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value | identifier stringlengths 1 140 | parameters stringlengths 0 87.7k | argument_list stringclasses 1
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hovel/pybbm | 6cf297ea7cf23d52baba2a76a73c5fdddf19cf8e | pybb/permissions.py | python | DefaultPermissionHandler.filter_forums | (self, user, qs) | return qs.filter(Q(hidden=False) & Q(category__hidden=False)) | return a queryset with forums `user` is allowed to see | return a queryset with forums `user` is allowed to see | [
"return",
"a",
"queryset",
"with",
"forums",
"user",
"is",
"allowed",
"to",
"see"
] | def filter_forums(self, user, qs):
""" return a queryset with forums `user` is allowed to see """
if user.is_superuser or user.is_staff:
# FIXME: is_staff only allow user to access /admin but does not mean user has extra
# permissions on pybb models. We should add pybb perm test
return qs
return qs.filter(Q(hidden=False) & Q(category__hidden=False)) | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/django/contrib/sitemaps/__init__.py | python | Sitemap.get_urls | (self, page=1, site=None, protocol=None) | return urls | [] | def get_urls(self, page=1, site=None, protocol=None):
# Determine protocol
if self.protocol is not None:
protocol = self.protocol
if protocol is None:
protocol = 'http'
# Determine domain
if site is None:
if django_apps.is_installed('django.contrib.sites'):
Site = django_apps.get_model('sites.Site')
try:
site = Site.objects.get_current()
except Site.DoesNotExist:
pass
if site is None:
raise ImproperlyConfigured(
"To use sitemaps, either enable the sites framework or pass "
"a Site/RequestSite object in your view."
)
domain = site.domain
if getattr(self, 'i18n', False):
urls = []
current_lang_code = translation.get_language()
for lang_code, lang_name in settings.LANGUAGES:
translation.activate(lang_code)
urls += self._urls(page, protocol, domain)
translation.activate(current_lang_code)
else:
urls = self._urls(page, protocol, domain)
return urls | [
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enthought/traitsui | b7c38c7a47bf6ae7971f9ddab70c8a358647dd25 | traitsui/wx/table_editor.py | python | TableEditor._create_toolbar | (self, parent, sizer) | Creates the table editing toolbar. | Creates the table editing toolbar. | [
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] | def _create_toolbar(self, parent, sizer):
"""Creates the table editing toolbar."""
factory = self.factory
if not factory.show_toolbar:
return
toolbar = TableEditorToolbar(parent=parent, editor=self)
if (toolbar.control is not None) or (len(factory.filters) > 0):
tb_sizer = wx.BoxSizer(wx.HORIZONTAL)
if len(factory.filters) > 0:
view = View(
[
Item(
"filter<250>{View}", editor=factory._filter_editor
),
"_",
Item(
"filter_summary<100>{Results}~",
object="model",
resizable=False,
),
"_",
"-",
],
resizable=True,
)
self.toolbar_ui = ui = view.ui(
context={"object": self, "model": self.model},
parent=parent,
kind="subpanel",
).trait_set(parent=self.ui)
tb_sizer.Add(ui.control, 0)
if toolbar.control is not None:
self.toolbar = toolbar
# add padding so the toolbar is right aligned
tb_sizer.Add((1, 1), 1, wx.EXPAND)
tb_sizer.Add(toolbar.control, 0)
sizer.Add(tb_sizer, 0, wx.EXPAND) | [
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marian-margeta/gait-recognition | a9d1a738d4ca9c37355e0de4768a32c5f040d7fc | gait_nn.py | python | GaitNN.restore | (self, checkpoint_path) | [] | def restore(self, checkpoint_path):
all_vars = tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope = 'GaitNN')
saver = tf.train.Saver(all_vars)
saver.restore(self.sess, checkpoint_path) | [
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optuna/optuna | 2c44c1a405ba059efd53f4b9c8e849d20fb95c0a | optuna/storages/_rdb/storage.py | python | RDBStorage.get_all_study_summaries | (self) | return study_summaries | [] | def get_all_study_summaries(self) -> List[StudySummary]:
with _create_scoped_session(self.scoped_session) as session:
summarized_trial = (
session.query(
models.TrialModel.study_id,
functions.min(models.TrialModel.datetime_start).label("datetime_start"),
functions.count(models.TrialModel.trial_id).label("n_trial"),
)
.group_by(models.TrialModel.study_id)
.with_labels()
.subquery()
)
study_summary_stmt = session.query(
models.StudyModel.study_id,
models.StudyModel.study_name,
summarized_trial.c.datetime_start,
functions.coalesce(summarized_trial.c.n_trial, 0).label("n_trial"),
).select_from(orm.outerjoin(models.StudyModel, summarized_trial))
study_summary = study_summary_stmt.all()
_directions = defaultdict(list)
for d in session.query(models.StudyDirectionModel).all():
_directions[d.study_id].append(d.direction)
_user_attrs = defaultdict(list)
for a in session.query(models.StudyUserAttributeModel).all():
_user_attrs[d.study_id].append(a)
_system_attrs = defaultdict(list)
for a in session.query(models.StudySystemAttributeModel).all():
_system_attrs[d.study_id].append(a)
study_summaries = []
for study in study_summary:
directions = _directions[study.study_id]
best_trial: Optional[models.TrialModel] = None
try:
if len(directions) > 1:
raise ValueError
elif directions[0] == StudyDirection.MAXIMIZE:
best_trial = models.TrialModel.find_max_value_trial(
study.study_id, 0, session
)
else:
best_trial = models.TrialModel.find_min_value_trial(
study.study_id, 0, session
)
except ValueError:
best_trial_frozen: Optional[FrozenTrial] = None
if best_trial:
value = models.TrialValueModel.find_by_trial_and_objective(
best_trial, 0, session
)
assert value
params = (
session.query(
models.TrialParamModel.param_name,
models.TrialParamModel.param_value,
models.TrialParamModel.distribution_json,
)
.filter(models.TrialParamModel.trial_id == best_trial.trial_id)
.all()
)
param_dict = {}
param_distributions = {}
for param in params:
distribution = distributions.json_to_distribution(param.distribution_json)
param_dict[param.param_name] = distribution.to_external_repr(
param.param_value
)
param_distributions[param.param_name] = distribution
user_attrs = models.TrialUserAttributeModel.where_trial_id(
best_trial.trial_id, session
)
system_attrs = models.TrialSystemAttributeModel.where_trial_id(
best_trial.trial_id, session
)
intermediate = models.TrialIntermediateValueModel.where_trial_id(
best_trial.trial_id, session
)
best_trial_frozen = FrozenTrial(
best_trial.number,
TrialState.COMPLETE,
value.value,
best_trial.datetime_start,
best_trial.datetime_complete,
param_dict,
param_distributions,
{i.key: json.loads(i.value_json) for i in user_attrs},
{i.key: json.loads(i.value_json) for i in system_attrs},
{value.step: value.intermediate_value for value in intermediate},
best_trial.trial_id,
)
user_attrs = _user_attrs.get(study.study_id, [])
system_attrs = _system_attrs.get(study.study_id, [])
study_summaries.append(
StudySummary(
study_name=study.study_name,
direction=None,
directions=directions,
best_trial=best_trial_frozen,
user_attrs={i.key: json.loads(i.value_json) for i in user_attrs},
system_attrs={i.key: json.loads(i.value_json) for i in system_attrs},
n_trials=study.n_trial,
datetime_start=study.datetime_start,
study_id=study.study_id,
)
)
return study_summaries | [
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mehdilauters/wifiScanMap | 9adcd08e43db5dcb7700450abed00c97e140ef21 | src/AirodumpPoller.py | python | AirodumpPoller.is_too_old | (self, date, sleep) | return diff.total_seconds() > sleep | [] | def is_too_old(self, date, sleep):
diff = datetime.datetime.now() - self.date_from_str(date)
return diff.total_seconds() > sleep | [
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dragonfly/dragonfly | a579b5eadf452e23b07d4caf27b402703b0012b7 | dragonfly/exd/experiment_caller.py | python | EuclideanFunctionCaller.__init__ | (self, func, *args, **kwargs) | Constructor. | Constructor. | [
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] | def __init__(self, func, *args, **kwargs):
""" Constructor. """
self.func = func
super(EuclideanFunctionCaller, self).__init__(func, *args, **kwargs) | [
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gpiozero/gpiozero | 2b6aa5314830fedf3701113b6713161086defa38 | gpiozero/input_devices.py | python | InputDevice.pull_up | (self) | If :data:`True`, the device uses a pull-up resistor to set the GPIO pin
"high" by default. | If :data:`True`, the device uses a pull-up resistor to set the GPIO pin
"high" by default. | [
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"""
If :data:`True`, the device uses a pull-up resistor to set the GPIO pin
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"""
pull = self.pin.pull
if pull == 'floating':
return None
else:
return pull == 'up' | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/nn/models/tgn.py | python | TGNMemory.__init__ | (self, num_nodes: int, raw_msg_dim: int, memory_dim: int,
time_dim: int, message_module: Callable,
aggregator_module: Callable) | [] | def __init__(self, num_nodes: int, raw_msg_dim: int, memory_dim: int,
time_dim: int, message_module: Callable,
aggregator_module: Callable):
super().__init__()
self.num_nodes = num_nodes
self.raw_msg_dim = raw_msg_dim
self.memory_dim = memory_dim
self.time_dim = time_dim
self.msg_s_module = message_module
self.msg_d_module = copy.deepcopy(message_module)
self.aggr_module = aggregator_module
self.time_enc = TimeEncoder(time_dim)
self.gru = GRUCell(message_module.out_channels, memory_dim)
self.register_buffer('memory', torch.empty(num_nodes, memory_dim))
last_update = torch.empty(self.num_nodes, dtype=torch.long)
self.register_buffer('last_update', last_update)
self.register_buffer('__assoc__',
torch.empty(num_nodes, dtype=torch.long))
self.msg_s_store = {}
self.msg_d_store = {}
self.reset_parameters() | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | examples/dgcnn_segmentation.py | python | Net.forward | (self, data) | return F.log_softmax(out, dim=1) | [] | def forward(self, data):
x, pos, batch = data.x, data.pos, data.batch
x0 = torch.cat([x, pos], dim=-1)
x1 = self.conv1(x0, batch)
x2 = self.conv2(x1, batch)
x3 = self.conv3(x2, batch)
out = self.mlp(torch.cat([x1, x2, x3], dim=1))
return F.log_softmax(out, dim=1) | [
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roclark/sportsipy | c19f545d3376d62ded6304b137dc69238ac620a9 | sportsipy/mlb/teams.py | python | Team.last_ten_games_record | (self) | return self._last_ten_games_record | Returns a ``string`` of the team's record over the last ten games.
Record is in the format 'W-L'. | Returns a ``string`` of the team's record over the last ten games.
Record is in the format 'W-L'. | [
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"""
Returns a ``string`` of the team's record over the last ten games.
Record is in the format 'W-L'.
"""
return self._last_ten_games_record | [
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/exprs/Exprs.py | python | is_pure_expr | (expr) | return is_Expr(expr) and not expr_is_Instance(expr) | is expr an Expr (py class or py instance), but not an Instance? | is expr an Expr (py class or py instance), but not an Instance? | [
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] | def is_pure_expr(expr): ##e why not is_pure_Expr? is lowercase expr a generic term of some sort? [070207 terminology Q]
"""
is expr an Expr (py class or py instance), but not an Instance?
"""
return is_Expr(expr) and not expr_is_Instance(expr) | [
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giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter11/volatility-2.3/build/lib/volatility/plugins/addrspaces/amd64.py | python | AMD64PagedMemory.get_pdpi | (self, vaddr, pml4e) | return self.read_long_long_phys(pdpte_paddr) | This method returns the Page Directory Pointer entry for the
virtual address. Bits 32:30 are used to select the appropriate
8 byte entry in the Page Directory Pointer table.
"Bits 51:12 are from the PML4E" [Intel]
"Bits 11:3 are bits 38:30 of the linear address" [Intel]
"Bits 2:0 are all 0" [Intel] | This method returns the Page Directory Pointer entry for the
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8 byte entry in the Page Directory Pointer table.
