sequence stringlengths 1.19k 35k | code stringlengths 75 8.58k |
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{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_keys'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'dat... | def get_keys(data_list, leading_columns=LEADING_COLUMNS):
all_keys = set().union(*(list(d.keys()) for d in data_list))
leading_keys = []
for key in leading_columns:
if key not in all_keys:
continue
leading_keys.append(key)
all_keys.remove(key)
return leading_keys + so... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'to_file'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [... | def to_file(self, f, sorted=True, relativize=True, nl=None):
if sys.hexversion >= 0x02030000:
str_type = basestring
else:
str_type = str
if nl is None:
opts = 'w'
else:
opts = 'wb'
if isinstance(f, str_type):
f = file(f,... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_sorted_keys'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def _sorted_keys(self, keys):
sorted_keys = []
if ('epoch' in keys) and ('epoch' not in self.keys_ignored_):
sorted_keys.append('epoch')
for key in sorted(keys):
if not (
(key in ('epoch', 'dur')) or
(key in self.keys_ignored_) or
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_values'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def sort_values(expr, by, ascending=True):
if not isinstance(by, list):
by = [by, ]
by = [it(expr) if inspect.isfunction(it) else it for it in by]
return SortedCollectionExpr(expr, _sorted_fields=by, _ascending=ascending,
_schema=expr._schema) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'reshuffle'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def reshuffle(expr, by=None, sort=None, ascending=True):
by = by or RandomScalar()
grouped = expr.groupby(by)
if sort:
grouped = grouped.sort_values(sort, ascending=ascending)
return ReshuffledCollectionExpr(_input=grouped, _schema=expr._schema) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cumsum'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'childr... | def cumsum(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
if expr._data_type == types.boolean:
output_type = types.int64
else:
output_type = expr._data_type
return _cumulative_op(expr, CumSum, sort=sort, ascending=ascending,
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cummax'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'childr... | def cummax(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
return _cumulative_op(expr, CumMax, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=following) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cummin'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'childr... | def cummin(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
return _cumulative_op(expr, CumMin, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=following) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cummean'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'child... | def cummean(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
data_type = _stats_type(expr)
return _cumulative_op(expr, CumMean, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=following, ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cummedian'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'chi... | def cummedian(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
data_type = _stats_type(expr)
return _cumulative_op(expr, CumMedian, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=follo... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cumcount'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'chil... | def cumcount(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
data_type = types.int64
return _cumulative_op(expr, CumCount, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=following, dat... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cumstd'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'childr... | def cumstd(expr, sort=None, ascending=True, unique=False,
preceding=None, following=None):
data_type = _stats_type(expr)
return _cumulative_op(expr, CumStd, sort=sort, ascending=ascending,
unique=unique, preceding=preceding,
following=following, dat... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'nth_value'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children':... | def nth_value(expr, nth, skip_nulls=False, sort=None, ascending=True):
return _cumulative_op(expr, NthValue, data_type=expr._data_type, sort=sort,
ascending=ascending, _nth=nth, _skip_nulls=skip_nulls) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'rank'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'e... | def rank(expr, sort=None, ascending=True):
return _rank_op(expr, Rank, types.int64, sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'dense_rank'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def dense_rank(expr, sort=None, ascending=True):
return _rank_op(expr, DenseRank, types.int64, sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'percent_rank'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def percent_rank(expr, sort=None, ascending=True):
return _rank_op(expr, PercentRank, types.float64, sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'row_number'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def row_number(expr, sort=None, ascending=True):
