content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_mentornet_network_hyperparameter(checkpoint_path):
"""Get MentorNet network configuration from the checkpoint file.
Args:
checkpoint_path: the file path to restore MentorNet.
Returns:
a named tuple MentorNetNetHParams.
"""
if checkpoint_path and tf.gfile.IsDirectory(checkpoint_path):
che... | 3f7c8f056cec574e11027178b82652e5daa25989 | 46,500 |
def densenet(images, num_classes=1001, is_training=False, dropout_keep_prob=0.8, scope='densenet'):
"""Creates a variant of the densenet model.
images: A batch of `Tensors` of size [batch_size, height, width, channels].
num_classes: the number of classes in the dataset.
is_training: specifies whether or not ... | 7b444de908bb13c0403082895451f87211c2793a | 46,501 |
from typing import List
from functools import reduce
def product(li: List[int]) -> int:
"""Calculates the product of all the numbers in a list.
doctests:
>>> product([2, 1, 4])
8
>>> product([3, 1, 4, 2, 5, 8])
960
"""
x = reduce(lambda a, b: a * b, li)
return x | 8afe00bb6056accc694ab955a48b6be85d8a30bf | 46,502 |
def CDLDOJI(data: xr.DataArray) -> xr.DataArray:
"""
Doji (Pattern Recognition)
Inputs:
data:['open', 'high', 'low', 'close']
Outputs:
double series (values are -1, 0 or 1)
"""
return multiple_series_call(talib.CDLDOJI, data, ds.TIME, ds.FIELD, [f.OPEN, f.HIGH, f.LOW, f.CLOSE],
... | 73130fe5309c279b79e12738acee0433a4f78aee | 46,503 |
def filename(N, epsilon, v0, D, Dr, phi, launch):
"""
Name of simulation output files.
Parameters
----------
N : int
Number of particles in the system.
epsilon : float
Coefficient parameter of potential.
v0 : float
Self-propulsion velocity.
D : float
Tran... | 7fb6c9cfc081caaacc447c4e1869030ef2b00ff3 | 46,504 |
def g2c(coordinate:tuple, pixel_unit:int) -> tuple:
"""
Convert pygame coordinate to cartesian coordinate. x pixel unit
refers to 1 unit in cartesian coordinate system. For example:
if pixel unit is 100 then 100 pixel is one unit in cartesian system
coordinate: game coordinate (x,... | 16c93158145edf1454c31aaede426f99639f34a7 | 46,505 |
async def site_base():
"""Return the configured site prefix. Useful for correct client-side routing."""
return conf().metador.site | 729c72428baf914a91e5970f3d7c931283690986 | 46,506 |
from typing import Dict
from typing import List
def filter_labels(
label_dict: Dict[str, np.ndarray], filter_dict: Dict[str, List[int]]
) -> Dict[str, np.ndarray]:
"""Filter out examples from arrays based on specified labels to filter.
The most common use of this method is to remove examples whose gold l... | f5d0402bee3947d4c248320842750ec3613c5b30 | 46,507 |
from typing import Sequence
from typing import Dict
from typing import Any
def get_view_report_fields_list(report: str) -> Sequence[Dict[str, Any]]:
"""Gets the fields in the report month view
:param report: the kind of the report
:returns: list of fields in this report's month view"""
report_fields ... | af6d96acaec6d7b7d44f402b8edd09f354bdfa91 | 46,508 |
def parse_options() -> Namespace:
""" Parse command line arguments and liftoff configuration.
"""
opt_parser = OptionParser(
"liftoff-status", ["experiment", "all", "timestamp_fmt", "results_path"]
)
return opt_parser.parse_args() | cff274096dd9a74bce62867f2519e93b11c4521b | 46,509 |
def generate_betas_inertia(time_periods: int) -> np.ndarray:
"""
Generate an array of beta_inertia values for t time periods
"""
betas = np.empty(time_periods)
taste_shocks = np.random.normal(loc=0, scale=src.const.taste_shock_std, size=time_periods)
b0 = np.clip(np.random.normal(loc=src.const.... | 1bbc2a9f29206f7086874e9bb0992a8ee3ce7927 | 46,510 |
def create_graph_hac(env, n_layers, n_steps, subtask_specs, level_algo_kwargs_list):
"""Create and return HAC graph."""
# If buffer size not specified, set it such that all transitions will fit into buffer
# (assuming that the episodes always last for env.max_episode_length which is not the
# case in ... | c0b8ac527bc03d2b30c1eb0709985c2365528a78 | 46,511 |
def add_cartesian_dummy(odf: dc_dd) -> dc_dd:
"""create a dummy dataframe with a column "cartesian" with the value
0 to force a cartesian join using merge.
