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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | scripts/cpp_lint.py | python | CheckStyle | (filename, clean_lines, linenum, file_extension, nesting_state,
error) | Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found. | Checks rules from the 'C++ style rules' section of cppguide.html. | [
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error):
"""Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw_lines = clean_lines.lines_without_raw_strings
line = raw_lines[linenum]
if line.find('\t') != -1:
error(filename, linenum, 'whitespace/tab', 1,
'Tab found; better to use spaces')
# One or three blank spaces at the beginning of the line is weird; it's
# hard to reconcile that with 2-space indents.
# NOTE: here are the conditions rob pike used for his tests. Mine aren't
# as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces
# if(RLENGTH > 20) complain = 0;
# if(match($0, " +(error|private|public|protected):")) complain = 0;
# if(match(prev, "&& *$")) complain = 0;
# if(match(prev, "\\|\\| *$")) complain = 0;
# if(match(prev, "[\",=><] *$")) complain = 0;
# if(match($0, " <<")) complain = 0;
# if(match(prev, " +for \\(")) complain = 0;
# if(prevodd && match(prevprev, " +for \\(")) complain = 0;
initial_spaces = 0
cleansed_line = clean_lines.elided[linenum]
while initial_spaces < len(line) and line[initial_spaces] == ' ':
initial_spaces += 1
if line and line[-1].isspace():
error(filename, linenum, 'whitespace/end_of_line', 4,
'Line ends in whitespace. Consider deleting these extra spaces.')
# There are certain situations we allow one space, notably for section labels
elif ((initial_spaces == 1 or initial_spaces == 3) and
not Match(r'\s*\w+\s*:\s*$', cleansed_line)):
error(filename, linenum, 'whitespace/indent', 3,
'Weird number of spaces at line-start. '
'Are you using a 2-space indent?')
# Check if the line is a header guard.
is_header_guard = False
if file_extension == 'h':
cppvar = GetHeaderGuardCPPVariable(filename)
if (line.startswith('#ifndef %s' % cppvar) or
line.startswith('#define %s' % cppvar) or
line.startswith('#endif // %s' % cppvar)):
is_header_guard = True
# #include lines and header guards can be long, since there's no clean way to
# split them.
#
# URLs can be long too. It's possible to split these, but it makes them
# harder to cut&paste.
#
# The "$Id:...$" comment may also get very long without it being the
# developers fault.
if (not line.startswith('#include') and not is_header_guard and
not Match(r'^\s*//.*http(s?)://\S*$', line) and
not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
line_width = GetLineWidth(line)
extended_length = int((_line_length * 1.25))
if line_width > extended_length:
error(filename, linenum, 'whitespace/line_length', 4,
'Lines should very rarely be longer than %i characters' %
extended_length)
elif line_width > _line_length:
error(filename, linenum, 'whitespace/line_length', 2,
'Lines should be <= %i characters long' % _line_length)
if (cleansed_line.count(';') > 1 and
# for loops are allowed two ;'s (and may run over two lines).
cleansed_line.find('for') == -1 and
(GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
# It's ok to have many commands in a switch case that fits in 1 line
not ((cleansed_line.find('case ') != -1 or
cleansed_line.find('default:') != -1) and
cleansed_line.find('break;') != -1)):
error(filename, linenum, 'whitespace/newline', 0,
'More than one command on the same line')
# Some more style checks
CheckBraces(filename, clean_lines, linenum, error)
CheckEmptyBlockBody(filename, clean_lines, linenum, error)
CheckAccess(filename, clean_lines, linenum, nesting_state, error)
CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
CheckCheck(filename, clean_lines, linenum, error)
CheckAltTokens(filename, clean_lines, linenum, error)
classinfo = nesting_state.InnermostClass()
if classinfo:
CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error) | [
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] | https://github.com/intel/caffe/blob/3f494b442ee3f9d17a07b09ecbd5fa2bbda00836/scripts/cpp_lint.py#L3463-L3567 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListCtrl.ClearAll | (*args, **kwargs) | return _controls_.ListCtrl_ClearAll(*args, **kwargs) | ClearAll(self) | ClearAll(self) | [
"ClearAll",
"(",
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] | def ClearAll(*args, **kwargs):
"""ClearAll(self)"""
return _controls_.ListCtrl_ClearAll(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/lib/io/file_io.py | python | create_dir_v2 | (path) | Creates a directory with the name given by `path`.
Args:
path: string, name of the directory to be created
Notes: The parent directories need to exist. Use `tf.io.gfile.makedirs`
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Raises:
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Args:
path: string, name of the directory to be created
Notes: The parent directories need to exist. Use `tf.io.gfile.makedirs`
instead if there is the possibility that the parent dirs don't exist.
Raises:
errors.OpError: If the operation fails.
"""
_pywrap_file_io.CreateDir(compat.path_to_bytes(path)) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/ftplib.py | python | FTP.login | (self, user = '', passwd = '', acct = '') | return resp | Login, default anonymous. | Login, default anonymous. | [
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if not user: user = 'anonymous'
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if not acct: acct = ''
if user == 'anonymous' and passwd in ('', '-'):
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# - We want to stop SPAM
# - We don't want to let ftp sites to discriminate by the user,
# host or country.
passwd = passwd + 'anonymous@'
resp = self.sendcmd('USER ' + user)
if resp[0] == '3': resp = self.sendcmd('PASS ' + passwd)
if resp[0] == '3': resp = self.sendcmd('ACCT ' + acct)
if resp[0] != '2':
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/util/resource_utils.py | python | RJavaBuildOptions.GenerateOnResourcesLoaded | (self, fake=False) | Generate an onResourcesLoaded() method.
This Java method will be called at runtime by the framework when
the corresponding library (which includes the R.java source file)
will be loaded at runtime. This corresponds to the --shared-resources
or --app-as-shared-lib flags of 'aapt package'.
if |fake|, then the method will be empty bodied to compile faster. This
useful for dummy R.java files that will eventually be replaced by real
ones. | Generate an onResourcesLoaded() method. | [
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"""Generate an onResourcesLoaded() method.
This Java method will be called at runtime by the framework when
the corresponding library (which includes the R.java source file)
will be loaded at runtime. This corresponds to the --shared-resources
or --app-as-shared-lib flags of 'aapt package'.
if |fake|, then the method will be empty bodied to compile faster. This
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"""
self.has_on_resources_loaded = True
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/coli.py | python | CommandlineWrapper.getID | (self) | Return the plugin ID | Return the plugin ID | [
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numworks/epsilon | 8952d2f8b1de1c3f064eec8ffcea804c5594ba4c | build/device/usb/backend/__init__.py | python | IBackend.get_interface_descriptor | (self, dev, intf, alt, config) | r"""Return an interface descriptor of the given device.
The object returned is required to have all the Interface Descriptor
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r"""Return an interface descriptor of the given device.
The object returned is required to have all the Interface Descriptor
fields accessible as member variables. They must be convertible (but
not required to be equal) to the int type.
The dev parameter is the device identification object.
The intf parameter is the interface logical index (not the bInterfaceNumber field)
and alt is the alternate setting logical index (not the bAlternateSetting value).
Not every interface has more than one alternate setting. In this case, the alt
parameter should be zero. config is the configuration logical index (not the
bConfigurationValue field).
"""
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/visitors/InstanceTopologyCppVisitor.py | python | InstanceTopologyCppVisitor.namespaceVisit | (self, obj) | Defined to generate namespace code within a file.
Also any pre-condition code is generated.
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | Unsqueeze.backward | (self, dy) | return singa.Reshape(dy, self.cache) | Args:
dy (CTensor): the gradient tensor from upper operations
Returns:
CTensor, the gradient over input | Args:
dy (CTensor): the gradient tensor from upper operations
Returns:
CTensor, the gradient over input | [
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"""
Args:
dy (CTensor): the gradient tensor from upper operations
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | sandbox/mintime/RRT_Smooth.py | python | linear_smooth_dichotomy | (robot,vp_list,coll_check_step) | Recursively smooth a list of via points | Recursively smooth a list of via points | [
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"""Recursively smooth a list of via points"""
if len(vp_list)==2:
return vp_list
else:
p1=vp_list[0]
p2=vp_list[-1]
d=linalg.norm(p2-p1)
v_unit=(p2-p1)/d
for t in linspace(0,d,d/coll_check_step+1):
p=p1+t*v_unit
with robot:
robot.SetDOFValues(p)
if robot.GetEnv().CheckCollision(robot):
l1=linear_smooth_dichotomy(robot,vp_list[0:len(vp_list)/2+1],coll_check_step)
l2=linear_smooth_dichotomy(robot,vp_list[len(vp_list)/2:len(vp_list)],coll_check_step)
l1.extend(l2[1:])
return l1
return [p1,p2] | [
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mickem/nscp | 79f89fdbb6da63f91bc9dedb7aea202fe938f237 | scripts/python/lib/google/protobuf/service.py | python | Service.GetDescriptor | () | Retrieves this service's descriptor. | Retrieves this service's descriptor. | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/coordinator.py | python | LooperThread.start_loop | (self) | Called when the thread starts. | Called when the thread starts. | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/tools/pretty_gyp.py | python | mask_comments | (input) | return [search_re.sub(comment_replace, line) for line in input] | Mask the quoted strings so we skip braces inside quoted strings. | Mask the quoted strings so we skip braces inside quoted strings. | [
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"""Mask the quoted strings so we skip braces inside quoted strings."""
search_re = re.compile(r'(.*?)(#)(.*)')
return [search_re.sub(comment_replace, line) for line in input] | [
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CanalTP/navitia | cb84ce9859070187e708818b058e6a7e0b7f891b | source/jormungandr/jormungandr/authentication.py | python | has_access | (region, api, abort, user) | Check the Authorization of the current user for this region and this API.
If abort is True, the request is aborted with the appropriate HTTP code.
Warning: Please this function is cached therefore it should not be
dependent of the request context, so keep it as a pure function. | Check the Authorization of the current user for this region and this API.
If abort is True, the request is aborted with the appropriate HTTP code.
Warning: Please this function is cached therefore it should not be
dependent of the request context, so keep it as a pure function. | [
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"""
Check the Authorization of the current user for this region and this API.
If abort is True, the request is aborted with the appropriate HTTP code.
Warning: Please this function is cached therefore it should not be
dependent of the request context, so keep it as a pure function.
