content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def _get_file_names():
"""Returns the file names expected to exist in the input_dir."""
file_names = {}
file_names['train'] = ['data_batch_%d' % i for i in xrange(1, 5)]
file_names['validation'] = ['data_batch_5']
file_names['eval'] = ['test_batch']
return file_names | c2fb7a28fb7e0a1da0ab914f008f580e0f4b17b0 | 3,625,237 |
def index_of_best(list):
"""
low distance is better
:param list:
:return:
"""
a_list, error_count, error = remove_errors(list)
return list.index(min(a_list)) | 03c484e0579cb78054ce9177cfe52520f692247a | 3,625,238 |
def mold(
content: VyList,
shape: VyList,
) -> VyList:
"""Mold one list to the shape of the other. Uses the mold function
that Jelly uses."""
# https://github.com/DennisMitchell/jellylanguage/blob/70c9fd93ab009c05dc396f8cc091f72b212fb188/jelly/interpreter.py#L578
if isinstance(content, str):
... | d28ccab5c3e3338b0e55dc1ff2be327721856919 | 3,625,239 |
import json
import torch
import copy
import pickle
def goal_optimization(model_exp_key, opt_exp_key=None, write_results=True):
"""
Optimize random goal states using a model-based estimator.
Note: tailored to HalfCheetah-v2 environment currently.
Args:
model_exp_key (str): model-based experime... | fbe668784003dbce61bfbd499dc80cb26fed3457 | 3,625,240 |
import gzip
import struct
from functools import reduce
import operator
def _read_datafile(path, expected_dims):
"""Utility function for reading mnist data files."""
base_magic_num = 2048
with gzip.GzipFile(path) as f:
magic_num = struct.unpack('>I', f.read(4))[0]
expected_magic_num = base_... | 12b0940e369769b8948867f30e7b0ee436723609 | 3,625,241 |
def _vsos(da, pos, method_sos="median"):
"""
vSOS = Value at the start of season
Params
-----
da : xarray.DataArray
method_sos : str,
If 'first' then vSOS is estimated
as the first positive slope on the
greening side of the curve. If 'median',
then vSOS is estima... | 6b4bf26d6c0d3b7d6fecfbed07dabf3ec202ae41 | 3,625,242 |
def parse_coordSys(config, coordSys=batoid.CoordSys()):
"""
@param config configuration dictionary
@param coordSys sys to which transformations in config are added
"""
shift = [0.0, 0.0, 0.0]
if any(x in config for x in ['x', 'y', 'z']):
if 'shift' in config:
raise ValueErr... | 461ca4fe295ffa5bb47dd40d2af00f4034c5222c | 3,625,243 |
import hashlib
def password_hasher(password):
"""
Just hashes the password
:param password:
:return: 32 byte hash
:rtype: bytes
"""
return hashlib.pbkdf2_hmac('sha256', password.encode('utf-8'), b'salt', 100000) | 44e921903c63f1703bbc79869f1a63a25bfe5916 | 3,625,244 |
def index_alpha_beta(i, ij, ik, points):
"""
Finds for each input point the index of it's bounding triangle
and the alpha and beta value for that point in the triangle. Note
this means that the following statements will always be true:
alpha + beta <= 1
alpha >= 0
beta >= 0
f... | 4799890b1e292bce19628779294e068f73744e76 | 3,625,245 |
import array
def flac_read_file_f32(filename: str) -> DecodedSoundFile:
"""Reads and decodes the whole flac audio file. Resulting sample format is 32 bits float."""
filenamebytes = _get_filename_bytes(filename)
with ffi.new("unsigned int *") as channels, \
ffi.new("unsigned int *") as sample_rate,... | d455a941ef27cdc7b696fae648556c31b3664135 | 3,625,246 |
import ctypes
def version() -> str:
"""
Version library
:return: version
"""
pfun = _lib.zmVersionLib
pfun.restype = None
pfun.argtypes = (ctypes.c_char_p,)
ver = ctypes.create_string_buffer(32)
pfun(ver)
return ver.value.decode("utf-8") | d91f04d7c614d11803df4d6d5b6f3b96c6d60202 | 3,625,247 |
def get_all_books(request):
"""
This view is to return all the books
"""
books = Book.objects.all()
context = {
'books': books
}
return render(request, 'books/books.html', context=context) | 059b9db54fdb52b1918de4e5c32f48f6400acf4e | 3,625,248 |
def _GetSchemaPath(release_track, for_help=False):
"""Returns the resource schema path."""
