nwo stringlengths 5 86 | sha stringlengths 40 40 | path stringlengths 4 189 | language stringclasses 1 value | identifier stringlengths 1 94 | parameters stringlengths 2 4.03k | argument_list stringclasses 1 value | return_statement stringlengths 0 11.5k | docstring stringlengths 1 33.2k | docstring_summary stringlengths 0 5.15k | docstring_tokens list | function stringlengths 34 151k | function_tokens list | url stringlengths 90 278 |
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/IndirectAnnulusAbsorption2.py | python | IndirectAnnulusAbsorption.validateInputs | (self) | return issues | Validate algorithm options. | Validate algorithm options. | [
"Validate",
"algorithm",
"options",
"."
] | def validateInputs(self):
"""
Validate algorithm options.
"""
self._setup()
issues = dict()
if self._use_can_corrections and self._can_chemical_formula == '':
issues['CanChemicalFormula'] = 'Must be set to use can corrections'
if self._use_can_corrections and self._can_ws_name is None:
issues['UseCanCorrections'] = 'Must specify a can workspace to use can corrections'
# Geometry validation: can inner < sample inner < sample outer < can outer
if self._sample_outer_radius <= self._sample_inner_radius:
issues['SampleOuterRadius'] = 'Must be greater than SampleInnerRadius'
if self._can_ws_name is not None:
if self._sample_inner_radius <= self._can_inner_radius:
issues['SampleInnerRadius'] = 'Must be greater than CanInnerRadius'
if self._can_outer_radius <= self._sample_outer_radius:
issues['CanOuterRadius'] = 'Must be greater than SampleOuterRadius'
return issues | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/tex.py | python | InternalLaTeXAuxAction | (XXXLaTeXAction, target = None, source= None, env=None) | return result | A builder for LaTeX files that checks the output in the aux file
and decides how many times to use LaTeXAction, and BibTeXAction. | A builder for LaTeX files that checks the output in the aux file
and decides how many times to use LaTeXAction, and BibTeXAction. | [
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] | def InternalLaTeXAuxAction(XXXLaTeXAction, target = None, source= None, env=None):
"""A builder for LaTeX files that checks the output in the aux file
and decides how many times to use LaTeXAction, and BibTeXAction."""
global must_rerun_latex
# This routine is called with two actions. In this file for DVI builds
# with LaTeXAction and from the pdflatex.py with PDFLaTeXAction
# set this up now for the case where the user requests a different extension
# for the target filename
if (XXXLaTeXAction == LaTeXAction):
callerSuffix = ".dvi"
else:
callerSuffix = env['PDFSUFFIX']
basename = SCons.Util.splitext(str(source[0]))[0]
basedir = os.path.split(str(source[0]))[0]
basefile = os.path.split(str(basename))[1]
abspath = os.path.abspath(basedir)
targetext = os.path.splitext(str(target[0]))[1]
targetdir = os.path.split(str(target[0]))[0]
saved_env = {}
for var in SCons.Scanner.LaTeX.LaTeX.env_variables:
saved_env[var] = modify_env_var(env, var, abspath)
# Create base file names with the target directory since the auxiliary files
# will be made there. That's because the *COM variables have the cd
# command in the prolog. We check
# for the existence of files before opening them--even ones like the
# aux file that TeX always creates--to make it possible to write tests
# with stubs that don't necessarily generate all of the same files.
targetbase = os.path.join(targetdir, basefile)
# if there is a \makeindex there will be a .idx and thus
# we have to run makeindex at least once to keep the build
# happy even if there is no index.
# Same for glossaries, nomenclature, and acronyms
src_content = source[0].get_text_contents()
run_makeindex = makeindex_re.search(src_content) and not os.path.isfile(targetbase + '.idx')
run_nomenclature = makenomenclature_re.search(src_content) and not os.path.isfile(targetbase + '.nlo')
run_glossary = makeglossary_re.search(src_content) and not os.path.isfile(targetbase + '.glo')
run_glossaries = makeglossaries_re.search(src_content) and not os.path.isfile(targetbase + '.glo')
run_acronyms = makeacronyms_re.search(src_content) and not os.path.isfile(targetbase + '.acn')
saved_hashes = {}
suffix_nodes = {}
for suffix in all_suffixes+sum(newglossary_suffix, []):
theNode = env.fs.File(targetbase + suffix)
suffix_nodes[suffix] = theNode
saved_hashes[suffix] = theNode.get_csig()
if Verbose:
print "hashes: ",saved_hashes
must_rerun_latex = True
# .aux files already processed by BibTex
already_bibtexed = []
#
# routine to update MD5 hash and compare
#
def check_MD5(filenode, suffix):
global must_rerun_latex
# two calls to clear old csig
filenode.clear_memoized_values()
filenode.ninfo = filenode.new_ninfo()
new_md5 = filenode.get_csig()
if saved_hashes[suffix] == new_md5:
if Verbose:
print "file %s not changed" % (targetbase+suffix)
return False # unchanged
saved_hashes[suffix] = new_md5
must_rerun_latex = True
if Verbose:
print "file %s changed, rerunning Latex, new hash = " % (targetbase+suffix), new_md5
return True # changed
# generate the file name that latex will generate
resultfilename = targetbase + callerSuffix
count = 0
while (must_rerun_latex and count < int(env.subst('$LATEXRETRIES'))) :
result = XXXLaTeXAction(target, source, env)
if result != 0:
return result
count = count + 1
must_rerun_latex = False
# Decide if various things need to be run, or run again.
# Read the log file to find warnings/errors
logfilename = targetbase + '.log'
logContent = ''
if os.path.isfile(logfilename):
logContent = open(logfilename, "rb").read()
# Read the fls file to find all .aux files
flsfilename = targetbase + '.fls'
flsContent = ''
auxfiles = []
if os.path.isfile(flsfilename):
flsContent = open(flsfilename, "rb").read()
auxfiles = openout_aux_re.findall(flsContent)
# remove duplicates
dups = {}
for x in auxfiles:
dups[x] = 1
auxfiles = list(dups.keys())
bcffiles = []
if os.path.isfile(flsfilename):
flsContent = open(flsfilename, "rb").read()
bcffiles = openout_bcf_re.findall(flsContent)
# remove duplicates
dups = {}
for x in bcffiles:
dups[x] = 1
bcffiles = list(dups.keys())
if Verbose:
print "auxfiles ",auxfiles
print "bcffiles ",bcffiles
# Now decide if bibtex will need to be run.
# The information that bibtex reads from the .aux file is
# pass-independent. If we find (below) that the .bbl file is unchanged,
# then the last latex saw a correct bibliography.
# Therefore only do this once
# Go through all .aux files and remember the files already done.
for auxfilename in auxfiles:
if auxfilename not in already_bibtexed:
already_bibtexed.append(auxfilename)
target_aux = os.path.join(targetdir, auxfilename)
if os.path.isfile(target_aux):
content = open(target_aux, "rb").read()
if content.find("bibdata") != -1:
if Verbose:
print "Need to run bibtex on ",auxfilename
bibfile = env.fs.File(SCons.Util.splitext(target_aux)[0])
result = BibTeXAction(bibfile, bibfile, env)
if result != 0:
check_file_error_message(env['BIBTEX'], 'blg')
must_rerun_latex = True
# Now decide if biber will need to be run.
# When the backend for biblatex is biber (by choice or default) the
# citation information is put in the .bcf file.
# The information that biber reads from the .bcf file is
# pass-independent. If we find (below) that the .bbl file is unchanged,
# then the last latex saw a correct bibliography.
# Therefore only do this once
# Go through all .bcf files and remember the files already done.
for bcffilename in bcffiles:
if bcffilename not in already_bibtexed:
already_bibtexed.append(bcffilename)
target_bcf = os.path.join(targetdir, bcffilename)
if os.path.isfile(target_bcf):
content = open(target_bcf, "rb").read()
if content.find("bibdata") != -1:
if Verbose:
print "Need to run biber on ",bcffilename
bibfile = env.fs.File(SCons.Util.splitext(target_bcf)[0])
result = BiberAction(bibfile, bibfile, env)
if result != 0:
check_file_error_message(env['BIBER'], 'blg')
must_rerun_latex = True
# Now decide if latex will need to be run again due to index.
if check_MD5(suffix_nodes['.idx'],'.idx') or (count == 1 and run_makeindex):
# We must run makeindex
if Verbose:
print "Need to run makeindex"
idxfile = suffix_nodes['.idx']
result = MakeIndexAction(idxfile, idxfile, env)
if result != 0:
check_file_error_message(env['MAKEINDEX'], 'ilg')
return result
# TO-DO: need to add a way for the user to extend this list for whatever
# auxiliary files they create in other (or their own) packages
# Harder is case is where an action needs to be called -- that should be rare (I hope?)
for index in check_suffixes:
check_MD5(suffix_nodes[index],index)
# Now decide if latex will need to be run again due to nomenclature.
if check_MD5(suffix_nodes['.nlo'],'.nlo') or (count == 1 and run_nomenclature):
# We must run makeindex
if Verbose:
print "Need to run makeindex for nomenclature"
nclfile = suffix_nodes['.nlo']
result = MakeNclAction(nclfile, nclfile, env)
if result != 0:
check_file_error_message('%s (nomenclature)' % env['MAKENCL'],
'nlg')
#return result
# Now decide if latex will need to be run again due to glossary.
if check_MD5(suffix_nodes['.glo'],'.glo') or (count == 1 and run_glossaries) or (count == 1 and run_glossary):
# We must run makeindex
if Verbose:
print "Need to run makeindex for glossary"
glofile = suffix_nodes['.glo']
result = MakeGlossaryAction(glofile, glofile, env)
if result != 0:
check_file_error_message('%s (glossary)' % env['MAKEGLOSSARY'],
'glg')
#return result
# Now decide if latex will need to be run again due to acronyms.
if check_MD5(suffix_nodes['.acn'],'.acn') or (count == 1 and run_acronyms):
# We must run makeindex
if Verbose:
print "Need to run makeindex for acronyms"
acrfile = suffix_nodes['.acn']
result = MakeAcronymsAction(acrfile, acrfile, env)
if result != 0:
check_file_error_message('%s (acronyms)' % env['MAKEACRONYMS'],
'alg')
return result
# Now decide if latex will need to be run again due to newglossary command.
for ig in range(len(newglossary_suffix)):
if check_MD5(suffix_nodes[newglossary_suffix[ig][2]],newglossary_suffix[ig][2]) or (count == 1):
# We must run makeindex
if Verbose:
print "Need to run makeindex for newglossary"
newglfile = suffix_nodes[newglossary_suffix[ig][2]]
MakeNewGlossaryAction = SCons.Action.Action("$MAKENEWGLOSSARYCOM ${SOURCE.filebase}%s -s ${SOURCE.filebase}.ist -t ${SOURCE.filebase}%s -o ${SOURCE.filebase}%s" % (newglossary_suffix[ig][2],newglossary_suffix[ig][0],newglossary_suffix[ig][1]), "$MAKENEWGLOSSARYCOMSTR")
result = MakeNewGlossaryAction(newglfile, newglfile, env)
if result != 0:
check_file_error_message('%s (newglossary)' % env['MAKENEWGLOSSARY'],
newglossary_suffix[ig][0])
return result
# Now decide if latex needs to be run yet again to resolve warnings.
if warning_rerun_re.search(logContent):
must_rerun_latex = True
if Verbose:
print "rerun Latex due to latex or package rerun warning"
if rerun_citations_re.search(logContent):
must_rerun_latex = True
if Verbose:
print "rerun Latex due to 'Rerun to get citations correct' warning"
if undefined_references_re.search(logContent):
must_rerun_latex = True
if Verbose:
print "rerun Latex due to undefined references or citations"
if (count >= int(env.subst('$LATEXRETRIES')) and must_rerun_latex):
print "reached max number of retries on Latex ,",int(env.subst('$LATEXRETRIES'))
# end of while loop
# rename Latex's output to what the target name is
if not (str(target[0]) == resultfilename and os.path.isfile(resultfilename)):
if os.path.isfile(resultfilename):
print "move %s to %s" % (resultfilename, str(target[0]), )
shutil.move(resultfilename,str(target[0]))
# Original comment (when TEXPICTS was not restored):
# The TEXPICTS enviroment variable is needed by a dvi -> pdf step
# later on Mac OSX so leave it
#
# It is also used when searching for pictures (implicit dependencies).
# Why not set the variable again in the respective builder instead
# of leaving local modifications in the environment? What if multiple
# latex builds in different directories need different TEXPICTS?
for var in SCons.Scanner.LaTeX.LaTeX.env_variables:
if var == 'TEXPICTS':
continue
if saved_env[var] is _null:
try:
del env['ENV'][var]
except KeyError:
pass # was never set
else:
env['ENV'][var] = saved_env[var]
return result | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/serve/push/search/util/search_schema_parser.py | python | SearchSchemaParser.__StartElement | (self, elem) | Start element handler.
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/suitesconfig.py | python | _get_suite_config | (suite_name_or_path) | return SuiteFinder.get_config_obj(suite_name_or_path) | Attempt to read YAML configuration from 'suite_path' for the suite. | Attempt to read YAML configuration from 'suite_path' for the suite. | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/git.py | python | Repository.get_merge_base | (self, commit) | return self._callgito("merge-base", ["HEAD", commit]).rstrip() | Get the merge base between 'commit' and HEAD. | Get the merge base between 'commit' and HEAD. | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/maximal-rectangle.py | python | Solution2.maximalRectangle | (self, matrix) | return result | :type matrix: List[List[str]]
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/ReflectometryILLSumForeground.py | python | ReflectometryILLSumForeground._directOnly | (self) | return self.getProperty(Prop.DIRECT_FOREGROUND_WS).isDefault | Return true if only the direct beam should be processed. | Return true if only the direct beam should be processed. | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/saving/saved_model/utils.py | python | get_training_arg_index | (call_fn) | Returns the index of 'training' in the layer call function arguments.
