebounds = None # The starts and lengths initialization self.starts = numpy.empty(shape=self.nrows, dtype=numpy.int32) """Where the values fulfiling conditions starts for every slice.""" self.lengths = numpy.empty(shape=self.nrows, dtype=numpy.int32) """Lengths of the values fulfilling conditions for every slice.""" # Finally, create a temporary file for indexes if needed if self.temp_required: self.create_temp() def initial_append(self, xarr, nrow, reduction): """Compute an initial indices arrays for data to be indexed.""" if profile: tref = time() if profile: show_stats("Entering initial_append", tref) arr = xarr.pop() indsize = self.indsize slicesize = self.slicesize nelementsILR = self.nelementsILR if profile: show_stats("Before creating idx", tref) if indsize == 8: idx = numpy.arange(0, len(arr), dtype="uint64") + nrow * slicesize elif indsize == 4: # For medium (32-bit) all the rows in tables should be # directly reachable. But as len(arr) < 2**31, we can # choose uint32 for representing indices. In this way, we # consume far less memory during the keysort process. The # offset will be added in self.final_idx32() later on. # # This optimization also prevents the values in LR to # participate in the ``swap_chunks`` process, and this is # the main reason to not allow the medium indexes to create # completely sorted indexes. However, I don't find this to # be a big limitation, as probably fully indexes are much # more suitable for producing completely sorted indexes # because in this case the indices part is usable for # getting the reverse indices of the index, and I forsee # this to be a common requirement in many operations (for # example, in table sorts). # # F. Alted 2008-09-15 idx = numpy.arange(0, len(arr), dtype="uint32") else: idx = numpy.empty(len(arr), "uint%d" % (indsize * 8)) lbucket = self.lbucket # Fill the idx with the bucket indices offset = int(lbucket - ((nrow * (slicesize % lbucket)) % lbucket)) idx[0:offset] = 0 for i in range(offset, slicesize, lbucket): idx[i:i + lbucket] = (i + lbucket - 1) // lbucket if indsize == 2: # Add a second offset in this case # First normalize the number of rows offset2 = (nrow % self.nslicesblock) * slicesize // lbucket idx += offset2 # Add the last row at the beginning of arr & idx (if needed) if (indsize == 8 and nelementsILR > 0): # It is possible that the values in LR are already sorted. # Fetch them and override existing values in arr and idx. assert len(arr) > nelementsILR self.read_slice_lr(self.sortedLR, arr[:nelementsILR]) self.read_slice_lr(self.indicesLR, idx[:nelementsILR]) # In-place sorting if profile: show_stats("Before keysort", tref) indexesextension.keysort(arr, idx) larr = arr[-1] if reduction > 1: # It's important to do a copy() here in order to ensure that # sorted._append() will receive a contiguous array. if profile: show_stats("Before reduction", tref) reduc = arr[::reduction].copy() if profile: show_stats("After reduction", tref) arr = reduc if profile: show_stats("After arr <-- reduc", tref) # A completely sorted index is not longer possible after an # append of an index with already one slice. if nrow > 0: self._v_attrs.is_csi = False if profile: show_stats("Exiting initial_append", tref) return larr, arr, idx def final_idx32(self, idx, offset): """Perform final operations in 32-bit indices.""" if profile: tref = time() if profile: show_stats("Entering final_idx32", tref) # Do an upcast first in order to add the offset. idx = idx.astype('uint64') idx += offset # The next partition is valid up to table sizes of # 2**30 * 2**18 = 2**48 bytes, that is, 256 Tera-elements, # which should be a safe figure, at least for a while. idx //= self.lbucket # After the division, we can downsize the indexes to 'uint32' idx = idx.astype('uint32') if profile: show_stats("Exiting final_idx32", tref) return idx def append(self, xarr, update=False): """Append the array to the index objects.""" if profile: tref = time() if profile: show_stats("Entering append", tref) if not update and self.temp_required: where = self.tmp # The reduction will take place *after* the optimization process reduction = 1 else: where = self reduction = self.reduction sorted = where.sorted indices = where.indices ranges = where.ranges mranges = where.mranges bounds = where.bounds mbounds = where.mbounds abounds = where.abounds zbounds = where.zbounds sortedLR = where.sortedLR indicesLR = where.indicesLR nrows = sorted.nrows # before sorted.append() larr, arr, idx = self.initial_append(xarr, nrows, reduction) # Save the sorted array sorted.append(arr.reshape(1, arr.size)) cs = self.chunksize // reduction ncs = self.nchunkslice # Save ranges & bounds ranges.append([[arr[0], larr]]) bounds.append([arr[cs::cs]]) abounds.append(arr[0::cs]) zbounds.append(arr[cs - 1::cs]) # Compute the medians smedian = arr[cs // 2::cs] mbounds.append(smedian) mranges.append([smedian[ncs // 2]]) if profile: show_stats("Before deleting arr & smedian", tref) del arr, smedian # delete references if profile: show_stats("After deleting arr & smedian", tref) # Now that arr is gone, we can upcast the indices and add the offset if self.indsize == 4: idx = self.final_idx32(idx, nrows * self.slicesize) indices.append(idx.reshape(1, idx.size)) if profile: show_stats("Before deleting idx", tref) del idx # Update counters after a successful append self.nrows = nrows + 1 self.nelements = self.nrows * self.slicesize self.nelementsSLR = 0 # reset the counter of the last row index to 0 self.nelementsILR = 0 # reset the counter of the last row index to 0 # The number of elements will be saved as an attribute. # This is necessary in case the LR arrays can remember its values # after a possible node preemtion/reload. sortedLR.attrs.nelements = self.nelementsSLR indicesLR.attrs.nelements = self.nelementsILR self.dirtycache = True # the cache is dirty now if profile: show_stats("Exiting append", tref) def append_last_row(self, xarr, update=False): """Append the array to the last row index objects.""" if profile: tref = time() if profile: show_stats("Entering appendLR", tref) # compute the elements in the last row sorted & bounds array nrows = self.nslices if not update and self.temp_required: where = self.tmp # The reduction will take place *after* the optimization process reduction = 1 else: where = self reduction = self.reduction indicesLR = where.indicesLR sortedLR = where.sortedLR larr, arr, idx = self.initial_append(xarr, nrows, reduction) nelementsSLR = len(arr) nelementsILR = len(idx) # Build the cache of bounds rchunksize = self.chunksize // reduction