return export_util.GetSchemaPath(
'compute',
_GetApiVersion(release_track),
'TargetHttpsProxy',
for_help=for_help) | 02ef6e67daf4337859716cbc71edf41bb14cfaa5 | 3,625,249 |
def login():
"""Login a user"""
if not request.is_json:
return jsonify(error="Missing JSON in request"), 400
username = request.json.get("username", None)
password = request.json.get("password", None)
remember = request.json.get("remember", False)
if not username:
return jsonify(... | 5e0af9233b15ac81918dbbd0459fa192905d52f7 | 3,625,250 |
def client_host(server_host):
"""Return the host on which a client can connect to the given listener."""
if server_host == '0.0.0.0':
# 0.0.0.0 is INADDR_ANY, which should answer on localhost.
return '127.0.0.1'
if server_host in ('::', '::0', '::0.0.0.0'):
# :: is IN6ADDR_ANY, which should answer on localhost... | 93d1b23c7714e5252efc866daa10dd5f7f53e0e7 | 3,625,252 |
def get_context(bot, update, session, user):
"""Create a context object for callback queries."""
context = CallbackContext(session, bot, update.callback_query, user)
add_breadcrumb(
crumb={
"query": update.callback_query,
"data": update.callback_query.data,
"user... | 4cb0259e8dd1ddcde8e4142c92da5ba5b2514d33 | 3,625,253 |
def customer_all_detail(request, pk):
"""客户详情（不能更改信息）"""
customer = get_object_or_404(Customer, pk=pk, is_valid=True)
form = CustomerForm(instance=customer)
# 添加上地址信息
# 下面这里引入异常处理，如果数据库里没有该字段，那么表单渲染为空
try:
customer_shop = CustomerShop.objects.get(customer=pk)
shopform = CustomerS... | a239f01a73a519880c6372bacd79370e07268841 | 3,625,254 |
import logging
def logmethod(func):
""" Decorator to add logging information around calls for use with . """
def _wrapper(self, *args, **kwds):
logging.info("Start::%s.%s:%s",
func.__module__,
self.__class__.__name__,
func.__name__)
... | 7f8223ab99d6f9101088bff050f79fd0669231d4 | 3,625,255 |
def to_isbn(ean):
"""Convert EAN to ISBN"""
clean = clean_isbn(ean)
isbn = clean[3:-1]
isbn.append(isbn_check_digit(isbn))
return ''.join(str(d) for d in isbn) | 3537e28de6c296bb07d791fb7b7a87eec6539057 | 3,625,257 |
def markdown_cell(content):
"""
Create a markdown cell with a given content.
"""
return nbformat.notebooknode.NotebookNode({"cell_type": "markdown", "source": content, "metadata": {}}) | fadb7ad9d2e3a1df3393aab5cef7f012612a99b0 | 3,625,258 |
def _replace_suffix(string, old, new):
"""Returns a string with an old suffix replaced by a new suffix."""
return string.endswith(old) and string[:-len(old)] + new or string | 4e487f62126b130d973f249cd34abc5a75df3eaa | 3,625,259 |
def compute_fig(grid, col_x, col_y, col_z, value_coupe, n_points, xlim, ylim):
"""uses user input parameters to compute the values to plot
Parameters
----------
grid : pandas.DataFrame
full data grid
col_x : string
horizontal column in final plot
col_y : string
vertical ... | 0e8614cbf625560bce8ac533184c36d7b464e72e | 3,625,260 |
def is_empty_json_response_from_s3(context):
"""Check if the JSON response from S3 is empty (but not None)."""
return context.s3_data == {} | 6ec3a41646de74d82f3786e772228da55d91b63a | 3,625,261 |
def vault_value(key, default=None):
"""
Returns the secret referenced by the key supplied.
If the vault has not been initialized, returns the provided default instead
:param key:
:param default:
:return:
"""
if not vault_ready():
return default
try:
return _get_from_... | e7c055a4e1e8f6a1b7c8fb9fe1f2d219a84fb5e6 | 3,625,262 |
def show_expression_of_KO_genes(
sample_meta_file: str,
normalized_matrix: str,
ko_list: list,
ko_dict: dict = "",
percentile: bool = False,
heat: bool = False,
):
"""
Show expression of the genes that were actually knocked out.
Parameters
----------
sample_meta_file
... | d647b2c54dd4049176667122196fcc8bd595549a | 3,625,263 |
from datetime import datetime
def preprocessEventData(eventData):
"""
Ensures that the event data dictionary is consistent before it reaches the template or event logic.
