code stringlengths 281 23.7M |
|---|
def assert_string_arrays_equal(expected: list[str], actual: list[str], msg: str) -> None:
actual = clean_up(actual)
if (expected != actual):
(expected_ranges, actual_ranges) = diff_ranges(expected, actual)
sys.stderr.write('Expected:\n')
red = ('\x1b[31m' if (sys.platform != 'win32') els... |
class CacheIndexableTest(unittest.TestCase):
def get_iter(self):
for i in range(100):
it = rorpiter.IndexedTuple((i,), list(range(i)))
self.d[(i,)] = it
(yield it)
def testCaching(self):
self.d = {}
ci = rorpiter.CacheIndexable(self.get_iter(), 3)
... |
class Migration(migrations.Migration):
dependencies = [('questions', '0084_catalog_sections')]
operations = [migrations.CreateModel(name='SectionPage', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order', models.IntegerField(default=0)), ('section'... |
def get_result_statistics(results, opts, num_gpus=None, include_failed_instances_in_duration=False, as_percentage_of=None):
ensure_backward_compatibility(opts)
results_stat = [(cost, tour, duration) for (cost, tour, duration) in results if (tour is not None)]
failed = [i for (i, (cost, tour, duration)) in e... |
def test_ipopt_solver_options():
solver = Solver.IPOPT()
assert (solver.type == SolverType.IPOPT)
assert (solver.show_online_optim is False)
assert (solver.show_options is None)
assert (solver.tol == 1e-06)
assert (solver.dual_inf_tol == 1.0)
assert (solver.constr_viol_tol == 0.0001)
ass... |
def format_time_brief(seconds: Union[(int, float)]) -> str:
s = int(np.rint(seconds))
if (s < 60):
return '{0}s'.format(s)
elif (s < (60 * 60)):
return '{0}m {1:02}s'.format((s // 60), (s % 60))
elif (s < ((24 * 60) * 60)):
return '{0}h {1:02}m'.format((s // (60 * 60)), ((s // 60... |
class Output():
def __init__(self, verbosity: Verbosity=Verbosity.NORMAL, decorated: bool=False, formatter: (Formatter | None)=None) -> None:
self._verbosity: Verbosity = verbosity
self._formatter = (formatter or Formatter())
self._formatter.decorated(decorated)
self._section_outputs... |
def strncat(state, dst, src, num):
(dlength, last) = state.mem_search(src, [BZERO])
dlength = state.evalcon(dlength).as_long()
(length, last) = state.mem_search(src, [BZERO])
length = z3.If((num < length), num, length)
state.mem_move((dst + dlength), src, (length + ONE))
return dst |
def create_toplevel_linklet_vars(forms_ls, linklet):
linkl_toplevels = {}
for form in forms_ls:
if isinstance(form, W_Correlated):
form = form.get_obj()
if (isinstance(form, values.W_List) and (form.car() is mksym('define-values'))):
ids = form.cdr().car()
(id... |
def create_hparams(FLAGS):
FLAGS = flat_config(FLAGS)
return tf.contrib.training.HParams(train_file=(FLAGS['train_file'] if ('train_file' in FLAGS) else None), eval_file=(FLAGS['eval_file'] if ('eval_file' in FLAGS) else None), test_file=(FLAGS['test_file'] if ('test_file' in FLAGS) else None), infer_file=(FLAG... |
def preprocess_pairwise_data(samples: List[ContextualizedExample], tokenizer: PreTrainedTokenizer, max_seq_length=64, disable_tqdm=False):
raw_sentences = []
for sample in samples:
(ent_ctx_a, ent_ctx_b) = sample.entities
raw_sentences.extend([ent_ctx_a.left_context, ent_ctx_a.entity, ent_ctx_a.... |
class ESILState():
def __init__(self, r2api: R2API, **kwargs):
self.kwargs = kwargs
self.r2api = r2api
self.pure_symbolic = kwargs.get('sym', False)
self.pcode = kwargs.get('pcode', False)
self.check_perms = kwargs.get('check', False)
if kwargs.get('optimize', False):... |
