content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import List
def readOneLineFileWithCommas(filepath: str) -> List[str]:
"""
Reads a file that is one line long, separated by commas
"""
try:
with open(filepath) as fp:
s: str = fp.readline()
return s.split(",")
except:
raise Exception(f"Failed to open {filepath}... | 4c181523192fab0ea01ae5da0883c543565119c6 | 23,400 |
from operator import or_
def package_search(filters, context, limit=None, catalog=False):
"""Search packages with different filters
Catalog param controls the base query creation. Catalog queries
only search packages a user can deploy. Non-catalog queries searches
packages a user can edit.
... | 0d15d2936f713437e3d9dad794cd07faf1ca3090 | 23,401 |
def is_valid(listener_tuple):
"""
There are a few rules that aws has when creating listeners,
this function ensures those rules are met before we try and create
or update a listener.
While these could be caught with boto exception handling, I would
rather be nice and catch these early before we... | d95db075e302753a373ed4e0efd7a3667dc2ecf3 | 23,402 |
def _jitter_boxes(gt_boxes, jitter=0.05):
"""
"""
jittered_boxes = gt_boxes.copy()
ws = jittered_boxes[:, 2] - jittered_boxes[:, 0] + 1.0
hs = jittered_boxes[:, 3] - jittered_boxes[:, 1] + 1.0
width_offset = (np.random.rand(jittered_boxes.shape[0]) - 0.5) * jitter * ws
height_offset = (np.ra... | 570fa7a6bd2f898ce1d64dd9f6e666e50251fcf5 | 23,403 |
import os
def last_model_path(exp_name):
"""
get path of the last model in the exp
"""
model_path = os.path.join(constants.ET_LOGS, exp_name, "latest.pth")
assert os.path.islink(model_path)
return model_path | 8948eb304047540fc988a55a9682c841f356eb72 | 23,404 |
def lcm_gcd(a, b):
"""Finds the least common multiple of two integers
Args:
a, b: integers greater than or equal to 1
"""
return a * b//greatest_common_divisor(a, b) | 3b23d04164c8e69eee26e48ab2b1a60e8e99fd14 | 23,405 |
def test_ahocorasick_rs_overlapping(benchmark, test_data):
"""ahocorasick_rs overlapping matches."""
patterns, haystacks = test_data
ac = ahocorasick_rs.AhoCorasick(patterns)
def run():
for haystack in haystacks:
x = ac.find_matches_as_strings(haystack, overlapping=True)
ret... | 3c53369e8006502a5071fb73a75ace4705421a84 | 23,406 |
import warnings
def merge_frames(frames):
"""
Merge the multiple data files downloaded from the M2M system or the Gold
Copy THREDDS server into a single xarray data set. Keep track of how many
files fail to merge.
:param frames: The data frames to concatenate/merge into a single data set
:ret... | b8b083d8f0e9360df325fbeb812b64fffc8d1d0f | 23,407 |
import re
def sorted_nicely(l):
""" This function sorts the given iterable in the way that is expected
Obtained from:
https://arcpy.wordpress.com/2012/05/11/sorting-alphanumeric-strings-in-python/
:param l: The iterable to be sorted
:return: Sorted iterable
"""
convert =... | c2e398e7a654a1a1ec7cc113fcad500beefd876a | 23,408 |
def run_board(effects: list, audio: np.array, sample_rate: float) -> np.array:
"""Run board on input audio data.
Args:
board (list): List of Pedalboard effects.
audio (np.array): Input audio data.
Returns:
Output (effected) audio data
"""
board = Pedalboard(effects, sample_... | 062f7d34aa7eadad5401e64df1e96857606cbcf6 | 23,409 |
def html_escape( s ):
"""
"""
s = s.replace( '&', '&' )
s = s.replace( '<', '<' )
s = s.replace( '>', '>' )
return s | eb47ba4d4651763cb74f081095b78d53ee9bebc1 | 23,410 |
def model_query(context, model, *args, **kwargs):
"""Query helper.
:param context: context to query under
:param session: if present, the session to use
"""
session = kwargs.get('session') or object_sqla.get_session()
query = session.query(model, *args)
return filter_by_project(context, q... | c6e5fb09b7e9a4d85ab6c6abc1e03e227010591f | 23,411 |
def bert_dropout_model(num_classes,
bert_config,
use_mc_dropout_mha=False,
use_mc_dropout_att=False,
use_mc_dropout_ffn=False,
use_mc_dropout_output=False,
channel_wise_dropout_mha=F... | 6ee1d09b2070e54ba631bd6e1b8e3e453960073a | 23,412 |
def calculate_monthly_sales(year: int, month: int, beer_style: str) -> int:
"""Calculates the sales of a particular type of beer in a given month.
param: month -- an int ranges from 1 to 12, beer_style;
return: total_sales
"""
total_sales = 0
for item in data:
if item[2].year =... | fa448a8e9dfb7186652a6dc3000d3a8465320994 | 23,413 |
def check_canopy_height(region_info, regional_lookup):
"""
Check the regional canopy height.