return _rank_op(expr, RowNumber, types.int64, sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'qcut'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def qcut(expr, bins, labels=False, sort=None, ascending=True):
if labels is None or labels:
raise NotImplementedError('Showing bins or customizing labels not supported')
return _rank_op(expr, QCut, types.int64, sort=sort, ascending=ascending,
_bins=bins) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'cume_dist'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'value... | def cume_dist(expr, sort=None, ascending=True):
return _rank_op(expr, CumeDist, types.float64, sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'lag'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def lag(expr, offset, default=None, sort=None, ascending=True):
return _shift_op(expr, Lag, offset, default=default,
sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '15']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'lead'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12']}; {'id': '4', 'type': 'identifier', 'children': [], ... | def lead(expr, offset, default=None, sort=None, ascending=True):
return _shift_op(expr, Lead, offset, default=default,
sort=sort, ascending=ascending) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'value_counts'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [... | def value_counts(expr, sort=True, ascending=False, dropna=False):
names = [expr.name, 'count']
typos = [expr.dtype, types.int64]
return ValueCounts(_input=expr, _schema=Schema.from_lists(names, typos),
_sort=sort, _ascending=ascending, _dropna=dropna) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'last'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def last(self, limit=1, **kwargs):
return self.collection_instance(
self.db_adapter(
db_name=kwargs.get('db'),
role=kwargs.get('role', 'replica')
).select(
where='created_at IS NOT NULL',
order='created_at DESC',... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '4']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_compile_itemsort'}, {'id': '3', 'type': 'parameters', 'children': []}; {'id': '4', 'type': 'block', 'children': ['5', '7', '16', '27'... | def _compile_itemsort():
'''return sort function of mappings'''
def is_extra(key_):
return key_ is Extra
def is_remove(key_):
return isinstance(key_, Remove)
def is_marker(key_):
return isinstance(key_, Marker)
def is_type(key_):
return inspect.isclass(key_)
def i... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_compile_dict'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value':... | def _compile_dict(self, schema):
base_validate = self._compile_mapping(
schema, invalid_msg='dictionary value')
groups_of_exclusion = {}
groups_of_inclusion = {}
for node in schema:
if isinstance(node, Exclusive):
g = groups_of_exclusion.setdefault... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'to_list'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def to_list(self, length):
if length is not None:
if not isinstance(length, int):
raise TypeError('length must be an int, not %r' % length)
elif length < 0:
raise ValueError('length must be non-negative')
if self._query_flags() & _QUERY_OPTIONS['ta... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'find'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '7']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'se... | def find(self, *args, **kwargs):
cursor = self.delegate.find(*args, **kwargs)
grid_out_cursor = create_class_with_framework(
AgnosticGridOutCursor, self._framework, self.__module__)
return grid_out_cursor(cursor, self.collection) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_serial_poller'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'se... | def _serial_poller(self):
while True:
_next = dict(self._poller.poll(POLLING_FREQUENCY_MS))
if self._halt_read_file.fileno() in _next:
log.debug("Poller [{}]: halt".format(hash(self)))
self._halt_read_file.read()
break
elif self... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '11']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'enqueue_at'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '9']}; {'id': '4', 'type': 'identifier', 'children':... | def enqueue_at(self, scheduled_time, func, *args, **kwargs):
timeout = kwargs.pop('timeout', None)
job_id = kwargs.pop('job_id', None)
job_ttl = kwargs.pop('job_ttl', None)
job_result_ttl = kwargs.pop('job_result_ttl', None)
job_description = kwargs.pop('job_description', None)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_getMostActiveCells'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value'... | def _getMostActiveCells(self):
poolingActivation = self._poolingActivation
nonZeroCells = numpy.argwhere(poolingActivation > 0)[:,0]
poolingActivationSubset = poolingActivation[nonZeroCells] + \
self._poolingActivation_tieBreaker[nonZeroCells]
potentialUnionSDR = nonZeroCel... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'printNetwork'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'netw... | def printNetwork(network):
print "The network has",len(network.regions.values()),"regions"
for p in range(network.getMaxPhase()):
print "=== Phase",p
for region in network.regions.values():
