def __init__(self, my_book):
# Save reference to the book
self.my_book = my_book
# Save reference to the metamanager
self.metamgr = my_book.get_meta_manager()
# Save reference to the edited document
self.document = my_book.get_edit_model()
# Save reference to a speller, which will be the default
# at this point.
self.speller = my_book.get_speller()
# The vocabulary list as a blist sorted dict.
self.vocab = blist.sorteddict()
# Key and Values views on the vocab list for indexing by table row.
self.vocab_kview = self.vocab.keys()
self.vocab_vview = self.vocab.values()
# The good- and bad-words sets and the scannos set.
self.good_words = set()
self.bad_words = set()
self.scannos = set()
# A dict of words that use an alt-dict tag. The key is a word and the
# value is the alt-dict tag string.
self.alt_tags = blist.sorteddict()
# Register metadata readers and writers.
self.metamgr.register(C.MD_GW, self.good_read, self.good_save)
self.metamgr.register(C.MD_BW, self.bad_read, self.bad_save)
self.metamgr.register(C.MD_SC, self.scanno_read, self.scanno_save)
self.metamgr.register(C.MD_VL, self.word_read, self.word_save)
def __init__(self):
""" Initializes trader by order books for the asset from different markets
"""
MultiAssetStrategy.__init__(self)
# order queues ordered by their best asks and bids
# something like std::map<Ticks, OrderQueue>[2]
self._bests = [sorteddict(), sorteddict()]
self._oldBests = {}
def __init__(self):
""" Initializes trader by order books for the asset from different markets
"""
MultiAssetStrategy.__init__(self)
from blist import sorteddict
# order queues ordered by their best asks and bids
# something like std::map<Ticks, OrderQueue>[2]
self._bests = [sorteddict(), sorteddict()]
self._oldBests = {}
def __init__(self):
self.index = sorteddict()
self.tag = []
self.pgid = None
self.title = None
self.title_words = sorteddict()
self.text = ''
self.page_map = sortedlist()
self.Infobox = sorteddict()
self.Links = sorteddict()
self.stopwords = open('stopwords.txt','r').readlines()[0].strip().split(',')
xml.sax.ContentHandler.__init__(self)
def __init__(self, db_filename, fields_to_index=[], columns=[], cluster=[]):
self.data = sorteddict()
self.keys = self.data.keys()
self.values = self.data.values()
self.indexes = {field: sorteddict() for field in fields_to_index}
self.columns = {field: sorteddict() for field in columns}
self.lock = TimeoutLock(0.5)
self._setup_cluster(cluster)
self._setup_persistence(db_filename)
def solution(counts):
agg = blist.sorteddict(lambda x: -x)
for elem in counts:
agg[elem] = agg.get(elem, 0) + 1
best = 10000
if next(agg.iterkeys()) >= 9:
cop = blist.sorteddict(agg)
num, count = cop.popitem()
cop[num // 3] = cop.get(num // 3, 0) + 3 * count
best = count * 2 + solve(cop)
return min(solve(agg), best)
def run_tests(names, num_items, num_its, type_=int):
fns = dict([
('btrees', lambda: BTrees.OOBTree.OOBTree()),
('blist', lambda: blist.sorteddict()),
('bintrees', lambda: bintrees.FastRBTree()), ('dict', lambda: dict()),
('banyan_red_black_tree',
lambda: banyan.SortedDict(key_type=type_, alg=banyan.RED_BLACK_TREE)),
('banyan_red_black_tree_rank_updator',
lambda: banyan.SortedDict(key_type=type_,
alg=banyan.RED_BLACK_TREE,
updator=banyan.RankUpdator)),
('banyan_red_black_tree_min_max_updator',
lambda: banyan.SortedDict(key_type=type_,
alg=banyan.RED_BLACK_TREE,
updator=banyan.MinMaxUpdator)),
('banyan_splay_tree',
lambda: banyan.SortedDict(key_type=type_, alg=banyan.SPLAY_TREE)),
('banyan_sorted_list',
lambda: banyan.SortedDict(key_type=type_, alg=banyan.SORTED_LIST)),
('banyan_red_black_tree_gen',
lambda: banyan.SortedDict(alg=banyan.RED_BLACK_TREE)),
('banyan_splay_tree_gen',
lambda: banyan.SortedDict(alg=banyan.SPLAY_TREE)),
('banyan_sorted_list_gen',
lambda: banyan.SortedDict(alg=banyan.SORTED_LIST))
])
t = dict([])
for name in names:
t[name] = _run_test(fns[name], type_, num_items, num_its)
return t
def loadPatched(self):
with open(self.patched_mgf, "r") as file_to_read:
for line in file_to_read:
if "BEGIN IONS" in line:
mD = mgfData()
mz_int_additional_data = blist.sorteddict()
elif "END IONS" in line:
mD.setMZ(mz_int_additional_data)
self.patched_Data[mD.title] = mD
else:
items = line.strip().split('=')
if items[0] == "TITLE":
mD.setTitle(items[1])
elif items[0] == "PEPMASS":
mD.setPepmass(items[1])
elif items[0] == "CHARGE":
mD.setCharge(items[1])
else: # peaks
mz_int_additional = items[0].split()
if len(mz_int_additional) == 0: # empty line
continue
else:
mz_int_additional_data[float(
mz_int_additional[0])] = [
mz_int_additional[1]
]
def __init__(self, size_of_cache=10000):
self._size_of_cache = size_of_cache
self._room_to_key = {}
self._cache = sorteddict()
self._earliest_key = None
self.name = "ReceiptsRoomChangeCache"
caches_by_name[self.name] = self._cache
def __init__(self, size_of_cache=10000):
self._size_of_cache = size_of_cache
self._room_to_key = {}
self._cache = sorteddict()
self._earliest_key = None
