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hakuna-m/wubiuefi
|
caec1af0a09c78fd5a345180ada1fe45e0c63493
|
src/pypack/altgraph/Dot.py
|
python
|
Dot.edge_style
|
(self, head, tail, **kwargs)
|
Modifies an edge style to the dot representation.
|
Modifies an edge style to the dot representation.
|
[
"Modifies",
"an",
"edge",
"style",
"to",
"the",
"dot",
"representation",
"."
] |
def edge_style(self, head, tail, **kwargs):
'''
Modifies an edge style to the dot representation.
'''
try:
if tail not in self.edges[head]:
self.edges[head][tail]= {}
self.edges[head][tail] = kwargs
except KeyError:
raise GraphError("invalid edge %s -> %s " % (head, tail) )
|
[
"def",
"edge_style",
"(",
"self",
",",
"head",
",",
"tail",
",",
"*",
"*",
"kwargs",
")",
":",
"try",
":",
"if",
"tail",
"not",
"in",
"self",
".",
"edges",
"[",
"head",
"]",
":",
"self",
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"edges",
"[",
"head",
"]",
"[",
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"]",
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"raise",
"GraphError",
"(",
"\"invalid edge %s -> %s \"",
"%",
"(",
"head",
",",
"tail",
")",
")"
] |
https://github.com/hakuna-m/wubiuefi/blob/caec1af0a09c78fd5a345180ada1fe45e0c63493/src/pypack/altgraph/Dot.py#L177-L186
|
||
include-what-you-use/include-what-you-use
|
208fbfffa5d69364b9f78e427caa443441279283
|
fix_includes.py
|
python
|
_CalculateMoveSpans
|
(file_lines, forward_declare_spans)
|
Fills each input_line's move_span field.
A 'move span' is a range of lines (from file_lines) that includes
an #include or forward-declare, and all the comments preceding it.
It is the unit we would move if we decided to move (or delete) this
#include or forward-declare.
For lines of type _INCLUDE_RE or _FORWARD_DECLARE_RE, the move span
is set to the tuple [start_of_span, end_of_span). All other lines
have the move span kept at None.
Arguments:
file_lines: an array of LineInfo objects, with .type fields filled in.
forward_declare_spans: a set of line-number pairs
[start_line, end_line), each representing a single namespace.
In practice this comes from iwyu_record.seen_forward_declare_lines.
|
Fills each input_line's move_span field.
|
[
"Fills",
"each",
"input_line",
"s",
"move_span",
"field",
"."
] |
def _CalculateMoveSpans(file_lines, forward_declare_spans):
"""Fills each input_line's move_span field.
A 'move span' is a range of lines (from file_lines) that includes
an #include or forward-declare, and all the comments preceding it.
It is the unit we would move if we decided to move (or delete) this
#include or forward-declare.
For lines of type _INCLUDE_RE or _FORWARD_DECLARE_RE, the move span
is set to the tuple [start_of_span, end_of_span). All other lines
have the move span kept at None.
Arguments:
file_lines: an array of LineInfo objects, with .type fields filled in.
forward_declare_spans: a set of line-number pairs
[start_line, end_line), each representing a single namespace.
In practice this comes from iwyu_record.seen_forward_declare_lines.
"""
# First let's do #includes.
for line_number in range(len(file_lines)):
if file_lines[line_number].type == _INCLUDE_RE:
span_begin = _LineNumberStartingPrecedingComments(file_lines, line_number)
for i in range(span_begin, line_number + 1):
file_lines[i].move_span = (span_begin, line_number + 1)
# Now forward-declares. These spans come as input to this function.
for (span_begin, span_end) in forward_declare_spans:
span_begin = _LineNumberStartingPrecedingComments(file_lines, span_begin)
for i in range(span_begin, span_end):
file_lines[i].move_span = (span_begin, span_end)
|
[
"def",
"_CalculateMoveSpans",
"(",
"file_lines",
",",
"forward_declare_spans",
")",
":",
"# First let's do #includes.",
"for",
"line_number",
"in",
"range",
"(",
"len",
"(",
"file_lines",
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":",
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"file_lines",
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"line_number",
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"type",
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"_INCLUDE_RE",
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"span_begin",
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"file_lines",
",",
"line_number",
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"span_begin",
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"line_number",
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"span_begin",
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"line_number",
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"# Now forward-declares. These spans come as input to this function.",
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",",
"span_end",
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":",
"file_lines",
"[",
"i",
"]",
".",
"move_span",
"=",
"(",
"span_begin",
",",
"span_end",
")"
] |
https://github.com/include-what-you-use/include-what-you-use/blob/208fbfffa5d69364b9f78e427caa443441279283/fix_includes.py#L860-L889
|
||
google/zooshi
|
05390b98f79eed8ef26ec4ab2c3aea3790b1165c
|
scripts/build_assets.py
|
python
|
main
|
()
|
return builder.main(
project_root=PROJECT_ROOT,
assets_path=ASSETS_PATH,
asset_meta=ASSET_META,
asset_roots=ASSET_ROOTS,
intermediate_path=INTERMEDIATE_TEXTURE_PATH,
overlay_dirs=OVERLAY_DIRS,
tga_files_to_convert=tga_files_to_convert,
png_files_to_convert=png_files_to_convert,
anim_files_to_convert=anim_files_to_convert,
fbx_files_to_convert=fbx_files_to_convert,
flatbuffers_conversion_data=lambda: FLATBUFFERS_CONVERSION_DATA,
schema_output_path='flatbufferschemas')
|
Builds or cleans the assets needed for the game.
To build all assets, either call this script without any arguments. Or
alternatively, call it with the argument 'all'. To just convert the
flatbuffer json files, call it with 'flatbuffers'. Likewise to convert the
png files to webp files, call it with 'webp'. To clean all converted files,
call it with 'clean'.
Returns:
Returns 0 on success.
|
Builds or cleans the assets needed for the game.
|
[
"Builds",
"or",
"cleans",
"the",
"assets",
"needed",
"for",
"the",
"game",
"."
] |
def main():
"""Builds or cleans the assets needed for the game.
To build all assets, either call this script without any arguments. Or
alternatively, call it with the argument 'all'. To just convert the
flatbuffer json files, call it with 'flatbuffers'. Likewise to convert the
png files to webp files, call it with 'webp'. To clean all converted files,
call it with 'clean'.
Returns:
Returns 0 on success.
"""
return builder.main(
project_root=PROJECT_ROOT,
assets_path=ASSETS_PATH,
asset_meta=ASSET_META,
asset_roots=ASSET_ROOTS,
intermediate_path=INTERMEDIATE_TEXTURE_PATH,
overlay_dirs=OVERLAY_DIRS,
tga_files_to_convert=tga_files_to_convert,
png_files_to_convert=png_files_to_convert,
anim_files_to_convert=anim_files_to_convert,
fbx_files_to_convert=fbx_files_to_convert,
flatbuffers_conversion_data=lambda: FLATBUFFERS_CONVERSION_DATA,
schema_output_path='flatbufferschemas')
|
[
"def",
"main",
"(",
")",
":",
"return",
"builder",
".",
"main",
"(",
"project_root",
"=",
"PROJECT_ROOT",
",",
"assets_path",
"=",
"ASSETS_PATH",
",",
"asset_meta",
"=",
"ASSET_META",
",",
"asset_roots",
"=",
"ASSET_ROOTS",
",",
"intermediate_path",
"=",
"INTERMEDIATE_TEXTURE_PATH",
",",
"overlay_dirs",
"=",
"OVERLAY_DIRS",
",",
"tga_files_to_convert",
"=",
"tga_files_to_convert",
",",
"png_files_to_convert",
"=",
"png_files_to_convert",
",",
"anim_files_to_convert",
"=",
"anim_files_to_convert",
",",
"fbx_files_to_convert",
"=",
"fbx_files_to_convert",
",",
"flatbuffers_conversion_data",
"=",
"lambda",
":",
"FLATBUFFERS_CONVERSION_DATA",
",",
"schema_output_path",
"=",
"'flatbufferschemas'",
")"
] |
https://github.com/google/zooshi/blob/05390b98f79eed8ef26ec4ab2c3aea3790b1165c/scripts/build_assets.py#L217-L241
|
|
mindspore-ai/mindspore
|
fb8fd3338605bb34fa5cea054e535a8b1d753fab
|
mindspore/python/mindspore/ops/_op_impl/tbe/sparse_apply_ftrl_v2.py
|
python
|
_sparse_apply_ftrl_v2_tbe
|
()
|
return
|
SparseApplyFtrlV2D TBE register
|
SparseApplyFtrlV2D TBE register
|
[
"SparseApplyFtrlV2D",
"TBE",
"register"
] |
def _sparse_apply_ftrl_v2_tbe():
"""SparseApplyFtrlV2D TBE register"""
return
|
[
"def",
"_sparse_apply_ftrl_v2_tbe",
"(",
")",
":",
"return"
] |
https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/_op_impl/tbe/sparse_apply_ftrl_v2.py#L48-L50
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/logging/handlers.py
|
python
|
BaseRotatingHandler.emit
|
(self, record)
|
Emit a record.
Output the record to the file, catering for rollover as described
in doRollover().
|
Emit a record.
|
[
"Emit",
"a",
"record",
"."
] |
def emit(self, record):
"""
Emit a record.
Output the record to the file, catering for rollover as described
in doRollover().
"""
try:
if self.shouldRollover(record):
self.doRollover()
logging.FileHandler.emit(self, record)
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record)
|
[
"def",
"emit",
"(",
"self",
",",
"record",
")",
":",
"try",
":",
"if",
"self",
".",
"shouldRollover",
"(",
"record",
")",
":",
"self",
".",
"doRollover",
"(",
")",
"logging",
".",
"FileHandler",
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"self",
",",
"record",
")",
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",",
"SystemExit",
")",
":",
"raise",
"except",
":",
"self",
".",
"handleError",
"(",
"record",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/logging/handlers.py#L68-L82
|
||
scylladb/dpdk
|
cc7e6ed22c0fc08e3ff37b3e68a61979d8214547
|
tools/dpdk_nic_bind.py
|
python
|
get_nic_details
|
()
|
This function populates the "devices" dictionary. The keys used are
the pci addresses (domain:bus:slot.func). The values are themselves
dictionaries - one for each NIC.
|
This function populates the "devices" dictionary. The keys used are
the pci addresses (domain:bus:slot.func). The values are themselves
dictionaries - one for each NIC.
|
[
"This",
"function",
"populates",
"the",
"devices",
"dictionary",
".",
"The",
"keys",
"used",
"are",
"the",
"pci",
"addresses",
"(",
"domain",
":",
"bus",
":",
"slot",
".",
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")",
".",
"The",
"values",
"are",
"themselves",
"dictionaries",
"-",
"one",
"for",
"each",
"NIC",
"."
] |
def get_nic_details():
'''This function populates the "devices" dictionary. The keys used are
the pci addresses (domain:bus:slot.func). The values are themselves
dictionaries - one for each NIC.'''
global devices
global dpdk_drivers
# clear any old data
devices = {}
# first loop through and read details for all devices
# request machine readable format, with numeric IDs
dev = {};
dev_lines = check_output(["lspci", "-Dvmmn"]).splitlines()
for dev_line in dev_lines:
if (len(dev_line) == 0):
if dev["Class"] == ETHERNET_CLASS:
#convert device and vendor ids to numbers, then add to global
dev["Vendor"] = int(dev["Vendor"],16)
dev["Device"] = int(dev["Device"],16)
devices[dev["Slot"]] = dict(dev) # use dict to make copy of dev
else:
name, value = dev_line.split("\t", 1)
dev[name.rstrip(":")] = value
# check what is the interface if any for an ssh connection if
# any to this host, so we can mark it later.
ssh_if = []
route = check_output(["ip", "-o", "route"])
# filter out all lines for 169.254 routes
route = "\n".join(filter(lambda ln: not ln.startswith("169.254"),
route.splitlines()))
rt_info = route.split()
for i in xrange(len(rt_info) - 1):
if rt_info[i] == "dev":
ssh_if.append(rt_info[i+1])
# based on the basic info, get extended text details
for d in devices.keys():
# get additional info and add it to existing data
devices[d] = dict(devices[d].items() +
get_pci_device_details(d).items())
for _if in ssh_if:
if _if in devices[d]["Interface"].split(","):
devices[d]["Ssh_if"] = True
devices[d]["Active"] = "*Active*"
break;
# add igb_uio to list of supporting modules if needed
if "Module_str" in devices[d]:
for driver in dpdk_drivers:
if driver not in devices[d]["Module_str"]:
devices[d]["Module_str"] = devices[d]["Module_str"] + ",%s" % driver
else:
devices[d]["Module_str"] = ",".join(dpdk_drivers)
# make sure the driver and module strings do not have any duplicates
if has_driver(d):
modules = devices[d]["Module_str"].split(",")
if devices[d]["Driver_str"] in modules:
modules.remove(devices[d]["Driver_str"])
devices[d]["Module_str"] = ",".join(modules)
|
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] |
https://github.com/scylladb/dpdk/blob/cc7e6ed22c0fc08e3ff37b3e68a61979d8214547/tools/dpdk_nic_bind.py#L216-L277
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/framework/tensor_shape.py
|
python
|
Dimension.__le__
|
(self, other)
|
Returns True if `self` is known to be less than or equal to `other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) <= tf.Dimension(n)) == (m <= n)
(tf.Dimension(m) <= tf.Dimension(None)) == None
(tf.Dimension(None) <= tf.Dimension(n)) == None
(tf.Dimension(None) <= tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value <= other.value` if both are known, otherwise
None.
|
Returns True if `self` is known to be less than or equal to `other`.
|
[
"Returns",
"True",
"if",
"self",
"is",
"known",
"to",
"be",
"less",
"than",
"or",
"equal",
"to",
"other",
"."
] |
def __le__(self, other):
"""Returns True if `self` is known to be less than or equal to `other`.
Dimensions are compared as follows:
```python
(tf.Dimension(m) <= tf.Dimension(n)) == (m <= n)
(tf.Dimension(m) <= tf.Dimension(None)) == None
(tf.Dimension(None) <= tf.Dimension(n)) == None
(tf.Dimension(None) <= tf.Dimension(None)) == None
```
Args:
other: Another Dimension.
Returns:
The value of `self.value <= other.value` if both are known, otherwise
None.
"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value <= other.value
|
[
"def",
"__le__",
"(",
"self",
",",
"other",
")",
":",
"other",
"=",
"as_dimension",
"(",
"other",
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"self",
".",
"_value",
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"None",
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"value",
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"return",
"None",
"else",
":",
"return",
"self",
".",
"_value",
"<=",
"other",
".",
"value"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/framework/tensor_shape.py#L305-L328
|
||
gem5/gem5
|
141cc37c2d4b93959d4c249b8f7e6a8b2ef75338
|
src/python/gem5/components/cachehierarchies/chi/private_l1_cache_hierarchy.py
|
python
|
PrivateL1CacheHierarchy.__init__
|
(self, size: str, assoc: int)
|
:param size: The size of the priavte I/D caches in the hierarchy.
:param assoc: The associativity of each cache.
|
:param size: The size of the priavte I/D caches in the hierarchy.
:param assoc: The associativity of each cache.
|
[
":",
"param",
"size",
":",
"The",
"size",
"of",
"the",
"priavte",
"I",
"/",
"D",
"caches",
"in",
"the",
"hierarchy",
".",
":",
"param",
"assoc",
":",
"The",
"associativity",
"of",
"each",
"cache",
"."
] |
def __init__(self, size: str, assoc: int) -> None:
"""
:param size: The size of the priavte I/D caches in the hierarchy.
:param assoc: The associativity of each cache.
"""
super().__init__()
self._size = size
self._assoc = assoc
|
[
"def",
"__init__",
"(",
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"size",
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"str",
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":",
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"(",
")",
"self",
".",
"_size",
"=",
"size",
"self",
".",
"_assoc",
"=",
"assoc"
] |
https://github.com/gem5/gem5/blob/141cc37c2d4b93959d4c249b8f7e6a8b2ef75338/src/python/gem5/components/cachehierarchies/chi/private_l1_cache_hierarchy.py#L71-L79
|
||
hakuna-m/wubiuefi
|
caec1af0a09c78fd5a345180ada1fe45e0c63493
|
src/pypack/modulegraph/pkg_resources.py
|
python
|
WorkingSet.iter_entry_points
|
(self, group, name=None)
|
Yield entry point objects from `group` matching `name`
If `name` is None, yields all entry points in `group` from all
distributions in the working set, otherwise only ones matching
both `group` and `name` are yielded (in distribution order).
|
Yield entry point objects from `group` matching `name`
|
[
"Yield",
"entry",
"point",
"objects",
"from",
"group",
"matching",
"name"
] |
def iter_entry_points(self, group, name=None):
"""Yield entry point objects from `group` matching `name`
If `name` is None, yields all entry points in `group` from all
distributions in the working set, otherwise only ones matching
both `group` and `name` are yielded (in distribution order).
"""
for dist in self:
entries = dist.get_entry_map(group)
if name is None:
for ep in entries.values():
yield ep
elif name in entries:
yield entries[name]
|
[
"def",
"iter_entry_points",
"(",
"self",
",",
"group",
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"name",
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"None",
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"elif",
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"entries",
":",
"yield",
"entries",
"[",
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] |
https://github.com/hakuna-m/wubiuefi/blob/caec1af0a09c78fd5a345180ada1fe45e0c63493/src/pypack/modulegraph/pkg_resources.py#L350-L363
|
||
baidu/Familia
|
958febfd5fe7a61e46a35bfb084e71f806dde6a6
|
python/familia_wrapper.py
|
python
|
TopicalWordEmbeddingsWrapper.__init__
|
(self, model_dir, emb_file)
|
初始化TWE对象
Args:
model_dir: 模型目录路径
emb_file: topical word embeddings模型文件
|
初始化TWE对象
Args:
model_dir: 模型目录路径
emb_file: topical word embeddings模型文件
|
[
"初始化TWE对象",
"Args",
":",
"model_dir",
":",
"模型目录路径",
"emb_file",
":",
"topical",
"word",
"embeddings模型文件"
] |
def __init__(self, model_dir, emb_file):
"""初始化TWE对象
Args:
model_dir: 模型目录路径
emb_file: topical word embeddings模型文件
"""
self._twe = familia.init_twe(model_dir, emb_file)
|
[
"def",
"__init__",
"(",
"self",
",",
"model_dir",
",",
"emb_file",
")",
":",
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".",
"_twe",
"=",
"familia",
".",
"init_twe",
"(",
"model_dir",
",",
"emb_file",
")"
] |
https://github.com/baidu/Familia/blob/958febfd5fe7a61e46a35bfb084e71f806dde6a6/python/familia_wrapper.py#L190-L196
|
||
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/eager/monitoring.py
|
python
|
CounterCell.increase_by
|
(self, value)
|
Atomically increments the value.
Args:
value: non-negative value.
|
Atomically increments the value.
|
[
"Atomically",
"increments",
"the",
"value",
"."
] |
def increase_by(self, value):
"""Atomically increments the value.
Args:
value: non-negative value.
