query stringlengths 9 9.05k | document stringlengths 10 222k | metadata dict | negatives listlengths 30 30 | negative_scores listlengths 30 30 | document_score stringlengths 4 10 | document_rank stringclasses 2
values |
|---|---|---|---|---|---|---|
Load certificate request from a bio. | def load_request_bio(bio, format=FORMAT_PEM):
if format == FORMAT_PEM:
cptr = m2.x509_req_read_pem(bio._ptr())
elif format == FORMAT_DER:
cptr = m2.d2i_x509_req(bio._ptr())
else:
raise ValueError("Unknown format. Must be either FORMAT_DER or FORMAT_PEM")
if cptr is None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_cert_bio(bio, format=FORMAT_PEM):\n if format == FORMAT_PEM:\n cptr = m2.x509_read_pem(bio._ptr())\n elif format == FORMAT_DER:\n cptr = m2.d2i_x509(bio._ptr())\n else:\n raise ValueError(\"Unknown format. Must be either FORMAT_DER or FORMAT_PEM\")\n if cptr is None:\n ... | [
"0.7187881",
"0.6071833",
"0.5961294",
"0.5895639",
"0.5752047",
"0.56425667",
"0.5607162",
"0.5551173",
"0.5539058",
"0.55196214",
"0.54728323",
"0.5370808",
"0.53546655",
"0.5348489",
"0.53252023",
"0.53093314",
"0.53057045",
"0.5222469",
"0.5218528",
"0.5218528",
"0.519756... | 0.7412514 | 0 |
Load certificate request from a string. | def load_request_string(string, format=FORMAT_PEM):
bio = BIO.MemoryBuffer(string)
return load_request_bio(bio, format) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_cert_string(string, format=FORMAT_PEM):\n bio = BIO.MemoryBuffer(string)\n return load_cert_bio(bio, format)",
"def load_cert_der_string(string):\n bio = BIO.MemoryBuffer(string)\n cptr = m2.d2i_x509(bio._ptr())\n if cptr is None:\n raise X509Error(Err.get_error())\n return X509... | [
"0.7479425",
"0.70367086",
"0.6870818",
"0.5943792",
"0.5939875",
"0.5774697",
"0.5653851",
"0.55566955",
"0.5535322",
"0.54895294",
"0.54457223",
"0.54409075",
"0.5393966",
"0.53898907",
"0.53844166",
"0.52580863",
"0.5256024",
"0.52432954",
"0.5231493",
"0.5229351",
"0.5227... | 0.7196225 | 1 |
Return CRL in PEM format in a string. | def as_text(self):
buf=BIO.MemoryBuffer()
m2.x509_crl_print(buf.bio_ptr(), self.crl)
return buf.read_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_pem(tls_cert):\n pem = ()\n if tls_cert.intermediates:\n for c in tls_cert.intermediates:\n pem = pem + (c,)\n if tls_cert.certificate:\n pem = pem + (tls_cert.certificate,)\n if tls_cert.private_key:\n pem = pem + (tls_cert.private_key,)\n return \"\\n\".j... | [
"0.67152685",
"0.5924512",
"0.5799669",
"0.5792997",
"0.56763786",
"0.5639472",
"0.55644107",
"0.55427945",
"0.552508",
"0.54778117",
"0.5394357",
"0.537613",
"0.5347132",
"0.5325517",
"0.5303099",
"0.5282787",
"0.52816725",
"0.5272289",
"0.52708054",
"0.52534777",
"0.5252673... | 0.73158324 | 0 |
Load CRL from file. | def load_crl(file):
f=BIO.openfile(file)
cptr= m2.x509_crl_read_pem(f.bio_ptr())
f.close()
if cptr is None:
raise X509Error(Err.get_error())
return CRL(cptr, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_file(path, encoding='pem'):\n try:\n with open(path, 'r') as f:\n return X509Csr.from_open_file(f, encoding)\n except IOError:\n raise X509CsrError(\"Could not read file %s\" % path)",
"def load(journal: Journal, file: Path) -> None:",
"def load_cr(lo... | [
"0.6195027",
"0.60231894",
"0.5959088",
"0.58282405",
"0.5799078",
"0.57490224",
"0.5677562",
"0.5667214",
"0.5539522",
"0.55323887",
"0.5513573",
"0.5513521",
"0.5507139",
"0.55067164",
"0.55038714",
"0.5502746",
"0.547065",
"0.547065",
"0.54239094",
"0.54205334",
"0.5407105... | 0.7959856 | 0 |
This pyunit is written to make sure PCA works with customer data. It is mainly used by customer to verify PCA operations and not to be used as a regular test since I do not want to expose customer data. | def pca_pubdev_4167_OOM():
h2o.remove_all()
transform_types = ["NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"] # make sure we check all tranforms
transformN = transform_types[randint(0, len(transform_types)-1)]
print("transform used on dataset is {0}.\n".format(transformN))
training_data = h2o.imp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runPCA(data, reducedDimensions, showScree):\n print(\"-->Running PCA.\")\n latent = gp.pca(data['features'], reducedDimensions, showScree, savePlots)\n plot(latent, data['colours'], reducedDimensions, \"Iris Dataset\", \"PCA\")",
"def get_pca():\n from sklearn.decomposition import PCA\n return... | [
"0.66180795",
"0.6507633",
"0.6497427",
"0.64842975",
"0.6418953",
"0.63032764",
"0.6280184",
"0.6269524",
"0.6225767",
"0.61714995",
"0.61530423",
"0.6147072",
"0.61040044",
"0.61023873",
"0.60824835",
"0.6067693",
"0.6058919",
"0.60552704",
"0.6040558",
"0.5988753",
"0.5975... | 0.65589345 | 1 |
Build dataframes containing evaluated symbolic functions. | def build_dataframes(self, ode_sols: list, used_fs: list):
# Create empty lists to house the stacked theta's and u's
dataframes = []
# Create the dataframes
for sol, f in zip(ode_sols, used_fs):
# Prepare independent variable
x_data = sol.t
x = Indepe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runCalculators(series, calculators=[]):\n df_obj = {}\n for ind, fn in enumerate(calculators):\n df_obj[fn.__name__] = fn(series)\n return pd.DataFrame(df_obj)",
"def make_df(raw_data, variables):\n vars = []\n for v in requirement_order(variables):\n vars.append(v(raw_data))\n ... | [
"0.65260476",
"0.5977529",
"0.58888173",
"0.5824303",
"0.5801469",
"0.5783029",
"0.5741829",
"0.5653855",
"0.5553497",
"0.55050355",
"0.548221",
"0.5481139",
"0.54733086",
"0.5439582",
"0.5430116",
"0.5391746",
"0.5342683",
"0.53277475",
"0.52953726",
"0.5292279",
"0.52898544... | 0.6206946 | 1 |
Assert that any of the model types support "equal" comparisons against expected types. | def test_model_supports_eq_with_expected_types(model_with_eq_bytes):
model_type, equal_bytes = model_with_eq_bytes
model = model_type(equal_bytes)
assert model == model_type(equal_bytes)
assert model == bytes(equal_bytes)
assert model == bytearray(equal_bytes)
assert model == memoryview(equal_b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_types_are_equal(self):\n self.assertEqual(True, comparator.types_are_equal(None, None))\n self.assertEqual(True, comparator.types_are_equal(True, True))\n self.assertEqual(True, comparator.types_are_equal(True, False))\n self.assertEqual(True, comparator.types_are_equal(int(), ... | [
"0.7583787",
"0.70162624",
"0.69057155",
"0.678198",
"0.6744791",
"0.669955",
"0.6672378",
"0.66065145",
"0.6586775",
"0.65678626",
"0.655128",
"0.6521056",
"0.65021616",
"0.64980686",
"0.64774406",
"0.6442611",
"0.640196",
"0.636395",
"0.6363593",
"0.63635164",
"0.6357821",
... | 0.74301857 | 1 |
Assert that any of the model types supports "not equal" comparisons against expected types. | def test_model_supports_ne_with_expected_types(model_with_ne_bytes):
model_type, equal_bytes, not_equal_bytes = model_with_ne_bytes
model = model_type(equal_bytes)
assert model != ulid.MemoryView(not_equal_bytes)
assert model != bytes(not_equal_bytes)
assert model != bytearray(not_equal_bytes)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_not_equal_to_other_types(self):\n from pedemath.quat import Quat\n\n self.assertNotEqual(Vec3(1, -2, 3), 5)\n self.assertNotEqual(Vec3(1, -2, 3), \"abc\")\n\n self.assertNotEqual(Vec3(1, 1, 1), Quat(1, 1, 1, 1))\n self.assertNotEqual(Quat(1, 1, 1, 1), Vec3(1, 1, 1))",
... | [
"0.69606763",
"0.69124705",
"0.675815",
"0.6716363",
"0.67063314",
"0.6688518",
"0.6646908",
"0.65396637",
"0.65251935",
"0.6522157",
"0.64143497",
"0.6391868",
"0.6382574",
"0.63788563",
"0.6349058",
"0.6341017",
"0.6304153",
"0.6301197",
"0.62666476",
"0.62429833",
"0.62392... | 0.7148142 | 0 |
Assert that any of the model types support "greater than or equal" comparisons against expected types. | def test_model_supports_ge_with_expected_types(model_with_ordered_bytes):
model_type, less_than_bytes, greater_than_bytes = model_with_ordered_bytes
model = model_type(greater_than_bytes)
assert model >= ulid.MemoryView(greater_than_bytes)
assert model >= bytes(greater_than_bytes)
assert model >= b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_model_supports_gt_with_expected_types(model_with_ordered_bytes):\n model_type, less_than_bytes, greater_than_bytes = model_with_ordered_bytes\n\n model = model_type(greater_than_bytes)\n assert model > ulid.MemoryView(less_than_bytes)\n assert model > bytes(less_than_bytes)\n assert model >... | [
"0.71447897",
"0.7004308",
"0.68969035",
"0.65432584",
"0.6526058",
"0.65056455",
"0.64575636",
"0.62672114",
"0.6266962",
"0.6030582",
"0.59935045",
"0.5972007",
"0.59685004",
"0.5953082",
"0.59211165",
"0.5906382",
"0.59022427",
"0.5893711",
"0.58105123",
"0.58095473",
"0.5... | 0.7128925 | 1 |
Builds an opener with a useful user agent. | def build_opener():
opener = urllib2.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_4) AppleWebKit/536.30.1 (KHTML, like Gecko) Version/6.0.5 Safari/536.30.1')]
return opener | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.opener = urllib2.build_opener()\n self.opener.addheaders = [('User-agent', 'Mozilla/5.0')]",
"def build_user_agent(application_name, version, url):\n return '%s/%s %s/%s (+%s)' % (application_name, version,\n 'python-simplemediawiki', _... | [
"0.63375366",
"0.620493",
"0.614195",
"0.604114",
"0.572189",
"0.5556321",
"0.55325794",
"0.55206364",
"0.5470152",
"0.5430915",
"0.54090637",
"0.5395895",
"0.53759587",
"0.53670514",
"0.5240311",
"0.51287067",
"0.51226544",
"0.5105934",
"0.5090448",
"0.50633335",
"0.50439775... | 0.7806084 | 0 |
Adds scrape metadata to the given context dict. | def add_scrape_metadata(context, url, date=None):
if not date:
date = datetime.datetime.now()
context['_scrape'] = {'url': url, 'date': date } | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeToMetadata(self, context):\n pass",
"def add_metadata(self, metadata: dict) -> None:",
"def add_context(self):\n return {}",
"def prepare_context(self, activity, context, typename=None):\n context.update({\n 'activity': activity,\n 'object': activity.snapsh... | [
"0.5974032",
"0.5931977",
"0.5540139",
"0.55156606",
"0.5514726",
"0.54949576",
"0.54839015",
"0.54758626",
"0.53552836",
"0.5349449",
"0.5281398",
"0.5266595",
"0.518954",
"0.5162245",
"0.51347166",
"0.5121527",
"0.5106837",
"0.50921",
"0.5073093",
"0.50536144",
"0.50370854"... | 0.7269503 | 0 |
Send Request to the gaia tap to retrieve the list of public tables | def test_get_public_tables(self, test_info):
list_of_public_tables = ['This is the list of public tables:']
test_results = test_info
# Execute the test
try:
tables = self.gaia.load_tables(only_names=True)
# Get current time
time = datetime.now()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n return {\"tables\": public_tables}",
"async def test_tornado_list_tables(self):\n\n tables = self.r.table_list().run(self.conn)\n assert isinstance(tables, list)",
"def get_tables(self):\n r = self.client.query(\"show tables\")\n if r:\n tables = [... | [
