content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def _detects_peaks(ecg_integrated, sample_rate):
"""
Detects peaks from local maximum
----------
Parameters
----------
ecg_integrated : ndarray
Array that contains the samples of the integrated signal.
sample_rate : int
Sampling rate at which the acquisition took place.
... | ab9dd461f65095f048942ab8b8c069c456ea4933 | 59,306 |
def FixedRate_ReadFileAdapter_Carousel(readmode = "bytes", **rateargs):
"""A file reading carousel, that reads at a fixed rate.
Takes filenames on its inbox
"""
return Graphline(RC = RateControl(**rateargs),
CAR = ReusableFileReader(readmode),
link... | b25735924841c61507e205e7249501dd849dfe70 | 59,307 |
def get(id, user=None):
"""# Retrieve a specific DictKey
Receive a single DictKey object by its id
## Parameters (required):
- id [string]: DictKey object unique id and PIX key itself. ex: "tony@starkbank.com", "722.461.430-04", "20.018.183/0001-80", "+5511988887777", "b6295ee1-f054-47d1-9e90-ee57b74f60... | f7cfc8bb94a1bdec437ce4387285de0f08c16c21 | 59,308 |
import re
def pascal2title(string: str) -> str:
"""Splits on whitespace between capital and lowercase letters."""
# https://stackoverflow.com/a/29922050
return " ".join(re.findall(r"[A-Z](?:[a-z]+|[A-Z]*(?=[A-Z]|$))", string)) | c91019552d1d0a3e58b65b3d2d84ec78a48fec52 | 59,309 |
def _topological_sort(data, head, top_node, raise_exception=False, result=None, visited=None):
""" Internal function """
if not result:
result = []
if not visited:
visited = []
deps = data.get(head, list())
if head in visited:
if head == top_node and raise_exception:
... | e20d4a62278b978489d708a77324a443b6199c5c | 59,310 |
from typing import List
from typing import Tuple
async def _compile_filters_devs(for_sets: List[ForSetDevelopers],
request: AthenianWebRequest,
account: int,
meta_ids: Tuple[int, ...],
) -> ... | 2ce1491dd606c3c718977d41ca25ec97f8fc5f3e | 59,311 |
import copy
def flatten_samplesets(samplesets):
"""
Takes a list of SampleSets (with one or multiple samples in each SampleSet)
and returns a list of SampleSets (with one sample in each SampleSet)
Parameters
----------
samplesets: list of SampleSets
Input list of SampleSets (with one o... | d89c5b58db69caa7570088fbfae4d7e414c5b9af | 59,312 |
import torch
def center_conv_point(bboxes, kernel_size=3, c_min=0, c_max=1, v3_form=False):
"""In a parallel manner also keeps the gradient during BP"""
#bboxes.clamp_(min=c_min, max=c_max)
if v3_form:
base = torch.cat([bboxes[:, :2][:, (1, 0)]] * (kernel_size ** 2), dim=1)
else:
base ... | a5b14c544525672f8f47fbdbb31a1ccb96ff8e8b | 59,313 |
def lambda_curry2(func):
"""
Returns a Curried version of a two-argument function FUNC.
>>> from operator import add
>>> curried_add = lambda_curry2(add)
>>> add_three = curried_add(3)
>>> add_three(5)
8
"""
"*** YOUR CODE HERE ***"
return lambda x: lambda y: func(x,y) | bb524c02756c57dcb82ce9dbf722113b7314871d | 59,314 |
def get_trailer_offset(line_list):
"""Determine the line number where coordinates end."""
i = 0
for i, line in enumerate(line_list):
if match_trailer.match(line):
break
return i | b87c73baecafd829ebde353c6dd3bae35f8a13bb | 59,316 |
def cosine_similarity_sklearn(x, y):
"""
:param x:
:param y:
:return:
"""
result = cosine_similarity(np.array(x).reshape(1, -1), np.array(y).reshape(1, -1))
return result[0][0]*100 | d5c2d2b0998787a58cc202bb8dde96fda38aeb27 | 59,317 |
def calcPubChemFingerPart2(mol):# 116-263
""" *Internal Use Only*
Calculate PubChem Fingerprints （116-263)
"""
bits=[0]*148
bits=func_1(mol,bits)[1]
bits=func_2(mol,bits)[1]
bits=func_3(mol,bits)[1]
bits=func_4(mol,bits)[1]
bits=func_5(mol,bits)[1]
bits=func_6(mol,bits)[1]
... | cf0d47671544bd9ff1db8339e28a1214e15b5493 | 59,318 |
def auto_type_convert(value):
"""Try to convert 'value' to a int, float, or bool. Otherwise leave
as a string. This is done recursively with complex values."""
