nwo stringlengths 5 86 | sha stringlengths 40 40 | path stringlengths 4 189 | language stringclasses 1 value | identifier stringlengths 1 94 | parameters stringlengths 2 4.03k | argument_list stringclasses 1 value | return_statement stringlengths 0 11.5k | docstring stringlengths 1 33.2k | docstring_summary stringlengths 0 5.15k | docstring_tokens list | function stringlengths 34 151k | function_tokens list | url stringlengths 90 278 |
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raspberrypi/tools | 13474ee775d0c5ec8a7da4fb0a9fa84187abfc87 | arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/share/gdb/python/gdb/command/explore.py | python | CompoundExplorer.explore_type | (name, datatype, is_child) | return False | Function to explore struct/class and union types.
See Explorer.explore_type for more information. | Function to explore struct/class and union types.
See Explorer.explore_type for more information. | [
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"explore_type",
"for",
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"information",
"."
] | def explore_type(name, datatype, is_child):
"""Function to explore struct/class and union types.
See Explorer.explore_type for more information.
"""
type_code = datatype.code
type_desc = ""
if type_code == gdb.TYPE_CODE_STRUCT:
type_desc = "struct/class"
else:
type_desc = "union"
fields = datatype.fields()
if CompoundExplorer._get_real_field_count(fields) == 0:
if is_child:
print ("%s is a %s of type '%s' with no fields." %
(name, type_desc, str(datatype)))
Explorer.return_to_enclosing_type_prompt()
else:
print ("'%s' is a %s with no fields." % (name, type_desc))
return False
if is_child:
print ("%s is a %s of type '%s' "
"with the following fields:\n" %
(name, type_desc, str(datatype)))
else:
print ("'%s' is a %s with the following "
"fields:\n" %
(name, type_desc))
has_explorable_fields = False
current_choice = 0
choice_to_compound_field_map = { }
print_list = [ ]
for field in fields:
if field.artificial:
continue
if field.is_base_class:
field_desc = "base class"
else:
field_desc = "field"
rhs = ("<Enter %d to explore this %s of type '%s'>" %
(current_choice, field_desc, str(field.type)))
print_list.append((field.name, rhs))
choice_to_compound_field_map[str(current_choice)] = (
field.name, field.type, field_desc)
current_choice = current_choice + 1
CompoundExplorer._print_fields(print_list)
print ("")
if len(choice_to_compound_field_map) > 0:
choice = raw_input("Enter the field number of choice: ")
if choice in choice_to_compound_field_map:
if is_child:
new_name = ("%s '%s' of %s" %
(choice_to_compound_field_map[choice][2],
choice_to_compound_field_map[choice][0],
name))
else:
new_name = ("%s '%s' of '%s'" %
(choice_to_compound_field_map[choice][2],
choice_to_compound_field_map[choice][0],
name))
Explorer.explore_type(new_name,
choice_to_compound_field_map[choice][1], True)
return True
else:
if is_child:
Explorer.return_to_enclosing_type()
else:
if is_child:
Explorer.return_to_enclosing_type_prompt()
return False | [
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"... | https://github.com/raspberrypi/tools/blob/13474ee775d0c5ec8a7da4fb0a9fa84187abfc87/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/share/gdb/python/gdb/command/explore.py#L473-L547 | |
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/layers/python/layers/feature_column_ops.py | python | _check_forbidden_sequence_columns | (feature_columns) | Recursively checks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`. | Recursively checks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`. | [
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] | def _check_forbidden_sequence_columns(feature_columns):
"""Recursively checks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`."""
all_feature_columns = _gather_feature_columns(feature_columns)
for feature_column in all_feature_columns:
if isinstance(feature_column, _FORBIDDEN_SEQUENCE_COLUMNS):
raise ValueError(
'Column {} is of type {}, which is not currently supported for '
'sequences.'.format(feature_column.name,
type(feature_column).__name__)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/inputsplitter.py | python | InputSplitter.__init__ | (self) | Create a new InputSplitter instance. | Create a new InputSplitter instance. | [
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] | def __init__(self):
"""Create a new InputSplitter instance.
"""
self._buffer = []
self._compile = codeop.CommandCompiler()
self.encoding = get_input_encoding() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/dataview.py | python | PyDataViewCustomRenderer.GetView | (*args, **kwargs) | return _dataview.PyDataViewCustomRenderer_GetView(*args, **kwargs) | GetView(self) -> DataViewCtrl | GetView(self) -> DataViewCtrl | [
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] | def GetView(*args, **kwargs):
"""GetView(self) -> DataViewCtrl"""
return _dataview.PyDataViewCustomRenderer_GetView(*args, **kwargs) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2_extras/securecookie.py | python | SecureCookieSerializer.__init__ | (self, secret_key) | Initiliazes the serializer/deserializer.
:param secret_key:
A random string to be used as the HMAC secret for the cookie
signature. | Initiliazes the serializer/deserializer. | [
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"/",
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] | def __init__(self, secret_key):
"""Initiliazes the serializer/deserializer.
:param secret_key:
A random string to be used as the HMAC secret for the cookie
signature.
"""
self.secret_key = secret_key | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib2to3/pgen2/grammar.py | python | Grammar.copy | (self) | return new | Copy the grammar. | Copy the grammar. | [
"Copy",
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"."
] | def copy(self):
"""
Copy the grammar.
"""
new = self.__class__()
for dict_attr in ("symbol2number", "number2symbol", "dfas", "keywords",
"tokens", "symbol2label"):
setattr(new, dict_attr, getattr(self, dict_attr).copy())
new.labels = self.labels[:]
new.states = self.states[:]
new.start = self.start
return new | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/psutil/psutil/__init__.py | python | Process.get_ext_memory_info | (self) | return self._platform_impl.get_ext_memory_info() | Return a namedtuple with variable fields depending on the
platform representing extended memory information about
the process. All numbers are expressed in bytes. | Return a namedtuple with variable fields depending on the
platform representing extended memory information about
the process. All numbers are expressed in bytes. | [
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] | def get_ext_memory_info(self):
"""Return a namedtuple with variable fields depending on the
platform representing extended memory information about
the process. All numbers are expressed in bytes.
"""
return self._platform_impl.get_ext_memory_info() | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/serial/urlhandler/protocol_socket.py | python | Serial.cts | (self) | return True | Read terminal status line: Clear To Send | Read terminal status line: Clear To Send | [
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":",
"Clear",
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] | def cts(self):
"""Read terminal status line: Clear To Send"""
if not self.is_open:
raise portNotOpenError
if self.logger:
self.logger.info('returning dummy for cts')
return True | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/gyp/pylib/gyp/msvs_emulation.py | python | GenerateEnvironmentFiles | (toplevel_build_dir, generator_flags,
system_includes, open_out) | return cl_paths | It's not sufficient to have the absolute path to the compiler, linker,
etc. on Windows, as those tools rely on .dlls being in the PATH. We also
need to support both x86 and x64 compilers within the same build (to support
msvs_target_platform hackery). Different architectures require a different
compiler binary, and different supporting environment variables (INCLUDE,
LIB, LIBPATH). So, we extract the environment here, wrap all invocations
of compiler tools (cl, link, lib, rc, midl, etc.) via win_tool.py which
sets up the environment, and then we do not prefix the compiler with
an absolute path, instead preferring something like "cl.exe" in the rule
which will then run whichever the environment setup has put in the path.
When the following procedure to generate environment files does not
meet your requirement (e.g. for custom toolchains), you can pass
"-G ninja_use_custom_environment_files" to the gyp to suppress file
generation and use custom environment files prepared by yourself. | It's not sufficient to have the absolute path to the compiler, linker,
etc. on Windows, as those tools rely on .dlls being in the PATH. We also
need to support both x86 and x64 compilers within the same build (to support
msvs_target_platform hackery). Different architectures require a different
compiler binary, and different supporting environment variables (INCLUDE,
LIB, LIBPATH). So, we extract the environment here, wrap all invocations
of compiler tools (cl, link, lib, rc, midl, etc.) via win_tool.py which
sets up the environment, and then we do not prefix the compiler with
an absolute path, instead preferring something like "cl.exe" in the rule
which will then run whichever the environment setup has put in the path.
When the following procedure to generate environment files does not
meet your requirement (e.g. for custom toolchains), you can pass
"-G ninja_use_custom_environment_files" to the gyp to suppress file
generation and use custom environment files prepared by yourself. | [
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... | def GenerateEnvironmentFiles(toplevel_build_dir, generator_flags,
system_includes, open_out):
"""It's not sufficient to have the absolute path to the compiler, linker,
etc. on Windows, as those tools rely on .dlls being in the PATH. We also
need to support both x86 and x64 compilers within the same build (to support
msvs_target_platform hackery). Different architectures require a different
compiler binary, and different supporting environment variables (INCLUDE,
LIB, LIBPATH). So, we extract the environment here, wrap all invocations
of compiler tools (cl, link, lib, rc, midl, etc.) via win_tool.py which
sets up the environment, and then we do not prefix the compiler with
an absolute path, instead preferring something like "cl.exe" in the rule
which will then run whichever the environment setup has put in the path.
When the following procedure to generate environment files does not
meet your requirement (e.g. for custom toolchains), you can pass
"-G ninja_use_custom_environment_files" to the gyp to suppress file
generation and use custom environment files prepared by yourself."""
archs = ('x86', 'x64')
if generator_flags.get('ninja_use_custom_environment_files', 0):
cl_paths = {}
for arch in archs:
cl_paths[arch] = 'cl.exe'
return cl_paths
vs = GetVSVersion(generator_flags)
cl_paths = {}
for arch in archs:
# Extract environment variables for subprocesses.
args = vs.SetupScript(arch)
args.extend(('&&', 'set'))
popen = subprocess.Popen(
args, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
variables, _ = popen.communicate()
if PY3:
variables = variables.decode('utf-8')
if popen.returncode != 0:
raise Exception('"%s" failed with error %d' % (args, popen.returncode))
env = _ExtractImportantEnvironment(variables)
# Inject system includes from gyp files into INCLUDE.
if system_includes:
system_includes = system_includes | OrderedSet(
env.get('INCLUDE', '').split(';'))
env['INCLUDE'] = ';'.join(system_includes)
env_block = _FormatAsEnvironmentBlock(env)
f = open_out(os.path.join(toplevel_build_dir, 'environment.' + arch), 'wb')
f.write(env_block)
f.close()
# Find cl.exe location for this architecture.
args = vs.SetupScript(arch)
args.extend(('&&',
'for', '%i', 'in', '(cl.exe)', 'do', '@echo', 'LOC:%~$PATH:i'))
popen = subprocess.Popen(args, shell=True, stdout=subprocess.PIPE)
output, _ = popen.communicate()
if PY3:
output = output.decode('utf-8')
cl_paths[arch] = _ExtractCLPath(output)
return cl_paths | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/core/_asarray.py | python | asfortranarray | (a, dtype=None) | return array(a, dtype, copy=False, order='F', ndmin=1) | Return an array (ndim >= 1) laid out in Fortran order in memory.