if network.getPhases(region.name)[0] == p:
print " ",region.name |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'argmaxMulti'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def argmaxMulti(a, groupKeys, assumeSorted=False):
if not assumeSorted:
sorter = np.argsort(groupKeys, kind="mergesort")
a = a[sorter]
groupKeys = groupKeys[sorter]
_, indices, lengths = np.unique(groupKeys, return_index=True,
return_counts=True)
maxValues = np.maximu... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'getAllCellsInColumns'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def getAllCellsInColumns(columns, cellsPerColumn):
return ((columns * cellsPerColumn).reshape((-1, 1)) +
np.arange(cellsPerColumn, dtype="uint32")).flatten() |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'shuffle_sparse_matrix_and_labels'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'chil... | def shuffle_sparse_matrix_and_labels(matrix, labels):
print "Shuffling data"
new_matrix = matrix.toDense()
rng_state = numpy.random.get_state()
numpy.random.shuffle(new_matrix)
numpy.random.set_state(rng_state)
numpy.random.shuffle(labels)
print "Data shuffled"
return SM32(new_matrix), numpy.asarray(lab... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compute'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def compute(self, sensorToBodyByColumn, sensorToSpecificObjectByColumn):
votesByCell = np.zeros(self.cellCount, dtype="int")
self.activeSegmentsByColumn = []
for (connections,
activeSensorToBodyCells,
activeSensorToSpecificObjectCells) in zip(self.connectionsByColumn,
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'metricCompute'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def metricCompute(self, sensorToBody, bodyToSpecificObject):
overlaps = self.metricConnections.computeActivity({
"bodyToSpecificObject": bodyToSpecificObject,
"sensorToBody": sensorToBody,
})
self.activeMetricSegments = np.where(overlaps >= 2)[0]
self.activeCells = np.unique(
self.metr... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compute'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'child... | def compute(self, feedforwardInput=(), lateralInputs=(),
feedforwardGrowthCandidates=None, learn=True,
predictedInput = None,):
if feedforwardGrowthCandidates is None:
feedforwardGrowthCandidates = feedforwardInput
if not learn:
self._computeInferenceMode(feedforwardInput... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_learn'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9', '10', '11', '12', '13']}; {'id': '4', 'type': ... | def _learn(
permanences, rng,
activeCells, activeInput, growthCandidateInput,
sampleSize, initialPermanence, permanenceIncrement,
permanenceDecrement, connectedPermanence):
permanences.incrementNonZerosOnOuter(
activeCells, activeInput, permanenceIncrement)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'compute'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'child... | def compute(self, deltaLocation=(), newLocation=(),
featureLocationInput=(), featureLocationGrowthCandidates=(),
learn=True):
prevActiveCells = self.activeCells
self.activeDeltaSegments = np.where(
(self.internalConnections.computeActivity(
prevActiveCells, self.connect... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'getmerge'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '10']}; {'id': '4', 'type': 'identifier', 'children': ... | def getmerge(self, path, dst, newline=False, check_crc=False):
''' Get all the files in the directories that
match the source file pattern and merge and sort them to only
one file on local fs.
:param paths: Directory containing files that will be merged
:type paths: string
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'countByValue'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self... | def countByValue(self):
return self.transform(
lambda rdd: self._context._context.parallelize(
rdd.countByValue().items())) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_contiguous_offsets'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'v... | def _contiguous_offsets(self, offsets):
offsets.sort()
for i in range(len(offsets) - 1):
assert offsets[i] + 1 == offsets[i + 1], \
"Offsets not contiguous: %s" % (offsets,)
return offsets |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_collapse'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'interva... | def _collapse(intervals):
span = None
for start, stop in intervals:
if span is None:
span = _Interval(start, stop)
elif start <= span.stop < stop:
span = _Interval(span.start, stop)
elif start > span.stop:
yield span
span = _Interval(start,... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'remove'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children': [], 'va... | def remove(self, index=None, hash=None, keepSorted=True):
if index is not None:
clibrebound.reb_remove(byref(self), index, keepSorted)
if hash is not None:
hash_types = c_uint32, c_uint, c_ulong
PY3 = sys.version_info[0] == 3
if PY3:
string... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'particles'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'},... | def particles(self):
sim = self._sim.contents
ps = []
if self.testparticle>=0:
N = 1
else:
N = sim.N-sim.N_var
ParticleList = Particle*N