if value is None:
return None
if isinstance(value, list):
return [auto_type_convert(item) for item in value]
elif isinstan... | 1fe873dca7017d25128c30cc5144c3cf890319a1 | 59,319 |
def norm_get_dly():
"""Get all daily climate normals data"""
prcp = norm_get_dly_prcp()
snow = norm_get_dly_snow()
tavg = norm_get_dly_tavg()
tmax = norm_get_dly_tmax()
tmin = norm_get_dly_tmin()
by = ["id", "month", "day"]
df = pd.merge(prcp, snow, how="outer", on=by)
df = pd.merge(... | 1894ca46bbc1853fc2d0952a85e36a62c64a3a85 | 59,321 |
def allowed_with_lowinverse(solution: Solution, other: Solution) -> bool:
"""Returns True if other can be combined with solution; Otherwise, False.
Args:
solution (Solution): a Solution with rule=Rule.Lowinverse.
other (Solution): a Solution.
Requires:
1. solution must have rule=Ru... | af6a8ee81079a4e57ecb7e57d9ff992b20aaadb0 | 59,322 |
import requests
def _create_upload_url(auth_control, name, size, ht_space='user',
ht_space_id=None, ht_id_path=ID_ROOT_PATH,
metadata=None):
"""
Create an upload URL to use to upload a file.
Parameters
----------
auth_control
HTAuthorizationCo... | 7f5d67f663dbca71f995fd3a0b7ac6c006000e97 | 59,324 |
def string(commands, data, node):
""" Компиляция выражения "строка" """
commands.add(Push, 0)
for character in node.characters:
commands.add(Push, ord(character))
commands.add(Push, len(node.characters))
StringCompiler.store(commands, data)
return commands.set_and_return_type(Types.STR... | eb9ca1a6774553918e285ffc39bea6fc8515c922 | 59,325 |
import json
def master_serializer(resource):
"""Function to return a resource json"""
data = resource.serialize()
user_json = json.dumps(
data, default=datetimeconverter, sort_keys=True
)
return user_json | 1d1ea730a0bb54963960016d945d09d871122dbd | 59,326 |
import itertools
def normalise_text(text):
"""
Removes leading and trailing whitespace from each line of text.
Removes leading and trailing blank lines from text.
"""
stripped = text.strip()
stripped_lines = [line.strip() for line in text.split("\n")]
# remove leading and trailing empty li... | 52ab40cfa4c39ed194fa71aa28d70f5fa737d70c | 59,327 |
def global_precision(actual, predicted, k = 5):
""" Computes global precision for recommendation systems using actual items and predicated items at k
If the argument `k` isn't passed in, the default k 5 is used.
Parameters
----------
actual : list
A list of lists of... | b3f0f78e04aa5595cea9ee2789278c0372c86e28 | 59,328 |
def export_module_part(request, pk):
""" Creates an excel sheet that contains all tests that are in a course. Used mainly to download sign off excel sheets.
:param request: Django request
:param pk: Module part ID
:return: A file response containing an .xlsx file.
"""
module_part = get_object_... | 70b443314c7c041231ab0f3dddb145990ccaa8d3 | 59,329 |
import re
import string
def stdtextpreprocessing(text):
""" Desc: Text preprocessing of social media text using
typical text cleaning methods such as lowercasing, removal of mentions, etc.
Input: text (string) - text to be cleaned
Output: text (string) - cleaned text """
# Instantiate wordnet le... | b7319ddca4c629aa77e34cd4220cc00b8c9bf0d0 | 59,330 |
def dedispersion_plan(nchan, dmmin, dmmax, start_freq, bandwidth, sample_time):
"""
Examples
--------
>>> tDM = dedispersion_plan(10, 0, 10, 1400, 128, 0.0005)
>>> np.isclose(tDM[0], 0)
True
>>> np.isclose(tDM[-1], 10., atol=1)
True
"""
# dfreq = bandwidth / nchan
stop_freq =... | 2cb09d31cd581e10593710c48096aac0e9bc1e65 | 59,331 |
def RMSpropConfig(argument_parser):
"""
Set CLI arguments
:param argument_parser: argument parser
:type argument_parser: ```ArgumentParser```
:returns: argument_parser
:rtype: ```ArgumentParser```
"""
argument_parser.description = """Implements RMSprop algorithm.
Proposed by G. Hinton... | 733faf8005c5260ee2443fe6d42bfb85c3ba5979 | 59,332 |
def create_from_input(fluid: Fluid, filename=""):
"""Adds Density and Velocity to a Fluid from an input file.
