content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_main_workflow_sql(project_id: str, dataset_id: str, merchant_id: str,
customer_id: str) -> str:
"""Returns main workflow sql.
Args:
project_id: A cloud project id.
dataset_id: BigQuery dataset id.
merchant_id: Merchant center id.
customer_id: Google Ads customer id... | f4ac6663a3a6246ef0fef4bb63134b088d499ce4 | 67,591 |
def dividend(
tickers, typ='all', start_date=None, end_date=None, **kwargs
) -> pd.DataFrame:
"""
Bloomberg dividend / split history
Args:
tickers: list of tickers
typ: dividend adjustment type
`all`: `DVD_Hist_All`
`dvd`: `DVD_Hist`
`... | 5e2c99e74557c53ed2dd6b9a6444fdb77771bc31 | 67,592 |
def get_next_mini_batch():
"""Return the next mini batch of training data.
Returns
x_trn_mb: mini batch of training x values
y_trn_mb: batch of training y values
"""
global idx_in_epoch, epoch, ids, x_trn, y_trn
start = idx_in_epoch
idx_in_epoch += mb_size
... | c0ba5720045b9452e54e74987011c3078c5ec765 | 67,593 |
def put(value):
"""Store an object in the object store.
The object may not be evicted while a reference to the returned ID exists.
Args:
value: The Python object to be stored.
Returns:
The object ref assigned to this value.
"""
worker = global_worker
worker.check_connected... | d5e245d63c162a4c72b20fec2ff04d281331ca32 | 67,594 |
def get_accuracy(data_loader, model, device):
"""
Calculates the accuracy of the model given (inference mode) applied in the dataset of the data_loader
:param data_loader: Data loader
:param model: Model to be used for predictions
:param device: Device to be used
:return: accuracy percentage %
... | afdd5fc792ba726969057c1d12c3287c15778d65 | 67,596 |
def grayscale(image):
"""
Change the image to be grayscale using the NTSC luminosity formula and return it.
"""
for pixel in image:
luminosity = compute_luminosity (pixel.red, pixel.green, pixel.blue)
pixel.red = luminosity
pixel.green = luminosity
pixel.blue = luminosity... | 155eb30b4a4da0d830e0f1b36a0adf13ddee4e65 | 67,597 |
import tensorflow as tf
from art.classifiers import TFClassifier
def get_classifier_tf():
"""
Standard Tensorflow classifier for unit testing.
The following hyper-parameters were used to obtain the weights and biases:
learning_rate: 0.01
batch size: 10
number of epochs: 2
optimizer: tf.tr... | 7df7f0d823da0285f6e82090eefa0cb23bbfbae6 | 67,598 |
def version():
"""Get the current version."""
return _version_info | 42c08ec4d051646ed3dc6bf2e59cd29944136723 | 67,599 |
import warnings
def wsgi_proxyfix(factory=None):
"""Fix Flask environment according to ``X-Forwarded-_`` headers.
.. note::
You must set ``WSGI_PROXIES`` to the correct number of proxies,
otherwise you application is susceptible to malicious attacks.
.. versionadded:: 1.0.0
.. note::... | 4fd7cc6048672db0e9f27669bcc56deeb84e29a7 | 67,600 |
from api.v2.serializers import keys_hyphens_to_underscores
import requests
def export_content_list(request):
"""
View for listing metadata for all exportable contents.
"""
proto = request.META['wsgi.url_scheme']
host = request.META['HTTP_HOST']
resp = requests.get('{}://{}/api/v2/exportab... | 6e81226260bbc438f87421e8b8695b5bd0a6fdd0 | 67,601 |
def make_dag1D_params():
"""Make parameters for a triangle graph with 1D treatment."""
alpha = np.array([0.9])
gamma = np.array([[1.9]])
beta = np.array([1.2])
eps = 0.3
nu = 0.3
return alpha, gamma, beta, eps, nu | fa169ce64277fe0e39b2c730613fd91f8966aa65 | 67,602 |
import json
def check_json_exists():
"""Loads courseId_folderId_pairs.json as dict if exists, returns empty dict if not
Returns:
[dict]: dict(courseId_folderId_pairs)
"""
try:
with open(f"{BASE}/data/courseId_folderId_pairs.json") as f:
courseId_folderId_pairs = json.load(f... | 49e7f70b1f0de780f407357738c1a52514df6b0f | 67,603 |
import requests
def info():
""" get api info """
res = requests.request("GET", URL, headers=HEADERS)
return _decode_response(res.content) | 6d6b886c68b89ad1f336bde87179834d7b5915a2 | 67,606 |
import copy
def plugin_init(config):
""" Initialise the plugin.
