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achael/eht-imaging | bbd3aeb06bef52bf89fa1c06de71e5509a5b0015 | ehtim/obsdata.py | python | Obsdata.reorder_baselines | (self) | return | Reorder baselines to match uvfits convention, based on the telescope array ordering | Reorder baselines to match uvfits convention, based on the telescope array ordering | [
"Reorder",
"baselines",
"to",
"match",
"uvfits",
"convention",
"based",
"on",
"the",
"telescope",
"array",
"ordering"
] | def reorder_baselines(self):
"""Reorder baselines to match uvfits convention, based on the telescope array ordering
"""
# Time partition the datatable
datatable = self.data.copy()
datalist = []
for key, group in it.groupby(datatable, lambda x: x['time']):
datalist.append(np.array([obs for obs in group]))
# Remove conjugate baselines
obsdata = []
for tlist in datalist:
blpairs = []
for dat in tlist:
if not (set((dat['t1'], dat['t2']))) in blpairs:
# Reverse the baseline in the right order for uvfits:
if(self.tkey[dat['t2']] < self.tkey[dat['t1']]):
(dat['t1'], dat['t2']) = (dat['t2'], dat['t1'])
(dat['tau1'], dat['tau2']) = (dat['tau2'], dat['tau1'])
dat['u'] = -dat['u']
dat['v'] = -dat['v']
if self.polrep == 'stokes':
dat['vis'] = np.conj(dat['vis'])
dat['qvis'] = np.conj(dat['qvis'])
dat['uvis'] = np.conj(dat['uvis'])
dat['vvis'] = np.conj(dat['vvis'])
elif self.polrep == 'circ':
dat['rrvis'] = np.conj(dat['rrvis'])
dat['llvis'] = np.conj(dat['llvis'])
# must switch l & r !!
rl = dat['rlvis'].copy()
lr = dat['lrvis'].copy()
dat['rlvis'] = np.conj(lr)
dat['lrvis'] = np.conj(rl)
else:
raise Exception("polrep must be either 'stokes' or 'circ'")
# Append the data point
blpairs.append(set((dat['t1'], dat['t2'])))
obsdata.append(dat)
obsdata = np.array(obsdata, dtype=self.poltype)
# Timesort data
obsdata = obsdata[np.argsort(obsdata, order=['time', 't1'])]
# Save the data
self.data = obsdata
return | [
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mvantellingen/python-zeep | 2f35b7d29355ba646f5e3c6e9925033d5d6df8bb | src/zeep/xsd/elements/indicators.py | python | OrderIndicator.accept | (self, values) | return max(results) | Return the number of values which are accepted by this choice.
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"""Return the number of values which are accepted by this choice.
If not all required elements are available then 0 is returned.
"""
if not self.accepts_multiple:
values = [values]
results = set()
for value in values:
num = 0
for name, element in self.elements_nested:
if isinstance(element, Element):
if element.name in value and value[element.name] is not None:
num += 1
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num += element.accept(value)
results.add(num)
return max(results) | [
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jcartledge/sublime-worksheet | 44b2ba96d02759b485adbf85c1a2c9d45cc39599 | repl/pexpect.py | python | spawnb.setwinsize | (self, r, c) | This sets the terminal window size of the child tty. This will cause
a SIGWINCH signal to be sent to the child. This does not change the
physical window size. It changes the size reported to TTY-aware
applications like vi or curses -- applications that respond to the
SIGWINCH signal. | This sets the terminal window size of the child tty. This will cause
a SIGWINCH signal to be sent to the child. This does not change the
physical window size. It changes the size reported to TTY-aware
applications like vi or curses -- applications that respond to the
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"""This sets the terminal window size of the child tty. This will cause
a SIGWINCH signal to be sent to the child. This does not change the
physical window size. It changes the size reported to TTY-aware
applications like vi or curses -- applications that respond to the
SIGWINCH signal. """
# Check for buggy platforms. Some Python versions on some platforms
# (notably OSF1 Alpha and RedHat 7.1) truncate the value for
# termios.TIOCSWINSZ. It is not clear why this happens.
# These platforms don't seem to handle the signed int very well;
# yet other platforms like OpenBSD have a large negative value for
# TIOCSWINSZ and they don't have a truncate problem.
# Newer versions of Linux have totally different values for TIOCSWINSZ.
# Note that this fix is a hack.
TIOCSWINSZ = getattr(termios, 'TIOCSWINSZ', -2146929561)
if TIOCSWINSZ == 2148037735: # L is not required in Python >= 2.2.
TIOCSWINSZ = -2146929561 # Same bits, but with sign.
# Note, assume ws_xpixel and ws_ypixel are zero.
s = struct.pack('HHHH', r, c, 0, 0)
fcntl.ioctl(self.fileno(), TIOCSWINSZ, s) | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/dependencies/qt.py | python | Qt6PkgConfigDependency.get_private_includes | (self, mod_inc_dir: str, module: str) | return _qt_get_private_includes(mod_inc_dir, module, self.version) | [] | def get_private_includes(self, mod_inc_dir: str, module: str) -> T.List[str]:
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/numpy/polynomial/polynomial.py | python | polyvalfromroots | (x, r, tensor=True) | return np.prod(x - r, axis=0) | Evaluate a polynomial specified by its roots at points x.
If `r` is of length `N`, this function returns the value
.. math:: p(x) = \\prod_{n=1}^{N} (x - r_n)
The parameter `x` is converted to an array only if it is a tuple or a
list, otherwise it is treated as a scalar. In either case, either `x`
or its elements must support multiplication and addition both with
themselves and with the elements of `r`.
If `r` is a 1-D array, then `p(x)` will have the same shape as `x`. If `r`
is multidimensional, then the shape of the result depends on the value of
`tensor`. If `tensor is ``True`` the shape will be r.shape[1:] + x.shape;
that is, each polynomial is evaluated at every value of `x`. If `tensor` is
``False``, the shape will be r.shape[1:]; that is, each polynomial is
evaluated only for the corresponding broadcast value of `x`. Note that
scalars have shape (,).
.. versionadded:: 1.12
Parameters
----------
x : array_like, compatible object
If `x` is a list or tuple, it is converted to an ndarray, otherwise
it is left unchanged and treated as a scalar. In either case, `x`
or its elements must support addition and multiplication with
with themselves and with the elements of `r`.
r : array_like
Array of roots. If `r` is multidimensional the first index is the
root index, while the remaining indices enumerate multiple
polynomials. For instance, in the two dimensional case the roots
of each polynomial may be thought of as stored in the columns of `r`.
tensor : boolean, optional
If True, the shape of the roots array is extended with ones on the
right, one for each dimension of `x`. Scalars have dimension 0 for this
action. The result is that every column of coefficients in `r` is
evaluated for every element of `x`. If False, `x` is broadcast over the
columns of `r` for the evaluation. This keyword is useful when `r` is
multidimensional. The default value is True.
Returns
-------
values : ndarray, compatible object
The shape of the returned array is described above.
See Also
--------
polyroots, polyfromroots, polyval
Examples
--------
>>> from numpy.polynomial.polynomial import polyvalfromroots
>>> polyvalfromroots(1, [1,2,3])
0.0
>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
[2, 3]])
>>> polyvalfromroots(a, [-1, 0, 1])
array([[ -0., 0.],
[ 6., 24.]])
>>> r = np.arange(-2, 2).reshape(2,2) # multidimensional coefficients
>>> r # each column of r defines one polynomial
array([[-2, -1],
[ 0, 1]])
>>> b = [-2, 1]
>>> polyvalfromroots(b, r, tensor=True)
array([[-0., 3.],
[ 3., 0.]])
>>> polyvalfromroots(b, r, tensor=False)
array([-0., 0.]) | Evaluate a polynomial specified by its roots at points x. | [
"Evaluate",
"a",
"polynomial",
"specified",
"by",
"its",
"roots",
"at",
"points",
"x",
"."
] | def polyvalfromroots(x, r, tensor=True):
"""
Evaluate a polynomial specified by its roots at points x.
If `r` is of length `N`, this function returns the value
.. math:: p(x) = \\prod_{n=1}^{N} (x - r_n)
The parameter `x` is converted to an array only if it is a tuple or a
list, otherwise it is treated as a scalar. In either case, either `x`
or its elements must support multiplication and addition both with
themselves and with the elements of `r`.
If `r` is a 1-D array, then `p(x)` will have the same shape as `x`. If `r`
is multidimensional, then the shape of the result depends on the value of
`tensor`. If `tensor is ``True`` the shape will be r.shape[1:] + x.shape;
that is, each polynomial is evaluated at every value of `x`. If `tensor` is
``False``, the shape will be r.shape[1:]; that is, each polynomial is
evaluated only for the corresponding broadcast value of `x`. Note that
scalars have shape (,).
.. versionadded:: 1.12
Parameters
----------
x : array_like, compatible object
If `x` is a list or tuple, it is converted to an ndarray, otherwise
it is left unchanged and treated as a scalar. In either case, `x`
or its elements must support addition and multiplication with
with themselves and with the elements of `r`.
r : array_like
Array of roots. If `r` is multidimensional the first index is the
root index, while the remaining indices enumerate multiple
polynomials. For instance, in the two dimensional case the roots
of each polynomial may be thought of as stored in the columns of `r`.
tensor : boolean, optional
If True, the shape of the roots array is extended with ones on the
right, one for each dimension of `x`. Scalars have dimension 0 for this
action. The result is that every column of coefficients in `r` is
evaluated for every element of `x`. If False, `x` is broadcast over the
columns of `r` for the evaluation. This keyword is useful when `r` is
multidimensional. The default value is True.
Returns
-------
values : ndarray, compatible object
The shape of the returned array is described above.
See Also
--------
polyroots, polyfromroots, polyval
Examples
--------
>>> from numpy.polynomial.polynomial import polyvalfromroots
>>> polyvalfromroots(1, [1,2,3])
0.0
>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
[2, 3]])
>>> polyvalfromroots(a, [-1, 0, 1])
array([[ -0., 0.],
[ 6., 24.]])
>>> r = np.arange(-2, 2).reshape(2,2) # multidimensional coefficients
>>> r # each column of r defines one polynomial
array([[-2, -1],
[ 0, 1]])
>>> b = [-2, 1]
>>> polyvalfromroots(b, r, tensor=True)
array([[-0., 3.],
[ 3., 0.]])
>>> polyvalfromroots(b, r, tensor=False)
array([-0., 0.])
