content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def build_delete(table, where):
"""
Build a delete request.
Parameters
----------
table : str
Table where query will be directed.
where: iterable
The list of conditions to constrain the query.
Returns
-------
str
Built query.
"""
sql_q = "DELETE "
... | 1f065b5905b6c7af4e19863ae48e228358278f06 | 32,370 |
def filter_resources_sets(used_resources_sets, resources_sets, expand_resources_set, reduce_resources_set):
""" Filter resources_set used with resources_sets defined.
It will block a resources_set from resources_sets if an used_resources_set in a subset of a resources_set"""
resources_expand = [expand_... | 2ecd752a0460fff99ecc6b8c34ed28782e848923 | 32,371 |
def get_index_base():
"""获取上海及深圳指数代码、名称表"""
url_fmt = 'http://quotes.money.163.com/hs/service/hsindexrank.php?host=/hs/service/'
url_fmt += 'hsindexrank.php&page={page}&query=IS_INDEX:true;EXCHANGE:CNSE{ex}&fields=no,SYMBOL,NAME&'
url_fmt += 'sort=SYMBOL&order=asc&count={count}&type=query'
one_big_i... | 4639e94d9412967c0a5403a5e49fc43c8033c40b | 32,372 |
def merge_list_of_dicts(old, new, key):
"""
Merge a list of dictionary items based on a specific key.
Dictionaries inside the list with a matching key get merged together.
Assumes that a value for the given key is unique and appears only once.
Example:
list1 = [{"name": "one", "data": "stuff"... | a56c0b3476ea67d6b77126a34c14005aad345cfa | 32,373 |
from masci_tools.util.schema_dict_util import read_constants, eval_simple_xpath
from masci_tools.util.schema_dict_util import evaluate_text, evaluate_attribute
from masci_tools.util.xml.common_functions import clear_xml
def get_kpoints_data_max4(xmltree, schema_dict, logger=None, convert_to_angstroem=True):
"""
... | 23001a430e8cb1b2434fce7de67e5249f345806c | 32,374 |
def contains_badwords(string):
"""
Return whether a string contains bad words
"""
return any([x in string for x in bad_words]) | 499e338599441e24845a19ba8504a77bd7838d8e | 32,376 |
def _learn_individual_mixture_weights(n_users, alpha, multinomials, max_iter, tol, val_mat, prior_strength, num_proc):
"""
Learns the mixing weights for each individual user, uses multiple-processes to make it faster.
:param n_users: Int, total number of users.
:param alpha: prior (learned through glob... | 7ee9020685ec8fc0538ce4695fcefedc6280d55e | 32,379 |
def banana(cls):
"""
A decorator for a class that adds the ability to create Permissions and Handlers
from their Checks.
"""
cls.__checks = set()
# Basically tell checks that we are the class, not a medium to pass things through
cls.__banana = True
cls_annotations = cls.__dict__.get("_... | 6392d5a7e029dca556c92f4d7546fb6f76078858 | 32,380 |
def residual_v2_conv(
kernel_size: int,
stride: int,
depth: int,
is_deconv: bool,
add_max_pool: bool,
add_bias: bool,
is_train: bool,
input_op: tf.Tensor,
name: str = None,
) -> tf.Tensor:
"""Creates a residual convolution in the style of He et al. April 2016.
This is the second... | 63d7589caed876ed9b0d3617442e6d676555c791 | 32,381 |
def dump_cups_with_first(cups: list[int]) -> str:
"""Dump list of cups with highlighting the first one
:param cups: list of digits
:return: list of cups in string format
"""
dump_cup = lambda i, cup: f'({cup})' if i == 0 else f' {cup} '
ret_val = ''.join([dump_cup(i, cup) for i, cup in enumerat... | 5fe4111f09044c6afc0fbd0870c2b5d548bd3c1a | 32,382 |
def init(strKernel, iKernelPar=1, iALDth=1e-4, iMaxDict=1e3):
"""
Function initializes krls dictionary. |br|
Args:
strKernel (string): Type of the kernel
iKernelPar (float): Kernel parameter [default = 1]
iALDth (float): ALD threshold [default = 1e-4]
iMaxDict (int): Max... | 652e0c498b4341e74bcd30ca7119163345c7f2cc | 32,383 |
def prune_scope():
"""Provides a scope in which Pruned layers and models can be deserialized.
For TF 2.X: this is not needed for SavedModel or TF checkpoints, which are
the recommended serialization formats.
For TF 1.X: if a tf.keras h5 model or layer has been pruned, it needs to be
within this
scope to b... | 64569464611640ac5c13cbb0bf41c3f7ba16424a | 32,384 |
import re
def is_valid_br_cnpj(cnpj):
"""
Accept an string parameter cnpj and
Check if is brazilian CNPJ valid.