self.name = "ReceiptsRoomChangeCache"
caches_by_name[self.name] = self._cache
def setInSlot(self, slot, key, value):
shape = self.inputs["shape"].value
eraseLabel = self.inputs["eraser"].value
neutralElement = 0
self.lock.acquire()
#fix slicing of single dimensions:
start, stop = sliceToRoi(key, shape, extendSingleton = False)
start = start.floor()
stop = stop.floor()
tempKey = roiToSlice(start-start, stop-start, hardBind = True)
stop += numpy.where(stop-start == 0,1,0)
key = roiToSlice(start,stop)
updateShape = tuple(stop-start)
update = self._denseArray[key].copy()
update[tempKey] = value
startRavel = numpy.ravel_multi_index(numpy.array(start, numpy.int32),shape)
#insert values into dict
updateNZ = numpy.nonzero(numpy.where(update != neutralElement,1,0))
updateNZRavelSmall = numpy.ravel_multi_index(updateNZ, updateShape)
if isinstance(value, numpy.ndarray):
valuesNZ = value.ravel()[updateNZRavelSmall]
else:
valuesNZ = value
updateNZRavel = numpy.ravel_multi_index(updateNZ, shape)
updateNZRavel += startRavel
self._denseArray.ravel()[updateNZRavel] = valuesNZ
valuesNZ = self._denseArray.ravel()[updateNZRavel]
self._denseArray.ravel()[updateNZRavel] = valuesNZ
td = blist.sorteddict(zip(updateNZRavel.tolist(),valuesNZ.tolist()))
self._sparseNZ.update(td)
#remove values to be deleted
updateNZ = numpy.nonzero(numpy.where(update == eraseLabel,1,0))
if len(updateNZ)>0:
updateNZRavel = numpy.ravel_multi_index(updateNZ, shape)
updateNZRavel += startRavel
self._denseArray.ravel()[updateNZRavel] = neutralElement
for index in updateNZRavel:
self._sparseNZ.pop(index)
self.lock.release()
self.outputs["Output"].setDirty(key)
def __init__(self,
descriptor=None,
filters=None,
sort=None,
encoding=None,
work_dir=None,
*args,
**kwargs):
super(CodecArticleIndex, self).__init__(descriptor=descriptor,
work_dir=work_dir,
*args,
**kwargs)
# Our Meta Content
self.decoded = NNTPMetaContent(work_dir=self.work_dir)
# Switch our content subvalue to be a sorteddict()
self.decoded.content = sorteddict()
# Filters
self.filters = filters
# Sort Order
self.sort = sort
if self.sort is None or self.sort not in XOVER_GROUPINGS:
self.sort = XoverGrouping.BY_POSTER_TIME
# The character set encoding usenet content is retrieved in
if encoding is None:
self.encoding = NNTP_DEFAULT_ENCODING
else:
self.encoding = encoding
def __init__(self,
db_filename,
fields_to_index=[],
columns=[],
cluster=[]):
self.data = sorteddict()
self.keys = self.data.keys()
self.values = self.data.values()
self.indexes = {field: sorteddict() for field in fields_to_index}
self.columns = {field: sorteddict() for field in columns}
self.lock = TimeoutLock(0.5)
self._setup_cluster(cluster)
self._setup_persistence(db_filename)
def run_tests(names, num_items, num_its, type_ = int):
fns = dict([
('btrees', lambda: BTrees.OOBTree.OOBTree()),
('blist', lambda: blist.sorteddict()),
('bintrees', lambda: bintrees.FastRBTree()),
('dict', lambda: dict()),
('banyan_red_black_tree',
lambda: banyan.SortedDict(key_type = type_, alg = banyan.RED_BLACK_TREE)),
('banyan_red_black_tree_rank_updator',
lambda: banyan.SortedDict(key_type = type_, alg = banyan.RED_BLACK_TREE, updator = banyan.RankUpdator)),
('banyan_red_black_tree_min_max_updator',
lambda: banyan.SortedDict(key_type = type_, alg = banyan.RED_BLACK_TREE, updator = banyan.MinMaxUpdator)),
('banyan_splay_tree',
lambda: banyan.SortedDict(key_type = type_, alg = banyan.SPLAY_TREE)),
('banyan_sorted_list',
lambda: banyan.SortedDict(key_type = type_, alg = banyan.SORTED_LIST)),
('banyan_red_black_tree_gen',
lambda: banyan.SortedDict(alg = banyan.RED_BLACK_TREE)),
('banyan_splay_tree_gen',
lambda: banyan.SortedDict(alg = banyan.SPLAY_TREE)),
('banyan_sorted_list_gen',
lambda: banyan.SortedDict(alg = banyan.SORTED_LIST))])
t = dict([])
for name in names:
t[name] = _run_test(fns[name], type_, num_items, num_its)
return t
def __init__(self, eps=1e-6):
# [remaining, jobid] queue for the *virtual* scheduler
self.queue = blist()
# Jobs that should have finished in the virtual time,
# but didn't in the real (happens only in case of estimation
# errors)
self.late = set()
# last time we run the schedule function
self.last_t = 0
# Jobs that are running in the real time
self.running = set()
# Jobs that have less than eps work to do are considered done
# (deals with floating point imprecision)
self.eps = eps
# queue for late jobs, sorted by attained service
self.late_queue = sorteddict()
# {jobid: att} where att is jobid's attained service
self.attained = {}
# last result of calling the schedule function
self.scheduled = {}
def __init__(self, eps=1e-6):
# [remaining, jobid] queue for the *virtual* scheduler
self.queue = blist()
# Jobs that should have finished in the virtual time,
# but didn't in the real (happens only in case of estimation
# errors)
self.late = set()
# last time we run the schedule function
self.last_t = 0
# Jobs that are running in the real time
self.running = set()
