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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/saved_model/load_v1_in_v2.py | python | _EagerSavedModelLoader.restore_variables | (self, wrapped, restore_from_saver) | Restores variables from the checkpoint. | Restores variables from the checkpoint. | [
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] | def restore_variables(self, wrapped, restore_from_saver):
"""Restores variables from the checkpoint."""
if restore_from_saver is not None:
initializer, _ = restore_from_saver(
constant_op.constant(self._variables_path))
if not ops.executing_eagerly_outside_functions():
# Add the initialization operation to the "saved_model_initializers"
# collection in case we don't have any lifted variables to attach it to.
ops.add_to_collection("saved_model_initializers", initializer)
one_unlifted = False
for variable in wrapped.graph.get_collection_ref(
ops.GraphKeys.GLOBAL_VARIABLES):
if variable.graph is wrapped.graph:
one_unlifted = True
# pylint: disable=protected-access
variable._initializer_op = initializer
# pylint: enable=protected-access
if one_unlifted:
logging.warning(
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/libmintsmolecule.py | python | LibmintsMolecule.input_units_to_au | (self) | return self.PYinput_units_to_au | Gets the geometry unit conversion. | Gets the geometry unit conversion. | [
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] | def input_units_to_au(self):
"""Gets the geometry unit conversion."""
return self.PYinput_units_to_au | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PGProperty.SetWasModified | (*args, **kwargs) | return _propgrid.PGProperty_SetWasModified(*args, **kwargs) | SetWasModified(self, bool set=True) | SetWasModified(self, bool set=True) | [
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] | def SetWasModified(*args, **kwargs):
"""SetWasModified(self, bool set=True)"""
return _propgrid.PGProperty_SetWasModified(*args, **kwargs) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/rospkg/manifest.py | python | _check_platform | (n, filename) | return [Platform(*v) for v in vals] | Validator for manifest platform.
:raise: :exc:`InvalidManifest` If validation fails | Validator for manifest platform.
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"""
Validator for manifest platform.
:raise: :exc:`InvalidManifest` If validation fails
"""
platforms = _get_nodes_by_name(n, 'platform')
try:
vals = [(p.attributes['os'].value, p.attributes['version'].value, p.getAttribute('notes')) for p in platforms]
except KeyError as e:
raise InvalidManifest("<platform> tag is missing required '%s' attribute"%str(e))
return [Platform(*v) for v in vals] | [
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twtygqyy/caffe-augmentation | c76600d247e5132fa5bd89d87bb5df458341fa84 | tools/extra/resize_and_crop_images.py | python | OpenCVResizeCrop.resize_and_crop_image | (self, input_file, output_file, output_side_length = 256) | Takes an image name, resize it and crop the center square | Takes an image name, resize it and crop the center square | [
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'''Takes an image name, resize it and crop the center square
'''
img = cv2.imread(input_file)
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
resized_img = cv2.resize(img, (new_width, new_height))
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = resized_img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
cv2.imwrite(output_file, cropped_img) | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0279-Perfect-Squares/0279.py | python | Solution.numSquares | (self, n) | return 3 | :type n: int
:rtype: int | :type n: int
:rtype: int | [
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"""
:type n: int
:rtype: int
"""
while n % 4 == 0:
n /= 4
if n % 8 == 7:
return 4
a = 0
while a**2 <= n:
b = int((n - a**2)**0.5)
if a**2 + b**2 == n:
return (not not a) + (not not b)
a += 1
return 3 | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/lib/scimath.py | python | arccos | (x) | return nx.arccos(x) | Compute the inverse cosine of x.
Return the "principal value" (for a description of this, see
`numpy.arccos`) of the inverse cosine of `x`. For real `x` such that
`abs(x) <= 1`, this is a real number in the closed interval
:math:`[0, \\pi]`. Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose arccos is (are) required.
Returns
-------
out : ndarray or scalar
The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so
is `out`, otherwise an array object is returned.
See Also
--------
numpy.arccos
Notes
-----
For an arccos() that returns ``NAN`` when real `x` is not in the
interval ``[-1,1]``, use `numpy.arccos`.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.emath.arccos(1) # a scalar is returned
0.0
>>> np.emath.arccos([1,2])
array([0.-0.j , 0.-1.317j]) | Compute the inverse cosine of x. | [
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] | def arccos(x):
"""
Compute the inverse cosine of x.
Return the "principal value" (for a description of this, see
`numpy.arccos`) of the inverse cosine of `x`. For real `x` such that
`abs(x) <= 1`, this is a real number in the closed interval
:math:`[0, \\pi]`. Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose arccos is (are) required.
Returns
-------
out : ndarray or scalar
The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so
is `out`, otherwise an array object is returned.
See Also
--------
numpy.arccos
Notes
-----
For an arccos() that returns ``NAN`` when real `x` is not in the
interval ``[-1,1]``, use `numpy.arccos`.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.emath.arccos(1) # a scalar is returned
0.0
>>> np.emath.arccos([1,2])
array([0.-0.j , 0.-1.317j])
"""
x = _fix_real_abs_gt_1(x)
return nx.arccos(x) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/drill/view/DrillExportDialog.py | python | DrillExportDialog.getAlgorithmCheckStates | (self) | return {a:w.isChecked() for a,w in self._widgets.items()} | Get the check state of algorithms.
Returns:
dict(str:bool): for each algorithm name, a bool to set its check
state | Get the check state of algorithms. | [
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"""
Get the check state of algorithms.
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mandiant/flare-wmi | b0a5a094ff9ca7d7a1c4fc711dc00c74dec4b6b1 | python-cim/cim/objects.py | python | ClassDefinition.keys | (self) | return ret | get names of Key properties for instances
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"""
get names of Key properties for instances
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ret.append(propname)
return ret | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/robotparser.py | python | RobotFileParser.parse | (self, lines) | parse the input lines from a robots.txt file.
We allow that a user-agent: line is not preceded by
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We allow that a user-agent: line is not preceded by
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"""parse the input lines from a robots.txt file.
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entry = Entry()
entry.useragents.append(line[1])
state = 1
elif line[0] == "disallow":
if state != 0:
entry.rulelines.append(RuleLine(line[1], False))
state = 2
elif line[0] == "allow":
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entry.rulelines.append(RuleLine(line[1], True))
state = 2
if state == 2:
self._add_entry(entry) | [
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wesnoth/wesnoth | 6ccac5a5e8ff75303c9190c0da60580925cb32c0 | data/tools/wesnoth/wmltools3.py | python | namespace_directory | (name) | Go from namespace to directory. | Go from namespace to directory. | [
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] | def namespace_directory(name):
"Go from namespace to directory."
if name == "core":
return "data/core/"
else:
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lballabio/quantlib-old | 136336947ed4fea9ecc1da6edad188700e821739 | gensrc/gensrc/addins/addin.py | python | Addin.objectIdSuffix | (self) | return self.objectIdSuffix_ | Return the text which is appended to variable names
in autogenerated source code after object IDs are converted
to the corresponding objects. | Return the text which is appended to variable names
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/plotting.py | python | PlotGroup.draw | (self, legendLabels, prefix=None, separate=False, saveFormat=".pdf", ratio=True, directory="") | return self._save(canvas, saveFormat, prefix=prefix, directory=directory) | Draw the histograms using values for a given algorithm.
Arguments:
legendLabels -- List of strings for legend labels (corresponding to the tdirectories in create())
prefix -- Optional string for file name prefix (default None)
separate -- Save the plots of a group to separate files instead of a file per group (default False)
saveFormat -- String specifying the plot format (default '.pdf')
ratio -- Add ratio to the plot (default True)
directory -- Directory where to save the file (default "") | Draw the histograms using values for a given algorithm. | [
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"""Draw the histograms using values for a given algorithm.
