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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/ec2/elb/__init__.py | python | ELBConnection.set_lb_policies_of_listener | (self, lb_name, lb_port, policies) | return self.get_status('SetLoadBalancerPoliciesOfListener', params) | Associates, updates, or disables a policy with a listener on the load
balancer. Currently only zero (0) or one (1) policy can be associated
with a listener. | Associates, updates, or disables a policy with a listener on the load
balancer. Currently only zero (0) or one (1) policy can be associated
with a listener. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/ndimage/interpolation.py | python | zoom | (input, zoom, output=None, order=3, mode='constant', cval=0.0,
prefilter=True) | return return_value | Zoom an array.
The array is zoomed using spline interpolation of the requested order.
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input : ndarray
The input array.
zoom : float or sequence, optional
The zoom factor along the axes. If a float, `zoom` is the same for each
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output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
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order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
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Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
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zoom : ndarray or None
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Zoom an array.
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Parameters
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The input array.
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The zoom factor along the axes. If a float, `zoom` is the same for each
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The array in which to place the output, or the dtype of the returned
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"""
if order < 0 or order > 5:
raise RuntimeError('spline order not supported')
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
if input.ndim < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numpy.float64)
else:
filtered = input
zoom = _ni_support._normalize_sequence(zoom, input.ndim)
output_shape = tuple(
[int(round(ii * jj)) for ii, jj in zip(input.shape, zoom)])
output_shape_old = tuple(
[int(ii * jj) for ii, jj in zip(input.shape, zoom)])
if output_shape != output_shape_old:
warnings.warn(
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zoom_div = numpy.array(output_shape, float) - 1
zoom = (numpy.array(input.shape) - 1) / zoom_div
# Zooming to non-finite values is unpredictable, so just choose
# zoom factor 1 instead
zoom[~numpy.isfinite(zoom)] = 1
output, return_value = _ni_support._get_output(output, input,
shape=output_shape)
zoom = numpy.asarray(zoom, dtype=numpy.float64)
zoom = numpy.ascontiguousarray(zoom)
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mickem/nscp | 79f89fdbb6da63f91bc9dedb7aea202fe938f237 | scripts/python/lib/google/protobuf/internal/python_message.py | python | _AddPropertiesForFields | (descriptor, cls) | Adds properties for all fields in this protocol message type. | Adds properties for all fields in this protocol message type. | [
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/xcodeproj_file.py | python | XCObject.Comment | (self) | return self.Name() | Return a comment string for the object.
Most objects just use their name as the comment, but PBXProject uses
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/inspector_protocol/jinja2/bccache.py | python | BytecodeCache.load_bytecode | (self, bucket) | Subclasses have to override this method to load bytecode into a
bucket. If they are not able to find code in the cache for the
bucket, it must not do anything. | Subclasses have to override this method to load bytecode into a
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rrwick/Unicycler | 96ffea71e3a78d63ade19d6124946773e65cf129 | unicycler/assembly_graph.py | python | AssemblyGraph.get_path_availability | (self, path) | Given a path, this function returns the fraction that is available. A segment is considered
fully available it has a depth of above 0.5. Below that, availability drops towards 0. A
single segment can have negative availability (if it has negative depth), but this function
will only return a number from 0 to 1. | Given a path, this function returns the fraction that is available. A segment is considered
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if seg.depth >= 0.5:
seg_availability = 1.0
else:
seg_availability = 2 * seg.depth
seg_len = seg.get_length() - self.overlap
total_bases += seg_len
available_bases += seg_len * seg_availability
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/asyncio/base_events.py | python | BaseEventLoop.call_later | (self, delay, callback, *args, context=None) | return timer | Arrange for a callback to be called at a given time.
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can be used to cancel the call.
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timer = self.call_at(self.time() + delay, callback, *args,
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moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | moderngl/program_members/uniform.py | python | Uniform.read | (self) | return self.mglo.data | Read the value of the uniform. | Read the value of the uniform. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/richtext.py | python | RichTextParagraphLayoutBox.ClearListStyle | (*args, **kwargs) | return _richtext.RichTextParagraphLayoutBox_ClearListStyle(*args, **kwargs) | ClearListStyle(self, RichTextRange range, int flags=RICHTEXT_SETSTYLE_WITH_UNDO) -> bool | ClearListStyle(self, RichTextRange range, int flags=RICHTEXT_SETSTYLE_WITH_UNDO) -> bool | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/richtext.py#L1765-L1767 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib2to3/pgen2/tokenize.py | python | untokenize | (iterable) | return ut.untokenize(iterable) | Transform tokens back into Python source code.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output text will tokenize the back to the input
t1 = [tok[:2] for tok in generate_tokens(f.readline)]
newcode = untokenize(t1)
readline = iter(newcode.splitlines(1)).next
t2 = [tok[:2] for tokin generate_tokens(readline)]
assert t1 == t2 | Transform tokens back into Python source code. | [
"Transform",
"tokens",
"back",
"into",
"Python",
"source",
"code",
"."
] | def untokenize(iterable):
"""Transform tokens back into Python source code.
Each element returned by the iterable must be a token sequence
with at least two elements, a token number and token value. If
only two tokens are passed, the resulting output is poor.
Round-trip invariant for full input:
Untokenized source will match input source exactly
Round-trip invariant for limited intput:
# Output text will tokenize the back to the input
t1 = [tok[:2] for tok in generate_tokens(f.readline)]
newcode = untokenize(t1)
readline = iter(newcode.splitlines(1)).next
t2 = [tok[:2] for tokin generate_tokens(readline)]
assert t1 == t2
"""
ut = Untokenizer()
return ut.untokenize(iterable) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/pycc/cc.py | python | CC.export | (self, exported_name, sig) | return decorator | Mark a function for exporting in the extension module. | Mark a function for exporting in the extension module. | [
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"""
Mark a function for exporting in the extension module.
"""
fn_args, fn_retty = sigutils.normalize_signature(sig)
sig = typing.signature(fn_retty, *fn_args)
if exported_name in self._exported_functions:
raise KeyError("duplicated export symbol %s" % (exported_name))
def decorator(func):
entry = ExportEntry(exported_name, sig, func)
self._exported_functions[exported_name] = entry
return func
return decorator | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/minimum-knight-moves.py | python | Solution2.minKnightMoves | (self, x, y) | return dp(x, y) | :type x: int
:type y: int
:rtype: int | :type x: int
:type y: int
:rtype: int | [
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"""
:type x: int
:type y: int
:rtype: int
"""
def dp(x, y):
x, y = abs(x), abs(y)
if x < y:
x, y = y, x
if (x, y) not in self.__lookup: # greedy, smaller x, y is always better if not special cases
self.__lookup[(x, y)] = min(dp(x-1, y-2), dp(x-2, y-1)) + 1
return self.__lookup[(x, y)]
return dp(x, y) | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/distributed/DoCollectionManager.py | python | DoCollectionManager.doFindAll | (self, str) | return matches | Returns list of distributed objects with matching str in value. | Returns list of distributed objects with matching str in value. | [
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] | def doFindAll(self, str):
"""
Returns list of distributed objects with matching str in value.
"""
matches = []
for value in self.doId2do.values():
if repr(value).find(str) >= 0:
matches.append(value)
return matches | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/plugins/db_manager/db_plugins/postgis/plugin.py | python | PGRasterTable.uri | (self, uri=None) | return uri | Returns the datasource URI for postgresraster provider | Returns the datasource URI for postgresraster provider | [
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"datasource",
"URI",
"for",
"postgresraster",
"provider"
] | def uri(self, uri=None):
"""Returns the datasource URI for postgresraster provider"""
if not uri:
uri = self.database().uri()
service = (u'service=\'%s\'' % uri.service()) if uri.service() else ''
dbname = (u'dbname=\'%s\'' % uri.database()) if uri.database() else ''
host = (u'host=%s' % uri.host()) if uri.host() else ''
user = (u'user=%s' % uri.username()) if uri.username() else ''
passw = (u'password=%s' % uri.password()) if uri.password() else ''
port = (u'port=%s' % uri.port()) if uri.port() else ''
schema = self.schemaName() if self.schemaName() else 'public'
table = '"%s"."%s"' % (schema, self.name)
if not dbname:
# postgresraster provider *requires* a dbname
connector = self.database().connector
r = connector._execute(None, "SELECT current_database()")
dbname = (u'dbname=\'%s\'' % connector._fetchone(r)[0])
connector._close_cursor(r)
# Find first raster field
col = ''
for fld in self.fields():
if fld.dataType == "raster":
col = u'column=\'%s\'' % fld.name
break
uri = u'%s %s %s %s %s %s %s table=%s' % \
(service, dbname, host, user, passw, port, col, table)
return uri | [
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zetavm/zetavm | 61af9cd317fa5629f570b30b61ea8c7ffc375e59 | espresso/e_parser.py | python | parse_atom | (input_handler) | Parsing an atomic expression | Parsing an atomic expression | [
"Parsing",
"an",
"atomic",
"expression"
] | def parse_atom(input_handler):
"""Parsing an atomic expression"""
input_handler.eat_ws()
if input_handler.peek_ch().isdigit():
return parse_num(input_handler)
if input_handler.match('"'):
return parse_string_literal(input_handler, '"')
if input_handler.match("'"):
return parse_string_literal(input_handler, "'")
if input_handler.match('['):
return ArrayExpr(parse_expr_list(input_handler, "]"))
if input_handler.match("{"):
return parse_obj_literal(input_handler)
if input_handler.match("("):
expr = parse_expr(input_handler)
input_handler.expect_ws(")")
return expr
op = match_op(input_handler, 0, True)
if op is not None:
expr = parse_expr_prec(input_handler, op.prec)
return UnOpExpr(op, expr)
if input_handler.peek_ch().isalnum():
if input_handler.match_kw("fun"):
return parse_fun_expr(input_handler)
return IdentExpr(parse_ident_expr(input_handler))
if input_handler.match("$"):
op_name = parse_ident_expr(input_handler)
input_handler.expect('(')
arg_exprs = parse_expr_list(input_handler, ')')
return IRExpr(op_name, arg_exprs)
raise ParseError(input_handler, "invalid expression") | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/virtualenv/files/virtualenv.py | python | install_python | (home_dir, lib_dir, inc_dir, bin_dir, site_packages, clear) | return py_executable | Install just the base environment, no distutils patches etc | Install just the base environment, no distutils patches etc | [
"Install",
"just",
"the",
"base",
"environment",
"no",
"distutils",
"patches",
"etc"
] | def install_python(home_dir, lib_dir, inc_dir, bin_dir, site_packages, clear):
"""Install just the base environment, no distutils patches etc"""
if sys.executable.startswith(bin_dir):
print('Please use the *system* python to run this script')
return
if clear:
rmtree(lib_dir)
## FIXME: why not delete it?
## Maybe it should delete everything with #!/path/to/venv/python in it
logger.notify('Not deleting %s', bin_dir)
if hasattr(sys, 'real_prefix'):
logger.notify('Using real prefix %r' % sys.real_prefix)
prefix = sys.real_prefix
else:
prefix = sys.prefix
mkdir(lib_dir)
fix_lib64(lib_dir)
stdlib_dirs = [os.path.dirname(os.__file__)]
if sys.platform == 'win32':
stdlib_dirs.append(join(os.path.dirname(stdlib_dirs[0]), 'DLLs'))
elif sys.platform == 'darwin':
stdlib_dirs.append(join(stdlib_dirs[0], 'site-packages'))
if hasattr(os, 'symlink'):
logger.info('Symlinking Python bootstrap modules')
else:
logger.info('Copying Python bootstrap modules')
logger.indent += 2
try:
