content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def stddev(data, ddof=0):
"""Calculates the population standard deviation
by default; specify ddof=1 to compute the sample
standard deviation."""
n = len(data)
if n < 2:
raise ValueError('variance requires at least two data points')
ss = _ss(data)
pvar = ss/(n-ddof)
return pvar**0.5 | 3ec3576aa14965b98d9ef44a3d77b69d9a2913a6 | 3,614,700 |
def build_model_with_cfg(
model_cls,
variant: str,
pretrained: bool,
default_cfg: dict,
model_cfg= None,
feature_cfg= None,
pretrained_strict: bool = True,
pretrained_filter_fn = None,
pretrained_custom_load = False,
kwargs_filter = None,
... | 4d127be55ecbc557c5a146ecf87d23307cb785e5 | 3,614,701 |
def load_data_and_labels_lemonde(filepathXml):
"""
Load data and label from Le Monde XML corpus file
the format is ENAMEX-style, as follow:
<sentence id="E14">Les ventes de micro-ordinateurs en <ENAMEX type="Location" sub_type="Country"
eid="2000000003017382" name="Republic of France">France</E... | a1d624d12a1f4ceda49c62cb2608f48301e62dd2 | 3,614,702 |
def view_user_issues(username):
"""
Shows the issues created or assigned to the specified user.
:param username: The username to retrieve the issues for
:type username: str
"""
if not pagure_config.get("ENABLE_TICKETS", True):
flask.abort(
404,
description="Tic... | dfbbc5932080a7e0fa18ebb04d2b553fe57d9862 | 3,614,703 |
def i0(x):
"""
Modified Bessel function of the first kind, order 0.
Usually denoted :math:`I_0`. This function does broadcast, but will *not*
"up-cast" int dtype arguments unless accompanied by at least one float or
complex dtype argument (see Raises below).
Parameters
----------
x : ... | 665861f872be896553b2b57f5de8ca09b6dc7bfb | 3,614,704 |
def generate_registers_sifive_clic0_clicintip(intr, addr):
"""Generate xml string for riscv_clic0 intip register for specific interrupt id"""
return """\
<register>
<name>clicintip_""" + intr + """</name>
<description>CLICINTIP Register for interrupt id """ + ... | d3260a11f670a90affaf0284c2f74afac9f6c4a4 | 3,614,705 |
import json
import time
def close_poll():
""" Closes a poll. """
form = DeletePost()
if form.validate():
try:
post = SubPost.get(SubPost.pid == form.post.data)
except SubPost.DoesNotExist:
return json.dumps({'status': 'error', 'error': _('Post does not exist')})
... | 1514e0430f808a6e9e9a7f0b377767bdc2436c2b | 3,614,706 |
def TUInt_JavaUIntToCppUInt(*args):
"""
TUInt_JavaUIntToCppUInt(uint const & JavaUInt) -> uint
Parameters:
JavaUInt: uint const &
"""
return _snap.TUInt_JavaUIntToCppUInt(*args) | 3d845fab201781d2d23a7d5d6dab2cedc94863cf | 3,614,707 |
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry):
"""Unload a config entry."""
if hass.data[DOMAIN].get(DISPATCHERS) is not None:
for cleanup in hass.data[DOMAIN][DISPATCHERS]:
cleanup()
if hass.data[DOMAIN].get(DATA_DISCOVERY_INTERVAL) is not None:
hass.dat... | bc862de99608e8f8668ddf5d22bc4cb526277447 | 3,614,708 |
def extract_pdbs(input_filelines):
""" Given a list of lines from SSM output txt, returns a list of upper case PDBs
The input file should have a range of lines enumerating the PDBS that looks like this:
.....
.....
## Structure Nres Nsse RMSD Q-score
1 PDB 5cxv... | 52270f03ba2a4eca6308327583467bc391346bea | 3,614,709 |
def result():
"""Route to results page after submitting form on index page"""
# declare form from form.py
form = pathToVideo()
# user option selections from form saved as sessions to be accessible via different routes
session['firstPath'] = form.firstPathSelect.data
session['secondPath'] = form... | c4f73bbdd749360c99d64d9106020b61da904235 | 3,614,710 |
def is_conv2d(module):
"""Determine Conv2d."""