class PageIterator(Iterator[_T]):
def __init__(self, operation: Callable, args: Any, kwargs: Dict[(str, Any)], rate_limit: Optional[float]=None) -> None:
self._operation = operation
self._args = args
self._kwargs = kwargs
self._last_evaluated_key = kwargs.get('exclusive_start_key')
... |
def run_test(case, m):
m.elaborate()
m.apply(BehavioralRTLIRGenPass(m))
m.apply(BehavioralRTLIRTypeCheckPass(m))
visitor = BehavioralRTLIRToVVisitorL2((lambda x: (x in verilog_reserved)))
upblks = m.get_metadata(BehavioralRTLIRGenPass.rtlir_upblks)
m_all_upblks = m.get_update_blocks()
assert... |
def test_validation_error(capsys):
testargs = ['--schema', '3.0.0', './tests/integration/data/v2.0/petstore.yaml']
with pytest.raises(SystemExit):
main(testargs)
(out, err) = capsys.readouterr()
assert (not err)
assert ('./tests/integration/data/v2.0/petstore.yaml: Validation Error:' in out)... |
class AverageMeter():
def __init__(self, *keys):
self.__data = dict()
for k in keys:
self.__data[k] = [0.0, 0]
def add(self, dict):
for (k, v) in dict.items():
self.__data[k][0] += v
self.__data[k][1] += 1
def get(self, *keys):
if (len(keys... |
class BERTweetMetrics():
def __init__(self, multiclass=True, weight=None, **kwargs):
self.multiclass = multiclass
self.weight = (weight is not None)
self.metrics = {}
self.metrics['loss'] = Average()
self.metrics['accuracy'] = Accuracy(is_multilabel=(not multiclass))
... |
.skipif((not pytensor.config.cxx), reason='G++ not available, so we need to skip this test.')
def test_local_mul_s_d():
mode = get_default_mode()
mode = mode.including('specialize', 'local_mul_s_d')
for sp_format in sparse.sparse_formats:
inputs = [getattr(pytensor.sparse, (sp_format + '_matrix'))()... |
class UpBlock(BaseModule):
def __init__(self, in_channels, out_channels, init_cfg=None):
super().__init__(init_cfg=init_cfg)
assert isinstance(in_channels, int)
assert isinstance(out_channels, int)
self.conv1x1 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)... |
class AttrVI_ATTR_USB_MAX_INTR_SIZE(RangeAttribute):
resources = [(constants.InterfaceType.usb, 'INSTR'), (constants.InterfaceType.usb, 'RAW')]
py_name = 'maximum_interrupt_size'
visa_name = 'VI_ATTR_USB_MAX_INTR_SIZE'
visa_type = 'ViUInt16'
default = NotAvailable
(read, write, local) = (True, T... |
def test_hello_ini_setting(testdir):
testdir.makeini('\n [pytest]\n HELLO = world\n ')
testdir.makepyfile("\n import pytest\n\n \n def hello(request):\n return request.config.getini('HELLO')\n\n def test_hello_world(hello):\n assert hello == 'wo... |
def get_embedding_names_by_table(tables: Union[(List[EmbeddingBagConfig], List[EmbeddingConfig])]) -> List[List[str]]:
shared_feature: Dict[(str, bool)] = {}
for embedding_config in tables:
for feature_name in embedding_config.feature_names:
if (feature_name not in shared_feature):
... |
class StandUpExecutor(ActionExecutor):
def execute(self, script: Script, state: EnvironmentState, info: ExecutionInfo, char_index, modify=True, in_place=False):
info.set_current_line(script[0])
char_node = _get_character_node(state, char_index)
if ((State.SITTING in char_node.states) or (Sta... |
_node(_caller_prototype_tags, _prototype_tag_select)
def node_prototype_tags(caller):
text = '\n |cPrototype-Tags|n can be used to classify and find prototypes in listings Tag names are not\n case-sensitive and can have not have a custom category.\n\n {current}\n '.format(current=_get_cu... |
def read_srml(filename, map_variables=True):