"""
r = np.array(r, ndmin=1, copy=0)
if r.dtype.char in '?bBhHiIlLqQpP':
r = r.astype(np.double)
if isinstance(x, (tuple, list)):
x = np.asarray(x)
if isinstance(x, np.ndarray):
if tensor:
r = r.reshape(r.shape + (1,)*x.ndim)
elif x.ndim >= r.ndim:
raise ValueError("x.ndim must be < r.ndim when tensor == False")
return np.prod(x - r, axis=0) | [
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rtheunissen/foobar | 06e91a741e44b662157bcafaf839aa62d1635895 | peculiar_balance.py | python | number_of_steps | (weight) | return int(log(weight * 2, 3)) + 1 | Returns the number of steps required to balance the scales | Returns the number of steps required to balance the scales | [
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] | def number_of_steps(weight):
"""
Returns the number of steps required to balance the scales
"""
# this formula was derived from boundary = (3 ** n - 1) / 2
return int(log(weight * 2, 3)) + 1 | [
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | common/djangoapps/edxmako/paths.py | python | save_lookups | () | A context manager to save and restore the Mako template lookup path.
Useful for testing. | A context manager to save and restore the Mako template lookup path. | [
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] | def save_lookups():
"""
A context manager to save and restore the Mako template lookup path.
Useful for testing.
"""
# Make a copy of the list of directories for each namespace.
namespace_dirs = {namespace: list(look.directories) for namespace, look in LOOKUP.items()}
try:
yield
finally:
# Get rid of all the lookups.
LOOKUP.clear()
# Re-create the lookups from our saved list.
for namespace, directories in namespace_dirs.items():
for directory in directories:
add_lookup(namespace, directory) | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1_job_status.py | python | V1JobStatus.active | (self, active) | Sets the active of this V1JobStatus.
The number of actively running pods. # noqa: E501
:param active: The active of this V1JobStatus. # noqa: E501
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:type: int
"""
self._active = active | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/root_system/root_space.py | python | RootSpace._name_string | (self, capitalize=True, base_ring=True, type=True) | return self._name_string_helper("root", capitalize=capitalize, base_ring=base_ring, type=type) | EXAMPLES::
sage: RootSystem(['A',4]).root_space()._name_string()
"Root space over the Rational Field of the Root system of type ['A', 4]" | EXAMPLES:: | [
"EXAMPLES",
"::"
] | def _name_string(self, capitalize=True, base_ring=True, type=True):
"""
EXAMPLES::
sage: RootSystem(['A',4]).root_space()._name_string()
"Root space over the Rational Field of the Root system of type ['A', 4]"
"""
return self._name_string_helper("root", capitalize=capitalize, base_ring=base_ring, type=type) | [
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ethereum/trinity | 6383280c5044feb06695ac2f7bc1100b7bcf4fe0 | trinity/components/builtin/new_block/component.py | python | NewBlockService._handle_imported_blocks | (self) | [] | async def _handle_imported_blocks(self) -> None:
async for event in self._event_bus.stream(NewBlockImported):
block = event.block
self.logger.debug("NewBlockImported: %s", block)
await self._broadcast_new_block_hashes(block) | [
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explosion/prodigy-recipes | 0037b32d954e0b1672f9dae1e8aa53ac0c9136e3 | image/tf_odapi/misc/exporter.py | python | replace_variable_values_with_moving_averages | (graph,
current_checkpoint_file,
new_checkpoint_file) | Replaces variable values in the checkpoint with their moving averages.
If the current checkpoint has shadow variables maintaining moving averages of
the variables defined in the graph, this function generates a new checkpoint
where the variables contain the values of their moving averages.
Args:
graph: a tf.Graph object.
current_checkpoint_file: a checkpoint containing both original variables and
their moving averages.
new_checkpoint_file: file path to write a new checkpoint. | Replaces variable values in the checkpoint with their moving averages. | [
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] | def replace_variable_values_with_moving_averages(graph,
current_checkpoint_file,
new_checkpoint_file):
"""Replaces variable values in the checkpoint with their moving averages.
If the current checkpoint has shadow variables maintaining moving averages of
the variables defined in the graph, this function generates a new checkpoint
where the variables contain the values of their moving averages.
Args:
graph: a tf.Graph object.
current_checkpoint_file: a checkpoint containing both original variables and
their moving averages.
new_checkpoint_file: file path to write a new checkpoint.
"""
with graph.as_default():
variable_averages = tf.train.ExponentialMovingAverage(0.0)
ema_variables_to_restore = variable_averages.variables_to_restore()
with tf.Session() as sess:
read_saver = tf.train.Saver(ema_variables_to_restore)
read_saver.restore(sess, current_checkpoint_file)
write_saver = tf.train.Saver()
write_saver.save(sess, new_checkpoint_file) | [
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... | https://github.com/explosion/prodigy-recipes/blob/0037b32d954e0b1672f9dae1e8aa53ac0c9136e3/image/tf_odapi/misc/exporter.py#L87-L109 | ||
rndusr/stig | 334f03e2e3eda7c1856dd5489f0265a47b9861b6 | stig/client/aiotransmission/rpc.py | python | TransmissionRPC.timeout | (self) | return self._timeout | Number of seconds to try to connect before giving up | Number of seconds to try to connect before giving up | [
"Number",
"of",
"seconds",
"to",
"try",
"to",
"connect",
"before",
"giving",
"up"
] | def timeout(self):
"""Number of seconds to try to connect before giving up"""
return self._timeout | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/motech/openmrs/atom_feed.py | python | get_case_block_for_indexed_case | (
mapping: ObservationMapping,
external_data: dict,
parent_case_attrs: CaseAttrs,
) | return case_block | [] | def get_case_block_for_indexed_case(
mapping: ObservationMapping,
external_data: dict,
parent_case_attrs: CaseAttrs,
) -> CaseBlock:
parent_case_id, parent_case_type, default_owner_id = parent_case_attrs
relationship = mapping.indexed_case_mapping.relationship
case_block_kwargs = {
"index": {
mapping.indexed_case_mapping.identifier: IndexAttrs(
parent_case_type,
parent_case_id,
relationship,
)
},
"update": {}
}
for value_source_config in mapping.indexed_case_mapping.case_properties:
value_source = as_value_source(value_source_config)
value = value_source.get_import_value(external_data)
if value_source.case_property in CASE_BLOCK_ARGS:
case_block_kwargs[value_source.case_property] = value
else:
case_block_kwargs["update"][value_source.case_property] = value
case_id = uuid.uuid4().hex
case_type = mapping.indexed_case_mapping.case_type
case_block_kwargs.setdefault("owner_id", default_owner_id)
if not case_block_kwargs["owner_id"]:
raise ConfigurationError(_(
f'Unable to determine mobile worker to own new "{case_type}" '
f'{relationship} case or parent case "{parent_case_id}"'
))
case_block = CaseBlock.deprecated_init(
create=True,
case_id=case_id,
case_type=case_type,
**case_block_kwargs
)
return case_block | [
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zdenop/lector | d44d316dc208a2f3f3efd51444f464340d178b02 | lector/editor/textwidget.py | python | TextWidget.togglewhiteSpace | (self, state=True) | Show or hide whitespace and line ending markers | Show or hide whitespace and line ending markers | [
"Show",
"or",
"hide",
"whitespace",
"and",
"line",
"ending",
"markers"
] | def togglewhiteSpace(self, state=True):
"""
Show or hide whitespace and line ending markers
"""
option = QTextOption()
if state:
option.setFlags(QTextOption.ShowTabsAndSpaces |
QTextOption.ShowLineAndParagraphSeparators)
else:
option.setFlags(option.flags() & ~option.ShowTabsAndSpaces &
~option.ShowLineAndParagraphSeparators)
self.document().setDefaultTextOption(option)
settings.set('editor:whiteSpace', state) | [
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python-escpos/python-escpos | b16f44257e7318060fbd0c2bf548239947734359 | src/escpos/magicencode.py | python | MagicEncode._handle_character_failed | (self, char) | Called when no codepage was found to render a character. | Called when no codepage was found to render a character. | [
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"."
] | def _handle_character_failed(self, char):
"""Called when no codepage was found to render a character."""
# Writing the default symbol via write() allows us to avoid
# unnecesary codepage switches.
self.write(self.defaultsymbol) | [
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datamllab/rlcard | c21ea82519c453a42e3bdc6848bd3356e9b6ac43 | rlcard/games/limitholdem/utils.py | python | determine_winner_four_of_a_kind | (hands, all_players, potential_winner_index) | return all_players | Find out who wins in the situation of having players which all have a four of a kind
Args:
key_index(int): the position of a card in a sorted handcard
hands(list): cards of those players with a four of a kind
e.g. hands = [['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CJ', 'SJ', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7']]
all_players(list): all the players in this round, 0 for losing and 1 for winning or draw
potential_winner_index(list): the positions of those players with same highest hand_catagory in all_players
Returns:
[0, 1, 0]: player1 wins
[1, 0, 0]: player0 wins
[1, 1, 1]: draw
[1, 1, 0]: player1 and player0 draws | Find out who wins in the situation of having players which all have a four of a kind
Args:
key_index(int): the position of a card in a sorted handcard
hands(list): cards of those players with a four of a kind
e.g. hands = [['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CJ', 'SJ', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7']]
all_players(list): all the players in this round, 0 for losing and 1 for winning or draw
potential_winner_index(list): the positions of those players with same highest hand_catagory in all_players
Returns:
[0, 1, 0]: player1 wins
[1, 0, 0]: player0 wins
[1, 1, 1]: draw
[1, 1, 0]: player1 and player0 draws | [
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":",
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"a",
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"han... | def determine_winner_four_of_a_kind(hands, all_players, potential_winner_index):
'''
Find out who wins in the situation of having players which all have a four of a kind
Args:
key_index(int): the position of a card in a sorted handcard
hands(list): cards of those players with a four of a kind
e.g. hands = [['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CJ', 'SJ', 'H9', 'B9', 'C2', 'C8', 'C7'], ['CT', 'ST', 'H9', 'B9', 'C2', 'C8', 'C7']]
all_players(list): all the players in this round, 0 for losing and 1 for winning or draw
potential_winner_index(list): the positions of those players with same highest hand_catagory in all_players
Returns:
[0, 1, 0]: player1 wins
[1, 0, 0]: player0 wins
[1, 1, 1]: draw
[1, 1, 0]: player1 and player0 draws
'''
ranks = []
for hand in hands:
rank_1 = hand.STRING_TO_RANK[hand.best_five[-1][1]] # rank of the four of a kind
rank_2 = hand.STRING_TO_RANK[hand.best_five[0][1]] # rank of the kicker
ranks.append((rank_1, rank_2))
max_rank = max(ranks)
for i, rank in enumerate(ranks):
if rank == max_rank:
all_players[potential_winner_index[i]] = 1
return all_players | [
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pypa/bandersnatch | 2e3eb53029ddb8f205f85242d724ae492040c1ce | src/bandersnatch/mirror.py | python | Mirror.sync_packages | (self) | [] | async def sync_packages(self) -> None:
try:
self.package_queue: asyncio.Queue = asyncio.Queue()