# depth-wise convolution not in pruned search space.
return isinstance(module, Conv2d) and not is_depth_wise_conv(module) | db7b1d883900a532e183332998ade2723d98f681 | 3,614,711 |
def make_date(d_str: str) -> Date:
""" Returns new Date instance from given date string
REQUIRES: d_str is in format 'yyyy-mm-dd'
"""
year = int(d_str[:4])
month = int(d_str[5:7])
day = int(d_str[8:])
return Date(year, month, day) | ea09489abba828edcdbc33ff545bf09b88b019ec | 3,614,712 |
def get_device_id():
""" GEt unique id for device"""
flash_id = '{0:x}'.format(esp.flash_id())
manufacturer = flash_id[-2:]
device_id = flash_id[2:4] + flash_id[0:2]
return (manufacturer, device_id) | 1f7ecc90b8ef6c844d64ee7acab79c69e7727a07 | 3,614,713 |
import torch
def view_complex_native(x: torch.FloatTensor) -> torch.Tensor:
"""Convert a PyKEEN complex tensor representation into a torch one using :func:`torch.view_as_complex`."""
return torch.view_as_complex(x.view(*x.shape[:-1], -1, 2)) | 14e74f1c8b5e6de673c962e4381e74026d3d3db2 | 3,614,714 |
def cross_corr_norm(patch_0, patch_1):
"""
Returns the normalized cross-correlation between two same-sized image
patches.
Parameters :
patch_0, patch_1 : image patches
"""
n = patch_0.shape[0] * patch_0.shape[1]
# Mean intensities
mu_0, mu_1 = patch_0.mean(), patch_1.... | 213100b174993baa07ea685b23541d3dfe49ace8 | 3,614,715 |
from docopt import docopt
import logging
import os
def main(args=None):
"""Run program."""
args = docopt(__doc__, version=__version__)
if args.get('--verbose'):
log.setLevel(logging.INFO)
elif args.get('--quiet'):
log.setLevel(logging.ERROR)
elif args.get('--debug'):
log.s... | 1a7482ecb441dcf89c2b6d3236828120ae83a71d | 3,614,716 |
from datetime import datetime
def datetime_to_W3CDTF(dt):
"""Convert from a datetime to a timestamp string."""
return datetime.datetime.strftime(dt, W3CDTF_FORMAT) | debdce838987c8815fec977761f760a4fba62aa1 | 3,614,717 |
def get_device_value_oid(ip, oid, community_string="public"):
"""
Get value from specified device with OID
Args:
ip: The device's IP address
oid: SNMP OID
community_string: The community string for the network. Usually 'public' or 'private'
Returns:
... | bf5ecb3519fca3d2d6343e35a540d37a70e698e6 | 3,614,718 |
def get_flip_set(index):
"""Make flip set"""
n = 1
while n <= index:
n *= 2
def get(n, j):
if j <= 1:
return {b for b in range(j)}
n_half = n // 2
if j < n_half:
return get(n_half, j)
f = {b + n_half for b in get(n_half, j - n_half)}
... | 3174b9bab59ae67e9f869fddd9c0a6669f742a45 | 3,614,719 |
def cosineDistance(a, b):
""" Calculates the cosine distance between lists of numbers `a` and `b`.
Args
---
`a : float[]` The first list of floats (or ints)
`b : float[]` The second list of floats (or ints)
Returns
---
`distance : float` The distance between `a` and `b`
"""
n =... | a697e17c2483441995c3a04652edd2bcac287539 | 3,614,720 |
def tensor2sparse(tensor):
"""Convert pytorch tensor to sparse csr matrix."""
return sparse.csr_matrix(tensor.detach().cpu().numpy()) | dbd33450abd534eafa48fdae896afefcf6bdefad | 3,614,721 |
from typing import Any
from typing import Callable
import click
def optional(*decls: str, nilstr: bool = False, **attrs: Any) -> Callable[[FC], FC]:
"""
Like `click.option`, but no value (not even `None`) is passed to the
command callback if the user doesn't use the option. If ``nilstr`` is
true, ``-... | dae9c0b60d4d7f0bdbab4966e30f8b237c6af17a | 3,614,722 |
def get_model_base() -> dict:
"""The base model for running a WLTC experiment.
It contains some default values for the experiment
but this model is not valid - you need to override its attributes.
:return: a tree with the default values for the experiment.