tsv_data = pd.read_csv(filename, delimiter='\t')
data = _format_index(tsv_data)
data = data[data.columns[2:]]
if map_variables:
data = data.rename(columns=_map_columns)
columns = data.columns
flag_label_map = {flag: (columns[(columns.get_loc(f... |
class ChannelAFG(ChannelBase):
def __init__(self, instrument, id):
super().__init__(instrument, id)
self.calculate_voltage_range()
self.frequency_values = [self.frequency_min, self.frequency_max]
self.phase_values = [self.phase_min, self.phase_max]
load_impedance = Instrument.con... |
class PlainVerticalTable(PrettyTable):
def get_string(self, **kwargs: (str | list[str])) -> str:
options = self._get_options(kwargs)
rows = self._get_rows(options)
output = ''
for row in rows:
for v in row:
output += '{}\n'.format(v)
output += ... |
def _ensure_unique_nodes_unique_edges(graph_dict):
nodes = graph_dict['nodes']
edges = graph_dict['edges']
new_nodes = {node['id']: node for node in nodes}
new_nodes = list(new_nodes.values())
new_edges = {'{}.{}.{}'.format(edge['from_id'], edge['relation_type'], edge['to_id']): edge for edge in edg... |
class BatchBase(futures.FutureBase):
def __init__(self):
futures.FutureBase.__init__(self)
self.items = []
def is_flushed(self):
return self.is_computed()
def is_cancelled(self):
return (self.is_computed() and (self.error() is not None))
def is_empty(self):
return... |
class RailsRoleTest(ProvyTestCase):
def setUp(self):
super(RailsRoleTest, self).setUp()
self.role = RailsRole(prov=None, context={'owner': 'some-owner'})
self.supervisor_role = SupervisorRole(prov=None, context=self.role.context)
def installs_necessary_packages_to_provision(self):
... |
_config
def test_ratiotile_alternative_calculation(manager):
manager.c.next_layout()
manager.c.next_layout()
for i in range(12):
manager.test_window(str(i))
print(manager.c.layout.info()['layout_info'])
if (i == 0):
assert (manager.c.layout.info()['layout_info'] == [(0, 0... |
def plt_fig(test_img, scores, img_scores, gts, threshold, cls_threshold, save_dir, class_name):
num = len(scores)
vmax = (scores.max() * 255.0)
vmin = (scores.min() * 255.0)
vmax = ((vmax * 0.5) + (vmin * 0.5))
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
for i in range(num):
... |
class Blur(nn.Module):
def __init__(self, in_filters, sfilter=(1, 1), pad_mode='replicate', **kwargs):
super(Blur, self).__init__()
filter_size = len(sfilter)
self.pad = SamePad(filter_size, pad_mode=pad_mode)
self.filter_proto = torch.tensor(sfilter, dtype=torch.float, requires_grad... |
def test_bmn():
model_cfg = dict(type='BMN', temporal_dim=100, boundary_ratio=0.5, num_samples=32, num_samples_per_bin=3, feat_dim=400, soft_nms_alpha=0.4, soft_nms_low_threshold=0.5, soft_nms_high_threshold=0.9, post_process_top_k=100)
if torch.cuda.is_available():
localizer_bmn = build_localizer(model... |
class VariableDeclarations(VersionBase):
def __init__(self):
self.variables = []
def parse(element):
variable_declarations = VariableDeclarations()
declarations = element.findall('VariableDeclaration')
for declaration in declarations:
variable = Variable.parse(declara... |
class ArrayList(Array2D):
def __init__(self, w, h, data=None):
self.width = w
self.height = h
self.data = [(array('d', [0]) * w) for y in range(h)]
if (data is not None):
self.setup(data)
def __getitem__(self, idx):
if isinstance(idx, tuple):
retur... |
class PLBartTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ['input_ids', 'attention_mask']
prefix_tokens: List[int] = []
