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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/setuptools/pkg_resources/_vendor/pyparsing.py | python | pyparsing_common.convertToDatetime | (fmt="%Y-%m-%dT%H:%M:%S.%f") | return cvt_fn | Helper to create a parse action for converting parsed datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"})
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] | Helper to create a parse action for converting parsed datetime string to Python datetime.datetime | [
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".",
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] | def convertToDatetime(fmt="%Y-%m-%dT%H:%M:%S.%f"):
"""
Helper to create a parse action for converting parsed datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"})
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
"""
def cvt_fn(s,l,t):
try:
return datetime.strptime(t[0], fmt)
except ValueError as ve:
raise ParseException(s, l, str(ve))
return cvt_fn | [
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jbjorne/TEES | caf19a4a1352ac59f5dc13a8684cc42ce4342d9d | Utils/Download.py | python | extractPackage | (path, destPath, subPath=None) | return names | [] | def extractPackage(path, destPath, subPath=None):
global pbar
if path.endswith('.zip'):
opener, mode = zipfile.ZipFile, 'r'
namelister = zipfile.ZipFile.namelist
elif path.endswith('.tar.gz') or path.endswith('.tgz'):
opener, mode = tarfile.open, 'r:gz'
namelister = tarfile.TarFile.getnames
elif path.endswith('.tar.bz2') or path.endswith('.tbz'):
opener, mode = tarfile.open, 'r:bz2'
namelister = tarfile.TarFile.getnames
else:
raise ValueError, "Could not extract `%s` as no appropriate extractor is found" % path
widgets = [' [', Bar(), '] ', Percentage(), ' ', ETA()]
pbar = ProgressBar(widgets=widgets, maxval=100)
pbar.start()
#package = opener(fileobj=SizeReportingFile(path), mode=mode)
package = opener(path, mode)
names = namelister(package)
if subPath == None:
extractWithProgress(package, names, destPath)
else:
tempdir = tempfile.mkdtemp()
extractWithProgress(package, names, tempdir)
if os.path.exists(destPath):
shutil.rmtree(destPath)
shutil.move(os.path.join(tempdir, subPath), destPath)
shutil.rmtree(tempdir)
package.close()
pbar.finish()
pbar = None
return names | [
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online-judge-tools/oj | 70f8a29ae6a1f551fee50b35b61354ccbc81505f | onlinejudge_command/pretty_printers.py | python | _tokens_from_line_diff_ops | (ops: List[_MergedDiffOp], *, char_in_line: int) | return tokens | [] | def _tokens_from_line_diff_ops(ops: List[_MergedDiffOp], *, char_in_line: int) -> List[_PrettyToken]:
if not ops:
return [_PrettyToken(_PrettyTokenType.HINT, '(no diff)')]
left_width = char_in_line // 2
# calculate the widths of lineno
max_left_linno = 0
max_right_linno = 0
for op in ops:
if op.left_lineno is not None:
max_left_linno = op.left_lineno
if op.right_lineno is not None:
max_right_linno = op.right_lineno
left_lineno_width = len(str(max_left_linno + 1))
right_lineno_width = len(str(max_right_linno + 1))
assert left_lineno_width + 2 + 10 <= left_width
assert right_lineno_width + 2 + 10
tokens = []
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, 'output:'.ljust(left_width) + 'expected:'))
tokens.append(_PrettyToken(_PrettyTokenType.NEWLINE, '\n'))
for op in ops:
if op == _MergedDiffOpDots:
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, '...'.ljust(left_width) + '...'))
tokens.append(_PrettyToken(_PrettyTokenType.NEWLINE, '\n'))
continue
left_exists = False
if op.left_lineno is not None:
tokens.append(_PrettyToken(_PrettyTokenType.LINENO, str(op.left_lineno + 1).rjust(left_lineno_width)))
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, '| '))
tokens.extend([_PrettyToken(token.type, token.value.replace('\n', '')) for token in op.left])
padding = left_width - (left_lineno_width + 2 + _len_of_tokens(op.left))
if padding >= 0:
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, ' ' * padding))
left_exists = True
else:
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, '\n'))
if op.right_lineno is not None:
if not left_exists:
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, ' ' * left_width))
tokens.append(_PrettyToken(_PrettyTokenType.LINENO, str(op.right_lineno + 1).rjust(right_lineno_width)))
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, '| '))
tokens.extend(op.right)
else:
if left_exists:
tokens.append(_PrettyToken(_PrettyTokenType.OTHERS, '\n'))
return tokens | [
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graphql-python/gql | 5440c6c14b74f0414551e0ebebeed187bdf4ae5a | gql/transport/phoenix_channel_websockets.py | python | PhoenixChannelWebsocketsTransport._find_existing_subscription | (self, query_id: int) | return subscription_id | Perform a reverse lookup to find the subscription id matching
a listener's query_id. | Perform a reverse lookup to find the subscription id matching
a listener's query_id. | [
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"""Perform a reverse lookup to find the subscription id matching
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"""
subscription_id, _listener_id = self._find_subscription(query_id)
if subscription_id is None:
raise TransportProtocolError(
f"No subscription registered for listener {query_id}"
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return subscription_id | [
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mynameisfiber/high_performance_python | 615341ff066772dc45125fce1866349d555524fe | 01_profiling/dowser/julia1_dowser.py | python | calc_pure_python | (draw_output, desired_width, max_iterations) | Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display | Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display | [
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] | def calc_pure_python(draw_output, desired_width, max_iterations):
"""Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display"""
x_step = (float(x2 - x1) / float(desired_width))
y_step = (float(y1 - y2) / float(desired_width))
x = []
y = []
ycoord = y2
while ycoord > y1:
y.append(ycoord)
ycoord += y_step
xcoord = x1
while xcoord < x2:
x.append(xcoord)
xcoord += x_step
# set width and height to the generated pixel counts, rather than the
# pre-rounding desired width and height
# build a list of co-ordinates and the initial condition for each cell.
# Note that our initial condition is a constant and could easily be removed,
# we use it to simulate a real-world scenario with several inputs to our
# function
zs = []
cs = []
for ycoord in y:
for xcoord in x:
zs.append(complex(xcoord, ycoord))
cs.append(complex(c_real, c_imag))
launch_memory_usage_server()
print "Length of x:", len(x)
print "Total elements:", len(zs)
start_time = time.time()
output = calculate_z_serial_purepython(max_iterations, zs, cs)
end_time = time.time()
secs = end_time - start_time
print calculate_z_serial_purepython.func_name + " took", secs, "seconds"
# this sum is expected for 1000^2 grid with 300 iterations
assert sum(output) == 33219980
print "now waiting..."
while True:
time.sleep(1) | [
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inventree/InvenTree | 4a5e4a88ac3e91d64a21e8cab3708ecbc6e2bd8b | InvenTree/company/models.py | python | SupplierPart.api_instance_filters | (self) | return {
'manufacturer_part': {
'part': self.part.pk
}
} | [] | def api_instance_filters(self):
return {
'manufacturer_part': {
'part': self.part.pk
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} | [
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bcbio/bcbio-nextgen | c80f9b6b1be3267d1f981b7035e3b72441d258f2 | bcbio/broad/picardrun.py | python | picard_fastq_to_bam | (picard, fastq_one, fastq_two, out_dir, names, order="queryname") | return out_bam | Convert fastq file(s) to BAM, adding sample, run group and platform information. | Convert fastq file(s) to BAM, adding sample, run group and platform information. | [
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"""Convert fastq file(s) to BAM, adding sample, run group and platform information.
"""
out_bam = os.path.join(out_dir, "%s-fastq.bam" %
os.path.splitext(os.path.basename(fastq_one))[0])
if not file_exists(out_bam):
with tx_tmpdir(picard._config) as tmp_dir:
with file_transaction(picard._config, out_bam) as tx_out_bam:
opts = [("--FASTQ", fastq_one),
("--READ_GROUP_NAME", names["rg"]),
("--SAMPLE_NAME", names["sample"]),
("--PLATFORM_UNIT", names["pu"]),
("--PLATFORM", names["pl"]),
("--TMP_DIR", tmp_dir),
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("--SORT_ORDER", order)]
if fastq_two:
opts.append(("--FASTQ2", fastq_two))
picard.run("FastqToSam", opts)
return out_bam | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-47/fabmetheus_utilities/geometry/manipulation_shapes/flip.py | python | getNewDerivation | (elementNode, prefix, sideLength) | return FlipDerivation(elementNode, prefix) | Get new derivation. | Get new derivation. | [
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'Get new derivation.'
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007gzs/dingtalk-sdk | 7979da2e259fdbc571728cae2425a04dbc65850a | dingtalk/client/api/taobao.py | python | TbWangWang.taobao_wangwang_eservice_abs_word_add | (
self,
word
) | return self._top_request(
"taobao.wangwang.eservice.abs.word.add",
{
"word": word
}
) | 添加关键词
为聊天记录查询接口添加关键词。
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=28017
:param word: 关键词 | 添加关键词
为聊天记录查询接口添加关键词。
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=28017 | [
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self,
word
):
"""
添加关键词
为聊天记录查询接口添加关键词。
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=28017
:param word: 关键词
"""
return self._top_request(
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"word": word
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aparo/pyes | 96965174760cb5aa5c92eac7ccff346fb5d53cf1 | pyes/managers.py | python | Indices.get_alias | (self, alias) | return status['indices'].keys() | Get the index or indices pointed to by a given alias.
(See :ref:`es-guide-reference-api-admin-indices-aliases`)
:param alias: the name of an alias
:return returns a list of index names.
:raise IndexMissingException if the alias does not exist. | Get the index or indices pointed to by a given alias.
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"""
Get the index or indices pointed to by a given alias.
(See :ref:`es-guide-reference-api-admin-indices-aliases`)
:param alias: the name of an alias
:return returns a list of index names.
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status = self.status([alias])
return status['indices'].keys() | [
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/dateutil/tz/tz.py | python | tzutc.is_ambiguous | (self, dt) | return False | Whether or not the "wall time" of a given datetime is ambiguous in this
zone.
:param dt:
A :py:class:`datetime.datetime`, naive or time zone aware.
:return:
Returns ``True`` if ambiguous, ``False`` otherwise.
.. versionadded:: 2.6.0 | Whether or not the "wall time" of a given datetime is ambiguous in this
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Whether or not the "wall time" of a given datetime is ambiguous in this
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:param dt:
A :py:class:`datetime.datetime`, naive or time zone aware.
:return:
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.. versionadded:: 2.6.0
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/packets/zigbee.py | python | RegisterDeviceStatusPacket._get_api_packet_spec_data_dict | (self) | return {DictKeys.STATUS: "%s (%s)" % (self.__status.code,
self.__status.description)} | Override method.
.. seealso::
| :meth:`.XBeeAPIPacket._get_api_packet_spec_data_dict` | Override method. | [
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] | def _get_api_packet_spec_data_dict(self):
"""
Override method.