"""
instance = {
"unladen_ma... | cfa1bb87a6f6ffb6c88863a27d75fd9bf2a13cff | 3,614,723 |
def triplet_loss(y_true:tf.Tensor, y_pred:tf.Tensor, alpha:float = 0.2):
"""
-- Explanation :
this function compares the triplet loss ()
-- Args:
y_true -- true labels,
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor images, of shape (None, 12... | c46161f5253a9969110d2867c309982d1f1db119 | 3,614,724 |
def x_view_function():
""" underspecified library view """
return Response(1.234) | a85460fd90978277cfc5c9c4253e2d056c8115c2 | 3,614,725 |
def mkXRDTag(t):
"""basestring -> basestring
Create a tag name in the XRD 2.0 XML namespace suitable for using
with ElementTree
"""
return nsTag(XRD_NS_2_0, t) | c770afdcf21576d761e111afc8556f7c37745099 | 3,614,726 |
def auth(
func=None,
roles=None,
permissions=None,
requires=None,
login_redirect=None,
should_remember_referrer=True):
"""Guards a controller action against unauthorized and unauthenticated access.
Args:
roles (list|string): A list of roles that are able ... | 43916198f76f2e840ba6b4535117a35a54d70294 | 3,614,727 |
def site_stat_stmt(table, site_col, values_col, fun):
"""
Function to produce an SQL statement to make a basic summary grouped by a sites column.
Parameters
----------
table : str
The database table.
site_col : str
The column containing the sites.
values_col : str
T... | c704d5687effd3c12abb3feecde9041eb88aae7a | 3,614,728 |
def to_time_units(obj, freq):
"""Multiply each element with `freq_delta` to get result in time units."""
if not checks.is_array(obj):
obj = np.asarray(obj)
return obj * freq_delta(freq) | 86ca6c833d32e9e03b4afcba8462f69fa42670d4 | 3,614,729 |
import random
def perform_learning_step(epoch):
""" Makes an action according to eps-greedy policy, observes the result
(next state, reward) and learns from the transition"""
def exploration_rate(epoch):
"""# Define exploration rate change over time"""
start_eps = 1.0
end_eps = 0.... | e10ccdaeffeb37800ca52db193dbfd6426ebc9dd | 3,614,730 |
def read_bhrc(filename, **kwargs):
"""Read the Iran BHRC strong motion data format.
Args:
filename (str): path to BHRC data file.
kwargs (ref):
Other arguments will be ignored.
Returns:
list: Sequence of one StationStream object containing 3
StationTrace objects... | 3d86478e22189b3910ca6e509911437fb2bfbb58 | 3,614,731 |
def random_pdf(x, dx, seed_i=False, n_iter=1000, silent=True):
"""
Created on 24/06/2016
Modified on 29/06/2016 to reverse shape
"""
len0 = len(x)
if not silent:
print(len0)
# Mod on 29/06/2016
x_pdf = np.zeros((len0, n_iter), dtype=np.float64)
# x_pdf = np.zeros((n_iter, l... | aa5940ae6913ab40aeb10d319b7e0bdab01a0453 | 3,614,732 |
import itertools
def vertical_split(im):
"""
Split an image vertically. This works for member list as well as (well, most times) scouting screenshots
:param im: screenshot data
:return: the ratio for resizing the screenshot, a list of (start, stop) chunks to use when looping
"""
im_gray = cv2... | 78b2c423f72f0c75ce7712ee8f74a1564caa2990 | 3,614,733 |
def interp2d(image):
"""
Bilinear interpolation method to be used for upscaling
Args:
tensor_nchw (tensor): tensor of shape (N, C, H, W)
Return:
tensor of shape (N, C, Hout, Wout), where Hout and Wout are computed
by applying the scale factor to H and W
"""
# return ... | 14a40dcf0b3a22d9493b99c0cf5c96e41f37fd2e | 3,614,734 |
def create_app(config_object="chaos_genius.settings"):
"""Create application factory, as explained here: http://flask.pocoo.org/docs/patterns/appfactories/.
:param config_object: The configuration object to use.
"""
app = Flask(__name__.split(".")[0])
app.config.from_object(config_object)
regis... | d3dab2a874518b1f942d91b3c0c76f2ddc50e212 | 3,614,735 |
def get(cls, key):
"""
Get an entity by key
"""
return build_key(cls, key).get() | 58113088582c61dc67bb4d0366a9f4f35256c959 | 3,614,736 |
def page_not_found(__=None):
"""
What to return if a user requests an endpoint that doesn't exist.
"""
print(app.root_path)
return transiter_error_handler(exceptions.PageNotFound(flask.request.path)) | 21dd68dc51607bee937f327c5c8ac864d5309029 | 3,614,737 |
import copy
def rational_diagonal_form(self, return_matrix=False):
"""
Returns a diagonal form equivalent to Q over the fraction field of
its defining ring. If the return_matrix is True, then we return
the transformation matrix performing the diagonalization as the
second argument.
