query stringlengths 10 2k | document stringlengths 20 51.2k | metadata dict | negatives listlengths 0 101 | negative_scores listlengths 0 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
States are not equal when substances are not the same. | def test_not_eq_sub(self):
st_1 = State(substance="water", T=Q_(400.0, "K"), p=Q_(101325.0, "Pa"))
st_2 = State(substance="ammonia", T=Q_(400.0, "K"), p=Q_(101325.0, "Pa"))
assert not st_1 == st_2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_eq_not_two_states(self):\n assert not State(substance=\"water\") == 3\n assert not 3 == State(substance=\"water\")",
"def test_not_eq(self):\n st_1 = State(substance=\"water\", T=Q_(400.0, \"K\"), p=Q_(101325.0, \"Pa\"))\n st_2 = State(substance=\"water\", T=Q_(300.0, \"K\"),... | [
"0.82808363",
"0.79227084",
"0.69984084",
"0.6873306",
"0.673153",
"0.66939735",
"0.6568405",
"0.6506088",
"0.64778894",
"0.64763105",
"0.6437843",
"0.63877356",
"0.6349658",
"0.6294364",
"0.62919545",
"0.6291824",
"0.62673426",
"0.6244693",
"0.62310535",
"0.6223238",
"0.6215... | 0.843503 | 0 |
Specifying too many properties should raise a ValueError. | def test_too_many_props(self):
with pytest.raises(ValueError):
State(
substance="water",
T=Q_(300, "K"),
p=Q_(101325, "Pa"),
u=Q_(100, "kJ/kg"),
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_too_few_props(self):\n with pytest.raises(ValueError):\n State(substance=\"water\", T=Q_(300, \"K\"))",
"def test_should_raise_error_if_inconsistent_properties(self):\r\n edge_spec = {\r\n 'type': 'edge',\r\n 'label': 'subscribed_to',\r\n 'primar... | [
"0.6713041",
"0.6603682",
"0.62248707",
"0.6221275",
"0.61699057",
"0.614503",
"0.610601",
"0.60891724",
"0.60261506",
"0.5967855",
"0.58871776",
"0.5837227",
"0.58025193",
"0.57816184",
"0.57575655",
"0.5751086",
"0.57486403",
"0.5737626",
"0.57250184",
"0.5681589",
"0.56664... | 0.71155095 | 0 |
Specifying too few properties should raise a value error. | def test_too_few_props(self):
with pytest.raises(ValueError):
State(substance="water", T=Q_(300, "K")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_too_many_props(self):\n with pytest.raises(ValueError):\n State(\n substance=\"water\",\n T=Q_(300, \"K\"),\n p=Q_(101325, \"Pa\"),\n u=Q_(100, \"kJ/kg\"),\n )",
"def test_should_raise_error_if_inconsistent_properti... | [
"0.691417",
"0.65681446",
"0.6393225",
"0.6357369",
"0.6268618",
"0.62368995",
"0.62219155",
"0.6212051",
"0.6203742",
"0.6201047",
"0.6150353",
"0.6140446",
"0.6125671",
"0.60866463",
"0.6051994",
"0.6046648",
"0.60387206",
"0.60310185",
"0.5975781",
"0.5974152",
"0.59425783... | 0.68810195 | 1 |
Negative absolute pressures should raise a StateError. | def test_negative_pressure(self):
with pytest.raises(StateError):
State(substance="water", T=Q_(300, "K"), p=Q_(-101325, "Pa")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_negative_volume(self):\n with pytest.raises(StateError):\n State(substance=\"water\", T=Q_(300, \"K\"), v=Q_(-10.13, \"m**3/kg\"))",
"def test_quality_lt_zero(self):\n with pytest.raises(StateError):\n State(substance=\"water\", x=Q_(-1.0, \"dimensionless\"), p=Q_(101... | [
"0.6810716",
"0.6713059",
"0.6393805",
"0.5986361",
"0.5780576",
"0.5770264",
"0.5723537",
"0.5716454",
"0.56924635",
"0.56550866",
"0.5608319",
"0.55696857",
"0.5552951",
"0.55477136",
"0.5508744",
"0.5501384",
"0.549518",
"0.54909736",
"0.54727924",
"0.5471732",
"0.54639775... | 0.72122383 | 0 |
Setting bad dimensions for the input property raises a StateError. | def test_bad_dimensions(self, prop: str):
kwargs = {prop: Q_(1.0, "dimensionless")}
if prop == "v":
kwargs["T"] = Q_(300.0, "K")
else:
kwargs["v"] = Q_(1.0, "m**3/kg")
with pytest.raises(StateError):
State(substance="water", **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_invalid_input_prop(self):\n with pytest.raises(ValueError):\n State(\n substance=\"water\", x=Q_(0.5, \"dimensionless\"), bad_prop=Q_(101325, \"Pa\")\n )",
"def test_bad_x_dimensions(self):\n with pytest.raises(StateError):\n State(substance=... | [
"0.70860714",
"0.678234",
"0.6377732",
"0.62351644",
"0.6171897",
"0.6063006",
"0.6034769",
"0.6015262",
"0.59894466",
"0.5987992",
"0.59762096",
"0.59451014",
"0.59425527",
"0.59303266",
"0.59287095",
"0.5853424",
"0.58482957",
"0.58220845",
"0.5759549",
"0.57540387",
"0.574... | 0.7068877 | 1 |
Setting bad dimensions for quality raises a StateError. Must be done in a separate test because the "dimensionless" sentinel value used in the other test is actually the correct dimension for quality. | def test_bad_x_dimensions(self):
with pytest.raises(StateError):
State(substance="water", T=Q_(300.0, "K"), x=Q_(1.01325, "K")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_bad_dimensions(self, prop: str):\n kwargs = {prop: Q_(1.0, \"dimensionless\")}\n if prop == \"v\":\n kwargs[\"T\"] = Q_(300.0, \"K\")\n else:\n kwargs[\"v\"] = Q_(1.0, \"m**3/kg\")\n with pytest.raises(StateError):\n State(substance=\"water\", *... | [
"0.711872",
"0.6767196",
"0.6627679",
"0.6209383",
"0.6091683",
"0.6090364",
"0.6045845",
"0.6036081",
"0.6023392",
"0.6005132",
"0.5998497",
"0.5950686",
"0.5938751",
"0.59303045",
"0.588569",
"0.58840954",
"0.5874641",
"0.58311474",
"0.5811564",
"0.5772429",
"0.5764034",
... | 0.694339 | 1 |
Accessing attributes that aren't one of the properties or pairs raises. | def test_bad_get_property(self):
s = State(substance="water", T=Q_(400.0, "K"), p=Q_(101325.0, "Pa"))
with pytest.raises(AttributeError):
s.bad_get | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getattribute__(self,name):\n try:\n return object.__getattribute__(self,name)\n except AttributeError:\n extraPO = object.__getattribute__(self,'_extraPO')\n\n if hasattr(extraPO,name):\n return getattr(extraPO,name) # HIDDEN!\n\n _attr... | [
"0.65133715",
"0.6202257",
"0.6189909",
"0.60724235",
"0.60174316",
"0.5985178",
"0.59813917",
"0.59092987",
"0.59054035",
"0.5887275",
"0.58833724",
"0.5864438",
"0.585859",
"0.5850074",
"0.58483046",
"0.5827751",
"0.58149964",
"0.58121717",
"0.5804015",
"0.57854235",
"0.578... | 0.6509815 | 1 |
Test that a remote home L{loadChildren} works. | def test_homechild_loadallobjects(self):
home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
self.assertTrue(home01 is not None)
children01 = yield home01.loadChildren()
names01 = [child.name() for child in children01]
ids01 = [chil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_homechild_listobjects(self):\n\n home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name=\"user01\", create=True)\n self.assertTrue(home01 is not None)\n children01 = yield home01.listChildren()\n yield self.commitTransaction(0)\n\n home = yield self.... | [
"0.69731224",
"0.61677164",
"0.6042571",
"0.6015302",
"0.59528166",
"0.58976364",
"0.5875181",
"0.587056",
"0.5750654",
"0.57373816",
"0.57092553",
"0.5671516",
"0.56219393",
"0.5621391",
"0.5576499",
"0.5576499",
"0.55605936",
"0.552817",
"0.55038",
"0.54879546",
"0.5454167"... | 0.75470066 | 0 |
Test that a remote home L{loadChildren} works. | def test_homechild_objectwith(self):
home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
self.assertTrue(home01 is not None)
calendar01 = yield home01.childWithName("calendar")
yield self.commitTransaction(0)
home = yield self._rem... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_homechild_loadallobjects(self):\n\n home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name=\"user01\", create=True)\n self.assertTrue(home01 is not None)\n children01 = yield home01.loadChildren()\n names01 = [child.name() for child in children01]\n ... | [
"0.7547497",
"0.6973483",
"0.61700195",
"0.6015915",
"0.59506714",
"0.5895117",
"0.5876951",
"0.5869469",
"0.5748563",
"0.5739482",
"0.5710386",
"0.56728685",
"0.5623981",
"0.5622312",
"0.55777234",
"0.55777234",
"0.5560308",
"0.5529578",
"0.5503385",
"0.5487204",
"0.5455855"... | 0.60419405 | 3 |
Test that a remote object resource L{setComponent} works. | def test_objectresource_setcomponent(self):
home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
self.assertTrue(home01 is not None)
calendar01 = yield home01.childWithName("calendar")
yield calendar01.createCalendarObjectWithName("1.ics", C... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_objectresource_component(self):\n\n home01 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name=\"user01\", create=True)\n self.assertTrue(home01 is not None)\n calendar01 = yield home01.childWithName(\"calendar\")\n yield calendar01.createCalendarObjectWithName... | [
"0.6539774",
"0.6298994",
"0.62562716",
"0.61385554",
"0.61363655",
"0.61001855",
"0.603852",
"0.59159076",
"0.5911475",
"0.5862129",
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"0.5762465",
"0.57464576",
"0.57112545",
"0.56989205",
"0.5681898",
"0.56... | 0.72049016 | 0 |
remove accents, upper and punctuation and split into list compare to stop_words reference and remove found items | def clean_string(self):
cleaned = unidecode(self.user_query).lower()
cleaned = re.compile("\w+").findall(cleaned)
return [item for item in cleaned if item not in stop_words] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stopword_filter(words):\n new_words = []\n for w in words:\n if w in stopwords.words(\"german\"): continue\n else: new_words += [w]\n return new_words",
"def remove_stopwords_fun(self):\n tokens = str(self.doc).split()\n cleaned_tokens = [token for token in tokens\n ... | [
"0.72972363",
"0.7257799",
"0.71417207",
"0.71125436",
"0.71042067",
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"0.7079376",
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"0.7013695",
"0.7000867",
"0.7000867",
"0.7000867",
"0.7000867",
"0.70000... | 0.72592735 | 1 |
Builds a fully connected capsule layer. Given an input tensor of shape `[batch, input_dim, input_atoms]`, this op | def capsule(input_tensor,
input_dim,
output_dim,
layer_name,
input_atoms=8,
output_atoms=8,
**routing_args):
with tf.variable_scope(layer_name):
# weights variable will hold the state of the weights for the layer
weights = varia... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conv_slim_capsule(input_tensor,\n input_dim,\n output_dim,\n layer_name,\n input_atoms=8,\n output_atoms=8,\n stride=2,\n kernel_size=5,\n padd... | [
"0.6867665",
"0.6325789",
"0.5803091",
"0.575039",
"0.5691336",
"0.56583816",
"0.56410503",
"0.56189513",
"0.5585061",
"0.558173",
"0.5563669",
"0.5554573",
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"0.5507673",
"0.5485954",
"0.54833984",
"0.54604906",
"0.54243445",
"0.5423439",
"0.54152966",
"0.5413802... | 0.7286712 | 0 |
