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def _pretty_print_target(self, target):
'\n Make target printouts more user-friendly.\n 1) builtins will be printed as `builtins.xyz`\n 2) operators will be printed as `operator.xyz`\n 3) other callables will be printed with qualfied name, e.g. torch.add\n '
if isinstance(target, str):
return target
if hasattr(target, '__module__'):
if (not hasattr(target, '__name__')):
return _get_qualified_name(target)
if (target.__module__ == 'builtins'):
return f'builtins.{target.__name__}'
elif (target.__module__ == '_operator'):
return f'operator.{target.__name__}'
return _get_qualified_name(target) | -4,326,301,714,006,745,600 | Make target printouts more user-friendly.
1) builtins will be printed as `builtins.xyz`
2) operators will be printed as `operator.xyz`
3) other callables will be printed with qualfied name, e.g. torch.add | venv/Lib/site-packages/torch/fx/node.py | _pretty_print_target | Westlanderz/AI-Plat1 | python | def _pretty_print_target(self, target):
'\n Make target printouts more user-friendly.\n 1) builtins will be printed as `builtins.xyz`\n 2) operators will be printed as `operator.xyz`\n 3) other callables will be printed with qualfied name, e.g. torch.add\n '
if isinstance(target, str):
return target
if hasattr(target, '__module__'):
if (not hasattr(target, '__name__')):
return _get_qualified_name(target)
if (target.__module__ == 'builtins'):
return f'builtins.{target.__name__}'
elif (target.__module__ == '_operator'):
return f'operator.{target.__name__}'
return _get_qualified_name(target) |
@compatibility(is_backward_compatible=True)
def format_node(self, placeholder_names: List[str]=None, maybe_return_typename: List[str]=None) -> Optional[str]:
"\n Return a descriptive string representation of ``self``.\n\n This method can be used with no arguments as a debugging\n utility.\n\n This function is also used internally in the ``__str__`` method\n of ``Graph``. Together, the strings in ``placeholder_names``\n and ``maybe_return_typename`` make up the signature of the\n autogenerated ``forward`` function in this Graph's surrounding\n GraphModule. ``placeholder_names`` and ``maybe_return_typename``\n should not be used otherwise.\n\n Args:\n placeholder_names: A list that will store formatted strings\n representing the placeholders in the generated\n ``forward`` function. Internal use only.\n maybe_return_typename: A single-element list that will store\n a formatted string representing the output of the\n generated ``forward`` function. Internal use only.\n\n Returns:\n str: If 1) we're using ``format_node`` as an internal helper\n in the ``__str__`` method of ``Graph``, and 2) ``self``\n is a placeholder Node, return ``None``. Otherwise,\n return a descriptive string representation of the\n current Node.\n "
if (self.op == 'placeholder'):
assert isinstance(self.target, str)
arg_str = self.target
arg_str += ((arg_str + f': {_type_repr(self.type)}') if self.type else '')
if placeholder_names:
placeholder_names.append(arg_str)
return None
maybe_typename = (f'{_type_repr(self.type)} ' if self.type else '')
default_val = ((('(default=' + str(self.args[0])) + ')') if self.args else '')
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
elif (self.op == 'get_attr'):
maybe_typename = (f'{_type_repr(self.type)} ' if (self.type is not None) else '')
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self._pretty_print_target(self.target)}]'
elif (self.op == 'output'):
if (self.type and maybe_return_typename):
maybe_return_typename[0] = f' -> {_type_repr(self.type)}'
return f'return {self.args[0]}'
else:
maybe_typename = (f'{_type_repr(self.type)} ' if (self.type is not None) else '')
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self._pretty_print_target(self.target)}](args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})' | 2,380,465,824,610,337,000 | Return a descriptive string representation of ``self``.
This method can be used with no arguments as a debugging
utility.
This function is also used internally in the ``__str__`` method
of ``Graph``. Together, the strings in ``placeholder_names``
and ``maybe_return_typename`` make up the signature of the
autogenerated ``forward`` function in this Graph's surrounding
GraphModule. ``placeholder_names`` and ``maybe_return_typename``
should not be used otherwise.
Args:
placeholder_names: A list that will store formatted strings
representing the placeholders in the generated
``forward`` function. Internal use only.
maybe_return_typename: A single-element list that will store
a formatted string representing the output of the
generated ``forward`` function. Internal use only.
Returns:
str: If 1) we're using ``format_node`` as an internal helper
in the ``__str__`` method of ``Graph``, and 2) ``self``
is a placeholder Node, return ``None``. Otherwise,
return a descriptive string representation of the
current Node. | venv/Lib/site-packages/torch/fx/node.py | format_node | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def format_node(self, placeholder_names: List[str]=None, maybe_return_typename: List[str]=None) -> Optional[str]:
"\n Return a descriptive string representation of ``self``.\n\n This method can be used with no arguments as a debugging\n utility.\n\n This function is also used internally in the ``__str__`` method\n of ``Graph``. Together, the strings in ``placeholder_names``\n and ``maybe_return_typename`` make up the signature of the\n autogenerated ``forward`` function in this Graph's surrounding\n GraphModule. ``placeholder_names`` and ``maybe_return_typename``\n should not be used otherwise.\n\n Args:\n placeholder_names: A list that will store formatted strings\n representing the placeholders in the generated\n ``forward`` function. Internal use only.\n maybe_return_typename: A single-element list that will store\n a formatted string representing the output of the\n generated ``forward`` function. Internal use only.\n\n Returns:\n str: If 1) we're using ``format_node`` as an internal helper\n in the ``__str__`` method of ``Graph``, and 2) ``self``\n is a placeholder Node, return ``None``. Otherwise,\n return a descriptive string representation of the\n current Node.\n "
if (self.op == 'placeholder'):
assert isinstance(self.target, str)
arg_str = self.target
arg_str += ((arg_str + f': {_type_repr(self.type)}') if self.type else )
if placeholder_names:
placeholder_names.append(arg_str)
return None
maybe_typename = (f'{_type_repr(self.type)} ' if self.type else )
default_val = ((('(default=' + str(self.args[0])) + ')') if self.args else )
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
elif (self.op == 'get_attr'):
maybe_typename = (f'{_type_repr(self.type)} ' if (self.type is not None) else )
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self._pretty_print_target(self.target)}]'
elif (self.op == 'output'):
if (self.type and maybe_return_typename):
maybe_return_typename[0] = f' -> {_type_repr(self.type)}'
return f'return {self.args[0]}'
else:
maybe_typename = (f'{_type_repr(self.type)} ' if (self.type is not None) else )
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self._pretty_print_target(self.target)}](args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})' |
@compatibility(is_backward_compatible=True)
def replace_all_uses_with(self, replace_with: 'Node') -> List['Node']:
'\n Replace all uses of ``self`` in the Graph with the Node ``replace_with``.\n\n Args:\n\n replace_with (Node): The node to replace all uses of ``self`` with.\n\n Returns:\n\n The list of Nodes on which this change was made.\n '
to_process = list(self.users)
for use_node in to_process:
def maybe_replace_node(n: Node) -> Node:
if (n == self):
return replace_with
else:
return n
new_args = map_arg(use_node.args, maybe_replace_node)
new_kwargs = map_arg(use_node.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
use_node.__update_args_kwargs(new_args, new_kwargs)
assert (len(self.users) == 0)
return to_process | -1,574,887,777,613,111,800 | Replace all uses of ``self`` in the Graph with the Node ``replace_with``.
Args:
replace_with (Node): The node to replace all uses of ``self`` with.
Returns:
The list of Nodes on which this change was made. | venv/Lib/site-packages/torch/fx/node.py | replace_all_uses_with | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def replace_all_uses_with(self, replace_with: 'Node') -> List['Node']:
'\n Replace all uses of ``self`` in the Graph with the Node ``replace_with``.\n\n Args:\n\n replace_with (Node): The node to replace all uses of ``self`` with.\n\n Returns:\n\n The list of Nodes on which this change was made.\n '
to_process = list(self.users)
for use_node in to_process:
def maybe_replace_node(n: Node) -> Node:
if (n == self):
return replace_with
else:
return n
new_args = map_arg(use_node.args, maybe_replace_node)
new_kwargs = map_arg(use_node.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
use_node.__update_args_kwargs(new_args, new_kwargs)
assert (len(self.users) == 0)
return to_process |
@compatibility(is_backward_compatible=False)
def is_impure(self):
'\n Returns whether this op is impure, i.e. if its op is a placeholder or\n output, or if a call_function or call_module which is impure.\n\n Returns:\n\n bool: If the op is impure or not.\n '
if (self.op in {'placeholder', 'output'}):
return True
if (self.op == 'call_function'):
return (self.target in _side_effectful_functions)
if (self.op == 'call_module'):
assert (self.graph.owning_module is not None), 'self.graph.owning_module not set for purity check'
target_mod = self.graph.owning_module.get_submodule(self.target)
assert (target_mod is not None), f'Did not find expected submodule target {self.target}'
return getattr(target_mod, '_is_impure', False)
return False | -7,435,228,998,158,217,000 | Returns whether this op is impure, i.e. if its op is a placeholder or
output, or if a call_function or call_module which is impure.
Returns:
bool: If the op is impure or not. | venv/Lib/site-packages/torch/fx/node.py | is_impure | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=False)
def is_impure(self):
'\n Returns whether this op is impure, i.e. if its op is a placeholder or\n output, or if a call_function or call_module which is impure.\n\n Returns:\n\n bool: If the op is impure or not.\n '
if (self.op in {'placeholder', 'output'}):
return True
if (self.op == 'call_function'):
return (self.target in _side_effectful_functions)
if (self.op == 'call_module'):
assert (self.graph.owning_module is not None), 'self.graph.owning_module not set for purity check'
target_mod = self.graph.owning_module.get_submodule(self.target)
assert (target_mod is not None), f'Did not find expected submodule target {self.target}'
return getattr(target_mod, '_is_impure', False)
return False |
@compatibility(is_backward_compatible=False)
def normalized_arguments(self, root: torch.nn.Module, arg_types: Optional[Tuple[Any]]=None, kwarg_types: Optional[Dict[(str, Any)]]=None, normalize_to_only_use_kwargs: bool=False) -> Optional[ArgsKwargsPair]:
"\n Returns normalized arguments to Python targets. This means that\n `args/kwargs` will be matched up to the module/functional's\n signature and return exclusively kwargs in positional order\n if `normalize_to_only_use_kwargs` is true.\n Also populates default values. Does not support positional-only\n parameters or varargs parameters.\n\n Supports module calls.\n\n May require `arg_types` and `kwarg_types` in order to disambiguate overloads.\n\n Args:\n root (torch.nn.Module): Module upon which to resolve module targets.\n arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args\n kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs\n normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.\n\n Returns:\n\n Returns NamedTuple ArgsKwargsPair, or `None` if not successful.\n "
if (self.op == 'call_function'):
assert callable(self.target)
return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types)
elif (self.op == 'call_module'):
assert isinstance(self.target, str)
return normalize_module(root, self.target, self.args, self.kwargs)
return None | 8,207,796,060,024,680,000 | Returns normalized arguments to Python targets. This means that
`args/kwargs` will be matched up to the module/functional's
signature and return exclusively kwargs in positional order
if `normalize_to_only_use_kwargs` is true.
Also populates default values. Does not support positional-only
parameters or varargs parameters.
Supports module calls.
May require `arg_types` and `kwarg_types` in order to disambiguate overloads.
Args:
root (torch.nn.Module): Module upon which to resolve module targets.
arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args
kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs
normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
Returns:
Returns NamedTuple ArgsKwargsPair, or `None` if not successful. | venv/Lib/site-packages/torch/fx/node.py | normalized_arguments | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=False)
def normalized_arguments(self, root: torch.nn.Module, arg_types: Optional[Tuple[Any]]=None, kwarg_types: Optional[Dict[(str, Any)]]=None, normalize_to_only_use_kwargs: bool=False) -> Optional[ArgsKwargsPair]:
"\n Returns normalized arguments to Python targets. This means that\n `args/kwargs` will be matched up to the module/functional's\n signature and return exclusively kwargs in positional order\n if `normalize_to_only_use_kwargs` is true.\n Also populates default values. Does not support positional-only\n parameters or varargs parameters.\n\n Supports module calls.\n\n May require `arg_types` and `kwarg_types` in order to disambiguate overloads.\n\n Args:\n root (torch.nn.Module): Module upon which to resolve module targets.\n arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args\n kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs\n normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.\n\n Returns:\n\n Returns NamedTuple ArgsKwargsPair, or `None` if not successful.\n "
if (self.op == 'call_function'):
assert callable(self.target)
return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types)
elif (self.op == 'call_module'):
assert isinstance(self.target, str)
return normalize_module(root, self.target, self.args, self.kwargs)
return None |
@compatibility(is_backward_compatible=True)
def replace_input_with(self, old_input: 'Node', new_input: 'Node'):
'\n Loop through input nodes of ``self``, and replace all instances of\n ``old_input`` with ``new_input``.\n\n Args:\n\n old_input (Node): The old input node to be replaced.\n new_input (Node): The new input node to replace ``old_input``.\n '
def maybe_replace_node(n: Node) -> Node:
return (new_input if (n == old_input) else n)
new_args = map_arg(self.args, maybe_replace_node)
new_kwargs = map_arg(self.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
self.__update_args_kwargs(new_args, new_kwargs) | -5,413,264,850,184,298,000 | Loop through input nodes of ``self``, and replace all instances of
``old_input`` with ``new_input``.
Args:
old_input (Node): The old input node to be replaced.
new_input (Node): The new input node to replace ``old_input``. | venv/Lib/site-packages/torch/fx/node.py | replace_input_with | Westlanderz/AI-Plat1 | python | @compatibility(is_backward_compatible=True)
def replace_input_with(self, old_input: 'Node', new_input: 'Node'):
'\n Loop through input nodes of ``self``, and replace all instances of\n ``old_input`` with ``new_input``.\n\n Args:\n\n old_input (Node): The old input node to be replaced.\n new_input (Node): The new input node to replace ``old_input``.\n '
def maybe_replace_node(n: Node) -> Node:
return (new_input if (n == old_input) else n)
new_args = map_arg(self.args, maybe_replace_node)
new_kwargs = map_arg(self.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
self.__update_args_kwargs(new_args, new_kwargs) |
def execute(argv):
'Generate genetic sequences for each benchmark'
del argv
FLAGS = flags.FLAGS
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if (not benchmarks):
logging.error('There are no benchmarks to process')
sys.exit(1)
if (not os.path.isdir(FLAGS.benchmarks_directory)):
logging.error('Benchmarks directory {} does not exist.'.format(FLAGS.benchmarks_directory))
sys.exit(1)
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
sga = SGA(FLAGS.generations, FLAGS.population, FLAGS.cr, FLAGS.m, FLAGS.param_m, FLAGS.param_s, FLAGS.crossover, FLAGS.mutation, FLAGS.selection, FLAGS.seed, FLAGS.dimension, FLAGS.passes_filename, Goals.prepare_goals(FLAGS.goals, FLAGS.weights), 'opt', FLAGS.benchmarks_directory, FLAGS.working_set, FLAGS.times, FLAGS.tool, FLAGS.verify_output)
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[(index + 1):]
bench_dir = os.path.join(FLAGS.results_directory, bench_dir)
try:
os.makedirs(bench_dir)
except FileExistsError:
pass
filename = '{}/{}.yaml'.format(bench_dir, bench_name)
if (FLAGS.verify_report and os.path.isfile(filename)):
continue
sga.run(benchmark)
if sga.results:
IO.dump_yaml(sga.results, filename, FLAGS.report_only_the_best) | 5,197,675,214,564,177,000 | Generate genetic sequences for each benchmark | examples/algorithms/sga.py | execute | ComputerSystemsLab/OptimizationCache | python | def execute(argv):
del argv
FLAGS = flags.FLAGS
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if (not benchmarks):
logging.error('There are no benchmarks to process')
sys.exit(1)
if (not os.path.isdir(FLAGS.benchmarks_directory)):
logging.error('Benchmarks directory {} does not exist.'.format(FLAGS.benchmarks_directory))
sys.exit(1)
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
sga = SGA(FLAGS.generations, FLAGS.population, FLAGS.cr, FLAGS.m, FLAGS.param_m, FLAGS.param_s, FLAGS.crossover, FLAGS.mutation, FLAGS.selection, FLAGS.seed, FLAGS.dimension, FLAGS.passes_filename, Goals.prepare_goals(FLAGS.goals, FLAGS.weights), 'opt', FLAGS.benchmarks_directory, FLAGS.working_set, FLAGS.times, FLAGS.tool, FLAGS.verify_output)
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[(index + 1):]
bench_dir = os.path.join(FLAGS.results_directory, bench_dir)
try:
os.makedirs(bench_dir)
except FileExistsError:
pass
filename = '{}/{}.yaml'.format(bench_dir, bench_name)
if (FLAGS.verify_report and os.path.isfile(filename)):
continue
sga.run(benchmark)
if sga.results:
IO.dump_yaml(sga.results, filename, FLAGS.report_only_the_best) |
def main():
'Run administrative tasks.'
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'apibox.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment variable? Did you forget to activate a virtual environment?") from exc
execute_from_command_line(sys.argv) | 4,307,895,462,495,865,300 | Run administrative tasks. | manage.py | main | woodonggyu/apibox | python | def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'apibox.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment variable? Did you forget to activate a virtual environment?") from exc
execute_from_command_line(sys.argv) |
def object_to_dict(obj, list_depth=1):
'Convert Suds object into serializable format.\n\n The calling function can limit the amount of list entries that\n are converted.\n '
d = {}
for (k, v) in suds.sudsobject.asdict(obj).iteritems():
if hasattr(v, '__keylist__'):
d[k] = object_to_dict(v, list_depth=list_depth)
elif isinstance(v, list):
d[k] = []
used = 0
for item in v:
used = (used + 1)
if (used > list_depth):
break
if hasattr(item, '__keylist__'):
d[k].append(object_to_dict(item, list_depth=list_depth))
else:
d[k].append(item)
else:
d[k] = v
return d | -3,044,999,984,033,977,300 | Convert Suds object into serializable format.
