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tendenci/tendenci
|
0f2c348cc0e7d41bc56f50b00ce05544b083bf1d
|
tendenci/apps/pages/feeds.py
|
python
|
LatestEntriesFeed.items
|
(self)
|
return items
|
[] |
def items(self):
items = Page.objects.filter(**PUBLIC_FILTER).filter(syndicate=True).order_by('-create_dt')[:20]
return items
|
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https://github.com/tendenci/tendenci/blob/0f2c348cc0e7d41bc56f50b00ce05544b083bf1d/tendenci/apps/pages/feeds.py#L16-L18
|
|||
OpenMDAO/OpenMDAO
|
f47eb5485a0bb5ea5d2ae5bd6da4b94dc6b296bd
|
openmdao/surrogate_models/nn_interpolators/nn_base.py
|
python
|
NNBase.__init__
|
(self, training_points, training_values, num_leaves=2)
|
Initialize nearest neighbor interpolant by scaling input to the unit hypercube.
|
Initialize nearest neighbor interpolant by scaling input to the unit hypercube.
|
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"."
] |
def __init__(self, training_points, training_values, num_leaves=2):
"""
Initialize nearest neighbor interpolant by scaling input to the unit hypercube.
"""
# training_points and training_values are the known points and their
# respective values which will be interpolated against.
# Grab the mins and ranges of each dimension
self._tpm = np.amin(training_points, axis=0)
self._tpr = (np.amax(training_points, axis=0) - self._tpm)
self._tvm = np.amin(training_values, axis=0)
self._tvr = (np.amax(training_values, axis=0) - self._tvm)
# This prevents against collinear data (range = 0)
self._tpr[self._tpr == 0] = 1
self._tvr[self._tvr == 0] = 1
# Normalize all points
self._tp = (training_points - self._tpm) / self._tpr
self._tv = (training_values - self._tvm) / self._tvr
# Record number of dimensions and points
self._indep_dims = training_points.shape[1]
self._dep_dims = training_values.shape[1]
self._ntpts = training_points.shape[0]
# Make training data into a Tree
leavesz = ceil(self._ntpts / float(num_leaves))
self._KData = cKDTree(self._tp, leafsize=leavesz)
# Cache for gradients
self._pt_cache = None
|
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https://github.com/OpenMDAO/OpenMDAO/blob/f47eb5485a0bb5ea5d2ae5bd6da4b94dc6b296bd/openmdao/surrogate_models/nn_interpolators/nn_base.py#L54-L84
|
||
statsmodels/statsmodels
|
debbe7ea6ba28fe5bdb78f09f8cac694bef98722
|
statsmodels/sandbox/tsa/example_arma.py
|
python
|
autocorr
|
(s, axis=-1)
|
return sxx/N
|
Returns the autocorrelation of signal s at all lags. Adheres to the
definition r(k) = E{s(n)s*(n-k)} where E{} is the expectation operator.
|
Returns the autocorrelation of signal s at all lags. Adheres to the
definition r(k) = E{s(n)s*(n-k)} where E{} is the expectation operator.
|
[
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] |
def autocorr(s, axis=-1):
"""Returns the autocorrelation of signal s at all lags. Adheres to the
definition r(k) = E{s(n)s*(n-k)} where E{} is the expectation operator.
"""
N = s.shape[axis]
S = np.fft.fft(s, n=2*N-1, axis=axis)
sxx = np.fft.ifft(S*S.conjugate(), axis=axis).real[:N]
return sxx/N
|
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|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/urllib3/packages/backports/makefile.py
|
python
|
backport_makefile
|
(self, mode="r", buffering=None, encoding=None,
errors=None, newline=None)
|
return text
|
Backport of ``socket.makefile`` from Python 3.5.
|
Backport of ``socket.makefile`` from Python 3.5.
|
[
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"makefile",
"from",
"Python",
"3",
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"5",
"."
] |
def backport_makefile(self, mode="r", buffering=None, encoding=None,
errors=None, newline=None):
"""
Backport of ``socket.makefile`` from Python 3.5.
"""
if not set(mode) <= set(["r", "w", "b"]):
raise ValueError(
"invalid mode %r (only r, w, b allowed)" % (mode,)
)
writing = "w" in mode
reading = "r" in mode or not writing
assert reading or writing
binary = "b" in mode
rawmode = ""
if reading:
rawmode += "r"
if writing:
rawmode += "w"
raw = SocketIO(self, rawmode)
self._makefile_refs += 1
if buffering is None:
buffering = -1
if buffering < 0:
buffering = io.DEFAULT_BUFFER_SIZE
if buffering == 0:
if not binary:
raise ValueError("unbuffered streams must be binary")
return raw
if reading and writing:
buffer = io.BufferedRWPair(raw, raw, buffering)
elif reading:
buffer = io.BufferedReader(raw, buffering)
else:
assert writing
buffer = io.BufferedWriter(raw, buffering)
if binary:
return buffer
text = io.TextIOWrapper(buffer, encoding, errors, newline)
text.mode = mode
return text
|
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https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/urllib3/packages/backports/makefile.py#L14-L53
|
|
liquidctl/liquidctl
|
73962574632f94050c2a75f517e929a29797b5e2
|
liquidctl/util.py
|
python
|
delta
|
(profile)
|
return [(cur[0]-prev[0], cur[1]-prev[1])
for cur, prev in zip(profile[1:], profile[:-1])]
|
Compute a profile's Δx and Δy.
|
Compute a profile's Δx and Δy.
|
[
"Compute",
"a",
"profile",
"s",
"Δx",
"and",
"Δy",
"."
] |
def delta(profile):
"""Compute a profile's Δx and Δy."""
return [(cur[0]-prev[0], cur[1]-prev[1])
for cur, prev in zip(profile[1:], profile[:-1])]
|
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] |
https://github.com/liquidctl/liquidctl/blob/73962574632f94050c2a75f517e929a29797b5e2/liquidctl/util.py#L169-L172
|
|
saltstack/salt-contrib
|
062355938ad1cced273056e9c23dc344c6a2c858
|
modules/syslog_ng.py
|
python
|
_parse_typed_parameter
|
(param)
|
Parses a TypedParameter and fills it with values.
|
Parses a TypedParameter and fills it with values.
|
[
"Parses",
"a",
"TypedParameter",
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"fills",
"it",
"with",
"values",
"."
] |
def _parse_typed_parameter(param):
'''
Parses a TypedParameter and fills it with values.
'''
global _current_parameter, _current_parameter_value
type, value = _expand_one_key_dictionary(param)
_current_parameter.type = type
if _is_simple_type(value) and value != '':
_current_parameter_value = SimpleParameterValue(value)
_current_parameter.add_value(_current_parameter_value)
elif isinstance(value, list):
for i in value:
if _is_simple_type(i):
_current_parameter_value = SimpleParameterValue(i)
_current_parameter.add_value(_current_parameter_value)
elif isinstance(i, dict):
_current_parameter_value = TypedParameterValue()
_parse_typed_parameter_typed_value(i)
_current_parameter.add_value(_current_parameter_value)
|
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https://github.com/saltstack/salt-contrib/blob/062355938ad1cced273056e9c23dc344c6a2c858/modules/syslog_ng.py#L427-L446
|
||
openstack/manila
|
142990edc027e14839d5deaf4954dd6fc88de15e
|
manila/db/api.py
|
python
|
share_group_snapshot_get
|
(context, share_group_snapshot_id)
|
return IMPL.share_group_snapshot_get(context, share_group_snapshot_id)
|
Get a share group snapshot.
|
Get a share group snapshot.
|
[
"Get",
"a",
"share",
"group",
"snapshot",
"."
] |
def share_group_snapshot_get(context, share_group_snapshot_id):
"""Get a share group snapshot."""
return IMPL.share_group_snapshot_get(context, share_group_snapshot_id)
|
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",",
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https://github.com/openstack/manila/blob/142990edc027e14839d5deaf4954dd6fc88de15e/manila/db/api.py#L1310-L1312
|
|
Delta-ML/delta
|
31dfebc8f20b7cb282b62f291ff25a87e403cc86
|
egs/sre16/v1/local/make_sre18_eval.py
|
python
|
wait_for_background_commands
|
()
|
This waits for all threads to exit. You will often want to
run this at the end of programs that have launched background
threads, so that the program will wait for its child processes
to terminate before it dies.
|
This waits for all threads to exit. You will often want to
run this at the end of programs that have launched background
threads, so that the program will wait for its child processes
to terminate before it dies.
|
[
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def wait_for_background_commands():
""" This waits for all threads to exit. You will often want to
run this at the end of programs that have launched background
threads, so that the program will wait for its child processes
to terminate before it dies."""
for t in threading.enumerate():
if not t == threading.current_thread():
t.join()
|
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https://github.com/Delta-ML/delta/blob/31dfebc8f20b7cb282b62f291ff25a87e403cc86/egs/sre16/v1/local/make_sre18_eval.py#L178-L185
|
||
kanzure/nanoengineer
|
874e4c9f8a9190f093625b267f9767e19f82e6c4
|
cad/src/commands/Extrude/ExtrudePropertyManager.py
|
python
|
ExtrudePropertyManager.show
|
(self)
|
Extends superclass method.
|
Extends superclass method.
|
[
"Extends",
"superclass",
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"."
] |
def show(self):
"""
Extends superclass method.
"""
_superclass.show(self)
self.updateMessage()
|
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https://github.com/kanzure/nanoengineer/blob/874e4c9f8a9190f093625b267f9767e19f82e6c4/cad/src/commands/Extrude/ExtrudePropertyManager.py#L40-L46
|
||
python-telegram-bot/python-telegram-bot
|
ade1529986f5b6d394a65372d6a27045a70725b2
|
telegram/ext/updater.py
|
python
|
Updater.__init__
|
(
self: 'Updater[CCT, UD, CD, BD]',
user_sig_handler: Callable = None,
dispatcher: Dispatcher[CCT, UD, CD, BD] = None,
)
|
[] |
def __init__(
self: 'Updater[CCT, UD, CD, BD]',
user_sig_handler: Callable = None,
dispatcher: Dispatcher[CCT, UD, CD, BD] = None,
):
...
|
[
"def",
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"self",
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"'Updater[CCT, UD, CD, BD]'",
",",
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",",
"CD",
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"BD",
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"=",
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",",
")",
":",
"..."
] |
https://github.com/python-telegram-bot/python-telegram-bot/blob/ade1529986f5b6d394a65372d6a27045a70725b2/telegram/ext/updater.py#L195-L200
|
||||
dimagi/commcare-hq
|
d67ff1d3b4c51fa050c19e60c3253a79d3452a39
|
corehq/apps/users/models.py
|
python
|
CouchUser.from_django_user
|
(cls, django_user, strict=False)
|
return cls.get_by_username(django_user.username, strict=strict)
|
[] |
def from_django_user(cls, django_user, strict=False):
return cls.get_by_username(django_user.username, strict=strict)
|
[
"def",
"from_django_user",
"(",
"cls",
",",
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"get_by_username",
"(",
"django_user",
".",
"username",
",",
"strict",
"=",
"strict",
")"
] |
https://github.com/dimagi/commcare-hq/blob/d67ff1d3b4c51fa050c19e60c3253a79d3452a39/corehq/apps/users/models.py#L1391-L1392
|
|||
mypaint/mypaint
|
90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33
|
lib/layer/group.py
|
python
|
LayerStack.__setitem__
|
(self, index, layer)
|
Replaces the layer at an index (notifies root)
|
Replaces the layer at an index (notifies root)
|
[
"Replaces",
"the",
"layer",
"at",
"an",
"index",
"(",
"notifies",
"root",
")"
] |
def __setitem__(self, index, layer):
"""Replaces the layer at an index (notifies root)"""
index = self._normidx(index)
oldlayer = self._layers[index]
self._layers[index] = layer
self._notify_disown(oldlayer, index)
updates = [oldlayer.get_full_redraw_bbox()]
self._notify_adopt(layer, index)
updates.append(layer.get_full_redraw_bbox())
self._content_changed(*tuple(core.combine_redraws(updates)))
|
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] |
https://github.com/mypaint/mypaint/blob/90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33/lib/layer/group.py#L349-L358
|
||
angr/claripy
|
4c961b4dc664706be8142fe4868f27655bc8da77
|
claripy/simplifications.py
|
python
|
SimplificationManager.rotate_shift_mask_simplifier
|
(a, b)
|
return expr
|
Handles the following case:
((A << a) | (A >> (_N - a))) & mask, where
A being a BVS,
a being a integer that is less than _N,
_N is either 32 or 64, and
mask can be evaluated to 0xffffffff (64-bit) or 0xffff (32-bit) after reversing the rotate-shift
operation.
It will be simplified to:
(A & (mask >>> a)) <<< a
|
Handles the following case:
((A << a) | (A >> (_N - a))) & mask, where
A being a BVS,
a being a integer that is less than _N,
_N is either 32 or 64, and
mask can be evaluated to 0xffffffff (64-bit) or 0xffff (32-bit) after reversing the rotate-shift
operation.
|
[
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"32",
"-",
"bit",
")",
"after",
"reversing",
"the",
"rotate",
"-",
"shift",
"operation",
"."
] |
def rotate_shift_mask_simplifier(a, b):
"""
Handles the following case:
((A << a) | (A >> (_N - a))) & mask, where
A being a BVS,
a being a integer that is less than _N,
_N is either 32 or 64, and
mask can be evaluated to 0xffffffff (64-bit) or 0xffff (32-bit) after reversing the rotate-shift
operation.
It will be simplified to:
(A & (mask >>> a)) <<< a
"""
# is the second argument a BVV?
if b.op != 'BVV':
return None
# is it a rotate-shift?
if a.op != '__or__' or len(a.args) != 2:
return None
a_0, a_1 = a.args
if a_0.op != '__lshift__':
return None
if a_1.op != 'LShR':
return None
a_00, a_01 = a_0.args
a_10, a_11 = a_1.args
if not a_00 is a_10:
return None
if a_01.op != 'BVV' or a_11.op != 'BVV':
return None
lshift_ = a_01.args[0]
rshift_ = a_11.args[0]
bitwidth = lshift_ + rshift_
if bitwidth not in (32, 64):
return None
# is the second argument a mask?
# Note: the following check can be further loosen if we want to support more masks.
if bitwidth == 32:
m = ((b.args[0] << rshift_) & 0xffffffff) | (b.args[0] >> lshift_)
if m != 0xffff:
return None
else: # bitwidth == 64
m = ((b.args[0] << rshift_) & 0xffffffffffffffff) | (b.args[0] >> lshift_)
if m != 0xffffffff:
return None
# Show our power!
masked_a = (a_00 & m)
expr = (masked_a << lshift_) | (masked_a >> rshift_)
return expr
|
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] |
https://github.com/angr/claripy/blob/4c961b4dc664706be8142fe4868f27655bc8da77/claripy/simplifications.py#L811-L864
|
|
shchur/gnn-benchmark
|
1e72912a0810cdf27ae54fd589a3b43358a2b161
|
gnnbench/models/gat.py
|
python
|
attention_layer
|
(inputs, output_dim, num_heads, graph_adj, adj_with_self_loops_indices, activation_fn,
use_averaging,
input_dropout_prob, coefficient_dropout_prob,
weight_decay,
name)
|
[] |
def attention_layer(inputs, output_dim, num_heads, graph_adj, adj_with_self_loops_indices, activation_fn,
use_averaging,
input_dropout_prob, coefficient_dropout_prob,
weight_decay,
name):
with tf.name_scope(name):
head_results = []
for i in range(num_heads):
# dims: num_nodes x num_features, output_dim, num_nodes x num_nodes -> num_nodes x output_dim
head_results.append(attention_head(inputs=inputs, output_dim=output_dim,
graph_adj=graph_adj,
adj_with_self_loops_indices=adj_with_self_loops_indices,
activation_fn=activation_fn,
input_dropout_prob=input_dropout_prob,
coefficient_dropout_prob=coefficient_dropout_prob,
weight_decay=weight_decay,
name="%s-head%d" % (name, i)))
if use_averaging:
return tf.add_n(head_results) / num_heads
else:
# dims: num_nodes x output_dim -> num_nodes x num_heads x output_dim
return tf.concat(head_results, axis=1)
|
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",",
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"=",
"1",
")"
] |
https://github.com/shchur/gnn-benchmark/blob/1e72912a0810cdf27ae54fd589a3b43358a2b161/gnnbench/models/gat.py#L118-L139
|
||||
iwonbigbro/gsync
|
03db22fe826f073e56877918b0acb7b4ba908f18
|
libgsync/output.py
|
python
|
Channel.disable
|
(self)
|
Disables the channel.
|
Disables the channel.
|
[
"Disables",
"the",
"channel",
"."
] |
def disable(self):
"""Disables the channel."""
self._priority = -1
|
[
"def",
"disable",
"(",
"self",
")",
":",
"self",
".",
"_priority",
"=",
"-",
"1"
] |
https://github.com/iwonbigbro/gsync/blob/03db22fe826f073e56877918b0acb7b4ba908f18/libgsync/output.py#L29-L32
|
||
uqfoundation/multiprocess
|
028cc73f02655e6451d92e5147d19d8c10aebe50
|
py3.10/multiprocess/resource_tracker.py
|
python
|
ResourceTracker.ensure_running
|
(self)
|
Make sure that resource tracker process is running.
This can be run from any process. Usually a child process will use
the resource created by its parent.
|
Make sure that resource tracker process is running.
|
[
"Make",
"sure",
"that",
"resource",
"tracker",
"process",
"is",
"running",
"."
] |
def ensure_running(self):
'''Make sure that resource tracker process is running.
