content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Callable
from typing import Any
from typing import List
import inspect
def get_parameters(
f: Callable[..., Any], allow_args: bool = False, allow_kwargs: bool = False
) -> List[str]:
"""Get names of function parameters."""
params = inspect.getfullargspec(f)
if not allow_args and par... | 218e187ae504ce18c0d240aee93480e275f4b8a4 | 70,400 |
def error_417(message = None):
"""JSON error handler for HTTP error 417"""
if not isinstance(message, str) or len(message) < 1:
message = 'Expectation Failed'
return _error(code = 417, message = message) | d72965ccd49c9e8d679dadde74d2ab76a0ea005b | 70,401 |
from typing import Optional
from typing import Generator
def barabasi_albert(num_nodes: int, num_edges: int, rng: Optional[Generator] = None) -> NDArray:
"""
Returns a graph sampled from the Barabasi-Albert (BA) model. The graph is built
incrementally by adding `num_edges` arcs from a new node to already ... | c1df5d4dfd29a2c7eabc2159d611d2186415c1d8 | 70,402 |
import functools
def split_and_combine_df(func=None, *, remove_type=False):
"""Split the data across sub state spaces and combine.
This function groups the data according to the dense variables. `remove_type` is
used to prevent grouping by types which might not be possible.
"""
def decorator_sp... | a472c9dff37dc2144b5938bdaf196fcb7cc2e514 | 70,403 |
import ctypes
def get_output_console_level(p_state):
"""Returns the level up to which the Log is output to the console.
The return value will be one of the integers defined above.
"""
return int(_Get_Output_Console_Level(ctypes.c_void_p(p_state))) | 0363de51919b6e09a6de6ad905967d62878be448 | 70,404 |
def create_beam_node(instrument, bounds, visible=False):
"""Creates node for beam
:param instrument: instrument object
:type instrument: Instrument
:param bounds: bounding box of the instrument scene
:type bounds: BoundingBox
:param visible: indicates node is visible
:type visible: bool
... | b6d7476498f68d5bf90141f0d2123c21f8cef4a7 | 70,405 |
def svn_diff_mem_string_output_unified(*args):
"""
svn_diff_mem_string_output_unified(svn_stream_t output_stream, svn_diff_t diff, char original_header,
char modified_header, char header_encoding,
svn_string_t original, svn_string_t modified,
apr_pool_t pool) -> svn_error_t
"""
re... | 2f20e704eb848b338268fff01bcbfb82c3a843dd | 70,406 |
def annotate_with_clonality(insertions):
"""Annotates an insertion dataframe with the clonality of insertions.
The clonality of each insertion is calculated by dividing the support
of the insertion by the support score of the strongest insertion in the
same tumor.
Parameters
----------
ins... | b78d770d0f295912859d375b0b974276e2704cf1 | 70,407 |
def _convertCtypeArrayToList(listCtype):
"""Returns a normal list from a ctypes list."""
return listCtype[:] | 89a408b796f2aba2f34bc20942574986abd66cd2 | 70,408 |
def expr_len(e: Expression) -> int:
"""
Computes the number of nodes in the AST of `e`.
:param e: The expression whose length is computed.
:return: The length of the expression.
"""
return 1 + sum(map(expr_len, e.children())) | c26355a56ce94c2f18a57d59d92c0bfe154355a9 | 70,409 |
def read_datafile(filename):
"""
Reads a langmuir csv file with the format:
time,header,Sweep voltage,Plasma voltage,Plasma temperature,Particles counter
2018-07-19 07:03:48,0x43434301,4.017525,4.1494875,291.78375,2 # Calibration
2018-07-19 07:03:48,0x43434302,4.0126375,3.470125000000... | 6bf3f0ded56563086ccd8f39aaeab23d3a233369 | 70,410 |
def selectFood():
"""Display caterering options"""
t = TenativeBooking.loadFromSession()
d = Dish.select().where(Dish.isDeleted == False)
c = Caterer.select().where(Caterer.isDeleted == False)
dishes = prefetch(d, c)
return render_template('event/caterer.html', dishes=dishes, booking=t) | 429e5e990f28a7954e50c3e440aa7d6bcbcb6eb8 | 70,411 |
from typing import List
def get_cost_vector(counts: dict) -> List[float]:
"""
This function simply gives values that represent how far away from our desired goal we are. Said desired goal is that
we get as close to 0 counts for the states |00> and |11>, and as close to 50% of the total counts for |01> and... | b7565de2e47ba99e93b387ba954fdc29f44805e8 | 70,412 |
def YcCbcCrc_to_RGB(YcCbcCrc,
in_bits=10,
in_legal=True,
in_int=False,
is_12_bits_system=False,
**kwargs):
"""
Converts an array of *Yc'Cbc'Crc'* colour encoding values to the
corresponding *RGB* array of lin... | b730dadfc38a1ad96f7ec0e795020b3239f1fcd8 | 70,413 |
import os
def exists(fname):
"""Returns True if `fname` relative to `ROOT` exists"""
return os.path.exists(os.path.join(ROOT, fname)) | 13d75f6f0af22fc283fa223a4005042a755cda9c | 70,414 |
import sqlite3
def getradecrangedb(dbfile):
""" Get RA/DEC ranges from database """
sqlite3.register_adapter(np.int16, int)
sqlite3.register_adapter(np.int64, int)
sqlite3.register_adapter(np.float64, float)
sqlite3.register_adapter(np.float32, float)
db = sqlite3.connect(dbfile, detect_types=... | 84553ab1a284b253026f1ae6d99e9ad4d7922c63 | 70,415 |
def compute_rhat_regression(samples, n_chains):
"""A wrapper function for computing R-hat statistics for regression tasks.
