code stringlengths 281 23.7M |
|---|
class AnritsuMS464xB(Instrument):
CHANNELS_MAX = 16
TRACES_MAX = 16
PORTS = 4
TRIGGER_TYPES = ['POIN', 'SWE', 'CHAN', 'ALL']
FREQUENCY_RANGE = [.0, .0]
SPARAM_LIST = ['S11', 'S12', 'S21', 'S22', 'S13', 'S23', 'S33', 'S31', 'S32', 'S14', 'S24', 'S34', 'S41', 'S42', 'S43', 'S44']
DISPLAY_LAYOU... |
def metrics(labels, logits, batchsize, reverse_ce=False):
with tf.variable_scope('metrics'):
labels_reshaped = _reshape_labels_like_logits(labels, logits, batchsize)
if (not reverse_ce):
xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels_reshaped, logits=logit... |
class ValueGradFunction():
def __init__(self, costs, grad_vars, extra_vars_and_values=None, *, dtype=None, casting='no', compute_grads=True, **kwargs):
if (extra_vars_and_values is None):
extra_vars_and_values = {}
names = [arg.name for arg in (grad_vars + list(extra_vars_and_values.keys... |
class TestHeaderTuple():
def test_is_tuple(self):
h = HeaderTuple('name', 'value')
assert isinstance(h, tuple)
def test_unpacks_properly(self):
h = HeaderTuple('name', 'value')
(k, v) = h
assert (k == 'name')
assert (v == 'value')
def test_header_tuples_are_in... |
def create_datasets(params_list):
if (not isinstance(params_list, list)):
params_list = [params_list]
datasets = [WatercolorsDataset(params=dp) for dp in params_list]
if (len(datasets) == 1):
dataset = datasets[0]
else:
dataset = dataset_utils.combine_dataset_names(datasets)
... |
.parametrize('ramp_symmetry', [0.01, 99.98])
def test_ramp_symmetry(ramp_symmetry):
with expected_protocol(Agilent33500, [('SOUR1:FUNC:RAMP:SYMM?', ramp_symmetry), ('SOUR2:FUNC:RAMP:SYMM?', ramp_symmetry), ('FUNC:RAMP:SYMM?', ramp_symmetry), (f'SOUR1:FUNC:RAMP:SYMM {ramp_symmetry:.6f}', None), (f'SOUR2:FUNC:RAMP:SY... |
_images
def test_addr(host):
non_resolvable = host.addr('some_non_resolvable_host')
assert (not non_resolvable.is_resolvable)
assert (not non_resolvable.is_reachable)
assert (not non_resolvable.port(80).is_reachable)
non_reachable_ip = host.addr('10.42.13.73')
assert non_reachable_ip.is_resolvab... |
class FGM():
def __init__(self, model, emb_name, epsilon=1.0):
self.model = model
self.epsilon = epsilon
self.emb_name = emb_name
self.backup = {}
def attack(self):
for (name, param) in self.model.named_parameters():
if (param.requires_grad and (self.emb_name ... |
def build_tries(entities: List[Entity]) -> Dict[(str, Trie)]:
tries = defaultdict(list)
labels = set()
for ent in entities:
tries[ent.label.name].append([ent.name])
if (ent.label.name not in labels):
labels.add(ent.label.name)
for label in labels:
tries[label] = Trie(... |
class Plane(Primitive):
def __init__(self, center, material, width, height, u_axis, v_axis, max_ray_depth=5, shadow=True):
super().__init__(center, material, max_ray_depth, shadow=shadow)
self.collider_list += [Plane_Collider(assigned_primitive=self, center=center, u_axis=u_axis, v_axis=v_axis, w=(w... |
class CountRelations(LogicalValue):
def __init__(self, from_nodes: NodeEnumerator, relation: Relation, to_nodes: NodeFilter, min_value: int):
self.from_nodes = from_nodes
self.relation = relation
self.to_nodes = to_nodes
self.min_value = min_value
def evaluate(self, state: Enviro... |
class NodePattern(BasePattern):
wildcards = False
def __init__(self, type=None, content=None, name=None):
if (type is not None):
assert (type >= 256), type
if (content is not None):
assert (not isinstance(content, str)), repr(content)
content = list(content)
... |
def evaluate(model, dataset, args, sess):
[train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)
NDCG = 0.0
HT = 0.0
valid_user = 0.0
if (usernum > 10000):
users = random.sample(range(1, (usernum + 1)), 10000)
else:
users = range(1, (usernum + 1))
for u in users:
... |
class ListConfig(ProductionCommand):
keyword = 'listconfig'
def assemble(self):
super().assemble()
self.parser.add_argument('-v', '--values', action='store_true', dest='print_values', help='prints the currently configured value')
self.parser.add_argument('-f', '--files', action='store_tr... |
def prefix_match(s1: str, s2: str) -> bool:
(i, j) = (0, 0)
for i in range(len(s1)):
if (not is_span_separator(s1[i])):
break
for j in range(len(s2)):
if (not is_span_separator(s2[j])):
break
if ((i < len(s1)) and (j < len(s2))):
return (s1[i] == s2[j])
... |
def normalize_data_storage(data_storage, offset=0.1, mul_factor=100, save_file='../data/mean_std.pkl'):
print('normalize_data_storage...')
