content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_short_description(dict, value):
"""Get layout class based on value."""
return dict.get(value, {}).get('short_description') | f5885f10ce009db925d552f8a0b0f40ab1ccaa2a | 3,623,900 |
def cdlconcealbabyswallow(
client,
symbol,
timeframe="6m",
opencol="open",
highcol="high",
lowcol="low",
closecol="close",
):
"""This will return a dataframe of conceal baby swallow for the given symbol across
the given timeframe
Args:
client (pyEX.Client): Client
... | 04cea2169f0fc1dc9e173fea66d5c341aac4224c | 3,623,901 |
def rectangular_hollow_section(b: float, d: float, t: float, r_out: float, n_r: int, material: pre.Material = pre.DEFAULT_MATERIAL) -> Geometry:
"""Constructs a rectangular hollow section (RHS) centered at *(b/2, d/2)*, with depth *d*, width *b*,
thickness *t* and outer radius *r_out*, using *n_r* points to con... | a0639d8674485ba31e2e7c7b4df1c78e5980dcc8 | 3,623,902 |
def _is_subexpansion_optional(query_metadata, parent_location, child_location):
"""Return True if child_location is the root of an optional subexpansion."""
child_optional_depth = query_metadata.get_location_info(child_location).optional_scopes_depth
parent_optional_depth = query_metadata.get_location_info(... | 29391226258e75d434e07c291fe9590c1810d85b | 3,623,903 |
import uuid
def get_uuid(key, value, is_list=False, is_optional=False, default=None, options=None):
"""
Get the value corresponding to the key and converts it to `uuid`/`list(uuid)`.
Args:
key: the dict key.
value: the value to parse.
is_list: If this is one element or a list of e... | 8325f288f580ed0b47fefc144cb16f71d4b1e742 | 3,623,904 |
import torch
from typing import Sequence
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`, :class:`int` and :class:`float`.
"""
if isinstance(data, torch.Tensor):
r... | 3d9aa7cb8424e6ee07412449ee890ce174cbc5b8 | 3,623,905 |
def _filter_labels(text, labels, allowed_labels):
"""Keep examples with approved labels.
:param text: list of text inputs.
:param labels: list of corresponding labels.
:param allowed_labels: list of approved label values.
:return: (final_text, final_labels). Filtered version of text and labels
... | e17ed7659acbdadc71a6b3f5b522af1e34d40370 | 3,623,906 |
def _Itype():
"""Loop iterator data type."""
return tf.int32 if use_xla() else tf.int64 | 484c51f53226f7ebb0cf9738c8b16ad99438289b | 3,623,907 |
import math
def Q(fastev,lagev,fastrc,lagrc):
"""Following Wuestefeld et al. 2010"""
omega = math.fabs((fastev - fastrc + 3645)%90 - 45) / 45
delta = lagrc / lagev
dnull = math.sqrt(delta**2 + (omega-1)**2) * math.sqrt(2)
dgood = math.sqrt((delta-1)**2 + omega**2) * math.sqrt(2)
if dnull < dgo... | fe975fb234297a6f25100bb85aa6639590a010d8 | 3,623,908 |
def w_median(a, weights):
"""
Compute the weighted median of a 1D numpy array.
Parameters
----------
a : ndarray
Input array (one dimension).
weights : ndarray
Array with the weights of the same size of `data`.
Returns
-------
median : float
The output value.
... | f259dd036777380e50f9030d728c1d80c1f5ebaa | 3,623,909 |
import os
def find_source_files(
path,
extensions=(".ts", ".py"),
skip_folders=("tests", "test", "node_modules", "lib", ".git", ".ipynb_checkpoints"),
):
"""
Find source files in given `path`.
Parameters
----------
extensions: sequence
FIXME:
skip_folders: sequence
... | 010af90119329cc1f69343900ef2f28d8f494136 | 3,623,910 |
def from_trends_top_query_by_category(n=NUM_KEYWORDS):
"""
Get a set of keyword objects by querying Google Trends
Each keyword obj is a dict with keys: keyword, category
"""
keyword_objs = []
for cid in POPULAR_CATEGORIES:
yearmonth = '2016'
pytrends = TrendReq(hl='en-US', t... | bf2582d60cf1f720b9f4b88838e9a22852f1f36f | 3,623,911 |
import re
def get_document_type(doc):
"""
Return the document type lowercased
Parameters
----------
doc : str
The document string
Returns
-------
doc_type : str
The document type lowercased
"""
type_pattern=re.compile(r'<TYPE>[^\n]+')
type_i=[x[len('<TYPE>... | ea9d25e1bb414fe16f9eb1cabfad1699aa90d79f | 3,623,912 |
from typing import List
def list_vpc_cidrs(vpc_id: str, account_id: str, region: str) -> List[str]:
"""
Returns a list of vpc cidrs associated with a given vpc.