This can be run from any process. Usually a child process will use
the resource created by its parent.'''
with self._lock:
if self._fd is not None:
# resource tracker was launched before, is it still running?
if self._check_alive():
# => still alive
return
# => dead, launch it again
os.close(self._fd)
# Clean-up to avoid dangling processes.
try:
# _pid can be None if this process is a child from another
# python process, which has started the resource_tracker.
if self._pid is not None:
os.waitpid(self._pid, 0)
except ChildProcessError:
# The resource_tracker has already been terminated.
pass
self._fd = None
self._pid = None
warnings.warn('resource_tracker: process died unexpectedly, '
'relaunching. Some resources might leak.')
fds_to_pass = []
try:
fds_to_pass.append(sys.stderr.fileno())
except Exception:
pass
cmd = 'from multiprocess.resource_tracker import main;main(%d)'
r, w = os.pipe()
try:
fds_to_pass.append(r)
# process will out live us, so no need to wait on pid
exe = spawn.get_executable()
args = [exe] + util._args_from_interpreter_flags()
args += ['-c', cmd % r]
# bpo-33613: Register a signal mask that will block the signals.
# This signal mask will be inherited by the child that is going
# to be spawned and will protect the child from a race condition
# that can make the child die before it registers signal handlers
# for SIGINT and SIGTERM. The mask is unregistered after spawning
# the child.
try:
if _HAVE_SIGMASK:
signal.pthread_sigmask(signal.SIG_BLOCK, _IGNORED_SIGNALS)
pid = util.spawnv_passfds(exe, args, fds_to_pass)
finally:
if _HAVE_SIGMASK:
signal.pthread_sigmask(signal.SIG_UNBLOCK, _IGNORED_SIGNALS)
except:
os.close(w)
raise
else:
self._fd = w
self._pid = pid
finally:
os.close(r)
|
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] |
https://github.com/uqfoundation/multiprocess/blob/028cc73f02655e6451d92e5147d19d8c10aebe50/py3.10/multiprocess/resource_tracker.py#L81-L143
|
||
Vector35/binaryninja-api
|
d9661f34eec6855d495a10eaafc2a8e2679756a7
|
python/plugin.py
|
python
|
PluginCommand.register_for_address
|
(cls,
name: str,
description: str,
action: Callable[[binaryview.BinaryView, int], None],
is_valid: Optional[Callable[[binaryview.BinaryView, int], bool]] = None
)
|
r"""
``register_for_address`` Register a plugin to be called with an address argument
:param str name: name of the plugin (use 'Folder\\Name' to have the menu item nested in a folder)
:param str description: description of the plugin
:param callback action: function to call with the :class:`~binaryview.BinaryView` and address as arguments
:param callback is_valid: optional argument of a function passed a :class:`~binaryview.BinaryView` and address to determine whether the plugin should be enabled for that view
:rtype: None
.. warning:: Calling ``register_for_address`` with the same function name will replace the existing function but will leak the memory of the original plugin.
|
r"""
``register_for_address`` Register a plugin to be called with an address argument
|
[
"r",
"register_for_address",
"Register",
"a",
"plugin",
"to",
"be",
"called",
"with",
"an",
"address",
"argument"
] |
def register_for_address(cls,
name: str,
description: str,
action: Callable[[binaryview.BinaryView, int], None],
is_valid: Optional[Callable[[binaryview.BinaryView, int], bool]] = None
):
r"""
``register_for_address`` Register a plugin to be called with an address argument
:param str name: name of the plugin (use 'Folder\\Name' to have the menu item nested in a folder)
:param str description: description of the plugin
:param callback action: function to call with the :class:`~binaryview.BinaryView` and address as arguments
:param callback is_valid: optional argument of a function passed a :class:`~binaryview.BinaryView` and address to determine whether the plugin should be enabled for that view
:rtype: None
.. warning:: Calling ``register_for_address`` with the same function name will replace the existing function but will leak the memory of the original plugin.
"""
binaryninja._init_plugins()
action_obj = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.POINTER(core.BNBinaryView), ctypes.c_ulonglong)(lambda ctxt, view, addr: cls._address_action(view, addr, action))
is_valid_obj = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p, ctypes.POINTER(core.BNBinaryView), ctypes.c_ulonglong)(lambda ctxt, view, addr: cls._address_is_valid(view, addr, is_valid))
cls._registered_commands.append((action_obj, is_valid_obj))
core.BNRegisterPluginCommandForAddress(name, description, action_obj, is_valid_obj, None)
|
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https://github.com/Vector35/binaryninja-api/blob/d9661f34eec6855d495a10eaafc2a8e2679756a7/python/plugin.py#L376-L397
|
||
AppScale/gts
|
46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9
|
AppServer/lib/google-api-python-client/oauth2client/client.py
|
python
|
Credentials.to_json
|
(self)
|
return self._to_json(Credentials.NON_SERIALIZED_MEMBERS)
|
Creating a JSON representation of an instance of Credentials.
Returns:
string, a JSON representation of this instance, suitable to pass to
from_json().
|
Creating a JSON representation of an instance of Credentials.
|
[
"Creating",
"a",
"JSON",
"representation",
"of",
"an",
"instance",
"of",
"Credentials",
"."
] |
def to_json(self):
"""Creating a JSON representation of an instance of Credentials.
Returns:
string, a JSON representation of this instance, suitable to pass to
from_json().
"""
return self._to_json(Credentials.NON_SERIALIZED_MEMBERS)
|
[
"def",
"to_json",
"(",
"self",
")",
":",
"return",
"self",
".",
"_to_json",
"(",
"Credentials",
".",
"NON_SERIALIZED_MEMBERS",
")"
] |
https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/lib/google-api-python-client/oauth2client/client.py#L134-L141
|
|
H4ckForJob/dirmap
|
d2000165efe01988fe55280175b0f87930f65d33
|
thirdlib/IPy/IPy.py
|
python
|
IPint.__cmp__
|
(self, other)
|
return 0
|
Called by comparison operations.
Should return a negative integer if self < other, zero if self
== other, a positive integer if self > other.
Order is first determined by the address family. IPv4 addresses
are always smaller than IPv6 addresses:
>>> IP('10.0.0.0') < IP('2001:db8::')
1
Then the first address is compared. Lower addresses are
always smaller:
>>> IP('10.0.0.0') > IP('10.0.0.1')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.1')
0
>>> IP('10.0.1.0') > IP('10.0.0.0/24')
1
>>> IP('10.0.1.0/24') > IP('10.0.0.0/24')
1
>>> IP('10.0.1.0/24') > IP('10.0.0.0')
1
Then the prefix length is compared. Shorter prefixes are
considered smaller than longer prefixes:
>>> IP('10.0.0.0/24') > IP('10.0.0.0')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.0/25')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.0/23')
1
|
Called by comparison operations.
|
[
"Called",
"by",
"comparison",
"operations",
"."
] |
def __cmp__(self, other):
"""Called by comparison operations.
Should return a negative integer if self < other, zero if self
== other, a positive integer if self > other.
Order is first determined by the address family. IPv4 addresses
are always smaller than IPv6 addresses:
>>> IP('10.0.0.0') < IP('2001:db8::')
1
Then the first address is compared. Lower addresses are
always smaller:
>>> IP('10.0.0.0') > IP('10.0.0.1')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.1')
0
>>> IP('10.0.1.0') > IP('10.0.0.0/24')
1
>>> IP('10.0.1.0/24') > IP('10.0.0.0/24')
1
>>> IP('10.0.1.0/24') > IP('10.0.0.0')
1
Then the prefix length is compared. Shorter prefixes are
considered smaller than longer prefixes:
>>> IP('10.0.0.0/24') > IP('10.0.0.0')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.0/25')
0
>>> IP('10.0.0.0/24') > IP('10.0.0.0/23')
1
"""
if not isinstance(other, IPint):
raise TypeError
# Lower version -> lower result
if self._ipversion != other._ipversion:
return self._ipversion < other._ipversion and -1 or 1
# Lower start address -> lower result
if self.ip != other.ip:
return self.ip < other.ip and -1 or 1
# Shorter prefix length -> lower result
if self._prefixlen != other._prefixlen:
return self._prefixlen < other._prefixlen and -1 or 1
# No differences found
return 0
|
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] |
https://github.com/H4ckForJob/dirmap/blob/d2000165efe01988fe55280175b0f87930f65d33/thirdlib/IPy/IPy.py#L706-L759
|
|
holzschu/Carnets
|
44effb10ddfc6aa5c8b0687582a724ba82c6b547
|
Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/collections.py
|
python
|
EventCollection.get_linewidth
|
(self)
|
return super(EventCollection, self).get_linewidth()[0]
|
Get the width of the lines used to mark each event.
|
Get the width of the lines used to mark each event.
|
[
"Get",
"the",
"width",
"of",
"the",
"lines",
"used",
"to",
"mark",
"each",
"event",
"."
] |
def get_linewidth(self):
"""Get the width of the lines used to mark each event."""
return super(EventCollection, self).get_linewidth()[0]
|
[
"def",
"get_linewidth",
"(",
"self",
")",
":",
"return",
"super",
"(",
"EventCollection",
",",
"self",
")",
".",
"get_linewidth",
"(",
")",
"[",
"0",
"]"
] |
https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/collections.py#L1530-L1532
|
|
sentinel-hub/eo-learn
|
cf964eaf173668d6a374675dbd7c1d244264c11d
|
visualization/eolearn/visualization/eoexecutor_visualization.py
|
python
|
EOExecutorVisualization._get_task_descriptions
|
(self)
|
return descriptions
|
Prepares a list of task names and their initialization parameters
|
Prepares a list of task names and their initialization parameters
|
[
"Prepares",
"a",
"list",
"of",
"task",
"names",
"and",
"their",
"initialization",
"parameters"
] |
def _get_task_descriptions(self):
""" Prepares a list of task names and their initialization parameters
"""
descriptions = []
for task_name, task in self.eoexecutor.workflow.get_tasks().items():
descriptions.append({
'name': f"{task_name} ({task.__class__.__name__}_{task.private_task_config.uuid[:6]})",
'args': {
key: value.replace('<', '<').replace('>', '>') for key, value in
task.private_task_config.init_args.items()
}
})
return descriptions
|
[
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"}",
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] |
https://github.com/sentinel-hub/eo-learn/blob/cf964eaf173668d6a374675dbd7c1d244264c11d/visualization/eolearn/visualization/eoexecutor_visualization.py#L88-L102
|
|
statsmodels/statsmodels
|
debbe7ea6ba28fe5bdb78f09f8cac694bef98722
|
statsmodels/stats/libqsturng/qsturng_.py
|
python
|
_interpolate_v
|
(p, r, v)
|
return y
|
interpolates v based on the values in the A table for the
scalar value of r and th
|
interpolates v based on the values in the A table for the
scalar value of r and th
|
[
"interpolates",
"v",
"based",
"on",
"the",
"values",
"in",
"the",
"A",
"table",
"for",
"the",
"scalar",
"value",
"of",
"r",
"and",
"th"
] |
def _interpolate_v(p, r, v):
"""
interpolates v based on the values in the A table for the
scalar value of r and th
"""
# interpolate v (p should be in table)
# ordinate: y**2
# abcissa: 1./v
# find the 3 closest v values
# only p >= .9 have table values for 1 degree of freedom.
# The boolean is used to index the tuple and append 1 when
# p >= .9
v0, v1, v2 = _select_vs(v, p)
# y = f - 1.
y0_sq = (_func(A[(p,v0)], p, r, v0) + 1.)**2.
y1_sq = (_func(A[(p,v1)], p, r, v1) + 1.)**2.
y2_sq = (_func(A[(p,v2)], p, r, v2) + 1.)**2.
# if v2 is inf set to a big number so interpolation
# calculations will work
if v2 > 1e38:
v2 = 1e38
# transform v
v_, v0_, v1_, v2_ = 1./v, 1./v0, 1./v1, 1./v2
# calculate derivatives for quadratic interpolation
d2 = 2.*((y2_sq-y1_sq)/(v2_-v1_) - \
(y0_sq-y1_sq)/(v0_-v1_)) / (v2_-v0_)
if (v2_ + v0_) >= (v1_ + v1_):
d1 = (y2_sq-y1_sq) / (v2_-v1_) - 0.5*d2*(v2_-v1_)
else:
d1 = (y1_sq-y0_sq) / (v1_-v0_) + 0.5*d2*(v1_-v0_)
d0 = y1_sq
# calculate y
y = math.sqrt((d2/2.)*(v_-v1_)**2. + d1*(v_-v1_)+ d0)
return y
|
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https://github.com/statsmodels/statsmodels/blob/debbe7ea6ba28fe5bdb78f09f8cac694bef98722/statsmodels/stats/libqsturng/qsturng_.py#L627-L667
|
|
jython/jython3
|
def4f8ec47cb7a9c799ea4c745f12badf92c5769
|
lib-python/3.5.1/_pydecimal.py
|
python
|
Decimal.__float__
|
(self)
|
return float(s)
|
Float representation.
|
Float representation.
|
[
"Float",
"representation",
"."
] |
def __float__(self):
"""Float representation."""
if self._isnan():
if self.is_snan():
raise ValueError("Cannot convert signaling NaN to float")
s = "-nan" if self._sign else "nan"
else:
s = str(self)
return float(s)
|
[
"def",
"__float__",
"(",
"self",
")",
":",
"if",
"self",
".",
"_isnan",
"(",
")",
":",
"if",
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"(",
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"ValueError",
"(",
"\"Cannot convert signaling NaN to float\"",
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"=",
"str",
"(",
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")",
"return",
"float",
"(",
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")"
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https://github.com/jython/jython3/blob/def4f8ec47cb7a9c799ea4c745f12badf92c5769/lib-python/3.5.1/_pydecimal.py#L1598-L1606
|
|
blurstudio/cross3d
|
277968d1227de740fc87ef61005c75034420eadf
|
cross3d/abstract/abstractscenelayer.py
|
python
|
AbstractSceneLayer.altMaterialCount
|
(self)
|
return len(self._nativeAltMaterials())
|
\remarks return the number of alternate materials that this layer is associated with
\return <int> count
|
\remarks return the number of alternate materials that this layer is associated with
|
[
"\\",
"remarks",
"return",
"the",
"number",
"of",
"alternate",
"materials",
"that",
"this",
"layer",
"is",
"associated",
"with"
] |
def altMaterialCount(self):
"""
\remarks return the number of alternate materials that this layer is associated with
\return <int> count
"""
return len(self._nativeAltMaterials())
|
[
"def",
"altMaterialCount",
"(",
"self",
")",
":",
"return",
"len",
"(",
"self",
".",
"_nativeAltMaterials",
"(",
")",
")"
] |
https://github.com/blurstudio/cross3d/blob/277968d1227de740fc87ef61005c75034420eadf/cross3d/abstract/abstractscenelayer.py#L155-L161
|
|
caktus/django-timepiece
|
52515dec027664890efbc535429e1ba1ee152f40
|
timepiece/entries/views.py
|
python
|
Dashboard.process_progress
|
(self, entries, assignments)
|
return project_progress
|
Returns a list of progress summary data (pk, name, hours worked, and
hours assigned) for each project either worked or assigned.
The list is ordered by project name.
|
Returns a list of progress summary data (pk, name, hours worked, and
hours assigned) for each project either worked or assigned.
The list is ordered by project name.
|
[
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"pk",
"name",
"hours",
"worked",
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"worked",
"or",
"assigned",
".",
"The",
"list",
"is",
"ordered",
"by",
"project",
"name",
"."
] |
def process_progress(self, entries, assignments):
"""
Returns a list of progress summary data (pk, name, hours worked, and
hours assigned) for each project either worked or assigned.
The list is ordered by project name.
"""
# Determine all projects either worked or assigned.
project_q = Q(id__in=assignments.values_list('project__id', flat=True))
project_q |= Q(id__in=entries.values_list('project__id', flat=True))
projects = Project.objects.filter(project_q).select_related('business')
# Hours per project.
project_data = {}
for project in projects:
try:
assigned = assignments.get(project__id=project.pk).hours
except ProjectHours.DoesNotExist:
assigned = Decimal('0.00')
project_data[project.pk] = {
'project': project,
'assigned': assigned,
'worked': Decimal('0.00'),
}
for entry in entries:
pk = entry.project_id
hours = Decimal('%.5f' % (entry.get_total_seconds() / 3600.0))
project_data[pk]['worked'] += hours
# Sort by maximum of worked or assigned hours (highest first).
key = lambda x: x['project'].name.lower()
project_progress = sorted(project_data.values(), key=key)
return project_progress
|
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] |
https://github.com/caktus/django-timepiece/blob/52515dec027664890efbc535429e1ba1ee152f40/timepiece/entries/views.py#L101-L134
|
|
leo-editor/leo-editor
|
383d6776d135ef17d73d935a2f0ecb3ac0e99494
|
leo/plugins/obsolete/swing_gui.py
|
python
|
leoSwingTree.allocateNodes
|
(self,where,lines)
|
Allocate Tk widgets in nodes that will become visible as the result of an upcoming scroll
|
Allocate Tk widgets in nodes that will become visible as the result of an upcoming scroll
|
[
"Allocate",
"Tk",
"widgets",
"in",
"nodes",
"that",
"will",
"become",
"visible",
"as",
"the",
"result",
"of",
"an",
"upcoming",
"scroll"
] |
def allocateNodes(self,where,lines):
"""Allocate Tk widgets in nodes that will become visible as the result of an upcoming scroll"""
assert(where in ("above","below"))
# print "allocateNodes: %d lines %s visible area" % (lines,where)
# Expand the visible area: a little extra delta is safer.
delta = lines * (self.line_height + 4)
y1,y2 = self.visibleArea
if where == "below":
y2 += delta
else:
y1 = max(0.0,y1-delta)
self.expandedVisibleArea=y1,y2
# print "expandedArea: %5.1f %5.1f" % (y1,y2)
# Allocate all nodes in expanded visible area.
self.updatedNodeCount = 0
self.updateTree(self.c.rootPosition(),self.root_left,self.root_top,0,0)
|
[
"def",
"allocateNodes",
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"self",
",",
"where",
",",
"lines",
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"assert",
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"\"above\"",
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"# print \"allocateNodes: %d lines %s visible area\" % (lines,where)",
"# Expand the visible area: a little extra delta is safer.",
"delta",
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"line_height",
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",",
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"# print \"expandedArea: %5.1f %5.1f\" % (y1,y2)",
"# Allocate all nodes in expanded visible area.",
"self",
".",
"updatedNodeCount",
"=",
"0",
"self",
".",
"updateTree",
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".",
"root_left",
",",
"self",
".",
"root_top",
",",
"0",
",",
"0",
")"
] |
https://github.com/leo-editor/leo-editor/blob/383d6776d135ef17d73d935a2f0ecb3ac0e99494/leo/plugins/obsolete/swing_gui.py#L6176-L6198
|
||
mfessenden/SceneGraph
|
0fa3429059c77c881d1b58b28e89dcb44c609909
|
ui/attributes.py
|
python
|
QIntEditor.initializeEditor
|
(self)
|
Set the widgets nodes values.
|
Set the widgets nodes values.
|
[
"Set",
"the",
"widgets",
"nodes",
"values",
"."