suffix_tokens... |
class MultiTextureSprite(pyglet.sprite.AdvancedSprite):
def __init__(self, imgs: Mapping[(str, pyglet.image.Texture)], x: float=0, y: float=0, z: float=0, blend_src: int=pyglet.gl.GL_SRC_ALPHA, blend_dest: int=pyglet.gl.GL_ONE_MINUS_SRC_ALPHA, batch: Optional[pyglet.graphics.Batch]=None, group: Optional[MultiTextur... |
class MyModel(ClassyModel):
def __init__(self, num_classes):
super().__init__()
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
num_channels = 3
self.fc = nn.Linear(num_channels, num_classes)
def forward(self, x):
out = self.avgpool(x)
out = out.reshape(out.size(0), (... |
class ViewPseudoFactory(ViewFactory):
def __init__(self, bookmark):
super().__init__(RegexPath('/', '/', {}), '')
self.bookmark = bookmark
def matches_view(self, view):
return False
def get_absolute_url(self, user_interface, **arguments):
return self.bookmark.href.as_network_... |
class LeNetContainer(nn.Module):
def __init__(self, num_filters, kernel_size, input_dim, hidden_dims, output_dim=10):
super(LeNetContainer, self).__init__()
self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size, 1)
self.conv2 = nn.Conv2d(num_filters[0], num_filters[1], kernel_size, 1)
... |
def getClusterLabelWithDisMatrix(dis_matrix, display_dis_matrix=False):
n_clusters = 7
linkage = 'complete'
if display_dis_matrix:
sns.heatmap(dis_matrix)
plt.show()
estimator = AgglomerativeClustering(n_clusters=n_clusters, linkage=linkage, affinity='precomputed')
estimator.fit(dis_... |
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.label_encoder = LabelEncoder()
curr_dim = 64
image_encoder = [nn.Conv2d(6, curr_dim, kernel_size=7, stride=1, padding=3, bias=True), nn.InstanceNorm2d(curr_dim), nn.ReLU(inplace=True)]
for ... |
def load_checkpoint(meta_path: str, file_name_prefix: str) -> QuantizationSimModel:
new_sess = utils.graph_saver.load_model_from_meta(meta_path=str((((meta_path + '/') + file_name_prefix) + '.meta')))
new_quant_sim = load_data_from_pickle_file((meta_path + '/orig_quantsim_config'))
new_quant_sim.session = n... |
def _get_localzone(_root: str='/') -> datetime.tzinfo:
tzenv = os.environ.get('TZ')
if tzenv:
return _tz_from_env(tzenv)
try:
link_dst = os.readlink('/etc/localtime')
except OSError:
pass
else:
pos = link_dst.find('/zoneinfo/')
if (pos >= 0):
zone_... |
def build_model(images, model_name, training, override_params=None, model_dir=None, fine_tuning=False):
assert isinstance(images, tf.Tensor)
if ((not training) or fine_tuning):
if (not override_params):
override_params = {}
override_params['batch_norm'] = utils.BatchNormalization
... |
class LoggerDepthProjection():
def __init__(self, step_size, name):
super(LoggerDepthProjection, self).__init__()
self.step_size = step_size
self.name = name
self.config = {'material': {'cls': 'PointsMaterial', 'size': 0.03}}
def tick(self, logger, step, ray_origins, ray_directio... |
class TimeParameteriseModel(TimeCreateExpression):
r: pybamm.SpatialVariable
geometry: dict
def setup(self):
set_random_seed()
TimeCreateExpression.time_create_expression(self)
def time_parameterise(self):
param = pybamm.ParameterValues({'Particle radius [m]': 1e-05, 'Diffusion c... |
def process_switch_inform(tokens, switch_pointer):
switch_idxs = [0]
while (switch_pointer[switch_idxs[(- 1)]] != 0):
switch_idxs.append(switch_pointer[switch_idxs[(- 1)]])
differ = [i for i in range(1, len(switch_idxs)) if ((switch_idxs[i] - switch_idxs[(i - 1)]) != 1)]
dif_len = len(differ)