Example use cases:
1. Get the CIDRs to install on other side of a peering.
2. See if there are any common CIDRs between two VPCs
:pa... | bed0b41ff5d86fa87a89ee2aa1770871545e745e | 3,623,913 |
from typing import Optional
def _cmd_debugger_getcontext(shell, _args: [str]) -> Result:
"""
Print basic set of registers for the active thread context."""
if not shell._debugger:
print("ERROR: /attach debugger first.")
return Result.HANDLED
info: Optional[Thread.Context] = shell._de... | d4cf6a4fd407a2575f065b1d1ab0825fc3ffd259 | 3,623,914 |
def _multi_dot(arrays, order, i, j, precision):
"""Actually do the multiplication with the given order."""
if i == j:
return arrays[i]
else:
return np.dot(_multi_dot(arrays, order, i, order[i, j], precision),
_multi_dot(arrays, order, order[i, j] + 1, j, precision),
... | fa3e01e611d2897387452af6afb785087a0e46eb | 3,623,915 |
import time
def creation_date_demographics(route, label):
"""Return tweet creation dates."""
dataset = {'hateval': Tweet.objects.filter(hateval=True),
'offenseval': Tweet.objects.filter(offenseval=True),
'all': Tweet.objects.all()}
db = dataset.get(route)
if label == 'ab... | 176f48ddf37b35d4acfd82e48e00e1be3b657072 | 3,623,916 |
def array_check(lst):
"""
Function to check whether 1,2,3 exists in given array
"""
for i in range(len(lst)-2):
if lst[i] == 1 and lst[i+1] == 2 and lst[i+2] == 3:
return True
return False | ef60b52a9d7300fa458b503f49996aec0f0831ad | 3,623,917 |
def fastmri_unet_transform_multicoil(
kspace=None, mask=None, ground_truth=None, attrs=None, fname=None, slice_id=None
):
"""Transform to use as input to fastMRI's Unet model for multicoil data.
This is an adapted version of the code found in
`fastMRI <https://github.com/facebookresearch/fastMRI/blob/m... | c420979fc7b329972abf62b5a13996d98d2f1038 | 3,623,918 |
def stylize_cartoon(image, blur_ksize=3, segmentation_size=1.0,
saturation=2.0, edge_prevalence=1.0,
suppress_edges=True,
from_colorspace=colorlib.CSPACE_RGB):
"""Convert the style of an image to a more cartoonish one.
This function was primarily desi... | 25db74f3ffb1548ccdc0f3af98c7ea0375150c94 | 3,623,919 |
def tca_model (image: Image.Image, order: int=2) -> ndarray:
"""
Compute a lens model which corrects transverse chromatic aberration.
Parameters:
image (PIL.Image): Input image.
order (int): Polynomial order of lens model. Quadratic or cubic model is ideal.
Returns:
ndarray: Re... | 2973e69d2896a918803bdaea3e2993048ecbcf2f | 3,623,920 |
def _group_result_from_fields(json, fields):
"""Helper that creates a group response object from the given fields.
:param json: original JSON string
:param fields: the JSON fields
:return: the created group response
:rtype: GroupResult
"""
result = api.GroupResult()
result.child_group... | a3ca4c71a4231b9a537d59c04a0c471db8785c83 | 3,623,921 |
def debounce(wait):
""" Decorator that will postpone a function's
execution until after `wait` seconds
have elapsed since the last time it was invoked. """
def decorator(fn):
timer = None
def debounced(*args, **kwargs):
nonlocal timer
def call_it():
... | 270dc56653afde86c7874ad06a18e51ef195a80c | 3,623,922 |
from typing import List
from typing import Any
def list_difference(list_1: List[Any], list_2: List[Any]) -> List[Any]:
""" This Function that takes two lists as parameters
and returns a new list with the values that are in l1, but NOT in l2"""
differ_list = [values for values in list_1 if values no... | 3831d799bb40080b828ee90e2929f77ff8aeb7ba | 3,623,923 |
def truncate_msg(msg, max_len=2000):
"""
Truncate a message string so it doesn't get lost (todo: automatically do this in realtime logger class)
"""
if len(msg) <= max_len:
return msg
else:
trunc_info = '<log message truncated to fit buffer>'
assert len(trunc_info) < max_len
... | 9084975e1eee570923c01a04e7c9f170e9904909 | 3,623,924 |
def rlsp(
_run,
mdp,
s_current,
p_0,
horizon,
temp=1,
epochs=1,
learning_rate=0.2,
r_prior=None,
r_vec=None,
threshold=1e-3,
check_grad_flag=False,
solver="value_iter",
reset_solver=False,
solver_iterations=1000,
):
"""The RLSP algorithm."""