"""
mean_canopy_height = region_info['mean_canopy_height']
if mean_canopy_height == 'no data':
mean_canopy_height = 0
return mean_canopy_height | 5f04ad71df7f0b1c9ef73e97bbe99bea1916ae5e | 23,414 |
def annotated_var(prs):
"""
Parser for annotated variable in parentheses.
Annotation is parsed with prs.
Parser output is a var token
annotation is stored in attribute 'annotation' of var token.
Sample input to parser:
(x : A)
"""
def trt(acc):
v,ann = acc
... | 42acdf6eb09952701d17fab73a2ee8fc20c7dc5e | 23,415 |
def action_from_json(project, value):
"""return a action from the given json
"""
json_type = value.get('type')
for class_ in sftoolbox.engine.action_classes_register:
if json_type == class_.json_type:
return class_.from_json(project, value)
return DummyAction.from_json(project, ... | 69658b53e839c7d112b7509e3ecdf57a82de817a | 23,416 |
def get_springer_doi(node):
"""
:param node:
:return:
"""
for elem in find_key(node, 'occurrence'):
if isinstance(elem, list):
for sub_elem in elem:
if isinstance(sub_elem, dict):
values = sub_elem.values()
if len(values) =... | ca8773f10e6fed6b41064a5a5ad6717afd540bb5 | 23,417 |
def check_versions(versions=[]):
""" Check if there are version to build the changelog. """
if len(versions) == 0:
raise NotEnoughVersionsError()
return True | f9c7f81c02f08a867f27f329554ed85eddc34243 | 23,418 |
def create_fnet(widths, nfeat, nfeato, orthoinit, llbias):
""" Creates feature-generating network, a multi-layer perceptron.
Parameters:
widths: list of widths of hidden layers
nfeat, nfeato: # input and output channels of the convolution
orthoinit: whether to use orthogonal weight initialization
... | 3bdfdd89d77b6ba172e2ac85df191b11e78ab049 | 23,419 |
def pytorch_array_setitem(op):
"""Implementation of array_setitem for pytorch."""
def _impl(array, begin, end, strides, value):
idx = tuple(slice(b, e, s) for b, e, s in zip(begin, end, strides))
ret = array.clone()
ret[idx] = value
return (ret,)
return _impl, op.inputs[1:] | b0c6504b2c0d1971ec16e5fdf198b20a911d4946 | 23,420 |
def time_series_seasonal_test(x: pd.Series, expected_lags: list):
"""
通过自相关系数来获取不同lag的相关系数，通过相关系数来判断时序数据的周期值
PS：需要列出lag的值的列表
:param x: 时序数据x，type: Series
:param expected_lags: 可供选择的的滞后值
:return: 返回滞后值值的自相关性排序序列
"""
acf_scores = []
for lag in expected_lags:
acf_score = acf(x.v... | 5c0614b986eb8dfe576821245e80ef0244c70c69 | 23,421 |
def comment_like():
"""
- 1.判断用户是否登陆
- 2.获取参数
- 3.校验参数,为空校验
- 4.操作类型校验
- 5.根据评论编号取出,评论对象
- 6.判断评论对象是否存在
- 7.根据操作类型,点赞,取消点赞
- 8.返回响应
:return:
"""
# - 1.判断用户是否登陆
if not g.user:
return jsonify(errno=RET.NODATA, errmsg="用户未登录")
# - 2.获取参数
comment_id = req... | 09564653f3d843c7d82e16946507c8a081374ce6 | 23,422 |
import os
def open_fits(subject, field, wavelength, size='2x2'):
"""Opens a FITS image of a subject.
Can be used as a context handler.
subject: RGZ subject dict, from the ATLAS survey.
field: 'elais' or 'cdfs'
wavelength: 'ir' or 'radio'
size: Optional. '2x2' or '5x5'.