ps = ParticleList.from_address(ctypes.addressof(sim._particles.contents)+self.index*ctypes.sizeof(Particle))
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'filter_queryset'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': ... | def filter_queryset(self, request, queryset, view):
self.ordering_param = view.SORT
ordering = self.get_ordering(request, queryset, view)
if ordering:
return queryset.order_by(*ordering)
return queryset |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_ordering'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children': [],... | def get_ordering(self, request, queryset, view):
params = view.get_request_feature(view.SORT)
if params:
fields = [param.strip() for param in params]
valid_ordering, invalid_ordering = self.remove_invalid_fields(
queryset, fields, view
)
if... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'remove_invalid_fields'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'child... | def remove_invalid_fields(self, queryset, fields, view):
valid_orderings = []
invalid_orderings = []
for term in fields:
stripped_term = term.lstrip('-')
reverse_sort_term = '' if len(stripped_term) is len(term) else '-'
ordering = self.ordering_for(stripped_t... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '13', '17']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_format_dataframe'}, {'id': '3', 'type': 'parameters', 'children': ['4', '10']}; {'id': '4', 'type': 'typed_parameter', 'childr... | def _format_dataframe(
df: pd.DataFrame, nautical_units=True
) -> pd.DataFrame:
if "callsign" in df.columns and df.callsign.dtype == object:
df.callsign = df.callsign.str.strip()
if nautical_units:
df.altitude = df.altitude / 0.3048
if "geoaltitude" in df.... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'find_package_indexes_in_dir'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children'... | def find_package_indexes_in_dir(self, simple_dir):
packages = sorted(
{
canonicalize_name(x)
for x in os.listdir(simple_dir)
}
)
packages = [x for x in packages if os.path.isdir(os.path.join(simple_dir, x))]
return packages |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '17']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'factorize'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '11', '14']}; {'id': '4', 'type': 'identifier', 'chil... | def factorize(train, test, features, na_value=-9999, full=False, sort=True):
for column in features:
if full:
vs = pd.concat([train[column], test[column]])
labels, indexer = pd.factorize(vs, sort=sort)
else:
labels, indexer = pd.factorize(train[column], sort=sort)... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'preferred_ordinal'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def preferred_ordinal(cls, attr_name):
attr_name = cls.map(attr_name)
if attr_name in cls.preferred_order:
ordinal = cls.preferred_order.index(attr_name)
else:
ordinal = len(cls.preferred_order)
return (ordinal, attr_name) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'patience_sort'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'xs'... | def patience_sort(xs):
'''Patience sort an iterable, xs.
This function generates a series of pairs (x, pile), where "pile"
is the 0-based index of the pile "x" should be placed on top of.
Elements of "xs" must be less-than comparable.
'''
pile_tops = list()
for x in xs:
pile = bisect... |
{'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': 'args'}, {'id... | def sort(args):
p = OptionParser(sort.__doc__)
p.add_option("--sizes", default=False, action="store_true",
help="Sort by decreasing size [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
fastafile, = args
sortedfastafile = ... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'posmap'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, {'... | def posmap(args):
p = OptionParser(posmap.__doc__)
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(p.print_help())
frgscffile, fastafile, scf = args
cmd = "faOneRecord {0} {1}".format(fastafile, scf)
scffastafile = scf + ".fasta"
if not op.exists(scffastafile):
sh... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'shred'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, {'i... | def shred(args):
p = OptionParser(shred.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
s, = args
u = UnitigLayout(s)
u.shred()
u.print_to_file(inplace=True) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'index'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, {'i... | def index(args):
p = OptionParser(index.__doc__)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(p.print_help())
frgscffile, = args
gzfile = frgscffile + ".gz"
cmd = "bgzip -c {0}".format(frgscffile)
if not op.exists(gzfile):
sh(cmd, outfile=gzfile)
tbifile = ... |
{'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': 'args'}, {'id... | def sort(args):
valid_sort_methods = ("unix", "topo")
p = OptionParser(sort.__doc__)
p.add_option("--method", default="unix", choices=valid_sort_methods,
help="Specify sort method [default: %default]")
p.add_option("-i", dest="inplace", default=False, action="store_true",
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '9']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_layout'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'valu... | def sort_layout(thread, listfile, column=0):