Args:
samples: numpy array [n_chains * n_samples, n_vars], the samples
taken from MCMC sampling.
n_chains: int, the number of sampling chains.
Return:
... | 436655868282421b702fe8e156c1e765af553744 | 70,416 |
import torch
def binary_loss_function(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes the binary loss function while summing over batch dimension, not averaged!
:param recon_x: shape: (batch_size, num_channels, pixel_width, pixel_height), bernoulli parameters p(x=1)
:param x: shape (bat... | 5009cc3c881f3b6b71495efe516dd038b9ca92f9 | 70,417 |
def update_create_front_page_groups(request_ctx, group_id, wiki_page_body, wiki_page_title=None, wiki_page_hide_from_students=None, wiki_page_editing_roles=None, wiki_page_notify_of_update=None, wiki_page_published=None, **request_kwargs):
"""
Update the title or contents of the front page
:param reque... | 64a6ecc66d6ec63c35c2d20e9610f9814dbd60d6 | 70,418 |
def default_copy(original_content):
"""Copy all fields of the original content object exactly as they are
and return a new content object which is different only in its pk.
NOTE: This will only work for very simple content objects. This will
throw exceptions on one2one and m2m relationships. And it mig... | d0a6fca858e34b8a7ae26ed9693a894cf2b2473f | 70,419 |
def build_normalized_request_string(ts, nonce, http_method, host, port, request_path, ext):
"""Implements the notion of a normalized request string as described in
http://tools.ietf.org/html/draft-ietf-oauth-v2-http-mac-02#section-3.2.1"""
normalized_request_string = \
ts + '\n' + \
nonce +... | 6a7f397738b852116cbaf249c846f58b482fdca1 | 70,420 |
def get_merged_coverage_eval():
"""Merges and returns all active coverage data as a single dict.
Returns: coverage eval dict.
"""
if not _coverage_eval:
return {}
coverage_eval_list = list(_coverage_eval.values())
merged_eval = deepcopy(coverage_eval_list.pop())
for coverage_eval in... | e4f5b227f7dab9ce50513bc29eec30172af7d77e | 70,421 |
from operator import setitem
def xmlparse(source, parsing_method=XMLParsingMethods.C_ELEMENTTREE, **kwargs):
"""Parses a XML document into a dictionary.
Details about how the XML is converted into this dictionary (json)
representation is described in reference [3] in the README.
:param str,file-like... | 155a1f44f3b0393235693626e53ce63a750fcce7 | 70,422 |
def print_exception(
type=None, value=None, traceback=None, limit=LIMIT, as_html=False,
with_filenames=False, stream=None
):
"""Print the exception to the given `stream`."""
info = format_exception(
type, value, traceback, limit, as_html, with_filenames
)
if stream is None:
... | ff6c184b40d6fba2ff197832a40456658d66ce04 | 70,423 |
def authenticate(f):
"""
Decorator for checking if a user is authenticated
"""
@wraps(f)
def wrapper(self):
"""
:type self: RequestHandler
"""
try:
self.request.session = Session.get(Session.secret == self.get_cookie('session'))
f(self)
... | 9814d4180cf0a11de7ede9d862a953b744d161a7 | 70,424 |
def _average_gradients(tower_grads):
"""Calculates average of gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list is
over individual gradients. T... | 67c143dbdae01ca1dfdeff345861e0e0aca0bc1c | 70,425 |
from datetime import datetime
def home():
"""Show user info"""
# Get today's date
date_now = datetime.datetime.strptime(get_time(), "%Y-%m-%d %H:%M")
# Get user event info
events = db.execute("SELECT * FROM events WHERE event_id IN (SELECT event_id FROM event_members WHERE user_id = ?) ORDER BY ... | 4b327ed7309909560ef59302a272ae4d8090c60a | 70,426 |
import ast
import re
def extract_from_grid(field_soup, form_info):
"""Return list of Samples, extracted from grid portion of form xml.
