content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def outline_to_mask(line, x, y):
"""Create mask from outline contour
Parameters
----------
line: array-like (N, 2)
x, y: 1-D grid coordinates (input for meshgrid)
Returns
-------
mask : 2-D boolean array (True inside)
Examples
--------
>>> from shapely.geometry import Poin... | 1c1ab70ed949b10a052aae1b17239a5d7a08da64 | 3,633,300 |
def check_ContentType():
"""
HowTo make Pre-Processing for all requests.
But: it also can be managed in View Class, just like django-rest-framework
"""
if request.method != 'GET':
if (not request.content_type) or ('application/json' not in request.content_type):
msg = jsonify(
... | 7fc35fe40621a1cf0486d681b294ff636f293c06 | 3,633,301 |
import hashlib
def _hash_feature(feature):
"""Calculate SHA256 hash of feature geometry as WKT"""
geom = shape(feature["geometry"])
return hashlib.sha256(geom.to_wkt().encode("utf-8")).hexdigest() | bd1dc2ad46f0960a042066152b5622febe530411 | 3,633,302 |
import socket
def ssdp_scan(address=None, service=None, timeout=None):
"""
Returns a list of responses to an SSDP request
"""
if address is None:
address = DEFAULT_ADDR
if service is None:
service = DEFAULT_SERVICE
if timeout is None:
timeout = DEFAULT_TIMOUT
mes... | 43a0f557ed3f8b5b8a9c8085ce71a0e5a45a2f31 | 3,633,303 |
def random_walk_timeseries(length: int = 10, freq: str = 'D', mean: float = 0, std: float = 1,
start_ts: pd.Timestamp = pd.Timestamp('2000-01-01')) -> 'TimeSeries':
"""
Creates a random walk TimeSeries by sampling a gaussian distribution with mean 'mean' and
standard deviation 's... | 754a52be186f6f05fd70c8019c61cf6c27059680 | 3,633,304 |
def provider_for(platform: str, source: str) -> ContentProvider:
"""
A factory method that returns the appropriate data provider. Throws an exception to let you know if the
arguments are unsupported.
:param platform: One of the PLATFORM_* constants above.
:param source: One of the PLATFORM_SOURCE>* ... | 8f77453c6ec02d9bae571f12cd2de2a3420976b5 | 3,633,305 |
def create_app(config_name: str = "development") -> Flask:
"""
Factory for the creation of a Flask app.
:param config_name: the key for the config setting to use
:type config_name: str
:return: app: a Flask app instance
"""
app = Flask(__name__)
app.config.from_object(config[config_name]... | ca38c3bb82e19db20aff7a26724f00e848d54b5b | 3,633,306 |
def oneorzero(argument):
"""Conversion function for the various options that let you choose between 1 and 0."""
return directives.choice(argument, ('0', '1')) | 4de687a99c56c5a7e2074d0ae58312690f115472 | 3,633,307 |
def encode(s, c):
"""
s is the scret code
c is the clear text
"""
secret_code_list = list(s)
clear_text_list = list(c)
encoded_text_list= []
count = 0
for letter in clear_text_list:
if letter == ' ':
encoded_text_list.append(' ')
continue
enc... | 3af6297fb79b77c542b19789ccf9bc0668f6afd5 | 3,633,308 |
import sqlite3
def test_ap_wpa2_eap_sql(dev, apdev, params):
"""WPA2-Enterprise connection using SQLite for user DB"""
try:
except ImportError:
return "skip"
dbfile = os.path.join(params['logdir'], "eap-user.db")
try:
os.remove(dbfile)
except:
pass
con = sqlite3.con... | cf5139cfc264e18fdb8e60eb4bce75797506db48 | 3,633,309 |
import numpy
def polyfit(data, time_axis, masked_array, outlier_threshold):
"""Fit polynomial to data."""
if not masked_array:
if outlier_threshold:
data, outlier_idx = timeseries.outlier_removal(data, outlier_threshold)
coeffs = numpy.ma.polyfit(time_axis, data, 3)[::-1]
... | c58b04e31bea8a028ee464d4b70515c4e5b1e7e2 | 3,633,310 |
def update_board(position, board, player):
"""
Update the board with the user input position if position not taken
returns board, True=position taken or False=position not taken and board updated
args: position (int 1-9, user input)
board (np.array 2d)
player ("X" or "O")
"""
... | eb53d24c4976499e6611c97757d0c33b4cb3254f | 3,633,311 |
def find_deployed_version(package_name, environment, version=None,
revision=None, apptypes=None, apptier=False):
"""Find a given deployed version for a given package in a given
environment for all related app types; search for full tier
or host only deployment specifically
... | 57bcd217e0a63a610f78a5ce49365578d7540d3e | 3,633,312 |
import itertools
import math
def stochastic_block_model(sizes, p, nodelist=None, seed=None,
directed=False, selfloops=False, sparse=True):
"""Returns a stochastic block model graph.
