content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def convert_coevalcube_to_sphere_surface_inpdict(inpdict):
"""
-----------------------------------------------------------------------------
Covert a cosmological coeval cube at a given resolution (in physical comoving
distance) to HEALPIX coordinates of a specified nside covering the whole sky
or... | e99f4ca3d6ff1a76ce95c4e929521ccf857148df | 3,637,734 |
def postmsg(message):
"""!Sends the message to the jlogfile logging stream at level INFO.
This is identical to:
@code
jlogger.info(message).
@endcode
@param message the message to log."""
return jlogger.info(message) | b7cad54650fd769ef9c56f8a03e68d0ef9fa485d | 3,637,735 |
def dec_lap_pyr(x, levs):
""" constructs batch of 'levs' level laplacian pyramids from x
Inputs:
x -- BxCxHxW pytorch tensor
levs -- integer number of pyramid levels to construct
Outputs:
pyr -- a list of pytorch tensors, each representing a pyramid level,
... | d0b48660b194c71e34e7f838525d0814081939fb | 3,637,736 |
def mif2amps(sh_mif_file, working_dir, dsi_studio_odf="odf8"):
"""Convert a MRTrix SH mif file to a NiBabel amplitudes image.
Parameters:
===========
sh_mif_file : str
path to the mif file with SH coefficients
"""
verts, _ = get_dsi_studio_ODF_geometry(dsi_studio_odf)
num_dirs, _... | 2defa9d0656bc6c884e6f0591041efdea743db95 | 3,637,738 |
import struct
import array
def write_nifti_header(hdrname, hdr, newfile=True):
#*************************************************
"""
filename is the name of the nifti header file.
hdr is a header dictionary. Contents of the native header
will be used if it is a nifti header.
Returns: 0 if no er... | 8b9239ff96d453f8bcb7a667e62434fa9f1bfbc6 | 3,637,739 |
import struct
def get_array_of_float(num, data):
"""Read array of floats
Parameters
----------
num : int
Number of values to be read (length of array)
data : str
4C binary data file
Returns
-------
str
Truncated 4C binary data file
list
List of flo... | 92a0a4cc653046826b14c2cd376a42045c4fa641 | 3,637,740 |
def AUcat(disk=None, first=1, last=1000, Aname=None, Aclass=None, Aseq=0,
giveList=False):
"""
Catalog listing of AIPS UV data files on disk disk
Strings use AIPS wild cards:
* blank => any
'?' => one of any character
"*" => arbitrary string
If giveList then r... | 501bb5a1eaa82fd162d17478f5bd9b14d8b76124 | 3,637,741 |
def process_threat_results(matching_threats, context):
""" prepare response from threat results """
threats = [ThreatSerializer(threat).data for threat in matching_threats]
response_data = {
"id": context.id,
"hits": threats,
}
status_code = status.HTTP_200_OK
if context.pending... | b6f763f1a2983967dd0ccc68237408bf3871f9ac | 3,637,742 |
def entropy_logits(logits):
"""
Computes the entropy of an unnormalized probability distribution.
"""
probs = F.softmax(logits, dim=-1)
return entropy(probs) | a9806dfbafbe77f74df55b81cc19603826e2d994 | 3,637,743 |
def convert_int_to_str(number: int, char: str = "'"):
"""Converts an ugly int into a beautiful and sweet str
Parameters:
nb: The number which is gonna be converted.
char: The characters which are gonna be inserted between every 3 digits.
Example: 2364735247 --> 2'364'735'247"""
number ... | ae8e2b0e4cc9a332e559e3128c440fff59cf6c78 | 3,637,744 |
def exists(index, doc_type, id, **kwargs):
"""
Returns a boolean indicating whether or not given document exists in Elasticsearch.
http://www.elastic.co/guide/en/elasticsearch/reference/current/docs-get.html
"""
res = request("exists", None, index, doc_type, id, **kwargs)
jsonprint(res)
retu... | fd5488acef16b22b0da7302345eab2de6073523c | 3,637,745 |
def deserialize_cookie(string):
"""Deserialize cookie"""
parts = string.split("#")
length = len(parts)
if length == 0 or length < 3:
return None
if not is_int(parts[2]):
return None
return create_internal_cookie(
unquote(parts[0]),
unquote(parts[1]),
pa... | 9887eb18c4cc91a13048b987ec962deb83a4da2b | 3,637,746 |
def choose(n, k):
"""This is a binomial coeficient nCk used in binomial probablilty
this funtion uses factorial()
Usage: choose(n, k)
args:
n = total number
k = total number of sub-groups """
try:
return factorial(n)/(factorial(k) * factorial(n - k))
except(ValueError, ZeroD... | 3e9fe5212a2ddf680fc6681c0a7d7bd1ec9a4de2 | 3,637,747 |
import grp
from typing import cast
def get_os_group(name: _STR_OR_INT_OR_NONE = None) -> grp.struct_group:
"""Get an operating system group object.
