def _get_ids_shelve(self, cual):
'''Return the ids index.'''
fname = os.path.join(self._directory, "compindex-%02d.ids.bz2" % cual)
fh = CompressedFile(fname, "rb")
idx = cPickle.load(fh)
fh.close()
return idx
def __init__(self, directory):
self._directory = directory
# open the key shelve
# Format:
# ( matrix, docsets )
# matrix = TermSimilitudeMatrix
# docsets = FrozenStringList
keyfilename = os.path.join(directory, "compindex.key.bz2")
fh = CompressedFile(keyfilename, "rb")
matrix, docsets = cPickle.load(fh)
fh.close()
matrix = TermSimilitudeMatrix.unpickle(matrix)
docsets = FrozenStringList.unpickle(docsets)
self.matrix, self.docsets = matrix, docsets
# see how many id files we have
idsfilename = os.path.join(directory, "compindex-*.ids.bz2")
filenames = []
for fn in os.listdir(directory):
if fn.startswith("compindex-") and \
fn.endswith(".ids.bz2"):
filenames.append(fn)
self.idfiles_count = len(filenames)
def __init__(self, directory):
self._directory = directory
# open the key shelve
keyfilename = os.path.join(directory, "easyindex.key.bz2")
fh = CompressedFile(keyfilename, "rb")
self.key_shelf = cPickle.load(fh)
fh.close()
# see how many id files we have
idsfilename = os.path.join(directory, "easyindex-*.ids.bz2")
filenames = []
for fn in os.listdir(directory):
if fn.startswith("easyindex-") and \
fn.endswith(".ids.bz2"):
filenames.append(fn)
self.idfiles_count = len(filenames)
def __init__(self, fname, verbose=False, manager=None):
if os.path.exists(fname):
self.fh = CompressedFile(fname, "rb")
self.header_size = struct.unpack("<l", self.fh.read(4))[0]
header_bytes = self.fh.read(self.header_size)
self.header = pickle.loads(header_bytes)
else:
# no hace falta definir self.fh ni self.header_size porque no va
# a llegar a usarlo porque nunca va a tener el item en el header
self.header = {}
self.verbose = verbose
self.manager = manager
def create(cls, directory, source):
'''Creates the index in the directory.
The "source" generates pairs (key, value) to store in the index. The
key must be a string, the value can be any hashable Python object.
It must return the quantity of pairs indexed.
'''
ids_shelf = {}
key_shelf = {}
ids_cnter = 0
tmp_reverse_id = {}
indexed_counter = 0
# fill them
for key, value in source:
indexed_counter += 1
# process key
if not isinstance(key, basestring):
raise TypeError("The key must be string or unicode")
# docid -> info final
if value in tmp_reverse_id:
docid = tmp_reverse_id[value]
else:
docid = ids_cnter
tmp_reverse_id[value] = docid
ids_cnter += 1
ids_shelf[docid] = value
# keys -> docid
key_shelf.setdefault(key, set()).add(docid)
# save key
keyfilename = os.path.join(directory, "easyindex.key.bz2")
fh = CompressedFile(keyfilename, "wb")
cPickle.dump(key_shelf, fh, 2)
fh.close()
# split ids_shelf in N dicts of about ~5k entries
N = int(round(len(ids_shelf) / 5000.0))
if not N:
N = 1
all_idshelves = [{} for i in range(N)]
for k,v in ids_shelf.iteritems():
cual = utiles.coherent_hash(k) % N
all_idshelves[cual][k] = v
# save dict where corresponds
for cual, shelf in enumerate(all_idshelves):
fname = "easyindex-%03d.ids.bz2" % cual
idsfilename = os.path.join(directory, fname)
fh = CompressedFile(idsfilename, "wb")
cPickle.dump(shelf, fh, 2)
fh.close()
return indexed_counter
def crear(self, redirects, bloqNum, top_filenames, verbose=False):
'''Genera el comprimido.'''