Args:
config: JSON configuration document for the South plugin configuration category
Returns:
data: JSON object to be used in future calls to the plugin
Raises:
"""
data = copy.deepcopy(config)
return data | 5bf5b8d160712de1305071605cedc10e82a23926 | 67,607 |
def _int_size(x):
"""Return the smallest size int that can store the value"""
if -0x80 <= x <= 0x7F:
return 1
elif -0x8000 <= x <= 0x7FFF:
return 2
elif -0x80000000 <= x <= 0x7FFFFFFF:
return 4
elif long(-0x8000000000000000) <= x <= long(0x7FFFFFFFFFFFFFFF):
return 8
... | c22b8a4789f8cf6150959b30fdf1c444b585e0e7 | 67,608 |
def _build_sudoku_instance_graph(elements):
"""Creates a graph representation of a sudoku instance.
Arguments:
elements (list[list[int]]): A matrix representation of the sudoku instance.
Returns:
A networkx.Graph object representation of the sudoku instance.
"""
size = len(elements)... | c10395318a5dd43920bb3c2644700e3434e3bc97 | 67,610 |
def send_contract(post_data):
"""
Due to the potentially sensitive nature of TAC case data, it is necessary (for the time being) to limit CASE API
access to Cisco employees and contractors, until such time as a more appropriate authentication method can be added
"""
# Check if user is cisco.com
... | 02aa3b95d5c9fdf98cc73e908a0f6c18fd25aa4c | 67,611 |
from operator import invert
def _select_period(detected, true_values):
"""For the detected values, we check when it goes above/below the
selection.
Parameters
----------
detected : ndarray (dtype='int')
N x 3 matrix with starting and ending times.
true_values : ndarray (dtype='bool')
... | 6f39bc04014b44d45bec08bcaa0dd441336adfb8 | 67,613 |
from typing import List
from typing import Type
from typing import Union
def gini_cols(
data: np.ndarray,
distributions: List[Type[Leaf]],
domains: List[Union[list, tuple]],
random_state: np.random.RandomState,
e: float = 0.3,
alpha: float = 0.1
) -> np.ndarray:
"""
Gini index column s... | a858966337caa68f1f39785428908a1969d957b4 | 67,614 |
def Rg(*args):
"""Earth return resistance value used to compute line impedances at power frequency"""
# Getter
if len(args) == 0:
return lib.Lines_Get_Rg()
# Setter
Value, = args
lib.Lines_Set_Rg(Value) | 3f7daa7da2e5b7fdf56375386603cfb94bbf55a8 | 67,615 |
def reshape_non_gcp_df(flight_line_df, multi_buffers):
"""
get the time column back where it belongs efore exporting result
"""
temp_df = remove_non_gcp(flight_line_df, multi_buffers)
if temp_df is None:
return None
temp_df.insert(loc=7,
column='time',
... | c0842587316367ea896af26a219a66e349f9dd1a | 67,616 |
def learn_pool(tickers: tuple, last_date: pd.Timestamp, freq: Freq, lags: int = 5):
"""Возвращает обучающие кейсы до указанной даты включительно в формате Pool
Кейсы состоят из значений дивидендной доходности за последние years лет с частотой freq за период с начала данных до
last_date
Parameters
... | ba84a9844d75dd59dc3748d311d8d5f7f350adc6 | 67,617 |
def chart(symbol, timeframe="1m", date=None, token="", version="", filter=""):
"""Historical price/volume data, daily and intraday
https://iexcloud.io/docs/api/#historical-prices
Data Schedule
1d: -9:30-4pm ET Mon-Fri on regular market trading days
-9:30-1pm ET on early close trading days
A... | 4df7842c3febab86216ef738e66a0c4628f85355 | 67,618 |
from typing import Any
async def async_setup(hass: HomeAssistant, config: dict[str, Any]) -> bool:
"""Set up the Verisure integration."""
if DOMAIN in config:
hass.async_create_task(
hass.config_entries.flow.async_init(
DOMAIN,
context={"source": SOURCE_IMPO... | f21f231d562a81efcc937f88d479aa0236741d0a | 67,619 |
def q_response(frequency, nmax):
"""
Compute the Q-response for a given conductivity model of Earth.
The conductivity model is loaded during the computation from
``basicConfig['file.Earth_conductivity']``.