"0.69629925",
"0.65512455",
"0.62498534",
"0.61649203",
"0.6152742",
"0.6142407",
"0.6099446",
"0.6097399",
"0.60967684",
"0.60932463",
"0.5949833",
"0.59212863",
"0.58989733",
"0.58647656",
"0.58346915",
"0.5800731",
"0.57881397",
"0.5782249",
"0.5773628",
"0.5772787",
"0.57... | 0.6710767 | 1 |
Test synchronous query. Type of request does not store the results at server side. Suitable when the amount of data to be retrieved is 'small' | def test_synchronous_query(self, test_info):
test_results = test_info
# STEP-1: get table selected for the test from the dict of conditions for this test
# -------------------------------------------------------------------------------------
full_qualified_name = test_common_cons.FULL_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def test_9() -> None:\n LOG.debug(\"Test get query (good query, empty response)\")\n params = {\"assemblyId\": \"GRCh99\", \"referenceName\": \"MT\", \"start\": 63, \"referenceBases\": \"CT\", \"alternateBases\": \"NN\", \"includeDatasetResponses\": \"HIT\"}\n async with aiohttp.ClientSession() as s... | [
"0.629921",
"0.6279578",
"0.6234913",
"0.621616",
"0.6092675",
"0.60624474",
"0.60507977",
"0.60354835",
"0.60340416",
"0.5941938",
"0.593374",
"0.59144413",
"0.5902323",
"0.589409",
"0.58759207",
"0.5869263",
"0.58567464",
"0.5823924",
"0.58184433",
"0.5817279",
"0.5798723",... | 0.6375462 | 0 |
Test synchronous_on_the_fly_query. This test shows how a table can be uploaded to the server in order to be used in a query. | def test_synchronous_on_the_fly_query(self, test_info):
test_results = test_info
# get table selected for the test from the dict of conditions for this test
table_name = test_results['test_table']
# Prepare the query for the test
test_query = test_results['test_query'] \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_synchronous_query(self, test_info):\n\n test_results = test_info\n\n # STEP-1: get table selected for the test from the dict of conditions for this test\n # -------------------------------------------------------------------------------------\n full_qualified_name = test_common... | [
"0.6659065",
"0.66310436",
"0.65996295",
"0.65570974",
"0.6497477",
"0.6484379",
"0.6409102",
"0.64074314",
"0.6379959",
"0.63628465",
"0.62215203",
"0.6161747",
"0.6119813",
"0.61117744",
"0.61036205",
"0.60550517",
"0.60440403",
"0.6024488",
"0.602106",
"0.60184824",
"0.597... | 0.7093079 | 0 |
Test asynchronous query. These type of queries save results at server side and can be accessed at any time. The results can be saved in memory (default) or in a file.' | def test_asynchronous_query(self, test_results):
test_results = test_results
# get table selected for the test from the dict of conditions for this test
full_qualified_name = test_common_cons.FULL_QUALIFIED_TABLE_NAME_PATTERN \
.replace(test_common_cons.SCHEMA_PATTERN, test_results... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_synchronous_query(self, test_info):\n\n test_results = test_info\n\n # STEP-1: get table selected for the test from the dict of conditions for this test\n # -------------------------------------------------------------------------------------\n full_qualified_name = test_common... | [
"0.70452106",
"0.69394165",
"0.6745088",
"0.6691548",
"0.6247566",
"0.6199555",
"0.613124",
"0.6117636",
"0.605641",
"0.6025256",
"0.60176057",
"0.59973246",
"0.5995443",
"0.59498656",
"0.59310764",
"0.59269315",
"0.5926482",
"0.59242433",
"0.59060663",
"0.5885044",
"0.585591... | 0.7363706 | 0 |
This test list all tables, public and shared (Tap + Capability). For the test to be correct the number of results returned must be major or equal to the number of public tables. | def test_shared_table(self, test_info):
test_results = test_info
# execute request
try:
# Step 1: Get the list of public tables
tables = self.gaia.load_tables(only_names=True)
# Now we will keep the number of results returned
n_public_tables = l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_public_tables(self, test_info):\n\n list_of_public_tables = ['This is the list of public tables:']\n test_results = test_info\n\n # Execute the test\n try:\n tables = self.gaia.load_tables(only_names=True)\n\n # Get current time\n time = dat... | [
"0.7000047",
"0.6719644",
"0.6495027",
"0.61799294",
"0.6146831",
"0.60357755",
"0.6034993",
"0.59896284",
"0.5958457",
"0.5955863",
"0.5933418",
"0.5901197",
"0.5866797",
"0.5835075",
"0.58206844",
"0.5818216",
"0.57765245",
"0.5730656",
"0.5730642",
"0.5729839",
"0.57256323... | 0.6815065 | 1 |
This test checks the possibility of persisting a table in the private user space from source. this source can be an url or a local file. | def test_upload_table_from_source(self, test_info, current_test):
test_results = test_info
if current_test == 'DELETE_TABLE':
log.debug('Executing Test - "Delete table from user area"')
else:
log.debug('Executing Test - "Upload table on the fly and query it"')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_example_test_table(conn):\n execute_sql_script(conn, \"06_create_example_test_table.sql\")",
"def test_db_schema(client):\n table_names = [\"user\", \"house\", \"user_role\", \"model_param\"]\n with db.engine.connect() as connexion:\n for table_name in table_names:\n assert ... | [
"0.57268167",
"0.5687335",
"0.56736815",
"0.5628419",
"0.56202555",
"0.560951",
"0.55935013",
"0.55869275",
"0.5577231",
"0.5531349",
"0.5467646",
"0.5409379",
"0.5390512",
"0.5385465",
"0.5384764",
"0.5380472",
"0.53698415",
"0.53694075",
"0.53531086",
"0.5340485",
"0.533752... | 0.6122918 | 0 |
This test checks the possibility of persisting a table in the private user space from source. In this case the source is an Astropy table. | def test_upload_table_from_astropy_table(self, test_info):
test_results = test_info
# execute request
try:
self.gaia.login(user=credentials.USERNAME, password=credentials.PASSWORD)
# Step 1: create an astroquery table. We will upload this table later
# ---... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_upload_table_from_source(self, test_info, current_test):\n\n test_results = test_info\n\n if current_test == 'DELETE_TABLE':\n log.debug('Executing Test - \"Delete table from user area\"')\n else:\n log.debug('Executing Test - \"Upload table on the fly and query ... | [
"0.5738934",
"0.56980634",
"0.56459063",
"0.5508142",
"0.54857105",
"0.54741836",
"0.54670304",
"0.53948617",
"0.5384693",
"0.53317964",
"0.53095365",
"0.5300424",
"0.52918273",
"0.52881664",
"0.52718246",
"0.5255498",
"0.5234632",
"0.5233685",
"0.5233491",
"0.5220224",
"0.52... | 0.6101739 | 0 |
This tests execute a cross match between tables based on distance | def test_cross_match(self, test_info):
test_results = test_info
# execute request
try:
self.gaia.login(user=credentials.USERNAME, password=credentials.PASSWORD)
# -----------------------------
# Step 1: Delete previous tables
# ------------------... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_distances(self):\n sf = make_classifier_data(n=10, d=2, seed=37)\n sf.remove_column(\"class\", inplace=True)\n\n numeric_features = [\"int0\", \"int1\", \"float0\", \"float1\"]\n array_features = [\"array0\"]\n string_features = [\"str0\"]\n dict_features = [\"dic... | [
"0.67952037",
"0.64480233",
"0.6285972",
"0.61816925",
"0.6124425",
"0.6124227",
"0.6109464",
"0.6099723",
"0.6093255",
"0.60920066",
"0.60861856",
"0.60195357",
"0.60088426",
"0.6003538",
"0.6001823",
"0.59931093",
"0.59834784",
"0.5981892",
"0.5967314",
"0.5953879",
"0.5939... | 0.64574254 | 1 |
Load clipboard with the given key. | def copy(self, key):
text = self.data.get(key)
if text:
pyperclip.copy(text)
else:
pyperclip.copy(key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Retrieve(self, key):\n\n pyperclip.copy(self.paris[key])\n pyperclip.paste()\n stdout.write(\"Password copied to clipboard\\n\\n\")",
"def load_key(self, key):\n self.key = key",
"def load(self, key: str) -> str:\n pass",
"def load_key():",
"def read_from_clipboard():... | [
"0.6782136",
"0.6230264",
"0.61726445",
"0.5792487",
"0.5710984",
"0.55823094",
"0.548936",
"0.5430834",
"0.5428954",
"0.5402148",
"0.5323533",
"0.52961814",
"0.52913374",
"0.5250666",
"0.52369",
"0.52177256",
"0.5199609",
"0.51991016",
"0.5176464",
"0.50925374",
"0.507247",
... | 0.70678526 | 0 |
Send request loop. Delays the initial request for a random interval to reduce load. Then sends a request to the datapath, waits the specified interval and checks that a response has been received in a loop. | def __call__(self):
hub.sleep(random.randint(1, self.interval))
while True:
self.send_req()
self.reply_pending = True
hub.sleep(self.interval)
if self.reply_pending:
self.no_response() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sendAndReceive(self, request):\n count = 0\n while count < 100: # 5 seconds\n try:\n count += 1\n self.sock.sendto(request, self.server_addr)\n reply, _ = self.sock.recvfrom(1024)\n return reply\n except:\n pa... | [
"0.671391",
"0.6681441",
"0.66344315",
"0.65068275",
"0.65068275",
"0.65068275",
"0.63012445",
"0.6274581",
"0.62608635",
"0.61249435",
"0.6105993",
"0.6019827",
"0.59919775",
"0.5986788",
"0.5948139",
"0.59058326",
"0.586756",
"0.5854614",
"0.5845046",
"0.58273923",
"0.58238... | 0.74421644 | 0 |
Testing "conf_interval" function with "ecdf" method | def test_conf_interval_ecdf_method(data):
df = data["df"]
new_df = data["new_df"]
# ``quantile_estimation_method = "ecdf"``
ci_model = conf_interval(
df=df,
value_col="y",
residual_col="residual",
conditional_cols=["x"],
quantiles=[0.005, 0.025, 0.975, 0.995],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_conf_interval_normal_method(data):\n df = data[\"df\"]\n new_df = data[\"new_df\"]\n # ``quantile_estimation_method = \"normal_fit\"``\n ci_model = conf_interval(\n df=df,\n value_col=\"y\",\n residual_col=\"residual\",\n conditional_cols=[\"x\"],\n quantiles... | [
"0.5594304",
"0.5566685",
"0.55178356",
"0.5485094",
"0.5464813",
"0.5437122",
"0.54301286",
"0.5403605",
"0.53533435",
"0.5314584",
"0.53059244",
"0.5303937",
"0.5301775",
"0.5297584",
"0.52942604",
"0.529139",
"0.5288953",
"0.52825075",
"0.5277701",
"0.5265392",
"0.52548724... | 0.72014546 | 0 |
Testing ``conf_interval`` function, normal method, multivariate conditional columns | def test_conf_interval_normal_method_multivar_conditionals(data):
df = data["df"]
new_df = data["new_df"]
# ``quantile_estimation_method = "normal_fit"``
# with multi-variate ``conditional_cols``
ci_model = conf_interval(
df=df,
value_col="y",
residual_col="residual",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_conf_interval_normal_method_no_conditionals(data):\n df = data[\"df\"]\n new_df = data[\"new_df\"]\n # ``quantile_estimation_method = \"normal_fit\"``;\n # with no ``conditional_cols``\n ci_model = conf_interval(\n df=df,\n value_col=\"y\",\n residual_col=\"residual\",\... | [
"0.70808744",
"0.6852093",
"0.6755946",
