content stringlengths 22 815k | id int64 0 4.91M |
|---|---|
def _generate_mesh_simplices(
*, limits, mesh_size, periodic=False, skip_origin=False
):
"""
Generate the starting simplices for given limits and mesh size.
"""
dim = len(limits)
if len(mesh_size) != dim:
raise ValueError(
"Inconsistent dimensions: {}, {}".format(dim,... | 5,289,100 |
def print_score_breakdown(extractor, score_breakdown):
"""
:type extractor: nlplingo.nn.extractor.Extractor
:type score_breakdown: dict
"""
for index, f1_score in score_breakdown.items():
et = extractor.domain.get_event_type_from_index(index)
print('{}\t{}'.format(et, f1_score.to_str... | 5,289,101 |
def register_cmaps(*args, user=None, local=None, default=False):
"""
Register named colormaps. This is called on import.
Parameters
----------
%(rc.cmap_args)s
%(rc.cmap_exts)s
%(rc.cmap_params)s
See also
--------
register_cycles
register_colors
register_fonts
pro... | 5,289,102 |
def stop_thread(thread):
"""未执行完线程终止."""
tid = ctypes.c_long(thread.ident)
exctype = SystemExit
if not inspect.isclass(exctype):
exctype = type(exctype)
res = ctypes.pythonapi.PyThreadState_SetAsyncExc(
tid, ctypes.py_object(exctype))
if res == 0:
raise ValueError("invali... | 5,289,103 |
def write_tfrecords(data_path,video_paths,action_labels,
n_vids_per_batch,subset,
n_frames_batch = 16,
n_frames_chunk = 512):
"""Function to write tfrecords.
:param data_path: name of tfrecords file to write
:param video_paths: list contain... | 5,289,104 |
def test_batch():
""" Testing the batch function into chunks of 100 """
full_list = list(range(0, 1000))
initial_batch = list(range(0, 100))
iteration = 0
batch_size = 100
for batch in batcher(full_list, batch_size):
expected_batch = [x + (batch_size * iteration) for x in initial_batch]
... | 5,289,105 |
def _set_up_thermo_mets(base_df: pd.DataFrame, mets_list: list, mets_conc_df: pd.DataFrame) -> tuple:
"""
Given the base excel input file, the list of metabolites in the model and a dataframe with metabolite
concentrations averages and respective stdev, fills in the thermoMets sheet.
First fills in conc... | 5,289,106 |
def setup_constant(prefix, **kwargs):
""" Set up a simple constant function model.
:param prefix: Model prefix
:type prefix: string
:param kwargs: Keyword arguments
:return: LMFIT model and parameters
:rtype: (lmfit.Model, lmfit.Parameters)
"""
amplitude = kwargs.pop('amplitude', 5)
... | 5,289,107 |
def subapp_css_static(filename):
"""
set css files.
:param filename: css file name.
:return: static path.
"""
return static_file(filename, root='kokemomo/plugins/subapp/view/resource/css') | 5,289,108 |
def graph1D(n: int, pbc: bool = True) -> Graph:
"""
1D PBC chain with n sites
:param n:
:return:
"""
g = nx.Graph()
for i in range(n):
g.add_node(i)
for i in range(n - 1):
g.add_edge(i, i + 1, weight=1.0)
if pbc is True:
g.add_edge(n - 1, 0, weight=1.0)
... | 5,289,109 |
def demo_4(win):
"""Print single lines."""
win.erase()
win.box()
win.bkgd(' ', pairs[('white','black')])
try:
win.move(1,1)
win.addstr("What is this??", ATTRS['uline'] | pairs['yellow','pink'])
win.move(3,1)
win.addstr("What is this??", ATTRS['uline'] | pairs['orange'... | 5,289,110 |
def test_wait_for_ready(main_container):
"""Wait for container to be ready."""
TIMEOUT = 10
for i in range(TIMEOUT):
if READY_MESSAGE in main_container.logs().decode("utf-8"):
break
time.sleep(1)
else:
raise Exception(
f"Container does not seem ready. "
... | 5,289,111 |
def _solve_triangular_right(a, b, left_side, lower, trans_a):
"""An unrolled right-looking triangular solve on (blocked) LapaxMatrices."""
n = a.shape[-1]
def solve(a, b):
return _solve_triangular_left(a, b, left_side, lower, trans_a)
if n == 1:
return solve(a.bview(1), b.bview(1))
out = full_like(... | 5,289,112 |
def _get_latlons(nlat, nlon):
"""Short summary.
