content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def cases_list(request):
"""List caseversions."""
return TemplateResponse(
request,
"manage/case/cases.html",
{
"caseversions": model.CaseVersion.objects.select_related(
"case",
"productversion",
"productversion__product",
... | f159de0330cc66155056101604400c424c510391 | 3,608,372 |
import functools
import errno
def wrap_exceptions(fun):
"""Decorator which translates bare OSError and WindowsError
exceptions into NoSuchProcess and AccessDenied.
"""
@functools.wraps(fun)
def wrapper(self, *args, **kwargs):
try:
return fun(self, *args, **kwargs)
exce... | f2ce0079b193065bae725b144cdd1d759af50c9c | 3,608,373 |
import random
def draw_box(pred, orig_img, cls, colors):
"""
draw the predicted bounding boxes on a given image.
designed for single images.
For multi batch support, supply singular image iteratively
"""
coords1 = tuple(pred[1:3].int())
coords2 = tuple(pred[3:5].int())
label = "{0}".... | d04494b8736f76f94ab69d491ba71b17dc40665f | 3,608,374 |
from typing import Tuple
def compute_climatology(
data: xr.DataArray,
base_period: Tuple = (None, None),
) -> xr.DataArray:
"""
Computes the seasonal mean of a DataArray that has a time
dimension
Parameters
----------
data
base_period
"""
_check_dimensions(data)
return... | e211800d0b7a0c4127e369a4c7132b3e8d0a5f64 | 3,608,375 |
def name_by_arg(sex: str, name: str = None, id: int = None):
"""Get name object by passed `name` or `id` argument.
Args:
sex (str): male/female/all
Returns:
dict: Name object if successful, otherwise error.
If no argument passed, return all names.
"""
if sex == "male":
... | 61a7db60c5360ece9f1457947ed027ba507f8d56 | 3,608,376 |
def plugin_info():
""" Returns information about the plugin
Args:
Returns:
dict: plugin information
Raises:
"""
return {
'name': 'ds18b20 Plugin',
'version': '1.0',
'mode': 'poll', ''
'type': 'south',
'interface': '1.0',
'config': _DEFAUL... | 97ce0e5b7e1f7cdd1eaa72b0258cd394ded65366 | 3,608,377 |
def LP():
"""
load prototype
"""
return builder.lp() | 124c3034fecbd7e3abf9186e1749bfa970668408 | 3,608,378 |
def maxkcolor_edges(num_vertices: int, num_edges: int):
"""Generates edges for MaxKColor problems randomly
Args:
num_vertices: Number of vertices
num_edges: Number of edges
Returns:
List of randomly generated undirected edges
`[..., (v1, v2) ,...]`. Note that (v1, v2) and
... | 1febf58f50102fb95317fc8b3680b555a6b442fc | 3,608,379 |
def _standard_normalize(values, axes=(0,)):
"""Standard normalizes values `values`.
Args:
values: Tensor with values to be standardized.
axes: Axes used to compute mean and variances.
Returns:
Standardized values (values - mean(values[axes])) / std(values[axes]).
"""
values_mean, values_var = tf... | d1103b725c462c01e37d503dfdcb68c936fea505 | 3,608,380 |
import array
def dimcollapse_csr(v, indexes=(), normalize=True):
"""dimensional collapse of a vector
:param v: csr vector
:param indexes: allowed dimensional indexes
:param normalize: logical, set True to rescale values to unit norm
:return: new csr vector, values outside indexes reset to zero
... | a12d065190c47728755e40c0ce5ac3fb53d77ac3 | 3,608,381 |
import requests
def openei_api_request(data, pause_duration=None, timeout=180,
error_pause_duration=None):
"""
Args:
data (dict or OrderedDict): key-value pairs of parameters to post to
the API
pause_duration:
timeout (int): how long to pause in seco... | ad228a80bd7d4c7d2ffe8db148a4ebd48fa4aa23 | 3,608,384 |
def downside_risk_nb(returns, ann_factor, required_return_arr):
"""2-dim version of `downside_risk_1d_nb`.
`required_return_arr` should be an array of shape `returns.shape[1]`."""
result = np.empty(returns.shape[1], dtype=np.float_)
for col in range(returns.shape[1]):
result[col] = downside_ris... | 4a56b95824dc9d13d6cd401b86d7eb700942309d | 3,608,385 |
import json
def shorten():
"""
Input a youtube id from the client and we will
return a jsonified array of subclips. [(s1, e1), (s2, e2)]
We cache all extracted yt hot clips with their respective yt
id's. We maintain the 10 most recent hot clips per yt id.
