sequence stringlengths 1.19k 35k | code stringlengths 75 8.58k |
|---|---|
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'select_segment'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'childr... | def select_segment(self, segs, segs_tips, segs_undecided) -> Tuple[int, int]:
scores_tips = np.zeros((len(segs), 4))
allindices = np.arange(self._adata.shape[0], dtype=int)
for iseg, seg in enumerate(segs):
if segs_tips[iseg][0] == -1: continue
if not isinstance(self.dist... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'unique_categories'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def unique_categories(categories):
categories = np.unique(categories)
categories = np.setdiff1d(categories, np.array(settings.categories_to_ignore))
categories = np.array(natsorted(categories, key=lambda v: v.upper()))
return categories |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '17']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'find_one_and_delete'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15']}; {'id': '4', 'type': 'identifi... | def find_one_and_delete(self, filter,
projection=None, sort=None, session=None, **kwargs):
kwargs['remove'] = True
return self.__find_and_modify(filter, projection, sort,
session=session, **kwargs) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'lines'}, {'i... | def sort(lines):
lines = list(lines)
new_lines = parse_block(lines, header=True)
for block in sorted(parse_blocks(lines), key=first_key):
if new_lines:
new_lines.append('')
new_lines.extend(block)
return new_lines |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'first_key'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'lines'}... | def first_key(lines):
for line in lines:
if line.startswith('
continue
if any(line.startswith(quote) for quote in QUOTES):
return line[1:]
return line
else:
return '' |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'geoadd'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9', '11']}; {'id': '4', 'type': 'identifier', 'chi... | def geoadd(self, key, longitude, latitude, member, *args, **kwargs):
return self.execute(
b'GEOADD', key, longitude, latitude, member, *args, **kwargs
) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '31']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'georadius'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9', '12', '13', '16', '19', '22', '25', '28']};... | def georadius(self, key, longitude, latitude, radius, unit='m', *,
with_dist=False, with_hash=False, with_coord=False,
count=None, sort=None, encoding=_NOTSET):
args = validate_georadius_options(
radius, unit, with_dist, with_hash, with_coord, count, sort
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '30']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'georadiusbymember'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '11', '12', '15', '18', '21', '24', '27'... | def georadiusbymember(self, key, member, radius, unit='m', *,
with_dist=False, with_hash=False, with_coord=False,
count=None, sort=None, encoding=_NOTSET):
args = validate_georadius_options(
radius, unit, with_dist, with_hash, with_coord, count, so... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '26']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '8', '11', '14', '17', '20', '23']}; {'id': '4', 'type': 'ident... | def sort(self, key, *get_patterns,
by=None, offset=None, count=None,
asc=None, alpha=False, store=None):
args = []
if by is not None:
args += [b'BY', by]
if offset is not None and count is not None:
args += [b'LIMIT', offset, count]
if ge... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zadd'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '10']}; {'id': '4', 'type': 'identifier', 'children':... | def zadd(self, key, score, member, *pairs, exist=None):
if not isinstance(score, (int, float)):
raise TypeError("score argument must be int or float")
if len(pairs) % 2 != 0:
raise TypeError("length of pairs must be even number")
scores = (item for i, item in enumerate(pa... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '22']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zcount'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '12', '18', '19']}; {'id': '4', 'type': 'identifier', 'childr... | def zcount(self, key, min=float('-inf'), max=float('inf'),
*, exclude=None):
if not isinstance(min, (int, float)):
raise TypeError("min argument must be int or float")
if not isinstance(max, (int, float)):
raise TypeError("max argument must be int or float")
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zincrby'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def zincrby(self, key, increment, member):
if not isinstance(increment, (int, float)):
raise TypeError("increment argument must be int or float")
fut = self.execute(b'ZINCRBY', key, increment, member)
return wait_convert(fut, int_or_float) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zrem'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def zrem(self, key, member, *members):
return self.execute(b'ZREM', key, member, *members) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '18']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zremrangebylex'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15']}; {'id': '4', 'type': 'identifier', ... | def zremrangebylex(self, key, min=b'-', max=b'+',
include_min=True, include_max=True):