Args:
fluid (Fluid): The Fluid object to be modified.
filename (str, optional): The name of the file to be read without the extension. Defaults to "".
Returns:
[str, str, l... | 3c4a3f99cb26b12d40185b3ac268e033bd66a0d7 | 59,333 |
def remove_stale_events(data):
"""
Parse an event dictionary and remove transitions to the same state:
.. code::
event_dict = {
(entity_id, cohort interval) : [(time, state), ..., (time, state)]
(entity_id, cohort interval) : (time, state), ..., (time, state)]
}
:para... | be80965efc7077e06d6e9b2ab5fb8ddc65df79a9 | 59,334 |
def get_L_dashdash_ba2_d(L_dashdash_ba2_d_t):
"""# 1日当たりの浴槽追焚時における太陽熱補正給湯負荷 (MJ/d)
Args:
L_dashdash_ba2_d_t(ndarray): 1時間当たりの浴槽追焚時における太陽熱補正給湯負荷 (MJ/h)
Returns:
ndarray: 1日当たりの浴槽追焚時における太陽熱補正給湯負荷 (MJ/d)
"""
return np.sum(L_dashdash_ba2_d_t.reshape((365, 24)), axis=1) | 0ba4ef903ef69cb755eeee657e1eb9d6c72f6ead | 59,335 |
def im2col_indices(x, field_height, field_width, padding=0, stride_y=1, stride_x=1):
""" An implementation of im2col based on some fancy indexing """
# Zero-pad the input
p = padding
x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant')
k, i, j = get_im2col_indices(x.shape, field_height, fi... | 717fae3eec42b5514f912be8abb71cc8b0f2c821 | 59,336 |
import sqlite3
def sqlite3get_table_names(sql_conn: sqlite3.Connection):
"""For a given sqlite3 database connection object, returns the table names within that database.
Examples:
>>> db_conn = sqlite3.connect('places.sqlite')\n
>>> sqlite3get_table_names(db_conn)\n
['moz_origins', 'm... | 0cb1ba2ba475268d39fef8bdbe044436f51c4f07 | 59,339 |
def add_eDOO_to_summary(df, delay_don=3, delay_dpl=2, blur=1):
"""Add eDOO column - estimated DOO - to summary dataframe.
Also add sDON and sDPL columns, which are estimated shifted
DON/DPL values (with blurring).
Parameters:
- delay_don: assumed delay (days) from date of notification -> day of o... | 7331f559d9933c617b044c1bba695c3435ba4215 | 59,340 |
from operator import and_
def vulns_by_product(vendor_id=None, product_id=None):
"""View vulns associated to product."""
if product_id is None or product_id is None:
return abort(404)
nvd_ids = (
db.session.query(Cpe.nvd_json_id)
.filter(and_(Cpe.vendor == vendor_id, Cpe.product ==... | 51041bdcb193b956deac619176e70b6e08a76912 | 59,341 |
def calc_hcore_matrix(Tuv_, Vuv_):
"""
calc_hcore_matrix - Computes the 1 electron core matrix
Arguments:
Tuv_: The Kinetic Energy 1e integral matrix
Vuv_: The Nuclear Repulsion 1e integrals matrix
Returns:
h_core: The one electron hamiltonian matrix
"""
h_core = Tuv_ +... | 5c2dbd61b938a235f6c11b7647e157777fa18428 | 59,342 |
def _fit_lines(spectrum, model, fitter=fitting.LevMarLSQFitter(),
exclude_regions=None, weights=None, window=None):
"""
Fit the input model (initial conditions) to the spectrum. Output will be
the same model with the parameters set based on the fitting.
spectrum, model -> model
Par... | 84bddc75c562bf9df44269c9b687a0212b512f8e | 59,343 |
def rivm(path="rivm/report.csv", **kwargs):
""" DataFrame: Daily new cases from RIVM (Netherlands) PDF report. """
kwargs.setdefault('parse_dates', ['date'])
kwargs.setdefault('index_col', 'date')
return (
read_csv(DATADIR / path, **kwargs)
.rename_axis(None).sort_index(axis=1)
... | d7117d9d33009c0f2915149c1f9db68913ceb1cb | 59,344 |
def vector(x, y, z):
"""
Make a 3-vector from components
x, y, z : vector components
"""
return tf.stack([x, y, z], axis=0) | 4e7ddb6a9f0de0ddfc8f72e4a874465d0075e080 | 59,345 |
def _to_sequence_example(image, decoder, vocab, config):
"""Builds a SequenceExample proto for an image-caption pair.
Args:
image: An ImageMetadata object.
decoder: An ImageDecoder object.
vocab: A Vocabulary object.