Returns True if command for .hs can be invoked | def is_enabled_haskell_command(view = None, must_be_project=True, must_be_main=False, must_be_file = False):
window, view, file_shown_in_view = get_haskell_command_window_view_file_project(view)
if not window or not view:
return False
if must_be_file and not file_shown_in_view:
return Fals... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hasCommand():\n args = sys.argv[1:]\n if '--help' in args:\n return False\n if '-h' in args:\n return False\n for arg in args:\n if arg and not arg.startswith('-'):\n return True\n return False",
"def is_executable(self):\n raise NotImplementedError()",
... | [
"0.6825338",
"0.63825595",
"0.63114893",
"0.6306956",
"0.6228417",
"0.6183935",
"0.61634946",
"0.6161326",
"0.6152913",
"0.6150453",
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"0.60518086",
"0.60512227",
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"0.6011512",
"0.5987021",
"0.59834313",
"0.59825546",
"0.5971442",
"0.5968849",
"0.59186... | 0.64808047 | 1 |
Returns current cabaldev sandbox or 'cabal' | def current_cabal():
if get_setting_async('use_cabal_dev'):
return get_setting_async('cabal_dev_sandbox')
else:
return 'cabal' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_sandbox():\n if get_setting_async('use_cabal_dev'):\n return get_setting_async('cabal_dev_sandbox')\n else:\n return None",
"def get_current_environment():\n # Search for the environment variable set by the hutch python setup\n env = os.getenv('CONDA_ENVNAME')\n # Otherwi... | [
"0.835338",
"0.6345757",
"0.6232153",
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"0.56610507",
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"0.5453962",
"0.54171276",
"0.5415763",
"0.5412264",
"0.5366288",
"0.5354206"... | 0.84304273 | 0 |
Returns current cabaldef sandbox or None | def current_sandbox():
if get_setting_async('use_cabal_dev'):
return get_setting_async('cabal_dev_sandbox')
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_cabal():\n if get_setting_async('use_cabal_dev'):\n return get_setting_async('cabal_dev_sandbox')\n else:\n return 'cabal'",
"def get_current_environment():\n # Search for the environment variable set by the hutch python setup\n env = os.getenv('CONDA_ENVNAME')\n # Otherw... | [
"0.72634137",
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"0.5275435",
"0.5256564",
"0.52563494",
"0.52349573",
"0.5218031",
"0.... | 0.7854318 | 0 |
Get setting from any thread Note, that setting must be loaded before by get_setting from main thread | def get_setting_async(key, default=None):
# Reload it in main thread for future calls of get_setting_async
sublime.set_timeout(lambda: update_setting(key), 0)
with sublime_haskell_settings as settings:
if key not in settings:
# Load it in main thread, but for now all we can do is result ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_setting(self, setting):\n return self.do_rpc(\"get_setting\", key=key)",
"def _value(self):\n # If we have not been initialized, it means we're in a thread: take the\n # value from the root thread.\n if not hasattr(self._local, '_value'):\n self._local._value = self... | [
"0.62968093",
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"0.53416437",
"0.53305244",
"0.53260916",
"0.53233534",
"0.53217125",
"0.52909034",
"0.5... | 0.6773107 | 0 |
Get the path to where this package is installed | def sublime_haskell_package_path():
return os.path.dirname(os.path.realpath(__file__)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path(self):\n installed_packages_folder_path = site.getsitepackages()[0]\n return f'{installed_packages_folder_path}/{SITE_PACKAGES_FOLDER_NAME}'",
"def get_package_dir():\n return Path(__file__).parent",
"def GetPackageDirectory():\n return os.path.dirname(__file__)",
"def get_instal... | [
"0.82700145",
"0.8006811",
"0.79788554",
"0.7880663",
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"0.76820534",
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"0.76764643",
"0.747839",
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"0.74449986",
"0.7433358",
"0.7414158",
"0.7296697",
"0.72786695",
"0.7267651",
"0.72396743",
"0.72396743",
"0.72273105",
"0.71784407",
"0.71461... | 0.6823971 | 40 |
Visualizes estimates from one image. | def visualize(
samples, predictions, pred_poses, im_ind, crop_size, output_scale,
model_store, renderer, vis_dir):
tf.logging.info('Visualization for: {}'.format(
samples[common.IMAGE_PATH][0].decode('utf8')))
# Size of a visualization grid tile.
tile_size = (300, 225)
# Extension of the saved... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_env(self, img):\n plt.figure(1)\n plt.subplot(111)\n plt.imshow(img, interpolation=\"nearest\")\n plt.show()",
"def visualize(image, preds, fps):\n # show inference info\n fps_text = \"FPS : {:.2f}\".format(fps)\n cv2.putText(image, fps_text, (11, 40), cv2.FONT_HERSH... | [
"0.68167734",
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"0.6232943",
"0.6194941",
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"0.6117432",
"0.61116505",
"0.60924476",
"0.6082084",
"0.6064306",
"0.60435134",
"0.6043386",
"0.60239655",
"0.60179704",
"0.6014... | 0.0 | -1 |
Retrieve the model stored in this checkpoint. | def get_model(self, model: Optional[torch.nn.Module] = None) -> torch.nn.Module:
with self.as_directory() as tempdir:
model_path = os.path.join(tempdir, self.MODEL_FILENAME)
if not os.path.exists(model_path):
raise RuntimeError(
"`model.pt` not found w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getModel(self):\n # Check if the dir is empty\n if os.path.exists(self.savedModelsPath) and os.listdir(self.savedModelsPath):\n log.info(\"Loading model from checkpoint %s\" % self.savedModelsPath)\n model = ModelFactory.loadFromCheckpoint(self.savedModelsPath)\n else... | [
"0.79251266",
"0.77934444",
"0.77934444",
"0.77373654",
"0.76767665",
"0.76767665",
"0.76767665",
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"0.7560977",
"0.7529371",
"0.7476861",
"0.7464899",
"0.7431397",
"0... | 0.7159487 | 31 |
Retrieve the model stored in this checkpoint. | def get_model(self, model: Optional[torch.nn.Module] = None) -> torch.nn.Module:
saved_model, _ = _load_checkpoint_dict(self, "TorchTrainer")
if isinstance(saved_model, torch.nn.Module):
if model:
warnings.warn(
"TorchCheckpoint already contains all infor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getModel(self):\n # Check if the dir is empty\n if os.path.exists(self.savedModelsPath) and os.listdir(self.savedModelsPath):\n log.info(\"Loading model from checkpoint %s\" % self.savedModelsPath)\n model = ModelFactory.loadFromCheckpoint(self.savedModelsPath)\n else... | [
"0.79251266",
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"0.77934444",
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"0.76767665",
"0.76767665",
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"0.7560977",
"0.7529371",
"0.7476861",
"0.7464899",
"0.7431397",
"0... | 0.70137835 | 40 |
Sets the dt_land_slide_time of this LandslideViewModel. | def dt_land_slide_time(self, dt_land_slide_time):
self._dt_land_slide_time = dt_land_slide_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dt_land_slide_time_end(self, dt_land_slide_time_end):\n\n self._dt_land_slide_time_end = dt_land_slide_time_end",
"def dt_off_land_slide_time(self, dt_off_land_slide_time):\n\n self._dt_off_land_slide_time = dt_off_land_slide_time",
"def dt_off_land_slide_time_end(self, dt_off_land_slide_time... | [
"0.7219535",
"0.69628906",
"0.5803215",
"0.56844133",
"0.56503665",
"0.56335217",
"0.5616314",
"0.5570946",
"0.55504394",
"0.5502447",
"0.54805285",
"0.5420679",
"0.54078424",
"0.536137",
"0.53596956",
"0.52255434",
"0.5212031",
"0.51737964",
"0.5143329",
"0.51330763",
"0.511... | 0.86971784 | 0 |
Sets the land_slide_tid of this LandslideViewModel. | def land_slide_tid(self, land_slide_tid):
self._land_slide_tid = land_slide_tid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_trigger_tid(self, land_slide_trigger_tid):\n\n self._land_slide_trigger_tid = land_slide_trigger_tid",
"def land_slide_size_tid(self, land_slide_size_tid):\n\n self._land_slide_size_tid = land_slide_size_tid",
"def land_slide_name(self, land_slide_name):\n\n self._land_slide... | [
"0.74662006",
"0.74592316",
"0.5954114",
"0.5786251",
"0.5719078",
"0.5188832",
"0.5188832",
"0.5188832",
"0.5188832",
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"0.49208993",
"0.4891214",
"0.48605734",
"0.48222616",
"0.48222616",
"0.48222616",
"0.4822... | 0.87264055 | 0 |
Sets the land_slide_name of this LandslideViewModel. | def land_slide_name(self, land_slide_name):
self._land_slide_name = land_slide_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_trigger_name(self, land_slide_trigger_name):\n\n self._land_slide_trigger_name = land_slide_trigger_name",
"def land_slide_size_name(self, land_slide_size_name):\n\n self._land_slide_size_name = land_slide_size_name",
"def set_name(self, _name):\n self.name = _name",
"def ... | [
"0.75669974",
"0.72538644",
"0.619062",
"0.61273307",
"0.6058072",
"0.6058072",
"0.6058072",
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"0.5952358",
"0.5950012",
"0.59441054",
"0.5933231",
"0.5923952",
"0.5905573... | 0.8808541 | 0 |
Sets the land_slide_trigger_tid of this LandslideViewModel. | def land_slide_trigger_tid(self, land_slide_trigger_tid):
self._land_slide_trigger_tid = land_slide_trigger_tid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_tid(self, land_slide_tid):\n\n self._land_slide_tid = land_slide_tid",
"def land_slide_trigger_name(self, land_slide_trigger_name):\n\n self._land_slide_trigger_name = land_slide_trigger_name",
"def trigger_id(self, trigger_id):\n\n self._trigger_id = trigger_id",
"def lan... | [
"0.7079232",
"0.62298405",
"0.598309",
"0.5893759",
"0.52889764",
"0.50392246",
"0.48819435",
"0.48145127",
"0.48001224",
"0.478176",
"0.4780856",
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"0.46451673",
"0.460823",
"0.460823",
"0.460823",
"0.460823",
"0.460823",
... | 0.87317926 | 0 |
Sets the land_slide_trigger_name of this LandslideViewModel. | def land_slide_trigger_name(self, land_slide_trigger_name):
self._land_slide_trigger_name = land_slide_trigger_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_name(self, land_slide_name):\n\n self._land_slide_name = land_slide_name",
"def trigger_name(self, trigger_name: \"str\"):\n self._attrs[\"triggerName\"] = trigger_name",
"def land_slide_trigger_tid(self, land_slide_trigger_tid):\n\n self._land_slide_trigger_tid = land_slide... | [