check_in("... | 12449a66b430f3ad6d869184a9e954e520066232 | 3,623,925 |
def load_vsmi() -> pd.DataFrame:
"""
"""
vsmi = pd.read_csv("../statistics/h_vsmi_30.csv", sep=";")
vsmi.columns = vsmi.columns.str.lower()
vsmi.rename(columns={"indexvalue": "VSMI"}, inplace=True)
vsmi["date"] = pd.to_datetime(vsmi["date"], format="%d.%m.%Y")
vsmi.set_index("date", inplace=... | ccd3ff8d0c728108b1556860e98c1723819cd778 | 3,623,926 |
def create_app(context: GhiaContext = None) -> Flask:
"""
Create the Flask app.
Args:
context (GhiaContext, optional): If no context is provided a new default one is automatically created. Defaults to None.
Returns:
Flask: Newly created Flask application.
"""
return ghia_web_logic.create_app(context=cont... | 7df41acbc2babf6f25debac1a035a74b95fdd606 | 3,623,927 |
def shift_until_PSD(M, tol):
""" Add the identity until a p x p matrix M has eigenvalues of at least tol"""
p = M.shape[0]
mineig = np.linalg.eigh(M)[0].min()
if mineig < tol:
M += (tol - mineig) * np.eye(p)
return M | de2cf1060a32a487c5f6c3024ce294803f60b6da | 3,623,928 |
def iris_to_df(iris):
""" Make dataframe for multiclass classification from iris data"""
X, y = iris.data, iris.target
iris_column_data = X.T.tolist()
iris_column_names = ["col" + str(idx) for idx in range(X.shape[1])]
data = {}
for ind, iris_data_column in enumerate(iris_column_data):
d... | f158502b90a9a1afa8ac2e48b5446f42d18ca9f0 | 3,623,929 |
def format_feedback(feedback_row, study):
"""Updates the feedback dict with the new information."""
formatted_feedback_row = {
"success": {
study.get_single_field(field["field_id"]).field_name: field["field_value"]
for field in feedback_row["success"]
},
"failed":... | 3d6d52d6a5340b13e81ca19ee28bff28b189cbc1 | 3,623,930 |
def build_geometry(self):
"""Compute the curve (Line) needed to plot the object.
The ending point of a curve is the starting point of the next curve
in the list
Parameters
----------
self : SlotW11
A SlotW11 object
Returns
-------
curve_list: list
A list of 7 Segmen... | c27bdfe1b1d80e4a1d9c664244cfc420b14c1742 | 3,623,931 |
def validcolor(c):
"""Takes a color and makes it valid by clamping each value between 0 and 255"""
try:
ret = [clamp(int(v+0.5), 0, 255) for v in c]
return type(c)(ret)
except TypeError:
return clamp(int(v+0.5), 0, 255) | 9a3b8e869c009c7c5c71ef8a4c9b1c0692abc856 | 3,623,932 |
def give_me_the_record(primary_id, swissprot_file):
"""
Return a single record given with the primary id
:param primary_id: A primary id
:param swissprot_file: A swissprot file
:return: A record with accession == primary id
"""
with open(swissprot_file, 'r') as fh:
for record in Swis... | 9272ae10c3fb6faa2abec4638a8edb36a9bd7985 | 3,623,933 |
import os
import re
import logging
def fetch_local_files(stime, etime, localdirfmt, localdict, outdir, fnamefmt,
back_time=relativedelta(years=1), remove=False):
"""
A routine to locate and retrieve file names from locally stored SuperDARN
radar files that fit the input criteria.
... | c11cfaa01cd3a022327b5ffbaa2da819ba965191 | 3,623,934 |
import requests
def ms_graph_users(licensed=False):
"""Query the Microsoft Graph REST API for on-premise user accounts in our tenancy.
Passing ``licensed=True`` will return only those users having >0 licenses assigned.
"""
token = ms_graph_client_token()
headers = {
"Authorization": "Beare... | 976ebf632e1dd6bb7eee6acc7379617e2eac082a | 3,623,935 |
def protect_def_name(defName):
"""Convert a DEF name to be supported in Webots."""
protectedDefName = clean_string(defName)
if len(protectedDefName) > 0 and protectedDefName[0].isdigit():
protectedDefName = "_" + protectedDefName
return protectedDefName | 838d66ec3d0e88bff99d63f933f079df635777fb | 3,623,936 |
from typing import Tuple
def get_blog(id: str) -> Tuple:
"""
Function used to fetch particular blog or
return error if it is doesn't exist.