"""
yt_id = request.form['yt_id']... | 76dc5f137957d7dbe8052eb6ce6c0823af620f3d | 3,608,386 |
import random
def generate_masked_image(image, boxes, masks, class_ids, class_names,
scores=None,
show_mask=True, show_bbox=True, show_score=True,
colors=None, captions=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coord... | 5155f9d0226f47fb6dc7e47435994f57da5ecc55 | 3,608,387 |
def msbe(P, R, Γ, v):
"""Mean squared Bellman error (MSBE)."""
assert linalg.is_ergodic(P)
d_pi = linalg.stationary(P)
return np.sum(d_pi * bellman_error(P, R, Γ, v) ** 2) | 2905987f5dcf83499d96c29a77c191d9da9ed683 | 3,608,388 |
def new_indicator_request(category):
"""
Create a new indicator
"""
url = '{}/indicators/{}'.format(BASE_PATH, category)
response = http_request(
'POST',
url,
headers=GET_HEADERS
)
try:
return response.json().get('data')
except Exception as e:
LO... | 3419b0e108d095799a9301ee521ebd8d5c4f96cf | 3,608,389 |
def parse_default(
filename, data=None, metadata=None, read_write='r'):
"""
Default parser for data. Assumes there is a single line of metadata at the top line in the form of a dictionary and the remainder is tabular and can be imported as a pandas DataFrame
:param filename: Name of the file to be ... | 839a845af95622a908f0c4f8958a72747b7bdbf3 | 3,608,390 |
from typing import Counter
def filter_adjoined_candidates(candidates, min_freq):
"""
Funcao que filtra apenas os candidatos proximos que aparecem com certa frequencia
"""
candidates_freq = Counter(candidates)
filtered_candidates = []
for candidate in candidates:
freq = candidates_freq[... | be9c8618dc9e8efd086cc3c8cc51d5ffdb5d8254 | 3,608,391 |
def tune_model(X_train, y_train, X_test, y_test, model,
space, metric, n_calls=25, minimize=True, min_func=gp_minimize):
"""
:param X_train: array-like, shape = [n_samples, n_features] The input samples.
:param y_train: array of shape = [n_samples] The training values.
:param X_train: arr... | 79d8f0f689e2d1d52b4346c5fc29a0f1e1eee074 | 3,608,392 |
def gcd(a, b):
"""Find the greatest common denominator of two integers.
Using Euclid's algorithm.
"""
b = abs(b)
while b != 0:
a, b = (b, a % b)
return a | 3c636a00c73fbc26a2dbcf5d3a99bc1ca26c887e | 3,608,393 |
from typing import Iterable
def load_data(pairs_db_obj):
""" Load currency pair's data
"""
data = _schema.dump(pairs_db_obj, many=isinstance(pairs_db_obj, Iterable))
return data | c8b7ffeffe6ccc45a3f61307e3f02347e9426bb0 | 3,608,394 |
def logout_view(request):
"""
logout
"""
logout(request)
return redirect('/') | 6a2a2dcbd904b19ccff6ffbc9cad3997439f78be | 3,608,396 |
def a2tf(ndp, angle):
"""
Wrapper for ERFA function ``eraA2tf``.
Parameters
----------
ndp : int array
angle : double array
Returns
-------
sign : char array
ihmsf : int array
Notes
-----
The ERFA documentation is below.
- - - - - - - -
e r a A 2 t f
... | 04070aef38a558146d7e3c68fc5769e28ebbd91e | 3,608,397 |
def calc_distance_matrix(all_seqs: list):
"""
Uses BLAST to calculate pairwise distances between sequences
:param all_seqs: A list of sequences to write to file
:return: a symmatrical matrix of distances and a list of the row/colnames
"""
# align_output, header = blast_seqs(all_seqs, all_seqs, b... | 8dd7f224a7bce14cd420892bfc51f7ea23f197c9 | 3,608,398 |
def getCache(request):
"""
测试方法
:param request:
:return:
"""
dict = settings.ZK_HARPC.get_resource()
return JsonResponse({'test': dict}, safe=False) | 145f25bba8625a61d67e8ae295c602e1886dc333 | 3,608,399 |
def create_tdm_tree():
"""Create tdm tree info"""
tree_info = [
[0, 0, 0, 1, 2],
[0, 1, 0, 3, 4],
[0, 1, 0, 5, 6],
[0, 2, 1, 7, 8],
[0, 2, 1, 9, 10],
[0, 2, 2, 11, 12],
[0, 2, 2, 13, 0],
[0, 3, 3, 14, 15],
[0, 3, 3, 16, 17],
[0, 3, ... | 03a44baa724135b07b88f8f5bec4834262498320 | 3,608,400 |
def find_all_key_entities(text):
"""
Find all key entity mentions in text, including locations, persons, and other types
"""
all_entities = []
try:
doc = Doc(text)
doc.segment(segmenter)
doc.tag_morph(morph_tagger)
doc.parse_syntax(syntax_parser)
doc.tag_ner(n... | 7efa5e9c2431638f158d1639adefb6376be7e467 | 3,608,402 |
def selftest_function(opts):
"""
Placeholder for selftest function. An example use would be to test package api connectivity.