"0.74336916",
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"0.5202438",
"0.5178088",
"0.51763123",
"0.51721793",
"0.5154824",
"0.51548... | 0.8938869 | 0 |
Sets the land_slide_size_tid of this LandslideViewModel. | def land_slide_size_tid(self, land_slide_size_tid):
self._land_slide_size_tid = land_slide_size_tid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_size_name(self, land_slide_size_name):\n\n self._land_slide_size_name = land_slide_size_name",
"def land_slide_tid(self, land_slide_tid):\n\n self._land_slide_tid = land_slide_tid",
"def set_size(self, size):\n \n self.width = size[0]\n self.height = size[1]",
... | [
"0.6876164",
"0.65713716",
"0.6247053",
"0.6006086",
"0.5982348",
"0.58198637",
"0.5738395",
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"0.53933614",
"0.53933614",
"0.5389313",
"0.5380527",
"0.5369201",
"0.536427",... | 0.87594175 | 0 |
Sets the land_slide_size_name of this LandslideViewModel. | def land_slide_size_name(self, land_slide_size_name):
self._land_slide_size_name = land_slide_size_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_name(self, land_slide_name):\n\n self._land_slide_name = land_slide_name",
"def land_slide_size_tid(self, land_slide_size_tid):\n\n self._land_slide_size_tid = land_slide_size_tid",
"def land_slide_trigger_name(self, land_slide_trigger_name):\n\n self._land_slide_trigger_nam... | [
"0.70535576",
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"0.5373643",
"0.5373643",
"0.535486",
"0.5337886",
"0.5326846",
... | 0.88934284 | 0 |
Sets the dt_land_slide_time_end of this LandslideViewModel. | def dt_land_slide_time_end(self, dt_land_slide_time_end):
self._dt_land_slide_time_end = dt_land_slide_time_end | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dt_off_land_slide_time_end(self, dt_off_land_slide_time_end):\n\n self._dt_off_land_slide_time_end = dt_off_land_slide_time_end",
"def end_date_time(self, end_date_time):\n\n self._end_date_time = end_date_time",
"def end_time(self, end_time):\n\n self._end_time = end_time",
"def end... | [
"0.79389036",
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"0.624188",
"0.624188",
"0.624188",
"0.6231079",
"0.6... | 0.8900883 | 0 |
Sets the geo_hazard_name of this LandslideViewModel. | def geo_hazard_name(self, geo_hazard_name):
self._geo_hazard_name = geo_hazard_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def land_slide_trigger_name(self, land_slide_trigger_name):\n\n self._land_slide_trigger_name = land_slide_trigger_name",
"def geo_hazard_tid(self, geo_hazard_tid):\n\n self._geo_hazard_tid = geo_hazard_tid",
"def geo_hazard_tid(self, geo_hazard_tid):\n\n self._geo_hazard_tid = geo_hazard_... | [
"0.6323422",
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"0.5356345",
"0.5337057",
"0.5329886",
"0.5327257",
"0.5286704",
... | 0.88625747 | 1 |
Sets the activity_influenced_tid of this LandslideViewModel. | def activity_influenced_tid(self, activity_influenced_tid):
self._activity_influenced_tid = activity_influenced_tid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activity_influenced_name(self, activity_influenced_name):\n\n self._activity_influenced_name = activity_influenced_name",
"def set_tid(self, tid):\n self.__tid = tid",
"def land_slide_tid(self, land_slide_tid):\n\n self._land_slide_tid = land_slide_tid",
"def damage_extent_tid(self, ... | [
"0.6191259",
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"0.45697528",
"0.45691535",
"0.45588946",
"0.45266706",
"0.45266706",
"0.... | 0.8416941 | 0 |
Sets the activity_influenced_name of this LandslideViewModel. | def activity_influenced_name(self, activity_influenced_name):
self._activity_influenced_name = activity_influenced_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activity_influenced_tid(self, activity_influenced_tid):\n\n self._activity_influenced_tid = activity_influenced_tid",
"def map_activity_name(self, name):\n newname = self.amappings.get(name)\n if newname is None:\n return 'Other'\n else:\n return newname",
... | [
"0.5815861",
"0.569146",
"0.5454217",
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"0.5171618",
"0.5165369",
"0.5165369",
"0.51511717",
"0.51440233"... | 0.8486172 | 0 |
Sets the forecast_accurate_name of this LandslideViewModel. | def forecast_accurate_name(self, forecast_accurate_name):
self._forecast_accurate_name = forecast_accurate_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forecast_accurate_tid(self, forecast_accurate_tid):\n\n self._forecast_accurate_tid = forecast_accurate_tid",
"def forecast_date(self, forecast_date):\n self._forecast_date = forecast_date.strftime(\"%a %b %d\")",
"def enable_forecast(self, enable_forecast):\n\n self._enable_forecast =... | [
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"0.47040147",
"0.46323523",
"0.46076682",
"0.4540731",
"0.45308232",
"0... | 0.8883575 | 0 |
Sets the damage_extent_tid of this LandslideViewModel. | def damage_extent_tid(self, damage_extent_tid):
self._damage_extent_tid = damage_extent_tid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def damage_extent_name(self, damage_extent_name):\n\n self._damage_extent_name = damage_extent_name",
"def tower_damage(self, tower_damage):\n\n self._tower_damage = tower_damage",
"def land_slide_size_tid(self, land_slide_size_tid):\n\n self._land_slide_size_tid = land_slide_size_tid",
... | [
"0.67297006",
"0.5751202",
"0.572754",
"0.5525234",
"0.51919156",
"0.51038945",
"0.50611943",
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"0.4926995",
"0.49112517",
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"0.4820465",
"0.48068848",
"0.48064297",
"0.4769242",
"0.47372717",
"0.47367844",
"0.47... | 0.8567385 | 0 |
Sets the damage_extent_name of this LandslideViewModel. | def damage_extent_name(self, damage_extent_name):
self._damage_extent_name = damage_extent_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def damage_extent_tid(self, damage_extent_tid):\n\n self._damage_extent_tid = damage_extent_tid",
"def land_slide_size_name(self, land_slide_size_name):\n\n self._land_slide_size_name = land_slide_size_name",
"def tower_damage(self, tower_damage):\n\n self._tower_damage = tower_damage",
... | [
"0.6534705",
"0.59690756",
"0.5644936",
"0.55477685",
"0.52566946",
"0.52185076",
"0.520311",
"0.51807725",
"0.5176838",
"0.5176838",
"0.51660454",
"0.51587",
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"0.51012367",
"0.51012367",
"0.5090892",
"0.5090892",
"0.5090892",
"0.5090892",
"0.507553... | 0.8837939 | 0 |
Sets the utm_zone_start of this LandslideViewModel. | def utm_zone_start(self, utm_zone_start):
self._utm_zone_start = utm_zone_start | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def utm_north_start(self, utm_north_start):\n\n self._utm_north_start = utm_north_start",
"def utm_east_start(self, utm_east_start):\n\n self._utm_east_start = utm_east_start",
"def start(self, value):\n self.set_property(\"start\", DateTimeTimeZone.parse(value))",
"def _set_start(self, ... | [
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"0.53537965",
"0.5269706",
"0.5269706",
"0.5269706"... | 0.83880484 | 0 |
Sets the dt_off_land_slide_time_end of this LandslideViewModel. | def dt_off_land_slide_time_end(self, dt_off_land_slide_time_end):
self._dt_off_land_slide_time_end = dt_off_land_slide_time_end | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dt_off_land_slide_time(self, dt_off_land_slide_time):\n\n self._dt_off_land_slide_time = dt_off_land_slide_time",
"def dt_land_slide_time_end(self, dt_land_slide_time_end):\n\n self._dt_land_slide_time_end = dt_land_slide_time_end",
"def end_date_time(self, end_date_time):\n\n self._en... | [
"0.8042958",
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"0.59637755",
"0.59442294",
"0.5923649",
"0.... | 0.89746404 | 0 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, LandslideViewModel):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
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"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
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"0.81410587",
"0.8132492",
"0.8093973",
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"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Print to STDERR (as opposed to STDOUT) | def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eprint(errmsg):\n print(errmsg, file=STDERR)",
"def print_to_stderr(msg):\n sys.stderr.write(msg)",
"def printerr(*args, **kwargs):\n console_print(sys.stderr, *args, **kwargs)",
"def err_print(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)",
"def printerr(msg):\n print(msg,... | [
"0.77311355",
"0.76423746",
"0.76404876",
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"0.6991108",
"0.6959241",
"0.6950453",
"0.69390243",
"0.69293225",
"0.691... | 0.6756693 | 29 |
Determine whether the requested algorithm name is compatible with this cipher | def validate_algorithm(self, algorithm):
# type: (Text) -> None
if not algorithm == self.java_name:
raise InvalidAlgorithmError(
'Requested algorithm "{requested}" is not compatible with cipher "{actual}"'.format(
requested=algorithm, actual=self.java_name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_compatibility(cipher, mode):\n\n chiper_obj = cipher_params(cipher, os.urandom(length_by_cipher[cipher]))[0] #need to be object, not interface, to validate_for_algorithm work\n if chiper_obj.name == \"ChaCha20\":\n return True\n mode_object = None\n if mode == 'CBC':\n mode_obje... | [
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"0.5707566",
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"0.5670079",
"0.5629978",
"0.5629978",
"0.56100786",
"0.5537851",
"0.5515106",
"0.5451762",
... | 0.71650994 | 0 |
Return a saved state value, None if item is undefined. | def __getitem__(self, item):
return self._state["data"].get(item, None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getattr__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getattr__(self, item):\r\n retur... | [
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"0.5684511",
"0.5684511",
"0.5684511",
"0.5684511... | 0.6358824 | 4 |
Return a saved state value, None if item is undefined. | def __getattr__(self, item):
return self._state["data"].get(item, None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\r\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return self._state[\"data\"].get(item, None)",
"def __getitem__(self, item):\n return se... | [
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"0.5721914",
"0.5684511",
"0.5684511",
"0.5684511",
"0.5684511",