] |
def initializeEditor(self):
"""
Set the widgets nodes values.
"""
if not self.nodes or not self.attribute:
return
editor_value = self.default_value
node_values = self.values
if node_values:
if len(node_values) > 1:
pass
elif len(node_values) == 1:
if node_values[0]:
editor_value = node_values[0]
# set the editor value
self.val1_edit.blockSignals(True)
editor_value = int(editor_value)
# set the current node values.
self.val1_edit.setText(str(editor_value))
self.val1_edit.blockSignals(False)
self.val1_edit.valueChanged.connect(self.valueUpdatedAction)
|
[
"def",
"initializeEditor",
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"self",
")",
":",
"if",
"not",
"self",
".",
"nodes",
"or",
"not",
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"attribute",
":",
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"val1_edit",
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"valueChanged",
".",
"connect",
"(",
"self",
".",
"valueUpdatedAction",
")"
] |
https://github.com/mfessenden/SceneGraph/blob/0fa3429059c77c881d1b58b28e89dcb44c609909/ui/attributes.py#L757-L783
|
||
allenai/allennlp
|
a3d71254fcc0f3615910e9c3d48874515edf53e0
|
allennlp/modules/conditional_random_field.py
|
python
|
ConditionalRandomField.forward
|
(
self, inputs: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor = None
)
|
return torch.sum(log_numerator - log_denominator)
|
Computes the log likelihood.
|
Computes the log likelihood.
|
[
"Computes",
"the",
"log",
"likelihood",
"."
] |
def forward(
self, inputs: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor = None
) -> torch.Tensor:
"""
Computes the log likelihood.
"""
if mask is None:
mask = torch.ones(*tags.size(), dtype=torch.bool, device=inputs.device)
else:
# The code below fails in weird ways if this isn't a bool tensor, so we make sure.
mask = mask.to(torch.bool)
log_denominator = self._input_likelihood(inputs, mask)
log_numerator = self._joint_likelihood(inputs, tags, mask)
return torch.sum(log_numerator - log_denominator)
|
[
"def",
"forward",
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"self",
",",
"inputs",
":",
"torch",
".",
"Tensor",
",",
"tags",
":",
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"sum",
"(",
"log_numerator",
"-",
"log_denominator",
")"
] |
https://github.com/allenai/allennlp/blob/a3d71254fcc0f3615910e9c3d48874515edf53e0/allennlp/modules/conditional_random_field.py#L318-L334
|
|
omerbsezer/LSTM_RNN_Tutorials_with_Demo
|
d3bb6b7dac10159b2e8a0a21fbf27e0078c3321b
|
SentimentAnalysisProject/emo_utils.py
|
python
|
read_glove_vecs
|
(glove_file)
|
return words_to_index, index_to_words, word_to_vec_map
|
[] |
def read_glove_vecs(glove_file):
with open(glove_file, 'r', encoding="utf8") as f:
words = set()
word_to_vec_map = {}
for line in f:
line = line.strip().split()
curr_word = line[0]
words.add(curr_word)
word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)
i = 1
words_to_index = {}
index_to_words = {}
for w in sorted(words):
words_to_index[w] = i
index_to_words[i] = w
i = i + 1
return words_to_index, index_to_words, word_to_vec_map
|
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"read_glove_vecs",
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":",
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"(",
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",",
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"i",
"+",
"1",
"return",
"words_to_index",
",",
"index_to_words",
",",
"word_to_vec_map"
] |
https://github.com/omerbsezer/LSTM_RNN_Tutorials_with_Demo/blob/d3bb6b7dac10159b2e8a0a21fbf27e0078c3321b/SentimentAnalysisProject/emo_utils.py#L8-L25
|
|||
wizyoung/googletranslate.popclipext
|
a3c465685a5a75213e2ec8517eb98d336984bc50
|
src/h2/settings.py
|
python
|
Settings.acknowledge
|
(self)
|
return changed_settings
|
The settings have been acknowledged, either by the user (remote
settings) or by the remote peer (local settings).
:returns: A dict of {setting: ChangedSetting} that were applied.
|
The settings have been acknowledged, either by the user (remote
settings) or by the remote peer (local settings).
|
[
"The",
"settings",
"have",
"been",
"acknowledged",
"either",
"by",
"the",
"user",
"(",
"remote",
"settings",
")",
"or",
"by",
"the",
"remote",
"peer",
"(",
"local",
"settings",
")",
"."
] |
def acknowledge(self):
"""
The settings have been acknowledged, either by the user (remote
settings) or by the remote peer (local settings).
:returns: A dict of {setting: ChangedSetting} that were applied.
"""
changed_settings = {}
# If there is more than one setting in the list, we have a setting
# value outstanding. Update them.
for k, v in self._settings.items():
if len(v) > 1:
old_setting = v.popleft()
new_setting = v[0]
changed_settings[k] = ChangedSetting(
k, old_setting, new_setting
)
return changed_settings
|
[
"def",
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"}",
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"(",
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https://github.com/wizyoung/googletranslate.popclipext/blob/a3c465685a5a75213e2ec8517eb98d336984bc50/src/h2/settings.py#L160-L179
|
|
Source-Python-Dev-Team/Source.Python
|
d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb
|
addons/source-python/Python3/asyncio/queues.py
|
python
|
Queue.put
|
(self, item)
|
return self.put_nowait(item)
|
Put an item into the queue.
Put an item into the queue. If the queue is full, wait until a free
slot is available before adding item.
This method is a coroutine.
|
Put an item into the queue.
|
[
"Put",
"an",
"item",
"into",
"the",
"queue",
"."
] |
def put(self, item):
"""Put an item into the queue.
Put an item into the queue. If the queue is full, wait until a free
slot is available before adding item.
This method is a coroutine.
"""
while self.full():
putter = self._loop.create_future()
self._putters.append(putter)
try:
yield from putter
except:
putter.cancel() # Just in case putter is not done yet.
if not self.full() and not putter.cancelled():
# We were woken up by get_nowait(), but can't take
# the call. Wake up the next in line.
self._wakeup_next(self._putters)
raise
return self.put_nowait(item)
|
[
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"(",
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] |
https://github.com/Source-Python-Dev-Team/Source.Python/blob/d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb/addons/source-python/Python3/asyncio/queues.py#L121-L141
|
|
twilio/twilio-python
|
6e1e811ea57a1edfadd5161ace87397c563f6915
|
twilio/rest/video/v1/room/room_participant/room_participant_subscribed_track.py
|
python
|
SubscribedTrackList.stream
|
(self, limit=None, page_size=None)
|
return self._version.stream(page, limits['limit'])
|
Streams SubscribedTrackInstance records from the API as a generator stream.
This operation lazily loads records as efficiently as possible until the limit
is reached.
The results are returned as a generator, so this operation is memory efficient.
:param int limit: Upper limit for the number of records to return. stream()
guarantees to never return more than limit. Default is no limit
:param int page_size: Number of records to fetch per request, when not set will use
the default value of 50 records. If no page_size is defined
but a limit is defined, stream() will attempt to read the
limit with the most efficient page size, i.e. min(limit, 1000)
:returns: Generator that will yield up to limit results
:rtype: list[twilio.rest.video.v1.room.room_participant.room_participant_subscribed_track.SubscribedTrackInstance]
|
Streams SubscribedTrackInstance records from the API as a generator stream.
This operation lazily loads records as efficiently as possible until the limit
is reached.
The results are returned as a generator, so this operation is memory efficient.
|
[
"Streams",
"SubscribedTrackInstance",
"records",
"from",
"the",
"API",
"as",
"a",
"generator",
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".",
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"efficient",
"."
] |
def stream(self, limit=None, page_size=None):
"""
Streams SubscribedTrackInstance records from the API as a generator stream.
This operation lazily loads records as efficiently as possible until the limit
is reached.
The results are returned as a generator, so this operation is memory efficient.
:param int limit: Upper limit for the number of records to return. stream()
guarantees to never return more than limit. Default is no limit
:param int page_size: Number of records to fetch per request, when not set will use
the default value of 50 records. If no page_size is defined
but a limit is defined, stream() will attempt to read the
limit with the most efficient page size, i.e. min(limit, 1000)
:returns: Generator that will yield up to limit results
:rtype: list[twilio.rest.video.v1.room.room_participant.room_participant_subscribed_track.SubscribedTrackInstance]
"""
limits = self._version.read_limits(limit, page_size)
page = self.page(page_size=limits['page_size'], )
return self._version.stream(page, limits['limit'])
|
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https://github.com/twilio/twilio-python/blob/6e1e811ea57a1edfadd5161ace87397c563f6915/twilio/rest/video/v1/room/room_participant/room_participant_subscribed_track.py#L36-L57
|
|
openstack/ironic
|
b392dc19bcd29cef5a69ec00d2f18a7a19a679e5
|
ironic/drivers/modules/image_utils.py
|
python
|
ImageHandler.publish_image
|
(self, image_file, object_name)
|
return image_url
|
Make image file downloadable.
Depending on ironic settings, pushes given file into Swift or copies
it over to local HTTP server's document root and returns publicly
accessible URL leading to the given file.
:param image_file: path to file to publish
:param object_name: name of the published file
:return: a URL to download published file
|
Make image file downloadable.
|
[
"Make",
"image",
"file",
"downloadable",
"."
] |
def publish_image(self, image_file, object_name):
"""Make image file downloadable.
Depending on ironic settings, pushes given file into Swift or copies
it over to local HTTP server's document root and returns publicly
accessible URL leading to the given file.
:param image_file: path to file to publish
:param object_name: name of the published file
:return: a URL to download published file
"""
if self.swift_enabled:
container = self._container
timeout = self._timeout
object_headers = {'X-Delete-After': str(timeout)}
swift_api = swift.SwiftAPI()
swift_api.create_object(container, object_name, image_file,
object_headers=object_headers)
image_url = swift_api.get_temp_url(container, object_name, timeout)
image_url = self._append_filename_param(
image_url, os.path.basename(image_file))
else:
public_dir = os.path.join(CONF.deploy.http_root,
self._image_subdir)
if not os.path.exists(public_dir):
os.mkdir(public_dir, 0o755)
published_file = os.path.join(public_dir, object_name)
try:
os.link(image_file, published_file)
os.chmod(image_file, self._file_permission)
except OSError as exc:
LOG.debug(
"Could not hardlink image file %(image)s to public "
"location %(public)s (will copy it over): "
"%(error)s", {'image': image_file,
'public': published_file,
'error': exc})
shutil.copyfile(image_file, published_file)
os.chmod(published_file, self._file_permission)
http_url = CONF.deploy.external_http_url or CONF.deploy.http_url
image_url = os.path.join(http_url, self._image_subdir, object_name)
return image_url
|
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https://github.com/openstack/ironic/blob/b392dc19bcd29cef5a69ec00d2f18a7a19a679e5/ironic/drivers/modules/image_utils.py#L172-L226
|
|
nottombrown/rl-teacher
|
b2c2201e9d2457b13185424a19da7209364f23df
|
agents/pposgd-mpi/pposgd_mpi/common/tf_util.py
|
python
|
is_placeholder
|
(x)
|
return type(x) is tf.Tensor and len(x.op.inputs) == 0
|
[] |
def is_placeholder(x):
return type(x) is tf.Tensor and len(x.op.inputs) == 0
|
[
"def",
"is_placeholder",
"(",
"x",
")",
":",
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"type",
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"x",
")",
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"and",
"len",
"(",
"x",
".",
"op",
".",
"inputs",
")",
"==",
"0"
] |
https://github.com/nottombrown/rl-teacher/blob/b2c2201e9d2457b13185424a19da7209364f23df/agents/pposgd-mpi/pposgd_mpi/common/tf_util.py#L100-L101
|
|||
numenta/nupic
|
b9ebedaf54f49a33de22d8d44dff7c765cdb5548
|
external/linux32/lib/python2.6/site-packages/matplotlib/patches.py
|
python
|
Rectangle.__init__
|
(self, xy, width, height, **kwargs)
|
*fill* is a boolean indicating whether to fill the rectangle
Valid kwargs are:
%(Patch)s
|
[] |
def __init__(self, xy, width, height, **kwargs):
"""
*fill* is a boolean indicating whether to fill the rectangle
Valid kwargs are:
%(Patch)s
"""
Patch.__init__(self, **kwargs)
self._x = xy[0]
self._y = xy[1]
self._width = width
self._height = height
# Note: This cannot be calculated until this is added to an Axes
self._rect_transform = transforms.IdentityTransform()
|
[
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",",
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"self",
".",
"_rect_transform",
"=",
"transforms",
".",
"IdentityTransform",
"(",
")"
] |
https://github.com/numenta/nupic/blob/b9ebedaf54f49a33de22d8d44dff7c765cdb5548/external/linux32/lib/python2.6/site-packages/matplotlib/patches.py#L424-L440
|
|||
IronLanguages/ironpython3
|
7a7bb2a872eeab0d1009fc8a6e24dca43f65b693
|
Src/StdLib/Lib/sunau.py
|
python
|
Au_write.setparams
|
(self, params)
|
[] |
def setparams(self, params):
nchannels, sampwidth, framerate, nframes, comptype, compname = params
self.setnchannels(nchannels)
self.setsampwidth(sampwidth)
self.setframerate(framerate)
self.setnframes(nframes)
self.setcomptype(comptype, compname)
|
[
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",",
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":",
"nchannels",
",",
"sampwidth",
",",
"framerate",
",",
"nframes",
",",
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",",
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"(",
"nframes",
")",
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"setcomptype",
"(",
"comptype",
",",
"compname",
")"
] |
https://github.com/IronLanguages/ironpython3/blob/7a7bb2a872eeab0d1009fc8a6e24dca43f65b693/Src/StdLib/Lib/sunau.py#L400-L406
|
||||
qutebrowser/qutebrowser
|
3a2aaaacbf97f4bf0c72463f3da94ed2822a5442
|
qutebrowser/browser/greasemonkey.py
|
python
|
GreasemonkeyManager.all_scripts
|
(self)
|
return self._run_start + self._run_end + self._run_idle
|
Return all scripts found in the configured script directory.
|
Return all scripts found in the configured script directory.
|
[
"Return",
"all",
"scripts",
"found",
"in",
"the",
"configured",
"script",
"directory",
"."
] |
def all_scripts(self):
"""Return all scripts found in the configured script directory."""
return self._run_start + self._run_end + self._run_idle
|
[
"def",
"all_scripts",
"(",
"self",
")",
":",
"return",
"self",
".",
"_run_start",
"+",
"self",
".",
"_run_end",
"+",
"self",
".",
"_run_idle"
] |
https://github.com/qutebrowser/qutebrowser/blob/3a2aaaacbf97f4bf0c72463f3da94ed2822a5442/qutebrowser/browser/greasemonkey.py#L445-L447
|
|
numenta/nupic
|
b9ebedaf54f49a33de22d8d44dff7c765cdb5548
|
external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/triangulate.py
|
python
|
Triangulation.nn_interpolator
|
(self, z, default_value=np.nan)
|
return NNInterpolator(self, z, default_value)
|
Get an object which can interpolate within the convex hull by
the natural neighbors method.
z -- an array of floats giving the known function values at each point
in the triangulation.
|
Get an object which can interpolate within the convex hull by
the natural neighbors method.
|
[
"Get",
"an",
"object",
"which",
"can",
"interpolate",
"within",
"the",
"convex",
"hull",
"by",
"the",
"natural",
"neighbors",
"method",
"."
] |
def nn_interpolator(self, z, default_value=np.nan):
"""Get an object which can interpolate within the convex hull by
the natural neighbors method.
z -- an array of floats giving the known function values at each point
in the triangulation.
