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fluentpython/example-code-2e | 80f7f84274a47579e59c29a4657691525152c9d5 | 21-async/mojifinder/bottle.py | python | _hkey | (key) | return key.title().replace('_', '-') | [] | def _hkey(key):
if '\n' in key or '\r' in key or '\0' in key:
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flask-admin/flask-admin | 7cff9c742d44d42a8d3495c73a6d71381c796396 | flask_admin/model/typefmt.py | python | bool_formatter | (view, value) | return Markup('<span class="fa %s glyphicon glyphicon-%s icon-%s"></span>' % (fa, glyph, glyph)) | Return check icon if value is `True` or empty string otherwise.
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/skybeacon/sensor.py | python | Monitor._update | (self, handle, value) | Notification callback from pygatt. | Notification callback from pygatt. | [
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uqfoundation/multiprocess | 028cc73f02655e6451d92e5147d19d8c10aebe50 | py3.6/multiprocess/pool.py | python | Pool.map | (self, func, iterable, chunksize=None) | return self._map_async(func, iterable, mapstar, chunksize).get() | Apply `func` to each element in `iterable`, collecting the results
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PaulSonOfLars/tgbot | 0ece72778b7772725ab214fe0929daaa2fc7d2d1 | tg_bot/modules/helper_funcs/chat_status.py | python | is_user_ban_protected | (chat: Chat, user_id: int, member: ChatMember = None) | return member.status in ('administrator', 'creator') | [] | def is_user_ban_protected(chat: Chat, user_id: int, member: ChatMember = None) -> bool:
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or user_id in SUDO_USERS \
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projectmesa/mesa | 246c69d592a82e89c75d555c79736c3f619d434a | mesa/space.py | python | ContinuousSpace.move_agent | (self, agent: Agent, pos: FloatCoordinate) | Move an agent from its current position to a new position.
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agent: The agent object to move.
pos: Coordinate tuple to move the agent to.
"""
pos = self.torus_adj(pos)
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metabrainz/picard | 535bf8c7d9363ffc7abb3f69418ec11823c38118 | picard/tagger.py | python | Tagger.extract_and_submit_acousticbrainz_features | (self, objs) | Extract AcousticBrainz features and submit them. | Extract AcousticBrainz features and submit them. | [
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return
for file in iter_files_from_objects(objs):
# Skip unmatched files
if not file.can_extract():
log.warning("AcousticBrainz requires a MusicBrainz Recording ID, but file does not have it: %s" % file.filename)
# And process matched ones
else:
file.set_pending()
# Check if file was either already processed or sent to the AcousticBrainz server
if file.acousticbrainz_features_file:
results = (file.acousticbrainz_features_file, 0, "Writing results")
ab_extractor_callback(self, file, results, False)
elif file.acousticbrainz_is_duplicate:
results = (None, 0, "Duplicate")
ab_extractor_callback(self, file, results, False)
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file.acousticbrainz_error = False
# Launch the acousticbrainz on a separate process
log.debug("Extracting AcousticBrainz features from %s" % file.filename)
ab_feature_extraction(
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microsoft/unilm | 65f15af2a307ebb64cfb25adf54375b002e6fe8d | xtune/src/transformers/file_utils.py | python | s3_etag | (url, proxies=None) | return s3_object.e_tag | Check ETag on S3 object. | Check ETag on S3 object. | [
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bucket_name, s3_path = split_s3_path(url)
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/speaklater.py | python | _LazyString.__setstate__ | (self, tup) | [] | def __setstate__(self, tup):
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wequant-org/liveStrategyEngine | 6e799e33cfa83b4496dd694e1d2bf30ea104e2c1 | exchangeConnection/huobi/huobiServiceETH.py | python | withdraw | (address_id, amount) | return api_key_post(params, url) | :param address_id:
:param amount:
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:param amount:
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"""
:param address_id:
:param amount:
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"""
params = {'address-id': address_id,
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url = '/v1/dw/withdraw-virtual/create'
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trailofbits/manticore | b050fdf0939f6c63f503cdf87ec0ab159dd41159 | manticore/native/cpu/x86.py | python | X86Cpu.PCMPGTD | (cpu, op0, op1) | PCMPGTD: Packed compare for greater than with double words
see PCMPEQB | PCMPGTD: Packed compare for greater than with double words
see PCMPEQB | [
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"""
PCMPGTD: Packed compare for greater than with double words
see PCMPEQB
"""
arg0 = op0.read()
arg1 = op1.read()
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/template/defaultfilters.py | python | get_digit | (value, arg) | Given a whole number, returns the requested digit of it, where 1 is the
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django/channels | 6af1bc3ab45f55e3f47d0d1d059d5db0a18a9581 | channels/generic/websocket.py | python | AsyncWebsocketConsumer.close | (self, code=None) | Closes the WebSocket from the server end | Closes the WebSocket from the server end | [
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meraki/dashboard-api-python | aef5e6fe5d23a40d435d5c64ff30580a28af07f1 | meraki/api/switch.py | python | Switch.getNetworkSwitchStack | (self, networkId: str, switchStackId: str) | return self._session.get(metadata, resource) | **Show a switch stack**
https://developer.cisco.com/meraki/api-v1/#!get-network-switch-stack
- networkId (string): (required)
- switchStackId (string): (required) | **Show a switch stack**
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- networkId (string): (required)
- switchStackId (string): (required)
"""
metadata = {
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resource = f'/networks/{networkId}/switch/stacks/{switchStackId}'
return self._session.get(metadata, resource) | [
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mlrun/mlrun | 4c120719d64327a34b7ee1ab08fb5e01b258b00a | mlrun/frameworks/pytorch/callbacks/logging_callback.py | python | LoggingCallback.on_epoch_end | (self, epoch: int) | Before the given epoch ends, this method will be called to log the dynamic hyperparameters as needed.
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"""
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"""
# Update the dynamic hyperparameters dictionary:
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self._logger.log_dynamic_hyperparameter(
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/codeintel/play/core.py | python | Sizer.ShowItems | (*args, **kwargs) | return _core.Sizer_ShowItems(*args, **kwargs) | ShowItems(bool show) | ShowItems(bool show) | [
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"""ShowItems(bool show)"""
return _core.Sizer_ShowItems(*args, **kwargs) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/backdoors/escalation/shell.py | python | Shell.do_exploit | (self, args) | [] | def do_exploit(self, args):
target = self.core.curtarget
print(GOOD + "Initializing backdoor...")
target.ssh.exec_command(self.get_command())
print(GOOD + "Shell Backdoor attempted.")
for mod in self.modules.keys():
print(INFO + "Attempting to execute " + mod.name + " module...")
mod.exploit() | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/minion.py | python | Minion._execute_job_function | (
self, function_name, function_args, executors, opts, data
) | return None | Executes a function within a job given it's name, the args and the executors.
It also checks if the function is allowed to run if 'blackout mode' is enabled. | Executes a function within a job given it's name, the args and the executors.
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):
"""
Executes a function within a job given it's name, the args and the executors.
It also checks if the function is allowed to run if 'blackout mode' is enabled.
"""
minion_blackout_violation = False
if self.connected and self.opts["pillar"].get("minion_blackout", False):
whitelist = self.opts["pillar"].get("minion_blackout_whitelist", [])
# this minion is blacked out. Only allow saltutil.refresh_pillar and the whitelist
if (
function_name != "saltutil.refresh_pillar"
and function_name not in whitelist
):
minion_blackout_violation = True
# use minion_blackout_whitelist from grains if it exists
if self.opts["grains"].get("minion_blackout", False):
whitelist = self.opts["grains"].get("minion_blackout_whitelist", [])
if (
function_name != "saltutil.refresh_pillar"
and function_name not in whitelist
):
minion_blackout_violation = True
if minion_blackout_violation:
raise SaltInvocationError(
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"to False in pillar or grains to resume operations. Only "
"saltutil.refresh_pillar allowed in blackout mode."
)
if function_name in self.functions:
func = self.functions[function_name]
args, kwargs = load_args_and_kwargs(func, function_args, data)
else:
# only run if function_name is not in minion_instance.functions and allow_missing_funcs is True
func = function_name
args, kwargs = function_args, data
self.functions.pack["__context__"]["retcode"] = 0
if isinstance(executors, str):
executors = [executors]
elif not isinstance(executors, list) or not executors:
raise SaltInvocationError(
"Wrong executors specification: {}. String or non-empty list expected".format(
executors
)
)
if opts.get("sudo_user", "") and executors[-1] != "sudo":
executors[-1] = "sudo" # replace the last one with sudo
log.trace("Executors list %s", executors) # pylint: disable=no-member
for name in executors:
fname = "{}.execute".format(name)
if fname not in self.executors:
raise SaltInvocationError("Executor '{}' is not available".format(name))
return_data = self.executors[fname](opts, data, func, args, kwargs)
if return_data is not None:
return return_data
return None | [
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burke-software/schooldriver | a07262ba864aee0182548ecceb661e49c925725f | ecwsp/discipline/forms.py | python | get_start_date_default | () | return default_date | Return default date for report. It should be X work days ago. | Return default date for report. It should be X work days ago. | [
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""" Return default date for report. It should be X work days ago. """
work_days = (0,1,2,3,4) # python day of weeks mon-fri
# Traverse back these many days
days_back = int(Configuration.get_or_default('Discipline Merit Default Days', default=14).value)
default_date = datetime.date.today()
while days_back > 0:
while not default_date.weekday() in work_days:
default_date = default_date - datetime.timedelta(days=1)
default_date = default_date - datetime.timedelta(days=1)
days_back -= 1
return default_date | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/api/v2010/account/conference/recording.py | python | RecordingInstance.account_sid | (self) | return self._properties['account_sid'] | :returns: The SID of the Account that created the resource
:rtype: unicode | :returns: The SID of the Account that created the resource
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"""
:returns: The SID of the Account that created the resource
:rtype: unicode
"""
return self._properties['account_sid'] | [
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laincloud/lain | 0fe5f93d06540300c5dafb94d81b3b69d2c8f1da | playbooks/roles/collectd/files/plugins/lain/cluster_monitor.py | python | ClusterPlugin._get_cali_veth_stat | (self) | Check the status of all network interfaces.
Value is 1 if any one of them is DOWN | Check the status of all network interfaces.
Value is 1 if any one of them is DOWN | [
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] | def _get_cali_veth_stat(self):
'''
Check the status of all network interfaces.