"0.65520185",
"0.65497816",
"0.6308374",
"0.6099223",
"0.6054645",
"0.60077924",
"0.5784047",
"0.5716202",
"0.57114714",
"0.566932",
"0.56619006",
"0.5587391",
"0.5570684",
"0.55646056",
"0.5526567",
"0.55166423",
"0.55066925",
"0.54996... | 0.74649847 | 0 |
Testing "conf_interval" function, normal method, no small sample size calculation | def test_conf_interval_normal_method_no_small_sample_calc(data):
df = data["df"]
new_df = data["new_df"]
# ``quantile_estimation_method = "normal_fit"``;
# with no small sample size calculation
ci_model = conf_interval(
df=df,
value_col="y",
residual_col="residual",
c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_conf_interval_normal_method(data):\n df = data[\"df\"]\n new_df = data[\"new_df\"]\n # ``quantile_estimation_method = \"normal_fit\"``\n ci_model = conf_interval(\n df=df,\n value_col=\"y\",\n residual_col=\"residual\",\n conditional_cols=[\"x\"],\n quantiles... | [
"0.6606613",
"0.6490512",
"0.648579",
"0.6397159",
"0.63010037",
"0.6299123",
"0.62481505",
"0.61707187",
"0.6122008",
"0.6107042",
"0.6066163",
"0.6031395",
"0.6017054",
"0.6014699",
"0.6004228",
"0.59864295",
"0.5981038",
"0.5938319",
"0.5931015",
"0.58754426",
"0.58591354"... | 0.68414664 | 0 |
Sets the cwe of this Finding. | def cwe(self, cwe):
if cwe is not None and cwe > 2147483647: # noqa: E501
raise ValueError("Invalid value for `cwe`, must be a value less than or equal to `2147483647`") # noqa: E501
if cwe is not None and cwe < -2147483648: # noqa: E501
raise ValueError("Invalid value for `cw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_c(self, c):\n self.c = c",
"def setC(self, c):\n\t\tself.c = int(c)",
"def set_coor(self, new_coor):\n self.__x_coor, self.__y_coor = new_coor",
"def cep(self, cep):\n self._cep = cep",
"def setFactura(self, caja): \n self.caja = caja",
"def bcp_set(self, **kwar... | [
"0.5688809",
"0.554394",
"0.54632795",
"0.53285086",
"0.5170759",
"0.51304007",
"0.51214826",
"0.512021",
"0.5022846",
"0.5015133",
"0.4990162",
"0.4948518",
"0.4948518",
"0.4945609",
"0.4887969",
"0.48668787",
"0.4852238",
"0.48455235",
"0.48343876",
"0.48337865",
"0.4820999... | 0.61058354 | 0 |
Sets the cve of this Finding. | def cve(self, cve):
if cve is not None and len(cve) > 20:
raise ValueError("Invalid value for `cve`, length must be less than or equal to `20`") # noqa: E501
if cve is not None and len(cve) < 1:
raise ValueError("Invalid value for `cve`, length must be greater than or equal to `... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_cpe(self, cpe_model):\n self.cpe_model = cpe_model",
"def cep(self, cep):\n self._cep = cep",
"def set_c(self, c):\n self.c = c",
"def setValveCoeff(self, Kv = -1.0):\n vv = zeros(1,'d')\n vv[0] = Kv\n if self._verbose:\n print\n print s... | [
"0.5929993",
"0.536457",
"0.52619433",
"0.5204143",
"0.51745",
"0.50967634",
"0.5045109",
"0.5033974",
"0.49600694",
"0.4951797",
"0.49439722",
"0.4928139",
"0.48799694",
"0.48693478",
"0.4851566",
"0.483571",
"0.4820047",
"0.48153067",
"0.48127368",
"0.48025617",
"0.47910535... | 0.6524324 | 0 |
Sets the mitigation of this Finding. | def mitigation(self, mitigation):
if mitigation is None:
raise ValueError("Invalid value for `mitigation`, must not be `None`") # noqa: E501
if mitigation is not None and len(mitigation) < 1:
raise ValueError("Invalid value for `mitigation`, length must be greater than or equal ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mitigated(self, mitigated):\n\n self._mitigated = mitigated",
"def mitigated_by(self, mitigated_by):\n\n self._mitigated_by = mitigated_by",
"def setImmunity(self, immune):\n self._immune = immune",
"def set_unmute_finding(finding_path: str) -> None:\n from google.cloud import sec... | [
"0.6370731",
"0.55640244",
"0.55121887",
"0.5153072",
"0.5148045",
"0.5035062",
"0.50184983",
"0.50184983",
"0.5002891",
"0.49091902",
"0.49087924",
"0.49018398",
"0.48800558",
"0.4870139",
"0.4867523",
"0.4864948",
"0.4850007",
"0.48348397",
"0.4830076",
"0.48254335",
"0.481... | 0.56995326 | 1 |
Sets the impact of this Finding. | def impact(self, impact):
if impact is None:
raise ValueError("Invalid value for `impact`, must not be `None`") # noqa: E501
if impact is not None and len(impact) < 1:
raise ValueError("Invalid value for `impact`, length must be greater than or equal to `1`") # noqa: E501
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setExperience(self, damage, enemyShip):\n experience = damage/25.0\n if experience > 0:\n self.myCaptain.addExperience(experience)\n enemyShip.myCaptain.addExperience(experience)",
"def set_experience(self):\n if self.__experience < 50:\n self.__experienc... | [
"0.59221643",
"0.58621556",
"0.5606283",
"0.5597777",
"0.5525032",
"0.5394613",
"0.53606826",
"0.5329237",
"0.5274561",
"0.525864",
"0.52578574",
"0.5237974",
"0.5220516",
"0.51929873",
"0.5154473",
"0.5139406",
"0.5138795",
"0.5132232",
"0.5124831",
"0.5107665",
"0.5101155",... | 0.61716855 | 0 |
Sets the steps_to_reproduce of this Finding. | def steps_to_reproduce(self, steps_to_reproduce):
self._steps_to_reproduce = steps_to_reproduce | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def steps(self, steps):\n\n self._steps = steps",
"def performStep(self):\n for ant in self.ants:\n if self.parameters['steps'] == self.parameters['step_to_reset']:\n for u,v in self.parameters['edges_to_reset']:\n self.graph_data[u]['pheromones'][v] = 0... | [
"0.623401",
"0.56161344",
"0.5354803",
"0.5269238",
"0.52536535",
"0.5173228",
"0.5131404",
"0.5121534",
"0.5096198",
"0.5082088",
"0.5073663",
"0.50319296",
"0.496523",
"0.4926089",
"0.48702627",
"0.4854846",
"0.4835863",
"0.48290208",
"0.48282164",
"0.48270118",
"0.4790628"... | 0.8589428 | 0 |
Sets the severity_justification of this Finding. | def severity_justification(self, severity_justification):
self._severity_justification = severity_justification | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def severity(self, severity):\n self._severity = severity",
"def severity(self, severity):\n\n self._severity = severity",
"def severity(self, severity):\n if severity is None:\n raise ValueError(\"Invalid value for `severity`, must not be `None`\") # noqa: E501\n\n self... | [
"0.64040995",
"0.6313406",
"0.5872749",
"0.5815683",
"0.5782054",
"0.55704063",
"0.5539979",
"0.54631597",
"0.54631597",
"0.54631597",
"0.54207903",
"0.53800726",
"0.53800726",
"0.53324807",
"0.5238413",
"0.51685005",
"0.5130509",
"0.5130509",
"0.5130509",
"0.49345392",
"0.48... | 0.8714417 | 0 |
Sets the references of this Finding. | def references(self, references):
self._references = references | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_references(self, references: IReferences):\n self._logger.set_references(references)\n self._connection_resolver.set_references(references)",
"def set_references(self, map_data):\n \n pass",
"def reference(self, reference):\n\n self._reference = reference",
"def ref... | [
"0.69170445",
"0.6718143",
"0.6479481",
"0.6479481",
"0.6309612",
"0.62870604",
"0.6254883",
"0.62312526",
"0.6216842",
"0.61540294",
"0.59833276",
"0.5980825",
"0.59533966",
"0.5946948",
"0.5894542",
"0.5882001",
"0.58238137",
"0.5820833",
"0.5799906",
"0.57940304",
"0.57829... | 0.76643914 | 0 |
Sets the is_template of this Finding. | def is_template(self, is_template):
self._is_template = is_template | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setTemplate(self, template):\n self.template = template",
"def template(self, template):\n self._template = template",
"def template(self, template):\n\n self._template = template",
"def template(self, template):\n\n self._template = template",
"def template_spec(self, templ... | [
"0.69381595",
"0.6622306",
"0.66119707",
"0.66119707",
"0.58835995",
"0.57484084",
"0.5578699",
"0.55410117",
"0.5504101",
"0.54682386",
"0.5420288",
"0.53855085",
"0.5373229",
"0.53705716",
"0.5341186",
"0.53302974",
"0.5310795",
"0.5299374",
"0.5293215",
"0.526132",
"0.5138... | 0.8170815 | 1 |
Sets the verified of this Finding. | def verified(self, verified):
self._verified = verified | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify(self, user: get_user_model(), verified: bool):\n if not self.verifications.filter(user=user).update(verified=verified):\n Verification.objects.create(\n annotation=self, user=user, verified=verified)",
"def set_user_verified(self, authenticator_id, uv):\n pass",... | [
"0.6685678",
"0.6417394",
"0.6392021",
"0.63076985",
"0.6282731",
"0.6227949",
"0.61350805",
"0.60798943",
"0.6070255",
"0.6070255",
"0.58693814",
"0.5845169",
"0.56802887",
"0.5658213",
"0.5647124",
"0.5639812",
"0.56168455",
"0.560145",
"0.5598253",
"0.5566235",
"0.55147266... | 0.7996484 | 0 |
Sets the false_p of this Finding. | def false_p(self, false_p):
self._false_p = false_p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setFalse(self):\n self.cond = CT.FALSE\n self.left = self.right = None\n self.z3 = BoolSort().cast(False)\n self.customSimplifiedValue = CE.FALSE\n self.cleaned = self.Z3Simplified = self.customSimplified = self.checked = True",
"def set(self, boolean):\n self._v... | [
"0.6360846",
"0.60413694",
"0.5882884",
"0.58073723",
"0.564068",
"0.5609894",
"0.5553763",
"0.5502876",
"0.54671377",
"0.5434651",
"0.5434651",
"0.5434651",
"0.5434651",
"0.5434651",
"0.54192686",
"0.5379097",
"0.5373385",
"0.5353504",
"0.53513646",
"0.53093076",
"0.5299857"... | 0.8441194 | 0 |
Sets the duplicate of this Finding. | def duplicate(self, duplicate):
self._duplicate = duplicate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def duplicate_finding(self, duplicate_finding):\n\n self._duplicate_finding = duplicate_finding",
"def copy(self):\n return set(self)",
"def duplicate(self,newName):\n raise AbstractError",
"def copy(self):\n pass",
"def copy(self):\n pass",
"def copy(self):\n pa... | [
"0.7817593",
"0.6339552",
"0.622764",
"0.60756475",
"0.60756475",
"0.60756475",
"0.6057",
"0.6044895",
"0.60136396",
"0.5995016",
"0.5982935",
"0.5980931",
"0.597355",
"0.59625727",
"0.5927888",
"0.5917736",
"0.59139246",
"0.5907133",
"0.5907133",
"0.5907133",
"0.5907133",
... | 0.7379165 | 1 |
Sets the out_of_scope of this Finding. | def out_of_scope(self, out_of_scope):
self._out_of_scope = out_of_scope | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def leaveScope(self, name):",
"def setOutside(self, *args):\n return _libsbml.Compartment_setOutside(self, *args)",
"def endScope():",
"def remove_scope(self, ):\n if self.AttributeNames.SCOPE in self.attrs:\n del self.attrs[self.AttributeNames.SCOPE]\n return self",
"def un... | [
"0.54191524",
"0.5267828",
"0.52066463",
"0.50985223",
"0.5060749",
"0.4911923",
"0.48993295",
"0.48969147",
"0.48820654",
"0.4856717",
"0.48263094",
"0.47996077",
"0.47797975",
"0.47635654",
"0.47547698",
"0.47399506",
"0.4734951",
"0.4687473",
"0.46658072",
"0.46342385",
"0... | 0.85201716 | 0 |
Sets the under_review of this Finding. | def under_review(self, under_review):
