content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def _default_link_table2table(left, right):
"""Find default reference link between left and right tables.
Returns (keyref, refop).
Raises exception.ConflictModel if no default can be found.
"""
if left == right:
raise exception.ConflictModel('Ambiguous self-link for table %s' % left)... | a7d5b589f811512f70bb5ba00d611745d1c3a353 | 3,626,521 |
def quit_() -> None:
"""Quits the program, returns None."""
win.quit()
win.destroy()
return None | e98fac70384f46c5a5abbf6dd9ca90ee2b846fad | 3,626,522 |
def create_lkas_ui(packer, main_on, enabled, steer_alert):
"""Creates a CAN message for the Ford Steer Ui."""
if not main_on:
lines = 0xf
elif enabled:
lines = 0x3
else:
lines = 0x6
values = {
"Set_Me_X80": 0x80,
"Set_Me_X45": 0x45,
"Set_Me_X30": 0x30,
"Lines_Hud": lines,
"Ha... | 16c226698160b14194daf63baf61bb3952e1cd59 | 3,626,523 |
def recover_password(user: schemas.UserBase) -> JSONResponse:
"""
Password Recovery
"""
db_user = get_active_user(email=user.email)
if db_user is None:
return JSONResponse(status_code=404, content={
"message": "The user with this email "
"does not exist in... | 03b270a743b8c262e8622860672493c649079b36 | 3,626,524 |
def plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
chromaticity_diagram_callable_CIE1960UCS=(
plot_chromaticity_diagram_CIE1960UCS),
chromaticity_diagram_clipping=False,
ellipse_kwargs=None,
**kwargs):
"""
Plots *MacAdam (1942) Ellipses (Observer PGN)* ... | 04157ae011619f6bc9cb4207ff09e7edf86d91b9 | 3,626,525 |
import torch
def get_pytorch_device() -> torch.device:
"""Checks if a CUDA enabled GPU is available, and returns the
approriate device, either CPU or GPU.
Returns
-------
device : torch.device
"""
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.devic... | 0109f3146c96bec08bbe6fa62e5a8bc5638e461c | 3,626,526 |
import logging
def test_execution_store(cfg):
"""
Creates a proper test_execution store based on the current configuration.
:param cfg: Config object. Mandatory.
:return: A test_execution store implementation.
"""
logger = logging.getLogger(__name__)
if cfg.opts("results_publishing", "data... | 8684f2ea65019e9c49444bbc25a8e1b0f59e687e | 3,626,527 |
import time
import random
def creat_order_num(user_id):
"""
生成订单号
:param user_id: 用户id
:return: 订单号
"""
time_stamp = int(round(time.time() * 1000))
randomnum = '%04d' % random.randint(0, 100000)
order_num = str(time_stamp) + str(randomnum) + str(user_id)
return order_num | 339764f7dc943c46d959a9bfdc6c48dc9ecd6bef | 3,626,528 |
def get_rst_title_char(level):
"""Return character used for the given title level in rst files.
:param level: Level of the title.
:type: int
:returns: Character used for the given title level in rst files.
:rtype: str
"""
chars = (u'=', u'-', u'`', u"'", u'.', u'~', u'*', u'+', u'^')
if... | b646b9e0010d87ece7f5c8c53c8abf89ca557a21 | 3,626,529 |
def python_to_ir(f, imports=None):
"""Compile a piece of python code to an ir module.
Args:
f (file-like-object): a file like object containing the python code
imports: Dictionary with symbols that are present.
Returns:
A :class:`ppci.ir.Module` module
.. doctest::
>>... | 67ae702485ad664667b80efc8c3ff3d9ec13ab57 | 3,626,530 |
def get_endpoint_url(env, endpoint='public'):
"""Gets the Endpoint to use."""
endpoint_type = env.input('Endpoint (public|private|custom)', default=endpoint)
endpoint_type = endpoint_type.lower()
if endpoint_type == 'public':
endpoint_url = SoftLayer.API_PUBLIC_ENDPOINT
elif endpoint_type ... | 37eec3f0d9499b079867af233922a49afe4c40cf | 3,626,531 |
def compute_temperature_high_altitude(altitude: pint.Quantity) -> pint.Quantity:
"""Compute temperature in high-altitude region.
Parameters
----------
altitude: quantity
Altitude.
Returns
-------
quantity
Temperature.
