content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import List
from re import T
def reverse(ls: List[T]) -> List[T]:
"""
Reverses a list.
:param ls: The list to be reversed
:return: The reversed list
"""
for i in range(len(ls) // 2):
ls[i], ls[len(ls) - 1 - i] = ls[len(ls) - 1 - i], ls[i]
return ls | eacee56b5325178ec27a13283d64d0155c7a97ed | 25,530 |
def test_get_annotations_not_5(
test_gb_file, test_accession, coordination_args, monkeypatch
):
"""Test get_annotations when length of protein data is not 5."""
def mock_get_gb_file(*args, **kwargs):
gb_file = test_gb_file
return gb_file
def mock_get_record(*args, **kwargs):
re... | a9021af24ecb339ebea89d6ad7beb6e4097c5519 | 25,531 |
def increment_with_offset(c: str, increment: int, offset: int) -> str:
""" Caesar shift cipher. """
return chr(((ord(c) - offset + increment) % 26) + offset) | 50b10b6d3aff3dff157dfc46c368ae251ed060bb | 25,532 |
import logging
def uploadfiles():
"""
function to upload csv to db
:return: renders success.html
"""
# get the uploaded file
uploaded_file = request.files['filename']
if uploaded_file.filename != '':
csv_to_db(uploaded_file)
return render_template('success.html')
loggin... | 5baa9dfb8930e70ebd37b502a211ae847194e08f | 25,533 |
def static_html(route):
"""
Route in charge of routing users to Pages.
:param route:
:return:
"""
page = get_page(route)
if page is None:
abort(404)
else:
if page.auth_required and authed() is False:
return redirect(url_for("auth.login", next=request.full_path... | 52c74b63c5856a04b294f8e539b4be26deec0209 | 25,534 |
import math
def getCenterFrequency(filterBand):
"""
Intermediate computation used by the mfcc function.
Compute the center frequency (fc) of the specified filter band (l)
This where the mel-frequency scaling occurs. Filters are specified so that their
center frequencies are equally spaced on the m... | e043774093c4417658cdfd052d486ea5e30efb81 | 25,535 |
import numpy
def phi_analytic(dist, t, t_0, k, phi_1, phi_2):
""" the analytic solution to the Gaussian diffusion problem """
phi = (phi_2 - phi_1)*(t_0/(t + t_0)) * \
numpy.exp(-0.25*dist**2/(k*(t + t_0))) + phi_1
return phi | 49fac597afa876f81ba5774bf82fedcfb88f6c7f | 25,536 |
def geometric_median(X, eps=1e-5):
"""
calculate the geometric median as implemented in https://stackoverflow.com/a/30305181
:param X: 2D dataset
:param eps:
:return: median value from X
"""
y = np.mean(X, 0)
while True:
D = cdist(X, [y])
nonzeros = (D != 0)[:, 0]
... | 9c8b0d69b4f66dc471bcb838b19ecac934493c54 | 25,538 |
def distance(bbox, detection):
"""docstring for distance"""
nDetections = detection.shape[0]
d = np.zeros(nDetections)
D = detection - np.ones([nDetections,1])*bbox
for i in xrange(nDetections):
d[i] = np.linalg.norm(D[i],1)
return d | 21c4beea66df1dde96cd91cff459bf10f1b7a41e | 25,539 |
from typing import TextIO
from typing import Tuple
def _read_float(line: str,
pos: int,
line_buffer: TextIO
) -> Tuple[float, str, int]:
"""Read float value from line.
Args:
line: line.
pos: current position.
line_buffer: line buffer for nnet3 file.
... | f0c76b2224a17854902aadbe7a715ca00da64932 | 25,540 |
def pk_to_p2wpkh_in_p2sh_addr(pk, testnet=False):
"""
Compressed public key (hex string) -> p2wpkh nested in p2sh address. 'SegWit address.'
"""
pk_bytes = bytes.fromhex(pk)
assert is_compressed_pk(pk_bytes), \
"Only compressed public keys are compatible with p2sh-p2wpkh addresses. See BIP49... | 10e9b2659df98b02b5030c1eec1820c9bbdd1a8b | 25,542 |
def remove_imaginary(pauli_sums):
"""
Remove the imaginary component of each term in a Pauli sum
:param PauliSum pauli_sums: The Pauli sum to process.
:return: a purely hermitian Pauli sum.
