content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def app_env():
"""
Create a dict of environment variables.
Mirrors the testing environment for use in subprocesses.
"""
return {
'DB_HOST': TEST_DATABASE_CONFIG.host,
'DB_PORT': str(TEST_DATABASE_CONFIG.port),
'DB_NAME': TEST_DATABASE_CONFIG.name,
'DB_USER': TEST_DAT... | ebc4c3e000a916c5506655f96047dd4c53714853 | 51,246 |
def determine_key_type(bucket, key, profile):
"""Return values : 'File', 'Folder' or 'Does not exist'"""
if profile:
s3 = boto3.session(profile_name=profile).client('s3')
else:
s3 = boto3.client('s3')
if not key:
raise Exception("Cannot determine key type - no key is specified")
... | a3c2f70da06e1cb1757454b8e7b8cdb9a1ffdc00 | 51,247 |
def correct_brenda_dtypes(r: pd.DataFrame):
"""Make sure the columns have the right dtypes.
:param r: dataframe of reports
"""
df_out = r.copy()
float_cols = ["ph", "mols", "temperature", "km", "kcat"]
for col in float_cols:
if col in r.columns:
df_out[col] = r[col].astype(f... | f5c083e3b4422df58dea5b4c6e797d2d04d2c0ab | 51,248 |
def vmtkmarchingcubes(image, level=0.0):
"""Generate an isosurface of given level from a 3D image.
Args:
image: vtkImageData object.
level: Graylevel at which to generate the isosurface.
Returns:
Surface mesh of the isosurface.
"""
marcher = vmtkscripts.vmtkMarchingCubes()... | 32182ff8cee665008ce84c4a8c18b0dd7840f370 | 51,249 |
def _update_dict(d, **kwargs):
"""little helper to modify and return a dict in one line"""
d.update(kwargs)
return d | 0a28c5f82d439ceb65aa3afc39d30a29b589004e | 51,250 |
def svg_icon(loader, name, css_class=""):
"""
Return inline SVG markup for an icon.
This is a helper for generating inline SVGs for rendering icons in HTML
that can be customized via CSS.
See https://github.com/blog/2112-delivering-octicons-with-svg
To color an icon dynamically (eg. on hover),... | 1cddcecc954c6d8956c7e029bd4a99c86a33d98b | 51,251 |
def right_stop(G, adorn=True):
"""
Compute the (adorned) right stop of G.
Parameters
----------
G : Game
The Game of interest.
adorn : bool
Whether to adorn the stop or not. Defaults to True.
Returns
-------
RS : Game
The right stop of G.
initiative : st... | 1c1c48cfacd1b86511b38fb91694cc892a9dbae4 | 51,253 |
def value_and_multigrad(fun, argnums=[0]):
"""Returns a function that returns both value and gradients wrt multiple
arguments simultaneously."""
def combined_arg_fun(multi_arg, *args, **kwargs):
extra_args_list = list(args)
for argnum_ix, arg_ix in enumerate(argnums):
extra_args_... | 5d55a547c6309d2db3324ebd05fe19f1c9d6c1f0 | 51,254 |
def deploy_disk_image(address, port, iqn, lun,
image_path, node_uuid, configdrive=None,
conv_flags=None):
"""All-in-one function to deploy a whole disk image to a node.
:param address: The iSCSI IP address.
:param port: The iSCSI port number.
:param iqn: The ... | 0eb796b5089597c916393bbbaf6f314460a8a856 | 51,255 |
from typing import Union
from pathlib import Path
from typing import Dict
from typing import Tuple
def read_tracking_data_txt(
filepath_txt: Union[str, Path],
links: Dict[str, Dict[int, int]] = None,
) -> Tuple[XY, XY, XY, XY, XY, XY]:
"""Parses a StatsPerform .txt file and extracts position data.
I... | 8ba9f17374eea860fa434e170e7d1ca6b6b22f9a | 51,256 |
from typing import Optional
from typing import Union
from typing import List
from typing import Dict
def _estimate_segments(df: pd.DataFrame, target_field: str = None, max_segments: int = 30) -> Optional[Union[List[Dict], List[str]]]:
"""
Estimates the most important features and values on which to segment
... | ebdd75b64616da497a73004d502acd320322935b | 51,257 |
def show_images(images, file_to_save, cols=1, titles=None):
"""Display a list of images in a single figure with matplotlib.