.. seealso::
| :meth:`.XBeeAPIPacket._get_api_packet_spec_data_dict`
"""
return {DictKeys.STATUS: "%s (%s)" % (self.__status.code,
self.__status.description)} | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/ipma/config_flow.py | python | IpmaFlowHandler.async_step_user | (self, user_input=None) | return await self._show_config_form(
name=HOME_LOCATION_NAME,
latitude=self.hass.config.latitude,
longitude=self.hass.config.longitude,
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self._errors = {}
if user_input is not None:
if user_input[CONF_NAME] not in self.hass.config_entries.async_entries(
DOMAIN
):
return self.async_create_entry(
title=user_input[CONF_NAME], data=user_input
)
self._errors[CONF_NAME] = "name_exists"
# default location is set hass configuration
return await self._show_config_form(
name=HOME_LOCATION_NAME,
latitude=self.hass.config.latitude,
longitude=self.hass.config.longitude,
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bloomberg/phabricator-tools | 09bd1587fe8945d93a891162fd4c89640c6fada7 | py/abd/abdt_fs.py | python | Accessor.repo_config_path_list | (self) | return [os.path.join(p, r) for r in os.listdir(p) if r != 'README'] | Return a list of string paths to repo configs.
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:returns: list of string
"""
p = self.layout.repository_config_dir
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/cluster/vq.py | python | py_vq | (obs, code_book, check_finite=True) | return code, min_dist | Python version of vq algorithm.
The algorithm computes the euclidian distance between each
observation and every frame in the code_book.
Parameters
----------
obs : ndarray
Expects a rank 2 array. Each row is one observation.
code_book : ndarray
Code book to use. Same format than obs. Should have same number of
features (eg columns) than obs.
check_finite : bool, optional
Whether to check that the input matrices contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Default: True
Returns
-------
code : ndarray
code[i] gives the label of the ith obversation, that its code is
code_book[code[i]].
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
Notes
-----
This function is slower than the C version but works for
all input types. If the inputs have the wrong types for the
C versions of the function, this one is called as a last resort.
It is about 20 times slower than the C version. | Python version of vq algorithm. | [
"Python",
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] | def py_vq(obs, code_book, check_finite=True):
""" Python version of vq algorithm.
The algorithm computes the euclidian distance between each
observation and every frame in the code_book.
Parameters
----------
obs : ndarray
Expects a rank 2 array. Each row is one observation.
code_book : ndarray
Code book to use. Same format than obs. Should have same number of
features (eg columns) than obs.
check_finite : bool, optional
Whether to check that the input matrices contain only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Default: True
Returns
-------
code : ndarray
code[i] gives the label of the ith obversation, that its code is
code_book[code[i]].
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
Notes
-----
This function is slower than the C version but works for
all input types. If the inputs have the wrong types for the
C versions of the function, this one is called as a last resort.
It is about 20 times slower than the C version.
"""
obs = _asarray_validated(obs, check_finite=check_finite)
code_book = _asarray_validated(code_book, check_finite=check_finite)
if obs.ndim != code_book.ndim:
raise ValueError("Observation and code_book should have the same rank")
if obs.ndim == 1:
obs = obs[:, np.newaxis]
code_book = code_book[:, np.newaxis]
dist = cdist(obs, code_book)
code = dist.argmin(axis=1)
min_dist = dist[np.arange(len(code)), code]
return code, min_dist | [
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datawire/forge | d501be4571dcef5691804c7db7008ee877933c8d | forge/kubernetes.py | python | Kubernetes.resources | (self, yaml_dir) | return sh(*cmd).output.split() | [] | def resources(self, yaml_dir):
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return []
cmd = "kubectl", "apply", "--dry-run", "-R", "-f", yaml_dir, "-o", "name"
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/idlelib/ClassBrowser.py | python | ClassBrowserTreeItem.__init__ | (self, name, classes, file) | [] | def __init__(self, name, classes, file):
self.name = name
self.classes = classes
self.file = file
try:
self.cl = self.classes[self.name]
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/polys/galoistools.py | python | gf_add_ground | (f, a, p, K) | Compute ``f + a`` where ``f`` in ``GF(p)[x]`` and ``a`` in ``GF(p)``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.galoistools import gf_add_ground
>>> gf_add_ground([3, 2, 4], 2, 5, ZZ)
[3, 2, 1] | Compute ``f + a`` where ``f`` in ``GF(p)[x]`` and ``a`` in ``GF(p)``. | [
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"""
Compute ``f + a`` where ``f`` in ``GF(p)[x]`` and ``a`` in ``GF(p)``.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.galoistools import gf_add_ground
>>> gf_add_ground([3, 2, 4], 2, 5, ZZ)
[3, 2, 1]
"""
if not f:
a = a % p
else:
a = (f[-1] + a) % p
if len(f) > 1:
return f[:-1] + [a]
if not a:
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/categories/baseclasses.py | python | Diagram.__new__ | (cls, *args) | return Basic.__new__(cls, Dict(premises), Dict(conclusions), objects) | Construct a new instance of Diagram.
If no arguments are supplied, an empty diagram is created.
If at least an argument is supplied, ``args[0]`` is
interpreted as the premises of the diagram. If ``args[0]`` is
a list, it is interpreted as a list of :class:`Morphism`'s, in
which each :class:`Morphism` has an empty set of properties.
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is interpreted as a dictionary associating to some
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interpreted as the conclusions of the diagram. The type of
``args[1]`` is interpreted in exactly the same way as the type
of ``args[0]``. If only one argument is supplied, the diagram
has no conclusions.
Examples
========
>>> from sympy.categories import Object, NamedMorphism
>>> from sympy.categories import IdentityMorphism, Diagram
>>> A = Object("A")
>>> B = Object("B")
>>> C = Object("C")
>>> f = NamedMorphism(A, B, "f")
>>> g = NamedMorphism(B, C, "g")
>>> d = Diagram([f, g])
>>> IdentityMorphism(A) in d.premises.keys()
True
>>> g * f in d.premises.keys()
True
>>> d = Diagram([f, g], {g * f: "unique"})
>>> d.conclusions[g * f]
FiniteSet(unique) | Construct a new instance of Diagram. | [
"Construct",
"a",
"new",
"instance",
"of",
"Diagram",
"."
] | def __new__(cls, *args):
"""
Construct a new instance of Diagram.
If no arguments are supplied, an empty diagram is created.
If at least an argument is supplied, ``args[0]`` is
interpreted as the premises of the diagram. If ``args[0]`` is
a list, it is interpreted as a list of :class:`Morphism`'s, in
which each :class:`Morphism` has an empty set of properties.
If ``args[0]`` is a Python dictionary or a :class:`Dict`, it
is interpreted as a dictionary associating to some
:class:`Morphism`'s some properties.
If at least two arguments are supplied ``args[1]`` is
interpreted as the conclusions of the diagram. The type of
``args[1]`` is interpreted in exactly the same way as the type
of ``args[0]``. If only one argument is supplied, the diagram
has no conclusions.
Examples
========
>>> from sympy.categories import Object, NamedMorphism
>>> from sympy.categories import IdentityMorphism, Diagram
>>> A = Object("A")
>>> B = Object("B")
>>> C = Object("C")
>>> f = NamedMorphism(A, B, "f")
>>> g = NamedMorphism(B, C, "g")
>>> d = Diagram([f, g])
>>> IdentityMorphism(A) in d.premises.keys()
True
>>> g * f in d.premises.keys()
True
>>> d = Diagram([f, g], {g * f: "unique"})
>>> d.conclusions[g * f]
FiniteSet(unique)
"""
premises = {}
conclusions = {}
# Here we will keep track of the objects which appear in the
# premises.
objects = EmptySet
if len(args) >= 1:
# We've got some premises in the arguments.
premises_arg = args[0]
if isinstance(premises_arg, list):
# The user has supplied a list of morphisms, none of
# which have any attributes.
empty = EmptySet
for morphism in premises_arg:
objects |= FiniteSet(morphism.domain, morphism.codomain)
Diagram._add_morphism_closure(premises, morphism, empty)
elif isinstance(premises_arg, dict) or isinstance(premises_arg, Dict):
# The user has supplied a dictionary of morphisms and
# their properties.
for morphism, props in premises_arg.items():
objects |= FiniteSet(morphism.domain, morphism.codomain)
Diagram._add_morphism_closure(
premises, morphism, FiniteSet(*props) if iterable(props) else FiniteSet(props))
if len(args) >= 2:
# We also have some conclusions.
conclusions_arg = args[1]
if isinstance(conclusions_arg, list):
# The user has supplied a list of morphisms, none of
# which have any attributes.
empty = EmptySet
for morphism in conclusions_arg:
# Check that no new objects appear in conclusions.
if ((sympify(objects.contains(morphism.domain)) is S.true) and
(sympify(objects.contains(morphism.codomain)) is S.true)):
# No need to add identities and recurse
# composites this time.
Diagram._add_morphism_closure(
conclusions, morphism, empty, add_identities=False,
recurse_composites=False)
elif isinstance(conclusions_arg, dict) or \
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# The user has supplied a dictionary of morphisms and
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for morphism, props in conclusions_arg.items():
# Check that no new objects appear in conclusions.
if (morphism.domain in objects) and \
(morphism.codomain in objects):
# No need to add identities and recurse
# composites this time.
Diagram._add_morphism_closure(
conclusions, morphism, FiniteSet(*props) if iterable(props) else FiniteSet(props),
add_identities=False, recurse_composites=False)
return Basic.__new__(cls, Dict(premises), Dict(conclusions), objects) | [
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tanghaibao/jcvi | 5e720870c0928996f8b77a38208106ff0447ccb6 | jcvi/apps/gbsubmit.py | python | t384 | (args) | %prog t384
Print out a table converting between 96 well to 384 well | %prog t384 | [
"%prog",
"t384"
] | def t384(args):
"""
%prog t384
Print out a table converting between 96 well to 384 well
"""
p = OptionParser(t384.__doc__)
p.parse_args(args)
plate, splate = get_plate()
fw = sys.stdout
for i in plate:
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if j != 0:
fw.write("|")
fw.write(p)
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/scmtools/clearcase.py | python | ClearCaseDynamicViewClient.__init__ | (self, path) | Initialize the client.
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/cffi/model.py | python | BaseType.__eq__ | (self, other) | return (self.__class__ == other.__class__ and
self._get_items() == other._get_items()) | [] | def __eq__(self, other):
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v9/services/services/conversion_custom_variable_service/client.py | python | ConversionCustomVariableServiceClient.conversion_custom_variable_path | (
customer_id: str, conversion_custom_variable_id: str,
) | return "customers/{customer_id}/conversionCustomVariables/{conversion_custom_variable_id}".format(
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) -> str:
"""Return a fully-qualified conversion_custom_variable string."""
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nltk/nltk | 3f74ac55681667d7ef78b664557487145f51eb02 | nltk/draw/tree.py | python | TreeSegmentWidget.replace_child | (self, oldchild, newchild) | Replace the child ``oldchild`` with ``newchild``. | Replace the child ``oldchild`` with ``newchild``. | [
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myquant/strategy | 17595e6bf4a118e1fa87c90bfb0fd78afa69a60b | AR_MA_STOCK/python/ar_ma_stock.py | python | AR_MA_STOCK.fixation_stop_profit_loss | (self, bar) | 功能：固定止盈、止损,盈利或亏损超过了设置的比率则执行止盈、止损 | 功能：固定止盈、止损,盈利或亏损超过了设置的比率则执行止盈、止损 | [
"功能：固定止盈、止损",
"盈利或亏损超过了设置的比率则执行止盈、止损"
] | def fixation_stop_profit_loss(self, bar):
"""
功能：固定止盈、止损,盈利或亏损超过了设置的比率则执行止盈、止损
"""
if self.is_fixation_stop == 0:
return
symbol = bar.exchange + '.' + bar.sec_id
pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)
if pos is not None:
if pos.fpnl > 0 and pos.fpnl / pos.cost >= self.stop_fixation_profit:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
'fixnation stop profit: close long, symbol:%s, time:%s, price:%.2f, vwap: %s, volume:%s' % (symbol,
bar.strtime,
bar.close,
pos.vwap,
pos.volume))
elif pos.fpnl < 0 and pos.fpnl / pos.cost <= -1 * self.stop_fixation_loss:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/feed_service/client.py | python | FeedServiceClient.common_project_path | (project: str,) | return "projects/{project}".format(project=project,) | Return a fully-qualified project string. | Return a fully-qualified project string. | [
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cortex-lab/phy | 9a330b9437a3d0b40a37a201d147224e6e7fb462 | phy/plot/gloo/globject.py | python | GLObject.need_update | (self) | return self._need_update | Whether object needs to be updated | Whether object needs to be updated | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/skeinforge_application/skeinforge_plugins/craft.py | python | addToCraftMenu | ( menu ) | Add a craft plugin menu. | Add a craft plugin menu. | [
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"Add a craft plugin menu."