:param id: blog id
:return: tuple of (blog object or any error)
"""
blog, error = _get_blog_obj(id)
if not error:
blog = [blog_schema.dum... | 73bd9d679d2dd50a37ff6bd46fd31be677e3f5cc | 3,623,937 |
def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
"""Calculate cross entropy loss while ignoring padding.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch_size, length_labels]
smoothing: Label smoothing constant, used to det... | 826a53fd6af931b33b7b7ac773310d2143db8597 | 3,623,938 |
import io
import os
import logging
import re
def ATL06_to_dataframe(FILENAME,
beams=DEFAULT_BEAMS,
groups=DEFAULT_GROUPS,
**kwargs):
"""
Reads ICESat-2 ATL06 (Land Ice Along-Track Height Product) data files
Arguments
---------
FILENAME: full path to ATL06 file
Keyword Arguments
... | 237af4c636465629050f6c55db6f2570efd44144 | 3,623,939 |
from typing import List
def get_neighboring_connectivity(cm: np.ndarray) -> List[float]:
"""
Get how strong neighboring classes are connected.
Parameters
----------
cm : np.ndarray
Returns
-------
con : List[float]
"""
con = []
n = len(cm)
for i in range(n - 1):
... | ba92b09ed1845eb5545d353df59f52f8032fab13 | 3,623,940 |
def autocov(x):
""" Calculate the auto-covariance of a signal.
This assumes that the signal is wide-sense stationary
Parameters
----------
x: 1-d float array
The signal
Returns
-------
nXn array (where n is x.shape[0]) with the autocovariance matrix of the
signal x
Not... | 6f4f1855b9e0b4f237fbcaecdd911f51cf9c8707 | 3,623,941 |
import os
import torch
def set_apex_params(local_rank):
"""
Setting distributed parameters for Apex
"""
if 'WORLD_SIZE' in os.environ:
world_size = int(os.environ['WORLD_SIZE'])
global_rank = int(os.environ['RANK'])
print('GPU {} has Rank {}'.format(
local_rank, gl... | f7fcf48c560e29e408286fa7923576b5c6086f83 | 3,623,942 |
def correlation(self, column_a, column_b):
"""
Calculate correlation for two columns of current frame.
Parameters
----------
:param column_a: (str) The name of the column from which to compute the correlation.
:param column_b: (str) The name of the column from which to compute the correlation.... | b8f1600e0b2968ca4013418b2fbfda0b13f5911a | 3,623,943 |
import platform
import os
import ctypes
def load_library():
"""Loads the MagickWand library.
:returns: the MagickWand library and the ImageMagick library
"""
libpath = None
system = platform.system()
magick_home = os.environ.get('MAGICK_HOME')
if magick_home:
if system == 'Windo... | d4037d7a8a404076f5a86a26ad28db0bb369b38d | 3,623,944 |
def get_ax(rows=1, cols=1, size=8):
"""Return a Matplotlib Axes array to be used in
all visualizations in the notebook. Provide a
central point to control graph sizes.
Change the default size attribute to control the size
of rendered images
"""
_, ax = plt.subplots(rows, cols, figsize=(size... | c5d16b65dc5e505143062c49847b54b1e84717e6 | 3,623,945 |
import logging
def workflow(func, **kwargs):
"""
Desc
:param func:
:param kwargs:
:return:
"""
def inner(*args, **kwargs):
"""
Desc
:param args:
:param kwargs:
:return:
"""
logging.debug("workflow args: %s %s",str(args), str(kwargs))
... | 3bb746bb2ab0190d9f5fc35e332ebcc3ee5407fe | 3,623,946 |
def multiply(a, b, out=None, increment=False, stream=None):
"""Element-wise product of `a` and `b`."""
dtype = a.dtype
if out is None:
out = gpuarray.zeros(a.shape, dtype=dtype)
assert a.size == b.size
assert a.dtype == b.dtype == out.dtype
block = (min(a._block[0], a.size), 1, 1)
... | 0886834e18d6c6ba7abab5e41943d752d9c8fd6c | 3,623,947 |
import numpy as np
def uv2spd_dir(u,v):
"""
converts u, v meteorological wind components to speed/direction
where u is velocity from N and v is velocity from E (90 deg)
usage spd, dir = uv2spd_dir(u, v)
"""
spd = np.zeros_like(u)
dir = np.zeros_like(u)
spd = np.sqrt(u**2 + v**2)
... | 21525616f97974ba7463fe137f07ef7e7a728fe1 | 3,623,948 |
def adjacency_mat(x_all, y_all, ox, oy, rr):
"""
Function that creates the adjacency matrix from the edges and points
with the no restriction method
"""
n = len(x_all)
A = np.zeros((n, n))
road_map = []
for i in range(n):
temp = []
for j in range(n):
if i ==... | f66af74b37d8d2385720e71be31cef3446155afd | 3,623,949 |
import os
import string
def get_file_list(dir, pattern, suffix, sort_order):
"""
Makes a sorted list of files with fully-qualified path which have
pattern in the filename, and end with suffix. Sorted in the
order specified. NOTE: does NOT use index files to make list.