"""
r0 = R0
a = -76.3232 # K
b = -19.94... | c626e901e291f66f03056b288ab05738adbb67d0 | 3,626,532 |
def _get_individual_lists(ws, ind_numbers = INDIVIDUAL_NUMBERS, behav_map = BEHAVIOR_MAPPING):
""" returns {1 : [time, behav, start/stop] }"""
start_row = _get_first_content_row(ws = ws)
ret_dict = {}
for j in range(start_row, ws.max_row + 1):
if j % 100 == 0:
print("******Proce... | 3a68795d1083abd6f876a014a1e873341ba04b79 | 3,626,535 |
def normalize_prob_dictionary(prob_dict):
"""
Given a dictionary that describes probabilities of parameter values,
normalize the probabilities so that they sum up to 1
:param dict:
:return:
"""
sum = np.sum(list(prob_dict.values()))
if sum > 0:
for key in prob_dict.keys():
... | 57432f66c5bf569d080e251e877632b9cfbec743 | 3,626,537 |
def stsci_extraction_ranges(x1d, seg=''):
"""
Parameters
----------
x1d
seg
Returns
-------
ysignal, yback
"""
cos, stis = _iscos(x1d), _isstis(x1d)
xh, xd = x1d[1].header, x1d[1].data
# below these will all be divided by 2 (except bk off). initially they specify the f... | 6e2843656ae1955da36a1bca059fec7ae454d624 | 3,626,538 |
def ending_at(row_key: str) -> pbt_C.RowRange:
"""Create a row range ending at given row (inclusive).
Args:
row_key (str): The ending row key of the range (inclusive).
Returns:
RowRange: The row range which ends at `row_key` (inclusive).
"""
return pbt_C.ending_at_row_range(row_key) | b15b65c74bcf8524d3d1932dcde8dca678d5c967 | 3,626,539 |
def train_model(model, X_data_train, y_target_train, early_stopping):
"""
:param model: compiled model
:param X_data_train: 3d array
:param y_target_train: 1d array
:param flag: true if googlnet (output expect 3d array) else false if 1d array for output
:return: fitted model
"""
if earl... | 1510c7887a503d9a1fca6faf7f2eadb583af70a1 | 3,626,540 |
import torch
import torch.nn as nn
def bn_model_pytorch():
"""Same as bn_model but with PyTorch."""
bounds = (0, 1)
num_classes = 10
class Net(nn.Module):
def forward(self, x):
assert isinstance(x.data, torch.FloatTensor)
x = torch.mean(x, 3)
x = torch.m... | f4e16cb8283fa5ad58a0c65bb94f295d14ebec0c | 3,626,542 |
def check_point(x, y, d, length, alpha):
"""
Проверяет точку на принадлежность волноводу
Точка в ск Федера
"""
if -d / 2 <= y <= d / 2:
return 0 <= x <= length or is_inside_cone(x - length, y, d, alpha) | 747db69665134096077bdc29ccd162de5c14bda5 | 3,626,543 |
def comments_list(request, locker_id, submission_id):
"""Returns a list of comments for the specified submission"""
submission = get_object_or_404(Submission, pk=submission_id)
if submission.locker.discussion_enabled():
is_owner = submission.locker.is_owner(request.user)
is_user = submission... | b3202414cf2174e9329538c1f617a3734e5c97b2 | 3,626,545 |
def massage_spectrum(cov, shape):
"""given a spectrum cov[nl] or cov[n,n,nl] and a shape
(stokes,ny,nx) or (ny,nx), return a new ocov that has
a shape compatible with shape, padded with zeros if necessary.
If shape is scalar (ny,nx), then ocov will be scalar (nl).
If shape is (stokes,ny,nx), then ocov will be (sto... | 18a7bf9b8704f1ada174b523fb9ddd3fdf135ff7 | 3,626,547 |
def get_device_by_label(session, label):
"""get Device by label
Args:
session: Active database session
label: label to get device that matches
Returns:
device found or None
"""
return session.query(Resource).filter(Resource.label == label).one_or_none() | 624acfd77be27d89f00f3e594f179c9a4f285639 | 3,626,548 |
from bs4 import BeautifulSoup
def fetch_MX_exchange(sorted_zone_keys, s):
"""
Finds current flow between two Mexican control areas.
Returns a float.
"""
req = s.get(MX_EXCHANGE_URL)
soup = BeautifulSoup(req.text, 'html.parser')
exchange_div = soup.find("div", attrs={'id': EXCHANGES[sorted... | c2316f6c3bbd4d823fd2e34057f2fa8df248e3a6 | 3,626,549 |
def mlp(X_train, targets):
"""Fully Connected Neural Network, known as MLP(Multi-Layer Perceptons).