... | 0.61004424 | 11 |
Objective function for recall score for multiclass classification using microaveraging. | def objective_function(self, y_true, y_predicted, X=None, sample_weight=None):
return metrics.recall_score(
y_true,
y_predicted,
average="micro",
zero_division=0.0,
sample_weight=sample_weight,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recall(y_true, y_pred, average, labels):\n\n y_true, y_pred = check_metric_args(y_true, y_pred, average, labels)\n\n result = None\n\n m = len(y_true)\n n = len(labels)\n\n confusion_matrix = get_confusion_matrix(y_true, y_pred, labels).T\n\n if average == \"micro\":\n numerator = np.t... | [
"0.7280571",
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"0.6576055",
"0.6576055",
"0.6576055",
"0.6576055",
"0.6576055",
"0.6576... | 0.61561096 | 60 |
If True, this objective is only valid for positive data. | def positive_only(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_Positive(self):\n return self.signature() > 0",
"def is_nonpositive(self, a):\n return a <= 0",
"def is_positive(self):\n return (self._num > 0)",
"def is_nonnegative(self, a):\n return a >= 0",
"def is_Negative(self):\n return self.signature() < 0",
"def is_nega... | [
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"0.6278503",
"0.62462217",
"0.6181479",
"0.6180418",
"0.61609... | 0.7482567 | 1 |
If True, this objective is only valid for positive data. | def positive_only(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_Positive(self):\n return self.signature() > 0",
"def is_nonpositive(self, a):\n return a <= 0",
"def is_positive(self):\n return (self._num > 0)",
"def is_nonnegative(self, a):\n return a >= 0",
"def is_Negative(self):\n return self.signature() < 0",
"def is_nega... | [
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"0.6278503",
"0.62462217",
"0.6181479",
"0.6180418",
"0.61609... | 0.7482567 | 2 |
If True, this objective is only valid for positive data. | def positive_only(self):
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_Positive(self):\n return self.signature() > 0",
"def is_nonpositive(self, a):\n return a <= 0",
"def is_positive(self):\n return (self._num > 0)",
"def is_nonnegative(self, a):\n return a >= 0",
"def is_Negative(self):\n return self.signature() < 0",
"def is_nega... | [
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"0.62790275",
"0.6278503",
"0.62462217",
"0.6181479",
"0.6180418",
"0.61609... | 0.7482567 | 0 |
Computes the precision for the specified values of k | def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_, indices = torch.topk(output, maxk, dim=1, largest=True, sorted=True)
res = []
for k in topk:
count = 0.0
for i, t in enumerate(target):
if t in indices[i, :k]:
count... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def precision(gt, pred, k):\n k = min(len(pred), k)\n den = min(len(gt), k)\n return sum([int(pred[i] in gt) for i in range(k)]) / den",
"def precision_at_k(r, k):\n assert k >= 1\n r = np.asarray(r)[:k] != 0\n if r.size != k:\n raise ValueError('Relevance score length < k')\... | [
"0.76427615",
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"0.67181736",
"0.67181736",
"0.67181736",
"0.67181736",
"0.67181736",
"0.... | 0.0 | -1 |
Returns the point record | def points(self) -> record.PackedPointRecord:
return self._points | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_point(self):\n return self.point",
"def getPoint(self):\n return self.point",
"def getPoint(self):\n return self.point",
"def getPoint(self):\n return self._point",
"def point(self) -> Point:\n return self._point",
"def point(self):\n return self.x, self.... | [
"0.72322273",
"0.71932346",
"0.71932346",
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"0.66636306",
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"0.6307685",
"0.6307685",
"0.623118",
"0.6206001",
"0.6119068",
"... | 0.64666396 | 14 |
Switch On/Off the led | def set_switch(self, node_uuid, index, data):
if data == "on":
self._bus.i2c_acquire()
try:
p = self.values['num'].get_data_index(index=index)
self._bus.pca9685_manager.set_pwm(p, 4096, 0)
self.values['level'].set_data_index(index=index, da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def led_toggle(self):\n if self.state == GPIO.LOW:\n self.state = GPIO.HIGH\n else:\n self.state = GPIO.LOW\n return self.update_status()",
"def set_led(self, on=True):\n if on:\n GPIO.output(self.LED, GPIO.HIGH)\n else:\n GPIO.output... | [
"0.81266165",
"0.79273415",
"0.79001373",
"0.7836904",
"0.7745392",
"0.75227135",
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"0.70422465",
"0.6980844",
"0.69754773",
"0.6973274",
"0.69589853",
"0.6... | 0.0 | -1 |
Sets the id of this BlogAuthorCloneRequestVNext. | def id(self, id):
if self.local_vars_configuration.client_side_validation and id is None: # noqa: E501
raise ValueError("Invalid value for `id`, must not be `None`") # noqa: E501
self._id = id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def author_id(self, author_id):\n\n self._author_id = author_id",
"def set_id(self, id_):\n\n self.id_ = id_",
"def set_id(self, id):\n self.__id = id",
"def set_id(self, id):\n self.data['id'] = id",
"def set_id(self, id_=None):\n if id_ is None:\n self.id = i... | [
"0.63493824",
"0.6147549",
"0.6126255",
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"0.5962226",
"0.59133136",
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"0.57300776",
"0.57300776",
"0.57300776",
"0.57300776",
"0.57300776",
"0... | 0.0 | -1 |
Sets the blog_author of this BlogAuthorCloneRequestVNext. | def blog_author(self, blog_author):
if self.local_vars_configuration.client_side_validation and blog_author is None: # noqa: E501
raise ValueError("Invalid value for `blog_author`, must not be `None`") # noqa: E501
self._blog_author = blog_author | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_author (self, author):\n self.author = author",
"def author(self, author):\n self._author = author",
"def author(self, author):\n\n self._author = author",
"def author(self, author):\n\n self._author = author",
"def author(self, author: str):\n\n self._author = au... | [
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"0.53739065",
"0.53106886",
"0.51514614",
"0.510211",
"0.510211",
"0... | 0.8142379 | 0 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, BlogAuthorCloneRequestVNext):
return False
return self.to_dict() == other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
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"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
if not isinstance(other, BlogAuthorCloneRequestVNext):
return True
return self.to_dict() != other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
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"0.8093973",
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"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
... | 0.0 | -1 |
Return the configuration of a bundle. | def get_bundle_config(code):
response = houston.get(f"/zipline/bundles/config/{code}")
houston.raise_for_status_with_json(response)
return response.json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def config_bundle(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config_bundle\")",
"def config_bundle(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"config_bundle\")",
"def config_bundle(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"config_bundle\")"... | [
"0.80415297",
"0.7907061",
"0.7877194",
"0.724372",
"0.66189295",
"0.65484405",
"0.65209943",
"0.6472185",
"0.6450467",
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"0.6332269",
"0.62882334",
"0.62876064",
"0.62843204",
"0.62843204",
"0.628... | 0.7280412 | 3 |
Returns a string representation of this `User`. This string is used when a `User` is printed in the console. | def __str__(self):
return self.username | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return str(self.user_id) + \": \" + \\\n str(self.user_name) + \": \" + str(self.user_email)",
"def __repr__(self):\n\n return '<User user_id={} email={} first_name={} last_name={}>'.format(self.user_id,\n ... | [
"0.84132314",
"0.8341488",
"0.82693446",
"0.8243925",
"0.822031",
"0.8203807",
"0.8101436",
"0.8099485",
"0.8096871",
"0.8094887",
"0.80931145",
"0.80913174",
"0.80913174",
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"0.80913174",
"0.80913174",
"0.80896246",
"0.80845183",
"0.8081162",
"0.8060837",
"0.8006... | 0.6927743 | 89 |
Returns true if the user is enrolled or is staff. | def has_object_permission(self, request, view, obj):
return has_team_api_access(request.user, obj.course_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_staff(self):\n # Simplest possible answer: All admins are staff\n return self.is_admin",
"def is_staff(self):\n # Simplest possible answer: All admins are staff\n return self.is_admin",
"def is_staff(self):\n # Simplest possible answer: All admins are staff\n re... | [
"0.7902633",
"0.7902633",
"0.7902633",
"0.7755686",
"0.7713276",
"0.763313",
"0.7598537",
"0.7598537",
"0.7501484",
"0.73213285",
"0.73213285",
"0.72612274",
"0.7220561",
"0.7215408",
"0.7178862",
"0.7169912",
"0.71099865",
"0.70865947",
"0.70788157",
"0.70566386",
"0.7048779... | 0.0 | -1 |
Return a filtered list of teamsets, removing any private teamsets that a user doesn't have access to. Follows the same logic as `has_specific_teamset_access` but in bulk rather than for one teamset at a time | def _filter_hidden_private_teamsets(user, teamsets, course_module):
if has_course_staff_privileges(user, course_module.id):
return teamsets
private_teamset_ids = [teamset.teamset_id for teamset in course_module.teamsets if teamset.is_private_managed]
teamset_ids_user_has_access_to = set(
Cou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_private_team_ids_to_exclude(self, course_module):\n if has_access(self.request.user, 'staff', course_module.id):\n return set()\n\n private_teamset_ids = [ts.teamset_id for ts in course_module.teamsets if ts.is_private_managed]\n excluded_team_ids = CourseTeam.objects.filte... | [
"0.6755524",
"0.63036036",
"0.6234977",
"0.59630865",
"0.57284164",
"0.56667364",
"0.56176966",
"0.5563463",
"0.5542023",
"0.5526518",
"0.54849756",
"0.54461503",
"0.5419549",
"0.53458124",
"0.5268576",
"0.52402765",
"0.52311945",
"0.52100325",
"0.51957524",
"0.5180716",
"0.5... | 0.8336481 | 0 |
Return a CourseBlock based on the `course_id` kwarg. If invalid or not found, raise 404. | def course(self):
course_id_string = self.kwargs.get('course_id')
if not course_id_string:
raise Http404('No course key provided.')