# copy required files...
for stdlib_dir in stdlib_dirs:
if not os.path.isdir(stdlib_dir):
continue
for fn in os.listdir(stdlib_dir):
bn = os.path.splitext(fn)[0]
if fn != 'site-packages' and bn in REQUIRED_FILES:
copyfile(join(stdlib_dir, fn), join(lib_dir, fn))
# ...and modules
copy_required_modules(home_dir)
finally:
logger.indent -= 2
mkdir(join(lib_dir, 'site-packages'))
import site
site_filename = site.__file__
if site_filename.endswith('.pyc'):
site_filename = site_filename[:-1]
elif site_filename.endswith('$py.class'):
site_filename = site_filename.replace('$py.class', '.py')
site_filename_dst = change_prefix(site_filename, home_dir)
site_dir = os.path.dirname(site_filename_dst)
writefile(site_filename_dst, SITE_PY)
writefile(join(site_dir, 'orig-prefix.txt'), prefix)
site_packages_filename = join(site_dir, 'no-global-site-packages.txt')
if not site_packages:
writefile(site_packages_filename, '')
else:
if os.path.exists(site_packages_filename):
logger.info('Deleting %s' % site_packages_filename)
os.unlink(site_packages_filename)
if is_pypy:
stdinc_dir = join(prefix, 'include')
else:
stdinc_dir = join(prefix, 'include', py_version + abiflags)
if os.path.exists(stdinc_dir):
copyfile(stdinc_dir, inc_dir)
else:
logger.debug('No include dir %s' % stdinc_dir)
# pypy never uses exec_prefix, just ignore it
if sys.exec_prefix != prefix and not is_pypy:
if sys.platform == 'win32':
exec_dir = join(sys.exec_prefix, 'lib')
elif is_jython:
exec_dir = join(sys.exec_prefix, 'Lib')
else:
exec_dir = join(sys.exec_prefix, 'lib', py_version)
for fn in os.listdir(exec_dir):
copyfile(join(exec_dir, fn), join(lib_dir, fn))
if is_jython:
# Jython has either jython-dev.jar and javalib/ dir, or just
# jython.jar
for name in 'jython-dev.jar', 'javalib', 'jython.jar':
src = join(prefix, name)
if os.path.exists(src):
copyfile(src, join(home_dir, name))
# XXX: registry should always exist after Jython 2.5rc1
src = join(prefix, 'registry')
if os.path.exists(src):
copyfile(src, join(home_dir, 'registry'), symlink=False)
copyfile(join(prefix, 'cachedir'), join(home_dir, 'cachedir'),
symlink=False)
mkdir(bin_dir)
py_executable = join(bin_dir, os.path.basename(sys.executable))
if 'Python.framework' in prefix:
if re.search(r'/Python(?:-32|-64)*$', py_executable):
# The name of the python executable is not quite what
# we want, rename it.
py_executable = os.path.join(
os.path.dirname(py_executable), 'python')
logger.notify('New %s executable in %s', expected_exe, py_executable)
if sys.executable != py_executable:
## FIXME: could I just hard link?
executable = sys.executable
if sys.platform == 'cygwin' and os.path.exists(executable + '.exe'):
# Cygwin misreports sys.executable sometimes
executable += '.exe'
py_executable += '.exe'
logger.info('Executable actually exists in %s' % executable)
shutil.copyfile(executable, py_executable)
make_exe(py_executable)
if sys.platform == 'win32' or sys.platform == 'cygwin':
pythonw = os.path.join(os.path.dirname(sys.executable), 'pythonw.exe')
if os.path.exists(pythonw):
logger.info('Also created pythonw.exe')
shutil.copyfile(pythonw, os.path.join(os.path.dirname(py_executable), 'pythonw.exe'))
if is_pypy:
# make a symlink python --> pypy-c
python_executable = os.path.join(os.path.dirname(py_executable), 'python')
logger.info('Also created executable %s' % python_executable)
copyfile(py_executable, python_executable)
if os.path.splitext(os.path.basename(py_executable))[0] != expected_exe:
secondary_exe = os.path.join(os.path.dirname(py_executable),
expected_exe)
py_executable_ext = os.path.splitext(py_executable)[1]
if py_executable_ext == '.exe':
# python2.4 gives an extension of '.4' :P
secondary_exe += py_executable_ext
if os.path.exists(secondary_exe):
logger.warn('Not overwriting existing %s script %s (you must use %s)'
% (expected_exe, secondary_exe, py_executable))
else:
logger.notify('Also creating executable in %s' % secondary_exe)
shutil.copyfile(sys.executable, secondary_exe)
make_exe(secondary_exe)
if 'Python.framework' in prefix:
logger.debug('MacOSX Python framework detected')
# Make sure we use the the embedded interpreter inside
# the framework, even if sys.executable points to
# the stub executable in ${sys.prefix}/bin
# See http://groups.google.com/group/python-virtualenv/
# browse_thread/thread/17cab2f85da75951
original_python = os.path.join(
prefix, 'Resources/Python.app/Contents/MacOS/Python')
shutil.copy(original_python, py_executable)
# Copy the framework's dylib into the virtual
# environment
virtual_lib = os.path.join(home_dir, '.Python')
if os.path.exists(virtual_lib):
os.unlink(virtual_lib)
copyfile(
os.path.join(prefix, 'Python'),
virtual_lib)
# And then change the install_name of the copied python executable
try:
call_subprocess(
["install_name_tool", "-change",
os.path.join(prefix, 'Python'),
'@executable_path/../.Python',
py_executable])
except:
logger.fatal(
"Could not call install_name_tool -- you must have Apple's development tools installed")
raise
# Some tools depend on pythonX.Y being present
py_executable_version = '%s.%s' % (
sys.version_info[0], sys.version_info[1])
if not py_executable.endswith(py_executable_version):
# symlinking pythonX.Y > python
pth = py_executable + '%s.%s' % (
sys.version_info[0], sys.version_info[1])
if os.path.exists(pth):
os.unlink(pth)
os.symlink('python', pth)
else:
# reverse symlinking python -> pythonX.Y (with --python)
pth = join(bin_dir, 'python')
if os.path.exists(pth):
os.unlink(pth)
os.symlink(os.path.basename(py_executable), pth)
if sys.platform == 'win32' and ' ' in py_executable:
# There's a bug with subprocess on Windows when using a first
# argument that has a space in it. Instead we have to quote
# the value:
py_executable = '"%s"' % py_executable
cmd = [py_executable, '-c', 'import sys; print(sys.prefix)']
logger.info('Testing executable with %s %s "%s"' % tuple(cmd))
try:
proc = subprocess.Popen(cmd,
stdout=subprocess.PIPE)
proc_stdout, proc_stderr = proc.communicate()
except OSError:
e = sys.exc_info()[1]
if e.errno == errno.EACCES:
logger.fatal('ERROR: The executable %s could not be run: %s' % (py_executable, e))
sys.exit(100)
else:
raise e
proc_stdout = proc_stdout.strip().decode(sys.getdefaultencoding())
proc_stdout = os.path.normcase(os.path.abspath(proc_stdout))
if proc_stdout != os.path.normcase(os.path.abspath(home_dir)):
logger.fatal(
'ERROR: The executable %s is not functioning' % py_executable)
logger.fatal(
'ERROR: It thinks sys.prefix is %r (should be %r)'
% (proc_stdout, os.path.normcase(os.path.abspath(home_dir))))
logger.fatal(
'ERROR: virtualenv is not compatible with this system or executable')
if sys.platform == 'win32':
logger.fatal(
'Note: some Windows users have reported this error when they installed Python for "Only this user". The problem may be resolvable if you install Python "For all users". (See https://bugs.launchpad.net/virtualenv/+bug/352844)')
sys.exit(100)
else:
logger.info('Got sys.prefix result: %r' % proc_stdout)
pydistutils = os.path.expanduser('~/.pydistutils.cfg')
if os.path.exists(pydistutils):
logger.notify('Please make sure you remove any previous custom paths from '
'your %s file.' % pydistutils)
## FIXME: really this should be calculated earlier
return py_executable | [
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kevinlin311tw/Caffe-DeepBinaryCode | 9eaa7662be47d49f475ecbeea2bd51be105270d2 | scripts/cpp_lint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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"-",
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"comments",
"."
] | def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '// dummy' | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/io/matlab/mio4.py | python | MatFile4Reader.list_variables | (self) | return vars | list variables from stream | list variables from stream | [
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''' list variables from stream '''
self.mat_stream.seek(0)
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vars = []
while not self.end_of_stream():
hdr, next_position = self.read_var_header()
name = asstr(hdr.name)
shape = self._matrix_reader.shape_from_header(hdr)
info = mclass_info.get(hdr.mclass, 'unknown')
vars.append((name, shape, info))
self.mat_stream.seek(next_position)
return vars | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/distributions/util.py | python | pad | (x, axis, front=False, back=False, value=0, count=1, name=None) | Pads `value` to the front and/or back of a `Tensor` dim, `count` times.
Args:
x: `Tensor` input.
axis: Scalar `int`-like `Tensor` representing the single dimension to pad.
(Negative indexing is supported.)
front: Python `bool`; if `True` the beginning of the `axis` dimension is
padded with `value`, `count` times. If `False` no front padding is made.
back: Python `bool`; if `True` the end of the `axis` dimension is padded
with `value`, `count` times. If `False` no end padding is made.
value: Scalar `int`-like `Tensor` representing the actual value added to the
front and/or back of the `axis` dimension of `x`.
count: Scalar `int`-like `Tensor` representing number of elements added to
the front and/or back of the `axis` dimension of `x`. E.g., if `front =
back = True` then `2 * count` elements are added.
name: Python `str` name prefixed to Ops created by this function.
Returns:
pad: The padded version of input `x`.
Raises:
ValueError: if both `front` and `back` are `False`.
TypeError: if `count` is not `int`-like. | Pads `value` to the front and/or back of a `Tensor` dim, `count` times. | [
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"""Pads `value` to the front and/or back of a `Tensor` dim, `count` times.
Args:
x: `Tensor` input.
axis: Scalar `int`-like `Tensor` representing the single dimension to pad.
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name: Python `str` name prefixed to Ops created by this function.
Returns:
pad: The padded version of input `x`.
Raises:
ValueError: if both `front` and `back` are `False`.
TypeError: if `count` is not `int`-like.
"""
with ops.name_scope(name, "pad", [x, value, count]):
x = ops.convert_to_tensor(x, name="x")
value = ops.convert_to_tensor(value, dtype=x.dtype, name="value")
count = ops.convert_to_tensor(count, name="count")
if not count.dtype.is_integer:
raise TypeError("`count.dtype` (`{}`) must be `int`-like.".format(
count.dtype.name))
if not front and not back:
raise ValueError("At least one of `front`, `back` must be `True`.")
ndims = (
x.shape.ndims if x.shape.ndims is not None else array_ops.rank(
x, name="ndims"))
axis = ops.convert_to_tensor(axis, name="axis")
axis_ = tensor_util.constant_value(axis)
if axis_ is not None:
axis = axis_
if axis < 0:
axis = ndims + axis
count_ = tensor_util.constant_value(count)
if axis_ >= 0 or x.shape.ndims is not None:
head = x.shape[:axis]
middle = tensor_shape.TensorShape(None if count_ is None else (
tensor_shape.dimension_at_index(x.shape, axis) + count_ *
(front + back)))
tail = x.shape[axis + 1:]
final_shape = head.concatenate(middle.concatenate(tail))
else:
final_shape = None
else:
axis = array_ops.where_v2(axis < 0, ndims + axis, axis)
final_shape = None
x = array_ops.pad(
x,
paddings=array_ops.one_hot(
indices=array_ops.stack(
[axis if front else -1, axis if back else -1]),
depth=ndims,
axis=0,
on_value=count,
dtype=dtypes.int32),
constant_values=value)
if final_shape is not None:
x.set_shape(final_shape)
return x | [
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ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/cpy/antlr3/recognizers.py | python | BaseRecognizer._getRuleInvocationStack | (cls, module) | return rules | A more general version of getRuleInvocationStack where you can
pass in, for example, a RecognitionException to get it's rule
stack trace. This routine is shared with all recognizers, hence,
static.
TODO: move to a utility class or something; weird having lexer call
this | A more general version of getRuleInvocationStack where you can
pass in, for example, a RecognitionException to get it's rule
stack trace. This routine is shared with all recognizers, hence,
static. | [
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"""
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stack trace. This routine is shared with all recognizers, hence,
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TODO: move to a utility class or something; weird having lexer call
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# mmmhhh,... perhaps look at the first argument
# (f_locals[co_varnames[0]]?) and test if it's a (sub)class of
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rules = []
for frame in reversed(inspect.stack()):
code = frame[0].f_code
codeMod = inspect.getmodule(code)
if codeMod is None:
continue
# skip frames not in requested module
if codeMod.__name__ != module:
continue
# skip some unwanted names
if code.co_name in ('nextToken', '<module>'):
continue
rules.append(code.co_name)
return rules | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/slim/quantization/cal_kl_threshold.py | python | expand_quantized_bins | (quantized_bins, reference_bins) | return expanded_quantized_bins | Expand hist bins. | Expand hist bins. | [
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"."
] | def expand_quantized_bins(quantized_bins, reference_bins):
'''
Expand hist bins.