"""
feature_number = X_train.shape[1]
output_number = targets.shape[1]
model = tf.keras.Sequential()
model.add(Dense((output_number+feature_number)/2+40,
input_dim=feature_numb... | 37a651a0007a6d31cc5c277db49085b9bd303c25 | 3,626,550 |
def make_ir_context() -> ir.Context:
"""Creates an MLIR context suitable for JAX IR."""
context = ir.Context()
mhlo.register_mhlo_dialect(context)
chlo.register_chlo_dialect(context)
return context | 7e34ade63d47e763784277d05fcb8d979b28715b | 3,626,551 |
def parse_patch(patch_string):
"""Parse a patch string and return the affected files."""
patch = DiffParser(patch_string.splitlines())
return patch.files | 8b53d5443290fc6cdb1162ab0a607d42313ed9ba | 3,626,553 |
def adjust_color_lightness_scalar(r, g, b, factor):
"""
r,g,b between 0 and 1
factor between 0 and +infty, but lightness bounded between 0 and 1
"""
h, l, s = rgb_to_hls_scalar(r, g, b)
l = max(min(l * factor, 1.0), 0.0)
r, g, b = hls_to_rgb_scalar(h, l, s)
return r,g,b | 0e541f92c3df11a8bce1e5988d2a329ee15d933b | 3,626,554 |
def get_sample_ids(fams):
""" create a ditionary mapping family ID to sample, to subID
Returns:
e.g {'10000': {'p': 'p1', 's': 's1'}, ...}
"""
sample_ids = {}
for i, row in fams.iterrows():
ids = set()
for col in ['CSHL', 'UW', 'YALE']:
col = 'SequencedA... | 443e63b486a1bdb8a64595beda30f75468af7560 | 3,626,555 |
def mesh_conway_join(mesh):
"""Generates the join mesh from a seed mesh.
Parameters
----------
mesh : Mesh
A seed mesh
Returns
-------
Mesh
The join mesh.
Examples
--------
>>> mesh = Mesh.from_polyhedron(6)
>>> join = conway_join(mesh)
>>> join.number_... | 53f6358798f3421f98524fbcf166834a579a5f45 | 3,626,556 |
def calcbw(K, N, srate):
"""Calculate the bandwidth given K."""
return float(K + 1) * srate / N | 13e99bcb729352feb66a34aac66e12b1c5e158ef | 3,626,557 |
import requests
import json
def get_all_devices(auth):
"""
Function to get all devices for the account linked to the token
:param auth: pyawair.auth.AwairAuth object which contains a valid authentication token
:return: Object of Dict type which contains a list of all devices for this account
"""
... | ec46bd1be167e8df4d6cce94bddd3725eca73b43 | 3,626,558 |
def supports_display(handler_input):
# type: (HandlerInput) -> bool
"""Check if display is supported by the skill."""
#check the incoming request to the skill from the AVS to determine if the device the user invoked the skill on has a screen
try:
if hasattr(handler_input.request_envelope.context... | 98c45257b034637549e687dfe5e500d5cc4ffcb7 | 3,626,559 |
def format_sqlexec(result_rows, maxlen):
"""
Format rows of a SQL query as a discord message, adhering to a maximum
length.
If the message needs to be truncated, a (truncated) note will be added.
"""
codeblock = "\n".join(str(row) for row in result_rows)
message = f"```\n{codeblock}```"
... | 3b98590c72245241ba488e07fdfdd20acae441cf | 3,626,560 |
def is_calibration_produced(drs4_pedestal_run_id: int, pedcal_run_id: int) -> bool:
"""
Check if both daily calibration (DRS4 baseline and
charge calibration) files are already produced.
"""
return (
drs4_pedestal_exists(drs4_pedestal_run_id)
and calibration_file_exists(pedcal_run_id... | 2ce3a469bb3fa84493379d758b9573c6170986da | 3,626,561 |
def generate_initial_population(network_info, network_layout):
"""
Generates the initial population for network optimization.
:param NetworkInfo network_info: Object storing global network information (information about the whole
optimization)
:param NetworkLayout ... | 0c07f873f162ed8c87f9335fc1eadf8e84990311 | 3,626,564 |
def split_zip(zip_code):
""" split the zip code into 5 and 4 digit codes """
if not valid_zip(zip_code):
return None, None
if len(zip_code) == 5:
return zip_code[:5], None
return zip_code[:5], zip_code[-4:] | 86d6c37e2dce4a840e6198c9aa86c52716eff256 | 3,626,565 |
from typing import Dict
from typing import Any
from typing import Iterable
import warnings
def parse_initial_conditions(
ic: Dict[str, Any],
start_date_simulation: pd.Timestamp,
virus_strains: Dict[str, Any],
) -> Dict[str, Any]:
"""Parse the initial conditions."""
ic = {**INITIAL_CONDITIONS} if i... | 2e47bcff366870425b738381ac7abfb4c860103c | 3,626,566 |
def rq2responses(request):
"""
Converts a request to a list of responses.