The calling function can limit the amount of list entries that
are converted. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | object_to_dict | Hybrid-Cloud/badam | python | def object_to_dict(obj, list_depth=1):
'Convert Suds object into serializable format.\n\n The calling function can limit the amount of list entries that\n are converted.\n '
d = {}
for (k, v) in suds.sudsobject.asdict(obj).iteritems():
if hasattr(v, '__keylist__'):
d[k] = object_to_dict(v, list_depth=list_depth)
elif isinstance(v, list):
d[k] = []
used = 0
for item in v:
used = (used + 1)
if (used > list_depth):
break
if hasattr(item, '__keylist__'):
d[k].append(object_to_dict(item, list_depth=list_depth))
else:
d[k].append(item)
else:
d[k] = v
return d |
def get_object_properties(vim, collector, mobj, type, properties):
'Gets the properties of the Managed object specified.'
client_factory = vim.client.factory
if (mobj is None):
return None
usecoll = collector
if (usecoll is None):
usecoll = vim.service_content.propertyCollector
property_filter_spec = client_factory.create('ns0:PropertyFilterSpec')
property_spec = client_factory.create('ns0:PropertySpec')
property_spec.all = ((properties is None) or (len(properties) == 0))
property_spec.pathSet = properties
property_spec.type = type
object_spec = client_factory.create('ns0:ObjectSpec')
object_spec.obj = mobj
object_spec.skip = False
property_filter_spec.propSet = [property_spec]
property_filter_spec.objectSet = [object_spec]
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(usecoll, specSet=[property_filter_spec], options=options) | -1,476,953,285,040,833,300 | Gets the properties of the Managed object specified. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_object_properties | Hybrid-Cloud/badam | python | def get_object_properties(vim, collector, mobj, type, properties):
client_factory = vim.client.factory
if (mobj is None):
return None
usecoll = collector
if (usecoll is None):
usecoll = vim.service_content.propertyCollector
property_filter_spec = client_factory.create('ns0:PropertyFilterSpec')
property_spec = client_factory.create('ns0:PropertySpec')
property_spec.all = ((properties is None) or (len(properties) == 0))
property_spec.pathSet = properties
property_spec.type = type
object_spec = client_factory.create('ns0:ObjectSpec')
object_spec.obj = mobj
object_spec.skip = False
property_filter_spec.propSet = [property_spec]
property_filter_spec.objectSet = [object_spec]
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(usecoll, specSet=[property_filter_spec], options=options) |
def get_dynamic_property(vim, mobj, type, property_name):
'Gets a particular property of the Managed Object.'
property_dict = get_dynamic_properties(vim, mobj, type, [property_name])
return property_dict.get(property_name) | 1,675,623,835,722,089,500 | Gets a particular property of the Managed Object. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_dynamic_property | Hybrid-Cloud/badam | python | def get_dynamic_property(vim, mobj, type, property_name):
property_dict = get_dynamic_properties(vim, mobj, type, [property_name])
return property_dict.get(property_name) |
def get_dynamic_properties(vim, mobj, type, property_names):
'Gets the specified properties of the Managed Object.'
obj_content = get_object_properties(vim, None, mobj, type, property_names)
if (obj_content is None):
return {}
if hasattr(obj_content, 'token'):
cancel_retrieve(vim, obj_content.token)
property_dict = {}
if obj_content.objects:
if hasattr(obj_content.objects[0], 'propSet'):
dynamic_properties = obj_content.objects[0].propSet
if dynamic_properties:
for prop in dynamic_properties:
property_dict[prop.name] = prop.val
if hasattr(obj_content.objects[0], 'missingSet'):
for m in obj_content.objects[0].missingSet:
LOG.warning(_('Unable to retrieve value for %(path)s Reason: %(reason)s'), {'path': m.path, 'reason': m.fault.localizedMessage})
return property_dict | -5,690,402,733,005,168,000 | Gets the specified properties of the Managed Object. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_dynamic_properties | Hybrid-Cloud/badam | python | def get_dynamic_properties(vim, mobj, type, property_names):
obj_content = get_object_properties(vim, None, mobj, type, property_names)
if (obj_content is None):
return {}
if hasattr(obj_content, 'token'):
cancel_retrieve(vim, obj_content.token)
property_dict = {}
if obj_content.objects:
if hasattr(obj_content.objects[0], 'propSet'):
dynamic_properties = obj_content.objects[0].propSet
if dynamic_properties:
for prop in dynamic_properties:
property_dict[prop.name] = prop.val
if hasattr(obj_content.objects[0], 'missingSet'):
for m in obj_content.objects[0].missingSet:
LOG.warning(_('Unable to retrieve value for %(path)s Reason: %(reason)s'), {'path': m.path, 'reason': m.fault.localizedMessage})
return property_dict |
def get_objects(vim, type, properties_to_collect=None, all=False):
'Gets the list of objects of the type specified.'
return vutil.get_objects(vim, type, CONF.vmware.maximum_objects, properties_to_collect, all) | 8,382,414,298,554,375,000 | Gets the list of objects of the type specified. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_objects | Hybrid-Cloud/badam | python | def get_objects(vim, type, properties_to_collect=None, all=False):
return vutil.get_objects(vim, type, CONF.vmware.maximum_objects, properties_to_collect, all) |
def get_inner_objects(vim, base_obj, path, inner_type, properties_to_collect=None, all=False):
'Gets the list of inner objects of the type specified.'
client_factory = vim.client.factory
base_type = base_obj._type
traversal_spec = vutil.build_traversal_spec(client_factory, 'inner', base_type, path, False, [])
object_spec = vutil.build_object_spec(client_factory, base_obj, [traversal_spec])
property_spec = vutil.build_property_spec(client_factory, type_=inner_type, properties_to_collect=properties_to_collect, all_properties=all)
property_filter_spec = vutil.build_property_filter_spec(client_factory, [property_spec], [object_spec])
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(vim.service_content.propertyCollector, specSet=[property_filter_spec], options=options) | 3,725,338,895,588,128,000 | Gets the list of inner objects of the type specified. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_inner_objects | Hybrid-Cloud/badam | python | def get_inner_objects(vim, base_obj, path, inner_type, properties_to_collect=None, all=False):
client_factory = vim.client.factory
base_type = base_obj._type
traversal_spec = vutil.build_traversal_spec(client_factory, 'inner', base_type, path, False, [])
object_spec = vutil.build_object_spec(client_factory, base_obj, [traversal_spec])
property_spec = vutil.build_property_spec(client_factory, type_=inner_type, properties_to_collect=properties_to_collect, all_properties=all)
property_filter_spec = vutil.build_property_filter_spec(client_factory, [property_spec], [object_spec])
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(vim.service_content.propertyCollector, specSet=[property_filter_spec], options=options) |
def cancel_retrieve(vim, token):
'Cancels the retrieve operation.'
return vim.CancelRetrievePropertiesEx(vim.service_content.propertyCollector, token=token) | -7,733,806,174,974,513,000 | Cancels the retrieve operation. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | cancel_retrieve | Hybrid-Cloud/badam | python | def cancel_retrieve(vim, token):
return vim.CancelRetrievePropertiesEx(vim.service_content.propertyCollector, token=token) |
def continue_to_get_objects(vim, token):
'Continues to get the list of objects of the type specified.'
return vim.ContinueRetrievePropertiesEx(vim.service_content.propertyCollector, token=token) | -4,253,159,307,787,871,000 | Continues to get the list of objects of the type specified. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | continue_to_get_objects | Hybrid-Cloud/badam | python | def continue_to_get_objects(vim, token):
return vim.ContinueRetrievePropertiesEx(vim.service_content.propertyCollector, token=token) |
def get_prop_spec(client_factory, spec_type, properties):
'Builds the Property Spec Object.'
prop_spec = client_factory.create('ns0:PropertySpec')
prop_spec.type = spec_type
prop_spec.pathSet = properties
return prop_spec | -2,410,048,619,987,145,000 | Builds the Property Spec Object. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_prop_spec | Hybrid-Cloud/badam | python | def get_prop_spec(client_factory, spec_type, properties):
prop_spec = client_factory.create('ns0:PropertySpec')
prop_spec.type = spec_type
prop_spec.pathSet = properties
return prop_spec |
def get_obj_spec(client_factory, obj, select_set=None):
'Builds the Object Spec object.'
obj_spec = client_factory.create('ns0:ObjectSpec')
obj_spec.obj = obj
obj_spec.skip = False
if (select_set is not None):
obj_spec.selectSet = select_set
return obj_spec | 3,381,832,873,053,010,000 | Builds the Object Spec object. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_obj_spec | Hybrid-Cloud/badam | python | def get_obj_spec(client_factory, obj, select_set=None):
obj_spec = client_factory.create('ns0:ObjectSpec')
obj_spec.obj = obj
obj_spec.skip = False
if (select_set is not None):
obj_spec.selectSet = select_set
return obj_spec |
def get_prop_filter_spec(client_factory, obj_spec, prop_spec):
'Builds the Property Filter Spec Object.'
prop_filter_spec = client_factory.create('ns0:PropertyFilterSpec')
prop_filter_spec.propSet = prop_spec
prop_filter_spec.objectSet = obj_spec
return prop_filter_spec | 2,964,159,198,052,121,600 | Builds the Property Filter Spec Object. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_prop_filter_spec | Hybrid-Cloud/badam | python | def get_prop_filter_spec(client_factory, obj_spec, prop_spec):
prop_filter_spec = client_factory.create('ns0:PropertyFilterSpec')
prop_filter_spec.propSet = prop_spec
prop_filter_spec.objectSet = obj_spec
return prop_filter_spec |
def get_properties_for_a_collection_of_objects(vim, type, obj_list, properties):
'Gets the list of properties for the collection of\n objects of the type specified.\n '
client_factory = vim.client.factory
if (len(obj_list) == 0):
return []
prop_spec = get_prop_spec(client_factory, type, properties)
lst_obj_specs = []
for obj in obj_list:
lst_obj_specs.append(get_obj_spec(client_factory, obj))
prop_filter_spec = get_prop_filter_spec(client_factory, lst_obj_specs, [prop_spec])
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(vim.service_content.propertyCollector, specSet=[prop_filter_spec], options=options) | -3,855,717,937,075,148,000 | Gets the list of properties for the collection of
objects of the type specified. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_properties_for_a_collection_of_objects | Hybrid-Cloud/badam | python | def get_properties_for_a_collection_of_objects(vim, type, obj_list, properties):
'Gets the list of properties for the collection of\n objects of the type specified.\n '
client_factory = vim.client.factory
if (len(obj_list) == 0):
return []
prop_spec = get_prop_spec(client_factory, type, properties)
lst_obj_specs = []
for obj in obj_list:
lst_obj_specs.append(get_obj_spec(client_factory, obj))
prop_filter_spec = get_prop_filter_spec(client_factory, lst_obj_specs, [prop_spec])
options = client_factory.create('ns0:RetrieveOptions')
options.maxObjects = CONF.vmware.maximum_objects
return vim.RetrievePropertiesEx(vim.service_content.propertyCollector, specSet=[prop_filter_spec], options=options) |
def get_about_info(vim):
'Get the About Info from the service content.'
return vim.service_content.about | -3,403,545,440,397,797,000 | Get the About Info from the service content. | fs_patches_of_hybrid_cloud/cherry_for_111T/nova_cascaded/nova/virt/vmwareapi/vim_util.py | get_about_info | Hybrid-Cloud/badam | python | def get_about_info(vim):
return vim.service_content.about |
def validate_view_arguments(project_name=None, ontology_key=None, document_type=None):
'\n extends the "validate_view_arguments" fn in "views_base"\n by adding a check that there is a default customization associated w/ this project/ontology/proxy\n :param project_name:\n :param ontology_key:\n :param document_type:\n :return:\n '
model_customization = None
(validity, project, ontology, model_proxy, msg) = validate_view_arguments_base(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return (validity, project, ontology, model_proxy, model_customization, msg)
try:
model_customization = QModelCustomization.objects.get(project=project, proxy=model_proxy, is_default=True)
except ObjectDoesNotExist:
msg = _("There is no default customization associated with this document type for this project.<br/>Please <a href='mailto:{0}?subject=Missing%20Customization&body=Please%20create%20a%20customization%20for%20the%20%22{1}%22%20document%20type.'>contact</a> the project administrator for assistance.").format(project.email, model_proxy.fully_qualified_name)
validity = False
return (validity, project, ontology, model_proxy, model_customization, msg)
return (validity, project, ontology, model_proxy, model_customization, msg) | 7,049,328,265,247,111,000 | extends the "validate_view_arguments" fn in "views_base"
by adding a check that there is a default customization associated w/ this project/ontology/proxy
:param project_name:
:param ontology_key:
:param document_type:
:return: | Q/questionnaire/views/views_realizations.py | validate_view_arguments | ES-DOC/esdoc-questionnaire | python | def validate_view_arguments(project_name=None, ontology_key=None, document_type=None):
'\n extends the "validate_view_arguments" fn in "views_base"\n by adding a check that there is a default customization associated w/ this project/ontology/proxy\n :param project_name:\n :param ontology_key:\n :param document_type:\n :return:\n '
model_customization = None
(validity, project, ontology, model_proxy, msg) = validate_view_arguments_base(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return (validity, project, ontology, model_proxy, model_customization, msg)
try:
model_customization = QModelCustomization.objects.get(project=project, proxy=model_proxy, is_default=True)
except ObjectDoesNotExist:
msg = _("There is no default customization associated with this document type for this project.<br/>Please <a href='mailto:{0}?subject=Missing%20Customization&body=Please%20create%20a%20customization%20for%20the%20%22{1}%22%20document%20type.'>contact</a> the project administrator for assistance.").format(project.email, model_proxy.fully_qualified_name)
validity = False
return (validity, project, ontology, model_proxy, model_customization, msg)
return (validity, project, ontology, model_proxy, model_customization, msg) |
@redirect_legacy_projects
def q_view_new(request, project_name=None, ontology_key=None, document_type=None):
'\n this is never exposed by templates\n but a user might still try to navigate explicitly to this URL\n just return an error telling them not to try that\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :return:\n '
context = add_parameters_to_context(request)
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
msg = 'The ES-DOC Questionnaire only supports viewing of <em>existing</em> documents.'
return q_error(request, msg) | -2,959,830,592,501,942,300 | this is never exposed by templates
but a user might still try to navigate explicitly to this URL
just return an error telling them not to try that
:param request:
:param project_name:
:param ontology_key:
:param document_type:
:return: | Q/questionnaire/views/views_realizations.py | q_view_new | ES-DOC/esdoc-questionnaire | python | @redirect_legacy_projects
def q_view_new(request, project_name=None, ontology_key=None, document_type=None):
'\n this is never exposed by templates\n but a user might still try to navigate explicitly to this URL\n just return an error telling them not to try that\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :return:\n '
context = add_parameters_to_context(request)
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
msg = 'The ES-DOC Questionnaire only supports viewing of <em>existing</em> documents.'
return q_error(request, msg) |
@redirect_legacy_projects
def q_view_existing(request, project_name=None, ontology_key=None, document_type=None, realization_pk=None):
'\n this is exactly the same as "q_edit_existing" except:\n there are no authentication checks,\n the template_context & template are different.\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :param realization_pk:\n :return:\n '
context = add_parameters_to_context(request)
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
try:
session_key = get_key_from_request(request)
cached_realizations_key = '{0}_realizations'.format(session_key)
model_realization = get_or_create_cached_object(request.session, cached_realizations_key, get_existing_realizations, **{'project': project, 'ontology': ontology, 'model_proxy': model_proxy, 'model_id': realization_pk})
except ObjectDoesNotExist:
msg = "Cannot find a document with an id of '{0}' for that project/ontology/document type combination.".format(realization_pk)
return q_error(request, msg)
view_url_dirname = request.path.rsplit('/', 1)[0]
api_url_dirname = reverse('realization-detail', kwargs={'pk': model_realization.pk}).rsplit('/', 2)[0]
template_context = {'project': project, 'ontology': ontology, 'proxy': model_proxy, 'view_url_dirname': view_url_dirname, 'api_url_dirname': api_url_dirname, 'session_key': session_key, 'customization': model_customization, 'realization': model_realization, 'read_only': 'true'}
return render_to_response('questionnaire/q_view.html', template_context, context_instance=context) | 7,441,352,269,681,091,000 | this is exactly the same as "q_edit_existing" except:
there are no authentication checks,
the template_context & template are different.
:param request:
:param project_name:
:param ontology_key:
:param document_type:
:param realization_pk:
:return: | Q/questionnaire/views/views_realizations.py | q_view_existing | ES-DOC/esdoc-questionnaire | python | @redirect_legacy_projects
def q_view_existing(request, project_name=None, ontology_key=None, document_type=None, realization_pk=None):
'\n this is exactly the same as "q_edit_existing" except:\n there are no authentication checks,\n the template_context & template are different.\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :param realization_pk:\n :return:\n '
context = add_parameters_to_context(request)
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
try:
session_key = get_key_from_request(request)
cached_realizations_key = '{0}_realizations'.format(session_key)
model_realization = get_or_create_cached_object(request.session, cached_realizations_key, get_existing_realizations, **{'project': project, 'ontology': ontology, 'model_proxy': model_proxy, 'model_id': realization_pk})
except ObjectDoesNotExist:
msg = "Cannot find a document with an id of '{0}' for that project/ontology/document type combination.".format(realization_pk)
return q_error(request, msg)
view_url_dirname = request.path.rsplit('/', 1)[0]
api_url_dirname = reverse('realization-detail', kwargs={'pk': model_realization.pk}).rsplit('/', 2)[0]
template_context = {'project': project, 'ontology': ontology, 'proxy': model_proxy, 'view_url_dirname': view_url_dirname, 'api_url_dirname': api_url_dirname, 'session_key': session_key, 'customization': model_customization, 'realization': model_realization, 'read_only': 'true'}
return render_to_response('questionnaire/q_view.html', template_context, context_instance=context) |
@redirect_legacy_projects
def q_get_existing(request, project_name=None, ontology_key=None, document_type=None):
'\n this is meant to be used from external requests (ie: further_info_url)\n where uniquely identifying model fields (including pk) are passed\n if a unique realization cannot be found then an error is returned\n otherwise the response is routed to "q_edit_existing"\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :param realization_pk:\n :return:\n '
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
model_realizations = QModelRealization.objects.filter(project=project, proxy=model_proxy)
additional_parameters = request.GET.copy()
for (key, value) in additional_parameters.iteritems():
if ((key == 'pk') or (key == 'guid')):
try:
return HttpResponseRedirect(reverse('edit_existing', kwargs={'project_name': project_name, 'ontology_key': ontology_key, 'document_type': document_type, 'realization_pk': model_realizations.get(**{key: value}).pk}))
except (ObjectDoesNotExist, ValueError):
msg = "There is no '{0}' document with a {1} of '{2}'".format(model_proxy, key, value)
return q_error(request, msg)
else:
try:
property_proxy = model_proxy.property_proxies.get(name=key)
if (property_proxy.field_type == 'ATOMIC'):
model_realizations = model_realizations.filter(properties__proxy=property_proxy).has_atomic_value(value)
elif (property_proxy.field_type == 'ENUMERATION'):
formatted_values = [fv for fv in map((lambda v: v.strip()), value.split(',')) if fv]
model_realizations = model_realizations.filter(properties__proxy=property_proxy).has_enumeration_values(formatted_values)
else:
msg = 'Unable to support getting a document by relationship_field'
return q_error(request, msg)
except ObjectDoesNotExist:
msg = "There is no '{0}' property for the '{0}' document_type".format(key, model_proxy)
return q_error(request, msg)
if (model_realizations.count() != 1):
msg = "Unable to uniquely identify '{0}' document_type with the following properties: '{1}'".format(model_proxy, ', '.join(['{0}: {1}'.format(p[0], p[1]) for p in additional_parameters.items()]))
return q_error(request, msg)
return HttpResponseRedirect(reverse('edit_existing', kwargs={'project_name': project_name, 'ontology_key': ontology_key, 'document_type': document_type, 'realization_pk': model_realizations.first().pk})) | -1,087,697,355,295,826,700 | this is meant to be used from external requests (ie: further_info_url)
where uniquely identifying model fields (including pk) are passed
if a unique realization cannot be found then an error is returned
otherwise the response is routed to "q_edit_existing"
:param request:
:param project_name:
:param ontology_key:
:param document_type:
:param realization_pk:
:return: | Q/questionnaire/views/views_realizations.py | q_get_existing | ES-DOC/esdoc-questionnaire | python | @redirect_legacy_projects
def q_get_existing(request, project_name=None, ontology_key=None, document_type=None):
'\n this is meant to be used from external requests (ie: further_info_url)\n where uniquely identifying model fields (including pk) are passed\n if a unique realization cannot be found then an error is returned\n otherwise the response is routed to "q_edit_existing"\n :param request:\n :param project_name:\n :param ontology_key:\n :param document_type:\n :param realization_pk:\n :return:\n '
(validity, project, ontology, model_proxy, model_customization, msg) = validate_view_arguments(project_name=project_name, ontology_key=ontology_key, document_type=document_type)
if (not validity):
return q_error(request, msg)
model_realizations = QModelRealization.objects.filter(project=project, proxy=model_proxy)
additional_parameters = request.GET.copy()
for (key, value) in additional_parameters.iteritems():
if ((key == 'pk') or (key == 'guid')):
try:
return HttpResponseRedirect(reverse('edit_existing', kwargs={'project_name': project_name, 'ontology_key': ontology_key, 'document_type': document_type, 'realization_pk': model_realizations.get(**{key: value}).pk}))
except (ObjectDoesNotExist, ValueError):
msg = "There is no '{0}' document with a {1} of '{2}'".format(model_proxy, key, value)
return q_error(request, msg)
else:
try:
property_proxy = model_proxy.property_proxies.get(name=key)
if (property_proxy.field_type == 'ATOMIC'):
model_realizations = model_realizations.filter(properties__proxy=property_proxy).has_atomic_value(value)
elif (property_proxy.field_type == 'ENUMERATION'):
formatted_values = [fv for fv in map((lambda v: v.strip()), value.split(',')) if fv]
model_realizations = model_realizations.filter(properties__proxy=property_proxy).has_enumeration_values(formatted_values)
else:
msg = 'Unable to support getting a document by relationship_field'
return q_error(request, msg)
except ObjectDoesNotExist:
msg = "There is no '{0}' property for the '{0}' document_type".format(key, model_proxy)
return q_error(request, msg)
if (model_realizations.count() != 1):
msg = "Unable to uniquely identify '{0}' document_type with the following properties: '{1}'".format(model_proxy, ', '.join(['{0}: {1}'.format(p[0], p[1]) for p in additional_parameters.items()]))
return q_error(request, msg)
return HttpResponseRedirect(reverse('edit_existing', kwargs={'project_name': project_name, 'ontology_key': ontology_key, 'document_type': document_type, 'realization_pk': model_realizations.first().pk})) |
def combineCommandLineOptionsDictIntoShellCommand(commandOptions):
'\n Write out the compas input parameters into a shell string.\n Ensure the Compas executable is first, and not repeated.\n Options are non-ordered.\n '
shellCommand = commandOptions['compas_executable']
del commandOptions['compas_executable']
for (key, val) in commandOptions.items():
shellCommand += (((' ' + key) + ' ') + val)
return shellCommand | -6,617,363,741,634,693,000 | Write out the compas input parameters into a shell string.
