content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def combine(main_busytime_list, merging_list):
"""
given two lists of busytimes, returns list of busytimes that have events of the same day together
"""
merged = [ ]
days = { }
current_date = ''
#this creates the dates dict
for busytime in main_busytime_list:
if busytime['date'] ... | 978946353dd4d7a57b57e66693a5d396f6e8d7f6 | 72,300 |
def update_speed(row_speed, column_speed, rows_direction, columns_direction, row_speed_limit=2, column_speed_limit=2, fading=0.8):
"""Update speed smootly to make control handy for player. Return new speed value (row_speed, column_speed)
rows_direction — is a force direction by rows axis. Possible values:
... | e17e8e7d5ecd8b1f0e4679ca3d5e70f8b99aadac | 72,301 |
def oc(*args, **kwargs):
"""
Run 'sh.oc' and print the command, show output, catch errors, etc.
Optional kwargs:
_reraise: if ErrorReturnCode is hit, don't exit, re-raise it
_exit_on_err: sys.exit(1) if this command fails (default True)
_silent: don't print command or resulting stdo... | 6b6c90a3aaf8d1d89c6abe7a9b6ee84765c8bad5 | 72,302 |
def label_to_membership(targets, num_classes=None):
"""Generate a true membership matrix, and assign value to current Pi.
Parameters:
targets (np.ndarray): matrix with one hot labels
Return:
Pi: membership matirx, shape (num_classes, num_samples, num_samples)
"""
targets = one_hot... | 9681fa8639b9403ec2e5b7637c5df0af571e6fe4 | 72,303 |
def open_fits(fitsfilename, n=0, header=False, ignore_missing_end=False,
precision=np.float32, verbose=True):
"""Loads a fits file into a memory as numpy array.
Parameters
----------
fitsfilename : string
Name of the fits file.
n : int
It chooses which HDU to open.... | ccb8c91ab2c1ca20d590028a4865e58d02baf0fe | 72,304 |
def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, num_already_masked,
vocab_words, rng, do_whole_word_mask=False):
"""Creates the predictions for the masked LM objective."""
assert 0 < len(tokens) <= 512, str(len(tokens))
cand_indexes = []
... | e30ba2438ffcdf8d1c154b90bdac1a9f106eaf10 | 72,305 |
def get_user_id(username):
"""Convenience method to look up the id for a username."""
try:
rv = User.select().where(User.username == username).get()
except User.DoesNotExist:
rv = None
return rv | 5bda6c59c6864d03755e4dd3330a925c56ba546a | 72,306 |
def performSA(
initLabels,
initRegionList,
initRegionSpatialAttr,
threshold_array,
weight,
distance_matrix,
threshold,
alpha,
tabuLength,
max_no_move,
):
"""...Needs a short description...
Parameters
----------
initLabels :
...
init... | a0c971fd0cb29309e954bd8bd720840cd1ea5907 | 72,307 |
def partition(data, threshold=0.5, verbose = False):
"""Partitions data into training and test sets. Assumes the last column of
data is y.
Parameters
----------
data : numpy ndarray, or pandas.DataFrame
Data to partition into training and test sets.
threshold : float
Determi... | 7231dacd783b13f45d0591908653694bc08bd669 | 72,308 |
def preprocess_data(closing_data):
"""Preprocesses data into time series.
Args:
closing_data (pandas.dataframe): dataframe with close values of tickers
Returns:
pandas.dataframe: dataframe with time series
"""
# transform into log return
log_return_data = pd.DataFrame()
tickers = [column_heade... | 475722431583c1299a209df8c6b69ef7b0d1270e | 72,309 |
def map_sinh(process):
"""
"""
return map_default(process, 'sinh', 'apply') | 39e91c99e95f244b2a7c08ba4949caefcdbbb930 | 72,310 |
from typing import Mapping
from typing import Hashable
from typing import Callable
from typing import Union
def xarray_block_reduce(
obj: T_DataArray_or_Dataset,
block_sizes: Mapping[Hashable, int],
reduction_function: Callable,
cval: float = np.nan,
coord_func: Union[str, CoordFunc] = coarsen_coo... | f82cc61e9a0b6afb6167b47a58d105119a125d80 | 72,311 |
def GroupNorm(x, group=32, gamma_initializer=tf.constant_initializer(1.)):
"""
More code that reproduces the paper can be found at https://github.com/ppwwyyxx/GroupNorm-reproduce/.