Arguments:
legendLabels -- List of strings for legend labels (corresponding to the tdirectories in create())
prefix -- Optional string for file name prefix (default None)
separate -- Save the plots of a group to separate files instead of a file per group (default False)
saveFormat -- String specifying the plot format (default '.pdf')
ratio -- Add ratio to the plot (default True)
directory -- Directory where to save the file (default "")
"""
if self._overrideLegendLabels is not None:
legendLabels = self._overrideLegendLabels
# Do not draw the group if it would be empty
onlyEmptyPlots = True
for plot in self._plots:
if not plot.isEmpty():
onlyEmptyPlots = False
break
if onlyEmptyPlots:
return []
if separate:
return self._drawSeparate(legendLabels, prefix, saveFormat, ratio, directory)
cwidth = 500*self._ncols
nrows = int((len(self._plots)+self._ncols-1)/self._ncols) # this should work also for odd n
cheight = 500 * nrows
if ratio:
cheight = int(cheight*self._ratioFactor)
canvas = _createCanvas(self._name, cwidth, cheight)
canvas.Divide(self._ncols, nrows)
if ratio:
for i, plot in enumerate(self._plots):
pad = canvas.cd(i+1)
self._modifyPadForRatio(pad)
# Draw plots to canvas
for i, plot in enumerate(self._plots):
pad = canvas.cd(i+1)
if not plot.isEmpty():
plot.draw(pad, ratio, self._ratioFactor, nrows)
# Setup legend
canvas.cd()
if len(self._plots) <= 4:
lx1 = 0.2
lx2 = 0.9
ly1 = 0.48
ly2 = 0.53
else:
lx1 = 0.1
lx2 = 0.9
ly1 = 0.64
ly2 = 0.67
if self._legendDx is not None:
lx1 += self._legendDx
lx2 += self._legendDx
if self._legendDy is not None:
ly1 += self._legendDy
ly2 += self._legendDy
if self._legendDw is not None:
lx2 += self._legendDw
if self._legendDh is not None:
ly1 -= self._legendDh
plot = max(self._plots, key=lambda p: p.getNumberOfHistograms())
denomUnc = sum([p.drawRatioUncertainty() for p in self._plots]) > 0
legend = self._createLegend(plot, legendLabels, lx1, ly1, lx2, ly2,
denomUncertainty=(ratio and denomUnc))
return self._save(canvas, saveFormat, prefix=prefix, directory=directory) | [
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/jedi/jedi/parser/fast.py | python | FastParser._scan_user_scope | (self, sub_module) | return None | Scan with self.user_position. | Scan with self.user_position. | [
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""" Scan with self.user_position. """
for scope in sub_module.statements + sub_module.subscopes:
if isinstance(scope, pr.Scope):
if scope.start_pos <= self.user_position <= scope.end_pos:
return self._scan_user_scope(scope) or scope
return None | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | DataViewCtrl.IsExpanded | (*args, **kwargs) | return _dataview.DataViewCtrl_IsExpanded(*args, **kwargs) | IsExpanded(self, DataViewItem item) -> bool | IsExpanded(self, DataViewItem item) -> bool | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/cookielib.py | python | split_header_words | (header_values) | return result | r"""Parse header values into a list of lists containing key,value pairs.
The function knows how to deal with ",", ";" and "=" as well as quoted
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If the header_values passed as argument contains multiple values, then they
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This means that this function is useful for parsing header fields that
follow this syntax (BNF as from the HTTP/1.1 specification, but we relax
the requirement for tokens).
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header = (token | parameter) *( [";"] (token | parameter))
token = 1*<any CHAR except CTLs or separators>
separators = "(" | ")" | "<" | ">" | "@"
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| "/" | "[" | "]" | "?" | "="
| "{" | "}" | SP | HT
quoted-string = ( <"> *(qdtext | quoted-pair ) <"> )
qdtext = <any TEXT except <">>
quoted-pair = "\" CHAR
parameter = attribute "=" value
attribute = token
value = token | quoted-string
Each header is represented by a list of key/value pairs. The value for a
simple token (not part of a parameter) is None. Syntactically incorrect
headers will not necessarily be parsed as you would want.
This is easier to describe with some examples:
>>> split_header_words(['foo="bar"; port="80,81"; discard, bar=baz'])
[[('foo', 'bar'), ('port', '80,81'), ('discard', None)], [('bar', 'baz')]]
>>> split_header_words(['text/html; charset="iso-8859-1"'])
[[('text/html', None), ('charset', 'iso-8859-1')]]
>>> split_header_words([r'Basic realm="\"foo\bar\""'])
[[('Basic', None), ('realm', '"foobar"')]] | r"""Parse header values into a list of lists containing key,value pairs. | [
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The function knows how to deal with ",", ";" and "=" as well as quoted
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If the header_values passed as argument contains multiple values, then they
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>>> split_header_words(['text/html; charset="iso-8859-1"'])
[[('text/html', None), ('charset', 'iso-8859-1')]]
>>> split_header_words([r'Basic realm="\"foo\bar\""'])
[[('Basic', None), ('realm', '"foobar"')]]
"""
assert not isinstance(header_values, basestring)
result = []
for text in header_values:
orig_text = text
pairs = []
while text:
m = HEADER_TOKEN_RE.search(text)
if m:
text = unmatched(m)
name = m.group(1)
m = HEADER_QUOTED_VALUE_RE.search(text)
if m: # quoted value
text = unmatched(m)
value = m.group(1)
value = HEADER_ESCAPE_RE.sub(r"\1", value)
else:
m = HEADER_VALUE_RE.search(text)
if m: # unquoted value
text = unmatched(m)
value = m.group(1)
value = value.rstrip()
else:
# no value, a lone token
value = None
pairs.append((name, value))
elif text.lstrip().startswith(","):
# concatenated headers, as per RFC 2616 section 4.2
text = text.lstrip()[1:]
if pairs: result.append(pairs)
pairs = []
else:
# skip junk
non_junk, nr_junk_chars = re.subn("^[=\s;]*", "", text)
assert nr_junk_chars > 0, (
"split_header_words bug: '%s', '%s', %s" %
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text = non_junk
if pairs: result.append(pairs)
return result | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/build/pymake/pymake/parser.py | python | itermakefilechars | (d, offset, tokenlist, it, ignorecomments=False) | Iterate over data in makefile syntax. Comments are found at unescaped # characters, and escaped newlines
are converted to single-space continuations. | Iterate over data in makefile syntax. Comments are found at unescaped # characters, and escaped newlines
are converted to single-space continuations. | [
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"""
Iterate over data in makefile syntax. Comments are found at unescaped # characters, and escaped newlines
are converted to single-space continuations.
"""
assert offset >= d.lstart and offset <= d.lend, "offset %i should be between %i and %i" % (offset, d.lstart, d.lend)
if offset == d.lend:
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return
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yield starttext + token[-l / 2:], None, None, mend
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yield starttext, token, mstart, mend
else:
yield starttext + token, None, None, mend
offset = mend
yield _makecontinuations.sub(_replacemakecontinuations, s[offset:d.lend]), None, None, None | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/third_party/jinja2/filters.py | python | do_format | (value, *args, **kwargs) | return soft_unicode(value) % (kwargs or args) | Apply python string formatting on an object:
.. sourcecode:: jinja
{{ "%s - %s"|format("Hello?", "Foo!") }}
-> Hello? - Foo! | Apply python string formatting on an object: | [
"Apply",
"python",
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"formatting",
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] | def do_format(value, *args, **kwargs):
"""
Apply python string formatting on an object:
.. sourcecode:: jinja
{{ "%s - %s"|format("Hello?", "Foo!") }}
-> Hello? - Foo!
"""
if args and kwargs:
raise FilterArgumentError('can\'t handle positional and keyword '
'arguments at the same time')
return soft_unicode(value) % (kwargs or args) | [
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jeog/TOSDataBridge | 6a5a08ca5cf3883db1f12e9bc89ef374d098df5a | python/tosdb/_common.py | python | _TOSDB_DataBlock.remove_items | () | Remove items (ex. 'IBM', 'SPY') from the block.
NOTE: if this call removes all items from the block the remaining topics
will be pre-cached and appear not to exist, until a valid item is re-added.
remove_items(self, *items)
*items :: *str :: any numer of item strings in the block
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""" Remove items (ex. 'IBM', 'SPY') from the block.
NOTE: if this call removes all items from the block the remaining topics
will be pre-cached and appear not to exist, until a valid item is re-added.
remove_items(self, *items)
*items :: *str :: any numer of item strings in the block
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"""
pass | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/metrics_impl.py | python | false_positives_at_thresholds | (labels,
predictions,
thresholds,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None) | Computes false positives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
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 `false_positives`
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:
false_positives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `false_positives` variable and
returns its current value.
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 false positives at provided threshold values. | [
"Computes",
"false",
"positives",
"at",
"provided",
"threshold",
"values",
"."
] | def false_positives_at_thresholds(labels,
predictions,
thresholds,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes false positives at provided threshold values.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
thresholds: A python list or tuple of float thresholds in `[0, 1]`.
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 `false_positives`
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:
false_positives: A float `Tensor` of shape `[len(thresholds)]`.
update_op: An operation that updates the `false_positives` variable and
returns its current value.
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.false_positives_at_thresholds is not '
'supported when eager execution is enabled.')
with variable_scope.variable_scope(name, 'false_positives',
(predictions, labels, weights)):
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights=weights, includes=('fp',))
fp_value = _aggregate_variable(values['fp'], metrics_collections)
if updates_collections:
ops.add_to_collections(updates_collections, update_ops['fp'])
return fp_value, update_ops['fp'] | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | config/configobj.py | python | ConfigObj._handle_repeat | (self, section, configspec) | Dynamically assign configspec for repeated section. | Dynamically assign configspec for repeated section. | [
"Dynamically",
"assign",
"configspec",
"for",
"repeated",
"section",
"."
] | def _handle_repeat(self, section, configspec):
"""Dynamically assign configspec for repeated section."""
try:
section_keys = configspec.sections
scalar_keys = configspec.scalars
except AttributeError:
section_keys = [entry for entry in configspec
if isinstance(configspec[entry], dict)]
scalar_keys = [entry for entry in configspec
if not isinstance(configspec[entry], dict)]
if '__many__' in section_keys and len(section_keys) > 1:
# FIXME: can we supply any useful information here ?
raise RepeatSectionError
scalars = {}
sections = {}
for entry in scalar_keys:
val = configspec[entry]
scalars[entry] = val
for entry in section_keys:
val = configspec[entry]
if entry == '__many__':
scalars[entry] = val
continue
sections[entry] = val
#
section.configspec = scalars
for entry in sections:
if not section.has_key(entry):
section[entry] = {}
self._handle_repeat(section[entry], sections[entry]) | [
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smartdevicelink/sdl_core | 68f082169e0a40fccd9eb0db3c83911c28870f07 | tools/InterfaceGenerator/generator/generators/SmartFactoryBase.py | python | CodeGenerator._indent_code | (self, code, indent_level) | return u"".join(
[u"{0}{1}\n".format(
self._indent_template * indent_level,
x) if x != u"" else u"\n" for x in code_lines]) | Indent given source code.