if not isinstance(min, bytes):
raise TypeError("min argument must be bytes")
if not isinstance(max, bytes):
raise TypeError("max argument must be bytes")
if not m... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zremrangebyrank'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': ... | def zremrangebyrank(self, key, start, stop):
if not isinstance(start, int):
raise TypeError("start argument must be int")
if not isinstance(stop, int):
raise TypeError("stop argument must be int")
return self.execute(b'ZREMRANGEBYRANK', key, start, stop) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '22']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zremrangebyscore'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '12', '18', '19']}; {'id': '4', 'type': 'identifier... | def zremrangebyscore(self, key, min=float('-inf'), max=float('inf'),
*, exclude=None):
if not isinstance(min, (int, float)):
raise TypeError("min argument must be int or float")
if not isinstance(max, (int, float)):
raise TypeError("max argument must be i... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zrevrange'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '11']}; {'id': '4', 'type': 'identifier', 'child... | def zrevrange(self, key, start, stop, withscores=False, encoding=_NOTSET):
if not isinstance(start, int):
raise TypeError("start argument must be int")
if not isinstance(stop, int):
raise TypeError("stop argument must be int")
if withscores:
args = [b'WITHSCOR... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '34']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zrevrangebyscore'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '12', '18', '19', '22', '25', '28', '31']}; {'id': ... | def zrevrangebyscore(self, key, max=float('inf'), min=float('-inf'),
*, exclude=None, withscores=False,
offset=None, count=None, encoding=_NOTSET):
if not isinstance(min, (int, float)):
raise TypeError("min argument must be int or float")
if ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zscore'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def zscore(self, key, member):
fut = self.execute(b'ZSCORE', key, member)
return wait_convert(fut, optional_int_or_float) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zunionstore'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '9', '12']}; {'id': '4', 'type': 'identifier', 'chi... | def zunionstore(self, destkey, key, *keys,
with_weights=False, aggregate=None):
keys = (key,) + keys
numkeys = len(keys)
args = []
if with_weights:
assert all(isinstance(val, (list, tuple)) for val in keys), (
"All key arguments must be (ke... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zscan'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [],... | def zscan(self, key, cursor=0, match=None, count=None):
args = []
if match is not None:
args += [b'MATCH', match]
if count is not None:
args += [b'COUNT', count]
fut = self.execute(b'ZSCAN', key, cursor, *args)
def _converter(obj):
return (int(... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zpopmin'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '10']}; {'id': '4', 'type': 'identifier', 'children': [... | def zpopmin(self, key, count=None, *, encoding=_NOTSET):
if count is not None and not isinstance(count, int):
raise TypeError("count argument must be int")
args = []
if count is not None:
args.extend([count])
fut = self.execute(b'ZPOPMIN', key, *args, encoding=enc... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zpopmax'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '10']}; {'id': '4', 'type': 'identifier', 'children': [... | def zpopmax(self, key, count=None, *, encoding=_NOTSET):
if count is not None and not isinstance(count, int):
raise TypeError("count argument must be int")
args = []
if count is not None:
args.extend([count])
fut = self.execute(b'ZPOPMAX', key, *args, encoding=enc... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'uniq'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'container'},... | def uniq(container):
try:
return len(set(unbool(i) for i in container)) == len(container)
except TypeError:
try:
sort = sorted(unbool(i) for i in container)
sliced = itertools.islice(sort, 1, None)
for i, j in zip(sort, sliced):
if i == j:
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'split_traversal'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def split_traversal(traversal,
edges,
edges_hash=None):
traversal = np.asanyarray(traversal,
dtype=np.int64)
if edges_hash is None:
edges_hash = grouping.hashable_rows(
np.sort(edges, axis=1))
trav_edge = np.column_sta... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'fill_traversals'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def fill_traversals(traversals, edges, edges_hash=None):
edges = np.asanyarray(edges, dtype=np.int64)
edges.sort(axis=1)
if len(traversals) == 0:
return edges.copy()
if edges_hash is None:
edges_hash = grouping.hashable_rows(edges)
splits = []
for nodes in traversals:
spl... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'hashable_rows'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def hashable_rows(data, digits=None):
if len(data) == 0:
return np.array([])
as_int = float_to_int(data, digits=digits)
if len(as_int.shape) == 1:
return as_int
if len(as_int.shape) == 2 and as_int.shape[1] <= 4:
precision = int(np.floor(64 / as_int.shape[1]))