Parameters
---------
images: List of np.arrays compatible with plt.imshow.
cols (Default = 1): Number of columns in figure (number of rows is
set to n... | 14f31b182ef2c80d1da720461cb994cb15b2493a | 51,258 |
def WT_EC50s(unkVec_2_15, Traf=True):
"""Returns dataframe of the Wild Type EC50s"""
df = pd.DataFrame(columns=["Time Point", "Cell Type", "IL", "Data Type", "EC50"])
x0 = [1, 2.0, 1000.0]
tps = np.array([0.5, 1.0, 2.0, 4.0]) * 60.0
data_types = []
cell_types = []
EC50s_2 = np.zeros(len(cel... | 88f8f57617ec9666a8c574e289bfccbcee0ee461 | 51,259 |
def implicit_op(op):
""" implicit constant operand promotion """
if isinstance(op, ML_Operation):
return op
elif isinstance(op, SollyaObject) or isinstance(op, int) or isinstance(op, float):
return Constant(op)
elif isinstance(op, ML_FloatingPointException):
return Constant(op, p... | 31e7c36d47e8885631b79d74ae1cfb003f5fd848 | 51,260 |
def ToHex(data):
"""Return a string representing data in hexadecimal format."""
s = ""
for c in data:
s += ("%02x" % ord(c))
return s | 1566962a89967ae0d812ef416d56867a85d824fb | 51,261 |
def set_legend(ax, size=12,
color=COLORS['black'], alpha=ALPHAS['translucent'],
loc='best', frame=False, ncol=1, nscatter=1, **kwargs):
"""
Add and modify legend settings.
:param ax: (mpl.axes) - plot axis
:param size: (str) - size of legend labels
:param color: (str) ... | e40db57e44692eb2c4852f3e173eef2f0cf137a9 | 51,262 |
from typing import List
from typing import Tuple
def fill_missing_test(parameters: List[Parameter]) -> Tuple[str]:
"""Convenience function for testing job parameter fill missing"""
param_set = JobParameterSet(parameters)
param_set.fill_missing_parameters()
return (
param_set.get_param_by_type(... | 5ef2566d535ef8dc877430337ecadc78d142a240 | 51,263 |
def test_PostPlot():
"""Validation of the PostPlot class to plot airgap flux automatically as postprocessing at the end of the simulation"""
IPMSM_A = load(join(DATA_DIR, "Machine", "IPMSM_A.json"))
simu = Simu1(name="PostPlot", machine=IPMSM_A)
# Definition of the enforced output of the electrical m... | 111e2ef836659057e3db452a1bcf3309caa8bf6e | 51,265 |
from datetime import datetime
def parse_date(date_str: str, sep: str = None, format: str = None) -> datetime.date:
"""Returns a ``datetime.date`` if possible
Args:
date_str (str): The date which may not be in standard ISO format
sep: Optional. The separator used to separate each time unit
... | 0b6725b60cd8ce945a2c5d625bc5afe911f88c04 | 51,266 |
def scrub_properties(orig_props):
"""Scrubs fields in API object properties for use in DM properties.
Scrubbed fields include:
- output-only fields common for most resources
- output-only fields specific to a particular resource type
Args:
orig_props: the resource properties that need to be scrubbed
... | d34d115c685123f88d921abc0d0a88bc49ed9bc3 | 51,267 |
def _trend_grid(concentration_cube, weight_cube, *, clipping_threshold):
"""Build a grid for a decadal trend."""
assert concentration_cube.shape == weight_cube.shape
assert clipping_threshold
rows, cols, periods = weight_cube.shape
# Flatten rows and cols of the concentration/weight cubes
# A ... | 24b76b0959713c837e7288faced8d98e15f274e1 | 51,268 |
def uniquify_reference_names(comp):
"""Replaces all the bound reference names in `comp` with unique names.
Notice that `uniquify_reference_names` simply leaves alone any reference
which is unbound under `comp`.
Args:
comp: The computation building block in which to perform the replacements.
Returns:
... | e5022b9ac377ff5397101ab75570faf0137f7dd7 | 51,269 |
def load_csv_data(train_path, test_path=None, sep=',', nrows=None,
x_cols=None, y_cols=None, drop_cols=None,
onehot_cols=None, n_cols=None, random_cols=False,
shuffle=False, scaling=None, dtype=None,
validation_split=None, return_dataframe=True,
... | 9a873f0a7acf7f39675f6865b9cda79c82613908 | 51,270 |
def fclayer(in_features, out_features):
"""
fully connected layers or dense layer
:param in_features: input_dimension => channels * features
:param out_features: output_dimension
:return: (batch, windows, channels * features) => (*, *, output_dimension)
"""
fc = nn.Linear(in_features, out_fe... | a723222d4ef045e329079832073302c077622761 | 51,271 |
def visual_mask(mask_img=None, mask_path=None):
"""
:param mask_path:
:param palette:
:return:
"""
palette = get_palette()
if mask_img is None:
mask_img = Image.open(mask_path)
else:
mask_img = Image.fromarray(np.array(mask_img, dtype=np.uint8))
mask_img.putpalette(p... | c65b612412b8c7ad6930da127a7a09dc6bb3e2e2 | 51,272 |
from typing import Callable
def window_replaced_message(callback: Callable[[HWND, HWND], None]) -> MessageEntry:
"""Listens for when a window is hung, and needs to be recreated.