"""
shape = x.get_shape().as_list()
ndims = len(shape)
assert ndims == 4, shape
chan = shape[1]
assert chan... | f4fd603fc35f8b17881f60088b74f5176970e9ce | 72,312 |
import traceback
def get_min_max_json(request):
"""
Returns a JsonResponse with min and max values
:param request:
:return:
"""
if request.method == 'POST':
try:
post_values = unravel_post(request.POST)
values = get_min_max(model=post_values.model,
... | 89f700e8d420c978bb00ccee2bba25026b531706 | 72,313 |
def regional_get_more(page, per_page, kindtype, status, merge):
""" 分页获取指定 votype 下的 ValueObject 信息
"""
if merge > 0:
return_method = lambda vos: [vo.merge() for vo in vos]
else:
return_method = 'model'
qry = Regional.get_qry(kindtype=kindtype, status=status)
rdata = get_page_re... | b3650a5f9dc2c29f52fd7c739162d49e50307a88 | 72,314 |
from typing import List
def chunked_conj(chunk_size=32) -> Fn:
"""
Like `conj` but buffers values into chunks and calls `conj` with chunks.
Returns a function which will conjoin in batches for the specified `chunk_size`.
To be used with tranducers. Must complete the fuction (call with 1 arg) to
g... | 84ebffaa6914711cb8bd6eb61dd16b9f47f0a945 | 72,315 |
def _make_final_gr(x):
"""Apply function to do graduation rates"""
race, sixyrgr, sixyrgraah, comments = x
first_gr = sixyrgraah if race in ["B", "H", "M", "I"] else sixyrgr
if comments == "Posse":
return (first_gr + 0.15) if first_gr < 0.7 else (1.0 - (1.0 - first_gr) / 2)
else:
ret... | 600157433efe8a1689d81c2e17114c1578c43c21 | 72,316 |
def truncate_pad(line, num_steps, padding_token):
"""Truncate or pad sequences.
Defined in :numref:`sec_machine_translation`
"""
if len(line) > num_steps:
return line[:num_steps] # Truncate
return line + [padding_token] * (num_steps - len(line)) | 2e9da312e50cea4bc2015612fa5aaccc66d3f704 | 72,317 |
def sparsenet264(**kwargs):
"""
SparseNet-264 model from 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
... | 8b8a3b8cc3e78c04dc190458effd0e4de46bf9bd | 72,318 |
from typing import Callable
def rmsle(series1: TimeSeries,
series2: TimeSeries,
intersect: bool = True,
reduction: Callable[[np.ndarray], float] = np.mean) -> float:
""" Root Mean Squared Log Error (RMSLE).
For two time series :math:`y^1` and :math:`y^2` of length :math:`T`, it ... | eb66e6a618c9f3a3373bbf57d66523cada197fb1 | 72,319 |
def extract(infile,filter="(tcp or udp or gre)",extension="", ip_layer=False):
"""Extract flows from given pcap file.
Parameters
----------
infile : string
Path to input file.
filter : string
Filter condition, which is the same with wireshark
extensio... | 660733e97caaaf11d58008f3214d9cf218f958ba | 72,320 |
import requests
def check_hash(h):
"""
Do the heavy lifting. Take the hash, poll the haveibeenpwned API, and check results.
:param h: The sha1 hash to check
:return: The number of times the password has been found (0 is good!)
"""
if len(h) != 40:
raise ValueError("A sha1 hash should be 30 characters.")
h = ... | 965dd75b5da095bc24ce6a6d733b271d9ec7aa80 | 72,321 |
import collections
import json
def _encode_log(namespace, time_ms, event_type, key, value, metadata):
"""Encodes an MLEvent as a string log line.
Args:
namespace: provides structure, e.g. "GPU0".
time_ms: milliseconds since unix epoch.
event_type: one of: 'INTERVAL_START', 'INTERVAL_END', 'POINT_IN_T... | 61de4ace883fe63ed3868487d73ea7288e44de75 | 72,322 |
import re
def load(path, mode='r', encoding='cp932'):
"""
otoiniを読み取ってオブジェクト生成
"""
# otoiniファイルを読み取る
path = path.strip('"')
with open(path, mode=mode, encoding=encoding) as f:
lines = [line.strip() for line in f.readlines()]
# Otoクラスオブジェクトのリストを作る
otoini = OtoIni()
for line... | 224939573e3e31cffbd21b0defba5d6effe4b83b | 72,323 |
def RF_get_feature(path):
"""
获取apk特征集
:param path:
:return:
"""
feature_list = get_permissions(path)+get_apis(path)
feature_list = list(set(feature_list))
return list(feature_list) | 7cbecd5396c1e69f9f20a4ee7f53303b866884d2 | 72,324 |
def create_unet_new(config, metrics=None):
"""
Does not work so far!!!