Ensure the Compas executable is first, and not repeated.
Options are non-ordered. | utils/example_plots/methods_paper_plots/fig_5_HR_diagram/pythonSubmit.py | combineCommandLineOptionsDictIntoShellCommand | IsobelMarguarethe/COMPAS | python | def combineCommandLineOptionsDictIntoShellCommand(commandOptions):
'\n Write out the compas input parameters into a shell string.\n Ensure the Compas executable is first, and not repeated.\n Options are non-ordered.\n '
shellCommand = commandOptions['compas_executable']
del commandOptions['compas_executable']
for (key, val) in commandOptions.items():
shellCommand += (((' ' + key) + ' ') + val)
return shellCommand |
def cleanStringParameter(str_param):
' clean up string parameters to avoid confusing Boost '
if (str_param is not None):
str_param = str_param.strip('\'"')
escapes = [' ', "'", '"']
for escape in escapes:
str_param = re.sub('(?<!\\\\){}'.format(escape), '\\{}'.format(escape), str_param)
return str_param | -4,032,103,620,565,185,500 | clean up string parameters to avoid confusing Boost | utils/example_plots/methods_paper_plots/fig_5_HR_diagram/pythonSubmit.py | cleanStringParameter | IsobelMarguarethe/COMPAS | python | def cleanStringParameter(str_param):
' '
if (str_param is not None):
str_param = str_param.strip('\'"')
escapes = [' ', "'", '"']
for escape in escapes:
str_param = re.sub('(?<!\\\\){}'.format(escape), '\\{}'.format(escape), str_param)
return str_param |
def generateCommandLineOptionsDict(self):
'\n This function generates a dictionary mapping COMPAS options to their specified \n values (or empty strings for boolean options). These can be combined into a string\n and run directly as a terminal command, or passed to the stroopwafel interface\n where some of them may be overwritten. Options not to be included in the command \n line should be set to pythons None (except booleans, which should be set to False)\n\n Parameters\n -----------\n self : pythonProgramOptions\n Contains program options\n\n Returns\n --------\n commands : str or list of strs\n '
booleanChoices = self.booleanChoices()
booleanCommands = self.booleanCommands()
nBoolean = len(booleanChoices)
assert (len(booleanCommands) == nBoolean)
numericalChoices = self.numericalChoices()
numericalCommands = self.numericalCommands()
nNumerical = len(numericalChoices)
assert (len(numericalCommands) == nNumerical)
stringChoices = self.stringChoices()
stringCommands = self.stringCommands()
nString = len(stringChoices)
assert (len(stringCommands) == nString)
listChoices = self.listChoices()
listCommands = self.listCommands()
nList = len(listChoices)
assert (len(listCommands) == nList)
command = {'compas_executable': self.compas_executable}
for i in range(nBoolean):
if (booleanChoices[i] == True):
command.update({booleanCommands[i]: ''})
elif (booleanChoices[i] == False):
command.update({booleanCommands[i]: 'False'})
for i in range(nNumerical):
if (not (numericalChoices[i] == None)):
command.update({numericalCommands[i]: str(numericalChoices[i])})
for i in range(nString):
if (not (stringChoices[i] == None)):
command.update({stringCommands[i]: cleanStringParameter(stringChoices[i])})
for i in range(nList):
if listChoices[i]:
command.update({listCommands[i]: ' '.join(map(str, listChoices[i]))})
return command | 400,721,765,994,395,800 | This function generates a dictionary mapping COMPAS options to their specified
values (or empty strings for boolean options). These can be combined into a string
and run directly as a terminal command, or passed to the stroopwafel interface
where some of them may be overwritten. Options not to be included in the command
line should be set to pythons None (except booleans, which should be set to False)
Parameters
-----------
self : pythonProgramOptions
Contains program options
Returns
--------
commands : str or list of strs | utils/example_plots/methods_paper_plots/fig_5_HR_diagram/pythonSubmit.py | generateCommandLineOptionsDict | IsobelMarguarethe/COMPAS | python | def generateCommandLineOptionsDict(self):
'\n This function generates a dictionary mapping COMPAS options to their specified \n values (or empty strings for boolean options). These can be combined into a string\n and run directly as a terminal command, or passed to the stroopwafel interface\n where some of them may be overwritten. Options not to be included in the command \n line should be set to pythons None (except booleans, which should be set to False)\n\n Parameters\n -----------\n self : pythonProgramOptions\n Contains program options\n\n Returns\n --------\n commands : str or list of strs\n '
booleanChoices = self.booleanChoices()
booleanCommands = self.booleanCommands()
nBoolean = len(booleanChoices)
assert (len(booleanCommands) == nBoolean)
numericalChoices = self.numericalChoices()
numericalCommands = self.numericalCommands()
nNumerical = len(numericalChoices)
assert (len(numericalCommands) == nNumerical)
stringChoices = self.stringChoices()
stringCommands = self.stringCommands()
nString = len(stringChoices)
assert (len(stringCommands) == nString)
listChoices = self.listChoices()
listCommands = self.listCommands()
nList = len(listChoices)
assert (len(listCommands) == nList)
command = {'compas_executable': self.compas_executable}
for i in range(nBoolean):
if (booleanChoices[i] == True):
command.update({booleanCommands[i]: })
elif (booleanChoices[i] == False):
command.update({booleanCommands[i]: 'False'})
for i in range(nNumerical):
if (not (numericalChoices[i] == None)):
command.update({numericalCommands[i]: str(numericalChoices[i])})
for i in range(nString):
if (not (stringChoices[i] == None)):
command.update({stringCommands[i]: cleanStringParameter(stringChoices[i])})
for i in range(nList):
if listChoices[i]:
command.update({listCommands[i]: ' '.join(map(str, listChoices[i]))})
return command |
def get_header(self, request):
'\n Extracts the header containing the JSON web token from the given\n request.\n '
header = request.META.get(api_settings.AUTH_HEADER_NAME)
if isinstance(header, str):
header = header.encode(HTTP_HEADER_ENCODING)
return header | 1,066,136,120,163,030,000 | Extracts the header containing the JSON web token from the given
request. | webpersonal/env/Lib/site-packages/rest_framework_simplejwt/authentication.py | get_header | BrianMarquez3/Python-Django | python | def get_header(self, request):
'\n Extracts the header containing the JSON web token from the given\n request.\n '
header = request.META.get(api_settings.AUTH_HEADER_NAME)
if isinstance(header, str):
header = header.encode(HTTP_HEADER_ENCODING)
return header |
def get_raw_token(self, header):
'\n Extracts an unvalidated JSON web token from the given "Authorization"\n header value.\n '
parts = header.split()
if (len(parts) == 0):
return None
if (parts[0] not in AUTH_HEADER_TYPE_BYTES):
return None
if (len(parts) != 2):
raise AuthenticationFailed(_('Authorization header must contain two space-delimited values'), code='bad_authorization_header')
return parts[1] | 4,438,926,674,893,976,000 | Extracts an unvalidated JSON web token from the given "Authorization"
header value. | webpersonal/env/Lib/site-packages/rest_framework_simplejwt/authentication.py | get_raw_token | BrianMarquez3/Python-Django | python | def get_raw_token(self, header):
'\n Extracts an unvalidated JSON web token from the given "Authorization"\n header value.\n '
parts = header.split()
if (len(parts) == 0):
return None
if (parts[0] not in AUTH_HEADER_TYPE_BYTES):
return None
if (len(parts) != 2):
raise AuthenticationFailed(_('Authorization header must contain two space-delimited values'), code='bad_authorization_header')
return parts[1] |
def get_validated_token(self, raw_token):
'\n Validates an encoded JSON web token and returns a validated token\n wrapper object.\n '
messages = []
for AuthToken in api_settings.AUTH_TOKEN_CLASSES:
try:
return AuthToken(raw_token)
except TokenError as e:
messages.append({'token_class': AuthToken.__name__, 'token_type': AuthToken.token_type, 'message': e.args[0]})
raise InvalidToken({'detail': _('Given token not valid for any token type'), 'messages': messages}) | -2,122,270,758,756,231,000 | Validates an encoded JSON web token and returns a validated token
wrapper object. | webpersonal/env/Lib/site-packages/rest_framework_simplejwt/authentication.py | get_validated_token | BrianMarquez3/Python-Django | python | def get_validated_token(self, raw_token):
'\n Validates an encoded JSON web token and returns a validated token\n wrapper object.\n '
messages = []
for AuthToken in api_settings.AUTH_TOKEN_CLASSES:
try:
return AuthToken(raw_token)
except TokenError as e:
messages.append({'token_class': AuthToken.__name__, 'token_type': AuthToken.token_type, 'message': e.args[0]})
raise InvalidToken({'detail': _('Given token not valid for any token type'), 'messages': messages}) |
def get_user(self, validated_token):
'\n Attempts to find and return a user using the given validated token.\n '
try:
user_id = validated_token[api_settings.USER_ID_CLAIM]
except KeyError:
raise InvalidToken(_('Token contained no recognizable user identification'))
try:
user = self.user_model.objects.get(**{api_settings.USER_ID_FIELD: user_id})
except self.user_model.DoesNotExist:
raise AuthenticationFailed(_('User not found'), code='user_not_found')
if (not user.is_active):
raise AuthenticationFailed(_('User is inactive'), code='user_inactive')
return user | 8,316,481,509,880,417,000 | Attempts to find and return a user using the given validated token. | webpersonal/env/Lib/site-packages/rest_framework_simplejwt/authentication.py | get_user | BrianMarquez3/Python-Django | python | def get_user(self, validated_token):
'\n \n '
try:
user_id = validated_token[api_settings.USER_ID_CLAIM]
except KeyError:
raise InvalidToken(_('Token contained no recognizable user identification'))
try:
user = self.user_model.objects.get(**{api_settings.USER_ID_FIELD: user_id})
except self.user_model.DoesNotExist:
raise AuthenticationFailed(_('User not found'), code='user_not_found')
if (not user.is_active):
raise AuthenticationFailed(_('User is inactive'), code='user_inactive')
return user |
def get_user(self, validated_token):
'\n Returns a stateless user object which is backed by the given validated\n token.\n '
if (api_settings.USER_ID_CLAIM not in validated_token):
raise InvalidToken(_('Token contained no recognizable user identification'))
return api_settings.TOKEN_USER_CLASS(validated_token) | 1,161,550,880,086,617,600 | Returns a stateless user object which is backed by the given validated
token. | webpersonal/env/Lib/site-packages/rest_framework_simplejwt/authentication.py | get_user | BrianMarquez3/Python-Django | python | def get_user(self, validated_token):
'\n Returns a stateless user object which is backed by the given validated\n token.\n '
if (api_settings.USER_ID_CLAIM not in validated_token):
raise InvalidToken(_('Token contained no recognizable user identification'))
return api_settings.TOKEN_USER_CLASS(validated_token) |
def _init_state(self, encoder_hidden):
' Initialize the encoder hidden state. '
if (encoder_hidden is None):
return None
if isinstance(encoder_hidden, tuple):
encoder_hidden = tuple([self._cat_directions(h) for h in encoder_hidden])
else:
encoder_hidden = self._cat_directions(encoder_hidden)
return encoder_hidden | -888,232,364,086,648,400 | Initialize the encoder hidden state. | seq2seq/models/DecoderRNN.py | _init_state | junyongk/pytorch-seq2seq | python | def _init_state(self, encoder_hidden):
' '
if (encoder_hidden is None):
return None
if isinstance(encoder_hidden, tuple):
encoder_hidden = tuple([self._cat_directions(h) for h in encoder_hidden])
else:
encoder_hidden = self._cat_directions(encoder_hidden)
return encoder_hidden |
def _cat_directions(self, h):
' If the encoder is bidirectional, do the following transformation.\n (#directions * #layers, #batch, hidden_size) -> (#layers, #batch, #directions * hidden_size)\n '
if self.bidirectional_encoder:
h = torch.cat([h[0:h.size(0):2], h[1:h.size(0):2]], 2)
return h | 1,430,779,244,302,292,700 | If the encoder is bidirectional, do the following transformation.
(#directions * #layers, #batch, hidden_size) -> (#layers, #batch, #directions * hidden_size) | seq2seq/models/DecoderRNN.py | _cat_directions | junyongk/pytorch-seq2seq | python | def _cat_directions(self, h):
' If the encoder is bidirectional, do the following transformation.\n (#directions * #layers, #batch, hidden_size) -> (#layers, #batch, #directions * hidden_size)\n '
if self.bidirectional_encoder:
h = torch.cat([h[0:h.size(0):2], h[1:h.size(0):2]], 2)
return h |
def parse(self) -> object:
' reads in the file, then parses record tables'
self.contents = self.f.read()
self.header = self.parseHeader()
self.records = self.parseRecordInfoList()
self.readRecord0() | 3,804,246,371,784,762,400 | reads in the file, then parses record tables | dbookbee/mobi/__init__.py | parse | cloudylan/dbooklib | python | def parse(self) -> object:
' '
self.contents = self.f.read()
self.header = self.parseHeader()
self.records = self.parseRecordInfoList()
self.readRecord0() |
def author(self):
'Returns the author of the book'
return self.config['exth']['records'][100] | 5,083,589,889,736,570,000 | Returns the author of the book | dbookbee/mobi/__init__.py | author | cloudylan/dbooklib | python | def author(self):
return self.config['exth']['records'][100] |
def title(self):
'Returns the title of the book'
return self.config['mobi']['Full Name'] | 943,860,609,546,996,200 | Returns the title of the book | dbookbee/mobi/__init__.py | title | cloudylan/dbooklib | python | def title(self):
return self.config['mobi']['Full Name'] |
async def begin_delete(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, **kwargs) -> AsyncLROPoller[None]:
'Deletes the specified network security rule.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_initial(resource_group_name=resource_group_name, network_security_group_name=network_security_group_name, security_rule_name=security_rule_name, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | -2,860,460,665,875,554,300 | Deletes the specified network security rule.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_security_group_name: The name of the network security group.
:type network_security_group_name: str
:param security_rule_name: The name of the security rule.
:type security_rule_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError: | sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_07_01/aio/operations/_security_rules_operations.py | begin_delete | Co0olboi/azure-sdk-for-python | python | async def begin_delete(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, **kwargs) -> AsyncLROPoller[None]:
'Deletes the specified network security rule.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[None]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._delete_initial(resource_group_name=resource_group_name, network_security_group_name=network_security_group_name, security_rule_name=security_rule_name, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
async def get(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, **kwargs) -> 'models.SecurityRule':
'Get the specified network security rule.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SecurityRule, or the result of cls(response)\n :rtype: ~azure.mgmt.network.v2018_07_01.models.SecurityRule\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2018-07-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url('network_security_group_name', network_security_group_name, 'str'), 'securityRuleName': self._serialize.url('security_rule_name', security_rule_name, 'str'), 'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SecurityRule', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized | -431,310,436,554,868,800 | Get the specified network security rule.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_security_group_name: The name of the network security group.
:type network_security_group_name: str
:param security_rule_name: The name of the security rule.
:type security_rule_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SecurityRule, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2018_07_01.models.SecurityRule
:raises: ~azure.core.exceptions.HttpResponseError | sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_07_01/aio/operations/_security_rules_operations.py | get | Co0olboi/azure-sdk-for-python | python | async def get(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, **kwargs) -> 'models.SecurityRule':
'Get the specified network security rule.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: SecurityRule, or the result of cls(response)\n :rtype: ~azure.mgmt.network.v2018_07_01.models.SecurityRule\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2018-07-01'
accept = 'application/json'
url = self.get.metadata['url']
path_format_arguments = {'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url('network_security_group_name', network_security_group_name, 'str'), 'securityRuleName': self._serialize.url('security_rule_name', security_rule_name, 'str'), 'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('SecurityRule', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized |
async def begin_create_or_update(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, security_rule_parameters: 'models.SecurityRule', **kwargs) -> AsyncLROPoller['models.SecurityRule']:
'Creates or updates a security rule in the specified network security group.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :param security_rule_parameters: Parameters supplied to the create or update network security\n rule operation.\n :type security_rule_parameters: ~azure.mgmt.network.v2018_07_01.models.SecurityRule\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either SecurityRule or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_07_01.models.SecurityRule]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_initial(resource_group_name=resource_group_name, network_security_group_name=network_security_group_name, security_rule_name=security_rule_name, security_rule_parameters=security_rule_parameters, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('SecurityRule', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) | -7,602,824,364,702,028,000 | Creates or updates a security rule in the specified network security group.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_security_group_name: The name of the network security group.
:type network_security_group_name: str
:param security_rule_name: The name of the security rule.
:type security_rule_name: str
:param security_rule_parameters: Parameters supplied to the create or update network security
rule operation.
:type security_rule_parameters: ~azure.mgmt.network.v2018_07_01.models.SecurityRule
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: True for ARMPolling, False for no polling, or a
polling object for personal polling strategy
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either SecurityRule or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_07_01.models.SecurityRule]
:raises ~azure.core.exceptions.HttpResponseError: | sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_07_01/aio/operations/_security_rules_operations.py | begin_create_or_update | Co0olboi/azure-sdk-for-python | python | async def begin_create_or_update(self, resource_group_name: str, network_security_group_name: str, security_rule_name: str, security_rule_parameters: 'models.SecurityRule', **kwargs) -> AsyncLROPoller['models.SecurityRule']:
'Creates or updates a security rule in the specified network security group.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :param security_rule_name: The name of the security rule.\n :type security_rule_name: str\n :param security_rule_parameters: Parameters supplied to the create or update network security\n rule operation.\n :type security_rule_parameters: ~azure.mgmt.network.v2018_07_01.models.SecurityRule\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: True for ARMPolling, False for no polling, or a\n polling object for personal polling strategy\n :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.\n :return: An instance of AsyncLROPoller that returns either SecurityRule or the result of cls(response)\n :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_07_01.models.SecurityRule]\n :raises ~azure.core.exceptions.HttpResponseError:\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = (await self._create_or_update_initial(resource_group_name=resource_group_name, network_security_group_name=network_security_group_name, security_rule_name=security_rule_name, security_rule_parameters=security_rule_parameters, cls=(lambda x, y, z: x), **kwargs))
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('SecurityRule', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = AsyncARMPolling(lro_delay, **kwargs)
elif (polling is False):
polling_method = AsyncNoPolling()
else:
polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) |
def list(self, resource_group_name: str, network_security_group_name: str, **kwargs) -> AsyncIterable['models.SecurityRuleListResult']:
'Gets all security rules in a network security group.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either SecurityRuleListResult or the result of cls(response)\n :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_07_01.models.SecurityRuleListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2018-07-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list.metadata['url']
path_format_arguments = {'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url('network_security_group_name', network_security_group_name, 'str'), 'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('SecurityRuleListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) | -2,965,344,165,562,121,700 | Gets all security rules in a network security group.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param network_security_group_name: The name of the network security group.