self._under_review = under_review | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def under_defect_review(self, under_defect_review):\n\n self._under_defect_review = under_defect_review",
"def review(self, review):\n self._review = review",
"def under_payout(self, under_payout):\n\n self._under_payout = under_payout",
"def over_under(self, over_under):\n\n self... | [
"0.80887806",
"0.65063375",
"0.6184821",
"0.6156164",
"0.6056587",
"0.59243995",
"0.5616105",
"0.5368503",
"0.5243998",
"0.51580375",
"0.51022154",
"0.50840384",
"0.5068778",
"0.50678664",
"0.50283754",
"0.4983441",
"0.4967108",
"0.49614212",
"0.4892614",
"0.48733404",
"0.485... | 0.89384973 | 0 |
Sets the under_defect_review of this Finding. | def under_defect_review(self, under_defect_review):
self._under_defect_review = under_defect_review | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def under_review(self, under_review):\n\n self._under_review = under_review",
"def review(self, review):\n self._review = review",
"def auto_review(self, auto_review):\n \n self._auto_review = auto_review",
"def review(self, review: object):\n\n self._review = review",
"d... | [
"0.74856985",
"0.6099573",
"0.57681555",
"0.56493217",
"0.5564493",
"0.5362523",
"0.5323876",
"0.5304445",
"0.5285012",
"0.52580965",
"0.5159914",
"0.49756",
"0.49215767",
"0.49064335",
"0.48911047",
"0.48828933",
"0.48604757",
"0.48057616",
"0.4725517",
"0.4684644",
"0.46299... | 0.87650913 | 0 |
Sets the is_mitigated of this Finding. | def is_mitigated(self, is_mitigated):
self._is_mitigated = is_mitigated | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mitigated(self, mitigated):\n\n self._mitigated = mitigated",
"def is_flagged(self, is_flagged):\n \n self._is_flagged = is_flagged",
"def set_mark(self, perception: Perception) -> None:\n if self.mark.set_mark_using_condition(self.condition, perception):\n self.ee = ... | [
"0.6235596",
"0.62330747",
"0.56150764",
"0.55545604",
"0.5541536",
"0.54872495",
"0.54105633",
"0.538814",
"0.53507006",
"0.5325853",
"0.52925426",
"0.52925426",
"0.52887464",
"0.5271636",
"0.52111655",
"0.5194795",
"0.5184096",
"0.51481634",
"0.5142245",
"0.51230884",
"0.51... | 0.79430884 | 0 |
Sets the thread_id of this Finding. | def thread_id(self, thread_id):
self._thread_id = thread_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_thread_name(self, thread_name: str):\n self.thread_name = thread_name",
"def set_tid(self, tid):\n self.__tid = tid",
"def thread(self, thread):\n\n self._thread = thread",
"async def set_pin_thread(self, thread: int) -> None:\n await self.AD.threading.set_pin_thread(self.... | [
"0.63807136",
"0.637374",
"0.63235277",
"0.62702554",
"0.61503106",
"0.6105179",
"0.5976203",
"0.57605016",
"0.5711145",
"0.5687795",
"0.56499785",
"0.5642432",
"0.5638906",
"0.562864",
"0.5615846",
"0.5571751",
"0.55335456",
"0.55335456",
"0.55335456",
"0.5528578",
"0.552136... | 0.8253602 | 1 |
Sets the mitigated of this Finding. | def mitigated(self, mitigated):
self._mitigated = mitigated | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mitigated_by(self, mitigated_by):\n\n self._mitigated_by = mitigated_by",
"def set_unmute_finding(finding_path: str) -> None:\n from google.cloud import securitycenter\n\n client = securitycenter.SecurityCenterClient()\n\n request = securitycenter.SetMuteRequest()\n request.name = finding_... | [
"0.60433817",
"0.53549194",
"0.5269847",
"0.5247048",
"0.51150465",
"0.50873536",
"0.50873244",
"0.50644",
"0.5025339",
"0.50055635",
"0.49971715",
"0.49953154",
"0.4965352",
"0.486616",
"0.48573276",
"0.48410597",
"0.4837211",
"0.48298788",
"0.48186752",
"0.48183027",
"0.480... | 0.68774295 | 0 |
Sets the numerical_severity of this Finding. | def numerical_severity(self, numerical_severity):
if numerical_severity is None:
raise ValueError("Invalid value for `numerical_severity`, must not be `None`") # noqa: E501
if numerical_severity is not None and len(numerical_severity) > 4:
raise ValueError("Invalid value for `nu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def severity(self, severity):\n self._severity = severity",
"def severity(self, severity):\n\n self._severity = severity",
"def severity(self, severity):\n if severity is None:\n raise ValueError(\"Invalid value for `severity`, must not be `None`\") # noqa: E501\n\n self... | [
"0.70312357",
"0.69504195",
"0.6056087",
"0.5866975",
"0.58487254",
"0.56784946",
"0.53979814",
"0.53979814",
"0.5347689",
"0.532361",
"0.5317774",
"0.53107584",
"0.52069896",
"0.5194592",
"0.51884854",
"0.5153899",
"0.5153899",
"0.5153899",
"0.5122025",
"0.509542",
"0.504196... | 0.7459507 | 0 |
Sets the last_reviewed of this Finding. | def last_reviewed(self, last_reviewed):
self._last_reviewed = last_reviewed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_reviewed_by(self, last_reviewed_by):\n\n self._last_reviewed_by = last_reviewed_by",
"def last_updated(self, last_updated):\n\n self._last_updated = last_updated",
"def last_updated(self, last_updated):\n\n self._last_updated = last_updated",
"def last_updated(self, last_updated... | [
"0.7755603",
"0.63661575",
"0.63661575",
"0.63661575",
"0.63661575",
"0.6272486",
"0.6263831",
"0.6116794",
"0.60889816",
"0.60505205",
"0.60140896",
"0.59420484",
"0.58359915",
"0.570513",
"0.56955117",
"0.56791735",
"0.56767905",
"0.5624951",
"0.56221116",
"0.5613982",
"0.5... | 0.89560175 | 0 |
Sets the sourcefilepath of this Finding. | def sourcefilepath(self, sourcefilepath):
if sourcefilepath is not None and len(sourcefilepath) < 1:
raise ValueError("Invalid value for `sourcefilepath`, length must be greater than or equal to `1`") # noqa: E501
self._sourcefilepath = sourcefilepath | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setSourcePath(self, sourcePath):\n self.__sourcePath = sourcePath",
"def set_source_file(self, source_file):\n self.set_attribute(\"source_file\", source_file)",
"def set_source_path(self, folder):\n self.source_path = folder",
"def set_source(self, source_name):\n self.source... | [
"0.78381664",
"0.7524008",
"0.74281013",
"0.7021528",
"0.6882114",
"0.67714393",
"0.6753047",
"0.6753047",
"0.6753047",
"0.6753047",
"0.6753047",
"0.6753047",
"0.6753047",
"0.6742166",
"0.65922105",
"0.6507826",
"0.63968766",
"0.6276455",
"0.62755287",
"0.6191163",
"0.6190613... | 0.7598891 | 1 |
Sets the static_finding of this Finding. | def static_finding(self, static_finding):
self._static_finding = static_finding | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dynamic_finding(self, dynamic_finding):\n\n self._dynamic_finding = dynamic_finding",
"def duplicate_finding(self, duplicate_finding):\n\n self._duplicate_finding = duplicate_finding",
"def static_assigned_driver_id(self, static_assigned_driver_id):\n\n self._static_assigned_driver_id ... | [
"0.65178263",
"0.55996287",
"0.5258209",
"0.523934",
"0.51157033",
"0.5071155",
"0.48656785",
"0.48148686",
"0.4775216",
"0.4733953",
"0.4729156",
"0.4716503",
"0.4605012",
"0.45946562",
"0.45564985",
"0.45060068",
"0.4503049",
"0.4503049",
"0.44868708",
"0.44686472",
"0.4453... | 0.8843208 | 0 |
Sets the dynamic_finding of this Finding. | def dynamic_finding(self, dynamic_finding):
self._dynamic_finding = dynamic_finding | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def static_finding(self, static_finding):\n\n self._static_finding = static_finding",
"def is_dynamic(self):\r\n return self.type == \"Dynamic\"",
"def set_dynamic_input(self, obj):\n self._executor.set_dynamic_input(obj)",
"def is_dynamic(self):\n return self.t == \"Dynamic\"",
... | [
"0.6116908",
"0.5537111",
"0.5501389",
"0.5491738",
"0.53399014",
"0.52900743",
"0.5267026",
"0.51532286",
"0.5024485",
"0.49915695",
"0.49892583",
"0.49367055",
"0.49165323",
"0.49104887",
"0.48870617",
"0.4885128",
"0.48765588",
"0.4876136",
"0.48627678",
"0.47566918",
"0.4... | 0.8963111 | 0 |
Sets the jira_creation of this Finding. | def jira_creation(self, jira_creation):
self._jira_creation = jira_creation | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def creation(self, creation):\n\n self._creation = creation",
"def creation_date(self, creation_date):\n\n self._creation_date = creation_date",
"def creation_date(self, creation_date):\n\n self._creation_date = creation_date",
"def creation_name(self, creation_name):\n\n self._cr... | [
"0.6149801",
"0.6030288",
"0.6030288",
"0.58517164",
"0.5837033",
"0.5734765",
"0.5699269",
"0.5699269",
"0.54844713",
"0.5321022",
"0.5303341",
"0.5220061",
"0.5220061",
"0.5220061",
"0.52131796",
"0.5141409",
"0.5141409",
"0.5141409",
"0.511682",
"0.511682",
"0.511682",
"... | 0.8764235 | 0 |
Sets the jira_change of this Finding. | def jira_change(self, jira_change):
self._jira_change = jira_change | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jira_creation(self, jira_creation):\n\n self._jira_creation = jira_creation",
"def change_password(self, change_password):\n if change_password is None:\n raise ValueError(\"Invalid value for `change_password`, must not be `None`\") # noqa: E501\n\n self._change_password = ch... | [
"0.56641996",
"0.54325026",
"0.53372353",
"0.5159689",
"0.48526832",
"0.48197427",
"0.48074037",
"0.48063827",
"0.47671103",
"0.4753339",
"0.4734146",
"0.4713386",
"0.4713386",
"0.4693393",
"0.46926972",
"0.46798125",
"0.4656048",
"0.4656048",
"0.45987073",
"0.45832437",
"0.4... | 0.8861954 | 0 |
Sets the scanner_confidence of this Finding. | def scanner_confidence(self, scanner_confidence):
self._scanner_confidence = scanner_confidence | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def confidence(self, confidence):\n\n self._confidence = confidence",
"def confidence(self, confidence):\n\n self._confidence = confidence",
"def confidence(self, confidence):\n self._confidence = confidence",
"def confidence(self, confidence):\n self._confidence = confidence",
... | [
"0.69514847",
"0.69514847",
"0.6947945",
"0.6947945",
"0.694054",
"0.6170676",
"0.54872805",
"0.54872805",
"0.5474622",
"0.5474622",
"0.54345167",
"0.54066634",
"0.5313893",
"0.5262962",
"0.5262962",
"0.50407827",
"0.5028977",
"0.5003185",
"0.49880823",
"0.4872491",
"0.486848... | 0.8823388 | 0 |
Sets the test of this Finding. | def test(self, test):
self._test = test | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_test(self):\n self.genes_test = self.__genes.copy()\n self.__fitness_test = self.__fitness",
"def test_type(self, test_type):\n self._test_type = test_type",
"def test_set_testing(self):\n old_value = Config.testing\n Config.set_testing(True)\n\n self.assertNot... | [
"0.64539975",
"0.6376605",
"0.6249061",
"0.61838406",
"0.60420793",
"0.60334736",
"0.60056776",
"0.59245753",
"0.58661824",
"0.5804414",
"0.5801666",
"0.5794919",
"0.5789537",
"0.5777487",
"0.57646495",
"0.5755397",
"0.5733128",
"0.5728654",
"0.57055455",
"0.5664977",
"0.5650... | 0.72610223 | 0 |
Sets the duplicate_finding of this Finding. | def duplicate_finding(self, duplicate_finding):