Args:
name (:obj:`str` or :obj:`int`, optional): The "group name" or ``gid``.
Defaults to the current users's group.
Raises:
OSE... | 6c359b46cdd2766cbdea7fb5412b1e03a3fbecac | 3,637,749 |
def _process_output(response, context):
"""Post-process TensorFlow Serving output before it is returned to the client.
Args:
response (obj): the TensorFlow serving response
context (Context): an object containing request and configuration details
Returns:
(bytes, string): data to r... | 19805fc9ce122b4c02a596167edbc01398dfa2ab | 3,637,750 |
from bs4 import BeautifulSoup
import requests
def make_soup(text: str, mode: str="url", parser: str=PARSER) -> BeautifulSoup:
""" Returns a soup. """
if mode == "url" or isinstance(mode, dict):
params = mode if isinstance(mode, dict) else {}
text = requests.get(text, params=params).text
el... | 9641a7a0807194c911614e2ac41551b04bdbe22d | 3,637,752 |
import ast
def _merge_inner_function(
class_def, infer_type, intermediate_repr, merge_inner_function
):
"""
Merge the inner function if found within the class, with the class IR
:param class_def: Class AST
:type class_def: ```ClassDef```
:param infer_type: Whether to try inferring the typ (f... | 5c891ba82cb5b41a5b5d311611f5d318d249a31e | 3,637,753 |
def pb_set_defaults():
"""Set board defaults. Must be called before using any other board functions."""
return spinapi.pb_set_defaults() | 30d360a15e4602c64a81900a581a2f4429f7d71e | 3,637,754 |
def count_routes_graph(graph, source_node, dest_node):
"""
classic tree-like graph traversal
"""
if dest_node == source_node or dest_node - source_node == 1:
return 1
else:
routes = 0
for child in graph[source_node]:
routes += count_routes_graph(graph, child, dest... | f952b35f101d9f1c42eb1d7444859493701c6838 | 3,637,755 |
from typing import Dict
def pluck_state(obj: Dict) -> str:
"""A wrapper to illustrate composing
the above two functions.
Args:
obj: The dictionary created from the json string.
"""
plucker = pipe(get_metadata, get_state_from_meta)
return plucker(obj) | d9517346b701f9ff434452992a4f3e8ca3dccf08 | 3,637,756 |
from typing import Callable
from typing import Mapping
from typing import Any
from typing import Optional
def value(
parser: Callable[[str, Mapping[str, str]], Any] = nop,
tag_: Optional[str] = None,
var: Optional[str] = None,
) -> Parser:
"""Return a parser to parse a simple value assignment XML tag.... | dcb2ad9b9e83015f1fd86323a156bbe92d505211 | 3,637,757 |
def compute_Rnorm(image, mask_field, cen, R=12, wid=1, mask_cross=True, display=False):
""" Compute (3 sigma-clipped) normalization using an annulus.
Note the output values of normalization contain background.
Paramters
----------
image : input image for measurement
mask_field : mask map wi... | 7c0b2aebf009b81c19de30e3a0d9f91fcfcebd52 | 3,637,758 |
import six
def inject_timeout(func):
"""Decorator which injects ``timeout`` parameter into request.
On client initiation, default timeout is set. This timeout will be
injected into any request if no explicit parameter is set.
:return: Value of decorated function.
"""
@six.wraps(func)
de... | 479ed7b6aa7005d528ace0ff662840d14c23035c | 3,637,759 |
def test_match_partial(values):
"""@match_partial allows not covering all the cases."""
v, v2 = values
@match_partial(MyType)
class get_partial_value(object):
def MyConstructor(x):
return x
assert get_partial_value(v) == 3 | 826a08066822e701c2077c2b71be48152c401b3f | 3,637,760 |
def assert_sim_of_model_with_itself_is_approx_one(mdl: nn.Module, X: Tensor,
layer_name: str,
metric_comparison_type: str = 'pwcca',
metric_as_sim_or_dist: str = 'dist') ... | 76d9b88063b69b69217f28cb98c985ff92f9b6e0 | 3,637,761 |
def cver(verstr):
"""Converts a version string into a number"""
if verstr.startswith("b"):
return float(verstr[1:])-100000
return float(verstr) | 1ad119049b9149efe7df74f5ac269d3dfafad4e2 | 3,637,762 |
import urllib
def _GetGaeCookie(host, service, auth_token, secure):
"""This function creates a login cookie using the authentication token
obtained after logging in successfully in the Google account.