Value is 1 if any one of them is DOWN
'''
cali_veth_up = 0
cali_veth_down = 0
cali_veth_total = 0
tmp_veth_up = 0
tmp_veth_down = 0
tmp_veth_total = 0
for name, stat in psutil.net_if_stats().iteritems():
if name.startswith('cali'):
cali_veth_total += 1
if stat.isup:
cali_veth_up += 1
else:
cali_veth_down += 1
elif name.startswith('tmp'):
tmp_veth_total += 1
if stat.isup:
tmp_veth_up += 1
else:
tmp_veth_down += 1
self._result.append(
GraphiteData("lain.cluster.calico.veth.cali.up",
self._endpoint, cali_veth_up, self._step, "val"))
self._result.append(
GraphiteData("lain.cluster.calico.veth.cali.down",
self._endpoint, cali_veth_down, self._step, "val"))
self._result.append(
GraphiteData("lain.cluster.calico.veth.cali.total",
self._endpoint, cali_veth_total, self._step, "val"))
self._result.append(
GraphiteData("lain.cluster.calico.veth.tmp.up",
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self._result.append(
GraphiteData("lain.cluster.calico.veth.tmp.down",
self._endpoint, tmp_veth_down, self._step, "val"))
self._result.append(
GraphiteData("lain.cluster.calico.veth.tmp.total",
self._endpoint, tmp_veth_total, self._step, "val")) | [
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markovmodel/PyEMMA | e9d08d715dde17ceaa96480a9ab55d5e87d3a4b3 | pyemma/coordinates/estimation/koopman.py | python | _KoopmanEstimator.weights | (self) | return _KoopmanWeights(u_input[0:-1], u_input[-1]) | weights in the input basis (encapsulated in an object) | weights in the input basis (encapsulated in an object) | [
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] | def weights(self):
'weights in the input basis (encapsulated in an object)'
self._check_estimated()
u_input = self.u
return _KoopmanWeights(u_input[0:-1], u_input[-1]) | [
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hungpham2511/toppra | 766a2c736319639c7b0176959d2baa624ef74415 | toppra/interpolator.py | python | UnivariateSplineInterpolator.eval | (self, ss_sam) | return np.array(data).T | Return the path position. | Return the path position. | [
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] | def eval(self, ss_sam):
"""Return the path position."""
data = []
for spl in self.uspl:
data.append(spl(ss_sam))
return np.array(data).T | [
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/venv/lib/python3.7/site-packages/pip/_vendor/ipaddress.py | python | IPv4Address.is_loopback | (self) | return self in self._constants._loopback_network | Test if the address is a loopback address.
Returns:
A boolean, True if the address is a loopback per RFC 3330. | Test if the address is a loopback address. | [
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] | def is_loopback(self):
"""Test if the address is a loopback address.
Returns:
A boolean, True if the address is a loopback per RFC 3330.
"""
return self in self._constants._loopback_network | [
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dwadden/dygiepp | 8faac5711489d4f5fb1189f8344c8ffb5548d2cb | scripts/data/genia/merge_coref.py | python | Corefs._assign_parent_indices | (corefs, coref_ids) | return corefs | Give each coref the index of it parent in the list of corefs. | Give each coref the index of it parent in the list of corefs. | [
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for coref in corefs:
if coref.ref is None:
coref.parent_ix = None
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coref.parent_ix = coref_ids.index(coref.ref)
return corefs | [
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yaksok/yaksok | 73f14863d04f054eef2926f25a091f72f60352a5 | yaksok/yacc.py | python | p_atom_paren | (t) | atom : LPAR expression RPAR
| LPAR call RPAR
| LPAR import_call RPAR | atom : LPAR expression RPAR
| LPAR call RPAR
| LPAR import_call RPAR | [
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'''atom : LPAR expression RPAR
| LPAR call RPAR
| LPAR import_call RPAR'''
t[0] = t[2] | [
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zsdlove/Hades | f3d8c43a40ccd7a1bca2a855d8cccc110c34448a | cfg/graphs.py | python | GraphManager.add_block_edge | (self, b1, b2, label="CONT") | join two pydot nodes / create nodes edge | join two pydot nodes / create nodes edge | [
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] | def add_block_edge(self, b1, b2, label="CONT"):
""" join two pydot nodes / create nodes edge """
# Edge color based on label text
if label=='false':
ecolor = "red"
elif label=='true':
ecolor = 'green'
elif label == 'exception':
ecolor = 'orange'
elif label == 'try':
ecolor = 'blue'
else:
ecolor = 'gray'
# node shape based on block type (First or Last instruction)
nodes = [None, None]
blocks = [b1,b2]
for i in range(2):
if re.search("^if-", blocks[i].instructiones[-1]) != None:
ncolor = "cornflowerblue"
elif re.search("^\:" , blocks[i].instructiones[0]) != None:
ncolor = "tan"
elif re.search("^goto", blocks[i].instructiones[-1]) != None:
ncolor = "darkgreen"
elif re.search("^invoke-", blocks[i].instructiones[-1]) != None:
ncolor = "lightyellow4"
else:
ncolor = "mediumaquamarine"
insTxt = "%s\r" % blocks[i].etiqueta
lidx = int(blocks[i].etiqueta.split(' ')[-1])
for ii in blocks[i].instructiones:
insTxt += str(lidx) + ": " + ii
lidx+=1
nodes[i] = pydot.Node(b1.etiqueta, color=ncolor, style="filled", shape="box", fontname="Courier", fontsize=8)
nodes[i].set_name(insTxt)#函数引用+指令
self.graph.add_node(nodes[i])
ed = pydot.Edge(nodes[0], nodes[1], color=ecolor, label=label, fontname="Courier", fontsize=8, arrowhead="open")
self.graph.add_edge(ed) | [
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NeuralEnsemble/python-neo | 34d4db8fb0dc950dbbc6defd7fb75e99ea877286 | neo/io/nwbio.py | python | EventProxy.load | (self, time_slice=None, strict_slicing=True) | return Event(times * pq.s,
labels=labels,
name=self.name,
description=self.description,
**self.annotations) | Load EventProxy args:
:param time_slice: None or tuple of the time slice expressed with quantities.
None is the entire signal.
:param strict_slicing: True by default.
Control if an error is raised or not when one of the time_slice members
(t_start or t_stop) is outside the real time range of the segment. | Load EventProxy args:
:param time_slice: None or tuple of the time slice expressed with quantities.
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"""
Load EventProxy args:
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None is the entire signal.
:param strict_slicing: True by default.
Control if an error is raised or not when one of the time_slice members
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"""
if time_slice:
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else:
times = self._timeseries.timestamps[:]
labels = self._timeseries.data[:]
return Event(times * pq.s,
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ckan/ckan | b3b01218ad88ed3fb914b51018abe8b07b07bff3 | ckanext/datastore/backend/postgres.py | python | DatastorePostgresqlBackend._is_postgresql_engine | (self) | return drivername.startswith('postgres') | Returns True if the read engine is a Postgresql Database.
According to
http://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql
all Postgres driver names start with `postgres`. | Returns True if the read engine is a Postgresql Database. | [
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''' Returns True if the read engine is a Postgresql Database.
According to
http://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql
all Postgres driver names start with `postgres`.
'''
drivername = self._get_read_engine().engine.url.drivername
return drivername.startswith('postgres') | [
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] | https://github.com/ckan/ckan/blob/b3b01218ad88ed3fb914b51018abe8b07b07bff3/ckanext/datastore/backend/postgres.py#L1719-L1727 | |
sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3/s3aaa.py | python | AuthS3.s3_link_to_human_resource | (self,
user,
person_id,
hr_type,
) | return hr_id | Link the user to a human resource record and make them owner
Args:
user: the user record
person_id: the person ID linked to that user
hr_type: the human resource type (staff/volunteer) | Link the user to a human resource record and make them owner | [
"Link",
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"user",
"to",
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"human",
"resource",
"record",
"and",
"make",
"them",
"owner"
] | def s3_link_to_human_resource(self,
user,
person_id,
hr_type,
):
"""
Link the user to a human resource record and make them owner
Args:
user: the user record
person_id: the person ID linked to that user
hr_type: the human resource type (staff/volunteer)
"""
db = current.db
s3db = current.s3db
settings = current.deployment_settings
user_id = user.id
organisation_id = user.organisation_id
htablename = "hrm_human_resource"
htable = s3db.table(htablename)
if not htable or (not organisation_id and \
settings.get_hrm_org_required()):
# Module disabled or no user organisation set
return None
def customise(hr_id):
""" Customise hrm_human_resource """
customise = settings.customise_resource(htablename)
if customise:
request = S3Request("hrm", "human_resource",
current.request,
args = [str(hr_id)] if hr_id else [],
)
customise(request, htablename)
# Determine the site ID
site_id = user.site_id if hr_type == 1 else None
# Get existing active HR record for this user
ptable = s3db.pr_person
ltable = s3db.pr_person_user
query = (ltable.user_id == user_id) & \
(ptable.pe_id == ltable.pe_id) & \
(htable.person_id == ptable.id) & \
(htable.type == hr_type) & \
(htable.status == 1) & \
(htable.deleted == False)
rows = db(query).select(htable.id,
limitby = (0, 2),
)
accepted = None
if len(rows) == 1:
# Single active HR record of this type
# => update organisation and site
record = rows.first()
hr_id = record.id
# Update the record
customise(hr_id)
db(htable.id == hr_id).update(organisation_id = organisation_id,
site_id = site_id,
)
accepted = "update"
# Update or create site link
hstable = s3db.hrm_human_resource_site
query = (hstable.human_resource_id == hr_id)
hstable.update_or_insert(query,
site_id = site_id,
human_resource_id = hr_id,
owned_by_user = user_id,
)
else:
# Multiple or no HR records of this type
if rows:
# Multiple records
# => check if there is one for this organisation and site
if type(person_id) is list:
person_id = person_id[0]
query = (htable.person_id == person_id) & \
(htable.organisation_id == organisation_id) & \
(htable.type == hr_type) & \
(htable.site_id == site_id) & \
(htable.deleted == False)
row = db(query).select(htable.id,
limitby = (0, 1),
).first()
else:
# No HR record exists at all
row = None
if row:
# At least one record for this organisation and site exists
# => pass
hr_id = row.id
else:
# Create new HR record
customise(hr_id = None)
record = Storage(person_id = person_id,
organisation_id = organisation_id,
site_id = site_id,
type = hr_type,
owned_by_user = user_id,
)
hr_id = htable.insert(**record)
record["id"] = hr_id
accepted = "create"
if hr_id and accepted:
# Update any super-records
s3db.update_super(htable, record)
# Set or update the record owner and realm entity
# (enforce update to change realm if organisation changed)
self.s3_set_record_owner(htable, hr_id, force_update=True)
# Run onaccept
s3db.onaccept(htablename, record, method=accepted)
return hr_id | [
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scrtlabs/catalyst | 2e8029780f2381da7a0729f7b52505e5db5f535b | catalyst/utils/numpy_utils.py | python | repeat_first_axis | (array, count) | return as_strided(array, (count,) + array.shape, (0,) + array.strides) | Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape (count,) + array.shape, composed of `array` repeated
`count` times along the first axis.
Example
-------
>>> from numpy import arange
>>> a = arange(3); a
array([0, 1, 2])
>>> repeat_first_axis(a, 2)
array([[0, 1, 2],
[0, 1, 2]])
>>> repeat_first_axis(a, 4)
array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
Notes
----
The resulting array will share memory with `array`. If you need to assign
to the input or output, you should probably make a copy first.