Arguments:
field_soup (BeautifulSoup object): The soup of the grid field.
form_info (api_types.CustomForm): dataclass holding information for the
... | 2fbd16c4f288733011eac6f2f06e59c2666e690e | 70,427 |
import os
def get_env_setting(setting, default=None):
""" Get the environment setting or return exception """
try:
return os.environ[setting]
except KeyError:
if default:
return default
error_msg = "Set the %s env variable" % setting
raise ImproperlyConfigured(e... | 7d78702178b629ae00d2faa42ca4f55e710712e6 | 70,428 |
def instance_get_oper_state(vm_state, task_state, power_state):
"""
Convert the nfvi vm states to an instance operational state
"""
oper_state = nfvi_objs.INSTANCE_OPER_STATE.ENABLED
if nova.VM_STATE.ERROR == vm_state:
oper_state = nfvi_objs.INSTANCE_OPER_STATE.DISABLED
elif nova.VM_ST... | 831ccfd9cc6e67a52d144297f8e851a9c1ae4176 | 70,429 |
def _force_list(value):
"""If the value is not a list, make it one."""
if value is None:
return []
try:
if isinstance(value, basestring):
raise TypeError()
return list(value)
except TypeError:
return [value] | 88a70d870a2c5c4a3c537b5b25247e852ed3af7c | 70,430 |
import uuid
def launch_v1(scan_id: uuid,
probes: comma_separated_list = None,
force: smart_boolean = False,
mimetype_filtering: smart_boolean = True,
resubmit_files: smart_boolean = True,
):
""" Launch a scan.
The request should be perf... | c82ab3545c7ad698d741e14ceb8c1531334f8429 | 70,431 |
import google.datalab.utils as du
def batch_predict_async(training_dir, prediction_input_file, output_dir,
mode, batch_size=16, shard_files=True, output_format='csv', cloud=False):
"""Local and cloud batch prediction.
Args:
training_dir: The output folder of training.
prediction_i... | bc1da3a7a1c1f6dc28f96bfad326eaf212da2531 | 70,432 |
import sys
def check_python_version():
"""Check python version and return version info.
Returns:
int: 2 if Python version is 2.x
3 if >= 3.5
else raises.
Raises:
UnsupportedPython: If current python version is not supported.
"""
vi = sys.version_info
... | 7cf326b9b5ccee1942fed1e6957b703d14001fb5 | 70,433 |
import sys
def os_arch():
"""
Return a tuple for the current the OS and architecture.
"""
if sys.maxsize > 2 ** 32:
arch = '64'
else:
arch = '32'
sys_platform = str(sys.platform).lower()
if 'linux' in sys_platform:
os = 'linux'
elif'win32' in sys_platform:
... | 0dd55b5a07ae853c700bd75ed43bab8d78da1dec | 70,434 |
def make_sphere_a():
""" Create a sphere"""
mesh_a = trimesh.creation.icosphere(subdivisions=1, radius=1.0)
return mesh_a | 69c034884c6280411764d86cadcb81308d957416 | 70,435 |
def title():
"""
(None) -> None
Function that loads the error page
"""
title = request.args.get('title')
if title in TITLES:
return render_template(f"books/{title}.html")
else:
return redirect(url_for(error)) | 32b9a317bcbb9905e63934b5ca61b2914da06865 | 70,436 |
from typing import Tuple
def load_sets(directory, suffix: str = '', file_type: str = 'csv') -> Tuple[
pd.DataFrame,
pd.DataFrame,
pd.Series,
pd.Series,
pd.DataFrame
]:
"""
"""
# Cater for empty suffix and not having a dangling underscore
if suffix:
suffix = f'_{suffix}... | 8ac7357a9e994d1cf6e025baeac35a0369dbbfbd | 70,437 |
def resnet101(**kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model | 5e40c374f07025d07ff385a229c487851e6464ec | 70,438 |
import json
def load_game(file_name):
"""
Loads game information from a json file and populates the input layer of the LSTM
Parameters
----------
file_name : str
Name of json file to be parsed and converted to input layer
Returns
-------
list
List of containing one el... | bc4da931ca469a32d544a988b103afe7f5c97981 | 70,439 |
def test_hard_requirements():
""" Returns any errors resulting from hard config requirement violations
"""
output = {}
for k in hardRequirements:
hardRequirements[k](output)
return output | 1805f54b58431840d80ffaef0b5d139299614176 | 70,440 |
from datetime import datetime
import pytz
import os
def load_precip():
""" Load the 5 minute precipitation data into our ginormus grid """
printt("load_precip() called...")