-> FITS image file... | ae101ca51d4a6687cec21d57749faf610850bbb5 | 23,423 |
def create_relationships(model_cls, data):
"""
Create the relationship dict of the specified model class with the data
:param model_cls:
:param data:
:return:
"""
relationships = model_cls.get_relationships()
relationship_map = {}
for key in relationships.keys():
relationship... | 6ed811b180141190cde5eaa20d4fca817647c970 | 23,424 |
import requests
def get_news_items_from_web(url):
"""
Calls the Athletics News RSS API, parses the resulting response and returns a list of parsed news_items to be
stored in DynamoDB
:param url: Url for the RSS API for UBCO Heat
:return: Parsed news items in a JSON formatted list
"""
try:... | aff75310b155475d185f15c5bbaadeda9902aae3 | 23,425 |
def get_node_model(manager, handle_id=None, node=None):
"""
:param manager: Context manager to handle transactions
:type manager: Neo4jDBSessionManager
:param handle_id: Nodes handle id
:type handle_id: str|unicode
:param node: Node object
:type node: neo4j.v1.types.Node
:return: Node mo... | a8c42b8e72b6ae96e897bd5c7f5a06b5820b4b56 | 23,426 |
from functools import reduce
def convert_hcp_plane(plane: list) -> np.ndarray:
"""
four index notion to three index notion for hcp and rhombohedral plane
Args:
plane (list): four index notion
Returns:
three index notion of plane
"""
u1 = plane[0]
v1 = plane[1]
w1 = p... | aa6d7527a55d8b14bd03b2f6660ed94c8cf760a8 | 23,427 |
from sentry.plugins import plugins
def should_process(data):
"""Quick check if processing is needed at all."""
for plugin in plugins.all(version=2):
processors = safe_execute(
plugin.get_event_preprocessors, data=data, _with_transaction=False
)
if processors:
r... | 8e6f013d54ac1e3a0b77f8969a3700c45efdc673 | 23,428 |
from typing import Tuple
from typing import List
import gzip
def load_fasta_file(input_file: str) -> Tuple[str, List]:
"""
Load a fasta file into a list of SeqRecords.
:param input_file: The path to the input fasta file.
:returns: A tuple of the sequence type ('protein' or 'dna'), and the list of Seq... | 8e62e7d7002d74da7a43315785f5ce663b5ba366 | 23,429 |
from typing import get_args
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT3 ...')
train_ds, valid_ds, test_ds = build_train_val... | 06f9532c6d60a3c3858dc08a43070b8aa4d19691 | 23,430 |
import requests
def get(username, start):
"""
Second level function to pull up to 50 reviews.
start - review number to start from
"""
r = requests.get(
'{}/user/beers/?start={}&&ba={}&order=dateD&view=R'.format(
BASE_URL, start, username
)
)
beers = []
pq = ... | 7aaccda46954b629bad37e0a77f834e5b3f40c27 | 23,431 |
def isInContinent(country_name: str, continent: str):
"""Permet de vérifier si le pays est dans un continent
Paramètres
----------
country_name : str
Le nom du pays
continent : str
Le code du continent (alpha2)
Retours
-------
is_in_continent : int
entier binair... | 5a78e181ace8574baa00eeadd21e7ecea8529f6c | 23,432 |
def encoder_decoder_archi(inputs, is_train):
"""
Input is assumed to be a 4-D Tensor, with [batch_size, phrase_len, 1, features]
"""
encoder_layers = []
encoded = inputs
encoder_layers.append(encoded)
for i in range(config.encoder_layers):
encoded = encoder_conv_block(encoded, i,... | 6b75ce8a31375173e01ccd7d33078c76aff6d2b8 | 23,433 |
def build_dict_conforming_to_schema(schema, **kwargs):
"""
Given a schema object (for example, TIMESTAMP_SCHEMA from this module) and
a set of keyword arguments, create a dictionary that conforms to the given
schema, using the keyword arguments to define the elements of the new dict.