# Jobs that have less than eps work to do are considered done
# (deals with floating point imprecision)
self.eps = eps
# queue for late jobs, sorted by attained service
self.late_queue = sorteddict()
# {jobid: att} where att is jobid's attained service
self.attained = {}
# last result of calling the schedule function
self.scheduled = {}
def __init__(self, time_window):
"""
:param time_window: time in milliseconds
"""
self.time_window = time_window
self.ts_to_values = sorteddict()
self.values = sortedlist()
def load_apls(self):
for f in self.apl_files:
file = self.apl_folder + f
print(file)
with open(file, "r") as file_to_read:
for line in file_to_read:
line = line.strip()
if "peaklist start" in line:
mD = mgfData()
mz_int_additional_data = blist.sorteddict()
elif "peaklist end" in line:
mD.setMZ(mz_int_additional_data)
self.apl_data[mD.title + "__tobi__" + mD.charge] = mD
else:
items = line.strip().split('=')
if items[0] == 'mz':
mD.setPepmass(items[1])
elif items[0] == "header":
mD.setTitle(items[1])
elif items[0] == "charge":
mD.setCharge(items[1])
elif items[0] == "fragmentation":
continue
else: # peaks
mz_int_additional = items[0].split("\t")
if len(mz_int_additional) == 1: # empty line
continue
else:
mz_int_additional_data[float(
mz_int_additional[0])] = [
mz_int_additional[1]
]
print(len(self.apl_data))
def __init__(self):
super(SortedList, self).__init__()
self.nodes = blist.sorteddict()
self.datas = blist.sortedlist()
self._max_id = 0
self._except_max_id = 0
return None
def compare2original(self):
firstLine = True
with open(self.original_mgf, "r") as file_to_read:
for line in file_to_read:
if firstLine:
firstLine = False
self.firstLine = line
continue
if "BEGIN IONS" in line:
mD = mgfData()
mz_int_additional_data = blist.sorteddict()
elif "END IONS" in line:
mD.setMZ(mz_int_additional_data)
for spectrum_keys in self.patched_Data[mD.title].mz.keys():
self.patched_Data[
mD.title].mz[spectrum_keys] = mD.mz[spectrum_keys]
else:
items = line.strip().split('=')
if items[0] == "TITLE":
mD.setTitle(items[1])
elif items[0] == "PEPMASS":
mD.setPepmass(items[1])
elif items[0] == "CHARGE":
mD.setCharge(items[1])
else: # peaks
mz_int_additional = items[0].split()
if len(mz_int_additional) == 0: # empty line
continue
else:
mz_int_additional_data[float(
mz_int_additional[0])] = [
mz_int_additional[1], mz_int_additional[2]
]
def __init__(self):
"""
Initialize your data structure here.
"""
# all ints map to the interval left
self.left = {}
# current intervals left to current intervals, pop and add
self.intervals = sorteddict()
def term_frequency_query(terms):
term_freq_dict = sorteddict({})
for term in terms:
try:
term_freq_dict[term] += 1
except KeyError:
term_freq_dict[term] = 1
return term_freq_dict
def set(self, key, col, val):
"""Sets the value at the given key/column.
In the average case, requires O(log(c)**2) operations, where c is the
number of columns associated with the key."""
assert all(isinstance(datum, basestring) for datum in (key, col, val))
self.kcv.setdefault(key, sorteddict())[col] = val
self._persist()
def params_from_postdata(data):
p = sorteddict()
for i in data.split('&'):
k, v = i.strip().split('=')
if k == 'oauth_signature':
oauth_signature = urllib.unquote(v)
continue
p[percent_encode(k)] = percent_encode(urllib.unquote_plus(v))
return p, oauth_signature
def params_from_authorization(auth):
p = sorteddict()
for i in auth.split(',')[1:]:
k,v = i.strip().split('=')
if k == 'oauth_signature':
oauth_signature = urllib.unquote(v[1:-1])
continue
p[percent_encode(k)] = percent_encode(urllib.unquote(v[1:-1]))
return p, oauth_signature
def params_from_postdata(data):
p = sorteddict()
for i in data.split('&'):
k,v = i.strip().split('=')
if k == 'oauth_signature':
oauth_signature = urllib.unquote(v)
continue
p[percent_encode(k)] = percent_encode(urllib.unquote_plus(v))
return p, oauth_signature
def params_from_authorization(auth):
p = sorteddict()
for i in auth.split(',')[1:]:
k, v = i.strip().split('=')
if k == 'oauth_signature':
oauth_signature = urllib.unquote(v[1:-1])
continue
p[percent_encode(k)] = percent_encode(urllib.unquote(v[1:-1]))
return p, oauth_signature
def __init__(self, description, rr_replay, source_pane):
self.rr_replay = rr_replay
self.description = description
self.spawn_cmd = "{} replay {}".format(shlex.quote(cli_args.rr),
shlex.quote(rr_replay))
self.source_pane = source_pane
self.breakpoints = {}
self.watches_set = 0
self.instr_to_checkpoint = sorteddict()
def __init__(self, name, current_stream_pos, max_size=10000, prefilled_cache={}):
self._max_size = int(max_size * CACHE_SIZE_FACTOR)
self._entity_to_key = {}
self._cache = sorteddict()
self._earliest_known_stream_pos = current_stream_pos
self.name = name
self.metrics = register_cache(self.name, self._cache)
for entity, stream_pos in prefilled_cache.items():
self.entity_has_changed(entity, stream_pos)
def __init__(self, name, current_stream_pos, max_size=10000, prefilled_cache={}):