# Sorting the packages alphabetically makes it more predictable:
# easier to debug and easier to follow in the logs.
for name in sorted(self.packages_to_sync):
serial = int(self.packages_to_sync[name])
await self.package_queue.put(Package(name, serial=serial))
sync_coros: List[Awaitable] = [
self.package_syncer(idx) for idx in range(self.workers)
]
try:
await asyncio.gather(*sync_coros)
except KeyboardInterrupt as e:
self.on_error(e)
except (ValueError, TypeError) as e:
# This is for when self.packages_to_sync isn't of type Dict[str, int]
# Which occurs during testing or if BandersnatchMirror's todolist is
# corrupted in determine_packages_to_sync()
# TODO Remove this check by following packages_to_sync's typing
self.on_error(e) | [
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ManiacalLabs/BiblioPixel | afb993fbbe56e75e7c98f252df402b0f3e83bb6e | bibliopixel/control/rest/decorator.py | python | single | (method) | return single | Decorator for RestServer methods that take a single address | Decorator for RestServer methods that take a single address | [
"Decorator",
"for",
"RestServer",
"methods",
"that",
"take",
"a",
"single",
"address"
] | def single(method):
"""Decorator for RestServer methods that take a single address"""
@functools.wraps(method)
def single(self, address, value=None):
address = urllib.parse.unquote_plus(address)
try:
error = NO_PROJECT_ERROR
if not self.project:
raise ValueError
error = BAD_ADDRESS_ERROR
ed = editor.Editor(address, self.project)
if value is None:
error = BAD_GETTER_ERROR
result = method(self, ed)
else:
error = BAD_SETTER_ERROR
result = method(self, ed, value)
result = {'value': result}
except Exception as e:
traceback.print_exc()
msg = '%s\n%s' % (error.format(**locals()), e)
result = {'error': msg}
return flask.jsonify(result)
return single | [
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cvjena/semantic-embeddings | 0d4177422bafbba685fb6a0f976675864f31e09f | models/DenseNet/densenet.py | python | DenseNetFCN | (input_shape, nb_dense_block=5, growth_rate=16, nb_layers_per_block=4,
reduction=0.0, dropout_rate=0.0, weight_decay=1e-4, init_conv_filters=48,
include_top=True, weights=None, input_tensor=None, classes=1, activation='softmax',
upsampling_conv=128, upsampling_type='deconv') | return model | Instantiate the DenseNet FCN architecture.
Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
# Arguments
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
reduction: reduction factor of transition blocks.
Note : reduction value is inverted to compute compression.
dropout_rate: dropout rate
init_conv_filters: number of layers in the initial convolution layer
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization) or
'cifar10' (pre-training on CIFAR-10)..
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(32, 32, 3)` (with `channels_last` dim ordering)
or `(3, 32, 32)` (with `channels_first` dim ordering).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 8.
E.g. `(200, 200, 3)` would be one valid value.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and
'subpixel'. Defines type of upsampling algorithm used.
batchsize: Fixed batch size. This is a temporary requirement for
computation of output shape in the case of Deconvolution2D layers.
Parameter will be removed in next iteration of Keras, which infers
output shape of deconvolution layers automatically.
# Returns
A Keras model instance. | Instantiate the DenseNet FCN architecture.
Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
# Arguments
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
reduction: reduction factor of transition blocks.
Note : reduction value is inverted to compute compression.
dropout_rate: dropout rate
init_conv_filters: number of layers in the initial convolution layer
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization) or
'cifar10' (pre-training on CIFAR-10)..
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(32, 32, 3)` (with `channels_last` dim ordering)
or `(3, 32, 32)` (with `channels_first` dim ordering).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 8.
E.g. `(200, 200, 3)` would be one valid value.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and
'subpixel'. Defines type of upsampling algorithm used.
batchsize: Fixed batch size. This is a temporary requirement for
computation of output shape in the case of Deconvolution2D layers.
Parameter will be removed in next iteration of Keras, which infers
output shape of deconvolution layers automatically.
# Returns
A Keras model instance. | [
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reduction=0.0, dropout_rate=0.0, weight_decay=1e-4, init_conv_filters=48,
include_top=True, weights=None, input_tensor=None, classes=1, activation='softmax',
upsampling_conv=128, upsampling_type='deconv'):
'''Instantiate the DenseNet FCN architecture.
Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
# Arguments
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
reduction: reduction factor of transition blocks.
Note : reduction value is inverted to compute compression.
dropout_rate: dropout rate
init_conv_filters: number of layers in the initial convolution layer
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization) or
'cifar10' (pre-training on CIFAR-10)..
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(32, 32, 3)` (with `channels_last` dim ordering)
or `(3, 32, 32)` (with `channels_first` dim ordering).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 8.
E.g. `(200, 200, 3)` would be one valid value.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and
'subpixel'. Defines type of upsampling algorithm used.
batchsize: Fixed batch size. This is a temporary requirement for
computation of output shape in the case of Deconvolution2D layers.
Parameter will be removed in next iteration of Keras, which infers
output shape of deconvolution layers automatically.
# Returns
A Keras model instance.
'''
if weights not in {None}:
raise ValueError('The `weights` argument should be '
'`None` (random initialization) as no '
'model weights are provided.')
upsampling_type = upsampling_type.lower()
if upsampling_type not in ['upsampling', 'deconv', 'subpixel']:
raise ValueError('Parameter "upsampling_type" must be one of "upsampling", '
'"deconv" or "subpixel".')
if input_shape is None:
raise ValueError('For fully convolutional models, input shape must be supplied.')
if type(nb_layers_per_block) is not list and nb_dense_block < 1:
raise ValueError('Number of dense layers per block must be greater than 1. Argument '
'value was %d.' % (nb_layers_per_block))
if activation not in ['softmax', 'sigmoid']:
raise ValueError('activation must be one of "softmax" or "sigmoid"')
if activation == 'sigmoid' and classes != 1:
raise ValueError('sigmoid activation can only be used when classes = 1')
# Determine proper input shape
min_size = 2 ** nb_dense_block
if K.image_data_format() == 'channels_first':
if input_shape is not None:
if ((input_shape[1] is not None and input_shape[1] < min_size) or
(input_shape[2] is not None and input_shape[2] < min_size)):
raise ValueError('Input size must be at least ' +
str(min_size) + 'x' + str(min_size) + ', got '
'`input_shape=' + str(input_shape) + '`')
else:
input_shape = (classes, None, None)
else:
if input_shape is not None:
if ((input_shape[0] is not None and input_shape[0] < min_size) or
(input_shape[1] is not None and input_shape[1] < min_size)):
raise ValueError('Input size must be at least ' +
str(min_size) + 'x' + str(min_size) + ', got '
'`input_shape=' + str(input_shape) + '`')
else:
input_shape = (None, None, classes)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
x = __create_fcn_dense_net(classes, img_input, include_top, nb_dense_block,
growth_rate, reduction, dropout_rate, weight_decay,
nb_layers_per_block, upsampling_conv, upsampling_type,
init_conv_filters, input_shape, activation)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='fcn-densenet')
return model | [
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ctxis/CAPE | dae9fa6a254ecdbabeb7eb0d2389fa63722c1e82 | utils/machine.py | python | update_conf | (machinery, args) | Writes the new machine to the relevant configuration file. | Writes the new machine to the relevant configuration file. | [
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] | def update_conf(machinery, args):
"""Writes the new machine to the relevant configuration file."""
path = os.path.join(CUCKOO_ROOT, "conf", "%s.conf" % machinery)
lines = []
for line in open(path, "rb"):
line = line.strip()
if line.split("=")[0].strip() == "machines":
# If there are already one or more labels then append the new
# label to the list, otherwise make a new list.
if line.split("=", 1)[1].strip():
line += ", %s" % args.vmname
else:
line += " %s" % args.vmname
lines.append(line)
lines += [
"",
"[%s]" % args.vmname,
"label = %s" % args.vmname,
"platform = %s" % args.platform,
"ip = %s" % args.ip,
]
if args.snapshot:
lines.append("snapshot = %s" % args.snapshot)
if args.interface:
lines.append("interface = %s" % args.interface)
if args.resultserver:
ip, port = args.resultserver.split(":")
lines.append("resultserver_ip = %s" % ip)
lines.append("resultserver_port = %s" % port)
if args.tags:
lines.append("tags = %s" % args.tags)
open(path, "wb").write("\n".join(lines)) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_adm_router.py | python | RoleBinding.add_user_name | (self, inc_user_name) | return True | add a username | add a username | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/idlelib/TreeWidget.py | python | TreeItem.IsExpandable | (self) | return 1 | Return whether there are subitems. | Return whether there are subitems. | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/requests/packages/urllib3/util/url.py | python | Url.request_uri | (self) | return uri | Absolute path including the query string. | Absolute path including the query string. | [
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"string",
"."