:rtype: PauliSum
"""
if not isinstance(pauli_sums, PauliSum):
raise TypeError("not a pauli su... | 2edd93f338d4e2dc1878953ced5edf954f509ccc | 25,543 |
def log_sigmoid_deprecated(z):
"""
Calculate the log of sigmod, avoiding overflow underflow
"""
if abs(z) < 30:
return np.log(sigmoid(z))
else:
if z > 0:
return -np.exp(-z)
else:
return z | 576d7de9bf61aa32c3e39fc5ca7f4428b43519bb | 25,544 |
def roty(t):
"""Rotation about the y-axis."""
c = np.cos(t)
s = np.sin(t)
return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]]) | 9c05a96c8c36fd3cd7eee1860574b9242d7543d6 | 25,545 |
def ranks_to_metrics_dict(ranks):
"""Calculates metrics, returns metrics as a dict."""
mean_rank = np.mean(ranks)
mean_reciprocal_rank = np.mean(1. / ranks)
hits_at = {}
for k in (1, 3, 10):
hits_at[k] = np.mean(ranks <= k)*100
return {
'MR': mean_rank,
'MRR': mean_reciprocal_rank,
'hi... | 60ee20fdf43240e3f0aa0e414fd49bcc52f83446 | 25,546 |
def bias_correction(input_data, output_filename='', mask_filename='', method="ants", command="/home/abeers/Software/ANTS/ANTs.2.1.0.Debian-Ubuntu_X64/N4BiasFieldCorrection", temp_dir='./'):
""" A catch-all function for motion correction. Will perform motion correction on an input volume
depending on the 'm... | 5236cff562dc50390146a5902a8f9924457e5426 | 25,547 |
def randperm2d(H, W, number, population=None, mask=None):
"""randperm 2d function
genarates diffrent random interges in range [start, end)
Parameters
----------
H : {integer}
height
W : {integer}
width
number : {integer}
random numbers
population : {list or num... | a3507c488740e0190673cb0bd920c0c0f15b77a1 | 25,548 |
def get_engine(db_credentials):
"""
Get SQLalchemy engine using credentials.
Input:
db: database name
user: Username
host: Hostname of the database server
port: Port number
passwd: Password for the database
"""
url = 'postgresql://{user}:{passwd}@{host}:{port}/{db}'.format(
... | ff66c10c7a79b0f5751979f0f5fc74c16d97eac0 | 25,549 |
def numpy_to_vtkIdTypeArray(num_array, deep=0):
"""
Notes
-----
This was pulled from VTK and modified to eliminate numpy 1.14 warnings.
VTK uses a BSD license, so it's OK to do that.
"""
isize = vtk.vtkIdTypeArray().GetDataTypeSize()
dtype = num_array.dtype
if isize == 4:
if... | 149da1f117968839801f2720c132451045b21fb6 | 25,550 |
def denormalize_ged(g1, g2, nged):
"""
Converts normalized ged into ged.
"""
return round(nged * (g1.num_nodes + g2.num_nodes) / 2) | 214813120d552ef5ece10349978238117fe26cf3 | 25,551 |
from datetime import datetime
import time
def get_current_time():
"""just returns time stamp
"""
time_stamp = datetime.datetime.fromtimestamp(
time()).strftime('%Y-%m-%d %H:%M:%S')
return time_stamp | 236bd2b141c3686bb4c05a18a6d0f0ef3b15ea6b | 25,552 |
import asyncio
async def test_script_mode_2(hass, hass_ws_client, script_mode, script_execution):
"""Test overlapping runs with max_runs > 1."""
id = 1
def next_id():
nonlocal id
id += 1
return id
flag = asyncio.Event()
@callback
def _handle_event(_):
flag.se... | 76a251dc4f2f7aa17e280ee1bcb76aa8333388cb | 25,554 |
def ease_of_movement(high, low, close, volume, n=20, fillna=False):
"""Ease of movement (EoM, EMV)
It relate an asset's price change to its volume and is particularly useful
for assessing the strength of a trend.
https://en.wikipedia.org/wiki/Ease_of_movement
Args:
high(pandas.Series): da... | c25720e866b1d4635d7e8256b9ace94f78b463ed | 25,555 |
def Document(docx=None, word_open_xml=None):
"""
Return a |Document| object loaded from *docx*, where *docx* can be
either a path to a ``.docx`` file (a string) or a file-like object.
Optionally, ``word_open_xml`` can be specified as a string of xml.
Either ``docx`` or `word_open_xml`` may be specif... | 565dd4f7f1d815f2e5ef97226d1175283ba942de | 25,556 |
def css_tag(parser, token):
"""
Renders a tag to include the stylesheet. It takes an optional second
parameter for the media attribute; the default media is "screen, projector".
