def test_clear():
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict(mapping)
assert len(temp) == 26
assert list(temp.items()) == mapping
temp.clear()
assert len(temp) == 0
def __init__(self, my_book):
super().__init__(None)
# 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 sorted dict.
self.vocab = 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 count of available words based on the latest sort
self.active_word_count = 0
# 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 = SortedDict()
# Cached sort vectors, see get_sort_vector()
self.sort_up_vectors = [None, None, None]
self.sort_down_vectors = [None, None, None]
self.sort_key_funcs = [None, None, None]
# 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 predict(self, X):
y = np.zeros(len(X))
for i,x in enumerate(X): # test points
sd = SortedDict() # distance -> class
for j,xt in enumerate(self.X): # training points
d = np.linalg.norm(x - xt)
# print d, sd
if len(sd) < self.k:
sd[d] = self.y[j]
else:
last = sd.viewkeys()[-1]
if d < last:
del sd[last]
sd[d] = self.y[j]
# print "sd:", sd
# vote
votes = {}
# print "viewvalues:", sd.viewvalues()
for v in sd.viewvalues():
# print "v:", v
votes[v] = votes.get(v,0) + 1
# print "votes:", votes, "true:", Ytest[i]
max_votes = 0
max_votes_class = -1
for v,count in votes.iteritems():
if count > max_votes:
max_votes = count
max_votes_class = v
y[i] = max_votes_class
return y
def get_sort_vector( self, col, order, key_func = None, filter_func = None ) :
if filter_func : # is not None,
# create a sort vector from scratch, filtered
getter_func = self._make_key_getter( col )
sorted_dict = SortedDict( key_func )
for j in range( len( self.vocab ) ) :
if filter_func( self.vocab_kview[j], self.vocab_vview[j][1] ) :
k = getter_func( j )
sorted_dict[ k ] = j
vector = sorted_dict.values()
if order != Qt.AscendingOrder :
vector = [j for j in reversed( vector ) ]
else : # no filter_func, try to reuse a cached vector
vector = self.sort_up_vectors[ col ]
if not vector or key_func is not self.sort_key_funcs[ col ] :
# there is no ascending vector for this column, or there
# is one but it was made with a different key_func.
getter_func = self._make_key_getter( col )
sorted_dict = SortedDict( key_func )
for j in range( len( self.vocab ) ) :
k = getter_func( j )
sorted_dict[ k ] = j
vector = self.sort_up_vectors[ col ] = sorted_dict.values()
self.sort_key_funcs[ col ] = key_func
if order != Qt.AscendingOrder :
# what is wanted is a descending order vector, do we have one?
if self.sort_down_vectors[ col ] is None :
# no, so create one from the asc. vector we now have
self.sort_down_vectors[ col ] = [ j for j in reversed( vector ) ]
# yes we do (now)
vector = self.sort_down_vectors[ col ]
# one way or another, vector is a sort vector
# note the actual word count available through that vector
self.active_word_count = len(vector)
return vector
def setUpClass(self):
self._vocab = SortedDict({
'1950_1959': [('w1', 1.0), ('w2', 1.0)],
'1951_1960': [('w3', 1.0), ('w4', 1.0)],
'1952_1961': [('w5', 1.0), ('w6', 1.0)],
'1953_1962': [('w7', 1.0), ('w8', 1.0)]
})
self._links = SortedDict({
'1950_1959': {'w1': [('w1', 0.0), ('w2', 1.0)]},
'1951_1960': {'w3': [('w3', 0.0), ('w4', 1.0)]},
'1952_1961': {'w5': [('w5', 0.0), ('w6', 1.0)]},
'1953_1962': {'w7': [('w7', 0.0), ('w8', 1.0)]}
})
self._aggVocab = SortedDict({
'1954': [('w1', 1.0), ('w2', 1.0)],
'1955': [('w3', 1.0), ('w4', 1.0)],
'1956': [('w5', 1.0), ('w6', 1.0)],
'1957': [('w7', 1.0), ('w8', 1.0)]
})
self._aggPeriods = SortedDict({
'1954': ['1950_1959'],
'1955': ['1951_1960'],
'1956': ['1952_1961'],
'1957': ['1953_1962']
})
def test_pickle():
import pickle
alpha = SortedDict(negate, zip(range(10000), range(10000)))
alpha._reset(500)
beta = pickle.loads(pickle.dumps(alpha))
assert alpha == beta
assert alpha._key == beta._key
def test_valuesview():
if hexversion < 0x02070000: return
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict(mapping[:13])
values = get_valuesview(temp)
assert len(values) == 13
assert 0 in values
assert list(values) == [pos for val, pos in mapping[:13]]
assert values[0] == 0
assert values[-3:] == [10, 11, 12]
assert list(reversed(values)) == list(reversed(range(13)))
assert values.index(5) == 5
assert values.count(10) == 1
temp.update(mapping[13:])
assert len(values) == 26
assert 25 in values
assert list(values) == [pos for val, pos in mapping]
that = dict(mapping)
that_values = get_valuesview(that)
values = get_valuesview(SortedDict(mapping[:2]))
assert repr(values) == "SortedDict_values([0, 1])"
def sort( self, col, order ) :
self.active_sort_vector = []
if 0 == len(self.message_tuples) : # nothing to display
return
self.layoutAboutToBeChanged.emit([],QAbstractItemModel.VerticalSortHint)
# treat columns 0 and 1 the same
if col : # is 1 or 2
col -= 1 # make it 0 or 1
# we need an ascending vector in all cases.
vector = self.sort_vectors_ascending[ col ]
if vector is None : # we need to create the ascending vector
sorted_dict = SortedDict()
for j in range( len( self.message_tuples ) ) :
line_col_msg_tuple = self.message_tuples[ j ]
if col : # is 1, meaning sort on messages
key = line_col_msg_tuple[2]+line_col_msg_tuple[0]
else : # col is 0, sort on line#+col#
key = line_col_msg_tuple[0]+line_col_msg_tuple[1]
key += str(j) # ensure uniqueness
sorted_dict[key] = j
vector = self.sort_vectors_ascending[ col ] = sorted_dict.values()
# vector now has an ascending sort vector which is cached..
if order == Qt.DescendingOrder : # ..but we need the descending one
if self.sort_vectors_descending[ col ] is None : # we need to make it
self.sort_vectors_descending[ col ] = [ j for j in reversed( vector ) ]
vector = self.sort_vectors_descending[ col ]
self.active_sort_vector = vector
self.layoutChanged.emit([],QAbstractItemModel.VerticalSortHint)
def test_irange():
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict(7, mapping)
for start in range(26):
for stop in range(start + 1, 26):
result = list(string.ascii_lowercase[start:stop])
assert list(temp.irange(result[0], result[-1])) == result
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.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", "device_messages", "pos_time",
]:
register(queue_name, getattr(self, queue_name))
self.clock.looping_call(self._clear_queue, 30 * 1000)
class PositionMapping:
__slots__ = ('_pos', '_posmap')
DUPLICATION_CHECK = True
def __init__(self):
self._pos = 0
self._posmap = SortedDict()
def items(self):
return self._posmap.items()
#
# Properties
#
@property
def pos(self):
return self._pos
@pos.setter
def pos(self, v):
self._pos = v
#
# Public methods
#
def add_mapping(self, start_pos, length, obj):
# duplication check
if self.DUPLICATION_CHECK:
try:
pre = next(self._posmap.irange(maximum=start_pos, reverse=True))
if start_pos in self._posmap[pre]:
raise ValueError("New mapping is overlapping with an existing element.")
except StopIteration:
pass
self._posmap[start_pos] = PositionMappingElement(start_pos, length, obj)
def tick_pos(self, delta):
self._pos += delta
def get_node(self, pos):
element = self.get_element(pos)
if element is None:
return None
return element.obj
def get_element(self, pos):
try:
pre = next(self._posmap.irange(maximum=pos, reverse=True))
except StopIteration:
return None
element = self._posmap[pre]
if pos in element:
return element
return None
def signal_crosses(short_moving_averages, long_moving_averages):
short_moving_averages = SortedDict(short_moving_averages)
long_moving_averages = SortedDict(long_moving_averages)
short_len = len(short_moving_averages.values())
long_len = len(long_moving_averages.values())
if(short_len != long_len):
print "[Error] signal_crosses: inputs must be same size"
return {}
signal_crosses = {}
last_diff_dir = 0
for date, short_average in short_moving_averages.iteritems():
long_average = long_moving_averages[date]
diff = short_average - long_average
if(last_diff_dir == 0):
signal_crosses[date] = HOLD
if(diff != 0):
last_diff_dir = sign(diff)
continue
if(sign(diff) != last_diff_dir):
signal_crosses[date] = BUY if last_diff_dir < 0 else SELL
last_diff_dir = -last_diff_dir
else:
signal_crosses[date] = HOLD
return SortedDict(signal_crosses)
def test_copy():
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict(mapping)
dup = temp.copy()
assert len(temp) == 26
dup.clear()
assert len(temp) == 0
def _adaptiveAggregation(V, n, yIntervals, weightF, param, freq):
'''Apply adaptive aggregation algorithm to the given vocabulary.
Algorithm 2 from paper.
'''
# Initialize returned parameters
finalVocabs = SortedDict()
periodGroups = SortedDict()
# Select weighting function
f = _selectWeightingFunction(weightF, param)
# Iterate over time frames
for t in _arrangeIntervals(V, yIntervals, freq):
mu_t = getRangeMiddle(t[0], t[-1])
V_prime = SortedDict({tx: V[tx] for tx in t})
score = defaultdict(float)
for years_v, words_v in V_prime.iteritems():
mu_v = getRangeMiddle(years_v)
fvt = f(mu_v, mu_t)
for word, score_wv in words_v:
score[word] += fvt * score_wv
# Top n terms w sorted by score_w
scoreList = [(k, v) for k, v in score.iteritems()]
scoreList = sorted(scoreList, key=lambda pair: pair[1], reverse=True)
topN = scoreList[:n]
finalVocabs[str(int(mu_t))] = topN
periodGroups[str(int(mu_t))] = t
return finalVocabs, periodGroups
class PrioritizedIntensity(object):
_MIN_VALUE = 0.005
def __init__(self):
self._values = SortedDict()
def set(self, value, priority=100):
value = float(value)
if value < self._MIN_VALUE and priority in self._values:
del self._values[priority]
else:
self._values[priority] = value
def eval(self):
if not self._values:
return 0.0
return self._values[self._values.iloc[- 1]]
def top_priority(self):
if not self._values:
return 0
return self._values.keys()[len(self._values) - 1]
def reset(self):
self._values.clear()
def dist_player_stats(cls, stats, strength=False):
"""Order the stats to create distribution
previously 'hand strength'"""
# logger.info('distributing the player stats')
dist = SortedDict(pos, {0: 'f'})
p = 0
for o in ['f', 's', 'l', 'k', 'c', 'b', 'r', 'a']:
if o in stats:
dist[p] = o
p += max(0.01, stats[o])
dist[p - 0.001] = o
dist[1] = 'a'
logger.info(f'dist = {dist}')
logger.debug(f'strength? {strength}')
if strength is False:
return dist
r = ''
logger.debug(f'dist = {type(dist)}')
for _ in range(20):
p = _ * 5 / 100
i_pos = dist.bisect_key_left(p)
logger.debug(f'i_pos {i_pos} / {len(dist)}')
k = dist.iloc[i_pos]
v = dist[k]
r += v.upper() if (1 - strength) <= p <= 1 else v.lower()
logger.debug(f'bisected {v} from {k} at {r}%')
logger.debug(f'dist_stats {r}')
return r
def test_islice():
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict(7, mapping)
for start in range(30):
for stop in range(30):
assert list(temp.islice(start, stop)) == list(string.ascii_lowercase[start:stop])
def test_stress(repeat=1000):
sdict = SortedDict((val, -val) for val in range(1000))
for rpt in range(repeat):
action = random.choice(actions)
action(sdict)
try:
sdict._check()
except AssertionError:
print(action)
raise
start_len = len(sdict)
while len(sdict) < 500:
key = random.randrange(0, 2000)
sdict[key] = -key
while len(sdict) > 2000:
key = random.randrange(0, 2000)
if key in sdict:
del sdict[key]
if start_len != len(sdict):
sdict._check()
def test_irange_key():
temp = SortedDict(modulo, 7, ((val, val) for val in range(100)))
values = sorted(range(100), key=modulo)
for start in range(10):
for stop in range(start, 10):
result = list(temp.irange_key(start, stop))
assert result == values[(start * 10):((stop + 1) * 10)]
def __init__(self):
self.median = 0
self.highMarker = TxGraph.WINDOW_SIZE
self.lowMarker = 1
self.txMap = SortedDict() #sorted by unix epoch (timestamp)
self.edgeMap = SortedDict() #sorted by edge name
self.nodeMap = SortedDict() #sorted by node name
self.degreeList = SortedList() #sorted by degreeList
def test_update():
mapping = [(val, pos) for pos, val in enumerate(string.ascii_lowercase)]
temp = SortedDict()
temp.update()
temp.update(mapping)
temp.update(dict(mapping))
temp.update(mapping[5:7])
assert list(temp.items()) == mapping
def __init__(self, product_id='BTC-USD', log_to=None):
super(OrderBook, self).__init__(products=product_id)
self._asks = SortedDict()
self._bids = SortedDict()
self._client = PublicClient()
self._sequence = -1
self._log_to = log_to
if self._log_to:
assert hasattr(self._log_to, 'write')
self._current_ticker = None
def occurences(iterable):
""" count number of occurences of each item in a finite iterable
:param iterable: finite iterable
:return: dict of int count indexed by item
"""
from sortedcontainers import SortedDict
occur=SortedDict()
for x in iterable:
occur[x]=occur.get(x, 0) + 1
return occur
def arrayRDP(arr, epsilon=0.0, n=None):
"""
This is a slightly modified version of the _aRDP function, that accepts
as arguments the tolerance in the distance and the maximum number of points
the algorithm can select.
**Note:** The results of this algoritm should be identical to the arrayRDP
function if the *n* parameter is not specified. In that case, the
performance is slightly worse, although the asymptotic complexity is the
same. For this reason, this function internally delegates the solution in
that function if the *n* parameter is missing.
Parameters
----------
arr:
Array of values of consecutive points.
epsilon:
Maximum difference allowed in the simplification process.
n:
Maximum number of points of the resulted simplificated array.
Returns
-------
out:
Array of indices of the selected points.