Args:
call_fn: Call function.
Returns:
- n: index of 'training' in the call function arguments.
- -1: if 'training' is not found in the arguments, but layer.call accepts
variable keyword arguments
- None: if layer doesn't expect a training argument. | Returns the index of 'training' in the layer call function arguments. | [
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"""Returns the index of 'training' in the layer call function arguments.
Args:
call_fn: Call function.
Returns:
- n: index of 'training' in the call function arguments.
- -1: if 'training' is not found in the arguments, but layer.call accepts
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- None: if layer doesn't expect a training argument.
"""
arg_list = tf_inspect.getfullargspec(call_fn).args
if tf_inspect.ismethod(call_fn):
arg_list = arg_list[1:]
if 'training' in arg_list:
return arg_list.index('training')
else:
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/examples/adding_an_op/zero_out_grad_2.py | python | _zero_out_grad | (op, grad) | return [to_zero_grad] | The gradients for `zero_out`.
Args:
op: The `zero_out` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `zero_out` op.
Returns:
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"""The gradients for `zero_out`.
Args:
op: The `zero_out` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `zero_out` op.
Returns:
Gradients with respect to the input of `zero_out`.
"""
to_zero = op.inputs[0]
shape = array_ops.shape(to_zero)
index = array_ops.zeros_like(shape)
first_grad = array_ops.reshape(grad, [-1])[0]
to_zero_grad = sparse_ops.sparse_to_dense([index], shape, first_grad, 0)
return [to_zero_grad] | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/mac_tool.py | python | MacTool._DetectInputEncoding | (self, file_name) | Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it. | Reads the first few bytes from file_name and tries to guess the text
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"""Reads the first few bytes from file_name and tries to guess the text
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with open(file_name, "rb") as fp:
try:
header = fp.read(3)
except Exception:
return None
if header.startswith(b"\xFE\xFF"):
return "UTF-16"
elif header.startswith(b"\xFF\xFE"):
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiTabArt.SetNormalFont | (*args, **kwargs) | return _aui.AuiTabArt_SetNormalFont(*args, **kwargs) | SetNormalFont(self, Font font) | SetNormalFont(self, Font font) | [
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OPAE/opae-sdk | 221124343c8275243a249eb72d69e0ea2d568d1b | python/opae.admin/opae/admin/tools/fpgasupdate.py | python | do_partial_reconf | (addr, filename) | return (0, output + '\nPartial Reconfiguration OK') | Call out to fpgaconf for Partial Reconfiguration.
addr - the canonical ssss:bb:dd.f of the device to PR.
filename - the GBS file path.
returns a 2-tuple of the process exit status and a message. | Call out to fpgaconf for Partial Reconfiguration. | [
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"""Call out to fpgaconf for Partial Reconfiguration.
addr - the canonical ssss:bb:dd.f of the device to PR.
filename - the GBS file path.
returns a 2-tuple of the process exit status and a message.
"""
conf_args = ['fpgaconf', '--segment', '0x' + addr[:4],
'--bus', '0x' + addr[5:7],
'--device', '0x' + addr[8:10],
'--function', '0x' + addr[11:], filename]
LOG.debug('command: %s', ' '.join(conf_args))
try:
output = subprocess.check_output(conf_args, stderr=subprocess.STDOUT)
output = output.decode(sys.getdefaultencoding())
except subprocess.CalledProcessError as exc:
return (exc.returncode,
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return (0, output + '\nPartial Reconfiguration OK') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/stc.py | python | StyledTextCtrl.StyleSetEOLFilled | (*args, **kwargs) | return _stc.StyledTextCtrl_StyleSetEOLFilled(*args, **kwargs) | StyleSetEOLFilled(self, int style, bool filled)
Set a style to have its end of line filled or not. | StyleSetEOLFilled(self, int style, bool filled) | [
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"""
StyleSetEOLFilled(self, int style, bool filled)
Set a style to have its end of line filled or not.
"""
return _stc.StyledTextCtrl_StyleSetEOLFilled(*args, **kwargs) | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | CalibMuon/DTCalibration/python/Workflow/DTT0WireWorkflow.py | python | DTT0WireWorkflow.prepare_workflow | (self) | Generalized function to prepare workflow dependent on workflow mode | Generalized function to prepare workflow dependent on workflow mode | [
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function_name = "prepare_" + self.options.workflow_mode + "_" + self.options.command
try:
fill_function = getattr(self, function_name)
except AttributeError:
errmsg = "Class `{}` does not implement `{}`"
raise NotImplementedError(errmsg.format(my_cls.__class__.__name__,
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log.debug("Preparing workflow with function %s" % function_name)
# call chosen function
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/_grad_ops.py | python | ConcatOffset.__init__ | (self, N=2, axis=0) | Initialize ConcatOffset | Initialize ConcatOffset | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/turtle.py | python | TPen.color | (self, *args) | Return or set the pencolor and fillcolor.
Arguments:
Several input formats are allowed.
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color()
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>>> turtle.color()
('red', 'green')
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>>> color((40, 80, 120), (160, 200, 240))
>>> color()
('#285078', '#a0c8f0') | Return or set the pencolor and fillcolor. | [
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color()
Return the current pencolor and the current fillcolor
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>>> turtle.color()
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>>> color()
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"""
if args:
l = len(args)
if l == 1:
pcolor = fcolor = args[0]
elif l == 2:
pcolor, fcolor = args
elif l == 3:
pcolor = fcolor = args
pcolor = self._colorstr(pcolor)
fcolor = self._colorstr(fcolor)
self.pen(pencolor=pcolor, fillcolor=fcolor)
else:
return self._color(self._pencolor), self._color(self._fillcolor) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/android_platform/development/scripts/stack_core.py | python | PreProcessLog.__init__ | (self, load_vaddrs) | Bind load_vaddrs to the PreProcessLog closure.
Args:
load_vaddrs: LOAD segment min_vaddrs keyed on mapped executable | Bind load_vaddrs to the PreProcessLog closure.
Args:
load_vaddrs: LOAD segment min_vaddrs keyed on mapped executable | [
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Args:
load_vaddrs: LOAD segment min_vaddrs keyed on mapped executable
"""
self._load_vaddrs = load_vaddrs;
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self._shared_libraries_mapping = dict()
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self._so_dirs = [] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/Paste/paste/proxy.py | python | parse_headers | (message) | return headers_out | Turn a Message object into a list of WSGI-style headers. | Turn a Message object into a list of WSGI-style headers. | [
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] | def parse_headers(message):
"""
Turn a Message object into a list of WSGI-style headers.
"""
headers_out = []
if six.PY3:
for header, value in message.items():
if header.lower() not in filtered_headers:
headers_out.append((header, value))
else:
for full_header in message.headers:
if not full_header:
# Shouldn't happen, but we'll just ignore
continue
if full_header[0].isspace():
# Continuation line, add to the last header
if not headers_out:
raise ValueError(
"First header starts with a space (%r)" % full_header)
last_header, last_value = headers_out.pop()
value = last_value + ' ' + full_header.strip()
headers_out.append((last_header, value))
continue
try:
header, value = full_header.split(':', 1)
except:
raise ValueError("Invalid header: %r" % full_header)
value = value.strip()
if header.lower() not in filtered_headers:
headers_out.append((header, value))
return headers_out | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/losses/util.py | python | get_regularization_losses | (scope=None) | return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope) | Gets the list of regularization losses.
Args:
scope: An optional scope name for filtering the losses to return.
Returns:
A list of regularization losses as Tensors. | Gets the list of regularization losses. | [
"Gets",
"the",
"list",
"of",
"regularization",
"losses",
"."
] | def get_regularization_losses(scope=None):
"""Gets the list of regularization losses.
Args:
scope: An optional scope name for filtering the losses to return.
Returns:
A list of regularization losses as Tensors.
"""
return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/engine/node.py | python | Node.iterate_inbound | (self) | Yields tuples representing the data inbound from other nodes.
Yields:
tuples like: (inbound_layer, node_index, tensor_index, tensor). | Yields tuples representing the data inbound from other nodes. | [
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] | def iterate_inbound(self):
"""Yields tuples representing the data inbound from other nodes.
Yields:
tuples like: (inbound_layer, node_index, tensor_index, tensor).
"""
for kt in self.keras_inputs:
keras_history = kt._keras_history
layer = keras_history.layer
node_index = keras_history.node_index
tensor_index = keras_history.tensor_index
yield layer, node_index, tensor_index, kt | [
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rodeofx/OpenWalter | 6116fbe3f04f1146c854afbfbdbe944feaee647e | walter/maya/scripts/walterVariantsMenu.py | python | VariantsMenu._createMenu | (self, primPath, index, variantSet) | return VariantSetMenu(primPath, variantSet, self) | Implementation of BaseVariantsMenu. | Implementation of BaseVariantsMenu. | [
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] | def _createMenu(self, primPath, index, variantSet):
"""Implementation of BaseVariantsMenu."""
return VariantSetMenu(primPath, variantSet, self) | [
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generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | deprecated/algorithms/sfm/OpenSfM/opensfm/commands/match_features.py | python | match_candidates_by_order | (images, max_neighbors) | return pairs | Find candidate matching pairs by sequence order. | Find candidate matching pairs by sequence order. | [
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] | def match_candidates_by_order(images, max_neighbors):
"""Find candidate matching pairs by sequence order."""
if max_neighbors <= 0:
return set()
n = (max_neighbors + 1) // 2
pairs = set()
for i, image in enumerate(images):
a = max(0, i - n)
b = min(len(images), i + n)
for j in range(a, b):
if i != j:
pairs.add(tuple(sorted((images[i], images[j]))))
return pairs | [
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uncrustify/uncrustify | 719cf9d153a9ec14be8d5f01536121ced71d8bc9 | scripts/option_reducer.py | python | write_config_file2 | (args) | Writes two option lists into a config file
Parameters
----------------------------------------------------------------------------
:param args: list< tuple< str, str > >,
list< tuple< str, str > >, str, int
this function is intended to be called by multiprocessing.pool.map()
therefore all arguments are inside a list / tuple:
config_list: list< tuple< str, str > >
the first list of tuples containing option names and values
test_list: list< tuple< str, str > >
the second list of tuples containing option names and values
tmp_dir: str
path to a directory in which the config file is going to be
written
idx: int
index that is going to be used for the filename | Writes two option lists into a config file | [
"Writes",
"two",
"option",
"lists",
"into",
"a",
"config",
"file"
] | def write_config_file2(args):
"""
Writes two option lists into a config file
Parameters
----------------------------------------------------------------------------
:param args: list< tuple< str, str > >,
list< tuple< str, str > >, str, int
this function is intended to be called by multiprocessing.pool.map()
therefore all arguments are inside a list / tuple:
config_list: list< tuple< str, str > >
the first list of tuples containing option names and values
test_list: list< tuple< str, str > >
the second list of tuples containing option names and values
tmp_dir: str
path to a directory in which the config file is going to be
written
idx: int
index that is going to be used for the filename
"""
config_list0, config_list1, tmp_dir, idx = args
with open("%s%suncr-r-%d.cfg" % (tmp_dir, os_path_sep, idx), 'w') as f:
print_config(config_list0, target_file_obj=f)
print("", end='\n', file=f)
print_config(config_list1, target_file_obj=f) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathDressupHoldingTags.py | python | Create | (baseObject, name="DressupTag") | return obj | Create(basePath, name='DressupTag') ... create tag dressup object for the given base path. | Create(basePath, name='DressupTag') ... create tag dressup object for the given base path. | [
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] | def Create(baseObject, name="DressupTag"):
"""
Create(basePath, name='DressupTag') ... create tag dressup object for the given base path.
"""
if not baseObject.isDerivedFrom("Path::Feature"):
PathLog.error(
translate("Path_DressupTag", "The selected object is not a path") + "\n"
)
return None
if baseObject.isDerivedFrom("Path::FeatureCompoundPython"):
PathLog.error(translate("Path_DressupTag", "Please select a Profile object"))
return None
obj = FreeCAD.ActiveDocument.addObject("Path::FeaturePython", name)
dbo = ObjectTagDressup(obj, baseObject)
job = PathUtils.findParentJob(baseObject)
job.Proxy.addOperation(obj, baseObject)
dbo.setup(obj, True)
return obj | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/RunDescriptor.py | python | RunList.get_last_ind2sum | (self,sum_runs) | return ind | Get last run number contributing to sum | Get last run number contributing to sum | [
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"last",
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"number",
"contributing",
"to",
"sum"
] | def get_last_ind2sum(self,sum_runs):
"""Get last run number contributing to sum"""
if self._last_ind2sum >= 0 and self._last_ind2sum < len(self._run_numbers):
ind = self._last_ind2sum
else:
if sum_runs:
ind = len(self._run_numbers) - 1
else:
ind = 0
return ind | [
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pskun/finance_news_analysis | 6ac13e32deede37a4cf57bba8b2897941ae3d80d | utils/file_operator.py | python | FileMultiReadWrite.read | (self) | return line | 从文件中读 | 从文件中读 | [
"从文件中读"
] | def read(self):
''' 从文件中读 '''
self.lock()
line = self.file.readline()
if line == "":
line = None
self.unlock()
return line | [
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telefonicaid/fiware-orion | 27c3202b9ddcfb9e3635a0af8d373f76e89b1d24 | scripts/cpplint.py | python | _ClassState.CheckFinished | (self, filename, error) | Checks that all classes have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found. | Checks that all classes have been completely parsed. | [
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] | def CheckFinished(self, filename, error):
"""Checks that all classes have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
if self.classinfo_stack:
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
error(filename, self.classinfo_stack[0].linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
self.classinfo_stack[0].name) | [
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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/input.py | python | GetIncludedBuildFiles | (build_file_path, aux_data, included=None) | return included | Return a list of all build files included into build_file_path.