Usage::
{% css "<somefile>.css" ["<projection type(s)>"] %}
Examples::
{% css "myfile.css" %}
... | b05deebf31c864408df33a41ba95016a06f48e2e | 25,557 |
def camelcase(path):
"""Applies mixedcase and capitalizes the first character"""
return mixedcase('_{0}'.format(path)) | 484bfcf8797637f56d5d0bdcad6c370f158773c0 | 25,558 |
import copy
def ImproveData_v2 (Lidar_DataOld,Lidar_Data,Data_Safe,Speed,orientation,orientationm1):
"""
The function calculates new positions for obstacles now taking into account the car's relative speed in relation to each point. We need the accelerometer for that.
Return:
... | 2bd6c0f167e65ad4a461d75a95539b68dc0b1a70 | 25,559 |
def label_by_track(mask, label_table):
"""Label objects in mask with track ID
Args:
mask (numpy.ndarray): uint8 np array, output from main model.
label_table (pandas.DataFrame): track table.
Returns:
numpy.ndarray: uint8/16 dtype based on track count.
"""
assert mask.s... | 9190714e8cfc3955d1aeffd22d20574d14889538 | 25,560 |
import zipfile
import xml
def load_guidata(filename, report):
"""Check if we have a GUI document."""
report({'INFO'}, "load guidata..")
guidata = None
zdoc = zipfile.ZipFile(filename)
if zdoc:
if "GuiDocument.xml" in zdoc.namelist():
gf = zdoc.open("GuiDocument.xml")
... | 3828d895a5abb9c6f783eee52d8c747f2f32c20c | 25,561 |
def question_answers(id2line, convos):
""" Divide the dataset into two sets: questions and answers. """
questions, answers = [], []
for convo in convos:
for index, line in enumerate(convo[:-1]):
questions.append(id2line[convo[index]])
answers.append(id2line[convo[index + 1]])... | f2654fcff2b9d90e78750cc8632eea9771361c4d | 25,562 |
import copy
def subgrid_kernel(kernel, subgrid_res, odd=False, num_iter=100):
"""
creates a higher resolution kernel with subgrid resolution as an interpolation of the original kernel in an
iterative approach
:param kernel: initial kernel
:param subgrid_res: subgrid resolution required
:retur... | 8c62e9a09052faf2f52dc2141b0432b115c79417 | 25,563 |
import spacy.en
import logging
def get_spacy():
"""
Loads the spaCy english processor.
Tokenizing, Parsing, and NER are enabled. All other features are disabled.
Returns:
A spaCy Language object for English
"""
logging.info('Loading spaCy...')
nlp = spacy.en.English(tagger=False,... | 6abe2c9cb8cb0027c53c5e013d4127829b339699 | 25,564 |
import astroobs as obs
import re
from datetime import datetime
def get_JDs(period='102', night=True, arrays=True, verbose=True):
"""
Get the Julian days for all ESPRESSO GTO runs in a given period. If
`night`=True, return the JD of sunset and sunrise. This function returns the
runs' start and end in ... | f21aea967e0d1a481d599bf7ffea2316d401a7ea | 25,565 |
def normalize_breton(breton_string: str) -> str:
"""Applies Breton mutations."""
return (breton_string.strip().lower() @
DO_PREPROCESSING @
DO_SOFT_MUTATION @
DO_HARD_MUTATION @
DO_SPIRANT_MUTATION @
DO_POSTPROCESSING).string() | f5536f98c881d854fc279b81b5a6e99e4811165f | 25,566 |
from keras.utils.data_utils import get_file
from art import DATA_PATH
def load_mnist(raw=False):
"""Loads MNIST dataset from `DATA_PATH` or downloads it if necessary.
:param raw: `True` if no preprocessing should be applied to the data. Otherwise, data is normalized to 1.
:type raw: `bool`
:return: `... | fc661afef4062e14a90a3cbc1a837cd6f68b6039 | 25,567 |
def word_flag(*args):
"""
word_flag() -> flags_t
Get a flags_t representing a word.
"""
return _ida_bytes.word_flag(*args) | 765051d3c51974f24cf71a846ab3ffed4767a3d0 | 25,568 |
from typing import Optional
from typing import Dict
from typing import Iterable
from typing import Union
from typing import List
def get_sequence_annotations(
sequence: str,
allow: Optional[set] = {"H", "K", "L"},
scheme: Optional[str] = "chothia",
cdr1_scheme: Optional[Dict[str, Iterable]] = {
... | 3f7d74693086e7603215d912083653005cdddb5a | 25,570 |
import stat
def skew(variable=None, weights=None, data=None):
"""Return the asymmetry coefficient of a sample.