'''
expanded_quantized_bins = [0] * len(reference_bins)
num_merged_bins = int(len(reference_bins) / len(quantized_bins))
j_start = 0
j_end = num_merged_bins
for idx in range(len(quantized_bins)):
zero_count = reference_bins[j_start:j_end].count(0)
num_merged_bins = j_end - j_start
if zero_count == num_merged_bins:
avg_bin_ele = 0
else:
avg_bin_ele = quantized_bins[idx] / (
num_merged_bins - zero_count + 0.0)
for idx1 in range(j_start, j_end):
expanded_quantized_bins[idx1] = (0 if reference_bins[idx1] == 0 else
avg_bin_ele)
j_start += num_merged_bins
j_end += num_merged_bins
if (idx + 1) == len(quantized_bins) - 1:
j_end = len(reference_bins)
return expanded_quantized_bins | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/crywaflib/branch_spec.py | python | spec_force_individual_build_directory | (ctx, spec_name = None, platform = None, configuration = None) | return force_individual_build_directory[0].lower() == "true" | Return true if the current platform|configuration should use it's own temporary build directory | Return true if the current platform|configuration should use it's own temporary build directory | [
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""" Return true if the current platform|configuration should use it's own temporary build directory """
force_individual_build_directory = _spec_entry_as_single_list(ctx, 'force_individual_build_directory', spec_name, platform, configuration)
if force_individual_build_directory == []:
return False
# _spec_entry always returns a list, but client code expects a single boolean
assert( len(force_individual_build_directory) == 1 )
return force_individual_build_directory[0].lower() == "true" | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/LoadVesuvio.py | python | LoadVesuvio._set_spectra_type | (self, spectrum_no) | Set whether this spectrum no is forward/backward scattering
and set the normalization range appropriately
@param spectrum_no The current spectrum no | Set whether this spectrum no is forward/backward scattering
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"""
Set whether this spectrum no is forward/backward scattering
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@param spectrum_no The current spectrum no
"""
if self._is_back_scattering(spectrum_no):
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self._period_sum1_start, self._period_sum1_end = self._back_period_sum1
self._period_sum2_start, self._period_sum2_end = self._back_period_sum2
self._foil_out_norm_start, self._foil_out_norm_end = self._back_foil_out_norm
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self._mon_norm_start, self._mon_norm_end = self._forw_mon_norm
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self._period_sum2_start, self._period_sum2_end = self._forw_period_sum2
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/lambda-code/SanitizationLambda/sanitization_lambda.py | python | Sanitizer.__validate_file | (self, key, file_path) | Validates plain text files like logs. | Validates plain text files like logs. | [
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] | def __validate_file(self, key, file_path):
''' Validates plain text files like logs. '''
if self.__validate_file_size(file_path):
return True
else:
self.rejected_files.append(FileStatus(key, False, "Invalid file size.", ''))
return False | [
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qt/qt | 0a2f2382541424726168804be2c90b91381608c6 | src/3rdparty/webkit/Source/ThirdParty/gyp/pylib/gyp/MSVSProject.py | python | Writer.AddConfig | (self, name, attrs=None, tools=None) | Adds a configuration to the project.
Args:
name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None. | Adds a configuration to the project. | [
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"""Adds a configuration to the project.
Args:
name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
"""
self._AddConfigToNode(self.n_configs, 'Configuration', name, attrs, tools) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/make.py | python | MakefileWriter.WriteTarget | (self, spec, configs, deps, link_deps, bundle_deps,
extra_outputs, part_of_all) | Write Makefile code to produce the final target of the gyp spec.
spec, configs: input from gyp.
deps, link_deps: dependency lists; see ComputeDeps()
extra_outputs: any extra outputs that our target should depend on
part_of_all: flag indicating this target is part of 'all' | Write Makefile code to produce the final target of the gyp spec. | [
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extra_outputs, part_of_all):
"""Write Makefile code to produce the final target of the gyp spec.
spec, configs: input from gyp.
deps, link_deps: dependency lists; see ComputeDeps()
extra_outputs: any extra outputs that our target should depend on
part_of_all: flag indicating this target is part of 'all'
"""
self.WriteLn('### Rules for final target.')
if extra_outputs:
self.WriteDependencyOnExtraOutputs(self.output_binary, extra_outputs)
self.WriteMakeRule(extra_outputs, deps,
comment=('Preserve order dependency of '
'special output on deps.'),
order_only = True)
target_postbuilds = {}
if self.type != 'none':
for configname in sorted(configs.keys()):
config = configs[configname]
if self.flavor == 'mac':
ldflags = self.xcode_settings.GetLdflags(configname,
generator_default_variables['PRODUCT_DIR'],
lambda p: Sourceify(self.Absolutify(p)))
# TARGET_POSTBUILDS_$(BUILDTYPE) is added to postbuilds later on.
gyp_to_build = gyp.common.InvertRelativePath(self.path)
target_postbuild = self.xcode_settings.AddImplicitPostbuilds(
configname,
QuoteSpaces(os.path.normpath(os.path.join(gyp_to_build,
self.output))),
QuoteSpaces(os.path.normpath(os.path.join(gyp_to_build,
self.output_binary))))
if target_postbuild:
target_postbuilds[configname] = target_postbuild
else:
ldflags = config.get('ldflags', [])
# Compute an rpath for this output if needed.
if any(dep.endswith('.so') or '.so.' in dep for dep in deps):
# We want to get the literal string "$ORIGIN" into the link command,
# so we need lots of escaping.
ldflags.append(r'-Wl,-rpath=\$$ORIGIN/lib.%s/' % self.toolset)
ldflags.append(r'-Wl,-rpath-link=\$(builddir)/lib.%s/' %
self.toolset)
library_dirs = config.get('library_dirs', [])
ldflags += [('-L%s' % library_dir) for library_dir in library_dirs]
self.WriteList(ldflags, 'LDFLAGS_%s' % configname)
if self.flavor == 'mac':
self.WriteList(self.xcode_settings.GetLibtoolflags(configname),
'LIBTOOLFLAGS_%s' % configname)
libraries = spec.get('libraries')
if libraries:
# Remove duplicate entries
libraries = gyp.common.uniquer(libraries)
if self.flavor == 'mac':
libraries = self.xcode_settings.AdjustLibraries(libraries)
self.WriteList(libraries, 'LIBS')
self.WriteLn('%s: GYP_LDFLAGS := $(LDFLAGS_$(BUILDTYPE))' %
QuoteSpaces(self.output_binary))
self.WriteLn('%s: LIBS := $(LIBS)' % QuoteSpaces(self.output_binary))
if self.flavor == 'mac':
self.WriteLn('%s: GYP_LIBTOOLFLAGS := $(LIBTOOLFLAGS_$(BUILDTYPE))' %
QuoteSpaces(self.output_binary))
# Postbuild actions. Like actions, but implicitly depend on the target's
# output.
postbuilds = []
if self.flavor == 'mac':
if target_postbuilds:
postbuilds.append('$(TARGET_POSTBUILDS_$(BUILDTYPE))')
postbuilds.extend(
gyp.xcode_emulation.GetSpecPostbuildCommands(spec))
if postbuilds:
# Envvars may be referenced by TARGET_POSTBUILDS_$(BUILDTYPE),
# so we must output its definition first, since we declare variables
# using ":=".
self.WriteSortedXcodeEnv(self.output, self.GetSortedXcodePostbuildEnv())
for configname in target_postbuilds:
self.WriteLn('%s: TARGET_POSTBUILDS_%s := %s' %
(QuoteSpaces(self.output),
configname,
gyp.common.EncodePOSIXShellList(target_postbuilds[configname])))
# Postbuilds expect to be run in the gyp file's directory, so insert an
# implicit postbuild to cd to there.
postbuilds.insert(0, gyp.common.EncodePOSIXShellList(['cd', self.path]))
for i in range(len(postbuilds)):
if not postbuilds[i].startswith('$'):
postbuilds[i] = EscapeShellArgument(postbuilds[i])
self.WriteLn('%s: builddir := $(abs_builddir)' % QuoteSpaces(self.output))
self.WriteLn('%s: POSTBUILDS := %s' % (
QuoteSpaces(self.output), ' '.join(postbuilds)))
# A bundle directory depends on its dependencies such as bundle resources
# and bundle binary. When all dependencies have been built, the bundle
# needs to be packaged.
if self.is_mac_bundle:
# If the framework doesn't contain a binary, then nothing depends
# on the actions -- make the framework depend on them directly too.
self.WriteDependencyOnExtraOutputs(self.output, extra_outputs)
# Bundle dependencies. Note that the code below adds actions to this
# target, so if you move these two lines, move the lines below as well.
self.WriteList([QuoteSpaces(dep) for dep in bundle_deps], 'BUNDLE_DEPS')
self.WriteLn('%s: $(BUNDLE_DEPS)' % QuoteSpaces(self.output))
# After the framework is built, package it. Needs to happen before
# postbuilds, since postbuilds depend on this.
if self.type in ('shared_library', 'loadable_module'):
self.WriteLn('\t@$(call do_cmd,mac_package_framework,,,%s)' %
self.xcode_settings.GetFrameworkVersion())
# Bundle postbuilds can depend on the whole bundle, so run them after
# the bundle is packaged, not already after the bundle binary is done.
if postbuilds:
self.WriteLn('\t@$(call do_postbuilds)')
postbuilds = [] # Don't write postbuilds for target's output.
# Needed by test/mac/gyptest-rebuild.py.
self.WriteLn('\t@true # No-op, used by tests')
# Since this target depends on binary and resources which are in
# nested subfolders, the framework directory will be older than
# its dependencies usually. To prevent this rule from executing
# on every build (expensive, especially with postbuilds), expliclity
# update the time on the framework directory.
self.WriteLn('\t@touch -c %s' % QuoteSpaces(self.output))
if postbuilds:
assert not self.is_mac_bundle, ('Postbuilds for bundles should be done '
'on the bundle, not the binary (target \'%s\')' % self.target)
assert 'product_dir' not in spec, ('Postbuilds do not work with '
'custom product_dir')
if self.type == 'executable':
self.WriteLn('%s: LD_INPUTS := %s' % (
QuoteSpaces(self.output_binary),
' '.join(QuoteSpaces(dep) for dep in link_deps)))
if self.toolset == 'host' and self.flavor == 'android':
self.WriteDoCmd([self.output_binary], link_deps, 'link_host',
part_of_all, postbuilds=postbuilds)
else:
self.WriteDoCmd([self.output_binary], link_deps, 'link', part_of_all,
postbuilds=postbuilds)
elif self.type == 'static_library':
for link_dep in link_deps:
assert ' ' not in link_dep, (
"Spaces in alink input filenames not supported (%s)" % link_dep)
if (self.flavor not in ('mac', 'openbsd', 'netbsd', 'win') and not
self.is_standalone_static_library):
self.WriteDoCmd([self.output_binary], link_deps, 'alink_thin',
part_of_all, postbuilds=postbuilds)
else:
self.WriteDoCmd([self.output_binary], link_deps, 'alink', part_of_all,
postbuilds=postbuilds)
elif self.type == 'shared_library':
self.WriteLn('%s: LD_INPUTS := %s' % (
QuoteSpaces(self.output_binary),
' '.join(QuoteSpaces(dep) for dep in link_deps)))
self.WriteDoCmd([self.output_binary], link_deps, 'solink', part_of_all,
postbuilds=postbuilds)
elif self.type == 'loadable_module':
for link_dep in link_deps:
assert ' ' not in link_dep, (
"Spaces in module input filenames not supported (%s)" % link_dep)
if self.toolset == 'host' and self.flavor == 'android':
self.WriteDoCmd([self.output_binary], link_deps, 'solink_module_host',
part_of_all, postbuilds=postbuilds)
else:
self.WriteDoCmd(
[self.output_binary], link_deps, 'solink_module', part_of_all,
postbuilds=postbuilds)
elif self.type == 'none':
# Write a stamp line.
self.WriteDoCmd([self.output_binary], deps, 'touch', part_of_all,
postbuilds=postbuilds)
else:
print("WARNING: no output for", self.type, self.target)
# Add an alias for each target (if there are any outputs).
# Installable target aliases are created below.
if ((self.output and self.output != self.target) and
(self.type not in self._INSTALLABLE_TARGETS)):
self.WriteMakeRule([self.target], [self.output],
comment='Add target alias', phony = True)
if part_of_all:
self.WriteMakeRule(['all'], [self.target],
comment = 'Add target alias to "all" target.',
phony = True)
# Add special-case rules for our installable targets.
# 1) They need to install to the build dir or "product" dir.
# 2) They get shortcuts for building (e.g. "make chrome").
# 3) They are part of "make all".
if (self.type in self._INSTALLABLE_TARGETS or
self.is_standalone_static_library):
if self.type == 'shared_library':
file_desc = 'shared library'
elif self.type == 'static_library':
file_desc = 'static library'
else:
file_desc = 'executable'
install_path = self._InstallableTargetInstallPath()
installable_deps = [self.output]
if (self.flavor == 'mac' and not 'product_dir' in spec and
self.toolset == 'target'):
# On mac, products are created in install_path immediately.
assert install_path == self.output, '%s != %s' % (
install_path, self.output)
# Point the target alias to the final binary output.
self.WriteMakeRule([self.target], [install_path],
comment='Add target alias', phony = True)
if install_path != self.output:
assert not self.is_mac_bundle # See comment a few lines above.
self.WriteDoCmd([install_path], [self.output], 'copy',
comment = 'Copy this to the %s output path.' %
file_desc, part_of_all=part_of_all)
installable_deps.append(install_path)
if self.output != self.alias and self.alias != self.target:
self.WriteMakeRule([self.alias], installable_deps,
comment = 'Short alias for building this %s.' %
file_desc, phony = True)
if part_of_all:
self.WriteMakeRule(['all'], [install_path],
comment = 'Add %s to "all" target.' % file_desc,
phony = True) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/multiprocessing/util.py | python | close_fds | (*fds) | Close each file descriptor given as an argument | Close each file descriptor given as an argument | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2.py | python | xmlTextReader.ReadOuterXml | (self) | return ret | Reads the contents of the current node, including child
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/chardet/cli/chardetect.py | python | description_of | (lines, name='stdin') | Return a string describing the probable encoding of a file or
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dmlc/treelite | df56babb6a4a2d7c29d719c28ce53acfa7dbab3c | runtime/python/treelite_runtime/libpath.py | python | find_lib_path | () | return lib_path | Find the path to Treelite runtime library files.