- dates should exist and be date objects if there is a value
- checkbaxes should be True or False
- if term is ... | ceea909651a706e2aef532855788205d9b962f8c | 3,625,264 |
def get_data(self):
"""Generate the PWM matrix
Parameters
----------
self : ImportGenPWM
An ImportGenPWM object
Returns
-------
matrix: ndarray
The generated PWM matrix
"""
# Tpwmu=np.arange(fs*duration)/fs,
Tpwmu = np.linspace(0, self.duration, self.fs * self.... | e2aea3c7acfdda09601a5e1d92192390b1276310 | 3,625,265 |
def set_code_by_activity_hash(db, overwrite=False):
"""Use ``activity_hash`` to set dataset code.
By default, won't overwrite existing codes, but will if ``overwrite`` is ``True``."""
for ds in db:
if 'code' not in ds or overwrite:
ds['code'] = activity_hash(ds)
return db | 539c4b0618ff9112136a4e697dce34efabf0bee6 | 3,625,266 |
def ca_time_sync(h_session, ultime):
"""
:param int h_session: Session handle
:param ultime:
"""
ret = CA_TimeSync(h_session, CK_ULONG(ultime))
return ret | c80a5a328826d63f661bf0a20156ddf6147a2a19 | 3,625,267 |
def add_subparser(parser):
"""Add the subparser that needs to be used for this command"""
plot_parser = parser.add_parser("plot", help=DESCRIPTION, description=DESCRIPTION)
cli.get_basic_args_group(plot_parser)
plot_parser.add_argument(
"kind",
type=str,
choices=SINGLE_EXPERIME... | 35f460076138655f7bc2db871bb1919fe29dfdaf | 3,625,268 |
def Potential_bruteforce_parallel(x,m,softening,G=1.):
"""Returns the exact mutually-interacting gravitational potential for a set of particles with positions x and masses m, evaluated by brute force.
Arguments:
x -- shape (N,3) array of particle positions
m -- shape (N,) array of particle masses
s... | 034f526d2597f9c418430ff26aeae2ee9adcf867 | 3,625,269 |
from typing import Mapping
from typing import Any
from typing import List
import collections
def _validate_plurals(mapping: Mapping[str, Any],
ref: str) -> List[SchemaError]:
"""
Check that no graph field conflicts with a instance registry field.
The instance registry field is iden... | b57096d223400385c2d1671e91359e6b52907a6a | 3,625,270 |
def mock_validator_execute_validator(*args, **kwargs):
"""
Mock method to just return the builded command line without
executing it.
"""
cls = args[0]
command = args[1]
return command | d03cc70f1f0a5bd59e7a116e3774b99aa8db5a03 | 3,625,271 |
def load_seq(seq_path):
"""Load HPatches sequences."""
seq = cv2.imread(seq_path, 0)
n_patch = seq.shape[0] / 65
seq = np.reshape(seq, (n_patch, 65, 65, 1)).astype(np.float32)
resized_seq = np.zeros((n_patch, 32, 32), np.float32)
for i in range(n_patch):
tmp_patch = cv2.resize(seq[i], (... | 2f605ab5fcdc6c336613d8397e87721ba762ecf1 | 3,625,272 |
def mode_check_decorator(func):
"""Decorate load()/save() CookieJar methods."""
def wrapper(cls, **kwargs):
try:
filename = kwargs['filename']
except KeyError:
filename = cls.filename
res = func(cls, **kwargs)
file_mode_checker(filename, mode=0o600)
... | 3c36b920b84dc14bd23bd189c15b7b5247705c61 | 3,625,273 |
def has_gap(k1, k2, min_gap=0.002):
"""判断 k1, k2 之间是否有缺口"""
assert k2['dt'] > k1['dt']
if k1['high'] < k2['low'] * (1-min_gap) \
or k2['high'] < k1['low'] * (1-min_gap):
return True
else:
return False | 642d9793dcc6f85d3bf21e1abf6f481292c053f1 | 3,625,274 |
def get_pronoun_lemmas(conll_document):
"""
Returns the list of lemmatized pronouns found in the given contents of a CONLL document (list of triples).