mean_std_values = {}
for modality_storage in data_storage:
means = []
pbar = ProgressBar().start()
print('calculate mean value...')
n_sub... |
def test_color():
assert (mmcv.color_val(mmcv.Color.blue) == (255, 0, 0))
assert (mmcv.color_val('green') == (0, 255, 0))
assert (mmcv.color_val((1, 2, 3)) == (1, 2, 3))
assert (mmcv.color_val(100) == (100, 100, 100))
assert (mmcv.color_val(np.zeros(3, dtype=np.int)) == (0, 0, 0))
with pytest.ra... |
def test_fgraph_rewrite(non_centered_rewrite):
with pm.Model(coords={'subject': range(10)}) as m_old:
group_mean = pm.Normal('group_mean')
group_std = pm.HalfNormal('group_std')
subject_mean = pm.Normal('subject_mean', group_mean, group_std, dims=('subject',))
obs = pm.Normal('obs', ... |
class RCompleter(Completer):
def __init__(self, timeout=0.02):
self.timeout = timeout
super(RCompleter, self).__init__()
def get_completions(self, document, complete_event):
word = document.get_word_before_cursor()
prefix_length = settings.completion_prefix_length
if ((le... |
def _handle_conv2d_transpose(callback):
(callback)
def _handle(cls, tensor):
if isinstance(cls.original_layer, tf.keras.layers.Conv2DTranspose):
if (len(tensor.shape) == 4):
permute = [0, 1, 3, 2]
tensor = K.permute_dimensions(tensor, permute)
... |
def import_dsprite_location_module():
script_path = os.path.split(__file__)[0]
module_name = 'create_dsprites_location_data_files'
module_path = os.path.join(script_path, f'{module_name}.py')
spec = importlib.util.spec_from_file_location(module_name, module_path)
module = importlib.util.module_from_... |
def test_swift_session_by_user_key():
def mock_init(self, session=None, swift_storage_url=None, swift_auth_token=None, swift_auth_v1_url=None, swift_user=None, swift_key=None):
self._creds = {'SWIFT_STORAGE_URL': 'foo', 'SWIFT_AUTH_TOKEN': 'bar'}
with mock.patch('rasterio.session.SwiftSession.__init__',... |
def disambiguate(items, nr, **kwds):
msgs = []
for (key, value) in iteritems(kwds):
msgs.append(('%s=%r' % (key, value)))
msg = ' '.join(msgs)
if (not items):
raise ItemNotFoundError(msg)
if (nr is None):
if (len(items) > 1):
raise AmbiguityError(msg)
nr =... |
def calculate_monthly_payment(total_amount, down_payment, interest_rate, amortization_period):
total_amount = float(total_amount)
amortization_period = int(amortization_period)
down_payment = float(down_payment)
interest_rate = float(interest_rate)
total_amount -= down_payment
if ((interest_rate... |
def train_model_swag(model, arch, opt, train_data, test_data, args, lamb_lr, verbose=False):
model.train()
MI_data = train_data
(train_accs, train_losses) = ([], [])
(test_accs, test_losses) = ([], [])
l_MIs = []
maxes = []
lambs = []
t = 0
lamb = args.lamb_init
analyse(model, gr... |
def make_dataset(dir, class_to_idx, extensions, num_instance_per_class):
images = []
dir = os.path.expanduser(dir)
for target in sorted(class_to_idx.keys()):
d = os.path.join(dir, target)
if (not os.path.isdir(d)):
continue
for (root, _, fnames) in sorted(os.walk(d)):
... |
def train(model, env, args):
STEPS = 10
LAMBDA = 0.99
vis = visdom.Visdom(env=(args.name + '[{}]'.format(args.phrase)))
pre_per_replay = [[] for _ in range(args.n_replays)]
gt_per_replay = [[] for _ in range(args.n_replays)]
acc = None
win = vis.line(X=np.zeros(1), Y=np.zeros(1))
loss_wi... |
def edit_task(name=None, location='\\', user_name=None, password=None, description=None, enabled=None, hidden=None, run_if_idle=None, idle_duration=None, idle_wait_timeout=None, idle_stop_on_end=None, idle_restart=None, ac_only=None, stop_if_on_batteries=None, wake_to_run=None, run_if_network=None, network_id=None, net... |