"""
if n is None:
return _aRDP(arr, epsilon)
if epsilon <= 0.0:
raise ValueError('Epsilon must be > 0.0')
n = n or len(arr)
if n < 3:
return arr
fragments = SortedDict()
#We store the distances as negative values due to the default order of
#sorteddict
dist, idx = max_vdist(arr, 0, len(arr) - 1)
fragments[(-dist, idx)] = (0, len(arr) - 1)
while len(fragments) < n-1:
(dist, idx), (first, last) = fragments.popitem(last=False)
if -dist <= epsilon:
#We have to put again the last item to prevent loss
fragments[(dist, idx)] = (first, last)
break
else:
#We have to break the fragment in the selected index
dist, newidx = max_vdist(arr, first, idx)
fragments[(-dist, newidx)] = (first, idx)
dist, newidx = max_vdist(arr, idx, last)
fragments[(-dist, newidx)] = (idx, last)
#Now we have to get all the indices in the keys of the fragments in order.
result = SortedList(i[0] for i in fragments.itervalues())
result.add(len(arr) - 1)
return np.array(result)
class TwoDimensionTable:
def __init__(self):
self.__rows = SortedDict()
@classmethod
def from_json(cls, input_file):
with open(input_file, 'r') as json_f:
json_data = json.load(json_f)
table = TwoDimensionTable()
for d in json_data:
speedup = d['speedup_mine_over_sol']
input_size = d['input_size']
proc_count = d['proc_count']
table.add(row=proc_count, col=input_size, val=speedup)
return table
def add(self, row, col, val):
if row not in self.__rows:
self.__add_new_row(row)
for r, c in self.__rows.items():
if r == row:
c[col] = val
elif col not in c:
c[col] = None
def row_items(self):
return self.__rows.items()
def __add_new_row(self, row_name):
self.__rows[row_name] = SortedDict()
for r, c in self.__rows.items():
if r is not row_name:
for c_title, _ in c.items():
self.__rows[row_name][c_title] = None
break
def __repr__(self):
return self.__str__()
def __str__(self):
table = ['rows ' + u'\u2193' + ' columns ' + u'\u2192']
for r_title, col in self.row_items():
row = [str(r_title), '->']
for c_title, val in col.items():
row.append('({0}, {1})'.format(c_title, val))
table.append(' '.join(row))
return '\n'.join(table)
def compute_pagerank(urls, inlinks, outlinks, b=.85, iters=20):
""" Return a dictionary mapping each url to its PageRank.
The formula is R(u) = (1/N)(1-b) + b * (sum_{w in B_u} R(w) / (|F_w|)
Initialize all scores to 1.0.
Params:
urls.......SortedList of urls (names)
inlinks....SortedDict mapping url to list of in links (backlinks)
outlinks...Sorteddict mapping url to list of outlinks
Returns:
A SortedDict mapping url to its final PageRank value (float)
>>> urls = SortedList(['a', 'b', 'c'])
>>> inlinks = SortedDict({'a': ['c'], 'b': set(['a']), 'c': set(['a', 'b'])})
>>> outlinks = SortedDict({'a': ['b', 'c'], 'b': set(['c']), 'c': set(['a'])})
>>> sorted(compute_pagerank(urls, inlinks, outlinks, b=.5, iters=0).items())
[('a', 1.0), ('b', 1.0), ('c', 1.0)]
>>> iter1 = compute_pagerank(urls, inlinks, outlinks, b=.5, iters=1)
>>> iter1['a'] # doctest:+ELLIPSIS
0.6666...
>>> iter1['b'] # doctest:+ELLIPSIS
0.333...
"""
###TODO
"""
R(u) = (1 - b)/N + b * sum( inlinks of u/outlink-number)
"""
Ru = SortedDict()
Rv = SortedDict()
size = len(urls)
# Initialize to 1.0 all URL's
for k in urls:
Ru.setdefault(k, 1.0)
# Page Rank definition
for i in range(iters):
for url in urls:
try:
Ru[url] = ((1-b)/size) + b * sum([Ru[x]/len(outlinks[x]) for x in inlinks[url] if len(outlinks[x])])
except:
pass
return Ru
def test_setitem():
temp = SortedDict()
for pos, key in enumerate(string.ascii_lowercase):
temp[key] = pos
temp._check()
assert len(temp) == 26
for pos, key in enumerate(string.ascii_lowercase):
temp[key] = pos
temp._check()
assert len(temp) == 26
def createTermIndex():
sortTepDic = SortedDict()
# Structure for each term
# sortTepDic['term']=({'DocId1':['Pos1','Pos2'],'DocId2':['Pos1','Pos2']},'termFreq','DocFreq')
for root, dirs, files in os.walk(Contants.DATA_DIRECTORY_NAME, topdown=True):
for name in files:
file_name = os.path.join(root, name)
# 'r' when the file will only be read
# 'w' for only writing (an existing file with the same name will be erased)
# 'a' opens the file for appending; any data written to the file is automatically added to the end.
# 'r+' opens the file for both reading and writing.
mode = "r"
file_object = open(file_name, mode)
DocId = os.path.split(file_name)[1]
wordPos = 0
for word in file_object.read().split():
wordPos = wordPos + 1 # increment word location
lamma = applyFilters(word)
if lamma:
if lamma not in sortTepDic:
sortTepDic[lamma] = [{DocId: [wordPos]}, 1, 1] # add a new term
else:
sortTepDic[lamma][1] = sortTepDic[lamma][1] + 1 # increment the term frequency
if DocId in sortTepDic[lamma][0]:
sortTepDic[lamma][0][DocId].append(
wordPos
) # add new word position for the existing document
else:
sortTepDic[lamma][0][DocId] = [wordPos] # add a new document ID and he word position
sortTepDic[lamma][2] = sortTepDic[lamma][2] + 1 # increment the document frequecy
# covert lists to tuples
for key in sortTepDic.keys():
for DocId in sortTepDic[key][0]:
sortTepDic[key][0][DocId] = tuple(sortTepDic[key][0][DocId])
sortTepDic[key] = tuple(sortTepDic[key])
Data.write_dataStruct_to_file(Contants.WORD_INDEX_FILE_NAME, sortTepDic)
createLexicon(sortTepDic)
createPostingList(sortTepDic)
def __init__(self):
self.price_map = SortedDict() # Dictionary containing price : OrderList object
self.prices = self.price_map.keys()
self.order_map = {} # Dictionary containing order_id : Order object
self.volume = 0 # Contains total quantity from all Orders in tree
self.num_orders = 0 # Contains count of Orders in tree
self.depth = 0 # Number of different prices in tree (http://en.wikipedia.org/wiki/Order_book_(trading)#Book_depth)
class ProductReport(object):
"""Read overview page of one job group and generate a report for the product."""
def __init__(self, browser, job_group_url, root_url, args):
"""Construct a product report object with options."""
self.args = args
self.job_group_url = job_group_url
self.group = job_group_url.split('/')[-1]
current_url, previous_url = get_build_urls_to_compare(browser, job_group_url, args.builds, args.against_reviewed, args.running_threshold)
# read last finished
current_details = browser.get_soup(current_url)
previous_details = browser.get_soup(previous_url)
for details in current_details, previous_details:
assert sum(int(badge.text) for badge in details.find_all(class_='badge')) > 0, \
"invalid page with no test results found, make sure you specified valid builds (leading zero missing?)"
current_summary = parse_summary(current_details)
previous_summary = parse_summary(previous_details)
changes = {k: v - previous_summary.get(k, 0) for k, v in iteritems(current_summary) if k != 'none' and k != 'incomplete'}
log.info("Changes since last build:\n\t%s" % '\n\t'.join("%s: %s" % (k, v) for k, v in iteritems(changes)))
self.build = get_build_nr(current_url)
self.ref_build = get_build_nr(previous_url)
# for each architecture iterate over all
cur_archs, prev_archs = (set(arch.text for arch in details.find_all('th', id=re.compile('flavor_'))) for details in [current_details, previous_details])
archs = cur_archs
if args.arch:
assert args.arch in cur_archs, "Selected arch {} was not found in test results {}".format(args.arch, cur_archs)
archs = [args.arch]
self.missing_archs = sorted(prev_archs - cur_archs)
if self.missing_archs:
log.info("%s missing completely from current run: %s" %
(pluralize(len(self.missing_archs), "architecture is", "architectures are"), ', '.join(self.missing_archs)))
# create arch reports
self.reports = SortedDict()
progress_browser = progress_browser_factory(args) if args.query_issue_status else None
bugzilla_browser = bugzilla_browser_factory(args) if args.query_issue_status else None
for arch in sorted(archs):
results = get_arch_state_results(arch, current_details, previous_details, args.output_state_results)
self.reports[arch] = ArchReport(arch, results, args, root_url, progress_browser, bugzilla_browser, browser)
def __str__(self):
"""Return report for product."""
now_str = datetime.datetime.now().strftime('%Y-%m-%d - %H:%M')
missing_archs_str = '\n * **Missing architectures**: %s' % ', '.join(self.missing_archs) if self.missing_archs else ''
build_str = self.build
if self.args.verbose_test and self.args.verbose_test > 1:
build_str += ' (reference %s)' % self.ref_build
openqa_review_report_product = openqa_review_report_product_template.substitute({
'now': now_str,
'build': build_str,
'common_issues': common_issues(missing_archs_str, self.args.show_empty),
'arch_report': '<hr>'.join(map(str, self.reports.values()))
})
return openqa_review_report_product
def calculateTrust():
if request.headers['Content-Type'] == 'application/json':
d = json.dumps(request.json)
d = json.loads(d)
vector = []
n = d['nodes'] #getting the number of nodes
for i in range(0, n):
temp = np.random.randint(0, 10, n)
vector.append(temp)
cosinedictionary = {}
for i in range(0, n + 1):
for j in range(i + i, n):
res = np.dot(vector[i], vector[j])
cosinedictionary[res] = [i, j]
#print(cosinedictionary)
numberofuntrystablenodes = np.random.randint(1, n - (n % 3))
trustfactor = {}
nodecombinationdict = {}
def remove_duplicatenodecombination():
for key, value in cosinedictionary.items():
if value not in nodecombinationdict.values():
nodecombinationdict[key] = value
remove_duplicatenodecombination()
def calculate_similarity():
for i in range(0, n):
sum = 0
for key, value in nodecombinationdict.items():
if int(value[0]) == i or int(value[1] == i):
sum = sum + key
trustfactor[sum] = i
calculate_similarity()
result = SortedDict(trustfactor)
factorsum = 0
for key, value in result.items():
factorsum = factorsum + key
#calculating the final trust factor of all the nodes and printing it
trustvaluedictres = {}
for key, value in result.items():
trustvaluedictres[key / factorsum] = value
print(trustvaluedictres)
count = 0
l = []
for key, value in trustvaluedictres.items():
if count != numberofuntrystablenodes:
print("untrustable node ", value, " Trustfactor :", key)
a = "untrustable node " + str(value) + " Trustfactor :" + str(key)
l.append(a)
count = count + 1
else:
break
return jsonify(l)
class OrderBook:
def __init__(self, enable_qos=True, enable_statistics=True):
self.bid_order_book = SortedDict()
self.ask_order_book = SortedDict()
self.snapshot_received = False
self._cond_len = 1000
self._mid_price_cond = deque(maxlen=self._cond_len)
self._bid_ask_cond = deque(maxlen=self._cond_len)
self.best_adjusted_bid = None
self.best_adjusted_ask = None
self.mid_price = None
self.qos = 1.0 # 1.0: perfect synchronised stream, <0.9 degraded stream.
self.ups = NumUpdatesPerSeconds()
self.enable_qos = enable_qos
self.enable_statistics = enable_statistics
@property
def best_bid(self):
if self.best_adjusted_bid is not None:
return self.best_adjusted_bid
try:
i = -1
while True:
val = self.bid_order_book.peekitem(i)
if val[1] != 0:
return val[0]
i -= 1
except IndexError:
return None
@property
def best_ask(self):
if self.best_adjusted_ask is not None:
return self.best_adjusted_ask
try:
i = 0
while True:
val = self.ask_order_book.peekitem(i)
if val[1] != 0:
return val[0]
i += 1
except IndexError:
return None
def updates_per_second(self):
return self.ups.rate
def liquidity_for(self, quantity: float):
if not self.snapshot_received:
return 0, 0, 0, 0
bid_total_value = 0
bid_total_quantity = 0
level_price = 0
for i in range(-1, -len(self.bid_order_book), -1):
level_price, liquidity = self.bid_order_book.peekitem(i)
if bid_total_quantity < quantity:
bid_total_value += level_price * liquidity
bid_total_quantity += liquidity
else:
break
bid_average_price = int(bid_total_value / bid_total_quantity)
bid_lowest_price = level_price
ask_total_value = 0
ask_total_quantity = 0
for i in range(len(self.ask_order_book)):
level_price, liquidity = self.ask_order_book.peekitem(i)
if ask_total_quantity < quantity:
ask_total_value += level_price * liquidity
ask_total_quantity += liquidity
else:
break
ask_average_price = int(ask_total_value / ask_total_quantity)
ask_highest_price = level_price
return bid_average_price, ask_average_price, bid_lowest_price, ask_highest_price
def snapshot_update(self, snapshot):
if self.enable_statistics:
self.ups.count()
self.best_adjusted_bid = None
self.best_adjusted_ask = None
self.mid_price = snapshot['mid_price']
self.bid_order_book = SortedDict()
self.ask_order_book = SortedDict()
for bid in snapshot['bids']:
self._single_book_update(bid['price'], bid['size'], is_bid=True)
for ask in snapshot['asks']:
self._single_book_update(ask['price'], ask['size'], is_bid=False)
assert self.best_bid <= snapshot['mid_price'] <= self.best_ask
self.snapshot_received = True
def _single_book_update(self, price, size, is_bid=True):
if self.enable_statistics:
self.ups.count()
price = int(price)
book = self.bid_order_book if is_bid else self.ask_order_book
book[price] = size
def book_update(self, update: dict):
self.best_adjusted_bid = None
self.best_adjusted_ask = None
self.mid_price = update['mid_price']
for bid in update['bids']:
self._single_book_update(bid['price'], bid['size'], is_bid=True)
for ask in update['asks']:
self._single_book_update(ask['price'], ask['size'], is_bid=False)
if self.enable_qos:
self._mid_price_cond.append(
self.best_bid <= update["mid_price"] <= self.best_ask)
self._bid_ask_cond.append(self.best_bid <= self.best_ask)
# It should never happen in practice.
# But sometimes the messages don't arrive sequentially.
if self.best_bid >= update["mid_price"]:
self.best_adjusted_bid = update["mid_price"] - 1
if update["mid_price"] >= self.best_ask:
self.best_adjusted_ask = update["mid_price"] + 1
if self.best_bid >= self.best_ask:
self.best_adjusted_bid = update["mid_price"] - 1
if self.best_ask <= self.best_bid:
self.best_adjusted_ask = update["mid_price"] + 1
assert self.best_bid < self.best_ask
assert self.best_bid <= update["mid_price"] <= self.best_ask
if self.enable_qos and len(self._mid_price_cond) == self._cond_len:
self.qos = (0.5 * np.mean(self._bid_ask_cond) +
0.5 * np.mean(self._mid_price_cond))
def loadjson(filename): with open(filename + '.json') as f: data = json.load(f) if filename == "suggestion" or filename == 'todo': data = SortedDict(data) return data
write_input(sys.argv[1], sys.argv[2])
def print_menu():
"""This script is used to stores user's favorite NHL teams. The user
passes in their name and favorite team, which then get stored in a dictionary
that can be used for analysis of the entries."""
print("1. Print Fans")
print("2. Add Fan")
print("3. View Team Fan Counts")
print("4. Graph Team Fan Counts")
print("5. Quit")
# Create dictionary with key = Names, value = user_name
nhlpicks = SortedDict()
nhlpicks['Tristan'] = 'San Jose Sharks'
nhlpicks['Daniel'] = 'Colorado Avalanche'
nhlpicks['Robby'] = 'San Jose Sharks'
nhlpicks['Robb'] = 'Colorado Avalanche'
nhlpicks['CJ'] = 'Pittsburgh Penguins'
nhlpicks['Katie'] = 'Chicago Blackhawks'
nhlteams = [
"Carolina Hurricanes", "Columbus Blue Jackets", "Colorado Avalanche",
"San Jose Sharks", "New Jersey Devils", "New York Islanders",
"New York Rangers", "Philadelphia Flyers", "Pittsburgh Penguins",
"Washington Capitals", "Boston Bruins", "Buffalo Sabres",
"Detroit Red Wings", "Florida Panthers", "Montreal Canadiens",
"Ottawa Senators", "Tampa Bay Lightning", "Toronto Maple Leafs",
"Chicago Blackhawks", "Dallas Stars", "Minnesota Wild",
class Numbers:
def __init__(self, reddit, settings) -> None:
"""
This class deals with the key number handling.