"""
z = np.asarray(z, dtype=np.float64)
if z.shape != self.old_shape:
raise ValueError("z must be the same shape as x and y")
if self.j_unique is not None:
z = z[self.j_unique]
return NNInterpolator(self, z, default_value)
|
[
"def",
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"(",
"self",
",",
"z",
",",
"default_value",
")"
] |
https://github.com/numenta/nupic/blob/b9ebedaf54f49a33de22d8d44dff7c765cdb5548/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/triangulate.py#L145-L158
|
|
lutris/lutris
|
66675a4d5537f6b2a2ba2b6df0b3cdf8924c823a
|
lutris/util/linux.py
|
python
|
LinuxSystem.has_steam
|
(self)
|
return bool(system.find_executable("steam"))
|
Return whether Steam is installed locally
|
Return whether Steam is installed locally
|
[
"Return",
"whether",
"Steam",
"is",
"installed",
"locally"
] |
def has_steam(self):
"""Return whether Steam is installed locally"""
return bool(system.find_executable("steam"))
|
[
"def",
"has_steam",
"(",
"self",
")",
":",
"return",
"bool",
"(",
"system",
".",
"find_executable",
"(",
"\"steam\"",
")",
")"
] |
https://github.com/lutris/lutris/blob/66675a4d5537f6b2a2ba2b6df0b3cdf8924c823a/lutris/util/linux.py#L219-L221
|
|
facebookresearch/pytorch_GAN_zoo
|
b75dee40918caabb4fe7ec561522717bf096a8cb
|
models/utils/image_transform.py
|
python
|
NumpyFlip.__init__
|
(self, p=0.5)
|
[] |
def __init__(self, p=0.5):
self.p = p
random.seed(None)
|
[
"def",
"__init__",
"(",
"self",
",",
"p",
"=",
"0.5",
")",
":",
"self",
".",
"p",
"=",
"p",
"random",
".",
"seed",
"(",
"None",
")"
] |
https://github.com/facebookresearch/pytorch_GAN_zoo/blob/b75dee40918caabb4fe7ec561522717bf096a8cb/models/utils/image_transform.py#L38-L40
|
||||
thinkle/gourmet
|
8af29c8ded24528030e5ae2ea3461f61c1e5a575
|
gourmet/threadManager.py
|
python
|
Terminated.__init__
|
(self, value)
|
[] |
def __init__ (self, value):
self.value=value
|
[
"def",
"__init__",
"(",
"self",
",",
"value",
")",
":",
"self",
".",
"value",
"=",
"value"
] |
https://github.com/thinkle/gourmet/blob/8af29c8ded24528030e5ae2ea3461f61c1e5a575/gourmet/threadManager.py#L55-L56
|
||||
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/sms/v20190711/models.py
|
python
|
ModifySmsTemplateResponse.__init__
|
(self)
|
r"""
:param ModifyTemplateStatus: 修改模板参数响应
:type ModifyTemplateStatus: :class:`tencentcloud.sms.v20190711.models.ModifyTemplateStatus`
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
|
r"""
:param ModifyTemplateStatus: 修改模板参数响应
:type ModifyTemplateStatus: :class:`tencentcloud.sms.v20190711.models.ModifyTemplateStatus`
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
|
[
"r",
":",
"param",
"ModifyTemplateStatus",
":",
"修改模板参数响应",
":",
"type",
"ModifyTemplateStatus",
":",
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":",
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"RequestId",
":",
"str"
] |
def __init__(self):
r"""
:param ModifyTemplateStatus: 修改模板参数响应
:type ModifyTemplateStatus: :class:`tencentcloud.sms.v20190711.models.ModifyTemplateStatus`
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
"""
self.ModifyTemplateStatus = None
self.RequestId = None
|
[
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")",
":",
"self",
".",
"ModifyTemplateStatus",
"=",
"None",
"self",
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"RequestId",
"=",
"None"
] |
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/sms/v20190711/models.py#L897-L905
|
||
pyparallel/pyparallel
|
11e8c6072d48c8f13641925d17b147bf36ee0ba3
|
Lib/imghdr.py
|
python
|
test_xbm
|
(h, f)
|
X bitmap (X10 or X11)
|
X bitmap (X10 or X11)
|
[
"X",
"bitmap",
"(",
"X10",
"or",
"X11",
")"
] |
def test_xbm(h, f):
"""X bitmap (X10 or X11)"""
if h.startswith(b'#define '):
return 'xbm'
|
[
"def",
"test_xbm",
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"h",
",",
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":",
"if",
"h",
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"startswith",
"(",
"b'#define '",
")",
":",
"return",
"'xbm'"
] |
https://github.com/pyparallel/pyparallel/blob/11e8c6072d48c8f13641925d17b147bf36ee0ba3/Lib/imghdr.py#L100-L103
|
||
nelson-liu/contextual-repr-analysis
|
1550abe0f2cad8586ac891e44dc2c5256d7d0685
|
contexteval/data/dataset_readers/conll2003_ner.py
|
python
|
_is_divider
|
(line: str)
|
[] |
def _is_divider(line: str) -> bool:
empty_line = line.strip() == ''
if empty_line:
return True
else:
first_token = line.split()[0]
if first_token == "-DOCSTART-":
return True
else:
return False
|
[
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":",
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"True",
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":",
"return",
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] |
https://github.com/nelson-liu/contextual-repr-analysis/blob/1550abe0f2cad8586ac891e44dc2c5256d7d0685/contexteval/data/dataset_readers/conll2003_ner.py#L17-L26
|
||||
MycroftAI/mycroft-core
|
3d963cee402e232174850f36918313e87313fb13
|
mycroft/enclosure/api.py
|
python
|
EnclosureAPI.register
|
(self, skill_name="")
|
Registers a skill as active. Used for speak() and speak_dialog()
to 'patch' a previous implementation. Somewhat hacky.
|
Registers a skill as active. Used for speak() and speak_dialog()
to 'patch' a previous implementation. Somewhat hacky.
|
[
"Registers",
"a",
"skill",
"as",
"active",
".",
"Used",
"for",
"speak",
"()",
"and",
"speak_dialog",
"()",
"to",
"patch",
"a",
"previous",
"implementation",
".",
"Somewhat",
"hacky",
"."
] |
def register(self, skill_name=""):
"""Registers a skill as active. Used for speak() and speak_dialog()
to 'patch' a previous implementation. Somewhat hacky.
"""
if self.name != "":
self.display_manager.set_active(self.name)
else:
self.display_manager.set_active(skill_name)
|
[
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"\"\"",
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"!=",
"\"\"",
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"else",
":",
"self",
".",
"display_manager",
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"set_active",
"(",
"skill_name",
")"
] |
https://github.com/MycroftAI/mycroft-core/blob/3d963cee402e232174850f36918313e87313fb13/mycroft/enclosure/api.py#L44-L51
|
||
kivy/python-for-android
|
4ecaa5fe01aa25e3bc8cadc52ae481645754f955
|
pythonforandroid/logger.py
|
python
|
shprint
|
(command, *args, **kwargs)
|
return output
|
Runs the command (which should be an sh.Command instance), while
logging the output.
|
Runs the command (which should be an sh.Command instance), while
logging the output.
|
[
"Runs",
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"which",
"should",
"be",
"an",
"sh",
".",
"Command",
"instance",
")",
"while",
"logging",
"the",
"output",
"."
] |
def shprint(command, *args, **kwargs):
'''Runs the command (which should be an sh.Command instance), while
logging the output.'''
kwargs["_iter"] = True
kwargs["_out_bufsize"] = 1
kwargs["_err_to_out"] = True
kwargs["_bg"] = True
is_critical = kwargs.pop('_critical', False)
tail_n = kwargs.pop('_tail', None)
full_debug = False
if "P4A_FULL_DEBUG" in os.environ:
tail_n = 0
full_debug = True
filter_in = kwargs.pop('_filter', None)
filter_out = kwargs.pop('_filterout', None)
if len(logger.handlers) > 1:
logger.removeHandler(logger.handlers[1])
columns = get_console_width()
command_path = str(command).split('/')
command_string = command_path[-1]
string = ' '.join(['{}->{} running'.format(Out_Fore.LIGHTBLACK_EX,
Out_Style.RESET_ALL),
command_string] + list(args))
# If logging is not in DEBUG mode, trim the command if necessary
if logger.level > logging.DEBUG:
logger.info('{}{}'.format(shorten_string(string, columns - 12),
Err_Style.RESET_ALL))
else:
logger.debug('{}{}'.format(string, Err_Style.RESET_ALL))
need_closing_newline = False
try:
msg_hdr = ' working: '
msg_width = columns - len(msg_hdr) - 1
output = command(*args, **kwargs)
for line in output:
if isinstance(line, bytes):
line = line.decode('utf-8', errors='replace')
if logger.level > logging.DEBUG:
if full_debug:
stdout.write(line)
stdout.flush()
continue
msg = line.replace(
'\n', ' ').replace(
'\t', ' ').replace(
'\b', ' ').rstrip()
if msg:
if "CI" not in os.environ:
stdout.write(u'{}\r{}{:<{width}}'.format(
Err_Style.RESET_ALL, msg_hdr,
shorten_string(msg, msg_width), width=msg_width))
stdout.flush()
need_closing_newline = True
else:
logger.debug(''.join(['\t', line.rstrip()]))
if need_closing_newline:
stdout.write('{}\r{:>{width}}\r'.format(
Err_Style.RESET_ALL, ' ', width=(columns - 1)))
stdout.flush()
except sh.ErrorReturnCode as err:
if need_closing_newline:
stdout.write('{}\r{:>{width}}\r'.format(
Err_Style.RESET_ALL, ' ', width=(columns - 1)))
stdout.flush()
if tail_n is not None or filter_in or filter_out:
def printtail(out, name, forecolor, tail_n=0,
re_filter_in=None, re_filter_out=None):
lines = out.splitlines()
if re_filter_in is not None:
lines = [line for line in lines if re_filter_in.search(line)]
if re_filter_out is not None:
lines = [line for line in lines if not re_filter_out.search(line)]
if tail_n == 0 or len(lines) <= tail_n:
info('{}:\n{}\t{}{}'.format(
name, forecolor, '\t\n'.join(lines), Out_Fore.RESET))
else:
info('{} (last {} lines of {}):\n{}\t{}{}'.format(
name, tail_n, len(lines),
forecolor, '\t\n'.join([s for s in lines[-tail_n:]]),
Out_Fore.RESET))
printtail(err.stdout.decode('utf-8'), 'STDOUT', Out_Fore.YELLOW, tail_n,
re.compile(filter_in) if filter_in else None,
re.compile(filter_out) if filter_out else None)
printtail(err.stderr.decode('utf-8'), 'STDERR', Err_Fore.RED)
if is_critical or full_debug:
env = kwargs.get("_env")
if env is not None:
info("{}ENV:{}\n{}\n".format(
Err_Fore.YELLOW, Err_Fore.RESET, "\n".join(
"export {}='{}'".format(n, v) for n, v in env.items())))
info("{}COMMAND:{}\ncd {} && {} {}\n".format(
Err_Fore.YELLOW, Err_Fore.RESET, os.getcwd(), command,
' '.join(args)))
warning("{}ERROR: {} failed!{}".format(
Err_Fore.RED, command, Err_Fore.RESET))
if is_critical:
exit(1)
else:
raise
return output
|
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] |
https://github.com/kivy/python-for-android/blob/4ecaa5fe01aa25e3bc8cadc52ae481645754f955/pythonforandroid/logger.py#L131-L233
|
|
DrSleep/tensorflow-deeplab-resnet
|
066023c033624e6c8154340e06e8fbad4f702bdf
|
train_msc.py
|
python
|
load
|
(saver, sess, ckpt_path)
|
Load trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
ckpt_path: path to checkpoint file with parameters.
|
Load trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
ckpt_path: path to checkpoint file with parameters.
|
[
"Load",
"trained",
"weights",
".",
"Args",
":",
"saver",
":",
"TensorFlow",
"Saver",
"object",
".",
"sess",
":",
"TensorFlow",
"session",
".",
"ckpt_path",
":",
"path",
"to",
"checkpoint",
"file",
"with",
"parameters",
"."
] |
def load(saver, sess, ckpt_path):
'''Load trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
ckpt_path: path to checkpoint file with parameters.
'''
saver.restore(sess, ckpt_path)
print("Restored model parameters from {}".format(ckpt_path))
|
[
"def",
"load",
"(",
"saver",
",",
"sess",
",",
"ckpt_path",
")",
":",
"saver",
".",
"restore",
"(",
"sess",
",",
"ckpt_path",
")",
"print",
"(",
"\"Restored model parameters from {}\"",
".",
"format",
"(",
"ckpt_path",
")",
")"
] |
https://github.com/DrSleep/tensorflow-deeplab-resnet/blob/066023c033624e6c8154340e06e8fbad4f702bdf/train_msc.py#L110-L119
|
||
choasup/SIN
|
4851efb7b1c64180026e51ab8abcd95265c0602c
|
lib/rpn_msr/generate.py
|
python
|
_vis_proposals
|
(im, dets, thresh=0.5)
|
Draw detected bounding boxes.
|
Draw detected bounding boxes.
|
[
"Draw",
"detected",
"bounding",
"boxes",
"."
] |
def _vis_proposals(im, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
class_name = 'obj'
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
|
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https://github.com/choasup/SIN/blob/4851efb7b1c64180026e51ab8abcd95265c0602c/lib/rpn_msr/generate.py#L14-L45
|
||
sympy/sympy
|
d822fcba181155b85ff2b29fe525adbafb22b448
|
sympy/polys/subresultants_qq_zz.py
|
python
|
sign_seq
|
(poly_seq, x)
|
return [sign(LC(poly_seq[i], x)) for i in range(len(poly_seq))]
|
Given a sequence of polynomials poly_seq, it returns
the sequence of signs of the leading coefficients of
the polynomials in poly_seq.
|
Given a sequence of polynomials poly_seq, it returns
the sequence of signs of the leading coefficients of
the polynomials in poly_seq.
|
[
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"in",
"poly_seq",
"."
] |
def sign_seq(poly_seq, x):
"""
Given a sequence of polynomials poly_seq, it returns
the sequence of signs of the leading coefficients of
the polynomials in poly_seq.
"""
return [sign(LC(poly_seq[i], x)) for i in range(len(poly_seq))]
|
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https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/polys/subresultants_qq_zz.py#L611-L618
|
|
WZMIAOMIAO/deep-learning-for-image-processing
|
a4502c284958d4bf78fb77b089a90e7688ddc196
|
pytorch_object_detection/faster_rcnn/network_files/rpn_function.py
|
python
|
concat_box_prediction_layers
|
(box_cls, box_regression)
|
return box_cls, box_regression
|
对box_cla和box_regression两个list中的每个预测特征层的预测信息
的tensor排列顺序以及shape进行调整 -> [N, -1, C]
Args:
box_cls: 每个预测特征层上的预测目标概率
box_regression: 每个预测特征层上的预测目标bboxes regression参数
Returns:
|
对box_cla和box_regression两个list中的每个预测特征层的预测信息
的tensor排列顺序以及shape进行调整 -> [N, -1, C]
Args:
box_cls: 每个预测特征层上的预测目标概率
box_regression: 每个预测特征层上的预测目标bboxes regression参数
|
[
"对box_cla和box_regression两个list中的每个预测特征层的预测信息",
"的tensor排列顺序以及shape进行调整",
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"box_regression",
":",
"每个预测特征层上的预测目标bboxes",
"regression参数"
] |
def concat_box_prediction_layers(box_cls, box_regression):
# type: (List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor]
"""
对box_cla和box_regression两个list中的每个预测特征层的预测信息
的tensor排列顺序以及shape进行调整 -> [N, -1, C]
Args:
box_cls: 每个预测特征层上的预测目标概率
box_regression: 每个预测特征层上的预测目标bboxes regression参数
Returns:
"""
box_cls_flattened = []
box_regression_flattened = []
# 遍历每个预测特征层
for box_cls_per_level, box_regression_per_level in zip(box_cls, box_regression):
# [batch_size, anchors_num_per_position * classes_num, height, width]
# 注意，当计算RPN中的proposal时，classes_num=1,只区分目标和背景
N, AxC, H, W = box_cls_per_level.shape
# # [batch_size, anchors_num_per_position * 4, height, width]
Ax4 = box_regression_per_level.shape[1]
# anchors_num_per_position
A = Ax4 // 4
# classes_num
C = AxC // A
# [N, -1, C]
box_cls_per_level = permute_and_flatten(box_cls_per_level, N, A, C, H, W)
box_cls_flattened.append(box_cls_per_level)
# [N, -1, C]
box_regression_per_level = permute_and_flatten(box_regression_per_level, N, A, 4, H, W)
box_regression_flattened.append(box_regression_per_level)
box_cls = torch.cat(box_cls_flattened, dim=1).flatten(0, -2) # start_dim, end_dim
box_regression = torch.cat(box_regression_flattened, dim=1).reshape(-1, 4)
return box_cls, box_regression
|
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https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/a4502c284958d4bf78fb77b089a90e7688ddc196/pytorch_object_detection/faster_rcnn/network_files/rpn_function.py#L272-L309
|
|
cornellius-gp/gpytorch
|
61f643eb8b487aef332c818f661fbcdb1df576ca
|
gpytorch/likelihoods/multitask_gaussian_likelihood.py
|
python
|
_MultitaskGaussianLikelihoodBase.marginal
|
(self, function_dist, *params, **kwargs)
|
return function_dist.__class__(mean, covar)
|
r"""
If `rank` == 0, adds the task noises to the diagonal of the covariance matrix of the supplied
:obj:`gpytorch.distributions.MultivariateNormal` or :obj:`gpytorch.distributions.MultitaskMultivariateNormal`.
Otherwise, adds a rank `rank` covariance matrix to it.
To accomplish this, we form a new :obj:`gpytorch.lazy.KroneckerProductLazyTensor` between :math:`I_{n}`,
an identity matrix with size equal to the data and a (not necessarily diagonal) matrix containing the task
noises :math:`D_{t}`.
We also incorporate a shared `noise` parameter from the base
:class:`gpytorch.likelihoods.GaussianLikelihood` that we extend.
The final covariance matrix after this method is then :math:`K + D_{t} \otimes I_{n} + \sigma^{2}I_{nt}`.
Args:
function_dist (:obj:`gpytorch.distributions.MultitaskMultivariateNormal`): Random variable whose covariance
matrix is a :obj:`gpytorch.lazy.LazyTensor` we intend to augment.
Returns:
:obj:`gpytorch.distributions.MultitaskMultivariateNormal`: A new random variable whose covariance
matrix is a :obj:`gpytorch.lazy.LazyTensor` with :math:`D_{t} \otimes I_{n}` and :math:`\sigma^{2}I_{nt}`
added.
|
r"""
If `rank` == 0, adds the task noises to the diagonal of the covariance matrix of the supplied
:obj:`gpytorch.distributions.MultivariateNormal` or :obj:`gpytorch.distributions.MultitaskMultivariateNormal`.
Otherwise, adds a rank `rank` covariance matrix to it.
|
[
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"."
] |
def marginal(self, function_dist, *params, **kwargs):
r"""
If `rank` == 0, adds the task noises to the diagonal of the covariance matrix of the supplied
:obj:`gpytorch.distributions.MultivariateNormal` or :obj:`gpytorch.distributions.MultitaskMultivariateNormal`.
Otherwise, adds a rank `rank` covariance matrix to it.
To accomplish this, we form a new :obj:`gpytorch.lazy.KroneckerProductLazyTensor` between :math:`I_{n}`,
an identity matrix with size equal to the data and a (not necessarily diagonal) matrix containing the task
noises :math:`D_{t}`.
We also incorporate a shared `noise` parameter from the base
:class:`gpytorch.likelihoods.GaussianLikelihood` that we extend.
The final covariance matrix after this method is then :math:`K + D_{t} \otimes I_{n} + \sigma^{2}I_{nt}`.
Args:
function_dist (:obj:`gpytorch.distributions.MultitaskMultivariateNormal`): Random variable whose covariance
matrix is a :obj:`gpytorch.lazy.LazyTensor` we intend to augment.