:param config:
:param metrics:
:return:
"""
unet = Unet(config, metrics)
loss_f = config.get('LOSS_FUNCTION', keras.losses.categorical_crossentropy)
unet.compile(optimizer=get_optimizer(config), loss=loss_f, metri... | 80dbac4ca81a1c216e342ac47411563ec48f63ed | 72,325 |
import argparse
def create_parser(prog_name):
"""Parse command line flags added to `rest_api` command.
"""
parent_parser = create_parent_parser(prog_name)
parser = argparse.ArgumentParser(
parents=[parent_parser],
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument... | 7bac1a9175c8135d2f93c91cbc1dccd760ad1eee | 72,326 |
def make_frequency_matrix(sequence_dict):
"""Makes list of base frequencies per position
- Skips any position where the maximum base is a '-'' or 'N'
:param sequence_dict - count of each base per position:
:returns frequency_matrix - list of dictionaries:
"""
frequency_matrix = []
base_pos... | 820f4387f778429890cb961c2ca76aa51a01d250 | 72,327 |
def create_ipv6_filter_rule(container_ip, bridge, proto, container_port):
"""return a iptables v4 filter rule for forwarding a host port to a container IP:port"""
return '-A FORWARD -d {container_ip} ! -i {bridge} -o {bridge}' \
' -p {proto} -m {proto} --dport {container_port}'\
' -j ACCEP... | 903e43c285c14e20aeb74133c3a9da14b21997b6 | 72,328 |
import re
def is_applicable_python_file(rel_path: str) -> bool:
"""
Determines if a file should be included in incremental coverage analysis.
Args:
rel_path: The repo-relative file path being considered.
Returns:
Whether to include the file.
"""
return rel_path.endswith('.py')... | a890ceaee78cc6a09cd3055dab0573d352d81166 | 72,329 |
def get_field_hint(config, field):
"""Get the hint given by __field_hint__ or the field name if not defined."""
return getattr(config, '__{field}_hint__'.format(field=field), field) | 0d374daf93646caf55fe436c8eb2913d22151bbc | 72,330 |
def optimal_epsilon_integral():
"""Fit optimal choice of epsilon for integral representation.
The integrand of
int_0^pi P(eps, a, b, x, phi) * dphi
can exhibit oscillatory behaviour. It stems from the cosine of P and can be
minimized by minimizing the arc length of the argument
f(phi) =... | 6d00fb2982b9210bf0aad1c67b88187a80cb0d7a | 72,331 |
def strikethrough(text):
"""Return the text, with strikethrough IRC formatting.
Note: This is a relatively new addition to IRC formatting conventions.
Use only when you can afford to have its meaning lost, as not many clients
support it yet."""
return ''.join([CONTROL_STRIKETHROUGH, text, CONTROL_S... | 1842fd3eeaf552f465a795969f67f3b3dce2440b | 72,332 |
def get_energy_level(sound, win_len):
""" Calculate energy signal of an audio object
Args:
sound: AudioSegment object with the audio signal
win_len: length of the window for the energy calculations
Returns:
energy: the energy of the signal
"""
loudness = list([])
len... | 70c693cef7f7e5cbe2ff6187312db3df97c7677e | 72,333 |
from galpy.potential import evaluateplanarPotentials
from galpy.potential import PotentialError
from galpy.potential import RZToplanarPotential
def vesc(Pot,R):
"""
NAME:
vesc
PURPOSE:
calculate the escape velocity at R for potential Pot
INPUT:
Pot - Potential instances or ... | fe5b3aead92acd6777a0babf71683e48a38c3df5 | 72,334 |
def match_major_version(semantic_version, major_version):
"""
:param semantic_version: (str)
:param major_version: (int)
"""
parts = semantic_version.split(".")
major = parse_int(parts[0])
return major_version == major | ebd3ec1fc2cb035f7e79f5027dd0a0aea890c43d | 72,335 |
import re
def escape_re(strg: str) -> str:
"""
Returns the string with all regular expression special characters escaped.
TODO: Remove this function in favor of re.escape()
"""
# assert isinstance(strg, str)
# re_chars = r"\.^$*+?{}[]()#<>=|!"
# for esc_ch in re_chars:
# strg = st... | df30603e374a277500cfb268d1a905ed84ca88c5 | 72,336 |
def array2tensor(z, dtype=tf.float32):
"""Converts numpy arrays into tensorflow tensors.