INPUT:
... | 9bee64eabd0a4550455e99542ff7db3c89526892 | 3,614,738 |
def _format_source_error(filename, lineno, block):
""" A helper function which generates an error string.
This function handles the work of reading the lines of the file
which bracket the error, and formatting a string which points to
the offending line. The output is similar to:
File "foo.py", li... | 32d093e53811415338877349ca8e64b0e9261b1d | 3,614,739 |
def calc_exposure(k, src_rate, bgd_rate, read_noise, neff):
"""
Compute the time to get to a given significance (k) given the source rate,
the background rate, the read noise, and the number
of effective background pixels
-----
time = calc_exposure(k, src_rate, bgd_rate, read_noise, neff)
... | 993853d244cfa5c6619300def02294a2497d78df | 3,614,740 |
def decode_image(contents, channels=None, name=None):
"""Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`.
Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate
operation to convert the input bytes `string` into a `Tensor` of type `uint8`.
Note: `decode_gif` return... | 2d875772abbd02b716922880c88b63b8c080eed8 | 3,614,741 |
import os
def exec_mkdir(dirname):
"""
Create a directory.
Parameters
----------
dirname: str
The full path of the directory.
Returns
-------
bool:
True on success, False otherwise.
"""
_logger.debug('__ Creating %s.', dirname)
try:
if not o... | 179d8aeb13c53710904ccc824046636ba38253aa | 3,614,742 |
def get_all_round_info_from_cache():
"""Returns a dictionary containing all the round information.
example: {"rounds": {"Round 1": {"start": start_date, "end": end_date,},},
"competition_start": start_date,
"competition_end": end_date}
"""
rounds_info = cache_mgr.get_cache('r... | e996b97290112116c65c66d9db3fe7c19f9b242c | 3,614,743 |
def type_or_null(names):
"""Return the list of types `names` + the name-or-null list for every type in `names`."""
return [[name, 'null'] for name in names] | 72cbefcbba08c98d3c4c11a126e22b6f83f4175b | 3,614,744 |
def scratch(request, slug=None):
"""
Get or create a scratch
"""
if request.method == "GET":
db_scratch = get_object_or_404(Scratch, slug=slug)
return Response(ScratchSerializer(db_scratch).data)
elif request.method == "POST":
data = request.data
if "target_as... | d7c13689f7f7a4bb97c1c4d8e2fb20636f1cbf7a | 3,614,745 |
def variables_pool(variable, question='Variable to analyze'):
"""
:param variable: the variable chosen from variable pool in your dataframe
:param question: default parameter ("Variable to analyze")
don't :return:
"""
def guide(diz):
"""
function that guides user to chose the ri... | fcff9eaa1467d96251ba08eaa433aa05b7b769f2 | 3,614,746 |
from sys import path
def add_topic():
"""
Endpunkt `/topic`.
Route zum hinzufügen eines Themas.
"""
try:
if "config" not in request.files:
err = flask.jsonify({"err_msg": "Missing File"})
return err, 400
if "name" not in request.form:
err = fla... | f42ff7a64f85672ccb3cd151fbb9967b74de02fa | 3,614,747 |
import torch
def quadratic_matmul(x: torch.Tensor, A: torch.Tensor) -> torch.Tensor:
"""Matrix quadratic multiplication.
Parameters
----------
x : torch.Tensor, shape=(..., X)
A batch of vectors.
A : torch.Tensor, shape=(..., X, X)
A batch of square matrices.
Returns
----... | 78335f6a57f34701f3f1fe9b8dd74e9b8be686a3 | 3,614,748 |
def get_global_step(estimator):
"""Return estimator's last checkpoint."""
return int(estimator.latest_checkpoint().split("-")[-1]) | 11b4a96f74d029f9d9cc5a0fcc93da7504729eb7 | 3,614,749 |
from src.priorityq import PriorityQ
def test_q():
"""Test fixtures of priority qs."""
q0 = PriorityQ()
q1 = PriorityQ()
q1.insert('sgds', 10)
q1.insert('another', 9)
q1.insert('another', 8)
q1.insert('another', 7)
q1.insert('another', 6)
return q0, q1 | fd5e9cb0a0110b96c34bbc9b85e44596d1180dc5 | 3,614,750 |
def make_mlb_classifier_and_data_with_feature_extraction_pipeline():
"""Create data set and classifier for testing a multi-label
classification scenario with a feature extraction pipeline.