"""
dir_list = os.listdi... | afe475dcfc06a1b207773cb55ed989e3a4429221 | 3,623,950 |
from typing import Counter
def calcEOAutomorphisms(tree) :
"""
Computes the size of the automorphism group of the input :py:obj:`tree`.
We think of :py:obj:`tree` as a rooted tree, whose vertices are decorated by degrees and which has additional "exterior" edges of two distinct types, corresponding to the bounda... | c10d579df582444339c0d0fe5859f37ed1bad4bd | 3,623,951 |
def cisco_ios_simple_config():
"""Creares raw cisco config of comments etc."""
with open(
CISCO_IOS_SIMPLE_CONFIG_PATH, mode="r", errors="ignore", encoding="ascii"
) as config_file:
raw_config = config_file.readlines()
config = []
for line in raw_config:
line = line.rstrip()
... | 48e7177a99b5b2b2f196a9fff6f8e175f37dd389 | 3,623,952 |
def RRIMAPublicDashboard(request,id=0):
"""
:param request:
:param id:
:return:
"""
## retrieve program
model = Program
program_id = id
getProgram = Program.objects.all().filter(id=program_id)
## retrieve the coutries the user has data access for
countries = getCountry(requ... | c5c9273d5017397eecdd99ee78bf318d45440f17 | 3,623,953 |
def get_workflows_requests(module):
"""Returns all requests for specified workflow"""
return Request.objects.filter(module_ref=module) | 6e634ded8f7c0b1dc147d276a8d9749205e9d991 | 3,623,954 |
def cwd_hg_version(short=False):
"""Get the Mercurial changeset hash of the repository that contains the
current working directory. If ``short`` is True, the short (12-character)
form of the changeset hash will be returned. If the current working copy of
the repository is modified, a plus sign is append... | 4a1f369cd635230c44d4725b13b9704700d5bf3b | 3,623,955 |
from typing import List
from typing import Dict
def parse_idf(content: str) -> dict:
"""Parse an IDF file into a dictionary."""
sections = content.rstrip().split(';')
sub_sections: List[List[str]] = []
obj_dict: Dict[str, List[List[str]]] = {}
for sec in sections:
sec_lines = sec.splitline... | 9232964d590745482bb0552520b7ad42ebce8d94 | 3,623,956 |
from pathlib import Path
import joblib
def _make_item_rec_sys_data(review_data):
"""
Generates item_rec_sys_data by obfuscating customer ids.
Parameters
----------
review_data: combined_data.
Yields
------
item_rec_sys_data.csv
Returns
-------
item_rec_sys_da... | 868cae8a54513be3a129fdf7f38320652f265733 | 3,623,957 |
def system_order(tf: scipysig.dlti) -> tuple:
"""Returns the order of the numerator and denominator
of a transfer function
Parameters
----------
tf : scipy.signal.dlti
discrete time rational transfer function
Returns
----------
(num, den): tuple
Tuple containing the o... | d432e61758ceed35f3f5f1eeadf41c2080d137c6 | 3,623,958 |
def drvit_small_patch16_384(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_visi... | e9c59e19c98f17318fb058a8c2262fb8d40f0e79 | 3,623,959 |
def pdist_block(pdist_vec, i, j):
"""Slice the pdist ndarray as if it were a squareform
matrix.
Args:
pdist_vec: ndarray output from pdist()
i: ndarray row index of matrix
j: ndarray col index
Returns:
(i.size, j.size) ndarray from distance matrix
"""
col_ind, row_ind = np.meshgrid(... | 4ad2ba8694aa1fb6396d1c396c33264642451d25 | 3,623,960 |
def ufile_put_url(bucket, key):
"""
采用普通上传方法上传UCloud UFile文件的url
:param bucket: string类型, 待创建的空间名称
:param key: string类型, 在空间中的文件名
:return: string类型, 普通上传UFile的url
"""
return 'http://{0}{1}/{2}'.format(bucket, config.get_default('upload_suffix'), key) | a5bc7ab0bed9dc6368b7ca985c9f5bcb004378e4 | 3,623,961 |
from typing import Optional
def build_assemblenet_model(
input_specs: tf.keras.layers.InputSpec,
model_config: cfg.AssembleNetModel,
num_classes: int,
l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None):
"""Builds assemblenet model."""
input_specs_dict = {'image': input_specs}
ba... | 017347267a768b58609c9bb24faefe0bcb0b5cd5 | 3,623,962 |
import httpx
async def post_donation(donation_id: str) -> tuple:
"""Post donations to their respective third party APIs
If the donation has already been posted, it will not be posted again.