Returns:
A SequenceExample proto.
"""
with tf.gf... | cdd8a0d6a1ec3a16e46a0dd2ab9828754f190f26 | 59,346 |
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
tok_answer_text = " ".join([sw for w in whitespace_tokenize(orig_answer_t... | 10ed5493260ba9f2fba51d3f0b3509ee35e4f684 | 59,347 |
def _step_const_v_1d(dt, x0, v, x_lim):
"""
Evaluate daisy pattern step at `x0` with step `dt`.
Parameters
----------
dt :
The step size.
x0 :
The initial position, signed.
v :
The velocity, signed
x_lim :
The limit of x such that -x_lim < x < x_lim.
... | 36dfd7cff56d3e69d58a121b6977080ebd3c8b00 | 59,348 |
def get_confusion_matrix_intersection_mats(groundtruth, predicted):
"""
Returns a dictionary of 4 boolean numpy arrays containing True at TP, FP, FN, TN.
"""
confusion_matrix_arrs = {}
groundtruth_inverse = np.logical_not(groundtruth)
predicted_inverse = np.logical_not(predicted)
confusion... | ad7a3351f18a412894ce6261f7cba57acc90bb18 | 59,349 |
def run_command(runtime, timeout=0.01):
"""
Run a command, read stdout and stderr, prefix with timestamp. The returned
runtime contains a merged stdout+stderr log with timestamps
http://stackoverflow.com/questions/4984549/merge-and-sync-stdout-and-stderr/5188359#5188359
"""
PIPE = subprocess.PI... | d66f8769e407417c24ffca74baf22368f760a233 | 59,350 |
from pathlib import Path
def parse_uri_path(uri: str) -> str:
"""Parse a URI starting with 'file:///' to a posix path."""
return Path(url2pathname(urlparse(unquote(uri)).path)).as_posix() | d5a35fad0388d2b534f871d985a590159b3f1927 | 59,351 |
def make_key_frames():
"""
make key frames for the regions
"""
key_frames = {}
key_frames[0] = [100, 200, 300]
key_frames[1] = [50, 150]
return key_frames | 3b551b7bb7fb22fab8e35abc54b2c524bdfa36fd | 59,352 |
from typing import Dict
def get_registered_commands() -> Dict[ str, Command ]:
"""
Gets the registered commands.
"""
global _lock, _plugin_importer
with _lock:
if _plugin_importer is None:
_plugin_importer = PluginImporter( plugins )
_plugin_importer.import_module... | 39eafb0c13f1c1face66e7bbcc72943a8ec89291 | 59,353 |
def _get_cov_blocks_ordering(probes):
"""
Utility function to get the ordering of the covariance matrix blocks
"""
cl_index = _get_cl_ordering(probes)
def find_index(a, b):
if (a, b) in cl_index:
return cl_index.index((a, b))
else:
return cl_index.index((b, a... | 660d255c6426172486914a0efe2d53f502b0e43c | 59,354 |
def use_uber_files(ctx, section_name, option_name, value):
""" Configure the usage of UberFiles """
if not _is_user_input_allowed(ctx, option_name, value):
Logs.info('\nUser Input disabled.\nUsing default value "%s" for option: "%s"' % (value, option_name))
return value
info_str = ['UberFil... | 5dc44c03268dbacca11132f01623653088922269 | 59,355 |
from typing import List
def _generate_list_of_keywords_to_prepend(
keywords_visible_to_user: List[str],
keywords_not_visible_to_user: List[str], title: str,
language: str) -> List[str]:
"""Determines which performant keywords need to be prepended and sorts them.
Args:
keywords_visible_to_user: ke... | ba9e1811d75a75bbee465f663706b3226f1f67f5 | 59,357 |
import re
def cal_re(raw_trajs: list, final_trajs: list, min_latitude: float, min_longitude: float,
len_latitude: float, len_longitude: float, epsilon: float) -> float:
"""
calculate RE
Args:
raw_trajs : original trajectory
final_trajs : generated trajectory
min_la... | 07b34a7a932dcb47c0c05214cc3b31db1eac4160 | 59,359 |
def initialize_empty_command(help=None, formatter_class=None): # pylint: disable=redefined-builtin
"""
Initialize empty command with script for tests
"""
script = Script()
command = EmptyCommand(script)
# pylint: disable=redefined-builtin
script.add_subcommand(command, help=help, formatter... | f7d37c9cdc67138c8497abc48ffecb1337bd9dd8 | 59,360 |
from typing import List
from typing import Any
def sort(xs: List[Any]) -> List[Any]:
"""
Sort the list `xs` with the python `sort` function
and return the sorted function. The given list is not modified.