Suggested return values are be unimplemented, success, or failure.
"""
app_configs = opts.get("fn_create_zoom_meeting", {})
zoom = ZoomCommon(opts, app_configs)
requ... | d18cdce7aebbf51ffc21aac988c8669f41c7c80d | 3,608,403 |
def conv_F2C(value):
"""Converts degree Fahrenheit to degree Celsius.
Input parameter: scalar or array
"""
value_c = (value-32)*(5/9)
if(hasattr(value_c, 'units')):
value_c.attrs.update(units='degC')
return(value_c) | 1f3ac169942c51b363a7ae7977890be237b95390 | 3,608,404 |
def get_db_tables_with_data() -> list:
"""Gets database tables. If table is empty pass"""
full_dbs = []
get_tables = cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
for table in get_tables:
check_table_rows = cursor_2.execute(F'SELECT count(*) FROM {table[0]}')
for ... | afd26c9a68c18ea547984d9b7812600e99231ff8 | 3,608,405 |
import time
def inner_loop(repo, cref, clock=time):
"""Fetches the config ref and runs single iteration of processing.
Returns:
tuple (bool success status, list of synthesized commits).
"""
repo.fetch()
cref.evaluate()
return process_repo(repo, cref, clock) | 2dd90ef7cdb84c3a032ba762027b42c7b4a316b1 | 3,608,406 |
def extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(dt64):
"""
Extract separate fields for year, monday, day, hour, min, sec from
a datetime64 object, or an array-like object of datetime64 objects
Parameters
----------
dt64 : xarray DataArray, np.ndarray, list of, or single numpy.datetime64
... | d41719fe92444bc4fff42c9cc496935e019409c7 | 3,608,407 |
import warnings
def postelToDirCos(u, v):
"""Convert Postel azimuthal equidistant tangent plane projection u,v to
direction cosines.
Parameters
----------
u, v : float
Postel tangent plane coordinates in radians.
Returns
-------
alpha, beta, gamma : float
Direction co... | 51819ce5b7d467acaf74bbb1f21895353fb46f42 | 3,608,408 |
import pandas
def protocol_df_chunk_to_pandas(df):
"""
Convert exchange protocol chunk to ``pandas.DataFrame``.
Parameters
----------
df : ProtocolDataframe
Returns
-------
pandas.DataFrame
"""
# We need a dict of columns here, with each column being a NumPy array (at
# l... | 99afa0e9a680da790c628d8770d1f71cf96f9828 | 3,608,409 |
def parse_dtype(s):
""" Parse text as numpy dtype
>>> parse_dtype('i4')
dtype('int32')
>>> parse_dtype("[('a', 'i4')]")
dtype([('a', '<i4')])
"""
if s.startswith(b'['):
return np.dtype(eval(s)) # Dangerous!
else:
return np.dtype(s) | 65dfe2331ea020a2860468029ef6318b618700d4 | 3,608,410 |
import re
def test_custom_s3_decorator():
"""Example of creating a custom decorator"""
bucket = "my-bucket"
prefix = "all-exceptions/"
httpretty.register_uri(httpretty.PUT, re.compile(r".*amazonaws\..*"), body="")
def my_exception_report(f):
storage_backend = S3ErrorStorage(access_key="a... | 17d8061cab2ca0b1cba50b00c8e93e4ce7b2545c | 3,608,413 |
def login():
"""Log user in"""
# Forget any user_id
session.clear()
# User reached route via POST (as by submitting a form via POST)
if request.method == "POST":
# Ensure username was submitted
if not request.form.get("username"):
return apology("must provide username"... | 9ddd7b76481f94239dee979cec44956db5aaf6cc | 3,608,414 |
def get_cutoff(obj, r_max=units.length(1, 'nm'), r_min=0, hyperparam='default', return_mask=False, reverse=False):
"""get cutoff"""
if obj is None or isinstance(obj, Cutoff):
return obj
if isinstance(obj, str):
if obj not in _CUTOFF_ALIAS.keys():
raise ValueError(
... | 8b1a0a104758ab9c60200cea1aad0f66b9d3283d | 3,608,417 |
def AggFormula(formula, formulaParam, *matchPairs):
"""
Create a formula aggregation.