Parameters
----------
frequency : ndarray, shape (N,)
Vector of `N` frequenc... | d484c0991d11d4f0441c91c6503727a5faee0952 | 67,620 |
def convert_bytes(size: float) -> str:
"""humanize size"""
if not size:
return ""
power = 1024
t_n = 0
power_dict = {0: " ", 1: "Ki", 2: "Mi", 3: "Gi", 4: "Ti"}
while size > power:
size /= power
t_n += 1
return "{:.2f} {}B".format(size, power_dict[t_n]) | cf73b50454c25ce32148697b520069aea6b44d23 | 67,621 |
def svn_txdelta_compose_windows(*args):
"""
svn_txdelta_compose_windows(svn_txdelta_window_t window_A, svn_txdelta_window_t window_B,
apr_pool_t pool) -> svn_txdelta_window_t
"""
return apply(_delta.svn_txdelta_compose_windows, args) | a33c4dca58e3a658739b78f038bba2511774dc86 | 67,622 |
def compute_metrics(preds):
"""Computes a set of metrics (accuracy, f1-score, precision and recall).
Args:
preds (transformers.trainer_utils.PredictionOutput): Predictions and potential metrics.
Returns:
A dictionary containing a set of metrics.
"""
# True labels
y_true = pre... | 21644eb9a30f5425d94482b241d4617c381849df | 67,623 |
from typing import List
def generate_additional_num_features(
X: pd.DataFrame,
num_features: List[str]
) -> pd.DataFrame:
"""
Generate additional numerical features by applying mathematical operations on combinations of features
:param X: Data
:param num_features: Numerical features in... | 6ab5de5adc298f03c28f985bd2d3f704699cf702 | 67,624 |
from typing import OrderedDict
def normalize_inp_config(inp_obects):
"""
Unpack the config details for each inp section and organize in a standard format.
This allows the YAML file to be more short hand and human readable.
:param inp_obects:
:return:
>>> from swmmio.defs import INP_OBJECTS
... | 568a83cd734f0ad5b613a83d9a35b2bfb145fb5a | 67,625 |
def normalize_image(img, circle_center, circle_radius, target_circle_radius):
"""
Transform an image such that the provided circle is moved to the image center
and scaled to the specified size.
:param img: An image
:param circle_center: Circle center
:param circle_radius: Circle radius
:par... | f8e6009f2f87effd90382d1500230d41eb0f38a7 | 67,627 |
from typing import Dict
from typing import Any
from typing import Callable
from typing import Generator
import tempfile
def run_lambda(
event: Dict,
context: Any,
parsing_function: Callable[[str, str, str], Generator[Dict, None, None]]):
"""
Encapsulates all of the work performed by a ... | 465bf5b77f8f3bdd0ca3bdcc88d51a183eda27b5 | 67,628 |
import six
import json
def read_key(key):
"""Returns the value of the specified key, or None if it is not present."""
clt = get_etcd_client()
val, meta = clt.get(key)
if val is not None:
val = six.ensure_text(val)
return json.loads(val) | 2231e188e1d4644cd53dab70d671393379f9006b | 67,629 |
def extract(typ, src):
"""
DataObject value type converter.
:param typ: the type to extract.
:param src: the source to extract as type typ.
:return: if the type has the ability to extract (convert), otherwise the
original version is returned.
"""
if hasattr(typ, 'extract'):
... | 4fd3636970ca0f3785414e436e3fab3156daa17f | 67,630 |
def ComputeErrorRates(label_counts, word_counts, seq_errors, num_seqs):
"""Returns an ErrorRates corresponding to the given counts.
Args:
label_counts: ErrorCounts for the character labels
word_counts: ErrorCounts for the words
seq_errors: Number of sequence errors
num_seqs: Total sequences
... | f95ed337f3f4dda019518b84b111994ff5396e3f | 67,631 |
def prediction_to_dict(instances, img_id):
"""
Args:
instances (Instances): the output of the model
img_id (str): the image id in COCO
Returns:
list[dict]: the results in densepose evaluation format
"""
scores = instances.scores.tolist()
segmentations = ToMaskConverter.c... | e9556de43cd9b193c174a447c9890869cfd965b8 | 67,632 |
def gen_keys_condition(allStar,elems,dr='16',uplim=0.05,downlim=-1000,
snrlim=50,gerrlim=0.2,terrlim=100,tuplim=5000,
tlolim=3500,guplim=4,glolim=0):
"""
allStar: numpy structured array or pandas dataframe with the
following keys:
... | 9bba937ac60c21c88c41bd2d442e508dbb655f71 | 67,633 |
def read_format(info):
"""input info and return readable format"""
info_name = info["name"]
info_desc = info["description"]
info_coun = info["country"]
return f"{info_name}, a {info_desc}, from {info_coun}" | 1804b25b8d1d01b2525887e9f4ba75d031cdf515 | 67,634 |
import zipfile
def test_zip(obj: zipfile.ZipFile) -> (str, bool):
"""
Test a zip file.
