content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import math
def cluster_assortativity(
sparkdf, src="src", dst="dst", cluster_id_colname="cluster_id"
):
"""calculate assortativity of a cluster
Args:
sparkdf: imput edgelist Spark DataFrame
src: src column name
dst: dst column name
cluster_id_colname: ... | edfc9944c800547d340893b6f1aad5ea5710f816 | 62,601 |
def repository_branch_url(namespace, repository, branch_name):
""" URL for retrieiving a specific branch under a repository. """
return _BASE_URL_V2 % ('repositories/%s/%s/refs/branches/%s' % (namespace, repository,
branch_name)) | d6b0259e94664f27ff7457fdae8a7249c082ee03 | 62,602 |
from inspect import currentframe, getframeinfo
def embed_kw(verbosity=2, **kwargs):
"""
Prepares header for IPython prompt showing current location in code
Use ``IPython.embed(**embed_kw())``.
Args:
verbosity (int): Level of verbose output
**kwargs: Additional keyword arguments
... | acff081bc5bd3705e66d3b6754566691c75a91e2 | 62,603 |
def add_2_numbers(a, b):
"""
Assume your function returns the addition of 2 numbers
"""
return a + b | fbc3a89fb16334594914ce9cdd388d240effc7ea | 62,604 |
import collections
def defer(func, *args, **kwargs):
"""Invoke the given function that may or not may be a Deferred.
If the return object of the function call is a Deferred return, it.
Otherwise wrap it into a Deferred.
>>> defer(lambda x: x, 10) #doctest: +ELLIPSIS
<defer.Deferr... | 4c310bc915976991834dec240459df05f338f090 | 62,605 |
from typing import List
from pathlib import Path
def filter_paths_to_existing(*iterables: str) -> List[str]:
"""
Filter paths to only existing.
"""
return [path for path in iterables if Path(path).exists()] | 32326b3578b754c361d11c24690709dd5d114e46 | 62,606 |
import warnings
def anomaly_tuning(X,
base_estimator=AverageKLPE,
parameters={'k': np.array([10])},
cv=None,
n_sim=10000,
alphas=np.arange(0.05, 1., 0.05),
random_state=42,
n_jobs=1,
... | a68557fe739cd06d4e0beedf73a2dfb8e34d1644 | 62,607 |
def map_pyon_exceptions(f):
"""Decorator that maps some Pyon exceptions to dashi
"""
def wrapped(*args, **kwargs):
try:
return f(*args, **kwargs)
except IonException, e:
dashi_exc = _PYON_DASHI_EXC_MAP.get(type(e))
if dashi_exc is not None:
... | 9ff32cd5963a30a4605dfced252bdc313428eec3 | 62,608 |
import time
import random
def _generate_secret():
"""
create a pseudo-random secret
This is used for message authentication.
"""
digest = sha(str(time()))
digest.update(str(random()))
digest.update('lorem foo ipsum')
return digest.hexdigest() | 8cadb73a2f8529aa9a68341e24fa77c5d4bd9195 | 62,609 |
def show_lat_lon_gps(
move_data,
kind="scatter",
figsize=(21, 9),
plot_start_and_end=True,
return_fig=True,
save_fig=False,
name="show_gps_points.png",
):
"""
Generate a visualization with points [lat, lon] of dataset.
Parameters
----------
move_data : pymove.core.MoveDa... | ebd8795168a4d22bd7482973fab5229691e9144b | 62,610 |
def dda_extended(hands, n_players: int, hand_owner=-1, times=1):
"""
Run alpha-beta search algorithm on multiple games. All games are analysed using the same hands, while each game has
a different declarer-trump combination.
:param hands:
:param n_players: number of players.
:param hand_owner: i... | f483c643a19e82ed9a2e36f864d8a943103ea189 | 62,611 |
def check_board(user_piece, board_state):
"""Check if the game is over and return the result if so"""
# List containing all possible winning combinations
possible_wins = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 4, 7],
[2, 5, 8], [3, 6, 9], [1, 5, 9], [3, 5, 7]]
# Iterate through ... | e5bc6543598b8076ce3cb67b5c1ff4929d0a24d0 | 62,612 |
async def add_jira_action(
request_data: JiraActionRequest,
current_user: User = Security(Authentication.get_current_user_and_bot, scopes=DESIGNER_ACCESS)
):
"""
Stores Jira action config.
"""
mongo_processor.add_jira_action(request_data.dict(), current_user.get_bot(), current_user.get_u... | a841d436d72e12f1cecd68ec2dd83737a3124a60 | 62,613 |
def on_right(p1, p2, p3):
"""
Returns
-------
out : Bool
Return true if the point p3 is on the right of the line defined by the
points p1 and p3.