try:
course_id = CourseKey.from_string(course_id_string)
except InvalidKeyError:
raise Http404(f'Invalid course key: {co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_course_by_id(course_key, depth=0):\r\n course = modulestore().get_course(course_key, depth=depth)\r\n if course:\r\n return course\r\n else:\r\n raise Http404(\"Course not found.\")",
"def get_course_by_id(course_id):\n course = Courses.query. \\\n filter_by(id=course_id)... | [
"0.74033624",
"0.7335829",
"0.71149474",
"0.7055479",
"0.6929176",
"0.68868697",
"0.6635517",
"0.6550209",
"0.62885416",
"0.62159735",
"0.61981195",
"0.61844164",
"0.618044",
"0.6148515",
"0.6139553",
"0.59785396",
"0.5965017",
"0.5909493",
"0.58715063",
"0.5865562",
"0.58369... | 0.68011206 | 6 |
return the version of pyzmq as a string | def pyzmq_version():
if __revision__:
return '@'.join([__version__,__revision__[:6]])
else:
return __version__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zmq_version():\n return \"%i.%i.%i\" % zmq_version_info()",
"def zmq_version():\n return \"%i.%i.%i\" % zmq_version_info()",
"def pyzmq_version_info():\n return version_info",
"def versionstring():\n return \"%i.%i.%i\" % __version__",
"def versionstring():\n return \"%i.%i.%i\" % __vers... | [
"0.8694501",
"0.8694501",
"0.8418009",
"0.72448",
"0.72448",
"0.72034657",
"0.7011062",
"0.69681036",
"0.6949386",
"0.69432604",
"0.6936414",
"0.6926458",
"0.69066197",
"0.69010836",
"0.6899091",
"0.689267",
"0.6848807",
"0.683185",
"0.683185",
"0.68175805",
"0.6794193",
"0... | 0.8830094 | 1 |
Return the address that should be used as coinbase for new blocks. The coinbase address is given by the config field pow.coinbase_hex. If this does not exist or is `None`, the address of the first account is used instead. If there are no accounts, the coinbase is `DEFAULT_COINBASE`. | def coinbase(self):
cb_hex = self.app.config.get('pow', {}).get('coinbase_hex')
if cb_hex is None:
if not self.accounts_with_address:
return DEFAULT_COINBASE
cb = self.accounts_with_address[0].address
else:
# [NOTE]: check it!
# if ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_coin_address(self, coin, idx=0):\n return self.coins[coin][idx]",
"def coinbase_transaction(self):\n return self.txns[0]",
"def _generate_new_address(self) -> str:\n while True:\n address = \"0x\" + \"\".join([str(hex(randint(0, 16)))[-1] for _ in range(20)])\n ... | [
"0.5670176",
"0.5642931",
"0.56078154",
"0.55952734",
"0.5577637",
"0.55478376",
"0.5547059",
"0.53384995",
"0.525013",
"0.52022433",
"0.51845676",
"0.51752895",
"0.50634557",
"0.5037472",
"0.4950477",
"0.4922192",
"0.48936066",
"0.48864445",
"0.48722824",
"0.48722824",
"0.48... | 0.8363927 | 0 |
Return the account with a given id. Note that accounts are not required to have an id. | def get_by_id(self, id):
accts = [acct for acct in self.accounts if UUID(acct.uuid) == UUID(id)]
assert len(accts) <= 1
if len(accts) == 0:
raise KeyError('account with id {} unknown'.format(id))
elif len(accts) > 1:
log.warning('multiple accounts with same UUID f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_account_by_id(self, id_):\n return next((account for account in self.accounts\n if account.id == id_), None)",
"def account(self, account_id: str):\n return get_from_list(self.accounts, \"id\", account_id)",
"def get(self, account_id):\n self.client.get_account(... | [
"0.87767506",
"0.8555859",
"0.7852226",
"0.7556572",
"0.7399255",
"0.73203254",
"0.73010284",
"0.7278642",
"0.72527444",
"0.7245869",
"0.69676787",
"0.6906095",
"0.6859446",
"0.68503404",
"0.6769051",
"0.67493343",
"0.6689509",
"0.6677988",
"0.6670694",
"0.665516",
"0.6605365... | 0.8190827 | 2 |
Annotate the provided axis. | def _multi_data_axis_annotator(co_plot_axis: mpa.Axes, file_data_list: List[Tuple[List[str], np.array, str]],
coplot_index: int, coplot_name: str, coplot_expression: str,
diff_toggle: bool = False, log_x: bool = False, log_y: bool = False,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def annotate(axis, text, x, y):\n text_annotation = Annotation(text, xy=(x, y), xycoords='data')\n axis.add_artist(text_annotation)",
"def label_axis(self, name, label):\n\n axis = self._find_axis(name)\n axis.axis_label = label",
"def annotate(self, ax):\n annotation = ax.annotate(s... | [
"0.74204445",
"0.60440093",
"0.6021231",
"0.6021231",
"0.5957967",
"0.5824709",
"0.5780858",
"0.57317555",
"0.57109356",
"0.5699475",
"0.5694785",
"0.56742615",
"0.5621013",
"0.5617761",
"0.56120455",
"0.55667603",
"0.5519272",
"0.5516718",
"0.55057997",
"0.5501435",
"0.54938... | 0.0 | -1 |
Initializes model input to Gaussian distribution. | def init_function(matrix_dimensions):
return numpy.random.normal(
loc=mean, scale=standard_deviation, size=matrix_dimensions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_init(self):\n likelihoods.Gaussian()\n self._standard_likelihood()",
"def model_setup(self):\n self.DNN = SganMLP(self.settings.number_of_bins)\n self.D = SganMLP(self.settings.number_of_bins)\n self.G = Generator()",
"def _initialize_model(self):\n max_value ... | [
"0.6706796",
"0.66954994",
"0.667302",
"0.66233027",
"0.6613511",
"0.65771985",
"0.6533759",
"0.65318936",
"0.65236276",
"0.6431872",
"0.6426711",
"0.6413443",
"0.6392847",
"0.6366323",
"0.63363624",
"0.6290338",
"0.6276084",
"0.6221373",
"0.6220845",
"0.62101674",
"0.6199422... | 0.0 | -1 |
Initializes model input to uniform random distribution. | def init_function(matrix_dimensions):
return numpy.random.uniform(
low=min_value, high=max_value, size=matrix_dimensions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_model(model):\n model(tf.random.uniform((1, 512, 512, 3)))",
"def WeightInitializer():\n return np.random.uniform(-1, 1)",
"def _init_model_params(self):\n super()._init_model_params()\n\n if 'e' in self.init_params:\n if self.init_type == 'uniform':\n if ... | [
"0.67373776",
"0.66805893",
"0.667121",
"0.6614928",
"0.6529763",
"0.65062696",
"0.6457459",
"0.64224577",
"0.6367217",
"0.6338167",
"0.6321726",
"0.6278144",
"0.6240916",
"0.62341267",
"0.6220254",
"0.6197383",
"0.6195375",
"0.6170335",
"0.6161947",
"0.615799",
"0.6146432",
... | 0.0 | -1 |
Initializes model input to constant value. | def init_function(matrix_dimensions):
return numpy.full(matrix_dimensions, constant_value, dtype=float) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_model(self):\n pass",
"def init_model(self):\n pass",
"def initialize(self, model):\n pass",
"def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n settings, notFound = paramInput.findNodesAndExtractValues(['C', 'dual', 'penalty', 'l1_ratio', 'tol'... | [
"0.6715645",
"0.66487795",
"0.6643774",
"0.6381518",
"0.6291614",
"0.62833273",
"0.61864454",
"0.61541927",
"0.61278045",
"0.6123635",
"0.6116088",
"0.6088965",
"0.6065569",
"0.60538226",
"0.6043925",
"0.6043925",
"0.60343534",
"0.596088",
"0.596088",
"0.5950421",
"0.5948329"... | 0.0 | -1 |
Test operations using a fully populated PUT | def test_set_state_full(self):
host = synthetic_host("myserver")
self.create_simple_filesystem(host)
mgt_uri = "/api/target/%s/" % self.mgt.id
with mock.patch("chroma_core.models.Command.set_state", mock.Mock(return_value=None)):
self.api_set_state_full(mgt_uri, "unmounted")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_put_method(self):\n self.getPage('/blah', method='PUT')\n self.assertStatus('200 OK')\n self.assertHeader('Content-Type', 'application/json')\n self.assertBody('{\"mystring\": \"blah\"}')",
"def taco_test_put_update(self):\n body = '{ \"id\": 400, \"name\": \"item4\", ... | [
"0.7729409",
"0.76213175",
"0.7475804",
"0.7419208",
"0.741161",
"0.7398777",
"0.73462284",
"0.72832346",
"0.71880955",
"0.7185516",
"0.70722806",
"0.70225215",
"0.7006779",
"0.70031375",
"0.699312",
"0.6883744",
"0.6860931",
"0.68156266",
"0.6803725",
"0.6776905",
"0.6762365... | 0.0 | -1 |
(DivideAndConquer, number, number, number, number) > number Return euclidean distance between a pair of two different points. | def get_euclidean_distance(self, x_coord_1, x_coord_2, y_coord_1, y_coord_2):
return math.sqrt(((x_coord_1 - x_coord_2) ** 2) + \
((y_coord_1 - y_coord_2) ** 2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def euclidean(p1, p2):\n return p1.distance(p2)",
"def euclidDist(pair1,pair2):\n return ((pair1[0]-pair2[0])**2+(pair1[1]-pair2[1])**2)**0.5",
"def euclidean_distance(point1, point2):\n\n return math.sqrt(sum([(x - y) ** 2 for x, y in zip(point1, point2)]))",
"def dist(a,b): # compute distance bet... | [
"0.7278617",
"0.7250074",
"0.7225641",
"0.71126026",
"0.70544463",
"0.7031144",
"0.7015865",
"0.69151855",
"0.68748003",
"0.6873702",
"0.6869098",
"0.6844026",
"0.6833971",
"0.68160766",
"0.68083674",
"0.68008876",
"0.6799511",
"0.67918587",
"0.67872196",
"0.67771673",
"0.677... | 0.0 | -1 |
(DivideAndConquer, list of numbers, list of numbers) > number Return the smallest distance between a pair of two different points. | def closest_points_naive(self, x, y):