if np.abs(a... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'interpolate_nans_1d'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [... | def interpolate_nans_1d(x, y, kind='linear'):
x_sort_args = np.argsort(x)
x = x[x_sort_args]
y = y[x_sort_args]
nans = np.isnan(y)
if kind == 'linear':
y[nans] = np.interp(x[nans], x[~nans], y[~nans])
elif kind == 'log':
y[nans] = np.interp(np.log(x[nans]), np.log(x[~nans]), y[~n... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'interpolate_1d'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '8']}; {'id': '4', 'type': 'identifier', 'children': ... | def interpolate_1d(x, xp, *args, **kwargs):
r
fill_value = kwargs.pop('fill_value', np.nan)
axis = kwargs.pop('axis', 0)
x = np.asanyarray(x).reshape(-1)
ndim = xp.ndim
sort_args = np.argsort(xp, axis=axis)
sort_x = np.argsort(x)
sorter = broadcast_indices(xp, sort_args, ndim, axis)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'log_interpolate_1d'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '8']}; {'id': '4', 'type': 'identifier', 'childre... | def log_interpolate_1d(x, xp, *args, **kwargs):
r
fill_value = kwargs.pop('fill_value', np.nan)
axis = kwargs.pop('axis', 0)
log_x = np.log(x)
log_xp = np.log(xp)
return interpolate_1d(log_x, log_xp, *args, axis=axis, fill_value=fill_value) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_find_append_zero_crossings'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children'... | def _find_append_zero_crossings(x, y):
r
crossings = find_intersections(x[1:], y[1:], np.zeros_like(y[1:]) * y.units)
x = concatenate((x, crossings[0]))
y = concatenate((y, crossings[1]))
sort_idx = np.argsort(x)
x = x[sort_idx]
y = y[sort_idx]
keep_idx = np.ediff1d(x, to_end=[1]) > 0
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_ordered_objects'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def sort_ordered_objects(items, getter=lambda x: x):
return sorted(items, key=lambda x: getattr(getter(x), OrderedBase.CREATION_COUNTER_FIELD, -1)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'remove_fewwords_paragraphs'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def remove_fewwords_paragraphs(self):
all_nodes = self.parser.getElementsByTags(self.get_top_node(), ['*'])
all_nodes.reverse()
for el in all_nodes:
tag = self.parser.getTag(el)
text = self.parser.getText(el)
stop_words = self.stopwords_class(language=self.get... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compare_baselines'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def compare_baselines(old_baseline_filename, new_baseline_filename):
if old_baseline_filename == new_baseline_filename:
raise RedundantComparisonError
old_baseline = _get_baseline_from_file(old_baseline_filename)
new_baseline = _get_baseline_from_file(new_baseline_filename)
_remove_nonexistent_f... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_GetEventIdentifiers'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def _GetEventIdentifiers(self, event):
attributes = []
attribute_string = 'data_type: {0:s}'.format(event.data_type)
attributes.append(attribute_string)
for attribute_name, attribute_value in sorted(event.GetAttributes()):
if attribute_name in self._IDENTIFIER_EXCLUDED_ATTRIBUTES:
continue... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'PopEvents'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'},... | def PopEvents(self):
event = self.PopEvent()
while event:
yield event
event = self.PopEvent() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_CheckStatusAnalysisProcess'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children'... | def _CheckStatusAnalysisProcess(self, pid):
self._RaiseIfNotRegistered(pid)
if pid in self._completed_analysis_processes:
status_indicator = definitions.STATUS_INDICATOR_COMPLETED
process_status = {
'processing_status': status_indicator}
used_memory = 0
else:
process = self... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_ExportEvent'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': []... | def _ExportEvent(self, output_module, event, deduplicate_events=True):
if event.timestamp != self._export_event_timestamp:
self._FlushExportBuffer(
output_module, deduplicate_events=deduplicate_events)
self._export_event_timestamp = event.timestamp
self._export_event_heap.PushEvent(event) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_FlushExportBuffer'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': []... | def _FlushExportBuffer(self, output_module, deduplicate_events=True):
last_macb_group_identifier = None
last_content_identifier = None
macb_group = []
generator = self._export_event_heap.PopEvents()
for macb_group_identifier, content_identifier, event in generator:
if deduplicate_events and la... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_MergeEventTag'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'v... | def _MergeEventTag(self, storage_writer, attribute_container):
if attribute_container.CONTAINER_TYPE != 'event_tag':
return
event_identifier = attribute_container.GetEventIdentifier()
if not event_identifier:
return
stored_event_tag = self._event_tag_index.GetEventTagByIdentifier(
st... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_StartAnalysisProcesses'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children... | def _StartAnalysisProcesses(self, storage_writer, analysis_plugins):
logger.info('Starting analysis plugins.')