ts = 30 * 24 # 2 minute
midnight = datetime.datetime(VALID.year, VALID.month, VALID.day, 12, 0)
midnight = midnight.replace... | 977869038c787bd590c6783b3c9ddc9c53a8447a | 70,441 |
def Lt(left: Expr, right: Expr) -> BinaryExpr:
"""Less than expression.
Checks if left < right.
Args:
left: Must evaluate to uint64.
right: Must evaluate to uint64.
"""
return BinaryExpr(Op.lt, TealType.uint64, TealType.uint64, left, right) | f982a1386ded20da9b8c13355512e83e0a786809 | 70,442 |
import math
def train_ann_test_batch(sess, ixs, data, summary_writer=None):
#inputs: label_classifier, label_offset, label_coldensity
"""
Perform training and estimate accuracy,RMSE after every iteration training
Parameters
----------
sess:tf.Session object
ixs:np.ndarray, indices f... | 510262cd46e0f9291fd7b3ad7978cf0e977009ac | 70,443 |
def first_prime_greater_than(k: int) -> int:
"""
return the first prime greater than k
Called by the InputParams constructor
"""
if k < 1:
raise FuzzyError("k < 1")
while True:
k += 1
if isprime(k):
return k | 92a563b97987b526a6e3d01ee73475cb60df24b8 | 70,444 |
import pickle
def to_bytes(graph: BELGraph, protocol: int = pickle.HIGHEST_PROTOCOL) -> bytes:
"""Convert a graph to bytes with pickle.
Note that the pickle module has some incompatibilities between Python 2 and 3. To export a universally importable
pickle, choose 0, 1, or 2.
:param graph: A BEL gra... | 5af28070f1a5eb4cf666c3014968dcf41f552251 | 70,445 |
import pickle
import math
def genr_item_univ(config: dict, size_res_path='data/item_size.pkl', cls_res_path='data/cls_item.pkl'):
"""Generate universe of items (queries).
Generate and save result as <Item Size Table>. Item number and size range
specified by params in config. Compute and save <Class Item... | c5716e03b43c178b9bdc44c79463ca33e8fef9a9 | 70,446 |
def authorize(*_, **kwargs):
"""OAuth2'orize."""
authorize_form = AuthorizeForm(request.form)
if request.method == 'GET':
client_id = kwargs.get('client_id')
client = Client.query.filter_by(client_id=client_id).first()
kwargs['client'] = client
has_authorized_scopes = session... | 91a3738537e88c325aa7f4e8d498f1175334525a | 70,447 |
def trace_stream(stream_port, request_message = None, response_message = None):
"""Allows tracing the input stream within the resulting stream."""
s = stream_port
expect_stream(stream_port)
model = s.get_model()
item_data_type = s.get_item_data_type()
code = 'return $ traceStream '
code += e... | c7a9d7c26c25ac3c050f312bd5b827022f6c832a | 70,448 |
from typing import List
import os
def setup_checkpointing(
config: CheckpointingConfig, run_path: Text
) -> List[tf.keras.callbacks.Callback]:
"""Set up model checkpointing callbacks from config."""
callbacks = []
if config.initial_model:
callbacks.append(
ModelCheckpointOnEvent(
... | 0602ea99a3798e1f29fe574b6ff412fde31b5e6a | 70,449 |
import requests
def get_mobility(city):
"""Gets the latest mobility data from city mapper
Arguments:
city {String} -- Which city to get data from
Returns:
Pandas Series -- Series of mobility index
"""
url = "https://www.data.gouv.fr/en/datasets/citymapper-mobility-index/"
u... | ded9e90604b346ebdc8bc702d26c64cbe4b8050a | 70,450 |
def rivers_with_station(stations):
"""Given list of stations (MonitoringStation object), return the
names of the rivers that are being monitored"""
# Collect all the names of the rivers in a set to avoid duplicate entries
rivers = {station.river for station in stations}
# Return a list for conveni... | c099af2eb0f869c6f1e3270ee089f54246779e2d | 70,451 |
from typing import List
def expand_time_windows(periods: List[TimeWindow]) -> List[pd.Timestamp]:
"""Return a list of days to exclude from a list of TimeWindows.