] | def request_uri(self):
"""Absolute path including the query string."""
uri = self.path or '/'
if self.query is not None:
uri += '?' + self.query
return uri | [
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sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/db/migrations/questioner.py | python | InteractiveMigrationQuestioner.ask_not_null_addition | (self, field_name, model_name) | return None | Adding a NOT NULL field to a model. | Adding a NOT NULL field to a model. | [
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] | def ask_not_null_addition(self, field_name, model_name):
"""Adding a NOT NULL field to a model."""
if not self.dry_run:
choice = self._choice_input(
"You are trying to add a non-nullable field '%s' to %s without a default; "
"we can't do that (the database needs something to populate existing rows).\n"
"Please select a fix:" % (field_name, model_name),
[
("Provide a one-off default now (will be set on all existing "
"rows with a null value for this column)"),
"Quit, and let me add a default in models.py",
]
)
if choice == 2:
sys.exit(3)
else:
return self._ask_default()
return None | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/tkinter/__init__.py | python | Spinbox.index | (self, index) | return self.tk.call(self._w, 'index', index) | Returns the numerical index corresponding to index | Returns the numerical index corresponding to index | [
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"""Returns the numerical index corresponding to index
"""
return self.tk.call(self._w, 'index', index) | [
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d2l-ai/d2l-en | 39a7d4174534740b2387b0dc5eb22f409b82ee10 | d2l/torch.py | python | _replace_mlm_tokens | (tokens, candidate_pred_positions, num_mlm_preds,
vocab) | return mlm_input_tokens, pred_positions_and_labels | Defined in :numref:`sec_bert-dataset` | Defined in :numref:`sec_bert-dataset` | [
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"-",
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] | def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
vocab):
"""Defined in :numref:`sec_bert-dataset`"""
# Make a new copy of tokens for the input of a masked language model,
# where the input may contain replaced '<mask>' or random tokens
mlm_input_tokens = [token for token in tokens]
pred_positions_and_labels = []
# Shuffle for getting 15% random tokens for prediction in the masked
# language modeling task
random.shuffle(candidate_pred_positions)
for mlm_pred_position in candidate_pred_positions:
if len(pred_positions_and_labels) >= num_mlm_preds:
break
masked_token = None
# 80% of the time: replace the word with the '<mask>' token
if random.random() < 0.8:
masked_token = '<mask>'
else:
# 10% of the time: keep the word unchanged
if random.random() < 0.5:
masked_token = tokens[mlm_pred_position]
# 10% of the time: replace the word with a random word
else:
masked_token = random.choice(vocab.idx_to_token)
mlm_input_tokens[mlm_pred_position] = masked_token
pred_positions_and_labels.append(
(mlm_pred_position, tokens[mlm_pred_position]))
return mlm_input_tokens, pred_positions_and_labels | [
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jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/wsgiref/handlers.py | python | BaseHandler.write | (self, data) | write()' callable as specified by PEP 333 | write()' callable as specified by PEP 333 | [
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assert type(data) is StringType,"write() argument must be string"
if not self.status:
raise AssertionError("write() before start_response()")
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# Before the first output, send the stored headers
self.bytes_sent = len(data) # make sure we know content-length
self.send_headers()
else:
self.bytes_sent += len(data)
# XXX check Content-Length and truncate if too many bytes written?
self._write(data)
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/Engine/Core/Compilers/circuit_to_bentayga.py | python | get_bentayga_pf_options | (opt: PowerFlowOptions) | return btg.PowerFlowOptions(solver=solver_type,
tolerance=opt.tolerance,
max_iter=opt.max_iter,
retry_with_other_methods=opt.retry_with_other_methods,
q_control_mode=q_control_dict[opt.control_Q]) | Translate GridCal power flow options to Bentayga power flow options
:param opt:
:return: | Translate GridCal power flow options to Bentayga power flow options
:param opt:
:return: | [
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":",
":",
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":"
] | def get_bentayga_pf_options(opt: PowerFlowOptions):
"""
Translate GridCal power flow options to Bentayga power flow options
:param opt:
:return:
"""
solver_dict = {SolverType.NR: btg.PowerFlowSolvers.NewtonRaphson,
SolverType.DC: btg.PowerFlowSolvers.LinearDc,
# SolverType.HELM: nn.NativeSolverType.HELM,
# SolverType.IWAMOTO: nn.NativeSolverType.IWAMOTO,
SolverType.LM: btg.PowerFlowSolvers.LevenbergMarquardt,
# SolverType.LACPF: nn.NativeSolverType.LACPF,
# SolverType.FASTDECOUPLED: nn.NativeSolverType.FD
}
q_control_dict = {ReactivePowerControlMode.NoControl: btg.QControlMode.NoControl,
ReactivePowerControlMode.Direct: btg.QControlMode.Direct}
if opt.solver_type in solver_dict.keys():
solver_type = solver_dict[opt.solver_type]
else:
solver_type = btg.PowerFlowSolvers.NewtonRaphson
return btg.PowerFlowOptions(solver=solver_type,
tolerance=opt.tolerance,
max_iter=opt.max_iter,
retry_with_other_methods=opt.retry_with_other_methods,
q_control_mode=q_control_dict[opt.control_Q]) | [
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mitogen-hq/mitogen | 5b505f524a7ae170fe68613841ab92b299613d3f | mitogen/core.py | python | Latch._make_cookie | (self) | return struct.pack(self.COOKIE_FMT, self.COOKIE_MAGIC,
os.getpid(), id(self), thread.get_ident()) | Return a string encoding the ID of the process, instance and thread.
This disambiguates legitimate wake-ups, accidental writes to the FD,
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/craft_plugins/multiply.py | python | MultiplySkein.parseLine | (self, line) | Parse a gcode line and add it to the multiply skein. | Parse a gcode line and add it to the multiply skein. | [
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'Parse a gcode line and add it to the multiply skein.'
splitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)
if len(splitLine) < 1:
return
firstWord = splitLine[0]
if firstWord == '(</layer>)':
self.addLayer()
self.distanceFeedRate.addLine(line)
return
elif firstWord == '(</crafting>)':
self.shouldAccumulate = False
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self.layerLines.append(line)
return
self.distanceFeedRate.addLine(line) | [
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cjdrake/pyeda | 554ee53aa678f4b61bcd7e07ba2c74ddc749d665 | pyeda/boolalg/expr.py | python | Expression.encode_cnf | (self) | Encode as a compact CNF. | Encode as a compact CNF. | [
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"""Encode as a compact CNF."""
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return self._encode_cnf()
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seantis/seantis-questionnaire | 698c77b3d707635f50bcd86e7f1c94e94061b0f5 | questionnaire/views.py | python | show_questionnaire | (request, runinfo, errors={}) | return r | Return the QuestionSet template
Also add the javascript dependency code. | Return the QuestionSet template | [
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] | def show_questionnaire(request, runinfo, errors={}):
"""
Return the QuestionSet template
Also add the javascript dependency code.
"""
request.runinfo = runinfo
if request.GET.get('show_all') == '1': # for debugging purposes.
questions = runinfo.questionset.questionnaire.questions()
else:
questions = runinfo.questionset.questions()
show_all = request.GET.get('show_all') == '1'
questionset = runinfo.questionset
questions = questionset.questionnaire.questions() if show_all \
else questionset.questions()
qlist = []
jsinclude = [] # js files to include
cssinclude = [] # css files to include
jstriggers = []
qvalues = {}
# initialize qvalues
cookiedict = runinfo.get_cookiedict()
for k, v in cookiedict.items():
qvalues[k] = v
substitute_answer(qvalues, runinfo.questionset)
for question in questions:
# if we got here the questionset will at least contain one question
# which passes, so this is all we need to check for
question_visible = question_satisfies_checks(question, runinfo) \
or show_all
Type = question.get_type()
_qnum, _qalpha = split_numal(question.number)
qdict = {
'css_style': '' if question_visible else 'display:none;',
'template': 'questionnaire/%s.html' % (Type),
'qnum': _qnum,
'qalpha': _qalpha,
'qtype': Type,
'qnum_class': (_qnum % 2 == 0) and " qeven" or " qodd",
'qalpha_class': _qalpha and (
ord(_qalpha[-1]) % 2 and ' alodd' or ' aleven'
) or '',
}
# substitute answer texts
substitute_answer(qvalues, question)
# add javascript dependency checks
cd = question.getcheckdict()
depon = cd.get('requiredif', None) or cd.get('dependent', None)
if depon:
# extra args to BooleanParser are not required for toString
parser = BooleanParser(dep_check)
qdict['checkstring'] = ' checks="%s"' % parser.toString(depon)
jstriggers.append('qc_%s' % question.number)
if 'default' in cd and question.number not in cookiedict:
qvalues[question.number] = cd['default']
if Type in QuestionProcessors:
qdict.update(QuestionProcessors[Type](request, question))
if 'jsinclude' in qdict:
if qdict['jsinclude'] not in jsinclude:
jsinclude.extend(qdict['jsinclude'])
if 'cssinclude' in qdict:
if qdict['cssinclude'] not in cssinclude:
cssinclude.extend(qdict['jsinclude'])
if 'jstriggers' in qdict:
jstriggers.extend(qdict['jstriggers'])
if 'qvalue' in qdict and question.number not in cookiedict:
qvalues[question.number] = qdict['qvalue']
qlist.append((question, qdict))
try:
has_progress = settings.QUESTIONNAIRE_PROGRESS in ('async', 'default')
async_progress = settings.QUESTIONNAIRE_PROGRESS == 'async'
except AttributeError:
has_progress = True
async_progress = False
if has_progress:
if async_progress:
progress = cache.get('progress' + runinfo.random, 1)
else:
progress = get_progress(runinfo)
else:
progress = 0
if request.POST:
for k, v in request.POST.items():
if k.startswith("question_"):
s = k.split("_")
if len(s) == 4:
qvalues[s[1] + '_' + v] = '1' # evaluates true in JS
elif len(s) == 3 and s[2] == 'comment':
qvalues[s[1] + '_' + s[2]] = v
else:
qvalues[s[1]] = v
if use_session:
prev_url = reverse('redirect_to_prev_questionnaire')
else:
prev_url = 'javascript:history.back();'
r = r2r(
"questionnaire/questionset.html",
request,
questionset=runinfo.questionset,
runinfo=runinfo,
errors=errors,
qlist=qlist,
progress=progress,
triggers=jstriggers,
qvalues=qvalues,
jsinclude=jsinclude,
cssinclude=cssinclude,
async_progress=async_progress,
async_url=reverse('progress', args=[runinfo.random]),
prev_url=prev_url,
)
r['Cache-Control'] = 'no-cache'
r['Expires'] = "Thu, 24 Jan 1980 00:00:00 GMT"
return r | [
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... | https://github.com/seantis/seantis-questionnaire/blob/698c77b3d707635f50bcd86e7f1c94e94061b0f5/questionnaire/views.py#L575-L704 | |
nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/sqlalchemy/sql/selectable.py | python | GenerativeSelect._simple_int_limit | (self) | return isinstance(self._limit_clause, _OffsetLimitParam) | True if the LIMIT clause is a simple integer, False
if it is not present or is a SQL expression. | True if the LIMIT clause is a simple integer, False
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yao8839836/kg-bert | 0b8f625108efcb9b43603b6840f2548ff3d3c973 | run_bert_triple_classifier.py | python | KGProcessor.get_train_examples | (self, data_dir) | return self._create_examples(
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/distlib/database.py | python | InstalledDistribution.check_installed_files | (self) | return mismatches | Checks that the hashes and sizes of the files in ``RECORD`` are
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roclark/sportsipy | c19f545d3376d62ded6304b137dc69238ac620a9 | sportsipy/mlb/boxscore.py | python | Boxscore.home_on_base_percentage | (self) | return self._home_on_base_percentage | Returns a ``float`` of the percentage of at bats that result in the
batter getting on base. | Returns a ``float`` of the percentage of at bats that result in the
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ReactionMechanismGenerator/RMG-Py | 2b7baf51febf27157def58fb3f6cee03fb6a684c | rmgpy/data/kinetics/library.py | python | KineticsLibrary.save_entry | (self, f, entry) | return save_entry(f, entry) | Write the given `entry` in the kinetics library to the file object `f`. | Write the given `entry` in the kinetics library to the file object `f`. | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | examples/research_projects/quantization-qdqbert/quant_trainer.py | python | set_quantizer_by_name | (model, names, **kwargs) | Set quantizer attributes for layers where name contains a substring in names. | Set quantizer attributes for layers where name contains a substring in names. | [
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if re.search(n, name):
set_quantizers(name, mod, **kwargs)
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for n in names:
if re.search(n, name):
s = f"Warning: changing {name:{name_width}}"
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trailofbits/manticore | b050fdf0939f6c63f503cdf87ec0ab159dd41159 | manticore/platforms/linux.py | python | Linux.sys_chmod | (self, filename, mode) | return 0 | Modify file permissions
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openstack/python-neutronclient | 517bef2c5454dde2eba5cc2194ee857be6be7164 | neutronclient/v2_0/client.py | python | Client.create_flavor | (self, body=None) | return self.post(self.flavors_path, body=body) | Creates a new Neutron service flavor. | Creates a new Neutron service flavor. | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/prompt_toolkit/layout/containers.py | python | is_container | (value) | return False | Checks whether the given value is a container object
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apache/libcloud | 90971e17bfd7b6bb97b2489986472c531cc8e140 | libcloud/dns/drivers/godaddy.py | python | GoDaddyDNSDriver.ex_check_availability | (self, domain, for_transfer=False) | return GoDaddyAvailability(
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Check the availability of the domain
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:type domain: ``str``
:param for_transfer: Check if domain is available for transfer
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"""
result = self.connection.request(
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return GoDaddyAvailability(
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py | python | RedisCache.__init__ | (self, conn) | [] | def __init__(self, conn):
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NervanaSystems/ngraph-python | ac032c83c7152b615a9ad129d54d350f9d6a2986 | ngraph/frontends/tensorflow/tf_importer/ops_bridge.py | python | OpsBridge.Range | (self, tf_node, inputs) | return ng.constant(range_val,
make_pos_axes(range_val.shape)).named(tf_node.name) | Creates a sequence of integers.