"""
# if jormungandr is on public mode or database is not accessible, we skip the authentication process
if current_app.config.get('PUBLIC', False) or (not can_connect_to_database()):
return True
if not user:
# no user --> no need to continue, we can abort, a user is mandatory even for free region
# To manage database error of the following type we should fetch one more time from database
# Can connect to database but at least one table/attribute is not accessible due to transaction problem
if can_read_user():
abort_request(user=user)
else:
return True
try:
model_instance = Instance.get_by_name(region)
except Exception as e:
logging.getLogger(__name__).error('No access to table Instance (error: {})'.format(e))
g.can_connect_to_database = False
return True
if not model_instance:
if abort:
raise RegionNotFound(region)
return False
if (model_instance.is_free and user.have_access_to_free_instances) or user.has_access(
model_instance.id, api
):
return True
else:
if abort:
abort_request(user=user)
else:
return False | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | chrome/common/extensions/docs/examples/apps/hello-python/oauth2/__init__.py | python | generate_timestamp | () | return int(time.time()) | Get seconds since epoch (UTC). | Get seconds since epoch (UTC). | [
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"""Get seconds since epoch (UTC)."""
return int(time.time()) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pydoc.py | python | splitdoc | (doc) | return '', '\n'.join(lines) | Split a doc string into a synopsis line (if any) and the rest. | Split a doc string into a synopsis line (if any) and the rest. | [
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lines = doc.strip().split('\n')
if len(lines) == 1:
return lines[0], ''
elif len(lines) >= 2 and not lines[1].rstrip():
return lines[0], '\n'.join(lines[2:])
return '', '\n'.join(lines) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.SetHotspotSingleLine | (*args, **kwargs) | return _stc.StyledTextCtrl_SetHotspotSingleLine(*args, **kwargs) | SetHotspotSingleLine(self, bool singleLine)
Limit hotspots to single line so hotspots on two lines don't merge. | SetHotspotSingleLine(self, bool singleLine) | [
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"""
SetHotspotSingleLine(self, bool singleLine)
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"""
return _stc.StyledTextCtrl_SetHotspotSingleLine(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | JoystickEvent.__init__ | (self, *args, **kwargs) | __init__(self, EventType type=wxEVT_NULL, int state=0, int joystick=JOYSTICK1,
int change=0) -> JoystickEvent | __init__(self, EventType type=wxEVT_NULL, int state=0, int joystick=JOYSTICK1,
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/maximal-network-rank.py | python | Solution2.maximalNetworkRank | (self, n, roads) | return result | :type n: int
:type roads: List[List[int]]
:rtype: int | :type n: int
:type roads: List[List[int]]
:rtype: int | [
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"""
:type n: int
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"""
degree = [0]*n
adj = collections.defaultdict(set)
for a, b in roads:
degree[a] += 1
degree[b] += 1
adj[a].add(b)
adj[b].add(a)
sorted_idx = range(n)
sorted_idx.sort(key=lambda x:-degree[x])
m = 2
while m < n:
if degree[sorted_idx[m]] != degree[sorted_idx[1]]:
break
m += 1
result = degree[sorted_idx[0]] + degree[sorted_idx[1]] - 1 # at least sum(top2 values) - 1
for i in xrange(m-1): # only need to check pairs of top2 values
for j in xrange(i+1, m):
if degree[sorted_idx[i]]+degree[sorted_idx[j]]-int(sorted_idx[i] in adj and sorted_idx[j] in adj[sorted_idx[i]]) > result: # if equal to ideal sum of top2 values, break
return degree[sorted_idx[i]]+degree[sorted_idx[j]]-int(sorted_idx[i] in adj and sorted_idx[j] in adj[sorted_idx[i]])
return result | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/gaussian_process/kernels.py | python | CompoundKernel.theta | (self) | return np.hstack([kernel.theta for kernel in self.kernels]) | Returns the (flattened, log-transformed) non-fixed hyperparameters.
Note that theta are typically the log-transformed values of the
kernel's hyperparameters as this representation of the search space
is more amenable for hyperparameter search, as hyperparameters like
length-scales naturally live on a log-scale.
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject.Copy | (self) | return that | Make a copy of this object.
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else:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/importlib/abc.py | python | SourceLoader.set_data | (self, path, data) | Write the bytes to the path (if possible).
Accepts a str path and data as bytes.
Any needed intermediary directories are to be created. If for some
reason the file cannot be written because of permissions, fail
silently. | Write the bytes to the path (if possible). | [
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Accepts a str path and data as bytes.
Any needed intermediary directories are to be created. If for some
reason the file cannot be written because of permissions, fail
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/autograd.py | python | _parse_head | (heads, head_grads) | return head_handles, hgrad_handles | parse head gradient for backward and grad. | parse head gradient for backward and grad. | [
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"""parse head gradient for backward and grad."""
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head_grads = [head_grads]
head_handles = c_handle_array(heads)
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msg = "heads and head_grads must be lists of the same length: {} vs. {}"
assert len(heads) == len(head_grads), msg.format(len(heads), len(head_grads))
hgrad_handles = c_array(NDArrayHandle,
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return head_handles, hgrad_handles | [
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ucbrise/confluo | 578883a4f7fbbb4aea78c342d366f5122ef598f7 | pyclient/confluo/rpc/rpc_service.py | python | Iface.predef_filter | (self, mid, filter_id, beg_ms, end_ms) | Parameters:
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- beg_ms
- end_ms | Parameters:
- mid
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netease-youdao/hex | d7b8773dae8dde63f3807cef1d48c017077db727 | tools/svn_util.py | python | get_svn_info | (path) | return {'url': url, 'revision': rev} | Retrieves the URL and revision from svn info. | Retrieves the URL and revision from svn info. | [
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if line[0:4] == "URL:":
url = check_url(line[5:-1])
elif line[0:9] == "Revision:":
rev = str(int(line[10:-1]))
except IOError, (errno, strerror):
sys.stderr.write('Failed to read svn info: '+strerror+"\n")
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vicaya/hypertable | e7386f799c238c109ae47973417c2a2c7f750825 | src/py/ThriftClient/gen-py/hyperthrift/gen/ClientService.py | python | Client.open_scanner | (self, name, scan_spec, retry_table_not_found) | return self.recv_open_scanner() | Open a table scanner
@param name - table name
@param scan_spec - scan specification
@param retry_table_not_found - whether to retry upon errors caused by
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Open a table scanner
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@param retry_table_not_found - whether to retry upon errors caused by
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Parameters:
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self.send_open_scanner(name, scan_spec, retry_table_not_found)
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baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/tf/layers/tf_layers.py | python | SoftsignLayer.__init__ | (self) | init function | init function | [
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | prj/app/lib/util/xml.py | python | single_text_child | (element) | return element.childNodes[0].data | Return the text contents of an element, assuming that the element contains a single text node.
Zero-length child nodes are also supported, for example:
<foo> </foo> -> returns string ' '
<foo></foo> -> returns string ''
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LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | python/lbann/plot/plot.py | python | _get_time_axis | (time_list, units='hours') | return time_axis | Convert time to sequential format and convert time units. | Convert time to sequential format and convert time units. | [
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] | def _get_time_axis(time_list, units='hours'):
"""Convert time to sequential format and convert time units."""
time_axis = []
for i in range(len(time_list)):
time_sum = sum(time_list[:i])
if units == 'seconds':
pass
elif units == 'minutes':
time_sum /= 60.0
elif units == 'hours':
time_sum /= 3600.0
time_axis.append(time_sum)
return time_axis | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/sparse/construct.py | python | spdiags | (data, diags, m, n, format=None) | return dia_matrix((data, diags), shape=(m,n)).asformat(format) | Return a sparse matrix from diagonals.
Parameters
----------
data : array_like
matrix diagonals stored row-wise
diags : diagonals to set
- k = 0 the main diagonal
- k > 0 the k-th upper diagonal
- k < 0 the k-th lower diagonal
m, n : int
shape of the result
format : str, optional
Format of the result. By default (format=None) an appropriate sparse
matrix format is returned. This choice is subject to change.
See Also
--------
diags : more convenient form of this function
dia_matrix : the sparse DIAgonal format.
Examples
--------
>>> from scipy.sparse import spdiags
>>> data = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
>>> diags = np.array([0, -1, 2])
>>> spdiags(data, diags, 4, 4).toarray()
array([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]]) | Return a sparse matrix from diagonals. | [
"Return",
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] | def spdiags(data, diags, m, n, format=None):
"""
Return a sparse matrix from diagonals.
Parameters
----------
data : array_like
matrix diagonals stored row-wise
diags : diagonals to set
- k = 0 the main diagonal
- k > 0 the k-th upper diagonal
- k < 0 the k-th lower diagonal
m, n : int
shape of the result
format : str, optional
Format of the result. By default (format=None) an appropriate sparse
matrix format is returned. This choice is subject to change.
See Also
--------
diags : more convenient form of this function
dia_matrix : the sparse DIAgonal format.
Examples
--------
>>> from scipy.sparse import spdiags
>>> data = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
>>> diags = np.array([0, -1, 2])
>>> spdiags(data, diags, 4, 4).toarray()
array([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
"""
return dia_matrix((data, diags), shape=(m,n)).asformat(format) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Image_HSVtoRGB | (*args, **kwargs) | return _core_.Image_HSVtoRGB(*args, **kwargs) | Image_HSVtoRGB(Image_HSVValue hsv) -> Image_RGBValue
Converts a color in HSV color space to RGB color space. | Image_HSVtoRGB(Image_HSVValue hsv) -> Image_RGBValue | [
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"""
Image_HSVtoRGB(Image_HSVValue hsv) -> Image_RGBValue
Converts a color in HSV color space to RGB color space.
"""
return _core_.Image_HSVtoRGB(*args, **kwargs) | [
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | vendor/pybind11/tools/clang/cindex.py | python | Cursor.get_tokens | (self) | return TokenGroup.get_tokens(self._tu, self.extent) | Obtain Token instances formulating that compose this Cursor.
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"""Obtain Token instances formulating that compose this Cursor.
This is a generator for Token instances. It returns all tokens which
occupy the extent this cursor occupies.
"""
return TokenGroup.get_tokens(self._tu, self.extent) | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | ci/detect-changes.py | python | get_windows_shell_eval | (env) | return "\n".join(('set "{0}={1}"'.format(k, v)
for k, v in env.items())) | Return a shell-evalable string to setup some environment variables. | Return a shell-evalable string to setup some environment variables. | [
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] | def get_windows_shell_eval(env):
"""
Return a shell-evalable string to setup some environment variables.
"""
return "\n".join(('set "{0}={1}"'.format(k, v)
for k, v in env.items())) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/sched.py | python | scheduler.enterabs | (self, time, priority, action, argument) | return event | Enter a new event in the queue at an absolute time.
Returns an ID for the event which can be used to remove it,
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] | def enterabs(self, time, priority, action, argument):
"""Enter a new event in the queue at an absolute time.
Returns an ID for the event which can be used to remove it,
if necessary.
"""
event = Event(time, priority, action, argument)
heapq.heappush(self._queue, event)
return event | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | demo/Main.py | python | LookForExternals | (externalDemos, demoName) | return pkg, overview | Checks if a demo name is in any of the external packages (like AGW) or
if the user clicked on one of the external packages parent items in the
tree, in which case it returns the html overview for the package. | Checks if a demo name is in any of the external packages (like AGW) or
if the user clicked on one of the external packages parent items in the
tree, in which case it returns the html overview for the package. | [
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] | def LookForExternals(externalDemos, demoName):
"""
Checks if a demo name is in any of the external packages (like AGW) or
if the user clicked on one of the external packages parent items in the
tree, in which case it returns the html overview for the package.
"""
pkg = overview = None
# Loop over all the external demos
for key, package in externalDemos.items():
# Get the tree item name for the package and its demos
treeName, treeDemos = package.GetDemos()
# Get the overview for the package
treeOverview = package.GetOverview()
if treeName == demoName:
# The user clicked on the parent tree item, return the overview
return pkg, treeOverview
elif demoName in treeDemos:
# The user clicked on a real demo, return the package
return key, overview
# No match found, return None for both
return pkg, overview | [
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axw/cmonster | 4bb5131add306abdd0481488764d8949ca7114d3 | lib/cmonster/_preprocessor.py | python | Preprocessor | (*args, **kwargs) | return parser.preprocessor | Convenience function for creating a Parser and returning its Preprocessor. | Convenience function for creating a Parser and returning its Preprocessor. | [
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] | def Preprocessor(*args, **kwargs):
"""
Convenience function for creating a Parser and returning its Preprocessor.