self._duplicate_finding = duplicate_finding | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def duplicate(self, duplicate):\n\n self._duplicate = duplicate",
"def expect_duplicate(self):\n # Reset everything for this record\n self._expect_duplicate = False\n self.__dupcntr = 0\n self.__maxdup = 0\n # Get the probability to generate duplicate for next record\n ... | [
"0.7073005",
"0.6097549",
"0.5721719",
"0.5710329",
"0.57082045",
"0.55761975",
"0.5515979",
"0.54847944",
"0.5427966",
"0.5352307",
"0.5258211",
"0.5257777",
"0.51621395",
"0.5117955",
"0.50949824",
"0.50704545",
"0.5065502",
"0.5063879",
"0.5055366",
"0.5023045",
"0.4982112... | 0.8920292 | 0 |
Sets the review_requested_by of this Finding. | def review_requested_by(self, review_requested_by):
self._review_requested_by = review_requested_by | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def defect_review_requested_by(self, defect_review_requested_by):\n\n self._defect_review_requested_by = defect_review_requested_by",
"def set_review_request_field(self, review_request, field, value):\r\n rid = review_request['id']\r\n\r\n self.debug('Attempting to set field \"%s\" to \"%s\" for rev... | [
"0.84883976",
"0.6535724",
"0.6335159",
"0.6081085",
"0.60185385",
"0.58516747",
"0.5819577",
"0.57748806",
"0.5771885",
"0.5715",
"0.56908035",
"0.5423405",
"0.5418873",
"0.5415813",
"0.54025936",
"0.5390402",
"0.5381158",
"0.53239805",
"0.5254904",
"0.5223348",
"0.5116721",... | 0.92076296 | 0 |
Sets the defect_review_requested_by of this Finding. | def defect_review_requested_by(self, defect_review_requested_by):
self._defect_review_requested_by = defect_review_requested_by | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def review_requested_by(self, review_requested_by):\n\n self._review_requested_by = review_requested_by",
"def set_review_request_field(self, review_request, field, value):\r\n rid = review_request['id']\r\n\r\n self.debug('Attempting to set field \"%s\" to \"%s\" for review request \"%s\"' %\r\n ... | [
"0.85946757",
"0.62267476",
"0.5612693",
"0.5512908",
"0.5471893",
"0.5252553",
"0.52187866",
"0.5193079",
"0.5181131",
"0.5145015",
"0.5111961",
"0.5109017",
"0.50910103",
"0.49924377",
"0.48887545",
"0.48134822",
"0.4811421",
"0.4805345",
"0.47687432",
"0.47640267",
"0.4762... | 0.93379503 | 0 |
Sets the mitigated_by of this Finding. | def mitigated_by(self, mitigated_by):
self._mitigated_by = mitigated_by | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mitigated(self, mitigated):\n\n self._mitigated = mitigated",
"def fixed_by(self, fixed_by):\n\n self._fixed_by = fixed_by",
"def found_by(self, found_by):\n\n self._found_by = found_by",
"def issued_by(self, issued_by):\n\n self._issued_by = issued_by",
"def changed_by(self... | [
"0.68558943",
"0.5654965",
"0.5443997",
"0.50412035",
"0.49280533",
"0.48663288",
"0.4804481",
"0.47543198",
"0.46727324",
"0.45902643",
"0.45902643",
"0.45658347",
"0.4559191",
"0.45233694",
"0.44822916",
"0.4472691",
"0.44713748",
"0.44697174",
"0.4420642",
"0.44196764",
"0... | 0.8268934 | 0 |
Sets the reporter of this Finding. | def reporter(self, reporter):
self._reporter = reporter | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_reporter(self, reporter):\n self.reporters.add(reporter)",
"def report(self, report):\n\n self._report = report",
"def __init__(self, reporter_config):\n self.string_save = None\n self.reporter_config = reporter_config",
"def remove_reporter(self, reporter):\n self.... | [
"0.62569094",
"0.56327236",
"0.56069857",
"0.5412691",
"0.53603697",
"0.5333382",
"0.5184835",
"0.50688535",
"0.50554264",
"0.5047336",
"0.49403504",
"0.48789456",
"0.48789456",
"0.48684776",
"0.48659042",
"0.48518023",
"0.4850572",
"0.4850572",
"0.4850572",
"0.4849047",
"0.4... | 0.8073061 | 0 |
Sets the last_reviewed_by of this Finding. | def last_reviewed_by(self, last_reviewed_by):
self._last_reviewed_by = last_reviewed_by | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_reviewed(self, last_reviewed):\n\n self._last_reviewed = last_reviewed",
"def last_modified_by(self, last_modified_by):\n\n self._last_modified_by = last_modified_by",
"def last_modified_by(self, last_modified_by):\n\n self._last_modified_by = last_modified_by",
"def last_modifi... | [
"0.79556274",
"0.7197687",
"0.7197687",
"0.66010195",
"0.6503434",
"0.6279238",
"0.6124746",
"0.58754367",
"0.5853754",
"0.5853754",
"0.5853754",
"0.5812513",
"0.5798303",
"0.5798303",
"0.5798303",
"0.5798303",
"0.57652587",
"0.56792426",
"0.559972",
"0.559972",
"0.559972",
... | 0.8904637 | 0 |
Sets the sonarqube_issue of this Finding. | def sonarqube_issue(self, sonarqube_issue):
self._sonarqube_issue = sonarqube_issue | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def problem(self, problem):\n\n self._problem = problem",
"def _apply_issue(self, issue):\n data = {\n \"title\" : issue._title,\n \"body\" : issue._desc,\n \"labels\" : issue._labels\n }\n state = issue._state\n resp = self._post(\n ... | [
"0.5561962",
"0.5458624",
"0.540904",
"0.5404084",
"0.53185093",
"0.52937216",
"0.5264972",
"0.5052823",
"0.5012604",
"0.49992844",
"0.49978098",
"0.49964926",
"0.49623737",
"0.48758945",
"0.47914398",
"0.47730893",
"0.4768244",
"0.4746505",
"0.4720768",
"0.47122902",
"0.4712... | 0.8616002 | 0 |
Sets the endpoints of this Finding. | def endpoints(self, endpoints):
self._endpoints = endpoints | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addEndpoints(self, endpoints):\n self.endpoints.extend(endpoints)\n self._connectOrBind(endpoints)",
"def add_endpoint_set(self, other_endpoint_set):\n for fep in other_endpoint_set.get_flask_endpoints():\n other_endpoint = other_endpoint_set.get_endpoint(fep)\n sel... | [
"0.6077575",
"0.60154194",
"0.599041",
"0.59231985",
"0.5911764",
"0.58548164",
"0.5830439",
"0.5782214",
"0.57668996",
"0.5713096",
"0.565779",
"0.56107736",
"0.55429935",
"0.55350053",
"0.5523469",
"0.548419",
"0.54149413",
"0.53945225",
"0.5336241",
"0.5336241",
"0.5336067... | 0.774177 | 0 |
Sets the duplicate_list of this Finding. | def duplicate_list(self, duplicate_list):
self._duplicate_list = duplicate_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def duplicate_finding(self, duplicate_finding):\n\n self._duplicate_finding = duplicate_finding",
"def duplicate(self, duplicate):\n\n self._duplicate = duplicate",
"def listDuplicate(self,permutations=True):\n ind,ok = self.testDuplicate(permutations)\n return ind[~ok]",
"def _cl... | [
"0.7094946",
"0.6712174",
"0.59677184",
"0.589752",
"0.57951343",
"0.57349366",
"0.5673172",
"0.56601197",
"0.5622501",
"0.5621235",
"0.56055266",
"0.5538224",
"0.5526309",
"0.5509451",
"0.5485976",
"0.5473682",
"0.54637426",
"0.5458277",
"0.5421908",
"0.54028994",
"0.539673"... | 0.80563396 | 0 |
Sets the reviewers of this Finding. | def reviewers(self, reviewers):
self._reviewers = reviewers | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reviews(self, reviews: object):\n\n self._reviews = reviews",
"def review(self, review):\n self._review = review",
"def _upd_reviewers(self, new_reviewers):\n if self.review[\"reviewers\"]:\n if new_reviewers and new_reviewers[0] not in self.review[\"reviewers\"]:\n self.review... | [
"0.7149326",
"0.66016865",
"0.653844",
"0.6431182",
"0.6363863",
"0.6168382",
"0.6114938",
"0.60685486",
"0.60613334",
"0.60613334",
"0.5854484",
"0.5761171",
"0.576108",
"0.57578826",
"0.56420034",
"0.562614",
"0.5604291",
"0.5501873",
"0.54779947",
"0.54476804",
"0.53713536... | 0.831613 | 0 |
Sets the notes of this Finding. | def notes(self, notes):
self._notes = notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notes(self, notes: str):\n self._notes = notes",
"def setNotes(self, *args):\n return _libsbml.SBase_setNotes(self, *args)",
"def notes(self, notes):\n if notes is None:\n raise ValueError(\"Invalid value for `notes`, must not be `None`\") # noqa: E501\n\n self._note... | [
"0.80854905",
"0.80669326",
"0.75870216",
"0.7008429",
"0.6639618",
"0.6604006",
"0.63941693",
"0.63941693",
"0.6379022",
"0.6339135",
"0.6311893",
"0.6286612",
"0.62642753",
"0.62642753",
"0.6254263",
"0.62482953",
"0.6235223",
"0.6212902",
"0.6205799",
"0.6172656",
"0.61156... | 0.81995916 | 0 |
Sets the found_by of this Finding. | def found_by(self, found_by):
self._found_by = found_by | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fixed_by(self, fixed_by):\n\n self._fixed_by = fixed_by",
"def updated_by(self, updated_by):\n\n self._updated_by = updated_by",
"def changed_by(self, changed_by):\n\n self._changed_by = changed_by",
"def issued_by(self, issued_by):\n\n self._issued_by = issued_by",
"def cre... | [
"0.56301457",
"0.56203693",
"0.55938286",
"0.55466205",
"0.52315575",
"0.52315575",
"0.52315575",
"0.52315575",
"0.52315575",
"0.52315575",
"0.5131663",
"0.5129135",
"0.5128363",
"0.5096805",
"0.5095075",
"0.5071412",
"0.49754062",
"0.49754062",
"0.49754062",
"0.49494028",
"0... | 0.85180235 | 0 |
Registers the App to receive a callback every time an App logs a message. | async def listen_log(self, callback: Callable, level="INFO", **kwargs) -> str:
namespace = kwargs.pop("namespace", "admin")
return await self.AD.logging.add_log_callback(namespace, self.name, callback, level, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_hook(self, callback: Callable[[BaseEvent], None]) -> None:\n if logger.isEnabledFor(logging.DEBUG):\n logger.debug(f\"Registering callback: {str(callback)}\")\n\n self._registered_hooks.add(callback)",
"def add_callback(self, on_msg_cb):\n self.callbacks.add(on_msg_cb... | [
"0.63341475",
"0.6273902",
"0.59880596",
"0.5976008",
"0.59268904",
"0.5904015",
"0.5892836",
"0.58703643",
"0.585239",
"0.5811212",
"0.57969373",
"0.5752889",
"0.57508385",
"0.57506573",
"0.5738102",
"0.57147175",
"0.56959236",
"0.567807",
"0.5652475",
"0.5646454",
"0.563767... | 0.64805794 | 0 |
Cancels the log callback for the App. | async def cancel_listen_log(self, handle: str) -> None:
self.logger.debug("Canceling listen_log for %s", self.name)
await self.AD.logging.cancel_log_callback(self.name, handle) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancel(self):\n self.on_cancel()",
"def cancel_callback(self):\n pass",
"def on_cancel(self) -> None:\n pass",
"def on_cancel(self) -> None:\n pass",
"def kill_log(self): \n # set the thread termination event\n self.kill_event.set()",
"def on_back(self):... | [
"0.66582906",
"0.64458466",
"0.6420648",
"0.6420648",
"0.62485707",
"0.6229325",
"0.62121",
"0.6180224",
"0.6172824",
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"0.6130699",
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"0.61006635",
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"0.6049521",
"0.60417944",
"0.6011838",
"0.6011838",
"0.6011838",
"0.6011838",
"0.5990302",... | 0.7434622 | 0 |
Sets the log level to send to the `error` logfile of the system. | def set_error_level(self, level: str) -> None:
self.err.setLevel(self._logging.log_levels[level]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_log_level_error(loggers=[\"vaex\"]):\n set_log_level(loggers, logging.ERROR)",
