sequence stringlengths 492 15.9k | code stringlengths 75 8.58k |
|---|---|
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 14; 2, function_name:sort; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:cmp; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:key; 10, None; 11, default_parameter; 11, 12; 11, 13; 12, identifi... | def sort(self, cmp=None, key=None, reverse=False):
if not key and self._keys:
key = self.KeyValue
super(CliTable, self).sort(cmp=cmp, key=key, reverse=reverse) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 14; 2, function_name:sort; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:cmp; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:key; 10, None; 11, default_parameter; 11, 12; 11, 13; 12, identifi... | def sort(self, cmp=None, key=None, reverse=False):
def _DefaultKey(value):
result = []
for key in self.header:
try:
result.append(float(value[key]))
except ValueError:
result.append(value[key])
return res... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:find_order; 3, parameters; 3, 4; 4, identifier:graph; 5, block; 5, 6; 5, 8; 6, expression_statement; 6, 7; 7, string:'''
Do a topological sort on the dependency graph dict.
'''; 8, while_statement; 8, 9; 8, 10; 9, identifier:graph; 10, ... | def find_order(graph):
'''
Do a topological sort on the dependency graph dict.
'''
while graph:
leftmost = [l for l, s in graph.items() if not s]
if not leftmost:
raise ValueError('Dependency cycle detected! %s' % graph)
leftmost.sort()
for result in leftmost:... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:do_sort; 3, parameters; 3, 4; 3, 5; 4, identifier:value; 5, default_parameter; 5, 6; 5, 7; 6, identifier:case_sensitive; 7, False; 8, block; 8, 9; 8, 41; 9, if_statement; 9, 10; 9, 12; 9, 35; 10, not_operator; 10, 11; 11, identifier:case_sensit... | def do_sort(value, case_sensitive=False):
if not case_sensitive:
def sort_func(item):
if isinstance(item, basestring):
item = item.lower()
return item
else:
sort_func = None
return sorted(seq, key=sort_func) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 13; 2, function_name:dedupe; 3, parameters; 3, 4; 3, 5; 3, 8; 4, identifier:contains_dupes; 5, default_parameter; 5, 6; 5, 7; 6, identifier:threshold; 7, integer:70; 8, default_parameter; 8, 9; 8, 10; 9, identifier:scorer; 10, attribute; 10, 11; 10, 12; 11, identi... | def dedupe(contains_dupes, threshold=70, scorer=fuzz.token_set_ratio):
extractor = []
for item in contains_dupes:
matches = extract(item, contains_dupes, limit=None, scorer=scorer)
filtered = [x for x in matches if x[1] > threshold]
if len(filtered) == 1:
extractor.append(fil... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:_process_and_sort; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:s; 5, identifier:force_ascii; 6, default_parameter; 6, 7; 6, 8; 7, identifier:full_process; 8, True; 9, block; 9, 10; 9, 25; 9, 33; 9, 45; 10, expression_statement; 10, 11; 11, a... | def _process_and_sort(s, force_ascii, full_process=True):
ts = utils.full_process(s, force_ascii=force_ascii) if full_process else s
tokens = ts.split()
sorted_string = u" ".join(sorted(tokens))
return sorted_string.strip() |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:token_sort_ratio; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 4, identifier:s1; 5, identifier:s2; 6, default_parameter; 6, 7; 6, 8; 7, identifier:force_ascii; 8, True; 9, default_parameter; 9, 10; 9, 11; 10, identifier:full_process; 11, True; 12, b... | def token_sort_ratio(s1, s2, force_ascii=True, full_process=True):
return _token_sort(s1, s2, partial=False, force_ascii=force_ascii, full_process=full_process) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:partial_token_sort_ratio; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 4, identifier:s1; 5, identifier:s2; 6, default_parameter; 6, 7; 6, 8; 7, identifier:force_ascii; 8, True; 9, default_parameter; 9, 10; 9, 11; 10, identifier:full_process; 11, Tru... | def partial_token_sort_ratio(s1, s2, force_ascii=True, full_process=True):
return _token_sort(s1, s2, partial=True, force_ascii=force_ascii, full_process=full_process) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:WRatio; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 4, identifier:s1; 5, identifier:s2; 6, default_parameter; 6, 7; 6, 8; 7, identifier:force_ascii; 8, True; 9, default_parameter; 9, 10; 9, 11; 10, identifier:full_process; 11, True; 12, block; 12, ... | def WRatio(s1, s2, force_ascii=True, full_process=True):
if full_process:
p1 = utils.full_process(s1, force_ascii=force_ascii)
p2 = utils.full_process(s2, force_ascii=force_ascii)
else:
p1 = s1
p2 = s2
if not utils.validate_string(p1):
return 0
if not utils.valida... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:sort_depth; 3, parameters; 3, 4; 3, 5; 4, identifier:vals; 5, default_parameter; 5, 6; 5, 7; 6, identifier:reverse; 7, False; 8, block; 8, 9; 8, 28; 8, 44; 9, expression_statement; 9, 10; 10, assignment; 10, 11; 10, 12; 11, identifier:lst; 12, ... | def sort_depth(vals, reverse=False):
lst = [[float(price), quantity] for price, quantity in vals.items()]
lst = sorted(lst, key=itemgetter(0), reverse=reverse)
return lst |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:_get_fields; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 4, identifier:attrs; 5, identifier:field_class; 6, default_parameter; 6, 7; 6, 8; 7, identifier:pop; 8, False; 9, default_parameter; 9, 10; 9, 11; 10, identifier:ordered; 11, False; 12, block... | def _get_fields(attrs, field_class, pop=False, ordered=False):
fields = [
(field_name, field_value)
for field_name, field_value in iteritems(attrs)
if is_instance_or_subclass(field_value, field_class)
]
if pop:
for field_name, _ in fields:
del attrs[field_name]
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 66; 2, function_name:extract_features; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 3, 20; 3, 23; 3, 28; 3, 33; 3, 38; 3, 43; 3, 48; 3, 53; 3, 58; 3, 63; 4, identifier:timeseries_container; 5, default_parameter; 5, 6; 5, 7; 6, identifier:default_fc_parame... | def extract_features(timeseries_container, default_fc_parameters=None,
kind_to_fc_parameters=None,
column_id=None, column_sort=None, column_kind=None, column_value=None,
chunksize=defaults.CHUNKSIZE,
n_jobs=defaults.N_PROCESSES, show_wa... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:convert_to_output_format; 3, parameters; 3, 4; 4, identifier:param; 5, block; 5, 6; 5, 34; 6, function_definition; 6, 7; 6, 8; 6, 10; 7, function_name:add_parenthesis_if_string_value; 8, parameters; 8, 9; 9, identifier:x; 10, block; 10, 11; 11,... | def convert_to_output_format(param):
def add_parenthesis_if_string_value(x):
if isinstance(x, string_types):
return '"' + str(x) + '"'