self.bebounds = numpy.concatenate((arr[::rchunksize], [larr])) # The number of elements will be saved as an attribute sortedLR.attrs.nelements = nelementsSLR indicesLR.attrs.nelements = nelementsILR # Save the number of elements, bounds and sorted values # at the end of the sorted array offset2 = len(self.bebounds) sortedLR[nelementsSLR:nelementsSLR + offset2] = self.bebounds sortedLR[:nelementsSLR] = arr del arr # Now that arr is gone, we can upcast the indices and add the offset if self.indsize == 4: idx = self.final_idx32(idx, nrows * self.slicesize) # Save the reverse index array indicesLR[:len(idx)] = idx del idx # Update counters after a successful append self.nrows = nrows + 1 self.nelements = nrows * self.slicesize + nelementsILR self.nelementsILR = nelementsILR self.nelementsSLR = nelementsSLR self.dirtycache = True # the cache is dirty now if profile: show_stats("Exiting appendLR", tref) def optimize(self, verbose=False): """Optimize an index so as to allow faster searches. verbose If True, messages about the progress of the optimization process are printed out. """ if not self.temp_required: return if verbose: self.verbose = True else: self.verbose = debug # Initialize last_tover and last_nover self.last_tover = 0 self.last_nover = 0 # Compute the correct optimizations for current optim level opts = calcoptlevels(self.nblocks, self.optlevel, self.indsize) optmedian, optstarts, optstops, optfull = opts if debug: print("optvalues:", opts) self.create_temp2() # Start the optimization process while True: if optfull: for niter in range(optfull): if self.swap('chunks', 'median'): break if self.nblocks > 1: # Swap slices only in the case that we have # several blocks if self.swap('slices', 'median'): break if self.swap('chunks', 'median'): break if self.swap('chunks', 'start'): break if self.swap('chunks', 'stop'): break else: if optmedian: if self.swap('chunks', 'median'): break if optstarts: if self.swap('chunks', 'start'): break if optstops: if self.swap('chunks', 'stop'): break break # If we reach this, exit the loop # Check if we require a complete sort. Important: this step # should be carried out *after* the optimization process has # been completed (this is to guarantee that the complete sort # does not take too much memory). if self.want_complete_sort: if self.noverlaps > 0: self.do_complete_sort() # Check that we have effectively achieved the complete sort if self.noverlaps > 0: warnings.warn( "OPSI was not able to achieve a completely sorted index." " Please report this to the authors.", UserWarning) # Close and delete the temporal optimization index file self.cleanup_temp() return def do_complete_sort(self): """Bring an already optimized index into a complete sorted state.""" if self.verbose: t1 = time() c1 = perf_counter() ss = self.slicesize tmp = self.tmp ranges = tmp.ranges[:] nslices = self.nslices nelementsLR = self.nelementsILR if nelementsLR > 0: # Add the ranges corresponding to the last row rangeslr = numpy.array([self.bebounds[0], self.bebounds[-1]]) ranges = numpy.concatenate((ranges, [rangeslr])) nslices += 1 sorted = tmp.sorted indices = tmp.indices sortedLR = tmp.sortedLR indicesLR = tmp.indicesLR sremain = numpy.array([], dtype=self.dtype) iremain = numpy.array([], dtype='u%d' % self.indsize) starts = numpy.zeros(shape=nslices, dtype=numpy.int_) for i in range(nslices): # Find the overlapping elements for slice i sover = numpy.array([], dtype=self.dtype) iover = numpy.array([], dtype='u%d' % self.indsize) prev_end = ranges[i, 1] for j in range(i + 1, nslices): stj = starts[j] if ((j < self.nslices and stj == ss) or (j == self.nslices and stj == nelementsLR)): # This slice has been already dealt with continue if j < self.nslices: assert stj < ss, \ "Two slices cannot overlap completely at this stage!" next_beg = sorted[j, stj] else: assert stj < nelementsLR, \ "Two slices cannot overlap completely at this stage!" next_beg = sortedLR[stj] next_end = ranges[j, 1] if prev_end > next_end: # Complete overlapping case if j < self.nslices: sover = numpy.concatenate((sover, sorted[j, stj:])) iover = numpy.concatenate((iover, indices[j, stj:])) starts[j] = ss else: n = nelementsLR sover = numpy.concatenate((sover, sortedLR[stj:n])) iover = numpy.concatenate((iover, indicesLR[stj:n])) starts[j] = nelementsLR elif prev_end > next_beg: idx = self.search_item_lt(tmp, prev_end, j, ranges[j], stj) if j < self.nslices: sover = numpy.concatenate((sover, sorted[j, stj:idx])) iover = numpy.concatenate((iover, indices[j, stj:idx])) else: sover = numpy.concatenate((sover, sortedLR[stj:idx])) iover = numpy.concatenate((iover, indicesLR[stj:idx])) starts[j] = idx # Build the extended slices to sort out if i < self.nslices: ssorted = numpy.concatenate( (sremain, sorted[i, starts[i]:], sover)) sindices = numpy.concatenate( (iremain, indices[i, starts[i]:], iover)) else: ssorted = numpy.concatenate( (sremain, sortedLR[starts[i]:nelementsLR], sover)) sindices = numpy.concatenate( (iremain, indicesLR[starts[i]:nelementsLR], iover)) # Sort the extended slices indexesextension.keysort(ssorted, sindices) # Save the first elements of extended slices in the slice i if i < self.nslices: sorted[i] = ssorted[:ss] indices[i] = sindices[:ss] # Update caches for this slice self.update_caches(i, ssorted[:ss]) # Save the remaining values in a separate array send = len(sover) + len(sremain) sremain = ssorted[ss:ss + send] iremain = sindices[ss:ss + send] else: # Still some elements remain for the last row n = len(ssorted) assert n == nelementsLR send = 0 sortedLR[:n] = ssorted indicesLR[:n] = sindices # Update the caches for last row sortedlr = sortedLR[:nelementsLR] bebounds = numpy.concatenate( (sortedlr[::self.chunksize], [sortedlr[-1]])) sortedLR[nelementsLR:nelementsLR + len(bebounds)] = bebounds self.bebounds = bebounds # Verify that we have dealt with all the remaining values assert send == 0 # Compute the overlaps in order to verify that we have achieved # a complete sort. This has to be executed always (and not only # in verbose mode!). self.compute_overlaps(self.tmp, "do_complete_sort()", self.verbose) if self.verbose: t = round(time() - t1, 4) c = round(perf_counter() - c1, 4) print("time: {}. clock: {}".format(t, c)) def swap(self, what, mode=None): """Swap chunks or slices using a certain bounds reference.""" # Thresholds for avoiding continuing the optimization # thnover = 4 * self.slicesize # minimum number of overlapping # # elements thnover = 40 thmult = 0.1 # minimum ratio of multiplicity (a 10%) thtover = 0.01 # minimum overlaping index for slices (a 1%) if self.verbose: t1 = time() c1 = perf_counter() if what == "chunks": self.swap_chunks(mode) elif what == "slices": self.swap_slices(mode) if mode: message = "swap_{}({})".format(what, mode) else: message = "swap_{}".format(what) (nover, mult, tover) = self.compute_overlaps( self.tmp, message, self.verbose) rmult = len(mult.nonzero()[0]) / float(len(mult)) if self.verbose: t = round(time() - t1, 4) c = round(perf_counter() - c1, 4) print("time: {}. clock: {}".format(t, c)) # Check that entropy is actually decreasing if what == "chunks" and self.last_tover > 0. and self.last_nover > 0: tover_var = (self.last_tover - tover) / self.last_tover nover_var = (self.last_nover - nover) / self.last_nover if tover_var < 0.05 and nover_var < 0.05: # Less than a 5% of improvement is too few return True self.last_tover = tover self.last_nover = nover # Check if some threshold has met if nover < thnover: return True if rmult < thmult: return True # Additional check for the overlap ratio if 0. <= tover < thtover: return True return False def create_temp(self): """Create some temporary objects for slice sorting purposes.""" # The index will be dirty during the index optimization process self.dirty = True # Build the name of the temporary file fd, self.tmpfilename = tempfile.mkstemp( ".tmp", "pytables-", self.tmp_dir) # Close the file descriptor so as to avoid leaks os.close(fd) # Create the proper PyTables file self.tmpfile = self._openFile(self.tmpfilename, "w") self.tmp = tmp = self.tmpfile.root cs = self.chunksize ss = self.slicesize filters = self.filters # temporary sorted & indices arrays shape = (0, ss) atom = Atom.from_dtype(self.dtype) EArray(tmp, 'sorted', atom, shape, "Temporary sorted", filters, chunkshape=(1, cs)) EArray(tmp, 'indices', UIntAtom(itemsize=self.indsize), shape, "Temporary indices", filters, chunkshape=(1, cs)) # temporary bounds nbounds_inslice = (ss - 1) // cs shape = (0, nbounds_inslice) EArray(tmp, 'bounds', atom, shape, "Temp chunk bounds", filters, chunkshape=(cs, nbounds_inslice)) shape = (0,) EArray(tmp, 'abounds', atom, shape, "Temp start bounds", filters, chunkshape=(cs,)) EArray(tmp, 'zbounds', atom, shape, "Temp end bounds", filters, chunkshape=(cs,)) EArray(tmp, 'mbounds', atom, shape, "Median bounds", filters, chunkshape=(cs,)) # temporary ranges EArray(tmp, 'ranges', atom, (0, 2), "Temporary range values", filters, chunkshape=(cs, 2)) EArray(tmp, 'mranges', atom, (0,), "Median ranges", filters, chunkshape=(cs,)) # temporary last row (sorted) shape = (ss + 2 + nbounds_inslice,) CArray(tmp, 'sortedLR', atom, shape, "Temp Last Row sorted values + bounds", filters, chunkshape=(cs,)) # temporary last row (indices) shape = (ss,) CArray(tmp, 'indicesLR', UIntAtom(itemsize=self.indsize), shape, "Temp Last Row indices", filters, chunkshape=(cs,)) def create_temp2(self): """Create some temporary objects for slice sorting purposes.""" # The algorithms for doing the swap can be optimized so that # one should be necessary to create temporaries for keeping just # the contents of a single superblock. # F. Alted 2007-01-03 cs = self.chunksize ss = self.slicesize filters = self.filters # temporary sorted & indices arrays shape = (self.nslices, ss) atom = Atom.from_dtype(self.dtype) tmp = self.tmp CArray(tmp, 'sorted2', atom, shape, "Temporary sorted 2", filters, chunkshape=(1, cs)) CArray(tmp, 'indices2', UIntAtom(itemsize=self.indsize), shape, "Temporary indices 2", filters, chunkshape=(1, cs)) # temporary bounds nbounds_inslice = (ss - 1) // cs shape = (self.nslices, nbounds_inslice) CArray(tmp, 'bounds2', atom, shape, "Temp chunk bounds 2", filters, chunkshape=(cs, nbounds_inslice)) shape = (self.nchunks,) CArray(tmp, 'abounds2', atom, shape, "Temp start bounds 2", filters, chunkshape=(cs,)) CArray(tmp, 'zbounds2', atom, shape, "Temp end bounds 2", filters, chunkshape=(cs,)) CArray(tmp, 'mbounds2', atom, shape, "Median bounds 2", filters, chunkshape=(cs,)) # temporary ranges CArray(tmp, 'ranges2', atom, (self.nslices, 2), "Temporary range values 2", filters, chunkshape=(cs, 2)) CArray(tmp, 'mranges2', atom, (self.nslices,), "Median ranges 2", filters, chunkshape=(cs,)) def cleanup_temp(self): """Copy the data and delete the temporaries for sorting purposes.""" if self.verbose: print("Copying temporary data...") # tmp -> index reduction = self.reduction cs = self.chunksize // reduction ncs = self.nchunkslice tmp = self.tmp for i in range(self.nslices): # Copy sorted & indices slices sorted = tmp.sorted[i][::reduction].copy() self.sorted.append(sorted.reshape(1, sorted.size)) # Compute ranges self.ranges.append([[sorted[0], sorted[-1]]]) # Compute chunk bounds self.bounds.append([sorted[cs::cs]]) # Compute start, stop & median bounds and ranges self.abounds.append(sorted[0::cs]) self.zbounds.append(sorted[cs - 1::cs]) smedian = sorted[cs // 2::cs] self.mbounds.append(smedian) self.mranges.append([smedian[ncs // 2]]) del sorted, smedian # delete references # Now that sorted is gone, we can copy the indices indices = tmp.indices[i] self.indices.append(indices.reshape(1, indices.size)) # Now it is the last row turn (if needed) if self.nelementsSLR > 0: # First, the sorted values sortedLR = self.sortedLR indicesLR = self.indicesLR nelementsLR = self.nelementsILR sortedlr = tmp.sortedLR[:nelementsLR][::reduction].copy() nelementsSLR = len(sortedlr) sortedLR[:nelementsSLR] = sortedlr # Now, the bounds self.bebounds = numpy.concatenate((sortedlr[::cs], [sortedlr[-1]])) offset2 = len(self.bebounds) sortedLR[nelementsSLR:nelementsSLR + offset2] = self.bebounds # Finally, the indices indicesLR[:] = tmp.indicesLR[:] # Update the number of (reduced) sorted elements self.nelementsSLR = nelementsSLR # The number of elements will be saved as an attribute self.sortedLR.attrs.nelements = self.nelementsSLR self.indicesLR.attrs.nelements = self.nelementsILR if self.verbose: print("Deleting temporaries...") self.tmp = None self.tmpfile.close() os.remove(self.tmpfilename) self.tmpfilename = None # The optimization process has finished, and the index is ok now self.dirty = False # ...but the memory data cache is dirty now self.dirtycache = True def get_neworder(self, neworder, src_disk, tmp_disk, lastrow, nslices, offset, dtype): """Get sorted & indices values in new order.""" cs = self.chunksize ncs = ncs2 = self.nchunkslice self_nslices = self.nslices tmp = numpy.empty(shape=self.slicesize, dtype=dtype) for i in range(nslices): ns = offset + i if ns == self_nslices: # The number of complete chunks in the last row ncs2 = self.nelementsILR // cs # Get slices