Keyword arguments:
z -- numpy array
dtype -- data type of tensor entries (default float32)
"""
if len(np.shape(z)) == 1: # special case where input is a vector
return tf.cast(np.reshape(z, (np.sh... | 4f8e2bb6a2f5a49ac257f14bbfbf3657d9df156c | 72,337 |
def _splitSentencesInternalRawString(body, name):
"""Splits a raw string."""
docLength = len(body)
docName = name
doc = nlp(unicode(body)) # Necessary in Python2.7
#doc = nlp(body) # Necessary in python3.6
sentences = []
for sent in doc.sents:
text = sent.text
span = (sent.st... | b658b49aedfed3431f39ab7471427428ab787326 | 72,338 |
def Fourier_series_approx(x_range, fx, n_deg):
"""
This function is used to create the Fourier series which be truncated
fourier_f(x) = sum(2*cn*exp(j*n*x/L))
where
cn is the Fourier coefficience
"""
y = fx(x_range)
period = 2*np.pi
time = x_range
... | 173679e7260c1b9860a001a8b50229780a13f95f | 72,339 |
def btc(value):
"""Format value as BTC."""
return f"{value:,.8f}" | 1d883384a6052788e8fa2bedcddd723b8765f44f | 72,340 |
def parse_sbd_devices(options):
"""Returns an array of all sbd devices.
Key arguments:
options -- options dictionary
Return Value:
devices -- array of device paths
"""
devices = [str.strip(dev) \
for dev in str.split(options["--devices"], ",")]
return devices | d706efd6a456e2279b30492b634e018ebd8e873f | 72,341 |
from src.praxxis.sqlite import sqlite_notebook
from src.praxxis.display import display_notebook
def search_notebook(args, library_db, current_scene_db, query_start, query_end):
"""searches and displays loaded notebooks"""
search_term = args.term
notebooks = sqlite_notebook.search_notebooks(library_db, se... | 4f36f3b4fd3f1057b729cbe6d4ef6bdcdf5e8b0a | 72,342 |
from typing import Mapping
import pandas
def create_attribute_types_dataframe(attribute_types: Mapping) -> pandas.DataFrame:
"""
Creates a pandas.Dataframe from a Mapping of attribute_types str:field=str:attribute_type
:param attribute_types: Mapping str:field=str:attribute_type
:return: pandas.DataF... | 7e7276542cb9a96f3aaa697cad3eba744527f98f | 72,343 |
from typing import List
from typing import Iterator
def _center_provider(contexts: 'List[Acronym]', min_freq: int,
max_rank: int) -> Iterator[str]:
"""
Provide unlimited center words for a given list of contexts.
:param contexts:
:param min_freq:
:param max_rank:
:return:... | 17e3d46d3e1dcf90a50e3680c8218d25603bf8bf | 72,344 |
def generateJopekMethodE(loaded_data, n_samples, q_std, node_std, e_std, peri_std, incl_std, show_plots=True,
get_more_data=False):
""" Generate synthetic orbits using the Jopek & Bronikowska (2016) method E approach.
Arguments:
loaded_data: [2D ndarray] numpy array containing the input data, colu... | c176c215656672b351ff7a1a5f4724134ea0d2bb | 72,345 |
def pull_from_js(content, key):
"""Pull a value from some ES6."""
# we have to parse fucking ES6 because ES6 cannot natively import fucking JSON
return list(filter(lambda x: f"{key}:" in x, content.split("\n")))[0].split('"')[1] | 0b5f7fae68e1f5d376c22cd18a6799c4c76a71f7 | 72,346 |
def multi_category_focal_loss2(gamma=2., alpha=.25):
"""
focal loss for multi category of multi label problem
适用于多分类或多标签问题的focal loss
alpha控制真值y_true为1/0时的权重
1的权重为alpha, 0的权重为1-alpha
当你的模型欠拟合，学习存在困难时，可以尝试适用本函数作为loss
当模型过于激进(无论何时总是倾向于预测出1),尝试将alpha调小
当模型过于惰性(无论何时总是倾向于预测出0,或是某一个固定的常数,说... | bb9ab631280dd4425c9517ee25e204b0f31712f4 | 72,347 |
import time
def nagios_from_file(results_file):
"""Returns a nagios-appropriate string and return code obtained by
parsing the desired file on disk. The file on disk should be of format
%s|%s % (timestamp, nagios_string)
This file is created by various nagios checking cron jobs such as
check-rab... | 02697105ad5e9d01dd0eb504e232314f4d15a6a9 | 72,348 |
import math
def _width2wing(width, x, min_wing=3):
"""Convert a fractional or absolute width to integer half-width ("wing").