This model partitions the nodes in blocks of arbitrary sizes, and places
edges between pairs of ... | 4beb0e20381aa65927a8b60278f8de6cb06f64ca | 3,633,313 |
def get_text_and_links(wikitext):
"""
Obtain text and links from a wikipedia text.
"""
parsed = wtp.parse(wikitext)
basic_info = parsed.sections[0]
saved_links = {}
num_links = len(basic_info.wikilinks)
for i in range(num_links):
index = num_links - i - 1
link = basic_in... | a0b05f72c12529b655dda216e32e129b5fcaad8f | 3,633,314 |
import signal
def SobelOperator(image, n):
""" 构建了 Sobel 平滑算子和差分算子后，通过这两个算子来完成图像矩阵与 Sobel 算子的 same 卷积，
函数 SobelOperator 实现该功能:
图像矩阵先与垂直方向上的平滑算子卷积得到的卷积结果，
再与水平方向上的差分算子卷积，
这样就得到了图像矩阵与sobel_x 核的卷积。
与该过程类似,图像矩阵先与水平方向上的平滑算子卷积得到的卷积结果,
再与垂直方向上的差分算子卷积,
这样就得到了图像矩阵与 sobe... | e39b807ecff2e78f289a918e6f85ef7fced84427 | 3,633,315 |
import typing
import pickle
def read_model(model_file: typing.IO) -> SVR:
"""Read the model from the given file."""
return pickle.loads(model_file.read()) | 32a49bd37da2b6fb33a64d0ff334b8e323030169 | 3,633,316 |
def create_nonfixations(stimuli, fixations, index, adjust_n = True, adjust_history=True):
"""Create nonfixations from fixations for given index
stimuli of different sizes will be rescaled to match the
target stimulus
"""
x_factors, y_factors = calculate_nonfixation_factors(stimuli, index)
non... | 5c4462dd4bb5a3565158a9b2b085b88651d8b5a5 | 3,633,317 |
def get_commit_log(url, revnum):
"""Return the log message for a specific integer revision
number."""
out = launchsvn("log --incremental -r%d %s" % (revnum, url))
return recode_stdout_to_file("".join(out[1:])) | fb079051926292fdabf69da99008c6dd5dab47c9 | 3,633,318 |
import torch
def cov(x, rowvar=False, bias=False, ddof=None, aweights=None):
"""
Estimates covariance matrix like numpy.cov
https://github.com/pytorch/pytorch/issues/19037
"""
# ensure at least 2D
if x.dim() == 1:
x = x.view(-1, 1)
# treat each column as a data point, each row as ... | 376e89804374979fc21b1412d9db5ed588555d40 | 3,633,319 |
def tle_fmt_int(num, digits=5):
""" Return an integer right-aligned string with DIGITS of precision, all blank if num=0
Ignores sign.
"""
if num:
num = abs(num)
else:
return " "*digits
string_int = "{:>{DIGITS}d}".format(num,DIGITS=digits)
return string_int | 8db7938e7a88e68c4a22013b10debbc4f5a9ca72 | 3,633,320 |
import six
def SignalCollection(metasignals): # noqa: C901
"""Class factory for ``SignalCollection`` objects."""
@six.add_metaclass(MetaCollection)
class SignalCollection(object):
_metasignals = metasignals
def __init__(self, psr):
self.psrname = psr.name
# insta... | 627959eb695979f1b8922a5da60ae4a88144ddb0 | 3,633,321 |
def get_status():
"""Return classifier status."""
return 'ok' | 84aedac3659ac2321867b02d3f6e7acb523923a3 | 3,633,322 |
def get_config_info() -> dict:
"""Gets the config from core sqlfluff and sqlfluff plugins and merges them."""
plugin_manager = get_plugin_manager()
configs_info = plugin_manager.hook.get_configs_info()
return {
k: v for config_info_dict in configs_info for k, v in config_info_dict.items()
} | c51f5853a54080189c37b1dea49da126d402854d | 3,633,323 |
def landing_page(request, page):
"""Return resource landing page context."""
edit_resource = check_resource_mode(request)
return get_page_context(page, request.user, resource_edit=edit_resource, request=request) | 1a78491694a004ed784c50e8205c7341cbe58452 | 3,633,324 |
import torch
def _smooth_l1_loss(pred: Tensor, target: Tensor, beta: float = 1.) -> Tensor:
"""(F.smooth_l1_loss())
:param pred: shape(N, In)
:param target: shape(N, In)
:param beta: smooth线
:return: ()"""
diff = torch.abs(target - pred)
return torch.mean(torch.where(diff < beta, 0.5 * d... | 33cbfbf66360f9dd9d473b82c0f2103af19df676 | 3,633,325 |
import scipy
def solve_assignment(weights, exclude_zero=False):
"""Finds matching that maximizes sum of edge weights.