"""
self.reddit = reddit
self.settings = settings
# Define the numbers container (this is number to username).
# This is also using an automatically sorted dictionary for efficiency.
self.numbers = SortedDict({})
# Load the numbers from Reddit.
self.load_numbers()
# Handle the current max number.
self.current_max_number = 0
self.set_max_number()
# Load the subclasses.
self.search = self.search(self)
self.generation = self.generation(self)
self.assignment = self.assignment(self)
self.checks = NumberChecks()
def load_numbers(self) -> None:
"""
Loads the numbers from Reddit.
"""
for flair in self.reddit.subreddit(
self.settings.reddit.subreddit).flair(limit=None):
try:
number = int("".join([
char for char in flair["flair_text"].lower().lstrip("#")
if char.isnumeric() or char == "-"
]))
self.numbers[number] = flair["user"].name
except Exception:
pass
print(f"Loaded {len(self.numbers)} numbers.")
def set_max_number(self) -> None:
"""
Sets the current max number based on the configuration.
"""
if self.settings.reddit.assignment.numbers["static_max"] is False:
numbers_assigned = len(list(self.numbers.keys()))
self.current_max_number = numbers_assigned + len(
self.settings.reddit.assignment.numbers["blacklist"]
) + self.settings.reddit.assignment.numbers["max"]
else:
self.current_max_number = self.settings.reddit.assignment.numbers[
"max"]
print(
f"Set max number to {self.settings.reddit.assignment.numbers['max']}"
)
class search:
def __init__(self, parent) -> None:
self.parent = parent
def num_to_user(self, number) -> str:
"""
Gets the username of the user who has a specific number (if any).
:param number: The number to search for.
:return: The user who has it or None.
"""
try:
return self.parent.numbers[number]
except Exception:
return None
def user_to_num(self, username) -> int:
"""
Gets the number of a specific user.
:param username: The username to find the number of.
:return: The number of that user (or None if they don't have one).
"""
try:
return int(
list(self.parent.numbers.keys())[[
user_with_number.lower()
for user_with_number in self.parent.numbers.values()
].index(username.lower())])
except Exception:
return None
class generation:
def __init__(self, parent) -> None:
self.parent = parent
def get_random_number(self) -> int:
"""
Generates a randomly avaliable number.
:return: The random number.
"""
random_number = randint(
self.parent.settings.reddit.assignment.numbers["min"],
self.parent.current_max_number)
if random_number in (
list(self.parent.numbers.keys()) +
self.parent.settings.reddit.assignment.blacklisted_numbers
):
return self.get_random_number()
else:
return random_number
def get_random_user(self) -> tuple:
"""
Gets a user with a number.
:return: A random user and their number.
"""
return choice(list(self.parent.numbers.items()))
class assignment:
def __init__(self, parent) -> None:
self.parent = parent
def assign_number(self, username: str, number: int = None) -> int:
"""
Assign a number to a user.
:param number: Optional. The number to assign the user.
:return: The number assigned.
"""
number = self.parent.generation.get_random_number(
) if number is None else number
# Remove a previous number (if the user had one)
old_number = self.parent.search.user_to_num(username)
if old_number is not None:
del self.parent.numbers[old_number]
# Assign the user a flair.
self.parent.reddit.subreddit(
self.parent.settings.reddit.subreddit
).flair.set(
username,
self.parent.settings.reddit.assignment.flair["text"].format(
number),
flair_template_id=(
self.parent.settings.reddit.assignment.flair["template_id"]
if
self.parent.settings.reddit.assignment.flair["template_id"]
else None),
)
# Add the user to the numbers dictionary.
self.parent.numbers[number] = username
# Approve on the relevant subreddits.
self.approve_number_subreddits(username, number)
# Increase the max possible number.
self.parent.current_max_number += 1
# Print a success message.
print(
f"Succesfully set a user's number. (u/{username} as #{number})"
)
return number
def approve_number_subreddits(self, username: str,
number: int) -> None:
"""
Approves a user on all the subreddits relevant to their number.
"""
subreddits = [f"NUM{get_number_nation(number)}"]
if self.parent.checks.is_descendant_of_3(number):
subreddits.append("descendantsof3")
if self.parent.checks.is_seven_seas(number):
subreddits.append("SevenSeasFaction")
if self.parent.checks.is_prime_number(number):
subreddits.append("the_primes")
# Approve the user on the subreddits.
for subreddit in subreddits:
try:
self.parent.reddit.subreddit(subreddit).contributor.add(
username)
except Exception:
pass
@property
def statistics(self) -> dict:
"""
Gets some statistics on the currently assigned numbers.
:return: The calculated statistics.
"""
number_list = self.numbers.keys()
stats = {
"numbers_given": 0, # Amount of Numbers Given
"sum_of_numbers": 0, # The sum of all the numbers.
"lowest_positive": 0, # The lowest positive number.
"mean": 0, # The mean of all the numbers.
"median": 0, # The median of all the numbers.
"evens": 0, # The amount of even numbers.
"odds": 0, # The amount of odd numbers.
"below_500": 0, # The amount of numbers below 500.
"below_1000": 0, # The amount of numbers below below 1000.
"below_2500": 0, # The amount of numbers below 2500.
}
# Find the lowest positive number.
for number in number_list:
if number >= 1:
stats["lowest_positive"] = number
break
# Gather some statistics by looking through the numbers.
for number in number_list:
# Odd or Even
if number % 2 == 0:
stats["evens"] += 1
else:
stats["odds"] += 1
# Below 500, 1000 or 2500
if number <= 500:
stats["below_500"] += 1
if number <= 1000:
stats["below_1000"] += 1
if number <= 2500:
stats["below_2500"] += 1
# Work out these statistics.
stats["numbers_given"] = len(number_list)
stats["sum"] = sum(number_list)
stats["mean"] = stats["sum"] / stats["numbers_given"]
stats["median"] = median(number_list)
# Switch these to percentages.
stats["below_500"] = "{0:.2f}%".format(
float((stats["below_500"] / stats["numbers_given"]) * 100))
stats["below_1000"] = "{0:.2f}%".format(
float((stats["below_1000"] / stats["numbers_given"]) * 100))
stats["below_2500"] = "{0:.2f}%".format(
float((stats["below_2500"] / stats["numbers_given"]) * 100))
# Get the highest and lowest numbers.
stats["highest"] = number_list[-1]
stats["lowest"] = number_list[0]
# Format these stats for the message.
stats["highest_info"] = "#{number} (u/{username})".format(
number=stats["highest"],
username=self.search.num_to_user(stats["highest"]),
)
stats["lowest_positive_info"] = "#{number} (u/{username})".format(
number=stats["lowest_positive"],
username=self.search.num_to_user(stats["lowest_positive"]),
)
stats["lowest_info"] = "#{number} (u/{username})".format(
number=stats["lowest"],
username=self.search.num_to_user(stats["lowest"]),
)
return stats
def __str__(self) -> str:
return str(self.numbers)
def __repr__(self) -> SortedDict:
return self.numbers
def entrenamiento(self, dataset, datosTrain):
# Cargamos todos los datos de la clase del dataset desde la matriz de datos
clasesTrain = dataset.extraeDatos(datosTrain)
self.numClases = clasesTrain[:, -1]
# Contamos las apariciones de cada uno para luego calcular la probabilidad a priori de cada clase
counter = Counter(self.numClases)
# Calculamos la probabilidad de la clase y lo metemos en un diccionario ordenado segun el numero
# correspondiente a cada clase asignado en el diccionario
self.dictPrioris = {}
for k in counter:
k = int(k)
counter[k] = counter[k] / len(self.numClases)
self.dictPrioris[k] = counter[k]
# Aqui ordenamos el diccionario para que esten en el mismo orden de como extraemos los datos del dataset
self.dictPrioris = SortedDict(self.dictPrioris)
# Calcular tablas de probabilidades del entrenamiento. Tenemos que calcular por cada atributo una cuenta
# de las apariciones en cada clase
# Creamos una lista de matrices, donde vamos almacenar todos los datos que hemos obtenido en los datos de Test
self.posteriori = np.zeros(len(dataset.nombreAtributos) - 1,
dtype=object)
# Recorremos todos los datos de la matriz sin llegar a la clase
for i in range(len(dataset.nombreAtributos) - 1):
# Si el dato que obtenemos es Nominal haremos el recuento de todas las veces que sale la P(D|H)
if dataset.nominalAtributos[i] == True:
# Creamos una matriz de tamaño X: Número de Atributos menos la clase Y: Número de clases
post = np.zeros(
(len(dataset.listaDicts[i]), len(dataset.listaDicts[-1])))
# Aqui contamos todos las datos que queremos del datos Train para construir la matriz de entrenamiento
for c in range(len(dataset.listaDicts[-1])):
datosEnt = dataset.extraeDatos(datosTrain)
dat = datosEnt[:, i]
repes = Counter(dat[datosEnt[:, -1] == c])
for r in repes:
post[int(r), c] = repes[r]
if self.laplace == True:
self.posteriori[i] = post + 1
else:
self.posteriori[i] = post
# Si el dato es Continuo obtendremos la media y la desviación tipica de la clase
else:
# Creamos una matriz de X: Los datos de Media y Desivación típica Y: Número de clases
post = np.zeros((2, len(dataset.listaDicts[-1])))
# Aqui obtenemos la media y desviación tipica de cada clase, despues de tener los datos de entrenamiento
for c in range(len(dataset.listaDicts[-1])):
datosEnt = dataset.extraeDatos(datosTrain)
dat = datosEnt[:, i]
datos = dat[datosEnt[:, -1] == c]
post[0][c] = np.mean(datos)
post[1][c] = np.std(datos)
self.posteriori[i] = post
# Calculamos los valores de los posteriori de todos las tablas anteriores
for i in range(len(dataset.listaDicts) - 1):
if dataset.nominalAtributos[i] == True:
self.posteriori[i] /= sum(self.posteriori[i])
def similarity_matrix(theta=None, M_T=10, M_O=10):
"""
Compute the similarity matrix for a corpus, given
the distribution of topics therein.
Arguments:
@theta: The document-topic distribution from LDA
@M_T : The number of topics to consider
@M_O : The number of other documents (opinions) to consider
Return:
NxN sparse matrix in CSR format (see function body for
explanation).
Notes:
This needs to be cleaned up bigtime, it's a waste.
"""
LOG.info("Building similarity matrix M_T="+str(M_T)+" M_O="+str(M_O));
# doc_id => M_T highest-probability topic_ids
DOC_TOPICS = {};
# topic_id => M_O highest-probability doc_ids
TOPIC_DOCS = {};
# For each document in the corpus
for doc_id in range(len(theta)):
topics = topicmodel.format_theta(theta[doc_id]);
DOC_TOPICS[doc_id] = [];
for i in range(len(topics)):
top_id = topics[i][0];
top_pr = topics[i][1];
#
# Build the collection of highest-probability
# documents for each topic.
#
if top_id not in TOPIC_DOCS:
TOPIC_DOCS[top_id] = SortedDict();
TOPIC_DOCS[top_id][top_pr] = doc_id;
# Don't bother attempting to insert if the probability
# is less than the lowest already in the collection.
elif top_pr >= TOPIC_DOCS[top_id].peekitem(0)[0]:
if top_pr not in TOPIC_DOCS[top_id]:
TOPIC_DOCS[top_id][top_pr] = doc_id;
else:
# If two docs have an equal probability
# of expressing the topic, then which
# should we favor? We can only choose a
# certain number. Ignore for now.
LOG.warn("Equal probabilities.");
if len(TOPIC_DOCS[top_id]) > M_O:
# Remember, dict is sorted, so this
# will only discard the least one.
TOPIC_DOCS[top_id].popitem(last=False);
#
# Build the collection of highest-probability
# topics for each document.
#
if i < M_O:
DOC_TOPICS[doc_id].append(top_id);
#
# Build this matrix thing to join docs to "similar" docs
#
MATRIX = {};
for doc_id in DOC_TOPICS:
MATRIX[doc_id] = [];
for i in range(len(DOC_TOPICS[doc_id])):
topic_id = DOC_TOPICS[doc_id][i];
MATRIX[doc_id].append(TOPIC_DOCS[topic_id].values());
#
# Build dictionary to count occurrences.
#
W = {};
for doc_id_A in DOC_TOPICS:
W[doc_id_A] = {};
# Count occurrences of doc_id_B in matrix of doc_id_A
for i in range(len(MATRIX[doc_id_A])):
for j in range(len(MATRIX[doc_id_A][i])):
doc_id_B = MATRIX[doc_id_A][i][j];
if doc_id_B not in W[doc_id_A]:
W[doc_id_A][doc_id_B] = 1;
else:
W[doc_id_A][doc_id_B] += 1;