Arguments:
tf_node: NodeDef object, the tensorflow node to convert.
inputs: List of ngraph Ops as inputs to this node.
Returns:
A ngraph Op corresponding to the tensorflow node.
Inputs to tf_node:
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"""
Creates a sequence of integers.
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tf_node: NodeDef object, the tensorflow node to convert.
inputs: List of ngraph Ops as inputs to this node.
Returns:
A ngraph Op corresponding to the tensorflow node.
Inputs to tf_node:
start, limit, delta, name
"""
# get inputs
start, limit, delta = inputs
# get range
try:
range_val = np.arange(start.const, limit.const, delta.const)
except:
raise NotImplementedError("[NON-NATIVE] Input to `Range` must all "
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# return
return ng.constant(range_val,
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kupferlauncher/kupfer | 1c1e9bcbce05a82f503f68f8b3955c20b02639b3 | waflib/Tools/c_preproc.py | python | c_parser.start | (self, node, env) | Preprocess a source file to obtain the dependencies, which are accumulated to :py:attr:`waflib.Tools.c_preproc.c_parser.nodes`
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:type env: :py:class:`waflib.ConfigSet.ConfigSet` | Preprocess a source file to obtain the dependencies, which are accumulated to :py:attr:`waflib.Tools.c_preproc.c_parser.nodes`
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state = self.state
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state.append(undefined)
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state[-1] = ignored
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state[-1] = accepted
elif token == 'include' or token == 'import':
(kind, inc) = extract_include(line, self.defs)
self.current_file = self.tryfind(inc, kind, env)
if token == 'import':
self.ban_includes.add(self.current_file)
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state[-1] = skipped
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state[-1] = skipped
elif state[-1] == ignored:
state[-1] = accepted
elif token == 'define':
try:
self.defs[self.define_name(line)] = line
except AttributeError:
raise PreprocError('Invalid define line %r' % line)
elif token == 'undef':
m = re_mac.match(line)
if m and m.group() in self.defs:
self.defs.__delitem__(m.group())
#print "undef %s" % name
elif token == 'pragma':
if re_pragma_once.match(line.lower()):
self.ban_includes.add(self.current_file)
except Exception as e:
if Logs.verbose:
Logs.debug('preproc: line parsing failed (%s): %s %s', e, line, traceback.format_exc()) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/idlelib/Debugger.py | python | StackViewer.popup_event | (self, event) | override base method | override base method | [
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ry/tensorflow-resnet | ccb9cc8cc2cc7021c67e7df4a70f96faab9553c5 | image_processing.py | python | distort_color | (image, thread_id=0, scope=None) | Distort the color of the image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: Tensor containing single image.
thread_id: preprocessing thread ID.
scope: Optional scope for op_scope.
Returns:
color-distorted image | Distort the color of the image. | [
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] | def distort_color(image, thread_id=0, scope=None):
"""Distort the color of the image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: Tensor containing single image.
thread_id: preprocessing thread ID.
scope: Optional scope for op_scope.
Returns:
color-distorted image
"""
with tf.op_scope([image], scope, 'distort_color'):
color_ordering = thread_id % 2
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
# The random_* ops do not necessarily clamp.
image = tf.clip_by_value(image, 0.0, 1.0)
return image | [
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nltk/nltk | 3f74ac55681667d7ef78b664557487145f51eb02 | nltk/metrics/association.py | python | NgramAssocMeasures._marginals | (*contingency) | Calculates values of contingency table marginals from its values. | Calculates values of contingency table marginals from its values. | [
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LuxCoreRender/BlendLuxCore | bf31ca58501d54c02acd97001b6db7de81da7cbf | properties/material.py | python | LuxCoreMaterialPreviewProps.update_preview | (self, context) | [] | def update_preview(self, context):
material = self.id_data
# A trick to force a material preview refresh (update_tag() does not work)
material.preview_render_type = material.preview_render_type | [
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cbrgm/telegram-robot-rss | 58fe98de427121fdc152c8df0721f1891174e6c9 | venv/lib/python2.7/site-packages/future/backports/email/message.py | python | Message.get_content_charset | (self, failobj=None) | return charset.lower() | Return the charset parameter of the Content-Type header.
The returned string is always coerced to lower case. If there is no
Content-Type header, or if that header has no charset parameter,
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"""Return the charset parameter of the Content-Type header.
The returned string is always coerced to lower case. If there is no
Content-Type header, or if that header has no charset parameter,
failobj is returned.
"""
missing = object()
charset = self.get_param('charset', missing)
if charset is missing:
return failobj
if isinstance(charset, tuple):
# RFC 2231 encoded, so decode it, and it better end up as ascii.
pcharset = charset[0] or 'us-ascii'
try:
# LookupError will be raised if the charset isn't known to
# Python. UnicodeError will be raised if the encoded text
# contains a character not in the charset.
as_bytes = charset[2].encode('raw-unicode-escape')
charset = str(as_bytes, pcharset)
except (LookupError, UnicodeError):
charset = charset[2]
# charset characters must be in us-ascii range
try:
charset.encode('us-ascii')
except UnicodeError:
return failobj
# RFC 2046, $4.1.2 says charsets are not case sensitive
return charset.lower() | [
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opendistro-for-elasticsearch/sample-code | 94abc715845d17fb4c24e70c7c328b2d4da0d581 | es_sink/es_sink/line_buffer.py | python | LineBuffer._dict_to_string | (dic) | Encode a dict as a string. Silently swallows errors | Encode a dict as a string. Silently swallows errors | [
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''' Encode a dict as a string. Silently swallows errors '''
try:
line = json.JSONEncoder().encode(dic)
return line
except UnicodeDecodeError as exc:
msg = "unicode problem {}, skipping line: {}"
print(msg.format(str(exc), dic))
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/SimpleXMLRPCServer.py | python | list_public_methods | (obj) | return [member for member in dir(obj)
if not member.startswith('_') and
hasattr(getattr(obj, member), '__call__')] | Returns a list of attribute strings, found in the specified
object, which represent callable attributes | Returns a list of attribute strings, found in the specified
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"""Returns a list of attribute strings, found in the specified
object, which represent callable attributes"""
return [member for member in dir(obj)
if not member.startswith('_') and
hasattr(getattr(obj, member), '__call__')] | [
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psychopy/psychopy | 01b674094f38d0e0bd51c45a6f66f671d7041696 | psychopy/visual/pie.py | python | Pie.setRadius | (self, end, operation='', log=None) | Usually you can use 'stim.attribute = value' syntax instead,
but use this method if you need to suppress the log message | Usually you can use 'stim.attribute = value' syntax instead,
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"""Usually you can use 'stim.attribute = value' syntax instead,
but use this method if you need to suppress the log message
"""
setAttribute(self, 'radius', end, log, operation) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sqlalchemy/dialects/postgresql/json.py | python | JSON.__init__ | (self, none_as_null=False) | Construct a :class:`.JSON` type.
:param none_as_null: if True, persist the value ``None`` as a
SQL NULL value, not the JSON encoding of ``null``. Note that
when this flag is False, the :func:`.null` construct can still
be used to persist a NULL value::
from sqlalchemy import null
conn.execute(table.insert(), data=null())
.. versionchanged:: 0.9.8 - Added ``none_as_null``, and :func:`.null`
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"""Construct a :class:`.JSON` type.
:param none_as_null: if True, persist the value ``None`` as a
SQL NULL value, not the JSON encoding of ``null``. Note that
when this flag is False, the :func:`.null` construct can still
be used to persist a NULL value::
from sqlalchemy import null
conn.execute(table.insert(), data=null())
.. versionchanged:: 0.9.8 - Added ``none_as_null``, and :func:`.null`
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aneisch/home-assistant-config | 86e381fde9609cb8871c439c433c12989e4e225d | custom_components/localtuya/pytuya/__init__.py | python | ContextualLogger.__init__ | (self) | Initialize a new ContextualLogger. | Initialize a new ContextualLogger. | [
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"""Initialize a new ContextualLogger."""