Args:
odf (dc_dd): original dask_cudf or dd
Returns:
dc_dd: dummy dask_cudf or dd
"""
odf["cartesian"] = 0
odf = du.sh... | 406d58869b2a49b7cbcb6528b9746b2320e8a8e0 | 46,512 |
import os
def parse_fname(fname):
"""
Returns dictionary of parameters encoded in file name.
Assumes structure:
der/<name1>-<unit1><val1>_<name2>-<unit2><val2>_<note1>_<note2>/trial_v<version>.txt
No numbers are allowed in the name, unit, or note fields
"""
#assumes <path tail>/data/<date>... | ac3923653af311c85da28d52ff75b1c7cd41506e | 46,513 |
def get_etl_pipeline_ids(client):
"""Return a dict mapping pipeline ids to their names, filtering on ETL pipelines."""
paginator = client.get_paginator("list_pipelines")
response_iterator = paginator.paginate()
filtered_iterator = response_iterator.search("pipelineIdList[?contains(@.name, 'ETL') == `tru... | 10dcd1d933ed8adabd75740a55d567cf786fffbb | 46,514 |
from typing import Optional
from typing import Dict
def GCBucket(bucket: str, prefix: Optional[str] = None, identifier: Optional[str] = None) -> Dict:
"""Get configuration object for Google Cloud storage volume.
Parameters
----------
bucket: string
Google Cloud Storage bucket identifier.
... | e93f2bab567a32a15b76278b18da3a7f001263cc | 46,515 |
import tqdm
def segment_threshold(eventfile,segment_length,demod,tbin_size,threshold,PI1,PI2,t1,t2):
"""
Using the .dat files, rebin them into 1s bins, to weed out the segments below
some desired threshold. Will return a *list* of *indices*! This is so that I
can filter out the *sorted* array of .dat ... | d9bbf34b6230df4257ed5a99d46292603d7f54f8 | 46,516 |
import tokenize
def build_model():
"""
Making a pipeline and using grid-search to optimize it.
Input: None
Output: Returns a model
"""
pipeline = Pipeline([
('vect', CountVectorizer(tokenizer=tokenize)),
('tfidf', TfidfTransformer()),
('clf', MultiOutputClassifier(RandomForestClassifier()))
])
para... | 4e27d35ce612a5bdea6ff2175db956d121777362 | 46,517 |
import json
def load_remote_settings_cache():
"""Load cached remote skill settings.
Returns:
(dict) Loaded remote settings cache or None of none exists.
"""
remote_settings = {}
if REMOTE_CACHE.exists():
try:
with open(str(REMOTE_CACHE)) as cache:
remot... | f2a58a5185a8b9d670489d6ad9eaa21abcb2e5d9 | 46,518 |
def get_blogroll(parser, token):
"""
{% get_blogroll %}
"""
return BlogRoll() | 9ebd58021e048f1d9d4610daadba4f98d427b76f | 46,519 |
def otsu(data, num=400):
"""
Generate threshold value based on Otsu.
:param data: cluster data
:param num: intensity number
:return:
selected threshold
"""
max_value = np.max(data)
min_value = np.min(data)
total_num = data.shape[1]
best_threshold = min_value
best_in... | 1d3c264ff059b4205dc8bbe02e45b1f267ba52d4 | 46,520 |
import inspect
def get_states(start=None, stop=None, state_keys=None, cmds=None, continuity=None,
reduce=True, merge_identical=False, scenario=None):
"""
Get table of states corresponding to intervals when ``state_keys`` parameters
are unchanged given the input commands ``cmds`` or ``start`... | f141f29f4e9f4d57a30a0869dd6043fdeedcbbad | 46,521 |
import os
import contextlib
import wave
def skip_long_utterance(wav_file, cut_off_len = 15):
""" from "Text-Free Image-to-Speech Synthesis Using Learned Segmental Units" Appendix A:
When computing duration statistics, we exclude utterances longer than 15s for SpokenCOCO...
"""
if os.path.isl... | 9f2d81f30aaa0e5325b6ec88a116810f4c6c7ae9 | 46,522 |
import zipfile
def read_data(filename):
"""Extract the first file enclosed in a zip file as a list of words."""
with zipfile.ZipFile(filename) as f:
data = tf.compat.as_str(f.read(f.namelist()[0])).split()
return data | e1b06f067cb22b71483033d32dddf454ce1c22ab | 46,523 |
import logging
def create_logger(name='', logging_config=logging_config):
"""Create a Logger object
Args:
name (str, optional): The name of the module.function
logging_config (TYPE, optional): Logging config
Returns:
TYPE: Description
"""
dictConfig(logging_config)
l... | f71bd7d96b50f2263c7b0175a89229d8c58abc92 | 46,524 |
def mark_all(request):
"""
Mark all notification as read or unread.