See Also
--------
repeat_last_axis | Restride `array` to repeat `count` times along the first axis. | [
"Restride",
"array",
"to",
"repeat",
"count",
"times",
"along",
"the",
"first",
"axis",
"."
] | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape (count,) + array.shape, composed of `array` repeated
`count` times along the first axis.
Example
-------
>>> from numpy import arange
>>> a = arange(3); a
array([0, 1, 2])
>>> repeat_first_axis(a, 2)
array([[0, 1, 2],
[0, 1, 2]])
>>> repeat_first_axis(a, 4)
array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
Notes
----
The resulting array will share memory with `array`. If you need to assign
to the input or output, you should probably make a copy first.
See Also
--------
repeat_last_axis
"""
return as_strided(array, (count,) + array.shape, (0,) + array.strides) | [
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aiven/pghoard | 1de0d2e33bf087b7ce3b6af556bbf941acfac3a4 | pghoard/webserver.py | python | RequestHandler.get_wal_or_timeline_file | (self, site, filename, filetype) | [] | def get_wal_or_timeline_file(self, site, filename, filetype):
target_path = self.headers.get("x-pghoard-target-path")
if not target_path:
raise HttpResponse("x-pghoard-target-path header missing from download", status=400)
self._process_completed_download_operations()
# See if we have already prefetched the file
site_config = self.server.config["backup_sites"][site]
xlog_dir = get_pg_wal_directory(site_config)
prefetch_target_path = os.path.join(xlog_dir, "{}.pghoard.prefetch".format(filename))
if os.path.exists(prefetch_target_path):
ex = self._try_save_and_verify_restored_file(filetype, filename, prefetch_target_path, target_path)
if not ex:
self._create_prefetch_operations(site, filetype, filename)
self.server.most_recently_served_files[filetype] = {
"name": filename,
"time": time.time(),
}
raise HttpResponse(status=201)
# After reaching a recovery_target and restart of a PG server, PG wants to replay and refetch
# files from the archive starting from the latest checkpoint. We have potentially fetched these files
# already earlier. Check if we have the files already and if we do, don't go over the network to refetch
# them yet again but just rename them to the path that PG is requesting.
xlog_path = os.path.join(xlog_dir, filename)
exists_on_disk = os.path.exists(xlog_path)
if exists_on_disk and filename not in self.server.served_from_disk:
self.server.log.info(
"Requested %r, found it in pg_xlog directory as: %r, returning directly", filename, xlog_path
)
ex = self._try_save_and_verify_restored_file(filetype, filename, xlog_path, target_path, unlink=False)
if ex:
self.server.log.warning("Found file: %r but it was invalid: %s", xlog_path, ex)
else:
self.server.served_from_disk.append(filename)
self.server.most_recently_served_files[filetype] = {
"name": filename,
"time": time.time(),
}
raise HttpResponse(status=201)
elif exists_on_disk:
self.server.log.info("Found file %r but it was recently already served from disk, fetching remote", xlog_path)
key = self._make_file_key(site, filetype, filename)
with suppress(ValueError):
self.server.prefetch_404.remove(key)
self._create_fetch_operation(key, site, filetype, filename, max_age=5, suppress_error=False)
self._create_prefetch_operations(site, filetype, filename)
last_schedule_call = time.monotonic()
start_time = time.monotonic()
retries = 2
while (time.monotonic() - start_time) <= 30:
self._process_completed_download_operations(timeout=0.01)
with self.server.lock:
if os.path.isfile(prefetch_target_path):
ex = self._try_save_and_verify_restored_file(filetype, filename, prefetch_target_path, target_path)
if not ex:
self.server.most_recently_served_files[filetype] = {
"name": filename,
"time": time.time(),
}
raise HttpResponse(status=201)
elif ex and retries == 0:
raise ex # pylint: disable=raising-bad-type
retries -= 1
if key in self.server.prefetch_404:
raise HttpResponse(status=404)
with self.server.lock:
if key not in self.server.pending_download_ops:
if retries == 0:
raise HttpResponse(status=500)
retries -= 1
self._create_fetch_operation(key, site, filetype, filename, suppress_error=False)
if time.monotonic() - last_schedule_call >= 1:
last_schedule_call = time.monotonic()
# Replace existing download operation if it has been executing for too long
self._create_fetch_operation(key, site, filetype, filename, max_age=10, suppress_error=False)
raise HttpResponse("TIMEOUT", status=500) | [
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PrefectHQ/prefect | 67bdc94e2211726d99561f6f52614bec8970e981 | src/prefect/client/client.py | python | Client.get_available_tenants | (self) | return result.data.tenant | Returns a list of available tenants.
NOTE: this should only be called by users who have provided a USER-scoped API token.
Returns:
- List[Dict]: a list of dictionaries containing the id, slug, and name of
available tenants | Returns a list of available tenants. | [
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"""
Returns a list of available tenants.
NOTE: this should only be called by users who have provided a USER-scoped API token.
Returns:
- List[Dict]: a list of dictionaries containing the id, slug, and name of
available tenants
"""
result = self.graphql(
{"query": {"tenant(order_by: {slug: asc})": {"id", "slug", "name"}}},
# API keys can see all available tenants. If not using an API key, we can't
# use the access token which is scoped to a single tenant
token=self.api_key or self._api_token,
)
return result.data.tenant | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/pickletools.py | python | read_unicodestring4 | (f) | r"""
>>> import StringIO
>>> s = u'abcd\uabcd'
>>> enc = s.encode('utf-8')
>>> enc
'abcd\xea\xaf\x8d'
>>> n = chr(len(enc)) + chr(0) * 3 # little-endian 4-byte length
>>> t = read_unicodestring4(StringIO.StringIO(n + enc + 'junk'))
>>> s == t
True
>>> read_unicodestring4(StringIO.StringIO(n + enc[:-1]))
Traceback (most recent call last):
...
ValueError: expected 7 bytes in a unicodestring4, but only 6 remain | r"""
>>> import StringIO
>>> s = u'abcd\uabcd'
>>> enc = s.encode('utf-8')
>>> enc
'abcd\xea\xaf\x8d'
>>> n = chr(len(enc)) + chr(0) * 3 # little-endian 4-byte length
>>> t = read_unicodestring4(StringIO.StringIO(n + enc + 'junk'))
>>> s == t
True | [
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r"""
>>> import StringIO
>>> s = u'abcd\uabcd'
>>> enc = s.encode('utf-8')
>>> enc
'abcd\xea\xaf\x8d'
>>> n = chr(len(enc)) + chr(0) * 3 # little-endian 4-byte length
>>> t = read_unicodestring4(StringIO.StringIO(n + enc + 'junk'))
>>> s == t
True
>>> read_unicodestring4(StringIO.StringIO(n + enc[:-1]))
Traceback (most recent call last):
...
ValueError: expected 7 bytes in a unicodestring4, but only 6 remain
"""
n = read_int4(f)
if n < 0:
raise ValueError("unicodestring4 byte count < 0: %d" % n)
data = f.read(n)
if len(data) == n:
return unicode(data, 'utf-8')
raise ValueError("expected %d bytes in a unicodestring4, but only %d "
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ring04h/wyportmap | c4201e2313504e780a7f25238eba2a2d3223e739 | sqlalchemy/sql/elements.py | python | ColumnElement.anon_label | (self) | return _anonymous_label(
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) | provides a constant 'anonymous label' for this ColumnElement.
This is a label() expression which will be named at compile time.
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that expressions can reference anon_label multiple times, producing
the same label name at compile time.
the compiler uses this function automatically at compile time
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return _anonymous_label(
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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/wallet_db.py | python | WalletDB._convert_version_37 | (self) | [] | def _convert_version_37(self):
if not self._is_upgrade_method_needed(36, 36):
return
payments = self.data.get('lightning_payments', {})
for k, v in list(payments.items()):
amount_sat, direction, status = v
amount_msat = amount_sat * 1000 if amount_sat is not None else None
payments[k] = amount_msat, direction, status
self.data['lightning_payments'] = payments
self.data['seed_version'] = 37 | [
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/decimal.py | python | _format_align | (sign, body, spec) | return result | Given an unpadded, non-aligned numeric string 'body' and sign
string 'sign', add padding and alignment conforming to the given
format specifier dictionary 'spec' (as produced by
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Also converts result to unicode if necessary. | Given an unpadded, non-aligned numeric string 'body' and sign
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result = padding[:half] + sign + body + padding[half:]
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tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/examples/penguin/penguin_utils_cloud_tuner.py | python | _input_fn | (file_pattern: List[str],
data_accessor: tfx.components.DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) | return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
tf_transform_output.transformed_metadata.schema).repeat() | Generates features and label for tuning/training.
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file_pattern: List of paths or patterns of input tfrecord files.
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tf_transform_output: A `TFTransformOutput` object, containing statistics
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batch_size: representing the number of consecutive elements of returned
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batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A `TFTransformOutput` object, containing statistics
and metadata from TFTransform component.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
tf_transform_output.transformed_metadata.schema).repeat() | [
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radiac/django-tagulous | 90c6ee5ac54ef1127f2edcfac10c5ef3ab09e255 | tagulous/models/models.py | python | BaseTagModel.__eq__ | (self, obj) | return super(BaseTagModel, self).__eq__(obj) | If comparing to a string, is equal if string value matches
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karanchahal/distiller | a17ec06cbeafcdd2aea19d7c7663033c951392f5 | models/vision/densenet.py | python | densenet161 | (pretrained=False, progress=True, **kwargs) | return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs) | r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ | r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ | [
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r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs) | [
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unknown-horizons/unknown-horizons | 7397fb333006d26c3d9fe796c7bd9cb8c3b43a49 | horizons/util/checkupdates.py | python | is_system_installed | (uh_path: Optional[PurePath] = None) | Returns whether UH is likely to have been installed with the systems package manager.
In typical usage, you don't need to pass any parameters. | Returns whether UH is likely to have been installed with the systems package manager. | [
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"""
Returns whether UH is likely to have been installed with the systems package manager.
In typical usage, you don't need to pass any parameters.