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Dentosal/python-sc2 | e816cce83772d1aee1291b86b300b69405aa96b4 | sc2/bot_ai.py | python | BotAI._prepare_first_step | (self) | First step extra preparations. Must not be called before _prepare_step. | First step extra preparations. Must not be called before _prepare_step. | [
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] | def _prepare_first_step(self):
"""First step extra preparations. Must not be called before _prepare_step."""
if self.townhalls:
self._game_info.player_start_location = self.townhalls.first.position
self._game_info.map_ramps = self._game_info._find_ramps() | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/future/backports/urllib/response.py | python | addbase.__repr__ | (self) | return '<%s at %r whose fp = %r>' % (self.__class__.__name__,
id(self), self.fp) | [] | def __repr__(self):
return '<%s at %r whose fp = %r>' % (self.__class__.__name__,
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | openedx/core/djangoapps/content/course_overviews/models.py | python | _invalidate_overview_cache | (**kwargs) | Invalidate the course overview request cache. | Invalidate the course overview request cache. | [
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"""
Invalidate the course overview request cache.
"""
RequestCache('course_overview').clear() | [
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dry-python/returns | dfc1613f22ef6cbc5d1c48e086affe16c1bd33bb | returns/_internal/futures/_future_result.py | python | async_bind_result | (
function: Callable[[_ValueType], Result[_NewValueType, _ErrorType]],
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"""Async binds a container returning ``Result`` over a value."""
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/jinja2/runtime.py | python | make_logging_undefined | (logger=None, base=None) | return LoggingUndefined | Given a logger object this returns a new undefined class that will
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Example::
logger = logging.getLogger(__name__)
LoggingUndefined = make_logging_undefined(
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logger.addHandler(logging.StreamHandler(sys.stderr))
if base is None:
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CloudBotIRC/CloudBot | 6c5a88f9961b6cd66a8f489a38feb2e8227ac9ea | plugins/wordnik.py | python | define | (text) | <word> -- Returns a dictionary definition from Wordnik for <word>. | <word> -- Returns a dictionary definition from Wordnik for <word>. | [
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"""<word> -- Returns a dictionary definition from Wordnik for <word>."""
if not api_key:
return "This command requires an API key from wordnik.com."
word = sanitize(text)
url = API_URL + "word.json/{}/definitions".format(word)
params = {
'api_key': api_key,
'limit': 1
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json = requests.get(url, params=params).json()
if json:
data = json[0]
data['word'] = " ".join(data['word'].split())
data['url'] = web.try_shorten(WEB_URL.format(data['word']))
data['attrib'] = ATTRIB_NAMES[data['sourceDictionary']]
return "\x02{word}\x02: {text} - {url} ({attrib})".format(**data)
else:
return "I could not find a definition for \x02{}\x02.".format(word) | [
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micahhausler/container-transform | 68223fae98f30b8bb2ce0f02ba9e58afbc80f196 | container_transform/compose.py | python | ComposeTransformer.emit_privileged | (self, privileged) | return privileged | [] | def emit_privileged(self, privileged):
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meduza-corp/interstellar | 40a801ccd7856491726f5a126621d9318cabe2e1 | gsutil/third_party/boto/boto/ec2/cloudwatch/alarm.py | python | MetricAlarms.__init__ | (self, connection=None) | Parses a list of MetricAlarms. | Parses a list of MetricAlarms. | [
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zopefoundation/Zope | ea04dd670d1a48d4d5c879d3db38fc2e9b4330bb | src/ZPublisher/BaseRequest.py | python | BaseRequest.values | (self) | return result | [] | def values(self):
result = []
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analysiscenter/batchflow | 294747da0bca309785f925be891441fdd824e9fa | batchflow/opensets/mnist.py | python | MNIST._extract_labels | (self, f) | Extract the labels into a 1D uint8 numpy array [index].
Args:
f: A file object that can be passed into a gzip reader.
Returns:
labels: a 1D uint8 numpy array.
Raises:
ValueError: If the bystream doesn't start with 2049. | Extract the labels into a 1D uint8 numpy array [index].
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f: A file object that can be passed into a gzip reader.
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labels: a 1D uint8 numpy array.
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num_items = self._read32(bytestream)
buf = bytestream.read(num_items)
labels = np.frombuffer(buf, dtype=np.uint8)
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python/mypy | 17850b3bd77ae9efb5d21f656c4e4e05ac48d894 | mypy/stubgen.py | python | StubGenerator.output | (self) | return imports + ''.join(self._output) | Return the text for the stub. | Return the text for the stub. | [
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pulp/pulp | a0a28d804f997b6f81c391378aff2e4c90183df9 | common/pulp/common/pic.py | python | create_repo | (id, name=None, arch='noarch', **kwargs) | return POST('/repositories/', kwargs) | Acceptable keyword arguments are any arguments for a new Repo model.
Common ones are: feed and sync_schedule | Acceptable keyword arguments are any arguments for a new Repo model.
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"""
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kwargs.update({'id': id, 'name': name or id, 'arch': arch})
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microsoft/MMdnn | 19562a381c27545984a216eda7591430e274e518 | mmdnn/conversion/examples/tensorflow/models/nasnet.py | python | _build_nasnet_base | (images,
normal_cell,
reduction_cell,
num_classes,
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] | def _build_nasnet_base(images,
normal_cell,
reduction_cell,
num_classes,
hparams,
is_training,
stem_type,
final_endpoint=None):
"""Constructs a NASNet image model."""
end_points = {}
def add_and_check_endpoint(endpoint_name, net):
end_points[endpoint_name] = net
return final_endpoint and (endpoint_name == final_endpoint)
# Find where to place the reduction cells or stride normal cells
reduction_indices = nasnet_utils.calc_reduction_layers(
hparams.num_cells, hparams.num_reduction_layers)
stem_cell = reduction_cell
if stem_type == 'imagenet':
stem = lambda: _imagenet_stem(images, hparams, stem_cell)
elif stem_type == 'cifar':
stem = lambda: _cifar_stem(images, hparams)
else:
raise ValueError('Unknown stem_type: ', stem_type)
net, cell_outputs = stem()
if add_and_check_endpoint('Stem', net): return net, end_points
# Setup for building in the auxiliary head.
aux_head_cell_idxes = []
if len(reduction_indices) >= 2:
aux_head_cell_idxes.append(reduction_indices[1] - 1)
# Run the cells
filter_scaling = 1.0
# true_cell_num accounts for the stem cells
true_cell_num = 2 if stem_type == 'imagenet' else 0
for cell_num in range(hparams.num_cells):
stride = 1
if hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
if cell_num in reduction_indices:
filter_scaling *= hparams.filter_scaling_rate
net = reduction_cell(
net,
scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
filter_scaling=filter_scaling,
stride=2,
prev_layer=cell_outputs[-2],
cell_num=true_cell_num)
if add_and_check_endpoint(
'Reduction_Cell_{}'.format(reduction_indices.index(cell_num)), net):
return net, end_points
true_cell_num += 1
cell_outputs.append(net)
if not hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
net = normal_cell(
net,
scope='cell_{}'.format(cell_num),
filter_scaling=filter_scaling,
stride=stride,
prev_layer=prev_layer,
cell_num=true_cell_num)
if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
return net, end_points
true_cell_num += 1
if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
num_classes and is_training):
aux_net = tf.nn.relu(net)
_build_aux_head(aux_net, end_points, num_classes, hparams,
scope='aux_{}'.format(cell_num))
cell_outputs.append(net)
# Final softmax layer
with tf.variable_scope('final_layer'):
net = tf.nn.relu(net)
net = nasnet_utils.global_avg_pool(net)
if add_and_check_endpoint('global_pool', net) or num_classes is None:
return net, end_points
net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
logits = slim.fully_connected(net, num_classes)
if add_and_check_endpoint('Logits', logits):
return net, end_points
predictions = tf.nn.softmax(logits, name='predictions')
if add_and_check_endpoint('Predictions', predictions):
return net, end_points
return logits, end_points | [
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plaid/plaid-python | 8c60fca608e426f3ff30da8857775946d29e122c | plaid/model/wallet_transaction_counterparty_bacs.py | python | WalletTransactionCounterpartyBACS.openapi_types | () | return {
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"""
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.4/django/template/loader.py | python | select_template | (template_name_list) | Given a list of template names, returns the first that can be loaded. | Given a list of template names, returns the first that can be loaded. | [
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"Given a list of template names, returns the first that can be loaded."
if not template_name_list:
raise TemplateDoesNotExist("No template names provided")
not_found = []
for template_name in template_name_list:
try:
return get_template(template_name)
except TemplateDoesNotExist, e:
if e.args[0] not in not_found:
not_found.append(e.args[0])
continue
# If we get here, none of the templates could be loaded
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fepegar/torchio | 3e07b78da16d6db4da7193325b3f9cb31fc0911a | torchio/data/subject.py | python | Subject.load | (self) | Load images in subject on RAM. | Load images in subject on RAM. | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/backend/xcodebackend.py | python | PbxItem.__init__ | (self, value, comment = '') | [] | def __init__(self, value, comment = ''):
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weewx/weewx | cb594fce224560bd8696050fc5c7843c7839320e | bin/weewx/manager.py | python | Manager.firstGoodStamp | (self) | return _row[0] if _row else None | Retrieves earliest timestamp in the archive.
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"""
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Azure/azure-cli | 6c1b085a0910c6c2139006fcbd8ade44006eb6dd | src/azure-cli/azure/cli/command_modules/keyvault/vendored_sdks/azure_keyvault_t1/key_vault_authentication.py | python | KeyVaultAuthBase._handle_redirect | (self, r, **kwargs) | Reset auth_attempted on redirects. | Reset auth_attempted on redirects. | [
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dowsnature/dowsDNS | d202e821112e1f3839de6df1a9c696fbd9502494 | dnslib/zoneresolver.py | python | ZoneResolver.resolve | (self,request,handler) | return reply | Respond to DNS request - parameters are request packet & handler.
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"""
Respond to DNS request - parameters are request packet & handler.