for analysis_plugin in analysis_plugins.values():
self._analysis_plugins[analysis_plugin.NAME] = analysis_plugin
process = self._StartWorkerProcess(analysis_plugin.NAME, storage_writer)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_StopAnalysisProcesses'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [],... | def _StopAnalysisProcesses(self, abort=False):
logger.debug('Stopping analysis processes.')
self._StopMonitoringProcesses()
if abort:
self._AbortTerminate()
if not self._use_zeromq:
logger.debug('Emptying queues.')
for event_queue in self._event_queues.values():
event_queue.Emp... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_UpdateForemanProcessStatus'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [],... | def _UpdateForemanProcessStatus(self):
used_memory = self._process_information.GetUsedMemory() or 0
display_name = getattr(self._merge_task, 'identifier', '')
self._processing_status.UpdateForemanStatus(
self._name, self._status, self._pid, used_memory, display_name,
self._number_of_consumed... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'workers_status'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'se... | def workers_status(self):
return [self._workers_status[identifier]
for identifier in sorted(self._workers_status.keys())] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'ProcessStorage'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'se... | def ProcessStorage(self):
self._CheckStorageFile(self._storage_file_path)
self._status_view.SetMode(self._status_view_mode)
self._status_view.SetStorageFileInformation(self._storage_file_path)
status_update_callback = (
self._status_view.GetAnalysisStatusUpdateCallback())
session = engine.Ba... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_state'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def sort_state(self, best_hyp_indices: mx.nd.NDArray):
self.states = [mx.nd.take(ds, best_hyp_indices) for ds in self.states] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '19', '27']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'rerank'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '15']}; {'id': '4', 'type': 'identifier', 'children': [], 'v... | def rerank(self, hypotheses: Dict[str, Any], reference: str) -> Dict[str, Any]:
scores = [self.scoring_function(hypothesis, reference) for hypothesis in hypotheses['translations']]
ranking = list(np.argsort(scores, kind='mergesort')[::-1])
reranked_hypotheses = self._sort_by_ranking(hypotheses, ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '18']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'load'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '9']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def load(self, path: str, k: Optional[int] = None):
load_time_start = time.time()
with open(path, 'rb') as inp:
_lex = np.load(inp)
loaded_k = _lex.shape[1]
if k is not None:
top_k = min(k, loaded_k)
if k > loaded_k:
logger.warning("Can... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_trg_ids'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def get_trg_ids(self, src_ids: np.ndarray) -> np.ndarray:
unique_src_ids = np.lib.arraysetops.unique(src_ids)
trg_ids = np.lib.arraysetops.union1d(self.always_allow, self.lex[unique_src_ids, :].reshape(-1))
return trg_ids |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'choice'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'... | def choice(self, obj):
tree_id = getattr(obj, self.queryset.model._mptt_meta.tree_id_attr, 0)
left = getattr(obj, self.queryset.model._mptt_meta.left_attr, 0)
return super(MPTTModelChoiceIterator,
self).choice(obj) + ((tree_id, left),) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_best_dataset_key'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def get_best_dataset_key(key, choices):
if key.wavelength is not None and choices:
nearest_wl = min([_wl_dist(key.wavelength, x.wavelength)
for x in choices if x.wavelength is not None])
choices = [c for c in choices
if _wl_dist(key.wavelength, c.waveleng... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '27']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_key'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15', '18', '21', '24']}; {'id': '4', 'type': 'id... | def get_key(key, key_container, num_results=1, best=True,
resolution=None, calibration=None, polarization=None,
level=None, modifiers=None):
if isinstance(key, numbers.Number):
key = DatasetID(wavelength=key, modifiers=None)
elif isinstance(key, (str, six.text_type)):
key... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'group_files'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'c... | def group_files(files_to_sort, reader=None, time_threshold=10,
group_keys=None, ppp_config_dir=None, reader_kwargs=None):
if reader is None:
raise ValueError("'reader' keyword argument is required.")