"""
action = request.data.get('action', None)
success = True
if action == 'read':
request.user.notifications.read_all()
msg = _("Marked all notifications as read")
elif action == 'unread':
request.us... | 495a39c5f1229477704826ebaa4bdf86d8d67afe | 46,525 |
import os
def GetUniqueName(path, name):
"""Make a file name that will be unique in case a file of the
same name already exists at that path.
@param path: Root path to folder of files destination
@param name: desired file name base
@return: string
"""
tmpname = os.path.join(path, name)
... | c22b5d897fcec291f4e1e69fae8d85a9601ba15e | 46,526 |
def _split_mime_type(mime_type):
"""Split MIME type into main type and subtype.
Args:
mime_type: The full MIME type string.
Returns:
(main, sub):
main: Main part of MIME type (e.g., application, image, text, etc).
sub: Subtype part of MIME type (e.g., pdf, png, html, etc).
Raises:
_In... | ce88fd75624060403aed44ddab9f044a577cfbd1 | 46,527 |
def handler_get_user_connections_from_id_from_event_id(userID, eventID):
"""Get the connections of the user with the given IDs (user and event).
.. :quickref: Users; Get the connections of the user with the given IDs (user and event).
:param int userID: The ID of the user to retrieve the collection fr... | 506904c23a23fd94f5894d2ede08ed37a8ae5ff5 | 46,528 |
import requests
def run(studata,cook):
"""获取处理后的数据
:param studatae:学生信息
:param cook:传入的cookie
:return :打卡结果
"""
# 读取个人提交信息
info = getinfo.data(studata, cook)
if info == 0:
print("今日打卡已完成，自动打卡取消\n")
return "已完成"
# 提交今日打卡
url = 'https://yq.weishao.com.cn/api/quest... | f7767bc6e0dfbf3d0c663eaed5ed1e9d7924ad34 | 46,529 |
def filter_label_2( context, label ):
"""Test Filter Label 2"""
return False | a31337f18cbec6d5b8e556b21603635732d17ffd | 46,530 |
def raise_404_if_no_object(fn):
"""
get_object_or_404 doesn't work with mongoengine,
so this is needed for similar purpose
"""
def wrapper(*args, **kwargs):
try:
return fn(*args, **kwargs)
except Exception as e:
if not e.__class__.__name__ == "DoesNotExist":
... | b50a204a8cc0ab2cb2c05eabbe62f9d9ea6c751d | 46,531 |
from typing import Tuple
async def generate_keys() -> Tuple[bytes, str]:
"""A helper function to generate the private and public keys.
backend - The value specified is default_backend(). This is because the
cryptography library used to support different backends, but now only uses
the default_backend... | 13654fa051eeddaad6682ee0525199500dee67fd | 46,532 |
import six
def _is_named_tuple(instance):
"""Returns True iff `instance` is a `namedtuple`.
Args:
instance: An instance of a Python object.
Returns:
True if `instance` is a `namedtuple`.
"""
if not isinstance(instance, tuple):
return False
return (hasattr(instance, "_fields") and
i... | 8ee641d4fb64926b807d74eaf40115e8608af301 | 46,533 |
import os
import joblib
def load_trained_model(pipeline_str='BDT', config='IC86.2012',
return_metadata=False):
""" Function to load pre-trained model to avoid re-training
Parameters
----------
pipeline_str : str, optional
Name of model to load (default is 'BDT').
co... | 92edd50dadf3741851218b0ecb6ffeb123d4b3a4 | 46,534 |
import urllib3
import json
import sys
def orsGetSnapToRoadLatLon(loc, APIkey):
"""
A function to get snapped latlng for one coordinate using ORS
Parameters
----------
loc: list
The location to be snapped to road
Returns
-------
list
A snapped location in the format of [lat, lon]. Note that this function ... | 6c770e59ce0fc5d76c1750e99bf3f087593989f2 | 46,535 |
def department_list_current():
"""Returns a list of all Departments as of today's date.
"""
resp = _call_organisations_service("GetDepartmentListCurrent")
return _parse_list_response(resp, "OrganisationsList", "Organisation") | dba40b7de228fdb196e46869499ed21db12fc569 | 46,536 |
def unique(ar, return_index=False, return_inverse=False, return_counts=False,
axis=None, aggregate_size=None):
"""
Find the unique elements of a tensor.
