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train | CSVLogger._init | CSV outputted with Headers as first set of results. | python/ray/tune/logger.py | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
self._file = open(progress_fil... | def _init(self):
"""CSV outputted with Headers as first set of results."""
# Note that we assume params.json was already created by JsonLogger
progress_file = os.path.join(self.logdir, "progress.csv")
self._continuing = os.path.exists(progress_file)
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train | UnifiedLogger.sync_results_to_new_location | Sends the current log directory to the remote node.
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"""Sends the current log directory to the remote node.
Syncing will not occur if the cluster is not started
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"""
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train | deep_insert | Inserts value into config by path, generating intermediate dictionaries.
Example:
>>> deep_insert(path.split("."), value, {}) | python/ray/tune/automl/search_policy.py | def deep_insert(path_list, value, config):
"""Inserts value into config by path, generating intermediate dictionaries.
Example:
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"""
if len(path_list) > 1:
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"""
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train | FunctionDescriptor.from_bytes_list | Create a FunctionDescriptor instance from list of bytes.
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Args:
cls: Current class which is required argument for classmethod.
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This function is used to create the function descriptor from
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Args:
cls: Current class which is required argument for classmethod.
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This function is used to create the function descriptor from
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train | FunctionDescriptor.from_function | Create a FunctionDescriptor from a function instance.
This function is used to create the function descriptor from
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train | FunctionDescriptor.from_class | Create a FunctionDescriptor from a class.
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cls: Current class which is required argument for classmethod.
target_class: the python class used to create the function
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train | FunctionDescriptor.is_for_driver_task | See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks. | python/ray/function_manager.py | def is_for_driver_task(self):
"""See whether this function descriptor is for a driver or not.
Returns:
True if this function descriptor is for driver tasks.
"""
return all(
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for x in [self.module_name, self.class_name, self.function_name]) | def is_for_driver_task(self):
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"""
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train | FunctionDescriptor._get_function_id | Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor. | python/ray/function_manager.py | def _get_function_id(self):
"""Calculate the function id of current function descriptor.
This function id is calculated from all the fields of function
descriptor.
Returns:
ray.ObjectID to represent the function descriptor.
"""
if self.is_for_driver_task:
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Returns:
ray.ObjectID to represent the function descriptor.
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train | FunctionDescriptor.get_function_descriptor_list | Return a list of bytes representing the function descriptor.
This function is used to pass this function descriptor to backend.
Returns:
A list of bytes. | python/ray/function_manager.py | def get_function_descriptor_list(self):
"""Return a list of bytes representing the function descriptor.
This function is used to pass this function descriptor to backend.
Returns:
A list of bytes.
"""
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train | FunctionActorManager.export_cached | Export cached remote functions
Note: this should be called only once when worker is connected. | python/ray/function_manager.py | def export_cached(self):
"""Export cached remote functions
Note: this should be called only once when worker is connected.
"""
for remote_function in self._functions_to_export:
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train | FunctionActorManager.export | Export a remote function.
Args:
remote_function: the RemoteFunction object. | python/ray/function_manager.py | def export(self, remote_function):
"""Export a remote function.
Args:
remote_function: the RemoteFunction object.
"""
if self._worker.mode is None:
# If the worker isn't connected, cache the function
# and export it later.
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# If the worker isn't connected, cache the function
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train | FunctionActorManager._do_export | Pickle a remote function and export it to redis.
Args:
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"""Pickle a remote function and export it to redis.
Args:
remote_function: the RemoteFunction object.
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if self._worker.load_code_from_local:
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train | FunctionActorManager.fetch_and_register_remote_function | Import a remote function. | python/ray/function_manager.py | def fetch_and_register_remote_function(self, key):
"""Import a remote function."""
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train | FunctionActorManager.get_execution_info | Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
A FunctionExecutionInfo object. | python/ray/function_manager.py | def get_execution_info(self, driver_id, function_descriptor):
"""Get the FunctionExecutionInfo of a remote function.
Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
Returns:
... | def get_execution_info(self, driver_id, function_descriptor):
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Args:
driver_id: ID of the driver that the function belongs to.
function_descriptor: The FunctionDescriptor of the function to get.
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train | FunctionActorManager._wait_for_function | Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
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If this wor... | python/ray/function_manager.py | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
"""Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a ... | def _wait_for_function(self, function_descriptor, driver_id, timeout=10):
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train | FunctionActorManager._publish_actor_class_to_key | Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
on cached actor class definitions when a worker connects for the first
time.
Args:
key: The key to store the actor class info at.
actor_class_info: Info... | python/ray/function_manager.py | def _publish_actor_class_to_key(self, key, actor_class_info):
"""Push an actor class definition to Redis.
The is factored out as a separate function because it is also called
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train | FunctionActorManager.load_actor_class | Load the actor class.
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function_descriptor: Function descriptor of the actor constructor.
Returns:
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"""Load the actor class.
Args:
driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
The actor class.
"""
function_id = funct... | def load_actor_class(self, driver_id, function_descriptor):
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driver_id: Driver ID of the actor.
function_descriptor: Function descriptor of the actor constructor.
Returns:
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train | FunctionActorManager._load_actor_from_local | Load actor class from local code. | python/ray/function_manager.py | def _load_actor_from_local(self, driver_id, function_descriptor):
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train | FunctionActorManager._load_actor_class_from_gcs | Load actor class from GCS. | python/ray/function_manager.py | def _load_actor_class_from_gcs(self, driver_id, function_descriptor):
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actor: The actor to checkpoint.
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Args:
actor: The actor to checkpoint.
Returns:
The result of the actor's user-defined `save_checkpoint` method.
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actor_id = self._worker.actor_id
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actor: The actor to restore from a checkpoint.
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train | _env_runner | This implements the common experience collection logic.
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extra_batch_callback (fn): function to send extra batch data to.
policies (dict): Map of policy ids to PolicyGraph instances.
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to_eval: map of policy_id to list of agent PolicyEvalData
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train | _do_policy_eval | Call compute actions on observation batches to get next actions.
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Records policy evaluation results into the given episode objects and
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train | _fetch_atari_metrics | Atari games have multiple logical episodes, one per life.
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unwrapped = base_env.get_unwrapped()
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train | FlatbuffersConan.package | Copy Flatbuffers' artifacts to package folder | conanfile.py | def package(self):
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train | FlatbuffersConan.package_info | Collect built libraries names and solve flatc path. | conanfile.py | def package_info(self):
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self.cpp_info.libs = tools.collect_libs(self)
self.user_info.flatc = os.path.join(self.package_folder, "bin", "flatc") | def package_info(self):
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train | Table.Offset | Offset provides access into the Table's vtable.
Deprecated fields are ignored by checking the vtable's length. | python/flatbuffers/table.py | def Offset(self, vtableOffset):
"""Offset provides access into the Table's vtable.
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train | Table.Get | Get retrieves a value of the type specified by `flags` at the
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Get retrieves a value of the type specified by `flags` at the
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train | dumps | Stringifies input dict as toml
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train | _is_env_truthy | An environment variable is truthy if it exists and isn't one of (0, false, no, off) | pipenv/environments.py | def _is_env_truthy(name):
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if name not in os.environ:
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train | is_in_virtualenv | Check virtualenv membership dynamically
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"""
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train | unpackb | Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options. | pipenv/patched/notpip/_vendor/msgpack/fallback.py | def unpackb(packed, **kwargs):
"""
Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
"""
unpacker = Unpacker(None, **kwargs)
unpacker.feed(packed)
try:
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Unpack an object from `packed`.
Raises `ExtraData` when `packed` contains extra bytes.
See :class:`Unpacker` for options.
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train | HTTPConnection._new_conn | Establish a socket connection and set nodelay settings on it.
