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Befzz/blender3d_import_psk_psa | 47f1418aef7642f300e0fccbe3c96654ab275a52 | addons/io_import_scene_unreal_psa_psk_270.py | python | obj_select_get | (obj) | return obj.select | [] | def obj_select_get(obj):
return obj.select | [
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|||
openvinotoolkit/training_extensions | e7aa33af94a1f8004d3ea2df259d99234dfca046 | ote_cli/ote_cli/utils/io.py | python | save_model_data | (model, folder) | Saves model data to folder. Folder is created if it does not exist. | Saves model data to folder. Folder is created if it does not exist. | [
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"""
Saves model data to folder. Folder is created if it does not exist.
"""
os.makedirs(folder, exist_ok=True)
for filename, model_adapter in model.model_adapters.items():
with open(os.path.join(folder, filename), "wb") as write_file:
write_file.write(model_adapter.data) | [
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||
aichallenge/aichallenge | 4237971f22767ef7f439d297e4e6ad7c458415dc | setup/worker_setup.py | python | main | (argv=["worker_setup.py"]) | Completely set everything up from a fresh ec2 instance | Completely set everything up from a fresh ec2 instance | [
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] | def main(argv=["worker_setup.py"]):
""" Completely set everything up from a fresh ec2 instance """
_, ubuntu_arch = check_ubuntu_version()
opts = get_options(argv)
opts.arch = ubuntu_arch
with Environ("DEBIAN_FRONTEND", "noninteractive"):
if opts.update_system:
run_cmd("apt-get update")
run_cmd("apt-get upgrade -y")
if opts.install_required:
install_required_packages()
if opts.install_utilities:
install_utility_packages()
if opts.install_pkg_languages:
install_packaged_languages()
if opts.install_languages:
install_all_languages(opts)
if opts.install_jailguard:
install_jailguard(opts)
if opts.create_jails:
setup_base_chroot(opts)
if opts.packages_only:
return
setup_contest_files(opts)
if opts.create_jails:
setup_base_jail(opts)
setup_jailusers(opts)
start_script = os.path.join(opts.root_dir, opts.local_repo,
"worker/start_worker.sh")
if opts.install_cronjob:
cron_file = "/etc/cron.d/ai-contest"
if not file_contains(cron_file, start_script):
append_line(cron_file, "@reboot %s %s"
% (opts.username, start_script,))
if opts.run_worker:
run_cmd("sudo -u %s %s" % (opts.username, start_script)) | [
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||
jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/scipy/optimize/_lsq/trf_linear.py | python | backtracking | (A, g, x, p, theta, p_dot_g, lb, ub) | return x, step, cost_change | Find an appropriate step size using backtracking line search. | Find an appropriate step size using backtracking line search. | [
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"step",
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"using",
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] | def backtracking(A, g, x, p, theta, p_dot_g, lb, ub):
"""Find an appropriate step size using backtracking line search."""
alpha = 1
while True:
x_new, _ = reflective_transformation(x + alpha * p, lb, ub)
step = x_new - x
cost_change = -evaluate_quadratic(A, g, step)
if cost_change > -0.1 * alpha * p_dot_g:
break
alpha *= 0.5
active = find_active_constraints(x_new, lb, ub)
if np.any(active != 0):
x_new, _ = reflective_transformation(x + theta * alpha * p, lb, ub)
x_new = make_strictly_feasible(x_new, lb, ub, rstep=0)
step = x_new - x
cost_change = -evaluate_quadratic(A, g, step)
return x, step, cost_change | [
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|
hidasib/GRU4Rec | 8184fd9b3769469dfc1a2b583e401c794016fbbf | baselines.py | python | ItemKNN.predict_next | (self, session_id, input_item_id, predict_for_item_ids) | return pd.Series(data=preds, index=predict_for_item_ids) | Gives predicton scores for a selected set of items on how likely they be the next item in the session.
Parameters
--------
session_id : int or string
The session IDs of the event.
input_item_id : int or string
The item ID of the event. Must be in the set of item IDs of the training set.
predict_for_item_ids : 1D array
IDs of items for which the network should give prediction scores. Every ID must be in the set of item IDs of the training set.
Returns
--------
out : pandas.Series
Prediction scores for selected items on how likely to be the next item of this session. Indexed by the item IDs. | Gives predicton scores for a selected set of items on how likely they be the next item in the session.
Parameters
--------
session_id : int or string
The session IDs of the event.
input_item_id : int or string
The item ID of the event. Must be in the set of item IDs of the training set.
predict_for_item_ids : 1D array
IDs of items for which the network should give prediction scores. Every ID must be in the set of item IDs of the training set.
Returns
--------
out : pandas.Series
Prediction scores for selected items on how likely to be the next item of this session. Indexed by the item IDs. | [
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'''
Gives predicton scores for a selected set of items on how likely they be the next item in the session.
Parameters
--------
session_id : int or string
The session IDs of the event.
input_item_id : int or string
The item ID of the event. Must be in the set of item IDs of the training set.
predict_for_item_ids : 1D array
IDs of items for which the network should give prediction scores. Every ID must be in the set of item IDs of the training set.
Returns
--------
out : pandas.Series
Prediction scores for selected items on how likely to be the next item of this session. Indexed by the item IDs.
'''
preds = np.zeros(len(predict_for_item_ids))
sim_list = self.sims[input_item_id]
mask = np.in1d(predict_for_item_ids, sim_list.index)
preds[mask] = sim_list[predict_for_item_ids[mask]]
return pd.Series(data=preds, index=predict_for_item_ids) | [
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|
securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/numpy/distutils/misc_util.py | python | default_config_dict | (name = None, parent_name = None, local_path=None) | return c.todict() | Return a configuration dictionary for usage in
configuration() function defined in file setup_<name>.py. | Return a configuration dictionary for usage in
configuration() function defined in file setup_<name>.py. | [
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] | def default_config_dict(name = None, parent_name = None, local_path=None):
"""Return a configuration dictionary for usage in
configuration() function defined in file setup_<name>.py.
"""
import warnings
warnings.warn('Use Configuration(%r,%r,top_path=%r) instead of '\
'deprecated default_config_dict(%r,%r,%r)'
% (name, parent_name, local_path,
name, parent_name, local_path,
), stacklevel=2)
c = Configuration(name, parent_name, local_path)
return c.todict() | [
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|
richq/folders2flickr | 0b735057dbf3c0ea132668af36d30ded52e7b6d9 | f2flickr/flickr.py | python | Blogs.getList | (self,auth=True) | return myReturn | blogs.getList requires READ authentication | blogs.getList requires READ authentication | [
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".",
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"requires",
"READ",
"authentication"
] | def getList(self,auth=True):
"""blogs.getList requires READ authentication"""
# please read documentation on how to use this
method = 'flickr.blogs.getList'
if auth==True : data = _doget(method, auth=True)
if not auth==True : data = _doget(method, auth=False)
bID = []
bName = []
bNeedsPword = []
bURL = []
try:
for plog in data.rsp.blogs.blog:
bID.append(plog.id)
bName.append(plog.name)
bNeedsPword.append(plog.needspassword)
bURL.append(plog.url)
except TypeError:
try:
bID.append(data.rsp.blogs.blog.id)
bName.append(data.rsp.blogs.blog.name)
bNeedsPword.append(data.rsp.blogs.blog.needspassword)
bURL.append(data.rsp.blogs.blog.url)
except AttributeError:
return "AttributeError, unexplained!"
except:
return "Unknown error!"
except AttributeError:
return "There are no blogs!"
myReturn = [bID,bName,bNeedsPword,bURL]
return myReturn | [
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] | https://github.com/richq/folders2flickr/blob/0b735057dbf3c0ea132668af36d30ded52e7b6d9/f2flickr/flickr.py#L1414-L1447 |
|
miketeo/pysmb | fc3faca073385b8abc4a503bb4439f849840f94c | python2/smb/SMBConnection.py | python | SMBConnection.echo | (self, data, timeout = 10) | return results[0] | Send an echo command containing *data* to the remote SMB/CIFS server. The remote SMB/CIFS will reply with the same *data*.
:param bytes data: Data to send to the remote server. Must be a bytes object.
:return: The *data* parameter | Send an echo command containing *data* to the remote SMB/CIFS server. The remote SMB/CIFS will reply with the same *data*. | [
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"""
Send an echo command containing *data* to the remote SMB/CIFS server. The remote SMB/CIFS will reply with the same *data*.
:param bytes data: Data to send to the remote server. Must be a bytes object.
:return: The *data* parameter
"""
if not self.sock:
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results = [ ]
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self.is_busy = False
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|
googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/gender_view_service/client.py | python | GenderViewServiceClient.parse_common_organization_path | (path: str) | return m.groupdict() if m else {} | Parse a organization path into its component segments. | Parse a organization path into its component segments. | [
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] | def parse_common_organization_path(path: str) -> Dict[str, str]:
"""Parse a organization path into its component segments."""
m = re.match(r"^organizations/(?P<organization>.+?)$", path)
return m.groupdict() if m else {} | [
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|
obspy/obspy | 0ee5a0d2db293c8d5d4c3b1f148a6c5a85fea55f | obspy/clients/filesystem/msriterator.py | python | _MSRIterator.__next__ | (self) | return self | Read next record from file. | Read next record from file. | [
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"""
Read next record from file.
"""
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if errcode == MS_ENDOFFILE:
raise StopIteration()
if self.raise_errors:
if errcode != MS_NOERROR:
raise Exception("Error %d in ms_readmsr_r" % errcode)
return self | [
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|
Qirky/FoxDot | 76318f9630bede48ff3994146ed644affa27bfa4 | FoxDot/lib/Workspace/TextBox.py | python | ThreadedText.update | (self) | return | Recursively called method that monitors as
queue of Tkinter tasks. | Recursively called method that monitors as
queue of Tkinter tasks. | [
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] | def update(self):
""" Recursively called method that monitors as
queue of Tkinter tasks.
"""
try:
while True:
task, args, kwargs = self.queue.get_nowait()
task(*args, **kwargs)
self.update_idletasks()
# Break when the queue is empty
except Queue.Empty:
pass
except Exception as e:
print(e)
# Recursive call
self.after(10, self.update)
return | [
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|
Net-ng/kansha | 85b5816da126b1c7098707c98f217d8b2e524ff2 | kansha/user/usermanager.py | python | UserManager.get_confirmed_users | () | return DataUser.get_confirmed_users() | Return confirmed user
Return all user which have email validated
Return:
- list of DataUser instance | Return confirmed user | [
"Return",
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] | def get_confirmed_users():
"""Return confirmed user
Return all user which have email validated
Return:
- list of DataUser instance
"""
return DataUser.get_confirmed_users() | [
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|
dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/messaging/smsbackends/telerivet/views.py | python | TelerivetSetupView.page_context | (self) | return {
'outgoing_sms_form': TelerivetOutgoingSMSForm(),
'test_sms_form': TelerivetPhoneNumberForm(),
'finalize_gateway_form': FinalizeGatewaySetupForm(
initial={
'name': 'TELERIVET',
'set_as_default': (FinalizeGatewaySetupForm.NO
if domain_has_default_gateway
else FinalizeGatewaySetupForm.YES),
}
),
'webhook_url': webhook_url,
'include_https_notice': webhook_url.startswith('https'),
'webhook_secret': webhook_secret,
'request_token': request_token,
'gateway_list_url': reverse(DomainSmsGatewayListView.urlname, args=[self.domain]),
} | [] | def page_context(self):
# The webhook secret is a piece of data that is sent to hq on each
# Telerivet inbound request. It's used to tie an inbound request to
# a Telerivet backend.
webhook_secret = uuid.uuid4().hex
# The request token is only used for the purposes of using this UI to
# setup a Telerivet backend. We need a way to post the webhook_secret
# to create the backend, but we want hq to be the origin of the secret
# generation. So instead, the request_token resolves to the webhook_secret
# via a redis lookup which expires in 1 week.
request_token = uuid.uuid4().hex
TelerivetSetupView.set_cached_webhook_secret(request_token, webhook_secret)
domain_has_default_gateway = SQLMobileBackend.get_domain_default_backend(
SQLMobileBackend.SMS,
self.domain,
id_only=True
) is not None
webhook_url = absolute_reverse('telerivet_in')
return {
'outgoing_sms_form': TelerivetOutgoingSMSForm(),
'test_sms_form': TelerivetPhoneNumberForm(),
'finalize_gateway_form': FinalizeGatewaySetupForm(
initial={
'name': 'TELERIVET',
'set_as_default': (FinalizeGatewaySetupForm.NO
if domain_has_default_gateway
else FinalizeGatewaySetupForm.YES),
}
),
'webhook_url': webhook_url,
'include_https_notice': webhook_url.startswith('https'),
'webhook_secret': webhook_secret,
'request_token': request_token,
'gateway_list_url': reverse(DomainSmsGatewayListView.urlname, args=[self.domain]),
} | [
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|||
cdhigh/KindleEar | 7c4ecf9625239f12a829210d1760b863ef5a23aa | lib/calibre/ebooks/BeautifulSoup.py | python | BeautifulStoneSoup._smartPop | (self, name) | We need to pop up to the previous tag of this type, unless
one of this tag's nesting reset triggers comes between this
tag and the previous tag of this type, OR unless this tag is a
generic nesting trigger and another generic nesting trigger
comes between this tag and the previous tag of this type.