:param conll_document: the contents of a CONLL file, as a list of triples
:return:
"""
pronouns = []
for (form, lemma, pos) in conll_document... | 17507968c67e145c754da940743cea554102f7f4 | 3,625,275 |
def isdisjoint(pth1, pth2):
""" returns 0 if disjoint
"""
edge1 = list(pairwise(pth1))
edge2 = list(pairwise(pth2))
for edge in edge1:
if edge in edge2:
return 1
return 0 | 2632d3c336871ecfb0cb3693db2f86d8ae527792 | 3,625,276 |
def delete_post(post_id):
"""function for deleting a business by id"""
business = Business.query.get_or_404(post_id)
if business.business != current_user:
abort(403)
db.session.delete(business)
db.session.commit()
flash('Your post has been deleted!', 'success')
return redirect(url_fo... | af445a24c71b57eb26b5cc2603ff6cc246f8859d | 3,625,277 |
def retract(request):
"""retracts a cash invitations"""
params = request.get_params(schemas.RetractSchema())
device = get_device(request)
customer = device.customer
access_token = get_wc_token(request, customer)
postParams = {
'reason': 'other'
}
response = wc_contact(
re... | 7d93210b985e36300adfe3e20006a5bec5918b8e | 3,625,278 |
def generate_fileattr_metadata(local_path, metadata):
# type: (blobxfer.models.upload.LocalPath, dict) -> dict
"""Generate file attribute metadata dict
:param blobxfer.models.upload.LocalPath local_path: local path
:param dict metadata: existing metadata dict
:rtype: dict
:return: merged metadat... | 4231af249ccc9f57f413ca02735ae57c3a91d8bc | 3,625,279 |
def build_estimator(model_dir):
"""Build an estimator."""
m = tf.estimator.LinearClassifier(
model_dir=model_dir, feature_columns=base_columns,
optimizer=tf.train.FtrlOptimizer(
learning_rate=0.1,
l1_regularization_strength=1.0,
l2_regularization_strength=1.0)
)
return m | 6ec9ad927d86aafc4bf8b20771ff8092ab652a50 | 3,625,280 |
import logging
def _set_logger(
logger_file_path: str,
logger_name: str = "default_logger",
write_to_console: bool = True,
) -> logging.Logger:
"""Set logger to log to the given path.
Modified from https://docs.python.org/3/howto/logging-cookbook.html
Args:
logger_file_path (str): Fi... | 338999fbcd34367f1dc4140143688965663bf486 | 3,625,281 |
def sort_and_print(body, num):
"""
Sorts the values of dictionaries and prints respective
top sentences
:param body: list of dictionaries of 'sentence': score
:param num: no of sentences to be printed
:return: prints
"""
result = []
rank = []
for sentdict in body:
for sen... | 400b11f84d0e68fb393c261736856257b4681c52 | 3,625,282 |
def get_trans_func(name):
"""
Retrieves the transformation module by name.
"""
trans_funcs = {
"bottleneck_transform": BottleneckTransform,
"basic_transform": BasicTransform,
}
assert (
name in trans_funcs.keys()
), "Transformation function '{}' not supported".format(... | 2fbad8e9034734bedccf6a21842c389b63b26336 | 3,625,283 |
import torch
def get_box_pair_info(box1, box2):
"""
input:
box1 [batch_size, (x1,y1,x2,y2,cx,cy,w,h)]
box2 [batch_size, (x1,y1,x2,y2,cx,cy,w,h)]
output:
32-digits: [box1, box2, unionbox, intersectionbox]
"""
# union box
unionbox = box1[:,:4].clone()
unionbox[:, 0]... | 6461d64ae2d5ef0338cb69dda40fad65882a8803 | 3,625,284 |
def bintogrid(
x,
y,
unc=None,
newx=None,
dx=None,
weighting="inversevariance",
drop_nans=True,
):
"""
x = the independent variable (wavelength)
y = the measurement (transit depth)
unc = the uncertainty on the measurement (sigmas on the transit depths)
newx = a (linearly)... | e392dadfda3eec08e32f2d74f1ac5729f00176c4 | 3,625,285 |
def parse_date(date_str: str) -> date:
"""Just a wrapper to avoid having to declare dateutil as a dependency in other projects."""
return dateutil_parser(date_str).date() | 8fdf6ae162f46547f488a4ccd1b7fe82a60ac731 | 3,625,286 |
import torch
def dagger(x: torch.Tensor) -> torch.Tensor:
"""Conjugate transpose of a batch of matrices. Matrix dimensions are
assumed to be the final two, with all preceding dimensions batched over."""
return x.conj().transpose(-2, -1) | 672d26333182803b18f3d1d10e49ef393e216f5f | 3,625,287 |
def node_factory(edge_model, children_null=True, base_model=models.Model):
"""
Dag Node factory
"""
class Node(base_model, NodeBase):
class Meta:
abstract = True
children = models.ManyToManyField(
"self",
blank=children_null,
symmetrical=... | 9b739490baab70a5b24a90b611e2db6e1a3664fa | 3,625,288 |
def geometry_mask(
geometries,
out_shape,
transform,
all_touched=False,
invert=False):
"""Create a mask from shapes.