Parameters
----------
nlat : type
Description of parameter `nlat`.
nlon : type
Description of parameter `nlon`.
Returns
-------
type
Description of returned object.
"""
from numpy import linspace, meshgrid... | 5,289,113 |
def abs(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in abs")
return Array._new(np.abs(x._array)) | 5,289,114 |
def challenge_hash(peer_challenge, auth_challenge, user_name):
"""
RFC 2759
section 8.2
ChallengeHash
:param peer_challenge:
:param auth_challenge:
:param user_name:
:return: challenge
example
peer_challenge = "\x21\x40\x23\x24\x25\x5E\x26\x2A\x28\x29\x5F\x2B\x3A\x33\x7C\x7E"
... | 5,289,115 |
def var_historic(r, level=5):
"""
Returns the historic Value at Risk at a specified level
i.e. returns the number such that "level" percent of the returns
fall below that number, and the (100-level) percent are above
"""
if isinstance(r, pd.DataFrame):
return r.aggregate(var_historic, le... | 5,289,116 |
def ignore_covcl():
"""Checking Gaussian covariances for lens0, lens0
"""
with open("tests/data/tjpcov_cl.pkl", "rb") as ff:
cov0cl = pickle.load(ff)
ccl_tracers = ccl_tracers
tracer_Noise = tracer_Noise
gcov_cl_0 = tjp.cl_gaussian_cov(tracer_comb1=('lens0', 'lens0'),
... | 5,289,117 |
def convert_to_km(distance: float, print_output: bool = True) -> Dict[str, float]:
"""Convert a miles distance into the double of km.
Args:
distance: a distance (in miles).
print_output: if True, prints the progress.
Returns:
A dictionary with two keys ('original' and 'converted').... | 5,289,118 |
def discriminator_loss(D, real_images, fake_images):
"""Loss computed to train the GAN discriminator.
Args:
D: The discriminator.
real_images of shape (batch_size, nc, 28, 28): Real images.
fake_images of shape (batch_size, nc, 28, 28): Fake images produces by the generator.
Returns:
... | 5,289,119 |
def Lucas_chain(n, f, g, x_0, x_1):
"""
Given an integer n, two functions f and g, and initial value (x_0, x_1),
compute (x_n, x_{n+1}), where the sequence {x_i} is defined as:
x_{2i} = f(x_i)
x_{2i+1} = g(x_i, x_{i+1})
"""
binary = arith1.expand(n, 2)
u = x_0
v = x_1
while b... | 5,289,120 |
def build_input_from_segments(
history, reply, tokenizer, lm_labels=False, with_eos=True
):
""" Build a sequence of input from 2 segments: history and candidate reply. """
bos, eos, speaker1, speaker2 = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS[:-1])
# build input sequence as:
# [bos, person2, ... | 5,289,121 |
async def setup_guardian_fixture(hass, config, data_ping):
"""Define a fixture to set up Guardian."""
with patch("aioguardian.client.Client.connect"), patch(
"aioguardian.commands.system.SystemCommands.ping",
return_value=data_ping,
), patch("aioguardian.client.Client.disconnect"), patch(
... | 5,289,122 |
def aiohttp_socket_timeout(socket_timeout_s):
""" Return a aiohttp.ClientTimeout object with only socket timeouts set. """
return aiohttp.ClientTimeout(total=None,
connect=None,
sock_connect=socket_timeout_s,
sock_read=sock... | 5,289,123 |
def get_contributions_from_file(site, username):
"""Get a list of Wikipedia-namespace contributions."""
with open("my-contribs.json") as my_contribs_file:
data = json.load(my_contribs_file)
for contrib in data:
time = datetime.datetime.strptime(contrib["timestamp"],
... | 5,289,124 |
def alignment_to_search_path(algn):
"""
Given an alignment, make searchpath.
Searchpath must step exactly one position in x XOR y at each time step.
In the case of a block of deletions, the order found by DP is not meaningful.