"""
donation = await get_donation(donation_id)
if not donation:
return (jsonify({"message": "Donati... | 84fd0e7a975cf913b79b2fa91a1569b25f4bdb8d | 3,623,963 |
import transformers
from typing import List
import torch
import tqdm
def predict_in_batches(
model: transformers.models, tokenizer, dataset: List[str], batch_size: int = 4
) -> List[int]:
"""Predicts the labels for the entries in dataset using the model passed
:param model: the model to use for prediction... | e21a668c2181acd076b565bbde379b028cbeead6 | 3,623,964 |
def remove_punctuation(sentence: str, punctuation: str = None):
"""
Default Punctuation -> [',', '!', '#', '$', '%', "'", '*', '+', '-', '.', '/', '?', '@', '\\', '^', '_', '~']
"""
punctuation = punctuation or ''.join(PUNCTUATION)
for x in punctuation:
sentence = sentence.replace(x, '')
... | 2b421a69efdb2bb58f1c778725953ceb2357394d | 3,623,965 |
def get_digest_for(changelogs,
before_date=None,
after_date=None,
limit_versions=5):
"""Before date and after date are inclusive."""
# search packages which have changes after given date
# we exclude unreleased changes from digest
# because they ... | 73dd266b3a02d9a4a1f499937f630605e2918582 | 3,623,966 |
import os
import time
def count_time(func):
"""类中使用的计时装饰器"""
def inner(*args, **kwargs):
print('{} 进程数据处理开始'.format(os.getpid()))
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
print('{} 进程数据处理完成，处理数据用时:{}'.format(os.getpid(), end_time -... | 2b7cdff3e308500b9c3509bc0d4c626b118eca36 | 3,623,967 |
import pkg_resources
def get_version():
"""Returns version"""
return pkg_resources.get_distribution("rosetta-cipher").version | 4a9e67434e8a1884e44dafaa30ffaf8c5ec5025d | 3,623,968 |
def good_fft_number(goal):
"""pick a number >= goal that has only factors of 2,3,5. FFT will be much
faster if I use such a number"""
assert goal < 1e5
choices = [2**a * 3**b * 5**c for a in range(17) for b in range(11)
for c in range(8)]
return min(x for x in choic... | e469a37f28869dca520aea3520fa0d763e9bb8ae | 3,623,969 |
def adjust_image_resolution(data):
"""Given image data, shrink it to no greater than 1024 for its larger
dimension."""
output_large = cStringIO.StringIO()
output_default = cStringIO.StringIO()
output_tiny = cStringIO.StringIO()
try:
im0 = Image.open(cStringIO.StringIO(data))
... | 06a6048efbc11fdc7c151feacbb4eddfa46ad90a | 3,623,970 |
def connect_to_ecs(env):
"""
Return boto connection to the ecs in the specified environment's region.
"""
rh = env.resource_handler.cast()
wrapper = rh.get_api_wrapper()
client = wrapper.get_boto3_client(
'ecs',
rh.serviceaccount,
rh.servicepasswd,
env.aws_region
... | e4c0b7ad80c18fd6d2a90df6670ca9bfa6f1cbe3 | 3,623,971 |
import random
def dsa_sign(message, private, constants=None):
"""DSA signs the bytestring `message` with the given private key and returns the signature (r, s)
using the hash SHA1"""
p, q, g = get_dsa_constants(constants)
while True:
k = random.randint(1, q - 1)
r = pow(g, k, p) % q
... | 780fd6370f0a73ca6ee3d761ffb21b5f6d6efaf1 | 3,623,972 |
def _pytesmo_to_qa4sm_results(results: dict) -> dict:
"""
Converts the new pytesmo results dictionary format to the old format that
is still used by QA4SM.
Parameters
----------
results : dict
Each key in the dictionary is a tuple of ``((ds1, col1), (d2, col2))``,
and the values... | f4f36562a65dd140a16b3969fe26a3450e7f9037 | 3,623,973 |
import argparse
import json
import os
from pathlib import Path
def provision_greengrass(arguments: argparse) -> dict:
"""Orchstrates and completes all provisioning processes based on
incoming validated argument list
:param arguments: Validated command line arguments
:type arguments: argparse
... | d51b90604962c89449df4cf5fe6a2bc8904f0fd3 | 3,623,974 |
def infotodict(seqinfo):
"""Heuristic evaluator for determining which runs belong where
allowed template fields - follow python string module:
item: index within category
subject: participant id
seqitem: run number during scanning
subindex: sub index within group
"""
info = {'test':[]... | ea5c334452cbc3e5fd700c38f258578b26f9cf56 | 3,623,975 |
def load_vgg(sess, vgg_path):
"""
Load Pretrained VGG Model into TensorFlow.