Returns
-------
lib_path: :py:class:`list <python:list>` of :py:class:`str <python:str>`
List of all found library path to Treelite | Find the path to Treelite runtime library files. | [
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"Treelite",
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] | def find_lib_path():
"""Find the path to Treelite runtime library files.
Returns
-------
lib_path: :py:class:`list <python:list>` of :py:class:`str <python:str>`
List of all found library path to Treelite
"""
if sys.platform == 'win32':
lib_name = f'treelite_runtime.dll'
elif sys.platform.startswith('linux'):
lib_name = f'libtreelite_runtime.so'
elif sys.platform == 'darwin':
lib_name = f'libtreelite_runtime.dylib'
else:
raise RuntimeError('Unsupported operating system')
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
# List possible locations for the library file
dll_path = [
# normal, after installation `lib` is copied into Python package tree.
os.path.join(curr_path, 'lib'),
# editable installation, no copying is performed.
os.path.join(curr_path, os.path.pardir, os.path.pardir, os.path.pardir, 'build')
]
# Windows hack: additional candidate locations
if sys.platform == 'win32':
if platform.architecture()[0] == '64bit':
dll_path.append(os.path.join(curr_path, '../../windows/x64/Release/'))
# hack for pip installation when copy all parent source directory here
dll_path.append(os.path.join(curr_path, './windows/x64/Release/'))
else:
dll_path.append(os.path.join(curr_path, '../../windows/Release/'))
# hack for pip installation when copy all parent source directory here
dll_path.append(os.path.join(curr_path, './windows/Release/'))
# Now examine all candidate locations for the library file
dll_path = [os.path.join(p, lib_name) for p in dll_path]
lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
if not lib_path:
candidate_list = '\n'.join(dll_path)
raise TreeliteRuntimeLibraryNotFound(
f'Cannot find library {lib_name} in the candidate path: ' +
f'List of candidates:\n{os.path.normpath(candidate_list)}\n')
return lib_path | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/descriptor_pool.py | python | DescriptorPool.FindFileByName | (self, file_name) | return self._ConvertFileProtoToFileDescriptor(file_proto) | Gets a FileDescriptor by file name.
Args:
file_name (str): The path to the file to get a descriptor for.
Returns:
FileDescriptor: The descriptor for the named file.
Raises:
KeyError: if the file cannot be found in the pool. | Gets a FileDescriptor by file name. | [
"Gets",
"a",
"FileDescriptor",
"by",
"file",
"name",
"."
] | def FindFileByName(self, file_name):
"""Gets a FileDescriptor by file name.
Args:
file_name (str): The path to the file to get a descriptor for.
Returns:
FileDescriptor: The descriptor for the named file.
Raises:
KeyError: if the file cannot be found in the pool.
"""
try:
return self._file_descriptors[file_name]
except KeyError:
pass
try:
file_proto = self._internal_db.FindFileByName(file_name)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileByName(file_name)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file named %s' % file_name)
return self._ConvertFileProtoToFileDescriptor(file_proto) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py | python | AdderStateSpaceModel.transition_power_noise_accumulator | (self, num_steps) | return _pack_and_reshape(
num_steps * a + sum_of_first_n * (b + c) + sum_of_first_n_squares * d,
num_steps * b + sum_of_first_n * d,
num_steps * c + sum_of_first_n * d,
num_steps * d) | Computes power sums in closed form. | Computes power sums in closed form. | [
"Computes",
"power",
"sums",
"in",
"closed",
"form",
"."
] | def transition_power_noise_accumulator(self, num_steps):
"""Computes power sums in closed form."""
def _pack_and_reshape(*values):
return array_ops.reshape(
array_ops.stack(axis=1, values=values),
array_ops.concat(values=[array_ops.shape(num_steps), [2, 2]], axis=0))
num_steps = math_ops.cast(num_steps, self.dtype)
noise_transitions = num_steps - 1
noise_transform = ops.convert_to_tensor(self.get_noise_transform(),
self.dtype)
noise_covariance_transformed = math_ops.matmul(
math_ops.matmul(noise_transform,
self.state_transition_noise_covariance),
noise_transform,
adjoint_b=True)
# Un-packing the transformed noise as:
# [[a b]
# [c d]]
a, b, c, d = array_ops.unstack(
array_ops.reshape(noise_covariance_transformed, [-1, 4]), axis=1)
sum_of_first_n = noise_transitions * (noise_transitions + 1) / 2
sum_of_first_n_squares = sum_of_first_n * (2 * noise_transitions + 1) / 3
return _pack_and_reshape(
num_steps * a + sum_of_first_n * (b + c) + sum_of_first_n_squares * d,
num_steps * b + sum_of_first_n * d,
num_steps * c + sum_of_first_n * d,
num_steps * d) | [
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] | https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/timeseries/python/timeseries/state_space_models/level_trend.py#L93-L120 | |
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/nn_ops.py | python | KLDivLoss.__init__ | (self, reduction='mean') | Initialize KLDivLoss. | Initialize KLDivLoss. | [
"Initialize",
"KLDivLoss",
"."
] | def __init__(self, reduction='mean'):
"""Initialize KLDivLoss."""
self.reduction = validator.check_string(reduction, ['none', 'mean', 'sum'], 'reduction', self.name) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/lib2to3/pytree.py | python | WildcardPattern._bare_name_matches | (self, nodes) | return count, r | Special optimized matcher for bare_name. | Special optimized matcher for bare_name. | [
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"optimized",
"matcher",
"for",
"bare_name",
"."
] | def _bare_name_matches(self, nodes):
"""Special optimized matcher for bare_name."""
count = 0
r = {}
done = False
max = len(nodes)
while not done and count < max:
done = True
for leaf in self.content:
if leaf[0].match(nodes[count], r):
count += 1
done = False
break
r[self.name] = nodes[:count]
return count, r | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/CommandBody.py | python | CommandBody.accept | (self, visitor) | The operation in Visitor design pattern that takes a visitor as an argument
and calls the visitor's method that corresponds to this element.
@raise Exception: if the given visitor is not a subclass of AbstractVisitor | The operation in Visitor design pattern that takes a visitor as an argument
and calls the visitor's method that corresponds to this element. | [
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"""
The operation in Visitor design pattern that takes a visitor as an argument
and calls the visitor's method that corresponds to this element.
@raise Exception: if the given visitor is not a subclass of AbstractVisitor
"""
# visitor should be extended from the AbstractVisitor class
if issubclass(visitor.__class__, AbstractVisitor.AbstractVisitor):
visitor.commandBodyVisit(self.__obj)
else:
DEBUG.error(
"startCommandVisit.accept() - the given visitor is not a subclass of AbstractVisitor!"
)
raise Exception(
"startCommandVisit.accept() - the given visitor is not a subclass of AbstractVisitor!"
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings._GetStandaloneBinaryPath | (self) | return target_prefix + target + target_ext | Returns the name of the non-bundle binary represented by this target.
E.g. hello_world. Only valid for non-bundles. | Returns the name of the non-bundle binary represented by this target.
E.g. hello_world. Only valid for non-bundles. | [
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"""Returns the name of the non-bundle binary represented by this target.
E.g. hello_world. Only valid for non-bundles."""
assert not self._IsBundle()
assert self.spec['type'] in (
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target = self.spec['target_name']
if self.spec['type'] == 'static_library':
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target = target[3:]
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target = target[3:]
target_prefix = self._GetStandaloneExecutablePrefix()
target = self.spec.get('product_name', target)
target_ext = self._GetStandaloneExecutableSuffix()
return target_prefix + target + target_ext | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/distributions/bijector_impl.py | python | Bijector._lookup | (self, x=None, y=None, kwargs=None) | return mapping | Helper which retrieves mapping info from forward/inverse dicts. | Helper which retrieves mapping info from forward/inverse dicts. | [
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"""Helper which retrieves mapping info from forward/inverse dicts."""
mapping = _Mapping(x=x, y=y, kwargs=kwargs)
# Since _cache requires both x,y to be set, we only need to do one cache
# lookup since the mapping is always in both or neither.
if mapping.x is not None:
return self._from_x.get(mapping.x_key, mapping)
if mapping.y is not None:
return self._from_y.get(mapping.y_key, mapping)
return mapping | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/ma/mrecords.py | python | fromtextfile | (fname, delimitor=None, commentchar='#', missingchar='',
varnames=None, vartypes=None) | return fromarrays(_datalist, dtype=mdescr) | Creates a mrecarray from data stored in the file `filename`.
Parameters
----------
fname : {file name/handle}
Handle of an opened file.
delimitor : {None, string}, optional
Alphanumeric character used to separate columns in the file.
If None, any (group of) white spacestring(s) will be used.
commentchar : {'#', string}, optional
Alphanumeric character used to mark the start of a comment.
missingchar : {'', string}, optional
String indicating missing data, and used to create the masks.
varnames : {None, sequence}, optional
Sequence of the variable names. If None, a list will be created from
the first non empty line of the file.
vartypes : {None, sequence}, optional
Sequence of the variables dtypes. If None, it will be estimated from
the first non-commented line.
Ultra simple: the varnames are in the header, one line | Creates a mrecarray from data stored in the file `filename`. | [
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varnames=None, vartypes=None):
"""
Creates a mrecarray from data stored in the file `filename`.
Parameters
----------
fname : {file name/handle}
Handle of an opened file.
delimitor : {None, string}, optional
Alphanumeric character used to separate columns in the file.
If None, any (group of) white spacestring(s) will be used.
commentchar : {'#', string}, optional
Alphanumeric character used to mark the start of a comment.
missingchar : {'', string}, optional
String indicating missing data, and used to create the masks.
varnames : {None, sequence}, optional
Sequence of the variable names. If None, a list will be created from
the first non empty line of the file.
vartypes : {None, sequence}, optional
Sequence of the variables dtypes. If None, it will be estimated from
the first non-commented line.
Ultra simple: the varnames are in the header, one line"""
# Try to open the file.
ftext = openfile(fname)
# Get the first non-empty line as the varnames
while True:
line = ftext.readline()
firstline = line[:line.find(commentchar)].strip()
_varnames = firstline.split(delimitor)
if len(_varnames) > 1:
break
if varnames is None:
varnames = _varnames
# Get the data.
_variables = masked_array([line.strip().split(delimitor) for line in ftext
if line[0] != commentchar and len(line) > 1])
(_, nfields) = _variables.shape
ftext.close()
# Try to guess the dtype.
if vartypes is None:
vartypes = _guessvartypes(_variables[0])
else:
vartypes = [np.dtype(v) for v in vartypes]
if len(vartypes) != nfields:
msg = "Attempting to %i dtypes for %i fields!"
msg += " Reverting to default."
warnings.warn(msg % (len(vartypes), nfields), stacklevel=2)
vartypes = _guessvartypes(_variables[0])
# Construct the descriptor.
mdescr = [(n, f) for (n, f) in zip(varnames, vartypes)]
mfillv = [ma.default_fill_value(f) for f in vartypes]
# Get the data and the mask.
# We just need a list of masked_arrays. It's easier to create it like that:
_mask = (_variables.T == missingchar)
_datalist = [masked_array(a, mask=m, dtype=t, fill_value=f)
for (a, m, t, f) in zip(_variables.T, _mask, vartypes, mfillv)]
return fromarrays(_datalist, dtype=mdescr) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/pipeline/sync/skip/skippable.py | python | Skippable.poppable | (self) | Iterates over namespaced skip names to be popped. | Iterates over namespaced skip names to be popped. | [
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"""Iterates over namespaced skip names to be popped."""
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yield self.namespaced(name) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/asyncio/sslproto.py | python | SSLProtocol.resume_writing | (self) | Called when the low-level transport's buffer drains below
the low-water mark. | Called when the low-level transport's buffer drains below
the low-water mark. | [
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"""Called when the low-level transport's buffer drains below
the low-water mark.