elif not isinstance(reader, (list, tuple)):
reader = [reader]
reader = reade... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sorted_filetype_items'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def sorted_filetype_items(self):
processed_types = []
file_type_items = deque(self.config['file_types'].items())
while len(file_type_items):
filetype, filetype_info = file_type_items.popleft()
requirements = filetype_info.get('requires')
if requirements is not... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'combine_hashes'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'ha... | def combine_hashes(hashes):
hasher = sha1()
for h in sorted(hashes):
h = ensure_binary(h)
hasher.update(h)
return hasher.hexdigest() if PY3 else hasher.hexdigest().decode('utf-8') |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '31']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'register_jvm_tool'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '10', '13', '16', '19', '22', '25', '28']}; {... | def register_jvm_tool(cls,
register,
key,
classpath_spec=None,
main=None,
custom_rules=None,
fingerprint=True,
classpath=None,
h... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_available_urls'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def get_available_urls(self, urls):
baseurl_to_urls = {self._baseurl(url): url for url in urls}
pingtimes = self._pinger.pings(list(baseurl_to_urls.keys()))
self._log.debug('Artifact cache server ping times: {}'
.format(', '.join(['{}: {:.6f} secs'.format(*p) for p in pingtimes])))
s... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'iterate'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def iterate(self, scopes):
scope_infos = [self._scope_to_info[s] for s in self._expand_tasks(scopes)]
if scope_infos:
for scope_info in self._expand_subsystems(scope_infos):
yield scope_info |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_expand_tasks'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def _expand_tasks(self, scopes):
expanded_scopes = set(scopes)
for scope, info in self._scope_to_info.items():
if info.category == ScopeInfo.TASK:
outer = enclosing_scope(scope)
while outer != GLOBAL_SCOPE:
if outer in expanded_scopes:
expanded_scopes.add(scope)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_all'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {... | def get_all(self):
return [{'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}
for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '4']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'all'}, {'id': '3', 'type': 'parameters', 'children': []}; {'id': '4', 'type': 'block', 'children': ['5']}, {'id': '5', 'type': 'return... | def all():
return [goal for _, goal in sorted(Goal._goal_by_name.items()) if goal.active] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'select_best_url'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def select_best_url(self):
best_url = self.parsed_urls[0]
try:
yield best_url
except Exception:
self.unsuccessful_calls[best_url] += 1
if self.unsuccessful_calls[best_url] > self.max_failures:
self.parsed_urls.rotate(-1)
self.unsuccessful_calls[best_url] = 0
raise
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_topological_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def _topological_sort(self, targets):
target_set = set(targets)
return [t for t in reversed(sort_targets(targets)) if t in target_set] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sortmerna_detailed_barplot'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def sortmerna_detailed_barplot (self):
keys = OrderedDict()
metrics = set()
for sample in self.sortmerna:
for key in self.sortmerna[sample]:
if not key in ["total", "rRNA", "non_rRNA"] and not "_pct" in key:
metrics.add(key)
for key in metr... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_short_chrom'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def _short_chrom(self, chrom):
default_allowed = set(["X"])
allowed_chroms = set(getattr(config, "goleft_indexcov_config", {}).get("chromosomes", []))
chrom_clean = chrom.replace("chr", "")
try:
chrom_clean = int(chrom_clean)
except ValueError:
if chrom_cl... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'deepvalues'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'mappin... | def deepvalues(mapping):
values = vals_sorted_by_key(mapping)
for obj in values:
mapping = False
try:
obj.items
except AttributeError:
pass
else:
mapping = True
for subobj in deepvalues(obj):
yield subobj
if ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'values'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'... | def values(self):
return [self.policy.header_fetch_parse(k, v)
for k, v in self._headers] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'items'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, {'i... | def items(self):
return [(k, self.policy.header_fetch_parse(k, v))
for k, v in self._headers] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_all'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def get_all(self, name, failobj=None):
values = []
name = name.lower()
for k, v in self._headers:
if k.lower() == name:
values.append(self.policy.header_fetch_parse(k, v))
if not values:
return failobj
return values |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'setup'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def setup(job, input_file_id, n, down_checkpoints):
job.fileStore.logToMaster("Starting the merge sort")
return job.addChildJobFn(down,
input_file_id, n,
down_checkpoints=down_checkpoints,