Return True or False
"""
# Extract dots, stroke, slash
cnpj = re.sub('[.|\-/|/]', '', str(cnpj))
# if does not contain numerical characters
if not re.match(r'^\d{14}$',... | f41f9814cfef7d75e287834ac2a5514d03cd8fdb | 32,386 |
def get_supported_locales():
"""
Returns a list of Locale objects that the Web Interfaces supports
"""
locales = BABEL.list_translations()
locales.append(Locale("en"))
sorted_locales = sorted(locales, key=lambda x: x.language)
return sorted_locales | 3068889d0c7888b23f207d3397e0aec58418cef2 | 32,387 |
from typing import Optional
from pathlib import Path
import site
def get_pipx_user_bin_path() -> Optional[Path]:
"""Returns None if pipx is not installed using `pip --user`
Otherwise returns parent dir of pipx binary
"""
# NOTE: using this method to detect pip user-installed pipx will return
# N... | ccf9b886af41b73c7e2060704d45781938d8e811 | 32,388 |
def _normalize_int_key(key, length, axis_name=None):
"""
Normalizes an integer signal key.
Leaves a nonnegative key as it is, but converts a negative key to
the equivalent nonnegative one.
"""
axis_text = '' if axis_name is None else axis_name + ' '
if key < -length o... | 9b58b09e70c20c9ac5ee0be059333dd5058802ef | 32,389 |
def create_interview_in_jobma(interview):
"""
Create a new interview on Jobma
Args:
interview (Interview): An interview object
"""
client = get_jobma_client()
url = urljoin(settings.JOBMA_BASE_URL, "interviews")
job = interview.job
first_name, last_name = get_first_and_last_name... | 36834c0e6557627a52a179b9d8529d5693cc92cb | 32,390 |
def get_solutions(N, K, W_hat, x):
"""
Get valid indices of x that sum up to S
"""
# Scalar form of y = W_hat * x
S = scalar(W_hat @ x)
# print(f'Scalar value = {S}')
solutions = []
for partition in sum_to_S(S, K):
if len(set(partition)) == len(partition) and max(partition) < N:
... | 6de6b0f77070b40f6e0028009f9b96264f6daa64 | 32,391 |
def get_actual_order(geometry, order):
"""
Return the actual integration order for given geometry.
Parameters
----------
geometry : str
The geometry key describing the integration domain,
see the keys of `quadrature_tables`.
Returns
-------
order : int
If `order... | 876c9a70418de7d4768ab0234abb86bf676884c0 | 32,392 |
def getcwd(*args,**kw):
"""getcwd() -> path
Return a unicode string representing the current working directory."""
return __BRYTHON__.brython_path | 1d0e9491a2a35b326ec87314887fb1dede23c927 | 32,393 |
def sample_graph(B, logvars, n_samp):
"""
Generate data given B matrix, variances
"""
p = len(logvars)
N = np.random.normal(0, np.sqrt(np.exp(logvars)), size=(n_samp, p))
return (np.linalg.inv(np.eye(p) - B.T)@N.T).T | 2e798035bcb807e670ff9b9f4a39236ffe6b1157 | 32,394 |
def rotate_ne_rt(n, e, ba):
"""
Rotates horizontal components of a seismogram.
The North- and East-Component of a seismogram will be rotated in Radial
and Transversal Component. The angle is given as the back-azimuth, that is
defined as the angle measured between the vector pointing from the statio... | c374ad762e122b519698bd1c199e2aa773e295cb | 32,395 |
def pwgen(pw_len=16):
""" Generate a random password with the given length.
Allowed chars does not have "I" or "O" or letters and
digits that look similar -- just to avoid confusion.
"""
return get_random_string(pw_len, 'abcdefghjkmnpqrstuvwxyz'
'ABCDEFGH... | 747bb049ad3cca47d3898f0ea6b52108938aa2b2 | 32,396 |
import requests
from bs4 import BeautifulSoup
def get_property_data(sch=""):
"""Get property id and return dictionary with data
Attributes:
sch: property id
"""
property_url = "http://ats.jeffco.us/ats/displaygeneral.do?sch={0}".format(sch)
r = requests.get(property_url)
property_page = ... | d7a0f462340c75d14f00a1712923988b415258fb | 32,398 |
def tcl_delta_remote(curef):
"""
Prepare remote version for delta scanning.
:param curef: PRD of the phone variant to check.