:param request: Flask Request object
:return: list of response strings
"""
i, responses = 0, []
for i in range(int(request.form['num_questions'])):
name = Question.ID_FORMAT % i
if request.form.get(nam... | 83e42a33d6f3a23134819fcba16b4422ac7cffc3 | 3,626,567 |
def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs):
"""
Taken from :
https://github.com/gmagannaDevelop/skimage-tutorials/blob/master/lectures/4_segmentation.ipynb
"""
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))
ax.imshow(image, cmap='gray')
ax.axis... | b2293d254a942b5f6534cc7394031dbefb87978b | 3,626,568 |
def normalize(arr):
"""Normalizes an array to its mean values.
Parameters
----------
arr : array-like | shape = [N]
The array to normalize.
Returns
-------
normalized_array : np.ndarray | shape = [arr.shape]
"""
return arr / np.mean(arr) | 6021296c5a86f770be59de259945e2ac28532154 | 3,626,569 |
from typing import List
from typing import Tuple
def find_matching_parens(
assertion_str, matched_quotes, errors: List[ValidationError]
) -> Tuple[List[Pair], List[ValidationError]]:
"""Find and return the location of the matching parentheses pairs in s.
Given a string, s, return a dictionary of start: e... | 07259774357cc04bd9ededdf0e1557207ed301ca | 3,626,570 |
import re
def regex_sub_groups_global(pattern, repl, string):
"""
Globally replace all groups inside pattern with `repl`.
If `pattern` doesn't have groups the whole match is replaced.
"""
for search in reversed(list(re.finditer(pattern, string))):
for i in range(len(search.groups()), 0 if ... | 67e909778d9565d498fc3e7b3c9522378e971846 | 3,626,571 |
def _parse( filepath : str ) -> list:
"""
[summary]
Arguments:
filepath {str} -- [description]
Returns:
list -- [description]
"""
with open( filepath, 'r' ) as f:
raw_data = f.read( ) # not readlines( ), as this needs to be one long string
data = list( map( int, raw_data.split( ) ) )
return data | 6814c4f45aee453f6387761ee7c4f3e64c669b1c | 3,626,572 |
from typing import Optional
def get_app_sec_eval(config_id: Optional[int] = None,
security_policy_id: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetAppSecEvalResult:
"""
Use this data source to access information about an existing r... | 2499b44e1b59ab989fb6b14a4d60145610a0d0b1 | 3,626,573 |
def write_merged_bioassembly(inpath, outdir, outname, force_rerun=False):
"""Utility to take as input a bioassembly file and merge all its models into multiple chains in a single model.
Args:
infile (str): Path to input PDB file with multiple models that represent an oligomeric form of a structure.
... | e53779b0c466a730028d91ff10779137a186c288 | 3,626,574 |
def get_final_loss(src_logits, src_one_hot_labels, dst_logits,
finetune_one_hot_labels, global_step, loss_weights,
inst_weights):
"""Gets the final loss for ."""
if FLAGS.uniform_weight:
inst_weights = 1.0
src_loss = get_loss(src_logits, inst_weights, src_one_hot_labels)
... | 2b87a024394e983eea9dbd5a1df9b30a9bff74f4 | 3,626,575 |
def causal_kernel(alpha):
"""
The causal kernel.
.. math::
w(\\tau) = [\\alpha^2 \\tau \\exp(- \\alpha \\tau)]_+
example:
.. code-block:: python
>>> kernel('causal', {'alpha': 0.4})
"""
def causal(t):
v = alpha**2 * t * np.exp(-alpha*t)
v[v<0] = 0
return v
return caus... | f49766f44f1532e11ef1ad88f162d1cf4bc03e0a | 3,626,576 |
import numpy
def read_nasa_planets(csv_filename,
eliminate=('SWEEPS-11',
'HD 41004 B',
'PSR J1719-1438',
'K2-22'),
fill_missing=manual_data,
need_ages=Tr... | a39f96be4f5b2b9f8e3e642117f5338db542fabb | 3,626,577 |
def e8(s: str) -> str:
"""
Encode Unicode string with stanard options
"""
return s.encode('utf-8', 'ignore') | ae28011d7d71c28399d81012fac26012693de9aa | 3,626,578 |
import requests
def download_seed_seqs(acc):
"""
Download seed sequences from PFAM.
Input
-----
acc : str
Accession number of a Pfam domain
Output
------
fasta : str
Seed sequences in fasta format
"""
url = "http://pfam.xfam.org/family/%s/alignment/seed" % acc... | e067e5e6e0669812151a680d6f785b19be01fb01 | 3,626,580 |
import numpy
def fit_spline(points, smoothing=None, order=None, force_endpoints=True):
"""Fit a parametric smoothing spline to a given set of x,y points. (Fits
x(p) and y(p) as functions for some parameter p.)