To make things consistent and to improve the probability of recoveri... | 5,289,125 |
def setLCData(*args):
"""Access and changes protected lc_data class data. The returned value representation is an array of data from
load cells
Parameters
----------
lc_data: an array containing the load cell data
"""
lcd.lc_data = args[0]
# return lcd.lc_data | 5,289,126 |
def getMulitples(indices):
"""Return a subset with no multiples (filters out the bad ones)."""
multiples = []
added = []
for i in range(0, len(indices) - 1):
if indices[i][0] == indices[i + 1][0]:
added.append(indices[i])
elif added:
added.append(indices[i])
... | 5,289,127 |
async def test_lock_locked(
hass: HomeAssistant,
mock_entry: MockEntityFixture,
doorlock: tuple[Doorlock, str],
):
"""Test lock entity locked."""
new_bootstrap = copy(mock_entry.api.bootstrap)
new_lock = doorlock[0].copy()
new_lock.lock_status = LockStatusType.CLOSED
mock_msg = Mock()
... | 5,289,128 |
def get_pourbaix_info(entry: dict) -> dict:
"""
Grabs the relevant pourbaix entries for a given mpid
from Materials Project and constructs a pourbaix diagram for it. This currently only supports MP materials.
Args:
entry: bulk structure entry as constructed by
catlas.load_bulk_st... | 5,289,129 |
def do_math(a, b, operator):
"""Helper function that performs computation between two numbers."""
if operator == "+":
return int(b) + int(a)
elif operator == "-":
return int(b) - int(a)
elif operator == "*":
return int(b) * int(a)
elif operator == "/":
return int(b)... | 5,289,130 |
def getFrequencyBands(sessionId):
"""
Get the frequency bands supported by this server.
Returns: A list of frequency bands for the sensors managed by this server.
"""
@testcase
def getFrequencyBandsWorker(sessionId):
try:
if not Config.isConfigured():
util.de... | 5,289,131 |
def test_Get_all_POST(client):
"""
**GIVEN** a Flask application configured for testing\n
**WHEN** a valid endpoint for retrieving all files of a type is hit with a POST request\n
**THEN** check that the response is valid for a 405 response (Method not Allowed)
"""
for endpoint in ['song', 'podc... | 5,289,132 |
def deg_to_arcsec(angle: float) -> float:
"""
Convert degrees to arcseconds.
Args:
angle: Angle in units of degrees.
Returns:
angle: Angle in units of arcseconds.
"""
return float(angle) * 3600. | 5,289,133 |
def pixel_level_peak_finder_1d(correlation_lines: np.ndarray) -> np.ndarray:
"""
This function returns a numpy array containing the location of the maximum surface value
to pixel level accuracy for each row of the input matrix.
:return: The location of the maximum surface values to pixel level accuracy... | 5,289,134 |
def Shift(**kwargs):
"""
Calculates register shifts between strands based on their pairing and length
"""
refbluefile = kwargs.get('refblue')
seg1 = kwargs.get('seg1')
seg2 = kwargs.get('seg2')
refblue = Blueprint(refbluefile)
nseg1 = len( refblue.segment_... | 5,289,135 |
def mapping_stats(output_dir):
"""This is UID level mapping summary, the config file is in parent dir"""
output_dir = pathlib.Path(output_dir).absolute()
config = get_configuration(output_dir.parent / 'mapping_config.ini')
mode = config['mode']
if mode == 'mc':
final_df = mc_mapping_stats(o... | 5,289,136 |
def ul_lands_download(save_dir = None):
"""
Downloads las files from University Lands Texas
This function downloads files from the university lands ftp
website located at publiftp.utlands.utsystem.edu. It inventories
readable logs into a csv file containing header data in the
save_dir. ... | 5,289,137 |
def cc_random_payoffs():
"""
Plotting for experiments with random 3x3 and 32x32 experiments if the results were
stored by run_single_config.py
"""
exp_dir = "Experiments/PaperExperiments/{}/".format("32x32")
baseline_dir = "Experiments/PaperExperiments/{}/".format("1598034745")
with open(ex... | 5,289,138 |
def copyExecutable(dir, name):
"""Copy the built exe to the output directory."""
kepath = "./Release"
keexe = "kinderegg.exe" # specified in CMakeLists.txt
shutil.copyfile(
os.path.join(kepath, keexe),
os.path.join('..', 'tools', dir, name+'.exe'))
# Copy SDL2.dll to output dir
s... | 5,289,139 |
async def test_ap_fails(injector):
"""Async providers must not be usable in sync consumers"""
class A:
pass
a = A()
@injector.consumer
def consumer(a_: A):
assert a_ is a
@injector.provider
async def bean() -> A:
return a
with pytest.raises(pyserp.InjectionErr... | 5,289,140 |
def gru_run(params, x_t):
"""Run the Vanilla RNN T steps, where T is shape[0] of input.