Checks the result to mak... | 8971b7c6e1df8fd16a1b0e0946c9f21a3c601512 | 23,434 |
def drop_non_channels(overlaps_df, filename):
""" Return the overlap dataframe with all channels dropped
and index reset. Save the df as a csv with the filename
passed this function. """
df = overlaps_df
channels_df_dict = {}
for column in df.columns:
# For eac... | 0cfa7f1ec86328179612c46c6b5f4b787984a7fa | 23,435 |
import os
def evaluate_all_flights(model, train_flights_dict, val_flights_dict, trial_folder, n_extreme_flights=10):
"""
Arguments
model: trained tf model to make the predictions
train_flights_dict: a dictionary whose key is flight name and value is a tuple of (features,labels)
... | eafbd5d7276ef503a2d609d3593e50751971af6e | 23,436 |
def _REOM(y,t,pot,l2):
"""
NAME:
_REOM
PURPOSE:
implements the EOM, i.e., the right-hand side of the differential
equation
INPUT:
y - current phase-space position
t - current time
pot - (list of) Potential instance(s)
l2 - angular momentum squared
OU... | 427393c1eeb89214603dc8363a9b39084e9030d4 | 23,437 |
def optimize_inst(module, inst):
"""Simplify one instruction"""
for operand in inst.operands:
if isinstance(operand, ir.Id):
if operand.inst.op_name not in ir.CONSTANT_INSTRUCTIONS:
return inst
if inst.op_name == 'OpCompositeConstruct':
inst = optimize_OpComposit... | 1de61b914bdac4076be4ffb27823ad9384504814 | 23,438 |
def table_3_3(M, lambd_nos, lambd_cil):
"""
Функция для вывода Су для оживальной ГЧ
arguments: число Маха, относительное удлинение носка и цилиндрической части
return: Значение Су ГЧ
"""
cy1iz_alf_0 = [0.0350, 0.0350, 0.0350, 0.0350, 0.0362, 0.0375, 0.0380, 0.0378,
0.0374... | d0d4b2e1fa65f3e8ad2cd39bee1d0d4878293090 | 23,439 |
def ms_to_timestamp(ms):
"""Convert ms to 'HH:MM:SS,mmm'"""
# XXX throw on overflow/underflow?
if ms < 0: ms = 0
if ms > MAX_REPRESENTABLE_TIME: ms = MAX_REPRESENTABLE_TIME
h, m, s, ms = ms_to_times(ms)
return "%02d:%02d:%02d,%03d" % (h, m, s, ms) | 514773d94f4e3b78594bed4f232f34bcd2956f4d | 23,440 |
import torch
def _lovasz_softmax_flat(y_pred, y_true, classes="present"):
"""
Multi-class Lovasz-Softmax loss
y_pred: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
y_true: [P] Tensor, ground truth y_true (between 0 and C - 1)
classes: 'all' for all, 'present' for ... | 9cdbab2873e198750079e560a559b1f4eb8f256c | 23,441 |
def quantum_state_encoding_circuit(bits):
"""根据`bits`构建并返回量子态编码线路."""
circuit = cirq.Circuit()
circuit.append(cirq.H.on_each(bits))
return circuit | 75734a349187af7ac32683d5faf6aec331f25713 | 23,442 |
from datetime import datetime
def parse_mov_date(date_str):
"""converts string to date"""
try:
return datetime.datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S%z")
except (TypeError, ValueError):
pass
return None | 6d4f1ad566f3e3914eeed7f9c29d914f1ced96df | 23,443 |
def get_settable_attr(attr):
"""
If attr is not settable, navigate upp in the connection hierarchy until we find the settable attribute.
For example, in RigSqueeze, the ikFk state attribute will be redirected to the root ctrl.
Note that in some case the attribute might have been piped in an utility node... | aca71e6e7f9e1312beaf1c4dcba897073ae3b3ea | 23,444 |
def adds(repo, subset, x):
"""Changesets that add a file matching pattern.
The pattern without explicit kind like ``glob:`` is expected to be
relative to the current directory and match against a file or a
directory.
"""
# i18n: "adds" is a keyword
pat = getstring(x, _(b"adds requires a pat... | 6d9d1879c77f64bb68d43483cc2d3095328fd26f | 23,445 |
def data_context_topology_context_topologyuuid_linklink_uuid_available_capacity_bandwidth_profile_committed_information_rate_get(uuid, link_uuid): # noqa: E501
"""data_context_topology_context_topologyuuid_linklink_uuid_available_capacity_bandwidth_profile_committed_information_rate_get
returns tapi.common.Ca... | b44e48aa0fff6b01da22576fc73352deba812636 | 23,446 |
import os
def CreateMD5ChecksumFile(filename, mangled_filename=None):
"""Create and upload an MD5 checksum file for filename."""
if not mangled_filename:
mangled_filename = os.path.basename(filename)
checksum = CalculateMD5Checksum(filename)
checksum_filename = '%s.md5sum' % filename
with open(checksu... | 8de75d2ab9e82ca663f29a6ca377bfe44576932d | 23,447 |
import argparse
def parse_arguments() -> argparse.Namespace:
"""Parse the arguments."""
parser = argparse.ArgumentParser(
description="Panoptic segmentation evaluation."