Parameters:
points: array of n points x,y; shape=(n,2)
smoothing: smoothing factor: 0 r... | e694736af14999881a9fd1cf3c8fd070802b137b | 3,626,581 |
def get_list_of_teams():
"""Get a list of all teams."""
teamlist = []
for team in cursor.execute('SELECT * from teams'):
teamlist.append(team[0]) # man isn't it cool that order matters
return teamlist | 82121e2fc8a652c4ee3630a4cba80aeb4f026520 | 3,626,582 |
def linear_activation_forward(A_prev, W, b, activation):
"""
activation of forward propagation
:param A_prev: np.array, activations from previous layer
:param W: np.array, weights matrix of current layer
:param b: np.array, biases vector of current layer
:param activation: str, activation mode o... | a8290d71b1660c5a154cc47e3674560a24c6d2e8 | 3,626,583 |
def stoll_auc_subjects(ds, skip_samples=0, dur_samples=10000):
""" Calculate AUC for each subject in a dataset
using the Stoll (2013) classification method """
aucs = []
for sub in np.unique(ds.trials_ppid):
tpr = []
fpr = []
tr = ds.trials[ds.trials_ppid == sub, :]
... | b3398c52d9f8d0fc8b90621b301e8d27770a18e6 | 3,626,584 |
def stations_level_over_threshold(stations, tol):
"""Returns a list of the tuples, each containing the name of a station at which the relative
water level is above tol and the relative water level at that station"""
output = []
for station in stations:
relative_level = station.relative_water_l... | 87be1329ea8b7e58796ebfe33a6f9e9503777227 | 3,626,585 |
def select_ensembl_species(species, table):
"""
Filters the species of interests, called group, from a table containing all the species in the Current release
"""
# Read the species table from Ensembl Genomes
df = pd.read_csv(table, sep='\t', index_col=False)
# Filter out the species that are ... | eb7f0a75842745a27b11296d602088770002eae8 | 3,626,588 |
def normalize_power_spectrum(Q):
"""transform spectrum to complex vectors with unit length
Parameters
----------
Q : np.array, size=(m,n), dtype=complex
cross-spectrum
Returns
-------
Qn : np.array, size=(m,n), dtype=complex
normalized cross-spectrum, that i... | 783fc5def698289f45721080c91ef74554604a4c | 3,626,589 |
import torch
def img2tensor(
img: pillow.Image,
size: tuple = None,
) -> torch.Tensor:
"""
Args:
img: image to convert
size: (W, H) of output tensor
Returns:
tensor of shape (1, 3, H, W)
the first dimension (batch size) is neccesary for the CNN
3 cha... | d90bc998d1a476287b0e2c2444d3dce4a035b7c6 | 3,626,590 |
def handler(event, context):
"""
function handler
"""
if 'imageDiscard' in event and event['imageDiscard']:
return None
if 'imageLocation' not in event or len(event['imageLocation']) == 0:
return None
if 'imageObjects' not in event or len(event['imageObjects']) == 0:
re... | 82d723d913f1c2d0631157a27702a889faa4c1b4 | 3,626,591 |
def set_smb_netbios_name(session, smb_netbios_name, force="YES", return_type=None, **kwargs):
"""
Get VPSA cache
:type session: zadarapy.session.Session
:param session: A valid zadarapy.session.Session object. Required.
:type smb_netbios_name: str
:param smb_netbios_name: The smb ne... | 964877297f8814da9ca9af92bb93b3bc7fd2718d | 3,626,592 |
def _random_correlated_image(mean, sigma, image_shape, alpha=0.3, rng=None):
"""
Creates a random image with correlated neighbors.
pixel covariance is sigma^2, direct neighors pixel covariance is alpha * sigma^2.
Parameters
----------
mean : the mean value of the image pixel values.
sigma :... | 71a81630c29e271265b8bace4136a4fbce83137b | 3,626,594 |
def diameter(aabb):
"""
Compute the length of the diameter of an AABB.
:param aabb: AABB defined by its min and max point.
:type aabb: Pair of n-dimensional vectors
:return: Length of the diameter of the AABB.
"""
if not is_valid(aabb):
return None
return np.linalg.norm(aabb[1] ... | 675c0a2a445c18b7d1da75f9f78cb7a529ed1a05 | 3,626,596 |
def deregister_device(device):
"""
Task that deregisters a device.
:param device: device to be deregistered.