"""
if 0 < width < 1:
wing = int(math.ceil(len(x) * width * 0.5))
elif width >= 2 and int(width) == width:
# Ensure window width <= len(x) to avoid TypeError
... | 38bdb809167b19b0ef5c7fad6858d2f7016ec310 | 72,349 |
def parse_timecode_str(s: str) -> Timecode:
"""Parses a timecode from all the numeric digits in s."""
digits = _NON_DIGIT_PATTERN.sub("", s) # Remove non-digits.
digits = f"{int(digits):08}" # Zero pad to 8 digits.
if len(digits) != 8:
print_error_exit(f"Invalid timecode pattern '{s}'")
h... | 35ce4e572e3f2064f09cb37305ed83aa5ca88cf2 | 72,350 |
def probplot(
data,
ax=None,
axtype="prob",
yscale="log",
xlabel=None,
ylabel=None,
bestfit=False,
scatter_kws=None,
line_kws=None,
return_results=False,
):
""" Probability, percentile, and quantile plots.
Parameters
----------
data : sequence or array-like
... | cdd5c8031cce44df9a2225eca7d501cf63f299aa | 72,351 |
from typing import Tuple
def concatenate(data, axis):
"""Concatenate the input tensors along the given axis.
Parameters
----------
data : Union(List[relay.Expr], Tuple[relay.Expr])
A list of tensors.
axis : int
The axis along which the tensors are concatenated.
Returns
--... | acafe6c2ed70f97e0e88312a5d5c71dae73f1f2e | 72,352 |
def mb_bl_ind(tr1, tr2):
"""Returns the baseline index for given track indices.
By convention, tr1 < tr2. Otherwise, a warning is printed,
and same baseline returned.
"""
if tr1 == tr2:
print("ERROR: no baseline between same tracks")
return None
if tr1 > tr2:
print("WARN... | 7d1bc958ca9928f54e51935510d62c45f7fc927f | 72,353 |
def run_cardiac_segmentation(img, settings=CARDIAC_SETTINGS_DEFAULTS):
"""Runs the atlas-based cardiac segmentation
Args:
img (sitk.Image):
settings (dict, optional): Dictionary containing settings for algorithm.
Defaults to default_settings.
Returns:
... | 0c6fa36b5525f4ddc19d891f2f86d17f3683ab60 | 72,354 |
from pathlib import Path
def check_installed(installed="/media/mmcblk0p1/installed"):
"""Check if LNCM-Box is installed"""
if Path(installed).is_file():
with open(installed, "r") as file:
lines = file.readlines()
for line in lines:
print(line)
return Tru... | 31f73b8927e0a23c6d70ab13791ead9cf5d55b8c | 72,355 |
def L(s, o, N=5):
"""
Likelihood
input:
s: simulated
o: observed
output:
L: likelihood
"""
# s,o = filter_nan(s,o)
return np.exp(-N*np.sum((s-o)**2)/np.sum((o-np.mean(o))**2)) | 05cb084ce392c009d69b172a72b8b13d9faad8bf | 72,356 |
def decipher(string):
"""
1. Record the index of the matched letter
2. Go back to correspond to the original ALPHABET
3. print
"""
ans = ''
for i in s: # W L L H A
j = string.find(i) # 0 15 15 11 4
for ch in range(len(ALPHABET)):
if j == ch:
ans ... | 8356cd82021f8166a526cb5cec1efad6cf3ddfa9 | 72,357 |
def sliding_window(image_width,image_height, patch_w,patch_h,adj_overlay_x=0,adj_overlay_y=0):
"""get the subset windows of each patch
Args:
image_width: width of input image
image_height: height of input image
patch_w: the width of the expected patch
patch_h: the height of the e... | 42af883927a72f313eb7804e9d99bd6ec37fa7f3 | 72,358 |
from typing import Tuple
def process_rows(df: pd.DataFrame) -> Tuple[pd.DataFrame, int, int, int]:
"""
Create various counts of rows of a dataframe.
Drop duplicate rows.
Parameters
----------
df : pd.DataFrame
The input dataframe.
Returns
-------
df : pd.DataFrame
... | 626dfe150128ad1e27708bfe2ac5b98f2c30e03e | 72,359 |
from FuXi.Rete.RuleStore import Formula
from FuXi.Rete.AlphaNode import AlphaNode
def ExtendN3Rules(network, horn_clause, constructNetwork=False):
"""
Extends the network with the given Horn clause (rule)
"""
rt = []
if constructNetwork:
ruleStore = network.ruleStore
lhs = BNode()
... | 03ad35d45264a84a25a42a76c7458adc08fc861d | 72,360 |
import getpass
def main(args: list, pipe: bool) -> Result:
"""
# TODO: Add docstring for manpage
"""
args, parser = parse_args(args)
if parser.error_message:
if not args.version:
return output(f"{__COMMAND__}: {parser.error_message}", pipe, success=False)
if args.version:... | c520a29143fbe6ca2d12921170e01786ad3e68a6 | 72,361 |
def generate_2d_neighbors(input_tensor, dilation=2):
"""Generate a feature map of 2D neighbors.