Args:
host: Host where the user wants to login.
service: Service code where the user wants to login.
... | 9bef7516f6b43c2b744e6bb0a75a488e8aee3934 | 3,637,763 |
async def ping_handler() -> data.PingResponse:
"""
Check server status.
"""
return data.PingResponse(status="ok") | 77d1130aa31f54fbcac351d58b8ae4e4b893c5e9 | 3,637,764 |
def create_session_cookie():
"""
Creates a cookie containing a session for a user
Stolen from https://stackoverflow.com/questions/22494583/login-with-code-when-using-liveservertestcase-with-django
:param username:
:param password:
:return:
"""
# First, create a new test user
user =... | d4d7eef96e7b0136aa888d362b3278eb24ae91b8 | 3,637,767 |
def _replace_oov(original_vocab, line):
"""Replace out-of-vocab words with "UNK".
This maintains compatibility with published results.
Args:
original_vocab: a set of strings (The standard vocabulary for the dataset)
line: a unicode string - a space-delimited sequence of words.
Returns:
a unicode ... | 2e2cb1464484806b79263a14fd32ed4d40d0c9ba | 3,637,770 |
def linear_CMD_fit(x,y,xerr,yerr):
"""
Does a linear fit to CMD data where x is color and y is amplitude, returning some fit
statistics
Parameters
----------
x : array-like
color
y : array-like
magnitude
xerr : array-like
color errors
yerr : array-like
... | fb145d5caf48d2ab1b49a17b1e05ddd32e97c3f1 | 3,637,771 |
def _verify_path_value(value, is_str, is_kind=False):
"""Verify a key path value: one of a kind, string ID or integer ID.
Args:
value (Union[str, int]): The value to verify
is_str (bool): Flag indicating if the ``value`` is a string. If
:data:`False`, then the ``value`` is assumed t... | 3d8db518f244e6d09826d29dfcc42769a0015c33 | 3,637,772 |
def _is_tipologia_header(row):
"""Controlla se la riga corrente e' una voce o l'header di una
nuova tipologia di voci ("Personale", "Noli", etc).
"""
if type(row.iloc[1]) is not str:
return False
if type(row.iloc[2]) is str:
if row.iloc[2] != HEADERS["units"]:
return Fal... | 0fdbc6bea8d961fbe990d607a175815ccc475f88 | 3,637,773 |
def validateFloat(
value,
blank=False,
strip=None,
allowRegexes=None,
blockRegexes=None,
min=None,
max=None,
lessThan=None,
greaterThan=None,
excMsg=None,
):
# type: (str, bool, Union[None, str, bool], Union[None, Sequence[Union[Pattern, str]]], Union[None, Sequence[Union[Pat... | e11bbef1b0f53fa803918f9871e9779549e3cdb8 | 3,637,774 |
from typing import Dict
from typing import Any
def send_sms(mobile: str, sms_code: str) -> Dict[str, Any]:
"""发送短信"""
sdk: SmsSDK = SmsSDK(
celery.app.config.get("SMS_ACCOUNT_ID"),
celery.app.config.get("SMS_ACCOUNT_TOKEN"),
celery.app.config.get("SMS_APP_ID")
)
try:
re... | f1117d0543cc84d0429ce67f1415e6ab371ef2a6 | 3,637,775 |
def from_dataframe(df, name='df', client=None):
"""
convenience function to construct an ibis table
from a DataFrame
EXPERIMENTAL API
Parameters
----------
df : DataFrame
name : str, default 'df'
client : Client, default new PandasClient
client dictionary will be mutated wi... | 23d64170f078652e60d65be5346293ea3c4aedb5 | 3,637,776 |
def filter_list(prev_list, current_list, zeta):
"""
apply filter to the all elements
of the list one by one
"""
filtered_list = []
for i, current_val in enumerate(current_list):
prev_val = prev_list[i]
filtered_list.append(
moving_average_filter(current_val, prev_val... | 842d71f58b07dbe771c7fdd43797f26e75565ef5 | 3,637,781 |
def has_prefix(sub_s):
"""
:param sub_s: (str) A substring that is constructed by neighboring letters on a 4x4 square grid
:return: (bool) If there is any words with prefix stored in sub_s
"""
for word in dict_list:
if word.startswith(sub_s):
return True
return False | 78900ed757d4a1a94832f5a2f6d19da784935966 | 3,637,782 |
import yaml
def main():
""" """
try:
# read parameters configuration file yaml
with open(setupcfg.extraParam, "r") as stream:
try:
param = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
# check parameters file
... | 67da82991e8ae5b36dae81c6ac107099a54ab7e4 | 3,637,784 |
def primary_key(field_type):
"""
* Returns the field to be treated as the "primary key" for this type
* Primary key is determined as the first of:
* - non-null ID field
* - ID field
* - first String field
* - first field
*
* @param {object_type_definition} type
*... | 5beef62f9311b013b6c6cbe3c36260783bc61506 | 3,637,785 |
def get_discussion_data_list_with_percentage(session: Session, doi, limit: int = 20, min_percentage: float = 1,
dd_type="lang"):
""" get discussion types with count an percentage from postgresql """
query = """
WITH result AS
(
... | 4842566f7a891ce53cfc8170cc0fb5db2a6b298b | 3,637,786 |
import collections
import torch
import time
def validate(config, model, val_iterator, criterion, scheduler=None):
"""Runs one standard validation pass over the val_iterator.