... |
.timeout(60)
.skipif((not with_distributed), reason='dask.distributed is not installed')
.skipif((OPERATING_SYSTEM == 'Windows'), reason='XXX: seems to always fail')
.skipif((OPERATING_SYSTEM == 'Darwin'), reason='XXX: intermittently fails')
.skipif((OPERATING_SYSTEM == 'Linux'), reason='XXX: intermittently fails')
def... |
class TupleSelector(object):
class _TupleWrapper(object):
def __init__(self, data, fields):
self._data = data
self._fields = fields
def get(self, field):
return self._data[self._fields.index(TupleSelector.tuple_reference_key(field))]
def tuple_reference_key(cl... |
def fixDelex(filename, data, data2, idx, idx_acts):
try:
turn = data2[filename.strip('.json')][str(idx_acts)]
except:
return data
if (not isinstance(turn, str)):
for (k, act) in turn.items():
if ('Attraction' in k):
if ('restaurant_' in data['log'][idx]['t... |
class Conv3DSimple(nn.Conv3d):
def __init__(self, in_planes, out_planes, midplanes=None, stride=1, padding=1):
super(Conv3DSimple, self).__init__(in_channels=in_planes, out_channels=out_planes, kernel_size=(3, 3, 3), stride=stride, padding=padding, bias=False)
def get_downsample_stride(stride):
... |
def test_detect_clearsky_arrays(detect_clearsky_data):
(expected, cs) = detect_clearsky_data
clear_samples = clearsky.detect_clearsky(expected['GHI'].values, cs['ghi'].values, times=cs.index, window_length=10)
assert isinstance(clear_samples, np.ndarray)
assert (clear_samples == expected['Clear or not']... |
class InitCatalogTestCase(unittest.TestCase):
def setUp(self):
self.olddir = os.getcwd()
os.chdir(data_dir)
self.dist = Distribution(TEST_PROJECT_DISTRIBUTION_DATA)
self.cmd = frontend.InitCatalog(self.dist)
self.cmd.initialize_options()
def tearDown(self):
for di... |
def extract_feature_from_samples(generator, inception, truncation, truncation_latent, batch_size, n_sample, device, info_print=False):
with torch.no_grad():
generator.eval()
inception.eval()
n_batch = (n_sample // batch_size)
resid = (n_sample - ((n_batch - 1) * batch_size))
... |
def test_filter_languages():
filtered_langs = dictcli.filter_languages(langs(), ['af-ZA'])
assert (filtered_langs == [afrikaans()])
filtered_langs = dictcli.filter_languages(langs(), ['pl-PL', 'en-US'])
assert (filtered_langs == [english(), polish()])
with pytest.raises(dictcli.InvalidLanguageError)... |
class W_BytePRegexp(W_AnyRegexp):
def tostring(self):
from pypy.objspace.std.bytesobject import string_escape_encode
out_encoded = string_escape_encode(self.source, '"')
return ('#px#%s' % out_encoded)
def obj_name(self):
return values.W_Bytes.from_string(self.source) |
class BaseAgent(object):
def __init__(self, env):
self.env = env
self.results = {}
def get_results(self, detailed_output=False):
output = []
for (k, v) in self.results.items():
output.append({'instr_id': k, 'trajectory': v['path']})
if detailed_output:
... |
def mn_encode(message):
assert ((len(message) % 8) == 0)
out = []
for i in range((len(message) // 8)):
word = message[(8 * i):((8 * i) + 8)]
x = int(word, 16)
w1 = (x % n)
w2 = (((x // n) + w1) % n)
w3 = ((((x // n) // n) + w2) % n)
out += [wordlist[w1], wordl... |
class TestSpatialSVD():
def test_spatial_svd_compression(self):
model = get_model()
eval_callback = MagicMock()
eval_callback.side_effect = [0.4, 0.6, 0.6, 0.5, 0.4, 0.6, 0.6, 0.5, 0.4, 0.6]
greedy_params = GreedySelectionParameters(0.5, 4)
auto_params = SpatialSvdParameters.... |
def prime_decode_image(prime_encoded_image):