in new order for j in range(ncs2): idx = neworder[i * ncs + j] ins = idx // ncs inc = (idx - ins * ncs) * cs ins += offset nc = j * cs if ins == self_nslices: tmp[nc:nc + cs] = lastrow[inc:inc + cs] else: tmp[nc:nc + cs] = src_disk[ins, inc:inc + cs] if ns == self_nslices: # The number of complete chunks in the last row lastrow[:ncs2 * cs] = tmp[:ncs2 * cs] # The elements in the last chunk of the last row will # participate in the global reordering later on, during # the phase of sorting of *two* slices at a time # (including the last row slice, see # self.reorder_slices()). The caches for last row will # be updated in self.reorder_slices() too. # F. Altet 2008-08-25 else: tmp_disk[ns] = tmp def swap_chunks(self, mode="median"): """Swap & reorder the different chunks in a block.""" boundsnames = { 'start': 'abounds', 'stop': 'zbounds', 'median': 'mbounds'} tmp = self.tmp sorted = tmp.sorted indices = tmp.indices tmp_sorted = tmp.sorted2 tmp_indices = tmp.indices2 sortedLR = tmp.sortedLR indicesLR = tmp.indicesLR cs = self.chunksize ncs = self.nchunkslice nsb = self.nslicesblock ncb = ncs * nsb ncb2 = ncb boundsobj = tmp._f_get_child(boundsnames[mode]) can_cross_bbounds = (self.indsize == 8 and self.nelementsILR > 0) for nblock in range(self.nblocks): # Protection for last block having less chunks than ncb remainingchunks = self.nchunks - nblock * ncb if remainingchunks < ncb: ncb2 = remainingchunks if ncb2 <= 1: # if only zero or one chunks remains we are done break nslices = ncb2 // ncs bounds = boundsobj[nblock * ncb:nblock * ncb + ncb2] # Do this only if lastrow elements can cross block boundaries if (nblock == self.nblocks - 1 and # last block can_cross_bbounds): nslices += 1 ul = self.nelementsILR // cs bounds = numpy.concatenate((bounds, self.bebounds[:ul])) sbounds_idx = bounds.argsort(kind=defsort) offset = int(nblock * nsb) # Swap sorted and indices following the new order self.get_neworder(sbounds_idx, sorted, tmp_sorted, sortedLR, nslices, offset, self.dtype) self.get_neworder(sbounds_idx, indices, tmp_indices, indicesLR, nslices, offset, 'u%d' % self.indsize) # Reorder completely the index at slice level self.reorder_slices(tmp=True) def read_slice(self, where, nslice, buffer, start=0): """Read a slice from the `where` dataset and put it in `buffer`.""" # Create the buffers for specifying the coordinates self.startl = numpy.array([nslice, start], numpy.uint64) self.stopl = numpy.array([nslice + 1, start + buffer.size], numpy.uint64) self.stepl = numpy.ones(shape=2, dtype=numpy.uint64) where._g_read_slice(self.startl, self.stopl, self.stepl, buffer) def write_slice(self, where, nslice, buffer, start=0): """Write a `slice` to the `where` dataset with the `buffer` data.""" self.startl = numpy.array([nslice, start], numpy.uint64) self.stopl = numpy.array([nslice + 1, start + buffer.size], numpy.uint64) self.stepl = numpy.ones(shape=2, dtype=numpy.uint64) countl = self.stopl - self.startl # (1, self.slicesize) where._g_write_slice(self.startl, self.stepl, countl, buffer) # Read version for LastRow def read_slice_lr(self, where, buffer, start=0): """Read a slice from the `where` dataset and put it in `buffer`.""" startl = numpy.array([start], dtype=numpy.uint64) stopl = numpy.array([start + buffer.size], dtype=numpy.uint64) stepl = numpy.array([1], dtype=numpy.uint64) where._g_read_slice(startl, stopl, stepl, buffer) # Write version for LastRow def write_sliceLR(self, where, buffer, start=0): """Write a slice from the `where` dataset with the `buffer` data.""" startl = numpy.array([start], dtype=numpy.uint64) countl = numpy.array([start + buffer.size], dtype=numpy.uint64) stepl = numpy.array([1], dtype=numpy.uint64) where._g_write_slice(startl, stepl, countl, buffer) def reorder_slice(self, nslice, sorted, indices, ssorted, sindices, tmp_sorted, tmp_indices): """Copy & reorder the slice in source to final destination.""" ss = self.slicesize # Load the second part in buffers self.read_slice(tmp_sorted, nslice, ssorted[ss:]) self.read_slice(tmp_indices, nslice, sindices[ss:]) indexesextension.keysort(ssorted, sindices) # Write the first part of the buffers to the regular leaves self.write_slice(sorted, nslice - 1, ssorted[:ss]) self.write_slice(indices, nslice - 1, sindices[:ss]) # Update caches self.update_caches(nslice - 1, ssorted[:ss]) # Shift the slice in the end to the beginning ssorted[:ss] = ssorted[ss:] sindices[:ss] = sindices[ss:] def update_caches(self, nslice, ssorted): """Update the caches for faster lookups.""" cs = self.chunksize ncs = self.nchunkslice tmp = self.tmp # update first & second cache bounds (ranges & bounds) tmp.ranges[nslice] = ssorted[[0, -1]] tmp.bounds[nslice] = ssorted[cs::cs] # update start & stop bounds tmp.abounds[nslice * ncs:(nslice + 1) * ncs] = ssorted[0::cs] tmp.zbounds[nslice * ncs:(nslice + 1) * ncs] = ssorted[cs - 1::cs] # update median bounds smedian = ssorted[cs // 2::cs] tmp.mbounds[nslice * ncs:(nslice + 1) * ncs] = smedian tmp.mranges[nslice] = smedian[ncs // 2] def reorder_slices(self, tmp): """Reorder completely the index at slice level. This method has to maintain the locality of elements in the ambit of ``blocks``, i.e. an element of a ``block`` cannot be sent to another ``block`` during this reordering. This is *critical* for ``light`` indexes to be able to use this. This version of reorder_slices is optimized in that *two* complete slices are taken at a time (including the last row slice) so as to sort them. Then, each new slice that is read is put at the end of this two-slice buffer, while the previous one is moved to the beginning of the buffer. This is in order to better reduce the entropy of the regular part (i.e. all except the last row) of the index. A secondary effect of this is that it takes at least *twice* of memory than a previous version of reorder_slices() that only reorders on a slice-by-slice basis. However, as this is more efficient than the old version, one can configure the slicesize to be smaller, so the memory consumption is barely similar. """ tmp = self.tmp sorted = tmp.sorted indices = tmp.indices if tmp: tmp_sorted = tmp.sorted2 tmp_indices = tmp.indices2 else: tmp_sorted = tmp.sorted tmp_indices = tmp.indices cs = self.chunksize ss = self.slicesize nsb = self.blocksize // self.slicesize nslices = self.nslices nblocks = self.nblocks nelementsLR = self.nelementsILR # Create the buffer for reordering 2 slices at a time ssorted = numpy.empty(shape=ss * 2, dtype=self.dtype) sindices = numpy.empty(shape=ss * 2, dtype=numpy.dtype('u%d' % self.indsize)) if self.indsize == 8: # Bootstrap the process for reordering # Read the first slice in buffers self.read_slice(tmp_sorted, 0, ssorted[:ss]) self.read_slice(tmp_indices, 0, sindices[:ss]) nslice = 0 # Just in case the loop behind executes nothing # Loop over the remainding slices in block for nslice in range(1, sorted.nrows): self.reorder_slice(nslice, sorted, indices, ssorted, sindices, tmp_sorted, tmp_indices) # End the process (enrolling the lastrow if necessary) if nelementsLR > 0: sortedLR = self.tmp.sortedLR indicesLR = self.tmp.indicesLR # Shrink the ssorted and sindices arrays to the minimum ssorted2 = ssorted[:ss + nelementsLR] sortedlr = ssorted2[ss:] sindices2 = sindices[:ss + nelementsLR] indiceslr = sindices2[ss:] # Read the last row info in the second part of the buffer self.read_slice_lr(sortedLR, sortedlr) self.read_slice_lr(indicesLR, indiceslr) indexesextension.keysort(ssorted2, sindices2) # Write the second part of the buffers to the lastrow indices self.write_sliceLR(sortedLR, sortedlr) self.write_sliceLR(indicesLR, indiceslr) # Update the caches for last row bebounds = numpy.concatenate((sortedlr[::cs], [sortedlr[-1]])) sortedLR[nelementsLR:nelementsLR + len(bebounds)] = bebounds self.bebounds = bebounds # Write the first part of the buffers to the regular leaves self.write_slice(sorted, nslice, ssorted[:ss]) self.write_slice(indices, nslice, sindices[:ss]) # Update caches for this slice self.update_caches(nslice, ssorted[:ss]) else: # Iterate over each block. No data should cross block # boundaries to avoid adressing problems with short indices. for nb in range(nblocks): # Bootstrap the process for reordering # Read the first slice in buffers nrow = nb * nsb self.read_slice(tmp_sorted, nrow, ssorted[:ss]) self.read_slice(tmp_indices, nrow, sindices[:ss]) # Loop over the remainding slices in block lrb = nrow + nsb if lrb > nslices: lrb = nslices nslice = nrow # Just in case the loop behind executes nothing for nslice in range(nrow + 1, lrb): self.reorder_slice(nslice, sorted, indices, ssorted, sindices, tmp_sorted, tmp_indices) # Write the first part of the buffers to the regular leaves self.write_slice(sorted, nslice, ssorted[:ss]) self.write_slice(indices, nslice, sindices[:ss]) # Update caches for this slice self.update_caches(nslice, ssorted[:ss]) def swap_slices(self, mode="median"): """Swap slices in a superblock.""" tmp = self.tmp sorted = tmp.sorted indices = tmp.indices tmp_sorted = tmp.sorted2 tmp_indices = tmp.indices2 ncs = self.nchunkslice nss = self.superblocksize // self.slicesize nss2 = nss for sblock in range(self.nsuperblocks): # Protection for last superblock having less slices than nss remainingslices = self.nslices - sblock * nss if remainingslices < nss: nss2 = remainingslices if nss2 <= 1: break if mode == "start": ranges = tmp.ranges[sblock * nss:sblock * nss + nss2, 0] elif mode == "stop": ranges = tmp.ranges[sblock * nss:sblock * nss + nss2, 1] elif mode == "median": ranges = tmp.mranges[sblock * nss:sblock * nss + nss2] sranges_idx = ranges.argsort(kind=defsort) # Don't swap the superblock at all if one doesn't need to ndiff = (sranges_idx != numpy.arange(nss2)).sum() / 2 if ndiff * 50 < nss2: # The number of slices to rearrange is less than 2.5%, # so skip the reordering of this superblock # (too expensive for such a little improvement) if self.verbose: print("skipping reordering of superblock ->", sblock) continue ns = sblock * nss2 # Swap sorted and indices slices following the new order for i in range(nss2): idx = sranges_idx[i] # Swap sorted & indices slices oi = ns + i oidx = ns + idx tmp_sorted[oi] = sorted[oidx] tmp_indices[oi] = indices[oidx] # Swap start, stop & median ranges tmp.ranges2[oi] = tmp.ranges[oidx] tmp.mranges2[oi] = tmp.mranges[oidx] # Swap chunk bounds tmp.bounds2[oi] = tmp.bounds[oidx] # Swap start, stop & median bounds j = oi * ncs jn = (oi + 1) * ncs xj = oidx * ncs xjn = (oidx + 1) * ncs tmp.abounds2[j:jn] = tmp.abounds[xj:xjn] tmp.zbounds2[j:jn] = tmp.zbounds[xj:xjn] tmp.mbounds2[j:jn] = tmp.mbounds[xj:xjn] # tmp -> originals for i in range(nss2): # Copy sorted & indices slices oi = ns + i sorted[oi] = tmp_sorted[oi] indices[oi] = tmp_indices[oi] # Copy start, stop & median ranges tmp.ranges[oi] = tmp.ranges2[oi] tmp.mranges[oi] = tmp.mranges2[oi] # Copy chunk bounds tmp.bounds[oi] = tmp.bounds2[oi] # Copy start, stop & median bounds j = oi * ncs jn = (oi + 1) * ncs tmp.abounds[j:jn] = tmp.abounds2[j:jn] tmp.zbounds[j:jn] = tmp.zbounds2[j:jn] tmp.mbounds[j:jn] = tmp.mbounds2[j:jn] def search_item_lt(self, where, item, nslice, limits, start=0): """Search a single item in a specific sorted slice.""" # This method will only works under the assumtion that item # *is to be found* in the nslice. assert nan_aware_lt(limits[0], item) and nan_aware_le(item, limits[1]) cs = self.chunksize ss = self.slicesize nelementsLR = self.nelementsILR bstart = start // cs # Find the chunk if nslice < self.nslices: nchunk = bisect_left(where.bounds[nslice], item, bstart) else: # We need to subtract 1 chunk here because bebounds # has a leading value nchunk = bisect_left(self.bebounds, item, bstart) - 1 assert nchunk >= 0 # Find the element in chunk pos = nchunk * cs if nslice < self.nslices: pos += bisect_left(where.sorted[nslice, pos:pos + cs], item) assert pos <= ss else: end = pos + cs if end > nelementsLR: end = nelementsLR pos += bisect_left(self.sortedLR[pos:end], item) assert pos <= nelementsLR assert pos > 0 return pos def compute_overlaps_finegrain(self, where, message, verbose): """Compute some statistics about overlaping of slices in index. It returns the following info: noverlaps : int The total number of elements that overlaps in index. multiplicity : array of int The number of times that a concrete slice overlaps with any other. toverlap : float An ovelap index: the sum of the values in segment slices that overlaps divided by the entire range of values. This index is only computed for numerical types. """ ss = self.slicesize ranges = where.ranges[:] sorted = where.sorted sortedLR = where.sortedLR nslices = self.nslices nelementsLR = self.nelementsILR if nelementsLR > 0: # Add the ranges corresponding to the last row rangeslr = numpy.array([self.bebounds[0], self.bebounds[-1]]) ranges = numpy.concatenate((ranges, [rangeslr])) nslices += 1 soverlap = 0. toverlap = -1. multiplicity = numpy.zeros(shape=nslices, dtype="int_") overlaps = multiplicity.copy() starts = multiplicity.copy() for i in range(nslices): prev_end = ranges[i, 1] for j in range(i + 1, nslices): stj = starts[j] assert stj <= ss if stj == ss: # This