# Running time: O(n ** 2)
dist = []
for i in range(len(x)):
for j in range(i+1, len(x)):
d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])
dist.append(d)
return min(dist) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def brute_force(points):\n if len(points) == 2:\n return points[0], points[1], calc_distance(points[0], points[1])\n\n return min(((pair[0], pair[1], calc_distance(pair[0], pair[1]))\n for pair in combinations(points, 2)), key=pair_key)",
"def get_distance(first: Point, second: Point)... | [
"0.6944849",
"0.6826158",
"0.67622733",
"0.6743852",
"0.6743762",
"0.67315394",
"0.6725385",
"0.66605836",
"0.6618714",
"0.6603629",
"0.6599424",
"0.6558483",
"0.6540006",
"0.6538834",
"0.6535681",
"0.6534919",
"0.6530343",
"0.6527055",
"0.6524328",
"0.6521089",
"0.6507946",
... | 0.6223005 | 75 |
The span of the genome covered by this interval, simply ``rightleft``. | def span(self) -> float | int:
return self.right - self.left | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def span(self):\n return self.right - self.left",
"def right(self):\n return self.left + self.width",
"def _rect_right(self):\n\treturn max(self.x, self.x + self.w)",
"def get_right(self):\n return self.right",
"def right(self):\n return self.points['topRight'].x",
"def right(... | [
"0.75360054",
"0.6977182",
"0.67229855",
"0.6658675",
"0.6636132",
"0.6636132",
"0.6634681",
"0.6588929",
"0.65714335",
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"0.64235145",
"0.63681877",
"0.6348362",
"0.6321899",
"0.6303899",
"0.62718725",
"0.621... | 0.7066523 | 1 |
Returns True if this node is a sample. This value is derived from the ``flag`` variable. | def is_sample(self):
return self.flags & NODE_IS_SAMPLE | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isSampleObject(self, object):\n return isinstance(object, Sample)",
"def should_sample(self):\n sampling_probability = 1.0 / self._trans_sample_rate\n log.debug(\"should_sample (sampling prob: %f)\", sampling_probability)\n\n return random() <= sampling_probability",
"def sample... | [
"0.672181",
"0.6518628",
"0.6260431",
"0.62490267",
"0.6131253",
"0.6130926",
"0.6098722",
"0.5927426",
"0.59075904",
"0.58247924",
"0.5785239",
"0.57314193",
"0.56839883",
"0.5620142",
"0.55811983",
"0.55382276",
"0.55382276",
"0.55382276",
"0.55382276",
"0.5444727",
"0.5442... | 0.84794223 | 0 |
Returns the span of this edge, i.e., the right position minus the left position | def span(self):
return self.right - self.left | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def span(self) -> float | int:\n return self.right - self.left",
"def right(self):\n return self.left + self.width",
"def get_rightmost_edge(self):\n return self.offset + sum(self.get_screen_widths())",
"def _rect_right(self):\n\treturn max(self.x, self.x + self.w)",
"def span(self):\r... | [
"0.81368136",
"0.701518",
"0.68822384",
"0.6815371",
"0.6714591",
"0.66649777",
"0.65827394",
"0.6555784",
"0.65373147",
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"0.6468958",
"0.64179003",
"0.64179003",
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"0.63606656",
"0.6330291",
"0.6309332",
"0.6306665",
"0.6282587",
"0.62755454",
"0.623... | 0.8796711 | 0 |
Returns a deep copy of this tree. The returned tree will have identical state to this tree. | def copy(self):
copy = type(self).__new__(type(self))
copy._tree_sequence = self._tree_sequence
copy._ll_tree = self._ll_tree.copy()
copy._make_arrays()
return copy | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __copy__(self) -> 'Tree':\n return non_recursive_tree_copy(self)",
"def copy(self):\n copy = Node(self.node_data, self.tree_type, self.parent)\n if self.children:\n copy.children = [c.copy() for c in self.children]\n for c in copy.children:\n c.parent... | [
"0.8742961",
"0.81864965",
"0.7994098",
"0.7778772",
"0.7703886",
"0.7692181",
"0.7601361",
"0.7560541",
"0.7549789",
"0.75461906",
"0.7540499",
"0.74829555",
"0.74829555",
"0.74829555",
"0.74829555",
"0.73787916",
"0.7355088",
"0.73443633",
"0.73249763",
"0.730428",
"0.73042... | 0.7937966 | 3 |
Returns the tree sequence that this tree is from. | def tree_sequence(self):
return self._tree_sequence | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sequence(self):\n return self._sequence",
"def sequence(self):\n return self._sequence",
"def seq(self):\n return self.__seq",
"def _get_sequence(self):\n return self.__sequence",
"def _get_sequence(self):\n return self.__sequence",
"def _get_sequence(self):\n return sel... | [
"0.71544707",
"0.71544707",
"0.7037959",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",
"0.69551474",... | 0.85810316 | 0 |
Returns the minimum number of samples that a node must be an ancestor of to be considered a potential root. This can be set, for example, when | def root_threshold(self):
return self._ll_tree.get_root_threshold() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def minimum_samples(cls):\n return DEFAULT_MINIMUM_SAMPLE_COUNT",
"def num_actual_nodes(tree):\n return (tree.n_node_samples > 0).sum()",
"def num_samples(self):\n return self._ll_tree_sequence.get_num_samples()",
"def min_node_count(self) -> Optional[pulumi.Input[int]]:\n return ... | [
"0.68396354",
"0.68336636",
"0.6548493",
"0.6411667",
"0.6349708",
"0.63305575",
"0.6200745",
"0.6110282",
"0.6082368",
"0.5992124",
"0.5870389",
"0.5864266",
"0.5836012",
"0.58313954",
"0.58162874",
"0.5796975",
"0.5789289",
"0.57675284",
"0.5766005",
"0.57572585",
"0.573571... | 0.63182706 | 6 |
Returns the sum of all the branch lengths in this tree (in units of time). This is equivalent to >>> sum(tree.branch_length(u) for u in tree.nodes()) Note that the branch lengths for root nodes are defined as zero. As this is defined by a traversal of the tree, technically we return the sum of all branch lengths that a... | def total_branch_length(self):
return self._ll_tree.get_total_branch_length() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tree_size(self, node):\n\n # If the tree has not been created yet.\n if node == None:\n return 0\n n_nodes = 1\n for child in node.children:\n n_nodes += self.get_tree_size(node.children[child])\n return n_nodes",
"def size(self) -> int:\n #... | [
"0.67232084",
"0.6657682",
"0.655308",
"0.6537849",
"0.65087956",
"0.6490195",
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"0.63538253",
"0.6294673",
"0.6291538",
"0.628126",
"0.62467563",
"0.62176985... | 0.7553698 | 0 |
Returns the leftmost child of the specified node. Returns | def left_child(self, u):
return self._ll_tree.get_left_child(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_leftmost_child(self):\n\t\tif self.left_child == None:\n\t\t\treturn self\n\t\telse:\n\t\t\treturn self.left_child.get_leftmost_child()",
"def left(self, node):\r\n if self._col(node.count) > 0:\r\n return self.nodes[node.count - 1]\r\n else:\r\n return None",
"def g... | [
"0.8476717",
"0.79886734",
"0.790321",
"0.7860517",
"0.7764364",
"0.77203244",
"0.7512164",
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"0.6981023",
"0.68979305",
"0.6881802",
"0.6878958",
"0.686598",
"0.686598",
"0.68631965",... | 0.7239283 | 12 |
A numpy array (dtype=np.int32) encoding the left child of each node in this tree, such that ``tree.left_child_array[u] == tree.left_child(u)`` | def left_child_array(self):
return self._left_child_array | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def left_root(self):\n return self.left_child(self.virtual_root)",
"def left(self, node):\n self._validate_node(node)\n idx = node._index\n left_idx = 2*idx + 1\n if left_idx >= self._N:\n return None # Exceeds length of array\n return ... | [
"0.67617476",
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"0.63455385",
"0.6335596",
"0.6332378",
"0.6331963",
"0.6280451",
"0.62632... | 0.80260164 | 0 |
Returns the rightmost child of the specified node. Returns | def right_child(self, u):
return self._ll_tree.get_right_child(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_rightmost_child(self):\n\t\tif self.right_child == None:\n\t\t\treturn self\n\t\telse:\n\t\t\treturn self.right_child.get_rightmost_child()",
"def get_rightchild(self):\n return self._rightchild",
"def getRightChild(self):\n return _libsbml.ASTNode_getRightChild(self)",
"def get_right_n... | [
"0.8499115",
"0.788694",
"0.7871831",
"0.75799596",
"0.7522379",
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"0.6765684",
"0.67513055",
"0.67340815",
"0.66999966",
"0.6694083",
"0.669283... | 0.71364003 | 8 |
A numpy array (dtype=np.int32) encoding the left sib of each node in this tree, such that ``tree.left_sib_array[u] == tree.left_sib(u)`` | def left_sib_array(self):
return self._left_sib_array | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lefts(self):\n lstack = [len(self.arr)]\n i = len(self.arr) - 1\n while i >= 0:\n if self.arr[i] > self.arr[lstack[-1] - 1]:\n while lstack and self.arr[i] > self.arr[lstack[-1] - 1]:\n x = lstack.pop()\n self.left[x - 1] = i ... | [