:type network_security_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either SecurityRuleListResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_07_01.models.SecurityRuleListResult]
:raises: ~azure.core.exceptions.HttpResponseError | sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_07_01/aio/operations/_security_rules_operations.py | list | Co0olboi/azure-sdk-for-python | python | def list(self, resource_group_name: str, network_security_group_name: str, **kwargs) -> AsyncIterable['models.SecurityRuleListResult']:
'Gets all security rules in a network security group.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param network_security_group_name: The name of the network security group.\n :type network_security_group_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either SecurityRuleListResult or the result of cls(response)\n :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_07_01.models.SecurityRuleListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
api_version = '2018-07-01'
accept = 'application/json'
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header('accept', accept, 'str')
if (not next_link):
url = self.list.metadata['url']
path_format_arguments = {'resourceGroupName': self._serialize.url('resource_group_name', resource_group_name, 'str'), 'networkSecurityGroupName': self._serialize.url('network_security_group_name', network_security_group_name, 'str'), 'subscriptionId': self._serialize.url('self._config.subscription_id', self._config.subscription_id, 'str')}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query('api_version', api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('SecurityRuleListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), AsyncList(list_of_elem))
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = (await self._client._pipeline.run(request, stream=False, **kwargs))
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(get_next, extract_data) |
@classmethod
def file_to_delete(cls, all_files=False):
'\n Return the list of file to delete.\n '
directory = '{0}{1}'.format(PyFunceble.OUTPUT_DIRECTORY, PyFunceble.OUTPUTS.parent_directory)
if (not directory.endswith(directory_separator)):
directory += directory_separator
result = []
for (root, _, files) in walk(directory):
for file in files:
if (file in ['.gitignore', '.keep']):
continue
if ((not all_files) and ('logs' in root) and ('.log' in file)):
continue
if root.endswith(directory_separator):
result.append((root + file))
else:
result.append(((root + directory_separator) + file))
return result | -7,448,717,146,557,944,000 | Return the list of file to delete. | PyFunceble/output/clean.py | file_to_delete | NeolithEra/PyFunceble | python | @classmethod
def file_to_delete(cls, all_files=False):
'\n \n '
directory = '{0}{1}'.format(PyFunceble.OUTPUT_DIRECTORY, PyFunceble.OUTPUTS.parent_directory)
if (not directory.endswith(directory_separator)):
directory += directory_separator
result = []
for (root, _, files) in walk(directory):
for file in files:
if (file in ['.gitignore', '.keep']):
continue
if ((not all_files) and ('logs' in root) and ('.log' in file)):
continue
if root.endswith(directory_separator):
result.append((root + file))
else:
result.append(((root + directory_separator) + file))
return result |
@classmethod
def databases_to_delete(cls):
'\n Set the databases files to delete.\n '
result = []
if (PyFunceble.CONFIGURATION.db_type == 'json'):
directory = PyFunceble.CONFIG_DIRECTORY
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.dir_structure))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.iana))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.public_suffix))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.inactive_db))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.mining))
return result | 2,857,564,988,682,521,600 | Set the databases files to delete. | PyFunceble/output/clean.py | databases_to_delete | NeolithEra/PyFunceble | python | @classmethod
def databases_to_delete(cls):
'\n \n '
result = []
if (PyFunceble.CONFIGURATION.db_type == 'json'):
directory = PyFunceble.CONFIG_DIRECTORY
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.dir_structure))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.iana))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.public_suffix))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.inactive_db))
result.append('{0}{1}'.format(directory, PyFunceble.OUTPUTS.default_files.mining))
return result |
def almost_everything(self, clean_all=False, file_path=False):
'\n Delete almost all discovered files.\n\n :param bool clean_all:\n Tell the subsystem if we have to clean everything instesd\n of almost everything.\n '
if (('do_not_clean' not in PyFunceble.INTERN) or (not PyFunceble.INTERN['do_not_clean'])):
to_delete = self.file_to_delete(clean_all)
if ((not PyFunceble.abstracts.Version.is_local_cloned()) and clean_all):
to_delete.extend(self.databases_to_delete())
for file in to_delete:
PyFunceble.helpers.File(file).delete()
PyFunceble.LOGGER.info(f'Deleted: {file}')
if clean_all:
to_avoid = ['whois']
else:
to_avoid = ['whois', 'auto_continue', 'inactive', 'mining']
if (not file_path):
query = 'DELETE FROM {0}'
else:
query = 'DELETE FROM {0} WHERE file_path = %(file_path)s'
if (PyFunceble.CONFIGURATION.db_type in ['mariadb', 'mysql']):
with PyFunceble.engine.MySQL() as connection:
for database_name in [y for (x, y) in PyFunceble.engine.MySQL.tables.items() if (x not in to_avoid)]:
lquery = query.format(database_name)
with connection.cursor() as cursor:
cursor.execute(lquery, {'file_path': file_path})
PyFunceble.LOGGER.info(f'Cleaned the data related to {repr(file_path)} from the {database_name} table.')
if ((not PyFunceble.abstracts.Version.is_local_cloned()) and clean_all):
PyFunceble.load_config()
PyFunceble.LOGGER.info(f'Reloaded configuration.') | -2,799,624,771,840,950,000 | Delete almost all discovered files.
:param bool clean_all:
Tell the subsystem if we have to clean everything instesd
of almost everything. | PyFunceble/output/clean.py | almost_everything | NeolithEra/PyFunceble | python | def almost_everything(self, clean_all=False, file_path=False):
'\n Delete almost all discovered files.\n\n :param bool clean_all:\n Tell the subsystem if we have to clean everything instesd\n of almost everything.\n '
if (('do_not_clean' not in PyFunceble.INTERN) or (not PyFunceble.INTERN['do_not_clean'])):
to_delete = self.file_to_delete(clean_all)
if ((not PyFunceble.abstracts.Version.is_local_cloned()) and clean_all):
to_delete.extend(self.databases_to_delete())
for file in to_delete:
PyFunceble.helpers.File(file).delete()
PyFunceble.LOGGER.info(f'Deleted: {file}')
if clean_all:
to_avoid = ['whois']
else:
to_avoid = ['whois', 'auto_continue', 'inactive', 'mining']
if (not file_path):
query = 'DELETE FROM {0}'
else:
query = 'DELETE FROM {0} WHERE file_path = %(file_path)s'
if (PyFunceble.CONFIGURATION.db_type in ['mariadb', 'mysql']):
with PyFunceble.engine.MySQL() as connection:
for database_name in [y for (x, y) in PyFunceble.engine.MySQL.tables.items() if (x not in to_avoid)]:
lquery = query.format(database_name)
with connection.cursor() as cursor:
cursor.execute(lquery, {'file_path': file_path})
PyFunceble.LOGGER.info(f'Cleaned the data related to {repr(file_path)} from the {database_name} table.')
if ((not PyFunceble.abstracts.Version.is_local_cloned()) and clean_all):
PyFunceble.load_config()
PyFunceble.LOGGER.info(f'Reloaded configuration.') |
async def get_thumb(self, message):
'\n Тупой алгоритм,\n который рекурсивно с конца ищет поле "thumb"\n и если находит, возвращает его\n '
if isinstance(message, list):
values = list(enumerate(message))
elif isinstance(message, dict):
values = list(message.items())
else:
return
values.reverse()
for (k, v) in values:
if (k == 'reply_to_message'):
continue
if isinstance(v, dict):
if ('thumb' in v):
return v['thumb']
if (result := (await self.get_thumb(v))):
return result | 1,209,851,858,328,211,700 | Тупой алгоритм,
который рекурсивно с конца ищет поле "thumb"
и если находит, возвращает его | tgquote/filegetters/base.py | get_thumb | Forevka/tgquote | python | async def get_thumb(self, message):
'\n Тупой алгоритм,\n который рекурсивно с конца ищет поле "thumb"\n и если находит, возвращает его\n '
if isinstance(message, list):
values = list(enumerate(message))
elif isinstance(message, dict):
values = list(message.items())
else:
return
values.reverse()
for (k, v) in values:
if (k == 'reply_to_message'):
continue
if isinstance(v, dict):
if ('thumb' in v):
return v['thumb']
if (result := (await self.get_thumb(v))):
return result |
async def get_document(self, message):
'\n Тупой алгоритм,\n который рекурсивно с конца ищет поле "file_id"\n и если находит, возвращает его родителя\n '
if isinstance(message, list):
values = list(enumerate(message))
elif isinstance(message, dict):
values = list(message.items())
else:
return
values.reverse()
for (k, v) in values:
if (k == 'reply_to_message'):
continue
if isinstance(v, dict):
if ('file_id' in v):
return v
if (result := (await self.get_document(v))):
return result | 1,139,342,667,563,544,700 | Тупой алгоритм,
который рекурсивно с конца ищет поле "file_id"
и если находит, возвращает его родителя | tgquote/filegetters/base.py | get_document | Forevka/tgquote | python | async def get_document(self, message):
'\n Тупой алгоритм,\n который рекурсивно с конца ищет поле "file_id"\n и если находит, возвращает его родителя\n '
if isinstance(message, list):
values = list(enumerate(message))
elif isinstance(message, dict):
values = list(message.items())
else:
return
values.reverse()
for (k, v) in values:
if (k == 'reply_to_message'):
continue
if isinstance(v, dict):
if ('file_id' in v):
return v
if (result := (await self.get_document(v))):
return result |
def gradient_penalty_loss(self, y_true, y_pred, averaged_samples):
'\n Computes gradient penalty based on prediction and weighted real / fake samples\n '
gradients = K.gradients(y_pred, averaged_samples)[0]
gradients_sqr = K.square(gradients)
gradients_sqr_sum = K.sum(gradients_sqr, axis=np.arange(1, len(gradients_sqr.shape)))
gradient_l2_norm = K.sqrt(gradients_sqr_sum)
gradient_penalty = K.square((1 - gradient_l2_norm))
return K.mean(gradient_penalty) | 2,655,758,970,492,790,300 | Computes gradient penalty based on prediction and weighted real / fake samples | wgan_gp/wgan_gp.py | gradient_penalty_loss | 311nguyenbaohuy/Keras-GAN | python | def gradient_penalty_loss(self, y_true, y_pred, averaged_samples):
'\n \n '
gradients = K.gradients(y_pred, averaged_samples)[0]
gradients_sqr = K.square(gradients)
gradients_sqr_sum = K.sum(gradients_sqr, axis=np.arange(1, len(gradients_sqr.shape)))
gradient_l2_norm = K.sqrt(gradients_sqr_sum)
gradient_penalty = K.square((1 - gradient_l2_norm))
return K.mean(gradient_penalty) |
def find_subsections(section: Element) -> List[nodes.section]:
'Return a list of subsections for the given ``section``.'
result = []
for child in section:
if isinstance(child, nodes.section):
result.append(child)
continue
elif isinstance(child, nodes.Element):
result.extend(find_subsections(child))
return result | 7,239,203,639,831,353,000 | Return a list of subsections for the given ``section``. | sphinx/writers/texinfo.py | find_subsections | Bibo-Joshi/sphinx | python | def find_subsections(section: Element) -> List[nodes.section]:
result = []
for child in section:
if isinstance(child, nodes.section):
result.append(child)
continue
elif isinstance(child, nodes.Element):
result.extend(find_subsections(child))
return result |
def smart_capwords(s: str, sep: str=None) -> str:
'Like string.capwords() but does not capitalize words that already\n contain a capital letter.'
words = s.split(sep)
for (i, word) in enumerate(words):
if all((x.islower() for x in word)):
words[i] = word.capitalize()
return (sep or ' ').join(words) | 8,820,783,055,573,011,000 | Like string.capwords() but does not capitalize words that already
contain a capital letter. | sphinx/writers/texinfo.py | smart_capwords | Bibo-Joshi/sphinx | python | def smart_capwords(s: str, sep: str=None) -> str:
'Like string.capwords() but does not capitalize words that already\n contain a capital letter.'
words = s.split(sep)
for (i, word) in enumerate(words):
if all((x.islower() for x in word)):
words[i] = word.capitalize()
return (sep or ' ').join(words) |
def collect_node_names(self) -> None:
'Generates a unique id for each section.\n\n Assigns the attribute ``node_name`` to each section.'
def add_node_name(name: str) -> str:
node_id = self.escape_id(name)
(nth, suffix) = (1, '')
while (((node_id + suffix) in self.written_ids) or ((node_id + suffix) in self.node_names)):
nth += 1
suffix = ('<%s>' % nth)
node_id += suffix
self.written_ids.add(node_id)
self.node_names[node_id] = name
return node_id
self.document['node_name'] = 'Top'
add_node_name('Top')
add_node_name('top')
self.indices = [(add_node_name(name), content) for (name, content) in self.indices]
for section in self.document.findall(nodes.section):
title = cast(nodes.TextElement, section.next_node(nodes.Titular))
name = (title.astext() if title else '<untitled>')
section['node_name'] = add_node_name(name) | 3,862,376,790,812,649,000 | Generates a unique id for each section.
Assigns the attribute ``node_name`` to each section. | sphinx/writers/texinfo.py | collect_node_names | Bibo-Joshi/sphinx | python | def collect_node_names(self) -> None:
'Generates a unique id for each section.\n\n Assigns the attribute ``node_name`` to each section.'
def add_node_name(name: str) -> str:
node_id = self.escape_id(name)
(nth, suffix) = (1, )
while (((node_id + suffix) in self.written_ids) or ((node_id + suffix) in self.node_names)):
nth += 1
suffix = ('<%s>' % nth)
node_id += suffix
self.written_ids.add(node_id)
self.node_names[node_id] = name
return node_id
self.document['node_name'] = 'Top'
add_node_name('Top')
add_node_name('top')
self.indices = [(add_node_name(name), content) for (name, content) in self.indices]
for section in self.document.findall(nodes.section):
title = cast(nodes.TextElement, section.next_node(nodes.Titular))
name = (title.astext() if title else '<untitled>')
section['node_name'] = add_node_name(name) |
def collect_node_menus(self) -> None:
'Collect the menu entries for each "node" section.'
node_menus = self.node_menus
targets: List[Element] = [self.document]
targets.extend(self.document.findall(nodes.section))
for node in targets:
assert (('node_name' in node) and node['node_name'])
entries = [s['node_name'] for s in find_subsections(node)]
node_menus[node['node_name']] = entries
title = self.document.next_node(nodes.title)
top = (title.parent if title else self.document)
if (not isinstance(top, (nodes.document, nodes.section))):
top = self.document
if (top is not self.document):
entries = node_menus[top['node_name']]
entries += node_menus['Top'][1:]
node_menus['Top'] = entries
del node_menus[top['node_name']]
top['node_name'] = 'Top'
for (name, _content) in self.indices:
node_menus[name] = []
node_menus['Top'].append(name) | -9,153,002,201,023,103,000 | Collect the menu entries for each "node" section. | sphinx/writers/texinfo.py | collect_node_menus | Bibo-Joshi/sphinx | python | def collect_node_menus(self) -> None:
node_menus = self.node_menus
targets: List[Element] = [self.document]
targets.extend(self.document.findall(nodes.section))
for node in targets:
assert (('node_name' in node) and node['node_name'])
entries = [s['node_name'] for s in find_subsections(node)]
node_menus[node['node_name']] = entries
title = self.document.next_node(nodes.title)
top = (title.parent if title else self.document)
if (not isinstance(top, (nodes.document, nodes.section))):
top = self.document
if (top is not self.document):
entries = node_menus[top['node_name']]
entries += node_menus['Top'][1:]
node_menus['Top'] = entries
del node_menus[top['node_name']]
top['node_name'] = 'Top'
for (name, _content) in self.indices:
node_menus[name] = []
node_menus['Top'].append(name) |
def collect_rellinks(self) -> None:
'Collect the relative links (next, previous, up) for each "node".'
rellinks = self.rellinks
node_menus = self.node_menus
for id in node_menus:
rellinks[id] = ['', '', '']
for (id, entries) in node_menus.items():
for e in entries:
rellinks[e][2] = id
for (id, entries) in node_menus.items():
for (i, id) in enumerate(entries):
if (i != 0):
rellinks[id][1] = entries[(i - 1)]
if (i != (len(entries) - 1)):
rellinks[id][0] = entries[(i + 1)]
try:
first = node_menus['Top'][0]
except IndexError:
pass
else:
rellinks['Top'][0] = first
rellinks[first][1] = 'Top' | 9,184,016,339,000,884,000 | Collect the relative links (next, previous, up) for each "node". | sphinx/writers/texinfo.py | collect_rellinks | Bibo-Joshi/sphinx | python | def collect_rellinks(self) -> None:
rellinks = self.rellinks
node_menus = self.node_menus
for id in node_menus:
rellinks[id] = [, , ]
for (id, entries) in node_menus.items():
for e in entries:
rellinks[e][2] = id
for (id, entries) in node_menus.items():
for (i, id) in enumerate(entries):
if (i != 0):
rellinks[id][1] = entries[(i - 1)]
if (i != (len(entries) - 1)):
rellinks[id][0] = entries[(i + 1)]
try:
first = node_menus['Top'][0]
except IndexError:
pass
else:
rellinks['Top'][0] = first
rellinks[first][1] = 'Top' |
def escape(self, s: str) -> str:
'Return a string with Texinfo command characters escaped.'
s = s.replace('@', '@@')
s = s.replace('{', '@{')
s = s.replace('}', '@}')
s = s.replace('``', '`@w{`}')
s = s.replace("''", "'@w{'}")
return s | 9,155,230,778,005,700,000 | Return a string with Texinfo command characters escaped. | sphinx/writers/texinfo.py | escape | Bibo-Joshi/sphinx | python | def escape(self, s: str) -> str:
s = s.replace('@', '@@')
s = s.replace('{', '@{')
s = s.replace('}', '@}')
s = s.replace('``', '`@w{`}')
s = s.replace(, "'@w{'}")
return s |
def escape_arg(self, s: str) -> str:
'Return an escaped string suitable for use as an argument\n to a Texinfo command.'
s = self.escape(s)
s = s.replace(',', '@comma{}')
s = ' '.join(s.split()).strip()
return s | 6,421,078,286,981,946,000 | Return an escaped string suitable for use as an argument
to a Texinfo command. | sphinx/writers/texinfo.py | escape_arg | Bibo-Joshi/sphinx | python | def escape_arg(self, s: str) -> str:
'Return an escaped string suitable for use as an argument\n to a Texinfo command.'
s = self.escape(s)
s = s.replace(',', '@comma{}')
s = ' '.join(s.split()).strip()
return s |
def escape_id(self, s: str) -> str:
'Return an escaped string suitable for node names and anchors.'
bad_chars = ',:()'
for bc in bad_chars:
s = s.replace(bc, ' ')
if re.search('[^ .]', s):
s = s.replace('.', ' ')
s = ' '.join(s.split()).strip()
return self.escape(s) | 2,745,504,349,594,490,400 | Return an escaped string suitable for node names and anchors. | sphinx/writers/texinfo.py | escape_id | Bibo-Joshi/sphinx | python | def escape_id(self, s: str) -> str:
bad_chars = ',:()'
for bc in bad_chars:
s = s.replace(bc, ' ')
if re.search('[^ .]', s):
s = s.replace('.', ' ')
s = ' '.join(s.split()).strip()
return self.escape(s) |
def escape_menu(self, s: str) -> str:
'Return an escaped string suitable for menu entries.'
s = self.escape_arg(s)
s = s.replace(':', ';')
s = ' '.join(s.split()).strip()
return s | -2,540,185,727,114,880,500 | Return an escaped string suitable for menu entries. | sphinx/writers/texinfo.py | escape_menu | Bibo-Joshi/sphinx | python | def escape_menu(self, s: str) -> str:
s = self.escape_arg(s)
s = s.replace(':', ';')
s = ' '.join(s.split()).strip()
return s |
def ensure_eol(self) -> None:
'Ensure the last line in body is terminated by new line.'
if (self.body and (self.body[(- 1)][(- 1):] != '\n')):
self.body.append('\n') | -8,811,531,292,864,465,000 | Ensure the last line in body is terminated by new line. | sphinx/writers/texinfo.py | ensure_eol | Bibo-Joshi/sphinx | python | def ensure_eol(self) -> None:
if (self.body and (self.body[(- 1)][(- 1):] != '\n')):
self.body.append('\n') |
def get_short_id(self, id: str) -> str:
"Return a shorter 'id' associated with ``id``."
try:
sid = self.short_ids[id]
except KeyError:
sid = hex(len(self.short_ids))[2:]
self.short_ids[id] = sid
return sid | -8,816,042,497,587,074,000 | Return a shorter 'id' associated with ``id``. | sphinx/writers/texinfo.py | get_short_id | Bibo-Joshi/sphinx | python | def get_short_id(self, id: str) -> str:
try:
sid = self.short_ids[id]
except KeyError:
sid = hex(len(self.short_ids))[2:]
self.short_ids[id] = sid
return sid |
def load_data(coh, thresh=False):
'Load in the hg38 and hg19 gistic thresholded data. Assume GISTIC runs \n for each tumor type live in a parent directory (hg38_gistic or hg19_gistic)\n one level up from this script.'
if thresh:
hg38 = (('../hg38_gistic/' + coh) + '/all_thresholded.by_genes.txt')
hg19 = (('../hg19_gistic/' + coh) + '/all_thresholded.by_genes.txt')
hg38drops = ['Cytoband', 'Locus ID']
else:
hg38 = (('../hg38_gistic/' + coh) + '/all_data_by_genes.txt')
hg19 = (('../hg19_gistic/' + coh) + '/all_data_by_genes.txt')
hg38drops = ['Cytoband', 'Gene ID']
df_hg19 = pd.read_table(hg19, index_col=[0]).drop(['Cytoband', 'Locus ID'], axis=1)
df_hg38 = pd.read_table(hg38, index_col=[0]).drop(hg38drops, axis=1)
same_samps = list((set(df_hg38.columns) & set(df_hg19.columns)))
same_genes = list((set(df_hg38.index) & set(df_hg19.index)))
print(coh, len(same_genes), len(same_samps))
return (df_hg38[same_samps].T[same_genes], df_hg19[same_samps].T[same_genes])
return (df_hg38, df_hg19) | 8,639,613,017,847,819,000 | Load in the hg38 and hg19 gistic thresholded data. Assume GISTIC runs
for each tumor type live in a parent directory (hg38_gistic or hg19_gistic)
one level up from this script. | scripts/AnalysisCode.py | load_data | gaog94/GDAN_QC_CopyNumber | python | def load_data(coh, thresh=False):
'Load in the hg38 and hg19 gistic thresholded data. Assume GISTIC runs \n for each tumor type live in a parent directory (hg38_gistic or hg19_gistic)\n one level up from this script.'
if thresh:
hg38 = (('../hg38_gistic/' + coh) + '/all_thresholded.by_genes.txt')
hg19 = (('../hg19_gistic/' + coh) + '/all_thresholded.by_genes.txt')
hg38drops = ['Cytoband', 'Locus ID']
else:
hg38 = (('../hg38_gistic/' + coh) + '/all_data_by_genes.txt')
hg19 = (('../hg19_gistic/' + coh) + '/all_data_by_genes.txt')
hg38drops = ['Cytoband', 'Gene ID']
df_hg19 = pd.read_table(hg19, index_col=[0]).drop(['Cytoband', 'Locus ID'], axis=1)
df_hg38 = pd.read_table(hg38, index_col=[0]).drop(hg38drops, axis=1)
same_samps = list((set(df_hg38.columns) & set(df_hg19.columns)))
same_genes = list((set(df_hg38.index) & set(df_hg19.index)))
print(coh, len(same_genes), len(same_samps))
return (df_hg38[same_samps].T[same_genes], df_hg19[same_samps].T[same_genes])
return (df_hg38, df_hg19) |
def raw_value_comparison(coh, plot=False):
"Return the average differences in raw copy number values between the\n gene-level calls in hg19 and hg38 for each gene for a given tumor type \n 'coh.' If plot=True, plot the genes' differences in a histogram."