Returns the sorted unique elements of a tensor. There are three optional
outputs in addition to the unique elements:
* the indices of ... | fb97f22416a5558cb334396d07546aa2c98d89b2 | 46,537 |
def first(n, min_length, max_length, floor, ceiling, min_slope, max_slope):
"""
Return the lexicographically smallest valid composition of ``n``
satisfying the conditions.
.. warning::
INTERNAL FUNCTION! DO NOT USE DIRECTLY!
.. TODO::
Move this into Cython.
Preconditions:
... | 75a48ebd937dd8278f72b07954b1e97c73c45c16 | 46,538 |
from typing import Sequence
from typing import Callable
from typing import Optional
from typing import Dict
def load_with_native_transform(
dss: Sequence[Dataset],
bands: Sequence[str],
geobox: GeoBox,
native_transform: Callable[[xr.Dataset], xr.Dataset],
basis: Optional[str] = None,
groupby: ... | 8625bf16a637c43169d57a775b9f5c293f9a41df | 46,539 |
def post_to_dto(post: Post, href: str = None, links: list = None) -> PostDto:
"""
Converts post resource into data transfer object.
:param post: Post resource to convert.
:type post: Post
:param href: Post resource href link.
:type href: str
:param links: Post resource links.
:type link... | 106d8b3dde351e52ed3df6b83eed3dd770e8f797 | 46,540 |
import logging
def get_dual_coding_stop_stop_location(
fs_coord, fs_type, strand, chr_seq, gencode):
"""Extend the starting coordinate (fs_coord) downstream until the stop-codon
in the main frame, shift the frame (fs_type) at this stop-codon and then
go upstream in the alternative until the second st... | cd5db26363585f93e234c36e158b0cd8046484ed | 46,541 |
def delete_restaurant(restaurant_id):
"""V 0.0.0 POST Delete a restaurant: GET render Delete a restaurant form"""
restaurant_to_delete = SESSION.query(
Restaurant).filter_by(id=restaurant_id).one()
if request.method == 'POST':
SESSION.delete(restaurant_to_delete)
SESSION.commit()
... | 1380fa2159a90c536c94967a226453efedeaeafc | 46,542 |
def get_clusters_from_file(path, ref_cluster_names=[]):
"""Get cluster names, labels, and cells from cluster file or metadata file
"""
clusters = {}
with open(path) as f:
lines = f.readlines()
headers = [line.strip().split('\t') for line in lines[:2]]
names = headers[0]
types = hea... | cabf14c72b0467b0f2b15e3c0b8c8bd1846e92b5 | 46,543 |
import gzip
def split_deltas(deltas_url):
"""Opens and decompress a gzipped text file and splits each line into two
deltas"""
left, right = [], []
with gzip.open(BytesIO(read_url(deltas_url))) as deltas:
for line in deltas:
line = line.decode().rsplit(" ", 1)
left.app... | ff43daced54f1c18f837a525424e79dcd23d91d2 | 46,544 |
def transform(image, parameter_maps, verbose=False):
"""Transform an image according to some vector of parameter maps
:param image: Image to be transformed
:param parameter_maps: Vector of 3 parameter maps used to dictate the
image transformation
:type image: SimpleITK.Image
... | d2ac4b61f79f2a8275aa0f63feb49c87f12294ba | 46,545 |
def put_lines(ax, cwaves, fluxes, ypos2, ypos3, labels,
bars=None, bscale=None, edges=None, barskwargs=dict(),
adjustkwargs=dict(), linekwargs=dict(), textkwargs=dict()):
"""
Automatic layout of labels for spectral lines in a plot.
Parameters
----------
ax : Matplotlib A... | b8cf1918eeb77871e42e4bc9691ccaf227373ca8 | 46,546 |
import os
def sip_to_pv(infile,outfile,tpv_format=True,preserve=False,extension=0,clobber=True):
""" Function which wraps the sip_to_pv conversion
Parameters:
-----------
infile (string) : name of input FITS file with TAN-SIP projection
outfile (string) : name of output FITS file with TAN-TPV p... | c13f4f7b7e1e1ddb2bed29365036d56d673789dd | 46,547 |
def _tracking(fcn):
""" Decorator to indicate the list has changed """
def new_fcn(self, *args):
self.changed = True
return fcn(self, *args)
return new_fcn | 3f6454190056f112134f01507b2bb353a8043790 | 46,548 |
def test(doorcount: int, switch: bool, trials: int) -> bool:
"""Returns a win rate for the Monty Hall problem with repeated trials."""
wins = sum(int(play(doorcount, switch) == CAR) for _ in range(trials))
return wins/trials | 0e02834f54570867412eb2c25892377a388c9287 | 46,549 |
def getLastStepMeanSpeed(detID):
"""getLastStepMeanSpeed(string) -> double
Returns the mean speed in m/s of vehicles that have been within the named multi-entry/multi-exit detector within the last simulation step.