Args:
obj: a ZipFile object
Returns:
str: HTML output
bool: test ok
"""
try:
result = obj.testzip()
except Exception as exception: # pylint: disable=broad-except
... | cb0836465278e46bc61827a0013faf864e252f0d | 67,635 |
def attach_from_parents(routing_tree, id_to_idx, parents, visited):
""" Attach children routing tree starting from list of parent routing trees.
routing_tree : RoutingTree
A node cache that contains all routing branches in the net.
id_to_idx : dict object id to int
Map of object id to idx ... | 58b441cb30fcd81dbc7c1bd9a330aec107448e8f | 67,636 |
def cvFirstType(*args):
"""cvFirstType() -> CvTypeInfo"""
return _cv.cvFirstType(*args) | daf0c19c706e9fa8b91f3c2d2154b6877d1617bf | 67,638 |
def flatten_issue(get_issue, issue, expand_subtasks=False):
"""
story is broken down into subtasks
"""
issues = [issue]
if is_parent_issue(issue):
if expand_subtasks:
issues += [
get_issue(s.key, expand='changelog')
for s in issue.fields.subtasks
... | 59e8f3fcd6ac4e27cbc269358f4c6393ca462497 | 67,639 |
def score_function(nom_cosine, w):
"""
最终得分值为归一化的余弦距离 * 权重项
:param nom_cosine:
:param w:
:return:
"""
return nom_cosine * w | 99468bca131a42148b0bf012f5f3c61215716b57 | 67,640 |
def load_motion_masks(motion_root):
"""Load motion masks from disk.
Args:
motion_root (Path): Points to a directory which contains a
subdirectory for each sequence, which in turn contains a .png for
each frame in the sequence.
Returns:
motion_masks (dict): Map seque... | babe7c83b7f2c7a1cade2d4fcb3de8461d88ba00 | 67,641 |
import numpy
def ltp(epj):
"""
Wrapper for ERFA function ``eraLtp``.
Parameters
----------
epj : double array
Returns
-------
rp : double array
Notes
-----
The ERFA documentation is below.
- - - - - - -
e r a L t p
- - - - - - -
Long-term precession ma... | 23cbc1d4b420d17553c720b4881d46167d79deee | 67,643 |
def parse_genetic_models(models_info, case_id):
"""Parse the genetic models entry of a vcf
Args:
models_info(str): The raw vcf information
case_id(str)
Returns:
genetic_models(list)
"""
genetic_models = []
if models_info:
for family_info in models_info.split(",... | 9d8d94d9008e2f287a875aa9bd15330c82bbf8b5 | 67,644 |
def get_watchdog_list(threadid):
"""Get the watchdog list of a thread.
Contrary to :func:`get_current_watchdog_list` this function does
not create a watchdog list, if it does not already exist.
"""
# The second argument of get_thread_info() is intentionally undocumented.
# See C source.
ret... | 65da0743642d13bdf402198693eb1baa54382d7d | 67,645 |
def make_points(reader):
"""Make the list of points from the CSV."""
points = []
for row in reader:
points.append((ip_address(row['start']), START_POINT, row['id']))
points.append((ip_address(row['end']), END_POINT, row['id']))
return sorted(points) | cf61989c09067806701a1e5bcd23b589a13c05a1 | 67,646 |
import copy
def bg_command(command_dict, bg_parameter, system_id):
"""Use the bg_command fixture instead."""
dict_copy = copy.deepcopy(command_dict)
dict_copy["parameters"] = [bg_parameter]
return Command(**dict_copy) | 1d84f6ba8d541fef839cddcad620ed0780e389a4 | 67,647 |
from typing import Tuple
def random_scaling_crop(
scale_range: Tuple[int, int] = (0.8, 1.0),
ratio_range: Tuple[int, int] = (3.0 / 4.0, 4.0 / 3.0),
):
"""
Random crop implementation which also randomly scales the crop taken
as well as the aspect ratio of the crop.
:param scale_range: the (min,... | 0ef554df2b94665dee4a9f2975925ae376695163 | 67,648 |
def aluno_detail_form(**kwargs):
"""
Function to get Aluno's detail form.