The returned list will contain build_file_path as well as all other files
that it included, either directly or indirectly. Note that the list may
contain files that were included into a conditional section that evaluated
to false and was not merged into build_file_path's dict.
aux_data is a dict containing a key for each build file or included build
file. Those keys provide access to dicts whose "included" keys contain
lists of all other files included by the build file.
included should be left at its default None value by external callers. It
is used for recursion.
The returned list will not contain any duplicate entries. Each build file
in the list will be relative to the current directory. | Return a list of all build files included into build_file_path. | [
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"list",
"of",
"all",
"build",
"files",
"included",
"into",
"build_file_path",
"."
] | def GetIncludedBuildFiles(build_file_path, aux_data, included=None):
"""Return a list of all build files included into build_file_path.
The returned list will contain build_file_path as well as all other files
that it included, either directly or indirectly. Note that the list may
contain files that were included into a conditional section that evaluated
to false and was not merged into build_file_path's dict.
aux_data is a dict containing a key for each build file or included build
file. Those keys provide access to dicts whose "included" keys contain
lists of all other files included by the build file.
included should be left at its default None value by external callers. It
is used for recursion.
The returned list will not contain any duplicate entries. Each build file
in the list will be relative to the current directory.
"""
if included == None:
included = []
if build_file_path in included:
return included
included.append(build_file_path)
for included_build_file in aux_data[build_file_path].get('included', []):
GetIncludedBuildFiles(included_build_file, aux_data, included)
return included | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/coverage/annotate.py | python | AnnotateReporter.report | (self, morfs, config, directory=None) | Run the report.
See `coverage.report()` for arguments. | Run the report. | [
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] | def report(self, morfs, config, directory=None):
"""Run the report.
See `coverage.report()` for arguments.
"""
self.report_files(self.annotate_file, morfs, config, directory) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/appstats.py | python | OutputBeautifier.__PrintHeaderHelper | (self,
snapshot,
show_labels,
show_timestamp,
show_mem,
show_net,
show_trailer) | return ' '.join(titles) | Helper method to concat various header entries together into one header.
This will line up with a entry built by __PrintStatsHelper if the same
values are passed to it. | Helper method to concat various header entries together into one header.
This will line up with a entry built by __PrintStatsHelper if the same
values are passed to it. | [
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titles = []
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titles.append(self.__PrintNetworkStatsHeader())
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titles.append(self.__PrintTrailingHeader(snapshot))
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apple/swift | 469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893 | utils/build_swift/build_swift/presets.py | python | PresetParser.presets | (self) | return self._presets.values() | Returns a list of all parsed presets in the order they were parsed. | Returns a list of all parsed presets in the order they were parsed. | [
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"""Returns a list of all parsed presets in the order they were parsed.
"""
return self._presets.values() | [
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hcdth011/ROS-Hydro-SLAM | 629448eecd2c9a3511158115fa53ea9e4ae41359 | rpg_vikit/vikit_py/src/vikit_py/transformations.py | python | projection_matrix | (point, normal, direction=None,
perspective=None, pseudo=False) | return M | Return matrix to project onto plane defined by point and normal.
Using either perspective point, projection direction, or none of both.
If pseudo is True, perspective projections will preserve relative depth
such that Perspective = dot(Orthogonal, PseudoPerspective).
>>> P = projection_matrix((0, 0, 0), (1, 0, 0))
>>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
True
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> P1 = projection_matrix(point, normal, direction=direct)
>>> P2 = projection_matrix(point, normal, perspective=persp)
>>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> is_same_transform(P2, numpy.dot(P0, P3))
True
>>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
>>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(P, v0)
>>> numpy.allclose(v1[1], v0[1])
True
>>> numpy.allclose(v1[0], 3.0-v1[1])
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perspective=None, pseudo=False):
"""Return matrix to project onto plane defined by point and normal.
Using either perspective point, projection direction, or none of both.
If pseudo is True, perspective projections will preserve relative depth
such that Perspective = dot(Orthogonal, PseudoPerspective).
>>> P = projection_matrix((0, 0, 0), (1, 0, 0))
>>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:])
True
>>> point = numpy.random.random(3) - 0.5
>>> normal = numpy.random.random(3) - 0.5
>>> direct = numpy.random.random(3) - 0.5
>>> persp = numpy.random.random(3) - 0.5
>>> P0 = projection_matrix(point, normal)
>>> P1 = projection_matrix(point, normal, direction=direct)
>>> P2 = projection_matrix(point, normal, perspective=persp)
>>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True)
>>> is_same_transform(P2, numpy.dot(P0, P3))
True
>>> P = projection_matrix((3, 0, 0), (1, 1, 0), (1, 0, 0))
>>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(P, v0)
>>> numpy.allclose(v1[1], v0[1])
True
>>> numpy.allclose(v1[0], 3.0-v1[1])
True
"""
M = numpy.identity(4)
point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
normal = unit_vector(normal[:3])
if perspective is not None:
# perspective projection
perspective = numpy.array(perspective[:3], dtype=numpy.float64,
copy=False)
M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal)
M[:3, :3] -= numpy.outer(perspective, normal)
if pseudo:
# preserve relative depth
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * (perspective+normal)
else:
M[:3, 3] = numpy.dot(point, normal) * perspective
M[3, :3] = -normal
M[3, 3] = numpy.dot(perspective, normal)
elif direction is not None:
# parallel projection
direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
scale = numpy.dot(direction, normal)
M[:3, :3] -= numpy.outer(direction, normal) / scale
M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
else:
# orthogonal projection
M[:3, :3] -= numpy.outer(normal, normal)
M[:3, 3] = numpy.dot(point, normal) * normal
return M | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/idl/idl_check_compatibility.py | python | get_field_type | (field: Union[syntax.Field, syntax.Command], idl_file: syntax.IDLParsedSpec,
idl_file_path: str) | return field_type | Resolve and get field type of a field from the IDL file. | Resolve and get field type of a field from the IDL file. | [
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idl_file_path: str) -> Optional[Union[syntax.Enum, syntax.Struct, syntax.Type]]:
"""Resolve and get field type of a field from the IDL file."""
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field_type = idl_file.spec.symbols.resolve_field_type(parser_ctxt, field, field.name,
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib2to3/pgen2/parse.py | python | Parser.classify | (self, type, value, context) | return ilabel | Turn a token into a label. (Internal) | Turn a token into a label. (Internal) | [
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if type == token.NAME:
# Keep a listing of all used names
self.used_names.add(value)
# Check for reserved words
ilabel = self.grammar.keywords.get(value)
if ilabel is not None:
return ilabel
ilabel = self.grammar.tokens.get(type)
if ilabel is None:
raise ParseError("bad token", type, value, context)
return ilabel | [
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | src/arch/micro_asm.py | python | t_ANY_COMMENT | (t) | r'\#[^\n]*(?=\n) | r'\#[^\n]*(?=\n) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | TextAttrBorder.SetStyle | (*args, **kwargs) | return _richtext.TextAttrBorder_SetStyle(*args, **kwargs) | SetStyle(self, int style) | SetStyle(self, int style) | [
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scribusproject/scribus | 41ec7c775a060912cf251682a8b1437f753f80f4 | scribus/plugins/scriptplugin/scripts/FontSample.py | python | create_empty_samplepage | (pageNum, getSizeOnly) | return pageNum | Creates a new page and increments page number by one.
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"""Creates a new page and increments page number by one.
Note getSizeOnly is now evaluated. Will still generate page number increment
but will not actually create the new page or place the number on the page."""
if not getSizeOnly:
scribus.newPage(-1)
pageNum = pageNum + 1
set_odd_even(pageNum)
if not getSizeOnly:
if userPrefs['wantPageNumbers']:
add_page_num(pageNum)
return pageNum | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/asyncio/base_events.py | python | BaseEventLoop.set_task_factory | (self, factory) | Set a task factory that will be used by loop.create_task().
If factory is None the default task factory will be set.
If factory is a callable, it should have a signature matching
'(loop, coro)', where 'loop' will be a reference to the active
event loop, 'coro' will be a coroutine object. The callable
must return a Future. | Set a task factory that will be used by loop.create_task(). | [
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"""Set a task factory that will be used by loop.create_task().
If factory is None the default task factory will be set.
If factory is a callable, it should have a signature matching
'(loop, coro)', where 'loop' will be a reference to the active
event loop, 'coro' will be a coroutine object. The callable
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"""
if factory is not None and not callable(factory):
raise TypeError('task factory must be a callable or None')
self._task_factory = factory | [
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apache/kudu | 90895ce76590f10730ad7aac3613b69d89ff5422 | build-support/check_compatibility.py | python | get_git_hash | (revname) | return check_output(["git", "rev-parse", revname],
cwd=get_repo_dir()).decode('utf-8').strip() | Convert 'revname' to its SHA-1 hash. | Convert 'revname' to its SHA-1 hash. | [
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] | def get_git_hash(revname):
""" Convert 'revname' to its SHA-1 hash. """
return check_output(["git", "rev-parse", revname],
cwd=get_repo_dir()).decode('utf-8').strip() | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | samples/networking/01-simple-connection/client.py | python | GameClientRepository.gotCreateReady | (self) | Ready to enter the world. Expand our interest to include
any other zones | Ready to enter the world. Expand our interest to include
any other zones | [
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] | def gotCreateReady(self):
""" Ready to enter the world. Expand our interest to include
any other zones """
# This method checks whether we actually have a valid doID range
# to create distributed objects yet
if not self.haveCreateAuthority():
# Not ready yet.
return
# we are ready now, so ignore further createReady events
self.ignore(self.uniqueName('createReady'))
print("Client Ready")
base.messenger.send("client-ready") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_windows.py | python | TopLevelWindow.GetDefaultSize | (*args, **kwargs) | return _windows_.TopLevelWindow_GetDefaultSize(*args, **kwargs) | GetDefaultSize() -> Size | GetDefaultSize() -> Size | [
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"""GetDefaultSize() -> Size"""
return _windows_.TopLevelWindow_GetDefaultSize(*args, **kwargs) | [
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bayandin/chromedriver | d40a2092b50f2fca817221eeb5ea093e0e642c10 | util.py | python | MaybeDelete | (path) | Deletes the given file or directory (recurisvely), if it exists. | Deletes the given file or directory (recurisvely), if it exists. | [
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] | def MaybeDelete(path):
"""Deletes the given file or directory (recurisvely), if it exists."""
if os.path.exists(path):
Delete(path) | [
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | ParseArguments | (args) | return filenames | Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint. | Parses the command line arguments. | [
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] | def ParseArguments(args):
"""Parses the command line arguments.
This may set the output format and verbosity level as side-effects.
Args:
args: The command line arguments:
Returns:
The list of filenames to lint.
"""
try:
(opts, filenames) = getopt.getopt(args, '', ['help', 'output=', 'verbose=',
'counting=',
'filter=',
'root='])
except getopt.GetoptError:
PrintUsage('Invalid arguments.')
verbosity = _VerboseLevel()
output_format = _OutputFormat()
filters = ''
counting_style = ''
for (opt, val) in opts:
if opt == '--help':
PrintUsage(None)
elif opt == '--output':
if not val in ('emacs', 'vs7', 'eclipse'):
PrintUsage('The only allowed output formats are emacs, vs7 and eclipse.')
output_format = val
elif opt == '--verbose':
verbosity = int(val)
elif opt == '--filter':
filters = val
if not filters:
PrintCategories()
elif opt == '--counting':
if val not in ('total', 'toplevel', 'detailed'):
PrintUsage('Valid counting options are total, toplevel, and detailed')
counting_style = val
elif opt == '--root':
global _root
_root = val
if not filenames:
PrintUsage('No files were specified.')
_SetOutputFormat(output_format)
_SetVerboseLevel(verbosity)
_SetFilters(filters)
_SetCountingStyle(counting_style)
return filenames | [
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yyzybb537/libgo | 4af17b7c67643c4d54aa354dcc77963ea07847d0 | third_party/boost.context/tools/build/src/build/type.py | python | set_scanner | (type, scanner) | Sets a scanner class that will be used for this 'type'. | Sets a scanner class that will be used for this 'type'. | [
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"that",
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"."
] | def set_scanner (type, scanner):
""" Sets a scanner class that will be used for this 'type'.
"""
if __debug__:
from .scanner import Scanner
assert isinstance(type, basestring)
assert issubclass(scanner, Scanner)
validate (type)
__types [type]['scanner'] = scanner | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/msw/gizmos.py | python | TreeListColumnInfo.IsShown | (*args, **kwargs) | return _gizmos.TreeListColumnInfo_IsShown(*args, **kwargs) | IsShown(self) -> bool | IsShown(self) -> bool | [
"IsShown",
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] | def IsShown(*args, **kwargs):
"""IsShown(self) -> bool"""
return _gizmos.TreeListColumnInfo_IsShown(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | PrintDialogData.IsOk | (*args, **kwargs) | return _windows_.PrintDialogData_IsOk(*args, **kwargs) | IsOk(self) -> bool | IsOk(self) -> bool | [
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"""IsOk(self) -> bool"""
return _windows_.PrintDialogData_IsOk(*args, **kwargs) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/core/android_action_runner.py | python | AndroidActionRunner.TurnScreenOff | (self) | If device screen is on, turn screen off.