(df_38, df_19) = load_data(coh, thresh=False)
df_s = (df_38 - df_19)
avg_diff = {g: np.average(df_s[g]) for g in df_s.columns.get_level_values('Gene Symbol')}
results = []
std = np.std([avg_diff[x] for x in avg_diff])
for g in avg_diff:
if (avg_diff[g] > (4 * std)):
results.append([coh, 'Pos', g, avg_diff[g]])
elif (avg_diff[g] < ((- 4) * std)):
results.append([coh, 'Neg', g, avg_diff[g]])
if plot:
plt.hist([avg_diff[x] for x in avg_diff], bins=1000)
plt.title(coh, fontsize=16)
plt.xlabel('Average CN Difference Between Alignments', fontsize=14)
plt.ylabel('Genes', fontsize=14)
sns.despine()
plt.savefig((('./genehists/' + coh) + '_genehist.pdf'))
plt.savefig((('./genehists/' + coh) + '_genehist.png'))
plt.clf()
return results | 7,161,945,418,783,088,000 | Return the average differences in raw copy number values between the
gene-level calls in hg19 and hg38 for each gene for a given tumor type
'coh.' If plot=True, plot the genes' differences in a histogram. | scripts/AnalysisCode.py | raw_value_comparison | gaog94/GDAN_QC_CopyNumber | python | def raw_value_comparison(coh, plot=False):
"Return the average differences in raw copy number values between the\n gene-level calls in hg19 and hg38 for each gene for a given tumor type \n 'coh.' If plot=True, plot the genes' differences in a histogram."
(df_38, df_19) = load_data(coh, thresh=False)
df_s = (df_38 - df_19)
avg_diff = {g: np.average(df_s[g]) for g in df_s.columns.get_level_values('Gene Symbol')}
results = []
std = np.std([avg_diff[x] for x in avg_diff])
for g in avg_diff:
if (avg_diff[g] > (4 * std)):
results.append([coh, 'Pos', g, avg_diff[g]])
elif (avg_diff[g] < ((- 4) * std)):
results.append([coh, 'Neg', g, avg_diff[g]])
if plot:
plt.hist([avg_diff[x] for x in avg_diff], bins=1000)
plt.title(coh, fontsize=16)
plt.xlabel('Average CN Difference Between Alignments', fontsize=14)
plt.ylabel('Genes', fontsize=14)
sns.despine()
plt.savefig((('./genehists/' + coh) + '_genehist.pdf'))
plt.savefig((('./genehists/' + coh) + '_genehist.png'))
plt.clf()
return results |
def sequential_cohort_test_raw_values(cohs, plot=False):
'Sequentially compare raw gene-level calls for the given tumor types.'
c_results = []
for coh in cohs:
c_results += raw_value_comparison(coh, plot=plot)
df_r = pd.DataFrame(c_results, columns=['Cohort', 'Direction', 'Gene', 'Difference'])
gcount = Counter(df_r['Gene'])
pos_gcount = Counter(df_r[(df_r['Direction'] == 'Pos')]['Gene'])
neg_gcount = Counter(df_r[(df_r['Direction'] == 'Neg')]['Gene'])
df = pd.DataFrame([gcount[x] for x in gcount], index=gcount.keys(), columns=['Count'])
df['Count_pos'] = [(pos_gcount[x] if (x in pos_gcount) else 0) for x in gcount]
df['Count_neg'] = [(neg_gcount[x] if (x in neg_gcount) else 0) for x in gcount]
if plot:
plt.plot(np.sort([gcount[x] for x in gcount])[::(- 1)], 'b-')
plt.xlabel('Gene by Rank', fontsize=16)
plt.ylabel('Number of Occurences', fontsize=16)
sns.despine()
plt.savefig('GeneDevianceDropoff.pdf')
plt.savefig('GeneDevianceDropoff.png')
df_r.to_csv('./genehists/LargestDifferences.tsv', sep='\t', index=False)
df.to_csv('./genehists/LargestDifferenceGenes_ByCount.tsv', sep='\t', index=True) | 3,576,139,177,949,071,400 | Sequentially compare raw gene-level calls for the given tumor types. | scripts/AnalysisCode.py | sequential_cohort_test_raw_values | gaog94/GDAN_QC_CopyNumber | python | def sequential_cohort_test_raw_values(cohs, plot=False):
c_results = []
for coh in cohs:
c_results += raw_value_comparison(coh, plot=plot)
df_r = pd.DataFrame(c_results, columns=['Cohort', 'Direction', 'Gene', 'Difference'])
gcount = Counter(df_r['Gene'])
pos_gcount = Counter(df_r[(df_r['Direction'] == 'Pos')]['Gene'])
neg_gcount = Counter(df_r[(df_r['Direction'] == 'Neg')]['Gene'])
df = pd.DataFrame([gcount[x] for x in gcount], index=gcount.keys(), columns=['Count'])
df['Count_pos'] = [(pos_gcount[x] if (x in pos_gcount) else 0) for x in gcount]
df['Count_neg'] = [(neg_gcount[x] if (x in neg_gcount) else 0) for x in gcount]
if plot:
plt.plot(np.sort([gcount[x] for x in gcount])[::(- 1)], 'b-')
plt.xlabel('Gene by Rank', fontsize=16)
plt.ylabel('Number of Occurences', fontsize=16)
sns.despine()
plt.savefig('GeneDevianceDropoff.pdf')
plt.savefig('GeneDevianceDropoff.png')
df_r.to_csv('./genehists/LargestDifferences.tsv', sep='\t', index=False)
df.to_csv('./genehists/LargestDifferenceGenes_ByCount.tsv', sep='\t', index=True) |
def thresholded_value_comparison(df_hg38, df_hg19, metric='hamming'):
"Compare -2,-1,0,1,2 gene-level thresholded calls. metric can be either\n hamming (number of discrepancies in each gene) or manhattan (sum of \n 'distances' between each gene so a 1 to -1 change is 2). Returns a vector\n of each gene's metric."
out = []
for (i, g) in enumerate(df_hg38.columns):
if (metric == 'hamming'):
out.append((sum((df_hg19[g] != df_hg38[g])) / len(df_hg19)))
elif (metric == 'manhattan'):
out.append(sum(abs((df_hg19[g] - df_hg38[g]))))
return pd.DataFrame(out, index=df_hg38.columns) | 8,923,828,903,387,390,000 | Compare -2,-1,0,1,2 gene-level thresholded calls. metric can be either
hamming (number of discrepancies in each gene) or manhattan (sum of
'distances' between each gene so a 1 to -1 change is 2). Returns a vector
of each gene's metric. | scripts/AnalysisCode.py | thresholded_value_comparison | gaog94/GDAN_QC_CopyNumber | python | def thresholded_value_comparison(df_hg38, df_hg19, metric='hamming'):
"Compare -2,-1,0,1,2 gene-level thresholded calls. metric can be either\n hamming (number of discrepancies in each gene) or manhattan (sum of \n 'distances' between each gene so a 1 to -1 change is 2). Returns a vector\n of each gene's metric."
out = []
for (i, g) in enumerate(df_hg38.columns):
if (metric == 'hamming'):
out.append((sum((df_hg19[g] != df_hg38[g])) / len(df_hg19)))
elif (metric == 'manhattan'):
out.append(sum(abs((df_hg19[g] - df_hg38[g]))))
return pd.DataFrame(out, index=df_hg38.columns) |
def sequential_cohort_test_thresholded_values(cohs):
'Compare thresholded gene-level calls for input tumor types.'
df_out = pd.DataFrame([])
for coh in cohs:
(df_hg38, df_hg19) = load_data(coh, thresh=True)
df_results = thresholded_value_comparison(df_hg38, df_hg19, metric='hamming')
df_results.columns = [coh]
df_out = df_out.join(df_results, how='outer')
df_out.to_csv('../readout/DiscordantSampleFractions_perGene_perCohort_thresholdedCalls.tsv', sep='\t')
return df_out | 2,946,206,052,754,461,000 | Compare thresholded gene-level calls for input tumor types. | scripts/AnalysisCode.py | sequential_cohort_test_thresholded_values | gaog94/GDAN_QC_CopyNumber | python | def sequential_cohort_test_thresholded_values(cohs):
df_out = pd.DataFrame([])
for coh in cohs:
(df_hg38, df_hg19) = load_data(coh, thresh=True)
df_results = thresholded_value_comparison(df_hg38, df_hg19, metric='hamming')
df_results.columns = [coh]
df_out = df_out.join(df_results, how='outer')
df_out.to_csv('../readout/DiscordantSampleFractions_perGene_perCohort_thresholdedCalls.tsv', sep='\t')
return df_out |
def plot_fractionDisagreements_perCohort(cohs):
'Visualize fraction of samples with disagreements in thresholded copy \n number for each gene. Run sequential_cohort_test_thresholded_values()\n before this function.'
df = sequential_cohort_test_thresholded_values(cohs)
df_box = pd.melt(df.reset_index(), id_vars='Gene Symbol').set_index('Gene Symbol')
df_box.columns = ['Tumor Type', 'Fraction of Samples with Disagreements']
dft = df.T
dft['med_degenerates'] = df.median(axis=0)
boxorder = dft.sort_values('med_degenerates', axis=0).index
df_cn = pd.read_table('../../PanCanAneuploidy/bin/PANCAN_armonly_ASandpuritycalls_092817_xcellcalls.txt', index_col=0, usecols=[0, 1, 2, 16])
coh_medians = [int(np.median(df_cn[(df_cn['Type'] == x)]['RecurrentSCNA'].dropna())) for x in df_cn.Type.unique()]
df_med = pd.DataFrame(coh_medians, index=df_cn.Type.unique(), columns=['med'])
pal = sns.color_palette('Blues', ((max(df_med.med) - min(df_med.med)) + 1))
my_pal = {c: pal[df_med.at[(c, 'med')]] for c in df_med.index}
g = sns.boxplot(x=df_box.columns[0], y=df_box.columns[1], data=df_box, order=boxorder, fliersize=1, palette=my_pal, linewidth=0.5)
newxticks = [(((x + ' (') + str(df_med.loc[x]['med'])) + ')') for x in boxorder]
g.set_xticklabels(newxticks, rotation=90)
plt.ylabel('Fraction with Disagreements', fontsize=12)
sns.despine()
plt.gcf().set_size_inches((8, 3))
plt.savefig('2_thresholdedCN_boxplot.pdf', bbox_inches='tight')
plt.savefig('2_thresholdedCN_boxplot.png', bbox_inches='tight') | -8,287,393,572,364,439,000 | Visualize fraction of samples with disagreements in thresholded copy
number for each gene. Run sequential_cohort_test_thresholded_values()
before this function. | scripts/AnalysisCode.py | plot_fractionDisagreements_perCohort | gaog94/GDAN_QC_CopyNumber | python | def plot_fractionDisagreements_perCohort(cohs):
'Visualize fraction of samples with disagreements in thresholded copy \n number for each gene. Run sequential_cohort_test_thresholded_values()\n before this function.'
df = sequential_cohort_test_thresholded_values(cohs)
df_box = pd.melt(df.reset_index(), id_vars='Gene Symbol').set_index('Gene Symbol')
df_box.columns = ['Tumor Type', 'Fraction of Samples with Disagreements']
dft = df.T
dft['med_degenerates'] = df.median(axis=0)
boxorder = dft.sort_values('med_degenerates', axis=0).index
df_cn = pd.read_table('../../PanCanAneuploidy/bin/PANCAN_armonly_ASandpuritycalls_092817_xcellcalls.txt', index_col=0, usecols=[0, 1, 2, 16])
coh_medians = [int(np.median(df_cn[(df_cn['Type'] == x)]['RecurrentSCNA'].dropna())) for x in df_cn.Type.unique()]
df_med = pd.DataFrame(coh_medians, index=df_cn.Type.unique(), columns=['med'])
pal = sns.color_palette('Blues', ((max(df_med.med) - min(df_med.med)) + 1))
my_pal = {c: pal[df_med.at[(c, 'med')]] for c in df_med.index}
g = sns.boxplot(x=df_box.columns[0], y=df_box.columns[1], data=df_box, order=boxorder, fliersize=1, palette=my_pal, linewidth=0.5)
newxticks = [(((x + ' (') + str(df_med.loc[x]['med'])) + ')') for x in boxorder]
g.set_xticklabels(newxticks, rotation=90)
plt.ylabel('Fraction with Disagreements', fontsize=12)
sns.despine()
plt.gcf().set_size_inches((8, 3))
plt.savefig('2_thresholdedCN_boxplot.pdf', bbox_inches='tight')
plt.savefig('2_thresholdedCN_boxplot.png', bbox_inches='tight') |
def peakgene_overlaps(combos, same_genes, normalize=False):
"Count the number of genes that overlap when examing the hg19 & hg38 \n GISTIC runs' focal peaks."
(venn_numbers, gsu, gsi) = ([], [], [])
for (coh, ad) in combos:
print(coh)
fnames = [((('../hg19_gistic/' + coh) + ad) + 'genes.conf_99.txt'), ((('../hg38_gistic/' + coh) + ad) + 'genes.txt')]
df38 = pd.read_table(fnames[0], index_col=0).drop(['q value', 'residual q value', 'wide peak boundaries'])
df19 = pd.read_table(fnames[1], index_col=0).drop(['q value', 'residual q value', 'wide peak boundaries'])
g_38 = (set([x for col in df38.columns for x in df38[col].dropna()]) & same_genes)
g_19 = (set([x for col in df19.columns for x in df19[col].dropna()]) & same_genes)
(intersect, union) = ((g_38 & g_19), (g_38 | g_19))
gsu.append(union)
gsi.append(intersect)
if normalize:
venn_numbers.append([(len((g_19 - intersect)) / len(union)), (len(intersect) / len(union)), (len((g_38 - intersect)) / len(union))])
else:
venn_numbers.append([len((g_19 - intersect)), len(intersect), len((g_38 - intersect))])
index = [((x[0] + '_') + x[1][1:(- 1)]) for x in combos]
return pd.DataFrame(venn_numbers, index=index, columns=['hg19 only', 'Intersection', 'hg38 only']) | 2,170,390,030,271,310,600 | Count the number of genes that overlap when examing the hg19 & hg38
GISTIC runs' focal peaks. | scripts/AnalysisCode.py | peakgene_overlaps | gaog94/GDAN_QC_CopyNumber | python | def peakgene_overlaps(combos, same_genes, normalize=False):
"Count the number of genes that overlap when examing the hg19 & hg38 \n GISTIC runs' focal peaks."
(venn_numbers, gsu, gsi) = ([], [], [])
for (coh, ad) in combos:
print(coh)
fnames = [((('../hg19_gistic/' + coh) + ad) + 'genes.conf_99.txt'), ((('../hg38_gistic/' + coh) + ad) + 'genes.txt')]
df38 = pd.read_table(fnames[0], index_col=0).drop(['q value', 'residual q value', 'wide peak boundaries'])
df19 = pd.read_table(fnames[1], index_col=0).drop(['q value', 'residual q value', 'wide peak boundaries'])
g_38 = (set([x for col in df38.columns for x in df38[col].dropna()]) & same_genes)
g_19 = (set([x for col in df19.columns for x in df19[col].dropna()]) & same_genes)
(intersect, union) = ((g_38 & g_19), (g_38 | g_19))
gsu.append(union)
gsi.append(intersect)
if normalize:
venn_numbers.append([(len((g_19 - intersect)) / len(union)), (len(intersect) / len(union)), (len((g_38 - intersect)) / len(union))])
else:
venn_numbers.append([len((g_19 - intersect)), len(intersect), len((g_38 - intersect))])
index = [((x[0] + '_') + x[1][1:(- 1)]) for x in combos]
return pd.DataFrame(venn_numbers, index=index, columns=['hg19 only', 'Intersection', 'hg38 only']) |
def plot_peakgene_overlaps(combos, same_genes, write=False):
'Visualize the results of peakgene_overlaps function in bargraph form.'
df_out = peakgene_overlaps(combos, same_genes, normalize=False)
(df_d, df_a) = (df_out[(df_out.index.str.split('_').str[(- 1)] == 'del')], df_out[(df_out.index.str.split('_').str[(- 1)] == 'amp')])
for x in zip((df_d, df_a), ('Deletion Peak Memberships', 'Amplification Peak Memberships')):
x[0].index = x[0].index.str.split('_').str[0]
x[0].plot.bar(stacked=True, color=['#af8dc3', '#f7f7f7', '#7fbf7b'], linewidth=1, edgecolor='k')
plt.gca().set_xticklabels(x[0].index, rotation=90)
plt.title(x[1], fontsize=18)
plt.gcf().set_size_inches(10, 8)
sns.despine()
plt.savefig((x[1].split(' ')[0] + '_peakMemberships.pdf'), bbox_inches='tight')
plt.savefig((x[1].split(' ')[0] + '_peakMemberships.png'), bbox_inches='tight')
plt.clf()
if write:
df_out.to_csv('VennStats_focalpeaks.tsv', sep='\t') | 6,568,916,132,747,667,000 | Visualize the results of peakgene_overlaps function in bargraph form. | scripts/AnalysisCode.py | plot_peakgene_overlaps | gaog94/GDAN_QC_CopyNumber | python | def plot_peakgene_overlaps(combos, same_genes, write=False):
df_out = peakgene_overlaps(combos, same_genes, normalize=False)
(df_d, df_a) = (df_out[(df_out.index.str.split('_').str[(- 1)] == 'del')], df_out[(df_out.index.str.split('_').str[(- 1)] == 'amp')])
for x in zip((df_d, df_a), ('Deletion Peak Memberships', 'Amplification Peak Memberships')):
x[0].index = x[0].index.str.split('_').str[0]
x[0].plot.bar(stacked=True, color=['#af8dc3', '#f7f7f7', '#7fbf7b'], linewidth=1, edgecolor='k')
plt.gca().set_xticklabels(x[0].index, rotation=90)
plt.title(x[1], fontsize=18)
plt.gcf().set_size_inches(10, 8)
sns.despine()
plt.savefig((x[1].split(' ')[0] + '_peakMemberships.pdf'), bbox_inches='tight')
plt.savefig((x[1].split(' ')[0] + '_peakMemberships.png'), bbox_inches='tight')
plt.clf()
if write:
df_out.to_csv('VennStats_focalpeaks.tsv', sep='\t') |
def documented_driver_differences():
'Scan and analyze manually currated DocumentedDriverDifferences.txt file.\n Returns: 1) Number of driver genes called in both hg19 & hg38 GISTIC peaks\n 2) Number of drivers missing in hg38 peaks that appeared in hg19 peaks and\n 3) Number of drivers present in hg38 peaks but absent from hg19 peaks.'
df = pd.read_table('../DocumentedDriverDifferences.txt', index_col=0)
df['hg19?'] = df['present in hg19?'].str.strip(')').str.strip('(').str.strip('[').str.strip(']')
df['hg38?'] = df['present in hg38?'].str.strip(')').str.strip('(').str.strip('[').str.strip(']')
matches = sum((df['hg19?'] == df['hg38?']))
lostdrivers = len(df[((df['hg19?'] == 'yes') & (df['hg38?'] == 'no'))])
recovereddrivers = len(df[((df['hg19?'] == 'no') & (df['hg38?'] == 'yes'))])
return (matches, lostdrivers, recovereddrivers) | 3,854,181,415,097,760,000 | Scan and analyze manually currated DocumentedDriverDifferences.txt file.