#
# Build the similarity matrix
#
# FIXME: Why dok?
T_sim = scipy.sparse.dok_matrix((len(theta), len(theta)), dtype=numpy.float);
for a in W:
total = 0;
for b in W[a]:
if W[a][b] > 0:
total += W[a][b];
for b in W[a]:
if W[a][b] > 0:
T_sim[a,b] = float(W[a][b])/total;
return T_sim.tocsr();
# irange(minimum=None, maximum=None, inclusive=True, True, reverse=False)
rangeList = list(sl.irange(10, 14, inclusive=[True, False]))
print(rangeList)
print(sl.index(10)) # 3
# print(sl.index(-99)) # Throw Error
s2 = SortedList([1, 7, 7, 7, 7, 10, 11, 13, 14])
print(f"left most idx: {s2.bisect_left(7)}")
print(f"right most idx: {s2.bisect_right(7)}")
print(f"out of boundary < min, idx={s2.bisect_left(-100)}")
print(f"out of boundary > max, len={len(s2)}, idx={s2.bisect_left(100)}")
# %% SortedDict
sd = SortedDict()
sd["c"] = 3
sd["a"] = 1
sd["b"] = 2
del sd["a"]
print(sd)
sd.pop("c") # return and remove
sd.popitem("b") # return key-value pair and remove
print(sd) # print {}
sd2 = SortedDict({1: "a", 2: "b", 4: "c"})
idx = sd2.bisect_left(3)
print(idx)
print(sd2.peekitem(index=idx))
def __init__(self):
# 两个TreeMap
self.timeToPrice = SortedDict() # 时间->价格，key已排序
self.priceToTimes = SortedDict() # 价格->Set<该价格的时间>，key已排序
def __init__(self):
self.mp = SortedDict()
class OrderBook(WebsocketClient):
def __init__(self,
url="wss://ws-feed.pro.coinbase.com",
product_id='BTC-USD',
api_key="",
api_secret="",
api_passphrase="",
channels=None,
log_to=None):
super(OrderBook, self).__init__(url=url, products=product_id,
api_key=api_key, api_secret=api_secret,
api_passphrase=api_passphrase, channels=channels)
self._asks = SortedDict()
self._bids = SortedDict()
self._client = PublicClient()
self._sequence = -1
self._log_to = log_to
if self._log_to:
assert hasattr(self._log_to, 'write')
self._current_ticker = None
@property
def product_id(self):
''' Currently OrderBook only supports a single product even though it is stored as a list of products. '''
return self.products[0]
def on_open(self):
self._sequence = -1
#print("-- Subscribed to OrderBook! --\n")
def on_close(self):
self.is_done = False
#print("\n-- OrderBook Socket Closed! --")
def reset_book(self):
self._asks = SortedDict()
self._bids = SortedDict()
res = self._client.get_product_order_book(product_id=self.product_id, level=3)
for bid in res['bids']:
self.add({
'id': bid[2],
'side': 'buy',
'price': Decimal(bid[0]),
'size': Decimal(bid[1])
})
for ask in res['asks']:
self.add({
'id': ask[2],
'side': 'sell',
'price': Decimal(ask[0]),
'size': Decimal(ask[1])
})
self._sequence = res['sequence']
def on_message(self, message):
if self._log_to:
pickle.dump(message, self._log_to)
sequence = message.get('sequence', -1)
if self._sequence == -1:
self.reset_book()
return
if sequence <= self._sequence:
# ignore older messages (e.g. before order book initialization from getProductOrderBook)
return
elif sequence > self._sequence + 1:
self.on_sequence_gap(self._sequence, sequence)
return
msg_type = message['type']
if msg_type == 'open':
self.add(message)
elif msg_type == 'done' and 'price' in message:
self.remove(message)
elif msg_type == 'match':
self.match(message)
self._current_ticker = message
elif msg_type == 'change':
self.change(message)
self._sequence = sequence
def on_sequence_gap(self, gap_start, gap_end):
self.reset_book()
print('Error: messages missing ({} - {}). Re-initializing book at sequence.'.format(
gap_start, gap_end, self._sequence))
def add(self, order):
order = {
'id': order.get('order_id') or order['id'],
'side': order['side'],
'price': Decimal(order['price']),
'size': Decimal(order.get('size') or order['remaining_size'])
}
if order['side'] == 'buy':
bids = self.get_bids(order['price'])
if bids is None:
bids = [order]
else:
bids.append(order)
self.set_bids(order['price'], bids)
else:
asks = self.get_asks(order['price'])
if asks is None:
asks = [order]
else:
asks.append(order)
self.set_asks(order['price'], asks)
def remove(self, order):
price = Decimal(order['price'])
if order['side'] == 'buy':
bids = self.get_bids(price)
if bids is not None:
bids = [o for o in bids if o['id'] != order['order_id']]
if len(bids) > 0:
self.set_bids(price, bids)
else:
self.remove_bids(price)
else:
asks = self.get_asks(price)
if asks is not None:
asks = [o for o in asks if o['id'] != order['order_id']]
if len(asks) > 0:
self.set_asks(price, asks)
else:
self.remove_asks(price)
def match(self, order):
size = Decimal(order['size'])
price = Decimal(order['price'])
if order['side'] == 'buy':
bids = self.get_bids(price)
if not bids:
return
assert bids[0]['id'] == order['maker_order_id']
if bids[0]['size'] == size:
self.set_bids(price, bids[1:])
else:
bids[0]['size'] -= size
self.set_bids(price, bids)
else:
asks = self.get_asks(price)
if not asks:
return
assert asks[0]['id'] == order['maker_order_id']
if asks[0]['size'] == size:
self.set_asks(price, asks[1:])
else:
asks[0]['size'] -= size
self.set_asks(price, asks)
def change(self, order):
try:
new_size = Decimal(order['new_size'])
except KeyError:
return
try:
price = Decimal(order['price'])
except KeyError:
return
if order['side'] == 'buy':
bids = self.get_bids(price)
if bids is None or not any(o['id'] == order['order_id'] for o in bids):
return
index = [b['id'] for b in bids].index(order['order_id'])
bids[index]['size'] = new_size
self.set_bids(price, bids)
else:
asks = self.get_asks(price)
if asks is None or not any(o['id'] == order['order_id'] for o in asks):
return
index = [a['id'] for a in asks].index(order['order_id'])
asks[index]['size'] = new_size
self.set_asks(price, asks)
tree = self._asks if order['side'] == 'sell' else self._bids
node = tree.get(price)
if node is None or not any(o['id'] == order['order_id'] for o in node):
return
def get_current_ticker(self):
return self._current_ticker
def get_current_book(self):
result = {
'sequence': self._sequence,
'asks': [],
'bids': [],
}
for ask in self._asks:
try:
# There can be a race condition here, where a price point is removed
# between these two ops
this_ask = self._asks[ask]
except KeyError:
continue
for order in this_ask:
result['asks'].append([order['price'], order['size'], order['id']])
for bid in self._bids:
try:
# There can be a race condition here, where a price point is removed
# between these two ops
this_bid = self._bids[bid]
except KeyError:
continue
for order in this_bid:
result['bids'].append([order['price'], order['size'], order['id']])
return result
def get_ask(self):
return self._asks.peekitem(0)[0]
def get_nasks(self, n):
return [self.get_asks(self._asks.peekitem(n)[0]) for i in range(n+1)]
def get_asks(self, price):
return self._asks.get(price)
def remove_asks(self, price):
del self._asks[price]
def set_asks(self, price, asks):
self._asks[price] = asks
def get_bid(self):
return self._bids.peekitem(-1)[0]
def get_nbids(self, n):
return [self.get_bids(self._bids.peekitem(-(i+1))[0]) for i in range(n+1)]
def get_bids(self, price):
return self._bids.get(price)
def remove_bids(self, price):
del self._bids[price]
def set_bids(self, price, bids):
self._bids[price] = bids
class RegionMap:
"""
Mostly used in SimAbstractMemory, RegionMap stores a series of mappings between concrete memory address ranges and
memory regions, like stack frames and heap regions.
"""
def __init__(self, is_stack):
"""
Constructor
:param is_stack: Whether this is a region map for stack frames or not. Different strategies apply for stack
regions.
"""
self.is_stack = is_stack
# A sorted list, which maps stack addresses to region IDs
self._address_to_region_id = SortedDict()
# A dict, which maps region IDs to memory address ranges
self._region_id_to_address = {}
#
# Properties
#
def __repr__(self):
return "RegionMap<%s>" % ("S" if self.is_stack else "H")
@property
def is_empty(self):
return len(self._address_to_region_id) == 0
@property
def stack_base(self):
if not self.is_stack:
raise SimRegionMapError(
'Calling "stack_base" on a non-stack region map.')
return next(self._address_to_region_id.irange(reverse=True))
@property
def region_ids(self):
return self._region_id_to_address.keys()
#
# Public methods
#
@SimStatePlugin.memo
def copy(self, memo): # pylint: disable=unused-argument
r = RegionMap(is_stack=self.is_stack)
# A shallow copy should be enough, since we never modify any RegionDescriptor object in-place
r._address_to_region_id = self._address_to_region_id.copy()
r._region_id_to_address = self._region_id_to_address.copy()
return r
def map(self, absolute_address, region_id, related_function_address=None):
"""
Add a mapping between an absolute address and a region ID. If this is a stack region map, all stack regions
beyond (lower than) this newly added regions will be discarded.
:param absolute_address: An absolute memory address.
:param region_id: ID of the memory region.
:param related_function_address: A related function address, mostly used for stack regions.
"""
if self.is_stack:
# Sanity check
if not region_id.startswith('stack_'):
raise SimRegionMapError(
'Received a non-stack memory ID "%d" in a stack region map'
% region_id)
# Remove all stack regions that are lower than the one to add
while True:
try:
addr = next(
self._address_to_region_id.irange(
maximum=absolute_address, reverse=True))
descriptor = self._address_to_region_id[addr]
# Remove this mapping
del self._address_to_region_id[addr]
# Remove this region ID from the other mapping
del self._region_id_to_address[descriptor.region_id]
except StopIteration:
break
else:
if absolute_address in self._address_to_region_id:
descriptor = self._address_to_region_id[absolute_address]
# Remove this mapping
del self._address_to_region_id[absolute_address]
del self._region_id_to_address[descriptor.region_id]
# Add this new region mapping
desc = RegionDescriptor(
region_id,
absolute_address,
related_function_address=related_function_address)
self._address_to_region_id[absolute_address] = desc
self._region_id_to_address[region_id] = desc
def unmap_by_address(self, absolute_address):
"""
Removes a mapping based on its absolute address.
:param absolute_address: An absolute address
"""
desc = self._address_to_region_id[absolute_address]
del self._address_to_region_id[absolute_address]
del self._region_id_to_address[desc.region_id]
def absolutize(self, region_id, relative_address):
"""
Convert a relative address in some memory region to an absolute address.
:param region_id: The memory region ID
:param relative_address: The relative memory offset in that memory region
:return: An absolute address if converted, or an exception is raised when region id does not
exist.
"""
if region_id == 'global':
# The global region always bases 0
return relative_address
if region_id not in self._region_id_to_address:
raise SimRegionMapError('Non-existent region ID "%s"' % region_id)
base_address = self._region_id_to_address[region_id].base_address
return base_address + relative_address
def relativize(self, absolute_address, target_region_id=None):
"""
Convert an absolute address to the memory offset in a memory region.
Note that if an address belongs to heap region is passed in to a stack region map, it will be converted to an
offset included in the closest stack frame, and vice versa for passing a stack address to a heap region.
Therefore you should only pass in address that belongs to the same category (stack or non-stack) of this region
map.
:param absolute_address: An absolute memory address
:return: A tuple of the closest region ID, the relative offset, and the related function
address.
"""
if target_region_id is None:
if self.is_stack:
# Get the base address of the stack frame it belongs to
base_address = next(
self._address_to_region_id.irange(minimum=absolute_address,
reverse=False))
else:
try:
base_address = next(
self._address_to_region_id.irange(
maximum=absolute_address, reverse=True))
except StopIteration:
# Not found. It belongs to the global region then.
return 'global', absolute_address, None
descriptor = self._address_to_region_id[base_address]
else:
if target_region_id == 'global':
# Just return the absolute address
return 'global', absolute_address, None
if target_region_id not in self._region_id_to_address:
raise SimRegionMapError(
'Trying to relativize to a non-existent region "%s"' %
target_region_id)
descriptor = self._region_id_to_address[target_region_id]
base_address = descriptor.base_address
return descriptor.region_id, absolute_address - base_address, descriptor.related_function_address
def __init__(self):
self._data = SortedDict() # type: SortedDict[K, deque[V]]
def cleaner(self):
self.log.debug('Cleaner started')
clean_flag = False
store_file_list = None
if int(self.cfg['cleaner_store_max_gb']) != 0:
store_file_list = glob2.glob(
os.path.join(self.cfg['cap_dir'], '**'))
self.log.debug('Found files: %s' % store_file_list)
store_file_total_size_bytes = 0
for store_file in store_file_list:
store_file_total_size_bytes += os.path.getsize(store_file)
store_file_total_size_gigabytes = 1.0 * store_file_total_size_bytes / 1024 / 1024 / 1024
self.log.debug('Store files size, Gb: %f' %
store_file_total_size_gigabytes)
if store_file_total_size_gigabytes > float(
self.cfg['cleaner_store_max_gb']):
self.log.info(
'Current store size / Configured max store size, Gb: %.3f/%.3f'
% (store_file_total_size_gigabytes,
self.cfg['cleaner_store_max_gb']))
clean_flag = True
if int(self.cfg['cleaner_store_keep_free_gb']) != 0:
store_stat = os.statvfs(self.cfg['cap_dir'])
store_free_gb = 1.0 * store_stat.f_bavail * store_stat.f_frsize / 1024 / 1024 / 1024
self.log.debug('Store free space, Gb: %f' % store_free_gb)
if store_free_gb < float(self.cfg['cleaner_store_keep_free_gb']):
self.log.info(
'Current store free space / Configured keep store free space, Gb: %.3f/%.3f'
% (store_free_gb, self.cfg['cleaner_store_keep_free_gb']))
clean_flag = True
if clean_flag:
self.log.info('Clean is active')
if store_file_list is None:
store_file_list = glob2.glob(
os.path.join(self.cfg['cap_dir'], '**'))
store_file_list_sorted = SortedDict()
for store_file in store_file_list:
store_file_list_sorted.update(
{os.path.getmtime(store_file): store_file})
self.log.debug(
'Sorted files list (with last modification date): %s' %
store_file_list_sorted)
self.log.debug(
'Sorted files list (by last modification date): %s' %
store_file_list_sorted.values())
removes = 0
for file_name in store_file_list_sorted.values():
if os.path.isfile(file_name):
file_size = os.path.getsize(file_name)
self.log.info('Remove file: ' + file_name)
os.remove(file_name)
if file_size > int(
self.cfg['cleaner_force_remove_file_less_bytes']):
removes += 1
else:
self.log.warn(
'Removed "%s" file with the "%s" bytes size' %
(file_name, file_size))
if removes == int(self.cfg['cleaner_max_removes_per_run']):
self.log.debug('Max removes reached: ' +
self.cfg['cleaner_max_removes_per_run'])
break
self.log.debug('Cleaner finished')
def _clear(self):
self.best_price = [MIN_PRICE, MAX_PRICE]
self.price_levels = [SortedDict(), SortedDict()]
self.time = 0
self.events = []
def load_gtf(
gtf_path
):
# open gtf file
try:
gtf_file = open(gtf_path, 'rU')
except IOError as err:
print (
'!error: '
'load_gtf(): '
'cannot open gtf file: '+gtf_path
)
print err
return False
# initialize dict data structure
gtf = {}
genes = {}
try:
while True:
line = gtf_file.next().strip()
line_elems = re.split(r'\t', line)
contig = line_elems[0]
start = int(line_elems[3])
stop = int(line_elems[4])
strand = line_elems[6]
frame = int(line_elems[7])
properties = line_elems[8]
regex = re.compile(r"gene_id \"(.*?)\"; transcript_id \"(.*?)\"; exon_number \"(.*?)\";")
matches = regex.search(properties).groups()
gene_id = matches[0]
transcript_id = matches[1]
exon_number = matches[2]
if contig not in gtf:
gtf[contig] = SortedDict()
gtf[contig][start] = {
'stop': stop,
'gene_id': gene_id,
'transcript_id': transcript_id,
'exon_number': exon_number,
'frame': frame
}
# keep track of gene start/stop
if gene_id not in genes:
genes[gene_id] = {
'start': start,
'stop': stop,
'contig': contig,
'strand': strand
}
else:
if start < genes[gene_id]['start']:
genes[gene_id]['start'] = start
if stop > genes[gene_id]['stop']:
genes[gene_id]['stop'] = stop
except(StopIteration):
gtf_file.close()
# create sorted dict for gene start/stop
genes_sorted = {}
for gene_id in genes:
contig = genes[gene_id]['contig']
if contig not in genes_sorted:
genes_sorted[contig] = SortedDict()
genes_sorted[contig][genes[gene_id]['start']] = {
'gene_id': gene_id,
'stop': genes[gene_id]['stop']
}
return (gtf, genes_sorted, genes)
def test_on_l2_updates(self):
client = BcexClient(symbols=["BTC-USD"])
msg = {
"seqnum":
2,
"event":
"snapshot",
"channel":
"l2",
"symbol":
"BTC-USD",
"bids": [
{
"px": 8723.45,
"qty": 1.45,
"num": 2
},
{
"px": 8124.45,
"qty": 123.45,
"num": 1
},
],
"asks": [
{
"px": 8730.0,
"qty": 1.55,
"num": 2
},
{
"px": 8904.45,
"qty": 13.66,
"num": 2
},
],
}
client._on_l2_updates(msg)
assert client.l2_book["BTC-USD"] == {
"bids": SortedDict({
8124.45: 123.45,
8723.45: 1.45
}),
"asks": SortedDict({
8730.0: 1.55,
8904.45: 13.66
}),
}
msg = {
"seqnum": 3,
"event": "updated",
"channel": "l2",
"symbol": "BTC-USD",
"bids": [{
"px": 8723.45,
"qty": 1.1,
"num": 1
}],
"asks": [],
}
client._on_l2_updates(msg)
assert client.l2_book["BTC-USD"] == {
"bids": SortedDict({
8124.45: 123.45,
8723.45: 1.1
}),
"asks": SortedDict({
8730.0: 1.55,
8904.45: 13.66
}),
}
msg = {
"seqnum": 3,
"event": "updated",
"channel": "l2",
"symbol": "BTC-USD",
"bids": [{
"px": 8124.45,
"qty": 0,
"num": 1
}],
"asks": [],
}
client._on_l2_updates(msg)
assert client.l2_book["BTC-USD"] == {
"bids": SortedDict({8723.45: 1.1}),
"asks": SortedDict({
8730.0: 1.55,
8904.45: 13.66
}),
}
msg = {
"seqnum": 2,
"event": "snapshot",
"channel": "l2",
"symbol": "BTC-USD",
"bids": [{
"px": 8723.45,
"qty": 1.45,
"num": 2
}],
"asks": [{
"px": 8730.0,
"qty": 1.55,
"num": 2
}],
}
client._on_l2_updates(msg)
assert client.l2_book["BTC-USD"] == {
"bids": SortedDict({8723.45: 1.45}),
"asks": SortedDict({8730.0: 1.55}),
}
# realloc(81d360600<6390>, 512) = 81d3b9800<6391> # 256 bytes, allocated 1 lines ago, prev 81d360300, gap 512, next 81d363800, gap 12544, pages [8508256-8508256], 0 fewer pages
# realloc(81d3b9800<6391>, 1024) = 81d3bbc00<6392> # 512 bytes, allocated 1 lines ago, prev 81d3b8590, gap 4708, next 81d3ba200, gap 2048, pages [8508345-8508345], 1 fewer pages
# free(81d220b28<6306>) # 6 bytes, allocated 87 lines ago, prev 81d220b20, gap 1, next 81d220b30, gap 2, pages [8507936-8507936], 0 fewer pages
# free(81d3b83b0<6307>) # 9 bytes, allocated 87 lines ago, prev 81d3b83a0, gap 4, next 81d3b83c0, gap 7, pages [8508344-8508344], 0 fewer pages
# free(81d3b83c0<6308>) # 10 bytes, allocated 87 lines ago, prev 81d3b83a0, gap 20, next 81d3b83d0, gap 6, pages [8508344-8508344], 0 fewer pages
# free(81d3b83d0<6309>) # 16 bytes, allocated 87 lines ago, prev 81d3b83a0, gap 36, next 81d3b83e0, gap 0, pages [8508344-8508344], 0 fewer pages
# free(81d3bd100<6310>) # 18 bytes, allocated 87 lines ago, prev 81d3bd0e0, gap 0, next 81d3bd120, gap 14, pages [8508349-8508349], 0 fewer pages
# free(81d3bd120<6311>) # 17 bytes, allocated 87 lines ago, prev 81d3bd0e0, gap 32, next 81d3bd140, gap 15, pages [8508349-8508349], 0 fewer pages
#
import re
import sys
from sortedcontainers import SortedDict
# Line number of the malloc(), indexed by pointer.
allocated = SortedDict()
# Size of the malloc(), indexed by pointer.
sizes = SortedDict()
def parse_ptr(l):
return int(re.split("[<>]+", l)[0], 16)
def atop(ptr):
return ptr >> 12
def ptoa(page):
return page << 12
class ClasificadorNaiveBayes(Clasificador):
def __init__(self, laplace):
self.laplace = laplace
self.lista_fpr = []
self.lista_tpr = []
def entrenamiento(self, dataset, datosTrain):
# Cargamos todos los datos de la clase del dataset desde la matriz de datos
clasesTrain = dataset.extraeDatos(datosTrain)
self.numClases = clasesTrain[:, -1]
# Contamos las apariciones de cada uno para luego calcular la probabilidad a priori de cada clase
counter = Counter(self.numClases)
# Calculamos la probabilidad de la clase y lo metemos en un diccionario ordenado segun el numero
# correspondiente a cada clase asignado en el diccionario
self.dictPrioris = {}
for k in counter:
k = int(k)
counter[k] = counter[k] / len(self.numClases)
self.dictPrioris[k] = counter[k]
# Aqui ordenamos el diccionario para que esten en el mismo orden de como extraemos los datos del dataset
self.dictPrioris = SortedDict(self.dictPrioris)
# Calcular tablas de probabilidades del entrenamiento. Tenemos que calcular por cada atributo una cuenta
# de las apariciones en cada clase
# Creamos una lista de matrices, donde vamos almacenar todos los datos que hemos obtenido en los datos de Test
self.posteriori = np.zeros(len(dataset.nombreAtributos) - 1,
dtype=object)
# Recorremos todos los datos de la matriz sin llegar a la clase
for i in range(len(dataset.nombreAtributos) - 1):
# Si el dato que obtenemos es Nominal haremos el recuento de todas las veces que sale la P(D|H)
if dataset.nominalAtributos[i] == True:
# Creamos una matriz de tamaño X: Número de Atributos menos la clase Y: Número de clases
post = np.zeros(
(len(dataset.listaDicts[i]), len(dataset.listaDicts[-1])))
# Aqui contamos todos las datos que queremos del datos Train para construir la matriz de entrenamiento
for c in range(len(dataset.listaDicts[-1])):
datosEnt = dataset.extraeDatos(datosTrain)
dat = datosEnt[:, i]
repes = Counter(dat[datosEnt[:, -1] == c])
for r in repes:
post[int(r), c] = repes[r]
if self.laplace == True:
self.posteriori[i] = post + 1
else:
self.posteriori[i] = post
# Si el dato es Continuo obtendremos la media y la desviación tipica de la clase
else:
# Creamos una matriz de X: Los datos de Media y Desivación típica Y: Número de clases
post = np.zeros((2, len(dataset.listaDicts[-1])))
# Aqui obtenemos la media y desviación tipica de cada clase, despues de tener los datos de entrenamiento
for c in range(len(dataset.listaDicts[-1])):
datosEnt = dataset.extraeDatos(datosTrain)
dat = datosEnt[:, i]
datos = dat[datosEnt[:, -1] == c]
post[0][c] = np.mean(datos)
post[1][c] = np.std(datos)
self.posteriori[i] = post
# Calculamos los valores de los posteriori de todos las tablas anteriores
for i in range(len(dataset.listaDicts) - 1):
if dataset.nominalAtributos[i] == True:
self.posteriori[i] /= sum(self.posteriori[i])
def clasifica(self, dataset, datosTest):
acum_probs = 1
self.prediccion = []
datTest = dataset.extraeDatos(datosTest)
# Ahora vamos a estudiar la probabilidad de la clase con los datos obtenidos en el entrenamiento
# Recorremos todos las datos de la matriz de los datos Test
for dato in datTest:
mapa = []
# Aqui obtenemos los prioris de cada clase para poder obtener la probabilidad de cada una
for clase in range(len(self.dictPrioris)):
listaVerosimilitudes = []
# Aqui obtenemos cada valor posteriori de nuestro entrenamiento de los datos, es decir, P(D|H)
for atributo in range(len(self.posteriori)):
if dataset.nominalAtributos[atributo] == True:
prob = self.posteriori[atributo][int(
dato[atributo])][clase]
listaVerosimilitudes.append(prob)
# Aqui obtenemos la probabilidad de los atibutos continuos
else:
# Hacemos la formula de la distribucion normal
exp1 = 1 / (self.posteriori[atributo][1][clase] *
math.sqrt(2 * math.pi))
exp2 = np.power((dato[atributo] -
self.posteriori[atributo][0][clase]),
2)
exp3 = np.power(self.posteriori[atributo][1][clase], 2)
exp4 = exp2 / exp3
exp4 = math.exp((-1 / 2) * exp4)
prob = exp1 * exp4
listaVerosimilitudes.append(prob)
for verosimilitud in listaVerosimilitudes:
acum_probs *= verosimilitud
acum_probs *= self.dictPrioris.get(clase)
mapa.append(acum_probs)
acum_probs = 1
# Aqui obtenemos la predicción de mayor probabilidad y la guardamos en nuestra lista de predicciones
self.prediccion.append(np.argmax(mapa))
# Devolvemos la lista con la predicción de nuestro clasifica
return self.prediccion
def __init__(self, name: str):
self.name: str = name
self.__collections: SortedDict[str, MyMongoCollection] = SortedDict()
def __init__(self):
self.sorted_map = SortedDict()
class google:
def __init__(self):
self.SCOPES = ['https://www.googleapis.com/auth/calendar']
self.calendars = []
self.calendar_names = {}
self.realevents = SortedDict({})
self.isfree = False
self.creds = {}
self.tag = "google"
def logger(self, msg, type="info", colour="none"):
mainlogger().logger(self.tag, msg, type, colour)
def timecleaner(self, time):
if "+" in time:
time = time[::-1].replace(":", "")[::-1]
time = datetime.datetime.strptime(time, "%Y-%m-%dT%H%M%S%z")
else:
time = datetime.datetime.strptime(time, "%Y-%m-%d")
time = pytz.timezone("Europe/Amsterdam").localize(time)
return time
def timedifference(self, duration):
days, seconds = duration.days, duration.seconds
hours = seconds // 3600
minutes = (seconds % 3600) // 60
seconds = seconds % 60
timelist = []
if days != 0:
timelist.append(days)
if hours != 0:
timelist.append(hours)
if minutes != 0:
timelist.append(minutes)
if seconds != 0:
timelist.append(seconds)
#pass
return timelist
def requestusercode(self, client_id):
data = {
'client_id': client_id,
'scope': 'https://www.googleapis.com/auth/calendar'
}
url = 'https://accounts.google.com/o/oauth2/device/code'
response = json.loads(requests.post(url, data=data).text)
return response
def poll(self, device_code, interval):
data = {
'client_id': client_id,
'client_secret': client_secret,
'code': device_code,
'grant_type': "http://oauth.net/grant_type/device/1.0"
}
url = 'https://oauth2.googleapis.com/token'
while True:
response = json.loads(requests.post(url, data=data).text)
if "access_token" in response.keys():
# figure out when the token expires:
expiredate = datetime.datetime.now() + datetime.timedelta(
seconds=response["expires_in"])
# fill creds with infomration to figure out if accesstoken is expired or not
olddict = {}
with open("components/google/credentials.json", "w") as f:
olddict["access_token"] = response["access_token"]
olddict["refresh_token"] = response["refresh_token"]
olddict["expires_at"] = str(expiredate)
tmpdict = {"installed": olddict}
f.write(json.dumps(tmpdict))
return olddict["access_token"]
else:
self.logger("not yet..", "debug", "yellow")
sleep(interval)
def refreshtoken(self, creddict):
refresh_token = creddict["refresh_token"]
data = {
'client_id': client_id,
'client_secret': client_secret,
'refresh_token': refresh_token,
'grant_type': "refresh_token"
}
url = 'https://oauth2.googleapis.com/token'
response = json.loads(requests.post(url, data=data).text)
expiredate = datetime.datetime.now() + datetime.timedelta(
seconds=response["expires_in"])
with open("components/google/credentials.json", "w") as f:
creddict["access_token"] = response["access_token"]
creddict["expires_at"] = str(expiredate)
tmpdict = {"installed": creddict}
f.write(json.dumps(tmpdict))
return creddict["access_token"]
def getcreds(self):
"""Shows basic usage of the Google Calendar API.
Prints the start and name of the next 10 events on the user's calendar.
