id int64 11 59.9k | original stringlengths 33 150k | modified stringlengths 37 150k |
|---|---|---|
45,714 | def forecast(
R,
metadata,
V,
timesteps,
n_ens_members=24,
n_cascade_levels=6,
win_size=256,
overlap=0.1,
war_thr=0.1,
extrap_method="semilagrangian",
decomp_method="fft",
bandpass_filter_method="gaussian",
noise_method="ssft",
ar_order=2,
vel_pert_method=None... | def forecast(
R,
metadata,
V,
timesteps,
n_ens_members=24,
n_cascade_levels=6,
win_size=256,
overlap=0.1,
war_thr=0.1,
extrap_method="semilagrangian",
decomp_method="fft",
bandpass_filter_method="gaussian",
noise_method="ssft",
ar_order=2,
vel_pert_method=None... |
31,588 | def get_dns_history_command(client, args):
hostname = args.get('hostname')
record_type = args.get('type')
page = int(args.get('page', 1))
res = client.get_dns_history(hostname=hostname, record_type=record_type, page=page)
res = {k: v for k, v in res.items() if k not in removed_keys}
records_list... | def get_dns_history_command(client, args):
hostname = args.get('hostname')
record_type = args.get('type')
page = int(args.get('page', 1))
res = client.get_dns_history(hostname=hostname, record_type=record_type, page=page)
res = {k: v for k, v in res.items() if k not in removed_keys}
records_list... |
25,614 | def _mix_latent_gp(W, g_mu, g_var, full_cov, full_output_cov):
r"""
Takes the mean and variance of a uncorrelated L-dimensional latent GP
and returns the mean and the variance of the mixed GP, `f = W \times g`,
where both f and g are GPs.
:param W: [P, L]
:param g_mu: [..., N, L]
:param g_v... | def _mix_latent_gp(W, g_mu, g_var, full_cov, full_output_cov):
r"""
Takes the mean and variance of a uncorrelated L-dimensional latent GP
and returns the mean and the variance of the mixed GP, `f = W \times g`,
where both f and g are GPs.
:param W: [P, L]
:param g_mu: [..., N, L]
:param g_v... |
28,068 | def build_stat_coll_cmd(action, config, source):
"""
Build the statistics collector analysis command.
"""
cmd = [config.analyzer_binary, '-c', '-x', action.lang, '--analyze',
# Do not warn about the unused gcc/g++ arguments.
'-Qunused-arguments',
'--analyzer-output', 't... | def build_stat_coll_cmd(action, config, source):
"""
Build the statistics collector analysis command.
"""
cmd = [config.analyzer_binary, '-c', '-x', action.lang, '--analyze',
# Do not warn about the unused gcc/g++ arguments.
'-Qunused-arguments',
'--analyzer-output', 't... |
41,176 | def _gen_gray_code(n: int):
"""Generate the Gray Code from 0 to 2^n-1.
Each iteration returns two elements. The first element is the decimal representation
of the gray code and the second one is the position of bits flipped for next gray code.
"""
gray_code = 0
for i in range(1, 2 ** n):
... | def _gen_gray_code(n: int):
"""Generate the Gray Code from 0 to 2^n-1.
Each iteration returns two elements. The first element is the decimal representation
of the gray code and `bit_flip` is the position of bits flipped for next gray code.
"""
gray_code = 0
for i in range(1, 2 ** n):
ne... |
45,350 | def _predict(
booster,
data,
**kwargs,
):
"""
Run distributed prediction with a trained booster on Ray backend.
During work it runs xgb.predict on each worker for row partition of `data`
and creates Modin DataFrame with prediction results.
Parameters
----------
booster : xgboos... | def _predict(
booster,
data,
**kwargs,
):
"""
Run distributed prediction with a trained booster on Ray backend.
During work it runs xgb.predict on each worker for row partition of `data`
and creates Modin DataFrame with prediction results.
Parameters
----------
booster : xgboos... |
27,786 | def _parse_ini_file(path: Path) -> PARSE_RESULT:
"""Parses .ini files with expected pytest.ini sections
todo: investigate if tool:pytest should be added
"""
iniconfig = _parse_ini_config(path)
if "pytest" in iniconfig:
return dict(iniconfig["pytest"].items())
return None
| def _parse_ini_file(path: Path) -> PARSE_RESULT:
"""Parses .ini files with expected pytest.ini sections
TODO: Investigate if tool:pytest should be added.
