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 |
|---|---|---|---|---|---|---|
Prueba el algoritmo de descenso de colinas con n repeticiones | def prueba_descenso_colinas(problema=ProblemaNreinas(8), repeticiones=10):
start = time.time()
print("\n\n" + "intento".center(10) +
"estado".center(60) + "costo".center(10))
for intento in range(repeticiones):
solucion = blocales.descenso_colinas(problema)
print(str(intento).cente... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def criaColuns():\n\n #Preencher Colunas\n numColunsVaz = 5\n numColuns = 2\n coluns = \"\"\n\n while(numColuns > 0): #preenchendo colunas de linha\n while(numColunsVaz > 0): #preenchendo espacos vazios das colunas de cada linha\n coluns += \" \"\n numColunsVaz -= 1\n\n coluns +... | [
"0.58503443",
"0.5545101",
"0.54143476",
"0.5391092",
"0.519753",
"0.51916754",
"0.5173583",
"0.5167264",
"0.51431435",
"0.51075566",
"0.5032127",
"0.5005249",
"0.49976033",
"0.49963444",
"0.49929944",
"0.4981939",
"0.49644017",
"0.49621123",
"0.49579847",
"0.49576473",
"0.49... | 0.7171292 | 0 |
If `dimension` is specified, create a vector of that size. `base_ring` must also be specified and `init` should be an int whose bits are decoded to elements of Galois field. If `dimension` is not specified, `init` should be a collection with length and the vector is initialized with it. If `base_ring` is specified, the... | def __init__(self, init, dimension=None, base_ring=None):
try:
if base_ring.algebra_name == 'Polynomial':
size = base_ring.base_ring.size
else:
size = base_ring.size
self.value = list(base_ring((init // size**_n) % size) for _n in range(dimension))
except (TypeError, AttributeError):
if isins... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, init=None, column_dimension=None, row_dimension=None, base_ring=None, vector_algebra=None):\n\t\t\n\t\ttry:\n\t\t\tcolumn_dimension = init.column_dimension\n\t\t\trow_dimension = init.row_dimension\n\t\texcept AttributeError:\n\t\t\tpass\n\t\t\n\t\tself.column_dimension = column_dimension\n\t\ts... | [
"0.6573724",
"0.61566955",
"0.6132931",
"0.6132931",
"0.60379446",
"0.5583378",
"0.5548514",
"0.54246706",
"0.5365157",
"0.5079334",
"0.4947213",
"0.493818",
"0.4912925",
"0.48869562",
"0.48804602",
"0.48549023",
"0.48542884",
"0.4829093",
"0.48226342",
"0.4808479",
"0.479004... | 0.7304109 | 0 |
`column_dimension` number of columns `row_dimension` number of rows `field_size` if specified, check if the provided values really belong to the field of the right size; throw exception if not. If `init` is `None` (or not specified), create a zero matrix. `field_size` must be specified. If `init` is a scalar, fill the ... | def __init__(self, init=None, column_dimension=None, row_dimension=None, base_ring=None, vector_algebra=None):
try:
column_dimension = init.column_dimension
row_dimension = init.row_dimension
except AttributeError:
pass
self.column_dimension = column_dimension
self.row_dimension = row_dimension
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self,nrows,ncols,fill=None):\n self.nrows = nrows \n self.ncols = ncols\n self.swapcount = 1\n self.initargs = str((self.nrows,self.ncols,fill))\n\n if type(nrows) != int or type(ncols) != int:\n raise TypeError('matrix number of rows and columns must be i... | [
"0.608386",
"0.5264823",
"0.5189937",
"0.5178625",
"0.51492167",
"0.51142234",
"0.50848687",
"0.5084529",
"0.50661117",
"0.5030533",
"0.5020849",
"0.50191116",
"0.5008367",
"0.49606284",
"0.49107182",
"0.49059767",
"0.4840349",
"0.4827868",
"0.48104033",
"0.4772353",
"0.47508... | 0.5403305 | 1 |
opens a SVG file and returns its main element, together with width and height of the viewport. | def openSVG(path):
from xml.dom import minidom
doc = minidom.parse(open(path))
svg = doc.getElementsByTagName("svg")[0]
sizeMatch = re.match(r"(\d+) (\d+) (\d+) (\d+)", svg.getAttribute("viewBox"))
w, h = int(sizeMatch.group(3)), int(sizeMatch.group(4))
return svg, w, h | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_svg(filename, size):\n try:\n import rsvg, cairo, array, cStringIO\n os.stat(filename)\n except (ImportError, OSError):\n return pygame.Surface((0,0))\n width, height = size\n csurface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height)\n context = cairo.Context(csurface)\n svg = rsvg.... | [
"0.67633545",
"0.63558894",
"0.6141794",
"0.590606",
"0.5414904",
"0.53030044",
"0.52899384",
"0.52899384",
"0.52681136",
"0.5229538",
"0.5183269",
"0.51638323",
"0.51307595",
"0.51009583",
"0.50976187",
"0.50906485",
"0.5036794",
"0.4985737",
"0.49677688",
"0.495371",
"0.493... | 0.7962191 | 0 |
fix the stroke width for SVG readers which cannot honor the attribute 'vectoreffect = "nonscalingstroke"' and render the oscilloscope's traces too wide. | def fixNonScalingStroke(path):
svg, w, h = openSVG(path)
groups = svg.getElementsByTagName("g")
# first remove childless groups
childless = [g for g in groups if len([el for el in g.childNodes if el.nodeType != 3]) == 0] # 3 is the value of TEXT_NODE
removed=[]
for g in childless:
g.par... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onChartSeriesLineWidthChanged(self, name, width):\n series = self.series[name]\n self.pen[name].setWidth(width)\n series.setPen(self.pen[name])",
"def pensize(self, width):\n self._penwidth = width",
"def setCurveWidth(w):\n dislin.thkcrv(w)",
"def remove_stroke(settings):\... | [
"0.59252506",
"0.54272324",
"0.54249233",
"0.5365047",
"0.5351435",
"0.52838856",
"0.52301836",
"0.52242607",
"0.52173424",
"0.51380944",
"0.51017445",
"0.50684786",
"0.5049057",
"0.49663606",
"0.4952353",
"0.4919379",
"0.4910415",
"0.48811263",
"0.48775443",
"0.4849431",
"0.... | 0.5534599 | 1 |
List of supported languages. | def supported_languages(self):
return SUPPORT_LANGUAGES | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def available_languages():\n utility = queryUtility(ILanguageAvailability)\n if utility is not None:\n return utility.getAvailableLanguages()\n return [DEFAULT_LANGUAGE]",
"def supported_languages(self) -> list[str]:\n return SUPPORTED_LANGUAGE_CODES",
"def available_languages(self):\n ... | [
"0.8483722",
"0.840621",
"0.8243387",
"0.8199635",
"0.8061957",
"0.7907665",
"0.77799827",
"0.77774334",
"0.7711223",
"0.7557256",
"0.7551935",
"0.7540216",
"0.74785465",
"0.74774396",
"0.7461168",
"0.7424731",
"0.73493814",
"0.73034257",
"0.72838485",
"0.7270892",
"0.7256779... | 0.841067 | 1 |
Return list of supported options like voice, emotionen. | def supported_options(self):
return ['person','filename','speed','pitch','volume'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def options(self):\n return list(self._moptions.keys())",
"def get_options(self):\n return []",
"def options(self) -> list[str]:\n return [BYPASS, ARMED]",
"def options() -> List:\n return list(c.value for c in Plugin)",
"def get_options(self):\n option_list = []\n ... | [
"0.7033087",
"0.68543774",
"0.67012095",
"0.65697294",
"0.6494509",
"0.6486308",
"0.6438346",
"0.6408869",
"0.6406022",
"0.6364897",
"0.63563967",
"0.6330588",
"0.62798905",
"0.6242719",
"0.62336355",
"0.62271035",
"0.6213618",
"0.62034756",
"0.61974275",
"0.6191447",
"0.6165... | 0.7531665 | 0 |
Loop and check for new followers for given people on Twitch | async def twitch_follower_checker_loop(self):
new_last_timestamp = dt.utcnow()
new_followers = collections.defaultdict(list) # channel user id: list of new follower usernames
db = await self.bot.database.get_connection()
data = await db("SELECT * FROM user_settings WHERE twitch_bearer_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow_followers(self):\n self.logger.log(\"starting follow_followers...\")\n follow = perform_with_ran_delay(self.instagram.get_followers, self.account.identifier, 150, 15, delayed=True)\n for acc in follow[\"accounts\"]:\n try:\n try:\n # prin... | [
"0.7179295",
"0.7176156",
"0.7139441",
"0.70234627",
"0.69152945",
"0.6737504",
"0.66895646",
"0.66658944",
"0.6626472",
"0.6562262",
"0.6528205",
"0.65006524",
"0.6485986",
"0.6449151",
"0.6440839",
"0.64056814",
"0.63817114",
"0.637028",
"0.6359783",
"0.6327252",
"0.6319632... | 0.8393314 | 0 |
Gives you a list of new followers from after a given datetime | async def get_new_followers(self, bearer_token:str, user_id:str, after:str) -> list:
headers = {"Client-Id": self.TWITCH_PARAMS['client_id'], "Authorization": f"Bearer {bearer_token}"}
params = {"to_id": user_id, "first": 100}
if after:
params["after"] = after
output = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_new_followers(self):\n sleeper()\n\n if self.api.get_news_inbox():\n news = self.api.last_json\n if news.get('counts', {}).get('relationships') > 0:\n stories = news.get('new_stories', []) + news.get('old_stories', [])\n stories = list(filte... | [
"0.68389374",
"0.66291773",
"0.658169",
"0.65321267",
"0.6396187",
"0.63941056",
"0.63932",
"0.6348139",
"0.6284659",
"0.62585175",
"0.62114143",
"0.6181787",
"0.6138993",
"0.61023927",
"0.6062513",
"0.60558367",
"0.60401285",
"0.6032618",
"0.60230297",
"0.6005495",
"0.600549... | 0.71898484 | 0 |
Checks if related config exists | def is_config_exist(self) -> bool:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_config_exist(self) -> bool:\n return True",
"def check_configs(self):\n\n pass",
"def __check_config(self):\n if not os.path.exists(self.__config_path):\n return False\n else:\n return True",
"def exists(self):\n if self.host.exists(self.remote_... | [
"0.815209",
"0.76055694",
"0.7587182",
"0.7469037",
"0.74612963",
"0.7461122",
"0.74430376",
"0.7387585",
"0.71708006",
"0.6983819",
"0.69818145",
"0.69528574",
"0.6796934",
"0.66908",
"0.66285247",
"0.66264105",
"0.6617301",
"0.6612457",
"0.65714",
"0.65323555",
"0.6514306",... | 0.82049394 | 0 |
Creates config based on connection parameters | def create_config(self) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_connection_params(self):\r\n settings_dict = self.settings_dict.copy()\r\n if settings_dict['NAME'] == '':\r\n from django.core.exceptions import ImproperlyConfigured\r\n raise ImproperlyConfigured(\r\n \"settings.DATABASES is improperly configured. \"\r\n... | [
"0.66714865",
"0.66658276",
"0.65769315",
"0.6562847",
"0.6513452",
"0.64851594",
"0.6482444",
"0.647544",
"0.64490384",
"0.6444649",
"0.6444649",
"0.6444367",
"0.6394916",
"0.63917106",
"0.6385805",
"0.6377325",
"0.6319609",
"0.626159",
"0.62571645",
"0.6244668",
"0.6221921"... | 0.73845017 | 1 |
Check if attribute exist in given section | def is_attr_exist(self, section_name: str, attr_name: str) -> bool:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_attr_exist(self, section_name: str, attr_name: str) -> bool:\n success = False\n try:\n response = self._vault_api.read_secret(path=section_name.upper(), mount_point=self.mount_point)\n keys = list(response[\"data\"][\"data\"].keys())\n success = attr_name.uppe... | [
"0.80059695",
"0.77579767",
"0.66706944",
"0.6560602",
"0.6518653",
"0.65135545",
"0.6459456",
"0.64363635",
"0.63487697",
"0.6343279",
"0.62790215",
"0.62653744",
"0.62300307",
"0.62162244",
"0.6169301",
"0.6116442",
"0.6098963",
"0.60900444",
"0.60861427",
"0.606089",
"0.60... | 0.8640172 | 0 |
Add new attribute in given section | def add_attr(self, section_name: str, attr_name: str, value: str) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_attr(self, section_name: str, attr_name: str, value: str) -> None:\n if self.is_section_exist(section_name):\n self._vault_api.patch(\n path=section_name.upper(),\n mount_point=self.mount_point,\n secret={\n attr_name.upper()... | [
"0.7537377",
"0.6775715",
"0.6360088",
"0.6340781",
"0.62840027",
"0.6233568",
"0.6212896",
"0.6212896",
"0.620235",
"0.6074416",
"0.6074416",
"0.6074416",
"0.6074416",
"0.6074416",
"0.60693836",
"0.6044737",
"0.60407203",
"0.60364676",
"0.6025874",
"0.5964739",
"0.5925738",
... | 0.8350567 | 0 |
Checks if related config exists | def is_config_exist(self) -> bool:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_config_exist(self) -> bool:\n pass",
"def check_configs(self):\n\n pass",
"def __check_config(self):\n if not os.path.exists(self.__config_path):\n return False\n else:\n return True",
"def exists(self):\n if self.host.exists(self.remote_path):\... | [
"0.82049394",
"0.76055694",
"0.7587182",
"0.7469037",
"0.74612963",
"0.7461122",
"0.74430376",
"0.7387585",
"0.71708006",
"0.6983819",
"0.69818145",
"0.69528574",
"0.6796934",
"0.66908",
"0.66285247",