Examples:
<p>Foo<b>Bar *<p>* should pop to 'p', not 'b'.
<p>Foo<table>Bar *<p>* should pop to 'table', not 'p'.
<p>Foo<table><tr>Bar *<p>* should pop to 'tr', not 'p'.
<li><ul><li> *<li>* should pop to 'ul', not the first 'li'.
<tr><table><tr> *<tr>* should pop to 'table', not the first 'tr'
<td><tr><td> *<td>* should pop to 'tr', not the first 'td' | We need to pop up to the previous tag of this type, unless
one of this tag's nesting reset triggers comes between this
tag and the previous tag of this type, OR unless this tag is a
generic nesting trigger and another generic nesting trigger
comes between this tag and the previous tag of this type. | [
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"""We need to pop up to the previous tag of this type, unless
one of this tag's nesting reset triggers comes between this
tag and the previous tag of this type, OR unless this tag is a
generic nesting trigger and another generic nesting trigger
comes between this tag and the previous tag of this type.
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<p>Foo<b>Bar *<p>* should pop to 'p', not 'b'.
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<li><ul><li> *<li>* should pop to 'ul', not the first 'li'.
<tr><table><tr> *<tr>* should pop to 'table', not the first 'tr'
<td><tr><td> *<td>* should pop to 'tr', not the first 'td'
"""
nestingResetTriggers = self.NESTABLE_TAGS.get(name)
isNestable = nestingResetTriggers != None
isResetNesting = self.RESET_NESTING_TAGS.has_key(name)
popTo = None
inclusive = True
for i in range(len(self.tagStack)-1, 0, -1):
p = self.tagStack[i]
if (not p or p.name == name) and not isNestable:
#Non-nestable tags get popped to the top or to their
#last occurance.
popTo = name
break
if (nestingResetTriggers is not None
and p.name in nestingResetTriggers) \
or (nestingResetTriggers is None and isResetNesting
and self.RESET_NESTING_TAGS.has_key(p.name)):
#If we encounter one of the nesting reset triggers
#peculiar to this tag, or we encounter another tag
#that causes nesting to reset, pop up to but not
#including that tag.
popTo = p.name
inclusive = False
break
p = p.parent
if popTo:
self._popToTag(popTo, inclusive) | [
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||
OneDrive/onedrive-sdk-python | e5642f8cad8eea37a4f653c1a23dfcfc06c37110 | src/python2/request/effective_roles_collection.py | python | EffectiveRolesCollectionRequest.get | (self) | return self._page_from_response(collection_response) | Gets the EffectiveRolesCollectionPage
Returns:
:class:`EffectiveRolesCollectionPage<onedrivesdk.request.effective_roles_collection.EffectiveRolesCollectionPage>`:
The EffectiveRolesCollectionPage | Gets the EffectiveRolesCollectionPage | [
"Gets",
"the",
"EffectiveRolesCollectionPage"
] | def get(self):
"""Gets the EffectiveRolesCollectionPage
Returns:
:class:`EffectiveRolesCollectionPage<onedrivesdk.request.effective_roles_collection.EffectiveRolesCollectionPage>`:
The EffectiveRolesCollectionPage
"""
self.method = "GET"
collection_response = EffectiveRolesCollectionResponse(json.loads(self.send().content))
return self._page_from_response(collection_response) | [
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|
nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | core/notification.py | python | Notification.add | (data) | return | Adds notification to core.NOTIFICATIONS
:param data: dict of notification information
Merges supplied 'data' with 'base' dict to ensure no fields are missing
Appends 'base' to core.NOTIFICATIONS
If data['param'] includes an on_click function, remember to add it to the
notifications javascript handler.
Does not return | Adds notification to core.NOTIFICATIONS
:param data: dict of notification information | [
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"notification",
"information"
] | def add(data):
''' Adds notification to core.NOTIFICATIONS
:param data: dict of notification information
Merges supplied 'data' with 'base' dict to ensure no fields are missing
Appends 'base' to core.NOTIFICATIONS
If data['param'] includes an on_click function, remember to add it to the
notifications javascript handler.
Does not return
'''
base = {'type': 'success',
'title': '',
'body': '',
'params': None
}
base.update(data)
logging.debug(u'Creating new notification:')
logging.debug(base)
# if it already exists, ignore it
if base in core.NOTIFICATIONS:
return
# if this is an update notif, remove other update notifs first
if base['type'] == u'update':
for i, v in enumerate(core.NOTIFICATIONS):
if v['type'] == u'update':
core.NOTIFICATIONS[i] = None
# if there is a None in the list, overwrite it. If not, just append
for i, v in enumerate(core.NOTIFICATIONS):
if v is None:
core.NOTIFICATIONS[i] = base
return
core.NOTIFICATIONS.append(base)
return | [
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|
rytilahti/python-miio | b6e53dd16fac77915426e7592e2528b78ef65190 | miio/integrations/vacuum/roborock/vacuum.py | python | RoborockVacuum.get_room_mapping | (self) | return self.send("get_room_mapping") | Retrieves a list of segments. | Retrieves a list of segments. | [
"Retrieves",
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"of",
"segments",
"."
] | def get_room_mapping(self):
"""Retrieves a list of segments."""
return self.send("get_room_mapping") | [
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|
rwth-i6/returnn | f2d718a197a280b0d5f0fd91a7fcb8658560dddb | returnn/tf/layers/basic.py | python | CtcLoss.get_soft_alignment | (self) | return soft_align | Also called the Baum-Welch-alignment.
This is basically p_t(s|x_1^T,w_1^N), where s are the output labels (including blank),
and w are the real target labels.
:return: shape (time, batch, dim)
:rtype: tf.Tensor | Also called the Baum-Welch-alignment.
This is basically p_t(s|x_1^T,w_1^N), where s are the output labels (including blank),
and w are the real target labels. | [
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"""
Also called the Baum-Welch-alignment.
This is basically p_t(s|x_1^T,w_1^N), where s are the output labels (including blank),
and w are the real target labels.
:return: shape (time, batch, dim)
:rtype: tf.Tensor
"""
assert self._ctc_loss is not None
assert isinstance(self._ctc_loss, tf.Tensor)
assert self._ctc_loss.op.type == "CTCLoss"
# See grad definition of CTCLoss.
# The op will calculate the gradient w.r.t. the logits.
# I.e. with y = softmax(z), this is \partial loss / \partial z = y - soft_align.
ctc_grad_z = self._ctc_loss.op.outputs[1] # time major, i.e. (time, batch, dim)
y = self.output.get_placeholder_as_time_major() # (time, batch, dim)
soft_align = y - ctc_grad_z
soft_align.set_shape(tf.TensorShape((None, None, self.output.dim)))
return soft_align | [
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|
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/PIL/ImageOps.py | python | _lut | (image, lut) | [] | def _lut(image, lut):
if image.mode == "P":
# FIXME: apply to lookup table, not image data
raise NotImplementedError("mode P support coming soon")
elif image.mode in ("L", "RGB"):
if image.mode == "RGB" and len(lut) == 256:
lut = lut + lut + lut
return image.point(lut)
else:
raise IOError("not supported for this image mode") | [
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||||
collinsctk/PyQYT | 7af3673955f94ff1b2df2f94220cd2dab2e252af | Practice_Lab/ms08_067_check/ms08_067_check.py | python | MS08_067.__connect | (self) | SMB connect to the Computer Browser service named pipe
Reference: http://www.hsc.fr/ressources/articles/win_net_srv/msrpc_browser.html | SMB connect to the Computer Browser service named pipe
Reference: http://www.hsc.fr/ressources/articles/win_net_srv/msrpc_browser.html | [
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'''
SMB connect to the Computer Browser service named pipe
Reference: http://www.hsc.fr/ressources/articles/win_net_srv/msrpc_browser.html
'''
try:
self.__trans = transport.DCERPCTransportFactory('ncacn_np:%s[\\pipe\\browser]' % self.target)
self.__trans.connect()
except smb.SessionError, _:
raise connectionException, 'access denied (RestrictAnonymous is probably set to 2)'
except:
#raise Exception, 'unhandled exception (%s)' % format_exc()
raise connectionException, 'unexpected exception' | [
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||
researchmm/SiamDW | c82b0599920e1fbd67ada9271aa4a63405e2d316 | lib/dataset/siamfc.py | python | SiamFCDataset._get_pairs | (self, index) | return self._get_image_anno(video_name, track, template_frame), \
self._get_image_anno(video_name, track, search_frame) | get training pairs | get training pairs | [
"get",
"training",
"pairs"
] | def _get_pairs(self, index):
"""
get training pairs
"""
video_name = self.videos[index]
video = self.labels[video_name]
track = random.choice(list(video.keys()))
track_info = video[track]
try:
frames = track_info['frames']
except:
frames = list(track_info.keys())
template_frame = random.randint(0, len(frames)-1)
left = max(template_frame - self.frame_range, 0)
right = min(template_frame + self.frame_range, len(frames)-1) + 1
search_range = frames[left:right]
template_frame = int(frames[template_frame])
search_frame = int(random.choice(search_range))
return self._get_image_anno(video_name, track, template_frame), \
self._get_image_anno(video_name, track, search_frame) | [
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|
pytorch/audio | 7b6b2d000023e2aa3365b769866c5f375e0d5fda | torchaudio/utils/sox_utils.py | python | set_verbosity | (verbosity: int) | Set libsox's verbosity
Args:
verbosity (int): Set verbosity level of libsox.
* ``1`` failure messages
* ``2`` warnings
* ``3`` details of processing
* ``4``-``6`` increasing levels of debug messages
See Also:
http://sox.sourceforge.net/sox.html | Set libsox's verbosity | [
"Set",
"libsox",
"s",
"verbosity"
] | def set_verbosity(verbosity: int):
"""Set libsox's verbosity
Args:
verbosity (int): Set verbosity level of libsox.
* ``1`` failure messages
* ``2`` warnings
* ``3`` details of processing
* ``4``-``6`` increasing levels of debug messages
See Also:
http://sox.sourceforge.net/sox.html
"""
torch.ops.torchaudio.sox_utils_set_verbosity(verbosity) | [
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||
kurolz/DjangoWeb | eb76629034089f65ed04632e2fbcecdef8095ff8 | webserver/views.py | python | serverList | (request,id = 0) | return render(request, 'serverlist.html') | 服务器列表 | 服务器列表 | [
"服务器列表"
] | def serverList(request,id = 0):
'''
服务器列表
'''
if id != 0:
hostinfo.objects.filter(id = id).delete()
if request.method == "POST":
getHostInfo()
print(request.POST)
pageSize = request.POST.get('pageSize') # how manufactoryy items per page
pageNumber = request.POST.get('pageNumber')
offset = request.POST.get('offset') # how many items in total in the DB
search = request.POST.get('search')
sort_column = request.POST.get('sort') # which column need to sort
order = request.POST.get('order') # ascending or descending
if search: # 判断是否有搜索字
all_records = hostinfo.objects.filter(id=search,asset_type=search,business_unit=search,idc=search)
else:
all_records = hostinfo.objects.all() # must be wirte the line code here
if sort_column: # 判断是否有排序需求
sort_column = sort_column.replace('asset_', '')
if sort_column in ['id','asset_type','sn','name','management_ip','manufactory','type']: # 如果排序的列表在这些内容里面
if order == 'desc': # 如果排序是反向
sort_column = '-%s' % (sort_column)
all_records = hostinfo.objects.all().order_by(sort_column)
elif sort_column in ['salt_minion_id','os_release',]:
# server__ 表示asset下的外键关联的表server下面的os_release或者其他的字段进行排序
sort_column = "server__%s" % (sort_column)
if order == 'desc':
sort_column = '-%s'%(sort_column)
all_records = hostinfo.objects.all().order_by(sort_column)
elif sort_column in ['cpu_model','cpu_count','cpu_core_count']:
sort_column = "cpu__%s" %(sort_column)
if order == 'desc':
sort_column = '-%s'%(sort_column)
all_records = hostinfo.objects.all().order_by(sort_column)
elif sort_column in ['rams_size',]:
if order == 'desc':
sort_column = '-rams_size'
else:
sort_column = 'rams_size'
all_records = hostinfo.objects.all().annotate(rams_size = Sum('ram__capacity')).order_by(sort_column)
elif sort_column in ['localdisks_size',]: # using variable of localdisks_size because there have a annotation below of this line
if order == "desc":
sort_column = '-localdisks_size'
else:
sort_column = 'localdisks_size'
