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django-haystack/django-haystack
|
b6dd72e6b5c97b782f5436b7bb4e8227ba6e3b06
|
haystack/query.py
|
python
|
ValuesListSearchQuerySet.post_process_results
|
(self, results)
|
return to_cache
|
[] |
def post_process_results(self, results):
to_cache = []
if self._flat:
accum = to_cache.extend
else:
accum = to_cache.append
for result in results:
accum([getattr(result, i, None) for i in self._fields])
return to_cache
|
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https://github.com/django-haystack/django-haystack/blob/b6dd72e6b5c97b782f5436b7bb4e8227ba6e3b06/haystack/query.py#L692-L703
|
|||
radlab/sparrow
|
afb8efadeb88524f1394d1abe4ea66c6fd2ac744
|
src/main/python/parse_per_task_logs.py
|
python
|
Request.queue_times
|
(self)
|
return queue_times
|
Returns a list of queue times for all complete __tasks.
|
Returns a list of queue times for all complete __tasks.
|
[
"Returns",
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"list",
"of",
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"for",
"all",
"complete",
"__tasks",
"."
] |
def queue_times(self):
""" Returns a list of queue times for all complete __tasks. """
queue_times = []
for task in self.__tasks.values():
if task.complete():
queue_times.append(task.queued_time())
return queue_times
|
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"(",
"task",
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"(",
")",
")",
"return",
"queue_times"
] |
https://github.com/radlab/sparrow/blob/afb8efadeb88524f1394d1abe4ea66c6fd2ac744/src/main/python/parse_per_task_logs.py#L359-L365
|
|
home-assistant/core
|
265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1
|
homeassistant/components/ads/binary_sensor.py
|
python
|
AdsBinarySensor.__init__
|
(self, ads_hub, name, ads_var, device_class)
|
Initialize ADS binary sensor.
|
Initialize ADS binary sensor.
|
[
"Initialize",
"ADS",
"binary",
"sensor",
"."
] |
def __init__(self, ads_hub, name, ads_var, device_class):
"""Initialize ADS binary sensor."""
super().__init__(ads_hub, name, ads_var)
self._attr_device_class = device_class or BinarySensorDeviceClass.MOVING
|
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https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/ads/binary_sensor.py#L51-L54
|
||
saltstack/salt
|
fae5bc757ad0f1716483ce7ae180b451545c2058
|
salt/modules/selinux.py
|
python
|
port_delete_policy
|
(name, protocol=None, port=None)
|
return _port_add_or_delete_policy("delete", name, None, protocol, port, None)
|
.. versionadded:: 2019.2.0
Deletes the SELinux policy for a given protocol and port.
Returns the result of the call to semanage.
name
The protocol and port spec. Can be formatted as ``(tcp|udp)/(port|port-range)``.
protocol
The protocol for the port, ``tcp`` or ``udp``. Required if name is not formatted.
port
The port or port range. Required if name is not formatted.
CLI Example:
.. code-block:: bash
salt '*' selinux.port_delete_policy tcp/8080
salt '*' selinux.port_delete_policy foobar protocol=tcp port=8091
|
.. versionadded:: 2019.2.0
|
[
"..",
"versionadded",
"::",
"2019",
".",
"2",
".",
"0"
] |
def port_delete_policy(name, protocol=None, port=None):
"""
.. versionadded:: 2019.2.0
Deletes the SELinux policy for a given protocol and port.
Returns the result of the call to semanage.
name
The protocol and port spec. Can be formatted as ``(tcp|udp)/(port|port-range)``.
protocol
The protocol for the port, ``tcp`` or ``udp``. Required if name is not formatted.
port
The port or port range. Required if name is not formatted.
CLI Example:
.. code-block:: bash
salt '*' selinux.port_delete_policy tcp/8080
salt '*' selinux.port_delete_policy foobar protocol=tcp port=8091
"""
return _port_add_or_delete_policy("delete", name, None, protocol, port, None)
|
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https://github.com/saltstack/salt/blob/fae5bc757ad0f1716483ce7ae180b451545c2058/salt/modules/selinux.py#L805-L829
|
|
pyscf/pyscf
|
0adfb464333f5ceee07b664f291d4084801bae64
|
pyscf/pbc/scf/khf.py
|
python
|
analyze
|
(mf, verbose=logger.DEBUG, with_meta_lowdin=WITH_META_LOWDIN,
**kwargs)
|
Analyze the given SCF object: print orbital energies, occupancies;
print orbital coefficients; Mulliken population analysis; Dipole moment
|
Analyze the given SCF object: print orbital energies, occupancies;
print orbital coefficients; Mulliken population analysis; Dipole moment
|
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";",
"Dipole",
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] |
def analyze(mf, verbose=logger.DEBUG, with_meta_lowdin=WITH_META_LOWDIN,
**kwargs):
'''Analyze the given SCF object: print orbital energies, occupancies;
print orbital coefficients; Mulliken population analysis; Dipole moment
'''
mf.dump_scf_summary(verbose)
mo_occ = mf.mo_occ
mo_coeff = mf.mo_coeff
ovlp_ao = mf.get_ovlp()
dm = mf.make_rdm1(mo_coeff, mo_occ)
if with_meta_lowdin:
return mf.mulliken_meta(mf.cell, dm, s=ovlp_ao, verbose=verbose)
else:
raise NotImplementedError
|
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https://github.com/pyscf/pyscf/blob/0adfb464333f5ceee07b664f291d4084801bae64/pyscf/pbc/scf/khf.py#L280-L294
|
||
aneisch/home-assistant-config
|
86e381fde9609cb8871c439c433c12989e4e225d
|
custom_components/monitor_docker/switch.py
|
python
|
DockerContainerSwitch.event_callback
|
(self, name="", remove=False)
|
Callback for update of container information.
|
Callback for update of container information.
|
[
"Callback",
"for",
"update",
"of",
"container",
"information",
"."
] |
def event_callback(self, name="", remove=False):
"""Callback for update of container information."""
if remove:
# If already called before, do not remove it again
if self._removed:
return
_LOGGER.info("[%s] %s: Removing switch entity", self._instance, self._cname)
self._loop.create_task(self.async_remove())
self._removed = True
return
state = None
try:
info = self._container.get_info()
except Exception as err:
_LOGGER.error(
"[%s] %s: Cannot request container info (%s)",
self._instance,
name,
str(err),
)
else:
if info is not None:
state = info.get(CONTAINER_INFO_STATE) == "running"
if state is not self._state:
self._state = state
self.async_schedule_update_ha_state()
|
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https://github.com/aneisch/home-assistant-config/blob/86e381fde9609cb8871c439c433c12989e4e225d/custom_components/monitor_docker/switch.py#L197-L227
|
||
HonglinChu/SiamTrackers
|
8471660b14f970578a43f077b28207d44a27e867
|
SiamFCpp/SiamFCpp-video_analyst/siamfcpp/utils/visualization.py
|
python
|
VideoWriter.__init__
|
(self, video_file, fps=25, scale=1.0)
|
:param video_file: path to write video. Perform nothing in case of None
:param fps: frame per second
:param scale: resize scale
|
[] |
def __init__(self, video_file, fps=25, scale=1.0):
"""
:param video_file: path to write video. Perform nothing in case of None
:param fps: frame per second
:param scale: resize scale
"""
self.video_file = video_file
self.fps = fps
self.writer = None
self.scale = scale
|
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https://github.com/HonglinChu/SiamTrackers/blob/8471660b14f970578a43f077b28207d44a27e867/SiamFCpp/SiamFCpp-video_analyst/siamfcpp/utils/visualization.py#L17-L27
|
|||
naftaliharris/tauthon
|
5587ceec329b75f7caf6d65a036db61ac1bae214
|
Lib/idlelib/CallTips.py
|
python
|
get_arg_text
|
(ob)
|
return argspec
|
Return a string describing the signature of a callable object, or ''.
For Python-coded functions and methods, the first line is introspected.
Delete 'self' parameter for classes (.__init__) and bound methods.
The next lines are the first lines of the doc string up to the first
empty line or _MAX_LINES. For builtins, this typically includes
the arguments in addition to the return value.
|
Return a string describing the signature of a callable object, or ''.
|
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] |
def get_arg_text(ob):
'''Return a string describing the signature of a callable object, or ''.
For Python-coded functions and methods, the first line is introspected.
Delete 'self' parameter for classes (.__init__) and bound methods.
The next lines are the first lines of the doc string up to the first
empty line or _MAX_LINES. For builtins, this typically includes
the arguments in addition to the return value.
'''
argspec = ""
try:
ob_call = ob.__call__
except BaseException:
if type(ob) is types.ClassType: # old-style
ob_call = ob
else:
return argspec
arg_offset = 0
if type(ob) in (types.ClassType, types.TypeType):
# Look for the first __init__ in the class chain with .im_func.
# Slot wrappers (builtins, classes defined in funcs) do not.
fob = _find_constructor(ob)
if fob is None:
fob = lambda: None
else:
arg_offset = 1
elif type(ob) == types.MethodType:
# bit of a hack for methods - turn it into a function
# and drop the "self" param for bound methods
fob = ob.im_func
if ob.im_self is not None:
arg_offset = 1
elif type(ob_call) == types.MethodType:
# a callable class instance
fob = ob_call.im_func
arg_offset = 1
else:
fob = ob
# Try to build one for Python defined functions
if type(fob) in [types.FunctionType, types.LambdaType]:
argcount = fob.func_code.co_argcount
real_args = fob.func_code.co_varnames[arg_offset:argcount]
defaults = fob.func_defaults or []
defaults = list(map(lambda name: "=%s" % repr(name), defaults))
defaults = [""] * (len(real_args) - len(defaults)) + defaults
items = map(lambda arg, dflt: arg + dflt, real_args, defaults)
for flag, pre, name in ((0x4, '*', 'args'), (0x8, '**', 'kwargs')):
if fob.func_code.co_flags & flag:
pre_name = pre + name
if name not in real_args:
items.append(pre_name)
else:
i = 1
while ((name+'%s') % i) in real_args:
i += 1
items.append((pre_name+'%s') % i)
argspec = ", ".join(items)
argspec = "(%s)" % re.sub("(?<!\d)\.\d+", "<tuple>", argspec)
lines = (textwrap.wrap(argspec, _MAX_COLS, subsequent_indent=_INDENT)
if len(argspec) > _MAX_COLS else [argspec] if argspec else [])
if isinstance(ob_call, types.MethodType):
doc = ob_call.__doc__
else:
doc = getattr(ob, "__doc__", "")
if doc:
for line in doc.split('\n', _MAX_LINES)[:_MAX_LINES]:
line = line.strip()
if not line:
break
if len(line) > _MAX_COLS:
line = line[: _MAX_COLS - 3] + '...'
lines.append(line)
argspec = '\n'.join(lines)
return argspec
|
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https://github.com/naftaliharris/tauthon/blob/5587ceec329b75f7caf6d65a036db61ac1bae214/Lib/idlelib/CallTips.py#L139-L215
|
|
hexway/apple_bleee
|
1f8022959be660b561e6004b808dd93fa252bc90
|
npyscreen/wgwidget.py
|
python
|
InputHandler.set_up_handlers
|
(self)
|
This function should be called somewhere during object initialisation (which all library-defined widgets do). You might like to override this in your own definition,
but in most cases the add_handers or add_complex_handlers methods are what you want.
|
This function should be called somewhere during object initialisation (which all library-defined widgets do). You might like to override this in your own definition,
but in most cases the add_handers or add_complex_handlers methods are what you want.
|
[
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"or",
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"."
] |
def set_up_handlers(self):
"""This function should be called somewhere during object initialisation (which all library-defined widgets do). You might like to override this in your own definition,
but in most cases the add_handers or add_complex_handlers methods are what you want."""
#called in __init__
self.handlers = {
curses.ascii.NL: self.h_exit_down,
curses.ascii.CR: self.h_exit_down,
curses.ascii.TAB: self.h_exit_down,
curses.KEY_BTAB: self.h_exit_up,
curses.KEY_DOWN: self.h_exit_down,
curses.KEY_UP: self.h_exit_up,
curses.KEY_LEFT: self.h_exit_left,
curses.KEY_RIGHT: self.h_exit_right,
# "^P": self.h_exit_up,
# "^N": self.h_exit_down,
curses.ascii.ESC: self.h_exit_escape,
curses.KEY_MOUSE: self.h_exit_mouse,
}
self.complex_handlers = []
|
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https://github.com/hexway/apple_bleee/blob/1f8022959be660b561e6004b808dd93fa252bc90/npyscreen/wgwidget.py#L103-L122
|
||
jython/jython3
|
def4f8ec47cb7a9c799ea4c745f12badf92c5769
|
lib-python/3.5.1/urllib/request.py
|
python
|
URLopener.open_http
|
(self, url, data=None)
|
return self._open_generic_http(http.client.HTTPConnection, url, data)
|
Use HTTP protocol.
|
Use HTTP protocol.
|
[
"Use",
"HTTP",
"protocol",
"."
] |
def open_http(self, url, data=None):
"""Use HTTP protocol."""
return self._open_generic_http(http.client.HTTPConnection, url, data)
|
[
"def",
"open_http",
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"_open_generic_http",
"(",
"http",
".",
"client",
".",
"HTTPConnection",
",",
"url",
",",
"data",
")"
] |
https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/urllib/request.py#L1861-L1863
|
|
freewym/espresso
|
6671c507350295269e38add57dbe601dcb8e6ecf
|
fairseq/models/roberta/model.py
|
python
|
RobertaEncoder.forward
|
(
self,
src_tokens,
features_only=False,
return_all_hiddens=False,
masked_tokens=None,
**unused,
)
|
return x, extra
|
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
features_only (bool, optional): skip LM head and just return
features. If True, the output will be of shape
`(batch, src_len, embed_dim)`.
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
Returns:
tuple:
- the LM output of shape `(batch, src_len, vocab)`
- a dictionary of additional data, where 'inner_states'
is a list of hidden states. Note that the hidden
states have shape `(src_len, batch, vocab)`.
|
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
features_only (bool, optional): skip LM head and just return
features. If True, the output will be of shape
`(batch, src_len, embed_dim)`.
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
|
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def forward(
self,
src_tokens,
features_only=False,
return_all_hiddens=False,
masked_tokens=None,
**unused,
):
"""
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
features_only (bool, optional): skip LM head and just return
features. If True, the output will be of shape
`(batch, src_len, embed_dim)`.
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
Returns:
tuple:
- the LM output of shape `(batch, src_len, vocab)`
- a dictionary of additional data, where 'inner_states'
is a list of hidden states. Note that the hidden
states have shape `(src_len, batch, vocab)`.
"""
x, extra = self.extract_features(
src_tokens, return_all_hiddens=return_all_hiddens
)
if not features_only:
x = self.output_layer(x, masked_tokens=masked_tokens)
return x, extra
|
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https://github.com/freewym/espresso/blob/6671c507350295269e38add57dbe601dcb8e6ecf/fairseq/models/roberta/model.py#L558-L587
|
|
guildai/guildai
|
1665985a3d4d788efc1a3180ca51cc417f71ca78
|
guild/external/pip/_vendor/requests/models.py
|
python
|
PreparedRequest.prepare_hooks
|
(self, hooks)
|
Prepares the given hooks.
|
Prepares the given hooks.
|
[
"Prepares",
"the",
"given",
"hooks",
"."
] |
def prepare_hooks(self, hooks):
"""Prepares the given hooks."""
# hooks can be passed as None to the prepare method and to this
# method. To prevent iterating over None, simply use an empty list
# if hooks is False-y
hooks = hooks or []
for event in hooks:
self.register_hook(event, hooks[event])
|
[
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"prepare_hooks",
"(",
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",",
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"# hooks can be passed as None to the prepare method and to this",
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"(",
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",",
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"[",
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"]",
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] |
https://github.com/guildai/guildai/blob/1665985a3d4d788efc1a3180ca51cc417f71ca78/guild/external/pip/_vendor/requests/models.py#L572-L579
|
||
fluentpython/example-code-2e
|
80f7f84274a47579e59c29a4657691525152c9d5
|
12-seq-hacking/vector_v2.py
|
python
|
Vector.__len__
|
(self)
|
return len(self._components)
|
[] |
def __len__(self):
return len(self._components)
|
[
"def",
"__len__",
"(",
"self",
")",
":",
"return",
"len",
"(",
"self",
".",
"_components",
")"
] |
https://github.com/fluentpython/example-code-2e/blob/80f7f84274a47579e59c29a4657691525152c9d5/12-seq-hacking/vector_v2.py#L149-L150
|
|||
proycon/pynlpl
|
7707f69a91caaa6cde037f0d0379f1d42500a68b
|
pynlpl/statistics.py
|
python
|
FrequencyList.__contains__
|
(self, type)
|
return type in self._count
|
Checks if the specified type is in the frequency list
|
Checks if the specified type is in the frequency list
|
[
"Checks",
"if",
"the",
"specified",
"type",
"is",
"in",
"the",
"frequency",
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] |
def __contains__(self, type):
"""Checks if the specified type is in the frequency list"""
if self.dovalidation: type = self._validate(type)
return type in self._count
|
[
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https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/statistics.py#L167-L170
|
|
Source-Python-Dev-Team/Source.Python
|
d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb
|
addons/source-python/packages/site-packages/sqlalchemy/orm/interfaces.py
|
python
|
MapperOption.process_query_conditionally
|
(self, query)
|
same as process_query(), except that this option may not
apply to the given query.
This is typically used during a lazy load or scalar refresh
operation to propagate options stated in the original Query to the
new Query being used for the load. It occurs for those options that
specify propagate_to_loaders=True.
|
same as process_query(), except that this option may not
apply to the given query.
|
[
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"process_query",
"()",
"except",
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"this",
"option",
"may",
"not",
"apply",
"to",
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"given",
"query",
"."
] |
def process_query_conditionally(self, query):
"""same as process_query(), except that this option may not
apply to the given query.
This is typically used during a lazy load or scalar refresh
operation to propagate options stated in the original Query to the
new Query being used for the load. It occurs for those options that
specify propagate_to_loaders=True.
"""
self.process_query(query)
|
[
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"(",
"self",
",",
"query",
")",
":",
"self",
".",
"process_query",
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https://github.com/Source-Python-Dev-Team/Source.Python/blob/d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb/addons/source-python/packages/site-packages/sqlalchemy/orm/interfaces.py#L568-L579
|
||
bytedance/byteps
|
d0bcf1a87ee87539ceb29bcc976d4da063ffc47b
|
byteps/torch/compression.py
|
python
|
Compressor.decompress
|
(tensor, ctx)
|
Decompress the tensor with the given context.
|
Decompress the tensor with the given context.
|
[
"Decompress",
"the",
"tensor",
"with",
"the",
"given",
"context",
"."