Args:
weights: 2D array of edge weights.
exclude_zero: Exclude pairs with zero weight from result.
Returns:
Integer array of pairs with shape [num_matches, 2].
"""
rs, cs = sci... | d73d2c8b5dc7d5c5cbd08f38a4cc21d0b1ebf62a | 3,633,326 |
import os
import logging
def _initXDG():
"""Initialize config path per XDG basedir-spec and resolve the final location of state file storage.
Returns:
str: file path to state file as per XDG spec and current env.
"""
# per the XDG basedir-spec we adhere to $XDG_CONFIG_HOME if it's set, other... | edb3a0355b16a339dcb3112f4fdd99492eb48457 | 3,633,327 |
import os
def get_stdout(jobname, jobid):
"""Get stdout for job <jobid>
Returns:
200 OK: file (on success)
404 Not Found: Job not found (on NotFoundWarning)
404 Not Found: Result not found (on NotFoundWarning)
500 Internal Server Error (on error)
"""
user = set_user()
... | 11c859cc2100ae08b7f2d1724de657c9ded54f1b | 3,633,328 |
def stepwise_kpca(X, gamma, n_components):
"""
Implementation of a RBF kernel PCA.
Arguments:
X: A MxN dataset as NumPy array where the samples are stored as rows (M),
and the attributes defined as columns (N).
gamma: A free parameter (coefficient) for the RBF kernel.
n_c... | 3e0abd47e5527191e681f68ccb9ed71587a5adb2 | 3,633,329 |
import functools
def make_val_and_grad_fn(value_fn):
"""Function decorator to compute both function value and gradient.
For example:
```
@tff.math.make_val_and_grad_fn
def quadratic(x):
return tf.reduce_sum(scales * (x - minimum) ** 2, axis=-1)
```
Turns `quadratic` into a function that accepts a... | f08e889d62ce5d7e94e70bf96ae0ec8bca31f931 | 3,633,330 |
def showCatalog():
"""This showCatalog handler will diplay the home page for both a user not
logged in and one for a logged in user.
"""
categories = session.query(Category).order_by(asc(Category.name))
# Limit the query to a maximum of 10 and order by the most recent
# items added, which is id... | e2a50c7478536fe573b6f7860e0976de3a832692 | 3,633,331 |
def is_fill_compute_seq(seq):
"""Test whether *seq* can be converted to a FillComputeSeq.
True only if it is a FillCompute element
or contains at least one such,
and it is not a Source sequence.
"""
if is_source(seq):
return False
is_fcseq = False
try:
is_fcseq = any(map... | f6cca52b2ed7065ae950eca48071120c83a735bc | 3,633,332 |
def upload_model(uploadfile, name=None): # noqa: E501
"""upload_model
# noqa: E501
:param uploadfile: The model YAML file to upload. Can be a GZip-compressed TAR file (.tgz, .tar.gz) or a YAML file (.yaml, .yml). Maximum size is 32MB.
:type uploadfile: werkzeug.datastructures.FileStorage
:param ... | 95a759bc6d5cc806a8a8df1945816275784ab132 | 3,633,333 |
from mpl_toolkits.mplot3d import Axes3D
import mcubes
def visual_callback_3d(fig=None, plot_each=1):
"""
Returns a callback than can be passed as the argument `iter_callback`
of `morphological_geodesic_active_contour` and
`morphological_chan_vese` for visualizing the evolution
of the levelsets. On... | 1f69691a983f9afabbe53e761e95138a1aa64e40 | 3,633,334 |
def _erfint(x):
"""
Integral of the error function.
Parameters
----------
x : float or array
Argument.
Returns
-------
float or array
Integral of the error function.
"""
return x * erf(x) - 1.0/np.sqrt(np.pi) * (1.0-np.exp(-x**2)) | e397f62fb35caf5d71eaebd3eb9dc8ce0ba5c9d5 | 3,633,335 |
def model(x, n_pos, mask_miss1, mask_miss2, is_train=False, reuse=None, data_format='channels_last'):
"""Defines the entire pose estimation model."""
def _conv2d(x, c, filter_size, strides, act, padding, name):
return Conv2d(
x, c, filter_size, strides, act, padding, W_init=W_init, b_init=b... | 895f9e8695b0564eb8fa3e2d5af320916d443025 | 3,633,336 |
from typing import Optional
from typing import Union
def zeros(shape: Optional[Union[int, tuple, list]] = None) -> 'ArrayInterval':
"""
Instantiate an `ArrayInterval` filled with zeros.