"""
if uh_path is None:
uh_path = PurePath(os.path.abspath(__file__))
home_directory = get_home_directory()
try:
uh_path.relative_to(home_directory)
return False
except ValueError:
return True | [
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cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/analysis/windowed.py | python | TestMockProcessor.process | (self, subStream) | return len(subStream.flatten().notesAndRests), None | Simply count the number of notes found | Simply count the number of notes found | [
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mme/vergeml | 3dc30ba4e0f3d038743b6d468860cbcf3681acc6 | vergeml/results.py | python | Results._sync | (self, force=False) | [] | def _sync(self, force=False):
now = datetime.datetime.now()
if force or (now - self.last_sync).total_seconds() > _SYNC_INTV:
self.last_sync = now
with open(self.path, "w") as f:
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/setuptools/command/bdist_egg.py | python | walk_egg | (egg_dir) | Walk an unpacked egg's contents, skipping the metadata directory | Walk an unpacked egg's contents, skipping the metadata directory | [
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"""Walk an unpacked egg's contents, skipping the metadata directory"""
walker = sorted_walk(egg_dir)
base, dirs, files = next(walker)
if 'EGG-INFO' in dirs:
dirs.remove('EGG-INFO')
yield base, dirs, files
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onaio/onadata | 89ad16744e8f247fb748219476f6ac295869a95f | onadata/apps/viewer/xls_writer.py | python | XlsWriter.save_workbook_to_file | (self) | return self._file | [] | def save_workbook_to_file(self):
self._workbook.save(self._file)
return self._file | [
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huawei-noah/vega | d9f13deede7f2b584e4b1d32ffdb833856129989 | vega/datasets/tensorflow/adapter.py | python | TfAdapter.loader | (self) | return self | Dataloader arrtribute which is a unified interface to generate the data.
:return: a batch data
:rtype: dict, list, optional | Dataloader arrtribute which is a unified interface to generate the data. | [
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:return: a batch data
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"""
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dipy/dipy | be956a529465b28085f8fc435a756947ddee1c89 | dipy/reconst/fwdti.py | python | nls_iter | (design_matrix, sig, S0, Diso=3e-3, mdreg=2.7e-3,
min_signal=1.0e-6, cholesky=False, f_transform=True, jac=False,
weighting=None, sigma=None) | return params | Applies non linear least squares fit of the water free elimination
model to single voxel signals.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients.
sig : array (g, )
Diffusion-weighted signal for a single voxel data.
S0 : float
Non diffusion weighted signal (i.e. signal for b-value=0).
Diso : float, optional
Value of the free water isotropic diffusion. Default is set to 3e-3
$mm^{2}.s^{-1}$. Please adjust this value if you are assuming different
units of diffusion.
mdreg : float, optimal
DTI's mean diffusivity regularization threshold. If standard DTI
diffusion tensor's mean diffusivity is almost near the free water
diffusion value, the diffusion signal is assumed to be only free water
diffusion (i.e. volume fraction will be set to 1 and tissue's diffusion
parameters are set to zero). Default md_reg is 2.7e-3 $mm^{2}.s^{-1}$
(corresponding to 90% of the free water diffusion value).
min_signal : float
The minimum signal value. Needs to be a strictly positive
number.
cholesky : bool, optional
If true it uses Cholesky decomposition to insure that diffusion tensor
is positive define.
Default: False
f_transform : bool, optional
If true, the water volume fractions is converted during the convergence
procedure to ft = arcsin(2*f - 1) + pi/2, insuring f estimates between
0 and 1.
Default: True
jac : bool
Use the Jacobian? Default: False
weighting: str, optional
the weighting scheme to use in considering the
squared-error. Default behavior is to use uniform weighting. Other
options: 'sigma' 'gmm'
sigma: float, optional
If the 'sigma' weighting scheme is used, a value of sigma needs to be
provided here. According to [Chang2005]_, a good value to use is
1.5267 * std(background_noise), where background_noise is estimated
from some part of the image known to contain no signal (only noise).
Returns
-------
All parameters estimated from the free water tensor model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the
first, second and third coordinates of the eigenvector
3) The volume fraction of the free water compartment. | Applies non linear least squares fit of the water free elimination
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min_signal=1.0e-6, cholesky=False, f_transform=True, jac=False,
weighting=None, sigma=None):
""" Applies non linear least squares fit of the water free elimination
model to single voxel signals.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients.
sig : array (g, )
Diffusion-weighted signal for a single voxel data.
S0 : float
Non diffusion weighted signal (i.e. signal for b-value=0).
Diso : float, optional
Value of the free water isotropic diffusion. Default is set to 3e-3
$mm^{2}.s^{-1}$. Please adjust this value if you are assuming different
units of diffusion.
mdreg : float, optimal
DTI's mean diffusivity regularization threshold. If standard DTI
diffusion tensor's mean diffusivity is almost near the free water
diffusion value, the diffusion signal is assumed to be only free water
diffusion (i.e. volume fraction will be set to 1 and tissue's diffusion
parameters are set to zero). Default md_reg is 2.7e-3 $mm^{2}.s^{-1}$
(corresponding to 90% of the free water diffusion value).
min_signal : float
The minimum signal value. Needs to be a strictly positive
number.
cholesky : bool, optional
If true it uses Cholesky decomposition to insure that diffusion tensor
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Default: False
f_transform : bool, optional
If true, the water volume fractions is converted during the convergence
procedure to ft = arcsin(2*f - 1) + pi/2, insuring f estimates between
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Default: True
jac : bool
Use the Jacobian? Default: False
weighting: str, optional
the weighting scheme to use in considering the
squared-error. Default behavior is to use uniform weighting. Other
options: 'sigma' 'gmm'
sigma: float, optional
If the 'sigma' weighting scheme is used, a value of sigma needs to be
provided here. According to [Chang2005]_, a good value to use is
1.5267 * std(background_noise), where background_noise is estimated
from some part of the image known to contain no signal (only noise).
Returns
-------
All parameters estimated from the free water tensor model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the
first, second and third coordinates of the eigenvector
3) The volume fraction of the free water compartment.
"""
# Initial guess
params = wls_iter(design_matrix, sig, S0,
min_signal=min_signal, Diso=Diso, mdreg=mdreg)
# Process voxel if it has significant signal from tissue
if params[12] < 0.99 and np.mean(sig) > min_signal and S0 > min_signal:
# converting evals and evecs to diffusion tensor elements
evals = params[:3]
evecs = params[3:12].reshape((3, 3))
dt = lower_triangular(vec_val_vect(evecs, evals))
# Cholesky decomposition if requested
if cholesky:
dt = lower_triangular_to_cholesky(dt)
# f transformation if requested
if f_transform:
f = np.arcsin(2*params[12] - 1) + np.pi/2
else:
f = params[12]
# Use the Levenberg-Marquardt algorithm wrapped in opt.leastsq
start_params = np.concatenate((dt, [-np.log(S0), f]), axis=0)
if jac:
this_tensor, status = opt.leastsq(_nls_err_func, start_params[:8],
args=(design_matrix, sig, Diso,
weighting, sigma, cholesky,
f_transform),
Dfun=_nls_jacobian_func)
else:
this_tensor, status = opt.leastsq(_nls_err_func, start_params[:8],
args=(design_matrix, sig, Diso,
weighting, sigma, cholesky,
f_transform))
# Process tissue diffusion tensor
if cholesky:
this_tensor[:6] = cholesky_to_lower_triangular(this_tensor[:6])
evals, evecs = _decompose_tensor_nan(
from_lower_triangular(this_tensor[:6]),
from_lower_triangular(start_params[:6]))
# Process water volume fraction f
f = this_tensor[7]
if f_transform:
f = 0.5 * (1 + np.sin(f - np.pi/2))
params = np.concatenate((evals, evecs[0], evecs[1], evecs[2],
np.array([f])), axis=0)
return params | [
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CastagnaIT/plugin.video.netflix | 5cf5fa436eb9956576c0f62aa31a4c7d6c5b8a4a | resources/lib/kodi/library.py | python | Library.import_library | (self, path) | Imports an already existing exported STRM library into the add-on library database,
allows you to restore an existing library, by avoiding to recreate it from scratch.
This operations also update the missing tv shows seasons and episodes, and automatically
converts old STRM format type from add-on version 0.13.x or before 1.7.0 to new format. | Imports an already existing exported STRM library into the add-on library database,
allows you to restore an existing library, by avoiding to recreate it from scratch.
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"""
Imports an already existing exported STRM library into the add-on library database,
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This operations also update the missing tv shows seasons and episodes, and automatically
converts old STRM format type from add-on version 0.13.x or before 1.7.0 to new format.
"""
# If set ask to user if want to export NFO files
nfo_settings = nfo.NFOSettings()
nfo_settings.show_export_dialog()
LOG.info('Start importing Kodi library')
remove_folders = [] # List of failed imports paths to be optionally removed
remove_titles = [] # List of failed imports titles to be optionally removed
# Start importing STRM files
folders = get_library_subfolders(FOLDER_NAME_MOVIES, path) + get_library_subfolders(FOLDER_NAME_SHOWS, path)
with ui.ProgressDialog(True, max_value=len(folders)) as progress_bar:
for folder_path in folders:
folder_name = os.path.basename(xbmcvfs.translatePath(folder_path))
progress_bar.set_message(folder_name)
try:
videoid = self.import_videoid_from_existing_strm(folder_path, folder_name)
if videoid is None:
# Failed to import, add folder to remove list
remove_folders.append(folder_path)
remove_titles.append(folder_name)
continue
# Successfully imported, Execute the task
for index, total_tasks, title in self.execute_library_task(videoid,
self.export_item,
nfo_settings=nfo_settings,
notify_errors=True):
label_partial_op = f' ({index + 1}/{total_tasks})' if total_tasks > 1 else ''
progress_bar.set_message(title + label_partial_op)
if progress_bar.is_cancelled():
LOG.warn('Import library interrupted by User')
return
if self.monitor.abortRequested():
LOG.warn('Import library interrupted by Kodi')
return
except ImportWarning:
# Ignore it, something was wrong in STRM file (see _import_videoid in library_jobs.py)
pass
progress_bar.perform_step()
progress_bar.set_wait_message()
delay_anti_ban()
ret = self._import_library_remove(remove_titles, remove_folders)
request_kodi_library_update(scan=True, clean=ret) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/inject/thirdparty/odict/odict.py | python | _OrderedDict.sort | (self, *args, **kwargs) | Sort the key order in the OrderedDict.
This method takes the same arguments as the ``list.sort`` method on
your version of Python.
>>> d = OrderedDict(((4, 1), (2, 2), (3, 3), (1, 4)))
>>> d.sort()
>>> d
OrderedDict([(1, 4), (2, 2), (3, 3), (4, 1)]) | Sort the key order in the OrderedDict. | [
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This method takes the same arguments as the ``list.sort`` method on
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>>> d = OrderedDict(((4, 1), (2, 2), (3, 3), (1, 4)))
>>> d.sort()
>>> d
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"""
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snarfed/granary | ab085de2aef0cff8ac31a99b5e21443a249e8419 | granary/reddit.py | python | Reddit.user_to_actor | (self, user) | return util.trim_nulls({
'objectType': 'person',
'displayName': username,
'image': {'url': image},
'id': self.tag_uri(username),
# numeric_id is our own custom field that always has the source's numeric
# user id, if available.
'numeric_id': user.get('id'),
'published': util.maybe_timestamp_to_iso8601(user.get('created_utc')),
'url': urls[0],
'urls': [{'value': u} for u in urls] if len(urls) > 1 else None,
'username': username,
'description': description,
}) | Converts a dict user to an actor.