else:
return str(x)
return "__".join(str(key) + "_" + add_parenthesis_if_string_value(param[key]) for key in sorted(param.keys())) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:end_profiling; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:profiler; 5, identifier:filename; 6, default_parameter; 6, 7; 6, 8; 7, identifier:sorting; 8, None; 9, block; 9, 10; 9, 16; 9, 24; 9, 41; 9, 47; 10, expression_statement; 10, 11; 11,... | def end_profiling(profiler, filename, sorting=None):
profiler.disable()
s = six.StringIO()
ps = pstats.Stats(profiler, stream=s).sort_stats(sorting)
ps.print_stats()
with open(filename, "w+") as f:
_logger.info("[calculate_ts_features] Finished profiling of time series feature extraction")
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 95; 2, function_name:extract_relevant_features; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 3, 15; 3, 18; 3, 21; 3, 24; 3, 27; 3, 32; 3, 37; 3, 42; 3, 47; 3, 52; 3, 57; 3, 62; 3, 67; 3, 72; 3, 77; 3, 82; 3, 87; 3, 92; 4, identifier:timeseries_container; 5, ident... | def extract_relevant_features(timeseries_container, y, X=None,
default_fc_parameters=None,
kind_to_fc_parameters=None,
column_id=None, column_sort=None, column_kind=None, column_value=None,
show_warni... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:user_agents; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 6, return_statement; 6, 7; 7, parenthesized_expression; 7, 8; 8, call; 8, 9; 8, 58; 9, attribute; 9, 10; 9, 57; 10, call; 10, 11; 10, 44; 11, attribute; 11, 12; 11, 43; 12, c... | def user_agents(self):
return (self.get_query()
.select(
PageView.headers['User-Agent'],
fn.Count(PageView.id))
.group_by(PageView.headers['User-Agent'])
.order_by(fn.Count(PageView.id).desc())
.tuples()) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:languages; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 5, 14; 5, 31; 6, expression_statement; 6, 7; 7, assignment; 7, 8; 7, 9; 8, identifier:language; 9, subscript; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 11, identifier:PageVi... | def languages(self):
language = PageView.headers['Accept-Language']
first_language = fn.SubStr(
language,
1,
fn.StrPos(language, ';'))
return (self.get_query()
.select(first_language, fn.Count(PageView.id))
.group_by(first_langu... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:error_router; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:original_handler; 6, identifier:e; 7, block; 7, 8; 7, 28; 8, if_statement; 8, 9; 8, 14; 9, call; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 11, identifier:self; ... | def error_router(self, original_handler, e):
if self._has_fr_route():
try:
return self.handle_error(e)
except Exception:
pass
return original_handler(e) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 15; 2, function_name:smooth_knn_dist; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 4, identifier:distances; 5, identifier:k; 6, default_parameter; 6, 7; 6, 8; 7, identifier:n_iter; 8, integer:64; 9, default_parameter; 9, 10; 9, 11; 10, identifier:local_connectivi... | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
target = np.log2(k) * bandwidth
rho = np.zeros(distances.shape[0])
result = np.zeros(distances.shape[0])
for i in range(distances.shape[0]):
lo = 0.0
hi = NPY_INFINITY
mid = 1.0
ith_dista... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:_visible_units; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 6, for_statement; 6, 7; 6, 8; 6, 43; 7, identifier:u; 8, call; 8, 9; 8, 10; 9, identifier:sorted; 10, argument_list; 10, 11; 10, 20; 11, attribute; 11, 12; 11, 19; 12, att... | def _visible_units(self):
for u in sorted(self._obs.observation.raw_data.units,
key=lambda u: (u.pos.z, u.owner != 16, -u.radius, u.tag)):
yield u, point.Point.build(u.pos) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 19; 2, function_name:interp; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:coords; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:method; 10, string:'linear'; 11, default_parame... | def interp(self, coords=None, method='linear', assume_sorted=False,
kwargs={}, **coords_kwargs):
if self.dtype.kind not in 'uifc':
raise TypeError('interp only works for a numeric type array. '
'Given {}.'.format(self.dtype))
ds = self._to_temp_data... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 15; 2, function_name:interp_like; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 4, identifier:self; 5, identifier:other; 6, default_parameter; 6, 7; 6, 8; 7, identifier:method; 8, string:'linear'; 9, default_parameter; 9, 10; 9, 11; 10, identifier:assume_sorted; 1... | def interp_like(self, other, method='linear', assume_sorted=False,
kwargs={}):
if self.dtype.kind not in 'uifc':
raise TypeError('interp only works for a numeric type array. '
'Given {}.'.format(self.dtype))
ds = self._to_temp_dataset().interp_... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 1, 9; 2, function_name:is_uniform_spaced; 3, parameters; 3, 4; 3, 5; 4, identifier:arr; 5, dictionary_splat_pattern; 5, 6; 6, identifier:kwargs; 7, type; 7, 8; 8, identifier:bool; 9, block; 9, 10; 9, 22; 9, 31; 10, expression_statement; 10, 11; 11, assignment; ... | def is_uniform_spaced(arr, **kwargs) -> bool:
arr = np.array(arr, dtype=float)
diffs = np.diff(arr)
return bool(np.isclose(diffs.min(), diffs.max(), **kwargs)) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:broadcast_variables; 3, parameters; 3, 4; 4, list_splat_pattern; 4, 5; 5, identifier:variables; 6, block; 6, 7; 6, 14; 6, 21; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:dims_map; 10, call; 10, 11; 10, 12; 11, ident... | def broadcast_variables(*variables):
dims_map = _unified_dims(variables)
dims_tuple = tuple(dims_map)
return tuple(var.set_dims(dims_map) if var.dims != dims_tuple else var
for var in variables) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 19; 2, function_name:interp; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:coords; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:method; 10, string:'linear'; 11, default_parame... | def interp(self, coords=None, method='linear', assume_sorted=False,
kwargs={}, **coords_kwargs):
from . import missing
coords = either_dict_or_kwargs(coords, coords_kwargs, 'interp')
indexers = OrderedDict(self._validate_indexers(coords))
obj = self if assume_sorted else s... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 15; 2, function_name:interp_like; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 4, identifier:self; 5, identifier:other; 6, default_parameter; 6, 7; 6, 8; 7, identifier:method; 8, string:'linear'; 9, default_parameter; 9, 10; 9, 11; 10, identifier:assume_sorted; 1... | def interp_like(self, other, method='linear', assume_sorted=False,