Returns: 1) Number of driver genes called in both hg19 & hg38 GISTIC peaks
2) Number of drivers missing in hg38 peaks that appeared in hg19 peaks and
3) Number of drivers present in hg38 peaks but absent from hg19 peaks. | scripts/AnalysisCode.py | documented_driver_differences | gaog94/GDAN_QC_CopyNumber | python | def documented_driver_differences():
'Scan and analyze manually currated DocumentedDriverDifferences.txt file.\n Returns: 1) Number of driver genes called in both hg19 & hg38 GISTIC peaks\n 2) Number of drivers missing in hg38 peaks that appeared in hg19 peaks and\n 3) Number of drivers present in hg38 peaks but absent from hg19 peaks.'
df = pd.read_table('../DocumentedDriverDifferences.txt', index_col=0)
df['hg19?'] = df['present in hg19?'].str.strip(')').str.strip('(').str.strip('[').str.strip(']')
df['hg38?'] = df['present in hg38?'].str.strip(')').str.strip('(').str.strip('[').str.strip(']')
matches = sum((df['hg19?'] == df['hg38?']))
lostdrivers = len(df[((df['hg19?'] == 'yes') & (df['hg38?'] == 'no'))])
recovereddrivers = len(df[((df['hg19?'] == 'no') & (df['hg38?'] == 'yes'))])
return (matches, lostdrivers, recovereddrivers) |
def read_raw_artemis123(input_fname, preload=False, verbose=None):
'Read Artemis123 data as raw object.\n\n Parameters\n ----------\n input_fname : str\n Path to the data file (extension ``.bin``). The header file with the\n same file name stem and an extension ``.txt`` is expected to be found\n in the same directory.\n preload : bool or str (default False)\n Preload data into memory for data manipulation and faster indexing.\n If True, the data will be preloaded into memory (fast, requires\n large amount of memory). If preload is a string, preload is the\n file name of a memory-mapped file which is used to store the data\n on the hard drive (slower, requires less memory).\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose).\n\n Returns\n -------\n raw : Instance of Raw\n A Raw object containing the data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n '
return RawArtemis123(input_fname, preload=preload, verbose=verbose) | 1,388,420,436,905,467,000 | Read Artemis123 data as raw object.
Parameters
----------
input_fname : str
Path to the data file (extension ``.bin``). The header file with the
same file name stem and an extension ``.txt`` is expected to be found
in the same directory.
preload : bool or str (default False)
Preload data into memory for data manipulation and faster indexing.
If True, the data will be preloaded into memory (fast, requires
large amount of memory). If preload is a string, preload is the
file name of a memory-mapped file which is used to store the data
on the hard drive (slower, requires less memory).
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
raw : Instance of Raw
A Raw object containing the data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods. | mne/io/artemis123/artemis123.py | read_raw_artemis123 | mvdoc/mne-python | python | def read_raw_artemis123(input_fname, preload=False, verbose=None):
'Read Artemis123 data as raw object.\n\n Parameters\n ----------\n input_fname : str\n Path to the data file (extension ``.bin``). The header file with the\n same file name stem and an extension ``.txt`` is expected to be found\n in the same directory.\n preload : bool or str (default False)\n Preload data into memory for data manipulation and faster indexing.\n If True, the data will be preloaded into memory (fast, requires\n large amount of memory). If preload is a string, preload is the\n file name of a memory-mapped file which is used to store the data\n on the hard drive (slower, requires less memory).\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose).\n\n Returns\n -------\n raw : Instance of Raw\n A Raw object containing the data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n '
return RawArtemis123(input_fname, preload=preload, verbose=verbose) |
def _get_artemis123_info(fname):
'Function for extracting info from artemis123 header files.'
fname = op.splitext(op.abspath(fname))[0]
header = (fname + '.txt')
logger.info('Reading header...')
chan_keys = ['name', 'scaling', 'FLL_Gain', 'FLL_Mode', 'FLL_HighPass', 'FLL_AutoReset', 'FLL_ResetLock']
header_info = dict()
header_info['filter_hist'] = []
header_info['comments'] = ''
header_info['channels'] = []
with open(header, 'r') as fid:
sectionFlag = 0
for line in fid:
if ((not line.strip()) or ((sectionFlag == 2) and line.startswith('DAQ Map'))):
continue
if line.startswith('<end'):
sectionFlag = 0
elif line.startswith('<start main header>'):
sectionFlag = 1
elif line.startswith('<start per channel header>'):
sectionFlag = 2
elif line.startswith('<start comments>'):
sectionFlag = 3
elif line.startswith('<start length>'):
sectionFlag = 4
elif line.startswith('<start filtering history>'):
sectionFlag = 5
elif (sectionFlag == 1):
values = line.strip().split('\t')
if (len(values) == 1):
values.append('')
header_info[values[0]] = values[1]
elif (sectionFlag == 2):
values = line.strip().split('\t')
if (len(values) != 7):
raise IOError((('Error parsing line \n\t:%s\n' % line) + ('from file %s' % header)))
tmp = dict()
for (k, v) in zip(chan_keys, values):
tmp[k] = v
header_info['channels'].append(tmp)
elif (sectionFlag == 3):
header_info['comments'] = ('%s%s' % (header_info['comments'], line.strip()))
elif (sectionFlag == 4):
header_info['num_samples'] = int(line.strip())
elif (sectionFlag == 5):
header_info['filter_hist'].append(line.strip())
for k in ['Temporal Filter Active?', 'Decimation Active?', 'Spatial Filter Active?']:
if (header_info[k] != 'FALSE'):
warn(('%s - set to but is not supported' % k))
if header_info['filter_hist']:
warn(('Non-Empty Filter histroy found, BUT is not supported' % k))
info = _empty_info(float(header_info['Rate Out']))
try:
date = datetime.datetime.strptime(op.basename(fname).split('_')[2], '%Y-%m-%d-%Hh-%Mm')
meas_date = calendar.timegm(date.utctimetuple())
except Exception:
meas_date = None
subject_info = {'id': header_info['Subject ID']}
desc = ''
for k in ['Purpose', 'Notes']:
desc += '{} : {}\n'.format(k, header_info[k])
desc += 'Comments : {}'.format(header_info['comments'])
info = _empty_info(float(header_info['Rate Out']))
info.update({'filename': fname, 'meas_date': meas_date, 'description': desc, 'buffer_size_sec': 1.0, 'subject_info': subject_info, 'proj_name': header_info['Project Name']})
ref_mag_names = ['REF_001', 'REF_002', 'REF_003', 'REF_004', 'REF_005', 'REF_006']
ref_grad_names = ['REF_007', 'REF_008', 'REF_009', 'REF_010', 'REF_011', 'REF_012']
loc_dict = _load_mne_locs()
info['chs'] = []
info['bads'] = []
for (i, chan) in enumerate(header_info['channels']):
t = {'cal': float(chan['scaling']), 'ch_name': chan['name'], 'logno': (i + 1), 'scanno': (i + 1), 'range': 1.0, 'unit_mul': FIFF.FIFF_UNITM_NONE, 'coord_frame': FIFF.FIFFV_COORD_DEVICE}
t['loc'] = loc_dict.get(chan['name'], np.zeros(12))
if chan['name'].startswith('MEG'):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_GRAD
t['kind'] = FIFF.FIFFV_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif (chan['name'] in ref_mag_names):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG
t['kind'] = FIFF.FIFFV_REF_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif (chan['name'] in ref_grad_names):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD
t['kind'] = FIFF.FIFFV_REF_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif chan['name'].startswith('REF'):
t['coil_type'] = FIFF.FIFFV_COIL_NONE
t['kind'] = FIFF.FIFFV_MISC_CH
t['unit'] = FIFF.FIFF_UNIT_V
info['bads'].append(t['ch_name'])
elif chan['name'].startswith(('AUX', 'TRG', 'MIO')):
t['coil_type'] = FIFF.FIFFV_COIL_NONE
t['unit'] = FIFF.FIFF_UNIT_V
if chan['name'].startswith('TRG'):
t['kind'] = FIFF.FIFFV_STIM_CH
else:
t['kind'] = FIFF.FIFFV_MISC_CH
else:
raise ValueError(('Channel does not match expected' + (' channel Types:"%s"' % chan['name'])))
t['cal'] *= (10 ** t['unit_mul'])
t['unit_mul'] = FIFF.FIFF_UNITM_NONE
info['chs'].append(t)
if (chan['FLL_ResetLock'] == 'TRUE'):
info['bads'].append(t['ch_name'])
info['bads'] = list(set(info['bads']))
info._update_redundant()
return (info, header_info) | 6,327,057,148,829,524,000 | Function for extracting info from artemis123 header files. | mne/io/artemis123/artemis123.py | _get_artemis123_info | mvdoc/mne-python | python | def _get_artemis123_info(fname):
fname = op.splitext(op.abspath(fname))[0]
header = (fname + '.txt')
logger.info('Reading header...')
chan_keys = ['name', 'scaling', 'FLL_Gain', 'FLL_Mode', 'FLL_HighPass', 'FLL_AutoReset', 'FLL_ResetLock']
header_info = dict()
header_info['filter_hist'] = []
header_info['comments'] =
header_info['channels'] = []
with open(header, 'r') as fid:
sectionFlag = 0
for line in fid:
if ((not line.strip()) or ((sectionFlag == 2) and line.startswith('DAQ Map'))):
continue
if line.startswith('<end'):
sectionFlag = 0
elif line.startswith('<start main header>'):
sectionFlag = 1
elif line.startswith('<start per channel header>'):
sectionFlag = 2
elif line.startswith('<start comments>'):
sectionFlag = 3
elif line.startswith('<start length>'):
sectionFlag = 4
elif line.startswith('<start filtering history>'):
sectionFlag = 5
elif (sectionFlag == 1):
values = line.strip().split('\t')
if (len(values) == 1):
values.append()
header_info[values[0]] = values[1]
elif (sectionFlag == 2):
values = line.strip().split('\t')
if (len(values) != 7):
raise IOError((('Error parsing line \n\t:%s\n' % line) + ('from file %s' % header)))
tmp = dict()
for (k, v) in zip(chan_keys, values):
tmp[k] = v
header_info['channels'].append(tmp)
elif (sectionFlag == 3):
header_info['comments'] = ('%s%s' % (header_info['comments'], line.strip()))
elif (sectionFlag == 4):
header_info['num_samples'] = int(line.strip())
elif (sectionFlag == 5):
header_info['filter_hist'].append(line.strip())
for k in ['Temporal Filter Active?', 'Decimation Active?', 'Spatial Filter Active?']:
if (header_info[k] != 'FALSE'):
warn(('%s - set to but is not supported' % k))
if header_info['filter_hist']:
warn(('Non-Empty Filter histroy found, BUT is not supported' % k))
info = _empty_info(float(header_info['Rate Out']))
try:
date = datetime.datetime.strptime(op.basename(fname).split('_')[2], '%Y-%m-%d-%Hh-%Mm')
meas_date = calendar.timegm(date.utctimetuple())
except Exception:
meas_date = None
subject_info = {'id': header_info['Subject ID']}
desc =
for k in ['Purpose', 'Notes']:
desc += '{} : {}\n'.format(k, header_info[k])
desc += 'Comments : {}'.format(header_info['comments'])
info = _empty_info(float(header_info['Rate Out']))
info.update({'filename': fname, 'meas_date': meas_date, 'description': desc, 'buffer_size_sec': 1.0, 'subject_info': subject_info, 'proj_name': header_info['Project Name']})
ref_mag_names = ['REF_001', 'REF_002', 'REF_003', 'REF_004', 'REF_005', 'REF_006']
ref_grad_names = ['REF_007', 'REF_008', 'REF_009', 'REF_010', 'REF_011', 'REF_012']
loc_dict = _load_mne_locs()
info['chs'] = []
info['bads'] = []
for (i, chan) in enumerate(header_info['channels']):
t = {'cal': float(chan['scaling']), 'ch_name': chan['name'], 'logno': (i + 1), 'scanno': (i + 1), 'range': 1.0, 'unit_mul': FIFF.FIFF_UNITM_NONE, 'coord_frame': FIFF.FIFFV_COORD_DEVICE}
t['loc'] = loc_dict.get(chan['name'], np.zeros(12))
if chan['name'].startswith('MEG'):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_GRAD
t['kind'] = FIFF.FIFFV_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif (chan['name'] in ref_mag_names):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG
t['kind'] = FIFF.FIFFV_REF_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif (chan['name'] in ref_grad_names):
t['coil_type'] = FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD
t['kind'] = FIFF.FIFFV_REF_MEG_CH
t['unit'] = FIFF.FIFF_UNIT_T
t['unit_mul'] = FIFF.FIFF_UNITM_F
elif chan['name'].startswith('REF'):
t['coil_type'] = FIFF.FIFFV_COIL_NONE
t['kind'] = FIFF.FIFFV_MISC_CH
t['unit'] = FIFF.FIFF_UNIT_V
info['bads'].append(t['ch_name'])
elif chan['name'].startswith(('AUX', 'TRG', 'MIO')):
t['coil_type'] = FIFF.FIFFV_COIL_NONE
t['unit'] = FIFF.FIFF_UNIT_V
if chan['name'].startswith('TRG'):
t['kind'] = FIFF.FIFFV_STIM_CH
else:
t['kind'] = FIFF.FIFFV_MISC_CH
else:
raise ValueError(('Channel does not match expected' + (' channel Types:"%s"' % chan['name'])))
t['cal'] *= (10 ** t['unit_mul'])
t['unit_mul'] = FIFF.FIFF_UNITM_NONE
info['chs'].append(t)
if (chan['FLL_ResetLock'] == 'TRUE'):
info['bads'].append(t['ch_name'])
info['bads'] = list(set(info['bads']))
info._update_redundant()
return (info, header_info) |
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
'Read a chunk of raw data.'
_read_segments_file(self, data, idx, fi, start, stop, cals, mult, dtype='>f4') | 6,048,899,956,598,921,000 | Read a chunk of raw data. | mne/io/artemis123/artemis123.py | _read_segment_file | mvdoc/mne-python | python | def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
_read_segments_file(self, data, idx, fi, start, stop, cals, mult, dtype='>f4') |
@singledispatch
def das(var, level=0):
'Single dispatcher that generates the DAS response.'
raise StopIteration | 4,591,123,262,229,556,000 | Single dispatcher that generates the DAS response. | src/pydap/responses/das.py | das | JohnMLarkin/pydap | python | @singledispatch
def das(var, level=0):
raise StopIteration |
def build_attributes(attr, values, level=0):
'Recursive function to build the DAS.'
if isinstance(values, dict):
(yield '{indent}{attr} {{\n'.format(indent=(level * INDENT), attr=attr))
for (k, v) in values.items():
for line in build_attributes(k, v, (level + 1)):
(yield line)
(yield '{indent}}}\n'.format(indent=(level * INDENT)))
else:
type = get_type(values)
if (isinstance(values, string_types) or (not isinstance(values, Iterable)) or (getattr(values, 'shape', None) == ())):
values = [encode(values)]
else:
values = map(encode, values)
(yield '{indent}{type} {attr} {values};\n'.format(indent=(level * INDENT), type=type, attr=quote(attr), values=', '.join(values))) | -7,251,863,667,847,830,000 | Recursive function to build the DAS. | src/pydap/responses/das.py | build_attributes | JohnMLarkin/pydap | python | def build_attributes(attr, values, level=0):
if isinstance(values, dict):
(yield '{indent}{attr} {{\n'.format(indent=(level * INDENT), attr=attr))
for (k, v) in values.items():
for line in build_attributes(k, v, (level + 1)):
(yield line)
(yield '{indent}}}\n'.format(indent=(level * INDENT)))
else:
type = get_type(values)
if (isinstance(values, string_types) or (not isinstance(values, Iterable)) or (getattr(values, 'shape', None) == ())):
values = [encode(values)]
else:
values = map(encode, values)
(yield '{indent}{type} {attr} {values};\n'.format(indent=(level * INDENT), type=type, attr=quote(attr), values=', '.join(values))) |
def get_type(values):
'Extract the type of a variable.\n\n This function tries to determine the DAP type of a Python variable using\n several methods. Returns the DAP type as a string.\n\n '
if hasattr(values, 'dtype'):
return NUMPY_TO_DAP2_TYPEMAP[values.dtype.char]
elif (isinstance(values, string_types) or (not isinstance(values, Iterable))):
return type_convert(values)
else:
types = [type_convert(val) for val in values]
precedence = ['String', 'Float64', 'Int32']
types.sort(key=precedence.index)
return types[0] | 5,853,865,236,788,224,000 | Extract the type of a variable.
This function tries to determine the DAP type of a Python variable using
several methods. Returns the DAP type as a string. | src/pydap/responses/das.py | get_type | JohnMLarkin/pydap | python | def get_type(values):
'Extract the type of a variable.\n\n This function tries to determine the DAP type of a Python variable using\n several methods. Returns the DAP type as a string.\n\n '
if hasattr(values, 'dtype'):
return NUMPY_TO_DAP2_TYPEMAP[values.dtype.char]
elif (isinstance(values, string_types) or (not isinstance(values, Iterable))):
return type_convert(values)
else:
types = [type_convert(val) for val in values]
precedence = ['String', 'Float64', 'Int32']
types.sort(key=precedence.index)
return types[0] |
def type_convert(obj):
'Map Python objects to the corresponding Opendap types.\n\n Returns the DAP representation of the type as a string.\n\n '
if isinstance(obj, float):
return 'Float64'
elif isinstance(obj, integer_types):
return 'Int32'
else:
return 'String' | -4,285,503,868,923,275,000 | Map Python objects to the corresponding Opendap types.
Returns the DAP representation of the type as a string. | src/pydap/responses/das.py | type_convert | JohnMLarkin/pydap | python | def type_convert(obj):
'Map Python objects to the corresponding Opendap types.\n\n Returns the DAP representation of the type as a string.\n\n '
if isinstance(obj, float):
return 'Float64'
elif isinstance(obj, integer_types):
return 'Int32'
else:
return 'String' |
def _parse__event_type_ids(self):
"turns the request's `event_type=operations__update_recents__global` into an id."
event_type_id = None
event_type = self.request.params.get('event_type', None)
if event_type:
try:
event_type_id = model_utils.OperationsEventType.from_string(event_type)
except AttributeError:
event_type = None
event_type_id = None
if event_type_id:
return (event_type_id,)
return None | 7,138,887,671,699,360,000 | turns the request's `event_type=operations__update_recents__global` into an id. | src/peter_sslers/web/views_admin/operation.py | _parse__event_type_ids | jvanasco/peter_sslers | python | def _parse__event_type_ids(self):
event_type_id = None
event_type = self.request.params.get('event_type', None)
if event_type:
try:
event_type_id = model_utils.OperationsEventType.from_string(event_type)
except AttributeError:
event_type = None
event_type_id = None
if event_type_id:
return (event_type_id,)
return None |
def get_queryset(self, request):
'\n read queryset if is superuser\n or read owns objects\n '
qs = super().get_queryset(request)
if request.user.is_superuser:
return qs
return qs.filter(created_by=request.user) | -5,192,438,078,885,395,000 | read queryset if is superuser
or read owns objects | schedules/mixins.py | get_queryset | dvek/scyp | python | def get_queryset(self, request):
'\n read queryset if is superuser\n or read owns objects\n '
qs = super().get_queryset(request)
if request.user.is_superuser:
return qs
return qs.filter(created_by=request.user) |
def response_change(self, request, obj):
"\n get from response change some custom action from post\n ej: '_custom_action' in request.POST:\n "
if ('_custom_action' in request.POST):
pass
return super().response_change(request, obj) | 5,401,525,779,719,651,000 | get from response change some custom action from post
ej: '_custom_action' in request.POST: | schedules/mixins.py | response_change | dvek/scyp | python | def response_change(self, request, obj):
"\n get from response change some custom action from post\n ej: '_custom_action' in request.POST:\n "
if ('_custom_action' in request.POST):
pass
return super().response_change(request, obj) |
def response_add(self, request, obj):
"\n get from response change some custom action from post\n ej: '_custom_action' in request.POST:\n "
if ('_custom_action' in request.POST):
pass
return super().response_add(request, obj) | -777,167,301,962,328,600 | get from response change some custom action from post
ej: '_custom_action' in request.POST: | schedules/mixins.py | response_add | dvek/scyp | python | def response_add(self, request, obj):
"\n get from response change some custom action from post\n ej: '_custom_action' in request.POST:\n "
if ('_custom_action' in request.POST):
pass
return super().response_add(request, obj) |
def get_queryset(self, request):
'\n read queryset if is superuser\n or read owns objects\n '
qs = super().get_queryset(request)
if request.user.is_superuser:
return qs
return qs.filter(created_by=request.user) | -5,192,438,078,885,395,000 | read queryset if is superuser
or read owns objects | schedules/mixins.py | get_queryset | dvek/scyp | python | def get_queryset(self, request):
'\n read queryset if is superuser\n or read owns objects\n '
qs = super().get_queryset(request)
if request.user.is_superuser:
return qs
return qs.filter(created_by=request.user) |
def _split_generators(self, dl_manager: nlp.DownloadManager):
'Returns SplitGenerators.'