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menu.add_separator()
directoryPath = skeinforge_craft.getPluginsDirectoryPath()
directoryFolders = settings.getFolders(directoryPath)
pluginFileNames = skeinforge_craft.getPluginFileNames()
for pluginFileName in pluginFileNames:
pluginFolderName = pluginFileName + '_plugins'
pluginPath = os.path.join( directoryPath, pluginFileName )
if pluginFolderName in directoryFolders:
addSubmenus( menu, pluginFileName, os.path.join( directoryPath, pluginFolderName ), pluginPath )
else:
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LexPredict/lexpredict-contraxsuite | 1d5a2540d31f8f3f1adc442cfa13a7c007319899 | sdk/python/sdk/openapi_client/model/task.py | python | Task.openapi_types | () | return {
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/dates.py | python | RRuleLocator._get_unit | (self) | return self.get_unit_generic(freq) | Return how many days a unit of the locator is; used for
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freq = self.rule._rrule._freq
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n1nj4sec/pupy | a5d766ea81fdfe3bc2c38c9bdaf10e9b75af3b39 | pupy/packages/windows/all/reg.py | python | Key.__getitem__ | (self, attr) | [] | def __getitem__(self, attr):
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CastagnaIT/plugin.video.netflix | 5cf5fa436eb9956576c0f62aa31a4c7d6c5b8a4a | packages/h2/connection.py | python | H2Connection.inbound_flow_control_window | (self) | return self._inbound_flow_control_window_manager.current_window_size | The size of the inbound flow control window for the connection. This is
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/ipaddress.py | python | _BaseV4._is_hostmask | (self, ip_str) | return False | Test if the IP string is a hostmask (rather than a netmask).
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Returns:
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emesene/emesene | 4548a4098310e21b16437bb36223a7f632a4f7bc | emesene/e3/xmpp/SleekXMPP/sleekxmpp/plugins/xep_0030/disco.py | python | XEP_0030.add_feature | (self, feature, node=None, jid=None) | Add a feature to a JID/node combination.
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feature -- The namespace of the supported feature.
node -- The node to modify.
jid -- The JID to modify.
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/logging/__init__.py | python | Logger.info | (self, msg, *args, **kwargs) | Log 'msg % args' with severity 'INFO'.
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derek-zhang123/MxShop | 22001da19b3d85424f48d78a844b8f424ae1e0e6 | extra_apps/rest_framework/request.py | python | Request._parse | (self) | Parse the request content, returning a two-tuple of (data, files)
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if self._supports_form_parsing():
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stream = None
if stream is None or media_type is None:
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self._data = QueryDict('', encoding=self._request._encoding)
self._files = MultiValueDict()
self._full_data = self._data
raise
# Parser classes may return the raw data, or a
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except AttributeError:
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/numpy/core/_internal.py | python | _ctypes.shape | (self) | return self.shape_as(_getintp_ctype()) | (c_intp*self.ndim): A ctypes array of length self.ndim where
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/whoosh/matching/wrappers.py | python | WrappingMatcher.next | (self) | [] | def next(self):
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dmlc/gluon-nlp | 5d4bc9eba7226ea9f9aabbbd39e3b1e886547e48 | src/gluonnlp/utils/parameter.py | python | deduplicate_param_dict | (param_dict) | return dedup_param_dict | Get a parameter dict that has been deduplicated
Parameters
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param_dict
The parameter dict returned by `model.collect_params()`
Returns
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dedup_param_dict | Get a parameter dict that has been deduplicated | [
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Parameters
----------
param_dict
The parameter dict returned by `model.collect_params()`
Returns
-------
dedup_param_dict
"""
dedup_param_dict = dict()
param_uuid_set = set()
for k in sorted(param_dict.keys()):
v = param_dict[k]
if v._uuid in param_uuid_set:
continue
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | core/state.py | python | Mem.Shift | (self, n) | [] | def Shift(self, n):
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/pip/utils/__init__.py | python | untar_file | (filename, location) | Untar the file (with path `filename`) to the destination `location`.
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broadinstitute/viral-ngs | e144969e4c57060d53f38a4c3a270e8227feace1 | util/vcf.py | python | VcfReader.get_snp_genos | (self, c, p, as_strings=True) | return len(snps) == 1 and dict(snps[0][3]) or {} | Read a single position from a VCF file and return the genotypes
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/cls/v20201016/models.py | python | RuleInfo.__init__ | (self) | r"""
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/rest/binary_sensor.py | python | RestBinarySensor.is_on | (self) | return self._is_on | Return true if the binary sensor is on. | Return true if the binary sensor is on. | [
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open-mmlab/mmdetection | ff9bc39913cb3ff5dde79d3933add7dc2561bab7 | mmdet/core/evaluation/class_names.py | python | get_classes | (dataset) | return labels | Get class names of a dataset. | Get class names of a dataset. | [
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nabeel-oz/qlik-py-tools | 09d0cd232fadcaa926bb11cebb37d5ae3051bc86 | core/__main__.py | python | ExtensionService.__init__ | (self, funcdef_file) | Class initializer.
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pycontribs/pyrax | a0c022981f76a4cba96a22ecc19bb52843ac4fbe | pyrax/object_storage.py | python | Container.move_object | (self, obj, new_container, new_obj_name=None,
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/encodings/cp720.py | python | getregentry | () | return codecs.CodecInfo(
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phimpme/phimpme-generator | ba6d11190b9016238f27672e1ad55e6a875b74a0 | Phimpme/site-packages/nose/plugins/base.py | python | IPluginInterface.loadTestsFromTestClass | (self, cls) | Return tests in this test class. Class will *not* be a
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Jack-Cherish/Deep-Learning | 5fd254b61ad45367fbae28c49976e82b14ff7110 | Tutorial/lesson-5/activators.py | python | TanhActivator.backward | (self, output) | return 1 - output * output | [] | def backward(self, output):
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/rabbitmq.py | python | set_user_tags | (name, tags, runas=None) | return _format_response(res, msg) | Add user tags via rabbitmqctl set_user_tags
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.. code-block:: bash
salt '*' rabbitmq.set_user_tags myadmin administrator | Add user tags via rabbitmqctl set_user_tags | [
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.. code-block:: bash
salt '*' rabbitmq.set_user_tags myadmin administrator
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NVIDIA/object-detection-tensorrt-example | feb3632ed289fb71acb84e28a4f51931fc8c7e13 | SSD_Model/utils/boxes.py | python | draw_bounding_box_on_image | (image,
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normalized coordinates by setting the use_normalized_coordinates argument.
The string passed in display_str is displayed above the
bounding box in black text on a rectangle filled with the input 'color'.
If the top of the bounding box extends to the edge of the image, the string
is displayed below the bounding box.
Args:
image (PIL.Image): PIL.Image object
ymin (float): ymin of bounding box
xmin (float): xmin of bounding box
ymax (float): ymax of bounding box
xmax (float): xmax of bounding box
color (int, int, int): RGB tuple describing color to draw bounding box
thickness (int): line thickness
display_str (str): string to display in box
use_normalized_coordinates (bool): If True, treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute | Adds a bounding box to an image. | [
"Adds",
"a",
"bounding",
"box",
"to",
"an",
"image",
"."
] | def draw_bounding_box_on_image(image,
ymin,
xmin,
ymax,
xmax,
color=(255, 0, 0),
thickness=4,
display_str='',
use_normalized_coordinates=True):
"""Adds a bounding box to an image.
Bounding box coordinates can be specified in either absolute (pixel) or
normalized coordinates by setting the use_normalized_coordinates argument.
The string passed in display_str is displayed above the
bounding box in black text on a rectangle filled with the input 'color'.
If the top of the bounding box extends to the edge of the image, the string
is displayed below the bounding box.