"0.66264105",
"0.6617301",
"0.6612457",
"0.65714",
"0.65323555",
"0.6514306... | 0.815209 | 1 |
Creates config based on connection parameters | def create_config(self) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_connection_params(self):\r\n settings_dict = self.settings_dict.copy()\r\n if settings_dict['NAME'] == '':\r\n from django.core.exceptions import ImproperlyConfigured\r\n raise ImproperlyConfigured(\r\n \"settings.DATABASES is improperly configured. \"\r\n... | [
"0.66714865",
"0.66658276",
"0.65769315",
"0.6562847",
"0.6513452",
"0.64851594",
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"0.647544",
"0.64490384",
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"0.63917106",
"0.6385805",
"0.6377325",
"0.6319609",
"0.626159",
"0.62571645",
"0.6244668",
"0.6221921"... | 0.73845017 | 0 |
Check if attribute exist in given section | def is_attr_exist(self, section_name: str, attr_name: str) -> bool:
success = False
try:
response = self._vault_api.read_secret(path=section_name.upper(), mount_point=self.mount_point)
keys = list(response["data"]["data"].keys())
success = attr_name.upper() in keys
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_attr_exist(self, section_name: str, attr_name: str) -> bool:\n pass",
"def is_attr_exist(self, section_name: str, attr_name: str) -> bool:\n config = ConfigParser(allow_no_value=True)\n config.read(self.connection_string)\n\n result = False\n for section in config.sectio... | [
"0.8640172",
"0.77579767",
"0.66706944",
"0.6560602",
"0.6518653",
"0.65135545",
"0.6459456",
"0.64363635",
"0.63487697",
"0.6343279",
"0.62790215",
"0.62653744",
"0.62300307",
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"0.6169301",
"0.6116442",
"0.6098963",
"0.60900444",
"0.60861427",
"0.606089",
"0.605... | 0.80059695 | 1 |
Add new attribute in given section | def add_attr(self, section_name: str, attr_name: str, value: str) -> None:
if self.is_section_exist(section_name):
self._vault_api.patch(
path=section_name.upper(),
mount_point=self.mount_point,
secret={
attr_name.upper(): value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_attr(self, section_name: str, attr_name: str, value: str) -> None:\n pass",
"def add_attr(self, section_name: str, attr_name: str, value: str) -> None:\n if self.is_attr_exist(section_name=section_name, attr_name=attr_name) is False:\n if value is not None:\n value... | [
"0.8350567",
"0.6775715",
"0.6360088",
"0.6340781",
"0.62840027",
"0.6233568",
"0.6212896",
"0.6212896",
"0.620235",
"0.6074416",
"0.6074416",
"0.6074416",
"0.6074416",
"0.6074416",
"0.60693836",
"0.6044737",
"0.60407203",
"0.60364676",
"0.6025874",
"0.5964739",
"0.5925738",
... | 0.7537377 | 1 |
Returns the trigger method of a named action. | def get_action(action_name):
action = justrok.Globals.action_collection.action(action_name)
if action is None:
justrok.logger.error('action %r not found', action_name)
return lambda: None
else:
return action.trigger | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_action_func(self):\n try:\n func = self.action_funcs[self.action]\n except KeyError:\n raise DaemonRunnerInvalidActionError(\n \"Unknown action: %(action)r\" % vars(self))\n return func",
"def get_action(self):\n return self.__action",
"... | [
"0.6982433",
"0.6882818",
"0.67911154",
"0.6699492",
"0.6672278",
"0.6668549",
"0.6646114",
"0.66295385",
"0.66295385",
"0.66295385",
"0.66169465",
"0.66112214",
"0.66112214",
"0.66112214",
"0.66112214",
"0.66112214",
"0.66112214",
"0.65433276",
"0.6513046",
"0.6490807",
"0.6... | 0.7507943 | 0 |
Getter for handle_once flag. | def handle_once(self):
return self.__handle_once | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def once(self) -> \"fn\":\n return self._mod.once(self._func)",
"def func_once(func):\n def decorated(*args, **kwargs):\n try:\n return decorated._once_result\n except AttributeError:\n decorated._once_result = func(*args, **kwargs)\n return decorated._onc... | [
"0.6371545",
"0.5818473",
"0.57973456",
"0.57973456",
"0.57973456",
"0.5707667",
"0.5707462",
"0.56246483",
"0.5416864",
"0.53649235",
"0.5337877",
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"0.52500623",
"0.51820797",
"0.51675594",
"0.512946",
"0.51011926",
"0.49968946",
"0.497... | 0.8319137 | 0 |
Return the string description of the handler. This should be overridden. | def handler_description(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetHandlerName(self):\n return u'VI'",
"def GetHandlerName(self):\n return u'NULL'",
"def hook_description(self) -> str:",
"def description(self):\n assert not self._expression_used\n self._handle_used = True\n return self._description",
"def get_description(self) -> str:\n ... | [
"0.67875755",
"0.67388743",
"0.6597192",
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"0.6570138",
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"0.64774513",
"0.6476568",
"0.6459005",
"0.6432333",
"0.6424122... | 0.8959061 | 0 |
Return the string description of the matcher. This should be overridden. | def matcher_description(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe(self) -> Tuple[Text, List[SomeEntitiesType]]:\n return (\n \"{}(matcher='{}', handler='{}', handle_once={})\".format(\n type(self).__name__,\n self.matcher_description,\n self.handler_description,\n self.handle_once\n ... | [
"0.7222027",
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"0.66901016",
"0.6674943",
"0.66633004",
"0.66633004",
"0.66633004... | 0.8797827 | 0 |
r"""Mean of variational posterior distribution of global bias `w0`. If the model is not fit yet, returns `None`. | def w0_mean(self) -> Optional[float]:
def _retrieve(fm: VariationalFM) -> float:
return fm.w0
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def w_mean(self) -> Optional[np.ndarray]:\n\n def _retrieve(fm: VariationalFM) -> np.ndarray:\n return fm.w\n\n return runtime_error_to_optional(self, _retrieve)",
"def w0_var(self) -> Optional[float]:\n\n def _retrieve(fm: VariationalFM) -> float:\n return fm.w0_var\n\... | [
"0.6405869",
"0.60896486",
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"0.53618115",
"0.53606606",
"0.53543854",
"0.5332366",
"0.531... | 0.7282462 | 0 |
r"""Variance of variational posterior distribution of global bias `w0`. If the model is not fit yet, returns `None`. | def w0_var(self) -> Optional[float]:
def _retrieve(fm: VariationalFM) -> float:
return fm.w0_var
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def w_var(self) -> Optional[np.ndarray]:\n\n def _retrieve(fm: VariationalFM) -> np.ndarray:\n return fm.w_var\n\n return runtime_error_to_optional(self, _retrieve)",
"def w0_mean(self) -> Optional[float]:\n\n def _retrieve(fm: VariationalFM) -> float:\n return fm.w0\n\... | [
"0.61224735",
"0.6091875",
"0.60470706",
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"0.5312088",
"0.5309289",
"0.52915287",
"0.5289387",
"0.5263444",
"0.5246979... | 0.6527798 | 0 |
r"""Mean of variational posterior distribution of linear coefficnent `w`. If the model is not fit yet, returns `None`. | def w_mean(self) -> Optional[np.ndarray]:
def _retrieve(fm: VariationalFM) -> np.ndarray:
return fm.w
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def w0_mean(self) -> Optional[float]:\n\n def _retrieve(fm: VariationalFM) -> float:\n return fm.w0\n\n return runtime_error_to_optional(self, _retrieve)",
"def objective(self,w):\n l = 0\n for i in range(len(self.x)):\n # Each example contributes log(sigma(y_i * x_i . w))\n ... | [
"0.63365054",
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"0.5467083",
"0.5455212",
"0.5455... | 0.6798231 | 0 |
r"""Variance of variational posterior distribution of linear coefficnent `w`. If the model is not fit yet, returns `None`. | def w_var(self) -> Optional[np.ndarray]:
def _retrieve(fm: VariationalFM) -> np.ndarray:
return fm.w_var
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def variance(y, w):\n # w = clip_normalize(w)\n # Compute the expectance (d_y, n_q)\n y_q_exp = np.dot(y.T, w)\n\n # Compute the expectance of squares (d_y, n_q)\n y_q_exp_sq = np.dot((y ** 2).T, w)\n\n # Compute the variance (d_y, n_q)\n return y_q_exp_sq - (y_q_exp ** 2)",
"def variance(y)... | [
"0.7128826",
"0.59807926",
"0.5975847",
"0.5946089",
"0.5852916",
"0.5801572",
"0.57856596",
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"0.54716337",
"0.5466638",
"0.54525745",
"0.54498744",
"0.5416106",
"0.5415863",
"0.541317... | 0.65654993 | 1 |
r"""Mean of variational posterior distribution of factorized quadratic coefficnent `V`. If the model is not fit yet, returns `None`. | def V_mean(self) -> Optional[np.ndarray]:
def _retrieve(fm: VariationalFM) -> np.ndarray:
return fm.V
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prob_V(self, V_array, mu, sd, v):\n from scipy.integrate import quad\n step = lambda x: 0.5 * (np.sign(x) + 1) # Heaviside step function\n red = lambda V: (V - mu) / sd # reduced voltage\n P_integrand = lambda u: step(u - red(self.V_r)) * np.exp(u**2) # int... | [
"0.59684515",
"0.59213054",
"0.57099384",
"0.5682134",
"0.55768394",
"0.54644245",
"0.54439634",
"0.5442008",
"0.535256",
"0.53346574",
"0.53267014",
"0.53226745",
"0.53215265",
"0.5310928",
"0.5304539",
"0.52508295",
"0.52419704",
"0.5230906",
"0.52225924",
"0.5216639",
"0.5... | 0.6388949 | 0 |
r"""Variance of variational posterior distribution of factorized quadratic coefficnent `V`. If the model is not fit yet, returns `None`. | def V_var(self) -> Optional[np.ndarray]:
def _retrieve(fm: VariationalFM) -> np.ndarray:
return fm.V_var
return runtime_error_to_optional(self, _retrieve) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def variance(self):\n if self.dirty:\n self._finalize()\n return self.vvar",
"def sd(self, v):\n return np.sqrt(np.dot(self.mat_var, v) + self.var_ext)",
"def variance(self):\n return self.k * self.theta ** 2",
"def prob_V(self, V_array, mu, sd, v):\n from scipy.... | [
"0.606957",
"0.6043539",
"0.5970838",
"0.58802533",
"0.58587074",
"0.5843937",
"0.5822379",
"0.58129424",
"0.57794935",
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"0.55880475",
"0.5587365",
"0.558583",
"0.55673707",
"0.5550404",
"0.55317974"... | 0.6556247 | 0 |
Plot ns state data. First plots, trajectory in 3d space, then | def plot_ins_state(time, state):
pylab.ion()
plot_trajectory(state[:,0], state[:,1], state[:,2])
# Plot position vs. time
pylab.figure()
pylab.subplot(311)
pylab.plot(time, state[:,0],'r')
pylab.xlabel('time (s)')
pylab.ylabel('$\\phi$, rad')
pylab.title('Latitude')
pylab.gr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_3d(grbdir,x, y, z, t, thetax, thetay, name):\n # Set ax.azim and ax.elev to ra, dec\n global runconf\n\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n plt.suptitle(r\"Visualisation of {name} in 3d:$\\theta_x$={tx:0.1f},$\\theta_y$={ty:0.1f}\".format(name=name, tx=theta... | [
"0.6716344",
"0.6626634",
"0.6409651",
"0.6173262",
"0.6151631",
"0.6128093",
"0.60930204",
"0.60878885",
"0.60637754",
"0.6048436",
"0.5970073",
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"0.5842628",
"0.5840308",
"0.58378845",
"0.58337605",
"0.5831986",
"0.582365",
"0.57777876... | 0.69922644 | 0 |
General settings for the plot. | def plot_settings():
# Color palette
import seaborn as sns
# sns.set()
# Axes font size
sns.set(font_scale=1.2)
# LaTeX
if find_executable('latex'):
plt.rc('text', usetex=True)
# Style sheet
sns.set_style("white")
sns.set_style("ticks")
# Font (Note: this should ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def PlotSettings():\n\n # Color palette\n import seaborn as sns\n # sns.set()\n\n # Axes font size\n sns.set(font_scale=1.2)\n\n # LaTeX\n if find_executable('latex'):\n plt.rc('text',usetex=True)\n matplotlib.font_manager._rebuild()\n\n # Style sheet\n sns.set_style(\"whit... | [
"0.7753699",
"0.7366803",
"0.7239848",
"0.7191062",
"0.71012586",
"0.71012586",
"0.7078925",
"0.7051272",
"0.70454264",
"0.7044571",
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"0.6768519",
"0.66768545",
"0.6614457",
"0.66003627",
"0.6590423"... | 0.7382368 | 1 |
hiredate used for calculating years of service. birthday to determine age min_age gotta be old enough req_years gotta work here a while magic_number there always is one. returns a tuple of possibly relevant info, or None if there is no ability to retire. | def retirement_main(g:argparse.Namespace) -> tuple:
now = datetime.date.today()