"""
return ccw_angle(p1, p2, p3) > 0 | 135e08def2073ec8792517e2667a15e7282a7bc1 | 62,614 |
from typing import Union
from typing import Sequence
def compute_invoke_hash(
contract_address: int,
entry_point_selector: Union[int, str],
calldata: Sequence[int],
chain_id: StarknetChainId,
) -> int:
"""
Computes invocation hash.
:param contract_address: int
:param entry_point_selec... | de56d6b08a78d9cafa6ee66a41d3c076f6fb6a65 | 62,616 |
def determine_img_center(img, center_left=True, center_up=True, center=100):
"""
Determines the center of an image.
Args:
img: An opencv instance of an image.
center_left: A boolean to determine whether the you should push the
center width to the left or push it to the right. I... | 9c567a0b398d106089858553b502477b3195d7e4 | 62,617 |
def numba_update_requestable(requestable, r_ct, c_ct, c_f, c_cf, c_dt):
"""
calculates: requestable *= (1 - request)
"""
for a, b, c, d, e in zip(r_ct, c_ct, c_f, c_cf, c_dt):
requestable[a, b, c, d, e] = 0
return requestable | d1cf9c3a8c07fdfb4cab4d12733d1900e31ffd6a | 62,618 |
def port_bind(endpoint_id, port, subnets, network=None, vm_port=None,
segmentation_id=None, driver=None, **kwargs):
"""Binds the Neutron port to the network interface on the host.
:param endpoint_id: the ID of the endpoint as string
:param port: the container Neutron port dictionary... | 084577ebae258414f91b0666c39aa6305a1cc8d3 | 62,619 |
def linearize_spectra(spectra: np.ndarray, x_klinear):
""" This method interpolate the input spectra with the input list.
Args:
:param spectra: OCT spectra of mirror.
:type spectra1: list
:name x_klinear: The fractional indexes.
:type x_klinear: list
Return:
:rname... | 4976a4fbb0bfa011d821c75b1b77b6435201bb82 | 62,620 |
def apply_gradient_descent_run(shape, dtype, attrs=None):
"""run function for dsl function apply_gradient_descent."""
shapes = [shape, (1,), shape]
dtypes = [dtype] * len(shapes)
mod = utils.op_build_test(apply_gradient_descent, shapes, dtypes,
kernel_name='apply_gradient_... | 80e09fbe5b80e72b017ef130f249da44e56f3741 | 62,621 |
def compute_bid(task, fog_node, phi_CPU=1.0, phi_RAM=1.0, phi_DISK=1.0):
"""Calculate the bid for the current node for the current task.
Args:
:param task (dataframe): A dataframe containing information about the current task.
:param fog_node (series): A series containing information about the ... | 5e4560454fd709d3e63a3c165da84ee71dd13a49 | 62,622 |
def customer_group(request):
"""团队客户，可筛选"""
up_name = request.session.get('user_id')
users = User.objects.filter(up_name=up_name)
if 'name' in request.GET and request.GET['name']:
name = request.GET['name']
customers = Customer.objects.filter(name__icontains=name, user__up_name=up_name).... | aa100bc2121906bed09ebd72fc2c1730f97b7ebf | 62,623 |
from typing import Dict
from typing import Any
from pathlib import Path
import json
def create_model_folder(model_config: Dict[str, Any]) -> None:
"""Create model asset folder and write config if it doesn't exist."""
model_path = get_model_path(model_config)
if model_path.exists():
return None
... | 87c9f91328788a87e5d0b5e17304040aaef4645c | 62,624 |
from typing import Union
from typing import List
import torch
def separate_parameters(module: Union[Parameter, nn.Module, List[nn.Module]]):
"""Separate BatchNorm2d, GroupNorm, and LayerNorm paremeters from others
Args:
module (Union[Parameter, nn.Module, List[nn.Module]]): to be separated.
Retu... | 7428dbc0e05fee868ae15ec84d13352e18c3d96f | 62,625 |
import random
def getCategories(l0keyP="", testTypeP=0):
"""
Depending on the provided testType, this function finds random child keys, which we use to generate Twitter hashtags. For our evaluation we mainly used testType 2 or 3.