"""
self._app_protocol.resume_writing() | [
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PixarAnimationStudios/USD | faed18ce62c8736b02413635b584a2f637156bad | pxr/usdImaging/usdviewq/selectionDataModel.py | python | Blocker.blocked | (self) | return self._count > 0 | Returns True if in the 'blocked' state, and False otherwise. | Returns True if in the 'blocked' state, and False otherwise. | [
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | pynest/nest/lib/hl_api_connections.py | python | Connect | (pre, post, conn_spec=None, syn_spec=None,
return_synapsecollection=False) | Connect `pre` nodes to `post` nodes.
Nodes in `pre` and `post` are connected using the specified connectivity
(`all-to-all` by default) and synapse type (:cpp:class:`static_synapse <nest::static_synapse>` by default).
Details depend on the connectivity rule.
Parameters
----------
pre : NodeCollection (or array-like object)
Presynaptic nodes, as object representing the IDs of the nodes
post : NodeCollection (or array-like object)
Postsynaptic nodes, as object representing the IDs of the nodes
conn_spec : str or dict, optional
Specifies connectivity rule, see below
syn_spec : str or dict, optional
Specifies synapse model, see below
return_synapsecollection: bool
Specifies whether or not we should return a :py:class:`.SynapseCollection` of pre and post connections
Raises
------
kernel.NESTError
Notes
-----
It is possible to connect NumPy arrays of node IDs one-to-one by passing the arrays as `pre` and `post`,
specifying `'one_to_one'` for `conn_spec`.
In that case, the arrays may contain non-unique IDs.
You may also specify weight, delay, and receptor type for each connection as NumPy arrays in the `syn_spec`
dictionary.
This feature is currently not available when MPI is used; trying to connect arrays with more than one
MPI process will raise an error.
If pre and post have spatial positions, a `mask` can be specified as a dictionary. The mask define which
nodes are considered as potential targets for each source node. Connections with spatial nodes can also
use `nest.spatial_distributions` as parameters, for instance for the probability `p`.
**Connectivity specification (conn_spec)**
Available rules and associated parameters::
- 'all_to_all' (default)
- 'one_to_one'
- 'fixed_indegree', 'indegree'
- 'fixed_outdegree', 'outdegree'
- 'fixed_total_number', 'N'
- 'pairwise_bernoulli', 'p'
- 'symmetric_pairwise_bernoulli', 'p'
See :ref:`conn_rules` for more details, including example usage.
**Synapse specification (syn_spec)**
The synapse model and its properties can be given either as a string
identifying a specific synapse model (default: :cpp:class:`static_synapse <nest::static_synapse>`) or
as a dictionary specifying the synapse model and its parameters.
Available keys in the synapse specification dictionary are::
- 'synapse_model'
- 'weight'
- 'delay'
- 'receptor_type'
- any parameters specific to the selected synapse model.
See :ref:`synapse_spec` for details, including example usage.
All parameters are optional and if not specified, the default values
of the synapse model will be used. The key 'synapse_model' identifies the
synapse model, this can be one of NEST's built-in synapse models
or a user-defined model created via :py:func:`.CopyModel`.
If `synapse_model` is not specified the default model :cpp:class:`static_synapse <nest::static_synapse>`
will be used.
Distributed parameters can be defined through NEST's different parametertypes. NEST has various
random parameters, spatial parameters and distributions (only accesseable for nodes with spatial positions),
logical expressions and mathematical expressions, which can be used to define node and connection parameters.
To see all available parameters, see documentation defined in distributions, logic, math,
random and spatial modules.
See Also
---------
:ref:`connection_management` | Connect `pre` nodes to `post` nodes. | [
"Connect",
"pre",
"nodes",
"to",
"post",
"nodes",
"."
] | def Connect(pre, post, conn_spec=None, syn_spec=None,
return_synapsecollection=False):
"""
Connect `pre` nodes to `post` nodes.
Nodes in `pre` and `post` are connected using the specified connectivity
(`all-to-all` by default) and synapse type (:cpp:class:`static_synapse <nest::static_synapse>` by default).
Details depend on the connectivity rule.
Parameters
----------
pre : NodeCollection (or array-like object)
Presynaptic nodes, as object representing the IDs of the nodes
post : NodeCollection (or array-like object)
Postsynaptic nodes, as object representing the IDs of the nodes
conn_spec : str or dict, optional
Specifies connectivity rule, see below
syn_spec : str or dict, optional
Specifies synapse model, see below
return_synapsecollection: bool
Specifies whether or not we should return a :py:class:`.SynapseCollection` of pre and post connections
Raises
------
kernel.NESTError
Notes
-----
It is possible to connect NumPy arrays of node IDs one-to-one by passing the arrays as `pre` and `post`,
specifying `'one_to_one'` for `conn_spec`.
In that case, the arrays may contain non-unique IDs.
You may also specify weight, delay, and receptor type for each connection as NumPy arrays in the `syn_spec`
dictionary.
This feature is currently not available when MPI is used; trying to connect arrays with more than one
MPI process will raise an error.
If pre and post have spatial positions, a `mask` can be specified as a dictionary. The mask define which
nodes are considered as potential targets for each source node. Connections with spatial nodes can also
use `nest.spatial_distributions` as parameters, for instance for the probability `p`.
**Connectivity specification (conn_spec)**
Available rules and associated parameters::
- 'all_to_all' (default)
- 'one_to_one'
- 'fixed_indegree', 'indegree'
- 'fixed_outdegree', 'outdegree'
- 'fixed_total_number', 'N'
- 'pairwise_bernoulli', 'p'
- 'symmetric_pairwise_bernoulli', 'p'
See :ref:`conn_rules` for more details, including example usage.
**Synapse specification (syn_spec)**
The synapse model and its properties can be given either as a string
identifying a specific synapse model (default: :cpp:class:`static_synapse <nest::static_synapse>`) or
as a dictionary specifying the synapse model and its parameters.
Available keys in the synapse specification dictionary are::
- 'synapse_model'
- 'weight'
- 'delay'
- 'receptor_type'
- any parameters specific to the selected synapse model.
See :ref:`synapse_spec` for details, including example usage.
All parameters are optional and if not specified, the default values
of the synapse model will be used. The key 'synapse_model' identifies the
synapse model, this can be one of NEST's built-in synapse models
or a user-defined model created via :py:func:`.CopyModel`.
If `synapse_model` is not specified the default model :cpp:class:`static_synapse <nest::static_synapse>`
will be used.
Distributed parameters can be defined through NEST's different parametertypes. NEST has various
random parameters, spatial parameters and distributions (only accesseable for nodes with spatial positions),
logical expressions and mathematical expressions, which can be used to define node and connection parameters.
To see all available parameters, see documentation defined in distributions, logic, math,
random and spatial modules.
See Also
---------
:ref:`connection_management`
"""
use_connect_arrays, pre, post = _process_input_nodes(pre, post, conn_spec)
# Converting conn_spec to dict, without putting it on the SLI stack.
processed_conn_spec = _process_conn_spec(conn_spec)
# If syn_spec is given, its contents are checked, and if needed converted
# to the right formats.
processed_syn_spec = _process_syn_spec(
syn_spec, processed_conn_spec, len(pre), len(post), use_connect_arrays)
# If pre and post are arrays of node IDs, and conn_spec is unspecified,
# the node IDs are connected one-to-one.
if use_connect_arrays:
if return_synapsecollection:
raise ValueError("SynapseCollection cannot be returned when connecting two arrays of node IDs")
if processed_syn_spec is None:
raise ValueError("When connecting two arrays of node IDs, the synapse specification dictionary must "
"be specified and contain at least the synapse model.")
# In case of misspelling
if "weights" in processed_syn_spec:
raise ValueError("To specify weights, use 'weight' in syn_spec.")
if "delays" in processed_syn_spec:
raise ValueError("To specify delays, use 'delay' in syn_spec.")
weights = numpy.array(processed_syn_spec['weight']) if 'weight' in processed_syn_spec else None
delays = numpy.array(processed_syn_spec['delay']) if 'delay' in processed_syn_spec else None
try:
synapse_model = processed_syn_spec['synapse_model']
except KeyError:
raise ValueError("When connecting two arrays of node IDs, the synapse specification dictionary must "
"contain a synapse model.")
# Split remaining syn_spec entries to key and value arrays
reduced_processed_syn_spec = {k: processed_syn_spec[k]
for k in set(processed_syn_spec.keys()).difference(
set(('weight', 'delay', 'synapse_model')))}
if len(reduced_processed_syn_spec) > 0:
syn_param_keys = numpy.array(list(reduced_processed_syn_spec.keys()), dtype=numpy.string_)
syn_param_values = numpy.zeros([len(reduced_processed_syn_spec), len(pre)])
for i, value in enumerate(reduced_processed_syn_spec.values()):
syn_param_values[i] = value
else:
syn_param_keys = None
syn_param_values = None
connect_arrays(pre, post, weights, delays, synapse_model, syn_param_keys, syn_param_values)
return
sps(pre)
sps(post)
if not isinstance(pre, NodeCollection):
raise TypeError("Not implemented, presynaptic nodes must be a NodeCollection")
if not isinstance(post, NodeCollection):
raise TypeError("Not implemented, postsynaptic nodes must be a NodeCollection")
# In some cases we must connect with ConnectLayers instead.
if _connect_layers_needed(processed_conn_spec, processed_syn_spec):
# Check that pre and post are layers
if pre.spatial is None:
raise TypeError("Presynaptic NodeCollection must have spatial information")
if post.spatial is None:
raise TypeError("Presynaptic NodeCollection must have spatial information")
# Create the projection dictionary
spatial_projections = _process_spatial_projections(
processed_conn_spec, processed_syn_spec)
# Connect using ConnectLayers
_connect_spatial(pre, post, spatial_projections)
else:
sps(processed_conn_spec)
if processed_syn_spec is not None:
sps(processed_syn_spec)
sr('Connect')
if return_synapsecollection:
return GetConnections(pre, post) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/sql.py | python | SQLTable._query_iterator | (
self, result, chunksize, columns, coerce_float=True, parse_dates=None
) | Return generator through chunked result set. | Return generator through chunked result set. | [
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] | def _query_iterator(
self, result, chunksize, columns, coerce_float=True, parse_dates=None
):
"""Return generator through chunked result set."""
while True:
data = result.fetchmany(chunksize)
if not data:
break
else:
self.frame = DataFrame.from_records(
data, columns=columns, coerce_float=coerce_float
)
self._harmonize_columns(parse_dates=parse_dates)
if self.index is not None:
self.frame.set_index(self.index, inplace=True)
yield self.frame | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | build/compare-mozconfig/compare-mozconfigs-wrapper.py | python | main | () | A wrapper script that calls compare-mozconfig.py
based on the platform that the machine is building for | A wrapper script that calls compare-mozconfig.py
based on the platform that the machine is building for | [
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""" A wrapper script that calls compare-mozconfig.py
based on the platform that the machine is building for"""
platform = determine_platform()
if platform is not None:
python_exe = substs['PYTHON']
topsrcdir = substs['top_srcdir']
# construct paths and args for compare-mozconfig
browser_dir = path.join(topsrcdir, 'browser')
script_path = path.join(topsrcdir, 'build/compare-mozconfig/compare-mozconfigs.py')
whitelist_path = path.join(browser_dir, 'config/mozconfigs/whitelist')
beta_mozconfig_path = path.join(browser_dir, 'config/mozconfigs', platform, 'beta')
release_mozconfig_path = path.join(browser_dir, 'config/mozconfigs', platform, 'release')
nightly_mozconfig_path = path.join(browser_dir, 'config/mozconfigs', platform, 'nightly')
log.info("Comparing beta against nightly mozconfigs")
ret_code = subprocess.call([python_exe, script_path, '--whitelist',
whitelist_path, '--no-download',
platform + ',' + beta_mozconfig_path +
',' + nightly_mozconfig_path])
if ret_code > 0:
return ret_code
log.info("Comparing release against nightly mozconfigs")
ret_code = subprocess.call([python_exe, script_path, '--whitelist',
whitelist_path, '--no-download',
platform + ',' + release_mozconfig_path +
',' + nightly_mozconfig_path])
return ret_code | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/idlelib/configHandler.py | python | IdleConfParser.Get | (self, section, option, type=None, default=None, raw=False) | Get an option value for given section/option or return default.
If type is specified, return as type. | Get an option value for given section/option or return default.
If type is specified, return as type. | [
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"""
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"""
if not self.has_option(section, option):
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if type=='bool':
return self.getboolean(section, option)
elif type=='int':
return self.getint(section, option)
else:
return self.get(section, option, raw=raw) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/data_selectors/cyclic_data_selector_view.py | python | CyclicDataSelectorView.set_data_combo_box_label | (self, text: str) | Sets the label text next to the data selector combobox. | Sets the label text next to the data selector combobox. | [
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bayandin/chromedriver | d40a2092b50f2fca817221eeb5ea093e0e642c10 | log_replay/client_replay.py | python | Replayer.__init__ | (self, logfile, server, chrome_binary, base_url=None) | Initialize the Replayer instance.