Returns:
:obj:`gpytorch.distributions.MultitaskMultivariateNormal`: A new random variable whose covariance
matrix is a :obj:`gpytorch.lazy.LazyTensor` with :math:`D_{t} \otimes I_{n}` and :math:`\sigma^{2}I_{nt}`
added.
"""
mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix
# ensure that sumKroneckerLT is actually called
if isinstance(covar, LazyEvaluatedKernelTensor):
covar = covar.evaluate_kernel()
covar_kron_lt = self._shaped_noise_covar(mean.shape, add_noise=self.has_global_noise)
covar = covar + covar_kron_lt
return function_dist.__class__(mean, covar)
|
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https://github.com/cornellius-gp/gpytorch/blob/61f643eb8b487aef332c818f661fbcdb1df576ca/gpytorch/likelihoods/multitask_gaussian_likelihood.py#L67-L99
|
|
square/pylink
|
a2d9fbd3add62ffd06ba737c5ea82b8491fdc425
|
pylink/jlink.py
|
python
|
JLink.__exit__
|
(self, exc_type, exc_val, exc_tb)
|
Closes the JLink connection on exit of the context manager.
Stops the SWO if enabled and closes the J-Link connection if one
exists.
Args:
self (JLink): the ``JLink`` instance
exc_type (BaseExceptionType, None): the exception class, if any
raised inside the context manager
exc_val (BaseException, None): the exception object, if any raised
inside the context manager
exc_tb (TracebackType, None): the exception traceback, if any
exception was raised inside the context manager.
Returns:
``True`` if exception raised inside the context manager was handled
and shall be suppressed (not propagated), ``None`` otherwise.
|
Closes the JLink connection on exit of the context manager.
|
[
"Closes",
"the",
"JLink",
"connection",
"on",
"exit",
"of",
"the",
"context",
"manager",
"."
] |
def __exit__(self, exc_type, exc_val, exc_tb):
"""Closes the JLink connection on exit of the context manager.
Stops the SWO if enabled and closes the J-Link connection if one
exists.
Args:
self (JLink): the ``JLink`` instance
exc_type (BaseExceptionType, None): the exception class, if any
raised inside the context manager
exc_val (BaseException, None): the exception object, if any raised
inside the context manager
exc_tb (TracebackType, None): the exception traceback, if any
exception was raised inside the context manager.
Returns:
``True`` if exception raised inside the context manager was handled
and shall be suppressed (not propagated), ``None`` otherwise.
"""
self._finalize()
|
[
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":",
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https://github.com/square/pylink/blob/a2d9fbd3add62ffd06ba737c5ea82b8491fdc425/pylink/jlink.py#L373-L392
|
||
FriedAppleTeam/FRAPL
|
89c14d57e0cc77b915fe1e95f60e9e1847699103
|
Framework/FridaLink/FridaLink/Core/HookEngine.py
|
python
|
HookEngineProtocol.showImportSymbolCPUForIdx
|
(self, import_idx)
|
[] |
def showImportSymbolCPUForIdx(self, import_idx):
if self.clientSocket is None:
fl_log("FridaLink: Frida not connected\n");
return
importEntry = self.imports[import_idx]
if importEntry.address not in self.importHookMap:
return
address = importEntry.address;
if self.cpuContextViews.hasView(address) == False:
entry = self.importHookMap[address]
newView = CPUContextView(self, entry.hook.id, entry.hook.symbol)
self.cpuContextViews.addView("CPU Context", newView)
self.cpuContextViews.setContent(entry.hook.id, {"arch":entry.arch, "context":entry.cpu_ctx})
self.cpuContextViews.showView(address)
|
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https://github.com/FriedAppleTeam/FRAPL/blob/89c14d57e0cc77b915fe1e95f60e9e1847699103/Framework/FridaLink/FridaLink/Core/HookEngine.py#L654-L670
|
||||
googleads/google-ads-python
|
2a1d6062221f6aad1992a6bcca0e7e4a93d2db86
|
google/ads/googleads/v9/services/services/google_ads_service/client.py
|
python
|
GoogleAdsServiceClient.parse_shopping_performance_view_path
|
(path: str)
|
return m.groupdict() if m else {}
|
Parse a shopping_performance_view path into its component segments.
|
Parse a shopping_performance_view path into its component segments.
|
[
"Parse",
"a",
"shopping_performance_view",
"path",
"into",
"its",
"component",
"segments",
"."
] |
def parse_shopping_performance_view_path(path: str) -> Dict[str, str]:
"""Parse a shopping_performance_view path into its component segments."""
m = re.match(
r"^customers/(?P<customer_id>.+?)/shoppingPerformanceView$", path
)
return m.groupdict() if m else {}
|
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https://github.com/googleads/google-ads-python/blob/2a1d6062221f6aad1992a6bcca0e7e4a93d2db86/google/ads/googleads/v9/services/services/google_ads_service/client.py#L2740-L2745
|
|
mithril-global/GoAgentX
|
788fbd5e1c824c75cf98a9aef8a6d4ec8df25e95
|
GoAgentX.app/Contents/PlugIns/shadowsocks.gxbundle/Contents/Resources/bin/python/M2Crypto/X509.py
|
python
|
X509_Store_Context.get_error
|
(self)
|
return m2.x509_store_ctx_get_error(self.ctx)
|
Get error code.
|
Get error code.
|
[
"Get",
"error",
"code",
"."
] |
def get_error(self):
"""
Get error code.
"""
return m2.x509_store_ctx_get_error(self.ctx)
|
[
"def",
"get_error",
"(",
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")",
":",
"return",
"m2",
".",
"x509_store_ctx_get_error",
"(",
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".",
"ctx",
")"
] |
https://github.com/mithril-global/GoAgentX/blob/788fbd5e1c824c75cf98a9aef8a6d4ec8df25e95/GoAgentX.app/Contents/PlugIns/shadowsocks.gxbundle/Contents/Resources/bin/python/M2Crypto/X509.py#L700-L704
|
|
nfvlabs/openmano
|
b09eabec0a168aeda8adc3ea99f734e45e810205
|
openvim/utils/RADclass.py
|
python
|
RADclass.set_os
|
(self,os)
|
return (True, "")
|
Sets the operating system.
Returns (True,Warning) in case of success and ('False',<error description>) in case of error
|
Sets the operating system.
Returns (True,Warning) in case of success and ('False',<error description>) in case of error
|
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"system",
".",
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"(",
"False",
"<error",
"description",
">",
")",
"in",
"case",
"of",
"error"
] |
def set_os(self,os):
"""Sets the operating system.
Returns (True,Warning) in case of success and ('False',<error description>) in case of error"""
if not isinstance(os,OpSys):
return (False, 'The variable \'os\' must be of class OpSys')
self.os.assign(os)
return (True, "")
|
[
"def",
"set_os",
"(",
"self",
",",
"os",
")",
":",
"if",
"not",
"isinstance",
"(",
"os",
",",
"OpSys",
")",
":",
"return",
"(",
"False",
",",
"'The variable \\'os\\' must be of class OpSys'",
")",
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".",
"os",
".",
"assign",
"(",
"os",
")",
"return",
"(",
"True",
",",
"\"\"",
")"
] |
https://github.com/nfvlabs/openmano/blob/b09eabec0a168aeda8adc3ea99f734e45e810205/openvim/utils/RADclass.py#L300-L307
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/PIL/Image.py
|
python
|
effect_mandelbrot
|
(size, extent, quality)
|
return Image()._new(core.effect_mandelbrot(size, extent, quality))
|
Generate a Mandelbrot set covering the given extent.
:param size: The requested size in pixels, as a 2-tuple:
(width, height).
:param extent: The extent to cover, as a 4-tuple:
(x0, y0, x1, y2).
:param quality: Quality.
|
Generate a Mandelbrot set covering the given extent.
|
[
"Generate",
"a",
"Mandelbrot",
"set",
"covering",
"the",
"given",
"extent",
"."
] |
def effect_mandelbrot(size, extent, quality):
"""
Generate a Mandelbrot set covering the given extent.
:param size: The requested size in pixels, as a 2-tuple:
(width, height).
:param extent: The extent to cover, as a 4-tuple:
(x0, y0, x1, y2).
:param quality: Quality.
"""
return Image()._new(core.effect_mandelbrot(size, extent, quality))
|
[
"def",
"effect_mandelbrot",
"(",
"size",
",",
"extent",
",",
"quality",
")",
":",
"return",
"Image",
"(",
")",
".",
"_new",
"(",
"core",
".",
"effect_mandelbrot",
"(",
"size",
",",
"extent",
",",
"quality",
")",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/PIL/Image.py#L2820-L2830
|
|
GetStream/stream-python
|
142b5b43c0a60a96c36f25b6fc5a224dd2e418cc
|
setup.py
|
python
|
PyTest.finalize_options
|
(self)
|
[] |
def finalize_options(self):
TestCommand.finalize_options(self)
self.test_args = []
self.test_suite = True
|
[
"def",
"finalize_options",
"(",
"self",
")",
":",
"TestCommand",
".",
"finalize_options",
"(",
"self",
")",
"self",
".",
"test_args",
"=",
"[",
"]",
"self",
".",
"test_suite",
"=",
"True"
] |
https://github.com/GetStream/stream-python/blob/142b5b43c0a60a96c36f25b6fc5a224dd2e418cc/setup.py#L23-L26
|
||||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/celery/schedules.py
|
python
|
crontab_parser._parse_part
|
(self, part)
|
return self._expand_range((part,))
|
[] |
def _parse_part(self, part):
for regex, handler in self.pats:
m = regex.match(part)
if m:
return handler(m.groups())
return self._expand_range((part,))
|
[
"def",
"_parse_part",
"(",
"self",
",",
"part",
")",
":",
"for",
"regex",
",",
"handler",
"in",
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".",
"pats",
":",
"m",
"=",
"regex",
".",
"match",
"(",
"part",
")",
"if",
"m",
":",
"return",
"handler",
"(",
"m",
".",
"groups",
"(",
")",
")",
"return",
"self",
".",
"_expand_range",
"(",
"(",
"part",
",",
")",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/celery/schedules.py#L263-L268
|
|||
AstroPrint/AstroBox
|
e7e3b8a7d33ea85fcb6b2696869c0d719ceb8b75
|
src/ext/makerbot_driver/GcodeProcessors/EmptyLayerProcessor.py
|
python
|
EmptyLayerProcessor.check_for_layer_end
|
(self, string)
|
[] |
def check_for_layer_end(self, string):
match = re.match(self.MG_layer_end, string)
if match is not None:
return 'mg'
match = re.match(self.SF_layer_end, string)
if match is not None:
return 'sf'
else:
return None
|
[
"def",
"check_for_layer_end",
"(",
"self",
",",
"string",
")",
":",
"match",
"=",
"re",
".",
"match",
"(",
"self",
".",
"MG_layer_end",
",",
"string",
")",
"if",
"match",
"is",
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"None",
":",
"return",
"'mg'",
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"(",
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"SF_layer_end",
",",
"string",
")",
"if",
"match",
"is",
"not",
"None",
":",
"return",
"'sf'",
"else",
":",
"return",
"None"
] |
https://github.com/AstroPrint/AstroBox/blob/e7e3b8a7d33ea85fcb6b2696869c0d719ceb8b75/src/ext/makerbot_driver/GcodeProcessors/EmptyLayerProcessor.py#L133-L141
|
||||
cobbler/cobbler
|
eed8cdca3e970c8aa1d199e80b8c8f19b3f940cc
|
cobbler/remote.py
|
python
|
CobblerXMLRPCInterface.remove_autoinstall_snippet
|
(self, file_path: str, token: str)
|
return True
|
Remove an automated OS installation snippet file
:param file_path: automated OS installation snippet file path
:param token: Cobbler token, obtained form login()
:return: bool if operation was successful
|
Remove an automated OS installation snippet file
|
[
"Remove",
"an",
"automated",
"OS",
"installation",
"snippet",
"file"
] |
def remove_autoinstall_snippet(self, file_path: str, token: str):
"""
Remove an automated OS installation snippet file
:param file_path: automated OS installation snippet file path
:param token: Cobbler token, obtained form login()
:return: bool if operation was successful
"""
what = "remove_autoinstall_snippet"
self._log(what, name=file_path, token=token)
self.check_access(token, what, file_path, True)
self.autoinstall_mgr.remove_autoinstall_snippet(file_path)
return True
|
[
"def",
"remove_autoinstall_snippet",
"(",
"self",
",",
"file_path",
":",
"str",
",",
"token",
":",
"str",
")",
":",
"what",
"=",
"\"remove_autoinstall_snippet\"",
"self",
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"_log",
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"what",
",",
"name",
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"file_path",
",",
"token",
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"token",
")",
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"token",
",",
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",",
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",",
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"self",
".",
"autoinstall_mgr",
".",
"remove_autoinstall_snippet",
"(",
"file_path",
")",
"return",
"True"
] |
https://github.com/cobbler/cobbler/blob/eed8cdca3e970c8aa1d199e80b8c8f19b3f940cc/cobbler/remote.py#L3517-L3532
|
|
mchristopher/PokemonGo-DesktopMap
|
ec37575f2776ee7d64456e2a1f6b6b78830b4fe0
|
app/pywin/Lib/mailbox.py
|
python
|
MaildirMessage.get_subdir
|
(self)
|
return self._subdir
|
Return 'new' or 'cur'.
|
Return 'new' or 'cur'.
|
[
"Return",
"new",
"or",
"cur",
"."
] |
def get_subdir(self):
"""Return 'new' or 'cur'."""
return self._subdir
|
[
"def",
"get_subdir",
"(",
"self",
")",
":",
"return",
"self",
".",
"_subdir"
] |
https://github.com/mchristopher/PokemonGo-DesktopMap/blob/ec37575f2776ee7d64456e2a1f6b6b78830b4fe0/app/pywin/Lib/mailbox.py#L1491-L1493
|
|
nameko/nameko
|
17ecee2bcfa90cb0f3a2f3328c5004f48e4e02a3
|
nameko/rpc.py
|
python
|
MethodProxy.__repr__
|
(self)
|
return '<proxy method: {}.{}>'.format(service_name, method_name)
|
[] |
def __repr__(self):
service_name = self.service_name
method_name = self.method_name
return '<proxy method: {}.{}>'.format(service_name, method_name)
|
[
"def",
"__repr__",
"(",
"self",
")",
":",
"service_name",
"=",
"self",
".",
"service_name",
"method_name",
"=",
"self",
".",
"method_name",
"return",
"'<proxy method: {}.{}>'",
".",
"format",
"(",
"service_name",
",",
"method_name",
")"
] |
https://github.com/nameko/nameko/blob/17ecee2bcfa90cb0f3a2f3328c5004f48e4e02a3/nameko/rpc.py#L463-L466
|
|||
nipy/nipy
|
d16d268938dcd5c15748ca051532c21f57cf8a22
|
nipy/externals/configobj.py
|
python
|
ConfigObj._handle_value
|
(self, value)
|
return (the_list, comment)
|
Given a value string, unquote, remove comment,
handle lists. (including empty and single member lists)
|
Given a value string, unquote, remove comment,
handle lists. (including empty and single member lists)
|
[
"Given",
"a",
"value",
"string",
"unquote",
"remove",
"comment",
"handle",
"lists",
".",
"(",
"including",
"empty",
"and",
"single",
"member",
"lists",
")"
] |
def _handle_value(self, value):
"""
Given a value string, unquote, remove comment,
handle lists. (including empty and single member lists)
"""
if self._inspec:
# Parsing a configspec so don't handle comments
return (value, '')
# do we look for lists in values ?
if not self.list_values:
mat = self._nolistvalue.match(value)
if mat is None:
raise SyntaxError()
# NOTE: we don't unquote here
return mat.groups()
#
mat = self._valueexp.match(value)
if mat is None:
# the value is badly constructed, probably badly quoted,
# or an invalid list
raise SyntaxError()
(list_values, single, empty_list, comment) = mat.groups()
if (list_values == '') and (single is None):
# change this if you want to accept empty values
raise SyntaxError()
# NOTE: note there is no error handling from here if the regex
# is wrong: then incorrect values will slip through
if empty_list is not None:
# the single comma - meaning an empty list
return ([], comment)
if single is not None:
# handle empty values
if list_values and not single:
# FIXME: the '' is a workaround because our regex now matches
# '' at the end of a list if it has a trailing comma
single = None
else:
single = single or '""'
single = self._unquote(single)
if list_values == '':
# not a list value
return (single, comment)
the_list = self._listvalueexp.findall(list_values)
the_list = [self._unquote(val) for val in the_list]
if single is not None:
the_list += [single]
return (the_list, comment)
|
[
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"[",
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"# FIXME: the '' is a workaround because our regex now matches",
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"single",
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":",
"single",
"=",
"single",
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"'\"\"'",
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"+=",
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"(",
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] |
https://github.com/nipy/nipy/blob/d16d268938dcd5c15748ca051532c21f57cf8a22/nipy/externals/configobj.py#L1851-L1897
|
|
MrH0wl/Cloudmare
|
65e5bc9888f9d362ab2abfb103ea6c1e869d67aa
|
thirdparty/dns/opcode.py
|
python
|
is_update
|
(flags)
|
return from_flags(flags) == Opcode.UPDATE
|
Is the opcode in flags UPDATE?
*flags*, an ``int``, the DNS message flags.
Returns a ``bool``.
|
Is the opcode in flags UPDATE?
|
[
"Is",
"the",
"opcode",
"in",
"flags",
"UPDATE?"
] |
def is_update(flags):
"""Is the opcode in flags UPDATE?
*flags*, an ``int``, the DNS message flags.
Returns a ``bool``.