"""
pywrap_tensorflow.TFE_MonitoringCounterCellIncrementBy(self._cell, value)
|
[
"def",
"increase_by",
"(",
"self",
",",
"value",
")",
":",
"pywrap_tensorflow",
".",
"TFE_MonitoringCounterCellIncrementBy",
"(",
"self",
".",
"_cell",
",",
"value",
")"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/eager/monitoring.py#L153-L159
|
||
CRYTEK/CRYENGINE
|
232227c59a220cbbd311576f0fbeba7bb53b2a8c
|
Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/database.py
|
python
|
EggInfoDistribution.check_installed_files
|
(self)
|
return mismatches
|
Checks that the hashes and sizes of the files in ``RECORD`` are
matched by the files themselves. Returns a (possibly empty) list of
mismatches. Each entry in the mismatch list will be a tuple consisting
of the path, 'exists', 'size' or 'hash' according to what didn't match
(existence is checked first, then size, then hash), the expected
value and the actual value.
|
Checks that the hashes and sizes of the files in ``RECORD`` are
matched by the files themselves. Returns a (possibly empty) list of
mismatches. Each entry in the mismatch list will be a tuple consisting
of the path, 'exists', 'size' or 'hash' according to what didn't match
(existence is checked first, then size, then hash), the expected
value and the actual value.
|
[
"Checks",
"that",
"the",
"hashes",
"and",
"sizes",
"of",
"the",
"files",
"in",
"RECORD",
"are",
"matched",
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"the",
"files",
"themselves",
".",
"Returns",
"a",
"(",
"possibly",
"empty",
")",
"list",
"of",
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".",
"Each",
"entry",
"in",
"the",
"mismatch",
"list",
"will",
"be",
"a",
"tuple",
"consisting",
"of",
"the",
"path",
"exists",
"size",
"or",
"hash",
"according",
"to",
"what",
"didn",
"t",
"match",
"(",
"existence",
"is",
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"first",
"then",
"size",
"then",
"hash",
")",
"the",
"expected",
"value",
"and",
"the",
"actual",
"value",
"."
] |
def check_installed_files(self):
"""
Checks that the hashes and sizes of the files in ``RECORD`` are
matched by the files themselves. Returns a (possibly empty) list of
mismatches. Each entry in the mismatch list will be a tuple consisting
of the path, 'exists', 'size' or 'hash' according to what didn't match
(existence is checked first, then size, then hash), the expected
value and the actual value.
"""
mismatches = []
record_path = os.path.join(self.path, 'installed-files.txt')
if os.path.exists(record_path):
for path, _, _ in self.list_installed_files():
if path == record_path:
continue
if not os.path.exists(path):
mismatches.append((path, 'exists', True, False))
return mismatches
|
[
"def",
"check_installed_files",
"(",
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")",
":",
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"=",
"[",
"]",
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",",
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",",
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")",
")",
"return",
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https://github.com/CRYTEK/CRYENGINE/blob/232227c59a220cbbd311576f0fbeba7bb53b2a8c/Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/database.py#L949-L966
|
|
FreeCAD/FreeCAD
|
ba42231b9c6889b89e064d6d563448ed81e376ec
|
src/Mod/Draft/draftguitools/gui_labels.py
|
python
|
Label.Activated
|
(self)
|
Execute when the command is called.
|
Execute when the command is called.
|
[
"Execute",
"when",
"the",
"command",
"is",
"called",
"."
] |
def Activated(self):
"""Execute when the command is called."""
super(Label, self).Activated(name="Label")
self.ghost = None
self.labeltype = utils.getParam("labeltype", "Custom")
self.sel = Gui.Selection.getSelectionEx()
if self.sel:
self.sel = self.sel[0]
self.ui.labelUi(title=translate("draft",self.featureName), callback=self.setmode)
self.ui.xValue.setFocus()
self.ui.xValue.selectAll()
self.ghost = trackers.lineTracker()
self.call = self.view.addEventCallback("SoEvent", self.action)
_msg(translate("draft", "Pick target point"))
self.ui.isCopy.hide()
|
[
"def",
"Activated",
"(",
"self",
")",
":",
"super",
"(",
"Label",
",",
"self",
")",
".",
"Activated",
"(",
"name",
"=",
"\"Label\"",
")",
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".",
"ghost",
"=",
"None",
"self",
".",
"labeltype",
"=",
"utils",
".",
"getParam",
"(",
"\"labeltype\"",
",",
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"sel",
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"Selection",
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"getSelectionEx",
"(",
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"if",
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",",
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"featureName",
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",",
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"self",
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")",
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"setFocus",
"(",
")",
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".",
"ui",
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"xValue",
".",
"selectAll",
"(",
")",
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"=",
"trackers",
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"lineTracker",
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"=",
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"addEventCallback",
"(",
"\"SoEvent\"",
",",
"self",
".",
"action",
")",
"_msg",
"(",
"translate",
"(",
"\"draft\"",
",",
"\"Pick target point\"",
")",
")",
"self",
".",
"ui",
".",
"isCopy",
".",
"hide",
"(",
")"
] |
https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Draft/draftguitools/gui_labels.py#L67-L81
|
||
klzgrad/naiveproxy
|
ed2c513637c77b18721fe428d7ed395b4d284c83
|
src/build/android/gyp/util/resource_utils.py
|
python
|
RJavaBuildOptions._IsResourceFinal
|
(self, entry)
|
return entry.name not in self.resources_allowlist
|
Determines whether a resource should be final or not.
Args:
entry: A _TextSymbolEntry instance.
Returns:
True iff the corresponding entry should be final.
|
Determines whether a resource should be final or not.
|
[
"Determines",
"whether",
"a",
"resource",
"should",
"be",
"final",
"or",
"not",
"."
] |
def _IsResourceFinal(self, entry):
"""Determines whether a resource should be final or not.
Args:
entry: A _TextSymbolEntry instance.
Returns:
True iff the corresponding entry should be final.
"""
if entry.resource_type == 'styleable' and entry.java_type != 'int[]':
# A styleable constant may be exported as non-final after all.
return not self.export_const_styleable
if not self.has_constant_ids:
# Every resource is non-final
return False
if not self.resources_allowlist:
# No allowlist means all IDs are non-final.
return True
# Otherwise, only those in the
return entry.name not in self.resources_allowlist
|
[
"def",
"_IsResourceFinal",
"(",
"self",
",",
"entry",
")",
":",
"if",
"entry",
".",
"resource_type",
"==",
"'styleable'",
"and",
"entry",
".",
"java_type",
"!=",
"'int[]'",
":",
"# A styleable constant may be exported as non-final after all.",
"return",
"not",
"self",
".",
"export_const_styleable",
"if",
"not",
"self",
".",
"has_constant_ids",
":",
"# Every resource is non-final",
"return",
"False",
"if",
"not",
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"resources_allowlist",
":",
"# No allowlist means all IDs are non-final.",
"return",
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"# Otherwise, only those in the",
"return",
"entry",
".",
"name",
"not",
"in",
"self",
".",
"resources_allowlist"
] |
https://github.com/klzgrad/naiveproxy/blob/ed2c513637c77b18721fe428d7ed395b4d284c83/src/build/android/gyp/util/resource_utils.py#L511-L529
|
|
tensorflow/tensorflow
|
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
|
tensorflow/python/tools/import_pb_to_tensorboard.py
|
python
|
import_to_tensorboard
|
(model_dir, log_dir, tag_set)
|
View an SavedModel as a graph in Tensorboard.
Args:
model_dir: The directory containing the SavedModel to import.
log_dir: The location for the Tensorboard log to begin visualization from.
tag_set: Group of tag(s) of the MetaGraphDef to load, in string format,
separated by ','. For tag-set contains multiple tags, all tags must be
passed in.
Usage: Call this function with your SavedModel location and desired log
directory. Launch Tensorboard by pointing it to the log directory. View your
imported SavedModel as a graph.
|
View an SavedModel as a graph in Tensorboard.
|
[
"View",
"an",
"SavedModel",
"as",
"a",
"graph",
"in",
"Tensorboard",
"."
] |
def import_to_tensorboard(model_dir, log_dir, tag_set):
"""View an SavedModel as a graph in Tensorboard.
Args:
model_dir: The directory containing the SavedModel to import.
log_dir: The location for the Tensorboard log to begin visualization from.
tag_set: Group of tag(s) of the MetaGraphDef to load, in string format,
separated by ','. For tag-set contains multiple tags, all tags must be
passed in.
Usage: Call this function with your SavedModel location and desired log
directory. Launch Tensorboard by pointing it to the log directory. View your
imported SavedModel as a graph.
"""
with session.Session(graph=ops.Graph()) as sess:
input_graph_def = saved_model_utils.get_meta_graph_def(model_dir,
tag_set).graph_def
importer.import_graph_def(input_graph_def)
pb_visual_writer = summary.FileWriter(log_dir)
pb_visual_writer.add_graph(sess.graph)
print("Model Imported. Visualize by running: "
"tensorboard --logdir={}".format(log_dir))
|
[
"def",
"import_to_tensorboard",
"(",
"model_dir",
",",
"log_dir",
",",
"tag_set",
")",
":",
"with",
"session",
".",
"Session",
"(",
"graph",
"=",
"ops",
".",
"Graph",
"(",
")",
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"as",
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"input_graph_def",
"=",
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"get_meta_graph_def",
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",",
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"(",
"\"Model Imported. Visualize by running: \"",
"\"tensorboard --logdir={}\"",
".",
"format",
"(",
"log_dir",
")",
")"
] |
https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/tools/import_pb_to_tensorboard.py#L40-L61
|
||
llvm/llvm-project
|
ffa6262cb4e2a335d26416fad39a581b4f98c5f4
|
llvm/examples/Kaleidoscope/MCJIT/lazy/genk-timing.py
|
python
|
KScriptGenerator.updateCalledFunctionList
|
(self, callee)
|
Maintains a list of functions that will actually be called
|
Maintains a list of functions that will actually be called
|
[
"Maintains",
"a",
"list",
"of",
"functions",
"that",
"will",
"actually",
"be",
"called"
] |
def updateCalledFunctionList(self, callee):
"""Maintains a list of functions that will actually be called"""
# Update the total call count
self.updateTotalCallCount(callee)
# If this function is already in the list, don't do anything else
if callee in self.calledFunctions:
return
# Add this function to the list of those that will be called.
self.calledFunctions.append(callee)
# If this function calls other functions, add them too
if callee in self.calledFunctionTable:
for subCallee in self.calledFunctionTable[callee]:
self.updateCalledFunctionList(subCallee)
|
[
"def",
"updateCalledFunctionList",
"(",
"self",
",",
"callee",
")",
":",
"# Update the total call count",
"self",
".",
"updateTotalCallCount",
"(",
"callee",
")",
"# If this function is already in the list, don't do anything else",
"if",
"callee",
"in",
"self",
".",
"calledFunctions",
":",
"return",
"# Add this function to the list of those that will be called.",
"self",
".",
"calledFunctions",
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"append",
"(",
"callee",
")",
"# If this function calls other functions, add them too",
"if",
"callee",
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"calledFunctionTable",
":",
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"calledFunctionTable",
"[",
"callee",
"]",
":",
"self",
".",
"updateCalledFunctionList",
"(",
"subCallee",
")"
] |
https://github.com/llvm/llvm-project/blob/ffa6262cb4e2a335d26416fad39a581b4f98c5f4/llvm/examples/Kaleidoscope/MCJIT/lazy/genk-timing.py#L68-L80
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/numpy/py3/numpy/polynomial/polyutils.py
|
python
|
_fromroots
|
(line_f, mul_f, roots)
|
Helper function used to implement the ``<type>fromroots`` functions.
Parameters
----------
line_f : function(float, float) -> ndarray
The ``<type>line`` function, such as ``polyline``
mul_f : function(array_like, array_like) -> ndarray
The ``<type>mul`` function, such as ``polymul``
roots
See the ``<type>fromroots`` functions for more detail
|
Helper function used to implement the ``<type>fromroots`` functions.
|
[
"Helper",
"function",
"used",
"to",
"implement",
"the",
"<type",
">",
"fromroots",
"functions",
"."
] |
def _fromroots(line_f, mul_f, roots):
"""
Helper function used to implement the ``<type>fromroots`` functions.
Parameters
----------
line_f : function(float, float) -> ndarray
The ``<type>line`` function, such as ``polyline``
mul_f : function(array_like, array_like) -> ndarray
The ``<type>mul`` function, such as ``polymul``
roots
See the ``<type>fromroots`` functions for more detail
"""
if len(roots) == 0:
return np.ones(1)
else:
[roots] = as_series([roots], trim=False)
roots.sort()
p = [line_f(-r, 1) for r in roots]
n = len(p)
while n > 1:
m, r = divmod(n, 2)
tmp = [mul_f(p[i], p[i+m]) for i in range(m)]
if r:
tmp[0] = mul_f(tmp[0], p[-1])
p = tmp
n = m
return p[0]
|
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"=",
"tmp",
"n",
"=",
"m",
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"[",
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] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/polynomial/polyutils.py#L456-L483
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/grid.py
|
python
|
GridTableBase.SetValueAsBool
|
(*args, **kwargs)
|
return _grid.GridTableBase_SetValueAsBool(*args, **kwargs)
|
SetValueAsBool(self, int row, int col, bool value)
|
SetValueAsBool(self, int row, int col, bool value)
|
[
"SetValueAsBool",
"(",
"self",
"int",
"row",
"int",
"col",
"bool",
"value",
")"
] |
def SetValueAsBool(*args, **kwargs):
"""SetValueAsBool(self, int row, int col, bool value)"""
return _grid.GridTableBase_SetValueAsBool(*args, **kwargs)
|
[
"def",
"SetValueAsBool",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_grid",
".",
"GridTableBase_SetValueAsBool",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/grid.py#L854-L856
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/arraypad.py
|
python
|
_set_wrap_both
|
(padded, axis, width_pair)
|
return new_left_pad, new_right_pad
|
Pad `axis` of `arr` with wrapped values.
Parameters
----------
padded : ndarray
Input array of arbitrary shape.
axis : int
Axis along which to pad `arr`.
width_pair : (int, int)
Pair of widths that mark the pad area on both sides in the given
dimension.
Returns
-------
pad_amt : tuple of ints, length 2
New index positions of padding to do along the `axis`. If these are
both 0, padding is done in this dimension.
|
Pad `axis` of `arr` with wrapped values.
|
[
"Pad",
"axis",
"of",
"arr",
"with",
"wrapped",
"values",
"."
] |
def _set_wrap_both(padded, axis, width_pair):
"""
Pad `axis` of `arr` with wrapped values.
Parameters
----------
padded : ndarray
Input array of arbitrary shape.
axis : int
Axis along which to pad `arr`.
width_pair : (int, int)
Pair of widths that mark the pad area on both sides in the given
dimension.
Returns
-------
pad_amt : tuple of ints, length 2
New index positions of padding to do along the `axis`. If these are
both 0, padding is done in this dimension.
"""
left_pad, right_pad = width_pair
period = padded.shape[axis] - right_pad - left_pad
# If the current dimension of `arr` doesn't contain enough valid values
# (not part of the undefined pad area) we need to pad multiple times.
# Each time the pad area shrinks on both sides which is communicated with
# these variables.
new_left_pad = 0
new_right_pad = 0
if left_pad > 0:
# Pad with wrapped values on left side
# First slice chunk from right side of the non-pad area.
# Use min(period, left_pad) to ensure that chunk is not larger than
# pad area
right_slice = _slice_at_axis(
slice(-right_pad - min(period, left_pad),
-right_pad if right_pad != 0 else None),
axis
)
right_chunk = padded[right_slice]
if left_pad > period:
# Chunk is smaller than pad area
pad_area = _slice_at_axis(slice(left_pad - period, left_pad), axis)
new_left_pad = left_pad - period
else:
# Chunk matches pad area
pad_area = _slice_at_axis(slice(None, left_pad), axis)
padded[pad_area] = right_chunk
if right_pad > 0:
# Pad with wrapped values on right side
# First slice chunk from left side of the non-pad area.
# Use min(period, right_pad) to ensure that chunk is not larger than
# pad area
left_slice = _slice_at_axis(
slice(left_pad, left_pad + min(period, right_pad),), axis)
left_chunk = padded[left_slice]
if right_pad > period:
# Chunk is smaller than pad area
pad_area = _slice_at_axis(
slice(-right_pad, -right_pad + period), axis)
new_right_pad = right_pad - period
else:
# Chunk matches pad area
pad_area = _slice_at_axis(slice(-right_pad, None), axis)
padded[pad_area] = left_chunk
return new_left_pad, new_right_pad
|
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"[",
"pad_area",
"]",
"=",
"left_chunk",
"return",
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",",
"new_right_pad"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/arraypad.py#L386-L456
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/graph_editor/subgraph.py
|
python
|
SubGraphView.input_index
|
(self, t)
|
return subgraph_id
|
Find the input index corresponding to the given input tensor t.
Args:
t: the input tensor of this subgraph view.
Returns:
The index in the self.inputs list.
Raises:
Error: if t in not an input tensor.
|
Find the input index corresponding to the given input tensor t.
|
[
"Find",
"the",
"input",
"index",
"corresponding",
"to",
"the",
"given",
"input",
"tensor",
"t",
"."
] |
def input_index(self, t):
"""Find the input index corresponding to the given input tensor t.
Args:
t: the input tensor of this subgraph view.
Returns:
The index in the self.inputs list.
Raises:
Error: if t in not an input tensor.
"""
try:
subgraph_id = self._input_ts.index(t)
except:
raise ValueError("Can't find {} in inputs of subgraph {}.".format(
t.name, self.name))
return subgraph_id
|
[
"def",
"input_index",
"(",
"self",
",",
"t",
")",
":",
"try",
":",
"subgraph_id",
"=",
"self",
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".",
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"t",
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".",
"name",
",",
"self",
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"name",
")",
")",
"return",
"subgraph_id"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/graph_editor/subgraph.py#L553-L568
|
|
ricardoquesada/Spidermonkey
|
4a75ea2543408bd1b2c515aa95901523eeef7858
|
ipc/ipdl/ipdl/parser.py
|
python
|
p_TranslationUnit
|
(p)
|
TranslationUnit : Preamble NamespacedStuff
|
TranslationUnit : Preamble NamespacedStuff
|
[
"TranslationUnit",
":",
"Preamble",
"NamespacedStuff"
] |
def p_TranslationUnit(p):
"""TranslationUnit : Preamble NamespacedStuff"""
tu = Parser.current.tu
tu.loc = Loc(tu.filename)
for stmt in p[1]:
if isinstance(stmt, CxxInclude):
tu.addCxxInclude(stmt)
elif isinstance(stmt, Include):
tu.addInclude(stmt)
elif isinstance(stmt, UsingStmt):
tu.addUsingStmt(stmt)
else:
assert 0
for thing in p[2]:
if isinstance(thing, StructDecl):
tu.addStructDecl(thing)
elif isinstance(thing, UnionDecl):
tu.addUnionDecl(thing)
elif isinstance(thing, Protocol):
if tu.protocol is not None:
_error(thing.loc, "only one protocol definition per file")
tu.protocol = thing
else:
assert(0)
# The "canonical" namespace of the tu, what it's considered to be
# in for the purposes of C++: |#include "foo/bar/TU.h"|
if tu.protocol:
assert tu.filetype == 'protocol'
tu.namespaces = tu.protocol.namespaces
tu.name = tu.protocol.name
else:
assert tu.filetype == 'header'
# There's not really a canonical "thing" in headers. So
# somewhat arbitrarily use the namespace of the last
# interesting thing that was declared.
for thing in reversed(tu.structsAndUnions):
tu.namespaces = thing.namespaces
break
p[0] = tu
|
[
"def",
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".",
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"0",
")",
"# The \"canonical\" namespace of the tu, what it's considered to be",
"# in for the purposes of C++: |#include \"foo/bar/TU.h\"|",
"if",
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".",
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":",
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".",
"filetype",
"==",
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":",
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"tu",
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"filetype",
"==",
"'header'",
"# There's not really a canonical \"thing\" in headers. So",
"# somewhat arbitrarily use the namespace of the last",
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"[",
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"]",
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] |
https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/ipc/ipdl/ipdl/parser.py#L188-L229
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/ops/batch_norm_benchmark.py
|
python
|
build_graph
|
(device, input_shape, axes, num_layers, mode, scale, train)
|
Build a graph containing a sequence of batch normalizations.
Args:
device: string, the device to run on.
input_shape: shape of the input tensor.
axes: axes that are to be normalized across.
num_layers: number of batch normalization layers in the graph.
mode: "op", "py" or "slow" depending on the implementation.
scale: scale after normalization.
train: if true, also run backprop.
Returns:
An array of tensors to run()
|
Build a graph containing a sequence of batch normalizations.
|
[
"Build",
"a",
"graph",
"containing",
"a",
"sequence",
"of",
"batch",
"normalizations",
"."
] |
def build_graph(device, input_shape, axes, num_layers, mode, scale, train):
"""Build a graph containing a sequence of batch normalizations.