:param sess: TensorFlow Session
:param vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb"
:return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
"""... | f754449adf355e3462bafb6ecb6fbed2ad0b5c6f | 3,623,976 |
def grab_svg(scene):
"""
Return a SVG rendering of the scene contents.
Parameters
----------
scene : :class:`CanvasScene`
"""
svg_buffer = QBuffer()
gen = QSvgGenerator()
gen.setOutputDevice(svg_buffer)
items_rect = scene.itemsBoundingRect().adjusted(-10, -10, 10, 10)
if ... | a4292ff99062be7a110f31aafc4ee50f88618b58 | 3,623,977 |
def get_bel_node_uniprot():
"""Get UniProt related eBEL nodes."""
b = Bel()
conf = {
'rid': "@rid.asString()",
'name': "name",
'namespace': "namespace",
'bel': "bel",
'uniprot_accession': "uniprot"
}
sql = "SELECT "
sql += ', '.join([f"{v} as {k}" for k, v... | ff38ba4f8daaa12549c482a2b7b7bd7444e21750 | 3,623,978 |
import torch
def decode_ori_batch(ori, b):
"""Decode a batch of orientation (ori) using the pre-computed orientation decode variable (b) based on the histogram
(see pre_compute_ori_decode)
"""
ori = ori.cpu()
batch_size = ori.size(0)
ori_avg = torch.zeros((batch_size, 4), dtype=torch.float32... | a6acac339d3cf0613b733d0258ba21d1c8c57d9f | 3,623,979 |
from datetime import datetime
def receive_email(msg_str):
"""
Given a string representation of an email message, parses it into a :class:`~kiki.message.KikiMessage`. Returns a (message, created) tuple, where ``created`` is False if the message was already in the database.
"""
received = datetime.now()
python_m... | 58a7bbc758be4eb48d71794452dab18a2ae307b4 | 3,623,980 |
def rotated_array_search(input_list, number):
"""
Find the index by searching in a rotated sorted array
"""
high = len(input_list) - 1
low = 0
while low <= high:
mid = (low + high) // 2
if input_list[mid] == number:
return mid
elif input_list[mid] < number <= ... | c3deb50e608c58e5e11665d949a602cc661305ae | 3,623,981 |
def parse_sentence_spacy(sentence_text, sentence_entities):
"""
:param sentence_text:
:param sentence_entities:
:return:
"""
# Use spacy to parse a sentence
for e in sentence_entities:
idx = sentence_entities[e][0]
sentence_text = sentence_text[:idx[0] - 1] + sentence_text[... | b6da3a1f5dc02196286c666fa268ad7cfadb4c40 | 3,623,982 |
def check_for_deprecated_generators(main, file):
""" Check if the conan file if using some deprecated generator
:param main: Output stream
:param file: Conanfile path
"""
conan_instance, _, _ = conan_api.Conan.factory()
dict_generators = conan_instance.inspect(path=file, attributes=["generators... | a281e6b7e3ff48a49830a19c2bbb2fe9907d1e8d | 3,623,983 |
def get_chat_members_count(chat_id, **kwargs):
"""
Use this method to get the number of members in a chat.
:param chat_id: Unique identifier for the target chat or username of the target channel (in the format @channelusername)
:param kwargs: Args that get passed down to :class:`TelegramBotRPCRequest`
... | 1cec0d216fd3c1f28c1592cf2be170c26f32780a | 3,623,984 |
def sample_tag(user, name='Sample tag'):
"""Create and reutn a sample tag"""
return Tag.objects.create(user=user, name=name) | 985d40292258efa4fb997a1f68a1ca523c43df02 | 3,623,985 |
def _unpack_keypoints(keypoints):
"""Unpack the keypoints into an array of coordinates.