Indents given source code right by given indentation level.
Keyword arguments:
code -- given source code.
indent_level -- desired indentation level.
Returns:
String with processed code. | Indent given source code. | [
"Indent",
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"source",
"code",
"."
] | def _indent_code(self, code, indent_level):
"""Indent given source code.
Indents given source code right by given indentation level.
Keyword arguments:
code -- given source code.
indent_level -- desired indentation level.
Returns:
String with processed code.
"""
code_lines = code.split("\n")
return u"".join(
[u"{0}{1}\n".format(
self._indent_template * indent_level,
x) if x != u"" else u"\n" for x in code_lines]) | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/barbican.py | python | assign_ports | (ctx, config, initial_port) | return role_endpoints | Assign port numbers starting from @initial_port | Assign port numbers starting from | [
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] | def assign_ports(ctx, config, initial_port):
"""
Assign port numbers starting from @initial_port
"""
port = initial_port
role_endpoints = {}
for remote, roles_for_host in ctx.cluster.remotes.items():
for role in roles_for_host:
if role in config:
role_endpoints[role] = (remote.name.split('@')[1], port)
port += 1
return role_endpoints | [
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deepmind/reverb | ef3c8f0be1b720a741d2dee335e15e44668c291a | reverb/server.py | python | Table.stack | (cls,
name: str,
max_size: int,
extensions: Sequence[TableExtensionBase] = (),
signature: Optional[reverb_types.SpecNest] = None) | return cls(
name=name,
sampler=item_selectors.Lifo(),
remover=item_selectors.Lifo(),
max_size=max_size,
max_times_sampled=1,
rate_limiter=rate_limiters.Stack(max_size),
extensions=extensions,
signature=signature) | Constructs a Table which acts like a stack.
Args:
name: Name of the priority table (aka stack).
max_size: Maximum number of items in the priority table (aka stack).
extensions: See documentation in the constructor.
signature: See documentation in the constructor.
Returns:
Table which behaves like a stack of size `max_size`. | Constructs a Table which acts like a stack. | [
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"which",
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"stack",
"."
] | def stack(cls,
name: str,
max_size: int,
extensions: Sequence[TableExtensionBase] = (),
signature: Optional[reverb_types.SpecNest] = None):
"""Constructs a Table which acts like a stack.
Args:
name: Name of the priority table (aka stack).
max_size: Maximum number of items in the priority table (aka stack).
extensions: See documentation in the constructor.
signature: See documentation in the constructor.
Returns:
Table which behaves like a stack of size `max_size`.
"""
return cls(
name=name,
sampler=item_selectors.Lifo(),
remover=item_selectors.Lifo(),
max_size=max_size,
max_times_sampled=1,
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Smorodov/Multitarget-tracker | bee300e8bfd660c86cbeb6892c65a5b7195c9381 | thirdparty/pybind11/tools/clang/cindex.py | python | CursorKind.is_expression | (self) | return conf.lib.clang_isExpression(self) | Test if this is an expression kind. | Test if this is an expression kind. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/thumbnailctrl.py | python | ScrolledThumbnail.SetCaption | (self, caption="") | Sets the current caption string.
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/learn/python/learn/monitors.py | python | CaptureVariable.values | (self) | return self._var_values | Returns the values captured so far.
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CoolProp/CoolProp | 381c8535e5dec3eec27ad430ebbfff8bc9dfc008 | dev/scripts/viscosity_builder.py | python | viscosity_linear | (fluid, T, rho, e_k, sigma) | return viscosity_dilute(fluid, T, e_k, sigma) * B_eta * rho / molemass * 1000 | Implements the method of Vogel 1998 (Propane) for the linear part | Implements the method of Vogel 1998 (Propane) for the linear part | [
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N_A = 6.02214129e23
molemass = Props(fluid, 'molemass')
Tstar = T / e_k
b = [-19.572881, 219.73999, -1015.3226, 2471.01251, -3375.1717, 2491.6597, -787.26086, 14.085455, -0.34664158]
s = sum([b[i] * pow(Tstar, -0.25 * i) for i in range(7)])
B_eta_star = s + b[7] * pow(Tstar, -2.5) + b[8] * pow(Tstar, -5.5) # //[no units]
B_eta = N_A * pow(sigma / 1e9, 3) * B_eta_star # [m3/mol]
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Window.Layout | (*args, **kwargs) | return _core_.Window_Layout(*args, **kwargs) | Layout(self) -> bool
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/requests/sessions.py | python | SessionRedirectMixin.rebuild_auth | (self, prepared_request, response) | When being redirected we may want to strip authentication from the
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kevinlin311tw/Caffe-DeepBinaryCode | 9eaa7662be47d49f475ecbeea2bd51be105270d2 | scripts/cpp_lint.py | python | _IncludeState.CanonicalizeAlphabeticalOrder | (self, header_path) | return header_path.replace('-inl.h', '.h').replace('-', '_').lower() | Returns a path canonicalized for alphabetical comparison.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/tarfile.py | python | TarFile._getmember | (self, name, tarinfo=None, normalize=False) | Find an archive member by name from bottom to top.
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/client/timeline.py | python | _TensorTracker.name | (self) | return self._name | Name of this tensor. | Name of this tensor. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | ArrayStringProperty.OnButtonClick | (*args, **kwargs) | return _propgrid.ArrayStringProperty_OnButtonClick(*args, **kwargs) | OnButtonClick(self, PropertyGrid propgrid, Window primary, wxChar cbt) -> bool | OnButtonClick(self, PropertyGrid propgrid, Window primary, wxChar cbt) -> bool | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | python | get_image_grad | (net, image, class_id=None) | return _get_grad(net, image, class_id, image_grad=True) | Get the gradients of the image.
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net: Block
Network to use for visualization.
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Preprocessed image to use for visualization.
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Network to use for visualization.
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Preprocessed image to use for visualization.
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/beta.py | python | Beta.b | (self) | return self._b | Shape parameter. | Shape parameter. | [
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] | def b(self):
"""Shape parameter."""
return self._b | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListItem.GetColumn | (*args, **kwargs) | return _controls_.ListItem_GetColumn(*args, **kwargs) | GetColumn(self) -> int | GetColumn(self) -> int | [
"GetColumn",
"(",
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")",
"-",
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] | def GetColumn(*args, **kwargs):
"""GetColumn(self) -> int"""
return _controls_.ListItem_GetColumn(*args, **kwargs) | [
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/minitaur/robots/safety/motor_action_validator.py | python | MotorActionValidator.__init__ | (
self,
motor_id: typing.Any,
position_bound: data_types.Bound,
position_gain_bound: data_types.Bound,
velocity_bound: data_types.Bound,
velocity_gain_bound: data_types.Bound,
torque_bound: data_types.Bound,
timestamp_delta_bound: data_types.Bound,
delta_position_bound: data_types.Bound,
average_abs_delta_position_bound: data_types.Bound,
state_buffer_size: int = _DEQUE_SIZE,
) | Initializes the class.
Args:
motor_id: Unique ID for the motor.
position_bound: The lower/upper bound of the motor angle.
position_gain_bound: The lower/upper bound of the motor position gain for
PD control.
velocity_bound: The lower/upper bound of the motor speed.
velocity_gain_bound: The lower/upper bound of the motor velocity gain for
PD control.
torque_bound: The lower/upper bound of the measured motor torque.
timestamp_delta_bound: The range of timestamp difference between two
consecutively received motor states.
delta_position_bound: The bound between the current motor position and the
command position, in position control mode.
average_abs_delta_position_bound: The bound for average motor position and
command poisition difference.
state_buffer_size: The buffer size used to calculate the average. | Initializes the class. | [
"Initializes",
"the",
"class",
"."
] | def __init__(
self,
motor_id: typing.Any,
position_bound: data_types.Bound,
position_gain_bound: data_types.Bound,
velocity_bound: data_types.Bound,
velocity_gain_bound: data_types.Bound,
torque_bound: data_types.Bound,
timestamp_delta_bound: data_types.Bound,
delta_position_bound: data_types.Bound,
average_abs_delta_position_bound: data_types.Bound,
state_buffer_size: int = _DEQUE_SIZE,
):
"""Initializes the class.
Args:
motor_id: Unique ID for the motor.
position_bound: The lower/upper bound of the motor angle.
position_gain_bound: The lower/upper bound of the motor position gain for
PD control.
velocity_bound: The lower/upper bound of the motor speed.
velocity_gain_bound: The lower/upper bound of the motor velocity gain for
PD control.
torque_bound: The lower/upper bound of the measured motor torque.
timestamp_delta_bound: The range of timestamp difference between two
consecutively received motor states.
delta_position_bound: The bound between the current motor position and the
command position, in position control mode.
average_abs_delta_position_bound: The bound for average motor position and
command poisition difference.
state_buffer_size: The buffer size used to calculate the average.