Args:
periods: List of time windows (first day, last day).
Returns:
days_exclude: a List of obtained by expanding the list in input.
"""
d... | 1179926b7eb335be3e606db19b33bb819069f69c | 70,452 |
def _clean_str(words):
"""
heavily borrowed from github
https://github.com/LukeZhuang/Hierarchical-Attention-Network/blob/master/yelp-preprocess.ipynb
:param sentence: is a str
:return:
"""
words_collection = []
for word in words:
if word in ['-lrb-', '-rrb-', '<sssss>', '-r', '... | 7b69764bbdab2c5226163f31f365194a7cb1fac5 | 70,453 |
def from_text(text, origin=None, rdclass=dns.rdataclass.IN,
relativize=True, zone_factory=Zone, filename=None,
allow_include=False, check_origin=True, idna_codec=None):
"""Build a zone object from a master file format string.
*text*, a ``str``, the master file format input.
*or... | f78d8ea626029c25e819bfe4e61f36a2f2be6de3 | 70,454 |
from MoinMoin.widget import html
def makeSelection(name, values, selectedval=None, size=1, multiple=False, enabled=True):
""" Make a HTML <select> element named `name` from a value list.
The list can either be a list of strings, or a list of
(value, label) tuples.
`selectedval` is the val... | 5aea7545d4213915bd3fba9cf0b2908ad938cb40 | 70,455 |
def find_anagrams(s, diction):
"""
:param s:
:param diction:
:return:
"""
ans = []
permutation(s, '', len(s), ans, diction)
return ans | a1ee705426963ea393479c366d6ff7e5a4a110a4 | 70,456 |
async def del_schema(event_type: str):
"""
Deletes event validation schema for given event type
"""
try:
result = await storage.driver.validation_schema.del_schema(event_type)
await storage.driver.validation_schema.refresh()
except ElasticsearchException as e:
raise HTTPExcep... | 9167a0424ba7dd6a83f66f49835640b7fe44e795 | 70,457 |
import requests
import json
def pinterest_shares(url):
"""pinterest share count"""
try:
pinterest_url = 'http://api.pinterest.com/v1/urls/count.json?url=' \
+ url
response = requests.get(pinterest_url).text\
.replace('receiveCount(', '')\
... | 097a587778b1b21ceb84664b644cdbc6bb4619c3 | 70,458 |
import math
def get_deltas(radians):
"""
gets delta x and y for any angle in radians
"""
dx = math.sin(radians)
dy = -math.cos(radians)
return dx, dy | 032acb537373d0ee18b721f04c95e75bb1572b1b | 70,459 |
def global_min_temp(year):
""" Finds the minimum temperature from the minimum temperatures from all
the observations for a given year from 1929 to 2009 inclusive.
"""
return sc.textFile(server_name + dir_name + str(year))\
.filter(lambda line: "STN" not in line)\
... | 8daad6ac0ac7f96ae672ae7ce8b0136f6c8e9a96 | 70,460 |
import logging
def tok_sentence_to_vec(tok_sentences, vocabulary, model, normalize_sentence=1,
normalize_word=True, alpha=1e-4, show_logs=0):
"""
Maps a list of tokenized sentences to their vectorial representation.
> tok_sentences: List of tokenized sentences
> vocabulary: Lis... | 6634262922235a3e8f21648200257d22fa1626c2 | 70,461 |
from typing import Dict
def mrr(benchmark: Dict, results: Dict, repo: str) -> float:
"""
Calculate the MRR of the prediction for a repo.
:param benchmark: dictionary with the real libraries from benchmark.
:param results: dictionary with the predicted libraries.
:param repo: full name of the repo.... | b057c93cc807244e4d5e8e94772877273ac1041c | 70,462 |
from typing import Dict
from typing import Optional
from typing import Tuple
from typing import Union
from typing import cast
def netlist(
*,
instances: Instances,
connections: Dict[str, str],
ports: Dict[str, str],
models: Optional[Models] = None,
settings: Optional[Settings] = None,
defa... | add686735ddebe819e7d494f0feb78008248e10c | 70,463 |
from datetime import datetime
import time
import requests
import json
def get_interlanguage_links(page_names,
languages_of_interest={'it', 'nl'},
sleep=1,
verbose=0):
"""
mapping of English page name to its equivalent pages in... | 8bcd458dd9ae6fb857d57535897701c017d11252 | 70,464 |
from typing import Callable
def is_similar_callables(t: Callable, s: Callable) -> bool:
"""Return True if t and s are equivalent and have identical numbers of
arguments, default arguments and varargs.