:param formula: (java.lang.String) - the formula to apply to each group
:param formulaParam: (java.lang.String) - the parameter name within the formula
:param matchPairs: (java.lang.String...) - the columns to... | 8ef974d0e1e7c0e5df8cd58a947766feb7a472b6 | 3,608,418 |
def status_in_range(value: int, lower: int = 100,
upper: int = 600) -> bool:
"""
Validates the status code of a HTTP call is within the given boundary,
inclusive.
"""
return value in range(lower, upper+1) | 43bb6f4824b7e42b6620e3ddff853aa65713f145 | 3,608,419 |
def combine_dicts(*args, exclude=(), deep_copy=False):
"""Returns a new dict with entries from all dicts passed, with later dicts overriding earlier ones.
:param exclude: Remove these keys from the result.
:param deepcopy: Perform a deepcopy of the dicts before combining them.
"""
if not len(args):
... | 49627b9f37c08be7f7d3f51f8a47ddb316c545d4 | 3,608,420 |
def get_FR_dev(start_time,end_time,sim_dt,spikemon,n):
"""
get firing rate given spikemonitor and time interval.
quantify deviation as well.
start_time: time of input start
end_time: time of input end
sim_dt: simulation time step
spikemon_t: time array from spike monitor
n: total number... | 579ba454cdbc924cca5b4789fcc6e57cd81c977c | 3,608,421 |
def _convert_from_roi(fname):
"""Convert a roi file to a numpy array [x, y, h, w].
Parameters
----------
fname : string
If ends with `.roi`, we assume a full path is given
"""
if not fname.endswith('.roi'):
fname = '%s.roi' % join(IJ_ROI_DIR, fname)
with open(fname, 'rb') ... | 80cc53ed8ac576100974e9d7843751fed0173141 | 3,608,422 |
def init_grid (mins=0, limits=np.array([-180, 180, -90, 90])):
"""
Initiates the grid variables based on the number of points wanted
within the given limits
gris step is in minutes, 60 min = 1 degree
"""
dim = limits * pi/180
if ( (mins < 1) or (mins > 600) ):
size_long = 5
... | 5aaf240aa4a958e9fa6df2de5abb545647b761c4 | 3,608,423 |
def author(request, author):
"""
View function for author
"""
return render(request, 'author.html', {}) | a1b713d8919d16728322dc478c1d46abb7f22ca9 | 3,608,424 |
def import_feed_from_text(text, filename=None) -> [str]:
"""
>>> text = "<opml> https://blog.guyskk.com/blog/1 https://blog.anyant.com"
>>> expect = set(['https://blog.guyskk.com/blog/1', 'https://blog.anyant.com'])
>>> set(import_feed_from_text(text)) == expect
True
>>> set(import_feed_from_tex... | 197f3551b90f328025b46d4c0a75d3fca0028ee6 | 3,608,425 |
def create_attachment(b64_data, name):
"""Create a Salesforce attachment object from a jpeg."""
attachment = Attachment()
attachment.update_fields({
"Body": b64_data,
"Name": name,
})
return attachment | a8aa9537535430a2237e569d24a28f70a710fd96 | 3,608,426 |
def getSteamAppDir(appid: int) -> str:
"""
A function that retrieves the folder of an app by searching in all possible libraries
\t
:param appid: The app's id to search for
:return: path to app's folder
:raises RuntimeError: raised when the app is not found
"""
for path in libraryFolders():
try:
logger.inf... | c93736aafa5440f530a3d025cf8e92313dcb2bf0 | 3,608,427 |
def lyapunov(A, Q=None):
"""
Solve the equation :math:`A^T X + X A = -Q`.
default Q is set to I
:param A: system matrix
:type A: np.ndarray | np.matrix | List[List]
:param Q: matrix
:type Q: np.ndarray | np.matrix
:return: the matrix X if there is a solution
:rtype: np.ndarray | No... | bfc65d7966e12b640b2e52e0bee28c1f23d95000 | 3,608,428 |
def _resize_cropped(image, image_size, thumbnail_display_size, thumbnail_image_size):
"""
Resizes the image to fit the desired size, preserving aspect ratio by
cropping, if required.
"""
# Resize with nice filter.
image_aspect = image_size.aspect
if image_aspect > thumbnail_image_size.aspect... | 7067c2348e6bcb35f3e6e33d2e7fe16a6b9e8855 | 3,608,429 |
def nodeOrdering(criteria = "const FieldList<%(Dimension)s, %(DataType)s>&"):
"""Compute the order that a given set of nodes should be stepped through
given a FieldList of things to sort them by.