:type curef: str
"""
remotedict = networkutilstcl.remote_prd_info()
fvver = remotedict.get(curef, "AAA000")
if fvver == "AAA000":
print("NO REMOTE VERSION... | 65d1aeb25ce58c066465c3b7eb3e560a54224ba7 | 32,400 |
from typing import Iterable
def prodi(items: Iterable[float]) -> float:
"""Imperative product
>>> prodi( [1,2,3,4,5,6,7] )
5040
"""
p: float = 1
for n in items:
p *= n
return p | 3b8e52f40a760939d5b291ae97c4d7134a5ab450 | 32,401 |
def transformer_prepare_encoder(inputs, target_space, hparams):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a Tensor, con... | af9c5e2ab8fe3508722af822f19671461e92e62d | 32,402 |
from typing import Tuple
def get_bottom_left_coords(
text_width: int,
text_height: int,
text_x: int,
text_y: int,
) -> Tuple[TextOrg, BoxCoords]:
"""Get coordinates for text and background in bottom left corner.
Args:
text_width: Width of the text to be drawn.
text_height: Hei... | 8cf1f88f98990bb727de86152ea5239b725c0fbc | 32,403 |
def read_gcs_zarr(zarr_url, token='/opt/gcsfuse_tokens/impactlab-data.json', check=False):
"""
takes in a GCSFS zarr url, bucket token, and returns a dataset
Note that you will need to have the proper bucket authentication.
"""
fs = gcsfs.GCSFileSystem(token=token)
store_path = fs.get_map... | f6ac1e149639afbc10af052a288eb6536a43a13a | 32,404 |
import operator
def bor(*args: int) -> int:
"""Bitwise or.
Example:
bor(0x01, 0x10) == 0x01 | 0x10
Returns:
int: Inputs.
"""
return list(accumulate(args, operator.or_))[-1] | 6e40612b0117fef5584857c947910fa4a0fa865f | 32,405 |
from Scikit.ML.DocHelperMlExt import MamlHelper
def mlnet_components_kinds():
"""
Retrieves all kinds.
"""
kinds = list(MamlHelper.GetAllKinds())
kinds += ["argument", "command"]
kinds = list(set(kinds))
titles = {
'anomalydetectortrainer': 'Anomaly Detection',
'binarycla... | f5a365f2054a263786ca17a96d58d9f39c7061fe | 32,406 |
def hexString(s):
"""
Output s' bytes in HEX
s -- string
return -- string with hex value
"""
return ":".join("{:02x}".format(ord(c)) for c in s) | 22c1e94f0d54ca3d430e0342aa5b714f28a5815b | 32,410 |
def hydrate_board_from_model(a, radius, rect_width):
"""
:type a: ndarray
:type radius: int
:return: Board
"""
b = Board(radius)
for cellId in b.cells:
thid = get_thid_from_cellId(cellId, rect_width)
value = a[thid.y][thid.x]
b.change_ownership(cellId, get_player_name_from_resource(value), in... | d7486e43beb2676aed32da627d03f018b4b91d65 | 32,411 |
from pathlib import Path
def tree_walk():
"""Walk the source folder using pathlib. Populate 3 dicts, a folder dict,
a file dict, and a stats dict.
- Returns:
- [dict]: k: folders; v: size
- [dict]: k: files; v: size
- [dict]:
'file_size'
'num_dirs'
... | e7edf9897560ee5d0ddab5344e0993e4185e6009 | 32,412 |
def handle_response(response, content_type, file_path=None):
"""handle response. Extract, transform and emit/write to file"""
if content_type == "application/json":
if file_path is None:
return response.json()
else:
save_json(response.json(), file_path)
elif conte... | 43291fdf367f27ee3c982235518d6bf28d600691 | 32,413 |
import random
def randomCaptchaText(char_set=CAPTCHA_LIST, captcha_size=CAPTCHA_LENGTH):
"""
随机生成定长字符串
:param char_set: 备选字符串列表
:param captcha_size: 字符串长度
:return: 字符串
"""
captcha_text = [random.choice(char_set) for _ in range(captcha_size)]
return ''.join(captcha_text) | ee426c26051e720636659cd013617abce2f77a5e | 32,415 |
def integral_total(Nstrips):
"""
The total integral.