"""
return (len(t.arg_types) == len(s.arg_types) and t.min_args == s.min_args
and t.is_v... | 861ae7265a7ad8e2b5b4c83d8e17a8c33195acf9 | 70,465 |
import hashlib
def hashmd5mod(*args):
"""
.. function:: hashmd5mod(args, divisor) -> int
Returns the *modulo* with divisor number of the MD5 hash of args.
Numbers are converted to text before hashing is performed.
Examples:
>>> sql("select hashmd5mod(65, 3)")
hashmd5mod(65, 3)
-----... | 13507127ab561a79b14ef5afac181760270226ba | 70,466 |
def get_total_gpu_memory(units="Mb"):
"""Get the memory of the GPUs in the system
Returns:
list: List of strings with the GPU memory in Mb
Examples:
>>> get_total_gpu_memory() #doctest: +SKIP
[16280.875]
"""
try:
memory_list = []
for gpu in cuda.gpus... | 848c208bc310ed641e441f096342326f6dc75905 | 70,467 |
def dictTagging(word, tag):
"""
Use language dictionaries to tag words
"""
global language_1_dicts
global language_2_dicts
global lang1
global lang2
dhin, den0, den1 = 0, 0, 0
word = word
if word.lower() in language_1_dicts["hindict1"].keys():
dhin = 1
if word.low... | 2a5fc15cbff2480c5c9e9336ac6a4feaa62112e1 | 70,468 |
def extract_dependencies(depends_content, exclude=None):
"""
Extract the Bazel project name from the BUILD file.
:param set depends_content: The Bazel BUILD files merged content.
:param str exclude: exclude self references.
:returns: List of dependencies, otherwise None.
:rtype: set
"""
... | 1d923a87f7ce7f5343242c756e5b0ab4bf59f90a | 70,469 |
def hook_get_load_batch_query(table: str, start_index: int,
end_index: int) -> str:
"""Returns the query that loads the BQ data.
Here it's possible to filter just new customers or other customer
groups.
Args:
table: A string representing the full path of the BQ table where th... | 6a99cf04214a815249a01b6cf5040bdb93b73fb8 | 70,470 |
def soft_nms(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold, topk_per_image):
"""
Performs soft non-maximum suppression algorithm on axis aligned boxes
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_... | 39c61258f913d6f943754c2f6a911d7bde577b5b | 70,471 |
def getSourceDefaultSettings(sourceKind: str):
"""
Get the default settings for a given source type.
:param sourceKind: String - Source kind. Also called "source id" in libobs terminology.
"""
return __createJSON("GetSourceDefaultSettings", {'sourceKind': sourceKind}) | 142f26c3e300b6f034f17ebfef4dba7d1eb1cb9e | 70,472 |
def get_spearman_rho(word2id1, embeddings1, path, lower,
word2id2=None, embeddings2=None):
"""
Compute monolingual or cross-lingual word similarity score.
"""
assert not ((word2id2 is None) ^ (embeddings2 is None))
word2id2 = word2id1 if word2id2 is None else word2id2
embedd... | 25921c17209fea11ae95f7a62858eb90ae198b6d | 70,473 |
def elf_segment_names(elf):
"""Return a hash of elf segment names, indexed by segment number."""
table = {}
seg_names_sect = elf.find_section_named(".segment_names")
if seg_names_sect is not None:
names = seg_names_sect.get_strings()[1:]
for i in range(len(names)):
table[i... | 4af46fb35c9808f8f90509417d52e7ce4eb2770e | 70,474 |
def line_line_closest_points(A1, C1, A2, C2):
"""
>>> print line_line_closest_points(Ux,Ux,Uy,Uy)
(Vec( 0.000000, 0.000000, 0.000000 ), Vec( 0.000000, 0.000000, 0.000000 ))
>>> print line_line_closest_points(Ux,Uy,Uy,Uz)
(Vec( 0.000000, 1.000000, 0.000000 ), Vec( 0.000000, 1.000000, 1.000000 ))
... | 211d19b67fb56d00ae1276ebd62e9e979c4d5295 | 70,475 |
import base64
def create_nova_keypair(identity_service, amp_key_name):
"""Create a nova keypair to use with Amphora images to allow ssh access
e.g. for debug purposes.