# Only the most cursory checking.
if not g.birthday < g.hiredate < now:
print("\n".join(["you must have been born before you were hired,",
"and you must have been hired before today."]))
sys.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def age(self, year=None):\n y, m = self.master['birthYear'], self.master['birthMonth']\n return (year if year else self.currentyear) - y - (m > 6)",
"def birth_date_or_min_year(individual):\n year = fuzzy_date_year(individual.birth_date)\n if year:\n return year\n return 0",
"def ... | [
"0.6080395",
"0.60442376",
"0.5978503",
"0.59045935",
"0.58265024",
"0.58012956",
"0.5790349",
"0.5786886",
"0.578189",
"0.5678376",
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"0.54875743",
"0.5457795",
"0.5446115",
"0.5427107",
"0.54030263",
"0.538782... | 0.60962236 | 0 |
Initializes schur complement petsc options. | def initialize_petsc_options():
petsc_options = PETSc.Options()
petsc_options.clear()
for k in petsc_options.getAll(): petsc_options.delValue(k)
petsc_options.setValue('ksp_type', 'fgmres')
petsc_options.setValue('ksp_rtol', 1.0e-8)
petsc_options.setValue('ksp_atol', 1.0e-8)
petsc_options.se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_options(self):\n pass",
"def initialize_options(self):\n pass",
"def initialize_options(self):",
"def initialize_options(self):\n pass",
"def init_opt(self):\n raise NotImplementedError",
"def init_opt(self):\n raise NotImplementedError",
"def init_opt(self... | [
"0.7096684",
"0.7096684",
"0.7089368",
"0.6831426",
"0.6557297",
"0.6557297",
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"0.61831236",
"0.61650866",
"0.6131956",
"0.6106735",
"0.60682225",
"0.6062325... | 0.73684335 | 0 |
Tests a KSP solve using the Sp Schur complement approximation. For this test, the global matrix does not have a null space. | def test_Schur_Sp_solve():
mat_A = load_matrix_step_noslip()
petsc_options = initialize_petsc_options()
b, x = create_petsc_vecs(mat_A)
solver_info = LS.ModelInfo('interlaced', 3)
schur_approx = LS.Schur_Sp(mat_A,
'',
solver_info=solver_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_symplectic(self, tol):\n r = 0.543\n phi = 0.123\n S = symplectic.squeezing(r, phi)\n\n # the symplectic matrix\n O = np.array([[0, 1], [-1, 0]])\n\n assert np.allclose(S @ O @ S.T, O, atol=tol, rtol=0)",
"def sp_tests(reg):\r\n if reg.w != None:\r\n t... | [
"0.61507964",
"0.60482126",
"0.599857",
"0.5963827",
"0.59315217",
"0.5923064",
"0.58068204",
"0.5800536",
"0.5778327",
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"0.5617532",
"0.56018865",
"0.55930185",
"0.55850023",
"0.55705285",
"0.554... | 0.7735858 | 0 |
Create PETSc index sets for the velocity components of a saddle point matrix | def build_amg_index_sets(L_sizes):
neqns = L_sizes[0][0]
velocityDOF=[]
for start in range(1,3):
velocityDOF.append(np.arange(start=start,
stop=1+neqns,
step=3,
dtype='i'))
velocityDOF_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_petsc_vecs(matrix_A):\n b = PETSc.Vec().create()\n x = PETSc.Vec().create()\n b.createWithArray(np.ones(matrix_A.getSizes()[0][0],'d'))\n x.createWithArray(np.zeros(matrix_A.getSizes()[0][0],'d'))\n return (b, x)",
"def _build_mcid_vectors(self, model: BDF, nplies: int):\n etype ... | [
"0.57111764",
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"0.56116736",
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"0.54044163",
"0.536448",
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"0.5219142",
"0.5218424",
"0.521225",
"0.5204978",
"0.5181996",
"0.51707155... | 0.69217116 | 0 |
Loads a medium sized backwards facing step matrix for studying different AMG preconditioners. | def load_matrix_step_slip():
A = LAT.petsc_load_matrix('dump_test_1_step2d_1.0par_j_0')
return A | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_matrix_step_noslip():\n A = LAT.petsc_load_matrix('dump_test_2_step2d_1.0par_j_0')\n return A",
"def __load_reward_machine(self):\n\t\t# load the data for the reward machine\n\t\treward_machine_dict = parse_json_reward_machine(self.file_rm, self.idx_env)\n\n\t\t## Setting the Deterministic finite ... | [
"0.5810148",
"0.52885616",
"0.52373576",
"0.5128013",
"0.5093277",
"0.50875396",
"0.5032695",
"0.50319135",
"0.50183755",
"0.49761423",
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"0.4895836",
"0.48767456",
"0.48729625",
"0.48645157",
"0.48639032",
"0.485... | 0.5837878 | 0 |
Loads a medium sized backwards facing step matrix for studying different AMG preconditioners. | def load_matrix_step_noslip():
A = LAT.petsc_load_matrix('dump_test_2_step2d_1.0par_j_0')
return A | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_matrix_step_slip():\n A = LAT.petsc_load_matrix('dump_test_1_step2d_1.0par_j_0')\n return A",
"def __load_reward_machine(self):\n\t\t# load the data for the reward machine\n\t\treward_machine_dict = parse_json_reward_machine(self.file_rm, self.idx_env)\n\n\t\t## Setting the Deterministic finite au... | [
"0.5839584",
"0.52905416",
"0.5236045",
"0.51241046",
"0.50932777",
"0.5088872",
"0.50343317",
"0.502773",
"0.5019657",
"0.4979421",
"0.49729094",
"0.49185368",
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"0.48933357",
"0.4893148",
"0.487765",
"0.48747078",
"0.48655993",
"0.48605072",
"0.48564... | 0.58103716 | 1 |
Implement this function to do things once scheduler starts | def post_scheduler_start(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schedule_start(self):\n self.initialize_scheduler()",
"def schedule_start(self):\n print(\"Scheduler for monitoring request is running\")\n self.initialize_scheduler()",
"def _init_scheduler(self):\n self._sched = BackgroundScheduler()\n self._sched.add_job(self._check_ra... | [
"0.8190096",
"0.7755541",
"0.7274012",
"0.72734946",
"0.72621554",
"0.7260336",
"0.7260336",
"0.7234157",
"0.720759",
"0.7145206",
"0.7046787",
"0.69850713",
"0.697608",
"0.696596",
"0.6863917",
"0.6790794",
"0.6753649",
"0.6717804",
"0.6715892",
"0.67149884",
"0.67115337",
... | 0.81872547 | 1 |
Implement this function to do things once scheduler stops | def post_scheduler_stop(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stopSchedule(self):\n DPxStopDinSched()",
"def post_scheduler_start(self):\n pass",
"def stop_owping_scheduler(self):\n if self.scheduler:\n self.scheduler.shutdown_owping_scheduler()",
"def stop(self):\n self._schedule(0, 0)\n self._started = False",
"def ... | [
"0.71613675",
"0.7047334",
"0.70426625",
"0.6991927",
"0.69695085",
"0.6907677",
"0.68956625",
"0.68622696",
"0.6857062",
"0.6857062",
"0.6857062",
"0.6857062",
"0.6857062",
"0.6857062",
"0.6857062",
"0.6767245",
"0.6767245",
"0.6767245",
"0.6767245",
"0.67516077",
"0.6728322... | 0.83008593 | 0 |
Load pretrained word2vec model from file | def load_word2vec(path):
model = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
return model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_word2vec_model():\n logging.basicConfig(\n format='%(asctime)s : %(levelname)s : %(message)s', \n level=logging.INFO)\n model_path = '/playpen/home/tongn/GoogleNews-vectors-negative300.bin'\n model = KeyedVectors.load_word2vec_format(fname=model_path, binary=True)\n return model"... | [
"0.8218622",
"0.782518",
"0.77834904",
"0.77406394",
"0.7639196",
"0.75672185",
"0.75614536",
"0.7465879",
"0.7444701",
"0.7219885",
"0.7110543",
"0.71005493",
"0.71002835",
"0.704719",
"0.7018251",
"0.6999417",
"0.69798297",
"0.6911457",
"0.68803114",
"0.68366563",
"0.683050... | 0.8274128 | 0 |
Using pretrained word2vec model and supported character dictionary generate a dictionary with valid words as keys and associated word2vec embeddings as values | def create_data_dict(model, chardict):
# Less than 25 isn't part of original paper, but word2vec has some outrageous entries
def include_word(word, chardict):
"""
Function to determine if word can be included and perform any parsing
"""
if (all(char in chardict.keys() for ch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word2vec_mapping_func():\n return {\"belonging to\": \"belonging\", \"parked on\": \"parked\", \"growing on\": \"growing\", \"standing on\": \"standing\",\n \"made of\": \"made\", \"attached to\": \"attached\", \"hanging from\": \"hanging\", \"in front of\": \"front\",\n \"lying on\": ... | [
"0.7135873",
"0.705784",
"0.6958729",
"0.69434714",
"0.6712796",
"0.66958296",
"0.6673992",
"0.6615525",
"0.6594129",
"0.6553697",
"0.65515494",
"0.6547762",
"0.65421236",
"0.65269893",
"0.6521589",
"0.65121514",
"0.65010345",
"0.64791167",
"0.6473052",
"0.64702034",
"0.64525... | 0.7718554 | 0 |
Return next book of format (ID, NAME), or None if no books | def next(self):
res = self._cursor.fetchone()
if res:
self.id = res[0]
self.book = res[1]
self.pos = res[2]
self.content = res[3]
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getBookById(self, id):\n cur = self.__execute__(\n '''select ID, NAME, AUTHOR from books where ID = %d;''' % id)\n return BookIter(cur)",
"def next(self):\n res = self._cursor.fetchone()\n if res:\n self.id = res[0]\n self.name = res[1]\n ... | [
"0.57146686",
"0.569733",
"0.5661543",
"0.56611097",
"0.55472445",
"0.5516916",
"0.54633266",
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"0.52704483",
"0.5255863",
"0.5255308",
"0.5245439",
"0.5228048",
"0.5222301... | 0.6123419 | 0 |