] |
def decompress(tensor, ctx):
"""Decompress the tensor with the given context."""
pass
|
[
"def",
"decompress",
"(",
"tensor",
",",
"ctx",
")",
":",
"pass"
] |
https://github.com/bytedance/byteps/blob/d0bcf1a87ee87539ceb29bcc976d4da063ffc47b/byteps/torch/compression.py#L29-L31
|
||
lovelylain/pyctp
|
fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d
|
futures/ctp/ApiStruct.py
|
python
|
ReqOpenAccount.__init__
|
(self, TradeCode='', BankID='', BankBranchID='', BrokerID='', BrokerBranchID='', TradeDate='', TradeTime='', BankSerial='', TradingDay='', PlateSerial=0, LastFragment=LF_Yes, SessionID=0, CustomerName='', IdCardType=ICT_EID, IdentifiedCardNo='', Gender=GD_Unknown, CountryCode='', CustType=CUSTT_Person, Address='', ZipCode='', Telephone='', MobilePhone='', Fax='', EMail='', MoneyAccountStatus=MAS_Normal, BankAccount='', BankPassWord='', AccountID='', Password='', InstallID=0, VerifyCertNoFlag=YNI_Yes, CurrencyID='', CashExchangeCode=CEC_Exchange, Digest='', BankAccType=BAT_BankBook, DeviceID='', BankSecuAccType=BAT_BankBook, BrokerIDByBank='', BankSecuAcc='', BankPwdFlag=BPWDF_NoCheck, SecuPwdFlag=BPWDF_NoCheck, OperNo='', TID=0, UserID='')
|
[] |
def __init__(self, TradeCode='', BankID='', BankBranchID='', BrokerID='', BrokerBranchID='', TradeDate='', TradeTime='', BankSerial='', TradingDay='', PlateSerial=0, LastFragment=LF_Yes, SessionID=0, CustomerName='', IdCardType=ICT_EID, IdentifiedCardNo='', Gender=GD_Unknown, CountryCode='', CustType=CUSTT_Person, Address='', ZipCode='', Telephone='', MobilePhone='', Fax='', EMail='', MoneyAccountStatus=MAS_Normal, BankAccount='', BankPassWord='', AccountID='', Password='', InstallID=0, VerifyCertNoFlag=YNI_Yes, CurrencyID='', CashExchangeCode=CEC_Exchange, Digest='', BankAccType=BAT_BankBook, DeviceID='', BankSecuAccType=BAT_BankBook, BrokerIDByBank='', BankSecuAcc='', BankPwdFlag=BPWDF_NoCheck, SecuPwdFlag=BPWDF_NoCheck, OperNo='', TID=0, UserID=''):
self.TradeCode = '' #业务功能码, char[7]
self.BankID = '' #银行代码, char[4]
self.BankBranchID = 'BankBrchID' #银行分支机构代码, char[5]
self.BrokerID = '' #期商代码, char[11]
self.BrokerBranchID = 'FutureBranchID' #期商分支机构代码, char[31]
self.TradeDate = '' #交易日期, char[9]
self.TradeTime = '' #交易时间, char[9]
self.BankSerial = '' #银行流水号, char[13]
self.TradingDay = 'TradeDate' #交易系统日期 , char[9]
self.PlateSerial = 'Serial' #银期平台消息流水号, int
self.LastFragment = '' #最后分片标志, char
self.SessionID = '' #会话号, int
self.CustomerName = 'IndividualName' #客户姓名, char[51]
self.IdCardType = '' #证件类型, char
self.IdentifiedCardNo = '' #证件号码, char[51]
self.Gender = '' #性别, char
self.CountryCode = '' #国家代码, char[21]
self.CustType = '' #客户类型, char
self.Address = '' #地址, char[101]
self.ZipCode = '' #邮编, char[7]
self.Telephone = '' #电话号码, char[41]
self.MobilePhone = '' #手机, char[21]
self.Fax = '' #传真, char[41]
self.EMail = '' #电子邮件, char[41]
self.MoneyAccountStatus = '' #资金账户状态, char
self.BankAccount = '' #银行帐号, char[41]
self.BankPassWord = 'Password' #银行密码, char[41]
self.AccountID = '' #投资者帐号, char[13]
self.Password = '' #期货密码, char[41]
self.InstallID = '' #安装编号, int
self.VerifyCertNoFlag = 'YesNoIndicator' #验证客户证件号码标志, char
self.CurrencyID = '' #币种代码, char[4]
self.CashExchangeCode = '' #汇钞标志, char
self.Digest = '' #摘要, char[36]
self.BankAccType = '' #银行帐号类型, char
self.DeviceID = '' #渠道标志, char[3]
self.BankSecuAccType = 'BankAccType' #期货单位帐号类型, char
self.BrokerIDByBank = 'BankCodingForFuture' #期货公司银行编码, char[33]
self.BankSecuAcc = 'BankAccount' #期货单位帐号, char[41]
self.BankPwdFlag = 'PwdFlag' #银行密码标志, char
self.SecuPwdFlag = 'PwdFlag' #期货资金密码核对标志, char
self.OperNo = '' #交易柜员, char[17]
self.TID = '' #交易ID, int
self.UserID = ''
|
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"=",
"0",
",",
"VerifyCertNoFlag",
"=",
"YNI_Yes",
",",
"CurrencyID",
"=",
"''",
",",
"CashExchangeCode",
"=",
"CEC_Exchange",
",",
"Digest",
"=",
"''",
",",
"BankAccType",
"=",
"BAT_BankBook",
",",
"DeviceID",
"=",
"''",
",",
"BankSecuAccType",
"=",
"BAT_BankBook",
",",
"BrokerIDByBank",
"=",
"''",
",",
"BankSecuAcc",
"=",
"''",
",",
"BankPwdFlag",
"=",
"BPWDF_NoCheck",
",",
"SecuPwdFlag",
"=",
"BPWDF_NoCheck",
",",
"OperNo",
"=",
"''",
",",
"TID",
"=",
"0",
",",
"UserID",
"=",
"''",
")",
":",
"self",
".",
"TradeCode",
"=",
"''",
"#业务功能码, char[7]",
"self",
".",
"BankID",
"=",
"''",
"#银行代码, char[4]",
"self",
".",
"BankBranchID",
"=",
"'BankBrchID'",
"#银行分支机构代码, char[5]",
"self",
".",
"BrokerID",
"=",
"''",
"#期商代码, char[11]",
"self",
".",
"BrokerBranchID",
"=",
"'FutureBranchID'",
"#期商分支机构代码, char[31]",
"self",
".",
"TradeDate",
"=",
"''",
"#交易日期, char[9]",
"self",
".",
"TradeTime",
"=",
"''",
"#交易时间, char[9]",
"self",
".",
"BankSerial",
"=",
"''",
"#银行流水号, char[13]",
"self",
".",
"TradingDay",
"=",
"'TradeDate'",
"#交易系统日期 , char[9]",
"self",
".",
"PlateSerial",
"=",
"'Serial'",
"#银期平台消息流水号, int",
"self",
".",
"LastFragment",
"=",
"''",
"#最后分片标志, char",
"self",
".",
"SessionID",
"=",
"''",
"#会话号, int",
"self",
".",
"CustomerName",
"=",
"'IndividualName'",
"#客户姓名, char[51]",
"self",
".",
"IdCardType",
"=",
"''",
"#证件类型, char",
"self",
".",
"IdentifiedCardNo",
"=",
"''",
"#证件号码, char[51]",
"self",
".",
"Gender",
"=",
"''",
"#性别, char",
"self",
".",
"CountryCode",
"=",
"''",
"#国家代码, char[21]",
"self",
".",
"CustType",
"=",
"''",
"#客户类型, char",
"self",
".",
"Address",
"=",
"''",
"#地址, char[101]",
"self",
".",
"ZipCode",
"=",
"''",
"#邮编, char[7]",
"self",
".",
"Telephone",
"=",
"''",
"#电话号码, char[41]",
"self",
".",
"MobilePhone",
"=",
"''",
"#手机, char[21]",
"self",
".",
"Fax",
"=",
"''",
"#传真, char[41]",
"self",
".",
"EMail",
"=",
"''",
"#电子邮件, char[41]",
"self",
".",
"MoneyAccountStatus",
"=",
"''",
"#资金账户状态, char",
"self",
".",
"BankAccount",
"=",
"''",
"#银行帐号, char[41]",
"self",
".",
"BankPassWord",
"=",
"'Password'",
"#银行密码, char[41]",
"self",
".",
"AccountID",
"=",
"''",
"#投资者帐号, char[13]",
"self",
".",
"Password",
"=",
"''",
"#期货密码, char[41]",
"self",
".",
"InstallID",
"=",
"''",
"#安装编号, int",
"self",
".",
"VerifyCertNoFlag",
"=",
"'YesNoIndicator'",
"#验证客户证件号码标志, char",
"self",
".",
"CurrencyID",
"=",
"''",
"#币种代码, char[4]",
"self",
".",
"CashExchangeCode",
"=",
"''",
"#汇钞标志, char",
"self",
".",
"Digest",
"=",
"''",
"#摘要, char[36]",
"self",
".",
"BankAccType",
"=",
"''",
"#银行帐号类型, char",
"self",
".",
"DeviceID",
"=",
"''",
"#渠道标志, char[3]",
"self",
".",
"BankSecuAccType",
"=",
"'BankAccType'",
"#期货单位帐号类型, char",
"self",
".",
"BrokerIDByBank",
"=",
"'BankCodingForFuture'",
"#期货公司银行编码, char[33]",
"self",
".",
"BankSecuAcc",
"=",
"'BankAccount'",
"#期货单位帐号, char[41]",
"self",
".",
"BankPwdFlag",
"=",
"'PwdFlag'",
"#银行密码标志, char",
"self",
".",
"SecuPwdFlag",
"=",
"'PwdFlag'",
"#期货资金密码核对标志, char",
"self",
".",
"OperNo",
"=",
"''",
"#交易柜员, char[17]",
"self",
".",
"TID",
"=",
"''",
"#交易ID, int",
"self",
".",
"UserID",
"=",
"''"
] |
https://github.com/lovelylain/pyctp/blob/fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d/futures/ctp/ApiStruct.py#L4757-L4801
|
||||
twilio/twilio-python
|
6e1e811ea57a1edfadd5161ace87397c563f6915
|
twilio/rest/conversations/v1/user/__init__.py
|
python
|
UserInstance.friendly_name
|
(self)
|
return self._properties['friendly_name']
|
:returns: The string that you assigned to describe the resource
:rtype: unicode
|
:returns: The string that you assigned to describe the resource
:rtype: unicode
|
[
":",
"returns",
":",
"The",
"string",
"that",
"you",
"assigned",
"to",
"describe",
"the",
"resource",
":",
"rtype",
":",
"unicode"
] |
def friendly_name(self):
"""
:returns: The string that you assigned to describe the resource
:rtype: unicode
"""
return self._properties['friendly_name']
|
[
"def",
"friendly_name",
"(",
"self",
")",
":",
"return",
"self",
".",
"_properties",
"[",
"'friendly_name'",
"]"
] |
https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/conversations/v1/user/__init__.py#L386-L391
|
|
IJDykeman/wangTiles
|
7c1ee2095ebdf7f72bce07d94c6484915d5cae8b
|
experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/pip/_internal/utils/misc.py
|
python
|
renames
|
(old, new)
|
Like os.renames(), but handles renaming across devices.
|
Like os.renames(), but handles renaming across devices.
|
[
"Like",
"os",
".",
"renames",
"()",
"but",
"handles",
"renaming",
"across",
"devices",
"."
] |
def renames(old, new):
"""Like os.renames(), but handles renaming across devices."""
# Implementation borrowed from os.renames().
head, tail = os.path.split(new)
if head and tail and not os.path.exists(head):
os.makedirs(head)
shutil.move(old, new)
head, tail = os.path.split(old)
if head and tail:
try:
os.removedirs(head)
except OSError:
pass
|
[
"def",
"renames",
"(",
"old",
",",
"new",
")",
":",
"# Implementation borrowed from os.renames().",
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",",
"tail",
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"try",
":",
"os",
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"removedirs",
"(",
"head",
")",
"except",
"OSError",
":",
"pass"
] |
https://github.com/IJDykeman/wangTiles/blob/7c1ee2095ebdf7f72bce07d94c6484915d5cae8b/experimental_code/tiles_3d/venv_mac_py3/lib/python2.7/site-packages/pip/_internal/utils/misc.py#L269-L283
|
||
kanzure/nanoengineer
|
874e4c9f8a9190f093625b267f9767e19f82e6c4
|
cad/src/commands/BuildCrystal/CrystalShape.py
|
python
|
CrystalShape._updateBBox
|
(self, curveList)
|
Recompute the bounding box for the list of curves
|
Recompute the bounding box for the list of curves
|
[
"Recompute",
"the",
"bounding",
"box",
"for",
"the",
"list",
"of",
"curves"
] |
def _updateBBox(self, curveList):
"""
Recompute the bounding box for the list of curves
"""
bbox = BBox()
for c in curveList[1:]:
bbox.merge(c.bbox)
curveList[0] = bbox
|
[
"def",
"_updateBBox",
"(",
"self",
",",
"curveList",
")",
":",
"bbox",
"=",
"BBox",
"(",
")",
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"in",
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"[",
"1",
":",
"]",
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"bbox",
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"merge",
"(",
"c",
".",
"bbox",
")",
"curveList",
"[",
"0",
"]",
"=",
"bbox"
] |
https://github.com/kanzure/nanoengineer/blob/874e4c9f8a9190f093625b267f9767e19f82e6c4/cad/src/commands/BuildCrystal/CrystalShape.py#L213-L220
|
||
billpmurphy/hask
|
4609cc8d9d975f51b6ecdbd33640cdffdc28f953
|
hask/Prelude.py
|
python
|
sequence_
|
(xs)
|
sequence_ :: Monad m => [m a] -> m None
Evaluate each action in the sequence from left to right, and ignore the
results.
|
sequence_ :: Monad m => [m a] -> m None
|
[
"sequence_",
"::",
"Monad",
"m",
"=",
">",
"[",
"m",
"a",
"]",
"-",
">",
"m",
"None"
] |
def sequence_(xs):
"""
sequence_ :: Monad m => [m a] -> m None
Evaluate each action in the sequence from left to right, and ignore the
results.
"""
raise NotImplementedError()
|
[
"def",
"sequence_",
"(",
"xs",
")",
":",
"raise",
"NotImplementedError",
"(",
")"
] |
https://github.com/billpmurphy/hask/blob/4609cc8d9d975f51b6ecdbd33640cdffdc28f953/hask/Prelude.py#L209-L216
|
||
minio/minio-py
|
b3ba3bf99fe6b9ff2b28855550d6ab5345c134e3
|
minio/minioadmin.py
|
python
|
MinioAdmin.kms_key_create
|
(self, key=None)
|
return self._run(
[
"kms", "key", "create", self._target, key
] + ([key] if key else []),
)
|
Create a new KMS master key.
|
Create a new KMS master key.
|
[
"Create",
"a",
"new",
"KMS",
"master",
"key",
"."
] |
def kms_key_create(self, key=None):
"""Create a new KMS master key."""
return self._run(
[
"kms", "key", "create", self._target, key
] + ([key] if key else []),
)
|
[
"def",
"kms_key_create",
"(",
"self",
",",
"key",
"=",
"None",
")",
":",
"return",
"self",
".",
"_run",
"(",
"[",
"\"kms\"",
",",
"\"key\"",
",",
"\"create\"",
",",
"self",
".",
"_target",
",",
"key",
"]",
"+",
"(",
"[",
"key",
"]",
"if",
"key",
"else",
"[",
"]",
")",
",",
")"
] |
https://github.com/minio/minio-py/blob/b3ba3bf99fe6b9ff2b28855550d6ab5345c134e3/minio/minioadmin.py#L206-L212
|
|
python-social-auth/social-core
|
1ea27e8989657bb35dd37b6ee2e038e1358fbc96
|
social_core/backends/douban.py
|
python
|
DoubanOAuth2.user_data
|
(self, access_token, *args, **kwargs)
|
return self.get_json(
'https://api.douban.com/v2/user/~me',
headers={'Authorization': f'Bearer {access_token}'}
)
|
Return user data provided
|
Return user data provided
|
[
"Return",
"user",
"data",
"provided"
] |
def user_data(self, access_token, *args, **kwargs):
"""Return user data provided"""
return self.get_json(
'https://api.douban.com/v2/user/~me',
headers={'Authorization': f'Bearer {access_token}'}
)
|
[
"def",
"user_data",
"(",
"self",
",",
"access_token",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"get_json",
"(",
"'https://api.douban.com/v2/user/~me'",
",",
"headers",
"=",
"{",
"'Authorization'",
":",
"f'Bearer {access_token}'",
"}",
")"
] |
https://github.com/python-social-auth/social-core/blob/1ea27e8989657bb35dd37b6ee2e038e1358fbc96/social_core/backends/douban.py#L54-L59
|
|
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/lib-python/3/xmlrpc/client.py
|
python
|
Marshaller.dump_int
|
(self, value, write)
|
[] |
def dump_int(self, value, write):
# in case ints are > 32 bits
if value > MAXINT or value < MININT:
raise OverflowError("int exceeds XML-RPC limits")
write("<value><int>")
write(str(value))
write("</int></value>\n")
|
[
"def",
"dump_int",
"(",
"self",
",",
"value",
",",
"write",
")",
":",
"# in case ints are > 32 bits",
"if",
"value",
">",
"MAXINT",
"or",
"value",
"<",
"MININT",
":",
"raise",
"OverflowError",
"(",
"\"int exceeds XML-RPC limits\"",
")",
"write",
"(",
"\"<value><int>\"",
")",
"write",
"(",
"str",
"(",
"value",
")",
")",
"write",
"(",
"\"</int></value>\\n\"",
")"
] |
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/lib-python/3/xmlrpc/client.py#L525-L531
|
||||
theotherp/nzbhydra
|
4b03d7f769384b97dfc60dade4806c0fc987514e
|
libs/requests/packages/urllib3/response.py
|
python
|
HTTPResponse._init_length
|
(self, request_method)
|
return length
|
Set initial length value for Response content if available.
|
Set initial length value for Response content if available.
|
[
"Set",
"initial",
"length",
"value",
"for",
"Response",
"content",
"if",
"available",
"."
] |
def _init_length(self, request_method):
"""
Set initial length value for Response content if available.
"""
length = self.headers.get('content-length')
if length is not None and self.chunked:
# This Response will fail with an IncompleteRead if it can't be
# received as chunked. This method falls back to attempt reading
# the response before raising an exception.
log.warning("Received response with both Content-Length and "
"Transfer-Encoding set. This is expressly forbidden "
"by RFC 7230 sec 3.3.2. Ignoring Content-Length and "
"attempting to process response as Transfer-Encoding: "
"chunked.")
return None
elif length is not None:
try:
# RFC 7230 section 3.3.2 specifies multiple content lengths can
# be sent in a single Content-Length header
# (e.g. Content-Length: 42, 42). This line ensures the values
# are all valid ints and that as long as the `set` length is 1,
# all values are the same. Otherwise, the header is invalid.
lengths = set([int(val) for val in length.split(',')])
if len(lengths) > 1:
raise InvalidHeader("Content-Length contained multiple "
"unmatching values (%s)" % length)
length = lengths.pop()
except ValueError:
length = None
else:
if length < 0:
length = None
# Convert status to int for comparison
# In some cases, httplib returns a status of "_UNKNOWN"
try:
status = int(self.status)
except ValueError:
status = 0
# Check for responses that shouldn't include a body
if status in (204, 304) or 100 <= status < 200 or request_method == 'HEAD':
length = 0
return length
|
[
"def",
"_init_length",
"(",
"self",
",",
"request_method",
")",
":",
"length",
"=",
"self",
".",
"headers",
".",
"get",
"(",
"'content-length'",
")",
"if",
"length",
"is",
"not",
"None",
"and",
"self",
".",
"chunked",
":",
"# This Response will fail with an IncompleteRead if it can't be",
"# received as chunked. This method falls back to attempt reading",
"# the response before raising an exception.",
"log",
".",
"warning",
"(",
"\"Received response with both Content-Length and \"",
"\"Transfer-Encoding set. This is expressly forbidden \"",
"\"by RFC 7230 sec 3.3.2. Ignoring Content-Length and \"",
"\"attempting to process response as Transfer-Encoding: \"",
"\"chunked.\"",
")",
"return",
"None",
"elif",
"length",
"is",
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"None",
":",
"try",
":",
"# RFC 7230 section 3.3.2 specifies multiple content lengths can",
"# be sent in a single Content-Length header",
"# (e.g. Content-Length: 42, 42). This line ensures the values",
"# are all valid ints and that as long as the `set` length is 1,",
"# all values are the same. Otherwise, the header is invalid.",
"lengths",
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"(",
"[",
"int",
"(",
"val",
")",
"for",
"val",
"in",
"length",
".",
"split",
"(",
"','",
")",
"]",
")",
"if",
"len",
"(",
"lengths",
")",
">",
"1",
":",
"raise",
"InvalidHeader",
"(",
"\"Content-Length contained multiple \"",
"\"unmatching values (%s)\"",
"%",
"length",
")",
"length",
"=",
"lengths",
".",
"pop",
"(",
")",
"except",
"ValueError",
":",
"length",
"=",
"None",
"else",
":",
"if",
"length",
"<",
"0",
":",
"length",
"=",
"None",
"# Convert status to int for comparison",
"# In some cases, httplib returns a status of \"_UNKNOWN\"",
"try",
":",
"status",
"=",
"int",
"(",
"self",
".",
"status",
")",
"except",
"ValueError",
":",
"status",
"=",
"0",
"# Check for responses that shouldn't include a body",
"if",
"status",
"in",
"(",
"204",
",",
"304",
")",
"or",
"100",
"<=",
"status",
"<",
"200",
"or",
"request_method",
"==",
"'HEAD'",
":",
"length",
"=",
"0",
"return",
"length"
] |
https://github.com/theotherp/nzbhydra/blob/4b03d7f769384b97dfc60dade4806c0fc987514e/libs/requests/packages/urllib3/response.py#L198-L244
|
|
openshift/openshift-tools
|
1188778e728a6e4781acf728123e5b356380fe6f
|
openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_adm_ca_server_cert.py
|
python
|
OpenShiftCLIConfig.config_options
|
(self)
|
return self._options
|
return config options
|
return config options
|
[
"return",
"config",
"options"
] |
def config_options(self):
''' return config options '''
return self._options
|
[
"def",
"config_options",
"(",
"self",
")",
":",
"return",
"self",
".",
"_options"
] |
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_adm_ca_server_cert.py#L1459-L1461
|
|
UniShared/videonotes
|
803cdd97b90823fb17f50dd55999aa7d1fec6c3a
|
lib/oauth2/__init__.py
|
python
|
Request.to_postdata
|
(self)
|
return urllib.urlencode(d, True).replace('+', '%20')
|
Serialize as post data for a POST request.
|
Serialize as post data for a POST request.
|
[
"Serialize",
"as",
"post",
"data",
"for",
"a",
"POST",
"request",
"."
] |
def to_postdata(self):
"""Serialize as post data for a POST request."""
d = {}
for k, v in self.iteritems():
d[k.encode('utf-8')] = to_utf8_optional_iterator(v)
# tell urlencode to deal with sequence values and map them correctly
# to resulting querystring. for example self["k"] = ["v1", "v2"] will
# result in 'k=v1&k=v2' and not k=%5B%27v1%27%2C+%27v2%27%5D
return urllib.urlencode(d, True).replace('+', '%20')
|
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https://github.com/UniShared/videonotes/blob/803cdd97b90823fb17f50dd55999aa7d1fec6c3a/lib/oauth2/__init__.py#L402-L411
|
|
smart-mobile-software/gitstack
|
d9fee8f414f202143eb6e620529e8e5539a2af56
|
python/Lib/site-packages/django/contrib/gis/db/models/sql/compiler.py
|
python
|
GeoSQLCompiler.get_default_columns
|
(self, with_aliases=False, col_aliases=None,
start_alias=None, opts=None, as_pairs=False, local_only=False)
|
return result, aliases
|
Computes the default columns for selecting every field in the base
model. Will sometimes be called to pull in related models (e.g. via
select_related), in which case "opts" and "start_alias" will be given
to provide a starting point for the traversal.
Returns a list of strings, quoted appropriately for use in SQL
directly, as well as a set of aliases used in the select statement (if
'as_pairs' is True, returns a list of (alias, col_name) pairs instead
of strings as the first component and None as the second component).
This routine is overridden from Query to handle customized selection of
geometry columns.
|
Computes the default columns for selecting every field in the base
model. Will sometimes be called to pull in related models (e.g. via
select_related), in which case "opts" and "start_alias" will be given
to provide a starting point for the traversal.
|
[
"Computes",
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] |
def get_default_columns(self, with_aliases=False, col_aliases=None,
start_alias=None, opts=None, as_pairs=False, local_only=False):
"""
Computes the default columns for selecting every field in the base
model. Will sometimes be called to pull in related models (e.g. via
select_related), in which case "opts" and "start_alias" will be given
to provide a starting point for the traversal.