If the screen is already off, log a warning and return immediately.
Raises:
Timeout: If the screen is on and device fails to turn screen off. | If device screen is on, turn screen off.
If the screen is already off, log a warning and return immediately. | [
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] | def TurnScreenOff(self):
"""If device screen is on, turn screen off.
If the screen is already off, log a warning and return immediately.
Raises:
Timeout: If the screen is on and device fails to turn screen off.
"""
def is_screen_off():
return not self._platform_backend.device.IsScreenOn()
self._platform_backend.device.SetScreen(False)
util.WaitFor(is_screen_off, 5) | [
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osrf/gazebo | f570338107862253229a0514ffea10deab4f4517 | tools/cpplint.py | python | _ClassState.CheckFinished | (self, filename, error) | Checks that all classes have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found. | Checks that all classes have been completely parsed. | [
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] | def CheckFinished(self, filename, error):
"""Checks that all classes have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
if self.classinfo_stack:
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
error(filename, self.classinfo_stack[0].linenum, 'build/class', 5,
'Failed to find complete declaration of class %s' %
self.classinfo_stack[0].name) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/customtreectrl.py | python | CustomTreeCtrl.OnMouse | (self, event) | Handles a bunch of ``wx.EVT_MOUSE_EVENTS`` events for :class:`CustomTreeCtrl`.
:param `event`: a :class:`MouseEvent` event to be processed. | Handles a bunch of ``wx.EVT_MOUSE_EVENTS`` events for :class:`CustomTreeCtrl`. | [
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"""
Handles a bunch of ``wx.EVT_MOUSE_EVENTS`` events for :class:`CustomTreeCtrl`.
:param `event`: a :class:`MouseEvent` event to be processed.
"""
if not self._anchor:
return
pt = self.CalcUnscrolledPosition(event.GetPosition())
# Is the mouse over a tree item button?
flags = 0
thisItem, flags = self._anchor.HitTest(pt, self, flags, 0)
underMouse = thisItem
underMouseChanged = underMouse != self._underMouse
if underMouse and (flags & TREE_HITTEST_ONITEM) and not event.LeftIsDown() and \
not self._isDragging and (not self._editTimer or not self._editTimer.IsRunning()):
underMouse = underMouse
else:
underMouse = None
if underMouse != self._underMouse:
if self._underMouse:
# unhighlight old item
self._underMouse = None
self._underMouse = underMouse
# Determines what item we are hovering over and need a tooltip for
hoverItem = thisItem
# We do not want a tooltip if we are dragging, or if the edit timer is running
if underMouseChanged and not self._isDragging and (not self._editTimer or not self._editTimer.IsRunning()):
if hoverItem is not None:
# Ask the tree control what tooltip (if any) should be shown
hevent = TreeEvent(wxEVT_TREE_ITEM_GETTOOLTIP, self.GetId())
hevent._item = hoverItem
hevent.SetEventObject(self)
if self.GetEventHandler().ProcessEvent(hevent) and hevent.IsAllowed():
self.SetToolTip(hevent._label)
elif self.HasAGWFlag(TR_TOOLTIP_ON_LONG_ITEMS):
tip = self.GetToolTipString()
if hoverItem.IsSeparator():
if tip:
self.SetToolTipString('')
else:
maxsize = self.GetItemSize(hoverItem)
itemText = hoverItem.GetText()
dc = wx.ClientDC(self)
if dc.GetMultiLineTextExtent(itemText)[0] > maxsize:
if tip != itemText:
self.SetToolTipString(itemText)
else:
if tip:
self.SetToolTipString('')
if hoverItem.IsHyperText() and (flags & TREE_HITTEST_ONITEMLABEL) and hoverItem.IsEnabled():
self.SetCursor(wx.StockCursor(wx.CURSOR_HAND))
self._isonhyperlink = True
else:
if self._isonhyperlink:
self.SetCursor(wx.StockCursor(wx.CURSOR_ARROW))
self._isonhyperlink = False
# we process left mouse up event (enables in-place edit), right down
# (pass to the user code), left dbl click (activate item) and
# dragging/moving events for items drag-and-drop
if not (event.LeftDown() or event.LeftUp() or event.RightDown() or event.LeftDClick() or \
event.Dragging() or ((event.Moving() or event.RightUp()) and self._isDragging)):
event.Skip()
return
flags = 0
item, flags = self._anchor.HitTest(pt, self, flags, 0)
if event.Dragging() and not self._isDragging and ((flags & TREE_HITTEST_ONITEMICON) or (flags & TREE_HITTEST_ONITEMLABEL)):
if self._dragCount == 0:
self._dragStart = pt
self._countDrag = 0
self._dragCount = self._dragCount + 1
if self._dragCount != 3:
# wait until user drags a bit further...
return
command = (event.RightIsDown() and [wxEVT_TREE_BEGIN_RDRAG] or [wxEVT_TREE_BEGIN_DRAG])[0]
nevent = TreeEvent(command, self.GetId())
nevent._item = self._current
nevent.SetEventObject(self)
newpt = self.CalcScrolledPosition(pt)
nevent.SetPoint(newpt)
# by default the dragging is not supported, the user code must
# explicitly allow the event for it to take place
nevent.Veto()
if self.GetEventHandler().ProcessEvent(nevent) and nevent.IsAllowed():
# we're going to drag this item
self._isDragging = True
# remember the old cursor because we will change it while
# dragging
self._oldCursor = self._cursor
# in a single selection control, hide the selection temporarily
if not (self.GetAGWWindowStyleFlag() & TR_MULTIPLE):
self._oldSelection = self.GetSelection()
if self._oldSelection:
self._oldSelection.SetHilight(False)
self.RefreshLine(self._oldSelection)
else:
selections = self.GetSelections()
if len(selections) == 1:
self._oldSelection = selections[0]
self._oldSelection.SetHilight(False)
self.RefreshLine(self._oldSelection)
if self._dragImage:
del self._dragImage
# Create the custom draw image from the icons and the text of the item
self._dragImage = DragImage(self, self._current)
self._dragImage.BeginDrag(wx.Point(0,0), self)
self._dragImage.Show()
self._dragImage.Move(self.CalcScrolledPosition(pt))
elif event.Dragging() and self._isDragging:
self._dragImage.Move(self.CalcScrolledPosition(pt))
if self._countDrag == 0 and item:
self._oldItem = item
if item != self._dropTarget:
# unhighlight the previous drop target
if self._dropTarget:
self._dropTarget.SetHilight(False)
self.RefreshLine(self._dropTarget)
if item:
item.SetHilight(True)
self.RefreshLine(item)
self._countDrag = self._countDrag + 1
self._dropTarget = item
self.Update()
if self._countDrag >= 3:
# Here I am trying to avoid ugly repainting problems... hope it works
self.RefreshLine(self._oldItem)
self._countDrag = 0
elif (event.LeftUp() or event.RightUp()) and self._isDragging:
if self._dragImage:
self._dragImage.EndDrag()
if self._dropTarget:
self._dropTarget.SetHilight(False)
if self._oldSelection:
self._oldSelection.SetHilight(True)
self.RefreshLine(self._oldSelection)
self._oldSelection = None
# generate the drag end event
event = TreeEvent(wxEVT_TREE_END_DRAG, self.GetId())
event._item = item
event._pointDrag = self.CalcScrolledPosition(pt)
event.SetEventObject(self)
self.GetEventHandler().ProcessEvent(event)
self._isDragging = False
self._dropTarget = None
self.SetCursor(self._oldCursor)
if wx.Platform in ["__WXMSW__", "__WXMAC__"]:
self.Refresh()
else:
# Probably this is not enough on GTK. Try a Refresh() if it does not work.
wx.YieldIfNeeded()
else:
# If we got to this point, we are not dragging or moving the mouse.
# Because the code in carbon/toplevel.cpp will only set focus to the tree
# if we skip for EVT_LEFT_DOWN, we MUST skip this event here for focus to work.
# We skip even if we didn't hit an item because we still should
# restore focus to the tree control even if we didn't exactly hit an item.
if event.LeftDown():
self._hasFocus = True
self.SetFocusIgnoringChildren()
event.Skip()
# here we process only the messages which happen on tree items
self._dragCount = 0
if item == None:
if self._editCtrl != None and item != self._editCtrl.item():
self._editCtrl.StopEditing()
return # we hit the blank area
if event.RightDown():
if self._editCtrl != None and item != self._editCtrl.item():
self._editCtrl.StopEditing()
self._hasFocus = True
self.SetFocusIgnoringChildren()
# If the item is already selected, do not update the selection.
# Multi-selections should not be cleared if a selected item is clicked.
if not self.IsSelected(item):
self.DoSelectItem(item, True, False)
nevent = TreeEvent(wxEVT_TREE_ITEM_RIGHT_CLICK, self.GetId())
nevent._item = item
nevent._pointDrag = self.CalcScrolledPosition(pt)
nevent.SetEventObject(self)
event.Skip(not self.GetEventHandler().ProcessEvent(nevent))
# Consistent with MSW (for now), send the ITEM_MENU *after*
# the RIGHT_CLICK event. TODO: This behaviour may change.
nevent2 = TreeEvent(wxEVT_TREE_ITEM_MENU, self.GetId())
nevent2._item = item
nevent2._pointDrag = self.CalcScrolledPosition(pt)
nevent2.SetEventObject(self)
self.GetEventHandler().ProcessEvent(nevent2)
elif event.LeftUp():
# this facilitates multiple-item drag-and-drop
if self.HasAGWFlag(TR_MULTIPLE):
selections = self.GetSelections()
if len(selections) > 1 and not event.CmdDown() and not event.ShiftDown():
self.DoSelectItem(item, True, False)
if self._lastOnSame:
if item == self._current and (flags & TREE_HITTEST_ONITEMLABEL) and self.HasAGWFlag(TR_EDIT_LABELS):
if self._editTimer:
if self._editTimer.IsRunning():
self._editTimer.Stop()
else:
self._editTimer = TreeEditTimer(self)
self._editTimer.Start(_DELAY, True)
self._lastOnSame = False
else: # !RightDown() && !LeftUp() ==> LeftDown() || LeftDClick()
if not item or not item.IsEnabled():
if self._editCtrl != None and item != self._editCtrl.item():
self._editCtrl.StopEditing()
return
if self._editCtrl != None and item != self._editCtrl.item():
self._editCtrl.StopEditing()
self._hasFocus = True
self.SetFocusIgnoringChildren()
if event.LeftDown():
self._lastOnSame = item == self._current
if flags & TREE_HITTEST_ONITEMBUTTON:
# only toggle the item for a single click, double click on
# the button doesn't do anything (it toggles the item twice)
if event.LeftDown():
self.Toggle(item)
# don't select the item if the button was clicked
return
if item.GetType() > 0 and (flags & TREE_HITTEST_ONITEMCHECKICON) and (flags & TREE_HITTEST_ONITEMLABEL == 0):
if event.LeftDown():
if flags & TREE_HITTEST_ONITEM and self.HasAGWFlag(TR_FULL_ROW_HIGHLIGHT):
self.DoSelectItem(item, not self.HasAGWFlag(TR_MULTIPLE))
if self.IsItem3State(item):
checked = self.GetItem3StateValue(item)
checked = (checked+1)%3
else:
checked = not self.IsItemChecked(item)
self.CheckItem(item, checked)
return
# clear the previously selected items, if the
# user clicked outside of the present selection.
# otherwise, perform the deselection on mouse-up.
# this allows multiple drag and drop to work.
# but if Cmd is down, toggle selection of the clicked item
if not self.IsSelected(item) or event.CmdDown():
if flags & TREE_HITTEST_ONITEM:
# how should the selection work for this event?
if item.IsHyperText():
self.SetItemVisited(item, True)
is_multiple, extended_select, unselect_others = EventFlagsToSelType(self.GetAGWWindowStyleFlag(),
event.ShiftDown(),
event.CmdDown())
self.DoSelectItem(item, unselect_others, extended_select)
# Handle hyperlink items... which are a bit odd sometimes
elif self.IsSelected(item) and item.IsHyperText():
self.HandleHyperLink(item)
# For some reason, Windows isn't recognizing a left double-click,
# so we need to simulate it here. Allow 200 milliseconds for now.
if event.LeftDClick():
# double clicking should not start editing the item label
if self._editTimer:
self._editTimer.Stop()
self._lastOnSame = False
# send activate event first
nevent = TreeEvent(wxEVT_TREE_ITEM_ACTIVATED, self.GetId())
nevent._item = item
nevent._pointDrag = self.CalcScrolledPosition(pt)
nevent.SetEventObject(self)
if not self.GetEventHandler().ProcessEvent(nevent):
# if the user code didn't process the activate event,
# handle it ourselves by toggling the item when it is
# double clicked
## if item.HasPlus():
self.Toggle(item) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/pkg_resources/__init__.py | python | ResourceManager.extraction_error | (self) | Give an error message for problems extracting file(s) | Give an error message for problems extracting file(s) | [
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"""Give an error message for problems extracting file(s)"""
old_exc = sys.exc_info()[1]
cache_path = self.extraction_path or get_default_cache()
tmpl = textwrap.dedent("""
Can't extract file(s) to egg cache
The following error occurred while trying to extract file(s)
to the Python egg cache:
{old_exc}
The Python egg cache directory is currently set to:
{cache_path}
Perhaps your account does not have write access to this directory?
You can change the cache directory by setting the PYTHON_EGG_CACHE
environment variable to point to an accessible directory.