"""
return from_flags(flags) == Opcode.UPDATE
|
[
"def",
"is_update",
"(",
"flags",
")",
":",
"return",
"from_flags",
"(",
"flags",
")",
"==",
"Opcode",
".",
"UPDATE"
] |
https://github.com/MrH0wl/Cloudmare/blob/65e5bc9888f9d362ab2abfb103ea6c1e869d67aa/thirdparty/dns/opcode.py#L99-L107
|
|
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/full/locale.py
|
python
|
_group
|
(s, monetary=False)
|
return (
left_spaces + thousands_sep.join(groups) + right_spaces,
len(thousands_sep) * (len(groups) - 1)
)
|
[] |
def _group(s, monetary=False):
conv = localeconv()
thousands_sep = conv[monetary and 'mon_thousands_sep' or 'thousands_sep']
grouping = conv[monetary and 'mon_grouping' or 'grouping']
if not grouping:
return (s, 0)
if s[-1] == ' ':
stripped = s.rstrip()
right_spaces = s[len(stripped):]
s = stripped
else:
right_spaces = ''
left_spaces = ''
groups = []
for interval in _grouping_intervals(grouping):
if not s or s[-1] not in "0123456789":
# only non-digit characters remain (sign, spaces)
left_spaces = s
s = ''
break
groups.append(s[-interval:])
s = s[:-interval]
if s:
groups.append(s)
groups.reverse()
return (
left_spaces + thousands_sep.join(groups) + right_spaces,
len(thousands_sep) * (len(groups) - 1)
)
|
[
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"False",
")",
":",
"conv",
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"+",
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",",
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")",
"*",
"(",
"len",
"(",
"groups",
")",
"-",
"1",
")",
")"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/full/locale.py#L138-L166
|
|||
DataDog/integrations-core
|
934674b29d94b70ccc008f76ea172d0cdae05e1e
|
citrix_hypervisor/datadog_checks/citrix_hypervisor/config_models/defaults.py
|
python
|
instance_kerberos_force_initiate
|
(field, value)
|
return False
|
[] |
def instance_kerberos_force_initiate(field, value):
return False
|
[
"def",
"instance_kerberos_force_initiate",
"(",
"field",
",",
"value",
")",
":",
"return",
"False"
] |
https://github.com/DataDog/integrations-core/blob/934674b29d94b70ccc008f76ea172d0cdae05e1e/citrix_hypervisor/datadog_checks/citrix_hypervisor/config_models/defaults.py#L85-L86
|
|||
akfamily/akshare
|
590e50eece9ec067da3538c7059fd660b71f1339
|
akshare/stock/stock_zh_a_sina.py
|
python
|
_get_zh_a_page_count
|
()
|
所有股票的总页数
http://vip.stock.finance.sina.com.cn/mkt/#hs_a
:return: 需要采集的股票总页数
:rtype: int
|
所有股票的总页数
http://vip.stock.finance.sina.com.cn/mkt/#hs_a
:return: 需要采集的股票总页数
:rtype: int
|
[
"所有股票的总页数",
"http",
":",
"//",
"vip",
".",
"stock",
".",
"finance",
".",
"sina",
".",
"com",
".",
"cn",
"/",
"mkt",
"/",
"#hs_a",
":",
"return",
":",
"需要采集的股票总页数",
":",
"rtype",
":",
"int"
] |
def _get_zh_a_page_count() -> int:
"""
所有股票的总页数
http://vip.stock.finance.sina.com.cn/mkt/#hs_a
:return: 需要采集的股票总页数
:rtype: int
"""
res = requests.get(zh_sina_a_stock_count_url)
page_count = int(re.findall(re.compile(r"\d+"), res.text)[0]) / 80
if isinstance(page_count, int):
return page_count
else:
return int(page_count) + 1
|
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https://github.com/akfamily/akshare/blob/590e50eece9ec067da3538c7059fd660b71f1339/akshare/stock/stock_zh_a_sina.py#L29-L41
|
||
dengdan/seglink
|
cc36732d78a637ac10587c11befe19944ec1c1ea
|
preprocessing/ssd_vgg_preprocessing.py
|
python
|
preprocess_for_eval
|
(image, labels, bboxes, xs, ys,
out_shape=EVAL_SIZE, data_format='NHWC',
difficults=None, resize=Resize.WARP_RESIZE,
scope='ssd_preprocessing_train')
|
Preprocess an image for evaluation.
Args:
image: A `Tensor` representing an image of arbitrary size.
out_shape: Output shape after pre-processing (if resize != None)
resize: Resize strategy.
Returns:
A preprocessed image.
|
Preprocess an image for evaluation.
|
[
"Preprocess",
"an",
"image",
"for",
"evaluation",
"."
] |
def preprocess_for_eval(image, labels, bboxes, xs, ys,
out_shape=EVAL_SIZE, data_format='NHWC',
difficults=None, resize=Resize.WARP_RESIZE,
scope='ssd_preprocessing_train'):
"""Preprocess an image for evaluation.
Args:
image: A `Tensor` representing an image of arbitrary size.
out_shape: Output shape after pre-processing (if resize != None)
resize: Resize strategy.
Returns:
A preprocessed image.
"""
with tf.name_scope(scope):
if image.get_shape().ndims != 3:
raise ValueError('Input must be of size [height, width, C>0]')
image = tf.to_float(image)
image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN])
if resize == Resize.NONE:
pass
else:
image = tf_image.resize_image(image, out_shape,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False)
# Image data format.
if data_format == 'NCHW':
image = tf.transpose(image, perm=(2, 0, 1))
return image, labels, bboxes, xs, ys
|
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",",
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https://github.com/dengdan/seglink/blob/cc36732d78a637ac10587c11befe19944ec1c1ea/preprocessing/ssd_vgg_preprocessing.py#L298-L329
|
||
openhatch/oh-mainline
|
ce29352a034e1223141dcc2f317030bbc3359a51
|
vendor/packages/gdata/src/atom/core.py
|
python
|
XmlElement.get_elements
|
(self, tag=None, namespace=None, version=1)
|
return matches
|
Find all sub elements which match the tag and namespace.
To find all elements in this object, call get_elements with the tag and
namespace both set to None (the default). This method searches through
the object's members and the elements stored in _other_elements which
did not match any of the XML parsing rules for this class.
Args:
tag: str
namespace: str
version: int Specifies the version of the XML rules to be used when
searching for matching elements.
Returns:
A list of the matching XmlElements.
|
Find all sub elements which match the tag and namespace.
|
[
"Find",
"all",
"sub",
"elements",
"which",
"match",
"the",
"tag",
"and",
"namespace",
"."
] |
def get_elements(self, tag=None, namespace=None, version=1):
"""Find all sub elements which match the tag and namespace.
To find all elements in this object, call get_elements with the tag and
namespace both set to None (the default). This method searches through
the object's members and the elements stored in _other_elements which
did not match any of the XML parsing rules for this class.
Args:
tag: str
namespace: str
version: int Specifies the version of the XML rules to be used when
searching for matching elements.
Returns:
A list of the matching XmlElements.
"""
matches = []
ignored1, elements, ignored2 = self.__class__._get_rules(version)
if elements:
for qname, element_def in elements.iteritems():
member = getattr(self, element_def[0])
if member:
if _qname_matches(tag, namespace, qname):
if element_def[2]:
# If this is a repeating element, copy all instances into the
# result list.
matches.extend(member)
else:
matches.append(member)
for element in self._other_elements:
if _qname_matches(tag, namespace, element._qname):
matches.append(element)
return matches
|
[
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] |
https://github.com/openhatch/oh-mainline/blob/ce29352a034e1223141dcc2f317030bbc3359a51/vendor/packages/gdata/src/atom/core.py#L190-L223
|
|
vivisect/vivisect
|
37b0b655d8dedfcf322e86b0f144b096e48d547e
|
cobra/__init__.py
|
python
|
SocketBuilder.setSslClientCert
|
(self, crtfile, keyfile)
|
Set the cert/key used by this client to negotiate SSL.
|
Set the cert/key used by this client to negotiate SSL.
|
[
"Set",
"the",
"cert",
"/",
"key",
"used",
"by",
"this",
"client",
"to",
"negotiate",
"SSL",
"."
] |
def setSslClientCert(self, crtfile, keyfile):
'''
Set the cert/key used by this client to negotiate SSL.
'''
self.ssl = True
self.sslcrt = crtfile
self.sslkey = keyfile
|
[
"def",
"setSslClientCert",
"(",
"self",
",",
"crtfile",
",",
"keyfile",
")",
":",
"self",
".",
"ssl",
"=",
"True",
"self",
".",
"sslcrt",
"=",
"crtfile",
"self",
".",
"sslkey",
"=",
"keyfile"
] |
https://github.com/vivisect/vivisect/blob/37b0b655d8dedfcf322e86b0f144b096e48d547e/cobra/__init__.py#L299-L305
|
||
dimagi/commcare-hq
|
d67ff1d3b4c51fa050c19e60c3253a79d3452a39
|
corehq/apps/userreports/models.py
|
python
|
ReportConfiguration.save
|
(self, *args, **kwargs)
|
[] |
def save(self, *args, **kwargs):
self.report_meta.last_modified = datetime.utcnow()
super(ReportConfiguration, self).save(*args, **kwargs)
|
[
"def",
"save",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
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",",
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")",
".",
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"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/dimagi/commcare-hq/blob/d67ff1d3b4c51fa050c19e60c3253a79d3452a39/corehq/apps/userreports/models.py#L744-L746
|
||||
frictionlessdata/datapackage-py
|
52416bd5be2e146490ee91f51b80d9b2178e0070
|
datapackage/resource.py
|
python
|
Resource.schema
|
(self)
|
return self.__get_table().schema
|
Resource's schema
> Only for tabular resources
For tabular resources it returns `Schema` instance to interact with data schema.
Read API documentation - [tableschema.Schema](https://github.com/frictionlessdata/tableschema-py#schema).
# Returns
tableschema.Schema: schema
|
Resource's schema
|
[
"Resource",
"s",
"schema"
] |
def schema(self):
"""Resource's schema
> Only for tabular resources
For tabular resources it returns `Schema` instance to interact with data schema.
Read API documentation - [tableschema.Schema](https://github.com/frictionlessdata/tableschema-py#schema).
# Returns
tableschema.Schema: schema
"""
if not self.tabular:
return None
return self.__get_table().schema
|
[
"def",
"schema",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"tabular",
":",
"return",
"None",
"return",
"self",
".",
"__get_table",
"(",
")",
".",
"schema"
] |
https://github.com/frictionlessdata/datapackage-py/blob/52416bd5be2e146490ee91f51b80d9b2178e0070/datapackage/resource.py#L252-L266
|
|
securesystemslab/zippy
|
ff0e84ac99442c2c55fe1d285332cfd4e185e089
|
zippy/benchmarks/src/benchmarks/sympy/sympy/polys/factortools.py
|
python
|
dup_zz_irreducible_p
|
(f, K)
|
Test irreducibility using Eisenstein's criterion.
|
Test irreducibility using Eisenstein's criterion.
|
[
"Test",
"irreducibility",
"using",
"Eisenstein",
"s",
"criterion",
"."
] |
def dup_zz_irreducible_p(f, K):
"""Test irreducibility using Eisenstein's criterion. """
lc = dup_LC(f, K)
tc = dup_TC(f, K)
e_fc = dup_content(f[1:], K)
if e_fc:
e_ff = factorint(int(e_fc))
for p in e_ff.keys():
if (lc % p) and (tc % p**2):
return True
|
[
"def",
"dup_zz_irreducible_p",
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"f",
",",
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"lc",
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"dup_LC",
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https://github.com/securesystemslab/zippy/blob/ff0e84ac99442c2c55fe1d285332cfd4e185e089/zippy/benchmarks/src/benchmarks/sympy/sympy/polys/factortools.py#L364-L376
|
||
HCIILAB/DeRPN
|
21e6738ee1f7d3f159ee48d435c543e773f8ce99
|
caffe/tools/extra/extract_seconds.py
|
python
|
extract_seconds
|
(input_file, output_file)
|
[] |
def extract_seconds(input_file, output_file):
with open(input_file, 'r') as f:
lines = f.readlines()
log_created_year = get_log_created_year(input_file)
start_datetime = get_start_time(lines, log_created_year)
assert start_datetime, 'Start time not found'
out = open(output_file, 'w')
for line in lines:
line = line.strip()
if line.find('Iteration') != -1:
dt = extract_datetime_from_line(line, log_created_year)
elapsed_seconds = (dt - start_datetime).total_seconds()
out.write('%f\n' % elapsed_seconds)
out.close()
|
[
"def",
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"input_file",
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"line",
",",
"log_created_year",
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"(",
"dt",
"-",
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"total_seconds",
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"write",
"(",
"'%f\\n'",
"%",
"elapsed_seconds",
")",
"out",
".",
"close",
"(",
")"
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https://github.com/HCIILAB/DeRPN/blob/21e6738ee1f7d3f159ee48d435c543e773f8ce99/caffe/tools/extra/extract_seconds.py#L44-L58
|
||||
hubblestack/hubble
|
763142474edcecdec5fd25591dc29c3536e8f969
|
hubblestack/modules/file.py
|
python
|
user_to_uid
|
(user)
|
Convert user name to a uid
user
user name to convert to its uid
CLI Example:
.. code-block:: bash
salt '*' file.user_to_uid root
|
Convert user name to a uid
|
[
"Convert",
"user",
"name",
"to",
"a",
"uid"
] |
def user_to_uid(user):
"""
Convert user name to a uid
user
user name to convert to its uid
CLI Example:
.. code-block:: bash
salt '*' file.user_to_uid root
"""
if user is None:
user = hubblestack.utils.user.get_user()
try:
if isinstance(user, int):
return user
return pwd.getpwnam(user).pw_uid
except KeyError:
return ""
|
[
"def",
"user_to_uid",
"(",
"user",
")",
":",
"if",
"user",
"is",
"None",
":",
"user",
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"hubblestack",
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"utils",
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"(",
")",
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"getpwnam",
"(",
"user",
")",
".",
"pw_uid",
"except",
"KeyError",
":",
"return",
"\"\""
] |
https://github.com/hubblestack/hubble/blob/763142474edcecdec5fd25591dc29c3536e8f969/hubblestack/modules/file.py#L60-L80
|
||
DataBiosphere/toil
|
2e148eee2114ece8dcc3ec8a83f36333266ece0d
|
src/toil/fileStores/abstractFileStore.py
|
python
|
AbstractFileStore.getLocalTempFile
|
(self)
|
return os.path.abspath(tmpFile)
|
Get a new local temporary file that will persist for the duration of the job.
:return: The absolute path to a local temporary file. This file will exist
for the duration of the job only, and is guaranteed to be deleted
once the job terminates.
|
Get a new local temporary file that will persist for the duration of the job.
|
[
"Get",
"a",
"new",
"local",
"temporary",
"file",
"that",
"will",
"persist",
"for",
"the",
"duration",
"of",
"the",
"job",
"."
] |
def getLocalTempFile(self) -> str:
"""
Get a new local temporary file that will persist for the duration of the job.
:return: The absolute path to a local temporary file. This file will exist
for the duration of the job only, and is guaranteed to be deleted
once the job terminates.
"""
handle, tmpFile = tempfile.mkstemp(
prefix="tmp", suffix=".tmp", dir=self.localTempDir
)
os.close(handle)
return os.path.abspath(tmpFile)
|
[
"def",
"getLocalTempFile",
"(",
"self",
")",
"->",
"str",
":",
"handle",
",",
"tmpFile",
"=",
"tempfile",
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"mkstemp",
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",",
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"\".tmp\"",
",",
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")",
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"close",
"(",
"handle",
")",
"return",
"os",
".",
"path",
".",
"abspath",
"(",
"tmpFile",
")"
] |
https://github.com/DataBiosphere/toil/blob/2e148eee2114ece8dcc3ec8a83f36333266ece0d/src/toil/fileStores/abstractFileStore.py#L213-L225
|
|
facebookresearch/detectron2
|
cb92ae1763cd7d3777c243f07749574cdaec6cb8
|
detectron2/export/shared.py
|
python
|
_generic_status_identifier
|
(
predict_net: caffe2_pb2.NetDef,
status_updater: Callable,
known_status: Dict[Tuple[str, int], Any],
)
|
return _known_status
|
Statically infer the status of each blob, the status can be such as device type
(CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here
is versioned blob (Tuple[str, int]) in the format compatible with ssa.
Inputs:
predict_net: the caffe2 network
status_updater: a callable, given an op and the status of its input/output,
it returns the updated status of input/output. `None` is used for
representing unknown status.
known_status: a dict containing known status, used as initialization.
Outputs:
A dict mapping from versioned blob to its status
|
Statically infer the status of each blob, the status can be such as device type
(CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here
is versioned blob (Tuple[str, int]) in the format compatible with ssa.
Inputs:
predict_net: the caffe2 network
status_updater: a callable, given an op and the status of its input/output,
it returns the updated status of input/output. `None` is used for
representing unknown status.
known_status: a dict containing known status, used as initialization.
Outputs:
A dict mapping from versioned blob to its status
|
[
"Statically",
"infer",
"the",
"status",
"of",
"each",
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] |
def _generic_status_identifier(
predict_net: caffe2_pb2.NetDef,
status_updater: Callable,
known_status: Dict[Tuple[str, int], Any],
) -> Dict[Tuple[str, int], Any]:
"""
Statically infer the status of each blob, the status can be such as device type
(CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here
is versioned blob (Tuple[str, int]) in the format compatible with ssa.
Inputs:
predict_net: the caffe2 network
status_updater: a callable, given an op and the status of its input/output,
it returns the updated status of input/output. `None` is used for
representing unknown status.
known_status: a dict containing known status, used as initialization.