"""
return integral_4(Nstrips) + integral_1(Nstrips) | cc2468c69a3e6c98ee139125afc2f4a571cc588b | 32,416 |
def calculate_amplitude(dem, Template, scale, age, angle):
"""Calculate amplitude and SNR of features using a template
Parameters
----------
dem : DEMGrid
Grid object of elevation data
Template : WindowedTemplate
Class representing template function
scale : float
Scale o... | b286ed97952667052a8ecfacb152b70a7a1be2ba | 32,417 |
from typing import OrderedDict
def number_limit_sub_validator(entity_config: OrderedDict) -> OrderedDict:
"""Validate a number entity configurations dependent on configured value type."""
value_type = entity_config[CONF_TYPE]
min_config: float | None = entity_config.get(NumberSchema.CONF_MIN)
max_conf... | 96c33af5e3764cc6cfe0f355de216945b0ab3920 | 32,419 |
from typing import Tuple
from typing import Union
def patch_2D_aggregator(
patches: np.ndarray,
orig_shape: Tuple[int],
patch_loc: np.array,
count_ndarray: Union[np.array, None] = None,
) -> np.ndarray:
"""
Aggregate patches to a whole 2D image.
Args:
patches: shape is [patch_num,... | 3fd7d98c7b792cb3df646045ad32d0cde2c94e56 | 32,420 |
from typing import Callable
def vmap_grad(forward_fn: Callable, params: PyTree, samples: Array) -> PyTree:
"""
compute the jacobian of forward_fn(params, samples) w.r.t params
as a pytree using vmapped gradients for efficiency
"""
complex_output = nkjax.is_complex(jax.eval_shape(forward_fn, params... | 411a3ce1a38c31fef9422ed740d1a7d0a4cf887b | 32,421 |
def random_crop_list(images, size, pad_size=0, order="CHW", boxes=None):
"""
Perform random crop on a list of images.
Args:
images (list): list of images to perform random crop.
size (int): size to crop.
pad_size (int): padding size.
order (string): order of the 'height', 'wi... | e4e7933a02c356c509bbd3d7dbc54814ec1f7bc1 | 32,422 |
from typing import List
def normalize_resource_paths(resource_paths: List[str]) -> List[str]:
"""
Takes a list of resource relative paths and normalizes to lowercase
and with the "ed-fi" namespace prefix removed.
Parameters
----------
resource_paths : List[str]
The list of resource re... | ec7e5020ae180cbbdc5b35519106c0cd0697a252 | 32,423 |
def GetDegenerateSites(seq1, seq2,
degeneracy=4,
position=3):
"""returns two new sequenes containing only degenerate sites.
Only unmutated positions are counted.
"""
new_seq1 = []
new_seq2 = []
for x in range(0, len(seq1), 3):
c1 = seq1[x:... | de9aa02b6ef46cb04e64094b67436922e86e10bb | 32,424 |
def ransac(a, b, model: str ='rigid', inlier_threshold: float = 1.0, ransac_it: int = 100):
"""Estimates parameters of given model by applying RANSAC on corresponding point sets A and B
(preserves handedness).
:param a: nx4 array of points
:param b: nx4 array of points
:param model: Specify the mod... | bbbf3c7695437ef00f4fc4570033575808d84604 | 32,425 |
from typing import List
from datetime import datetime
def create_telescope_types(session: scoped_session, telescope_types: List, created: datetime):
"""Create a list of TelescopeType objects.
:param session: the SQLAlchemy session.
:param telescope_types: a list of tuples of telescope type id and names.
... | 011a8f3950fd0f4bdd3809085d74c45ae5756716 | 32,426 |
from functools import reduce
def update(*p):
""" Update dicts given in params with its precessor param dict
in reverse order """
return reduce(lambda x, y: x.update(y) or x,
(p[i] for i in range(len(p)-1,-1,-1)), {}) | de7f5adbe5504dd9b1be2bbe52e14d11e05ae86f | 32,427 |
def alphabet_to_use(three_letter_code, parity, direction):
"""Return tuple of alphabet to be used for glue in given direction on tile of
given parity.
Note that this refers to the alphabet used for the CANONICAL direction, which
may be the opposite of direction."""