Check if book is already in store, and returns a tuple to indicate if | def __addEntry__(self, book, author, pos, typ, date, clip):
new_book = False
book_id = self._book_cache.get(book, None)
if book_id is None:
cur = self.__execute__(
'''select id, author from books where NAME = '%s' ''' % book)
row = cur.fetchone()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_book_exist(self, book_info):\n for type, link in book_info.links.items():\n try:\n bookfile = BookFile.objects.get( link_hash = md5(link).hexdigest() )\n books = bookfile.book_set.all()\n if books:\n return True, books[0]\n ... | [
"0.65658265",
"0.6113058",
"0.61119384",
"0.61030245",
"0.60722727",
"0.5834233",
"0.5815799",
"0.5796884",
"0.57684106",
"0.5752261",
"0.5731847",
"0.5720351",
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"0.5689362",
"0.5639724",
"0.56176865",
"0.5561191",
"0.556069",
"0.55386555",
"0.5518699",
"0.551334... | 0.6146573 | 1 |
Return iterator of books. | def getBooks(self, showAll=False):
if showAll:
sql = '''select ID, NAME from books;'''
else:
sql = '''
select books.id, books.name, books.author
from books where exists (
select * from clippings where books.id = clippings.book);'''
cur = self.__execute__(sql)
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def demo_get_all_books(self):\n results = []\n self.cursor.execute(\"\"\"SELECT ISBN FROM book\"\"\")\n for book in self.cursor.fetchall():\n results.append(book[0])\n return results",
"def get_all_books() -> List[Dict]:\n pass",
"def getBookById(self, id):\n cu... | [
"0.66596466",
"0.6630247",
"0.65911406",
"0.6541217",
"0.64870906",
"0.6426697",
"0.64078766",
"0.62198365",
"0.61020017",
"0.6070597",
"0.6040379",
"0.6030183",
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"0.5925863",
"0.5909802",
"0.59034723",
"0.58813566",
"0.5779025",
"0.57681143",
"0.5733... | 0.67631567 | 0 |
Update clip if text is changed. | def updateClip(self, clip, text):
text = handleStr(text)
if clip.content == text:
return False
SQL = ''' update clippings set content = '%s' where ID = %d ''' % (
text, clip.id)
self.__execute__(SQL)
clip.content = text
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text_update(self):\n if self.stext is not None:\n # Get index of non-masked sources :\n # idx = self._select_unmasked()[-1]\n\n # Set masked-sources text to '':\n text = np.array(self.stext)\n # text[np.array(~idx, dtype=bool)] = ''\n\n #... | [
"0.6832454",
"0.6269757",
"0.6191828",
"0.5960741",
"0.5954705",
"0.59475875",
"0.5859353",
"0.58367366",
"0.57988596",
"0.57988596",
"0.5741981",
"0.5720842",
"0.56694096",
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"0.5636248",
"0.5633924",
"0.55790514",
"0.55586195",
"0.55477554",
"0.55013055",
"0.5462... | 0.7515564 | 0 |
Move specified clip into blacklist. | def dropClip(self, clip):
SQL = ''' insert into blacklist select * from clippings where ID = %d''' % clip.id
PDEBUG('SQL: %s', SQL)
self.__execute__(SQL)
SQL = '''delete from clippings where ID = %d''' % clip.id
PDEBUG('SQL: %s', SQL)
self.__execute__(SQL)
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pclip(data,pclip):\n data[data > pclip] = pclip\n return data",
"def clip(data,clip):\n data[data > clip] = clip\n data[data < -clip] = -clip\n return data",
"def nclip(data,nclip):\n data[data < nclip] = nclip\n return data",
"def bakeClip(*args, blend: List[int, int]=None, clipIn... | [
"0.58963823",
"0.57906437",
"0.5744762",
"0.5741313",
"0.5641685",
"0.5633234",
"0.5589184",
"0.554548",
"0.5473608",
"0.54658484",
"0.53366137",
"0.53206825",
"0.5293567",
"0.5275759",
"0.5271679",
"0.52522594",
"0.5248824",
"0.51448643",
"0.5129438",
"0.51247317",
"0.510995... | 0.7428678 | 0 |
Add a new clip into database. | def newClip(self, book, content, typ, date):
sql = ''' insert into clippings values (NULL, '%s', '%s', '%s', '%s', '%s')
''' % (book, '0', typ, date, content)
self.__execute__(sql)
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_clip(md5=None, pos=None):\n m = request.args.get('md5', None) if md5 is None else md5\n u = Upload.objects.filter(md5=m).first()\n if not u:\n abort(404)\n p = request.args.get('pos', None) if pos is None else pos\n xt, t, xb, b = parse_pos(p)\n # make the form\n form = Referenc... | [
"0.64640534",
"0.6188294",
"0.5897324",
"0.55828196",
"0.5502853",
"0.54831064",
"0.5403506",
"0.53474206",
"0.52728707",
"0.52669656",
"0.51315546",
"0.5129336",
"0.5055349",
"0.5050209",
"0.5049412",
"0.50438815",
"0.50288737",
"0.500915",
"0.4997684",
"0.49823377",
"0.4970... | 0.73653305 | 0 |
Prints an OTUlike table of reads and taxonomies assigned to contigs. Because taxon ids are not necessarily unique, must check and merge same taxonomies. | def print_kraken_otu_table(linked_dict, tax_ids, out="contig_taxonomy_table.txt"):
unique_tax = get_unique_dict(tax_ids)
sorted_tax = sorted(unique_tax.keys())
with open(out, 'w') as OUT:
OUT.write("\t".join(["contig"] + sorted_tax) + "\n")
for contig, tid_dict in linked_dict.it... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_systematics_overview(outfile: TextIO, do_print: bool, taxon_ranks: list, higher_taxa_list: list,\n specieslist: list, refdict: dict) -> None:\n\n # def write_species_table(header: list, data_list: list) -> None:\n # outfile.write(\" <table>\\n\")\n # ou... | [
"0.67589146",
"0.59809923",
"0.59603786",
"0.5779224",
"0.56466323",
"0.56435305",
"0.55965203",
"0.5521878",
"0.5500584",
"0.54970276",
"0.54721093",
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"0.5403291",
"0.53955436",
"0.53844565",
"0.5356315",
"0.5347432",
"0.5341753",
"0.5328785",
"0.5... | 0.7302376 | 0 |
Computes the elements of the batch sequentially by penalizing the acquisition. | def compute_batch(self, duplicate_manager=None,context_manager=None):
from ...acquisitions import AcquisitionTS
assert isinstance(self.acquisition, AcquisitionTS)
X_batch,_ = self.acquisition.optimize()
k=1
# --- GET the remaining elements
while k<self.b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\n if self.numbatches is None:\n pool = self.pooler()\n if self.batchsize is None:\n self.batchsize = self.pooler.nInPool()\n self.numbatches = self.pooler.nInPool()//self.batchsize\n for i in xrange(self.numbatches):\n poo... | [
"0.6296725",
"0.58579063",
"0.5793344",
"0.5788031",
"0.5779636",
"0.5741037",
"0.57315445",
"0.56771463",
"0.5661104",
"0.5637373",
"0.56152445",
"0.5593507",
"0.5577737",
"0.5562872",
"0.55451375",
"0.55381125",
"0.5533099",
"0.5506441",
"0.5498843",
"0.54656684",
"0.546566... | 0.6400441 | 0 |
Restore an existing wallet | def restore_wallet(
simple_config: SimpleConfig, storage: WalletStorage
) -> AbstractWallet:
text = simple_config.get("text").strip()
passphrase = simple_config.get("passphrase", "")
password = None
if keystore.is_private(text):
password = prompt_create_wallet_password()
if keystore.is_a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def restore_wallet(network):\n\n # ask user for desired M and N\n policy = await choose_policy()\n if policy is None: # user canceled selection\n return\n M, N = policy\n\n Mnem = Mnemonic()\n # Warn user about private key data on-screen\n if not await sensitive_data_warning():\n ... | [
"0.73510367",
"0.6350453",
"0.62652797",
"0.6113398",
"0.6101865",
"0.6031631",
"0.60292965",
"0.60286236",
"0.59924567",
"0.5940678",
"0.5924004",
"0.5871187",
"0.58419615",
"0.5764305",
"0.5748415",
"0.5723382",
"0.5712464",
"0.56713593",
"0.5602117",
"0.5600646",
"0.557184... | 0.7081722 | 1 |
Helper function updating a dictionary with set elements. Adds a new set if key is not present and adds element to the setvalue of the key. Make sure that h (host) is always short host name (split at '.'). | def addHostToJob(d, h, j):
if j not in d:
d[j] = set()
d[j].add(h.split(".")[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set(self, key, value):",
"def set(self, key, value):",
"def __setitem__(self, key, value):\r\n self.setdefault(key, []).append(value)",
"def hset(self, key, dictionary=None, **keyval):\n if dictionary:\n keyval = {**dictionary, **keyval}\n args = [a for kv in keyval.items(... | [
"0.6408249",
"0.6408249",
"0.6371761",
"0.63667107",
"0.6353907",
"0.632047",
"0.632047",
"0.62933385",
"0.62562346",
"0.62433106",
"0.61929286",
"0.61929286",
"0.61201346",
"0.60378045",
"0.6002755",
"0.59714854",
"0.59475684",
"0.59443396",
"0.59426975",
"0.5930565",
"0.591... | 0.70885074 | 0 |
Test case for get_ao_image Obtains the AO Image | def test_get_ao_image(self):
response = self.client.open(
'/rui-support/ao-image',
method='GET',
content_type='application/ld+json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getimage(self):",
"def test_post_ao_image(self):\n aoImageObj = AoImage()\n response = self.client.open(\n '/rui-support/ao-image',\n method='POST',\n data=json.dumps(aoImageObj),\n content_type='application/ld+json')\n self.assert200(response,... | [
"0.6691601",
"0.66877824",
"0.64691186",
"0.6237087",
"0.61862767",
"0.6183003",
"0.61762977",
"0.61159146",
"0.6107655",
"0.61052036",
"0.60473704",
"0.60473704",
"0.60473704",
"0.6046605",
"0.6035938",
"0.5984879",
"0.593931",
"0.5916387",
"0.5912639",
"0.5894874",
"0.58619... | 0.78551143 | 0 |
Test case for post_ao_image Stores the AO Image | def test_post_ao_image(self):