Args:
user: JSON user
Returns:
an ActivityStreams actor dict, ready to be JSON-encoded | Converts a dict user to an actor. | [
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] | def user_to_actor(self, user):
"""Converts a dict user to an actor.
Args:
user: JSON user
Returns:
an ActivityStreams actor dict, ready to be JSON-encoded
"""
username = user.get('name')
if not username:
return {}
# trying my best to grab all the urls from the profile description
urls = [f'{self.BASE_URL}/user/{username}/']
description = None
subreddit = user.get('subreddit')
if subreddit:
url = subreddit.get('url')
if url:
urls.append(self.BASE_URL + url)
description = subreddit.get('description')
urls += util.trim_nulls(util.extract_links(description))
image = user.get('icon_img')
return util.trim_nulls({
'objectType': 'person',
'displayName': username,
'image': {'url': image},
'id': self.tag_uri(username),
# numeric_id is our own custom field that always has the source's numeric
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'numeric_id': user.get('id'),
'published': util.maybe_timestamp_to_iso8601(user.get('created_utc')),
'url': urls[0],
'urls': [{'value': u} for u in urls] if len(urls) > 1 else None,
'username': username,
'description': description,
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RiotGames/cloud-inquisitor | 29a26c705381fdba3538b4efedb25b9e09b387ed | backend/cloud_inquisitor/plugins/types/resources.py | python | BaseResource.update | (self, data) | Updates the resource object with the information from `data`. The changes will be added to the current
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alanhamlett/pip-update-requirements | ce875601ef278c8ce00ad586434a978731525561 | pur/packages/pip/_vendor/urllib3/contrib/securetransport.py | python | WrappedSocket._set_ciphers | (self) | Sets up the allowed ciphers. By default this matches the set in
util.ssl_.DEFAULT_CIPHERS, at least as supported by macOS. This is done
custom and doesn't allow changing at this time, mostly because parsing
OpenSSL cipher strings is going to be a freaking nightmare. | Sets up the allowed ciphers. By default this matches the set in
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custom and doesn't allow changing at this time, mostly because parsing
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"""
ciphers = (Security.SSLCipherSuite * len(CIPHER_SUITES))(*CIPHER_SUITES)
result = Security.SSLSetEnabledCiphers(
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tcgoetz/GarminDB | 55796eb621df9f6ecd92f16b3a53393d23c0b4dc | garmindb/garmindb/monitoring_db.py | python | Monitoring.get_stats | (cls, session, func, start_ts, end_ts) | return {
'steps': func(session, cls.steps, start_ts, end_ts),
'calories_active_avg': (
cls.get_active_calories(session, fitfile.field_enums.ActivityType.running, start_ts, end_ts)
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} | Return a dict of stats for table entries within the time span. | Return a dict of stats for table entries within the time span. | [
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'calories_active_avg': (
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IntelLabs/coach | dea46ae0d22b0a0cd30b9fc138a4a2642e1b9d9d | rl_coach/architectures/tensorflow_components/architecture.py | python | TensorFlowArchitecture.get_model | (self) | Constructs the model using `network_parameters` and sets `input_embedders`, `middleware`,
`output_heads`, `outputs`, `losses`, `total_loss`, `adaptive_learning_rate_scheme`,
`current_learning_rate`, and `optimizer`.
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"""
Constructs the model using `network_parameters` and sets `input_embedders`, `middleware`,
`output_heads`, `outputs`, `losses`, `total_loss`, `adaptive_learning_rate_scheme`,
`current_learning_rate`, and `optimizer`.
:return: A list of the model's weights
"""
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EOA-AILab/NER-Chinese | 6df4a63a67936c356c578ac68ad49841ac0c9ebc | bert_ner/bert_ner/bert_base/server/zmq_decor.py | python | _SocketDecorator.process_decorator_args | (self, *args, **kwargs) | return kw_name, args, kwargs | Also grab context_name out of kwargs | Also grab context_name out of kwargs | [
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"""Also grab context_name out of kwargs"""
kw_name, args, kwargs = super(_SocketDecorator, self).process_decorator_args(*args, **kwargs)
self.context_name = kwargs.pop('context_name', 'context')
return kw_name, args, kwargs | [
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inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/cgitb.py | python | reset | () | return '''<!--: spam
Content-Type: text/html
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> -->
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> --> -->
</font> </font> </font> </script> </object> </blockquote> </pre>
</table> </table> </table> </table> </table> </font> </font> </font>''' | Return a string that resets the CGI and browser to a known state. | Return a string that resets the CGI and browser to a known state. | [
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"""Return a string that resets the CGI and browser to a known state."""
return '''<!--: spam
Content-Type: text/html
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> -->
<body bgcolor="#f0f0f8"><font color="#f0f0f8" size="-5"> --> -->
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django-oscar/django-oscar | ffcc530844d40283b6b1552778a140536b904f5f | src/oscar/apps/payment/bankcards.py | python | luhn | (card_number) | return (sum % 10) == 0 | Test whether a bankcard number passes the Luhn algorithm. | Test whether a bankcard number passes the Luhn algorithm. | [
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"""
Test whether a bankcard number passes the Luhn algorithm.
"""
card_number = str(card_number)
sum = 0
num_digits = len(card_number)
odd_even = num_digits & 1
for i in range(0, num_digits):
digit = int(card_number[i])
if not ((i & 1) ^ odd_even):
digit = digit * 2
if digit > 9:
digit = digit - 9
sum = sum + digit
return (sum % 10) == 0 | [
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EmuKit/emukit | cdcb0d070d7f1c5585260266160722b636786859 | emukit/core/optimization/anchor_points_generator.py | python | ObjectiveAnchorPointsGenerator.get_anchor_point_scores | (self, X: np.ndarray) | return scores | :param X: The samples at which to evaluate the criterion
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"""
are_constraints_satisfied = np.all([np.ones(X.shape[0])] + [c.evaluate(X) for c in self.space.constraints], axis=0)
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scores[~are_constraints_satisfied] = -np.inf
scores[are_constraints_satisfied] = self.acquisition.evaluate(X[are_constraints_satisfied, :])[:, 0]
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/contextlib.py | python | ExitStack._push_cm_exit | (self, cm, cm_exit) | Helper to correctly register callbacks to __exit__ methods | Helper to correctly register callbacks to __exit__ methods | [
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def _exit_wrapper(*exc_details):
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.2/django/db/models/sql/query.py | python | Query.get_loaded_field_names | (self) | return collection | If any fields are marked to be deferred, returns a dictionary mapping
models to a set of names in those fields that will be loaded. If a
model is not in the returned dictionary, none of it's fields are
deferred.
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/reports/filters/base.py | python | BaseMultipleOptionFilter.get_value | (cls, request, domain) | return request.GET.getlist(cls.slug) | [] | def get_value(cls, request, domain):
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/plat-mac/ic.py | python | IC.launchurl | (self, url, hint="") | [] | def launchurl(self, url, hint=""):
# Work around a bug in ICLaunchURL: file:/foo does
# not work but file:///foo does.
if url[:6] == 'file:/' and url[6] != '/':
url = 'file:///' + url[6:]
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/gdata/src/gdata/client.py | python | GDClient.client_login | (self, email, password, source, service=None,
account_type='HOSTED_OR_GOOGLE',
auth_url=atom.http_core.Uri.parse_uri(
'https://www.google.com/accounts/ClientLogin'),
captcha_token=None, captcha_response=None) | return self.auth_token | Performs an auth request using the user's email address and password.
In order to modify user specific data and read user private data, your
application must be authorized by the user. One way to demonstrage
authorization is by including a Client Login token in the Authorization
HTTP header of all requests. This method requests the Client Login token
by sending the user's email address, password, the name of the
application, and the service code for the service which will be accessed
by the application. If the username and password are correct, the server
will respond with the client login code and a new ClientLoginToken
object will be set in the client's auth_token member. With the auth_token
set, future requests from this client will include the Client Login
token.
For a list of service names, see
http://code.google.com/apis/gdata/faq.html#clientlogin
For more information on Client Login, see:
http://code.google.com/apis/accounts/docs/AuthForInstalledApps.html
Args:
email: str The user's email address or username.
password: str The password for the user's account.
source: str The name of your application. This can be anything you
like but should should give some indication of which app is
making the request.
service: str The service code for the service you would like to access.
For example, 'cp' for contacts, 'cl' for calendar. For a full
list see
http://code.google.com/apis/gdata/faq.html#clientlogin
If you are using a subclass of the gdata.client.GDClient, the
service will usually be filled in for you so you do not need
to specify it. For example see BloggerClient,
SpreadsheetsClient, etc.
account_type: str (optional) The type of account which is being
authenticated. This can be either 'GOOGLE' for a Google
Account, 'HOSTED' for a Google Apps Account, or the
default 'HOSTED_OR_GOOGLE' which will select the Google
Apps Account if the same email address is used for both
a Google Account and a Google Apps Account.
auth_url: str (optional) The URL to which the login request should be
sent.
captcha_token: str (optional) If a previous login attempt was reponded
to with a CAPTCHA challenge, this is the token which
identifies the challenge (from the CAPTCHA's URL).
captcha_response: str (optional) If a previous login attempt was
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Returns:
Generated token, which is also stored in this object.
Raises:
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BadAuthenticationServiceURL, ClientLoginFailed,
ClientLoginTokenMissing, or CaptchaChallenge | Performs an auth request using the user's email address and password.
In order to modify user specific data and read user private data, your
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authorization is by including a Client Login token in the Authorization
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by the application. If the username and password are correct, the server
will respond with the client login code and a new ClientLoginToken
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set, future requests from this client will include the Client Login
token.
For a list of service names, see
http://code.google.com/apis/gdata/faq.html#clientlogin
For more information on Client Login, see:
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captcha_token=captcha_token, captcha_response=captcha_response)
if self.alt_auth_service is not None:
self.alt_auth_token = self.request_client_login_token(
email, password, source, service=self.alt_auth_service,
account_type=account_type, auth_url=auth_url,
captcha_token=captcha_token, captcha_response=captcha_response)
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/paramiko/agent.py | python | Agent.close | (self) | Close the SSH agent connection. | Close the SSH agent connection. | [
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DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow | cca574844df5fc8bbf380227725a4e3106fbc48c | libs/networks/slim_nets/mobilenet_v1.py | python | _reduced_kernel_size_for_small_input | (input_tensor, kernel_size) | return kernel_size_out | Define kernel size which is automatically reduced for small input.