if not parity in (0,1):
r... | b7bb02d5a5b9d5144ab8a6026600bd16096680aa | 32,428 |
def get_tipranks_sentiment(collection):
"""
:param collection: "100-most-popular", "upcoming-earnings", "new-on-robinhood", "technology", "oil-and-gas",
"finance", "software-service", "energy", "manufacturing", "consumer-products", "etf", "video-games", "social-media",
"health", "entertainme... | a6a6527314d2610f20de640aa8e17ad9234d5664 | 32,429 |
def lBoundedForward(x, lower):
"""
Transform from transformed (unconstrained) parameters to physical ones with upper limit
Args:
x (float): vector of transformed parameters
lower (float): vector with lower limits
Returns:
Float: transformed variables and log Jacobian
... | af3b7613f4b08917c835c51c38b6e506f619ab6a | 32,430 |
from datetime import datetime
def date_range(begin_date, end_date):
"""
:param begin_date: 起始日期，string
:param end_date: 结束日期，string
:return: dates: 指定日期范围内日期列表，元素类型string
"""
dates = []
dt = datetime.datetime.strptime(begin_date, "%Y-%m-%d")
date = begin_date[:]
while date <= e... | e168f291226fa00806992f85b3ac2c89f96b8426 | 32,431 |
import collections
def createNeighDict(rP, lP, b, c):
"""Finds the neighbours nearest to a lost packet in a particular tensor plane
# Arguments
rP: packets received in that tensor plane
lp: packets lost in that tensor plane
b,c : batch and channel number denoting the tensor plane
... | 0b67024dac04678b8cb084eb18312ed8df468d93 | 32,433 |
def get_norm_3d(norm: str, out_channels: int, bn_momentum: float = 0.1) -> nn.Module:
"""Get the specified normalization layer for a 3D model.
Args:
norm (str): one of ``'bn'``, ``'sync_bn'`` ``'in'``, ``'gn'`` or ``'none'``.
out_channels (int): channel number.
bn_momentum (float): the ... | 2def355c8b775512fec9d58a4fa43a0b54734f96 | 32,434 |
import json
def cancel_cheque():
"""取消支票"""
user_id = '96355632'
sn = request.values['sn']
result = pay_client.app_cancel_cheque(user_id, sn, ret_result=True)
return render_template('sample/info.html', title='取消支票结果',
msg=json.dumps({'status_code': result.status_code, '... | 6734aa86a1300f678a22b45407a8597255ad0a33 | 32,435 |
def to_relative_engagement(lookup_table, duration, wp_score, lookup_keys=None):
""" Convert watch percentage to relative engagement.
:param lookup_table: duration ~ watch percentage table, in format of dur: [1st percentile, ..., 1000th percentile]
:param duration: target input duration
:param wp_score: ... | cfecebe5830a7681417d6fbd14485adc6908cb5d | 32,436 |
import dmsky.factory
def factory(ptype, **kwargs):
"""Factory method to build `DenityProfile` objects
Keyword arguments are passed to class c'tor
Parameters
----------
ptype : str
Density profile type
Returns
-------
profile : `DensityProfile`
Newly created object
... | 9acb4b93fc3e82e22ec0360a38def9058cea5640 | 32,437 |
def r2_score(y, y_predicted):
"""Calculate the R2 score.
Parameters
----------
y : array-like of shape = number_of_outputs
Represent the target values.
y_predicted : array-like of shape = number_of_outputs
Target values predicted by the model.
Returns
-------
loss : flo... | 00e8004f076e8147f70896bd5304cd73c389522e | 32,439 |
def vcg_solve(goal):
"""Compute the verification conditions for a hoare triple, then
solves the verification conditions using SMT.
"""
assert goal.is_comb("Valid", 3), "vcg_solve"
P, c, Q = goal.args
T = Q.get_type().domain_type()
pt = vcg_norm(T, goal)
vc_pt = [ProofTerm("z3", vc,... | 9f496edd1a3725640582f4016a086fd5dfe70d72 | 32,440 |
def ism_extinction(av_mag: float,
rv_red: float,
wavelengths: np.ndarray) -> np.ndarray:
"""
Function for calculating the optical and IR extinction with the empirical relation from
Cardelli et al. (1989).
Parameters
----------
av_mag : float
Extinct... | cb2bba0cbb396fbac900492fe9b49d70646a4255 | 32,441 |
def rangify(values):
"""
Given a list of integers, returns a list of tuples of ranges (interger pairs).
:param values:
:return:
"""
previous = None
start = None
ranges = []
for r in values:
if previous is None:
previous = r
start = r
elif r ==... | 672b30d4a4ce98d2203b84db65ccebd53d1f73f5 | 32,442 |
def load_balancers_with_instance(ec2_id):
"""
@param ec2_id: ec2 instance id
@return: list of elb names with the ec2 instance attached
"""
elbs = []
client = boto3.client('elb')
paginator = client.get_paginator('describe_load_balancers')
for resp in paginator.paginate():
for elb ... | b9ad53b7cafdbc44f88044e976a700a187605b2d | 32,443 |
def parse_json_frequency_high(df, column, key):
"""
Takes a JETS dataframe and column containing JSON strings
and finds the highest 'Mode' or 'Config' frequency.
Excludes intermediate frequencies
Parameters
----------
df : pandas dataframe
JETS dataframe
column : str
... | e8489ed7bca2357d4be3421932898696daf27bac | 32,444 |
def adapter_checker(read, args):
"""
Retrieves the end sequences and sorts adapter information for each end.