Args:
logfile: log file handle object to replay from.
options: command-line options; see below. Needs at least
options.chromedriver for the ChromeDriver binary.
base_url: string, base of the url to replace in the logged urls (useful
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"""Initialize the Replayer instance.
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logfile: log file handle object to replay from.
options: command-line options; see below. Needs at least
options.chromedriver for the ChromeDriver binary.
base_url: string, base of the url to replace in the logged urls (useful
for when ports change). If any value is passed here, it overrides any
base url passed in options.
"""
# TODO(cwinstanley) Add Android support and perhaps support for other
# chromedriver command line options.
self.executor = command_executor.CommandExecutor(server.GetUrl())
self.command_sequence = CommandSequence(logfile, base_url=base_url,
chrome_binary=chrome_binary) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/wxPIA_book/Chapter-14/grid_editor.py | python | UpCaseCellEditor.SetSize | (self, rect) | Called to position/size the edit control within the cell rectangle.
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self._tc.SetDimensions(rect.x, rect.y, rect.width+2, rect.height+2,
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/samples/wxPIA_book/Chapter-14/grid_editor.py#L23-L30 | ||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/cloud/common/google_instance_helper.py | python | GoogleInstanceHelper.DeleteInstanceGroup | (self, tag) | return False | Deletes the instance group identified by tag. If instances are still
running in this group, they are deleted as well. | Deletes the instance group identified by tag. If instances are still
running in this group, they are deleted as well. | [
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] | def DeleteInstanceGroup(self, tag):
"""Deletes the instance group identified by tag. If instances are still
running in this group, they are deleted as well.
"""
group_name = self._GetInstanceGroupName(tag)
request = self._compute_api.instanceGroupManagers().delete(
project=self._project, zone=self._zone,
instanceGroupManager=group_name)
(success, result) = self._ExecuteApiRequest(request)
if success:
return True
if google_error_helper.GetErrorReason(result) == \
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# The group does not exist, nothing to do.
self._logger.warning('Instance group not found: ' + group_name)
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/tempfile.py | python | mkdtemp | (suffix="", prefix=template, dir=None) | User-callable function to create and return a unique temporary
directory. The return value is the pathname of the directory.
Arguments are as for mkstemp, except that the 'text' argument is
not accepted.
The directory is readable, writable, and searchable only by the
creating user.
Caller is responsible for deleting the directory when done with it. | User-callable function to create and return a unique temporary
directory. The return value is the pathname of the directory. | [
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] | def mkdtemp(suffix="", prefix=template, dir=None):
"""User-callable function to create and return a unique temporary
directory. The return value is the pathname of the directory.
Arguments are as for mkstemp, except that the 'text' argument is
not accepted.
The directory is readable, writable, and searchable only by the
creating user.
Caller is responsible for deleting the directory when done with it.
"""
if dir is None:
dir = gettempdir()
names = _get_candidate_names()
for seq in xrange(TMP_MAX):
name = names.next()
file = _os.path.join(dir, prefix + name + suffix)
try:
_os.mkdir(file, 0700)
return file
except OSError, e:
if e.errno == _errno.EEXIST:
continue # try again
raise
raise IOError, (_errno.EEXIST, "No usable temporary directory name found") | [
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ivansafrin/Polycode | 37a40fefe194ec7f6e9d1257f3bb3517b0a168bc | Bindings/Scripts/create_lua_library/zipfile.py | python | _ZipDecrypter.__call__ | (self, c) | return c | Decrypt a single character. | Decrypt a single character. | [
"Decrypt",
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"single",
"character",
"."
] | def __call__(self, c):
"""Decrypt a single character."""
c = ord(c)
k = self.key2 | 2
c = c ^ (((k * (k^1)) >> 8) & 255)
c = chr(c)
self._UpdateKeys(c)
return c | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/html5lib-python/html5lib/treebuilders/etree_lxml.py | python | TreeBuilder.insertRoot | (self, token) | Create the document root | Create the document root | [
"Create",
"the",
"document",
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] | def insertRoot(self, token):
"""Create the document root"""
# Because of the way libxml2 works, it doesn't seem to be possible to
# alter information like the doctype after the tree has been parsed.
# Therefore we need to use the built-in parser to create our iniial
# tree, after which we can add elements like normal
docStr = ""
if self.doctype:
assert self.doctype.name
docStr += "<!DOCTYPE %s" % self.doctype.name
if (self.doctype.publicId is not None or
self.doctype.systemId is not None):
docStr += (' PUBLIC "%s" ' %
(self.infosetFilter.coercePubid(self.doctype.publicId or "")))
if self.doctype.systemId:
sysid = self.doctype.systemId
if sysid.find("'") >= 0 and sysid.find('"') >= 0:
warnings.warn("DOCTYPE system cannot contain single and double quotes", DataLossWarning)
sysid = sysid.replace("'", 'U00027')
if sysid.find("'") >= 0:
docStr += '"%s"' % sysid
else:
docStr += "'%s'" % sysid
else:
docStr += "''"
docStr += ">"
if self.doctype.name != token["name"]:
warnings.warn("lxml cannot represent doctype with a different name to the root element", DataLossWarning)
docStr += "<THIS_SHOULD_NEVER_APPEAR_PUBLICLY/>"
root = etree.fromstring(docStr)
# Append the initial comments:
for comment_token in self.initial_comments:
root.addprevious(etree.Comment(comment_token["data"]))
# Create the root document and add the ElementTree to it
self.document = self.documentClass()
self.document._elementTree = root.getroottree()
# Give the root element the right name
name = token["name"]
namespace = token.get("namespace", self.defaultNamespace)
if namespace is None:
etree_tag = name
else:
etree_tag = "{%s}%s" % (namespace, name)
root.tag = etree_tag
# Add the root element to the internal child/open data structures
root_element = self.elementClass(name, namespace)
root_element._element = root
self.document._childNodes.append(root_element)
self.openElements.append(root_element)
# Reset to the default insert comment function
self.insertComment = self.insertCommentMain | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/signaltools.py | python | correlate | (in1, in2, mode='full', method='auto') | r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')``
then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> import matplotlib.pyplot as plt
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show() | r"""
Cross-correlate two N-dimensional arrays. | [
"r",
"Cross",
"-",
"correlate",
"two",
"N",
"-",
"dimensional",
"arrays",
"."
] | def correlate(in1, in2, mode='full', method='auto'):
r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')``
then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> import matplotlib.pyplot as plt
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
in1 = asarray(in1)
in2 = asarray(in2)
if in1.ndim == in2.ndim == 0:
return in1 * in2.conj()
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
# Don't use _valfrommode, since correlate should not accept numeric modes
try:
val = _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
# this either calls fftconvolve or this function with method=='direct'
if method in ('fft', 'auto'):
return convolve(in1, _reverse_and_conj(in2), mode, method)
elif method == 'direct':
# fastpath to faster numpy.correlate for 1d inputs when possible
if _np_conv_ok(in1, in2, mode):
return np.correlate(in1, in2, mode)
# _correlateND is far slower when in2.size > in1.size, so swap them
# and then undo the effect afterward if mode == 'full'. Also, it fails
# with 'valid' mode if in2 is larger than in1, so swap those, too.
# Don't swap inputs for 'same' mode, since shape of in1 matters.
swapped_inputs = ((mode == 'full') and (in2.size > in1.size) or
_inputs_swap_needed(mode, in1.shape, in2.shape))
if swapped_inputs:
in1, in2 = in2, in1
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = tuple(slice(0, i) for i in in1.shape)
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
if swapped_inputs:
# Reverse and conjugate to undo the effect of swapping inputs
z = _reverse_and_conj(z)
return z
else:
raise ValueError("Acceptable method flags are 'auto',"
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/markdown/extensions/smart_strong.py | python | SmartEmphasisExtension.extendMarkdown | (self, md, md_globals) | Modify inline patterns. | Modify inline patterns. | [
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"inline",
"patterns",
"."
] | def extendMarkdown(self, md, md_globals):
""" Modify inline patterns. """
md.inlinePatterns['strong'] = SimpleTagPattern(STRONG_RE, 'strong')
md.inlinePatterns.add('strong2', SimpleTagPattern(SMART_STRONG_RE, 'strong'), '>emphasis2') | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/win/split_link/install_split_link.py | python | EscapeForCommandLineAndCString | (path) | return path.replace('\\', '\\\\').replace('"', '\\"') | Quoted sufficiently to be passed on the compile command line as a define
to be turned into a string in the target C program. | Quoted sufficiently to be passed on the compile command line as a define
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"""Quoted sufficiently to be passed on the compile command line as a define
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path = '"' + path + '"'
return path.replace('\\', '\\\\').replace('"', '\\"') | [
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Ewenwan/MVision | 97b394dfa48cb21c82cd003b1a952745e413a17f | CNN/MobileNet/MobileNet_v1_ssd_caffe/ssd_detect.py | python | parse_args | () | return parser.parse_args() | parse args | parse args | [
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] | def parse_args():
'''parse args'''
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=1, help='gpu id')
parser.add_argument('--labelmap_file',
default='labelmap_voc.prototxt')
parser.add_argument('--model_def',
default='ssd_33_deploy.prototxt')
parser.add_argument('--image_resize', default=300, type=int)
parser.add_argument('--model_weights',
default='models/ssd_33_iter_50000.caffemodel')
#parser.add_argument('--image_file', default='fish-bike.jpg')
#parser.add_argument('--image_file', default='dog.jpg')
parser.add_argument('--image_file', default='person.jpg')
return parser.parse_args() | [
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martinmoene/expected-lite | 6284387cb117ea78d973fb5b1cbff1651a8d5d9a | script/create-vcpkg.py | python | createPortfile | ( args ) | Create vcpkg portfile | Create vcpkg portfile | [
"Create",
"vcpkg",
"portfile"
] | def createPortfile( args ):
"""Create vcpkg portfile"""
output = tpl_vcpkg_portfile.format(
optpfx=cfg_cmake_optpfx, usr=args.user, prj=args.project, ref=to_ref(args.version), sha=args.sha, lic=cfg_license )
if args.verbose:
print( "Creating portfile '{f}':".format( f=portfile_path( args ) ) )
if args.verbose > 1:
print( output )
os.makedirs( os.path.dirname( portfile_path( args ) ), exist_ok=True )
with open( portfile_path( args ), 'w') as f:
print( output, file=f ) | [
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indutny/candor | 48e7260618f5091c80a3416828e2808cad3ea22e | tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCConfigurationList.HasBuildSetting | (self, key) | return 1 | Determines the state of a build setting in all XCBuildConfiguration
child objects.
If all child objects have key in their build settings, and the value is the
same in all child objects, returns 1.
If no child objects have the key in their build settings, returns 0.
If some, but not all, child objects have the key in their build settings,
or if any children have different values for the key, returns -1. | Determines the state of a build setting in all XCBuildConfiguration
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If all child objects have key in their build settings, and the value is the
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If no child objects have the key in their build settings, returns 0.
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has = None
value = None
for configuration in self._properties['buildConfigurations']:
configuration_has = configuration.HasBuildSetting(key)
if has is None:
has = configuration_has
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return -1
if configuration_has:
configuration_value = configuration.GetBuildSetting(key)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/indentation.py | python | IndentationRules._AllFunctionPropertyAssignTokens | (self, start_token, end_token) | return True | Checks if tokens are (likely) a valid function property assignment.
Args:
start_token: Start of the token range.
end_token: End of the token range.
Returns:
True if all tokens between start_token and end_token are legal tokens
within a function declaration and assignment into a property. | Checks if tokens are (likely) a valid function property assignment. | [
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] | def _AllFunctionPropertyAssignTokens(self, start_token, end_token):
"""Checks if tokens are (likely) a valid function property assignment.
Args:
start_token: Start of the token range.
end_token: End of the token range.
Returns:
True if all tokens between start_token and end_token are legal tokens
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"""
for token in tokenutil.GetTokenRange(start_token, end_token):
fn_decl_tokens = (Type.FUNCTION_DECLARATION,
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if (token.type not in fn_decl_tokens and
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francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | ctpx/ctp3/ctptd.py | python | CtpTd.onErrRtnFutureToBankByFuture | (self, ReqTransferField, RspInfoField) | 期货发起期货资金转银行错误回报 | 期货发起期货资金转银行错误回报 | [
"期货发起期货资金转银行错误回报"
] | def onErrRtnFutureToBankByFuture(self, ReqTransferField, RspInfoField):
"""期货发起期货资金转银行错误回报"""
pass | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/array_ops.py | python | DynamicShape.__init__ | (self) | init Shape | init Shape | [
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] | def __init__(self):
"""init Shape"""
self.init_prim_io_names(inputs=['tensor'], outputs=['output'])
self.add_prim_attr('is_dynamic_shape', True) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/eager/function.py | python | _GraphModeFunction._build_call_outputs | (self, func_outputs, result) | return tuple(outputs) if type(func_outputs) is tuple else outputs | Maps the fdef output list to actual output structure.