memory='600M').rv() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'down'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def down(job, input_file_id, n, down_checkpoints):
input_file = job.fileStore.readGlobalFile(input_file_id, cache=False)
length = os.path.getsize(input_file)
if length > n:
job.fileStore.logToMaster("Splitting file: %s of size: %s"
% (input_file_id, length), level=l... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'in_file... | def sort(in_file, out_file):
filehandle = open(in_file, 'r')
lines = filehandle.readlines()
filehandle.close()
lines.sort()
filehandle = open(out_file, 'w')
for line in lines:
filehandle.write(line)
filehandle.close() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'merge'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'f... | def merge(filehandle_1, filehandle_2, output_filehandle):
line2 = filehandle_2.readline()
for line1 in filehandle_1.readlines():
while line2 != '' and line2 <= line1:
output_filehandle.write(line2)
line2 = filehandle_2.readline()
output_filehandle.write(line1)
while l... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'decorateSubHeader'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [],... | def decorateSubHeader(title, columnWidths, options):
title = title.lower()
if title != options.sortCategory:
s = "| %*s%*s%*s%*s%*s " % (
columnWidths.getWidth(title, "min"), "min",
columnWidths.getWidth(title, "med"), "med",
columnWidths.getWidth(title, "ave"), "ave"... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sortJobs'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'job... | def sortJobs(jobTypes, options):
longforms = {"med": "median",
"ave": "average",
"min": "min",
"total": "total",
"max": "max",}
sortField = longforms[options.sortField]
if (options.sortCategory == "time" or
options.sortCategory == "... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_sort_tau_by_y'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def _sort_tau_by_y(self, y):
tau_y = self.tau_matrix[:, y]
tau_y[y] = np.NaN
temp = np.empty([self.n_nodes, 3])
temp[:, 0] = np.arange(self.n_nodes)
temp[:, 1] = tau_y
temp[:, 2] = abs(tau_y)
temp[np.isnan(temp)] = -10
tau_sorted = temp[temp[:, 2].argsort(... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_edge'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'edges'}... | def sort_edge(edges):
return sorted(edges, key=lambda x: (x.L, x.R)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '25']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'stanc'}, {'id': '3', 'type': 'parameters', 'children': ['4', '7', '10', '13', '16', '19', '22']}; {'id': '4', 'type': 'default_parame... | def stanc(file=None, charset='utf-8', model_code=None, model_name="anon_model",
include_paths=None, verbose=False, obfuscate_model_name=True):
if file and model_code:
raise ValueError("Specify stan model with `file` or `model_code`, "
"not both.")
if file is None and m... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'suggestion_list'}, {'id': '3', 'type': 'parameters', 'children': ['4', '8']}; {'id': '4', 'type': 'typed_parameter', 'children': ['5'... | def suggestion_list(input_: str, options: Collection[str]):
options_by_distance = {}
input_threshold = len(input_) // 2
for option in options:
distance = lexical_distance(input_, option)
threshold = max(input_threshold, len(option) // 2, 1)
if distance <= threshold:
optio... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '12', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'lexical_distance'}, {'id': '3', 'type': 'parameters', 'children': ['4', '8']}; {'id': '4', 'type': 'typed_parameter', 'children... | def lexical_distance(a_str: str, b_str: str) -> int:
if a_str == b_str:
return 0
a, b = a_str.lower(), b_str.lower()
a_len, b_len = len(a), len(b)
if a == b:
return 1
d = [[j for j in range(0, b_len + 1)]]
for i in range(1, a_len + 1):
d.append([i] + [0] * b_len)
for ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16', '22']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_suggested_type_names'}, {'id': '3', 'type': 'parameters', 'children': ['4', '8', '12']}; {'id': '4', 'type': 'typed_paramet... | def get_suggested_type_names(
schema: GraphQLSchema, type_: GraphQLOutputType, field_name: str
) -> List[str]:
if is_abstract_type(type_):
type_ = cast(GraphQLAbstractType, type_)
suggested_object_types = []
interface_usage_count: Dict[str, int] = defaultdict(int)
for possible_ty... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'instances_get'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': []... | def instances_get(opts, plugins, url_file_input, out):
instances = OrderedDict()
preferred_order = ['wordpress', 'joomla', 'drupal']
for cms_name in preferred_order:
for plugin in plugins:
plugin_name = plugin.__name__.lower()
if cms_name == plugin_name:
insta... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_signed_headers'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def get_signed_headers(headers):
signed_headers = []
for header in headers:
signed_headers.append(header.lower().strip())
return sorted(signed_headers) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'add_from_names_to_locals'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [... | def add_from_names_to_locals(self, node):
_key_func = lambda node: node.fromlineno
def sort_locals(my_list):
my_list.sort(key=_key_func)
for (name, asname) in node.names:
if name == "*":
try:
imported = node.do_import_module()
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'nearest'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def nearest(self, nodes):
myroot = self.root()
mylineno = self.fromlineno