if verbose:
print "Procesando el bloque", bloqNum
header = {}
# Llenamos el header con archivos reales, con la pag como
# clave, y la posición/tamaño como valor
seek = 0
for dir3, filename in top_filenames:
fullName = path.join(config.DIR_PAGSLISTAS, dir3, filename)
size = path.getsize(fullName)
header[filename] = (seek, size)
seek += size
# Ponemos en el header también los redirects, apuntando en este caso
# ael nombre de la página a la que se redirecciona
for orig, dest in redirects:
header[orig] = dest
headerBytes = pickle.dumps(header)
if verbose:
print " archivos: %d seek total: %d largo header: %d" % (
len(top_filenames), seek, len(headerBytes))
# abro el archivo a comprimir
nomfile = path.join(config.DIR_BLOQUES, "%08x.cdp" % bloqNum)
if verbose:
print " grabando en", nomfile
f = CompressedFile(nomfile, "wb")
# grabo la longitud del header, y el header
f.write( struct.pack("<l", len(headerBytes) ) )
f.write( headerBytes )
# grabo cada uno de los articulos
for dir3, filename in top_filenames:
fullName = path.join(config.DIR_PAGSLISTAS, dir3, filename)
f.write(open( fullName, "rb" ).read())
def setup_function(self):
self.data.representation = self.calc_mode
#
# Init arrays
#
# Prepare some array shapes
gradient_params_shape = (len(self.gradient_param_names), )
if self.data.is_map:
# speed up calculation by adding links
# as nominal flux doesn't depend on the (outgoing) flavour
self.data.link_containers('nu', [
'nue_cc', 'numu_cc', 'nutau_cc', 'nue_nc', 'numu_nc',
'nutau_nc'
])
self.data.link_containers('nubar', [
'nuebar_cc', 'numubar_cc', 'nutaubar_cc', 'nuebar_nc',
'numubar_nc', 'nutaubar_nc'
])
# Loop over containers
for container in self.data:
# Define shapes for containers
# TODO maybe include toggles for nutau (only needed if prompt
# considered) and for nu+nubar (only needed if nu->nubar
# oscillations included) for better speed/memory performance
# [ N events, 2 flavors in flux, nu vs nubar ]
# SDB - reduced flavours to 2 (nue, numu) since nutau flux not
# stored in MCEq splines
flux_container_shape = (container.size, 2)
gradients_shape = tuple(
list(flux_container_shape) + list(gradient_params_shape))
container["nu_flux"] = np.full(flux_container_shape,
np.NaN,
dtype=FTYPE)
container["gradients"] = np.full(gradients_shape,
np.NaN,
dtype=FTYPE)
# Also create an array container to hold the gradient parameter values
# Only want this once, e.g. not once per container
self.gradient_params = np.empty(gradient_params_shape, dtype=FTYPE)
#
# Load MCEq splines
#
# Have splines for each Barr parameter, plus +/- versions of each
# Barr parameter corresponding to mesons/antimesons.
# For a given Barr parameter, an underlying dictionary have the following
# keywords: "dnumu", "dnumubar", "dnue", dnuebar"
# Units are changed to m^-2 in creates_splines.., rather than cm^2 which
# is the unit of calculation in MCEq!!!!
# Note that doing this all on CPUs, since the splines reside on the CPUs
# The actual `compute_function` computation can be done on GPUs though
# Load the MCEq splines
spline_file = find_resource(self.table_file)
logging.info("Loading MCEq spline tables from : %s", spline_file)
# Encoding is to support pickle files created with python v2
self.spline_tables_dict = pickle.load(BZ2File(spline_file),
encoding="latin1")
# Ensure that the user is not loading an incompatible spline
for bp in self.barr_param_names:
bp_p = bp + '+' # meson
bp_m = bp + '-' # antimeson
assert bp_p in self.spline_tables_dict.keys(), (
"Gradient parameter '%s' missing from table" % bp_p)
assert bp_m in self.spline_tables_dict.keys(), (
"Gradient parameter '%s' missing from table" % bp_m)
# Loop over containers
for container in self.data:
# Grab containers here once to save time
# TODO make spline generation script store splines directly in
# terms of energy, not ln(energy)
true_log_energy = np.log(container["true_energy"])
true_abs_coszen = np.abs(container["true_coszen"])
gradients = container["gradients"]
nubar = container["nubar"]
#
# Flux gradients
#
# Evaluate splines to get the flux graidents w.r.t the Barr parameter values
# Need to correctly map nu/nubar and flavor to the output arrays
# Loop over parameters
for (
gradient_param_name,
gradient_param_idx,
) in self.gradient_param_indices.items():
# nue(bar)
self._eval_spline(
true_log_energy=true_log_energy,
true_abs_coszen=true_abs_coszen,
spline=self.spline_tables_dict[gradient_param_name]
["dnue" if nubar > 0 else "dnuebar"],
out=gradients[:, 0, gradient_param_idx],
)
# numu(bar)
self._eval_spline(
true_log_energy=true_log_energy,
true_abs_coszen=true_abs_coszen,
spline=self.spline_tables_dict[gradient_param_name]
["dnumu" if nubar > 0 else "dnumubar"],
out=gradients[:, 1, gradient_param_idx],
)
# nutau(bar)
# TODO include nutau flux in splines
# SDB - there is no nutau flux in splines
## gradients[:, 2, gradient_param_idx].fill(0.0)
# Tell the smart arrays we've changed the flux gradient values on the host
container.mark_changed("gradients")
# don't forget to un-link everything again
self.data.unlink_containers()
def testReadLineMultiStream(self):
self.createTempFile(streams=5)
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.readline, None)
for line in self.TEXT_LINES * 5:
self.assertEqual(bz2f.readline(), line)
class Comprimido(Bloque):
"""Un bloque de artículos.
