content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def all_acc(y_true, y_pred):
"""
All Accuracy
https://github.com/rasmusbergpalm/normalization/blob/master/train.py#L10
"""
return K.mean(
K.all(
K.equal(
K.max(y_true, axis=-1),
K.cast(K.argmax(y_pred, axis=-1), K.floatx())
),
... | f707ac258d21bb789bc0c4b8e792b8e42ba03e6f | 48,310 |
def generate_identifier(signingkey):
"""Generates encoded version of the public key associated with
signingkey.
Args:
signingkey (str): A private key.
Returns:
str: An encoded 'address' associated with the public key.
"""
return pybitcointools.pubtoaddr(pybitcointools.privtopub... | 6c8311aee007d2732447f27b30b2cc628709bec9 | 48,312 |
def get_page(paginated_list):
"""
Take a github.PaginatedList.PaginatedList and then iterate through the
pages to get all of its entries
Args:
paginated_list (github.PaginatedList.PaginatedList): PyGithub paginated
list object
Returns:
`list`: All entries in the paginated ... | 0510537b20c18b6b1be5b10ca014e13be7a19a1f | 48,314 |
def acnt2(chain: pv.Chain, wallet: pv.Wallet) -> pv.Account:
"""
acnt2 is the fixture that returns the account of nonce 2.
Args:
chain (pv.Chain): The Chain object.
wallet (pv.Wallet): The wallet object.
Returns:
pv.Account: The account.
"""
return wallet.get_account(ch... | 3156dc4a140f6130682675a92d649d1f5d41d646 | 48,315 |
def _get_acc_data():
""" Датасет для графика акселерометра """
time = now()
datax, datay, dataz = [_get_data_abstract("IMU", x, time) for x in ("xacc", "yacc", "zacc")]
for x, y, z in zip(datax, datay, dataz):
x["y"] /= 1000.0
y["y"] /= 1000.0
z["y"] /= 1000.0
latestUpdateT... | 6c7fbf18bb5992f255a568af15268463d5ed893d | 48,316 |
def git_ref(check=True):
"""
return name for current branch / tag
If `check` is True, the execution end if git root not found.
Otherwise `None` is returned.
"""
branch = _exec("git rev-parse --abbrev-ref HEAD".split(), check)
if branch != "HEAD":
return branch
tag_ref = _exec("g... | 6c4ac95859785404ced853739187bd7da4ebac83 | 48,317 |
def get_metadata_from_asset_name(asset_name: str) -> dict:
"""Get metadata for given asset"""
url = f"{POOL_PM_URL}/asset/{POLICY_ID}.{asset_name}"
response = get_request(url, headers=None)
return response.get("metadata") | 37068d9f3b021dc5c66ef9aa6e6ad13135bb010d | 48,318 |
import math
def prime(n):
"""Primality test by trial division."""
if n == 2:
return True
elif n < 2 or n % 2 == 0:
return False
else:
return not any(n % x == 0
for x in range(3, math.ceil(math.sqrt(n)) + 1, 2)) | 3504217a7e8149867ec16ddf9c54f4fac736d592 | 48,321 |
def is_sukun(archar):
"""Checks for Arabic Sukun Mark.
@param archar: arabic unicode char
@type archar: unicode
@return:
@rtype:Boolean
"""
return archar == SUKUN | 3ccedfb4b73c7d80bf24e0c9b8d04c6309eb9a29 | 48,323 |
def radio_util(
data # type: "XDR Data"
):
"""Radio Utilization Counter - Type: Counter, Enterprise: 0, Format: 1002"""
sample_data = {} # Cache
sample_data["Elapsed Time Milliseconds"] = int(data.unpack_uint())
sample_data["On Channel Time Milliseconds"] = int(data.unpack_uint())
sample_data["On Channel Busy Ti... | 5e5c90ca511db1babda427ea7218375542f77bce | 48,324 |
import re
from typing import OrderedDict
def getNames(xml):
"""
get person, organization and place tags from PageXML.
"""
content = re.sub("<\?xml version.*?>", "", xml)
try:
root = et.fromstring(content)
except Exception as e:
print(e)
return {"XMLERROR":"XMLERROR"}
... | 5a0ba6ba9b3ec6c303adc1d27680e01dadecd64c | 48,325 |
def get_xls(es_result, column_mapping):
"""
Creates a stream with the Excel file, the column headers, and the result rows
:param es_result: The result of the ElasticSearch search
:param column_mapping (dict): A dict mapping the column names in the original data, to the desired column headers
for th... | 8caa799586ef910818338f0e0e7300df267dbdd0 | 48,326 |
def average(numbers):
"""
:param list[float] numbers: a list of numbers
:returns: the average of the given number sequence. an empty list returns 0.
