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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/pylinters.py | python | _get_build_dir | () | return os.path.join(git.get_base_dir(), "build") | Get the location of the scons' build directory in case we need to download clang-format. | Get the location of the scons' build directory in case we need to download clang-format. | [
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# type: () -> str
"""Get the location of the scons' build directory in case we need to download clang-format."""
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ptrkrysik/gr-gsm | 2de47e28ce1fb9a518337bfc0add36c8e3cff5eb | python/qa_tch_h_decoder.py | python | qa_tch_h_decoder.test_facch_th | (self) | FACCH/TH test | FACCH/TH test | [
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FACCH/TH test
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timeslots = [ 6, 6, 6, 6, 6, 6, 6, 6],
bursts = [ b[14], b[15], b[16], b[17], b[16], b[17], b[18], b[19]]),
['02 04 01 06 00 00 00 00 00 03 f4 8b 06 00 00 00 03 60 09 03 0f 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b 2b']) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/traceback.py | python | format_tb | (tb, limit = None) | return format_list(extract_tb(tb, limit)) | A shorthand for 'format_list(extract_tb(tb, limit))'. | A shorthand for 'format_list(extract_tb(tb, limit))'. | [
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"""A shorthand for 'format_list(extract_tb(tb, limit))'."""
return format_list(extract_tb(tb, limit)) | [
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PixarAnimationStudios/USD | faed18ce62c8736b02413635b584a2f637156bad | pxr/usdImaging/usdviewq/appController.py | python | AppController._HUDInfoChanged | (self) | Called when a HUD setting that requires info refresh has changed. | Called when a HUD setting that requires info refresh has changed. | [
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"""Called when a HUD setting that requires info refresh has changed."""
if self._isHUDVisible():
self._updateHUDPrimStats()
self._updateHUDGeomCounts() | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/internal/_parameterized.py | python | Parameters | (*testcases) | return _ParameterDecorator(_ARGUMENT_REPR, testcases) | A decorator for creating parameterized tests.
See the module docstring for a usage example.
Args:
*testcases: Parameters for the decorated method, either a single
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Returns:
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"""A decorator for creating parameterized tests.
See the module docstring for a usage example.
Args:
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iterable, or a list of tuples/dicts/objects (for tests
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Returns:
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"""
return _ParameterDecorator(_ARGUMENT_REPR, testcases) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/fnmatch.py | python | fnmatchcase | (name, pat) | return match(name) is not None | Test whether FILENAME matches PATTERN, including case.
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"""
match = _compile_pattern(pat)
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esa/pagmo | 80281d549c8f1b470e1489a5d37c8f06b2e429c0 | PyGMO/util/_analysis.py | python | analysis._scale_sample | (self) | Scales the sample in x and f after sampling, so all values are [0,1]. If constraints
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/PeacockMainWindow.py | python | PeacockMainWindow._setTitle | (self) | Sets the title of the window. | Sets the title of the window. | [
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"""
Sets the title of the window.
"""
title = "Peacock"
if self.exe_path:
title += " : executable - %s" % os.path.abspath(self.exe_path)
if self.input_file_path:
title += " : input file - %s" % os.path.abspath(self.input_file_path)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Misc._displayof | (self, displayof) | return () | Internal function. | Internal function. | [
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if displayof:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/ttk.py | python | LabeledScale.__init__ | (self, master=None, variable=None, from_=0, to=10, **kw) | Construct a horizontal LabeledScale with parent master, a
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If variable is not specified, a tkinter.IntVar is created.
WIDGET-SPECIFIC OPTIONS
compound: 'top' or 'bottom'
Specifies how to display the label relative to the scale.
Defaults to 'top'. | Construct a horizontal LabeledScale with parent master, a
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label_side = 'top' if scale_side == 'bottom' else 'bottom'
self.scale.pack(side=scale_side, fill='x')
tmp = Label(self).pack(side=label_side) # place holder
self.label.place(anchor='n' if label_side == 'top' else 's')
# update the label as scale or variable changes
self.__tracecb = self._variable.trace_variable('w', self._adjust)
self.bind('<Configure>', self._adjust)
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus2.in.py | python | exodus.put_node_set | (self, id, nodeSetNodes) | exo.put_node_set(node_set_id, ns_nodes)
-> store a node set by its id and the list of node *INDICES* in
the node set (see exo.get_node_id_map() for explanation of node
*INDEX* versus node *ID*)
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<list<int>> ns_nodes | exo.put_node_set(node_set_id, ns_nodes) | [
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-> store a node set by its id and the list of node *INDICES* in
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<list<int>> ns_nodes
"""
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ku-nlp/jumanpp | 008e73b9cf876ce50ba5e751ac7108e68796cb1f | script/git-clang-format.py | python | filter_by_extension | (dictionary, allowed_extensions) | Delete every key in `dictionary` that doesn't have an allowed extension.
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"""Delete every key in `dictionary` that doesn't have an allowed extension.
`allowed_extensions` must be a collection of lowercase file extensions,
excluding the period."""
allowed_extensions = frozenset(allowed_extensions)
for filename in list(dictionary.keys()):
base_ext = filename.rsplit('.', 1)
if len(base_ext) == 1 and '' in allowed_extensions:
continue
if len(base_ext) == 1 or base_ext[1].lower() not in allowed_extensions:
del dictionary[filename] | [
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cinder/Cinder | e83f5bb9c01a63eec20168d02953a0879e5100f7 | docs/libs/pystache/loader.py | python | Loader.load_object | (self, obj) | return self.read(path) | Find and return the template associated to the given object.
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obj: an instance of a user-defined class.
search_dirs: the list of directories in which to search. | Find and return the template associated to the given object. | [
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Find and return the template associated to the given object.
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obj: an instance of a user-defined class.
search_dirs: the list of directories in which to search.
"""
locator = self._make_locator()
path = locator.find_object(obj, self.search_dirs)
return self.read(path) | [
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qt/qt | 0a2f2382541424726168804be2c90b91381608c6 | src/3rdparty/webkit/Source/ThirdParty/gyp/pylib/gyp/xcodeproj_file.py | python | XCConfigurationList.AppendBuildSetting | (self, key, value) | Appends value to the build setting for key, which is treated as a list,
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for configuration in self._properties['buildConfigurations']:
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ROCmSoftwarePlatform/hipCaffe | 4ec5d482515cce532348553b6db6d00d015675d5 | python/caffe/coord_map.py | python | crop_params | (fn) | return (axis, offset) | Extract the crop layer parameters with defaults. | Extract the crop layer parameters with defaults. | [
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"""
Extract the crop layer parameters with defaults.
"""
params = fn.params.get('crop_param', fn.params)
axis = params.get('axis', 2) # default to spatial crop for N, C, H, W
offset = np.array(params.get('offset', 0), ndmin=1)
return (axis, offset) | [
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greenheartgames/greenworks | 3ea4ab490b56676de3f0a237c74bcfdb17323e60 | deps/cpplint/cpplint.py | python | FilesBelongToSameModule | (filename_cc, filename_h) | return files_belong_to_same_module, common_path | Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
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some/path/public/xyzzy and some/path/internal/xyzzy are also considered
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Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
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some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file. | Check if these two filenames belong to the same module. | [
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"""Check if these two filenames belong to the same module.
The concept of a 'module' here is a as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
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'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
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so we need this guesswork here.
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according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the .cc file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
fileinfo = FileInfo(filename_cc)
if not fileinfo.IsSource():
return (False, '')
filename_cc = filename_cc[:-len(fileinfo.Extension())]
matched_test_suffix = Search(_TEST_FILE_SUFFIX, fileinfo.BaseName())
if matched_test_suffix:
filename_cc = filename_cc[:-len(matched_test_suffix.group(1))]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
if not filename_h.endswith('.h'):
return (False, '')
filename_h = filename_h[:-len('.h')]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path | [
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indutny/candor | 48e7260618f5091c80a3416828e2808cad3ea22e | tools/gyp/pylib/gyp/generator/msvs.py | python | _EscapeCommandLineArgumentForMSBuild | (s) | return s | Escapes a Windows command-line argument for use by MSBuild. | Escapes a Windows command-line argument for use by MSBuild. | [
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] | def _EscapeCommandLineArgumentForMSBuild(s):
"""Escapes a Windows command-line argument for use by MSBuild."""
def _Replace(match):
return (len(match.group(1))/2*4)*'\\' + '\\"'
# Escape all quotes so that they are interpreted literally.
s = quote_replacer_regex2.sub(_Replace, s)
return s | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/framework/ops.py | python | value_text | (tensor, is_repr=False) | return text | Either the NumPy value or a custom TensorFlow formatting of `tensor`.
Custom formatting is used for custom device tensors, e.g. parallel tensors
with multiple components on different devices.
Args:
tensor: The tensor to format.
is_repr: Controls the style/verbosity of formatting.
Returns:
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"""Either the NumPy value or a custom TensorFlow formatting of `tensor`.
Custom formatting is used for custom device tensors, e.g. parallel tensors
with multiple components on different devices.
Args:
tensor: The tensor to format.
is_repr: Controls the style/verbosity of formatting.
Returns:
The formatted tensor.
"""
# pylint: disable=protected-access # friend access
if tensor._prefer_custom_summarizer():
text = tensor._summarize_value()
# pylint: enable=protected-access
if is_repr:
text = "value=" + text
else:
text = numpy_text(tensor, is_repr=is_repr)
if is_repr:
text = "numpy=" + text
return text | [
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scylladb/seastar | 0cdd2329beb1cc4c0af8828598c26114397ffa9c | scripts/perftune.py | python | DiskPerfTuner.__write_cache_config | (self) | return "write back" if self.args.set_write_back else "write through" | :return: None - if write cache mode configuration is not requested or the corresponding write cache
configuration value string | :return: None - if write cache mode configuration is not requested or the corresponding write cache
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"""
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return "write back" if self.args.set_write_back else "write through" | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | PlatformInformation.IsOk | (*args, **kwargs) | return _misc_.PlatformInformation_IsOk(*args, **kwargs) | IsOk(self) -> bool | IsOk(self) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/email/mime/application.py | python | MIMEApplication.__init__ | (self, _data, _subtype='octet-stream',
_encoder=encoders.encode_base64, *, policy=None, **_params) | Create an application/* type MIME document.
_data is a string containing the raw application data.
_subtype is the MIME content type subtype, defaulting to
'octet-stream'.
_encoder is a function which will perform the actual encoding for
transport of the application data, defaulting to base64 encoding.
Any additional keyword arguments are passed to the base class
constructor, which turns them into parameters on the Content-Type
header. | Create an application/* type MIME document. | [
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"""Create an application/* type MIME document.
_data is a string containing the raw application data.
_subtype is the MIME content type subtype, defaulting to
'octet-stream'.
_encoder is a function which will perform the actual encoding for
transport of the application data, defaulting to base64 encoding.
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self.set_payload(_data)
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | clang/bindings/python/clang/cindex.py | python | Type.get_declaration | (self) | return conf.lib.clang_getTypeDeclaration(self) | Return the cursor for the declaration of the given type. | Return the cursor for the declaration of the given type. | [
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Return the cursor for the declaration of the given type.