Parameters
----------
xs
A list.
Returns
-------
xs_sorted
... | 706190a533c19b6a7ea4baf540823ca73406560b | 59,361 |
def _parse_chained_types(ctxt, node):
# type: (errors.ParserContext, yaml.nodes.MappingNode) -> List[syntax.ChainedType]
"""Parse a chained types section in a struct in the IDL file."""
chained_items = []
field_name_set = set() # type: Set[str]
for [first_node, second_node] in node.value:
... | 605fa7c45fcce3dbc3e7bb1aa37d3648553ea636 | 59,362 |
def sameQ(v1, v2) -> bool:
"""Mathics SameQ"""
return get_type(v1) == get_type(v2) and v1 == v2 | f7e00876cafeca6955a03f2e8836d7e76e87b60a | 59,363 |
def read_from_file(file: str) -> list[list[str]]:
"""Read row data from the selected file."""
with open(file, 'r', encoding='utf-8') as read_file:
csv = reader(read_file)
return [row for row in csv] | f55da44817eea8c7fa01883947dead57c7a8b52e | 59,364 |
def response_200(result=None):
"""GET에 대한 반환"""
return {
'msg': 'success',
'result': result
}, 200 | 2c2acbfed5ee678536671a367c67cc7d0b1fb6ba | 59,365 |
def get_all_players():
"""Returns a Dict with all players"""
return {x.player_name: x for x in soco.discover()} | 329ec97a1a91f62e5c2aec080c010736cff97336 | 59,366 |
def zernike(img, radius, degree=8, cm=None):
"""Zernike moments.
This geometric moment derivate is based on alternative orthogonal
polynomials, which makes it more optimal wrt. information redundancy. These
are invariant to rotation.
"""
img = np.asarray(img, dtype=np.float32)
assert img.nd... | dc206872a04900e553e83b52634fb71e13de6d46 | 59,367 |
def _list_agave(uri, agave):
"""
List contents of agave URI.
Args:
uri: parsed URI to list.
agave: dict that contains:
agave_wrapper: Agave wrapper object.
Returns:
On success: a list of filenames (basenames only).
On failure: False.
"""
file_list =... | 4c721f710b26687926a613a2346b19fc8fe18cf6 | 59,368 |
def overview_of_specific_year(request, year):
"""
Get the overview of a year
"""
data_of_year = DATA.get(str(year))
context_data = {}
if not data_of_year:
return Response({'error': f'Data not found for the year {year}'}, status=status.HTTP_400_BAD_REQUEST)
else:
context_data[... | fa1b67b75947e34fd85b00b46febc500d39a7554 | 59,369 |
import ast
def unparse(tree: ast.AST):
"""`ast.unparse`, but cached."""
return ast_unparse(tree) | 0690df059cb6c5f16c401e6474ba8bda6f9c8a6a | 59,371 |
def limit():
"""The number of corpus files to process, e.g ``--limit 100`."""
return '--limit 50' | 45638e81c16cc9a89a7137566581b43d7e952f46 | 59,372 |
import regex
def len_img_block(string):
""" Length of image html blocks from a string. """
all_oc = regex.findall(r'<img\s[^<>]*+>', string)
tot_len = 0
for oc in all_oc:
tot_len += len(oc)
return tot_len | 73caeed3112164a4259a38b92b2041a8a0c6dfe4 | 59,373 |
def detect_noise(sp, spt, sp_win, type="positive", max_spikes=None,
resample=1):
"""Find noisy spikes"""
spike_waves = extract.extract_spikes(sp, spt, sp_win)
if type == "positive":
threshold = calc_noise_threshold(spike_waves, 1)
spt_noise = extract.detect_spikes(sp, threshold... | f3159ef0ae7abd1612e04acaf9f9542bdce0baf5 | 59,374 |
import itertools
def check_package_attributes(container, additional_requirements=[]):
"""
container may be a ConfigurationDescription or MetadataDescription
additional_requirements are context-specific attributes to be required
Returns an AttrErrorString of problems found. Caller can treat this like a... | 064cc6e1aec3fcdf00c5e84fd187239d1c93d27b | 59,375 |
def formatArrayTrinary(*arr, **kwargs):
"""Converts all elems to -1 (if elem < 0), 0 (if elem == 0), or 1 (if elem > 1) then formats the results as densely as possible.