"""
from ._parser import Parser
parser = Parser(*args, **kwargs)
return parser.preprocessor | [
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syoyo/tinygltf | e7f1ff5c59d3ca2489923beb239bdf93d863498f | deps/cpplint.py | python | _BlockInfo.IsBlockInfo | (self) | return self.__class__ == _BlockInfo | Returns true if this block is a _BlockInfo.
This is convenient for verifying that an object is an instance of
a _BlockInfo, but not an instance of any of the derived classes.
Returns:
True for this class, False for derived classes. | Returns true if this block is a _BlockInfo. | [
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"""Returns true if this block is a _BlockInfo.
This is convenient for verifying that an object is an instance of
a _BlockInfo, but not an instance of any of the derived classes.
Returns:
True for this class, False for derived classes.
"""
return self.__class__ == _BlockInfo | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | lldb/examples/python/file_extract.py | python | FileExtract.set_byte_order | (self, b) | Set the byte order, valid values are "big", "little", "swap", "native", "<", ">", "@", "=" | Set the byte order, valid values are "big", "little", "swap", "native", "<", ">", " | [
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] | def set_byte_order(self, b):
'''Set the byte order, valid values are "big", "little", "swap", "native", "<", ">", "@", "="'''
if b == 'big':
self.byte_order = '>'
elif b == 'little':
self.byte_order = '<'
elif b == 'swap':
# swap what ever the current byte order is
self.byte_order = swap_unpack_char()
elif b == 'native':
self.byte_order = '='
elif b == '<' or b == '>' or b == '@' or b == '=':
self.byte_order = b
else:
print("error: invalid byte order specified: '%s'" % b) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Node.py | python | Node.relpath | (self) | return self.srcpath() | If a file in the build directory, bldpath, else srcpath | If a file in the build directory, bldpath, else srcpath | [
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"If a file in the build directory, bldpath, else srcpath"
cur = self
x = id(self.ctx.bldnode)
while cur.parent:
if id(cur) == x:
return self.bldpath()
cur = cur.parent
return self.srcpath() | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | PNEANet.DelAttrN | (self, *args) | return _snap.PNEANet_DelAttrN(self, *args) | DelAttrN(PNEANet self, TStr attr) -> int
Parameters:
attr: TStr const & | DelAttrN(PNEANet self, TStr attr) -> int | [
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"""
DelAttrN(PNEANet self, TStr attr) -> int
Parameters:
attr: TStr const &
"""
return _snap.PNEANet_DelAttrN(self, *args) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/logging/__init__.py | python | LoggerAdapter.isEnabledFor | (self, level) | return self.logger.isEnabledFor(level) | Is this logger enabled for level 'level'? | Is this logger enabled for level 'level'? | [
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"""
Is this logger enabled for level 'level'?
"""
return self.logger.isEnabledFor(level) | [
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MhLiao/TextBoxes_plusplus | 39d4898de1504c53a2ed3d67966a57b3595836d0 | scripts/cpp_lint.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
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
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
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. | Check if these two filenames belong to the same module. | [
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] | def FilesBelongToSameModule(filename_cc, filename_h):
"""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
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/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
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
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.
"""
if not filename_cc.endswith('.cc'):
return (False, '')
filename_cc = filename_cc[:-len('.cc')]
if filename_cc.endswith('_unittest'):
filename_cc = filename_cc[:-len('_unittest')]
elif filename_cc.endswith('_test'):
filename_cc = filename_cc[:-len('_test')]
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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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/tools/inspector_protocol/jinja2/ext.py | python | InternationalizationExtension.parse | (self, parser) | Parse a translatable tag. | Parse a translatable tag. | [
"Parse",
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"""Parse a translatable tag."""
lineno = next(parser.stream).lineno
num_called_num = False
# find all the variables referenced. Additionally a variable can be
# defined in the body of the trans block too, but this is checked at
# a later state.
plural_expr = None
plural_expr_assignment = None
variables = {}
trimmed = None
while parser.stream.current.type != 'block_end':
if variables:
parser.stream.expect('comma')
# skip colon for python compatibility
if parser.stream.skip_if('colon'):
break
name = parser.stream.expect('name')
if name.value in variables:
parser.fail('translatable variable %r defined twice.' %
name.value, name.lineno,
exc=TemplateAssertionError)
# expressions
if parser.stream.current.type == 'assign':
next(parser.stream)
variables[name.value] = var = parser.parse_expression()
elif trimmed is None and name.value in ('trimmed', 'notrimmed'):
trimmed = name.value == 'trimmed'
continue
else:
variables[name.value] = var = nodes.Name(name.value, 'load')
if plural_expr is None:
if isinstance(var, nodes.Call):
plural_expr = nodes.Name('_trans', 'load')
variables[name.value] = plural_expr
plural_expr_assignment = nodes.Assign(
nodes.Name('_trans', 'store'), var)
else:
plural_expr = var
num_called_num = name.value == 'num'
parser.stream.expect('block_end')
plural = None
have_plural = False
referenced = set()
# now parse until endtrans or pluralize
singular_names, singular = self._parse_block(parser, True)
if singular_names:
referenced.update(singular_names)
if plural_expr is None:
plural_expr = nodes.Name(singular_names[0], 'load')
num_called_num = singular_names[0] == 'num'
# if we have a pluralize block, we parse that too
if parser.stream.current.test('name:pluralize'):
have_plural = True
next(parser.stream)
if parser.stream.current.type != 'block_end':
name = parser.stream.expect('name')
if name.value not in variables:
parser.fail('unknown variable %r for pluralization' %
name.value, name.lineno,
exc=TemplateAssertionError)
plural_expr = variables[name.value]
num_called_num = name.value == 'num'
parser.stream.expect('block_end')
plural_names, plural = self._parse_block(parser, False)
next(parser.stream)
referenced.update(plural_names)
else:
next(parser.stream)
# register free names as simple name expressions
for var in referenced:
if var not in variables:
variables[var] = nodes.Name(var, 'load')
if not have_plural:
plural_expr = None
elif plural_expr is None:
parser.fail('pluralize without variables', lineno)
if trimmed is None:
trimmed = self.environment.policies['ext.i18n.trimmed']
if trimmed:
singular = self._trim_whitespace(singular)
if plural:
plural = self._trim_whitespace(plural)
node = self._make_node(singular, plural, variables, plural_expr,
bool(referenced),
num_called_num and have_plural)
node.set_lineno(lineno)
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return [plural_expr_assignment, node]
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | pynest/nest/lib/hl_api_parallel_computing.py | python | Rank | () | return spp() | Return the MPI rank of the local process.
Returns
-------
int:
MPI rank of the local process
Note
----
DO NOT USE `Rank()` TO EXECUTE ANY FUNCTION IMPORTED FROM THE `nest`
MODULE ON A SUBSET OF RANKS IN AN MPI-PARALLEL SIMULATION.
This will lead to unpredictable behavior. Symptoms may be an
error message about non-synchronous global random number generators
or deadlocks during simulation. In the worst case, the simulation
may complete but generate nonsensical results. | Return the MPI rank of the local process. | [
"Return",
"the",
"MPI",
"rank",
"of",
"the",
"local",
"process",
"."
] | def Rank():
"""Return the MPI rank of the local process.
Returns
-------
int:
MPI rank of the local process
Note
----
DO NOT USE `Rank()` TO EXECUTE ANY FUNCTION IMPORTED FROM THE `nest`
MODULE ON A SUBSET OF RANKS IN AN MPI-PARALLEL SIMULATION.
This will lead to unpredictable behavior. Symptoms may be an
error message about non-synchronous global random number generators
or deadlocks during simulation. In the worst case, the simulation
may complete but generate nonsensical results.
"""
sr("Rank")
return spp() | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_grad/grad_array_ops.py | python | get_bprop_select | (self) | return bprop | Generate bprop for Select | Generate bprop for Select | [
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"bprop",
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"Select"
] | def get_bprop_select(self):
"""Generate bprop for Select"""
select = P.Select()
def bprop(cond, x, y, out, dout):
return zeros_like(cond), select(cond, dout, zeros_like(x)), select(cond, zeros_like(y), dout)
return bprop | [
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gnina/gnina | b9ae032f52fc7a8153987bde09c0efa3620d8bb6 | caffe/python/caffe/net_spec.py | python | to_proto | (*tops) | return net | Generate a NetParameter that contains all layers needed to compute
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"""Generate a NetParameter that contains all layers needed to compute
all arguments."""
layers = OrderedDict()
autonames = Counter()
for top in tops:
top.fn._to_proto(layers, {}, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/s3/connection.py | python | S3Connection.head_bucket | (self, bucket_name, headers=None) | Determines if a bucket exists by name.
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:type bucket_name: string
:param bucket_name: The name of the bucket
:type headers: dict
:param headers: Additional headers to pass along with the request to
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:returns: A <Bucket> object | Determines if a bucket exists by name. | [
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"""
Determines if a bucket exists by name.
If the bucket does not exist, an ``S3ResponseError`` will be raised.
:type bucket_name: string
:param bucket_name: The name of the bucket
:type headers: dict
:param headers: Additional headers to pass along with the request to
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:returns: A <Bucket> object
"""
response = self.make_request('HEAD', bucket_name, headers=headers)
body = response.read()
if response.status == 200:
return self.bucket_class(self, bucket_name)
elif response.status == 403:
# For backward-compatibility, we'll populate part of the exception
# with the most-common default.
err = self.provider.storage_response_error(
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err.error_code = 'AccessDenied'
err.error_message = 'Access Denied'
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err = self.provider.storage_response_error(
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err.error_code = 'NoSuchBucket'
err.error_message = 'The specified bucket does not exist'
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pythonwin/pywin/framework/scriptutils.py | python | IsOnPythonPath | (path) | return 0 | Given a path only, see if it is on the Pythonpath. Assumes path is a full path spec. | Given a path only, see if it is on the Pythonpath. Assumes path is a full path spec. | [
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for syspath in sys.path:
try:
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/relu_grad_v2_ds.py | python | _relu_grad_v2_ds_tbe | () | return | ReluGradV2 TBE register | ReluGradV2 TBE register | [
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] | def _relu_grad_v2_ds_tbe():
"""ReluGradV2 TBE register"""
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jtv/libpqxx | c0b7e682b629241fc53d037920fb33dfa4ed873d | tools/template2mak.py | python | expand_foreach | (globs, block, outfile) | Expand a foreach block for each file matching one of globs.
Write the results to outfile. | Expand a foreach block for each file matching one of globs. | [
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"""Expand a foreach block for each file matching one of globs.
Write the results to outfile.
"""
# We'll be iterating over block a variable number of times. Turn it
# from a generic iterable into an immutable array.
block = tuple(block)
for path in match_globs(globs):
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/graphviz/py3/graphviz/tools.py | python | attach | (object: typing.Any, name: str) | return decorator | Return a decorator doing ``setattr(object, name)`` with its argument.
>>> spam = type('Spam', (object,), {})()
>>> @attach(spam, 'eggs')
... def func():
... pass
>>> spam.eggs # doctest: +ELLIPSIS
<function func at 0x...> | Return a decorator doing ``setattr(object, name)`` with its argument. | [
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"""Return a decorator doing ``setattr(object, name)`` with its argument.