Outputs:
A dict mapping from versioned blob to its status
"""
ssa, versions = core.get_ssa(predict_net)
versioned_ext_input = [(b, 0) for b in predict_net.external_input]
versioned_ext_output = [(b, versions[b]) for b in predict_net.external_output]
all_versioned_blobs = set().union(*[set(x[0] + x[1]) for x in ssa])
allowed_vbs = all_versioned_blobs.union(versioned_ext_input).union(versioned_ext_output)
assert all(k in allowed_vbs for k in known_status)
assert all(v is not None for v in known_status.values())
_known_status = copy.deepcopy(known_status)
def _check_and_update(key, value):
assert value is not None
if key in _known_status:
if not _known_status[key] == value:
raise RuntimeError(
"Confilict status for {}, existing status {}, new status {}".format(
key, _known_status[key], value
)
)
_known_status[key] = value
def _update_i(op, ssa_i):
versioned_inputs = ssa_i[0]
versioned_outputs = ssa_i[1]
inputs_status = [_known_status.get(b, None) for b in versioned_inputs]
outputs_status = [_known_status.get(b, None) for b in versioned_outputs]
new_inputs_status, new_outputs_status = status_updater(op, inputs_status, outputs_status)
for versioned_blob, status in zip(
versioned_inputs + versioned_outputs, new_inputs_status + new_outputs_status
):
if status is not None:
_check_and_update(versioned_blob, status)
for op, ssa_i in zip(predict_net.op, ssa):
_update_i(op, ssa_i)
for op, ssa_i in zip(reversed(predict_net.op), reversed(ssa)):
_update_i(op, ssa_i)
# NOTE: This strictly checks all the blob from predict_net must be assgined
# a known status. However sometimes it's impossible (eg. having deadend op),
# we may relax this constraint if
for k in all_versioned_blobs:
if k not in _known_status:
raise NotImplementedError(
"Can not infer the status for {}. Currently only support the case where"
" a single forward and backward pass can identify status for all blobs.".format(k)
)
return _known_status
|
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"k",
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"return",
"_known_status"
] |
https://github.com/facebookresearch/detectron2/blob/cb92ae1763cd7d3777c243f07749574cdaec6cb8/detectron2/export/shared.py#L376-L445
|
|
oracle/oci-python-sdk
|
3c1604e4e212008fb6718e2f68cdb5ef71fd5793
|
src/oci/cloud_guard/cloud_guard_client_composite_operations.py
|
python
|
CloudGuardClientCompositeOperations.update_target_and_wait_for_state
|
(self, target_id, update_target_details, wait_for_states=[], operation_kwargs={}, waiter_kwargs={})
|
Calls :py:func:`~oci.cloud_guard.CloudGuardClient.update_target` and waits for the :py:class:`~oci.cloud_guard.models.Target` acted upon
to enter the given state(s).
:param str target_id: (required)
OCID of target
:param oci.cloud_guard.models.UpdateTargetDetails update_target_details: (required)
The information to be updated.
:param list[str] wait_for_states:
An array of states to wait on. These should be valid values for :py:attr:`~oci.cloud_guard.models.Target.lifecycle_state`
:param dict operation_kwargs:
A dictionary of keyword arguments to pass to :py:func:`~oci.cloud_guard.CloudGuardClient.update_target`
:param dict waiter_kwargs:
A dictionary of keyword arguments to pass to the :py:func:`oci.wait_until` function. For example, you could pass ``max_interval_seconds`` or ``max_interval_seconds``
as dictionary keys to modify how long the waiter function will wait between retries and the maximum amount of time it will wait
|
Calls :py:func:`~oci.cloud_guard.CloudGuardClient.update_target` and waits for the :py:class:`~oci.cloud_guard.models.Target` acted upon
to enter the given state(s).
|
[
"Calls",
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"CloudGuardClient",
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"enter",
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"given",
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"(",
"s",
")",
"."
] |
def update_target_and_wait_for_state(self, target_id, update_target_details, wait_for_states=[], operation_kwargs={}, waiter_kwargs={}):
"""
Calls :py:func:`~oci.cloud_guard.CloudGuardClient.update_target` and waits for the :py:class:`~oci.cloud_guard.models.Target` acted upon
to enter the given state(s).
:param str target_id: (required)
OCID of target
:param oci.cloud_guard.models.UpdateTargetDetails update_target_details: (required)
The information to be updated.
:param list[str] wait_for_states:
An array of states to wait on. These should be valid values for :py:attr:`~oci.cloud_guard.models.Target.lifecycle_state`
:param dict operation_kwargs:
A dictionary of keyword arguments to pass to :py:func:`~oci.cloud_guard.CloudGuardClient.update_target`
:param dict waiter_kwargs:
A dictionary of keyword arguments to pass to the :py:func:`oci.wait_until` function. For example, you could pass ``max_interval_seconds`` or ``max_interval_seconds``
as dictionary keys to modify how long the waiter function will wait between retries and the maximum amount of time it will wait
"""
operation_result = self.client.update_target(target_id, update_target_details, **operation_kwargs)
if not wait_for_states:
return operation_result
lowered_wait_for_states = [w.lower() for w in wait_for_states]
wait_for_resource_id = operation_result.data.id
try:
waiter_result = oci.wait_until(
self.client,
self.client.get_target(wait_for_resource_id),
evaluate_response=lambda r: getattr(r.data, 'lifecycle_state') and getattr(r.data, 'lifecycle_state').lower() in lowered_wait_for_states,
**waiter_kwargs
)
result_to_return = waiter_result
return result_to_return
except Exception as e:
raise oci.exceptions.CompositeOperationError(partial_results=[operation_result], cause=e)
|
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https://github.com/oracle/oci-python-sdk/blob/3c1604e4e212008fb6718e2f68cdb5ef71fd5793/src/oci/cloud_guard/cloud_guard_client_composite_operations.py#L785-L824
|
||
Alexey-T/CudaText
|
6a8b9a974c5d5029c6c273bde83198c83b3a5fb9
|
app/py/sys/chardet/cli/chardetect.py
|
python
|
description_of
|
(lines, name='stdin')
|
Return a string describing the probable encoding of a file or
list of strings.
:param lines: The lines to get the encoding of.
:type lines: Iterable of bytes
:param name: Name of file or collection of lines
:type name: str
|
Return a string describing the probable encoding of a file or
list of strings.
|
[
"Return",
"a",
"string",
"describing",
"the",
"probable",
"encoding",
"of",
"a",
"file",
"or",
"list",
"of",
"strings",
"."
] |
def description_of(lines, name='stdin'):
"""
Return a string describing the probable encoding of a file or
list of strings.
:param lines: The lines to get the encoding of.
:type lines: Iterable of bytes
:param name: Name of file or collection of lines
:type name: str
"""
u = UniversalDetector()
for line in lines:
line = bytearray(line)
u.feed(line)
# shortcut out of the loop to save reading further - particularly useful if we read a BOM.
if u.done:
break
u.close()
result = u.result
if PY2:
name = name.decode(sys.getfilesystemencoding(), 'ignore')
if result['encoding']:
return '{}: {} with confidence {}'.format(name, result['encoding'],
result['confidence'])
else:
return '{}: no result'.format(name)
|
[
"def",
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"'stdin'",
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https://github.com/Alexey-T/CudaText/blob/6a8b9a974c5d5029c6c273bde83198c83b3a5fb9/app/py/sys/chardet/cli/chardetect.py#L25-L50
|
||
Cue/scales
|
0aced26eb050ceb98ee9d5d6cdca8db448666986
|
src/greplin/scales/aggregation.py
|
python
|
MeterFormat.getCount
|
(self, data)
|
return data['count']
|
Get the count
|
Get the count
|
[
"Get",
"the",
"count"
] |
def getCount(self, data):
"""Get the count"""
assert data['type'] == "meter"
return data['count']
|
[
"def",
"getCount",
"(",
"self",
",",
"data",
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"assert",
"data",
"[",
"'type'",
"]",
"==",
"\"meter\"",
"return",
"data",
"[",
"'count'",
"]"
] |
https://github.com/Cue/scales/blob/0aced26eb050ceb98ee9d5d6cdca8db448666986/src/greplin/scales/aggregation.py#L104-L107
|
|
ym2011/POC-EXP
|
206b22d3a6b2a172359678df33bbc5b2ad04b6c3
|
K8/Web-Exp/sqlmap/thirdparty/socks/socks.py
|
python
|
wrapmodule
|
(module)
|
wrapmodule(module)
Attempts to replace a module's socket library with a SOCKS socket. Must set
a default proxy using setdefaultproxy(...) first.
This will only work on modules that import socket directly into the namespace;
most of the Python Standard Library falls into this category.
|
wrapmodule(module)
Attempts to replace a module's socket library with a SOCKS socket. Must set
a default proxy using setdefaultproxy(...) first.
This will only work on modules that import socket directly into the namespace;
most of the Python Standard Library falls into this category.
|
[
"wrapmodule",
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"Attempts",
"to",
"replace",
"a",
"module",
"s",
"socket",
"library",
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"SOCKS",
"socket",
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"default",
"proxy",
"using",
"setdefaultproxy",
"(",
"...",
")",
"first",
".",
"This",
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"most",
"of",
"the",
"Python",
"Standard",
"Library",
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"into",
"this",
"category",
"."
] |
def wrapmodule(module):
"""wrapmodule(module)
Attempts to replace a module's socket library with a SOCKS socket. Must set
a default proxy using setdefaultproxy(...) first.
This will only work on modules that import socket directly into the namespace;
most of the Python Standard Library falls into this category.
"""
if _defaultproxy != None:
module.socket.socket = socksocket
module.socket.create_connection = create_connection
else:
raise GeneralProxyError((4, "no proxy specified"))
|
[
"def",
"wrapmodule",
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"module",
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"if",
"_defaultproxy",
"!=",
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".",
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"else",
":",
"raise",
"GeneralProxyError",
"(",
"(",
"4",
",",
"\"no proxy specified\"",
")",
")"
] |
https://github.com/ym2011/POC-EXP/blob/206b22d3a6b2a172359678df33bbc5b2ad04b6c3/K8/Web-Exp/sqlmap/thirdparty/socks/socks.py#L103-L114
|
||
Azure/azure-cli
|
6c1b085a0910c6c2139006fcbd8ade44006eb6dd
|
src/azure-cli/azure/cli/command_modules/acs/_validators.py
|
python
|
validate_linux_host_name
|
(namespace)
|
Validates a string as a legal host name component.
This validation will also occur server-side in the ARM API, but that may take
a minute or two before the user sees it. So it's more user-friendly to validate
in the CLI pre-flight.
|
Validates a string as a legal host name component.
|
[
"Validates",
"a",
"string",
"as",
"a",
"legal",
"host",
"name",
"component",
"."
] |
def validate_linux_host_name(namespace):
"""Validates a string as a legal host name component.
This validation will also occur server-side in the ARM API, but that may take
a minute or two before the user sees it. So it's more user-friendly to validate
in the CLI pre-flight.
"""
# https://stackoverflow.com/questions/106179/regular-expression-to-match-dns-hostname-or-ip-address
rfc1123_regex = re.compile(r'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])(\.([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\-]{0,61}[a-zA-Z0-9]))*$') # pylint:disable=line-too-long
found = rfc1123_regex.findall(namespace.name)
if not found:
raise CLIError('--name cannot exceed 63 characters and can only contain '
'letters, numbers, or dashes (-).')
|
[
"def",
"validate_linux_host_name",
"(",
"namespace",
")",
":",
"# https://stackoverflow.com/questions/106179/regular-expression-to-match-dns-hostname-or-ip-address",
"rfc1123_regex",
"=",
"re",
".",
"compile",
"(",
"r'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\-]{0,61}[a-zA-Z0-9])(\\.([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\-]{0,61}[a-zA-Z0-9]))*$'",
")",
"# pylint:disable=line-too-long",
"found",
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"rfc1123_regex",
".",
"findall",
"(",
"namespace",
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"if",
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"found",
":",
"raise",
"CLIError",
"(",
"'--name cannot exceed 63 characters and can only contain '",
"'letters, numbers, or dashes (-).'",
")"
] |
https://github.com/Azure/azure-cli/blob/6c1b085a0910c6c2139006fcbd8ade44006eb6dd/src/azure-cli/azure/cli/command_modules/acs/_validators.py#L139-L151
|
||
biolab/orange2
|
db40a9449cb45b507d63dcd5739b223f9cffb8e6
|
distribute_setup.py
|
python
|
_patch_file
|
(path, content)
|
return True
|
Will backup the file then patch it
|
Will backup the file then patch it
|
[
"Will",
"backup",
"the",
"file",
"then",
"patch",
"it"
] |
def _patch_file(path, content):
"""Will backup the file then patch it"""
f = open(path)
existing_content = f.read()
f.close()
if existing_content == content:
# already patched
log.warn('Already patched.')
return False
log.warn('Patching...')
_rename_path(path)
f = open(path, 'w')
try:
f.write(content)
finally:
f.close()
return True
|
[
"def",
"_patch_file",
"(",
"path",
",",
"content",
")",
":",
"f",
"=",
"open",
"(",
"path",
")",
"existing_content",
"=",
"f",
".",
"read",
"(",
")",
"f",
".",
"close",
"(",
")",
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")",
"finally",
":",
"f",
".",
"close",
"(",
")",
"return",
"True"
] |
https://github.com/biolab/orange2/blob/db40a9449cb45b507d63dcd5739b223f9cffb8e6/distribute_setup.py#L250-L266
|
|
amymcgovern/pyparrot
|
bf4775ec1199b282e4edde1e4a8e018dcc8725e0
|
pyparrot/utils/vlc.py
|
python
|
MediaList.unlock
|
(self)
|
return libvlc_media_list_unlock(self)
|
Release lock on media list items
The L{lock} should be held upon entering this function.
|
Release lock on media list items
The L{lock} should be held upon entering this function.
|
[
"Release",
"lock",
"on",
"media",
"list",
"items",
"The",
"L",
"{",
"lock",
"}",
"should",
"be",
"held",
"upon",
"entering",
"this",
"function",
"."
] |
def unlock(self):
'''Release lock on media list items
The L{lock} should be held upon entering this function.
'''
return libvlc_media_list_unlock(self)
|
[
"def",
"unlock",
"(",
"self",
")",
":",
"return",
"libvlc_media_list_unlock",
"(",
"self",
")"
] |
https://github.com/amymcgovern/pyparrot/blob/bf4775ec1199b282e4edde1e4a8e018dcc8725e0/pyparrot/utils/vlc.py#L2986-L2990
|
|
aliyun/aliyun-oss-python-sdk
|
5f2afa0928a58c7c1cc6317ac147f3637481f6fd
|
oss2/models.py
|
python
|
BucketQosInfo.__init__
|
(self,
total_upload_bw = None,
intranet_upload_bw = None,
extranet_upload_bw = None,
total_download_bw = None,
intranet_download_bw = None,
extranet_download_bw = None,
total_qps = None,
intranet_qps = None,
extranet_qps = None)
|
[] |
def __init__(self,
total_upload_bw = None,
intranet_upload_bw = None,
extranet_upload_bw = None,
total_download_bw = None,
intranet_download_bw = None,
extranet_download_bw = None,
total_qps = None,
intranet_qps = None,
extranet_qps = None):
self.total_upload_bw = total_upload_bw
self.intranet_upload_bw = intranet_upload_bw
self.extranet_upload_bw = extranet_upload_bw
self.total_download_bw = total_download_bw
self.intranet_download_bw = intranet_download_bw
self.extranet_download_bw = extranet_download_bw
self.total_qps = total_qps
self.intranet_qps = intranet_qps
self.extranet_qps = extranet_qps
|
[
"def",
"__init__",
"(",
"self",
",",
"total_upload_bw",
"=",
"None",
",",
"intranet_upload_bw",
"=",
"None",
",",
"extranet_upload_bw",
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"None",
",",
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"=",
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",",
"intranet_download_bw",
"=",
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",",
"extranet_download_bw",
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"None",
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"None",
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")",
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"=",
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"intranet_download_bw",
"=",
"intranet_download_bw",
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"=",
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"total_qps",
"=",
"total_qps",
"self",
".",
"intranet_qps",
"=",
"intranet_qps",
"self",
".",
"extranet_qps",
"=",
"extranet_qps"
] |
https://github.com/aliyun/aliyun-oss-python-sdk/blob/5f2afa0928a58c7c1cc6317ac147f3637481f6fd/oss2/models.py#L1616-L1635
|
||||
nucleic/enaml
|
65c2a2a2d765e88f2e1103046680571894bb41ed
|
enaml/core/code_generator.py
|
python
|
CodeGenerator.add_map
|
(self)
|
Store the key/value pair on the TOS into the map at 3rd pos.
|
Store the key/value pair on the TOS into the map at 3rd pos.
|
[
"Store",
"the",
"key",
"/",
"value",
"pair",
"on",
"the",
"TOS",
"into",
"the",
"map",
"at",
"3rd",
"pos",
"."
] |
def add_map(self):
""" Store the key/value pair on the TOS into the map at 3rd pos.
"""
# WARNING in Python 3.8 the order is # TOS -> map -> key -> value
self.code_ops.append( # TOS -> map -> value -> key
bc.Instr("MAP_ADD", 1),
)
|
[
"def",
"add_map",
"(",
"self",
")",
":",
"# WARNING in Python 3.8 the order is # TOS -> map -> key -> value",
"self",
".",
"code_ops",
".",
"append",
"(",
"# TOS -> map -> value -> key",
"bc",
".",
"Instr",
"(",
"\"MAP_ADD\"",
",",
"1",
")",
",",
")"
] |
https://github.com/nucleic/enaml/blob/65c2a2a2d765e88f2e1103046680571894bb41ed/enaml/core/code_generator.py#L286-L293
|
||
openrazer/openrazer
|
1615f8516e8014bad7f78c781c91e6529679718f
|
pylib/openrazer/client/devices/mousemat.py
|
python
|
RazerMousemat.trigger_reactive
|
(self)
|
return self._dbus_interfaces['device'].triggerReactive()
|
Trigger a reactive flash
|
Trigger a reactive flash
|
[
"Trigger",
"a",
"reactive",
"flash"
] |
def trigger_reactive(self) -> bool:
"""
Trigger a reactive flash
"""
return self._dbus_interfaces['device'].triggerReactive()
|
[
"def",
"trigger_reactive",
"(",
"self",
")",
"->",
"bool",
":",
"return",
"self",
".",
"_dbus_interfaces",
"[",
"'device'",
"]",
".",
"triggerReactive",
"(",
")"
] |
https://github.com/openrazer/openrazer/blob/1615f8516e8014bad7f78c781c91e6529679718f/pylib/openrazer/client/devices/mousemat.py#L5-L9
|
|
joshfriend/flask-restful-demo
|
4571c5dd3c38c1563894eb9558f44f8c4ddd8c7d
|
migrations/env.py
|
python
|
run_migrations_offline
|
()
|
Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
|
Run migrations in 'offline' mode.
|
[
"Run",
"migrations",
"in",
"offline",
"mode",
"."