This function automatically measures timing for various operations such
as host to device transfer and processing time for the batc... | 4f10e68c2e863e11e33f4f49b8378de51ff2b8fe | 3,637,787 |
def geq_indicate(var, indicator, var_max, thr):
"""Generates constraints that make indicator 1 iff var >= thr, else 0.
Parameters
----------
var : str
Variable on which thresholding is performed.
indicator : str
Identifier of the indicator variable.
var_max : int
An uppe... | 319f18f5343b806b7108dd9c02ca5d647e132dab | 3,637,790 |
import re
def parse_manpage_number(path):
"""
Parse number of man page group.
"""
# Create regular expression
number_regex = re.compile(r".*/man(\d).*")
# Get number of manpage group
number = number_regex.search(path)
only_number = ""
if number is not None:
number = nu... | b45edb65705592cd18fd1fd8ee30bb389dbd8dff | 3,637,791 |
def sample_coordinates_from_coupling(c, row_points, column_points, num_samples=None, return_all = False, thr = 10**(-6)):
"""
Generates [x, y] samples from the coupling c.
If return_all is True, returns [x,y] coordinates of every pair with coupling value >thr
"""
index_samples = sample_indices_fro... | a8343291a34ff31a2fc7b86c9b83872e7c787b76 | 3,637,792 |
import ast
def is_suppress_importerror(node: ast.With):
"""
Returns whether the given ``with`` block contains a
:func:`contextlib.suppress(ImportError) <contextlib.suppress>` contextmanager.
.. versionadded:: 0.5.0 (private)
:param node:
""" # noqa: D400
item: ast.withitem
for item in node.items:
if not... | 341d106b62d7940e4d84a359cd2f2ca254d3434e | 3,637,793 |
def random_flip_left_right(data):
""" Randomly flip an image or batch of image left/right uniformly
Args:
data: tensor of shape (H, W, C) or (N, H, W, C)
Returns:
Randomly flipped data
"""
data_con, C, N = _concat_batch(data)
data_con = tf.image.random_flip_left_right(data_con)... | bcdd0dfd35ff7ee0237d585d5a6cd70f92d7df2b | 3,637,794 |
def get_all_lobbyists(official_id, cycle=None, api_key=None):
"""
https://www.opensecrets.org/api/?method=candContrib&cid=N00007360&cycle=2020&apikey=__apikey__
"""
if cycle is None:
cycle = 2020 # I don't actually know how the cycles work; I assume you can't just take the current year?
#... | a2d8267881e871cb54201d243357739e689f187e | 3,637,798 |
def get_sale(this_line):
"""Convert the input into a dictionary, with keys matching
the CSV column headers in the scrape_util module.
"""
sale = {}
sale['consignor_name'] = this_line.pop(0)
sale['consignor_city'] = this_line.pop(0).title()
try:
maybe_head = this_line[0].split()
... | 39fee66b4c92a2cb459722f238e4a3b6e5848f4d | 3,637,799 |
def validate_besseli(nu, z, n):
"""
Compares the results of besseli function with scipy.special. If the return
is zero, the result matches with scipy.special.
.. note::
Scipy cannot compute this special case: ``scipy.special.iv(nu, 0)``,
where nu is negative and non-integer. The correc... | a8102c014fdcb2d256adf94aea842d1e5733ba72 | 3,637,800 |
from typing import Any
from typing import List
def delete_by_ip(*ip_address: Any) -> List:
"""
Remove the rules connected to specific ip_address.