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/AI/AIstate.py | python | AIstate.__split_new_fleets | (self) | Split any new fleets.
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] | def __split_new_fleets(self):
"""Split any new fleets.
This function is supposed to be called once at the beginning of the turn.
Splitting the auto generated fleets at game start or those created by
recently built ships allows the AI to assign correct roles to all ships.
"""
# TODO: check length of fleets for losses or do in AIstate.__cleanRoles
universe = fo.getUniverse()
known_fleets = self.get_fleet_roles_map()
self.newlySplitFleets.clear()
fleets_to_split = [fleet_id for fleet_id in FleetUtilsAI.get_empire_fleet_ids() if fleet_id not in known_fleets]
if fleets_to_split:
debug("Trying to split %d new fleets" % len(fleets_to_split))
for fleet_id in fleets_to_split:
fleet = universe.getFleet(fleet_id)
if not fleet:
warning("Trying to split fleet %d but seemingly does not exist" % fleet_id)
continue
fleet_len = len(fleet.shipIDs)
if fleet_len == 1:
continue
new_fleets = FleetUtilsAI.split_fleet(fleet_id)
debug("Split fleet %d with %d ships into %d new fleets:" % (fleet_id, fleet_len, len(new_fleets))) | [
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hszhao/PSPNet | cf7e5a99ba37e46118026e96be5821a9bc63bde0 | scripts/cpp_lint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum. | If input points to ) or } or ] or >, finds the position that opens it. | [
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"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
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Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
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# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
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if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
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if start_pos > -1:
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mysql/mysql-workbench | 2f35f9034f015cbcd22139a60e1baa2e3e8e795c | plugins/wb.sqlide/sqlide_schematree_ext.py | python | CodeGenerator.build_cascaded_delete | (self) | To delete a row from a table all references to that row from other tables must be deleted first,
otherwise the FK constraint won't let it be done.
So this function will build the DELETE statements needed to do that, by deleting
references to the given row all the way up to the wanted table. | To delete a row from a table all references to that row from other tables must be deleted first,
otherwise the FK constraint won't let it be done.
So this function will build the DELETE statements needed to do that, by deleting
references to the given row all the way up to the wanted table. | [
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"""
To delete a row from a table all references to that row from other tables must be deleted first,
otherwise the FK constraint won't let it be done.
So this function will build the DELETE statements needed to do that, by deleting
references to the given row all the way up to the wanted table.
"""
editor = self.editor
object = self.selection[0]
info = dependencyInfoForSchemaInEditor(editor, object.schemaName)
if not info:
return
info.scan_tables()
the_table = info.table_with_name(object.schemaName, object.name)
if not the_table:
mforms.Utilities.show_error("Cascading DELETE", "Could not find reverse engineered %s" % object.name, "OK", "", "")
return
if not the_table.primaryKey:
mforms.Utilities.show_error("Cascading DELETE", "Unable to create a DELETE statement for %s because the table has no primary key." % object.name,
"OK", "", "")
return
# expression to match the row to be deleted
pk = []
for c in the_table.primaryKey.columns:
pk.append("%s.%s = @%s_to_delete" % (the_table.name, c.referencedColumn.name, c.referencedColumn.name))
pk = " AND ".join(pk)
def create_delete(table, info, deletes, reference_chain, visited):
if table in visited:
return
visited.add(table)
def add_to_list(list, item):
if item not in list:
return list + [item]
return list
for fk in info.get_foreign_keys_to_table(table):
create_delete(fk.owner, info, deletes, reference_chain + [fk], visited)
query = "DELETE FROM %s\n" % table.name
query += " USING %s\n" % ", ".join(add_to_list([fk.owner.name for fk in reference_chain], the_table.name))
if reference_chain:
query += " WHERE %s\n" % "\n AND ".join([format_fk_join(fk) for fk in reference_chain])
query += " AND %s;" % pk
else:
query += " WHERE %s;" % pk
deletes.append(query)
deletes = []
create_delete(the_table, info, deletes, [], set())
varsetup = []
for c in the_table.primaryKey.columns:
varsetup.append("SET @%s_to_delete = <{row_id}>;" % c.referencedColumn.name)
query = """
-- All objects that reference that row (directly or indirectly) will be deleted when this snippet is executed.
-- To preview the rows to be deleted, use Select Row Dependencies
START TRANSACTION;
-- Provide the values of the primary key of the row to delete.
%s
%s
COMMIT;
""" % ("\n".join(varsetup), "\n".join(deletes))
self.send(query)
if not self.to_editor:
mforms.App.get().set_status_text("DELETE statement for %s was copied to clipboard" % the_table.name) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/SConf.py | python | SConfBase._shutdown | (self) | Private method. Reset to non-piped spawn | Private method. Reset to non-piped spawn | [
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"""Private method. Reset to non-piped spawn"""
global sconf_global, _ac_config_hs
if not self.active:
raise SCons.Errors.UserError("Finish may be called only once!")
if self.logstream is not None and not dryrun:
self.logstream.write("\n")
self.logstream.close()
self.logstream = None
# remove the SConfSourceBuilder from the environment
blds = self.env['BUILDERS']
del blds['SConfSourceBuilder']
self.env.Replace( BUILDERS=blds )
self.active = 0
sconf_global = None
if not self.config_h is None:
_ac_config_hs[self.config_h] = self.config_h_text
self.env.fs = self.lastEnvFs | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/sdist.py | python | sdist.write_manifest | (self) | Write the file list in 'self.filelist' (presumably as filled in
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named by 'self.manifest'. | Write the file list in 'self.filelist' (presumably as filled in
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"""Write the file list in 'self.filelist' (presumably as filled in
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named by 'self.manifest'.
"""
if self._manifest_is_not_generated():
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return
content = self.filelist.files[:]
content.insert(0, '# file GENERATED by distutils, do NOT edit')
self.execute(file_util.write_file, (self.manifest, content),
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/functools.py | python | _le_from_gt | (self, other, NotImplemented=NotImplemented) | return not op_result | Return a <= b. Computed by @total_ordering from (not a > b). | Return a <= b. Computed by | [
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op_result = type(self).__gt__(self, other)
if op_result is NotImplemented:
return op_result
return not op_result | [
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mapequation/infomap | 5f56b94fe0f956483f61a03ef07e94d8def2205e | interfaces/python/infomap.py | python | Infomap.num_leaf_modules | (self) | return num_leaf_modules | Get the number of leaf modules in the tree
Returns
-------
int
The number of leaf modules | Get the number of leaf modules in the tree | [
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Returns
-------
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The number of leaf modules
"""
num_leaf_modules = 0
for _ in self.leaf_modules:
num_leaf_modules += 1
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/json_schema_compiler/schema_bundle_generator.py | python | SchemaBundleGenerator.GenerateHeader | (self, file_base, body_code) | return c | Generates a code.Code object for a header file
Parameters:
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Parameters:
- |file_base| - the base of the filename, e.g. 'foo' (for 'foo.h')
- |body_code| - the code to put in between the multiple inclusion guards"""
c = code.Code()
c.Append(cpp_util.CHROMIUM_LICENSE)
c.Append()
c.Append(cpp_util.GENERATED_BUNDLE_FILE_MESSAGE % SOURCE_BASE_PATH)
ifndef_name = cpp_util.GenerateIfndefName(SOURCE_BASE_PATH, file_base)
c.Append()
c.Append('#ifndef %s' % ifndef_name)
c.Append('#define %s' % ifndef_name)
c.Append('#pragma once')
c.Append()
c.Concat(body_code)
c.Append()
c.Append('#endif // %s' % ifndef_name)
c.Append()
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/nntplib.py | python | NNTP.xhdr | (self, hdr, str, file=None) | return resp, lines | Process an XHDR command (optional server extension). Arguments:
- hdr: the header type (e.g. 'subject')
- str: an article nr, a message id, or a range nr1-nr2
Returns:
- resp: server response if successful
- list: list of (nr, value) strings | Process an XHDR command (optional server extension). Arguments:
- hdr: the header type (e.g. 'subject')
- str: an article nr, a message id, or a range nr1-nr2
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resp, lines = self.longcmd('XHDR ' + hdr + ' ' + str, file)
for i in range(len(lines)):
line = lines[i]
m = pat.match(line)
if m:
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | src/python/m5/ext/pyfdt/pyfdt.py | python | FdtPropertyBytes.json_represent | (self, depth=0) | return result | Ouput JSON | Ouput JSON | [
"Ouput",
"JSON"
] | def json_represent(self, depth=0):
"""Ouput JSON"""
result = '%s: ["bytes", "' % json.dumps(self.name)
result += '", "'.join(["%02x" % byte
for byte in self.bytes])
result += '"]'
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | NavigationKeyEvent.SetFromTab | (*args, **kwargs) | return _core_.NavigationKeyEvent_SetFromTab(*args, **kwargs) | SetFromTab(self, bool bIs)
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"""
SetFromTab(self, bool bIs)
Set to true under MSW if the event was generated using the tab key.
This is required for proper navogation over radio buttons.
"""
return _core_.NavigationKeyEvent_SetFromTab(*args, **kwargs) | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_contrib/condor_pigeon/src/condor_pigeon_client/skype_linux_tools/Skype4Py/skype.py | python | ISkypeEvents.ConnectionStatus | (self, Status) | This event is caused by a connection status change.
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/code_coverage/croc.py | python | Coverage.__init__ | (self) | Constructor. | Constructor. | [
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self.files = {} # Map filename --> CoverageFile
self.root_dirs = [] # (root, altname)
self.rules = [] # (regexp, dict of RHS attrs)
self.tree = CoveredDir('')
self.print_stats = [] # Dicts of args to PrintStat()
# Functions which need to be replaced for unit testing
self.add_files_walk = os.walk # Walk function for AddFiles()
self.scan_file = croc_scan.ScanFile | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Build.py | python | BuildContext.get_all_task_gen | (self) | return lst | Utility method, returns a list of all task generators - if you need something more complicated, implement your own | Utility method, returns a list of all task generators - if you need something more complicated, implement your own | [
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"""
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"""
lst = []
for g in self.groups:
lst.extend(t for t in g if isinstance(t, TaskGen.task_gen))
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ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/rocksdb-master/linters/cpp_linter/cpplint.py | python | CheckIncludeLine | (filename, clean_lines, linenum, include_state, error) | Check rules that are applicable to #include lines.
Strings on #include lines are NOT removed from elided line, to make
certain tasks easier. However, to prevent false positives, checks
applicable to #include lines in CheckLanguage must be put here.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
include_state: An _IncludeState instance in which the headers are inserted.
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Strings on #include lines are NOT removed from elided line, to make
certain tasks easier. However, to prevent false positives, checks
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
include_state: An _IncludeState instance in which the headers are inserted.
error: The function to call with any errors found.