The first HWND will be set to the HWND of the new window handle, and
the second is the HWND of the replaced window.
The cal... | 01404005f7591673509fc6ae0891a941c533df7d | 51,273 |
from typing import Iterable
def get_sse_mat(mats: Iterable[sp.spmatrix]) -> sp.spmatrix:
"""
Given multiple matrices, return the matrix of position-wise sum of
squared errors to median. All input matrices must have the same shape,
nonzero coordinates and format. Output matrix will have the same format... | 8fb8bd6b4ef162399a2ef87fd9a7377910302fc7 | 51,274 |
def energy_sum(
df: pd.DataFrame, network: NetworkSchema, power_column: str = "generated"
) -> pd.DataFrame:
"""Takes the energy sum for a series of raw duid intervals
and returns a fresh dataframe to be imported"""
# These are the networks that run through the compat func
COMPAT_NETWORKS = [Networ... | a4733c70f6e3d710e2d5f83a33aafd1b6ba01b39 | 51,275 |
import unicodedata
def tokenise_word(string,
strict=False, replace=False, tones=False, unknown=False):
"""
Tokenise the string into a list of tokens or raise ValueError if it cannot
be tokenised (relatively) unambiguously. The string should not include
whitespace, i.e. it is assumed ... | 0c2ed6fc6f0f3748b63922a7743da3350ba58f4f | 51,276 |
def latsrf(method, target, et, fixref, lonlat):
"""latsrf(ConstSpiceChar * method, ConstSpiceChar * target, SpiceDouble et, ConstSpiceChar * fixref, ConstSpiceDouble [1][2] lonlat)"""
return _cspyce0.latsrf(method, target, et, fixref, lonlat) | a8f8705918caedb7477e6fa022fc1deead6cf6e6 | 51,277 |
def _parse_common(xml, the_dict):
"""
Parse things in common for both variables and functions. This should
be run after a more specific function like _parse_func or
_parse_variable because it needs a member dictionary as an input.
Parameters
----------
xml : etree.Element
The xml re... | a6606366ce9b0e4d2c848b16bc868532684b4abe | 51,278 |
import collections
import itertools
def sliding_window(n, seq):
"""
Copied from toolz
https://toolz.readthedocs.io/en/latest/_modules/toolz/itertoolz.html#sliding_window
A sequence of overlapping subsequences
>>> list(sliding_window(2, [1, 2, 3, 4]))
[(1, 2), (2, 3), (3, 4)]
This functi... | 67e65fa6690ecc6974713a0559d596718e7c6361 | 51,279 |
def change_course(request, pk):
""" Изменение данных в курсе """
course = get_object_or_404(Course, pk=pk)
if request.method == 'POST':
form = CourseForm(request.POST, request.FILES, instance=course)
if form.is_valid():
course = form.save()
for csa in CourseStudentAc... | 64c244eee6ea687d88757a621809f0b90f911229 | 51,280 |
def interaction(multiindex):
"""
Return tuple of interaction between variables which this multiindex
represents. Eg. (a) (1,1,1) -> (), (b) (1,2,1) -> (1,), (c) (2,3,1) -> (0,1)
Tuple is always ordered smallest dim-index to largest.
"""
return tuple(np.where(np.array(multiindex, dtype=np.int8)... | 28dbe2161bc30f4bfba72625434b7d170b68722f | 51,281 |
def home(request):
"""Dashboard home."""
return render(
request,
'dashboard/home.html',
{
'quests': Questionnaire.objects.filter(
created_at__year=date.today().year,
)
},
) | 765582081a8d23a90dad7962a662f325be608203 | 51,282 |
def get_entities(config):
"""
Get a dictionary of lambda functions that create entity URLs
:param config: dict
:return: dict
"""
server = config['api']
entities = dict(project=lambda project: server + '/projects/{0}'.format(project),
subject=lambda project, subject: serve... | 2fa3aa0d42ecb027dee4f2610c5eaf5c72f55d44 | 51,283 |
def crypto_secretbox(message, nonce, key):
"""
Encrypts and returns the message ``message`` with the secret ``key`` and
the nonce ``nonce``.