from jcvi.formats.base import DictFile
outfile = listfile.rsplit(".", 1)[0] + ".sorted.list"
threadorder = thread.order
fw = open(outfile, "w")
lt = DictFile(listfile, keypos=column, valuepos=None)
threaded = []
imported = set()
for t in threa... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'fromgroups'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}... | def fromgroups(args):
from jcvi.formats.bed import Bed
p = OptionParser(fromgroups.__doc__)
opts, args = p.parse_args(args)
if len(args) < 2:
sys.exit(not p.print_help())
groupsfile = args[0]
bedfiles = args[1:]
beds = [Bed(x) for x in bedfiles]
fp = open(groupsfile)
groups =... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'merge_paths'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def merge_paths(paths, weights=None):
G = make_paths(paths, weights=weights)
G = reduce_paths(G)
return G |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'build'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, {'i... | def build(args):
from jcvi.apps.cdhit import deduplicate
from jcvi.apps.vecscreen import mask
from jcvi.formats.fasta import sort
p = OptionParser(build.__doc__)
p.add_option("--nodedup", default=False, action="store_true",
help="Do not deduplicate [default: deduplicate]")
opts,... |
{'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': 'args'}, {'id... | def sort(args):
import jcvi.formats.blast
return jcvi.formats.blast.sort(args + ["--coords"]) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'coverage'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'args'}, ... | def coverage(args):
p = OptionParser(coverage.__doc__)
p.add_option("--format", default="bigwig",
choices=("bedgraph", "bigwig", "coverage"),
help="Output format")
p.add_option("--nosort", default=False, action="store_true",
help="Do not sort BAM")
p.se... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': '_number_finder'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], 'v... | def _number_finder(s, regex, numconv):
s = regex.split(s)
if len(s) == 1:
return tuple(s)
s = remove_empty(s)
for i in range(len(s)):
try:
s[i] = numconv(s[i])
except ValueError:
pass
if not isinstance(s[0], six.string_types):
return [''] + s
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '20']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'index_natsorted'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '11', '14', '17']}; {'id': '4', 'type': 'identifier', 'ch... | def index_natsorted(seq, key=lambda x: x, number_type=float, signed=True, exp=True):
from operator import itemgetter
item1 = itemgetter(1)
index_seq_pair = [[x, key(y)] for x, y in zip(range(len(seq)), seq)]
index_seq_pair.sort(key=lambda x: natsort_key(item1(x),
... |
{'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': 'args'}, {'id... | def sort(args):
p = OptionParser(sort.__doc__)
p.add_option("-i", "--inplace", dest="inplace",
default=False, action="store_true",
help="Sort bed file in place [default: %default]")
p.add_option("-u", dest="unique",
default=False, action="store_true",
help="Un... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'by_image_seq'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': ... | def by_image_seq(blocks, image_seq):
return list(filter(lambda block: blocks[block].ec_hdr.image_seq == image_seq, blocks)) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'by_vol_id'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'bl... | def by_vol_id(blocks, slist=None):
vol_blocks = {}
for i in blocks:
if slist and i not in slist:
continue
elif not blocks[i].is_valid:
continue
if blocks[i].vid_hdr.vol_id not in vol_blocks:
vol_blocks[blocks[i].vid_hdr.vol_id] = []
vol_blocks[... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'by_type'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'bloc... | def by_type(blocks, slist=None):
layout = []
data = []
int_vol = []
unknown = []
for i in blocks:
if slist and i not in slist:
continue
if blocks[i].is_vtbl and blocks[i].is_valid:
layout.append(i)
elif blocks[i].is_internal_vol and blocks[i].is_valid:... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'group_pairs'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': '... | def group_pairs(blocks, layout_blocks_list):
image_dict={}
for block_id in layout_blocks_list:
image_seq=blocks[block_id].ec_hdr.image_seq
if image_seq not in image_dict:
image_dict[image_seq]=[block_id]
else:
image_dict[image_seq].append(block_id)
log(group_p... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'read_cBpack'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'filen... | def read_cBpack(filename):
with gzip.open(filename, 'rb') as infile:
data = msgpack.load(infile, raw=False)
header = data[0]
if (
not isinstance(header, dict) or header.get('format') != 'cB'
or header.get('version') != 1
):
raise ValueError("Unexpected header: %r" % heade... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'addDiscreteOutcomeConstantMean'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifie... | def addDiscreteOutcomeConstantMean(distribution, x, p, sort = False):
'''
Adds a discrete outcome of x with probability p to an existing distribution,
holding constant the relative probabilities of other outcomes and overall mean.