# annotate 是注释的功能,localdisks_size前端传过来的是这个值,后端也必须这样写,Sum方法是django里面的,不是小写的sum方法,
# 两者的区别需要注意,Sum('disk__capacity‘)表示对disk表下面的capacity进行加法计算,返回一个总值.
all_records = hostinfo.objects.all().annotate(localdisks_size=Sum('disk__capacity')).order_by(sort_column)
elif sort_column in ['idc',]:
sort_column = "idc__%s" % (sort_column)
if order == 'desc':
sort_column = '-%s'%(sort_column)
all_records = hostinfo.objects.all().order_by(sort_column)
elif sort_column in ['trade_date','create_date']:
if order == 'desc':
sort_column = '-%s'%sort_column
all_records = User.objects.all().order_by(sort_column)
all_records_count=all_records.count()
if not offset:
offset = 0
if not pageSize:
pageSize = 10 # 默认是每页20行的内容,与前端默认行数一致
pageinator = Paginator(all_records, pageSize) # 开始做分页
page = int(int(offset) / int(pageSize) + 1)
response_data = {'total': all_records_count, 'rows': []}
for server_li in pageinator.page(page):
response_data['rows'].append({
"id": server_li.id if server_li.id else "",
"hostname": server_li.hostname if server_li.hostname else "",
"IP":server_li.IP if server_li.IP else "",
"Mem":server_li.Mem if server_li.Mem else "",
"CPU": server_li.CPU if server_li.CPU else "",
"CPUS": server_li.CPUS if server_li.CPUS else "",
"OS": server_li.OS if server_li.OS else "",
"virtual1": server_li.virtual1 if server_li.virtual1 else "",
"status": server_li.status if server_li.status else "",
})
return HttpResponse(json.dumps(response_data))
return render(request, 'serverlist.html') | [
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|
johntruckenbrodt/pyroSAR | efac51134ba42d20120b259f968afe5a4ddcc46a | pyroSAR/gamma/parser_demo.py | python | par_S1_SLC | (GeoTIFF, annotation_XML, calibration_XML, noise_XML, SLC_par, SLC, TOPS_par='-', dtype='-', sc_dB='-',
noise_pwr='-', logpath=None, outdir=None, shellscript=None) | | Generate SLC parameter and image files for Sentinel-1 SLC data
| Copyright 2018, Gamma Remote Sensing, v4.0 30-Apr-2018 awi/clw/cm
Parameters
----------
GeoTIFF:
(input) image data file in GeoTIFF format (enter - for none, \*.tiff)
annotation_XML:
(input) Sentinel-1 L1 XML annotation file
calibration_XML:
(input) Sentinel-1 L1 radiometric calibration XML file (enter - for no radiometric calibration)
noise_XML:
(input) Sentinel-1 L1 noise XML file (enter - to not subtract thermal noise power level)
SLC_par:
(output) ISP SLC parameter file (example: yyyymmdd_iw1_vv.slc.par)
SLC:
(output) SLC data file (enter - for none, example: yyyymmdd_iw1_vv.slc)
TOPS_par:
(output) SLC burst annotation file, TOPS and EW SLC data only (enter - for none, example: yyyymmdd_iw1_vv.tops_par)
dtype:
output data type:
* 0: FCOMPLEX (default)
* 1: SCOMPLEX
sc_dB:
scale factor for FCOMPLEX -> SCOMPLEX, (enter - for default: HH,VV (dB): 60.0000, VH,HV: 70.0000)
noise_pwr:
noise intensity for each SLC sample in slant range using data from noise_XML
* NOTE: when the noise_pwr file is specified, noise power will NOT be subtracted from the image data values
logpath: str or None
a directory to write command logfiles to
outdir: str or None
the directory to execute the command in
shellscript: str or None
a file to write the Gamma commands to in shell format | | Generate SLC parameter and image files for Sentinel-1 SLC data
| Copyright 2018, Gamma Remote Sensing, v4.0 30-Apr-2018 awi/clw/cm | [
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noise_pwr='-', logpath=None, outdir=None, shellscript=None):
"""
| Generate SLC parameter and image files for Sentinel-1 SLC data
| Copyright 2018, Gamma Remote Sensing, v4.0 30-Apr-2018 awi/clw/cm
Parameters
----------
GeoTIFF:
(input) image data file in GeoTIFF format (enter - for none, \*.tiff)
annotation_XML:
(input) Sentinel-1 L1 XML annotation file
calibration_XML:
(input) Sentinel-1 L1 radiometric calibration XML file (enter - for no radiometric calibration)
noise_XML:
(input) Sentinel-1 L1 noise XML file (enter - to not subtract thermal noise power level)
SLC_par:
(output) ISP SLC parameter file (example: yyyymmdd_iw1_vv.slc.par)
SLC:
(output) SLC data file (enter - for none, example: yyyymmdd_iw1_vv.slc)
TOPS_par:
(output) SLC burst annotation file, TOPS and EW SLC data only (enter - for none, example: yyyymmdd_iw1_vv.tops_par)
dtype:
output data type:
* 0: FCOMPLEX (default)
* 1: SCOMPLEX
sc_dB:
scale factor for FCOMPLEX -> SCOMPLEX, (enter - for default: HH,VV (dB): 60.0000, VH,HV: 70.0000)
noise_pwr:
noise intensity for each SLC sample in slant range using data from noise_XML
* NOTE: when the noise_pwr file is specified, noise power will NOT be subtracted from the image data values
logpath: str or None
a directory to write command logfiles to
outdir: str or None
the directory to execute the command in
shellscript: str or None
a file to write the Gamma commands to in shell format
"""
process(
['/usr/local/GAMMA_SOFTWARE-20180703/ISP/bin/par_S1_SLC', GeoTIFF, annotation_XML, calibration_XML, noise_XML,
SLC_par, SLC, TOPS_par, dtype, sc_dB, noise_pwr], logpath=logpath, outdir=outdir, shellscript=shellscript) | [
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||
knipknap/exscript | a20e83ae3a78ea7e5ba25f07c1d9de4e9b961e83 | Exscript/util/event.py | python | Event.listen | (self, callback, *args, **kwargs) | return ref | Like :class:`connect()`, but uses a weak reference instead of a
normal reference.
The signal is automatically disconnected as soon as the handler
is garbage collected.
.. HINT::
Storing signal handlers as weak references means that if
your handler is a local function, it may be garbage collected. To
prevent this, use :class:`connect()` instead.
:type callback: object
:param callback: The callback function.
:type args: tuple
:param args: Optional arguments passed to the callback.
:type kwargs: dict
:param kwargs: Optional keyword arguments passed to the callback.
:rtype: :class:`Exscript.util.weakmethod.WeakMethod`
:return: The newly created weak reference to the callback. | Like :class:`connect()`, but uses a weak reference instead of a
normal reference.
The signal is automatically disconnected as soon as the handler
is garbage collected. | [
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"""
Like :class:`connect()`, but uses a weak reference instead of a
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The signal is automatically disconnected as soon as the handler
is garbage collected.
.. HINT::
Storing signal handlers as weak references means that if
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prevent this, use :class:`connect()` instead.
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if self.lock is None:
self.lock = Lock()
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self.weak_subscribers = []
ref = weakmethod.ref(callback, self._try_disconnect)
self.weak_subscribers.append((ref, args, kwargs))
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|
huawei-noah/vega | d9f13deede7f2b584e4b1d32ffdb833856129989 | evaluate_service/evaluate_service/security/zmq_op.py | python | connect_security | (ip, port, temp_path) | return socket | Connect to server. | Connect to server. | [
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] | def connect_security(ip, port, temp_path):
"""Connect to server."""
ctx = zmq.Context.instance()
socket = ctx.socket(zmq.REQ)
client_name = uuid.uuid1().hex[:8]
client_secret_key = os.path.join(temp_path, "{}.key_secret".format(client_name))
if not os.path.exists(client_secret_key):
client_public_key, client_secret_key = zmq.auth.create_certificates(temp_path, client_name)
client_public, client_secret = zmq.auth.load_certificate(client_secret_key)
socket.curve_secretkey = client_secret
socket.curve_publickey = client_public
server_public_key = os.path.join(temp_path, "server.key")
if not os.path.exists(server_public_key):
server_public_key, _ = zmq.auth.create_certificates(temp_path, "server")
server_public, _ = zmq.auth.load_certificate(server_public_key)
socket.curve_serverkey = server_public
socket.connect(f"tcp://{ip}:{port}")
if os.path.exists(client_secret_key):
os.remove(client_secret_key)
if os.path.exists(client_public_key):
os.remove(client_public_key)
return socket | [
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|
cloudinary/pycloudinary | a61a9687c8933f23574c38e27f201358e540ee64 | cloudinary/poster/encode.py | python | MultipartParam.from_file | (cls, paramname, filename) | return cls(paramname, filename=os.path.basename(filename),
filetype=mimetypes.guess_type(filename)[0],
filesize=os.path.getsize(filename),
fileobj=open(filename, "rb")) | Returns a new MultipartParam object constructed from the local
file at ``filename``.
``filesize`` is determined by os.path.getsize(``filename``)
``filetype`` is determined by mimetypes.guess_type(``filename``)[0]
``filename`` is set to os.path.basename(``filename``) | Returns a new MultipartParam object constructed from the local
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``filesize`` is determined by os.path.getsize(``filename``)
``filetype`` is determined by mimetypes.guess_type(``filename``)[0]
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return cls(paramname, filename=os.path.basename(filename),
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openstack/keystone | 771c943ad2116193e7bb118c74993c829d93bd71 | keystone/credential/backends/base.py | python | CredentialDriverBase.delete_credentials_for_user | (self, user_id) | Delete all credentials for a user. | Delete all credentials for a user. | [
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ramonhagenaars/jsons | a5150cdd2704e83fe5f8798822a1c901b54dcb1c | jsons/classes/json_serializable.py | python | JsonSerializable.dump | (self, **kwargs) | return dump(self, fork_inst=self.__class__, **kwargs) | See ``jsons.dump``.
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/mqtt/climate.py | python | MqttClimate.target_temperature_low | (self) | return self._target_temp_low | Return the low target temperature we try to reach. | Return the low target temperature we try to reach. | [
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|
agile-geoscience/bruges | 64f162dbf8b94ff265108f6eb85e0d4e28ecb2cb | bruges/reflection/reflection.py | python | shuey | (vp1, vs1, rho1, vp2, vs2, rho2, theta1=0,
terms=False,
return_gradient=False) | Compute Shuey approximation with 3 terms.
http://subsurfwiki.org/wiki/Shuey_equation
Args:
vp1 (ndarray): The upper P-wave velocity; float or 1D array length m.
vs1 (ndarray): The upper S-wave velocity; float or 1D array length m.
rho1 (ndarray): The upper layer's density; float or 1D array length m.
vp2 (ndarray): The lower P-wave velocity; float or 1D array length m.
vs2 (ndarray): The lower S-wave velocity; float or 1D array length m.
rho2 (ndarray): The lower layer's density; float or 1D array length m.
theta1 (ndarray): The incidence angle; float or 1D array length n.
terms (bool): Whether or not to return a tuple of the terms of the
equation. The first term is the acoustic impedance.
return_gradient (bool): Whether to return a tuple of the intercept
and gradient (i.e. the second term divided by sin^2(theta)).
Returns:
ndarray. The Aki-Richards approximation for P-P reflectivity at the
interface. Will be a float (for float inputs and one angle), a
1 x n array (for float inputs and an array of angles), a 1 x m
array (for float inputs and one angle), or an n x m array (for
array inputs and an array of angles). | Compute Shuey approximation with 3 terms.
http://subsurfwiki.org/wiki/Shuey_equation | [
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"""
Compute Shuey approximation with 3 terms.
http://subsurfwiki.org/wiki/Shuey_equation
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vp1 (ndarray): The upper P-wave velocity; float or 1D array length m.
vs1 (ndarray): The upper S-wave velocity; float or 1D array length m.
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array (for float inputs and one angle), or an n x m array (for
array inputs and an array of angles).
"""
theta1 = np.real(theta1)
drho = rho2-rho1
dvp = vp2-vp1
dvs = vs2-vs1
rho = (rho1+rho2)/2.0
vp = (vp1+vp2)/2.0
vs = (vs1+vs2)/2.0
# Compute three-term reflectivity
r0 = 0.5 * (dvp/vp + drho/rho)
g = 0.5 * dvp/vp - 2 * (vs**2/vp**2) * (drho/rho + 2 * dvs/vs)
f = 0.5 * dvp/vp
term1 = r0
term2 = g * np.sin(theta1)**2
term3 = f * (np.tan(theta1)**2 - np.sin(theta1)**2)
if return_gradient:
fields = ['intercept', 'gradient']
Shuey = namedtuple('Shuey', fields)
return Shuey(np.squeeze(r0), np.squeeze(g))
elif terms:
fields = ['R0', 'Rg', 'Rf']
Shuey = namedtuple('Shuey', fields)
return Shuey(np.squeeze([term1 for _ in theta1]),
np.squeeze(term2),
np.squeeze(term3)
)
else:
return np.squeeze(term1 + term2 + term3) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/uwsgidecorators.py | python | mule_msg_dispatcher | (message) | [] | def mule_msg_dispatcher(message):
msg = pickle.loads(message)
if msg['service'] == 'uwsgi_mulefunc':
return mule_functions[msg['func']](*msg['args'], **msg['kwargs']) | [
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||||
edgewall/trac | beb3e4eaf1e0a456d801a50a8614ecab06de29fc | trac/loader.py | python | _enable_plugin | (env, module) | Enable the given plugin module if it wasn't disabled explicitly. | Enable the given plugin module if it wasn't disabled explicitly. | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/words/protocols/irc.py | python | ServerSupportedFeatures.hasFeature | (self, feature) | return self.getFeature(feature) is not None | Determine whether a feature is supported or not.