:param message: bytes
:param nonce: bytes
:param key: bytes
:rtype: bytes
"""
if len(key) != crypto_secretbox_KEYBYTES:
raise exc.Valu... | 6ce8bc311427e52568110bccec6747cc6b5f3149 | 51,284 |
def create_bins(bin_info_list, shuffle):
"""Creates a list of bins based on a string list of bin info."""
bins = []
for bin_info in bin_info_list:
bins.append(DataBin(bin_info[0], bin_info[1], shuffle))
return bins | 89273361df4fd8e1dff6145400685faf5a788818 | 51,285 |
def getEmissionSelf(dataDirBase, NCVariable, RCPScenario, decade):
"""This function uses makeEmissionNCFile() to get the nc file path
of the desired species arguemnt and returns the nc connection to
that species.
Parameters: Same as makeEmissionNCFile()
dataDirBase: Working hardrive
NCVariable... | b4f93bc4266020a4044a3a1f4c4cf50a288402e8 | 51,286 |
def from_plus(path):
"""
Given the path to a CoNLL-U Plus file, return the corresponding plain
CoNLL-U string.
"""
with open(path) as f:
plus_fields = [field.upper() for field in parse_conllu_plus_fields(f)]
plus_content = f.read()
plus_sentences = parse(plus_content, plus_f... | d2c7e8dccf63c0d90416dbff7237cc6b7f6216a3 | 51,287 |
def __coord_n(n, **_kwargs):
"""Get bare positions"""
return np.arange(n + 1) | 697963a802598e4d3d1ec20c610760398eecbf3f | 51,288 |
def vectorized_gaussian_logpdf(X, means, covariances):
"""
Compute log N(x_i; mu_i, sigma_i) for each x_i, mu_i, sigma_i
https://stackoverflow.com/a/50809036/8395138
Args:
X : shape (n, d)
Data points
means : shape (n, d)
Mean vectors
covariances : shape (... | 80dfd7618647e2b00ff5b98693820aad5226c342 | 51,289 |
def elementWise(A, B, operation):
"""
execute an operate element wise and return result
A and B are lists of lists (all lists of same lengths)
operation is a function of two arguments and one return value
"""
return [[operation(x, y)
for x, y in zip(rowA, rowB)]
for row... | 39e78ca7730bf8367daf3a55aeb617b2c0707a44 | 51,290 |
def create_test_baymodel(context, **kw):
"""Create and return a test baymodel object.
Create a baymodel in the DB and return a BayModel object with appropriate
attributes.
"""
baymodel = get_test_baymodel(context, **kw)
try:
baymodel.create()
except exception.BayModelAlreadyExists:
... | eeed0905d907856593faa2b992492cbb0250b4fc | 51,291 |
def test_forward_with_parameter_in_sub_cell():
"""
Feature: Custom cell bprop
Description: Get the gradients of inputs when the forward net using Parameter in the sub-cell.
Expectation: Get the correct gradients.
"""
class Net(nn.Cell):
def __init__(self):
super(Net, self)._... | aaf570a83c29ec98e09b96377c2179a869f1d89c | 51,292 |
def _GetRollbackRelease(release_id, pipeline_id, target_ref):
"""Gets the release that will be used by promote API to create the rollback rollout."""
ref_dict = target_ref.AsDict()
if release_id:
return resources.REGISTRY.Parse(
release_id,
collection='clouddeploy.projects.locations.deliveryPi... | a2d978093a4475df07a3dd56816c0a914daf5a58 | 51,293 |
def find_spaces(string_to_check):
"""Returns a list of string indexes for each string this finds.
Args:
string_to_check; string: The string to scan.
Returns:
A list of string indexes.
"""
spaces = list()
for index, character in enumerate(string_to_check):
if character == ' ':
spaces.ap... | 8bcd1d9911efab3c65e08524293b11afd449efa0 | 51,295 |
def calculate_activation_statistics(images, batch_size):
"""Calculates the statistics used by FID
Args:
images: torch.tensor, shape: (N, 3, H, W), dtype: torch.float32 in range 0 - 1
batch_size: batch size to use to calculate inception scores
Returns:
mu: mean over all activation... | 9d1760c3aba636004bf158d2efaf021a035484a6 | 51,296 |
def _should_recurse(start, config_lines, initial_indent):
"""Sometimes object are not expected to document every parameter and contain all the description at the top.