The FieldList returned is the one to N indexing corresponding to sorting the
input in increasing order."""
return "... | 70af2ca0549a03e3e6bf9cfd08b6d831770c4a31 | 3,608,430 |
def xtile(lst, lo=0, hi=1,
width=50,
chops=[0.1, 0.3, 0.5, 0.7, 0.9],
marks=[" ", ".", ".", " ", " "],
bar="",
star="o",
show=" %5.3f"):
"""
Pretty print a large list of numbers.
Take a list of numbers, sort them,
then print them as a
horizontal
xt... | e9c548a3d9bdf1fd015f9e11b692afa1e35a509d | 3,608,431 |
import six
def format_decimal(decimal):
"""
Formats a decimal number using the same precision as Excel
:param decimal: the decimal value
:return: the formatted string value
"""
getcontext().rounding = ROUND_HALF_UP
# strip trailing zeros
normalized = decimal.normalize()
sign, digi... | e4907e613832856802de7a2fa933877012aaaec2 | 3,608,432 |
def avg_Nc(log10mhalo,z,log10mstellar_thresh,sig_log_mstellar):
"""<Nc(m)>"""
log10mstar = Mstellar_halo(z,log10mhalo)
num = log10mstellar_thresh - log10mstar
denom = np.sqrt(2.) * sig_log_mstellar
return 0.5*(1. - erf(num/denom)) | 9575d43ba1477e697937916094345020380cbab3 | 3,608,433 |
def get_hiragana(word: str) -> str:
"""文字列をひらがなに変換します。
Args:
word (str): 変換する文字列
Returns:
str: ひらがな文字列
"""
converted_word = jaconv.kata2hira(word)
return converted_word | de2597a1846e8da038bcb8108fe7ca0357af3f9e | 3,608,434 |
def get_activity_polylines(before=None, after=None, limit=None):
"""Gets activity polylines for user.
Args:
before (datetime or str): Optional. Retrieve activities before
this date. Format YYYY-MM-DD.
after (datetime or str): Optional. Retrieve activities after this
date... | 4ee542936b4dad7bca0702c1f7ea11a13fd1a125 | 3,608,435 |
import ast
def processLambda(arg, exp, fail=ast.FAIL):
"""Destructure lambda arguments.
f(λa1 a2 ... an.body) → f(λa1.f(λa2. ... f(λan.body)))
f(λ[x . y].body) → λv.if pair? v then (f(λx y.body)) (head v) (tail v) else FAIL
→ λv.if pair? v then (λx.λy.body) (head v) (tail v) else FAIL... | c29a7a88885e4abada9c1e1ec4358875ac7b4cd7 | 3,608,436 |
def tails_concentration():
"""Calculate the possible enrichment given tails U234 concentrations
"""
# Split product in two parts to have a higher granularity in the
# first part.
size = 50
xp = np.concatenate((np.linspace(1, 20, size, endpoint=False),
np.linspace(20, 9... | 00463b6cc6a470ebce87e55da7cc70316071162a | 3,608,438 |
async def crime_plot(current_city:City):
"""
Visualize crime information for city
- see overall crime breakdown
- visualize breakdown of violent crime and property crime
### Query Parameters
- city
### Response
JSON string to render with react-plotly.js
"""
city = validate_city... | 477aa54ff87a6470757636a5ea3d7b2acd4f6df1 | 3,608,439 |
def _float_to_str(x):
"""
Converts a float to str making. For most numbers this results in a
decimal representation (for xs:decimal) while for very large or very
small numbers this results in an exponential representation suitable for
xs:float and xs:double.
"""
return "%s" % x | cb795b9c4778b9a3fda7398024166d86d8458bd3 | 3,608,440 |
def create_ips(file1_content, file2_content):
"""
Creates a new Patch object based on the differences between file1 and file2
:Parameters:
file1_content : bytearray
Contents of the first file
file2_content : bytearray
Contents of the second file
rtype: Patch
return: The newly created Pat... | cff7e01025b93057cc952d23629f81a63ed3adc2 | 3,608,442 |
def unique_cluster_indices(cluster_indx):
"""
Return a unique list of cluster indices
:param cluster_indx: Cluster index list of ClusterExpansionSetting
"""
unique_indx = []
for symmgroup in cluster_indx:
for sizegroup in symmgroup:
for cluster in sizegroup:
... | 36bc5a287d49c6abbd552b9edc0e72675ba82eca | 3,608,443 |
from datetime import datetime
def get_slmtag_str(dbpath, timestamp=None):
"""Read the database and return an SLMTag string.
dbpath - the path to the sqlite database.
timestamp - to be used for testing only, where we want to ensure that
log times are always the same.