"""
removed_rules = []
counter = 1
for rule in rules():
if rule.src in ip_address:
removed_rules.append(rule)
execut... | 88b430b83a5c3c82491f210e218a10719b5b75df | 3,637,801 |
def findMaxWindow(a, w):
"""
:param a: input array of integers
:param w: window size
:return: array of max val in every window
"""
max = [0] * (len(a)-w+1)
maxPointer = 0
maxCount = 0
q = Queue()
for i in range(0, w):
if a[i] > max[maxPointer]:
max[maxPointer... | af3e7f010b162e8f378e541be32a2d295e31e51c | 3,637,802 |
import logging
def filtering_news(news: list, filtered_news: list):
"""
Filters news to remove unwanted removed articles
Args:
news (list): List of articles to remove from
filtered_news (list): List of titles to filter the unwanted news with
Returns:
news (list): List of arti... | 98049b6bd826109fe7bc8e2e42de4c50970988a9 | 3,637,803 |
def extract_subsequence(sequence, start_time, end_time):
"""Extracts a subsequence from a NoteSequence.
Notes starting before `start_time` are not included. Notes ending after
`end_time` are truncated.
Args:
sequence: The NoteSequence to extract a subsequence from.
start_time: The float time in second... | cf8e1be638163a6cb7c6fd6e69121ccc7100afd6 | 3,637,804 |
import re
def read_data(filename):
"""Read the raw tweet data from a file. Replace Emails etc with special tokens """
with open(filename, 'r') as f:
all_lines=f.readlines()
padded_lines=[]
for line in all_lines:
line = emoticonsPattern.sub(lambda m: rep[re.escape(m.group(0)... | 8e15d6e4bd9e4a6b3b01ea5baffad8e6bc390034 | 3,637,805 |
def client():
"""AlgodClient for testing"""
client = _algod_client()
client.flat_fee = True
client.fee = 1000
print("fee ", client.fee)
return client | ad51102a58d9ffad4a9dd43c3e2b4bd5adc0f467 | 3,637,806 |
def GRU_sent_encoder(batch_size, max_len, vocab_size, hidden_dim, wordembed_dim,
dropout=0.0, is_train=True, n_gpus=1):
"""
Implementing the GRU of skip-thought vectors.
Use masks so that sentences at different lengths can be put into the same batch.
sent_seq: sequence of tokens c... | fe7090efe78ec97ba88651ecf8f7918bb5277eec | 3,637,807 |
def process_contours(frame_resized):
"""Get contours of the object detected"""
blurred = cv2.GaussianBlur(frame_resized, (11, 9), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, constants.blueLower, constants.blueUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = ... | 5725b12a3e5e0447a3b587d091f4fdeae1f5bac9 | 3,637,808 |
from typing import Optional
from typing import List
import itertools
def add_ignore_file_arguments(files: Optional[List[str]] = None) -> List[str]:
"""Adds ignore file variables to the scope of the deployment"""
default_ignores = ["config.json", "Dockerfile", ".dockerignore"]
# Combine default files and ... | f7e7487c4a17a761f23628cbb79cbade64237ce6 | 3,637,809 |
import torch
def compute_accuracy(logits, targets):
"""Compute the accuracy"""
with torch.no_grad():
_, predictions = torch.max(logits, dim=1)
accuracy = torch.mean(predictions.eq(targets).float())
return accuracy.item() | af15e4d077209ff6e790d6fdaa7642bb65ff8dbf | 3,637,810 |
def division_by_zero(number: int):
"""Divide by zero. Should raise exception.
Try requesting http://your-app/_divide_by_zero/7
"""
result = -1
try:
result = number / 0
except ZeroDivisionError:
logger.exception("Failed to divide by zero", exc_info=True)
return f"{number} divi... | b97d7f38aea43bfb6ee4db23549e89799bd299b7 | 3,637,811 |
def is_ELF_got_pointer_to_external(ea):
"""Similar to `is_ELF_got_pointer`, but requires that the eventual target
of the pointer is an external."""
if not is_ELF_got_pointer(ea):
return False
target_ea = get_reference_target(ea)
return is_external_segment(target_ea) | cd62d43bb266d229ae31e477dc60d21f73b8850a | 3,637,812 |
from pathlib import Path
def _normalise_dataset_path(input_path: Path) -> Path:
"""
Dataset path should be either the direct imagery folder (mtl+bands) or a tar path.
Translate other inputs (example: the MTL path) to one of the two.
>>> tmppath = Path(tempfile.mkdtemp())
>>> ds_path = tmppath.jo... | cf61da9a043db9c67714d7437c7ef18ee6235acb | 3,637,813 |
def get_customers():
"""returns an array of dicts with the customers
Returns:
Array[Dict]: returns an array of dicts of the customers
"""
try:
openConnection
with conn.cursor() as cur:
result = cur.run_query('SELECT * FROM customer')
cur.close()
... | 4440fb5d226070facb4e5c1b854535e40f42d607 | 3,637,814 |
def fixtureid_es_server(fixture_value):
"""
Return a fixture ID to be used by pytest for fixture `es_server()`.