slice has already been counted continue if j < self.nslices: next_beg = sorted[j, stj] else: next_beg = sortedLR[stj] next_end = ranges[j, 1] if prev_end > next_end: # Complete overlapping case multiplicity[j - i] += 1 if j < self.nslices: overlaps[i] += ss - stj starts[j] = ss # a sentinel else: overlaps[i] += nelementsLR - stj starts[j] = nelementsLR # a sentinel elif prev_end > next_beg: multiplicity[j - i] += 1 idx = self.search_item_lt( where, prev_end, j, ranges[j], stj) nelem = idx - stj overlaps[i] += nelem starts[j] = idx if self.type != "string": # Convert ranges into floats in order to allow # doing operations with them without overflows soverlap += float(ranges[i, 1]) - float(ranges[j, 0]) # Return the overlap as the ratio between overlaps and entire range if self.type != "string": erange = float(ranges[-1, 1]) - float(ranges[0, 0]) # Check that there is an effective range of values # Beware, erange can be negative in situations where # the values are suffering overflow. This can happen # specially on big signed integer values (on overflows, # the end value will become negative!). # Also, there is no way to compute overlap ratios for # non-numerical types. So, be careful and always check # that toverlap has a positive value (it must have been # initialized to -1. before) before using it. # F. Alted 2007-01-19 if erange > 0: toverlap = soverlap / erange if verbose and message != "init": print("toverlap (%s):" % message, toverlap) print("multiplicity:\n", multiplicity, multiplicity.sum()) print("overlaps:\n", overlaps, overlaps.sum()) noverlaps = overlaps.sum() # For full indexes, set the 'is_csi' flag if self.indsize == 8 and self._v_file._iswritable(): self._v_attrs.is_csi = (noverlaps == 0) # Save the number of overlaps for future references self.noverlaps = noverlaps return (noverlaps, multiplicity, toverlap) def compute_overlaps(self, where, message, verbose): """Compute some statistics about overlaping of slices in index. It returns the following info: noverlaps : int The total number of slices that overlaps in index. multiplicity : array of int The number of times that a concrete slice overlaps with any other. toverlap : float An ovelap index: the sum of the values in segment slices that overlaps divided by the entire range of values. This index is only computed for numerical types. """ ranges = where.ranges[:] nslices = self.nslices if self.nelementsILR > 0: # Add the ranges corresponding to the last row rangeslr = numpy.array([self.bebounds[0], self.bebounds[-1]]) ranges = numpy.concatenate((ranges, [rangeslr])) nslices += 1 noverlaps = 0 soverlap = 0. toverlap = -1. multiplicity = numpy.zeros(shape=nslices, dtype="int_") for i in range(nslices): for j in range(i + 1, nslices): if ranges[i, 1] > ranges[j, 0]: noverlaps += 1 multiplicity[j - i] += 1 if self.type != "string": # Convert ranges into floats in order to allow # doing operations with them without overflows soverlap += float(ranges[i, 1]) - float(ranges[j, 0]) # Return the overlap as the ratio between overlaps and entire range if self.type != "string": erange = float(ranges[-1, 1]) - float(ranges[0, 0]) # Check that there is an effective range of values # Beware, erange can be negative in situations where # the values are suffering overflow. This can happen # specially on big signed integer values (on overflows, # the end value will become negative!). # Also, there is no way to compute overlap ratios for # non-numerical types. So, be careful and always check # that toverlap has a positive value (it must have been # initialized to -1. before) before using it. # F. Altet 2007-01-19 if erange > 0: toverlap = soverlap / erange if verbose: print("overlaps (%s):" % message, noverlaps, toverlap) print(multiplicity) # For full indexes, set the 'is_csi' flag if self.indsize == 8 and self._v_file._iswritable(): self._v_attrs.is_csi = (noverlaps == 0) # Save the number of overlaps for future references self.noverlaps = noverlaps return (noverlaps, multiplicity, toverlap) def read_sorted_indices(self, what, start, stop, step): """Return the sorted or indices values in the specified range.""" (start, stop, step) = self._process_range(start, stop, step) if start >= stop: return numpy.empty(0, self.dtype) # Correction for negative values of step (reverse indices) if step < 0: tmp = start start = self.nelements - stop stop = self.nelements - tmp if what == "sorted": values = self.sorted valuesLR = self.sortedLR buffer_ = numpy.empty(stop - start, dtype=self.dtype) else: values = self.indices valuesLR = self.indicesLR buffer_ = numpy.empty(stop - start, dtype="u%d" % self.indsize) ss = self.slicesize nrow_start = start // ss istart = start % ss nrow_stop = stop // ss tlen = stop - start bstart = 0 ilen = 0 for nrow in range(nrow_start, nrow_stop + 1): blen = ss - istart if ilen + blen > tlen: blen = tlen - ilen if blen <= 0: break if nrow < self.nslices: self.read_slice( values, nrow, buffer_[bstart:bstart + blen], istart) else: self.read_slice_lr( valuesLR, buffer_[bstart:bstart + blen], istart) istart = 0 bstart += blen ilen += blen return buffer_[::step]
[docs] def read_sorted(self, start=None, stop=None, step=None): """Return the sorted values of index in the specified range. The meaning of the start, stop and step arguments is the same as in :meth:`Table.read_sorted`. """ return self.read_sorted_indices('sorted', start, stop, step)
[docs] def read_indices(self, start=None, stop=None, step=None): """Return the indices values of index in the specified range. The meaning of the start, stop and step arguments is the same as in :meth:`Table.read_sorted`. """ return self.read_sorted_indices('indices', start, stop, step)
def _process_range(self, start, stop, step): """Get a range specifc for the index usage.""" if start is not None and stop is None: # Special case for the behaviour of PyTables iterators stop = idx2long(start + 1) if start is None: start = 0 else: start = idx2long(start) if stop is None: stop = idx2long(self.nelements) else: stop = idx2long(stop) if step is None: step = 1 else: step = idx2long(step) return (start, stop, step)
[docs] def __getitem__(self, key): """Return the indices values of index in the specified range. If key argument is an integer, the corresponding index is returned. If key is a slice, the range of indices determined by it is returned. A negative value of step in slice is supported, meaning that the results will be returned in reverse order. This method is equivalent to :meth:`Index.read_indices`. """ if is_idx(key): key = operator.index(key) if key < 0: # To support negative values key += self.nelements return self.read_indices(key, key + 1, 1)[0] elif isinstance(key, slice): return self.read_indices(key.start, key.stop, key.step)
