content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def gen_cookie(username, hash_password):
"""
Build secure cookie content as a string containing:
- content length (excluding length itself)
- role_name
- 16 first chars of the hash password
Part of hash password is there for 2 main reasons:
1/ If the cookie secret key is st... | 44b0ec056bd7002aea7b63fb57901dc1180cb45c | 3,623,837 |
def line_generic(pos, color=(1, 1, 1, 1), width=0.1, antialias=True, mode='line_strip'):
"""
Add a pyqtgraph.opengl.GLLinePlotItem to GPGLViewWidget, with exactly the same arguments.
For detail, consult documentation of pyqtgraph.opengl.GLLinePlotItem:
http://www.pyqtgraph.org/documentation/3dgraphic... | 749189775a45cf78e20000e17edc734eed6b6bfa | 3,623,838 |
def create_dict(obj, columns):
"""
Create a dict, given a database record and an ordered list of columns.
Mind the order of column names in the columns list
"""
try:
new_obj = {}
for col in range(len(columns)):
new_obj[columns[col]] = obj[col]
return new_obj
... | 9ac55086e8e6e584849deb93d79e148054e835f7 | 3,623,839 |
def filepath_result_format(classifier_name, task, res, filepath):
"""
Helper function to produce result dictionary using filepath
filepath (str) : (e.g. '../../inferred_features/code_ss_w2v_multiclass_7.pkl'
"""
filepath = filepath.split("/")[-1]
dims = filepath.split(".")[0].split("_")
if d... | 3b2d508f02963840ca7c467b40edfc2813fae73d | 3,623,840 |
def get_optimisation_status():
"""Get the status of an optimisation job."""
opt_job_id = request.args.get('opt-job-id')
opt_job = database.opt_jobs.find_one({'_id': ObjectId(opt_job_id)})
return jsonify({'_id': opt_job_id, 'status': opt_job['status']}) | c02707aedc5dea50389c0ef061b3df89f9024b95 | 3,623,841 |
def makeCorrelationMatrixFromDictionary(value_dictionary, keys=[], matrix_file_name="temporary_matrix_file.txt", key_file_name="temporary_key_file.txt"):
"""
Returns numpy.matrix: matrix object of correlation coefficients, symmetric matrix with a diagonal of ones
Returns list: list of keys that correspond to the row... | 6faa71bd32050ed5b9005de7f199e8ed05622f89 | 3,623,842 |
import itertools
def pad(value, seq: Seq, size: int = None, step: int = None) -> Iter:
"""
Fill resulting sequence with value after the first sequence terminates.
Args:
value:
Value used to pad sequence.
seq:
Input sequence.
size:
Optional minim... | b2ee68adf9e0bb760547fcc4c029b16df3f7dff3 | 3,623,843 |
def str_to_state(str_state):
""" Reads a sequence of 9 digits and returns the corresponding state. """
assert len(str_state) == 9 and sorted(str_state) == list('012345678')
return tuple(int(c) for c in str_state) | 8e9e8c2b70f86aa4798f9be14d39c43404336e7a | 3,623,844 |
def load_10x_h5(file, genome):
"""Load count matrix in 10x H5 format
Adapted from:
https://support.10xgenomics.com/single-cell-gene-expression/software/
pipelines/latest/advanced/h5_matrices
Args:
file (str): Path to H5 file
genome (str): genome, top level h5 group
Ret... | 743da441f08eceb211340d1078ad95ba0be99afc | 3,623,846 |
from typing import Optional
def to_dataset_entity_id(
full_name: str, platform: DataPlatform, account: Optional[str] = None
) -> EntityId:
"""
converts a dataset name, platform and account into a dataset entity ID
"""
return EntityId(
EntityType.DATASET,
DatasetLogicalID(name=full_... | cd93b34e43ef8f2b8cbda47ed1958430183fd57c | 3,623,847 |
def get_collection_table_name(node, intermine_model):
"""
Get the table name for this collection
:param node:
:param intermine_model:
:return: (table-name, reference-column-name).
table-name will be null if there isn't a collection table for this node
"""
if 'reverse-reference' in ... | 06aff0058450263131ad27afbe136dd63eaf4320 | 3,623,849 |
import time
import gzip
def parse_xml(path: str) -> Element:
"""Parse an XML file from a path to a GZIP file."""