"""
newsgroups_train = fetch_20newsgroups(subset="train")
X, Y = newsgroups_train.data, newsgroups_train.targe... | 478077dbecde141e7d92f00cfe9e8d961d14a1b0 | 3,614,751 |
def read_volume(filepath, dtype=None, return_affine=False):
"""Return numpy array of data from a neuroimaging file.
Args:
filepath: path-like, path to volume file.
dtype: dtype-like or str, data type of the volume data.
return_affine: boolean, if true, return tuple of volume data and
... | d581155c06599b6e987db5a7fdc2f62e4918e25b | 3,614,752 |
import traceback
import json
def Import(context, request):
""" FOSS FIAStar analysis results
"""
infile = request.form['data_file']
fileformat = request.form['format']
artoapply = request.form['artoapply']
override = request.form['override']
sample = request.form.get('sample',
... | 7536c920b36c8158976b02f6c75cbce1ac8e3bf7 | 3,614,753 |
def meantime_blockedby_pp_hat(arr_rate, pp_mean_svctime, pp_cap, pp_cv2_svctime):
"""
Approximate unconditional mean time blocked in ldr or csect waiting for a pp bed.
Modeling pp as an M/G/c queue and using approximation by Kimura.
"""
pp_svcrate = 1.0 / pp_mean_svctime
meantime = qng.mgc_mean... | 5c7e8de26b1f131e7e9a02743f51e2c62ef5023d | 3,614,754 |
def _combine_grad(evoked, picks):
"""Create a new instance of Evoked with combined gradiometers (RMSE)."""
def pair_and_combine(data):
data = data ** 2
data = (data[::2, :] + data[1::2, :]) / 2
return np.sqrt(data)
picks, ch_names = _grad_pair_pick_and_name(evoked.info, picks)
th... | 5469cc60f27be8646f9f20fa28b3e305d6d1bf38 | 3,614,755 |
def each_segment(time_ci, energy_ci, rate_ref, meta_dict,\
start_time, end_time):
"""
Turns the event list into a populated histogram, stacks the reference band,
and makes the cross spectrum, per segment of light curve.
Parameters
----------
time_ci : np.array of floats
1-D array of... | 043fa5e18021f54ddc4eb7da1e1c45c50453ee8c | 3,614,756 |
import torch
def box_iou(boxes1, boxes2):
"""Compute pairwise IoU across two lists of anchor or bounding boxes.
Defined in :numref:`sec_anchor`"""
def box_area(boxes): return ((boxes[:, 2] - boxes[:, 0]) *
(boxes[:, 3] - boxes[:, 1]))
# Shape of `boxes1`, `boxes2`, `a... | c358c15b99d0e742487a92630ff927606ad6d896 | 3,614,757 |
import os
def getBranchPath(path):
"""Get a path rooted in the current branch.
@param path: A path relative to the current branch.
@return: A fully-qualified path.
"""
currentPath = os.path.dirname(__file__)
fullyQualifiedPath = os.path.join(currentPath, '..', path)
return os.path.abspath... | 57897e58b57c704cea63549d437f22f96830d226 | 3,614,758 |
import horovod.torch as hvd
import horovod.torch as hvd
from typing import Tuple
from typing import Optional
import torch
def _get_train_sampler(val_ratio:float, val_fold:int, trainset, horovod,
target_lb:int=-1)->Tuple[Optional[Sampler], Sampler]:
"""Splits train set into train, validation sets, stratifi... | 3cf56ed690a4f43795d961f0eb669bc3d133baf5 | 3,614,759 |
def feature_reconstruction_loss(base, output):
"""
Compute the content loss for style transfer.
Inputs:
- output: features of the generated image, Tensor with shape [height, width, channels]
- base: features of the content image, Tensor with shape [height, width, channels]
Returns:
- scala... | aab47546c68a83e687eeabe77d89fdb7aa51ab8b | 3,614,760 |
import time
def attempt_to_acquire_lock(s3_conn, lock_uri, sync_wait_time, job_name,
mins_to_expiration=None):
"""Returns True if this session successfully took ownership of the lock
specified by ``lock_uri``.
"""
key = _lock_acquire_step_1(s3_conn, lock_uri, job_name, mins... | 714eb9daaaa242aeb6d877a10b1fffaf353a606e | 3,614,761 |
import torch
def multiclass_nms(
multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None,
multi_attrs=None,
multi_feats=None,
):
"""NMS for multi-class bboxes.