Args:
image (PIL.Image): PIL.Image object
ymin (float): ymin of bounding box
xmin (float): xmin of bounding box
ymax (float): ymax of bounding box
xmax (float): xmax of bounding box
color (int, int, int): RGB tuple describing color to draw bounding box
thickness (int): line thickness
display_str (str): string to display in box
use_normalized_coordinates (bool): If True, treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute
"""
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom),
(right, top), (left, top)], width=thickness, fill=tuple(color))
try:
font = ImageFont.truetype('arial.ttf', 24)
except IOError:
font = ImageFont.load_default()
# If the total height of the display string added to the top of the bounding
# box exceeds the top of the image, move the string below the bounding box
# instead of above
display_str_height = font.getsize(display_str)[1]
# Each display_str has a top and bottom margin of 0.05x
total_display_str_height = (1 + 2 * 0.05) * display_str_height
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle(
[(left, text_bottom - text_height - 2 * margin), (left + text_width,
text_bottom)],
fill=tuple(color))
draw.text(
(left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin | [
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... | https://github.com/NVIDIA/object-detection-tensorrt-example/blob/feb3632ed289fb71acb84e28a4f51931fc8c7e13/SSD_Model/utils/boxes.py#L35-L104 | ||
kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_custom_resource_definition.py | python | V1CustomResourceDefinition.__init__ | (self, api_version=None, kind=None, metadata=None, spec=None, status=None, local_vars_configuration=None) | V1CustomResourceDefinition - a model defined in OpenAPI | V1CustomResourceDefinition - a model defined in OpenAPI | [
"V1CustomResourceDefinition",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] | def __init__(self, api_version=None, kind=None, metadata=None, spec=None, status=None, local_vars_configuration=None): # noqa: E501
"""V1CustomResourceDefinition - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._api_version = None
self._kind = None
self._metadata = None
self._spec = None
self._status = None
self.discriminator = None
if api_version is not None:
self.api_version = api_version
if kind is not None:
self.kind = kind
if metadata is not None:
self.metadata = metadata
self.spec = spec
if status is not None:
self.status = status | [
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PINTO0309/PINTO_model_zoo | 2924acda7a7d541d8712efd7cc4fd1c61ef5bddd | 082_MediaPipe_Meet_Segmentation/02_segm_full_v679_tflite_to_pb_saved_model.py | python | make_graph | (ops, op_types, interpreter) | [] | def make_graph(ops, op_types, interpreter):
height = 144
width = 256
tensors = {}
input_details = interpreter.get_input_details()
# output_details = interpreter.get_output_details()
print(input_details)
for input_detail in input_details:
tensors[input_detail['index']] = tf.placeholder(
dtype=input_detail['dtype'],
shape=input_detail['shape'],
name=input_detail['name'])
for op in ops:
print('@@@@@@@@@@@@@@ op:', op)
op_type = op_types[op['opcode_index']]
if op_type == 'CONV_2D':
input_tensor = tensors[op['inputs'][0]]
weights = tensors[op['inputs'][1]].transpose(1,2,3,0)
bias = tensors[op['inputs'][2]]
output_detail = interpreter._get_tensor_details(op['outputs'][0])
options = op['builtin_options']
output_tensor = tf.nn.conv2d(
input_tensor,
weights,
strides=[1, options['stride_h'], options['stride_w'], 1],
padding=options['padding'],
dilations=[
1, options['dilation_h_factor'],
options['dilation_w_factor'], 1
],
name=output_detail['name'] + '/conv2d')
output_tensor = tf.add(
output_tensor, bias, name=output_detail['name'])
if output_detail['name'].split('/')[-1]=='Relu6':
output_tensor = tf.nn.relu6(output_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'DEPTHWISE_CONV_2D':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
weights = tensors[op['inputs'][1]].transpose(1,2,3,0)
bias = tensors[op['inputs'][2]]
options = op['builtin_options']
output_tensor = tf.nn.depthwise_conv2d(
input_tensor,
weights,
strides=[1, options['stride_h'], options['stride_w'], 1],
padding=options['padding'],
# dilations=[1, options['dilation_h_factor'], options['dilation_w_factor'], 1],
name=output_detail['name'] + '/depthwise_conv2d')
output_tensor = tf.add(output_tensor, bias, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'MAX_POOL_2D':
input_tensor = tensors[op['inputs'][0]]
output_detail = interpreter._get_tensor_details(op['outputs'][0])
options = op['builtin_options']
output_tensor = tf.nn.max_pool(
input_tensor,
ksize=[
1, options['filter_height'], options['filter_width'], 1
],
strides=[1, options['stride_h'], options['stride_w'], 1],
padding=options['padding'],
name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'PAD':
input_tensor = tensors[op['inputs'][0]]
output_detail = interpreter._get_tensor_details(op['outputs'][0])
paddings_detail = interpreter._get_tensor_details(op['inputs'][1])
paddings_array = interpreter.get_tensor(paddings_detail['index'])
paddings = tf.Variable(
paddings_array, name=paddings_detail['name'])
output_tensor = tf.pad(
input_tensor, paddings, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'RELU':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
output_tensor = tf.nn.relu(input_tensor, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'PRELU':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
alpha_detail = interpreter._get_tensor_details(op['inputs'][1])
alpha_array = interpreter.get_tensor(alpha_detail['index'])
with tf.variable_scope(name_or_scope=output_detail['name']):
alphas = tf.Variable(alpha_array, name=alpha_detail['name'])
output_tensor = tf.maximum(alphas * input_tensor, input_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'RELU6':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
output_tensor = tf.nn.relu6(input_tensor, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'RESHAPE':
input_tensor = tensors[op['inputs'][0]]
output_detail = interpreter._get_tensor_details(op['outputs'][0])
options = op['builtin_options']
output_tensor = tf.reshape(input_tensor, options['new_shape'], name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'ADD':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor_0 = tensors[op['inputs'][0]]
try:
input_tensor_1 = tensors[op['inputs'][1]]
except:
param = interpreter._get_tensor_details(op['inputs'][1])
input_tensor_1 = interpreter.get_tensor(param['index'])
output_tensor = tf.add(input_tensor_0, input_tensor_1, name=output_detail['name'])
if output_detail['name'].split('/')[-1]=='Relu6':
output_tensor = tf.nn.relu6(output_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'CONCATENATION':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor_0 = tensors[op['inputs'][0]]
input_tensor_1 = tensors[op['inputs'][1]]
try:
input_tensor_2 = tensors[op['inputs'][2]]
options = op['builtin_options']
output_tensor = tf.concat([input_tensor_0, input_tensor_1, input_tensor_2],
options['axis'],
name=output_detail['name'])
except:
options = op['builtin_options']
output_tensor = tf.concat([input_tensor_0, input_tensor_1],
options['axis'],
name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'LOGISTIC':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
output_tensor = tf.math.sigmoid(input_tensor, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'TRANSPOSE_CONV':
input_tensor = tensors[op['inputs'][2]]
weights_detail = interpreter._get_tensor_details(op['inputs'][1])
output_shape_detail = interpreter._get_tensor_details(op['inputs'][0])
output_detail = interpreter._get_tensor_details(op['outputs'][0])
weights_array = interpreter.get_tensor(weights_detail['index'])
weights_array = np.transpose(weights_array, (1, 2, 0, 3))
output_shape_array = interpreter.get_tensor(output_shape_detail['index'])
weights = tf.Variable(weights_array, name=weights_detail['name'])
shape = tf.Variable(output_shape_array, name=output_shape_detail['name'])
options = op['builtin_options']
output_tensor = tf.nn.conv2d_transpose(input_tensor,
weights,
shape,
[1, options['stride_h'], options['stride_w'], 1],
padding=options['padding'],
name=output_detail['name'] + '/conv2d_transpose')
tensors[output_detail['index']] = output_tensor
elif op_type == 'MUL':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor_0 = tensors[op['inputs'][0]]
input_tensor_1 = None
try:
input_tensor_1 = tensors[op['inputs'][1]]
except:
param = interpreter._get_tensor_details(op['inputs'][1])
input_tensor_1 = interpreter.get_tensor(param['index'])
output_tensor = tf.multiply(input_tensor_0, input_tensor_1, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'HARD_SWISH':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
output_tensor = optimizing_hardswish_for_edgetpu(input_tensor, name=output_detail['name'])
tensors[output_detail['index']] = output_tensor
elif op_type == 'AVERAGE_POOL_2D':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
options = op['builtin_options']
pool_size = [options['filter_height'], options['filter_width']]
strides = [options['stride_h'], options['stride_w']]
padding = options['padding']
output_tensor = tf.keras.layers.AveragePooling2D(pool_size=pool_size,
strides=strides,
padding=padding,
name=output_detail['name'])(input_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'FULLY_CONNECTED':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
weights = tensors[op['inputs'][1]].transpose(1,0)
bias = tensors[op['inputs'][2]]
output_shape_detail = interpreter._get_tensor_details(op['inputs'][0])
output_shape_array = interpreter.get_tensor(output_shape_detail['index'])
output_tensor = tf.keras.layers.Dense(units=output_shape_array.shape[3],
use_bias=True,
kernel_initializer=tf.keras.initializers.Constant(weights),
bias_initializer=tf.keras.initializers.Constant(bias))(input_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'RESIZE_BILINEAR':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
input_tensor = tensors[op['inputs'][0]]
size_detail = interpreter._get_tensor_details(op['inputs'][1])
size = interpreter.get_tensor(size_detail['index'])
size_height = size[0]
size_width = size[1]
def upsampling2d_bilinear(x, size_height, size_width):
if optimizing_for_edgetpu_flg:
return tf.image.resize_bilinear(x, (size_height, size_width))
else:
return tfv2.image.resize(x, [size_height, size_width], method='bilinear')
output_tensor = tf.keras.layers.Lambda(upsampling2d_bilinear, arguments={'size_height': size_height, 'size_width': size_width})(input_tensor)
tensors[output_detail['index']] = output_tensor
elif op_type == 'DEQUANTIZE':
output_detail = interpreter._get_tensor_details(op['outputs'][0])
weights_detail = interpreter._get_tensor_details(op['inputs'][0])
weights = interpreter.get_tensor(weights_detail['index'])
output_tensor = weights.astype(np.float32)
tensors[output_detail['index']] = output_tensor
else:
raise ValueError(op_type)
# Convolution2DTransposeBias
input_tensor = tensors[241]
weights = np.load('weights/segment_Kernel').transpose(1,2,0,3).astype(np.float32)
bias = np.load('weights/segment_Bias').astype(np.float32)
custom_trans = tf.nn.conv2d_transpose(input=input_tensor,
filters=weights,
output_shape=[1, height, width, 2],
strides=[2, 2],
padding='SAME',
dilations=[1, 1])
output_tensor = tf.math.add(custom_trans, bias, name='segment')
tensors[999] = output_tensor | [
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facebookresearch/mmf | fb6fe390287e1da12c3bd28d4ab43c5f7dcdfc9f | tools/sweeps/lib/slurm.py | python | has_started | (save_dir) | return True | [] | def has_started(save_dir):
train_log = os.path.join(save_dir, "train.log")
if not os.path.exists(train_log):
return False
return True | [
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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/scapy/build/lib.linux-i686-2.7/scapy/utils6.py | python | in6_iseui64 | (x) | return x == eui64 | Return True if provided address has an interface identifier part
created in modified EUI-64 format (meaning it matches *::*:*ff:fe*:*).
Otherwise, False is returned. Address must be passed in printable
format. | Return True if provided address has an interface identifier part
created in modified EUI-64 format (meaning it matches *::*:*ff:fe*:*).
Otherwise, False is returned. Address must be passed in printable
format. | [
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"""
Return True if provided address has an interface identifier part
created in modified EUI-64 format (meaning it matches *::*:*ff:fe*:*).
Otherwise, False is returned. Address must be passed in printable
format.
"""
eui64 = inet_pton(socket.AF_INET6, '::ff:fe00:0')
x = in6_and(inet_pton(socket.AF_INET6, x), eui64)
return x == eui64 | [
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dieseldev/diesel | 8d48371fce0b79d6631053594bce06e4b9628499 | diesel/protocols/riak.py | python | RiakClient.get | (self, bucket, key) | Get the value of key from named bucket.
Returns a dictionary with a list of the content for the key
and the vector clock (vclock) for the key. | Get the value of key from named bucket. | [
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] | def get(self, bucket, key):
"""Get the value of key from named bucket.
Returns a dictionary with a list of the content for the key
and the vector clock (vclock) for the key.
"""
request = riak_palm.RpbGetReq(bucket=bucket, key=key)
self._send(request)
response = self._receive()
if response:
return _to_dict(response) | [
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BerkeleyAutomation/meshrender | 25b6fb711ef7a7871a5908459e6be5c76a04b631 | meshrender/trackball.py | python | Trackball.__init__ | (self, T_camera_world, size, scale,
target=np.array([0.0, 0.0, 0.0])) | Initialize a trackball with an initial camera-to-world pose
and the given parameters.
Parameters
----------
T_camera_world : autolab_core.RigidTransform
An initial camera-to-world pose for the trackball.
size : (float, float)
The width and height of the camera image in pixels.
scale : float
The diagonal of the scene's bounding box --
used for ensuring translation motions are sufficiently
fast for differently-sized scenes.
target : (3,) float
The center of the scene in world coordinates.
The trackball will revolve around this point. | Initialize a trackball with an initial camera-to-world pose
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] | def __init__(self, T_camera_world, size, scale,
target=np.array([0.0, 0.0, 0.0])):
"""Initialize a trackball with an initial camera-to-world pose
and the given parameters.
Parameters
----------
T_camera_world : autolab_core.RigidTransform
An initial camera-to-world pose for the trackball.
size : (float, float)
The width and height of the camera image in pixels.
scale : float
The diagonal of the scene's bounding box --
used for ensuring translation motions are sufficiently
fast for differently-sized scenes.
target : (3,) float
The center of the scene in world coordinates.
The trackball will revolve around this point.