t = time.time()
log.info('parsing xml from %s', path)
with gzip.open(path) as xml_file:
tree = ET.parse(xml_file)
log.info('parsed xml in %.2f seconds', time.time() - t)
re... | 526908cc950339c5471c53613a096b3fb615a51e | 3,623,850 |
import string
def apply_qos():
"""POST QOS configuration from form data"""
find_int_num = [i for i in request.form.get("interface") if i not in string.ascii_letters]
find_int_type = [i for i in request.form.get("interface") if i in string.ascii_letters]
build_config = BuildConfig.build_interface_qos(... | c426b0c3bcd9ec75306475c43600cfff1f5da7ad | 3,623,851 |
def _readdir(DIR):
"""Implementation of perl readdir in scalar context"""
try:
result = (DIR[0])[DIR[1]]
DIR[1] += 1
return result
except IndexError:
return None | 0ebb237de9ea32fd11f7c6fc6e8a365420c65655 | 3,623,852 |
def compute_normalization(data):
"""
Write a function to take in a dataset and compute the means, and stds.
Return 6 elements: mean of s_t, std of s_t, mean of (s_t+1 - s_t), std of (s_t+1 - s_t), mean of actions, std of actions
"""
l = []
for a in [data['observations'], (data['next_observatio... | 521995f2611f66dc11bbe5cc3d3d40e04c97ff19 | 3,623,854 |
def convert_cidr_to_canonical_format(value):
"""CIDR is validated and converted to canonical format.
:param value: The CIDR which needs to be checked.
:returns: - 'value' if 'value' is CIDR with IPv4 address,
- CIDR with canonical IPv6 address if 'value' is IPv6 CIDR.
:raises: InvalidInpu... | a105c1eece32c0dd615fbe1e0082be95ade6666a | 3,623,856 |
def g_xyz_eclip_planet_eqxdate(name, jde):
"""
Parameters
----------
name : str
Name of the planet
jde : float
Julian Day of the ephemeris
Returns
-------
np.array[3]
"""
h_xyz_eclipt_earth = h_xyz_eclip_eqxdate("Earth",jde)
h_xyz_eclipt_planet = h_... | 9d8f29ca4af12cb369d89cc71e0428f571393d3d | 3,623,857 |
def get_cannot_db(state_brief_db):
"""
Determine for each position register (identified by acceptance_id) the set of
position registers. The condition for this is given at the entrance of this file.
RETURNS:
map:
acceptance_id --> list of pattern_ids that it cannot be c... | c889ef602ff7caff59974cbb2aa5cf47dafa7581 | 3,623,858 |
def _unique_numpy_dtype_string(dtype):
"""Private function providing a standardized string used to characterize
a Numpy dtype
"""
dt = np.dtype(dtype)
try:
s = dt[0].str
except KeyError:
s = dt.str
return s[1:] | 9764a029da8a947b0e04da11e822df00a6d7be21 | 3,623,859 |
from re import S
def powsimp(expr, deep=False):
"""
Usage
=====
powsimp(expr, deep) -> reduces expression by combining powers with
similar bases and exponents.
Notes
=====
If deep is True then powsimp() will also simplify arguments of
functions. By default deep... | 55b3a28e7fbeca72ad07d588151a29fa3a4cc4ed | 3,623,860 |
def dictize(aniter, mode, initial=None):
"""iter must contain (key,value) pairs. mode is a string, one of: replace, keep,
tally, sum, append, or a custom function that takes two arguments.
replace: default dict behavior. New value overwrites old if key exists. This
is essentially a pass-thru.
... | c56a2ad83ec9a45e87caa7def33c6b51f63655cb | 3,623,861 |
def flatten_composition(EXX):
"""Convert the ternary composition of B2 into
a binary by taking out the vacancy composition
degree of freedom.
:EXX: ndarray (E, xa, xb)
:returns: ndarray (E,x)
"""
E=EXX[:,0]
a=EXX[:,1]
b=EXX[:,2]
xNi=1+a-b
xAl=1-a
xVa=b
x=xNi/(xNi+... | 3f2c2340214bfb36c97ee5e78189202fef12d921 | 3,623,862 |
def param_Valide_Algo_3(Lambda) :
"""Il faut que lambda soit valide"""
return valide_Lambda(Lambda) | 8b93ec29b1bdc0c9e6e55b6c640e7a0ed6e3e760 | 3,623,863 |
def extract_from(treatment):
"""Extract the data from the genus treatment.
Parameters:
treatment - a pdf file name of the genus treatment.
data_type - "locations" or "classifiers"
Returns a dict of results with the following format
"locations" - a string of species names and locat... | fdae872f08757b990b3bd13cf3ab298b11caa9cf | 3,623,864 |
from typing import Dict
from typing import Any
from typing import List
def make_snapshots_of_each_scope_vars(
*, locals_: Dict[str, Any], globals_: Dict[str, Any]) -> str:
"""
Make snapshots of each scope's variables.