"""
iniconfig = _parse_ini_config(path)
if "pytest" in iniconfig:
return dict(iniconfig["pytest"].items())
return None
|
13,416 | def test_13_verify_logs_collection_still_work_after_moving_the_system_dataset_to_the_second_pool(logs_data):
cmd = "cat /var/log/middlewared.log"
middlewared_log = SSH_TEST(cmd, user, password, ip)
assert middlewared_log['result'] is True, str(middlewared_log)
logs_data['middleware_log_5'] = middlewared... | def test_13_verify_logs_after_sysds_is_moved_to_second_pool(logs_data):
cmd = "cat /var/log/middlewared.log"
middlewared_log = SSH_TEST(cmd, user, password, ip)
assert middlewared_log['result'] is True, str(middlewared_log)
logs_data['middleware_log_5'] = middlewared_log['output'].splitlines()[-1]
a... |
13,908 | def parse_coverage(
lines: List[str],
*,
filename: str,
exclude_lines_by_pattern: Optional[str],
exclude_branches_by_pattern: Optional[str],
exclude_pattern_prefix: Optional[str],
flags: ParserFlags,
) -> FileCoverage:
"""
Extract coverage data from a gcov report.
Logging:
P... | def parse_coverage(
lines: List[str],
*,
filename: str,
exclude_lines_by_pattern: Optional[str],
exclude_branches_by_pattern: Optional[str],
exclude_pattern_prefix: Optional[str],
flags: ParserFlags,
) -> FileCoverage:
"""
Extract coverage data from a gcov report.
Logging:
P... |
13,742 | def _enqueue_recompute_grades_task(course_key, grading_policy_hash=None):
kwargs = {
'course_key': six.text_type('course_key'),
'event_transaction_id': six.text_type(get_event_transaction_id()),
'event_transaction_type': six.text_type(get_event_transaction_type()),
}
if grading_polic... | def _enqueue_recompute_grades_task(course_key, grading_policy_hash=None):
kwargs = {
'course_key': str('course_key'),
'event_transaction_id': six.text_type(get_event_transaction_id()),
'event_transaction_type': six.text_type(get_event_transaction_type()),
}
if grading_policy_hash is ... |
54,082 | def _get_maintenance_config(cmd, client, file_path):
# get models
MaintenanceConfiguration = cmd.get_models('MaintenanceConfiguration', resource_type=CUSTOM_MGMT_AKS_PREVIEW, operation_group='maintenance_configurations')
TimeInWeek = cmd.get_models('TimeInWeek', resource_type=CUSTOM_MGMT_AKS_PREVIEW, operat... | def _get_maintenance_config(cmd, file_path):
maintenance_config = get_file_json(file_path)
return maintenance_config
# get models
MaintenanceConfiguration = cmd.get_models('MaintenanceConfiguration', resource_type=CUSTOM_MGMT_AKS_PREVIEW, operation_group='maintenance_configurations')
TimeInWeek = cm... |
43,928 | def expansion(la, lb, ra, rb, alpha, beta, t):
r"""Compute Hermite Gaussian expansion coefficients recursively for two Gaussian functions.
An overlap distribution, which defines the product of two Gaussians, can be written as a Hermite
expansion as [`Helgaker (1995) p798 <https://www.worldscientific.com/do... | def expansion(la, lb, ra, rb, alpha, beta, t):
r"""Compute Hermite Gaussian expansion coefficients recursively for two Gaussian functions.