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@rtype: C{bool}
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|
donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | xonsh/tools.py | python | is_string_or_callable | (x) | return is_string(x) or is_callable(x) | Tests if something is a string or callable | Tests if something is a string or callable | [
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|
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/util/_collections.py | python | IdentitySet.difference_update | (self, iterable) | [] | def difference_update(self, iterable):
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||||
inkcut/inkcut | d3dea58e13de9f9babb5ed9b562c7326b4efdcd7 | inkcut/preview/plugin.py | python | PreviewPlugin.set_live_preview | (self, *items) | Set the items that will be displayed in the live plot preview.
After set, use live_preview.update(position) to update it.
Parameters
----------
items: list of kwargs
A list of kwargs to to pass to each plot item | Set the items that will be displayed in the live plot preview.
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Parameters
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items: list of kwargs
A list of kwargs to to pass to each plot item
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view_items = [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/export/management/commands/update_export_with_newest_data.py | python | Command.add_arguments | (self, parser) | [] | def add_arguments(self, parser):
parser.add_argument('export_id')
parser.add_argument('-d', '--download_path', help="Path to download export to.")
parser.add_argument(
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||||
pybrain/pybrain | dcdf32ba1805490cefbc0bdeb227260d304fdb42 | pybrain/rl/environments/ode/tools/xmltools.py | python | XMLstruct.up | (self, steps=1) | traverse upward a number of steps in tag stack | traverse upward a number of steps in tag stack | [
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"""traverse upward a number of steps in tag stack"""
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biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/data/owdiscretize.py | python | IncreasingNumbersListValidator.fixup | (self, string) | return ", ".join(parts) | Fixup the input. Remove empty parts from the string. | Fixup the input. Remove empty parts from the string. | [
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# type: (str) -> str
"""
Fixup the input. Remove empty parts from the string.
"""
parts = [string[start: end] for start, end in self.itersplit(string)]
parts = [part for part in parts if part.strip()]
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|
securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | intelbot/requests/sessions.py | python | Session.get_adapter | (self, url) | Returns the appropriate connection adapter for the given URL. | Returns the appropriate connection adapter for the given URL. | [
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] | def get_adapter(self, url):
"""Returns the appropriate connection adapter for the given URL."""
for (prefix, adapter) in self.adapters.items():
if url.lower().startswith(prefix):
return adapter
# Nothing matches :-/
raise InvalidSchema("No connection adapters were found for '%s'" % url) | [
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||
jgilhutton/PyxieWPS | ba590343a73db98f898bda8b56b43928ac90947f | Version-1.1/pyxiewps-EN-swearing-version.py | python | Config.get_binaries | (self) | Installs reaver, pixiewps and other stuff | Installs reaver, pixiewps and other stuff | [
"Installs",
"reaver",
"pixiewps",
"and",
"other",
"stuff"
] | def get_binaries(self):
"""
Installs reaver, pixiewps and other stuff
"""
if not self.internet_on():
print
print ALERT + "You are not connected to the internet."
print " Please check your connection so that Pyxiewps"
print " can install all the required programs."
print
engine.exit_clean()
git = 'apt-get -y install git'
reaver_dep = 'apt-get -y install build-essential libpcap-dev sqlite3 libsqlite3-dev aircrack-ng'
pixie_dep = 'sudo apt-get -y install libssl-dev'
reaver_apt = 'apt-get -y install reaver'
reaver = 'git clone https://github.com/t6x/reaver-wps-fork-t6x.git'
pixiewps = 'git clone https://github.com/wiire/pixiewps.git'
aircrack = 'apt-get -y install aircrack-ng'
if not engine.GIT:
print INFO + "Installing git..."
proc4 = system(git)
if not engine.AIRMON:
print INFO + "Installing aircrack..."
proc5 = system(aircrack)
if not engine.PIXIEWPS:
print INFO + "Installing pixiewps dependencies..."
proc2 = system(pixie_dep)
print INFO + "Downloading pixiewps..."
proc3 = system(pixiewps)
if not engine.REAVER:
print INFO + "Installing reaver dependencies..."
proc = system(reaver_dep)
print INFO + "Downloading reaver..."
if 'kali' in subprocess.check_output('uname -a', shell = True):
proc1 = system(reaver_apt)
else:
proc1 = system(reaver)
if path.isdir('pixiewps') and not engine.PIXIEWPS:
print INFO + "Installing pixiewps..."
system('cd pixiewps/src && make && make install')
print INFO + "Done"
if path.isdir('reaver-wps-fork-t6x') and not engine.REAVER:
print INFO + "Installing reaver..."
system('cd reaver-wps-fork-t6x* && cd src && ./configure && make && make install')
print INFO + "Done"
engine.check(check_again = True) | [
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||
microsoft/debugpy | be8dd607f6837244e0b565345e497aff7a0c08bf | src/debugpy/_vendored/pydevd/third_party/pep8/pycodestyle.py | python | comparison_type | (logical_line, noqa) | r"""Object type comparisons should always use isinstance().
Do not compare types directly.
Okay: if isinstance(obj, int):
E721: if type(obj) is type(1):
When checking if an object is a string, keep in mind that it might be a
unicode string too! In Python 2.3, str and unicode have a common base
class, basestring, so you can do:
Okay: if isinstance(obj, basestring):
Okay: if type(a1) is type(b1): | r"""Object type comparisons should always use isinstance(). | [
"r",
"Object",
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"comparisons",
"should",
"always",
"use",
"isinstance",
"()",
"."
] | def comparison_type(logical_line, noqa):
r"""Object type comparisons should always use isinstance().
Do not compare types directly.
Okay: if isinstance(obj, int):
E721: if type(obj) is type(1):
When checking if an object is a string, keep in mind that it might be a
unicode string too! In Python 2.3, str and unicode have a common base
class, basestring, so you can do:
Okay: if isinstance(obj, basestring):
Okay: if type(a1) is type(b1):
"""
match = COMPARE_TYPE_REGEX.search(logical_line)
if match and not noqa:
inst = match.group(1)
if inst and isidentifier(inst) and inst not in SINGLETONS:
return # Allow comparison for types which are not obvious
yield match.start(), "E721 do not compare types, use 'isinstance()'" | [
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||
dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/team_log.py | python | LoginMethod.is_web_session | (self) | return self._tag == 'web_session' | Check if the union tag is ``web_session``.
:rtype: bool | Check if the union tag is ``web_session``. | [
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] | def is_web_session(self):
"""
Check if the union tag is ``web_session``.
:rtype: bool
"""
return self._tag == 'web_session' | [
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|
trailofbits/manticore | b050fdf0939f6c63f503cdf87ec0ab159dd41159 | manticore/platforms/linux_syscall_stubs.py | python | SyscallStubs.sys_splice | (self, fd_in, off_in, fd_out, off_out, len, flags) | return self.complicated_success(275) | AUTOGENERATED UNIMPLEMENTED STUB | AUTOGENERATED UNIMPLEMENTED STUB | [
"AUTOGENERATED",
"UNIMPLEMENTED",
"STUB"
] | def sys_splice(self, fd_in, off_in, fd_out, off_out, len, flags) -> int:
""" AUTOGENERATED UNIMPLEMENTED STUB """
return self.complicated_success(275) | [
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|
ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/ntpath.py | python | isabs | (s) | return s != '' and s[:1] in '/\\' | Test whether a path is absolute | Test whether a path is absolute | [
"Test",
"whether",
"a",
"path",
"is",
"absolute"
] | def isabs(s):
"""Test whether a path is absolute"""
s = splitdrive(s)[1]
return s != '' and s[:1] in '/\\' | [
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|
gentoo/portage | e5be73709b1a42b40380fd336f9381452b01a723 | lib/portage/util/socks5.py | python | ProxyManager.stop | (self) | Stop the SOCKSv5 server. | Stop the SOCKSv5 server. | [
"Stop",
"the",
"SOCKSv5",
"server",
"."
] | def stop(self):
"""
Stop the SOCKSv5 server.
"""
for p in self._pids:
os.kill(p, signal.SIGINT)
os.waitpid(p, 0)
self.socket_path = None
self._pids = [] | [
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||
tornadoweb/tornado | 208672f3bf6cbb7e37f54c356e02a71ca29f1e02 | tornado/ioloop.py | python | IOLoop.call_later | (
self, delay: float, callback: Callable, *args: Any, **kwargs: Any
) | return self.call_at(self.time() + delay, callback, *args, **kwargs) | Runs the ``callback`` after ``delay`` seconds have passed.
Returns an opaque handle that may be passed to `remove_timeout`
to cancel. Note that unlike the `asyncio` method of the same
name, the returned object does not have a ``cancel()`` method.
See `add_timeout` for comments on thread-safety and subclassing.
.. versionadded:: 4.0 | Runs the ``callback`` after ``delay`` seconds have passed. | [
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] | def call_later(
self, delay: float, callback: Callable, *args: Any, **kwargs: Any
) -> object:
"""Runs the ``callback`` after ``delay`` seconds have passed.
Returns an opaque handle that may be passed to `remove_timeout`
to cancel. Note that unlike the `asyncio` method of the same
name, the returned object does not have a ``cancel()`` method.
See `add_timeout` for comments on thread-safety and subclassing.
.. versionadded:: 4.0
"""
return self.call_at(self.time() + delay, callback, *args, **kwargs) | [
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GNS3/gns3-server | aff06572d4173df945ad29ea8feb274f7885d9e4 | gns3server/compute/qemu/utils/qcow2.py | python | Qcow2.backing_file | (self) | return path | When using linked clone this will return the path to the base image
:returns: None if it's not a linked clone, the path otherwise | When using linked clone this will return the path to the base image | [
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"""
When using linked clone this will return the path to the base image
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"""
with open(self._path, 'rb') as f:
f.seek(self.backing_file_offset)
content = f.read(self.backing_file_size)
path = content.decode()
if len(path) == 0:
return None
return path | [
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|
git-cola/git-cola | b48b8028e0c3baf47faf7b074b9773737358163d | cola/widgets/diff.py | python | Viewer.render | (self) | [] | def render(self):
# Update images
if self.pixmaps:
mode = self.options.image_mode.currentIndex()
if mode == self.options.SIDE_BY_SIDE:
image = self.render_side_by_side()
elif mode == self.options.DIFF:
image = self.render_diff()
elif mode == self.options.XOR:
image = self.render_xor()
elif mode == self.options.PIXEL_XOR:
image = self.render_pixel_xor()
else:
image = self.render_side_by_side()
else:
image = QtGui.QPixmap()
self.image.pixmap = image
# Apply zoom
zoom_mode = self.options.zoom_mode.currentIndex()
zoom_factor = self.options.zoom_factors[zoom_mode][1]
if zoom_factor > 0.0:
self.image.resetTransform()
self.image.scale(zoom_factor, zoom_factor)
poly = self.image.mapToScene(self.image.viewport().rect())
self.image.last_scene_roi = poly.boundingRect() | [
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] | https://github.com/git-cola/git-cola/blob/b48b8028e0c3baf47faf7b074b9773737358163d/cola/widgets/diff.py#L483-L508 |
||||
python-diamond/Diamond | 7000e16cfdf4508ed9291fc4b3800592557b2431 | src/diamond/handler/tsdb.py | python | TSDBHandler.process | (self, metric) | Process a metric by sending it to TSDB | Process a metric by sending it to TSDB | [
"Process",
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"metric",
"by",
"sending",
"it",
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"TSDB"
] | def process(self, metric):
"""
Process a metric by sending it to TSDB
"""
entry = {'timestamp': metric.timestamp, 'value': metric.value,
"tags": {}}
entry["tags"]["hostname"] = metric.host
if self.cleanMetrics:
metric = MetricWrapper(metric, self.log)
if self.skipAggregates and metric.isAggregate():
return
for tagKey in metric.getTags():
entry["tags"][tagKey] = metric.getTags()[tagKey]
entry['metric'] = (self.prefix + metric.getCollectorPath() +
'.' + metric.getMetricPath())
for [key, value] in self.tags:
entry["tags"][key] = value
self.entrys.append(entry)
# send data if list is long enough
if (len(self.entrys) >= self.batch):
# Compress data
if self.compression >= 1:
data = StringIO.StringIO()
with contextlib.closing(gzip.GzipFile(fileobj=data,
compresslevel=self.compression,
mode="w")) as f:
f.write(json.dumps(self.entrys))
self._send(data.getvalue())
else:
# no compression
data = json.dumps(self.entrys)
self._send(data) | [
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||
daoluan/decode-Django | d46a858b45b56de48b0355f50dd9e45402d04cfd | Django-1.5.1/django/template/loaders/eggs.py | python | Loader.load_template_source | (self, template_name, template_dirs=None) | Loads templates from Python eggs via pkg_resource.resource_string.
For every installed app, it tries to get the resource (app, template_name). | Loads templates from Python eggs via pkg_resource.resource_string. | [
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"templates",
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"eggs",
"via",
"pkg_resource",
".",
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"."
] | def load_template_source(self, template_name, template_dirs=None):
"""
Loads templates from Python eggs via pkg_resource.resource_string.