"Bits 51:12 are from the PML4E" [Intel]
"Bits 11:3 are bits 38:30 of the linear address" [Intel]
"Bits 2:0 are all 0" [Intel] | [
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"Bits 51:12 are from the PML4E" [Intel]
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pdpte_paddr = (pml4e & 0xffffffffff000) | ((vaddr & 0x7FC0000000) >> 27)
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aaronjanse/asciidots | 9c6a7d6a68c5e7870d574ab8ae94aa9ad7f53012 | dots/__main__.py | python | DefaultIOCallbacks.get_input | (self, ascii_char=False) | Get an input from the user. | Get an input from the user. | [
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"""Get an input from the user."""
if not self.debug or self.compat_debug:
if ascii_char:
return getch()
else:
if not sys.stdin.isatty():
return input('')
else:
return input('?: ')
else:
return self.curses_input(self.stdscr, curses.LINES - 3, 2, '?: ', ascii_char) | [
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svenkreiss/pysparkling | f0e8e8d039f3313c2693b7c7576cb1b7ba5a6d78 | pysparkling/sql/types.py | python | MapType.__init__ | (self, keyType, valueType, valueContainsNull=True) | >>> (MapType(StringType(), IntegerType())
... == MapType(StringType(), IntegerType(), True))
True
>>> (MapType(StringType(), IntegerType(), False)
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False | >>> (MapType(StringType(), IntegerType())
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>>> (MapType(StringType(), IntegerType(), False)
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False | [
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>>> (MapType(StringType(), IntegerType())
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True
>>> (MapType(StringType(), IntegerType(), False)
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assert isinstance(keyType, DataType), \
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assert isinstance(valueType, DataType), \
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self.keyType = keyType
self.valueType = valueType
self.valueContainsNull = valueContainsNull | [
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nodesign/weio | 1d67d705a5c36a2e825ad13feab910b0aca9a2e8 | weioLib/weioUserApi.py | python | WeioSharedVar.__init__ | (self, procFnc, procArgs) | [] | def __init__ (self, procFnc, procArgs) :
self.dict = None
self.Val = None
self.Array = None | [
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aws/sagemaker-python-sdk | 9d259b316f7f43838c16f35c10e98a110b56735b | src/sagemaker/tuner.py | python | HyperparameterTuner._get_best_training_job | (self) | Return the best training job for the latest hyperparameter tuning job.
Raises:
Exception: If there is no best training job available for the
hyperparameter tuning job. | Return the best training job for the latest hyperparameter tuning job. | [
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"""Return the best training job for the latest hyperparameter tuning job.
Raises:
Exception: If there is no best training job available for the
hyperparameter tuning job.
"""
self._ensure_last_tuning_job()
tuning_job_describe_result = self.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job( # noqa: E501 # pylint: disable=line-too-long
HyperParameterTuningJobName=self.latest_tuning_job.name
)
try:
return tuning_job_describe_result["BestTrainingJob"]
except KeyError:
raise Exception(
"Best training job not available for tuning job: {}".format(
self.latest_tuning_job.name
)
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PyHDI/veriloggen | 2382d200deabf59cfcfd741f5eba371010aaf2bb | veriloggen/types/fixed.py | python | _FixedGreaterThan.__init__ | (self, left, right) | [] | def __init__(self, left, right):
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/pip/index.py | python | Link.is_artifact | (self) | return True | Determines if this points to an actual artifact (e.g. a tarball) or if
it points to an "abstract" thing like a path or a VCS location. | Determines if this points to an actual artifact (e.g. a tarball) or if
it points to an "abstract" thing like a path or a VCS location. | [
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] | def is_artifact(self):
"""
Determines if this points to an actual artifact (e.g. a tarball) or if
it points to an "abstract" thing like a path or a VCS location.
"""
from pip.vcs import vcs
if self.scheme in vcs.all_schemes:
return False
return True | [
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Blazemeter/taurus | 6e36b20397cf3e730e181cfebde0c8f19eb31fed | bzt/utils.py | python | get_host_ips | (filter_loopbacks=True) | return ips | Returns a list of all IP addresses assigned to this host.
:param filter_loopbacks: filter out loopback addresses | Returns a list of all IP addresses assigned to this host. | [
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"""
Returns a list of all IP addresses assigned to this host.
:param filter_loopbacks: filter out loopback addresses
"""
ips = []
for _, interfaces in iteritems(psutil.net_if_addrs()):
for iface in interfaces:
addr = str(iface.address)
try:
ip = ipaddress.ip_address(addr)
if filter_loopbacks and ip.is_loopback:
continue
except ValueError:
continue
ips.append(iface.address)
return ips | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/xml/dom/expatbuilder.py | python | ExpatBuilder.createParser | (self) | return expat.ParserCreate() | Create a new parser object. | Create a new parser object. | [
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"""Create a new parser object."""
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/site-packages/win32/lib/win32timezone.py | python | resolveMUITimeZone | (spec) | return result | Resolve a multilingual user interface resource for the time zone name
>>> #some pre-amble for the doc-tests to be py2k and py3k aware)
>>> try: unicode and None
... except NameError: unicode=str
...
>>> result = resolveMUITimeZone('@tzres.dll,-110')
>>> expectedResultType = [type(None),unicode][sys.getwindowsversion() >= (6,)]
>>> type(result) is expectedResultType
True
spec should be of the format @path,-stringID[;comment]
see http://msdn2.microsoft.com/en-us/library/ms725481.aspx for details | Resolve a multilingual user interface resource for the time zone name
>>> #some pre-amble for the doc-tests to be py2k and py3k aware)
>>> try: unicode and None
... except NameError: unicode=str
...
>>> result = resolveMUITimeZone(' | [
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"""Resolve a multilingual user interface resource for the time zone name
>>> #some pre-amble for the doc-tests to be py2k and py3k aware)
>>> try: unicode and None
... except NameError: unicode=str
...
>>> result = resolveMUITimeZone('@tzres.dll,-110')
>>> expectedResultType = [type(None),unicode][sys.getwindowsversion() >= (6,)]
>>> type(result) is expectedResultType
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spec should be of the format @path,-stringID[;comment]
see http://msdn2.microsoft.com/en-us/library/ms725481.aspx for details
"""
pattern = re.compile('@(?P<dllname>.*),-(?P<index>\d+)(?:;(?P<comment>.*))?')
matcher = pattern.match(spec)
assert matcher, 'Could not parse MUI spec'
try:
handle = DLLCache[matcher.groupdict()['dllname']]
result = win32api.LoadString(handle, int(matcher.groupdict()['index']))
except win32api.error, e:
result = None
return result | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/fileserver/roots.py | python | find_file | (path, saltenv="base", **kwargs) | return fnd | Search the environment for the relative path. | Search the environment for the relative path. | [
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"""
Search the environment for the relative path.
"""
if "env" in kwargs:
# "env" is not supported; Use "saltenv".
kwargs.pop("env")
path = os.path.normpath(path)
fnd = {"path": "", "rel": ""}
if os.path.isabs(path):
return fnd
if saltenv not in __opts__["file_roots"]:
if "__env__" in __opts__["file_roots"]:
log.debug(
"salt environment '%s' maps to __env__ file_roots directory", saltenv
)
saltenv = "__env__"
else:
return fnd
def _add_file_stat(fnd):
"""
Stat the file and, assuming no errors were found, convert the stat
result to a list of values and add to the return dict.
Converting the stat result to a list, the elements of the list
correspond to the following stat_result params:
0 => st_mode=33188
1 => st_ino=10227377
2 => st_dev=65026
3 => st_nlink=1
4 => st_uid=1000
5 => st_gid=1000
6 => st_size=1056233
7 => st_atime=1468284229
8 => st_mtime=1456338235
9 => st_ctime=1456338235
"""
try:
fnd["stat"] = list(os.stat(fnd["path"]))
except Exception as exc: # pylint: disable=broad-except
log.error("Unable to stat file: %s", exc)
return fnd
if "index" in kwargs:
try:
root = __opts__["file_roots"][saltenv][int(kwargs["index"])]
except IndexError:
# An invalid index was passed
return fnd
except ValueError:
# An invalid index option was passed
return fnd
full = os.path.join(root, path)
if os.path.isfile(full) and not salt.fileserver.is_file_ignored(__opts__, full):
fnd["path"] = full
fnd["rel"] = path
return _add_file_stat(fnd)
return fnd
for root in __opts__["file_roots"][saltenv]:
full = os.path.join(root, path)
if os.path.isfile(full) and not salt.fileserver.is_file_ignored(__opts__, full):
fnd["path"] = full
fnd["rel"] = path
return _add_file_stat(fnd)
return fnd | [
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pculture/mirovideoconverter3 | 27efad91845c8ae544dc27034adb0d3e18ca8f1f | mvc/widgets/gtk/tableview.py | python | TableView.get_path | (self, iter_) | return self._model.get_path(iter_) | Always use this rather than the model's get_path directly -
if the iter isn't valid, a GTK assertion causes us to exit
without warning; this wrapper changes that to a much more useful
AssertionError. Example related bug: #17362. | Always use this rather than the model's get_path directly -
if the iter isn't valid, a GTK assertion causes us to exit
without warning; this wrapper changes that to a much more useful
AssertionError. Example related bug: #17362. | [
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without warning; this wrapper changes that to a much more useful
AssertionError. Example related bug: #17362.
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assert self.model.iter_is_valid(iter_)
return self._model.get_path(iter_) | [
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chrippa/python-librtmp | 6efefd5edd76cad7a3b53f7c87c1c7350448224d | librtmp/packet.py | python | RTMPPacket.timestamp | (self) | return self.packet.m_nTimeStamp | Timestamp of the packet. | Timestamp of the packet. | [
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"""Timestamp of the packet."""
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | examples/pytorch/pointcloud/pointnet/pointnet2.py | python | FixedRadiusNearNeighbors.forward | (self, pos, centroids) | return group_idx | Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch | Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch | [
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'''
Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch
'''
device = pos.device
B, N, _ = pos.shape
center_pos = index_points(pos, centroids)
_, S, _ = center_pos.shape
group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
sqrdists = square_distance(center_pos, pos)
group_idx[sqrdists > self.radius ** 2] = N
group_idx = group_idx.sort(dim=-1)[0][:, :, :self.n_neighbor]
group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, self.n_neighbor])
mask = group_idx == N
group_idx[mask] = group_first[mask]
return group_idx | [
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giswqs/whitebox-python | b4df0bbb10a1dee3bd0f6b3482511f7c829b38fe | whitebox/whitebox_tools.py | python | WhiteboxTools.difference | (self, i, overlay, output, callback=None) | return self.run_tool('difference', args, callback) | Outputs the features that occur in one of the two vector inputs but not both, i.e. no overlapping features.
Keyword arguments:
i -- Input vector file.
overlay -- Input overlay vector file.
output -- Output vector file.
callback -- Custom function for handling tool text outputs. | Outputs the features that occur in one of the two vector inputs but not both, i.e. no overlapping features. | [
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"""Outputs the features that occur in one of the two vector inputs but not both, i.e. no overlapping features.
Keyword arguments:
i -- Input vector file.
overlay -- Input overlay vector file.
output -- Output vector file.
callback -- Custom function for handling tool text outputs.
"""
args = []
args.append("--input='{}'".format(i))
args.append("--overlay='{}'".format(overlay))
args.append("--output='{}'".format(output))
return self.run_tool('difference', args, callback) | [
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007gzs/dingtalk-sdk | 7979da2e259fdbc571728cae2425a04dbc65850a | dingtalk/client/api/taobao.py | python | TbZhiHuiYuanQu.alibaba_campus_acl_updategrantroletouser | (
self,
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system_id,
campus_id,
account_id,
role,
user_id=''
) | return self._top_request(
"alibaba.campus.acl.updategrantroletouser",
{
"company_id": company_id,
"system_id": system_id,
"campus_id": campus_id,
"account_id": account_id,
"role": role,
"user_id": user_id
}
) | 修改用户到角色关系
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=32231
:param company_id: 公司id
:param system_id: 系统id
:param campus_id: 园区id
:param account_id: 用户账号
:param role: 角色
:param user_id: 操作人id(不填默认appCode) | 修改用户到角色关系
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=32231 | [
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self,
company_id,
system_id,
campus_id,
account_id,
role,
user_id=''
):
"""
修改用户到角色关系
文档地址：https://open-doc.dingtalk.com/docs/api.htm?apiId=32231
:param company_id: 公司id
:param system_id: 系统id
:param campus_id: 园区id
:param account_id: 用户账号
:param role: 角色
:param user_id: 操作人id(不填默认appCode)
"""
return self._top_request(
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{
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"system_id": system_id,
"campus_id": campus_id,
"account_id": account_id,
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/db/utils.py | python | ConnectionHandler.__setitem__ | (self, key, value) | [] | def __setitem__(self, key, value):
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /scripts/sshbackdoors/rpyc/experimental/splitbrain.py | python | RoutedModule.__init__ | (self, realmod) | [] | def __init__(self, realmod):
ModuleType.__init__(self, realmod.__name__, getattr(realmod, "__doc__", None))
object.__setattr__(self, "__realmod__", realmod)
object.__setattr__(self, "__file__", getattr(realmod, "__file__", None)) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | scripts/monitoring/cron-send-create-app.py | python | getPod | (name) | return result | get Pod from all possible pods | get Pod from all possible pods | [
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""" get Pod from all possible pods """
pods = ocutil.get_pods()
result = None
for pod in pods['items']:
if pod and pod['metadata']['name'] and pod['metadata']['name'].startswith(name):
# if we have a pod already, and this one is a build or deploy pod, don't worry about it
# we want podname-xyz12 to be priority
# if we dont already have a pod, then this one will do
if result:
if pod['metadata']['name'].endswith("build"):
continue
if pod['metadata']['name'].endswith("deploy"):
continue
result = pod
return result | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/encodings/mac_greek.py | python | getregentry | () | return codecs.CodecInfo(
name='mac-greek',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
streamwriter=StreamWriter,
) | [] | def getregentry():
return codecs.CodecInfo(
name='mac-greek',
encode=Codec().encode,
decode=Codec().decode,
incrementalencoder=IncrementalEncoder,
incrementaldecoder=IncrementalDecoder,
streamreader=StreamReader,
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/db/models/query.py | python | QuerySet.db | (self) | return self._db or router.db_for_read(self.model) | Return the database that will be used if this query is executed now | Return the database that will be used if this query is executed now | [
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return self._db or router.db_for_write(self.model)
return self._db or router.db_for_read(self.model) | [
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sopel-irc/sopel | 787baa6e39f9dad57d94600c92e10761c41b21ef | sopel/modules/wikipedia.py | python | wpclang | (bot, trigger) | [] | def wpclang(bot, trigger):
if not (trigger.admin or bot.channels[trigger.sender.lower()].privileges[trigger.nick.lower()] >= plugin.OP):
bot.reply("You don't have permission to change this channel's Wikipedia language setting.")