Args:
func_outputs: The outputs originally defined by the graph function. It
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result: Output lists defined by FunctionDef.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PropertyGridInterface.BeginAddChildren | (*args, **kwargs) | return _propgrid.PropertyGridInterface_BeginAddChildren(*args, **kwargs) | BeginAddChildren(self, PGPropArg id) | BeginAddChildren(self, PGPropArg id) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/auibar.py | python | AuiToolBar.SetToolOrientation | (self, orientation) | Sets the tool orientation for the toolbar items.
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Sets the tool orientation for the toolbar items.
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self._tool_orientation = orientation
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tkinter.py | python | LabelFrame.__init__ | (self, master=None, cnf={}, **kw) | Construct a labelframe widget with the parent MASTER.
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borderwidth, cursor, font, foreground,
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highlightthickness, padx, pady, relief,
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WIDGET-SPECIFIC OPTIONS
background, class, colormap, container,
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highlightthickness, padx, pady, relief,
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | ipc/ipdl/ipdl/parser.py | python | p_OptionalMessageCompress | (p) | OptionalMessageCompress : COMPRESS
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timi-liuliang/echo | 40a5a24d430eee4118314459ab7e03afcb3b8719 | thirdparty/protobuf/python/google/protobuf/text_format.py | python | ParseBool | (text) | Parse a boolean value.
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text: Text to parse.
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Boolean values parsed
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text: Text to parse.
Returns:
Boolean values parsed
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"""
if text in ('true', 't', '1'):
return True
elif text in ('false', 'f', '0'):
return False
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apple/swift | 469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893 | utils/build_swift/build_swift/shell.py | python | copy | (source, dest, echo=False) | Emulates the `cp` command to copy a file or directory. | Emulates the `cp` command to copy a file or directory. | [
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"""
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if os.path.isfile(source):
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if os.path.isdir(source):
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Window.SetOwnBackgroundColour | (*args, **kwargs) | return _core_.Window_SetOwnBackgroundColour(*args, **kwargs) | SetOwnBackgroundColour(self, Colour colour) | SetOwnBackgroundColour(self, Colour colour) | [
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"""SetOwnBackgroundColour(self, Colour colour)"""
return _core_.Window_SetOwnBackgroundColour(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | PreCheckBox | (*args, **kwargs) | return val | PreCheckBox() -> CheckBox
Precreate a CheckBox for 2-phase creation. | PreCheckBox() -> CheckBox | [
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"""
PreCheckBox() -> CheckBox
Precreate a CheckBox for 2-phase creation.
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val = _controls_.new_PreCheckBox(*args, **kwargs)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/polynomial/_polybase.py | python | ABCPolyBase.has_sametype | (self, other) | return isinstance(other, self.__class__) | Check if types match.
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Parameters
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other : object
Class instance.
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-------
bool : boolean
True if other is same class as self | Check if types match. | [
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"""Check if types match.
.. versionadded:: 1.7.0
Parameters
----------
other : object
Class instance.
Returns
-------
bool : boolean
True if other is same class as self
"""
return isinstance(other, self.__class__) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/cluster/hierarchy.py | python | ClusterNode.get_left | (self) | return self.left | Return a reference to the left child tree object.
Returns
-------
left : ClusterNode
The left child of the target node. If the node is a leaf,
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Return a reference to the left child tree object.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/nonlin.py | python | LowRankMatrix.collapse | (self) | Collapse the low-rank matrix to a full-rank one. | Collapse the low-rank matrix to a full-rank one. | [
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"""Collapse the low-rank matrix to a full-rank one."""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | Locale.GetName | (*args, **kwargs) | return _gdi_.Locale_GetName(*args, **kwargs) | GetName(self) -> String | GetName(self) -> String | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/tree.py | python | listicons | (icondir=ICONDIR) | Utility to display the available icons. | Utility to display the available icons. | [
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"""Utility to display the available icons."""
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import glob
list = glob.glob(os.path.join(icondir, "*.gif"))
list.sort()
images = []
row = column = 0
for file in list:
name = os.path.splitext(os.path.basename(file))[0]
image = PhotoImage(file=file, master=root)
images.append(image)
label = Label(root, image=image, bd=1, relief="raised")
label.grid(row=row, column=column)
label = Label(root, text=name)
label.grid(row=row+1, column=column)
column = column + 1
if column >= 10:
row = row+2
column = 0
root.images = images | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/window/rolling.py | python | _Window._create_blocks | (self) | return blocks, obj | Split data into blocks & return conformed data. | Split data into blocks & return conformed data. | [
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"""
Split data into blocks & return conformed data.
"""
obj = self._selected_obj
# filter out the on from the object
if self.on is not None and not isinstance(self.on, Index):
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obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False)
blocks = obj._to_dict_of_blocks(copy=False).values()
return blocks, obj | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/wms/ogc/implementation/impl_v130.py | python | GetCapabilitiesRequest.SetLayers | (self) | Set the layers for the capabilities xml. | Set the layers for the capabilities xml. | [
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"""Set the layers for the capabilities xml."""
# This outer, inaccessible layer is to give information just once;
# the sub-layers inherit it.
outer_layer = capabilities_wms.Layer(
# 7.2.4.7.4 The layer is area-filling => opaque
opaque=True,
# 7.2.4.7.5 -- we can subset.
noSubsets=False,
# whether we support GetFeatureInfo
queryable=False,
# -- can't request it, this is just a container.
Name=None,
Title=_TITLE)
server_layers_by_name = self.layer_obj.GetLayers(
utils.GetValue(self.parameters, "server-url"),
utils.GetValue(self.parameters, "TargetPath"))
if not server_layers_by_name:
# Raise ServiceException here.
raise ServiceException(None, "Database type is not supported.")
for layer_name, server_layer in server_layers_by_name.iteritems():
proj = server_layer.projection
wms_layer = capabilities_wms.Layer(
# 7.2.4.7.4 - Even for vector maps we always get data from
# the server, even if it's just a transparent tile. By
# jeffdonner's reading, this means that even the vector
# layers are 'opaque'.
opaque=True,
# 7.2.4.7.5 - we can subset.
noSubsets=False,
queryable=False,
Name=layer_name,
Title=server_layer.label,
# ex geo bounding box is required.
EX_GeographicBoundingBox=
capabilities_wms.EX_GeographicBoundingBox(
westBoundLongitude=-proj.MAX_LONGITUDE,
eastBoundLongitude=proj.MAX_LONGITUDE,
southBoundLatitude=-proj.MAX_LATITUDE,
northBoundLatitude=proj.MAX_LATITUDE))
for epsg_name in proj.EPSG_NAMES:
wms_layer.add_CRS(epsg_name)
map_limits = proj.AdvertizedLogOuterBounds()
bounding_boxes = []
for epsg_name in proj.EPSG_NAMES:
(min_x, min_y, max_x, max_y) = self._GetMapLimitsForEpsg(
map_limits, epsg_name)
bounding_box_object = capabilities_wms.BoundingBox(
CRS=epsg_name,
minx=min_x,
miny=min_y,
maxx=max_x,
maxy=max_y)
bounding_boxes.append(bounding_box_object)
wms_layer.set_BoundingBox(bounding_boxes)
outer_layer.add_Layer(wms_layer)
self.capabilities_xml.get_Capability().set_Layer(outer_layer) | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/v8/third_party/jinja2/lexer.py | python | Lexer.tokenize | (self, source, name=None, filename=None, state=None) | return TokenStream(self.wrap(stream, name, filename), name, filename) | Calls tokeniter + tokenize and wraps it in a token stream. | Calls tokeniter + tokenize and wraps it in a token stream. | [
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"""
stream = self.tokeniter(source, name, filename, state)
return TokenStream(self.wrap(stream, name, filename), name, filename) | [
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/wubi/backends/common/metalink.py | python | Metalink.get_dict | (self) | return dict | Return all the data as a dictionary. Used by __eq__. | Return all the data as a dictionary. Used by __eq__. | [
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"""Return all the data as a dictionary. Used by __eq__."""
dict = copy.copy(self.__dict__)
files = []
for f in self.files:
files.append(f.get_dict())
dict['files'] = files
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/scons/khEnvironment.py | python | khEnvironment.bash_escape | (value) | return "'{0}'".format(value.replace("'", "'\\''")) | Escapes a given value as a BASH string. | Escapes a given value as a BASH string. | [
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] | def bash_escape(value):
"""Escapes a given value as a BASH string."""
return "'{0}'".format(value.replace("'", "'\\''")) | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/dask_cudf/dask_cudf/core.py | python | DataFrame.to_parquet | (self, path, *args, **kwargs) | return to_parquet(self, path, *args, **kwargs) | Calls dask.dataframe.io.to_parquet with CudfEngine backend | Calls dask.dataframe.io.to_parquet with CudfEngine backend | [
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"""Calls dask.dataframe.io.to_parquet with CudfEngine backend"""
from dask_cudf.io import to_parquet
return to_parquet(self, path, *args, **kwargs) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/function.py | python | get_extra_args | () | Returns the corresponding function arguments for the captured inputs.
Returns:
If the default graph is being used to define a function, the
returned list of place holders are those used inside the function
body corresponding those returned by get_extra_inputs(). Otherwise,
returns an empty list. | Returns the corresponding function arguments for the captured inputs. | [
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"""Returns the corresponding function arguments for the captured inputs.
Returns:
If the default graph is being used to define a function, the
returned list of place holders are those used inside the function
body corresponding those returned by get_extra_inputs(). Otherwise,
returns an empty list.
"""
g = ops.get_default_graph()
if isinstance(g, _FuncGraph):
return g.extra_args
else:
return [] | [
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llvm-mirror/lldb | d01083a850f577b85501a0902b52fd0930de72c7 | third_party/Python/module/pexpect-4.6/pexpect/spawnbase.py | python | SpawnBase.read_nonblocking | (self, size=1, timeout=None) | return s | This reads data from the file descriptor.
This is a simple implementation suitable for a regular file. Subclasses using ptys or pipes should override it.
The timeout parameter is ignored. | This reads data from the file descriptor. | [
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The timeout parameter is ignored.
"""
try:
s = os.read(self.child_fd, size)
except OSError as err:
if err.args[0] == errno.EIO:
# Linux-style EOF
self.flag_eof = True
raise EOF('End Of File (EOF). Exception style platform.')
raise
if s == b'':
# BSD-style EOF
self.flag_eof = True
raise EOF('End Of File (EOF). Empty string style platform.')
s = self._decoder.decode(s, final=False)
self._log(s, 'read')
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/frame.py | python | DataFrame._ixs | (self, i: int, axis: int = 0) | Parameters
----------
i : int
axis : int
Notes
-----
If slice passed, the resulting data will be a view. | Parameters
----------
i : int
axis : int | [
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"""
Parameters
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i : int
axis : int
Notes
-----
If slice passed, the resulting data will be a view.
"""
# irow
if axis == 0:
new_values = self._data.fast_xs(i)
# if we are a copy, mark as such
copy = isinstance(new_values, np.ndarray) and new_values.base is None
result = self._constructor_sliced(
new_values,
index=self.columns,
name=self.index[i],
dtype=new_values.dtype,
)
result._set_is_copy(self, copy=copy)
return result
# icol
else:
label = self.columns[i]
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values = self._data.iget(i)
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result = self._box_col_values(values, label)
# this is a cached value, mark it so
result._set_as_cached(label, self)
return result | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | Examples/ReinforcementLearning/DeepQNeuralNetwork.py | python | DeepQAgent.observe | (self, old_state, action, reward, done) | This allows the agent to observe the output of doing the action it selected through act() on the old_state
Attributes:
old_state (Tensor[input_shape]): Previous environment state
action (int): Action done by the agent
reward (float): Reward for doing this action in the old_state environment
done (bool): Indicate if the action has terminated the environment | This allows the agent to observe the output of doing the action it selected through act() on the old_state | [
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""" This allows the agent to observe the output of doing the action it selected through act() on the old_state
Attributes:
old_state (Tensor[input_shape]): Previous environment state
action (int): Action done by the agent
reward (float): Reward for doing this action in the old_state environment
done (bool): Indicate if the action has terminated the environment
"""
self._episode_rewards.append(reward)
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetComputedDefines | (self, config) | return defines | Returns the set of defines that are injected to the defines list based
on other VS settings. | Returns the set of defines that are injected to the defines list based
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"""Returns the set of defines that are injected to the defines list based
on other VS settings."""
config = self._TargetConfig(config)
defines = []
if self._ConfigAttrib(['CharacterSet'], config) == '1':
defines.extend(('_UNICODE', 'UNICODE'))
if self._ConfigAttrib(['CharacterSet'], config) == '2':
defines.append('_MBCS')
defines.extend(self._Setting(
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | ListItem.SetWidth | (*args, **kwargs) | return _controls_.ListItem_SetWidth(*args, **kwargs) | SetWidth(self, int width) | SetWidth(self, int width) | [
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"""SetWidth(self, int width)"""
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | lldb/third_party/Python/module/pexpect-4.6/pexpect/expect.py | python | searcher_string.__init__ | (self, strings) | This creates an instance of searcher_string. This argument 'strings'
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self.eof_index = -1
self.timeout_index = -1
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opencv/opencv | 76aff8478883858f0e46746044348ebb16dc3c67 | modules/java/generator/gen_java.py | python | JavaWrapperGenerator.isSmartClass | (self, ci) | return ci.smart | Check if class stores Ptr<T>* instead of T* in nativeObj field | Check if class stores Ptr<T>* instead of T* in nativeObj field | [
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'''
Check if class stores Ptr<T>* instead of T* in nativeObj field
'''
if ci.smart != None:
return ci.smart
ci.smart = True # smart class is not properly handled in case of base/derived classes
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/vendored/requests/utils.py | python | should_bypass_proxies | (url) | return False | Returns whether we should bypass proxies or not. | Returns whether we should bypass proxies or not. | [
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Returns whether we should bypass proxies or not.