:rtype: float
"""
return float(sum(numbers)) / max(len(numbers), 1) | d86c6f24733d3032b82cb6c64c02eba37cc34a04 | 48,327 |
import six
def _IsIdentityTypeMapping(type_mappings):
"""\
An identity type mapping is a special case where each of the input types
matches the output type.
"""
for input_type, output_types in six.iteritems(type_mappings):
if output_types != [input_type]:
return False
ret... | 27f13266dc23c5d4bd78a27c240664208d4f8c8f | 48,328 |
def inv_rotate(vec: Vector3, quat: Quaternion):
"""Rotates a vector by the inverse of a unit quaternion.
Args:
vec: (3,) a vector
quat: (4,) a quaternion
Returns:
A vector rotated by quat^{-1}
"""
return rotate(vec, quat_inv(quat)) | f77e301b5feff76bf73850189c48a14c00d61339 | 48,329 |
def play_manche_joueur():
"""
cette fonction propose au joueur de jouier une manche si il fauit 1 , son score est annulé et la fonction retourne -1
Returns:
le total de la cagnote
"""
cagnote=0
if input("voulez vous lancer un dé ? (y pour relancer, ou anykey pour arréter)\n")=="y":
... | 0c0bc49093ba8d602e6617f10d616ad3f290d14f | 48,330 |
def crossProduct(u, v):
"""
Calculates the cross product of two 3d vectors (as 1-d arrays).
"""
return np.cross(u, v) | e6da5b203f6617a7400f9b3f327434f70ca3f686 | 48,331 |
def vgg13_bn(backbone='vgg13_bn', pretrained=False, **kwargs):
"""VGG 13-layer model (configuration "B") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
kwargs['cfg_key'] = 'B'
kwargs['batch_norm'] = True
kwargs['backbone'] = "vgg13... | a56f6b5d4899c1f6eb02c12d92ad4e2f57228c5d | 48,332 |
from operator import sub
def get_resource_type_by_url(url: str):
"""Get the resource type definition from a resource URL."""
url_parts = urlparse(url)
url_path = url_parts.path
resource_type = sub(r'/(core|rest|identity|auth|product-metadata)/v\d+/([^/]+)/?.*', r'\2', url_path)
if resource_type ==... | 9f525eb5f841d61db29d2ec2413cfa687c2c6287 | 48,333 |
def general_pool_fn(x):
"""
x[0]: function to call
x[1] to x[n]: arguments of the function
"""
return x[0](*x[1:]) | d398378d3d1671f0e58bff2bc8737ff07da0c3e3 | 48,334 |
def getfullname(val):
"""Get the module-qualified name of type or function val."""
return (val.__module__ or '<UNKNOWN_MODULE>') + '.' + getqualname(val) | 3f8e9570219d5dcae45313e5bc530f8892b33a34 | 48,335 |
def dt_hms(d, n=3):
"""
Allow negative times
"""
try:
sign = '+' if d > 0. else '-'
x = abs(d)
h = int(x)
x = (x-h) * 60.
m = int(x)
s = (x-m) * 60.
w = n + 3
return f"{sign}{h:02d}h {m:02d}m {s:0{w}.{n}f}s"
except:
return None | 30c04b23ba304a1d8dffcdbe38035bdde24b1848 | 48,336 |
def get_file_contents(repo_path, file_path):
"""
Given an absolute file path, return the text contents of that file
as a string.
"""
binary = get_file_contents_binary(repo_path, file_path)
try:
return binary.decode('utf-8')
except UnicodeDecodeError:
return binary.decode('lat... | b6d622724c1bb6675eb0c5ec43093d1bfca4bfbe | 48,338 |
import torch
def generate_param(n_dimensions: int):
"""Simulation of model's parameters"""
nnz = 20
idx = np.arange(n_dimensions)
W = torch.FloatTensor((-1) ** (idx + 1) * np.exp(-idx / 10.)).to(dtype=torch.float64)
W[nnz:] = 0.
return W | d1fd5f13d65de1368e7ccede89028ec46687c505 | 48,339 |
def cleanDf(df, badaa=None):
"""Remove all invalid sequences (containing badaa) from
the alignment
badaa is '-*BX#Z' by default"""
return df.loc[[isvalidpeptide(s, badaa) for s in df.seq]] | 73f485630828ed555502a8e810d3662ffe7512a8 | 48,340 |
def minify_css(s):
"""
Minify CSS code.
@param s: css to minify
@type s: L{str}
@return: the minfied css
@rtype: L{str}
"""
if csscompressor is None:
raise NotImplementedError("Dependency 'csscompressor' required, but not found!")
return csscompressor.compress(
s... | 82f78b63418c51b58b7e392db54d0d471de15fc3 | 48,342 |
def cumsum(a, axis=None, dtype=None, out=None):
"""Returns the cumulative sum of an array along a given axis.