Parameters
----------
data : pandas.DataFrame
variable : array-like, str
weights : array-like, str
data : pandas.DataFrame
Object which stores ``variable`` and ``weights`... | 08be7f2e9741855b699e847307c61b14ab6b3009 | 25,571 |
def deep_initial_state(batch_size, h_size, stack_size):
""" Function to make a stack of inital state for a multi-layer GRU.
"""
return tuple(static_initial_state(batch_size, h_size) for layer in range(stack_size)) | 4d6bc65d2fcb158a99a08d88c755c81ca08433f3 | 25,572 |
def create_element(pan_elem, elem_type=None)->Element:
"""
Find the element type and call constructor specified by it.
"""
etype = 'ELEMENT TYPE MISSING'
if elem_type is not None:
etype = elem_type
elif 't' in pan_elem:
etype = pan_elem['t']
elif 'pandoc-api-version' in pa... | c5507a35e7a75676e450d0f960fd3b70c873440d | 25,573 |
def load_weights(variables, file_name):
"""Reshapes and loads official pretrained Yolo weights.
Args:
variables: A list of tf.Variable to be assigned.
file_name: A name of a file containing weights.
Returns:
A list of assign operations.
"""
with open(file_name, "rb") as f:
... | 3d953792ae1e13285044f40dd840fe2400f20243 | 25,574 |
def parsing_sa_class_id_response(pdu: list) -> int:
"""Parsing TaiSEIA class ID response protocol data."""
packet = SAInfoResponsePacket.from_pdu(pdu=pdu)
if packet.service_id != SARegisterServiceIDEnum.READ_CLASS_ID:
raise ValueError(f'pdu service id invalid, {pdu}')
return int.from_bytes(packe... | e55c6e7041349f036babfd7e9699bfcfe1ff5dea | 25,575 |
def wrr(self) -> int:
"""
Name: Write ROM port.
Function: The content of the accumulator is transferred to the ROM
output port of the previously selected ROM chip.
The data is available on the output pins until a new WRR
is execute... | a019f176bba0e50d73906abd8a20862c4993b75f | 25,576 |
def convert_to_signed_int_32_bit(hex_str):
"""
Utility function to convert a hex string into a 32 bit signed hex integer value
:param hex_str: hex String
:return: signed 32 bit integer
"""
val = int(hex_str, 16)
if val > 0x7FFFFFFF:
val = ((val+0x80000000) & 0xFFFFFFFF) - 0x80000000
... | f8d39b20475c30f162948167f8534e367d9c58e8 | 25,577 |
def parent_node(max_child_node, max_parent_node):
"""
Parents child node into parent node hierarchy
:param max_child_node: MaxPlus.INode
:param max_parent_node: MaxPlus.INode
"""
max_child_node.SetParent(max_parent_node)
return max_child_node | 1a54d4c485e61361633165da0f05c8f871296ae6 | 25,578 |
import tensorflow as tf
import torch
def to_numpy_or_python_type(tensors):
"""Converts a structure of `Tensor`s to `NumPy` arrays or Python scalar types.
For each tensor, it calls `tensor.numpy()`. If the result is a scalar value,
it converts it to a Python type, such as a float or int, by calling
`r... | 34ea32fb2cf4fe8e45c429139876e7f1afc9f794 | 25,580 |
def _get_flow(args):
"""Ensure the same flow is used in hello world example and system test."""
return (
Flow(cors=True)
.add(uses=MyTransformer, replicas=args.replicas)
.add(uses=MyIndexer, workspace=args.workdir)
) | 625164c400f420cbb255cfdaa32f79c4862e23ea | 25,581 |
def get_group_id(
client: AlgodClient,
txids: list
) -> list:
"""
Gets Group IDs from Transaction IDs
:param client: an AlgodClient (GET)
:param txids: Transaction IDs
:return: gids - Group IDs
"""
# Get Group IDs
gids = []
print("Getting gids...")
try:
w... | 937b29f6b482ed1e62612a07cc80c17c6737c143 | 25,582 |
import logging
import tqdm
import multiprocessing
def _simple_proc(st, sampling_rate=10, njobs=1):
"""
A parallel version of `_proc`, i.e., Basic processing including downsampling, detrend, and demean.
:param st: an obspy stream
:param sampling_rate: expected sampling rate
:param njobs: number of... | aa24340d0d43ad8f6c042ed5e04bc94f2ec28cc3 | 25,583 |
from datetime import datetime
def closing_time(date=datetime.date.today()):
"""
Get closing time of the current date.