"""
assert state_buffer_size > 1
self._last_motor_state = None
self._motor_id = motor_id
self._position_bound = position_bound
self._position_gain_bound = position_gain_bound
self._velocity_bound = velocity_bound
self._velocity_gain_bound = velocity_gain_bound
self._torque_bound = torque_bound
self._timestamp_delta_bound = timestamp_delta_bound
self._delta_position_bound = delta_position_bound
self._average_abs_delta_position_bound = average_abs_delta_position_bound
self._abs_delta_position_filter = moving_window_filter.MovingWindowFilter(
state_buffer_size) | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/hpmc/update.py | python | BoxMC.counter | (self) | Trial move counters.
The counter object has the following attributes:
* ``volume``: `tuple` [`int`, `int`] - Number of accepted and rejected
volume and length moves.
* ``shear``: `tuple` [`int`, `int`] - Number of accepted and rejected
shear moves.
* ``aspect``: `tuple` [`int`, `int`] - Number of accepted and rejected
aspect moves.
Note:
The counts are reset to 0 at the start of each call to
`hoomd.Simulation.run`. Before the first call to `Simulation.run`,
`counter` is `None`. | Trial move counters. | [
"Trial",
"move",
"counters",
"."
] | def counter(self):
"""Trial move counters.
The counter object has the following attributes:
* ``volume``: `tuple` [`int`, `int`] - Number of accepted and rejected
volume and length moves.
* ``shear``: `tuple` [`int`, `int`] - Number of accepted and rejected
shear moves.
* ``aspect``: `tuple` [`int`, `int`] - Number of accepted and rejected
aspect moves.
Note:
The counts are reset to 0 at the start of each call to
`hoomd.Simulation.run`. Before the first call to `Simulation.run`,
`counter` is `None`.
"""
if not self._attached:
return None
else:
return self._cpp_obj.getCounters(1) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/core/programs.py | python | genny_program | (logger, job_num, test_id=None, executable=None, process_kwargs=None, **kwargs) | return generic_program(logger, args, job_num, test_id=test_id, process_kwargs=process_kwargs,
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"""Return a Process instance that starts a genny executable with arguments constructed from 'kwargs'."""
executable = utils.default_if_none(executable, config.DEFAULT_GENNY_EXECUTABLE)
args = [executable]
return generic_program(logger, args, job_num, test_id=test_id, process_kwargs=process_kwargs,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/datetime.py | python | time._tzstr | (self) | return _format_offset(off) | Return formatted timezone offset (+xx:xx) or an empty string. | Return formatted timezone offset (+xx:xx) or an empty string. | [
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"""Return formatted timezone offset (+xx:xx) or an empty string."""
off = self.utcoffset()
return _format_offset(off) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/interpolate/fitpack2.py | python | BivariateSpline._from_tck | (cls, tck) | return self | Construct a spline object from given tck and degree | Construct a spline object from given tck and degree | [
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] | def _from_tck(cls, tck):
"""Construct a spline object from given tck and degree"""
self = cls.__new__(cls)
if len(tck) != 5:
raise ValueError("tck should be a 5 element tuple of tx,"
" ty, c, kx, ky")
self.tck = tck[:3]
self.degrees = tck[3:]
return self | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | uCSIsBuhid | (code) | return ret | Check whether the character is part of Buhid UCS Block | Check whether the character is part of Buhid UCS Block | [
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"""Check whether the character is part of Buhid UCS Block """
ret = libxml2mod.xmlUCSIsBuhid(code)
return ret | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/debug/debug_data.py | python | has_inf_or_nan | (datum, tensor) | A predicate for whether a tensor consists of any bad numerical values.
This predicate is common enough to merit definition in this module.
Bad numerical values include nans and infs.
The signature of this function follows the requiremnet of DebugDumpDir's
find() method.
Args:
datum: (DebugTensorDatum) Datum metadata.
tensor: (numpy.ndarray or None) Value of the tensor. None represents
an uninitialized tensor.
Returns:
(bool) True if and only if tensor consists of any nan or inf values. | A predicate for whether a tensor consists of any bad numerical values. | [
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"""A predicate for whether a tensor consists of any bad numerical values.
This predicate is common enough to merit definition in this module.
Bad numerical values include nans and infs.
The signature of this function follows the requiremnet of DebugDumpDir's
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tensor: (numpy.ndarray or None) Value of the tensor. None represents
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Returns:
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_ = datum # Datum metadata is unused in this predicte.
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# Uninitialized tensor doesn't have bad numerical values.
return False
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/ops/random_ops.py | python | random_uniform | (shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None) | Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
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In the integer case, the random integers are slightly biased unless
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`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified. | Outputs random values from a uniform distribution. | [
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minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
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Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
shape = _ShapeTensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
seed1, seed2 = random_seed.get_seed(seed)
if dtype.is_integer:
return gen_random_ops._random_uniform_int(shape,
minval,
maxval,
seed=seed1,
seed2=seed2,
name=name)
else:
rnd = gen_random_ops._random_uniform(shape,
dtype,
seed=seed1,
seed2=seed2)
return math_ops.add(rnd * (maxval - minval), minval, name=name) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros/roslib/src/roslib/packages.py | python | _update_rospack_cache | (env=None) | return _read_rospack_cache(cache, ros_root, ros_package_path) | Internal routine to update global package directory cache
@return: True if cache is valid
@rtype: bool | Internal routine to update global package directory cache | [
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"""
Internal routine to update global package directory cache
@return: True if cache is valid
@rtype: bool
"""
if env is None:
env = os.environ
cache = _pkg_dir_cache
if cache:
return True
ros_root = env[ROS_ROOT]
ros_package_path = env.get(ROS_PACKAGE_PATH, '')
return _read_rospack_cache(cache, ros_root, ros_package_path) | [
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andr3wmac/Torque6 | db6cd08f18f4917e0c6557b2766fb40d8e2bee39 | lib/freetype/android/freetype-2.4.12/src/tools/docmaker/tohtml.py | python | HtmlFormatter.make_html_words | ( self, words ) | return line | convert a series of simple words into some HTML text | convert a series of simple words into some HTML text | [
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""" convert a series of simple words into some HTML text """
line = ""
if words:
line = html_quote( words[0] )
for w in words[1:]:
line = line + " " + html_quote( w )
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/geodesy/utm.py | python | UTMPoint.is2D | (self) | return self.altitude != self.altitude | :returns: True if altitude is not defined. | :returns: True if altitude is not defined. | [
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TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | scripts/kat/spectra.py | python | Spectra.analyse | (self, min_elements=1, verbose=False) | Analyse the histogram for peaks
:param verbose: Prints additional information about progress to stdout | Analyse the histogram for peaks
:param verbose: Prints additional information about progress to stdout | [
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"""
Analyse the histogram for peaks
:param verbose: Prints additional information about progress to stdout
"""
if verbose:
print()
print("Creating initial peaks ... ", end="", flush=True)
self._createInitialPeaks()
if self.peaks:
if verbose:
print("done.", len(self.peaks), "peaks initially created")
print()
self.printPeaks()
print()
print("Locally optimising each peak ... ", end="")
for p_i, p in enumerate(self.peaks):
try:
p.optimise(self.histogram)
except Exception as inst:
print("Problem locally optimising peak", p_i+1, file=sys.stderr)
print(inst, file=sys.stderr)
# Just carry on from here... maybe the problem will fix itself...?
# For debugging
if False:
plt.plot(self.histogram, color='black')
for p_i, p in enumerate(self.peaks):
plt.plot(p.Ty)
plt.xlim(0, 70)
plt.ylim(0, 200000000)
plt.show()
# Remove any peaks that contain little to no content
self.peaks = list(filter(lambda p: p.elements() >= min_elements, self.peaks))
if verbose:
print("done.")
print()
self.printPeaks()
print()
print("Fitting cumulative distribution to histogram by adjusting peaks ... ", end="", flush=True)
try:
self.optimise(fmin=self.fmin if type(self) == KmerSpectra else 0)
# Remove any peaks that contain little to no content
self.peaks = list(filter(lambda p: p.elements() >= min_elements, self.peaks))
if verbose:
print("done.")
print()
self.printPeaks()
except Exception as inst:
print(
"WARNING: problem optimising peaks. It is likely that the spectra is too complex to analyse properly. Output for this spectra may not be valid.",
file=sys.stderr)
print(inst, file=sys.stderr)
pass
elif verbose:
print("done. No peaks created") | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/wsgiref/handlers.py | python | BaseHandler.write | (self, data) | write()' callable as specified by PEP 333 | write()' callable as specified by PEP 333 | [
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] | def write(self, data):
"""'write()' callable as specified by PEP 333"""
assert type(data) is StringType,"write() argument must be string"
if not self.status:
raise AssertionError("write() before start_response()")
elif not self.headers_sent:
# Before the first output, send the stored headers
self.bytes_sent = len(data) # make sure we know content-length
self.send_headers()
else:
self.bytes_sent += len(data)
# XXX check Content-Length and truncate if too many bytes written?
self._write(data)
self._flush() | [
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s5z/zsim | fb4d6e0475a25cffd23f0687ede2d43d96b4a99f | misc/cpplint.py | python | ResetNolintSuppressions | () | Resets the set of NOLINT suppressions to empty. | Resets the set of NOLINT suppressions to empty. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/html.py | python | HtmlTag.HasParam | (*args, **kwargs) | return _html.HtmlTag_HasParam(*args, **kwargs) | HasParam(self, String par) -> bool | HasParam(self, String par) -> bool | [
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"""HasParam(self, String par) -> bool"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/outwin.py | python | OutputWindow.write | (self, s, tags=(), mark="insert") | return len(s) | Write text to text widget.