"""
(authva... | 5165ffc51a782b76e69a8575fc902101b0753b21 | 3,608,444 |
def makeExternalLink(url, anchor):
"""Function applied to wikiLinks"""
if options.keepLinks:
return '<a href="%s">%s</a>' % (quote(url.encode('utf-8')), anchor)
else:
return anchor | cf7d4895b2ca40ea780e18b95fd2aa28787ecee6 | 3,608,445 |
def _calculate_atr(atr_length, highs, lows, closes):
"""Calculate the average true range
atr_length : time period to calculate over
all_highs : list of highs
all_lows : list of lows
all_closes : list of closes
"""
if atr_length < 1:
raise ValueError("Specified atr_length may not be l... | f5878eda22c09fa8c428122bd013f9c6088ea0f8 | 3,608,446 |
def combine_cdd_path(*, resp, **_):
"""Call function to generate combined dataframe from csv file and
excel dataset, bringing only those flows from the excel file that are
not in the csv file
"""
df_csv = write_cdd_path_from_csv()
df_excel = epa_cddpath_call(resp=resp)
df_excel = df_excel[~d... | 752ad0305cc34b07789a019b1939726eeadb7f2f | 3,608,447 |
def generate_handles(labels, colors, edge='k', alpha=1):
"""
Generate handles for the legend map
:param labels: name of legend entry
:param colors: color of legend box
:param edge: edge colour, default is black
:param alpha: transparency of legend, default is not transparent
:return: handles... | 0a97ec997bb0ea03b412eebedadb1617cadbb90d | 3,608,448 |
def get_reviewers(request):
""" All assigned reviewers, staff or admin """
return User.objects.filter(Q(submissions_reviewer__isnull=False) | Q(groups__name=STAFF_GROUP_NAME) | Q(is_superuser=True)).distinct() | fb0e7a7a34643ebbe02be5c5b61e01e585aaa2b9 | 3,608,449 |
import io
def _load_data(fov_data, exp):
"""
Load a field of view dataset
Parameters
----------
fov_data : pd.DataFrame
Table of file locations for the spot results and mask label image
corresponding to the dataset to be viewed
exp : Experiment
Experiment ... | 95f95f9d6e9e28cec138757714d4686205f024cc | 3,608,450 |
from typing import Any
def move_file(client: Any, team_drive_id: str, file_id: str):
"""Moves a file from one team drive to another"""
f = make_call(client.files(), "get", fileId=file_id, fields="parents", supportsTeamDrives=True)
previous_parents = ",".join(f.get("parents"))
return make_call(
... | 6cbdd56b2898bf0c972413110cf2bd229504101e | 3,608,451 |
def requested() -> bool:
"""Return whether listeners should shut down."""
return _flag.is_set() | b95736b7c1f31999c6e34828cf5d8d23fa4d16a2 | 3,608,453 |
import sh
def extract_sac(data, ctable, processes):
"""
extract the downloaded data to the sac and pz format.
"""
# * get dirname
thedir = dirname(data)
# * mkdir for the sac and pz files
sh.mkdir("-p", join(thedir, "SAC"))
sh.mkdir("-p", join(thedir, "PZ"))
# * extract sac
win... | e58dab1eb747b9f6f727584f53598cf8f1e54b18 | 3,608,454 |
def _spin_echo_gates(idx: int) -> cirq.ops:
"""Outputs one of 4 single-qubit pi rotations which is used for spin echoes."""
pi_pulses = [
cirq.PhasedXPowGate(phase_exponent=0.0, exponent=1.0),
cirq.PhasedXPowGate(phase_exponent=0.5, exponent=1.0),
cirq.PhasedXPowGate(phase_exponent=1.0, ... | bf780d3c1de3caee4543e2eb4e5c5dfd7aaa3e08 | 3,608,455 |
def peak_normalization(spectrum, peak_range=[3.05, 4.05], verbose=False):
"""
Each spectrum is divided by its mean so that its median becomes 1
:param spectrum: dataframe of the spectrun values index are the cases and columns are the ppm value
... | 475c4df7bc12ff6e7d32318cda104c42f66b3226 | 3,608,456 |
def get_node_text_tags_preserved(xml_node):
"""Get the body of an XML node as a string, avoiding a specific blacklist
of bad tags."""
xml_node = deepcopy(xml_node)
etree.strip_tags(xml_node, *_tag_black_list)
# Remove the wrapping tag
node_text = xml_node.text or ''
node_text += ''.join(etr... | 2611eea7e5fc484d9750cd420361e9ce8335c0db | 3,608,458 |
def _getopt_size(py_obj, option):
"""Gets the specified size option"""
i = pynng.ffi.new('size_t []', 1)
opt_as_char = pynng.nng.to_char(option)
# attempt to accept floats that are exactly int
obj, lib_func = _get_inst_and_func(py_obj, 'size', 'get')
ret = lib_func(obj, opt_as_char, i)
pynng... | 90a99ec0edbbfbac7f90e65b531382756ad3761f | 3,608,460 |
def fileFromItem(item):
"""
Return the file contained in an item. Raise an exception if the
item doesn't contain exactly one file.