Method is expected to return DNS response
"""
reply = request.reply()
qname = request.q.qname
qtype = QTYPE[request.q.qtype]
for name,rtype,rr in self.zone:
# Check if label & type match
if getattr(qname,self.eq)(name) and (qtype == rtype or
qtype == 'ANY' or
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# If we have a glob match fix reply label
if self.glob:
a = copy.copy(rr)
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reply.add_answer(a)
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reply.add_answer(rr)
# Check for A/AAAA records associated with reply and
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reply.add_ar(a_rr)
if not reply.rr:
reply.header.rcode = RCODE.NXDOMAIN
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/gdata/Crypto/PublicKey/pubkey.py | python | pubkey.decrypt | (self, ciphertext) | decrypt(ciphertext:tuple|string|long): string
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"""decrypt(ciphertext:tuple|string|long): string
Decrypt 'ciphertext' using this key.
"""
wasString=0
if not isinstance(ciphertext, types.TupleType):
ciphertext=(ciphertext,)
if isinstance(ciphertext[0], types.StringType):
ciphertext=tuple(map(bytes_to_long, ciphertext)) ; wasString=1
plaintext=self._decrypt(ciphertext)
if wasString: return long_to_bytes(plaintext)
else: return plaintext | [
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Coalfire-Research/Slackor | aa32a7f9250bd8b107d48fd573f26176b527b2a5 | impacket/impacket/dns.py | python | DNS.get_qdcount_tcp | (self) | return self.header.get_word(6) | Get Unsigned 16 bit integer specifying the number of entries in the question section. | Get Unsigned 16 bit integer specifying the number of entries in the question section. | [
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/messaging/jsonld/routes.py | python | sign | (request: web.BaseRequest) | return web.json_response(response) | Request handler for signing a jsonld doc.
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request: aiohttp request object | Request handler for signing a jsonld doc. | [
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"""
Request handler for signing a jsonld doc.
Args:
request: aiohttp request object
"""
response = {}
body = await request.json()
doc = body.get("doc")
try:
context: AdminRequestContext = request["context"]
async with context.session() as session:
doc_with_proof = await sign_credential(
session, doc.get("credential"), doc.get("options"), body.get("verkey")
)
response["signed_doc"] = doc_with_proof
except (BaseJSONLDMessagingError) as err:
response["error"] = str(err)
except (WalletError, InjectionError):
raise web.HTTPForbidden(reason="No wallet available")
return web.json_response(response) | [
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frescobaldi/frescobaldi | 301cc977fc4ba7caa3df9e4bf905212ad5d06912 | frescobaldi_app/extensions/__init__.py | python | ExtensionMixin.current_document | (self) | return self.extension().current_document() | Return the current document. | Return the current document. | [
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eliben/pyelftools | 8f7a0becaface09435c4374947548b7851e3d1a2 | elftools/construct/core.py | python | RepeatUntil._parse | (self, stream, context) | return obj | [] | def _parse(self, stream, context):
obj = []
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/pkg_resources/__init__.py | python | NullProvider._fn | (self, base, resource_name) | return base | [] | def _fn(self, base, resource_name):
self._validate_resource_path(resource_name)
if resource_name:
return os.path.join(base, *resource_name.split('/'))
return base | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/batch/v20170312/batch_client.py | python | BatchClient.DescribeAvailableCvmInstanceTypes | (self, request) | 查看可用的CVM机型配置信息
:param request: Request instance for DescribeAvailableCvmInstanceTypes.
:type request: :class:`tencentcloud.batch.v20170312.models.DescribeAvailableCvmInstanceTypesRequest`
:rtype: :class:`tencentcloud.batch.v20170312.models.DescribeAvailableCvmInstanceTypesResponse` | 查看可用的CVM机型配置信息 | [
"查看可用的CVM机型配置信息"
] | def DescribeAvailableCvmInstanceTypes(self, request):
"""查看可用的CVM机型配置信息
:param request: Request instance for DescribeAvailableCvmInstanceTypes.
:type request: :class:`tencentcloud.batch.v20170312.models.DescribeAvailableCvmInstanceTypesRequest`
:rtype: :class:`tencentcloud.batch.v20170312.models.DescribeAvailableCvmInstanceTypesResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeAvailableCvmInstanceTypes", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeAvailableCvmInstanceTypesResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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minivision-ai/photo2cartoon | 6204e27a71620500c2f5b2ffd36646b0e2e5c110 | models/networks.py | python | adaLIN.__init__ | (self, num_features, eps=1e-5) | [] | def __init__(self, num_features, eps=1e-5):
super(adaLIN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/lib2to3/btm_utils.py | python | MinNode.leaves | (self) | Generator that returns the leaves of the tree | Generator that returns the leaves of the tree | [
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yield x
if not self.children:
yield self | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | golismero/api/data/vulnerability/__init__.py | python | Vulnerability.nessus | (self, nessus) | :param nessus: Nessus Plugin IDs.
:type nessus: tuple( str, ... ) | :param nessus: Nessus Plugin IDs.
:type nessus: tuple( str, ... ) | [
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"""
:param nessus: Nessus Plugin IDs.
:type nessus: tuple( str, ... )
"""
self.__nessus = sanitize_vuln_ids(nessus) | [
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dexy/dexy | 323c1806e51f75435e11d2265703e68f46c8aef3 | dexy/filters/confluence.py | python | ConfluenceRESTAPI.credentials_with_json_content_type | (self) | return self.credentials_with_headers(json_content_type) | [] | def credentials_with_json_content_type(self):
json_content_type = {'content-type': 'application/json'}
return self.credentials_with_headers(json_content_type) | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/tornado/auth.py | python | FacebookGraphMixin.facebook_request | (
self,
path: str,
access_token: str = None,
post_args: Dict[str, Any] = None,
**args: Any
) | return await self.oauth2_request(
url, access_token=access_token, post_args=post_args, **args
) | Fetches the given relative API path, e.g., "/btaylor/picture"
If the request is a POST, ``post_args`` should be provided. Query
string arguments should be given as keyword arguments.
An introduction to the Facebook Graph API can be found at
http://developers.facebook.com/docs/api
Many methods require an OAuth access token which you can
obtain through `~OAuth2Mixin.authorize_redirect` and
`get_authenticated_user`. The user returned through that
process includes an ``access_token`` attribute that can be
used to make authenticated requests via this method.
Example usage:
.. testcode::
class MainHandler(tornado.web.RequestHandler,
tornado.auth.FacebookGraphMixin):
@tornado.web.authenticated
async def get(self):
new_entry = await self.facebook_request(
"/me/feed",
post_args={"message": "I am posting from my Tornado application!"},
access_token=self.current_user["access_token"])
if not new_entry:
# Call failed; perhaps missing permission?
yield self.authorize_redirect()
return
self.finish("Posted a message!")
.. testoutput::
:hide:
The given path is relative to ``self._FACEBOOK_BASE_URL``,
by default "https://graph.facebook.com".
This method is a wrapper around `OAuth2Mixin.oauth2_request`;
the only difference is that this method takes a relative path,
while ``oauth2_request`` takes a complete url.
.. versionchanged:: 3.1
Added the ability to override ``self._FACEBOOK_BASE_URL``.
.. versionchanged:: 6.0
The ``callback`` argument was removed. Use the returned awaitable object instead. | Fetches the given relative API path, e.g., "/btaylor/picture" | [
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self,
path: str,
access_token: str = None,
post_args: Dict[str, Any] = None,
**args: Any
) -> Any:
"""Fetches the given relative API path, e.g., "/btaylor/picture"
If the request is a POST, ``post_args`` should be provided. Query
string arguments should be given as keyword arguments.
An introduction to the Facebook Graph API can be found at
http://developers.facebook.com/docs/api
Many methods require an OAuth access token which you can
obtain through `~OAuth2Mixin.authorize_redirect` and
`get_authenticated_user`. The user returned through that
process includes an ``access_token`` attribute that can be
used to make authenticated requests via this method.
Example usage:
.. testcode::
class MainHandler(tornado.web.RequestHandler,
tornado.auth.FacebookGraphMixin):
@tornado.web.authenticated
async def get(self):
new_entry = await self.facebook_request(
"/me/feed",
post_args={"message": "I am posting from my Tornado application!"},
access_token=self.current_user["access_token"])
if not new_entry:
# Call failed; perhaps missing permission?
yield self.authorize_redirect()
return
self.finish("Posted a message!")
.. testoutput::
:hide:
The given path is relative to ``self._FACEBOOK_BASE_URL``,
by default "https://graph.facebook.com".
This method is a wrapper around `OAuth2Mixin.oauth2_request`;
the only difference is that this method takes a relative path,
while ``oauth2_request`` takes a complete url.
.. versionchanged:: 3.1
Added the ability to override ``self._FACEBOOK_BASE_URL``.
.. versionchanged:: 6.0
The ``callback`` argument was removed. Use the returned awaitable object instead.
"""
url = self._FACEBOOK_BASE_URL + path
return await self.oauth2_request(
url, access_token=access_token, post_args=post_args, **args
) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/asymptotic/misc.py | python | Locals.__getitem__ | (self, key) | r"""
Return an item.
TESTS::
sage: from sage.rings.asymptotic.misc import Locals
sage: locals = Locals()
sage: locals
{}
sage: locals['log'] # indirect doctest
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Return an item.
TESTS::
sage: from sage.rings.asymptotic.misc import Locals
sage: locals = Locals()
sage: locals
{}
sage: locals['log'] # indirect doctest
<function log at 0x...>
"""
try:
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except KeyError:
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/core/losses.py | python | BootstrappedSigmoidClassificationLoss.__init__ | (self, alpha, bootstrap_type='soft') | Constructor.
Args:
alpha: a float32 scalar tensor between 0 and 1 representing interpolation
weight
bootstrap_type: set to either 'hard' or 'soft' (default)
Raises:
ValueError: if bootstrap_type is not either 'hard' or 'soft' | Constructor. | [
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] | def __init__(self, alpha, bootstrap_type='soft'):
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Args:
alpha: a float32 scalar tensor between 0 and 1 representing interpolation
weight
bootstrap_type: set to either 'hard' or 'soft' (default)
Raises:
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"""
if bootstrap_type != 'hard' and bootstrap_type != 'soft':
raise ValueError('Unrecognized bootstrap_type: must be one of '
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self._alpha = alpha
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fboender/ansible-cmdb | 3f3e412d2a7be91c97c5a1842f4e57cc85b06961 | lib/yaml3/__init__.py | python | load | (stream, Loader=Loader) | Parse the first YAML document in a stream
and produce the corresponding Python object. | Parse the first YAML document in a stream
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loader = Loader(stream)
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | lts/ctp/ApiStruct.py | python | Exchange.__init__ | (self, ExchangeID='', ExchangeName='', ExchangeProperty=EXP_Normal) | [] | def __init__(self, ExchangeID='', ExchangeName='', ExchangeProperty=EXP_Normal):
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self.ExchangeName = '' #交易所名称, char[31]
self.ExchangeProperty = '' | [
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catap/namebench | 9913a7a1a7955a3759eb18cbe73b421441a7a00f | nb_third_party/graphy/backends/google_chart_api/util.py | python | JoinLists | (**args) | return out | Take a dictionary of {long_name:values}, and join the values.