Parameters
----------
locals_ : dict
Local scope's variables.
... | 13747146eb30ffaf8629d30e8033adcd25ec9fe2 | 3,623,865 |
def get_registered_themes():
"""Get registered themes.
Gets a list of registered themes in form of tuple (plugin name, plugin
description). If not yet auto-discovered, auto-discovers them.
:return list:
"""
return get_registered_plugins(theme_registry) | 582dc847c1e7a1118669ee1fe991ac2546201f23 | 3,623,866 |
def polyfit2d(pmap):
"""
Fit a 2nd order polynomial surface (paraboloid) to the map of Pearson's correlation
coefficients (pmap) and return a list (a) containing the fit parameters.
Model: C(i, j) = a0*i*i + a1*j*j + a2*i*j + a3*i + a4*j + a5
where (i, j) are the rows and columns in pmap.
:pa... | dbf8416537219c8ad64b0dbb4909a07982d765db | 3,623,867 |
def apriori_zc(data_set, data_set_dict, min_support=5):
"""
Apriori算法过程
:param data_set: 数据集
:param min_support: 最小支持度，默认值 0.5
:return:
"""
c1 = init_c1(data_set_dict, min_support)
data = map(set, data_set) # 将dataSet集合化，以满足scanD的格式要求
freq_items = {}
l1 = scan_data(data, c1, min... | 5396c045e3e80bd3b2a5ca78b0cf4cca18c622b7 | 3,623,868 |
def find_replace_line_endings(fp_readlines):
"""
special find and replace function
to clean up line endings in the file
from end or starttag characters
"""
clean = []
for line in fp_readlines:
if line.endswith("=<\n"):
line = line.replace("<\n", "lt\n")
clean.appe... | 1fa3703b6d244d6b51c17c95a8cd71e48d5ebc9d | 3,623,869 |
def process_properties(partition, vfunction, params):
"""
Process the properties specified in the 'properties' module parameter,
and return two dictionaries (create_props, update_props) that contain
the properties that can be created, and the properties that can be updated,
respectively. If the reso... | 0bb17cca4f742fb909987d948145ce1bfb52285e | 3,623,870 |
def resize_and_project(features,
resize_size,
num_projection_layers,
num_projection_channels):
"""Resizes input features and passes them through a projection head.
Args:
features: A [batch_size, height, width, num_channels] tensor
resize_... | 1fcd54728f4ffc582d9863fe549ce21570b5e245 | 3,623,871 |
import requests
def _download_file_from_google_drive(id_file, destination, proxy=None):
"""
From https://stackoverflow.com/a/39225272/8195528.
"""
def get_confirm_token(response):
for key, value in response.cookies.items():
if key.startswith('download_warning'):
re... | 95647eeaa58cef7367adbae9015badde0aeeb4c6 | 3,623,872 |
from typing import Optional
def generate_text_consumer(filter_pattern: Optional[str]) -> ObservabilityEventConsumer:
"""
Creates a console event consumer, which is used to display events in the user's console
Parameters
----------
filter_pattern : str
Filter pattern is used to display cer... | 3c5d10b95b05bd2b61ad3b7be7381a16c3a9ee1b | 3,623,873 |
def verify_lacp_link_state(device,
interface,
links,
state_name,
expected_state,
max_time=30,
check_interval=10):
""" Verify links of lag interface
... | 900aee30781374e1ce61e38af6fb6c3393eec08a | 3,623,874 |
def wfc3_bandpass(request):
"""Fixture to read in the pysynphot bandpass for a WFC3 filter"""
return nebulio.Bandpass(','.join(['wfc3', 'uvis1', request.param])) | 62b461a5ffc9e063b43caec9942e8c6bcb1513dd | 3,623,875 |
def exists(profile, name):
"""Check if a role exists.
Args:
profile
A profile to connect to AWS with.
name
The name of a role.
Returns:
True if it exists, False if it doesn't.
"""
result = fetch_by_name(profile, name)
return len(result) > 0 | bebf4cd514e9cbb2896b439c245c75b9b2c1f019 | 3,623,877 |
def _right_h5(value: list, fmt: str, meta: dict) -> dict:
"""Right-aligned header 5."""
return Plain([RawInline(fmt, '<h5 style="text-align:right !important">')]
+ value + [RawInline(fmt, '</h5>')]) | 811c89673d88cc090861e23551481a85f9f5bbd7 | 3,623,878 |
from typing import List
def find_long_period(bool_array: List, min_duration:int, scale:int) -> List:
"""find_long_period. identify long period of motion
:param bool_array: bool array with check of motion
:type bool_array: List
:param min_duration: minimum duration in index unit
:type min_duration... | ab30167289045cc4ed7ee06acec669f933a1461f | 3,623,879 |
import torch
def nnc_compile(model: torch.nn.Module, example_inputs) -> torch.nn.Module:
"""
nnc_compile(model, example_inputs) returns a function with the same args
as `model.forward`, with an extra argument corresponding to where the
output is stored. This function takes the inputs (which must be Py... | 15c686ec6a2b850ca0d2ef50e0e90a15392bb289 | 3,623,880 |
def _DeleteGridCellMetaData(zoom, x, y, uss_id):
"""Removes the USS entry in the metadata stored in a specific GridCell.