Args:
device: string, the device to run on.
input_shape: shape of the input tensor.
axes: axes that are to be normalized across.
num_layers: number of batch normalization layers in the graph.
mode: "op", "py" or "slow" depending on the implementation.
scale: scale after normalization.
train: if true, also run backprop.
Returns:
An array of tensors to run()
"""
moment_shape = []
keep_dims = mode == "py" or mode == "slow"
if keep_dims:
for axis in range(len(input_shape)):
if axis in axes:
moment_shape.append(1)
else:
moment_shape.append(input_shape[axis])
else:
for axis in range(len(input_shape)):
if axis not in axes:
moment_shape.append(input_shape[axis])
with ops.device("/%s:0" % device):
tensor = variables.Variable(random_ops.truncated_normal(input_shape))
for _ in range(num_layers):
if train:
mean, variance = nn_impl.moments(tensor, axes, keep_dims=keep_dims)
else:
mean = array_ops.zeros(moment_shape)
variance = array_ops.ones(moment_shape)
beta = variables.Variable(array_ops.zeros(moment_shape))
gamma = variables.Variable(constant_op.constant(1.0, shape=moment_shape))
if mode == "py":
tensor = batch_norm_py(tensor, mean, variance, beta, gamma, scale)
elif mode == "op":
tensor = batch_norm_op(tensor, mean, variance, beta, gamma, scale)
elif mode == "slow":
tensor = batch_norm_slow(tensor, mean, variance, beta, gamma, scale)
if train:
return gradients_impl.gradients([tensor], variables.trainable_variables())
else:
return [tensor]
|
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"=",
"variables",
".",
"Variable",
"(",
"constant_op",
".",
"constant",
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"1.0",
",",
"shape",
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"moment_shape",
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")",
"if",
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"==",
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"tensor",
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"mean",
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"variance",
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",",
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"[",
"tensor",
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",",
"variables",
".",
"trainable_variables",
"(",
")",
")",
"else",
":",
"return",
"[",
"tensor",
"]"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/ops/batch_norm_benchmark.py#L68-L114
|
||
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/saved_model/builder_impl.py
|
python
|
_add_asset_to_metagraph
|
(meta_graph_def, asset_filename, asset_tensor)
|
Builds an asset proto and adds it to the meta graph def.
Args:
meta_graph_def: The meta graph def to which the asset will be added.
asset_filename: The filename of the asset to be added.
asset_tensor: The asset tensor used to populate the tensor info of the asset
proto.
|
Builds an asset proto and adds it to the meta graph def.
|
[
"Builds",
"an",
"asset",
"proto",
"and",
"adds",
"it",
"to",
"the",
"meta",
"graph",
"def",
"."
] |
def _add_asset_to_metagraph(meta_graph_def, asset_filename, asset_tensor):
"""Builds an asset proto and adds it to the meta graph def.
Args:
meta_graph_def: The meta graph def to which the asset will be added.
asset_filename: The filename of the asset to be added.
asset_tensor: The asset tensor used to populate the tensor info of the asset
proto.
"""
asset_proto = meta_graph_def.asset_file_def.add()
asset_proto.filename = asset_filename
asset_proto.tensor_info.name = asset_tensor.name
|
[
"def",
"_add_asset_to_metagraph",
"(",
"meta_graph_def",
",",
"asset_filename",
",",
"asset_tensor",
")",
":",
"asset_proto",
"=",
"meta_graph_def",
".",
"asset_file_def",
".",
"add",
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")",
"asset_proto",
".",
"filename",
"=",
"asset_filename",
"asset_proto",
".",
"tensor_info",
".",
"name",
"=",
"asset_tensor",
".",
"name"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/saved_model/builder_impl.py#L742-L753
|
||
ceph/ceph
|
959663007321a369c83218414a29bd9dbc8bda3a
|
qa/tasks/cephfs/filesystem.py
|
python
|
Filesystem.wait_for_state
|
(self, goal_state, reject=None, timeout=None, mds_id=None, rank=None)
|
Block until the MDS reaches a particular state, or a failure condition
is met.
When there are multiple MDSs, succeed when exaclty one MDS is in the
goal state, or fail when any MDS is in the reject state.
:param goal_state: Return once the MDS is in this state
:param reject: Fail if the MDS enters this state before the goal state
:param timeout: Fail if this many seconds pass before reaching goal
:return: number of seconds waited, rounded down to integer
|
Block until the MDS reaches a particular state, or a failure condition
is met.
|
[
"Block",
"until",
"the",
"MDS",
"reaches",
"a",
"particular",
"state",
"or",
"a",
"failure",
"condition",
"is",
"met",
"."
] |
def wait_for_state(self, goal_state, reject=None, timeout=None, mds_id=None, rank=None):
"""
Block until the MDS reaches a particular state, or a failure condition
is met.
When there are multiple MDSs, succeed when exaclty one MDS is in the
goal state, or fail when any MDS is in the reject state.
:param goal_state: Return once the MDS is in this state
:param reject: Fail if the MDS enters this state before the goal state
:param timeout: Fail if this many seconds pass before reaching goal
:return: number of seconds waited, rounded down to integer
"""
started_at = time.time()
while True:
status = self.status()
if rank is not None:
try:
mds_info = status.get_rank(self.id, rank)
current_state = mds_info['state'] if mds_info else None
log.debug("Looked up MDS state for mds.{0}: {1}".format(rank, current_state))
except:
mdsmap = self.get_mds_map(status=status)
if rank in mdsmap['failed']:
log.debug("Waiting for rank {0} to come back.".format(rank))
current_state = None
else:
raise
elif mds_id is not None:
# mds_info is None if no daemon with this ID exists in the map
mds_info = status.get_mds(mds_id)
current_state = mds_info['state'] if mds_info else None
log.debug("Looked up MDS state for {0}: {1}".format(mds_id, current_state))
else:
# In general, look for a single MDS
states = [m['state'] for m in status.get_ranks(self.id)]
if [s for s in states if s == goal_state] == [goal_state]:
current_state = goal_state
elif reject in states:
current_state = reject
else:
current_state = None
log.debug("mapped states {0} to {1}".format(states, current_state))
elapsed = time.time() - started_at
if current_state == goal_state:
log.debug("reached state '{0}' in {1}s".format(current_state, elapsed))
return elapsed
elif reject is not None and current_state == reject:
raise RuntimeError("MDS in reject state {0}".format(current_state))
elif timeout is not None and elapsed > timeout:
log.error("MDS status at timeout: {0}".format(status.get_fsmap(self.id)))
raise RuntimeError(
"Reached timeout after {0} seconds waiting for state {1}, while in state {2}".format(
elapsed, goal_state, current_state
))
else:
time.sleep(1)
|
[
"def",
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"goal_state",
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"None",
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"\"Looked up MDS state for {0}: {1}\"",
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"current_state",
")",
")",
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":",
"time",
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"sleep",
"(",
"1",
")"
] |
https://github.com/ceph/ceph/blob/959663007321a369c83218414a29bd9dbc8bda3a/qa/tasks/cephfs/filesystem.py#L1199-L1257
|
||
deepmind/open_spiel
|
4ca53bea32bb2875c7385d215424048ae92f78c8
|
open_spiel/python/pytorch/policy_gradient.py
|
python
|
PolicyGradient._add_transition
|
(self, time_step)
|
Adds intra-episode transition to the `_episode_data` buffer.
Adds the transition from `self._prev_time_step` to `time_step`.
Args:
time_step: an instance of rl_environment.TimeStep.
|
Adds intra-episode transition to the `_episode_data` buffer.
|
[
"Adds",
"intra",
"-",
"episode",
"transition",
"to",
"the",
"_episode_data",
"buffer",
"."
] |
def _add_transition(self, time_step):
"""Adds intra-episode transition to the `_episode_data` buffer.
Adds the transition from `self._prev_time_step` to `time_step`.
Args:
time_step: an instance of rl_environment.TimeStep.
"""
assert self._prev_time_step is not None
legal_actions = (
self._prev_time_step.observations["legal_actions"][self.player_id])
legal_actions_mask = np.zeros(self._num_actions)
legal_actions_mask[legal_actions] = 1.0
transition = Transition(
info_state=(
self._prev_time_step.observations["info_state"][self.player_id][:]),
action=self._prev_action,
reward=time_step.rewards[self.player_id],
discount=time_step.discounts[self.player_id],
legal_actions_mask=legal_actions_mask)
self._episode_data.append(transition)
|
[
"def",
"_add_transition",
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"self",
",",
"time_step",
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":",
"assert",
"self",
".",
"_prev_time_step",
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"None",
"legal_actions",
"=",
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"_prev_time_step",
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"observations",
"[",
"\"legal_actions\"",
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"player_id",
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")",
"legal_actions_mask",
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"np",
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"_num_actions",
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"legal_actions_mask",
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"legal_actions",
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"=",
"1.0",
"transition",
"=",
"Transition",
"(",
"info_state",
"=",
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"_prev_time_step",
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"observations",
"[",
"\"info_state\"",
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"[",
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"player_id",
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":",
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",",
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"discounts",
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",",
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"=",
"legal_actions_mask",
")",
"self",
".",
"_episode_data",
".",
"append",
"(",
"transition",
")"
] |
https://github.com/deepmind/open_spiel/blob/4ca53bea32bb2875c7385d215424048ae92f78c8/open_spiel/python/pytorch/policy_gradient.py#L383-L404
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/_windows.py
|
python
|
ColourData.GetChooseFull
|
(*args, **kwargs)
|
return _windows_.ColourData_GetChooseFull(*args, **kwargs)
|
GetChooseFull(self) -> bool
Under Windows, determines whether the Windows colour dialog will
display the full dialog with custom colour selection controls. Has no
meaning under other platforms. The default value is true.
|
GetChooseFull(self) -> bool
|
[
"GetChooseFull",
"(",
"self",
")",
"-",
">",
"bool"
] |
def GetChooseFull(*args, **kwargs):
"""
GetChooseFull(self) -> bool
Under Windows, determines whether the Windows colour dialog will
display the full dialog with custom colour selection controls. Has no
meaning under other platforms. The default value is true.
"""
return _windows_.ColourData_GetChooseFull(*args, **kwargs)
|
[
"def",
"GetChooseFull",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_windows_",
".",
"ColourData_GetChooseFull",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_windows.py#L2931-L2939
|
|
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/contrib/layers/python/layers/feature_column_ops.py
|
python
|
_create_embedding_lookup
|
(column,
columns_to_tensors,
embedding_lookup_arguments,
num_outputs,
trainable,
weight_collections)
|
Creates variables and returns predictions for linear weights in a model.
Args:
column: the column we're working on.
columns_to_tensors: a map from column name to tensors.
embedding_lookup_arguments: arguments for embedding lookup.
num_outputs: how many outputs.
trainable: whether the variable we create is trainable.
weight_collections: weights will be placed here.
Returns:
variables: the created embeddings.
predictions: the computed predictions.
|
Creates variables and returns predictions for linear weights in a model.
|
[
"Creates",
"variables",
"and",
"returns",
"predictions",
"for",
"linear",
"weights",
"in",
"a",
"model",
"."
] |
def _create_embedding_lookup(column,
columns_to_tensors,
embedding_lookup_arguments,
num_outputs,
trainable,
weight_collections):
"""Creates variables and returns predictions for linear weights in a model.
Args:
column: the column we're working on.
columns_to_tensors: a map from column name to tensors.
embedding_lookup_arguments: arguments for embedding lookup.
num_outputs: how many outputs.
trainable: whether the variable we create is trainable.
weight_collections: weights will be placed here.
Returns:
variables: the created embeddings.
predictions: the computed predictions.
"""
with variable_scope.variable_scope(
None, default_name=column.name, values=columns_to_tensors.values()):
variable = contrib_variables.model_variable(
name='weights',
shape=[embedding_lookup_arguments.vocab_size, num_outputs],
dtype=dtypes.float32,
initializer=embedding_lookup_arguments.initializer,
trainable=trainable,
collections=weight_collections)
if fc._is_variable(variable): # pylint: disable=protected-access
variable = [variable]
else:
variable = variable._get_variable_list() # pylint: disable=protected-access
predictions = embedding_ops.safe_embedding_lookup_sparse(
variable,
embedding_lookup_arguments.input_tensor,
sparse_weights=embedding_lookup_arguments.weight_tensor,
combiner=embedding_lookup_arguments.combiner,
name=column.name + '_weights')
return variable, predictions
|
[
"def",
"_create_embedding_lookup",
"(",
"column",
",",
"columns_to_tensors",
",",
"embedding_lookup_arguments",
",",
"num_outputs",
",",
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",",
"weight_collections",
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":",
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"model_variable",
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",",
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"]",
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":",
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"=",
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".",
"_get_variable_list",
"(",
")",
"# pylint: disable=protected-access",
"predictions",
"=",
"embedding_ops",
".",
"safe_embedding_lookup_sparse",
"(",
"variable",
",",
"embedding_lookup_arguments",
".",
"input_tensor",
",",
"sparse_weights",
"=",
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"weight_tensor",
",",
"combiner",
"=",
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"combiner",
",",
"name",
"=",
"column",
".",
"name",
"+",
"'_weights'",
")",
"return",
"variable",
",",
"predictions"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/contrib/layers/python/layers/feature_column_ops.py#L261-L300
|
||
nasa/meshNetwork
|
ff4bd66e0ca6bd424fd8897a97252bb3925d8b3c
|
python/mesh/generic/serialComm.py
|
python
|
SerialComm.readBytes
|
(self, bufferFlag=False)
|
Reads raw bytes from radio
|
Reads raw bytes from radio
|
[
"Reads",
"raw",
"bytes",
"from",
"radio"
] |
def readBytes(self, bufferFlag=False):
"""Reads raw bytes from radio"""
self.radio.readBytes(bufferFlag)
|
[
"def",
"readBytes",
"(",
"self",
",",
"bufferFlag",
"=",
"False",
")",
":",
"self",
".",
"radio",
".",
"readBytes",
"(",
"bufferFlag",
")"
] |
https://github.com/nasa/meshNetwork/blob/ff4bd66e0ca6bd424fd8897a97252bb3925d8b3c/python/mesh/generic/serialComm.py#L77-L79
|
||
tinyobjloader/tinyobjloader
|
8322e00ae685ea623ab6ac5a6cebcfa2d22fbf93
|
deps/cpplint.py
|
python
|
CheckForBadCharacters
|
(filename, lines, error)
|
Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
|
Logs an error for each line containing bad characters.
|
[
"Logs",
"an",
"error",
"for",
"each",
"line",
"containing",
"bad",
"characters",
"."
] |
def CheckForBadCharacters(filename, lines, error):
"""Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
for linenum, line in enumerate(lines):
if u'\ufffd' in line:
error(filename, linenum, 'readability/utf8', 5,
'Line contains invalid UTF-8 (or Unicode replacement character).')
if '\0' in line:
error(filename, linenum, 'readability/nul', 5, 'Line contains NUL byte.')
|
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https://github.com/tinyobjloader/tinyobjloader/blob/8322e00ae685ea623ab6ac5a6cebcfa2d22fbf93/deps/cpplint.py#L1800-L1822
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/grid.py
|
python
|
Grid.CalcColLabelsExposed
|
(*args, **kwargs)
|
return _grid.Grid_CalcColLabelsExposed(*args, **kwargs)
|
CalcColLabelsExposed(self, Region reg) -> wxArrayInt
|
CalcColLabelsExposed(self, Region reg) -> wxArrayInt
|
[
"CalcColLabelsExposed",
"(",
"self",
"Region",
"reg",
")",
"-",
">",
"wxArrayInt"
] |
def CalcColLabelsExposed(*args, **kwargs):
"""CalcColLabelsExposed(self, Region reg) -> wxArrayInt"""
return _grid.Grid_CalcColLabelsExposed(*args, **kwargs)
|
[
"def",
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",",
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/grid.py#L1243-L1245
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/tools/python3/src/Lib/pyclbr.py
|
python
|
readmodule_ex
|
(module, path=None)
|
return _readmodule(module, path or [])
|
Return a dictionary with all functions and classes in module.
Search for module in PATH + sys.path.
If possible, include imported superclasses.
Do this by reading source, without importing (and executing) it.
|
Return a dictionary with all functions and classes in module.
|
[
"Return",
"a",
"dictionary",
"with",
"all",
"functions",
"and",
"classes",
"in",
"module",
"."
] |
def readmodule_ex(module, path=None):
"""Return a dictionary with all functions and classes in module.
Search for module in PATH + sys.path.
If possible, include imported superclasses.
Do this by reading source, without importing (and executing) it.
"""
return _readmodule(module, path or [])
|
[
"def",
"readmodule_ex",
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"module",
",",
"path",
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"None",
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":",
"return",
"_readmodule",
"(",
"module",
",",
"path",
"or",
"[",
"]",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/pyclbr.py#L109-L116
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/grid.py
|
python
|
GridTableBase._setOORInfo
|
(*args, **kwargs)
|
return _grid.GridTableBase__setOORInfo(*args, **kwargs)
|
_setOORInfo(self, PyObject _self)
|
_setOORInfo(self, PyObject _self)
|
[
"_setOORInfo",
"(",
"self",
"PyObject",
"_self",
")"
] |
def _setOORInfo(*args, **kwargs):
"""_setOORInfo(self, PyObject _self)"""
return _grid.GridTableBase__setOORInfo(*args, **kwargs)
|
[
"def",
"_setOORInfo",
"(",
"*",
"args",
",",
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"_grid",
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"GridTableBase__setOORInfo",
"(",
"*",
"args",
",",
"*",
"*",
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")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/grid.py#L770-L772
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/py3/scipy/special/basic.py
|
python
|
jnyn_zeros
|
(n, nt)
|
return specfun.jyzo(abs(n), nt)
|
Compute nt zeros of Bessel functions Jn(x), Jn'(x), Yn(x), and Yn'(x).
Returns 4 arrays of length `nt`, corresponding to the first `nt` zeros of
Jn(x), Jn'(x), Yn(x), and Yn'(x), respectively.
Parameters
----------
n : int
Order of the Bessel functions
nt : int
Number (<=1200) of zeros to compute
See jn_zeros, jnp_zeros, yn_zeros, ynp_zeros to get separate arrays.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996, chapter 5.
https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html
|
Compute nt zeros of Bessel functions Jn(x), Jn'(x), Yn(x), and Yn'(x).
|
[
"Compute",
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"Bessel",
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"(",
"x",
")",
"Yn",
"(",
"x",
")",
"and",
"Yn",
"(",
"x",
")",
"."
] |
def jnyn_zeros(n, nt):
"""Compute nt zeros of Bessel functions Jn(x), Jn'(x), Yn(x), and Yn'(x).
Returns 4 arrays of length `nt`, corresponding to the first `nt` zeros of
Jn(x), Jn'(x), Yn(x), and Yn'(x), respectively.
Parameters
----------
n : int
Order of the Bessel functions
nt : int
Number (<=1200) of zeros to compute
See jn_zeros, jnp_zeros, yn_zeros, ynp_zeros to get separate arrays.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996, chapter 5.
https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html
"""
if not (isscalar(nt) and isscalar(n)):
raise ValueError("Arguments must be scalars.")
if (floor(n) != n) or (floor(nt) != nt):
raise ValueError("Arguments must be integers.")
if (nt <= 0):
raise ValueError("nt > 0")
return specfun.jyzo(abs(n), nt)
|
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py3/scipy/special/basic.py#L199-L227
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/email/_header_value_parser.py
|
python
|
get_dtext
|
(value)
|
return ptext, value
|
r""" dtext = <printable ascii except \ [ ]> / obs-dtext
obs-dtext = obs-NO-WS-CTL / quoted-pair
We allow anything except the excluded characters, but if we find any
ASCII other than the RFC defined printable ASCII, a NonPrintableDefect is
added to the token's defects list. Quoted pairs are converted to their
unquoted values, so what is returned is a ptext token, in this case a
ValueTerminal. If there were quoted-printables, an ObsoleteHeaderDefect is
added to the returned token's defect list.
|
r""" dtext = <printable ascii except \ [ ]> / obs-dtext
obs-dtext = obs-NO-WS-CTL / quoted-pair
|
[
"r",
"dtext",
"=",
"<printable",
"ascii",
"except",
"\\",
"[",
"]",
">",
"/",
"obs",
"-",
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"obs",
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"dtext",
"=",
"obs",
"-",
"NO",
"-",
"WS",
"-",
"CTL",
"/",
"quoted",
"-",
"pair"
] |
def get_dtext(value):
r""" dtext = <printable ascii except \ [ ]> / obs-dtext
obs-dtext = obs-NO-WS-CTL / quoted-pair
We allow anything except the excluded characters, but if we find any
ASCII other than the RFC defined printable ASCII, a NonPrintableDefect is
added to the token's defects list. Quoted pairs are converted to their
unquoted values, so what is returned is a ptext token, in this case a
ValueTerminal. If there were quoted-printables, an ObsoleteHeaderDefect is
added to the returned token's defect list.
"""
ptext, value, had_qp = _get_ptext_to_endchars(value, '[]')
ptext = ValueTerminal(ptext, 'ptext')
if had_qp:
ptext.defects.append(errors.ObsoleteHeaderDefect(
"quoted printable found in domain-literal"))
_validate_xtext(ptext)
return ptext, value
|
[
"def",
"get_dtext",
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"value",
")",
":",
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",",
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/email/_header_value_parser.py#L1523-L1541
|
|
adobe/chromium
|
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
|
ppapi/generators/idl_parser.py
|
python
|
IDLParser.p_integer
|
(self, p)
|
integer : HEX
| INT
| OCT
|
integer : HEX
| INT
| OCT
|
[
"integer",
":",
"HEX",
"|",
"INT",
"|",
"OCT"
] |
def p_integer(self, p):
"""integer : HEX
| INT
| OCT"""
p[0] = p[1]
if self.parse_debug: DumpReduction('integer', p)
|
[
"def",
"p_integer",
"(",
"self",
",",
"p",
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"[",
"0",
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":",
"DumpReduction",
"(",
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",",
"p",
")"
] |
https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/ppapi/generators/idl_parser.py#L450-L455
|
||
windystrife/UnrealEngine_NVIDIAGameWorks
|
b50e6338a7c5b26374d66306ebc7807541ff815e
|
Engine/Source/ThirdParty/CEF3/cef_source/tools/make_distrib.py
|
python
|
combine_libs
|
(build_dir, libs, dest_lib)
|
Combine multiple static libraries into a single static library.
|
Combine multiple static libraries into a single static library.
|
[
"Combine",
"multiple",
"static",
"libraries",
"into",
"a",
"single",
"static",
"library",
"."
] |
def combine_libs(build_dir, libs, dest_lib):
""" Combine multiple static libraries into a single static library. """
cmdline = 'msvs_env.bat win%s python combine_libs.py -o "%s"' % (platform_arch, dest_lib)
for lib in libs:
lib_path = os.path.join(build_dir, lib)
for path in get_files(lib_path): # Expand wildcards in |lib_path|.
if not path_exists(path):
raise Exception('File not found: ' + path)
cmdline = cmdline + ' "%s"' % path
run(cmdline, os.path.join(cef_dir, 'tools'))
|
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Source/ThirdParty/CEF3/cef_source/tools/make_distrib.py#L242-L251
|
||
bulletphysics/bullet3
|
f0f2a952e146f016096db6f85cf0c44ed75b0b9a
|
examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py
|
python
|
MinitaurTrottingEnv._get_observation
|
(self)
|
return self._observation
|
Get observations of this environment.
It includes the base roll, pitch, roll dot and pitch dot which may contain
noises, bias, and latency. Also includes the disired/observed motor angles
if the relevant flags are set.
Returns:
The observation list.
|
Get observations of this environment.
|
[
"Get",
"observations",
"of",
"this",
"environment",
"."
] |
def _get_observation(self):
"""Get observations of this environment.
It includes the base roll, pitch, roll dot and pitch dot which may contain
noises, bias, and latency. Also includes the disired/observed motor angles
if the relevant flags are set.
Returns:
The observation list.
"""
observation = []
roll, pitch, _ = self.minitaur.GetBaseRollPitchYaw()
roll_rate, pitch_rate, _ = self.minitaur.GetBaseRollPitchYawRate()
observation.extend([roll, pitch, roll_rate, pitch_rate])
if self._use_signal_in_observation:
observation.extend(self._transform_action_to_motor_command([0] * 8))
if self._use_angle_in_observation:
observation.extend(self.minitaur.GetMotorAngles().tolist())
self._observation = np.array(observation)
return self._observation
|
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https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py#L254-L273
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/tools/python3/src/Lib/smtplib.py
|
python
|
SMTP.getreply
|
(self)
|
return errcode, errmsg
|
Get a reply from the server.
Returns a tuple consisting of:
- server response code (e.g. '250', or such, if all goes well)
Note: returns -1 if it can't read response code.
- server response string corresponding to response code (multiline
responses are converted to a single, multiline string).
Raises SMTPServerDisconnected if end-of-file is reached.
|
Get a reply from the server.
|
[
"Get",
"a",
"reply",
"from",
"the",
"server",
"."
] |
def getreply(self):
"""Get a reply from the server.
Returns a tuple consisting of:
- server response code (e.g. '250', or such, if all goes well)
Note: returns -1 if it can't read response code.
- server response string corresponding to response code (multiline
responses are converted to a single, multiline string).
Raises SMTPServerDisconnected if end-of-file is reached.
"""
resp = []
if self.file is None:
self.file = self.sock.makefile('rb')
while 1:
try:
line = self.file.readline(_MAXLINE + 1)
except OSError as e:
self.close()
raise SMTPServerDisconnected("Connection unexpectedly closed: "
+ str(e))
if not line:
self.close()
raise SMTPServerDisconnected("Connection unexpectedly closed")
if self.debuglevel > 0:
self._print_debug('reply:', repr(line))
if len(line) > _MAXLINE:
self.close()
raise SMTPResponseException(500, "Line too long.")
resp.append(line[4:].strip(b' \t\r\n'))
code = line[:3]
# Check that the error code is syntactically correct.
# Don't attempt to read a continuation line if it is broken.
try:
errcode = int(code)
except ValueError:
errcode = -1
break
# Check if multiline response.
if line[3:4] != b"-":
break
errmsg = b"\n".join(resp)
if self.debuglevel > 0:
self._print_debug('reply: retcode (%s); Msg: %a' % (errcode, errmsg))
return errcode, errmsg
|
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"line",
"[",
":",
"3",
"]",
"# Check that the error code is syntactically correct.",
"# Don't attempt to read a continuation line if it is broken.",
"try",
":",
"errcode",
"=",
"int",
"(",
"code",
")",
"except",
"ValueError",
":",
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"# Check if multiline response.",
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":",
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"(",
"errcode",
",",
"errmsg",
")",
")",
"return",
"errcode",
",",
"errmsg"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/smtplib.py#L380-L427
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/ipython/py3/IPython/utils/text.py
|
python
|
columnize
|
(items, row_first=False, separator=' ', displaywidth=80, spread=False)
|
return '\n'.join(map(sjoin, fmatrix))+'\n'
|
Transform a list of strings into a single string with columns.
Parameters
----------
items : sequence of strings
The strings to process.
row_first : (default False)
Whether to compute columns for a row-first matrix instead of
column-first (default).
separator : str, optional [default is two spaces]
The string that separates columns.
displaywidth : int, optional [default is 80]
Width of the display in number of characters.
Returns
-------
The formatted string.
|
Transform a list of strings into a single string with columns.
|
[
"Transform",
"a",
"list",
"of",
"strings",
"into",
"a",
"single",
"string",
"with",
"columns",
"."
] |
def columnize(items, row_first=False, separator=' ', displaywidth=80, spread=False):
""" Transform a list of strings into a single string with columns.
Parameters
----------
items : sequence of strings
The strings to process.
row_first : (default False)
Whether to compute columns for a row-first matrix instead of
column-first (default).
separator : str, optional [default is two spaces]
The string that separates columns.
displaywidth : int, optional [default is 80]
Width of the display in number of characters.
Returns
-------
The formatted string.
"""
if not items:
return '\n'
matrix, info = compute_item_matrix(items, row_first=row_first, separator_size=len(separator), displaywidth=displaywidth)
if spread:
separator = separator.ljust(int(info['optimal_separator_width']))
fmatrix = [filter(None, x) for x in matrix]
sjoin = lambda x : separator.join([ y.ljust(w, ' ') for y, w in zip(x, info['column_widths'])])
return '\n'.join(map(sjoin, fmatrix))+'\n'
|
[
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"columnize",
"(",
"items",
",",
"row_first",
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"False",
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"separator",
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"displaywidth",
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",",
"spread",
"=",
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"(",
"map",
"(",
"sjoin",
",",
"fmatrix",
")",
")",
"+",
"'\\n'"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/ipython/py3/IPython/utils/text.py#L701-L730
|
|
microsoft/AirSim
|
8057725712c0cd46979135396381784075ffc0f3
|
PythonClient/airsim/client.py
|
python
|
VehicleClient.getGpsData
|
(self, gps_name = '', vehicle_name = '')
|
return GpsData.from_msgpack(self.client.call('getGpsData', gps_name, vehicle_name))
|
Args:
gps_name (str, optional): Name of GPS to get data from, specified in settings.json
vehicle_name (str, optional): Name of vehicle to which the sensor corresponds to
Returns:
GpsData:
|
Args:
gps_name (str, optional): Name of GPS to get data from, specified in settings.json
vehicle_name (str, optional): Name of vehicle to which the sensor corresponds to
|
[
"Args",
":",
"gps_name",
"(",
"str",
"optional",
")",
":",
"Name",
"of",
"GPS",
"to",
"get",
"data",
"from",
"specified",
"in",
"settings",
".",
"json",
"vehicle_name",
"(",
"str",
"optional",
")",
":",
"Name",
"of",
"vehicle",
"to",
"which",
"the",
"sensor",
"corresponds",
"to"
] |
def getGpsData(self, gps_name = '', vehicle_name = ''):
"""
Args:
gps_name (str, optional): Name of GPS to get data from, specified in settings.json
vehicle_name (str, optional): Name of vehicle to which the sensor corresponds to
Returns:
GpsData:
"""
return GpsData.from_msgpack(self.client.call('getGpsData', gps_name, vehicle_name))
|
[
"def",
"getGpsData",
"(",
"self",
",",
"gps_name",
"=",
"''",
",",
"vehicle_name",
"=",
"''",
")",
":",
"return",
"GpsData",
".",
"from_msgpack",
"(",
"self",
".",
"client",
".",
"call",
"(",
"'getGpsData'",
",",
"gps_name",
",",
"vehicle_name",
")",
")"
] |
https://github.com/microsoft/AirSim/blob/8057725712c0cd46979135396381784075ffc0f3/PythonClient/airsim/client.py#L872-L881
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py
|
python
|
Canvas.type
|
(self, tagOrId)
|
return self.tk.call(self._w, 'type', tagOrId) or None
|
Return the type of the item TAGORID.
|
Return the type of the item TAGORID.
|
[
"Return",
"the",
"type",
"of",
"the",
"item",
"TAGORID",
"."
] |
def type(self, tagOrId):
"""Return the type of the item TAGORID."""
return self.tk.call(self._w, 'type', tagOrId) or None
|
[
"def",
"type",
"(",
"self",
",",
"tagOrId",
")",
":",
"return",
"self",
".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'type'",
",",
"tagOrId",
")",
"or",
"None"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py#L2630-L2632
|
|
s9xie/hed
|
94fb22f10cbfec8d84fbc0642b224022014b6bd6
|
python/caffe/pycaffe.py
|
python
|
_Net_blobs
|
(self)
|
return OrderedDict(zip(self._blob_names, self._blobs))
|
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
|
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
|
[
"An",
"OrderedDict",
"(",
"bottom",
"to",
"top",
"i",
".",
"e",
".",
"input",
"to",
"output",
")",
"of",
"network",
"blobs",
"indexed",
"by",
"name"
] |
def _Net_blobs(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
"""
return OrderedDict(zip(self._blob_names, self._blobs))
|
[
"def",
"_Net_blobs",
"(",
"self",
")",
":",
"return",
"OrderedDict",
"(",
"zip",
"(",
"self",
".",
"_blob_names",
",",
"self",
".",
"_blobs",
")",
")"
] |
https://github.com/s9xie/hed/blob/94fb22f10cbfec8d84fbc0642b224022014b6bd6/python/caffe/pycaffe.py#L22-L27
|
|
mindspore-ai/mindspore
|
fb8fd3338605bb34fa5cea054e535a8b1d753fab
|
mindspore/python/mindspore/nn/layer/basic.py
|
python
|
Dense.__init__
|
(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None)
|
Initialize Dense.
|
Initialize Dense.
|
[
"Initialize",
"Dense",
"."
] |
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None):
"""Initialize Dense."""
super(Dense, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels, "in_channels", self.cls_name)
self.out_channels = Validator.check_positive_int(out_channels, "out_channels", self.cls_name)
self.has_bias = Validator.check_bool(has_bias, "has_bias", self.cls_name)
self.reshape = P.Reshape()
self.shape_op = P.Shape()
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError(f"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' should "
f"be equal to 2, and the first dim should be equal to 'out_channels', and the "
f"second dim should be equal to 'in_channels'. But got 'weight_init': {weight_init}, "
f"'out_channels': {out_channels}, 'in_channels': {in_channels}.")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
self.bias = None
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError(f"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' should "
f"be equal to 1, and the first dim should be equal to 'out_channels'. But got "
f"'bias_init': {bias_init}, 'out_channels': {out_channels}.")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul(transpose_b=True)
self.activation = get_activation(activation) if isinstance(activation, str) else activation
if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
raise TypeError(f"For '{self.cls_name}', the 'activation' must be str or Cell or Primitive, but got "
f"{type(activation).__name__}.")
self.activation_flag = self.activation is not None
|
[
"def",
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",",
"in_channels",
",",
"out_channels",
",",
"weight_init",
"=",
"'normal'",
",",
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",",
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",",
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",",
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",",
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",",
"\"has_bias\"",
",",
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"P",
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"self",
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"shape_op",
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".",
"Shape",
"(",
")",
"if",
"isinstance",
"(",
"weight_init",
",",
"Tensor",
")",
":",
"if",
"weight_init",
".",
"ndim",
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"2",
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"weight_init",
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"shape",
"[",
"0",
"]",
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"out_channels",
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"weight_init",
".",
"shape",
"[",
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"]",
"!=",
"in_channels",
":",
"raise",
"ValueError",
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"f\"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' should \"",
"f\"be equal to 2, and the first dim should be equal to 'out_channels', and the \"",
"f\"second dim should be equal to 'in_channels'. But got 'weight_init': {weight_init}, \"",
"f\"'out_channels': {out_channels}, 'in_channels': {in_channels}.\"",
")",
"self",
".",
"weight",
"=",
"Parameter",
"(",
"initializer",
"(",
"weight_init",
",",
"[",
"out_channels",
",",
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"]",
")",
",",
"name",
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"\"weight\"",
")",
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".",
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"=",
"None",
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"has_bias",
":",
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",",
"Tensor",
")",
":",
"if",
"bias_init",
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"ndim",
"!=",
"1",
"or",
"bias_init",
".",
"shape",
"[",
"0",
"]",
"!=",
"out_channels",
":",
"raise",
"ValueError",
"(",
"f\"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' should \"",
"f\"be equal to 1, and the first dim should be equal to 'out_channels'. But got \"",
"f\"'bias_init': {bias_init}, 'out_channels': {out_channels}.\"",
")",
"self",
".",
"bias",
"=",
"Parameter",
"(",
"initializer",
"(",
"bias_init",
",",
"[",
"out_channels",
"]",
")",
",",
"name",
"=",
"\"bias\"",
")",
"self",
".",
"bias_add",
"=",
"P",
".",
"BiasAdd",
"(",
")",
"self",
".",
"matmul",
"=",
"P",
".",
"MatMul",
"(",
"transpose_b",
"=",
"True",
")",
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".",
"activation",
"=",
"get_activation",
"(",
"activation",
")",
"if",
"isinstance",
"(",
"activation",
",",
"str",
")",
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"(",
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",",
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",",
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")",
")",
":",
"raise",
"TypeError",
"(",
"f\"For '{self.cls_name}', the 'activation' must be str or Cell or Primitive, but got \"",
"f\"{type(activation).__name__}.\"",
")",
"self",
".",
"activation_flag",
"=",
"self",
".",
"activation",
"is",
"not",
"None"
] |
https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/nn/layer/basic.py#L278-L317
|
||
natanielruiz/android-yolo
|
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
|
jni-build/jni/include/tensorflow/python/ops/image_ops.py
|
python
|
grayscale_to_rgb
|
(images, name=None)
|
Converts one or more images from Grayscale to RGB.
Outputs a tensor of the same `DType` and rank as `images`. The size of the
last dimension of the output is 3, containing the RGB value of the pixels.
Args:
images: The Grayscale tensor to convert. Last dimension must be size 1.
name: A name for the operation (optional).
Returns:
The converted grayscale image(s).
|
Converts one or more images from Grayscale to RGB.
|
[
"Converts",
"one",
"or",
"more",
"images",
"from",
"Grayscale",
"to",
"RGB",
"."
] |
def grayscale_to_rgb(images, name=None):
"""Converts one or more images from Grayscale to RGB.
Outputs a tensor of the same `DType` and rank as `images`. The size of the
last dimension of the output is 3, containing the RGB value of the pixels.
Args:
images: The Grayscale tensor to convert. Last dimension must be size 1.
name: A name for the operation (optional).
Returns:
The converted grayscale image(s).
"""
with ops.op_scope([images], name, 'grayscale_to_rgb') as name:
images = ops.convert_to_tensor(images, name='images')
rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
shape_list = (
[array_ops.ones(rank_1,
dtype=dtypes.int32)] + [array_ops.expand_dims(3, 0)])
multiples = array_ops.concat(0, shape_list)
rgb = array_ops.tile(images, multiples, name=name)
rgb.set_shape(images.get_shape()[:-1].concatenate([3]))
return rgb
|
[
"def",
"grayscale_to_rgb",
"(",
"images",
",",
"name",
"=",
"None",
")",
":",
"with",
"ops",
".",
"op_scope",
"(",
"[",
"images",
"]",
",",
"name",
",",
"'grayscale_to_rgb'",
")",
"as",
"name",
":",
"images",
"=",
"ops",
".",
"convert_to_tensor",
"(",
"images",
",",
"name",
"=",
"'images'",
")",
"rank_1",
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"array_ops",
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"-",
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",",
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"[",
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",",
"dtype",
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"+",
"[",
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"(",
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",",
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"]",
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"array_ops",
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",",
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"tile",
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",",
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",",
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"name",
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"rgb",
".",
"set_shape",
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"images",
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"(",
")",
"[",
":",
"-",
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"]",
".",
"concatenate",
"(",
"[",
"3",
"]",
")",
")",
"return",
"rgb"
] |
https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/ops/image_ops.py#L1158-L1180
|
||
BitMEX/api-connectors
|
37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812
|
auto-generated/python/swagger_client/models/wallet.py
|
python
|
Wallet.timestamp
|
(self, timestamp)
|
Sets the timestamp of this Wallet.
:param timestamp: The timestamp of this Wallet. # noqa: E501
:type: datetime
|
Sets the timestamp of this Wallet.
|
[
"Sets",
"the",
"timestamp",
"of",
"this",
"Wallet",
"."
] |
def timestamp(self, timestamp):
"""Sets the timestamp of this Wallet.
:param timestamp: The timestamp of this Wallet. # noqa: E501
:type: datetime
"""
self._timestamp = timestamp
|
[
"def",
"timestamp",
"(",
"self",
",",
"timestamp",
")",
":",
"self",
".",
"_timestamp",
"=",
"timestamp"
] |
https://github.com/BitMEX/api-connectors/blob/37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812/auto-generated/python/swagger_client/models/wallet.py#L624-L632
|
||
nucleic/atom
|
9f0cb2a8101dd63c354a98ebc7489b2c616dc82a
|
docs/source/examples/example_doc_generator.py
|
python
|
main
|
()
|
Generate documentation for all atom examples.
|
Generate documentation for all atom examples.
|
[
"Generate",
"documentation",
"for",
"all",
"atom",
"examples",
"."
] |
def main():
"""Generate documentation for all atom examples."""
docs_path = os.path.dirname(__file__)
base_path = "../../../examples"
base_path = os.path.realpath(os.path.join(docs_path, base_path))
# Find all the files in the examples directory with a .enaml extension
# that contain the pragma '<< autodoc-me >>', and generate .rst files for
# them.
for dirname, dirnames, filenames in os.walk(base_path):
files = [os.path.join(dirname, f) for f in filenames if f.endswith(".py")]
for fname in files:
generate_example_doc(docs_path, fname)
|
[
"def",
"main",
"(",
")",
":",
"docs_path",
"=",
"os",
".",
"path",
".",
"dirname",
"(",
"__file__",
")",
"base_path",
"=",
"\"../../../examples\"",
"base_path",
"=",
"os",
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"path",
".",
"realpath",
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"os",
".",
"path",
".",
"join",
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"docs_path",
",",
"base_path",
")",
")",
"# Find all the files in the examples directory with a .enaml extension",
"# that contain the pragma '<< autodoc-me >>', and generate .rst files for",
"# them.",
"for",
"dirname",
",",
"dirnames",
",",
"filenames",
"in",
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"\".py\"",
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"for",
"fname",
"in",
"files",
":",
"generate_example_doc",
"(",
"docs_path",
",",
"fname",
")"
] |
https://github.com/nucleic/atom/blob/9f0cb2a8101dd63c354a98ebc7489b2c616dc82a/docs/source/examples/example_doc_generator.py#L109-L121
|
||
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/python/saved_model/utils_impl.py
|
python
|
build_tensor_info
|
(tensor)
|
return tensor_info
|
Utility function to build TensorInfo proto.
Args:
tensor: Tensor or SparseTensor whose name, dtype and shape are used to
build the TensorInfo. For SparseTensors, the names of the three
constitutent Tensors are used.
Returns:
A TensorInfo protocol buffer constructed based on the supplied argument.
|
Utility function to build TensorInfo proto.
|
[
"Utility",
"function",
"to",
"build",
"TensorInfo",
"proto",
"."
] |
def build_tensor_info(tensor):
"""Utility function to build TensorInfo proto.
Args:
tensor: Tensor or SparseTensor whose name, dtype and shape are used to
build the TensorInfo. For SparseTensors, the names of the three
constitutent Tensors are used.
Returns:
A TensorInfo protocol buffer constructed based on the supplied argument.
"""
tensor_info = meta_graph_pb2.TensorInfo(
dtype=dtypes.as_dtype(tensor.dtype).as_datatype_enum,
tensor_shape=tensor.get_shape().as_proto())
if isinstance(tensor, sparse_tensor.SparseTensor):
tensor_info.coo_sparse.values_tensor_name = tensor.values.name
tensor_info.coo_sparse.indices_tensor_name = tensor.indices.name
tensor_info.coo_sparse.dense_shape_tensor_name = tensor.dense_shape.name
else:
tensor_info.name = tensor.name
return tensor_info
|
[
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"build_tensor_info",
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"tensor",
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"tensor_info",
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"name",
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"tensor_info"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/saved_model/utils_impl.py#L30-L50
|
|
weolar/miniblink49
|
1c4678db0594a4abde23d3ebbcc7cd13c3170777
|
third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/coverage/results.py
|
python
|
Numbers.pc_str_width
|
(cls)
|
return width
|
How many characters wide can pc_covered_str be?
|
How many characters wide can pc_covered_str be?
|
[
"How",
"many",
"characters",
"wide",
"can",
"pc_covered_str",
"be?"
] |
def pc_str_width(cls):
"""How many characters wide can pc_covered_str be?"""
width = 3 # "100"
if cls._precision > 0:
width += 1 + cls._precision
return width
|
[
"def",
"pc_str_width",
"(",
"cls",
")",
":",
"width",
"=",
"3",
"# \"100\"",
"if",
"cls",
".",
"_precision",
">",
"0",
":",
"width",
"+=",
"1",
"+",
"cls",
".",
"_precision",
"return",
"width"
] |
https://github.com/weolar/miniblink49/blob/1c4678db0594a4abde23d3ebbcc7cd13c3170777/third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/coverage/results.py#L224-L229
|
|
intel/caffe
|
3f494b442ee3f9d17a07b09ecbd5fa2bbda00836
|
scripts/cpp_lint.py
|
python
|
_NestingState.Update
|
(self, filename, clean_lines, linenum, error)
|
Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
|
Update nesting state with current line.
|
[
"Update",
"nesting",
"state",
"with",
"current",
"line",
"."
] |
def Update(self, filename, clean_lines, linenum, error):
"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Update pp_stack first
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
if (depth_change != 0 and
inner_block.open_parentheses == 1 and
_MATCH_ASM.match(line)):
# Enter assembly block
inner_block.inline_asm = _INSIDE_ASM
else:
# Not entering assembly block. If previous line was _END_ASM,
# we will now shift to _NO_ASM state.
inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
#
# Templates with class arguments may confuse the parser, for example:
# template <class T
# class Comparator = less<T>,
# class Vector = vector<T> >
# class HeapQueue {
#
# Because this parser has no nesting state about templates, by the
# time it saw "class Comparator", it may think that it's a new class.
# Nested templates have a similar problem:
# template <
# typename ExportedType,
# typename TupleType,
# template <typename, typename> class ImplTemplate>
#
# To avoid these cases, we ignore classes that are followed by '=' or '>'
class_decl_match = Match(
r'\s*(template\s*<[\w\s<>,:]*>\s*)?'
r'(class|struct)\s+([A-Z_]+\s+)*(\w+(?:::\w+)*)'
r'(([^=>]|<[^<>]*>|<[^<>]*<[^<>]*>\s*>)*)$', line)
if (class_decl_match and
(not self.stack or self.stack[-1].open_parentheses == 0)):
self.stack.append(_ClassInfo(
class_decl_match.group(4), class_decl_match.group(2),
clean_lines, linenum))
line = class_decl_match.group(5)
# If we have not yet seen the opening brace for the innermost block,
# run checks here.
if not self.SeenOpenBrace():
self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
# Update access control if we are inside a class/struct
if self.stack and isinstance(self.stack[-1], _ClassInfo):
classinfo = self.stack[-1]
access_match = Match(
r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
r':(?:[^:]|$)',
line)
if access_match:
classinfo.access = access_match.group(2)
# Check that access keywords are indented +1 space. Skip this
# check if the keywords are not preceded by whitespaces.
indent = access_match.group(1)
if (len(indent) != classinfo.class_indent + 1 and
Match(r'^\s*$', indent)):
if classinfo.is_struct:
parent = 'struct ' + classinfo.name
else:
parent = 'class ' + classinfo.name
slots = ''
if access_match.group(3):
slots = access_match.group(3)
error(filename, linenum, 'whitespace/indent', 3,
'%s%s: should be indented +1 space inside %s' % (
access_match.group(2), slots, parent))
# Consume braces or semicolons from what's left of the line
while True:
# Match first brace, semicolon, or closed parenthesis.
matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
if not matched:
break
token = matched.group(1)
if token == '{':
# If namespace or class hasn't seen a opening brace yet, mark
# namespace/class head as complete. Push a new block onto the
# stack otherwise.
if not self.SeenOpenBrace():
self.stack[-1].seen_open_brace = True
else:
self.stack.append(_BlockInfo(True))
if _MATCH_ASM.match(line):
self.stack[-1].inline_asm = _BLOCK_ASM
elif token == ';' or token == ')':
# If we haven't seen an opening brace yet, but we already saw
# a semicolon, this is probably a forward declaration. Pop
# the stack for these.
#
# Similarly, if we haven't seen an opening brace yet, but we
# already saw a closing parenthesis, then these are probably
# function arguments with extra "class" or "struct" keywords.
# Also pop these stack for these.
if not self.SeenOpenBrace():
self.stack.pop()
else: # token == '}'
# Perform end of block checks and pop the stack.
if self.stack:
self.stack[-1].CheckEnd(filename, clean_lines, linenum, error)
self.stack.pop()
line = matched.group(2)
|
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"(",
"2",
")"
] |
https://github.com/intel/caffe/blob/3f494b442ee3f9d17a07b09ecbd5fa2bbda00836/scripts/cpp_lint.py#L2008-L2162
|
||
pmq20/node-packer
|
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
|
current/tools/gyp/pylib/gyp/xcode_emulation.py
|
python
|
XcodeSettings.GetExecutableName
|
(self)
|
Returns the executable name of the bundle represented by this target.
E.g. Chromium.
|
Returns the executable name of the bundle represented by this target.
E.g. Chromium.
|
[
"Returns",
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"executable",
"name",
"of",
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"bundle",
"represented",
"by",
"this",
"target",
".",
"E",
".",
"g",
".",
"Chromium",
"."
] |
def GetExecutableName(self):
"""Returns the executable name of the bundle represented by this target.
E.g. Chromium."""
if self._IsBundle():
return self.spec.get('product_name', self.spec['target_name'])
else:
return self._GetStandaloneBinaryPath()
|
[
"def",
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")",
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":",
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"_GetStandaloneBinaryPath",
"(",
")"
] |
https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/current/tools/gyp/pylib/gyp/xcode_emulation.py#L471-L477
|
||
windystrife/UnrealEngine_NVIDIAGameWorks
|
b50e6338a7c5b26374d66306ebc7807541ff815e
|
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/calendar.py
|
python
|
Calendar.itermonthdays
|
(self, year, month)
|
Like itermonthdates(), but will yield day numbers. For days outside
the specified month the day number is 0.
|
Like itermonthdates(), but will yield day numbers. For days outside
the specified month the day number is 0.
|
[
"Like",
"itermonthdates",
"()",
"but",
"will",
"yield",
"day",
"numbers",
".",
"For",
"days",
"outside",
"the",
"specified",
"month",
"the",
"day",
"number",
"is",
"0",
"."
] |
def itermonthdays(self, year, month):
"""
Like itermonthdates(), but will yield day numbers. For days outside
the specified month the day number is 0.
"""
for date in self.itermonthdates(year, month):
if date.month != month:
yield 0
else:
yield date.day
|
[
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":",
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/calendar.py#L183-L192
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/__init__.py
|
python
|
_handle_ns
|
(packageName, path_item)
|
return subpath
|
Ensure that named package includes a subpath of path_item (if needed)
|
Ensure that named package includes a subpath of path_item (if needed)
|
[
"Ensure",
"that",
"named",
"package",
"includes",
"a",
"subpath",
"of",
"path_item",
"(",
"if",
"needed",
")"
] |
def _handle_ns(packageName, path_item):
"""Ensure that named package includes a subpath of path_item (if needed)"""
importer = get_importer(path_item)
if importer is None:
return None
# use find_spec (PEP 451) and fall-back to find_module (PEP 302)
try:
loader = importer.find_spec(packageName).loader
except AttributeError:
# capture warnings due to #1111
with warnings.catch_warnings():
warnings.simplefilter("ignore")
loader = importer.find_module(packageName)
if loader is None:
return None
module = sys.modules.get(packageName)
if module is None:
module = sys.modules[packageName] = types.ModuleType(packageName)
module.__path__ = []
_set_parent_ns(packageName)
elif not hasattr(module, '__path__'):
raise TypeError("Not a package:", packageName)
handler = _find_adapter(_namespace_handlers, importer)
subpath = handler(importer, path_item, packageName, module)
if subpath is not None:
path = module.__path__
path.append(subpath)
loader.load_module(packageName)
_rebuild_mod_path(path, packageName, module)
return subpath
|
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",",
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",",
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")",
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/__init__.py#L2193-L2225
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/datetimelike.py
|
python
|
DatetimeTimedeltaMixin._set_freq
|
(self, freq)
|
Set the _freq attribute on our underlying DatetimeArray.
Parameters
----------
freq : DateOffset, None, or "infer"
|
Set the _freq attribute on our underlying DatetimeArray.
|
[
"Set",
"the",
"_freq",
"attribute",
"on",
"our",
"underlying",
"DatetimeArray",
"."
] |
def _set_freq(self, freq):
"""
Set the _freq attribute on our underlying DatetimeArray.
Parameters
----------
freq : DateOffset, None, or "infer"
"""
# GH#29843
if freq is None:
# Always valid
pass
elif len(self) == 0 and isinstance(freq, DateOffset):
# Always valid. In the TimedeltaIndex case, we assume this
# is a Tick offset.
pass
else:
# As an internal method, we can ensure this assertion always holds
assert freq == "infer"
freq = to_offset(self.inferred_freq)
self._data._freq = freq
|
[
"def",
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",",
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"if",
"freq",
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"inferred_freq",
")",
"self",
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"_data",
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"_freq",
"=",
"freq"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/datetimelike.py#L575-L596
|
||
miyosuda/TensorFlowAndroidDemo
|
35903e0221aa5f109ea2dbef27f20b52e317f42d
|
jni-build/jni/include/tensorflow/python/ops/array_ops.py
|
python
|
_OneHotShape
|
(op)
|
return [tensor_shape.TensorShape(new_shape)]
|
Shape function for the OneHot op.
It closely follows the code in the .cc implementation.
Args:
op: A OneHot Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: if axis < -1.
|
Shape function for the OneHot op.
|
[
"Shape",
"function",
"for",
"the",
"OneHot",
"op",
"."
] |
def _OneHotShape(op):
"""Shape function for the OneHot op.
It closely follows the code in the .cc implementation.
Args:
op: A OneHot Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: if axis < -1.
"""
indices_shape = op.inputs[0].get_shape()
indices_dims = indices_shape.ndims
depth = tensor_util.constant_value(op.inputs[1])
axis = op.get_attr("axis")
if axis < -1:
raise ValueError("axis must be >= -1")
new_shape = None
if indices_dims is not None:
new_shape = indices_shape.as_list()
new_shape.insert(axis % (indices_dims + 1), depth)
return [tensor_shape.TensorShape(new_shape)]
|
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] |
https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/ops/array_ops.py#L2721-L2748
|
|
Kitware/kwiver
|
7ed70308905698b6e88d27ae3dc028c9b016ca0a
|
python/kwiver/sprokit/processes/pytorch/utils/parse_gpu_list.py
|
python
|
get_device
|
(gpu_list=None)
|
return torch.device('cuda:{}'.format(gpu_list[0])), True
|
Get a Pytorch device corresponding to one of the GPU indices listed
in gpu_list. If gpu_list is empty, get the device corresponding
to the CPU instead. If gpu_list is None (the default), enumerate
the available GPU indices and pick one as though the list had been
passed directly, except that in the case of there being no GPUs,
an IndexError will be thrown.
The return value is a pair of the device and a boolean that is
true if the returned device is a GPU device.
Note that we currently return the first listed device.
|
Get a Pytorch device corresponding to one of the GPU indices listed
in gpu_list. If gpu_list is empty, get the device corresponding
to the CPU instead. If gpu_list is None (the default), enumerate
the available GPU indices and pick one as though the list had been
passed directly, except that in the case of there being no GPUs,
an IndexError will be thrown.
|
[
"Get",
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"Pytorch",
"device",
"corresponding",
"to",
"one",
"of",
"the",
"GPU",
"indices",
"listed",
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"being",
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"GPUs",
"an",
"IndexError",
"will",
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"thrown",
"."
] |
def get_device(gpu_list=None):
"""Get a Pytorch device corresponding to one of the GPU indices listed
in gpu_list. If gpu_list is empty, get the device corresponding
to the CPU instead. If gpu_list is None (the default), enumerate
the available GPU indices and pick one as though the list had been
passed directly, except that in the case of there being no GPUs,
an IndexError will be thrown.
The return value is a pair of the device and a boolean that is
true if the returned device is a GPU device.
Note that we currently return the first listed device.
"""
if gpu_list is None:
gpu_list = list(range(torch.cuda.device_count()))
elif not gpu_list:
return torch.device('cpu'), False
return torch.device('cuda:{}'.format(gpu_list[0])), True
|
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https://github.com/Kitware/kwiver/blob/7ed70308905698b6e88d27ae3dc028c9b016ca0a/python/kwiver/sprokit/processes/pytorch/utils/parse_gpu_list.py#L61-L79
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/build/waf-1.7.13/lmbrwaflib/third_party.py
|
python
|
evaluate_node_alias_map
|
(lib_root, lib_name)
|
return evaluated_name_alias_map
|
Given a root of a lib config, perform an evaluation of all the aliases and produce a map of its value
:param lib_root: The lib root
:return: The map of aliased values
|
Given a root of a lib config, perform an evaluation of all the aliases and produce a map of its value
:param lib_root: The lib root
:return: The map of aliased values
|
[
"Given",
"a",
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":",
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"values"
] |
def evaluate_node_alias_map(lib_root, lib_name):
"""
Given a root of a lib config, perform an evaluation of all the aliases and produce a map of its value
:param lib_root: The lib root
:return: The map of aliased values
"""
def _process_alias_value(name_alias_map, name_alias_value, visit_stack):
alias_match = ALIAS_SEARCH_PATTERN.search(name_alias_value)
if alias_match is not None:
# This contains an aliased value
alias_key = alias_match.group(2)
# If the value is another alias
if alias_key not in name_alias_map:
# If the alias is not in the map, this is an error
raise RuntimeError('Invalid alias value {} for 3rd party library {}'.format(name_alias_value, lib_name))
if alias_key in visit_stack:
# If the alias is in the stack, this is a recursive error
raise RuntimeError('Recursive alias value error {} for 3rd party library {}'.format(name_alias_value, lib_name))
visit_stack.append(alias_key)
aliased_values = []
aliased_names = name_alias_map[alias_key] if isinstance(name_alias_map[alias_key], list) else [name_alias_map[alias_key]]
for alias_value in aliased_names:
pre_expansion_aliases = _process_alias_value(name_alias_map, alias_value, visit_stack)
for pre_expansion_alias in pre_expansion_aliases:
aliased_values.append(ALIAS_SEARCH_PATTERN.sub(pre_expansion_alias, name_alias_value))
visit_stack.pop()
return aliased_values
else:
return [name_alias_value]
# Scan the optional 'aliases' node off of the parent to look for aliases to build the map
evaluated_name_alias_map = {}
if 'aliases' not in list(lib_root.keys()):
return evaluated_name_alias_map
alias_node = lib_root['aliases']
# The first pass is to collect the pre-evaluated alias names
for node_key in alias_node:
node_alias_value = alias_node[node_key]
if isinstance(node_alias_value, str):
node_alias_value = [node_alias_value]
evaluated_name_alias_map[node_key] = node_alias_value
# Second pass, go through each list (possible recursively) and expand any aliased values in the list
stack = []
for aliased_node_key in evaluated_name_alias_map:
aliased_node_values = evaluated_name_alias_map[aliased_node_key]
stack.append(aliased_node_key)
values = []
for aliased_node_value in aliased_node_values:
values += _process_alias_value(evaluated_name_alias_map, aliased_node_value, stack)
evaluated_name_alias_map[aliased_node_key] = values
stack.pop()
return evaluated_name_alias_map
|
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] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/build/waf-1.7.13/lmbrwaflib/third_party.py#L112-L172
|
|
lballabio/quantlib-old
|
136336947ed4fea9ecc1da6edad188700e821739
|
gensrc/gensrc/rules/rule.py
|
python
|
Wrap.serialize
|
(self, serializer)
|
Load/unload class state to/from serializer object.
|
Load/unload class state to/from serializer object.
|
[
"Load",
"/",
"unload",
"class",
"state",
"to",
"/",
"from",
"serializer",
"object",
"."
] |
def serialize(self, serializer):
"""Load/unload class state to/from serializer object."""
serializer.serializeValue(self)
serializer.serializeAttribute(self, common.CODE_ID)
|
[
"def",
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"(",
"self",
",",
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"CODE_ID",
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] |
https://github.com/lballabio/quantlib-old/blob/136336947ed4fea9ecc1da6edad188700e821739/gensrc/gensrc/rules/rule.py#L140-L143
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/dashboard/dashboard/edit_config_handler.py
|
python
|
_ChangeTestPatterns
|
(old_patterns, new_patterns)
|
return _RemoveOverlapping(added_test_paths, removed_test_paths)
|
Updates tests that are different between old_patterns and new_patterns.
The two arguments both represent sets of test paths (i.e. sets of data
series). Any tests that are different between these two sets need to be
updated.
Some properties of TestMetadata entities are updated when they are put in the
|_pre_put_hook| method of TestMetadata, so any TestMetadata entity that might
need to be updated should be re-put.
Args:
old_patterns: An iterable of test path pattern strings.
new_patterns: Another iterable of test path pattern strings.
Returns:
A pair (added_test_paths, removed_test_paths), which are, respectively,
the test paths that are in the new set but not the old, and those that
are in the old set but not the new.
|
Updates tests that are different between old_patterns and new_patterns.
|
[
"Updates",
"tests",
"that",
"are",
"different",
"between",
"old_patterns",
"and",
"new_patterns",
"."
] |
def _ChangeTestPatterns(old_patterns, new_patterns):
"""Updates tests that are different between old_patterns and new_patterns.
The two arguments both represent sets of test paths (i.e. sets of data
series). Any tests that are different between these two sets need to be
updated.
Some properties of TestMetadata entities are updated when they are put in the
|_pre_put_hook| method of TestMetadata, so any TestMetadata entity that might
need to be updated should be re-put.
Args:
old_patterns: An iterable of test path pattern strings.
new_patterns: Another iterable of test path pattern strings.
Returns:
A pair (added_test_paths, removed_test_paths), which are, respectively,
the test paths that are in the new set but not the old, and those that
are in the old set but not the new.
"""
added_patterns, removed_patterns = _ComputeDeltas(old_patterns, new_patterns)
added_test_paths = _AllTestPathsMatchingPatterns(added_patterns)
removed_test_paths = _AllTestPathsMatchingPatterns(removed_patterns)
_AddTestsToPutToTaskQueue(added_test_paths + removed_test_paths)
return _RemoveOverlapping(added_test_paths, removed_test_paths)
|
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/dashboard/dashboard/edit_config_handler.py#L165-L189
|
|
idaholab/moose
|
9eeebc65e098b4c30f8205fb41591fd5b61eb6ff
|
python/MooseDocs/base/renderers.py
|
python
|
Renderer.getRoot
|
(self)
|
Return the rendered content root node.
Called by the Translator prior to beginning rendering.
|
Return the rendered content root node.
|
[
"Return",
"the",
"rendered",
"content",
"root",
"node",
"."
] |
def getRoot(self):
"""
Return the rendered content root node.
Called by the Translator prior to beginning rendering.
"""
raise NotImplementedError()
|
[
"def",
"getRoot",
"(",
"self",
")",
":",
"raise",
"NotImplementedError",
"(",
")"
] |
https://github.com/idaholab/moose/blob/9eeebc65e098b4c30f8205fb41591fd5b61eb6ff/python/MooseDocs/base/renderers.py#L59-L65
|
||
NVIDIA/DALI
|
bf16cc86ba8f091b145f91962f21fe1b6aff243d
|
dali/python/nvidia/dali/math.py
|
python
|
clamp
|
(value, lo, hi)
|
return _arithm_op("clamp", value, lo, hi)
|
Produces a tensor of values from ``value`` clamped to the range ``[lo, hi]``.
:rtype: TensorList of the type that is calculated based on the type promotion rules.
|
Produces a tensor of values from ``value`` clamped to the range ``[lo, hi]``.
|
[
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"values",
"from",
"value",
"clamped",
"to",
"the",
"range",
"[",
"lo",
"hi",
"]",
"."
] |
def clamp(value, lo, hi):
"""Produces a tensor of values from ``value`` clamped to the range ``[lo, hi]``.
:rtype: TensorList of the type that is calculated based on the type promotion rules.
"""
return _arithm_op("clamp", value, lo, hi)
|
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https://github.com/NVIDIA/DALI/blob/bf16cc86ba8f091b145f91962f21fe1b6aff243d/dali/python/nvidia/dali/math.py#L245-L250
|
|
libornovax/master_thesis_code
|
6eca474ed3cae673afde010caef338cf7349f839
|
caffe/python/caffe/pycaffe.py
|
python
|
_Net_forward_all
|
(self, blobs=None, **kwargs)
|
return all_outs
|
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
|
Run net forward in batches.
|
[
"Run",
"net",
"forward",
"in",
"batches",
"."
] |
def _Net_forward_all(self, blobs=None, **kwargs):
"""
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
for out, out_blob in six.iteritems(outs):
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
return all_outs
|
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https://github.com/libornovax/master_thesis_code/blob/6eca474ed3cae673afde010caef338cf7349f839/caffe/python/caffe/pycaffe.py#L175-L203
|
|
ApolloAuto/apollo-platform
|
86d9dc6743b496ead18d597748ebabd34a513289
|
ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/matrixlib/defmatrix.py
|
python
|
matrix.all
|
(self, axis=None, out=None)
|
return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
|
Test whether all matrix elements along a given axis evaluate to True.
Parameters
----------
See `numpy.all` for complete descriptions
See Also
--------
numpy.all
Notes
-----
This is the same as `ndarray.all`, but it returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]], dtype=bool)
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]], dtype=bool)
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]], dtype=bool)
|
Test whether all matrix elements along a given axis evaluate to True.
|
[
"Test",
"whether",
"all",
"matrix",
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"along",
"a",
"given",
"axis",
"evaluate",
"to",
"True",
"."
] |
def all(self, axis=None, out=None):
"""
Test whether all matrix elements along a given axis evaluate to True.
Parameters
----------
See `numpy.all` for complete descriptions
See Also
--------
numpy.all
Notes
-----
This is the same as `ndarray.all`, but it returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]], dtype=bool)
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]], dtype=bool)
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]], dtype=bool)
"""
return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
|
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"_collapse",
"(",
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] |
https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/matrixlib/defmatrix.py#L615-L653
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
samples/doodle/superdoodle.py
|
python
|
ControlPanel.OnSetThickness
|
(self, event)
|
Use the event ID to set the thickness in the doodle.
|
Use the event ID to set the thickness in the doodle.
|
[
"Use",
"the",
"event",
"ID",
"to",
"set",
"the",
"thickness",
"in",
"the",
"doodle",
"."
] |
def OnSetThickness(self, event):
"""
Use the event ID to set the thickness in the doodle.
"""
thickness = event.GetId()
if thickness != self.doodle.thickness:
# untoggle the old thickness button
self.thknsBtns[self.doodle.thickness].SetToggle(False)
# set the new colour
self.doodle.SetThickness(thickness)
|
[
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"self",
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] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/samples/doodle/superdoodle.py#L283-L292
|
||
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
buildconfig/python_export_maker.py
|
python
|
get_namespace
|
(headerfile)
|
return namespace
|
Returns the Mantid namespace that the header resides in
|
Returns the Mantid namespace that the header resides in
|
[
"Returns",
"the",
"Mantid",
"namespace",
"that",
"the",
"header",
"resides",
"in"
] |
def get_namespace(headerfile):
"""
Returns the Mantid namespace that the header resides in
"""
matches = re.match(r".*inc(/|\\)Mantid(\w+)(/|\\).*\.h", headerfile)
if matches:
namespace = matches.group(2)
else:
raise RuntimeError("Unknown header path style. Cannot extract Mantid namespace name.")
return namespace
|
[
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")",
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] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/buildconfig/python_export_maker.py#L41-L50
|
|
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/klampt/math/autodiff/kinematics_ad.py
|
python
|
KinematicsBuilder.world_angular_velocity
|
(self,link)
|
return self.link_angular_velocities[link]
|
Returns an autodiff expression for the world angular velocity of the
given link.
Expression evaluates to a 9-D so3_ad array.
|
Returns an autodiff expression for the world angular velocity of the
given link.
|
[
"Returns",
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"for",
"the",
"world",
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"velocity",
"of",
"the",
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"."
] |
def world_angular_velocity(self,link):
"""Returns an autodiff expression for the world angular velocity of the
given link.
Expression evaluates to a 9-D so3_ad array.
"""
link = self._link_index(link)
return self.link_angular_velocities[link]
|
[
"def",
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"(",
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"link_angular_velocities",
"[",
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"]"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/math/autodiff/kinematics_ad.py#L551-L558
|
|
y123456yz/reading-and-annotate-mongodb-3.6
|
93280293672ca7586dc24af18132aa61e4ed7fcf
|
mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/packaging/msi.py
|
python
|
build_wxsfile_default_gui
|
(root)
|
This function adds a default GUI to the wxs file
|
This function adds a default GUI to the wxs file
|
[
"This",
"function",
"adds",
"a",
"default",
"GUI",
"to",
"the",
"wxs",
"file"
] |
def build_wxsfile_default_gui(root):
""" This function adds a default GUI to the wxs file
"""
factory = Document()
Product = root.getElementsByTagName('Product')[0]
UIRef = factory.createElement('UIRef')
UIRef.attributes['Id'] = 'WixUI_Mondo'
Product.childNodes.append(UIRef)
UIRef = factory.createElement('UIRef')
UIRef.attributes['Id'] = 'WixUI_ErrorProgressText'
Product.childNodes.append(UIRef)
|
[
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https://github.com/y123456yz/reading-and-annotate-mongodb-3.6/blob/93280293672ca7586dc24af18132aa61e4ed7fcf/mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/packaging/msi.py#L417-L429
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/mailbox.py
|
python
|
_singlefileMailbox.__len__
|
(self)
|
return len(self._toc)
|
Return a count of messages in the mailbox.
|
Return a count of messages in the mailbox.
|
[
"Return",
"a",
"count",
"of",
"messages",
"in",
"the",
"mailbox",
"."
] |
def __len__(self):
"""Return a count of messages in the mailbox."""
self._lookup()
return len(self._toc)
|
[
"def",
"__len__",
"(",
"self",
")",
":",
"self",
".",
"_lookup",
"(",
")",
"return",
"len",
"(",
"self",
".",
"_toc",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/mailbox.py#L617-L620
|
|
gem5/gem5
|
141cc37c2d4b93959d4c249b8f7e6a8b2ef75338
|
src/python/gem5/components/processors/complex_generator_core.py
|
python
|
ComplexGeneratorCore._create_linear_traffic
|
(
self,
duration: str,
rate: str,
block_size: int,
min_addr: int,
max_addr: int,
rd_perc: int,
data_limit: int,
)
|
This function yields (creates) a linear traffic based on the input
params. Then it will yield (create) an exit traffic (exit traffic is
used to exit the simulation).
:param duration: The number of ticks for the generator core to generate
traffic.
:param rate: The rate at which the synthetic data is read/written.
:param block_size: The number of bytes to be read/written with each
request.
:param min_addr: The lower bound of the address range the generator
will read/write from/to.
:param max_addr: The upper bound of the address range the generator
will read/write from/to.
:param rd_perc: The percentage of read requests among all the generated
requests. The write percentage would be equal to 100 - rd_perc.
:param data_limit: The amount of data in bytes to read/write by the
generator before stopping generation.
|
This function yields (creates) a linear traffic based on the input
params. Then it will yield (create) an exit traffic (exit traffic is
used to exit the simulation).
|
[
"This",
"function",
"yields",
"(",
"creates",
")",
"a",
"linear",
"traffic",
"based",
"on",
"the",
"input",
"params",
".",
"Then",
"it",
"will",
"yield",
"(",
"create",
")",
"an",
"exit",
"traffic",
"(",
"exit",
"traffic",
"is",
"used",
"to",
"exit",
"the",
"simulation",
")",
"."
] |
def _create_linear_traffic(
self,
duration: str,
rate: str,
block_size: int,
min_addr: int,
max_addr: int,
rd_perc: int,
data_limit: int,
) -> None:
"""
This function yields (creates) a linear traffic based on the input
params. Then it will yield (create) an exit traffic (exit traffic is
used to exit the simulation).
:param duration: The number of ticks for the generator core to generate
traffic.
:param rate: The rate at which the synthetic data is read/written.
:param block_size: The number of bytes to be read/written with each
request.
:param min_addr: The lower bound of the address range the generator
will read/write from/to.
:param max_addr: The upper bound of the address range the generator
will read/write from/to.
:param rd_perc: The percentage of read requests among all the generated
requests. The write percentage would be equal to 100 - rd_perc.
:param data_limit: The amount of data in bytes to read/write by the
generator before stopping generation.
"""
duration = fromSeconds(toLatency(duration))
rate = toMemoryBandwidth(rate)
period = fromSeconds(block_size / rate)
min_period = period
max_period = period
yield self.generator.createLinear(
duration,
min_addr,
max_addr,
block_size,
min_period,
max_period,
rd_perc,
data_limit,
)
yield self.generator.createExit(0)
|
[
"def",
"_create_linear_traffic",
"(",
"self",
",",
"duration",
":",
"str",
",",
"rate",
":",
"str",
",",
"block_size",
":",
"int",
",",
"min_addr",
":",
"int",
",",
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",",
"data_limit",
",",
")",
"yield",
"self",
".",
"generator",
".",
"createExit",
"(",
"0",
")"
] |
https://github.com/gem5/gem5/blob/141cc37c2d4b93959d4c249b8f7e6a8b2ef75338/src/python/gem5/components/processors/complex_generator_core.py#L242-L286
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/contrib/learn/python/learn/estimators/head.py
|
python
|
_assert_class_id
|
(class_id, num_classes=None)
|
Average label value for class `class_id`.
|
Average label value for class `class_id`.
|
[
"Average",
"label",
"value",
"for",
"class",
"class_id",
"."
] |
def _assert_class_id(class_id, num_classes=None):
"""Average label value for class `class_id`."""
if (class_id is None) or (class_id < 0):
raise ValueError("Invalid class_id %s." % class_id)
if num_classes is not None:
if num_classes < 2:
raise ValueError("Invalid num_classes %s." % num_classes)
if class_id >= num_classes:
raise ValueError("Invalid class_id %s." % class_id)
|
[
"def",
"_assert_class_id",
"(",
"class_id",
",",
"num_classes",
"=",
"None",
")",
":",
"if",
"(",
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"is",
"None",
")",
"or",
"(",
"class_id",
"<",
"0",
")",
":",
"raise",
"ValueError",
"(",
"\"Invalid class_id %s.\"",
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"if",
"num_classes",
"is",
"not",
"None",
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"if",
"num_classes",
"<",
"2",
":",
"raise",
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"\"Invalid num_classes %s.\"",
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"if",
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">=",
"num_classes",
":",
"raise",
"ValueError",
"(",
"\"Invalid class_id %s.\"",
"%",
"class_id",
")"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/contrib/learn/python/learn/estimators/head.py#L2017-L2025
|
||
apple/swift
|
469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893
|
utils/swift_build_support/swift_build_support/targets.py
|
python
|
Platform.cmake_options
|
(self, args)
|
return ''
|
CMake flags to build for a platform, useful for cross-compiling
|
CMake flags to build for a platform, useful for cross-compiling
|
[
"CMake",
"flags",
"to",
"build",
"for",
"a",
"platform",
"useful",
"for",
"cross",
"-",
"compiling"
] |
def cmake_options(self, args):
"""
CMake flags to build for a platform, useful for cross-compiling
"""
return ''
|
[
"def",
"cmake_options",
"(",
"self",
",",
"args",
")",
":",
"return",
"''"
] |
https://github.com/apple/swift/blob/469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893/utils/swift_build_support/swift_build_support/targets.py#L77-L81
|
|
infinit/memo
|
3a8394d0f647efe03ccb8bfe885a7279cb8be8a6
|
elle/drake/src/drake/__init__.py
|
python
|
FunctionExpander.function
|
(self)
|
return self.__function
|
The function.
|
The function.
|
[
"The",
"function",
"."
] |
def function(self):
"""The function."""
return self.__function
|
[
"def",
"function",
"(",
"self",
")",
":",
"return",
"self",
".",
"__function"
] |
https://github.com/infinit/memo/blob/3a8394d0f647efe03ccb8bfe885a7279cb8be8a6/elle/drake/src/drake/__init__.py#L2842-L2844
|
|
koth/kcws
|
88efbd36a7022de4e6e90f5a1fb880cf87cfae9f
|
third_party/setuptools/pkg_resources.py
|
python
|
DistInfoDistribution._compute_dependencies
|
(self)
|
return dm
|
Recompute this distribution's dependencies.
|
Recompute this distribution's dependencies.
|
[
"Recompute",
"this",
"distribution",
"s",
"dependencies",
"."
] |
def _compute_dependencies(self):
"""Recompute this distribution's dependencies."""
from _markerlib import compile as compile_marker
dm = self.__dep_map = {None: []}
reqs = []
# Including any condition expressions
for req in self._parsed_pkg_info.get_all('Requires-Dist') or []:
distvers, mark = self._preparse_requirement(req)
parsed = next(parse_requirements(distvers))
parsed.marker_fn = compile_marker(mark)
reqs.append(parsed)
def reqs_for_extra(extra):
for req in reqs:
if req.marker_fn(override={'extra':extra}):
yield req
common = frozenset(reqs_for_extra(None))
dm[None].extend(common)
for extra in self._parsed_pkg_info.get_all('Provides-Extra') or []:
extra = safe_extra(extra.strip())
dm[extra] = list(frozenset(reqs_for_extra(extra)) - common)
return dm
|
[
"def",
"_compute_dependencies",
"(",
"self",
")",
":",
"from",
"_markerlib",
"import",
"compile",
"as",
"compile_marker",
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"=",
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"[",
"]",
"}",
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"[",
"]",
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"get_all",
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"'Requires-Dist'",
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"(",
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"]",
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"(",
"frozenset",
"(",
"reqs_for_extra",
"(",
"extra",
")",
")",
"-",
"common",
")",
"return",
"dm"
] |
https://github.com/koth/kcws/blob/88efbd36a7022de4e6e90f5a1fb880cf87cfae9f/third_party/setuptools/pkg_resources.py#L2577-L2602
|
|
trilinos/Trilinos
|
6168be6dd51e35e1cd681e9c4b24433e709df140
|
packages/seacas/scripts/exodus3.in.py
|
python
|
exodus.get_node_set_name
|
(self, object_id)
|
return self.__ex_get_name('EX_NODE_SET', object_id)
|
get the name of a node set
>>> node_set_name = exo.get_node_set_name(node_set_id)
Parameters
----------
<int> node_set_id node set *ID* (not *INDEX*)
Returns
-------
<string> node_set_name
|
get the name of a node set
|
[
"get",
"the",
"name",
"of",
"a",
"node",
"set"
] |
def get_node_set_name(self, object_id):
"""
get the name of a node set
>>> node_set_name = exo.get_node_set_name(node_set_id)
Parameters
----------
<int> node_set_id node set *ID* (not *INDEX*)
Returns
-------
<string> node_set_name
"""
return self.__ex_get_name('EX_NODE_SET', object_id)
|
[
"def",
"get_node_set_name",
"(",
"self",
",",
"object_id",
")",
":",
"return",
"self",
".",
"__ex_get_name",
"(",
"'EX_NODE_SET'",
",",
"object_id",
")"
] |
https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/seacas/scripts/exodus3.in.py#L3180-L3194
|
|
sofa-framework/sofa
|
70628e35a44fcc258cf8250109b5e4eba8c5abe9
|
applications/plugins/PSL/python/pslengine.py
|
python
|
populateFrame
|
(cname, frame, stack)
|
Initialize a frame from the current attributes of the 'self' object
This is needed to expose the data as first class object.
|
Initialize a frame from the current attributes of the 'self' object
This is needed to expose the data as first class object.
|
[
"Initialize",
"a",
"frame",
"from",
"the",
"current",
"attributes",
"of",
"the",
"self",
"object",
"This",
"is",
"needed",
"to",
"expose",
"the",
"data",
"as",
"first",
"class",
"object",
"."
] |
def populateFrame(cname, frame, stack):
"""Initialize a frame from the current attributes of the 'self' object
This is needed to expose the data as first class object.
"""
fself = getFromStack("self", stack)
if fself == None:
return
|
[
"def",
"populateFrame",
"(",
"cname",
",",
"frame",
",",
"stack",
")",
":",
"fself",
"=",
"getFromStack",
"(",
"\"self\"",
",",
"stack",
")",
"if",
"fself",
"==",
"None",
":",
"return"
] |
https://github.com/sofa-framework/sofa/blob/70628e35a44fcc258cf8250109b5e4eba8c5abe9/applications/plugins/PSL/python/pslengine.py#L112-L118
|
||
natanielruiz/android-yolo
|
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
|
jni-build/jni/include/tensorflow/contrib/factorization/examples/mnist.py
|
python
|
run_training
|
()
|
Train MNIST for a number of steps.
|
Train MNIST for a number of steps.
|
[
"Train",
"MNIST",
"for",
"a",
"number",
"of",
"steps",
"."
] |
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
train_dir = tempfile.mkdtemp()
data_sets = input_data.read_data_sets(train_dir, FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels.
images_placeholder, labels_placeholder = placeholder_inputs()
# Build a Graph that computes predictions from the inference model.
logits, clustering_loss, kmeans_training_op = inference(images_placeholder,
FLAGS.num_clusters,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = tf.group(mnist.training(loss, FLAGS.learning_rate),
kmeans_training_op)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Add the variable initializer Op.
init = tf.initialize_all_variables()
# Create a session for running Ops on the Graph.
sess = tf.Session()
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder,
batch_size=5000)
# Run the Op to initialize the variables.
sess.run(init, feed_dict=feed_dict)
# Start the training loop.
max_test_prec = 0
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder,
FLAGS.batch_size)
# Run one step of the model.
_, loss_value, clustering_loss_value = sess.run([train_op,
loss,
clustering_loss],
feed_dict=feed_dict)
duration = time.time() - start_time
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f, clustering_loss = %.2f (%.3f sec)' % (
step, loss_value, clustering_loss_value, duration))
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
test_prec = do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.test)
max_test_prec = max(max_test_prec, test_prec)
return max_test_prec
|
[
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"'Step %d: loss = %.2f, clustering_loss = %.2f (%.3f sec)'",
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"# Save a checkpoint and evaluate the model periodically.",
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"max",
"(",
"max_test_prec",
",",
"test_prec",
")",
"return",
"max_test_prec"
] |
https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/contrib/factorization/examples/mnist.py#L192-L281
|
||
lrjconan/GRAN
|
43cb4433e6f69401c3a4a6e946ea75da6ec35d72
|
utils/dist_helper.py
|
python
|
gaussian_emd
|
(x, y, sigma=1.0, distance_scaling=1.0)
|
return np.exp(-emd * emd / (2 * sigma * sigma))
|
Gaussian kernel with squared distance in exponential term replaced by EMD
Args:
x, y: 1D pmf of two distributions with the same support
sigma: standard deviation
|
Gaussian kernel with squared distance in exponential term replaced by EMD
Args:
x, y: 1D pmf of two distributions with the same support
sigma: standard deviation
|
[
"Gaussian",
"kernel",
"with",
"squared",
"distance",
"in",
"exponential",
"term",
"replaced",
"by",
"EMD",
"Args",
":",
"x",
"y",
":",
"1D",
"pmf",
"of",
"two",
"distributions",
"with",
"the",
"same",
"support",
"sigma",
":",
"standard",
"deviation"
] |
def gaussian_emd(x, y, sigma=1.0, distance_scaling=1.0):
''' Gaussian kernel with squared distance in exponential term replaced by EMD
Args:
x, y: 1D pmf of two distributions with the same support
sigma: standard deviation
'''
support_size = max(len(x), len(y))
d_mat = toeplitz(range(support_size)).astype(np.float)
distance_mat = d_mat / distance_scaling
# convert histogram values x and y to float, and make them equal len
x = x.astype(np.float)
y = y.astype(np.float)
if len(x) < len(y):
x = np.hstack((x, [0.0] * (support_size - len(x))))
elif len(y) < len(x):
y = np.hstack((y, [0.0] * (support_size - len(y))))
emd = pyemd.emd(x, y, distance_mat)
return np.exp(-emd * emd / (2 * sigma * sigma))
|
[
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] |
https://github.com/lrjconan/GRAN/blob/43cb4433e6f69401c3a4a6e946ea75da6ec35d72/utils/dist_helper.py#L57-L76
|
|
qboticslabs/mastering_ros
|
d83e78f30acc45b0f18522c1d5fae3a7f52974b9
|
chapter_4_codes/seven_dof_arm_gazebo/scripts/pick_and_place_both_working_good.py
|
python
|
CokeCanPickAndPlace._place
|
(self, group, target, place)
|
return True
|
Place a target using the planning group
|
Place a target using the planning group
|
[
"Place",
"a",
"target",
"using",
"the",
"planning",
"group"
] |
def _place(self, group, target, place):
"""
Place a target using the planning group
"""
# Obtain possible places:
places = self._generate_places(place)
# Create and send Place goal:
goal = self._create_place_goal(group, target, places)
state = self._place_ac.send_goal_and_wait(goal)
if state != GoalStatus.SUCCEEDED:
rospy.logerr('Place goal failed!: %s' % self._place_ac.get_goal_status_text())
return None
result = self._place_ac.get_result()
# Check for error:
err = result.error_code.val
if err != MoveItErrorCodes.SUCCESS:
rospy.logwarn('Group %s cannot place target %s!: %s' % (group, target, str(moveit_error_dict[err])))
return False
return True
|
[
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"[",
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"return",
"False",
"return",
"True"
] |
https://github.com/qboticslabs/mastering_ros/blob/d83e78f30acc45b0f18522c1d5fae3a7f52974b9/chapter_4_codes/seven_dof_arm_gazebo/scripts/pick_and_place_both_working_good.py#L324-L349
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py3/pandas/io/pytables.py
|
python
|
HDFStore._identify_group
|
(self, key: str, append: bool)
|
return group
|
Identify HDF5 group based on key, delete/create group if needed.
|
Identify HDF5 group based on key, delete/create group if needed.
|
[
"Identify",
"HDF5",
"group",
"based",
"on",
"key",
"delete",
"/",
"create",
"group",
"if",
"needed",
"."
] |
def _identify_group(self, key: str, append: bool) -> Node:
"""Identify HDF5 group based on key, delete/create group if needed."""
group = self.get_node(key)
# we make this assertion for mypy; the get_node call will already
# have raised if this is incorrect
assert self._handle is not None
# remove the node if we are not appending
if group is not None and not append:
self._handle.remove_node(group, recursive=True)
group = None
if group is None:
group = self._create_nodes_and_group(key)
return group
|
[
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"(",
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")",
"return",
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] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py3/pandas/io/pytables.py#L1796-L1812
|
|
yuxng/PoseCNN
|
9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04
|
lib/utils/bbox_transform.py
|
python
|
clip_boxes
|
(boxes, im_shape)
|
return boxes
|
Clip boxes to image boundaries.
|
Clip boxes to image boundaries.
|
[
"Clip",
"boxes",
"to",
"image",
"boundaries",
"."
] |
def clip_boxes(boxes, im_shape):
"""
Clip boxes to image boundaries.
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
# y2 < im_shape[0]
boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
return boxes
|
[
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",",
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"[",
"0",
"]",
"-",
"1",
")",
",",
"0",
")",
"return",
"boxes"
] |
https://github.com/yuxng/PoseCNN/blob/9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04/lib/utils/bbox_transform.py#L67-L80
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/mailbox.py
|
python
|
Babyl.get_file
|
(self, key)
|
return io.BytesIO(self.get_bytes(key).replace(b'\n', linesep))
|
Return a file-like representation or raise a KeyError.
|
Return a file-like representation or raise a KeyError.
|
[
"Return",
"a",
"file",
"-",
"like",
"representation",
"or",
"raise",
"a",
"KeyError",
"."
] |
def get_file(self, key):
"""Return a file-like representation or raise a KeyError."""
return io.BytesIO(self.get_bytes(key).replace(b'\n', linesep))
|
[
"def",
"get_file",
"(",
"self",
",",
"key",
")",
":",
"return",
"io",
".",
"BytesIO",
"(",
"self",
".",
"get_bytes",
"(",
"key",
")",
".",
"replace",
"(",
"b'\\n'",
",",
"linesep",
")",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/mailbox.py#L1317-L1319
|
|
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
scripts/SANS/sans/algorithm_detail/batch_execution.py
|
python
|
rename_group_workspace
|
(name_of_workspace, name_of_group_workspace)
|
Rename a group workspace
:param name_of_workspace: current name of group workspace
:param name_of_group_workspace: target name of group workspace
|
Rename a group workspace
:param name_of_workspace: current name of group workspace
:param name_of_group_workspace: target name of group workspace
|
[
"Rename",
"a",
"group",
"workspace",
":",
"param",
"name_of_workspace",
":",
"current",
"name",
"of",
"group",
"workspace",
":",
"param",
"name_of_group_workspace",
":",
"target",
"name",
"of",
"group",
"workspace"
] |
def rename_group_workspace(name_of_workspace, name_of_group_workspace):
"""
Rename a group workspace
:param name_of_workspace: current name of group workspace
:param name_of_group_workspace: target name of group workspace
"""
rename_name = "RenameWorkspace"
rename_options = {"InputWorkspace": name_of_workspace,
"OutputWorkspace": name_of_group_workspace}
rename_alg = create_unmanaged_algorithm(rename_name, **rename_options)
rename_alg.setAlwaysStoreInADS(True)
rename_alg.execute()
|
[
"def",
"rename_group_workspace",
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"name_of_workspace",
",",
"name_of_group_workspace",
")",
":",
"rename_name",
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"\"RenameWorkspace\"",
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"{",
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"setAlwaysStoreInADS",
"(",
"True",
")",
"rename_alg",
".",
"execute",
"(",
")"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/SANS/sans/algorithm_detail/batch_execution.py#L1067-L1078
|
||
intel/llvm
|
e6d0547e9d99b5a56430c4749f6c7e328bf221ab
|
lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py
|
python
|
screen.scroll_down
|
(self)
|
Scroll display down one line.
|
Scroll display down one line.
|
[
"Scroll",
"display",
"down",
"one",
"line",
"."
] |
def scroll_down (self): # <ESC>D
'''Scroll display down one line.'''
# Screen is indexed from 1, but arrays are indexed from 0.
s = self.scroll_row_start - 1
e = self.scroll_row_end - 1
self.w[s+1:e+1] = copy.deepcopy(self.w[s:e])
|
[
"def",
"scroll_down",
"(",
"self",
")",
":",
"# <ESC>D",
"# Screen is indexed from 1, but arrays are indexed from 0.",
"s",
"=",
"self",
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"scroll_row_start",
"-",
"1",
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"deepcopy",
"(",
"self",
".",
"w",
"[",
"s",
":",
"e",
"]",
")"
] |
https://github.com/intel/llvm/blob/e6d0547e9d99b5a56430c4749f6c7e328bf221ab/lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py#L360-L366
|
||
mindspore-ai/mindspore
|
fb8fd3338605bb34fa5cea054e535a8b1d753fab
|
mindspore/python/mindspore/ops/operations/math_ops.py
|
python
|
Invert.__init__
|
(self)
|
Initialize Invert
|
Initialize Invert
|
[
"Initialize",
"Invert"
] |
def __init__(self):
"""Initialize Invert"""
self.init_prim_io_names(inputs=['x'], outputs=['y'])
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"self",
".",
"init_prim_io_names",
"(",
"inputs",
"=",
"[",
"'x'",
"]",
",",
"outputs",
"=",
"[",
"'y'",
"]",
")"
] |
https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/operations/math_ops.py#L5177-L5179
|
||
FreeCAD/FreeCAD
|
ba42231b9c6889b89e064d6d563448ed81e376ec
|
src/Tools/generateBase/generateTools.py
|
python
|
temporary_exec
|
(text, globals, locals)
|
this function is a dirty hack to allow using the copier from
python2 and python3. Once the support of python2 has stopped
feel free to remove this function and use the std exec function
instead
|
this function is a dirty hack to allow using the copier from
python2 and python3. Once the support of python2 has stopped
feel free to remove this function and use the std exec function
instead
|
[
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"the",
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"std",
"exec",
"function",
"instead"
] |
def temporary_exec(text, globals, locals):
"""this function is a dirty hack to allow using the copier from
python2 and python3. Once the support of python2 has stopped
feel free to remove this function and use the std exec function
instead"""
# maybe this should be fixed by rewriting the generators.
if sys.version_info[0] < 3:
from .__exec_old import __exec_old__
__exec_old__(text, globals, locals)
else:
from .__exec_new import __exec_new__
__exec_new__(text, globals, locals)
|
[
"def",
"temporary_exec",
"(",
"text",
",",
"globals",
",",
"locals",
")",
":",
"# maybe this should be fixed by rewriting the generators.",
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"sys",
".",
"version_info",
"[",
"0",
"]",
"<",
"3",
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"__exec_old",
"import",
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"import",
"__exec_new__",
"__exec_new__",
"(",
"text",
",",
"globals",
",",
"locals",
")"
] |
https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Tools/generateBase/generateTools.py#L10-L21
|
||
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/contrib/learn/python/learn/models.py
|
python
|
bidirectional_rnn
|
(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
scope=None)
|
return outputs, array_ops_.concat(1, [state_fw, state_bw])
|
Creates a bidirectional recurrent neural network.
Similar to the unidirectional case (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states
are ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not
given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: A length T list of inputs, each a tensor of shape
[batch_size, cell.input_size].
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
[batch_size x cell.state_size].
initial_state_bw: (optional) Same as for initial_state_fw.
dtype: (optional) The data type for the initial state. Required if
either of the initial states are not provided.
sequence_length: (optional) An int64 vector (tensor) of size
[batch_size],
containing the actual lengths for each of the sequences.
scope: VariableScope for the created subgraph; defaults to "BiRNN"
Returns:
A pair (outputs, state) where:
outputs is a length T list of outputs (one for each input), which
are depth-concatenated forward and backward outputs
state is the concatenated final state of the forward and backward RNN
Raises:
TypeError: If "cell_fw" or "cell_bw" is not an instance of RNNCell.
ValueError: If inputs is None or an empty list.
|
Creates a bidirectional recurrent neural network.
|
[
"Creates",
"a",
"bidirectional",
"recurrent",
"neural",
"network",
"."
] |
def bidirectional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
scope=None):
"""Creates a bidirectional recurrent neural network.
Similar to the unidirectional case (rnn) but takes input and builds
independent forward and backward RNNs with the final forward and backward
outputs depth-concatenated, such that the output will have the format
[time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
forward and backward cell must match. The initial state for both directions
is zero by default (but can be set optionally) and no intermediate states
are ever returned -- the network is fully unrolled for the given (passed in)
length(s) of the sequence(s) or completely unrolled if length(s) is not
given.
Args:
cell_fw: An instance of RNNCell, to be used for forward direction.
cell_bw: An instance of RNNCell, to be used for backward direction.
inputs: A length T list of inputs, each a tensor of shape
[batch_size, cell.input_size].
initial_state_fw: (optional) An initial state for the forward RNN.
This must be a tensor of appropriate type and shape
[batch_size x cell.state_size].
initial_state_bw: (optional) Same as for initial_state_fw.
dtype: (optional) The data type for the initial state. Required if
either of the initial states are not provided.
sequence_length: (optional) An int64 vector (tensor) of size
[batch_size],
containing the actual lengths for each of the sequences.
scope: VariableScope for the created subgraph; defaults to "BiRNN"
Returns:
A pair (outputs, state) where:
outputs is a length T list of outputs (one for each input), which
are depth-concatenated forward and backward outputs
state is the concatenated final state of the forward and backward RNN
Raises:
TypeError: If "cell_fw" or "cell_bw" is not an instance of RNNCell.
ValueError: If inputs is None or an empty list.
"""
if not isinstance(cell_fw, nn.rnn_cell.RNNCell):
raise TypeError('cell_fw must be an instance of RNNCell')
if not isinstance(cell_bw, nn.rnn_cell.RNNCell):
raise TypeError('cell_bw must be an instance of RNNCell')
if not isinstance(inputs, list):
raise TypeError('inputs must be a list')
if not inputs:
raise ValueError('inputs must not be empty')
name = scope or 'BiRNN'
# Forward direction
with vs.variable_scope(name + '_FW'):
output_fw, state_fw = nn.rnn(cell_fw, inputs, initial_state_fw, dtype,
sequence_length)
# Backward direction
with vs.variable_scope(name + '_BW'):
tmp, state_bw = nn.rnn(cell_bw, _reverse_seq(inputs, sequence_length),
initial_state_bw, dtype, sequence_length)
output_bw = _reverse_seq(tmp, sequence_length)
# Concat each of the forward/backward outputs
outputs = [array_ops_.concat(1, [fw, bw])
for fw, bw in zip(output_fw, output_bw)]
return outputs, array_ops_.concat(1, [state_fw, state_bw])
|
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] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/contrib/learn/python/learn/models.py#L266-L336
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_gdi.py
|
python
|
Colour.Alpha
|
(*args, **kwargs)
|
return _gdi_.Colour_Alpha(*args, **kwargs)
|
Alpha(self) -> byte
Returns the Alpha value.
|
Alpha(self) -> byte
|
[
"Alpha",
"(",
"self",
")",
"-",
">",
"byte"
] |
def Alpha(*args, **kwargs):
"""
Alpha(self) -> byte
Returns the Alpha value.
"""
return _gdi_.Colour_Alpha(*args, **kwargs)
|
[
"def",
"Alpha",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_gdi_",
".",
"Colour_Alpha",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_gdi.py#L157-L163
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/turtle.py
|
python
|
RawTurtle.window_height
|
(self)
|
return self.screen._window_size()[1]
|
Return the height of the turtle window.
No argument.
Example (for a TurtleScreen instance named screen):
>>> screen.window_height()
480
|
Return the height of the turtle window.
|
[
"Return",
"the",
"height",
"of",
"the",
"turtle",
"window",
"."
] |
def window_height(self):
""" Return the height of the turtle window.
No argument.
Example (for a TurtleScreen instance named screen):
>>> screen.window_height()
480
"""
return self.screen._window_size()[1]
|
[
"def",
"window_height",
"(",
"self",
")",
":",
"return",
"self",
".",
"screen",
".",
"_window_size",
"(",
")",
"[",
"1",
"]"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/turtle.py#L3396-L3405
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/logging/handlers.py
|
python
|
TimedRotatingFileHandler.shouldRollover
|
(self, record)
|
return 0
|
Determine if rollover should occur.
record is not used, as we are just comparing times, but it is needed so
the method signatures are the same
|
Determine if rollover should occur.
|
[
"Determine",
"if",
"rollover",
"should",
"occur",
"."