>>> spam = type('Spam', (object,), {})()
>>> @attach(spam, 'eggs')
... def func():
... pass
>>> spam.eggs # doctest: +ELLIPSIS
<function func at 0x...>
"""
def decorator(func):
setattr(object, name, func)
return func
return decorator | [
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RobotLocomotion/drake | 0e18a34604c45ed65bc9018a54f7610f91cdad5b | tools/workspace/drake_visualizer/_drake_visualizer_builtin_scripts/show_hydroelastic_contact.py | python | HydroelasticContactVisualizer.update_max_pressure | (self, pressure) | Tests to see if the maximum pressure needs to be increased. If so,
updates the dialog. | Tests to see if the maximum pressure needs to be increased. If so,
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"""Tests to see if the maximum pressure needs to be increased. If so,
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if pressure > self.max_pressure_observed:
self.max_pressure_observed = pressure
# Note: This is a *horrible* hack rendered necessary because
# PythonQt doesn't provide QtCore.QObject *or* QtCore.pyqtSignal so
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/AI/freeorion_tools/_freeorion_tools.py | python | get_ship_part | (part_name: str) | return part_type | Return the shipPart object (fo.getShipPart(part_name)) of the given part_name.
As the function in late game may be called some thousand times, the results are cached. | Return the shipPart object (fo.getShipPart(part_name)) of the given part_name. | [
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"""Return the shipPart object (fo.getShipPart(part_name)) of the given part_name.
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"""
if not part_name:
return None
part_type = fo.getShipPart(part_name)
if not part_type:
warning("Could not find part %s" % part_name)
return part_type | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/plat-mac/lib-scriptpackages/Netscape/Mozilla_suite.py | python | Mozilla_suite_Events.Go | (self, _object, _attributes={}, **_arguments) | Go: navigate a window: back, forward, again(reload), home)
Required argument: window
Keyword argument direction: undocumented, typecode 'dire'
Keyword argument _attributes: AppleEvent attribute dictionary | Go: navigate a window: back, forward, again(reload), home)
Required argument: window
Keyword argument direction: undocumented, typecode 'dire'
Keyword argument _attributes: AppleEvent attribute dictionary | [
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"""Go: navigate a window: back, forward, again(reload), home)
Required argument: window
Keyword argument direction: undocumented, typecode 'dire'
Keyword argument _attributes: AppleEvent attribute dictionary
"""
_code = 'MOSS'
_subcode = 'gogo'
aetools.keysubst(_arguments, self._argmap_Go)
_arguments['----'] = _object
aetools.enumsubst(_arguments, 'dire', _Enum_dire)
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----'] | [
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tensorflow/io | 92b44e180674a8af0e12e405530f7343e3e693e4 | tensorflow_io/python/experimental/serial_ops.py | python | _bytes_feature | (value) | return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) | value: list | value: list | [
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":",
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] | def _bytes_feature(value):
"""value: list"""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/tools/build/src/build/project.py | python | ProjectRegistry.push_current | (self, project) | Temporary changes the current project to 'project'. Should
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"""Temporary changes the current project to 'project'. Should
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if __debug__:
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assert isinstance(project, ProjectTarget)
self.saved_current_project.append(self.current_project)
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syoyo/tinygltf | e7f1ff5c59d3ca2489923beb239bdf93d863498f | deps/cpplint.py | python | CheckInvalidIncrement | (filename, clean_lines, linenum, error) | Checks for invalid increment *count++.
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void increment_counter(int* count) {
*count++;
}
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filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
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] | def CheckInvalidIncrement(filename, clean_lines, linenum, error):
"""Checks for invalid increment *count++.
For example following function:
void increment_counter(int* count) {
*count++;
}
is invalid, because it effectively does count++, moving pointer, and should
be replaced with ++*count, (*count)++ or *count += 1.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if _RE_PATTERN_INVALID_INCREMENT.match(line):
error(filename, linenum, 'runtime/invalid_increment', 5,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/ir/builder.py | python | IRBuilder.fence | (self, ordering, targetscope=None, name='') | return inst | Add a memory barrier, preventing certain reorderings of load and/or store accesses with
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inst = instructions.Fence(self.block, ordering, targetscope, name=name)
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/fitpropertybrowser/fitpropertybrowser.py | python | FitPropertyBrowser.update_legend | (self) | This needs to be called to update plot's legend after removing lines. | This needs to be called to update plot's legend after removing lines. | [
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"""
This needs to be called to update plot's legend after removing lines.
"""
ax = self.get_axes()
# only create a legend if the plot already had one
if ax.get_legend():
ax.make_legend() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/linalg/_interpolative_backend.py | python | idzp_rid | (eps, m, n, matveca) | return k, idx, proj | Compute ID of a complex matrix to a specified relative precision using
random matrix-vector multiplication.
:param eps:
Relative precision.
:type eps: float
:param m:
Matrix row dimension.
:type m: int
:param n:
Matrix column dimension.
:type n: int
:param matveca:
Function to apply the matrix adjoint to a vector, with call signature
`y = matveca(x)`, where `x` and `y` are the input and output vectors,
respectively.
:type matveca: function
:return:
Rank of ID.
:rtype: int
:return:
Column index array.
:rtype: :class:`numpy.ndarray`
:return:
Interpolation coefficients.
:rtype: :class:`numpy.ndarray` | Compute ID of a complex matrix to a specified relative precision using
random matrix-vector multiplication. | [
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] | def idzp_rid(eps, m, n, matveca):
"""
Compute ID of a complex matrix to a specified relative precision using
random matrix-vector multiplication.
:param eps:
Relative precision.
:type eps: float
:param m:
Matrix row dimension.
:type m: int
:param n:
Matrix column dimension.
:type n: int
:param matveca:
Function to apply the matrix adjoint to a vector, with call signature
`y = matveca(x)`, where `x` and `y` are the input and output vectors,
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:type matveca: function
:return:
Rank of ID.
:rtype: int
:return:
Column index array.
:rtype: :class:`numpy.ndarray`
:return:
Interpolation coefficients.
:rtype: :class:`numpy.ndarray`
"""
proj = np.empty(
m + 1 + 2*n*(min(m, n) + 1),
dtype=np.complex128, order='F')
k, idx, proj, ier = _id.idzp_rid(eps, m, n, matveca, proj)
if ier:
raise _RETCODE_ERROR
proj = proj[:k*(n-k)].reshape((k, n-k), order='F')
return k, idx, proj | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextObject.GetPosition | (*args, **kwargs) | return _richtext.RichTextObject_GetPosition(*args, **kwargs) | GetPosition(self) -> Point | GetPosition(self) -> Point | [
"GetPosition",
"(",
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")",
"-",
">",
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] | def GetPosition(*args, **kwargs):
"""GetPosition(self) -> Point"""
return _richtext.RichTextObject_GetPosition(*args, **kwargs) | [
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OpenChemistry/tomviz | 0a903679318f191cb7dd3eb5ff5bc3a7d3320d9a | tomviz/python/tomviz/state/_views.py | python | Camera.zoom | (self, factor) | Decrease the view angle by the specified factor. A value greater than 1
is a zoom-in, a value less than 1 is a zoom-out. | Decrease the view angle by the specified factor. A value greater than 1
is a zoom-in, a value less than 1 is a zoom-out. | [
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"""
Decrease the view angle by the specified factor. A value greater than 1
is a zoom-in, a value less than 1 is a zoom-out.
"""
self._camera.Zoom(factor)
Render(self._render_view) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/s3fs/core.py | python | S3FileSystem.touch | (self, path, truncate=True, data=None, **kwargs) | Create empty file or truncate | Create empty file or truncate | [
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"""Create empty file or truncate"""
bucket, key, version_id = self.split_path(path)
if version_id:
raise ValueError("S3 does not support touching existing versions of files")
if not truncate and self.exists(path):
raise ValueError("S3 does not support touching existent files")
try:
self._call_s3(self.s3.put_object, kwargs, Bucket=bucket, Key=key)
except ClientError as ex:
raise translate_boto_error(ex)
self.invalidate_cache(self._parent(path)) | [
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IDI-Systems/acre2 | 821bd25e7eb748e35aaf48b06002d564cc60a88d | tools/make.py | python | color | (color) | Set the color. Works on Win32 and normal terminals. | Set the color. Works on Win32 and normal terminals. | [
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"and",
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"."
] | def color(color):
"""Set the color. Works on Win32 and normal terminals."""
if sys.platform == "win32":
if color == "green":
set_text_attr(FOREGROUND_GREEN | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)
elif color == "yellow":
set_text_attr(FOREGROUND_YELLOW | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)
elif color == "red":
set_text_attr(FOREGROUND_RED | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)
elif color == "blue":
set_text_attr(FOREGROUND_BLUE | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)
elif color == "reset":
set_text_attr(FOREGROUND_GREY | get_text_attr() & 0x0070)
elif color == "grey":
set_text_attr(FOREGROUND_GREY | get_text_attr() & 0x0070)
else :
if color == "green":
sys.stdout.write('\033[92m')
elif color == "red":
sys.stdout.write('\033[91m')
elif color == "blue":
sys.stdout.write('\033[94m')
elif color == "reset":
sys.stdout.write('\033[0m') | [
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MegaGlest/megaglest-source | e3af470288a3c9cc179f63b5a1eb414a669e3772 | mk/windoze/symbolstore.py | python | GetPlatformSpecificDumper | (**kwargs) | return {'Windows': Dumper_Win32,
'Microsoft': Dumper_Win32,
'Linux': Dumper_Linux,
'Sunos5': Dumper_Solaris,
'Darwin': Dumper_Mac}[platform.system()](**kwargs) | This function simply returns a instance of a subclass of Dumper
that is appropriate for the current platform. | This function simply returns a instance of a subclass of Dumper
that is appropriate for the current platform. | [
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] | def GetPlatformSpecificDumper(**kwargs):
"""This function simply returns a instance of a subclass of Dumper
that is appropriate for the current platform."""
# Python 2.5 has a bug where platform.system() returns 'Microsoft'.
# Remove this when we no longer support Python 2.5.
return {'Windows': Dumper_Win32,
'Microsoft': Dumper_Win32,
'Linux': Dumper_Linux,
'Sunos5': Dumper_Solaris,
'Darwin': Dumper_Mac}[platform.system()](**kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/_distn_infrastructure.py | python | rv_continuous.fit_loc_scale | (self, data, *args) | return Lhat, Shat | Estimate loc and scale parameters from data using 1st and 2nd moments.
Parameters
----------
data : array_like
Data to fit.
arg1, arg2, arg3,... : array_like
The shape parameter(s) for the distribution (see docstring of the
instance object for more information).
Returns
-------
Lhat : float
Estimated location parameter for the data.
Shat : float
Estimated scale parameter for the data. | Estimate loc and scale parameters from data using 1st and 2nd moments. | [
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] | def fit_loc_scale(self, data, *args):
"""
Estimate loc and scale parameters from data using 1st and 2nd moments.
Parameters
----------
data : array_like
Data to fit.
arg1, arg2, arg3,... : array_like
The shape parameter(s) for the distribution (see docstring of the
instance object for more information).
Returns
-------
Lhat : float
Estimated location parameter for the data.
Shat : float
Estimated scale parameter for the data.