return plugin.NOLIMIT
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trigger.sender,
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bot.config.wikipedia.default_lang)
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return
bot.db.set_channel_value(trigger.sender, 'wikipedia_lang', trigger.group(3))
bot.say(
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Project-Platypus/Platypus | a4e56410a772798e905407e99f80d03b86296ad3 | examples/plot_operators.py | python | generate | (variator, parents) | return to_points(result) | [] | def generate(variator, parents):
result = []
while len(result) < 10000:
result.extend(variator.evolve(parents))
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scipy/scipy | e0a749f01e79046642ccfdc419edbf9e7ca141ad | scipy/stats/_continuous_distns.py | python | crystalball_gen._munp | (self, n, beta, m) | return N * _lazywhere(n + 1 < m, (n, beta, m),
np.vectorize(n_th_moment, otypes=[np.float64]),
np.inf) | Returns the n-th non-central moment of the crystalball function. | Returns the n-th non-central moment of the crystalball function. | [
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"""
N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
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"""
Returns n-th moment. Defined only if n+1 < m
Function cannot broadcast due to the loop over n
"""
A = (m/beta)**m * np.exp(-beta**2 / 2.0)
B = m/beta - beta
rhs = (2**((n-1)/2.0) * sc.gamma((n+1)/2) *
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lhs = np.zeros(rhs.shape)
for k in range(n + 1):
lhs += (sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) *
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return A * lhs + rhs
return N * _lazywhere(n + 1 < m, (n, beta, m),
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Yelp/kafka-utils | 74831206648512db1a29426c6ebb428b33820d04 | kafka_utils/util/config.py | python | iter_configurations | (kafka_topology_base_path=None) | Cluster topology iterator.
Iterate over all the topologies available in config. | Cluster topology iterator.
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Iterate over all the topologies available in config.
"""
if not kafka_topology_base_path:
config_dirs = get_conf_dirs()
else:
config_dirs = [kafka_topology_base_path]
types = set()
for config_dir in config_dirs:
new_types = [x for x in map(
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for cluster_type in new_types:
try:
topology = TopologyConfiguration(
cluster_type,
config_dir,
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types.add(cluster_type)
yield topology | [
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brython-dev/brython | 9cba5fb7f43a9b52fff13e89b403e02a1dfaa5f3 | www/src/Lib/sysconfig.py | python | _init_non_posix | (vars) | Initialize the module as appropriate for NT | Initialize the module as appropriate for NT | [
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vars['BINLIBDEST'] = get_path('platstdlib')
vars['INCLUDEPY'] = get_path('include')
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wanggrun/Adaptively-Connected-Neural-Networks | e27066ef52301bdafa5932f43af8feeb23647edb | tensorpack-installed/tensorpack/dataflow/raw.py | python | DataFromList.__init__ | (self, lst, shuffle=True) | Args:
lst (list): input list. Each element is a datapoint.
shuffle (bool): shuffle data. | Args:
lst (list): input list. Each element is a datapoint.
shuffle (bool): shuffle data. | [
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"""
Args:
lst (list): input list. Each element is a datapoint.
shuffle (bool): shuffle data.
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super(DataFromList, self).__init__()
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stanislavkozlovski/Red-Black-Tree | cb3cefb420bfa6c1d1fc703cefad54e209c7438c | rb_tree.py | python | RedBlackTree._remove | (self, node) | Receives a node with 0 or 1 children (typically some sort of successor)
and removes it according to its color/children
:param node: Node with 0 or 1 children | Receives a node with 0 or 1 children (typically some sort of successor)
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"""
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:param node: Node with 0 or 1 children
"""
left_child = node.left
right_child = node.right
not_nil_child = left_child if left_child != self.NIL_LEAF else right_child
if node == self.root:
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self.root = not_nil_child
self.root.parent = None
self.root.color = BLACK
else:
self.root = None
elif node.color == RED:
if not node.has_children():
# Red node with no children, the simplest remove
self._remove_leaf(node)
else:
"""
Since the node is red he cannot have a child.
If he had a child, it'd need to be black, but that would mean that
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"""
raise Exception('Unexpected behavior')
else: # node is black!
if right_child.has_children() or left_child.has_children(): # sanity check
raise Exception('The red child of a black node with 0 or 1 children'
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if not_nil_child.color == RED:
"""
Swap the values with the red child and remove it (basically un-link it)
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"""
node.value = not_nil_child.value
node.left = not_nil_child.left
node.right = not_nil_child.right
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self._remove_black_node(node) | [
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boto/boto | b2a6f08122b2f1b89888d2848e730893595cd001 | boto/s3/bucket.py | python | Bucket.set_as_logging_target | (self, headers=None) | Setup the current bucket as a logging target by granting the necessary
permissions to the LogDelivery group to write log files to this bucket. | Setup the current bucket as a logging target by granting the necessary
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"""
Setup the current bucket as a logging target by granting the necessary
permissions to the LogDelivery group to write log files to this bucket.
"""
policy = self.get_acl(headers=headers)
g1 = Grant(permission='WRITE', type='Group', uri=self.LoggingGroup)
g2 = Grant(permission='READ_ACP', type='Group', uri=self.LoggingGroup)
policy.acl.add_grant(g1)
policy.acl.add_grant(g2)
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openstack/manila | 142990edc027e14839d5deaf4954dd6fc88de15e | manila/share/drivers/nexenta/ns4/nexenta_nfs_helper.py | python | NFSHelper.update_access | (self, share_name, access_rules) | Update access to the share. | Update access to the share. | [
"Update",
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"share",
"."
] | def update_access(self, share_name, access_rules):
"""Update access to the share."""
rw_list = []
ro_list = []
for rule in access_rules:
if rule['access_type'].lower() != 'ip':
msg = _('Only IP access type is supported.')
raise exception.InvalidShareAccess(reason=msg)
else:
if rule['access_level'] == common.ACCESS_LEVEL_RW:
rw_list.append(rule['access_to'])
else:
ro_list.append(rule['access_to'])
share_opts = {
'auth_type': 'none',
'read_write': ':'.join(rw_list),
'read_only': ':'.join(ro_list),
'recursive': 'true',
'anonymous_rw': 'true',
'anonymous': 'true',
'extra_options': 'anon=0',
}
self.nms.netstorsvc.share_folder(
'svc:/network/nfs/server:default',
self._get_share_path(share_name), share_opts) | [
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/boto/vpc/vpc_peering_connection.py | python | VpcPeeringConnection.__init__ | (self, connection=None) | Represents a VPC peering connection.
:ivar id: The unique ID of the VPC peering connection.
:ivar accepter_vpc_info: Information on peer Vpc.
:ivar requester_vpc_info: Information on requester Vpc.
:ivar expiration_time: The expiration date and time for the VPC peering connection.
:ivar status_code: The status of the VPC peering connection.
:ivar status_message: A message that provides more information about the status of the VPC peering connection, if applicable. | Represents a VPC peering connection. | [
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] | def __init__(self, connection=None):
"""
Represents a VPC peering connection.
:ivar id: The unique ID of the VPC peering connection.
:ivar accepter_vpc_info: Information on peer Vpc.
:ivar requester_vpc_info: Information on requester Vpc.
:ivar expiration_time: The expiration date and time for the VPC peering connection.
:ivar status_code: The status of the VPC peering connection.
:ivar status_message: A message that provides more information about the status of the VPC peering connection, if applicable.
"""
super(VpcPeeringConnection, self).__init__(connection)
self.id = None
self.accepter_vpc_info = VpcInfo()
self.requester_vpc_info = VpcInfo()
self.expiration_time = None
self._status = VpcPeeringConnectionStatus() | [
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Pyomo/pyomo | dbd4faee151084f343b893cc2b0c04cf2b76fd92 | pyomo/contrib/pyros/util.py | python | substitute_ssv_in_dr_constraints | (model, constraint) | return model.dr_substituted_constraints[max(model.dr_substituted_constraints.keys())] | Generate the standard_repn for the dr constraints. Generate new expression with replace_expression to ignore
the ssv component.
Then, replace_expression with substitution_map between ssv and the new expression.
Deactivate or del_component the original dr equation.
Then, return modified model and do coefficient matching as normal.
:param model: the working_model
:param constraint: an equality constraint from the working model identified to be of the form h(x,z,q) = 0.
:return: | Generate the standard_repn for the dr constraints. Generate new expression with replace_expression to ignore
the ssv component.
Then, replace_expression with substitution_map between ssv and the new expression.
Deactivate or del_component the original dr equation.
Then, return modified model and do coefficient matching as normal.
:param model: the working_model
:param constraint: an equality constraint from the working model identified to be of the form h(x,z,q) = 0.
:return: | [
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'''
Generate the standard_repn for the dr constraints. Generate new expression with replace_expression to ignore
the ssv component.
Then, replace_expression with substitution_map between ssv and the new expression.
Deactivate or del_component the original dr equation.
Then, return modified model and do coefficient matching as normal.
:param model: the working_model
:param constraint: an equality constraint from the working model identified to be of the form h(x,z,q) = 0.
:return:
'''
dr_eqns = model.util.decision_rule_eqns
fsv = ComponentSet(model.util.first_stage_variables)
if not hasattr(model, "dr_substituted_constraints"):
model.dr_substituted_constraints = ConstraintList()
for eqn in dr_eqns:
repn = generate_standard_repn(eqn.body, compute_values=False)
new_expression = 0
map_linear_coeff_to_var = [x for x in zip(repn.linear_coefs, repn.linear_vars) if x[1] in ComponentSet(fsv)]
map_quad_coeff_to_var = [x for x in zip(repn.quadratic_coefs, repn.quadratic_vars) if x[1] in ComponentSet(fsv)]
if repn.linear_coefs:
for coeff, var in map_linear_coeff_to_var:
new_expression += coeff * var
if repn.quadratic_coefs:
for coeff, var in map_quad_coeff_to_var:
new_expression += coeff * var[0] * var[1] # var here is a 2-tuple
model.no_ssv_dr_expr = Expression(expr=new_expression)
substitution_map = {}
substitution_map[id(repn.linear_vars[-1])] = model.no_ssv_dr_expr.expr
model.dr_substituted_constraints.add(
replace_expressions(expr=constraint.lower,
substitution_map=substitution_map) ==
replace_expressions(expr=constraint.body,
substitution_map=substitution_map))
# === Delete the original constraint
model.del_component(constraint.name)
model.del_component("no_ssv_dr_expr")
return model.dr_substituted_constraints[max(model.dr_substituted_constraints.keys())] | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/urllib3/contrib/appengine.py | python | AppEngineManager.__exit__ | (self, exc_type, exc_val, exc_tb) | return False | [] | def __exit__(self, exc_type, exc_val, exc_tb):
# Return False to re-raise any potential exceptions
return False | [
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OpenEndedGroup/Field | 4f7c8edfb01bb0ccc927b78d3c500f018a4ae37c | Contents/lib/python/optparse.py | python | OptionParser.parse_args | (self, args=None, values=None) | return self.check_values(values, args) | parse_args(args : [string] = sys.argv[1:],
values : Values = None)
-> (values : Values, args : [string])
Parse the command-line options found in 'args' (default:
sys.argv[1:]). Any errors result in a call to 'error()', which
by default prints the usage message to stderr and calls
sys.exit() with an error message. On success returns a pair
(values, args) where 'values' is an Values instance (with all
your option values) and 'args' is the list of arguments left
over after parsing options. | parse_args(args : [string] = sys.argv[1:],
values : Values = None)
-> (values : Values, args : [string]) | [
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"""
parse_args(args : [string] = sys.argv[1:],
values : Values = None)
-> (values : Values, args : [string])
Parse the command-line options found in 'args' (default:
sys.argv[1:]). Any errors result in a call to 'error()', which
by default prints the usage message to stderr and calls
sys.exit() with an error message. On success returns a pair
(values, args) where 'values' is an Values instance (with all
your option values) and 'args' is the list of arguments left
over after parsing options.
"""
rargs = self._get_args(args)
if values is None:
values = self.get_default_values()
# Store the halves of the argument list as attributes for the
# convenience of callbacks:
# rargs
# the rest of the command-line (the "r" stands for
# "remaining" or "right-hand")
# largs
# the leftover arguments -- ie. what's left after removing
# options and their arguments (the "l" stands for "leftover"
# or "left-hand")
self.rargs = rargs
self.largs = largs = []
self.values = values
try:
stop = self._process_args(largs, rargs, values)
except (BadOptionError, OptionValueError), err:
self.error(str(err))
args = largs + rargs
return self.check_values(values, args) | [
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vlachoudis/bCNC | 67126b4894dabf6579baf47af8d0f9b7de35e6e3 | bCNC/plugins/intersection.py | python | Tool.__init__ | (self, master) | [] | def __init__(self, master):
Plugin.__init__(self, master,"Intersection")
#Helical_Descent: is the name of the plugin show in the tool ribbon button
self.icon = "intersection" #<<< This is the name of png file used as icon for the ribbon button. It will be search in the "icons" subfolder
self.group = "Development" #<<< This is the name of group that plugin belongs
self.oneshot = True
#Here we are creating the widgets presented to the user inside the plugin
#Name, Type , Default value, Description
self.variables = [ #<<< Define a list of components for the GUI
("name" , "db" , "", _("Name")), #used to store plugin settings in the internal database
]
self.buttons.append("exe") | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/pygments/util.py | python | make_analysator | (f) | return staticmethod(text_analyse) | Return a static text analysation function that
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"""
Return a static text analysation function that
returns float values.