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"""
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | itn | (n, digits=8, format=DEFAULT_FORMAT) | return s | Convert a python number to a number field. | Convert a python number to a number field. | [
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s = ("%0*o" % (digits - 1, n)).encode("ascii") + NUL
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LinuxCNC/simple-gcode-generators | d06c2750fe03c41becd6bd5863ace54b52920155 | counterbore/counterbore.py | python | Application.__init__ | (self, master=None) | [] | def __init__(self, master=None):
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | get_table_column_statistics_args.__init__ | (self, db_name=None, tbl_name=None, col_name=None,) | [] | def __init__(self, db_name=None, tbl_name=None, col_name=None,):
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docker/docker-py | a48a5a9647761406d66e8271f19fab7fa0c5f582 | docker/models/plugins.py | python | Plugin.remove | (self, force=False) | return self.client.api.remove_plugin(self.name, force=force) | Remove the plugin from the server.
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bdarnell/plop | 1f026fce98d52508f42e67444c1a3a046f4901db | plop/callgraph.py | python | Node.__repr__ | (self) | return 'Node(%s)' % (self.id,) | [] | def __repr__(self):
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/dna/commands/InsertDna/InsertDna_PropertyManager.py | python | InsertDna_PropertyManager.__init__ | ( self, command ) | Constructor for the DNA Duplex property manager. | Constructor for the DNA Duplex property manager. | [
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davidbau/ganseeing | 93cea2c8f391aef001ddf9dcb35c43990681a47c | seeing/workerpool.py | python | WorkerPool.__init__ | (self, worker=WorkerBase, process_count=None, **initargs) | [] | def __init__(self, worker=WorkerBase, process_count=None, **initargs):
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process_count = cpu_count()
if process_count == 0:
# zero process_count uses only main process, for debugging.
self.queue = None
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signal.signal(signal.SIGINT, original_sigint_handler) | [
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/tkinter/__init__.py | python | Canvas.icursor | (self, *args) | Set cursor at position POS in the item identified by TAGORID.
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Fantomas42/django-blog-zinnia | 881101a9d1d455b2fc581d6f4ae0947cdd8126c6 | zinnia/feeds.py | python | DiscussionFeed.item_link | (self, item) | return item.get_absolute_url() | URL of the discussion item. | URL of the discussion item. | [
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nosmokingbandit/Watcher3 | 0217e75158b563bdefc8e01c3be7620008cf3977 | lib/sqlalchemy/orm/events.py | python | MapperEvents.before_insert | (self, mapper, connection, target) | Receive an object instance before an INSERT statement
is emitted corresponding to that instance.
This event is used to modify local, non-object related
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The event is often called for a batch of objects of the
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.. warning::
Mapper-level flush events only allow **very limited operations**,
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at :ref:`session_persistence_mapper` for guidelines on using
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:param mapper: the :class:`.Mapper` which is the target
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:return: No return value is supported by this event.
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"""Receive an object instance before an INSERT statement
is emitted corresponding to that instance.
This event is used to modify local, non-object related
attributes on the instance before an INSERT occurs, as well
as to emit additional SQL statements on the given
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The event is often called for a batch of objects of the
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Mapper-level flush events only allow **very limited operations**,
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.. seealso::
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theAIGuysCode/yolov3_deepsort | a325e9f93d406b2e137e5e071b4b350ca9380545 | deep_sort/kalman_filter.py | python | KalmanFilter.predict | (self, mean, covariance) | return mean, covariance | Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
The 8 dimensional mean vector of the object state at the previous
time step.
covariance : ndarray
The 8x8 dimensional covariance matrix of the object state at the
previous time step.
Returns
-------
(ndarray, ndarray)
Returns the mean vector and covariance matrix of the predicted
state. Unobserved velocities are initialized to 0 mean. | Run Kalman filter prediction step. | [
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] | def predict(self, mean, covariance):
"""Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
The 8 dimensional mean vector of the object state at the previous
time step.
covariance : ndarray
The 8x8 dimensional covariance matrix of the object state at the
previous time step.
Returns
-------
(ndarray, ndarray)
Returns the mean vector and covariance matrix of the predicted
state. Unobserved velocities are initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[3],
self._std_weight_position * mean[3],
1e-2,
self._std_weight_position * mean[3]]
std_vel = [
self._std_weight_velocity * mean[3],
self._std_weight_velocity * mean[3],
1e-5,
self._std_weight_velocity * mean[3]]
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
mean = np.dot(self._motion_mat, mean)
covariance = np.linalg.multi_dot((
self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
return mean, covariance | [
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adamcaudill/EquationGroupLeak | 52fa871c89008566c27159bd48f2a8641260c984 | windows/Resources/Python/Override/Lib/multiprocessing/sharedctypes.py | python | Value | (typecode_or_type, *args, **kwds) | return synchronized(obj, lock) | Return a synchronization wrapper for a Value | Return a synchronization wrapper for a Value | [
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'''
lock = kwds.pop('lock', None)
if kwds:
raise ValueError('unrecognized keyword argument(s): %s' % kwds.keys())
obj = RawValue(typecode_or_type, *args)
if lock is False:
return obj
if lock in (True, None):
lock = RLock()
if not hasattr(lock, 'acquire'):
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boxeehacks/boxeehack | bdfa63187f662b542261dce64aff68548f63ecaf | hack/boxee/scripts/OpenSubtitles/resources/lib/plugins/BeautifulSoup.py | python | BeautifulStoneSoup.handle_decl | (self, data) | Handle DOCTYPEs and the like as Declaration objects. | Handle DOCTYPEs and the like as Declaration objects. | [
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google-research/mixmatch | 1011a1d51eaa9ca6f5dba02096a848d1fe3fc38e | libml/utils.py | python | ilog2 | (x) | return int(np.ceil(np.log2(x))) | Integer log2. | Integer log2. | [
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"""Integer log2."""
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/groups/generic.py | python | structure_description | (G, latex=False) | return description | r"""
Return a string that tries to describe the structure of ``G``.
This methods wraps GAP's ``StructureDescription`` method.
For full details, including the form of the returned string and the
algorithm to build it, see `GAP's documentation
<https://www.gap-system.org/Manuals/doc/ref/chap39.html>`_.
INPUT:
- ``latex`` -- a boolean (default: ``False``). If ``True`` return a
LaTeX formatted string.
OUTPUT:
- string
.. WARNING::
From GAP's documentation: The string returned by
``StructureDescription`` is **not** an isomorphism invariant:
non-isomorphic groups can have the same string value, and two
isomorphic groups in different representations can produce different
strings.
EXAMPLES::
sage: G = CyclicPermutationGroup(6)
sage: G.structure_description()
'C6'
sage: G.structure_description(latex=True)
'C_{6}'
sage: G2 = G.direct_product(G, maps=False)
sage: LatexExpr(G2.structure_description(latex=True))
C_{6} \times C_{6}
This method is mainly intended for small groups or groups with few
normal subgroups. Even then there are some surprises::
sage: D3 = DihedralGroup(3)
sage: D3.structure_description()
'S3'
We use the Sage notation for the degree of dihedral groups::
sage: D4 = DihedralGroup(4)
sage: D4.structure_description()
'D4'
Works for finitely presented groups (:trac:`17573`)::
sage: F.<x, y> = FreeGroup()
sage: G=F / [x^2*y^-1, x^3*y^2, x*y*x^-1*y^-1]
sage: G.structure_description()
'C7'
And matrix groups (:trac:`17573`)::
sage: groups.matrix.GL(4,2).structure_description()
'A8' | r"""
Return a string that tries to describe the structure of ``G``. | [
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r"""
Return a string that tries to describe the structure of ``G``.
This methods wraps GAP's ``StructureDescription`` method.
For full details, including the form of the returned string and the
algorithm to build it, see `GAP's documentation
<https://www.gap-system.org/Manuals/doc/ref/chap39.html>`_.
INPUT:
- ``latex`` -- a boolean (default: ``False``). If ``True`` return a
LaTeX formatted string.
OUTPUT:
- string
.. WARNING::
From GAP's documentation: The string returned by
``StructureDescription`` is **not** an isomorphism invariant:
non-isomorphic groups can have the same string value, and two
isomorphic groups in different representations can produce different
strings.
EXAMPLES::
sage: G = CyclicPermutationGroup(6)
sage: G.structure_description()
'C6'
sage: G.structure_description(latex=True)
'C_{6}'
sage: G2 = G.direct_product(G, maps=False)
sage: LatexExpr(G2.structure_description(latex=True))
C_{6} \times C_{6}
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sage: D3 = DihedralGroup(3)
sage: D3.structure_description()
'S3'
We use the Sage notation for the degree of dihedral groups::
sage: D4 = DihedralGroup(4)
sage: D4.structure_description()
'D4'
Works for finitely presented groups (:trac:`17573`)::
sage: F.<x, y> = FreeGroup()
sage: G=F / [x^2*y^-1, x^3*y^2, x*y*x^-1*y^-1]
sage: G.structure_description()
'C7'
And matrix groups (:trac:`17573`)::
sage: groups.matrix.GL(4,2).structure_description()
'A8'
"""
import re
def correct_dihedral_degree(match):
return "%sD%d" % (match.group(1), int(match.group(2)) // 2)
description = str(G._gap_().StructureDescription())
description = re.sub(r"(\A|\W)D(\d+)", correct_dihedral_degree, description)
if not latex:
return description
description = description.replace("x", r"\times").replace(":", r"\rtimes")
description = re.sub(r"([A-Za-z]+)([0-9]+)", r"\g<1>_{\g<2>}", description)
description = re.sub(r"O([+-])", r"O^{\g<1>}", description)
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mFIZ/scripts/mFIZ_extern/serial/serialcli.py | python | Serial.reset_output_buffer | (self) | \
Clear output buffer, aborting the current output and
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | common/lib/xmodule/xmodule/modulestore/__init__.py | python | BulkOperationsMixin._start_outermost_bulk_operation | (self, bulk_ops_record, course_key, ignore_case=False) | The outermost nested bulk_operation call: do the actual begin of the bulk operation.
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"""
The outermost nested bulk_operation call: do the actual begin of the bulk operation.
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/http/server.py | python | CGIHTTPRequestHandler.do_POST | (self) | Serve a POST request.
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"""Serve a POST request.
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | source/addons/account/account_bank_statement.py | python | account_bank_statement_line._get_exchange_lines | (self, cr, uid, st_line, mv_line, currency_diff, currency_id, move_id, context=None) | return (move_line, move_line_counterpart) | Prepare the two lines in company currency due to currency rate difference.
:param line: browse record of the voucher.line for which we want to create currency rate difference accounting
entries
:param move_id: Account move wher the move lines will be.
:param currency_diff: Amount to be posted.
:param company_currency: id of currency of the company to which the voucher belong
:param current_currency: id of currency of the voucher
:return: the account move line and its counterpart to create, depicted as mapping between fieldname and value
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'''
Prepare the two lines in company currency due to currency rate difference.
:param line: browse record of the voucher.line for which we want to create currency rate difference accounting
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:param move_id: Account move wher the move lines will be.
:param currency_diff: Amount to be posted.