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line as it is added.
Args:
s: Text to insert into text widget.
tags: Tuple of tag strings to apply on the insert.
mark: Index for the insert.
Return:
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"""Write text to text widget.
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and updated to display it, giving the effect of seeing each
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Args:
s: Text to insert into text widget.
tags: Tuple of tag strings to apply on the insert.
mark: Index for the insert.
Return:
Length of text inserted.
"""
if isinstance(s, bytes):
s = s.decode(iomenu.encoding, "replace")
self.text.insert(mark, s, tags)
self.text.see(mark)
self.text.update()
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/SIP/sipspectrum.py | python | SIPSpectrum.unifyData | (self, onlydown=False) | Unify data (only one value per frequency) by mean or selection. | Unify data (only one value per frequency) by mean or selection. | [
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"""Unify data (only one value per frequency) by mean or selection."""
if self.f is None:
return
fu = np.unique(self.f)
if len(fu) < len(self.f) or onlydown:
if onlydown:
nonzero = np.nonzero(np.diff(self.f) > 0)[0]
if len(nonzero) > 0:
wende = min(nonzero)
if wende > 0:
self.f = self.f[wende::-1]
self.amp = self.amp[wende::-1]
self.phi = self.phi[wende::-1]
else:
amp = np.zeros(fu.shape)
phi = np.zeros(fu.shape)
for i in range(len(fu)):
ind = np.nonzero(self.f == fu[i])[0]
amp[i] = np.mean(self.amp[ind])
phi[i] = np.mean(self.phi[ind])
self.f = fu
self.amp = amp
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | gpu/command_buffer/build_gles2_cmd_buffer.py | python | PUTnHandler.WriteGLES2ImplementationUnitTest | (self, func, file) | Writes the GLES2 Implemention unit test. | Writes the GLES2 Implemention unit test. | [
"Writes",
"the",
"GLES2",
"Implemention",
"unit",
"test",
"."
] | def WriteGLES2ImplementationUnitTest(self, func, file):
"""Writes the GLES2 Implemention unit test."""
code = """
TEST_F(GLES2ImplementationTest, %(name)s) {
%(type)s data[%(count_param)d][%(count)d] = {{0}};
struct Cmds {
cmds::%(name)sImmediate cmd;
%(type)s data[%(count_param)d][%(count)d];
};
Cmds expected;
for (int ii = 0; ii < %(count_param)d; ++ii) {
for (int jj = 0; jj < %(count)d; ++jj) {
data[ii][jj] = static_cast<%(type)s>(ii * %(count)d + jj);
}
}
expected.cmd.Init(%(cmd_args)s, &data[0][0]);
gl_->%(name)s(%(args)s, &data[0][0]);
EXPECT_EQ(0, memcmp(&expected, commands_, sizeof(expected)));
}
"""
cmd_arg_strings = []
for count, arg in enumerate(func.GetCmdArgs()[0:-2]):
cmd_arg_strings.append(arg.GetValidClientSideCmdArg(func, count, 0))
gl_arg_strings = []
count_param = 0
for count, arg in enumerate(func.GetOriginalArgs()[0:-1]):
gl_arg_strings.append(arg.GetValidClientSideArg(func, count, 0))
if arg.name == "count":
count_param = int(arg.GetValidClientSideArg(func, count, 0))
file.Write(code % {
'name': func.name,
'type': func.GetInfo('data_type'),
'count': func.GetInfo('count'),
'args': ", ".join(gl_arg_strings),
'cmd_args': ", ".join(cmd_arg_strings),
'count_param': count_param,
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ptrkrysik/gr-gsm | 2de47e28ce1fb9a518337bfc0add36c8e3cff5eb | python/qa_gsm_bcch_ccch_sdcch4_demapper.py | python | qa_bcch_ccch_sdcch4_demapper.test_uplink | (self) | BCCH_CCCH_SDCCH4 demapper uplink test | BCCH_CCCH_SDCCH4 demapper uplink test | [
"BCCH_CCCH_SDCCH4",
"demapper",
"uplink",
"test"
] | def test_uplink (self):
"""
BCCH_CCCH_SDCCH4 demapper uplink test
"""
src = grgsm.burst_source(test_data.frames, test_data.timeslots, test_data.bursts)
src.set_arfcn(0x2240); #uplink flag is 40
demapper = grgsm.gsm_bcch_ccch_sdcch4_demapper(timeslot_nr=0)
dst = grgsm.burst_sink()
self.tb.msg_connect(src, "out", demapper, "bursts")
self.tb.msg_connect(demapper, "bursts", dst, "in")
self.tb.run ()
b = test_data.bursts
self.assertEqual(b, list(dst.get_burst_data()))
self.assertEqual([
7, 7, 7, 7, #SDCCH 3
3, 3, #RACCH
135, 135, 135, 135, #SACCH 2
135, 135, 135, 135, #SACCH 3
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, #RACCH
7, 7, 7, 7, #SDCCH 0
7, 7, 7, 7, #SDCCH 1
3, 3, #RACCH
7, 7, 7, 7, #SDCCH 2
7, 7, 7, 7, #SDCCH 3
3, 3, #RACCH
135, 135, 135, 135, #SACCH 0
135, 135, 135, 135, #SACCH 1
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, 3, #RACCH
3, 3, 3, #RACCH
7, 7, 7, 7, #SDCCH 0
7, 7, 7, 7, #SDCCH 1
3, 3, #RACCH
7, 7, 7, 7, #SDCCH 2
7, 7, 7, 7, #SDCCH 3
3, 3, #RACCH
], list(dst.get_sub_types()))
self.assertEqual([
3, 3, 3, 3, #SDCCH 3
0, 0, #RACCH
2, 2, 2, 2, #SACCH 2
3, 3, 3, 3, #SACCH 3
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, #RACCH
0, 0, 0, 0, #SDCCH 0
1, 1, 1, 1, #SDCCH 1
0, 0, #RACCH
2, 2, 2, 2, #SDCCH 2
3, 3, 3, 3, #SDCCH 3
0, 0, #RACCH
0, 0, 0, 0, #SACCH 0
1, 1, 1, 1, #SACCH 1
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, 0, #RACCH
0, 0, 0, #RACCH
0, 0, 0, 0, #SDCCH 0
1, 1, 1, 1, #SDCCH 1
0, 0, #RACCH
2, 2, 2, 2, #SDCCH 2
3, 3, 3, 3, #SDCCH 3
0, 0, #RACCH
], list(dst.get_sub_slots())) | [
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xhzdeng/crpn | a5aef0f80dbe486103123f740c634fb01e6cc9a1 | caffe-fast-rcnn/scripts/cpp_lint.py | python | _CppLintState.SetFilters | (self, filters) | Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter" | Sets the error-message filters. | [
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] | def SetFilters(self, filters):
"""Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
"""
# Default filters always have less priority than the flag ones.
self.filters = _DEFAULT_FILTERS[:]
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError('Every filter in --filters must start with + or -'
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/buffer.py | python | Buffer._set_cursor_position | (self, value) | return value != original_position | Set cursor position. Return whether it changed. | Set cursor position. Return whether it changed. | [
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""" Set cursor position. Return whether it changed. """
original_position = self.__cursor_position
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microsoft/EdgeML | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | tf/edgeml_tf/graph/rnn.py | python | EMI_UGRNN.addBaseAssignOps | (self, graph, initVarList, **kwargs) | Adds Tensorflow assignment operations to all of the model tensors.
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initVarList: A list of numpy matrices that will be used for
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numpy matrices by running these operators
initVarList: A list of numpy matrices that will be used for
initialization by the assignment operation. For EMI_UGRNN, this
should be list of numpy matrices corresponding to [kernel, bias]
'''
assert initVarList is not None
assert len(initVarList) == 2
k_ = graph.get_tensor_by_name('rnn/ugrnn_cell/kernel:0')
b_ = graph.get_tensor_by_name('rnn/ugrnn_cell/bias:0')
kernel, bias = initVarList[-2], initVarList[-1]
k_op = tf.assign(k_, kernel)
b_op = tf.assign(b_, bias)
self.assignOps.extend([k_op, b_op]) | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/mythproto.py | python | FileOps.forgetRecording | (self, program) | FileOps.forgetRecording(program) -> None | FileOps.forgetRecording(program) -> None | [
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"""FileOps.forgetRecording(program) -> None"""
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program.toString()])) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/ttk.py | python | Notebook.identify | (self, x, y) | return self.tk.call(self._w, "identify", x, y) | Returns the name of the tab element at position x, y, or the
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/dtypes/common.py | python | is_timedelta64_dtype | (arr_or_dtype) | return _is_dtype_type(arr_or_dtype, classes(np.timedelta64)) | Check whether an array-like or dtype is of the timedelta64 dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean
Whether or not the array-like or dtype is of the timedelta64 dtype.