"""
return datetime.time(13, 0) if date in nyse_close_early_dates(date.year) else datetime.time(16, 0) | 40670512dbebfe65c3eb2b2790881fc91415aa40 | 25,584 |
def cos_fp16(x: tf.Tensor) -> tf.Tensor:
"""Run cos(x) in FP16, first running mod(x, 2*pi) for range safety."""
if x.dtype == tf.float16:
return tf.cos(x)
x_16 = tf.cast(tf.mod(x, 2 * np.pi), tf.float16)
return tf.cos(x_16) | 3212eb19e43fa733490d2cfcfffcc0094715022b | 25,585 |
from typing import Callable
def is_documented_by(original: Callable) -> Callable[[_F], _F]:
"""
Decorator to set the docstring of the ``target`` function to that of the ``original`` function.
This may be useful for subclasses or wrappers that use the same arguments.
:param original:
"""
def wrapper(target: _... | acd582112371ccfffd53762546415353abbd3129 | 25,586 |
def check_if_bst(root, min, max):
"""Given a binary tree, check if it follows binary search tree property
To start off, run `check_if_bst(BT.root, -math.inf, math.inf)`"""
if root == None:
return True
if root.key < min or root.key >= max:
return False
return check_if_bst(root.left,... | 1bb4b601ef548aec9a4ab2cf5242bc5875c587a2 | 25,587 |
from typing import Union
import pathlib
from typing import Sequence
from typing import Any
import torchvision
def create_video_file(
root: Union[pathlib.Path, str],
name: Union[pathlib.Path, str],
size: Union[Sequence[int], int] = (1, 3, 10, 10),
fps: float = 25,
**kwargs: Any,
) -> pathlib.Path:
... | f11748ae86a80a5f4d9c859c313837fac7effa32 | 25,589 |
def aggregate(collection, pipeline):
"""Executes an aggregation on a collection.
Args:
collection: a `pymongo.collection.Collection` or
`motor.motor_tornado.MotorCollection`
pipeline: a MongoDB aggregation pipeline
Returns:
a `pymongo.command_cursor.CommandCursor` or
... | 03ea889ea23fb81c6a329ee270df2ac253e90d69 | 25,590 |
def decryptAES(key, data, mode=2):
"""decrypt data with aes key"""
return aes.decryptData(key, data, mode) | 30f5b4173a8ed388a13481a2fd41293cd2304b21 | 25,591 |
import requests
def __ipv6_safe_get(endpoint: str, addr: str) -> str:
"""HTTP GET from endpoint with IPv6-safe Host: header
Args:
endpoint: The endpoint path starting with /
addr: full address (IPV6 or IPv4) of server
Notes:
* This is needed because the Py... | adb1c7c2300e9e41049a9eda957f264322095d9c | 25,592 |
def format_advertisement(data):
""" format advertisement data and scan response data. """
resolve_dict = {
# FLAGS AD type
st_constant.AD_TYPE_FLAGS: 'FLAGS',
# Service UUID AD types
st_constant.AD_TYPE_16_BIT_SERV_UUID: '16_BIT_SERV_UUID',
st_constant.AD_TYPE_16_BIT_SERV... | a2b2740c45debe6c801ac80d99c8ed2b4537c205 | 25,593 |
def is_dicom_file(path):
"""Check if the given path appears to be a dicom file.
Only looks at the extension, not the contents.
Args:
path (str): The path to the dicom file
Returns:
bool: True if the file appears to be a dicom file
"""
path = path.lower()
for ext in DICOM_E... | 2bd20b0f9bf40db24e9c6df4591127f59d07f882 | 25,594 |
import math
def build_graph(df_list, sens='ST', top=410, min_sens=0.01,
edge_cutoff=0.0, edge_width=150, log=False):
"""
Initializes and constructs a graph where vertices are the parameters
selected from the first dataframe in 'df_list', subject to the
constraints set by 'sens', 'top',... | b17b3f57ab21df0117e61a12005f401f81620368 | 25,595 |
def ranking_scores(prng=None, mix=False, permute=False, gamma=0.01, beta=5., N=100, l=1, means=None, stds=None):
"""
Generate the ranking scores.