Args:
multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
multi_scores (Tenso... | 4f7075c93b0c4c7fc5d4905237a35bb48f445314 | 3,614,762 |
import torch
def get_device():
""" Get the device on which running."""
return torch.device("cuda" if torch.cuda.is_available() else "cpu") | 9ab3e98a98f9f6c1630ee4bd76c170efae029d68 | 3,614,763 |
def make_png_thumbnail():
"""Make a thumbail of the first page of a PDF and return it.
:return: A response containing our file and any errors
:type: HTTPS response
"""
f = request.files["file"]
max_dimension = int(request.args.get("max_dimension"))
with NamedTemporaryFile(suffix=".%s" % "pd... | fefc14618bea13693878983c9630280b32584115 | 3,614,764 |
import uuid
def rand_uuid():
"""Generate a random UUID string
:return: a random UUID (e.g. '1dc12c7d-60eb-4b61-a7a2-17cf210155b6')
:rtype: string
"""
return str(uuid.uuid4()) | fc35e154eeab62988bcd96799ce0f688f4ec427a | 3,614,765 |
import logging
def get_logfile_name():
"""
Return the current logfile name
"""
return logging.getLoggerClass().root.handlers[0].baseFilename | 28b5d6628890a6cccb09a68f24e6257d425c3833 | 3,614,766 |
def sign(x, y):
"""Fortran's sign transfer function"""
return x * tf.math.sign(y) | 05f44a6f8955b50318e65e3ad0a633c2acaf8d8d | 3,614,767 |
def filter_linksearchtotals(queryset, filter_dict):
"""
Adds filter conditions to a LinkSearchTotal queryset based on form results.
queryset -- a LinkSearchTotal queryset
filter_dict -- a dictionary of data from the user filter form
Returns a queryset
"""
if "start_date" in filter_dict:
... | 96a7e816e7e2d6632db6e6fb20dc50a56a273be9 | 3,614,768 |
def mean(samps):
"""
Find the mean point forecasts.
"""
return np.mean(samps, axis=0) | 8821fe547e1b1f12626544ca2d8056b725cf00e1 | 3,614,769 |
import os
def merge_data(path_data, filename_participants):
"""
Merge the different datasets.
:param path_data: string
:param filename_participants: string
:return: dataframe, dictionary
"""
d = {}
d_features = {}
for i in os.listdir(path_data):
path0 = os.path.join(path_d... | fa72cf58f008d8d79dbf56664edd10163d53cbf9 | 3,614,770 |
def all():
"""
Returns all W2S Scenarios in [GWh/a]
:return:
"""
sc = (read("szenarien_w2s.xlsx")
.pipe(start_pipeline)
.pipe(NaNtoZero)
.pipe(format_df)
.pipe(convert_PJ_to_GWH)
)
return sc | 7cac87baf53c7bfeda5bd2158801628c989178ce | 3,614,771 |
def test_download_cache_hit(mocker):
"""Check that download is not repeated on cache hit."""
data = b"Hello, world"
data_checksum = "4ae7c3b6ac0beff671efa8cf57386151c06e58ca53a78d83f36107316cec125f"
cached_path = cache_path(f"downloads/{data_checksum}")
# Tidy up from a previous test, if applicable... | 244e483ccef50c877e5023d0dd857e2f92ca34b9 | 3,614,772 |
import transformers
import torch
import tqdm
def eval_two_span(
val_data: data.DataLoader,
model: transformers.PreTrainedModel,
loss_func: nn.modules.loss._Loss,
dev: torch.device=None
) -> float:
"""Evaluate a two span edge probing model.
Args:
val_data: valid... | 6873dc6e5dcbd5d19ee7321b42f5bac7aa63d2f8 | 3,614,773 |
import json
import logging
def gen_tensorflow_client_string(generated_tensor_data, model_name):
"""
Generate TensorFlow SDK in Python.