Args:
a (clpy.ndarray): Input array.
axis (int): Axis along which the cumulative sum is taken. If it is not
specified, the input is flattened.
dtype: Data type specifier... | 145fd87072a7a196fb609b9d0d57e2321f0eab10 | 48,343 |
import math
async def async_api_adjust_volume(hass, config, directive, context):
"""Process an adjust volume request."""
volume_delta = int(directive.payload["volume"])
entity = directive.entity
current_level = entity.attributes.get(media_player.const.ATTR_MEDIA_VOLUME_LEVEL)
# read current stat... | c71a67450860181799e9fac56379443b5df9fef2 | 48,344 |
import random
def random_database():
"""
Creates a random database in the testing Postgres instance and returns the
name of the database.
"""
# Setup connection with default credentials for testing.
with connect(dbname='holo', user='holocleanuser', password='abcd1234', host='localhost') as con... | 1fdf929d2531a00e92a3b6801edea5d57cb8d622 | 48,345 |
def fscore(target_mat, decision_mat, beta=1., event_wise=False):
"""
Args:
target_mat: n_hot matrix indicating ground truth events/labels
(num_frames times num_labels)
decision_mat: n_hot matrix indicating detected events/labels
(num_frames times num_labels)
even... | 5bfc11a38d766797cbf5b93aa09b3dca31793f80 | 48,346 |
def _EraseTombstone(device, tombstone_file):
"""Deletes a tombstone from the device.
Args:
device: An instance of DeviceUtils.
tombstone_file: the tombstone to delete.
"""
return device.RunShellCommand(
'rm /data/tombstones/' + tombstone_file, root=True) | 00e6f316062785d7465f501ea743a2dc94864aef | 48,347 |
def graphite_electrolyte_reaction_rate_Kim2011(T, T_inf, E_r, R_g):
"""
Reaction rate for Butler-Volmer reactions between graphite and LiPF6 in EC:DMC
[1].
References
----------
.. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
(2011). Multi-domain modeling of lit... | 15efdc98d2f4b63b2d9482c404f465ec5bd52194 | 48,348 |
def initialize_node_details(storage_gui_ip, user, key_file):
""" Initialize node details for cluster definition.
:args: storage_gui_ip (str), user (string), key_file (string)
"""
node_details, node = [], {}
node = {'ip_addr': storage_gui_ip, 'is_quorum': True, 'is_manager': True,
'is_gui... | cb3c82a5c0e9c418fac88f61473b44a60c0d7c12 | 48,349 |
def do_filter(parser, input_list):
"""
Filter flavor by applying parser on the flavor list
:param input_list: list of input data in JSON format
:param parser: jsonpath parser
:return: list of matched flavors in JSON
"""
return [match.value for match in parser.find(input_list)] | 9a5efe4e80a6fdf3afb0cff9616e4c480de8e26d | 48,350 |
def module(script_in):
"""Decorate a python function or class as tvm script.
Alias for tvm.script.tir for now.
Returns
-------
output : Union[Function, Module]
The Function or Module in IR.
"""
return tir(script_in) | c9857bdc4a444090928a17f18b980eb03f73c99c | 48,352 |
def shorten_class(class_name: str) -> str:
"""Returns a shortened version of the fully qualilied class name."""
return class_name.replace('org.chromium.',
'.').replace('chrome.browser.', 'c.b.') | 2064e6e0dc159bc130f84ce4a830857455d12ba4 | 48,353 |
from typing import List
def get_words(content: str) -> List[str]:
"""
文字列内に出現する名詞のリスト（重複含む）を取得する関数。
"""
words = [] # 出現した名詞を格納するリスト。
node = tagger.parseToNode(content)
while node:
# node.featureはカンマで区切られた文字列なので、split()で分割して
# 最初の2項目をposとpos_sub1に代入する。posはPart of Speech（品詞）の略... | cc05e06cef47fc3cce4b1ce79c4fd8d0a755cd59 | 48,354 |
import json
def create_initial_parameters(instances, Ns):
"""
Create a list of initial parameters for spectra loaded from task instances.
:param instances:
A list of task instances where spectra were loaded. This should
be length `N` long.
:param Ns:
A list containing the... | 078ecf7ecc6c140d6fe2cb68cac28dca98526b56 | 48,356 |
def load_snps_by_region(chrom, start, end):
"""Retrieve snp information by region"""
index = _get_index_from_chr(chrom)
search_snps = Search().using(es).doc_type('snps').index(index).filter("range", position={"lte": end, "gte":start})
return {snp.position: snp.to_dict() for snp in search_snps.scan() } | e512eade4e5ca6cc8358306e4843258297566eef | 48,357 |
def weight_function(run_params, displacement_norm):
"""Determine motion-dependent prediction weight of given supporter point.