"""
files = Item().childFiles(item, limit=2)
if files.count() != 1:
raise DanesfieldWorkflowException(
"Item must contain %d files, but should co... | 16a86fe51f67b5df44370b247d5d7f777b2f53d9 | 3,608,461 |
def capacity_factor(pudl_out, min_cap_fact=0, max_cap_fact=1.5):
"""Calculate the capacity factor for each generator.
Capacity Factor is calculated by using the net generation from eia923 and
the nameplate capacity from eia860. The net gen and capacity are pulled
into one dataframe and then run through... | 87cf99cb19795e718d3cd43b76a858f27b13c61a | 3,608,462 |
import json
def get_minister_positions(minister, lang_code):
"""
Return all positions for the minister
"""
file_name = settings.MINISTER_JSON_FILE
if lang_code == 'fr':
status_prefix = 'Précédent '
else:
status_prefix = 'Former '
positions = []
with open(file_name, 'r'... | 2979ff7a59b9f47cd93fc6caa9cf5591c20e7d49 | 3,608,463 |
def home():
"""Home page."""
current_app.logger.info("Hello from the home page!")
return render_template("index.html") | 3f9054e945eae7f377c3e3edc0fbe820aa021f59 | 3,608,464 |
def compare_interior_kaplan(obs, var_pair, rescale_kaplan=False,
rescale_interior=False):
"""
Interior vs kaplan est for `multi_locus_analysis.finite_window.ab_window`.
Compare the Kaplan-Meier estimator to the empirical distribution function
(eCDF) of interior times of data... | 9c53818c5fc873ae53b7a33cc57c2535898f6fbf | 3,608,465 |
def all_intervals(text, elements, config):
"""From the slices of elements and units create an intervaltree.
time consuming and may unnessesary to search the whole txt
1. search all elements in text
2. search all elements in paragraph!
3. links > 40: search with aho-coressio search algo for speed
... | 06f4e764c74984ab71667417033331688ff5090f | 3,608,466 |
def multigauss(x, mu, cov, norm=True):
"""
Evaluates multivariate Gaussian distributions, each at different data
points. This code is optimised to evaluate M Gaussians of dimension N
at M points. The distribution is normalised by default but there is an
option to turn off normalisation.
... | 036f4f7104b65a8b049839457bc43bf9b9720c97 | 3,608,467 |
import torch
def preprocess(camera_value): # Preprocessing for camera format --> Network input format
"""
Preprocessing function for camera format --> network input format
param camera_value: input from Camera
type camera_value: np.ndarray
"""
global device, normalize
x = camera_value
... | 29d9ebe362172be617f609d2707daaee4abdaf69 | 3,608,468 |
def number_of_pending_jobs():
"""Return the number of jobs in the slurm queue."""
cmd = ["squeue", "-u", "lstanalyzer", "-h", "-t", "pending", "-r"]
output = sp.check_output(cmd)
return output.count(b"\n") | 054e77f553746f37d02eeeb8ed05771b263f5bb6 | 3,608,469 |
def copy_weights(model, source, target, num_layers, shared=False):
"""Copies the weight values of mixture weights and head from source to target
domain.
Arguments:
model: a tf.keras.Model object.
source: source domain name.
target: target domain name.
shared: whether the model is shared.
num_layers: nu... | 227ea9a45a3c9af2086ded56ce5f9a6c6780d2c9 | 3,608,473 |
def load_csv(file_name):
""" Load a csv-file generated by Audio2LED.
PARAMETER
---------
file_name: str
the path to the csv file
RETURNS
-------
led_time: ndarray
the time in seconds from the start
led_channel_levels: ndarray
2D array for the LED channels and fra... | f7985d6c00f7bdb8d381dcfe468f4b9e4bd39aa9 | 3,608,474 |
def error_in_assigned_energy(predictions, ground_truth):
"""Compute error in assigned energy.
.. math::
error^{(n)} =
\\left | \\sum_t y^{(n)}_t - \\sum_t \\hat{y}^{(n)}_t \\right |
Parameters
----------
predictions, ground_truth : nilmtk.MeterGroup
Returns
-------
er... | 92891991241bfbd5d583b11e8583ac59204b4af0 | 3,608,476 |
import torch
from typing import Tuple
def get_tot_objf_and_finite_mask(tot_scores: torch.Tensor, reduction: str) -> Tuple[torch.Tensor, torch.Tensor]:
"""Figures out the total score(log-prob) over all successful supervision segments
(i.e. those for which the total score wasn't -infinity).