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, 'ijcnlp_dailydialog')
for name in ('train', 'validation', 'test'):
zip_fpath = os.path.join(data_dir, f'{name}.zip')
with ZipFile(zip_fpath) as zip_file:
zip_file.extractall(path=data_dir)
zip_file.close()
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={'file_path': os.path.join(data_dir, 'train', 'dialogues_train.txt'), 'act_path': os.path.join(data_dir, 'train', 'dialogues_act_train.txt'), 'emotion_path': os.path.join(data_dir, 'train', 'dialogues_emotion_train.txt'), 'split': 'train'}), nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={'file_path': os.path.join(data_dir, 'test', 'dialogues_test.txt'), 'act_path': os.path.join(data_dir, 'test', 'dialogues_act_test.txt'), 'emotion_path': os.path.join(data_dir, 'test', 'dialogues_emotion_test.txt'), 'split': 'test'}), nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={'file_path': os.path.join(data_dir, 'validation', 'dialogues_validation.txt'), 'act_path': os.path.join(data_dir, 'validation', 'dialogues_act_validation.txt'), 'emotion_path': os.path.join(data_dir, 'validation', 'dialogues_emotion_validation.txt'), 'split': 'dev'})] | -3,751,968,599,593,737,700 | Returns SplitGenerators. | datasets/daily_dialog/daily_dialog.py | _split_generators | vinayya/nlp | python | def _split_generators(self, dl_manager: nlp.DownloadManager):
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, 'ijcnlp_dailydialog')
for name in ('train', 'validation', 'test'):
zip_fpath = os.path.join(data_dir, f'{name}.zip')
with ZipFile(zip_fpath) as zip_file:
zip_file.extractall(path=data_dir)
zip_file.close()
return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={'file_path': os.path.join(data_dir, 'train', 'dialogues_train.txt'), 'act_path': os.path.join(data_dir, 'train', 'dialogues_act_train.txt'), 'emotion_path': os.path.join(data_dir, 'train', 'dialogues_emotion_train.txt'), 'split': 'train'}), nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={'file_path': os.path.join(data_dir, 'test', 'dialogues_test.txt'), 'act_path': os.path.join(data_dir, 'test', 'dialogues_act_test.txt'), 'emotion_path': os.path.join(data_dir, 'test', 'dialogues_emotion_test.txt'), 'split': 'test'}), nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={'file_path': os.path.join(data_dir, 'validation', 'dialogues_validation.txt'), 'act_path': os.path.join(data_dir, 'validation', 'dialogues_act_validation.txt'), 'emotion_path': os.path.join(data_dir, 'validation', 'dialogues_emotion_validation.txt'), 'split': 'dev'})] |
def _generate_examples(self, file_path, act_path, emotion_path, split):
' Yields examples. '
with open(file_path, 'r', encoding='utf-8') as f, open(act_path, 'r', encoding='utf-8') as act, open(emotion_path, 'r', encoding='utf-8') as emotion:
for (i, (line_f, line_act, line_emotion)) in enumerate(zip(f, act, emotion)):
if (len(line_f.strip()) == 0):
break
dialog = line_f.split(self.__EOU__)[:(- 1)]
act = line_act.split(' ')[:(- 1)]
emotion = line_emotion.split(' ')[:(- 1)]
assert (len(dialog) == len(act) == len(emotion)), 'Different turns btw dialogue & emotion & action'
(yield (f'{split}-{i}', {'dialog': dialog, 'act': [act_label[x] for x in act], 'emotion': [emotion_label[x] for x in emotion]})) | 3,076,678,954,509,529,600 | Yields examples. | datasets/daily_dialog/daily_dialog.py | _generate_examples | vinayya/nlp | python | def _generate_examples(self, file_path, act_path, emotion_path, split):
' '
with open(file_path, 'r', encoding='utf-8') as f, open(act_path, 'r', encoding='utf-8') as act, open(emotion_path, 'r', encoding='utf-8') as emotion:
for (i, (line_f, line_act, line_emotion)) in enumerate(zip(f, act, emotion)):
if (len(line_f.strip()) == 0):
break
dialog = line_f.split(self.__EOU__)[:(- 1)]
act = line_act.split(' ')[:(- 1)]
emotion = line_emotion.split(' ')[:(- 1)]
assert (len(dialog) == len(act) == len(emotion)), 'Different turns btw dialogue & emotion & action'
(yield (f'{split}-{i}', {'dialog': dialog, 'act': [act_label[x] for x in act], 'emotion': [emotion_label[x] for x in emotion]})) |
def access_log_decorate(func):
'\n 用于记录用户登录后访问网址行为的装饰器\n :param func:\n :return:\n '
@wraps(func)
def wrapper(*args, **kwargs):
access_user = request.headers.get('X-Real-IP ', request.remote_addr)
access_method = request.method
access_path = request.path
access_time = timestamp_format(time.time())
resp = func(*args, **kwargs)
access_result = resp[0].get('status')
access_message = (resp[0].get('message', 'Internal Server Error') if resp else 'Internal Server Error')
SfoServerAccessLog.add_access_log(access_user, access_method, access_path, access_time, access_result, access_message)
return resp
return wrapper | -7,364,709,911,023,220,000 | 用于记录用户登录后访问网址行为的装饰器
:param func:
:return: | sfo_server/decorate.py | access_log_decorate | SF-Technology/SFO | python | def access_log_decorate(func):
'\n 用于记录用户登录后访问网址行为的装饰器\n :param func:\n :return:\n '
@wraps(func)
def wrapper(*args, **kwargs):
access_user = request.headers.get('X-Real-IP ', request.remote_addr)
access_method = request.method
access_path = request.path
access_time = timestamp_format(time.time())
resp = func(*args, **kwargs)
access_result = resp[0].get('status')
access_message = (resp[0].get('message', 'Internal Server Error') if resp else 'Internal Server Error')
SfoServerAccessLog.add_access_log(access_user, access_method, access_path, access_time, access_result, access_message)
return resp
return wrapper |
def login_required(func):
'\n 验证是否登录\n :param func:\n :return:\n '
@wraps(func)
def wrapper(*args, **kwargs):
user_account = session.get('username', '')
if user_account:
login_user = SfoServerUser.query_user_by_account(user_account)
g.user = login_user
return func(*args, **kwargs)
else:
return ResponseBase(json.dumps({'status': 401, 'message': u'请先登录'}), status=401, content_type='application/json')
return wrapper | 2,283,296,594,586,760,200 | 验证是否登录
:param func:
:return: | sfo_server/decorate.py | login_required | SF-Technology/SFO | python | def login_required(func):
'\n 验证是否登录\n :param func:\n :return:\n '
@wraps(func)
def wrapper(*args, **kwargs):
user_account = session.get('username', )
if user_account:
login_user = SfoServerUser.query_user_by_account(user_account)
g.user = login_user
return func(*args, **kwargs)
else:
return ResponseBase(json.dumps({'status': 401, 'message': u'请先登录'}), status=401, content_type='application/json')
return wrapper |
def permission_required(*resources):
'\n 权限验证的前提是用户已经登录\n 权限验证\n :param resources: 控制的资源对象\n '
def decorate(func):
@wraps(func)
def wrapper(*args, **kwargs):
method = func.__name__
resource_names = [resource.__tablename__ for resource in resources]
need_permission = set([((method + '_') + resource_name) for resource_name in resource_names])
user = getattr(g, 'user', '')
has_permission_set = set()
is_clusteradmin = (user.is_clusteradmin if user else 0)
if is_clusteradmin:
return func(*args, **kwargs)
if user:
for role in user.roles:
for permission in role.permissions:
has_permission_set.add(permission.permission_name)
if (not need_permission.issubset(has_permission_set)):
return ResponseBase(json.dumps({'status': 403, 'message': u'权限不足，请联系管理员'}), status=403, content_type='application/json')
else:
return func(*args, **kwargs)
else:
return ResponseBase(json.dumps({'status': 401, 'message': u'请先登录'}), status=401, content_type='application/json')
return wrapper
return decorate | 2,981,514,981,444,468,700 | 权限验证的前提是用户已经登录
权限验证
:param resources: 控制的资源对象 | sfo_server/decorate.py | permission_required | SF-Technology/SFO | python | def permission_required(*resources):
'\n 权限验证的前提是用户已经登录\n 权限验证\n :param resources: 控制的资源对象\n '
def decorate(func):
@wraps(func)
def wrapper(*args, **kwargs):
method = func.__name__
resource_names = [resource.__tablename__ for resource in resources]
need_permission = set([((method + '_') + resource_name) for resource_name in resource_names])
user = getattr(g, 'user', )
has_permission_set = set()
is_clusteradmin = (user.is_clusteradmin if user else 0)
if is_clusteradmin:
return func(*args, **kwargs)
if user:
for role in user.roles:
for permission in role.permissions:
has_permission_set.add(permission.permission_name)
if (not need_permission.issubset(has_permission_set)):
return ResponseBase(json.dumps({'status': 403, 'message': u'权限不足，请联系管理员'}), status=403, content_type='application/json')
else:
return func(*args, **kwargs)
else:
return ResponseBase(json.dumps({'status': 401, 'message': u'请先登录'}), status=401, content_type='application/json')
return wrapper
return decorate |
@when('@:2.4.99')
def patch(self):
' see https://github.com/spack/spack/issues/13559 '
filter_file('import sys', 'import sys; return "{0}"'.format(self.prefix.include), 'pybind11/__init__.py', string=True) | -4,811,059,582,925,944,000 | see https://github.com/spack/spack/issues/13559 | var/spack/repos/builtin/packages/py-pybind11/package.py | patch | ikitayama/spack | python | @when('@:2.4.99')
def patch(self):
' '
filter_file('import sys', 'import sys; return "{0}"'.format(self.prefix.include), 'pybind11/__init__.py', string=True) |
def _DashboardJsonToRawRows(dash_json_dict):
"Formats a Dashboard JSON dict as a list of row dicts.\n\n For the dashboard to begin accepting the Telemetry Dashboard JSON format\n as per go/telemetry-json, this function chunks a Dashboard JSON literal\n into rows and passes the resulting list to _AddTasks.\n\n Args:\n dash_json_dict: A dashboard JSON v1.0 dict.\n\n Returns:\n A list of dicts, each of which represents a point.\n\n Raises:\n AssertionError: The given argument wasn't a dict.\n BadRequestError: The content of the input wasn't valid.\n "
assert (type(dash_json_dict) is dict)
if (not dash_json_dict.get('master')):
raise BadRequestError('No master name given.')
if (not dash_json_dict.get('bot')):
raise BadRequestError('No bot name given.')
if (not dash_json_dict.get('point_id')):
raise BadRequestError('No point_id number given.')
if (not dash_json_dict.get('chart_data')):
raise BadRequestError('No chart data given.')
test_suite_name = _TestSuiteName(dash_json_dict)
chart_data = dash_json_dict.get('chart_data', {})
charts = chart_data.get('charts', {})
if (not charts):
return []
tracing_links = None
if ('trace' in charts):
tracing_links = charts['trace'].copy()
del charts['trace']
row_template = _MakeRowTemplate(dash_json_dict)
benchmark_description = chart_data.get('benchmark_description', '')
trace_rerun_options = dict(chart_data.get('trace_rerun_options', []))
is_ref = bool(dash_json_dict.get('is_ref'))
rows = []
for chart in charts:
for trace in charts[chart]:
row = copy.deepcopy(row_template)
specific_vals = _FlattenTrace(test_suite_name, chart, trace, charts[chart][trace], is_ref, tracing_links, benchmark_description)
if (not (math.isnan(specific_vals['value']) or math.isnan(specific_vals['error']))):
if specific_vals['tracing_uri']:
row['supplemental_columns']['a_tracing_uri'] = specific_vals['tracing_uri']
if trace_rerun_options:
row['supplemental_columns']['a_trace_rerun_options'] = trace_rerun_options
row.update(specific_vals)
rows.append(row)
return rows | 2,263,905,534,619,643,600 | Formats a Dashboard JSON dict as a list of row dicts.
For the dashboard to begin accepting the Telemetry Dashboard JSON format
as per go/telemetry-json, this function chunks a Dashboard JSON literal
into rows and passes the resulting list to _AddTasks.
Args:
dash_json_dict: A dashboard JSON v1.0 dict.
Returns:
A list of dicts, each of which represents a point.
Raises:
AssertionError: The given argument wasn't a dict.
BadRequestError: The content of the input wasn't valid. | dashboard/dashboard/add_point.py | _DashboardJsonToRawRows | bopopescu/catapult-2 | python | def _DashboardJsonToRawRows(dash_json_dict):
"Formats a Dashboard JSON dict as a list of row dicts.\n\n For the dashboard to begin accepting the Telemetry Dashboard JSON format\n as per go/telemetry-json, this function chunks a Dashboard JSON literal\n into rows and passes the resulting list to _AddTasks.\n\n Args:\n dash_json_dict: A dashboard JSON v1.0 dict.\n\n Returns:\n A list of dicts, each of which represents a point.\n\n Raises:\n AssertionError: The given argument wasn't a dict.\n BadRequestError: The content of the input wasn't valid.\n "
assert (type(dash_json_dict) is dict)
if (not dash_json_dict.get('master')):
raise BadRequestError('No master name given.')
if (not dash_json_dict.get('bot')):
raise BadRequestError('No bot name given.')
if (not dash_json_dict.get('point_id')):
raise BadRequestError('No point_id number given.')
if (not dash_json_dict.get('chart_data')):
raise BadRequestError('No chart data given.')
test_suite_name = _TestSuiteName(dash_json_dict)
chart_data = dash_json_dict.get('chart_data', {})
charts = chart_data.get('charts', {})
if (not charts):
return []
tracing_links = None
if ('trace' in charts):
tracing_links = charts['trace'].copy()
del charts['trace']
row_template = _MakeRowTemplate(dash_json_dict)
benchmark_description = chart_data.get('benchmark_description', )
trace_rerun_options = dict(chart_data.get('trace_rerun_options', []))
is_ref = bool(dash_json_dict.get('is_ref'))
rows = []
for chart in charts:
for trace in charts[chart]:
row = copy.deepcopy(row_template)
specific_vals = _FlattenTrace(test_suite_name, chart, trace, charts[chart][trace], is_ref, tracing_links, benchmark_description)
if (not (math.isnan(specific_vals['value']) or math.isnan(specific_vals['error']))):
if specific_vals['tracing_uri']:
row['supplemental_columns']['a_tracing_uri'] = specific_vals['tracing_uri']
if trace_rerun_options:
row['supplemental_columns']['a_trace_rerun_options'] = trace_rerun_options
row.update(specific_vals)
rows.append(row)
return rows |
def _TestSuiteName(dash_json_dict):
'Extracts a test suite name from Dashboard JSON.\n\n The dashboard JSON may contain a field "test_suite_name". If this is not\n present or it is None, the dashboard will fall back to using "benchmark_name"\n in the "chart_data" dict.\n '
if dash_json_dict.get('test_suite_name'):
return dash_json_dict['test_suite_name']
try:
return dash_json_dict['chart_data']['benchmark_name']
except KeyError as e:
raise BadRequestError(('Could not find test suite name. ' + e.message)) | -8,499,947,017,492,508,000 | Extracts a test suite name from Dashboard JSON.
The dashboard JSON may contain a field "test_suite_name". If this is not
present or it is None, the dashboard will fall back to using "benchmark_name"
in the "chart_data" dict. | dashboard/dashboard/add_point.py | _TestSuiteName | bopopescu/catapult-2 | python | def _TestSuiteName(dash_json_dict):
'Extracts a test suite name from Dashboard JSON.\n\n The dashboard JSON may contain a field "test_suite_name". If this is not\n present or it is None, the dashboard will fall back to using "benchmark_name"\n in the "chart_data" dict.\n '
if dash_json_dict.get('test_suite_name'):
return dash_json_dict['test_suite_name']
try:
return dash_json_dict['chart_data']['benchmark_name']
except KeyError as e:
raise BadRequestError(('Could not find test suite name. ' + e.message)) |
def _AddTasks(data):
'Puts tasks on queue for adding data.\n\n Args:\n data: A list of dictionaries, each of which represents one point.\n '
task_list = []
for data_sublist in _Chunk(data, _TASK_QUEUE_SIZE):
task_list.append(taskqueue.Task(url='/add_point_queue', params={'data': json.dumps(data_sublist)}))
queue = taskqueue.Queue(_TASK_QUEUE_NAME)
for task_sublist in _Chunk(task_list, taskqueue.MAX_TASKS_PER_ADD):
queue.add_async(task_sublist).get_result() | -2,097,049,842,708,797,200 | Puts tasks on queue for adding data.
Args:
data: A list of dictionaries, each of which represents one point. | dashboard/dashboard/add_point.py | _AddTasks | bopopescu/catapult-2 | python | def _AddTasks(data):
'Puts tasks on queue for adding data.\n\n Args:\n data: A list of dictionaries, each of which represents one point.\n '
task_list = []
for data_sublist in _Chunk(data, _TASK_QUEUE_SIZE):
task_list.append(taskqueue.Task(url='/add_point_queue', params={'data': json.dumps(data_sublist)}))
queue = taskqueue.Queue(_TASK_QUEUE_NAME)
for task_sublist in _Chunk(task_list, taskqueue.MAX_TASKS_PER_ADD):
queue.add_async(task_sublist).get_result() |
def _Chunk(items, chunk_size):
'Breaks a long list into sub-lists of a particular size.'
chunks = []
for i in range(0, len(items), chunk_size):
chunks.append(items[i:(i + chunk_size)])
return chunks | 3,657,317,635,363,090,400 | Breaks a long list into sub-lists of a particular size. | dashboard/dashboard/add_point.py | _Chunk | bopopescu/catapult-2 | python | def _Chunk(items, chunk_size):
chunks = []
for i in range(0, len(items), chunk_size):
chunks.append(items[i:(i + chunk_size)])
return chunks |
def _MakeRowTemplate(dash_json_dict):
'Produces a template for rows created from a Dashboard JSON v1.0 dict.\n\n _DashboardJsonToRawRows adds metadata fields to every row that it creates.\n These include things like master, bot, point ID, versions, and other\n supplementary data. This method produces a dict containing this metadata\n to which row-specific information (like value and error) can be added.\n Some metadata needs to be transformed to conform to the v0 format, and this\n method is also responsible for that transformation.\n\n Some validation is deferred until after the input is converted to a list\n of row dicts, since revision format correctness is checked on a per-point\n basis.\n\n Args:\n dash_json_dict: A dashboard JSON v1.0 dict.\n\n Returns:\n A dict containing data to include in each row dict that is created from\n |dash_json_dict|.\n '
row_template = dash_json_dict.copy()
del row_template['chart_data']
del row_template['point_id']
row_template['revision'] = dash_json_dict['point_id']
annotations = row_template['supplemental']
versions = row_template['versions']
del row_template['supplemental']
del row_template['versions']
row_template['supplemental_columns'] = {}
supplemental = row_template['supplemental_columns']
for annotation in annotations:
supplemental[('a_' + annotation)] = annotations[annotation]
for version in versions:
supplemental[('r_' + version)] = versions[version]
return row_template | 4,264,561,190,868,986,000 | Produces a template for rows created from a Dashboard JSON v1.0 dict.
_DashboardJsonToRawRows adds metadata fields to every row that it creates.
These include things like master, bot, point ID, versions, and other
supplementary data. This method produces a dict containing this metadata
to which row-specific information (like value and error) can be added.
Some metadata needs to be transformed to conform to the v0 format, and this
method is also responsible for that transformation.
Some validation is deferred until after the input is converted to a list
of row dicts, since revision format correctness is checked on a per-point
basis.
Args:
dash_json_dict: A dashboard JSON v1.0 dict.