)
parser.add_argument(
"--gt", "-g", required=True, help="path to panseg ground truth"
)
parser.add_argument... | 5d9b968015282340973fb0b631f0fe9539d08f50 | 23,448 |
from typing import Union
import os
import tqdm
def get_similarity_graph(
*,
fullgraph: Union[str, BELGraph] = DEFAULT_FULLGRAPH_WITHOUT_CHEMSIM_PICKLE,
rebuild: bool = False,
mapping_file: str = DEFAULT_CHEMICALS_MAPPING_PATH,
chemsim_graph_path=None,
clustered: bool = True,
weighted: bool... | 768ffa099af70e6a0fc12416dded6776a048d3f2 | 23,449 |
from typing import List
from typing import Dict
from operator import and_
def update_mlwh_with_cog_uk_ids(samples: List[Dict[str, str]]) -> None:
"""Update the MLWH to write the COG UK barcode for each sample.
Arguments:
samples {List[Dict[str, str]]} -- list of samples to be updated
"""
if l... | b4d6dfaec4bb40a59cbdfef619f7f4542e55e2a9 | 23,450 |
def make_09f9():
"""倉庫インベントリーフッタ"""
return "" | 91d21aeb58fc004865db91846d73f978f48f9be4 | 23,451 |
def get_last_successful_hour_or_start_hour():
"""Get the last hour that ran successfully or the start hour."""
last_hour = crash_stats.get_last_successful_hour()
if last_hour:
return last_hour
return get_start_hour() | 86518100bafe3296d63a8ac3612de1fa2c2ed8d4 | 23,452 |
import copy
from datetime import datetime
def encode_jwt(payload, secret):
"""
Return ``payload`` as a JWT encoded with ``secret``.
Return a JWT whose payload is ``payload`` and that is signed using
``secret``.
:arg payload: the payload to encode
:type payload: dict
:arg secret: the secr... | 497d5180e8956a737ad6edbd1113d73aeb915e80 | 23,453 |
def make_model():
"""
Loads pretrained torchvision model and redefines fc layer for car classification
"""
# uses about 1 GiB of GPU memory
model = models.vgg19(pretrained = True)
#model = models.resnet50(pretrained = True)
in_feat_num = model.classifier[3].in_features
mid_feat_num = int... | cd189f4b4d4dcadf6dd686aad08e2e494e0c2200 | 23,454 |
def empty_call_false(*args, **kwargs) -> bool:
"""
Do nothing and return False
"""
return False | 3b3964c859a47698f0000e1b26963953980fad51 | 23,455 |
def cookie_is_encoded(data):
""" Tests whether or not a cookie is encoded / HMAC signed
-> #bool True if encoded
..
from vital.security import cookie_is_encoded
cookie_is_encoded(
"!YuOoKwDp8GhrwwojdjTxSCj1c2Z+7yz7r6cC7E3hBWo=?IkhlbGxvLCB3b3JsZC4i")
... | baf2a05b516a23cacca4985944974112019abfda | 23,456 |
import torch
def l2_normalize(x: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:
"""Normalizes the input tensor using L2-norm.
Args:
x: Tensor to be normalized.
eps: Small value to avoid division by zero.
Returns:
Normalized tensor.
"""
return x / (torch.norm(x, p=2, ... | 22273bbbda7bece511d31d517790bfa14427d76f | 23,457 |
from re import S
def ssq_cwt(x, wavelet='gmw', scales='log-piecewise', nv=None, fs=None, t=None,
ssq_freqs=None, padtype='reflect', squeezing='sum', maprange='peak',
difftype='trig', difforder=None, gamma=None, vectorized=True,
preserve_transform=None, astensor=True, order=0, patie... | 2776e85dde171b1c47fdce028bf9c845298b3a93 | 23,458 |
import torch
def predict_image_classification(model: nn.Module, input_: torch.Tensor):
"""
Predict using an image classification model.
Args:
model (`nn.Module`):
Pytorch model.
input_ (`Tensor`):
Input image tensor.
Returns:
(`tuple`)
Predic... | 2343d4db9b93910337e0e55b9935783714710330 | 23,459 |
def _id_to_box(id_, dim):
"""Convert id to box ID"""
row = id_ // (dim ** 3)
col = (id_ % (dim ** 2)) // dim
return row * dim + col | 8e6c4779872fff5cdc5a6ca6b4143a1519d8aaf2 | 23,460 |
import string
def _load_hex(instream):
"""Load font from a .hex file."""
global_comment = []
glyphs = []
comment = []
for line in instream:
line = line.rstrip('\r\n')
if ':' in line:
# parse code line
key, value = line.rsplit(':', 1)
value = valu... | 6e5980e53ee598d813f10bdbcb775e8d47102fa8 | 23,461 |
def make_small_graph(graph_description, create_using=None):
"""
Return the small graph described by graph_description.
graph_description is a list of the form [ltype,name,n,xlist]
Here ltype is one of "adjacencylist" or "edgelist",
name is the name of the graph and n the number of nodes.