Returns a list of strings, quoted appropriately for use in SQL
directly, as well as a set of aliases used in the select statement (if
'as_pairs' is True, returns a list of (alias, col_name) pairs instead
of strings as the first component and None as the second component).
This routine is overridden from Query to handle customized selection of
geometry columns.
"""
result = []
if opts is None:
opts = self.query.model._meta
aliases = set()
only_load = self.deferred_to_columns()
# Skip all proxy to the root proxied model
proxied_model = opts.concrete_model
if start_alias:
seen = {None: start_alias}
for field, model in opts.get_fields_with_model():
if local_only and model is not None:
continue
if start_alias:
try:
alias = seen[model]
except KeyError:
if model is proxied_model:
alias = start_alias
else:
link_field = opts.get_ancestor_link(model)
alias = self.query.join((start_alias, model._meta.db_table,
link_field.column, model._meta.pk.column))
seen[model] = alias
else:
# If we're starting from the base model of the queryset, the
# aliases will have already been set up in pre_sql_setup(), so
# we can save time here.
alias = self.query.included_inherited_models[model]
table = self.query.alias_map[alias][TABLE_NAME]
if table in only_load and field.column not in only_load[table]:
continue
if as_pairs:
result.append((alias, field.column))
aliases.add(alias)
continue
# This part of the function is customized for GeoQuery. We
# see if there was any custom selection specified in the
# dictionary, and set up the selection format appropriately.
field_sel = self.get_field_select(field, alias)
if with_aliases and field.column in col_aliases:
c_alias = 'Col%d' % len(col_aliases)
result.append('%s AS %s' % (field_sel, c_alias))
col_aliases.add(c_alias)
aliases.add(c_alias)
else:
r = field_sel
result.append(r)
aliases.add(r)
if with_aliases:
col_aliases.add(field.column)
return result, aliases
|
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https://github.com/smart-mobile-software/gitstack/blob/d9fee8f414f202143eb6e620529e8e5539a2af56/python/Lib/site-packages/django/contrib/gis/db/models/sql/compiler.py#L96-L163
|
|
omz/PythonistaAppTemplate
|
f560f93f8876d82a21d108977f90583df08d55af
|
PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/transforms.py
|
python
|
CompositeGenericTransform.__init__
|
(self, a, b, **kwargs)
|
Create a new composite transform that is the result of
applying transform *a* then transform *b*.
You will generally not call this constructor directly but use
the :func:`composite_transform_factory` function instead,
which can automatically choose the best kind of composite
transform instance to create.
|
Create a new composite transform that is the result of
applying transform *a* then transform *b*.
|
[
"Create",
"a",
"new",
"composite",
"transform",
"that",
"is",
"the",
"result",
"of",
"applying",
"transform",
"*",
"a",
"*",
"then",
"transform",
"*",
"b",
"*",
"."
] |
def __init__(self, a, b, **kwargs):
"""
Create a new composite transform that is the result of
applying transform *a* then transform *b*.
You will generally not call this constructor directly but use
the :func:`composite_transform_factory` function instead,
which can automatically choose the best kind of composite
transform instance to create.
"""
assert a.output_dims == b.input_dims
self.input_dims = a.input_dims
self.output_dims = b.output_dims
Transform.__init__(self, **kwargs)
self._a = a
self._b = b
self.set_children(a, b)
|
[
"def",
"__init__",
"(",
"self",
",",
"a",
",",
"b",
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"*",
"*",
"kwargs",
")",
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https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/transforms.py#L2135-L2152
|
||
pythonql/pythonql
|
10ded9473eee8dc75630c2c67f06b6f86a0af305
|
pythonql/parser/PythonQLParser.py
|
python
|
Parser.p_let_clause_entry
|
(self, p)
|
let_clause_entry : expr_list '=' test
|
let_clause_entry : expr_list '=' test
|
[
"let_clause_entry",
":",
"expr_list",
"=",
"test"
] |
def p_let_clause_entry(self, p):
"""let_clause_entry : expr_list '=' test"""
p[0] = make_node('let_clause_entry', p)
|
[
"def",
"p_let_clause_entry",
"(",
"self",
",",
"p",
")",
":",
"p",
"[",
"0",
"]",
"=",
"make_node",
"(",
"'let_clause_entry'",
",",
"p",
")"
] |
https://github.com/pythonql/pythonql/blob/10ded9473eee8dc75630c2c67f06b6f86a0af305/pythonql/parser/PythonQLParser.py#L714-L716
|
||
dmlc/dgl
|
8d14a739bc9e446d6c92ef83eafe5782398118de
|
python/dgl/_deprecate/graph.py
|
python
|
DGLBaseGraph.edge_id
|
(self, u, v, force_multi=None, return_array=False)
|
Return the edge ID, or an array of edge IDs, between source node
`u` and destination node `v`.
Parameters
----------
u : int
The source node ID.
v : int
The destination node ID.
force_multi : bool
Deprecated (Will be deleted in the future).
If False, will return a single edge ID.
If True, will always return an array.
return_array : bool
If False, will return a single edge ID.
If True, will always return an array.
Returns
-------
int or tensor
The edge ID if return_array is False.
The edge ID array otherwise.
Notes
-----
If multiply edges exist between `u` and `v` and return_array is False,
the result is undefined.
Examples
--------
The following example uses PyTorch backend.
For simple graphs:
>>> G = dgl.DGLGraph()
>>> G.add_node(3)
>>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
>>> G.edge_id(0, 2)
1
>>> G.edge_id(0, 1)
0
For multigraphs:
>>> G = dgl.DGLGraph()
>>> G.add_nodes(3)
Adding edges (0, 1), (0, 2), (0, 1), (0, 2), so edge ID 0 and 2 both
connect from 0 and 1, while edge ID 1 and 3 both connect from 0 and 2.
>>> G.add_edges([0, 0, 0, 0], [1, 2, 1, 2])
>>> G.edge_id(0, 1, return_array=True)
tensor([0, 2])
See Also
--------
edge_ids
|
Return the edge ID, or an array of edge IDs, between source node
`u` and destination node `v`.
|
[
"Return",
"the",
"edge",
"ID",
"or",
"an",
"array",
"of",
"edge",
"IDs",
"between",
"source",
"node",
"u",
"and",
"destination",
"node",
"v",
"."
] |
def edge_id(self, u, v, force_multi=None, return_array=False):
"""Return the edge ID, or an array of edge IDs, between source node
`u` and destination node `v`.
Parameters
----------
u : int
The source node ID.
v : int
The destination node ID.
force_multi : bool
Deprecated (Will be deleted in the future).
If False, will return a single edge ID.
If True, will always return an array.
return_array : bool
If False, will return a single edge ID.
If True, will always return an array.
Returns
-------
int or tensor
The edge ID if return_array is False.
The edge ID array otherwise.
Notes
-----
If multiply edges exist between `u` and `v` and return_array is False,
the result is undefined.
Examples
--------
The following example uses PyTorch backend.
For simple graphs:
>>> G = dgl.DGLGraph()
>>> G.add_node(3)
>>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
>>> G.edge_id(0, 2)
1
>>> G.edge_id(0, 1)
0
For multigraphs:
>>> G = dgl.DGLGraph()
>>> G.add_nodes(3)
Adding edges (0, 1), (0, 2), (0, 1), (0, 2), so edge ID 0 and 2 both
connect from 0 and 1, while edge ID 1 and 3 both connect from 0 and 2.
>>> G.add_edges([0, 0, 0, 0], [1, 2, 1, 2])
>>> G.edge_id(0, 1, return_array=True)
tensor([0, 2])
See Also
--------
edge_ids
"""
idx = self._graph.edge_id(u, v)
if force_multi is not None:
dgl_warning("force_multi will be deprecated." \
"Please use return_array instead")
return_array = force_multi
if return_array:
return idx.tousertensor()
else:
assert len(idx) == 1, "For return_array=False, there should be one and " \
"only one edge between u and v, but get {} edges. " \
"Please use return_array=True instead".format(len(idx))
return idx[0]
|
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"idx",
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"return",
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"[",
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] |
https://github.com/dmlc/dgl/blob/8d14a739bc9e446d6c92ef83eafe5782398118de/python/dgl/_deprecate/graph.py#L322-L393
|
||
bugy/script-server
|
9a57ce15903c81bcb537b872f1330ee55ba31563
|
tools/build.py
|
python
|
BuildInfo.get_files
|
(self)
|
return self.files
|
[] |
def get_files(self):
return self.files
|
[
"def",
"get_files",
"(",
"self",
")",
":",
"return",
"self",
".",
"files"
] |
https://github.com/bugy/script-server/blob/9a57ce15903c81bcb537b872f1330ee55ba31563/tools/build.py#L47-L48
|
|||
MeanEYE/Sunflower
|
1024bbdde3b8e202ddad3553b321a7b6230bffc9
|
sunflower/plugin_base/plugin.py
|
python
|
PluginBase._drag_drop
|
(self, widget, drag_context, x, y, timestamp)
|
return True
|
Handle dropping data over widget
|
Handle dropping data over widget
|
[
"Handle",
"dropping",
"data",
"over",
"widget"
] |
def _drag_drop(self, widget, drag_context, x, y, timestamp):
"""Handle dropping data over widget"""
return True
|
[
"def",
"_drag_drop",
"(",
"self",
",",
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",",
"drag_context",
",",
"x",
",",
"y",
",",
"timestamp",
")",
":",
"return",
"True"
] |
https://github.com/MeanEYE/Sunflower/blob/1024bbdde3b8e202ddad3553b321a7b6230bffc9/sunflower/plugin_base/plugin.py#L161-L163
|
|
smart-mobile-software/gitstack
|
d9fee8f414f202143eb6e620529e8e5539a2af56
|
python/Lib/idlelib/configDialog.py
|
python
|
ConfigDialog.SaveNewTheme
|
(self,themeName,theme)
|
save a newly created theme.
themeName - string, the name of the new theme
theme - dictionary containing the new theme
|
save a newly created theme.
themeName - string, the name of the new theme
theme - dictionary containing the new theme
|
[
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"new",
"theme",
"theme",
"-",
"dictionary",
"containing",
"the",
"new",
"theme"
] |
def SaveNewTheme(self,themeName,theme):
"""
save a newly created theme.
themeName - string, the name of the new theme
theme - dictionary containing the new theme
"""
if not idleConf.userCfg['highlight'].has_section(themeName):
idleConf.userCfg['highlight'].add_section(themeName)
for element in theme.keys():
value=theme[element]
idleConf.userCfg['highlight'].SetOption(themeName,element,value)
|
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https://github.com/smart-mobile-software/gitstack/blob/d9fee8f414f202143eb6e620529e8e5539a2af56/python/Lib/idlelib/configDialog.py#L1075-L1085
|
||
osmr/imgclsmob
|
f2993d3ce73a2f7ddba05da3891defb08547d504
|
pytorch/pytorchcv/models/sparsenet.py
|
python
|
sparsenet201
|
(**kwargs)
|
return get_sparsenet(num_layers=201, model_name="sparsenet201", **kwargs)
|
SparseNet-201 model from 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.
|
SparseNet-201 model from 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
|
[
"SparseNet",
"-",
"201",
"model",
"from",
"Sparsely",
"Aggregated",
"Convolutional",
"Networks",
"https",
":",
"//",
"arxiv",
".",
"org",
"/",
"abs",
"/",
"1801",
".",
"05895",
"."
] |
def sparsenet201(**kwargs):
"""
SparseNet-201 model from 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.
"""
return get_sparsenet(num_layers=201, model_name="sparsenet201", **kwargs)
|
[
"def",
"sparsenet201",
"(",
"*",
"*",
"kwargs",
")",
":",
"return",
"get_sparsenet",
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"num_layers",
"=",
"201",
",",
"model_name",
"=",
"\"sparsenet201\"",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/osmr/imgclsmob/blob/f2993d3ce73a2f7ddba05da3891defb08547d504/pytorch/pytorchcv/models/sparsenet.py#L309-L320
|
|
openbmc/openbmc
|
5f4109adae05f4d6925bfe960007d52f98c61086
|
poky/bitbake/lib/toaster/orm/models.py
|
python
|
Project.get_available_recipes
|
(self)
|
return queryset
|
Returns QuerySet of all the recipes that are provided by layers
added to this project
|
Returns QuerySet of all the recipes that are provided by layers
added to this project
|
[
"Returns",
"QuerySet",
"of",
"all",
"the",
"recipes",
"that",
"are",
"provided",
"by",
"layers",
"added",
"to",
"this",
"project"
] |
def get_available_recipes(self):
""" Returns QuerySet of all the recipes that are provided by layers
added to this project """
queryset = Recipe.objects.filter(
layer_version__in=self.get_project_layer_versions())
return queryset
|
[
"def",
"get_available_recipes",
"(",
"self",
")",
":",
"queryset",
"=",
"Recipe",
".",
"objects",
".",
"filter",
"(",
"layer_version__in",
"=",
"self",
".",
"get_project_layer_versions",
"(",
")",
")",
"return",
"queryset"
] |
https://github.com/openbmc/openbmc/blob/5f4109adae05f4d6925bfe960007d52f98c61086/poky/bitbake/lib/toaster/orm/models.py#L347-L353
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/Python-2.7.9/Lib/argparse.py
|
python
|
ArgumentParser._add_action
|
(self, action)
|
return action
|
[] |
def _add_action(self, action):
if action.option_strings:
self._optionals._add_action(action)
else:
self._positionals._add_action(action)
return action
|
[
"def",
"_add_action",
"(",
"self",
",",
"action",
")",
":",
"if",
"action",
".",
"option_strings",
":",
"self",
".",
"_optionals",
".",
"_add_action",
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"action",
")",
"else",
":",
"self",
".",
"_positionals",
".",
"_add_action",
"(",
"action",
")",
"return",
"action"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/Python-2.7.9/Lib/argparse.py#L1680-L1685
|
|||
sideeffects/SideFXLabs
|
956bc1eef6710882ae8d3a31b4a33dd631a56d5f
|
scripts/python/hou_settings.py
|
python
|
Settings._save
|
(self)
|
[] |
def _save(self):
with open(self.filename, 'w') as fp:
json.dump(self._settings_dic, fp)
|
[
"def",
"_save",
"(",
"self",
")",
":",
"with",
"open",
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"self",
".",
"filename",
",",
"'w'",
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"as",
"fp",
":",
"json",
".",
"dump",
"(",
"self",
".",
"_settings_dic",
",",
"fp",
")"
] |
https://github.com/sideeffects/SideFXLabs/blob/956bc1eef6710882ae8d3a31b4a33dd631a56d5f/scripts/python/hou_settings.py#L57-L59
|
||||
facebookresearch/hydra
|
9b2f4d54b328d1551aa70a241a1d638cbe046367
|
examples/patterns/multi-select/my_app.py
|
python
|
my_app
|
(cfg: DictConfig)
|
[] |
def my_app(cfg: DictConfig) -> None:
print(OmegaConf.to_yaml(cfg))
|
[
"def",
"my_app",
"(",
"cfg",
":",
"DictConfig",
")",
"->",
"None",
":",
"print",
"(",
"OmegaConf",
".",
"to_yaml",
"(",
"cfg",
")",
")"
] |
https://github.com/facebookresearch/hydra/blob/9b2f4d54b328d1551aa70a241a1d638cbe046367/examples/patterns/multi-select/my_app.py#L8-L9
|
||||
edisonlz/fastor
|
342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3
|
base/site-packages/pymongo/master_slave_connection.py
|
python
|
MasterSlaveConnection._send_message_with_response
|
(self, message, _connection_to_use=None,
_must_use_master=False, **kwargs)
|
Receive a message from Mongo.
Sends the given message and returns a (connection_id, response) pair.
:Parameters:
- `operation`: opcode of the message to send
- `data`: data to send
|
Receive a message from Mongo.
|
[
"Receive",
"a",
"message",
"from",
"Mongo",
"."
] |
def _send_message_with_response(self, message, _connection_to_use=None,
_must_use_master=False, **kwargs):
"""Receive a message from Mongo.
Sends the given message and returns a (connection_id, response) pair.
:Parameters:
- `operation`: opcode of the message to send
- `data`: data to send
"""
if _connection_to_use is not None:
if _connection_to_use == -1:
return (-1,
self.__master._send_message_with_response(message,
**kwargs))
else:
return (_connection_to_use,
self.__slaves[_connection_to_use]
._send_message_with_response(message, **kwargs))
# _must_use_master is set for commands, which must be sent to the
# master instance. any queries in a request must be sent to the
# master since that is where writes go.
if _must_use_master or self.in_request():
return (-1, self.__master._send_message_with_response(message,
**kwargs))
# Iterate through the slaves randomly until we have success. Raise
# reconnect if they all fail.
for connection_id in helpers.shuffled(xrange(len(self.__slaves))):
try:
slave = self.__slaves[connection_id]
return (connection_id,
slave._send_message_with_response(message, **kwargs))
except AutoReconnect:
pass
raise AutoReconnect("failed to connect to slaves")
|
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"AutoReconnect",
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"pass",
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"AutoReconnect",
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")"
] |
https://github.com/edisonlz/fastor/blob/342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3/base/site-packages/pymongo/master_slave_connection.py#L169-L206
|
||
ReactionMechanismGenerator/RMG-Py
|
2b7baf51febf27157def58fb3f6cee03fb6a684c
|
rmgpy/tools/globaluncertainty.py
|
python
|
ReactorPCEFactory.analyze_results
|
(self, log=True)
|
return mean, var, cov, main_sens, total_sens
|
Obtain the results: the prediction mean and variance, as well as the global sensitivity indices
Returns a tuple containing the following statistics
(mean species mole fractions, variance, covariance, main sensitivity indices, total sensitivity indices)
|
Obtain the results: the prediction mean and variance, as well as the global sensitivity indices
Returns a tuple containing the following statistics
(mean species mole fractions, variance, covariance, main sensitivity indices, total sensitivity indices)
|
[
"Obtain",
"the",
"results",
":",
"the",
"prediction",
"mean",
"and",
"variance",
"as",
"well",
"as",
"the",
"global",
"sensitivity",
"indices",
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"tuple",
"containing",
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"mean",
"species",
"mole",
"fractions",
"variance",
"covariance",
"main",
"sensitivity",
"indices",
"total",
"sensitivity",
"indices",
")"
] |
def analyze_results(self, log=True):
"""
Obtain the results: the prediction mean and variance, as well as the global sensitivity indices
Returns a tuple containing the following statistics
(mean species mole fractions, variance, covariance, main sensitivity indices, total sensitivity indices)
"""
# Compute the mean and variance for each of the uncertain parameters
mean = np.array(self.pce.Mean())
var = np.array(self.pce.Variance())
stddev = np.sqrt(var)
stddev_percent = stddev / mean * 100.0
cov = self.pce.Covariance()
# Extract the global sensitivity indices
main_sens = np.array(self.pce.MainSensitivity())
total_sens = np.array(self.pce.TotalSensitivity())
output = ''
for i in range(self.reactor_mod.num_conditions):
output += """============================================================
Condition {0}
------------------------------------------------------------
{1!s}
============================================================
Condition {0} {2}Mole Fractions
------------------------------------------------------------
Species Mean Stddev Stddev (%)
------------------------------------------------------------
""".format(i + 1, self.reactor_mod.cantera.conditions[i], 'Log ' if self.logx else '')
for j, output_species in enumerate(self.reactor_mod.output_species_list):
output_index = i * self.reactor_mod.num_output_species + j
output += '{0:<15}{1:>15.3e}{2:>15.3e}{3:>15.3f}\n'.format(output_species.to_chemkin(),
mean[output_index],
stddev[output_index],
stddev_percent[output_index])
output += '============================================================\n\n'
if self.reactor_mod.k_params:
output += """====================================================================================================
Condition {0} Reaction Rate Sensitivity Indices
----------------------------------------------------------------------------------------------------
Description sens_main sens_total
""".format(i + 1)
for j, output_species in enumerate(self.reactor_mod.output_species_list):
output += '----------------------------------------------------------------------------------------------------\n'
output_index = i * self.reactor_mod.num_output_species + j
for k, descriptor in enumerate(self.reactor_mod.k_params):
parameter_index = k
if not self.reactor_mod.correlated:
description = 'dln[{0}]/dln[{1}]'.format(output_species.to_chemkin(),
self.reactor_mod.cantera.reaction_list[descriptor].to_chemkin(kinetics=False))
else:
description = 'dln[{0}]/dln[{1}]'.format(output_species.to_chemkin(), descriptor)
output += '{0:<70}{1:>14.3f}%{2:>14.3f}%\n'.format(description,
100 * main_sens[output_index][parameter_index],
100 * total_sens[output_index][parameter_index])
output += '====================================================================================================\n\n'
if self.reactor_mod.g_params:
output += """====================================================================================================
Condition {0} Thermochemistry Sensitivity Indices
----------------------------------------------------------------------------------------------------
Description sens_main sens_total
""".format(i + 1)
for j, output_species in enumerate(self.reactor_mod.output_species_list):
output += '----------------------------------------------------------------------------------------------------\n'
output_index = i * self.reactor_mod.num_output_species + j
for g, descriptor in enumerate(self.reactor_mod.g_params):
parameter_index = len(self.reactor_mod.k_params) + g
if not self.reactor_mod.correlated:
description = 'dln[{0}]/dG[{1}]'.format(output_species.to_chemkin(),
self.reactor_mod.cantera.species_list[descriptor].to_chemkin())
else:
description = 'dln[{0}]/dG[{1}]'.format(output_species.to_chemkin(), descriptor)
output += '{0:<70}{1:>14.3f}%{2:>14.3f}%\n'.format(description,
100 * main_sens[output_index][parameter_index],
100 * total_sens[output_index][parameter_index])
output += '====================================================================================================\n\n'
if log:
logging.info(output)
else:
print(output)
return mean, var, cov, main_sens, total_sens
|
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"'====================================================================================================\\n\\n'",
"if",
"log",
":",
"logging",
".",
"info",
"(",
"output",
")",
"else",
":",
"print",
"(",
"output",
")",
"return",
"mean",
",",
"var",
",",
"cov",
",",
"main_sens",
",",
"total_sens"
] |
https://github.com/ReactionMechanismGenerator/RMG-Py/blob/2b7baf51febf27157def58fb3f6cee03fb6a684c/rmgpy/tools/globaluncertainty.py#L417-L508
|
|
biolab/orange2
|
db40a9449cb45b507d63dcd5739b223f9cffb8e6
|
Orange/evaluation/scoring.py
|
python
|
split_by_iterations
|
(res)
|
return ress
|
Split ExperimentResults of a multiple iteratation test into a list
of ExperimentResults, one for each iteration.
|
Split ExperimentResults of a multiple iteratation test into a list
of ExperimentResults, one for each iteration.
|
[
"Split",
"ExperimentResults",
"of",
"a",
"multiple",
"iteratation",
"test",
"into",
"a",
"list",
"of",
"ExperimentResults",
"one",
"for",
"each",
"iteration",
"."