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
By default, the data-type is inferred from the input data.
Returns
-------
out : ndarray
The input `a` in Fortran, or column-major, order.
See Also
--------
ascontiguousarray : Convert input to a contiguous (C order) array.
asanyarray : Convert input to an ndarray with either row or
column-major memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> y = np.asfortranarray(x)
>>> x.flags['F_CONTIGUOUS']
False
>>> y.flags['F_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
so it will not preserve 0-d arrays. | Return an array (ndim >= 1) laid out in Fortran order in memory. | [
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"."
] | def asfortranarray(a, dtype=None):
"""
Return an array (ndim >= 1) laid out in Fortran order in memory.
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
By default, the data-type is inferred from the input data.
Returns
-------
out : ndarray
The input `a` in Fortran, or column-major, order.
See Also
--------
ascontiguousarray : Convert input to a contiguous (C order) array.
asanyarray : Convert input to an ndarray with either row or
column-major memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> y = np.asfortranarray(x)
>>> x.flags['F_CONTIGUOUS']
False
>>> y.flags['F_CONTIGUOUS']
True
Note: This function returns an array with at least one-dimension (1-d)
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"""
return array(a, dtype, copy=False, order='F', ndmin=1) | [
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google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/python/slots.py | python | GenSetItem | (setitem_slots) | Combine __setitem__ / __delitem__ funcs into one xx_setitem slot. | Combine __setitem__ / __delitem__ funcs into one xx_setitem slot. | [
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"."
] | def GenSetItem(setitem_slots):
"""Combine __setitem__ / __delitem__ funcs into one xx_setitem slot."""
assert len(setitem_slots) == 2, 'Need __setitem__ / __delitem__ funcs.'
setitem, delitem = setitem_slots
assert setitem or delitem, 'Need one or both __setitem__ / __delitem__ funcs.'
yield ''
yield 'int slot_seti(PyObject* self, Py_ssize_t idx, PyObject* value) {'
yield I+'idx = slot::item_index(self, idx);'
yield I+'if (idx < 0) return -1;'
yield I+'PyObject* i = PyLong_FromSize_t(idx);'
yield I+'if (i == nullptr) return -1;'
yield I+'if (value != nullptr) {'
if setitem:
yield I+I+'PyObject* args = PyTuple_Pack(2, i, value);'
yield I+I+'Py_DECREF(i);'
yield I+I+'if (args == nullptr) return -1;'
yield I+I+'PyObject* res = %s(self, args, nullptr);' % setitem
yield I+I+'Py_DECREF(args);'
yield I+I+'return slot::ignore(res);'
else:
yield I+I+'PyErr_SetNone(PyExc_NotImplementedError);'
yield I+I+'return -1;'
yield I+'} else {'
if delitem:
yield I+I+'PyObject* args = PyTuple_Pack(1, i);'
yield I+I+'Py_DECREF(i);'
yield I+I+'if (args == nullptr) return -1;'
yield I+I+'PyObject* res = %s(self, args, nullptr);' % delitem
yield I+I+'Py_DECREF(args);'
yield I+I+'return slot::ignore(res);'
else:
yield I+I+'PyErr_SetNone(PyExc_NotImplementedError);'
yield I+I+'return -1;'
yield I+'}'
yield '}' | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Misc._report_exception | (self) | Internal function. | Internal function. | [
"Internal",
"function",
"."
] | def _report_exception(self):
"""Internal function."""
exc, val, tb = sys.exc_info()
root = self._root()
root.report_callback_exception(exc, val, tb) | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/generator/make.py | python | MakefileWriter.WritePchTargets | (self, pch_commands) | Writes make rules to compile prefix headers. | Writes make rules to compile prefix headers. | [
"Writes",
"make",
"rules",
"to",
"compile",
"prefix",
"headers",
"."
] | def WritePchTargets(self, pch_commands):
"""Writes make rules to compile prefix headers."""
if not pch_commands:
return
for gch, lang_flag, lang, input in pch_commands:
extra_flags = {
"c": "$(CFLAGS_C_$(BUILDTYPE))",
"cc": "$(CFLAGS_CC_$(BUILDTYPE))",
"m": "$(CFLAGS_C_$(BUILDTYPE)) $(CFLAGS_OBJC_$(BUILDTYPE))",
"mm": "$(CFLAGS_CC_$(BUILDTYPE)) $(CFLAGS_OBJCC_$(BUILDTYPE))",
}[lang]
var_name = {
"c": "GYP_PCH_CFLAGS",
"cc": "GYP_PCH_CXXFLAGS",
"m": "GYP_PCH_OBJCFLAGS",
"mm": "GYP_PCH_OBJCXXFLAGS",
}[lang]
self.WriteLn(
f"{gch}: {var_name} := {lang_flag} " + "$(DEFS_$(BUILDTYPE)) "
"$(INCS_$(BUILDTYPE)) "
"$(CFLAGS_$(BUILDTYPE)) " + extra_flags
)
self.WriteLn(f"{gch}: {input} FORCE_DO_CMD")
self.WriteLn("\t@$(call do_cmd,pch_%s,1)" % lang)
self.WriteLn("")
assert " " not in gch, "Spaces in gch filenames not supported (%s)" % gch
self.WriteLn("all_deps += %s" % gch)
self.WriteLn("") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/docview.py | python | DocTemplate.GetDocumentName | (self) | return self._docTypeName | Returns the document type name, as passed to the document template
constructor. | Returns the document type name, as passed to the document template
constructor. | [
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"""
Returns the document type name, as passed to the document template
constructor.
"""
return self._docTypeName | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/bitstring/bitstring.py | python | Bits._assertsanity | (self) | return True | Check internal self consistency as a debugging aid. | Check internal self consistency as a debugging aid. | [
"Check",
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"self",
"consistency",
"as",
"a",
"debugging",
"aid",
"."
] | def _assertsanity(self):
"""Check internal self consistency as a debugging aid."""
assert self.len >= 0
assert 0 <= self._offset, "offset={0}".format(self._offset)
assert (self.len + self._offset + 7) // 8 == self._datastore.bytelength + self._datastore.byteoffset
return True | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/plugins/db_manager/dlg_table_properties.py | python | DlgTableProperties.addColumn | (self) | open dialog to set column info and add column to table | open dialog to set column info and add column to table | [
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] | def addColumn(self):
""" open dialog to set column info and add column to table """
dlg = DlgFieldProperties(self, None, self.table)
if not dlg.exec_():
return
fld = dlg.getField()
with OverrideCursor(Qt.WaitCursor):
self.aboutToChangeTable.emit()
try:
# add column to table
self.table.addField(fld)
self.refresh()
except BaseError as e:
DlgDbError.showError(e, self) | [
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NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | cmake/ReverseDDPostProcess.py | python | configure_root_dirs | (test_dir: str) | return os.path.join(test_dir, 'Regular'), os.path.join(test_dir, 'Reversed') | Set up the test directories based on prescribed names | Set up the test directories based on prescribed names | [
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] | def configure_root_dirs(test_dir: str) -> Tuple[str, str]:
"""Set up the test directories based on prescribed names"""
return os.path.join(test_dir, 'Regular'), os.path.join(test_dir, 'Reversed') | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/basislist.py | python | BasisFamily.add_rifit | (self, fit) | Function to add basis *fit* as associated fitting basis
member *rifit* to a BasisFamily object. | Function to add basis *fit* as associated fitting basis
member *rifit* to a BasisFamily object. | [
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] | def add_rifit(self, fit):
"""Function to add basis *fit* as associated fitting basis
member *rifit* to a BasisFamily object.
"""
self.rifit = sanitize_basisname(fit) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/summary_io.py | python | SummaryWriter.close | (self) | Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore. | Flushes the event file to disk and close the file. | [
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] | def close(self):
"""Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
"""
self.flush()
self._ev_writer.Close()
self._closed = True | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/layers/python/layers/feature_column.py | python | _get_feature_config | (feature_column) | Returns configuration for the base feature defined in feature_column. | Returns configuration for the base feature defined in feature_column. | [
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] | def _get_feature_config(feature_column):
"""Returns configuration for the base feature defined in feature_column."""
if not isinstance(feature_column, _FeatureColumn):
raise TypeError(
"feature_columns should only contain instances of _FeatureColumn. "
"Given column is {}".format(feature_column))
if isinstance(feature_column, (_SparseColumn, _WeightedSparseColumn,
_EmbeddingColumn, _RealValuedColumn,
_RealValuedVarLenColumn,
_BucketizedColumn, _CrossedColumn,
_OneHotColumn, _ScatteredEmbeddingColumn)):
return feature_column.config
raise TypeError("Not supported _FeatureColumn type. "
"Given column is {}".format(feature_column)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/resource_manager/stack.py | python | Monitor.wait | (self) | Waits for the operation to complete, displaying events as they occur. | Waits for the operation to complete, displaying events as they occur. | [
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"."
] | def wait(self):
"""Waits for the operation to complete, displaying events as they occur."""
errors = []
failed_resources = set([])
done = False
while not done:
for monitored_stack_id in self.monitored_stacks:
try:
res = self.client.describe_stack_events(StackName=monitored_stack_id)
stack_events = reversed(res['StackEvents'])
except ClientError as e:
if e.response['Error']['Code'] == 'Throttling':
time.sleep(MONITOR_WAIT_SECONDS)
stack_events = []
else:
raise HandledError('Could not get events for {0} stack.'.format(self.stack_id), e)
for event in stack_events:
if event['EventId'] not in self.events_seen:
resource_status = event.get('ResourceStatus', None)
self.events_seen[event['EventId']] = True
self.context.view.sack_event(event)
if resource_status.endswith('_FAILED'):
errors.append(
'{status} for {logical} ({type} with id "{physical}") - {reason}'.format(
status=event.get('ResourceStatus', ''),
type=event.get('ResourceType', 'unknown type'),
logical=event.get('LogicalResourceId', 'unknown resource'),
reason=event.get('ResourceStatusReason', 'No reason reported.'),
physical=event.get('PhysicalResourceId', '{unknown}')
)
)
if event['StackId'] == self.stack_id:
if resource_status in self.finished_status and event['PhysicalResourceId'] == self.stack_id:
if errors:
self.context.view.stack_event_errors(errors, resource_status == self.success_status)
if resource_status == self.success_status:
done = True
else:
if len(failed_resources) > 0:
return failed_resources
raise HandledError("The operation failed.")
if event['ResourceType'] == 'AWS::CloudFormation::Stack':
if resource_status in self.start_nested_stack_status and resource_status not in self.monitored_stacks:
if event['PhysicalResourceId'] is not None and event['PhysicalResourceId'] != '':
self.monitored_stacks.append(event['PhysicalResourceId'])
if resource_status in self.end_nested_stack_status and resource_status in self.monitored_stacks:
self.monitored_stacks.remove(event['PhysicalResourceId'])
else:
# return resources ids for resources that failed to delete
logical_resource_id = event.get('LogicalResourceId', None)
if logical_resource_id is not None:
if resource_status != self.context.stack.STATUS_DELETE_COMPLETE:
failed_resources.add(logical_resource_id)
elif logical_resource_id in failed_resources:
failed_resources.remove(logical_resource_id)
if not done:
time.sleep(MONITOR_WAIT_SECONDS) # seconds
else:
return [] | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/requests/packages/urllib3/util/url.py | python | get_host | (url) | return p.scheme or 'http', p.hostname, p.port | Deprecated. Use :func:`.parse_url` instead. | Deprecated. Use :func:`.parse_url` instead. | [
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] | def get_host(url):
"""
Deprecated. Use :func:`.parse_url` instead.