"""
creds = os.path.exists("components/google/credentials.json")
#creddict = json.loads(open("components/google/credentials.json").read())["installed"]
if creds:
self.logger("creds found!", colour="yellow")
creddict = json.loads(
open("components/google/credentials.json").read())["installed"]
now = datetime.datetime.now()
expiredate = datetime.datetime.strptime(creddict["expires_at"],
"%Y-%m-%d %H:%M:%S.%f")
if now < expiredate:
self.logger("Getting from credentials.json", "debug", "yellow")
return creddict["access_token"]
else:
self.logger("creds need to be refreshed!", "debug", "yellow")
creds = self.refreshtoken(creddict)
return creds
# If there are no (valid) credentials available, let the user log in.
if not creds:
self.logger("No creds found!", "debug", "red")
response = self.requestusercode(client_id)
self.logger("Please go to {} and enter this code: {}".format(
response["verification_url"], response["user_code"]),
colour="blue")
device_code, interval = response["device_code"], response[
"interval"]
creds = self.poll(device_code, interval)
self.logger("Got access token, ready to rumble.", colour="green")
return creds
#creds = flow.run_local_server(port=0)
# Save the credentials for the next run
def main(self, numberofevents=1):
creds = self.getcreds()
service = Service(creds)
# Call the Calendar API
now = datetime.datetime.utcnow().isoformat(
) + 'Z' # 'Z' indicates UTC time
#print("getting calendars")
page_token = None
while True:
calendar_list = service.calendarList()
#sys.exit()
for calendar_list_entry in calendar_list['items']:
id = calendar_list_entry["id"]
name = calendar_list_entry['summary']
#print("calendar \"{}\" has id: {}".format(name ,id))
self.calendars.append(id)
self.calendar_names[id] = name
page_token = calendar_list.get('nextPageToken')
if not page_token:
break
#print('Getting the upcoming 10 events')
for calendar_id in self.calendars:
events_result = service.eventList(calendarId=calendar_id,
timeMin=now,
maxResults=10,
singleEvents=True,
orderBy='startTime')
events = events_result.get('items', [])
if not events:
#print('No upcoming events found for calendar {}'.format(self.calendar_names[calendar_id]))
pass
for index, event in enumerate(events): #[:numberofevents + 1]):
start = event['start'].get('dateTime',
event['start'].get('date'))
end = event['end'].get('dateTime', event['end'].get('date'))
try:
nextstart = events[index + 1]['start'].get(
'dateTime', event['start'].get('date'))
nextstart = self.timecleaner(nextstart)
event[
"nextstart"] = nextstart # when the next event starts
except Exception as e:
pass
start = self.timecleaner(start)
end = self.timecleaner(end)
event["start"] = start
event["end"] = end
#event["until_next"] = timelist # so you know how long it takes until the next event. not implemented on flutter yet.
self.realevents[start] = event
#print(start, event['summary'])
now = pytz.timezone("Europe/Amsterdam").localize(
datetime.datetime.now())
#self.isfree = not (start < now < end)
sortedlist = list(self.realevents.keys())
for time in sortedlist:
eventsummary = self.realevents[time]["summary"]
return sortedlist, self.realevents
uc_dict = SortedDict({
"SNEK": "0x1f40d", ## Error
## qwertz
"ACUTE": "0x00b4",
## QWERTZ
"INV_EXCL": "0x00a1",
"INV_QST": "0x00bf",
## numpad
"SUP1": "0x00b9",
"SUP2": "0x00b2",
"SUP3": "0x00b3",
"SUP_MIN": "0x207b",
"ROOT": "0x221a",
"DEGREE": "0x00b0",
"MINUTE": "0x02b9",
"SECOND": "0x02ba",
"CURRENCY": "0x00a4",
"CENT": "0x00a2",
"YEN": "0x00a5",
"POUND": "0x00a3",
"EURO": "0x20ac",
"DIVISION": "0x00f7",
"MICRO": "0x00b5",
"OHM": "0x2126",
"BITCOIN": "0x20bf",
"SECTION": "0x00a7",
## NAV_TYPO
"SQT1": "0x203a",
"SQT2": "0x201a",
"SQT3": "0x2018",
"SQT4": "0x2019",
"SQT5": "0x2039",
"SUP_A": "0x00aa",
"SUP_O": "0x00ba",
"TMARK": "0x2122",
"COPYR": "0x00a9",
"REGTM": "0x00ae",
"DOTS": "0x2026",
"CROSS": "0x2020",
"BULLET": "0x2022",
"EMDASH": "0x2014",
"PERMIL": "0x2030",
"DQT1": "0x00bb",
"DQT2": "0x201e",
"DQT3": "0x201c",
"DQT4": "0x201d",
"DQT5": "0x00ab",
## lang
"ae": "0x00e4",
"oe": "0x00f6",
"ue": "0x00fc",
"sz": "0x00df",
"n_tilde": "0x00f1",
## LANG
"AE": "0x00c4",
"OE": "0x00d6",
"UE": "0x00dc",
"SZ": "0x1e9e",
"N_TILDE": "0x00d1",
## greek
"alpha": "0x03b1",
"beta": "0x03b2",
"gamma": "0x03b3",
"delta": "0x03b4",
"epsilon": "0x03b5",
"zeta": "0x03b6",
"eta": "0x03b7",
"theta": "0x03b8",
"iota": "0x03b9",
"kappa": "0x03ba",
"lambda": "0x03bb",
"my": "0x03bc",
"ny": "0x03bd",
"xi": "0x03be",
"omikron": "0x03bf",
"pi": "0x03c0",
"rho": "0x03c1",
"sigma": "0x03c3",
"tau": "0x03c4",
"ypsilon": "0x03c5",
"phi": "0x03c6",
"chi": "0x03c7",
"psi": "0x03c8",
"omega": "0x03c9",
# variants
"beta2": "0x03d0",
"epsi2": "0x03f5",
"theta2": "0x03d1",
"kappa2": "0x03f0",
"pi2": "0x03d6",
"rho2": "0x03f1",
"sigma2": "0x03c2",
"sigma3": "0x03f2",
"phi2": "0x03d5",
# non-standard
"digamma": "0x03dd",
"stigma": "0x03db",
"heta": "0x0371",
"san": "0x03fb",
"koppa": "0x03d9",
"sampi": "0x0373",
"scho": "0x03f8",
## GREEK
"ALPHA": "0x0391",
"BETA": "0x0392",
"GAMMA": "0x0393",
"DELTA": "0x0394",
"EPSILON": "0x0395",
"ZETA": "0x0396",
"ETA": "0x0397",
"THETA": "0x0398",
"IOTA": "0x0399",
"KAPPA": "0x039a",
"LAMBDA": "0x039b",
"MY": "0x039c",
"NY": "0x039d",
"XI": "0x039e",
"OMIKRON": "0x039f",
"PI": "0x03a0",
"RHO": "0x03a1",
"SIGMA": "0x03a3",
"TAU": "0x03a4",
"YPSILON": "0x03a5",
"PHI": "0x03a6",
"CHI": "0x03a7",
"PSI": "0x03a8",
"OMEGA": "0x03a9",
# variants
"YPSI2": "0x03d2",
# non-standard
"DIGAMMA": "0x03dc",
"STIGMA": "0x03da",
"HETA": "0x0370",
"SAN": "0x03fa",
"KOPPA": "0x03d8",
"SAMPI": "0x0372",
"SCHO": "0x03f7",
## symbol
"SHOGI_WD": "0x26c9",
"FLAG_W": "0x2690",
"LETTER": "0x2709",
"PHONE": "0x2706",
"INFO": "0x1f6c8",
"REWIND": "0x23ee",
"PLAY": "0x23ef",
"FORWARD": "0x23ed",
"STOP": "0x25fc",
"EJECT": "0x23cf",
"SHOGI_W": "0x2616",
"FEMALE": "0x2640",
"MALE": "0x2642",
"NO": "0x2717",
"YES": "0x2713",
"FLAT": "0x266d",
"NATURAL": "0x266e",
"SHARP": "0x266f",
"COMMON_T": "0x1d134",
"ALLA_BR": "0x1d135",
"PAWN_W": "0x2659",
"DIAMS_W": "0x2662",
"HEARTS_W": "0x2661",
"SPADES_W": "0x2664",
"CLUBS_W": "0x2667",
"WHOLE_N": "0x1d15d",
"HALF_N": "0x1d15e",
"QUART_N": "0x1d15f",
"EIGHTH_N": "0x1d160",
"SIXT_N": "0x1d161",
"ROOK_W": "0x2656",
"KNIGHT_W": "0x2658",
"BISHOP_W": "0x2657",
"QUEEN_W": "0x2655",
"KING_W": "0x2654",
"C_CLEF": "0x1d121",
"G_CLEF": "0x1d11e",
"F_CLEF": "0x1d122",
"PEDAL": "0x1d1ae",
"PEDAL_UP": "0x1d1af",
## SYMBOL
"SHOGI_BD": "0x26ca",
"FLAG_B": "0x2691",
"SPEAKER_UP": "0x1f50a",
"SPEAKER_TG": "0x1f507",
"SPEAKER_DN": "0x1f509",
"SHOGI_B": "0x2617",
"KEY": "0x1f511",
"LOCK": "0x1f512",
"LOCK_OPEN": "0x1f513",
"STAFF": "0x1d11a",
"BARLINE": "0x1d100",
"BARLINE_D": "0x1d101",
"BARLINE_FIN": "0x1d102",
"CODA": "0x1d10c",
"PAWN_B": "0x265f",
"DIAMS_B": "0x2666",
"HEARTS_B": "0x2665",
"SPADES_B": "0x2660",
"CLUBS_B": "0x2663",
"WHOLE_P": "0x1d13b",
"HALF_P": "0x1d13c",
"QUART_P": "0x1d13d",
"EIGHTH_P": "0x1d13e",
"SIXT_P": "0x1d13f",
"ROOK_B": "0x265c",
"KNIGHT_B": "0x265e",
"BISHOP_B": "0x265d",
"QUEEN_B": "0x265b",
"KING_B": "0x265a",
"REPEAT_S": "0x1d106",
"REPEAT_E": "0x1d107",
"SEGNO": "0x1d10b",
"DALSEGNO": "0x1d109",
"DACAPO": "0x1d10a",
## math
"EXISTS": "0x2203",
"FORALL": "0x2200",
"EQUIV": "0x2261",
"CORRESP": "0x2259",
"INEQUAL": "0x2260",
"DEFINE": "0x2254",
"TO": "0x27f6",
"MAPSTO": "0x27fc",
"SETMINUS": "0x2216",
"QED": "0x220e",
"OR_": "0x22c1",
"AND_": "0x22c0",
"OR": "0x2228",
"AND": "0x2227",
"NOT": "0x00ac",
"BICOND": "0x21d4",
"IMPL_REV": "0x21d0",
"IMPL": "0x21d2",
"TOP": "0x22a4",
"BOTTOM": "0x22a5",
"SUM_": "0x2211",
"PROD_": "0x220f",
"MDOT": "0x22c5",
"NOTIN": "0x2209",
"IN": "0x2208",
"LS_EQ": "0x2264",
"GR_EQ": "0x2265",
"INFTY": "0x221e",
"UNION_": "0x22c3",
"INTERS_": "0x22c2",
"UNION": "0x222a",
"INTERS": "0x2229",
"NOTSUBS": "0x2288",
"PR_SUBSET": "0x2282",
"SUBSET": "0x2286",
"SUBSET_REV": "0x2287",
"EMPTYSET": "0x2205",
## MATHSCRIPT
"LEFT_CEIL": "0x2308",
"REAL_P": "0x211c",
"NABLA": "0x2207",
"RIGHT_CEIL": "0x2309",
"LEFT_FLOOR": "0x230a",
"BRA": "0x27e8",
"IMAG_P": "0x2111",
"KET": "0x27e9",
"RIGHT_FLOOR": "0x230b",
"Q_SET": "0x211a",
"e_FUN": "0x0065",
"R_SET": "0x211d",
"Z_SET": "0x2124",
"U_SET": "0x1d54c",
"i_UNIT": "0x0069",
"BIG_O": "0x1d4aa",
"POWERSET": "0x1d4ab",
"A_SET": "0x1d538",
"INTEGRAL": "0x222b",
"PARTIAL": "0x2202",
"F_SET": "0x1d53d",
"H_SET": "0x210d",
"K_SET": "0x1d542",
"LENGTH": "0x2113",
"ALEPH": "0x2135",
"BETH": "0x2136",
"C_SET": "0x2102",
"B_SET": "0x1d539",
"N_SET": "0x2115",
"INDICATOR": "0x1d7cf",
"FOURIER": "0x2131",
"HAMILTON": "0x210b",
"LAPLACE": "0x2112",
## MATHEXT
"RIGHT_TACK": "0x22a2",
"MODELS": "0x22a7",
"AB_VEC": "0x20d7",
"AB_LINE": "0x0305",
"AB_ARC": "0x0361",
"LESS_LESS": "0x226a",
"PRED": "0x227a",
"EMBED": "0x21aa",
"SUCC": "0x227b",
"GREAT_GREAT": "0x226b",
"TRIANGLE": "0x25b3",
"SQUARE": "0x25a1",
"CIRCLE": "0x25cb",
"DOT_OP": "0x2299",
"PLUS_OP": "0x2295",
"MINUS_OP": "0x2296",
"ROUGHLY": "0x2248",
"ISOMORPH": "0x2245",
"LTIMES": "0x22c9",
"RTIMES": "0x22ca",
"BOWTIE": "0x22c8",
"M_ANGLE": "0x2221",
"ANGLE": "0x2220",
"CIRC_MID": "0x2218",
"AST_MID": "0x2217",
"TIMES": "0x00d7",
"PLUS_MINUS": "0x00b1",
"MINUS_PLUS": "0x2213",
"LEFT_OUTER": "0x27d5",
"RIGHT_OUTER": "0x27d6",
"FULL_OUTER": "0x27d7",
"SQ_LS": "0x228f",
"SQ_LS_EQ": "0x2291",
"PROP_TO": "0x221d",
"PARALLEL": "0x2225",
"NOT_PARA": "0x2226",
"TIMES_OP": "0x2297",
"NOT_DIV": "0x2224"
})
class FederationRemoteSendQueue(object):
"""A drop in replacement for FederationSender"""
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 -> list[user_id]