"def set_log_level_to_error():\n logging.setLevel(default_logging.ERROR)\n\n if os.path.exists(\"gen3tests.logs\"):\n os.remove(\"gen3tests.logs\")\n logfile_handler = default_logging.FileHandler(\"gen3tests... | [
"0.7884033",
"0.78725624",
"0.78603655",
"0.74651957",
"0.7167047",
"0.7157125",
"0.70500237",
"0.6971392",
"0.68343633",
"0.67313653",
"0.671256",
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"0.663434",
"0.6600697",
"0.6596445",
"0.65570927",
"0.65306485",
"0.6490433",
"0.64620... | 0.83446234 | 0 |
Sets an App to be pinned or unpinned. | async def set_app_pin(self, pin: bool) -> None:
await self.AD.threading.set_app_pin(self.name, pin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pinned(self, pinned):\n if pinned is None:\n raise ValueError(\"Invalid value for `pinned`, must not be `None`\") # noqa: E501\n\n self._pinned = pinned",
"def set_application(self, app):\n \n self.app = app",
"def set_all_pins(self, ap):\n self.allpins = ap",... | [
"0.58168226",
"0.5742242",
"0.56856865",
"0.55636704",
"0.5159775",
"0.5078989",
"0.5036599",
"0.5036471",
"0.50056607",
"0.49910948",
"0.49754983",
"0.4926896",
"0.49026492",
"0.48561022",
"0.4800552",
"0.4794215",
"0.47908068",
"0.47760552",
"0.47625145",
"0.47483006",
"0.4... | 0.68459177 | 0 |
Finds out if the current App is currently pinned or not. | async def get_app_pin(self) -> bool:
return await self.AD.threading.get_app_pin(self.name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pinned(self):\n sql = \"SELECT is_pinned FROM hive_posts WHERE id = :id\"\n return bool(DB.query_one(sql, id=self.post_id))",
"def has_pinned_content(self):\n if \"query\" in self.query:\n q = self.query[\"query\"]\n else:\n q = self.query\n if \"pinn... | [
"0.6886633",
"0.6475159",
"0.604358",
"0.55474627",
"0.5492122",
"0.5449889",
"0.5443055",
"0.53712463",
"0.53160864",
"0.52480054",
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"0.5208957",
"0.5203377",
"0.51976275",
"0.519241",
"0.51919484",
"0.51706934",
"0.5160064"... | 0.74043727 | 0 |
Sets the thread that the App will be pinned to. | async def set_pin_thread(self, thread: int) -> None:
await self.AD.threading.set_pin_thread(self.name, thread) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def thread(self, thread):\n\n self._thread = thread",
"def pin_thread(request, course_id, thread_id):\r\n user = cc.User.from_django_user(request.user)\r\n thread = cc.Thread.find(thread_id)\r\n thread.pin(user, thread_id)\r\n return JsonResponse(utils.safe_content(thread.to_dict()))",
"asyn... | [
"0.60138106",
"0.60135186",
"0.5912089",
"0.58179",
"0.58179",
"0.58179",
"0.5484811",
"0.54823303",
"0.5443001",
"0.5283483",
"0.5252374",
"0.52245486",
"0.5202273",
"0.51888347",
"0.51873344",
"0.51796937",
"0.51657015",
"0.5145812",
"0.51124924",
"0.5089819",
"0.508948",
... | 0.8124858 | 0 |
Finds out which thread the App is pinned to. | async def get_pin_thread(self) -> int:
return await self.AD.threading.get_pin_thread(self.name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_thread(self):\n return self.threads[self.thread_id]",
"def getSpimThreadID(self):\n\n\t\ttry:\n\t\t\tapp = api.getDesktopObject()\n\n\t\t\t# Eliminate items with a \"None\" name - these cause the list comprehension to fail.\n\t\t\tapp = filter(lambda x: x.name != None, app.children)\n\t\t\tapp = f... | [
"0.6722103",
"0.65724087",
"0.6558115",
"0.61347806",
"0.6069823",
"0.59864855",
"0.58905303",
"0.58905303",
"0.5868452",
"0.58665264",
"0.58599204",
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"0.57680374",
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"0.5752738",
"0.5584569",
"0.55697256",
"0.55651814",
"0.5538622",
"0.5511172",
"0.55... | 0.79157025 | 0 |
Sets a new namespace for the App to use from that point forward. | def set_namespace(self, namespace: str) -> None:
self._namespace = namespace | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_target_namespace(self, namespace):\n # do shit\n self.target_namespace = namespace.strip(\":\")",
"def namespace(self, namespace: str):\n\n self._namespace = namespace",
"def namespace(self, namespace):\n\n self._namespace = namespace",
"def namespace(self, namespace):\n\n... | [
"0.67648345",
"0.644635",
"0.63194656",
"0.63194656",
"0.6101525",
"0.6086145",
"0.59994686",
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"0.57523435",
"0.5741521",
"0.57008475",
"0.56457096",
"0.5640391",
"0.5636354",
"0.563282... | 0.68038756 | 0 |
Returns the App's namespace. | def get_namespace(self) -> str:
return self._namespace | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def app_id_namespace(self):\n return self.__key.app_id_namespace()",
"def namespace(self) -> str:\n return pulumi.get(self, \"namespace\")",
"def namespace (self) :\n\n return self.__namespace__",
"def namespace(self) -> Optional[str]:\n return pulumi.get(self, \"namespace\")",
"def... | [
"0.7818359",
"0.7581196",
"0.7523769",
"0.744232",
"0.744232",
"0.744232",
"0.73690593",
"0.73176533",
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"0.71245193",
"0.71245193",
"0.71245193",
"0.7035851",
"0.702897",
"0.702897",
"0.702897",
... | 0.7737416 | 1 |
Checks the existence of a namespace in AppDaemon. | async def namespace_exists(self, namespace: str) -> bool:
return await self.AD.state.namespace_exists(namespace) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hasNS(self, *args):\n return _libsbml.XMLNamespaces_hasNS(self, *args)",
"def hasNamespaceNS(self, *args):\n return _libsbml.XMLToken_hasNamespaceNS(self, *args)",
"def check(self):\n BadNamespaces = list()\n\n for namespace in pm.listNamespaces():\n BadNamespaces.app... | [
"0.6678822",
"0.6416857",
"0.6351905",
"0.62983334",
"0.60599357",
"0.60576105",
"0.5898541",
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"0.58569133",
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"0.5697997",
"0.5660734",
"0.5619079",
"0.55770165",
"0.5546747",
"0.5542397",
"0.549885... | 0.79155934 | 0 |
Used to add a userdefined namespaces from apps, which has a database file associated with it. This way, when AD restarts these entities will be reloaded into AD with its previous states within the namespace. This can be used as a basic form of nonvolatile storage of entity data. Depending on the configuration of the na... | async def add_namespace(self, namespace: str, **kwargs) -> Union[str, None]:
if namespace == self.get_namespace(): # if it belongs to this app's namespace
raise ValueError("Cannot add namespace with the same name as operating namespace")
writeback = kwargs.get("writeback", "safe")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_namespace():\n global namespace\n with open('/run/secrets/kubernetes.io/serviceaccount/namespace') as f:\n namespace = f.read()",
"async def save_namespace(self, **kwargs) -> None:\n namespace = self._get_namespace(**kwargs)\n await self.AD.state.save_namespace(namespace)",
... | [
"0.613976",
"0.5538623",
"0.5495671",
"0.548917",
"0.5464456",
"0.5422924",
"0.5369365",
"0.5311823",
"0.52479595",
"0.5178134",
"0.5107621",
"0.5090314",
"0.50848407",
"0.50365907",
"0.5014212",
"0.49451938",
"0.49414486",
"0.49187905",
"0.49094176",
"0.49069646",
"0.4889842... | 0.5946513 | 1 |
Returns a list of available namespaces. | async def list_namespaces(self) -> list:
return await self.AD.state.list_namespaces() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_namespaces():\n cmds.namespace(setNamespace=':')\n return cmds.namespaceInfo(listOnlyNamespaces=True, recurse=True)",
"def get_namespaces():\r\n\r\n print 'Getting namespaces'\r\n tree = etree.parse('http://lesswrong.wikia.com/wiki/Special:AllPages', parser)\r\n options = tree.xpath('/... | [
"0.7923025",
"0.7840596",
"0.77799517",
"0.7765428",
"0.77018833",
"0.7682345",
"0.7644465",
"0.76041424",
"0.7557412",
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"0.7351902",
"0.71664965",
"0.7122117",
"0.70671463",
"0.69921726",
"0.6988... | 0.825262 | 0 |
Saves entities created in userdefined namespaces into a file. This way, when AD restarts these entities will be reloaded into AD with its previous states within the namespace. This can be used as a basic form of nonvolatile storage of entity data. Depending on the configuration of the namespace, this function can be se... | async def save_namespace(self, **kwargs) -> None:
namespace = self._get_namespace(**kwargs)
await self.AD.state.save_namespace(namespace) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize_namespaces(namespaces, connection, path, directory):\n from pybel import from_lines\n from .definition_utils import export_namespaces\n\n graph = from_lines(path, manager=connection)\n export_namespaces(namespaces, graph, directory)",
"def save(self):\r\n # os.mkdirs(DATADIR, exi... | [
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"0.5492503",
"0.54217833",
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"0.5374697",
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"0.5200681",
"0.51950413",
"0.5164241",
"0.5158492",
"0.5157759",
"0.51365006",
"0.5127894"... | 0.62214833 | 0 |
Checks the existence of an entity in AD. When working with multiple AD namespaces, it is possible to specify the namespace, so that it checks within the right namespace in in the event the app is working in a different namespace. Also when using this function, it is also possible to check if an AppDaemon entity exists. | async def entity_exists(self, entity_id: str, **kwargs: Optional[Any]) -> bool:
namespace = self._get_namespace(**kwargs)
return await self.get_entity_api(namespace, entity_id).exists() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def namespace_exists(self, namespace: str) -> bool:\n return await self.AD.state.namespace_exists(namespace)",
"def entity_exists(entity):\n return entity.get_by_id(entity.key.id())",
"def exists_entry(self, dn, filterstr='(objectclass=*)'):\n try:\n r = self.search(dn, scope=ld... | [
"0.675481",
"0.6085755",
"0.54766536",
"0.5419351",
"0.5417385",
"0.5385195",
"0.535328",
"0.53413415",
"0.5106101",
"0.5094099",
"0.50853413",
"0.50526357",
"0.5052361",
"0.50144935",
"0.50019693",
"0.49810308",
"0.49527466",
"0.493786",
"0.49239162",
"0.49062037",
"0.489847... | 0.65881133 | 1 |
Splits an entity into parts. This utility function will take a fully qualified entity id of the form ``light.hall_light`` and split it into 2 values, the device and the entity, e.g. light and hall_light. | async def split_entity(self, entity_id: str, **kwargs) -> list:
await self._check_entity(self._get_namespace(**kwargs), entity_id)
return entity_id.split(".") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_entity_ids(hass, service_call, expand_group=True):\n if not (service_call.data and ATTR_ENTITY_ID in service_call.data):\n return []\n\n group = hass.components.group\n\n # Entity ID attr can be a list or a string\n service_ent_id = service_call.data[ATTR_ENTITY_ID]\n\n if expand_... | [