Returns:
List -- List of two lists. First, the provided or random l... | d41ebf94d5a87980e2a50dc5db39033f6aa3b7ec | 62,626 |
from typing import Optional
def frame(
*,
scene_name: Optional[str] = None,
frame_name: str, # pylint: disable=redefined-outer-name
frame_data: Json,
intrinsics: sunds.specs_utils.CameraIntrinsics,
color_image: Optional[np.ndarray] = None,
) -> sunds.specs_utils.Frame:
"""Extract the frame ... | b3739fc68c46a9b587545f5a594556b3a9bc19fb | 62,627 |
def add_grad(orig, grad):
"""Returns [grad, grad]"""
return [collapse_sum_like(grad, orig.args[0]),
collapse_sum_like(grad, orig.args[1])] | 9cc1450c6867735e5833b251b0c93396a5bb098a | 62,628 |
def CustomFancyAnalyzer(expression=r"\s+", stoplist=STOP_WORDS, minsize=2,
maxsize=None, gaps=True, splitwords=False, splitnums=False,
mergewords=False, mergenums=False):
"""Composes a RegexTokenizer with an IntraWordFilter, LowercaseFilter, and
StopFilter.
>>> ana = Fan... | b02e61a53670e3e3dfb30d8209a1a637327bf950 | 62,629 |
from typing import Callable
def get_piecewise_rollout(start_time: float, end_times: list, vaccination_rates: list) -> Callable:
"""
Turn the vaccination rates and end times into a piecewise roll-out function.
"""
def get_vaccination_rate(time, computed_values):
if time > start_time:
... | fcea805aa563efe7cd6b2dd5b38b30d039cd7027 | 62,630 |
def generate_ballot_style_contests(manifest: Manifest, ballot_style_id: str) -> dict:
"""
Generate the dict structure for the relevant contests for a given ballot style.
Args:
manifest: election manifest
ballot_style_id: ballot style to reference from the manifest
Returns:
Dict ... | e523fd8b5fc463ef10647399eef4ae335e4ea3e2 | 62,631 |
def analysis_kindex(analysis):
"""
Returns a dictionary of keywords by (namespace, name).
'kindex' stands for 'keyword index'.
"""
return analysis.get("kindex", {}) | 7f8c48cf0b2acdd28466827cea9ca512e30479d8 | 62,632 |
def format_percent(
number, format=None, locale=LC_NUMERIC, decimal_quantization=True):
"""Return formatted percent value for a specific locale.
>>> format_percent(0.34, locale='en_US')
u'34%'
>>> format_percent(25.1234, locale='en_US')
u'2,512%'
>>> format_percent(25.1234, locale='sv_S... | 05ff3e9bcb1b3babe53cc061a767841e432e0348 | 62,633 |
def inputs(dataset, is_training=False, num_epochs=None):
"""Reads input data num_epochs times.
Args:
dataset:
num_epochs: Number of times to read the input data, or 0/None to train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PI... | d126b6db2dcb03574764893bda4a4bb5df2736ea | 62,635 |
import numpy as np
def aspect_sentiment_accuracy(true_aspects,aspect_preds,true_sentiment,sentiment_preds):
"""
Compute the micro and macro accuracy for the aspect sentiment predictions
"""
#Ensure all the inputted values contain lists and not strings
true_aspects = true_aspects.app... | c7bda4660befa0b6d9d0b741a84f520cf278d66a | 62,638 |
def table_exists():
""" Helper: Checks for table. """
print("Checking for table 'journalEntries' in database...")
if engine.dialect.has_table(engine, "journal"):
return True
else:
return False | 0cba98cadf192787a91b0eee37fe40a506b6349e | 62,639 |
import string
import random
import time
def get_random_string(length: int = 30, allowed_chars: str = string.ascii_letters + string.digits) -> str:
"""
Heavily inspired by Plone/Django
Returns a securely generated random string.
>>> get_random_string(length=10)
:param length:
:param allowed_c... | cd7ef6fa8073b1df7a3c0235597d5c89f4192446 | 62,640 |
def unwrapped_data_ids_list(args):
"""
Transform all found data ID objects to their underlying value in an iterable object.
Parameters
----------
args : iterable
Sequence of data.
Returns
-------
list
Transformed list with underlying ``DataID`` values.
"""
if ar... | dbf0dfa2bb1f068b150bb0ac4739242fee6618e5 | 62,641 |
def mean_change(x):
"""
Returns the mean over the absolute differences between subsequent time series values which is
.. math::
\\frac{1}{n} \\sum_{i=1,\ldots, n-1} x_{i+1} - x_{i}
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this... | dffc27bec10b551e7a2f05926d0f8f2fd7399afe | 62,643 |
def spectral_whitening(tr, smooth=None, filter=None,
waterlevel=1e-8, corners=2, zerophase=True):
"""
Apply spectral whitening to data
Data is divided by its smoothed (Default: None) amplitude spectrum.