:param kwargs: form properties
:return: Form
"""
return AlunoDetailForm(**kwargs) | c07121adfaa24b57639ea9d37a25b8da604b9040 | 67,649 |
def MeshPlane1(mesh, planes, multiple=False):
"""
Intersects a mesh with a collection of (infinite) planes.
Args:
mesh (Mesh): Mesh to intersect.
planes (IEnumerable<Plane>): Planes to intersect with.
Returns:
Polyline[]: An array of polylines describing the intersection loops ... | 43ff8e7003c331bd62045b5ca7279a2204845b66 | 67,650 |
def _normalize_au_netref(network, ref):
"""
Take the network and ref of an Australian road and normalise them so that
the network is in the form 'AU:road-type' and the ref is numeric. This is
based on a bunch of logic about what kinds of Australian roads exist.
Returns new (network, ref) values.
... | f4d38ef0b7a2a611a918fd8a3126d8dab4c6b8e2 | 67,651 |
def get_table(nb_name, n_cell, n_output=0):
"""Get output table (or any html) from an IPython notebook on disk."""
outputs = get_outputs(nb_name, n_cell)
html = outputs[n_output]['data']['text/html']
return HTML(html) | 32f47baa8733723cf3182d48e96f87f4aa5fa7ba | 67,652 |
def loadGroupActFlowFC_NoHidden(inputtype,fcdir):
"""Load direct input 2 output FC mappings -- control analysis"""
if inputtype in ['VERTICAL','RED','HIGH','CONSTANT']:
h5f = h5py.File(fcdir + inputtype + 'ToOutputLayer_FC_Group.h5','r')
fcmapping = h5f['sourceToTargetMapping'][:].copy()
... | 2903ef04fb48b027ad258171fe898c30fe2faafa | 67,653 |
def bundle_selector(bundle_dict, colors, b):
"""
Selects bundle and color
from the given bundle dictionary and color informaiton
Helper function
Parameters
----------
bundle_dict : dict, optional
Keys are names of bundles and values are dicts that should include
a key `'uid'... | 3aa51f30e6e5fae6ed6b201f229f33c249e4bc3f | 67,655 |
def func_api_view_snippet_detail(request, pk):
"""
Retrieve, update or delete a code snippet.
"""
try:
snippet = Snippet.objects.get(pk=pk)
except Snippet.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = SnippetSeri... | 89c12e138a60d8d4764256745a5747e341df3f19 | 67,656 |
def plotRegParam(sc, data, regParam_list = [0,0.001, 0.003, 0.006, 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24],\
iterations=50, iter_sgd=10, alpha=0.01, factorLength=4):
""" Plot the error (rtv_pr_auc, rtv_auc, logl, mse) function of the regularization parameters ... | 3f158aceb5f9d914c63073692e71ec767346148b | 67,657 |
def generate_partial_productions(segment):
"""
短语树拆分
:param segment:
:return:
"""
pos_root = BASE_DIR + "/toolkit/stanford/stanford-corenlp-full-2017-06-09/"
par_model = pos_root + "models/lexparser/chinesePCFG.ser.gz"
opttype = 'penn'
parser = StanfordParser(par_model, pos_root, opt... | 8dced355e5a85fb152e68d93df95a95b930e6537 | 67,658 |
def zzX_abs(f):
"""Make all coefficients positive in Z[X]. """
if poly_univariate_p(f):
return zzx_abs(f)
else:
return [ zzX_abs(coeff) for coeff in f ] | 5b985deb4ef0d68f9ac8be752e23cb61436dc0bf | 67,659 |
def stress_to_speed(x, y):
"""Convert ocean wind stress to wind speed at 10 m over the ocean.
This expects that tau is in dyn/cm2.
.. math::
\\tau = 0.0027 * U + 0.000142 * U2 + 0.0000764 * U3
.. note::
This is useful for looking at wind speed on the native ocean grid, rather than
... | a13761f4fdfd01b22493493730aa59d9d3f229de | 67,661 |
def _form_save_redirect(pkg_name, action, package_type=None):
"""This redirects the user to the CKAN package/read page,
unless there is request parameter giving an alternate location,
perhaps an external website.
@param pkg_name - Name of the package just edited
@param action - What the action of th... | fecee05f2ec8524c96eed735c65ebeaad96e86f6 | 67,662 |
def _get_source_pulse(pulse_length, offset):
"""
Define source pulse start and end.
By default, the source pulse is at distance 0.