"""
cigar = read.cigartuples
seq = read.query_sequence
leftend, check_in_softl, left_match = get_left_end(seq, cigar, args)
rightend, check_in_softr, right_match = get_right_end(seq, ... | 096fff89eafe7ce7915daac55e95a2ea51e7f302 | 32,445 |
def create_pane(widgets, horizontal, parent_widget=None, compact=False,
compact_spacing=2):
"""Create a widget containing an aligned set of widgets.
Args:
widgets (list of `QWidget`).
horizontal (bool).
align (str): One of:
- 'left', 'right' (horizontal);
... | f291b6482c8d5bb8ecb312b5f5747cf6c4e36e53 | 32,446 |
def image_TOKEN_search_by_word_query_TOKEN(query_snd_ix, multi_distances,
snd_fnames, img_fnames,
id2pic):
"""map a word token query into the embedding space and find images in the same space
return rank of first neighbor whos... | 81fcd8ef466e4712cbab396ed55626e61f297fac | 32,448 |
from packaging import version
def _evolve_angles_forwards(
mass_1, mass_2, a_1, a_2, tilt_1, tilt_2, phi_12, f_start, final_velocity,
tolerance, dt, evolution_approximant
):
"""Wrapper function for the SimInspiralSpinTaylorPNEvolveOrbit function
Parameters
----------
mass_1: float
pri... | b4a000db741aab65076ca2257230aaac45634465 | 32,451 |
import logging
def get_execution(execution):
"""Get an execution"""
logging.info('[ROUTER]: Getting execution: '+execution)
include = request.args.get('include')
include = include.split(',') if include else []
exclude = request.args.get('exclude')
exclude = exclude.split(',') if exclude else [... | dfaba70a41e74423f86eca2c645f71dd2c4117ac | 32,452 |
def fz_Kd_singlesite(K: float, p: np.ndarray, x: np.ndarray) -> np.ndarray:
"""Fit function for Cl titration."""
return (p[0] + p[1] * x / K) / (1 + x / K) | 8054447e87c70adb4d6f505c45336ccd839a69c9 | 32,453 |
def show_exam_result(request, course_id, submission_id):
""" Returns exam result template """
course_obj = get_object_or_404(Course, pk=course_id)
submission_obj = get_object_or_404(Submission, pk=submission_id)
submission_choices = submission_obj.choices.all()
choice_ids = [choice_obj.id for choic... | e61ffc8748e3a9aa2cb62e4ed277a08f0be05c07 | 32,454 |
def createInvoiceObject(account_data: dict, invoice_data: dict) -> dict:
"""
example: https://wiki.wayforpay.com/view/852498
param: account_data: dict
merchant_account: str
merchant_password: str
param: invoice_data
reqularMode -> one of [
... | 0ea61d68916c5b6f43e568cb1978bcb05b8eba04 | 32,455 |
from sklearn.cluster import KMeans
def _kmeans_seed_points(points, D, d, C, K, trial=0):
"""A seed point generation function that puts the seed points at customer
node point cluster centers using k-Means clustering."""
kmeans = KMeans(n_clusters=K, random_state=trial).fit(points[1:])
return kmean... | 7131b53b9cf0c8719daa577f7a03c41f068df90d | 32,456 |
def site_geolocation(site):
""" Obtain lat-lng coordinate of active trials in the Cancer NCI API"""
try:
latitude = site['org_coordinates']['lat']
longitude = site['org_coordinates']['lon']
lat_lng = tuple((latitude, longitude))
return lat_lng
except KeyError: # key ['org... | 9fefcd3f49d82233005c88e645efd1c00e1db564 | 32,457 |
def get_scaling_desired_nodes(sg):
"""
Returns the numb of desired nodes the scaling group will have in the future
"""
return sg.get_state()["desired_capacity"] | 5a417f34d89c357e12d760b28243714a50a96f02 | 32,458 |
def _BitmapFromBufferRGBA(*args, **kwargs):
"""_BitmapFromBufferRGBA(int width, int height, buffer data) -> Bitmap"""
return _gdi_._BitmapFromBufferRGBA(*args, **kwargs) | 91fc08c42726ad101e1d060bf4e60d498d1f0b0f | 32,459 |
def get_user_analysis_choice():
"""
Function gets the user input to determine what kind of data
quality metrics s/he wants to investigate.
:return:
analytics_type (str): the data quality metric the user wants to
investigate
percent_bool (bool): determines whether the data will be seen
... | 58ebda03cd4eb12c92951649fc946b00eb1a8075 | 32,460 |
def points_to_segments(points):
"""Convert a list of points, given in clockwise order compared to the inside of the system to a list of segments.