:param company_currency: id of currency of the company to which the voucher belong
:param current_currency: id of currency of the voucher
:return: the account move line and its counterpart to create, depicted as mapping between fieldname and value
:rtype: tuple of dict
'''
if currency_diff > 0:
exchange_account_id = st_line.company_id.expense_currency_exchange_account_id.id
else:
exchange_account_id = st_line.company_id.income_currency_exchange_account_id.id
# Even if the amount_currency is never filled, we need to pass the foreign currency because otherwise
# the receivable/payable account may have a secondary currency, which render this field mandatory
if mv_line.account_id.currency_id:
account_currency_id = mv_line.account_id.currency_id.id
else:
account_currency_id = st_line.company_id.currency_id.id != currency_id and currency_id or False
move_line = {
'journal_id': st_line.journal_id.id,
'period_id': st_line.statement_id.period_id.id,
'name': _('change') + ': ' + (st_line.name or '/'),
'account_id': mv_line.account_id.id,
'move_id': move_id,
'partner_id': st_line.partner_id.id,
'currency_id': account_currency_id,
'amount_currency': 0.0,
'quantity': 1,
'credit': currency_diff > 0 and currency_diff or 0.0,
'debit': currency_diff < 0 and -currency_diff or 0.0,
'date': st_line.date,
'counterpart_move_line_id': mv_line.id,
}
move_line_counterpart = {
'journal_id': st_line.journal_id.id,
'period_id': st_line.statement_id.period_id.id,
'name': _('change') + ': ' + (st_line.name or '/'),
'account_id': exchange_account_id,
'move_id': move_id,
'amount_currency': 0.0,
'partner_id': st_line.partner_id.id,
'currency_id': account_currency_id,
'quantity': 1,
'debit': currency_diff > 0 and currency_diff or 0.0,
'credit': currency_diff < 0 and -currency_diff or 0.0,
'date': st_line.date,
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hustlzp/Flask-Boost | d0308408ebb248dd752b77123b845f8ec637fab2 | flask_boost/project/application/controllers/account.py | python | signup | () | return render_template('account/signup/signup.html', form=form) | Signup | Signup | [
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] | def signup():
"""Signup"""
form = SignupForm()
if form.validate_on_submit():
params = form.data.copy()
params.pop('repassword')
user = User(**params)
db.session.add(user)
db.session.commit()
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/python27/1.0/lib/urllib2.py | python | parse_http_list | (s) | return [part.strip() for part in res] | Parse lists as described by RFC 2068 Section 2.
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escape = False
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escape = True
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ehForwarderBot/efb-wechat-slave | 36a94c5e5f2fe5dc7247448cf37d5e8b653e8990 | efb_wechat_slave/vendor/itchat/core.py | python | Core.set_alias | (self, userName, alias) | set alias for a friend
for options
- userName: 'UserName' key of info dict
- alias: new alias
it is defined in components/contact.py | set alias for a friend
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] | https://github.com/ehForwarderBot/efb-wechat-slave/blob/36a94c5e5f2fe5dc7247448cf37d5e8b653e8990/efb_wechat_slave/vendor/itchat/core.py#L227-L234 | ||
facebookresearch/pytext | 1a4e184b233856fcfb9997d74f167cbf5bbbfb8d | pytext/models/semantic_parsers/rnng/rnng_parser.py | python | RNNGParserBase.__init__ | (
self,
ablation: Config.AblationParams,
constraints: Config.RNNGConstraints,
lstm_num_layers: int,
lstm_dim: int,
max_open_NT: int,
dropout: float,
actions_vocab,
shift_idx: int,
reduce_idx: int,
ignore_subNTs_roots: List[int],
valid_NT_idxs: List[int],
valid_IN_idxs: List[int],
valid_SL_idxs: List[int],
embedding: EmbeddingList,
p_compositional,
) | Initialize the model
Args:
ablation : AblationParams
Features/RNNs to use
constraints : RNNGConstraints
Constraints to use when computing valid actions
lstm_num_layers : int
number of layers in the LSTMs
lstm_dim : int
size of LSTM
max_open_NT : int
number of maximum open non-terminals allowed on the stack.
After that, the only valid actions are SHIFT and REDUCE
dropout : float
dropout parameter
beam_size : int
beam size for beam search; run only during inference
top_k : int
top k results from beam search
actions_vocab : Vocab (right now torchtext.vocab.Vocab)
dictionary of actions
shift_idx : int
index of shift action
reduce_idx : int
index of reduce action
ignore_subNTs_roots : List[int]
for these top non-terminals, ignore loss for all subsequent actions
valid_NT_idxs : List[int]
indices of all non-terminals
valid_IN_idxs : List[int]
indices of intent non-terminals
valid_SL_idxs : List[int]
indices of slot non-terminals
embedding : EmbeddingList
embeddings for the tokens
p_compositional : CompositionFunction
Composition function to use to get embedding of a sub-tree
Returns:
None | Initialize the model | [
"Initialize",
"the",
"model"
] | def __init__(
self,
ablation: Config.AblationParams,
constraints: Config.RNNGConstraints,
lstm_num_layers: int,
lstm_dim: int,
max_open_NT: int,
dropout: float,
actions_vocab,
shift_idx: int,
reduce_idx: int,
ignore_subNTs_roots: List[int],
valid_NT_idxs: List[int],
valid_IN_idxs: List[int],
valid_SL_idxs: List[int],
embedding: EmbeddingList,
p_compositional,
) -> None:
"""
Initialize the model
Args:
ablation : AblationParams
Features/RNNs to use
constraints : RNNGConstraints
Constraints to use when computing valid actions
lstm_num_layers : int
number of layers in the LSTMs
lstm_dim : int
size of LSTM
max_open_NT : int
number of maximum open non-terminals allowed on the stack.
After that, the only valid actions are SHIFT and REDUCE
dropout : float
dropout parameter
beam_size : int
beam size for beam search; run only during inference
top_k : int
top k results from beam search
actions_vocab : Vocab (right now torchtext.vocab.Vocab)
dictionary of actions
shift_idx : int
index of shift action
reduce_idx : int
index of reduce action
ignore_subNTs_roots : List[int]
for these top non-terminals, ignore loss for all subsequent actions
valid_NT_idxs : List[int]
indices of all non-terminals
valid_IN_idxs : List[int]
indices of intent non-terminals
valid_SL_idxs : List[int]
indices of slot non-terminals
embedding : EmbeddingList
embeddings for the tokens
p_compositional : CompositionFunction
Composition function to use to get embedding of a sub-tree
Returns:
None
"""
super().__init__()
self.embedding = embedding
# self.embedding.config: FeatureConfig object cannot be pickled but,
# we require the model to be pickled for passing from one worker process
# for Hogwild training. Hence, setting the config to None
self.embedding.config = None
self.p_compositional = p_compositional
self.ablation_use_last_open_NT_feature = ablation.use_last_open_NT_feature
self.ablation_use_buffer = ablation.use_buffer
self.ablation_use_stack = ablation.use_stack
self.ablation_use_action = ablation.use_action
self.constraints_intent_slot_nesting = constraints.intent_slot_nesting
self.constraints_no_slots_inside_unsupported = (
constraints.no_slots_inside_unsupported
)
self.constraints_ignore_loss_for_unsupported = (
constraints.ignore_loss_for_unsupported
)
self.lstm_num_layers = lstm_num_layers
self.lstm_dim = lstm_dim
self.max_open_NT = max_open_NT
self.actions_vocab = actions_vocab
self.shift_idx = shift_idx
self.reduce_idx = reduce_idx
self.ignore_subNTs_roots = ignore_subNTs_roots
self.valid_NT_idxs = valid_NT_idxs
self.valid_IN_idxs = valid_IN_idxs
self.valid_SL_idxs = valid_SL_idxs
num_actions = len(actions_vocab)
lstm_count = ablation.use_buffer + ablation.use_stack + ablation.use_action
if lstm_count == 0:
raise ValueError("Need at least one of the LSTMs to be true")
self.action_linear = nn.Sequential(
nn.Linear(
lstm_count * lstm_dim + num_actions * ablation.use_last_open_NT_feature,
lstm_dim,
),
nn.ReLU(),
nn.Linear(lstm_dim, num_actions),
)
self.dropout_layer = nn.Dropout(p=dropout)
self.buff_rnn = nn.LSTM(
embedding.embedding_dim,
lstm_dim,
num_layers=lstm_num_layers,
dropout=dropout,
)
self.stack_rnn = nn.LSTM(
lstm_dim, lstm_dim, num_layers=lstm_num_layers, dropout=dropout
)
self.action_rnn = nn.LSTM(
lstm_dim, lstm_dim, num_layers=lstm_num_layers, dropout=dropout
)
self.actions_lookup = nn.Embedding(num_actions, lstm_dim)
self.loss_func = nn.CrossEntropyLoss()
log_class_usage(__class__) | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/albert/modeling_flax_albert.py | python | FlaxAlbertLayer.__call__ | (
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
) | return outputs | [] | def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
):
attention_outputs = self.attention(
hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
)
attention_output = attention_outputs[0]
ffn_output = self.ffn(attention_output)
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(ffn_output)
ffn_output = self.dropout(ffn_output, deterministic=deterministic)
hidden_states = self.full_layer_layer_norm(ffn_output + attention_output)
outputs = (hidden_states,)
if output_attentions:
outputs += (attention_outputs[1],)
return outputs | [
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beancount/beancount | cb3526a1af95b3b5be70347470c381b5a86055fe | beancount/core/position.py | python | Position.__str__ | (self) | return self.to_string() | Return a string representation of the position.
Returns:
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"""Return a string representation of the position.
Returns:
A string, a printable representation of the position.
"""
return self.to_string() | [
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jlhutch/pylru | 6411a3cf50ea96f513784c49b7154e49f54df3e8 | pylru.py | python | lrucache.get | (self, key, default=None) | return self[key] | Get an item - return default (None) if not present | Get an item - return default (None) if not present | [
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] | def get(self, key, default=None):
"""Get an item - return default (None) if not present"""
if key not in self.table:
return default
return self[key] | [
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goace/personal-file-sharing-center | 4a5b903b003f2db1306e77c5e51b6660fc5dbc6a | web/webapi.py | python | UnsupportedMediaType.__init__ | (self, message=None) | [] | def __init__(self, message=None):
status = "415 Unsupported Media Type"
headers = {'Content-Type': 'text/html'}
HTTPError.__init__(self, status, headers, message or self.message) | [
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happinesslz/TANet | 2d4b2ab99b8e57c03671b0f1531eab7dca8f3c1f | second.pytorch_with_TANet/second/kittiviewer/control_panel.py | python | QColorButton.setColor | (self, color) | [] | def setColor(self, color):
if color != self._color:
self._color = color
self.colorChanged.emit()
if self._color is not None:
self.setStyleSheet(f"background-color: {QColor(self._color).name()};")
else:
self.setStyleSheet("") | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | cfuncptr_t.set_user_cmt | (self, *args) | return _idaapi.cfuncptr_t_set_user_cmt(self, *args) | set_user_cmt(self, loc, cmt) | set_user_cmt(self, loc, cmt) | [
"set_user_cmt",
"(",
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] | def set_user_cmt(self, *args):
"""
set_user_cmt(self, loc, cmt)
"""
return _idaapi.cfuncptr_t_set_user_cmt(self, *args) | [
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jaraco/irc | 19859340b2ad3bffc58d842a7657fae9ec3563f8 | irc/client.py | python | ServerConnection.get_server_name | (self) | return self.real_server_name or "" | Get the (real) server name.