"""
x0 = (0.0 * sc.units.us) + offset
x1 = pulse_length + offset
y0 = 0.0 * sc.units.m
return x0, x1, y0 | d1a8231d498b016c76215c772c354e3497e92e94 | 67,663 |
def read_labels(labels_file):
"""Get the labels from names file"""
with open(labels_file, 'r') as f:
lines = f.readlines()
return [lab for lab in lines if len(lab) > 0] | a1b4ce5c0b5c613db5ec0d7d619910210d88106e | 67,666 |
def id3_sort_key(mp3_path):
"""Create the sort key for an MP3 file.
"""
id3r = id3reader.Reader(mp3_path)
return (
get_value(id3r, 'performer'),
get_value(id3r, 'year'),
get_value(id3r, 'album'),
get_track(id3r),
get_value(id3r, 'title'),
# Provide the ori... | a1faba94f7e9ab13efb7abe0ac0c5338bcbcda25 | 67,667 |
import torch
def breg_mixture_divergence(p, Y, q, X, kernel, symmetric=False):
# NOTE: if you make changes in this function, do them in *_stable function under this as well.
"""
Compute similarity sensitive GAIT divergence of between a pair of empirical distributions
p and q with supports Y and X, res... | 7ce29b78841fe11d5d1b6571439367f58b674eaa | 67,668 |
def create_example(inputs, outputs, word_vocab, char_vocab):
"""Given a string and a label (and a vocab dict), returns a tf.Example"""
inp_seq, inp_sl, inp_maxword = encode_line_charwise(inputs, char_vocab)
out_seq, out_sl1, out_maxword = encode_line_charwise(outputs, char_vocab,
... | 507b8d4dbbe0173de848f12db257b080b464e047 | 67,670 |
def has_owner_rank(user, club):
"""Checks for if user is owner."""
return is_rank(user, club, ClubMembership.UserRoles.OWNER) | c33207d700d4b17adbb44d99d5b2f80d05a33212 | 67,671 |
from typing import List
from typing import Any
from typing import Sequence
def _make_cat(
labels: List[List[Any]], n_states: int, state_names: Sequence[str]
) -> Series:
"""Get categorical from list of lists."""
labels_new = np.repeat(np.nan, n_states)
for i, c in enumerate(labels):
labels_ne... | 0dd3e8e824f7e281672880534918fa229133459f | 67,672 |
def get_incidence_flattened(pred, c):
"""Converts "raw" predictions for use in generate_scenarios_* methods.
This method
* rolls together model and sample dimensions into a single sample_flattened
dimension.
* restricts forecasts to the time period of the trial
* sorts by cumulative incidence
Args:
... | a0a89ba3307c829577216ca78f113c271ae77114 | 67,673 |
def triang_inds(pts_w_imgs, gb1_inds, inds_arr):
"""
Function finds the indices of atoms which make tetrahedrons with at least one GB atom.
Parameters
-------------
pts_w_imgs :
The position of atoms which are inside the main box and within rCut to the main box.
gb1_inds :
Indic... | 554219f5e4b67c22fc3c31f4ca577fa1ad491564 | 67,675 |
from typing import Optional
from typing import Set
def enrich_pmc_citations(
graph: BELGraph,
*,
manager: Optional[Manager] = None,
group_size: Optional[int] = None,
offline: bool = False,
) -> Set[str]:
"""Overwrite all PubMed citations with values from NCBI's eUtils lookup service.
:par... | 2ec80e3083c94acd3953bdc9134be9982bbd8c11 | 67,676 |
def read_file(filename):
"""
Reads in the file I created manually (by recompiling and adding timers)
Not used any more but left for historical reasons (the first 'speedup'
plots were generated with this function)
"""
dtype = np.dtype([('same_cell', np.int32),
('N1', np... | 206a9c8122fcca20de012e8dc94c136013a1ba5b | 67,677 |
def welcome():
"""List all available api routes."""
return (
f"Welcome to Climate App<br/>"
f"Available Routes:<br/>"
f"Precipitation Data: /api/v1.0/precipitation<br/>"
f"Stations List: /api/v1.0/stations<br/>"
f"Temperature Observation for the most active station: /api/... | 3fead56e744b1399b20ab24da4d2c1c9b901379e | 67,678 |
def accum_array(group_idx, arr, func=None, default_value=None, min_n_group=None, rebuild_index=False):
"""groups a by indices, and then applies func to each group in turn.