Parameters:
fixture_value (:class:`~easy_server.Server`):
The server the test runs against.
"""
es_obj = fixture_value
assert isinstance(es_obj, easy_server.Server)
... | f795a8e909354e0004ea81ebdf71f7da81153a64 | 3,637,815 |
def topn_vocabulary(document, TFIDF_model, topn=100):
"""
Find the top n most important words in a document.
Parameters
----------
`document` : The document to find important words in.
`TFIDF_model` : The TF-IDF model that will be used.
`topn`: Default = 100. Amount of top words.
... | 4c58e2f041c76407bb2e7c686713b12e2c1e8256 | 3,637,816 |
def embedding_table(inputs, vocab_size, embed_size, zero_pad=False,
trainable=True, scope="embedding", reuse=None):
""" Generating Embedding Table with given parameters
:param inputs: A 'Tensor' with type 'int8' or 'int16' or 'int32' or 'int64'
containing the ids to be looked up in '... | bc509e18048230372b8f52dc5bbb77295014aec8 | 3,637,817 |
def get_trading_dates(start_date, end_date):
"""
获取某个国家市场的交易日列表（起止日期加入判断）。目前仅支持中国市场。
:param start_date: 开始日期
:type start_date: `str` | `date` | `datetime` | `pandas.Timestamp`
:param end_date: 结束如期
:type end_date: `str` | `date` | `datetime` | `pandas.Timestamp`
:return: list[`datetime.date`... | 5b0bf331376c5b2f9d1c8308be285b54fa053e5f | 3,637,818 |
def gm_put(state, b1, b2):
"""
If goal is ('pos',b1,b2) and we're holding b1,
Generate either a putdown or a stack subtask for b1.
b2 is b1's destination: either the table or another block.
"""
if b2 != 'hand' and state.pos[b1] == 'hand':
if b2 == 'table':
return [('a_putdown... | c9076ac552529c60b5460740c74b1602c42414f2 | 3,637,819 |
def pad_to_shape_label(label, shape):
"""
Pad the label array to the given shape by 0 and 1.
:param label: The label for padding, of shape [n_batch, *vol_shape, n_class].
:param shape: The shape of the padded array, of value [n_batch, *vol_shape, n_class].
:return: The padded label array.
"""
... | e40d7c1949cc891353c9899767c92419202c325d | 3,637,821 |
def download_report(
bucket_name: str, client: BaseClient, report: str, location: str
) -> bool:
"""
Downloads the original report
to the temporary work area
"""
response = client.download_file(
Bucket=bucket_name, FileName=report, Location=location
)
return response | d46fb279d5a315c60f1908664951436edc997ab8 | 3,637,822 |
def get_service(hass, config):
"""Get the Google Voice SMS notification service."""
if not validate_config({DOMAIN: config},
{DOMAIN: [CONF_USERNAME,
CONF_PASSWORD]},
_LOGGER):
return None
return GoogleVoiceS... | c7fda936ca9448587e2c4167d9c765186344fb43 | 3,637,825 |
import random
import time
def hammer_op(context, chase_duration):
"""what better way to do a lot of gnarly work than to pointer chase?"""
ptr_length = context.op_config["chase_size"]
data = list(range(0, ptr_length))
random.shuffle(data)
curr = random.randint(0, ptr_length - 1)
# and away we... | f4a51fe1e2f89443b79fd4c9a5b3f5ee459e79ca | 3,637,826 |
from typing import Callable
from typing import Mapping
import copy
import torch
def generate_optimization_fns(
loss_fn: Callable,
opt_fn: Callable,
k_fn: Callable,
normalize_grad: bool = False,
optimizations: Mapping = None,
):
"""Directly generates upper/outer bilevel program derivative funct... | 5e70f05c5aa0e754e5c1fbe585e4a0856a732006 | 3,637,828 |
def get_weighted_spans(doc, vec, feature_weights):
# type: (Any, Any, FeatureWeights) -> Optional[WeightedSpans]
""" If possible, return a dict with preprocessed document and a list
of spans with weights, corresponding to features in the document.
"""
if isinstance(vec, FeatureUnion):
return... | 0896a8449690895d922ae409c7e278f38002f111 | 3,637,829 |
def get_child(parent, child_index):
"""
Get the child at the given index, or return None if it doesn't exist.
"""
if child_index < 0 or child_index >= len(parent.childNodes):
return None
return parent.childNodes[child_index] | 37f7752a4a77f3d750413e54659f907b5531848c | 3,637,830 |
def extinction(species, adj, z, independent):
"""
Returns the presence/absence of each species after taking into account
the secondary extinctions.