By default, mask is intended for use as a
numpy mask, where pixels that overlap shapes are False.
Parameters
----------
geometries : iter... | 726865aff256bdb73c3215f57dd2c7bfa0a58dd6 | 3,625,289 |
def detection_area(self):
"""
Checks the area to have obstacles
:return: float
"""
image = ImageGrab.grab(self.area)
gray_img = ImageOps.grayscale(image)
arr = np.array(gray_img.getcolors())
# print(arr.mean())
return arr.mean() | 4893e33696a3e11db290dae28a8f346c21c02a51 | 3,625,290 |
from pathlib import Path
import requests
def _get_data_by_filename(fname: str) -> Path:
"""
Download file or used cached version.
Args:
fname (str): name of file to download
Returns:
Path: path at which file lives
"""
full_fname = DATA_PATH.joinpath(fname)
# check if fil... | 11b908b801db2c4002a837c78a3266608cbe3056 | 3,625,291 |
def build_input(data_path, batch_size, size, mode):
"""Build image and targets.
Args:
data_path: Filename for data.
batch_size: Input batch size.
mode: Either 'train' or 'eval'.
Returns:
volumes: Batches of volumes. [batch_size, volume_size, volume_size, volume_size]
targets: Batches of targe... | 20bce2efcbb7a4f1998b754daf1577af1c885507 | 3,625,292 |
def _get_publication(paper_entry: dict) -> Publication:
"""
Using a paper entry provided, this method builds a publication instance
Parameters
----------
paper_entry : dict
A paper entry retrieved from arXiv API
Returns
-------
Publication, or None
A publication instanc... | bb530e8e4d6fbb5dad7438e18a1a63dc028b44b8 | 3,625,293 |
def get_table_13():
"""表 13 玄関ポーチに設置された照明設備の人感センサーによる補正係数
Args:
Returns:
list: 表 13 玄関ポーチに設置された照明設備の人感センサーによる補正係数
"""
table_13 = (0.9, 1.0)
return table_13 | 91a89343800ad5e3c587371c91e7d256d9ed85a1 | 3,625,294 |
def Parse(text: str) -> ParsedUniversalDependencies:
"""Parses the provided text."""
doc = nlp(text)
return __parse_tokens(doc) | 9878eb1cfd82d2590663635ba659f997c5619f23 | 3,625,295 |
def Log_GetTimestamp(*args):
"""Log_GetTimestamp() -> wxChar"""
return _misc_.Log_GetTimestamp(*args) | ec3574361a50cf12c8fd21bd9b51885905e61477 | 3,625,296 |
def find_left_anchor_index(fragment_info, fragments):
"""
Description:
Use the fragment information to find which fragment is the left anchor
:param fragment_info: [list[dict]] the list of fragment information
:param fragments: [list] the list of fragments being searched
:return: left_anchor_ind... | 502473f7fee00a5ccd1ed3d6cf5c938e8c4d2041 | 3,625,299 |
def get_all_preconditions(action_set):
"""
Returns a set of all preconditions for the actions in the given set
"""
all_precons = set()
for a in action_set:
all_precons.update(a.precondition)
return all_precons | 1697c2d013b07bbfc24ca6273d16523eb1d83acf | 3,625,300 |
def confusionmatrix(tt, tp, gn=['', '', ''], plot=False, title='',
cmm='Blues', fontsize=20, ylabel='True', xlabel='Prediction'):
"""
Calculates and/or plots the confusion matrix for machine learning algorithm
results.
:type tt: list[float]
:param tt: Real targets.
:type t... | 47e16c6df881e18f6cdf3317a5db575453269b9c | 3,625,301 |
def _to_string(*args: Object) -> Object:
"""Convert an integer to a string"""
if len(args) != 1:
return Error("It must recieve one parameter")
number: Object = args[0]
if not isinstance(number, Integer):
return Error("It must be an integer")
return String(str(number.value)) | 397a3deae6464602e04732e940e65a87f9976261 | 3,625,302 |
from typing import Optional
def compose_conformers(*conformers: Optional[Conformer]) -> Optional[Conformer]:
"""
Return a single conformer which is the composition of the input conformers.
If a single conformer is given, return the conformer.