"""
fileinfo = FileInfo(filename)
line = clean_lines.lines[linenum]
# "include" should use the new style "foo/bar.h" instead of just "bar.h"
if _RE_PATTERN_INCLUDE_NEW_STYLE.search(line):
error(filename, linenum, 'build/include', 4,
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# we shouldn't include a file more than once. actually, there are a
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match = _RE_PATTERN_INCLUDE.search(line)
if match:
include = match.group(2)
is_system = (match.group(1) == '<')
if include in include_state:
error(filename, linenum, 'build/include', 4,
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else:
include_state[include] = linenum
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# 2) c system files
# 3) cpp system files
# 4) for foo.cc, foo.h (deprecated location)
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#
# We classify each include statement as one of those 5 types
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error_message = include_state.CheckNextIncludeOrder(
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if error_message:
error(filename, linenum, 'build/include_order', 4,
'%s. Should be: %s.h, c system, c++ system, other.' %
(error_message, fileinfo.BaseName()))
canonical_include = include_state.CanonicalizeAlphabeticalOrder(include)
if not include_state.IsInAlphabeticalOrder(
clean_lines, linenum, canonical_include):
error(filename, linenum, 'build/include_alpha', 4,
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include_state.SetLastHeader(canonical_include)
# Look for any of the stream classes that are part of standard C++.
match = _RE_PATTERN_INCLUDE.match(line)
if match:
include = match.group(2)
if Match(r'(f|ind|io|i|o|parse|pf|stdio|str|)?stream$', include):
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if not _IsTestFilename(filename):
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/pycaffe/layers/pascal_multilabel_datalayers.py | python | load_pascal_annotation | (index, pascal_root) | return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'index': index} | This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083).
Thanks Ross! | This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083). | [
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"""
This code is borrowed from Ross Girshick's FAST-RCNN code
(https://github.com/rbgirshick/fast-rcnn).
It parses the PASCAL .xml metadata files.
See publication for further details: (http://arxiv.org/abs/1504.08083).
Thanks Ross!
"""
classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
class_to_ind = dict(zip(classes, xrange(21)))
filename = osp.join(pascal_root, 'Annotations', index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, 21), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
# Make pixel indexes 0-based
x1 = float(get_data_from_tag(obj, 'xmin')) - 1
y1 = float(get_data_from_tag(obj, 'ymin')) - 1
x2 = float(get_data_from_tag(obj, 'xmax')) - 1
y2 = float(get_data_from_tag(obj, 'ymax')) - 1
cls = class_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'index': index} | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/lib/io/tf_record.py | python | TFRecordWriter.close | (self) | Close the file. | Close the file. | [
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] | def close(self):
"""Close the file."""
self._writer.Close() | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | CondCore/Utilities/scripts/uploadConditions.py | python | HTTP.query | (self, url, data = None, files = None, keepCookies = True) | Queries a URL, optionally with some data (dictionary).
If no data is specified, a GET request will be used.
If some data is specified, a POST request will be used.
If files is specified, it must be a dictionary like data but
the values are filenames.
By default, cookies are kept in-between requests.
A HTTPError exception is raised if the response's HTTP code is not 200. | Queries a URL, optionally with some data (dictionary). | [
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"a",
"URL",
"optionally",
"with",
"some",
"data",
"(",
"dictionary",
")",
"."
] | def query(self, url, data = None, files = None, keepCookies = True):
'''Queries a URL, optionally with some data (dictionary).
If no data is specified, a GET request will be used.
If some data is specified, a POST request will be used.
If files is specified, it must be a dictionary like data but
the values are filenames.
By default, cookies are kept in-between requests.
A HTTPError exception is raised if the response's HTTP code is not 200.
'''
if not keepCookies:
self.discardCookies()
url = self.baseUrl + url
# make sure the logs are safe ... at least somewhat :)
data4log = copy.copy(data)
if data4log:
if 'password' in data4log.keys():
data4log['password'] = '*'
retries = [0] + list(self.retries)
while True:
logging.debug('Querying %s with data %s and files %s (retries left: %s, current sleep: %s)...', url, data4log, files, len(retries), retries[0])
time.sleep(retries.pop(0))
try:
self.curl.setopt(self.curl.URL, url)
self.curl.setopt(self.curl.HTTPGET, 1)
# from now on we use the token we got from the login
self.curl.setopt(pycurl.USERPWD, '%s:""' % ( str(self.token), ) )
self.curl.setopt(pycurl.HTTPHEADER, ['Accept: application/json'])
if data is not None or files is not None:
# If there is data or files to send, use a POST request
finalData = {}
if data is not None:
finalData.update(data)
if files is not None:
for (key, fileName) in files.items():
finalData[key] = (self.curl.FORM_FILE, fileName)
self.curl.setopt( self.curl.HTTPPOST, list(finalData.items()) )
self.curl.setopt(pycurl.VERBOSE, 0)
response = io.BytesIO()
self.curl.setopt(self.curl.WRITEFUNCTION, response.write)
self.curl.perform()
code = self.curl.getinfo(self.curl.RESPONSE_CODE)
if code in self.retryCodes and len(retries) > 0:
logging.debug('Retrying since we got the %s error code...', code)
continue
if code != 200:
raise HTTPError(code, response.getvalue())
return response.getvalue()
except pycurl.error as e:
if len(retries) == 0:
raise e
logging.debug('Retrying since we got the %s pycurl exception...', str(e)) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/distribute/distribute_coordinator_utils.py | python | _WorkerContext.has_barrier | (self) | return self._worker_barrier is not None | Whether the barrier is set or not. | Whether the barrier is set or not. | [
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"barrier",
"is",
"set",
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] | def has_barrier(self):
"""Whether the barrier is set or not."""
return self._worker_barrier is not None | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/_quant_ops.py | python | BatchNormFold2.__init__ | (self, freeze_bn=0) | Initialize conv2d fold layer | Initialize conv2d fold layer | [
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"fold",
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] | def __init__(self, freeze_bn=0):
"""Initialize conv2d fold layer"""
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
self.init_prim_io_names(inputs=['x', 'beta', 'gamma', 'batch_std', 'batch_mean',
'running_std', 'running_mean', 'global_step'],
outputs=['y']) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/pathlib.py | python | Path.is_mount | (self) | return ino == parent_ino | Check if this path is a POSIX mount point | Check if this path is a POSIX mount point | [
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] | def is_mount(self):
"""
Check if this path is a POSIX mount point
"""
# Need to exist and be a dir
if not self.exists() or not self.is_dir():
return False
try:
parent_dev = self.parent.stat().st_dev
except OSError:
return False
dev = self.stat().st_dev
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return True
ino = self.stat().st_ino
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l4ka/pistachio | 8be66aa9b85a774ad1b71dbd3a79c5c745a96273 | contrib/cml2/cmlcompile.py | python | intern_symbol | (input, name=None, oktypes=None, record=0) | return ref | Attempt to read and intern a symbol. | Attempt to read and intern a symbol. | [
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] | def intern_symbol(input, name=None, oktypes=None, record=0):
"Attempt to read and intern a symbol."
if name is None:
tok = input.lex_token()
if tok.type == "word":
name = tok.attr
else:
input.push_token(tok)
return None
# If symbol is a constant just pass it through.
if name == "y":
return cml.y
elif name == "m":
return cml.m
elif name == "n":
return cml.n
# If we have not seen the symbol before, create an entry for it.
if not rulebase.dictionary.has_key(name):
ref = rulebase.dictionary[name] = cml.ConfigSymbol(name,
None, None, None,
input.infile,
input.lineno)
compstate.explicit_ancestors[ref] = []
else:
ref = rulebase.dictionary[name]
if ref.type and oktypes is not None and ref.type not in oktypes:
input.complain('incompatible previous declaration of %s as %s (see "%s", %d)' % (name, ref.type, ref.file, ref.lineno))
if record:
if record:
ref.file = input.infile
ref.lineno = input.lineno
else:
input.complain('duplicate symbol %s (see "%s", line %d)'
% (name, ref.file, ref.lineno))
return ref | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/jira/client.py | python | JIRA.search_allowed_users_for_issue | (self, user, issueKey=None, projectKey=None, startAt=0, maxResults=50) | return self._fetch_pages(User, None, 'user/viewissue/search', startAt, maxResults, params) | Get a list of user Resources that match a username string and have browse permission for the issue or project.
:param user: a string to match usernames against.
:param issueKey: find users with browse permission for this issue.
:param projectKey: find users with browse permission for this project.
:param startAt: index of the first user to return.
:param maxResults: maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches. | Get a list of user Resources that match a username string and have browse permission for the issue or project. | [
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] | def search_allowed_users_for_issue(self, user, issueKey=None, projectKey=None, startAt=0, maxResults=50):
"""Get a list of user Resources that match a username string and have browse permission for the issue or project.
:param user: a string to match usernames against.
:param issueKey: find users with browse permission for this issue.
:param projectKey: find users with browse permission for this project.
:param startAt: index of the first user to return.
:param maxResults: maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches.
"""
params = {
'username': user}
if issueKey is not None:
params['issueKey'] = issueKey
if projectKey is not None:
params['projectKey'] = projectKey
return self._fetch_pages(User, None, 'user/viewissue/search', startAt, maxResults, params) | [
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bryanyzhu/Hidden-Two-Stream | f7f684adbdacb6df6b1cf196c3a476cd23484a0f | scripts/cpp_lint.py | python | _OutputFormat | () | return _cpplint_state.output_format | Gets the module's output format. | Gets the module's output format. | [
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"""Gets the module's output format."""
return _cpplint_state.output_format | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/asyncio/windows_events.py | python | IocpProactor._unregister | (self, ov) | Unregister an overlapped object.
Call this method when its future has been cancelled. The event can
already be signalled (pending in the proactor event queue). It is also
safe if the event is never signalled (because it was cancelled). | Unregister an overlapped object. | [
"Unregister",
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"overlapped",
"object",
"."
] | def _unregister(self, ov):
"""Unregister an overlapped object.
Call this method when its future has been cancelled. The event can
already be signalled (pending in the proactor event queue). It is also
safe if the event is never signalled (because it was cancelled).
"""
self._check_closed()
self._unregistered.append(ov) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Font.GetFaceName | (*args, **kwargs) | return _gdi_.Font_GetFaceName(*args, **kwargs) | GetFaceName(self) -> String
Returns the typeface name associated with the font, or the empty
string if there is no typeface information. | GetFaceName(self) -> String | [
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"""
GetFaceName(self) -> String
Returns the typeface name associated with the font, or the empty
string if there is no typeface information.
"""
return _gdi_.Font_GetFaceName(*args, **kwargs) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/ndarray/ndarray.py | python | imdecode | (str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None) | DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing. | DEPRECATED, use mx.img instead | [
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] | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing.
"""
# pylint: disable= no-member, protected-access, too-many-arguments
if mean is None:
mean = NDArray(_new_empty_handle())
if out is None:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img)
else:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img,
out=out) | [
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apiaryio/snowcrash | b5b39faa85f88ee17459edf39fdc6fe4fc70d2e3 | tools/gyp/pylib/gyp/common.py | python | EncodePOSIXShellList | (list) | return ' '.join(encoded_arguments) | Encodes |list| suitably for consumption by POSIX shells.