Args:
params: dict of GRU parameters
x_t: np array of inputs with dim ntime x u
Returns:
2-tuple of np arrays (hidden states w dim ntime x n, outputs w dim ntim x o)
"""
return gru_run_with_h0(params, x_... | 5,289,141 |
def set_window_width(image, MIN_BOUND=-1000.0, MAX_BOUND=400.0):
"""设置窗宽"""
image[image > MAX_BOUND] = MAX_BOUND
image[image < MIN_BOUND] = MIN_BOUND
return image | 5,289,142 |
def assert_allclose(
actual: numpy.ndarray, desired: List[Union[int, float]], atol: float
):
"""
usage.scipy: 5
usage.statsmodels: 1
"""
... | 5,289,143 |
def _process_merge(p1_ctx, p2_ctx, ctx):
"""compute the appropriate changed files for a changeset with two parents
This is a more advance case. The information we need to record is summarise
in the following table:
┌──────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
│ diff... | 5,289,144 |
def create_mmt_encoder_inputs(data_cfg: cfg.DataConfig):
"""Creates inputs for `MmtEncoder`."""
patch_embedding_size = (data_cfg.patch_size ** 2) * 3
num_patch_per_row = data_cfg.image_size // data_cfg.patch_size
num_patches = num_patch_per_row ** 2
max_seq_len = data_cfg.max_seq_len
word_ids = tf.keras.... | 5,289,145 |
def add_course_without_teacher_and_students(access_token):
""" Adding a course just setting course name as ADMIN"""
data = {
"name": "math"
}
headers = {
'Authorization': f'JWT {access_token}',
'Content-Type': 'application/json'
}
data = json.dumps(data)
response = r... | 5,289,146 |
def update_chart_info(_figure, chart_data_json_str):
"""
A callback function to set the sample count for the number of samples that
have been displayed on the chart.
Args:
_figure (object): A figure object for a dash-core-components Graph for
the strip chart - triggers the callback.... | 5,289,147 |
def compare_samples(samples, pipeline, dask_client, verbose):
"""Compare several samples in a All vs All fashion.
"""
if len(samples) == 1:
raise ValueError(
"It's necessary to have at least two samples for the comparison"
)
sample_sets = [
SampleSet(
[De... | 5,289,148 |
def delete_vnet_lxc_profile(name):
"""
Deletes a VNet specific LXC profile
Profile must not be used when deleting
:param str name: The LXC profile to delete
"""
# Check if the profile even exists
if not check_if_lxc_profile_exists(name):
logger.warning("Tried to delete LXC profile {}... | 5,289,149 |
def record_copy_all(node, **kwargs):
""" A default rcd implementation that copies all kwargs to the tape.
the impl is used for the vjp and vjp primitives.
the impl is can be used for the apl primitives if no rcd is given;
but we use 'record_copy_autodiff' to save a much smaller subset
... | 5,289,150 |
def _merge_annotations(curr,new):
"""
Private function. Don't use directly.
This function will modify the annotations
and docstring of `curr` to incorporate those
in `new`
"""
# Merge annotations by items in new
for k,v in new.__annotations__.items():
# Get a set of the annotati... | 5,289,151 |
def test_raw_html_648a():
"""
Test case 648a: variation of 644 with shorter
"""
# Arrange
source_markdown = """foo <!ELEMENT>"""
expected_tokens = [
"[para(1,1):]",
"[text(1,1):foo \a<\a<\a!ELEMENT\a>\a>\a:]",
"[end-para:::True]",
]
expected_gfm = """<p>fo... | 5,289,152 |
def split_df_train_test(data: pd.DataFrame, perc_train: float) -> Tuple[list, list]:
"""Create train test split"""
# get percentages
assert perc_train >= 0.0
assert perc_train <= 1.0
# get number of examples to use for training
num_train = int(perc_train * len(data))
# get number of exampl... | 5,289,153 |
def read_file(filename):
""" Return the content of a file as a list of strings, each corresponding to a line
:param filename: string: location and name of the file
:return: content of filename
"""
with open(filename, 'r') as ofile:
content = ofile.read().splitlines()
return content | 5,289,154 |
def parse_record(raw_record):
"""Parses a record containing a training example of an image.