"""
p = parse_url(url)
return p.scheme or 'http', p.hostname, p.port | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBBreakpoint.GetIgnoreCount | (self) | return _lldb.SBBreakpoint_GetIgnoreCount(self) | GetIgnoreCount(self) -> uint32_t | GetIgnoreCount(self) -> uint32_t | [
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"-",
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] | def GetIgnoreCount(self):
"""GetIgnoreCount(self) -> uint32_t"""
return _lldb.SBBreakpoint_GetIgnoreCount(self) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/image_processing/frame_generator.py | python | FrameGenerator.__init__ | (self) | Initializes the FrameGenerator object. | Initializes the FrameGenerator object. | [
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"."
] | def __init__(self):
""" Initializes the FrameGenerator object. """
self._generator = self._CreateGenerator() | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/factorization/python/ops/clustering_ops.py | python | KMeans._mini_batch_training_op | (self, inputs, cluster_idx_list,
cluster_centers, cluster_centers_var,
total_counts) | return tf.group(*update_ops) | Creates an op for training for mini batch case.
Args:
inputs: list of input Tensors.
cluster_idx_list: A vector (or list of vectors). Each element in the
vector corresponds to an input row in 'inp' and specifies the cluster id
corresponding to the input.
cluster_centers: Tensor of cluster centers, possibly normalized.
cluster_centers_var: Tensor Ref of cluster centers.
total_counts: Tensor Ref of cluster counts.
Returns:
An op for doing an update of mini-batch k-means. | Creates an op for training for mini batch case. | [
"Creates",
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"op",
"for",
"training",
"for",
"mini",
"batch",
"case",
"."
] | def _mini_batch_training_op(self, inputs, cluster_idx_list,
cluster_centers, cluster_centers_var,
total_counts):
"""Creates an op for training for mini batch case.
Args:
inputs: list of input Tensors.
cluster_idx_list: A vector (or list of vectors). Each element in the
vector corresponds to an input row in 'inp' and specifies the cluster id
corresponding to the input.
cluster_centers: Tensor of cluster centers, possibly normalized.
cluster_centers_var: Tensor Ref of cluster centers.
total_counts: Tensor Ref of cluster counts.
Returns:
An op for doing an update of mini-batch k-means.
"""
update_ops = []
for inp, cluster_idx in zip(inputs, cluster_idx_list):
with ops.colocate_with(inp):
assert total_counts is not None
cluster_idx = tf.reshape(cluster_idx, [-1])
# Dedupe the unique ids of cluster_centers being updated so that updates
# can be locally aggregated.
unique_ids, unique_idx = tf.unique(cluster_idx)
num_unique_cluster_idx = tf.size(unique_ids)
# Fetch the old values of counts and cluster_centers.
with ops.colocate_with(total_counts):
old_counts = tf.gather(total_counts, unique_ids)
with ops.colocate_with(cluster_centers):
old_cluster_centers = tf.gather(cluster_centers, unique_ids)
# Locally aggregate the increment to counts.
count_updates = tf.unsorted_segment_sum(
tf.ones_like(unique_idx, dtype=total_counts.dtype),
unique_idx,
num_unique_cluster_idx)
# Locally compute the sum of inputs mapped to each id.
# For a cluster with old cluster value x, old count n, and with data
# d_1,...d_k newly assigned to it, we recompute the new value as
# x += (sum_i(d_i) - k * x) / (n + k).
# Compute sum_i(d_i), see comment above.
cluster_center_updates = tf.unsorted_segment_sum(
inp,
unique_idx,
num_unique_cluster_idx)
# Shape to enable broadcasting count_updates and learning_rate to inp.
# It extends the shape with 1's to match the rank of inp.
broadcast_shape = tf.concat(
0,
[tf.reshape(num_unique_cluster_idx, [1]),
tf.ones(tf.reshape(tf.rank(inp) - 1, [1]), dtype=tf.int32)])
# Subtract k * x, see comment above.
cluster_center_updates -= tf.cast(
tf.reshape(count_updates, broadcast_shape),
inp.dtype) * old_cluster_centers
learning_rate = tf.inv(tf.cast(old_counts + count_updates, inp.dtype))
learning_rate = tf.reshape(learning_rate, broadcast_shape)
# scale by 1 / (n + k), see comment above.
cluster_center_updates *= learning_rate
# Apply the updates.
update_counts = tf.scatter_add(
total_counts,
unique_ids,
count_updates)
update_cluster_centers = tf.scatter_add(
cluster_centers_var,
unique_ids,
cluster_center_updates)
update_ops.extend([update_counts, update_cluster_centers])
return tf.group(*update_ops) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/ops.py | python | Graph.finalized | (self) | return self._finalized | True if this graph has been finalized. | True if this graph has been finalized. | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/contrib/tensorrt.py | python | set_use_fp16 | (status) | Set an environment variable which will enable or disable the use of FP16 precision in
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Note: The mode FP16 force the whole TRT node to be executed in FP16
:param status: Boolean, True if TensorRT should run in FP16, False for FP32 | Set an environment variable which will enable or disable the use of FP16 precision in
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simon-anders/htseq | 5ba0507ea237e2e067ea79fb28febbc56a37f0d4 | python3/HTSeq/StepVector.py | python | StepVector.__setitem__ | ( self, index, value ) | To set element i of StepVector sv to the value v, write
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cuda/cudadrv/driver.py | python | load_module_image | (context, image) | return Module(weakref.proxy(context), handle, info_log,
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/log_det.py | python | log_det.is_atom_concave | (self) | return True | Is the atom concave? | Is the atom concave? | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/coverage/results.py | python | Analysis.total_branches | (self) | return sum([count for count in exit_counts.values() if count > 1]) | How many total branches are there? | How many total branches are there? | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/knobctrl.py | python | KnobCtrl.Draw | (self, dc) | Draws everything on the empty bitmap.
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may0324/DeepCompression-caffe | 0aff6c1287bda4cfc7f378ed8a16524e1afabd8c | scripts/download_model_binary.py | python | reporthook | (count, block_size, total_size) | From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/ | From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/ | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/bayesflow/python/ops/stochastic_variables.py | python | make_stochastic_variable_getter | (dist_cls,
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NVlabs/fermat | 06e8c03ac59ab440cbb13897f90631ef1861e769 | contrib/assimp-4.1.0/port/PyAssimp/pyassimp/core.py | python | _init | (self, target = None, parent = None) | return self | Custom initialize() for C structs, adds safely accessible member functionality.
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continue
if m.startswith('m'):
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setattr(target, name, parent)
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setattr(target, name, _get_properties(obj, length))
continue
if not length: # empty!
setattr(target, name, [])
logger.debug(str(self) + ": " + name + " is an empty list.")
continue
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setattr(target, name, numpy.array([make_tuple(obj[i]) for i in range(length)], dtype=numpy.float32))
logger.debug(str(self) + ": Added an array of numpy arrays (type "+ str(type(obj)) + ") as self." + name)
else:
setattr(target, name, [make_tuple(obj[i]) for i in range(length)])
logger.debug(str(self) + ": Added a list of lists (type "+ str(type(obj)) + ") as self." + name)
else:
setattr(target, name, [obj[i] for i in range(length)]) #TODO: maybe not necessary to recreate an array?
logger.debug(str(self) + ": Added list of " + str(obj) + " " + name + " as self." + name + " (type: " + str(type(obj)) + ")")
# initialize array elements
try:
init = assimp_struct_inits[type(obj[0])]
except KeyError:
if _is_init_type(obj[0]):
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call_init(e, target)
else:
for e in getattr(target, name):
init(e)
except IndexError:
logger.error("in " + str(self) +" : mismatch between mNum" + name + " and the actual amount of data in m" + name + ". This may be due to version mismatch between libassimp and pyassimp. Quitting now.")
sys.exit(1)
except ValueError as e:
logger.error("In " + str(self) + "->" + name + ": " + str(e) + ". Quitting now.")
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | DateSpan.__mul__ | (*args, **kwargs) | return _misc_.DateSpan___mul__(*args, **kwargs) | __mul__(self, int n) -> DateSpan | __mul__(self, int n) -> DateSpan | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/importlib/_bootstrap_external.py | python | SourceLoader.path_stats | (self, path) | return {'mtime': self.path_mtime(path)} | Optional method returning a metadata dict for the specified path
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/utils/text.py | python | SList.sort | (self,field= None, nums = False) | return SList([t[1] for t in dsu]) | sort by specified fields (see fields())
Example::
a.sort(1, nums = True)
Sorts a by second field, in numerical order (so that 21 > 3) | sort by specified fields (see fields()) | [
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""" sort by specified fields (see fields())
Example::
a.sort(1, nums = True)
Sorts a by second field, in numerical order (so that 21 > 3)
"""
#decorate, sort, undecorate
if field is not None:
dsu = [[SList([line]).fields(field), line] for line in self]
else:
dsu = [[line, line] for line in self]
if nums:
for i in range(len(dsu)):
numstr = "".join([ch for ch in dsu[i][0] if ch.isdigit()])
try:
n = int(numstr)
except ValueError:
n = 0
dsu[i][0] = n
dsu.sort()
return SList([t[1] for t in dsu]) | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/mac_tool.py | python | MacTool._CommandifyName | (self, name_string) | return name_string.title().replace('-', '') | Transforms a tool name like copy-info-plist to CopyInfoPlist | Transforms a tool name like copy-info-plist to CopyInfoPlist | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/pkg_resources/__init__.py | python | WorkingSet.run_script | (self, requires, script_name) | Locate distribution for `requires` and run `script_name` script | Locate distribution for `requires` and run `script_name` script | [
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] | def run_script(self, requires, script_name):
"""Locate distribution for `requires` and run `script_name` script"""
ns = sys._getframe(1).f_globals
name = ns['__name__']
ns.clear()
ns['__name__'] = name
self.require(requires)[0].run_script(script_name, ns) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/idlelib/macosxSupport.py | python | addOpenEventSupport | (root, flist) | This ensures that the application will respond to open AppleEvents, which
makes is feasible to use IDLE as the default application for python files. | This ensures that the application will respond to open AppleEvents, which
makes is feasible to use IDLE as the default application for python files. | [
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"""
This ensures that the application will respond to open AppleEvents, which
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"""
def doOpenFile(*args):
for fn in args:
flist.open(fn)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Tools/msvc.py | python | gather_icl_versions | (conf, versions) | Checks ICL compilers
:param versions: list to modify
:type versions: list | Checks ICL compilers | [
"Checks",
"ICL",
"compilers"
] | def gather_icl_versions(conf, versions):
"""
Checks ICL compilers
:param versions: list to modify
:type versions: list
"""
version_pattern = re.compile('^...?.?\....?.?')