Removes the USS entry in the metadata using optimistic locking behavior.
Args:
zoom: zoom level in slippy tile format
x: x tile number in slippy tile format
y: y tile number in sl... | c106116efe48ffdeb6e9093c04bb5fe1a5fc81b6 | 3,623,881 |
def compute_speed(pos, pos_tt):
"""Compute boolean of whether the speed of the animal was above a threshold
for each time point
Parameters
----------
pos: np.ndarray(dtype=float)
in meters
pos_tt: np.ndarray(dtype=float)
in seconds
smooth_param: float, optional
Returns
... | f3176e459108127f79790bb67057cd316b3092c6 | 3,623,882 |
def unitsapi_Check(*args):
"""
* Checks the coherence between the quantity <aQuantity> and the unit <aUnits> in the current system and //! returns False when it's WRONG.
:param aQuantity:
:type aQuantity: char *
:param aUnit:
:type aUnit: char *
:rtype: bool
"""
return _UnitsAPI.unit... | c5f5d7c1730ed7953cbda2f758186e3e39877c41 | 3,623,883 |
def read_txt_file(file_path, n_num=-1, code_type='utf-8'):
"""
read .txt files, get all text or the previous n_num lines
:param file_path: string, the path of this file
:param n_num: int, denote the row number decided by \n, but -1 means all text
:param code_type: string, the code of this file
... | 9c55d370d8e8610965e0f8c4b1bed85e6adcdc5b | 3,623,884 |
import json
import re
def load_cluster(args):
"""
Load a single CourtListener cluster with its opinions from disk, and return metadata.
This is called within a process pool; see ingest_courtlistener for how it's used.
"""
cluster_member, opinions_dir = args
with cluster_member.open() a... | 9d5335d72172ae933f9d2e94a8bb5a3f316fc5b9 | 3,623,885 |
import re
import logging
def ParseSuccessMsg(msg):
"""Attempt to parse the message for a user_op_manager SUCCESS line and extract user, device, op, class, and method.
Return None otherwise.
"""
parsed = re.match(kSuccessMsgRe, msg)
if not parsed:
return None
try:
user, device, op, class_name, meth... | 2e7edccb1a8d15e16aadfaa3fcf4465d755b46c8 | 3,623,887 |
import math
def pow(x, y):
"""Return the logarithm of x with base y (default to e)"""
if x <= 0 and not isinstance(y, int):
raise ValueError(f"Exponent must be an integer if negative base. Received base {x} with exponent {y}.")
return math.pow(x, y) | 6ceb9957be3805c44db7c6b00e170db7ce27354f | 3,623,888 |
def GetWavelength():
""" Get a single frequency reading """
return getwave(DZERO) | 4b60655932762ae5e8157f2d7df12691e8162eed | 3,623,889 |
def sum_while_same(xs, x):
"""Sum points for same date representation"""
if not xs:
return [x]
if xs[-1][0] == x[0]:
return xs[:-1] + [(xs[-1][0], xs[-1][1] + x[1])]
else:
return xs + [x] | 646fc1873b582b3d9825cc69816e97de6f91bf3b | 3,623,890 |
def download_artifact_from_aml_uri(uri: str, destination: str, datastore_operation: DatastoreOperations):
"""Downloads artifact pointed to by URI of the form `azureml://...` to destination
:param str uri: AzureML uri of artifact to download
:param str destination: Path to download artifact to
:param Da... | 5a59f74ed03b348960905c5f579ffb87a4edd076 | 3,623,891 |
import torch
def l1_loss(pred_traj, pred_traj_gt):
"""
Input:
:param pred_traj: Tensor of shape (batch, seq_len)/(batch, seq_len). Predicted trajectory along one dimension.
:param pred_traj_gt: Tensor of shape (batch, seq_len)/(batch, seq_len).