] |
def shouldRollover(self, record):
"""
Determine if rollover should occur.
record is not used, as we are just comparing times, but it is needed so
the method signatures are the same
"""
t = int(time.time())
if t >= self.rolloverAt:
return 1
#print "No need to rollover: %d, %d" % (t, self.rolloverAt)
return 0
|
[
"def",
"shouldRollover",
"(",
"self",
",",
"record",
")",
":",
"t",
"=",
"int",
"(",
"time",
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"(",
")",
")",
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">=",
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".",
"rolloverAt",
":",
"return",
"1",
"#print \"No need to rollover: %d, %d\" % (t, self.rolloverAt)",
"return",
"0"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/logging/handlers.py#L281-L292
|
|
geemaple/leetcode
|
68bc5032e1ee52c22ef2f2e608053484c487af54
|
leetcode/338.counting-bits.py
|
python
|
Solution2.countBits
|
(self, num)
|
:type num: int
:rtype: List[int]
|
:type num: int
:rtype: List[int]
|
[
":",
"type",
"num",
":",
"int",
":",
"rtype",
":",
"List",
"[",
"int",
"]"
] |
def countBits(self, num):
"""
:type num: int
:rtype: List[int]
"""
table = []
for i in range(num + 1):
table.append(i % 2)
pre = i >> 1
if pre > 0:
table[i] += table[pre]
|
[
"def",
"countBits",
"(",
"self",
",",
"num",
")",
":",
"table",
"=",
"[",
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"0",
":",
"table",
"[",
"i",
"]",
"+=",
"table",
"[",
"pre",
"]"
] |
https://github.com/geemaple/leetcode/blob/68bc5032e1ee52c22ef2f2e608053484c487af54/leetcode/338.counting-bits.py#L58-L70
|
||
apple/swift-lldb
|
d74be846ef3e62de946df343e8c234bde93a8912
|
scripts/Python/static-binding/lldb.py
|
python
|
SBCommandReturnObject.SetImmediateErrorFile
|
(self, fh)
|
return _lldb.SBCommandReturnObject_SetImmediateErrorFile(self, fh)
|
SetImmediateErrorFile(SBCommandReturnObject self, FILE * fh)
|
SetImmediateErrorFile(SBCommandReturnObject self, FILE * fh)
|
[
"SetImmediateErrorFile",
"(",
"SBCommandReturnObject",
"self",
"FILE",
"*",
"fh",
")"
] |
def SetImmediateErrorFile(self, fh):
"""SetImmediateErrorFile(SBCommandReturnObject self, FILE * fh)"""
return _lldb.SBCommandReturnObject_SetImmediateErrorFile(self, fh)
|
[
"def",
"SetImmediateErrorFile",
"(",
"self",
",",
"fh",
")",
":",
"return",
"_lldb",
".",
"SBCommandReturnObject_SetImmediateErrorFile",
"(",
"self",
",",
"fh",
")"
] |
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L2950-L2952
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py3/pandas/io/pytables.py
|
python
|
IndexCol.take_data
|
(self)
|
return self.values
|
return the values
|
return the values
|
[
"return",
"the",
"values"
] |
def take_data(self):
"""return the values"""
return self.values
|
[
"def",
"take_data",
"(",
"self",
")",
":",
"return",
"self",
".",
"values"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py3/pandas/io/pytables.py#L2095-L2097
|
|
NVIDIA/MDL-SDK
|
aa9642b2546ad7b6236b5627385d882c2ed83c5d
|
src/mdl/jit/generator_jit/gen_intrinsic_func.py
|
python
|
SignatureParser.is_debug_supported
|
(self, name, signature)
|
return False
|
Checks if the given debug intrinsic is supported.
|
Checks if the given debug intrinsic is supported.
|
[
"Checks",
"if",
"the",
"given",
"debug",
"intrinsic",
"is",
"supported",
"."
] |
def is_debug_supported(self, name, signature):
"""Checks if the given debug intrinsic is supported."""
ret_type, params = self.split_signature(signature)
if name == "breakpoint":
if len(params) == 0:
# support breakpoint()
self.intrinsic_modes[name + signature] = "debug::breakpoint"
return True
elif name == "assert":
if len(params) == 5:
# support assert(expr, reason)
self.intrinsic_modes[name + signature] = "debug::assert"
return True
elif name == "print":
if len(params) == 1 or len(params) == 3:
# support print()
self.intrinsic_modes[name + signature] = "debug::print"
return True
return False
|
[
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"+",
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"=",
"\"debug::print\"",
"return",
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"return",
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] |
https://github.com/NVIDIA/MDL-SDK/blob/aa9642b2546ad7b6236b5627385d882c2ed83c5d/src/mdl/jit/generator_jit/gen_intrinsic_func.py#L706-L725
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/_misc.py
|
python
|
Joystick.ReleaseCapture
|
(*args, **kwargs)
|
return _misc_.Joystick_ReleaseCapture(*args, **kwargs)
|
ReleaseCapture(self) -> bool
|
ReleaseCapture(self) -> bool
|
[
"ReleaseCapture",
"(",
"self",
")",
"-",
">",
"bool"
] |
def ReleaseCapture(*args, **kwargs):
"""ReleaseCapture(self) -> bool"""
return _misc_.Joystick_ReleaseCapture(*args, **kwargs)
|
[
"def",
"ReleaseCapture",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
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"_misc_",
".",
"Joystick_ReleaseCapture",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_misc.py#L2290-L2292
|
|
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/basic_fitting/fit_function_options_view.py
|
python
|
FitFunctionOptionsView.plot_guess_points
|
(self)
|
return int(self.plot_guess_points_spin_box.value())
|
Returns the points for the guess fit.
|
Returns the points for the guess fit.
|
[
"Returns",
"the",
"points",
"for",
"the",
"guess",
"fit",
"."
] |
def plot_guess_points(self) -> int:
"""Returns the points for the guess fit."""
return int(self.plot_guess_points_spin_box.value())
|
[
"def",
"plot_guess_points",
"(",
"self",
")",
"->",
"int",
":",
"return",
"int",
"(",
"self",
".",
"plot_guess_points_spin_box",
".",
"value",
"(",
")",
")"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/basic_fitting/fit_function_options_view.py#L272-L274
|
|
ycm-core/ycmd
|
fc0fb7e5e15176cc5a2a30c80956335988c6b59a
|
ycmd/completers/language_server/language_server_completer.py
|
python
|
LanguageServerCompleter.Format
|
( self, request_data )
|
return responses.BuildFixItResponse( [ responses.FixIt(
responses.Location( request_data[ 'line_num' ],
request_data[ 'column_num' ],
request_data[ 'filepath' ] ),
chunks ) ] )
|
Issues the formatting or rangeFormatting request (depending on the
presence of a range) and returns the result as a FixIt response.
|
Issues the formatting or rangeFormatting request (depending on the
presence of a range) and returns the result as a FixIt response.
|
[
"Issues",
"the",
"formatting",
"or",
"rangeFormatting",
"request",
"(",
"depending",
"on",
"the",
"presence",
"of",
"a",
"range",
")",
"and",
"returns",
"the",
"result",
"as",
"a",
"FixIt",
"response",
"."
] |
def Format( self, request_data ):
"""Issues the formatting or rangeFormatting request (depending on the
presence of a range) and returns the result as a FixIt response."""
if not self.ServerIsReady():
raise RuntimeError( 'Server is initializing. Please wait.' )
self._UpdateServerWithFileContents( request_data )
request_data[ 'options' ].update(
self.AdditionalFormattingOptions( request_data ) )
request_id = self.GetConnection().NextRequestId()
if 'range' in request_data:
message = lsp.RangeFormatting( request_id, request_data )
else:
message = lsp.Formatting( request_id, request_data )
response = self.GetConnection().GetResponse( request_id,
message,
REQUEST_TIMEOUT_COMMAND )
filepath = request_data[ 'filepath' ]
contents = GetFileLines( request_data, filepath )
chunks = [ responses.FixItChunk( text_edit[ 'newText' ],
_BuildRange( contents,
filepath,
text_edit[ 'range' ] ) )
for text_edit in response[ 'result' ] or [] ]
return responses.BuildFixItResponse( [ responses.FixIt(
responses.Location( request_data[ 'line_num' ],
request_data[ 'column_num' ],
request_data[ 'filepath' ] ),
chunks ) ] )
|
[
"def",
"Format",
"(",
"self",
",",
"request_data",
")",
":",
"if",
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"self",
".",
"ServerIsReady",
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")",
":",
"raise",
"RuntimeError",
"(",
"'Server is initializing. Please wait.'",
")",
"self",
".",
"_UpdateServerWithFileContents",
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"request_data",
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".",
"update",
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"AdditionalFormattingOptions",
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"GetResponse",
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"=",
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"filepath",
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"responses",
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"FixItChunk",
"(",
"text_edit",
"[",
"'newText'",
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",",
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"(",
"contents",
",",
"filepath",
",",
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"'range'",
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")",
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"'result'",
"]",
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"[",
"]",
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"FixIt",
"(",
"responses",
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"Location",
"(",
"request_data",
"[",
"'line_num'",
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",",
"request_data",
"[",
"'column_num'",
"]",
",",
"request_data",
"[",
"'filepath'",
"]",
")",
",",
"chunks",
")",
"]",
")"
] |
https://github.com/ycm-core/ycmd/blob/fc0fb7e5e15176cc5a2a30c80956335988c6b59a/ycmd/completers/language_server/language_server_completer.py#L2648-L2679
|
|
Tencent/Pebble
|
68315f176d9e328a233ace29b7579a829f89879f
|
tools/blade/src/blade/fbthrift_library.py
|
python
|
FBThriftLibrary.scons_rules
|
(self)
|
scons_rules.
It outputs the scons rules according to user options.
|
scons_rules.
|
[
"scons_rules",
"."
] |
def scons_rules(self):
"""scons_rules.
It outputs the scons rules according to user options.
"""
self._prepare_to_generate_rule()
# Build java source according to its option
env_name = self._env_name()
self.options = self.blade.get_options()
self.direct_targets = self.blade.get_direct_targets()
self._setup_cc_flags()
sources = []
obj_names = []
for src in self.srcs:
thrift_cpp_files = self._thrift_gen_cpp_files(self.path, src)
thrift_cpp_src_files = [f for f in thrift_cpp_files if f.endswith('.cpp')]
thrift_cpp2_files = self._thrift_gen_cpp2_files(self.path, src)
thrift_cpp2_src_files = [f for f in thrift_cpp2_files if f.endswith('.cpp')]
self._write_rule('%s.FBThrift1(%s, "%s")' % (
env_name,
str(thrift_cpp_files),
os.path.join(self.path, src)))
self._write_rule('%s.FBThrift2(%s, "%s")' % (
env_name,
str(thrift_cpp2_files),
os.path.join(self.path, src)))
for thrift_cpp_src in thrift_cpp_src_files:
obj_name = '%s_object' % self._generate_variable_name(
self.path, thrift_cpp_src)
obj_names.append(obj_name)
self._write_rule(
'%s = %s.SharedObject(target="%s" + top_env["OBJSUFFIX"], '
'source="%s")' % (obj_name,
env_name,
thrift_cpp_src,
thrift_cpp_src))
sources.append(thrift_cpp_src)
for thrift_cpp_src in thrift_cpp2_src_files:
obj_name = '%s_object' % self._generate_variable_name(
self.path, thrift_cpp_src)
obj_names.append(obj_name)
self._write_rule(
'%s = %s.SharedObject(target="%s" + top_env["OBJSUFFIX"], '
'source="%s")' % (obj_name,
env_name,
thrift_cpp_src,
thrift_cpp_src))
sources.append(thrift_cpp_src)
self._write_rule('%s = [%s]' % (self._objs_name(), ','.join(obj_names)))
self._write_rule('%s.Depends(%s, %s)' % (
env_name, self._objs_name(), sources))
self._cc_library()
options = self.blade.get_options()
if (getattr(options, 'generate_dynamic', False) or
self.data.get('build_dynamic')):
self._dynamic_cc_library()
|
[
"def",
"scons_rules",
"(",
"self",
")",
":",
"self",
".",
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")",
"# Build java source according to its option",
"env_name",
"=",
"self",
".",
"_env_name",
"(",
")",
"self",
".",
"options",
"=",
"self",
".",
"blade",
".",
"get_options",
"(",
")",
"self",
".",
"direct_targets",
"=",
"self",
".",
"blade",
".",
"get_direct_targets",
"(",
")",
"self",
".",
"_setup_cc_flags",
"(",
")",
"sources",
"=",
"[",
"]",
"obj_names",
"=",
"[",
"]",
"for",
"src",
"in",
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".",
"srcs",
":",
"thrift_cpp_files",
"=",
"self",
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"(",
"self",
".",
"path",
",",
"src",
")",
"thrift_cpp_src_files",
"=",
"[",
"f",
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"if",
"f",
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"'.cpp'",
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"self",
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",",
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"=",
"[",
"f",
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",",
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"(",
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"str",
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")",
")",
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":",
"obj_name",
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"'%s_object'",
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"self",
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"_generate_variable_name",
"(",
"self",
".",
"path",
",",
"thrift_cpp_src",
")",
"obj_names",
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"append",
"(",
"obj_name",
")",
"self",
".",
"_write_rule",
"(",
"'%s = %s.SharedObject(target=\"%s\" + top_env[\"OBJSUFFIX\"], '",
"'source=\"%s\")'",
"%",
"(",
"obj_name",
",",
"env_name",
",",
"thrift_cpp_src",
",",
"thrift_cpp_src",
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")",
"sources",
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"in",
"thrift_cpp2_src_files",
":",
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"'%s_object'",
"%",
"self",
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"(",
"self",
".",
"path",
",",
"thrift_cpp_src",
")",
"obj_names",
".",
"append",
"(",
"obj_name",
")",
"self",
".",
"_write_rule",
"(",
"'%s = %s.SharedObject(target=\"%s\" + top_env[\"OBJSUFFIX\"], '",
"'source=\"%s\")'",
"%",
"(",
"obj_name",
",",
"env_name",
",",
"thrift_cpp_src",
",",
"thrift_cpp_src",
")",
")",
"sources",
".",
"append",
"(",
"thrift_cpp_src",
")",
"self",
".",
"_write_rule",
"(",
"'%s = [%s]'",
"%",
"(",
"self",
".",
"_objs_name",
"(",
")",
",",
"','",
".",
"join",
"(",
"obj_names",
")",
")",
")",
"self",
".",
"_write_rule",
"(",
"'%s.Depends(%s, %s)'",
"%",
"(",
"env_name",
",",
"self",
".",
"_objs_name",
"(",
")",
",",
"sources",
")",
")",
"self",
".",
"_cc_library",
"(",
")",
"options",
"=",
"self",
".",
"blade",
".",
"get_options",
"(",
")",
"if",
"(",
"getattr",
"(",
"options",
",",
"'generate_dynamic'",
",",
"False",
")",
"or",
"self",
".",
"data",
".",
"get",
"(",
"'build_dynamic'",
")",
")",
":",
"self",
".",
"_dynamic_cc_library",
"(",
")"
] |
https://github.com/Tencent/Pebble/blob/68315f176d9e328a233ace29b7579a829f89879f/tools/blade/src/blade/fbthrift_library.py#L130-L197
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/PixMapWrapper.py
|
python
|
PixMapWrapper.fromImage
|
(self,im)
|
Initialize this PixMap from a PIL Image object.
|
Initialize this PixMap from a PIL Image object.
|
[
"Initialize",
"this",
"PixMap",
"from",
"a",
"PIL",
"Image",
"object",
"."
] |
def fromImage(self,im):
"""Initialize this PixMap from a PIL Image object."""
# We need data in ARGB format; PIL can't currently do that,
# but it can do RGBA, which we can use by inserting one null
# up frontpm =
if im.mode != 'RGBA': im = im.convert('RGBA')
data = chr(0) + im.tostring()
self.fromstring(data, im.size[0], im.size[1])
|
[
"def",
"fromImage",
"(",
"self",
",",
"im",
")",
":",
"# We need data in ARGB format; PIL can't currently do that,",
"# but it can do RGBA, which we can use by inserting one null",
"# up frontpm =",
"if",
"im",
".",
"mode",
"!=",
"'RGBA'",
":",
"im",
"=",
"im",
".",
"convert",
"(",
"'RGBA'",
")",
"data",
"=",
"chr",
"(",
"0",
")",
"+",
"im",
".",
"tostring",
"(",
")",
"self",
".",
"fromstring",
"(",
"data",
",",
"im",
".",
"size",
"[",
"0",
"]",
",",
"im",
".",
"size",
"[",
"1",
"]",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/PixMapWrapper.py#L191-L198
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
tools/usb_gadget/usb_descriptors.py
|
python
|
Descriptor.total_size
|
(self)
|
return self.struct_size
|
Returns the total size of this descriptor.
|
Returns the total size of this descriptor.
|
[
"Returns",
"the",
"total",
"size",
"of",
"this",
"descriptor",
"."
] |
def total_size(self):
"""Returns the total size of this descriptor."""
return self.struct_size
|
[
"def",
"total_size",
"(",
"self",
")",
":",
"return",
"self",
".",
"struct_size"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/usb_gadget/usb_descriptors.py#L172-L174
|
|
KratosMultiphysics/Kratos
|
0000833054ed0503424eb28205d6508d9ca6cbbc
|
kratos/python_scripts/deprecation_management.py
|
python
|
DeprecationManager.ReplaceDeprecatedVariableName
|
(parameters, old_variable_name, new_variable_name)
|
Replace a key by another. The old key is assumed to be present.
|
Replace a key by another. The old key is assumed to be present.
|
[
"Replace",
"a",
"key",
"by",
"another",
".",
"The",
"old",
"key",
"is",
"assumed",
"to",
"be",
"present",
"."
] |
def ReplaceDeprecatedVariableName(parameters, old_variable_name, new_variable_name):
""" Replace a key by another. The old key is assumed to be present."""
parameters.AddEmptyValue(new_variable_name)
if parameters[old_variable_name].IsInt():
parameters[new_variable_name].SetInt(parameters[old_variable_name].GetInt())
elif parameters[old_variable_name].IsDouble():
parameters[new_variable_name].SetDouble(parameters[old_variable_name].GetDouble())
elif parameters[old_variable_name].IsString():
parameters[new_variable_name].SetString(parameters[old_variable_name].GetString())
elif parameters[old_variable_name].IsBool():
parameters[new_variable_name].SetBool(parameters[old_variable_name].GetBool())
else:
pass
parameters.RemoveValue(old_variable_name)
|
[
"def",
"ReplaceDeprecatedVariableName",
"(",
"parameters",
",",
"old_variable_name",
",",
"new_variable_name",
")",
":",
"parameters",
".",
"AddEmptyValue",
"(",
"new_variable_name",
")",
"if",
"parameters",
"[",
"old_variable_name",
"]",
".",
"IsInt",
"(",
")",
":",
"parameters",
"[",
"new_variable_name",
"]",
".",
"SetInt",
"(",
"parameters",
"[",
"old_variable_name",
"]",
".",
"GetInt",
"(",
")",
")",
"elif",
"parameters",
"[",
"old_variable_name",
"]",
".",
"IsDouble",
"(",
")",
":",
"parameters",
"[",
"new_variable_name",
"]",
".",
"SetDouble",
"(",
"parameters",
"[",
"old_variable_name",
"]",
".",
"GetDouble",
"(",
")",
")",
"elif",
"parameters",
"[",
"old_variable_name",
"]",
".",
"IsString",
"(",
")",
":",
"parameters",
"[",
"new_variable_name",
"]",
".",
"SetString",
"(",
"parameters",
"[",
"old_variable_name",
"]",
".",
"GetString",
"(",
")",
")",
"elif",
"parameters",
"[",
"old_variable_name",
"]",
".",
"IsBool",
"(",
")",
":",
"parameters",
"[",
"new_variable_name",
"]",
".",
"SetBool",
"(",
"parameters",
"[",
"old_variable_name",
"]",
".",
"GetBool",
"(",
")",
")",
"else",
":",
"pass",
"parameters",
".",
"RemoveValue",
"(",
"old_variable_name",
")"
] |
https://github.com/KratosMultiphysics/Kratos/blob/0000833054ed0503424eb28205d6508d9ca6cbbc/kratos/python_scripts/deprecation_management.py#L37-L52
|
||
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py
|
python
|
VectorSinhArcsinhDiag.scale
|
(self)
|
return self._scale
|
The `LinearOperator` `scale` in `Y := loc + scale @ F(Z) * (2 / F(2)).
|
The `LinearOperator` `scale` in `Y := loc + scale
|
[
"The",
"LinearOperator",
"scale",
"in",
"Y",
":",
"=",
"loc",
"+",
"scale"
] |
def scale(self):
"""The `LinearOperator` `scale` in `Y := loc + scale @ F(Z) * (2 / F(2))."""
return self._scale
|
[
"def",
"scale",
"(",
"self",
")",
":",
"return",
"self",
".",
"_scale"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/distributions/python/ops/vector_sinh_arcsinh_diag.py#L262-L264
|
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