""").lstrip()
err = ExtractionError(tmpl.format(**locals()))
err.manager = self
err.cache_path = cache_path
err.original_error = old_exc
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mingchen/protobuf-ios | 0958df34558cd54cb7b6e6ca5c8855bf3d475046 | compiler/python/mox.py | python | And.__init__ | (self, *args) | Initialize.
Args:
*args: One or more Comparator | Initialize. | [
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"""Initialize.
Args:
*args: One or more Comparator
"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | TextAreaBase.GetStyle | (*args, **kwargs) | return _core_.TextAreaBase_GetStyle(*args, **kwargs) | GetStyle(self, long position, wxTextAttr style) -> bool | GetStyle(self, long position, wxTextAttr style) -> bool | [
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adventuregamestudio/ags | efa89736d868e9dfda4200149d33ba8637746399 | Common/libsrc/freetype-2.1.3/src/tools/docmaker/tohtml.py | python | HtmlFormatter.make_html_para | ( self, words ) | return "<p>" + line + "</p>" | convert a paragraph's words into tagged HTML text, handle xrefs | convert a paragraph's words into tagged HTML text, handle xrefs | [
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""" convert a paragraph's words into tagged HTML text, handle xrefs """
line = ""
if words:
line = self.make_html_word( words[0] )
for word in words[1:]:
line = line + " " + self.make_html_word( word )
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/html.py | python | HtmlHelpController.ReadCustomization | (*args, **kwargs) | return _html.HtmlHelpController_ReadCustomization(*args, **kwargs) | ReadCustomization(self, ConfigBase cfg, String path=EmptyString) | ReadCustomization(self, ConfigBase cfg, String path=EmptyString) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/multiprocessing/connection.py | python | arbitrary_address | (family) | Return an arbitrary free address for the given family | Return an arbitrary free address for the given family | [
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'''
Return an arbitrary free address for the given family
'''
if family == 'AF_INET':
return ('localhost', 0)
elif family == 'AF_UNIX':
# Prefer abstract sockets if possible to avoid problems with the address
# size. When coding portable applications, some implementations have
# sun_path as short as 92 bytes in the sockaddr_un struct.
if util.abstract_sockets_supported:
return f"\0listener-{os.getpid()}-{next(_mmap_counter)}"
return tempfile.mktemp(prefix='listener-', dir=util.get_temp_dir())
elif family == 'AF_PIPE':
return tempfile.mktemp(prefix=r'\\.\pipe\pyc-%d-%d-' %
(os.getpid(), next(_mmap_counter)), dir="")
else:
raise ValueError('unrecognized family') | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/turtle.py | python | _turtle_docrevise | (docstr) | return newdocstr | To reduce docstrings from RawTurtle class for functions | To reduce docstrings from RawTurtle class for functions | [
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"from",
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"for",
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] | def _turtle_docrevise(docstr):
"""To reduce docstrings from RawTurtle class for functions
"""
import re
if docstr is None:
return None
turtlename = _CFG["exampleturtle"]
newdocstr = docstr.replace("%s." % turtlename,"")
parexp = re.compile(r' \(.+ %s\):' % turtlename)
newdocstr = parexp.sub(":", newdocstr)
return newdocstr | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftfunctions/svgtext.py | python | _get_text_techdraw | (text, tcolor, fontsize, anchor,
align, fontname, angle, base,
linespacing) | return svg | Return the SVG representation of text for TechDraw display.
`text` is a list of textual elements; they are iterated, styled,
and added around a `<text>` tag.
::
<text ...> text[0] </text>
<text ...> text[1] </text> | Return the SVG representation of text for TechDraw display. | [
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align, fontname, angle, base,
linespacing):
"""Return the SVG representation of text for TechDraw display.
`text` is a list of textual elements; they are iterated, styled,
and added around a `<text>` tag.
::
<text ...> text[0] </text>
<text ...> text[1] </text>
"""
svg = ""
for i in range(len(text)):
_t = text[i].replace("&", "&")
_t = _t.replace("<", "<")
t = _t.replace(">", ">")
# TODO: remove when Python 2 is no longer supported
if six.PY2 and not isinstance(t, six.text_type):
t = t.decode("utf8")
# possible workaround if UTF8 is unsupported
# import unicodedata as U
# v = list()
# for c in U.normalize("NFKD", t):
# if not U.combining(c):
# v.append(c)
#
# t = u"".join(v)
# t = t.encode("utf8")
svg += '<text '
svg += 'stroke-width="0" stroke="{}" '.format(tcolor)
svg += 'fill="{}" font-size="{}" '.format(tcolor, fontsize)
svg += 'style="text-anchor:{};text-align:{};'.format(anchor,
align.lower())
svg += 'font-family:{}" '.format(fontname)
svg += 'transform="'
svg += 'rotate({},{},{}) '.format(math.degrees(angle),
base.x,
base.y - i * linespacing)
svg += 'translate({},{}) '.format(base.x,
base.y - i * linespacing)
svg += 'scale(1,-1)"'
# svg += 'freecad:skip="1"'
svg += '>\n'
svg += t
svg += '</text>\n'
return svg | [
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apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | Gather.forward | (self, x) | return singa.ConcatOn(xs, self.axis) | forward of Gather
Args:
x (CTensor): input tensor.
Returns:
the output CTensor. | forward of Gather
Args:
x (CTensor): input tensor.
Returns:
the output CTensor. | [
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"""
forward of Gather
Args:
x (CTensor): input tensor.
Returns:
the output CTensor.
"""
self.x_shape = list(x.shape())
self.axis = self.axis % len(self.x_shape) # handle the negative value
_shape = self.x_shape[self.axis]
xs = []
for indice in self.indices:
# each indice is a sub-indice
if isinstance(indice, (tuple, list, np.ndarray)):
sub_xs = []
for idx in indice:
idx = int(idx % _shape)
tmp_tensor = singa.SliceOn(x, idx, idx + 1, self.axis)
sub_xs.append(tmp_tensor)
sub_xs = singa.VecTensor(sub_xs)
tmp_tensor = singa.ConcatOn(sub_xs, self.axis)
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_slice_shape.insert(self.axis, 1) # add a new axis to concat
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indice = int(indice % _shape)
tmp_tensor = singa.SliceOn(x, indice, indice + 1, self.axis)
xs.append(tmp_tensor)
xs = singa.VecTensor(xs)
return singa.ConcatOn(xs, self.axis) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/ipaddress.py | python | _BaseV6._string_from_ip_int | (cls, ip_int=None) | return ':'.join(hextets) | Turns a 128-bit integer into hexadecimal notation.
Args:
ip_int: An integer, the IP address.
Returns:
A string, the hexadecimal representation of the address.
Raises:
ValueError: The address is bigger than 128 bits of all ones. | Turns a 128-bit integer into hexadecimal notation. | [
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] | def _string_from_ip_int(cls, ip_int=None):
"""Turns a 128-bit integer into hexadecimal notation.
Args:
ip_int: An integer, the IP address.
Returns:
A string, the hexadecimal representation of the address.
Raises:
ValueError: The address is bigger than 128 bits of all ones.
"""
if ip_int is None:
ip_int = int(cls._ip)
if ip_int > cls._ALL_ONES:
raise ValueError('IPv6 address is too large')
hex_str = '%032x' % ip_int
hextets = ['%x' % int(hex_str[x:x+4], 16) for x in range(0, 32, 4)]
hextets = cls._compress_hextets(hextets)
return ':'.join(hextets) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | HyperlinkCtrl.GetVisitedColour | (*args, **kwargs) | return _controls_.HyperlinkCtrl_GetVisitedColour(*args, **kwargs) | GetVisitedColour(self) -> Colour | GetVisitedColour(self) -> Colour | [
"GetVisitedColour",
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] | def GetVisitedColour(*args, **kwargs):
"""GetVisitedColour(self) -> Colour"""
return _controls_.HyperlinkCtrl_GetVisitedColour(*args, **kwargs) | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/ninja.py | python | Define | (d, flavor) | return QuoteShellArgument(ninja_syntax.escape("-D" + d), flavor) | Takes a preprocessor define and returns a -D parameter that's ninja- and
shell-escaped. | Takes a preprocessor define and returns a -D parameter that's ninja- and
shell-escaped. | [
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] | def Define(d, flavor):
"""Takes a preprocessor define and returns a -D parameter that's ninja- and
shell-escaped."""
if flavor == "win":
# cl.exe replaces literal # characters with = in preprocessor definitions for
# some reason. Octal-encode to work around that.
d = d.replace("#", "\\%03o" % ord("#"))
return QuoteShellArgument(ninja_syntax.escape("-D" + d), flavor) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | AboutDialogInfo.GetIcon | (*args, **kwargs) | return _misc_.AboutDialogInfo_GetIcon(*args, **kwargs) | GetIcon(self) -> Icon
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"""
GetIcon(self) -> Icon
Return the current icon value.
"""
return _misc_.AboutDialogInfo_GetIcon(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Position.__eq__ | (*args, **kwargs) | return _core_.Position___eq__(*args, **kwargs) | __eq__(self, PyObject other) -> bool
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dmlc/xgboost | 2775c2a1abd4b5b759ff517617434c8b9aeb4cc0 | python-package/xgboost/core.py | python | _ProxyDMatrix._set_data_from_csr | (self, csr) | Set data from scipy csr | Set data from scipy csr | [
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self.handle,
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epiqc/ScaffCC | 66a79944ee4cd116b27bc1a69137276885461db8 | llvm/utils/benchmark/tools/strip_asm.py | python | process_identifiers | (l) | return new_line | process_identifiers - process all identifiers and modify them to have
consistent names across all platforms; specifically across ELF and MachO.
For example, MachO inserts an additional understore at the beginning of
names. This function removes that. | process_identifiers - process all identifiers and modify them to have
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netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/utils/group/cubic.py | python | Fd3m | () | return (_inv_group().change_origin([1 / 8, 1 / 8, 1 / 8]) @ Td()).replace(
unit_cell=np.asarray([[0, 0.5, 0.5], [0.5, 0, 0.5], [0.5, 0.5, 0]])
) | Nonsymmorphic "point group" of the diamond and pyrochlore lattices with a
cubic unit cell of side length 1. | Nonsymmorphic "point group" of the diamond and pyrochlore lattices with a
cubic unit cell of side length 1. | [
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"""Nonsymmorphic "point group" of the diamond and pyrochlore lattices with a
cubic unit cell of side length 1."""
return (_inv_group().change_origin([1 / 8, 1 / 8, 1 / 8]) @ Td()).replace(
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olliw42/storm32bgc | 99d62a6130ae2950514022f50eb669c45a8cc1ba | old/betacopter/old/betacopter36dev-v003/modules/uavcan/libuavcan/dsdl_compiler/pyuavcan/uavcan/dsdl/parser.py | python | parse_namespaces | (source_dirs, search_dirs=None) | return output_types | Use only this function to parse DSDL definitions.
This function takes a list of root namespace directories (containing DSDL definition files to parse) and an
optional list of search directories (containing DSDL definition files that can be referenced from the types
that are going to be parsed).
Returns the list of parsed type definitions, where type of each element is CompoundType.
Args:
source_dirs List of root namespace directories to parse.
search_dirs List of root namespace directories with referenced types (optional). This list is
automaitcally extended with source_dirs.
Example:
>>> import uavcan
>>> a = uavcan.dsdl.parse_namespaces(['../dsdl/uavcan'])
>>> len(a)
77
>>> a[0]
uavcan.Timestamp
>>> a[0].fields
[truncated uint48 husec]
>>> a[0].constants
[saturated uint48 UNKNOWN = 0, saturated uint48 USEC_PER_LSB = 100] | Use only this function to parse DSDL definitions.
This function takes a list of root namespace directories (containing DSDL definition files to parse) and an
optional list of search directories (containing DSDL definition files that can be referenced from the types
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'''
Use only this function to parse DSDL definitions.
This function takes a list of root namespace directories (containing DSDL definition files to parse) and an
optional list of search directories (containing DSDL definition files that can be referenced from the types
that are going to be parsed).
Returns the list of parsed type definitions, where type of each element is CompoundType.
Args:
source_dirs List of root namespace directories to parse.
search_dirs List of root namespace directories with referenced types (optional). This list is
automaitcally extended with source_dirs.
Example:
>>> import uavcan
>>> a = uavcan.dsdl.parse_namespaces(['../dsdl/uavcan'])
>>> len(a)
77
>>> a[0]
uavcan.Timestamp
>>> a[0].fields
[truncated uint48 husec]
>>> a[0].constants
[saturated uint48 UNKNOWN = 0, saturated uint48 USEC_PER_LSB = 100]
'''
def walk():
import fnmatch
from functools import partial
def on_walk_error(directory, ex):
raise DsdlException('OS error in [%s]: %s' % (directory, str(ex)))
for source_dir in source_dirs:
walker = os.walk(source_dir, onerror=partial(on_walk_error, source_dir), followlinks=True)
for root, _dirnames, filenames in walker:
for filename in fnmatch.filter(filenames, '*.uavcan'):
filename = os.path.join(root, filename)
yield filename
all_default_dtid = {} # (kind, dtid) : filename
def ensure_unique_dtid(t, filename):
if t.default_dtid is None:
return
key = t.kind, t.default_dtid
if key in all_default_dtid:
first = pretty_filename(all_default_dtid[key])
second = pretty_filename(filename)
error('Default data type ID collision: [%s] [%s]', first, second)
all_default_dtid[key] = filename
parser = Parser(source_dirs + (search_dirs or []))
output_types = []
for filename in walk():
t = parser.parse(filename)
ensure_unique_dtid(t, filename)
output_types.append(t)
return output_types | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | TreeCtrl.AssignImageList | (*args, **kwargs) | return _controls_.TreeCtrl_AssignImageList(*args, **kwargs) | AssignImageList(self, ImageList imageList) | AssignImageList(self, ImageList imageList) | [
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"""AssignImageList(self, ImageList imageList)"""
return _controls_.TreeCtrl_AssignImageList(*args, **kwargs) | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0917-Reverse-Only-Letters/0917.py | python | Solution.reverseOnlyLetters | (self, S) | return ''.join([i if not i.isalpha() else p.pop() for i in S]) | :type S: str
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:rtype: str | [
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"""
:type S: str
:rtype: str
"""
if S == '':
return ''
p = [i for i in S if i.isalpha()]
return ''.join([i if not i.isalpha() else p.pop() for i in S]) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/tools/api/generator/create_python_api.py | python | _ModuleInitCodeBuilder.format_import | (self, source_module_name, source_name, dest_name) | Formats import statement.