Este es un bloque en el que todo el archivo, header y datos por igual,
va al disco comprimido con bz2.
"""
def __init__(self, fname, verbose=False, manager=None):
if os.path.exists(fname):
self.fh = CompressedFile(fname, "rb")
self.header_size = struct.unpack("<l", self.fh.read(4))[0]
header_bytes = self.fh.read(self.header_size)
self.header = pickle.loads(header_bytes)
else:
# no hace falta definir self.fh ni self.header_size porque no va
# a llegar a usarlo porque nunca va a tener el item en el header
self.header = {}
self.verbose = verbose
self.manager = manager
@classmethod
def crear(self, redirects, bloqNum, top_filenames, verbose=False):
'''Genera el comprimido.'''
if verbose:
print "Procesando el bloque", bloqNum
header = {}
# Llenamos el header con archivos reales, con la pag como
# clave, y la posición/tamaño como valor
seek = 0
for dir3, filename in top_filenames:
fullName = path.join(config.DIR_PAGSLISTAS, dir3, filename)
size = path.getsize(fullName)
header[filename] = (seek, size)
seek += size
# Ponemos en el header también los redirects, apuntando en este caso
# ael nombre de la página a la que se redirecciona
for orig, dest in redirects:
header[orig] = dest
headerBytes = pickle.dumps(header)
if verbose:
print " archivos: %d seek total: %d largo header: %d" % (
len(top_filenames), seek, len(headerBytes))
# abro el archivo a comprimir
nomfile = path.join(config.DIR_BLOQUES, "%08x.cdp" % bloqNum)
if verbose:
print " grabando en", nomfile
f = CompressedFile(nomfile, "wb")
# grabo la longitud del header, y el header
f.write( struct.pack("<l", len(headerBytes) ) )
f.write( headerBytes )
# grabo cada uno de los articulos
for dir3, filename in top_filenames:
fullName = path.join(config.DIR_PAGSLISTAS, dir3, filename)
f.write(open( fullName, "rb" ).read())
def testRead0(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.read, None)
self.assertEqual(bz2f.read(0), b"")
def testReadMultiStream(self):
self.createTempFile(streams=5)
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.read, None)
self.assertEqual(bz2f.read(), self.TEXT * 5)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Este programa demonstra a leitura de um
pickle comprimido.