"""
return _getUniversal(tc.LAST_STEP_MEAN_SPEED, detID) | dd0e79e0639ee6141ba2abb8f66398bc4cbe4341 | 46,550 |
def get_random_experiment_histories_from_file(experiment_path_prefix, net_number):
""" Read history-arrays from the specified npz-file and return them as ExperimentHistories object. """
histories_file_path = generate_random_experiment_histories_file_path(experiment_path_prefix, net_number)
with np.load(hist... | ec65f7bb1580f166218554a28fbe40b629d3bd11 | 46,551 |
import sys
def caller_module(level=2, sys=sys):
"""This function is taken from Pyramid Web Framework - ``pyramid.path.caller_module``."""
module_globals = sys._getframe(level).f_globals
module_name = module_globals.get('__name__') or '__main__'
module = sys.modules[module_name]
return module | 4bc6d73f656c98be185f7b5aaa869d7fb6ca841c | 46,552 |
def qr_householder(A):
"""Return a QR-decomposition of the matrix A using Householder reflection.
The QR-decomposition decomposes the matrix A of shape (m, n) into an
orthogonal matrix Q of shape (m, m) and an upper triangular matrix R of
shape (m, n). Note that the matrix A does not have to be square... | ce2e64c55e4476be1048c88b66bb89ce0c6697f5 | 46,553 |
def login():
"""
Página de entrada de la app
"""
if 'email' in session:
# Obtenemos el ID del user
_id = elastic.getIDByMail(session['email'])
return render_template('index_sup.html', random_num = WebScraper.getRandomNumber(), msg = session['user'] + ' is already online!',... | ea534c69727332958fe4fe7a32e0979f5a674d87 | 46,554 |
def load_data(database_filepath):
"""
Load data from database
Arguments:
database_filepath -> Path to SQLite destination database
Output:
X -> a dataframe containing features
Y -> a dataframe containing labels
category_names -> List of categories name
"""
# l... | 142eed54d527225a7208a35b2631333ef842c934 | 46,555 |
def cholesteric_droplet_data(shape, radius, pitch, hand = "left", no = 1.5, ne = 1.6, nhost = 1.5):
"""Returns cholesteric droplet optical block.
This function returns a thickness, material_eps, angles, info tuple
of a cholesteric droplet, suitable for light propagation calculation tests.
Pa... | 3c478afad445066ade233461500b072bd756836f | 46,556 |
def getInterpolatedFzeroPzero( f, g, p ):
""" look at the points of f, g and p surrounding where
g crosses zero and interpolate f and p at 0
"""
ndx = getIndexZeroDbCrossover( g )
f_zero_db = None
g_zero_db = None
p_zero_db = None
if ndx != None:
f_zero_db = f[ndx]
g_z... | 7dbf5b0372e0c81c771c077b6badbe42d2c4eb55 | 46,557 |
from typing import Mapping
def create_tag(entity: Entity, source_name: str, color: str) -> Mapping[str, str]:
"""Create a tag."""
value = entity.value
if value is None:
value = f"NO_VALUE_{entity.id}"
return {"tag_type": source_name, "value": value, "color": color} | f510ddc698f9d6c491fda63e33a16fe2cfda1021 | 46,558 |
import re
def javacode_to_tokens(code:str):
"""
Starting on method level, without javadocs
returns a touple of ([code-tokens],code-string)
"""
code_tokens = re.findall(r"\w+(?:'\w+)*|[^\w\s]", code)
#print("Javacode to tokens to be done!",code,code_tokens)
return (code_tokens,code) | 85c0dccb06936326929493edc5192ccf01bf59ea | 46,559 |
def static(type='artist', artist_pick='song_hotttnesss-desc', variety=.5, artist_id=None, artist=None, \
song_id=None, description=None, results=15, max_tempo=None, min_tempo=None, max_duration=None, \
min_duration=None, max_loudness=None, min_loudness=None, max_danceability=None... | 95961b0d4c865e86a636682bbb0f335a43e57a27 | 46,560 |
def floor_to(value: float, target: float) -> float:
"""
Similar to math.floor function, but to target float number.
"""
value: Decimal = Decimal(str(value))
target: Decimal = Decimal(str(target))
result: float = float(int(floor(value / target)) * target)
return result | c58341913e294b68d38328fe42441e6942e94b8d | 46,561 |
def registration():
"""Processes registration when user provides email and password, or
displays the form."""
if request.method == 'POST':
email = request.form.get("email")
password = request.form.get("password")
# Create a possible user object to query if the user's email is in t... | e52fab05c05ad945b39d2f1e9d9f102efc5f395e | 46,562 |
from typing import Dict
def generate_meta(favicon: Dict[str, str]) -> str:
"""Generate metatag based on favicon data.