Parameters
----------
species : numpy array of shape (nbsimu, S) with nbsimu being the number
of simulations (decompositions). This ar... | 2a9cb1884cfceb3a7c06aede60191d8a86f4741b | 3,637,832 |
def fix_variable_mana(card):
"""
This function was created to fix a problem in the dataset.
We're currently pretty up against the wall and I realized
that 'Variable' mana texts were not correctly converted to {X}
so this function is fed cards and corrects their mana values
if it detects this pro... | de0a0fe10d7ebbe02cd36088765be373c7dd9789 | 3,637,833 |
def cli_arg(
runner: CliRunner,
notebook_path: Path,
mock_terminal: Mock,
remove_link_ids: Callable[[str], str],
mock_tempfile_file: Mock,
mock_stdin_tty: Mock,
mock_stdout_tty: Mock,
) -> Callable[..., str]:
"""Return function that applies arguments to cli."""
def _cli_arg(
... | 5d7e02b11ace8ee44fa85ce7d2dc4c5a24fb72cf | 3,637,834 |
def distinguish_system_application(vulner_info):
"""
Test whether CVE has system CIA loss or application CIA loss.
:param vulner_info: object of class Vulnerability from cve_parser.py
:return: result impact or impacts
"""
result_impacts = []
if system_confidentiality_changed(
vu... | c10ec04a761b038fe3c0d6408a31660ccf23a205 | 3,637,836 |
from typing import Tuple
def nearest_with_mask_regrid(
distances: ndarray,
indexes: ndarray,
surface_type_mask: ndarray,
in_latlons: ndarray,
out_latlons: ndarray,
in_classified: ndarray,
out_classified: ndarray,
vicinity: float,
) -> Tuple[ndarray, ndarray]:
"""
Main regriddin... | 75b69ddbbdca4c316ecf2d4e3933f6e3a55ff0e1 | 3,637,840 |
def get_renaming(mappers, year):
"""Get original to final column namings."""
renamers = {}
for code, attr in mappers.items():
renamers[code] = attr['df_name']
return renamers | 33197b5c748b3ecc43783d5f1f3a3b5a071d3a4e | 3,637,842 |
async def clap(text, args):
""" Puts clap emojis between words. """
if args != []:
clap_str = args[0]
else:
clap_str = "👏"
words = text.split(" ")
clappy_text = f" {clap_str} ".join(words)
return clappy_text | 09865461e658213a2f048b89757b75b2a37c0602 | 3,637,843 |
from typing import Union
from typing import Callable
from typing import List
def apply_binary_str(
a: Union[pa.Array, pa.ChunkedArray],
b: Union[pa.Array, pa.ChunkedArray],
*,
func: Callable,
output_dtype,
parallel: bool = False,
):
"""
Apply an element-wise numba-jitted function on tw... | 853cd326b5812314bb6595fee191ca1c6e1f89f6 | 3,637,844 |
def product_review(product_id: str):
"""
Shows review statistics for a product.
Returns a python dictionary with content-type: application/json
"""
session = Session()
date = request.args.get('date') # parse a query string formatted as BIGINT unixReviewTime
# SELECT AVG(overall)... | 945f29a536a5645b602633c4558ac3d68affe85a | 3,637,845 |
def remove_extra_two_spaces(text: str) -> str:
"""Replaces two consecutive spaces with one wherever they occur in a text"""
return text.replace(" ", " ") | d8b9600d3b442216b1fbe85918f313fec8a5c9cb | 3,637,846 |
def reflect_table(table_name, engine):
"""
Gets the table with the given name from the sqlalchemy engine.
Args:
table_name (str): Name of the table to extract.
engine (sqlalchemy.engine.base.Engine): Engine to extract from.
Returns:
table (sqlalchemy.ext.declarative.api.Declara... | 414a04172cec7e840bf257eaf5b15b1fc3fa9d59 | 3,637,847 |
def load_utt_list(utt_list):
"""Load a list of utterances.
Args:
utt_list (str): path to a file containing a list of utterances
Returns:
List[str]: list of utterances
"""
with open(utt_list) as f:
utt_ids = f.readlines()
utt_ids = map(lambda utt_id: utt_id.strip(), utt_... | 6a77e876b0cc959ac4151b328b718ae45522448b | 3,637,848 |
def kfunc_vals(points, area):
"""
Input
points: a list of Point objects
area: an Extent object
Return
ds: list of radii
lds: L(d) values for each radius in ds
"""
# This function is taken from kfunction file in spatialanalysis library
n = len(points)
density = n/area... | 2fd56da45f8fb4ede38a219b158dce802d68ae44 | 3,637,849 |
from datetime import datetime
async def get_locations():
"""
Retrieves the locations from the categories. The locations are cached for 1 hour.