self._max_size = max_size
self._entity_to_key = {}
self._cache = sorteddict()
self._earliest_known_stream_pos = current_stream_pos
self.name = name
caches_by_name[self.name] = self._cache
for entity, stream_pos in prefilled_cache.items():
self.entity_has_changed(entity, stream_pos)
def __init__(self, name, current_stream_pos, max_size=10000, prefilled_cache={}):
self._max_size = int(max_size * CACHE_SIZE_FACTOR)
self._entity_to_key = {}
self._cache = sorteddict()
self._earliest_known_stream_pos = current_stream_pos
self.name = name
self.metrics = register_cache(self.name, self._cache)
for entity, stream_pos in prefilled_cache.items():
self.entity_has_changed(entity, stream_pos)
def __init__(self, description, rr_replay, source_pane):
self.rr_replay = rr_replay
self.description = description
self.spawn_cmd = "{} replay {}".format(
shlex.quote(cli_args.rr), shlex.quote(rr_replay))
self.source_pane = source_pane
self.breakpoints = {}
self.watches_set = 0
self.instr_to_checkpoint = sorteddict()
def refresh(self,progress):
global re_lang_attr, re_token
count = 0
end_count = self.document.blockCount()
progress.setMaximum(end_count)
progress.setValue(0)
# get a reference to the dictionary to use
self.speller = self.my_book.get_speller()
# clear the alt-dict list.
self.alt_tags = blist.sorteddict()
# Zero out all counts and property sets that we have so far. We will
# develop new properties when each word is first seen. Properties
# such as HY will not have changed, but both AD and XX might have
# changed while the word text remains the same.
for j in range(len(self.vocab)) :
self.vocab_vview[j][0] = 0
self.vocab_vview[j][1] = set()
# iterate over all lines extracting tokens and processing them.
alt_dict = None
alt_tag = None
for line in self.document.all_lines():
count += 1
if 0 == (count % 20):
progress.setValue(count)
QCoreApplication.processEvents()
j = 0
m = re_token.search(line,0)
while m : # while match is not None
if m.group(6) : # start-tag; has it lang= ?
d = re_lang_attr.search(m.group(8))
if d :
alt_dict = d.group(1)
alt_tag = m.group(7)
elif m.group(9) :
if m.group(10) == alt_tag :
# end tag of a lang= start tag
alt_dict = None
alt_tag = None
else :
self._add_token(m.group(0),alt_dict)
j = m.end()
m = re_token.search(line,j)
# look for zero counts and delete those items. In order not to
# confuse the value and keys views, make a list of the actual word
# tokens to be deleted, then use del.
togo = []
for j in range(len(self.vocab)) :
if self.vocab_vview[j][0] == 0 :
togo.append(self.vocab_kview[j])
for key in togo:
del self.vocab[key]
progress.setValue(end_count)
def order(self):
order_attr = self._order_attr
context = aq_base(self.context)
# We store ordering on the context. This could get quite
# large, but it will be used often enough that including it on
# the object probably makes sense.
if not hasattr(context, self._order_attr):
setattr(context, order_attr, sorteddict())
# Force initial ordering
self.orderObjects()
return getattr(context, order_attr)
def reset(self):
"""
Reset our decoded content
"""
super(CodecArticleIndex, self).reset()
# Our Meta Content
self.decoded = NNTPMetaContent(work_dir=self.work_dir)
# Switch our decoded subvalue to be a sorteddict()
self.decoded.content = sorteddict()
def reset(self):
"""
Reset our decoded content
"""
super(CodecArticleIndex, self).reset()
# Our Meta Content
self.decoded = NNTPMetaContent(work_dir=self.work_dir)
# Switch our decoded subvalue to be a sorteddict()
self.decoded.content = sorteddict()
def __init__(self,my_book):
super().__init__()
# Save access to the book, from which we learn the metadata
# manager and the edit data model.
self.my_book = my_book
# The character list as a dict 'x':count.
self.census = sorteddict()
# Key and Values views on the census dict for indexed access.
self.k_view = None # supplied in char_read() or refresh()
self.v_view = None
# Register to handle metadata.
self.my_book.get_meta_manager().register('CHARCENSUS', self.char_read, self.char_save)
def token_map(self):
"""
returns a map of token -> {key[1], ...key[n]}
:return:
"""
#TODO: keep track of this during normal storage operation
# using a set in case there are token collisions
token_map = defaultdict(set)
for key in self._data.keys():
token_map[self.partitioner.get_key_token(key)].add(key)
token_map = sorteddict(token_map)
return token_map
def get_rescore_kaldi_n_best(kaldi_n_best_sentence, kaldi_n_best_acoustics,
session, m, fasttext_model, max_word_length,
char_vocab, rnn_state):
rescore_kaldi_n_best = {}
for sentence, acoustics in zip(kaldi_n_best_sentence,
kaldi_n_best_acoustics):
sentence_wer = get_sentence_wer(sentence, acoustics, session, m,
fasttext_model, max_word_length,
char_vocab, rnn_state)
rescore_kaldi_n_best[sentence] = sentence_wer
return sorteddict(rescore_kaldi_n_best).keys()
def setupOutputs(self):
if (numpy.array(self._oldShape) != self.inputs["shape"].value).any():