:return: response from SNS
"""
return device.deregister() | 44bec0e3ac356f150a0c4a6a0632939a20caafb0 | 3,626,597 |
async def peko(message):
"""peko"""
"""returns [content, embed, view]"""
url = "https://holodex.net/api/v2/users/live"
params = {
"channels": "UC1DCedRgGHBdm81E1llLhOQ,UCdn5BQ06XqgXoAxIhbqw5Rg,UC5CwaMl1eIgY8h02uZw7u8A,UChAnqc_AY5_I3Px5dig3X1Q"
}
headers = {"Content-Type": "application/js... | 674927c77fc6e4dc54a7eab6c4f42f7b64d84c00 | 3,626,598 |
def sequential_colors(n):
"""
Between 3 and 9 sequential colors.
.. seealso:: `<https://personal.sron.nl/~pault/#sec:sequential>`_
"""
# https://personal.sron.nl/~pault/
# as implemented by drmccloy here https://github.com/drammock/colorblind
assert 3 <= n <= 9
cols = ['#FFFFE5', '#FFFB... | d2ad5f8993f8c7dac99a577b6115a8452ad30024 | 3,626,599 |
def _get_mask(Size, idxi, idxj, win_type):
"""Compute a mask of zeros with a window at a given position.
"""
idxi = np.array(idxi).astype(int)
idxj = np.array(idxj).astype(int)
win_size = (idxi[1] - idxi[0] , idxj[1] - idxj[0])
wind = build_2D_tapering_function(win_size, win_type)
mask =... | cf6426646703601c23ab9d8491eff656e2effaf0 | 3,626,602 |
import shutil
import logging
def _check_tool(cfg: config.Config, bin_name: str, name: str,
min_version: Version) -> bool:
"""Check availability and version of a tool."""
if not _check_availability(bin_name, name):
return False
bin_path = shutil.which(bin_name)
assert bin_path i... | 15520d3b94ffac6957ca297aa1f4ae49bff8a45d | 3,626,604 |
def init_func_global() -> JobInitStateReturn:
"""
Init function for the job
:return: INIT or NOT_INIT state for the job
"""
return JobInitStateReturn(True) | e00c448dcc3f84cd53844d9d4a425ff4545a8135 | 3,626,605 |
def get_orbit_from_metadata(mds):
"""Get orbit for a set of SLC ids. They need to belong to the same day."""
day_dt, all_dts, mission = util.get_date_from_metadata(mds)
logger.info("get_orbit_from_metadata : day_dt %s, all_dts %s, mission %s" %(day_dt, all_dts, mission))
return fetch("%s.0" % all_dts[0... | ff0b9d1a2f2d99dc5332abcd0fc4d402e0dd693f | 3,626,606 |
def fibonacci(n):
"""Return the n-th Fibonacci number"""
if n in (0,1):
return n
return (fibonacci(n - 2) + fibonacci(n - 1)) # Can trace the recursive function with a decorator | ab01aabbc2ef5c5bf2f3f0b83f44675254b555c7 | 3,626,607 |
def random_color() -> np.ndarray:
"""Generate a random color (RGB)."""
return np.array(np.random.rand(3)) | f77b72075fbbd89d87f6507d06a2ac713d49daa2 | 3,626,608 |
def get_king_moves():
"""Generate the king's movement range."""
return [(1, 0), (1, -1), (-1, 0), (-1, -1),
(0, 1), (0, -1), (-1, 1), (1, 1)] | 5e6f5fcb8c57846b9b2ab112c27f90fc13a6d6b4 | 3,626,609 |
def _unpack_topk(topk, lp, hists, attn=None):
"""unpack the decoder output"""
beam, _ = topk.size()
topks = [t for t in topk]
lps = [l for l in lp]
k_hists = [(hists[0][:, i, :], hists[1][:, i, :], hists[2][i, :])
for i in range(beam)]
if attn is None:
return topks, lps, ... | 7e9012b62f25ec7f4a09387235a0161143ec5029 | 3,626,611 |
def share_map(map_url_hash):
"""Create shareable version of current map using crypotgraphic hash at end of address"""
user_map = Map.query.filter(Map.map_url_hash == map_url_hash).one()
map_id = user_map.map_id
places_on_map = Place.query.filter(Place.map_id == map_id, Place.place_active == True).all()
... | a92384b52e0b9beb36c783d5fec4036983289aef | 3,626,612 |
def _darknet_reshape(inputs, params, attrs, prefix):
"""Process the reshape operation."""
new_attrs = {}
new_attrs['shape'] = attrs.get('shape')
return get_relay_op('reshape')(*inputs, **new_attrs) | 90049d596aeca37074e2d9946eb0f2d0c6bc8513 | 3,626,613 |
def CreateVectorObject(volumeRef, name):
"""
Creates a c4d.VolumeObject with the VolumeRef passed.