"""
defaults = dict([
('delimiter', ','),
('fmt', '%1d'),
('newline', '\n'),
('squeeze', False),
... | b0e75db41ecd8c44da5c6c6eeb1f788335bc282b | 59,376 |
def protected_dict_attribute(
types=(),
subtypes=False,
checked=None,
module=None,
factory=None,
serialized=None,
serializer=None,
deserializer=None,
represented=True,
key_types=(),
key_subtypes=False,
key_factory=None,
child=True,
history=None,
data=None,
... | b0e70405d039e479b06b7d7f58edb7a271b82a06 | 59,377 |
def getAllV2ELBs():
"""
This function grabs each v2 elb from each region and returns
a list of them.
"""
regions = boto3.client('ec2').describe_regions()['Regions']
logger.info(f"searching {len(regions)} region(s) for modern load balancers.")
# get list of all load balancers in each region
... | 9dcfe50a64d6e71af8b7e5800801604c03fbd06a | 59,378 |
def class_is_meter_band(cls):
"""
Return True if cls_name is an instruction object
"""
return _unified_by_name(cls).is_instanceof("of_meter_band") | c417d3e09e1868ed68c838e6325341265cb93726 | 59,380 |
import re
def GetRepositoryFromEbuildInfo(info):
"""Parse output of the result of `ebuild <ebuild_path> info`
This command should return output that looks a lot like:
CROS_WORKON_SRCDIR=("/mnt/host/source/src/platform2")
CROS_WORKON_PROJECT=("chromiumos/platform2")
"""
srcdir_match = re.search(r'^CROS_... | dbc609f7d2c56f0d5e231cb0a1e2eb2ab7659f44 | 59,381 |
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
... | ac40034800e4b87670cb8e1d1ea508edcd178162 | 59,382 |
def add_pacient(ime, priimek, cepivo=None):
"""Funkcija v bazo vstavlja novega pacienta za sprejem v bolnišnico."""
return None | 18d302d4a926d7af194850f68425137f5ef48968 | 59,383 |
def cloud_shadow_confidence(arr):
"""
00 = "Not Determined" = Algorithm did not determine the status of this condition
01 = "No" = Algorithm has low to no confidence that this condition exists (0-33 percent confidence)
10 = "Maybe" = Algorithm has medium confidence that this condition exists (34-66 perc... | 7b5afe6a5787b6ef03b3acbf7ef2abaa81497282 | 59,385 |
from datetime import datetime
def load_sequence_and_metadata(kind="csv"):
"""
Returns the sequences as a list of SeqRecords, and metadata as a pandas
DataFrame.
"""
starttime = datetime.now()
sequences = [
s for s in SeqIO.parse("data/20170531-H3N2-global.fasta", "fasta")
]
if ... | 3c68327040debf0ca897185cdd5a63bf64c62ac9 | 59,386 |
def itmMatchingThree_collate(inputs):
"""Text-Image-Audio Matching dataset collate function"""
(input_ids, img_feats, img_pos_feats, attn_masks, audio_feats
) = map(list, unzip(inputs))
# text batches
txt_lens = [i.shape[0] for i in input_ids]
input_ids = pad_sequence(input_ids, batch_first=Tru... | 3226805e1c25ec5db0ca19e9f7f233a9be4f65ea | 59,387 |
def R_ratio_local(omega_par, disp_vector, passive_el, ind_passive, coord, connect, E, v, rho, const_func, aux_R=True):
""" Calculates the local strain-to-kinetic energy ratio function.
Args:
omega_par (:obj:`float`): 2 * pi * frequency.
disp_vector (:obj:`numpy.array`): Displacement.
pa... | 64eabbf73175f2fb65cd966ad7b98827b856bf2b | 59,388 |
def align(sentence, span):
"""align. Searches for span in sentence, to create character offsets
Uses heuristics if the span happens more than once in a sentence.
Prefers the earliest span that also ends on a word boundary, which
is defined as a non-alpha numeric character (e.g. a space character)
r... | eeb9e9b3aabca4c3b39efc4bd24c42547cb2e754 | 59,389 |
def profile(request, username):
"""
This function sets a profile.
:param request: A request
:param username: A user name
:return: A render
"""
user = User.objects.get(username=username)
treasures = Treasure.objects.filter(user=user)
return render(request, 'profile.html', {'username':... | 311c827703197f82a9523b9ffe5dfbca64e8d485 | 59,391 |
def derivatives_close(x, y):
"""
Returns True iff the AffineScalarFunc objects x and y have
derivatives that are close to each other (they must depend
on the same variables).