An overlap distribution, which defines the product of two Gaussians, can be written as a Hermite
expansion as [`Helgaker (1995) p798 <https://www.worldscientific.com/do... |
24,594 | def thermal_speed_coefficients(method: str, ndim: int) -> float:
r"""
Get the appropriate coefficient for calculating the thermal speed :math:`v_{th}`
based on the given ``method`` and ``ndim``. (See the
`~plasmapy.formulary.parameters.thermal_speed` :ref:`Notes <thermal-speed-notes>`
section for f... | def thermal_speed_coefficients(method: str, ndim: int) -> float:
r"""
Get the appropriate coefficient for calculating the thermal speed :math:`v_{th}`
based on the given ``method`` and ``ndim``. (See the
`~plasmapy.formulary.parameters.thermal_speed` :ref:`Notes <thermal-speed-notes>`
section for f... |
41,696 | def unpack_buffer(
buffer: JsProxy,
*,
filename: str = "",
format: str = None,
target: Literal["site", "lib", None] = None,
extract_dir: str = None,
calculate_dynlibs: bool = False,
) -> Optional[JsProxy]:
"""Used to install a package either into sitepackages or into the standard
lib... | def unpack_buffer(
buffer: JsProxy,
*,
filename: str = "",
format: str = None,
target: Literal["site", "lib", None] = None,
extract_dir: str = None,
calculate_dynlibs: bool = False,
) -> Optional[JsProxy]:
"""Used to install a package either into sitepackages or into the standard
lib... |
2,155 | def mean_variance_axis(X, axis, weights=None, return_sum_weights=False):
"""Compute mean and variance along an axix on a CSR or CSC matrix
Parameters
----------
X : CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
axis : int (either 0 or 1)
Axis along which the a... | def mean_variance_axis(X, axis, weights=None, return_sum_weights=False):
"""Compute mean and variance along an axix on a CSR or CSC matrix
Parameters
----------
X : CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
axis : int (either 0 or 1)
Axis along which the a... |
22,443 | def arg_parser():
parser = argparse.ArgumentParser(description=DESCRIPTION)
parser.add_argument('-u', '--galaxy-url', default="http://localhost:8080", help='Galaxy URL')
parser.add_argument('-k', '--key', default=None, help='Galaxy User API Key')
parser.add_argument('-a', '--admin-key', default=None, he... | def arg_parser():
parser = argparse.ArgumentParser(description=DESCRIPTION)
parser.add_argument('-u', '--galaxy-url', default="http://localhost:8080", help='Galaxy URL')
parser.add_argument('-k', '--key', default=None, help='Galaxy User API Key')
parser.add_argument('-a', '--admin-key', default=None, he... |
42,678 | def _populate_db_with_balances(connection, ts: Timestamp):
cursor = connection.cursor()
cursor.execute('INSERT OR IGNORE INTO assets(identifier) VALUES(?)', (NFT_TOKEN_ID,))
cursor.execute(
"""
INSERT INTO "timed_balances" ("category", "time", "currency", "amount", "usd_value") VALUES
... | def _populate_db_with_balances(connection, ts: Timestamp):
cursor = connection.cursor()
cursor.execute('INSERT OR IGNORE INTO assets(identifier) VALUES(?)', (NFT_TOKEN_ID,))
cursor.execute(
"""
INSERT INTO timed_balances ("category", "time", "currency", "amount", "usd_value") VALUES
... |
31,753 | def collect_entries_data_from_response(parsed_feed_data: FeedParserDict) -> List[Dict[str, Any]]:
"""Collects relevant data from the parsed RSS feed entries.
Args:
parsed_feed_data (FeedParserDict): Parsed RSS feed data.
Returns:
List[Dict[str, Any]]: The data from the RSS feed relevant fo... | def collect_entries_data_from_response(parsed_feed_data: FeedParserDict) -> List[Dict[str, Any]]:
"""Collects relevant data from the parsed RSS feed entries.
Args:
parsed_feed_data (FeedParserDict): Parsed RSS feed data.
Returns:
List[Dict[str, Any]]: The data from the RSS feed relevant fo... |
32,051 | def relationships_manager(client: Client, entity_a: str, entity_a_type: str, indicator_type: str,
indicator: str, field_for_passive_dns_rs: str, feed_indicator_type_for_passive_dns_rs: str):
"""
manage the relationships creation
Args:
client: Client object with request
... | def relationships_manager(client: Client, entity_a: str, entity_a_type: str, indicator_type: str,
indicator: str, field_for_passive_dns_rs: str, feed_indicator_type_for_passive_dns_rs: str):
"""
manage the relationships creation
Args:
client: Client object with request
... |
23,006 | def test_read_csv_skiprows_range():
with filetext(csv_text) as fn:
f = dd.read_csv(fn, skiprows=range(5))
result = f.compute(scheduler='sync')
expected = pd.read_csv(fn, skiprows=range(5))
assert_eq(result, expected)
| def test_read_csv_skiprows_range():
with filetext(csv_text) as fn:
f = dd.read_csv(fn, skiprows=range(5))
result = f
expected = pd.read_csv(fn, skiprows=range(5))
assert_eq(result, expected)
|
34,875 | def keras_op_to_relay(inexpr, keras_layer, outname, etab):
"""Convert keras layer to relay expr, and update etab.
Parameters
----------
inexpr : relay.expr.Expr or a list of it
The input relay expr(s)
keras_layer : keras.layers
The keras layer to be converted
outname : str
... | def keras_op_to_relay(inexpr, keras_layer, outname, etab):
"""Convert keras layer to relay expr, and update etab.