The last point being linked to the first one.
Args:
points (list): list of lists of size 2
Returns:
[np.ndarray]: 2D-array of segments - ea... | 1e560d8e752d34250f73c6e2305c7741a14afe04 | 32,461 |
def resnet_v1_34(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_34', **kwargs):
"""ResNet-34 model of [1]. See ... | 26e7d866a14d6a17acf92c10e4c5d48883c6b5c7 | 32,462 |
from typing import Union
def from_dlpack(x: Union[ivy.Array, ivy.NativeArray]) -> ivy.Array:
"""Returns a new array containing the data from another (array) object with a
``__dlpack__`` method.
Parameters
----------
x object
input (array) object.
Returns
-------
ret
... | c3213a607eb150791e74ffb8a5781e789dcd989f | 32,463 |
def render_webpage_string(vegalite_spec: str) -> str:
"""
Renders the given Vega-lite specification into a string of an HTML webpage
that displays the specified plots.
:param vegalite_spec str: The Vega-lite plot specification to create a
webpage for.
:returns: A string of a webpage with th... | 6b9c410046d6aba3b3de04bcb2cce4779f55b3d1 | 32,464 |
def _parse_blog(element):
"""
Parse and return genral blog data (title, tagline etc).
"""
title = element.find("./title").text
tagline = element.find("./description").text
language = element.find("./language").text
site_url = element.find("./{%s}base_site_url" % WP_NAMESPACE).text
blog_... | a2678c0e55a8db5aee042744f1f343c96c7fe6f1 | 32,465 |
def summarize_block(block):
"""
Return the sentence that best summarizes block.
"""
sents = nltk.sent_tokenize(block)
word_sents = map(nltk.word_tokenize, sents)
d = dict((compute_score(word_sent, word_sents), sent)
for sent, word_sent in zip(sents, word_sents))
return d[max(d.k... | 991d389366f0587f7dc7fe2eaf6966d0b531012f | 32,466 |
def get_club_result() -> list:
""" Returns the club's page. """
d = api_call("ion", "activities")
while "next" in d and d["next"] is not None:
for result in d["results"]:
if "cube" in result["name"].lower():
return result
d = api_call("ion", d["next"], False) | 3f335aeb2c476dc29e0d335b00722e5b56eb6716 | 32,468 |
def DiffuserConst_get_decorator_type_name():
"""DiffuserConst_get_decorator_type_name() -> std::string"""
return _RMF.DiffuserConst_get_decorator_type_name() | 97c9a68d35b079a1ecbf71a742c6799c4b6411bb | 32,469 |
import tqdm
def lemmatizer():
"""
Substitutes words by their lemma
"""
lemmatizer = WordNetLemmatizer()
preprocessor = lambda text: [lemmatizer.lemmatize(w) for w in \
text.split(" ")]
def preprocess(name, dataset):
description = " Running NLTK Lemmatizer - preprocessing dataset "
description += "{}..."... | 627ce460abb71969ac3f19832f2854a1a00db7c3 | 32,470 |
import io
def convert_numpy_array(numpy_array: np.ndarray):
"""
Converts a numpy array into compressed bytes
:param numpy_array: An array that is going to be converted into bytes
:return: A BytesIO object that contains compressed bytes
"""
compressed_array = io.BytesIO() # np.savez_compressed... | 1fe24003d00736b86361cf5eef03da304edc6bf6 | 32,471 |
def notas(* valores, sit=False):
"""
-> Função para analisar notas e situações de vários alunos.
:param valores: uma ou mais notas dos alunos (aceita várias)
:param sit: valor opcional, indicando se deve ou não adicionar a situação
:return: dicionário com várias informações sobre a situação da turma... | a6915e9b7b1feef0db2be6fdf97b6f236d73f282 | 32,472 |
def append_OrbitSection(df):
"""Use OrbitDirection flags to identify 4 sections in each orbit."""
df["OrbitSection"] = 0
ascending = (df["OrbitDirection"] == 1) & (df["QDOrbitDirection"] == 1)
descending = (df["OrbitDirection"] == -1) & (df["QDOrbitDirection"] == -1)
df["OrbitSection"].mask(
... | 4f2cad6cb2facf6a7a8c7a89ed7b3df0a56a54c2 | 32,473 |
def _get_old_time(request):
"""
Get's the alarm time the user wants to change
Args:
request (Request): contains info about the conversation up to this point
(e.g. domain, intent, entities, etc)
Returns:
string: resolved 24-hour time in XX:XX:XX format
"""
old_time_entit... | 6a2929ccffb4b397bd9f1dd044e70c871e302e33 | 32,474 |
def sxxxxx(p, nss):
"""
Defines a scalar wavefunction. Input momenta have shape (num events, 4).