def __len__(self): return self.nelements def restorecache(self): """Clean the limits cache and resize starts and lengths arrays""" params = self._v_file.params # The sorted IndexArray is absolutely required to be in memory # at the same time than the Index instance, so create a strong # reference to it. We are not introducing leaks because the # strong reference will disappear when this Index instance is # to be closed. self._sorted = self.sorted self._sorted.boundscache = ObjectCache(params['BOUNDS_MAX_SLOTS'], params['BOUNDS_MAX_SIZE'], 'non-opt types bounds') self.sorted.boundscache = ObjectCache(params['BOUNDS_MAX_SLOTS'], params['BOUNDS_MAX_SIZE'], 'non-opt types bounds') """A cache for the bounds (2nd hash) data. Only used for non-optimized types searches.""" self.limboundscache = ObjectCache(params['LIMBOUNDS_MAX_SLOTS'], params['LIMBOUNDS_MAX_SIZE'], 'bounding limits') """A cache for bounding limits.""" self.sortedLRcache = ObjectCache(params['SORTEDLR_MAX_SLOTS'], params['SORTEDLR_MAX_SIZE'], 'last row chunks') """A cache for the last row chunks. Only used for searches in the last row, and mainly useful for small indexes.""" self.starts = numpy.empty(shape=self.nrows, dtype=numpy.int32) self.lengths = numpy.empty(shape=self.nrows, dtype=numpy.int32) self.sorted._init_sorted_slice(self) self.dirtycache = False def search(self, item): """Do a binary search in this index for an item.""" if profile: tref = time() if profile: show_stats("Entering search", tref) if self.dirtycache: self.restorecache() # An empty item or if left limit is larger than the right one # means that the number of records is always going to be empty, # so we avoid further computation (including looking up the # limits cache). if not item or item[0] > item[1]: self.starts[:] = 0 self.lengths[:] = 0 return 0 tlen = 0 # Check whether the item tuple is in the limits cache or not nslot = self.limboundscache.getslot(item) if nslot >= 0: startlengths = self.limboundscache.getitem(nslot) # Reset the lengths array (not necessary for starts) self.lengths[:] = 0 # Now, set the interesting rows for nrow2, start, length in startlengths: self.starts[nrow2] = start self.lengths[nrow2] = length tlen = tlen + length return tlen # The item is not in cache. Do the real lookup. sorted = self.sorted if self.nslices > 0: if self.type in self.opt_search_types: # The next are optimizations. However, they hide the # CPU functions consumptions from python profiles. # You may want to de-activate them during profiling. if self.type == "int32": tlen = sorted._search_bin_na_i(*item) elif self.type == "int64": tlen = sorted._search_bin_na_ll(*item) elif self.type == "float16": tlen = sorted._search_bin_na_e(*item) elif self.type == "float32": tlen = sorted._search_bin_na_f(*item) elif self.type == "float64": tlen = sorted._search_bin_na_d(*item) elif self.type == "float96": tlen = sorted._search_bin_na_g(*item) elif self.type == "float128": tlen = sorted._search_bin_na_g(*item) elif self.type == "uint32": tlen = sorted._search_bin_na_ui(*item) elif self.type == "uint64": tlen = sorted._search_bin_na_ull(*item) elif self.type == "int8": tlen = sorted._search_bin_na_b(*item) elif self.type == "int16": tlen = sorted._search_bin_na_s(*item) elif self.type == "uint8": tlen = sorted._search_bin_na_ub(*item) elif self.type == "uint16": tlen = sorted._search_bin_na_us(*item) else: assert False, "This can't happen!" else: tlen = self.search_scalar(item, sorted) # Get possible remaining values in last row if self.nelementsSLR > 0: # Look for more indexes in the last row (start, stop) = self.search_last_row(item) self.starts[-1] = start self.lengths[-1] = stop - start tlen += stop - start if self.limboundscache.couldenablecache(): # Get a startlengths tuple and save it in cache. # This is quite slow, but it is a good way to compress # the bounds info. Moreover, the .couldenablecache() # is doing a good work so as to avoid computing this # when it is not necessary to do it. startlengths = [] for nrow, length in enumerate(self.lengths): if length > 0: startlengths.append((nrow, self.starts[nrow], length)) # Compute the size of the recarray (aproximately) # The +1 at the end is important to avoid 0 lengths # (remember, the object headers take some space) size = len(startlengths) * 8 * 2 + 1 # Put this startlengths list in cache self.limboundscache.setitem(item, startlengths, size) if profile: show_stats("Exiting search", tref) return tlen # This is an scalar version of search. It works with strings as well. def search_scalar(self, item, sorted): """Do a binary search in this index for an item.""" tlen = 0 # Do the lookup for values fullfilling the conditions for i in range(self.nslices): (start, stop) = sorted._search_bin(i, item) self.starts[i] = start self.lengths[i] = stop - start tlen += stop - start return tlen def search_last_row(self, item): # Variable initialization item1, item2 = item bebounds = self.bebounds b0, b1 = bebounds[0], bebounds[-1] bounds = bebounds[1:-1] itemsize = self.dtype.itemsize sortedLRcache = self.sortedLRcache hi = self.nelementsSLR # maximum number of elements rchunksize = self.chunksize // self.reduction nchunk = -1 # Lookup for item1 if nan_aware_gt(item1, b0): if nan_aware_le(item1, b1): # Search the appropriate chunk in bounds cache nchunk = bisect_left(bounds, item1) # Lookup for this chunk in cache nslot = sortedLRcache.getslot(nchunk) if nslot >= 0: chunk = sortedLRcache.getitem(nslot) else: begin = rchunksize * nchunk end = rchunksize * (nchunk + 1) if end > hi: end = hi # Read the chunk from disk chunk = self.sortedLR._read_sorted_slice( self.sorted, begin, end) # Put it in cache. It's important to *copy* # the buffer, as it is reused in future reads! sortedLRcache.setitem(nchunk, chunk.copy(), (end - begin) * itemsize) start = bisect_left(chunk, item1) start += rchunksize * nchunk else: start = hi else: start = 0 # Lookup for item2 if nan_aware_ge(item2, b0): if nan_aware_lt(item2, b1): # Search the appropriate chunk in bounds cache nchunk2 = bisect_right(bounds, item2) if nchunk2 != nchunk: # Lookup for this chunk in cache nslot = sortedLRcache.getslot(nchunk2) if nslot >= 0: chunk = sortedLRcache.getitem(nslot) else: begin = rchunksize * nchunk2 end = rchunksize * (nchunk2 + 1) if end > hi: end = hi # Read the chunk from disk chunk = self.sortedLR._read_sorted_slice( self.sorted, begin, end) # Put it