] |
def split_by_iterations(res):
"""Split ExperimentResults of a multiple iteratation test into a list
of ExperimentResults, one for each iteration.
"""
if res.number_of_iterations < 2:
return [res]
ress = [Orange.evaluation.testing.ExperimentResults(
1, res.classifier_names, res.class_values,
res.weights, classifiers=res.classifiers,
loaded=res.loaded, test_type=res.test_type, labels=res.labels)
for _ in range(res.number_of_iterations)]
for te in res.results:
ress[te.iteration_number].results.append(te)
return ress
|
[
"def",
"split_by_iterations",
"(",
"res",
")",
":",
"if",
"res",
".",
"number_of_iterations",
"<",
"2",
":",
"return",
"[",
"res",
"]",
"ress",
"=",
"[",
"Orange",
".",
"evaluation",
".",
"testing",
".",
"ExperimentResults",
"(",
"1",
",",
"res",
".",
"classifier_names",
",",
"res",
".",
"class_values",
",",
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"weights",
",",
"classifiers",
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".",
"classifiers",
",",
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"loaded",
",",
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",",
"labels",
"=",
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"labels",
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")",
"]",
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":",
"ress",
"[",
"te",
".",
"iteration_number",
"]",
".",
"results",
".",
"append",
"(",
"te",
")",
"return",
"ress"
] |
https://github.com/biolab/orange2/blob/db40a9449cb45b507d63dcd5739b223f9cffb8e6/Orange/evaluation/scoring.py#L84-L98
|
|
JiYou/openstack
|
8607dd488bde0905044b303eb6e52bdea6806923
|
packages/source/nova/nova/virt/xenapi/driver.py
|
python
|
XenAPIDriver.inject_file
|
(self, instance, b64_path, b64_contents)
|
Create a file on the VM instance. The file path and contents
should be base64-encoded.
|
Create a file on the VM instance. The file path and contents
should be base64-encoded.
|
[
"Create",
"a",
"file",
"on",
"the",
"VM",
"instance",
".",
"The",
"file",
"path",
"and",
"contents",
"should",
"be",
"base64",
"-",
"encoded",
"."
] |
def inject_file(self, instance, b64_path, b64_contents):
"""Create a file on the VM instance. The file path and contents
should be base64-encoded.
"""
self._vmops.inject_file(instance, b64_path, b64_contents)
|
[
"def",
"inject_file",
"(",
"self",
",",
"instance",
",",
"b64_path",
",",
"b64_contents",
")",
":",
"self",
".",
"_vmops",
".",
"inject_file",
"(",
"instance",
",",
"b64_path",
",",
"b64_contents",
")"
] |
https://github.com/JiYou/openstack/blob/8607dd488bde0905044b303eb6e52bdea6806923/packages/source/nova/nova/virt/xenapi/driver.py#L212-L216
|
||
openshift/openshift-tools
|
1188778e728a6e4781acf728123e5b356380fe6f
|
openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_route.py
|
python
|
OpenShiftCLI._create
|
(self, fname)
|
return self.openshift_cmd(['create', '-f', fname])
|
call oc create on a filename
|
call oc create on a filename
|
[
"call",
"oc",
"create",
"on",
"a",
"filename"
] |
def _create(self, fname):
'''call oc create on a filename'''
return self.openshift_cmd(['create', '-f', fname])
|
[
"def",
"_create",
"(",
"self",
",",
"fname",
")",
":",
"return",
"self",
".",
"openshift_cmd",
"(",
"[",
"'create'",
",",
"'-f'",
",",
"fname",
"]",
")"
] |
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_openshift/library/oc_route.py#L1003-L1005
|
|
scikit-learn/scikit-learn
|
1d1aadd0711b87d2a11c80aad15df6f8cf156712
|
examples/svm/plot_iris_svc.py
|
python
|
plot_contours
|
(ax, clf, xx, yy, **params)
|
return out
|
Plot the decision boundaries for a classifier.
Parameters
----------
ax: matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
|
Plot the decision boundaries for a classifier.
|
[
"Plot",
"the",
"decision",
"boundaries",
"for",
"a",
"classifier",
"."
] |
def plot_contours(ax, clf, xx, yy, **params):
"""Plot the decision boundaries for a classifier.
Parameters
----------
ax: matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
"""
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
out = ax.contourf(xx, yy, Z, **params)
return out
|
[
"def",
"plot_contours",
"(",
"ax",
",",
"clf",
",",
"xx",
",",
"yy",
",",
"*",
"*",
"params",
")",
":",
"Z",
"=",
"clf",
".",
"predict",
"(",
"np",
".",
"c_",
"[",
"xx",
".",
"ravel",
"(",
")",
",",
"yy",
".",
"ravel",
"(",
")",
"]",
")",
"Z",
"=",
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".",
"reshape",
"(",
"xx",
".",
"shape",
")",
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"ax",
".",
"contourf",
"(",
"xx",
",",
"yy",
",",
"Z",
",",
"*",
"*",
"params",
")",
"return",
"out"
] |
https://github.com/scikit-learn/scikit-learn/blob/1d1aadd0711b87d2a11c80aad15df6f8cf156712/examples/svm/plot_iris_svc.py#L61-L75
|
|
digidotcom/xbee-python
|
0757f4be0017530c205175fbee8f9f61be9614d1
|
digi/xbee/devices.py
|
python
|
DigiMeshNetwork.__init__
|
(self, device)
|
Class constructor. Instantiates a new `DigiMeshNetwork`.
Args:
device (:class:`.DigiMeshDevice`): Local DigiMesh node to get the
network from.
Raises:
ValueError: If `device` is `None`.
|
Class constructor. Instantiates a new `DigiMeshNetwork`.
|
[
"Class",
"constructor",
".",
"Instantiates",
"a",
"new",
"DigiMeshNetwork",
"."
] |
def __init__(self, device):
"""
Class constructor. Instantiates a new `DigiMeshNetwork`.
Args:
device (:class:`.DigiMeshDevice`): Local DigiMesh node to get the
network from.
Raises:
ValueError: If `device` is `None`.
"""
super().__init__(device)
self.__saved_no = None
self.__saved_so = None
self.__sync_sleep_enabled = False
# Calculated timeout based on the 'N?' value of the local XBee and the
# sleep configuration of the network.
self.__real_node_timeout = None
# Dictionary to store the neighbor find processes per node, so they
# can be stop when required.
# The dictionary uses as key the 64-bit address string representation (to be thread-safe)
self.__neighbor_finders = {}
|
[
"def",
"__init__",
"(",
"self",
",",
"device",
")",
":",
"super",
"(",
")",
".",
"__init__",
"(",
"device",
")",
"self",
".",
"__saved_no",
"=",
"None",
"self",
".",
"__saved_so",
"=",
"None",
"self",
".",
"__sync_sleep_enabled",
"=",
"False",
"# Calculated timeout based on the 'N?' value of the local XBee and the",
"# sleep configuration of the network.",
"self",
".",
"__real_node_timeout",
"=",
"None",
"# Dictionary to store the neighbor find processes per node, so they",
"# can be stop when required.",
"# The dictionary uses as key the 64-bit address string representation (to be thread-safe)",
"self",
".",
"__neighbor_finders",
"=",
"{",
"}"
] |
https://github.com/digidotcom/xbee-python/blob/0757f4be0017530c205175fbee8f9f61be9614d1/digi/xbee/devices.py#L11620-L11645
|
||
JacquesLucke/animation_nodes
|
b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1
|
animation_nodes/auto_load.py
|
python
|
iter_my_classes
|
(modules)
|
[] |
def iter_my_classes(modules):
base_types = get_register_base_types()
for cls in get_classes_in_modules(modules):
if any(base in base_types for base in cls.__bases__):
if not getattr(cls, "is_registered", False):
yield cls
|
[
"def",
"iter_my_classes",
"(",
"modules",
")",
":",
"base_types",
"=",
"get_register_base_types",
"(",
")",
"for",
"cls",
"in",
"get_classes_in_modules",
"(",
"modules",
")",
":",
"if",
"any",
"(",
"base",
"in",
"base_types",
"for",
"base",
"in",
"cls",
".",
"__bases__",
")",
":",
"if",
"not",
"getattr",
"(",
"cls",
",",
"\"is_registered\"",
",",
"False",
")",
":",
"yield",
"cls"
] |
https://github.com/JacquesLucke/animation_nodes/blob/b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1/animation_nodes/auto_load.py#L106-L111
|
||||
shlomif/PySolFC
|
780c399e6f68a95916d84e7e88a067e8fcbec1cc
|
pysollib/game/__init__.py
|
python
|
Game._calcMouseBind
|
(self, binding_format)
|
return self.app.opt.calcCustomMouseButtonsBinding(binding_format)
|
docstring for _calcMouseBind
|
docstring for _calcMouseBind
|
[
"docstring",
"for",
"_calcMouseBind"
] |
def _calcMouseBind(self, binding_format):
"""docstring for _calcMouseBind"""
return self.app.opt.calcCustomMouseButtonsBinding(binding_format)
|
[
"def",
"_calcMouseBind",
"(",
"self",
",",
"binding_format",
")",
":",
"return",
"self",
".",
"app",
".",
"opt",
".",
"calcCustomMouseButtonsBinding",
"(",
"binding_format",
")"
] |
https://github.com/shlomif/PySolFC/blob/780c399e6f68a95916d84e7e88a067e8fcbec1cc/pysollib/game/__init__.py#L677-L679
|
|
bgshih/aster
|
0c74b9b9cd22a021c1d57b75a3041bcbd7ace3cc
|
trainer.py
|
python
|
train
|
(create_tensor_dict_fn_list, create_model_fn, train_config, master, task,
num_clones, worker_replicas, clone_on_cpu, ps_tasks, worker_job_name,
is_chief, train_dir)
|
Training function for models.
Args:
create_tensor_dict_fn: a function to create a tensor input dictionary.
create_model_fn: a function that creates a DetectionModel and generates
losses.
train_config: a train_pb2.TrainConfig protobuf.
master: BNS name of the TensorFlow master to use.
task: The task id of this training instance.
num_clones: The number of clones to run per machine.
worker_replicas: The number of work replicas to train with.
clone_on_cpu: True if clones should be forced to run on CPU.
ps_tasks: Number of parameter server tasks.
worker_job_name: Name of the worker job.
is_chief: Whether this replica is the chief replica.
train_dir: Directory to write checkpoints and training summaries to.
|
Training function for models.
Args:
create_tensor_dict_fn: a function to create a tensor input dictionary.
create_model_fn: a function that creates a DetectionModel and generates
losses.
train_config: a train_pb2.TrainConfig protobuf.
master: BNS name of the TensorFlow master to use.
task: The task id of this training instance.
num_clones: The number of clones to run per machine.
worker_replicas: The number of work replicas to train with.
clone_on_cpu: True if clones should be forced to run on CPU.
ps_tasks: Number of parameter server tasks.
worker_job_name: Name of the worker job.
is_chief: Whether this replica is the chief replica.
train_dir: Directory to write checkpoints and training summaries to.
|
[
"Training",
"function",
"for",
"models",
".",
"Args",
":",
"create_tensor_dict_fn",
":",
"a",
"function",
"to",
"create",
"a",
"tensor",
"input",
"dictionary",
".",
"create_model_fn",
":",
"a",
"function",
"that",
"creates",
"a",
"DetectionModel",
"and",
"generates",
"losses",
".",
"train_config",
":",
"a",
"train_pb2",
".",
"TrainConfig",
"protobuf",
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"master",
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"BNS",
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"of",
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"TensorFlow",
"master",
"to",
"use",
".",
"task",
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"The",
"task",
"id",
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"training",
"instance",
".",
"num_clones",
":",
"The",
"number",
"of",
"clones",
"to",
"run",
"per",
"machine",
".",
"worker_replicas",
":",
"The",
"number",
"of",
"work",
"replicas",
"to",
"train",
"with",
".",
"clone_on_cpu",
":",
"True",
"if",
"clones",
"should",
"be",
"forced",
"to",
"run",
"on",
"CPU",
".",
"ps_tasks",
":",
"Number",
"of",
"parameter",
"server",
"tasks",
".",
"worker_job_name",
":",
"Name",
"of",
"the",
"worker",
"job",
".",
"is_chief",
":",
"Whether",
"this",
"replica",
"is",
"the",
"chief",
"replica",
".",
"train_dir",
":",
"Directory",
"to",
"write",
"checkpoints",
"and",
"training",
"summaries",
"to",
"."
] |
def train(create_tensor_dict_fn_list, create_model_fn, train_config, master, task,
num_clones, worker_replicas, clone_on_cpu, ps_tasks, worker_job_name,
is_chief, train_dir):
"""Training function for models.
Args:
create_tensor_dict_fn: a function to create a tensor input dictionary.
create_model_fn: a function that creates a DetectionModel and generates
losses.
train_config: a train_pb2.TrainConfig protobuf.
master: BNS name of the TensorFlow master to use.
task: The task id of this training instance.
num_clones: The number of clones to run per machine.
worker_replicas: The number of work replicas to train with.
clone_on_cpu: True if clones should be forced to run on CPU.
ps_tasks: Number of parameter server tasks.
worker_job_name: Name of the worker job.
is_chief: Whether this replica is the chief replica.
train_dir: Directory to write checkpoints and training summaries to.
"""
data_augmentation_options = [
preprocessor_builder.build(step)
for step in train_config.data_augmentation_options
]
with tf.Graph().as_default():
# Build a configuration specifying multi-GPU and multi-replicas.
deploy_config = model_deploy.DeploymentConfig(
num_clones=num_clones,
clone_on_cpu=clone_on_cpu,
replica_id=task,
num_replicas=worker_replicas,
num_ps_tasks=ps_tasks,
worker_job_name=worker_job_name
)
# Place the global step on the device storing the variables.
with tf.device(deploy_config.variables_device()):
global_step = tf.train.create_global_step()
with tf.device(deploy_config.inputs_device()), \
tf.name_scope('Input'):
input_queue_list = []
for i, create_tensor_dict_fn in enumerate(create_tensor_dict_fn_list):
input_queue_list.append(_create_input_queue(
train_config.batch_size[i] // num_clones,
create_tensor_dict_fn,
train_config.batch_queue_capacity,
train_config.num_batch_queue_threads,
train_config.prefetch_queue_capacity,
data_augmentation_options
))
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
global_summaries = set([])
model_fn = functools.partial(_create_losses, create_model_fn=create_model_fn)
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue_list])
first_clone_scope = clones[0].scope
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
with tf.device(deploy_config.optimizer_device()), \
tf.name_scope('Optimizer'):
training_optimizer = optimizer_builder.build(
train_config.optimizer,
global_summaries
)
sync_optimizer = None
if train_config.sync_replicas:
training_optimizer = tf.train.SyncReplicasOptimizer(
training_optimizer,
replicas_to_aggregate=train_config.replicas_to_aggregate,
total_num_replicas=train_config.worker_replicas
)
sync_optimizer = training_optimizer
# Create ops required to initialize the model from a given checkpoint.
init_fn = None
if train_config.fine_tune_checkpoint:
var_map = detection_model.restore_map(
from_detection_checkpoint=train_config.from_detection_checkpoint
)
available_var_map = variables_helper.get_variables_available_in_checkpoint(
var_map,
train_config.fine_tune_checkpoint
)
init_saver = tf.train.Saver(available_var_map)
def initializer_fn(sess):
init_saver.restore(sess, train_config.fine_tune_checkpoint)
init_fn = initializer_fn
with tf.device(deploy_config.optimizer_device()), \
tf.variable_scope('OptimizeClones'):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones,
training_optimizer,
regularization_losses=None
)
total_loss = tf.check_numerics(total_loss, 'LossTensor is inf or nan.')
# Optionally multiply bias gradients by train_config.bias_grad_multiplier.
if train_config.bias_grad_multiplier:
biases_regex_list = [r'.*bias(?:es)?', r'.*beta']
grads_and_vars = variables_helper.multiply_gradients_matching_regex(
grads_and_vars,
biases_regex_list,
multiplier=train_config.bias_grad_multiplier
)
# Optionally freeze some layers by setting their gradients to be zero.
if train_config.freeze_variables:
grads_and_vars = variables_helper.freeze_gradients_matching_regex(
grads_and_vars, train_config.freeze_variables
)
# Optionally clip gradients
if train_config.gradient_clipping_by_norm > 0:
with tf.name_scope('clip_grads'):
grads_and_vars = tf.contrib.training.clip_gradient_norms(
grads_and_vars, train_config.gradient_clipping_by_norm
)
# Create gradient updates.
grad_updates = training_optimizer.apply_gradients(
grads_and_vars,
global_step=global_step
)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add summaries.
for (grad, var) in grads_and_vars:
var_name = var.op.name
grad_name = 'grad/' + var_name
global_summaries.add(tf.summary.histogram(grad_name, grad))
global_summaries.add(tf.summary.histogram(var_name, var))
# for model_var in tf.contrib.framework.get_model_variables():
# global_summaries.add(tf.summary.histogram(model_var.op.name, model_var))
for loss_tensor in tf.losses.get_losses():
global_summaries.add(tf.summary.scalar(loss_tensor.op.name, loss_tensor))
global_summaries.add(
tf.summary.scalar('TotalLoss', tf.losses.get_total_loss())
)
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
summaries |= global_summaries
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
# Save checkpoints regularly.
keep_checkpoint_every_n_hours = train_config.keep_checkpoint_every_n_hours
saver = tf.train.Saver(
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours
)
scaffold = tf.train.Scaffold(
init_fn=init_fn,
summary_op=summary_op,
saver=saver
)
stop_hook = tf.train.StopAtStepHook(
num_steps=(train_config.num_steps if train_config.num_steps else None),
)
profile_hook = profile_session_run_hooks.ProfileAtStepHook(
at_step=200,
checkpoint_dir=train_dir)
tf.contrib.training.train(
train_tensor,
train_dir,
master=master,
is_chief=is_chief,
scaffold=scaffold,
hooks=[stop_hook, profile_hook],
chief_only_hooks=None,
save_checkpoint_secs=train_config.save_checkpoint_secs,
save_summaries_steps=train_config.save_summaries_steps,
config=session_config
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https://github.com/bgshih/aster/blob/0c74b9b9cd22a021c1d57b75a3041bcbd7ace3cc/trainer.py#L77-L269
|
||
worthwhile/django-herald
|
c0b0f4e894b26ec46647d1c70a6a62a89f34b06a
|
herald/base.py
|
python
|
NotificationBase.get_extra_data
|
(self)
|
return {}
|
Returns a dictionary of extra data to be stored, and used for sending.
MUST BE JSON SERIALIZABLE
|
Returns a dictionary of extra data to be stored, and used for sending.
MUST BE JSON SERIALIZABLE
|
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"MUST",
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"JSON",
"SERIALIZABLE"
] |
def get_extra_data(self):
"""
Returns a dictionary of extra data to be stored, and used for sending.
MUST BE JSON SERIALIZABLE
"""
return {}
|
[
"def",
"get_extra_data",
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"self",
")",
":",
"return",
"{",
"}"
] |
https://github.com/worthwhile/django-herald/blob/c0b0f4e894b26ec46647d1c70a6a62a89f34b06a/herald/base.py#L140-L146
|
|
smicallef/spiderfoot
|
fd4bf9394c9ab3ecc90adc3115c56349fb23165b
|
modules/sfp_subdomain_takeover.py
|
python
|
sfp_subdomain_takeover.watchedEvents
|
(self)
|
return ["AFFILIATE_INTERNET_NAME", "AFFILIATE_INTERNET_NAME_UNRESOLVED"]
|
[] |
def watchedEvents(self):
return ["AFFILIATE_INTERNET_NAME", "AFFILIATE_INTERNET_NAME_UNRESOLVED"]
|
[
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"[",
"\"AFFILIATE_INTERNET_NAME\"",
",",
"\"AFFILIATE_INTERNET_NAME_UNRESOLVED\"",
"]"
] |
https://github.com/smicallef/spiderfoot/blob/fd4bf9394c9ab3ecc90adc3115c56349fb23165b/modules/sfp_subdomain_takeover.py#L72-L73
|
|||
missionpinball/mpf
|
8e6b74cff4ba06d2fec9445742559c1068b88582
|
mpf/core/ball_controller.py
|
python
|
BallController._collecting_balls_complete
|
(self)
|
event: collecting_balls_complete
desc: Posted by the ball controller when it has finished the collecting
balls process.
|
event: collecting_balls_complete
|
[
"event",
":",
"collecting_balls_complete"
] |
def _collecting_balls_complete(self) -> None:
self.machine.events.remove_handler(self._collecting_balls_entered_callback)
self.machine.events.post('collecting_balls_complete')
'''event: collecting_balls_complete
desc: Posted by the ball controller when it has finished the collecting
balls process.