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train | HTTPConnection.request_chunked | Alternative to the common request method, which sends the
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train | VerifiedHTTPSConnection.set_cert | This method should only be called once, before the connection is used. | pipenv/vendor/urllib3/connection.py | def set_cert(self, key_file=None, cert_file=None,
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"""
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train | prettify_exc | Catch known errors and prettify them instead of showing the
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"""Catch known errors and prettify them instead of showing the
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train | get_stream_handle | Get the OS appropriate handle for the corresponding output stream.
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Get the OS appropriate handle for the corresponding output stream.
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train | hide_cursor | Hide the console cursor on the given stream
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:return: None
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Hide the console cursor on the given stream
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cursor... | def hide_cursor(stream=sys.stdout):
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Hide the console cursor on the given stream
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train | choice_complete | Returns the completion results for click.core.Choice
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ctx : click.core.Context
The current context
incomplete :
The string to complete
Returns
-------
[(str, str)]
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Parameters
----------
ctx : click.core.Context
The current context
incomplete :
The string to complete
Returns
-------
[(str, str)]
A list of completion results
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ctx : click.core.Context
The current context
incomplete :
The string to complete
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train | _shellcomplete | Internal handler for the bash completion support.
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The main click Command of the program
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The program name on the command line
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train | patch | Patch click | pipenv/vendor/click_completion/patch.py | def patch():
"""Patch click"""
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click.types.ParamType.complete = param_type_complete
click.types.Choice.complete = choice_complete
click.core.MultiCommand.get_command_short_help = multicommand_get_command_short_help
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train | parse_expr | expr ::= seq ( '|' seq )* ; | pipenv/vendor/docopt.py | def parse_expr(tokens, options):
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train | parse_seq | seq ::= ( atom [ '...' ] )* ; | pipenv/vendor/docopt.py | def parse_seq(tokens, options):
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train | parse_argv | Parse command-line argument vector.
If options_first:
argv ::= [ long | shorts ]* [ argument ]* [ '--' [ argument ]* ] ;
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argv ::= [ long | shorts | argument ]* [ '--' [ argument ]* ] ; | pipenv/vendor/docopt.py | def parse_argv(tokens, options, options_first=False):
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train | unnest | Flatten an arbitrarily nested iterable
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"""Flatten an arbitrarily nested iterable
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train | run | Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd: A list representing the command you want to run.
:param dict env: Additional environment settings to pass through to the subprocess.
:param bool return_object: When True, returns the whole subprocess instance
:param b... | pipenv/vendor/vistir/misc.py | def run(
cmd,
env=None,
return_object=False,
block=True,
cwd=None,
verbose=False,
nospin=False,
spinner_name=None,
combine_stderr=True,
display_limit=200,
write_to_stdout=True,
):
"""Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd... | def run(
cmd,
env=None,
return_object=False,
block=True,
cwd=None,
verbose=False,
nospin=False,
spinner_name=None,
combine_stderr=True,
display_limit=200,
write_to_stdout=True,
):
"""Use `subprocess.Popen` to get the output of a command and decode it.
:param list cmd... | [
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train | load_path | Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenvs/requirementslib-5MhGuG3C/bin... | pipenv/vendor/vistir/misc.py | def load_path(python):
"""Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenv... | def load_path(python):
"""Load the :mod:`sys.path` from the given python executable's environment as json
:param str python: Path to a valid python executable
:return: A python representation of the `sys.path` value of the given python executable.
:rtype: list
>>> load_path("/home/user/.virtualenv... | [
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train | to_bytes | Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: Corresponding byte representation (for us... | pipenv/vendor/vistir/misc.py | def to_bytes(string, encoding="utf-8", errors="ignore"):
"""Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str,... | def to_bytes(string, encoding="utf-8", errors="ignore"):
"""Force a value to bytes.
:param string: Some input that can be converted to a bytes.