Note: The difference from numpy is that the argument shape is optional.
When shape is None, some operations a... | 2d6348e327798b3019c6fd2d42827c64660cad02 | 3,633,337 |
def anova_total_mean_square(Ns, means, sigmas):
"""
This function performs an average over multiple sets of observations, each with its own standard deviation.
For example: 5 simulations compute RMSDs+-Sigma of the same protein. What is the aggregate?
See: http://www.burtonsys.com/climate/composite_stan... | b3cc0aa7fd66521f6692bd10c66b1e7b00e59566 | 3,633,338 |
def compute_lsb(n_bits, fsr_min, fsr_max, half_bit=None):
"""
Computes the least significant bit (LSB) magnitude in the stage MDAC and
sub-ADC to achieve a desired full scale range (FSR). The input FSR and
output FSR are assumed to be the same so only one value is returned.
:param n_bits: Number of... | 03520c71de04e654841c47c40a109431d315163d | 3,633,339 |
def proximity_matrix(rf, X, normalize=True):
"""
Calculate proximity matrix
:param rf:
:param X:
:param normalize:
:return:
"""
leaves = rf.apply(X)
n_trees = leaves.shape[1]
prox_mat = np.zeros((leaves.shape[0], leaves.shape[0]))
for i in range(n_trees):
a = leaves... | 781c08f242e52afa7430761124a344456c0efec1 | 3,633,340 |
def upload_to_s3_v2(local_path: str, bucket_name: str, object_name: str):
"""
path_output: local dir file path
bucket_name: name of s3 bucket
key_path: key path + file name = object name
"""
s3 = boto3.client("s3")
response = s3.upload_file(local_path, bucket_name, object_name)
return re... | 844b1c07daeb44f49e1071516da0802d843d4790 | 3,633,341 |
def marks(category, mark=None, category_marks=None, public=False):
"""Assign marks to a test or suite of tests, grouped by a category."""
def decorator(test_item):
if mark is None and category_marks is None:
raise ValueError("One of mark or category_marks must be defined")
test_item... | 2d47a8df4f610dbc081dd57fce169e2f89b88ca4 | 3,633,342 |
import secrets
def get_random_ua():
"""return a random user-agent string from file"""
# stop condition, file does not exists, not readable...
# + file operation
with open('headers.txt') as hbuffer:
all = hbuffer.readlines()
return secrets.choice(all).strip() | 0d6a924c07bbad2398966bed590bf3307f5c475d | 3,633,343 |
def is_nondecreasing(arr):
""" Returns true if the sequence is non-decreasing. """
return all([x <= y for x, y in zip(arr, arr[1:])]) | 593ac54669ef217e258380bf41ce067935ee53f0 | 3,633,344 |
def underscore_to_camelcase(value):
"""
Converts underscore notation (something_named_this) to camelcase notation (somethingNamedThis)
>>> underscore_to_camelcase('country_code')
'countryCode'
>>> underscore_to_camelcase('country')
'country'
>>> underscore_to_camelcase('price_GBP')
'pri... | 94bb5c007d3b50112c62ca9b3e97c5bf4f155fff | 3,633,345 |
def findCenter(S):
"""Find the approximate center atom of a structure.
The center of the structure is the atom closest to (0.5, 0.5, 0.5)
Returns the index of the atom.
"""
best = -1
bestd = len(S)
center = [0.5, 0.5, 0.5] # the cannonical center
for i in range(len(S)):
d = S.... | 634945a5560b3791f3835f3da090decd1b06b933 | 3,633,346 |
def add_noise(rots, level):
"""adds random noise to a rotation matrix."""
noised_rots = [[np.random.uniform(-level,level,4)]*23]*rots.shape[0]
noised_rots = Quaternions(np.array(noised_rots))
return rots+noised_rots | 8886f11b90a2d0098b4776100ee9834d433012fb | 3,633,347 |
def content_tree_update(request):
"""Returns all content of a given contenttree."""
assert request.contenttree.patched, "contenttree should be patched here..."
# SANITIZE JSON DATA
jsoncontent = request.json_body['content']
data = remove_unvalidated_fields(jsoncontent, peerreview_update_schema)
... | dcfc1932aa9fec431e138f205a143b792b6130f7 | 3,633,348 |
def compute_overlap_region(db_ref, db_new):
"""
Computes the overlapping/shared region between two images.