This ... | deb1cf0d08bba91a538c7d2c47c1d89e2c2a28da | 23,462 |
def get_masksize(mask, labelnum = None):
"""
Compute mask size in surface space
Parameters:
----------
mask: label image (mask)
labelnum: mask's label number, use for group analysis
Return:
--------
masksize: mask size of each roi
Example:
--------
>>> masksize = g... | c8ccd82d9887f923e3d2581f97dd2a8f016cc182 | 23,463 |
def _context_py2rpmversion(context):
"""get a python PEP0440 compatible version and translate it to an RPM
version"""
# the context needs a variable set via {% set upstream_version = 'ver' %}
_context_check_variable(context, CONTEXT_VAR_UPSTREAM_VERSION,
'py2rpmversion')
... | 3f9110dff377a6c819e6b87ab5fd9c81a7532694 | 23,464 |
def check_and_format_address(address):
"""
check address
"""
try:
formatted_address = to_checksum_address(address)
return formatted_address
except Exception as e:
raise ArgumentsError("invalid address {}, reason: {}"
.format(address, e)) | 1b0c88aede34386d1ccd5facd1bdbd4724538ab7 | 23,465 |
from typing import Optional
def get_cache_name(cache_type: str, tag: Optional[str] = None) -> str:
"""
Get the canonical cache name (e.g., "tmp.cache.mem.tag") for a type of
cache.
:param cache_type: type of a cache
:param tag: optional unique tag of the cache, empty by default
:return: name ... | ff933829314dd1794406ca4282eaf4efdf860b39 | 23,466 |
def _aves2_cfg():
""" Read aipctl config
"""
config = ConfigObj()
# The result is a merge of all the files as they appear in the list
f_list = cfg_files()
if not f_list:
print("error: configuration file not found")
exit(1)
for f in cfg_files():
_cfg = ConfigObj(f, e... | 527f1e94d5ec2c5cd13aa1a886d4c56914828f4d | 23,467 |
import os
def create_readme(top_dir,package_name,description="",docs=False):
"""
README requires the name of the package and the directory in which to write the file in.
Optionally, give a description and whether or not to create a 'docs' directory.
"""
readme_str="""
# {package}
## Descript... | 70f7221536078a5d5c13eb97b28c394b12621941 | 23,468 |
def estimate_responsivity(mis_MU, norm_MU):
"""from the estimated base intensities, we return onlu users which have zero base intensity for misinformation
and greater than zero base intensity for normal content. """
no_bad_intentions_ids = []
for id in range(len(mis_MU)):
if mis_MU[id] == 0 and... | 4d944478694f1be1474eea963fad284079d5fe57 | 23,469 |
from typing import Union
from typing import Any
from datetime import datetime
def parse_field_constraint(
x: Union[str, int, float, bool, list],
constraint: str,
type: str = "string",
**field: Any,
) -> Union[str, int, float, bool, list, datetime.datetime, ConstraintTypeError]:
"""
Parse field... | 531e33a1bc79e8a232032ebe9d340f829a3f513c | 23,470 |
def compute_ab_cycles(c_cycles, linear_combinations, g, tretkoff_graph):
"""
Returns the a- and b-cycles of the Riemann surface given the
intermediate 'c-cycles' and linear combinations matrix.
Input:
- c_cycles
- linear_combinations: output of the Frobenius transform of the
"""
linco... | 645d569ee06cb87161b12158603b1b6dcfb92077 | 23,471 |
import pickle
import pathlib
def pmlb_multiclass_classification_dataset_names():
"""Returns list of multiclass classification datasets in PMLB."""
try:
name = pickle.load(open(".pmlb/mcdn.pkl", "rb"))
except FileNotFoundError:
pathlib.Path(".pmlb").mkdir(parents=True, exist_ok=True)
... | d3030441c119de0c96c9d83df026b7f922fe21e6 | 23,472 |
from losses.loss_functions import BalancedCrossEntropyLoss
from losses.loss_functions import SoftMaxwithLoss
from losses.loss_functions import NormalsLoss
from losses.loss_functions import BalancedCrossEntropyLoss
from losses.loss_functions import DepthLoss
def get_loss(p, task=None):
""" Return loss function for... | 6284d2e40fc8aa220c153307fc7199a47549d15d | 23,473 |
def compute_embeddings(image):
"""A mock function for a call to a deep learning model or a web service."""