Parameters
----------
inexpr : relay.expr.Expr or a list of it
The input Relay expression(s).
keras_layer : keras.layers
The keras layer to be converted
outname : ... |
38,971 | def field_singleton_schema( # noqa: C901 (ignore complexity)
field: ModelField,
*,
by_alias: bool,
model_name_map: Dict[Type['BaseModel'], str],
schema_overrides: bool = False,
ref_prefix: Optional[str] = None,
known_models: Set[Type['BaseModel']],
) -> Tuple[Dict[str, Any], Dict[str, Any],... | def field_singleton_schema( # noqa: C901 (ignore complexity)
field: ModelField,
*,
by_alias: bool,
model_name_map: Dict[Type['BaseModel'], str],
schema_overrides: bool = False,
ref_prefix: Optional[str] = None,
known_models: Set[Type['BaseModel']],
) -> Tuple[Dict[str, Any], Dict[str, Any],... |
3,543 | def delete_versions_from_db(project, version_data):
"""
Delete all versions not in the current repo.
:returns: The slug of the deleted versions from the database,
and the slug of active versions that where deleted from the repository.
"""
# We use verbose_name for tags
# because several tag... | def delete_versions_from_db(project, version_data):
"""
Delete all versions not in the current repo.
:returns: The slug of the deleted versions from the database,
and the slug of active versions that were deleted from the repository.
"""
# We use verbose_name for tags
# because several tags... |
57,817 | def main():
try:
args = demisto.args()
last_seen_gte = args.get('from')
last_seen_lte = args.get('to')
limit = args.get('limit', '100')
get_endpoints_args = {'limit': limit}
if last_seen_gte:
get_endpoints_args['last_seen_gte'] = last_seen_gte
if ... | def main():
try:
args = demisto.args()
last_seen_gte = args.get('from')
last_seen_lte = args.get('to')
limit = args.get('limit', '100')
get_endpoints_args = {'limit': limit}
if last_seen_gte:
get_endpoints_args['last_seen_gte'] = last_seen_gte
if ... |
12,767 | def _preprocessed_interpreter_search_paths(
env_tgt: EnvironmentTarget,
_search_paths: Iterable[str],
is_default: bool,
) -> tuple[str, ...]:
"""Checks for special search path strings, and errors if any are invalid for the environment.
This will return:
* The search paths, unaltered, for local/... | def _preprocessed_interpreter_search_paths(
env_tgt: EnvironmentTarget,
_search_paths: Iterable[str],
is_default: bool,
) -> tuple[str, ...]:
"""Checks for special search path strings, and errors if any are invalid for the environment.
This will return:
* The search paths, unaltered, for local/... |
5,252 | def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
limit=None, datatype=REAL, binary_chunk_size=100 * 1024):
"""Load the input-hidden weight matrix from the original C word2vec-tool format.
Note that the information stored in the... | def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
limit=None, datatype=REAL, binary_chunk_size=100 * 1024):
"""Load the input-hidden weight matrix from the original C word2vec-tool format.
Note that the information stored in the... |
26,112 | def test_view_change_not_happen_if_ic_is_discarded(looper, txnPoolNodeSet,
sdk_pool_handle,
sdk_wallet_client,
tconf, tdir, allPluginsPath):
"""
1. panic_node ... | def test_view_change_not_happen_if_ic_is_discarded(looper, txnPoolNodeSet,
sdk_pool_handle,
sdk_wallet_client,
tconf, tdir, allPluginsPath):
"""
1. panic_node ... |
4,224 | def annotate_muscle(raw, threshold=1.5, picks=None, min_length_good=.1):
"""Detect segments with muscle artifacts.
Detects segments periods that contains high frequency activity beyond the
specified threshold. Muscle artifacts are most notable in the range of 110-
140Hz.
Raw data is band pass filt... | def annotate_muscle(raw, threshold=1.5, picks=None, min_length_good=.1):
"""Detect segments with muscle artifacts.
Detects segments periods that contains high frequency activity beyond the
specified threshold. Muscle artifacts are most notable in the range of 110-
140Hz.
Raw data is band pass filt... |
23,114 | def check_index(axis, ind, dimension):
"""Check validity of index for a given dimension
Examples
--------
>>> check_index(0, 3, 5)
>>> check_index(0, 5, 5)
Traceback (most recent call last):
...
IndexError: Index 5 is out of bounds for axis 0 with size 5
>>> check_index(1, 6, 5)
... | def check_index(axis, ind, dimension):
"""Check validity of index for a given dimension
Examples
--------
>>> check_index(0, 3, 5)
>>> check_index(0, 5, 5)
Traceback (most recent call last):
...