shape = self.inputs["shape"].value
self._oldShape = shape
self.outputs["Output"].meta.dtype = numpy.uint8
self.outputs["Output"].meta.shape = shape
# FIXME: Don't give arbitrary axistags. Specify them correctly if you need them.
#self.outputs["Output"].meta.axistags = vigra.defaultAxistags(len(shape))
self.inputs["Input"].meta.shape = shape
self.outputs["nonzeroValues"].meta.dtype = object
self.outputs["nonzeroValues"].meta.shape = (1, )
self.outputs["nonzeroCoordinates"].meta.dtype = object
self.outputs["nonzeroCoordinates"].meta.shape = (1, )
self._denseArray = numpy.zeros(shape, numpy.uint8)
self._sparseNZ = blist.sorteddict()
if self.inputs["deleteLabel"].ready(
) and self.inputs["deleteLabel"].value != -1:
labelNr = self.inputs["deleteLabel"].value
neutralElement = 0
self.inputs["deleteLabel"].setValue(-1) #reset state of inputslot
self.lock.acquire()
# Find the entries to remove
updateNZ = numpy.nonzero(
numpy.where(self._denseArray == labelNr, 1, 0))
if len(updateNZ) > 0:
# Convert to 1-D indexes for the raveled version
updateNZRavel = numpy.ravel_multi_index(
updateNZ, self._denseArray.shape)
# Zero out the entries we don't want any more
self._denseArray.ravel()[updateNZRavel] = neutralElement
# Remove the zeros from the sparse list
for index in updateNZRavel:
self._sparseNZ.pop(index)
# Labels are continuous values: Shift all higher label values down by 1.
self._denseArray[:] = numpy.where(self._denseArray > labelNr,
self._denseArray - 1,
self._denseArray)
self._maxLabel = self._denseArray.max()
self.lock.release()
self.outputs["nonzeroValues"].setDirty(slice(None))
self.outputs["nonzeroCoordinates"].setDirty(slice(None))
self.outputs["Output"].setDirty(slice(None))
self.outputs["maxLabel"].setValue(self._maxLabel)
def writeBinaryFileIndex(self,index_file):
with open(self.indexFolder+index_file, 'wb') as file_index:
with open(self.termFolder+index_file+'.txt','w') as file_term:
#Trier l'indexe par terme
inverse_index = sorteddict(self.index)
self.index.clear()
pos_term = 0
#Lire l'indexe par terme
for elem in inverse_index.items():
term = elem[0]
nb_doc = 0
#Trier la postingslist par docID
docs_list = sorteddict(elem[1])
#Lire la postingslist par docID
for doc in docs_list.items():
nb_doc += 1
doc_id = doc[0]
nb_occur = doc[1]
#Écrire en binaire le docID et le nombre d'occurences
file_index.write(struct.pack('ii',doc_id,nb_occur))
#Écire une ligne correspondante dans le fichier de vocabulaire avec le terme (vocabulaire), le nombre de doc contenant ce terme et la position débute de sa postingslist dans le fichier binaire
file_term.write(term+"|"+str(nb_doc)+"|"+str(pos_term)+"\n")
pos_term += nb_doc #La position débute du terme suivant
def order(self):
order_attr = self._order_attr
context = aq_base(self.context)
# We store ordering on the context. This could get quite
# large, but it will be used often enough that including it on
# the object probably makes sense.
order_obj = getattr(context, order_attr, None)
if order_obj is None or getattr(order_obj, 'viewkeys', None) is None:
setattr(context, order_attr, sorteddict())
# Force initial ordering
self.orderObjects()
# Override CSRF protection if we can
request = getattr(self.context, 'REQUEST', None)
if request is not None:
alsoProvides(request, IDisableCSRFProtection)
return getattr(context, order_attr)
def setupOutputs(self):
if (self._oldShape != self.inputs["shape"].value).any():
shape = self.inputs["shape"].value
self._oldShape = shape
self.outputs["Output"].meta.dtype = numpy.uint8
self.outputs["Output"].meta.shape = shape
# FIXME: Don't give arbitrary axistags. Specify them correctly if you need them.
#self.outputs["Output"].meta.axistags = vigra.defaultAxistags(len(shape))
self.inputs["Input"].meta.shape = shape
self.outputs["nonzeroValues"].meta.dtype = object
self.outputs["nonzeroValues"].meta.shape = (1,)
self.outputs["nonzeroCoordinates"].meta.dtype = object
self.outputs["nonzeroCoordinates"].meta.shape = (1,)
self._denseArray = numpy.zeros(shape, numpy.uint8)
self._sparseNZ = blist.sorteddict()
if self.inputs["deleteLabel"].ready() and self.inputs["deleteLabel"].value != -1:
labelNr = self.inputs["deleteLabel"].value
neutralElement = 0
self.inputs["deleteLabel"].setValue(-1) #reset state of inputslot
self.lock.acquire()
# Find the entries to remove
updateNZ = numpy.nonzero(numpy.where(self._denseArray == labelNr,1,0))
if len(updateNZ)>0:
# Convert to 1-D indexes for the raveled version
updateNZRavel = numpy.ravel_multi_index(updateNZ, self._denseArray.shape)
# Zero out the entries we don't want any more
self._denseArray.ravel()[updateNZRavel] = neutralElement
# Remove the zeros from the sparse list
for index in updateNZRavel:
self._sparseNZ.pop(index)