For every installed app, it tries to get the resource (app, template_name).
"""
if resource_string is not None:
pkg_name = 'templates/' + template_name
for app in settings.INSTALLED_APPS:
try:
resource = resource_string(app, pkg_name)
except Exception:
continue
if not six.PY3:
resource = resource.decode(settings.FILE_CHARSET)
return (resource, 'egg:%s:%s' % (app, pkg_name))
raise TemplateDoesNotExist(template_name) | [
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||
IvanFon/xinput-gui | 900f4dc0fe8020eb106881cf7e489862f532b9a1 | xinput_gui/xinput/xinput.py | python | Xinput.get_device_by_id | (self, id_: int) | return None | Get a device by it's ID.
Args:
id_: xinput device ID. | Get a device by it's ID. | [
"Get",
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"device",
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"."
] | def get_device_by_id(self, id_: int) -> Device:
'''Get a device by it's ID.
Args:
id_: xinput device ID.
'''
for device in self.devices:
if int(device.id) == id_:
return device
return None | [
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|
binaryage/drydrop | 2f27e15befd247255d89f9120eeee44851b82c4a | dryapp/jinja2/lexer.py | python | TokenStream.close | (self) | Close the stream. | Close the stream. | [
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] | def close(self):
"""Close the stream."""
self.current = Token(self.current.lineno, 'eof', '')
self._next = None
self.closed = True | [
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||
securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/pandas/io/stata.py | python | StataWriter._prepare_categoricals | (self, data) | return DataFrame.from_items(data_formatted) | Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int | Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int | [
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] | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int"""
is_cat = [is_categorical_dtype(data[col]) for col in data]
self._is_col_cat = is_cat
self._value_labels = []
if not any(is_cat):
return data
get_base_missing_value = StataMissingValue.get_base_missing_value
data_formatted = []
for col, col_is_cat in zip(data, is_cat):
if col_is_cat:
self._value_labels.append(StataValueLabel(data[col]))
dtype = data[col].cat.codes.dtype
if dtype == np.int64:
raise ValueError('It is not possible to export '
'int64-based categorical data to Stata.')
values = data[col].cat.codes.values.copy()
# Upcast if needed so that correct missing values can be set
if values.max() >= get_base_missing_value(dtype):
if dtype == np.int8:
dtype = np.int16
elif dtype == np.int16:
dtype = np.int32
else:
dtype = np.float64
values = np.array(values, dtype=dtype)
# Replace missing values with Stata missing value for type
values[values == -1] = get_base_missing_value(dtype)
data_formatted.append((col, values))
else:
data_formatted.append((col, data[col]))
return DataFrame.from_items(data_formatted) | [
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|
khalim19/gimp-plugin-export-layers | b37255f2957ad322f4d332689052351cdea6e563 | export_layers/pygimplib/_lib/future/future/backports/http/cookiejar.py | python | DefaultCookiePolicy.set_ok_port | (self, cookie, request) | return True | [] | def set_ok_port(self, cookie, request):
if cookie.port_specified:
req_port = request_port(request)
if req_port is None:
req_port = "80"
else:
req_port = str(req_port)
for p in cookie.port.split(","):
try:
int(p)
except ValueError:
_debug(" bad port %s (not numeric)", p)
return False
if p == req_port:
break
else:
_debug(" request port (%s) not found in %s",
req_port, cookie.port)
return False
return True | [
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|||
cylc/cylc-flow | 5ec221143476c7c616c156b74158edfbcd83794a | cylc/flow/config.py | python | WorkflowConfig._close_families | (l_id, r_id, clf_map) | return lret, rret | Turn (name, point) to 'name.point' for edge.
Replace close family members with family nodes if relevant. | Turn (name, point) to 'name.point' for edge. | [
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"(",
"name",
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")",
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"name",
".",
"point",
"for",
"edge",
"."
] | def _close_families(l_id, r_id, clf_map):
"""Turn (name, point) to 'name.point' for edge.
Replace close family members with family nodes if relevant.
"""
lret = None
lname, lpoint = None, None
if l_id:
lname, lpoint = l_id
lret = TaskID.get(lname, lpoint)
rret = None
rname, rpoint = None, None
if r_id:
rname, rpoint = r_id
rret = TaskID.get(rname, rpoint)
for fam_name, fam_members in clf_map.items():
if lname in fam_members and rname in fam_members:
# l and r are both members
lret = TaskID.get(fam_name, lpoint)
rret = TaskID.get(fam_name, rpoint)
break
elif lname in fam_members:
# l is a member
lret = TaskID.get(fam_name, lpoint)
elif rname in fam_members:
# r is a member
rret = TaskID.get(fam_name, rpoint)
return lret, rret | [
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|
MrH0wl/Cloudmare | 65e5bc9888f9d362ab2abfb103ea6c1e869d67aa | thirdparty/xlsxwriter/worksheet.py | python | Worksheet.add_table | (self, first_row, first_col, last_row, last_col,
options=None) | return table | Add an Excel table to a worksheet.
Args:
first_row: The first row of the cell range. (zero indexed).
first_col: The first column of the cell range.
last_row: The last row of the cell range. (zero indexed).
last_col: The last column of the cell range.
options: Table format options. (Optional)
Returns:
0: Success.
-1: Not supported in constant_memory mode.
-2: Row or column is out of worksheet bounds.
-3: Incorrect parameter or option. | Add an Excel table to a worksheet. | [
"Add",
"an",
"Excel",
"table",
"to",
"a",
"worksheet",
"."
] | def add_table(self, first_row, first_col, last_row, last_col,
options=None):
"""
Add an Excel table to a worksheet.
Args:
first_row: The first row of the cell range. (zero indexed).
first_col: The first column of the cell range.
last_row: The last row of the cell range. (zero indexed).
last_col: The last column of the cell range.
options: Table format options. (Optional)
Returns:
0: Success.
-1: Not supported in constant_memory mode.
-2: Row or column is out of worksheet bounds.
-3: Incorrect parameter or option.
"""
table = {}
col_formats = {}
if options is None:
options = {}
else:
# Copy the user defined options so they aren't modified.
options = options.copy()
if self.constant_memory:
warn("add_table() isn't supported in 'constant_memory' mode")
return -1
# Check that row and col are valid without storing the values.
if self._check_dimensions(first_row, first_col, True, True):
return -2
if self._check_dimensions(last_row, last_col, True, True):
return -2
# Swap last row/col for first row/col as necessary.
if first_row > last_row:
(first_row, last_row) = (last_row, first_row)
if first_col > last_col:
(first_col, last_col) = (last_col, first_col)
# Valid input parameters.
valid_parameter = {
'autofilter': True,
'banded_columns': True,
'banded_rows': True,
'columns': True,
'data': True,
'first_column': True,
'header_row': True,
'last_column': True,
'name': True,
'style': True,
'total_row': True,
}
# Check for valid input parameters.
for param_key in options.keys():
if param_key not in valid_parameter:
warn("Unknown parameter '%s' in add_table()" % param_key)
return -3
# Turn on Excel's defaults.
options['banded_rows'] = options.get('banded_rows', True)
options['header_row'] = options.get('header_row', True)
options['autofilter'] = options.get('autofilter', True)
# Check that there are enough rows.
num_rows = last_row - first_row
if options['header_row']:
num_rows -= 1
if num_rows < 0:
warn("Must have at least one data row in in add_table()")
return -3
# Set the table options.
table['show_first_col'] = options.get('first_column', False)
table['show_last_col'] = options.get('last_column', False)
table['show_row_stripes'] = options.get('banded_rows', False)
table['show_col_stripes'] = options.get('banded_columns', False)
table['header_row_count'] = options.get('header_row', 0)
table['totals_row_shown'] = options.get('total_row', False)
# Set the table name.
if 'name' in options:
name = options['name']
table['name'] = name
if ' ' in name:
warn("Name '%s' in add_table() cannot contain spaces"
% force_unicode(name))
return -3
# Warn if the name contains invalid chars as defined by Excel.
if (not re.match(r'^[\w\\][\w\\.]*$', name, re.UNICODE)
or re.match(r'^\d', name)):
warn("Invalid Excel characters in add_table(): '%s'"
% force_unicode(name))
return -1
# Warn if the name looks like a cell name.
if re.match(r'^[a-zA-Z][a-zA-Z]?[a-dA-D]?[0-9]+$', name):
warn("Name looks like a cell name in add_table(): '%s'"
% force_unicode(name))
return -1
# Warn if the name looks like a R1C1 cell reference.
if (re.match(r'^[rcRC]$', name)
or re.match(r'^[rcRC]\d+[rcRC]\d+$', name)):
warn("Invalid name '%s' like a RC cell ref in add_table()"
% force_unicode(name))
return -1
# Set the table style.
if 'style' in options:
table['style'] = options['style']
if table['style'] is None:
table['style'] = ''
# Remove whitespace from style name.
table['style'] = table['style'].replace(' ', '')
else:
table['style'] = "TableStyleMedium9"
# Set the data range rows (without the header and footer).
first_data_row = first_row
last_data_row = last_row
if options.get('header_row'):
first_data_row += 1
if options.get('total_row'):
last_data_row -= 1
# Set the table and autofilter ranges.
table['range'] = xl_range(first_row, first_col,
last_row, last_col)
table['a_range'] = xl_range(first_row, first_col,
last_data_row, last_col)
# If the header row if off the default is to turn autofilter off.
if not options['header_row']:
options['autofilter'] = 0
# Set the autofilter range.
if options['autofilter']:
table['autofilter'] = table['a_range']
# Add the table columns.
col_id = 1
table['columns'] = []
seen_names = {}
for col_num in range(first_col, last_col + 1):
# Set up the default column data.
col_data = {
'id': col_id,
'name': 'Column' + str(col_id),
'total_string': '',
'total_function': '',
'total_value': 0,
'formula': '',
'format': None,
'name_format': None,
}
# Overwrite the defaults with any user defined values.
if 'columns' in options:
# Check if there are user defined values for this column.
if col_id <= len(options['columns']):
user_data = options['columns'][col_id - 1]
else:
user_data = None
if user_data:
# Get the column format.
xformat = user_data.get('format', None)
# Map user defined values to internal values.
if user_data.get('header'):
col_data['name'] = user_data['header']
# Excel requires unique case insensitive header names.
header_name = col_data['name']
name = header_name.lower()
if name in seen_names:
warn("Duplicate header name in add_table(): '%s'"
% force_unicode(name))
return -1
else:
seen_names[name] = True
col_data['name_format'] = user_data.get('header_format')
# Handle the column formula.
if 'formula' in user_data and user_data['formula']:
formula = user_data['formula']
# Remove the formula '=' sign if it exists.
if formula.startswith('='):
formula = formula.lstrip('=')
# Covert Excel 2010 "@" ref to 2007 "#This Row".
formula = formula.replace('@', '[#This Row],')
col_data['formula'] = formula
for row in range(first_data_row, last_data_row + 1):
self._write_formula(row, col_num, formula, xformat)
# Handle the function for the total row.
if user_data.get('total_function'):
function = user_data['total_function']
# Massage the function name.
function = function.lower()
function = function.replace('_', '')
function = function.replace(' ', '')
if function == 'countnums':
function = 'countNums'
if function == 'stddev':
function = 'stdDev'
col_data['total_function'] = function
formula = \
self._table_function_to_formula(function,
col_data['name'])
value = user_data.get('total_value', 0)
self._write_formula(last_row, col_num, formula,
xformat, value)
elif user_data.get('total_string'):
# Total label only (not a function).
total_string = user_data['total_string']
col_data['total_string'] = total_string
self._write_string(last_row, col_num, total_string,
user_data.get('format'))
# Get the dxf format index.
if xformat is not None:
col_data['format'] = xformat._get_dxf_index()
# Store the column format for writing the cell data.
# It doesn't matter if it is undefined.
col_formats[col_id - 1] = xformat
# Store the column data.
table['columns'].append(col_data)
# Write the column headers to the worksheet.
if options['header_row']:
self._write_string(first_row, col_num, col_data['name'],
col_data['name_format'])
col_id += 1
# Write the cell data if supplied.
if 'data' in options:
data = options['data']
i = 0 # For indexing the row data.
for row in range(first_data_row, last_data_row + 1):
j = 0 # For indexing the col data.
for col in range(first_col, last_col + 1):
if i < len(data) and j < len(data[i]):
token = data[i][j]
if j in col_formats:
self._write(row, col, token, col_formats[j])
else:
self._write(row, col, token, None)
j += 1
i += 1
# Store the table data.
self.tables.append(table)
return table | [
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|
catalyst-cooperative/pudl | 40d176313e60dfa9d2481f63842ed23f08f1ad5f | src/pudl/transform/eia923.py | python | _map_prime_mover_sets | (prime_mover_set: np.ndarray) | Map unique prime mover combinations to a single prime mover code.
In 2001-2019 data, the .value_counts() of the combinations is:
(CA, CT) 750
(ST, CA) 101
(ST) 60
(CA) 17
(CS, ST, CT) 2
Args:
prime_mover_set (np.ndarray): unique combinations of prime_mover_code
Returns:
str: single prime mover code | Map unique prime mover combinations to a single prime mover code. | [
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] | def _map_prime_mover_sets(prime_mover_set: np.ndarray) -> str:
"""Map unique prime mover combinations to a single prime mover code.