:returns: The locations.
:rtype: List[Location]
"""
# Get all of the data categories locations.
confirmed = await get_category("confirmed")
de... | 24272f06ca3732f053d6efcc41a31ec205603a27 | 3,637,850 |
def MDAPE(y_true, y_pred, multioutput='raw_values'):
"""
calculate Median Absolute Percentage Error (MDAPE).
:param y_true: array-like of shape = (n_samples, *)
Ground truth (correct) target values.
:param y_pred: array-like of shape = (n_samples, *)
Estimated target values.
:param m... | 05cfbef6bd3e63ca151a584dc25b9b6574d2aa37 | 3,637,851 |
def read_line1(line):
"""! Function read_line1
Reads as argument a string formatted as a Line 1 in SEISAN's Nordic format
Returns a Hypocenter dataclass with all the fields in a SEISAN's Line 1
@param[in] line string with SEISAN's Nordic hypocenter format (Line 1)
@return Hypocenter... | 871f468c2ec4dd9e0a5e8784d2beb7dd958d068d | 3,637,854 |
def getInfo_insert(sql : str, tableInfo : table_info_module.TableInfo) -> tuple:
"""테이블 이름과 컬럼을 반환합니다."""
sql = string_module.removeNoise(sql)
tableName = string_module.getParenthesesContext2(sql, "INSERT INTO ", " ")
columns = tableInfo[tableName]
return (tableName, columns) | 25f2087b5fbb15ab1012d3f37749430a74e6faaa | 3,637,858 |
def compute_flow_for_supervised_loss(
feature_model,
flow_model,
batch,
training
):
"""Compute flow for an image batch.
Args:
feature_model: A model to compute features for flow.
flow_model: A model to compute flow.
batch: A tf.tensor of shape [b, seq, h, w, c] holding a batch of triple... | a74f392c1d4e234fdb66d18e63d7c733ec6669a7 | 3,637,859 |
def farey_sequence(n):
"""Return the nth Farey sequence as order pairs of the form (N,D) where `N' is the numerator and `D' is the denominator."""
a, b, c, d = 0, 1, 1, n
sequence=[(a,b)]
while (c <= n):
k = int((n + b) / d)
a, b, c, d = c, d, (k*c-a), (k*d-b)
sequence.append( (a... | d55bb90d05b4930d05a83dac9feb58e747288754 | 3,637,861 |
def make_vgg19_block(block):
"""Builds a vgg19 block from a dictionary
Args:
block: a dictionary
"""
layers = []
for i in range(len(block)):
one_ = block[i]
for k, v in one_.items():
if 'pool' in k:
layers += [nn.MaxPool2d(kernel_size=v[0], stride=... | 512543dfb32f9ed97b6ce99dd6ffc692d0ffa3b8 | 3,637,862 |
def tld():
"""
Return a random tld (Top Level Domain) from the tlds list below
:return: str
"""
tlds = ('com', 'org', 'edu', 'gov', 'co.uk', 'net', 'io', 'ru', 'eu',)
return pickone(tlds) | 8e9341058ccf79d991aab6317ab3c29858f00fdf | 3,637,864 |
def validate_boolean(option, value):
"""Validates that 'value' is 'true' or 'false'.
"""
if isinstance(value, bool):
return value
elif isinstance(value, basestring):
if value not in ('true', 'false'):
raise ConfigurationError("The value of '%s' must be "
... | 85b9a256e57ce7715fceea556ff7ad48b05bd996 | 3,637,865 |
def A2RT(room_size, A_wall_all, F_abs, c=343, A_air=None, estimator='Norris_Eyring'):
""" Estimate reverberation time based on room acoustic parameters,
translated from matlab code developed by Douglas R Campbell
Args:
room_size: three-dimension measurement of shoebox room
A_wall_all: sound ... | 8a8df0bf8f91c93dfb7480775ea9eadc552edcfe | 3,637,866 |
def GetVideoFromRate(content):
"""
从视频搜索源码页面提取视频信息
"""
#av号和标题
regular1 = r'<a href="/video/av(\d+)/" target="_blank" class="title" [^>]*>(.*)</a>'
info1 = GetRE(content, regular1)
#观看数
regular2 = r'<i class="b-icon b-icon-v-play" title=".+"></i><span number="([^"]+)">\1</span>'
info2 = ... | 446343bc3f2597310b7e4b22dd784bb0bc9b06ea | 3,637,867 |
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