:param tr: trace to manipulate
:param smooth: length of smoothing window... | 9fcdc74e3ac4e82f2764489084caa7f4f4432960 | 62,645 |
from pathlib import Path
def good_maps(ref_map):
"""Creates a ref map with only good satellites"""
pointings = ["0", "2", "4"]
# load data from map .npz file
f = Path(f"{map_dir}/{ref_map}")
tile_data = np.load(f, allow_pickle=True)
tile_data = {key: tile_data[key].item() for key in tile_dat... | 2050a0aadea6cb83036e0c8d7314083882451e36 | 62,646 |
def _load_variable(metadata, var_name):
"""
Load data for the variable listed in metadata of the diagnostic variable.
Argument:
--------
metadata - nested dictionary of metadata
Return:
------
iris cube of the data
"""
candidates = select_metadata(metadata, short_name=v... | 107d160bc3a9571f0f446a5506eb644ec990277d | 62,647 |
def use_case_3():
"""Processes results from the <Gather> prompt in /voice"""
global n
global data
global bot_resp
global q_repeat
global conv_6_1
global conv_6_2
global texts
global recheck_repeat
global payable
# To check whether 'no' is replied twice
global repeat
... | 719a099e85f71d5808ab402f45621c81eafc880a | 62,649 |
def analysemathmsg(msg):#
"""this function analyses the message sennd by the user and calls the function
that extracts the actual values of the message"""
if msg[0] == "help" or msg[0] == "info":
return MATHBOTINFOEMBED
classes = []
for index, m in enumerate(msg):
if index % 2 == ... | f9afb7f44cf82a42f1bad97b3043976185aa5cd3 | 62,651 |
from typing import Union
from datetime import datetime
import time
def is_aware(value: Union[datetime, time]) -> bool:
"""
Determine if a given datetime.datetime is aware.
The concept is defined in Python's docs:
https://docs.python.org/library/datetime.html#datetime.tzinfo
Assuming value.tzinfo... | e808139c1c21f4b422022639e3a78f259e27e042 | 62,652 |
def local_client(start_connected=True):
"""Create a mqtt client connected to the local broker
This method is a *coroutine*
"""
return LocalClient(start_connected) | f97b82e1139087cb4bdfcc35c1a9dbf52bc6489c | 62,653 |
import copy
def calculate_sectional_lewis(B_s:np.ndarray, T_s:np.ndarray, S_s:np.ndarray):
"""
Lewis form approximation' is obtained.
Given the section's area, S_s, b B and draught T, the constants a, a a_3 are uniquely defined
by von Kerczek and Tuck18 as:
Parameters
----------
B_s : arr... | a784b31339c500b447b065ea61bca80d9c4090f6 | 62,654 |
def win32com_is_installed():
"""returns True if win32com is installed"""
try:
return True
except ImportError:
return False | 9b29a8f97a560d48d7a7e24f8f86770ded3c7792 | 62,655 |
import struct
def is_float_broken():
"""
Older versions of Python (<=2.5) and the Windows platform are renowned for
mixing up 'special' floats. This function determines whether this is the
case.
@since: 0.4
@rtype: C{bool}
@return: Boolean indicating whether floats are broken on this plat... | d29f5b1d1467c17b33f7074a6e10cef8708b8d9c | 62,656 |
def _proxg_SNN(X, Mu):
"""
"""
# SVD decomposition
m, n, B = X.shape
U, s, Vh = np.linalg.svd(X.reshape(m*n, B).T, full_matrices=False)
# Soft-thresholding the singular values.
s_bar = _soft_thresholding(s, Mu)
# Prox output
prox = U @ np.diag(s_bar) @ Vh
return prox.T.reshap... | 5b59166b988fd7b56f621d97ad52a96cf93d2699 | 62,657 |
def equilibrate_temperature_mpi(sim_dir, pos, cell_dim, param, comm, size=1, rank=0, inc=0.1, thresh=5E-2):
"""
equilibrate_temperature(pos, vel, cell_dim, bond_matrix, vdw_matrix, param, thresh=2E-2)
Equilibrate temperature of system
Parameters
----------
pos: array_like (float); shape=(n_bead, n_dim)
Upd... | 3ce8a1044b6a356be0a607c5e5e5fdadc1d4f847 | 62,658 |
def get_distance(point1, point2, metric="euclidean", testing=None):
"""
Returns ditance between point1 and point2 based on dist type that is passed
from the dist parameter
Parameters
----------
point1 : list
Values defining a single observation
to compute distances for.
poi... | bc663757c3d3077e61ff4651b72da9d6bd699b6d | 62,659 |
def import_nd(path):
"""
Import Kea 1d, 2d, 3d, 4d files
Args:
path (str): Path to file
Returns:
tuple:
x (None, numpy.array): Axes if included in binary file, None otherwise
data (numpy.array): Numpy array of data
"""
ascii_header_size = 12
ascii_h... | 02f79d057eb94d905d188c73c3bbadb406af8d8d | 62,660 |
import yaml
def create_cli_parser(
file: PathLike, frontend: Frontend = Frontend.ARGPARSE, **kwargs
) -> clis.AbstractCLIParser:
"""
Create and return a CLI parser.