Examples
--------
>>> is_timedelta64_dtype(object)
False
>>> is_timedelta64_dtype(np.timedelta64)
True
>>> is_timedelta64_dtype([1, 2, 3])
False
>>> is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
True
>>> is_timedelta64_dtype('0 days')
False | Check whether an array-like or dtype is of the timedelta64 dtype. | [
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Check whether an array-like or dtype is of the timedelta64 dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
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"""
if isinstance(arr_or_dtype, np.dtype):
# GH#33400 fastpath for dtype object
return arr_or_dtype.kind == "m"
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | AboutDialogInfo.SetLicense | (*args, **kwargs) | return _misc_.AboutDialogInfo_SetLicense(*args, **kwargs) | SetLicense(self, String licence)
This is the same as `SetLicence`. | SetLicense(self, String licence) | [
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TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | deps/boost/tools/build/src/tools/common.py | python | check_init_parameters | (toolset, requirement, *args) | return ['/'.join(condition)] | The rule for checking toolset parameters. Trailing parameters should all be
parameter name/value pairs. The rule will check that each parameter either has
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""" The rule for checking toolset parameters. Trailing parameters should all be
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"""
assert isinstance(toolset, basestring)
assert is_iterable_typed(requirement, basestring) or requirement is None
from b2.build import toolset as b2_toolset
if requirement is None:
requirement = []
sig = toolset
condition = replace_grist(toolset, '<toolset>')
subcondition = []
for arg in args:
assert(isinstance(arg, tuple))
assert(len(arg) == 2)
name = arg[0]
value = arg[1]
assert(isinstance(name, str))
assert(isinstance(value, str) or value is None)
str_toolset_name = str((toolset, name))
# FIXME: is this the correct translation?
### if $(value)-is-not-empty
if value is not None:
condition = condition + '-' + value
if str_toolset_name in __had_unspecified_value:
raise BaseException("'%s' initialization: parameter '%s' inconsistent\n" \
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"an explicit value is specified now" % (toolset, name))
# The logic below is for intel compiler. It calls this rule
# with 'intel-linux' and 'intel-win' as toolset, so we need to
# get the base part of toolset name.
# We can't pass 'intel' as toolset, because it that case it will
# be impossible to register versionles intel-linux and
# intel-win of specific version.
t = toolset
m = __re__before_first_dash.match(toolset)
if m:
t = m.group(1)
if str_toolset_name not in __had_value:
if str((t, name)) not in __declared_subfeature:
feature.subfeature('toolset', t, name, [], ['propagated'])
__declared_subfeature[str((t, name))] = True
__had_value[str_toolset_name] = True
feature.extend_subfeature('toolset', t, name, [value])
subcondition += ['<toolset-' + t + ':' + name + '>' + value ]
else:
if str_toolset_name in __had_value:
raise BaseException ("'%s' initialization: parameter '%s' inconsistent\n" \
"an explicit value was specified in an earlier initialization\n" \
"no value is specified now" % (toolset, name))
__had_unspecified_value[str_toolset_name] = True
if value == None: value = ''
sig = sig + value + '-'
# if a requirement is specified, the signature should be unique
# with that requirement
if requirement:
sig += '-' + '-'.join(requirement)
if sig in __all_signatures:
message = "duplicate initialization of '%s' with the following parameters: " % toolset
for arg in args:
name = arg[0]
value = arg[1]
if value == None: value = '<unspecified>'
message += "'%s' = '%s'\n" % (name, value)
raise BaseException(message)
__all_signatures[sig] = True
# FIXME
__init_loc[sig] = "User location unknown" #[ errors.nearest-user-location ] ;
# If we have a requirement, this version should only be applied under that
# condition. To accomplish this we add a toolset requirement that imposes
# the toolset subcondition, which encodes the version.
if requirement:
r = ['<toolset>' + toolset] + requirement
r = ','.join(r)
b2_toolset.add_requirements([r + ':' + c for c in subcondition])
# We add the requirements, if any, to the condition to scope the toolset
# variables and options to this specific version.
condition = [condition]
if requirement:
condition += requirement
if __show_configuration:
print "notice:", condition
return ['/'.join(condition)] | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/ma/core.py | python | _arraymethod.getdoc | (self) | Return the doc of the function (from the doc of the method). | Return the doc of the function (from the doc of the method). | [
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methdoc = getattr(ndarray, self.__name__, None) or \
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fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/pychan/widgets/common.py | python | gui2str | (text) | This function returns an 8-bit representation of the
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to SWIG functions. | This function returns an 8-bit representation of the
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] | def gui2str(text):
"""
This function returns an 8-bit representation of the
unicode string. This is useful for passing strings
to SWIG functions.
"""
try:
return text.__str__()
except:
# String contains non-ascii characters
return text.encode("utf-8") | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | amalgamation/python/mxnet_predict.py | python | _find_lib_path | () | return lib_path | Find mxnet library. | Find mxnet library. | [
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] | def _find_lib_path():
"""Find mxnet library."""
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
api_path = os.path.join(curr_path, '../../lib/')
dll_path = [curr_path, api_path]
dll_path = [os.path.join(p, 'libmxnet.so') for p in dll_path] + \
[os.path.join(p, 'libmxnet_predict.so') for p in dll_path]
lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
if len(lib_path) == 0:
raise RuntimeError('Cannot find the files.\n' +
'List of candidates:\n' + str('\n'.join(dll_path)))
return lib_path | [
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garbear/kodi-steamlink | 3f8e5970b01607cdb3c2688fbaa78e08f2d9c561 | tools/EventClients/lib/python/xbmcclient.py | python | PacketHELO.__init__ | (self, devicename=None, icon_type=ICON_NONE, icon_file=None) | Keyword arguments:
devicename -- the string that identifies the client
icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF
icon_file -- location of icon file with respect to current working
directory if icon_type is not ICON_NONE | Keyword arguments:
devicename -- the string that identifies the client
icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF
icon_file -- location of icon file with respect to current working
directory if icon_type is not ICON_NONE | [
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"""
Keyword arguments:
devicename -- the string that identifies the client
icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF
icon_file -- location of icon file with respect to current working
directory if icon_type is not ICON_NONE
"""
Packet.__init__(self)
self.packettype = PT_HELO
self.icontype = icon_type
self.set_payload ( format_string(devicename)[0:128] )
self.append_payload( chr (icon_type) )
self.append_payload( format_uint16 (0) ) # port no
self.append_payload( format_uint32 (0) ) # reserved1
self.append_payload( format_uint32 (0) ) # reserved2
if icon_type != ICON_NONE and icon_file:
with open(icon_file, 'rb') as f:
self.append_payload(f.read()) | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | Feature.SetNativeData | (self, *args) | return _ogr.Feature_SetNativeData(self, *args) | r"""
SetNativeData(Feature self, char const * nativeData)
void
OGR_F_SetNativeData(OGRFeatureH hFeat, const char *pszNativeData)
Sets the native data for the feature.
The native data is the representation in a "natural" form that comes
from the driver that created this feature, or that is aimed at an
output driver. The native data may be in different format, which is
indicated by OGR_F_GetNativeMediaType().
This function is the same as the C++ method
OGRFeature::SetNativeData().
Parameters:
-----------
hFeat: handle to the feature.
pszNativeData: a string with the native data, or NULL if there is
none.
GDAL 2.1
See:
https://trac.osgeo.org/gdal/wiki/rfc60_improved_roundtripping_in_ogr | r"""
SetNativeData(Feature self, char const * nativeData)
void
OGR_F_SetNativeData(OGRFeatureH hFeat, const char *pszNativeData) | [
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r"""
SetNativeData(Feature self, char const * nativeData)
void
OGR_F_SetNativeData(OGRFeatureH hFeat, const char *pszNativeData)
Sets the native data for the feature.
The native data is the representation in a "natural" form that comes
from the driver that created this feature, or that is aimed at an
output driver. The native data may be in different format, which is
indicated by OGR_F_GetNativeMediaType().
This function is the same as the C++ method
OGRFeature::SetNativeData().
Parameters:
-----------
hFeat: handle to the feature.
pszNativeData: a string with the native data, or NULL if there is
none.
GDAL 2.1
See:
https://trac.osgeo.org/gdal/wiki/rfc60_improved_roundtripping_in_ogr
"""
return _ogr.Feature_SetNativeData(self, *args) | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/Tpm.py | python | Tpm.GetTestResult | (self) | return self.processResponse(respBuf, GetTestResultResponse) | This command returns manufacturer-specific information regarding the
results of a self-test and an indication of the test status.
Returns:
outData - Test result data
contains manufacturer-specific information
testResult - TBD | This command returns manufacturer-specific information regarding the
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] | def GetTestResult(self):
""" This command returns manufacturer-specific information regarding the
results of a self-test and an indication of the test status.
Returns:
outData - Test result data
contains manufacturer-specific information
testResult - TBD
"""
req = TPM2_GetTestResult_REQUEST()
respBuf = self.dispatchCommand(TPM_CC.GetTestResult, req)
return self.processResponse(respBuf, GetTestResultResponse) | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | driver/python/bson/objectid.py | python | ObjectId.__getstate__ | (self) | return self.__id | return value of object for pickling.
needed explicitly because __slots__() defined. | return value of object for pickling.
needed explicitly because __slots__() defined. | [
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] | def __getstate__(self):
"""return value of object for pickling.
needed explicitly because __slots__() defined.