'''
from bz2 import BZ2File
from pickle import load
from pprint import pprint
bzip = BZ2File('temporeal_pickle.bz2','r')
livros = load(bzip)
bzip.close()
print 'temporeal_pickle.bz2 lido'
print len(livros), 'livros na lista'
print '_' * 70
print 'O primeiro livro:'
pprint(livros[0])
meio = len(livros)/2
print '_' * 70
print 'O livro do meio (#%s):' % meio
pprint(livros[meio])
print '_' * 70
print 'O último livro:'
pprint(livros[-1])
def testSeekForwardBytesIO(self):
with BytesIO(self.DATA) as bio:
with BZ2File(bio) as bz2f:
self.assertRaises(TypeError, bz2f.seek)
bz2f.seek(150)
self.assertEqual(bz2f.read(), self.TEXT[150:])
def testSeekForward(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.seek)
bz2f.seek(150)
self.assertEqual(bz2f.read(), self.TEXT[150:])
def testWriteNonDefaultCompressLevel(self):
expected = bz2.compress(self.TEXT, compresslevel=5)
with BZ2File(self.filename, "w", compresslevel=5) as bz2f:
bz2f.write(self.TEXT)
with open(self.filename, "rb") as f:
self.assertEqual(f.read(), expected)
def testWrite(self):
with BZ2File(self.filename, "w") as bz2f:
self.assertRaises(TypeError, bz2f.write)
bz2f.write(self.TEXT)
with open(self.filename, 'rb') as f:
self.assertEqual(ext_decompress(f.read()), self.TEXT)
def testIteratorMultiStream(self):
self.createTempFile(streams=5)
with BZ2File(self.filename) as bz2f:
self.assertEqual(list(iter(bz2f)), self.TEXT_LINES * 5)
def testIterator(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertEqual(list(iter(bz2f)), self.TEXT_LINES)
def testReadLines(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.readlines, None)
self.assertEqual(bz2f.readlines(), self.TEXT_LINES)
def testOpenPathLikeFilename(self):
filename = pathlib.Path(self.filename)
with BZ2File(filename, "wb") as f:
f.write(self.DATA)
with BZ2File(filename, "rb") as f:
self.assertEqual(f.read(), self.DATA)
def testReadBytesIO(self):
with BytesIO(self.DATA) as bio:
with BZ2File(bio) as bz2f:
self.assertRaises(TypeError, bz2f.read, float())
self.assertEqual(bz2f.read(), self.TEXT)
self.assertFalse(bio.closed)
def testSeekForwardAcrossStreams(self):
self.createTempFile(streams=2)
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.seek)
bz2f.seek(len(self.TEXT) + 150)
self.assertEqual(bz2f.read(), self.TEXT[150:])
def testSeekBackwardsBytesIO(self):
with BytesIO(self.DATA) as bio:
with BZ2File(bio) as bz2f:
bz2f.read(500)
bz2f.seek(-150, 1)
self.assertEqual(bz2f.read(), self.TEXT[500 - 150:])
def testSeekBackwards(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
bz2f.read(500)
bz2f.seek(-150, 1)
self.assertEqual(bz2f.read(), self.TEXT[500 - 150:])
def testReadBadFile(self):
self.createTempFile(streams=0, suffix=self.BAD_DATA)
with BZ2File(self.filename) as bz2f:
self.assertRaises(OSError, bz2f.read)
def testSeekBackwardsFromEnd(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
bz2f.seek(-150, 2)
self.assertEqual(bz2f.read(), self.TEXT[len(self.TEXT) - 150:])
def testReadMultiStreamTrailingJunk(self):
self.createTempFile(streams=5, suffix=self.BAD_DATA)
with BZ2File(self.filename) as bz2f:
self.assertEqual(bz2f.read(), self.TEXT * 5)
def testSeekBackwardsFromEndAcrossStreams(self):
self.createTempFile(streams=2)
with BZ2File(self.filename) as bz2f:
bz2f.seek(-1000, 2)
self.assertEqual(bz2f.read(), (self.TEXT * 2)[-1000:])
def testRead100(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertEqual(bz2f.read(100), self.TEXT[:100])
def _from_file(clazz, filename, header_only=False, strict=False):
"""
:param filename: name of the file to read from
:type filename: string
:param header_only: read header only
:rtype: Graph
:return: imported hypergraph
"""
num_edges = None
num_verts = None
is_dimacs = False
stream = None
graph = clazz()
try:
mtype = mimetypes.guess_type(filename)[1]
if mtype is None:
stream = open(filename, 'r')
elif mtype == 'bzip2':
stream = BZ2File(filename, 'r')
elif mtype == 'gz' or mtype == 'gzip':
stream = gzip.open(filename, 'r')
elif mtype == 'xz' and xz:
stream = xz.open(filename, 'r')
else:
raise IOError('Unknown input type "%s" for file "%s"' %
(mtype, filename))
nr = 0
header_seen = False
for line in stream:
nr += 1
line = line.split()
if line == [] or line[0] in ('x', 'n'):
continue
elif line[0] == 'p':
if header_seen:
logging.critical('L(%s). Duplicate header. Exiting.' %
nr)
exit(3)
if len(line) > 4:
logging.critical(
'L(%s). Too many arguments. Exiting.' % nr)
exit(3)
is_dimacs = line[1] == 'edge'
is_formula = line[1] == 'cnf'
num_verts = int(line[2])
num_edges = int(line[3])
if header_only:
return num_verts, num_edges
if num_verts == 0:
logging.warning("Empty graph.")