# Labels are continuous values: Shift all higher label values down by 1.
self._denseArray[:] = numpy.where(self._denseArray > labelNr, self._denseArray - 1, self._denseArray)
self._maxLabel = self._denseArray.max()
self.lock.release()
self.outputs["nonzeroValues"].setDirty(slice(None))
self.outputs["nonzeroCoordinates"].setDirty(slice(None))
self.outputs["Output"].setDirty(slice(None))
self.outputs["maxLabel"].setValue(self._maxLabel)
def endElement(self, name):
global titlePageMapper
global pageNumber
if name=="title":
wikipediaHandler.title=self.bufferObject
wikipediaHandler.titleWords=tokenizeTitle(wikipediaHandler.title, str(pageNumber))
if name=="text":
wikipediaHandler.text=self.bufferObject
#wikipediaHandler.textWords=tokenizeText(wikipediaHandler.text,str(pageNumber))
if name=="page":
if pageNumber%7000==0:
for key in titlePageMapper:
title_page_map_fp.write(str(key) + " " + titlePageMapper[key].strip().encode('utf-8') + "\n")
titlePageMapper = blist.sorteddict({})
self.bufferObject=""
def findsol(mol, target, steps, maxlen, replacements):
branches = [(mol, steps)]
# print(branches)
index = blist.sorteddict()
i = 0
solution = -1
while branches:
i += 1
# print(branch)
if i % 100000 == 0:
print(len(index), len(branches), end='\r')
branch = branches.pop()
steps = branch[1] + 1
# if i == 500000:
# for key, val in index.items():
# print(key, val)
# If no other branch reached here first
if branch[0] not in index or (index[branch[0]] > branch[1]):
index[branch[0]] = branch[1]
for rep in replacements:
done = False
off = 0
while not done:
done = True
pos = branch[0].find(rep[1], off)
# print(rep)
# If found
if pos != -1:
done = False
newmol = branch[0][:pos] + rep[0] +\
branch[0][pos+len(rep[1]):]
if newmol == target:
# print('Target reached in', steps, 'steps!')
if solution == -1 or solution > steps:
solution = steps
print('Solution:', solution)
sys.exit(0)
else:
if rep[0] != 'e':
branches.append((newmol, steps))
off = pos + len(rep[1])
# print(branches)
# input()
return solution
def indexText(words, pageNumber):
word_length = len(words)
if word_length:
term = round((1/float(word_length)), 4)
global title_size, indexTitle
for word in words:
if word not in indexTitle:
indexTitle[word]={}
indexTitle[word][pageNumber]=term
elif pageNumber in indexTitle[word]:
indexTitle[word][pageNumber]+=term
else:
indexTitle[word][pageNumber]=term
title_size = title_size + sys.getsizeof(pageNumber) + sys.getsizeof(indexTitle[word][pageNumber])
if title_size>=file_size_limit:
writeOutput(indexTitle, 'title')
indexTitle = blist.sorteddict({})
title_size=0
def __init__(self, eps=1e-6):
# job attained service is represented as (real attained service // eps)
# (not perfectly precise but avoids problems with floats)
self.eps = eps
# sorted dictionary for {attained: {jobid}}
self.queue = sorteddict()
# {jobid: attained} dictionary
self.attained = {}
# result of the last time the schedule() method was called
# grouped by {attained: [service, {jobid}]}
self.scheduled = {}
# This is the entry point for doing XXX + LAS schedulers:
# it's sufficient to touch here
# last time when the schedule was changed
self.last_t = 0
def indexText(words, pageNumber):
word_length = len(words)
if word_length:
term = round((1 / float(word_length)), 4)
global title_size, indexTitle
for word in words:
if word not in indexTitle:
indexTitle[word] = {}
indexTitle[word][pageNumber] = term
elif pageNumber in indexTitle[word]:
indexTitle[word][pageNumber] += term
else:
indexTitle[word][pageNumber] = term
title_size = title_size + sys.getsizeof(
pageNumber) + sys.getsizeof(indexTitle[word][pageNumber])
if title_size >= file_size_limit:
writeOutput(indexTitle, 'title')
indexTitle = blist.sorteddict({})
title_size = 0
def __init__(self, eps=1e-6):
# job attained service is represented as (real attained service // eps)
# (not perfectly precise but avoids problems with floats)
self.eps = eps
# sorted dictionary for {attained: {jobid}}
self.queue = sorteddict()
# {jobid: attained} dictionary
self.attained = {}
# result of the last time the schedule() method was called
# grouped by {attained: [service, {jobid}]}
self.scheduled = {}
# This is the entry point for doing XXX + LAS schedulers:
# it's sufficient to touch here
# last time when the schedule was changed
self.last_t = 0
def run_tests(names, num_items, num_its):
# Tmp Ami - make key-type separate labels below
fns = dict([
('btrees', lambda es: BTrees.OOBTree.OOBTree([(e, 1) for e in es])),
('blist', lambda es: blist.sorteddict([(e, 1) for e in es])),
('bintrees', lambda es: bintrees.FastRBTree([(e, 1) for e in es])),
('set', lambda es: set([(e, 1) for e in es])),
('banyan_red_black_tree', lambda es: banyan.SortedDict([(e, 1) for e in es], alg = banyan.RED_BLACK_TREE)),
('banyan_splay_tree', lambda es: banyan.SortedDict([(e, 1) for e in es], alg = banyan.SPLAY_TREE)),
('banyan_sorted_list', lambda es: banyan.SortedDict([(e, 1) for e in es], alg = banyan.SORTED_LIST)),
('banyan_red_black_tree_gen',
lambda es: banyan.SortedDict([(e, 1) for e in es], key_type = int, alg = banyan.RED_BLACK_TREE)),
('banyan_splay_tree_gen',
lambda es: banyan.SortedDic([(e, 1) for e in es], key_type = int, alg = banyan.SPLAY_TREE)),
('banyan_sorted_list_gen',
lambda es: banyan.SortedDict([(e, 1) for e in es], key_type = int, alg = banyan.SORTED_LIST))])
t = dict([])
for name in names:
t[name] = _run_test(fns[name], int, num_items, num_its)
return t
def output_to_file(self, out_filename, tags):
print "=== Making file %s" % out_filename
clauses = ["%s ='%s'" % (tag, value) for (tag, value) in tags.iteritems()]
query = 'select value from /^cpu.trace.*/ where %s' % " and ".join(clauses)
if self.start:
query += " and time>=\'{}\'".format(self.start)
if self.end:
query += " and time<=\'{}\'".format(self.end)
print "running query: %s" % query
metrics = self.client.query(query)
try:
series = metrics.raw['series']
except KeyError:
print "got an empty recordset"
return
print "putting metrics into a sorted dictionary..."