Parameters
----------
prng : random generator container
Seed for the random number generator.
mix : bool
... | 40801599ab67d852740d5219d22debdbed91de39 | 25,596 |
def calculate_direction(G, cutoff, normalize=True):
""" Calculate direction for entire network
Parameters
----------
G : nx.graph
Fault network
cutoff : int, float
Cutoff distance for direction
normalize : bolean
Normalize direction (default: True)
Returns
... | 9b64e0e8226579728f76ab510e672372cb708338 | 25,597 |
from datetime import datetime
def generateVtBar(row):
"""生成K线"""
bar = VtBarData()
bar.symbol = row['code']
bar.exchange = ''
bar.vtSymbol = bar.symbol
bar.open = row['open']
bar.high = row['high']
bar.low = row['low']
bar.close = row['close']
bar.volume = row['volume']
... | 8431b313927692743d727ef9225e33899cc6c916 | 25,598 |
def coords_to_id(traversed):
"""calculate the id in level-order from the coordinates
Args:
input: traversed tree as list of dict
Returns:
traversed tree (dict) with id as key
"""
traversed_id = {}
#print('coords to id, traversed ', traversed)
for node in traversed:
... | 91f993f9693e01983de1f7fa124dcb5cb39a92f9 | 25,600 |
def dataframe_to_ipy_image(df, f=None, **kwargs):
"""Create IPython Image from PIL Image.
Args:
df - dataframe to render
f - operation to perform on PIL Image (e.g. f=lambda img: img.rotate(-90, expand=True))
kwargs - arguments to IPython.display.Image, such as width and height for html display
... | 44348ac041067620bfa37cdedb22f3544e6bc940 | 25,601 |
def read_dmarkov(columns, rows, D, symbolization_type, division_order, suffix=["normal", "gaussian002"]):
"""
Reads the result files for the D-Markov algorithm. The function requires a configuration for the parameters of
the D-Markov. The suffix parameter indicates if the non-modified files should be loaded... | c9fec2d46cbc8c3f4bcf7fc112779432dd6e9155 | 25,603 |
import torch
def compute_output_shape(observation_space, layers):
"""Compute the size of the output after passing an observation from
`observation_space` through the given `layers`."""
# [None] adds a batch dimension to the random observation
torch_obs = torch.tensor(observation_space.sample()[None])
... | 865b9b90f39f5726feb16da70afc515071991fd7 | 25,604 |
def tenure_type():
""" RESTful CRUD controller """
return s3_rest_controller(#rheader = s3db.stdm_rheader,
) | bfee3c2be579e1db6e8799b4a9d3156130b802a9 | 25,605 |
def _format_port(port):
"""
compute the right port type str
Arguments
-------
port: input/output port object
Returns
-------
list
a list of ports with name and type
"""
all_ports = []
for key in port:
one_port = {}
one_port['name'] = key
port... | 2fa65686b6b764afc97a200a02baec65645c9879 | 25,606 |
import io
def proc_cgroups(proc='self'):
"""Read a process' cgroups
:returns:
``dict`` - Dictionary of all the process' subsystem and cgroups.
"""
assert isinstance(proc, int) or '/' not in proc
cgroups = {}
with io.open(_PROC_CGROUP.format(proc), 'r') as f:
for cgroup_line i... | 95cb24cbbb4167dd2fa26ce36d78e5f532f10c1a | 25,608 |
import csv
def load_csv_data(
data_file_name,
*,
data_module=DATA_MODULE,
descr_file_name=None,
descr_module=DESCR_MODULE,
):
"""Loads `data_file_name` from `data_module with `importlib.resources`.
Parameters
----------
data_file_name : str
Name of csv file to be loaded fr... | 3629dded45954c25e538c53b5c7bc5d0dfec0a39 | 25,609 |
def ptFromSudakov(sudakovValue):
"""Returns the pt value that solves the relation
Sudakov = sudakovValue (for 0 < sudakovValue < 1)
"""
norm = (2*CA/pi)
# r = Sudakov = exp(-alphas * norm * L^2)
# --> log(r) = -alphas * norm * L^2
# --> L^2 = log(r)/(-alphas*norm)
L2 = log(sudakovVal... | 8ba504749f13ed1046799b5456d1f6f3c74bfc1e | 25,610 |
def _set_lod_2(gml_bldg, length, width, height, bldg_center):
"""Adds a LOD 2 representation of the building based on building length,
width and height
alternative way to handle building position
Parameters
----------
gml_bldg : bldg.Building() object
A building object, where bldg is ... | 309f66319c5cce07adbcb456548b3c29f707d96c | 25,611 |
def send_mail(subject, message, from_email, recipient_list, html_message='',
scheduled_time=None, headers=None, priority=PRIORITY.medium):
"""
Add a new message to the mail queue. This is a replacement for Django's
``send_mail`` core email method.