kwargs={}):
coords = alignment.reindex_like_indexers(self, other)
numeric_coords = OrderedDict()
object_coords = OrderedDict()
for k, v in coords.items():
if v.dtype.kind in 'uifcMm':
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:transpose; 3, parameters; 3, 4; 3, 5; 4, identifier:self; 5, list_splat_pattern; 5, 6; 6, identifier:dims; 7, block; 7, 8; 7, 40; 7, 48; 7, 90; 8, if_statement; 8, 9; 8, 10; 9, identifier:dims; 10, block; 10, 11; 11, if_statement; 11, 12; 11, 2... | def transpose(self, *dims):
if dims:
if set(dims) ^ set(self.dims):
raise ValueError('arguments to transpose (%s) must be '
'permuted dataset dimensions (%s)'
% (dims, tuple(self.dims)))
ds = self.copy()
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 11; 2, function_name:to_dask_dataframe; 3, parameters; 3, 4; 3, 5; 3, 8; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:dim_order; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:set_index; 10, False; 11, block; 11, 12; 11, 18; 11, 2... | def to_dask_dataframe(self, dim_order=None, set_index=False):
import dask.array as da
import dask.dataframe as dd
if dim_order is None:
dim_order = list(self.dims)
elif set(dim_order) != set(self.dims):
raise ValueError(
'dim_order {} does not matc... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:interp; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:var; 5, identifier:indexes_coords; 6, identifier:method; 7, dictionary_splat_pattern; 7, 8; 8, identifier:kwargs; 9, block; 9, 10; 9, 20; 9, 37; 9, 49; 9, 56; 9, 72; 9, 80; 9, 94; 9, ... | def interp(var, indexes_coords, method, **kwargs):
if not indexes_coords:
return var.copy()
if method in ['linear', 'nearest']:
var, indexes_coords = _localize(var, indexes_coords)
kwargs['bounds_error'] = kwargs.get('bounds_error', False)
dims = list(indexes_coords)
x, new_x = zip(*... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:coerce_pandas_values; 3, parameters; 3, 4; 4, identifier:objects; 5, block; 5, 6; 5, 13; 5, 20; 5, 24; 5, 102; 6, import_from_statement; 6, 7; 6, 11; 7, relative_import; 7, 8; 7, 9; 8, import_prefix; 9, dotted_name; 9, 10; 10, identifier:datase... | def coerce_pandas_values(objects):
from .dataset import Dataset
from .dataarray import DataArray
out = []
for obj in objects:
if isinstance(obj, Dataset):
variables = obj
else:
variables = OrderedDict()
if isinstance(obj, PANDAS_TYPES):
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:unique_value_groups; 3, parameters; 3, 4; 3, 5; 4, identifier:ar; 5, default_parameter; 5, 6; 5, 7; 6, identifier:sort; 7, True; 8, block; 8, 9; 8, 23; 8, 37; 8, 60; 9, expression_statement; 9, 10; 10, assignment; 10, 11; 10, 14; 11, pattern_li... | def unique_value_groups(ar, sort=True):
inverse, values = pd.factorize(ar, sort=sort)
groups = [[] for _ in range(len(values))]
for n, g in enumerate(inverse):
if g >= 0:
groups[g].append(n)
return values, groups |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:order; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 5, 46; 6, if_statement; 6, 7; 6, 10; 6, 36; 7, attribute; 7, 8; 7, 9; 8, identifier:self; 9, identifier:stage; 10, block; 10, 11; 11, for_statement; 11, 12; 11, 13; 11, 14; 12, ide... | def order(self):
if self.stage:
for st in STAGES:
if st in self.stage:
stage = (STAGES.index(st), self.stage)
break
else:
stage = (len(STAGES),)
return (int(self.major), int(self.minor), int(self.patch)) + stage |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 19; 2, function_name:zadd; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 3, 13; 3, 16; 4, identifier:self; 5, identifier:name; 6, identifier:mapping; 7, default_parameter; 7, 8; 7, 9; 8, identifier:nx; 9, False; 10, default_parameter; 10, 11; 10, 12; 11, identifie... | def zadd(self, name, mapping, nx=False, xx=False, ch=False, incr=False):
if not mapping:
raise DataError("ZADD requires at least one element/score pair")
if nx and xx:
raise DataError("ZADD allows either 'nx' or 'xx', not both")
if incr and len(mapping) != 1:
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:zincrby; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:name; 6, identifier:amount; 7, identifier:value; 8, block; 8, 9; 8, 11; 9, expression_statement; 9, 10; 10, string:"Increment the score of ``value`` in sorted set... | def zincrby(self, name, amount, value):
"Increment the score of ``value`` in sorted set ``name`` by ``amount``"
return self.execute_command('ZINCRBY', name, amount, value) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 10; 2, function_name:zinterstore; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:dest; 6, identifier:keys; 7, default_parameter; 7, 8; 7, 9; 8, identifier:aggregate; 9, None; 10, block; 10, 11; 11, return_statement; 11, 12; 12, call; 12, ... | def zinterstore(self, dest, keys, aggregate=None):
return self._zaggregate('ZINTERSTORE', dest, keys, aggregate) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:zlexcount; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:name; 6, identifier:min; 7, identifier:max; 8, block; 8, 9; 9, return_statement; 9, 10; 10, call; 10, 11; 10, 14; 11, attribute; 11, 12; 11, 13; 12, identifier:... | def zlexcount(self, name, min, max):
return self.execute_command('ZLEXCOUNT', name, min, max) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:zpopmax; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:name; 6, default_parameter; 6, 7; 6, 8; 7, identifier:count; 8, None; 9, block; 9, 10; 9, 22; 9, 29; 10, expression_statement; 10, 11; 11, assignment; 11, 12; 11, 13; 1... | def zpopmax(self, name, count=None):
args = (count is not None) and [count] or []
options = {
'withscores': True
}
return self.execute_command('ZPOPMAX', name, *args, **options) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:zpopmin; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:name; 6, default_parameter; 6, 7; 6, 8; 7, identifier:count; 8, None; 9, block; 9, 10; 9, 22; 9, 29; 10, expression_statement; 10, 11; 11, assignment; 11, 12; 11, 13; 1... | def zpopmin(self, name, count=None):
args = (count is not None) and [count] or []
options = {
'withscores': True
}
return self.execute_command('ZPOPMIN', name, *args, **options) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:bzpopmax; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:keys; 6, default_parameter; 6, 7; 6, 8; 7, identifier:timeout; 8, integer:0; 9, block; 9, 10; 9, 19; 9, 27; 9, 34; 10, if_statement; 10, 11; 10, 14; 11, comparison_ope... | def bzpopmax(self, keys, timeout=0):
if timeout is None:
timeout = 0
keys = list_or_args(keys, None)
keys.append(timeout)