"""
return self.__id | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/lib2to3/fixes/fix_urllib.py | python | FixUrllib.transform_dot | (self, node, results) | Transform for calls to module members in code. | Transform for calls to module members in code. | [
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] | def transform_dot(self, node, results):
"""Transform for calls to module members in code."""
module_dot = results.get("bare_with_attr")
member = results.get("member")
new_name = None
if isinstance(member, list):
member = member[0]
for change in MAPPING[module_dot.value]:
if member.value in change[1]:
new_name = change[0]
break
if new_name:
module_dot.replace(Name(new_name,
prefix=module_dot.prefix))
else:
self.cannot_convert(node, "This is an invalid module element") | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/dis.py | python | dis | (x=None) | Disassemble classes, methods, functions, or code.
With no argument, disassemble the last traceback. | Disassemble classes, methods, functions, or code. | [
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"classes",
"methods",
"functions",
"or",
"code",
"."
] | def dis(x=None):
"""Disassemble classes, methods, functions, or code.
With no argument, disassemble the last traceback.
"""
if x is None:
distb()
return
if type(x) is types.InstanceType:
x = x.__class__
if hasattr(x, 'im_func'):
x = x.im_func
if hasattr(x, 'func_code'):
x = x.func_code
if hasattr(x, '__dict__'):
items = x.__dict__.items()
items.sort()
for name, x1 in items:
if type(x1) in (types.MethodType,
types.FunctionType,
types.CodeType,
types.ClassType):
print "Disassembly of %s:" % name
try:
dis(x1)
except TypeError, msg:
print "Sorry:", msg
print
elif hasattr(x, 'co_code'):
disassemble(x)
elif isinstance(x, str):
disassemble_string(x)
else:
raise TypeError, \
"don't know how to disassemble %s objects" % \
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/SANSUserFileParser.py | python | BackCommandParser._parse | (self, arguments) | Parse the arguments and store the results
@param arguments: the string containing the arguments
@raise RuntimeError: If the argument cannot be parsed correctly | Parse the arguments and store the results | [
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'''
Parse the arguments and store the results
@param arguments: the string containing the arguments
@raise RuntimeError: If the argument cannot be parsed correctly
'''
to_parse = self._prepare_argument(arguments)
if not self.can_attempt_to_parse(arguments):
raise RuntimeError("BackCommandParser: Cannot parse provided arguments."
"They are not compatible")
index = 0
for element in to_parse:
key = self._evaluation_chain[index]
evaluation_method = self._get_method(key)
evaluation_method(element)
index += 1 | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/control_flow_ops.py | python | _GetLoopConstantEnter | (value) | return op if _IsLoopConstantEnter(op) else None | Return the enter op if we can infer `value` to be a loop invariant. | Return the enter op if we can infer `value` to be a loop invariant. | [
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] | def _GetLoopConstantEnter(value):
"""Return the enter op if we can infer `value` to be a loop invariant."""
id_ops = {"Switch", "RefSwitch", "Identity", "RefIdentity"}
op = value.op
while op.type in id_ops:
op = op.inputs[0].op
return op if _IsLoopConstantEnter(op) else None | [
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libfive/libfive | ab5e354cf6fd992f80aaa9432c52683219515c8a | libfive/bind/python/libfive/stdlib/transforms.py | python | twirl_y | (shape, amount, radius, center=(0, 0, 0)) | return Shape(stdlib.twirl_y(
args[0].ptr,
args[1].ptr,
args[2].ptr,
tvec3(*[a.ptr for a in args[3]]))) | Twirls the shape in the y axis about the (optional) center point | Twirls the shape in the y axis about the (optional) center point | [
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""" Twirls the shape in the y axis about the (optional) center point
"""
args = [Shape.wrap(shape), Shape.wrap(amount), Shape.wrap(radius), list([Shape.wrap(i) for i in center])]
return Shape(stdlib.twirl_y(
args[0].ptr,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/py/buffer.py | python | Buffer.addEditor | (self, editor) | Add an editor. | Add an editor. | [
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"""Add an editor."""
self.editor = editor
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magazino/move_base_flex | a8ca484e354a0196b5e9fbda5f0eae311d9b72d3 | git-clang-format.py | python | interpret_args | (args, dash_dash, default_commit) | return commits, files | Interpret `args` as "[commits] [--] [files]" and return (commits, files).
It is assumed that "--" and everything that follows has been removed from
args and placed in `dash_dash`.
If "--" is present (i.e., `dash_dash` is non-empty), the arguments to its
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"""Interpret `args` as "[commits] [--] [files]" and return (commits, files).
It is assumed that "--" and everything that follows has been removed from
args and placed in `dash_dash`.
If "--" is present (i.e., `dash_dash` is non-empty), the arguments to its
left (if present) are taken as commits. Otherwise, the arguments are checked
from left to right if they are commits or files. If commits are not given,
a list with `default_commit` is used."""
if dash_dash:
if len(args) == 0:
commits = [default_commit]
else:
commits = args
for commit in commits:
object_type = get_object_type(commit)
if object_type not in ('commit', 'tag'):
if object_type is None:
die("'%s' is not a commit" % commit)
else:
die("'%s' is a %s, but a commit was expected" % (commit, object_type))
files = dash_dash[1:]
elif args:
commits = []
while args:
if not disambiguate_revision(args[0]):
break
commits.append(args.pop(0))
if not commits:
commits = [default_commit]
files = args
else:
commits = [default_commit]
files = []
return commits, files | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/profiledir.py | python | ProfileDir.find_profile_dir | (cls, profile_dir, config=None) | return cls(location=profile_dir, config=config) | Find/create a profile dir and return its ProfileDir.
This will create the profile directory if it doesn't exist.
Parameters
----------
profile_dir : unicode or str
The path of the profile directory. | Find/create a profile dir and return its ProfileDir. | [
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] | def find_profile_dir(cls, profile_dir, config=None):
"""Find/create a profile dir and return its ProfileDir.
This will create the profile directory if it doesn't exist.
Parameters
----------
profile_dir : unicode or str
The path of the profile directory.
"""
profile_dir = expand_path(profile_dir)
if not os.path.isdir(profile_dir):
raise ProfileDirError('Profile directory not found: %s' % profile_dir)
return cls(location=profile_dir, config=config) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2.py | python | parserCtxt.parseCDSect | (self) | Parse escaped pure raw content. [18] CDSect ::= CDStart
CData CDEnd [19] CDStart ::= '<![CDATA[' [20] Data ::=
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Runtime/VART/vart/rnn-runner/apps/imdb_sentiment_detection/utils/hdf5_format.py | python | load_weights_from_hdf5_group | (f, layers) | Implements topological (order-based) weight loading.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file. | Implements topological (order-based) weight loading. | [
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] | def load_weights_from_hdf5_group(f, layers):
"""Implements topological (order-based) weight loading.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version']
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend']
else:
original_backend = None
filtered_layers = []
for layer in layers:
weights = _legacy_weights(layer)
if weights:
filtered_layers.append(layer)
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(filtered_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' + str(len(filtered_layers)) +
' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
layer = filtered_layers[k]
symbolic_weights = _legacy_weights(layer)
weight_values = preprocess_weights_for_loading(
layer, weight_values, original_keras_version, original_backend)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name + ' in the save file. '
'However the new layer ' + layer.name + ' expects ' +
str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) + ' elements.')
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListMainWindow.HideWindows | (self) | Hides the windows associated to the items. Used internally. | Hides the windows associated to the items. Used internally. | [
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] | def HideWindows(self):
""" Hides the windows associated to the items. Used internally. """
for child in self._itemWithWindow:
wnd = child.GetWindow()
if wnd:
wnd.Hide() | [
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ivansafrin/Polycode | 37a40fefe194ec7f6e9d1257f3bb3517b0a168bc | Bindings/Scripts/create_lua_library/CppHeaderParser3.py | python | is_namespace | (nameStack) | return False | Determines if a namespace is being specified | Determines if a namespace is being specified | [
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"""Determines if a namespace is being specified"""
if len(nameStack) == 0:
return False
if nameStack[0] == "namespace":
return True
return False | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/internal/python_message.py | python | _AddEqualsMethod | (message_descriptor, cls) | Helper for _AddMessageMethods(). | Helper for _AddMessageMethods(). | [
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] | def _AddEqualsMethod(message_descriptor, cls):
"""Helper for _AddMessageMethods()."""
def __eq__(self, other):
if (not isinstance(other, message_mod.Message) or
other.DESCRIPTOR != self.DESCRIPTOR):
return False
if self is other:
return True
if self.DESCRIPTOR.full_name == _AnyFullTypeName:
any_a = _InternalUnpackAny(self)
any_b = _InternalUnpackAny(other)
if any_a and any_b:
return any_a == any_b
if not self.ListFields() == other.ListFields():
return False
# Sort unknown fields because their order shouldn't affect equality test.
unknown_fields = list(self._unknown_fields)
unknown_fields.sort()
other_unknown_fields = list(other._unknown_fields)
other_unknown_fields.sort()
return unknown_fields == other_unknown_fields
cls.__eq__ = __eq__ | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_PolicyGetDigest_REQUEST.__init__ | (self, policySession = TPM_HANDLE()) | This command returns the current policyDigest of the session. This
command allows the TPM to be used to perform the actions required to
pre-compute the authPolicy for an object.