Parameters
----------
distribution : [np.array]
Two eleme... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '10']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'runStickyEregressionsInStata'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9']}; {'id': '4', 'type': 'i... | def runStickyEregressionsInStata(infile_name,interval_size,meas_err,sticky,all_specs,stata_exe):
'''
Runs regressions for the main tables of the StickyC paper in Stata and produces a
LaTeX table with results for one "panel". Running in Stata allows production of
the KP-statistic, for which there is curr... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'capability_functions'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def capability_functions(self, fn):
if _debug: Collector._debug("capability_functions %r", fn)
fns = []
for cls in self.capabilities:
xfn = getattr(cls, fn, None)
if _debug: Collector._debug(" - cls, xfn: %r, %r", cls, xfn)
if xfn:
fns.appen... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'add'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'}, ... | def add(self, *items):
for item in items:
self.unsorted.append(item)
key = item[0]
self.index[key] = item
return self |
{'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': 'self'}, {'id... | def sort(self):
self.sorted = list()
self.pushed = set()
for item in self.unsorted:
popped = []
self.push(item)
while len(self.stack):
try:
top = self.top()
ref = next(top[1])
refd = s... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '6']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'push'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'},... | def push(self, item):
if item in self.pushed:
return
frame = (item, iter(item[1]))
self.stack.append(frame)
self.pushed.add(item) |
{'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': 'self'},... | def sort(self, content):
v = content.value
if isinstance(v, Object):
md = v.__metadata__
md.ordering = self.ordering(content.real)
return self |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'alerts'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'self'... | def alerts(self, alert_level='High'):
alerts = self.zap.core.alerts()
alert_level_value = self.alert_levels[alert_level]
alerts = sorted((a for a in alerts if self.alert_levels[a['risk']] >= alert_level_value),
key=lambda k: self.alert_levels[k['risk']], reverse=True)
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'assortativity_bin'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5']}; {'id': '4', 'type': 'identifier', 'children': [], 'val... | def assortativity_bin(CIJ, flag=0):
'''
The assortativity coefficient is a correlation coefficient between the
degrees of all nodes on two opposite ends of a link. A positive
assortativity coefficient indicates that nodes tend to link to other
nodes with the same or similar degree.
Parameters
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sort_coords'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 'coord... | def sort_coords(coord):
import iris
order = {'T': -2, 'Z': -1, 'X': 1, 'Y': 2}
axis = iris.util.guess_coord_axis(coord)
return (order.get(axis, 0), coord and coord.name()) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '7']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'separate_groups'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def separate_groups(groups, key, total):
optimum, extra = compute_optimum(len(groups), total)
over_loaded, under_loaded, optimal = _smart_separate_groups(groups, key, total)
if not extra:
return over_loaded, under_loaded
potential_under_loaded = [
group for group in optimal
if ke... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '17']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zrange'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '11', '14']}; {'id': '4', 'type': 'identifier', 'ch... | async def zrange(self, name, start, end, desc=False, withscores=False,
score_cast_func=float):
if desc:
return await self.zrevrange(name, start, end, withscores,
score_cast_func)
pieces = ['ZRANGE', name, start, end]
if wit... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '8']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'zremrangebyscore'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7']}; {'id': '4', 'type': 'identifier', 'children':... | async def zremrangebyscore(self, name, min, max):
return await self.execute_command('ZREMRANGEBYSCORE', name, min, max) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '33']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'georadius'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '9', '12', '15', '18', '21', '24', '27', '30']};... | async def georadius(self, name, longitude, latitude, radius, unit=None,
withdist=False, withcoord=False, withhash=False, count=None,
sort=None, store=None, store_dist=None):
return await self._georadiusgeneric('GEORADIUS',
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '32']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'georadiusbymember'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '8', '11', '14', '17', '20', '23', '26', '29'... | async def georadiusbymember(self, name, member, radius, unit=None,
withdist=False, withcoord=False, withhash=False,
count=None, sort=None, store=None, store_dist=None):
return await self._georadiusgeneric('GEORADIUSBYMEMBER',