return graph
header_seen = True
elif line[0] != 'c' and (
is_dimacs or (line[0] != 'a' and line[0] != 'e')
): #now also ignores forAll and Exists :P
if not header_seen:
logging.critical(
'L(%s). Lines before header. Exiting.' % nr)
exit(3)
try:
if is_dimacs:
graph.add_edge(int(line[1]), int(line[2]))
elif is_formula:
atoms = list(map(lambda x: abs(int(x)),
line[0:-1]))
#print("formula{0}".format(atoms))
for i in atoms:
for j in atoms:
if i < j:
graph.add_edge(
i, j
) #abs -> then it also works for qbf
num_edges += (len(atoms) *
(len(atoms) - 1)) / 2 - 1
else:
graph.add_edge(int(line[0]), int(line[1]))
assert (0 not in graph.nodes())
except ValueError as e:
logging.critical('L(%s). Invalid integer. Exiting.' %
nr)
logging.critical('Error was: %s' % e)
exit(3)
except IndexError as e:
logging.critical('L(%s). Incomplete edge. Exiting' %
nr)
logging.critical('Error was: %s' % e)
exit(3)
clazz._parsed_file_line(graph, line)
finally:
if stream:
stream.close()
if graph.number_of_edges() > num_edges:
logging.error("Edges overmuch: read=%s expected=%s" %
(graph.number_of_edges(), num_edges))
exit(3)
if strict and graph.number_of_edges() < num_edges:
logging.error("Edges missing: read=%s expected=%s" %
(graph.number_of_edges(), num_edges))
exit(3)
if graph.number_of_nodes() > num_verts:
logging.error("Vertices overmuch: read=%s expected=%s" %
(graph.number_of_nodes(), num_verts))
#print(graph.nodes())
exit(3)
if strict and graph.number_of_nodes() < num_verts:
logging.error("Vertices missing: read=%s expected=%s" %
(graph.number_of_nodes(), num_verts))
exit(3)
return graph
def test_silently_closes(self):
from bz2 import BZ2File
self.create_broken_temp_file()
BZ2File(self.temppath)
def testRead(self):
self.createTempFile()
with BZ2File(self.filename) as bz2f:
self.assertRaises(TypeError, bz2f.read, float())
self.assertEqual(bz2f.read(), self.TEXT)
def testSeekPostEndMultiStream(self):
self.createTempFile(streams=5)
with BZ2File(self.filename) as bz2f:
bz2f.seek(150000)
self.assertEqual(bz2f.tell(), len(self.TEXT) * 5)
self.assertEqual(bz2f.read(), b"")
def test_read_broken_file(self):
from bz2 import BZ2File
self.create_broken_temp_file()
bz2f = BZ2File(self.temppath)
raises(EOFError, bz2f.read)
del bz2f # delete from this frame, which is captured in the traceback
def testSeekPreStartMultiStream(self):
self.createTempFile(streams=2)
with BZ2File(self.filename) as bz2f:
bz2f.seek(-150)
self.assertEqual(bz2f.tell(), 0)
self.assertEqual(bz2f.read(), self.TEXT * 2)
def testOpenDel(self):
self.createTempFile()
for i in range(10000):
o = BZ2File(self.filename)
del o
def create(cls, directory, source):
'''Creates the index in the directory.
The "source" generates pairs (key, value) to store in the index. The
key must be a string, the value can be any hashable Python object.
It must return the quantity of pairs indexed.
'''
ids_shelf = {}
key_shelf = {}
ids_cnter = 0
tmp_reverse_id = {}
indexed_counter = 0
# fill them
for key, value in source:
indexed_counter += 1
# process key
if not isinstance(key, basestring):
raise TypeError("The key must be string or unicode")
if '\n' in key:
raise ValueError("Key cannot contain newlines")
# docid -> info final
if value in tmp_reverse_id:
docid = tmp_reverse_id[value]
else:
docid = ids_cnter
tmp_reverse_id[value] = docid
ids_shelf[docid] = value
ids_cnter += 1
# keys -> docid
if key in key_shelf:
bucket = key_shelf[key]
else:
# Lets use array, it's more compact in memory, and given that it
# should be easy for the caller to remove most repetitions,
# it should only get very little overhead
#
# NOTE: right now, at most one repetition per property is sent
# by cdpindex.py
bucket = key_shelf[key] = array.array('l')
bucket.append(docid)
# prepare for serialization:
# turn docsets into lists if delta-encoded integers (they're more compressible)
print " Delta-encoding index buckets...",
sys.stdout.flush()
bucket_bytes = 0
bucket_entries = 0
bucket_maxentries = 0
for key, docset in key_shelf.iteritems():
key_shelf[key] = delta_encode(docset)
bucket_entries += len(docset)
bucket_bytes += len(key_shelf[key])
bucket_maxentries = max(bucket_maxentries, len(docset))
assert delta_decode(key_shelf[key]) == set(docset), \
("Delta-encoding error", docset)
print "done"
# print statistics
print " Index contains:"
print " ", len(key_shelf), "terms"
print " ", bucket_entries, "entries"
print " ", len(ids_shelf), "documents"
print
print " ", len(key_shelf) // max(1,len(ids_shelf)), "terms on avg per documents"
print
print " Bucket bytes", bucket_bytes
print " Bucket entries", bucket_entries
print " Bucket maximum size", bucket_maxentries
print " Avg bytes per entry", (float(bucket_bytes) / max(1,bucket_entries))