Args:
keypoints: a list of `cv2.KeyPoint`s
Returns:
an n x 2 array of [row, col] coordinates
"""
return np.array([[kp.pt[1], kp.pt[0]] for kp in keypoints]) | a594c580729750f493703310e067abe3196650a4 | 3,623,986 |
def get_videos():
"""Return a json array of all videos available on the site, built
by fetching each page sequentially until there are no more pages."""
videos = []
page = 0
end_of_pages = False
while not end_of_pages:
page += 1
url = "http://pyvideo.org/api/v2/video?page=%s" % s... | 8e3e5e2cea320fcdaf9ce819d06d59db97881ec5 | 3,623,987 |
import click
def input_mon(ctx):
""" Monitors all pins for changes. Expects ANSI terminal color. """
click.echo("Ctrl-\\ to quit")
d = CM119_IO(ctx.obj["vid"], ctx.obj["pid"])
d.set_dir({pin: "I" for pin in range(1, 9)}) # All GPIOs as inputs
def pin_formatter(pin_name, state):
"""Helpe... | 905ad5433fcb5c774f43b0ac143326367ab46bbd | 3,623,988 |
def make_fake_symbol_from_data_type(
data_type, symbol_pragma_text, *, name='$(SYMBOL)',
pragma_name: str = 'pytmc',
data_area_index=0, tmc=None,
create_data_area_if_needed=True):
"""
Create a :class:`_FakeSymbol` from the given data type.
Parameters
----------
data_... | d3bfde296814d4ba3327707225f40aa19ee08357 | 3,623,989 |
from typing import Optional
def has_at_least_one_share_class(filing_json, filing_type) -> Optional[str]: # pylint: disable=too-many-branches
"""Ensure that share structure contain at least 1 class by the end of the alteration or IA Correction filing."""
if filing_type in filing_json['filing'] and 'shareStruc... | ce0e324edd1fb4e427aafdedcdecafc736fdb8d0 | 3,623,990 |
def get_image_table(nova_endpoint=None):
"""
:param nova_endpoint:
:return:
"""
image_list = PrettyTable()
image_list.field_names = ["ID", "Name", "Format", "Size", "Min Ram", "Mind Disk", "location",
"adpter type", "status", "checksum"]
glance_list = nova_en... | 4d2b0c7d0d2bc222c3d57e38f2483e5165999a5f | 3,623,991 |
def color_thresholding(img, thresh_h=None, thresh_s=None, thresh_l=None):
"""
Crete image mask using color thresholding.
:param img: source bgr image
:param thresh_h: tuple(min,max), hue threshold in HSL color space
:param thresh_s: tuple(min,max), saturation threshold in HSL color space
:retur... | 387de7a3935431659de8b45e56efed0e4d38dc0f | 3,623,992 |
import builtins
def no_matplotlib(monkeypatch):
""" Mock an import error for matplotlib"""
import_orig = builtins.__import__
def mocked_import(name, globals, locals, fromlist, level):
""" """
if name == 'matplotlib.pyplot':
raise ImportError("This is a mocked import error")
... | 681ba8c0e70387e46ad7ed42ffb11ce8aa7f23bc | 3,623,993 |
def _get_center_context(
context_window_type, walks, n_walks, walk_len, window_length, padding_id
):
"""Get center and context pairs from a sequence
window_type = {-1,0,1} specifies the type of context window.
window_type = 0 specifies a context window of length window_length that extends both
left ... | d09b224341e3ceec2eb69ad3e7d0a03a1ea2035b | 3,623,994 |
def get_files(pga_id):
"""
Get all uploaded YAML files as a dictionary.
:return: dict of uploaded YAML files as JSON
"""
files_dict = utils.get_uploaded_files_dict(pga_id)
return jsonify(files_dict) | b2ab923de32a9ac9eee42dbebb218f075f4ea266 | 3,623,995 |
def convert_mask_to_pick(mask, sample_rate, threshold):
"""Convert a first breaks `mask` into an array of arrival times.
The mask has shape (n_traces, trace_length), each its value represents a probability of corresponding index along
the trace to follow the first break. A naive approach is to define the f... | 615c168bb119a8db1acb5581fa391ef6384c3f44 | 3,623,996 |
import tqdm
def create_tqdm_reader(reader, max_reads=None):
"""Wrap an iterable in a tqdm progress bar.
Args:
reader: The iterable to wrap.
max_reads: Max number of items, if known in advance.
Returns:
The wrapped iterable.
"""
return tqdm.tqdm(reader, total=max_r... | 29bd93b85ace167f5586ac275510e82f7bbe8223 | 3,623,997 |
def calc_U_slip_quasisteady(eps, E, x, mu):
"""
Slip velocity (quasi-steady limit)
"""
u_slip_quasisteady = -eps*E**2*x/(2*mu)
return u_slip_quasisteady | 0b5eebc9333b36cbd4b8179731e5998f7cb2739a | 3,623,998 |
def momentum(df, column='close', n=20, add_col=False, return_struct='numpy'):
""" Momentum
Parameters
----------
df : Pandas DataFrame
A Dataframe containing the columns open/high/low/close/volume
with the index being a date. open/high/low/close should all
be floats. volume ... | 72d4c5746e40d2ea70a9527d443249fe850401f7 | 3,623,999 |
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