"""
mu, mu2 = self.stats(*args, **{'moments': 'mv'})
tmp = asarray(data)
muhat = tmp.mean()
mu2hat = tmp.var()
Shat = sqrt(mu2hat / mu2)
Lhat = muhat - Shat*mu
if not np.isfinite(Lhat):
Lhat = 0
if not (np.isfinite(Shat) and (0 < Shat)):
Shat = 1
return Lhat, Shat | [
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esa/pykep | b410363653623730b577de257c04b0e0289f2014 | pykep/trajopt/_indirect.py | python | indirect_pt2pt.__init__ | (self,
x0=[-51051524893.335152, -142842795180.97464, 1139935.2553601924,
30488.847061907356, -10612.482697050367, -204.23284335657095, 1000],
xf=[24753885674.871033, 231247560000.17883, 4236305010.4256544, -
23171.900670190855, 4635.6817290400222, 666.44019588506023, 910.48383959441833],
thrust=0.3,
isp=3000,
mu=pk.MU_SUN,
tof=[276.15166075931495, 276.15166075931495],
freetime=False,
alpha=0, # quadratic control
bound=False,
atol=1e-12,
rtol=1e-12) | Constructs an instance of the ``pykep.trajopt.indirect_pt2pt`` problem.
Args:
- x0 (``list``, ``tuple``, ``numpy.ndarray``): Departure state [m, m, m, m/s, m/s, m/s, kg].
- xf (``list``, ``tuple``, ``numpy.ndarray``): Arrival state [m, m, m, m/s, m/s, m/s, kg].
- tof (``list``): Transfer time bounds [days].
- thrust (``float``, ``int``): Spacecraft maximum thrust [N].
- isp (``float``, ``int``): Spacecraft specific impulse [s].
- mu (``float``): Gravitational parameter of primary body [m^3/s^2].
- freetime (``bool``): Activates final time transversality condition. Allows final time to vary.
- alpha (``float``, ``int``): Homotopy parameter (0 -quadratic control, 1 - mass optimal)
- bound (``bool``): Activates bounded control, in which the control throttle is bounded between 0 and 1, otherwise the control throttle is allowed to unbounded.
- atol (``float``, ``int``): Absolute integration solution tolerance.
- rtol (``float``, ``int``): Relative integration solution tolerance. | Constructs an instance of the ``pykep.trajopt.indirect_pt2pt`` problem. | [
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x0=[-51051524893.335152, -142842795180.97464, 1139935.2553601924,
30488.847061907356, -10612.482697050367, -204.23284335657095, 1000],
xf=[24753885674.871033, 231247560000.17883, 4236305010.4256544, -
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thrust=0.3,
isp=3000,
mu=pk.MU_SUN,
tof=[276.15166075931495, 276.15166075931495],
freetime=False,
alpha=0, # quadratic control
bound=False,
atol=1e-12,
rtol=1e-12):
"""
Constructs an instance of the ``pykep.trajopt.indirect_pt2pt`` problem.
Args:
- x0 (``list``, ``tuple``, ``numpy.ndarray``): Departure state [m, m, m, m/s, m/s, m/s, kg].
- xf (``list``, ``tuple``, ``numpy.ndarray``): Arrival state [m, m, m, m/s, m/s, m/s, kg].
- tof (``list``): Transfer time bounds [days].
- thrust (``float``, ``int``): Spacecraft maximum thrust [N].
- isp (``float``, ``int``): Spacecraft specific impulse [s].
- mu (``float``): Gravitational parameter of primary body [m^3/s^2].
- freetime (``bool``): Activates final time transversality condition. Allows final time to vary.
- alpha (``float``, ``int``): Homotopy parameter (0 -quadratic control, 1 - mass optimal)
- bound (``bool``): Activates bounded control, in which the control throttle is bounded between 0 and 1, otherwise the control throttle is allowed to unbounded.
- atol (``float``, ``int``): Absolute integration solution tolerance.
- rtol (``float``, ``int``): Relative integration solution tolerance.
"""
# Cartesian states
if not all([(isinstance(x, list) or isinstance(x, tuple) or isinstance(x, np.ndarray)) for x in [x0, xf]]):
raise TypeError(
"Both x0 and xf must be supplied as an instance of either list, tuple, or numpy.ndarray.")
elif not all([len(x) == 7 for x in [x0, xf]]):
raise TypeError(
"Both x0 and xf must be supplied with 7 dimensions.")
else:
self.x0 = pk.sims_flanagan.sc_state()
self.x0.set(x0)
self.xf = pk.sims_flanagan.sc_state()
self.xf.set(xf)
self.tof = tof
# initialise base
_indirect_base.__init__(
self, x0[-1], thrust, isp, mu, True, freetime, alpha, bound,
atol, rtol
) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/saved_model/loader_impl.py | python | SavedModelLoader.load_graph | (self, graph, tags, import_scope=None, **saver_kwargs) | Load ops and nodes from SavedModel MetaGraph into graph.
Args:
graph: tf.Graph object.
tags: a set of string tags identifying a MetaGraphDef.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to
tensor instances loaded into the passed session, but it is *not* written
through to the static `MetaGraphDef` protocol buffer that is returned.
**saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph.
Returns:
A tuple of
* Saver defined by the MetaGraph, which can be used to restore the
variable values.
* List of `Operation`/`Tensor` objects returned from
`tf.import_graph_def` (may be `None`). | Load ops and nodes from SavedModel MetaGraph into graph. | [
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] | def load_graph(self, graph, tags, import_scope=None, **saver_kwargs):
"""Load ops and nodes from SavedModel MetaGraph into graph.
Args:
graph: tf.Graph object.
tags: a set of string tags identifying a MetaGraphDef.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to
tensor instances loaded into the passed session, but it is *not* written
through to the static `MetaGraphDef` protocol buffer that is returned.
**saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph.
Returns:
A tuple of
* Saver defined by the MetaGraph, which can be used to restore the
variable values.
* List of `Operation`/`Tensor` objects returned from
`tf.import_graph_def` (may be `None`).
"""
meta_graph_def = self.get_meta_graph_def_from_tags(tags)
if sys.byteorder == "big":
saved_model_utils.swap_function_tensor_content(meta_graph_def, "little",
"big")
with graph.as_default():
return tf_saver._import_meta_graph_with_return_elements( # pylint: disable=protected-access
meta_graph_def, import_scope=import_scope, **saver_kwargs) | [
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/i-pi/ipi/inputs/forces.py | python | InputForces.store | (self, flist) | Stores a list of the output objects, creating a sequence of
dynamic containers.
Args:
flist: A list of tuples, with each tuple being of the form
('type', 'object') where 'type' is the type of forcefield
and 'object' is a forcefield object of that type. | Stores a list of the output objects, creating a sequence of
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] | def store(self, flist):
"""Stores a list of the output objects, creating a sequence of
dynamic containers.
Args:
flist: A list of tuples, with each tuple being of the form
('type', 'object') where 'type' is the type of forcefield
and 'object' is a forcefield object of that type.
"""
super(InputForces, self).store()
self.extra = []
for el in flist:
if el[0]=="socket":
iff = InputFBSocket()
iff.store(el[1])
self.extra.append(("socket", iff)) | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | win32/scripts/ce/pysynch.py | python | delete | (args) | delete file, ...
Delete one or more remote files | delete file, ...
Delete one or more remote files | [
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"...",
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] | def delete(args):
"""delete file, ...
Delete one or more remote files
"""
for arg in args:
try:
wincerapi.CeDeleteFile(arg)
print("Deleted: %s" % arg)
except win32api.error as details:
print_error(details, "Error deleting '%s'" % arg) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/gradients_util.py | python | _HasAnyNotNoneGrads | (grads, op) | return False | Return true iff op has real gradient. | Return true iff op has real gradient. | [
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] | def _HasAnyNotNoneGrads(grads, op):
"""Return true iff op has real gradient."""
out_grads = _GetGrads(grads, op)
for out_grad in out_grads:
if isinstance(out_grad, (ops.Tensor, indexed_slices.IndexedSlices)):
return True
if out_grad and isinstance(out_grad, collections_abc.Sequence):
if any(g is not None for g in out_grad):
return True
return False | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/dask_cudf/dask_cudf/accessors.py | python | StructMethods.field | (self, key) | return self.d_series.map_partitions(
lambda s: s.struct.field(key),
meta=self.d_series._meta._constructor([], dtype=typ),
) | Extract children of the specified struct column
in the Series
Parameters
----------
key: int or str
index/position or field name of the respective
struct column
Returns
-------
Series
Examples
--------
>>> s = cudf.Series([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
>>> ds = dask_cudf.from_cudf(s, 2)
>>> ds.struct.field(0).compute()
0 1
1 3
dtype: int64
>>> ds.struct.field('a').compute()
0 1
1 3
dtype: int64 | Extract children of the specified struct column
in the Series
Parameters
----------
key: int or str
index/position or field name of the respective
struct column
Returns
-------
Series
Examples
--------
>>> s = cudf.Series([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
>>> ds = dask_cudf.from_cudf(s, 2)
>>> ds.struct.field(0).compute()
0 1
1 3
dtype: int64
>>> ds.struct.field('a').compute()
0 1
1 3
dtype: int64 | [
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key: int or str
index/position or field name of the respective
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Returns
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Examples
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>>> ds = dask_cudf.from_cudf(s, 2)
>>> ds.struct.field(0).compute()
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>>> ds.struct.field('a').compute()
0 1
1 3
dtype: int64
"""
typ = self.d_series._meta.struct.field(key).dtype
return self.d_series.map_partitions(
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meta=self.d_series._meta._constructor([], dtype=typ),
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gwaldron/osgearth | 4c521857d59a69743e4a9cedba00afe570f984e8 | src/third_party/tinygltf/deps/cpplint.py | python | IsInitializerList | (clean_lines, linenum) | return False | Check if current line is inside constructor initializer list.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line appears to be inside constructor initializer
list, False otherwise. | Check if current line is inside constructor initializer list. | [
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] | def IsInitializerList(clean_lines, linenum):
"""Check if current line is inside constructor initializer list.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
Returns:
True if current line appears to be inside constructor initializer
list, False otherwise.
"""
for i in xrange(linenum, 1, -1):
line = clean_lines.elided[i]
if i == linenum:
remove_function_body = Match(r'^(.*)\{\s*$', line)
if remove_function_body:
line = remove_function_body.group(1)
if Search(r'\s:\s*\w+[({]', line):
# A lone colon tend to indicate the start of a constructor
# initializer list. It could also be a ternary operator, which
# also tend to appear in constructor initializer lists as
# opposed to parameter lists.
return True
if Search(r'\}\s*,\s*$', line):
# A closing brace followed by a comma is probably the end of a
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return True
if Search(r'[{};]\s*$', line):
# Found one of the following:
# - A closing brace or semicolon, probably the end of the previous
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# - An opening brace, probably the start of current class or namespace.
#
# Current line is probably not inside an initializer list since
# we saw one of those things without seeing the starting colon.
return False
# Got to the beginning of the file without seeing the start of
# constructor initializer list.
return False | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/training/saver.py | python | BaseSaverBuilder._AddShardedSaveOps | (self, filename_tensor, per_device) | Add ops to save the params per shard.
Args:
filename_tensor: a scalar String Tensor.
per_device: A list of (device, BaseSaverBuilder.SaveableObject) pairs, as
returned by _GroupByDevices().