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '32']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'search'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17', '20', '23', '26', '29']}; {'id': '4', 'type... | def search(self, page=0, per_page=50, sort=None, q=None,
include_totals=True, fields=None, from_param=None, take=None,
include_fields=True):
params = {
'per_page': per_page,
'page': page,
'include_totals': str(include_totals).lower(),
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '32']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'list'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17', '20', '23', '26', '29']}; {'id': '4', 'type':... | def list(self, page=0, per_page=25, sort=None, connection=None, q=None,
search_engine=None, include_totals=True, fields=None,
include_fields=True):
params = {
'per_page': per_page,
'page': page,
'include_totals': str(include_totals).lower(),
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '18']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_log_events'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15']}; {'id': '4', 'type': 'identifier', ... | def get_log_events(self, user_id, page=0, per_page=50, sort=None,
include_totals=False):
params = {
'per_page': per_page,
'page': page,
'include_totals': str(include_totals).lower(),
'sort': sort
}
url = self._url('{}/logs'.f... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '14']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_activities'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11']}; {'id': '4', 'type': 'identifier', 'children':... | def get_activities(self, before=None, after=None, limit=None):
if before:
before = self._utc_datetime_to_epoch(before)
if after:
after = self._utc_datetime_to_epoch(after)
params = dict(before=before, after=after)
result_fetcher = functools.partial(self.protocol.g... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '33']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_segment_leaderboard'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15', '18', '21', '24', '27', '30... | def get_segment_leaderboard(self, segment_id, gender=None, age_group=None, weight_class=None,
following=None, club_id=None, timeframe=None, top_results_limit=None,
page=None, context_entries = None):
params = {}
if gender is not None:
... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '18']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'get_segment_efforts'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9', '12', '15']}; {'id': '4', 'type': 'identifi... | def get_segment_efforts(self, segment_id, athlete_id=None,
start_date_local=None, end_date_local=None,
limit=None):
params = {"segment_id": segment_id}
if athlete_id is not None:
params['athlete_id'] = athlete_id
if start_date_l... |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '16']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'range'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '7', '10', '13']}; {'id': '4', 'type': 'identifier', 'children... | def range(self, low, high, with_scores=False, desc=False, reverse=False):
if reverse:
return self.database.zrevrange(self.key, low, high, with_scores)
else:
return self.database.zrange(self.key, low, high, desc, with_scores) |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '5']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'sfiles_to_event'}, {'id': '3', 'type': 'parameters', 'children': ['4']}; {'id': '4', 'type': 'identifier', 'children': [], 'value': 's... | def sfiles_to_event(sfile_list):
event_list = []
sort_list = [(readheader(sfile).origins[0].time, sfile)
for sfile in sfile_list]
sort_list.sort(key=lambda tup: tup[0])
sfile_list = [sfile[1] for sfile in sort_list]
catalog = Catalog()
for i, sfile in enumerate(sfile_list):
... |
{'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': 'self'}, {'id... | def sort(self):
self.families.sort(key=lambda x: x.template.name)
return self |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '12']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'decluster'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '6', '9']}; {'id': '4', 'type': 'identifier', 'children': [], '... | def decluster(self, trig_int, timing='detect', metric='avg_cor'):
all_detections = []
for fam in self.families:
all_detections.extend(fam.detections)
if timing == 'detect':
if metric == 'avg_cor':
detect_info = [(d.detect_time, d.detect_val / d.no_chans)
... |
{'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': 'self'}, {'id... | def sort(self):
self.detections = sorted(self.detections, key=lambda d: d.detect_time)
return self |
{'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': 'self'}, {'id... | def sort(self):
self.templates = sorted(self.templates, key=lambda x: x.name)
return self |
{'id': '0', 'type': 'module', 'children': ['1']}, {'id': '1', 'type': 'function_definition', 'children': ['2', '3', '23']}; {'id': '2', 'type': 'function_name', 'children': [], 'value': 'filter_picks'}, {'id': '3', 'type': 'parameters', 'children': ['4', '5', '8', '11', '14', '17', '20']}; {'id': '4', 'type': 'identifi... | def filter_picks(catalog, stations=None, channels=None, networks=None,
locations=None, top_n_picks=None, evaluation_mode='all'):
filtered_catalog = catalog.copy()
if stations:
for event in filtered_catalog:
if len(event.picks) == 0:
continue
event... |
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