This is valid, therefore we should not recurse and analyze what's next after the whole object. Returns whether or
not to recurse as long a po... | a89c5a641f13cba8b487a1e41b707450a5998e00 | 51,297 |
def unwrap_eth():
"""
Withdraw ETH by depositing wETH
"""
account = get_account()
# Need to get the ABI and the Address of the contract
weth = interface.IWeth(config["networks"][network.show_active()]["weth_token"])
# Deposit
amount = 0.1
tx = weth.withdraw(
Web3.toWei(amo... | 277bbf8a23e5d0d14f27e53b5d7787e9b6f991b2 | 51,298 |
def client() -> User:
"""Construct the User (client) class"""
return User(username=USERNAME, auth=AUTH) | 8c217f2ed6cd4a5e60948bdfcac1594b28e2197f | 51,299 |
from typing import OrderedDict
def dict_from_lst(lst, ordered=False):
"""
Return a dictionary from a Geosoft `geosoft.gxapi.GXLST` instance.
:param lst: `geosoft.gxapi.GXLST` instance
:param ordered: True to return an OrderedDict
:returns: python dictionary from a Geosoft GXLST
... | 54c87468f6eb633fda64d1373dc7a8c621d7991b | 51,300 |
def parse_external_id(output, type=EXTERNAL_ID_TYPE_ANY):
"""
Attempt to parse the output of job submission commands for an external id.__doc__
>>> parse_external_id("12345.pbsmanager")
'12345.pbsmanager'
>>> parse_external_id('Submitted batch job 185')
'185'
>>> parse_external_id('Submitte... | 9af3c73ffbfeac3273251230fe220f381b402214 | 51,301 |
def ldns_pkt_set_ra(*args):
"""LDNS buffer."""
return _ldns.ldns_pkt_set_ra(*args) | 159dde50ee549980a2a92d21f5dce23327c4d6b3 | 51,302 |
def rehash(path, blocksize=1 << 20):
# type: (str, int) -> Tuple[str, str]
"""Return (encoded_digest, length) for path using hashlib.sha256()"""
h, length = hash_file(path, blocksize)
digest = 'sha256=' + urlsafe_b64encode(
h.digest()
).decode('latin1').rstrip('=')
# unicode/str python2 ... | 15c1f620d45cef116af6c221b326a208ab27acc4 | 51,303 |
import re
def CombineLogFiles(list_of_lists, logger):
"""Splices together multiple logcats from the same device.
Args:
list_of_lists: list of pairs (filename, list of timestamped lines)
logger: handler to log events
Returns:
list of lines with duplicates removed
"""
cur_device_log = ['']
for... | a749e34e63163c8493e0d4923e78367a6396327f | 51,304 |
from typing import Callable
from typing import Any
def step_decrease(f: Callable[[Any], Any], x, step=1e-2, step_multiplier=2, iteration_limit=100000000):
"""
Continues to increment x with x + step till f(x) returns a value that is bigger than the previous value
i.e
stop when f(x) > f(x+s)
step on... | 03b08e33ac6eaacc1e6939f9239058d90b9a6b04 | 51,305 |
def lcs_naive(first: str, index_f: int, second: str, index_s: int) -> int:
"""
Time Complexity: O(2^n)
"""
if index_f < 0 or index_s < 0:
return 0
if first[index_f] == second[index_s]:
return 1 + lcs_naive(first, index_f - 1, second, index_s - 1)
return max(
lcs_naive(f... | be2ef4013732d75ab14c69e8f93d38cc7f011e7c | 51,306 |
from typing import List
def cf_explain(examples: List[Example], nmols: int = 3) -> List[Example]:
"""From given :obj:`Examples`, find best counterfactuals using :ref:`readme_link:counterfactual generation`
:param examples: Output from :func:`sample_space`
:param nmols: Desired number of molecules
"""... | ebaadd0d7d235bcece99257e6d0debd02f6bb79a | 51,308 |
def quantile(a, q, axis=None, out=None,
overwrite_input=False, interpolation='linear', keepdims=False):
"""
Compute the q-th quantile of the data along the specified axis.
..versionadded:: 1.15.0
Parameters
----------
a : array_like
Input array or object that can be convert... | 30af34a0806daf25370bb202aa2fe6dd6d2b5cb2 | 51,309 |
def build_classifier(img_shape, num_classes):
"""
Function: build_classifier
Input: img_shape: image shape, num_classes: the number of image classes
Output: CNN-based classifier model
"""
cinput = Input(shape=img_shape, name='classifier_input')
H = Conv2D(64, kernel_size=(5, 5), acti... | ead6fa1190bf8a2e79b2f2b354853072ca5a7513 | 51,310 |
def filter_pair_by_len(p, maxlen=c.MAX_LENGTH):
""" Filter out sentences if they are greater than maximum length.