] |
def run_migrations_offline():
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(url=url)
with context.begin_transaction():
context.run_migrations()
|
[
"def",
"run_migrations_offline",
"(",
")",
":",
"url",
"=",
"config",
".",
"get_main_option",
"(",
"\"sqlalchemy.url\"",
")",
"context",
".",
"configure",
"(",
"url",
"=",
"url",
")",
"with",
"context",
".",
"begin_transaction",
"(",
")",
":",
"context",
".",
"run_migrations",
"(",
")"
] |
https://github.com/joshfriend/flask-restful-demo/blob/4571c5dd3c38c1563894eb9558f44f8c4ddd8c7d/migrations/env.py#L27-L43
|
||
krintoxi/NoobSec-Toolkit
|
38738541cbc03cedb9a3b3ed13b629f781ad64f6
|
NoobSecToolkit /tools/sqli/thirdparty/xdot/xdot.py
|
python
|
ZoomAction.drag
|
(self, deltax, deltay)
|
[] |
def drag(self, deltax, deltay):
self.dot_widget.zoom_ratio *= 1.005 ** (deltax + deltay)
self.dot_widget.zoom_to_fit_on_resize = False
self.dot_widget.queue_draw()
|
[
"def",
"drag",
"(",
"self",
",",
"deltax",
",",
"deltay",
")",
":",
"self",
".",
"dot_widget",
".",
"zoom_ratio",
"*=",
"1.005",
"**",
"(",
"deltax",
"+",
"deltay",
")",
"self",
".",
"dot_widget",
".",
"zoom_to_fit_on_resize",
"=",
"False",
"self",
".",
"dot_widget",
".",
"queue_draw",
"(",
")"
] |
https://github.com/krintoxi/NoobSec-Toolkit/blob/38738541cbc03cedb9a3b3ed13b629f781ad64f6/NoobSecToolkit /tools/sqli/thirdparty/xdot/xdot.py#L1335-L1338
|
||||
heynemann/motorengine
|
5e1fea7cc15060f768a697fe4c3593d20f23c4ed
|
motorengine/query_builder/field_list.py
|
python
|
QueryFieldList.__add__
|
(self, f)
|
return self
|
[] |
def __add__(self, f):
if isinstance(f.value, dict):
for field in f.fields:
self.slice[field] = f.value
if not self.fields:
self.fields = f.fields
elif not self.fields:
self.fields = f.fields
self.value = f.value
self.slice = {}
elif self.value is self.ONLY and f.value is self.ONLY:
self._clean_slice()
if self._only_called:
self.fields = self.fields.union(f.fields)
else:
self.fields = f.fields
elif self.value is self.EXCLUDE and f.value is self.EXCLUDE:
self.fields = self.fields.union(f.fields)
self._clean_slice()
elif self.value is self.ONLY and f.value is self.EXCLUDE:
self.fields -= f.fields
self._clean_slice()
elif self.value is self.EXCLUDE and f.value is self.ONLY:
self.value = self.ONLY
self.fields = f.fields - self.fields
self._clean_slice()
# _id should be saved separately to avoid situation such as
# exclude('_id').only('other') so the previous code of this method
# remove _id from self.fields (its a normal behavior for any field
# except for _id because _id field cannot be removed with only)
if '_id' in f.fields:
self._id = f.value
if self.always_include:
if self.value is self.ONLY and self.fields:
if sorted(self.slice.keys()) != sorted(self.fields):
self.fields = self.fields.union(self.always_include)
else:
# if this is exclude - remove from fields values from
# always included fields
self.fields -= self.always_include
if getattr(f, '_only_called', False):
self._only_called = True
return self
|
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",",
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"# except for _id because _id field cannot be removed with only)",
"if",
"'_id'",
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":",
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"(",
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"always_include",
")",
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":",
"# if this is exclude - remove from fields values from",
"# always included fields",
"self",
".",
"fields",
"-=",
"self",
".",
"always_include",
"if",
"getattr",
"(",
"f",
",",
"'_only_called'",
",",
"False",
")",
":",
"self",
".",
"_only_called",
"=",
"True",
"return",
"self"
] |
https://github.com/heynemann/motorengine/blob/5e1fea7cc15060f768a697fe4c3593d20f23c4ed/motorengine/query_builder/field_list.py#L30-L75
|
|||
danmacnish/cartoonify
|
39ea84d96b3e93f0480e6d6158bea506d01278ca
|
cartoonify/app/object_detection/meta_architectures/faster_rcnn_meta_arch.py
|
python
|
FasterRCNNMetaArch._unpad_proposals_and_sample_box_classifier_batch
|
(
self,
proposal_boxes,
proposal_scores,
num_proposals,
groundtruth_boxlists,
groundtruth_classes_with_background_list)
|
return (tf.stack(single_image_proposal_box_sample),
tf.stack(single_image_proposal_score_sample),
tf.stack(single_image_num_proposals_sample))
|
Unpads proposals and samples a minibatch for second stage.
Args:
proposal_boxes: A float tensor with shape
[batch_size, num_proposals, 4] representing the (potentially zero
padded) proposal boxes for all images in the batch. These boxes are
represented as normalized coordinates.
proposal_scores: A float tensor with shape
[batch_size, num_proposals] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
of the groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
Returns:
proposal_boxes: A float tensor with shape
[batch_size, second_stage_batch_size, 4] representing the (potentially
zero padded) proposal boxes for all images in the batch. These boxes
are represented as normalized coordinates.
proposal_scores: A float tensor with shape
[batch_size, second_stage_batch_size] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
|
Unpads proposals and samples a minibatch for second stage.
|
[
"Unpads",
"proposals",
"and",
"samples",
"a",
"minibatch",
"for",
"second",
"stage",
"."
] |
def _unpad_proposals_and_sample_box_classifier_batch(
self,
proposal_boxes,
proposal_scores,
num_proposals,
groundtruth_boxlists,
groundtruth_classes_with_background_list):
"""Unpads proposals and samples a minibatch for second stage.
Args:
proposal_boxes: A float tensor with shape
[batch_size, num_proposals, 4] representing the (potentially zero
padded) proposal boxes for all images in the batch. These boxes are
represented as normalized coordinates.
proposal_scores: A float tensor with shape
[batch_size, num_proposals] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
groundtruth_boxlists: A list of BoxLists containing (absolute) coordinates
of the groundtruth boxes.
groundtruth_classes_with_background_list: A list of 2-D one-hot
(or k-hot) tensors of shape [num_boxes, num_classes+1] containing the
class targets with the 0th index assumed to map to the background class.
Returns:
proposal_boxes: A float tensor with shape
[batch_size, second_stage_batch_size, 4] representing the (potentially
zero padded) proposal boxes for all images in the batch. These boxes
are represented as normalized coordinates.
proposal_scores: A float tensor with shape
[batch_size, second_stage_batch_size] representing the (potentially zero
padded) proposal objectness scores for all images in the batch.
num_proposals: A Tensor of type `int32`. A 1-D tensor of shape [batch]
representing the number of proposals predicted for each image in
the batch.
"""
single_image_proposal_box_sample = []
single_image_proposal_score_sample = []
single_image_num_proposals_sample = []
for (single_image_proposal_boxes,
single_image_proposal_scores,
single_image_num_proposals,
single_image_groundtruth_boxlist,
single_image_groundtruth_classes_with_background) in zip(
tf.unstack(proposal_boxes),
tf.unstack(proposal_scores),
tf.unstack(num_proposals),
groundtruth_boxlists,
groundtruth_classes_with_background_list):
static_shape = single_image_proposal_boxes.get_shape()
sliced_static_shape = tf.TensorShape([tf.Dimension(None),
static_shape.dims[-1]])
single_image_proposal_boxes = tf.slice(
single_image_proposal_boxes,
[0, 0],
[single_image_num_proposals, -1])
single_image_proposal_boxes.set_shape(sliced_static_shape)
single_image_proposal_scores = tf.slice(single_image_proposal_scores,
[0],
[single_image_num_proposals])
single_image_boxlist = box_list.BoxList(single_image_proposal_boxes)
single_image_boxlist.add_field(fields.BoxListFields.scores,
single_image_proposal_scores)
sampled_boxlist = self._sample_box_classifier_minibatch(
single_image_boxlist,
single_image_groundtruth_boxlist,
single_image_groundtruth_classes_with_background)
sampled_padded_boxlist = box_list_ops.pad_or_clip_box_list(
sampled_boxlist,
num_boxes=self._second_stage_batch_size)
single_image_num_proposals_sample.append(tf.minimum(
sampled_boxlist.num_boxes(),
self._second_stage_batch_size))
bb = sampled_padded_boxlist.get()
single_image_proposal_box_sample.append(bb)
single_image_proposal_score_sample.append(
sampled_padded_boxlist.get_field(fields.BoxListFields.scores))
return (tf.stack(single_image_proposal_box_sample),
tf.stack(single_image_proposal_score_sample),
tf.stack(single_image_num_proposals_sample))
|
[
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",",
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",",
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",",
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"_second_stage_batch_size",
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".",
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")",
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"append",
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"single_image_proposal_score_sample",
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".",
"BoxListFields",
".",
"scores",
")",
")",
"return",
"(",
"tf",
".",
"stack",
"(",
"single_image_proposal_box_sample",
")",
",",
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"stack",
"(",
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")",
",",
"tf",
".",
"stack",
"(",
"single_image_num_proposals_sample",
")",
")"
] |
https://github.com/danmacnish/cartoonify/blob/39ea84d96b3e93f0480e6d6158bea506d01278ca/cartoonify/app/object_detection/meta_architectures/faster_rcnn_meta_arch.py#L941-L1023
|
|
ipython/ipython
|
c0abea7a6dfe52c1f74c9d0387d4accadba7cc14
|
IPython/core/interactiveshell.py
|
python
|
InteractiveShell.init_history
|
(self)
|
Sets up the command history, and starts regular autosaves.
|
Sets up the command history, and starts regular autosaves.
|
[
"Sets",
"up",
"the",
"command",
"history",
"and",
"starts",
"regular",
"autosaves",
"."
] |
def init_history(self):
"""Sets up the command history, and starts regular autosaves."""
self.history_manager = HistoryManager(shell=self, parent=self)
self.configurables.append(self.history_manager)
|
[
"def",
"init_history",
"(",
"self",
")",
":",
"self",
".",
"history_manager",
"=",
"HistoryManager",
"(",
"shell",
"=",
"self",
",",
"parent",
"=",
"self",
")",
"self",
".",
"configurables",
".",
"append",
"(",
"self",
".",
"history_manager",
")"
] |
https://github.com/ipython/ipython/blob/c0abea7a6dfe52c1f74c9d0387d4accadba7cc14/IPython/core/interactiveshell.py#L1687-L1690
|
||
marionmari/pyGPs
|
792f3c6cb91126ade9f23a8e39d9cbcd30cfbc7b
|
pyGPs/GraphExtensions/nodeKernels.py
|
python
|
psInvLapKernel
|
(A)
|
return K
|
Pseudo inverse of the normalized Laplacian.
:param A: adjacency matrix
:return: kernel matrix
|
Pseudo inverse of the normalized Laplacian.
|
[
"Pseudo",
"inverse",
"of",
"the",
"normalized",
"Laplacian",
"."
] |
def psInvLapKernel(A):
'''
Pseudo inverse of the normalized Laplacian.
:param A: adjacency matrix
:return: kernel matrix
'''
L = normLap(A)
K = np.linalg.pinv(L)
return K
|
[
"def",
"psInvLapKernel",
"(",
"A",
")",
":",
"L",
"=",
"normLap",
"(",
"A",
")",
"K",
"=",
"np",
".",
"linalg",
".",
"pinv",
"(",
"L",
")",
"return",
"K"
] |
https://github.com/marionmari/pyGPs/blob/792f3c6cb91126ade9f23a8e39d9cbcd30cfbc7b/pyGPs/GraphExtensions/nodeKernels.py#L51-L60
|
|
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/flaskbb/forum/views.py
|
python
|
NewPost.get
|
(self, topic_id, slug=None, post_id=None)
|
return render_template(
'forum/new_post.html', topic=topic, form=form
)
|
[] |
def get(self, topic_id, slug=None, post_id=None):
topic = Topic.query.filter_by(id=topic_id).first_or_404()
form = self.form()
if post_id is not None:
post = Post.query.filter_by(id=post_id).first_or_404()
form.content.data = format_quote(post.username, post.content)
return render_template(
'forum/new_post.html', topic=topic, form=form
)
|
[
"def",
"get",
"(",
"self",
",",
"topic_id",
",",
"slug",
"=",
"None",
",",
"post_id",
"=",
"None",
")",
":",
"topic",
"=",
"Topic",
".",
"query",
".",
"filter_by",
"(",
"id",
"=",
"topic_id",
")",
".",
"first_or_404",
"(",
")",
"form",
"=",
"self",
".",
"form",
"(",
")",
"if",
"post_id",
"is",
"not",
"None",
":",
"post",
"=",
"Post",
".",
"query",
".",
"filter_by",
"(",
"id",
"=",
"post_id",
")",
".",
"first_or_404",
"(",
")",
"form",
".",
"content",
".",
"data",
"=",
"format_quote",
"(",
"post",
".",
"username",
",",
"post",
".",
"content",
")",
"return",
"render_template",
"(",
"'forum/new_post.html'",
",",
"topic",
"=",
"topic",
",",
"form",
"=",
"form",
")"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/flaskbb/forum/views.py#L431-L441
|
|||
cloudera/impyla
|
0c736af4cad2bade9b8e313badc08ec50e81c948
|
impala/_thrift_gen/hive_metastore/ttypes.py
|
python
|
GetPrincipalsInRoleRequest.__repr__
|
(self)
|
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
|
[] |
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
|
[
"def",
"__repr__",
"(",
"self",
")",
":",
"L",
"=",
"[",
"'%s=%r'",
"%",
"(",
"key",
",",
"value",
")",
"for",
"key",
",",
"value",
"in",
"self",
".",
"__dict__",
".",
"items",
"(",
")",
"]",
"return",
"'%s(%s)'",
"%",
"(",
"self",
".",
"__class__",
".",
"__name__",
",",
"', '",
".",
"join",
"(",
"L",
")",
")"
] |
https://github.com/cloudera/impyla/blob/0c736af4cad2bade9b8e313badc08ec50e81c948/impala/_thrift_gen/hive_metastore/ttypes.py#L1876-L1879
|
|||
microsoft/NimbusML
|
f6be39ce9359786976429bab0ccd837e849b4ba5
|
src/python/nimbusml/preprocessing/normalization/lpscaler.py
|
python
|
LpScaler._nodes_with_presteps
|
(self)
|
return [
TypeConverter(
result_type='R4')._steal_io(self),
self]
|
Inserts preprocessing before this one.
|
Inserts preprocessing before this one.
|
[
"Inserts",
"preprocessing",
"before",
"this",
"one",
"."
] |
def _nodes_with_presteps(self):
"""
Inserts preprocessing before this one.
"""
from ..schema import TypeConverter
return [
TypeConverter(
result_type='R4')._steal_io(self),
self]
|
[
"def",
"_nodes_with_presteps",
"(",
"self",
")",
":",
"from",
".",
".",
"schema",
"import",
"TypeConverter",
"return",
"[",
"TypeConverter",
"(",
"result_type",
"=",
"'R4'",
")",
".",
"_steal_io",
"(",
"self",
")",
",",
"self",
"]"
] |
https://github.com/microsoft/NimbusML/blob/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/preprocessing/normalization/lpscaler.py#L60-L68
|
|
timkpaine/tdameritrade
|
f877088d0c3aca9fef03e92b62074153c66194c1
|
tdameritrade/client.py
|
python
|
TDClient.instrument
|
(self, cusip)
|
return self._request(GET_INSTRUMENT.format(cusip=cusip)).json()
|
get instrument info from cusip
Args:
cusip (str): the cusip to use, can find it by looking up in search
|
get instrument info from cusip
|
[
"get",
"instrument",
"info",
"from",
"cusip"
] |
def instrument(self, cusip):
"""get instrument info from cusip
Args:
cusip (str): the cusip to use, can find it by looking up in search
"""
return self._request(GET_INSTRUMENT.format(cusip=cusip)).json()
|
[
"def",
"instrument",
"(",
"self",
",",
"cusip",
")",
":",
"return",
"self",
".",
"_request",
"(",
"GET_INSTRUMENT",
".",
"format",
"(",
"cusip",
"=",
"cusip",
")",
")",
".",
"json",
"(",
")"
] |
https://github.com/timkpaine/tdameritrade/blob/f877088d0c3aca9fef03e92b62074153c66194c1/tdameritrade/client.py#L216-L222
|
|
CvvT/dumpDex
|
92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1
|
python/idaapi.py
|
python
|
set_compiler_id
|
(*args)
|
return _idaapi.set_compiler_id(*args)
|
set_compiler_id(id) -> bool
|
set_compiler_id(id) -> bool
|
[
"set_compiler_id",
"(",
"id",
")",
"-",
">",
"bool"
] |
def set_compiler_id(*args):
"""
set_compiler_id(id) -> bool
"""
return _idaapi.set_compiler_id(*args)
|
[
"def",
"set_compiler_id",
"(",
"*",
"args",
")",
":",
"return",
"_idaapi",
".",
"set_compiler_id",
"(",
"*",
"args",
")"
] |
https://github.com/CvvT/dumpDex/blob/92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1/python/idaapi.py#L29472-L29476
|
|
samuelclay/NewsBlur
|
2c45209df01a1566ea105e04d499367f32ac9ad2
|
archive/fabfile.py
|
python
|
celery_reload
|
()
|
[] |
def celery_reload():
with virtualenv():
run('sudo supervisorctl reload celery')
run('tail logs/newsblur.log')
|
[
"def",
"celery_reload",
"(",
")",
":",
"with",
"virtualenv",
"(",
")",
":",
"run",
"(",
"'sudo supervisorctl reload celery'",
")",
"run",
"(",
"'tail logs/newsblur.log'",
")"
] |
https://github.com/samuelclay/NewsBlur/blob/2c45209df01a1566ea105e04d499367f32ac9ad2/archive/fabfile.py#L1880-L1883
|
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