This method returns the (real) server name, or, more
specifically, what the server calls itself. | Get the (real) server name. | [
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] | def get_server_name(self):
"""Get the (real) server name.
This method returns the (real) server name, or, more
specifically, what the server calls itself.
"""
return self.real_server_name or "" | [
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datactive/bigbang | d4fef7eb41ae04e51f4e369de5a721c66231202b | bigbang/ingress/utils.py | python | get_website_content | (
url: str,
session: Optional[requests.Session] = None,
) | Get HTML code from website
Note
----
Servers don't like it when one is sending too many requests from same
ip address in short period of time. Therefore we need to:
a) catch 'requests.exceptions.RequestException' errors
(includes all possible errors to be on the safe side),
b) safe intermediate results,
c) continue where we left off at a later stage. | Get HTML code from website | [
"Get",
"HTML",
"code",
"from",
"website"
] | def get_website_content(
url: str,
session: Optional[requests.Session] = None,
) -> Union[str, BeautifulSoup]:
"""
Get HTML code from website
Note
----
Servers don't like it when one is sending too many requests from same
ip address in short period of time. Therefore we need to:
a) catch 'requests.exceptions.RequestException' errors
(includes all possible errors to be on the safe side),
b) safe intermediate results,
c) continue where we left off at a later stage.
"""
# TODO: include option to change BeautifulSoup args
try:
if session is None:
sauce = requests.get(url)
if sauce.status_code == 200:
soup = BeautifulSoup(sauce.content, "html.parser")
return soup
elif sauce.status_code == 404:
logger.exception(f"HTTP 404 Error for {url}.")
return "RequestException"
else:
sauce = session.get(url)
soup = BeautifulSoup(sauce.text, "html.parser")
return soup
except requests.exceptions.RequestException as e:
if "A2=" in url:
# if URL of mboxMessage
logger.exception(f"{e} for {url}.")
return "RequestException"
else:
SystemExit() | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/permutation.py | python | Permutation.retract_plain | (self, m) | return Permutations(m)(p[:m]) | r"""
Return the plain retract of the permutation ``self`` in `S_n`
to `S_m`, where `m \leq n`. If this retract is undefined, then
``None`` is returned.
If `p \in S_n` is a permutation, and `m` is a nonnegative integer
less or equal to `n`, then the plain retract of `p` to `S_m` is
defined only if every `i > m` satisfies `p(i) = i`. In this case,
it is defined as the permutation written
`(p(1), p(2), \ldots, p(m))` in one-line notation.
EXAMPLES::
sage: Permutation([4,1,2,3,5]).retract_plain(4)
[4, 1, 2, 3]
sage: Permutation([4,1,2,3,5]).retract_plain(3)
sage: Permutation([1,3,2,4,5,6]).retract_plain(3)
[1, 3, 2]
sage: Permutation([1,3,2,4,5,6]).retract_plain(2)
sage: Permutation([1,2,3,4,5]).retract_plain(1)
[1]
sage: Permutation([1,2,3,4,5]).retract_plain(0)
[]
sage: all( p.retract_plain(3) == p for p in Permutations(3) )
True
.. SEEALSO::
:meth:`retract_direct_product`, :meth:`retract_okounkov_vershik`,
:meth:`remove_extra_fixed_points` | r"""
Return the plain retract of the permutation ``self`` in `S_n`
to `S_m`, where `m \leq n`. If this retract is undefined, then
``None`` is returned. | [
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r"""
Return the plain retract of the permutation ``self`` in `S_n`
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``None`` is returned.
If `p \in S_n` is a permutation, and `m` is a nonnegative integer
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defined only if every `i > m` satisfies `p(i) = i`. In this case,
it is defined as the permutation written
`(p(1), p(2), \ldots, p(m))` in one-line notation.
EXAMPLES::
sage: Permutation([4,1,2,3,5]).retract_plain(4)
[4, 1, 2, 3]
sage: Permutation([4,1,2,3,5]).retract_plain(3)
sage: Permutation([1,3,2,4,5,6]).retract_plain(3)
[1, 3, 2]
sage: Permutation([1,3,2,4,5,6]).retract_plain(2)
sage: Permutation([1,2,3,4,5]).retract_plain(1)
[1]
sage: Permutation([1,2,3,4,5]).retract_plain(0)
[]
sage: all( p.retract_plain(3) == p for p in Permutations(3) )
True
.. SEEALSO::
:meth:`retract_direct_product`, :meth:`retract_okounkov_vershik`,
:meth:`remove_extra_fixed_points`
"""
n = len(self)
p = list(self)
for i in range(m, n):
if p[i] != i + 1:
return None
return Permutations(m)(p[:m]) | [
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napari/napari | dbf4158e801fa7a429de8ef1cdee73bf6d64c61e | napari/utils/colormaps/vendored/colors.py | python | LightSource.__init__ | (self, azdeg=315, altdeg=45, hsv_min_val=0, hsv_max_val=1,
hsv_min_sat=1, hsv_max_sat=0) | Specify the azimuth (measured clockwise from south) and altitude
(measured up from the plane of the surface) of the light source
in degrees.
Parameters
----------
azdeg : number, optional
The azimuth (0-360, degrees clockwise from North) of the light
source. Defaults to 315 degrees (from the northwest).
altdeg : number, optional
The altitude (0-90, degrees up from horizontal) of the light
source. Defaults to 45 degrees from horizontal.
Notes
-----
For backwards compatibility, the parameters *hsv_min_val*,
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initialization as well. However, these parameters will only be used if
"blend_mode='hsv'" is passed into :meth:`shade` or :meth:`shade_rgb`.
See the documentation for :meth:`blend_hsv` for more details. | Specify the azimuth (measured clockwise from south) and altitude
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hsv_min_sat=1, hsv_max_sat=0):
"""
Specify the azimuth (measured clockwise from south) and altitude
(measured up from the plane of the surface) of the light source
in degrees.
Parameters
----------
azdeg : number, optional
The azimuth (0-360, degrees clockwise from North) of the light
source. Defaults to 315 degrees (from the northwest).
altdeg : number, optional
The altitude (0-90, degrees up from horizontal) of the light
source. Defaults to 45 degrees from horizontal.
Notes
-----
For backwards compatibility, the parameters *hsv_min_val*,
*hsv_max_val*, *hsv_min_sat*, and *hsv_max_sat* may be supplied at
initialization as well. However, these parameters will only be used if
"blend_mode='hsv'" is passed into :meth:`shade` or :meth:`shade_rgb`.
See the documentation for :meth:`blend_hsv` for more details.
"""
self.azdeg = azdeg
self.altdeg = altdeg
self.hsv_min_val = hsv_min_val
self.hsv_max_val = hsv_max_val
self.hsv_min_sat = hsv_min_sat
self.hsv_max_sat = hsv_max_sat | [
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"azdeg"... | https://github.com/napari/napari/blob/dbf4158e801fa7a429de8ef1cdee73bf6d64c61e/napari/utils/colormaps/vendored/colors.py#L1378-L1407 | ||
jparkhill/TensorMol | d52104dc7ee46eec8301d332a95d672270ac0bd1 | TensorMol/Containers/TensorMolData.py | python | TensorMolData.RawBatch | (self,nmol = 4096) | return Ins,Outs | Shimmy Shimmy Ya Shimmy Ya Shimmy Yay.
This type of batch is not built beforehand
because there's no real digestion involved.
Args:
nmol: number of molecules to put in the output.
Returns:
Ins: a #atomsX4 tensor (AtNum,x,y,z)
Outs: output of the digester
Keys: (nmol)X(MaxNAtoms) tensor listing each molecule's place in the input. | Shimmy Shimmy Ya Shimmy Ya Shimmy Yay.
This type of batch is not built beforehand
because there's no real digestion involved. | [
"Shimmy",
"Shimmy",
"Ya",
"Shimmy",
"Ya",
"Shimmy",
"Yay",
".",
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"type",
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"is",
"not",
"built",
"beforehand",
"because",
"there",
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"."
] | def RawBatch(self,nmol = 4096):
"""
Shimmy Shimmy Ya Shimmy Ya Shimmy Yay.
This type of batch is not built beforehand
because there's no real digestion involved.
Args:
nmol: number of molecules to put in the output.
Returns:
Ins: a #atomsX4 tensor (AtNum,x,y,z)
Outs: output of the digester
Keys: (nmol)X(MaxNAtoms) tensor listing each molecule's place in the input.
"""
ndone = 0
natdone = 0
self.MaxNAtoms = self.set.MaxNAtoms()
Ins = np.zeros(tuple([nmol,self.MaxNAtoms,4]))
Outs = np.zeros(tuple([nmol,self.MaxNAtoms,3]))
while (ndone<nmol):
try:
m = next(self.raw_it)
# print "m props", m.properties.keys()
# print "m coords", m.coords
ti, to = self.dig.Emb(m, True, False)
n=ti.shape[0]
Ins[ndone,:n,:] = ti.copy()
Outs[ndone,:n,:] = to.copy()
ndone += 1
natdone += n
except StopIteration:
self.raw_it = iter(self.set.mols)
return Ins,Outs | [
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"... | https://github.com/jparkhill/TensorMol/blob/d52104dc7ee46eec8301d332a95d672270ac0bd1/TensorMol/Containers/TensorMolData.py#L139-L172 | |
Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/pdb2pqr/contrib/numpy-1.1.0/numpy/f2py/lib/extgen/setup_py.py | python | SetupPy.execute | (self, *args) | return r | Run generated setup.py file with given arguments. | Run generated setup.py file with given arguments. | [
"Run",
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] | def execute(self, *args):
"""
Run generated setup.py file with given arguments.
"""
if not args:
raise ValueError('need setup.py arguments')
self.info(self.generate(dry_run=False))
cmd = [sys.executable,'setup.py'] + list(args)
self.info('entering %r directory' % (self.path))
self.info('executing command %r' % (' '.join(cmd)))
try:
r = exec_command(cmd, execute_in=self.path, use_tee=False)
except:
self.info('leaving %r directory' % (self.path))
raise
else:
self.info('leaving %r directory' % (self.path))
return r | [
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... | https://github.com/Pymol-Scripts/Pymol-script-repo/blob/bcd7bb7812dc6db1595953dfa4471fa15fb68c77/modules/pdb2pqr/contrib/numpy-1.1.0/numpy/f2py/lib/extgen/setup_py.py#L99-L116 |
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