Args:
source_module_name: (string) Source module to import from.
source_name: (string) Source symbol name to import.
dest_name: (string) Destination alias name.
Returns:
An import statement string. | Formats import statement. | [
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] | def format_import(self, source_module_name, source_name, dest_name):
"""Formats import statement.
Args:
source_module_name: (string) Source module to import from.
source_name: (string) Source symbol name to import.
dest_name: (string) Destination alias name.
Returns:
An import statement string.
"""
if self._lazy_loading:
return " '%s': ('%s', '%s')," % (dest_name, source_module_name,
source_name)
else:
if source_module_name:
if source_name == dest_name:
return 'from %s import %s' % (source_module_name, source_name)
else:
return 'from %s import %s as %s' % (source_module_name, source_name,
dest_name)
else:
if source_name == dest_name:
return 'import %s' % source_name
else:
return 'import %s as %s' % (source_name, dest_name) | [
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gv22ga/dlib-face-recognition-android | 42d6305cbd85833f2b85bb79b70ab9ab004153c9 | tools/lint/cpplint.py | python | CheckSectionSpacing | (filename, clean_lines, class_info, linenum, error) | Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found. | [
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
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# - We are forward-declaring an inner class that is semantically
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prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
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if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/metrics_impl.py | python | mean_squared_error | (labels,
predictions,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None) | return mean(squared_error, weights, metrics_collections, updates_collections,
name or 'mean_squared_error') | Computes the mean squared error between the labels and predictions.
The `mean_squared_error` function creates two local variables,
`total` and `count` that are used to compute the mean squared error.
This average is weighted by `weights`, and it is ultimately returned as
`mean_squared_error`: an idempotent operation that simply divides `total` by
`count`.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`mean_squared_error`. Internally, a `squared_error` operation computes the
element-wise square of the difference between `predictions` and `labels`. Then
`update_op` increments `total` with the reduced sum of the product of
`weights` and `squared_error`, and it increments `count` with the reduced sum
of `weights`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` of the same shape as `predictions`.
predictions: A `Tensor` of arbitrary shape.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that
`mean_squared_error` should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
mean_squared_error: A `Tensor` representing the current mean, the value of
`total` divided by `count`.
update_op: An operation that increments the `total` and `count` variables
appropriately and whose value matches `mean_squared_error`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
RuntimeError: If eager execution is enabled. | Computes the mean squared error between the labels and predictions. | [
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] | def mean_squared_error(labels,
predictions,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes the mean squared error between the labels and predictions.
The `mean_squared_error` function creates two local variables,
`total` and `count` that are used to compute the mean squared error.
This average is weighted by `weights`, and it is ultimately returned as
`mean_squared_error`: an idempotent operation that simply divides `total` by
`count`.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`mean_squared_error`. Internally, a `squared_error` operation computes the
element-wise square of the difference between `predictions` and `labels`. Then
`update_op` increments `total` with the reduced sum of the product of
`weights` and `squared_error`, and it increments `count` with the reduced sum
of `weights`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` of the same shape as `predictions`.
predictions: A `Tensor` of arbitrary shape.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that
`mean_squared_error` should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
mean_squared_error: A `Tensor` representing the current mean, the value of
`total` divided by `count`.
update_op: An operation that increments the `total` and `count` variables
appropriately and whose value matches `mean_squared_error`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
RuntimeError: If eager execution is enabled.
"""
if context.executing_eagerly():
raise RuntimeError('tf.metrics.mean_squared_error is not supported when '
'eager execution is enabled.')
predictions, labels, weights = _remove_squeezable_dimensions(
predictions=predictions, labels=labels, weights=weights)
squared_error = math_ops.squared_difference(labels, predictions)
return mean(squared_error, weights, metrics_collections, updates_collections,
name or 'mean_squared_error') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | demo/agw/AUI.py | python | ProgressGauge.GetPath | (self, gc, rc, r) | return path | Returns a rounded GraphicsPath. | Returns a rounded GraphicsPath. | [
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] | def GetPath(self, gc, rc, r):
""" Returns a rounded GraphicsPath. """
x, y, w, h = rc
path = gc.CreatePath()
path.AddRoundedRectangle(x, y, w, h, r)
path.CloseSubpath()
return path | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/_vendor/appdirs.py | python | _get_win_folder_from_registry | (csidl_name) | return dir | This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
names. | This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
names. | [
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] | def _get_win_folder_from_registry(csidl_name):
"""This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
names.
"""
if PY3:
import winreg as _winreg
else:
import _winreg
shell_folder_name = {
"CSIDL_APPDATA": "AppData",
"CSIDL_COMMON_APPDATA": "Common AppData",
"CSIDL_LOCAL_APPDATA": "Local AppData",
}[csidl_name]
key = _winreg.OpenKey(
_winreg.HKEY_CURRENT_USER,
r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders"
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dir, type = _winreg.QueryValueEx(key, shell_folder_name)
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PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | Tools/mavlink_px4.py | python | MAVLink.set_gps_global_origin_encode | (self, target_system, latitude, longitude, altitude) | return msg | As local waypoints exist, the global MISSION reference allows to
transform between the local coordinate frame and the
global (GPS) coordinate frame. This can be necessary
when e.g. in- and outdoor settings are connected and
the MAV should move from in- to outdoor.
target_system : System ID (uint8_t)
latitude : global position * 1E7 (int32_t)
longitude : global position * 1E7 (int32_t)
altitude : global position * 1000 (int32_t) | As local waypoints exist, the global MISSION reference allows to
transform between the local coordinate frame and the
global (GPS) coordinate frame. This can be necessary
when e.g. in- and outdoor settings are connected and
the MAV should move from in- to outdoor. | [
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] | def set_gps_global_origin_encode(self, target_system, latitude, longitude, altitude):
'''
As local waypoints exist, the global MISSION reference allows to
transform between the local coordinate frame and the
global (GPS) coordinate frame. This can be necessary
when e.g. in- and outdoor settings are connected and
the MAV should move from in- to outdoor.
target_system : System ID (uint8_t)
latitude : global position * 1E7 (int32_t)
longitude : global position * 1E7 (int32_t)
altitude : global position * 1000 (int32_t)
'''
msg = MAVLink_set_gps_global_origin_message(target_system, latitude, longitude, altitude)
msg.pack(self)
return msg | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/sparse_ops.py | python | sparse_slice | (sp_input, start, size, name=None) | Slice a `SparseTensor` based on the `start` and `size`.
For example, if the input is
input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are:
sparse.slice([0, 0], [2, 4]) = shape = [2, 4]
[ a ]
[b c ]
sparse.slice([0, 4], [2, 3]) = shape = [2, 3]
[ d e ]
[ ]
Args:
sp_input: The `SparseTensor` to split.
start: 1-D. tensor represents the start of the slice.
size: 1-D. tensor represents the size of the slice.
name: A name for the operation (optional).
Returns:
A `SparseTensor` objects resulting from splicing.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`. | Slice a `SparseTensor` based on the `start` and `size`. | [
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"size",
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] | def sparse_slice(sp_input, start, size, name=None):
"""Slice a `SparseTensor` based on the `start` and `size`.
For example, if the input is
input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are:
sparse.slice([0, 0], [2, 4]) = shape = [2, 4]
[ a ]
[b c ]
sparse.slice([0, 4], [2, 3]) = shape = [2, 3]
[ d e ]
[ ]
Args:
sp_input: The `SparseTensor` to split.
start: 1-D. tensor represents the start of the slice.
size: 1-D. tensor represents the size of the slice.
name: A name for the operation (optional).
Returns:
A `SparseTensor` objects resulting from splicing.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
start = ops.convert_to_tensor(start, dtypes.int64)
size = ops.convert_to_tensor(size, dtypes.int64)
with ops.name_scope(name, "SparseSlice", [sp_input]) as name:
output_indices, output_values, output_shape = gen_sparse_ops.sparse_slice(
sp_input.indices,
sp_input.values,
sp_input.dense_shape,
start,
size,
name=name)
return sparse_tensor.SparseTensor(output_indices, output_values,
output_shape) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/encodings/zlib_codec.py | python | zlib_encode | (input,errors='strict') | return (output, len(input)) | Encodes the object input and returns a tuple (output
object, length consumed).
errors defines the error handling to apply. It defaults to
'strict' handling which is the only currently supported
error handling for this codec. | Encodes the object input and returns a tuple (output
object, length consumed). | [
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""" Encodes the object input and returns a tuple (output
object, length consumed).
errors defines the error handling to apply. It defaults to
'strict' handling which is the only currently supported
error handling for this codec.
"""
assert errors == 'strict'
output = zlib.compress(input)
return (output, len(input)) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/utils/registry.py | python | Registry.list | (self) | return self._registry.keys() | Lists registered items.
Returns:
A list of names of registered objects. | Lists registered items. | [
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"""Lists registered items.
Returns:
A list of names of registered objects.
"""
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/training/saver.py | python | BaseSaverBuilder.OpListToDict | (op_list) | return names_to_saveables | Create a dictionary of names to operation lists.
Args:
op_list: A list, tuple, or set of Variables or SaveableObjects.
Returns:
A dictionary of names to the operations that must be saved under
that name. Variables with save_slice_info are grouped together under the
same key in no particular order.
Raises:
TypeError: If the type of op_list or its elements is not supported.
ValueError: If at least two saveables share the same name. | Create a dictionary of names to operation lists. | [
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"""Create a dictionary of names to operation lists.
Args:
op_list: A list, tuple, or set of Variables or SaveableObjects.
Returns:
A dictionary of names to the operations that must be saved under
that name. Variables with save_slice_info are grouped together under the
same key in no particular order.
Raises:
TypeError: If the type of op_list or its elements is not supported.
ValueError: If at least two saveables share the same name.
"""
if not isinstance(op_list, (list, tuple, set)):
raise TypeError("Variables to save should be passed in a dict or a "
"list: %s" % op_list)
op_list = set(op_list)
names_to_saveables = {}
# pylint: disable=protected-access
for var in op_list:
if isinstance(var, BaseSaverBuilder.SaveableObject):
names_to_saveables[var.name] = var
elif isinstance(var, variables.PartitionedVariable):
if var.name in names_to_saveables:
raise ValueError("At least two variables have the same name: %s" %
var.name)
names_to_saveables[var.name] = var
elif isinstance(var, variables.Variable) and var._save_slice_info:
name = var._save_slice_info.full_name
if name in names_to_saveables:
if not isinstance(names_to_saveables[name], list):
raise ValueError("Mixing slices and non-slices with the same name: "
"%s" % name)
names_to_saveables[name].append(var)
else:
names_to_saveables[name] = [var]
else:
if context.in_graph_mode():
var = ops.internal_convert_to_tensor(var, as_ref=True)
if not BaseSaverBuilder._IsVariable(var):
raise TypeError("Variable to save is not a Variable: %s" % var)
if var.op.type == "ReadVariableOp":
name = var.op.inputs[0].op.name
else:
name = var.op.name
if name in names_to_saveables:
raise ValueError("At least two variables have the same name: %s" %
name)
names_to_saveables[name] = var
else:
if not isinstance(var, resource_variable_ops.ResourceVariable):
raise ValueError("Can only save/restore ResourceVariable eager "
"mode is enabled, type: %s." % type(var))
set_var = names_to_saveables.setdefault(var._shared_name, var)
if set_var is not var:
raise ValueError(
("Two different ResourceVariable objects with the same "
"shared_name '%s' were passed to the Saver. This likely means "
"that they were created in different Graphs or isolation "
"contexts, and may not be checkpointed together.") % (
var._shared_name,))
# pylint: enable=protected-access
return names_to_saveables | [
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stellar-deprecated/stellard | 67eabb2217bdfa9a6ea317f62338fb6bca458c90 | src/protobuf/python/google/protobuf/internal/cpp_message.py | python | InitMessage | (message_descriptor, cls) | Constructs a new message instance (called before instance's __init__). | Constructs a new message instance (called before instance's __init__). | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/markdown/extensions/admonition.py | python | AdmonitionExtension.extendMarkdown | (self, md, md_globals) | Add Admonition to Markdown instance. | Add Admonition to Markdown instance. | [
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""" Add Admonition to Markdown instance. """
md.registerExtension(self)
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/vm/caffe/misc.py | python | set_mode_cpu | () | Set to the CPU mode. [**PyCaffe Style**]
Returns
-------
None
References
----------
The implementation of `set_mode_cpu(_caffe.cpp, L51)`_. | Set to the CPU mode. [**PyCaffe Style**] | [
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"""Set to the CPU mode. [**PyCaffe Style**]
Returns
-------
None
References
----------
The implementation of `set_mode_cpu(_caffe.cpp, L51)`_.