prime_generator = generate_primes()
structure_list = []
num_nonzero_voxels = 1
for prime in prime_generator:
print(prime)
s_img = sitk.Equal(sitk.Modulus(prime_encoded_image, prime), 0)
num_nonzero_voxels = sitk.GetArrayViewFromImage(s... |
def test_RandomVariable_bcast_specify_shape():
rv = RandomVariable('normal', 0, [0, 0], config.floatX, inplace=True)
s1 = pt.as_tensor(1, dtype=np.int64)
s2 = iscalar()
s2.tag.test_value = 2
s3 = iscalar()
s3.tag.test_value = 3
s3 = Assert('testing')(s3, eq(s1, 1))
size = specify_shape(p... |
class OrderSplitLoader(torch.utils.data.IterableDataset):
def __init__(self, contents, summaries, tokenizer_model, append_mask_token=False, **kwargs):
super(OrderSplitLoader).__init__()
if append_mask_token:
raise NotImplementedError
self.contents = contents
self.tokenize... |
def test_remove_by_full_path_to_python(tmp_path: Path, manager: EnvManager, poetry: Poetry, config: Config, mocker: MockerFixture, venv_name: str) -> None:
config.merge({'virtualenvs': {'path': str(tmp_path)}})
(tmp_path / f'{venv_name}-py3.7').mkdir()
(tmp_path / f'{venv_name}-py3.6').mkdir()
mocker.pa... |
def test_reusing_nonce_from_a_mined_transaction_raises(deploy_client: JSONRPCClient) -> None:
(contract_proxy, _) = deploy_rpc_test_contract(deploy_client, 'RpcTest')
client_invalid_nonce = JSONRPCClient(deploy_client.web3, deploy_client.privkey)
estimated_transaction = deploy_client.estimate_gas(contract_p... |
def emissivity(ndvi_image: np.ndarray, landsat_band_4: np.ndarray=None, emissivity_method: str='avdan'):
if (not (ndvi_image.shape == landsat_band_4.shape)):
raise InputShapesNotEqual(f'Shapes of input images should be equal: {ndvi_image.shape}, {landsat_band_4.shape}')
if ((emissivity_method == 'xiaole... |
def example_generator(data_path, single_pass):
while True:
filelist = glob.glob(data_path)
assert filelist, ('Error: Empty filelist at %s' % data_path)
if single_pass:
filelist = sorted(filelist)
else:
random.shuffle(filelist)
for f in filelist:
... |
class InceptionA(nn.Module):
def __init__(self, in_channels, pool_features):
super(InceptionA, self).__init__()
self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)
self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
self.branch5x5_2 = BasicConv2d(48, 64, kernel_si... |
def unpack_cmk_args(args, name):
(args, prop_keys, prop_vals) = unpack_properties(args, name)
if (len(args) != 3):
raise SchemeException((name + ': not give three required arguments'))
(key, get, set) = args
if (not isinstance(key, values.W_ContinuationMarkKey)):
raise SchemeException((n... |
def virtual_scane_one_model(model_dir, worker_id):
print(('Scanning ' + model_dir))
tmp_model_name = (('tmp' + str(worker_id)) + '.ply')
TMP_DATA_PATH = ('./tmp' + str(worker_id))
TMP_PLY_POINTCLOUD_PATH = (('./tmp' + str(worker_id)) + '.ply_output')
if (not os.path.exists(TMP_DATA_PATH)):
o... |
_test
def test_model_custom_target_tensors():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
y = K.placeholder([10, 4], name='y')
y1 = K.placeholder([10, 3], name='y1')
y2 = K.pla... |
class TestMapNotify(EndianTest):
def setUp(self):
self.evt_args_0 = {'event': , 'override': 1, 'sequence_number': 6027, 'type': 244, 'window': }
self.evt_bin_0 = b'\xf4\x00\x17\x8b(C\x19! O\x9b\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
def testPack0(sel... |
class StackOverflowDupQuestions(AbsTaskReranking):
def description(self):