traces = sorteddict()
for metric in series:
if re.match(r'cpu\.trace\.\d+', metric['name']):
continue
name = self._format_metric_name(metric['name'], 'cpu.trace.')
value = sum([v[1] for v in metric['values']])
if name in traces:
traces[name] = traces[name] + value
else:
traces[name] = value
print "output this dictionary to the file..."
with open(out_filename, "w") as f:
for t in traces:
found = False
for filter_string in self.filter_exclude:
if filter_string in t:
found = True
break
if not found:
v = traces[t]
if t != v: # this is Andrew's cpu.trace.23 = 23 measures; I don't know what are they for
f.write('%s %d\n' % (t, v))
print "output finished."
def endElement(self, name):
global titlePageMapper
global pageNumber
if name == "title":
wikipediaHandler.title = self.bufferObject
wikipediaHandler.titleWords = tokenizeTitle(
wikipediaHandler.title, str(pageNumber))
if name == "text":
wikipediaHandler.text = self.bufferObject
#wikipediaHandler.textWords=tokenizeText(wikipediaHandler.text,str(pageNumber))
if name == "page":
if pageNumber % 7000 == 0:
for key in titlePageMapper:
title_page_map_fp.write(
str(key) + " " +
titlePageMapper[key].strip().encode('utf-8') + "\n")
titlePageMapper = blist.sorteddict({})
self.bufferObject = ""
def __init__(self, *args, **kargs):
"""Construct the typeHintedDict.
Args:
*args, **kargs: Pass any parameters through to the dict() constructor.
Calls the dict() constructor, then runs through the keys of the
created dictionary and either uses the string type embedded in
the keyname to generate the type hint (and remove the
embedded string type from the keyname) or determines the likely
type hint from the value of the dict element.
"""
self._typehints = sorteddict()
super(typeHintedDict, self).__init__(*args, **kargs)
for key in list(self.keys()): # Chekc through all the keys and see if they contain
# type hints. If they do, move them to the
# _typehint dict
value = super(typeHintedDict, self).__getitem__(key)
super(typeHintedDict, self).__delitem__(key)
self[key] = value # __Setitem__ has the logic to handle embedded type hints correctly
def output_to_file(self, out_filename, tags):
print "=== Making file %s" % out_filename
clauses = [
"%s ='%s'" % (tag, value) for (tag, value) in tags.iteritems()
] + self.extra_clauses
query = 'select value from /^cpu.trace.*/ where %s' % " and ".join(
clauses)
print "running query: %s" % query
metrics = self.client.query(query)
try:
series = metrics.raw['series']
except KeyError:
print "got an empty recordset"
return
print "putting metrics into a sorted dictionary..."
traces = sorteddict()
for metric in series:
if re.match(r'cpu\.trace\.\d+', metric['name']):
continue
name = self._format_metric_name(metric['name'], 'cpu.trace.')
value = sum([v[1] for v in metric['values']])
if name in traces:
traces[name] = traces[name] + value
else:
traces[name] = value
print "output this dictionary to the file..."
with open(out_filename, "w") as f:
for t in traces:
found = False
for filter_string in self.filter_exclude:
if filter_string in t:
found = True
break
if not found:
v = traces[t]
if t != v: # this is Andrew's cpu.trace.23 = 23 measures; I don't know what are they for
f.write('%s %d\n' % (t, v))
print "output finished."