aoImageObj = AoImage()
response = self.client.open(
'/rui-support/ao-image',
method='POST',
data=json.dumps(aoImageObj),
content_type='application/ld+json')
self.assert200(response,
'Response bod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_image(self):\n pass",
"def test__put_image_into():\n image = EmbedImage(url = 'https://orindance.party/')\n \n for input_value, defaults, expected_output in (\n (None, False, {}),\n (image, False, {'image': image.to_data()}),\n (image, True, {'image': image.to... | [
"0.67802465",
"0.64644206",
"0.6407517",
"0.6344646",
"0.6327962",
"0.63004494",
"0.6270897",
"0.62683135",
"0.6266138",
"0.62150717",
"0.61016697",
"0.6081908",
"0.6080614",
"0.6042422",
"0.6041929",
"0.60279566",
"0.6012855",
"0.59381664",
"0.58961374",
"0.589126",
"0.58848... | 0.7459596 | 0 |
Returns the ratios of prime numbers on the diagonals of the Ulam spiral | def _ratios() -> Iterable[float]:
index = 0
primes = 0
while True:
primes += 1 if is_prime(_first_spiral_arm(index)) else 0
primes += 1 if is_prime(_second_spiral_arm(index)) else 0
primes += 1 if is_prime(_third_spiral_arm(index)) else 0
primes += 1 if is_prime(_fourth_spir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solver(spiral_size):\n square_length = 0\n diagonal_sum = 1\n last_value = 1\n size = 1\n\n while size < spiral_size:\n square_length += 2\n vals_to_sum = [last_value + i * square_length for i in range(1,5)]\n diagonal_sum += sum(vals_to_sum)\n last_value = vals_to_su... | [
"0.62379575",
"0.60954773",
"0.6029247",
"0.5945784",
"0.5779223",
"0.56642926",
"0.5655853",
"0.55377144",
"0.5525278",
"0.5523059",
"0.54860795",
"0.5468318",
"0.5466531",
"0.5462835",
"0.53987575",
"0.5388846",
"0.5386984",
"0.53869027",
"0.5381663",
"0.5368754",
"0.536235... | 0.7022402 | 0 |
Checks the the specified fullylabelled tree rooted at the specified node is equivalent to the specified Dendropy tree. | def assert_fully_labelled_trees_equal(tree, root, node_labels, dpy_tree):
label_map = {}
for node in dpy_tree:
label_map[str(node.taxon.label)] = node
for u in tree.nodes(root, order="postorder"):
# Consume the nodes in the dendropy node map one-by-one
dpy_node = label_map.pop(node_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, node):\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right",
"def test_tree_with_one_node_has_correct_value(one_t):\n assert one_t.root.value == 10",
"def assert_sample_labelled_trees_equal(t... | [
"0.63657284",
"0.6276306",
"0.62623096",
"0.6223688",
"0.6206919",
"0.618042",
"0.6162651",
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"0.59970343",
"0.59726435",
"0.59310246",
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"0.5844909",
"0.58431613",
"0.58295834",
"0.5797285",
"0.57932127",
"0.57726514",
"0.575... | 0.71304715 | 0 |
Checks that the specified trees are equivalent, where the dendropy tree only has labels identifying the samples. | def assert_sample_labelled_trees_equal(tree, dpy_tree):
for sample in tree.samples():
dpy_node = dpy_tree.find_node_with_taxon_label(f"n{sample}")
# Check the branch length paths to root are equal
p1 = []
u = sample
while tree.parent(u) != tskit.NULL:
p1.append(tr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assert_fully_labelled_trees_equal(tree, root, node_labels, dpy_tree):\n label_map = {}\n for node in dpy_tree:\n label_map[str(node.taxon.label)] = node\n\n for u in tree.nodes(root, order=\"postorder\"):\n # Consume the nodes in the dendropy node map one-by-one\n dpy_node = label... | [
"0.7184704",
"0.69209236",
"0.68368334",
"0.66966563",
"0.6592166",
"0.6590124",
"0.6531683",
"0.64659524",
"0.6303335",
"0.6158508",
"0.61347437",
"0.6016036",
"0.59612226",
"0.5931298",
"0.5929848",
"0.5880334",
"0.5869122",
"0.58330977",
"0.5789325",
"0.5788625",
"0.578534... | 0.7154204 | 1 |
Extracts the answer area from a TIF or PNG file | def extract_template_answer_area():
tif_files = encode.get_files_in_directory(TEMPLATE_DIR, ".tif")
png_files = encode.get_files_in_directory(TEMPLATE_DIR, ".png")
files = tif_files + png_files
# If there is at least one tif or png file in the template dir
if files:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ascii_to_tiff(infile, outfile, refIm):",
"def extract(img):\n # perform selective search\n img_lbl, regions = selectivesearch.selective_search(\n img, scale=500, sigma=0.9, min_size=1)\n\n # delete the region which contains whole image\n regions = sorted(regions, key=lambda x: x['size'], r... | [
"0.5865341",
"0.5584032",
"0.55547786",
"0.54898316",
"0.5484277",
"0.5394131",
"0.5374201",
"0.5367118",
"0.5355357",
"0.53319436",
"0.5282989",
"0.5251234",
"0.5230001",
"0.5180067",
"0.5170245",
"0.5159567",
"0.5115918",
"0.5108504",
"0.51075417",
"0.5105012",
"0.5096202",... | 0.6935976 | 0 |
Returns a list of all of the tif files (diaries) present in DIARIES_TO_ENCODE_DIR | def on_get(self, req, resp):
resp.set_header('Content-Type', 'text/json')
tif_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, ".tif")
zip_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, ".zip")
diaries_paths = tif_paths + zip_paths
def extract_file_name(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_image_paths(self):\n image_paths, image_labels = [], []\n for directory_name, subdirectory_list, file_list in os.walk(self.root_directory):\n for file_name in file_list:\n if file_name.endswith(('.jpg',)):\n image_paths.append(os.path.join(dire... | [
"0.628644",
"0.62454176",
"0.61320674",
"0.6116978",
"0.6085372",
"0.6031725",
"0.60101855",
"0.60101503",
"0.60044205",
"0.6000042",
"0.59953046",
"0.59837866",
"0.5980264",
"0.59779716",
"0.5973886",
"0.5972864",
"0.5968313",
"0.59581745",
"0.5953689",
"0.59462696",
"0.5938... | 0.62769246 | 1 |
Returns a list of all of the PDF files (templates) present in DIARIES_TO_CREATE_DIR | def on_get(self, req, resp):
resp.set_header('Content-Type', 'text/json')
diaries_paths = encode.get_files_in_directory(DIARIES_TO_CREATE_DIR, ".pdf")
def extract_file_name(path): return os.path.basename(path)
resp.body = json.dumps({"templates_file_names": list(map(extract_file_name, d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_pdfs():\n\n return filter(lambda f: fnmatch.fnmatch(f, '*.pdf'), os.listdir(cwd))",
"def _readFiles(self):\n template_files = []\n for file in os.listdir(self.template_folder):\n if file.endswith(\".xml\"):\n template_files.append(file)\n return template_files",
"def find_us... | [
"0.69231814",
"0.6141186",
"0.6118949",
"0.6074224",
"0.60071635",
"0.6001381",
"0.5915208",
"0.5844267",
"0.58433694",
"0.5763481",
"0.5746568",
"0.57192653",
"0.57173747",
"0.5704806",
"0.5699734",
"0.5699564",
"0.56631935",
"0.565441",
"0.5640205",
"0.5640205",
"0.5635952"... | 0.6476985 | 1 |
r = radial_data(data,annulus_width,working_mask,x,y) A function to reduce an image to a radial crosssection. | def radial_data(data,annulus_width=1,working_mask=None,x=None,y=None,rmax=None):
# 2010-03-10 19:22 IJC: Ported to python from Matlab
# 2005/12/19 Added 'working_region' option (IJC)
# 2005/12/15 Switched order of outputs (IJC)
# 2005/12/12 IJC: Removed decifact, changed name, wrote comments.
# 2005/11/04 by Ian C... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calcdata(self):\n bot = max(0, self.r - self.radius)\n top = min(self.img.shape[0], self.r + self.radius)\n left = max(0, self.c - self.radius)\n right = min(self.img.shape[1], self.c + self.radius)\n self.data = self.img[bot:top, left:right]",
"def calc_radial_profile(fits... | [
"0.635511",
"0.6350057",
"0.6086051",
"0.6034714",
"0.59491116",
"0.5870748",
"0.58705753",
"0.58676606",
"0.5803789",
"0.5592001",
"0.5586976",
"0.55823505",
"0.5542319",
"0.54842496",
"0.5477918",
"0.545632",
"0.54207975",
"0.5419271",
"0.5408651",
"0.5403409",
"0.5370979",... | 0.77806413 | 0 |
compute KMT based on missing values | def compute_kmt(da):
nk = len(da.z_t)
KMT = np.zeros(da.shape[-2:]).astype(int)
#-- where surface is missing, KMT = 0, else full depth
KMT = np.where(np.isnan(da.data[0, :, :]), 0, nk)
# where level k is missing: KMT = k, i.e. the level above in 1-based indexing
for k in range(1, nk):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_result_missing(att_trees, data, k=DEFAULT_K, n=DEFAULT_K):\n data_back = copy.deepcopy(data)\n length = len(data_back)\n qi_len = len(data[0]) - 1\n raw_missing = raw_missing_record = 0\n print \"K=%d\" % k\n for record in data:\n flag = False\n for value in record:\n ... | [
"0.6027694",
"0.59880894",
"0.5656967",
"0.56132144",
"0.54714584",
"0.544704",
"0.54354364",
"0.5401745",
"0.538166",
"0.53478956",
"0.5319351",
"0.5316531",
"0.53137183",
"0.531049",
"0.5292198",
"0.5286385",
"0.5275297",
"0.5232497",
"0.523211",
"0.52316475",
"0.52261186",... | 0.6935543 | 0 |
Write ds to a temporary netCDF file, compute zonal mean for a given variable based on Keith L's fortran program, read resulting netcdf file, and return the new xarray dataset If three_ocean_regions=True, use a region mask that extends the Pacific, Indian, and Atlantic to the coast of Antarctica (and does not provide se... | def zonal_mean_via_fortran(ds, var=None, grid=None, region_mask=None, replace_kmt=False):
if replace_kmt and (var is None or ',' in var):
raise ValueError('if "replace_kmt" is True, a single "var" must be specified.')