The input record is parsed into a label and image, and the image is passed
through preprocessing steps (cropping, flipping, and so on).
Args:
raw_record: scalar Tensor tf.string containing a serialized
... | 5,289,155 |
def helicsFederateRegisterGlobalTypeInput(fed: HelicsFederate, name: str, type: str, units: str) -> HelicsInput:
"""
Register a global publication with an arbitrary type.
The publication becomes part of the federate and is destroyed when the federate is freed so there are no separate free functions for subs... | 5,289,156 |
def main():
""" Main function
Returns:
None
"""
configfile = "config.json"
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
try:
logging.info("Opening config file.")
f = open(configfile, "tr")
except FileNotFoundError:
logging.info("File not found... | 5,289,157 |
def get_cluster(cluster_identifier: Optional[str] = None,
tags: Optional[Mapping[str, str]] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetClusterResult:
"""
Provides details about a specific redshift cluster.
## Example Usage
```python
import p... | 5,289,158 |
def fiRna(exName: str,
filePath: str = './',
copyPath: str = './copy',
pattern: str = "0:0>4",
boolCopy: bool = True,
boolExist: bool = False):
"""fiRna.
批量重命名
传入扩展名，文件路径及复制路径
"""
# 检查 filePath 文件夹是否存在，否则报错退出
pathCheck(filePath)
# 提取文件夹下所有文件... | 5,289,159 |
def predict(
mixture: str,
results_dir: str,
split: str,
batch_size: int,
model_parallelism: int,
tpu_name: str,
tpu_topology: str,
) -> None:
"""Predict with the model located at RESULTS_DIR on MIXTURE."""
# Validate arguments.
if not results_dir.startswith("gs://"):
ra... | 5,289,160 |
def translate_to_origin(structure, weight="mass", atom_mask=None, dimension=None):
"""Translate a structure to the origin based on the centroid of the whole system or a subset of atom(s).
Parameters
----------
structure : str or :class:`parmed.Structure`
Molecular structure containing coordinat... | 5,289,161 |
def score_1(game, player): # 82.14%
"""
Heuristics computing score using #player moves - k * #opponent moves
:param game: game
:param player: player
:return: score
"""
if game.is_winner(player) or game.is_loser(player):
return game.utility(player)
opponent = game.get_opponent(p... | 5,289,162 |
def parse_line( line ):
"""Returns data located in the line."""
tokens = line.split()
return int(tokens[1]), tokens[2], (
int(tokens[4]), # n_i
int(tokens[6]), # s_i
) | 5,289,163 |
def find_ancestors(starting_resource, full_name):
"""Find the ancestors for a given resource.
Take advantage of the full name from the data model which has
the entire hierarchy.
Keeping this outside of the class, because the class is mocked out during
testing.
Args:
starting_resource ... | 5,289,164 |
def select_from_existing_derivations():
"""
Which derivation does the user want to review?
Alternatively, the user can generate a PDF
>>> select_from_existing_derivations()
"""
trace_id = str(random.randint(1000000, 9999999))
logger.info("[trace page start " + trace_id + "]")
try:
... | 5,289,165 |
async def is_streamable(filename: str) -> bool:
"""Return if a file is streamable by pyatv.
This method will return if the file format of the given file is supported
and streamable by pyatv. It will never raise an exception, e.g. because the
file is missing or lack of permissions.
"""
try:
... | 5,289,166 |
def forward_pass(x: np.ndarray, weights: np.ndarray, bias: float) -> np.ndarray:
"""
Method that calculates the activation of the sigmoid function
from a given input, weights and biases.
:param x: input data
:param weights: tuple with the weights
:param bias: value of the biases
:return: ac... | 5,289,167 |
def get_long_description():
"""
Provides the long description.
"""
with open("README.rst", "r", encoding="utf-8") as file_stream:
return file_stream.read() | 5,289,168 |
def get_connections(ccs_path):
""" Returns list of installed connection names.
Searches "<ccs_path>/ccs_base/common/targetdb/connections" directory for
installed connection names.