"0.65817153",
"0.62442845",
"0.6027532",
"0.59312594",
"0.5828023",
"0.5823536",
"0.5681712",
"0.5678256",
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"0.54819995",
"0.5470235",
"0.54533005",
"0.53899395",
"0.5374653",
"0.5356871",
"0.533722... | 0.7775731 | 0 |
The total number of edges in this tree. This is equal to the number of tree sequence edges that intersect with this tree's genomic interval. Note that this may be greater than the number of branches that are reachable from the tree's roots, since we can have topology that is not associated with any samples. | def num_edges(self):
return self._ll_tree.get_num_edges() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_edges(self) -> int:\n return self.graph.number_of_edges()",
"def num_edges(self):\n return sum(1 for _ in self.iteredges())",
"def num_edges(self):\r\n return len(self.__generate_edges())",
"def num_edges(self):\n return self._top_exp.number_of_edges()",
"def edge_... | [
"0.83892447",
"0.82065594",
"0.8164331",
"0.8142472",
"0.7962753",
"0.79174477",
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"0.7510252",
"0.7501979",
"0.7486827",
"0.7431027",
"0.73827606",
"0.7374399... | 0.8547094 | 0 |
The leftmost root in this tree. If there are multiple roots in this tree, they are siblings of this node, and so we can | def left_root(self):
return self.left_child(self.virtual_root) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getLeftmost(self, root):\n current = root\n while current.left is not None:\n current = current.left\n return current",
"def root(self):\n if self.has_multiple_roots:\n raise ValueError(\"More than one root exists. Use tree.roots instead\")\n return se... | [
"0.8530952",
"0.8295624",
"0.8063771",
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"0.77275914",
"0.7617638",
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"0.72581136",
"0.7250919",
"0.7238245",
"0.7197334",
"0.7139504",
"0.7093866... | 0.84995955 | 1 |
Returns the children of the specified node ``u`` as a tuple of integer node IDs. If ``u`` is a leaf, return the empty tuple. The ordering of children is arbitrary and should not be depended on; see the | def children(self, u):
return self._ll_tree.get_children(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def siblings(self, u):\n if u == self.virtual_root:\n return tuple()\n parent = self.parent(u)\n if self.is_root(u):\n parent = self.virtual_root\n if parent != tskit.NULL:\n return tuple(v for v in self.children(parent) if u != v)\n return tuple(... | [
"0.70097744",
"0.6166763",
"0.569762",
"0.5613714",
"0.5411131",
"0.5312682",
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"0.4931148",
"0.49260312",
"0.49067977",
"0.4898888",
"0.48970637",
"0.4877235",
"0.4828... | 0.7239634 | 0 |
Returns the population associated with the specified node. Equivalent to ``tree.tree_sequence.node(u).population``. | def population(self, u):
return self._ll_tree.get_population(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nodes_population(self):\n return self._nodes_population",
"def getPopulation(self):\n\n return self.population",
"def population(self):\n return self._population",
"def getPopulation(self):\n\n return self.p",
"def individuals_population(self):\n if self._individuals_... | [
"0.6784931",
"0.66868186",
"0.6671219",
"0.6613403",
"0.6545175",
"0.6408209",
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"0.51789737",
"0.51076955",
"0.5102596",
"0.50667673",
"0.5046358",
"0.5045376",
"0.49939802",... | 0.7531694 | 0 |
Returns the index this tree occupies in the parent tree sequence. This index is zero based, so the first tree in the sequence has index 0. | def index(self):
return self._ll_tree.get_index() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parent_index(self):\n return (self.index - 1) // 2",
"def _parent(self, index):\r\n # Declaring the \"root\" its own parent, otherwise usual math\r\n return (index - 1) // 2 if index else 0",
"def get_parent_index(i):\n # Indexing for i_parent == i // 2 is NOT ZERO-INDEXED\n... | [
"0.78220755",
"0.76860297",
"0.7536931",
"0.74937487",
"0.74726325",
"0.739073",
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"0.6986071",
"0.6937188",
"0.6864419",
"0.6846415",
"0.6821681",
"0.679533",
"0.67841625",... | 0.65180373 | 29 |
Returns the coordinates of the genomic interval that this tree represents the history of. The interval is returned as a tuple | def interval(self):
return Interval(self._ll_tree.get_left(), self._ll_tree.get_right()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interval(self):\n return (self.start, S.Infinity)",
"def _get_range(self):\n return tuple((0, m, 1) for m in self.level_shapes[0])",
"def range_(self):\n return tuple((e[0], e[-1]) for e in self.edges)",
"def coordinates(self) -> Tuple[int, int]:\n return self.x, self.y",
"d... | [
"0.635856",
"0.63123256",
"0.6307057",
"0.6160977",
"0.6151618",
"0.60858923",
"0.6057322",
"0.60464025",
"0.60396624",
"0.5965232",
"0.59336257",
"0.5932911",
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"0.5882035",
"0.58681595",
"0.58621997",
"0.5852146",
"0.58426297",
"0.5829853",
"0.5806959",
"0.579053... | 0.6236991 | 3 |
Returns the genomic distance that this tree spans. | def span(self):
return self.interval.span | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance(self):\n return self.value * len(self.alignment.query)",
"def get_distance(self) -> int:\n return self.get_measurement_data().distance",
"def distance(self):\n return self._distance",
"def dist(gene1, gene2):\n return abs(len(gene1.goal) - len(gene2.goal))",
"de... | [
"0.6617728",
"0.6580375",
"0.6476354",
"0.622307",
"0.61035866",
"0.6077471",
"0.5980814",
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"0.5970761",
"0.5955885",
"0.593995",
"0.59126115",
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"0.5795944",
"0.5778257",
"0.5765529",
"0.5732435",
"0.5730154",
"0.5664341",
... | 0.0 | -1 |
Return an SVG representation of a single tree. By default, numeric | def draw_svg(
self,
path=None,
*,
size=None,
time_scale=None,
tree_height_scale=None,
max_time=None,
min_time=None,
max_tree_height=None,
node_labels=None,
mutation_labels=None,
root_svg_attributes=None,
style=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tree(self):\n return self.to_geom()",
"def tree2gv(tree: TreeNode) -> graphviz.Graph:\n result = graphviz.Graph(\"ni\")\n # result.attr(size='12,0')\n tree2gv_helper(tree, result, \"\")\n return result",
"def _repr_svg_(self):\n pass",
"def svg(self, scale_factor=1., stroke_colo... | [
"0.71188587",
"0.6867975",
"0.63219464",
"0.6240237",
"0.6191095",
"0.6191095",
"0.61648273",
"0.61648273",
"0.61631405",
"0.6092333",
"0.6087477",
"0.60554206",
"0.60355574",
"0.60297",
"0.60161185",
"0.5973121",
"0.59649783",
"0.5924637",
"0.58717585",
"0.5852425",
"0.58154... | 0.58262193 | 20 |
Returns the total number of mutations across all sites on this tree. | def num_mutations(self):
return sum(len(site.mutations) for site in self.sites()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNNodesTot(self):\n nNodesTot = 0\n for iElt in Elements._all:\n nNodesTot += len(iElt.coord)\n return nNodesTot",
"def n(self):\n return len(self.data.sites)",
"def count_fragments(self):\n n = 0\n for chain in self.iter_chains():\n n += ch... | [
"0.67348415",
"0.662731",
"0.6496893",
"0.6496893",
"0.6428826",
"0.6404178",
"0.6404178",
"0.63672894",
"0.6320409",
"0.6299668",
"0.62946934",
"0.62711215",
"0.6259283",
"0.6250409",
"0.6248343",
"0.623999",
"0.62160534",
"0.62148464",
"0.61757725",
"0.61757725",
"0.6095577... | 0.8007304 | 0 |
Returns the number of sites on this tree. | def num_sites(self):
return self._ll_tree.get_num_sites() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def countSites(self):\n self.ni = len(self.sites)\n return self.ni",
"def number_of_sites(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"number_of_sites\")",
"def number_of_sites(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"number_of_sites\")",
"def n(self):\... | [
"0.8169985",
"0.8153402",
"0.8153402",
"0.8040949",
"0.74710983",
"0.72724086",
"0.7252431",
"0.72318196",
"0.72081715",
"0.7180082",
"0.71778214",
"0.7073089",
"0.7046339",
"0.7017032",
"0.70002794",
"0.69706094",
"0.692727",
"0.6909764",
"0.6909764",
"0.68881255",
"0.688632... | 0.8723763 | 0 |
Returns an iterator over all the leaves in this tree that descend from | def leaves(self, u=None):
roots = [u]
if u is None:
roots = self.roots
for root in roots:
for v in self.nodes(root):
if self.is_leaf(v):
yield v | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def leaves_iterator(node):\n if 0 == node.children_count:\n yield node\n else:\n for child in node.children:\n yield from leaves_iterator(child)",
"def iter_leaf(self):\n for elem in self[1:]:\n if isinstance(elem, Tree):\n for elem2 in elem.iter_le... | [