ds_in_file = tempfile.NamedTemporaryFile(suffix='.nc')
ds_out_file = temp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zonal_avg(data,Log=False):\n print 'computing zonal average'\n # get lat and lon for new regular grid\n# fpin = Nio.open_file('/home/ivan/Python/data/lat_t.nc','r')\n fpin = Nio.open_file('/home/emunoz/Python/mapping/model_grid/lat_t.nc','r')\n lat_t = fpin.variables['lat_t'][... | [
"0.62033606",
"0.6046982",
"0.5713098",
"0.5647565",
"0.56335545",
"0.55833864",
"0.5557668",
"0.5525196",
"0.5524461",
"0.54355425",
"0.5342466",
"0.53356504",
"0.5275262",
"0.5273372",
"0.5256116",
"0.5239368",
"0.520873",
"0.5206049",
"0.52029455",
"0.5174636",
"0.5165332"... | 0.8066713 | 0 |
This check to see if the number of biological sex files is twice the number of country files. If not, the preprocessing.py is envoked. | def runpreprocessing(numofcountryfiles, numofsexfiles):
if numofsexfiles/float(numofcountryfiles) == 2.0:
return False
else:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_num_profiles(self):\n for fp in [self.solar_fpath, self.wind_fpath]:\n with Resource(fp) as res:\n profile_dset_names = [\n n for n in res.dsets\n if self.__profile_reg_check.match(n)\n ]\n if not pro... | [
"0.59162104",
"0.55463576",
"0.54650545",
"0.53903943",
"0.5361821",
"0.53002745",
"0.5264159",
"0.521888",
"0.51619226",
"0.5090941",
"0.50624096",
"0.5008013",
"0.50047386",
"0.4991225",
"0.49887285",
"0.49823475",
"0.49728993",
"0.49693215",
"0.49222106",
"0.49124843",
"0.... | 0.79496104 | 0 |
This function takes a filename as a argument and creates new files according for each biological sex. | def parse_file(filename):
print(filename)
fh = open(filename)
header = next(fh)
output_file = open("Sex" + "1_" + filename, "a")
output_file.write(header)
output_file2 = open("Sex" + "2_" + filename, "a")
output_file2.write(header)
for line in fh:
line = line.strip().s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_profiles(datafolder, profilefolder, size):\n files = os.listdir(datafolder) \n for file in files:\n languagename = file.split(\"-\")[0]\n encodering = file.split(\"-\")[1]\n bestand = open('training/' + file,'r' , encoding=encodering) #Reads with the correct encoding.... | [
"0.60337716",
"0.5824258",
"0.5712114",
"0.5637219",
"0.5616942",
"0.54691935",
"0.54080874",
"0.5404639",
"0.5402783",
"0.539661",
"0.53558767",
"0.5329414",
"0.5248186",
"0.5240414",
"0.5169949",
"0.51663136",
"0.5159333",
"0.51430047",
"0.5139185",
"0.5125148",
"0.51231974... | 0.58292013 | 1 |
This function will find all the icd codes (represented as numbers) not in a given set. | def find_not_in_icd_set(lenoforginalset, numbers_set):
originalset = [str(i) for i in range(1, lenoforginalset+1, 1)]
numbers_not_in_set = list(set(originalset)-set(numbers_set))
numbers_not_in_set = sorted([int(num) for num in numbers_not_in_set])
numbers_not_in_set = [str(num) for num in numbers... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def non_mcnugget():\n nugget = [0, 6, 9, 20]\n mcnugget = set([6, 9, 20])\n\n while True:\n mcnugget = set([m+n for m in mcnugget for n in nugget])\n\n for m in mcnugget:\n found = all([m+j in mcnugget for j in range(6)])\n if found:\n return [k for k in ... | [
"0.5973054",
"0.5931591",
"0.5805185",
"0.56852686",
"0.56828356",
"0.5678099",
"0.565661",
"0.565661",
"0.56236756",
"0.5613788",
"0.560155",
"0.556826",
"0.5538928",
"0.5527729",
"0.5484155",
"0.54743135",
"0.54513276",
"0.54469943",
"0.5412802",
"0.5405012",
"0.5405012",
... | 0.8056438 | 0 |
the request code to unlock this license only | def unlockRequestCode(self)->str:
from .core import Core
req = Core().createRequest()
req.addAction(ActionId.ACT_UNLOCK, self.entity)
return req.code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def whmcs_license(username=None, lkey=None, action=\"view\"):\n actions_list = ['view', 'add', 'transfer', 'del']\n\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # build request\n action = acti... | [
"0.6147328",
"0.6136427",
"0.6115494",
"0.6097576",
"0.6063442",
"0.6028758",
"0.60115236",
"0.6009872",
"0.5928525",
"0.5906322",
"0.587585",
"0.5870189",
"0.5852557",
"0.58431053",
"0.5827284",
"0.5827265",
"0.58003414",
"0.5759984",
"0.57596606",
"0.5743199",
"0.5739569",
... | 0.7380096 | 0 |
when the license will be expired? (alias of property 'timeEnd') | def expireDate(self)->datetime:
return self.timeEnd | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expiration_time(self) -> str:\n return pulumi.get(self, \"expiration_time\")",
"def expiration(self) -> Optional[str]:\n return pulumi.get(self, \"expiration\")",
"def expired_time(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"expired_time\")",
"def expired(self):\n\n ... | [
"0.67641264",
"0.6696843",
"0.66515046",
"0.66509277",
"0.6610403",
"0.656929",
"0.65620375",
"0.65516114",
"0.6507877",
"0.64807945",
"0.6467765",
"0.6453672",
"0.6418264",
"0.639546",
"0.6387627",
"0.63807845",
"0.63801044",
"0.6378822",
"0.63490885",
"0.63476145",
"0.63427... | 0.71000206 | 0 |
Session time elapsed in seconds | def secondsPassed(self)->int:
return self._lic.params['sessionTimeUsed'].value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time_elapsed(session, player):\n #TODO (also needs to be added to bot logic)",
"def session_time(self):\n\n time_in_seconds = time.time() - self.__start_time\n return time.strftime('%H:%M:%S', time.gmtime(time_in_seconds))",
"def get_session_age(self):\n return (time.time() - self._... | [
"0.7663617",
"0.7661305",
"0.71520996",
"0.71286315",
"0.70924604",
"0.70045185",
"0.68992764",
"0.6816028",
"0.68099844",
"0.66990674",
"0.667257",
"0.66703564",
"0.66690665",
"0.66660154",
"0.66075814",
"0.66066486",
"0.65905344",
"0.6560364",
"0.6550616",
"0.6514697",
"0.6... | 0.80138737 | 0 |
how many seconds has elapsed since entity was first accessed return 0 if entity is never accessed before | def secondsPassed(self)->int:
return 0 if not self.used else int((datetime.utcnow() - self.firstAccessDate).total_seconds()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_elapsed_time(self):\n if hasattr(self, 'starttime'):\n return monotonic() - self.starttime\n else:\n return 0",
"def time(self) -> int:\n pass",
"def elapsed():\n global start_time\n return time.time() - start_time",
"def cpu_time(self):",
"d... | [
"0.6617167",
"0.652798",
"0.65166646",
"0.64807063",
"0.6445269",
"0.63923824",
"0.63572663",
"0.63527673",
"0.6337014",
"0.63277555",
"0.629772",
"0.627285",
"0.6263756",
"0.62381625",
"0.6231359",
"0.6217502",
"0.61960274",
"0.6195111",
"0.61842424",
"0.61770594",
"0.612822... | 0.71971196 | 0 |
New mote detected by SWAP server 'mote' Mote detected | def newMoteDetected(self, mote):
if self._printSWAP == True:
print "New mote with address " + str(mote.address) + " : " + mote.definition.product + \
" (by " + mote.definition.manufacturer + ")" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def do_motd():\n\n download = urllib.request.urlopen(server_api)\n data = json.loads(download.read())\n motd = data['motd']\n await bot.send_message(c, f'MOTD: {motd}')",
"def moteStateChanged(self, mote):\n if self._printSWAP == True:\n print \"Mote with addre... | [
"0.5557874",
"0.5552091",
"0.55492103",
"0.54637843",
"0.5407886",
"0.53313434",
"0.53221536",
"0.5275365",
"0.5193926",
"0.5179351",
"0.5169468",
"0.5085447",
"0.50776786",
"0.5025695",
"0.50255686",
"0.49901018",
"0.498371",
"0.4967981",
"0.4963014",
"0.49628928",
"0.496031... | 0.7501466 | 0 |
New endpoint detected by SWAP server 'endpoint' Endpoint detected | def newEndpointDetected(self, endpoint):
if self._printSWAP == True:
print "New endpoint with Reg ID = " + str(endpoint.getRegId()) + " : " + endpoint.name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def LocalEndpoint(self) -> _n_5_t_1:",
"def on_endpoint_set(self, response, name):\n try:\n hostname, orchestrator, workload_id, endpoint_id = \\\n split_endpoint_name(name)\n except ValueError:\n # For some reason this endpoint's name does not have the expected... | [
"0.6910449",
"0.6479605",
"0.6428298",
"0.6342632",
"0.6326875",
"0.6287056",
"0.6282815",
"0.6206631",
"0.61930865",
"0.6168928",
"0.61675835",
"0.6129168",
"0.6121042",
"0.6089144",
"0.604219",
"0.60197675",
"0.60182434",
"0.5984431",
"0.596734",
"0.5959731",
"0.5956604",
... | 0.77883375 | 0 |
Mote state changed 'mote' Mote having changed | def moteStateChanged(self, mote):
if self._printSWAP == True:
print "Mote with address " + str(mote.address) + " switched to \"" + \
SwapState.toString(mote.state) + "\""