return self.execute_command('BZPOPMAX', *keys) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:bzpopmin; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:keys; 6, default_parameter; 6, 7; 6, 8; 7, identifier:timeout; 8, integer:0; 9, block; 9, 10; 9, 19; 9, 27; 9, 34; 10, if_statement; 10, 11; 10, 14; 11, comparison_ope... | def bzpopmin(self, keys, timeout=0):
if timeout is None:
timeout = 0
keys = list_or_args(keys, None)
keys.append(timeout)
return self.execute_command('BZPOPMIN', *keys) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:zremrangebylex; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:name; 6, identifier:min; 7, identifier:max; 8, block; 8, 9; 9, return_statement; 9, 10; 10, call; 10, 11; 10, 14; 11, attribute; 11, 12; 11, 13; 12, identi... | def zremrangebylex(self, name, min, max):
return self.execute_command('ZREMRANGEBYLEX', name, min, max) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:zremrangebyrank; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:name; 6, identifier:min; 7, identifier:max; 8, block; 8, 9; 9, return_statement; 9, 10; 10, call; 10, 11; 10, 14; 11, attribute; 11, 12; 11, 13; 12, ident... | def zremrangebyrank(self, name, min, max):
return self.execute_command('ZREMRANGEBYRANK', name, min, max) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:argsort_k_smallest; 3, parameters; 3, 4; 3, 5; 4, identifier:x; 5, identifier:k; 6, block; 6, 7; 6, 24; 6, 43; 6, 57; 6, 63; 7, if_statement; 7, 8; 7, 11; 8, comparison_operator:==; 8, 9; 8, 10; 9, identifier:k; 10, integer:0; 11, block; 11, 12... | def argsort_k_smallest(x, k):
if k == 0:
return np.array([], dtype=np.intp)
if k is None or k >= len(x):
return np.argsort(x)
indices = np.argpartition(x, k)[:k]
values = x[indices]
return indices[np.argsort(values)] |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:lookup; 3, parameters; 3, 4; 3, 8; 4, typed_parameter; 4, 5; 4, 6; 5, identifier:source_id; 6, type; 6, 7; 7, identifier:str; 8, typed_parameter; 8, 9; 8, 10; 9, identifier:schema_id; 10, type; 10, 11; 11, identifier:str; 12, block; 12, 13; 13... | async def lookup(source_id: str, schema_id: str):
try:
schema = Schema(source_id, '', '', [])
if not hasattr(Schema.lookup, "cb"):
schema.logger.debug("vcx_schema_get_attributes: Creating callback")
Schema.lookup.cb = create_cb(CFUNCTYPE(None, c_uint32, c_... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:interleave_keys; 3, parameters; 3, 4; 3, 5; 4, identifier:a; 5, identifier:b; 6, block; 6, 7; 6, 30; 7, function_definition; 7, 8; 7, 9; 7, 11; 8, function_name:interleave; 9, parameters; 9, 10; 10, identifier:args; 11, block; 11, 12; 12, retur... | def interleave_keys(a, b):
def interleave(args):
return ''.join([x for t in zip(*args) for x in t])
return int(''.join(interleave(format(x, '016b') for x in (a, b))), base=2) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 24; 2, function_name:pool; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 15; 3, 18; 3, 21; 4, identifier:data; 5, identifier:batch_size; 6, identifier:key; 7, default_parameter; 7, 8; 7, 9; 8, identifier:batch_size_fn; 9, lambda; 9, 10; 9, 14; 10, lambda_parameters; 1... | def pool(data, batch_size, key, batch_size_fn=lambda new, count, sofar: count,
random_shuffler=None, shuffle=False, sort_within_batch=False):
if random_shuffler is None:
random_shuffler = random.shuffle
for p in batch(data, batch_size * 100, batch_size_fn):
p_batch = batch(sorted(p, key... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:data; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 5, 63; 6, if_statement; 6, 7; 6, 10; 6, 25; 6, 55; 7, attribute; 7, 8; 7, 9; 8, identifier:self; 9, identifier:sort; 10, block; 10, 11; 11, expression_statement; 11, 12; 12, assignm... | def data(self):
if self.sort:
xs = sorted(self.dataset, key=self.sort_key)
elif self.shuffle:
xs = [self.dataset[i] for i in self.random_shuffler(range(len(self.dataset)))]
else:
xs = self.dataset
return xs |
0, module; 0, 1; 0, 302; 1, function_definition; 1, 2; 1, 3; 1, 20; 2, function_name:color_table; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 4, identifier:color; 5, default_parameter; 5, 6; 5, 7; 6, identifier:N; 7, integer:1; 8, default_parameter; 8, 9; 8, 10; 9, identifier:sort; 10, False; 11, default_para... | def color_table(color, N=1, sort=False, sort_values=False, inline=False, as_html=False):
if isinstance(color, list):
c_ = ''
rgb_tup = [normalize(c) for c in color]
if sort:
rgb_tup.sort()
elif isinstance(color, dict):
c_ = ''
items = [(k, normalize(v), hex_to... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:get_nearest_edge; 3, parameters; 3, 4; 3, 5; 4, identifier:G; 5, identifier:point; 6, block; 6, 7; 6, 15; 6, 28; 6, 43; 6, 69; 6, 84; 6, 92; 6, 99; 6, 119; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:start_time; 10,... | def get_nearest_edge(G, point):
start_time = time.time()
gdf = graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)
graph_edges = gdf[["geometry", "u", "v"]].values.tolist()
edges_with_distances = [
(
graph_edge,
Point(tuple(reversed(point))).distance(graph_edge[0])
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 13; 2, function_name:get_http_headers; 3, parameters; 3, 4; 3, 7; 3, 10; 4, default_parameter; 4, 5; 4, 6; 5, identifier:user_agent; 6, None; 7, default_parameter; 7, 8; 7, 9; 8, identifier:referer; 9, None; 10, default_parameter; 10, 11; 10, 12; 11, identifier:ac... | def get_http_headers(user_agent=None, referer=None, accept_language=None):
if user_agent is None:
user_agent = settings.default_user_agent
if referer is None:
referer = settings.default_referer
if accept_language is None:
accept_language = settings.default_accept_language
headers... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:_has_sorted_sa_indices; 3, parameters; 3, 4; 3, 5; 4, identifier:s_indices; 5, identifier:a_indices; 6, block; 6, 7; 6, 14; 6, 60; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:L; 10, call; 10, 11; 10, 12; 11, identif... | def _has_sorted_sa_indices(s_indices, a_indices):
L = len(s_indices)
for i in range(L-1):
if s_indices[i] > s_indices[i+1]:
return False
if s_indices[i] == s_indices[i+1]:
if a_indices[i] >= a_indices[i+1]:
return False
return True |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:_generate_a_indptr; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:num_states; 5, identifier:s_indices; 6, identifier:out; 7, block; 7, 8; 7, 12; 7, 18; 7, 47; 8, expression_statement; 8, 9; 9, assignment; 9, 10; 9, 11; 10, identifier:idx; 11, ... | def _generate_a_indptr(num_states, s_indices, out):
idx = 0
out[0] = 0
for s in range(num_states-1):
while(s_indices[idx] == s):
idx += 1
out[s+1] = idx
out[num_states] = len(s_indices) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:sort_topologically; 3, parameters; 3, 4; 4, identifier:dag; 5, block; 5, 6; 5, 15; 5, 19; 5, 29; 5, 86; 5, 106; 6, expression_statement; 6, 7; 7, assignment; 7, 8; 7, 9; 8, identifier:dag; 9, call; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 1... | def sort_topologically(dag):