:param file:
The file declaring the CLI.
:param frontend:
The frontend to use for parsing the CLI args.
:param k... | 423967cac621b9504e3424eefd5d0d5d2778f0b4 | 62,661 |
def _get_entity_name(entity_dictionary):
"""
Extract the entity name from the specified entity dictionary if it can
be found. The entity dictionary must contain at least 'type'
:param entity_dictionary: An entity dictionary to extract the name from
:returns: The name of the ent... | d6a926400c80a3517309f26a5d0e1c903a5f8110 | 62,662 |
def shift_f(f, tau):
"""
shifts a curve f by tau
:param f: numpy ndarray of shape (2,M) of M samples
:param tau: scalar
:rtype: numpy ndarray
:return fn: shifted curve
"""
n, T = f.shape
fn = zeros((n, T))
fn[:, 0:(T - 1)] = roll(f[:, 0:(T - 1)], tau, axis=1)
fn[:, T - 1] ... | 63dc7aed0c2f782dcbafa509d95635826b944fe6 | 62,663 |
def dst_main_directory(library: str) -> str:
"""
Main directory for report files resulting from
the reconciliation process.
"""
return f"./files/{library}" | cea4ea7bcd2c37fd97302cb02185ac2090413a4d | 62,664 |
def wing_weight_gradient(samples):
"""
Gradient of weight of a light aircraft wing
Parameters
----------
samples : np.ndarray (nsamples, nvars)
The samples at which the gradient is requested
Returns
-------
grads : np.ndarray (nsamples, nvars)
"""
S_w, W_fw, A, Lamda, q... | 66a61630fd658d396c2c1e8f4bdffdc653fe277f | 62,665 |
def amsoftmax_angular_triplet_loss(features, labels, margin, triplet_type):
"""Compute the triplet loss (using amsoftmax loss).
Args:
features: The input embeddings.
labels: The input labels.
margin: The margin.
triplet_type: all or hard
:return: The triplet loss
"""
... | bb065576a33f736d5e7cf78ab73f39dd67157ecd | 62,666 |
def aggregate_to_dataset(fields, grid_params, cf_attrs, analysis_time, *subgrid_data_and_weights):
"""Aggregate results from individual radar subgrids onto main grid."""
grid_shape = (grid_params['nz'], grid_params['ny'], grid_params['nx'], len(fields))
grid_sum = np.zeros(grid_shape, dtype='float32')
g... | c62b5b403e815e51b6bfdb5a0deb5486856781c6 | 62,667 |
from typing import Tuple
def get_dataframes(fname: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""Takes the path to a FIT file (as a string) and returns two Pandas
DataFrames: one containing data about the laps, and one containing
data about the individual points.
"""
record_data = []
lap_data... | 14cf1f66c867c36252832c2071a1718297c1a1e4 | 62,668 |
def normalize(*args):
"""" takes 1 or multiple lists of n coordinates and returns it normalized (getting rid of negatives)
"""
dtype = type(args[0][0]) # support list(s) of lists and list(s) of tuples
if len(args)==1: # only 1 argument passed
dim = dimensions(args[0])
obj = args[0]
... | e5430e6ffcbeb9ee9dc7f737516b2301d9fe5ca5 | 62,669 |
def swap_values(d: Map[K, V], key1: K, key2: K) -> Map[K, V]:
"""
Swap values for given keys creating new mapping.
>>> swap_values({'k1': 1, 'k2': 2}, 'k1', 'k2')
{'k1': 2, 'k2': 1}
Original mapping is returned if at least one key does not exist in `d`.
>>> swap_values({'k1': 1}, 'k1', 'k2')
... | a3237b298da9aa4f9b5203c15dc3bf51752f3548 | 62,670 |
def event(timing=True):
"""
Create a CUDA event. Timing data is only recorded by the event if it is
created with ``timing=True``.