Args:
generated_tensor_data: Example is {"keys": [[1.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}
"""
code_template = """#!/usr/... | 63f197459d1995f4621523973ade157838836b7f | 3,614,774 |
import csv
def get_column(path, c=0, r=1, sep='\t'):
""" extracts column specified by column index
assumes that first row as a header
"""
try:
reader = csv.reader(open(path, "r"), delimiter=sep)
return [row[c] for row in reader] [r :]
except IOError:
print('list_rows: f... | 036a1630417224474e8bfe7a9c038a04bd3ea0d5 | 3,614,775 |
def part2(lines):
"""
>>> part2(load_example(__file__, "24"))
19
"""
return run(lines, Part2) | bfd2aaf0aff01365c3825a1b4608670739f56875 | 3,614,776 |
import uuid
def get_example_comments():
""" returns example comments on a submission. """
user1 = 24601
user2 = 42
user_ids_to_uuids = {user1: uuid.uuid4(), user2: uuid.uuid4()}
user_ids_to_names = {user1: 'Atlassian_bot', user2: 'Cool_McJones_ASE'}
return {'tester_messages': [
{
... | b64b9878f375440cd6745e98f064175e80145610 | 3,614,777 |
def add_md_padding(data, endian='big'):
"""Merkle-Damgard padding
Args: data(string)
Returns: data+padding(string)
"""
size = len(data) & 0x3f # len_in_bytes % 64
if size < 56:
size = 56 - size
else:
size = 120 - size
p = bytes(b'\x80') + bytes(b'\x00'*63)
p = p[:si... | 6138c66f854b8746db78ba5e8979e88d831d3a84 | 3,614,778 |
def list_fridge():
"""
List all items in the frigde.
:return: dict with all items and amounts
:rtype: dict
"""
return MOCK_FRIDGE | 56988ae3f27e92b2afdfcd404f15ab396c89468b | 3,614,779 |
def evaluate_hessian_val(A, point, direction):
"""
Returns the value of Hessian function in the given direction.
"""
hess_p = (A - np.diag((A.dot(point)).dot(point.T))).dot(direction)
return np.sum(hess_p * direction) | 92c9c62d2e80bf172b2e04c3e533b0cbf021e244 | 3,614,780 |
import numpy
def frustrated_loop(graph, num_cycles, R=float('inf'), cycle_predicates=tuple(),
max_failed_cycles=100, planted_solution=None, seed=None):
"""Generate a frustrated-loop problem.
A generic frustrated-loop (FL) problem is a sum of Hamiltonians, each generated
from a single ... | f1e7bccdb17c9703c9b4fa78bc3da4ce583c88a9 | 3,614,781 |
def _projection_unit_simplex(x: jnp.ndarray) -> jnp.ndarray:
"""Projection onto the unit simplex."""
s = 1.0
n_features = x.shape[0]
u = jnp.sort(x)[::-1]
cssv = jnp.cumsum(u) - s
ind = jnp.arange(n_features) + 1
cond = u - cssv / ind > 0
idx = jnp.count_nonzero(cond)
threshold = cssv[idx - 1] / idx.a... | 9d0cae071d27b2a105da9be945f59c50d5462e60 | 3,614,782 |
def channel_id_str_to_bytes(channel_id_str):
"""
Args:
channel_id_str: string representation of channel id
Returns:
bytes representation of channel id
"""
assert type(channel_id_str) in [str, bytes]
if isinstance(channel_id_str, bytes):
return channel_id_str
qid_byte... | 2267fa7d810ca09a6498c8ab9cfffc0d6c8fcb9e | 3,614,783 |
from typing import Optional
def compiled(
model: tf.keras.Model,
loss=None,
metrics=None,
optimizer=None,
run_eagerly: Optional[bool] = None,
# steps_per_execution: Optional[int] = None,
) -> tf.keras.Model:
"""Mutate model in-place by compiling and return the model for convenience."""
... | 809497352df4ac3ed609e525c3916bf6e506b80a | 3,614,784 |
def vtkVariantExtract(v, t=None):
"""
Extract the specified value type from the vtkVariant, where the type is
in the following format: 'int', 'unsigned int', etc. for numeric types,
and 'string' or 'unicode string' for strings. You can also use an
integer VTK type constant for the type. Set the ty... | 9d34e1b0e989d6b36b78f97eab7bbc9d1e49740e | 3,614,785 |
import collections
import itertools
def _create_region_groups(const_regions, psvs, genome, max_dist=1000):
"""
Groups closeby const regions if they have the same CN even if there is a region with a different CN between them.
"""
psv_ix = 0
n_psvs = len(psvs)
# Key: copy_num, value: list of Pl... | 5f75d088d31ea5b889ab5f77dbd7ee7abbe18dad | 3,614,786 |
import imp
def _LoadConfigModule(name: str, path: str):
"""Loads a script from external file specified by path.
Unprefixed path is looked for in the current working directory using
regular file open operation. This should work with relative config paths.