Default behavior:
- If favicon data contains no ``rel`` attribute, sets ``rel="icon"``
- If no ``size`` attribute is provided, ``size`` will be omitted
- If no favicon MIME type i... | 940e7f7ab7b2fb7207cba287f6f6ec7424749f98 | 46,563 |
import re
def select_disk(prompt='Which disk?'):
"""Select a disk from the attached disks"""
disks = get_attached_disk_info()
# Build menu
disk_options = []
for disk in disks:
display_name = '{Size}\t[{Table}] ({Type}) {Name}'.format(**disk)
if len(disk['Partitions']) > 0:
... | f5fd59ab6c45b1af303bfa453ecdde8c7df326a3 | 46,564 |
def imageToVTK(
path,
origin=(0.0, 0.0, 0.0),
spacing=(1.0, 1.0, 1.0),
cellData=None,
pointData=None,
fieldData=None,
):
"""
Export data values as a rectangular image.
Parameters
----------
path : str
name of the file without extension where data should be saved.
... | f263f1a7613e331fd46c336446be50d84e7110c6 | 46,565 |
def compute_capacities(n, demands_across_cuts):
"""
Computes the capacities as given in the paper. Takes O(n^2) time.
:param n: ring size
:param demands_across_cuts: SymmetricMatrix containing all demands across cuts.
:return: np.array containing capacities
"""
c = np.zeros((n,), dtype=np.fl... | 7a8936e057577473919302274b6c72c44d5b47fc | 46,566 |
def needs_nibabel_data(subdir=None):
""" Decorator for tests needing nibabel-data
Parameters
----------
subdir : None or str
Subdirectory we need in nibabel-data directory. If None, only require
nibabel-data directory itself.
Returns
-------
skip_dec : decorator
De... | 2d45fe47fae9ac6e574d33e97c556c7dabac9215 | 46,567 |
def get_ranks_lsq(df_teams, df_schedule, year, week, B_w=30., B_r=35., dS_max=35., beta_w=2.2, show=False):
"""Calculate iterative LSQ rankings, and save plot
:param df_teams: data frame wtih team_ids
:param df_schedule: data frame with data for each matchup
:param year: current year
:param week: current wee... | 3f2ba7d6aa1648b250a46cd0ecbcff9d6cb706df | 46,568 |
def __create_pyramid_features(C3, C4, C5, feature_size=256):
""" Creates the FPN layers on top of the backbone features.
Args
C3 : Feature stage C3 from the backbone.
C4 : Feature stage C4 from the backbone.
C5 : Feature stage C5 from the backbone.
... | 754acc903e767179096c5e84ec43e6df54970c3e | 46,569 |
import copy
def parse_compare(*tests, justeq=False):
"""Decorate a function to run it against pure python.
This will run and compare the function using all available backends.
"""
def decorate(fn):
def test(backend_opt, args):
if not isinstance(args, tuple):
args =... | 3e503ab27bdb5fedc62a82ddace9f12a4e9da3c2 | 46,570 |
def check_brand_parameters(request):
"""
Function to check if the parameters of a given brand are valid
:param request: request object containing all the parameters
:return: Error if exists, if not, None
"""
if not check_parameter(request.json, NAME, 2, 15) \
or not check_parameter(r... | c59b94c044d81fa29d338d51ff19a44beca0da6b | 46,571 |
def command_exists(cmd):
""" does the command exists in current system? """
path = which(cmd)
if path is None:
return False
return True | d84a843cb478c7ae63f1ec3abba0621a794dabd5 | 46,572 |
from typing import Optional
from typing import List
def _convert_names(
names, max_levels: Optional[int] = None, err_msg: Optional[str] = None
) -> List[str]:
"""Helper function that converts arguments of index, columns, values to list.
Also performs check on number of levels. If it exceeds `max_levels`,... | d67fb93b039306e7dac973abffe1e08089993c0d | 46,573 |
import time
import pickle
import json
import csv
import sys
def importCandidatePairs(input_file, log):
"""
Import candidate pairs from a file.
Supported filetypes: txt, json, pickle (python) and csv.
"""
tim = time.clock()
logprint(log, True, "Importing candidate pairs...")
candidatePairs = dict()
# Input fil... | e531e86874971e8a29a3cde24e4999d263c8b507 | 46,574 |
from backend.caffe.path_loader import PathLoader
def extractNetFromSolver(solverstring):
"""Read a protoxt string(!) of a solver and return the network protoxt string(!).