Returns:
An op to save the variables. | Add ops to save the params per shard. | [
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] | def _AddShardedSaveOps(self, filename_tensor, per_device):
"""Add ops to save the params per shard.
Args:
filename_tensor: a scalar String Tensor.
per_device: A list of (device, BaseSaverBuilder.SaveableObject) pairs, as
returned by _GroupByDevices().
Returns:
An op to save the variables.
"""
if self._write_version == saver_pb2.SaverDef.V2:
return self._AddShardedSaveOpsForV2(filename_tensor, per_device)
num_shards = len(per_device)
sharded_saves = []
num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
for shard, (device, saveables) in enumerate(per_device):
with ops.device(device):
sharded_filename = self.sharded_filename(filename_tensor, shard,
num_shards_tensor)
sharded_saves.append(self._AddSaveOps(sharded_filename, saveables))
# Return the sharded name for the save path.
with ops.control_dependencies([x.op for x in sharded_saves]):
return gen_io_ops.sharded_filespec(filename_tensor, num_shards_tensor) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/factorization/python/ops/gmm.py | python | GMM.__init__ | (self,
num_clusters,
model_dir=None,
random_seed=0,
params='wmc',
initial_clusters='random',
covariance_type='full',
batch_size=128,
steps=10,
continue_training=False,
config=None,
verbose=1) | Creates a model for running GMM training and inference.
Args:
num_clusters: number of clusters to train.
model_dir: the directory to save the model results and log files.
random_seed: Python integer. Seed for PRNG used to initialize centers.
params: Controls which parameters are updated in the training process.
Can contain any combination of "w" for weights, "m" for means,
and "c" for covars.
initial_clusters: specifies how to initialize the clusters for training.
See gmm_ops.gmm for the possible values.
covariance_type: one of "full", "diag".
batch_size: See TensorFlowEstimator
steps: See TensorFlowEstimator
continue_training: See TensorFlowEstimator
config: See TensorFlowEstimator
verbose: See TensorFlowEstimator | Creates a model for running GMM training and inference. | [
"Creates",
"a",
"model",
"for",
"running",
"GMM",
"training",
"and",
"inference",
"."
] | def __init__(self,
num_clusters,
model_dir=None,
random_seed=0,
params='wmc',
initial_clusters='random',
covariance_type='full',
batch_size=128,
steps=10,
continue_training=False,
config=None,
verbose=1):
"""Creates a model for running GMM training and inference.
Args:
num_clusters: number of clusters to train.
model_dir: the directory to save the model results and log files.
random_seed: Python integer. Seed for PRNG used to initialize centers.
params: Controls which parameters are updated in the training process.
Can contain any combination of "w" for weights, "m" for means,
and "c" for covars.
initial_clusters: specifies how to initialize the clusters for training.
See gmm_ops.gmm for the possible values.
covariance_type: one of "full", "diag".
batch_size: See TensorFlowEstimator
steps: See TensorFlowEstimator
continue_training: See TensorFlowEstimator
config: See TensorFlowEstimator
verbose: See TensorFlowEstimator
"""
super(GMM, self).__init__(
model_dir=model_dir,
config=config)
self.batch_size = batch_size
self.steps = steps
self.continue_training = continue_training
self.verbose = verbose
self._num_clusters = num_clusters
self._params = params
self._training_initial_clusters = initial_clusters
self._covariance_type = covariance_type
self._training_graph = None
self._random_seed = random_seed | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/skia_gold_common/skia_gold_session.py | python | SkiaGoldSession._GetDiffGoldInstance | (self) | return str(self._instance) + '-public' | Gets the Skia Gold instance to use for the Diff step.
This can differ based on how a particular instance is set up, mainly
depending on whether it is set up for internal results or not. | Gets the Skia Gold instance to use for the Diff step. | [
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] | def _GetDiffGoldInstance(self):
"""Gets the Skia Gold instance to use for the Diff step.
This can differ based on how a particular instance is set up, mainly
depending on whether it is set up for internal results or not.
"""
# TODO(skbug.com/10610): Decide whether to use the public or
# non-public instance once authentication is fixed for the non-public
# instance.
return str(self._instance) + '-public' | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | PyAuiTabArt._setCallbackInfo | (*args, **kwargs) | return _aui.PyAuiTabArt__setCallbackInfo(*args, **kwargs) | _setCallbackInfo(self, PyObject self, PyObject _class) | _setCallbackInfo(self, PyObject self, PyObject _class) | [
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return _aui.PyAuiTabArt__setCallbackInfo(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | PageSetupDialogData.SetDefaultInfo | (*args, **kwargs) | return _windows_.PageSetupDialogData_SetDefaultInfo(*args, **kwargs) | SetDefaultInfo(self, bool flag) | SetDefaultInfo(self, bool flag) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mailbox.py | python | Mailbox.__init__ | (self, path, factory=None, create=True) | Initialize a Mailbox instance. | Initialize a Mailbox instance. | [
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] | def __init__(self, path, factory=None, create=True):
"""Initialize a Mailbox instance."""
self._path = os.path.abspath(os.path.expanduser(path))
self._factory = factory | [
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jsk-ros-pkg/jsk_visualization | 278abbff394c1275925b0419a4f359cda07fde61 | jsk_rviz_plugins/scripts/contact_state_marker.py | python | callback | (msgs) | msgs = ContactStatesStamped | msgs = ContactStatesStamped | [
"msgs",
"=",
"ContactStatesStamped"
] | def callback(msgs):
"msgs = ContactStatesStamped"
global g_config
if g_config.use_parent_link:
urdf_robot = URDF.from_parameter_server()
marker_array = MarkerArray()
for msg, i in zip(msgs.states, range(len(msgs.states))):
marker = Marker()
link_name = msg.header.frame_id
if g_config.use_parent_link:
# lookup parent link
chain = urdf_robot.get_chain(urdf_robot.get_root(), link_name)
link_name = chain[-3]
mesh_file, offset = find_mesh(link_name)
marker.header.frame_id = link_name
marker.header.stamp = rospy.Time.now()
marker.type = Marker.MESH_RESOURCE
if msg.state.state == ContactState.ON:
marker.color.a = g_config.on_alpha
marker.color.r = g_config.on_red
marker.color.g = g_config.on_green
marker.color.b = g_config.on_blue
elif msg.state.state == ContactState.OFF:
marker.color.a = g_config.off_alpha
marker.color.r = g_config.off_red
marker.color.g = g_config.off_green
marker.color.b = g_config.off_blue
marker.scale.x = g_config.marker_scale
marker.scale.y = g_config.marker_scale
marker.scale.z = g_config.marker_scale
marker.pose = offset
marker.mesh_resource = mesh_file
marker.frame_locked = True
marker.id = i
if msg.state.state == ContactState.OFF:
if not g_config.visualize_off:
marker.action = Marker.DELETE
marker_array.markers.append(marker)
pub.publish(marker_array) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/basic_fitting/fit_function_options_view.py | python | FitFunctionOptionsView.set_slot_for_exclude_range_state_changed | (self, slot) | Connect the slot for the exclude range checkbox. | Connect the slot for the exclude range checkbox. | [
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] | def set_slot_for_exclude_range_state_changed(self, slot) -> None:
"""Connect the slot for the exclude range checkbox."""
self.exclude_range_checkbox.stateChanged.connect(slot) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/signal/filter_design.py | python | lp2hp_zpk | (z, p, k, wo=1.0) | return z_hp, p_hp, k_hp | r"""
Transform a lowpass filter prototype to a highpass filter.
Return an analog high-pass filter with cutoff frequency `wo`
from an analog low-pass filter prototype with unity cutoff frequency,
using zeros, poles, and gain ('zpk') representation.
Parameters
----------
z : array_like
Zeros of the analog filter transfer function.
p : array_like
Poles of the analog filter transfer function.
k : float
System gain of the analog filter transfer function.
wo : float
Desired cutoff, as angular frequency (e.g. rad/s).
Defaults to no change.
Returns
-------
z : ndarray
Zeros of the transformed high-pass filter transfer function.
p : ndarray
Poles of the transformed high-pass filter transfer function.
k : float
System gain of the transformed high-pass filter.
See Also
--------
lp2lp_zpk, lp2bp_zpk, lp2bs_zpk, bilinear
lp2hp
Notes
-----
This is derived from the s-plane substitution
.. math:: s \rightarrow \frac{\omega_0}{s}
This maintains symmetry of the lowpass and highpass responses on a
logarithmic scale.
.. versionadded:: 1.1.0 | r"""
Transform a lowpass filter prototype to a highpass filter. | [
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"Transform",
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"lowpass",
"filter",
"prototype",
"to",
"a",
"highpass",
"filter",
"."
] | def lp2hp_zpk(z, p, k, wo=1.0):
r"""
Transform a lowpass filter prototype to a highpass filter.
Return an analog high-pass filter with cutoff frequency `wo`
from an analog low-pass filter prototype with unity cutoff frequency,
using zeros, poles, and gain ('zpk') representation.
Parameters
----------
z : array_like
Zeros of the analog filter transfer function.
p : array_like
Poles of the analog filter transfer function.
k : float
System gain of the analog filter transfer function.
wo : float
Desired cutoff, as angular frequency (e.g. rad/s).
Defaults to no change.
Returns
-------
z : ndarray
Zeros of the transformed high-pass filter transfer function.
p : ndarray
Poles of the transformed high-pass filter transfer function.
k : float
System gain of the transformed high-pass filter.
See Also
--------
lp2lp_zpk, lp2bp_zpk, lp2bs_zpk, bilinear
lp2hp
Notes
-----
This is derived from the s-plane substitution
.. math:: s \rightarrow \frac{\omega_0}{s}
This maintains symmetry of the lowpass and highpass responses on a
logarithmic scale.
.. versionadded:: 1.1.0
"""
z = atleast_1d(z)
p = atleast_1d(p)
wo = float(wo)
degree = _relative_degree(z, p)
# Invert positions radially about unit circle to convert LPF to HPF
# Scale all points radially from origin to shift cutoff frequency
z_hp = wo / z
p_hp = wo / p
# If lowpass had zeros at infinity, inverting moves them to origin.
z_hp = append(z_hp, zeros(degree))
# Cancel out gain change caused by inversion
k_hp = k * real(prod(-z) / prod(-p))
return z_hp, p_hp, k_hp | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/amp/grad_scaler.py | python | GradScaler.minimize | (self, optimizer, *args, **kwargs) | return super(GradScaler, self).minimize(optimizer, *args, **kwargs) | This function is similar as `optimizer.minimize()`, which performs parameters updating.
If the scaled gradients of parameters contains NAN or INF, the parameters updating is skipped.
Otherwise, if `unscale_()` has not been called, it first unscales the scaled gradients of parameters, then updates the parameters.
Finally, the loss scaling ratio is updated.
Args:
optimizer(Optimizer): The optimizer used to update parameters.
args: Arguments, which will be forward to `optimizer.minimize()`.
kwargs: Keyword arguments, which will be forward to `optimizer.minimize()`.
Examples:
.. code-block:: python
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True)
optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
data = paddle.rand([10, 3, 32, 32])
with paddle.amp.auto_cast():
conv = model(data)
loss = paddle.mean(conv)
scaled = scaler.scale(loss) # scale the loss
scaled.backward() # do backward
scaler.minimize(optimizer, scaled) # update parameters
optimizer.clear_grad() | This function is similar as `optimizer.minimize()`, which performs parameters updating.