Parameters
----------
p: tf.Tensor, scalar boson four-momenta of shape=(None,4)
nss: tf.Tensor, final|initial state of shape=(), values=(+1|-1)
Returns
-------
phi: tf.Tenso... | 429fe82c9781ec8918fe57a68e899f899df8f32f | 32,475 |
def target_risk_contributions(target_risk, cov):
"""
Returns the weights of the portfolio that gives you the weights such
that the contributions to portfolio risk are as close as possible to
the target_risk, given the covariance matrix
"""
n = cov.shape[0]
init_guess = np.repeat(1 / n, n)
... | 82d338f2bc8c6b712e7489b70a3122eee21d0aab | 32,477 |
import urllib, datetime
import xarray as xr
import numpy as np
def read_monthly_indices_from_CLIMEXP(name_of_index):
"""
Try reading various monthly indices from KNMI's Climate Explorer
"""
name_to_url = {
'M1i': 'http://climexp.knmi.nl/data/iM1.dat', # 1910 ->
'M2i': 'http://clim... | 8ea42dbca11e587267ef8e2c13ee1787be9db430 | 32,478 |
def generate_xdataEMX(parm):
"""
Generate the x data from the parameters dictionary
Parameters:
parm: [dict] parameters
Returns:
xdata = nd.array[XNbPoints]
"""
# Extracts the x axis data from the parameter file
try:
xpoints = parm['SSX']
except KeyError:
... | a65b48e51f5013fe82d0b9baafe70330b15f0477 | 32,479 |
import csv
def csv_2d_cartesian(filename, polar=False, scan=False):
"""extract 2d cartesian coordinates from a file"""
x_values = []
y_values = []
with open(filename) as data_file:
odom_data = csv.reader(data_file)
for row in odom_data:
# if scan:
# row[1] =... | 648a8f9bbee8b0b61284bf5a8b93c729bb085c9d | 32,480 |
def get_edge_similarity(node_pos,neighbor_positions):
"""
useful for finding approximate colinear neighbors.
"""
displacements = get_displacement_to_neighbors(node_pos,neighbor_positions)
n_neighbors = neighbor_positions.shape[0]
# Quick and dirty, can reduce computation by factor 2.
similar... | b1b64384d84ffbdd6a042b1e2c3a8a9a2212e61e | 32,481 |
def high_pass_filter(x_vals, y_vals, cutoff, inspectPlots=True):
"""
Replicate origin directy
http://www.originlab.com/doc/Origin-Help/Smooth-Algorithm
"rotate" the data set so it ends at 0,
enforcing a periodicity in the data. Otherwise
oscillatory artifacts result at the ends
This uses a ... | 8a114e1868c28f1de8ee4ac445bd620cb45482ff | 32,482 |
def hyetograph(dataframe, col="precipitation", freq="hourly", ax=None, downward=True):
"""Plot showing rainfall depth over time.
Parameters
----------
dataframe : pandas.DataFrame
Must have a datetime index.
col : string, optional (default = 'precip')
The name of the column in *data... | 17deb837058ddd8ad8db9ed47c960cacfde957db | 32,483 |
def objective(z, x):
""" Objective. """
return park2_3_mf(z, x) | fb65c09f084b0af8848e78582703a1bb4e11e735 | 32,484 |
import json
import re
def validate_config(crawler_path):
"""
Validates config
"""
with open(crawler_path) as file:
config = json.load(file)
if 'total_articles_to_find_and_parse' not in config:
raise IncorrectNumberOfArticlesError
if 'seed_urls' not in config:
raise I... | ba46667fcc0d75be6b28d19c0f5fa2d41f9123dd | 32,485 |
def parse_FORCE_SETS(natom=None, filename="FORCE_SETS", to_type2=False):
"""Parse FORCE_SETS from file.
to_type2 : bool
dataset of type2 is returned when True.
Returns
-------
dataset : dict
Displacement dataset. See Phonopy.dataset.
"""
with open(filename, "r") as f:
... | 54b39f8b111292f53c6231facafb177109972965 | 32,486 |
import time
def DeserializeFileAttributesFromObjectMetadata(obj_metadata, url_str):
"""Parses the POSIX attributes from the supplied metadata.
Args:
obj_metadata: The metadata for an object.
url_str: File/object path that provides context if a warning is thrown.
Returns:
A POSIXAttribute object wi... | 3fb3d3f4e45a622cc12ce1d65b6f02d59efd3f58 | 32,487 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.