IndexError: Index 5 is out of bounds for axis 0 with size 5
>>> check_index(1, 6, 5)
... |
5,444 | def install(name=None, refresh=False, pkgs=None, version=None, test=False, **kwargs):
"""
Install the named fileset(s)/rpm package(s).
.. versionadded:: 3005
preference to install rpm packages are to use in the following order:
/opt/freeware/bin/dnf
/opt/freeware/bin/yum
... | def install(name=None, refresh=False, pkgs=None, version=None, test=False, **kwargs):
"""
Install the named fileset(s)/rpm package(s).
.. versionadded:: 3005
preference to install rpm packages are to use in the following order:
/opt/freeware/bin/dnf
/opt/freeware/bin/yum
... |
24,881 | def _loop_exits_early(loop):
"""
Returns true if a loop mays end up in a break statement.
Args:
loop (astroid.For, astroid.While): the loop node inspected.
Returns:
bool: True if the loop mays end up in a break statement, False otherwise.
"""
loop_nodes = (nodes.For, nodes.Whil... | def _loop_exits_early(loop):
"""
Returns true if a loop mays end up in a break statement.
Args:
loop (astroid.For, astroid.While): the loop node inspected.
Returns:
bool: True if the loop may end with a break statement, False otherwise.
"""
loop_nodes = (nodes.For, nodes.While)... |
13,564 | def QR_iteration(H, shifts):
"""Perform the QR iteration.
Performs a QR step for each shift provided in `shifts`. `H` is assumed to be an
unreduced upper Hessenberg matrix. If a complex shift occurs a double step is
peformed in order to avoid complex arithmetic.
Parameters
----------
H
... | def QR_iteration(H, shifts):
"""Perform the QR iteration.
Performs a QR step for each shift provided in `shifts`. `H` is assumed to be an
unreduced upper Hessenberg matrix. If a complex shift occurs a double step is
peformed in order to avoid complex arithmetic.
Parameters
----------
H
... |
28,583 | def plot_ppc(
data,
kind="kde",
alpha=None,
mean=True,
observed=True,
color=None,
colors=None,
grid=None,
figsize=None,
textsize=None,
data_pairs=None,
var_names=None,
filter_vars=None,
coords=None,
flatten=None,
flatten_pp=None,
num_pp_samples=None,
... | def plot_ppc(
data,
kind="kde",
alpha=None,
mean=True,
observed=True,
color=None,
colors=None,
grid=None,
figsize=None,
textsize=None,
data_pairs=None,
var_names=None,
filter_vars=None,
coords=None,
flatten=None,
flatten_pp=None,
num_pp_samples=None,
... |
38,271 | def build_node_set(node, s=None):
"""Build a set of all the nodes in a rapidz graph
Parameters
----------
node : Stream
The node to use as a starting point for building the set
s : set or None
The set to put the nodes into. If None return a new set full of nodes
Returns
---... | def build_node_set(node, s=None):
"""Build a set of all the nodes in a streamz graph
Parameters
----------
node : Stream
The node to use as a starting point for building the set
s : set or None
The set to put the nodes into. If None return a new set full of nodes
Returns
--... |
3,425 | def _symbolicate(profile: MutableMapping[str, Any], project: Project) -> MutableMapping[str, Any]:
symbolicator = Symbolicator(project=project, event_id=profile["profile_id"])
modules = profile["debug_meta"]["images"]
stacktraces = [
{
"registers": {},
"frames": s["frames"],
... | def _symbolicate(profile: MutableMapping[str, Any], project: Project) -> MutableMapping[str, Any]:
symbolicator = Symbolicator(project=project, event_id=profile["profile_id"])
modules = profile["debug_meta"]["images"]
stacktraces = [
{
"registers": {},
"frames": s["frames"],
... |
31,062 | def main():
try:
if demisto.command() == 'test-module':
# Tests connectivity and credentails on login
# generateStartEndDates(1)
return "ok"
elif demisto.command() == 'ironportQuarantineReleaseEmail':
mesId = demisto.args().get('mid')
ir... | def main():
try:
if demisto.command() == 'test-module':
# Tests connectivity and credentails on login
# generateStartEndDates(1)
return "ok"
elif demisto.command() == 'iron-port-quarantine-release-email':
mesId = demisto.args().get('mid')
... |
42,781 | def test_dataframe_multiIndex_index():