In 2001-2019 data, the .value_counts() of the combinations is:
(CA, CT) 750
(ST, CA) 101
(ST) 60
(CA) 17
(CS, ST, CT) 2
Args:
prime_mover_set (np.ndarray): unique combinations of prime_mover_code
Returns:
str: single prime mover code
"""
if len(prime_mover_set) == 1: # single valued
return prime_mover_set[0]
elif 'CA' in prime_mover_set:
return 'CA' # arbitrary choice
elif 'CS' in prime_mover_set:
return 'CS'
else:
raise ValueError(
"Dataset contains new kinds of duplicate boiler_fuel rows. "
f"Prime movers are {prime_mover_set}"
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/future/backports/datetime.py | python | timezone.fromutc | (self, dt) | [] | def fromutc(self, dt):
if isinstance(dt, datetime):
if dt.tzinfo is not self:
raise ValueError("fromutc: dt.tzinfo "
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return dt + self._offset
raise TypeError("fromutc() argument must be a datetime instance"
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syang1993/gst-tacotron | f28635c539d6a3a9ceece7be2acf8aa2fe3477b0 | models/multihead_attention.py | python | MultiheadAttention._split_last_dimension | (self, x, num_heads) | return tf.reshape(x, x_shape[:-1] + [num_heads, dim // num_heads]) | Reshape x to num_heads
Returns:
a Tensor with shape [batch, length_x, num_heads, dim_x/num_heads] | Reshape x to num_heads | [
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'''Reshape x to num_heads
Returns:
a Tensor with shape [batch, length_x, num_heads, dim_x/num_heads]
'''
x_shape = shape_list(x)
dim = x_shape[-1]
assert dim % num_heads == 0
return tf.reshape(x, x_shape[:-1] + [num_heads, dim // num_heads]) | [
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|
osmr/imgclsmob | f2993d3ce73a2f7ddba05da3891defb08547d504 | tensorflow2/tf2cv/models/sepreresnet.py | python | sepreresnet200 | (**kwargs) | return get_sepreresnet(blocks=200, model_name="sepreresnet200", **kwargs) | SE-PreResNet-200 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507. It's an
experimental model.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters. | SE-PreResNet-200 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507. It's an
experimental model. | [
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"""
SE-PreResNet-200 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507. It's an
experimental model.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
return get_sepreresnet(blocks=200, model_name="sepreresnet200", **kwargs) | [
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|
lazylibrarian/LazyLibrarian | ae3c14e9db9328ce81765e094ab2a14ed7155624 | lib/gntp/core.py | python | _GNTPBase.decode | (self, data, password=None) | Decode GNTP Message
:param string data: | Decode GNTP Message | [
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:param string data:
"""
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self.raw = shim.u(data)
parts = self.raw.split('\r\n\r\n')
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||
JimmXinu/FanFicFare | bc149a2deb2636320fe50a3e374af6eef8f61889 | fanficfare/adapters/adapter_chireadscom.py | python | ChireadsComSiteAdapter.getSiteExampleURLs | (cls) | return 'https://%s/category/translatedtales/story-name' % cls.getSiteDomain() | [] | def getSiteExampleURLs(cls):
return 'https://%s/category/translatedtales/story-name' % cls.getSiteDomain() | [
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|||
webrecorder/pywb | 7ff789f1a8e246720dab7744617824aa1e7d06c9 | pywb/utils/binsearch.py | python | iter_range | (reader, start, end, prev_size=0) | return end_iter | Creates an iterator which iterates over lines where
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"""
Creates an iterator which iterates over lines where
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"""
iter_ = search(reader, start, prev_size=prev_size)
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|
LumaPictures/pymel | fa88a3f4fa18e09bb8aa9bdf4dab53d984bada72 | pymel/util/utilitytypes.py | python | LazyLoadModule._lazyModule_update | (self) | Used to update the contents of the LazyLoadModule with the contents of another dict. | Used to update the contents of the LazyLoadModule with the contents of another dict. | [
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Used to update the contents of the LazyLoadModule with the contents of another dict.
"""
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cupy/cupy | a47ad3105f0fe817a4957de87d98ddccb8c7491f | cupy/_core/_fusion_interface.py | python | _set_dtype_to_astype_dict | () | Set a dict with dtypes and astype ufuncs to `_dtype_to_astype_dict`.
Creates a ufunc for type cast operations, and set a dict with keys
as the dtype of the output array and values as astype ufuncs.
This function is called at most once. | Set a dict with dtypes and astype ufuncs to `_dtype_to_astype_dict`. | [
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"""Set a dict with dtypes and astype ufuncs to `_dtype_to_astype_dict`.
Creates a ufunc for type cast operations, and set a dict with keys
as the dtype of the output array and values as astype ufuncs.
This function is called at most once.
"""
global _dtype_to_astype_dict
_dtype_to_astype_dict = {}
dtype_list = [numpy.dtype(type_char) for type_char in '?bhilqBHILQefdFD']
for t in dtype_list:
name = 'astype_{}'.format(t)
rules = tuple(['{}->{}'.format(s.char, t.char) for s in dtype_list])
command = 'out0 = static_cast< {} >(in0)'.format(get_typename(t))
_dtype_to_astype_dict[t] = core.create_ufunc(name, rules, command) | [
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mesalock-linux/mesapy | ed546d59a21b36feb93e2309d5c6b75aa0ad95c9 | rpython/translator/driver.py | python | TranslationDriver.task_annotate | (self) | return s | Annotate | Annotate | [
"Annotate"
] | def task_annotate(self):
""" Annotate
"""
# includes annotation and annotatation simplifications
translator = self.translator
policy = self.policy
self.log.info('with policy: %s.%s' % (policy.__class__.__module__, policy.__class__.__name__))
annotator = translator.buildannotator(policy=policy)
if self.secondary_entrypoints is not None:
for func, inputtypes in self.secondary_entrypoints:
if inputtypes == Ellipsis:
continue
annotator.build_types(func, inputtypes, False)
if self.entry_point:
s = annotator.build_types(self.entry_point, self.inputtypes)
translator.entry_point_graph = annotator.bookkeeper.getdesc(self.entry_point).getuniquegraph()
else:
s = None
self.sanity_check_annotation()
if self.entry_point and self.standalone and s.knowntype != int:
raise Exception("stand-alone program entry point must return an "
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annotator.complete()
annotator.simplify()
return s | [
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|
taigaio/taiga-ncurses | 65312098f2d167762e0dbd1c16019754ab64d068 | taiga_ncurses/data.py | python | us_ref | (us) | return us.get("ref", "--") | [] | def us_ref(us):
return us.get("ref", "--") | [
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|||
opendatateam/udata | a295cab3c0e8f086fea1853655011f361ac81b77 | udata/rdf.py | python | want_rdf | () | return mimetype in ACCEPTED_MIME_TYPES | Check wether client prefer RDF over the default HTML | Check wether client prefer RDF over the default HTML | [
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] | def want_rdf():
'''Check wether client prefer RDF over the default HTML'''
mimetype = request.accept_mimetypes.best
return mimetype in ACCEPTED_MIME_TYPES | [
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|
kneufeld/alkali | 0b5d423ea584ae3d627fcf37b801898364e19c71 | alkali/manager.py | python | Manager.pks | (self) | return list(self._instances.keys()) | **property**: return all primary keys
:rtype: ``list`` | **property**: return all primary keys | [
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"""
**property**: return all primary keys
:rtype: ``list``
"""
return list(self._instances.keys()) | [
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|
rolandshoemaker/CommonMark-py | c0786fbc05eb2276c9ccc6f8d9b47c261b854155 | CommonMark/CommonMark.py | python | InlineParser.parseEntity | (self, inlines) | Attempt to parse an entity, adding to inlines if successful. | Attempt to parse an entity, adding to inlines if successful. | [
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if m:
inlines.append(Block(t="Entity", c=m))
return len(m)
else:
return 0 | [
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||
aajanki/yle-dl | b0aa1bb5d943fdbd9a18da2604f21bb2094eadd7 | yledl/streamprobe.py | python | FullHDFlavorProber._drop_duplicates | (self, stream_flavors) | return unique.values() | [] | def _drop_duplicates(self, stream_flavors):
def flavor_key(s):
return (s.width, s.height, s.bitrate, next((x.url for x in s.streams), None))
unique = {flavor_key(s): s for s in stream_flavors}
return unique.values() | [
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|||
cool-RR/python_toolbox | cb9ef64b48f1d03275484d707dc5079b6701ad0c | python_toolbox/wx_tools/widgets/third_party/customtreectrl.py | python | CustomTreeCtrl.GetStateImageList | (self) | return self._imageListState | Returns the state image list associated with L{CustomTreeCtrl} (from which
application-defined state images are taken). | Returns the state image list associated with L{CustomTreeCtrl} (from which
application-defined state images are taken). | [
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"""
Returns the state image list associated with L{CustomTreeCtrl} (from which
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"""
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|
armadillica/flamenco | 7c3a5559ea200f9fd42ef61d3a09986fc0c44040 | flamenco/managers/api.py | python | tasks_cancel_requested | (manager_id) | return task_ids | Returns a set of tasks of status cancel-requested. | Returns a set of tasks of status cancel-requested. | [
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"""Returns a set of tasks of status cancel-requested."""
from flamenco import current_flamenco, eve_settings
tasks_coll = current_flamenco.db('tasks')
task_ids = {
task['_id']
for task in tasks_coll.find({'manager': manager_id, 'status': 'cancel-requested'},
projection={'_id': 1})
}
log.debug('Returning %i tasks to be canceled by manager %s', len(task_ids), manager_id)
return task_ids | [
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|
ansible-community/molecule | be98c8db07666fd1125f69020419b67fda48b559 | src/molecule/provisioner/ansible.py | python | Ansible.check | (self) | Execute ``ansible-playbook`` against the converge playbook with the \
``--check`` flag and returns None.
:return: None | Execute ``ansible-playbook`` against the converge playbook with the \
``--check`` flag and returns None. | [
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"""
Execute ``ansible-playbook`` against the converge playbook with the \
``--check`` flag and returns None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.converge)
pb.add_cli_arg("check", True)
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||
AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0/pyqtgraph/graphicsItems/GradientEditorItem.py | python | TickSliderItem.setTickValue | (self, tick, val) | Set the position (along the slider) of the tick.
============== ==================================================================
**Arguments:**
tick Can be either an integer corresponding to the index of the tick
or a Tick object. Ex: if you had a slider with 3 ticks and you
wanted to change the middle tick, the index would be 1.
val The desired position of the tick. If val is < 0, position will be
set to 0. If val is > 1, position will be set to 1.
============== ================================================================== | Set the position (along the slider) of the tick.
============== ==================================================================
**Arguments:**
tick Can be either an integer corresponding to the index of the tick
or a Tick object. Ex: if you had a slider with 3 ticks and you
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val The desired position of the tick. If val is < 0, position will be
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"""
Set the position (along the slider) of the tick.
============== ==================================================================
**Arguments:**
tick Can be either an integer corresponding to the index of the tick
or a Tick object. Ex: if you had a slider with 3 ticks and you
wanted to change the middle tick, the index would be 1.
val The desired position of the tick. If val is < 0, position will be
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============== ==================================================================
"""
tick = self.getTick(tick)
val = min(max(0.0, val), 1.0)
x = val * self.length
pos = tick.pos()
pos.setX(x)
tick.setPos(pos)
self.ticks[tick] = val
self.updateGradient() | [
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||
dmlc/gluon-cv | 709bc139919c02f7454cb411311048be188cde64 | gluoncv/model_zoo/residual_attentionnet.py | python | residualattentionnet236 | (**kwargs) | return get_residualAttentionModel(224, 236, **kwargs) | r"""AttentionModel model from
`"Residual Attention Network for Image Classification"
<https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
Parameters
----------
input_size : int
Input size of net. Options are 32,224.
num_layers : int
Numbers of layers. Options are 56, 92, 128, 164, 200, 236, 452.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`. | r"""AttentionModel model from
`"Residual Attention Network for Image Classification"
<https://arxiv.org/pdf/1704.06904.pdf>`_ paper. | [
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r"""AttentionModel model from
`"Residual Attention Network for Image Classification"
<https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
Parameters
----------
input_size : int
Input size of net. Options are 32,224.
num_layers : int
Numbers of layers. Options are 56, 92, 128, 164, 200, 236, 452.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
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Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_residualAttentionModel(224, 236, **kwargs) | [
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|
catalyst-team/catalyst | 678dc06eda1848242df010b7f34adb572def2598 | catalyst/callbacks/metrics/scikit_learn.py | python | SklearnBatchCallback.__init__ | (
self,
keys: Mapping[str, Any],
metric_fn: Union[Callable, str],
metric_key: str,
log_on_batch: bool = True,
**metric_kwargs
) | Init. | Init. | [
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self,
keys: Mapping[str, Any],
metric_fn: Union[Callable, str],
metric_key: str,
log_on_batch: bool = True,
**metric_kwargs
):
"""Init."""
if isinstance(metric_fn, str):
metric_fn = sklearn.metrics.__dict__[metric_fn]
metric_fn = partial(metric_fn, **metric_kwargs)
super().__init__(
metric=FunctionalBatchMetric(metric_fn=metric_fn, metric_key=metric_key),
input_key=keys,
target_key=keys,
log_on_batch=log_on_batch,
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||
hzlzh/AlfredWorkflow.com | 7055f14f6922c80ea5943839eb0caff11ae57255 | Sources/Workflows/Pinboard-Tools/BeautifulSoup.py | python | UnicodeDammit._toUnicode | (self, data, encoding) | return newdata | Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases | Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases | [
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'''Given a string and its encoding, decodes the string into Unicode.