"""
pubkey = ch_core.hookenv.config('amp-ssh-pub-key')
if not pubkey:
ch_core.hookenv.log('No pub key provided - cannot cr... | 08e299b631e2a4fbd86b1686823ace7229b75913 | 70,476 |
def load_data(args):
"""
Load dataset
Args:
args: arguments set by the user
"""
dst = args.dataset
if dst == 'mnist':
return load_mnist(args)
if dst == 'fashion-mnist':
return load_fmnist(args)
if dst == 'cifar10':
return load_cifar10(args)
if dst ==... | 9215008fb0545b79b3b8da14b21520908e1fe321 | 70,477 |
def segment_spectrum_batch(spectra_mat, w=50, dw=25):
"""
Segment multiple raman spectra into overlapping windows
Args:
spectra_mat (2D numpy array): array of input raman spectrum
w (int, optional): length of window. Defaults to 50.
dw (int, optional): step size. Defaults to 25.
... | 956791e2957f4810726d1d94549f79f3d83c8d21 | 70,478 |
from pathlib import Path
def _get_params() -> dict:
"""
Parse Commandline Arguments and return them as options.
Returns:
dict
"""
return EntryPointParameters(
inputdir=dict(
type=Path,
required=True,
help="Measurements path."
),
... | 2a208207a09a2de87f51d5e5432893e4feeb5941 | 70,479 |
import pandas as pd
import os
def leukemia(path):
"""Acute Myelogenous Leukemia survival data
Survival in patients with Acute Myelogenous Leukemia. The question at
the time was whether the standard course of chemotherapy should be
extended ('maintainance') for additional cycles.
time:
survival or censo... | d539586caae0c0acc1adf39d4153e9f47cffec58 | 70,480 |
def complete_annotation(program, dist_context=None):
""" Complete annotation for the partial annotated program.
Arguments:
program: partial annotated program.
dist_context: the distributed context is used to store distributed attributes for program.
If not provided, the default one ... | 5abb68e06243b935f0dec0070758390d3b513d7f | 70,481 |
def generate_hashes(peaks, hash_style):
"""
Hash list structure:
sha1_hash[0:30] time_offset
[(e05b341a9b77a51fd26..., 32), ... ]
"""
peaks = list(peaks)
if peak_sort:
peaks = sorted(peaks, key=lambda x: x[1])
# print("Length of Peaks List is: {}".format(len(peaks)))
i... | 9d6919dac3e1910eb173028ad0cc0bef6c265c12 | 70,482 |
def _create_serialized_example(features, label):
"""Helper for creating a serialized Example proto."""
example = tf.train.Example(features=tf.train.Features(feature={
"features": _float_feature(features),
"label": _int64_feature(label),
}))
return example.SerializeToString() | 835558f340ee23bd8280e26de7b128c3ee824eed | 70,483 |
from typing import Dict
def sparsity_loss(X_hat_cf: tf.Tensor, X: tf.Tensor) -> Dict[str, tf.Tensor]:
"""
Default L1 sparsity loss.
Parameters
----------
X_hat_cf
Autoencoder counterfactual reconstruction.
X
Input instance
Returns
-------
L1 sparsity loss.
... | 2ef5e83277b541506b030ca56c1690aad6f4d22b | 70,484 |
def proxies_get_number():
"""
获取代理IP数量
:return:
"""
number_10 = redis_pool.redis_llen(GRADEKEY10)
number_8 = redis_pool.redis_llen(GRADEKEY8)
number_6 = redis_pool.redis_llen(GRADEKEY6)
number_4 = redis_pool.redis_llen(GRADEKEY4)
number_grade = redis_pool.redis_llen(GRADEKEY) # 使用完待... | 1efff3b0cefa9b90d3d5674a66ce544d5eae7132 | 70,485 |
from typing import Union
from typing import Tuple
from typing import Dict
from typing import List
def rnns(
draw, return_kwargs: bool = False
) -> Union[
st.SearchStrategy[rnn_pb2.RNN], st.SearchStrategy[Tuple[rnn_pb2.RNN, Dict]]
]:
"""Returns a SearchStrategy for RNN plus maybe the kwargs."""
kwargs ... | ebfbbb2c1e04a51f504a0bd9b278602f2ac045e2 | 70,486 |
def readvar(fni,varname,latrange=[],prange=[]):
"""reads variable from netcdf object in varname
fni is a Dataset variable"""
tsv = fni.variables[varname]
ts=tsv[:]
# try:
# ts = np.array(ts,dtype='float32')*float(tsv.scale_factor)+float(tsv.add_offset)
# except AttributeError:
# x=0 # dummy statement
tr... | 2c987fa20c76911442cdcbba48f7b215350e0484 | 70,487 |
import time
import email
def date_time_string(timestamp=None):
"""Return the current date and time formatted for a message header."""
if timestamp is None:
timestamp = time.time()
return email.utils.formatdate(timestamp, usegmt=True) | 0b2c319f341979b29ad6ca76eb6a93771e48e56e | 70,488 |
def dot_product_attention_relative(q,
k,
v,
bias,
max_relative_position,
dropout_rate=0.0,
image_shapes=None,
... | 211725c6a62c8258db6d6ec77c1bc726a5dccb90 | 70,489 |
from operator import add
def do_query(documents, config_file=None, logger=None, context=None):
"""
Iterate through documents and count total size of document files
in bytes.