del image # this is just a mock and doesn't do anything with the input
return 42 | 31536d4a2371140e962aadb63b8645685328b3df | 23,474 |
from pathlib import Path
import os
def dir(path: str) -> Path:
"""Get equivalent directory in cache"""
_, filename = os.path.split(path)
if filename: # TODO fix; this won't work as intended
# If file
return __get_cache_filepath(path).parent
else:
# If directory
return _... | 203ebec92b19e82fe92d0cc4043db3aad2278a2f | 23,475 |
def text_to_string(filename):
"""Read a text file and return a string."""
with open(filename) as infile:
return infile.read() | dbd79e78c84c3374c0252544086885b909ae9bd9 | 23,476 |
def lgsvlToScenicElevation(pos):
"""Convert LGSVL positions to Scenic elevations."""
return pos.y | d90f7509285b08c791eac56c1a119f91120cf556 | 23,477 |
import jinja2
def render_to_string(backend, filename, context):
# type: (str, str, Dict) -> str
"""
Render a template using the specified context
:param backend: The backend for which the template is rendered
:param filename: The template name
:param context: The data to use when rendering the... | c645a9867acdb50236a5604144a104cb38e841f9 | 23,478 |
def customfield_by_name(self, name):
"""
Get the value of a customfield by name
"""
# Get all fields from Jira. This is expensive, so only do it once
if not hasattr(self, '_fields'):
response = self._session.get(
self._base_url.format(
server=self._options['server... | 35f7ee1e88029201086fc75bbc280beb386cca44 | 23,479 |
from pathlib import Path
def download_images(imgs):
"""Save any images on page to local directory"""
had_download_issue = False
for img in imgs:
image_url = 'https://projecteuler.net/{}'.format(img.get('src'))
logger.info(f'downloading image {image_url}')
image_name = Path(image_ur... | 7d39dff40797a698215a589f9ff65f3df4a85e9f | 23,480 |
def admin_order_pdf(request, order_id):
"""
1. Get data (and templates for displaying data)
2. Set type (cuz you'll need to download it, right?)
3. Using the module (configuring stuff, e.g. the CSS :P)
"""
order = get_object_or_404(Order, id=order_id)
html = render_to_string... | c4cf5a38743f573ef8dfa704cfe2d12bb47a679c | 23,481 |
import traceback
def delete_container(request, container):
""" Deletes a container """
storage_url = request.session.get('storage_url', '')
#meta_storage_url = request.session.get('meta_storage_url', '')
auth_token = request.session.get('auth_token', '')
#meta_auth_token = request.session.get('me... | ce205d6112239905707064f0357b6c19fe3bd688 | 23,482 |
def dense_encoder(X, params):
"""Dense model encoder subgraph that produces latent matrix.
Given data matrix tensor X and dictionary of parameters, process through dense
model encoder subgraph and return encoder latent vector for each example in
batch.
Args:
X: tf.float64 matrix tensor of input data.
... | 1dfe2b876cb32b5d8b89e70e451a732730762a14 | 23,483 |
def __asset_inventory_espanol(asset):
""" Renombra los encabezados del inventario de bases de datos de Datos \
Abiertos Colombia a términos en español.
:param asset: (pandas.DataFrame) - Tabla de inventario del portal de datos\
abiertos Colombia (https://www.datos.gov.co).
:return: base de ... | dfb508cec458ecb63c371849d84cb3b3d79335ba | 23,484 |
def end_of_sign_found(token: str, preceding_token: str):
"""
This function receives a token and its preceding token and returns whether that token ends an Akkadian sign.
"""
if not preceding_token:
return False
if '-' in token or '.' in token:
return True
if not preceding_token.e... | 30024ddad31c3149d1d2363842b085d2923c1387 | 23,485 |
import sys
def main(argv=None):
"""Execute the application from CLI."""
if argv is None:
argv = sys.argv[1:]
if not argv:
argv = [curdir]
args = _parse_args(argv)
data = csft2data(args.path)
if args.top:
data = data.head(args.top)
if args.with_raw:
data['r... | c6a0200c26373ca6f7ea2544608b5a9382ea9d96 | 23,486 |
import os
def get_base_path(node=None):
"""
get the base path for the system
"""
if node==None: node = get_system()
##
## Base path
try:
path = os.environ['sdss_catl_path']
assert(os.path.exists(path))
except:
proj_dict = cookiecutter_paths(__file__)
##
... | 35f321f93e1bfcbe32cb3dd0fd3497da19bfa264 | 23,487 |
from typing import Optional
from typing import Dict
import datasets
def get_loaders(
dataset: str, batch_size: int, num_workers: Optional[int]
) -> Dict[str, DataLoader]:
"""Init loaders based on parsed parametrs.