"""
# x and y must depend on the same variables:
if set(x.derivatives) != set(y.derivatives):
return False ... | 5555348253ce7e64a348c41ce9b65517b985bf5b | 59,392 |
from re import M
def get_norm(norm):
"""
Args:
norm (str): currently support "BN", "SyncBN", "FrozenBN" and "GN"
Returns:
M.Module or None: the normalization layer
"""
if norm is None:
return None
norm = {
"BN": M.BatchNorm2d,
"SyncBN": M.SyncBatchNorm,... | 80bdc5b1327f6718ddb371f2bda4c2ffc179077f | 59,393 |
def extraerWrfoutSerie(wrf_file: str, variable: str, x: int, y: int):
""" extrae de los arechivos wrfout listados en file_paths
para la posicion (x, y) toda la serie de la variable
seleccionada"""
dfData = pd.DataFrame()
t2 = wrf.getvar(wrf_file, variable, timeidx=wrf.ALL_TIMES)
t2_loc = t2[:,... | 445034862c38dbbb23803354e35114208af8ad8b | 59,394 |
def grad_logloss_theta_lr_weighted(label,ypred,x,weight_ar=None,C=0.03,has_l2=True,scale_factor=1.0,weights=None):
"""Return d l_i / d_theta = d l_i / d_ypred * d y_pred / d theta
Args:
label: a scalar (one sample) or an array of shape [None,]
ypred: a scalar (one sample) or an array of shape [N... | 9d718a9b46648f257a30192cee0eb5905d87802a | 59,395 |
import pickle
def read_solver(filename):
"""
This function reads a .pk1 file.
Parameters:
filename (str): A file name or path to file excluding file extension.
Returns:
Content of the loaded .pk1 file.
"""
try:
file = open(filename+'.pk1', 'rb')
except IOError:
... | da7bcfb75af1edde3d43091fdbc5c1829c13e5e0 | 59,396 |
def chi_square(logu, name=None):
"""The chi-Square Csiszar-function in log-space.
A Csiszar-function is a member of,
```none
F = { f:R_+ to R : f convex }.
```
The Chi-square Csiszar-function is:
```none
f(u) = u**2 - 1
```
Warning: this function makes non-log-space calculations and may therefo... | 9c221e7e9d0c08a671b00c6809014a42d04031bd | 59,397 |
def get_ir_grgrid_map(rotations, D_diag, PS=None):
"""Return grid point index mapping table to irreducible grid points
(Generalized-regular-grid version)
Parameters
----------
rotations : array_like
SNF transformed reciprocal rotation matrices. See some more detail
in the docstring... | 8d945ffaaefeb633446dc5ca23da41bc468e01c7 | 59,398 |
def schurs_complement(H, g, m, r, precond=False):
""" Shurs-complement """
assert H.shape[0] == (m + r)
# H = [Hmm, Hmr
# Hrm, Hrr];
Hmm = H[0:m, 0:m]
Hmr = H[0:m, m:]
Hrm = Hmr.T
Hrr = H[m:, m:]
# g = [gmm, grr]
gmm = g[1:]
grr = g[m:]
# Precondition Hmm
if precond:
Hmm = 0.5 * (H... | 08d14e0ab5a275230ca926ce20a5ba7bb7cc8c71 | 59,400 |
def markdown(text):
"""
Return match object if text is Markdown-formatted, otherwise None.
@param text: The message to check for Markdown formatting.
@type text: L{str}
"""
return mdre_code.match(text) or mdre_reply.match(text) or \
mdre_mono.search(text) or mdre_link.search(text) or \
... | a658a2932e952deebe140b9369e50a77fe056813 | 59,401 |
def neighborSelect(a, x, y):
"""
finds (local) minima in a 2d grid
:param a: 1d array of displacements from the source positions
:type a: numpy array with length numPix**2 in float
:returns: array of indices of local minima, values of those minima
:raises: AttributeError, KeyError
"""
... | 283066456ab5e9d93402ba24e1623e9d99ef17d2 | 59,402 |
from typing import Optional
from typing import Set
def extract_active_themes(raw_themes: str, graph: dict) -> Optional[Set[str]]:
"""Safely parse and extract theme uuids."""
if raw_themes != constants.ALL_THEMES:
active_themes = set()
candidates = [
x.strip()
for x in c... | 5144361ebee59038c97afe5d44b76f7e73ed7a93 | 59,404 |
def hash_password(plain_text_password, salt=None):
"""Convert a plain text password to salted/hashed form."""
plain_text_password = plain_text_password.encode('utf-8')
if salt is None:
salt = bcrypt.gensalt()
hashed_password = bcrypt.hashpw(plain_text_password, salt)
return hashed_password | 1fdb95265f535eac868c8486052b0803e9c71146 | 59,405 |
def pairwise_cos_similarity_np(features):
"""Compute the pairwise cosine similarity.
Args:
features: The input embeddings.