try:
all_versions = Utils.winreg.OpenKey(Utils.winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\Wow6432node\\Intel\\Compilers\\C++')
except WindowsError:
try:
all_versions = Utils.winreg.OpenKey(Utils.winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\Intel\\Compilers\\C++')
except WindowsError:
return
index = 0
while 1:
try:
version = Utils.winreg.EnumKey(all_versions, index)
except WindowsError:
break
index = index + 1
if not version_pattern.match(version):
continue
targets = []
for target,arch in all_icl_platforms:
try:
if target=='intel64': targetDir='EM64T_NATIVE'
else: targetDir=target
Utils.winreg.OpenKey(all_versions,version+'\\'+targetDir)
icl_version=Utils.winreg.OpenKey(all_versions,version)
path,type=Utils.winreg.QueryValueEx(icl_version,'ProductDir')
batch_file=os.path.join(path,'bin','iclvars.bat')
if os.path.isfile(batch_file):
try:
targets.append((target,(arch,conf.get_msvc_version('intel',version,target,batch_file))))
except conf.errors.ConfigurationError:
pass
except WindowsError:
pass
for target,arch in all_icl_platforms:
try:
icl_version = Utils.winreg.OpenKey(all_versions, version+'\\'+target)
path,type = Utils.winreg.QueryValueEx(icl_version,'ProductDir')
batch_file=os.path.join(path,'bin','iclvars.bat')
if os.path.isfile(batch_file):
try:
targets.append((target, (arch, conf.get_msvc_version('intel', version, target, batch_file))))
except conf.errors.ConfigurationError:
pass
except WindowsError:
continue
major = version[0:2]
versions.append(('intel ' + major, targets)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | TarFile.extractfile | (self, member) | Extract a member from the archive as a file object. `member' may be
a filename or a TarInfo object. If `member' is a regular file, a
file-like object is returned. If `member' is a link, a file-like
object is constructed from the link's target. If `member' is none of
the above, None is returned.
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methods: read(), readline(), readlines(), seek() and tell() | Extract a member from the archive as a file object. `member' may be
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"""Extract a member from the archive as a file object. `member' may be
a filename or a TarInfo object. If `member' is a regular file, a
file-like object is returned. If `member' is a link, a file-like
object is constructed from the link's target. If `member' is none of
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self._check("r")
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tarinfo = member
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# If a member's type is unknown, it is treated as a
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return self.fileobject(self, tarinfo)
elif tarinfo.islnk() or tarinfo.issym():
if isinstance(self.fileobj, _Stream):
# A small but ugly workaround for the case that someone tries
# to extract a (sym)link as a file-object from a non-seekable
# stream of tar blocks.
raise StreamError("cannot extract (sym)link as file object")
else:
# A (sym)link's file object is its target's file object.
return self.extractfile(self._find_link_target(tarinfo))
else:
# If there's no data associated with the member (directory, chrdev,
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return None | [
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codilime/veles | e65de5a7c268129acffcdb03034efd8d256d025c | python/veles/async_conn/conn.py | python | AsyncConnection.get_node | (self, id) | return self.get_node_norefresh(id).refresh() | Fetches an AsyncNode with the given id from the server. If this
node is already available, it is refreshed. Returns an awaitable
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"""
Fetches an AsyncNode with the given id from the server. If this
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return self.get_node_norefresh(id).refresh() | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/android/pylib/forwarder.py | python | Forwarder._GetInstanceLocked | (tool) | return Forwarder._instance | Returns the singleton instance.
Note that the global lock must be acquired before calling this method.
Args:
tool: Tool class to use to get wrapper, if necessary, for executing the
forwarder (see valgrind_tools.py). | Returns the singleton instance. | [
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] | def _GetInstanceLocked(tool):
"""Returns the singleton instance.
Note that the global lock must be acquired before calling this method.
Args:
tool: Tool class to use to get wrapper, if necessary, for executing the
forwarder (see valgrind_tools.py).
"""
if not Forwarder._instance:
Forwarder._instance = Forwarder(tool)
return Forwarder._instance | [
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sonyxperiadev/WebGL | 0299b38196f78c6d5f74bcf6fa312a3daee6de60 | Tools/Scripts/webkitpy/style/checkers/cpp.py | python | _process_lines | (filename, file_extension, lines, error, min_confidence) | Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is termined with a newline.
error: A callable to which errors are reported, which takes 4 arguments: | Performs lint checks and reports any errors to the given error function. | [
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] | def _process_lines(filename, file_extension, lines, error, min_confidence):
"""Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is termined with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
"""
lines = (['// marker so line numbers and indices both start at 1'] + lines +
['// marker so line numbers end in a known way'])
include_state = _IncludeState()
function_state = _FunctionState(min_confidence)
class_state = _ClassState()
check_for_copyright(lines, error)
if file_extension == 'h':
check_for_header_guard(filename, lines, error)
remove_multi_line_comments(lines, error)
clean_lines = CleansedLines(lines)
file_state = _FileState(clean_lines, file_extension)
for line in xrange(clean_lines.num_lines()):
process_line(filename, file_extension, clean_lines, line,
include_state, function_state, class_state, file_state, error)
class_state.check_finished(error)
check_for_include_what_you_use(filename, clean_lines, include_state, error)
# We check here rather than inside process_line so that we see raw
# lines rather than "cleaned" lines.
check_for_unicode_replacement_characters(lines, error)
check_for_new_line_at_eof(lines, error) | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/models/TopoFactory.py | python | TopoFactory.__set_base_id_list | (self, id, size, inst) | return [
n,
b,
w,
inst,
component_calculated_window_range,
self.__compute_component_ID_amount(comp),
] | Routine to set up the base id and window size with actual or instance set values.
Routine also checks window size against component max IDs needed if they are found. | Routine to set up the base id and window size with actual or instance set values.
Routine also checks window size against component max IDs needed if they are found. | [
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"""
Routine to set up the base id and window size with actual or instance set values.
Routine also checks window size against component max IDs needed if they are found.
"""
comp = inst.get_component_object()
# set instance name
n = inst.get_name()
#
"""
Logic for calculating base ids
1) If the user did not specify the base ID within an instance, set it to None
2) If the user did specify the base ID within an instance, check if it is greater than the base ID for the entire topology
a) if it is greater, use the base ID from the instance
b) if it is not greater, add the base ID from the instance and the base ID from the topology model and use the sum
"""
if inst.get_base_id() is None:
b = None
else:
if id > abs(int(inst.get_base_id(), 0)):
b = abs(int(inst.get_base_id(), 0)) + id
PRINT.info(
"WARNING: {} instance adding instance supplied base ID to the topology supplied base ID (New ID is {}) because instance supplied base ID is smaller than the topology supplied base ID.".format(
n, b
)
)
else:
b = abs(int(inst.get_base_id(), 0))
PRINT.info("WARNING: %s instance resetting base id to %d" % (n, b))
#
# set window size or override it on instance basis
component_calculated_window_range = self.__compute_component_base_id_range(comp)
"""
Note: The calculated window range is really the largest ID (plus one) found in the component object.
Logic for calculating window size
1) If user specifies window size in instance tag, use that.
2) If the user does not specify the window size in the instance tag, use the larger of the default window size and the calculated window size
3) If the calculated window size is larger than the new window size, thrown an error
"""
if inst.get_base_id_window() is not None:
w = abs(int(inst.get_base_id_window(), 0))
PRINT.info(
"{} instance resetting base id window range to instance specified size ({})".format(
n, w
)
)
else:
if size > component_calculated_window_range:
w = size
PRINT.info(
"{} instance resetting base id window range to default topology size ({})".format(
n, w
)
)
else:
w = component_calculated_window_range
PRINT.info(
"{} instance resetting base id window range to size calculated from the component XML file ({})".format(
n, w
)
)
if (
component_calculated_window_range is not None
and w < component_calculated_window_range
):
PRINT.info(
"ERROR: The specified window range for component {} is {}, which is smaller than the calculated window range of {}. Please check the instance definitions in the topology xml file.".format(
n, w, component_calculated_window_range
)
)
return [
n,
b,
w,
inst,
component_calculated_window_range,
self.__compute_component_ID_amount(comp),
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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/cccc.py | python | Isotxs._read_file_control | (self) | Reads the file control block. This block is always present and gives
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] | def _read_file_control(self):
"""Reads the file control block. This block is always present and gives
many parameters for the file including number of energy groups, number
of isotopes, etc.
"""
# Get file control record
fc = self.get_fortran_record()
# Read data from file control record
self.fc['ngroup'] = fc.get_int()[0] # Number of energy groups in file
self.fc['niso'] = fc.get_int()[0] # Number of isotopes in file
# Maximum number of upscatter groups
self.fc['maxup'] = fc.get_int()[0]
# Maximum number of downscatter groups
self.fc['maxdown'] = fc.get_int()[0]
self.fc['maxord'] = fc.get_int()[0] # Maximum scattering order
self.fc['ichidst'] = fc.get_int()[0] # File-wide fission spectrum flag
# Max blocks of scatter data (seems to be actual number)
self.fc['nscmax'] = fc.get_int()[0]
self.fc['nsblok'] = fc.get_int()[0] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGridInterface.GetPropertyParent | (*args, **kwargs) | return _propgrid.PropertyGridInterface_GetPropertyParent(*args, **kwargs) | GetPropertyParent(self, PGPropArg id) -> PGProperty | GetPropertyParent(self, PGPropArg id) -> PGProperty | [
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"""GetPropertyParent(self, PGPropArg id) -> PGProperty"""
return _propgrid.PropertyGridInterface_GetPropertyParent(*args, **kwargs) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/math_ops.py | python | _BatchMatMulShape | (op) | return [batch_dims.concatenate([output_rows, output_cols])] | Shape function for BatchMatMul op. | Shape function for BatchMatMul op. | [
"Shape",
"function",
"for",
"BatchMatMul",
"op",
"."