"""
evt = current_context().create_event(timing=timing)
return evt | 064af6c9e2968098c7031375250b852bef50f2c2 | 62,671 |
from .dataset import SingleSentenceDataset
from .dataset import PairedSentenceDataset
from .dataset import Dataset
def create_dataset(config):
"""Create dataset according to :attr:`config['model']` and :attr:`config['MODEL_TYPE']`.
Args:
config (Config): An instance object of Config, used to record p... | 71107bba50c47a9c0520f1865d1a9d2180f5e375 | 62,673 |
import array
def gridFromList(lists: list):
""" gridFromList coputes a grid of all combinations of values provided in lists.
Parameters
----------
lists : list of lists, each containing elements per dimension
Returns
-------
grid : list o... | 5e711ffe29535cad28445e5e9e1c126d37cde1e8 | 62,674 |
def preference_GP_gradient(f, data, labels, GP_prior_cov_inv, preference_noise):
"""
Evaluate the gradient of the optimization objective function for finding
the posterior mean of the GP preference model (at a given point).
Inputs:
1) f: the "point" at which to evaluate the gradient. This ... | c9205a6982cb42d49f3595e0f36e014d2b61e474 | 62,675 |
import csv
def read_csv_as_nested_dict(filename, keyfield, separator, quote):
"""
Inputs:
filename - Name of CSV file
keyfield - Field to use as key for rows
separator - Character that separates fields
quote - Character used to optionally quote fields
Output:
Returns a... | b3a16bf52c783b5b40cab35242fde41be16c6e40 | 62,676 |
def object_id_parser(*args, **kwargs):
"""Object (resource in storage) ID parser
"""
return resource_authz.resource_id_parser(*args, **kwargs) | aee215cd31f1119192f68b157ec781f6f9cb4f81 | 62,677 |
def is_urls_module_valid_constant(node):
"""Suppress warnings for valid constants in urls module."""
if node.name not in ('urlpatterns', 'app_name'):
return False
parent = node.parent
while not isinstance(parent, Module):
parent = parent.parent
if not parent.name.endswith('urls'):
... | 866410bbce40298457a4c702b560f8f20dff47f9 | 62,678 |
def make_cluster(mcluster, massfunc='kroupa', verbose=False, silent=False,
tolerance=0.0, stop_criterion='nearest', mmax=120, mmin=None):
"""
Sample from an IMF to make a cluster. Returns the masses of all stars in the cluster
massfunc must be a string
tolerance is how close the clust... | 4432dfb01eb8795622648c63ee8a6622ff33326f | 62,679 |
def GetRangeMOverNSegmentedData(DataDirectory,fname_prefix,basin_list=[]):
"""
This function performs a linear regression on each segment of the segmented
SA data and finds the median and interquartile range from the segments for each basin.
The number of removed segments is also reported (segments are ... | ca08d0e578f09c9a23b413cf917fa76794f227af | 62,681 |
def loadMR(mr):
"""
Load meaning representation using Dag class.
Store only the set of triples, which will be required throughout
"""
dag = Dag.from_string(mr) #.stringify()
triples = []
for triple in dag.triples(instances=False):
triples.append((triple[0],Edge(triple[1],1,1),triple... | c0bec6cb35176769117edde10705c4fa6f9789e3 | 62,682 |
from pydantic import BaseModel # noqa: E0611
def apply(blueprint, args, args_suffix=[]):
"""Adjusts Rendering arguments.
:param blueprint: field/model/container/function
:param args: render arguments
:param args_prefix: args_prefix
:return: rendered record
"""
# num arguments adjustment ... | 3384946eb32c797f2956cb79e92bf2cbbf1cfe5f | 62,684 |
from typing import Optional
from typing import Dict
from typing import Any
def create_session_factory(uri: str, base: Optional[DeclarativeMeta] = None,
**engine_kwargs: Dict[Any, Any]) -> SessionFactoryType:
"""
Create an SQLAlchemy session factory.
:param uri: The URI to the database to use
:par... | 53cce4c55a17515ce7cf1269b91c414dc74966f9 | 62,685 |
def get_duration(wav, sr):
"""
Compute the duration (in seconds) of an audio time series.
Arguments:
wav (np.ndarray):
Audio time series to get the duration of.
sampling_rate (int):
Sampling rate of `wav`.
Returns:
float: Duration in seconds.