Note: This op makes 8 (# of neighbors) as the leading dimension so that
following ops on TPU won't have to pad the last dimension to 128.
Args:
input_tensor: A float tensor of shape [height, width, channels].
... | a7d14fe83b552f990b64b06ef8e4545cfaf11a87 | 72,362 |
def datetime_format_to_js_datetime_format(format):
"""
Convert a Python datetime format to a time format suitable for use with
the datetime picker we use, http://www.malot.fr/bootstrap-datetimepicker/.
"""
converted = format
replacements = {
'%Y': 'yyyy',
'%y': 'yy',
'%m'... | a037146b17aae21831bc4c76d4500b12bc34feba | 72,363 |
import os
import configparser
def load_security_setting():
"""Load security settings."""
home = os.environ['HOME']
config_file = os.path.join(home, ".vega/vega.ini")
if not check_risky_files([config_file]):
return False
cfg = configparser.ConfigParser()
cfg.read(config_file)
config... | c82c3fafc7b5b65a6ba2769970f54ce2dd564af5 | 72,364 |
import typing
def search(
apikey: str, query: str = "", limit: int = DEFAULT_LIMIT, exchange: str = ""
) -> typing.List[typing.Dict]:
"""
Query FMP /search/ API.
Search via ticker and company name.
:param apikey: Your API key.
:param query: Whole or fragment of Ticker or Name of company.
... | 977fd6ebb129a311249042ebe310b8fb5fddbabf | 72,365 |
import click
import subprocess
def _join(client, network):
"""Join the given network.
Args:
client (two1.server.rest_client.TwentyOneRestClient) an object
for sending authenticated requests to the TwentyOne
backend
network (str): the name of the network being joined. D... | 3e735faac3b70c77cb97bafa46f35aa69b81847d | 72,366 |
import numpy
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""
Function to convert mallet model to gensim LdaModel. This works by copying the
training model weights (alpha, beta...) from a trained mallet model into the
gensim model.
Args:
mallet_model : Trained... | c00bc1ece7171c58bea33e5738afee3a0374f310 | 72,367 |
def get_sample_counts(tree_sequence, st):
"""
Returns a list of the sample node counts for the specified tree.
"""
nu = [0 for j in range(st.get_num_nodes())]
for j in range(tree_sequence.get_num_samples()):
u = j
while u != _tskit.NULL:
nu[u] += 1
u = st.get_... | 5d0aa413466ceb0ce7e41074680b434a9e663c30 | 72,368 |
def mgls(freqs):
"""
"""
if Globals.ar:
fitting_coeffs, A_matrix = mMGLS_AR.mdim_gls(Globals.time, Globals.rv, Globals.rv_err, freqs, Globals.params, Globals.nar, Globals.time[0])
#model = mMGLS_AR.model(Globals.time, Globals.rv, freqs, fitting_coeffs, Globals.params)
#chi2 = mMGLS_A... | 26c9ce981eb616565e1a795a4fe247f84ae1e17e | 72,369 |
def get_physical_wwpns(adapter, force_refresh=True):
"""Returns the active WWPNs of the FC ports across all VIOSes on system.
:param adapter: pypowervm.adapter.Adapter for REST API communication.
:param force_refresh: The value discovered by this method is cached. If
force_refres... | 93131e889d7e059e72fbed0cbd1ff5f2e606ee15 | 72,370 |
def _data_aug(image, box, jitter, hue, sat, val, image_input_size, max_boxes, max_trial=10, device_num=1):
"""Crop an image randomly with bounding box constraints.
This data augmentation is used in training of
Single Shot Multibox Detector [#]_. More details can be found in
data augmentatio... | f969448f44de730616d3ddee0e90b187d7ad2083 | 72,371 |
def get_metric_info(f):
"""Return metric type and metric name as tuple.
results/*_div/*/*/effect_sizes.tsv
"""
path_parts = PurePath(f).parts
return path_parts[2], path_parts[3] | 8420ac8663c423414153e786c49e854801d3ed0b | 72,372 |
def donation(f):
"""This decorator passes donate request """
return add_response_headers({'BTC':
'1aeuaAijzwK4Jk2ixomRkqjF6Q3JxXp9Q',
"Patreon": "patreon.com/jarbasAI",
"Paypal": "paypal.me/jarbasAI"})(
... | 2ad516b45d3ba1e6d6752fc9b0c2846281e36285 | 72,373 |
def collect_reuters_financials(universes=None, sids=None, force=True):
"""
Collect Reuters financial statements from Interactive Brokers and save
to database.