] | def _BatchMatMulShape(op):
"""Shape function for BatchMatMul op."""
a_shape = op.inputs[0].get_shape()
adj_a = op.get_attr("adj_x")
b_shape = op.inputs[1].get_shape()
adj_b = op.get_attr("adj_y")
if a_shape.dims is None and b_shape.dims is None:
return [tensor_shape.unknown_shape()]
batch_dims = a_shape[:-2].merge_with(b_shape[:-2])
output_rows = a_shape[-1] if adj_a else a_shape[-2]
output_cols = b_shape[-2] if adj_b else b_shape[-1]
inner_a = a_shape[-2] if adj_a else a_shape[-1]
inner_b = b_shape[-1] if adj_b else b_shape[-2]
inner_a.assert_is_compatible_with(inner_b)
return [batch_dims.concatenate([output_rows, output_cols])] | [
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infinidb/infinidb | 6c9f5dfdabc41ad80e81ba9e1a4eb0d7271a5d23 | writeengine/bulk/checkidx.py | python | main | () | Validate indexes.. | Validate indexes.. | [
"Validate",
"indexes",
".."
] | def main():
"""
Validate indexes..
"""
if not os.access('.', os.W_OK):
os.chdir('/tmp')
print 'Changing to /tmp to have permission to write files'
if len(os.getenv('LD_LIBRARY_PATH'))<5:
print 'Suspicous LD_LIBRARY_PATH: %s'%os.getenv('LD_LIBRARY_PATH')
home = os.getenv('HOME')
genii = home+'/genii'
(bulkroot, dbroot) = find_paths()
if len(glob.glob(bulkroot+'/job/Job_300.xml')) == 0:
sys.exit("No Job_300.xml exist ")
indexes = validate_indexes(bulkroot+'/job/Job_300.xml')
for idxCmdArg in indexes:
print idxCmdArg
exec_cmd( genii + '/tools/evalidx/evalidx', idxCmdArg ) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/estimator/__init__.py | python | model_to_estimator_v2 | (
keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None,
checkpoint_format='checkpoint') | return keras_lib.model_to_estimator( # pylint:disable=unexpected-keyword-arg
keras_model=keras_model,
keras_model_path=keras_model_path,
custom_objects=custom_objects,
model_dir=model_dir,
config=config,
checkpoint_format=checkpoint_format,
use_v2_estimator=True) | Constructs an `Estimator` instance from given keras model.
For usage example, please see:
[Creating estimators from Keras
Models](https://tensorflow.org/guide/estimators#model_to_estimator).
__Sample Weights__
Estimators returned by `model_to_estimator` are configured to handle sample
weights (similar to `keras_model.fit(x, y, sample_weights)`). To pass sample
weights when training or evaluating the Estimator, the first item returned by
the input function should be a dictionary with keys `features` and
`sample_weights`. Example below:
```
keras_model = tf.keras.Model(...)
keras_model.compile(...)
estimator = tf.keras.estimator.model_to_estimator(keras_model)
def input_fn():
return dataset_ops.Dataset.from_tensors(
({'features': features, 'sample_weights': sample_weights},
targets))
estimator.train(input_fn, steps=1)
```
Args:
keras_model: A compiled Keras model object. This argument is mutually
exclusive with `keras_model_path`.
keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
format, which can be generated with the `save()` method of a Keras model.
This argument is mutually exclusive with `keras_model`.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save `Estimator` model parameters, graph, summary
files for TensorBoard, etc.
config: `RunConfig` to config `Estimator`.
checkpoint_format: Sets the format of the checkpoint saved by the estimator
when training. May be `saver` or `checkpoint`, depending on whether to
save checkpoints from `tf.compat.v1.train.Saver` or `tf.train.Checkpoint`.
The default is `checkpoint`. Estimators use name-based `tf.train.Saver`
checkpoints, while Keras models use object-based checkpoints from
`tf.train.Checkpoint`. Currently, saving object-based checkpoints from
`model_to_estimator` is only supported by Functional and Sequential
models.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
ValueError: if an invalid checkpoint_format was given. | Constructs an `Estimator` instance from given keras model. | [
"Constructs",
"an",
"Estimator",
"instance",
"from",
"given",
"keras",
"model",
"."
] | def model_to_estimator_v2(
keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None,
checkpoint_format='checkpoint'):
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see:
[Creating estimators from Keras
Models](https://tensorflow.org/guide/estimators#model_to_estimator).
__Sample Weights__
Estimators returned by `model_to_estimator` are configured to handle sample
weights (similar to `keras_model.fit(x, y, sample_weights)`). To pass sample
weights when training or evaluating the Estimator, the first item returned by
the input function should be a dictionary with keys `features` and
`sample_weights`. Example below:
```
keras_model = tf.keras.Model(...)
keras_model.compile(...)
estimator = tf.keras.estimator.model_to_estimator(keras_model)
def input_fn():
return dataset_ops.Dataset.from_tensors(
({'features': features, 'sample_weights': sample_weights},
targets))
estimator.train(input_fn, steps=1)
```
Args:
keras_model: A compiled Keras model object. This argument is mutually
exclusive with `keras_model_path`.
keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
format, which can be generated with the `save()` method of a Keras model.
This argument is mutually exclusive with `keras_model`.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save `Estimator` model parameters, graph, summary
files for TensorBoard, etc.
config: `RunConfig` to config `Estimator`.
checkpoint_format: Sets the format of the checkpoint saved by the estimator
when training. May be `saver` or `checkpoint`, depending on whether to
save checkpoints from `tf.compat.v1.train.Saver` or `tf.train.Checkpoint`.
The default is `checkpoint`. Estimators use name-based `tf.train.Saver`
checkpoints, while Keras models use object-based checkpoints from
`tf.train.Checkpoint`. Currently, saving object-based checkpoints from
`model_to_estimator` is only supported by Functional and Sequential
models.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
ValueError: if an invalid checkpoint_format was given.
"""
try:
from tensorflow_estimator.python.estimator import keras as keras_lib # pylint: disable=g-import-not-at-top
except ImportError:
raise NotImplementedError(
'tf.keras.estimator.model_to_estimator function not available in your '
'installation.')
return keras_lib.model_to_estimator( # pylint:disable=unexpected-keyword-arg
keras_model=keras_model,
keras_model_path=keras_model_path,
custom_objects=custom_objects,
model_dir=model_dir,
config=config,
checkpoint_format=checkpoint_format,
use_v2_estimator=True) | [
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alibaba/MNN | c4d9566171d589c3ded23aa18ffb197016995a12 | pymnn/pip_package/MNN/expr/__init__.py | python | log | (x) | return _F.log(x) | log(x)
Return the log(x), element-wise.
Parameters
----------
x : var_like, input value.
Returns
-------
y : Var. The log of `x`.
Example:
-------
>>> expr.log([9., 4.5])
var([2.1972246, 1.5040774]) | log(x)
Return the log(x), element-wise. | [
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"(",
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] | def log(x):
'''
log(x)
Return the log(x), element-wise.
Parameters
----------
x : var_like, input value.
Returns
-------
y : Var. The log of `x`.
Example:
-------
>>> expr.log([9., 4.5])
var([2.1972246, 1.5040774])
'''
x = _to_var(x)
return _F.log(x) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/operator.py | python | add | (a, b) | return a + b | Same as a + b. | Same as a + b. | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/boosted_trees/examples/mnist.py | python | get_input_fn | (dataset_split,
batch_size,
capacity=10000,
min_after_dequeue=3000) | return _input_fn | Input function over MNIST data. | Input function over MNIST data. | [
"Input",
"function",
"over",
"MNIST",
"data",
"."
] | def get_input_fn(dataset_split,
batch_size,
capacity=10000,
min_after_dequeue=3000):
"""Input function over MNIST data."""
def _input_fn():
"""Prepare features and labels."""
images_batch, labels_batch = tf.train.shuffle_batch(
tensors=[dataset_split.images,
dataset_split.labels.astype(np.int32)],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
enqueue_many=True,
num_threads=4)
features_map = {"images": images_batch}
return features_map, labels_batch
return _input_fn | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/ftplib.py | python | parse227 | (resp) | return host, port | Parse the '227' response for a PASV request.
Raises error_proto if it does not contain '(h1,h2,h3,h4,p1,p2)'
Return ('host.addr.as.numbers', port#) tuple. | Parse the '227' response for a PASV request.
Raises error_proto if it does not contain '(h1,h2,h3,h4,p1,p2)'
Return ('host.addr.as.numbers', port#) tuple. | [
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'''Parse the '227' response for a PASV request.
Raises error_proto if it does not contain '(h1,h2,h3,h4,p1,p2)'
Return ('host.addr.as.numbers', port#) tuple.'''
if resp[:3] != '227':
raise error_reply, resp
global _227_re
if _227_re is None:
import re
_227_re = re.compile(r'(\d+),(\d+),(\d+),(\d+),(\d+),(\d+)')
m = _227_re.search(resp)
if not m:
raise error_proto, resp
numbers = m.groups()
host = '.'.join(numbers[:4])
port = (int(numbers[4]) << 8) + int(numbers[5])
return host, port | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/external/bazel_tools/tools/build_defs/pkg/make_deb.py | python | AddArFileEntry | (fileobj, filename,
content='', timestamp=0,
owner_id=0, group_id=0, mode=0o644) | Add a AR file entry to fileobj. | Add a AR file entry to fileobj. | [
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"a",
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"file",
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"to",
"fileobj",
"."
] | def AddArFileEntry(fileobj, filename,
content='', timestamp=0,
owner_id=0, group_id=0, mode=0o644):
"""Add a AR file entry to fileobj."""
fileobj.write((filename + '/').ljust(16)) # filename (SysV)
fileobj.write(str(timestamp).ljust(12)) # timestamp
fileobj.write(str(owner_id).ljust(6)) # owner id
fileobj.write(str(group_id).ljust(6)) # group id
fileobj.write(oct(mode).ljust(8)) # mode
fileobj.write(str(len(content)).ljust(10)) # size
fileobj.write('\x60\x0a') # end of file entry
fileobj.write(content)
if len(content) % 2 != 0:
fileobj.write('\n') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/foldpanelbar.py | python | CaptionBarStyle.GetCaptionStyle | (self) | return self._captionStyle | Returns the caption style for the caption bar.
:note: Please be warned this will result in an assertion failure
when this property is not previously set.
:see: :meth:`~CaptionBarStyle.SetCaptionStyle`, :meth:`~CaptionBarStyle.CaptionStyleUsed` | Returns the caption style for the caption bar.
:note: Please be warned this will result in an assertion failure
when this property is not previously set.