"""
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/cloudsearch/optionstatus.py | python | OptionStatus.save | (self) | Write the current state of the local object back to the
CloudSearch service. | Write the current state of the local object back to the
CloudSearch service. | [
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"""
Write the current state of the local object back to the
CloudSearch service.
"""
if self.save_fn:
data = self.save_fn(self.domain.name, self.to_json())
self.refresh(data) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/android/pylib/host_driven/setup.py | python | _GetTestsFromModule | (test_module, **kwargs) | return tests | Gets a list of test objects from |test_module|.
Args:
test_module: Module from which to get the set of test methods.
kwargs: Keyword args to pass into the constructor of test cases.
Returns:
A list of test case objects each initialized for a particular test method
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"""Gets a list of test objects from |test_module|.
Args:
test_module: Module from which to get the set of test methods.
kwargs: Keyword args to pass into the constructor of test cases.
Returns:
A list of test case objects each initialized for a particular test method
defined in |test_module|.
"""
tests = []
for name in dir(test_module):
attr = getattr(test_module, name)
if _IsTestCaseClass(attr):
tests.extend(_GetTestsFromClass(attr, **kwargs))
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/server/wsgi/search/common/utils.py | python | SearchUtils.NoSearchResults | (self, folder_name, style, response_type, callback=None) | return search_results | Prepares KML/JSONP output in case of no search results.
Args:
folder_name: Name of the folder which is used in folder->name
in the response.
style: Style information for KML.
response_type: Response type can be KML or JSONP, depending on the client.
callback: Call back function for the JSONP response.
Returns:
search_results: An empty KML/JSONP file with no search results. | Prepares KML/JSONP output in case of no search results. | [
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"""Prepares KML/JSONP output in case of no search results.
Args:
folder_name: Name of the folder which is used in folder->name
in the response.
style: Style information for KML.
response_type: Response type can be KML or JSONP, depending on the client.
callback: Call back function for the JSONP response.
Returns:
search_results: An empty KML/JSONP file with no search results.
"""
search_results = ""
if response_type == "KML":
search_results = self.kml_template.substitute(
foldername=folder_name, style=style, lookat="", placemark="")
elif response_type == "JSONP":
search_results = self.json_template.substitute(
foldername=folder_name, json_placemark="")
search_results = self.jsonp_functioncall % (callback, search_results)
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self.logger.debug("%s response_type not supported", response_type)
self.logger.info("search didn't return any results")
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Zilliqa/Zilliqa | 9c464cd6910cc4e83e84f49ea44d6bb7a7074ecc | scripts/run-clang-tidy.py | python | get_tidy_invocation | (f, clang_tidy_binary, checks, tmpdir, build_path,
header_filter, extra_arg, extra_arg_before, quiet,
config, warn_as_erro) | return start | Gets a command line for clang-tidy. | Gets a command line for clang-tidy. | [
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header_filter, extra_arg, extra_arg_before, quiet,
config, warn_as_erro):
"""Gets a command line for clang-tidy."""
start = [clang_tidy_binary]
if header_filter is not None:
start.append('-header-filter=' + header_filter)
else:
# Show warnings in all in-project headers by default.
start.append('-header-filter=^' + build_path + '/.*')
if checks:
start.append('-checks=' + checks)
if tmpdir is not None:
start.append('-export-fixes')
# Get a temporary file. We immediately close the handle so clang-tidy can
# overwrite it.
(handle, name) = tempfile.mkstemp(suffix='.yaml', dir=tmpdir)
os.close(handle)
start.append(name)
for arg in extra_arg:
start.append('-extra-arg=%s' % arg)
for arg in extra_arg_before:
start.append('-extra-arg-before=%s' % arg)
start.append('-p=' + build_path)
if quiet:
start.append('-quiet')
if config:
start.append('-config=' + config)
if warn_as_erro:
start.append('-warnings-as-errors=' + warn_as_erro)
start.append(f)
return start | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/roslaunch/src/roslaunch/parent.py | python | ROSLaunchParent.__init__ | (self, run_id, roslaunch_files, is_core=False, port=None, local_only=False, process_listeners=None,
verbose=False, force_screen=False, is_rostest=False, roslaunch_strs=None, num_workers=NUM_WORKERS, timeout=None) | @param run_id: UUID of roslaunch session
@type run_id: str
@param roslaunch_files: list of launch configuration
files to load
@type roslaunch_files: [str]
@param is_core bool: if True, this launch is a roscore
instance. This affects the error behavior if a master is
already running (i.e. it fails).
@type is_core: bool
@param process_listeners: (optional) list of process listeners
to register with process monitor once launch is running
@type process_listeners: [L{roslaunch.pmon.ProcessListener}]
@param port: (optional) override master port number from what is specified in the master URI.
@type port: int
@param verbose: (optional) print verbose output
@type verbose: boolean
@param force_screen: (optional) force output of all nodes to screen
@type force_screen: boolean
@param is_rostest bool: if True, this launch is a rostest
instance. This affects validation checks.
@type is_rostest: bool
@param num_workers: If this is the core, the number of worker-threads to use.
@type num_workers: int
@param timeout: If this is the core, the socket-timeout to use.
@type timeout: Float or None
@throws RLException | [] | def __init__(self, run_id, roslaunch_files, is_core=False, port=None, local_only=False, process_listeners=None,
verbose=False, force_screen=False, is_rostest=False, roslaunch_strs=None, num_workers=NUM_WORKERS, timeout=None):
"""
@param run_id: UUID of roslaunch session
@type run_id: str
@param roslaunch_files: list of launch configuration
files to load
@type roslaunch_files: [str]
@param is_core bool: if True, this launch is a roscore
instance. This affects the error behavior if a master is
already running (i.e. it fails).
@type is_core: bool
@param process_listeners: (optional) list of process listeners
to register with process monitor once launch is running
@type process_listeners: [L{roslaunch.pmon.ProcessListener}]
@param port: (optional) override master port number from what is specified in the master URI.
@type port: int
@param verbose: (optional) print verbose output
@type verbose: boolean
@param force_screen: (optional) force output of all nodes to screen
@type force_screen: boolean
@param is_rostest bool: if True, this launch is a rostest
instance. This affects validation checks.
@type is_rostest: bool
@param num_workers: If this is the core, the number of worker-threads to use.
@type num_workers: int
@param timeout: If this is the core, the socket-timeout to use.
@type timeout: Float or None
@throws RLException
"""
self.logger = logging.getLogger('roslaunch.parent')
self.run_id = run_id
self.process_listeners = process_listeners
self.roslaunch_files = roslaunch_files
self.roslaunch_strs = roslaunch_strs
self.is_core = is_core
self.is_rostest = is_rostest
self.port = port
self.local_only = local_only
self.verbose = verbose
self.num_workers = num_workers
self.timeout = timeout
# I don't think we should have to pass in so many options from
# the outside into the roslaunch parent. One possibility is to
# allow alternate config loaders to be passed in.
self.force_screen = force_screen
# flag to prevent multiple shutdown attempts
self._shutting_down = False
self.config = self.runner = self.server = self.pm = self.remote_runner = None | [
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clementine-player/Clementine | 111379dfd027802b59125829fcf87e3e1d0ad73b | dist/cpplint.py | python | CleansedLines.NumLines | (self) | return self.num_lines | Returns the number of lines represented. | Returns the number of lines represented. | [
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makefile/frcnn | 8d9b9ebf8be8315ba2f374d460121b0adf1df29c | python/caffe/draw.py | python | get_layer_label | (layer, rankdir) | return node_label | Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer | Define node label based on layer type. | [
"Define",
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] | def get_layer_label(layer, rankdir):
"""Define node label based on layer type.
Parameters
----------
layer : ?
rankdir : {'LR', 'TB', 'BT'}
Direction of graph layout.
Returns
-------
string :
A label for the current layer
"""
if rankdir in ('TB', 'BT'):
# If graph orientation is vertical, horizontal space is free and
# vertical space is not; separate words with spaces
separator = ' '
else:
# If graph orientation is horizontal, vertical space is free and
# horizontal space is not; separate words with newlines
separator = '\\n'
if layer.type == 'Convolution' or layer.type == 'Deconvolution':
# Outer double quotes needed or else colon characters don't parse
# properly
node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
layer.type,
separator,
layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size) else 1,
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layer.convolution_param.pad[0] if len(layer.convolution_param.pad) else 0)
elif layer.type == 'Pooling':
pooling_types_dict = get_pooling_types_dict()
node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
(layer.name,
separator,
pooling_types_dict[layer.pooling_param.pool],
layer.type,
separator,
layer.pooling_param.kernel_size,
separator,
layer.pooling_param.stride,
separator,
layer.pooling_param.pad)
else:
node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
return node_label | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/processor/conversion/ror/ability_subprocessor.py | python | RoRAbilitySubprocessor.game_entity_stance_ability | (line) | return ability_forward_ref | Adds the GameEntityStance ability to a line.
:param line: Unit/Building line that gets the ability.
:type line: ...dataformat.converter_object.ConverterObjectGroup
:returns: The forward reference for the ability.
:rtype: ...dataformat.forward_ref.ForwardRef | Adds the GameEntityStance ability to a line. | [
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] | def game_entity_stance_ability(line):
"""
Adds the GameEntityStance ability to a line.
:param line: Unit/Building line that gets the ability.
:type line: ...dataformat.converter_object.ConverterObjectGroup
:returns: The forward reference for the ability.
:rtype: ...dataformat.forward_ref.ForwardRef
"""
current_unit = line.get_head_unit()
current_unit_id = line.get_head_unit_id()
dataset = line.data
name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)
game_entity_name = name_lookup_dict[current_unit_id][0]
ability_ref = f"{game_entity_name}.GameEntityStance"
ability_raw_api_object = RawAPIObject(ability_ref,
"GameEntityStance",
dataset.nyan_api_objects)
ability_raw_api_object.add_raw_parent("engine.ability.type.GameEntityStance")
ability_location = ForwardRef(line, game_entity_name)
ability_raw_api_object.set_location(ability_location)
# Stances
search_range = current_unit["search_radius"].get_value()
stance_names = ["Aggressive", "StandGround"]
# Attacking is preferred
ability_preferences = []
if line.is_projectile_shooter():
ability_preferences.append(ForwardRef(line, f"{game_entity_name}.Attack"))
elif line.is_melee() or line.is_ranged():
if line.has_command(7):
ability_preferences.append(ForwardRef(line, f"{game_entity_name}.Attack"))
if line.has_command(105):
ability_preferences.append(ForwardRef(line, f"{game_entity_name}.Heal"))
# Units are preferred before buildings
type_preferences = [
dataset.pregen_nyan_objects["util.game_entity_type.types.Unit"].get_nyan_object(),
dataset.pregen_nyan_objects["util.game_entity_type.types.Building"].get_nyan_object(),
]
stances = []
for stance_name in stance_names:
stance_api_ref = f"engine.util.game_entity_stance.type.{stance_name}"
stance_ref = f"{game_entity_name}.GameEntityStance.{stance_name}"
stance_raw_api_object = RawAPIObject(stance_ref, stance_name, dataset.nyan_api_objects)
stance_raw_api_object.add_raw_parent(stance_api_ref)
stance_location = ForwardRef(line, ability_ref)
stance_raw_api_object.set_location(stance_location)
# Search range
stance_raw_api_object.add_raw_member("search_range",
search_range,
"engine.util.game_entity_stance.GameEntityStance")
# Ability preferences
stance_raw_api_object.add_raw_member("ability_preference",
ability_preferences,
"engine.util.game_entity_stance.GameEntityStance")
# Type preferences
stance_raw_api_object.add_raw_member("type_preference",
type_preferences,
"engine.util.game_entity_stance.GameEntityStance")
line.add_raw_api_object(stance_raw_api_object)
stance_forward_ref = ForwardRef(line, stance_ref)
stances.append(stance_forward_ref)
ability_raw_api_object.add_raw_member("stances",
stances,
"engine.ability.type.GameEntityStance")
line.add_raw_api_object(ability_raw_api_object)
ability_forward_ref = ForwardRef(line, ability_raw_api_object.get_id())
return ability_forward_ref | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/fftpack/basic.py | python | rfft | (x, n=None, axis=-1, overwrite_x=False) | return _raw_fft(tmp,n,axis,1,overwrite_x,work_function) | Discrete Fourier transform of a real sequence.
Parameters
----------
x : array_like, real-valued
The data to transform.
n : int, optional
Defines the length of the Fourier transform. If `n` is not specified
(the default) then ``n = x.shape[axis]``. If ``n < x.shape[axis]``,
`x` is truncated, if ``n > x.shape[axis]``, `x` is zero-padded.
axis : int, optional
The axis along which the transform is applied. The default is the
last axis.
overwrite_x : bool, optional
If set to true, the contents of `x` can be overwritten. Default is
False.
Returns
-------
z : real ndarray
The returned real array contains::
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2))] if n is even
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2))] if n is odd
where::
y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
j = 0..n-1
See Also
--------
fft, irfft, numpy.fft.rfft
Notes
-----
Within numerical accuracy, ``y == rfft(irfft(y))``.
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
To get an output with a complex datatype, consider using the related
function `numpy.fft.rfft`.
Examples
--------
>>> from scipy.fftpack import fft, rfft
>>> a = [9, -9, 1, 3]
>>> fft(a)
array([ 4. +0.j, 8.+12.j, 16. +0.j, 8.-12.j])
>>> rfft(a)
array([ 4., 8., 12., 16.]) | Discrete Fourier transform of a real sequence. | [
"Discrete",
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"."
] | def rfft(x, n=None, axis=-1, overwrite_x=False):
"""
Discrete Fourier transform of a real sequence.