"""
def text_analyse(text):
rv = f(text)
if not rv:
return 0.0
return min(1.0, max(0.0, float(rv)))
text_analyse.__doc__ = f.__doc__
return staticmethod(text_analyse) | [
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GNS3/gns3-gui | da8adbaa18ab60e053af2a619efd468f4c8950f3 | gns3/local_server_config.py | python | LocalServerConfig.saveSettings | (self, section, settings) | Save all the settings in a given section.
:param section: section name
:param settings: settings to save (dict) | Save all the settings in a given section. | [
"Save",
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] | def saveSettings(self, section, settings):
"""
Save all the settings in a given section.
:param section: section name
:param settings: settings to save (dict)
"""
changed = False
if section not in self._config:
self._config[section] = {}
changed = True
for name, value in settings.items():
if name not in self._config[section] or self._config[section][name] != str(value):
self._config[section][name] = str(value)
changed = True
if changed:
self.writeConfig() | [
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/outtakes/MMKit.py | python | MMKit.show_hybrid_btngrp | (self) | Show the hybrid button group and label above it.
This is a companion method to hide_hybrid_btngrp().
It includes workarounds for Qt layout issues that crop up
when hiding/showing the hybrid button groupbox using Qt's
hide() and show() methods. See bug 2407 for more information. | Show the hybrid button group and label above it.
This is a companion method to hide_hybrid_btngrp().
It includes workarounds for Qt layout issues that crop up
when hiding/showing the hybrid button groupbox using Qt's
hide() and show() methods. See bug 2407 for more information. | [
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"""Show the hybrid button group and label above it.
This is a companion method to hide_hybrid_btngrp().
It includes workarounds for Qt layout issues that crop up
when hiding/showing the hybrid button groupbox using Qt's
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"""
if 1:
self.hybrid_btngrp.show()
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self.atomic_hybrids_label.show() | [
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getavalon/core | 31e8cb4760e00e3db64443f6f932b7fd8e96d41d | avalon/vendor/qtawesome/iconic_font.py | python | CharIconPainter._paint_icon | (self, iconic, painter, rect, mode, state, options) | Paint a single icon | Paint a single icon | [
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"single",
"icon"
] | def _paint_icon(self, iconic, painter, rect, mode, state, options):
"""Paint a single icon"""
painter.save()
color, char = options['color'], options['char']
if mode == QtGui.QIcon.Disabled:
color = options.get('color_disabled', color)
char = options.get('disabled', char)
elif mode == QtGui.QIcon.Active:
color = options.get('color_active', color)
char = options.get('active', char)
elif mode == QtGui.QIcon.Selected:
color = options.get('color_selected', color)
char = options.get('selected', char)
painter.setPen(QtGui.QColor(color))
# A 16 pixel-high icon yields a font size of 14, which is pixel perfect
# for font-awesome. 16 * 0.875 = 14
# The reason for not using full-sized glyphs is the negative bearing of
# fonts.
draw_size = 0.875 * round(rect.height() * options['scale_factor'])
prefix = options['prefix']
# Animation setup hook
animation = options.get('animation')
if animation is not None:
animation.setup(self, painter, rect)
painter.setFont(iconic.font(prefix, draw_size))
if 'offset' in options:
rect = QtCore.QRect(rect)
rect.translate(options['offset'][0] * rect.width(),
options['offset'][1] * rect.height())
painter.setOpacity(options.get('opacity', 1.0))
painter.drawText(rect,
QtCore.Qt.AlignCenter | QtCore.Qt.AlignVCenter,
char)
painter.restore() | [
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dagrz/aws_pwn | e90d6089adc99a0298549d173d17f01b76b22449 | reconnaissance/validate_accounts.py | python | validate_account_signin | (account) | return result | [] | def validate_account_signin(account):
result = {
'accountAlias': None,
'accountId': None,
'signinUri': 'https://' + account + '.signin.aws.amazon.com/',
'exists': False,
'error': None
}
if re.match(r'\d{12}', account):
result['accountId'] = account
else:
result['accountAlias'] = account
if not validators.url(result['signinUri']):
result['error'] = 'Invalid URI'
return result
try:
r = requests.get(result['signinUri'], allow_redirects=False)
if r.status_code == 302:
result['exists'] = True
except requests.exceptions.RequestException as e:
result['error'] = e
return result | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/dcdb/v20180411/models.py | python | CreateDCDBInstanceRequest.__init__ | (self) | r"""
:param Zones: 分片节点可用区分布，最多可填两个可用区。当分片规格为一主两从时，其中两个节点在第一个可用区。
注意当前可售卖的可用区需要通过DescribeDCDBSaleInfo接口拉取。
:type Zones: list of str
:param Period: 欲购买的时长，单位：月。
:type Period: int
:param ShardMemory: 分片内存大小，单位：GB，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardMemory: int
:param ShardStorage: 分片存储空间大小，单位：GB，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardStorage: int
:param ShardNodeCount: 单个分片节点个数，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardNodeCount: int
:param ShardCount: 实例分片个数，可选范围2-8，可以通过升级实例进行新增分片到最多64个分片。
:type ShardCount: int
:param Count: 欲购买实例的数量
:type Count: int
:param ProjectId: 项目 ID，可以通过查看项目列表获取，不传则关联到默认项目
:type ProjectId: int
:param VpcId: 虚拟私有网络 ID，不传或传空表示创建为基础网络
:type VpcId: str
:param SubnetId: 虚拟私有网络子网 ID，VpcId不为空时必填
:type SubnetId: str
:param DbVersionId: 数据库引擎版本，当前可选：8.0.18，10.1.9，5.7.17。
8.0.18 - MySQL 8.0.18；
10.1.9 - Mariadb 10.1.9；
5.7.17 - Percona 5.7.17。
如果不填的话，默认为5.7.17，表示Percona 5.7.17。
:type DbVersionId: str
:param AutoVoucher: 是否自动使用代金券进行支付，默认不使用。
:type AutoVoucher: bool
:param VoucherIds: 代金券ID列表，目前仅支持指定一张代金券。
:type VoucherIds: list of str
:param SecurityGroupId: 安全组id
:type SecurityGroupId: str
:param InstanceName: 实例名称， 可以通过该字段自主的设置实例的名字
:type InstanceName: str
:param Ipv6Flag: 是否支持IPv6
:type Ipv6Flag: int
:param ResourceTags: 标签键值对数组
:type ResourceTags: list of ResourceTag
:param InitParams: 参数列表。本接口的可选值为：character_set_server（字符集，必传），lower_case_table_names（表名大小写敏感，必传，0 - 敏感；1-不敏感），innodb_page_size（innodb数据页，默认16K），sync_mode（同步模式：0 - 异步； 1 - 强同步；2 - 强同步可退化。默认为强同步可退化）。
:type InitParams: list of DBParamValue
:param DcnRegion: DCN源地域
:type DcnRegion: str
:param DcnInstanceId: DCN源实例ID
:type DcnInstanceId: str
:param AutoRenewFlag: 自动续费标记，0表示默认状态(用户未设置，即初始状态即手动续费，用户开通了预付费不停服特权也会进行自动续费)， 1表示自动续费，2表示明确不自动续费(用户设置)，若业务无续费概念或无需自动续费，需要设置为0
:type AutoRenewFlag: int | r"""
:param Zones: 分片节点可用区分布，最多可填两个可用区。当分片规格为一主两从时，其中两个节点在第一个可用区。
注意当前可售卖的可用区需要通过DescribeDCDBSaleInfo接口拉取。
:type Zones: list of str
:param Period: 欲购买的时长，单位：月。
:type Period: int
:param ShardMemory: 分片内存大小，单位：GB，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardMemory: int
:param ShardStorage: 分片存储空间大小，单位：GB，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardStorage: int
:param ShardNodeCount: 单个分片节点个数，可以通过 DescribeShardSpec
查询实例规格获得。
:type ShardNodeCount: int
:param ShardCount: 实例分片个数，可选范围2-8，可以通过升级实例进行新增分片到最多64个分片。
:type ShardCount: int
:param Count: 欲购买实例的数量
:type Count: int
:param ProjectId: 项目 ID，可以通过查看项目列表获取，不传则关联到默认项目
:type ProjectId: int
:param VpcId: 虚拟私有网络 ID，不传或传空表示创建为基础网络
:type VpcId: str
:param SubnetId: 虚拟私有网络子网 ID，VpcId不为空时必填
:type SubnetId: str
:param DbVersionId: 数据库引擎版本，当前可选：8.0.18，10.1.9，5.7.17。
8.0.18 - MySQL 8.0.18；
10.1.9 - Mariadb 10.1.9；
5.7.17 - Percona 5.7.17。
如果不填的话，默认为5.7.17，表示Percona 5.7.17。
:type DbVersionId: str
:param AutoVoucher: 是否自动使用代金券进行支付，默认不使用。
:type AutoVoucher: bool
:param VoucherIds: 代金券ID列表，目前仅支持指定一张代金券。
:type VoucherIds: list of str
:param SecurityGroupId: 安全组id
:type SecurityGroupId: str
:param InstanceName: 实例名称， 可以通过该字段自主的设置实例的名字
:type InstanceName: str
:param Ipv6Flag: 是否支持IPv6
:type Ipv6Flag: int
:param ResourceTags: 标签键值对数组
:type ResourceTags: list of ResourceTag
:param InitParams: 参数列表。本接口的可选值为：character_set_server（字符集，必传），lower_case_table_names（表名大小写敏感，必传，0 - 敏感；1-不敏感），innodb_page_size（innodb数据页，默认16K），sync_mode（同步模式：0 - 异步； 1 - 强同步；2 - 强同步可退化。默认为强同步可退化）。
:type InitParams: list of DBParamValue
:param DcnRegion: DCN源地域
:type DcnRegion: str
:param DcnInstanceId: DCN源实例ID
:type DcnInstanceId: str
:param AutoRenewFlag: 自动续费标记，0表示默认状态(用户未设置，即初始状态即手动续费，用户开通了预付费不停服特权也会进行自动续费)， 1表示自动续费，2表示明确不自动续费(用户设置)，若业务无续费概念或无需自动续费，需要设置为0
:type AutoRenewFlag: int | [
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:param SubnetId: 虚拟私有网络子网 ID，VpcId不为空时必填
:type SubnetId: str
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8.0.18 - MySQL 8.0.18；
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5.7.17 - Percona 5.7.17。
如果不填的话，默认为5.7.17，表示Percona 5.7.17。
:type DbVersionId: str
:param AutoVoucher: 是否自动使用代金券进行支付，默认不使用。
:type AutoVoucher: bool
:param VoucherIds: 代金券ID列表，目前仅支持指定一张代金券。
:type VoucherIds: list of str
:param SecurityGroupId: 安全组id
:type SecurityGroupId: str
:param InstanceName: 实例名称， 可以通过该字段自主的设置实例的名字
:type InstanceName: str
:param Ipv6Flag: 是否支持IPv6
:type Ipv6Flag: int
:param ResourceTags: 标签键值对数组
:type ResourceTags: list of ResourceTag
:param InitParams: 参数列表。本接口的可选值为：character_set_server（字符集，必传），lower_case_table_names（表名大小写敏感，必传，0 - 敏感；1-不敏感），innodb_page_size（innodb数据页，默认16K），sync_mode（同步模式：0 - 异步； 1 - 强同步；2 - 强同步可退化。默认为强同步可退化）。
:type InitParams: list of DBParamValue
:param DcnRegion: DCN源地域
:type DcnRegion: str
:param DcnInstanceId: DCN源实例ID
:type DcnInstanceId: str
:param AutoRenewFlag: 自动续费标记，0表示默认状态(用户未设置，即初始状态即手动续费，用户开通了预付费不停服特权也会进行自动续费)， 1表示自动续费，2表示明确不自动续费(用户设置)，若业务无续费概念或无需自动续费，需要设置为0
:type AutoRenewFlag: int
"""
self.Zones = None
self.Period = None
self.ShardMemory = None
self.ShardStorage = None
self.ShardNodeCount = None
self.ShardCount = None
self.Count = None
self.ProjectId = None
self.VpcId = None
self.SubnetId = None
self.DbVersionId = None
self.AutoVoucher = None
self.VoucherIds = None
self.SecurityGroupId = None
self.InstanceName = None
self.Ipv6Flag = None
self.ResourceTags = None
self.InitParams = None
self.DcnRegion = None
self.DcnInstanceId = None
self.AutoRenewFlag = None | [
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out0fmemory/GoAgent-Always-Available | c4254984fea633ce3d1893fe5901debd9f22c2a9 | server/lib/google/appengine/ext/blobstore/blobstore.py | python | BlobReader.__read_from_buffer | (self, size) | return data, size | Reads at most size bytes from the buffer.