"""
... | 44d3b712dbfeff628efac36a1dde4f6bdad1d0e0 | 62,686 |
def g_nu_fig():
"""
plot g(nu)
"""
N = 100
nu = np.linspace(.00001,1,N)
s = np.linspace(0,10.,100)
ds = (s[-1]-s[0])/len(s)
g1 = np.zeros(N)
g2 = np.zeros(N)
for i in range(N):
tot = 0
tot2 = 0
# find integral of exp(-s)*H(nu s)
for j in rang... | 16755668d981b7c95424adf4022da30643ff970a | 62,687 |
import time
from datetime import datetime
def parse_date(s):
"""parse string mode date
date format must be `YYYY-mm-dd`, return the start seconds of day
"""
if '-' in s:
sd = s.split('-')
else:
raise ValueError('invalid date: %s' % s)
if len(sd) != 3:
raise ValueError... | 30a82084b347b063b5dea3ff1b13d61f244a73f7 | 62,689 |
def contrast_and_reshape(mouse, size=128):
"""
Enhance image contrast.
:param mouse: Slices of the mouse we want to segment
:type mouse: np.array
:param size: Size of the images (we assume images are squares)
:type size: int
:return: Images list with readjusted contrast
:rtype: np.array... | 775157ed9fb5301e574887aec30b0074593e241b | 62,690 |
import json
def json_options_to_metadata(options, add_brackets=True):
"""Read metadata from its json representation"""
try:
options = json.loads('{' + options + '}' if add_brackets else options)
return options
except ValueError:
return {} | 60b796f804334ab095f103a56997b35a6a71271f | 62,691 |
def resize(transit, size=256):
""" Resizes a 1D-array with linear interpolation
:param transit: (1-D np.array) input data
:param size: (int) target size
:return: (1-D np.array) resized input
"""
x_original = np.linspace(0, 100, len(transit), endpoint=True)
x_target = np.linspace(0, 100, siz... | 67cf12a723f21dace8aa89ec9997ad3f4b74188c | 62,693 |
from dcspy.aircrafts import Aircraft
def aircraft(lcd_mono: LcdInfo):
"""
Return instance of Aircraft base class for Logitech mono LCD.
:param lcd_mono:
:return: Aircraft instance
"""
return Aircraft(lcd_mono) | 00ecf81b0f42108b94ac7eef2ef451bfc5d815b1 | 62,694 |
def calc_duty_cycle(reg_dict):
"""
Calculate the duty cycle of the illumination. The duty cycle might want to be varied
because of the rise time of the VCSEL or laser. Changing the duty cycle also changes
the harmonic content of the illumination waveform which will change the wiggle/circular
erro... | 0c9a772b58d0d0aa684d2e5ba872796d11749760 | 62,695 |
from typing import Tuple
import re
def parse_cmd(cmd_str: str) -> Tuple[str, ...]:
"""
Parse command from text
"""
if not is_valid_cmd(cmd_str):
return None
if re.match(CMD_RE_LIST[0], cmd_str) is not None:
action, loc_x, loc_y, direction = re.match(CMD_RE_LIST[0], cmd_str).groups... | 38ad630fc83da9ad0d45893a8afdea95c7e2d0ab | 62,696 |
import tempfile
from pathlib import Path
import json
def _copy_hyperparameters(container: _LocalJobContainer, **creation_kwargs) -> bool:
"""If hyperpameters are present, this function will store them as a JSON object in the
container in the appropriate location on disk.
Args:
container(_LocalJo... | 57fc06d75b4ac5722cceec3b761488546769009f | 62,697 |
import json
def englishToFrench(englishText):
"""Translates English to French"""
if englishText == '':
return json.dumps(dict(message='Input cannot be null.'))
translation = language_translator.translate(
text=englishText,
model_id='en-fr'
).get_result()
# return print(json... | 25aa18dbc87559d423032edab88faa3a23c24b7a | 62,698 |
def start(game, game_state):
"""
Response:
- taunt
"""
payload = serialize_game(game, game_state)
return __call_snakes(game_state.snakes, 'POST', 'start', payload, game.turn_time) | 5705f44ae3adbc0102c3bf2c5f8b0c14f14fbc0e | 62,699 |
def series_phrase_serial(items, separator=', ', last_separator=' and '):
"""Join items into a grammatical series phrase using a serial separator,
e.g., ``"item1, item2, item3, and item4"``.