Parameters
----------
x : array_like, real-valued
The data to transform.
n : int, optional
Defines the length of the Fourier transform. If `n` is not specified
(the default) then ``n = x.shape[axis]``. If ``n < x.shape[axis]``,
`x` is truncated, if ``n > x.shape[axis]``, `x` is zero-padded.
axis : int, optional
The axis along which the transform is applied. The default is the
last axis.
overwrite_x : bool, optional
If set to true, the contents of `x` can be overwritten. Default is
False.
Returns
-------
z : real ndarray
The returned real array contains::
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2))] if n is even
[y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2))] if n is odd
where::
y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
j = 0..n-1
See Also
--------
fft, irfft, numpy.fft.rfft
Notes
-----
Within numerical accuracy, ``y == rfft(irfft(y))``.
Both single and double precision routines are implemented. Half precision
inputs will be converted to single precision. Non floating-point inputs
will be converted to double precision. Long-double precision inputs are
not supported.
To get an output with a complex datatype, consider using the related
function `numpy.fft.rfft`.
Examples
--------
>>> from scipy.fftpack import fft, rfft
>>> a = [9, -9, 1, 3]
>>> fft(a)
array([ 4. +0.j, 8.+12.j, 16. +0.j, 8.-12.j])
>>> rfft(a)
array([ 4., 8., 12., 16.])
"""
tmp = _asfarray(x)
if not numpy.isrealobj(tmp):
raise TypeError("1st argument must be real sequence")
try:
work_function = _DTYPE_TO_RFFT[tmp.dtype]
except KeyError:
raise ValueError("type %s is not supported" % tmp.dtype)
overwrite_x = overwrite_x or _datacopied(tmp, x)
return _raw_fft(tmp,n,axis,1,overwrite_x,work_function) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/mailbox.py | python | Maildir.add_folder | (self, folder) | return result | Create a folder and return a Maildir instance representing it. | Create a folder and return a Maildir instance representing it. | [
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"""Create a folder and return a Maildir instance representing it."""
path = os.path.join(self._path, '.' + folder)
result = Maildir(path, factory=self._factory)
maildirfolder_path = os.path.join(path, 'maildirfolder')
if not os.path.exists(maildirfolder_path):
os.close(os.open(maildirfolder_path, os.O_CREAT | os.O_WRONLY,
0666))
return result | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/fsm/FSM.py | python | FSM.demand | (self, request, *args) | Requests a state transition, by code that does not expect
the request to be denied. If the request is denied, raises a
`RequestDenied` exception.
Unlike `request()`, this method allows a new request to be made
while the FSM is currently in transition. In this case, the
request is queued up and will be executed when the current
transition finishes. Multiple requests will queue up in
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The return value of this function can be used in an `await`
expression to suspend the current coroutine until the
transition is done. | Requests a state transition, by code that does not expect
the request to be denied. If the request is denied, raises a
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Unlike `request()`, this method allows a new request to be made
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The return value of this function can be used in an `await`
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self.fsmLock.acquire()
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self.notify.debug("%s.demand(%s, %s" % (
self._name, request, str(args)[1:]))
if not self.state:
# Queue up the request.
fut = AsyncFuture()
self.__requestQueue.append((PythonUtil.Functor(
self.demand, request, *args), fut))
return fut
result = self.request(request, *args)
if not result:
raise RequestDenied("%s (from state: %s)" % (request, self.state))
return result._future or self.__doneFuture
finally:
self.fsmLock.release() | [
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koth/kcws | 88efbd36a7022de4e6e90f5a1fb880cf87cfae9f | third_party/python/cpplint/cpplint.py | python | _VerboseLevel | () | return _cpplint_state.verbose_level | Returns the module's verbosity setting. | Returns the module's verbosity setting. | [
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"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Alignment/OfflineValidation/python/TkAlAllInOneTool/overlapValidation.py | python | OverlapValidation.appendToPlots | (self) | return '("{file}", "{title}", {color}, {style}),'.format(file=self.getCompareStrings(plain=True)["DEFAULT"], **self.getRepMap()) | if no argument or "" is passed a string with an instantiation is
returned, else the validation is appended to the list | if no argument or "" is passed a string with an instantiation is
returned, else the validation is appended to the list | [
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"""
if no argument or "" is passed a string with an instantiation is
returned, else the validation is appended to the list
"""
return '("{file}", "{title}", {color}, {style}),'.format(file=self.getCompareStrings(plain=True)["DEFAULT"], **self.getRepMap()) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Menu.InsertMenu | (*args, **kwargs) | return _core_.Menu_InsertMenu(*args, **kwargs) | InsertMenu(self, size_t pos, int id, String text, Menu submenu, String help=EmptyString) -> MenuItem | InsertMenu(self, size_t pos, int id, String text, Menu submenu, String help=EmptyString) -> MenuItem | [
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"""InsertMenu(self, size_t pos, int id, String text, Menu submenu, String help=EmptyString) -> MenuItem"""
return _core_.Menu_InsertMenu(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/layers/local.py | python | get_locallyconnected_mask | (input_shape, kernel_shape, strides, padding,
data_format) | return mask | Return a mask representing connectivity of a locally-connected operation.
This method returns a masking numpy array of 0s and 1s (of type `np.float32`)
that, when element-wise multiplied with a fully-connected weight tensor, masks
out the weights between disconnected input-output pairs and thus implements
local connectivity through a sparse fully-connected weight tensor.
Assume an unshared convolution with given parameters is applied to an input
having N spatial dimensions with `input_shape = (d_in1, ..., d_inN)`
to produce an output with spatial shape `(d_out1, ..., d_outN)` (determined
by layer parameters such as `strides`).
This method returns a mask which can be broadcast-multiplied (element-wise)
with a 2*(N+1)-D weight matrix (equivalent to a fully-connected layer between
(N+1)-D activations (N spatial + 1 channel dimensions for input and output)
to make it perform an unshared convolution with given `kernel_shape`,
`strides`, `padding` and `data_format`.
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)` spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
data_format: a string, `"channels_first"` or `"channels_last"`.
Returns:
a `np.float32`-type `np.ndarray` of shape
`(1, d_in1, ..., d_inN, 1, d_out1, ..., d_outN)`
if `data_format == `"channels_first"`, or
`(d_in1, ..., d_inN, 1, d_out1, ..., d_outN, 1)`
if `data_format == "channels_last"`.
Raises:
ValueError: if `data_format` is neither `"channels_first"` nor
`"channels_last"`. | Return a mask representing connectivity of a locally-connected operation. | [
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"""Return a mask representing connectivity of a locally-connected operation.
This method returns a masking numpy array of 0s and 1s (of type `np.float32`)
that, when element-wise multiplied with a fully-connected weight tensor, masks
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Assume an unshared convolution with given parameters is applied to an input
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to produce an output with spatial shape `(d_out1, ..., d_outN)` (determined
by layer parameters such as `strides`).
This method returns a mask which can be broadcast-multiplied (element-wise)
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Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)` spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
data_format: a string, `"channels_first"` or `"channels_last"`.
Returns:
a `np.float32`-type `np.ndarray` of shape
`(1, d_in1, ..., d_inN, 1, d_out1, ..., d_outN)`
if `data_format == `"channels_first"`, or
`(d_in1, ..., d_inN, 1, d_out1, ..., d_outN, 1)`
if `data_format == "channels_last"`.
Raises:
ValueError: if `data_format` is neither `"channels_first"` nor
`"channels_last"`.
"""
mask = conv_utils.conv_kernel_mask(
input_shape=input_shape,
kernel_shape=kernel_shape,
strides=strides,
padding=padding)
ndims = int(mask.ndim / 2)
if data_format == 'channels_first':
mask = np.expand_dims(mask, 0)
mask = np.expand_dims(mask, -ndims - 1)
elif data_format == 'channels_last':
mask = np.expand_dims(mask, ndims)
mask = np.expand_dims(mask, -1)
else:
raise ValueError('Unrecognized data_format: ' + str(data_format))
return mask | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/browser/web_contents.py | python | WebContents.HasReachedQuiescence | (self) | return has_reached_quiescence | Determine whether the page has reached quiescence after loading.
Returns:
True if 2 seconds have passed since last resource received, false
otherwise.
Raises:
exceptions.Error: See EvaluateJavaScript() for a detailed list of
possible exceptions. | Determine whether the page has reached quiescence after loading. | [
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"""Determine whether the page has reached quiescence after loading.
Returns:
True if 2 seconds have passed since last resource received, false
otherwise.
Raises:
exceptions.Error: See EvaluateJavaScript() for a detailed list of
possible exceptions.
"""
# Inclusion of the script that provides
# window.__telemetry_testHasReachedNetworkQuiescence()
# is idempotent, it's run on every call because WebContents doesn't track
# page loads and we need to execute anew for every newly loaded page.
has_reached_quiescence = (
self.EvaluateJavaScript(self._quiescence_js +
"window.__telemetry_testHasReachedNetworkQuiescence()"))
return has_reached_quiescence | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/pytorch/losses/rl_losses.py | python | BatchQPGLoss.loss | (self, policy_logits, action_values) | return total_loss | Constructs a PyTorch Crierion that computes the QPG loss for batches.
Args:
policy_logits: `B x A` tensor corresponding to policy logits.
action_values: `B x A` tensor corresponding to Q-values.
Returns:
loss: A 0-D `float` tensor corresponding the loss. | Constructs a PyTorch Crierion that computes the QPG loss for batches. | [
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"""Constructs a PyTorch Crierion that computes the QPG loss for batches.
Args:
policy_logits: `B x A` tensor corresponding to policy logits.
action_values: `B x A` tensor corresponding to Q-values.
Returns:
loss: A 0-D `float` tensor corresponding the loss.
"""
_assert_rank_and_shape_compatibility([policy_logits, action_values], 2)
advantages = compute_advantages(policy_logits, action_values)
_assert_rank_and_shape_compatibility([advantages], 1)
total_adv = torch.mean(advantages, dim=0)
total_loss = total_adv
if self._entropy_cost:
policy_entropy = torch.mean(compute_entropy(policy_logits))
entropy_loss = torch.mul(float(self._entropy_cost), policy_entropy)
total_loss = torch.add(total_loss, entropy_loss)
return total_loss | [
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opengauss-mirror/openGauss-server | e383f1b77720a00ddbe4c0655bc85914d9b02a2b | src/gausskernel/dbmind/tools/predictor/python/run.py | python | configure_training | () | API for configuring model, needs to be called before prediction or train
CURL format:
curl -X POST -d '{"max_epoch":"200", "learning_rate":"0.01", "hidden_units":"60", "batch_size": "1000",
"dim_red": "0.7","model_name":"rlstm"}' -H 'Content-Type: application/json' 'https://127.0.0.1:5000/configure'
JSON Parameters:
'max_epoch': [OPTIONAL] default 500
'learning_rate': [OPTIONAL] default 1
'hidden_units': [OPTIONAL] default 50
'batch_size': [OPTIONAL] default 5000
'template_name' [OPTIONAL] network type of the target model, default rlstm
model
'model_name': [COMPULSORY] model name to be saved, can be a already trained model
'labels': [COMPULSORY] target labels to predict
'dim_red': [OPTIONAL] part of variance explained by PCA, default -1 means no PCA
:return: 0: Success
F: TypeError
I: Invalid parameter type
M: Missing compulsory argument | API for configuring model, needs to be called before prediction or train
CURL format:
curl -X POST -d '{"max_epoch":"200", "learning_rate":"0.01", "hidden_units":"60", "batch_size": "1000",
"dim_red": "0.7","model_name":"rlstm"}' -H 'Content-Type: application/json' 'https://127.0.0.1:5000/configure'
JSON Parameters:
'max_epoch': [OPTIONAL] default 500
'learning_rate': [OPTIONAL] default 1
'hidden_units': [OPTIONAL] default 50
'batch_size': [OPTIONAL] default 5000
'template_name' [OPTIONAL] network type of the target model, default rlstm
model
'model_name': [COMPULSORY] model name to be saved, can be a already trained model
'labels': [COMPULSORY] target labels to predict
'dim_red': [OPTIONAL] part of variance explained by PCA, default -1 means no PCA
:return: 0: Success
F: TypeError
I: Invalid parameter type
M: Missing compulsory argument | [
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] | def configure_training():
'''
API for configuring model, needs to be called before prediction or train
CURL format:
curl -X POST -d '{"max_epoch":"200", "learning_rate":"0.01", "hidden_units":"60", "batch_size": "1000",
"dim_red": "0.7","model_name":"rlstm"}' -H 'Content-Type: application/json' 'https://127.0.0.1:5000/configure'
JSON Parameters:
'max_epoch': [OPTIONAL] default 500
'learning_rate': [OPTIONAL] default 1
'hidden_units': [OPTIONAL] default 50
'batch_size': [OPTIONAL] default 5000
'template_name' [OPTIONAL] network type of the target model, default rlstm
model
'model_name': [COMPULSORY] model name to be saved, can be a already trained model
'labels': [COMPULSORY] target labels to predict
'dim_red': [OPTIONAL] part of variance explained by PCA, default -1 means no PCA
:return: 0: Success
F: TypeError
I: Invalid parameter type
M: Missing compulsory argument
'''
global model_config
global req_logger
global model_logger
global tb_url
if request.method == 'POST':
req_logger.info(request.data)
arg_json = request.get_json()
if 'model_name' in arg_json:
model_name = arg_json['model_name']
model_config = ModelInfo(model_name)
else:
return 'M'
return model_config.get_info(arg_json) | [
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] | https://github.com/opengauss-mirror/openGauss-server/blob/e383f1b77720a00ddbe4c0655bc85914d9b02a2b/src/gausskernel/dbmind/tools/predictor/python/run.py#L72-L105 |
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