Args:
size: Number of bytes to read, or negative to read the entire buffer.
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Returns:
Tuple (data, size):
data: The bytes read from the buffer.
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isocpp/CppCoreGuidelines | 171fda35972cb0678c26274547be3d5dfaf73156 | scripts/python/cpplint.py | python | NestingState.SeenOpenBrace | (self) | return (not self.stack) or self.stack[-1].seen_open_brace | Check if we have seen the opening brace for the innermost block.
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pkg_resources/_vendor/pyparsing.py | python | ParserElement.transformString | ( self, instring ) | Extension to C{L{scanString}}, to modify matching text with modified tokens that may
be returned from a parse action. To use C{transformString}, define a grammar and
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Prints::
Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York. | Extension to C{L{scanString}}, to modify matching text with modified tokens that may
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print(wd.transformString("now is the winter of our discontent made glorious summer by this sun of york."))
Prints::
Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York. | [
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Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York.
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out = []
lastE = 0
# force preservation of <TAB>s, to minimize unwanted transformation of string, and to
# keep string locs straight between transformString and scanString
self.keepTabs = True
try:
for t,s,e in self.scanString( instring ):
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return "".join(map(_ustr,_flatten(out)))
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openbmc/openbmc | 5f4109adae05f4d6925bfe960007d52f98c61086 | poky/meta/lib/oe/utils.py | python | ThreadedPool.add_task | (self, func, *args, **kargs) | Add a task to the queue | Add a task to the queue | [
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merenlab/anvio | 9b792e2cedc49ecb7c0bed768261595a0d87c012 | anvio/structureops.py | python | ExternalStructuresFile.is_gene_caller_ids_ok | (self) | Returns True if all gene_callers_ids in external structures file are in the contigs database and are coding | Returns True if all gene_callers_ids in external structures file are in the contigs database and are coding | [
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table = contigs_db.get_table_as_dataframe('gene_amino_acid_sequences')
genes_in_contigs_db_with_aa_seqs = table.loc[table['sequence'] != '', 'gene_callers_id'].tolist()
missing_in_contigs = [x for x in self.content['gene_callers_id'] if x not in genes_in_contigs_db_with_aa_seqs]
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Anaconda-Platform/anaconda-project | df5ec33c12591e6512436d38d36c6132fa2e9618 | anaconda_project/internal/cli/download_commands.py | python | main_add | (args) | return add_download(args.directory, args.env_spec, args.filename_variable, args.download_url, args.filename,
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python-telegram-bot/python-telegram-bot | ade1529986f5b6d394a65372d6a27045a70725b2 | telegram/base.py | python | TelegramObject.de_json | (cls: Type[TO], data: Optional[JSONDict], bot: 'Bot') | return cls(bot=bot, **data) | Converts JSON data to a Telegram object.
Args:
data (Dict[:obj:`str`, ...]): The JSON data.
bot (:class:`telegram.Bot`): The bot associated with this object.
Returns:
The Telegram object. | Converts JSON data to a Telegram object. | [
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] | def de_json(cls: Type[TO], data: Optional[JSONDict], bot: 'Bot') -> Optional[TO]:
"""Converts JSON data to a Telegram object.
Args:
data (Dict[:obj:`str`, ...]): The JSON data.
bot (:class:`telegram.Bot`): The bot associated with this object.
Returns:
The Telegram object.
"""
data = cls._parse_data(data)
if data is None:
return None
if cls == TelegramObject:
return cls()
return cls(bot=bot, **data) | [
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niosus/EasyClangComplete | 3b16eb17735aaa3f56bb295fc5481b269ee9f2ef | plugin/clang/cindex35.py | python | AccessSpecifier.name | (self) | return self._name_map[self] | Get the enumeration name of this access specifier. | Get the enumeration name of this access specifier. | [
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self._name_map = {}
for key,value in list(AccessSpecifier.__dict__.items()):
if isinstance(value,AccessSpecifier):
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/whoosh/query/qcore.py | python | Highest.__eq__ | (self, other) | return self.__class__ is type(other) | [] | def __eq__(self, other):
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analyticalmindsltd/smote_variants | dedbc3d00b266954fedac0ae87775e1643bc920a | smote_variants/_smote_variants.py | python | kmeans_SMOTE.get_params | (self, deep=False) | return {'proportion': self.proportion,
'n_neighbors': self.n_neighbors,
'n_clusters': self.n_clusters,
'irt': self.irt,
'n_jobs': self.n_jobs,
'random_state': self._random_state_init} | Returns:
dict: the parameters of the current sampling object | Returns:
dict: the parameters of the current sampling object | [
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"""
Returns:
dict: the parameters of the current sampling object
"""
return {'proportion': self.proportion,
'n_neighbors': self.n_neighbors,
'n_clusters': self.n_clusters,
'irt': self.irt,
'n_jobs': self.n_jobs,
'random_state': self._random_state_init} | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/sklearn/decomposition/factor_analysis.py | python | FactorAnalysis.transform | (self, X) | return X_transformed | Apply dimensionality reduction to X using the model.
Compute the expected mean of the latent variables.
See Barber, 21.2.33 (or Bishop, 12.66).
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
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"""Apply dimensionality reduction to X using the model.
Compute the expected mean of the latent variables.
See Barber, 21.2.33 (or Bishop, 12.66).
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
The latent variables of X.
"""
check_is_fitted(self, 'components_')
X = check_array(X)
Ih = np.eye(len(self.components_))
X_transformed = X - self.mean_
Wpsi = self.components_ / self.noise_variance_
cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))
tmp = fast_dot(X_transformed, Wpsi.T)
X_transformed = fast_dot(tmp, cov_z)
return X_transformed | [
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ring04h/wyportmap | c4201e2313504e780a7f25238eba2a2d3223e739 | sqlalchemy/sql/selectable.py | python | Select.append_from | (self, fromclause) | append the given FromClause expression to this select() construct's
FROM clause.
This is an **in-place** mutation method; the
:meth:`~.Select.select_from` method is preferred, as it provides
standard :term:`method chaining`. | append the given FromClause expression to this select() construct's
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] | def append_from(self, fromclause):
"""append the given FromClause expression to this select() construct's
FROM clause.
This is an **in-place** mutation method; the
:meth:`~.Select.select_from` method is preferred, as it provides
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"""
self._reset_exported()
fromclause = _interpret_as_from(fromclause)
self._from_obj = self._from_obj.union([fromclause]) | [
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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/mailbox.py | python | _PartialFile.tell | (self) | return _ProxyFile.tell(self) - self._start | Return the position with respect to start. | Return the position with respect to start. | [
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"""Return the position with respect to start."""
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/distlib/util.py | python | EventMixin.add | (self, event, subscriber, append=True) | Add a subscriber for an event.
:param event: The name of an event.
:param subscriber: The subscriber to be added (and called when the
event is published).
:param append: Whether to append or prepend the subscriber to an
existing subscriber list for the event. | Add a subscriber for an event. | [
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"""
Add a subscriber for an event.
:param event: The name of an event.
:param subscriber: The subscriber to be added (and called when the
event is published).
:param append: Whether to append or prepend the subscriber to an
existing subscriber list for the event.
"""
subs = self._subscribers
if event not in subs:
subs[event] = deque([subscriber])
else:
sq = subs[event]
if append:
sq.append(subscriber)
else:
sq.appendleft(subscriber) | [
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PanJinquan/tensorflow_models_learning | e7a2773d526e01c76fc8366868099ca3d7a819b4 | slim/nets/inception_resnet_v2.py | python | inception_resnet_v2_arg_scope | (
weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu,
batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS) | Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float added to variance to avoid dividing by zero.
activation_fn: Activation function for conv2d.
batch_norm_updates_collections: Collection for the update ops for
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Returns:
a arg_scope with the parameters needed for inception_resnet_v2. | Returns the scope with the default parameters for inception_resnet_v2. | [
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] | def inception_resnet_v2_arg_scope(
weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu,
batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS):
"""Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float added to variance to avoid dividing by zero.
activation_fn: Activation function for conv2d.
batch_norm_updates_collections: Collection for the update ops for
batch norm.
Returns:
a arg_scope with the parameters needed for inception_resnet_v2.
"""
# Set weight_decay for weights in conv2d and fully_connected layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_regularizer=slim.l2_regularizer(weight_decay)):
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'updates_collections': batch_norm_updates_collections,
'fused': None, # Use fused batch norm if possible.
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as scope:
return scope | [
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matrix-org/synapse | 8e57584a5859a9002759963eb546d523d2498a01 | synapse/storage/databases/main/search.py | python | SearchBackgroundUpdateStore._background_reindex_gin_search | (self, progress, batch_size) | return 1 | This handles old synapses which used GIST indexes, if any;
converting them back to be GIN as per the actual schema. | This handles old synapses which used GIST indexes, if any;
converting them back to be GIN as per the actual schema. | [
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"""This handles old synapses which used GIST indexes, if any;
converting them back to be GIN as per the actual schema.
"""
def create_index(conn):
conn.rollback()
# we have to set autocommit, because postgres refuses to
# CREATE INDEX CONCURRENTLY without it.
conn.set_session(autocommit=True)
try:
c = conn.cursor()
# if we skipped the conversion to GIST, we may already/still
# have an event_search_fts_idx; unfortunately postgres 9.4
# doesn't support CREATE INDEX IF EXISTS so we just catch the
# exception and ignore it.
import psycopg2
try:
c.execute(
"CREATE INDEX CONCURRENTLY event_search_fts_idx"
" ON event_search USING GIN (vector)"
)
except psycopg2.ProgrammingError as e:
logger.warning(
"Ignoring error %r when trying to switch from GIST to GIN", e
)
# we should now be able to delete the GIST index.
c.execute("DROP INDEX IF EXISTS event_search_fts_idx_gist")
finally:
conn.set_session(autocommit=False)
if isinstance(self.database_engine, PostgresEngine):
await self.db_pool.runWithConnection(create_index)
await self.db_pool.updates._end_background_update(
self.EVENT_SEARCH_USE_GIN_POSTGRES_NAME
)
return 1 | [
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OpenEndedGroup/Field | 4f7c8edfb01bb0ccc927b78d3c500f018a4ae37c | Contents/lib/python/urllib2.py | python | request_host | (request) | return host.lower() | Return request-host, as defined by RFC 2965.
Variation from RFC: returned value is lowercased, for convenient
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] | def request_host(request):
"""Return request-host, as defined by RFC 2965.
Variation from RFC: returned value is lowercased, for convenient
comparison.
"""
url = request.get_full_url()
host = urlparse.urlparse(url)[1]
if host == "":
host = request.get_header("Host", "")
# remove port, if present
host = _cut_port_re.sub("", host, 1)
return host.lower() | [
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mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pylibs/win32/gevent/hub.py | python | Hub.run | (self) | Entry-point to running the loop. This method is called automatically
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:raises LoopExit: If the loop finishes running. This means
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"""
Entry-point to running the loop. This method is called automatically
when the hub greenlet is scheduled; do not call it directly.
:raises LoopExit: If the loop finishes running. This means
that there are no other scheduled greenlets, and no active
watchers or servers. In some situations, this indicates a
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"""
assert self is getcurrent(), 'Do not call Hub.run() directly'
while True:
loop = self.loop
loop.error_handler = self
try:
loop.run()
finally:
loop.error_handler = None # break the refcount cycle
self.parent.throw(LoopExit('This operation would block forever', self)) | [
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openstack/cinder | 23494a6d6c51451688191e1847a458f1d3cdcaa5 | cinder/api/v3/snapshots.py | python | SnapshotsController.create | (self, req, body) | return self._view_builder.detail(req, new_snapshot) | Creates a new snapshot. | Creates a new snapshot. | [
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] | def create(self, req, body):
"""Creates a new snapshot."""
kwargs = {}
context = req.environ['cinder.context']
snapshot = body['snapshot']
kwargs['metadata'] = snapshot.get('metadata', None)
volume_id = snapshot['volume_id']
volume = self.volume_api.get(context, volume_id)
req_version = req.api_version_request
force_flag = snapshot.get('force')
force = False
if force_flag is not None:
# note: this won't raise because it passed schema validation
force = strutils.bool_from_string(force_flag, strict=True)
if req_version.matches(mv.SNAPSHOT_IN_USE):
# strictly speaking, the 'force' flag is invalid for
# mv.SNAPSHOT_IN_USE, but we silently ignore a True
# value for backward compatibility
if force is False:
raise exc.HTTPBadRequest(
explanation=SNAPSHOT_IN_USE_FLAG_MSG)
LOG.info("Create snapshot from volume %s", volume_id)
self.validate_name_and_description(snapshot, check_length=False)
if 'name' in snapshot:
snapshot['display_name'] = snapshot.pop('name')
if force:
new_snapshot = self.volume_api.create_snapshot_force(
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**kwargs)
else:
if req_version.matches(mv.SNAPSHOT_IN_USE):
kwargs['allow_in_use'] = True
new_snapshot = self.volume_api.create_snapshot(
context,
volume,
snapshot.get('display_name'),
snapshot.get('description'),
**kwargs)
req.cache_db_snapshot(new_snapshot)
return self._view_builder.detail(req, new_snapshot) | [
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tartiflette/tartiflette | e292c28ed4fa279ecedb8980fc3741965bd28c87 | tartiflette/schema/schema.py | python | GraphQLSchema._validate_arguments_have_valid_type | (self) | return errors | Validates that argument definitions of fields and directives refer to
an input type.