"0.79767954",
"0.7772598",
"0.7745253",
"0.727287",
"0.72725004",
"0.7209723",
"0.7190232",
"0.7110553",
"0.70794207",
"0.7070099",
"0.7054885",
"0.7043993",
"0.6982216",
"0.69281703",
"0.68062425",
"0.6785666",
"0.67855394",
"0.6764764",
"0.67610675",
"0.67289233",
"0.672814... | 0.74338263 | 3 |
Returns the number of sample nodes in this tree underneath the specified node (including the node itself). If u is not specified return the total number of samples in the tree. This is a constant time operation. | def num_samples(self, u=None):
u = self.virtual_root if u is None else u
return self._ll_tree.get_num_samples(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_tracked_samples(self, u=None):\n u = self.virtual_root if u is None else u\n return self._ll_tree.get_num_tracked_samples(u)",
"def get_tree_size(self, node):\n\n # If the tree has not been created yet.\n if node == None:\n return 0\n n_nodes = 1\n for... | [
"0.71008927",
"0.6282148",
"0.60840005",
"0.5997149",
"0.5956238",
"0.5820769",
"0.57902974",
"0.57880586",
"0.57478213",
"0.57129973",
"0.5681754",
"0.5680819",
"0.5680611",
"0.56796664",
"0.5657526",
"0.56563616",
"0.56504655",
"0.56347567",
"0.5630829",
"0.5618458",
"0.560... | 0.7710853 | 0 |
Returns a numpy array of node ids. Starting at `u`, returns the reachable descendant nodes in order of decreasing time (least recent first), falling back to decreasing ID if times are equal. Also see | def timedesc(self, u=NULL):
return self.timeasc(u)[::-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def node_ids(self):\n # From: https://stackoverflow.com/questions/17415086/combine-consecutive-numbers-into-range-tuples\n groups = groupby(self.nodes.keys(), key=lambda item, c=count():item-next(c))\n temp = [list(g) for k, g in groups]\n return \"+\".join(\"{}-{}\".format(x[0], x[-1])... | [
"0.5754769",
"0.56354403",
"0.54743624",
"0.5459384",
"0.5416696",
"0.5404233",
"0.5378445",
"0.53768516",
"0.53543156",
"0.53155124",
"0.5300701",
"0.528117",
"0.5260044",
"0.52522194",
"0.52271116",
"0.5220997",
"0.51906085",
"0.5173995",
"0.5168041",
"0.5150069",
"0.514496... | 0.0 | -1 |
Returns the path length between two nodes (i.e., the number of edges between two nodes in this tree). If the two nodes have a most recent common ancestor, then this is defined as ``tree.depth(u) + tree.depth(v) 2 tree.depth(tree.mrca(u, v))``. If the nodes do not have an MRCA (i.e., they are in disconnected subtrees) t... | def path_length(self, u, v):
mrca = self.mrca(u, v)
if mrca == -1:
return math.inf
return self.depth(u) + self.depth(v) - 2 * self.depth(mrca) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lineLength(node1, node2):\n return ((node2[1] - node1[1])**2 + (node2[0] - node1[0])**2)**(1/2)",
"def node_distance(self, node1, node2):\n if node1 == node2:\n return 0.0\n for i, (n1, n2) in enumerate(zip(self.paths[node1], self.paths[node2])):\n if n1 != n2:\n ... | [
"0.6622066",
"0.6376726",
"0.6292443",
"0.6278159",
"0.6270519",
"0.617931",
"0.6169958",
"0.61558205",
"0.61325884",
"0.61075735",
"0.6069739",
"0.60078466",
"0.60040367",
"0.59875345",
"0.58789146",
"0.58705115",
"0.58697885",
"0.58439183",
"0.5839832",
"0.5817393",
"0.5802... | 0.69861245 | 0 |
Returns True if `self` and `other` are equal. Uses the underlying table | def equals(
self,
other,
*,
ignore_metadata=False,
ignore_ts_metadata=False,
ignore_provenance=False,
ignore_timestamps=False,
ignore_tables=False,
ignore_reference_sequence=False,
):
return self.tables.equals(
other.tables,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n if len(self.table) != len(other.table):\n return False\n for i in range(len(self.table)):\n if self.table[i] != other.table[i]:\n return False\n\n return True",
"def __eq__(self, other):\n return self.table == other",
"... | [
"0.87902474",
"0.8447956",
"0.79683846",
"0.78841156",
"0.78567225",
"0.78567225",
"0.78440946",
"0.7818776",
"0.7814475",
"0.7804798",
"0.77775574",
"0.7767973",
"0.7760221",
"0.77429354",
"0.7735221",
"0.7731506",
"0.7715449",
"0.7705749",
"0.7683314",
"0.7668625",
"0.76659... | 0.0 | -1 |
Returns the trees in this tree sequence as a list. Each tree is | def aslist(self, **kwargs):
return [tree.copy() for tree in self.trees(**kwargs)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subtrees(self):\n return list(iter(self))",
"def treeToList(self, root):\n if not root: return []\n jobs, res = [root], []\n while jobs:\n r = jobs.pop(0)\n if r:\n res.append(r.val)\n jobs += [r.left, r.right]\n return re... | [
"0.77387506",
"0.7168374",
"0.7150617",
"0.70441175",
"0.6884794",
"0.68606216",
"0.6812322",
"0.676793",
"0.67629874",
"0.6742403",
"0.6698996",
"0.6681821",
"0.66711295",
"0.6632985",
"0.66328776",
"0.6626858",
"0.6574341",
"0.65683067",
"0.6555661",
"0.654803",
"0.6541393"... | 0.7106193 | 3 |
Returns the total number of bytes required to store the data in this tree sequence. Note that this may not be equal to the actual memory footprint. | def nbytes(self):
return self.tables.nbytes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_size(self):\n return (\n sys.getsizeof(self.children) +\n sys.getsizeof(self.parent) +\n sys.getsizeof(self.dataset_id) +\n sys.getsizeof(self.k) +\n self.filter.get_size()\n )",
"def size(self):\n size = 0\n ... | [
"0.78447497",
"0.77019143",
"0.76310134",
"0.74393994",
"0.7427939",
"0.7404596",
"0.73535043",
"0.7322948",
"0.731892",
"0.731844",
"0.73066205",
"0.7300838",
"0.72691184",
"0.7248013",
"0.7241198",
"0.71995896",
"0.7196493",
"0.7185785",
"0.71830535",
"0.7171429",
"0.716757... | 0.6766097 | 64 |
Returns the number of sample nodes in this tree sequence. This is also the number of sample nodes in each tree. | def num_samples(self):
return self._ll_tree_sequence.get_num_samples() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_actual_nodes(tree):\n return (tree.n_node_samples > 0).sum()",
"def count(self):\n return self.__tree.node_count",
"def count_nodes(self):\n if self.children is None:\n return 0\n\n total_count = 0\n for child in self.children:\n if child is None... | [
"0.75340533",
"0.72002333",
"0.7187151",
"0.71474844",
"0.706328",
"0.7060709",
"0.7043023",
"0.7026021",
"0.702282",
"0.69940025",
"0.6987939",
"0.69874567",
"0.69519013",
"0.69288397",
"0.6921411",
"0.6905505",
"0.6892658",
"0.6860774",
"0.68527967",
"0.67772985",
"0.676849... | 0.8037324 | 0 |
Returns True if all time coordinates in this TreeSequence are | def discrete_time(self):
return bool(self._ll_tree_sequence.get_discrete_time()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_t(self):\n return any(map(lambda s: s.is_temporal, self))",
"def is_time(self) -> bool:\n return self.times > 1",
"def isstationary(self):\n if np.all(np.abs(self.arroots) > 1.0):\n return True\n else:\n return False",
"def isSetTimeUnits(self):\n ... | [
"0.7170942",
"0.68281794",
"0.63524956",
"0.628753",
"0.62724304",
"0.6269105",
"0.62313247",
"0.62220967",
"0.6207779",
"0.61885995",
"0.618821",
"0.6140443",
"0.6119052",
"0.61179096",
"0.6116701",
"0.608508",
"0.6036869",
"0.597261",
"0.5961411",
"0.5946981",
"0.5895624",
... | 0.6305888 | 3 |
Returns the min time in this tree sequence. This is the minimum of the node times and mutation times. Note that mutation times with the value ``tskit.UNKNOWN_TIME`` are ignored. | def min_time(self):
return self._ll_tree_sequence.get_min_time() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_min_tim(self):\n return self.get_shortest_mode().tim",
"def min_time(self) -> str:\n return self._min_time",
"def min(self):\n\n return time_stat(self, stat=\"min\")",
"def min_time(self):\n #{{{ function to return time of first sample\n\n return self.mintime",
"def initi... | [
"0.76301086",
"0.7540868",
"0.7337586",
"0.7212607",
"0.7120911",
"0.7066559",
"0.6846319",
"0.67451185",
"0.6688156",
"0.6661619",
"0.6616743",
"0.6535071",
"0.65190476",
"0.64759356",
"0.6434894",
"0.6425683",
"0.6409702",
"0.6400449",
"0.6400449",
"0.63828295",
"0.63797426... | 0.8721078 | 0 |
Returns the max time in this tree sequence. This is the maximum of the node times and mutation times. Note that mutation times with the value ``tskit.UNKNOWN_TIME`` are ignored. | def max_time(self):
return self._ll_tree_sequence.get_max_time() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max_time(self) -> float:\r\n if(len(self.operations_by_name) == 0):\r\n return -1\r\n return max(map(lambda x: x[\"time_step\"], self.operations_by_name.values()))",
"def max_time(self):\n return self._max_time",
"def _get_max_t(self):\n \"\"\"\n if hasattr(sel... | [
"0.7535206",
"0.74210066",
"0.7383867",
"0.7319022",
"0.7315086",
"0.7299319",
"0.70352024",
"0.70182174",
"0.6946768",
"0.6593718",
"0.651949",
"0.65012175",
"0.64540786",
"0.64530516",
"0.6345061",
"0.6335531",
"0.6329964",
"0.63199276",
"0.6315849",
"0.62969947",
"0.627882... | 0.850162 | 0 |
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