class NominationCreateForm(NominationForm):
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request', None)
super().__init__(*args, **kwargs)
self_nomination = forms.BooleanField(required=False, help_text='If you are nominating yourself, we will automatically associate the nomina... |
class LxTaskByPidFunc(gdb.Function):
def __init__(self):
super(LxTaskByPidFunc, self).__init__('lx_task_by_pid')
def invoke(self, pid):
task = get_task_by_pid(pid)
if task:
return task.dereference()
else:
raise gdb.GdbError(('No task of PID ' + str(pid))) |
class TestAlignMixinInitializationMethods(unittest.TestCase):
def setUp(self):
self.mixin_mock = mock.Mock()
def test_set_align_with_valid_config(self):
for align in ['right', 'center', 'left']:
with self.subTest():
AlignMixin._set_align(self.mixin_mock, align)
... |
class L2TPAttr(TypeEnum):
MsgType = 0
RandomVector = 36
Result = 1
Version = 2
FramingCap = 3
BearerCap = 4
TieBreaker = 5
Firmware = 6
HostName = 7
VendorName = 8
TunnelID = 9
WindowSize = 10
Challenge = 11
Response = 13
CauseCode = 12
SessionID = 14
... |
class Cmvn(object):
def __init__(self, dim=None):
self.init(dim)
def accumulate(self, feats, weights=None):
if (not self.stats):
raise ValueError('CMVN stats matrix is not initialized. Initialize it either by reading it from file or by calling the init method to accumulate new statis... |
class RenderThread(Thread):
def __init__(self, source_path: Path, target_path: Path, variables: TerraformVariableStore):
super().__init__()
self.source_path = source_path
self.target_path = target_path
self.target_name = target_path.name
self.variables = variables
sel... |
class Curric_Dataset(Dataset):
def __init__(self, root, txt, transform=None):
self.img_path = []
self.labels = []
self.transform = transform
with open(txt) as f:
for line in f:
self.img_path.append(os.path.join(root, line.split()[0]))
self.... |
class PercentileFilter(SingleInputMixin, Filter):
window_length = 0
def __new__(cls, factor, min_percentile, max_percentile, mask):
return super(PercentileFilter, cls).__new__(cls, inputs=(factor,), mask=mask, min_percentile=min_percentile, max_percentile=max_percentile)
def _init(self, min_percenti... |
def get_agents_action(o_n, sess, noise_rate=0):
agent1_action = (agent1_ddpg.action(state=[o_n[0]], sess=sess) + (np.random.randn(2) * noise_rate))
agent2_action = (agent2_ddpg.action(state=[o_n[1]], sess=sess) + (np.random.randn(2) * noise_rate))
agent3_action = (agent3_ddpg.action(state=[o_n[2]], sess=ses... |
def main(args=None):
p = argparse.ArgumentParser(description='Count how often each token is used by the lexers')
p.add_argument('-v', '--verbose', dest='verbose', help='Give more output.', default=False, action='store_true')
p.add_argument('--minfiles', dest='minfiles', metavar='COUNT', type=int, help='Repo... |
class ParseException(ParseBaseException):
def explain(exc, depth=16):
import inspect
if (depth is None):
depth = sys.getrecursionlimit()
ret = []
if isinstance(exc, ParseBaseException):
ret.append(exc.line)
ret.append(((' ' * (exc.col - 1)) + '^'))... |
def NetGap(pattern):
print(pattern)
count = 0
for i in range(SeqNum):
Nettree = [[] for k in range(len(pattern))]
CreatNettree(Nettree, pattern, sdb[i])
UpdateNettree(Nettree)
print(Nettree)
while (Nettree[0] != []):
ShowNettree(Nettree)