:return: a list of errors
:rtype: List[str] | Validates that argument definitions of fields and directives refer to
an input type.
:return: a list of errors
:rtype: List[str] | [
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] | def _validate_arguments_have_valid_type(self) -> List[str]:
"""
Validates that argument definitions of fields and directives refer to
an input type.
:return: a list of errors
:rtype: List[str]
"""
errors = []
for gqltype in self.type_definitions.values():
try:
for field in gqltype.implemented_fields.values():
for arg in field.arguments.values():
errors.extend(
self._validate_type_is_an_input_types(
arg,
f"Argument < {arg.name} > of Field < {gqltype}.{field.name} >",
)
)
except AttributeError:
pass
for directive in self._directive_definitions.values():
for arg in directive.arguments.values():
errors.extend(
self._validate_type_is_an_input_types(
arg,
f"Argument < {arg.name} > of Directive < {directive.name} >",
)
)
return errors | [
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sveitser/kaggle_diabetic | e6f1cf3109eaf87db12d77d48f73e62a084fd10a | data.py | python | fast_warp | (img, tf, output_shape, mode='constant', order=0) | return t_img | This wrapper function is faster than skimage.transform.warp | This wrapper function is faster than skimage.transform.warp | [
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] | def fast_warp(img, tf, output_shape, mode='constant', order=0):
"""
This wrapper function is faster than skimage.transform.warp
"""
m = tf.params
t_img = np.zeros((img.shape[0],) + output_shape, img.dtype)
for i in range(t_img.shape[0]):
t_img[i] = _warp_fast(img[i], m, output_shape=output_shape,
mode=mode, order=order)
return t_img | [
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harpribot/deep-summarization | 9b3bb1daae11a1db2386dbe4a71848714e6127f8 | models/stacked_bidirectional.py | python | StackedBidirectional._split_train_tst | (self) | divide the data into training and testing data
Create the X_trn, X_tst, for both forward and backward, and Y_trn and Y_tst
Note that only the reviews are changed, and not the summary.
:return: None | divide the data into training and testing data
Create the X_trn, X_tst, for both forward and backward, and Y_trn and Y_tst
Note that only the reviews are changed, and not the summary. | [
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... | def _split_train_tst(self):
"""
divide the data into training and testing data
Create the X_trn, X_tst, for both forward and backward, and Y_trn and Y_tst
Note that only the reviews are changed, and not the summary.
:return: None
"""
num_samples = self.Y.shape[0]
mapper_file = self.checkpointer.get_mapper_file_location()
if not self.checkpointer.is_mapper_checkpointed():
print 'No mapper checkpoint found. Fresh loading in progress ...'
# Now shuffle the data
sample_id = range(num_samples)
random.shuffle(sample_id)
print 'Dumping the mapper shuffle for reuse.'
Pickle.dump(sample_id, open(mapper_file, 'wb'))
print 'Dump complete. Moving Forward...'
else:
print 'Mapper Checkpoint found... Reading from mapper dump'
sample_id = Pickle.load(open(mapper_file, 'rb'))
print 'Mapping unpickling complete.. Moving forward...'
self.X_fwd = self.X_fwd[sample_id]
self.X_bwd = self.X_bwd[sample_id]
self.Y = self.Y[sample_id]
# Now divide the data into test ans train set
test_fraction = 0.01
self.test_size = int(test_fraction * num_samples)
self.train_size = num_samples - self.test_size
# Forward review
self.X_trn_fwd = self.X_fwd[0:self.train_size]
self.X_tst_fwd = self.X_fwd[self.train_size:num_samples]
# Backward review
self.X_trn_bwd = self.X_bwd[0:self.train_size]
self.X_tst_bwd = self.X_bwd[self.train_size:num_samples]
# Summary
self.Y_trn = self.Y[0:self.train_size]
self.Y_tst = self.Y[self.train_size:num_samples] | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/idna/compat.py | python | ToUnicode | (label) | return decode(label) | [] | def ToUnicode(label):
return decode(label) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/schemes/elliptic_curves/ell_generic.py | python | EllipticCurve_generic.rst_transform | (self, r, s, t) | return self.change_weierstrass_model(1, r, s, t) | r"""
Return the transform of the curve by `(r,s,t)` (with `u=1`).
INPUT:
- ``r``, ``s``, ``t`` -- three elements of the base ring.
OUTPUT:
The elliptic curve obtained from self by the standard
Weierstrass transformation `(u,r,s,t)` with `u=1`.
.. note::
This is just a special case of
:meth:`change_weierstrass_model`, with `u=1`.
EXAMPLES::
sage: R.<r,s,t>=QQ[]
sage: E = EllipticCurve([1,2,3,4,5])
sage: E.rst_transform(r,s,t)
Elliptic Curve defined by y^2 + (2*s+1)*x*y + (r+2*t+3)*y = x^3 + (-s^2+3*r-s+2)*x^2 + (3*r^2-r*s-2*s*t+4*r-3*s-t+4)*x + (r^3+2*r^2-r*t-t^2+4*r-3*t+5) over Multivariate Polynomial Ring in r, s, t over Rational Field | r"""
Return the transform of the curve by `(r,s,t)` (with `u=1`). | [
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")",
"(",
"with",
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"=",
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"."
] | def rst_transform(self, r, s, t):
r"""
Return the transform of the curve by `(r,s,t)` (with `u=1`).
INPUT:
- ``r``, ``s``, ``t`` -- three elements of the base ring.
OUTPUT:
The elliptic curve obtained from self by the standard
Weierstrass transformation `(u,r,s,t)` with `u=1`.
.. note::
This is just a special case of
:meth:`change_weierstrass_model`, with `u=1`.
EXAMPLES::
sage: R.<r,s,t>=QQ[]
sage: E = EllipticCurve([1,2,3,4,5])
sage: E.rst_transform(r,s,t)
Elliptic Curve defined by y^2 + (2*s+1)*x*y + (r+2*t+3)*y = x^3 + (-s^2+3*r-s+2)*x^2 + (3*r^2-r*s-2*s*t+4*r-3*s-t+4)*x + (r^3+2*r^2-r*t-t^2+4*r-3*t+5) over Multivariate Polynomial Ring in r, s, t over Rational Field
"""
return self.change_weierstrass_model(1, r, s, t) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/contrib/gis/gdal/srs.py | python | SpatialReference.local | (self) | return bool(capi.islocal(self.ptr)) | Returns True if this SpatialReference is local (root node is LOCAL_CS). | Returns True if this SpatialReference is local (root node is LOCAL_CS). | [
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] | def local(self):
"Returns True if this SpatialReference is local (root node is LOCAL_CS)."
return bool(capi.islocal(self.ptr)) | [
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thiagopena/djangoSIGE | e32186b27bfd8acf21b0fa400e699cb5c73e5433 | djangosige/apps/compras/views/compras.py | python | OrcamentoCompraVencidosListView.view_context | (self, context) | return context | [] | def view_context(self, context):
context['title_complete'] = 'ORÇAMENTOS DE COMPRA VENCIDOS'
context['add_url'] = reverse_lazy('compras:addorcamentocompraview')
return context | [
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araffin/srl-zoo | 438a05ab625a2c5ada573b47f73469d92de82132 | losses/losses.py | python | perceptualSimilarityLoss | (encoded_real, encoded_prediction, next_encoded_real, next_encoded_prediction,
weight, loss_manager) | return weight * pretrained_dae_encoding_loss | Perceptual similarity Loss for VAE as in
# "DARLA: Improving Zero-Shot Transfer in Reinforcement Learning", Higgins et al.
# see https://arxiv.org/pdf/1707.08475.pdf
:param loss_manager: loss criterion needed to log the loss value (LossManager)
:param encoded_real: states encoding the real observation by the DAE (th.Tensor)
:param encoded_prediction: states encoding the vae's predicted observation by the DAE (th.Tensor)
:param next_encoded_real: states encoding the next real observation by the DAE (th.Tensor)
:param next_encoded_prediction: states encoding the vae's predicted next observation by the DAE (th.Tensor)
:param weight: loss for the DAE's embedding l2 distance (float)
:return: (th.Tensor) | Perceptual similarity Loss for VAE as in
# "DARLA: Improving Zero-Shot Transfer in Reinforcement Learning", Higgins et al.
# see https://arxiv.org/pdf/1707.08475.pdf | [
"Perceptual",
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":",
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"/",
"pdf",... | def perceptualSimilarityLoss(encoded_real, encoded_prediction, next_encoded_real, next_encoded_prediction,
weight, loss_manager):
"""
Perceptual similarity Loss for VAE as in
# "DARLA: Improving Zero-Shot Transfer in Reinforcement Learning", Higgins et al.
# see https://arxiv.org/pdf/1707.08475.pdf
:param loss_manager: loss criterion needed to log the loss value (LossManager)
:param encoded_real: states encoding the real observation by the DAE (th.Tensor)
:param encoded_prediction: states encoding the vae's predicted observation by the DAE (th.Tensor)
:param next_encoded_real: states encoding the next real observation by the DAE (th.Tensor)
:param next_encoded_prediction: states encoding the vae's predicted next observation by the DAE (th.Tensor)
:param weight: loss for the DAE's embedding l2 distance (float)
:return: (th.Tensor)
"""
pretrained_dae_encoding_loss = F.mse_loss(encoded_real, encoded_prediction, reduction='sum')
pretrained_dae_encoding_loss += F.mse_loss(next_encoded_real, next_encoded_prediction, reduction='sum')
loss_manager.addToLosses("denoising perceptual similarity", weight, pretrained_dae_encoding_loss)
return weight * pretrained_dae_encoding_loss | [
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | paddlex/ppdet/modeling/backbones/lite_hrnet.py | python | Stem.__init__ | (self,
in_channel,
stem_channel,
out_channel,
expand_ratio,
norm_type='bn',
freeze_norm=False,
norm_decay=0.) | [] | def __init__(self,
in_channel,
stem_channel,
out_channel,
expand_ratio,
norm_type='bn',
freeze_norm=False,
norm_decay=0.):
super(Stem, self).__init__()
self.conv1 = ConvNormLayer(
in_channel,
stem_channel,
filter_size=3,
stride=2,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
mid_channel = int(round(stem_channel * expand_ratio))
branch_channel = stem_channel // 2
if stem_channel == out_channel:
inc_channel = out_channel - branch_channel
else:
inc_channel = out_channel - stem_channel
self.branch1 = nn.Sequential(
ConvNormLayer(
ch_in=branch_channel,
ch_out=branch_channel,
filter_size=3,
stride=2,
groups=branch_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay),
ConvNormLayer(
ch_in=branch_channel,
ch_out=inc_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay), )
self.expand_conv = ConvNormLayer(
ch_in=branch_channel,
ch_out=mid_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.depthwise_conv = ConvNormLayer(
ch_in=mid_channel,
ch_out=mid_channel,
filter_size=3,
stride=2,
groups=mid_channel,
norm_type=norm_type,
freeze_norm=freeze_norm,
norm_decay=norm_decay)
self.linear_conv = ConvNormLayer(
ch_in=mid_channel,
ch_out=branch_channel
if stem_channel == out_channel else stem_channel,
filter_size=1,
stride=1,
norm_type=norm_type,
act='relu',
freeze_norm=freeze_norm,
norm_decay=norm_decay) | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/clients/render/renderer.py | python | RichMediaRenderer.extract_card_info | (self, tag) | return data | [] | def extract_card_info(self, tag):
image = None
title = None
subtitle = None
buttons = []
for child in tag.children:
if child.name is None:
pass
elif child.name == 'image':
image = child.text
elif child.name == 'title':
title = child.text
elif child.name == 'subtitle':
subtitle = child.text
elif child.name == 'button':
button = self.extract_button_info(child)
buttons.append(button)
else:
outputLog(self, "Unknown card tag [%s]" % child.name)
data = {"type": "card", "image": image, "title": title, "subtitle": subtitle, "buttons": buttons}
self.extract_class_attr(tag, data)
self.extract_id_attr(tag, data)
return data | [
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snakeztc/NeuralDialog-ZSDG | 1d1548457a16a2e07567dc8532ea8b2fba178540 | zsdg/nn_lib.py | python | AttnConnector.forward | (self, queries, keys, contents) | return new_s | [] | def forward(self, queries, keys, contents):
batch_size = keys.size(0)
num_key = keys.size(1)
query_embeded = self.query_embed(queries)
key_embeded = self.key_embed(keys)
tiled_query = query_embeded.unsqueeze(1).repeat(1, num_key, 1)
fc1 = F.tanh(tiled_query + key_embeded)
attn = self.attn_w(fc1).squeeze(-1)
attn = F.sigmoid(attn.view(-1, num_key)).view(batch_size, -1, num_key)
mix = torch.bmm(attn, contents).squeeze(1)
out = torch.cat([mix, queries], dim=1)
if self.rnn_cell == 'lstm':
h = self.project_h(out).unsqueeze(0)
c = self.project_c(out).unsqueeze(0)
new_s = (h, c)
else:
new_s = self.project(out).unsqueeze(0)
return new_s | [
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... | https://github.com/snakeztc/NeuralDialog-ZSDG/blob/1d1548457a16a2e07567dc8532ea8b2fba178540/zsdg/nn_lib.py#L77-L98 | |||
caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/numpy/ma/extras.py | python | unique | (ar1, return_index=False, return_inverse=False) | return output | Finds the unique elements of an array.