Args:
name: Name of the new module.
path: Pat... | cd2d15bdd357c5efcfe15648a5fca437f3cdda8b | 3,614,787 |
def _parse_icq(fileobj):
"""Parse a International Comet Quarterly (ICQ) format file."""
df = pd.read_fwf(fileobj, colspecs=list(ICQ_COLUMNS.values()),
names=ICQ_COLUMNS.keys(), header=None)
return df | 40bd2302991e40e014caff6e3b4e4020ff1fa82b | 3,614,788 |
def IperfTCP(target_src, target_dst, dst, length, window=None):
"""Convenience method for starting a TCP IperfSet.
See IperfSet for more details.
Args:
target_src: A single host or list of hosts.
target_dst: A single host or list of hosts (1:1 with target_src).
dst: A single address/hostname or a li... | 98a94718e29d116acfaadd18bf20855fa2dfadb5 | 3,614,789 |
def plot_riemann(states, s, riemann_eval, t, fig=None, color='b', layout='horizontal',conserved_variables=None):
"""
Take an array of states and speeds s and plot the solution at time t.
For rarefaction waves, the corresponding entry in s should be tuple of two values,
which are the wave speeds that bou... | af1a53cd84e4e51ee0934c2fe303465fab338636 | 3,614,790 |
def find_in_list(list_one, list_two):
"""Find and return an element from list_one that is in list_two, or None otherwise."""
for element in list_one:
if element in list_two:
return element
return None | 9376b38a06cadbb3e06c19cc895eff46fd09f5c1 | 3,614,791 |
from typing import List
from typing import Tuple
def getElementById(idName: str, fileName: str) -> List[Tuple[int, str]]:
"""Returns first matching tag from an HTML/XML document"""
nonN: List[str] = []
with open(fileName, "r+") as f:
html: List[str] = f.readlines()
for line in html:
... | e0888a41c7cbdf4a0ef449bbbfb23ed650e2ab12 | 3,614,792 |
def skip_after(timeout: int or float):
"""
Creates an async pool with an associated timeout, but
without raising a TooSlowError exception. The pool
is simply cancelled and code execution moves on
"""
assert timeout > 0, "The timeout must be greater than 0"
mgr = TaskManager(timeout, False)
... | 1015389638164a2d1131c6a634e918e66645b2d2 | 3,614,793 |
from pathlib import Path
def read_peak_correlations(file_name):
"""
Read in the custom peak correlation Excel file provided by JAX.
"""
df = pd.DataFrame(pd.read_excel(file_name))
tab = Table.from_pandas(df) #read(file_name, format="ascii.csv")
# tab.rename_column('\ufeffMm_chr', 'Mm_chr')
... | f1fc20cfec167cf9aaa0284e4cb08e7cf4fdb74e | 3,614,794 |
import sys
import pickle
def load_pickle(fname):
"""Loads a pickle file to memory.
Parameters
----------
fname : str
File name + path.
Returns
-------
dict/list
Data structure of the input file.
"""
assert fname, 'Must input a valid file name.'
if sys.version... | d5890aec8fd491b89b81e9e6c01ee658b1a843bd | 3,614,795 |
import torch
def sample(model, block_size, x, steps, temperature=1.0, sample=False, top_k=None):
"""
take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in
the sequence, feeding the predictions back into the model each time. Clearly the sampling
has quadratic compl... | 7d5888a961b9f29c7345fe207f71e26aaf646eef | 3,614,796 |
def explore_result_coverage_detail():
"""Get coverage detail info
Get coverage detail info loading from disk
:return: a list, each element is dict map in list, if coverage file not exist,
will return [],for example:
[{
"fileName": "1.jpg",
"sampleNum": 5,
... | e7a6cf085414743f59d24da790d8115c36a35047 | 3,614,797 |
def gradient_dx(fx: tf.Tensor) -> tf.Tensor:
"""
Function to calculate gradients on x-axis of a 3D tensor using central finite difference.
It moves the tensor along axis 1 to calculate the approximate gradient, the x axis,
dx[i] = (x[i+1] - x[i-1]) / 2
:param fx: shape = (batch, m_dim1, m_dim2, m_d... | 452bb2240db6d155f072288aa32751167f7eb943 | 3,614,798 |
from ..main import app
def form_edit_permissions(formId):
"""Set form permissions of a particular user to an array.
POST request, with body: (either userId or email is required.)
{
"userId": "cm:cognitoUserPool:.....",
"email": "a@b.com",
"permissions": ["Responses_Edit", "Responses_View", ""] or st... | 02c53a057176150484c8b76e787b89b97b976ba7 | 3,614,799 |
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