Args:
... | d09b95056c2635be22a7db1baaa64b6667955007 | 3,608,478 |
def dct2d(a):
"""
Computes the 2D Normalized DCT-II.
Arguments
---------
X: (m, n) numpy array
2D image.
Returns
-------
(m, n) numpy array
DCT coefficient matrix.
"""
return fp.dctn(a, norm='ortho') | c16c04687b060485e1a4314daae8972da86cd40f | 3,608,479 |
def generate_embedding_mat(dict_size, emb_len, init_mat=None, extra_symbol=None,
scope=None, reuse=None, trainable=False):
"""
generate embedding matrix for looking up
:param dict_size: indices 0 and 1 corresponding to empty and unknown token
:param emb_len:
:param init_mat: init mat matching for [di... | c29efb8be60cb1153aa80774abf0283cd0304526 | 3,608,480 |
def user_settings(request):
"""
Let a user change its settings, like email preferences.
:param request:
:return:
"""
try:
meta = request.user.usermeta
except UserMeta.DoesNotExist:
meta = UserMeta()
request.user.usermeta = meta
meta.save()
request.use... | 0233c57baf6e07d46a0214964746fb6e38b2e641 | 3,608,481 |
import graphviz # noqa: F401
def render_graph(graph_specification, render_distributions=False):
"""
Create a graphviz object given a graph specification.
:param bool render_distributions: Show distribution of each RV in plot.
"""
try:
except ImportError as e:
raise ImportError(
... | 8fd01c67423e7303e11cac613e8265e75c7cc1c2 | 3,608,483 |
import dateutil
from operator import sub
def donationParser(inLoc, header=True):
"""Takes a donation csv formatted from twitch alerts. Returns a list of lists for each entry.
0-Date (datetime object)
1-Name
2-Email
3-Amount (Decimal type)
4-Comment
"""
donations=[]
file =open(inLoc... | 59f318c4570b0ebf06102bc99b4dc37889847a75 | 3,608,484 |
def VaporPressure(tempc, phase="liquid"):
"""Water vapor pressure over liquid water or ice.
INPUTS:
tempc: (C) OR dwpt (C), if SATURATION vapour pressure is desired.
phase: ['liquid'],'ice'. If 'liquid', do simple dew point. If 'ice',
return saturation vapour pressure as follows:
Tc>=0: es = ... | 058caabe6db9c6506daa6e1fa0071c3903a2ca9a | 3,608,485 |
def fft_detect(resultImage, hostImage, alpha):
"""Starting from a watermarked image, detects the watermark image embedded
into the host image by using the Fast Fourier Transform.\n
Arguments:
resultImage (NumPy array) -- the watermarked image
hostImage (NumPy array) -- the original im... | 9a45d7bf0af3278071d71bda79e005ab834bc9d9 | 3,608,486 |
def get_auth_twitter() -> tweepy.OAuthHandler:
"""auth twitter with saved tokens.
Returns:
tweepy.OAuthHandler:
"""
return tweepy.OAuthHandler(SETTINGS['TWITTER_API_KEY'], SETTINGS['TWITTER_API_SECRET_KEY']) | 2e9911a3d6f3aaf9e08a33ca7e4caff592494b01 | 3,608,487 |
def post_statistics():
""" get the data of posts """
post_statistic = dict()
post_statistic['total_posts'] = len(Post.query.filter_by(post_type='post').all())
post_statistic['total_comments'] = len(Comment.query.filter_by(user_id=0).all())
post_statistic['total_pages'] = len(Post.query.filter_by(pos... | a2c125b725c5361739a6f05957a92918e6ba2911 | 3,608,488 |
import pandas
def get_variant_ann_by_chrom_pos(db, chrom, start, ref, alt):
"""
e.g.,
UI:
https://biobankengine.stanford.edu/variant/1-39381448
MongoDB:
db.variants.find({'xpos': '1039381448'}, fields={'_id': False})
SciDB:
between(vairant, 1, 39381448,
1... | 19397af50d0c0ea2b871069959066c8b7e20aa83 | 3,608,489 |
def get_answers(candidate_tokens,predict_set,predict_result,dictionary):
"""
Build the dictionary of the selected answer for a QA-based network.
:param candidate_tokens: the dictionary of the documents and their candidate KPs
:param predict_set: the input of the network
:param predict_result: the o... | 3a6e82c34bb398c9ede673ad2e9a3c6fb672a8f3 | 3,608,490 |
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