Masked values are considered the same element (masked). The output array
is always a masked array. See `numpy.unique` for more details.
See Also
--------
numpy.unique : Equivalent function for ndarrays. | Finds the unique elements of an array. | [
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] | def unique(ar1, return_index=False, return_inverse=False):
"""
Finds the unique elements of an array.
Masked values are considered the same element (masked). The output array
is always a masked array. See `numpy.unique` for more details.
See Also
--------
numpy.unique : Equivalent function for ndarrays.
"""
output = np.unique(ar1,
return_index=return_index,
return_inverse=return_inverse)
if isinstance(output, tuple):
output = list(output)
output[0] = output[0].view(MaskedArray)
output = tuple(output)
else:
output = output.view(MaskedArray)
return output | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/github/Team.py | python | Team.get_repos | (self) | return github.PaginatedList.PaginatedList(
github.Repository.Repository, self._requester, self.url + "/repos", None
) | :calls: `GET /teams/:id/repos <http://developer.github.com/v3/orgs/teams>`_
:rtype: :class:`github.PaginatedList.PaginatedList` of :class:`github.Repository.Repository` | :calls: `GET /teams/:id/repos <http://developer.github.com/v3/orgs/teams>`_
:rtype: :class:`github.PaginatedList.PaginatedList` of :class:`github.Repository.Repository` | [
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"""
:calls: `GET /teams/:id/repos <http://developer.github.com/v3/orgs/teams>`_
:rtype: :class:`github.PaginatedList.PaginatedList` of :class:`github.Repository.Repository`
"""
return github.PaginatedList.PaginatedList(
github.Repository.Repository, self._requester, self.url + "/repos", None
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/poplib.py | python | POP3.retr | (self, which) | return self._longcmd('RETR %s' % which) | Retrieve whole message number 'which'.
Result is in form ['response', ['line', ...], octets]. | Retrieve whole message number 'which'. | [
"Retrieve",
"whole",
"message",
"number",
"which",
"."
] | def retr(self, which):
"""Retrieve whole message number 'which'.
Result is in form ['response', ['line', ...], octets].
"""
return self._longcmd('RETR %s' % which) | [
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EtienneCmb/visbrain | b599038e095919dc193b12d5e502d127de7d03c9 | visbrain/objects/roi_obj.py | python | RoiObj.reset | (self) | Reset the RoiObject. | Reset the RoiObject. | [
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] | def reset(self):
"""Reset the RoiObject."""
self.analysis = self._analysis_backup | [
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/Object detection/Mask RCNN/CharlesShang_FastMaskRCNN/libs/layers/anchor.py | python | decode | (boxes, scores, all_anchors, ih, iw) | return final_boxes, classes, scores | Decode outputs into boxes
Parameters
---------
boxes: an array of shape (1, h, w, Ax4)
scores: an array of shape (1, h, w, Ax2),
all_anchors: an array of shape (1, h, w, Ax4), [x1, y1, x2, y2]
Returns
--------
final_boxes: of shape (R x 4)
classes: of shape (R) in {0,1,2,3... K-1}
scores: of shape (R) in [0 ~ 1] | Decode outputs into boxes
Parameters
---------
boxes: an array of shape (1, h, w, Ax4)
scores: an array of shape (1, h, w, Ax2),
all_anchors: an array of shape (1, h, w, Ax4), [x1, y1, x2, y2]
Returns
--------
final_boxes: of shape (R x 4)
classes: of shape (R) in {0,1,2,3... K-1}
scores: of shape (R) in [0 ~ 1] | [
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"""Decode outputs into boxes
Parameters
---------
boxes: an array of shape (1, h, w, Ax4)
scores: an array of shape (1, h, w, Ax2),
all_anchors: an array of shape (1, h, w, Ax4), [x1, y1, x2, y2]
Returns
--------
final_boxes: of shape (R x 4)
classes: of shape (R) in {0,1,2,3... K-1}
scores: of shape (R) in [0 ~ 1]
"""
# h, w = boxes.shape[1], boxes.shape[2]
# if all_anchors is None:
# stride = 2 ** int(round(np.log2((iw + 0.0) / w)))
# all_anchors = anchors_plane(h, w, stride=stride)
all_anchors = all_anchors.reshape((-1, 4))
boxes = boxes.reshape((-1, 4))
scores = scores.reshape((-1, 2))
assert scores.shape[0] == boxes.shape[0] == all_anchors.shape[0], \
'Anchor layer shape error %d vs %d vs %d' % (scores.shape[0],boxes.shape[0],all_anchors.reshape[0])
boxes = bbox_transform_inv(all_anchors, boxes)
classes = np.argmax(scores, axis=1)
scores = scores[:, 1]
final_boxes = boxes
final_boxes = clip_boxes(final_boxes, (ih, iw))
classes = classes.astype(np.int32)
return final_boxes, classes, scores | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | python/dgl/nn/pytorch/conv/edgeconv.py | python | EdgeConv.forward | (self, g, feat) | Description
-----------
Forward computation
Parameters
----------
g : DGLGraph
The graph.
feat : Tensor or pair of tensors
:math:`(N, D)` where :math:`N` is the number of nodes and
:math:`D` is the number of feature dimensions.
If a pair of tensors is given, the graph must be a uni-bipartite graph
with only one edge type, and the two tensors must have the same
dimensionality on all except the first axis.
Returns
-------
torch.Tensor
New node features.
Raises
------
DGLError
If there are 0-in-degree nodes in the input graph, it will raise DGLError
since no message will be passed to those nodes. This will cause invalid output.
The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``. | [] | def forward(self, g, feat):
"""
Description
-----------
Forward computation
Parameters
----------
g : DGLGraph
The graph.
feat : Tensor or pair of tensors
:math:`(N, D)` where :math:`N` is the number of nodes and
:math:`D` is the number of feature dimensions.
If a pair of tensors is given, the graph must be a uni-bipartite graph
with only one edge type, and the two tensors must have the same
dimensionality on all except the first axis.
Returns
-------
torch.Tensor
New node features.
Raises
------
DGLError
If there are 0-in-degree nodes in the input graph, it will raise DGLError
since no message will be passed to those nodes. This will cause invalid output.
The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
"""
with g.local_scope():
if not self._allow_zero_in_degree:
if (g.in_degrees() == 0).any():
raise DGLError('There are 0-in-degree nodes in the graph, '
'output for those nodes will be invalid. '
'This is harmful for some applications, '
'causing silent performance regression. '
'Adding self-loop on the input graph by '
'calling `g = dgl.add_self_loop(g)` will resolve '
'the issue. Setting ``allow_zero_in_degree`` '
'to be `True` when constructing this module will '
'suppress the check and let the code run.')
h_src, h_dst = expand_as_pair(feat, g)
g.srcdata['x'] = h_src
g.dstdata['x'] = h_dst
g.apply_edges(fn.v_sub_u('x', 'x', 'theta'))
g.edata['theta'] = self.theta(g.edata['theta'])
g.dstdata['phi'] = self.phi(g.dstdata['x'])
if not self.batch_norm:
g.update_all(fn.e_add_v('theta', 'phi', 'e'), fn.max('e', 'x'))
else:
g.apply_edges(fn.e_add_v('theta', 'phi', 'e'))
# Although the official implementation includes a per-edge
# batch norm within EdgeConv, I choose to replace it with a
# global batch norm for a number of reasons:
#
# (1) When the point clouds within each batch do not have the
# same number of points, batch norm would not work.
#
# (2) Even if the point clouds always have the same number of
# points, the points may as well be shuffled even with the
# same (type of) object (and the official implementation
# *does* shuffle the points of the same example for each
# epoch).
#
# For example, the first point of a point cloud of an
# airplane does not always necessarily reside at its nose.
#
# In this case, the learned statistics of each position
# by batch norm is not as meaningful as those learned from
# images.
g.edata['e'] = self.bn(g.edata['e'])
g.update_all(fn.copy_e('e', 'e'), fn.max('e', 'x'))
return g.dstdata['x'] | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/svg_reader.py | python | SVGReader.getRotatedLoopLayer | (self) | return self.rotatedLoopLayers[-1] | Return the rotated loop layer. | Return the rotated loop layer. | [
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] | def getRotatedLoopLayer(self):
"Return the rotated loop layer."
if self.z != None:
rotatedLoopLayer = euclidean.RotatedLoopLayer( self.z )
self.rotatedLoopLayers.append( rotatedLoopLayer )
rotatedLoopLayer.rotation = self.bridgeRotation
self.z = None
return self.rotatedLoopLayers[-1] | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/redis/client.py | python | BasePipeline.__exit__ | (self, exc_type, exc_value, traceback) | [] | def __exit__(self, exc_type, exc_value, traceback):
self.reset() | [
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python-discord/bot | 26c5587ac13e5414361bb6e7ada42983b81014d2 | bot/exts/info/doc/_cog.py | python | DocCog.set_command | (
self,
ctx: commands.Context,
package_name: PackageName,
inventory: Inventory,
base_url: ValidURL = "",
) | Adds a new documentation metadata object to the site's database.
The database will update the object, should an existing item with the specified `package_name` already exist.
If the base url is not specified, a default created by removing the last segment of the inventory url is used.
Example:
!docs setdoc \
python \
https://docs.python.org/3/objects.inv | Adds a new documentation metadata object to the site's database. | [
"Adds",
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"new",
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"metadata",
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"to",
"the",
"site",
"s",
"database",
"."
] | async def set_command(
self,
ctx: commands.Context,
package_name: PackageName,
inventory: Inventory,
base_url: ValidURL = "",
) -> None:
"""
Adds a new documentation metadata object to the site's database.
The database will update the object, should an existing item with the specified `package_name` already exist.
If the base url is not specified, a default created by removing the last segment of the inventory url is used.
Example:
!docs setdoc \
python \
https://docs.python.org/3/objects.inv
"""
if base_url and not base_url.endswith("/"):
raise commands.BadArgument("The base url must end with a slash.")
inventory_url, inventory_dict = inventory
body = {
"package": package_name,
"base_url": base_url,
"inventory_url": inventory_url
}
try:
await self.bot.api_client.post("bot/documentation-links", json=body)
except ResponseCodeError as err:
if err.status == 400 and "already exists" in err.response_json.get("package", [""])[0]:
log.info(f"Ignoring HTTP 400 as package {package_name} has already been added.")
await ctx.send(f"Package {package_name} has already been added.")
return
raise
log.info(
f"User @{ctx.author} ({ctx.author.id}) added a new documentation package:\n"
+ "\n".join(f"{key}: {value}" for key, value in body.items())
)
if not base_url:
base_url = self.base_url_from_inventory_url(inventory_url)
self.update_single(package_name, base_url, inventory_dict)
await ctx.send(f"Added the package `{package_name}` to the database and updated the inventories.") | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/nose/tools/nontrivial.py | python | nottest | (func) | return func | Decorator to mark a function or method as *not* a test | Decorator to mark a function or method as *not* a test | [
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"""Decorator to mark a function or method as *not* a test
"""
func.__test__ = False
return func | [
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mcneel/rhinoscriptsyntax | c49bd0bf24c2513bdcb84d1bf307144489600fd9 | Scripts/rhinoscript/document.py | python | RenderMeshMaxAspectRatio | (ratio=None) | return rc | Returns or sets the render mesh maximum aspect ratio property of the active document.
For more information on render meshes, see the Document Properties: Mesh topic in the Rhino help file.
Parameters:
ratio (number, optional): the render mesh maximum aspect ratio. The suggested range, when not zero, is from 1 to 100.
Returns:
number: if ratio is not specified, the current render mesh maximum aspect ratio if successful.
number: if ratio is specified, the previous render mesh maximum aspect ratio if successful.
None: if not successful, or on error.
Example:
import rhinoscriptsyntax as rs
print("Quality: %s" % rs.RenderMeshQuality())
print("Mesh density: %s" % rs.RenderMeshDensity())
print("Maximum angle: %s" % rs.RenderMeshMaxAngle())
print("Maximum aspect ratio: %s" % rs.RenderMeshMaxAspectRatio())
print("Minimun edge length: %s" % rs.RenderMeshMinEdgeLength())
print("Maximum edge length: %s" % rs.RenderMeshMaxEdgeLength())
print("Maximum distance, edge to surface: %s" % rs.RenderMeshMaxDistEdgeToSrf())
print("Minumum initial grid quads: %s" % rs.RenderMeshMinInitialGridQuads())
print("Other settings: %s" % rs.RenderMeshSettings())
See Also:
RenderMeshDensity
RenderMeshMaxAngle
RenderMeshMaxAspectRatio
RenderMeshMaxDistEdgeToSrf
RenderMeshMaxEdgeLength
RenderMeshMinEdgeLength
RenderMeshMinInitialGridQuads
RenderMeshQuality
RenderMeshSettings | Returns or sets the render mesh maximum aspect ratio property of the active document.
For more information on render meshes, see the Document Properties: Mesh topic in the Rhino help file.