This data provides cash flow, balance sheet, and income metrics.
Parameters
----------
universes : list of str, optional
li... | 6699563ddde7759f73794312e8df4f5f31434dae | 72,374 |
def do_admins(bot, msg, **kwargs):
"""
Return the usernames of those who currently have admin priviledges over
the bot receiving this message.
"""
channel = kwargs.get('event').get('channel')
bot.post_msg(
text='My admins are: {admins}'.format(
admins=', '.join([bot.format_us... | fdd7ec3cbca1ab68de4a7d653d52b95d9f254b66 | 72,375 |
def api_get_document(project_id): # noqa: F401
"""Retrieve documents in order of review.
After these documents were retrieved, the queue on the client side is
updated.
This resuest can get triggered after each document classification.
Although it might be better to call this function after 20 requ... | 5eb291aa5bb7f2cffb674764440262fee9bdd1f0 | 72,376 |
def bray_curtis_distance_0(x_list, data_list):
"""
:param
x_list :
[(x0, y0), (x1, y1), (x2, y2), ..., (xn-2, yn-2), (xn-1, yn-1)]
data_list :
[x_list_0 (same data structure as x_list), x_list_1, x_list_2, ..., x_list_n-2, x_list_n-1]
:return:
bcd_list
"""... | ee61ac9b55d1ff264a7bdc6ccb2f17ca8b9164e8 | 72,377 |
def get_sigma(maj_error, min_error, pos_angle,
radio_ra, radio_dec,
opt_ra, opt_dec, opt_ra_err, opt_dec_err,
additonal_error=0.6):
"""
Get the combined error between an elongated radio source and an
optical source.
Input:
* maj_error: error in the ... | aa8dfac1fa55a19c99aea7113813a8be74f95fb8 | 72,378 |
def layer_sizes(X, Y):
"""
Arguments:
X -- input dataset of shape (input size, number of examples)
Y -- labels of shape (output size, number of examples)
Returns:
n_x -- the size of the input layer
n_h -- the size of the hidden layer
n_y -- the size of the output layer
"""
... | 0f790d3cafe746f1f01f79782c41747c0a89c3a6 | 72,379 |
def measure_optimise(budget, liste):
""" measures run time."""
return knap_sack_optimise(budget, liste) | d20d8010e1dbf66e57e56a80332345200064ac59 | 72,380 |
def delete_cloudformation_stack(stack_id, aws_config):
"""
Delete a CloudFormation stack.
:param unicode stack_id: The AWS cloudformation stack ID.
:param dict aws_config: environment variables to be merged with the current
process environment before running the ``aws`` sub-command.
:return... | 41df0166bad64afaa9da143b05971f64ef0753f6 | 72,381 |
from typing import Type
from typing import List
from typing import Dict
def _get_device_info(cls: Type[ophyd.Device]) -> List[Dict]:
"""
Get Device information that can easily be rendered as a table.
Parameters
----------
cls : ophyd.Device subclass
"""
if not issubclass(cls, ophyd.Device... | 8bd170cd11aea75344c91285a0c6f7db3c677e75 | 72,382 |
import os
def screenshot_fa_peaks(fa, peaks, directory='.'):
"""
Compute 3 view screenshot with peaks on FA.
Parameters
----------
fa : string
FA filename.
peaks : string
Peak filename.
directory : string
Directory to save the mosaic.
Returns
-------
n... | 048bc652c3968783bf70f837a5701828ab92e56a | 72,383 |
def make_data(current_data):
""" Formats the given data into the required form """
x = []
n = len(current_data)
for i in range(n - 1):
x.append(current_data[i])
x.append(1)
return x, current_data[n - 1] | 04ff4ba93445895451c4c710e7aae59b9787a07d | 72,384 |
def _process_centroids(k, num_features, observations, comm):
""" Helper method to distribute provided k if necessary and resolve whether
or not the input is seeded.
Returns
-------
centroids : Replicated MPIArray
Array of cluster centroids generated from provided set of observations.
... | 9985936817a5ff097448b25eb0e1c45abc9ab480 | 72,385 |
def create_issue(self, request, form):
""" Create a new issue.
This view is only visible by a publisher.