"0.5421866",
"0.5310347",
"0.52229977",
"0.5156989",
"0.50385386",
"0.50330603",
"0.5018565",
"0.49255732",
"0.49005437",
"0.4857359",
"0.4844326",
"0.47844097",
"0.47261876",
"0.46915856",
"0.46879286",
"0.46319097",
"0.4617935",
"0.4617935",
"0.46139228",
"0.460867",
"0.460... | 0.6903694 | 0 |
Deactivates or activates the production mode in AppDaemon. When called without declaring passing any arguments, mode defaults to ``True``. | async def set_production_mode(self, mode=True) -> bool:
if not isinstance(mode, bool):
self.logger.warning("%s not a valid parameter for Production Mode", mode)
return None
await self.AD.utility.set_production_mode(mode)
return mode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_production_mode(self):\n return getattr(self.env, 'mode', None) == 'production'",
"def maintenance_mode():\n pass",
"def production_mode(self):\n self.monitor_leaks = False",
"def debug(mode=True):\r\n global DEBUG\r\n DEBUG = bool(mode)",
"def production():\n env.run = run... | [
"0.5558557",
"0.5394914",
"0.53670967",
"0.52118254",
"0.51826936",
"0.5112336",
"0.51068693",
"0.51040536",
"0.5056953",
"0.50327426",
"0.50306594",
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"0.4946107",
"0.49417943",
"0.48948374",
"0.4836972",
"0.48098108",
"0.4796393",
"0.475... | 0.6782759 | 0 |
Starts an App which can either be running or not. This Api call cannot start an app which has already been disabled in the App Config. It essentially only runs the initialize() function in the app, and changes to attributes like class name or app config is not taken into account. | def start_app(self, app: str, **kwargs) -> None:
kwargs["app"] = app
kwargs["namespace"] = "admin"
kwargs["__name"] = self.name
self.call_service("app/start", **kwargs)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_and_init_app_instance(self, instance_id=None, is_system_startup=False):\n log.info(\"Initializing app instance . Instance id = %s\" % instance_id)\n\n for app_config in self.app_instances_configs:\n if (instance_id is None or app_config[\"id\"] == instance_id) and (\n ... | [
"0.7005094",
"0.6918287",
"0.6608379",
"0.6585847",
"0.6438439",
"0.64325726",
"0.6352084",
"0.6253124",
"0.6201502",
"0.61889666",
"0.6105854",
"0.61037916",
"0.60995805",
"0.60945755",
"0.6081408",
"0.60680276",
"0.60400134",
"0.6017223",
"0.60140574",
"0.60134786",
"0.6006... | 0.69768655 | 1 |
Stops an App which is running. | def stop_app(self, app: str, **kwargs) -> None:
kwargs["app"] = app
kwargs["namespace"] = "admin"
kwargs["__name"] = self.name
self.call_service("app/stop", **kwargs)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmd_stop(self, app_name=None):\n rc = self.socket_command_with_project('stop', app_name)\n return rc",
"def stop_app(package):\n G.DEVICE.stop_app(package)",
"def stop_app():\n try:\n get_gui_controller(create_if_missing=False).stop()\n except threadprop.NoControllerThreadErro... | [
"0.7469202",
"0.74690974",
"0.731208",
"0.7285554",
"0.7120908",
"0.7112286",
"0.7082136",
"0.70231986",
"0.67516595",
"0.674922",
"0.67296076",
"0.66493344",
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"0.6510905",
"0.6467532",
"0.6428413",
"0.63855183",
"0.6346779",
"0.6276602",
"0.6263856",
"0.6251857"... | 0.7716424 | 0 |
Reloads the apps, and loads up those that have changes made to their .yaml or .py files. This utility function can be used if AppDaemon is running in production mode, and it is needed to reload apps that changes have been made to. | def reload_apps(self, **kwargs) -> None:
kwargs["namespace"] = "admin"
kwargs["__name"] = self.name
self.call_service("app/reload", **kwargs)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reload_app(self, app_full_name):\n log.info(\"Reloading app module %s\" % app_full_name)\n developer, app_name, version = split_app_full_name(app_full_name)\n mod_ref = sys.modules[\"apps.lib.%s.%s\" % (app_full_name, app_name)]\n reload_success = False\n try:\n re... | [
"0.6995275",
"0.6879308",
"0.68698454",
"0.66720265",
"0.64311534",
"0.6430104",
"0.6358366",
"0.6339026",
"0.6266213",
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"0.5967183",
"0.59291697",
"0.5918248",
"0.5903974",
"0.5892981",
"0.58757",
"0.5863623",... | 0.7566606 | 0 |
Gets the intent's action from the Google Home response. | def get_dialogflow_intent(self, data: dict) -> Union[Any, None]:
if "result" in data and "action" in data["result"]:
self.dialogflow_v = 1
return data["result"]["action"]
elif "queryResult" in data and "action" in data["queryResult"]:
self.dialogflow_v = 2
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_intent_action(self, intent_keyword):\n pass",
"def action(self):\n return self._get_field(\"action\")",
"def on_intent(request, session):\n\n intent_name = request['intent']['name']\n\n # process the intents\n if intent_name == \"comenzar\":\n return get_fact_response()\n ... | [
"0.6907287",
"0.61972785",
"0.6172728",
"0.60447145",
"0.59265095",
"0.59227943",
"0.59227943",
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"0.571086",
"0.571086",
"0.571086",
"0.5689414",
"0.5669524",
... | 0.65663946 | 1 |
Formats a response to be returned to Google Home, including speech. | def format_dialogflow_response(self, speech=None) -> Union[Any, None]:
if self.dialogflow_v == 1:
speech = {"speech": speech, "source": "Appdaemon", "displayText": speech}
elif self.dialogflow_v == 2:
speech = {"fulfillmentText": speech, "source": "Appdaemon"}
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _format_response(self, response):\n texts = []\n for result in response.results: \n texts.append(result.alternatives[0].transcript)\n return texts",
"def response(speech_message):\n return {\n 'version': '1.0',\n 'response': speech_message\n }",
"def resp... | [
"0.6798622",
"0.6716875",
"0.6716875",
"0.6521428",
"0.6515013",
"0.6515013",
"0.649683",
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"0.616386",
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"0.5703039",
"0.5671744",
"0.5637685",
"0.56352043",
"0.56342554",
"0.5633292",
... | 0.6957498 | 0 |
Formats a response to be returned to Alex including speech and a card. | def format_alexa_response(speech=None, card=None, title=None) -> dict:
response = {"shouldEndSession": True}
if speech is not None:
response["outputSpeech"] = {"type": "PlainText", "text": speech}
if card is not None:
response["card"] = {"type": "Simple", "title": title... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def response(speech_response, should_end_session, \\\n reprompt_text=HELP_MESSAGE, attributes={}, card_text=\"\"):\n \n if not card_text:\n card_text = build_card_content(attributes)\n \n response = {\n \"version\": \"1.0\",\n \"sessionAttributes\": attributes,\n ... | [
"0.68182623",
"0.66497594",
"0.6642409",
"0.6570228",
"0.6487334",
"0.6384228",
"0.6357908",
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"0.6309073",
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"0.62482196",
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"0.58573526",
"0.58474827",
"0.5822615",
"0.5822615",
"0.5822615",
"0.5822449... | 0.7844901 | 0 |
Gets the Intent's name from the Alexa response. | def get_alexa_intent(data: dict) -> Union[str, None]:
if "request" in data and "intent" in data["request"] and "name" in data["request"]["intent"]:
return data["request"]["intent"]["name"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_response(self, response):\n\t\tself.context = response['context']\n\t\ttext = response['output']['text']\n\t\tintents = response['intents'] #is a list, should filter\n\t\tif len(intents) > 0:\n\t\t\tintent = intents[0]['intent'] #get the intent of the message\n\t\telse:\n\t\t\tintent = \"\"\n\t\t\t\n\t\t... | [
"0.65670735",
"0.6469158",
"0.62201214",
"0.61625844",
"0.6052376",
"0.5908137",
"0.5899395",
"0.58798957",
"0.58771354",
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"0.5622344",
"0.5607147",
"0.5604467",
"0.55703795",
"0.55530256",
"0.55324084",
"0.552... | 0.7110038 | 0 |
Cancels a ``listen_state()`` callback. This will mean that the App will no longer be notified for the specific state change that has been cancelled. Other state changes will continue to be monitored. | async def cancel_listen_state(self, handle: str) -> bool:
self.logger.debug("Canceling listen_state for %s", self.name)
return await self.AD.state.cancel_state_callback(handle, self.name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancel_callback(self):\n pass",
"def cancel(self):\n self.stop()\n self.make_callback('canceled')",
"def on_cancel(self):\n self.state = CANCELED\n self._reject()",
"async def cancel_handler(message: types.Message, state, raw_state=None):\n # Cancel state and inform ... | [
"0.6543459",
"0.6523783",
"0.6393634",
"0.6368258",
"0.62629086",
"0.61568445",
"0.61565655",
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"0.5692046",
"0.5643817",
"0.5643817",
"0.5628906",
"0.5598516... | 0.75374764 | 0 |
Gets information on state a callback from its handle. | async def info_listen_state(self, handle: str) -> dict:
self.logger.debug("Calling info_listen_state for %s", self.name)
return await self.AD.state.info_state_callback(handle, self.name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getstate__(self) -> dict:\n return self.__handle__.get_state()",
"def _get_state(self):",
"def _get_state(self):\n return self.__state",
"def _get_state(self):\n return self.__state",
"def _get_state(self):\n return self.__state",
"def _get_state(self):\n return self.__state",
... | [
"0.7011817",
"0.66188353",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
"0.6540452",
... | 0.7534334 | 0 |
Registers a service that can be called from other apps, the REST API and the Event Stream Using this function, an App can register a function to be available in the service registry. This will automatically make it available to other apps using the `call_service()` API call, as well as publish it as a service in the RE... | def register_service(self, service: str, cb: Callable, **kwargs: Optional[Any]) -> None:
self._check_service(service)
d, s = service.split("/")
self.logger.debug("register_service: %s/%s, %s", d, s, kwargs)
namespace = self._get_namespace(**kwargs)
if "namespace" in kwargs:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_service(service, iface, name):",
"def RegisterService():\n hooks.RegisterHook(SERVICE_NAME, 'file-exists', hook_class=HookForExists)\n hooks.RegisterHook(SERVICE_NAME, 'file-write',\n hook_class=HookForWriteAndTouch)\n hooks.RegisterHook(SERVICE_NAME, 'file-touch',\n ... | [
"0.6868112",
"0.68612844",
"0.6832488",
"0.6699347",
"0.6653247",
"0.64174247",
"0.6350392",
"0.6272634",
"0.6219395",
"0.6145342",
"0.6135437",
"0.6096262",
"0.60629535",
"0.60539436",
"0.6040758",
"0.59498125",
"0.59318525",
"0.58569527",
"0.5771658",
"0.57683605",
"0.57614... | 0.6956896 | 0 |