count ... |
class SettingsEntry(BaseModel, FieldRequiring):
name: ClassVar[str] = FieldRequiring.MUST_SET_UNIQUE
description: ClassVar[(str | dict[(str, str)])] = FieldRequiring.MUST_SET
_overrides: set[str] = PrivateAttr(default_factory=set)
def __init__(self, defaults: (SettingsEntry | None)=None, /, **data):
... |
def evalTime(f, v, script=False, loops=1000):
min = .0
for i in range(0, loops):
t0 = time.perf_counter()
f(v)
dt = (time.perf_counter() - t0)
min = (dt if (dt < min) else min)
if (not script):
print(f' run time in {int(loops)} loops was {min:2.9f} sec')
return mi... |
def test_object_feature_values():
(obj, _) = create_test_object()
properties = create_test_properties()
obj.properties = properties
keys = list(properties.keys())
obj.set_features(keys)
raw_features = np.ctypeslib.as_array(obj.features, shape=(obj.n_features,))
flat_properties = np.concatena... |
class ZipMemoryFile(MemoryFile):
def __init__(self, file_or_bytes=None):
super().__init__(file_or_bytes, ext='zip')
_env
def open(self, path, driver=None, sharing=False, **kwargs):
zippath = _UnparsedPath('/vsizip{0}/{1}'.format(self.name, path.lstrip('/')))
if self.closed:
... |
class TaskGroupHandler(TaskNewHandler):
.authenticated
async def get(self, taskid):
user = self.current_user
groupNow = (await self.db.task.get(taskid, fields=('_groups',)))['_groups']
_groups = []
for task in (await self.db.task.list(user['id'], fields=('_groups',), limit=None))... |
_required
_POST
def user_block(request, username):
user = get_object_or_404(User, username=username, is_staff=False)
user.is_active = False
user.save()
msg = _(('The user %s is now blocked.' % user))
messages.success(request, msg, fail_silently=True)
return HttpResponseRedirect(reverse('user_det... |
def test_session(server_app):
server_app.sio = MagicMock()
with server_app.app.test_request_context():
flask.request.sid = 1234
result = server_app.session()
assert (result == server_app.sio.server.session.return_value)
server_app.sio.server.session.assert_called_once_with(1234, namespac... |
class Test_prev_next_history(unittest.TestCase):
t = u'test text'
def setUp(self):
self.q = q = LineHistory()
for x in [u'aaaa', u'aaba', u'aaca', u'akca', u'bbb', u'ako']:
q.add_history(RL(x))
def test_previous_history(self):
hist = self.q
assert (hist.history_cu... |
def get_dataloaders(args):
(train_loader, val_loader, test_loader) = (None, None, None)
if (args.data == 'cifar10'):
normalize = transforms.Normalize(mean=[0.4914, 0.4824, 0.4467], std=[0.2471, 0.2435, 0.2616])
train_set = datasets.CIFAR10(args.data_root, train=True, transform=transforms.Compose... |
def compute_predictions_logits(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, verbose_logging, version_2_with_negative, null_score_diff_threshold, tokenizer):
logger.info(('Writing predictions to: %s' % ou... |
class CIBHash(Base_Model):
def __init__(self, hparams):
super().__init__(hparams=hparams)
def define_parameters(self):
self.vgg = torchvision.models.vgg16(pretrained=True)
self.vgg.classifier = nn.Sequential(*list(self.vgg.classifier.children())[:6])
for param in self.vgg.paramet... |
def se_resnet152(num_classes, loss, pretrained='imagenet', **kwargs):
model = SENet(num_classes=num_classes, loss=loss, block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, dropout_p=None, inplanes=64, input_3x3=False, downsample_kernel_size=1, downsample_padding=0, last_stride=2, fc_dims=None, *... |