def notifyConnectAll(self):
if (self._oldShape != self.inputs["shape"].value).all():
shape = self.inputs["shape"].value
self._oldShape = shape
self.outputs["Output"]._dtype = numpy.uint8
self.outputs["Output"]._shape = shape
self.outputs["Output"]._axistags = vigra.defaultAxistags(len(shape))
self.inputs["Input"].meta.shape = shape
self.outputs["nonzeroValues"]._dtype = object
self.outputs["nonzeroValues"]._shape = (1,)
self.outputs["nonzeroValues"]._axistags = vigra.defaultAxistags(1)
self.outputs["nonzeroCoordinates"]._dtype = object
self.outputs["nonzeroCoordinates"]._shape = (1,)
self.outputs["nonzeroCoordinates"]._axistags = vigra.defaultAxistags(1)
self._denseArray = numpy.zeros(shape, numpy.uint8)
self._sparseNZ = blist.sorteddict()
if self.inputs["deleteLabel"].connected() and self.inputs["deleteLabel"].value != -1:
labelNr = self.inputs["deleteLabel"].value
neutralElement = 0
self.inputs["deleteLabel"].setValue(-1) #reset state of inputslot
self.lock.acquire()
#remove values to be deleted
updateNZ = numpy.nonzero(numpy.where(self._denseArray == labelNr,1,0))
if len(updateNZ)>0:
updateNZRavel = numpy.ravel_multi_index(updateNZ, self._denseArray.shape)
self._denseArray.ravel()[updateNZRavel] = neutralElement
for index in updateNZRavel:
self._sparseNZ.pop(index)
self._denseArray[:] = numpy.where(self._denseArray > labelNr, self._denseArray - 1, self._denseArray)
self.lock.release()
self.outputs["nonzeroValues"][0] = numpy.array(self._sparseNZ.values())
self.outputs["nonzeroCoordinates"][0] = numpy.array(self._sparseNZ.keys())
self.outputs["Output"][:] = self._denseArray #set output dirty
def __init__(self,
memory_limit=1000000,
filter_tags=False,
remove_stopwords=False,
case_sensitive=False,
with_stemming=False):
self.memory_limit = memory_limit # Max size of self.inv_index in bytes, if reached it we use merge-based methode
self.filter_tags = filter_tags
self.inv_index = {} # Contains the whole index in in-memory mode
self.remove_stopwords = remove_stopwords
self.case_sensitive = case_sensitive
self.with_stemming = with_stemming
self.indexed = False
self._doc_id_list = [] # Contains the ids of the documents indexed
self._partial_files_names = [
] # Contains the filenames of the partial index in merge-based mode
self.dict_terms_offset = dict(
) # Keep the line where each term is in InvertedFile
self.offset = 1
self.dict_term_pl = sorteddict() # Used in merging
def __init__(self, hs):
self.server_name = hs.hostname
self.clock = hs.get_clock()
self.notifier = hs.get_notifier()
self.is_mine_id = hs.is_mine_id
self.presence_map = {
} # Pending presence map user_id -> UserPresenceState
self.presence_changed = sorteddict() # Stream position -> user_id
self.keyed_edu = {} # (destination, key) -> EDU
self.keyed_edu_changed = sorteddict(
) # stream position -> (destination, key)
self.edus = sorteddict() # stream position -> Edu
self.failures = sorteddict(
) # stream position -> (destination, Failure)
self.device_messages = sorteddict() # stream position -> destination
self.pos = 1
self.pos_time = sorteddict()
# EVERYTHING IS SAD. In particular, python only makes new scopes when
# we make a new function, so we need to make a new function so the inner
# lambda binds to the queue rather than to the name of the queue which
# changes. ARGH.
def register(name, queue):
LaterGauge(
"synapse_federation_send_queue_%s_size" % (queue_name, ), "",
[], lambda: len(queue))
for queue_name in [
"presence_map",
"presence_changed",
"keyed_edu",
"keyed_edu_changed",
"edus",
"failures",
"device_messages",
"pos_time",
]:
register(queue_name, getattr(self, queue_name))
self.clock.looping_call(self._clear_queue, 30 * 1000)
def __init__(self, eps=1e-6):
# job that should have finished, but didn't
# (because of estimation errors)
self.late = set()
# [remaining, jobid] heap for the SRPT scheduler
self.queue = sortedlist()
# last time we run the update function
self.last_t = 0
# Jobs that have less than eps work to do are considered done
# (deals with floating point imprecision)
self.eps = eps
# {jobid: att} where att is jobid's attained service
self.attained = {}
# queue for late jobs, sorted by attained service
self.late_queue = sorteddict()
# last result of calling the schedule function
self.scheduled = {}
def run_tests(names, num_items, num_its):
# Tmp Ami - make key-type separate labels below
fns = dict([
('btrees', lambda es: BTrees.OOBTree.OOBTree([(e, 1) for e in es])),
('blist', lambda es: blist.sorteddict([(e, 1) for e in es])),
('bintrees', lambda es: bintrees.FastRBTree([(e, 1) for e in es])),
('set', lambda es: set([(e, 1) for e in es])),
('banyan_red_black_tree', lambda es: banyan.SortedDict(
[(e, 1) for e in es], alg=banyan.RED_BLACK_TREE)),
('banyan_splay_tree', lambda es: banyan.SortedDict(
[(e, 1) for e in es], alg=banyan.SPLAY_TREE)),
('banyan_sorted_list', lambda es: banyan.SortedDict(
[(e, 1) for e in es], alg=banyan.SORTED_LIST)),
('banyan_red_black_tree_gen', lambda es: banyan.SortedDict(
[(e, 1) for e in es], key_type=int, alg=banyan.RED_BLACK_TREE)),
('banyan_splay_tree_gen', lambda es: banyan.SortedDic(
[(e, 1) for e in es], key_type=int, alg=banyan.SPLAY_TREE)),
('banyan_sorted_list_gen', lambda es: banyan.SortedDict(
[(e, 1) for e in es], key_type=int, alg=banyan.SORTED_LIST))
])
t = dict([])
for name in names:
t[name] = _run_test(fns[name], int, num_items, num_its)
return t
def __init__(self, eps=1e-6):
# job that should have finished, but didn't
# (because of estimation errors)
self.late = set()
# [remaining, jobid] heap for the SRPT scheduler
self.queue = sortedlist()
# last time we run the update function
self.last_t = 0
# Jobs that have less than eps work to do are considered done
# (deals with floating point imprecision)
self.eps = eps
# {jobid: att} where att is jobid's attained service
self.attained = {}
# queue for late jobs, sorted by attained service
self.late_queue = sorteddict()
# last result of calling the schedule function
self.scheduled = {}