Calls a Service within AppDaemon. This function can call any service and provide any required parameters. By default, there are standard services that can be called within AD. Other services that can be called, are dependent on the plugin used, or those registered by individual apps using the `register_service` api. In... | async def call_service(self, service: str, **kwargs: Optional[Any]) -> Any:
self._check_service(service)
d, s = service.split("/")
self.logger.debug("call_service: %s/%s, %s", d, s, kwargs)
namespace = self._get_namespace(**kwargs)
if "namespace" in kwargs:
del kwarg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def call_service(self, domain: str, service: str, service_data: dict = None):\n if not self.connected:\n raise NotConnected(\"Please call connect first.\")\n msg = {\"type\": \"call_service\", \"domain\": domain, \"service\": service}\n if service_data:\n msg[\"serv... | [
"0.7188079",
"0.6660593",
"0.6483296",
"0.5956217",
"0.5917856",
"0.5823332",
"0.5821186",
"0.58166564",
"0.5762257",
"0.57456964",
"0.57009673",
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"0.5514843",
"0.5457582",
"0.54486424",
"0.53693956",
"0.53167707",
"0.53167... | 0.757336 | 0 |
Gets a `datetime` object for the specified UTC. Home Assistant provides timestamps of several different sorts that may be used to gain additional insight into state changes. These timestamps are in UTC and are coded as `ISO 8601` combined date and time strings. This function will accept one of these strings and convert... | def convert_utc(utc) -> dt.datetime:
return iso8601.parse_date(utc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_utc_string(self, utc_string: str) -> dt.datetime:\n return dt.datetime(*map(int, re.split(r\"[^\\d]\", utc_string)[:-1])).timestamp() + self.get_tz_offset() * 60",
"def dt(*args, **kwargs):\n \n if 'tz' in kwargs:\n tzinfo = kwargs.pop('tz')\n else:\n tzinfo = kwargs.pop(... | [
"0.68134195",
"0.6664888",
"0.66034484",
"0.6412319",
"0.6279774",
"0.62740326",
"0.6213979",
"0.61867625",
"0.6155598",
"0.6061952",
"0.60599995",
"0.60480607",
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"0.5976109",
"0.59397435",
"0.5923152",
"0.5887481",
"0.58699644",
"0.5865... | 0.6755258 | 1 |
Determines if the sun is currently up. | async def sun_up(self) -> bool:
return await self.AD.sched.sun_up() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def sun_down(self) -> bool:\n return await self.AD.sched.sun_down()",
"def isUp ( self ) :\n return not self.isDown()",
"def is_up(self):\n \n return self.is_level('up')",
"def sun_is_down(check_time, observatory):\n sun = get_sun(check_time).transform_to(AltAz(obst... | [
"0.71763796",
"0.70919764",
"0.6911622",
"0.6804483",
"0.64416325",
"0.6440125",
"0.6200408",
"0.6200408",
"0.6149095",
"0.61095977",
"0.6108394",
"0.6080327",
"0.60548663",
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"0.6003695",
"0.59825486",
"0.5969954",
"0.59674424",
"0.5955185",
"0.5944579",
"0.59422... | 0.82950145 | 0 |
Determines if the sun is currently down. | async def sun_down(self) -> bool:
return await self.AD.sched.sun_down() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def sun_up(self) -> bool:\n return await self.AD.sched.sun_up()",
"def sun_is_down(check_time, observatory):\n sun = get_sun(check_time).transform_to(AltAz(obstime=check_time, location=observatory))\n return sun.alt.value < -14",
"def isDown ( self ) :\n return self._ilhcbmagnet.i... | [
"0.72871107",
"0.71315265",
"0.7014768",
"0.67834306",
"0.6729245",
"0.66027176",
"0.6356098",
"0.6347879",
"0.6346034",
"0.6280802",
"0.617958",
"0.6176804",
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"0.6095312",
"0.6049554",
"0.59639126",
"0.5839895",
"0.5727842",
"0.5719048",
"0.57141346",... | 0.8151937 | 0 |
Runs the callback at the same time every hour. | async def run_hourly(self, callback, start, **kwargs):
now = await self.get_now()
if start is None:
event = now + dt.timedelta(hours=1)
else:
event = now
event = event.replace(minute=start.minute, second=start.second)
if event < now:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def every_hour(self, time, function, args=None, kwargs=None, name=None):\n if args is None:\n args = list()\n if kwargs is None:\n kwargs = dict()\n if name is None:\n name = function.__name__+(f'_{len(self.config)+1}' if function.__name__ in self.config else '... | [
"0.73441684",
"0.64827365",
"0.64259183",
"0.62893593",
"0.62426174",
"0.62047297",
"0.6178456",
"0.61633664",
"0.615241",
"0.6020063",
"0.59799314",
"0.58927983",
"0.5867584",
"0.577256",
"0.5770753",
"0.57435054",
"0.5740845",
"0.57096153",
"0.56963587",
"0.56589067",
"0.56... | 0.735296 | 0 |
Runs a callback every day at or around sunset. | async def run_at_sunset(self, callback: Callable, **kwargs) -> str:
name = self.name
self.logger.debug("Registering run_at_sunset with kwargs = %s for %s", kwargs, name)
handle = await self._schedule_sun(name, "next_setting", callback, **kwargs)
return handle | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def run_at_sunrise(self, callback: Callable, **kwargs) -> str:\n name = self.name\n self.logger.debug(\"Registering run_at_sunrise with kwargs = %s for %s\", kwargs, name)\n handle = await self._schedule_sun(name, \"next_rising\", callback, **kwargs)\n return handle",
"def runs_... | [
"0.5926864",
"0.553329",
"0.5518025",
"0.5481334",
"0.54740477",
"0.5453187",
"0.5444437",
"0.5378735",
"0.5362679",
"0.53471375",
"0.5345242",
"0.53364813",
"0.533033",
"0.53231233",
"0.52388597",
"0.52199906",
"0.5171505",
"0.5165467",
"0.5154495",
"0.50502825",
"0.5049983"... | 0.56279874 | 1 |
Runs a callback every day at or around sunrise. | async def run_at_sunrise(self, callback: Callable, **kwargs) -> str:
name = self.name
self.logger.debug("Registering run_at_sunrise with kwargs = %s for %s", kwargs, name)
handle = await self._schedule_sun(name, "next_rising", callback, **kwargs)
return handle | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_at_sunrise(self, callback, *args, auto_constraints=False, **kwargs):\n if not auto_constraints:\n return super().run_at_sunrise(callback, **kwargs)\n\n return self._attach_constraints(super().run_at_sunrise, callback, **kwargs)",
"async def run_daily(self, callback: Callable, sta... | [
"0.6983089",
"0.69290894",
"0.63026947",
"0.6112787",
"0.6083349",
"0.59162414",
"0.5849472",
"0.5835096",
"0.5784527",
"0.57355666",
"0.57296747",
"0.57231414",
"0.5714134",
"0.56055045",
"0.55700827",
"0.5559175",
"0.5406871",
"0.53821194",
"0.5375414",
"0.5271202",
"0.5255... | 0.7599588 | 0 |
Submits a Sync function from within another Sync function to be executed using Executor threads. The function is not waited to be executed. As it submits and continues the rest of the code. This can be useful if wanting to execute a long running code, and don't want it to hold up the thread for other callbacks. | def submit_to_executor(self, func: Callable, *args, **kwargs) -> Future:
callback = kwargs.pop("callback", None)
# get stuff we'll need to fake scheduler call
sched_data = {
"id": uuid.uuid4().hex,
"name": self.name,
"objectid": self.AD.app_management.object... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_sync(func: Callable[..., Any]) -> Awaitable[Any]:\n\n @wraps(func)\n async def wrapper(*args: Any, **kwargs: Any):\n loop = asyncio.get_running_loop()\n return await loop.run_in_executor(None, partial(func, *args, **kwargs))\n\n return wrapper",
"def call_in_thread_sync(self, func,... | [
"0.6136952",
"0.5974381",
"0.5962316",
"0.5843436",
"0.58426213",
"0.5818142",
"0.5792016",
"0.5762429",
"0.57322854",
"0.57224387",
"0.57163393",
"0.5710141",
"0.57021195",
"0.56933194",
"0.5679087",
"0.5661091",
"0.5589211",
"0.55503947",
"0.5530906",
"0.5525657",
"0.549081... | 0.63751614 | 0 |
Gets information on AppDaemon worker threads. | async def get_thread_info(self) -> Any:
return await self.AD.threading.get_thread_info() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def threads(self):\n return self.rpc.call(MsfRpcMethod.CoreThreadList)",
"def background_worker_pool(self):\r\n return self.run_tracker.background_worker_pool()",
"def getWorkers(self):\n return self.workers",
"def workers(self):\n return self.worker_list",
"def give_workers_list(se... | [
"0.63627106",
"0.6355825",
"0.6337276",
"0.60910183",
"0.60172534",
"0.6011731",
"0.6009246",
"0.5970024",
"0.59085363",
"0.57874316",
"0.5783962",
"0.5733812",
"0.5704435",
"0.56788737",
"0.5631702",
"0.5616941",
"0.5612163",
"0.5526096",
"0.5520087",
"0.5487907",
"0.5422177... | 0.7383696 | 0 |
Gets information on AppDaemon scheduler entries. | async def get_scheduler_entries(self):
return await self.AD.sched.get_scheduler_entries() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, controller, data, *args, **kwargs):\n scheduler = controller.get_scheduler()\n data = scheduler.get_entries()\n res = [i[1].info() for i in data]\n resp = {\n u'schedules':res,\n u'count':len(res)\n }\n return resp",
"def get_feed_entr... | [
"0.68324393",
"0.6112934",
"0.5880765",
"0.56930625",
"0.56135625",
"0.5607324",
"0.5594731",
"0.5529565",
"0.5525851",
"0.5525851",
"0.5458208",
"0.5426336",
"0.54089224",
"0.54025614",
"0.5351196",
"0.53386474",
"0.5286497",
"0.52792615",
"0.5276673",
"0.52350324",
"0.52164... | 0.7508746 | 0 |
Gets information on AppDaemon callback entries. | async def get_callback_entries(self) -> list:
return await self.AD.callbacks.get_callback_entries() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def callback_map(self):\n return self._callback_map",
"def get_user_hook_info ( self ):\n return [\n ( s.name, [ ( s.event or \"undef\", s.priority ) ] )\n for event, s in self.user_hooks.iter_scripts()\n ]",
"def getAddressOfCallBacks(self) -> long:\n ...",
"def get_C... | [
"0.56981975",
"0.56015867",
"0.5585036",
"0.55384374",
"0.5431633",
"0.5420188",
"0.54048175",
"0.5330089",
"0.53189105",
"0.5289201",
"0.5268539",
"0.521257",
"0.51890236",
"0.51678306",
"0.516292",
"0.51493365",
"0.5122892",
"0.5091725",
"0.5084271",
"0.5050801",
"0.5033081... | 0.70758593 | 0 |
Registers a global_modules dependency for an app. | async def depends_on_module(self, *modules: str) -> None:
return await self.AD.app_management.register_module_dependency(self.name, *modules) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_app_modules(apps, submodules):\n for app in apps:\n mod = import_module(app)\n for submodule in submodules:\n import_submodule(mod, app, submodule)",
"def _setup_modules(self):\r\n module_registry = AppModule.module_registry()\r\n for bundle in topological_sort(AppModul... | [
"0.58119065",
"0.5715995",
"0.5632839",
"0.5578374",
"0.55624",
"0.55556947",
"0.5482154",
"0.54607236",
"0.54435194",
"0.5438451",
"0.54278237",
"0.5420232",
"0.53925306",
"0.53865993",
"0.53849673",
"0.53510743",
"0.5327831",
"0.53090245",
"0.52816886",
"0.5281256",
"0.5269... | 0.62192947 | 0 |
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