If the scaled gradients of parameters contains NAN or INF, the parameters updating is skipped.
Otherwise, if `unscale_()` has not been called, it first unscales the scaled gradients of parameters, then updates the parameters. | [
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This function is similar as `optimizer.minimize()`, which performs parameters updating.
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optimizer(Optimizer): The optimizer used to update parameters.
args: Arguments, which will be forward to `optimizer.minimize()`.
kwargs: Keyword arguments, which will be forward to `optimizer.minimize()`.
Examples:
.. code-block:: python
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True)
optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
data = paddle.rand([10, 3, 32, 32])
with paddle.amp.auto_cast():
conv = model(data)
loss = paddle.mean(conv)
scaled = scaler.scale(loss) # scale the loss
scaled.backward() # do backward
scaler.minimize(optimizer, scaled) # update parameters
optimizer.clear_grad()
"""
return super(GradScaler, self).minimize(optimizer, *args, **kwargs) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/third_party/jinja2/filters.py | python | do_title | (s) | return ''.join(
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Font.MakeSmaller | (*args, **kwargs) | return _gdi_.Font_MakeSmaller(*args, **kwargs) | MakeSmaller(self) -> Font | MakeSmaller(self) -> Font | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Netscape/Standard_Suite.py | python | Standard_Suite_Events.set | (self, _object, _attributes={}, **_arguments) | set: Set an object\xd5s data
Required argument: the object to change
Keyword argument to: the new value
Keyword argument _attributes: AppleEvent attribute dictionary | set: Set an object\xd5s data
Required argument: the object to change
Keyword argument to: the new value
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/parallel_for/control_flow_ops.py | python | _pfor_impl | (loop_fn, iters, parallel_iterations=None, pfor_config=None) | Implementation of pfor. | Implementation of pfor. | [
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"""Implementation of pfor."""
loop_fn_has_config = _loop_fn_has_config(loop_fn)
existing_ops = set(ops.get_default_graph().get_operations())
# Run the loop body
with ops.name_scope("loop_body"):
loop_var = array_ops.placeholder(dtypes.int32, shape=[])
if loop_fn_has_config:
if pfor_config is None:
pfor_config = PForConfig()
pfor_config._set_iters(iters) # pylint: disable=protected-access
loop_fn_outputs = loop_fn(loop_var, **{PFOR_CONFIG_ARG: pfor_config})
else:
assert pfor_config is None
loop_fn_outputs = loop_fn(loop_var)
# Convert outputs to Tensor if needed.
tmp_loop_fn_outputs = []
for loop_fn_output in nest.flatten(loop_fn_outputs):
if (loop_fn_output is not None and not isinstance(
loop_fn_output,
(ops.Operation, ops.Tensor, sparse_tensor.SparseTensor))):
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logging.warn("Converting %s to a dense representation may make it slow."
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loop_fn_output = ops.convert_to_tensor(loop_fn_output)
tmp_loop_fn_outputs.append(loop_fn_output)
loop_fn_outputs = nest.pack_sequence_as(loop_fn_outputs, tmp_loop_fn_outputs)
new_ops = set(ops.get_default_graph().get_operations()) - existing_ops
iters = ops.convert_to_tensor(iters)
if parallel_iterations is not None:
if parallel_iterations < 1:
raise ValueError("parallel_iterations must be None or a positive integer")
if parallel_iterations == 1:
raise ValueError("Found parallel_iterations == 1. Use for_loop instead.")
iters_value = tensor_util.constant_value(iters)
if iters_value is not None and iters_value < parallel_iterations:
parallel_iterations = None
if parallel_iterations is None:
with ops.name_scope("pfor"):
converter = PFor(loop_var, iters, new_ops, pfor_config=pfor_config)
outputs = []
for loop_fn_output in nest.flatten(loop_fn_outputs):
outputs.append(converter.convert(loop_fn_output))
return nest.pack_sequence_as(loop_fn_outputs, outputs)
else:
if pfor_config is not None and pfor_config._has_reductions(): # pylint: disable=protected-access
raise ValueError("Setting parallel_iterations currently unsupported if"
" reductions across iterations are performed.")
num_tiled_iterations = iters // parallel_iterations
num_remaining_iterations = iters % parallel_iterations
# TODO(agarwal): Avoid calling loop_fn twice. Generate the loop body inside
# a tf.function and extract the graph from there to vectorize it.
with ops.name_scope("pfor_untiled"):
converter = PFor(loop_var, num_remaining_iterations, new_ops,
pfor_config=pfor_config)
remaining_outputs = []
flattened_loop_fn_outputs = nest.flatten(loop_fn_outputs)
for loop_fn_output in flattened_loop_fn_outputs:
remaining_outputs.append(converter.convert(loop_fn_output))
with ops.name_scope("pfor_tiled"):
loop_fn_dtypes = [ops.convert_to_tensor(x).dtype
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def tiled_loop_body(j):
offset = j * parallel_iterations + num_remaining_iterations
def tiled_loop_fn(i, pfor_config=None):
if loop_fn_has_config:
return nest.flatten(loop_fn(i + offset, pfor_config=pfor_config))
else:
return nest.flatten(loop_fn(i + offset))
return _pfor_impl(
tiled_loop_fn, parallel_iterations, pfor_config=pfor_config)
tiled_outputs = for_loop(tiled_loop_body, loop_fn_dtypes,
num_tiled_iterations, parallel_iterations=1)
tiled_outputs = [_flatten_first_two_dims(y) for y in tiled_outputs]
with ops.name_scope("pfor"):
iters_value = tensor_util.constant_value(iters)
if iters_value is None or iters_value % parallel_iterations:
outputs = control_flow_ops.cond(
math_ops.equal(num_remaining_iterations, 0),
lambda: tiled_outputs,
lambda: [array_ops.concat([x, y], axis=0)
for x, y in zip(remaining_outputs, tiled_outputs)])
else:
outputs = tiled_outputs
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Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | veles/external/pydot.py | python | Graph.del_node | (self, name, index=None) | return False | Delete a node from the graph.
Given a node's name all node(s) with that same name
will be deleted if 'index' is not specified or set
to None.
If there are several nodes with that same name and
'index' is given, only the node in that position
will be deleted.
'index' should be an integer specifying the position
of the node to delete. If index is larger than the
number of nodes with that name, no action is taken.
If nodes are deleted it returns True. If no action
is taken it returns False. | Delete a node from the graph.
Given a node's name all node(s) with that same name
will be deleted if 'index' is not specified or set
to None.
If there are several nodes with that same name and
'index' is given, only the node in that position
will be deleted.
'index' should be an integer specifying the position
of the node to delete. If index is larger than the
number of nodes with that name, no action is taken.
If nodes are deleted it returns True. If no action
is taken it returns False. | [
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"""Delete a node from the graph.
Given a node's name all node(s) with that same name
will be deleted if 'index' is not specified or set
to None.
If there are several nodes with that same name and
'index' is given, only the node in that position
will be deleted.
'index' should be an integer specifying the position
of the node to delete. If index is larger than the
number of nodes with that name, no action is taken.
If nodes are deleted it returns True. If no action
is taken it returns False.
"""
if isinstance(name, Node):
name = name.get_name()
if name in self.obj_dict['nodes']:
if index is not None and index < len(self.obj_dict['nodes'][name]):
del self.obj_dict['nodes'][name][index]
return True
else:
del self.obj_dict['nodes'][name]
return True
return False | [
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | pynest/nest/spatial/hl_api_spatial.py | python | source_pos.n | (dimension) | return CreateParameter('position',
{'dimension': dimension, 'synaptic_endpoint': 1}) | Position of source node in given dimension.
Parameters
----------
dimension : int
Dimension in which to get the position.
Returns
-------
Parameter:
Object yielding the position in the given dimension. | Position of source node in given dimension. | [
"Position",
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] | def n(dimension):
"""
Position of source node in given dimension.
Parameters
----------
dimension : int
Dimension in which to get the position.
Returns
-------
Parameter:
Object yielding the position in the given dimension.
"""
return CreateParameter('position',
{'dimension': dimension, 'synaptic_endpoint': 1}) | [
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RLBot/RLBot | 34332b12cf158b3ef8dbf174ae67c53683368a9d | src/main/python/rlbot/setup_manager.py | python | setup_manager_context | (launcher_preference: RocketLeagueLauncherPreference = None) | Creates a initialized context manager which shuts down at the end of the
`with` block.
usage:
>>> with setup_manager_context() as setup_manager:
... setup_manager.load_config(...)
... # ... Run match | Creates a initialized context manager which shuts down at the end of the
`with` block. | [
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] | def setup_manager_context(launcher_preference: RocketLeagueLauncherPreference = None):
"""
Creates a initialized context manager which shuts down at the end of the
`with` block.
usage:
>>> with setup_manager_context() as setup_manager:
... setup_manager.load_config(...)
... # ... Run match
"""
setup_manager = SetupManager()
setup_manager.connect_to_game(launcher_preference)
try:
yield setup_manager
except Exception as e:
get_logger(DEFAULT_LOGGER).error(e)
raise e
finally:
setup_manager.shut_down(kill_all_pids=True) | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/mox.py | python | UnorderedGroup.MethodCalled | (self, mock_method) | Remove a method call from the group.
If the method is not in the set, an UnexpectedMethodCallError will be
raised.
Args:
mock_method: a mock method that should be equal to a method in the group.
Returns:
The mock method from the group
Raises:
UnexpectedMethodCallError if the mock_method was not in the group. | Remove a method call from the group. | [
"Remove",
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"group",
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] | def MethodCalled(self, mock_method):
"""Remove a method call from the group.
If the method is not in the set, an UnexpectedMethodCallError will be
raised.
Args:
mock_method: a mock method that should be equal to a method in the group.
Returns:
The mock method from the group
Raises:
UnexpectedMethodCallError if the mock_method was not in the group.
"""
# Check to see if this method exists, and if so, remove it from the set
# and return it.
for method in self._methods:
if method == mock_method:
# Remove the called mock_method instead of the method in the group.
# The called method will match any comparators when equality is checked
# during removal. The method in the group could pass a comparator to
# another comparator during the equality check.
self._methods.remove(mock_method)
# If this group is not empty, put it back at the head of the queue.
if not self.IsSatisfied():
mock_method._call_queue.appendleft(self)
return self, method
raise UnexpectedMethodCallError(mock_method, self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | distrib/propgrid/cpp_header_parser.py | python | class_obj.find_super_class | (self, classes_dict, any_of_these) | return None | \
Tries to find a specific class in one of the super classes.
classes_dict: dictionary of classes available (should include self).
any_of_these: One of these is acceptable name of super class. | \
Tries to find a specific class in one of the super classes. | [
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] | def find_super_class(self, classes_dict, any_of_these):
"""\
Tries to find a specific class in one of the super classes.
classes_dict: dictionary of classes available (should include self).
any_of_these: One of these is acceptable name of super class.