Args:
ccs_path (str): full path to ccs installation to use
Returns:
list: connection names
Raise... | 5,289,169 |
def escape_latex(strng):
"""Consistently escape LaTeX special characters for _repr_latex_ in IPython
Implementation taken from the IPython magic `format_latex`
Examples
--------
escape_latex('disease_rate') # 'disease\_rate'
Parameters
----------
strng : str
string to esc... | 5,289,170 |
def list_subnets(sort_by='name'):
"""
Get list of subnets from maas server
CLI Example:
.. code-block:: bash
salt 'maas-node' maasng.list_subnets
"""
subnets = {}
maas = _create_maas_client()
json_res = json.loads(maas.get(u'api/2.0/subnets/').read())
for item in json_res:... | 5,289,171 |
def stream_profile_sample(self, stream_data): # FileData object
"""Profiles a stream container: look at it as one big blob"""
tracks = {}
stream_data.crc32 = 0x0 # start value crc
meta_length = 0
stream_data.crc32 = calc_crc32(stream_data.name)
# do track stuff
track = TrackData()
track.track_number = 1
tr... | 5,289,172 |
def multinomial_as_basic(multinomial, *symbols):
"""
Converts the multinomial to Add/Mul/Pow instances.
multinomial is a dict of {powers: coefficient} pairs, powers is a tuple of
python integers, coefficient is a python integer.
"""
l = []
for powers, k in multinomial.iteritems():
t... | 5,289,173 |
def nvme_discover(uri):
"""Discover target."""
u = urlparse(uri)
port = u.port
host = u.hostname
command = "sudo nvme discover -t tcp -s {0} -a {1}".format(port, host)
output = subprocess.run(
command, check=True, shell=True, capture_output=True, encoding="utf-8"
)
if not u.path... | 5,289,174 |
def display_summary(user, sender_id, callback=send_message):
"""displays a submit score summary
Parameters:
user: the user dictionary (dict)
sender_id: the sender facebook id (? something)
callback: the thing to call with a result (function)
"""
summary = game_summary(user)
... | 5,289,175 |
def topic_modelling(all_data_t: List[List[float]],
tfidf: TfidfVectorizer, n_keywords: int,
param_grid=TRAIN_PARAM_GRID_LDA,
retrain=False, plot=False,
random_seed=42) -> Tuple[BaseEstimator,
Lis... | 5,289,176 |
def get_gpu_memory() -> List[int]:
"""Query GPU's for amount of VRAM
Modified from:
https://stackoverflow.com/questions/59567226/how-to-programmatically-determine-available-gpu-memory-with-tensorflow
Returns
-------
VRAM, List[int]
Total GPU VRAM in bits for each GPU.
"""
_outp... | 5,289,177 |
def die_screaming(instr):
"""
Make sure we exit in a way that will get Jenkins's attention.
"""
LOG.error(instr)
sys.exit(1) | 5,289,178 |
def smiles2inchi(smiles):
"""
Converts SMILES to InChi. This method accepts urlsafe_base64 encoded string containing single or multiple SMILES
optionally containing header line, specific to *.smi format.
cURL examples:
curl -X GET ${BEAKER_ROOT_URL}smiles2inchi/$(cat aspirin_with_header.smi | base64 -w 0 | tr ... | 5,289,179 |
def list_vaults_command(client: Client, args: Dict) -> Tuple[str, Dict, Dict]:
"""Lists all vaults.
"""
max_results = int(args.get('max_results', 0))
raw_response = client.list_vaults(max_results)
vaults = raw_response.get('vault')
if vaults:
title = f'{INTEGRATION_NAME} - Total of {len(... | 5,289,180 |
def strip_ddp_state_dict(state_dict):
""" Workaround the fact that DistributedDataParallel prepends 'module.' to
every key, but the sampler models will not be wrapped in
DistributedDataParallel. (Solution from PyTorch forums.)"""
clean_state_dict = type(state_dict)()
for k, v in state_dict.items():
... | 5,289,181 |
def test_container_count(dockerc):
"""Verify correct number of containers are running."""
# stopped parameter allows non-running containers in results
assert (
len(dockerc.containers(stopped=True)) == 1
), "Wrong number of containers were started." | 5,289,182 |
def parameter_attention(x,
total_key_depth,
total_value_depth,
output_depth,
memory_rows,
num_heads,
dropout_rate,
name=None):
"""Attention over param... | 5,289,183 |
def plot_gp_plotly(
model: GPModel, mins: TensorType, maxs: TensorType, grid_density=20
) -> go.Figure:
"""
Plots 2-dimensional plot of a GP model's predictions with mean and 2 standard deviations.