Returns:
A dict containing data to include in each row dict that is created from
|dash_json_dict|. | dashboard/dashboard/add_point.py | _MakeRowTemplate | bopopescu/catapult-2 | python | def _MakeRowTemplate(dash_json_dict):
'Produces a template for rows created from a Dashboard JSON v1.0 dict.\n\n _DashboardJsonToRawRows adds metadata fields to every row that it creates.\n These include things like master, bot, point ID, versions, and other\n supplementary data. This method produces a dict containing this metadata\n to which row-specific information (like value and error) can be added.\n Some metadata needs to be transformed to conform to the v0 format, and this\n method is also responsible for that transformation.\n\n Some validation is deferred until after the input is converted to a list\n of row dicts, since revision format correctness is checked on a per-point\n basis.\n\n Args:\n dash_json_dict: A dashboard JSON v1.0 dict.\n\n Returns:\n A dict containing data to include in each row dict that is created from\n |dash_json_dict|.\n '
row_template = dash_json_dict.copy()
del row_template['chart_data']
del row_template['point_id']
row_template['revision'] = dash_json_dict['point_id']
annotations = row_template['supplemental']
versions = row_template['versions']
del row_template['supplemental']
del row_template['versions']
row_template['supplemental_columns'] = {}
supplemental = row_template['supplemental_columns']
for annotation in annotations:
supplemental[('a_' + annotation)] = annotations[annotation]
for version in versions:
supplemental[('r_' + version)] = versions[version]
return row_template |
def _FlattenTrace(test_suite_name, chart_name, trace_name, trace, is_ref=False, tracing_links=None, benchmark_description=''):
"Takes a trace dict from dashboard JSON and readies it for display.\n\n Traces can be either scalars or lists; if scalar we take the value directly;\n if list we average the values and compute their standard deviation. We also\n extract fields that are normally part of v0 row dicts that are uploaded\n using add_point but are actually part of traces in the v1.0 format.\n\n Args:\n test_suite_name: The name of the test suite (benchmark).\n chart_name: The name of the chart to which this trace belongs.\n trace_name: The name of the passed trace.\n trace: A trace dict extracted from a dashboard JSON chart.\n is_ref: A boolean which indicates whether this trace comes from a\n reference build.\n tracing_links: A dictionary mapping trace names to about:tracing trace\n urls in cloud storage\n benchmark_description: A string documenting the benchmark suite to which\n this trace belongs.\n\n Returns:\n A dict containing units, value, and error for this trace.\n\n Raises:\n BadRequestError: The data wasn't valid.\n "
if ('@@' in chart_name):
(tir_label, chart_name) = chart_name.split('@@')
chart_name = ((chart_name + '/') + tir_label)
(value, error) = _ExtractValueAndError(trace)
tracing_uri = None
if (tracing_links and (trace_name in tracing_links) and ('cloud_url' in tracing_links[trace_name])):
tracing_uri = tracing_links[trace_name]['cloud_url'].replace('\\/', '/')
trace_name = _EscapeName(trace_name)
if (trace_name == 'summary'):
subtest_name = chart_name
else:
subtest_name = ((chart_name + '/') + trace_name)
name = ((test_suite_name + '/') + subtest_name)
if ((trace_name == 'summary') and is_ref):
name += '/ref'
elif ((trace_name != 'summary') and is_ref):
name += '_ref'
row_dict = {'test': name, 'value': value, 'error': error, 'units': trace['units'], 'tracing_uri': tracing_uri, 'benchmark_description': benchmark_description}
if ('improvement_direction' in trace):
improvement_direction_str = trace['improvement_direction']
if (improvement_direction_str is None):
raise BadRequestError('improvement_direction must not be None')
row_dict['higher_is_better'] = _ImprovementDirectionToHigherIsBetter(improvement_direction_str)
return row_dict | -3,075,484,346,601,531,000 | Takes a trace dict from dashboard JSON and readies it for display.
Traces can be either scalars or lists; if scalar we take the value directly;
if list we average the values and compute their standard deviation. We also
extract fields that are normally part of v0 row dicts that are uploaded
using add_point but are actually part of traces in the v1.0 format.
Args:
test_suite_name: The name of the test suite (benchmark).
chart_name: The name of the chart to which this trace belongs.
trace_name: The name of the passed trace.
trace: A trace dict extracted from a dashboard JSON chart.
is_ref: A boolean which indicates whether this trace comes from a
reference build.
tracing_links: A dictionary mapping trace names to about:tracing trace
urls in cloud storage
benchmark_description: A string documenting the benchmark suite to which
this trace belongs.
Returns:
A dict containing units, value, and error for this trace.
Raises:
BadRequestError: The data wasn't valid. | dashboard/dashboard/add_point.py | _FlattenTrace | bopopescu/catapult-2 | python | def _FlattenTrace(test_suite_name, chart_name, trace_name, trace, is_ref=False, tracing_links=None, benchmark_description=):
"Takes a trace dict from dashboard JSON and readies it for display.\n\n Traces can be either scalars or lists; if scalar we take the value directly;\n if list we average the values and compute their standard deviation. We also\n extract fields that are normally part of v0 row dicts that are uploaded\n using add_point but are actually part of traces in the v1.0 format.\n\n Args:\n test_suite_name: The name of the test suite (benchmark).\n chart_name: The name of the chart to which this trace belongs.\n trace_name: The name of the passed trace.\n trace: A trace dict extracted from a dashboard JSON chart.\n is_ref: A boolean which indicates whether this trace comes from a\n reference build.\n tracing_links: A dictionary mapping trace names to about:tracing trace\n urls in cloud storage\n benchmark_description: A string documenting the benchmark suite to which\n this trace belongs.\n\n Returns:\n A dict containing units, value, and error for this trace.\n\n Raises:\n BadRequestError: The data wasn't valid.\n "
if ('@@' in chart_name):
(tir_label, chart_name) = chart_name.split('@@')
chart_name = ((chart_name + '/') + tir_label)
(value, error) = _ExtractValueAndError(trace)
tracing_uri = None
if (tracing_links and (trace_name in tracing_links) and ('cloud_url' in tracing_links[trace_name])):
tracing_uri = tracing_links[trace_name]['cloud_url'].replace('\\/', '/')
trace_name = _EscapeName(trace_name)
if (trace_name == 'summary'):
subtest_name = chart_name
else:
subtest_name = ((chart_name + '/') + trace_name)
name = ((test_suite_name + '/') + subtest_name)
if ((trace_name == 'summary') and is_ref):
name += '/ref'
elif ((trace_name != 'summary') and is_ref):
name += '_ref'
row_dict = {'test': name, 'value': value, 'error': error, 'units': trace['units'], 'tracing_uri': tracing_uri, 'benchmark_description': benchmark_description}
if ('improvement_direction' in trace):
improvement_direction_str = trace['improvement_direction']
if (improvement_direction_str is None):
raise BadRequestError('improvement_direction must not be None')
row_dict['higher_is_better'] = _ImprovementDirectionToHigherIsBetter(improvement_direction_str)
return row_dict |
def _ExtractValueAndError(trace):
'Returns the value and measure of error from a chartjson trace dict.\n\n Args:\n trace: A dict that has one "result" from a performance test, e.g. one\n "value" in a Telemetry test, with the keys "trace_type", "value", etc.\n\n Returns:\n A pair (value, error) where |value| is a float and |error| is some measure\n of variance used to show error bars; |error| could be None.\n\n Raises:\n BadRequestError: Data format was invalid.\n '
trace_type = trace.get('type')
if (trace_type == 'scalar'):
value = trace.get('value')
if ((value is None) and trace.get('none_value_reason')):
return (float('nan'), 0)
try:
return (float(value), 0)
except:
raise BadRequestError(('Expected scalar value, got: %r' % value))
if (trace_type == 'list_of_scalar_values'):
values = trace.get('values')
if ((not isinstance(values, list)) and (values is not None)):
raise BadRequestError(('Expected list of scalar values, got: %r' % values))
if ((not values) or (None in values)):
if trace.get('none_value_reason'):
return (float('nan'), float('nan'))
raise BadRequestError(('Expected list of scalar values, got: %r' % values))
if (not all((_IsNumber(v) for v in values))):
raise BadRequestError(('Non-number found in values list: %r' % values))
value = math_utils.Mean(values)
std = trace.get('std')
if (std is not None):
error = std
else:
error = math_utils.StandardDeviation(values)
return (value, error)
if (trace_type == 'histogram'):
return _GeomMeanAndStdDevFromHistogram(trace)
raise BadRequestError(('Invalid value type in chart object: %r' % trace_type)) | -1,784,660,418,561,223,700 | Returns the value and measure of error from a chartjson trace dict.
Args:
trace: A dict that has one "result" from a performance test, e.g. one
"value" in a Telemetry test, with the keys "trace_type", "value", etc.
Returns:
A pair (value, error) where |value| is a float and |error| is some measure
of variance used to show error bars; |error| could be None.
Raises:
BadRequestError: Data format was invalid. | dashboard/dashboard/add_point.py | _ExtractValueAndError | bopopescu/catapult-2 | python | def _ExtractValueAndError(trace):
'Returns the value and measure of error from a chartjson trace dict.\n\n Args:\n trace: A dict that has one "result" from a performance test, e.g. one\n "value" in a Telemetry test, with the keys "trace_type", "value", etc.\n\n Returns:\n A pair (value, error) where |value| is a float and |error| is some measure\n of variance used to show error bars; |error| could be None.\n\n Raises:\n BadRequestError: Data format was invalid.\n '
trace_type = trace.get('type')
if (trace_type == 'scalar'):
value = trace.get('value')
if ((value is None) and trace.get('none_value_reason')):
return (float('nan'), 0)
try:
return (float(value), 0)
except:
raise BadRequestError(('Expected scalar value, got: %r' % value))
if (trace_type == 'list_of_scalar_values'):
values = trace.get('values')
if ((not isinstance(values, list)) and (values is not None)):
raise BadRequestError(('Expected list of scalar values, got: %r' % values))
if ((not values) or (None in values)):
if trace.get('none_value_reason'):
return (float('nan'), float('nan'))
raise BadRequestError(('Expected list of scalar values, got: %r' % values))
if (not all((_IsNumber(v) for v in values))):
raise BadRequestError(('Non-number found in values list: %r' % values))
value = math_utils.Mean(values)
std = trace.get('std')
if (std is not None):
error = std
else:
error = math_utils.StandardDeviation(values)
return (value, error)
if (trace_type == 'histogram'):
return _GeomMeanAndStdDevFromHistogram(trace)
raise BadRequestError(('Invalid value type in chart object: %r' % trace_type)) |
def _EscapeName(name):
'Escapes a trace name so it can be stored in a row.\n\n Args:\n name: A string representing a name.\n\n Returns:\n An escaped version of the name.\n '
return re.sub('[\\:|=/#&,]', '_', name) | 4,860,297,647,989,263,000 | Escapes a trace name so it can be stored in a row.
Args:
name: A string representing a name.
Returns:
An escaped version of the name. | dashboard/dashboard/add_point.py | _EscapeName | bopopescu/catapult-2 | python | def _EscapeName(name):
'Escapes a trace name so it can be stored in a row.\n\n Args:\n name: A string representing a name.\n\n Returns:\n An escaped version of the name.\n '
return re.sub('[\\:|=/#&,]', '_', name) |
def _GeomMeanAndStdDevFromHistogram(histogram):
"Generates the geom. mean and std. dev. for a histogram.\n\n A histogram is a collection of numerical buckets with associated\n counts; a bucket can either represent a number of instances of a single\n value ('low'), or from within a range of values (in which case 'high' will\n specify the upper bound). We compute the statistics by treating the\n histogram analogously to a list of individual values, where the counts tell\n us how many of each value there are.\n\n Args:\n histogram: A histogram dict with a list 'buckets' of buckets.\n\n Returns:\n The geometric mean and standard deviation of the given histogram.\n "
if ('buckets' not in histogram):
return (0.0, 0.0)
count = 0
sum_of_logs = 0
for bucket in histogram['buckets']:
if ('high' in bucket):
bucket['mean'] = ((bucket['low'] + bucket['high']) / 2.0)
else:
bucket['mean'] = bucket['low']
if (bucket['mean'] > 0):
sum_of_logs += (math.log(bucket['mean']) * bucket['count'])
count += bucket['count']
if (count == 0):
return (0.0, 0.0)
sum_of_squares = 0
geom_mean = math.exp((sum_of_logs / count))
for bucket in histogram['buckets']:
if (bucket['mean'] > 0):
sum_of_squares += (((bucket['mean'] - geom_mean) ** 2) * bucket['count'])
return (geom_mean, math.sqrt((sum_of_squares / count))) | 7,263,711,009,653,982,000 | Generates the geom. mean and std. dev. for a histogram.
A histogram is a collection of numerical buckets with associated
counts; a bucket can either represent a number of instances of a single
value ('low'), or from within a range of values (in which case 'high' will
specify the upper bound). We compute the statistics by treating the
histogram analogously to a list of individual values, where the counts tell
us how many of each value there are.
Args:
histogram: A histogram dict with a list 'buckets' of buckets.
Returns:
The geometric mean and standard deviation of the given histogram. | dashboard/dashboard/add_point.py | _GeomMeanAndStdDevFromHistogram | bopopescu/catapult-2 | python | def _GeomMeanAndStdDevFromHistogram(histogram):
"Generates the geom. mean and std. dev. for a histogram.\n\n A histogram is a collection of numerical buckets with associated\n counts; a bucket can either represent a number of instances of a single\n value ('low'), or from within a range of values (in which case 'high' will\n specify the upper bound). We compute the statistics by treating the\n histogram analogously to a list of individual values, where the counts tell\n us how many of each value there are.\n\n Args:\n histogram: A histogram dict with a list 'buckets' of buckets.\n\n Returns:\n The geometric mean and standard deviation of the given histogram.\n "
if ('buckets' not in histogram):
return (0.0, 0.0)
count = 0
sum_of_logs = 0
for bucket in histogram['buckets']:
if ('high' in bucket):
bucket['mean'] = ((bucket['low'] + bucket['high']) / 2.0)
else:
bucket['mean'] = bucket['low']
if (bucket['mean'] > 0):
sum_of_logs += (math.log(bucket['mean']) * bucket['count'])
count += bucket['count']
if (count == 0):
return (0.0, 0.0)
sum_of_squares = 0
geom_mean = math.exp((sum_of_logs / count))
for bucket in histogram['buckets']:
if (bucket['mean'] > 0):
sum_of_squares += (((bucket['mean'] - geom_mean) ** 2) * bucket['count'])
return (geom_mean, math.sqrt((sum_of_squares / count))) |
def _ImprovementDirectionToHigherIsBetter(improvement_direction_str):
"Converts an improvement direction string to a higher_is_better boolean.\n\n Args:\n improvement_direction_str: a string, either 'up' or 'down'.\n\n Returns:\n A boolean expressing the appropriate higher_is_better value.\n\n Raises:\n BadRequestError: if improvement_direction_str is invalid.\n "
if (improvement_direction_str == 'up'):
return True
elif (improvement_direction_str == 'down'):
return False
else:
raise BadRequestError(('Invalid improvement direction string: ' + improvement_direction_str)) | 5,229,265,490,068,555,000 | Converts an improvement direction string to a higher_is_better boolean.
Args:
improvement_direction_str: a string, either 'up' or 'down'.
Returns:
A boolean expressing the appropriate higher_is_better value.
Raises:
BadRequestError: if improvement_direction_str is invalid. | dashboard/dashboard/add_point.py | _ImprovementDirectionToHigherIsBetter | bopopescu/catapult-2 | python | def _ImprovementDirectionToHigherIsBetter(improvement_direction_str):
"Converts an improvement direction string to a higher_is_better boolean.\n\n Args:\n improvement_direction_str: a string, either 'up' or 'down'.\n\n Returns:\n A boolean expressing the appropriate higher_is_better value.\n\n Raises:\n BadRequestError: if improvement_direction_str is invalid.\n "
if (improvement_direction_str == 'up'):
return True
elif (improvement_direction_str == 'down'):
return False
else:
raise BadRequestError(('Invalid improvement direction string: ' + improvement_direction_str)) |
def _ConstructTestPathMap(row_dicts):
'Makes a mapping from test paths to last added revision.'
last_added_revision_keys = []
for row in row_dicts:
if (not (('master' in row) and ('bot' in row) and ('test' in row))):
continue
path = ('%s/%s/%s' % (row['master'], row['bot'], row['test'].strip('/')))
if (len(path) > _MAX_TEST_PATH_LENGTH):
continue
last_added_revision_keys.append(ndb.Key('LastAddedRevision', path))
try:
last_added_revision_entities = ndb.get_multi(last_added_revision_keys)
except datastore_errors.BadRequestError:
logging.warn('Datastore BadRequestError when getting %s', repr(last_added_revision_keys))
return {}
return {r.key.string_id(): r.revision for r in last_added_revision_entities if (r is not None)} | -3,003,235,259,617,169,400 | Makes a mapping from test paths to last added revision. | dashboard/dashboard/add_point.py | _ConstructTestPathMap | bopopescu/catapult-2 | python | def _ConstructTestPathMap(row_dicts):
last_added_revision_keys = []
for row in row_dicts:
if (not (('master' in row) and ('bot' in row) and ('test' in row))):
continue
path = ('%s/%s/%s' % (row['master'], row['bot'], row['test'].strip('/')))
if (len(path) > _MAX_TEST_PATH_LENGTH):
continue
last_added_revision_keys.append(ndb.Key('LastAddedRevision', path))
try:
last_added_revision_entities = ndb.get_multi(last_added_revision_keys)
except datastore_errors.BadRequestError:
logging.warn('Datastore BadRequestError when getting %s', repr(last_added_revision_keys))
return {}
return {r.key.string_id(): r.revision for r in last_added_revision_entities if (r is not None)} |
def _ValidateRowDict(row, test_map):
'Checks all fields in the input dictionary.\n\n Args:\n row: A dictionary which represents one point.\n test_map: A dictionary mapping test paths to last added revision.\n\n Raises:\n BadRequestError: The input was not valid.\n '
required_fields = ['master', 'bot', 'test']
for field in required_fields:
if (field not in row):
raise BadRequestError(('No "%s" field in row dict.' % field))
_ValidateMasterBotTest(row['master'], row['bot'], row['test'])
_ValidateRowId(row, test_map)
GetAndValidateRowProperties(row) | 789,831,051,503,380,000 | Checks all fields in the input dictionary.
Args:
row: A dictionary which represents one point.
test_map: A dictionary mapping test paths to last added revision.
Raises:
BadRequestError: The input was not valid. | dashboard/dashboard/add_point.py | _ValidateRowDict | bopopescu/catapult-2 | python | def _ValidateRowDict(row, test_map):
'Checks all fields in the input dictionary.\n\n Args:\n row: A dictionary which represents one point.\n test_map: A dictionary mapping test paths to last added revision.\n\n Raises:\n BadRequestError: The input was not valid.\n '
required_fields = ['master', 'bot', 'test']
for field in required_fields:
if (field not in row):
raise BadRequestError(('No "%s" field in row dict.' % field))
_ValidateMasterBotTest(row['master'], row['bot'], row['test'])
_ValidateRowId(row, test_map)
GetAndValidateRowProperties(row) |
def _ValidateMasterBotTest(master, bot, test):
'Validates the master, bot, and test properties of a row dict.'
test = test.strip('/')
if ('/' not in test):
raise BadRequestError('Test name must have more than one part.')
if (len(test.split('/')) > graph_data.MAX_TEST_ANCESTORS):
raise BadRequestError(('Invalid test name: %s' % test))
if (('/' in master) or ('/' in bot)):
raise BadRequestError('Illegal slash in master or bot name.')
_ValidateTestPath(('%s/%s/%s' % (master, bot, test))) | 4,406,785,683,133,111,300 | Validates the master, bot, and test properties of a row dict. | dashboard/dashboard/add_point.py | _ValidateMasterBotTest | bopopescu/catapult-2 | python | def _ValidateMasterBotTest(master, bot, test):
test = test.strip('/')
if ('/' not in test):
raise BadRequestError('Test name must have more than one part.')
if (len(test.split('/')) > graph_data.MAX_TEST_ANCESTORS):
raise BadRequestError(('Invalid test name: %s' % test))
if (('/' in master) or ('/' in bot)):
raise BadRequestError('Illegal slash in master or bot name.')
_ValidateTestPath(('%s/%s/%s' % (master, bot, test))) |
def _ValidateTestPath(test_path):
'Checks whether all the parts of the test path are valid.'
if (len(test_path) > _MAX_TEST_PATH_LENGTH):
raise BadRequestError(('Test path too long: %s' % test_path))
if ('*' in test_path):
raise BadRequestError('Illegal asterisk in test name.')
for name in test_path.split('/'):
_ValidateTestPathPartName(name) | -8,827,850,601,999,613,000 | Checks whether all the parts of the test path are valid. | dashboard/dashboard/add_point.py | _ValidateTestPath | bopopescu/catapult-2 | python | def _ValidateTestPath(test_path):
if (len(test_path) > _MAX_TEST_PATH_LENGTH):
raise BadRequestError(('Test path too long: %s' % test_path))
if ('*' in test_path):
raise BadRequestError('Illegal asterisk in test name.')
for name in test_path.split('/'):
_ValidateTestPathPartName(name) |
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