Groud truth predictions along on... | 3fb4dd2b7fc85e8f32610065078aa3dc98d728d5 | 3,623,893 |
import calendar
def calculate_summary_statistics(processed_fx_obs, categories):
"""
Calculate summary statistics for the processed data using the provided
categories and all metrics defined in :py:mod:`.summary`.
Parameters
----------
proc_fx_obs : datamodel.ProcessedForecastObservation
c... | 2871a0655e3a6b3a3df1ec137d536e3863299e72 | 3,623,894 |
import time
def splitting_division_semi(f, group, table, sample_indices, splitting_fold):
"""Saving indices of each splitting group to a list that will be fed later to the deep learning model.
Specific for the semi_resampling strategy, since there only training and validation need to be splitted."""
t0 = ... | 5179caf3ec3205fc56f0e1c6b77687cd15842556 | 3,623,895 |
from typing import Tuple
from typing import Any
def extract(keys: Tuple[str, ...], state: State) -> Tuple[Any, ...]:
"""Extract multiple values from dictionary.
Args:
keys: Tuple of key whose values should be extracted from the dictionary.
state: The dictionary where values need to be extract... | 917ba523594d10a7c4af40d7f15af6815e0a2e1b | 3,623,896 |
def distorted_bounding_box_crop(image,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100):
"""Generate... | e788245b874c472368cba27389d15bfcf5ea0c88 | 3,623,897 |
def constructBoard(numCards=52):
""""Create a board out of a shuffled deck of numCards
numCards(default 52): number of cards in the board (even #, 8-52)"""
deck = pd.Deck()
## Split the deck using the initial set of cards if numCards < 52
if numCards < 52:
deck = splitDeck(deck, numCards)
... | 9e8a26685ec01c1b35f269b50aa417bb1f08255e | 3,623,898 |
def get_short_description(dict, value):
"""Get layout class based on value."""
return dict.get(value, {}).get('short_description') | f5885f10ce009db925d552f8a0b0f40ab1ccaa2a | 3,623,900 |
def cdlconcealbabyswallow(
client,
symbol,
timeframe="6m",
opencol="open",
highcol="high",
lowcol="low",
closecol="close",
):
"""This will return a dataframe of conceal baby swallow for the given symbol across
the given timeframe
Args:
client (pyEX.Client): Client
... | 04cea2169f0fc1dc9e173fea66d5c341aac4224c | 3,623,901 |
def rectangular_hollow_section(b: float, d: float, t: float, r_out: float, n_r: int, material: pre.Material = pre.DEFAULT_MATERIAL) -> Geometry:
"""Constructs a rectangular hollow section (RHS) centered at *(b/2, d/2)*, with depth *d*, width *b*,
thickness *t* and outer radius *r_out*, using *n_r* points to con... | a0639d8674485ba31e2e7c7b4df1c78e5980dcc8 | 3,623,902 |
def _is_subexpansion_optional(query_metadata, parent_location, child_location):
"""Return True if child_location is the root of an optional subexpansion."""
child_optional_depth = query_metadata.get_location_info(child_location).optional_scopes_depth
parent_optional_depth = query_metadata.get_location_info(... | 29391226258e75d434e07c291fe9590c1810d85b | 3,623,903 |
import uuid
def get_uuid(key, value, is_list=False, is_optional=False, default=None, options=None):
"""
Get the value corresponding to the key and converts it to `uuid`/`list(uuid)`.
Args:
key: the dict key.
value: the value to parse.
is_list: If this is one element or a list of e... | 8325f288f580ed0b47fefc144cb16f71d4b1e742 | 3,623,904 |
import torch
from typing import Sequence
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`, :class:`int` and :class:`float`.
"""
if isinstance(data, torch.Tensor):
r... | 3d9aa7cb8424e6ee07412449ee890ce174cbc5b8 | 3,623,905 |
def _filter_labels(text, labels, allowed_labels):
"""Keep examples with approved labels.
:param text: list of text inputs.
:param labels: list of corresponding labels.
:param allowed_labels: list of approved label values.
:return: (final_text, final_labels). Filtered version of text and labels
... | e17ed7659acbdadc71a6b3f5b522af1e34d40370 | 3,623,906 |
def _Itype():
"""Loop iterator data type."""
return tf.int32 if use_xla() else tf.int64 | 484c51f53226f7ebb0cf9738c8b16ad99438289b | 3,623,907 |
import math
def Q(fastev,lagev,fastrc,lagrc):
"""Following Wuestefeld et al. 2010"""
omega = math.fabs((fastev - fastrc + 3645)%90 - 45) / 45
delta = lagrc / lagev
dnull = math.sqrt(delta**2 + (omega-1)**2) * math.sqrt(2)
dgood = math.sqrt((delta-1)**2 + omega**2) * math.sqrt(2)
if dnull < dgo... | fe975fb234297a6f25100bb85aa6639590a010d8 | 3,623,908 |
def w_median(a, weights):
"""
Compute the weighted median of a 1D numpy array.