dag = copy.deepcopy(dag)
sorted_nodes = []
independent_nodes = deque(get_independent_nodes(dag))
while independent_nodes:
node = independent_nodes.popleft()
sorted_nodes.append(node)
downstream_nodes = dag[node]
while downstream_nodes:
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 8; 2, function_name:set_topological_dag_upstreams; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:dag; 5, identifier:ops; 6, identifier:op_runs; 7, identifier:runs_by_ops; 8, block; 8, 9; 8, 20; 9, expression_statement; 9, 10; 10, assignment; 10, 11; 10, 12;... | def set_topological_dag_upstreams(dag, ops, op_runs, runs_by_ops):
sorted_ops = dags.sort_topologically(dag=dag)
for op_id in sorted_ops:
op_run_id = runs_by_ops[op_id]
op_run = op_runs[op_run_id]
set_op_upstreams(op_run=op_run, op=ops[op_id]) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:generate_from_text; 3, parameters; 3, 4; 3, 5; 4, identifier:self; 5, identifier:text; 6, block; 6, 7; 6, 16; 6, 23; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:words; 10, call; 10, 11; 10, 14; 11, attribute; 11, 12... | def generate_from_text(self, text):
words = self.process_text(text)
self.generate_from_frequencies(words)
return self |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:_update_pods_metrics; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:instance; 6, identifier:pods; 7, block; 7, 8; 7, 15; 7, 101; 7, 111; 8, expression_statement; 8, 9; 9, assignment; 9, 10; 9, 11; 10, identifier:tags_map; 1... | def _update_pods_metrics(self, instance, pods):
tags_map = defaultdict(int)
for pod in pods['items']:
pod_meta = pod.get('metadata', {})
pod_tags = self.kubeutil.get_pod_creator_tags(pod_meta, legacy_rep_controller_tag=True)
services = self.kubeutil.match_services_for... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:get_agent_tags; 3, parameters; 3, 4; 3, 5; 4, identifier:since; 5, identifier:to; 6, block; 6, 7; 6, 23; 6, 42; 6, 49; 6, 62; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:agent_tags; 10, call; 10, 11; 10, 12; 11, ide... | def get_agent_tags(since, to):
agent_tags = sorted(parse_version_info(t) for t in git_tag_list(r'^\d+\.\d+\.\d+$'))
if to:
to = parse_version_info(to)
else:
to = agent_tags[-1]
since = parse_version_info(since)
agent_tags = [t for t in agent_tags if since <= t <= to]
return [str(... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:sort; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:key_or_list; 6, default_parameter; 6, 7; 6, 8; 7, identifier:direction; 8, None; 9, block; 9, 10; 9, 16; 9, 26; 9, 37; 10, expression_statement; 10, 11; 11, call; 11, 12; ... | def sort(self, key_or_list, direction=None):
self.__check_okay_to_chain()
keys = helpers._index_list(key_or_list, direction)
self.__ordering = helpers._index_document(keys)
return self |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 23; 2, function_name:find_one_and_replace; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 3, 13; 3, 16; 3, 21; 4, identifier:self; 5, identifier:filter; 6, identifier:replacement; 7, default_parameter; 7, 8; 7, 9; 8, identifier:projection; 9, None; 10, default_para... | def find_one_and_replace(self, filter, replacement,
projection=None, sort=None, upsert=False,
return_document=ReturnDocument.BEFORE, **kwargs):
common.validate_ok_for_replace(replacement)
kwargs['update'] = replacement
return self.__find_... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 23; 2, function_name:find_one_and_update; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 3, 13; 3, 16; 3, 21; 4, identifier:self; 5, identifier:filter; 6, identifier:update; 7, default_parameter; 7, 8; 7, 9; 8, identifier:projection; 9, None; 10, default_parameter;... | def find_one_and_update(self, filter, update,
projection=None, sort=None, upsert=False,
return_document=ReturnDocument.BEFORE, **kwargs):
common.validate_ok_for_update(update)
kwargs['update'] = update
return self.__find_and_modify(filter, ... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 26; 2, function_name:feature_correlation; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 3, 15; 3, 18; 3, 21; 3, 24; 4, identifier:X; 5, identifier:y; 6, default_parameter; 6, 7; 6, 8; 7, identifier:ax; 8, None; 9, default_parameter; 9, 10; 9, 11; 10, identifier:me... | def feature_correlation(X, y, ax=None, method='pearson',
labels=None, sort=False, feature_index=None,
feature_names=None, **kwargs):
viz = FeatureCorrelation(ax, method, labels, sort,
feature_index, feature_names, **kwargs)
viz.fit(X, ... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 29; 2, function_name:dispersion; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 3, 15; 3, 18; 3, 21; 3, 24; 3, 27; 4, identifier:words; 5, identifier:corpus; 6, default_parameter; 6, 7; 6, 8; 7, identifier:y; 8, None; 9, default_parameter; 9, 10; 9, 11; 10, identif... | def dispersion(words, corpus, y=None, ax=None, colors=None, colormap=None,
labels=None, annotate_docs=False, ignore_case=False, **kwargs):
visualizer = DispersionPlot(
words, ax=ax, colors=colors, colormap=colormap,
ignore_case=ignore_case, labels=labels,
annotate_docs=annotat... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:sorted_product_set; 3, parameters; 3, 4; 3, 5; 4, identifier:array_a; 5, identifier:array_b; 6, block; 6, 7; 7, return_statement; 7, 8; 8, subscript; 8, 9; 8, 37; 9, call; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 11, identifier:np; 12, iden... | def sorted_product_set(array_a, array_b):
return np.sort(
np.concatenate(
[array_a[i] * array_b for i in xrange(len(array_a))], axis=0)
)[::-1] |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:_get_sorted_inputs; 3, parameters; 3, 4; 4, identifier:filename; 5, block; 5, 6; 5, 59; 5, 81; 5, 99; 5, 103; 5, 107; 5, 133; 6, with_statement; 6, 7; 6, 20; 7, with_clause; 7, 8; 8, with_item; 8, 9; 9, as_pattern; 9, 10; 9, 18; 10, call; 10, 1... | def _get_sorted_inputs(filename):
with tf.gfile.Open(filename) as f:
records = f.read().split("\n")
inputs = [record.strip() for record in records]
if not inputs[-1]:
inputs.pop()
input_lens = [(i, len(line.split())) for i, line in enumerate(inputs)]
sorted_input_lens = sorted(input_lens, key=la... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:games_by_time; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:start_game; 6, identifier:end_game; 7, block; 7, 8; 7, 12; 7, 44; 7, 88; 8, expression_statement; 8, 9; 9, assignment; 9, 10; 9, 11; 10, identifier:move_count; 11... | def games_by_time(self, start_game, end_game):
move_count = b'move_count'
rows = self.bt_table.read_rows(
ROWCOUNT_PREFIX.format(start_game),