"""
return len(p[0].split(" ")) < maxlen and len(p[1].split(" ")) < maxlen | 6e659746f193b3a420fd8189f324b166d4374b70 | 51,311 |
def run_model(params, rollout_size=50, num_steps=50):
"""Perform the training operation.
Parameters
----------
params : dict
flow-specific parameters (see flow/utils/registry.py)
rollout_size : int
length of a single rollout
num_steps : int
total number of training steps... | 86786ac27de7b4bd9a9ec75de745dfb45a00edf0 | 51,312 |
def func_lineno(func):
"""Get the line number of a function.
"""
try:
return func.compat_co_firstlineno
except AttributeError:
try:
if PYTHON_VERSION_MAJOR == 3:
return func.__code__.co_firstlineno
return func.func_code.co_firstlineno
excep... | 884f4fdfeeb22634d46f4f2651b0c5f93cb687e0 | 51,313 |
def calc_cl_metrics(acc_matrix):
"""
Calculate metrics based on an accuracy matrix of N train tasks on N test tasks
The lower triangular matrix is the BWT, the higher triangular matrix is the FWT
The metrics below are taking into account the accuracy of the model at every timestep / task
by dividin... | 4b1a1e12a6e2f06227f244ec8ba882ee634b135a | 51,314 |
def ElectricField(q, r0, x, y):
""" Return the electric field vector E=(Ex, Ey) due to charge q at r0.
"""
den = np.hypot(x - r0[0], y - r0[1]) ** 1.5
return q * (x - r0[0]) / den, q * (y - r0[1]) / den | 76aee7a4c8c0254eee8fc0c99793c86d501514fe | 51,315 |
import shelve
import contextlib
def readMirror(fileLocation):
"""Returns saved Model (mirror) from Pickled file at 'fileLocation'
Needed to transfer model from IPY to PY3
"""
#f = open(fileLocation,"rb")
with contextlib.closing(shelve.open(fileLocation, 'r')) as shelf:
mir = shelf['... | f6d6c56dbe99d544ccdc041f35e48e87883166c8 | 51,316 |
import json
import httpx
def get_api_es_client(session):
"""
Returns an Elasticsearch client for the catalogue cluster.
"""
secrets = session.client("secretsmanager")
credentials = json.loads(
secrets.get_secret_value(SecretId="elasticsearch/api_cleanup/credentials")[
"SecretS... | 5adcdb58a8eabe8d84834f730ab080a2f0b227c9 | 51,317 |
def insertionsort(x, count = False):
"""
For each element e of x, move through the array until you come to a value
that is less than e, or the end of the array, then place e at the new location.
"""
assignments, conditionals = 0, 0
for i in range(1, len(x)):
element = x[i]
j = i... | 4a8ae9bda1dfee0cb41ae7544cb45d089e11f703 | 51,319 |
def vm_impl_concatOffset(self):
"""Generate vm_impl function for ConcatOffset"""
def vm_impl(x):
out = vm.ConcatOffset(x) # out is tuple
return out
return vm_impl | 533041af510acf55c1337f6c2052053aa9669645 | 51,321 |
import requests
def get_assimilator_data(mode, assimilator, text, link):
"""
This function is used for get parsed web/local document from a Tika-service
mode: get metadata or content body
assimilator: api address of the text assimilator, running apache tika
text: piece of raw text
link: web ur... | 4af3970f8fd51e565c0d6bc5c2e92bf29eac98f5 | 51,322 |
def list_channel_messages_since_delta(session, *, deltaLink):
"""List channel messages for the team and channel
session = requests.Session() instance with Graph access token
deltaLink = the leftover delta you were given the last time you called list_channel_messages_since_time
we will p... | fc51b10d6cbd5207d150fa5579c3c367f77a09ba | 51,323 |
def get_default_title():
""" See description in Bootloader class below """
return __bootloader.get_default_title() | 13ca4ce0e74994f993830c20ca6b0bb920918998 | 51,324 |
def parse_config(app):
"""Process the Sphinx Gallery configuration"""
try:
plot_gallery = eval(app.builder.config.plot_gallery)
except TypeError:
plot_gallery = bool(app.builder.config.plot_gallery)
src_dir = app.builder.srcdir
abort_on_example_error = app.builder.config.abort_on_exa... | c90f17a7e6912fc94f9b97463db2ec570c9d0aab | 51,325 |
import json
def get_leader(cluster_name, role_type):
"""
:param cluster_name: 集群名称
:param role_type: 角色类型
:return: overlord or coordinator
"""
cluster = model_manager.get_cluster_obj_by_name_type(cluster_name, DRUID)
conn_info = json.loads(cluster.connection_info)
zk_addr = conn_info[Z... | 8d83f2f9625b92e4f1b355eaac5dd8e503155b7b | 51,326 |
from typing import Any
def _async_get_diagnostics(
hass: HomeAssistant,
entry: ConfigEntry,
) -> dict[str, Any]:
"""Return diagnostics for a config entry."""