"""
self._size = np.array(size)
self._scale = float(scale)
self._T_camera_world = T_camera_world
self._n_T_camera_world = T_camera_world
self._target = target
self._n_target = target
self._state = Trackball.STATE_ROTATE | [
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"... | https://github.com/BerkeleyAutomation/meshrender/blob/25b6fb711ef7a7871a5908459e6be5c76a04b631/meshrender/trackball.py#L15-L46 | ||
explosion/srsly | 8617ecc099d1f34a60117b5287bef5424ea2c837 | srsly/ruamel_yaml/comments.py | python | CommentedMap._unmerged_contains | (self, key) | return None | [] | def _unmerged_contains(self, key):
# type: (Any) -> Any
if key in self._ok:
return True
return None | [
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TheAlgorithms/Python | 9af2eef9b3761bf51580dedfb6fa7136ca0c5c2c | conversions/pressure_conversions.py | python | pressure_conversion | (value: float, from_type: str, to_type: str) | return (
value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
) | Conversion between pressure units.
>>> pressure_conversion(4, "atm", "pascal")
405300
>>> pressure_conversion(1, "pascal", "psi")
0.00014401981999999998
>>> pressure_conversion(1, "bar", "atm")
0.986923
>>> pressure_conversion(3, "kilopascal", "bar")
0.029999991892499998
>>> pressure_conversion(2, "megapascal", "psi")
290.074434314
>>> pressure_conversion(4, "psi", "torr")
206.85984
>>> pressure_conversion(1, "inHg", "atm")
0.0334211
>>> pressure_conversion(1, "torr", "psi")
0.019336718261000002
>>> pressure_conversion(4, "wrongUnit", "atm")
Traceback (most recent call last):
File "/usr/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest __main__.pressure_conversion[8]>", line 1, in <module>
pressure_conversion(4, "wrongUnit", "atm")
File "<string>", line 67, in pressure_conversion
ValueError: Invalid 'from_type' value: 'wrongUnit' Supported values are:
atm, pascal, bar, kilopascal, megapascal, psi, inHg, torr | Conversion between pressure units.
>>> pressure_conversion(4, "atm", "pascal")
405300
>>> pressure_conversion(1, "pascal", "psi")
0.00014401981999999998
>>> pressure_conversion(1, "bar", "atm")
0.986923
>>> pressure_conversion(3, "kilopascal", "bar")
0.029999991892499998
>>> pressure_conversion(2, "megapascal", "psi")
290.074434314
>>> pressure_conversion(4, "psi", "torr")
206.85984
>>> pressure_conversion(1, "inHg", "atm")
0.0334211
>>> pressure_conversion(1, "torr", "psi")
0.019336718261000002
>>> pressure_conversion(4, "wrongUnit", "atm")
Traceback (most recent call last):
File "/usr/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest __main__.pressure_conversion[8]>", line 1, in <module>
pressure_conversion(4, "wrongUnit", "atm")
File "<string>", line 67, in pressure_conversion
ValueError: Invalid 'from_type' value: 'wrongUnit' Supported values are:
atm, pascal, bar, kilopascal, megapascal, psi, inHg, torr | [
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"""
Conversion between pressure units.
>>> pressure_conversion(4, "atm", "pascal")
405300
>>> pressure_conversion(1, "pascal", "psi")
0.00014401981999999998
>>> pressure_conversion(1, "bar", "atm")
0.986923
>>> pressure_conversion(3, "kilopascal", "bar")
0.029999991892499998
>>> pressure_conversion(2, "megapascal", "psi")
290.074434314
>>> pressure_conversion(4, "psi", "torr")
206.85984
>>> pressure_conversion(1, "inHg", "atm")
0.0334211
>>> pressure_conversion(1, "torr", "psi")
0.019336718261000002
>>> pressure_conversion(4, "wrongUnit", "atm")
Traceback (most recent call last):
File "/usr/lib/python3.8/doctest.py", line 1336, in __run
exec(compile(example.source, filename, "single",
File "<doctest __main__.pressure_conversion[8]>", line 1, in <module>
pressure_conversion(4, "wrongUnit", "atm")
File "<string>", line 67, in pressure_conversion
ValueError: Invalid 'from_type' value: 'wrongUnit' Supported values are:
atm, pascal, bar, kilopascal, megapascal, psi, inHg, torr
"""
if from_type not in PRESSURE_CONVERSION:
raise ValueError(
f"Invalid 'from_type' value: {from_type!r} Supported values are:\n"
+ ", ".join(PRESSURE_CONVERSION)
)
if to_type not in PRESSURE_CONVERSION:
raise ValueError(
f"Invalid 'to_type' value: {to_type!r}. Supported values are:\n"
+ ", ".join(PRESSURE_CONVERSION)
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return (
value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
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nlloyd/SubliminalCollaborator | 5c619e17ddbe8acb9eea8996ec038169ddcd50a1 | libs/twisted/news/database.py | python | INewsStorage.articleRequest | (group, index, id = None) | Returns a deferred whose callback will be passed a file-like object
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kevinw/pyflakes | b08949f6cc123eb96f05051a1e29abc8457a6799 | pyflakes/checker.py | python | Checker.isDocstring | (self, node) | return isinstance(node, _ast.Str) or \
(isinstance(node, _ast.Expr) and
isinstance(node.value, _ast.Str)) | Determine if the given node is a docstring, as long as it is at the
correct place in the node tree. | Determine if the given node is a docstring, as long as it is at the
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Determine if the given node is a docstring, as long as it is at the
correct place in the node tree.
"""
return isinstance(node, _ast.Str) or \
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/numbers.py | python | Integral.__rxor__ | (self, other) | other ^ self | other ^ self | [
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jhpyle/docassemble | b90c84e57af59aa88b3404d44d0b125c70f832cc | docassemble_base/docassemble/base/util.py | python | DAFile.uses_acroform | (self) | return self.file_info.get('acroform', False) | Returns True if the file uses AcroForm, otherwise returns False. | Returns True if the file uses AcroForm, otherwise returns False. | [
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if not hasattr(self, 'file_info'):
self.retrieve()
return self.file_info.get('acroform', False) | [
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junsukchoe/ADL | dab2e78163bd96970ec9ae41de62835332dbf4fe | tensorpack/dataflow/imgaug/imgproc.py | python | Hue.__init__ | (self, range=(0, 180), rgb=True) | Args:
range(list or tuple): range from which the applied hue offset is selected (maximum [-90,90] or [0,180])
rgb (bool): whether input is RGB or BGR. | Args:
range(list or tuple): range from which the applied hue offset is selected (maximum [-90,90] or [0,180])
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rgb (bool): whether input is RGB or BGR.
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super(Hue, self).__init__()
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/provenance_event_dto.py | python | ProvenanceEventDTO.output_content_claim_container | (self, output_content_claim_container) | Sets the output_content_claim_container of this ProvenanceEventDTO.
The container in which the output content claim lives.
:param output_content_claim_container: The output_content_claim_container of this ProvenanceEventDTO.
:type: str | Sets the output_content_claim_container of this ProvenanceEventDTO.
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Sets the output_content_claim_container of this ProvenanceEventDTO.
The container in which the output content claim lives.
:param output_content_claim_container: The output_content_claim_container of this ProvenanceEventDTO.
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self._output_content_claim_container = output_content_claim_container | [
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mitogen-hq/mitogen | 5b505f524a7ae170fe68613841ab92b299613d3f | ansible_mitogen/connection.py | python | optional_int | (value) | Convert `value` to an integer if it is not :data:`None`, otherwise return
:data:`None`. | Convert `value` to an integer if it is not :data:`None`, otherwise return
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almarklein/visvis | 766ed97767b44a55a6ff72c742d7385e074d3d55 | wobjects/sliceTextures.py | python | SliceTexture._GetData | (self) | return self._dataRef3D | _GetData()
Get a reference to the raw data. For internal use. | _GetData()
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""" _GetData()
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OpenEndedGroup/Field | 4f7c8edfb01bb0ccc927b78d3c500f018a4ae37c | Contents/lib/python/cgitb.py | python | reset | () | return '''<!--: spam
Content-Type: text/html
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> -->
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> --> -->
</font> </font> </font> </script> </object> </blockquote> </pre>
</table> </table> </table> </table> </table> </font> </font> </font>''' | Return a string that resets the CGI and browser to a known state. | Return a string that resets the CGI and browser to a known state. | [
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"""Return a string that resets the CGI and browser to a known state."""
return '''<!--: spam
Content-Type: text/html
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> -->
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> --> -->
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BichenWuUCB/SqueezeSeg | 6dc53c8849eed91a4463ca22371474caeaab5827 | src/nn_skeleton.py | python | ModelSkeleton._bilateral_filter_layer | (
self, layer_name, inputs, thetas=[0.9, 0.01], sizes=[3, 5], stride=1,
padding='SAME') | return out | Computing pairwise energy with a bilateral filter for CRF.
Args:
layer_name: layer name
inputs: input tensor with shape [batch_size, zenith, azimuth, 2] where the
last 2 elements are intensity and range of a lidar point.
thetas: theta parameter for bilateral filter.
sizes: filter size for zenith and azimuth dimension.
strides: kernel strides.
padding: padding.
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out: bilateral filter weight output with size
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[b, z, a, :, cls] represents filter weights around the center position
for each class. | Computing pairwise energy with a bilateral filter for CRF. | [
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self, layer_name, inputs, thetas=[0.9, 0.01], sizes=[3, 5], stride=1,
padding='SAME'):
"""Computing pairwise energy with a bilateral filter for CRF.
Args:
layer_name: layer name
inputs: input tensor with shape [batch_size, zenith, azimuth, 2] where the
last 2 elements are intensity and range of a lidar point.
thetas: theta parameter for bilateral filter.
sizes: filter size for zenith and azimuth dimension.
strides: kernel strides.
padding: padding.
Returns:
out: bilateral filter weight output with size
[batch_size, zenith, azimuth, sizes[0]*sizes[1]-1, num_class]. Each
[b, z, a, :, cls] represents filter weights around the center position
for each class.
"""
assert padding == 'SAME', 'currently only supports "SAME" padding stategy'
assert stride == 1, 'currently only supports striding of 1'
assert sizes[0] % 2 == 1 and sizes[1] % 2 == 1, \
'Currently only support odd filter size.'
mc = self.mc
theta_a, theta_r = thetas
size_z, size_a = sizes
pad_z, pad_a = size_z//2, size_a//2
half_filter_dim = (size_z*size_a)//2
batch, zenith, azimuth, in_channel = inputs.shape.as_list()
# assert in_channel == 1, 'Only support input channel == 1'
with tf.variable_scope(layer_name) as scope:
condensing_kernel = tf.constant(
util.condensing_matrix(size_z, size_a, in_channel),
dtype=tf.float32,
name='condensing_kernel'
)
condensed_input = tf.nn.conv2d(
inputs, condensing_kernel, [1, 1, stride, 1], padding=padding,
name='condensed_input'
)
# diff_intensity = tf.reshape(
# inputs[:, :, :], [batch, zenith, azimuth, 1]) \
# - condensed_input[:, :, :, ::in_channel]
diff_x = tf.reshape(
inputs[:, :, :, 0], [batch, zenith, azimuth, 1]) \
- condensed_input[:, :, :, 0::in_channel]
diff_y = tf.reshape(
inputs[:, :, :, 1], [batch, zenith, azimuth, 1]) \
- condensed_input[:, :, :, 1::in_channel]
diff_z = tf.reshape(
inputs[:, :, :, 2], [batch, zenith, azimuth, 1]) \
- condensed_input[:, :, :, 2::in_channel]
bi_filters = []
for cls in range(mc.NUM_CLASS):
theta_a = mc.BILATERAL_THETA_A[cls]
theta_r = mc.BILATERAL_THETA_R[cls]
bi_filter = tf.exp(-(diff_x**2+diff_y**2+diff_z**2)/2/theta_r**2)
bi_filters.append(bi_filter)
out = tf.transpose(
tf.stack(bi_filters),
[1, 2, 3, 4, 0],
name='bilateral_filter_weights'
)
return out | [
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benedekrozemberczki/GEMSEC | c023122bdafe88278cdbd24b7fcf9dafe8e95b34 | src/calculation_helper.py | python | unit | (g, node_1, node_2) | return 1 | Function to calculate the "unit" weight.