"""Test for multiIndex dataframe"""
data = {
"x":
pd.DataFrame([[2, 3], [6, 7]],
index=pd.MultiIndex.from_arrays([['a', 'b'], ['y', 'z']]))
}
with pytest.raises(ValueError):
assert expand_grid(others=data)
| def test_dataframe_multi_index_index():
"""Test for multiIndex dataframe"""
data = {
"x":
pd.DataFrame([[2, 3], [6, 7]],
index=pd.MultiIndex.from_arrays([['a', 'b'], ['y', 'z']]))
}
with pytest.raises(ValueError):
assert expand_grid(others=data)
|
47,982 | def init_telemetry():
try:
import openvino_telemetry as tm # pylint:disable=C0415
except ImportError:
return None
try:
telemetry = tm.Telemetry('Accuracy Checker', version=__version__, tid='UA-194864834-1')
return telemetry
except Exception: # pylint:disable=W0703
... | def init_telemetry():
try:
import openvino_telemetry as tm # pylint:disable=C0415
except ImportError:
return None
try:
telemetry = tm.Telemetry('Accuracy Checker', app_version= __version__, tid='UA-194864834-1')
return telemetry
except Exception: # pylint:disable=W0703
... |
31,766 | def main():
params = demisto.params()
args = demisto.args()
url = params.get('url')
verify_certificate = not params.get('insecure', False)
proxy = params.get('proxy', False)
headers = {}
headers['PRIVATE-TOKEN'] = f'{params["api_key"]}'
command = demisto.command()
LOG(f'Command bein... | def main():
params = demisto.params()
args = demisto.args()
url = params.get('url')
verify_certificate = not params.get('insecure', False)
proxy = params.get('proxy', False)
headers = {}
headers['PRIVATE-TOKEN'] = f'{params["api_key"]}'
command = demisto.command()
LOG(f'Command bein... |
27,941 | def main():
parser = argparse.ArgumentParser('Train a neural network on MNIST dataset')
parser.add_argument(
'--batchsize', '-B', type=int, default=100, help='Batch size')
parser.add_argument(
'--epoch', '-E', type=int, default=20,
help='Number of epochs to train')
parser.add_arg... | def main():
parser = argparse.ArgumentParser('Train a neural network on MNIST dataset')
parser.add_argument(
'--batchsize', '-B', type=int, default=100, help='Batch size')
parser.add_argument(
'--epoch', '-E', type=int, default=20,
help='Number of epochs to train')
parser.add_arg... |
52,237 | def app_ssowatconf():
"""
Regenerate SSOwat configuration file
"""
from yunohost.domain import domain_list, _get_maindomain, domain_config_get
from yunohost.permission import user_permission_list
main_domain = _get_maindomain()
domains = domain_list()["domains"]
all_permissions = user... | def app_ssowatconf():
"""
Regenerate SSOwat configuration file
"""
from yunohost.domain import domain_list, _get_maindomain, domain_config_get
from yunohost.permission import user_permission_list
main_domain = _get_maindomain()
domains = domain_list()["domains"]
all_permissions = user... |
566 | def _get_feature_flag_items(domain, couch_user):
user_is_admin = couch_user.is_domain_admin(domain)
from corehq.apps.domain.views.fixtures import LocationFixtureConfigView
feature_flag_items = []
if user_is_admin and toggles.SYNC_SEARCH_CASE_CLAIM.enabled(domain):
feature_flag_items.append({
... | def _get_feature_flag_items(domain, couch_user):
user_is_admin = couch_user.is_domain_admin(domain)
from corehq.apps.domain.views.fixtures import LocationFixtureConfigView
feature_flag_items = []
if user_is_admin and toggles.SYNC_SEARCH_CASE_CLAIM.enabled(domain):
feature_flag_items.append({
... |
25,578 | def queue_channel_open(
nodeaddress_to_channelopenqueue: OpenQueue,
nodeaddress_to_channeldepositqueue: DepositQueue,
channel: Dict,
token_address: str,
node_to_address: Dict,
node_to_endpoint: Dict,
) -> None:
node1 = channel["node1"]
node2 = channel["node2"]
participant1 = node_to... | def queue_channel_open(
nodeaddress_to_channelopenqueue: OpenQueue,
nodeaddress_to_channeldepositqueue: DepositQueue,
channel: Dict,
token_address: str,
node_to_address: Dict,
node_to_endpoint: Dict,
) -> None:
node1 = channel["node1"]
node2 = channel["node2"]
participant1 = node_to... |
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