in cache. It's important to *copy* # the buffer, as it is reused in future reads! # See bug #60 in xot.carabos.com sortedLRcache.setitem(nchunk2, chunk.copy(), (end - begin) * itemsize) stop = bisect_right(chunk, item2) stop += rchunksize * nchunk2 else: stop = hi else: stop = 0 return (start, stop) def get_chunkmap(self): """Compute a map with the interesting chunks in index.""" if profile: tref = time() if profile: show_stats("Entering get_chunkmap", tref) ss = self.slicesize nsb = self.nslicesblock nslices = self.nslices lbucket = self.lbucket indsize = self.indsize bucketsinblock = float(self.blocksize) / lbucket nchunks = int(math.ceil(float(self.nelements) / lbucket)) chunkmap = numpy.zeros(shape=nchunks, dtype="bool") reduction = self.reduction starts = (self.starts - 1) * reduction + 1 stops = (self.starts + self.lengths) * reduction starts[starts < 0] = 0 # All negative values set to zero indices = self.indices for nslice in range(self.nrows): start = starts[nslice] stop = stops[nslice] if stop > start: idx = numpy.empty(shape=stop - start, dtype='u%d' % indsize) if nslice < nslices: indices._read_index_slice(nslice, start, stop, idx) else: self.indicesLR._read_index_slice(start, stop, idx) if indsize == 8: idx //= lbucket elif indsize == 2: # The chunkmap size cannot be never larger than 'int_' idx = idx.astype("int_") offset = int((nslice // nsb) * bucketsinblock) idx += offset elif indsize == 1: # The chunkmap size cannot be never larger than 'int_' idx = idx.astype("int_") offset = (nslice * ss) // lbucket idx += offset chunkmap[idx] = True # The case lbucket < nrowsinchunk should only happen in tests nrowsinchunk = self.nrowsinchunk if lbucket != nrowsinchunk: # Map the 'coarse grain' chunkmap into the 'true' chunkmap nelements = self.nelements tnchunks = int(math.ceil(float(nelements) / nrowsinchunk)) tchunkmap = numpy.zeros(shape=tnchunks, dtype="bool") ratio = float(lbucket) / nrowsinchunk idx = chunkmap.nonzero()[0] starts = (idx * ratio).astype('int_') stops = numpy.ceil((idx + 1) * ratio).astype('int_') for start, stop in zip(starts, stops): tchunkmap[start:stop] = True chunkmap = tchunkmap if profile: show_stats("Exiting get_chunkmap", tref) return chunkmap def get_lookup_range(self, ops, limits): assert len(ops) in [1, 2] assert len(limits) in [1, 2] assert len(ops) == len(limits) column = self.column coldtype = column.dtype.base itemsize = coldtype.itemsize if len(limits) == 1: assert ops[0] in ['lt', 'le', 'eq', 'ge', 'gt'] limit = limits[0] op = ops[0] if op == 'lt': range_ = (inftype(coldtype, itemsize, sign=-1), nextafter(limit, -1, coldtype, itemsize)) elif op == 'le': range_ = (inftype(coldtype, itemsize, sign=-1), limit) elif op == 'gt': range_ = (nextafter(limit, +1, coldtype, itemsize), inftype(coldtype, itemsize, sign=+1)) elif op == 'ge': range_ = (limit, inftype(coldtype, itemsize, sign=+1)) elif op == 'eq': range_ = (limit, limit) elif len(limits) == 2: assert ops[0] in ('gt', 'ge') and ops[1] in ('lt', 'le') lower, upper = limits if lower > upper: # ``a <[=] x <[=] b`` is always false if ``a > b``. return () if ops == ('gt', 'lt'): # lower < col < upper range_ = (nextafter(lower, +1, coldtype, itemsize), nextafter(upper, -1, coldtype, itemsize)) elif ops == ('ge', 'lt'): # lower <= col < upper range_ = (lower, nextafter(upper, -1, coldtype, itemsize)) elif ops == ('gt', 'le'): # lower < col <= upper range_ = (nextafter(lower, +1, coldtype, itemsize), upper) elif ops == ('ge', 'le'): # lower <= col <= upper range_ = (lower, upper) return range_ def _f_remove(self, recursive=False): """Remove this Index object.""" # Index removal is always recursive, # no matter what `recursive` says. super()._f_remove(True) def __str__(self): """This provides a more compact representation than __repr__""" # The filters filters = "" if self.filters.complevel: if self.filters.shuffle: filters += ", shuffle" if self.filters.bitshuffle: filters += ", bitshuffle" filters += ", {}({})".format(self.filters.complib, self.filters.complevel) return "Index(%s, %s%s).is_csi=%s" % \ (self.optlevel, self.kind, filters, self.is_csi) def __repr__(self): """This provides more metainfo than standard __repr__""" cpathname = self.table._v_pathname + ".cols." + self.column.pathname retstr = """{} (Index for column {}) optlevel := {} kind := {} filters := {} is_csi := {} nelements := {} chunksize := {} slicesize := {} blocksize := {} superblocksize := {} dirty := {} byteorder := {!r}""".format(self._v_pathname, cpathname, self.optlevel, self.kind, self.filters, self.is_csi, self.nelements, self.chunksize, self.slicesize, self.blocksize, self.superblocksize, self.dirty, self.byteorder) retstr += "\n sorted := %s" % self.sorted retstr += "\n indices := %s" % self.indices retstr += "\n ranges := %s" % self.ranges retstr += "\n bounds := %s" % self.bounds retstr += "\n sortedLR := %s" % self.sortedLR retstr += "\n indicesLR := %s" % self.indicesLR return retstr
class IndexesDescG(NotLoggedMixin, Group): _c_classid = 'DINDEX' def _g_width_warning(self): warnings.warn( "the number of indexed columns on a single description group " "is exceeding the recommended maximum (%d); " "be ready to see PyTables asking for *lots* of memory " "and possibly slow I/O" % self._v_max_group_width, PerformanceWarning) class IndexesTableG(NotLoggedMixin, Group): _c_classid = 'TINDEX' @property def auto(self): if 'AUTO_INDEX' not in self._v_attrs: return default_auto_index return self._v_attrs.AUTO_INDEX @auto.setter def auto(self, auto): self._v_attrs.AUTO_INDEX = bool(auto) @auto.deleter def auto(self): del self._v_attrs.AUTO_INDEX def _g_width_warning(self): warnings.warn( "the number of indexed columns on a single table " "is exceeding the recommended maximum (%d); " "be ready to see PyTables asking for *lots* of memory " "and possibly slow I/O" % self._v_max_group_width, PerformanceWarning) def _g_check_name(self, name): if not name.startswith('_i_'): raise ValueError( "names of index groups must start with ``_i_``: %s" % name) @property def table(self): """Accessor for the `Table` object of this `IndexesTableG` container.""" names = self._v_pathname.split("/") tablename = names.pop()[3:] # "_i_" is at the beginning parentpathname = "/".join(names) tablepathname = join_path(parentpathname, tablename) table = self._v_file._get_node(tablepathname) return table class OldIndex(NotLoggedMixin, Group): """This is meant to hide indexes of PyTables 1.x files.""" _c_classid = 'CINDEX' ## Local Variables: ## mode: python ## py-indent-offset: 4 ## tab-width: 4 ## fill-column: 72 ## End: