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pygae/clifford | 0b6cffb9a6d44ecb7a666f6b08b1788fc94a0ab6 | clifford/tools/g3c/rotor_parameterisation.py | python | interpolate_TR_rotors | (R_n_plus_1, R_n, interpolation_fraction) | return R_n_lambda | Interpolates TR type rotors
From :cite:`wareham-interpolation`. | Interpolates TR type rotors | [
"Interpolates",
"TR",
"type",
"rotors"
] | def interpolate_TR_rotors(R_n_plus_1, R_n, interpolation_fraction):
"""
Interpolates TR type rotors
From :cite:`wareham-interpolation`.
"""
if interpolation_fraction < np.finfo(float).eps:
return R_n
delta_R = R_n_plus_1 * ~R_n
delta_bivector = ga_log(delta_R)(2)
R_n_lambda = ga_exp(interpolation_fraction * delta_bivector) * R_n
return R_n_lambda | [
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beeware/ouroboros | a29123c6fab6a807caffbb7587cf548e0c370296 | ouroboros/pydoc.py | python | resolve | (thing, forceload=0) | Given an object or a path to an object, get the object and its name. | Given an object or a path to an object, get the object and its name. | [
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"""Given an object or a path to an object, get the object and its name."""
if isinstance(thing, str):
object = locate(thing, forceload)
if object is None:
raise ImportError('no Python documentation found for %r' % thing)
return object, thing
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name = getattr(thing, '__name__', None)
return thing, name if isinstance(name, str) else None | [
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fluentpython/example-code-2e | 80f7f84274a47579e59c29a4657691525152c9d5 | 13-protocol-abc/tombola_subhook.py | python | Tombola.pick | (self) | Remove item at random, returning it.
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/MicrosoftGraphApplications/Integrations/MicrosoftGraphApplications/MicrosoftGraphApplications.py | python | Client.delete_service_principals | (
self,
service_id: str
) | Deletes a given id from authorized apps.
Arguments:
service_id: the service to remove
Returns:
True if removed successfully
Raises:
DemistoException if no app exists or any other requests error.
Docs:
https://docs.microsoft.com/en-us/graph/api/serviceprincipal-delete?view=graph-rest-1.0&tabs=http | Deletes a given id from authorized apps. | [
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self,
service_id: str
):
"""Deletes a given id from authorized apps.
Arguments:
service_id: the service to remove
Returns:
True if removed successfully
Raises:
DemistoException if no app exists or any other requests error.
Docs:
https://docs.microsoft.com/en-us/graph/api/serviceprincipal-delete?view=graph-rest-1.0&tabs=http
"""
self.ms_client.http_request(
'DELETE', f'v1.0/servicePrincipals/{service_id}', return_empty_response=True
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cenkalti/kuyruk | 1052334e804c137245ddbbed31c75fa0aac46a71 | kuyruk/worker.py | python | Worker._handle_sigusr2 | (self, signum: int, frame: Any) | Drop current task. | Drop current task. | [
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quantopian/trading_calendars | 19c4b677f13147928d34be5a3da50ba4161be45d | trading_calendars/calendar_utils.py | python | TradingCalendarDispatcher.__init__ | (self, calendars, calendar_factories, aliases) | [] | def __init__(self, calendars, calendar_factories, aliases):
self._calendars = calendars
self._calendar_factories = dict(calendar_factories)
self._aliases = dict(aliases) | [
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google/ctfscoreboard | 28a8f6c30e401e07031741d5bafea3003e2d100e | scoreboard/models.py | python | Challenge.get_joined_query | (cls) | return cls.query.options(
orm.joinedload(cls.answers).joinedload(Answer.team)) | Get a prejoined-query with answers and teams. | Get a prejoined-query with answers and teams. | [
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"""Get a prejoined-query with answers and teams."""
return cls.query.options(
orm.joinedload(cls.answers).joinedload(Answer.team)) | [
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dragonfly/dragonfly | a579b5eadf452e23b07d4caf27b402703b0012b7 | dragonfly/exd/worker_manager.py | python | MultiProcessingWorkerManager._rwm_set_up | (self) | Sets things up for the child. | Sets things up for the child. | [
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""" Sets things up for the child. """
# Create the result directories. """
self.result_dir_names = {wid:'%s/result_%s'%(self.tmp_dir, str(wid)) for wid in
self.worker_ids}
# Create the working directories
self.working_dir_names = {wid:'%s/working_%s/tmp'%(self.tmp_dir,
str(wid)) for wid in self.worker_ids}
# Create the last receive times
self.last_receive_times = {wid:0.0 for wid in self.worker_ids}
# Create file names
self._result_file_name = 'result.p'
self._num_file_read_attempts = 10 | [
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/com.py | python | Variant.guess_type_and_set_value | (self, value) | [] | def guess_type_and_set_value(self, value):
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HaoZhang95/Python24 | b897224b8a0e6a5734f408df8c24846a98c553bf | 00Python/venv/Lib/site-packages/pip-10.0.1-py3.7.egg/pip/_vendor/distlib/_backport/tarfile.py | python | ExFileObject.close | (self) | Close the file object. | Close the file object. | [
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llSourcell/AI_For_Music_Composition | 795f8879158b238e7dc78d07e4451dcf54a2126c | musegan/model.py | python | Model.save_statistics | (self, filepath=None) | Save model statistics to file. Default to save to the log directory
given as a global variable. | Save model statistics to file. Default to save to the log directory
given as a global variable. | [
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"""Save model statistics to file. Default to save to the log directory
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if filepath is None:
filepath = os.path.join(self.config['log_dir'],
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with open(filepath, 'w') as f:
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/admin/server.py | python | AdminServer.liveliness_handler | (self, request: web.BaseRequest) | Request handler for liveliness check.
Args:
request: aiohttp request object
Returns:
The web response, always indicating True | Request handler for liveliness check. | [
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Request handler for liveliness check.
Args:
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Returns:
The web response, always indicating True
"""
app_live = self.app._state["alive"]
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | PyTorch/LanguageModeling/Transformer-XL/pytorch/utils/distributed.py | python | get_world_size | () | return world_size | Gets total number of distributed workers or returns one if distributed is
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] | def get_world_size():
"""
Gets total number of distributed workers or returns one if distributed is
not initialized.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
else:
world_size = 1
return world_size | [
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thinkle/gourmet | 8af29c8ded24528030e5ae2ea3461f61c1e5a575 | gourmet/gtk_extras/treeview_extras.py | python | get_row | (mod, iter) | return row | Grab all values in iter and return as a list suitable for
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n = 0
flag = True
row = []
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n += 1
return row | [
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mihaip/mail-trends | 312b5be7f8f0a5933b05c75e229eda8e44c3c920 | main.py | python | GetOptsMap | () | return opts_map | [] | def GetOptsMap():
opts, args = getopt.getopt(sys.argv[1:], "", [
# Standard options
"username=", "password=", "use_ssl", "server=",
# Other params
"filter_out=", "me=",
# Development options
"record", "replay",
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opts_map = {}
for name, value in opts:
opts_map[name[2:]] = value
assert "username" in opts_map
if "password" not in opts_map:
opts_map["password"] = getpass.getpass(
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assert "password" in opts_map
assert "server" in opts_map
return opts_map | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/twisted/conch/insults/insults.py | python | ITerminalTransport.nextLine | () | Move the cursor to the first position on the next line, performing scrolling if necessary. | Move the cursor to the first position on the next line, performing scrolling if necessary. | [
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nerdvegas/rez | d392c65bf63b4bca8106f938cec49144ba54e770 | src/rez/package_help.py | python | PackageHelp.open | (self, section_index=0) | Launch a help section. | Launch a help section. | [
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] | def open(self, section_index=0):
"""Launch a help section."""
uri = self._sections[section_index][1]
if len(uri.split()) == 1:
self._open_url(uri)
else:
if self._verbose:
print("running command: %s" % uri)
with Popen(uri, shell=True) as p:
p.wait() | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/parser/template/nodes/authorise.py | python | TemplateAuthoriseNode.__init__ | (self) | [] | def __init__(self):
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] | https://github.com/keiffster/program-y/blob/8c99b56f8c32f01a7b9887b5daae9465619d0385/src/programy/parser/template/nodes/authorise.py#L24-L27 | ||||
khalim19/gimp-plugin-export-layers | b37255f2957ad322f4d332689052351cdea6e563 | export_layers/pygimplib/setting/presenters_gtk.py | python | GimpUiPatternSelectButtonPresenter._create_gui_element | (self, setting) | return gimpui.PatternSelectButton(setting.display_name, setting.value) | [] | def _create_gui_element(self, setting):
return gimpui.PatternSelectButton(setting.display_name, setting.value) | [
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movidius/ncappzoo | 5d06afdbfce51b0627fab05d1941a30ecffee172 | apps/driving_pi/car_pi.py | python | DriveBrickPi3.speed | (self) | return self.__speed | Get speed value of the car
:return: speed value | Get speed value of the car
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Get speed value of the car
:return: speed value
"""
return self.__speed | [
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/visualize/ownomogram.py | python | RulerItem.__init__ | (self, name, values, scale, name_offset, offset, labels=None) | [] | def __init__(self, name, values, scale, name_offset, offset, labels=None):
super().__init__()
# leading label
font = name.document().defaultFont()
if self.bold_label:
font.setWeight(QFont.Bold)
name.setFont(font)
name.setPos(name_offset, -10)
name.setParentItem(self)
# prediction marker
self.dot = self.DOT_ITEM_CLS(self.DOT_RADIUS, scale, offset, values[0],
values[-1])
self.dot.setParentItem(self)
# pylint: disable=unused-variable
# line
line = QGraphicsLineItem(min(values) * scale + offset, 0,
max(values) * scale + offset, 0,
self)
if labels is None:
labels = [str(abs(v) if v == -0 else v) for v in values]
old_x_tick = None
shown_items = []
w = QGraphicsSimpleTextItem(labels[0]).boundingRect().width()
text_finish = values[0] * scale - w + offset - 10
for i, (label, value) in enumerate(zip(labels, values)):
text = QGraphicsSimpleTextItem(label)
x_text = value * scale - text.boundingRect().width() / 2 + offset
if text_finish > x_text - 10:
y_text, y_tick = self.DOT_RADIUS * 0.7, 0
text_finish = values[0] * scale + offset
else:
y_text = - text.boundingRect().height() - self.DOT_RADIUS * 0.7
y_tick = - self.tick_height
text_finish = x_text + text.boundingRect().width()
text.setPos(x_text, y_text)
if not collides(text, shown_items):
text.setParentItem(self)
shown_items.append(text)
x_tick = value * scale - self.tick_width / 2 + offset
tick = QGraphicsRectItem(
x_tick, y_tick, self.tick_width, self.tick_height,
self)
tick.setBrush(QColor(Qt.black))
if self.half_tick_height and i:
x = x_tick - (x_tick - old_x_tick) / 2
half_tick = QGraphicsLineItem(x, - self.half_tick_height, x, 0,
self)
old_x_tick = x_tick | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/socketserver.py | python | TCPServer.get_request | (self) | return self.socket.accept() | Get the request and client address from the socket.
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/http/multipartparser.py | python | ChunkIter.__iter__ | (self) | return self | [] | def __iter__(self):
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/idlelib/GrepDialog.py | python | grep | (text, io=None, flist=None) | [] | def grep(text, io=None, flist=None):
root = text._root()
engine = SearchEngine.get(root)
if not hasattr(engine, "_grepdialog"):
engine._grepdialog = GrepDialog(root, engine, flist)
dialog = engine._grepdialog
searchphrase = text.get("sel.first", "sel.last")
dialog.open(text, searchphrase, io) | [
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IdentityPython/pysaml2 | 6badb32d212257bd83ffcc816f9b625f68281b47 | src/saml2/httputil.py | python | Response._response | (self, message="", **argv) | [] | def _response(self, message="", **argv):
if self.template:
message = self.template % message
elif self.mako_lookup and self.mako_template:
argv["message"] = message
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iduta/iresnet | babdc4f5946f64905710cd64a5bd6c164a805c9e | models/resgroupfix.py | python | conv1x1 | (in_planes, out_planes, stride=1) | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | 1x1 convolution | 1x1 convolution | [
"1x1",
"convolution"
] | def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | [
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vibora-io/vibora | 4cda888f89aec6bfb2541ee53548ae1bf50fbf1b | vibora/client/response.py | python | Response.receive_headers | (self) | :return: | [] | async def receive_headers(self):
"""
:return:
"""
if self._parser_status != ResponseStatus.PENDING_HEADERS:
return
try:
self._parser.feed(await self._connection.read_until(b'\r\n\r\n'))
if self._headers.get('content-encoding') == 'gzip':
self._decoder = GzipDecoder()
self._parser_status = ResponseStatus.PENDING_BODY
except OSError as error:
# In case any network error occurs we should discard this connection.
self._parser_status = ResponseStatus.ERROR
self._connection.close()
raise error | [
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carla-simulator/scenario_runner | f4d00d88eda4212a1e119515c96281a4be5c234e | srunner/scenarios/background_activity.py | python | BackgroundBehavior.__init__ | (self, ego_actor, route, night_mode=False, debug=False, name="BackgroundBehavior") | Setup class members | Setup class members | [
"Setup",
"class",
"members"
] | def __init__(self, ego_actor, route, night_mode=False, debug=False, name="BackgroundBehavior"):
"""
Setup class members
"""
super(BackgroundBehavior, self).__init__(name)
self.debug = debug
self._map = CarlaDataProvider.get_map()
self._world = CarlaDataProvider.get_world()
timestep = self._world.get_snapshot().timestamp.delta_seconds
self._tm = CarlaDataProvider.get_client().get_trafficmanager(
CarlaDataProvider.get_traffic_manager_port())
self._tm.global_percentage_speed_difference(0.0)
self._night_mode = night_mode
# Global variables
self._ego_actor = ego_actor
self._ego_state = 'road'
self._route_index = 0
self._get_route_data(route)
self._spawn_vertical_shift = 0.2
self._reuse_dist = 10 # When spawning actors, might reuse actors closer to this distance
self._spawn_free_radius = 20 # Sources closer to the ego will not spawn actors
self._fake_junction_ids = []
self._fake_lane_pair_keys = []
# Road variables
self._road_actors = []
self._road_back_actors = {} # Dictionary mapping the actors behind the ego to their lane
self._road_ego_key = None
self._road_extra_front_actors = 0
self._road_sources = []
self._road_checker_index = 0
self._road_ego_key = ""
self._road_front_vehicles = 3 # Amount of vehicles in front of the ego
self._road_back_vehicles = 3 # Amount of vehicles behind the ego
self._road_vehicle_dist = 8 # Distance road vehicles leave betweeen each other[m]
self._road_spawn_dist = 11 # Initial distance between spawned road vehicles [m]
self._road_new_sources_dist = 20 # Distance of the source to the start of the new lanes
self._radius_increase_ratio = 1.8 # Meters the radius increases per m/s of the ego
self._extra_radius = 0.0 # Extra distance to avoid the road behavior from blocking
self._extra_radius_increase_ratio = 0.5 * timestep # Distance the radius increases per tick (0.5 m/s)
self._max_extra_radius = 10 # Max extra distance
self._base_min_radius = 0
self._base_max_radius = 0
self._min_radius = 0
self._max_radius = 0
self._junction_detection_dist = 0
self._get_road_radius()
# Junction variables
self._junctions = []
self._active_junctions = []
self._junction_sources_dist = 40 # Distance from the entry sources to the junction [m]
self._junction_vehicle_dist = 8 # Distance junction vehicles leave betweeen each other[m]
self._junction_spawn_dist = 10 # Initial distance between spawned junction vehicles [m]
self._junction_sources_max_actors = 5 # Maximum vehicles alive at the same time per source
# Opposite lane variables
self._opposite_actors = []
self._opposite_sources = []
self._opposite_route_index = 0
self._opposite_removal_dist = 30 # Distance at which actors are destroyed
self._opposite_sources_dist = 60 # Distance from the ego to the opposite sources [m]
self._opposite_vehicle_dist = 10 # Distance opposite vehicles leave betweeen each other[m]
self._opposite_spawn_dist = 20 # Initial distance between spawned opposite vehicles [m]
self._opposite_sources_max_actors = 8 # Maximum vehicles alive at the same time per source
# Scenario 2 variables
self._is_scenario_2_active = False
self._scenario_2_actors = []
self._activate_break_scenario = False
self._break_duration = 7 # Duration of the scenario
self._next_scenario_time = float('inf')
# Scenario 4 variables
self._is_scenario_4_active = False
self._scenario_4_actors = []
self._ego_exitted_junction = False
self._crossing_dist = None # Distance between the crossing object and the junction exit
self._start_ego_wp = None
# Junction scenario variables
self.scenario_info = {
'direction': None,
'remove_entries': False,
'remove_middle': False,
'remove_exits': False,
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analysiscenter/batchflow | 294747da0bca309785f925be891441fdd824e9fa | batchflow/models/torch/base.py | python | TorchModel.combine_configs | (self) | return config | Combine default configuration and the external one. | Combine default configuration and the external one. | [
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""" Combine default configuration and the external one. """
config = self.default_config() + self.external_config
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/mapbox/layer/_line.py | python | Line.dashsrc | (self) | return self["dashsrc"] | Sets the source reference on Chart Studio Cloud for `dash`.
The 'dashsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | Sets the source reference on Chart Studio Cloud for `dash`.
The 'dashsrc' property must be specified as a string or
as a plotly.grid_objs.Column object | [
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"""
Sets the source reference on Chart Studio Cloud for `dash`.
The 'dashsrc' property must be specified as a string or
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sergiocorreia/panflute | b9546cf7b88fdc9f00117fca395c4d3590f45769 | panflute/base.py | python | Element._set_ica | (self, identifier, classes, attributes) | [] | def _set_ica(self, identifier, classes, attributes):
self.identifier = check_type(identifier, str)
self.classes = [check_type(cl, str) for cl in classes]
self.attributes = dict(attributes) | [
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lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/cherrypy/lib/cptools.py | python | SessionAuth.do_check | (self) | Assert username. May raise redirect, or return True if request handled. | Assert username. May raise redirect, or return True if request handled. | [
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"""Assert username. May raise redirect, or return True if request handled."""
sess = cherrypy.session
request = cherrypy.serving.request
response = cherrypy.serving.response
username = sess.get(self.session_key)
if not username:
sess[self.session_key] = username = self.anonymous()
if self.debug:
cherrypy.log('No session[username], trying anonymous', 'TOOLS.SESSAUTH')
if not username:
url = cherrypy.url(qs=request.query_string)
if self.debug:
cherrypy.log('No username, routing to login_screen with '
'from_page %r' % url, 'TOOLS.SESSAUTH')
response.body = self.login_screen(url)
if "Content-Length" in response.headers:
# Delete Content-Length header so finalize() recalcs it.
del response.headers["Content-Length"]
return True
if self.debug:
cherrypy.log('Setting request.login to %r' % username, 'TOOLS.SESSAUTH')
request.login = username
self.on_check(username) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_obj.py | python | OCObject.run_ansible | (params, check_mode=False) | run the oc_obj module | run the oc_obj module | [
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"oc_obj",
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] | def run_ansible(params, check_mode=False):
'''run the oc_obj module'''
ocobj = OCObject(params['kind'],
params['namespace'],
params['name'],
params['selector'],
kubeconfig=params['kubeconfig'],
verbose=params['debug'],
all_namespaces=params['all_namespaces'],
field_selector=params['field_selector'])
state = params['state']
api_rval = ocobj.get()
#####
# Get
#####
if state == 'list':
if api_rval['returncode'] != 0:
return {'changed': False, 'failed': True, 'msg': api_rval}
return {'changed': False, 'results': api_rval, 'state': state}
########
# Delete
########
if state == 'absent':
# verify it's not in our results
# pylint: disable=too-many-boolean-expressions
if (params['name'] is not None or params['selector'] is not None) and \
(len(api_rval['results']) == 0 or \
(not api_rval['results'][0]) or \
('items' in api_rval['results'][0] and len(api_rval['results'][0]['items']) == 0)):
return {'changed': False, 'state': state}
if check_mode:
return {'changed': True, 'msg': 'CHECK_MODE: Would have performed a delete'}
api_rval = ocobj.delete()
if api_rval['returncode'] != 0:
return {'failed': True, 'msg': api_rval}
return {'changed': True, 'results': api_rval, 'state': state}
# create/update: Must define a name beyond this point
if not params['name']:
return {'failed': True, 'msg': 'Please specify a name when state is present.'}
if state == 'present':
########
# Create
########
if not Utils.exists(api_rval['results'], params['name']):
if check_mode:
return {'changed': True, 'msg': 'CHECK_MODE: Would have performed a create'}
# Create it here
api_rval = ocobj.create(params['files'], params['content'])
if api_rval['returncode'] != 0:
return {'failed': True, 'msg': api_rval}
# return the created object
api_rval = ocobj.get()
if api_rval['returncode'] != 0:
return {'failed': True, 'msg': api_rval}
# Remove files
if params['files'] and params['delete_after']:
Utils.cleanup(params['files'])
return {'changed': True, 'results': api_rval, 'state': state}
########
# Update
########
# if a file path is passed, use it.
update = ocobj.needs_update(params['files'], params['content'])
if not isinstance(update, bool):
return {'failed': True, 'msg': update}
# No changes
if not update:
if params['files'] and params['delete_after']:
Utils.cleanup(params['files'])
return {'changed': False, 'results': api_rval['results'][0], 'state': state}
if check_mode:
return {'changed': True, 'msg': 'CHECK_MODE: Would have performed an update.'}
api_rval = ocobj.update(params['files'],
params['content'],
params['force'])
if api_rval['returncode'] != 0:
return {'failed': True, 'msg': api_rval}
# return the created object
api_rval = ocobj.get()
if api_rval['returncode'] != 0:
return {'failed': True, 'msg': api_rval}
return {'changed': True, 'results': api_rval, 'state': state} | [
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magic-wormhole/magic-wormhole | e522a3992217b433ac2c36543bf85c27a7226ed9 | src/wormhole/observer.py | python | SequenceObserver.fire | (self, result) | [] | def fire(self, result):
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duckduckgo/zeroclickinfo-fathead | 477c5652f6576746618dbb8158b67f0960ae9f56 | lib/fathead/python/redirect.py | python | Entry.parse | (self, input_obj) | return self.data | Try to parse given input into a valid entry.
Args:
input_obj: TSV string or list of data.
Returns:
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Raises:
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Args:
input_obj: TSV string or list of data.
Returns:
List of data
Raises:
Throws BadEntryException if data is invalid.
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self.data = processed
elif isinstance(input_obj, list):
self.data = input_obj
try:
self.key = self.data[0].strip()
self.entry_type = self.data[1].strip()
self.reference = self.data[2].strip()
if len(self.data) > 3:
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self.abstract = self.data[11].strip()
self.anchor = self.data[12].strip()
elif self.entry_type == 13 and len(self.data) != 13:
raise BadEntryException
except Exception as e:
raise BadEntryException('Article had invalid number of elements.')
if self.entry_type == 'A':
self.parse_alternative_keys()
return self.data | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/simulators/bullet.py | python | Bullet.compute_view_matrix_from_ypr | (self, target_position, distance, yaw, pitch, roll, up_axis_index=2) | return np.asarray(view).reshape(4, 4).T | Compute the view matrix from the yaw, pitch, and roll angles.
The view matrix is the 4x4 matrix that maps the world coordinates into the camera coordinates. Basically,
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of turning the camera to capture what we want in the world, we keep the camera fixed and turn the world.
Args:
target_position (np.array[float[3]]): target focus point in Cartesian world coordinates
distance (float): distance from eye to focus point
yaw (float): yaw angle in radians left/right around up-axis
pitch (float): pitch in radians up/down.
roll (float): roll in radians around forward vector
up_axis_index (int): either 1 for Y or 2 for Z axis up.
Returns:
np.array[float[4,4]]: the view matrix
More info:
[1] http://www.codinglabs.net/article_world_view_projection_matrix.aspx
[2] http://www.thecodecrate.com/opengl-es/opengl-transformation-matrices/ | Compute the view matrix from the yaw, pitch, and roll angles. | [
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"""Compute the view matrix from the yaw, pitch, and roll angles.
The view matrix is the 4x4 matrix that maps the world coordinates into the camera coordinates. Basically,
it applies a rotation and translation such that the world is in front of the camera. That is, instead
of turning the camera to capture what we want in the world, we keep the camera fixed and turn the world.
Args:
target_position (np.array[float[3]]): target focus point in Cartesian world coordinates
distance (float): distance from eye to focus point
yaw (float): yaw angle in radians left/right around up-axis
pitch (float): pitch in radians up/down.
roll (float): roll in radians around forward vector
up_axis_index (int): either 1 for Y or 2 for Z axis up.
Returns:
np.array[float[4,4]]: the view matrix
More info:
[1] http://www.codinglabs.net/article_world_view_projection_matrix.aspx
[2] http://www.thecodecrate.com/opengl-es/opengl-transformation-matrices/
"""
view = self.sim.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=target_position, distance=distance,
yaw=np.rad2deg(yaw), pitch=np.rad2deg(pitch),
roll=np.rad2deg(roll), upAxisIndex=up_axis_index)
return np.asarray(view).reshape(4, 4).T | [
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lvpengyuan/masktextspotter.caffe2 | da99ef31f5ccb4de5248bb881d5b4a291910c8ae | lib/utils/segms.py | python | rle_mask_voting | (
top_masks, all_masks, all_dets, iou_thresh, binarize_thresh, method='AVG'
) | return top_segms_out | Returns new masks (in correspondence with `top_masks`) by combining
multiple overlapping masks coming from the pool of `all_masks`. Two methods
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overlapping mask pixels; 'UNION' takes the union of all mask pixels. | [
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):
"""Returns new masks (in correspondence with `top_masks`) by combining
multiple overlapping masks coming from the pool of `all_masks`. Two methods
for combining masks are supported: 'AVG' uses a weighted average of
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"""
if len(top_masks) == 0:
return
all_not_crowd = [False] * len(all_masks)
top_to_all_overlaps = mask_util.iou(top_masks, all_masks, all_not_crowd)
decoded_all_masks = [
np.array(mask_util.decode(rle), dtype=np.float32) for rle in all_masks
]
decoded_top_masks = [
np.array(mask_util.decode(rle), dtype=np.float32) for rle in top_masks
]
all_boxes = all_dets[:, :4].astype(np.int32)
all_scores = all_dets[:, 4]
# Fill box support with weights
mask_shape = decoded_all_masks[0].shape
mask_weights = np.zeros((len(all_masks), mask_shape[0], mask_shape[1]))
for k in range(len(all_masks)):
ref_box = all_boxes[k]
x_0 = max(ref_box[0], 0)
x_1 = min(ref_box[2] + 1, mask_shape[1])
y_0 = max(ref_box[1], 0)
y_1 = min(ref_box[3] + 1, mask_shape[0])
mask_weights[k, y_0:y_1, x_0:x_1] = all_scores[k]
mask_weights = np.maximum(mask_weights, 1e-5)
top_segms_out = []
for k in range(len(top_masks)):
# Corner case of empty mask
if decoded_top_masks[k].sum() == 0:
top_segms_out.append(top_masks[k])
continue
inds_to_vote = np.where(top_to_all_overlaps[k] >= iou_thresh)[0]
# Only matches itself
if len(inds_to_vote) == 1:
top_segms_out.append(top_masks[k])
continue
masks_to_vote = [decoded_all_masks[i] for i in inds_to_vote]
if method == 'AVG':
ws = mask_weights[inds_to_vote]
soft_mask = np.average(masks_to_vote, axis=0, weights=ws)
mask = np.array(soft_mask > binarize_thresh, dtype=np.uint8)
elif method == 'UNION':
# Any pixel that's on joins the mask
soft_mask = np.sum(masks_to_vote, axis=0)
mask = np.array(soft_mask > 1e-5, dtype=np.uint8)
else:
raise NotImplementedError('Method {} is unknown'.format(method))
rle = mask_util.encode(np.array(mask[:, :, np.newaxis], order='F'))[0]
top_segms_out.append(rle)
return top_segms_out | [
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kevoreilly/CAPEv2 | 6cf79c33264624b3604d4cd432cde2a6b4536de6 | modules/machinery/vsphere.py | python | vSphere._stop_virtual_machine | (self, vm) | Power off a virtual machine | Power off a virtual machine | [
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log.info("Powering off virtual machine %s", vm.summary.config.name)
task = vm.PowerOffVM_Task()
try:
self._wait_task(task)
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/httplib2/__init__.py | python | HTTPResponse__getheaders | (self) | return self.msg.items() | Return list of (header, value) tuples. | Return list of (header, value) tuples. | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/gree/climate.py | python | GreeClimateEntity.async_set_temperature | (self, **kwargs) | Set new target temperature. | Set new target temperature. | [
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"""Set new target temperature."""
if ATTR_TEMPERATURE not in kwargs:
raise ValueError(f"Missing parameter {ATTR_TEMPERATURE}")
temperature = kwargs[ATTR_TEMPERATURE]
_LOGGER.debug(
"Setting temperature to %d for %s",
temperature,
self._name,
)
self.coordinator.device.target_temperature = round(temperature)
await self.coordinator.push_state_update()
self.async_write_ha_state() | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/module/operator/interp_operator.py | python | setitem | (space, w_obj, w_key, w_value) | setitem(a, b, c) -- Same as a[b] = c. | setitem(a, b, c) -- Same as a[b] = c. | [
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danielfm/pybreaker | 1dbcbda76dda1d1d3a9df25cbe1912655dbbf6cc | src/pybreaker.py | python | CircuitBreaker.call_async | (self, func, *args, **kwargs) | return wrapped() | Calls async `func` with the given `args` and `kwargs` according to the rules
implemented by the current state of this circuit breaker.
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"""
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implemented by the current state of this circuit breaker.
Return a closure to prevent import errors when using without tornado present
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@gen.coroutine
def wrapped():
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styxit/HTPC-Manager | 490697460b4fa1797106aece27d873bc256b2ff1 | libs/cherrypy/lib/sessions.py | python | Session.save | (self) | Save session data. | Save session data. | [
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try:
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pyvista/pyvista | 012dbb95a9aae406c3cd4cd94fc8c477f871e426 | pyvista/utilities/errors.py | python | send_errors_to_logging | () | return obs.observe(error_output) | Send all VTK error/warning messages to Python's logging module. | Send all VTK error/warning messages to Python's logging module. | [
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"""Send all VTK error/warning messages to Python's logging module."""
error_output = _vtk.vtkStringOutputWindow()
error_win = _vtk.vtkOutputWindow()
error_win.SetInstance(error_output)
obs = Observer()
return obs.observe(error_output) | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-darwin/x64/mako/_ast_util.py | python | SourceGenerator.__init__ | (self, indent_with) | [] | def __init__(self, indent_with):
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hydroshare/hydroshare | 7ba563b55412f283047fb3ef6da367d41dec58c6 | hs_modflow_modelinstance/models.py | python | MODFLOWModelInstanceMetaData.stress_period | (self) | return self._stress_period.all().first() | [] | def stress_period(self):
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] | https://github.com/hydroshare/hydroshare/blob/7ba563b55412f283047fb3ef6da367d41dec58c6/hs_modflow_modelinstance/models.py#L708-L709 | |||
blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/studiomax/cachelib/transformcache.py | python | toggleCaches | ( object, state ) | return False | \remarks enables/disables the caches on the inputed object based on the given state
\param object <Py3dsMax.mxs.Object>
\param state <bool>
\return <bool> success | \remarks enables/disables the caches on the inputed object based on the given state
\param object <Py3dsMax.mxs.Object>
\param state <bool>
\return <bool> success | [
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"""
\remarks enables/disables the caches on the inputed object based on the given state
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\param state <bool>
\return <bool> success
"""
is_prop = mxs.isproperty
classof = mxs.classof
if ( is_prop( object, 'controller' ) and classof( object.controller ) == mxs.Transform_Cache ):
control = object.controller
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haiwen/seahub | e92fcd44e3e46260597d8faa9347cb8222b8b10d | seahub/api2/views.py | python | RepoHistoryLimit.put | (self, request, repo_id, format=None) | [] | def put(self, request, repo_id, format=None):
repo = seafile_api.get_repo(repo_id)
if not repo:
error_msg = 'Library %s not found.' % repo_id
return api_error(status.HTTP_404_NOT_FOUND, error_msg)
# check permission
if is_org_context(request):
repo_owner = seafile_api.get_org_repo_owner(repo_id)
else:
repo_owner = seafile_api.get_repo_owner(repo_id)
username = request.user.username
# no settings for virtual repo
if repo.is_virtual or not config.ENABLE_REPO_HISTORY_SETTING:
error_msg = 'Permission denied.'
return api_error(status.HTTP_403_FORBIDDEN, error_msg)
if '@seafile_group' in repo_owner:
group_id = get_group_id_by_repo_owner(repo_owner)
if not is_group_admin(group_id, username):
error_msg = 'Permission denied.'
return api_error(status.HTTP_403_FORBIDDEN, error_msg)
else:
if username != repo_owner:
error_msg = 'Permission denied.'
return api_error(status.HTTP_403_FORBIDDEN, error_msg)
# check arg validation
keep_days = request.data.get('keep_days', None)
if not keep_days:
error_msg = 'keep_days invalid.'
return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
try:
keep_days = int(keep_days)
except ValueError:
error_msg = 'keep_days invalid.'
return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
try:
# days <= -1, keep full history
# days = 0, not keep history
# days > 0, keep a period of days
res = seafile_api.set_repo_history_limit(repo_id, keep_days)
except SearpcError as e:
logger.error(e)
error_msg = 'Internal Server Error'
return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, error_msg)
if res == 0:
new_limit = seafile_api.get_repo_history_limit(repo_id)
return Response({'keep_days': new_limit})
else:
error_msg = 'Failed to set library history limit.'
return api_error(status.HTTP_520_OPERATION_FAILED, error_msg) | [
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | whois_lambda/dns/rdatatype.py | python | from_text | (text) | return value | Convert text into a DNS rdata type value.
@param text: the text
@type text: string
@raises dns.rdatatype.UnknownRdatatype: the type is unknown
@raises ValueError: the rdata type value is not >= 0 and <= 65535
@rtype: int | Convert text into a DNS rdata type value. | [
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] | def from_text(text):
"""Convert text into a DNS rdata type value.
@param text: the text
@type text: string
@raises dns.rdatatype.UnknownRdatatype: the type is unknown
@raises ValueError: the rdata type value is not >= 0 and <= 65535
@rtype: int"""
value = _by_text.get(text.upper())
if value is None:
match = _unknown_type_pattern.match(text)
if match is None:
raise UnknownRdatatype
value = int(match.group(1))
if value < 0 or value > 65535:
raise ValueError("type must be between >= 0 and <= 65535")
return value | [
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isce-framework/isce2 | 0e5114a8bede3caf1d533d98e44dfe4b983e3f48 | components/isceobj/Planet/Planet.py | python | Planet.polar_axis | (self, vector) | return None | Give me a vector that is parallel to my spin axis | Give me a vector that is parallel to my spin axis | [
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] | def polar_axis(self, vector):
"""Give me a vector that is parallel to my spin axis"""
from isceobj.Util.geo.euclid import Vector
if not isinstance(vector, Vector):
try:
vector = Vector(*vector)
except Exception:
raise ValueError(
"polar axis must a Vector or length 3 container"
)
pass
self._polar_axis = vector.hat()
return None | [
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lightforever/mlcomp | c78fdb77ec9c4ec8ff11beea50b90cab20903ad9 | mlcomp/contrib/segmentation/deeplabv3/aspp.py | python | ASPP._init_weight | (self) | [] | def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_() | [
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chenguanyou/weixin_YiQi | ad86ed8061f4f5fa88b6600a9b0809e5bb3bfd08 | backend/Yiqi/Yiqi/partyApp/xadmin/views/list.py | python | ListAdminView.get_list_display | (self) | return list(self.base_list_display) | Return a sequence containing the fields to be displayed on the list. | Return a sequence containing the fields to be displayed on the list. | [
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"""
Return a sequence containing the fields to be displayed on the list.
"""
self.base_list_display = (COL_LIST_VAR in self.request.GET and self.request.GET[COL_LIST_VAR] != "" and \
self.request.GET[COL_LIST_VAR].split('.')) or self.list_display
return list(self.base_list_display) | [
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errbotio/errbot | 66e1de8e1d7daa62be7f9ed1b2ac8832f09fa25d | errbot/core_plugins/utils.py | python | Utils.render_test | (self, _, args) | Tests / showcases the markdown rendering on your current backend | Tests / showcases the markdown rendering on your current backend | [
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] | def render_test(self, _, args):
"""Tests / showcases the markdown rendering on your current backend"""
with open(path.join(path.dirname(path.realpath(__file__)), "test.md")) as f:
return f.read() | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/WidgetRedirector.py | python | WidgetRedirector.dispatch | (self, operation, *args) | Callback from Tcl which runs when the widget is referenced.
If an operation has been registered in self._operations, apply the
associated function to the args passed into Tcl. Otherwise, pass the
operation through to Tk via the original Tcl function.
Note that if a registered function is called, the operation is not
passed through to Tk. Apply the function returned by self.register()
to *args to accomplish that. For an example, see ColorDelegator.py. | Callback from Tcl which runs when the widget is referenced. | [
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] | def dispatch(self, operation, *args):
'''Callback from Tcl which runs when the widget is referenced.
If an operation has been registered in self._operations, apply the
associated function to the args passed into Tcl. Otherwise, pass the
operation through to Tk via the original Tcl function.
Note that if a registered function is called, the operation is not
passed through to Tk. Apply the function returned by self.register()
to *args to accomplish that. For an example, see ColorDelegator.py.
'''
m = self._operations.get(operation)
try:
if m:
return m(*args)
else:
return self.tk.call((self.orig, operation) + args)
except TclError:
return "" | [
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/fields.py | python | IPField.any2i | (self, pkt, x) | return self.h2i(pkt,x) | [] | def any2i(self, pkt, x):
return self.h2i(pkt,x) | [
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Huangying-Zhan/DF-VO | 6a2ec43fc6209d9058ae1709d779c5ada68a31f3 | libs/deep_models/flow/deep_flow.py | python | DeepFlow.generate_images_pred_flow | (self, inputs, outputs) | return outputs | Generate the warped (reprojected) color images using optical flow for a minibatch.
Generated images are saved into the `outputs` dictionary. | Generate the warped (reprojected) color images using optical flow for a minibatch.
Generated images are saved into the `outputs` dictionary. | [
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] | def generate_images_pred_flow(self, inputs, outputs):
"""Generate the warped (reprojected) color images using optical flow for a minibatch.
Generated images are saved into the `outputs` dictionary.
"""
source_scale = 0
for _, f_i in enumerate(self.frame_ids[1:]):
if f_i != "s":
for scale in self.flow_scales:
# Warp image using forward flow
flow = outputs[("flow", 0, f_i, scale)]
# flow = self.resize_dense_flow(flow, self.height, self.width) # have been resized in inference()
pix_coords = self.flow_to_pix(flow)
outputs[("sample_flow", 0, f_i, scale)] = pix_coords
outputs[("color_flow", 0, f_i, scale)] = F.grid_sample(
inputs[("color", f_i, source_scale)],
pix_coords,
padding_mode="border")
if self.flow_forward_backward:
# Warp image using backward flow
flow = outputs[("flow", f_i, 0, scale)]
# flow = self.resize_dense_flow(flow, self.height, self.width) # have been resized in inference()
pix_coords = self.flow_to_pix(flow)
outputs[("sample_flow", f_i, 0, scale)] = pix_coords
outputs[("color_flow", f_i, 0, scale)] = F.grid_sample(
inputs[("color", 0, source_scale)],
pix_coords,
padding_mode="border")
return outputs | [
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arsaboo/homeassistant-config | 53c998986fbe84d793a0b174757154ab30e676e4 | custom_components/alexa_media/sensor.py | python | AlexaMediaNotificationSensor.__init__ | (
self,
client,
n_dict,
sensor_property: Text,
account,
name="Next Notification",
icon=None,
) | Initialize the Alexa sensor device. | Initialize the Alexa sensor device. | [
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] | def __init__(
self,
client,
n_dict,
sensor_property: Text,
account,
name="Next Notification",
icon=None,
):
"""Initialize the Alexa sensor device."""
# Class info
self._client = client
self._n_dict = n_dict
self._sensor_property = sensor_property
self._account = account
self._dev_id = client.unique_id
self._name = name
self._unit = None
self._device_class = DEVICE_CLASS_TIMESTAMP
self._icon = icon
self._all = []
self._active = []
self._next = None
self._prior_value = None
self._timestamp: Optional[datetime.datetime] = None
self._tracker: Optional[Callable] = None
self._state: Optional[datetime.datetime] = None | [
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mardix/assembly | 4c993d19bc9d33c1641323e03231e9ecad711b38 | assembly/_extensions.py | python | _Mailer.send | (self, to, subject=None, body=None, reply_to=None, template=None, **kwargs) | To send email
:param to: the recipients, list or string
:param subject: the subject
:param body: the body
:param reply_to: reply_to
:param template: template, will use the templates instead
:param kwargs: context args
:return: bool - True if everything is ok | To send email
:param to: the recipients, list or string
:param subject: the subject
:param body: the body
:param reply_to: reply_to
:param template: template, will use the templates instead
:param kwargs: context args
:return: bool - True if everything is ok | [
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"""
To send email
:param to: the recipients, list or string
:param subject: the subject
:param body: the body
:param reply_to: reply_to
:param template: template, will use the templates instead
:param kwargs: context args
:return: bool - True if everything is ok
"""
sender = self.config.get("MAIL_SENDER")
recipients = [to] if not isinstance(to, list) else to
kwargs.update({
"subject": subject,
"body": body,
"reply_to": reply_to
})
if not self.validated:
raise Error("Mail configuration error")
if self.provider == "SES":
kwargs["to"] = recipients
if template:
self.mail.send_template(template=template, **kwargs)
else:
self.mail.send(**kwargs)
elif self.provider == "SMTP":
if template:
data = self._template(template=template, **kwargs)
kwargs["subject"] = data["subject"]
kwargs["body"] = data["body"]
kwargs["recipients"] = recipients
kwargs["sender"] = sender
# Remove invalid Messages keys
_safe_keys = ["recipients", "subject", "body", "html", "alts",
"cc", "bcc", "attachments", "reply_to", "sender",
"date", "charset", "extra_headers", "mail_options",
"rcpt_options"]
for k in kwargs.copy():
if k not in _safe_keys:
del kwargs[k]
message = flask_mail.Message(**kwargs)
self.mail.send(message)
else:
raise Error("Invalid mail provider. Must be 'SES' or 'SMTP'") | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/gui/gui_common.py | python | GuiCommon.__init__ | (self, **kwds) | fmt_order, html_logging, inputs, parent=None, | fmt_order, html_logging, inputs, parent=None, | [
"fmt_order",
"html_logging",
"inputs",
"parent",
"=",
"None"
] | def __init__(self, **kwds):
"""
fmt_order, html_logging, inputs, parent=None,
"""
# this will reset the background color/label color if things break
#super(QMainWindow, self).__init__(self)
if qt_version == 'pyqt5':
super(GuiCommon, self).__init__(**kwds)
elif qt_version == 'pyside2':
QMainWindow.__init__(self)
GuiVTKCommon.__init__(self, **kwds)
else: #: pragma: no cover
raise NotImplementedError(qt_version)
self.format_class_map = CLASS_MAP
fmt_order = kwds['fmt_order']
inputs = kwds['inputs']
#self.app = inputs['app']
#del inputs['app']
if inputs['log'] is not None:
html_logging = False
else:
html_logging = kwds['html_logging']
del kwds['html_logging']
#-----------------------------------------------------------------------
self._active_background_image = None
self.reset_settings = False
self.fmts = fmt_order
self.base_window_title = f'pyNastran v{pyNastran.__version__}'
#defaults
self.wildcard_delimited = 'Delimited Text (*.txt; *.dat; *.csv)'
# initializes tools/checkables
self.set_tools()
self.html_logging = html_logging
self.execute_python = True
self.scalar_bar = ScalarBar(self.legend_obj.is_horizontal_scalar_bar)
# in,lb,s
self.input_units = ['', '', ''] # '' means not set
self.display_units = ['', '', ''] | [
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daiquocnguyen/Graph-Transformer | f9dc818a06ff0490f3d1bcc2e7b9ccd188950dcd | UGformerV1_TF/universal_transformer_modified.py | python | update_hparams_for_universal_transformer | (hparams) | return hparams | Adds deault hparams for all of the variants of the Universal Transformer.
Args:
hparams: default hparams (usually one of the standard hparams from
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"""Adds deault hparams for all of the variants of the Universal Transformer.
Args:
hparams: default hparams (usually one of the standard hparams from
transformer model (like "transformer_base")
Returns:
hparams with default values for Universal Transformers hyper-parameters
"""
hparams.daisy_chain_variables = False # Breaks multi-gpu in while loops.
# If not None, mixes vanilla transformer with Universal Transformer.
# Options: None, "before_ut", and "after_ut".
hparams.add_hparam("mix_with_transformer", None)
# Number of vanilla transformer layers used to be mixed with u-transofmer.
hparams.add_hparam("num_mixedin_layers", 2)
# Type of recurrency:
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hparams.add_hparam("recurrence_type", "basic")
# Number of steps (which is equivalent to num layer in transformer).
hparams.add_hparam("num_rec_steps", hparams.num_hidden_layers)
# Add the positional mebedding at each step(horisontal timing)
hparams.add_hparam("add_position_timing_signal", True)
if hparams.add_position_timing_signal:
hparams.pos = None
# Logic of position shifting when using timing signal:
# None, "random", "step"
hparams.add_hparam("position_start_index", None)
# Add an step embedding at each step (vertical timing)
hparams.add_hparam("add_step_timing_signal", True)
# Either "learned" or "sinusoid"
hparams.add_hparam("step_timing_signal_type", "learned")
# Add or concat the timing signal (applied both on position and step timing).
# Options: "add" and "concat".
hparams.add_hparam("add_or_concat_timing_signal", "add")
# Add SRU at the beginning of each Universal Transformer step.
# This can be considered as a position timing signal
hparams.add_hparam("add_sru", False)
# Default ffn layer is separable convolution.
# Options: "fc" and "sepconv".
hparams.add_hparam("transformer_ffn_type", "fc")
# Transform bias (in models with highway or skip connection).
hparams.add_hparam("transform_bias_init", -1.0)
hparams.add_hparam("couple_carry_transform_gates", True)
# Depth-wise attention (grid-transformer!) hparams:
# Adds depth embedding, if true.
hparams.add_hparam("depth_embedding", True)
# Learns attention weights for elements (instead of positions), if true.
hparams.add_hparam("dwa_elements", True)
# Type of ffn_layer used for gate in skip, highway, etc.
# "dense" or "dense_dropconnect".
# With dense_relu_dense, the bias/kernel initializations will not be applied.
hparams.add_hparam("gate_ffn_layer", "dense")
# LSTM forget bias for lstm style recurrence.
hparams.add_hparam("lstm_forget_bias", 1.0)
# Uses the memory at the last step as the final output, if true.
hparams.add_hparam("use_memory_as_final_state", True)
# if also add a ffn unit to the transition function when using gru/lstm
hparams.add_hparam("add_ffn_unit_to_the_transition_function", False)
# Type of act: basic/accumulated/global (instead of position-wise!)/random.
hparams.add_hparam("act_type", "basic")
# Max number of steps (forces halting at this step).
hparams.add_hparam("act_max_steps", 2 * hparams.num_hidden_layers)
hparams.add_hparam("act_halting_bias_init", 1.0)
hparams.add_hparam("act_epsilon", 0.01)
hparams.add_hparam("act_loss_weight", 0.01)
return hparams | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/cookielib.py | python | unmatched | (match) | return match.string[:start]+match.string[end:] | Return unmatched part of re.Match object. | Return unmatched part of re.Match object. | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/inspect.py | python | getmembers | (object, predicate=None) | return results | Return all members of an object as (name, value) pairs sorted by name.
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niosus/EasyClangComplete | 3b16eb17735aaa3f56bb295fc5481b269ee9f2ef | plugin/clang/cindex33.py | python | Token.spelling | (self) | return conf.lib.clang_getTokenSpelling(self._tu, self) | The spelling of this token.
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python-attrs/cattrs | 50ba769c8349f5891b157d2bb7f06602822ac0a3 | src/cattr/converters.py | python | Converter._structure_dict | (self, obj, cl) | Convert a mapping into a potentially generic dict. | Convert a mapping into a potentially generic dict. | [
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val_conv = self._structure_func.dispatch(val_type)
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elif val_type is Any:
key_conv = self._structure_func.dispatch(key_type)
return {key_conv(k, key_type): v for k, v in obj.items()}
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key_conv = self._structure_func.dispatch(key_type)
val_conv = self._structure_func.dispatch(val_type)
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cagbal/ros_people_object_detection_tensorflow | 982ffd4a54b8059638f5cd4aa167299c7fc9e61f | src/object_detection/metrics/tf_example_parser.py | python | BoundingBoxParser.__init__ | (self, xmin_field_name, ymin_field_name, xmax_field_name,
ymax_field_name) | [] | def __init__(self, xmin_field_name, ymin_field_name, xmax_field_name,
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/pyparsing/core.py | python | ParserElement.__rmul__ | (self, other) | return self.__mul__(other) | [] | def __rmul__(self, other):
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conda/conda-build | 2a19925f2b2ca188f80ffa625cd783e7d403793f | versioneer.py | python | versions_from_parentdir | (parentdir_prefix, root, verbose) | Try to determine the version from the parent directory name.
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Source tarballs conventionally unpack into a directory that includes both
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two directory levels for an appropriately named parent directory
"""
rootdirs = []
for i in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
else:
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix") | [
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runawayhorse001/LearningApacheSpark | 67f3879dce17553195f094f5728b94a01badcf24 | pyspark/sql/context.py | python | SQLContext.getOrCreate | (cls, sc) | return cls._instantiatedContext | Get the existing SQLContext or create a new one with given SparkContext.
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jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
sparkSession = SparkSession(sc, jsqlContext.sparkSession())
cls(sc, sparkSession, jsqlContext)
return cls._instantiatedContext | [
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onaio/onadata | 89ad16744e8f247fb748219476f6ac295869a95f | onadata/libs/utils/viewer_tools.py | python | image_urls_for_form | (xform) | return sum(
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stepjam/PyRep | d778d5d4ffa3be366d4e699f6e2941553fd47ecc | pyrep/robots/mobiles/nonholonomic_base.py | python | NonHolonomicBase.get_base_velocities | (self) | return [lv, av] | Gets linear and angular velocities of the mobile robot base
calculated from kinematics equations. Left joint should have index 1,
right joint should have index.
:return: A list with linear and angular velocity of the robot base. | Gets linear and angular velocities of the mobile robot base
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lv = sum(wv) * self.wheel_radius / 2.0
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X-Plane/XPlane2Blender | 5face69fbb828a414b167dcb063fabf5182a54bc | io_xplane2blender/xplane_types/xplane_material.py | python | XPlaneMaterial.isValid | (self, exportType:str) | return io_xplane2blender.xplane_types.xplane_material_utils.validate(self, exportType) | # Method: isValid
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# exportType <string> - one of "aircraft", "cockpit", "scenery", "instanced_scenery"
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# Returns:
# Tuple[List[str],Liststr]] A tuple of a list of errors and a list of warnings
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/CPython/27/Lib/decimal.py | python | Context.__repr__ | (self) | return ', '.join(s) + ')' | Show the current context. | Show the current context. | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/graphgym/checkpoint.py | python | get_ckpt_dir | () | return osp.join(cfg.run_dir, 'ckpt') | [] | def get_ckpt_dir() -> str:
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/Pillow-6.0.0-py3.7-macosx-10.9-x86_64.egg/PIL/Image.py | python | open | (fp, mode="r") | Opens and identifies the given image file.
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exclusive_fp = True
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preinit()
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# logger.debug("", exc_info=True)
continue
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fp.close()
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/cherrypy/wsgiserver/wsgiserver2.py | python | SizeCheckWrapper.next | (self) | return data | [] | def next(self):
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spotify/luigi | c3b66f4a5fa7eaa52f9a72eb6704b1049035c789 | examples/top_artists.py | python | Streams.run | (self) | Generates bogus data and writes it into the :py:meth:`~.Streams.output` target. | Generates bogus data and writes it into the :py:meth:`~.Streams.output` target. | [
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nosmokingbandit/Watcher3 | 0217e75158b563bdefc8e01c3be7620008cf3977 | lib/sqlalchemy/dialects/mssql/base.py | python | MSExecutionContext.pre_exec | (self) | Activate IDENTITY_INSERT if needed. | Activate IDENTITY_INSERT if needed. | [
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mkusner/grammarVAE | ffffe272a8cf1772578dfc92254c55c224cddc02 | equation_optimization/simulation1/grammar/run_bo.py | python | save_object | (obj, filename) | Function that saves an object to a file using pickle | Function that saves an object to a file using pickle | [
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/db/fields/json_field.py | python | JSONField.to_python | (self, value) | return value | Return a value suitable for using in Python code.
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Args:
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redhat-imaging/imagefactory | 176f6e045e1df049d50f33a924653128d5ab8b27 | imgfac/rest/bottle.py | python | BaseResponse.set_header | (self, name, value) | Create a new response header, replacing any previously defined
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/requests/packages/urllib3/connection.py | python | _match_hostname | (cert, asserted_hostname) | [] | def _match_hostname(cert, asserted_hostname):
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log.error(
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frePPLe/frepple | 57aa612030b4fcd03cb9c613f83a7dac4f0e8d6d | freppledb/common/report.py | python | GridReport._filter_in | (query, reportrow, data, database=DEFAULT_DB_ALIAS) | [] | def _filter_in(query, reportrow, data, database=DEFAULT_DB_ALIAS):
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/httplib.py | python | HTTPResponse._safe_read | (self, amt) | return ''.join(s) | Read the number of bytes requested, compensating for partial reads.
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s = []
while amt > 0:
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Sceptre/sceptre | b44797ae712e1e57c2421f2d93c35a4e8104c5ee | sceptre/diffing/stack_differ.py | python | repr_str | (dumper: Dumper, data: str) | return dumper.represent_str(data) | A YAML Representer that handles strings, breaking multi-line strings into something a lot
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getsentry/sentry-python | f92e9707ea73765eb9fdcf6482dc46aed4221a7a | sentry_sdk/scope.py | python | Scope.fingerprint | (self, value) | When set this overrides the default fingerprint. | When set this overrides the default fingerprint. | [
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apple/coremltools | 141a83af482fcbdd5179807c9eaff9a7999c2c49 | coremltools/models/neural_network/builder.py | python | _get_lstm_weight_fields | (lstm_wp) | return [
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] | def _get_lstm_weight_fields(lstm_wp):
"""
Get LSTM weight fields.
lstm_wp: _NeuralNetwork_pb2.LSTMWeightParams
"""
return [
lstm_wp.inputGateWeightMatrix,
lstm_wp.forgetGateWeightMatrix,
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lstm_wp.outputGateWeightMatrix,
lstm_wp.inputGateRecursionMatrix,
lstm_wp.forgetGateRecursionMatrix,
lstm_wp.blockInputRecursionMatrix,
lstm_wp.outputGateRecursionMatrix,
lstm_wp.inputGateBiasVector,
lstm_wp.forgetGateBiasVector,
lstm_wp.blockInputBiasVector,
lstm_wp.outputGateBiasVector,
lstm_wp.inputGatePeepholeVector,
lstm_wp.forgetGatePeepholeVector,
lstm_wp.outputGatePeepholeVector,
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/aui/aui_utilities.py | python | FindFocusDescendant | (ancestor) | return focusWin | Find a window with the focus, that is also a descendant of the given window.
This is used to determine the window to initially send commands to.
:param wx.Window `ancestor`: the window to check for ancestry. | Find a window with the focus, that is also a descendant of the given window.
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"""
Find a window with the focus, that is also a descendant of the given window.
This is used to determine the window to initially send commands to.
:param wx.Window `ancestor`: the window to check for ancestry.
"""
# Process events starting with the window with the focus, if any.
focusWin = wx.Window.FindFocus()
win = focusWin
# Check if this is a descendant of this frame.
# If not, win will be set to NULL.
while win:
if win == ancestor:
break
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win = win.GetParent()
if win is None:
focusWin = None
return focusWin | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pymongo/pool.py | python | Pool.__init__ | (self, address, options, handshake=True) | :Parameters:
- `address`: a (hostname, port) tuple
- `options`: a PoolOptions instance
- `handshake`: whether to call ismaster for each new SocketInfo | :Parameters:
- `address`: a (hostname, port) tuple
- `options`: a PoolOptions instance
- `handshake`: whether to call ismaster for each new SocketInfo | [
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"""
:Parameters:
- `address`: a (hostname, port) tuple
- `options`: a PoolOptions instance
- `handshake`: whether to call ismaster for each new SocketInfo
"""
# Check a socket's health with socket_closed() every once in a while.
# Can override for testing: 0 to always check, None to never check.
self._check_interval_seconds = 1
# LIFO pool. Sockets are ordered on idle time. Sockets claimed
# and returned to pool from the left side. Stale sockets removed
# from the right side.
self.sockets = collections.deque()
self.lock = threading.Lock()
self.active_sockets = 0
# Monotonically increasing connection ID required for CMAP Events.
self.next_connection_id = 1
self.closed = False
# Track whether the sockets in this pool are writeable or not.
self.is_writable = None
# Keep track of resets, so we notice sockets created before the most
# recent reset and close them.
self.pool_id = 0
self.pid = os.getpid()
self.address = address
self.opts = options
self.handshake = handshake
# Don't publish events in Monitor pools.
self.enabled_for_cmap = (
self.handshake and
self.opts.event_listeners is not None and
self.opts.event_listeners.enabled_for_cmap)
if (self.opts.wait_queue_multiple is None or
self.opts.max_pool_size is None):
max_waiters = None
else:
max_waiters = (
self.opts.max_pool_size * self.opts.wait_queue_multiple)
self._socket_semaphore = thread_util.create_semaphore(
self.opts.max_pool_size, max_waiters)
self.socket_checker = SocketChecker()
if self.enabled_for_cmap:
self.opts.event_listeners.publish_pool_created(
self.address, self.opts.non_default_options) | [
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regel/loudml | 0008baef02259a8ae81dd210d3f91a51ffc9ed9f | loudml/storage.py | python | Storage.get_model_hook | (self, model_name, hook_name) | Get model hook | Get model hook | [
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NeuralEnsemble/python-neo | 34d4db8fb0dc950dbbc6defd7fb75e99ea877286 | neo/rawio/baserawio.py | python | BaseRawIO.source_name | (self) | return self._source_name() | Return fancy name of file source | Return fancy name of file source | [
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"""Return fancy name of file source"""
return self._source_name() | [
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meduza-corp/interstellar | 40a801ccd7856491726f5a126621d9318cabe2e1 | gsutil/third_party/boto/boto/fps/connection.py | python | FPSConnection.get_recipient_verification_status | (self, action, response, **kw) | return self.get_object(action, kw, response) | Returns the recipient status. | Returns the recipient status. | [
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] | def get_recipient_verification_status(self, action, response, **kw):
"""
Returns the recipient status.
"""
return self.get_object(action, kw, response) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/boto-2.46.1/boto/gs/key.py | python | Key.set_xml_acl | (self, acl_str, headers=None, generation=None,
if_generation=None, if_metageneration=None) | Sets this objects's ACL to an XML string.
:type acl_str: string
:param acl_str: A string containing the ACL XML.
:type headers: dict
:param headers: Additional headers to set during the request.
:type generation: int
:param generation: If specified, sets the ACL for a specific generation
of a versioned object. If not specified, the current version is
modified.
:type if_generation: int
:param if_generation: (optional) If set to a generation number, the acl
will only be updated if its current generation number is this value.
:type if_metageneration: int
:param if_metageneration: (optional) If set to a metageneration number,
the acl will only be updated if its current metageneration number is
this value. | Sets this objects's ACL to an XML string. | [
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] | def set_xml_acl(self, acl_str, headers=None, generation=None,
if_generation=None, if_metageneration=None):
"""Sets this objects's ACL to an XML string.
:type acl_str: string
:param acl_str: A string containing the ACL XML.
:type headers: dict
:param headers: Additional headers to set during the request.
:type generation: int
:param generation: If specified, sets the ACL for a specific generation
of a versioned object. If not specified, the current version is
modified.
:type if_generation: int
:param if_generation: (optional) If set to a generation number, the acl
will only be updated if its current generation number is this value.
:type if_metageneration: int
:param if_metageneration: (optional) If set to a metageneration number,
the acl will only be updated if its current metageneration number is
this value.
"""
if self.bucket is not None:
return self.bucket.set_xml_acl(acl_str, self.name, headers=headers,
generation=generation,
if_generation=if_generation,
if_metageneration=if_metageneration) | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/future/backports/misc.py | python | ChainMap.popitem | (self) | Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty. | Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty. | [
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'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
try:
return self.maps[0].popitem()
except KeyError:
raise KeyError('No keys found in the first mapping.') | [
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MaybeShewill-CV/lanenet-lane-detection | 94be8d9a66c7bbda5ad99b10492bc406c8aee45f | lanenet_model/lanenet_discriminative_loss.py | python | discriminative_loss_single | (
prediction,
correct_label,
feature_dim,
label_shape,
delta_v,
delta_d,
param_var,
param_dist,
param_reg) | return loss, l_var, l_dist, l_reg | discriminative loss
:param prediction: inference of network
:param correct_label: instance label
:param feature_dim: feature dimension of prediction
:param label_shape: shape of label
:param delta_v: cut off variance distance
:param delta_d: cut off cluster distance
:param param_var: weight for intra cluster variance
:param param_dist: weight for inter cluster distances
:param param_reg: weight regularization | discriminative loss
:param prediction: inference of network
:param correct_label: instance label
:param feature_dim: feature dimension of prediction
:param label_shape: shape of label
:param delta_v: cut off variance distance
:param delta_d: cut off cluster distance
:param param_var: weight for intra cluster variance
:param param_dist: weight for inter cluster distances
:param param_reg: weight regularization | [
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prediction,
correct_label,
feature_dim,
label_shape,
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delta_d,
param_var,
param_dist,
param_reg):
"""
discriminative loss
:param prediction: inference of network
:param correct_label: instance label
:param feature_dim: feature dimension of prediction
:param label_shape: shape of label
:param delta_v: cut off variance distance
:param delta_d: cut off cluster distance
:param param_var: weight for intra cluster variance
:param param_dist: weight for inter cluster distances
:param param_reg: weight regularization
"""
correct_label = tf.reshape(
correct_label, [label_shape[1] * label_shape[0]]
)
reshaped_pred = tf.reshape(
prediction, [label_shape[1] * label_shape[0], feature_dim]
)
# calculate instance nums
unique_labels, unique_id, counts = tf.unique_with_counts(correct_label)
counts = tf.cast(counts, tf.float32)
num_instances = tf.size(unique_labels)
# calculate instance pixel embedding mean vec
segmented_sum = tf.unsorted_segment_sum(
reshaped_pred, unique_id, num_instances)
mu = tf.div(segmented_sum, tf.reshape(counts, (-1, 1)))
mu_expand = tf.gather(mu, unique_id)
distance = tf.norm(tf.subtract(mu_expand, reshaped_pred), axis=1, ord=1)
distance = tf.subtract(distance, delta_v)
distance = tf.clip_by_value(distance, 0., distance)
distance = tf.square(distance)
l_var = tf.unsorted_segment_sum(distance, unique_id, num_instances)
l_var = tf.div(l_var, counts)
l_var = tf.reduce_sum(l_var)
l_var = tf.divide(l_var, tf.cast(num_instances, tf.float32))
mu_interleaved_rep = tf.tile(mu, [num_instances, 1])
mu_band_rep = tf.tile(mu, [1, num_instances])
mu_band_rep = tf.reshape(
mu_band_rep,
(num_instances *
num_instances,
feature_dim))
mu_diff = tf.subtract(mu_band_rep, mu_interleaved_rep)
intermediate_tensor = tf.reduce_sum(tf.abs(mu_diff), axis=1)
zero_vector = tf.zeros(1, dtype=tf.float32)
bool_mask = tf.not_equal(intermediate_tensor, zero_vector)
mu_diff_bool = tf.boolean_mask(mu_diff, bool_mask)
mu_norm = tf.norm(mu_diff_bool, axis=1, ord=1)
mu_norm = tf.subtract(2. * delta_d, mu_norm)
mu_norm = tf.clip_by_value(mu_norm, 0., mu_norm)
mu_norm = tf.square(mu_norm)
l_dist = tf.reduce_mean(mu_norm)
l_reg = tf.reduce_mean(tf.norm(mu, axis=1, ord=1))
param_scale = 1.
l_var = param_var * l_var
l_dist = param_dist * l_dist
l_reg = param_reg * l_reg
loss = param_scale * (l_var + l_dist + l_reg)
return loss, l_var, l_dist, l_reg | [
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hgrecco/pint | befdffb9d767fb354fc756660a33268c0f8d48e1 | pint/numpy_func.py | python | convert_arg | (arg, pre_calc_units) | return arg | Convert quantities and sequences of quantities to pre_calc_units and strip units.
Helper function for convert_to_consistent_units. pre_calc_units must be given as a
pint Unit or None. | Convert quantities and sequences of quantities to pre_calc_units and strip units. | [
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"""Convert quantities and sequences of quantities to pre_calc_units and strip units.
Helper function for convert_to_consistent_units. pre_calc_units must be given as a
pint Unit or None.
"""
if pre_calc_units is not None:
if _is_quantity(arg):
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elif _is_sequence_with_quantity_elements(arg):
return [convert_arg(item, pre_calc_units) for item in arg]
elif arg is not None:
if pre_calc_units.dimensionless:
return pre_calc_units._REGISTRY.Quantity(arg).m_as(pre_calc_units)
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return arg
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return [convert_arg(item, pre_calc_units) for item in arg]
return arg | [
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IBM/lale | b4d6829c143a4735b06083a0e6c70d2cca244162 | lale/lib/autoai_libs/cat_encoder.py | python | _CatEncoderImpl.__init__ | (
self,
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categories,
dtype,
handle_unknown,
sklearn_version_family=None,
activate_flag=True,
encode_unknown_with="auto",
) | [] | def __init__(
self,
encoding,
categories,
dtype,
handle_unknown,
sklearn_version_family=None,
activate_flag=True,
encode_unknown_with="auto",
):
self._hyperparams = {
"encoding": encoding,
"categories": categories,
"dtype": dtype,
"handle_unknown": handle_unknown,
"sklearn_version_family": sklearn_version_family,
"activate_flag": activate_flag,
}
self.encode_unknown_with = encode_unknown_with
self._wrapped_model = autoai_libs.transformers.exportable.CatEncoder(
**self._hyperparams
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Lib/heapq.py | python | heapreplace | (heap, item) | return returnitem | Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
this routine unless written as part of a conditional replacement:
if item > heap[0]:
item = heapreplace(heap, item) | Pop and return the current smallest value, and add the new item. | [
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"""Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
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if item > heap[0]:
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"""
returnitem = heap[0] # raises appropriate IndexError if heap is empty
heap[0] = item
_siftup(heap, 0)
return returnitem | [
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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/plugins/cosigner_pool/qt.py | python | Listener.__init__ | (self, parent) | [] | def __init__(self, parent):
util.DaemonThread.__init__(self)
self.daemon = True
self.parent = parent
self.received = set()
self.keyhashes = [] | [
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... | https://github.com/spesmilo/electrum/blob/bdbd59300fbd35b01605e66145458e5f396108e8/electrum/plugins/cosigner_pool/qt.py#L59-L64 | ||||
NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/tts/modules/melgan_modules.py | python | MelGANMultiScaleDiscriminator.reset_parameters | (self) | Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py | Reset parameters. | [
"Reset",
"parameters",
"."
] | def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
def _reset_parameters(module):
if isinstance(module, torch.nn.Conv1d) or isinstance(module, torch.nn.ConvTranspose1d):
module.weight.data.normal_(0.0, 0.02)
logging.debug(f"Reset parameters in {module}.")
self.apply(_reset_parameters) | [
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"Con... | https://github.com/NVIDIA/NeMo/blob/5b0c0b4dec12d87d3cd960846de4105309ce938e/nemo/collections/tts/modules/melgan_modules.py#L495-L508 | ||
IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/asyncio/futures.py | python | Future.result | (self) | return self._result | Return the result this future represents.
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self._log_traceback = False
if self._tb_logger is not None:
self._tb_logger.clear()
self._tb_logger = None
if self._exception is not None:
raise self._exception
return self._result | [
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