'''
|
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https://github.com/missionpinball/mpf/blob/8e6b74cff4ba06d2fec9445742559c1068b88582/mpf/core/ball_controller.py#L354-L362
|
||
deepfakes/faceswap
|
09c7d8aca3c608d1afad941ea78e9fd9b64d9219
|
lib/gui/custom_widgets.py
|
python
|
ContextMenu.cm_bind
|
(self)
|
Bind the menu to the given widgets Right Click event
After associating a widget with this :class:`ContextMenu` this function should be called
to bind it to the right click button
|
Bind the menu to the given widgets Right Click event
|
[
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"event"
] |
def cm_bind(self):
""" Bind the menu to the given widgets Right Click event
After associating a widget with this :class:`ContextMenu` this function should be called
to bind it to the right click button
"""
button = "<Button-2>" if platform.system() == "Darwin" else "<Button-3>"
logger.debug("Binding '%s' to '%s'", button, self._widget.winfo_class())
self._widget.bind(button, lambda event: self.tk_popup(event.x_root, event.y_root))
|
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https://github.com/deepfakes/faceswap/blob/09c7d8aca3c608d1afad941ea78e9fd9b64d9219/lib/gui/custom_widgets.py#L53-L61
|
||
mgrankin/ru_transformers
|
e698092ed31b340dc39ecea26f1b0be87cdbd022
|
sp_encoder.py
|
python
|
SPEncoder.convert_tokens_to_ids
|
(self, tokens)
|
return [self.sp.PieceToId(token) for token in tokens]
|
[] |
def convert_tokens_to_ids(self, tokens):
return [self.sp.PieceToId(token) for token in tokens]
|
[
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] |
https://github.com/mgrankin/ru_transformers/blob/e698092ed31b340dc39ecea26f1b0be87cdbd022/sp_encoder.py#L53-L54
|
|||
mozillazg/pypy
|
2ff5cd960c075c991389f842c6d59e71cf0cb7d0
|
rpython/annotator/classdesc.py
|
python
|
ClassDesc.mergeattrfamilies
|
(self, others, attrname)
|
return changed
|
Merge the attr families of the given Descs into one.
|
Merge the attr families of the given Descs into one.
|
[
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"the",
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"into",
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"."
] |
def mergeattrfamilies(self, others, attrname):
"""Merge the attr families of the given Descs into one."""
access_sets = self.bookkeeper.get_classpbc_attr_families(attrname)
changed, rep, attrfamily = access_sets.find(self)
for desc in others:
changed1, rep, attrfamily = access_sets.union(rep, desc)
changed = changed or changed1
return changed
|
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https://github.com/mozillazg/pypy/blob/2ff5cd960c075c991389f842c6d59e71cf0cb7d0/rpython/annotator/classdesc.py#L935-L942
|
|
openstack/nova
|
b49b7663e1c3073917d5844b81d38db8e86d05c4
|
nova/scheduler/utils.py
|
python
|
_log_compute_error
|
(instance_uuid, retry)
|
If the request contained an exception from a previous compute
build/resize operation, log it to aid debugging
|
If the request contained an exception from a previous compute
build/resize operation, log it to aid debugging
|
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"debugging"
] |
def _log_compute_error(instance_uuid, retry):
"""If the request contained an exception from a previous compute
build/resize operation, log it to aid debugging
"""
exc = retry.get('exc') # string-ified exception from compute
if not exc:
return # no exception info from a previous attempt, skip
hosts = retry.get('hosts', None)
if not hosts:
return # no previously attempted hosts, skip
last_host, last_node = hosts[-1]
LOG.error(
'Error from last host: %(last_host)s (node %(last_node)s): %(exc)s',
{'last_host': last_host, 'last_node': last_node, 'exc': exc},
instance_uuid=instance_uuid)
|
[
"def",
"_log_compute_error",
"(",
"instance_uuid",
",",
"retry",
")",
":",
"exc",
"=",
"retry",
".",
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"(",
"'exc'",
")",
"# string-ified exception from compute",
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":",
"return",
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".",
"get",
"(",
"'hosts'",
",",
"None",
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"hosts",
":",
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"# no previously attempted hosts, skip",
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"[",
"-",
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"'Error from last host: %(last_host)s (node %(last_node)s): %(exc)s'",
",",
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"'last_host'",
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":",
"last_node",
",",
"'exc'",
":",
"exc",
"}",
",",
"instance_uuid",
"=",
"instance_uuid",
")"
] |
https://github.com/openstack/nova/blob/b49b7663e1c3073917d5844b81d38db8e86d05c4/nova/scheduler/utils.py#L992-L1008
|
||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/celery/backends/redis.py
|
python
|
RedisBackend.incr
|
(self, key)
|
return self.client.incr(key)
|
[] |
def incr(self, key):
return self.client.incr(key)
|
[
"def",
"incr",
"(",
"self",
",",
"key",
")",
":",
"return",
"self",
".",
"client",
".",
"incr",
"(",
"key",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/celery/backends/redis.py#L218-L219
|
|||
flairNLP/flair
|
b774774752c8338aab3d620f7e5062f66ec7a69d
|
flair/datasets/entity_linking.py
|
python
|
WSD_MASC.__init__
|
(
self,
base_path: Union[str, Path] = None,
in_memory: bool = True,
columns={0: "text", 3: "wn30_key"},
tag_to_bioes=None,
label_name_map: Dict[str, str] = None,
banned_sentences: List[str] = None,
sample_missing_splits: Union[bool, str] = True,
cut_multisense: bool = True,
use_raganato_ALL_as_test_data: bool = False,
)
|
Initialize MASC (Manually Annotated Sub-Corpus) provided in UFSAC https://github.com/getalp/UFSAC
When first initializing the corpus the whole UFSAC data is downloaded.
|
Initialize MASC (Manually Annotated Sub-Corpus) provided in UFSAC https://github.com/getalp/UFSAC
When first initializing the corpus the whole UFSAC data is downloaded.
|
[
"Initialize",
"MASC",
"(",
"Manually",
"Annotated",
"Sub",
"-",
"Corpus",
")",
"provided",
"in",
"UFSAC",
"https",
":",
"//",
"github",
".",
"com",
"/",
"getalp",
"/",
"UFSAC",
"When",
"first",
"initializing",
"the",
"corpus",
"the",
"whole",
"UFSAC",
"data",
"is",
"downloaded",
"."
] |
def __init__(
self,
base_path: Union[str, Path] = None,
in_memory: bool = True,
columns={0: "text", 3: "wn30_key"},
tag_to_bioes=None,
label_name_map: Dict[str, str] = None,
banned_sentences: List[str] = None,
sample_missing_splits: Union[bool, str] = True,
cut_multisense: bool = True,
use_raganato_ALL_as_test_data: bool = False,
):
"""
Initialize MASC (Manually Annotated Sub-Corpus) provided in UFSAC https://github.com/getalp/UFSAC
When first initializing the corpus the whole UFSAC data is downloaded.
"""
if not base_path:
base_path = flair.cache_root / "datasets"
else:
base_path = Path(base_path)
dataset_name = "wsd_ufsac"
# default dataset folder is the cache root
data_folder = base_path / dataset_name
original_data_folder = data_folder / "original_data"
# We check if the the UFSAC data has already been downloaded. If not, we download it.
# Note that this downloads more datasets than just MASC. But the size of the download is only around 190 Mb (around 4.5 Gb unpacked)
if not original_data_folder.exists():
# create folder
data_folder.mkdir(parents=True)
# download data
import gdown
url = "https://drive.google.com/uc?id=1Oigo3kzRosz2VjyA44vpJZ58tDFyLRMO"
output = data_folder / (dataset_name + ".tar")
gdown.download(url, str(output), quiet=False)
output = data_folder / (dataset_name + ".tar")
unpack_file(file=output, unpack_to=data_folder, mode="tar", keep=False)
os.rename(data_folder / "ufsac-public-2.1", original_data_folder)
if use_raganato_ALL_as_test_data:
# in this case no test data should be generated by sampling from train data. But if sample_missing_splits is true, the dev set will be sampled.
if sample_missing_splits:
sample_missing_splits = "only_dev"
# generate the test file
test_file = determine_tsv_file(
filename="raganato_ALL",
data_folder=data_folder,
cut_multisense=cut_multisense,
)
else:
test_file = None
train_file = determine_tsv_file(filename="masc", data_folder=data_folder, cut_multisense=cut_multisense)
super(WSD_MASC, self).__init__(
data_folder=data_folder,
columns=columns,
train_file=train_file,
test_file=test_file,
in_memory=in_memory,
document_separator_token="-DOCSTART-",
column_delimiter="\t",
autofind_splits=False,
tag_to_bioes=tag_to_bioes,
label_name_map=label_name_map,
banned_sentences=banned_sentences,
sample_missing_splits=sample_missing_splits,
)
|
[
"def",
"__init__",
"(",
"self",
",",
"base_path",
":",
"Union",
"[",
"str",
",",
"Path",
"]",
"=",
"None",
",",
"in_memory",
":",
"bool",
"=",
"True",
",",
"columns",
"=",
"{",
"0",
":",
"\"text\"",
",",
"3",
":",
"\"wn30_key\"",
"}",
",",
"tag_to_bioes",
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"None",
",",
"label_name_map",
":",
"Dict",
"[",
"str",
",",
"str",
"]",
"=",
"None",
",",
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":",
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"=",
"False",
",",
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":",
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"/",
"\"datasets\"",
"else",
":",
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"Path",
"(",
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")",
"dataset_name",
"=",
"\"wsd_ufsac\"",
"# default dataset folder is the cache root",
"data_folder",
"=",
"base_path",
"/",
"dataset_name",
"original_data_folder",
"=",
"data_folder",
"/",
"\"original_data\"",
"# We check if the the UFSAC data has already been downloaded. If not, we download it.",
"# Note that this downloads more datasets than just MASC. But the size of the download is only around 190 Mb (around 4.5 Gb unpacked)",
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"not",
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".",
"exists",
"(",
")",
":",
"# create folder",
"data_folder",
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"mkdir",
"(",
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"True",
")",
"# download data",
"import",
"gdown",
"url",
"=",
"\"https://drive.google.com/uc?id=1Oigo3kzRosz2VjyA44vpJZ58tDFyLRMO\"",
"output",
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"/",
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"+",
"\".tar\"",
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",",
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"+",
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"(",
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",",
"unpack_to",
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"data_folder",
",",
"mode",
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"\"tar\"",
",",
"keep",
"=",
"False",
")",
"os",
".",
"rename",
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"data_folder",
"/",
"\"ufsac-public-2.1\"",
",",
"original_data_folder",
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"if",
"use_raganato_ALL_as_test_data",
":",
"# in this case no test data should be generated by sampling from train data. But if sample_missing_splits is true, the dev set will be sampled.",
"if",
"sample_missing_splits",
":",
"sample_missing_splits",
"=",
"\"only_dev\"",
"# generate the test file",
"test_file",
"=",
"determine_tsv_file",
"(",
"filename",
"=",
"\"raganato_ALL\"",
",",
"data_folder",
"=",
"data_folder",
",",
"cut_multisense",
"=",
"cut_multisense",
",",
")",
"else",
":",
"test_file",
"=",
"None",
"train_file",
"=",
"determine_tsv_file",
"(",
"filename",
"=",
"\"masc\"",
",",
"data_folder",
"=",
"data_folder",
",",
"cut_multisense",
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")",
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"(",
"WSD_MASC",
",",
"self",
")",
".",
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"(",
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"=",
"data_folder",
",",
"columns",
"=",
"columns",
",",
"train_file",
"=",
"train_file",
",",
"test_file",
"=",
"test_file",
",",
"in_memory",
"=",
"in_memory",
",",
"document_separator_token",
"=",
"\"-DOCSTART-\"",
",",
"column_delimiter",
"=",
"\"\\t\"",
",",
"autofind_splits",
"=",
"False",
",",
"tag_to_bioes",
"=",
"tag_to_bioes",
",",
"label_name_map",
"=",
"label_name_map",
",",
"banned_sentences",
"=",
"banned_sentences",
",",
"sample_missing_splits",
"=",
"sample_missing_splits",
",",
")"
] |
https://github.com/flairNLP/flair/blob/b774774752c8338aab3d620f7e5062f66ec7a69d/flair/datasets/entity_linking.py#L1617-L1693
|
||
007gzs/dingtalk-sdk
|
7979da2e259fdbc571728cae2425a04dbc65850a
|
dingtalk/client/api/taobao.py
|
python
|
TbWuDaoKou.alibaba_price_promotion_activity_query
|
(
self,
page,
outer_promotion_code,
page_size,
ou_code=''
)
|
return self._top_request(
"alibaba.price.promotion.activity.query",
{
"page": page,
"outer_promotion_code": outer_promotion_code,
"page_size": page_size,
"ou_code": ou_code
}
)
|
查询盒马帮档期活动详情
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=36865
:param page: 页码
:param outer_promotion_code: 外部档期code
:param page_size: 页码大小
:param ou_code: TOB店仓编码
|
查询盒马帮档期活动详情
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=36865
|
[
"查询盒马帮档期活动详情",
"文档地址：https",
":",
"//",
"open",
"-",
"doc",
".",
"dingtalk",
".",
"com",
"/",
"docs",
"/",
"api",
".",
"htm?apiId",
"=",
"36865"
] |
def alibaba_price_promotion_activity_query(
self,
page,
outer_promotion_code,
page_size,
ou_code=''
):
"""
查询盒马帮档期活动详情
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=36865
:param page: 页码
:param outer_promotion_code: 外部档期code
:param page_size: 页码大小
:param ou_code: TOB店仓编码
"""
return self._top_request(
"alibaba.price.promotion.activity.query",
{
"page": page,
"outer_promotion_code": outer_promotion_code,
"page_size": page_size,
"ou_code": ou_code
}
)
|
[
"def",
"alibaba_price_promotion_activity_query",
"(",
"self",
",",
"page",
",",
"outer_promotion_code",
",",
"page_size",
",",
"ou_code",
"=",
"''",
")",
":",
"return",
"self",
".",
"_top_request",
"(",
"\"alibaba.price.promotion.activity.query\"",
",",
"{",
"\"page\"",
":",
"page",
",",
"\"outer_promotion_code\"",
":",
"outer_promotion_code",
",",
"\"page_size\"",
":",
"page_size",
",",
"\"ou_code\"",
":",
"ou_code",
"}",
")"
] |
https://github.com/007gzs/dingtalk-sdk/blob/7979da2e259fdbc571728cae2425a04dbc65850a/dingtalk/client/api/taobao.py#L63810-L63834
|
|
eoyilmaz/stalker
|
a35c041b79d953d00dc2a09cf8206956ca269bef
|
stalker/models/shot.py
|
python
|
Shot._validate_fps
|
(self, fps)
|
return float(fps)
|
validates the given fps_in value
|
validates the given fps_in value
|
[
"validates",
"the",
"given",
"fps_in",
"value"
] |
def _validate_fps(self, fps):
"""validates the given fps_in value
"""
if fps is None:
# fps = self.project.fps
return None
if not isinstance(fps, (int, float)):
raise TypeError(
'%s.fps should be a positive float or int, not %s' % (
self.__class__.__name__,
fps.__class__.__name__
)
)
fps = float(fps)
if fps <= 0:
raise ValueError(
'%s.fps should be a positive float or int, not %s' %
(self.__class__.__name__, fps)
)
return float(fps)
|
[
"def",
"_validate_fps",
"(",
"self",
",",
"fps",
")",
":",
"if",
"fps",
"is",
"None",
":",
"# fps = self.project.fps",
"return",
"None",
"if",
"not",
"isinstance",
"(",
"fps",
",",
"(",
"int",
",",
"float",
")",
")",
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"TypeError",
"(",
"'%s.fps should be a positive float or int, not %s'",
"%",
"(",
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"__class__",
".",
"__name__",
",",
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"'%s.fps should be a positive float or int, not %s'",
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"__class__",
".",
"__name__",
",",
"fps",
")",
")",
"return",
"float",
"(",
"fps",
")"
] |
https://github.com/eoyilmaz/stalker/blob/a35c041b79d953d00dc2a09cf8206956ca269bef/stalker/models/shot.py#L350-L371
|
|
ukdtom/ExportTools.bundle
|
49aba4292a2897f640162a833c2792480aa4f0b6
|
Contents/Libraries/Shared/xlsxwriter/worksheet.py
|
python
|
Worksheet.set_print_scale
|
(self, scale)
|
Set the scale factor for the printed page.
Args:
scale: Print scale. 10 <= scale <= 400.
Returns:
Nothing.
|
Set the scale factor for the printed page.
|
[
"Set",
"the",
"scale",
"factor",
"for",
"the",
"printed",
"page",
"."
] |
def set_print_scale(self, scale):
"""
Set the scale factor for the printed page.
Args:
scale: Print scale. 10 <= scale <= 400.
Returns:
Nothing.
"""
# Confine the scale to Excel's range.
if scale < 10 or scale > 400:
warn("Print scale '%d' outside range: 10 <= scale <= 400" % scale)
return
# Turn off "fit to page" option when print scale is on.
self.fit_page = 0
self.print_scale = int(scale)
self.page_setup_changed = True
|
[
"def",
"set_print_scale",
"(",
"self",
",",
"scale",
")",
":",
"# Confine the scale to Excel's range.",
"if",
"scale",
"<",
"10",
"or",
"scale",
">",
"400",
":",
"warn",
"(",
"\"Print scale '%d' outside range: 10 <= scale <= 400\"",
"%",
"scale",
")",
"return",
"# Turn off \"fit to page\" option when print scale is on.",
"self",
".",
"fit_page",
"=",
"0",
"self",
".",
"print_scale",
"=",
"int",
"(",
"scale",
")",
"self",
".",
"page_setup_changed",
"=",
"True"
] |
https://github.com/ukdtom/ExportTools.bundle/blob/49aba4292a2897f640162a833c2792480aa4f0b6/Contents/Libraries/Shared/xlsxwriter/worksheet.py#L3481-L3501
|
||
pikpikcu/Pentest-Tools-Framework
|
cd6e6107764a809943dc4e073cde8149c1a2cd03
|
modules/xsser/core/curlcontrol.py
|
python
|
Curl.post
|
(self, cgi, params)
|
return self.__request(cgi)
|
Post a url.
|
Post a url.
|
[
"Post",
"a",
"url",
"."
] |
def post(self, cgi, params):
"""
Post a url.
"""
self.set_option(pycurl.POST, 1)
self.set_option(pycurl.POSTFIELDS, params)
return self.__request(cgi)
|
[
"def",
"post",
"(",
"self",
",",
"cgi",
",",
"params",
")",
":",
"self",
".",
"set_option",
"(",
"pycurl",
".",
"POST",
",",
"1",
")",
"self",
".",
"set_option",
"(",
"pycurl",
".",
"POSTFIELDS",
",",
"params",
")",
"return",
"self",
".",
"__request",
"(",
"cgi",
")"
] |
https://github.com/pikpikcu/Pentest-Tools-Framework/blob/cd6e6107764a809943dc4e073cde8149c1a2cd03/modules/xsser/core/curlcontrol.py#L420-L426
|
|
EnterpriseDB/barman
|
487bad92edec72712531ead4746fad72bb310270
|
barman/clients/cloud_backup_delete.py
|
python
|
main
|
(args=None)
|
The main script entry point
:param list[str] args: the raw arguments list. When not provided
it defaults to sys.args[1:]
|
The main script entry point
|
[
"The",
"main",
"script",
"entry",
"point"
] |
def main(args=None):
"""
The main script entry point
:param list[str] args: the raw arguments list. When not provided
it defaults to sys.args[1:]
"""
config = parse_arguments(args)
configure_logging(config)
try:
cloud_interface = get_cloud_interface(config)
with closing(cloud_interface):
if not cloud_interface.test_connectivity():
raise SystemExit(1)
# If test is requested, just exit after connectivity test
elif config.test:
raise SystemExit(0)
if not cloud_interface.bucket_exists:
logging.error("Bucket %s does not exist", cloud_interface.bucket_name)
raise SystemExit(1)
catalog = CloudBackupCatalog(
cloud_interface=cloud_interface, server_name=config.server_name
)
# Call catalog.get_backup_list now so we know we can read the whole catalog
# (the results are cached so this does not result in extra calls to cloud
# storage)
catalog.get_backup_list()
if len(catalog.unreadable_backups) > 0:
logging.error(
"Cannot read the following backups: %s\n"
"Unsafe to proceed with deletion due to failure reading backup catalog"
% catalog.unreadable_backups
)
raise SystemExit(1)
if config.backup_id:
# Because we only care about one backup, skip the annotation cache
# because it is only helpful when dealing with multiple backups
if catalog.should_keep_backup(config.backup_id, use_cache=False):
logging.error(
"Skipping delete of backup %s for server %s "
"as it has a current keep request. If you really "
"want to delete this backup please remove the keep "
"and try again.",
config.backup_id,
config.server_name,
)
raise SystemExit(1)
_delete_backup(
cloud_interface, catalog, config.backup_id, config.dry_run
)
elif config.retention_policy:
retention_policy = RetentionPolicyFactory.create(
"retention_policy",
config.retention_policy,
server_name=config.server_name,
catalog=catalog,
)
# Sort to ensure that we delete the backups in ascending order, that is
# from oldest to newest. This ensures that the relevant WALs will be cleaned
# up after each backup is deleted.
backups_to_delete = sorted(
[
backup_id
for backup_id, status in retention_policy.report().items()
if status == "OBSOLETE"
]
)
for backup_id in backups_to_delete:
_delete_backup(
cloud_interface,
catalog,
backup_id,
config.dry_run,
skip_wal_cleanup_if_standalone=False,
)
except Exception as exc:
logging.error("Barman cloud backup delete exception: %s", force_str(exc))
logging.debug("Exception details:", exc_info=exc)
raise SystemExit(1)
|
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] |
https://github.com/EnterpriseDB/barman/blob/487bad92edec72712531ead4746fad72bb310270/barman/clients/cloud_backup_delete.py#L196-L279
|
||
Nuitka/Nuitka
|
39262276993757fa4e299f497654065600453fc9
|
nuitka/build/inline_copy/lib/scons-2.3.2/SCons/Node/Python.py
|
python
|
Value.write
|
(self, built_value)
|
Set the value of the node.
|
Set the value of the node.
|
[
"Set",
"the",
"value",
"of",
"the",
"node",
"."