:param model: a gpflow model
:param mins: list of 2 lower bounds
:param maxs: list of 2 upper bou... | 5,289,184 |
def pareto(all_y, maximize=False):
"""
Returns the indices of Pareto-optimal solutions.
Args:
Y [list]: A list of lists containing values to be evaluated for Pareto-
optimality
Returns:
list - The indices of the entries which are Pareto-optimal
"""
all_y = np.asarra... | 5,289,185 |
def _import(module_name, class_name):
"""
Return class of the module_name, where module_name is of the form
package.module (testoob's Asserter, which does simple __import__, returns
package, not package.module in this situation).
"""
mod = __import__(module_name)
components = module_name.spl... | 5,289,186 |
def test_compute_id_2NN_wprior():
"""Test that the id estimation with a Bayesian 2NN works correctly."""
de = DensityEstimation(coordinates=X)
de.compute_id_2NN_wprior()
assert pytest.approx(de.intrinsic_dim, abs=0.0001) == 1.8722 | 5,289,187 |
def plot_roc(labels, score, title='ROC', verbose=True):
"""Plot ROC curve
Parameters
----------
labels : np.ndarray
vector of ground truth
score : np.ndarray
vector of scores assigned by classifier (i.e.
clf.pred_proba(...)[-1] in sklearn)
title : str
title of p... | 5,289,188 |
def delete_user(username: str):
"""
Parameters
----------
username : str, required
Returns
-------
Response:
{
"deleted": true,
}
Raises
------
HTTPException
"""
response = False
try:
username_constraint(username)
... | 5,289,189 |
def Scan_Mono_Go(scanDIM,start,stop,step,settling_time=0.2,**kwargs):
"""Starts a mono scan for a given scan dimension
Logging is automatic: use **kwargs or the optional logging arguments see scanlog() for details
"""
scanIOC=BL_ioc()
current_energy=caget('29idmono:ENERGY_SP')
Scan_Mono(s... | 5,289,190 |
def p_primary(p):
"""primary: variable_access
| unsigned_constant
| function_designator
| set_constructor
| LPAREN expression RPAREN
| NOT primary""" | 5,289,191 |
def imread(image_name, colorspace="RGB"):
"""
Reads image and returns RGB numpy array
Parameters:
image_name: Image path , str
colorspace: RGB or BGR, str
Returns:
img: numpy array
"""
img = cv2.imread(image_name)
if colorspace == "RGB":
img = cv2.cvtColor(img... | 5,289,192 |
def _extract_time_series(data, time_series_name='values'):
"""
Extract specific time series from provided pandas DataFrame and return as a pandas Series
"""
# If it's already a series, just return it
if isinstance(data, pd.Series):
return data
if time_series_name not in data:
r... | 5,289,193 |
def residual_resampling(weights):
"""Residual resampling. The counts in each bin are floor(w*N) + N' where
N' is sampled from a multinomial with the residual weights."""
N = weights.shape[0]
counts = np.floor(weights*N)
R = int(np.sum(counts))
new_weights = (weights*N - counts)/(N-R)
counts ... | 5,289,194 |
def get_reftypes(exp_type, cal_ver=None, context=None):
"""Based on the exposure type, CAL s/w version, and CRDS context, determine
the list of applicable reference file types.
"""
return [] | 5,289,195 |
def filter_config_set_by_host_name( cfgD, hostname ):
"""
"""
for mach,specD in list( cfgD.items() ):
if not satisfies_host_match( hostname, specD ):
cfgD.pop( mach ) | 5,289,196 |
def mix(n = 1):
"""Accumulate n composite samples from the active generators."""
# Gather samples and count notes.
s = np.zeros(n, dtype=np.float32)
n_notes = 0
for note in set(notemap):
e = note.envelope()
if e == None:
# Release is complete. Get rid of the note.
... | 5,289,197 |
def train_step(grid, weights, optimizer, mov, fix):
"""
Train step function for backprop using gradient tape
:param grid: reference grid return from layer_util.get_reference_grid
:param weights: trainable affine parameters [1, 4, 3]
:param optimizer: tf.optimizers
:param mov: moving image [1, m... | 5,289,198 |
def register_pooling(key, module):
"""
Register a customized GNN pooling layer (for graph classification).
After registeration, the module can be directly called by GraphGym.
Args:
key (string): Name of the module
module: PyTorch module
"""
register(key, module, pooling_dict) | 5,289,199 |
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