# SYNC mode entered?
if mote.state == SwapState.SYNC:
self._addrInSyncMode = mote.address | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newMoteDetected(self, mote):\n if self._printSWAP == True:\n print \"New mote with address \" + str(mote.address) + \" : \" + mote.definition.product + \\\n \" (by \" + mote.definition.manufacturer + \")\"",
"def moteAddressChanged(self, mote):\n if self._printSWAP == True... | [
"0.6542815",
"0.63512164",
"0.60423607",
"0.60275704",
"0.6016302",
"0.5954982",
"0.5873663",
"0.585289",
"0.585289",
"0.5751477",
"0.5728968",
"0.566748",
"0.5664831",
"0.5644767",
"0.5635829",
"0.5627798",
"0.5625904",
"0.56214094",
"0.56030554",
"0.5602899",
"0.559459",
... | 0.7259847 | 0 |
Mote address changed 'mote' Mote having changed | def moteAddressChanged(self, mote):
if self._printSWAP == True:
print "Mote changed address to " + str(mote.address) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newMoteDetected(self, mote):\n if self._printSWAP == True:\n print \"New mote with address \" + str(mote.address) + \" : \" + mote.definition.product + \\\n \" (by \" + mote.definition.manufacturer + \")\"",
"def moteStateChanged(self, mote):\n if self._printSWAP == True:\... | [
"0.6768314",
"0.6620103",
"0.5359817",
"0.5238512",
"0.52165043",
"0.51508856",
"0.51445705",
"0.50726837",
"0.5047917",
"0.50477576",
"0.50385183",
"0.503132",
"0.5030789",
"0.50236905",
"0.5023457",
"0.50191927",
"0.5016769",
"0.49775064",
"0.49714828",
"0.49086824",
"0.488... | 0.79074264 | 0 |
Return the address of the mote currently in SYNC mode | def getAddressInSync(self):
return self._addrInSyncMode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sync_mode():\n return sync_mode",
"def getSyncObj(self):\n \n return self.sync_obj",
"def address(self):\n if self.con_strategy == \"local\":\n return self.address_local()\n if self.con_strategy == \"remote\":\n return self.address_remote()\n ... | [
"0.65469795",
"0.5887931",
"0.5824689",
"0.5764095",
"0.5753812",
"0.5723248",
"0.5640087",
"0.56286067",
"0.5594644",
"0.557762",
"0.5572857",
"0.5525033",
"0.54591423",
"0.54430777",
"0.5436679",
"0.5426568",
"0.5413674",
"0.54002976",
"0.53833145",
"0.5343736",
"0.53009164... | 0.7199365 | 0 |
Class constructor 'verbose' Print out SWAP frames or not 'monitor' Print out network events or not | def __init__(self, verbose=False, monitor=False):
# Superclass call
SwapInterface.__init__(self, None, verbose)
# Print SWAP activity
self._printSWAP = monitor
# Mote address in SYNC mode
self._addrInSyncMode = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, enable_verbose=True):\n self.enable_verbose = enable_verbose\n if self.enable_verbose:\n self.show = self._print_screen\n else:\n self.show = self._not_print_screen",
"def __init__(self, ip, debug):\n self.ip = ip\n self.debug = debug\n ... | [
"0.65296584",
"0.6060873",
"0.5938087",
"0.5928273",
"0.5824343",
"0.57283795",
"0.572683",
"0.5692821",
"0.5672617",
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"0.5616095",
"0.56014776",
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"0.55422336",
"0.5537475",
"0.5529403",
"0.5493745",
"0.54905796",
"0.54905796",
"0.547719... | 0.70198196 | 0 |
populate g demo user | def set_demo_user() -> None:
g.demo_user = _DEMO_USER | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_demo_user() -> None:\n global _DEMO_USER # populate after everything is set up pylint: disable=global-statement\n _DEMO_USER = user_datastore.find_user(email=\"demo@example.com\")",
"def sample_user(self):",
"def populate_user_data():\n try:\n db = mongo_client.MongoClient(co... | [
"0.74438804",
"0.7091167",
"0.70666105",
"0.69992507",
"0.6940794",
"0.68723917",
"0.66967756",
"0.66967756",
"0.6555618",
"0.6500795",
"0.647871",
"0.64348245",
"0.64325756",
"0.6402391",
"0.6374278",
"0.6349041",
"0.6346974",
"0.6338629",
"0.63265276",
"0.6324837",
"0.63198... | 0.7655008 | 0 |
Gets the affiliate_oid of this Customer. Affiliate oid | def affiliate_oid(self):
return self._affiliate_oid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def affiliation_id(self):\n return self._aff_id",
"def affiliate_oid(self, affiliate_oid):\n\n self._affiliate_oid = affiliate_oid",
"def affiliate_oid(self, affiliate_oid):\n\n self._affiliate_oid = affiliate_oid",
"def affiliation_current(self):\n return self._json.get('affiliat... | [
"0.7088198",
"0.6908923",
"0.6908923",
"0.6726395",
"0.65425396",
"0.63074726",
"0.59236395",
"0.5833018",
"0.5681659",
"0.56454664",
"0.5631452",
"0.55850416",
"0.55468804",
"0.5510164",
"0.5475057",
"0.547227",
"0.5468918",
"0.5458377",
"0.54531825",
"0.54170895",
"0.538797... | 0.8791251 | 0 |
Sets the affiliate_oid of this Customer. Affiliate oid | def affiliate_oid(self, affiliate_oid):
self._affiliate_oid = affiliate_oid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def affiliate_ledger_oid(self, affiliate_ledger_oid):\n\n self._affiliate_ledger_oid = affiliate_ledger_oid",
"def affiliate_link_oid(self, affiliate_link_oid):\n\n self._affiliate_link_oid = affiliate_link_oid",
"def affiliate_click_oid(self, affiliate_click_oid):\n\n self._affiliate_clic... | [
"0.76087534",
"0.7270813",
"0.7161406",
"0.7089725",
"0.5732011",
"0.5672323",
"0.5389598",
"0.53142256",
"0.5264382",
"0.52219224",
"0.5128206",
"0.5111839",
"0.50619966",
"0.4943211",
"0.4943211",
"0.4943211",
"0.4943211",
"0.49403697",
"0.49381366",
"0.49210244",
"0.491582... | 0.86643445 | 1 |
Gets the allow_3rd_party_billing of this Customer. Allow 3rd party billing | def allow_3rd_party_billing(self):
return self._allow_3rd_party_billing | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_3rd_party_billing(self, allow_3rd_party_billing):\n\n self._allow_3rd_party_billing = allow_3rd_party_billing",
"def allow_purchase_order(self):\n return self._allow_purchase_order",
"def billing(self):\n return self._billing",
"def is_billing_admin(self):\n return self.... | [
"0.7842041",
"0.5967459",
"0.5716493",
"0.5525604",
"0.53802145",
"0.5296603",
"0.5183098",
"0.5169147",
"0.5169147",
"0.51587415",
"0.51280606",
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"0.51015633",
"0.5096496",
"0.5028336",
"0.49712607",
"0.49688616",
"0.49688616",
"0.4943... | 0.8492231 | 0 |
Sets the allow_3rd_party_billing of this Customer. Allow 3rd party billing | def allow_3rd_party_billing(self, allow_3rd_party_billing):
self._allow_3rd_party_billing = allow_3rd_party_billing | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_3rd_party_billing(self):\n return self._allow_3rd_party_billing",
"def allow_purchase_order(self, allow_purchase_order):\n\n self._allow_purchase_order = allow_purchase_order",
"def enable_third_party_auth():\r\n\r\n from third_party_auth import settings as auth_settings\r\n auth_... | [
"0.8132025",
"0.6221755",
"0.55566573",
"0.5517311",
"0.5347566",
"0.5285754",
"0.5279152",
"0.5249024",
"0.5139754",
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"0.495924",
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"0.49080312",
"0.48960945",
"0.4870046",
"0.48430172",
"0.4819699",
"0.4815291",
"0.47918865... | 0.8973995 | 0 |
Gets the allow_cod of this Customer. Allow COD | def allow_cod(self):
return self._allow_cod | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_cod(self):\n return self._auto_approve_cod",
"def allow_cod(self, allow_cod):\n\n self._allow_cod = allow_cod",
"def get_beneficiary_customer_option(self):\n option = 'A'\n bic = ''\n counterparty_reference = self.acm_obj.CounterpartyAccountRef()\n if ... | [
"0.7140277",
"0.6337631",
"0.6162207",
"0.58091205",
"0.5691653",
"0.56432354",
"0.56432354",
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"0.56432354",
"0.5619494",
"0.56113744",
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"0.5525852",
"0.54850113",
"0.54638743",
"0.5450603",
"0.5444634",
"0.54375887",
"0.54375887",
"0.5... | 0.720659 | 0 |
Sets the allow_cod of this Customer. Allow COD | def allow_cod(self, allow_cod):
self._allow_cod = allow_cod | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_cod(self):\n return self._allow_cod",
"def auto_approve_cod(self, auto_approve_cod):\n\n self._auto_approve_cod = auto_approve_cod",
"def _setbeneficiary_customer_no_option_59(self, val):\n self.swift_obj.BeneficiaryCustomer = val\n self.swift_obj.BeneficiaryCustomer.swift... | [
"0.729959",
"0.6988769",
"0.6086145",
"0.5983467",
"0.58541125",
"0.57755184",
"0.5744954",
"0.5741068",
"0.56729275",
"0.55307794",
"0.5419211",
"0.5359062",
"0.53455514",
"0.5340476",
"0.52812076",
"0.5260953",
"0.524598",
"0.5190892",
"0.51724166",
"0.5171934",
"0.51629126... | 0.8570402 | 0 |
Gets the allow_purchase_order of this Customer. Allow purchase orders by this customer | def allow_purchase_order(self):
return self._allow_purchase_order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_purchase_order(self):\n return self._auto_approve_purchase_order",
"def allow_purchase_order(self, allow_purchase_order):\n\n self._allow_purchase_order = allow_purchase_order",
"def is_billing_admin(self):\n return self._is_billing_admin",
"def allow_3rd_party_billing(s... | [
"0.67609143",
"0.67143464",
"0.56554574",
"0.5336498",
"0.5225903",
"0.52243364",
"0.51814884",
"0.5143573",
"0.50079423",
"0.49739778",
"0.4837262",
"0.48365095",
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"0.47648245",
"0.47585806",
"0.47585806",
"0.47018978",
"0.46895516",
"0.46895516",
"0.46760073",
... | 0.7888027 | 0 |
Sets the allow_purchase_order of this Customer. Allow purchase orders by this customer | def allow_purchase_order(self, allow_purchase_order):
self._allow_purchase_order = allow_purchase_order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_purchase_order(self):\n return self._allow_purchase_order",
"def auto_approve_purchase_order(self, auto_approve_purchase_order):\n\n self._auto_approve_purchase_order = auto_approve_purchase_order",
"def auto_approve_purchase_order(self):\n return self._auto_approve_purchase_orde... | [
"0.7423001",
"0.67228186",
"0.6052454",
"0.5980167",
"0.5460318",
"0.5276938",
"0.5170954",
"0.5079696",
"0.50667834",
"0.50303876",
"0.50279343",
"0.5015787",
"0.49910057",
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"0.49309596",
"0.49174166",
"0.48882842",
"0.48680675",
"0.48383403",
"0.48383403",
"0.4... | 0.8618124 | 0 |
Gets the allow_quote_request of this Customer. Allow quote request | def allow_quote_request(self):
return self._allow_quote_request | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_quote_request(self, allow_quote_request):\n\n self._allow_quote_request = allow_quote_request",
"def allow_purchase_order(self):\n return self._allow_purchase_order",
"def get_enable_sgxquotehelper(self) -> bool:\n # determine the value of constants\n addon_consts = self.g... | [
"0.72689456",
"0.6126457",
"0.5860586",
"0.5839955",
"0.577016",
"0.5698457",
"0.56263775",
"0.5525105",
"0.53959376",
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"0.5181369",
"0.515366",
"0.5148207",
"0.5140561",
"0.51130056",
"0.51130056",
"0.505698... | 0.7837449 | 0 |
Sets the allow_quote_request of this Customer. Allow quote request | def allow_quote_request(self, allow_quote_request):
self._allow_quote_request = allow_quote_request | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_quote_request(self):\n return self._allow_quote_request",
"def allow_purchase_order(self, allow_purchase_order):\n\n self._allow_purchase_order = allow_purchase_order",
"def allow_3rd_party_billing(self, allow_3rd_party_billing):\n\n self._allow_3rd_party_billing = allow_3rd_part... | [
"0.7633311",
"0.6629769",
"0.62486017",
"0.6129808",
"0.5815442",
"0.5715786",
"0.5680128",
"0.55917376",
"0.558616",
"0.54518247",
"0.5450312",
"0.53812516",
"0.53521806",
"0.52422243",
"0.52322334",
"0.51129276",
"0.5105395",
"0.5093881",
"0.50309175",
"0.49818915",
"0.4974... | 0.8899058 | 0 |
Gets the allow_selection_of_address_type of this Customer. Allow selection of residential or business address type | def allow_selection_of_address_type(self):
return self._allow_selection_of_address_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_selection_of_address_type(self, allow_selection_of_address_type):\n\n self._allow_selection_of_address_type = allow_selection_of_address_type",