"""
conformers = tuple(filter(None, conformers))
... | 7f6b2856ea1cc79d287a864b9ff07d8fc15a69ed | 3,625,303 |
def model_fn_builder(config: Configs, tasks,
num_train_steps, pretraining_config=None):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params):
"""The `model_fn` for TPUEstimator."""
log("Building model...")
is_training = (mode == tf.estimat... | 6eced6c3f1ffd6ad01ef1ff8c65096ea1cbf7ce6 | 3,625,304 |
def get_ul_inds(udeg, ls):
"""
"""
# Turn `ls` into a slice
if isinstance(ls, integers):
ls = slice(ls, ls + 1)
if isinstance(ls, slice):
# List of indices user is accessing
inds = []
# Fill in the `None`s
if ls.start is None:
ls = slice(0, ls... | 49132020728129ca0288047f998b06a567b78164 | 3,625,305 |
def f_line(x, m, b):
"""
Line fit with equation y = mx + b
Parameters
----------
x : array
x values
m : float
slope
b : float
y-intercept
Returns
-------
y : array
y values
"""
y = m*x + b
return y | daf437461c2e8723a824e4083794d1f6ea6b368b | 3,625,306 |
def extractLostInTranslation(item):
"""
"""
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or 'preview' in item['title'].lower():
return None
if 'Third Prince Elmer' in item['tags']:
return buildReleaseMessageWithType(item, 'Third Prince Elmer', vol, chp, frag=fr... | ed7802c168626e5d87a7dcade175ff1b8a54fd54 | 3,625,307 |
import random
def determinize(perspective: cs.Perspective, mode=0) -> GameState:
"""Determinize the given perspective into a deterministic state.
The mode parameter determines the way in which determinization happens.
"""
if mode == 0:
is_declarer = perspective.player == perspective.declarer
... | 6964a75dbafb784ea6e1ef9d0c03fdc774d3881e | 3,625,308 |
def parse_value(file, section, key):
""" Read ini file and returns unique value
:param file: File to be parsed
:type file: str
:param section: Section targeted
:type section: str
:param key: Parameter to be read
:type key: str
:return: Read value
:rtype: str
"""
config = Con... | f3658f28323a19836f41da86527c35a99dcffe1a | 3,625,309 |
def demo_JmE(X, Y, DPiters=1000):
"""
Explore the posterior of our model.
"""
""" Create expert shell """
expert = MixtureOfExperts.experts.IndpendentRBF(input_dim=X.shape[1], output_dim=Y.shape[1],
process_mean='Constant', ard=True)
expert._p... | e7957d72dbb575d87cb5cb83f857432b40d98071 | 3,625,310 |
from bs4 import BeautifulSoup
import json
def magazineluiza_parser(html):
"""
Parser for magazineluiza.com.br.
There's an attribute with a JSON containing all the info we need when
the item is available. When it isn't, we must retrieve the info from
another JSON, one much less complete (without b... | 634f38ce32662594a9f14e8d27d835205317591a | 3,625,311 |
def getDistance(sensor):
"""
Return the distance of an obstacle for a sensor.
The value returned by the getValue() method of the distance sensors
corresponds to a physical value (here we have a sonar, so it is the
strength of the sonar ray). This function makes a conversion to a
distance value ... | d00b1d201fb59939d2b8a2e57bfa35588780b6d5 | 3,625,312 |
import warnings
def sjoin(
left_df,
right_df,
how="inner",
predicate="intersects",
lsuffix="left",
rsuffix="right",
**kwargs,
):
"""Spatial join of two GeoDataFrames.
See the User Guide page :doc:`../../user_guide/mergingdata` for details.
Parameters
----------
left_... | aa27217a006d2cfc77ff1012ed1814f059da1717 | 3,625,313 |
def compute_BtBinv(B, C):
"""Create block inverses.
Helper function that creates inv(B_i.T B_i) for each block row i in C,
where B_i is B restricted to the sparsity pattern of block row i.
Parameters
----------
B : {array}
(M,k) array, typically near-nullspace modes for coarse grid, i.... | 65871a8bb1a4491aa3a7bc1b112ea1312717bd49 | 3,625,314 |
import random
def fixed_onepoint(p_0, p_1):
"""
Given two individuals, create two children using one-point crossover and
return them. The same point is selected on both genomes for crossover
to occur. Crossover points are selected within the used portion of the
genome by default (i.e. crossover do... | 1db8157bbd3434c91fe82c64221a56c53bb56b36 | 3,625,315 |
def get_pipeline_ids(db_name='./grades.sqlite3'):
"""
:param db_name:
:return:
"""
with lite.connect(db_name) as con:
cur = con.cursor()
result = cur.execute("SELECT pipeline_id FROM students")
try:
resut = (ids[0] for ids in result.fetchall())
except Exc... | 7091d6098b33bbfbfba00f3e134b4d23b07f3b34 | 3,625,316 |
def update_individual_metadata(ts):
"""Update individual metadata in ts object.