"""
for scls in self.base_classes:
if scls in any_of_these:
return scls
try:
return classes_dict[scls].find_super_class(classes_dict, any_of_these)
except KeyError:
pass
return None | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/meta_graph.py | python | import_scoped_meta_graph_with_return_elements | (
meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs",
restore_collections_predicate=(lambda key: True),
return_elements=None) | return var_list, imported_return_elements | Imports graph from `MetaGraphDef` and returns vars and return elements.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates the desired collections, and returns a dictionary of
all the Variables imported into the name scope.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
restore_collections_predicate: a predicate on collection names. A collection
named c (i.e whose key is c) will be restored iff
1) `restore_collections_predicate(c)` is True, and
2) `c != unbound_inputs_col_name`.
return_elements: A list of strings containing operation names in the
`MetaGraphDef` that will be returned as `Operation` objects; and/or
tensor names in `MetaGraphDef` that will be returned as `Tensor` objects.
Returns:
A tuple of (
dictionary of all the `Variables` imported into the name scope,
list of `Operation` or `Tensor` objects from the `return_elements` list).
Raises:
ValueError: If the graph_def contains unbound inputs. | Imports graph from `MetaGraphDef` and returns vars and return elements. | [
"Imports",
"graph",
"from",
"MetaGraphDef",
"and",
"returns",
"vars",
"and",
"return",
"elements",
"."
] | def import_scoped_meta_graph_with_return_elements(
meta_graph_or_file,
clear_devices=False,
graph=None,
import_scope=None,
input_map=None,
unbound_inputs_col_name="unbound_inputs",
restore_collections_predicate=(lambda key: True),
return_elements=None):
"""Imports graph from `MetaGraphDef` and returns vars and return elements.
This function takes a `MetaGraphDef` protocol buffer as input. If
the argument is a file containing a `MetaGraphDef` protocol buffer ,
it constructs a protocol buffer from the file content. The function
then adds all the nodes from the `graph_def` field to the
current graph, recreates the desired collections, and returns a dictionary of
all the Variables imported into the name scope.
In combination with `export_scoped_meta_graph()`, this function can be used to
* Serialize a graph along with other Python objects such as `QueueRunner`,
`Variable` into a `MetaGraphDef`.
* Restart training from a saved graph and checkpoints.
* Run inference from a saved graph and checkpoints.
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.
clear_devices: Boolean which controls whether to clear device information
from graph_def. Default false.
graph: The `Graph` to import into. If `None`, use the default graph.
import_scope: Optional `string`. Name scope into which to import the
subgraph. If `None`, the graph is imported to the root name scope.
input_map: A dictionary mapping input names (as strings) in `graph_def` to
`Tensor` objects. The values of the named input tensors in the imported
graph will be re-mapped to the respective `Tensor` values.
unbound_inputs_col_name: Collection name for looking up unbound inputs.
restore_collections_predicate: a predicate on collection names. A collection
named c (i.e whose key is c) will be restored iff
1) `restore_collections_predicate(c)` is True, and
2) `c != unbound_inputs_col_name`.
return_elements: A list of strings containing operation names in the
`MetaGraphDef` that will be returned as `Operation` objects; and/or
tensor names in `MetaGraphDef` that will be returned as `Tensor` objects.
Returns:
A tuple of (
dictionary of all the `Variables` imported into the name scope,
list of `Operation` or `Tensor` objects from the `return_elements` list).
Raises:
ValueError: If the graph_def contains unbound inputs.
"""
if context.executing_eagerly():
raise ValueError("Exporting/importing meta graphs is not supported when "
"eager execution is enabled.")
if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
meta_graph_def = meta_graph_or_file
else:
meta_graph_def = read_meta_graph_file(meta_graph_or_file)
if unbound_inputs_col_name:
for key, col_def in meta_graph_def.collection_def.items():
if key == unbound_inputs_col_name:
kind = col_def.WhichOneof("kind")
field = getattr(col_def, kind)
if field.value and (
not input_map or
sorted([compat.as_str(v) for v in field.value]) !=
sorted(input_map)):
raise ValueError("Graph contains unbound inputs: %s. Must "
"provide these inputs through input_map." %
",".join([compat.as_str(v) for v in field.value
if not input_map or v not in input_map]))
break
# Sets graph to default graph if it's not passed in.
graph = graph or ops.get_default_graph()
# Gathers the list of nodes we are interested in.
with graph.as_default():
producer_op_list = None
if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
input_graph_def = meta_graph_def.graph_def
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
scope_to_prepend_to_names = graph.unique_name(
import_scope or "", mark_as_used=False)
imported_return_elements = importer.import_graph_def(
input_graph_def,
name=(import_scope or scope_to_prepend_to_names),
input_map=input_map,
producer_op_list=producer_op_list,
return_elements=return_elements)
# TensorFlow versions before 1.9 (not inclusive) exported SavedModels
# without a VariableDef.trainable field set.
tf_version = meta_graph_def.meta_info_def.tensorflow_version
if not tf_version:
variables_have_trainable = True
else:
variables_have_trainable = (
distutils_version.LooseVersion(tf_version)
>= distutils_version.LooseVersion("1.9"))
# Sort collections so we see TRAINABLE_VARIABLES first and can default these
# variables to trainable if the value is not set in their VariableDef.
sorted_collections = []
if ops.GraphKeys.TRAINABLE_VARIABLES in meta_graph_def.collection_def:
sorted_collections.append(
(ops.GraphKeys.TRAINABLE_VARIABLES,
meta_graph_def.collection_def[ops.GraphKeys.TRAINABLE_VARIABLES]))
for key, value in sorted(meta_graph_def.collection_def.items()):
if key != ops.GraphKeys.TRAINABLE_VARIABLES:
sorted_collections.append((key, value))
# Restores all the other collections.
variable_objects = {}
for key, col_def in sorted_collections:
# Don't add unbound_inputs to the new graph.
if key == unbound_inputs_col_name:
continue
if not restore_collections_predicate(key):
continue
kind = col_def.WhichOneof("kind")
if kind is None:
logging.error("Cannot identify data type for collection %s. Skipping.",
key)
continue
from_proto = ops.get_from_proto_function(key)
# Temporary change to allow the TFMA evaluator to read metric variables
# saved as a bytes list.
# TODO(kathywu): Remove this hack once cl/248406059 has been submitted.
if key == ops.GraphKeys.METRIC_VARIABLES:
# Metric variables will use the same proto functions as GLOBAL_VARIABLES
from_proto = ops.get_from_proto_function(ops.GraphKeys.GLOBAL_VARIABLES)
if from_proto and kind == "bytes_list":
proto_type = ops.get_collection_proto_type(key)
if key in ops.GraphKeys._VARIABLE_COLLECTIONS: # pylint: disable=protected-access
for value in col_def.bytes_list.value:
variable = variable_objects.get(value, None)
if variable is None:
proto = proto_type()
proto.ParseFromString(value)
if not variables_have_trainable:
# If the VariableDef proto does not contain a "trainable"
# property because it was exported before that property was
# added, we default it to whether the variable is in the
# TRAINABLE_VARIABLES collection. We've sorted
# TRAINABLE_VARIABLES to be first, so trainable variables will
# be created from that collection.
proto.trainable = (key == ops.GraphKeys.TRAINABLE_VARIABLES)
variable = from_proto(
proto, import_scope=scope_to_prepend_to_names)
variable_objects[value] = variable
graph.add_to_collection(key, variable)
else:
for value in col_def.bytes_list.value:
proto = proto_type()
proto.ParseFromString(value)
graph.add_to_collection(
key, from_proto(
proto, import_scope=scope_to_prepend_to_names))
else:
field = getattr(col_def, kind)
if key in _COMPAT_COLLECTION_LIST:
logging.warning(
"The saved meta_graph is possibly from an older release:\n"
"'%s' collection should be of type 'byte_list', but instead "
"is of type '%s'.", key, kind)
if kind == "node_list":
for value in field.value:
col_op = graph.as_graph_element(
ops.prepend_name_scope(value, scope_to_prepend_to_names))
graph.add_to_collection(key, col_op)
elif kind == "int64_list":
# NOTE(opensource): This force conversion is to work around the fact
# that Python2 distinguishes between int and long, while Python3 has
# only int.
for value in field.value:
graph.add_to_collection(key, int(value))
else:
for value in field.value:
graph.add_to_collection(
key, ops.prepend_name_scope(value, scope_to_prepend_to_names))
var_list = {}
variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
scope=scope_to_prepend_to_names)
for v in variables:
var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v
return var_list, imported_return_elements | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/v8/tools/sanitizers/sancov_formatter.py | python | write_instrumented | (options) | Implements the 'all' action of this tool. | Implements the 'all' action of this tool. | [
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"""Implements the 'all' action of this tool."""
exe_list = list(executables(options.build_dir))
logging.info('Reading instrumented lines from %d executables.',
len(exe_list))
pool = Pool(CPUS)
try:
results = pool.imap_unordered(get_instrumented_lines, exe_list)
finally:
pool.close()
# Merge multiprocessing results and prepare output data.
data = merge_instrumented_line_results(exe_list, results)
logging.info('Read data from %d executables, which covers %d files.',
len(data['tests']), len(data['files']))
logging.info('Writing results to %s', options.json_output)
# Write json output.
with open(options.json_output, 'w') as f:
json.dump(data, f, sort_keys=True) | [
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zyq8709/DexHunter | 9d829a9f6f608ebad26923f29a294ae9c68d0441 | art/tools/cpplint.py | python | CheckPosixThreading | (filename, clean_lines, linenum, error) | Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for calls to thread-unsafe functions. | [
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"""Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
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they have learned posix before threading extensions were added. These
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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.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for single_thread_function, multithread_safe_function in threading_list:
ix = line.find(single_thread_function)
# Comparisons made explicit for clarity -- pylint: disable-msg=C6403
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'runtime/threadsafe_fn', 2,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pkg_resources/__init__.py | python | file_ns_handler | (importer, path_item, packageName, module) | Compute an ns-package subpath for a filesystem or zipfile importer | Compute an ns-package subpath for a filesystem or zipfile importer | [
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"""Compute an ns-package subpath for a filesystem or zipfile importer"""
subpath = os.path.join(path_item, packageName.split('.')[-1])
normalized = _normalize_cached(subpath)
for item in module.__path__:
if _normalize_cached(item) == normalized:
break
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# Only return the path if it's not already there
return subpath | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/asynchat.py | python | async_chat.writable | (self) | return self.producer_fifo or (not self.connected) | predicate for inclusion in the writable for select() | predicate for inclusion in the writable for select() | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/debug/cli/analyzer_cli.py | python | DebugAnalyzer.__init__ | (self, debug_dump, config) | DebugAnalyzer constructor.
Args:
debug_dump: A DebugDumpDir object.
config: A `cli_config.CLIConfig` object that carries user-facing
configurations. | DebugAnalyzer constructor. | [
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] | def __init__(self, debug_dump, config):
"""DebugAnalyzer constructor.
Args:
debug_dump: A DebugDumpDir object.
config: A `cli_config.CLIConfig` object that carries user-facing
configurations.
"""
self._debug_dump = debug_dump
self._evaluator = evaluator.ExpressionEvaluator(self._debug_dump)
# Initialize tensor filters state.
self._tensor_filters = {}
self._build_argument_parsers(config)
config.set_callback("graph_recursion_depth",
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/libs/metaparse/tools/benchmark/generate.py | python | Template.property | (self, name) | return self._get_line(in_comment(name + ':[ \t]*(.*)')) | Parses and returns a property | Parses and returns a property | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/xml/sax/xmlreader.py | python | InputSource.getSystemId | (self) | return self.__system_id | Returns the system identifier of this InputSource. | Returns the system identifier of this InputSource. | [
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