This works only, if the solver specifies a network using the "net_param" parameter. A reference to a file using the
"net" parameter ca... | a78e35798a48ef19272993183e73f314e5f7fd14 | 46,575 |
from datetime import datetime
def expand_date_param(param, lower_upper):
"""
Expands a (possibly) incomplete date string to either the lowest
or highest possible contained date and returns
datetime.datetime for that string.
0753 (lower) => 0753-01-01
2012 (upper) => 2012-12-31
2012 (lower... | 1a2c34a0abace3b521be093de361ddcae0006ea7 | 46,576 |
def appliedMediumData(modelFunction,cellSpace,initialPercentageInfected,states,n_iterations,n_simulations,theSystemHasAges = False, systemAges = None):
"""Aplica el modelo epidemiológico en n_simulations
modelFunction => Función basica del modelo epidemiológico
initialPercentageInfected => Porcentaje de inf... | 2237c93fdd318db6dc3bcd77ed7b396041938f18 | 46,577 |
import glob
import os
import subprocess
def HD_BET_bet(niix_root,
first_level_code='PID*PNAME*', second_level_code='20*',
filename_pattern='CTP_*_mc_ncct.nii.gz', out_ext='hdbet',
low_thresh=-50, high_thresh=100, gpu='4'):
"""
"""
# retrieving the study root
... | 09d5b3c0e09b096726ed6cd06b230c483b4b8317 | 46,578 |
def make_id(ref: extmodule.Ref) -> Text:
"""Convert a ref to a string iddentifier."""
return "{}/{}".format(ref.type, ref.ident) | b3dd53a68dc98230576839f9ced28c220e044e80 | 46,579 |
import math
def get_torch_datagens(data_dir, feature_len=300, num_video_per_person=1e4, num_audio_per_video=1e4, split_by='audio', split_size=0.2, txt_dirs=None, ratios=[1.0, 1.0]):
"""
Returns datagens for torch
Params:
data_dir: Parent directory for the pickle files. Assumed that each person has... | 59fc414365d44551b32c262f6268161e7d462529 | 46,580 |
def create_grouped_df_adv(group_param, df, ticker_name='symbol',
agg_func=np.count_nonzero, show_value_counts=False):
"""
Receive the `group_param`, `df`, `ticker_name`, `agg_func`
and return grouped by group_param DataFrame
with values aggregated by agg_func and Dict with
... | 22fc6d243cb64df906c143bee1b3d4ff8f4cb376 | 46,581 |
import argparse
def read_args(argv):
"""Print the splash screen, parse, and return the command-line arguments"""
print(splash)
help_formatter = lambda prog: argparse.HelpFormatter(prog, max_help_position=45, width=200)
parser = argparse.ArgumentParser(description="",
... | 45eccec5688079443316566df145891b20c89604 | 46,582 |
def query_history_to_json(query_history: QueryHistory):
"""
The function converts an object of QueryHistory to a json string.
:param query_history: A QueryHistory object.
:type query_history: ibmpairs.query.QueryHistory
:rtype: str
"""
return QueryHi... | 766d0918a7cea0868cdab6c672d14c7c5504dbd1 | 46,583 |
def is_MC(parcels):
"""
Dummy for Maricopa County.
"""
return (parcels.county == 'MC').astype(int) | 6e8af2675f1ba40d642ada0d07e133aeb9dd0d70 | 46,584 |
import re
def getMatchingCompetence(dictionary, lastmessage):
"""
Searches for a competence in a string
"""
allCompetences = getAllCompetences(dictionary)
searchedCompetence = []
for word in re.split('[ .!?]', lastmessage):
if stemmer.stem(word.strip().lower()) in [
ste... | 2b6fbd95597db71a60a914443d2998ad093acb33 | 46,585 |
def objScale(obj,factor):
"""
Object scaling function, gets obj and scale factor, returns an array of the scaled size
"""
oldSize = obj.get_size()
newSize = []
for i in oldSize:
newSize.append(int(i/float(factor)))
return newSize | 3104fc4e126299400a5a119fff0d8bc9d3ea32f7 | 46,586 |
def tiny_shakespeare(c: NLPAutoRegressionConfigs):
"""
### Tiny Shakespeare dataset
It will download from the url if not present
"""
return TextFileDataset(
lab.get_data_path() / 'tiny_shakespeare.txt',
c.tokenizer,
url='https://raw.githubusercontent.com/karpathy/char-rnn/ma... | 7a22a359c5306e73e94701ce91645c31cf87c380 | 46,587 |
def preprocess_encoder_input(arr):
"""
Simple method to handle the complex MFCC coefs that are produced during preprocessing. This means:
1. (For now), discarding one of the channels of the MFCC coefs
2. Collapsing any empty dimensions
:param arr: the array of MFCC coefficients.