return {'name': 'StackOverflowDupQuestions', 'hf_hub_name': 'mteb/stackoverflowdupquestions-reranking', 'description': 'Stack Overflow Duplicate Questions Task for questions with the tags Java, JavaScript and Python', 'reference': ' '... |
class TestStochasticTMLE():
def df(self):
df = ze.load_sample_data(False)
df[['cd4_rs1', 'cd4_rs2']] = ze.spline(df, 'cd40', n_knots=3, term=2, restricted=True)
df[['age_rs1', 'age_rs2']] = ze.spline(df, 'age0', n_knots=3, term=2, restricted=True)
return df.drop(columns=['cd4_wk45'])... |
class Tracker():
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
name2module: Dict[(str, nn.Module)] = field(default_factory=OrderedDict)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
has_not_submod... |
def make_iterable_unstructure_fn(cl: Any, converter: BaseConverter, unstructure_to: Any=None) -> IterableUnstructureFn:
handler = converter.unstructure
fn_name = 'unstructure_iterable'
if (getattr(cl, '__args__', None) not in (None, ())):
type_arg = cl.__args__[0]
if (not isinstance(type_arg... |
def alu_prediction(A, B, op, error=False):
assert isinstance(op, Ops), 'The tinyalu op must be of type Ops'
if (op == Ops.ADD):
result = (A + B)
elif (op == Ops.AND):
result = (A & B)
elif (op == Ops.XOR):
result = (A ^ B)
elif (op == Ops.MUL):
result = (A * B)
if... |
class BaseRequiredImgAsset(BaseRequiredAsset):
ASSET_CLASS = ImgAsset
min_width = models.PositiveIntegerField()
max_width = models.PositiveIntegerField()
min_height = models.PositiveIntegerField()
max_height = models.PositiveIntegerField()
class Meta(BaseRequiredAsset.Meta):
abstract = T... |
def test_read_commandline_bad_cmd(dataframe):
temp_dir = tempfile.gettempdir()
dataframe.to_csv(f'{temp_dir}/dataframe.csv')
with pytest.raises(TypeError):
janitor.io.read_commandline(6)
with pytest.raises(CalledProcessError):
janitor.io.read_commandline('bad command')
cmd = 'cat'
... |
def test_sia_uses_ces_distances(s):
with config.override(REPERTOIRE_DISTANCE='EMD', CES_DISTANCE='EMD'):
sia = compute.subsystem.sia(s)
assert (sia.phi == 2.3125)
with config.override(REPERTOIRE_DISTANCE='EMD', CES_DISTANCE='SUM_SMALL_PHI'):
sia = compute.subsystem.sia(s)
assert ... |
def test_life_list(requests_mock):
requests_mock.get(f'{API_V1}/observations/taxonomy', json=j_life_list_2, status_code=200)
client = iNatClient()
results = client.observations.life_list(taxon_id=52775)
assert isinstance(results, LifeList)
assert (len(results) == 31)
t = results[8]
assert (t... |
def get_stanford_models():
jar_name = os.path.join(SPICEDIR, SPICELIB, '{}.jar'.format(JAR))
if (not os.path.exists(jar_name)):
print('Downloading {} for SPICE ...'.format(JAR))
url = '
(zip_file, headers) = urlretrieve(url, reporthook=print_progress)
print()
print('Extra... |
class PlanParser(object):
def __init__(self, domain_file_path):
self.domain = domain_file_path
self.problem_id = (- 1)
self.process_pool = multiprocessing.Pool(3)
def get_plan(self):
parsed_plans = self.process_pool.map(get_plan_async, zip(([self.domain] * 3), ([self.problem_id] ... |
class UdpTransport(BaseTransport):
def __init__(self, beaver_config, logger=None):
super(UdpTransport, self).__init__(beaver_config, logger=logger)
self._sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self._address = (beaver_config.get('udp_host'), beaver_config.get('udp_port'))
... |
class BuildMn(BuildMnBase):
def Run(self, argv):
if (len(argv) < 6):
((print >> sys.stderr), 'BuildMn.Run(<ARCADIA_ROOT> <archiver> <mninfo> <mnname> <mnrankingSuffix> <cppOutput> [params...])')