Outputs:
- Corner coordinates of the overlapping region in the SRS
- Corresponding pixel indexes in both input images
"""
cornerCoord_ref = gdal.Info(db_ref, format='json')['co... | 664f1825c75c0b33d0b0c5ba4686d132790ad363 | 3,633,349 |
from typing import List
import math
def _calculate_team_size_score(
projects: List[dict], assignments: List[AssignmentTuple], project: dict, student: dict
) -> int:
"""Calculates the weighted score based on how far away from the average team size this project would
be after assigning the student
A... | 47fcb0ea3feb637b558e6df10abd0aa993dbf965 | 3,633,350 |
import string
def encode(data):
""" Encodes a string to the 'cstring' encoding supported by the replay DTD.
Args:
data: string value to be encoded
Returns:
String containing the encoded value
Raises:
None
"""
chars = string.letters + string.digits + " ?!:."
retur... | 9fc3482c53eed42678aa3c7cbbf915c85b28cc68 | 3,633,351 |
import types
from typing import Dict
from typing import Any
from typing import List
def gen_frame_symbol(
data: types.PulseInstruction, formatter: Dict[str, Any], device: device_info.DrawerBackendInfo
) -> List[drawings.TextData]:
"""Generate a frame change symbol with instruction meta data from provided fram... | 8b38222cd294f42eb58dfcc36b10361ce7869a81 | 3,633,352 |
def leanlauncher_download_version(version=LATEST_VERSION, path=DEFAULT_INSTALL_PATH):
"""
Installs the specified version of Minecraft to the specified path
Parameters:
version (str): the version of Minecraft to be installed, by default the latest
path (str): the path to install Minecraft to, by default ~... | 35193ec3ca5d1ad1b23b84d35fec002997bb948e | 3,633,353 |
def Weierstrass_Enneper(f, g, z, imag_unit=1j):
"""
Compute the Weierstrass Enneper parametrization for given 'Weierstrass data'.
:param sympy expression f: h'/g, with h the height function
:param sympy expression g: Gauss map.
:param sympy variable z: Complex variable.
:param imag_unit: Represe... | a47648e52600fd5fff37fb0ee7477b67c587ad0b | 3,633,354 |
import os
def build_lstm(seq_length):
"""Builds an LSTM in Keras."""
model = tf.keras.Sequential([
tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(22),
input_shape=(seq_length, 3)), # output_shape=(batch, 44)
tf.keras.layers.Dense(4, activation="sigmoid") # (batch, 4)
])
... | 392fbb4491c5469499a714e4e4f2a8d65c62c1e3 | 3,633,355 |
def human_size(size_bytes):
"""
format a size in bytes into a 'human' file size, e.g. B, KB, MB, GB, TB, PB
Note that bytes will be reported in whole numbers but KB and above will have
greater precision. e.g. 43 B, 443 KB, 4.3 MB, 4.43 GB, etc
"""
suffixes_table = [('B', 0), ('KB', 1), ('MB', ... | d3489ee85c419711d82e35003426ef2915143c17 | 3,633,356 |
def html_color_to_rgba(html_colour, alpha):
"""
:param html_colour: Colour string like FF0088
:param alpha: Alpha value (opacity)
:return: RGBA semitransparent version of colour for use in css
"""
html_colour = html_colour.upper()
if html_colour[0] == '#':
html_colour = html_colour[1... | 4f28938aa89d62198cc3052a480e0e0744560a79 | 3,633,357 |
from typing import OrderedDict
def _assign_category_colors(uses, cmap, use_colors=None, assigned_colors=None):
"""Set a dictionary of nice colors for the use blocks.
Options allow specifing pre-defined elements for some categories."""
use_colors = OrderedDict() if use_colors is None else use_colors
a... | 085d0ca707990c84cd51464ee8f65f90500b7060 | 3,633,358 |
def GetClientContext(client_id, token):
"""Get context for the given client id.
Get platform, os release, and arch contexts for the client.
Args:
client_id: The client_id of the host to use.
token: Token to use for access.