] |
def write(self, built_value):
"""Set the value of the node."""
self.built_value = built_value
|
[
"def",
"write",
"(",
"self",
",",
"built_value",
")",
":",
"self",
".",
"built_value",
"=",
"built_value"
] |
https://github.com/Nuitka/Nuitka/blob/39262276993757fa4e299f497654065600453fc9/nuitka/build/inline_copy/lib/scons-2.3.2/SCons/Node/Python.py#L80-L82
|
||
pypa/pip
|
7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4
|
src/pip/_vendor/urllib3/util/proxy.py
|
python
|
connection_requires_http_tunnel
|
(
proxy_url=None, proxy_config=None, destination_scheme=None
)
|
return True
|
Returns True if the connection requires an HTTP CONNECT through the proxy.
:param URL proxy_url:
URL of the proxy.
:param ProxyConfig proxy_config:
Proxy configuration from poolmanager.py
:param str destination_scheme:
The scheme of the destination. (i.e https, http, etc)
|
Returns True if the connection requires an HTTP CONNECT through the proxy.
|
[
"Returns",
"True",
"if",
"the",
"connection",
"requires",
"an",
"HTTP",
"CONNECT",
"through",
"the",
"proxy",
"."
] |
def connection_requires_http_tunnel(
proxy_url=None, proxy_config=None, destination_scheme=None
):
"""
Returns True if the connection requires an HTTP CONNECT through the proxy.
:param URL proxy_url:
URL of the proxy.
:param ProxyConfig proxy_config:
Proxy configuration from poolmanager.py
:param str destination_scheme:
The scheme of the destination. (i.e https, http, etc)
"""
# If we're not using a proxy, no way to use a tunnel.
if proxy_url is None:
return False
# HTTP destinations never require tunneling, we always forward.
if destination_scheme == "http":
return False
# Support for forwarding with HTTPS proxies and HTTPS destinations.
if (
proxy_url.scheme == "https"
and proxy_config
and proxy_config.use_forwarding_for_https
):
return False
# Otherwise always use a tunnel.
return True
|
[
"def",
"connection_requires_http_tunnel",
"(",
"proxy_url",
"=",
"None",
",",
"proxy_config",
"=",
"None",
",",
"destination_scheme",
"=",
"None",
")",
":",
"# If we're not using a proxy, no way to use a tunnel.",
"if",
"proxy_url",
"is",
"None",
":",
"return",
"False",
"# HTTP destinations never require tunneling, we always forward.",
"if",
"destination_scheme",
"==",
"\"http\"",
":",
"return",
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"# Support for forwarding with HTTPS proxies and HTTPS destinations.",
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"use_forwarding_for_https",
")",
":",
"return",
"False",
"# Otherwise always use a tunnel.",
"return",
"True"
] |
https://github.com/pypa/pip/blob/7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4/src/pip/_vendor/urllib3/util/proxy.py#L4-L34
|
|
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework
|
cb692f527e4e819b6c228187c5702d990a180043
|
external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/email/message.py
|
python
|
Message.set_param
|
(self, param, value, header='Content-Type', requote=True,
charset=None, language='')
|
Set a parameter in the Content-Type header.
If the parameter already exists in the header, its value will be
replaced with the new value.
If header is Content-Type and has not yet been defined for this
message, it will be set to "text/plain" and the new parameter and
value will be appended as per RFC 2045.
An alternate header can specified in the header argument, and all
parameters will be quoted as necessary unless requote is False.
If charset is specified, the parameter will be encoded according to RFC
2231. Optional language specifies the RFC 2231 language, defaulting
to the empty string. Both charset and language should be strings.
|
Set a parameter in the Content-Type header.
|
[
"Set",
"a",
"parameter",
"in",
"the",
"Content",
"-",
"Type",
"header",
"."
] |
def set_param(self, param, value, header='Content-Type', requote=True,
charset=None, language=''):
"""Set a parameter in the Content-Type header.
If the parameter already exists in the header, its value will be
replaced with the new value.
If header is Content-Type and has not yet been defined for this
message, it will be set to "text/plain" and the new parameter and
value will be appended as per RFC 2045.
An alternate header can specified in the header argument, and all
parameters will be quoted as necessary unless requote is False.
If charset is specified, the parameter will be encoded according to RFC
2231. Optional language specifies the RFC 2231 language, defaulting
to the empty string. Both charset and language should be strings.
"""
if not isinstance(value, tuple) and charset:
value = (charset, language, value)
if header not in self and header.lower() == 'content-type':
ctype = 'text/plain'
else:
ctype = self.get(header)
if not self.get_param(param, header=header):
if not ctype:
ctype = _formatparam(param, value, requote)
else:
ctype = SEMISPACE.join(
[ctype, _formatparam(param, value, requote)])
else:
ctype = ''
for old_param, old_value in self.get_params(header=header,
unquote=requote):
append_param = ''
if old_param.lower() == param.lower():
append_param = _formatparam(param, value, requote)
else:
append_param = _formatparam(old_param, old_value, requote)
if not ctype:
ctype = append_param
else:
ctype = SEMISPACE.join([ctype, append_param])
if ctype != self.get(header):
del self[header]
self[header] = ctype
|
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"[",
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"]",
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"ctype"
] |
https://github.com/ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework/blob/cb692f527e4e819b6c228187c5702d990a180043/external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/email/message.py#L571-L617
|
||
mathics/Mathics
|
318e06dea8f1c70758a50cb2f95c9900150e3a68
|
mathics/builtin/system.py
|
python
|
Share.apply_1
|
(self, symbol, evaluation)
|
return Integer0
|
Share[symbol_Symbol]
|
Share[symbol_Symbol]
|
[
"Share",
"[",
"symbol_Symbol",
"]"
] |
def apply_1(self, symbol, evaluation) -> Integer:
"""Share[symbol_Symbol]"""
# TODO: implement a routine that swap all the definitions,
# collecting repeated symbols and expressions, and then
# remplace them by references.
# Return the amount of memory recovered.
return Integer0
|
[
"def",
"apply_1",
"(",
"self",
",",
"symbol",
",",
"evaluation",
")",
"->",
"Integer",
":",
"# TODO: implement a routine that swap all the definitions,",
"# collecting repeated symbols and expressions, and then",
"# remplace them by references.",
"# Return the amount of memory recovered.",
"return",
"Integer0"
] |
https://github.com/mathics/Mathics/blob/318e06dea8f1c70758a50cb2f95c9900150e3a68/mathics/builtin/system.py#L608-L614
|
|
selfteaching/selfteaching-python-camp
|
9982ee964b984595e7d664b07c389cddaf158f1e
|
19100205/Ceasar1978/pip-19.0.3/src/pip/_vendor/html5lib/treebuilders/base.py
|
python
|
Node.__init__
|
(self, name)
|
Creates a Node
:arg name: The tag name associated with the node
|
Creates a Node
|
[
"Creates",
"a",
"Node"
] |
def __init__(self, name):
"""Creates a Node
:arg name: The tag name associated with the node
"""
# The tag name assocaited with the node
self.name = name
# The parent of the current node (or None for the document node)
self.parent = None
# The value of the current node (applies to text nodes and comments)
self.value = None
# A dict holding name -> value pairs for attributes of the node
self.attributes = {}
# A list of child nodes of the current node. This must include all
# elements but not necessarily other node types.
self.childNodes = []
# A list of miscellaneous flags that can be set on the node.
self._flags = []
|
[
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"self",
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] |
https://github.com/selfteaching/selfteaching-python-camp/blob/9982ee964b984595e7d664b07c389cddaf158f1e/19100205/Ceasar1978/pip-19.0.3/src/pip/_vendor/html5lib/treebuilders/base.py#L25-L43
|
||
kanzure/nanoengineer
|
874e4c9f8a9190f093625b267f9767e19f82e6c4
|
cad/src/graphics/drawing/GLPrimitiveBuffer.py
|
python
|
Hunk.changedRange
|
(self, chgLowID, chgHighID)
|
return
|
Record a range of data changes, for later flushing.
chgLowID and chgHighID are primitive IDs, possibly spanning many hunks.
The high end is the index of the one *after* the end of the range, as
usual in Python.
|
Record a range of data changes, for later flushing.
|
[
"Record",
"a",
"range",
"of",
"data",
"changes",
"for",
"later",
"flushing",
"."
] |
def changedRange(self, chgLowID, chgHighID):
"""
Record a range of data changes, for later flushing.
chgLowID and chgHighID are primitive IDs, possibly spanning many hunks.
The high end is the index of the one *after* the end of the range, as
usual in Python.
"""
(lowHunk, lowIndex) = decodePrimID(chgLowID)
(highHunk, highIndex) = decodePrimID(chgHighID)
if self.hunkNumber < lowHunk or self.hunkNumber > highHunk:
return # This hunk is not in the hunk range.
if lowHunk < self.hunkNumber:
self.low = 0
else:
self.low = min(self.low, lowIndex) # Maybe extend the range.
pass
if highHunk > self.hunkNumber:
self.high = HUNK_SIZE
else:
self.high = max(self.high, highIndex) # Maybe extend the range.
pass
return
|
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https://github.com/kanzure/nanoengineer/blob/874e4c9f8a9190f093625b267f9767e19f82e6c4/cad/src/graphics/drawing/GLPrimitiveBuffer.py#L637-L664
|
|
yxgeee/MMT
|
057e1ea5d3054c9d7e5fa72c727298d8e4c5f668
|
mmt/utils/data/__init__.py
|
python
|
IterLoader.__len__
|
(self)
|
return len(self.loader)
|
[] |
def __len__(self):
if (self.length is not None):
return self.length
return len(self.loader)
|
[
"def",
"__len__",
"(",
"self",
")",
":",
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")",
":",
"return",
"self",
".",
"length",
"return",
"len",
"(",
"self",
".",
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")"
] |
https://github.com/yxgeee/MMT/blob/057e1ea5d3054c9d7e5fa72c727298d8e4c5f668/mmt/utils/data/__init__.py#L12-L15
|
|||
wandb/client
|
3963364d8112b7dedb928fa423b6878ea1b467d9
|
wandb/vendor/watchdog/observers/api.py
|
python
|
BaseObserver.schedule
|
(self, event_handler, path, recursive=False)
|
return watch
|
Schedules watching a path and calls appropriate methods specified
in the given event handler in response to file system events.
:param event_handler:
An event handler instance that has appropriate event handling
methods which will be called by the observer in response to
file system events.
:type event_handler:
:class:`watchdog.events.FileSystemEventHandler` or a subclass
:param path:
Directory path that will be monitored.
:type path:
``str``
:param recursive:
``True`` if events will be emitted for sub-directories
traversed recursively; ``False`` otherwise.
:type recursive:
``bool``
:return:
An :class:`ObservedWatch` object instance representing
a watch.
|
Schedules watching a path and calls appropriate methods specified
in the given event handler in response to file system events.
|
[
"Schedules",
"watching",
"a",
"path",
"and",
"calls",
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"methods",
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"in",
"the",
"given",
"event",
"handler",
"in",
"response",
"to",
"file",
"system",
"events",
"."
] |
def schedule(self, event_handler, path, recursive=False):
"""
Schedules watching a path and calls appropriate methods specified
in the given event handler in response to file system events.
:param event_handler:
An event handler instance that has appropriate event handling
methods which will be called by the observer in response to
file system events.
:type event_handler:
:class:`watchdog.events.FileSystemEventHandler` or a subclass
:param path:
Directory path that will be monitored.
:type path:
``str``
:param recursive:
``True`` if events will be emitted for sub-directories
traversed recursively; ``False`` otherwise.
:type recursive:
``bool``
:return:
An :class:`ObservedWatch` object instance representing
a watch.
"""
with self._lock:
watch = ObservedWatch(path, recursive)
self._add_handler_for_watch(event_handler, watch)
# If we don't have an emitter for this watch already, create it.
if self._emitter_for_watch.get(watch) is None:
emitter = self._emitter_class(event_queue=self.event_queue,
watch=watch,
timeout=self.timeout)
self._add_emitter(emitter)
if self.is_alive():
emitter.start()
self._watches.add(watch)
return watch
|
[
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https://github.com/wandb/client/blob/3963364d8112b7dedb928fa423b6878ea1b467d9/wandb/vendor/watchdog/observers/api.py#L258-L295
|
|
dragonfly/dragonfly
|
a579b5eadf452e23b07d4caf27b402703b0012b7
|
dragonfly/exd/domains.py
|
python
|
DiscreteEuclideanDomain.get_dim
|
(self)
|
return self.dim
|
Return the dimensions.
|
Return the dimensions.
|
[
"Return",
"the",
"dimensions",
"."
] |
def get_dim(self):
""" Return the dimensions. """
return self.dim
|
[
"def",
"get_dim",
"(",
"self",
")",
":",
"return",
"self",
".",
"dim"
] |
https://github.com/dragonfly/dragonfly/blob/a579b5eadf452e23b07d4caf27b402703b0012b7/dragonfly/exd/domains.py#L234-L236
|
|
IBM/lale
|
b4d6829c143a4735b06083a0e6c70d2cca244162
|
lale/lib/aif360/util.py
|
python
|
_SymmetricDisparateImpact.__call__
|
(self, estimator, X, y)
|
return self.score_estimator(estimator, X, y)
|
[] |
def __call__(self, estimator, X, y):
return self.score_estimator(estimator, X, y)
|
[
"def",
"__call__",
"(",
"self",
",",
"estimator",
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"X",
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".",
"score_estimator",
"(",
"estimator",
",",
"X",
",",
"y",
")"
] |
https://github.com/IBM/lale/blob/b4d6829c143a4735b06083a0e6c70d2cca244162/lale/lib/aif360/util.py#L844-L845
|
|||
awslabs/sockeye
|
ec2d13f7beb42d8c4f389dba0172250dc9154d5a
|
sockeye/utils.py
|
python
|
check_condition
|
(condition: bool, error_message: str)
|
Check the condition and if it is not met, exit with the given error message
and error_code, similar to assertions.
:param condition: Condition to check.
:param error_message: Error message to show to the user.
|
Check the condition and if it is not met, exit with the given error message
and error_code, similar to assertions.
|
[
"Check",
"the",
"condition",
"and",
"if",
"it",
"is",
"not",
"met",
"exit",
"with",
"the",
"given",
"error",
"message",
"and",
"error_code",
"similar",
"to",
"assertions",
"."
] |
def check_condition(condition: bool, error_message: str):
"""
Check the condition and if it is not met, exit with the given error message
and error_code, similar to assertions.
:param condition: Condition to check.
:param error_message: Error message to show to the user.
"""
if not condition:
raise SockeyeError(error_message)
|
[
"def",
"check_condition",
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"condition",
":",
"bool",
",",
"error_message",
":",
"str",
")",
":",
"if",
"not",
"condition",
":",
"raise",
"SockeyeError",
"(",
"error_message",
")"
] |
https://github.com/awslabs/sockeye/blob/ec2d13f7beb42d8c4f389dba0172250dc9154d5a/sockeye/utils.py#L144-L153
|
||
beeware/ouroboros
|
a29123c6fab6a807caffbb7587cf548e0c370296
|
ouroboros/datetime.py
|
python
|
date.ctime
|
(self)
|
return "%s %s %2d 00:00:00 %04d" % (
_DAYNAMES[weekday],
_MONTHNAMES[self._month],
self._day, self._year)
|
Return ctime() style string.
|
Return ctime() style string.
|
[
"Return",
"ctime",
"()",
"style",
"string",
"."
] |
def ctime(self):
"Return ctime() style string."
weekday = self.toordinal() % 7 or 7
return "%s %s %2d 00:00:00 %04d" % (
_DAYNAMES[weekday],
_MONTHNAMES[self._month],
self._day, self._year)
|
[
"def",
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"weekday",
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"]",
",",
"self",
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"_day",
",",
"self",
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"_year",
")"
] |
https://github.com/beeware/ouroboros/blob/a29123c6fab6a807caffbb7587cf548e0c370296/ouroboros/datetime.py#L733-L739
|
|
makinacorpus/landez
|
6e5c71ded6071158e7943df204cd7bd1ed623a30
|
landez/sources.py
|
python
|
TileDownloader.tile
|
(self, z, x, y)
|
Download the specified tile from `tiles_url`
|
Download the specified tile from `tiles_url`
|
[
"Download",
"the",
"specified",
"tile",
"from",
"tiles_url"
] |
def tile(self, z, x, y):
"""
Download the specified tile from `tiles_url`
"""
logger.debug(_("Download tile %s") % ((z, x, y),))
# Render each keyword in URL ({s}, {x}, {y}, {z}, {size} ... )
size = self.tilesize
s = self.tiles_subdomains[(x + y) % len(self.tiles_subdomains)];
try:
url = self.tiles_url.format(**locals())
except KeyError as e:
raise DownloadError(_("Unknown keyword %s in URL") % e)
logger.debug(_("Retrieve tile at %s") % url)
r = DOWNLOAD_RETRIES
sleeptime = 1
while r > 0:
try:
request = requests.get(url, headers=self.headers)
if request.status_code == 200:
return request.content
raise DownloadError(_("Status code : %s, url : %s") % (request.status_code, url))
except requests.exceptions.ConnectionError as e:
logger.debug(_("Download error, retry (%s left). (%s)") % (r, e))
r -= 1
time.sleep(sleeptime)
# progressivly sleep longer to wait for this tile
if (sleeptime <= 10) and (r % 2 == 0):
sleeptime += 1 # increase wait
raise DownloadError(_("Cannot download URL %s") % url)
|
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"\"Cannot download URL %s\"",
")",
"%",
"url",
")"
] |
https://github.com/makinacorpus/landez/blob/6e5c71ded6071158e7943df204cd7bd1ed623a30/landez/sources.py#L163-L192
|
||
GoogleCloudPlatform/PerfKitBenchmarker
|
6e3412d7d5e414b8ca30ed5eaf970cef1d919a67
|
perfkitbenchmarker/providers/azure/azure_blob_storage.py
|
python
|
AzureBlobStorageService.ListTopLevelSubfolders
|
(self, bucket)
|
return list(unique_folders)
|
Lists the top level folders (not files) in a bucket.
Each listed item is a full file name, eg. "supplier/supplier.csv", so just
the high level folder name is extracted, and repetitions are eliminated for
when there's multiple files in a folder.
Args:
bucket: Name of the bucket to list the top level subfolders of.
Returns:
A list of top level subfolder names. Can be empty if there are no folders.
|
Lists the top level folders (not files) in a bucket.
|
[
"Lists",
"the",
"top",
"level",
"folders",
"(",
"not",
"files",
")",
"in",
"a",
"bucket",
"."
] |
def ListTopLevelSubfolders(self, bucket):
"""Lists the top level folders (not files) in a bucket.
Each listed item is a full file name, eg. "supplier/supplier.csv", so just
the high level folder name is extracted, and repetitions are eliminated for
when there's multiple files in a folder.
Args:
bucket: Name of the bucket to list the top level subfolders of.
Returns:
A list of top level subfolder names. Can be empty if there are no folders.
"""
unique_folders = set([
obj.split('/')[0].strip()
for obj in self.List(bucket)
if obj and obj.contains('/')
])
return list(unique_folders)
|
[
"def",
"ListTopLevelSubfolders",
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",",
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"(",
"'/'",
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"]",
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"return",
"list",
"(",
"unique_folders",
")"
] |
https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/6e3412d7d5e414b8ca30ed5eaf970cef1d919a67/perfkitbenchmarker/providers/azure/azure_blob_storage.py#L197-L215
|
|
GaoQ1/rasa_nlu_gq
|
aea2d037220f022b2773d0852cbcfbad582e8e25
|
rasa_nlu_gao/models/bert/modeling.py
|
python
|
reshape_to_matrix
|
(input_tensor)
|
return output_tensor
|
Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).
|
Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).
|
[
"Reshapes",
"a",
">",
"=",
"rank",
"2",
"tensor",
"to",
"a",
"rank",
"2",
"tensor",
"(",
"i",
".",
"e",
".",
"a",
"matrix",
")",
"."
] |
def reshape_to_matrix(input_tensor):
"""Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
ndims = input_tensor.shape.ndims
if ndims < 2:
raise ValueError("Input tensor must have at least rank 2. Shape = %s" %
(input_tensor.shape))
if ndims == 2:
return input_tensor
width = input_tensor.shape[-1]
output_tensor = tf.reshape(input_tensor, [-1, width])
return output_tensor
|
[
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",",
"[",
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"1",
",",
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https://github.com/GaoQ1/rasa_nlu_gq/blob/aea2d037220f022b2773d0852cbcfbad582e8e25/rasa_nlu_gao/models/bert/modeling.py#L942-L953
|
|
jgagneastro/coffeegrindsize
|
22661ebd21831dba4cf32bfc6ba59fe3d49f879c
|
App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/pandas/core/strings.py
|
python
|
str_encode
|
(arr, encoding, errors="strict")
|
return _na_map(f, arr)
|
Encode character string in the Series/Index using indicated encoding.
Equivalent to :meth:`str.encode`.
Parameters
----------
encoding : str
errors : str, optional
Returns
-------
encoded : Series/Index of objects
|
Encode character string in the Series/Index using indicated encoding.