sys.exit(1)
self.SrcRoot = argv[0]
self.archiver = argv[1]
mninfo... |
def mock_plugin_installation(mocker):
subprocess_run = subprocess.run
mocked_subprocess_run = mocker.MagicMock(returncode=0)
def _mock(command, **kwargs):
if isinstance(command, list):
if (command[:5] == [sys.executable, '-u', '-m', 'pip', 'install']):
mocked_subprocess_r... |
class EmailBackend(BaseEmailBackend):
def __init__(self, token=None, channel=None, sender_name=None, author=None, archive=False, **kwargs):
super().__init__(**kwargs)
self.token = (token or settings.FRONT_TOKEN)
self.channel = (channel or settings.FRONT_CHANNEL)
if ((not self.token) ... |
class OutageSection(Section):
keyword = b'OUTAGE'
outages_header = b'NET Sta Chan Aux Start Date Time End Date Time Duration Comment'
report_period = OutageReportPeriod.T()
outages = List.T(Outage.T())
def read(cls, reader):
DataType.read(reader)
report_pe... |
def get_bit_vector(system):
if config.with_bit_all:
reservable = [len(value) for (entity, value) in system.state['reservation_informed'].items()]
reservable = np.all(reservable)
small_value = config.small_value
if (len(system.state['informed']['name']) > 0):
bit_vecs = ([... |
(2, 'where', 'filter')
def getItemsByCategory(filter, where=None, eager=None):
if isinstance(filter, int):
filter = (Category.ID == filter)
elif isinstance(filter, str):
filter = (Category.name == filter)
else:
raise TypeError('Need integer or string as argument')
filter = proces... |
class TableProcessor(object):
def __init__(self, table_linearize_func: TableLinearize, table_truncate_funcs: List[TableTruncate], target_delimiter: str=', '):
self.table_linearize_func = table_linearize_func
self.table_truncate_funcs = table_truncate_funcs
self.target_delimiter = target_deli... |
def test_jsonify_behaves():
assert (Jsonify.yaml_tag == '!jsonify')
jsonify = Jsonify({'a': 'string here', 'b': 123, 'c': False})
assert (jsonify == Jsonify({'a': 'string here', 'b': 123, 'c': False}))
assert jsonify
assert (str(jsonify) == "{'a': 'string here', 'b': 123, 'c': False}")
assert (r... |
def serialize_key_and_certificates(name: (bytes | None), key: (PKCS12PrivateKeyTypes | None), cert: (x509.Certificate | None), cas: (typing.Iterable[_PKCS12CATypes] | None), encryption_algorithm: serialization.KeySerializationEncryption) -> bytes:
if ((key is not None) and (not isinstance(key, (rsa.RSAPrivateKey, d... |
def set_interval(interval):
def decorator(function):
def wrapper(*args, **kwargs):
stopped = threading.Event()
def loop():
while (not stopped.wait(interval)):
function(*args, **kwargs)
t = threading.Thread(target=loop)
t.dae... |
def main() -> None:
import argparse
import configparser
import re
NODE_SECTION_RE = re.compile('^node[0-9]+')
parser = argparse.ArgumentParser()
parser.add_argument('--nodes-data-dir', default=os.getcwd())
parser.add_argument('--wait-after-first-sync', default=False, action='store_true')
... |
def do_autopaginate(parser, token):
split = token.split_contents()
as_index = None
context_var = None
for (i, bit) in enumerate(split):
if (bit == 'as'):
as_index = i
break
if (as_index is not None):
try:
context_var = split[(as_index + 1)]
... |
class TeleporterList(location_list.LocationList):
def nodes_list(cls, game: RandovaniaGame) -> list[NodeIdentifier]:
game_description = default_database.game_description_for(game)
teleporter_dock_types = game_description.dock_weakness_database.all_teleporter_dock_types
region_list = game_des... |
class IBContract(Contract):
security_type_map = {SecurityType.FUTURE: 'FUT', SecurityType.STOCK: 'STK', SecurityType.INDEX: 'IND', SecurityType.SPREAD: 'BAG', SecurityType.CONTFUT: 'CONTFUT'}
def __init__(self, symbol: str, security_type: SecurityType, exchange: str, multiplier: Optional[str]='', currency: str=... |
class VSA_Module(nn.Module):
def __init__(self, opt={}):
super(VSA_Module, self).__init__()
channel_size = opt['multiscale']['multiscale_input_channel']
out_channels = opt['multiscale']['multiscale_output_channel']
embed_dim = opt['embed']['embed_dim']
self.LF_conv = nn.Conv2... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.