Returns EncodePOSIXShellArgument for each item in list, and joins them
together using the space character as an argument separator. | Encodes |list| suitably for consumption by POSIX shells. | [
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"shells",
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] | def EncodePOSIXShellList(list):
"""Encodes |list| suitably for consumption by POSIX shells.
Returns EncodePOSIXShellArgument for each item in list, and joins them
together using the space character as an argument separator.
"""
encoded_arguments = []
for argument in list:
encoded_arguments.append(EncodePOSIXShellArgument(argument))
return ' '.join(encoded_arguments) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/stc.py | python | StyledTextCtrl.StyleSetChangeable | (*args, **kwargs) | return _stc.StyledTextCtrl_StyleSetChangeable(*args, **kwargs) | StyleSetChangeable(self, int style, bool changeable)
Set a style to be changeable or not (read only).
Experimental feature, currently buggy. | StyleSetChangeable(self, int style, bool changeable) | [
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"""
StyleSetChangeable(self, int style, bool changeable)
Set a style to be changeable or not (read only).
Experimental feature, currently buggy.
"""
return _stc.StyledTextCtrl_StyleSetChangeable(*args, **kwargs) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/layer/conv.py | python | Conv2d.__init__ | (self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW') | Initialize Conv2d. | Initialize Conv2d. | [
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] | def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW'):
"""Initialize Conv2d."""
kernel_size = twice(kernel_size)
stride = twice(stride)
self._dilation = dilation
dilation = twice(dilation)
super(Conv2d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
data_format)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
data_format=self.format)
self.bias_add = P.BiasAdd(data_format=self.format) | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/minimum-number-of-operations-to-reinitialize-a-permutation.py | python | Solution2.reinitializePermutation | (self, n) | return result | :type n: int
:rtype: int | :type n: int
:rtype: int | [
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"""
:type n: int
:rtype: int
"""
if n == 2:
return 1
result, i = 0, 1
while not result or i != 1:
i = (i*2)%(n-1)
result += 1
return result | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/boost_1_66_0/tools/build/src/util/set.py | python | intersection | (set1, set2) | return result | Removes from set1 any items which don't appear in set2 and returns the result. | Removes from set1 any items which don't appear in set2 and returns the result. | [
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""" Removes from set1 any items which don't appear in set2 and returns the result.
"""
assert is_iterable(set1)
assert is_iterable(set2)
result = []
for v in set1:
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return result | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/apitools/apitools/base/py/app2.py | python | CommandLoop.onecmd | (self, line) | return False | Process a single command.
Runs a single command, and stores the return code in
self._last_return_code. Always returns False unless the command
was EOF.
Args:
line: (str) Command line to process.
Returns:
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"""Process a single command.
Runs a single command, and stores the return code in
self._last_return_code. Always returns False unless the command
was EOF.
Args:
line: (str) Command line to process.
Returns:
A bool signaling whether or not the command loop should terminate.
"""
try:
self._last_return_code = cmd.Cmd.onecmd(self, line)
except CommandLoop.TerminateSignal:
return True
except BaseException as e:
name = line.split(' ')[0]
print('Error running %s:' % name)
print(e)
self._last_return_code = 1
return False | [
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/python/mox.py | python | IsA.equals | (self, rhs) | Check to see if the RHS is an instance of class_name.
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# rhs: the right hand side of the test
rhs: object
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rhs: object
Returns:
bool
"""
try:
return isinstance(rhs, self._class_name)
except TypeError:
# Check raw types if there was a type error. This is helpful for
# things like cStringIO.StringIO.
return type(rhs) == type(self._class_name) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Util/number.py | python | getStrongPrime | (N, e=0, false_positive_prob=1e-6, randfunc=None) | return X | r"""
Return a random strong *N*-bit prime number.
In this context, *p* is a strong prime if *p-1* and *p+1* have at
least one large prime factor.
Args:
N (integer): the exact length of the strong prime.
It must be a multiple of 128 and > 512.
e (integer): if provided, the returned prime (minus 1)
will be coprime to *e* and thus suitable for RSA where
*e* is the public exponent.
false_positive_prob (float):
The statistical probability for the result not to be actually a
prime. It defaults to 10\ :sup:`-6`.
Note that the real probability of a false-positive is far less. This is
just the mathematically provable limit.
randfunc (callable):
A function that takes a parameter *N* and that returns
a random byte string of such length.
If omitted, :func:`Crypto.Random.get_random_bytes` is used.
Return:
The new strong prime.
.. deprecated:: 3.0
This function is for internal use only and may be renamed or removed in
the future. | r"""
Return a random strong *N*-bit prime number.
In this context, *p* is a strong prime if *p-1* and *p+1* have at
least one large prime factor. | [
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r"""
Return a random strong *N*-bit prime number.
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N (integer): the exact length of the strong prime.
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.. deprecated:: 3.0
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"""
# This function was implemented following the
# instructions found in the paper:
# "FAST GENERATION OF RANDOM, STRONG RSA PRIMES"
# by Robert D. Silverman
# RSA Laboratories
# May 17, 1997
# which by the time of writing could be freely downloaded here:
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.2713&rep=rep1&type=pdf
if randfunc is None:
randfunc = Random.get_random_bytes
# Use the accelerator if available
if _fastmath is not None:
return _fastmath.getStrongPrime(long(N), long(e), false_positive_prob,
randfunc)
if (N < 512) or ((N % 128) != 0):
raise ValueError ("bits must be multiple of 128 and > 512")
rabin_miller_rounds = int(math.ceil(-math.log(false_positive_prob)/math.log(4)))
# calculate range for X
# lower_bound = sqrt(2) * 2^{511 + 128*x}
# upper_bound = 2^{512 + 128*x} - 1
x = (N - 512) >> 7;
# We need to approximate the sqrt(2) in the lower_bound by an integer
# expression because floating point math overflows with these numbers
lower_bound = (14142135623730950489 * (2 ** (511 + 128*x))) // 10000000000000000000
upper_bound = (1 << (512 + 128*x)) - 1
# Randomly choose X in calculated range
X = getRandomRange (lower_bound, upper_bound, randfunc)
# generate p1 and p2
p = [0, 0]
for i in (0, 1):
# randomly choose 101-bit y
y = getRandomNBitInteger (101, randfunc)
# initialize the field for sieving
field = [0] * 5 * len (sieve_base)
# sieve the field
for prime in sieve_base:
offset = y % prime
for j in iter_range((prime - offset) % prime, len (field), prime):
field[j] = 1
# look for suitable p[i] starting at y
result = 0
for j in range(len(field)):
composite = field[j]
# look for next canidate
if composite:
continue
tmp = y + j
result = _rabinMillerTest (tmp, rabin_miller_rounds)
if result > 0:
p[i] = tmp
break
if result == 0:
raise RuntimeError ("Couln't find prime in field. "
"Developer: Increase field_size")
# Calculate R
# R = (p2^{-1} mod p1) * p2 - (p1^{-1} mod p2) * p1
tmp1 = inverse (p[1], p[0]) * p[1] # (p2^-1 mod p1)*p2
tmp2 = inverse (p[0], p[1]) * p[0] # (p1^-1 mod p2)*p1
R = tmp1 - tmp2 # (p2^-1 mod p1)*p2 - (p1^-1 mod p2)*p1
# search for final prime number starting by Y0
# Y0 = X + (R - X mod p1p2)
increment = p[0] * p[1]
X = X + (R - (X % increment))
while 1:
is_possible_prime = 1
# first check candidate against sieve_base
for prime in sieve_base:
if (X % prime) == 0:
is_possible_prime = 0
break
# if e is given make sure that e and X-1 are coprime
# this is not necessarily a strong prime criterion but useful when
# creating them for RSA where the p-1 and q-1 should be coprime to
# the public exponent e
if e and is_possible_prime:
if e & 1:
if GCD(e, X-1) != 1:
is_possible_prime = 0
else:
if GCD(e, (X-1) // 2) != 1:
is_possible_prime = 0
# do some Rabin-Miller-Tests
if is_possible_prime:
result = _rabinMillerTest (X, rabin_miller_rounds)
if result > 0:
break
X += increment
# abort when X has more bits than requested
# TODO: maybe we shouldn't abort but rather start over.
if X >= 1 << N:
raise RuntimeError ("Couln't find prime in field. "
"Developer: Increase field_size")
return X | [
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AstarLight/Lets_OCR | b2af7120a34d785434c96e820b6eb1aa69269d20 | detector/ctpn/infer.py | python | get_text_lines | (text_proposals, im_size, scores=0) | return text_recs | text_proposals:boxes | text_proposals:boxes | [
"text_proposals",
":",
"boxes"
] | def get_text_lines(text_proposals, im_size, scores=0):
"""
text_proposals:boxes
"""
#tp_groups = neighbour_connector(text_proposals, im_size) # 首先还是建图，获取到文本行由哪几个小框构成
#print(tp_groups)
text_lines = np.zeros((len(text_proposals), 8), np.float32)
for index, tp_indices in enumerate(text_proposals):
text_line_boxes = np.array(tp_indices) # 每个文本行的全部小框
#print(text_line_boxes)
#print(type(text_line_boxes))
#print(text_line_boxes.shape)
X = (text_line_boxes[:, 0] + text_line_boxes[:, 2]) / 2 # 求每一个小框的中心x，y坐标
Y = (text_line_boxes[:, 1] + text_line_boxes[:, 3]) / 2
#print(X)
#print(Y)
z1 = np.polyfit(X, Y, 1) # 多项式拟合，根据之前求的中心店拟合一条直线（最小二乘）
x0 = np.min(text_line_boxes[:, 0]) # 文本行x坐标最小值
x1 = np.max(text_line_boxes[:, 2]) # 文本行x坐标最大值
offset = (text_line_boxes[0, 2] - text_line_boxes[0, 0]) * 0.5 # 小框宽度的一半
# 以全部小框的左上角这个点去拟合一条直线，然后计算一下文本行x坐标的极左极右对应的y坐标
lt_y, rt_y = fit_y(text_line_boxes[:, 0], text_line_boxes[:, 1], x0 + offset, x1 - offset)
# 以全部小框的左下角这个点去拟合一条直线，然后计算一下文本行x坐标的极左极右对应的y坐标
lb_y, rb_y = fit_y(text_line_boxes[:, 0], text_line_boxes[:, 3], x0 + offset, x1 - offset)
#score = scores[list(tp_indices)].sum() / float(len(tp_indices)) # 求全部小框得分的均值作为文本行的均值
text_lines[index, 0] = x0
text_lines[index, 1] = min(lt_y, rt_y) # 文本行上端 线段 的y坐标的小值
text_lines[index, 2] = x1
text_lines[index, 3] = max(lb_y, rb_y) # 文本行下端 线段 的y坐标的大值
text_lines[index, 4] = scores # 文本行得分
text_lines[index, 5] = z1[0] # 根据中心点拟合的直线的k，b
text_lines[index, 6] = z1[1]
height = np.mean((text_line_boxes[:, 3] - text_line_boxes[:, 1])) # 小框平均高度
text_lines[index, 7] = height + 2.5
text_recs = np.zeros((len(text_lines), 9), np.float32)
index = 0
for line in text_lines:
b1 = line[6] - line[7] / 2 # 根据高度和文本行中心线，求取文本行上下两条线的b值
b2 = line[6] + line[7] / 2
x1 = line[0]
y1 = line[5] * line[0] + b1 # 左上
x2 = line[2]
y2 = line[5] * line[2] + b1 # 右上
x3 = line[0]
y3 = line[5] * line[0] + b2 # 左下
x4 = line[2]
y4 = line[5] * line[2] + b2 # 右下
disX = x2 - x1
disY = y2 - y1
width = np.sqrt(disX * disX + disY * disY) # 文本行宽度
fTmp0 = y3 - y1 # 文本行高度
fTmp1 = fTmp0 * disY / width
x = np.fabs(fTmp1 * disX / width) # 做补偿
y = np.fabs(fTmp1 * disY / width)
if line[5] < 0:
x1 -= x
y1 += y
x4 += x
y4 -= y
else:
x2 += x
y2 += y
x3 -= x
y3 -= y
# clock-wise order
text_recs[index, 0] = x1
text_recs[index, 1] = y1
text_recs[index, 2] = x2
text_recs[index, 3] = y2
text_recs[index, 4] = x4
text_recs[index, 5] = y4
text_recs[index, 6] = x3
text_recs[index, 7] = y3
text_recs[index, 8] = line[4]
index = index + 1
text_recs = clip_boxes(text_recs, im_size)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGrid.DoHidePropertyError | (*args, **kwargs) | return _propgrid.PropertyGrid_DoHidePropertyError(*args, **kwargs) | DoHidePropertyError(self, PGProperty property) | DoHidePropertyError(self, PGProperty property) | [
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takemaru/graphillion | 51879f92bb96b53ef8f914ef37a05252ce383617 | graphillion/graphset.py | python | GraphSet.complement | (self) | return GraphSet(ss) | Returns a new GraphSet with complement graphs of `self`.