Equivalent to :meth:`str.encode`.
|
[
"Encode",
"character",
"string",
"in",
"the",
"Series",
"/",
"Index",
"using",
"indicated",
"encoding",
".",
"Equivalent",
"to",
":",
"meth",
":",
"str",
".",
"encode",
"."
] |
def str_encode(arr, encoding, errors="strict"):
"""
Encode character string in the Series/Index using indicated encoding.
Equivalent to :meth:`str.encode`.
Parameters
----------
encoding : str
errors : str, optional
Returns
-------
encoded : Series/Index of objects
"""
if encoding in _cpython_optimized_encoders:
# CPython optimized implementation
f = lambda x: x.encode(encoding, errors)
else:
encoder = codecs.getencoder(encoding)
f = lambda x: encoder(x, errors)[0]
return _na_map(f, arr)
|
[
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https://github.com/jgagneastro/coffeegrindsize/blob/22661ebd21831dba4cf32bfc6ba59fe3d49f879c/App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/pandas/core/strings.py#L1713-L1733
|
|
scrapinghub/frontera
|
84f9e1034d2868447db88e865596c0fbb32e70f6
|
frontera/core/manager.py
|
python
|
ComponentsPipelineMixin.middlewares
|
(self)
|
return self._middlewares
|
A list of :class:`Middleware <frontera.core.components.Middleware>` objects to be used by the frontier. \
Can be defined with :setting:`MIDDLEWARES` setting.
|
A list of :class:`Middleware <frontera.core.components.Middleware>` objects to be used by the frontier. \
Can be defined with :setting:`MIDDLEWARES` setting.
|
[
"A",
"list",
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"\\",
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":",
"setting",
":",
"MIDDLEWARES",
"setting",
"."
] |
def middlewares(self):
"""
A list of :class:`Middleware <frontera.core.components.Middleware>` objects to be used by the frontier. \
Can be defined with :setting:`MIDDLEWARES` setting.
"""
return self._middlewares
|
[
"def",
"middlewares",
"(",
"self",
")",
":",
"return",
"self",
".",
"_middlewares"
] |
https://github.com/scrapinghub/frontera/blob/84f9e1034d2868447db88e865596c0fbb32e70f6/frontera/core/manager.py#L97-L102
|
|
stratosphereips/Manati
|
20e55d49edf00f8503807c62397d02a0dad9ddff
|
manati/api_manager/core/modules_manager.py
|
python
|
ModulesManager.bulk_labeling_by_whois_relation
|
(username, analysis_session_id, domain,verdict)
|
[] |
def bulk_labeling_by_whois_relation(username, analysis_session_id, domain,verdict):
queue = django_rq.get_queue('high')
queue.enqueue(__bulk_labeling_by_whois_relation_aux__,
username,
analysis_session_id,
domain,verdict)
|
[
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",",
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",",
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] |
https://github.com/stratosphereips/Manati/blob/20e55d49edf00f8503807c62397d02a0dad9ddff/manati/api_manager/core/modules_manager.py#L301-L306
|
||||
GoogleCloudPlatform/PerfKitBenchmarker
|
6e3412d7d5e414b8ca30ed5eaf970cef1d919a67
|
perfkitbenchmarker/windows_virtual_machine.py
|
python
|
BaseWindowsMixin.WaitForBootCompletion
|
(self)
|
Waits until VM is has booted.
|
Waits until VM is has booted.
|
[
"Waits",
"until",
"VM",
"is",
"has",
"booted",
"."
] |
def WaitForBootCompletion(self):
"""Waits until VM is has booted."""
to_wait_for = [self._WaitForWinRmCommand]
if FLAGS.cluster_boot_test_rdp_port_listening:
to_wait_for.append(self._WaitForRdpPort)
vm_util.RunParallelThreads([(method, [], {}) for method in to_wait_for], 2)
|
[
"def",
"WaitForBootCompletion",
"(",
"self",
")",
":",
"to_wait_for",
"=",
"[",
"self",
".",
"_WaitForWinRmCommand",
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"if",
"FLAGS",
".",
"cluster_boot_test_rdp_port_listening",
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"to_wait_for",
".",
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"_WaitForRdpPort",
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"vm_util",
".",
"RunParallelThreads",
"(",
"[",
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"]",
",",
"{",
"}",
")",
"for",
"method",
"in",
"to_wait_for",
"]",
",",
"2",
")"
] |
https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/6e3412d7d5e414b8ca30ed5eaf970cef1d919a67/perfkitbenchmarker/windows_virtual_machine.py#L410-L415
|
||
WerWolv/EdiZon_CheatsConfigsAndScripts
|
d16d36c7509c01dca770f402babd83ff2e9ae6e7
|
Scripts/lib/python3.5/email/headerregistry.py
|
python
|
HeaderRegistry.map_to_type
|
(self, name, cls)
|
Register cls as the specialized class for handling "name" headers.
|
Register cls as the specialized class for handling "name" headers.
|
[
"Register",
"cls",
"as",
"the",
"specialized",
"class",
"for",
"handling",
"name",
"headers",
"."
] |
def map_to_type(self, name, cls):
"""Register cls as the specialized class for handling "name" headers.
"""
self.registry[name.lower()] = cls
|
[
"def",
"map_to_type",
"(",
"self",
",",
"name",
",",
"cls",
")",
":",
"self",
".",
"registry",
"[",
"name",
".",
"lower",
"(",
")",
"]",
"=",
"cls"
] |
https://github.com/WerWolv/EdiZon_CheatsConfigsAndScripts/blob/d16d36c7509c01dca770f402babd83ff2e9ae6e7/Scripts/lib/python3.5/email/headerregistry.py#L566-L570
|
||
facebookresearch/ParlAI
|
e4d59c30eef44f1f67105961b82a83fd28d7d78b
|
parlai/core/teachers.py
|
python
|
ChunkTeacher._setup_data
|
(self, datatype)
|
Passthrough.
|
Passthrough.
|
[
"Passthrough",
"."
] |
def _setup_data(self, datatype):
"""
Passthrough.
"""
pass
|
[
"def",
"_setup_data",
"(",
"self",
",",
"datatype",
")",
":",
"pass"
] |
https://github.com/facebookresearch/ParlAI/blob/e4d59c30eef44f1f67105961b82a83fd28d7d78b/parlai/core/teachers.py#L2395-L2399
|
||
vtr0n/TelegramTUI
|
a64f910971187d0d9f4192fb2ad369599d9d87c5
|
telegramtui/src/npyscreen/proto_fm_screen_area.py
|
python
|
ScreenArea._max_physical
|
(self)
|
return (mxy-1, mxx-1)
|
How big is the physical screen?
|
How big is the physical screen?
|
[
"How",
"big",
"is",
"the",
"physical",
"screen?"
] |
def _max_physical(self):
"How big is the physical screen?"
# On OS X newwin does not correctly get the size of the screen.
# let's see how big we could be: create a temp screen
# and see the size curses makes it. No good to keep, though
try:
mxy, mxx = struct.unpack('hh', fcntl.ioctl(sys.stderr.fileno(), termios.TIOCGWINSZ, 'xxxx'))
if (mxy, mxx) == (0,0):
raise ValueError
except (ValueError, NameError):
mxy, mxx = curses.newwin(0,0).getmaxyx()
# return safe values, i.e. slightly smaller.
return (mxy-1, mxx-1)
|
[
"def",
"_max_physical",
"(",
"self",
")",
":",
"# On OS X newwin does not correctly get the size of the screen.",
"# let's see how big we could be: create a temp screen",
"# and see the size curses makes it. No good to keep, though",
"try",
":",
"mxy",
",",
"mxx",
"=",
"struct",
".",
"unpack",
"(",
"'hh'",
",",
"fcntl",
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"ioctl",
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"sys",
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"fileno",
"(",
")",
",",
"termios",
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"TIOCGWINSZ",
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"'xxxx'",
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")",
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"mxy",
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"mxx",
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"0",
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"# return safe values, i.e. slightly smaller.",
"return",
"(",
"mxy",
"-",
"1",
",",
"mxx",
"-",
"1",
")"
] |
https://github.com/vtr0n/TelegramTUI/blob/a64f910971187d0d9f4192fb2ad369599d9d87c5/telegramtui/src/npyscreen/proto_fm_screen_area.py#L111-L124
|
|
VirtueSecurity/aws-extender
|
d123b7e1a845847709ba3a481f11996bddc68a1c
|
BappModules/boto/redshift/layer1.py
|
python
|
RedshiftConnection.modify_cluster
|
(self, cluster_identifier, cluster_type=None,
node_type=None, number_of_nodes=None,
cluster_security_groups=None,
vpc_security_group_ids=None,
master_user_password=None,
cluster_parameter_group_name=None,
automated_snapshot_retention_period=None,
preferred_maintenance_window=None,
cluster_version=None, allow_version_upgrade=None,
hsm_client_certificate_identifier=None,
hsm_configuration_identifier=None,
new_cluster_identifier=None)
|
return self._make_request(
action='ModifyCluster',
verb='POST',
path='/', params=params)
|
Modifies the settings for a cluster. For example, you can add
another security or parameter group, update the preferred
maintenance window, or change the master user password.
Resetting a cluster password or modifying the security groups
associated with a cluster do not need a reboot. However,
modifying a parameter group requires a reboot for parameters
to take effect. For more information about managing clusters,
go to `Amazon Redshift Clusters`_ in the Amazon Redshift
Management Guide
You can also change node type and the number of nodes to scale
up or down the cluster. When resizing a cluster, you must
specify both the number of nodes and the node type even if one
of the parameters does not change. If you specify the same
number of nodes and node type that are already configured for
the cluster, an error is returned.
:type cluster_identifier: string
:param cluster_identifier: The unique identifier of the cluster to be
modified.
Example: `examplecluster`
:type cluster_type: string
:param cluster_type: The new cluster type.
When you submit your cluster resize request, your existing cluster goes
into a read-only mode. After Amazon Redshift provisions a new
cluster based on your resize requirements, there will be outage for
a period while the old cluster is deleted and your connection is
switched to the new cluster. You can use DescribeResize to track
the progress of the resize request.
Valid Values: ` multi-node | single-node `
:type node_type: string
:param node_type: The new node type of the cluster. If you specify a
new node type, you must also specify the number of nodes parameter
also.
When you submit your request to resize a cluster, Amazon Redshift sets
access permissions for the cluster to read-only. After Amazon
Redshift provisions a new cluster according to your resize
requirements, there will be a temporary outage while the old
cluster is deleted and your connection is switched to the new
cluster. When the new connection is complete, the original access
permissions for the cluster are restored. You can use the
DescribeResize to track the progress of the resize request.
Valid Values: ` dw1.xlarge` | `dw1.8xlarge` | `dw2.large` |
`dw2.8xlarge`.
:type number_of_nodes: integer
:param number_of_nodes: The new number of nodes of the cluster. If you
specify a new number of nodes, you must also specify the node type
parameter also.
When you submit your request to resize a cluster, Amazon Redshift sets
access permissions for the cluster to read-only. After Amazon
Redshift provisions a new cluster according to your resize
requirements, there will be a temporary outage while the old
cluster is deleted and your connection is switched to the new
cluster. When the new connection is complete, the original access
permissions for the cluster are restored. You can use
DescribeResize to track the progress of the resize request.
Valid Values: Integer greater than `0`.
:type cluster_security_groups: list
:param cluster_security_groups:
A list of cluster security groups to be authorized on this cluster.
This change is asynchronously applied as soon as possible.
Security groups currently associated with the cluster, and not in the
list of groups to apply, will be revoked from the cluster.
Constraints:
+ Must be 1 to 255 alphanumeric characters or hyphens
+ First character must be a letter
+ Cannot end with a hyphen or contain two consecutive hyphens
:type vpc_security_group_ids: list
:param vpc_security_group_ids: A list of virtual private cloud (VPC)
security groups to be associated with the cluster.
:type master_user_password: string
:param master_user_password:
The new password for the cluster master user. This change is
asynchronously applied as soon as possible. Between the time of the
request and the completion of the request, the `MasterUserPassword`
element exists in the `PendingModifiedValues` element of the
operation response.
Default: Uses existing setting.
Constraints:
+ Must be between 8 and 64 characters in length.
+ Must contain at least one uppercase letter.
+ Must contain at least one lowercase letter.
+ Must contain one number.
+ Can be any printable ASCII character (ASCII code 33 to 126) except '
(single quote), " (double quote), \, /, @, or space.
:type cluster_parameter_group_name: string
:param cluster_parameter_group_name: The name of the cluster parameter
group to apply to this cluster. This change is applied only after
the cluster is rebooted. To reboot a cluster use RebootCluster.
Default: Uses existing setting.
Constraints: The cluster parameter group must be in the same parameter
group family that matches the cluster version.
:type automated_snapshot_retention_period: integer
:param automated_snapshot_retention_period: The number of days that
automated snapshots are retained. If the value is 0, automated
snapshots are disabled. Even if automated snapshots are disabled,
you can still create manual snapshots when you want with
CreateClusterSnapshot.
If you decrease the automated snapshot retention period from its
current value, existing automated snapshots that fall outside of
the new retention period will be immediately deleted.
Default: Uses existing setting.
Constraints: Must be a value from 0 to 35.
:type preferred_maintenance_window: string
:param preferred_maintenance_window: The weekly time range (in UTC)
during which system maintenance can occur, if necessary. If system
maintenance is necessary during the window, it may result in an
outage.
This maintenance window change is made immediately. If the new
maintenance window indicates the current time, there must be at
least 120 minutes between the current time and end of the window in
order to ensure that pending changes are applied.
Default: Uses existing setting.
Format: ddd:hh24:mi-ddd:hh24:mi, for example `wed:07:30-wed:08:00`.
Valid Days: Mon | Tue | Wed | Thu | Fri | Sat | Sun
Constraints: Must be at least 30 minutes.
:type cluster_version: string
:param cluster_version: The new version number of the Amazon Redshift
engine to upgrade to.
For major version upgrades, if a non-default cluster parameter group is
currently in use, a new cluster parameter group in the cluster
parameter group family for the new version must be specified. The
new cluster parameter group can be the default for that cluster
parameter group family. For more information about managing
parameter groups, go to `Amazon Redshift Parameter Groups`_ in the
Amazon Redshift Management Guide .
Example: `1.0`
:type allow_version_upgrade: boolean
:param allow_version_upgrade: If `True`, upgrades will be applied
automatically to the cluster during the maintenance window.
Default: `False`
:type hsm_client_certificate_identifier: string
:param hsm_client_certificate_identifier: Specifies the name of the HSM
client certificate the Amazon Redshift cluster uses to retrieve the
data encryption keys stored in an HSM.
:type hsm_configuration_identifier: string
:param hsm_configuration_identifier: Specifies the name of the HSM
configuration that contains the information the Amazon Redshift
cluster can use to retrieve and store keys in an HSM.
:type new_cluster_identifier: string
:param new_cluster_identifier: The new identifier for the cluster.
Constraints:
+ Must contain from 1 to 63 alphanumeric characters or hyphens.
+ Alphabetic characters must be lowercase.
+ First character must be a letter.
+ Cannot end with a hyphen or contain two consecutive hyphens.
+ Must be unique for all clusters within an AWS account.
Example: `examplecluster`
|
Modifies the settings for a cluster. For example, you can add
another security or parameter group, update the preferred
maintenance window, or change the master user password.
Resetting a cluster password or modifying the security groups
associated with a cluster do not need a reboot. However,
modifying a parameter group requires a reboot for parameters
to take effect. For more information about managing clusters,
go to `Amazon Redshift Clusters`_ in the Amazon Redshift
Management Guide
|
[
"Modifies",
"the",
"settings",
"for",
"a",
"cluster",
".",
"For",
"example",
"you",
"can",
"add",
"another",
"security",
"or",
"parameter",
"group",
"update",
"the",
"preferred",
"maintenance",
"window",
"or",
"change",
"the",
"master",
"user",
"password",
".",
"Resetting",
"a",
"cluster",
"password",
"or",
"modifying",
"the",
"security",
"groups",
"associated",
"with",
"a",
"cluster",
"do",
"not",
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"a",
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".",
"However",
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"a",
"parameter",
"group",
"requires",
"a",
"reboot",
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"to",
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".",
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"go",
"to",
"Amazon",
"Redshift",
"Clusters",
"_",
"in",
"the",
"Amazon",
"Redshift",
"Management",
"Guide"
] |
def modify_cluster(self, cluster_identifier, cluster_type=None,
node_type=None, number_of_nodes=None,
cluster_security_groups=None,
vpc_security_group_ids=None,
master_user_password=None,
cluster_parameter_group_name=None,
automated_snapshot_retention_period=None,
preferred_maintenance_window=None,
cluster_version=None, allow_version_upgrade=None,
hsm_client_certificate_identifier=None,
hsm_configuration_identifier=None,
new_cluster_identifier=None):
"""
Modifies the settings for a cluster. For example, you can add
another security or parameter group, update the preferred
maintenance window, or change the master user password.
Resetting a cluster password or modifying the security groups
associated with a cluster do not need a reboot. However,
modifying a parameter group requires a reboot for parameters
to take effect. For more information about managing clusters,
go to `Amazon Redshift Clusters`_ in the Amazon Redshift
Management Guide
You can also change node type and the number of nodes to scale
up or down the cluster. When resizing a cluster, you must
specify both the number of nodes and the node type even if one
of the parameters does not change. If you specify the same
number of nodes and node type that are already configured for
the cluster, an error is returned.
:type cluster_identifier: string
:param cluster_identifier: The unique identifier of the cluster to be
modified.
Example: `examplecluster`
:type cluster_type: string
:param cluster_type: The new cluster type.
When you submit your cluster resize request, your existing cluster goes
into a read-only mode. After Amazon Redshift provisions a new
cluster based on your resize requirements, there will be outage for
a period while the old cluster is deleted and your connection is
switched to the new cluster. You can use DescribeResize to track
the progress of the resize request.
Valid Values: ` multi-node | single-node `
:type node_type: string
:param node_type: The new node type of the cluster. If you specify a
new node type, you must also specify the number of nodes parameter
also.
When you submit your request to resize a cluster, Amazon Redshift sets
access permissions for the cluster to read-only. After Amazon
Redshift provisions a new cluster according to your resize
requirements, there will be a temporary outage while the old
cluster is deleted and your connection is switched to the new
cluster. When the new connection is complete, the original access
permissions for the cluster are restored. You can use the
DescribeResize to track the progress of the resize request.
Valid Values: ` dw1.xlarge` | `dw1.8xlarge` | `dw2.large` |
`dw2.8xlarge`.
:type number_of_nodes: integer
:param number_of_nodes: The new number of nodes of the cluster. If you
specify a new number of nodes, you must also specify the node type
parameter also.
When you submit your request to resize a cluster, Amazon Redshift sets
access permissions for the cluster to read-only. After Amazon
Redshift provisions a new cluster according to your resize
requirements, there will be a temporary outage while the old
cluster is deleted and your connection is switched to the new
cluster. When the new connection is complete, the original access
permissions for the cluster are restored. You can use
DescribeResize to track the progress of the resize request.
Valid Values: Integer greater than `0`.
:type cluster_security_groups: list
:param cluster_security_groups:
A list of cluster security groups to be authorized on this cluster.
This change is asynchronously applied as soon as possible.
Security groups currently associated with the cluster, and not in the
list of groups to apply, will be revoked from the cluster.
Constraints:
+ Must be 1 to 255 alphanumeric characters or hyphens
+ First character must be a letter
+ Cannot end with a hyphen or contain two consecutive hyphens
:type vpc_security_group_ids: list
:param vpc_security_group_ids: A list of virtual private cloud (VPC)
security groups to be associated with the cluster.
:type master_user_password: string
:param master_user_password:
The new password for the cluster master user. This change is
asynchronously applied as soon as possible. Between the time of the
request and the completion of the request, the `MasterUserPassword`
element exists in the `PendingModifiedValues` element of the
operation response.
Default: Uses existing setting.
Constraints:
+ Must be between 8 and 64 characters in length.
+ Must contain at least one uppercase letter.
+ Must contain at least one lowercase letter.
+ Must contain one number.
+ Can be any printable ASCII character (ASCII code 33 to 126) except '
(single quote), " (double quote), \, /, @, or space.
:type cluster_parameter_group_name: string
:param cluster_parameter_group_name: The name of the cluster parameter
group to apply to this cluster. This change is applied only after
the cluster is rebooted. To reboot a cluster use RebootCluster.
Default: Uses existing setting.
Constraints: The cluster parameter group must be in the same parameter
group family that matches the cluster version.
:type automated_snapshot_retention_period: integer
:param automated_snapshot_retention_period: The number of days that
automated snapshots are retained. If the value is 0, automated
snapshots are disabled. Even if automated snapshots are disabled,
you can still create manual snapshots when you want with
CreateClusterSnapshot.
If you decrease the automated snapshot retention period from its
current value, existing automated snapshots that fall outside of
the new retention period will be immediately deleted.
Default: Uses existing setting.
Constraints: Must be a value from 0 to 35.
:type preferred_maintenance_window: string
:param preferred_maintenance_window: The weekly time range (in UTC)
during which system maintenance can occur, if necessary. If system
maintenance is necessary during the window, it may result in an
outage.
This maintenance window change is made immediately. If the new
maintenance window indicates the current time, there must be at
least 120 minutes between the current time and end of the window in
order to ensure that pending changes are applied.
Default: Uses existing setting.
Format: ddd:hh24:mi-ddd:hh24:mi, for example `wed:07:30-wed:08:00`.
Valid Days: Mon | Tue | Wed | Thu | Fri | Sat | Sun
Constraints: Must be at least 30 minutes.
:type cluster_version: string
:param cluster_version: The new version number of the Amazon Redshift
engine to upgrade to.