%encoding is a string recognized by encodings.aliases'''
# strip Byte Order Mark (if present)
if (len(data) >= 4) and (data[:2] == '\xfe\xff') \
and (data[2:4] != '\x00\x00'):
encoding = 'utf-16be'
data = data[2:]
elif (len(data) >= 4) and (data[:2] == '\xff\xfe') \
and (data[2:4] != '\x00\x00'):
encoding = 'utf-16le'
data = data[2:]
elif data[:3] == '\xef\xbb\xbf':
encoding = 'utf-8'
data = data[3:]
elif data[:4] == '\x00\x00\xfe\xff':
encoding = 'utf-32be'
data = data[4:]
elif data[:4] == '\xff\xfe\x00\x00':
encoding = 'utf-32le'
data = data[4:]
newdata = unicode(data, encoding)
return newdata | [
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|
omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/tri/triplot.py | python | triplot | (ax, *args, **kwargs) | Draw a unstructured triangular grid as lines and/or markers.
The triangulation to plot can be specified in one of two ways;
either::
triplot(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
triplot(x, y, ...)
triplot(x, y, triangles, ...)
triplot(x, y, triangles=triangles, ...)
triplot(x, y, mask=mask, ...)
triplot(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of these
possibilities.
The remaining args and kwargs are the same as for
:meth:`~matplotlib.axes.Axes.plot`.
**Example:**
.. plot:: mpl_examples/pylab_examples/triplot_demo.py | Draw a unstructured triangular grid as lines and/or markers. | [
"Draw",
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"unstructured",
"triangular",
"grid",
"as",
"lines",
"and",
"/",
"or",
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] | def triplot(ax, *args, **kwargs):
"""
Draw a unstructured triangular grid as lines and/or markers.
The triangulation to plot can be specified in one of two ways;
either::
triplot(triangulation, ...)
where triangulation is a :class:`matplotlib.tri.Triangulation`
object, or
::
triplot(x, y, ...)
triplot(x, y, triangles, ...)
triplot(x, y, triangles=triangles, ...)
triplot(x, y, mask=mask, ...)
triplot(x, y, triangles, mask=mask, ...)
in which case a Triangulation object will be created. See
:class:`~matplotlib.tri.Triangulation` for a explanation of these
possibilities.
The remaining args and kwargs are the same as for
:meth:`~matplotlib.axes.Axes.plot`.
**Example:**
.. plot:: mpl_examples/pylab_examples/triplot_demo.py
"""
import matplotlib.axes
tri, args, kwargs = Triangulation.get_from_args_and_kwargs(*args, **kwargs)
x = tri.x
y = tri.y
edges = tri.edges
# If draw both lines and markers at the same time, e.g.
# ax.plot(x[edges].T, y[edges].T, *args, **kwargs)
# then the markers are drawn more than once which is incorrect if alpha<1.
# Hence draw lines and markers separately.
# Decode plot format string, e.g., 'ro-'
fmt = ''
if len(args) > 0:
fmt = args[0]
linestyle, marker, color = matplotlib.axes._process_plot_format(fmt)
# Draw lines without markers, if lines are required.
if linestyle is not None and linestyle is not 'None':
kw = kwargs.copy()
kw.pop('marker', None) # Ignore marker if set.
kw['linestyle'] = ls_mapper[linestyle]
kw['edgecolor'] = color
kw['facecolor'] = None
vertices = np.column_stack((x[edges].flatten(), y[edges].flatten()))
codes = ([Path.MOVETO] + [Path.LINETO])*len(edges)
path = Path(vertices, codes)
pathpatch = PathPatch(path, **kw)
ax.add_patch(pathpatch)
# Draw markers without lines.
# Should avoid drawing markers for points that are not in any triangle?
kwargs['linestyle'] = ''
ax.plot(x, y, *args, **kwargs) | [
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||
researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | python | greater | (attrs, inputs, proto_obj) | return 'broadcast_greater', attrs, inputs | Logical Greater operator with broadcasting. | Logical Greater operator with broadcasting. | [
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"Greater",
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"with",
"broadcasting",
"."
] | def greater(attrs, inputs, proto_obj):
"""Logical Greater operator with broadcasting."""
return 'broadcast_greater', attrs, inputs | [
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|
mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/httplib.py | python | HTTPConnection._output | (self, s) | Add a line of output to the current request buffer.
Assumes that the line does *not* end with \\r\\n. | Add a line of output to the current request buffer. | [
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] | def _output(self, s):
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Assumes that the line does *not* end with \\r\\n.
"""
self._buffer.append(s) | [
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||
ckan/ckan | b3b01218ad88ed3fb914b51018abe8b07b07bff3 | ckan/lib/config_tool.py | python | parse_config | (input_lines) | return options | Returns a dict of Option objects, keyed by Option.id, given the lines in a
config file.
(Not using ConfigParser.set() as it does not store all the comments and
ordering) | Returns a dict of Option objects, keyed by Option.id, given the lines in a
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'''
Returns a dict of Option objects, keyed by Option.id, given the lines in a
config file.
(Not using ConfigParser.set() as it does not store all the comments and
ordering)
'''
section = 'app:main' # default (for merge config files)
options = {}
for line in input_lines:
# ignore blank lines
if line.strip() == '':
continue
# section heading
section_match = SECTION_RE.match(line)
if section_match:
section = section_match.group('header')
continue
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option = parse_option_string(section, line)
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options[option.id] = option
return options | [
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|
demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Exabeam/Integrations/Exabeam/Exabeam.py | python | list_top_domains | (client: Client, args: Dict) | return human_readable, entry_context, top_domains_raw_data | Return session information for given session ID
Args:
client: Client
args: Dict | Return session information for given session ID | [
"Return",
"session",
"information",
"for",
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"session",
"ID"
] | def list_top_domains(client: Client, args: Dict) -> Tuple[Any, Dict[str, Dict[Any, Any]], Optional[Any]]:
""" Return session information for given session ID
Args:
client: Client
args: Dict
"""
sequence_id = args.get('sequence_id')
sequence_type = args.get('sequence_type')
top_domains_raw_data = client.list_top_domains_request(sequence_id, sequence_type)
top_domains = top_domains_raw_data.get('topDomains', [])
entry_context = {'Exabeam.DataFeed(val.sequenceId && val.sequenceId === obj.sequenceId)': top_domains_raw_data}
human_readable = tableToMarkdown(f'Sequence {sequence_id} Top Domains', top_domains,
removeNull=True, headerTransform=pascalToSpace)
return human_readable, entry_context, top_domains_raw_data | [
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rdiff-backup/rdiff-backup | 321e0cd6e5e47d4c158a0172e47ab38240a8b653 | src/rdiff_backup/SetConnections.py | python | get_connected_rpath | (cmd_pair) | Return normalized RPath from command pair (remote_cmd, filename) | Return normalized RPath from command pair (remote_cmd, filename) | [
"Return",
"normalized",
"RPath",
"from",
"command",
"pair",
"(",
"remote_cmd",
"filename",
")"
] | def get_connected_rpath(cmd_pair):
"""
Return normalized RPath from command pair (remote_cmd, filename)
"""
cmd, filename = cmd_pair
if cmd:
conn = _init_connection(cmd)
else:
conn = Globals.local_connection
if conn:
return rpath.RPath(conn, filename).normalize()
else:
return None | [
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||
ericmjl/nxviz | 2979723238f2b68ea29cc07baa13b8d79d37729d | nxviz/polcart.py | python | to_polar | (x, y) | return r, theta | Converts cartesian x, y to polar r, theta. | Converts cartesian x, y to polar r, theta. | [
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] | def to_polar(x, y):
"""
Converts cartesian x, y to polar r, theta.
"""
theta = atan2(y, x)
r = sqrt(x ** 2 + y ** 2)
if theta < 0:
theta += 2 * np.pi
return r, theta | [
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|
cleverhans-lab/cleverhans | e5d00e537ce7ad6119ed5a8db1f0e9736d1f6e1d | cleverhans_v3.1.0/cleverhans/attack_bundling.py | python | AttackGoal.get_criteria | (self, sess, model, advx, y, batch_size=BATCH_SIZE) | return out | Returns a dictionary mapping the name of each criterion to a NumPy
array containing the value of that criterion for each adversarial
example.
Subclasses can add extra criteria by implementing the `extra_criteria`
method.
:param sess: tf.session.Session
:param model: cleverhans.model.Model
:param adv_x: numpy array containing the adversarial examples made so far
by earlier work in the bundling process
:param y: numpy array containing true labels
:param batch_size: int, batch size | Returns a dictionary mapping the name of each criterion to a NumPy
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example.
Subclasses can add extra criteria by implementing the `extra_criteria`
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"""
Returns a dictionary mapping the name of each criterion to a NumPy
array containing the value of that criterion for each adversarial
example.
Subclasses can add extra criteria by implementing the `extra_criteria`
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:param y: numpy array containing true labels
:param batch_size: int, batch size
"""
names, factory = self.extra_criteria()
factory = _CriteriaFactory(model, factory)
results = batch_eval_multi_worker(
sess, factory, [advx, y], batch_size=batch_size, devices=devices
)
names = ["correctness", "confidence"] + names
out = dict(safe_zip(names, results))
return out | [
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|
buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/lib-tk/Tkinter.py | python | BaseWidget.__init__ | (self, master, widgetName, cnf={}, kw={}, extra=()) | Construct a widget with the parent widget MASTER, a name WIDGETNAME
and appropriate options. | Construct a widget with the parent widget MASTER, a name WIDGETNAME
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if kw:
cnf = _cnfmerge((cnf, kw))
self.widgetName = widgetName
BaseWidget._setup(self, master, cnf)
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self._tclCommands = []
classes = []
for k in cnf.keys():
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classes.append((k, cnf[k]))
del cnf[k]
self.tk.call(
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||
robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/simulators/dart.py | python | Dart.get_link_world_angular_velocities | (self, body_id, link_ids) | return [skeleton.getBodyNode(link + 1).getAngularVelocity.reshape(-1) for link in link_ids] | Return the angular velocity of the link(s) in the Cartesian world space coordinates.
Args:
body_id (int): unique body id.
link_ids (list[int]): list of link indices.
Returns:
if 1 link:
np.array[float[3]]: angular velocity of the link in the Cartesian world space
if multiple links:
np.array[float[N,3]]: angular velocity of each link | Return the angular velocity of the link(s) in the Cartesian world space coordinates. | [
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"""
Return the angular velocity of the link(s) in the Cartesian world space coordinates.
Args:
body_id (int): unique body id.
link_ids (list[int]): list of link indices.
Returns:
if 1 link:
np.array[float[3]]: angular velocity of the link in the Cartesian world space
if multiple links:
np.array[float[N,3]]: angular velocity of each link
"""
skeleton = self.world.getSkeleton(body_id)
if isinstance(link_ids, int):
return skeleton.getBodyNode(link_ids + 1).getAngularVelocity().reshape(-1)
return [skeleton.getBodyNode(link + 1).getAngularVelocity.reshape(-1) for link in link_ids] | [
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|
kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_secret.py | python | V1Secret.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
"Returns",
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] | def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result | [
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|
1040003585/WebScrapingWithPython | a770fa5b03894076c8c9539b1ffff34424ffc016 | ResourceCode/wswp-places-c573d29efa3a/web2py/gluon/widget.py | python | run_system_tests | (options) | Runs unittests for gluon.tests | Runs unittests for gluon.tests | [
"Runs",
"unittests",
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"gluon",
".",
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] | def run_system_tests(options):
"""
Runs unittests for gluon.tests
"""
import subprocess
major_version = sys.version_info[0]
minor_version = sys.version_info[1]
if major_version == 2:
if minor_version in (6,):
sys.stderr.write('Python 2.6\n')
ret = subprocess.call(['unit2', '-v', 'gluon.tests'])
elif minor_version in (7,):
call_args = [sys.executable, '-m', 'unittest', '-v', 'gluon.tests']
if options.with_coverage:
try:
import coverage
coverage_config = os.environ.get(
"COVERAGE_PROCESS_START",
os.path.join('gluon', 'tests', 'coverage.ini'))
call_args = ['coverage', 'run', '--rcfile=%s' %
coverage_config,
'-m', 'unittest', '-v', 'gluon.tests']
except:
sys.stderr.write('Coverage was not installed, skipping\n')
sys.stderr.write("Python 2.7\n")
ret = subprocess.call(call_args)
else:
sys.stderr.write("unknown python 2.x version\n")
ret = 256
else:
sys.stderr.write("Only Python 2.x supported.\n")
ret = 256
sys.exit(ret and 1) | [
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||
google/trax | d6cae2067dedd0490b78d831033607357e975015 | trax/layers/research/rel_attention.py | python | RelativeAttentionLMLayer | (d_feature,
context_bias_layer,
location_bias_layer,
total_kv_pooling,
separate_cls=False,
n_heads=1,
dropout=0.0,
mode='train') | return cb.Serial(
CreateAttentionMaskLayer(), # q, k, v, mask
attention, # vecs, mask
cb.Select([0], n_in=2), # vecs
) | Returns a layer that maps (q, k, v) to (activations).