:param g: NX graph.
:param node_1: Node 1. of a pair.
:param node_2: Node 2. of a pair. | Function to calculate the "unit" weight.
:param g: NX graph.
:param node_1: Node 1. of a pair.
:param node_2: Node 2. of a pair. | [
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huawei-noah/CV-Backbones | 03e8cdfe92494a55ddfb11cc875ff2e1c33f91da | tnt_pytorch/pyramid_tnt.py | python | Attention.__init__ | (self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1) | [] | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.pool = nn.AvgPool2d(sr_ratio, stride=sr_ratio)
self.linear = nn.Linear(dim, dim)
self.norm = nn.LayerNorm(dim) | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/utils/data_structures/orderedset.py | python | OrderedSet._check_index | (self, idx) | return idx | Check the given index; if it is in the range of the ordered set, and if it is negative return the
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"""
if not isinstance(idx, int):
raise TypeError("idx should be an integer.")
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return KeyError(idx)
if idx < 0:
idx = len(self._list) + idx
return idx | [
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spulec/moto | a688c0032596a7dfef122b69a08f2bec3be2e481 | moto/config/models.py | python | validate_tag_key | (tag_key, exception_param="tags.X.member.key") | Validates the tag key.
:param tag_key: The tag key to check against.
:param exception_param: The exception parameter to send over to help
format the message. This is to reflect
the difference between the tag and untag APIs.
:return: | Validates the tag key. | [
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:param tag_key: The tag key to check against.
:param exception_param: The exception parameter to send over to help
format the message. This is to reflect
the difference between the tag and untag APIs.
:return:
"""
# Validate that the key length is correct:
if len(tag_key) > 128:
raise TagKeyTooBig(tag_key, param=exception_param)
# Validate that the tag key fits the proper Regex:
# [\w\s_.:/=+\-@]+ SHOULD be the same as the Java regex on the AWS
# documentation: [\p{L}\p{Z}\p{N}_.:/=+\-@]+
match = re.findall(r"[\w\s_.:/=+\-@]+", tag_key)
# Kudos if you can come up with a better way of doing a global search :)
if not match or len(match[0]) < len(tag_key):
raise InvalidTagCharacters(tag_key, param=exception_param) | [
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princewen/leetcode_python | 79e6e760e4d81824c96903e6c996630c24d01932 | sort_by_leetcode/math/easy/7. Reverse Integer.py | python | Solution.reverse | (self, x) | return res if x > 0 else -res | :type x: int
:rtype: int | :type x: int
:rtype: int | [
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"""
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n = x if x > 0 else -x
res = 0
while n:
res = res * 10 + n % 10
n = n / 10
if res > 0x7fffffff:
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return res if x > 0 else -res | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/rpyc/experimental/splitbrain.py | python | RemoteModule.__delattr__ | (self, name, val) | return setattr(self.__currmod__, name, val) | [] | def __delattr__(self, name, val):
return setattr(self.__currmod__, name, val) | [
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ipython/ipython | c0abea7a6dfe52c1f74c9d0387d4accadba7cc14 | IPython/terminal/debugger.py | python | TerminalPdb.cmdloop | (self, intro=None) | Repeatedly issue a prompt, accept input, parse an initial prefix
off the received input, and dispatch to action methods, passing them
the remainder of the line as argument.
override the same methods from cmd.Cmd to provide prompt toolkit replacement. | Repeatedly issue a prompt, accept input, parse an initial prefix
off the received input, and dispatch to action methods, passing them
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"""Repeatedly issue a prompt, accept input, parse an initial prefix
off the received input, and dispatch to action methods, passing them
the remainder of the line as argument.
override the same methods from cmd.Cmd to provide prompt toolkit replacement.
"""
if not self.use_rawinput:
raise ValueError('Sorry ipdb does not support use_rawinput=False')
# In order to make sure that prompt, which uses asyncio doesn't
# interfere with applications in which it's used, we always run the
# prompt itself in a different thread (we can't start an event loop
# within an event loop). This new thread won't have any event loop
# running, and here we run our prompt-loop.
self.preloop()
try:
if intro is not None:
self.intro = intro
if self.intro:
print(self.intro, file=self.stdout)
stop = None
while not stop:
if self.cmdqueue:
line = self.cmdqueue.pop(0)
else:
self._ptcomp.ipy_completer.namespace = self.curframe_locals
self._ptcomp.ipy_completer.global_namespace = self.curframe.f_globals
# Run the prompt in a different thread.
try:
line = self.thread_executor.submit(self.pt_app.prompt).result()
except EOFError:
line = "EOF"
line = self.precmd(line)
stop = self.onecmd(line)
stop = self.postcmd(stop, line)
self.postloop()
except Exception:
raise | [
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vmware/pyvcloud | d72c615fa41b8ea5ab049a929e18d8ba6460fc59 | pyvcloud/vcd/client.py | python | find_link | (resource, rel, media_type, fail_if_absent=True, name=None) | Returns the link of the specified rel and type in the resource.
:param lxml.objectify.ObjectifiedElement resource: the resource with the
link.
:param RelationType rel: the rel of the desired link.
:param str media_type: media type of content.
:param bool fail_if_absent: if True raise an exception if there's
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:return: an object containing Link XML element representing the desired
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:rtype: lxml.objectify.ObjectifiedElement
:raises MissingLinkException: if no link of the specified rel and media
type is found
:raises MultipleLinksException: if multiple links of the specified rel
and media type are found | Returns the link of the specified rel and type in the resource. | [
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"""Returns the link of the specified rel and type in the resource.
:param lxml.objectify.ObjectifiedElement resource: the resource with the
link.
:param RelationType rel: the rel of the desired link.
:param str media_type: media type of content.
:param bool fail_if_absent: if True raise an exception if there's
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:return: an object containing Link XML element representing the desired
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:rtype: lxml.objectify.ObjectifiedElement
:raises MissingLinkException: if no link of the specified rel and media
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:raises MultipleLinksException: if multiple links of the specified rel
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"""
links = get_links(resource, rel, media_type, name)
num_links = len(links)
if num_links == 0:
if fail_if_absent:
raise MissingLinkException(resource.get('href'), rel, media_type)
else:
return None
elif num_links == 1:
return links[0]
else:
raise MultipleLinksException(resource.get('href'), rel, media_type) | [
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jupyter/nbgrader | 1ae2886e4e734554d8667c6e86861e83cc161451 | nbgrader/api.py | python | Gradebook.find_submission | (self, assignment: str, student: str) | return submission | Find a student's submission for a given assignment.
Parameters
----------
assignment : string
the name of an assignment
student : string
the unique id of a student
Returns
-------
submission : :class:`~nbgrader.api.SubmittedAssignment` | Find a student's submission for a given assignment. | [
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"""Find a student's submission for a given assignment.
Parameters
----------
assignment : string
the name of an assignment
student : string
the unique id of a student
Returns
-------
submission : :class:`~nbgrader.api.SubmittedAssignment`
"""
try:
submission = self.db.query(SubmittedAssignment)\
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except NoResultFound:
raise MissingEntry("No such submission: {} for {}".format(
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return submission | [
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ray-project/ray | 703c1610348615dcb8c2d141a0c46675084660f5 | rllib/examples/models/rnn_spy_model.py | python | SpyLayer.spy | (inputs, seq_lens, h_in, c_in, h_out, c_out) | return SpyLayer.output | The actual spy operation: Store inputs in internal_kv. | The actual spy operation: Store inputs in internal_kv. | [
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"""The actual spy operation: Store inputs in internal_kv."""
if len(inputs) == 1:
# don't capture inference inputs
return SpyLayer.output
# TF runs this function in an isolated context, so we have to use
# redis to communicate back to our suite
ray.experimental.internal_kv._internal_kv_put(
"rnn_spy_in_{}".format(RNNSpyModel.capture_index),
pickle.dumps({
"sequences": inputs,
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RNNSpyModel.capture_index += 1
return SpyLayer.output | [
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vstinner/hachoir | 8fb142ed4ab8e3603e5b613a75714f79b372b3fe | hachoir/parser/archive/rar.py | python | formatRARVersion | (field) | return "%u.%u" % divmod(field.value, 10) | Decodes the RAR version stored on 1 byte | Decodes the RAR version stored on 1 byte | [
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"""
Decodes the RAR version stored on 1 byte
"""
return "%u.%u" % divmod(field.value, 10) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/path.py | python | Path.circle | (cls, center=(0., 0.), radius=1., readonly=False) | return Path(vertices * radius + center, codes, readonly=readonly) | Return a Path representing a circle of a given radius and center.
Parameters
----------
center : pair of floats
The center of the circle. Default ``(0, 0)``.
radius : float
The radius of the circle. Default is 1.
readonly : bool
Whether the created path should have the "readonly" argument
set when creating the Path instance.
Notes
-----
The circle is approximated using cubic Bezier curves. This
uses 8 splines around the circle using the approach presented
here:
Lancaster, Don. `Approximating a Circle or an Ellipse Using Four
Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_. | Return a Path representing a circle of a given radius and center. | [
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] | def circle(cls, center=(0., 0.), radius=1., readonly=False):
"""
Return a Path representing a circle of a given radius and center.
Parameters
----------
center : pair of floats
The center of the circle. Default ``(0, 0)``.
radius : float
The radius of the circle. Default is 1.
readonly : bool
Whether the created path should have the "readonly" argument
set when creating the Path instance.
Notes
-----
The circle is approximated using cubic Bezier curves. This
uses 8 splines around the circle using the approach presented
here:
Lancaster, Don. `Approximating a Circle or an Ellipse Using Four
Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_.
"""
MAGIC = 0.2652031
SQRTHALF = np.sqrt(0.5)
MAGIC45 = SQRTHALF * MAGIC
vertices = np.array([[0.0, -1.0],
[MAGIC, -1.0],
[SQRTHALF-MAGIC45, -SQRTHALF-MAGIC45],
[SQRTHALF, -SQRTHALF],
[SQRTHALF+MAGIC45, -SQRTHALF+MAGIC45],
[1.0, -MAGIC],
[1.0, 0.0],
[1.0, MAGIC],
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[SQRTHALF, SQRTHALF],
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[0.0, 1.0],
[-MAGIC, 1.0],
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[-1.0, -MAGIC],
[-SQRTHALF-MAGIC45, -SQRTHALF+MAGIC45],
[-SQRTHALF, -SQRTHALF],
[-SQRTHALF+MAGIC45, -SQRTHALF-MAGIC45],
[-MAGIC, -1.0],
[0.0, -1.0],
[0.0, -1.0]],
dtype=float)
codes = [cls.CURVE4] * 26
codes[0] = cls.MOVETO
codes[-1] = cls.CLOSEPOLY
return Path(vertices * radius + center, codes, readonly=readonly) | [
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"SQRTHA... | https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/path.py#L717-L785 | |
bjmayor/hacker | e3ce2ad74839c2733b27dac6c0f495e0743e1866 | venv/lib/python3.5/site-packages/pip/utils/outdated.py | python | pip_version_check | (session) | Check for an update for pip.