e.g. func example: [func=lambda g: g] or [func=np.sum] or None for speed
based on https://github.com/ml31415/numpy-groupies
"""
... | bec604dd48e651c29454c30917253af57e39241a | 67,679 |
def get_ec2_instances(AwsInfo=None): # noqa: E501
"""List AWS EC2 instances
Get all EC2 instances in one region with match state # noqa: E501
:param AwsInfo: Request body
:type AwsInfo: dict | bytes
:rtype: None
"""
if connexion.request.is_json:
AwsInfo = AwsInfo.from_dict(connex... | f1debf4d84e5261744751ed8f3a230b4555b188d | 67,680 |
def custom_mapping_repr(
mapping, # type: Mapping
prefix="{", # type: str
template="{key}: {value}", # type: str
separator=", ", # type: str
suffix="}", # type: str
sorting=False, # type: bool
sort_key=None, # type: Optional[Callable[[Any], Any]]
reverse=False, # type: bool
k... | 890b1a18e0776dc5791ceec512ad026f1b94f331 | 67,682 |
from bs4 import BeautifulSoup
def get_string_from_tag(row_with_tag):
"""Given a row with a tag, return the string from it.
Ex. Given, <a href="http://www.example.com/page/index.html">Example Page</a>
return the string "Example Page" """
row = BeautifulSoup(str(row_with_tag))
return u''.join([r... | 7c154fa4752ae59a2a33c45aaded394db2cb42f3 | 67,684 |
def get_ami_list_from_ec2(main_region, regions, owner, credentials, filters):
"""Get the AMI mappings structure given the constraints represented by the args."""
amis_json = get_initialized_mappings_dicts()
for region_name in regions:
images_for_region = get_images_ec2(filters, owner, region_name)
... | 367247948dff95bc9bab996e96216d1cf4b8b9d7 | 67,685 |
def norm_angle(a):
"""Return the given angle normalized to -180 < *a* <= 180 degrees."""
a = (a + 360) % 360
if a > 180:
a = a - 360
return a | 2f0e77a4567ca566bb958cd4efb4db24b74e37b7 | 67,686 |
import re
def slug_sub(match):
"""Assigns id-less headers a slug that is derived from their
titles. Slugs are generated by lower-casing the titles, stripping
all punctuation, and converting spaces to hyphens (-).
"""
level = match.group(1)
title = match.group(2)
slug = title.lower()
sl... | 58c40e4ca23bd76a0e25d746aeb4ccdbed6640d7 | 67,687 |
def load_csv_data(data_path, sub_sample=False):
"""Loads data and returns y (class labels), tX (features) and ids (event ids)"""
y = np.genfromtxt(data_path, delimiter=",", skip_header=1, dtype=str, usecols=1)
x = np.genfromtxt(data_path, delimiter=",", skip_header=1)
ids = x[:, 0].astype(np.int)
input_data = x[:,... | 496d9ffa7b1b975bf5d742bd00daabae7ac4d7cb | 67,688 |
def sum_1_column(column,var):
"""
Arg: column --> df column name
var --> column name abbreviation used
Return: sum of occurrences of variable in column
count of values in column
"""
return np.sum((column == var)) | e711e427e203a5f8d1c65f535b6c826e630c744d | 67,689 |
def validateIsNotEmpty(data: str, fieldName: str):
"""Validate if data empty"""
if data == '' or data == None:
return "{} Must be not empty".format(fieldName)
return None | 26bec771bb881e32ded022acc57e62b81b2991f9 | 67,690 |
def search2(request, template_name='searchv2/search.html'):
"""
Searches in Grids and Packages
"""
return homepage(request, template_name=template_name) | fa9d3a8e5e0b7bd9318268abb9a822180c5fe1ee | 67,691 |
def get_message_after(response, index):
"""Returns next message of search response after index or None"""
try:
return response[index + 1].object
except IndexError:
return None | 0208fd1f4397e77636df11b62054e86eb84fb682 | 67,692 |
from typing import Union
from typing import Sequence
def average_false_discovery_score(
y_true: Union[Sequence[int], np.ndarray, pd.Series],
y_pred: Union[Sequence[int], np.ndarray, pd.Series],
) -> float:
"""Calculates the average false discovery score. Used for when we have more than 2 classes and want ... | e742996870e640bd15f03206b9a6b260387b34d1 | 67,693 |
from typing import Dict
from typing import Any
import torch
def evaluate(args: Namespace,
model: nn.Module,
dataloader: DataLoader,
output_composer: OutputComposer,
sequence_metrics: SequenceMetrics,
reset: bool = True,
) -> Dict[str, Any]:... | d8f9e5ee00daa8b0b3383829a974b7330ecd6c96 | 67,694 |
import json
def new_s3_bucket(s3, elb_name, account_id, region):
"""
Create new S3 Bucket
"""
bucket_name = 'elblogs-' + account_id + '-' + region
try:
if region == 'us-east-1':
result = s3.create_bucket(
ACL = 'private',
Bucket = bucket_name
)
else:
result = s3.c... | 92e2e699a789b203bd6125d8745defd32807ba3c | 67,695 |
def should_backport(labels=None, milestone=None):
"""return set of PRs marked for backport"""
if labels is None and milestone is None:
raise ValueError("Specify one of labels or milestone.")