"""
return a... | ccd754783377e9fe257e423f9099d6dbef21d11b | 46,588 |
def findTreeRoute(observations, tree, startNodeID, startLayer, stopLayer, alternateLeafActivation = False):
"""Classifies the observations according to the tree."""
col = 0
treeRoute = []
for i in range(startLayer,stopLayer+2):
if i == startLayer:
currentNode = tree[startNodeID]
... | e206902c6edec2eff100f2ff77bb8a57d2a6c99f | 46,589 |
def determineWinCardIndex(pile):
"""
@param list pile: pile of cards in center
@return int: index of winning card -
highest index of leading suit, or if there are spades, highest spades index
"""
bestCard = (pile[0], 0)
for i in range(1, len(pile)):
bestCardSuit = bestCard[0].ind... | c105ade59b79482de0f2d3e6b4d25c4353bb1dcf | 46,590 |
import time
def sagemaker_timestamp():
"""Return a timestamp with millisecond precision."""
moment = time.time()
moment_ms = repr(moment).split('.')[1][:3]
return time.strftime("%Y-%m-%d-%H-%M-%S-{}".format(moment_ms), time.gmtime(moment)) | a0c09ed9f419ef519ca22b219e1bc0922f7f7f7c | 46,591 |
def EmitEval(deque_graph, # type: collections.deque
ctx, # type: Context
):
"""
Emit evaluation code(eval layers)
"""
ret = '''
bool NetworkForwardExecute(Buffer *buffer) {
'''
for layer_name in deque_graph:
node = ctx.graph[layer_name]
if node["op"] == "N... | d2215da9db38d53e7a5fd1f6b3da01ef310cf357 | 46,592 |
from ._tornadoserver import TornadoServer # noqa - circular dependency
from ._flaskserver import FlaskServer
def create_server(host=None, port=None, loop=None, backend='tornado',
**server_kwargs):
"""
Create a new server object. This is automatically called; users generally
don't need t... | e4806cd389287c6aeab2e9ad824317fc34ec13e5 | 46,593 |
def build_dataset_info(cfg):
"""Build information struct about the dataset from a configuration.
This performs type conversion to the expected types. Paths contained in
cfg are expected to be alread expanded. That is, it should not contain
global variables or other system dependent abbreviations.
... | 576e71057bcaf38c1720d5b70f21557c608700e0 | 46,594 |
import os
def is_database_available(name: str) -> bool:
"""
Basic check for available files, does not recognize if some files
are missing.
"""
base_name = os.path.join(BLASTDB, name)
# It may be from multiple files, so check for the first one.
return are_database_files_available(base_name)... | 2c9f281a3d35db1f3cbee8e51f228ce2f235a321 | 46,595 |
def hdr2srgb(im):
"""[Tonemap a linear image with quasi srgb (clip and gamma 1/2.2)]
Args:
im ([[hxwx3 ndarray]): [linear image]
Returns:
[hxwx3 ndarray]: [srgb image]
"""
return (np.clip(im,0,1) ** (1/2.2) * 255).astype(np.uint8) | ab932f0ea5061f549bbb5d85ab8e1cdbeabb4466 | 46,596 |
def greyList(n):
"""
生成格雷编码序列
参考：https://www.jb51.net/article/133575.htm
:param n: 长度
:return: 范围 2 ** n的格雷序列
"""
def get_grace(list_grace, n):
if n == 1:
return list_grace
list_before, list_after = [], []
for i in range(len(list_grace)):
list... | eab1f00ec2cdd62fbbffbb78a2c69c6fe9177e66 | 46,597 |
from typing import Union
def get_feature(feature: Union[str, Feature]) -> Feature:
"""Get feature from name.
Args:
name (str): Name of feature.
Returns:
Feature
"""
if isinstance(feature, str):
feature = get_feature_class(feature)()
elif not isinstance(feature, Featur... | 92a0938ff426f66bc61e10e9f7b3f6cdc64ede33 | 46,598 |
from sys import path
def format_seq_timing(txt_loc, edge_type=1):
"""format the hold and setup time as per .lib requirement """
attr_names = ['rise_hold', 'fall_hold', 'rise_setup', 'fall_setup']
lut_table_list = []
seq_timing_type = ''
if edge_type == 1:
clk_type = 'rising'
else:
... | 31d7f845b6dfc8e90579e80c68b800c6969d2aff | 46,599 |
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