:type string: str or bytes unicode or a memoryview subclass
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str,... | [
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train | to_text | Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, optional
:return: The unicode representation of the string
... | pipenv/vendor/vistir/misc.py | def to_text(string, encoding="utf-8", errors=None):
"""Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, option... | def to_text(string, encoding="utf-8", errors=None):
"""Force a value to a text-type.
:param string: Some input that can be converted to a unicode representation.
:type string: str or bytes unicode
:param encoding: The encoding to use for conversions, defaults to "utf-8"
:param encoding: str, option... | [
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train | divide | split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
:rtype: list | pipenv/vendor/vistir/misc.py | def divide(n, iterable):
"""
split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
:rtype: lis... | def divide(n, iterable):
"""
split an iterable into n groups, per https://more-itertools.readthedocs.io/en/latest/api.html#grouping
:param int n: Number of unique groups
:param iter iterable: An iterable to split up
:return: a list of new iterables derived from the original iterable
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train | getpreferredencoding | Determine the proper output encoding for terminal rendering | pipenv/vendor/vistir/misc.py | def getpreferredencoding():
"""Determine the proper output encoding for terminal rendering"""
# Borrowed from Invoke
# (see https://github.com/pyinvoke/invoke/blob/93af29d/invoke/runners.py#L881)
_encoding = locale.getpreferredencoding(False)
if six.PY2 and not sys.platform == "win32":
_def... | def getpreferredencoding():
"""Determine the proper output encoding for terminal rendering"""
# Borrowed from Invoke
# (see https://github.com/pyinvoke/invoke/blob/93af29d/invoke/runners.py#L881)
_encoding = locale.getpreferredencoding(False)
if six.PY2 and not sys.platform == "win32":
_def... | [
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] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L522-L532 | [
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train | decode_for_output | Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A mapping of unicode character ordinals to replacement strings.
:return: A re-... | pipenv/vendor/vistir/misc.py | def decode_for_output(output, target_stream=None, translation_map=None):
"""Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A ... | def decode_for_output(output, target_stream=None, translation_map=None):
"""Given a string, decode it for output to a terminal
:param str output: A string to print to a terminal
:param target_stream: A stream to write to, we will encode to target this stream if possible.
:param dict translation_map: A ... | [
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"terminal"
] | pypa/pipenv | python | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/vistir/misc.py#L574-L597 | [
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"... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
train | get_canonical_encoding_name | Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str | pipenv/vendor/vistir/misc.py | def get_canonical_encoding_name(name):
# type: (str) -> str
"""
Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str
"""
import codecs
try:
codec = ... | def get_canonical_encoding_name(name):
# type: (str) -> str
"""
Given an encoding name, get the canonical name from a codec lookup.
:param str name: The name of the codec to lookup
:return: The canonical version of the codec name
:rtype: str
"""
import codecs
try:
codec = ... | [
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... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
train | get_wrapped_stream | Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper` | pipenv/vendor/vistir/misc.py | def get_wrapped_stream(stream):
"""
Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper`
"""
if stream is None:
raise TypeError("must provide a st... | def get_wrapped_stream(stream):
"""
Given a stream, wrap it in a `StreamWrapper` instance and return the wrapped stream.
:param stream: A stream instance to wrap
:returns: A new, wrapped stream
:rtype: :class:`StreamWrapper`
"""
if stream is None:
raise TypeError("must provide a st... | [
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train | is_connection_dropped | Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycling transparently for us. | pipenv/vendor/urllib3/util/connection.py | def is_connection_dropped(conn): # Platform-specific
"""
Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycli... | def is_connection_dropped(conn): # Platform-specific
"""
Returns True if the connection is dropped and should be closed.
:param conn:
:class:`httplib.HTTPConnection` object.
Note: For platforms like AppEngine, this will always return ``False`` to
let the platform handle connection recycli... | [
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"Non... | cae8d76c210b9777e90aab76e9c4b0e53bb19cde |
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