Parameters
----------
a : ndarray
Input array (one dimension).
weights : ndarray
Array with the weights of the same size of `data`.
Returns
-------
median : float
The output value.
... | f259dd036777380e50f9030d728c1d80c1f5ebaa | 3,623,909 |
def from_trends_top_query_by_category(n=NUM_KEYWORDS):
"""
Get a set of keyword objects by querying Google Trends
Each keyword obj is a dict with keys: keyword, category
"""
keyword_objs = []
for cid in POPULAR_CATEGORIES:
yearmonth = '2016'
pytrends = TrendReq(hl='en-US', t... | bf2582d60cf1f720b9f4b88838e9a22852f1f36f | 3,623,911 |
from typing import List
def list_vpc_cidrs(vpc_id: str, account_id: str, region: str) -> List[str]:
"""
Returns a list of vpc cidrs associated with a given vpc.
Example use cases:
1. Get the CIDRs to install on other side of a peering.
2. See if there are any common CIDRs between two VPCs
:pa... | bed0b41ff5d86fa87a89ee2aa1770871545e745e | 3,623,913 |
def _multi_dot(arrays, order, i, j, precision):
"""Actually do the multiplication with the given order."""
if i == j:
return arrays[i]
else:
return np.dot(_multi_dot(arrays, order, i, order[i, j], precision),
_multi_dot(arrays, order, order[i, j] + 1, j, precision),
... | fa3e01e611d2897387452af6afb785087a0e46eb | 3,623,915 |
import time
def creation_date_demographics(route, label):
"""Return tweet creation dates."""
dataset = {'hateval': Tweet.objects.filter(hateval=True),
'offenseval': Tweet.objects.filter(offenseval=True),
'all': Tweet.objects.all()}
db = dataset.get(route)
if label == 'ab... | 176f48ddf37b35d4acfd82e48e00e1be3b657072 | 3,623,916 |
def array_check(lst):
"""
Function to check whether 1,2,3 exists in given array
"""
for i in range(len(lst)-2):
if lst[i] == 1 and lst[i+1] == 2 and lst[i+2] == 3:
return True
return False | ef60b52a9d7300fa458b503f49996aec0f0831ad | 3,623,917 |
def fastmri_unet_transform_multicoil(
kspace=None, mask=None, ground_truth=None, attrs=None, fname=None, slice_id=None
):
"""Transform to use as input to fastMRI's Unet model for multicoil data.
This is an adapted version of the code found in
`fastMRI <https://github.com/facebookresearch/fastMRI/blob/m... | c420979fc7b329972abf62b5a13996d98d2f1038 | 3,623,918 |
def stylize_cartoon(image, blur_ksize=3, segmentation_size=1.0,
saturation=2.0, edge_prevalence=1.0,
suppress_edges=True,
from_colorspace=colorlib.CSPACE_RGB):
"""Convert the style of an image to a more cartoonish one.
This function was primarily desi... | 25db74f3ffb1548ccdc0f3af98c7ea0375150c94 | 3,623,919 |
def tca_model (image: Image.Image, order: int=2) -> ndarray:
"""
Compute a lens model which corrects transverse chromatic aberration.
Parameters:
image (PIL.Image): Input image.
order (int): Polynomial order of lens model. Quadratic or cubic model is ideal.
Returns:
ndarray: Re... | 2973e69d2896a918803bdaea3e2993048ecbcf2f | 3,623,920 |
def _group_result_from_fields(json, fields):
"""Helper that creates a group response object from the given fields.
:param json: original JSON string
:param fields: the JSON fields
:return: the created group response
:rtype: GroupResult
"""
result = api.GroupResult()
result.child_group... | a3ca4c71a4231b9a537d59c04a0c471db8785c83 | 3,623,921 |
def debounce(wait):
""" Decorator that will postpone a function's
execution until after `wait` seconds
have elapsed since the last time it was invoked. """
def decorator(fn):
timer = None
def debounced(*args, **kwargs):
nonlocal timer
def call_it():
... | 270dc56653afde86c7874ad06a18e51ef195a80c | 3,623,922 |
from typing import List
from typing import Any
def list_difference(list_1: List[Any], list_2: List[Any]) -> List[Any]:
""" This Function that takes two lists as parameters
and returns a new list with the values that are in l1, but NOT in l2"""
differ_list = [values for values in list_1 if values no... | 3831d799bb40080b828ee90e2929f77ff8aeb7ba | 3,623,923 |
def rlsp(
_run,
mdp,
s_current,
p_0,
horizon,
temp=1,
epochs=1,
learning_rate=0.2,
r_prior=None,
r_vec=None,
threshold=1e-3,
check_grad_flag=False,
solver="value_iter",
reset_solver=False,
solver_iterations=1000,
):
"""The RLSP algorithm."""