pv_entry: PowerviewEntryData = hass.data[DOMAIN][entry.entry_id]
shade_data = pv_entry.coordinator.data.get_all_raw_data()
hub_info = as... | fa6cd381a0b2a2578fa8aaa9276715deb70e8de9 | 51,327 |
import requests
import logging
def execute_dax_query(credential, dataset_id, daxQuery):
"""Execute DAX query"""
url = f"https://api.powerbi.com/v1.0/myorg/datasets/{dataset_id}/executeQueries"
try:
token = credential.get_token("https://analysis.windows.net/powerbi/api/.default").token
... | 9adcd81d0bcfc642b0c0336d351f5ad9b71aad7a | 51,328 |
import time
def wait_for_tasks(session, tasks, timeout):
"""returns true if all tasks are no longer pending (ie success/failure/cancelled)
and false if a timeout occurs"""
finished = False
start = time.time ()
while not(finished) and ((time.time () - start) < timeout):
finished = True
... | 8a4652060621ce3a7f3890578dea307f7e9bc8b5 | 51,330 |
import requests
import json
def get_previous(pair, end, qty):
"""get the previous pairs
Arguments:
pair {string} -- the name of the pair eg. EOSUSD
end {int} -- time of the last records to request
qty {int} -- the number of records to request
Returns:
list -- qty prices b... | f09845b2ae75e7ae3f9eca1da26f2d88790a1478 | 51,331 |
import copy
def handle_multiple_variantcallers(data):
"""Split samples that potentially require multiple variant calling approaches.
"""
assert len(data) == 1
callers = get_variantcaller(data[0])
if isinstance(callers, basestring):
return [data]
elif not callers:
return []
... | 93ea4dff458ad2aa937e776fe45acfca3d91298e | 51,332 |
import yaml
import re
def guess_host_name():
""" Just a handy function for external use
"""
with open(join(package_path(), 'paths.yml')) as f:
loc_by_name = yaml.load(f, Loader=yaml.FullLoader)
for ld in loc_by_name.values():
if 'host_pattern' in ld:
if re.match(ld['host_pa... | 1d5d0503f4ac28d6bb96ad12469c19931065c082 | 51,333 |
def main():
"""Runs the tests."""
defaults = {
'TEST_ID':
'MySimpleTest-' + KeystoreTestScenario.DEFAULT_TEST_ID
}
return citest.base.TestRunner.main(
default_binding_overrides=defaults,
test_case_list=[SynchronousKeystoreTest]) | d7c51db5bf56de8f5c923b9c968707bb7962a61a | 51,335 |
def get_maze(filename):
"""
Convenience function to get a grid from a file.
Takes the filename of the maze to load, loads the maze from the file,
and builds it into a grid. Then returns that grid.
Args:
filename: the file to load the maze from
Returns:
the grid representation ... | c9d0d3c3d7501afd4cd4a2fe178ad3f26877dfc3 | 51,336 |
def get_loss(name):
"""Returns loss_fn(outputs, ground_truths, **kwargs), where "outputs" are the model outputs.
Args:
name: The name of the loss function.
Returns:
loss_fn: A function that computes the loss between the inputs and the ground_truths
Raises:
ValueError: If Preprocessing `name` is n... | 33ef3a864ded30265cf3641ace5128c6271333cc | 51,337 |
def get_processed_invoices_df():
"""
this function will read the dataframe and create a text string for each row separated by new line '\n'
to match with resultant data for testing.
"""
process_invoices = spark.sql("select * from processedInvoiceTbl")
text = ""
for i in range(1, process_in... | e3ee7d2bc0880f1f8bd0fbd55338228814c01c2c | 51,338 |
def resistance(v='x', i='x', p='x', l='x', a='x', pw='x'):
"""
Calculate and return the value of resistance using given values of the params
How to Use:
Give arguments for v and i params,
or, give arguments for p,l and a params,
or, give arguments for pw and i params
or, giv... | d411cc6bb348e66d0f4eedab3a6ae7550a4cd4a7 | 51,339 |
async def edit_a_group(id: int, group: Group):
"""Updates the information stored about a group."""
updated_group = await Group.edit(id, group)
if not updated_group:
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="group not found.")