:see: :meth:`~CaptionBarStyle.SetCaptionStyle`, :meth:`~CaptionBarStyle.CaptionStyleUsed` | [
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"""
Returns the caption style for the caption bar.
:note: Please be warned this will result in an assertion failure
when this property is not previously set.
:see: :meth:`~CaptionBarStyle.SetCaptionStyle`, :meth:`~CaptionBarStyle.CaptionStyleUsed`
"""
return self._captionStyle | [
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/python/visualization.py | python | SvgWrapper.AddXScale | (self, step=1) | Add an scale on the x axis. | Add an scale on the x axis. | [
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"scale",
"on",
"the",
"x",
"axis",
"."
] | def AddXScale(self, step=1):
"""Add an scale on the x axis."""
o = self.__offset
s = self.__scaling
y = self.__sizey * s + o / 2.0 + o
dy = self.__offset / 4.0
self.__dwg.add(
self.__dwg.line((o, y), (self.__sizex * s + o, y), stroke='black'))
for i in range(0, int(self.__sizex) + 1, step):
self.__dwg.add(
self.__dwg.line((o + i * s, y - dy), (o + i * s, y + dy),
stroke='black')) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/crywaflib/msvs.py | python | compile_template | (line) | return Task.funex(fun) | Compile a template expression into a python function (like jsps, but way shorter) | Compile a template expression into a python function (like jsps, but way shorter) | [
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"""
Compile a template expression into a python function (like jsps, but way shorter)
"""
extr = []
def repl(match):
g = match.group
if g('dollar'): return "$"
elif g('backslash'):
return "\\"
elif g('subst'):
extr.append(g('code'))
return "<<|@|>>"
return None
line2 = reg_act.sub(repl, line)
params = line2.split('<<|@|>>')
assert(extr)
indent = 0
buf = []
app = buf.append
def app(txt):
buf.append(indent * '\t' + txt)
for x in range(len(extr)):
if params[x]:
app("lst.append(%r)" % params[x])
f = extr[x]
if f.startswith('if') or f.startswith('for'):
app(f + ':')
indent += 1
elif f.startswith('py:'):
app(f[3:])
elif f.startswith('endif') or f.startswith('endfor'):
indent -= 1
elif f.startswith('else') or f.startswith('elif'):
indent -= 1
app(f + ':')
indent += 1
elif f.startswith('xml:'):
app('lst.append(xml_escape(%s))' % f[4:])
else:
#app('lst.append((%s) or "cannot find %s")' % (f, f))
app('lst.append(%s)' % f)
if extr:
if params[-1]:
app("lst.append(%r)" % params[-1])
fun = COMPILE_TEMPLATE % "\n\t".join(buf)
#print(fun)
return Task.funex(fun) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/learn/python/learn/dataframe/transform.py | python | TensorFlowTransform._produce_output_series | (self, input_series=None) | return [TransformedSeries(input_series, self, output_name)
for output_name in self.output_names] | Apply this `Transform` to the provided `Series`, producing `Series`.
Args:
input_series: None, a `Series`, or a list of input `Series`, acting as
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Returns:
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] | def _produce_output_series(self, input_series=None):
"""Apply this `Transform` to the provided `Series`, producing `Series`.
Args:
input_series: None, a `Series`, or a list of input `Series`, acting as
positional arguments.
Returns:
A namedtuple of the output `Series`.
"""
return [TransformedSeries(input_series, self, output_name)
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/plotting/markers.py | python | RangeMarker.is_marker_moving | (self) | return self.min_marker.is_marker_moving() or self.max_marker.is_marker_moving() | Returns true if one of the markers is being moved
:return: True if one of the markers is being moved. | Returns true if one of the markers is being moved
:return: True if one of the markers is being moved. | [
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"""
Returns true if one of the markers is being moved
:return: True if one of the markers is being moved.
"""
return self.min_marker.is_marker_moving() or self.max_marker.is_marker_moving() | [
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gromacs/gromacs | 7dec3a3f99993cf5687a122de3e12de31c21c399 | python_packaging/src/gmxapi/simulation/fileio.py | python | read_tpr | (tprfile: typing.Union[str, TprFile]) | return _SimulationInput(tprfile) | Get a simulation input object from a TPR run input file.
Arguments:
tprfile : TPR input object or filename
Returns:
simulation input object
The returned object may be inspected by the user. Simulation input parameters
may be extracted through the `parameters` attribute.
Example:
>>> sim_input = gmx.fileio.read_tpr(tprfile=tprfilename)
>>> params = sim_input.parameters.extract()
>>> print(params['init-step'])
0
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] | def read_tpr(tprfile: typing.Union[str, TprFile]):
"""
Get a simulation input object from a TPR run input file.
Arguments:
tprfile : TPR input object or filename
Returns:
simulation input object
The returned object may be inspected by the user. Simulation input parameters
may be extracted through the `parameters` attribute.
Example:
>>> sim_input = gmx.fileio.read_tpr(tprfile=tprfilename)
>>> params = sim_input.parameters.extract()
>>> print(params['init-step'])
0
Supports the `read_tpr` gmxapi work graph operation. (not yet implemented)
"""
if not isinstance(tprfile, TprFile):
try:
tprfile = TprFile(os.fsencode(tprfile), mode='r')
except Exception as e:
raise exceptions.UsageError("TPR object or file name is required.") from e
return _SimulationInput(tprfile) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/tensor_array_ops.py | python | TensorArray.concat | (self, name=None) | Return the values in the TensorArray as a concatenated `Tensor`.
All of the values must have been written, their ranks must match, and
and their shapes must all match for all dimensions except the first.
Args:
name: A name for the operation (optional).
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"""Return the values in the TensorArray as a concatenated `Tensor`.
All of the values must have been written, their ranks must match, and
and their shapes must all match for all dimensions except the first.
Args:
name: A name for the operation (optional).
Returns:
All the tensors in the TensorArray concatenated into one tensor.
"""
if self._elem_shape and self._elem_shape[0].dims is not None:
element_shape_except0 = tensor_shape.TensorShape(self._elem_shape[0].dims[
1:])
else:
element_shape_except0 = tensor_shape.TensorShape(None)
with ops.colocate_with(self._handle):
value, _ = gen_data_flow_ops._tensor_array_concat(
handle=self._handle,
flow_in=self._flow,
dtype=self._dtype,
name=name,
element_shape_except0=element_shape_except0)
if self._elem_shape and self._elem_shape[0].dims is not None:
value.set_shape([None] + self._elem_shape[0].dims[1:])
return value | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | ComboBox.GetClassDefaultAttributes | (*args, **kwargs) | return _controls_.ComboBox_GetClassDefaultAttributes(*args, **kwargs) | GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
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"""
GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this.
"""
return _controls_.ComboBox_GetClassDefaultAttributes(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/asyncio/selector_events.py | python | BaseSelectorEventLoop._remove_writer | (self, fd) | Remove a writer callback. | Remove a writer callback. | [
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if self.is_closed():
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try:
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except KeyError:
return False
else:
mask, (reader, writer) = key.events, key.data
# Remove both writer and connector.
mask &= ~selectors.EVENT_WRITE
if not mask:
self._selector.unregister(fd)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/dynamodb2/layer1.py | python | DynamoDBConnection.create_table | (self, attribute_definitions, table_name, key_schema,
provisioned_throughput, local_secondary_indexes=None,
global_secondary_indexes=None) | return self.make_request(action='CreateTable',
body=json.dumps(params)) | The CreateTable operation adds a new table to your account. In
an AWS account, table names must be unique within each region.
That is, you can have two tables with same name if you create
the tables in different regions.
CreateTable is an asynchronous operation. Upon receiving a
CreateTable request, DynamoDB immediately returns a response
with a TableStatus of `CREATING`. After the table is created,
DynamoDB sets the TableStatus to `ACTIVE`. You can perform
read and write operations only on an `ACTIVE` table.
You can optionally define secondary indexes on the new table,
as part of the CreateTable operation. If you want to create
multiple tables with secondary indexes on them, you must
create the tables sequentially. Only one table with secondary
indexes can be in the `CREATING` state at any given time.
You can use the DescribeTable API to check the table status.
:type attribute_definitions: list
:param attribute_definitions: An array of attributes that describe the
key schema for the table and indexes.
:type table_name: string
:param table_name: The name of the table to create.
:type key_schema: list
:param key_schema: Specifies the attributes that make up the primary
key for a table or an index. The attributes in KeySchema must also
be defined in the AttributeDefinitions array. For more information,
see `Data Model`_ in the Amazon DynamoDB Developer Guide .
Each KeySchemaElement in the array is composed of:
+ AttributeName - The name of this key attribute.
+ KeyType - Determines whether the key attribute is `HASH` or `RANGE`.
For a primary key that consists of a hash attribute, you must specify
exactly one element with a KeyType of `HASH`.
For a primary key that consists of hash and range attributes, you must
specify exactly two elements, in this order: The first element must
have a KeyType of `HASH`, and the second element must have a
KeyType of `RANGE`.
For more information, see `Specifying the Primary Key`_ in the Amazon
DynamoDB Developer Guide .
:type local_secondary_indexes: list
:param local_secondary_indexes:
One or more local secondary indexes (the maximum is five) to be created
on the table. Each index is scoped to a given hash key value. There
is a 10 GB size limit per hash key; otherwise, the size of a local
secondary index is unconstrained.
Each local secondary index in the array includes the following:
+ IndexName - The name of the local secondary index. Must be unique
only for this table.
+ KeySchema - Specifies the key schema for the local secondary index.
The key schema must begin with the same hash key attribute as the
table.
+ Projection - Specifies attributes that are copied (projected) from
the table into the index. These are in addition to the primary key
attributes and index key attributes, which are automatically
projected. Each attribute specification is composed of:
+ ProjectionType - One of the following:
+ `KEYS_ONLY` - Only the index and primary keys are projected into the
index.
+ `INCLUDE` - Only the specified table attributes are projected into
the index. The list of projected attributes are in NonKeyAttributes
.
+ `ALL` - All of the table attributes are projected into the index.
+ NonKeyAttributes - A list of one or more non-key attribute names that
are projected into the secondary index. The total count of
attributes specified in NonKeyAttributes , summed across all of the
secondary indexes, must not exceed 20. If you project the same
attribute into two different indexes, this counts as two distinct
attributes when determining the total.
:type global_secondary_indexes: list
:param global_secondary_indexes:
One or more global secondary indexes (the maximum is five) to be
created on the table. Each global secondary index in the array
includes the following:
+ IndexName - The name of the global secondary index. Must be unique
only for this table.
+ KeySchema - Specifies the key schema for the global secondary index.