check_in("... | 12449a66b430f3ad6d869184a9e954e520066232 | 3,623,925 |
def load_vsmi() -> pd.DataFrame:
"""
"""
vsmi = pd.read_csv("../statistics/h_vsmi_30.csv", sep=";")
vsmi.columns = vsmi.columns.str.lower()
vsmi.rename(columns={"indexvalue": "VSMI"}, inplace=True)
vsmi["date"] = pd.to_datetime(vsmi["date"], format="%d.%m.%Y")
vsmi.set_index("date", inplace=... | ccd3ff8d0c728108b1556860e98c1723819cd778 | 3,623,926 |
def create_app(context: GhiaContext = None) -> Flask:
"""
Create the Flask app.
Args:
context (GhiaContext, optional): If no context is provided a new default one is automatically created. Defaults to None.
Returns:
Flask: Newly created Flask application.
"""
return ghia_web_logic.create_app(context=cont... | 7df41acbc2babf6f25debac1a035a74b95fdd606 | 3,623,927 |
def shift_until_PSD(M, tol):
""" Add the identity until a p x p matrix M has eigenvalues of at least tol"""
p = M.shape[0]
mineig = np.linalg.eigh(M)[0].min()
if mineig < tol:
M += (tol - mineig) * np.eye(p)
return M | de2cf1060a32a487c5f6c3024ce294803f60b6da | 3,623,928 |
def iris_to_df(iris):
""" Make dataframe for multiclass classification from iris data"""
X, y = iris.data, iris.target
iris_column_data = X.T.tolist()
iris_column_names = ["col" + str(idx) for idx in range(X.shape[1])]
data = {}
for ind, iris_data_column in enumerate(iris_column_data):
d... | f158502b90a9a1afa8ac2e48b5446f42d18ca9f0 | 3,623,929 |
def format_feedback(feedback_row, study):
"""Updates the feedback dict with the new information."""
formatted_feedback_row = {
"success": {
study.get_single_field(field["field_id"]).field_name: field["field_value"]
for field in feedback_row["success"]
},
"failed":... | 3d6d52d6a5340b13e81ca19ee28bff28b189cbc1 | 3,623,930 |
def build_geometry(self):
"""Compute the curve (Line) needed to plot the object.
The ending point of a curve is the starting point of the next curve
in the list
Parameters
----------
self : SlotW11
A SlotW11 object
Returns
-------
curve_list: list
A list of 7 Segmen... | c27bdfe1b1d80e4a1d9c664244cfc420b14c1742 | 3,623,931 |
def validcolor(c):
"""Takes a color and makes it valid by clamping each value between 0 and 255"""
try:
ret = [clamp(int(v+0.5), 0, 255) for v in c]
return type(c)(ret)
except TypeError:
return clamp(int(v+0.5), 0, 255) | 9a3b8e869c009c7c5c71ef8a4c9b1c0692abc856 | 3,623,932 |
def give_me_the_record(primary_id, swissprot_file):
"""
Return a single record given with the primary id
:param primary_id: A primary id
:param swissprot_file: A swissprot file
:return: A record with accession == primary id
"""
with open(swissprot_file, 'r') as fh:
for record in Swis... | 9272ae10c3fb6faa2abec4638a8edb36a9bd7985 | 3,623,933 |
import requests
def ms_graph_users(licensed=False):
"""Query the Microsoft Graph REST API for on-premise user accounts in our tenancy.
Passing ``licensed=True`` will return only those users having >0 licenses assigned.
"""
token = ms_graph_client_token()
headers = {
"Authorization": "Beare... | 976ebf632e1dd6bb7eee6acc7379617e2eac082a | 3,623,935 |
def protect_def_name(defName):
"""Convert a DEF name to be supported in Webots."""
protectedDefName = clean_string(defName)
if len(protectedDefName) > 0 and protectedDefName[0].isdigit():
protectedDefName = "_" + protectedDefName
return protectedDefName | 838d66ec3d0e88bff99d63f933f079df635777fb | 3,623,936 |
from typing import Tuple
def get_blog(id: str) -> Tuple:
"""
Function used to fetch particular blog or
return error if it is doesn't exist.