Attributes:
policySession (TPM_HANDLE): Handle for the policy session
Auth Index: None | This command returns the current policyDigest of the session. This
command allows the TPM to be used to perform the actions required to
pre-compute the authPolicy for an object. | [
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Attributes:
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smartdevicelink/sdl_core | 68f082169e0a40fccd9eb0db3c83911c28870f07 | tools/InterfaceGenerator/MsgVersionGenerate.py | python | store_data_to_file | (path_to_storage, data_for_storage) | Stores data with major and minor version
to file generated_msg_version.h | Stores data with major and minor version
to file generated_msg_version.h | [
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"""
path_to_storage = path_to_storage + "/generated_msg_version.h"
fh = open(path_to_storage, 'w')
fh.write(data_for_storage)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/dist.py | python | DistributionMetadata.write_pkg_file | (self, file) | Write the PKG-INFO format data to a file object. | Write the PKG-INFO format data to a file object. | [
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"""Write the PKG-INFO format data to a file object.
"""
version = '1.0'
if (self.provides or self.requires or self.obsoletes or
self.classifiers or self.download_url):
version = '1.1'
file.write('Metadata-Version: %s\n' % version)
file.write('Name: %s\n' % self.get_name())
file.write('Version: %s\n' % self.get_version())
file.write('Summary: %s\n' % self.get_description())
file.write('Home-page: %s\n' % self.get_url())
file.write('Author: %s\n' % self.get_contact())
file.write('Author-email: %s\n' % self.get_contact_email())
file.write('License: %s\n' % self.get_license())
if self.download_url:
file.write('Download-URL: %s\n' % self.download_url)
long_desc = rfc822_escape(self.get_long_description())
file.write('Description: %s\n' % long_desc)
keywords = ','.join(self.get_keywords())
if keywords:
file.write('Keywords: %s\n' % keywords)
self._write_list(file, 'Platform', self.get_platforms())
self._write_list(file, 'Classifier', self.get_classifiers())
# PEP 314
self._write_list(file, 'Requires', self.get_requires())
self._write_list(file, 'Provides', self.get_provides())
self._write_list(file, 'Obsoletes', self.get_obsoletes()) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/molpro2.py | python | psi4_list | () | return procedures['energy'].keys() | Return an array of Psi4 methods with energies. | Return an array of Psi4 methods with energies. | [
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"""Return an array of Psi4 methods with energies.
"""
return procedures['energy'].keys() | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/fibonacci-number.py | python | Solution.fib | (self, N) | return matrix_mult([[1, 0]], matrix_expo(T, N))[0][1] | :type N: int
:rtype: int | :type N: int
:rtype: int | [
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"""
:type N: int
:rtype: int
"""
def matrix_expo(A, K):
result = [[int(i==j) for j in xrange(len(A))] \
for i in xrange(len(A))]
while K:
if K % 2:
result = matrix_mult(result, A)
A = matrix_mult(A, A)
K /= 2
return result
def matrix_mult(A, B):
ZB = zip(*B)
return [[sum(a*b for a, b in itertools.izip(row, col)) \
for col in ZB] for row in A]
T = [[1, 1],
[1, 0]]
return matrix_mult([[1, 0]], matrix_expo(T, N))[0][1] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ebmlib/fileutil.py | python | GetPathName | (path) | return os.path.split(path)[0] | Gets the path minus filename
@param path: full path to get base of | Gets the path minus filename
@param path: full path to get base of | [
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"""Gets the path minus filename
@param path: full path to get base of
"""
return os.path.split(path)[0] | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/composite/multitype_ops/negative_impl.py | python | _neg_scalar | (x) | return F.scalar_usub(x) | Returns the negative value of scalar x.
Outputs:
Number, negative value of x. | Returns the negative value of scalar x. | [
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] | def _neg_scalar(x):
"""
Returns the negative value of scalar x.
Outputs:
Number, negative value of x.
"""
return F.scalar_usub(x) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiPaneInfo.HasMinimizeButton | (*args, **kwargs) | return _aui.AuiPaneInfo_HasMinimizeButton(*args, **kwargs) | HasMinimizeButton(self) -> bool | HasMinimizeButton(self) -> bool | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/urllib3/request.py | python | RequestMethods.request | (self, method, url, fields=None, headers=None, **urlopen_kw) | Make a request using :meth:`urlopen` with the appropriate encoding of
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This is a convenience method that requires the least amount of manual
effort. It can be used in most situations, while still having the
option to drop down to more specific methods when necessary, such as
:meth:`request_encode_url`, :meth:`request_encode_body`,
or even the lowest level :meth:`urlopen`. | Make a request using :meth:`urlopen` with the appropriate encoding of
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | Event.GetEventCategory | (*args, **kwargs) | return _core_.Event_GetEventCategory(*args, **kwargs) | GetEventCategory(self) -> int | GetEventCategory(self) -> int | [
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"""GetEventCategory(self) -> int"""
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/serial/urlhandler/protocol_alt.py | python | serial_class_for_url | (url) | return (''.join([parts.netloc, parts.path]), cls) | extract host and port from an URL string | extract host and port from an URL string | [
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"""extract host and port from an URL string"""
parts = urlparse.urlsplit(url)
if parts.scheme != 'alt':
raise serial.SerialException(
'expected a string in the form "alt://port[?option[=value][&option[=value]]]": '
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class_name = 'Serial'
try:
for option, values in urlparse.parse_qs(parts.query, True).items():
if option == 'class':
class_name = values[0]
else:
raise ValueError('unknown option: {!r}'.format(option))
except ValueError as e:
raise serial.SerialException(
'expected a string in the form '
'"alt://port[?option[=value][&option[=value]]]": {!r}'.format(e))
if not hasattr(serial, class_name):
raise ValueError('unknown class: {!r}'.format(class_name))
cls = getattr(serial, class_name)
if not issubclass(cls, serial.Serial):
raise ValueError('class {!r} is not an instance of Serial'.format(class_name))
return (''.join([parts.netloc, parts.path]), cls) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | NDArray._fresh_grad | (self) | return out.value | Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array). | Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset. | [
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"been",
"updated",
"by",
"autograd",
".",
"backward",
"since",
"last",
"reset",
"."
] | def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
"""
out = ctypes.c_int()
check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out)))
return out.value | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/RNN/rnn_quantizer/pytorch_binding/pytorch_nndct/parse/parser.py | python | TorchParser._convert_blob_tensor_type | (graph) | r"""convert torch tensor info to nndct tensor info | r"""convert torch tensor info to nndct tensor info | [
"r",
"convert",
"torch",
"tensor",
"info",
"to",
"nndct",
"tensor",
"info"
] | def _convert_blob_tensor_type(graph):
r"""convert torch tensor info to nndct tensor info"""
for blob_tensor in graph.tensors:
# tensor_util.convert_blob_tensor_format(blob_tensor,
# tensor_util.FrameworkType.TORCH,
# tensor_util.FrameworkType.NNDCT)
blob_tensor.dtype = convert_dtype(blob_tensor.dtype) | [
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MegEngine/MegEngine | ce9ad07a27ec909fb8db4dd67943d24ba98fb93a | imperative/python/megengine/core/tensor/array_method.py | python | ArrayMethodMixin.sum | (self, axis=None, keepdims: bool = False) | return _reduce("sum")(self, axis, keepdims) | r"""Returns the sum of each row of the input tensor in the given dimension ``axis``.
If ``axis`` is a list of axises, reduce over all of them.
If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
except in the dimension(s) ``axis`` where it is of size 1.
Otherwise, ``axis`` is squeezed (see :func:`~.squeeze`).
Args:
axis: the dimension or dimensions to reduce.
keepdims: whether the output tensor has ndim retained or not.
Returns:
output tensor.
Examples:
.. testcode::
from megengine import tensor
a = tensor([False, True, True, False])
b = tensor([1.0, 2.0, 3.0, 4.0])
print(a.sum().numpy())
print(b.sum().numpy())
Outputs:
.. testoutput::
2
10.0 | r"""Returns the sum of each row of the input tensor in the given dimension ``axis``. | [
"r",
"Returns",
"the",
"sum",
"of",
"each",
"row",
"of",
"the",
"input",
"tensor",
"in",
"the",
"given",
"dimension",
"axis",
"."
] | def sum(self, axis=None, keepdims: bool = False):
r"""Returns the sum of each row of the input tensor in the given dimension ``axis``.
If ``axis`` is a list of axises, reduce over all of them.
If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
except in the dimension(s) ``axis`` where it is of size 1.
Otherwise, ``axis`` is squeezed (see :func:`~.squeeze`).
Args:
axis: the dimension or dimensions to reduce.
keepdims: whether the output tensor has ndim retained or not.
Returns:
output tensor.
Examples:
.. testcode::
from megengine import tensor
a = tensor([False, True, True, False])
b = tensor([1.0, 2.0, 3.0, 4.0])
print(a.sum().numpy())
print(b.sum().numpy())
Outputs:
.. testoutput::
2
10.0
"""
return _reduce("sum")(self, axis, keepdims) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/backend.py | python | depthwise_conv2d | (x,
depthwise_kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)) | return x | 2D convolution with separable filters.
Args:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`. | 2D convolution with separable filters. | [
"2D",
"convolution",
"with",
"separable",
"filters",
"."
] | def depthwise_conv2d(x,
depthwise_kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D convolution with separable filters.
Args:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last` or
`channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format: ' + str(data_format))
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = nn.depthwise_conv2d(
x,
depthwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x | [
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