ROWCOUNT_PREFIX.format(end_game),
filter_=bigtable_row_filters.ColumnRangeFilter(
METADATA, move_count, move_c... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:bleakest_moves; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:start_game; 6, identifier:end_game; 7, block; 7, 8; 7, 12; 7, 44; 7, 98; 8, expression_statement; 8, 9; 9, assignment; 9, 10; 9, 11; 10, identifier:bleak; 11, st... | def bleakest_moves(self, start_game, end_game):
bleak = b'bleakest_q'
rows = self.bt_table.read_rows(
ROW_PREFIX.format(start_game),
ROW_PREFIX.format(end_game),
filter_=bigtable_row_filters.ColumnRangeFilter(
METADATA, bleak, bleak))
def parse... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 13; 2, function_name:_generate_subtokens; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 4, identifier:token_counts; 5, identifier:alphabet; 6, identifier:min_count; 7, default_parameter; 7, 8; 7, 9; 8, identifier:num_iterations; 9, integer:4; 10, default_parameter... | def _generate_subtokens(
token_counts, alphabet, min_count, num_iterations=4,
reserved_tokens=None):
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
subtoken_list = reserved_tokens + list(alphabet)
max_subtoken_length = 1
for i in xrange(num_iterations):
tf.logging.info("\tGenerati... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:sparse_svd; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:sparse_matrix; 5, identifier:num_values; 6, identifier:max_iter; 7, block; 7, 8; 7, 20; 7, 36; 7, 55; 7, 76; 8, if_statement; 8, 9; 8, 12; 9, comparison_operator:<=; 9, 10; 9, 11; 10, i... | def sparse_svd(sparse_matrix, num_values, max_iter):
if num_values <= 0:
raise ValueError("num_values should be > 0 but instead is %d." % num_values)
if max_iter is not None and max_iter < 0:
raise ValueError("max_iter should be >= 0 but instead is %d." % max_iter)
if max_iter is None:
max_iter = FLAG... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 13; 2, function_name:build_collate_fn; 3, parameters; 3, 4; 3, 7; 3, 10; 4, default_parameter; 4, 5; 4, 6; 5, identifier:batch_first; 6, False; 7, default_parameter; 7, 8; 7, 9; 8, identifier:parallel; 9, True; 10, default_parameter; 10, 11; 10, 12; 11, identifier... | def build_collate_fn(batch_first=False, parallel=True, sort=False):
def collate_seq(seq):
lengths = [len(s) for s in seq]
batch_length = max(lengths)
shape = (batch_length, len(seq))
seq_tensor = torch.full(shape, config.PAD, dtype=torch.int64)
for i, s in enumerate(seq):
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 4; 2, function_name:get_golden_chunk_records; 3, parameters; 4, block; 4, 5; 4, 21; 5, expression_statement; 5, 6; 6, assignment; 6, 7; 6, 8; 7, identifier:pattern; 8, call; 8, 9; 8, 14; 9, attribute; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 11, identifier:os;... | def get_golden_chunk_records():
pattern = os.path.join(fsdb.golden_chunk_dir(), '*.zz')
return sorted(tf.gfile.Glob(pattern), reverse=True)[:FLAGS.window_size] |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:_sorted_results; 3, parameters; 3, 4; 3, 5; 4, identifier:self; 5, identifier:results_dicts; 6, block; 6, 7; 6, 13; 6, 28; 6, 32; 6, 45; 7, expression_statement; 7, 8; 8, call; 8, 9; 8, 10; 9, identifier:print; 10, argument_list; 10, 11; 10, 12... | def _sorted_results(self, results_dicts):
print('results dicts:', results_dicts)
sorted_dict = sorted(results_dicts, key=lambda k: k['start_time'])
results = []
for entry in sorted_dict:
results.append(entry['dt'])
return results |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 4; 2, function_name:get_models; 3, parameters; 4, block; 4, 5; 4, 24; 4, 39; 4, 62; 5, expression_statement; 5, 6; 6, assignment; 6, 7; 6, 8; 7, identifier:all_models; 8, call; 8, 9; 8, 12; 9, attribute; 9, 10; 9, 11; 10, identifier:gfile; 11, identifier:Glob; 12,... | def get_models():
all_models = gfile.Glob(os.path.join(models_dir(), '*.meta'))
model_filenames = [os.path.basename(m) for m in all_models]
model_numbers_names = sorted([
(shipname.detect_model_num(m), shipname.detect_model_name(m))
for m in model_filenames])
return model_numbers_names |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 17; 1, 19; 2, function_name:sorted_by; 3, parameters; 3, 4; 4, typed_parameter; 4, 5; 4, 6; 5, identifier:key; 6, type; 6, 7; 7, generic_type; 7, 8; 7, 9; 8, identifier:Callable; 9, type_parameter; 9, 10; 9, 15; 10, type; 10, 11; 11, list:[raw_types.Qid]; 11, 12; ... | def sorted_by(key: Callable[[raw_types.Qid], Any]) -> 'QubitOrder':
return QubitOrder(lambda qubits: tuple(sorted(qubits, key=key))) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 32; 1, 36; 2, function_name:diagonalize_real_symmetric_and_sorted_diagonal_matrices; 3, parameters; 3, 4; 3, 10; 3, 16; 3, 17; 3, 22; 3, 27; 4, typed_parameter; 4, 5; 4, 6; 5, identifier:symmetric_matrix; 6, type; 6, 7; 7, attribute; 7, 8; 7, 9; 8, identifier:np; ... | def diagonalize_real_symmetric_and_sorted_diagonal_matrices(
symmetric_matrix: np.ndarray,
diagonal_matrix: np.ndarray,
*,
rtol: float = 1e-5,
atol: float = 1e-8,
check_preconditions: bool = True) -> np.ndarray:
if check_preconditions:
if (np.any(np.imag(symme... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 34; 1, 48; 2, function_name:findall_operations_between; 3, parameters; 3, 4; 3, 5; 3, 17; 3, 29; 4, identifier:self; 5, typed_parameter; 5, 6; 5, 7; 6, identifier:start_frontier; 7, type; 7, 8; 8, generic_type; 8, 9; 8, 10; 9, identifier:Dict; 10, type_parameter; ... | def findall_operations_between(self,
start_frontier: Dict[ops.Qid, int],
end_frontier: Dict[ops.Qid, int],
omit_crossing_operations: bool = False
) -> List[Tuple[int, ops.Operation... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:_GetUnsortedNotifications; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:queue_shard; 6, default_parameter; 6, 7; 6, 8; 7, identifier:notifications_by_session_id; 8, None; 9, block; 9, 10; 9, 19; 9, 33; 9, 121; 10, if_state... | def _GetUnsortedNotifications(self,
queue_shard,
notifications_by_session_id=None):
if notifications_by_session_id is None:
notifications_by_session_id = {}
end_time = self.frozen_timestamp or rdfvalue.RDFDatetime.Now()
for notification i... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 11; 2, function_name:Dump; 3, parameters; 3, 4; 3, 5; 3, 8; 4, identifier:obj; 5, default_parameter; 5, 6; 5, 7; 6, identifier:sort_keys; 7, False; 8, default_parameter; 8, 9; 8, 10; 9, identifier:encoder; 10, None; 11, block; 11, 12; 11, 36; 11, 56; 12, expressio... | def Dump(obj,
sort_keys = False,
encoder = None):
text = json.dumps(
obj,
indent=2,
sort_keys=sort_keys,
ensure_ascii=False,
cls=encoder,
separators=_SEPARATORS)
if compatibility.PY2 and isinstance(text, bytes):
text = text.decode("utf-8")