(params=[(10.0, 10.0, 10.0), (5.0, 5.0, 1.0)])
def simple_piecewise_model(request):
(in_flow, out_flow, benefit) = request.param
min_flow_req = 5.0
model = pywr.core.Model()
inpt = pywr.core.Input(model, name='Input', max_flow=in_flow)
lnk = pywr.core.PiecewiseLink(model, name='Link', nsteps=2, cost... |
class PyDemoPlugin(QPyDesignerCustomWidgetPlugin):
def __init__(self, parent=None):
super(PyDemoPlugin, self).__init__(parent)
self._initialized = False
def initialize(self, formEditor):
if self._initialized:
return
self._initialized = True
def isInitialized(self)... |
class SerializeMemoizer(Serialize):
__serialize_fields__ = ('memoized',)
def __init__(self, types_to_memoize: List) -> None:
self.types_to_memoize = tuple(types_to_memoize)
self.memoized = Enumerator()
def in_types(self, value: Serialize) -> bool:
return isinstance(value, self.types_... |
def test_write_pinned_buffer(tmpdir):
data_fname = tmpdir.join('test_read.sigmf-data')
meta_fname = tmpdir.join('test_read.sigmf-meta')
actual = cp.random.rand(100).astype(cp.complex64)
meta = {'core:datatype': 'cf32'}
cusignal.write_bin(str(data_fname), actual)
meta_fname.write(json.dumps(meta)... |
.parametrize('mark', [None, '', 'skip', 'xfail'])
def test_parameterset_for_parametrize_marks(pytester: Pytester, mark: Optional[str]) -> None:
if (mark is not None):
pytester.makeini('\n [pytest]\n {}={}\n '.format(EMPTY_PARAMETERSET_OPTION, mark))
config = pytester.parseconfig()
... |
def write_shapefile(df, filename, geomtype='line', prj=None):
import shapefile
df['Name'] = df.index
if (geomtype == 'point'):
w = shapefile.Writer(filename, shapefile.POINT, autoBalance=True)
elif (geomtype == 'line'):
w = shapefile.Writer(filename, shapefile.POLYLINE, autoBalance=True)... |
def list_killable_nodes(label_selector=None):
nodes = []
try:
if label_selector:
ret = cli.list_node(pretty=True, label_selector=label_selector)
else:
ret = cli.list_node(pretty=True)
except ApiException as e:
logging.error(('Exception when calling CoreV1Api->... |
def main():
warnings.filterwarnings('error')
with open('./pursuit-op.json') as f:
op = json.load(f)
self = CO.Object()
self.pool = None
self.cache = Cache(tmp_dir=op['options']['tmp_dir'])
self.runner = QueueMaster(op['options']['network']['qhost'], op['options']['network']['qport'])
... |
class ListControl(Control):
_label = None
def __init__(self, type, name, attrs={}, select_default=False, called_as_base_class=False, index=None):
if (not called_as_base_class):
raise NotImplementedError()
self.__dict__['type'] = type.lower()
self.__dict__['name'] = name
... |
.parametrize('holder', make_holder())
def test_hookrecorder_basic(holder) -> None:
pm = PytestPluginManager()
pm.add_hookspecs(holder)
rec = HookRecorder(pm, _ispytest=True)
pm.hook.pytest_xyz(arg=123)
call = rec.popcall('pytest_xyz')
assert (call.arg == 123)
assert (call._name == 'pytest_xy... |
def imitation_learning_loss(player):
episode_loss = torch.tensor(0)
with torch.cuda.device(player.gpu_id):
episode_loss = episode_loss.cuda()
for i in player.il_update_actions:
step_optimal_action = torch.tensor(player.il_update_actions[i]).reshape([1]).long()
with torch.cuda.device(... |
class CodeBlockPreprocessor(Preprocessor):
pattern = re.compile('\\[sourcecode:(.+?)\\](.+?)\\[/sourcecode\\]', re.S)
formatter = HtmlFormatter(noclasses=INLINESTYLES)
def run(self, lines):
def repl(m):
try:
lexer = get_lexer_by_name(m.group(1))
except ValueEr... |
class HotpotGoldParagraph(HotpotParagraph):