Returns:
array of client_context strings
"""
client_context = []
clie... | 3ecddfeb58e99d06951aab4fe359bc1291b43a2a | 3,633,359 |
import math
def calculate_slope_intercept(line):
"""
Calculating slope nd intercept for a line
"""
for x1, y1, x2, y2 in line:
if x2-x1 == 0:
return math.inf, 0
slope = (y2-y1)/(x2-x1)
intercept = y1 - slope * x1
return slope, intercept | e21ff81a36cef7a995a98f7adf0302e8397a8139 | 3,633,360 |
def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
speech_output = "I'm the Magic Conch Shell. Ask me a question"
# If the user either does not reply to the welcome messag... | 8a900efd3ef7129c1a9ff408e49591945dad83a1 | 3,633,361 |
from typing import Optional
def rmcgs(A: np.ndarray, m: Optional[int] = None, r: Optional[int] = None) -> np.ndarray:
"""
Compute the product: B <- G * S * A where G has size m * r and elements from the standard
normal distribution, rescaled by 1/sqrt(m), and S is a CountSketch of size r * n. The matrix
... | 25325d1b2c75a07468fdce63fb5481da99c93105 | 3,633,362 |
def start_multi_svf():
"""
This is function for satrt multi SVF GUI
:return: result, details
"""
app.logger.info("Try to start multi SVF GUI for test")
cli_rest_port_list = []
svf_num = int(request.form.get("svf_num"))
try:
cli_rest_port_list = StartMultiSvf(svf_num=svf_num, log... | 1dd3585e5fcfa96d225645d382ee5ab064bc4073 | 3,633,363 |
import glob
def get_files_by_pattern(root, pattern='a/b/*.ext', strip_root=False):
"""Optionally to only return matched sub paths."""
# Get the abspath of each directory images.
ret = glob.glob(osp.join(root, pattern))
# exclude the root str, so the ret is spec['patterns']. such as ['images/train/*.jp... | 905e4c4d08d228074a8036cdf5511f9eb7330f8c | 3,633,364 |
import torch
def gumbel_softmax(logits, temperature=1, hard=False):
"""
ST-gumple-softmax
input: [*, n_class]
return: flatten --> [*, n_class] an one-hot vector
"""
y = gumbel_softmax_sample(logits, temperature)
if not hard:
return y
shape = y.size()
_, ind = y.max(dim=-1... | 4ce2b64115c4a4ce87677aa99e0422c00524c5d3 | 3,633,365 |
def _load_augmentation_aug_all():
""" Load image augmentation model """
def sometimes(aug):
return iaa.Sometimes(0.5, aug)
return iaa.Sequential(
[
# apply the following augmenters to most images
iaa.Fliplr(0.5), # horizontally flip 50% of all images
ia... | e39a08f0166d8a6a379427a895b196891bf70fe1 | 3,633,366 |
import logging
import traceback
def zk_get_mq_servers(zookeeper_servers, logger = logging.getLogger(__name__)):
"""
Get list of mq servers from zookeeper
:param zookeeper_servers: list of zookeeper servers
:param logger: logger to use
:return: list of mq servers or None
"""
mq_servers = No... | 2201ac305ea1b44fe8baa0582bfc84bf39472dbe | 3,633,367 |
import os
def create_list_of_file_lists(possible_storage_locations, dir_path_to_files, dir_name_appendix):
"""This function creates a list of the files within each of the file storage locations so the user can inspect them to look for comparison options."""
# Output list of files
files_to_compare = []
... | 11886751557e6994e3994f820dee21848584e265 | 3,633,368 |
from datetime import datetime
def _make_todays_date() -> str:
""" build today's date as a standard format """
return datetime.now().strftime("%a %d-%b") | fdb9bc420689081586ac19fe91a17ea871576d59 | 3,633,369 |
import re
def add_review_suggestion_flags(df,
text_col,
result_col='result_binary'):
"""
attempt to add on some logical "manual review suggested" flags onto cases to reduce false positive/negative classifications. currently
flags cases w... | e2083d65f54b82dd9eba19b6b2d32806e2cd086d | 3,633,370 |
def create3DMatrix(data, trialTable, events, trialList, trialDur, fs, normalize, baselineDur=0.1):
"""
"""
trials = trialTable.copy()
trials = trials[trials['trialNum'].isin(trialList)]
totalTrialNum = np.max(trials['trialNum'])
m = trials.shape[0]
print m, totalTrialNum
electrodeNumber... | 1627f506a03bb7f07c30c6c70581ac40bd64d7fc | 3,633,371 |
def followed_list(username):
"""关注列表
"""
current_user = models.get_current_user()
user = models.get_user(username=username)
page = request.args.get('page', 1, type=int)
followed_list = user.followed.paginate(page, error_out=False)
user_list = [i.followed for i in followed_list.items]
ret... | 5634ce5e7fc6b344f7ad2b3461e9f294cee225d3 | 3,633,372 |
def add_edge_degree(graph, k=3):
"""
Add k edges to defend based on top edge degree centrality entries :cite:`tong2012gelling`.