return text |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:NamedPlaceholders; 3, parameters; 3, 4; 4, identifier:iterable; 5, block; 5, 6; 5, 26; 6, expression_statement; 6, 7; 7, assignment; 7, 8; 7, 9; 8, identifier:placeholders; 9, call; 9, 10; 9, 13; 10, attribute; 10, 11; 10, 12; 11, string:", "; ... | def NamedPlaceholders(iterable):
placeholders = ", ".join("%({})s".format(key) for key in sorted(iterable))
return "({})".format(placeholders) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:Columns; 3, parameters; 3, 4; 4, identifier:iterable; 5, block; 5, 6; 5, 13; 6, expression_statement; 6, 7; 7, assignment; 7, 8; 7, 9; 8, identifier:columns; 9, call; 9, 10; 9, 11; 10, identifier:sorted; 11, argument_list; 11, 12; 12, identifie... | def Columns(iterable):
columns = sorted(iterable)
return "({})".format(", ".join("`{}`".format(col) for col in columns)) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:GetArtifactsForCollection; 3, parameters; 3, 4; 3, 5; 4, identifier:os_name; 5, identifier:artifact_list; 6, block; 6, 7; 6, 15; 6, 23; 7, expression_statement; 7, 8; 8, assignment; 8, 9; 8, 10; 9, identifier:artifact_arranger; 10, call; 10, 11... | def GetArtifactsForCollection(os_name, artifact_list):
artifact_arranger = ArtifactArranger(os_name, artifact_list)
artifact_names = artifact_arranger.GetArtifactsInProperOrder()
return artifact_names |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:_FilterOutPathInfoDuplicates; 3, parameters; 3, 4; 4, identifier:path_infos; 5, block; 5, 6; 5, 10; 5, 39; 5, 66; 5, 94; 6, expression_statement; 6, 7; 7, assignment; 7, 8; 7, 9; 8, identifier:pi_dict; 9, dictionary; 10, for_statement; 10, 11; ... | def _FilterOutPathInfoDuplicates(path_infos):
pi_dict = {}
for pi in path_infos:
path_key = (pi.path_type, pi.GetPathID())
pi_dict.setdefault(path_key, []).append(pi)
def _SortKey(pi):
return (
pi.stat_entry.st_ctime,
pi.stat_entry.st_mtime,
pi.stat_entry.st_atime,
pi.s... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:DrainTaskSchedulerQueueForClient; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:client; 6, default_parameter; 6, 7; 6, 8; 7, identifier:max_count; 8, None; 9, block; 9, 10; 9, 21; 9, 28; 9, 37; 9, 45; 9, 230; 9, 245; 9, 267... | def DrainTaskSchedulerQueueForClient(self, client, max_count=None):
if max_count is None:
max_count = self.max_queue_size
if max_count <= 0:
return []
client = rdf_client.ClientURN(client)
start_time = time.time()
if data_store.RelationalDBEnabled():
action_requests = data_store.RE... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 71; 2, function_name:federated_query; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 3, 12; 3, 15; 3, 18; 3, 21; 3, 24; 3, 27; 3, 30; 3, 33; 3, 36; 3, 39; 3, 42; 3, 45; 3, 48; 3, 51; 3, 54; 3, 57; 3, 60; 3, 63; 3, 66; 3, 69; 4, identifier:self; 5, identifier:environment_i... | def federated_query(self,
environment_id,
filter=None,
query=None,
natural_language_query=None,
passages=None,
aggregation=None,
count=None,
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 27; 2, function_name:query_relations; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 3, 13; 3, 16; 3, 19; 3, 22; 3, 25; 4, identifier:self; 5, identifier:environment_id; 6, identifier:collection_id; 7, default_parameter; 7, 8; 7, 9; 8, identifier:entities; 9, None;... | def query_relations(self,
environment_id,
collection_id,
entities=None,
context=None,
sort=None,
filter=None,
count=None,
eviden... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 22; 2, function_name:query_log; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 3, 20; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:filter; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:query; 10, None; 11, default_paramete... | def query_log(self,
filter=None,
query=None,
count=None,
offset=None,
sort=None,
**kwargs):
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_heade... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 22; 2, function_name:list_workspaces; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 3, 20; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:page_limit; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:include_count; 10, None; 11... | def list_workspaces(self,
page_limit=None,
include_count=None,
sort=None,
cursor=None,
include_audit=None,
**kwargs):
headers = {}
if 'headers' in kwargs:
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 55; 2, function_name:list_feedback; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 3, 14; 3, 17; 3, 20; 3, 23; 3, 26; 3, 29; 3, 32; 3, 35; 3, 38; 3, 41; 3, 44; 3, 47; 3, 50; 3, 53; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:feedback_type; 7, None... | def list_feedback(self,
feedback_type=None,
before=None,
after=None,
document_title=None,
model_id=None,
model_version=None,
category_removed=None,
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 7; 2, function_name:multi_index_insert_row; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:df; 5, identifier:index_row; 6, identifier:values_row; 7, block; 7, 8; 7, 32; 7, 49; 7, 60; 7, 88; 8, expression_statement; 8, 9; 9, assignment; 9, 10; 9, 11; 10, identifier... | def multi_index_insert_row(df, index_row, values_row):
row_index = pd.MultiIndex(levels=[[i] for i in index_row],
labels=[[0] for i in index_row])
row = pd.DataFrame(values_row, index=row_index, columns=df.columns)
df = pd.concat((df, row))
if df.index.lexsort_depth == len(... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:substring_search; 3, parameters; 3, 4; 3, 5; 4, identifier:word; 5, identifier:collection; 6, block; 6, 7; 7, return_statement; 7, 8; 8, list_comprehension; 8, 9; 8, 10; 8, 16; 9, identifier:item; 10, for_in_clause; 10, 11; 10, 12; 11, identifi... | def substring_search(word, collection):
return [item for item in sorted(collection) if item.startswith(word)] |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 5; 2, function_name:_nodes; 3, parameters; 3, 4; 4, identifier:self; 5, block; 5, 6; 6, return_statement; 6, 7; 7, call; 7, 8; 7, 9; 8, identifier:list; 9, argument_list; 9, 10; 10, call; 10, 11; 10, 12; 11, identifier:set; 12, argument_list; 12, 13; 13, list_comp... | def _nodes(self):
return list(set([node for node, timeslice in
super(DynamicBayesianNetwork, self).nodes()])) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:add_edge; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 4, identifier:self; 5, identifier:start; 6, identifier:end; 7, dictionary_splat_pattern; 7, 8; 8, identifier:kwargs; 9, block; 9, 10; 9, 144; 9, 211; 9, 225; 10, try_statement; 10, 11; 10, 136; 1... | def add_edge(self, start, end, **kwargs):
try:
if len(start) != 2 or len(end) != 2:
raise ValueError('Nodes must be of type (node, time_slice).')