"""
subject = force_text(subject)
... | a97103e5e56463170122252073ebcc873306c708 | 25,613 |
def proxy_a_distance(source_X, target_X):
"""
Compute the Proxy-A-Distance of a source/target representation
"""
nb_source = np.shape(source_X)[0]
nb_target = np.shape(target_X)[0]
train_X = np.vstack((source_X, target_X))
train_Y = np.hstack((np.zeros(nb_source, dtype=int), np.ones(nb_targe... | fe0102cfd2a5a3cadb64a5ddfb7705e7b8440028 | 25,614 |
import json
def load_metadata(stock_model_name="BlackScholes", time_id=None):
"""
load the metadata of a dataset specified by its name and id
:return: dict (with hyperparams of the dataset)
"""
time_id = _get_time_id(stock_model_name=stock_model_name, time_id=time_id)
path = '{}{}-{}/'.format(... | 1171bf3a06327e907449872755315db8c34565c8 | 25,615 |
from typing import List
import torch
def evaluate(env: AlfEnvironment, algorithm: RLAlgorithm,
num_episodes: int) -> List[alf.metrics.StepMetric]:
"""Perform one round of evaluation.
Args:
env: the environment
algorithm: the training algorithm
num_episodes: number of epis... | 0218f1a38be8f897ac3b2a70036213877f5f7654 | 25,616 |
from pagure.hooks import BaseHook
def get_plugin_names(blacklist=None, without_backref=False):
"""Return the list of plugins names.
:arg blacklist: name or list of names to not return
:type blacklist: string or list of strings
:arg without_backref: whether or not to include hooks that
have ba... | 7f3b560334a5680fdcb4a47929613706bb699393 | 25,617 |
def is_autosync(*args):
"""
is_autosync(name, type) -> bool
is_autosync(name, tif) -> bool
Is the specified idb type automatically synchronized?
@param name (C++: const char *)
@param type (C++: const type_t *)
"""
return _ida_typeinf.is_autosync(*args) | 0f7eacc9931897f5fc0f076d0e07e0f1e1e01bce | 25,618 |
def scanboards(dirpath):
"""Scans the directory for board files and returns an array"""
print("Scanning for JSON board data files...", end = "")
files = [x for x in subfiles(dirpath) if x.endswith(".json") and not x.endswith("index.json")]
print("Found {} in \"{}\"".format(len(files), dir... | 9cfce78b06fef0b8f7ebaa3d1c5904dfd3e0ec56 | 25,619 |
def router_get_notification() -> dict:
"""Lista todas as configurações do BOT Telegram."""
logger.log('LOG ROTA', "Chamada rota /get_all.")
return {"configuracoes": TelegramNotifier.make_current_cfg_dict()} | 46faf67e02d537de49616085a1bcbb30f3087805 | 25,620 |
def script_filter_maximum_value(config):
""" The scripting version of `filter_maximum_value`. This
function applies the filter to the entire directory (or single
file). It also adds the tags to the header file of each fits file
indicating the number of pixels filtered for this filter.
Parame... | 8eccc2356c803d63c1ddfc7603e1dc784ccc49fe | 25,621 |
import time
def pretty_date(d):
""" returns a html formatted pretty date """
special_suffixs = {1 : "st", 2 : "nd" , 3 : "rd", 21 : "st", 22 : "nd", 23 : "rd", 31 : "st"}
suffix = "th"
if d.tm_mday in special_suffixs:
suffix = special_suffixs[d.tm_mday]
suffix = "<sup>" + suffix + "</sup>"
day = ti... | 7d6675f115021ddd46b2a614e831c9fae8faf7ad | 25,622 |
from datetime import datetime
import dateutil
def update(model, gcs_bucket, gcs_object):
"""Updates the given GCS object with new data from the given model.
Uses last_modified to determine the date to get items from. Bases the
identity of entities in the GCS object on their 'id' field -- existing
ent... | 66bde1371383f16c9449a3aec29e894e6a473d44 | 25,623 |
def member_requests_list(context, data_dict):
""" Show request access check """
return _only_registered_user() | c3ffdf798aabc80b3bd91160e9a580ff38c9540d | 25,626 |
def get_conductivity(sw_tdep,mesh,rvec,ham_r,ndegen,avec,fill,temp_max,temp_min,tstep,sw_tau,idelta=1e-3,tau0=100):
"""
this function calculates conductivity at tau==1 from Boltzmann equation in metal
"""
def calc_Kn(eig,veloc,temp,mu,tau):
dfermi=0.25*(1.-np.tanh(0.5*(eig-mu)/temp)**2)/temp
... | 0304781bac6160b353a90e5bce061faa89075bc0 | 25,627 |
from typing import List
from typing import Union
import time
def time_match(
data: List,
times: Union[List[str], List[int], int, str],
conv_codes: List[str],
strptime_attr: str,
name: str,
) -> np.ndarray:
"""
Match times by applying conversion codes to filtering list.