For major version upgrades, if a non-default cluster parameter group is
currently in use, a new cluster parameter group in the cluster
parameter group family for the new version must be specified. The
new cluster parameter group can be the default for that cluster
parameter group family. For more information about managing
parameter groups, go to `Amazon Redshift Parameter Groups`_ in the
Amazon Redshift Management Guide .
Example: `1.0`
:type allow_version_upgrade: boolean
:param allow_version_upgrade: If `True`, upgrades will be applied
automatically to the cluster during the maintenance window.
Default: `False`
:type hsm_client_certificate_identifier: string
:param hsm_client_certificate_identifier: Specifies the name of the HSM
client certificate the Amazon Redshift cluster uses to retrieve the
data encryption keys stored in an HSM.
:type hsm_configuration_identifier: string
:param hsm_configuration_identifier: Specifies the name of the HSM
configuration that contains the information the Amazon Redshift
cluster can use to retrieve and store keys in an HSM.
:type new_cluster_identifier: string
:param new_cluster_identifier: The new identifier for the cluster.
Constraints:
+ Must contain from 1 to 63 alphanumeric characters or hyphens.
+ Alphabetic characters must be lowercase.
+ First character must be a letter.
+ Cannot end with a hyphen or contain two consecutive hyphens.
+ Must be unique for all clusters within an AWS account.
Example: `examplecluster`
"""
params = {'ClusterIdentifier': cluster_identifier, }
if cluster_type is not None:
params['ClusterType'] = cluster_type
if node_type is not None:
params['NodeType'] = node_type
if number_of_nodes is not None:
params['NumberOfNodes'] = number_of_nodes
if cluster_security_groups is not None:
self.build_list_params(params,
cluster_security_groups,
'ClusterSecurityGroups.member')
if vpc_security_group_ids is not None:
self.build_list_params(params,
vpc_security_group_ids,
'VpcSecurityGroupIds.member')
if master_user_password is not None:
params['MasterUserPassword'] = master_user_password
if cluster_parameter_group_name is not None:
params['ClusterParameterGroupName'] = cluster_parameter_group_name
if automated_snapshot_retention_period is not None:
params['AutomatedSnapshotRetentionPeriod'] = automated_snapshot_retention_period
if preferred_maintenance_window is not None:
params['PreferredMaintenanceWindow'] = preferred_maintenance_window
if cluster_version is not None:
params['ClusterVersion'] = cluster_version
if allow_version_upgrade is not None:
params['AllowVersionUpgrade'] = str(
allow_version_upgrade).lower()
if hsm_client_certificate_identifier is not None:
params['HsmClientCertificateIdentifier'] = hsm_client_certificate_identifier
if hsm_configuration_identifier is not None:
params['HsmConfigurationIdentifier'] = hsm_configuration_identifier
if new_cluster_identifier is not None:
params['NewClusterIdentifier'] = new_cluster_identifier
return self._make_request(
action='ModifyCluster',
verb='POST',
path='/', params=params)
|
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"params",
",",
"cluster_security_groups",
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"'VpcSecurityGroupIds.member'",
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"params",
"[",
"'MasterUserPassword'",
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"'ClusterParameterGroupName'",
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"'AutomatedSnapshotRetentionPeriod'",
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"[",
"'ClusterVersion'",
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"=",
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"allow_version_upgrade",
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"[",
"'AllowVersionUpgrade'",
"]",
"=",
"str",
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"allow_version_upgrade",
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".",
"lower",
"(",
")",
"if",
"hsm_client_certificate_identifier",
"is",
"not",
"None",
":",
"params",
"[",
"'HsmClientCertificateIdentifier'",
"]",
"=",
"hsm_client_certificate_identifier",
"if",
"hsm_configuration_identifier",
"is",
"not",
"None",
":",
"params",
"[",
"'HsmConfigurationIdentifier'",
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"None",
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"params",
"[",
"'NewClusterIdentifier'",
"]",
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"(",
"action",
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",",
"verb",
"=",
"'POST'",
",",
"path",
"=",
"'/'",
",",
"params",
"=",
"params",
")"
] |
https://github.com/VirtueSecurity/aws-extender/blob/d123b7e1a845847709ba3a481f11996bddc68a1c/BappModules/boto/redshift/layer1.py#L2252-L2489
|
|
yianjiajia/django_web_ansible
|
1103343082a65abf9d37310f5048514d74930753
|
devops/apps/ansible/elfinder/volumes/base.py
|
python
|
ElfinderVolumeDriver._mimetype
|
(self, path)
|
Attempt to read the file's mimetype. Should return ``None``
on fail.
.. warning::
**Not implemented**, each driver must provide its own
imlementation.
|
Attempt to read the file's mimetype. Should return ``None``
on fail.
.. warning::
**Not implemented**, each driver must provide its own
imlementation.
|
[
"Attempt",
"to",
"read",
"the",
"file",
"s",
"mimetype",
".",
"Should",
"return",
"None",
"on",
"fail",
".",
"..",
"warning",
"::",
"**",
"Not",
"implemented",
"**",
"each",
"driver",
"must",
"provide",
"its",
"own",
"imlementation",
"."
] |
def _mimetype(self, path):
"""
Attempt to read the file's mimetype. Should return ``None``
on fail.
.. warning::
**Not implemented**, each driver must provide its own
imlementation.
"""
raise NotImplementedError
|
[
"def",
"_mimetype",
"(",
"self",
",",
"path",
")",
":",
"raise",
"NotImplementedError"
] |
https://github.com/yianjiajia/django_web_ansible/blob/1103343082a65abf9d37310f5048514d74930753/devops/apps/ansible/elfinder/volumes/base.py#L1931-L1941
|
||
datactive/bigbang
|
d4fef7eb41ae04e51f4e369de5a721c66231202b
|
bigbang/analysis/graph.py
|
python
|
ascendancy
|
(am)
|
return A
|
Ulanowicz ecosystem health measures
Input is weighted adjacency matrix.
|
Ulanowicz ecosystem health measures
Input is weighted adjacency matrix.
|
[
"Ulanowicz",
"ecosystem",
"health",
"measures",
"Input",
"is",
"weighted",
"adjacency",
"matrix",
"."
] |
def ascendancy(am):
"""
Ulanowicz ecosystem health measures
Input is weighted adjacency matrix.
"""
# should these be normalized?!?!
# output rates
s0 = np.tile(np.sum(am, 0).T, (am.shape[0], 1))
# input rates
s1 = np.tile(np.sum(am, 1).T, (am.shape[1], 1)).T
logs = np.nan_to_num(np.log(am * np.sum(am) / (s0 * s1)))
# ascendancy!
A = np.sum(am * logs)
return A
|
[
"def",
"ascendancy",
"(",
"am",
")",
":",
"# should these be normalized?!?!",
"# output rates",
"s0",
"=",
"np",
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"np",
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"(",
"am",
"*",
"logs",
")",
"return",
"A"
] |
https://github.com/datactive/bigbang/blob/d4fef7eb41ae04e51f4e369de5a721c66231202b/bigbang/analysis/graph.py#L113-L129
|
|
DylanWusee/pointconv
|
f39dc3e101af2f52544181ee20c14f73279b48ae
|
utils/provider.py
|
python
|
random_scale_point_cloud
|
(batch_data, scale_low=0.8, scale_high=1.25)
|
return batch_data
|
Randomly scale the point cloud. Scale is per point cloud.
Input:
BxNx3 array, original batch of point clouds
Return:
BxNx3 array, scaled batch of point clouds
|
Randomly scale the point cloud. Scale is per point cloud.
Input:
BxNx3 array, original batch of point clouds
Return:
BxNx3 array, scaled batch of point clouds
|
[
"Randomly",
"scale",
"the",
"point",
"cloud",
".",
"Scale",
"is",
"per",
"point",
"cloud",
".",
"Input",
":",
"BxNx3",
"array",
"original",
"batch",
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"point",
"clouds",
"Return",
":",
"BxNx3",
"array",
"scaled",
"batch",
"of",
"point",
"clouds"
] |
def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25):
""" Randomly scale the point cloud. Scale is per point cloud.
Input:
BxNx3 array, original batch of point clouds
Return:
BxNx3 array, scaled batch of point clouds
"""
B, N, C = batch_data.shape
scales = np.random.uniform(scale_low, scale_high, B)
for batch_index in range(B):
batch_data[batch_index,:,:] *= scales[batch_index]
return batch_data
|
[
"def",
"random_scale_point_cloud",
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"batch_data",
",",
"scale_low",
"=",
"0.8",
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"scale_high",
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",",
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"]",
"*=",
"scales",
"[",
"batch_index",
"]",
"return",
"batch_data"
] |
https://github.com/DylanWusee/pointconv/blob/f39dc3e101af2f52544181ee20c14f73279b48ae/utils/provider.py#L152-L163
|
|
mailpile/Mailpile
|
b5e4b85fd1e584951d6d13af362ab28821466eea
|
mailpile/config/validators.py
|
python
|
HostNameValid
|
(host)
|
Tests whether a string is a valid host-name, return a boolean value
>>> HostNameValid("127.0.0.1")
True
>>> HostNameValid("::1")
True
>>> HostNameValid("localhost")
True
>>> HostNameValid("22.45")
False
|
Tests whether a string is a valid host-name, return a boolean value
|
[
"Tests",
"whether",
"a",
"string",
"is",
"a",
"valid",
"host",
"-",
"name",
"return",
"a",
"boolean",
"value"
] |
def HostNameValid(host):
"""
Tests whether a string is a valid host-name, return a boolean value
>>> HostNameValid("127.0.0.1")
True
>>> HostNameValid("::1")
True
>>> HostNameValid("localhost")
True
>>> HostNameValid("22.45")
False
"""
valid = False
for attr in ["AF_INET","AF_INET6"]:
try:
socket.inet_pton(socket.__getattribute__(attr), host)
valid = True
break
except (socket.error):
pass
if not valid:
# the host is not an IP so check if its a hostname i.e. 'localhost' or 'site.com'
if not host or (not DnsNameValid(host) and not ALPHA_RE.match(host)):
return False
else:
return True
else:
return True
|
[
"def",
"HostNameValid",
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"host",
")",
":",
"valid",
"=",
"False",
"for",
"attr",
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"[",
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",",
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"inet_pton",
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":",
"return",
"False",
"else",
":",
"return",
"True",
"else",
":",
"return",
"True"
] |
https://github.com/mailpile/Mailpile/blob/b5e4b85fd1e584951d6d13af362ab28821466eea/mailpile/config/validators.py#L104-L135
|
||
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/filedb/gae.py
|
python
|
DatastoreFile.read
|
(self, length)
|
return self.data.read(length)
|
[] |
def read(self, length):
return self.data.read(length)
|
[
"def",
"read",
"(",
"self",
",",
"length",
")",
":",
"return",
"self",
".",
"data",
".",
"read",
"(",
"length",
")"
] |
https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/filedb/gae.py#L69-L70
|
|||
NervanaSystems/neon
|
8c3fb8a93b4a89303467b25817c60536542d08bd
|
neon/backends/nervanacpu.py
|
python
|
CPUTensor.raw
|
(self)
|
return self._tensor.ctypes.data
|
Access the raw buffer.
Returns:
pointer: A device specific pointer
|
Access the raw buffer.
|
[
"Access",
"the",
"raw",
"buffer",
"."
] |
def raw(self):
"""
Access the raw buffer.
Returns:
pointer: A device specific pointer
"""
return self._tensor.ctypes.data
|
[
"def",
"raw",
"(",
"self",
")",
":",
"return",
"self",
".",
"_tensor",
".",
"ctypes",
".",
"data"
] |
https://github.com/NervanaSystems/neon/blob/8c3fb8a93b4a89303467b25817c60536542d08bd/neon/backends/nervanacpu.py#L261-L268
|
|
googlecolab/colabtools
|
b7b3566363e2f0bad9ad256ae317314ba5bd42f8
|
google/colab/html/_background_server.py
|
python
|
_BackgroundServer.__init__
|
(self, app)
|
Initialize (but do not start) background server.
Args:
app: server application to run.
|
Initialize (but do not start) background server.
|
[
"Initialize",
"(",
"but",
"do",
"not",
"start",
")",
"background",
"server",
"."
] |
def __init__(self, app):
"""Initialize (but do not start) background server.
Args:
app: server application to run.
"""
self._app = app
# These will be initialized when starting the server.
self._port = None
self._server_thread = None
self._ioloop = None
self._server = None
|
[
"def",
"__init__",
"(",
"self",
",",
"app",
")",
":",
"self",
".",
"_app",
"=",
"app",
"# These will be initialized when starting the server.",
"self",
".",
"_port",
"=",
"None",
"self",
".",
"_server_thread",
"=",
"None",
"self",
".",
"_ioloop",
"=",
"None",
"self",
".",
"_server",
"=",
"None"
] |
https://github.com/googlecolab/colabtools/blob/b7b3566363e2f0bad9ad256ae317314ba5bd42f8/google/colab/html/_background_server.py#L32-L44
|
||
tomplus/kubernetes_asyncio
|
f028cc793e3a2c519be6a52a49fb77ff0b014c9b
|
kubernetes_asyncio/client/models/v1_typed_local_object_reference.py
|
python
|
V1TypedLocalObjectReference.to_str
|
(self)
|
return pprint.pformat(self.to_dict())
|
Returns the string representation of the model
|
Returns the string representation of the model
|
[
"Returns",
"the",
"string",
"representation",
"of",
"the",
"model"
] |
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
|
[
"def",
"to_str",
"(",
"self",
")",
":",
"return",
"pprint",
".",
"pformat",
"(",
"self",
".",
"to_dict",
"(",
")",
")"
] |
https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/models/v1_typed_local_object_reference.py#L160-L162
|
|
gnuradio/pybombs
|
17044241bf835b93571026b112f179f2db7448a4
|
pybombs/fetchers/wget.py
|
python
|
Wget.update_src
|
(self, src, dest, dirname, args=None)
|
return self.fetch_url(src, dest, dirname, args)
|
For an update, we grab the archive and copy it over into the existing
directory. Luckily, that's exactly the same as fetch_url().
|
For an update, we grab the archive and copy it over into the existing
directory. Luckily, that's exactly the same as fetch_url().
|
[
"For",
"an",
"update",
"we",
"grab",
"the",
"archive",
"and",
"copy",
"it",
"over",
"into",
"the",
"existing",
"directory",
".",
"Luckily",
"that",
"s",
"exactly",
"the",
"same",
"as",
"fetch_url",
"()",
"."
] |
def update_src(self, src, dest, dirname, args=None):
"""
For an update, we grab the archive and copy it over into the existing
directory. Luckily, that's exactly the same as fetch_url().
"""
return self.fetch_url(src, dest, dirname, args)
|
[
"def",
"update_src",
"(",
"self",
",",
"src",
",",
"dest",
",",
"dirname",
",",
"args",
"=",
"None",
")",
":",
"return",
"self",
".",
"fetch_url",
"(",
"src",
",",
"dest",
",",
"dirname",
",",
"args",
")"
] |
https://github.com/gnuradio/pybombs/blob/17044241bf835b93571026b112f179f2db7448a4/pybombs/fetchers/wget.py#L144-L149
|
|
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/volume/_colorbar.py
|
python
|
ColorBar.thickness
|
(self)
|
return self["thickness"]
|
Sets the thickness of the color bar This measure excludes the
size of the padding, ticks and labels.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
|
Sets the thickness of the color bar This measure excludes the
size of the padding, ticks and labels.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
|
[
"Sets",
"the",
"thickness",
"of",
"the",
"color",
"bar",
"This",
"measure",
"excludes",
"the",
"size",
"of",
"the",
"padding",
"ticks",
"and",
"labels",
".",
"The",
"thickness",
"property",
"is",
"a",
"number",
"and",
"may",
"be",
"specified",
"as",
":",
"-",
"An",
"int",
"or",
"float",
"in",
"the",
"interval",
"[",
"0",
"inf",
"]"
] |
def thickness(self):
"""
Sets the thickness of the color bar This measure excludes the
size of the padding, ticks and labels.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["thickness"]
|
[
"def",
"thickness",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"thickness\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/volume/_colorbar.py#L564-L576
|
|
1012598167/flask_mongodb_game
|
60c7e0351586656ec38f851592886338e50b4110
|
python_flask/venv/Lib/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/pkg_resources/__init__.py
|
python
|
WorkingSet.require
|
(self, *requirements)
|
return needed
|
Ensure that distributions matching `requirements` are activated
`requirements` must be a string or a (possibly-nested) sequence
thereof, specifying the distributions and versions required. The
return value is a sequence of the distributions that needed to be
activated to fulfill the requirements; all relevant distributions are
included, even if they were already activated in this working set.
|
Ensure that distributions matching `requirements` are activated
|
[
"Ensure",
"that",
"distributions",
"matching",
"requirements",
"are",
"activated"
] |
def require(self, *requirements):
"""Ensure that distributions matching `requirements` are activated
`requirements` must be a string or a (possibly-nested) sequence
thereof, specifying the distributions and versions required. The
return value is a sequence of the distributions that needed to be
activated to fulfill the requirements; all relevant distributions are
included, even if they were already activated in this working set.
"""
needed = self.resolve(parse_requirements(requirements))
for dist in needed:
self.add(dist)
return needed
|
[
"def",
"require",
"(",
"self",
",",
"*",
"requirements",
")",
":",
"needed",
"=",
"self",
".",
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"parse_requirements",
"(",
"requirements",
")",
")",
"for",
"dist",
"in",
"needed",
":",
"self",
".",
"add",
"(",
"dist",
")",
"return",
"needed"
] |
https://github.com/1012598167/flask_mongodb_game/blob/60c7e0351586656ec38f851592886338e50b4110/python_flask/venv/Lib/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/pkg_resources/__init__.py#L889-L903
|
|
dropbox/changes
|
37e23c3141b75e4785cf398d015e3dbca41bdd56
|
changes/vcs/base.py
|
python
|
Vcs._construct_subprocess
|
(self, *args, **kwargs)
|
return proc
|
Construct a subprocess with the correct arguments and environment
|
Construct a subprocess with the correct arguments and environment
|
[
"Construct",
"a",
"subprocess",
"with",
"the",
"correct",
"arguments",
"and",
"environment"
] |
def _construct_subprocess(self, *args, **kwargs):
# type: (*Any, **Any) -> Popen
"""Construct a subprocess with the correct arguments and environment"""
env = os.environ.copy()
for key, value in self.get_default_env().iteritems():
env.setdefault(key, value)
env.setdefault('CHANGES_SSH_REPO', self.url)
for key, value in kwargs.pop('env', {}):
env[key] = value
kwargs['env'] = env
kwargs['close_fds'] = True
kwargs.setdefault('stdout', PIPE)
kwargs.setdefault('stderr', PIPE)
kwargs.setdefault('stdin', PIPE)
proc = Popen(*args, **kwargs)
return proc
|
[
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"(",
"*",
"args",
",",
"*",
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"return",
"proc"
] |
https://github.com/dropbox/changes/blob/37e23c3141b75e4785cf398d015e3dbca41bdd56/changes/vcs/base.py#L127-L148
|
|
cloudera/hue
|
23f02102d4547c17c32bd5ea0eb24e9eadd657a4
|
desktop/core/ext-py/oauth2client-4.1.3/oauth2client/_helpers.py
|
python
|
scopes_to_string
|
(scopes)
|
Converts scope value to a string.
If scopes is a string then it is simply passed through. If scopes is an
iterable then a string is returned that is all the individual scopes
concatenated with spaces.
Args:
scopes: string or iterable of strings, the scopes.
Returns:
The scopes formatted as a single string.
|
Converts scope value to a string.
|
[
"Converts",
"scope",
"value",
"to",
"a",
"string",
"."
] |
def scopes_to_string(scopes):
"""Converts scope value to a string.
If scopes is a string then it is simply passed through. If scopes is an
iterable then a string is returned that is all the individual scopes
concatenated with spaces.
Args:
scopes: string or iterable of strings, the scopes.
Returns:
The scopes formatted as a single string.
"""
if isinstance(scopes, six.string_types):
return scopes
else:
return ' '.join(scopes)
|
[
"def",
"scopes_to_string",
"(",
"scopes",
")",
":",
"if",
"isinstance",
"(",
"scopes",
",",
"six",
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"string_types",
")",
":",
"return",
"scopes",
"else",
":",
"return",
"' '",
".",
"join",
"(",
"scopes",
")"
] |
https://github.com/cloudera/hue/blob/23f02102d4547c17c32bd5ea0eb24e9eadd657a4/desktop/core/ext-py/oauth2client-4.1.3/oauth2client/_helpers.py#L143-L159
|
||
stopstalk/stopstalk-deployment
|
10c3ab44c4ece33ae515f6888c15033db2004bb1
|
aws_lambda/spoj_aws_lambda_function/lambda_code/pkg_resources/__init__.py
|
python
|
_find_adapter
|
(registry, ob)
|
Return an adapter factory for `ob` from `registry`
|
Return an adapter factory for `ob` from `registry`
|
[
"Return",
"an",
"adapter",
"factory",
"for",
"ob",
"from",
"registry"
] |
def _find_adapter(registry, ob):
"""Return an adapter factory for `ob` from `registry`"""
types = _always_object(inspect.getmro(getattr(ob, '__class__', type(ob))))
for t in types:
if t in registry:
return registry[t]
|
[
"def",
"_find_adapter",
"(",
"registry",
",",
"ob",
")",
":",
"types",
"=",
"_always_object",
"(",
"inspect",
".",
"getmro",
"(",
"getattr",
"(",
"ob",
",",
"'__class__'",
",",
"type",
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"ob",
")",
")",
")",
")",
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"t",
"in",
"types",
":",
"if",
"t",
"in",
"registry",
":",
"return",
"registry",
"[",
"t",
"]"
] |
https://github.com/stopstalk/stopstalk-deployment/blob/10c3ab44c4ece33ae515f6888c15033db2004bb1/aws_lambda/spoj_aws_lambda_function/lambda_code/pkg_resources/__init__.py#L3023-L3028
|
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