elif labels is not None and milestone is not None:
raise ValueError("Specify only one of labels or mi... | 1e0794bd0a8fc57d70ecd4060a0bc5ebb86898b9 | 67,696 |
def returns(matcher):
"""
Matches a no-argument callable that returns a value matched by the given
matcher.
"""
return _Returns(matcher) | 0b92f04bb8d73da42c19de5e7f389eacf74ac1c4 | 67,697 |
from re import T
def _get_charge_state_icon(entity: RenaultSensor[T]) -> str:
"""Return the icon of this entity."""
if entity.data == ChargeState.CHARGE_IN_PROGRESS.value:
return "mdi:flash"
return "mdi:flash-off" | dc75a1c4695fc0e214c8b5d15038f21bb183f881 | 67,698 |
def is_noiseless(model: Model) -> bool:
"""Check if a given (single-task) botorch model is noiseless"""
if isinstance(model, ModelListGP): # pyre-ignore: [16]
raise ModelError(
"Checking for noisless models only applies to sub-models of ModelListGP"
)
return model.__class__ in N... | a358b62b872fcd797a9f4ae70142df510e012356 | 67,699 |
from datetime import datetime
from typing import Optional
from re import L
def day_label(dt: datetime, *, now: Optional[datetime] = None) -> str:
"""Returns nice date label (today, Monday, 11.12.2013)."""
if now is None:
now = datetime.now
if now.date() == dt.date():
return L(32327, 'Today... | 900b7c2e787beea3877c4f5a5384d4776ce500e5 | 67,700 |
def combine_codon_labels(c1, c2):
"""
Combines codon labels, assuming bases mixed randomly
Ex: AAA + CCC + GGG + TTT -> NNN = (AAA, AAC, ....)
Ex: ABC + CCC --> MBC
"""
letters = []
for b1, b2 in zip(c1, c2):
b1 = b1.upper()
b2 = b2.upper()
_ = DEGENERATE_NUCLEOTIDE_C... | 6b9731e0fda82f6c46fe4edb9a00b1a6e6ff1bf0 | 67,701 |
import time
import json
def result_running_callback(request, context):
# pylint: disable=unused-argument
"""
Callback function returning 'running' JSON.
"""
job_result_running = {
'status': 'running',
}
time.sleep(1)
return json.dumps(job_result_running) | cb38b44b86cdcc96706dbf40a764d8d2059de449 | 67,702 |
def polylinear_gradient(colors, n):
"""Returns a list of colors forming linear gradients between
all sequential pairs of colors. `n` specifies the total
number of desired output colors.
Code from http://bsou.io/posts/color-gradients-with-python
"""
# The number of colors per individual linear g... | 305949f20f96899c003c61d77d58668332b57d23 | 67,703 |
def applyXformToBounds(curXform, curBounds):
"""
Utility function to calculate the effect of an Xform (list of 16 values) to a bounds (list of 6 values)
"""
newBounds = None
# loop through the 8 corners of the bounding box
for ix, iy, iz in ( (0,2,4), (1,2,4), (0,3,4), (1,3,4), (0,2,5), (1,2,5),... | 228520db56b75a2e831d7443e20bce461a121dcc | 67,705 |
def _convert_to_binary_classification_predictions(predictions):
"""Converts a `Tensor` into a set of binary classification predictions.
This function checks that the given `Tensor` is floating-point, and that it is
trivially convertible to rank-1, i.e. has only one "nontrivial" dimension
(e.g. the shapes [1000... | bd72f62ce793f398306161868c467fb8ae953fb5 | 67,706 |
from typing import Union
def value_counts_v2(s: Union[pd.Series, pd.DataFrame]) -> pd.DataFrame:
"""
Returns that normalised and unnormalised columns in value_counts()
Useful when you want to know absoluoute number any percentage
"""
if isinstance(s, pd.Series):
s = s.to_frame()
df =... | 3bb053499c42b0b70c6b11b52dadcc2725a36204 | 67,708 |
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