Names this VolumeObject with the passed argument.
:param volumeRef: The VolumeRef to use within the VolumeObject.
:type volumeRef: maxon.frameworks.volume.VolumeRef
:param name: The name of the in... | 11099d54def62f560227d3a97ea628bf15c55931 | 3,626,615 |
def box2start_row_col(box_num):
"""
Converts the box number to the corresponding row and column number of the square in the upper left corner of the box
:param box_num: Int
:return: len(2) tuple
[0] start_row_num: Int
[1] start_col_num: Int
"""
start_row_num = 3 * (box_num // 3... | b11e7d4317e1dd32e896f1541b8a0cf05a342487 | 3,626,616 |
def safe_referrer(meta, default):
"""
Takes request.META and a default URL. Returns HTTP_REFERER if it's safe
to use and set, and the default URL otherwise.
The default URL can be a model with get_absolute_url defined, a urlname
or a regular URL
"""
referrer = meta.get('HTTP_REFERER')
i... | b70f7799bf13fc2ee9229f037553047dd60334be | 3,626,617 |
import re
def create_title(chunk, sep, tags):
"""Helper function to allow doconce jupyterbook to automatically assign titles in the TOC
If a chunk of text starts with the section specified in sep, lift it up
to a chapter section. This allows doconce jupyterbook to automatically use the
section's text... | 67bd80c10733d79f84ba38cd44155e73bb2a2efd | 3,626,618 |
import math
def get_marker_size(number):
"""Same scaling as for year 2019. Produces more overlap between circles.
But this was considered OK, since it does reflect the reality.
"""
return SCALE * (5 * math.sqrt(number) + 5) | 252c9549fa0c568dd88361cbf1a8cbc467972e08 | 3,626,619 |
import scipy
import numpy
def graph2VSM(graph):
"""
Convert an igraph model to a VSM
Params:
model the igraph model
"""
terms = graph.vcount()
edges = graph.ecount()
# Use sparse matrix representation if the matrix is less than 50% full
# if terms > 20000 and graph.density() <... | e89b682d539879945fe5a1a970db1f8c56c27652 | 3,626,621 |
def recurrence_memo(initial):
"""
Memo decorator for sequences defined by recurrence
See usage examples e.g. in the specfun/combinatorial module
"""
cache = initial
def decorator(f):
@wraps(f)
def g(n):
L = len(cache)
if n <= L - 1:
retur... | 6cb4ea4a2183bb98b915d966692fc5be40cdf494 | 3,626,624 |
def update_project_users(post_data=None, slug=None):
"""update-project-users (Site admins/Site managers/Project managers only)
Usage: update-project-users [-h] <slug> (<username> <access_mode>) ...
Arguments:
<slug> The slug of the project
<username> The username of the user to either update o... | 71548b9203f1ce42cb469dd1ea0c37eaff722b8c | 3,626,625 |
def _gaussian(y_pred, y_true, scale):
"""
Computes the log-likelihood of each sample for a Gaussian GLM given the predicted expected values.
"""
return - 0.5 * (y_pred - y_true) ** 2 / scale ** 2 \
- 0.5 * np.log(2 * np.pi * scale**2) | 0d3d1df54e1f785e696e39d649f6d8c1b10c80c4 | 3,626,627 |
from typing import Sequence
def regression_report(
y_true: Sequence,
y_pred: Sequence,
*,
precision: int = 4,
width: int = 32,
use_percentage: bool = False,
) -> str:
"""
Returns detailed regression report as string
:param y_true: sequence of ground truth values for regression pro... | 6572f117b045414822ec391af292759d4dc9d7ea | 3,626,628 |
def _extract_feats(data_test, model, what, skip=1, batch_size=16):
"""
:param data_test: test Dataset
:param model: network model
:param what: SK or IM
:param skip: skip a certain number of image/sketches to reduce computation
:return: a two-element list [extracted_labels, extracted_features]
... | 5549c3131d98ae876609f52ad0dc581ec7c9bc76 | 3,626,629 |
def _getbkfile(repo):
"""Hook so that extensions that mess with the store can hook bm storage.
For core, this just handles wether we should see pending
bookmarks or the committed ones. Other extensions (like share)
may need to tweak this behavior further.
"""
fp, pending = txnutil.trypending(re... | 1c04da01ed277ab790f02cae0e282d8dc853e8a0 | 3,626,630 |
def perturb(graph_list, p):
""" Perturb the list of (sparse) graphs by adding/removing edges.
Args:
p: proportion of added edges based on current number of edges.
Returns:
A list of graphs that are perturbed from the original graphs.