def __init__(self, title: str, sentences: List[List[str]], question_id: str, supporting_sentence_ids: List[int]):
super().__init__(title, sentences)
self.question_id = question_id
self.supporting_sentence_ids = supporting_sentence_ids
def repr_... |
class QueryExtension(rq.ReplyRequest):
_request = rq.Struct(rq.Opcode(98), rq.Pad(1), rq.RequestLength(), rq.LengthOf('name', 2), rq.Pad(2), rq.String8('name'))
_reply = rq.Struct(rq.ReplyCode(), rq.Pad(1), rq.Card16('sequence_number'), rq.ReplyLength(), rq.Card8('present'), rq.Card8('major_opcode'), rq.Card8('... |
def save_to_files(path, eight_bit_pan, eight_bit_rgb, eight_bit_rgbn, eight_bit_ps):
tiff.imwrite(f'{path}_pan_8bit.tiff', eight_bit_pan)
tiff.imwrite(f'{path}_rgbn_8bit.tiff', eight_bit_rgbn)
tiff.imwrite(f'{path}_ps_8bit.tiff', eight_bit_ps)
tiff.imwrite(f'{path}_rgb_8bit.png', eight_bit_rgb) |
def fn_amp_aware_filtering(t_input, amp, name='Amplitude_aware_filtering'):
with tf.variable_scope(name):
blurkernel = tf.constant(np.array([[0.002969, 0.013306, 0.021938, 0.013306, 0.002969], [0.013306, 0.059634, 0.09832, 0.059634, 0.013306], [0.021938, 0.09832, 0.162103, 0.09832, 0.021938], [0.013306, 0.0... |
def format_received_item(item_name: str, player_name: str) -> str:
special = {'Locked Missile Expansion': 'Received Missile Expansion from {provider_name}, but the Missile Launcher is required to use it.', 'Locked Ship Missile Expansion': 'Received Ship Missile Expansion from {provider_name}, but the main launcher ... |
class W_Path(W_Object):
errorname = 'path'
_attrs_ = _immutable_fields_ = ['path']
def __init__(self, p):
self.path = p
def equal(self, other):
if (not isinstance(other, W_Path)):
return False
return (self.path == other.path)
def write(self, port, env):
po... |
class TestCustomCircuitOracle(QiskitAquaTestCase):
def test_using_dj_with_constant_func(self):
q_v = QuantumRegister(2, name='v')
q_o = QuantumRegister(1, name='o')
circuit = QuantumCircuit(q_v, q_o)
circuit.x(q_o[0])
oracle = CustomCircuitOracle(variable_register=q_v, output... |
class Solution(object):
def isPerfectSquare(self, num):
(low, high) = (1, num)
while (low <= high):
mid = ((low + high) / 2)
mid_square = (mid * mid)
if (mid_square == num):
return True
elif (mid_square < num):
low = (mi... |
class KotlinLexer(RegexLexer):
name = 'Kotlin'
url = '
aliases = ['kotlin']
filenames = ['*.kt', '*.kts']
mimetypes = ['text/x-kotlin']
version_added = '1.5'
flags = (re.MULTILINE | re.DOTALL)
kt_name = ((((('?[_' + uni.combine('Lu', 'Ll', 'Lt', 'Lm', 'Nl')) + ']') + '[') + uni.combine('... |
def test_invalid_parent(qtmodeltester):
class Model(qt_api.QtGui.QStandardItemModel):
def parent(self, index):
if (index == self.index(0, 0, parent=self.index(0, 0))):
return self.index(0, 0)
else:
return qt_api.QtCore.QModelIndex()
model = Model()... |
def _extract_patches(x, kernel_size, stride, padding):
if ((padding[0] + padding[1]) > 0):
x = F.pad(x, (padding[1], padding[1], padding[0], padding[0])).data
x = x.unfold(2, kernel_size[0], stride[0])
x = x.unfold(3, kernel_size[1], stride[1])
x = x.transpose_(1, 2).transpose_(2, 3).contiguous(... |
class SawyerHandlePullV2Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'handle_pos': obs[4:7], 'unused_info': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort': 3})
action[... |
class Item():
def __init__(self, control, attrs, index=None):
label = _get_label(attrs)