Returns new list of individuals with updated metadata
"""
individuals = []
oldname = None
i = 0
for ind in ts.individuals():
popindex = ts.nodes()[ind.nodes[0]].population
popname = ts.population... | 4cfaf24fc4e68203e8206163fcb0686ac9fd9692 | 3,625,317 |
def get_trt_logger():
"""
Get the global TensorRT logger created by Polygraphy.
Returns:
trt.Logger: The TensorRT logger.
"""
global TRT_LOGGER
if TRT_LOGGER is None:
TRT_LOGGER = trt.Logger()
return TRT_LOGGER | 06ca9e41932f936c3b31ae210de040954916816c | 3,625,318 |
from pathlib import Path
import torch
def _convert_rx101_to_mobile(
model_path: str,
num_classes: int,
output_path: str or Path = "./rx101_optimized_scripted1.ptl",
):
"""Function handling conversion of MSCG-Net Rx101 Models
:param model_path:
:param num_classes:
:param output_path:
:... | a8b92ca86d893266b5eab5ebe8a03112ffa74d1f | 3,625,319 |
from datetime import datetime
def unify_index_info_couch_dates_fmt(index_info):
"""Applies standardization to secondary keys 'date' type keys.
@param index_info: is a index data to workout for
"""
clean_info = {}
index_keys = [key for key in index_info.iterkeys()]
for index_key in index_keys:... | 3ae4ba10be563082ae60a0636ff5a1b4e464ff97 | 3,625,320 |
def stellar_dist(row: pd.Series, push: float) -> pd.Series:
"""Calculate the fluxes for the stellar objects at a given distance.
For use with pandas.DataFrame.apply(). Requires the columns:
- 'distance'
- 'radio'
- 'optical_in_mJy'
Args:
row: The pd.Series object containing... | 401ce8b51df995b3e62a025750d5a0871c6e3b3f | 3,625,321 |
import platform
def mysql_config_path():
"""
根据平台查询mysql配置文件
:return:
"""
_platform = platform.system()
print('当前平台为：{}'.format(_platform))
mysql_config = None
if _platform == 'Darwin':
mysql_config = "/Users/yuxiang/Documents/Developer/WeChat/baixiaotu-mini/sqlconfig.json"
... | c24632e4262ea1cfb903492b5d261a8cd5cbcd8e | 3,625,322 |
import collections
def calculate_case_distances(
graph: nx.DiGraph, case: Case, *, additional_attributes: "list[str]" = []
) -> "dict[str, float]":
"""
Extracts the distances between the current graph and a given case across trace,
time, and the additional attributes provided.
Arguments:
... | 3e7383a983d611564edfa0689d22595e38ef83d6 | 3,625,323 |
import numpy
def index_to_position(index, nelx, nely):
"""
Convert the index of a element to the centroid of the element
"""
return numpy.array([(index % nelx)+.5, index/nelx+.5]) | 1402251581df9e346097dbd561ef5f782f18da1e | 3,625,324 |
import http
def imdb(inp, api_key=None):
""".imdb <movie> -- gets information about <movie> from IMDb"""
if not api_key:
return None
content = http.get_json("https://www.omdbapi.com/", t=inp, apikey=api_key)
if content["Response"] == "Movie Not Found":
return "movie not found"
e... | 44fb0e2d4ae7940c4fc59c2788062072420a3b5b | 3,625,325 |
def smooth(sig, window_size):
"""
Apply a uniform moving average filter to a signal.
Parameters
----------
sig : ndarray
The signal to smooth.
window_size : int
The width of the moving average filter.
Returns
-------
ndarray
The convolved input signal with the... | f91cbbeff3402c730d7b4f711c05c1992eed9e3a | 3,625,327 |
def filter_by_oid_alt(instructions, oid):
""" For a given list of instructions and an Order ID,
return the list of instructions that reference that Order ID.
:param instructions: list of instructions
:type instructions: list or tuple
:param oid: Order ID
:type oid: int
:return: list of inst... | c7d6b1cfd2218adfba1612bf42ac19a15e98e776 | 3,625,328 |
def get_sequence_lineage(request, upi):
"""
Internal API.
Get the lineage for an RNA sequence based on the
classifications from all database cross-references.