:param graph: an undirected NetworkX graph
:param k: number of edges to add
:return: a list of edges to add
"""
info = defaultdict(list)
info['added'] = get_c... | eded75ffc4eabe155124fe95023694df367f3d01 | 3,633,373 |
def number_of_fishers():
""" Prompt the user for the number of fishermen entering the draw."""
try:
number = int(input("How many fishermen will enter the competition: "))
return number
except ValueError:
print("Please enter an integer for the number of competing fishermen") | bd3ff25865d67851c8a1742a8cfa808a317716f0 | 3,633,374 |
def plot_histogram(df, x, bins, xlabel=None, ylabel=None, title=None, figsize=(8, 5)):
"""
"""
fig = plt.figure(figsize=figsize)
ax = fig.gca()
ax.hist(df[x], bins=bins, color='#8d1a93')
ax.set_xlabel(xlabel, fontsize=16)
ax.set_ylabel(ylabel, fontsize=16)
ax.set_title(title, fontsize ... | 42801361386edea3065974bdca2a589be0b964ef | 3,633,375 |
def pygmo_gaco(
criterion,
x,
lower_bounds,
upper_bounds,
*,
population_size=None,
batch_evaluator=None,
n_cores=1,
seed=None,
discard_start_params=False,
#
stopping_max_iterations=STOPPING_MAX_ITERATIONS_GENETIC,
kernel_size=63,
speed_parameter_q=1.0,
oracle=... | f8cf4e423b928393e4c028412da4f302376fb248 | 3,633,376 |
def get_project_by_id(project_id: str) -> Project:
"""
Get a project by its project_id, with project model and project data joined.
:param project_id: project id of the project
:return: Project with the project id
"""
query = (
Project.select(Project, ProjectModel, ProjectData)
... | 2a8986c8e2541f43d30bee2cb1bcac14df68b716 | 3,633,377 |
def hist_similarity(image_1, image_2):
"""color hist based image similarity
@param image_1: np.array(the first input image)
@param image_2: np.array(the second input image)
@return similarity: float(range from [0,1], the bigger the more similar)
"""
if image_1.ndim == 2 and image_2.ndim == ... | 76358cff7b3a33f44fecefd289805a1fea88e1c4 | 3,633,378 |
def slotter_obj():
""" Return basic slotter object """
return Slotter() | f4b8805c8ca26bfc22da79b49a9c287c35428f86 | 3,633,379 |
def get_ncbi_enrichment_domains():
""" Find all domains matched to given node id, then return dictionary with all domains as
result. All domains should have matching indices e.g. regulon[1] should be data from
matching same node as uniprot[1].
"""
# TODO: Validate incoming data using webargs... | 2a60168fe953b00aca1870deb1692e018d9297ac | 3,633,380 |
def gaussian(wavelength, w, sigma, amp=1., norm=True):
"""
Computes a gaussian for a given central wavelength, sigma and amp
.. math::
G = \\frac{A}{\sigma \sqrt{2 \pi}} \exp{\left( \\frac{ (w - w_0)^2 }{2 \sigma^2 } \\right) }
Args:
wavelength (np.ndarray): wavelength array to cal... | 7e757691fbe27641a4cfd983678dd0ccd2cbdfbd | 3,633,381 |
def summarize_samples(samples, run_parallel):
"""Back compatibility for existing pipelines. Should be replaced with summary when ready.
"""
return samples | 20c742e751f9ea1f783572f031fe144baf73293e | 3,633,382 |
def get_keywords(string):
"""Get keywords for a given string.
Args:
string (str): A string to get keywords for.
Returns:
(list): A list of keywords.
"""
keywords = string.lower().split(' ')
keywords = [x.strip() for x in keywords if x]
keywords = list(set(keywords))
retur... | 8d4e0781701dc3574583baf417c573967638e86f | 3,633,383 |
def calc_mass_loading_factor(OIII_results, OIII_error, hbeta_results, hbeta_error, hbeta_no_outflow_results, hbeta_no_outflow_error, statistical_results, z, header):
"""
Calculates the mass loading factor
eta = M_out/SFR
Using the calc_sfr.calc_sfr_koffee and the calc_mass_outflow_rate functions
... | 6504b328e749e98eb4b3612533701e562973d882 | 3,633,384 |
import os as _os
from ..io import json as _json
import _pickle
def _write_calccache(calc_cache, key_fn, val_fn, json_too=False, comm=None):
"""
Write `caclcache`, a dictionary of compact polys, to disk in two files,
one for the keys and one for the values.
This function can be called by multiple rank... | d18f913c61c025acb294bda6b48457f57319ff8f | 3,633,385 |
def truecircle(radius, rho):
"""Create a "true" circular mask with anti-aliasing.