elif not isinstance(start[1], int) or not isinstance(end[1], int):
raise ValueError('Nodes must be of type (no... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 14; 2, function_name:estimate; 3, parameters; 3, 4; 3, 5; 3, 8; 3, 11; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:start; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:tabu_length; 10, integer:0; 11, default_parameter; 11, 12; 1... | def estimate(self, start=None, tabu_length=0, max_indegree=None):
epsilon = 1e-8
nodes = self.state_names.keys()
if start is None:
start = DAG()
start.add_nodes_from(nodes)
elif not isinstance(start, DAG) or not set(start.nodes()) == set(nodes):
raise ... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:add_node; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:node; 6, default_parameter; 6, 7; 6, 8; 7, identifier:weight; 8, None; 9, block; 9, 10; 9, 64; 10, if_statement; 10, 11; 10, 31; 10, 55; 11, boolean_operator:and; 11, ... | def add_node(self, node, weight=None):
if isinstance(node, tuple) and len(node) == 2 and isinstance(node[1], dict):
node, attrs = node
if attrs.get('weight', None) is not None:
attrs['weight'] = weight
else:
attrs = {'weight': weight}
super(DAG... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:add_nodes_from; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:nodes; 6, default_parameter; 6, 7; 6, 8; 7, identifier:weights; 8, None; 9, block; 9, 10; 9, 17; 10, expression_statement; 10, 11; 11, assignment; 11, 12; 11, 13... | def add_nodes_from(self, nodes, weights=None):
nodes = list(nodes)
if weights:
if len(nodes) != len(weights):
raise ValueError("The number of elements in nodes and weights"
"should be equal.")
for index in range(len(nodes)):
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 11; 2, function_name:rank_items; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 8; 4, identifier:self; 5, identifier:userid; 6, identifier:user_items; 7, identifier:selected_items; 8, default_parameter; 8, 9; 8, 10; 9, identifier:recalculate_user; 10, False; 11, block;... | def rank_items(self, userid, user_items, selected_items, recalculate_user=False):
if max(selected_items) >= user_items.shape[1] or min(selected_items) < 0:
raise IndexError("Some of selected itemids are not in the model")
liked_vector = user_items[userid]
recommendations = liked_vect... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 11; 2, function_name:get_sorted_structure; 3, parameters; 3, 4; 3, 5; 3, 8; 4, identifier:self; 5, default_parameter; 5, 6; 5, 7; 6, identifier:key; 7, None; 8, default_parameter; 8, 9; 8, 10; 9, identifier:reverse; 10, False; 11, block; 11, 12; 11, 25; 12, expres... | def get_sorted_structure(self, key=None, reverse=False):
sites = sorted(self, key=key, reverse=reverse)
return self.__class__.from_sites(sites, charge=self._charge) |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 16; 2, function_name:from_str; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 7; 3, 10; 3, 13; 4, identifier:cls; 5, identifier:input_string; 6, identifier:fmt; 7, default_parameter; 7, 8; 7, 9; 8, identifier:primitive; 9, False; 10, default_parameter; 10, 11; 10, 12; 11, id... | def from_str(cls, input_string, fmt, primitive=False, sort=False,
merge_tol=0.0):
from pymatgen.io.cif import CifParser
from pymatgen.io.vasp import Poscar
from pymatgen.io.cssr import Cssr
from pymatgen.io.xcrysden import XSF
from pymatgen.io.atat import Mcsqs
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 6; 2, function_name:get_transition_chempots; 3, parameters; 3, 4; 3, 5; 4, identifier:self; 5, identifier:element; 6, block; 6, 7; 6, 21; 6, 25; 6, 49; 6, 53; 6, 99; 6, 105; 7, if_statement; 7, 8; 7, 13; 8, comparison_operator:not; 8, 9; 8, 10; 9, identifier:eleme... | def get_transition_chempots(self, element):
if element not in self.elements:
raise ValueError("get_transition_chempots can only be called with "
"elements in the phase diagram.")
critical_chempots = []
for facet in self.facets:
chempots = self... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 12; 2, function_name:from_dir; 3, parameters; 3, 4; 3, 5; 3, 6; 3, 9; 4, identifier:cls; 5, identifier:top; 6, default_parameter; 6, 7; 6, 8; 7, identifier:exts; 8, None; 9, default_parameter; 9, 10; 9, 11; 10, identifier:exclude_dirs; 11, string:"_*"; 12, block; ... | def from_dir(cls, top, exts=None, exclude_dirs="_*"):
pseudos = []
if exts == "all_files":
for f in [os.path.join(top, fn) for fn in os.listdir(top)]:
if os.path.isfile(f):
try:
p = Pseudo.from_file(f)
if p:
... |
0, module; 0, 1; 1, function_definition; 1, 2; 1, 3; 1, 9; 2, function_name:sorted; 3, parameters; 3, 4; 3, 5; 3, 6; 4, identifier:self; 5, identifier:attrname; 6, default_parameter; 6, 7; 6, 8; 7, identifier:reverse; 8, False; 9, block; 9, 10; 9, 14; 9, 48; 10, expression_statement; 10, 11; 11, assignment; 11, 12; 11,... | def sorted(self, attrname, reverse=False):
attrs = []
for i, pseudo in self:
try:
a = getattr(pseudo, attrname)
except AttributeError:
a = np.inf
attrs.append((i, a))
return self.__class__([self[a[0]] for a in sorted(attrs, key=... |
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