Limit the frequency of checks to once per week. State is stored either in
the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix
of the pip script path. | Check for an update for pip. | [
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] | def pip_version_check(session):
"""Check for an update for pip.
Limit the frequency of checks to once per week. State is stored either in
the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix
of the pip script path.
"""
installed_version = get_installed_version("pip")
if installed_version is None:
return
pip_version = packaging_version.parse(installed_version)
pypi_version = None
try:
state = load_selfcheck_statefile()
current_time = datetime.datetime.utcnow()
# Determine if we need to refresh the state
if "last_check" in state.state and "pypi_version" in state.state:
last_check = datetime.datetime.strptime(
state.state["last_check"],
SELFCHECK_DATE_FMT
)
if total_seconds(current_time - last_check) < 7 * 24 * 60 * 60:
pypi_version = state.state["pypi_version"]
# Refresh the version if we need to or just see if we need to warn
if pypi_version is None:
resp = session.get(
PyPI.pip_json_url,
headers={"Accept": "application/json"},
)
resp.raise_for_status()
pypi_version = [
v for v in sorted(
list(resp.json()["releases"]),
key=packaging_version.parse,
)
if not packaging_version.parse(v).is_prerelease
][-1]
# save that we've performed a check
state.save(pypi_version, current_time)
remote_version = packaging_version.parse(pypi_version)
# Determine if our pypi_version is older
if (pip_version < remote_version and
pip_version.base_version != remote_version.base_version):
# Advise "python -m pip" on Windows to avoid issues
# with overwriting pip.exe.
if WINDOWS:
pip_cmd = "python -m pip"
else:
pip_cmd = "pip"
logger.warning(
"You are using pip version %s, however version %s is "
"available.\nYou should consider upgrading via the "
"'%s install --upgrade pip' command.",
pip_version, pypi_version, pip_cmd
)
except Exception:
logger.debug(
"There was an error checking the latest version of pip",
exc_info=True,
) | [
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getpatchwork/patchwork | 60a7b11d12f9e1a6bd08d787d37066c8d89a52ae | patchwork/management/commands/cron.py | python | Command.handle | (self, *args, **kwargs) | [] | def handle(self, *args, **kwargs):
errors = send_notifications()
for (recipient, error) in errors:
self.stderr.write("Failed sending to %s: %s" %
(recipient.email, error))
expire_notifications() | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/urllib/parse.py | python | splitattr | (url) | return words[0], words[1:] | splitattr('/path;attr1=value1;attr2=value2;...') ->
'/path', ['attr1=value1', 'attr2=value2', ...]. | splitattr('/path;attr1=value1;attr2=value2;...') ->
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"""splitattr('/path;attr1=value1;attr2=value2;...') ->
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words = url.split(';')
return words[0], words[1:] | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_node_system_info.py | python | V1NodeSystemInfo.os_image | (self, os_image) | Sets the os_image of this V1NodeSystemInfo.
OS Image reported by the node from /etc/os-release (e.g. Debian GNU/Linux 7 (wheezy)). # noqa: E501
:param os_image: The os_image of this V1NodeSystemInfo. # noqa: E501
:type: str | Sets the os_image of this V1NodeSystemInfo. | [
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] | def os_image(self, os_image):
"""Sets the os_image of this V1NodeSystemInfo.
OS Image reported by the node from /etc/os-release (e.g. Debian GNU/Linux 7 (wheezy)). # noqa: E501
:param os_image: The os_image of this V1NodeSystemInfo. # noqa: E501
:type: str
"""
if self.local_vars_configuration.client_side_validation and os_image is None: # noqa: E501
raise ValueError("Invalid value for `os_image`, must not be `None`") # noqa: E501
self._os_image = os_image | [
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tensorflow/estimator | edb6e18703a0fa00182bcc72a056da6f5ce45e70 | tensorflow_estimator/python/estimator/tpu/util.py | python | parse_iterations_per_loop | (iterations_per_loop) | return IterationsPerLoopCounter(value, unit_value) | Parses the `iterations_per_loop` value.
The parser expects the value of the `iterations_per_loop` value to be a
positive integer value with unit:`count` or time-based value `<N><s|m|h>`
where <N> is any positive integer and `s`, `m`, `h` are unit of time in
seconds, minutes, hours respectively. Examples of valid values: `3600s`, `60m`
, `1h`.
Args:
iterations_per_loop: Number of iterations or time alloted to spend on per
device loop.
Returns:
A dictionary of `value` and `unit`. The `unit` value can be either a raw
`count`, or time in `seconds`.
{
"value": <positive-integer>,
"unit": <unit: `count` | `seconds`>
} | Parses the `iterations_per_loop` value. | [
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] | def parse_iterations_per_loop(iterations_per_loop):
"""Parses the `iterations_per_loop` value.
The parser expects the value of the `iterations_per_loop` value to be a
positive integer value with unit:`count` or time-based value `<N><s|m|h>`
where <N> is any positive integer and `s`, `m`, `h` are unit of time in
seconds, minutes, hours respectively. Examples of valid values: `3600s`, `60m`
, `1h`.
Args:
iterations_per_loop: Number of iterations or time alloted to spend on per
device loop.
Returns:
A dictionary of `value` and `unit`. The `unit` value can be either a raw
`count`, or time in `seconds`.
{
"value": <positive-integer>,
"unit": <unit: `count` | `seconds`>
}
"""
m = _ITERATIONS_PER_LOOP_VALUE_REGEX.match(str(iterations_per_loop))
if m is None:
raise ValueError(
'Invalid TPUConfig `iterations_per_loop` value. Value must be positive '
'integer value or time-based value `<N><s|m|h>` where <N> is any'
'positive integer and `s`, `m`, `h` are unit of time in seconds, '
'minutes, hours respectively. Examples of valid values: `3600s`, `60m`,'
' `1h`.')
unit_value = 'seconds' if m.group('suffix') in ['h', 'm', 's'] else 'count'
value = int(m.group('value'))
if m.group('suffix') == 'm':
value *= 60
elif m.group('suffix') == 'h':
value *= 3600
return IterationsPerLoopCounter(value, unit_value) | [
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polyaxon/polyaxon | e28d82051c2b61a84d06ce4d2388a40fc8565469 | src/core/polyaxon/cli/artifacts.py | python | delete | (ctx, project, version) | Delete a artifact version.
\b
$ polyaxon artifacts delete // delete `latest` in current project
\b
$ polyaxon artifacts delete --project=my-project --version=test-version
\b
$ polyaxon artifacts get -p owner/my-project -ver rc12 | Delete a artifact version.
\b
$ polyaxon artifacts delete // delete `latest` in current project | [
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"""Delete a artifact version.
\b
$ polyaxon artifacts delete // delete `latest` in current project
\b
$ polyaxon artifacts delete --project=my-project --version=test-version
\b
$ polyaxon artifacts get -p owner/my-project -ver rc12
"""
version = version or ctx.obj.get("version") or "latest"
owner, project_name = get_project_or_local(
project or ctx.obj.get("project"), is_cli=True
)
delete_project_version(
owner=owner,
project_name=project_name,
kind=V1ProjectVersionKind.ARTIFACT,
version=version,
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/importlib/_bootstrap_external.py | python | cache_from_source | (path, debug_override=None, *, optimization=None) | return _path_join(head, _PYCACHE, filename) | Given the path to a .py file, return the path to its .pyc file.
The .py file does not need to exist; this simply returns the path to the
.pyc file calculated as if the .py file were imported.
The 'optimization' parameter controls the presumed optimization level of
the bytecode file. If 'optimization' is not None, the string representation
of the argument is taken and verified to be alphanumeric (else ValueError
is raised).
The debug_override parameter is deprecated. If debug_override is not None,
a True value is the same as setting 'optimization' to the empty string
while a False value is equivalent to setting 'optimization' to '1'.
If sys.implementation.cache_tag is None then NotImplementedError is raised. | Given the path to a .py file, return the path to its .pyc file. | [
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] | def cache_from_source(path, debug_override=None, *, optimization=None):
"""Given the path to a .py file, return the path to its .pyc file.
The .py file does not need to exist; this simply returns the path to the
.pyc file calculated as if the .py file were imported.
The 'optimization' parameter controls the presumed optimization level of
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The debug_override parameter is deprecated. If debug_override is not None,
a True value is the same as setting 'optimization' to the empty string
while a False value is equivalent to setting 'optimization' to '1'.
If sys.implementation.cache_tag is None then NotImplementedError is raised.
"""
if debug_override is not None:
_warnings.warn('the debug_override parameter is deprecated; use '
"'optimization' instead", DeprecationWarning)
if optimization is not None:
message = 'debug_override or optimization must be set to None'
raise TypeError(message)
optimization = '' if debug_override else 1
path = _os.fspath(path)
head, tail = _path_split(path)
base, sep, rest = tail.rpartition('.')
tag = sys.implementation.cache_tag
if tag is None:
raise NotImplementedError('sys.implementation.cache_tag is None')
almost_filename = ''.join([(base if base else rest), sep, tag])
if optimization is None:
if sys.flags.optimize == 0:
optimization = ''
else:
optimization = sys.flags.optimize
optimization = str(optimization)
if optimization != '':
if not optimization.isalnum():
raise ValueError('{!r} is not alphanumeric'.format(optimization))
almost_filename = '{}.{}{}'.format(almost_filename, _OPT, optimization)
filename = almost_filename + BYTECODE_SUFFIXES[0]
if sys.pycache_prefix is not None:
# We need an absolute path to the py file to avoid the possibility of
# collisions within sys.pycache_prefix, if someone has two different
# `foo/bar.py` on their system and they import both of them using the
# same sys.pycache_prefix. Let's say sys.pycache_prefix is
# `C:\Bytecode`; the idea here is that if we get `Foo\Bar`, we first
# make it absolute (`C:\Somewhere\Foo\Bar`), then make it root-relative
# (`Somewhere\Foo\Bar`), so we end up placing the bytecode file in an
# unambiguous `C:\Bytecode\Somewhere\Foo\Bar\`.
if not _path_isabs(head):
head = _path_join(_os.getcwd(), head)
# Strip initial drive from a Windows path. We know we have an absolute
# path here, so the second part of the check rules out a POSIX path that
# happens to contain a colon at the second character.
if head[1] == ':' and head[0] not in path_separators:
head = head[2:]
# Strip initial path separator from `head` to complete the conversion
# back to a root-relative path before joining.
return _path_join(
sys.pycache_prefix,
head.lstrip(path_separators),
filename,
)
return _path_join(head, _PYCACHE, filename) | [
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tdamdouni/Pythonista | 3e082d53b6b9b501a3c8cf3251a8ad4c8be9c2ad | weather/WeatherAnywhereScene-coomlata.py | python | SceneViewer.format_plot_weather | (self, forecast) | return twf, icon_y, y1_y2 | If this is Pythonista 2, and an iPhone 6+ or better there is more screen to work with, as Pythonista recognizes the native screen eesolutions of the iOS device being used. | If this is Pythonista 2, and an iPhone 6+ or better there is more screen to work with, as Pythonista recognizes the native screen eesolutions of the iOS device being used. | [
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'''
If this is Pythonista 2, and an iPhone 6+ or better there is more screen to work with, as Pythonista recognizes the native screen eesolutions of the iOS device being used.