nearest = None, 0
for node in nodes:
assert node.root() is myroot, (
"nodes %s and %s are not from the same module" % (self, node)
)
lineno = node.fromlineno
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'object_type'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def object_type(node, context=None):
try:
types = set(_object_type(node, context))
except exceptions.InferenceError:
return util.Uninferable
if len(types) > 1 or not types:
return util.Uninferable
return list(types)[0] |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_deffacts'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def get_deffacts(self):
return sorted(self._get_by_type(DefFacts), key=lambda d: d.order) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'json_dumps'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def json_dumps(self, data):
return json.dumps(
data,
separators=(',', ':'),
sort_keys=True,
cls=self.json_encoder,
ensure_ascii=False
).encode('utf8') |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'create_waveform_generator'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifi... | def create_waveform_generator(variable_params, data,
recalibration=None, gates=None,
**static_params):
try:
approximant = static_params['approximant']
except KeyError:
raise ValueError("no approximant provided in the static args")
g... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '17']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'rst_dict_table'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14']}; {'id': '4', 'type': 'identifier', 'chil... | def rst_dict_table(dict_, key_format=str, val_format=str, header=None,
sort=True):
keys, values = zip(*dict_.items())
keys = map(key_format, keys)
values = map(val_format, values)
nckey = max(map(len, keys))
ncval = max(map(len, values))
if header:
khead, vhead = heade... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'read_transforms_from_config'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children'... | def read_transforms_from_config(cp, section="transforms"):
trans = []
for subsection in cp.get_subsections(section):
name = cp.get_opt_tag(section, "name", subsection)
t = transforms[name].from_config(cp, section, subsection)
trans.append(t)
return order_transforms(trans) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'samples_from_cli'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9']}; {'id': '4', 'type': 'identifier', 'children'... | def samples_from_cli(self, opts, parameters=None, **kwargs):
if parameters is None and opts.parameters is None:
parameters = self.variable_args
elif parameters is None:
parameters = opts.parameters
_, extra_actions = self.extra_args_parser()
extra_args = [act.dest... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'data_from_cli'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'opt... | def data_from_cli(opts):
gates = gates_from_cli(opts)
psd_gates = psd_gates_from_cli(opts)
instruments = opts.instruments if opts.instruments is not None else []
strain_dict = strain_from_cli_multi_ifos(opts, instruments,
precision="double")
if not opts.g... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def sort(self, axis=-1, kind='quicksort', order=None):
try:
numpy.recarray.sort(self, axis=axis, kind=kind, order=order)
except ValueError:
if isinstance(order, list):
raise ValueError("Cannot process more than one order field")
self[:] = self[numpy.ar... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'emit_containers'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def emit_containers(self, containers, verbose=True):
containers = sorted(containers, key=lambda c: c.get('name'))
task_definition = {
'family': self.family,
'containerDefinitions': containers,
'volumes': self.volumes or []
}
if verbose:
ret... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_completions'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def sort_completions(completions_gen):
from knack.help import REQUIRED_TAG
def _get_weight(val):
priority = ''
if val.display_meta and val.display_meta.startswith(REQUIRED_TAG):
priority = ' '
return priority + val.text
return sorted(completions_gen, key=_get_weight) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'attr_list'}, {'id': '3', 'type': 'parameters', 'children': ['4', '7', '10']}; {'id': '4', 'type': 'default_parameter', 'children': ['... | def attr_list(label=None, kwargs=None, attributes=None):
content = a_list(label, kwargs, attributes)
if not content:
return ''
return ' [%s]' % content |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'mapping_items'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def mapping_items(mapping, _iteritems=_compat.iteritems):
if type(mapping) is dict:
return iter(sorted(_iteritems(mapping)))
return _iteritems(mapping) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'convert_tensor_to_probability_map'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier',... | def convert_tensor_to_probability_map(scope, operator, container):
'''
This converter tries to convert a special operator 'TensorToProbabilityMap' into a sequence of some ONNX operators.
Those operators are used to create a dictionary in which keys are class labels and values are the associated
probabil... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'group_and_sort_nodes'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def group_and_sort_nodes(self):
if self.node_grouping and not self.node_order:
if self.group_order == "alphabetically":
self.nodes = [
n
for n, d in sorted(
self.graph.nodes(data=True),
key=lambda... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.