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GraphSet([[], [(1, 4)]])
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verilog-to-routing/vtr-verilog-to-routing | d9719cf7374821156c3cee31d66991cb85578562 | vtr_flow/scripts/python_libs/vtr/log_parse.py | python | determine_lut_size | (architecture_file) | return lut_size | Determines the maximum LUT size (K) in an architecture file.
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lut_size = max(lut_size, input_width)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/pickletools.py | python | read_floatnl | (f) | return float(s) | r"""
>>> import StringIO
>>> read_floatnl(StringIO.StringIO("-1.25\n6"))
-1.25 | r"""
>>> import StringIO
>>> read_floatnl(StringIO.StringIO("-1.25\n6"))
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/lib/io/file_io.py | python | copy_v2 | (src, dst, overwrite=False) | Copies data from `src` to `dst`.
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dst: string, name of the file to which to copy to
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pywrap_tensorflow.CopyFile(
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/fractions.py | python | Fraction.__abs__ | (a) | return Fraction(abs(a._numerator), a._denominator) | abs(a) | abs(a) | [
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] | def __abs__(a):
"""abs(a)"""
return Fraction(abs(a._numerator), a._denominator) | [
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dgobbi/vtk-dicom | 9078df24cfeceead466a785d675b7a4afa034bba | Utilities/chartables.py | python | readdict | (fname) | return d | Read a text file that contains a mapping table.
The table is assumed to have two or more columns,
with '#' for comments. | Read a text file that contains a mapping table.
The table is assumed to have two or more columns,
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"""Read a text file that contains a mapping table.
The table is assumed to have two or more columns,
with '#' for comments.
"""
d = {}
f = open(fname, 'r')
for l in f.readlines():
l = l.strip()
if l and l[0] != '#':
columns = l.split()
if len(columns) > 1:
d[int(columns[0],base=0)] = int(columns[1],base=0)
f.close()
return d | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/optimize/_tstutils.py | python | aps05_f | (x) | return np.sin(x) - 1.0 / 2 | r"""Simple Trigonometric function | r"""Simple Trigonometric function | [
"r",
"Simple",
"Trigonometric",
"function"
] | def aps05_f(x):
r"""Simple Trigonometric function"""
return np.sin(x) - 1.0 / 2 | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/framework.py | python | Block._to_readable_code | (self, skip_op_callstack=True) | return block_str | Get readable debug string of Block.
.. note::
If you want to get the debug string in protobuf format,
please use :code:`to_string` method.
Args:
skip_op_callstack(bool): whether to skip parsing Operator's attribute
op_callstack, default value is True
Returns:
string: The formatted Block string.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cur_program = fluid.Program()
cur_block = cur_program.current_block()
new_var = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
new_op = cur_block.append_op(type="abs",
inputs={"X": [new_var]},
outputs={"Out": [new_var]})
print(cur_block._to_readable_code()) | Get readable debug string of Block. | [
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] | def _to_readable_code(self, skip_op_callstack=True):
"""
Get readable debug string of Block.
.. note::
If you want to get the debug string in protobuf format,
please use :code:`to_string` method.
Args:
skip_op_callstack(bool): whether to skip parsing Operator's attribute
op_callstack, default value is True
Returns:
string: The formatted Block string.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cur_program = fluid.Program()
cur_block = cur_program.current_block()
new_var = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
new_op = cur_block.append_op(type="abs",
inputs={"X": [new_var]},
outputs={"Out": [new_var]})
print(cur_block._to_readable_code())
"""
assert isinstance(
skip_op_callstack, bool
), "skip_op_callstack parameter's type is error, expect bool, received {}".format(
type(skip_op_callstack))
block_str = "{ // block "
block_str += "{}\n".format(self.idx)
for var in list(self.vars.values()):
block_str += " {}\n".format(var._to_readable_code())
block_str += "\n"
for op in self.ops:
block_str += " {}\n".format(
op._to_readable_code(skip_op_callstack))
block_str += "}"
return block_str | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/imaplib.py | python | IMAP4._CRAM_MD5_AUTH | (self, challenge) | return self.user + " " + hmac.HMAC(self.password, challenge).hexdigest() | Authobject to use with CRAM-MD5 authentication. | Authobject to use with CRAM-MD5 authentication. | [
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] | def _CRAM_MD5_AUTH(self, challenge):
""" Authobject to use with CRAM-MD5 authentication. """
import hmac
return self.user + " " + hmac.HMAC(self.password, challenge).hexdigest() | [
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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/google/protobuf/internal/python_message.py | python | _AddSlots | (message_descriptor, dictionary) | Adds a __slots__ entry to dictionary, containing the names of all valid
attributes for this message type.
Args:
message_descriptor: A Descriptor instance describing this message type.
dictionary: Class dictionary to which we'll add a '__slots__' entry. | Adds a __slots__ entry to dictionary, containing the names of all valid
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"""Adds a __slots__ entry to dictionary, containing the names of all valid
attributes for this message type.
Args:
message_descriptor: A Descriptor instance describing this message type.
dictionary: Class dictionary to which we'll add a '__slots__' entry.
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/tactics_api.py | python | arg_initial_node | () | return _ivy_ag.states[0] | Add an initial node to the abstract reachability graph (arg) and
return it. | Add an initial node to the abstract reachability graph (arg) and
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"""
Add an initial node to the abstract reachability graph (arg) and
return it.
"""
# right now, the initial state is already added as the first
# state, so just return it
return _ivy_ag.states[0] | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/samplelogs/model.py | python | SampleLogsModel.getItemModel | (self, searched_key='') | return model | Return a QModel made from the current workspace. This should be set
onto a QTableView. The searched_key allows for filtering log entries. | Return a QModel made from the current workspace. This should be set
onto a QTableView. The searched_key allows for filtering log entries. | [
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"""Return a QModel made from the current workspace. This should be set
onto a QTableView. The searched_key allows for filtering log entries.
"""
def create_table_item(column, itemname, invalid_value_count, log_size, callable, *args):
item = QStandardItem()
item.setEditable(False)
#format if there is invalid data entries
if invalid_value_count == -1:
item.setData(DEEP_RED, Qt.BackgroundRole)
item.setToolTip("All of the values in the log are marked invalid, none of them are filtered.")
elif invalid_value_count > 0:
saturation = 10 + (170 * (invalid_value_count/(log_size+invalid_value_count)))
item.setData(QColor.fromHsv(0, saturation, 255), Qt.BackgroundRole)
aux_verb = "is" if invalid_value_count == 1 else "are"
item.setToolTip(f"{invalid_value_count}/{log_size+invalid_value_count} of the values in the log"
f" {aux_verb} marked invalid, and {aux_verb} filtered.")
try:
item.setText(callable(*args))
except Exception as exc:
logger.warning("Error setting column {} for log {}: {}".format(column, itemname, str(exc)))
return item
model = QStandardItemModel()
model.setHorizontalHeaderLabels(["Name", "Type", "Value", "Units"])
model.setColumnCount(4)
logs_to_highlight = self.get_logs_with_invalid_data()
logs_to_hide = self.get_hidden_logs()
for key in self.get_log_names():
if key in logs_to_hide:
continue
if searched_key.casefold() not in key.casefold():
continue
invalid_value_count = 0
if key in logs_to_highlight.keys():
invalid_value_count = logs_to_highlight[key]
log = self.run.getLogData(key)
size = log.size() if hasattr(log, 'size') else 0
name = create_table_item("Name", key, invalid_value_count, size, lambda: log.name)
log_type = create_table_item("Type", key, invalid_value_count, size, get_type, log)
value = create_table_item("Value", key, invalid_value_count, size, lambda log: get_value(log), log)
unit = create_table_item("Units", key, invalid_value_count, size, lambda: log.units)
model.appendRow((name, log_type, value, unit))
model.sort(0)
return model | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/ticker.py | python | Ticker.ShouldInheritColours | (self) | return False | Don't get colours from our parent... | Don't get colours from our parent... | [
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] | def ShouldInheritColours(self):
"""Don't get colours from our parent..."""
return False | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/layers/utils.py | python | object_list_uid | (object_list) | return ', '.join([str(abs(id(x))) for x in object_list]) | Creates a single string from object ids. | Creates a single string from object ids. | [
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] | def object_list_uid(object_list):
"""Creates a single string from object ids."""
object_list = nest.flatten(object_list)
return ', '.join([str(abs(id(x))) for x in object_list]) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_queue_runner.py | python | FeedingQueueRunner._run | (self, sess, enqueue_op, feed_fn, coord=None) | Execute the enqueue op in a loop, close the queue in case of error.
Args:
sess: A `Session`.
enqueue_op: The `Operation` to run.
feed_fn: the feed function to pass to `sess.run`.
coord: Optional `Coordinator` object for reporting errors and checking
for stop conditions. | Execute the enqueue op in a loop, close the queue in case of error. | [
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enqueue_op: The `Operation` to run.
feed_fn: the feed function to pass to `sess.run`.
coord: Optional `Coordinator` object for reporting errors and checking
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# TODO(jamieas): Reduce code duplication with `QueueRunner`.
decremented = False
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break
try:
feed_dict = None if feed_fn is None else feed_fn()
sess.run(enqueue_op, feed_dict=feed_dict)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/xml/etree/ElementTree.py | python | Element.keys | (self) | return self.attrib.keys() | Get list of attribute names.