"""
try:
queryset = Xref.objects.filter(upi=upi).select_related('accession')
results = queryset.filter(deleted='N')
... | 2339a648a5bf12b033dd9fdfb86f667ce6b92210 | 3,625,329 |
import requests
def get_function_fb_graph():
"""Get call - facebook."""
return requests.get('http://graph.facebook.com') | 38f790a006a3d8de981e66936e50b7be743e4d1b | 3,625,330 |
def mat_to_pose_wxyz(mat):
"""Convert matrix to pos and wxyz quaternion."""
p = mat[:3, 3].tolist()
p += tra.quaternion_from_matrix(mat).tolist()
return np.array(p) | a98da080207daeec40657bf57fd5c44c1063d780 | 3,625,331 |
def setDefaultCompressionCodecName(compressionCodecName):
"""
:param compressionCodecName: java.lang.String
"""
return _java_type_.setDefaultCompressionCodecName(compressionCodecName) | 8b8fbad360826c9a4a11e0e7b2c18ae473ea1e03 | 3,625,332 |
def do_ajax_update_no_effect(parser, token):
"""
Updates a target div by binding a onclick event to a link which uses jquery.metadata to parse for the target div and data source url
"""
try:
args_list = token.split_contents()
except ValueError:
raise template.TemplateSyntaxError, "... | 61fd3dcc3a176b70c9b670bc91f0f055a6f8f84d | 3,625,333 |
def standardize_rows(M):
""" Distribute the rows of the cost matrix normally to allow for accurate comparisons of error and description
cost. """
rv = np.matrix(M)
for i in range(rv.shape[0]):
mean = np.mean(M[i, :])
stdev = np.std(M[i, :])
rv[i, :]= (M[i, :]- mean)/stdev
ret... | dd3abb0eebc15dd72c9b64b66217018c0295bd9f | 3,625,334 |
def refine_group_join_event_msg(ctx: EventMsg) -> _GroupJoinEventMsg:
"""某人进群事件"""
if not isinstance(ctx, EventMsg):
raise ContextTypeError('Expected `EventMsg`, but got `%s`' % ctx.__class__)
if ctx.EventName == EventNames.ON_EVENT_GROUP_JOIN:
return _GroupJoinEventMsg(ctx)
return None | 4cd23b709e4d68e813d99d9ffd59a18b7cd87331 | 3,625,335 |
def parse_dlna(dlna_server):
"""
Return a dict of title: url from dlna
"""
res = [
s
for s in upnpclient.Device("http://{}/rootDesc.xml".format(dlna_server)).services
if s.name == "ContentDirectory"
][0].Browse(
ObjectID="2$8",
BrowseFlag="BrowseDirectChildren... | 77eeed8cd2200cfa96c76e15b444cc511eb8c5f3 | 3,625,336 |
import random
import pickle
def sax_optimization(locator, data, time_series_len, BOUND_LOW, BOUND_UP, NGEN, MU, CXPB, start_gen, building_name):
"""
A multi-objective problem set for three objectives to maximize using the DEAP library and NSGAII algorithm:
1. Compound function of accurracy, complexity and... | f224e6039a30a444f94e3730acfa318344e384dc | 3,625,337 |
def randomize_and_play_circuit(n_gates: int, init_state: str = "z"):
"""
:param n_gates: the depth of the circuit
:param init_state: starting position on the bloch sphere
:return:
"""
state = init_state
for ind in range(n_gates):
state = play_clifford(cliffords[np.random.randint(0, ... | c82620401fd9510c1d8b425613c013cd4cb831cc | 3,625,338 |
def pair(dice):
"""Score the given roll in the 'Pair' category"""
counts = dice_counts(dice)
for i in [6, 5, 4, 3, 2, 1]:
if counts[i] >= 2:
return 2*i
return 0 | 1076e00dd877e0381d990a4abe4942dd24c533fb | 3,625,339 |
def get_data_for_fit(expt, mask):
"""
exlucdes pre-bleach frame (T[0]) from the array
Parameters
----------
data: aicsimageio.aics_image.AICSImage
mask: np.ndarray
Returns
-------
data_for_fit: np.ndarray
"""
norm_inside = _norm_extract(expt, mask)
data_for_fit = norm_... | e6a02374f2d141f88beb5cc3157272af3424aafa | 3,625,340 |
def gen_docker_image_reference():
"""Generating a docker image reference including image ID.
returns the docker image reference and image ID"""
image_id = 'sha256:some-long-fake-id-with-numbers-{}'
docker_image_refrence = 'this.is.some.fake.{}/registry:{}@{}'.format(
fauxfactory.gen_alpha().lowe... | 1b10c10992efdb991b5ea97b734d07e53181f647 | 3,625,341 |
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