Same as standard Relative attention layer but additionally based on sizes
of queries and keys prepares a mask that masks out the future.
Masking the future is the concept primarily used for Language Modelling.
Args:
d_feature: Depth/dimensionality of feature embedding.
context_bias_layer: Global context bias from Transformer XL's attention.
There should be one such layer shared for all relative attention layers
location_bias_layer: Global location bias from Transformer XL's attention.
There should be one such layer shared for all relative attention layers.
total_kv_pooling: Accumulated pool size of keys/values used at this layer.
separate_cls: True/False if we separate_cls in calculations.
n_heads: Number of attention heads.
dropout: Probabilistic rate for internal dropout applied to attention
activations (based on query-key pairs) before dotting them with values.
mode: One of `'train'`, `'eval'`, or `'predict'`. | Returns a layer that maps (q, k, v) to (activations). | [
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] | def RelativeAttentionLMLayer(d_feature,
context_bias_layer,
location_bias_layer,
total_kv_pooling,
separate_cls=False,
n_heads=1,
dropout=0.0,
mode='train'):
"""Returns a layer that maps (q, k, v) to (activations).
Same as standard Relative attention layer but additionally based on sizes
of queries and keys prepares a mask that masks out the future.
Masking the future is the concept primarily used for Language Modelling.
Args:
d_feature: Depth/dimensionality of feature embedding.
context_bias_layer: Global context bias from Transformer XL's attention.
There should be one such layer shared for all relative attention layers
location_bias_layer: Global location bias from Transformer XL's attention.
There should be one such layer shared for all relative attention layers.
total_kv_pooling: Accumulated pool size of keys/values used at this layer.
separate_cls: True/False if we separate_cls in calculations.
n_heads: Number of attention heads.
dropout: Probabilistic rate for internal dropout applied to attention
activations (based on query-key pairs) before dotting them with values.
mode: One of `'train'`, `'eval'`, or `'predict'`.
"""
attention = RelativeAttentionLayer(
d_feature,
context_bias_layer,
location_bias_layer,
total_kv_pooling,
separate_cls,
n_heads=n_heads,
dropout=dropout,
mode=mode)
return cb.Serial(
CreateAttentionMaskLayer(), # q, k, v, mask
attention, # vecs, mask
cb.Select([0], n_in=2), # vecs
) | [
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|
xtiankisutsa/MARA_Framework | ac4ac88bfd38f33ae8780a606ed09ab97177c562 | tools/Smali-CFGs/Flow.py | python | Block.add_inst | (self, inst) | Just add one instruction to our set of instructions. | Just add one instruction to our set of instructions. | [
"Just",
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] | def add_inst(self, inst):
""" Just add one instruction to our set of instructions. """
self.instructions.append(inst) | [
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||
bitcraft/pyscroll | da826cd81abd48de8929601b07f39d4375b9c33e | pyscroll/orthographic.py | python | BufferedRenderer.translate_point | (self, point: Vector2D) | Translate world coordinates and return screen coordinates.
Args:
point: point to translate | Translate world coordinates and return screen coordinates. | [
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] | def translate_point(self, point: Vector2D) -> Vector2DInt:
"""
Translate world coordinates and return screen coordinates.
Args:
point: point to translate
"""
mx, my = self.get_center_offset()
if self._zoom_level == 1.0:
return int(point[0] + mx), int(point[1] + my)
else:
return (
int(round((point[0] + mx)) * self._real_ratio_x),
int(round((point[1] + my) * self._real_ratio_y))
) | [
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||
chipmuenk/pyfda | 665310b8548a940a575c0e5ff4bba94608d9ac26 | pyfda/filter_factory.py | python | FilterFactory.call_fil_method | (self, method, fil_dict, fc = None) | return self.err_code | Instantiate the filter design class passed as string ``fc`` with the
globally accessible handle ``fil_inst``. If ``fc = None``, use the previously
instantiated filter design class.
Next, call the design method passed as string ``method`` of the instantiated
filter design class.
Parameters
----------
method : string
The name of the design method to be called (e.g. 'LPmin')
fil_dict : dictionary
A dictionary with all the filter specs that is passed to the actual
filter design routine. This is usually a copy of ``fb.fil[0]``
The results of the filter design routine are written back to the same dict.
fc : string (optional, default: None)
The name of the filter design class to be instantiated. When nothing
is specified, the last filter selection is used.
Returns
-------
err_code : int
one of the following error codes:
:-1: filter design operation has been cancelled by user
:0: filter design method exists and is callable
:16: passed method name is not a string
:17: filter design method does not exist in class
:18: filter design error containing "order is too high"
:19: filter design error containing "failure to converge"
:99: unknown error
Examples
--------
>>> call_fil_method("LPmin", fil[0], fc="cheby1")
The example first creates an instance of the filter class 'cheby1' and
then performs the actual filter design by calling the method 'LPmin',
passing the global filter dictionary ``fil[0]`` as the parameter. | Instantiate the filter design class passed as string ``fc`` with the
globally accessible handle ``fil_inst``. If ``fc = None``, use the previously
instantiated filter design class. | [
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"""
Instantiate the filter design class passed as string ``fc`` with the
globally accessible handle ``fil_inst``. If ``fc = None``, use the previously
instantiated filter design class.
Next, call the design method passed as string ``method`` of the instantiated
filter design class.
Parameters
----------
method : string
The name of the design method to be called (e.g. 'LPmin')
fil_dict : dictionary
A dictionary with all the filter specs that is passed to the actual
filter design routine. This is usually a copy of ``fb.fil[0]``
The results of the filter design routine are written back to the same dict.
fc : string (optional, default: None)
The name of the filter design class to be instantiated. When nothing
is specified, the last filter selection is used.
Returns
-------
err_code : int
one of the following error codes:
:-1: filter design operation has been cancelled by user
:0: filter design method exists and is callable
:16: passed method name is not a string
:17: filter design method does not exist in class
:18: filter design error containing "order is too high"
:19: filter design error containing "failure to converge"
:99: unknown error
Examples
--------
>>> call_fil_method("LPmin", fil[0], fc="cheby1")
The example first creates an instance of the filter class 'cheby1' and
then performs the actual filter design by calling the method 'LPmin',
passing the global filter dictionary ``fil[0]`` as the parameter.
"""
if self.err_code >= 16 or self.err_code < 0:
self.err_code = 0 # # clear previous method call error
err_string = ""
if fc: # filter design class was part of the argument, (re-)create class instance
self.err_code = self.create_fil_inst(fc)
# Error during filter design class instantiation (class fc could not be instantiated)
if self.err_code > 0:
err_string = "Filter design class could not be instantiated, see previous error message."
# Test whether 'method' is a string (Py3):
elif not isinstance(method, str):
err_string = "Method name '{0}' is not a string.".format(method)
self.err_code = 16
# method does not exist in filter class:
elif not hasattr(fil_inst, method):
err_string = "Method '{0}' doesn't exist in class '{1}'.".format(method, fil_inst)
self.err_code = 17
else: # everything ok so far, try calling method with the filter dict as argument
# err_code = -1 means "operation cancelled"
try:
#------------------------------------------------------------------
self.err_code = getattr(fil_inst, method)(fil_dict)
#------------------------------------------------------------------
except Exception as e:
err_string = "Method '{0}' of class '{1}':\n{2}"\
.format(method, type(fil_inst).__name__, e)
if e:
err_string += "\n" # add line break to error message
if "order n is too high" in str(e).lower():
self.err_code = 18
err_string += "Try relaxing the specifications."
elif "failure to converge" in str(e).lower():
self.err_code = 19
err_string += "Try relaxing the specifications."
else:
self.err_code = 99
if self.err_code is None:
self.err_code = 0
elif self.err_code > 0:
logger.error("ErrCode {0}: {1}".format(self.err_code, err_string))
return self.err_code | [
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|
replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | dist/lib/python2.7/logging/handlers.py | python | RotatingFileHandler.__init__ | (self, filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=0) | Open the specified file and use it as the stream for logging.
By default, the file grows indefinitely. You can specify particular
values of maxBytes and backupCount to allow the file to rollover at
a predetermined size.
Rollover occurs whenever the current log file is nearly maxBytes in
length. If backupCount is >= 1, the system will successively create
new files with the same pathname as the base file, but with extensions
".1", ".2" etc. appended to it. For example, with a backupCount of 5
and a base file name of "app.log", you would get "app.log",
"app.log.1", "app.log.2", ... through to "app.log.5". The file being
written to is always "app.log" - when it gets filled up, it is closed
and renamed to "app.log.1", and if files "app.log.1", "app.log.2" etc.
exist, then they are renamed to "app.log.2", "app.log.3" etc.
respectively.
If maxBytes is zero, rollover never occurs. | Open the specified file and use it as the stream for logging. | [
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"it",
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] | def __init__(self, filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=0):
"""
Open the specified file and use it as the stream for logging.
By default, the file grows indefinitely. You can specify particular
values of maxBytes and backupCount to allow the file to rollover at
a predetermined size.
Rollover occurs whenever the current log file is nearly maxBytes in
length. If backupCount is >= 1, the system will successively create
new files with the same pathname as the base file, but with extensions
".1", ".2" etc. appended to it. For example, with a backupCount of 5
and a base file name of "app.log", you would get "app.log",
"app.log.1", "app.log.2", ... through to "app.log.5". The file being
written to is always "app.log" - when it gets filled up, it is closed
and renamed to "app.log.1", and if files "app.log.1", "app.log.2" etc.
exist, then they are renamed to "app.log.2", "app.log.3" etc.
respectively.
If maxBytes is zero, rollover never occurs.
"""
# If rotation/rollover is wanted, it doesn't make sense to use another
# mode. If for example 'w' were specified, then if there were multiple
# runs of the calling application, the logs from previous runs would be
# lost if the 'w' is respected, because the log file would be truncated
# on each run.
if maxBytes > 0:
mode = 'a'
BaseRotatingHandler.__init__(self, filename, mode, encoding, delay)
self.maxBytes = maxBytes
self.backupCount = backupCount | [
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||
securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/gettext.py | python | NullTranslations.lgettext | (self, message) | return message | [] | def lgettext(self, message):
if self._fallback:
return self._fallback.lgettext(message)
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|||
facebookresearch/CrypTen | 90bf38b4f80726c808f322efb0ce430dcdf5e5ec | examples/bandits/plain_contextual_bandits.py | python | epsilon_greedy | (
sampler,
epsilon=0.0,
dtype=torch.double,
device="cpu",
monitor_func=None,
checkpoint_func=None,
checkpoint_every=0,
) | Run epsilon-greedy linear least squares learner on dataset.
The `sampler` is expected to be an iterator that returns one sample at a time.
Samples are assumed to be `dict`s with a `'context'` and a `'rewards'` field.
The function takes a hyperpameter `epsilon`, `dtype`, and `device` as optional
arguments. It also takes an optional `monitor_func` closure that does logging,
and an optional `checkpoint_func` that does checkpointing. | Run epsilon-greedy linear least squares learner on dataset. | [
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"greedy",
"linear",
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"learner",
"on",
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"."
] | def epsilon_greedy(
sampler,
epsilon=0.0,
dtype=torch.double,
device="cpu",
monitor_func=None,
checkpoint_func=None,
checkpoint_every=0,
):
"""
Run epsilon-greedy linear least squares learner on dataset.
The `sampler` is expected to be an iterator that returns one sample at a time.
Samples are assumed to be `dict`s with a `'context'` and a `'rewards'` field.
The function takes a hyperpameter `epsilon`, `dtype`, and `device` as optional
arguments. It also takes an optional `monitor_func` closure that does logging,
and an optional `checkpoint_func` that does checkpointing.
"""
# define scoring function:
def score_func(scores, A_inv, b, context):
# Implement as (p < epsilon) * scores + (p > epsilon) * random
# in order to match private version
explore = random.random() < epsilon
rand_scores = torch.rand_like(scores)
scores.mul_(1 - explore).add_(rand_scores.mul(explore))
# run online learner:
online_learner(
sampler,
dtype=dtype,
device=device,
score_func=score_func,
monitor_func=monitor_func,
checkpoint_func=checkpoint_func,
checkpoint_every=checkpoint_every,
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||
openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_route.py | python | Utils.create_tmpfile | (prefix='tmp') | Generates and returns a temporary file name | Generates and returns a temporary file name | [
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] | def create_tmpfile(prefix='tmp'):
''' Generates and returns a temporary file name '''
with tempfile.NamedTemporaryFile(prefix=prefix, delete=False) as tmp:
return tmp.name | [
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