Parameters
... | 0480f5ca3e29ebcc4f44bef5a81db8fb36f78616 | 25,628 |
def find_best_input_size(sizes=[40]):
""" Returns the average and variance of the models """
accuracies = []
accuracy = []
t = []
sigma = []
time = []
#sizes = np.arange(5, 80, 5)
for size in sizes:
#for size in [80]:
accuracy = []
N = 20
for j in range(N):
... | 3d22441d07b44779cde6c4347669a435568f0378 | 25,629 |
import torch
def eval_acc(trainer, dataset="val"):
"""
"""
trainer.model.eval()
with torch.no_grad():
shot_count = 0
total_count = 0
for inputs,targets in trainer.val_dataset():
inputs = nested_to_cuda(inputs, trainer.device)
targets = nested_to_cuda(tar... | 452861ccb5805778d5dd0bc83226b73539b8aebb | 25,630 |
def _floor(n, base=1):
"""Floor `n` to a multiple of `base`"""
return n // base * base | 49019e4aa925b4f77a7f13f9919d36948bd132cc | 25,632 |
def fixed_timezone(offset): # type: (int) -> _FixedTimezone
"""
Return a Timezone instance given its offset in seconds.
"""
if offset in _tz_cache:
return _tz_cache[offset]
tz = _FixedTimezone(offset)
_tz_cache[offset] = tz
return tz | 401303d1893bc2ab7bee19ba09161549a2cc7fb2 | 25,634 |
def getFactoriesInfo():
"""
Returns a dictionary with information on how to create an object Sensor from its factory
"""
return {'Stitcher':
{
'factory':'createStitcher'
}
} | 75806002b1ada6bd1a87c9bde6b2e47f587d988d | 25,635 |
from typing import Dict
from typing import List
from typing import Optional
def pending_observations_as_array(
pending_observations: Dict[str, List[ObservationFeatures]],
outcome_names: List[str],
param_names: List[str],
) -> Optional[List[np.ndarray]]:
"""Re-format pending observations.
Args:
... | bc9bfff51b991b413b5861f55c8b0f55331ab763 | 25,636 |
def inverted_conditional_planar(input_dim, context_dim, hidden_dims=None):
"""
A helper function to create a
:class:`~pyro.distributions.transforms.ConditionalPlanar` object that takes care
of constructing a dense network with the correct input/output dimensions.
:param input_dim: Dimension of inpu... | 8bf5ae5dd6d8743a3eb1506b26dec5cf51af2bde | 25,638 |
def create_category_index(categories):
"""Creates dictionary of COCO compatible categories keyed by category id.
Args:
categories: a list of dicts, each of which has the following keys:
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category... | 226a39189d4203e2861bbba7334d5b8bbaa3b7df | 25,639 |
import torch
def n_step_returns(q_values, rewards, kls, discount=0.99):
"""
Calculates all n-step returns.
Args:
q_values (torch.Tensor): the Q-value estimates at each time step [time_steps+1, batch_size, 1]
rewards (torch.Tensor): the rewards at each time step [time_steps, batch_size, 1]... | 3bbd6026046328dc8ef63ab3e871f6c47636cb80 | 25,640 |
import random
def random_split_exact(iterable, split_fractions=None):
"""Randomly splits items into multiple sample lists according to the given
split fractions.
The number of items in each sample list will be given exactly by the
specified fractions.
Args:
iterable: a finite iterable
... | 2b7ae86e55b9be225e94cfc983295beeb3ed08cf | 25,642 |
def computeMaskIntra(inputFilename, outputFilename, m=0.2, M=0.9, cc=1):
""" Depreciated, see compute_mask_intra.
"""
print "here we are"
return compute_mask_intra(inputFilename, outputFilename,
m=m, M=M, cc=cc) | 0eaf8b8845c12b1fc90cb032881dacf53a2c7d12 | 25,644 |
def read_space_delimited(filename, skiprows=None, class_labels=True):
"""Read an space-delimited file
skiprows: list of rows to skip when reading the file.
Note: we can't use automatic comment detection, as
`#` characters are also used as data labels.
class_labels: boolean
if true, the last ... | be25b4f6c3c775f12fdfef7f334b4886c85a514e | 25,645 |
def get_gas_price(endpoint=_default_endpoint, timeout=_default_timeout) -> int:
"""
Get network gas price
Parameters
----------
endpoint: :obj:`str`, optional
Endpoint to send request to
timeout: :obj:`int`, optional
Timeout in seconds
Returns
-------
int
Ne... | b7f18a5a5044d8aeee7a63b702b01944cbff597b | 25,646 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.