Parameters
----------
samples : `int`, optional
number of samples in the square output array
radius : `float`, optional
radius of the shape in the square output array. radius=1 will fill the
rho :... | e721d99d99b89ca24637e20d9577b2725cc29525 | 3,633,386 |
def distancia(ponto1, ponto2):
"""
Calcula a distância entre dois pontos
"""
xdif = ponto2.getx() - ponto1.getx()
ydif = ponto2.gety() - ponto1.gety()
dif = (xdif**2 + ydif**2)**0.5
return dif | 36a980a1081133fb6496585c25cca5782ceef06d | 3,633,387 |
import time
def foo(x, sleep_time):
"""Dummy function for the tests"""
time.sleep(sleep_time)
return [{"type": "objective", "name": "objective", "value": x}] | 3d55a0b0776acec0badd10e38be724afc3015c2f | 3,633,388 |
def aug_ims(ims, fliplr=0, flipud=0, T=0):
"""Augment images with flips and transposition."""
ims_aug = np.array(ims, copy=True)
for i in range(len(ims_aug)):
if fliplr: # flip left right
ims_aug[i] = np.fliplr(ims_aug[i])
if flipud: # flip up down
ims_aug[i] = np.f... | 59f8c44f0efcb70c17f828351b0f59787c3dd677 | 3,633,389 |
def bt_search(btree, key):
"""基于二叉树查询操作"""
bt = btree
while bt is not None:
entry = bt.data
if key < entry.key:
bt = bt.left
elif key > entry.key:
bt = bt.right
else:
return entry.values
return None | 1b358087c10a4d0d6fe79b023340fafeafb81914 | 3,633,390 |
def make_reply(msgname, types, arguments, major):
"""Helper method for constructing a reply message from a list or tuple
Parameters
----------
msgname : str
Name of the reply message.
types : list of kattypes
The types of the reply message parameters (in order).
arguments : list... | 9c55089e1d6fe6b5a4345f444f2551a2c493f2e3 | 3,633,391 |
def empty_coord():
"""Return an empty coordinate tensor representing 1 residue-level pad character."""
coord_padding = np.zeros((NUM_COORDS_PER_RES, 3))
coord_padding[:] = GLOBAL_PAD_CHAR
return coord_padding | e4d8c4f24ebed354f5b083a4fc072e354c79a149 | 3,633,392 |
def grib_clone(msgid_src):
"""
@brief Create a copy of a message.
Create a copy of a given message (\em msgid_src) resulting in a new
message in memory (\em msgid_dest) identical to the original one.
\b Examples: \ref grib_clone.py "grib_clone.py"
@param msgid_src id of message to be cloned... | c01b3f626d11be8d218fdcd598e472c5fe748272 | 3,633,393 |
def layernorm(x, epsilon=1e-5, name='lnconv'):
"""Layer Normalization for conv. x must be [NCHW]"""
shape = x.get_shape().as_list()
with tf.variable_scope(name):
beta = tf.get_variable("beta", [1, shape[1], 1, 1], initializer=tf.constant_initializer(0.))
gamma = tf.get_variable("gamma", [1, ... | 9ae3bb3f6e0238de92f167bf45b05e3095541665 | 3,633,394 |
def create_graph_from_edges(edges):
"""
Create a graph from the `edges`
"""
G = nx.Graph()
for e in edges:
p1 = e[0]
p2 = e[1]
dist = LA.norm(np.array(p2) - np.array(p1))
G.add_edge(p1, p2, weight=dist)
return G | ac06c424fcfde720fbb4457c0baced0a0a41567d | 3,633,395 |
def far_field(frequency, radius, current, r, theta):
"""
Calculate the electric and magnetic far fields for a small circular loop.
:param r: The range to the field point (m).
:param theta: The angle to the field point (rad).
:param frequency: The operating frequency (Hz).
:param radius: The radi... | 29940432e3e4dbc427398a18e1d026d9e4c205c3 | 3,633,396 |
def rng_laplace(lambd=1, trunc=None):
"""
Generate random numbers from a Laplace distribution
Parameters
----------
lambd: float
The scale of the distribution
trunc: None, tuple
Specifies whether the distribution is truncated. If it's
not None then it must be a 2-tuple i... | d4ad9c23c5edef20babe59671e0464ef70ed74ae | 3,633,397 |
from typing import Sequence
from typing import Dict
from typing import List
def settings_to_connections(
settings: amicus.options.Configuration,
suffixes: Sequence[str]) -> Dict[str, List[str]]:
"""[summary]
Args:
settings (amicus.options.Configuration): [description]
suffixes (Sequen... | e2ec469014d5d26848d011feda717191ae8c452b | 3,633,398 |
def radec_from_pointing_object(pointing,
# default output in degrees
as_radians=False,
as_string=False):
"""Astropy object to ICRS format as strings"""
pnt_radec = pointing.transform_to(ICRS())
if as_string:
... | 8cdd5c6671ccfd12624df88dfdfce0f6e0f41856 | 3,633,399 |
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