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get_proxy = lambda k: os.environ.get(k) or os.environ.get(k.upper())
# First check whether no_proxy is defined. If it is, check that the URL
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no_proxy = get_proxy('no_proxy')
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if no_proxy:
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# the end of the netloc, both with and without the port.
no_proxy = no_proxy.replace(' ', '').split(',')
ip = netloc.split(':')[0]
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try:
bypass = proxy_bypass(netloc)
except (TypeError, socket.gaierror):
bypass = False
if bypass:
return True
return False | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/sans/algorithm_detail/batch_execution.py | python | single_reduction_for_batch | (state, use_optimizations, output_mode, plot_results, output_graph, save_can=False) | return out_scale_factors, out_shift_factors | Runs a single reduction.
This function creates reduction packages which essentially contain information for a single valid reduction, run it
and store the results according to the user specified setting (output_mode). Although this is considered a single
reduction it can contain still several reductions since the SANSState object can at this point contain slice
settings which require on reduction per time slice.
:param state: a SANSState object
:param use_optimizations: if true then the optimizations of child algorithms are enabled.
:param output_mode: the output mode
:param plot_results: bool. Whether or not workspaces should be plotted as they are reduced. Currently only works
with event slice compatibility
:param output_graph: The graph object for plotting workspaces.
:param save_can: bool. whether or not to save out can workspaces | Runs a single reduction. | [
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] | def single_reduction_for_batch(state, use_optimizations, output_mode, plot_results, output_graph, save_can=False):
"""
Runs a single reduction.
This function creates reduction packages which essentially contain information for a single valid reduction, run it
and store the results according to the user specified setting (output_mode). Although this is considered a single
reduction it can contain still several reductions since the SANSState object can at this point contain slice
settings which require on reduction per time slice.
:param state: a SANSState object
:param use_optimizations: if true then the optimizations of child algorithms are enabled.
:param output_mode: the output mode
:param plot_results: bool. Whether or not workspaces should be plotted as they are reduced. Currently only works
with event slice compatibility
:param output_graph: The graph object for plotting workspaces.
:param save_can: bool. whether or not to save out can workspaces
"""
# ------------------------------------------------------------------------------------------------------------------
# Load the data
# ------------------------------------------------------------------------------------------------------------------
workspace_to_name = {SANSDataType.SAMPLE_SCATTER: "SampleScatterWorkspace",
SANSDataType.SAMPLE_TRANSMISSION: "SampleTransmissionWorkspace",
SANSDataType.SAMPLE_DIRECT: "SampleDirectWorkspace",
SANSDataType.CAN_SCATTER: "CanScatterWorkspace",
SANSDataType.CAN_TRANSMISSION: "CanTransmissionWorkspace",
SANSDataType.CAN_DIRECT: "CanDirectWorkspace"}
workspace_to_monitor = {SANSDataType.SAMPLE_SCATTER: "SampleScatterMonitorWorkspace",
SANSDataType.CAN_SCATTER: "CanScatterMonitorWorkspace"}
workspaces, monitors = provide_loaded_data(state, use_optimizations, workspace_to_name, workspace_to_monitor)
# ------------------------------------------------------------------------------------------------------------------
# Get reduction settings
# Split into individual bundles which can be reduced individually. We split here if we have multiple periods.
# ------------------------------------------------------------------------------------------------------------------
reduction_packages = get_reduction_packages(state, workspaces, monitors)
split_for_event_slices = reduction_packages_require_splitting_for_event_slices(reduction_packages)
event_slice_optimisation, reduction_packages = select_reduction_alg(split_for_event_slices,
state.compatibility.use_compatibility_mode,
state.compatibility.use_event_slice_optimisation,
reduction_packages)
# ------------------------------------------------------------------------------------------------------------------
# Run reductions (one at a time)
# ------------------------------------------------------------------------------------------------------------------
single_reduction_name = "SANSSingleReduction"
single_reduction_options = {"UseOptimizations": use_optimizations,
"SaveCan": save_can}
alg_version = 2 if event_slice_optimisation else 1
reduction_alg = create_managed_non_child_algorithm(single_reduction_name, version=alg_version,
**single_reduction_options)
reduction_alg.setChild(False)
reduction_alg.setAlwaysStoreInADS(False)
# Perform the data reduction
for reduction_package in reduction_packages:
# -----------------------------------
# Set the properties on the algorithm
# -----------------------------------
set_properties_for_reduction_algorithm(reduction_alg, reduction_package,
workspace_to_name, workspace_to_monitor,
event_slice_optimisation=event_slice_optimisation)
# -----------------------------------
# Run the reduction
# -----------------------------------
reduction_alg.execute()
# -----------------------------------
# Get the output of the algorithm
# -----------------------------------
_get_ws_from_alg(reduction_alg, reduction_package)
set_alg_output_names(reduction_package, event_slice_optimisation)
out_scale_factor, out_shift_factor = get_shift_and_scale_factors_from_algorithm(reduction_alg,
event_slice_optimisation)
reduction_package.out_scale_factor = out_scale_factor
reduction_package.out_shift_factor = out_shift_factor
if not event_slice_optimisation and plot_results:
# Plot results is intended to show the result of each workspace/slice as it is reduced
# as we reduce in bulk, it is not possible to plot live results while in event_slice mode
plot_workspace(reduction_package, output_graph)
# -----------------------------------
# The workspaces are already on the ADS, but should potentially be grouped
# -----------------------------------
group_workspaces_if_required(reduction_package, output_mode, save_can,
event_slice_optimisation=event_slice_optimisation)
data = state.data
additional_run_numbers = {"SampleTransmissionRunNumber":
"" if data.sample_transmission is None else str(data.sample_transmission),
"SampleDirectRunNumber":
"" if data.sample_direct is None else str(data.sample_direct),
"CanScatterRunNumber":
"" if data.can_scatter is None else str(data.can_scatter),
"CanDirectRunNumber":
"" if data.can_direct is None else str(data.can_direct)}
# --------------------------------
# Perform output of all workspaces
# --------------------------------
# We have three options here
# 1. PublishToADS:
# * This means we can leave it as it is
# 2. SaveToFile:
# * This means we need to save out the reduced data
# * Then we need to delete the reduced data from the ADS
# 3. Both:
# * This means that we need to save out the reduced data
# * The data is already on the ADS, so do nothing
if output_mode is OutputMode.SAVE_TO_FILE:
save_to_file(reduction_packages, save_can, additional_run_numbers, event_slice_optimisation=event_slice_optimisation)
delete_reduced_workspaces(reduction_packages)
elif output_mode is OutputMode.BOTH:
save_to_file(reduction_packages, save_can, additional_run_numbers, event_slice_optimisation=event_slice_optimisation)
# -----------------------------------------------------------------------
# Clean up other workspaces if the optimizations have not been turned on.
# -----------------------------------------------------------------------
if not use_optimizations:
delete_optimization_workspaces(reduction_packages, workspaces, monitors, save_can)
out_scale_factors = []
out_shift_factors = []
for reduction_package in reduction_packages:
out_scale_factors.extend(reduction_package.out_scale_factor)
out_shift_factors.extend(reduction_package.out_shift_factor)
return out_scale_factors, out_shift_factors | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/xml/sax/xmlreader.py | python | XMLReader.setContentHandler | (self, handler) | Registers a new object to receive document content events. | Registers a new object to receive document content events. | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/Maya_AnimationRiggingTools/ARTv1/MayaTools/General/Scripts/P4.py | python | Map.is_empty | (self) | return self.count() == 0 | Returns True if this map has no entries yet, otherwise False | Returns True if this map has no entries yet, otherwise False | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | demo/VART/resnet50_mt_py/resnet50.py | python | main | (argv) | image list to be run | image list to be run | [
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global threadnum
listimage = os.listdir(calib_image_dir)
threadAll = []
threadnum = int(argv[1])
i = 0
global runTotall
runTotall = len(listimage)
g = xir.Graph.deserialize(argv[2])
subgraphs = get_child_subgraph_dpu(g)
assert len(subgraphs) == 1 # only one DPU kernel
all_dpu_runners = []
for i in range(int(threadnum)):
all_dpu_runners.append(vart.Runner.create_runner(subgraphs[0], "run"))
input_fixpos = all_dpu_runners[0].get_input_tensors()[0].get_attr("fix_point")
input_scale = 2**input_fixpos
"""image list to be run """
img = []
for i in range(runTotall):
path = os.path.join(calib_image_dir, listimage[i])
img.append(preprocess_one_image_fn(path, input_scale))
"""
The cnt variable is used to control the number of times a single-thread DPU runs.
Users can modify the value according to actual needs. It is not recommended to use
too small number when there are few input images, for example:
1. If users can only provide very few images, e.g. only 1 image, they should set
a relatively large number such as 360 to measure the average performance;
2. If users provide a huge dataset, e.g. 50000 images in the directory, they can
use the variable to control the test time, and no need to run the whole dataset.
"""
cnt = 360
"""run with batch """
time_start = time.time()
for i in range(int(threadnum)):
t1 = threading.Thread(target=runResnet50, args=(all_dpu_runners[i], img, cnt))
threadAll.append(t1)
for x in threadAll:
x.start()
for x in threadAll:
x.join()
del all_dpu_runners
time_end = time.time()
timetotal = time_end - time_start
total_frames = cnt * int(threadnum)
fps = float(total_frames / timetotal)
print(
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% (fps, total_frames, timetotal)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/jira/client.py | python | JIRA.assign_issue | (self, issue, assignee) | return True | Assign an issue to a user. None will set it to unassigned. -1 will set it to Automatic.
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"""
url = self._options['server'] + \
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payload = {'name': assignee}
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raise_on_error(r)
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/build/android/pylib/valgrind_tools.py | python | MemcheckTool.GetTestWrapper | (self) | return ValgrindTool.VG_DIR + '/' + 'vg-chrome-wrapper.sh' | Returns a string that is to be prepended to the test command line. | Returns a string that is to be prepended to the test command line. | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/decimal.py | python | Decimal.__rpow__ | (self, other, context=None) | return other.__pow__(self, context=context) | Swaps self/other and returns __pow__. | Swaps self/other and returns __pow__. | [
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other = _convert_other(other)
if other is NotImplemented:
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0xPhoeniX/MazeWalker | 4da160908419fdbf6fadf2c8b4794325f5cbcfba | MazeUI/mazeui.py | python | MazeUIPluginForm.__init__ | (self) | Initialization. | Initialization. | [
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"""
Initialization.
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idaapi.PluginForm.__init__(self)
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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/fispact.py | python | parse_spectra | (data) | return spectra | reads gamma spectra data for each timestep
returns list of length 24 corresponding to 24 gamma energy groups
data is in gamma/s/cc | reads gamma spectra data for each timestep
returns list of length 24 corresponding to 24 gamma energy groups
data is in gamma/s/cc | [
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""" reads gamma spectra data for each timestep
returns list of length 24 corresponding to 24 gamma energy groups
data is in gamma/s/cc
"""
p1 = find_ind(data, "GAMMA SPECTRUM AND ENERGIES/SECOND")
data = data[p1+7:p1+31]
spectra = []
for l in data:
spectra.append(float(l[130:141]))
return spectra | [
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lukasmonk/lucaschess | 13e2e5cb13b38a720ccf897af649054a64bcb914 | Code/SQL/DBFcache.py | python | DBFcache.buscarID | (self, xid) | return -1 | Busca el recno de un ID.
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Busca el recno de un ID.
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for r in range(self.reccount()):
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"self",
".",
"rowid",
"(",
"r",
")",
"==",
"xid",
":",
"return",
"r",
"return",
"-",
"1"
] | https://github.com/lukasmonk/lucaschess/blob/13e2e5cb13b38a720ccf897af649054a64bcb914/Code/SQL/DBFcache.py#L187-L196 |
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