Names are returned in an arbitrary order, just like an ordinary
Python dict. Equivalent to attrib.keys() | Get list of attribute names. | [
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"""Get list of attribute names.
Names are returned in an arbitrary order, just like an ordinary
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-js/mx.graal-js/mx_graal_js.py | python | testv8 | (args, nonZeroIsFatal=True) | run the testV8 conformance suite | run the testV8 conformance suite | [
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"""run the testV8 conformance suite"""
_location = join(_suite.dir, 'lib', 'testv8')
_stack_size = '3m' if mx.get_arch() in ('aarch64', 'sparcv9') else '1m'
_run_test_suite(
location=_location,
library_names=['TESTV8'],
custom_args=args,
default_vm_args=[],
max_heap='8g',
stack_size=_stack_size,
main_class='com.oracle.truffle.js.test.external.testv8.TestV8',
nonZeroIsFatal=nonZeroIsFatal,
cwd=_suite.dir
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/turtle.py | python | TNavigator.ycor | (self) | return self._position[1] | Return the turtle's y coordinate
---
No arguments.
Example (for a Turtle instance named turtle):
>>> reset()
>>> turtle.left(60)
>>> turtle.forward(100)
>>> print turtle.ycor()
86.6025403784 | Return the turtle's y coordinate
---
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""" Return the turtle's y coordinate
---
No arguments.
Example (for a Turtle instance named turtle):
>>> reset()
>>> turtle.left(60)
>>> turtle.forward(100)
>>> print turtle.ycor()
86.6025403784
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/generator/ninja.py | python | NinjaWriter.GetToolchainEnv | (self, additional_settings=None) | return env | Returns the variables toolchain would set for build steps. | Returns the variables toolchain would set for build steps. | [
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] | def GetToolchainEnv(self, additional_settings=None):
"""Returns the variables toolchain would set for build steps."""
env = self.GetSortedXcodeEnv(additional_settings=additional_settings)
if self.flavor == 'win':
env = self.GetMsvsToolchainEnv(
additional_settings=additional_settings)
return env | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | IdleEvent.RequestMore | (*args, **kwargs) | return _core_.IdleEvent_RequestMore(*args, **kwargs) | RequestMore(self, bool needMore=True)
Tells wxWidgets that more processing is required. This function can be
called by an EVT_IDLE handler for a window to indicate that the
application should forward the EVT_IDLE event once more to the
application windows. If no window calls this function during its
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system. | RequestMore(self, bool needMore=True) | [
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"""
RequestMore(self, bool needMore=True)
Tells wxWidgets that more processing is required. This function can be
called by an EVT_IDLE handler for a window to indicate that the
application should forward the EVT_IDLE event once more to the
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"""
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/catkin_pkg/cmake.py | python | configure_file | (template_file, environment) | Evaluate a .in template file used in CMake with configure_file().
:param template_file: path to the template, ``str``
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Evaluate a .in template file used in CMake with configure_file().
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:param environment: dictionary of placeholders to substitute,
``dict``
:returns: string with evaluates template
:raises: KeyError for placeholders in the template which are not
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with open(template_file, 'r') as f:
template = f.read()
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/cpp_wrappers/knowledge_gradient_mcmc.py | python | KnowledgeGradientMCMC.compute_grad_knowledge_gradient_mcmc | (self, force_monte_carlo=False) | return grad_knowledge_gradient_mcmc | r"""Compute the gradient of knowledge gradient at ``points_to_sample`` wrt ``points_to_sample``, with ``points_being_sampled`` concurrent samples.
.. Note:: These comments were copied from
:meth:`moe.optimal_learning.python.interfaces.expected_improvement_interface.ExpectedImprovementInterface.compute_grad_expected_improvement`
``points_to_sample`` is the "q" and ``points_being_sampled`` is the "p" in q,p-EI.
In general, the expressions for gradients of EI are complex and difficult to evaluate; hence we use
Monte-Carlo simulation to approximate it. When faster (e.g., analytic) techniques are available, we will prefer them.
The MC computation of grad EI is similar to the computation of EI (decsribed in
compute_expected_improvement). We differentiate ``y = \mu + Lw`` wrt ``points_to_sample``;
only terms from the gradient of ``\mu`` and ``L`` contribute. In EI, we computed:
``improvement_per_step = max(max(best_so_far - y), 0.0)``
and noted that only the smallest component of ``y`` may contribute (if it is > 0.0).
Call this index ``winner``. Thus in computing grad EI, we only add gradient terms
that are attributable to the ``winner``-th component of ``y``.
:param force_monte_carlo: whether to force monte carlo evaluation (vs using fast/accurate analytic eval when possible)
:type force_monte_carlo: boolean
:return: gradient of EI, ``\pderiv{EI(Xq \cup Xp)}{Xq_{i,d}}`` where ``Xq`` is ``points_to_sample``
and ``Xp`` is ``points_being_sampled`` (grad EI from sampling ``points_to_sample`` with
``points_being_sampled`` concurrent experiments wrt each dimension of the points in ``points_to_sample``)
:rtype: array of float64 with shape (num_to_sample, dim) | r"""Compute the gradient of knowledge gradient at ``points_to_sample`` wrt ``points_to_sample``, with ``points_being_sampled`` concurrent samples. | [
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r"""Compute the gradient of knowledge gradient at ``points_to_sample`` wrt ``points_to_sample``, with ``points_being_sampled`` concurrent samples.
.. Note:: These comments were copied from
:meth:`moe.optimal_learning.python.interfaces.expected_improvement_interface.ExpectedImprovementInterface.compute_grad_expected_improvement`
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and noted that only the smallest component of ``y`` may contribute (if it is > 0.0).
Call this index ``winner``. Thus in computing grad EI, we only add gradient terms
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:param force_monte_carlo: whether to force monte carlo evaluation (vs using fast/accurate analytic eval when possible)
:type force_monte_carlo: boolean
:return: gradient of EI, ``\pderiv{EI(Xq \cup Xp)}{Xq_{i,d}}`` where ``Xq`` is ``points_to_sample``
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``points_being_sampled`` concurrent experiments wrt each dimension of the points in ``points_to_sample``)
:rtype: array of float64 with shape (num_to_sample, dim)
"""
grad_knowledge_gradient_mcmc = C_GP.compute_grad_knowledge_gradient_mcmc(
self._gaussian_process_mcmc._gaussian_process_mcmc,
self._num_fidelity,
self._inner_optimizer.optimizer_parameters,
cpp_utils.cppify(self._inner_optimizer.domain.domain_bounds),
cpp_utils.cppify(self._discrete_pts_list),
cpp_utils.cppify(self._points_to_sample),
cpp_utils.cppify(self._points_being_sampled),
self.discrete,
self.num_to_sample,
self.num_being_sampled,
self._num_mc_iterations,
cpp_utils.cppify(self._best_so_far_list),
self._randomness,
)
return grad_knowledge_gradient_mcmc | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py | python | MultivariateNormalLinearOperator.scale | (self) | return self.bijector.scale | The `scale` `LinearOperator` in `Y = scale @ X + loc`. | The `scale` `LinearOperator` in `Y = scale | [
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"""The `scale` `LinearOperator` in `Y = scale @ X + loc`."""
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google/nucleus | 68d3947fafba1337f294c0668a6e1c7f3f1273e3 | nucleus/util/variant_utils.py | python | has_calls | (variant) | return bool(variant.calls) | Does variant have any genotype calls?
Args:
variant: nucleus.genomics.v1.Variant.
Returns:
True if variant has one or more VariantCalls. | Does variant have any genotype calls? | [
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"""Does variant have any genotype calls?
Args:
variant: nucleus.genomics.v1.Variant.
Returns:
True if variant has one or more VariantCalls.
"""
return bool(variant.calls) | [
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] | https://github.com/google/nucleus/blob/68d3947fafba1337f294c0668a6e1c7f3f1273e3/nucleus/util/variant_utils.py#L608-L617 | |
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/SimpleXMLRPCServer.py | python | SimpleXMLRPCDispatcher.register_multicall_functions | (self) | Registers the XML-RPC multicall method in the system
namespace.
see http://www.xmlrpc.com/discuss/msgReader$1208 | Registers the XML-RPC multicall method in the system
namespace. | [
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] | def register_multicall_functions(self):
"""Registers the XML-RPC multicall method in the system
namespace.
see http://www.xmlrpc.com/discuss/msgReader$1208"""
self.funcs.update({'system.multicall' : self.system_multicall}) | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinema/paraview/tpl/cinema_python/database/store.py | python | Store.add_field | (self, name, properties, parent_layer, parents_values) | A field is a component of the final color for a layer. Examples
include: depth, normal, color, scalar values. | A field is a component of the final color for a layer. Examples
include: depth, normal, color, scalar values. | [
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"""
A field is a component of the final color for a layer. Examples
include: depth, normal, color, scalar values.
"""
properties['type'] = 'hidden'
properties['role'] = 'field'
self.add_parameter(name, properties)
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/best-team-with-no-conflicts.py | python | Solution4.bestTeamScore | (self, scores, ages) | return max(dp.itervalues()) | :type scores: List[int]
:type ages: List[int]
:rtype: int | :type scores: List[int]
:type ages: List[int]
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"""
:type scores: List[int]
:type ages: List[int]
:rtype: int
"""
players = sorted(zip(ages, scores))
sorted_scores = sorted(set(scores))
dp = collections.defaultdict(int)
result = 0
for age, score in players:
dp[score] = max(dp[s] for s in sorted_scores if s <= score) + score
return max(dp.itervalues()) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/rpc/backend_registry.py | python | backend_registered | (backend_name) | return backend_name in BackendType.__members__.keys() | Checks if backend_name is registered as an RPC backend.
Args:
backend_name (str): string to identify the RPC backend.
Returns:
True if the backend has been registered with ``register_backend``, else
False. | Checks if backend_name is registered as an RPC backend. | [
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] | def backend_registered(backend_name):
"""
Checks if backend_name is registered as an RPC backend.
Args:
backend_name (str): string to identify the RPC backend.
Returns:
True if the backend has been registered with ``register_backend``, else
False.
"""
return backend_name in BackendType.__members__.keys() | [
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taskflow/taskflow | f423a100a70b275f6e7331bc96537a3fe172e8d7 | 3rd-party/tbb/python/tbb/pool.py | python | Pool.join | (self) | Wait for the worker processes to exit. One must call
close() or terminate() before using join(). | Wait for the worker processes to exit. One must call
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"""Wait for the worker processes to exit. One must call
close() or terminate() before using join()."""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | PyApp.GetTraits | (*args, **kwargs) | return _core_.PyApp_GetTraits(*args, **kwargs) | GetTraits(self) -> wxAppTraits
Return (and create if necessary) the app traits object to which we
delegate for everything which either should be configurable by the
user (then he can change the default behaviour simply by overriding
CreateTraits() and returning his own traits object) or which is
GUI/console dependent as then wx.AppTraits allows us to abstract the
differences behind the common facade.