+ Projection - Specifies attributes that are copied (projected) from
the table into the index. These are in addition to the primary key
attributes and index key attributes, which are automatically
projected. Each attribute specification is composed of:
+ ProjectionType - One of the following:
+ `KEYS_ONLY` - Only the index and primary keys are projected into the
index.
+ `INCLUDE` - Only the specified table attributes are projected into
the index. The list of projected attributes are in NonKeyAttributes
.
+ `ALL` - All of the table attributes are projected into the index.
+ NonKeyAttributes - A list of one or more non-key attribute names that
are projected into the secondary index. The total count of
attributes specified in NonKeyAttributes , summed across all of the
secondary indexes, must not exceed 20. If you project the same
attribute into two different indexes, this counts as two distinct
attributes when determining the total.
+ ProvisionedThroughput - The provisioned throughput settings for the
global secondary index, consisting of read and write capacity
units.
:type provisioned_throughput: dict
:param provisioned_throughput: Represents the provisioned throughput
settings for a specified table or index. The settings can be
modified using the UpdateTable operation.
For current minimum and maximum provisioned throughput values, see
`Limits`_ in the Amazon DynamoDB Developer Guide . | The CreateTable operation adds a new table to your account. In
an AWS account, table names must be unique within each region.
That is, you can have two tables with same name if you create
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provisioned_throughput, local_secondary_indexes=None,
global_secondary_indexes=None):
"""
The CreateTable operation adds a new table to your account. In
an AWS account, table names must be unique within each region.
That is, you can have two tables with same name if you create
the tables in different regions.
CreateTable is an asynchronous operation. Upon receiving a
CreateTable request, DynamoDB immediately returns a response
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DynamoDB sets the TableStatus to `ACTIVE`. You can perform
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You can optionally define secondary indexes on the new table,
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multiple tables with secondary indexes on them, you must
create the tables sequentially. Only one table with secondary
indexes can be in the `CREATING` state at any given time.
You can use the DescribeTable API to check the table status.
:type attribute_definitions: list
:param attribute_definitions: An array of attributes that describe the
key schema for the table and indexes.
:type table_name: string
:param table_name: The name of the table to create.
:type key_schema: list
:param key_schema: Specifies the attributes that make up the primary
key for a table or an index. The attributes in KeySchema must also
be defined in the AttributeDefinitions array. For more information,
see `Data Model`_ in the Amazon DynamoDB Developer Guide .
Each KeySchemaElement in the array is composed of:
+ AttributeName - The name of this key attribute.
+ KeyType - Determines whether the key attribute is `HASH` or `RANGE`.
For a primary key that consists of a hash attribute, you must specify
exactly one element with a KeyType of `HASH`.
For a primary key that consists of hash and range attributes, you must
specify exactly two elements, in this order: The first element must
have a KeyType of `HASH`, and the second element must have a
KeyType of `RANGE`.
For more information, see `Specifying the Primary Key`_ in the Amazon
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:type local_secondary_indexes: list
:param local_secondary_indexes:
One or more local secondary indexes (the maximum is five) to be created
on the table. Each index is scoped to a given hash key value. There
is a 10 GB size limit per hash key; otherwise, the size of a local
secondary index is unconstrained.
Each local secondary index in the array includes the following:
+ IndexName - The name of the local secondary index. Must be unique
only for this table.
+ KeySchema - Specifies the key schema for the local secondary index.
The key schema must begin with the same hash key attribute as the
table.
+ Projection - Specifies attributes that are copied (projected) from
the table into the index. These are in addition to the primary key
attributes and index key attributes, which are automatically
projected. Each attribute specification is composed of:
+ ProjectionType - One of the following:
+ `KEYS_ONLY` - Only the index and primary keys are projected into the
index.
+ `INCLUDE` - Only the specified table attributes are projected into
the index. The list of projected attributes are in NonKeyAttributes
.
+ `ALL` - All of the table attributes are projected into the index.
+ NonKeyAttributes - A list of one or more non-key attribute names that
are projected into the secondary index. The total count of
attributes specified in NonKeyAttributes , summed across all of the
secondary indexes, must not exceed 20. If you project the same
attribute into two different indexes, this counts as two distinct
attributes when determining the total.
:type global_secondary_indexes: list
:param global_secondary_indexes:
One or more global secondary indexes (the maximum is five) to be
created on the table. Each global secondary index in the array
includes the following:
+ IndexName - The name of the global secondary index. Must be unique
only for this table.
+ KeySchema - Specifies the key schema for the global secondary index.
+ Projection - Specifies attributes that are copied (projected) from
the table into the index. These are in addition to the primary key
attributes and index key attributes, which are automatically
projected. Each attribute specification is composed of:
+ ProjectionType - One of the following:
+ `KEYS_ONLY` - Only the index and primary keys are projected into the
index.
+ `INCLUDE` - Only the specified table attributes are projected into
the index. The list of projected attributes are in NonKeyAttributes
.
+ `ALL` - All of the table attributes are projected into the index.
+ NonKeyAttributes - A list of one or more non-key attribute names that
are projected into the secondary index. The total count of
attributes specified in NonKeyAttributes , summed across all of the
secondary indexes, must not exceed 20. If you project the same
attribute into two different indexes, this counts as two distinct
attributes when determining the total.
+ ProvisionedThroughput - The provisioned throughput settings for the
global secondary index, consisting of read and write capacity
units.
:type provisioned_throughput: dict
:param provisioned_throughput: Represents the provisioned throughput
settings for a specified table or index. The settings can be
modified using the UpdateTable operation.
For current minimum and maximum provisioned throughput values, see
`Limits`_ in the Amazon DynamoDB Developer Guide .
"""
params = {
'AttributeDefinitions': attribute_definitions,
'TableName': table_name,
'KeySchema': key_schema,
'ProvisionedThroughput': provisioned_throughput,
}
if local_secondary_indexes is not None:
params['LocalSecondaryIndexes'] = local_secondary_indexes
if global_secondary_indexes is not None:
params['GlobalSecondaryIndexes'] = global_secondary_indexes
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funnyzhou/Adaptive_Feeding | 9c78182331d8c0ea28de47226e805776c638d46f | lib/rpn/generate_anchors.py | python | generate_anchors | (base_size=16, ratios=[0.5, 1, 2],
scales=2**np.arange(3, 6)) | return anchors | Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window. | Generate anchor (reference) windows by enumerating aspect ratios X
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papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/google/protobuf/internal/containers.py | python | MessageMap.get_or_create | (self, key) | return self[key] | get_or_create() is an alias for getitem (ie. map[key]).
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key: The key to get or create in the map.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | GraphicsBrush.__init__ | (self, *args, **kwargs) | __init__(self) -> GraphicsBrush
A wx.GraphicsBrush is a native representation of a brush. It is used
for filling a path on a `wx.GraphicsContext`. The contents are
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/OpenSSL/crypto.py | python | CRL.set_nextUpdate | (self, when) | return self._set_boundary_time(_lib.X509_CRL_get_nextUpdate, when) | Set when the CRL will next be udpated.
The timestamp is formatted as an ASN.1 TIME::
YYYYMMDDhhmmssZ
.. versionadded:: 16.1.0
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Set when the CRL will next be udpated.
The timestamp is formatted as an ASN.1 TIME::
YYYYMMDDhhmmssZ
.. versionadded:: 16.1.0
:param bytes when: A timestamp string.
:return: ``None``
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | clang/utils/check_cfc/check_cfc.py | python | is_normal_compile | (args) | return compile_step and not bitcode and not query and not dependency and input_is_valid | Check if this is a normal compile which will output an object file rather
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compile_step = '-c' in args
# Bitcode cannot be disassembled in the same way
bitcode = '-flto' in args or '-emit-llvm' in args
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query = '--version' in args or '--help' in args
# Options to output dependency files for make
dependency = '-M' in args or '-MM' in args
# Check if the input is recognised as a source file (this may be too
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input_is_valid = bool(get_input_file(args))
return compile_step and not bitcode and not query and not dependency and input_is_valid | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/process/plugins.py | python | Monitor.graceful | (self) | Stop the callback's background task thread and restart it. | Stop the callback's background task thread and restart it. | [
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numworks/epsilon | 8952d2f8b1de1c3f064eec8ffcea804c5594ba4c | build/device/usb/legacy.py | python | DeviceHandle.resetEndpoint | (self, endpoint) | r"""Reset all states for the specified endpoint.
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self.clearHalt(endpoint) | [
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limbo018/DREAMPlace | 146c3b9fd003d1acd52c96d9fd02e3f0a05154e4 | dreamplace/ops/dct/discrete_spectral_transform.py | python | idsct2 | (x, expk_0=None, expk_1=None) | return idxt(idxt(x, 0, expk_1).transpose_(dim0=-2, dim1=-1), 1, expk_0).transpose_(dim0=-2, dim1=-1) | Batch 2D Inverse Discrete Sine-Cosine Transformation without normalization to coefficients.
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y_{u, v} = \sum_p \sum_q x_{p, q} sin(pi/M*p*(u+0.5)) cos(pi/N*q*(v+0.5))
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@param expk_0 with length M, 2*exp(-1j*pi*k/(2M))
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y_{u, v} = \sum_p \sum_q x_{p, q} sin(pi/M*p*(u+0.5)) cos(pi/N*q*(v+0.5))
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return idxt(idxt(x, 0, expk_1).transpose_(dim0=-2, dim1=-1), 1, expk_0).transpose_(dim0=-2, dim1=-1) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/psutil/__init__.py | python | net_if_stats | () | return _psplatform.net_if_stats() | Return information about each NIC (network interface card)
installed on the system as a dictionary whose keys are the
NIC names and value is a namedtuple with the following fields:
- isup: whether the interface is up (bool)
- duplex: can be either NIC_DUPLEX_FULL, NIC_DUPLEX_HALF or
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- speed: the NIC speed expressed in mega bits (MB); if it can't
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- mtu: the maximum transmission unit expressed in bytes. | Return information about each NIC (network interface card)
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/generator/android.py | python | AndroidMkWriter.ExtractIncludesFromCFlags | (self, cflags) | return (clean_cflags, include_paths) | Extract includes "-I..." out from cflags
Args:
cflags: A list of compiler flags, which may be mixed with "-I.."
Returns:
A tuple of lists: (clean_clfags, include_paths). "-I.." is trimmed. | Extract includes "-I..." out from cflags | [
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"""Extract includes "-I..." out from cflags
Args:
cflags: A list of compiler flags, which may be mixed with "-I.."
Returns:
A tuple of lists: (clean_clfags, include_paths). "-I.." is trimmed.
"""
clean_cflags = []
include_paths = []
if cflags:
for flag in cflags:
if flag.startswith('-I'):
include_paths.append(flag[2:])
else:
clean_cflags.append(flag)
return (clean_cflags, include_paths) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Text.delete | (self, index1, index2=None) | Delete the characters between INDEX1 and INDEX2 (not included). | Delete the characters between INDEX1 and INDEX2 (not included). | [
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"""Delete the characters between INDEX1 and INDEX2 (not included)."""
