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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/teleport/webroot/app/base/db.py | python | SQL._make_sql_query_string | (self) | return ' '.join(sql) | [] | def _make_sql_query_string(self):
sql = list()
sql.append('SELECT {}'.format(','.join(self._select_fields)))
sql.append('FROM {}{} AS {}'.format(self._db.table_prefix, self._from['n'], self._from['a']))
for _l in self._left_join:
sql.append('LEFT JOIN {}{} AS {} ON {}'.format(self._db.table_prefix, _l['n'], _l['a'], _l['on']))
if len(self._where) > 0:
sql.append('WHERE {}'.format(self._where))
if len(self._group_by) > 0:
sql.append('GROUP BY {}'.format(self._group_by))
if len(self._order_by) > 0:
sql.append('ORDER BY {}'.format(','.join(self._order_by)))
if self._limit is not None:
if self._ret_total_recorder <= self._limit['page_index'] * self._limit['per_page']:
self._ret_page_index = int(self._ret_total_recorder / self._limit['per_page'])
if self._ret_page_index < 0:
self._ret_page_index = 0
else:
self._ret_page_index = self._limit['page_index']
sql.append('LIMIT {},{}'.format(self._ret_page_index * self._limit['per_page'], self._limit['per_page']))
sql.append(';')
return ' '.join(sql) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/contrib/gis/gdal/raster/band.py | python | GDALBand.__init__ | (self, source, index) | [] | def __init__(self, source, index):
self.source = source
self._ptr = capi.get_ds_raster_band(source._ptr, index) | [
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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/scapy/scapy/contrib/gsm_um.py | python | alertingNetToMs | (Facility_presence=0, ProgressIndicator_presence=0,
UserUser_presence=0) | return packet | ALERTING Section 9.3.1.1 | ALERTING Section 9.3.1.1 | [
"ALERTING",
"Section",
"9",
".",
"3",
".",
"1",
".",
"1"
] | def alertingNetToMs(Facility_presence=0, ProgressIndicator_presence=0,
UserUser_presence=0):
"""ALERTING Section 9.3.1.1"""
a = TpPd(pd=0x3)
b = MessageType(mesType=0x1) # 00000001
packet = a / b
if Facility_presence is 1:
c = FacilityHdr(ieiF=0x1C)
packet = packet / c
if ProgressIndicator_presence is 1:
d = ProgressIndicatorHdr(ieiPI=0x1E)
packet = packet / d
if UserUser_presence is 1:
e = UserUserHdr(ieiUU=0x7E)
packet = packet / e
return packet | [
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ChiCheng123/SRN | fc07d7499bb5d8291cec68124fc50879f2f68147 | srn/utils/anchor_helper.py | python | get_anchors_over_plane | (featmap_h, featmap_w, anchor_ratios, anchor_scales, anchor_stride) | return anchors_overplane.reshape((K * A, 4)) | [] | def get_anchors_over_plane(featmap_h, featmap_w, anchor_ratios, anchor_scales, anchor_stride):
# get anchors over one grid
anchors_overgrid = get_anchors_over_grid(anchor_ratios, anchor_scales, anchor_stride)
# spread anchors over each grid
shift_x = np.arange(0, featmap_w) * anchor_stride
shift_y = np.arange(0, featmap_h) * anchor_stride
# [featmap_h, featmap_w]
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
A = anchors_overgrid.shape[0]
K = shifts.shape[0]
anchors_overplane = (anchors_overgrid.reshape((1, A, 4)) +
shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
return anchors_overplane.reshape((K * A, 4)) | [
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NervanaSystems/ngraph-python | ac032c83c7152b615a9ad129d54d350f9d6a2986 | ngraph/op_graph/axes.py | python | TensorDescription.base | (self) | return self.__base or self | The viewed tensor description or None if not a view. | The viewed tensor description or None if not a view. | [
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"""The viewed tensor description or None if not a view."""
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JoinMarket-Org/joinmarket-clientserver | 8b3d21f226185e31aa10e8e16cdfc719cea4a98e | jmclient/jmclient/blockchaininterface.py | python | ElectrumWalletInterface.query_utxo_set | (self, txout, includeconf=False) | return result | Behaves as for Core; TODO make it faster if possible.
Note in particular a failed connection should result in
a result list containing at least one "None" which the
caller can use as a flag for failure. | Behaves as for Core; TODO make it faster if possible.
Note in particular a failed connection should result in
a result list containing at least one "None" which the
caller can use as a flag for failure. | [
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"""Behaves as for Core; TODO make it faster if possible.
Note in particular a failed connection should result in
a result list containing at least one "None" which the
caller can use as a flag for failure.
"""
self.current_height = self.wallet.network.blockchain.local_height
if not isinstance(txout, list):
txout = [txout]
utxos = [[t[:64], int(t[65:])] for t in txout]
result = []
for ut in utxos:
address = self.wallet.network.synchronous_get((
'blockchain.utxo.get_address', ut))
try:
utxo_info = self.wallet.network.synchronous_get((
"blockchain.address.listunspent", [address]))
except Exception as e:
log.debug("Got exception calling listunspent: " + repr(e))
raise
utxo = None
for u in utxo_info:
if u['tx_hash'] == ut[0] and u['tx_pos'] == ut[1]:
utxo = u
if utxo is None:
result.append(None)
continue
r = {
'value': u['value'],
'address': address,
'script': btc.address_to_script(address)
}
if includeconf:
if int(u['height']) in [0, -1]:
#-1 means unconfirmed inputs
r['confirms'] = 0
else:
#+1 because if current height = tx height, that's 1 conf
r['confirms'] = int(self.current_height) - int(u['height']) + 1
result.append(r)
return result | [
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WikidPad/WikidPad | 558109638807bc76b4672922686e416ab2d5f79c | WikidPad/lib/pwiki/timeView/WikiWideHistory.py | python | HistoryEntry.serializeFromXml | (self, xmlNode) | Set object state from data in xmlNode) | Set object state from data in xmlNode) | [
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")"
] | def serializeFromXml(self, xmlNode):
"""
Set object state from data in xmlNode)
"""
self.xmlNode = xmlNode
self.unifiedPageName = serFromXmlUnicode(xmlNode, "unifiedName")
timeStr = serFromXmlUnicode(xmlNode, "visitedTime")
self.visitedTimeStamp = timegm(time.strptime(timeStr,
"%Y-%m-%d/%H:%M:%S")) | [
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openstack/cinder | 23494a6d6c51451688191e1847a458f1d3cdcaa5 | cinder/volume/drivers/dell_emc/powermax/performance.py | python | PowerMaxPerformance._get_port_group_performance_stats | (
self, array_id, port_group_id, f_date, l_date, metric,
data_format) | return self._process_load(result, metric) | Get performance data for a given port group and performance metric.
:param array_id: the array serial number -- str
:param port_group_id: the port group id -- str
:param f_date: first date for stats -- int
:param l_date: last date for stats -- int
:param metric: performance metric -- str
:param data_format: performance data format -- str
:returns: range average, range total, interval count -- float, float,
int | Get performance data for a given port group and performance metric. | [
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"data",
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"given",
"port",
"group",
"and",
"performance",
"metric",
"."
] | def _get_port_group_performance_stats(
self, array_id, port_group_id, f_date, l_date, metric,
data_format):
"""Get performance data for a given port group and performance metric.
:param array_id: the array serial number -- str
:param port_group_id: the port group id -- str
:param f_date: first date for stats -- int
:param l_date: last date for stats -- int
:param metric: performance metric -- str
:param data_format: performance data format -- str
:returns: range average, range total, interval count -- float, float,
int
"""
request_body = {
utils.SYMM_ID: array_id, utils.PORT_GROUP_ID: port_group_id,
utils.S_DATE: f_date, utils.E_DATE: l_date,
utils.DATA_FORMAT: data_format, utils.METRICS: [metric]}
port_group_uri = self.rest.build_uri(
category=utils.PERFORMANCE, resource_level=utils.PORT_GROUP,
resource_type=utils.METRICS, no_version=True)
result = self.rest.post_request(
port_group_uri, 'Port Group performance metrics',
request_body)
return self._process_load(result, metric) | [
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eggnogdb/eggnog-mapper | d6e6cdf0a829f2bd85480f3f3f16e38c213cd091 | eggnogmapper/search/hits_io.py | python | output_hits | (cmds, hits, out_file, resume, no_file_comments, outfmt_short) | return | [] | def output_hits(cmds, hits, out_file, resume, no_file_comments, outfmt_short):
start_time = time.time()
if resume == True:
file_mode = 'a'
else:
file_mode = 'w'
with open(out_file, file_mode) as OUT:
# comments
if not no_file_comments:
print(get_call_info(), file=OUT)
if cmds is not None:
for cmd in cmds:
print('##'+cmd, file=OUT)
# header (only first time, not for further resume)
if file_mode == 'w':
if outfmt_short == True:
print('#'+"\t".join("qseqid sseqid evalue bitscore".split(" ")), file=OUT)
else:
print('#'+"\t".join(("qseqid sseqid evalue bitscore qstart qend "
"sstart send pident qcov scov").split(" ")), file=OUT)
qn = 0
# rows
# (hit, skip): hits are wrapped in a tuple with a boolean flag
# to be output or not
for hit, skip in hits:
# only print the hit if not already present and --resume
if skip == False:
print('\t'.join(map(str, hit)), file=OUT)
# always yield the hit
yield hit
qn += 1
elapsed_time = time.time() - start_time
if not no_file_comments:
print('## %d queries scanned' % (qn), file=OUT)
print('## Total time (seconds):', elapsed_time, file=OUT)
print('## Rate:', "%0.2f q/s" % ((float(qn) / elapsed_time)), file=OUT)
return | [
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/slim/nets/mobilenet_v1_train.py | python | train_model | () | Trains mobilenet_v1. | Trains mobilenet_v1. | [
"Trains",
"mobilenet_v1",
"."
] | def train_model():
"""Trains mobilenet_v1."""
g, train_tensor = build_model()
with g.as_default():
slim.learning.train(
train_tensor,
FLAGS.checkpoint_dir,
is_chief=(FLAGS.task == 0),
master=FLAGS.master,
log_every_n_steps=FLAGS.log_every_n_steps,
graph=g,
number_of_steps=FLAGS.number_of_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
init_fn=get_checkpoint_init_fn(),
global_step=tf.train.get_global_step()) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/inspectlib/collector.py | python | Inspector._get_managed_files | (self) | return list(), list(), list() | Build a in-memory data of all managed files. | Build a in-memory data of all managed files. | [
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"""
Build a in-memory data of all managed files.
"""
if self.grains_core.os_data().get("os_family") == "Debian":
return self.__get_managed_files_dpkg()
elif self.grains_core.os_data().get("os_family") in ["Suse", "redhat"]:
return self.__get_managed_files_rpm()
return list(), list(), list() | [
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igushev/city_visit_planner | 95e868d24df1435a6f2cbbb64b9d74050aeddfe7 | router/cost_accumulator.py | python | CostAccumulatorInterface.AddPointVisit | (self, point) | Add visiting point to the cost. | Add visiting point to the cost. | [
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] | def AddPointVisit(self, point):
"""Add visiting point to the cost."""
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mozillazg/baidu-pcs-python-sdk | 12fe3f13b2ecda8f8bdcc5334c876e934776a5cc | baidupcs/api.py | python | PCS.video_convert | (self, remote_path, video_type, **kwargs) | return self._request('file', 'streaming', extra_params=params,
**kwargs) | 对视频文件进行转码，实现实时观看视频功能.
可下载支持 HLS/M3U8 的 `媒体云播放器 SDK <HLSSDK_>`__ 配合使用.
.. _HLSSDK:
http://developer.baidu.com/wiki/index.php?title=docs/cplat/media/sdk
:param remote_path: 需要下载的视频文件路径，以/开头的绝对路径，
需含源文件的文件名。
.. warning::
* 路径长度限制为1000；
* 径中不能包含以下字符：``\\\\ ? | " > < : *``；
* 文件名或路径名开头结尾不能是 ``.``
或空白字符，空白字符包括：
``\\r, \\n, \\t, 空格, \\0, \\x0B`` 。
:type remote_path: str
:param video_type: 目前支持以下格式：
M3U8_320_240、M3U8_480_224、M3U8_480_360、
M3U8_640_480和M3U8_854_480
:type video_type: str
:return: Response 对象
.. warning::
目前这个接口支持的源文件格式如下：
+--------------------------+------------+--------------------------+
|格式名称 |扩展名 |备注 |
+==========================+============+==========================+
|Apple HTTP Live Streaming |m3u8/m3u |iOS支持的视频格式 |
+--------------------------+------------+--------------------------+
|ASF |asf |视频格式 |
+--------------------------+------------+--------------------------+
|AVI |avi |视频格式 |
+--------------------------+------------+--------------------------+
|Flash Video (FLV) |flv |Macromedia Flash视频格式 |
+--------------------------+------------+--------------------------+
|GIF Animation |gif |视频格式 |
+--------------------------+------------+--------------------------+
|Matroska |mkv |Matroska/WebM视频格式 |
+--------------------------+------------+--------------------------+
|MOV/QuickTime/MP4 |mov/mp4/m4a/|支持3GP、3GP2、PSP、iPod |
| |3gp/3g2/mj2 |之类视频格式 |
+--------------------------+------------+--------------------------+
|MPEG-PS (program stream) |mpeg |也就是VOB文件/SVCD/DVD格式|
+--------------------------+------------+--------------------------+
|MPEG-TS (transport stream)|ts | 即DVB传输流 |
+--------------------------+------------+--------------------------+
|RealMedia |rm/rmvb | Real视频格式 |
+--------------------------+------------+--------------------------+
|WebM |webm | Html视频格式 |
+--------------------------+------------+--------------------------+ | 对视频文件进行转码，实现实时观看视频功能.
可下载支持 HLS/M3U8 的 `媒体云播放器 SDK <HLSSDK_>`__ 配合使用. | [
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"""对视频文件进行转码，实现实时观看视频功能.
可下载支持 HLS/M3U8 的 `媒体云播放器 SDK <HLSSDK_>`__ 配合使用.
.. _HLSSDK:
http://developer.baidu.com/wiki/index.php?title=docs/cplat/media/sdk
:param remote_path: 需要下载的视频文件路径，以/开头的绝对路径，
需含源文件的文件名。
.. warning::
* 路径长度限制为1000；
* 径中不能包含以下字符：``\\\\ ? | " > < : *``；
* 文件名或路径名开头结尾不能是 ``.``
或空白字符，空白字符包括：
``\\r, \\n, \\t, 空格, \\0, \\x0B`` 。
:type remote_path: str
:param video_type: 目前支持以下格式：
M3U8_320_240、M3U8_480_224、M3U8_480_360、
M3U8_640_480和M3U8_854_480
:type video_type: str
:return: Response 对象
.. warning::
目前这个接口支持的源文件格式如下：
+--------------------------+------------+--------------------------+
|格式名称 |扩展名 |备注 |
+==========================+============+==========================+
|Apple HTTP Live Streaming |m3u8/m3u |iOS支持的视频格式 |
+--------------------------+------------+--------------------------+
|ASF |asf |视频格式 |
+--------------------------+------------+--------------------------+
|AVI |avi |视频格式 |
+--------------------------+------------+--------------------------+
|Flash Video (FLV) |flv |Macromedia Flash视频格式 |
+--------------------------+------------+--------------------------+
|GIF Animation |gif |视频格式 |
+--------------------------+------------+--------------------------+
|Matroska |mkv |Matroska/WebM视频格式 |
+--------------------------+------------+--------------------------+
|MOV/QuickTime/MP4 |mov/mp4/m4a/|支持3GP、3GP2、PSP、iPod |
| |3gp/3g2/mj2 |之类视频格式 |
+--------------------------+------------+--------------------------+
|MPEG-PS (program stream) |mpeg |也就是VOB文件/SVCD/DVD格式|
+--------------------------+------------+--------------------------+
|MPEG-TS (transport stream)|ts | 即DVB传输流 |
+--------------------------+------------+--------------------------+
|RealMedia |rm/rmvb | Real视频格式 |
+--------------------------+------------+--------------------------+
|WebM |webm | Html视频格式 |
+--------------------------+------------+--------------------------+
"""
params = {
'path': remote_path,
'type': video_type,
}
return self._request('file', 'streaming', extra_params=params,
**kwargs) | [
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mike01/pypacker | 7e98b2cb00c465f726490580761ad6261302b6a6 | pypacker/pypacker.py | python | Packet._update_header_format | (self) | Update format of this packet header. Needs to be called on changes to dynamic fields. | Update format of this packet header. Needs to be called on changes to dynamic fields. | [
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] | def _update_header_format(self):
"""
Update format of this packet header. Needs to be called on changes to dynamic fields.
"""
header_format = [self._header_format_order]
self_getattr = self.__getattribute__
for name in self._header_field_names:
if not self_getattr(name + "_active"):
continue
val = self.__getattribute__(name)
if val.__class__ in HEADER_TYPES_SIMPLE: # assume bytes/int
header_format.append(self_getattr(name + "_format"))
# logger.debug("adding format for (simple): %r, %s, val: %s format: %s",
# self.__class__, name, self_getattr(name), self_getattr(name + "_format"))
else: # assume TriggerList
if val.__class__ == list:
# TriggerList not yet initiated: take cached value
header_format.append("%ds" % len(val[0]))
#logger.debug("adding format for: %r, %s, val: %s", self.__class__, name, val[0])
else:
try:
header_format.append("%ds" % len(val.bin()))
except AttributeError as err:
raise InvalidValuetypeException("Unknown value-type in %r for header"
" field %r: %r, allowed: int, bytes, Packets, tuples"
" (depending on context)" % (self.__class__, name[1:], type(val))) from err
self._header_format = Struct("".join(header_format))
self._header_len = self._header_format.size
self._header_format_changed = False | [
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SeldonIO/alibi-detect | b5ec53cfadcd8e3463d400259f2ea1b752ed1812 | alibi_detect/od/sr.py | python | SpectralResidual.__init__ | (self,
threshold: float = None,
window_amp: int = None,
window_local: int = None,
padding_amp_method: Literal['constant', 'replicate', 'reflect'] = 'reflect',
padding_local_method: Literal['constant', 'replicate', 'reflect'] = 'reflect',
padding_amp_side: Literal['bilateral', 'left', 'right'] = 'bilateral',
n_est_points: int = None,
n_grad_points: int = 5,
) | Outlier detector for time-series data using the spectral residual algorithm.
Based on "Time-Series Anomaly Detection Service at Microsoft" (Ren et al., 2019)
https://arxiv.org/abs/1906.03821
Parameters
----------
threshold
Threshold used to classify outliers. Relative saliency map distance from the moving average.
window_amp
Window for the average log amplitude.
window_local
Window for the local average of the saliency map. Note that the averaging is performed over the
previous `window_local` data points (i.e., is a local average of the preceding `window_local` points for
the current index).
padding_amp_method
Padding method to be used prior to each convolution over log amplitude.
Possible values: `constant` | `replicate` | `reflect`. Default value: `replicate`.
- `constant` - padding with constant 0.
- `replicate` - repeats the last/extreme value.
- `reflect` - reflects the time series.
padding_local_method
Padding method to be used prior to each convolution over saliency map.
Possible values: `constant` | `replicate` | `reflect`. Default value: `replicate`.
- `constant` - padding with constant 0.
- `replicate` - repeats the last/extreme value.
- `reflect` - reflects the time series.
padding_amp_side
Whether to pad the amplitudes on both sides or only on one side.
Possible values: `bilateral` | `left` | `right`.
n_est_points
Number of estimated points padded to the end of the sequence.
n_grad_points
Number of points used for the gradient estimation of the additional points padded
to the end of the sequence. | Outlier detector for time-series data using the spectral residual algorithm.
Based on "Time-Series Anomaly Detection Service at Microsoft" (Ren et al., 2019)
https://arxiv.org/abs/1906.03821 | [
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threshold: float = None,
window_amp: int = None,
window_local: int = None,
padding_amp_method: Literal['constant', 'replicate', 'reflect'] = 'reflect',
padding_local_method: Literal['constant', 'replicate', 'reflect'] = 'reflect',
padding_amp_side: Literal['bilateral', 'left', 'right'] = 'bilateral',
n_est_points: int = None,
n_grad_points: int = 5,
) -> None:
"""
Outlier detector for time-series data using the spectral residual algorithm.
Based on "Time-Series Anomaly Detection Service at Microsoft" (Ren et al., 2019)
https://arxiv.org/abs/1906.03821
Parameters
----------
threshold
Threshold used to classify outliers. Relative saliency map distance from the moving average.
window_amp
Window for the average log amplitude.
window_local
Window for the local average of the saliency map. Note that the averaging is performed over the
previous `window_local` data points (i.e., is a local average of the preceding `window_local` points for
the current index).
padding_amp_method
Padding method to be used prior to each convolution over log amplitude.
Possible values: `constant` | `replicate` | `reflect`. Default value: `replicate`.
- `constant` - padding with constant 0.
- `replicate` - repeats the last/extreme value.
- `reflect` - reflects the time series.
padding_local_method
Padding method to be used prior to each convolution over saliency map.
Possible values: `constant` | `replicate` | `reflect`. Default value: `replicate`.
- `constant` - padding with constant 0.
- `replicate` - repeats the last/extreme value.
- `reflect` - reflects the time series.
padding_amp_side
Whether to pad the amplitudes on both sides or only on one side.
Possible values: `bilateral` | `left` | `right`.
n_est_points
Number of estimated points padded to the end of the sequence.
n_grad_points
Number of points used for the gradient estimation of the additional points padded
to the end of the sequence.
"""
super().__init__()
if threshold is None:
logger.warning("No threshold level set. Need to infer threshold using `infer_threshold`.")
self.threshold = threshold
self.window_amp = window_amp
self.window_local = window_local
self.conv_amp = np.ones((1, window_amp)).reshape(-1,) / window_amp
# conv_local needs a special treatment since the paper says that:
# \bar{S}(xi) is the local average of the preceding z points of S(xi).
# To use the same padding implementation that includes the current point we convolving, we define a modified
# filter given by: [0, 1, 1, 1, ... ,1] / window_local of size `window_local + 1`. In this way
# the current value is multiplied by 0 and thus neglected. Note that the 0 goes first since before the
# element-wise multiplication, the filter is flipped. We only do this since the filter is asymmetric.
self.conv_local = np.ones((1, window_local + 1)).reshape(-1,) / window_local
self.conv_local[0] = 0
self.n_est_points = n_est_points
self.n_grad_points = n_grad_points
self.padding_amp_method = padding_amp_method
self.padding_local_method = padding_local_method
self.padding_amp_side = padding_amp_side
# set metadata
self.meta['detector_type'] = 'online'
self.meta['data_type'] = 'time-series' | [
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francisck/DanderSpritz_docs | 86bb7caca5a957147f120b18bb5c31f299914904 | Python/Core/Lib/lib2to3/patcomp.py | python | PatternCompiler.compile_node | (self, node) | Compiles a node, recursively.
This is one big switch on the node type. | Compiles a node, recursively.
This is one big switch on the node type. | [
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"""Compiles a node, recursively.
This is one big switch on the node type.
"""
if node.type == self.syms.Matcher:
node = node.children[0]
if node.type == self.syms.Alternatives:
alts = [ self.compile_node(ch) for ch in node.children[::2] ]
if len(alts) == 1:
return alts[0]
p = pytree.WildcardPattern([ [a] for a in alts ], min=1, max=1)
return p.optimize()
else:
if node.type == self.syms.Alternative:
units = [ self.compile_node(ch) for ch in node.children ]
if len(units) == 1:
return units[0]
p = pytree.WildcardPattern([units], min=1, max=1)
return p.optimize()
if node.type == self.syms.NegatedUnit:
pattern = self.compile_basic(node.children[1:])
p = pytree.NegatedPattern(pattern)
return p.optimize()
name = None
nodes = node.children
if len(nodes) >= 3 and nodes[1].type == token.EQUAL:
name = nodes[0].value
nodes = nodes[2:]
repeat = None
if len(nodes) >= 2 and nodes[-1].type == self.syms.Repeater:
repeat = nodes[-1]
nodes = nodes[:-1]
pattern = self.compile_basic(nodes, repeat)
if repeat is not None:
children = repeat.children
child = children[0]
if child.type == token.STAR:
min = 0
max = pytree.HUGE
elif child.type == token.PLUS:
min = 1
max = pytree.HUGE
elif child.type == token.LBRACE:
min = max = self.get_int(children[1])
if len(children) == 5:
max = self.get_int(children[3])
if min != 1 or max != 1:
pattern = pattern.optimize()
pattern = pytree.WildcardPattern([[pattern]], min=min, max=max)
if name is not None:
pattern.name = name
return pattern.optimize() | [
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grnet/synnefo | d06ec8c7871092131cdaabf6b03ed0b504c93e43 | snf-pithos-backend/pithos/backends/lib/sqlalchemy/node.py | python | Node.node_account_usage | (self, account=None, project=None, cluster=0) | return d | Return a dict of dicts with the project usage for a specific account
Keyword arguments:
account -- (default None: list usage for all accounts)
project -- (default None: list usage for all projects)
cluster -- list current, history or deleted usage (default 0: normal) | Return a dict of dicts with the project usage for a specific account | [
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] | def node_account_usage(self, account=None, project=None, cluster=0):
"""Return a dict of dicts with the project usage for a specific account
Keyword arguments:
account -- (default None: list usage for all accounts)
project -- (default None: list usage for all projects)
cluster -- list current, history or deleted usage (default 0: normal)
"""
n1 = self.nodes.alias('n1')
n2 = self.nodes.alias('n2')
n3 = self.nodes.alias('n3')
s = select([n3.c.path, self.policy.c.value,
func.sum(self.versions.c.size)])
s = s.where(self.policy.c.key == 'project')
s = s.where(self.policy.c.node == n2.c.node)
s = s.where(n1.c.node == self.versions.c.node)
s = s.where(self.versions.c.cluster == cluster)
s = s.where(n1.c.parent == n2.c.node)
s = s.where(n2.c.parent == n3.c.node)
s = s.where(n3.c.parent == 0)
s = s.where(n3.c.node != 0)
s = s.group_by(n3.c.path, self.policy.c.value)
if account:
s = s.where(n3.c.path == account)
if project:
s = s.where(self.policy.c.value == project)
r = self.conn.execute(s)
rows = r.fetchall()
r.close()
d = defaultdict(lambda: defaultdict(dict))
for account, project, usage in rows:
d[account][project][DEFAULT_DISKSPACE_RESOURCE] = \
safe_long(usage)
return d | [
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jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/plat-mac/pimp.py | python | PimpPackage_source.unpackPackageOnly | (self, output=None) | Unpack a source package and check that setup.py exists | Unpack a source package and check that setup.py exists | [
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] | def unpackPackageOnly(self, output=None):
"""Unpack a source package and check that setup.py exists"""
PimpPackage.unpackPackageOnly(self, output)
# Test that a setup script has been create
self._buildDirname = os.path.join(self._db.preferences.buildDir, self.basename)
setupname = os.path.join(self._buildDirname, "setup.py")
if not os.path.exists(setupname) and not NO_EXECUTE:
return "no setup.py found after unpack of archive" | [
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mynameisfiber/high_performance_python | 615341ff066772dc45125fce1866349d555524fe | 07_compiling/cython/nparray_memoryview/2/julia1.py | python | calc_pure_python | (draw_output, desired_width, max_iterations) | Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display | Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display | [
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] | def calc_pure_python(draw_output, desired_width, max_iterations):
"""Create a list of complex co-ordinates (zs) and complex parameters (cs), build Julia set and display"""
x_step = (float(x2 - x1) / float(desired_width))
y_step = (float(y1 - y2) / float(desired_width))
x = []
y = []
ycoord = y2
while ycoord > y1:
y.append(ycoord)
ycoord += y_step
xcoord = x1
while xcoord < x2:
x.append(xcoord)
xcoord += x_step
# build a list of co-ordinates and the initial condition for each cell.
# Note that our initial condition is a constant and could easily be removed,
# we use it to simulate a real-world scenario with several inputs to our
# function
zs = []
cs = []
for ycoord in y:
for xcoord in x:
zs.append(complex(xcoord, ycoord))
cs.append(complex(c_real, c_imag))
zs_np = np.array(zs, np.complex128)
cs_np = np.array(cs, np.complex128)
print "Length of x:", len(x)
print "Total elements:", len(zs)
start_time = time.time()
output = calculate.calculate_z(max_iterations, zs_np, cs_np)
end_time = time.time()
secs = end_time - start_time
print "Took", secs, "seconds"
validation_sum = sum(output)
print "Total sum of elements (for validation):", validation_sum | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/redis/client.py | python | PubSub.reset | (self) | [] | def reset(self):
if self.connection:
self.connection.disconnect()
self.connection.clear_connect_callbacks()
self.connection_pool.release(self.connection)
self.connection = None
self.channels = {}
self.patterns = {} | [
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/chardet/universaldetector.py | python | UniversalDetector.close | (self) | return self.result | Stop analyzing the current document and come up with a final
prediction.
:returns: The ``result`` attribute, a ``dict`` with the keys
`encoding`, `confidence`, and `language`. | Stop analyzing the current document and come up with a final
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] | def close(self):
"""
Stop analyzing the current document and come up with a final
prediction.
:returns: The ``result`` attribute, a ``dict`` with the keys
`encoding`, `confidence`, and `language`.
"""
# Don't bother with checks if we're already done
if self.done:
return self.result
self.done = True
if not self._got_data:
self.logger.debug('no data received!')
# Default to ASCII if it is all we've seen so far
elif self._input_state == InputState.PURE_ASCII:
self.result = {'encoding': 'ascii',
'confidence': 1.0,
'language': ''}
# If we have seen non-ASCII, return the best that met MINIMUM_THRESHOLD
elif self._input_state == InputState.HIGH_BYTE:
prober_confidence = None
max_prober_confidence = 0.0
max_prober = None
for prober in self._charset_probers:
if not prober:
continue
prober_confidence = prober.get_confidence()
if prober_confidence > max_prober_confidence:
max_prober_confidence = prober_confidence
max_prober = prober
if max_prober and (max_prober_confidence > self.MINIMUM_THRESHOLD):
charset_name = max_prober.charset_name
lower_charset_name = max_prober.charset_name.lower()
confidence = max_prober.get_confidence()
# Use Windows encoding name instead of ISO-8859 if we saw any
# extra Windows-specific bytes
if lower_charset_name.startswith('iso-8859'):
if self._has_win_bytes:
charset_name = self.ISO_WIN_MAP.get(lower_charset_name,
charset_name)
self.result = {'encoding': charset_name,
'confidence': confidence,
'language': max_prober.language}
# Log all prober confidences if none met MINIMUM_THRESHOLD
if self.logger.getEffectiveLevel() == logging.DEBUG:
if self.result['encoding'] is None:
self.logger.debug('no probers hit minimum threshold')
for group_prober in self._charset_probers:
if not group_prober:
continue
if isinstance(group_prober, CharSetGroupProber):
for prober in group_prober.probers:
self.logger.debug('%s %s confidence = %s',
prober.charset_name,
prober.language,
prober.get_confidence())
else:
self.logger.debug('%s %s confidence = %s',
prober.charset_name,
prober.language,
prober.get_confidence())
return self.result | [
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Adnios/Tensorflow | 69d6e04fef9d7061bef9e32f807f3f2dc132cf8e | Tensorflow 6 fc2/mnist_forward.py | python | get_bias | (shape) | return b | [] | def get_bias(shape):
# 初始化的一维数组，初始化值为全 0
b = tf.Variable(tf.zeros(shape))
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pjkundert/cpppo | 4c217b6c06b88bede3888cc5ea2731f271a95086 | server/enip/parser.py | python | octets_noop.__init__ | ( self, name=None, octets_state=state, **kwds ) | [] | def __init__( self, name=None, octets_state=state, **kwds ):
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nschloe/pygmsh | 3e7eea6fae3b4cd5e9f2c2d52b3686d3e5a1a725 | src/pygmsh/common/geometry.py | python | CommonGeometry.rotate | (
self,
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point: tuple[float, float, float],
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axis: tuple[float, float, float],
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Rotation axis has to be specified.
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"""Rotate input_entity around a given point with a given angle.
Rotation axis has to be specified.
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yandex/gixy | 641060d6355fbb5bd71695928a2bf14a9bcb8bf2 | gixy/parser/raw_parser.py | python | _fix_comment | (string, location, tokens) | return [comment] | Returns "cleared" comment text
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:param tokens: list of the matched tokens, packaged as a ParseResults_ object
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:param tokens: list of the matched tokens, packaged as a ParseResults_ object
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TristenHarr/MyPyBuilder | 5823ae9e5fcd2d745a70425c37a3aaa432c0389d | GuiBuilder/BUILDER/PROJECTBUILDER/Demo/MainGuiBuilderDemo.py | python | DemoGui.is_int | (data, default=0) | This is used as simple form validation to check if the input is a valid integer.
:param data: The raw data from a form
:type data: str
:param default: The lowest allowed integer
:type default: int
:return: integer if valid, False otherwise | This is used as simple form validation to check if the input is a valid integer. | [
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"""
This is used as simple form validation to check if the input is a valid integer.
:param data: The raw data from a form
:type data: str
:param default: The lowest allowed integer
:type default: int
:return: integer if valid, False otherwise
"""
neg_flag = False
if default < 0:
if '-' == data[0]:
neg_flag = True
data = data.lstrip('-')
if data.isdigit():
if neg_flag:
data = '-' + data
if int(data) >= default:
return int(data)
else:
return False
else:
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csujedihy/lc-all-solutions | b9fd938a7b3c164f28096361993600e338c399c3 | 308.range-sum-query-2d-mutable/range-sum-query-2d-mutable.py | python | NumMatrix.__init__ | (self, matrix) | initialize your data structure here.
:type matrix: List[List[int]] | initialize your data structure here.
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initialize your data structure here.
:type matrix: List[List[int]]
"""
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col = 0
else:
col = len(matrix[0])
self.c = [[0] * (col + 1) for _ in range(0, len(matrix) + 1)]
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for i in range(0, len(matrix)):
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self.update(i, j, matrix[i][j]) | [
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etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/treeview/templates.py | python | apply_template | (tree_style, template) | [] | def apply_template(tree_style, template):
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/lib2to3/pytree.py | python | Base.get_lineno | (self) | return node.lineno | Return the line number which generated the invocant node. | Return the line number which generated the invocant node. | [
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return
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slackapi/python-slack-sdk | 2dee6656ffacb7de0c29bb2a6c2b51ec6b5dbce7 | slack_sdk/web/legacy_client.py | python | LegacyWebClient.files_sharedPublicURL | (
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) | return self.api_call("files.sharedPublicURL", params=kwargs) | Enables a file for public/external sharing.
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"""Enables a file for public/external sharing.
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kwargs.update({"file": file})
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LabPy/lantz | 3e878e3f765a4295b0089d04e241d4beb7b8a65b | lantz/drivers/legacy/newport/powermeter1830c.py | python | powermeter1830c.filter | (self) | return int(self.query('F?')) | How many measurements are averaged for the displayed reading.
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medium: 4 measurements
fast: 1 measurement. | How many measurements are averaged for the displayed reading.
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medium: 4 measurements
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/sqli/thirdparty/gprof2dot/gprof2dot.py | python | Profile.prune | (self, node_thres, edge_thres) | Prune the profile | Prune the profile | [
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openstack/rally | 58b12c2e0bfa541bdb038be6e1679c5117b98484 | rally/task/sla.py | python | SLAChecker.add_iteration | (self, iteration) | return all([sla.add_iteration(iteration) for sla in self.sla_criteria]) | Process the result of a single iteration.
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/idlelib/tree.py | python | TreeItem.GetIconName | (self) | Return name of icon to be displayed normally. | Return name of icon to be displayed normally. | [
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isnowfy/pydown | 71ecc891868cd2a34b7e5fe662c99474f2d0fd7f | pygments/filters/__init__.py | python | RaiseOnErrorTokenFilter.__init__ | (self, **options) | [] | def __init__(self, **options):
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choasup/SIN | 4851efb7b1c64180026e51ab8abcd95265c0602c | lib/datasets/imagenet3d.py | python | imagenet3d._get_default_path | (self) | return os.path.join(datasets.ROOT_DIR, 'data', 'ImageNet3D') | Return the default path where imagenet3d is expected to be installed. | Return the default path where imagenet3d is expected to be installed. | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/_pydecimal.py | python | Context.copy_decimal | (self, a) | return Decimal(a) | Returns a copy of the decimal object.
>>> ExtendedContext.copy_decimal(Decimal('2.1'))
Decimal('2.1')
>>> ExtendedContext.copy_decimal(Decimal('-1.00'))
Decimal('-1.00')
>>> ExtendedContext.copy_decimal(1)
Decimal('1') | Returns a copy of the decimal object. | [
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] | def copy_decimal(self, a):
"""Returns a copy of the decimal object.
>>> ExtendedContext.copy_decimal(Decimal('2.1'))
Decimal('2.1')
>>> ExtendedContext.copy_decimal(Decimal('-1.00'))
Decimal('-1.00')
>>> ExtendedContext.copy_decimal(1)
Decimal('1')
"""
a = _convert_other(a, raiseit=True)
return Decimal(a) | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/express/_core.py | python | process_dataframe_timeline | (args) | return args | Massage input for bar traces for px.timeline() | Massage input for bar traces for px.timeline() | [
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"""
Massage input for bar traces for px.timeline()
"""
args["is_timeline"] = True
if args["x_start"] is None or args["x_end"] is None:
raise ValueError("Both x_start and x_end are required")
try:
x_start = pd.to_datetime(args["data_frame"][args["x_start"]])
x_end = pd.to_datetime(args["data_frame"][args["x_end"]])
except (ValueError, TypeError):
raise TypeError(
"Both x_start and x_end must refer to data convertible to datetimes."
)
# note that we are not adding any columns to the data frame here, so no risk of overwrite
args["data_frame"][args["x_end"]] = (x_end - x_start).astype("timedelta64[ms]")
args["x"] = args["x_end"]
del args["x_end"]
args["base"] = args["x_start"]
del args["x_start"]
return args | [
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/v5_0/feed/feed_client.py | python | FeedClient.get_recycle_bin_package_versions | (self, feed_id, package_id, include_urls=None) | return self._deserialize('[RecycleBinPackageVersion]', self._unwrap_collection(response)) | GetRecycleBinPackageVersions.
[Preview API] Get a list of package versions within the recycle bin.
:param str feed_id: Name or Id of the feed.
:param str package_id: The package Id (GUID Id, not the package name).
:param bool include_urls: True to return REST Urls with the response. Default is True.
:rtype: [RecycleBinPackageVersion] | GetRecycleBinPackageVersions.
[Preview API] Get a list of package versions within the recycle bin.
:param str feed_id: Name or Id of the feed.
:param str package_id: The package Id (GUID Id, not the package name).
:param bool include_urls: True to return REST Urls with the response. Default is True.
:rtype: [RecycleBinPackageVersion] | [
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[Preview API] Get a list of package versions within the recycle bin.
:param str feed_id: Name or Id of the feed.
:param str package_id: The package Id (GUID Id, not the package name).
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route_values = {}
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route_values['feedId'] = self._serialize.url('feed_id', feed_id, 'str')
if package_id is not None:
route_values['packageId'] = self._serialize.url('package_id', package_id, 'str')
query_parameters = {}
if include_urls is not None:
query_parameters['includeUrls'] = self._serialize.query('include_urls', include_urls, 'bool')
response = self._send(http_method='GET',
location_id='aceb4be7-8737-4820-834c-4c549e10fdc7',
version='5.0-preview.1',
route_values=route_values,
query_parameters=query_parameters)
return self._deserialize('[RecycleBinPackageVersion]', self._unwrap_collection(response)) | [
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PythonCharmers/python-future | 80523f383fbba1c6de0551e19d0277e73e69573c | src/future/backports/email/_header_value_parser.py | python | _validate_xtext | (xtext) | If input token contains ASCII non-printables, register a defect. | If input token contains ASCII non-printables, register a defect. | [
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] | def _validate_xtext(xtext):
"""If input token contains ASCII non-printables, register a defect."""
non_printables = _non_printable_finder(xtext)
if non_printables:
xtext.defects.append(errors.NonPrintableDefect(non_printables))
if utils._has_surrogates(xtext):
xtext.defects.append(errors.UndecodableBytesDefect(
"Non-ASCII characters found in header token")) | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/attack/db/sqlmap/thirdparty/xdot/xdot.py | python | Lexer.__init__ | (self, buf = None, pos = 0, filename = None, fp = None) | [] | def __init__(self, buf = None, pos = 0, filename = None, fp = None):
if fp is not None:
try:
fileno = fp.fileno()
length = os.path.getsize(fp.name)
import mmap
except:
# read whole file into memory
buf = fp.read()
pos = 0
else:
# map the whole file into memory
if length:
# length must not be zero
buf = mmap.mmap(fileno, length, access = mmap.ACCESS_READ)
pos = os.lseek(fileno, 0, 1)
else:
buf = ''
pos = 0
if filename is None:
try:
filename = fp.name
except AttributeError:
filename = None
self.buf = buf
self.pos = pos
self.line = 1
self.col = 1
self.filename = filename | [
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markokr/rarfile | 55fe778b5207ebba48b8158d2c25d9d536ec89de | rarfile.py | python | RarExtFile._read | (self, cnt) | Actual read that gets sanitized cnt. | Actual read that gets sanitized cnt. | [
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"""Actual read that gets sanitized cnt."""
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idc.py | python | RunTo | (ea) | return idaapi.run_to(ea) | Execute the process until the given address is reached.
If no process is active, a new process is started.
See the important note to the StepInto() function
@return: success | Execute the process until the given address is reached.
If no process is active, a new process is started.
See the important note to the StepInto() function | [
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"""
Execute the process until the given address is reached.
If no process is active, a new process is started.
See the important note to the StepInto() function
@return: success
"""
return idaapi.run_to(ea) | [
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/template/caches.py | python | clear_template_caches | () | Clear the templates caches.
This clears any caches for template parse trees and related state, forcing
templates to be re-parsed and re-rendered. | Clear the templates caches. | [
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] | def clear_template_caches():
"""Clear the templates caches.
This clears any caches for template parse trees and related state, forcing
templates to be re-parsed and re-rendered.
"""
template_loaders = []
for engine in engines.all():
template_loaders += engine.engine.template_loaders
for template_loader in template_loaders:
template_loader.reset() | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_objectvalidator.py | python | Utils.exists | (results, _name) | return False | Check to see if the results include the name | Check to see if the results include the name | [
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''' Check to see if the results include the name '''
if not results:
return False
if Utils.find_result(results, _name):
return True
return False | [
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mpatacchiola/dissecting-reinforcement-learning | 38660b0a0d5aed077a46acb4bcb2013565304d9c | environments/mountain_car.py | python | MountainCar.step | (self, action) | return [position_t1, velocity_t1], reward, done | Perform one step in the environment following the action.
@param action: an integer representing one of three actions [0, 1, 2]
where 0=move_left, 1=do_not_move, 2=move_right
@return: (postion_t1, velocity_t1), reward, done
where reward is always negative but when the goal is reached
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"""Perform one step in the environment following the action.
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where 0=move_left, 1=do_not_move, 2=move_right
@return: (postion_t1, velocity_t1), reward, done
where reward is always negative but when the goal is reached
done is True when the goal is reached
"""
if(action >= 3):
raise ValueError("[MOUNTAIN CAR][ERROR] The action value "
+ str(action) + " is out of range.")
done = False
reward = -0.01
action_list = [-0.2, 0, +0.2]
action_t = action_list[action]
velocity_t1 = self.velocity_t + \
(-self.gravity * self.mass * np.cos(3*self.position_t)
+ (action_t/self.mass)
- (self.friction*self.velocity_t)) * self.delta_t
position_t1 = self.position_t + (velocity_t1 * self.delta_t)
# Check the limit condition (car outside frame)
if position_t1 < -1.2:
position_t1 = -1.2
velocity_t1 = 0
# Assign the new position and velocity
self.position_t = position_t1
self.velocity_t= velocity_t1
self.position_list.append(position_t1)
# Reward and done when the car reaches the goal
if position_t1 >= 0.5:
reward = +1.0
done = True
# Return state_t1, reward, done
return [position_t1, velocity_t1], reward, done | [
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qiufengyuyi/sequence_tagging | ad6b3299ebbe32837f3d6e20c2c9190d6a8c934a | bert/modeling.py | python | BertModel.get_embedding_output | (self) | return self.embedding_output | Gets output of the embedding lookup (i.e., input to the transformer).
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
to the output of the embedding layer, after summing the word
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dhconnelly/paip-python | e6784004bea1d16c90a4ca75c798ae31e6ab698a | paip/othello.py | python | alphabeta | (player, board, alpha, beta, depth, evaluate) | return alpha, best_move | Find the best legal move for player, searching to the specified depth. Like
minimax, but uses the bounds alpha and beta to prune branches. | Find the best legal move for player, searching to the specified depth. Like
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"""
Find the best legal move for player, searching to the specified depth. Like
minimax, but uses the bounds alpha and beta to prune branches.
"""
if depth == 0:
return evaluate(player, board), None
def value(board, alpha, beta):
# Like in `minimax`, the value of a board is the opposite of its value
# to the opponent. We pass in `-beta` and `-alpha` as the alpha and
# beta values, respectively, for the opponent, since `alpha` represents
# the best score we know we can achieve and is therefore the worst score
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# achieve.
return -alphabeta(opponent(player), board, -beta, -alpha, depth-1, evaluate)[0]
moves = legal_moves(player, board)
if not moves:
if not any_legal_move(opponent(player), board):
return final_value(player, board), None
return value(board, alpha, beta), None
best_move = moves[0]
for move in moves:
if alpha >= beta:
# If one of the legal moves leads to a better score than beta, then
# the opponent will avoid this branch, so we can quit looking.
break
val = value(make_move(move, player, list(board)), alpha, beta)
if val > alpha:
# If one of the moves leads to a better score than the current best
# achievable score, then replace it with this one.
alpha = val
best_move = move
return alpha, best_move | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_openshift/library/oc_adm_router.py | python | Router.serviceaccount | (self) | return self._serviceaccount | property for serviceaccount | property for serviceaccount | [
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''' property for serviceaccount '''
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ctxis/CAPE | dae9fa6a254ecdbabeb7eb0d2389fa63722c1e82 | analyzer/windows/analyzer.py | python | remove_pid | (pid) | Remove a process to process list. | Remove a process to process list. | [
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] | def remove_pid(pid):
"""Remove a process to process list."""
if isinstance(pid, (int, long, str)):
log.info("Process with pid %s has terminated", pid)
PROCESS_LIST.remove(int(pid))
del_pid_from_aux_modules(int(pid)) | [
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/urllib.py | python | getproxies_environment | () | return proxies | Return a dictionary of scheme -> proxy server URL mappings.
Scan the environment for variables named <scheme>_proxy;
this seems to be the standard convention. If you need a
different way, you can pass a proxies dictionary to the
[Fancy]URLopener constructor. | Return a dictionary of scheme -> proxy server URL mappings. | [
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] | def getproxies_environment():
"""Return a dictionary of scheme -> proxy server URL mappings.
Scan the environment for variables named <scheme>_proxy;
this seems to be the standard convention. If you need a
different way, you can pass a proxies dictionary to the
[Fancy]URLopener constructor.
"""
proxies = {}
for name, value in os.environ.items():
name = name.lower()
if value and name[-6:] == '_proxy':
proxies[name[:-6]] = value
return proxies | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/chat/v2/credential.py | python | CredentialInstance.date_updated | (self) | return self._properties['date_updated'] | :returns: The ISO 8601 date and time in GMT when the resource was last updated
:rtype: datetime | :returns: The ISO 8601 date and time in GMT when the resource was last updated
:rtype: datetime | [
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"""
:returns: The ISO 8601 date and time in GMT when the resource was last updated
:rtype: datetime
"""
return self._properties['date_updated'] | [
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django/django | 0a17666045de6739ae1c2ac695041823d5f827f7 | django/template/defaultfilters.py | python | capfirst | (value) | return value and value[0].upper() + value[1:] | Capitalize the first character of the value. | Capitalize the first character of the value. | [
"Capitalize",
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] | def capfirst(value):
"""Capitalize the first character of the value."""
return value and value[0].upper() + value[1:] | [
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trezor/trezor-core | 18c3a6a5bd45923380312b064be96155f5a7377d | src/apps/monero/xmr/key_image.py | python | _export_key_image | (
creds, subaddresses, pkey, tx_pub_key, additional_tx_pub_keys, out_idx, test=True
) | return ki, sig | Generates key image for the TXO + signature for the key image | Generates key image for the TXO + signature for the key image | [
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creds, subaddresses, pkey, tx_pub_key, additional_tx_pub_keys, out_idx, test=True
):
"""
Generates key image for the TXO + signature for the key image
"""
r = monero.generate_tx_spend_and_key_image_and_derivation(
creds, subaddresses, pkey, tx_pub_key, additional_tx_pub_keys, out_idx
)
xi, ki, recv_derivation = r[:3]
phash = crypto.encodepoint(ki)
sig = generate_ring_signature(phash, ki, [pkey], xi, 0, test)
return ki, sig | [
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mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pylibs/osx64/gevent/hub.py | python | iwait | (objects, timeout=None, count=None) | Iteratively yield *objects* as they are ready, until all (or *count*) are ready
or *timeout* expired.
:param objects: A sequence (supporting :func:`len`) containing objects
implementing the wait protocol (rawlink() and unlink()).
:keyword int count: If not `None`, then a number specifying the maximum number
of objects to wait for. If ``None`` (the default), all objects
are waited for.
:keyword float timeout: If given, specifies a maximum number of seconds
to wait. If the timeout expires before the desired waited-for objects
are available, then this method returns immediately.
.. seealso:: :func:`wait`
.. versionchanged:: 1.1a1
Add the *count* parameter.
.. versionchanged:: 1.1a2
No longer raise :exc:`LoopExit` if our caller switches greenlets
in between items yielded by this function. | Iteratively yield *objects* as they are ready, until all (or *count*) are ready
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"""
Iteratively yield *objects* as they are ready, until all (or *count*) are ready
or *timeout* expired.
:param objects: A sequence (supporting :func:`len`) containing objects
implementing the wait protocol (rawlink() and unlink()).
:keyword int count: If not `None`, then a number specifying the maximum number
of objects to wait for. If ``None`` (the default), all objects
are waited for.
:keyword float timeout: If given, specifies a maximum number of seconds
to wait. If the timeout expires before the desired waited-for objects
are available, then this method returns immediately.
.. seealso:: :func:`wait`
.. versionchanged:: 1.1a1
Add the *count* parameter.
.. versionchanged:: 1.1a2
No longer raise :exc:`LoopExit` if our caller switches greenlets
in between items yielded by this function.
"""
# QQQ would be nice to support iterable here that can be generated slowly (why?)
if objects is None:
yield get_hub().join(timeout=timeout)
return
count = len(objects) if count is None else min(count, len(objects))
waiter = _MultipleWaiter()
switch = waiter.switch
if timeout is not None:
timer = get_hub().loop.timer(timeout, priority=-1)
timer.start(switch, _NONE)
try:
for obj in objects:
obj.rawlink(switch)
for _ in xrange(count):
item = waiter.get()
waiter.clear()
if item is _NONE:
return
yield item
finally:
if timeout is not None:
timer.stop()
for obj in objects:
unlink = getattr(obj, 'unlink', None)
if unlink:
try:
unlink(switch)
except:
traceback.print_exc() | [
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jazzband/django-ddp | 1e1954b06fe140346acea43582515991685e4e01 | dddp/migrations/__init__.py | python | set_default_forwards | (app_name, operation, apps, schema_editor) | Set default value for AleaIdField. | Set default value for AleaIdField. | [
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] | def set_default_forwards(app_name, operation, apps, schema_editor):
"""Set default value for AleaIdField."""
model = apps.get_model(app_name, operation.model_name)
for obj_pk in model.objects.values_list('pk', flat=True):
model.objects.filter(pk=obj_pk).update(**{
operation.name: get_meteor_id(model, obj_pk),
}) | [
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spartan-array/spartan | fdcf059ce7e48688648d793d632dc5961f4e72b5 | spartan/examples/ssvd/ssvd.py | python | svd | (A, k=None) | return U, S, V.T | Stochastic SVD.
Parameters
----------
A : spartan matrix
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.
The operations include matrix multiplication and QR decomposition.
We parallelize both of them.
Returns
--------
U : Spartan array of shape (M, k)
S : numpy array of shape (k,)
V : numpy array of shape (k, k) | Stochastic SVD. | [
"Stochastic",
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"."
] | def svd(A, k=None):
"""
Stochastic SVD.
Parameters
----------
A : spartan matrix
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.
The operations include matrix multiplication and QR decomposition.
We parallelize both of them.
Returns
--------
U : Spartan array of shape (M, k)
S : numpy array of shape (k,)
V : numpy array of shape (k, k)
"""
if k is None: k = A.shape[1]
Omega = expr.randn(A.shape[1], k)
Y = expr.dot(A, Omega)
Q, R = qr(Y)
B = expr.dot(expr.transpose(Q), A)
BTB = expr.dot(B, expr.transpose(B)).optimized().glom()
S, U_ = np.linalg.eig(BTB)
S = np.sqrt(S)
# Sort by eigen values from large to small
si = np.argsort(S)[::-1]
S = S[si]
U_ = U_[:, si]
U = expr.dot(Q, U_).optimized().evaluate()
V = np.dot(np.dot(expr.transpose(B).optimized().glom(), U_), np.diag(np.ones(S.shape[0]) / S))
return U, S, V.T | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/gdata/src/gdata/Crypto/Protocol/AllOrNothing.py | python | AllOrNothing.digest | (self, text) | return map(long_to_bytes, blocks) | digest(text:string) : [string]
Perform the All-or-Nothing package transform on the given
string. Output is a list of message blocks describing the
transformed text, where each block is a string of bit length equal
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"""digest(text:string) : [string]
Perform the All-or-Nothing package transform on the given
string. Output is a list of message blocks describing the
transformed text, where each block is a string of bit length equal
to the ciphermodule's block_size.
"""
# generate a random session key and K0, the key used to encrypt the
# hash blocks. Rivest calls this a fixed, publically-known encryption
# key, but says nothing about the security implications of this key or
# how to choose it.
key = self._inventkey(self.__key_size)
K0 = self.__K0digit * self.__key_size
# we need two cipher objects here, one that is used to encrypt the
# message blocks and one that is used to encrypt the hashes. The
# former uses the randomly generated key, while the latter uses the
# well-known key.
mcipher = self.__newcipher(key)
hcipher = self.__newcipher(K0)
# Pad the text so that its length is a multiple of the cipher's
# block_size. Pad with trailing spaces, which will be eliminated in
# the undigest() step.
block_size = self.__ciphermodule.block_size
padbytes = block_size - (len(text) % block_size)
text = text + ' ' * padbytes
# Run through the algorithm:
# s: number of message blocks (size of text / block_size)
# input sequence: m1, m2, ... ms
# random key K' (`key' in the code)
# Compute output sequence: m'1, m'2, ... m's' for s' = s + 1
# Let m'i = mi ^ E(K', i) for i = 1, 2, 3, ..., s
# Let m's' = K' ^ h1 ^ h2 ^ ... hs
# where hi = E(K0, m'i ^ i) for i = 1, 2, ... s
#
# The one complication I add is that the last message block is hard
# coded to the number of padbytes added, so that these can be stripped
# during the undigest() step
s = len(text) / block_size
blocks = []
hashes = []
for i in range(1, s+1):
start = (i-1) * block_size
end = start + block_size
mi = text[start:end]
assert len(mi) == block_size
cipherblock = mcipher.encrypt(long_to_bytes(i, block_size))
mticki = bytes_to_long(mi) ^ bytes_to_long(cipherblock)
blocks.append(mticki)
# calculate the hash block for this block
hi = hcipher.encrypt(long_to_bytes(mticki ^ i, block_size))
hashes.append(bytes_to_long(hi))
# Add the padbytes length as a message block
i = i + 1
cipherblock = mcipher.encrypt(long_to_bytes(i, block_size))
mticki = padbytes ^ bytes_to_long(cipherblock)
blocks.append(mticki)
# calculate this block's hash
hi = hcipher.encrypt(long_to_bytes(mticki ^ i, block_size))
hashes.append(bytes_to_long(hi))
# Now calculate the last message block of the sequence 1..s'. This
# will contain the random session key XOR'd with all the hash blocks,
# so that for undigest(), once all the hash blocks are calculated, the
# session key can be trivially extracted. Calculating all the hash
# blocks requires that all the message blocks be received, thus the
# All-or-Nothing algorithm succeeds.
mtick_stick = bytes_to_long(key) ^ reduce(operator.xor, hashes)
blocks.append(mtick_stick)
# we convert the blocks to strings since in Python, byte sequences are
# always represented as strings. This is more consistent with the
# model that encryption and hash algorithms always operate on strings.
return map(long_to_bytes, blocks) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/email/charset.py | python | Charset.encoded_header_len | (self, s) | Return the length of the encoded header string. | Return the length of the encoded header string. | [
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cset = self.get_output_charset()
# The len(s) of a 7bit encoding is len(s)
if self.header_encoding == BASE64:
return email.base64mime.base64_len(s) + len(cset) + MISC_LEN
elif self.header_encoding == QP:
return email.quoprimime.header_quopri_len(s) + len(cset) + MISC_LEN
elif self.header_encoding == SHORTEST:
lenb64 = email.base64mime.base64_len(s)
lenqp = email.quoprimime.header_quopri_len(s)
return min(lenb64, lenqp) + len(cset) + MISC_LEN
else:
return len(s) | [
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glutanimate/cloze-overlapper | 9eabb6a9d2a6807595478647fd968e8e4bac86fc | src/cloze_overlapper/reviewer.py | python | newKeyHandler20 | (reviewer, evt) | Bind mask reveal to a hotkey | Bind mask reveal to a hotkey | [
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"""Bind mask reveal to a hotkey"""
if (evt.key() == olc_keycode_reveal):
onHintRevealHotkey(reviewer) | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/inspect.py | python | BoundArguments.signature | (self) | return self._signature | [] | def signature(self):
return self._signature | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/pegasus/modeling_flax_pegasus.py | python | FlaxPegasusModule._get_encoder_module | (self) | return self.encoder | [] | def _get_encoder_module(self):
return self.encoder | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/groups/galois_group.py | python | _alg_key | (self, algorithm=None, recompute=False) | r"""
Return a key for use in cached_method calls.
If recompute is false, will cache using ``None`` as the key, so no recomputation will be done.
If recompute is true, will cache by algorithm, yielding a recomputation for each different algorithm.
EXAMPLES::
sage: from sage.groups.galois_group import _alg_key
sage: R.<x> = ZZ[]
sage: K.<a> = NumberField(x^3 + 2*x + 2)
sage: G = K.galois_group()
sage: _alg_key(G, algorithm="pari", recompute=True)
'pari' | r"""
Return a key for use in cached_method calls. | [
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] | def _alg_key(self, algorithm=None, recompute=False):
r"""
Return a key for use in cached_method calls.
If recompute is false, will cache using ``None`` as the key, so no recomputation will be done.
If recompute is true, will cache by algorithm, yielding a recomputation for each different algorithm.
EXAMPLES::
sage: from sage.groups.galois_group import _alg_key
sage: R.<x> = ZZ[]
sage: K.<a> = NumberField(x^3 + 2*x + 2)
sage: G = K.galois_group()
sage: _alg_key(G, algorithm="pari", recompute=True)
'pari'
"""
if recompute:
algorithm = self._get_algorithm(algorithm)
return algorithm | [
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datadesk/census-data-downloader | 7922e3f99566a0af0afef629c682bd6c522ccaa6 | census_data_downloader/__init__.py | python | download_everything | (*args, **kwargs) | Download all the data. | Download all the data. | [
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] | def download_everything(*args, **kwargs):
"""
Download all the data.
"""
logger.debug("Downloading all datasets for all geographies")
for klass in TABLE_LIST:
obj = klass(*args, **kwargs)
logger.debug(f"Downloading {klass.PROCESSED_TABLE_NAME} dataset")
obj.download_everything() | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/tkinter/ttk.py | python | Treeview.insert | (self, parent, index, iid=None, **kw) | return res | Creates a new item and return the item identifier of the newly
created item.
parent is the item ID of the parent item, or the empty string
to create a new top-level item. index is an integer, or the value
end, specifying where in the list of parent's children to insert
the new item. If index is less than or equal to zero, the new node
is inserted at the beginning, if index is greater than or equal to
the current number of children, it is inserted at the end. If iid
is specified, it is used as the item identifier, iid must not
already exist in the tree. Otherwise, a new unique identifier
is generated. | Creates a new item and return the item identifier of the newly
created item. | [
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"."
] | def insert(self, parent, index, iid=None, **kw):
"""Creates a new item and return the item identifier of the newly
created item.
parent is the item ID of the parent item, or the empty string
to create a new top-level item. index is an integer, or the value
end, specifying where in the list of parent's children to insert
the new item. If index is less than or equal to zero, the new node
is inserted at the beginning, if index is greater than or equal to
the current number of children, it is inserted at the end. If iid
is specified, it is used as the item identifier, iid must not
already exist in the tree. Otherwise, a new unique identifier
is generated."""
opts = _format_optdict(kw)
if iid is not None:
res = self.tk.call(self._w, "insert", parent, index,
"-id", iid, *opts)
else:
res = self.tk.call(self._w, "insert", parent, index, *opts)
return res | [
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gawel/irc3 | 112e474c126f84b509c8262840d29eadcdd878ed | irc3/__init__.py | python | IrcBot.dcc | (self) | return self._dcc | return the :class:`~irc3.dcc.DCCManager` | return the :class:`~irc3.dcc.DCCManager` | [
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"""return the :class:`~irc3.dcc.DCCManager`"""
if self._dcc is None:
self._dcc = DCCManager(self)
return self._dcc | [
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benoitc/gunicorn | 76f8da24cbb992d168e01bda811452bcf3b8f5b3 | gunicorn/arbiter.py | python | Arbiter.reexec | (self) | \
Relaunch the master and workers. | \
Relaunch the master and workers. | [
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] | def reexec(self):
"""\
Relaunch the master and workers.
"""
if self.reexec_pid != 0:
self.log.warning("USR2 signal ignored. Child exists.")
return
if self.master_pid != 0:
self.log.warning("USR2 signal ignored. Parent exists.")
return
master_pid = os.getpid()
self.reexec_pid = os.fork()
if self.reexec_pid != 0:
return
self.cfg.pre_exec(self)
environ = self.cfg.env_orig.copy()
environ['GUNICORN_PID'] = str(master_pid)
if self.systemd:
environ['LISTEN_PID'] = str(os.getpid())
environ['LISTEN_FDS'] = str(len(self.LISTENERS))
else:
environ['GUNICORN_FD'] = ','.join(
str(l.fileno()) for l in self.LISTENERS)
os.chdir(self.START_CTX['cwd'])
# exec the process using the original environment
os.execvpe(self.START_CTX[0], self.START_CTX['args'], environ) | [
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wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/lib/python-3.7.2-embed-win32/Crypto/Hash/HMAC.py | python | HMAC._pbkdf2_hmac_assist | (self, first_digest, iterations) | return result | Carry out the expensive inner loop for PBKDF2-HMAC | Carry out the expensive inner loop for PBKDF2-HMAC | [
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"""Carry out the expensive inner loop for PBKDF2-HMAC"""
result = self._digestmod._pbkdf2_hmac_assist(
self._inner,
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iterations)
return result | [
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lfz/Guided-Denoise | 8881ab768d16eaf87342da4ff7dc8271e183e205 | Attackset/Iter2_v3_resv2_inresv2_random/nets/inception_v1.py | python | inception_v1_base | (inputs,
final_endpoint='Mixed_5c',
scope='InceptionV1') | Defines the Inception V1 base architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
scope: Optional variable_scope.
Returns:
A dictionary from components of the network to the corresponding activation.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values. | Defines the Inception V1 base architecture. | [
"Defines",
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"Inception",
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"base",
"architecture",
"."
] | def inception_v1_base(inputs,
final_endpoint='Mixed_5c',
scope='InceptionV1'):
"""Defines the Inception V1 base architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
scope: Optional variable_scope.
Returns:
A dictionary from components of the network to the corresponding activation.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values.
"""
end_points = {}
with tf.variable_scope(scope, 'InceptionV1', [inputs]):
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
weights_initializer=trunc_normal(0.01)):
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
stride=1, padding='SAME'):
end_point = 'Conv2d_1a_7x7'
net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_2a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Conv2d_2b_1x1'
net = slim.conv2d(net, 64, [1, 1], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Conv2d_2c_3x3'
net = slim.conv2d(net, 192, [3, 3], scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_3a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_3b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_3c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_4a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4e'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_4f'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'MaxPool_5a_2x2'
net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_5b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
end_point = 'Mixed_5c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(
axis=3, values=[branch_0, branch_1, branch_2, branch_3])
end_points[end_point] = net
if final_endpoint == end_point: return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint) | [
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... | https://github.com/lfz/Guided-Denoise/blob/8881ab768d16eaf87342da4ff7dc8271e183e205/Attackset/Iter2_v3_resv2_inresv2_random/nets/inception_v1.py#L29-L254 | ||
joelgrus/data-science-from-scratch | d5d0f117f41b3ccab3b07f1ee1fa21cfcf69afa1 | scratch/nlp.py | python | fix_unicode | (text: str) | return text.replace(u"\u2019", "'") | [] | def fix_unicode(text: str) -> str:
return text.replace(u"\u2019", "'") | [
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GDQuest/krita-batch-exporter | d1f51af32f7fd026aa82e040710594516bbd9540 | batch_exporter/Utils/Tree.py | python | iterLevel | (node, maxDepth=-1) | return go([node]) | Visit nodes in level order.
Parameters
----------
node: Node
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Maximum depth level at which traversal will stop.
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Visit nodes in level order.
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node: Node
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Maximum depth level at which traversal will stop.
Returns
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out: iter(Node)
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/rum/v20210622/models.py | python | DescribeDataLogUrlStatisticsRequest.__init__ | (self) | r"""
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:param Os: 操作系统
:type Os: str
:param Browser: 浏览器
:type Browser: str
:param Env: 环境区分
:type Env: str | r"""
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"""
self.StartTime = None
self.Type = None
self.EndTime = None
self.ID = None
self.ExtSecond = None
self.Engine = None
self.Isp = None
self.From = None
self.Level = None
self.Brand = None
self.Area = None
self.VersionNum = None
self.Platform = None
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self.Device = None
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tobspr/RenderPipeline | d8c38c0406a63298f4801782a8e44e9c1e467acf | rplibs/progressbar/__init__.py | python | ProgressBar._env_size | (self) | return int(os.environ.get('COLUMNS', self._DEFAULT_TERMSIZE)) - 1 | Tries to find the term_width from the environment. | Tries to find the term_width from the environment. | [
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j91321/rext | 5f0f6266eec445b00ef4aeba3cfffbca022e7f43 | modules/misc/adb/alice_cpe_backdoor.py | python | Misc.do_set | (self, e) | [] | def do_set(self, e):
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if args[0] == "mac":
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self.mac = args[1]
print_info("MAC set to: " + self.mac + " " + lookup_mac(self.mac))
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/distutils/command/build_ext.py | python | build_ext.get_ext_fullpath | (self, ext_name) | return os.path.join(package_dir, filename) | Returns the path of the filename for a given extension.
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"""
fullname = self.get_ext_fullname(ext_name)
modpath = fullname.split('.')
filename = self.get_ext_filename(modpath[-1])
if not self.inplace:
# no further work needed
# returning :
# build_dir/package/path/filename
filename = os.path.join(*modpath[:-1]+[filename])
return os.path.join(self.build_lib, filename)
# the inplace option requires to find the package directory
# using the build_py command for that
package = '.'.join(modpath[0:-1])
build_py = self.get_finalized_command('build_py')
package_dir = os.path.abspath(build_py.get_package_dir(package))
# returning
# package_dir/filename
return os.path.join(package_dir, filename) | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models_pytorch/classifier_pytorch/transformers/configuration_ctrl.py | python | CTRLConfig.num_attention_heads | (self) | return self.n_head | [] | def num_attention_heads(self):
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/_vendor/requests/cookies.py | python | MockResponse.__init__ | (self, headers) | Make a MockResponse for `cookielib` to read.
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/stats/crv_types.py | python | UniformDistribution.pdf | (self, x) | return Piecewise(
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left, right = self.left, self.right
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hubblestack/hubble | 763142474edcecdec5fd25591dc29c3536e8f969 | hubblestack/hec/dq.py | python | DiskQueue.pop | (self) | remove the next item from the queue (do not return it); useful with .peek() | remove the next item from the queue (do not return it); useful with .peek() | [
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GalSim-developers/GalSim | a05d4ec3b8d8574f99d3b0606ad882cbba53f345 | galsim/config/process.py | python | ProcessTemplate | (config, base, logger=None) | If the config dict has a 'template' item, read in the appropriate file and
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base: The base configuration dict.
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base: The base configuration dict.
logger: If given, a logger object to log progress. [default: None]
"""
logger = LoggerWrapper(logger)
if 'template' in config:
template_string = config.pop('template')
logger.debug("Processing template specified as %s",template_string)
# Parse the template string
if ':' in template_string:
config_file, field = template_string.split(':')
else:
config_file, field = template_string, None
# Read the config file if appropriate
if config_file != '':
template = ReadConfig(config_file, logger=logger)[0]
else:
template = base
# Pull out the specified field, if any
if field is not None:
template = GetFromConfig(template, field)
# Copy over the template config into this one.
new_params = config.copy() # N.B. Already popped config['template'].
config.clear()
config.update(template)
# Update the config with the requested changes
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zyfra/ebonite | b01b662c43709d152940f488574d78ff25f89ecf | src/ebonite/build/provider/utils.py | python | BuildableWithServer.__init__ | (self, server_type: str) | [] | def __init__(self, server_type: str):
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dexy/dexy | 323c1806e51f75435e11d2265703e68f46c8aef3 | dexy/filters/standard.py | python | ClojureWhitespaceFilter.process | (self) | [] | def process(self):
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kubeflow/pipelines | bea751c9259ff0ae85290f873170aae89284ba8e | sdk/python/kfp/containers/entrypoint_utils.py | python | import_func_from_source | (source_path: str, fn_name: str) | Imports a function from a Python file.
The implementation is borrowed from
https://github.com/tensorflow/tfx/blob/8f25a4d1cc92dfc8c3a684dfc8b82699513cafb5/tfx/utils/import_utils.py#L50
Args:
source_path: The local path to the Python source file.
fn_name: The function name, which can be found in the source file.
Return: A Python function object.
Raises:
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The implementation is borrowed from
https://github.com/tensorflow/tfx/blob/8f25a4d1cc92dfc8c3a684dfc8b82699513cafb5/tfx/utils/import_utils.py#L50
Args:
source_path: The local path to the Python source file.
fn_name: The function name, which can be found in the source file.
Return: A Python function object.
Raises:
ImportError when failed to load the source file or cannot find the function
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"""
if any([source_path.startswith(prefix) for prefix in _REMOTE_FS_PREFIX]):
raise RuntimeError(
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try:
loader = importlib.machinery.SourceFileLoader(
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path=source_path,
)
spec = importlib.util.spec_from_loader(
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module = importlib.util.module_from_spec(spec)
sys.modules[loader.name] = module
loader.exec_module(module)
except IOError:
raise ImportError(
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fn_name, source_path))
try:
return getattr(module, fn_name)
except AttributeError:
raise ImportError(
'{} in {} not found in import_func_from_source()'.format(
fn_name, source_path)) | [
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/webapi/resources/review_general_comment.py | python | ReviewGeneralCommentResource.get_list | (self, *args, **kwargs) | Returns the list of general comments made on a review. | Returns the list of general comments made on a review. | [
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niosus/EasyClangComplete | 3b16eb17735aaa3f56bb295fc5481b269ee9f2ef | plugin/clang/cindex.py | python | Index.read | (self, path) | return TranslationUnit(ptr) if ptr else None | Load the translation unit from the given AST file. | Load the translation unit from the given AST file. | [
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] | def read(self, path):
"""Load the translation unit from the given AST file."""
ptr = TranslationUnit_read(self, path)
return TranslationUnit(ptr) if ptr else None | [
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collinsctk/PyQYT | 7af3673955f94ff1b2df2f94220cd2dab2e252af | ExtentionPackages/scapy/contrib/gsm_um.py | python | recall | () | return packet | RECALL Section 9.3.18a | RECALL Section 9.3.18a | [
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"""RECALL Section 9.3.18a"""
a = TpPd(pd=0x3)
b = MessageType(mesType=0xb) # 00001011
c = RecallType()
d = Facility()
packet = a / b / c / d
return packet | [
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Alex-Fabbri/Multi-News | f6476d1f114662eb93db32e9b704b7c4fe047217 | code/OpenNMT-py-baselines/onmt/decoders/transformer.py | python | TransformerDecoder.__init__ | (self, num_layers, d_model, heads, d_ff, attn_type,
copy_attn, self_attn_type, dropout, embeddings) | [] | def __init__(self, num_layers, d_model, heads, d_ff, attn_type,
copy_attn, self_attn_type, dropout, embeddings):
super(TransformerDecoder, self).__init__()
# Basic attributes.
self.decoder_type = 'transformer'
self.num_layers = num_layers
self.embeddings = embeddings
self.self_attn_type = self_attn_type
# Build TransformerDecoder.
self.transformer_layers = nn.ModuleList(
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self_attn_type=self_attn_type)
for _ in range(num_layers)])
# TransformerDecoder has its own attention mechanism.
# Set up a separated copy attention layer, if needed.
self._copy = False
if copy_attn:
self.copy_attn = onmt.modules.GlobalAttention(
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self._copy = True
self.layer_norm = onmt.modules.LayerNorm(d_model) | [
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mittagessen/kraken | 9882ba0743797a2d5b0a7b562fcc7235e87323da | kraken/lib/vgsl.py | python | TorchVGSLModel.one_channel_mode | (self) | return self.user_metadata['one_channel_mode'] | [] | def one_channel_mode(self):
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ctxis/CAPE | dae9fa6a254ecdbabeb7eb0d2389fa63722c1e82 | lib/cuckoo/common/utils.py | python | store_temp_file | (filedata, filename, path=None) | return tmp_file_path | Store a temporary file.
@param filedata: content of the original file.
@param filename: name of the original file.
@param path: optional path for temp directory.
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@param filename: name of the original file.
@param path: optional path for temp directory.
@return: path to the temporary file.
"""
filename = get_filename_from_path(filename).encode("utf-8", "replace")
# Reduce length (100 is arbitrary).
filename = filename[:100]
options = Config()
# Create temporary directory path.
if path:
target_path = path
else:
tmp_path = options.cuckoo.get("tmppath", "/tmp")
target_path = os.path.join(tmp_path, "cuckoo-tmp")
if not os.path.exists(target_path):
os.mkdir(target_path)
tmp_dir = tempfile.mkdtemp(prefix="upload_", dir=target_path)
tmp_file_path = os.path.join(tmp_dir, filename)
with open(tmp_file_path, "wb") as tmp_file:
# If filedata is file object, do chunked copy.
if hasattr(filedata, "read"):
chunk = filedata.read(1024)
while chunk:
tmp_file.write(chunk)
chunk = filedata.read(1024)
else:
tmp_file.write(filedata)
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Borda/pyImSegm | 7584b40a8d5bba04d3bf46f540f22b5d923e4b03 | imsegm/descriptors.py | python | cython_img2d_color_std | (img, seg, means=None) | return std | wrapper for fast implementation of colour features
:param ndarray img: input RGB image
:param ndarray seg: segmentation og the image
:param ndarray means: precomputed feature means
:return: np.array<nb_lbs, 3> matrix features per segment
.. seealso:: :func:`imsegm.descriptors.numpy_img2d_color_std`
>>> image = np.zeros((2, 10, 3))
>>> image[:, 2:6, 0] = 1
>>> image[:, 3:7, 1] = 3
>>> image[:, 4:9, 2] = 2
>>> segm = np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
... [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]])
>>> cython_img2d_color_std(image, segm)
array([[ 0.48989794, 1.46969383, 0.80000003],
[ 0.40000001, 1.46969383, 0.80000001]]) | wrapper for fast implementation of colour features | [
"wrapper",
"for",
"fast",
"implementation",
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] | def cython_img2d_color_std(img, seg, means=None):
""" wrapper for fast implementation of colour features
:param ndarray img: input RGB image
:param ndarray seg: segmentation og the image
:param ndarray means: precomputed feature means
:return: np.array<nb_lbs, 3> matrix features per segment
.. seealso:: :func:`imsegm.descriptors.numpy_img2d_color_std`
>>> image = np.zeros((2, 10, 3))
>>> image[:, 2:6, 0] = 1
>>> image[:, 3:7, 1] = 3
>>> image[:, 4:9, 2] = 2
>>> segm = np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
... [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]])
>>> cython_img2d_color_std(image, segm)
array([[ 0.48989794, 1.46969383, 0.80000003],
[ 0.40000001, 1.46969383, 0.80000001]])
"""
logging.debug(
'Cython: computing Colour STD for image %r & segm %r with %i segments', img.shape, seg.shape, np.max(seg)
)
_check_color_image_segm(img, seg)
if means is None:
means = cython_img2d_color_mean(img, seg)
var = fts_cython.computeColorImage2dVariance(
np.array(img, dtype=np.float32), np.array(seg, dtype=np.int32), np.array(means, dtype=np.float32)
)
std = np.sqrt(var)
return std | [
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/pkg_resources/_vendor/pyparsing.py | python | _MultipleMatch.__init__ | ( self, expr, stopOn=None) | [] | def __init__( self, expr, stopOn=None):
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/setuptools/setuptools/_vendor/pyparsing.py | python | ParserElement.setParseAction | ( self, *fns, **kwargs ) | return self | Define one or more actions to perform when successfully matching parse element definition.
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integer = Word(nums)
date_str = integer + '/' + integer + '/' + integer
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# use parse action to convert to ints at parse time
integer = Word(nums).setParseAction(lambda toks: int(toks[0]))
date_str = integer + '/' + integer + '/' + integer
# note that integer fields are now ints, not strings
date_str.parseString("1999/12/31") # -> [1999, '/', 12, '/', 31] | Define one or more actions to perform when successfully matching parse element definition.
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amphibian-dev/toad | 3d932169112e8474525262af15440af7f2cf8029 | toad/selection.py | python | drop_vif | (frame, threshold = 3, return_drop = False, exclude = None) | return unpack_tuple(res) | variance inflation factor
Args:
frame (DataFrame)
threshold (float): drop features until all vif is less than threshold
return_drop (bool): if need to return features' name who has been dropped
exclude (array-like): list of feature names that will not be dropped
Returns:
DataFrame: selected dataframe
array: list of feature names that has been dropped | variance inflation factor | [
"variance",
"inflation",
"factor"
] | def drop_vif(frame, threshold = 3, return_drop = False, exclude = None):
"""variance inflation factor
Args:
frame (DataFrame)
threshold (float): drop features until all vif is less than threshold
return_drop (bool): if need to return features' name who has been dropped
exclude (array-like): list of feature names that will not be dropped
Returns:
DataFrame: selected dataframe
array: list of feature names that has been dropped
"""
cols = frame.columns.copy()
if exclude is not None:
cols = cols.drop(exclude)
drop_list = []
while(True):
vif = VIF(frame[cols])
ix = vif.idxmax()
max = vif[ix]
if max < threshold:
break
cols = cols.drop(ix)
drop_list.append(ix)
r = frame.drop(columns = drop_list)
res = (r,)
if return_drop:
res += (drop_list,)
return unpack_tuple(res) | [
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keenlabs/KeenClient-Python | 0cd942152977ee37a0c828249c95cf3a27c8c80f | keen/client.py | python | KeenClient.median | (self, event_collection, target_property, timeframe=None, timezone=None, interval=None,
filters=None, group_by=None, order_by=None, max_age=None, limit=None) | return self.api.query("median", params) | Performs a median query
Finds the median of a target property for events that meet the given criteria.
:param event_collection: string, the name of the collection to query
:param target_property: string, the name of the event property you would like use
:param timeframe: string or dict, the timeframe in which the events
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:param timezone: int, the timezone you'd like to use for the timeframe
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:param interval: string, the time interval used for measuring data over
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:param limit: positive integer limiting the displayed results of a query using order_by
:param max_age: an integer, greater than 30 seconds, the maximum 'staleness' you're
willing to trade for increased query performance, in seconds | Performs a median query | [
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filters=None, group_by=None, order_by=None, max_age=None, limit=None):
""" Performs a median query
Finds the median of a target property for events that meet the given criteria.
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:param target_property: string, the name of the event property you would like use
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"""
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return self.api.query("median", params) | [
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openstack/barbican | a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce | barbican/model/migration/alembic_migrations/projects_init_ops.py | python | upgrade | () | [] | def upgrade():
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neptune-ai/open-solution-salt-identification | 394f16b23b6e30543aee54701f81a06b5dd92a98 | common_blocks/models.py | python | SegmentationModelWithDepth._transform | (self, datagen, validation_datagen=None, **kwargs) | return outputs | [] | def _transform(self, datagen, validation_datagen=None, **kwargs):
self.model.eval()
batch_gen, steps = datagen
outputs = {}
for batch_id, data in enumerate(batch_gen):
X = data[0]
D = data[1]
if torch.cuda.is_available():
X = Variable(X, volatile=True).cuda()
D = Variable(D, volatile=True).cuda()
else:
X = Variable(X, volatile=True)
D = Variable(D, volatile=True)
outputs_batch = self.model(X, D)
if len(self.output_names) == 1:
outputs.setdefault(self.output_names[0], []).append(outputs_batch.data.cpu().numpy())
else:
for name, output in zip(self.output_names, outputs_batch):
output_ = output.data.cpu().numpy()
outputs.setdefault(name, []).append(output_)
if batch_id == steps:
break
self.model.train()
outputs = {'{}_prediction'.format(name): get_list_of_image_predictions(outputs_) for name, outputs_ in
outputs.items()}
return outputs | [
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"bat... | https://github.com/neptune-ai/open-solution-salt-identification/blob/394f16b23b6e30543aee54701f81a06b5dd92a98/common_blocks/models.py#L258-L286 | |||
pytroll/satpy | 09e51f932048f98cce7919a4ff8bd2ec01e1ae98 | satpy/readers/sar_c_safe.py | python | AzimuthNoiseReader._create_dask_slices_from_blocks | (self, chunks) | return slices | Create full-width slices from azimuth noise blocks. | Create full-width slices from azimuth noise blocks. | [
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"-",
"width",
"slices",
"from",
"azimuth",
"noise",
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] | def _create_dask_slices_from_blocks(self, chunks):
"""Create full-width slices from azimuth noise blocks."""
current_line = 0
slices = []
while current_line < self._image_shape[0]:
new_slice = self._create_dask_slice_from_block_line(current_line, chunks)
slices.append(new_slice)
current_line += new_slice.shape[0]
return slices | [
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"_create_dask_sli... | https://github.com/pytroll/satpy/blob/09e51f932048f98cce7919a4ff8bd2ec01e1ae98/satpy/readers/sar_c_safe.py#L272-L280 | |
PaddlePaddle/Research | 2da0bd6c72d60e9df403aff23a7802779561c4a1 | ST_DM/KDD2021-MSTPAC/code/MST-PAC/frame/core/cpu_trainer.py | python | CPUTrainer.init_model_params | (self, exe, main_program, FLAGS) | return | load params of pretrained model, NOT including moment, learning_rate | load params of pretrained model, NOT including moment, learning_rate | [
"load",
"params",
"of",
"pretrained",
"model",
"NOT",
"including",
"moment",
"learning_rate"
] | def init_model_params(self, exe, main_program, FLAGS):
"""
load params of pretrained model, NOT including moment, learning_rate
"""
if FLAGS.init_train_params is not None:
place = self.create_places(FLAGS)[0]
self.paddle_env['factory']['net'].init_params(place)
logging.info("Load pretrain params from {}.".format(
FLAGS.init_train_params))
elif FLAGS.init_pretrain_model is not None:
fluid.io.load_persistables(
exe,
FLAGS.init_pretrain_model,
main_program=main_program)
logging.info("Load pretrain persistables from {}.".format(
FLAGS.init_pretrain_model))
return | [
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google/personfinder | 475f4c0ce916036d39bae2d480cde07126550875 | app/views/admin/api_keys.py | python | ApiKeyListView.get | (self, request, *args, **kwargs) | return self.render(
'admin_api_keys_list.html',
admin_api_keys_url=self.build_absolute_path(
'/admin/api_keys', self.env.repo or 'global'),
user=self.env.user,
user_email=self.env.user.email(),
authorizations=auths,
xsrf_token=self.xsrf_tool.generate_token(
self.env.user.user_id(), 'admin_api_keys')) | Serves a view with a list of API keys. | Serves a view with a list of API keys. | [
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"view",
"with",
"a",
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"API",
"keys",
"."
] | def get(self, request, *args, **kwargs):
"""Serves a view with a list of API keys."""
del request, args, kwargs # unused
auths = model.Authorization.all().filter(
'repo = ', self.env.repo or '*')
return self.render(
'admin_api_keys_list.html',
admin_api_keys_url=self.build_absolute_path(
'/admin/api_keys', self.env.repo or 'global'),
user=self.env.user,
user_email=self.env.user.email(),
authorizations=auths,
xsrf_token=self.xsrf_tool.generate_token(
self.env.user.user_id(), 'admin_api_keys')) | [
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apache/tvm | 6eb4ed813ebcdcd9558f0906a1870db8302ff1e0 | python/tvm/driver/tvmc/target.py | python | tokenize_target | (target) | return re.findall(target_pattern, target) | Extract a list of tokens from a target specification text.
It covers some corner-cases that are not covered by the built-in
module 'shlex', such as the use of "+" as a punctuation character.
Example
-------
For the input `foo -op1=v1 -op2="v ,2", bar -op3=v-4` we
should obtain:
["foo", "-op1=v1", "-op2="v ,2"", ",", "bar", "-op3=v-4"]
Parameters
----------
target : str
Target options sent via CLI arguments
Returns
-------
list of str
a list of parsed tokens extracted from the target string | Extract a list of tokens from a target specification text. | [
"Extract",
"a",
"list",
"of",
"tokens",
"from",
"a",
"target",
"specification",
"text",
"."
] | def tokenize_target(target):
"""
Extract a list of tokens from a target specification text.
It covers some corner-cases that are not covered by the built-in
module 'shlex', such as the use of "+" as a punctuation character.
Example
-------
For the input `foo -op1=v1 -op2="v ,2", bar -op3=v-4` we
should obtain:
["foo", "-op1=v1", "-op2="v ,2"", ",", "bar", "-op3=v-4"]
Parameters
----------
target : str
Target options sent via CLI arguments
Returns
-------
list of str
a list of parsed tokens extracted from the target string
"""
# Regex to tokenize the "--target" value. It is split into five parts
# to match with:
# 1. target and option names e.g. llvm, -mattr=, -mcpu=
# 2. option values, all together, without quotes e.g. -mattr=+foo,+opt
# 3. option values, when single quotes are used e.g. -mattr='+foo, +opt'
# 4. option values, when double quotes are used e.g. -mattr="+foo ,+opt"
# 5. commas that separate different targets e.g. "my-target, llvm"
target_pattern = (
r"(\-{0,2}[\w\-]+\=?"
r"(?:[\w\+\-\.]+(?:,[\w\+\-\.])*"
r"|[\'][\w\+\-,\s\.]+[\']"
r"|[\"][\w\+\-,\s\.]+[\"])*"
r"|,)"
)
return re.findall(target_pattern, target) | [
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