:todo: Add support for overriding CreateAppTraits in wxPython. | GetTraits(self) -> wxAppTraits | [
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"""
GetTraits(self) -> wxAppTraits
Return (and create if necessary) the app traits object to which we
delegate for everything which either should be configurable by the
user (then he can change the default behaviour simply by overriding
CreateTraits() and returning his own traits object) or which is
GUI/console dependent as then wx.AppTraits allows us to abstract the
differences behind the common facade.
:todo: Add support for overriding CreateAppTraits in wxPython.
"""
return _core_.PyApp_GetTraits(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/losses.py | python | _ragged_tensor_msle | (y_true, y_pred) | return _ragged_tensor_apply_loss(mean_squared_logarithmic_error, y_true,
y_pred) | Implements support for handling RaggedTensors. | Implements support for handling RaggedTensors. | [
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"""Implements support for handling RaggedTensors."""
return _ragged_tensor_apply_loss(mean_squared_logarithmic_error, y_true,
y_pred) | [
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | algorithms/src/LocalizationAndMapping/registration_localization/fast_gicp/thirdparty/Sophus/py/sophus/quaternion.py | python | Quaternion.__mul__ | (self, right) | return Quaternion(self[3] * right[3] - self.vec.dot(right.vec),
self[3] * right.vec + right[3] * self.vec +
self.vec.cross(right.vec)) | quaternion multiplication | quaternion multiplication | [
"quaternion",
"multiplication"
] | def __mul__(self, right):
""" quaternion multiplication """
return Quaternion(self[3] * right[3] - self.vec.dot(right.vec),
self[3] * right.vec + right[3] * self.vec +
self.vec.cross(right.vec)) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/functional_ops.py | python | scan | (fn, elems, initializer=None, parallel_iterations=10, back_prop=True,
swap_memory=False, infer_shape=True, name=None) | scan on the list of tensors unpacked from `elems` on dimension 0.
The simplest version of `scan` repeatedly applies the callable `fn` to a
sequence of elements from first to last. The elements are made of the tensors
unpacked from `elems` on dimension 0. The callable fn takes two tensors as
arguments. The first argument is the accumulated value computed from the
preceding invocation of fn. If `initializer` is None, `elems` must contain
at least one element, and its first element is used as the initializer.
Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
This method also allows multi-arity `elems` and accumulator. If `elems`
is a (possibly nested) list or tuple of tensors, then each of these tensors
must have a matching first (unpack) dimension. The second argument of
`fn` must match the structure of `elems`.
If no `initializer` is provided, the output structure and dtypes of `fn`
are assumed to be the same as its input; and in this case, the first
argument of `fn` must match the structure of `elems`.
If an `initializer` is provided, then the output of `fn` must have the same
structure as `initializer`; and the first argument of `fn` must match
this structure.
For example, if `elems` is `(t1, [t2, t3])` and `initializer` is
`[i1, i2]` then an appropriate signature for `fn` in `python2` is:
`fn = lambda (acc_p1, acc_p2), (t1 [t2, t3]):` and `fn` must return a list,
`[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the
one that works in `python3`, is:
`fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.
Args:
fn: The callable to be performed. It accepts two arguments. The first
will have the same structure as `initializer` if one is provided,
otherwise it will have the same structure as `elems`. The second
will have the same (possibly nested) structure as `elems`. Its output
must have the same structure as `initializer` if one is provided,
otherwise it must have the same structure as `elems`.
elems: A tensor or (possibly nested) sequence of tensors, each of which
will be unpacked along their first dimension. The nested sequence
of the resulting slices will be the first argument to `fn`.
initializer: (optional) A tensor or (possibly nested) sequence of tensors,
initial value for the accumulator, and the expected output type of `fn`.
parallel_iterations: (optional) The number of iterations allowed to run
in parallel.
back_prop: (optional) True enables support for back propagation.
swap_memory: (optional) True enables GPU-CPU memory swapping.
infer_shape: (optional) False disables tests for consistent output shapes.
name: (optional) Name prefix for the returned tensors.
Returns:
A tensor or (possibly nested) sequence of tensors. Each tensor packs the
results of applying `fn` to tensors unpacked from `elems` along the first
dimension, and the previous accumulator value(s), from first to last.
Raises:
TypeError: if `fn` is not callable or the structure of the output of
`fn` and `initializer` do not match.
ValueError: if the lengths of the output of `fn` and `initializer`
do not match.
Examples:
```python
elems = np.array([1, 2, 3, 4, 5, 6])
sum = scan(lambda a, x: a + x, elems)
# sum == [1, 3, 6, 10, 15, 21]
```
```python
elems = np.array([1, 2, 3, 4, 5, 6])
initializer = np.array(0)
sum_one = scan(
lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)
# sum_one == [1, 2, 3, 4, 5, 6]
```
```python
elems = np.array([1, 0, 0, 0, 0, 0])
initializer = (np.array(0), np.array(1))
fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
# fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
``` | scan on the list of tensors unpacked from `elems` on dimension 0. | [
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] | def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True,
swap_memory=False, infer_shape=True, name=None):
"""scan on the list of tensors unpacked from `elems` on dimension 0.
The simplest version of `scan` repeatedly applies the callable `fn` to a
sequence of elements from first to last. The elements are made of the tensors
unpacked from `elems` on dimension 0. The callable fn takes two tensors as
arguments. The first argument is the accumulated value computed from the
preceding invocation of fn. If `initializer` is None, `elems` must contain
at least one element, and its first element is used as the initializer.
Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
This method also allows multi-arity `elems` and accumulator. If `elems`
is a (possibly nested) list or tuple of tensors, then each of these tensors
must have a matching first (unpack) dimension. The second argument of
`fn` must match the structure of `elems`.
If no `initializer` is provided, the output structure and dtypes of `fn`
are assumed to be the same as its input; and in this case, the first
argument of `fn` must match the structure of `elems`.
If an `initializer` is provided, then the output of `fn` must have the same
structure as `initializer`; and the first argument of `fn` must match
this structure.
For example, if `elems` is `(t1, [t2, t3])` and `initializer` is
`[i1, i2]` then an appropriate signature for `fn` in `python2` is:
`fn = lambda (acc_p1, acc_p2), (t1 [t2, t3]):` and `fn` must return a list,
`[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the
one that works in `python3`, is:
`fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.
Args:
fn: The callable to be performed. It accepts two arguments. The first
will have the same structure as `initializer` if one is provided,
otherwise it will have the same structure as `elems`. The second
will have the same (possibly nested) structure as `elems`. Its output
must have the same structure as `initializer` if one is provided,
otherwise it must have the same structure as `elems`.
elems: A tensor or (possibly nested) sequence of tensors, each of which
will be unpacked along their first dimension. The nested sequence
of the resulting slices will be the first argument to `fn`.
initializer: (optional) A tensor or (possibly nested) sequence of tensors,
initial value for the accumulator, and the expected output type of `fn`.
parallel_iterations: (optional) The number of iterations allowed to run
in parallel.
back_prop: (optional) True enables support for back propagation.
swap_memory: (optional) True enables GPU-CPU memory swapping.
infer_shape: (optional) False disables tests for consistent output shapes.
name: (optional) Name prefix for the returned tensors.
Returns:
A tensor or (possibly nested) sequence of tensors. Each tensor packs the
results of applying `fn` to tensors unpacked from `elems` along the first
dimension, and the previous accumulator value(s), from first to last.
Raises:
TypeError: if `fn` is not callable or the structure of the output of
`fn` and `initializer` do not match.
ValueError: if the lengths of the output of `fn` and `initializer`
do not match.
Examples:
```python
elems = np.array([1, 2, 3, 4, 5, 6])
sum = scan(lambda a, x: a + x, elems)
# sum == [1, 3, 6, 10, 15, 21]
```
```python
elems = np.array([1, 2, 3, 4, 5, 6])
initializer = np.array(0)
sum_one = scan(
lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)
# sum_one == [1, 2, 3, 4, 5, 6]
```
```python
elems = np.array([1, 0, 0, 0, 0, 0])
initializer = (np.array(0), np.array(1))
fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
# fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
```
"""
if not callable(fn):
raise TypeError("fn must be callable.")
input_is_sequence = nest.is_sequence(elems)
input_flatten = lambda x: nest.flatten(x) if input_is_sequence else [x]
def input_pack(x):
return nest.pack_sequence_as(elems, x) if input_is_sequence else x[0]
if initializer is None:
output_is_sequence = input_is_sequence
output_flatten = input_flatten
output_pack = input_pack
else:
output_is_sequence = nest.is_sequence(initializer)
output_flatten = lambda x: nest.flatten(x) if output_is_sequence else [x]
def output_pack(x):
return (nest.pack_sequence_as(initializer, x)
if output_is_sequence else x[0])
elems_flat = input_flatten(elems)
with ops.name_scope(name, "scan", elems_flat):
# Any get_variable calls in fn will cache the first call locally
# and not issue repeated network I/O requests for each iteration.
varscope = vs.get_variable_scope()
varscope_caching_device_was_none = False
if varscope.caching_device is None:
# TODO(ebrevdo): Change to using colocate_with here and in other methods.
varscope.set_caching_device(lambda op: op.device)
varscope_caching_device_was_none = True
# Convert elems to tensor array.
elems_flat = [
ops.convert_to_tensor(elem, name="elem") for elem in elems_flat]
n = array_ops.shape(elems_flat[0])[0]
# TensorArrays are always flat
elems_ta = [
tensor_array_ops.TensorArray(dtype=elem.dtype, size=n,
dynamic_size=False,
infer_shape=True)
for elem in elems_flat]
# Unpack elements
elems_ta = [
elem_ta.unstack(elem) for elem_ta, elem in zip(elems_ta, elems_flat)]
if initializer is None:
a_flat = [elem.read(0) for elem in elems_ta]
i = constant_op.constant(1)
else:
initializer_flat = output_flatten(initializer)
a_flat = [ops.convert_to_tensor(init) for init in initializer_flat]
i = constant_op.constant(0)
# Create a tensor array to store the intermediate values.
accs_ta = [
tensor_array_ops.TensorArray(
dtype=init.dtype, size=n,
element_shape=init.shape if infer_shape else None,
dynamic_size=False,
infer_shape=infer_shape)
for init in a_flat]
if initializer is None:
accs_ta = [acc_ta.write(0, a) for (acc_ta, a) in zip(accs_ta, a_flat)]
def compute(i, a_flat, tas):
"""The loop body of scan.
Args:
i: the loop counter.
a_flat: the accumulator value(s), flattened.
tas: the output accumulator TensorArray(s), flattened.
Returns:
[i + 1, a_flat, tas]: the updated counter + new accumulator values +
updated TensorArrays
Raises:
TypeError: if initializer and fn() output structure do not match
ValueType: if initializer and fn() output lengths do not match
"""
packed_elems = input_pack([elem_ta.read(i) for elem_ta in elems_ta])
packed_a = output_pack(a_flat)
a_out = fn(packed_a, packed_elems)
nest.assert_same_structure(
elems if initializer is None else initializer, a_out)
flat_a_out = output_flatten(a_out)
tas = [ta.write(i, value) for (ta, value) in zip(tas, flat_a_out)]
return (i + 1, flat_a_out, tas)
_, _, r_a = control_flow_ops.while_loop(
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/hmac.py | python | HMAC.copy | (self) | return other | Return a separate copy of this hashing object.