Args:
dataset: dataset for the experiment
batch_size: batch size for loader... | 2340d05f69057bcb034a8ec4ad5515055d0bde71 | 23,488 |
def pipeline():
""" Creates a pipeline configured to use a given model with a specified configuration.
Notes
-----
Pipeline can be executed only if its config contains the following parameters:
model_class : TFModel
Architecture of model. List of available models is defined at 'AVAILABLE_M... | f8fbbe3898b58b1b1621d742e4acdf80f17ba11c | 23,489 |
import copy
def get_screen_point_array(width: float, height: float):
"""Get screen points(corners) in pixels from normalized points_in_square
:param width: screen width
:param height: screen height
:return:
"""
points = copy.deepcopy(points_in_square)
for i in range(len(points_in_square))... | 34d88ddb1a24e4e3ebc81f0c7e99530548ed8a8b | 23,490 |
def get_spacing_matrix(size, spacing, offset):
"""Returns a sparse matrix LinOp that spaces out an expression.
Parameters
----------
size : tuple
(rows in matrix, columns in matrix)
spacing : int
The number of rows between each non-zero.
offset : int
The number of zero r... | 5871385bcdcb9ce538fe1e4525c947c2cfa582c9 | 23,491 |
def next_power2(x):
"""
:param x: an integer number
:return: the power of 2 which is the larger than x but the smallest possible
>>> result = next_power2(5)
>>> np.testing.assert_equal(result, 8)
"""
return 2 ** np.ceil(np.log2(x)).astype(int) | 379c2170d0dbd25ee01a47eb0765f4dfd143efbb | 23,492 |
def category_induced_page():
"""Form to compute the Category induced."""
return render_template('category-induced.html') | 176af8bbbb67afce78c11483f66b3d5ac15f6d76 | 23,493 |
import array
from operator import concat
def zext(value, n):
"""Extend `value` by `n` zeros"""
assert (isinstance(value, (UInt, SInt, Bits)) or
(isinstance(value, Array) and issubclass(value.T, Digital)))
if not is_int(n) or n < 0:
raise TypeError(f"Expected non-negative integer, got '... | dfd666446f1b93ebdeeb94b932d8de7b243f6a4e | 23,494 |
import math
def _distance(point0, point1, point2, seg_len):
"""Compute distance between point0 and segment [point1, point2]. Based on Mark McClure's
PolylineEncoder.js."""
if (point1[0] == point2[0]) and (point1[1] == point2[1]):
out = _dist(point0, point2)
else:
uuu = ((point0[0] - po... | 1927a5fe46dcb0245031b395aade67ec01270930 | 23,495 |
def delete_node(
graph: xpb2.GraphProto,
node_name: str = "",
**kwargs):
""" Add node appends a node to graph g and returns the extended graph
Prints a message and returns False if fails.
Args:
graph: A graph, onnx.onnx_ml_pb2.GraphProto.
node_name: Name of the node... | 620e325a0ea9da7cd83e897fee49fb6ef9183da4 | 23,496 |
from PIL import Image
def image_to_term256(pil_image):
"""Convert image to a string that resembles it when printed on a terminal
Needs a PIL image as input and a 256-color xterm for output.
"""
result = []
im = pil_image.convert('RGBA')
try:
except ImportError:
im.thumbnail((80, 8... | 482f6c868adf5f302d88898abeff426d9ed000e7 | 23,497 |
def false_discovery(alpha,beta,rho):
"""The false discovery rate.
The false discovery rate is the probability that an observed edge is
incorrectly identified, namely that is doesn't exist in the 'true' network.
This is one measure of how reliable the results are.
Parameters
----------
... | 849c236157070c5d1becfec3e4e5f46a63d232d2 | 23,498 |
def add_default_legend(axes, subplots, traces):
"""
Add legend to the axes of the plot. This is needed to be done using matplotlib shapes
rather than the build in matplotlib legend because otherwise the animation will add
a legend at each time step rather than just once.
Parameters
----------
... | d352c1d90dac882f687be426d63dea35dca4ba46 | 23,499 |
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