"""
layout = Layout(self, request)
if form.submitted(request):
issue = Issue()
form.update_model(issue)
request.session.add(issue)
request.message(_("Issue adde... | b3bf32095d35a5948a943aea664d1361713493b8 | 72,386 |
def average_over_dictionary(mydict):
"""
Average over dictionary values.
"""
ave = sum([x for x in mydict.values()])/len(mydict)
return ave | 584e8cb073c298b3790a96e2649dbacfd5716987 | 72,387 |
from datetime import datetime
def login():
"""
form = LoginForm()
if request.method == 'POST':
if form.validate_on_submit():
{}
return render_template('auth/login.html', form=form)
"""
form = LoginForm()
user = User.authenticate(form.email.data, form.password.data)
... | 5948324df10c14cf6c16036bea847064688945c2 | 72,388 |
from typing import Optional
from typing import Dict
from typing import Union
from pathlib import Path
import logging
import tqdm
def prepare_mls(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
opus: bool = True,
num_jobs: int = 1,
) -> Dict[str, Dict[str, Dict[str, Union[RecordingSet, Su... | cd31b43b9e86e2516b4cc389d45daeff59470927 | 72,389 |
def _project_partial_joint_monotonicity(weights, lattice_sizes,
joint_monotonicity, constraint_group):
"""Applies exact joint monotonicity projection to given constraint group.
Algorithm details:
For the joint monotonicity projection, we follow a similar approach to the
... | b166b23ddb8889fc3725d3a21331c62eef1b3135 | 72,390 |
def mae(forecasted, observed, average_axis=None):
"""
Calculate MAE
:param forecasted: Forecast array
:param observed: Observation array
:param average_axis: The axis to average. `None` for averaging all axes.
:return: MAE array
"""
stats = np.nanmean(np.abs(forecasted - observed), axis... | d9968dfb0c1e5e15e0b8a5534d77ce65a39217a5 | 72,391 |
def get_datanodes_list():
"""Function for getting the datanodes identities from the configuration file.
Parameters
----------
None
Returns
-------
conf['datanodes'] --> list, the list of the datanodes
"""
return conf['datanodes'] | a0207676e4f42cde46333de60a6192535fb05c42 | 72,392 |
from datetime import datetime
import dateutil
def is_later(timestamp, period):
"""Return true if the current time > timestamp + period.
Checks if the timestamp given is later than the current time + the
period given in seconds.
Attributes:
timestamp -- integer, EPOCH.
period -- integer, seco... | 73d51bbb0294094107d7a2ee4068dcd753d01b4d | 72,393 |
def parse_puzzle(puzzle, digits='123456789', nulls='0.'):
"""
Parses a string describing a Sudoku puzzle board into a dictionary with each cell mapped to its relevant
coordinate, i.e. A1, A2, A3...
"""
# Serialise the input into a string, let the position define the grid location and .0 can be empty positions
# ... | 874bf52f708faf483c2b18118cad2c4fd7305c78 | 72,394 |
import os
def schema_activity(arch):
""" Check the activity view against its schema
:type arch: etree._Element
"""
global _activity_validator
if _activity_validator is None:
with misc.file_open(os.path.join('mail', 'views', 'activity.rng')) as f:
_activity_validator = etree.R... | 2e976060db1946fe1146ed7657e5844140c93825 | 72,395 |
def visualize(history, regret_periods=None, time_labels=None, model_labels={},
style_algs={}, ax=[None, None, None], params=["lam"], subset_time=None, legend=True):
""" Visualize online learning losses, weights, and parameters.
Args:
history (History): online learning History object
regret... | 954d64687b803dbac68fcb34ed03e404e5d1436f | 72,396 |
def import_read_depth(
gene, depth_of_coverage
):
"""
Import read depth data for target gene.
Parameters
----------
gene : str
Gene name.
depth_of_coverage : str or pypgx.Archive
Archive file or object with the semantic type
CovFrame[DepthOfCoverage].
Returns
... | ac9437cf92a120728276adb6748ff65497318f2e | 72,397 |
from typing import Any
from typing import Callable
from typing import Dict
def authenticate(jwthenticator_server: str) -> Any:
"""
Wrapper function to verify user performing the request is authorized.
The function extracts the JWT token from the header and verifies it against
an jwthenticator serv... | 3b6b38db3c1b0605839e89195dabb0b33ea997b2 | 72,398 |
def concat_complex(list_complex, width_in_bits, imreOrder=True):
"""
Concatenates the real and imaginary part into one integer.
The specifed width counts for both the real and imaginary part.
Real part is mapped on the LSB. Imaginary part is shifted to the MSB.
"""
# PD
if imreOrder:
... | 831275da0695a477f9f75da1e238f231ea8d39df | 72,399 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.