# Stores the destinations we need to explicitly send presence to about a
# given user.
# Stream position -> (user_id, destinations)
self.presence_destinations = SortedDict()
self.keyed_edu = {} # (destination, key) -> EDU
self.keyed_edu_changed = SortedDict(
) # stream position -> (destination, key)
self.edus = SortedDict() # stream position -> Edu
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",
"device_messages",
"pos_time",
"presence_destinations",
]:
register(queue_name, getattr(self, queue_name))
self.clock.looping_call(self._clear_queue, 30 * 1000)
def _next_pos(self):
pos = self.pos
self.pos += 1
self.pos_time[self.clock.time_msec()] = pos
return pos
def _clear_queue(self):
"""Clear the queues for anything older than N minutes"""
FIVE_MINUTES_AGO = 5 * 60 * 1000
now = self.clock.time_msec()
keys = self.pos_time.keys()
time = self.pos_time.bisect_left(now - FIVE_MINUTES_AGO)
if not keys[:time]:
return
position_to_delete = max(keys[:time])
for key in keys[:time]:
del self.pos_time[key]
self._clear_queue_before_pos(position_to_delete)
def _clear_queue_before_pos(self, position_to_delete):
"""Clear all the queues from before a given position"""
with Measure(self.clock, "send_queue._clear"):
# Delete things out of presence maps
keys = self.presence_changed.keys()
i = self.presence_changed.bisect_left(position_to_delete)
for key in keys[:i]:
del self.presence_changed[key]
user_ids = set(user_id for uids in self.presence_changed.values()
for user_id in uids)
keys = self.presence_destinations.keys()
i = self.presence_destinations.bisect_left(position_to_delete)
for key in keys[:i]:
del self.presence_destinations[key]
user_ids.update(
user_id for user_id, _ in self.presence_destinations.values())
to_del = [
user_id for user_id in self.presence_map
if user_id not in user_ids
]
for user_id in to_del:
del self.presence_map[user_id]
# Delete things out of keyed edus
keys = self.keyed_edu_changed.keys()
i = self.keyed_edu_changed.bisect_left(position_to_delete)
for key in keys[:i]:
del self.keyed_edu_changed[key]
live_keys = set()
for edu_key in self.keyed_edu_changed.values():
live_keys.add(edu_key)
to_del = [
edu_key for edu_key in self.keyed_edu
if edu_key not in live_keys
]
for edu_key in to_del:
del self.keyed_edu[edu_key]
# Delete things out of edu map
keys = self.edus.keys()
i = self.edus.bisect_left(position_to_delete)
for key in keys[:i]:
del self.edus[key]
# Delete things out of device map
keys = self.device_messages.keys()
i = self.device_messages.bisect_left(position_to_delete)
for key in keys[:i]:
del self.device_messages[key]
def notify_new_events(self, current_id):
"""As per FederationSender"""
# We don't need to replicate this as it gets sent down a different
# stream.
pass
def build_and_send_edu(self, destination, edu_type, content, key=None):
"""As per FederationSender"""
if destination == self.server_name:
logger.info("Not sending EDU to ourselves")
return
pos = self._next_pos()
edu = Edu(
origin=self.server_name,
destination=destination,
edu_type=edu_type,
content=content,
)
if key:
assert isinstance(key, tuple)
self.keyed_edu[(destination, key)] = edu
self.keyed_edu_changed[pos] = (destination, key)
else:
self.edus[pos] = edu
self.notifier.on_new_replication_data()
def send_read_receipt(self, receipt):
"""As per FederationSender
Args:
receipt (synapse.types.ReadReceipt):
"""
# nothing to do here: the replication listener will handle it.
pass
def send_presence(self, states):
"""As per FederationSender
Args:
states (list(UserPresenceState))
"""
pos = self._next_pos()
# We only want to send presence for our own users, so lets always just
# filter here just in case.
local_states = list(
filter(lambda s: self.is_mine_id(s.user_id), states))
self.presence_map.update(
{state.user_id: state
for state in local_states})
self.presence_changed[pos] = [state.user_id for state in local_states]
self.notifier.on_new_replication_data()
def send_presence_to_destinations(self, states, destinations):
"""As per FederationSender
Args:
states (list[UserPresenceState])
destinations (list[str])
"""
for state in states:
pos = self._next_pos()
self.presence_map.update(
{state.user_id: state
for state in states})
self.presence_destinations[pos] = (state.user_id, destinations)
self.notifier.on_new_replication_data()
def send_device_messages(self, destination):
"""As per FederationSender"""
pos = self._next_pos()
self.device_messages[pos] = destination
self.notifier.on_new_replication_data()
def get_current_token(self):
return self.pos - 1
def federation_ack(self, token):
self._clear_queue_before_pos(token)
def get_replication_rows(self,
from_token,
to_token,
limit,
federation_ack=None):
"""Get rows to be sent over federation between the two tokens
Args:
from_token (int)
to_token(int)
limit (int)
federation_ack (int): Optional. The position where the worker is
explicitly acknowledged it has handled. Allows us to drop
data from before that point
"""
# TODO: Handle limit.
# To handle restarts where we wrap around
if from_token > self.pos:
from_token = -1
# list of tuple(int, BaseFederationRow), where the first is the position
# of the federation stream.
rows = []
# There should be only one reader, so lets delete everything its
# acknowledged its seen.
if federation_ack:
self._clear_queue_before_pos(federation_ack)
# Fetch changed presence
i = self.presence_changed.bisect_right(from_token)
j = self.presence_changed.bisect_right(to_token) + 1
dest_user_ids = [
(pos, user_id)
for pos, user_id_list in self.presence_changed.items()[i:j]
for user_id in user_id_list
]
for (key, user_id) in dest_user_ids:
rows.append((key, PresenceRow(state=self.presence_map[user_id], )))
# Fetch presence to send to destinations
i = self.presence_destinations.bisect_right(from_token)
j = self.presence_destinations.bisect_right(to_token) + 1
for pos, (user_id, dests) in self.presence_destinations.items()[i:j]:
rows.append((pos,
PresenceDestinationsRow(
state=self.presence_map[user_id],
destinations=list(dests),
)))
# Fetch changes keyed edus
i = self.keyed_edu_changed.bisect_right(from_token)
j = self.keyed_edu_changed.bisect_right(to_token) + 1
# We purposefully clobber based on the key here, python dict comprehensions
# always use the last value, so this will correctly point to the last
# stream position.
keyed_edus = {v: k for k, v in self.keyed_edu_changed.items()[i:j]}
for ((destination, edu_key), pos) in iteritems(keyed_edus):
rows.append((pos,
KeyedEduRow(
key=edu_key,
edu=self.keyed_edu[(destination, edu_key)],
)))
# Fetch changed edus
i = self.edus.bisect_right(from_token)
j = self.edus.bisect_right(to_token) + 1
edus = self.edus.items()[i:j]
for (pos, edu) in edus:
rows.append((pos, EduRow(edu)))
# Fetch changed device messages
i = self.device_messages.bisect_right(from_token)
j = self.device_messages.bisect_right(to_token) + 1
device_messages = {v: k for k, v in self.device_messages.items()[i:j]}
for (destination, pos) in iteritems(device_messages):
rows.append((pos, DeviceRow(destination=destination, )))
# Sort rows based on pos
rows.sort()
return [(pos, row.TypeId, row.to_data()) for pos, row in rows]
""" ""
from sortedcontainers import SortedDict
def print_menu():
print('1. Print Users')
print('2. Add a User')
print('3. Remove a User')
print('4. Lookup a User')
print('5. Quit')
print()
# Create dictionary with key = Names, value = user_name
usernames = SortedDict()
usernames['Summer'] = 'summerela'
usernames['William'] = 'GoofyFish'
usernames['Steven'] = 'LoLCat'
usernames['Zara'] = 'zanyZara'
usernames['Renato'] = 'songDude'
# setup counter to store menu choice
menu_choice = 0
# display your menu
print_menu()
# as long as the menu choice isn't "quit" get user options
while menu_choice != 5:
# get menu choice from user
class Mentor(Mozillian):
def __init__(self, row):
super().__init__(
row
) #row['Email Address'], row['Organizational level (i.e. P3, M2, etc.)'], row['Organization'], row['Participant full name'], row['Manager email'])
self.submitted_dt = row['Timestamp']
self.time_availability_to_set(row['Time Availability'])
self.expertise_to_set(row['Areas of expertise'])
# if missing outside_org value, set to unbias value of 2
self.outside_org = safe_cast(
row['Interest in mentoring or learning from someone outside your own organization?'],
int, 2)
assert self.outside_org in [
1, 2, 3
], self.get_id() + " has invalid outside_org value " + str(
self.outside_org)
self.requests = row['Any particular requests?']
self.identify_as = row['Do you identify yourself as']
self.welcome_email_dt = row['Sent Wecome Email (date)']
self.notify_manager_dt = row['Sent Manager Approval Email (date)']
self.manager_approved_dt = row['Manager Approved (date)']
self.mentee_limit = safe_cast(
row['Mentee Limit'], int,
2) # by default mentors have a max of two mentees
def time_availability_to_set(self, availability):
""" Set times person is available in set form """
self.availability_set = set(
[x.strip() for x in availability.split(',')])
def expertise_to_set(self, expertise):
""" Set person's expertise in set form """
self.expertise_set = set([x.strip() for x in expertise.split(',')])
def get_submitted_dt(self) -> datetime:
return self.submitted_dt
def get_expertise(self) -> set:
return self.expertise_set
def get_times_available(self) -> set:
return self.availability_set
def get_mentee_limit(self) -> int:
return self.mentee_limit
def set_mentee_limit(self, mentee_limit: int):
self.mentee_limit = mentee_limit
def get_outside_org_score(self, learner) -> int:
# outside org 1=prefer, 3=rather not
assert learner.org is not None, "Learner " + learner.get_id(
) + " has invalid org value " + learner.track
if self.outside_org == 1:
if self.org == learner.org:
return 1
else:
return 3
elif self.outside_org == 2:
if self.org == learner.org:
return 2
else:
return 2
elif self.outside_org == 3:
if self.org == learner.org:
return 3
else:
return 1
def calc_score(self, learner, is_requested):
# get count of overlapping times
available_times = len(
self.availability_set.intersection(learner.get_times_available()))
# If constraints are satisfied, calculate preference rankings for learners based on feature vector
score = 0
# add bias for requested mentor
if is_requested:
score = score + 50
if available_times > 0:
# match expertise to interest, max score of 7
# need to account for "Other:" somehow
score = score + len(
self.expertise_set.intersection(learner.get_interests()))
score = score + self.get_outside_org_score(learner)
return score
def set_preferences(self, learners):
self.preferences = SortedDict(neg) #, enumerate('abc', start=1))
for learner in learners:
# Filter on constraints
# cannot match to themselves
if self.get_id() == learner.get_id():
continue
# mentor-learner should not be in the same management reporting chain - will need ppl dataset info
# for now just check that learner's manager = mentor
if self.get_manager_email() == learner.get_id():
continue
# unless manager says "no", manager approved column has no impact
# get count of overlapping times
available_times = len(
self.availability_set.intersection(
learner.get_times_available()))
# If constraints are satisfied, calculate preference rankings for learners based on feature vector
score = 0
if available_times > 0:
# match expertise to interest, max score of 7
# need to account for "Other:" somehow
score = score + len(
self.expertise_set.intersection(learner.get_interests()))
#print("interests intersection score:" + str(score))
# outside org 1=prefer, 3=rather not
# add 1 if prefer and orgs not the same
if self.outside_org == 1 and self.org != learner.org:
#print("outside org add 1: " + mentor_org)
score = score + 1
# add 1 if rather not and orgs are the same
if self.outside_org == 3 and self.org == learner.org:
#print("outside org add 1: " + mentor_org)
score = score + 1
# so far ranks range is [0,8]
# be careful matching those in relationship/married/dating/familial - How??
# option to constrain mentor org level > learner org level?
# if score is the same, order by date_submitted? no i think this is used in the apposite & global draw
#print(mentor.get_id() + ": " + str(score))
if score > 0:
if self.preferences.__contains__(score):
self.preferences[score].append(learner)
else:
self.preferences[score] = [learner]
def set_preferences_subscribed(self,
subscribed_learners: dict,
requested_learner=""):
self.preferences = SortedDict(neg) #, enumerate('abc', start=1))
self.learner_score = {}
for subscribed_learner_id, subscribed_learner in subscribed_learners.items(
):
# Filter on constraints
# cannot match to themselves
if self.get_id() == subscribed_learner_id:
continue
# mentor-learner should not be in the same management reporting chain - will need ppl dataset info
# for now just check that learner's manager = mentor
if self.get_manager_email(
) == subscribed_learner_id or subscribed_learner.get_manager_email(
) == self.get_id():
continue
# unless manager says "no", manager approved column has no impact
# get count of overlapping times
available_times = len(
self.availability_set.intersection(
subscribed_learner.get_times_available()))
# If constraints are satisfied, calculate preference rankings for learners based on feature vector
score = 0
# add bias for requested mentor
if subscribed_learner_id == requested_learner:
print(self.get_id() + ' requested learner: ' +
requested_learner)
score = score + 50
if available_times > 0:
# match expertise to interest, max score of 7
# need to account for "Other:" somehow
score = score + len(
self.expertise_set.intersection(
subscribed_learner.get_interests()))
#print("interests intersection score:" + str(score))
score = score + self.get_outside_org_score(subscribed_learner)
# so far ranks range is [2,13]
# be careful matching those in relationship/married/dating/familial - How??
# option to constrain mentor org level > learner org level? do levels translate across M/P?
# if score is the same, order by date_submitted? no i think this is used in the apposite & global draw
#print(mentor.get_id() + ": " + str(score))
#if score > 0:
if self.preferences.__contains__(score):
self.preferences[score].append(subscribed_learner)
else:
self.preferences[score] = [subscribed_learner]
self.learner_score[subscribed_learner_id] = score
def get_preferences(self) -> SortedDict:
return self.preferences
def get_ranked_learners(self) -> list:
ranked_learners = []
# add weights to those scores that are the same?
for value in self.preferences.values():
ranked_learners.extend([x.get_id() for x in value])
#for score, learners_list in self.preferences.items():
# print("score: " + str(score) + ", learners: " + ','.join([x.get_id() for x in learners_list]))
return ranked_learners
def get_learner_score(self) -> dict:
return self.learner_score
def get_learner_rank(self, learner, is_requested) -> int:
#scores = [score for subscribed_learner_id, score in self.learner_score.items() if subscribed_learner_id == learner.get_id()]
return self.calc_score(learner, is_requested)
def dead_screen(score_table, resent_result):
print(resent_result)
pygame.init()
size = (500, 500)
sc = pygame.display.set_mode(size)
pygame.display.set_caption("Snake")
img = pygame.image.load("snake image.jpg")
pygame.display.set_icon(img)
gameover_surf = pygame.image.load("game over.jpg")
gameover_surf = pygame.transform.scale(gameover_surf, (600, 300))
x_image = 260
y_image = 100
gameover_rect = gameover_surf.get_rect(center=(x_image, y_image))
sc.blit(gameover_surf, gameover_rect)
font = pygame.font.Font('freesansbold.ttf', 32)
text = font.render('SCORE TABLE', False, (255, 255, 255))
textRect = text.get_rect()
textRect.center = (260, 200)
sc.blit(text, textRect)
#score tablle
global name_win
score_table.update({resent_result: name_win})
score_table = SortedDict(score_table)
print(score_table)
font = pygame.font.Font('freesansbold.ttf', 20)
name1 = font.render(score_table[list(score_table.keys())[-1]], False,
(255, 255, 255))
score = str(list(score_table.keys())[-1])
score1 = font.render(score, False, (255, 255, 255))
name1Rect = name1.get_rect()
score1Rect = score1.get_rect()
name1Rect.center = (230, 250)
score1Rect.center = (300, 250)
sc.blit(name1, name1Rect)
sc.blit(score1, score1Rect)
name2 = font.render(score_table[list(score_table.keys())[-2]], False,
(255, 255, 255))
score = str(list(score_table.keys())[-2])
score2 = font.render(score, False, (255, 255, 255))
name2Rect = name2.get_rect()
score2Rect = score2.get_rect()
name2Rect.center = (230, 290)
score2Rect.center = (300, 290)
sc.blit(name2, name2Rect)
sc.blit(score2, score2Rect)
name3 = font.render(score_table[list(score_table.keys())[-3]], False,
(255, 255, 255))
score = str(list(score_table.keys())[-3])
score3 = font.render(score, False, (255, 255, 255))
name3Rect = name3.get_rect()
score3Rect = score3.get_rect()
name3Rect.center = (230, 330)
score3Rect.center = (300, 330)
sc.blit(name3, name3Rect)
sc.blit(score3, score3Rect)
pygame.display.update()
clock = pygame.time.Clock()
waiting = True
while waiting:
clock.tick(100)
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
if event.type == pygame.K_ESCAPE:
start_screen()
elif event.type == pygame.KEYDOWN:
waiting = False
def set_preferences_subscribed(self,
subscribed_learners: dict,
requested_learner=""):
self.preferences = SortedDict(neg) #, enumerate('abc', start=1))
self.learner_score = {}
for subscribed_learner_id, subscribed_learner in subscribed_learners.items(
):
# Filter on constraints
# cannot match to themselves
if self.get_id() == subscribed_learner_id:
continue
# mentor-learner should not be in the same management reporting chain - will need ppl dataset info
# for now just check that learner's manager = mentor
if self.get_manager_email(
) == subscribed_learner_id or subscribed_learner.get_manager_email(
) == self.get_id():
continue
# unless manager says "no", manager approved column has no impact
# get count of overlapping times
available_times = len(
self.availability_set.intersection(
subscribed_learner.get_times_available()))
# If constraints are satisfied, calculate preference rankings for learners based on feature vector
score = 0
# add bias for requested mentor
if subscribed_learner_id == requested_learner:
print(self.get_id() + ' requested learner: ' +
requested_learner)
score = score + 50
if available_times > 0:
# match expertise to interest, max score of 7
# need to account for "Other:" somehow
score = score + len(
self.expertise_set.intersection(
subscribed_learner.get_interests()))
#print("interests intersection score:" + str(score))
score = score + self.get_outside_org_score(subscribed_learner)