An update to this copy won't affect the original object. | Return a separate copy of this hashing object. | [
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"""Return a separate copy of this hashing object.
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other = self.__class__(_secret_backdoor_key)
other.digest_cons = self.digest_cons
other.digest_size = self.digest_size
other.inner = self.inner.copy()
other.outer = self.outer.copy()
return other | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/Tkinter.py | python | PanedWindow.identify | (self, x, y) | return self.tk.call(self._w, 'identify', x, y) | Identify the panedwindow component at point x, y
If the point is over a sash or a sash handle, the result
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is a two element list containing the index of the sash or
handle, and a word indicating whether it is over a sash
or a handle, such as {0 sash} or {2 handle}. If the point
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return self.tk.call(self._w, 'identify', x, y) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | LogStderr.__init__ | (self, *args, **kwargs) | __init__(self) -> LogStderr | __init__(self) -> LogStderr | [
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stack-of-tasks/pinocchio | 593d4d43fded997bb9aa2421f4e55294dbd233c4 | bindings/python/pinocchio/visualize/gepetto_visualizer.py | python | GepettoVisualizer.displayCollisions | (self,visibility) | Set whether to display collision objects or not | Set whether to display collision objects or not | [
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if visibility:
visibility_mode = "ON"
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visibility_mode = "OFF"
for collision in self.collision_model.geometryObjects:
nodeName = self.getViewerNodeName(collision,pin.GeometryType.COLLISION)
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/symbol/symbol.py | python | Symbol.__mul__ | (self, other) | x.__mul__(y) <=> x*y
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return _internal._Mul(self, other)
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/dist_aar.py | python | _MergeRTxt | (r_paths, include_globs) | return ''.join(sorted(all_lines)) | Merging the given R.txt files and returns them as a string. | Merging the given R.txt files and returns them as a string. | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/system.py | python | Grabber.search | (self, phrase, subtitle=None, tolerance=None, func=None) | obj.search(phrase, subtitle=None, tolerance=None) -> result generator
Returns a generator of results matching the given search
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if not func:
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func = lambda p,s,r: levenshtein('%s : %s' % \
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get('MetadataLookupTolerance', 5))
if subtitle is not None:
res = self.command('-N', '"%s" "%s"' % (phrase, subtitle))
else:
res = self.command('-M', '"%s"' % phrase)
for r in res:
r.levenshtein = func(phrase, subtitle, r)
if r.levenshtein > tolerance:
continue
yield r | [
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] | https://github.com/MythTV/mythtv/blob/d282a209cb8be85d036f85a62a8ec971b67d45f4/mythtv/bindings/python/MythTV/system.py#L353-L383 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/linalg/linalg.py | python | lstsq | (a, b, rcond="warn") | return wrap(x), wrap(resids), rank, s | r"""
Return the least-squares solution to a linear matrix equation.
Solves the equation :math:`a x = b` by computing a vector `x` that
minimizes the squared Euclidean 2-norm :math:`\| b - a x \|^2_2`.
The equation may be under-, well-, or over-determined (i.e., the
number of linearly independent rows of `a` can be less than, equal
to, or greater than its number of linearly independent columns).
If `a` is square and of full rank, then `x` (but for round-off error)
is the "exact" solution of the equation.
Parameters
----------
a : (M, N) array_like
"Coefficient" matrix.
b : {(M,), (M, K)} array_like
Ordinate or "dependent variable" values. If `b` is two-dimensional,
the least-squares solution is calculated for each of the `K` columns
of `b`.
rcond : float, optional
Cut-off ratio for small singular values of `a`.
For the purposes of rank determination, singular values are treated
as zero if they are smaller than `rcond` times the largest singular
value of `a`.
.. versionchanged:: 1.14.0
If not set, a FutureWarning is given. The previous default
of ``-1`` will use the machine precision as `rcond` parameter,
the new default will use the machine precision times `max(M, N)`.
To silence the warning and use the new default, use ``rcond=None``,
to keep using the old behavior, use ``rcond=-1``.
Returns
-------
x : {(N,), (N, K)} ndarray
Least-squares solution. If `b` is two-dimensional,
the solutions are in the `K` columns of `x`.
residuals : {(1,), (K,), (0,)} ndarray
Sums of residuals; squared Euclidean 2-norm for each column in
``b - a*x``.
If the rank of `a` is < N or M <= N, this is an empty array.
If `b` is 1-dimensional, this is a (1,) shape array.
Otherwise the shape is (K,).
rank : int
Rank of matrix `a`.
s : (min(M, N),) ndarray
Singular values of `a`.
Raises
------
LinAlgError
If computation does not converge.
Notes
-----
If `b` is a matrix, then all array results are returned as matrices.
Examples
--------
Fit a line, ``y = mx + c``, through some noisy data-points:
>>> x = np.array([0, 1, 2, 3])
>>> y = np.array([-1, 0.2, 0.9, 2.1])
By examining the coefficients, we see that the line should have a
gradient of roughly 1 and cut the y-axis at, more or less, -1.
We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
and ``p = [[m], [c]]``. Now use `lstsq` to solve for `p`:
>>> A = np.vstack([x, np.ones(len(x))]).T
>>> A
array([[ 0., 1.],
[ 1., 1.],
[ 2., 1.],
[ 3., 1.]])
>>> m, c = np.linalg.lstsq(A, y, rcond=None)[0]
>>> m, c
(1.0 -0.95) # may vary
Plot the data along with the fitted line:
>>> import matplotlib.pyplot as plt
>>> _ = plt.plot(x, y, 'o', label='Original data', markersize=10)
>>> _ = plt.plot(x, m*x + c, 'r', label='Fitted line')
>>> _ = plt.legend()
>>> plt.show() | r"""
Return the least-squares solution to a linear matrix equation. | [
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"Return",
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"to",
"a",
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"matrix",
"equation",
"."
] | def lstsq(a, b, rcond="warn"):
r"""
Return the least-squares solution to a linear matrix equation.
Solves the equation :math:`a x = b` by computing a vector `x` that
minimizes the squared Euclidean 2-norm :math:`\| b - a x \|^2_2`.
The equation may be under-, well-, or over-determined (i.e., the
number of linearly independent rows of `a` can be less than, equal
to, or greater than its number of linearly independent columns).
If `a` is square and of full rank, then `x` (but for round-off error)
is the "exact" solution of the equation.
Parameters
----------
a : (M, N) array_like
"Coefficient" matrix.
b : {(M,), (M, K)} array_like
Ordinate or "dependent variable" values. If `b` is two-dimensional,
the least-squares solution is calculated for each of the `K` columns
of `b`.
rcond : float, optional
Cut-off ratio for small singular values of `a`.
For the purposes of rank determination, singular values are treated
as zero if they are smaller than `rcond` times the largest singular
value of `a`.
.. versionchanged:: 1.14.0
If not set, a FutureWarning is given. The previous default
of ``-1`` will use the machine precision as `rcond` parameter,
the new default will use the machine precision times `max(M, N)`.
To silence the warning and use the new default, use ``rcond=None``,
to keep using the old behavior, use ``rcond=-1``.
Returns
-------
x : {(N,), (N, K)} ndarray
Least-squares solution. If `b` is two-dimensional,
the solutions are in the `K` columns of `x`.
residuals : {(1,), (K,), (0,)} ndarray
Sums of residuals; squared Euclidean 2-norm for each column in
``b - a*x``.
If the rank of `a` is < N or M <= N, this is an empty array.
If `b` is 1-dimensional, this is a (1,) shape array.
Otherwise the shape is (K,).
rank : int
Rank of matrix `a`.
s : (min(M, N),) ndarray
Singular values of `a`.
Raises
------
LinAlgError
If computation does not converge.
Notes
-----
If `b` is a matrix, then all array results are returned as matrices.
Examples
--------
Fit a line, ``y = mx + c``, through some noisy data-points:
>>> x = np.array([0, 1, 2, 3])
>>> y = np.array([-1, 0.2, 0.9, 2.1])
By examining the coefficients, we see that the line should have a
gradient of roughly 1 and cut the y-axis at, more or less, -1.
We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
and ``p = [[m], [c]]``. Now use `lstsq` to solve for `p`:
>>> A = np.vstack([x, np.ones(len(x))]).T
>>> A
array([[ 0., 1.],
[ 1., 1.],
[ 2., 1.],
[ 3., 1.]])
>>> m, c = np.linalg.lstsq(A, y, rcond=None)[0]
>>> m, c
(1.0 -0.95) # may vary
Plot the data along with the fitted line:
>>> import matplotlib.pyplot as plt
>>> _ = plt.plot(x, y, 'o', label='Original data', markersize=10)
>>> _ = plt.plot(x, m*x + c, 'r', label='Fitted line')
>>> _ = plt.legend()
>>> plt.show()
"""
a, _ = _makearray(a)
b, wrap = _makearray(b)
is_1d = b.ndim == 1
if is_1d:
b = b[:, newaxis]
_assert_2d(a, b)
m, n = a.shape[-2:]
m2, n_rhs = b.shape[-2:]
if m != m2:
raise LinAlgError('Incompatible dimensions')
t, result_t = _commonType(a, b)
# FIXME: real_t is unused
real_t = _linalgRealType(t)
result_real_t = _realType(result_t)
# Determine default rcond value
if rcond == "warn":
# 2017-08-19, 1.14.0
warnings.warn("`rcond` parameter will change to the default of "
"machine precision times ``max(M, N)`` where M and N "
"are the input matrix dimensions.\n"
"To use the future default and silence this warning "
"we advise to pass `rcond=None`, to keep using the old, "
"explicitly pass `rcond=-1`.",
FutureWarning, stacklevel=3)
rcond = -1
if rcond is None:
rcond = finfo(t).eps * max(n, m)
if m <= n:
gufunc = _umath_linalg.lstsq_m
else:
gufunc = _umath_linalg.lstsq_n
signature = 'DDd->Ddid' if isComplexType(t) else 'ddd->ddid'
extobj = get_linalg_error_extobj(_raise_linalgerror_lstsq)
if n_rhs == 0:
# lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
if m == 0:
x[...] = 0
if n_rhs == 0:
# remove the item we added
x = x[..., :n_rhs]
resids = resids[..., :n_rhs]
# remove the axis we added
if is_1d:
x = x.squeeze(axis=-1)
# we probably should squeeze resids too, but we can't
# without breaking compatibility.
# as documented
if rank != n or m <= n:
resids = array([], result_real_t)
# coerce output arrays
s = s.astype(result_real_t, copy=False)
resids = resids.astype(result_real_t, copy=False)
x = x.astype(result_t, copy=True) # Copying lets the memory in r_parts be freed
return wrap(x), wrap(resids), rank, s | [
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