'''
if py_ver == '2' and is_P6:
# Variables to aid in plotting coordinates for text & icons
wrap_len = 75
icon_y = [-245]
y1_y2 = [-245]
else:
wrap_len = 58
icon_y = [-410]
y1_y2 = [-410]
new_line = count = z = blanks = x = 0
twf = []
blank_line = []
section_lines = []
forecast = forecast.split('\n')
# Loop through each forecast line
for line in forecast:
# Look for long lines
if len(line) > wrap_len and line.find('Precip:') == -1:
# Estimate how many wrapped lines here
new_line = int((len(line)/wrap_len))
# Wrap the text
line = textwrap.fill(line,width = wrap_len)
# Append everything to a new list
twf.append(line)
# Get new line count after added wrap
count += 1 + new_line
# Clear value for next computation
new_line = 0
# Blank lines
if not line:
# Record line #
blank_line.append(count)
# If 2 line numbers exist in list
if len(blank_line) == 2:
'''
Subtract the difference between the 2 blank
lines, which gives you the number of lines
in a forecast section, multiply that by
11.35, which gives you an equivalent y
point to match the end line number of the
section, & subtract that from the icon y
anchor point on screen to get the approx
point to move the icon on the y axis to
align it with it's forecast text. The point
is then stored in a list to use as one of
the y coordinates for the icons in this
forecast.
'''
icon_y.append(icon_y[z] - ((blank_line[1] - blank_line[0]) * 11.35))
# Clear list for next section of text
blank_line = []
# Store blank line number that starts next forecast section
blank_line.append(count)
# Increment icon_y list counter
z += 1
# Increment blank line counter
blanks += 1
'''
Odd numbered blank lines indicate the end of
one forecast date section & the start of
another so we use the same process as above
to determine the y points to draw section
lines on the screen.
'''
if self.is_odd(blanks):
section_lines.append(count)
if len(section_lines) == 2:
line_factor = 11.5
num_lines = section_lines[1] - section_lines[0]
#print num_lines
if num_lines >= 12:
line_factor = 11.63
y1_y2.append(y1_y2[x] - (num_lines) * line_factor)
section_lines = []
section_lines.append(count)
x += 1
twf = '\n'.join(twf)
'''
Replace anchor point y value with y point for the
icon that goes with the current weather section.
'''
if py_ver == '2' and is_P6:
icon_y = [110 if x == -245 else x for x in icon_y]
else:
icon_y = [35 if x == -410 else x for x in icon_y]
return twf, icon_y, y1_y2 | [
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pyparsing/pyparsing | 1ccf846394a055924b810faaf9628dac53633848 | examples/pymicko.py | python | SymbolTable.insert_constant | (self, cname, ctype) | return index | Inserts a constant (or returns index if the constant already exists)
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"""
Inserts a constant (or returns index if the constant already exists)
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"""
index = self.lookup_symbol(cname, stype=ctype)
if index == None:
num = int(cname)
if ctype == SharedData.TYPES.INT:
if (num < SharedData.MIN_INT) or (num > SharedData.MAX_INT):
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elif ctype == SharedData.TYPES.UNSIGNED:
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raise SemanticException(
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index = self.insert_symbol(cname, SharedData.KINDS.CONSTANT, ctype)
return index | [
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CGCookie/retopoflow | 3d8b3a47d1d661f99ab0aeb21d31370bf15de35e | retopoflow/rf/rf_target.py | python | RetopoFlow_Target.select_edge_loop | (self, edge, only=True, **kwargs) | [] | def select_edge_loop(self, edge, only=True, **kwargs):
eloop,connected = self.get_edge_loop(edge)
self.rftarget.select(eloop, only=only, **kwargs) | [
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",... | https://github.com/CGCookie/retopoflow/blob/3d8b3a47d1d661f99ab0aeb21d31370bf15de35e/retopoflow/rf/rf_target.py#L741-L743 | ||||
uber-research/UPSNet | aa8434e5a721ed217849607815304f68dfd7720a | lib/nn/optimizer.py | python | SGD.step | (self, lr, closure=None) | return loss | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss. | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss. | [
"Performs",
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"single",
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"model",
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"."
] | def step(self, lr, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = p.data.new().resize_as_(p.data).zero_()
buf.mul_(momentum).add_(group['lr'] * lr, d_p)
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(group['lr'] * lr, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
p.data.add_(-1, d_p)
return loss | [
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... | https://github.com/uber-research/UPSNet/blob/aa8434e5a721ed217849607815304f68dfd7720a/lib/nn/optimizer.py#L70-L106 | |
CGCookie/retopoflow | 3d8b3a47d1d661f99ab0aeb21d31370bf15de35e | addon_common/common/utils.py | python | kwargopts | (kwargs, defvals=None, **mykwargs) | return factory() | [] | def kwargopts(kwargs, defvals=None, **mykwargs):
opts = defvals.copy() if defvals else {}
opts.update(mykwargs)
opts.update(kwargs)
if 'opts' in kwargs: opts.update(opts['opts'])
def factory():
class Opts():
''' pretend to be a dictionary, but also add . access fns '''
def __init__(self):
self.touched = set()
def __getattr__(self, opt):
self.touched.add(opt)
return opts[opt]
def __getitem__(self, opt):
self.touched.add(opt)
return opts[opt]
def __len__(self): return len(opts)
def has_key(self, opt): return opt in opts
def keys(self): return opts.keys()
def values(self): return opts.values()
def items(self): return opts.items()
def __contains__(self, opt): return opt in opts
def __iter__(self): return iter(opts)
def print_untouched(self):
print('untouched: %s' % str(set(opts.keys()) - self.touched))
def pass_through(self, *args):
return {key:self[key] for key in args}
return Opts()
return factory() | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-0.96/django/core/servers/basehttp.py | python | ServerHandler.start_response | (self, status, headers,exc_info=None) | return self.write | start_response()' callable as specified by PEP 333 | start_response()' callable as specified by PEP 333 | [
"start_response",
"()",
"callable",
"as",
"specified",
"by",
"PEP",
"333"
] | def start_response(self, status, headers,exc_info=None):
"""'start_response()' callable as specified by PEP 333"""
if exc_info:
try:
if self.headers_sent:
# Re-raise original exception if headers sent
raise exc_info[0], exc_info[1], exc_info[2]
finally:
exc_info = None # avoid dangling circular ref
elif self.headers is not None:
raise AssertionError("Headers already set!")
assert type(status) is StringType,"Status must be a string"
assert len(status)>=4,"Status must be at least 4 characters"
assert int(status[:3]),"Status message must begin w/3-digit code"
assert status[3]==" ", "Status message must have a space after code"
if __debug__:
for name,val in headers:
assert type(name) is StringType,"Header names must be strings"
assert type(val) is StringType,"Header values must be strings"
assert not is_hop_by_hop(name),"Hop-by-hop headers not allowed"
self.status = status
self.headers = self.headers_class(headers)
return self.write | [
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"headers_sent",
":",
"# Re-raise original exception if headers sent",
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"[",
"... | https://github.com/AppScale/gts/blob/46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9/AppServer/lib/django-0.96/django/core/servers/basehttp.py#L342-L366 | |
snare/binjatron | 4bbff5c4fa489a6718037126e2ea46816875e268 | __init__.py | python | _get_function | (view, address) | return func | [] | def _get_function(view, address):
func = view.get_function_at(address)
if func is None:
return view.get_function_at(view.get_previous_function_start_before(address))
return func | [
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"get_previous_function_start_before",
... | https://github.com/snare/binjatron/blob/4bbff5c4fa489a6718037126e2ea46816875e268/__init__.py#L42-L46 | |||
9miao/Firefly | fd2795b8c26de6ab63bbec23d11f18c3dfb39a50 | firefly/dbentrust/util.py | python | DeleteFromDB | (tablename,props) | return bool(count) | 从数据库中删除 | 从数据库中删除 | [
"从数据库中删除"
] | def DeleteFromDB(tablename,props):
'''从数据库中删除
'''
prers = FormatCondition(props)
sql = """DELETE FROM %s WHERE %s ;"""%(tablename,prers)
conn = dbpool.connection()
cursor = conn.cursor()
count = 0
try:
count = cursor.execute(sql)
conn.commit()
except Exception,e:
log.err(e)
log.err(sql)
cursor.close()
conn.close()
return bool(count) | [
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"%",
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"connection",
"(",
... | https://github.com/9miao/Firefly/blob/fd2795b8c26de6ab63bbec23d11f18c3dfb39a50/firefly/dbentrust/util.py#L119-L135 | |
roclark/sportsipy | c19f545d3376d62ded6304b137dc69238ac620a9 | sportsipy/fb/roster.py | python | SquadPlayer.penalty_kicks_saved | (self) | return self._penalty_kicks_saved | Returns an ``int`` of the number of penalty kicks a keeper has saved
during regular play. | Returns an ``int`` of the number of penalty kicks a keeper has saved
during regular play. | [
"Returns",
"an",
"int",
"of",
"the",
"number",
"of",
"penalty",
"kicks",
"a",
"keeper",
"has",
"saved",
"during",
"regular",
"play",
"."
] | def penalty_kicks_saved(self):
"""
Returns an ``int`` of the number of penalty kicks a keeper has saved
during regular play.
"""
return self._penalty_kicks_saved | [
"def",
"penalty_kicks_saved",
"(",
"self",
")",
":",
"return",
"self",
".",
"_penalty_kicks_saved"
] | https://github.com/roclark/sportsipy/blob/c19f545d3376d62ded6304b137dc69238ac620a9/sportsipy/fb/roster.py#L726-L731 | |
chb/indivo_server | 9826c67ab17d7fc0df935db327344fb0c7d237e5 | indivo/migrations/0021_old_problems_to_smart_problems.py | python | Migration.forwards | (self, orm) | Write your forwards methods here. | Write your forwards methods here. | [
"Write",
"your",
"forwards",
"methods",
"here",
"."
] | def forwards(self, orm):
"Write your forwards methods here."
for p in orm.Problem.objects.all():
p.startDate = p.date_onset
p.endDate = p.date_resolution
p.name_identifier = p.name_value
p.name_system = p.name_type
p.name_title = p.name
p.notes = p.comments
p.save() | [
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"... | https://github.com/chb/indivo_server/blob/9826c67ab17d7fc0df935db327344fb0c7d237e5/indivo/migrations/0021_old_problems_to_smart_problems.py#L9-L18 | ||
FuYanzhe2/Name-Entity-Recognition | 598b264262d667257c9e26646c49df45f7d76547 | BERT-BiLSTM-CRF-NER/bert/run_squad.py | python | get_final_text | (pred_text, orig_text, do_lower_case) | return output_text | Project the tokenized prediction back to the original text. | Project the tokenized prediction back to the original text. | [
"Project",
"the",
"tokenized",
"prediction",
"back",
"to",
"the",
"original",
"text",
"."
] | def get_final_text(pred_text, orig_text, do_lower_case):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if FLAGS.verbose_logging:
tf.logging.info(
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if FLAGS.verbose_logging:
tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if FLAGS.verbose_logging:
tf.logging.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if FLAGS.verbose_logging:
tf.logging.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text | [
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"(",
"pred_text",
",",
"orig_text",
",",
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")",
":",
"# When we created the data, we kept track of the alignment between original",
"# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So",
"# now `orig_text` contains the span of our or... | https://github.com/FuYanzhe2/Name-Entity-Recognition/blob/598b264262d667257c9e26646c49df45f7d76547/BERT-BiLSTM-CRF-NER/bert/run_squad.py#L926-L1019 |
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