Parameters:
ratio (number, optional): the render mesh maximum aspect ratio. The suggested range, when not zero, is from 1 to 100.
Returns:
number: if ratio is not specified, the current render mesh maximum aspect ratio if successful.
number: if ratio is specified, the previous render mesh maximum aspect ratio if successful.
None: if not successful, or on error.
Example:
import rhinoscriptsyntax as rs
print("Quality: %s" % rs.RenderMeshQuality())
print("Mesh density: %s" % rs.RenderMeshDensity())
print("Maximum angle: %s" % rs.RenderMeshMaxAngle())
print("Maximum aspect ratio: %s" % rs.RenderMeshMaxAspectRatio())
print("Minimun edge length: %s" % rs.RenderMeshMinEdgeLength())
print("Maximum edge length: %s" % rs.RenderMeshMaxEdgeLength())
print("Maximum distance, edge to surface: %s" % rs.RenderMeshMaxDistEdgeToSrf())
print("Minumum initial grid quads: %s" % rs.RenderMeshMinInitialGridQuads())
print("Other settings: %s" % rs.RenderMeshSettings())
See Also:
RenderMeshDensity
RenderMeshMaxAngle
RenderMeshMaxAspectRatio
RenderMeshMaxDistEdgeToSrf
RenderMeshMaxEdgeLength
RenderMeshMinEdgeLength
RenderMeshMinInitialGridQuads
RenderMeshQuality
RenderMeshSettings | [
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"""Returns or sets the render mesh maximum aspect ratio property of the active document.
For more information on render meshes, see the Document Properties: Mesh topic in the Rhino help file.
Parameters:
ratio (number, optional): the render mesh maximum aspect ratio. The suggested range, when not zero, is from 1 to 100.
Returns:
number: if ratio is not specified, the current render mesh maximum aspect ratio if successful.
number: if ratio is specified, the previous render mesh maximum aspect ratio if successful.
None: if not successful, or on error.
Example:
import rhinoscriptsyntax as rs
print("Quality: %s" % rs.RenderMeshQuality())
print("Mesh density: %s" % rs.RenderMeshDensity())
print("Maximum angle: %s" % rs.RenderMeshMaxAngle())
print("Maximum aspect ratio: %s" % rs.RenderMeshMaxAspectRatio())
print("Minimun edge length: %s" % rs.RenderMeshMinEdgeLength())
print("Maximum edge length: %s" % rs.RenderMeshMaxEdgeLength())
print("Maximum distance, edge to surface: %s" % rs.RenderMeshMaxDistEdgeToSrf())
print("Minumum initial grid quads: %s" % rs.RenderMeshMinInitialGridQuads())
print("Other settings: %s" % rs.RenderMeshSettings())
See Also:
RenderMeshDensity
RenderMeshMaxAngle
RenderMeshMaxAspectRatio
RenderMeshMaxDistEdgeToSrf
RenderMeshMaxEdgeLength
RenderMeshMinEdgeLength
RenderMeshMinInitialGridQuads
RenderMeshQuality
RenderMeshSettings
"""
current = scriptcontext.doc.GetCurrentMeshingParameters()
rc = current.GridAspectRatio
if ratio is not None:
current.GridAspectRatio = ratio
_SetRenderMeshAndUpdateStyle(current)
return rc | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/pip/_vendor/requests/models.py | python | Response.json | (self, **kwargs) | return complexjson.loads(self.text, **kwargs) | Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes. | Returns the json-encoded content of a response, if any. | [
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] | def json(self, **kwargs):
"""Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes.
"""
if not self.encoding and self.content and len(self.content) > 3:
# No encoding set. JSON RFC 4627 section 3 states we should expect
# UTF-8, -16 or -32. Detect which one to use; If the detection or
# decoding fails, fall back to `self.text` (using chardet to make
# a best guess).
encoding = guess_json_utf(self.content)
if encoding is not None:
try:
return complexjson.loads(
self.content.decode(encoding), **kwargs
)
except UnicodeDecodeError:
# Wrong UTF codec detected; usually because it's not UTF-8
# but some other 8-bit codec. This is an RFC violation,
# and the server didn't bother to tell us what codec *was*
# used.
pass
return complexjson.loads(self.text, **kwargs) | [
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"(",
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")",
">",
"3",
":",
"# No encoding set. JSON RFC 4627 section 3 states we should ex... | https://github.com/khanhnamle1994/natural-language-processing/blob/01d450d5ac002b0156ef4cf93a07cb508c1bcdc5/assignment1/.env/lib/python2.7/site-packages/pip/_vendor/requests/models.py#L803-L826 | |
asweigart/PythonStdioGames | 8bdabf93e6b1bb6af3e26fea24da93f85e8314b6 | src/gamesbyexample/chancecheckers.py | python | displayBoard | (board) | Display the board data structure on the screen. | Display the board data structure on the screen. | [
"Display",
"the",
"board",
"data",
"structure",
"on",
"the",
"screen",
"."
] | def displayBoard(board):
"""Display the board data structure on the screen."""
spaces = [] # Contains all the characters to display at each space.
for row in range(1, 9):
if row % 2 == 0:
for column in EVEN_CHECKER_COLUMNS:
spaces.append(board[column + str(row)])
else:
for column in ODD_CHECKER_COLUMNS:
spaces.append(board[column + str(row)])
print("""
A B C D E F G H
+---+---+---+---+---+---+---+---+
1 | | {} | | {} | | {} | | {} | 1
+---+---+---+---+---+---+---+---+
2 | {} | | {} | | {} | | {} | | 2
+---+---+---+---+---+---+---+---+
3 | | {} | | {} | | {} | | {} | 3
+---+---+---+---+---+---+---+---+
4 | {} | | {} | | {} | | {} | | 4
+---+---+---+---+---+---+---+---+
5 | | {} | | {} | | {} | | {} | 5
+---+---+---+---+---+---+---+---+
6 | {} | | {} | | {} | | {} | | 6
+---+---+---+---+---+---+---+---+
7 | | {} | | {} | | {} | | {} | 7
+---+---+---+---+---+---+---+---+
8 | {} | | {} | | {} | | {} | | 8
+---+---+---+---+---+---+---+---+
A B C D E F G H""".format(*spaces)) | [
"def",
"displayBoard",
"(",
"board",
")",
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"[",
"]",
"# Contains all the characters to display at each space.",
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")",
":",
"if",
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"2",
"==",
"0",
":",
"for",
"column",
"in",
"E... | https://github.com/asweigart/PythonStdioGames/blob/8bdabf93e6b1bb6af3e26fea24da93f85e8314b6/src/gamesbyexample/chancecheckers.py#L72-L102 | ||
flaskbb/flaskbb | de13a37fcb713b9c627632210ab9a7bb980d591f | flaskbb/plugins/spec.py | python | flaskbb_email_updated | (user, email_update) | Hook for responding to a user updating their email. This hook is called after
the email change has been persisted::
@impl
def flaskbb_email_updated(app):
send_email(
"Email changed",
[email_change.old_email],
text_body=...,
html_body=...
)
See also :class:`~flaskbb.core.changesets.ChangeSetPostProcessor`.
:param user: The user whose email was updated.
:param email_update: The change set applied to the user. | Hook for responding to a user updating their email. This hook is called after
the email change has been persisted:: | [
"Hook",
"for",
"responding",
"to",
"a",
"user",
"updating",
"their",
"email",
".",
"This",
"hook",
"is",
"called",
"after",
"the",
"email",
"change",
"has",
"been",
"persisted",
"::"
] | def flaskbb_email_updated(user, email_update):
"""
Hook for responding to a user updating their email. This hook is called after
the email change has been persisted::
@impl
def flaskbb_email_updated(app):
send_email(
"Email changed",
[email_change.old_email],
text_body=...,
html_body=...
)
See also :class:`~flaskbb.core.changesets.ChangeSetPostProcessor`.
:param user: The user whose email was updated.
:param email_update: The change set applied to the user.
""" | [
"def",
"flaskbb_email_updated",
"(",
"user",
",",
"email_update",
")",
":"
] | https://github.com/flaskbb/flaskbb/blob/de13a37fcb713b9c627632210ab9a7bb980d591f/flaskbb/plugins/spec.py#L672-L691 | ||
wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/Object detection/YOLO/YOLO_tensorflow/YOLO_face_tf.py | python | YOLO_TF.conv_layer | (self,idx,inputs,filters,size,stride) | return tf.maximum(self.alpha*conv_biased,conv_biased,name=str(idx)+'_leaky_relu') | [] | def conv_layer(self,idx,inputs,filters,size,stride):
channels = inputs.get_shape()[3]
weight = tf.Variable(tf.truncated_normal([size,size,int(channels),filters], stddev=0.1))
biases = tf.Variable(tf.constant(0.1, shape=[filters]))
pad_size = size//2
pad_mat = np.array([[0,0],[pad_size,pad_size],[pad_size,pad_size],[0,0]])
inputs_pad = tf.pad(inputs,pad_mat)
conv = tf.nn.conv2d(inputs_pad, weight, strides=[1, stride, stride, 1], padding='VALID',name=str(idx)+'_conv')
conv_biased = tf.add(conv,biases,name=str(idx)+'_conv_biased')
if self.disp_console : print ' Layer %d : Type = Conv, Size = %d * %d, Stride = %d, Filters = %d, Input channels = %d' % (idx,size,size,stride,filters,int(channels))
return tf.maximum(self.alpha*conv_biased,conv_biased,name=str(idx)+'_leaky_relu') | [
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"conv_layer",
"(",
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",",
"inputs",
",",
"filters",
",",
"size",
",",
"stride",
")",
":",
"channels",
"=",
"inputs",
".",
"get_shape",
"(",
")",
"[",
"3",
"]",
"weight",
"=",
"tf",
".",
"Variable",
"(",
"tf",
".",
"truncate... | https://github.com/wucng/TensorExpand/blob/4ea58f64f5c5082b278229b799c9f679536510b7/TensorExpand/Object detection/YOLO/YOLO_tensorflow/YOLO_face_tf.py#L71-L83 | |||
HazyResearch/fonduer | c9fd6b91998cd708ab95aeee3dfaf47b9e549ffd | src/fonduer/parser/visual_parser/visual_parser.py | python | VisualParser.is_parsable | (self, document_name: str) | Check if visual information can be parsed.
:param document_name: the document name.
:return: Whether visual information is parsable. | Check if visual information can be parsed. | [
"Check",
"if",
"visual",
"information",
"can",
"be",
"parsed",
"."
] | def is_parsable(self, document_name: str) -> bool:
"""Check if visual information can be parsed.
:param document_name: the document name.
:return: Whether visual information is parsable.
"""
pass | [
"def",
"is_parsable",
"(",
"self",
",",
"document_name",
":",
"str",
")",
"->",
"bool",
":",
"pass"
] | https://github.com/HazyResearch/fonduer/blob/c9fd6b91998cd708ab95aeee3dfaf47b9e549ffd/src/fonduer/parser/visual_parser/visual_parser.py#L26-L32 | ||
pyglet/pyglet | 2833c1df902ca81aeeffa786c12e7e87d402434b | pyglet/extlibs/png.py | python | rescale_rows | (rows, rescale) | Take each row in rows (an iterator) and yield
a fresh row with the pixels scaled according to
the rescale parameters in the list `rescale`.
Each element of `rescale` is a tuple of
(source_bitdepth, target_bitdepth),
with one element per channel. | Take each row in rows (an iterator) and yield
a fresh row with the pixels scaled according to
the rescale parameters in the list `rescale`.
Each element of `rescale` is a tuple of
(source_bitdepth, target_bitdepth),
with one element per channel. | [
"Take",
"each",
"row",
"in",
"rows",
"(",
"an",
"iterator",
")",
"and",
"yield",
"a",
"fresh",
"row",
"with",
"the",
"pixels",
"scaled",
"according",
"to",
"the",
"rescale",
"parameters",
"in",
"the",
"list",
"rescale",
".",
"Each",
"element",
"of",
"res... | def rescale_rows(rows, rescale):
"""
Take each row in rows (an iterator) and yield
a fresh row with the pixels scaled according to
the rescale parameters in the list `rescale`.
Each element of `rescale` is a tuple of
(source_bitdepth, target_bitdepth),
with one element per channel.
"""
# One factor for each channel
fs = [float(2 ** s[1] - 1)/float(2 ** s[0] - 1)
for s in rescale]
# Assume all target_bitdepths are the same
target_bitdepths = set(s[1] for s in rescale)
assert len(target_bitdepths) == 1
(target_bitdepth, ) = target_bitdepths
typecode = 'BH'[target_bitdepth > 8]
# Number of channels
n_chans = len(rescale)
for row in rows:
rescaled_row = array(typecode, iter(row))
for i in range(n_chans):
channel = array(
typecode,
(int(round(fs[i] * x)) for x in row[i::n_chans]))
rescaled_row[i::n_chans] = channel
yield rescaled_row | [
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"rescale_rows",
"(",
"rows",
",",
"rescale",
")",
":",
"# One factor for each channel",
"fs",
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"[",
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"(",
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"/",
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"(",
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"[",
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"]",
"-",
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"... | https://github.com/pyglet/pyglet/blob/2833c1df902ca81aeeffa786c12e7e87d402434b/pyglet/extlibs/png.py#L928-L958 |
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