self.__dict__.update({'name': attrs['value'], '_labels': ((label and [label]) or []), 'attrs': attrs, '_control': control, 'disabled': ('disabled' in attrs), '_selected': False, 'id': attrs.get('id'), '_index': i... |
def detect_python_on_windows():
try:
p = subprocess.run('python -c "import sys;print(sys.version_info.major)"', capture_output=True)
output = p.stdout.decode('utf-8')
if (int(output) == 3):
return ['python']
except FileNotFoundError:
pass
try:
p = subproce... |
class IndexedRawTextDataset(FairseqDataset):
def __init__(self, path, dictionary, append_eos=True, reverse_order=False):
self.tokens_list = []
self.lines = []
self.sizes = []
self.append_eos = append_eos
self.reverse_order = reverse_order
self.read_data(path, dictiona... |
class SimulScorer(object):
def __init__(self, args):
self.tokenizer = args.tokenizer
self.output_dir = args.output
if (args.output is not None):
self.output_files = {'text': os.path.join(args.output, 'text'), 'delay': os.path.join(args.output, 'delay'), 'scores': os.path.join(arg... |
def shortest_layer_path(start, end, layers):
links_from = {}
for layer in layers:
for bot in layer.bottom:
if (bot not in links_from):
links_from[bot] = []
links_from[bot].append(layer)
queue = [(s, []) for s in start]
visited = set(start)
while queue:... |
class VerificationRequest(Requirement):
__tablename__ = 'verificationrequest'
__mapper_args__ = {'polymorphic_identity': 'verificationrequest'}
id = Column(Integer, ForeignKey(Requirement.id, ondelete='CASCADE'), primary_key=True)
salt = Column(String(10), nullable=False)
mailer = None
def by_se... |
def get_token_network_registry_by_token_network_address(chain_state: ChainState, token_network_address: TokenNetworkAddress) -> Optional[TokenNetworkRegistryState]:
for token_network_registry in chain_state.identifiers_to_tokennetworkregistries.values():
if (token_network_address in token_network_registry.t... |
def mk_VTranslator(_RTLIRTranslator, _STranslator, _BTranslator):
class _VTranslator(_RTLIRTranslator, _STranslator, _BTranslator):
def get_pretty(s, namespace, attr, newline=True):
ret = getattr(namespace, attr, '')
if (newline and (ret and (ret[(- 1)] != '\n'))):
re... |
class Comment(Object):
id = Counter.T(optional=True, xmlstyle='attribute')
value = Unicode.T(xmltagname='Value')
begin_effective_time = DummyAwareOptionalTimestamp.T(optional=True, xmltagname='BeginEffectiveTime')
end_effective_time = DummyAwareOptionalTimestamp.T(optional=True, xmltagname='EndEffective... |
def downgrade(op, tables, tester):
op.drop_constraint(op.f('fk_repositorybuildtrigger_disabled_reason_id_disablereason'), 'repositorybuildtrigger', type_='foreignkey')
op.drop_index('repositorybuildtrigger_disabled_reason_id', table_name='repositorybuildtrigger')
op.drop_column('repositorybuildtrigger', 'en... |
_task('legacy_masked_lm')
class LegacyMaskedLMTask(FairseqTask):
def add_args(parser):
parser.add_argument('data', help='colon separated path to data directories list, will be iterated upon during epochs in round-robin manner')
parser.add_argument('--tokens-per-sample', d... |
.parametrize('map_variables', [True, False])
.parametrize('endpoint,function,params,json_response', [('live/radiation_and_weather', pvlib.iotools.get_solcast_live, dict(api_key='1234', latitude=(- 33.856784), longitude=151.215297, output_parameters='dni,ghi'), {'estimated_actuals': [{'dni': 836, 'ghi': 561, 'period_end... |
class ConfigureRequest(rq.Event):
_code = X.ConfigureRequest