:return: The pairwise cosine similarity matrix.
"""
feature_norm = np.sqrt(np.sum(features ** 2, axis=1, keepdims=True))
features = features / feature_norm
retu... | f894666722ea05bd22007f641d1d1f4916d879fb | 59,407 |
def disqus_show_comments(context, shortname=''):
"""
Return the HTML code to display DISQUS comments.
"""
shortname = getattr(settings, 'DISQUS_WEBSITE_SHORTNAME', shortname)
return {
'shortname': shortname,
'config': get_config(context),
} | 6ba672b887c5dfb395558943d2110a8ee195b1ac | 59,408 |
def sentence_length_feature(sentences):
""" List of values from 0 to 1 rating the length of the sentence in comparation with the longest one """
sentence_length_feature_values = []
max_length_sentence = len(sentences[0].original.split(' '))
# Find the longest sentence
for sentence in sentences[1:... | 89e705a6b12b30f29352d70977bd80db45a14b6a | 59,409 |
def radar_coords_to_cart_track_relative(rng, rot, roll, drift, tilt, pitch):
"""
Calculate track-relative Cartesian coordinates from radar coordinates.
Parameters
----------
rng : array
Distances to the center of the radar gates (bins) in kilometers.
rot : array
Rotation angle o... | d44912656223c54e6f2e1c94e598b479ee6acfe8 | 59,411 |
def _get_agreement_violations(agreement_id, term=None):
"""
:rtype : list[wsag_model.Violation]
"""
violations_client = _get_violations_client()
violations, response = violations_client.getbyagreement(agreement_id, term)
return violations | f0b9e24731acda7233d6dd1dd80f5890641af897 | 59,412 |
def processHis(pdb,his_tautomers,default_his=2,
valid_his={"CD1":0,"ND1":1,"NE2":2}):
"""
Take the calc_param.his_tautomers and generate proper input for the
generation of an input file. Either:
1) An input file is specified; read the file but return an error if it
is mang... | 55f2d030168b97b749e613ef11de1912dfaa3d8a | 59,414 |
def serialize(selector: Selector) -> str:
"""
# Equality:
>>> serialize({"a": "b", "c__neq": "d"})
'a=b,c!=d'
# Inclusion:
>>> serialize({"a__in": {"x", "y"}, "b__notin": ("z",)})
'a in (x,y),b notin (z)'
# Existence:
>>> serialize({"a": Is.PRESENT, "b": Is.ABSENT})
'a,!b'
... | 4fb83bf019ab3dff7761d86d1878542f9f463782 | 59,415 |
def __retrieve_linear_data(location=None):
""" Testdata from intervention cases. Univariable (here: at one location)"""
subsample = list(map(int, np.loadtxt('{}/intervention/subsample.csv'.format(testdata_path), delimiter=",")))
predictor = pd.read_csv('{}/intervention/reference.csv'.format(testdata_pa... | 93727086814c0882bccec5e54a50fc72ea018eeb | 59,416 |
import inspect
def get_argument_help_string(param: inspect.Parameter) -> str:
"""Get a default help string for a parameter
:param param: Parameter object
:type param: inspect.Parameter
:return: String describing the parameter based on the annotation and default value
:rtype: str
"""
help_... | 7381591ce45591bf1aef62280fc4b98e57969576 | 59,417 |
def _inTkStep(window):
"""Return true if the TkStep patches have been applied to Tk."""
global _in_tkstep
try:
return _in_tkstep
except NameError:
try:
f = Frame(window, text='is TkStep installed?')
except TclError:
_in_tkstep = 0
else:
... | 62c6fd896677ac447f4e31d006fb9ffdcf536744 | 59,418 |
def _dask_array_fft_has_norm_kwarg():
"""returns True if dask.array's fft supports the norm keyword argument
"""
return False | 93e111839aa5a175ebcdb995193c7774c45e25ec | 59,420 |
import six
def generic_signature(name_tensor_map):
"""Creates a generic signature of name to Tensor name.
Args:
name_tensor_map: Map from logical name to Tensor.
Returns:
A Signature message.
"""
signature = manifest_pb2.Signature()
for name, tensor in six.iteritems(name_tensor_map):
signatu... | bf652746645348efa0ebabec2bb59c3190edef9a | 59,421 |
def preprocess(input_sentence, en_vocab, en_word2index, output_sentence = None, de_vocab= None):
"""
:param input_sentence: test sample of input language
:param en_vocab: English language vocabulary
:param en_word2index: word-to-index dictionary
:param output_sentence: target language sentence for c... | f1798a38bebb80b4ecad3a237648b3f1f6f2e33b | 59,422 |
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