"""
perturbed_graph_list = []
for G_original i... | c3358dc0bbdcd75d5619ed1ee372d206ea7651b0 | 3,626,631 |
def hold_out_val(data, target, include_self=True, class_weight=None,
features=None, cl='rf', verbose=False, random_state=None):
"""
performs simple hold-out validation
:param data: list of datasets to evaluate
:param verbose: if true print confusion matrix and classification report for ... | b4e934fa87add641067ff16cb3709f384d1675e8 | 3,626,632 |
import torch
def train(model, train_loader, loss_func, optimizer, device):
"""
训练模型
train model using loss_fn and optimizer in an epoch.
model: CNN networks
train_loader: a Dataloader object with training data
loss_func: loss function
device: train on cpu or gpu device
"""
total_lo... | 610b03e10ebce886743a4b06c91a46de30ef586d | 3,626,633 |
def sq_dist(x: np.ndarray, y: np.ndarray):
"""
distance functions for point-point distances
:param x: nd array, T1 x D
:param y: nd array, T2 x D
:return: matrix with shape of (T1, T2)
"""
return np.sum((x[:, None, :] - y[None, :, :]) ** 2, axis=2) | 97f3b6fc10dbf5f18c5eea4403f3662be8580097 | 3,626,634 |
import uuid
def get_uuid():
"""
Returns a uuid4 (random UUID) specified by RFC 4122 as a
32-character hexadecimal string
"""
return uuid.uuid4().hex | ce3f00569c3fa12aa203246bd5d3ae098f2dab2a | 3,626,635 |
async def register_users(app, users):
"""Create multiple users from a list.
Parameters
----------
app : aiohttp.web.Application
The aiohttp application instance.
users : list(dict)
The list of new users with each user having username, password, permissions and cards (optional).
... | a7ca4cb451da4d542d9740f7e2d12511514a7612 | 3,626,636 |
def radmat_from_df_filter(
df, radmat_field="signal", channel_i=None, return_df=False, **kwargs
):
"""
Apply the filter args from df_filter and return a radmat
"""
_df = df_filter(df, channel_i=channel_i, radmat_field=radmat_field, **kwargs)
radmat = df_to_radmat(_df, channel_i=channel_i, radmat... | 61782b513be331da08d7c9ceaada7c12ba83d3e6 | 3,626,637 |
def _populate_number_fields(data_dict):
"""Returns a dict with the number fields N_NODE, N_EDGE filled in.
The N_NODE field is filled if the graph contains a non-`None` NODES field;
otherwise, it is set to 0.
The N_EDGE field is filled if the graph contains a non-`None` RECEIVERS field;
otherwise, it is set ... | e80e506057ecbb9ab4987ce4383da494824f1d66 | 3,626,638 |
def tree_pretty_print(tree):
"""Pretty-print a tree of python objects.
Examples::
>>> from pptree import Node
>>> users = Node("users")
>>> group = Node("group", users)
>>> _ = Node("roles", group)
>>> _ = Node("permissions", group)
>>> _ = Node("comments", users)
>>> print(tree_pr... | 2598ae29d3d019c1c9613f4944d16c01f149529f | 3,626,639 |
def branch_edit(request, branch_id):
"""/branch_edit/<branch> - Edit a Branch record."""
branch = models.Branch.get_by_id(int(branch_id))
if branch.owner != request.user:
return HttpTextResponse('You do not own this branch', status=403)
if request.method != 'POST':
form = BranchForm(initial={'category':... | f9ce2ba616899174b4765c889d28a2cfa7d39787 | 3,626,640 |
def eul(f, u, x_0, d, param):
"""Integrate dynamics using forward Euler method
x[k+1] = x[k] + delta*(f(x[k], u[k]))
Input: dynamics f, control input u, initial condition x_0, step d
Output: trajectory x
"""
if u.ndim == 1:
return x_0 + d*(f(x_0, u, param))
N... | 9ac8ad1fef149c96144ea9d2730752e0d18bc0bd | 3,626,641 |
def input_network_name():
"""
Prompt user to choose a network
"""
while True:
network_name = input('network ({0})?'.format(','.join(NETWORK_NAMES)))
if network_name in NETWORK_NAMES:
return network_name | 0a467259652a12aa2f4c71c57019aba8b02af889 | 3,626,642 |
import re
def hashtag(phrase, plain=False):
"""
Generate hashtags from phrases. Camelcase the resulting hashtag, strip punct.
Allow suppression of style changes, e.g. for two-letter state codes.
"""
words = phrase.split(' ')
if not plain:
for i in range(len(words)):
try:
if not words[i]:
del word... | b6e7ab647330a42cf9b7417469565ce5198edd4f | 3,626,643 |
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