_fields = rq.Struct(rq.Card8('type'), rq.Card8('stack_mode'), rq.Card16('sequence_number'), rq.Window('parent'), rq.Window('window'), rq.Window('sibling', (X.NONE,)), rq.Int16('x'), rq.Int16('y'), rq.Card16('width'), rq.Card16('height'), rq.Card16('bo... |
class JobManager():
def __init__(self, num_threads: int=2):
self._jobs: defaultdict[(str, Dict[(str, Event)])] = defaultdict(dict)
self._loop = asyncio.get_event_loop()
self._sem = Semaphore(num_threads)
if (sys.version_info < (3, 8)):
from .watcher import ThreadedChildWa... |
.skipif((sys.version_info < (3, 3)), reason='Mock class not available')
def test_v3_custom_session():
from unittest.mock import Mock
response = Mock()
response.content = SUCCESS_RESPONSE
session = Mock()
session.get = Mock(return_value=response)
client = cas.CASClient(version='3', server_url=' s... |
class CoverGridContainer(ScrolledWindow):
def __init__(self, fb):
super().__init__(hscrollbar_policy=Gtk.PolicyType.NEVER, vscrollbar_policy=Gtk.PolicyType.AUTOMATIC, shadow_type=Gtk.ShadowType.IN)
self._fb = fb
fb.set_hadjustment(self.props.hadjustment)
fb.set_vadjustment(self.props... |
class AdditionalSkipNamesTest(fake_filesystem_unittest.TestCase):
def setUp(self):
self.setUpPyfakefs(additional_skip_names=['pyfakefs.tests.import_as_example'])
def test_path_exists(self):
self.assertTrue(pyfakefs.tests.import_as_example.exists_this_file())
def test_fake_path_does_not_exist... |
class _AnnotationExtractor():
__slots__ = ['sig']
def __init__(self, callable):
try:
self.sig = inspect.signature(callable)
except (ValueError, TypeError):
self.sig = None
def get_first_param_type(self):
if (not self.sig):
return None
param... |
.parametrize('obj, raising, exc_reason, exc_str', [(QtObject(valid=True, null=True), False, None, None), (QtObject(valid=True, null=False), False, None, None), (QtObject(valid=False, null=True), True, None, '<QtObject> is not valid'), (QtObject(valid=False, null=False), True, None, '<QtObject> is not valid'), (QtObject... |
class CEGCN(nn.Module):
def __init__(self, height: int, width: int, changel: int, class_count: int, Q: torch.Tensor, A: torch.Tensor, model='normal'):
super(CEGCN, self).__init__()
self.class_count = class_count
self.channel = changel
self.height = height
self.width = width
... |
class Command(BaseCommand):
leave_locale_alone = True
server_class = BeatServer
def add_arguments(self, parser):
super(Command, self).add_arguments(parser)
parser.add_argument('--layer', action='store', dest='layer', default=DEFAULT_CHANNEL_LAYER, help='Channel layer alias to use, if not the... |
def get_loss_landscape(model, n_ff, dataset, bases=None, cutoffs=(0.0, 0.9), bins=np.linspace(0.0, 1.0, 11), verbose=False, period=10, gpu=True, x_min=(- 1.0), x_max=1.0, n_x=11, y_min=(- 1.0), y_max=1.0, n_y=11):
model = (model.cuda() if gpu else model.cpu())
model = copy.deepcopy(model)
ws0 = copy.deepcop... |
def get_arguments():
parser = argparse.ArgumentParser(description='Code for domain adaptation (DA) training')
parser.add_argument('--cfg', type=str, default=None, help='optional config file')
parser.add_argument('--random-train', action='store_true', help='not fixing random seed.')
parser.add_argument('... |
class HighResolutionNet(nn.Module):
def __init__(self, cfg, **kwargs):
self.inplanes = 64
super(HighResolutionNet, self).__init__()
use_old_impl = cfg.get('use_old_impl')
self.use_old_impl = use_old_impl
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=F... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.