For each long_name, join the values into a string according to
JOIN_DELIMS. If values is empty or None, replace with an empty string.
Returns:
A dictionary {long_name:joined_value} entries. | Take a dictionary of {long_name:values}, and join the values. | [
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] | def JoinLists(**args):
"""Take a dictionary of {long_name:values}, and join the values.
For each long_name, join the values into a string according to
JOIN_DELIMS. If values is empty or None, replace with an empty string.
Returns:
A dictionary {long_name:joined_value} entries.
"""
out = {}
for key, val in args.items():
if val:
out[key] = StrJoin(JOIN_DELIMS[key], val)
else:
out[key] = ''
return out | [
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ym2011/POC-EXP | 206b22d3a6b2a172359678df33bbc5b2ad04b6c3 | windows/SYNfulKnock/scapy-2.3.1/scapy/crypto/cert.py | python | Cert.isSelfSigned | (self) | return False | Return True if the certificate is self signed:
- issuer and subject are the same
- the signature of the certificate is valid. | Return True if the certificate is self signed:
- issuer and subject are the same
- the signature of the certificate is valid. | [
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"""
Return True if the certificate is self signed:
- issuer and subject are the same
- the signature of the certificate is valid.
"""
if self.issuer == self.subject:
return self.isIssuerCert(self)
return False | [
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gui/plug/_guioptions.py | python | GuiDestinationOption.__init__ | (self, option, dbstate, uistate, track, override) | @param option: The option to display.
@type option: gen.plug.menu.DestinationOption
@return: nothing | [] | def __init__(self, option, dbstate, uistate, track, override):
"""
@param option: The option to display.
@type option: gen.plug.menu.DestinationOption
@return: nothing
"""
Gtk.Box.__init__(self)
self.__option = option
self.__uistate = uistate
self.__entry = Gtk.Entry()
self.__entry.set_text(self.__option.get_value())
self.__button = Gtk.Button()
img = Gtk.Image()
img.set_from_icon_name('document-open', Gtk.IconSize.BUTTON)
self.__button.add(img)
self.__button.connect('clicked', self.__select_file)
self.pack_start(self.__entry, True, True, 0)
self.pack_end(self.__button, False, False, 0)
# Set up signal handlers when the widget value is changed
# from user interaction or programmatically. When handling
# a specific signal, we need to temporarily block the signal
# that would call the other signal handler.
self.changekey = self.__entry.connect('changed', self.__text_changed)
self.valuekey = self.__option.connect('value-changed',
self.__value_changed)
self.conkey1 = self.__option.connect('options-changed',
self.__option_changed)
self.conkey2 = self.__option.connect('avail-changed',
self.__update_avail)
self.__update_avail()
self.set_tooltip_text(self.__option.get_help()) | [
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joxeankoret/nightmare | 11b22bb7c346611de90f479ee781c9228af453ea | mutators/OleFileIO_PL.py | python | _OleDirectoryEntry.build_storage_tree | (self) | Read and build the red-black tree attached to this _OleDirectoryEntry
object, if it is a storage.
Note that this method builds a tree of all subentries, so it should
only be called for the root object once. | Read and build the red-black tree attached to this _OleDirectoryEntry
object, if it is a storage.
Note that this method builds a tree of all subentries, so it should
only be called for the root object once. | [
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"""
Read and build the red-black tree attached to this _OleDirectoryEntry
object, if it is a storage.
Note that this method builds a tree of all subentries, so it should
only be called for the root object once.
"""
debug('build_storage_tree: SID=%d - %s - sid_child=%d'
% (self.sid, repr(self.name), self.sid_child))
if self.sid_child != NOSTREAM:
# if child SID is not NOSTREAM, then this entry is a storage.
# Let's walk through the tree of children to fill the kids list:
self.append_kids(self.sid_child)
# Note from OpenOffice documentation: the safest way is to
# recreate the tree because some implementations may store broken
# red-black trees...
# in the OLE file, entries are sorted on (length, name).
# for convenience, we sort them on name instead:
# (see rich comparison methods in this class)
self.kids.sort() | [
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/aui/framemanager.py | python | AuiPaneInfo.IsLeftSnappable | (self) | return self.HasFlag(self.optionLeftSnapped) | Returns ``True`` if the pane can be snapped on the left of the managed frame. | Returns ``True`` if the pane can be snapped on the left of the managed frame. | [
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] | def IsLeftSnappable(self):
""" Returns ``True`` if the pane can be snapped on the left of the managed frame. """
return self.HasFlag(self.optionLeftSnapped) | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/data/misc/cookie_jar.py | python | ImprovedMozillaCookieJar._really_load | (self, f, filename, ignore_discard, ignore_expires) | Override this method in order to provide better error handling. | Override this method in order to provide better error handling. | [
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] | def _really_load(self, f, filename, ignore_discard, ignore_expires):
"""
Override this method in order to provide better error handling.
"""
now = time.time()
magic = f.readline()
if not re.search(self.magic_re, magic):
f.close()
msg = "%r does not look like a Netscape format cookies file"
raise LoadError(msg % filename)
try:
while 1:
line = f.readline()
if line == "":
break
# last field may be absent, so keep any trailing tab
if line.endswith("\n"):
line = line[:-1]
# skip comments and blank lines XXX what is $ for?
if line.strip().startswith(("#", "$")) or line.strip() == "":
continue
split_values = line.split("\t")
if len(split_values) != 7:
msg = 'Expected seven tab delimited fields, got %s in %s: %s'
args = (len(split_values), filename, line)
raise LoadError(msg % args)
domain, domain_specified, path, secure, expires, name, value = split_values
secure = (secure == "TRUE")
domain_specified = (domain_specified == "TRUE")
if name == "":
# cookies.txt regards 'Set-Cookie: foo' as a cookie
# with no name, whereas cookielib regards it as a
# cookie with no value.
name = value
value = None
initial_dot = domain.startswith(".")
if domain_specified != initial_dot:
if domain_specified:
msg = ('The second tab delimited field (domain_specified) is'
' set to %s and the domain does NOT start with a dot (%s).'
' This is not acceptable by the Mozilla Cookie format.'
' Issue found at %s: %s')
args = (domain_specified, domain, filename, line)
raise LoadError(msg % args)
else:
msg = ('The second tab delimited field (domain_specified) is'
' set to %s and the domain starts with a dot (%s).'
' This is not acceptable by the Mozilla Cookie format.'
' Issue found at %s: %s')
args = (domain_specified, domain, filename, line)
raise LoadError(msg % args)
discard = False
if expires == "":
expires = None
discard = True
# assume path_specified is false
c = Cookie(0, name, value,
None, False,
domain, domain_specified, initial_dot,
path, False,
secure,
expires,
discard,
None,
None,
{})
if not ignore_discard and c.discard:
continue
if not ignore_expires and c.is_expired(now):
continue
self.set_cookie(c)
except IOError:
raise
except Exception:
_warn_unhandled_exception()
raise LoadError("invalid Netscape format cookies file %r: %r" % (filename, line)) | [
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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/gui/qt/util.py | python | FixedAspectRatioLayout.count | (self) | return len(self.items) | [] | def count(self) -> int:
return len(self.items) | [
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jxhe/vae-lagging-encoder | cdc4eb9d9599a026bf277db74efc2ba1ec203b15 | data/text_data.py | python | MonoTextData.data_sample | (self, nsample, device, batch_first=False, shuffle=True) | return batch_data, sents_len | sample a subset of data (like data_iter)
Returns:
batch_data: LongTensor with shape (seq_len, batch_size)
sents_len: list of data length, this is the data length
after counting start and stop symbols | sample a subset of data (like data_iter)
Returns:
batch_data: LongTensor with shape (seq_len, batch_size)
sents_len: list of data length, this is the data length
after counting start and stop symbols | [
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"the",
"data",
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"""sample a subset of data (like data_iter)
Returns:
batch_data: LongTensor with shape (seq_len, batch_size)
sents_len: list of data length, this is the data length
after counting start and stop symbols
"""
index_arr = np.arange(len(self.data))
if shuffle:
np.random.shuffle(index_arr)
batch_ids = index_arr[: nsample]
batch_data = [self.data[index] for index in batch_ids]
# uncomment this line if the dataset has variable length
batch_data.sort(key=lambda e: -len(e))
batch_data, sents_len = self._to_tensor(batch_data, batch_first, device)
return batch_data, sents_len | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/preview/hosted_numbers/authorization_document/dependent_hosted_number_order.py | python | DependentHostedNumberOrderList.__init__ | (self, version, signing_document_sid) | Initialize the DependentHostedNumberOrderList
:param Version version: Version that contains the resource
:param signing_document_sid: LOA document sid.
:returns: twilio.rest.preview.hosted_numbers.authorization_document.dependent_hosted_number_order.DependentHostedNumberOrderList
:rtype: twilio.rest.preview.hosted_numbers.authorization_document.dependent_hosted_number_order.DependentHostedNumberOrderList | Initialize the DependentHostedNumberOrderList | [
"Initialize",
"the",
"DependentHostedNumberOrderList"
] | def __init__(self, version, signing_document_sid):
"""
Initialize the DependentHostedNumberOrderList
:param Version version: Version that contains the resource
:param signing_document_sid: LOA document sid.
:returns: twilio.rest.preview.hosted_numbers.authorization_document.dependent_hosted_number_order.DependentHostedNumberOrderList
:rtype: twilio.rest.preview.hosted_numbers.authorization_document.dependent_hosted_number_order.DependentHostedNumberOrderList
"""
super(DependentHostedNumberOrderList, self).__init__(version)
# Path Solution
self._solution = {'signing_document_sid': signing_document_sid, }
self._uri = '/AuthorizationDocuments/{signing_document_sid}/DependentHostedNumberOrders'.format(**self._solution) | [
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