"def get_type(self):\n types = dict(ADDRESS_TYPE_CHOICES)\n return types.get(self.address_type, \"N/A\")",
"def address_type(self) -> st... | [
"0.69965714",
"0.6991451",
"0.6518308",
"0.64159715",
"0.5924022",
"0.5737748",
"0.55365396",
"0.49901265",
"0.4929045",
"0.49041137",
"0.48814216",
"0.48382127",
"0.48241264",
"0.4792106",
"0.4775736",
"0.4775736",
"0.47209972",
"0.4680714",
"0.46388882",
"0.4633582",
"0.463... | 0.75989187 | 0 |
Sets the allow_selection_of_address_type of this Customer. Allow selection of residential or business address type | def allow_selection_of_address_type(self, allow_selection_of_address_type):
self._allow_selection_of_address_type = allow_selection_of_address_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def allow_selection_of_address_type(self):\n return self._allow_selection_of_address_type",
"def address_type(self, address_type):\n\n self._address_type = address_type",
"def _set_address_type(self, v, load=False):\n try:\n t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name=\"addre... | [
"0.7869283",
"0.6493774",
"0.5815577",
"0.55564404",
"0.5498073",
"0.5162351",
"0.51533425",
"0.50732017",
"0.5067141",
"0.50300115",
"0.49828735",
"0.49612227",
"0.4907661",
"0.48751763",
"0.4806331",
"0.4792663",
"0.4764711",
"0.47517994",
"0.47366992",
"0.47317573",
"0.471... | 0.8617997 | 0 |
Gets the auto_approve_cod of this Customer. Auto approve COD | def auto_approve_cod(self):
return self._auto_approve_cod | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_cod(self, auto_approve_cod):\n\n self._auto_approve_cod = auto_approve_cod",
"def auto_approve_purchase_order(self):\n return self._auto_approve_purchase_order",
"def cauto(self):\n return self['cauto']",
"def cauto(self):\n return self[\"cauto\"]",
"def get_ben... | [
"0.75147635",
"0.61069083",
"0.602281",
"0.59706914",
"0.5576524",
"0.5553883",
"0.5525107",
"0.551598",
"0.5461789",
"0.5355563",
"0.5319641",
"0.5279175",
"0.5263035",
"0.5213337",
"0.5209038",
"0.51998585",
"0.51813096",
"0.51149994",
"0.5029494",
"0.5011473",
"0.5005755",... | 0.8591699 | 0 |
Sets the auto_approve_cod of this Customer. Auto approve COD | def auto_approve_cod(self, auto_approve_cod):
self._auto_approve_cod = auto_approve_cod | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_cod(self):\n return self._auto_approve_cod",
"def auto_approve_purchase_order(self, auto_approve_purchase_order):\n\n self._auto_approve_purchase_order = auto_approve_purchase_order",
"def set_confirmation_code(cls, user_obj: User) -> str:\n confirmation_code = cls.generat... | [
"0.7658426",
"0.6928077",
"0.58672255",
"0.57855976",
"0.57577187",
"0.57492524",
"0.5673078",
"0.5587819",
"0.55171114",
"0.5507733",
"0.55027765",
"0.5407907",
"0.538749",
"0.5338425",
"0.52956164",
"0.5280978",
"0.5180923",
"0.5180101",
"0.5059513",
"0.5049081",
"0.5029308... | 0.9048027 | 0 |
Gets the auto_approve_purchase_order of this Customer. Auto approve purchase orders by this customer | def auto_approve_purchase_order(self):
return self._auto_approve_purchase_order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_purchase_order(self, auto_approve_purchase_order):\n\n self._auto_approve_purchase_order = auto_approve_purchase_order",
"def auto_approve_cod(self):\n return self._auto_approve_cod",
"def make_order_active(self, active_customer_id):\n active_order = self.retrieve_order_by_pay... | [
"0.74536115",
"0.6011969",
"0.56908983",
"0.56183755",
"0.5414985",
"0.5375504",
"0.533279",
"0.5222742",
"0.51791126",
"0.5083033",
"0.50701916",
"0.5031225",
"0.49730587",
"0.4965312",
"0.49168998",
"0.49168998",
"0.4907944",
"0.48955142",
"0.4877225",
"0.48304304",
"0.4825... | 0.8056145 | 0 |
Sets the auto_approve_purchase_order of this Customer. Auto approve purchase orders by this customer | def auto_approve_purchase_order(self, auto_approve_purchase_order):
self._auto_approve_purchase_order = auto_approve_purchase_order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_approve_purchase_order(self):\n return self._auto_approve_purchase_order",
"def auto_approve_cod(self, auto_approve_cod):\n\n self._auto_approve_cod = auto_approve_cod",
"def allow_purchase_order(self, allow_purchase_order):\n\n self._allow_purchase_order = allow_purchase_order",
... | [
"0.7533648",
"0.7108664",
"0.62216985",
"0.60340595",
"0.5921183",
"0.58375263",
"0.5813257",
"0.57364",
"0.5665899",
"0.56618285",
"0.559713",
"0.5568333",
"0.5536843",
"0.55078036",
"0.5493933",
"0.548854",
"0.54638684",
"0.53764236",
"0.53443635",
"0.5342011",
"0.5317043",... | 0.91480243 | 0 |
Gets the automatic_merchant_notes of this Customer. Automatic merchant notes are added to every order placed | def automatic_merchant_notes(self):
return self._automatic_merchant_notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def automatic_merchant_notes(self, automatic_merchant_notes):\n\n self._automatic_merchant_notes = automatic_merchant_notes",
"def getNotes(self):\n return self.__notes",
"def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes",
"def business_notes(se... | [
"0.7668428",
"0.5766891",
"0.57028675",
"0.56388897",
"0.55522865",
"0.55253977",
"0.55190694",
"0.54513943",
"0.54033065",
"0.5367845",
"0.53548414",
"0.53113383",
"0.53113383",
"0.5238234",
"0.51829404",
"0.5145786",
"0.51306903",
"0.5063472",
"0.50511765",
"0.5024999",
"0.... | 0.83806545 | 0 |
Sets the automatic_merchant_notes of this Customer. Automatic merchant notes are added to every order placed | def automatic_merchant_notes(self, automatic_merchant_notes):
self._automatic_merchant_notes = automatic_merchant_notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def automatic_merchant_notes(self):\n return self._automatic_merchant_notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes ... | [
"0.703403",
"0.58362395",
"0.58362395",
"0.58362395",
"0.58362395",
"0.58362395",
"0.5670964",
"0.5481556",
"0.5418983",
"0.5361266",
"0.5322753",
"0.5322753",
"0.52970904",
"0.5255368",
"0.5028528",
"0.50151944",
"0.499326",
"0.4979043",
"0.49085563",
"0.49059683",
"0.488207... | 0.9199884 | 0 |
Sets the billing of this Customer. Billing addresses for this customer | def billing(self, billing):
self._billing = billing | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def billing_contact(self, billing_contact):\n\n self._billing_contact = billing_contact",
"def billing_currency(self, billing_currency):\n\n self._billing_currency = billing_currency",
"def billing_info(self, billing_info):\n\n self._billing_info = billing_info",
"def default_billing(sel... | [
"0.7252403",
"0.69182295",
"0.6791327",
"0.67907035",
"0.5948151",
"0.5948151",
"0.5920023",
"0.58599746",
"0.58599746",
"0.58096415",
"0.5740131",
"0.564657",
"0.56100035",
"0.5595598",
"0.55726916",
"0.55726916",
"0.5554115",
"0.5554115",
"0.5554115",
"0.54979783",
"0.54383... | 0.7718957 | 0 |
Gets the business_notes of this Customer. Business notes (internally visible only) | def business_notes(self):
return self._business_notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNotes(self):\n return self.__notes",
"def getNotes(self):\n logger.debug(\"Func: getNotes\")\n\n return self._currentNotes",
"def notes(self):\n return self._notes",
"def notes(self):\n return reapy.NoteList(self)",
"def get_notes(self, note_limit=200):\n re... | [
"0.69043094",
"0.6661534",
"0.659805",
"0.6562779",
"0.6497148",
"0.64939034",
"0.6433012",
"0.63636637",
"0.63366354",
"0.6201325",
"0.6201325",
"0.6086248",
"0.60721266",
"0.60382587",
"0.6000788",
"0.59460074",
"0.5917981",
"0.5908841",
"0.5891362",
"0.5873272",
"0.5763616... | 0.85966206 | 0 |
Sets the business_notes of this Customer. Business notes (internally visible only) | def business_notes(self, business_notes):
if business_notes is not None and len(business_notes) > 2000:
raise ValueError("Invalid value for `business_notes`, length must be less than or equal to `2000`")
self._business_notes = business_notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def business_no... | [
"0.64642525",
"0.64642525",
"0.64642525",
"0.64642525",
"0.64642525",
"0.6354019",
"0.63490003",
"0.61379",
"0.60320604",
"0.60020274",
"0.5867036",
"0.58275306",
"0.56988704",
"0.56658614",
"0.5663424",
"0.5643399",
"0.5632016",
"0.56203383",
"0.55994624",
"0.5532145",
"0.53... | 0.79783094 | 0 |
Sets the cards of this Customer. Credit Cards for this customer | def cards(self, cards):
self._cards = cards | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cards(\n self,\n cards: Union[List[Tuple[int, str, str]], List[Any]]\n ) -> None:\n self._cards: List[List[Tuple[int, str, str]]] = [cards]",
"def card(self, c=None):\n if c: self._card = c\n return self._card",
"def cards(self) -> None:\n self._cards = ... | [
"0.6608554",
"0.6414294",
"0.63057697",
"0.6276307",
"0.61572474",
"0.61043686",
"0.6075938",
"0.6067061",
"0.60300416",
"0.6012156",
"0.5965581",
"0.5942135",
"0.5937874",
"0.5890377",
"0.5870525",
"0.5861895",
"0.58576655",
"0.58041275",
"0.5801309",
"0.57943904",
"0.574153... | 0.7751188 | 1 |
Gets the cc_emails of this Customer. Additional emails to CC notification | def cc_emails(self):
return self._cc_emails | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cc_email_address(self):\n return self._cc_recipients",
"def bcc_email_address(self):\n return self._bcc_recipients",
"def get_referral_emails(self):\n profiles = self.profiles.filter(should_get_notifications=True)\n return [profile.user.email for profile in profiles]",
"def li... | [
"0.75888234",
"0.64689773",
"0.64344084",
"0.62742186",
"0.6156105",
"0.6056084",
"0.60027933",
"0.600019",
"0.5974224",
"0.5797421",
"0.5784598",
"0.57692915",
"0.5691581",
"0.56686693",
"0.56602067",
"0.56108487",
"0.560231",
"0.557099",
"0.54601586",
"0.5456341",
"0.542864... | 0.8322627 | 0 |
Sets the cc_emails of this Customer. Additional emails to CC notification | def cc_emails(self, cc_emails):
self._cc_emails = cc_emails | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cc_emails(self):\n return self._cc_emails",
"def cc_email_address(self, val: list):\n self._cc_recipients = []\n if val is not None:\n for item in val:\n if isinstance(item, EmailAddress):\n self._cc_recipients.append(item)",
"def customer_e... | [
"0.6630792",
"0.64610815",
"0.62725186",
"0.5873671",
"0.5845759",
"0.5813783",
"0.54804736",
"0.53381747",
"0.5235436",
"0.5226068",
"0.5226068",
"0.51918876",
"0.5156741",
"0.51527923",
"0.5096081",
"0.50736475",
"0.5039553",
"0.50049907",
"0.49940872",
"0.49940872",
"0.499... | 0.8490713 | 0 |
Gets the customer_profile_oid of this Customer. Customer profile object identifier | def customer_profile_oid(self):
return self._customer_profile_oid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_profile_id(self, profile):\n return profile['id']",
"def customer_profile_oid(self, customer_profile_oid):\n\n self._customer_profile_oid = customer_profile_oid",
"def get_profile_id(self):\n # get list of user profile\n results = self.service.userProfiles().list().execute()... | [
"0.7198418",
"0.717386",
"0.65548843",
"0.6479222",
"0.6393283",
"0.6392906",
"0.6084148",
"0.6040575",
"0.5961779",
"0.58965856",
"0.5801002",
"0.57967854",
"0.57367575",
"0.572492",
"0.5722918",
"0.57189894",
"0.5676127",
"0.56760955",
"0.5643734",
"0.5589623",
"0.55654556"... | 0.91109216 | 0 |
Sets the customer_profile_oid of this Customer. Customer profile object identifier | def customer_profile_oid(self, customer_profile_oid):
self._customer_profile_oid = customer_profile_oid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def customer_profile_oid(self):\n return self._customer_profile_oid",
"def set_customer_id(self, case_obj: Family) -> None:\n self._set_customer_id(case_obj.customer.internal_id)",
"def test_update_profile(self):\n self.cim.update_profile(\n customer_id=u\"222\",\n de... | [
"0.7076937",
"0.67138827",
"0.6116765",
"0.6058371",
"0.5934972",
"0.5934972",
"0.5934972",
"0.5934972",
"0.5908919",
"0.5619792",
"0.56103975",
"0.547714",
"0.5429392",
"0.5404184",
"0.53489465",
"0.53489465",
"0.5315293",
"0.5237773",
"0.5218106",
"0.5215164",
"0.5189874",
... | 0.90416175 | 0 |
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