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intel-iot-devkit/how-to-code-samples | b4ea616f36bbfa2e042beb1698f968cfd651d79f | close-call-reporter/python/iot_close_call_reporter/__main__.py | python | main | () | Start main function. | Start main function. | [
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"""
Start main function.
"""
runner = Runner()
print("Running {0} example.".format(runner.project_name))
runner.start()
def signal_handle(sig, frame):
reactor.stop()
_exit(0)
signal(SIGINT, signal_handle)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/platform.py | python | _platform | (*args) | return platform | Helper to format the platform string in a filename
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""" Helper to format the platform string in a filename
compatible format e.g. "system-version-machine".
"""
# Format the platform string
platform = string.join(
map(string.strip,
filter(len, args)),
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# Cleanup some possible filename obstacles...
replace = string.replace
platform = replace(platform,' ','_')
platform = replace(platform,'/','-')
platform = replace(platform,'\\','-')
platform = replace(platform,':','-')
platform = replace(platform,';','-')
platform = replace(platform,'"','-')
platform = replace(platform,'(','-')
platform = replace(platform,')','-')
# No need to report 'unknown' information...
platform = replace(platform,'unknown','')
# Fold '--'s and remove trailing '-'
while 1:
cleaned = replace(platform,'--','-')
if cleaned == platform:
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platform = cleaned
while platform[-1] == '-':
platform = platform[:-1]
return platform | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Configure.py | python | ConfigurationContext.eval_rules | (self, rules) | Execute the configuration tests. The method :py:meth:`waflib.Configure.ConfigurationContext.err_handler`
is used to process the eventual exceptions
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/media/webrtc/trunk/tools/gyp/pylib/gyp/MSVSVersion.py | python | VisualStudioVersion.Path | (self) | return self.path | Returns the path to Visual Studio installation. | Returns the path to Visual Studio installation. | [
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | algorithms/src/LocalizationAndMapping/icp_lidar_localization/fast_gicp/thirdparty/pybind11/pybind11/setup_helpers.py | python | has_flag | (compiler, flag) | Return the flag if a flag name is supported on the
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/binding/initfini.py | python | initialize_all_asmprinters | () | Initialize all code generators. Necessary before generating
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | IconLocation.IsOk | (*args, **kwargs) | return _gdi_.IconLocation_IsOk(*args, **kwargs) | IsOk(self) -> bool | IsOk(self) -> bool | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/calibration.py | python | _CalibratedClassifier.predict_proba | (self, X) | return proba | Posterior probabilities of classification
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Parameters
----------
X : array-like, shape (n_samples, n_features)
The samples.
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-------
C : array, shape (n_samples, n_classes)
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"""Posterior probabilities of classification
This function returns posterior probabilities of classification
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Parameters
----------
X : array-like, shape (n_samples, n_features)
The samples.
Returns
-------
C : array, shape (n_samples, n_classes)
The predicted probas. Can be exact zeros.
"""
n_classes = len(self.classes_)
proba = np.zeros((X.shape[0], n_classes))
df, idx_pos_class = self._preproc(X)
for k, this_df, calibrator in \
zip(idx_pos_class, df.T, self.calibrators_):
if n_classes == 2:
k += 1
proba[:, k] = calibrator.predict(this_df)
# Normalize the probabilities
if n_classes == 2:
proba[:, 0] = 1. - proba[:, 1]
else:
proba /= np.sum(proba, axis=1)[:, np.newaxis]
# XXX : for some reason all probas can be 0
proba[np.isnan(proba)] = 1. / n_classes
# Deal with cases where the predicted probability minimally exceeds 1.0
proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PGChoices.AllocExclusive | (*args, **kwargs) | return _propgrid.PGChoices_AllocExclusive(*args, **kwargs) | AllocExclusive(self) | AllocExclusive(self) | [
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facebook/watchman | 0917460c71b000b96be9b9575d77f06f2f6053bb | build/fbcode_builder/getdeps/builder.py | python | CargoBuilder._resolve_dep_to_crates | (build_source_dir, dep_to_git) | return dep_to_crates | This function traverse the build_source_dir in search of Cargo.toml
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/window/ewm.py | python | EWM.mean | (self, *args, **kwargs) | return self._apply("ewma", **kwargs) | Exponential weighted moving average.
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*args, **kwargs
Arguments and keyword arguments to be passed into func. | Exponential weighted moving average. | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/otci/otci/otci.py | python | OTCI.get_ifconfig_state | (self) | return self.__parse_values(self.execute_command('ifconfig'), up=True, down=False) | Get the status of the IPv6 interface. | Get the status of the IPv6 interface. | [
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lballabio/quantlib-old | 136336947ed4fea9ecc1da6edad188700e821739 | gensrc/gensrc/serialization/serializable.py | python | Serializable.groupName | (self) | return self.groupName_ | Return unique identifier for this object. | Return unique identifier for this object. | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/tools/android/merge_manifests.py | python | MergeManifests._RemoveAndroidLabel | (self, node) | Remove android:label.
We do this because it is not required by merger manifest,
and it might contain @string references that will not allow compilation.
Args:
node: Node for which to remove Android labels. | Remove android:label. | [
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"""Remove android:label.
We do this because it is not required by merger manifest,
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Args:
node: Node for which to remove Android labels.
"""
if node.hasAttribute(self._ANDROID_LABEL):
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/kdshTreeRings.py | python | kdshTreeRings.__del__ | (self) | unload the dialog that we loaded | unload the dialog that we loaded | [
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/util/utils.py | python | detect_ptype | (runtime_value) | Detect the default PType type for a runtime value
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runtime_value (object): a runtime value, cannot be PType
Returns:
class: detected PType class | Detect the default PType type for a runtime value | [
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"""
Detect the default PType type for a runtime value
Args:
runtime_value (object): a runtime value, cannot be PType
Returns:
class: detected PType class
"""
import collections
def helper(nested_level, v):
if isinstance(v, collections.MutableMapping):
return helper(nested_level + 1, v.values()[0])
elif isinstance(v, list):
return nested_level, pcollection.PCollection
else:
return nested_level, pobject.PObject
if isinstance(runtime_value, ptype.PType):
raise ValueError("Input cannot be PType")
if isinstance(runtime_value, collections.MutableMapping):
return helper(0, runtime_value.values()[0])
elif isinstance(runtime_value, list):
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib2to3/refactor.py | python | get_all_fix_names | (fixer_pkg, remove_prefix=True) | return fix_names | Return a sorted list of all available fix names in the given package. | Return a sorted list of all available fix names in the given package. | [
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fixer_dir = os.path.dirname(pkg.__file__)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/html.py | python | HtmlCell.FindCellByPos | (*args, **kwargs) | return _html.HtmlCell_FindCellByPos(*args, **kwargs) | FindCellByPos(self, int x, int y, unsigned int flags=HTML_FIND_EXACT) -> HtmlCell | FindCellByPos(self, int x, int y, unsigned int flags=HTML_FIND_EXACT) -> HtmlCell | [
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] | def FindCellByPos(*args, **kwargs):
"""FindCellByPos(self, int x, int y, unsigned int flags=HTML_FIND_EXACT) -> HtmlCell"""
return _html.HtmlCell_FindCellByPos(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/html.py#L714-L716 | |
p4lang/behavioral-model | 81ce0163f0770c6b9d6056a28ce2e0cc035bb6e9 | tools/cpplint.py | python | _IsParentOrSame | (parent, child) | return child == os.path.join(prefix, child_suffix) | Return true if child is subdirectory of parent.
Assumes both paths are absolute and don't contain symlinks. | Return true if child is subdirectory of parent.
Assumes both paths are absolute and don't contain symlinks. | [
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"are",
"absolute",
"and",
"don",
"t",
"contain",
"symlinks",
"."
] | def _IsParentOrSame(parent, child):
"""Return true if child is subdirectory of parent.
Assumes both paths are absolute and don't contain symlinks.
"""
parent = os.path.normpath(parent)
child = os.path.normpath(child)
if parent == child:
return True
prefix = os.path.commonprefix([parent, child])
if prefix != parent:
return False
# Note: os.path.commonprefix operates on character basis, so
# take extra care of situations like '/foo/ba' and '/foo/bar/baz'
child_suffix = child[len(prefix):]
child_suffix = child_suffix.lstrip(os.sep)
return child == os.path.join(prefix, child_suffix) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/debug/lib/grpc_debug_server.py | python | EventListenerBaseStreamHandler.__init__ | (self) | Constructor of EventListenerBaseStreamHandler. | Constructor of EventListenerBaseStreamHandler. | [
"Constructor",
"of",
"EventListenerBaseStreamHandler",
"."
] | def __init__(self):
"""Constructor of EventListenerBaseStreamHandler.""" | [
"def",
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"(",
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")",
":"
] | https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/debug/lib/grpc_debug_server.py#L53-L54 | ||
deeplearningais/CUV | 4e920ad1304af9de3e5f755cc2e9c5c96e06c324 | examples/mlp/multi_layer_perceptron.py | python | MLP.__init__ | (self, neurons, batch_size) | Constructor
@param neurons -- array of sizes of layers.
@param batch_size -- size of batch being used for training. | Constructor | [
"Constructor"
] | def __init__(self, neurons, batch_size):
"""
Constructor
@param neurons -- array of sizes of layers.
@param batch_size -- size of batch being used for training.
"""
self.n_layers = len(neurons) - 1
self.batch_size = batch_size
self.neuron_layers = []
self.weight_layers = []
print("Training MLP with %d hidden layer(s)." % (self.n_layers - 1))
for i in xrange(self.n_layers + 1):
dim1 = neurons[i]
self.neuron_layers.append(neuron_layer(dim1,
self.batch_size))
for i in xrange(self.n_layers):
self.weight_layers.append(weight_layer(self.neuron_layers[i],
self.neuron_layers[i + 1])) | [
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"... | https://github.com/deeplearningais/CUV/blob/4e920ad1304af9de3e5f755cc2e9c5c96e06c324/examples/mlp/multi_layer_perceptron.py#L23-L42 | ||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py | python | VectorSinhArcsinhDiag.loc | (self) | return self._loc | The `loc` in `Y := loc + scale @ F(Z) * (2 / F(2)). | The `loc` in `Y := loc + scale | [
"The",
"loc",
"in",
"Y",
":",
"=",
"loc",
"+",
"scale"
] | def loc(self):
"""The `loc` in `Y := loc + scale @ F(Z) * (2 / F(2))."""
return self._loc | [
"def",
"loc",
"(",
"self",
")",
":",
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] | https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py#L257-L259 |
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