Returns result of form:
{"total_size": total_size}
:param documents: RDD of defoe.document.Document
:type docu... | f505940b0c5b628bb2eea6c7bbbf3be28da41734 | 70,490 |
def get_bond(bond_id: str) -> Bond:
"""Return a specific bond.
Args:
bond_id (str): The bond id of the bond to fetch.
Raises:
HTTPException(400) if the bond does not exist.
Returns:
The requested bond."""
logger.info(f"Get bond: bond_id={bond_id}")
try:
bond: B... | d7883c89a4c625f71e2286787892a2c54ff2195a | 70,491 |
import sys
import os
def find_cloudenv_secret_key(filename='.cloudenv-secret-key', raise_error_if_not_found=True):
"""
Search in increasingly higher folders for the given file
Returns path to the file if found, or an empty string otherwise
"""
# will work for .py files
frame = sys._getframe(... | 0e3dee7ae0d270c07a7cda9a5c8eb3da0f2c9946 | 70,492 |
def _convert_dtype_string(dtype):
"""Get the String from type.
# Arguments
dtype: Type.
# Returns
A stromg representation of a type.
# Raises
ValueError: if `dtype` is not supported.
"""
mapping = {np.float16: 'float16',
np.float32: 'float32',
... | f49673e5f66b2f433c1932abaeaefe6a40f30dcc | 70,493 |
import time
import os
def dmd_s(D_1,D_2,n_Modes, F_S, SAVE_T_DMD=False, FOLDER_OUT='./'):
"""
This method computes the Dynamic Mode Decomposition (DMD).
https://youtu.be/8fhupzhAR_M
--------------------------------------------------------------------------------------------------------------------
... | 3e8fa21344475751970a27470204817c6b0d1ecb | 70,494 |
import os
def map_one_param(param, context=None):
"""
Analyze the pattern in the given string and find out its transformed value.
Available tags and replacement values:
[CONF:xxxx] Value from the config dict in context.project_config for the key xxxx (dot notation is used
for keys, e.g. ke... | 91cc82082b7474d6f77b83943a8e8037d33712f2 | 70,495 |
def splitCubicAtT(pt1, pt2, pt3, pt4, *ts):
"""Split the cubic curve between pt1, pt2, pt3 and pt4 at one or more
values of t. Return a list of curve segments.
>>> printSegments(splitCubicAtT((0, 0), (25, 100), (75, 100), (100, 0), 0.5))
((0.0, 0.0), (12.5, 50.0), (31.25, 75.0), (50.0, 75.0))
((50.0, 75.0), (6... | 8e21ee2c71456b4bedd3304df83868e0471ef380 | 70,496 |
import zipfile
def validate_zip(path_to_zip, raise_on_fail=True):
"""
validates a zip file
:param path_to_zip:
:param raise_on_fail:
:return:
"""
log.debug('validate_zip: zip={}'.format(path_to_zip))
zip_ref = zipfile.ZipFile(path_to_zip, "r", zipfile.ZIP_DEFLATED)
try:
ret... | a612d23156fbee925f1b3956d9048a814bf8941c | 70,497 |
def optimal_assignment_adv(adv_input):
"""
Calculate optimal assignment based on euclidean distance
"""
a_1 = calc_distance((adv_input[0], adv_input[1]),
(adv_input[4], adv_input[5]))
b_1 = calc_distance((adv_input[2], adv_input[3]),
(adv_input[4], adv... | 5b265f2601e123ad4a07b017d3da93460e3131e0 | 70,498 |
import os
def get_csci_salt():
"""Returns the appropriate salt for CSCI E-29
:rtype: bytes
"""
# Hint: use os.environment and bytes.fromhex
raw_salt = os.getenv(SALT_ENV_VAR, default='')
if not raw_salt:
raise ValueError("Empty salt!")
return bytes.fromhex(raw_salt) | 78b14488479087d3b9f4ea232431ba5cc765bf3a | 70,499 |
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