:param id: blog id
:return: tuple of (blog object or any error)
"""
blog, error = _get_blog_obj(id)
if not error:
blog = [blog_schema.dum... | 73bd9d679d2dd50a37ff6bd46fd31be677e3f5cc | 3,623,937 |
def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
"""Calculate cross entropy loss while ignoring padding.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch_size, length_labels]
smoothing: Label smoothing constant, used to det... | 826a53fd6af931b33b7b7ac773310d2143db8597 | 3,623,938 |
from typing import List
def get_neighboring_connectivity(cm: np.ndarray) -> List[float]:
"""
Get how strong neighboring classes are connected.
Parameters
----------
cm : np.ndarray
Returns
-------
con : List[float]
"""
con = []
n = len(cm)
for i in range(n - 1):
... | ba92b09ed1845eb5545d353df59f52f8032fab13 | 3,623,940 |
def autocov(x):
""" Calculate the auto-covariance of a signal.
This assumes that the signal is wide-sense stationary
Parameters
----------
x: 1-d float array
The signal
Returns
-------
nXn array (where n is x.shape[0]) with the autocovariance matrix of the
signal x
Not... | 6f4f1855b9e0b4f237fbcaecdd911f51cf9c8707 | 3,623,941 |
def correlation(self, column_a, column_b):
"""
Calculate correlation for two columns of current frame.
Parameters
----------
:param column_a: (str) The name of the column from which to compute the correlation.
:param column_b: (str) The name of the column from which to compute the correlation.... | b8f1600e0b2968ca4013418b2fbfda0b13f5911a | 3,623,943 |
def get_ax(rows=1, cols=1, size=8):
"""Return a Matplotlib Axes array to be used in
all visualizations in the notebook. Provide a
central point to control graph sizes.
Change the default size attribute to control the size
of rendered images
"""
_, ax = plt.subplots(rows, cols, figsize=(size... | c5d16b65dc5e505143062c49847b54b1e84717e6 | 3,623,945 |
def multiply(a, b, out=None, increment=False, stream=None):
"""Element-wise product of `a` and `b`."""
dtype = a.dtype
if out is None:
out = gpuarray.zeros(a.shape, dtype=dtype)
assert a.size == b.size
assert a.dtype == b.dtype == out.dtype
block = (min(a._block[0], a.size), 1, 1)
... | 0886834e18d6c6ba7abab5e41943d752d9c8fd6c | 3,623,947 |
import numpy as np
def uv2spd_dir(u,v):
"""
converts u, v meteorological wind components to speed/direction
where u is velocity from N and v is velocity from E (90 deg)
usage spd, dir = uv2spd_dir(u, v)
"""
spd = np.zeros_like(u)
dir = np.zeros_like(u)
spd = np.sqrt(u**2 + v**2)
... | 21525616f97974ba7463fe137f07ef7e7a728fe1 | 3,623,948 |
def adjacency_mat(x_all, y_all, ox, oy, rr):
"""
Function that creates the adjacency matrix from the edges and points
with the no restriction method
"""
n = len(x_all)
A = np.zeros((n, n))
road_map = []
for i in range(n):
temp = []
for j in range(n):
if i ==... | f66af74b37d8d2385720e71be31cef3446155afd | 3,623,949 |
from typing import Counter
def calcEOAutomorphisms(tree) :
"""
Computes the size of the automorphism group of the input :py:obj:`tree`.
We think of :py:obj:`tree` as a rooted tree, whose vertices are decorated by degrees and which has additional "exterior" edges of two distinct types, corresponding to the bounda... | c10d579df582444339c0d0fe5859f37ed1bad4bd | 3,623,951 |
def cisco_ios_simple_config():
"""Creares raw cisco config of comments etc."""
with open(
CISCO_IOS_SIMPLE_CONFIG_PATH, mode="r", errors="ignore", encoding="ascii"
) as config_file:
raw_config = config_file.readlines()
config = []
for line in raw_config:
line = line.rstrip()
... | 48e7177a99b5b2b2f196a9fff6f8e175f37dd389 | 3,623,952 |
def RRIMAPublicDashboard(request,id=0):
"""
:param request:
:param id:
:return:
"""
## retrieve program
model = Program
program_id = id
getProgram = Program.objects.all().filter(id=program_id)
## retrieve the coutries the user has data access for
countries = getCountry(requ... | c5c9273d5017397eecdd99ee78bf318d45440f17 | 3,623,953 |
def get_workflows_requests(module):
"""Returns all requests for specified workflow"""
return Request.objects.filter(module_ref=module) | 6e634ded8f7c0b1dc147d276a8d9749205e9d991 | 3,623,954 |
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