# so far ranks range is [2,13]
# be careful matching those in relationship/married/dating/familial - How??
# option to constrain mentor org level > learner org level? do levels translate across M/P?
# if score is the same, order by date_submitted? no i think this is used in the apposite & global draw
#print(mentor.get_id() + ": " + str(score))
#if score > 0:
if self.preferences.__contains__(score):
self.preferences[score].append(subscribed_learner)
else:
self.preferences[score] = [subscribed_learner]
self.learner_score[subscribed_learner_id] = score
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 -> list[user_id]
# Stores the destinations we need to explicitly send presence to about a
# given user.
# Stream position -> (user_id, destinations)
self.presence_destinations = SortedDict()
self.keyed_edu = {} # (destination, key) -> EDU
self.keyed_edu_changed = SortedDict(
) # stream position -> (destination, key)
self.edus = SortedDict() # stream position -> Edu
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",
"device_messages",
"pos_time",
"presence_destinations",
]:
register(queue_name, getattr(self, queue_name))
self.clock.looping_call(self._clear_queue, 30 * 1000)
class DictionaryBuilder(object):
__BYTE_MAX_VALUE = 127
__MAX_LENGTH = 255
__MIN_REQUIRED_COLS_NUM = 18
__BUFFER_SIZE = 1024 * 1024
__PATTERN_UNICODE_LITERAL = re.compile(r"\\u([0-9a-fA-F]{4}|{[0-9a-fA-F]+})")
__ARRAY_MAX_LENGTH = __BYTE_MAX_VALUE # max value of byte in Java
__STRING_MAX_LENGTH = 32767 # max value of short in Java
is_user_dictionary = False
class WordEntry:
headword = None
parameters = None
wordinfo = None
aunit_split_string = None
bunit_split_string = None
cunit_split_string = None
class PosTable(object):
def __init__(self):
self.table = []
def get_id(self, str_):
id_ = self.table.index(str_) if str_ in self.table else -1
if id_ < 0:
id_ = len(self.table)
self.table.append(str_)
return id_
def get_list(self):
return self.table
@staticmethod
def __default_logger():
handler = StreamHandler()
handler.terminator = ""
handler.setLevel(DEBUG)
logger = getLogger(__name__)
logger.setLevel(DEBUG)
logger.addHandler(handler)
logger.propagate = False
return logger
def __init__(self, *, logger=None):
self.byte_buffer = JTypedByteBuffer()
self.trie_keys = SortedDict()
self.entries = []
self.is_dictionary = False
self.pos_table = self.PosTable()
self.logger = logger or self.__default_logger()
def build(self, lexicon_paths, matrix_input_stream, out_stream):
self.logger.info('reading the source file...')
for path in lexicon_paths:
with open(path, 'r', encoding='utf-8') as rf:
self.build_lexicon(rf)
self.logger.info('{} words\n'.format(len(self.entries)))
self.write_grammar(matrix_input_stream, out_stream)
self.write_lexicon(out_stream)
def build_lexicon(self, lexicon_input_stream):
line_no = -1
try:
for i, row in enumerate(csv.reader(lexicon_input_stream)):
line_no = i
entry = self.parse_line(row)
if entry.headword:
self.add_to_trie(entry.headword, len(self.entries))
self.entries.append(entry)
except Exception as e:
if line_no >= 0:
self.logger.error(
'{} at line {} in {}\n'.format(e.args[0], line_no, lexicon_input_stream.name))
raise e
def parse_line(self, cols):
if len(cols) < self.__MIN_REQUIRED_COLS_NUM:
raise ValueError('invalid format')
cols = [self.decode(col) for col in cols]
if not self.__is_length_valid(cols):
raise ValueError('string is too long')
if not cols[0]:
raise ValueError('headword is empty')
entry = self.WordEntry()
# head word for trie
if cols[1] != '-1':
entry.headword = cols[0]
# left-id, right-id, connect_cost
entry.parameters = [int(cols[i]) for i in [1, 2, 3]]
# part of speech
pos_id = self.get_posid(cols[5:11])
if pos_id < 0:
raise ValueError('invalid part of speech')
entry.aunit_split_string = cols[15]
entry.bunit_split_string = cols[16]
entry.cunit_split_string = cols[17]
self.check_splitinfo_format(entry.aunit_split_string)
self.check_splitinfo_format(entry.bunit_split_string)
self.check_splitinfo_format(entry.cunit_split_string)
if cols[14] == 'A' and \
not (entry.aunit_split_string == '*' and entry.bunit_split_string == '*'):
raise ValueError('invalid splitting')
synonym_group_ids = []
if len(cols) > 18:
synonym_group_ids = self.parse_synonym_group_ids(cols[18])
head_length = len(cols[0].encode('utf-8'))
dict_from_wordid = -1 if cols[13] == '*' else int(cols[13])
entry.wordinfo = WordInfo(
cols[4], head_length, pos_id, cols[12], dict_from_wordid, '', cols[11], None, None, None, synonym_group_ids)
return entry
def __is_length_valid(self, cols):
head_length = len(cols[0].encode('utf-8'))
return head_length <= self.__STRING_MAX_LENGTH \
and len(cols[4]) <= self.__STRING_MAX_LENGTH \
and len(cols[11]) <= self.__STRING_MAX_LENGTH \
and len(cols[12]) <= self.__STRING_MAX_LENGTH
def add_to_trie(self, headword, word_id):
key = headword.encode('utf-8')
if key not in self.trie_keys:
self.trie_keys[key] = []
self.trie_keys[key].append(word_id)
def get_posid(self, strs):
return self.pos_table.get_id(','.join(strs))
def write_grammar(self, matrix_input_stream, output_stream):
self.logger.info('writing the POS table...')
self.convert_postable(self.pos_table.get_list())
self.byte_buffer.seek(0)
output_stream.write(self.byte_buffer.read())
self.__logging_size(self.byte_buffer.tell())
self.byte_buffer.clear()
self.logger.info('writing the connection matrix...')
if not matrix_input_stream:
self.byte_buffer.write_int(0, 'short')
self.byte_buffer.write_int(0, 'short')
self.byte_buffer.seek(0)
output_stream.write(self.byte_buffer.read())
self.__logging_size(self.byte_buffer.tell())
self.byte_buffer.clear()
return
matrix = self.convert_matrix(matrix_input_stream)
self.byte_buffer.seek(0)
output_stream.write(self.byte_buffer.read())
self.byte_buffer.clear()
output_stream.write(matrix.read())
self.__logging_size(matrix.tell() + 4)
def convert_postable(self, pos_list):
self.byte_buffer.write_int(len(pos_list), 'short')
for pos in pos_list:
for text in pos.split(','):
self.write_string(text)
def convert_matrix(self, matrix_input):
header = matrix_input.readline().strip()
if re.fullmatch(r"\s*", header):
raise ValueError('invalid format at line 0')
lr = header.split()
lsize, rsize = [int(x) for x in lr]
self.byte_buffer.write_int(lsize, 'short')
self.byte_buffer.write_int(rsize, 'short')
matrix = JTypedByteBuffer()
for i, line in enumerate(matrix_input.readlines()):
line = line.strip()
if re.fullmatch(r"\s*", line) or re.match("#", line):
continue
cols = line.split()
if len(cols) < 3:
self.logger.warn('invalid format at line {}'.format(i))
continue
l, r, cost = [int(col) for col in cols]
pos = matrix.tell()
matrix.seek(2 * (l + lsize * r))
matrix.write_int(cost, 'short')
matrix.seek(pos)
return matrix
def write_lexicon(self, io_out):
trie = DoubleArray()
wordid_table = JTypedByteBuffer()
keys = []
vals = []
for key, word_ids in self.trie_keys.items():
keys.append(key)
vals.append(wordid_table.tell())
wordid_table.write_int(len(word_ids), 'byte')
for wid in word_ids:
wordid_table.write_int(wid, 'int')
self.logger.info('building the trie...')
trie.build(keys, lengths=[len(k) for k in keys], values=vals)
self.logger.info('done\n')
self.logger.info('writing the trie...')
self.byte_buffer.clear()
self.byte_buffer.write_int(trie.size(), 'int')
self.byte_buffer.seek(0)
io_out.write(self.byte_buffer.read())
self.byte_buffer.clear()
io_out.write(trie.array())
self.__logging_size(trie.size() * 4 + 4)
trie.clear()
del trie
self.logger.info('writing the word-ID table...')
self.byte_buffer.write_int(wordid_table.tell(), 'int')
self.byte_buffer.seek(0)
io_out.write(self.byte_buffer.read())
self.byte_buffer.clear()
wordid_table.seek(0)
io_out.write(wordid_table.read())
self.__logging_size(wordid_table.tell() + 4)
del wordid_table
self.logger.info('writing the word parameters...')
self.byte_buffer.write_int(len(self.entries), 'int')
for entry in self.entries:
self.byte_buffer.write_int(entry.parameters[0], 'short')
self.byte_buffer.write_int(entry.parameters[1], 'short')
self.byte_buffer.write_int(entry.parameters[2], 'short')
self.byte_buffer.seek(0)
io_out.write(self.byte_buffer.read())
self.byte_buffer.clear()
self.__logging_size(len(self.entries) * 6 + 4)
self.write_wordinfo(io_out)
def write_wordinfo(self, io_out):
mark = io_out.tell()
io_out.seek(mark * 4 + len(self.entries))
offsets = JTypedByteBuffer()
self.logger.info('writing the word_infos...')
base = io_out.tell()
for entry in self.entries:
wi = entry.wordinfo
offsets.write_int(io_out.tell(), 'int')
self.write_string(wi.surface)
self.write_stringlength(wi.length())
self.byte_buffer.write_int(wi.pos_id, 'short')
if wi.normalized_form == wi.surface:
self.write_string('')
else:
self.write_string(wi.normalized_form)
self.byte_buffer.write_int(wi.dictionary_form_word_id, 'int')
if wi.reading_form == wi.surface:
self.write_string('')
else:
self.write_string(wi.reading_form)
self.write_intarray(self.parse_splitinfo(entry.aunit_split_string))
self.write_intarray(self.parse_splitinfo(entry.bunit_split_string))
self.write_intarray(self.parse_splitinfo(entry.cunit_split_string))
self.write_intarray(wi.synonym_group_ids)
self.byte_buffer.seek(0)
io_out.write(self.byte_buffer.read())
self.byte_buffer.clear()
self.__logging_size(io_out.tell() - base)
self.logger.info('writing word_info offsets...')
io_out.seek(mark)
offsets.seek(0)
io_out.write(offsets.read())
self.__logging_size(offsets.tell())
def decode(self, str_):
def replace(match):
uni_text = match.group()
uni_text = uni_text.replace('{', '').replace('}', '')
if len(uni_text) > 6:
uni_text = ('\\U000{}'.format(uni_text[2:]))
return uni_text.encode('ascii').decode('unicode-escape')
return re.sub(self.__PATTERN_UNICODE_LITERAL, replace, str_)
def check_splitinfo_format(self, str_):
if str_.count('/') + 1 > self.__ARRAY_MAX_LENGTH:
raise ValueError('too many units')
def parse_splitinfo(self, info):
if info == '*':
return []
words = info.split('/')
if len(words) > self.__ARRAY_MAX_LENGTH:
raise ValueError('too many units')
ids = []
for word in words:
if self.__is_id(word):
ids.append(self.parse_id(word))
else:
ids.append(self.word_to_id(word))
if ids[-1] < 0:
raise ValueError('not found such a word: {}'.format(word))
return ids
@staticmethod
def __is_id(text):
return re.match(r'U?\d+$', text)
def parse_id(self, text):
if text.startswith('U'):
id_ = int(text[1:])
if self.is_user_dictionary:
id_ |= (1 << 28)
else:
id_ = int(text)
self.check_wordid(id_)
return id_
def word_to_id(self, text):
cols = text.split(',')
if len(cols) < 8:
raise ValueError('too few columns')
headword = self.decode(cols[0])
pos_id = self.get_posid([cols[i] for i in range(1, 7)])
if pos_id < 0:
raise ValueError('invalid part of speech')
reading = self.decode(cols[7])
return self.get_wordid(headword, pos_id, reading)
def get_wordid(self, headword, pos_id, reading_form):
for i in range(len(self.entries)):
info = self.entries[i].wordinfo
if info.surface == headword \
and info.pos_id == pos_id \
and info.reading_form == reading_form:
return i
return -1
def check_wordid(self, wid):
if wid < 0 or wid >= len(self.entries):
raise ValueError('invalid word ID')
def parse_synonym_group_ids(self, text):
if text == '*':
return []
synonym_group_ids = text.split('/')
if len(synonym_group_ids) > self.__ARRAY_MAX_LENGTH:
raise ValueError("too many units")
return [int(synonym_group_id) for synonym_group_id in synonym_group_ids]
def write_string(self, text):
len_ = 0
for c in text:
if 0x10000 <= ord(c) <= 0x10FFFF:
len_ += 2
else:
len_ += 1
self.write_stringlength(len_)
self.byte_buffer.write_str(text)
def write_stringlength(self, len_):
if len_ <= self.__BYTE_MAX_VALUE:
self.byte_buffer.write_int(len_, 'byte')
else:
self.byte_buffer.write_int((len_ >> 8) | 0x80, 'byte')
self.byte_buffer.write_int((len_ & 0xFF), 'byte')
def write_intarray(self, array):
self.byte_buffer.write_int(len(array), 'byte')
for item in array:
self.byte_buffer.write_int(item, 'int')
def __logging_size(self, size):
self.logger.info('{} bytes\n'.format(size))