return updated_group | e0e4d5faaf1cb9b071c899ab165c37cc6925bbfa | 51,340 |
from typing import AnyStr
from typing import Tuple
def derive_password(password: AnyStr) -> Tuple[bytes, bytes]:
"""
Args:
password: Password
Returns:
Encryption key, Mac key
"""
if isinstance(password, str):
password = password.encode()
tmp = PBKDF2HMAC(hashes.SHA25... | 8b3268006d556bcd97c936ce0390ab18ecb12939 | 51,341 |
def get_setting(varname):
""" Returns the value of a configuration variable. """
gl = globals()
if varname not in gl.keys():
raise ValueError("Unknown setting %s"%varname)
# Here, possibly add some code to raise exceptions if some
# parameter isn't set set properly, explaining on how to set... | ed5d831a7f96439dfe8439750c96062eae434f43 | 51,343 |
def get_lr(curr_epoch, hparams, iteration=None):
"""Returns the learning rate during training based on the current epoch."""
assert iteration is not None
batches_per_epoch = int(hparams.train_size / hparams.batch_size)
if 'svhn' in hparams.dataset and 'wrn' in hparams.model_name:
lr = step_lr(hp... | ebe5dc79bc03007dbffa8ee5c8d878d10cb5e690 | 51,344 |
def _timestamp_to_date_string(timestamp: pd.Timestamp):
"""Return a date-string (YYYY-MM-DD) from a pandas Timestamp."""
if timestamp.tzinfo:
timestamp = timestamp.tz_convert('UTC')
timestamp = timestamp.tz_localize(None)
date = pd.Timestamp.date(timestamp)
if pd.Timestamp(date) != timestamp... | c6b69be196cec2a0d3c9a7a246307227f6faba18 | 51,345 |
def beta_tcon(s_gkg,t_cels,p_dbar,tp_cels=None,chkvals=False,
chktol=_CHKTOL,tp_cels0=None,chkbnd=False,useext=False,
mathargs=None):
"""Calculate haline contraction wrt conservative temperature.
Calculate the haline contraction coefficient of seawater at constant
conservative temperature.
... | af4e6bb6903644ec7f25771240723b14429b1aaf | 51,346 |
def workspace_list_table_format(result):
"""Format workspace list as a table"""
table = []
for item in result:
table.append(workspace_show_table_format(item))
return table | b8af25ff21930bed92fec15e4f11323f3270a3b9 | 51,347 |
def get_items(wiki, only_undone=False) -> list:
""" all items page list """
items = []
for page in wiki.pages():
content: str = wiki.page_info(page["pageid"])["*"]
if content.startswith("{{面包屑|物品信息"):
if only_undone and content.find("{{施工中}}") == -1:
continue
... | 1637c04aa61f5efe882af6c7f1bed80c3cde3185 | 51,348 |
def cycle(n):
"""Return the cycle graph on n vertices."""
return circulant(n, [1]) | 25ea83b0c61fb653caca6e899dfade52304df8e7 | 51,349 |
from typing import Any
def _attempt_cast_as_float(value: Any) -> float:
"""
:param vale: a value
:return: the value as a float if casting is possible, otherwise return 1
"""
try:
return float(value)
except (ValueError, TypeError):
return 1.0 | 8616bf4d59a08f8e98d26231f7aa51d419d52550 | 51,350 |
from typing import Any
from typing import Dict
from typing import List
import json
def adjust_dict_property(property_value: Any, model_field: ModelField, depth: int):
"""Convert a dict property for the BigQuery schema"""
dict_value_type = ModelFieldUtils.get_dict_value_type(model_field=model_field)
# Scal... | 29df072365e0c0b216744f790b627c868dbcd81e | 51,351 |
def show_permission(permission_id):
"""Get permission."""
data = _get_request_args()
return utils.make_json_response(
200,
permission_api.get_permission(permission_id, user=current_user, **data)
) | c1f06c1c055d09bc195323e2a66b74ae06dc9fd9 | 51,353 |
import gzip
def train_markov_gz(fn):
""" trains a Markov model on gzipped text data """
with gzip.open(fn, "rt", encoding="utf-8") as f:
text = f.read()
return markovify.Text(text, retain_original=False, state_size=3) | dfd6470cef3254f3521d62d81c96afc61b781418 | 51,354 |
import sqlite3
from typing import List
from typing import Any
from typing import Sequence
from typing import Optional
from typing import Type
from re import T
from typing import Tuple
def execute_sql_by_id(db: sqlite3.Connection, sql: str,
sql_values: List[Any], ids: Sequence[T2], return_type: Optional[Type[T... | beabdfaca28e78d9633b137ce98aceed64bdc9ae | 51,355 |
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