# save key
# Format:
# ( matrix, docsets )
# Putting all keys togeter makes them more compressible.
# Sorting them (skeys) further helps.
# Joining them in a single string avoids pickling overhead
# (50% average with so many small strings)
# And keeping them joined in memory (FrozenStringList) helps
# avoid referencing overhead.
sitems = sorted([ (k.encode("utf8"),v)
for k,v in key_shelf.iteritems() ])
assert all('\n' not in k for k,v in sitems), \
"Terms cannot contain newlines"
# free the big dict... eats up a lot
del key_shelf
print " Computing similitude matrix...",
sys.stdout.flush()
def progress_cb(p):
print >> sys.stderr, "\r Computing similitude matrix... %d%%\t" % int(p),
sys.stderr.flush()
matrix = TermSimilitudeMatrix(map(operator.itemgetter(0), sitems),
progress_callback = progress_cb)
docsets = FrozenStringList(map(operator.itemgetter(1), sitems))
del sitems
print "done"
print " Saving:"
keyfilename = os.path.join(directory, "compindex.key.bz2")
fh = CompressedFile(keyfilename, "wb")
cPickle.dump( (matrix.pickle(), docsets.pickle()), fh, 2)
print " Uncompressed keystore bytes", fh.tell()
fh.close()
fh = open(keyfilename, "rb")
fh.seek(0,2)
print " Final keystore bytes", fh.tell()
print
fh.close()
# split ids_shelf in N dicts of about ~16M pickled data each,
# this helps get better compression ratios
NB = sum( len(cPickle.dumps(item,2)) for item in ids_shelf.iteritems() )
print " Total docstore bytes", NB
N = int((NB + DOCSTORE_BUCKET_SIZE/2) // DOCSTORE_BUCKET_SIZE)
if not N:
N = 1
print " Docstore buckets", N, "(", NB//N, " bytes per bucket)"
all_idshelves = [{} for i in xrange(N)]
for k,v in ids_shelf.iteritems():
cual = k % N
all_idshelves[cual][k] = v
# save dict where corresponds
docucomp = 0
doccomp = 0
for cual, shelf in enumerate(all_idshelves):
fname = "compindex-%02d.ids.bz2" % cual
idsfilename = os.path.join(directory, fname)
fh = CompressedFile(idsfilename, "wb")
cPickle.dump(shelf, fh, 2)
docucomp += fh.tell()
fh.close()
fh = open(idsfilename, "rb")
fh.seek(0,2)
doccomp += fh.tell()
fh.close()
print " Docstore uncompressed bytes", docucomp
print " Docstore compressed bytes", doccomp
print
return indexed_counter
'datavalue'] and type(key_item['mainsnak']['datavalue']
['value']) is str else ''
datavalue_type = key_item['mainsnak']['datavalue'][
'type'] if 'type' in key_item['mainsnak'][
'datavalue'] else ''
sql = "insert into claim VALUES(%s,%s,%s,%s)"
params = (qid, pid, datavalue_value, datavalue_type)
cur.execute(sql, params)
conn.commit()
# if i%10000==0:
print("the %s th line insert claim table ok" % i)
bz2_file_path = r'./latest-all.json.bz2'
bz2_file = BZ2File(bz2_file_path)
def main():
i = 1
count = 1
for line in bz2_file:
line_str = line.decode()
if count < 2:
print("正在跳过第%s行" % count)
count += 1
continue
if len(line_str) > 2:
json_object = json.loads(line_str[:-2])
insert_entity(i, json_object)
# insert_property(i, json_object)