Args:
items (list): List of string items to join.
separator (str, optional): Item separator. Defaults... | 7cc023500193407dddfedfb9872bc0e0e99a7a38 | 62,700 |
def file_deleted_from_playlist(*args, **kwargs) -> str:
"""
Audio-file deletion validation message
:param args:
:param kwargs:
:return: Generated validation message
"""
text = f"{_check_mark_button} " \
f"فایل با موفقیت حذف شد"
return text | f76e550009e64bdc66609567d192be9fb9361f6a | 62,701 |
def focal_plane_to_sky(cartrep):
"""
Convert physical position in focal plane to sky offset from FoV
"""
cartrep = cartrep.transform(flip_y)
with u.set_enabled_equivalencies(gtc_focalplane_equivalencies):
return CartesianRepresentation(cartrep.xyz.to(u.arcsec)) | f5c7bba935f58ef4f4b72356c0956a3ffc60fdea | 62,702 |
from pathlib import Path
def find_file(search_file: str, search_path: str):
"""This api find the file in sub-directories and returns
absolute path of file, if file exists"""
for File in Path(search_path).glob(f"**/{search_file}"):
return File | a776bb599c60d4bd532cdd05fe5e2b8b9ef5ccf0 | 62,703 |
import torch
from typing import List
def flatten2float_list(t: torch.Tensor) -> List[float]:
"""
Maps a tensor to a flatten list of floats
:param t:
:return:
"""
t = t.view(-1).detach().numpy().tolist()
# t = torch_uu.flatten(t).numpy().tolist()
return t | 4feb1d702106bcfc45472c21903c38fb71e45d21 | 62,704 |
def pad_image(image, pad=0, pad_mode='constant', pad_value=0.0):
"""Pads a batched image array."""
batch_shape = image.shape[:-3]
padding = [
*[(0, 0) for _ in batch_shape],
(pad, pad), (pad, pad), (0, 0),
]
if pad_mode == 'constant':
return np.pad(image, padding, pad_mode, constant_values=pad... | 744d6c00f85eafb720ead9711cb5d121d4eb4a88 | 62,705 |
def scalar_cast(input_x, input_y):
"""
Casts the input scalar to another type.
Args:
input_x (scalar): The input scalar. Only constant value is allowed.
input_y (mindspore.dtype): The type to be cast. Only constant value is allowed.
Returns:
Scalar. The type is the same as the ... | e9aed0f58c3294626cfdb684976659517c4dfd5f | 62,706 |
def get_directory_instance(ns, directory):
"""
Retrieve LMIInstance of a directory.
:type directory: string
:param directory: Full path to the directory.
:rtype: LMIInstance
"""
name = ns.LMI_UnixDirectory.new_instance_name(get_directory_name_properties(ns, directory))
return name.to_in... | 1301138a8b11820b832027f68d016bb59cac3a6c | 62,707 |
import torch
def compute_scores(users_embeds: torch.Tensor, items_embeds: torch.Tensor, items_bias: torch.Tensor) -> torch.Tensor:
"""
Args:
users_embeds(torch.Tensor): shape (batch_size, items_total, embed_dim)
items_embeds(torch.Tensor): shape (items_total, embed_dim)
items_bias(torc... | 8528963a23efef270b467ec6f039b5a8733d3b4f | 62,708 |
def max_height(target):
"""
Solved using this racionale:
ignore x -- it's independent and can adapt to any value we specify for each starting y velocity (vy)
with positive vy necessary for reaching height, vy will be -vy when y is 0 again
so, the heighest initial velocity is reaching the... | feeba57a4abaf7359ff2bf32a6b95ebe818c2d62 | 62,709 |
def extract_fplus_include_file(code_line):
"""
Extracts the included file path from an include statement
"""
return (
code_line
.replace("#include <fplus/", "")
.replace("#include \"", "")
.replace("\"", "")
.replace(">", "")[:-1]
) | c969fcb633332b05aec93f2998065d34b2d99c2f | 62,710 |
def merge_sort(lst):
"""Merge Sort."""
if len(lst) <= 1:
return lst
mid = len(lst) // 2
left = lst[mid:]
right = lst[:mid]
left = merge_sort(left)
right = merge_sort(right)
output = []
while left and right:
if right[0] < left[0]:
output.append(right.po... | 16bb2351efc133c11e0b6d51b7dae908260c0422 | 62,711 |
def gene_group_replacement(x1, x2, group, config):
"""
Metoda buduje genom dziecka przekazując mu geny z wyselekcjonowanej grupy od rodzica x2
i pozostałe geny od rodzica x1.
:param x1: list
:param x2: list
:param group: list
:return: new_x: list
"""
interval_in_indices =... | 770eebd91b4e5c82303d974e5545874c4f1aad49 | 62,713 |
import scipy
def compute_elastic_score(xyz,mean=None,neighbour_radius=5.0):
""" compute_elastic_score :
mean should not be None, but maybe something could be worked out for that case too...
Description
-----------
the idea is to compute the elastic energy of the mode.
For atom pairs within a... | 79ddcfcdb8b428abec616eb71481a37c1ae9bc39 | 62,714 |
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