This method determines the weight to apply to each supporter point when
using it for prediction of a target point based on the norm of its
displacement vector. The larger the di... | 2fdea32511ae8b4cedd47e79d7f8517a08a6b457 | 48,358 |
def _read_cp1251_file(fname):
"""
Returns:
Возвращает список и ответ на вопрос - угадали ли мы кодировку?
"""
this_is_it = True
sets = iow.get_utf8_template()
sets['coding'] = 'cp1251'
sets['name'] = fname
# Если не та кодировка - возвращает пустой список
readed_list =... | 55834a71a4607a6f9bf6fc9f8216e6d17eef6bbd | 48,359 |
def mixed_grad_color2gray(rgb_image):
"""EC: Convert an RGB image to gray image using mixed gradients."""
img = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HSV)
s = img[:, :, 1].reshape((img.shape[0], img.shape[1], 1)) / 255.
v = img[:, :, 2].reshape((img.shape[0], img.shape[1], 1)) / 255.
mask = np.ones_... | f8aefe4c836377f87b6d8ce0c4784da0c4115490 | 48,360 |
def PReadRec (inHis, recno, err):
"""
Read History record
returns string
* inHis = input Python History
* recno = desired record
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inHi... | e315f91fa46b8ed99071f681dc490630c256c04a | 48,361 |
def remove_leading_garbage_lines_from_reference_section(ref_sectn):
"""Sometimes, the first lines of the extracted references are completely
blank or email addresses. These must be removed as they are not
references.
@param ref_sectn: (list) of strings - the reference section lines
@retu... | 09dc153b064cd8fcf613520ef432e51350928307 | 48,362 |
def ensure_csrf_cookie_cross_domain(func):
"""View decorator for sending a cross-domain CSRF cookie.
This works like Django's `@ensure_csrf_cookie`, but
will also set an additional CSRF cookie for use
cross-domain.
Arguments:
func (function): The view function to decorate.
"""
def... | ecdeae3ebff37976c841887083c53f088f2b8b34 | 48,363 |
def _algorithm_kwargs(request):
"""Auto-parametrizes `_rl_algorithm_cls` for the `trainer` fixture."""
return dict(request.param) | fddb2a376449973f49d5a27cef04b3596e9cc3dd | 48,364 |
from typing import Dict
def convert_tsv_entries_to_dataframe(tsv_dict: Dict, header: str) -> pd.DataFrame:
"""
Converts entries from TSV file to Pandas DataFrame for faster processing.
"""
header_fields = header.strip().split("\t")
data = {header: [] for header in header_fields}
for line in tsv_dict.valu... | 2fbbb125f5dfa8bad2efabc420295f0925d9786e | 48,365 |
def decode_and_upload_base64_file(data, file_name):
""" Function to decode base64 files
Parameters
----------
date : str
string of user inputted graduation date.
Returns
-------
string
string of graduation date only with Month and Year.
"... | 3503ad48de0aa1278c859cd096e735e114eed9c3 | 48,366 |
def calc_sthovl_by_hkl_abc_cosines(
h: float, k: float, l: float, a: float, b: float, c: float,
cos_alpha: float, cos_beta: float, cos_gamma: float):
"""
Calculate sin(theta)/lambda for given reflections h, k, l
and unit cell parameters defined as a, b, c, cos(alpha), cos(beta), cos(gamma).
... | a5b8d01b970fdd5fd09a8b157b925952987a14d7 | 48,367 |
def RE2Str(RE):
"""Helper for del_gnfa_states
---
Given an RE as a tree, return the string equivalent of the RE.
"""
if type(RE) == str:
if (RE == ""):
return '""' # was return '@', but now no more '@'
else:
return RE
elif type(RE) == tuple:
... | 966d4dc06e597518ab7306ea3f5c970cf4534efa | 48,368 |
import json
def list_observations(model):
"""Return a JSON list of observations"""
obs = model.get_observations()
response.content_type = "application/json"
return json.dumps(obs) | a82f1148ee72d5690a7488b26f2030da8cd85911 | 48,369 |
def add_api_config_to_queries(generated_query_strings, search_engines):
"""
Merges the two parameters and returns a list of dicts that include the
api config.
If only 1 API key is provided, it is assumed this is valid for many searches and is used for all queries
If more than 1 is p... | 209b14e98c2cb339f958fc7dfe456a4a40876c8c | 48,370 |
def _get_first_model(structure: PdbStructure) -> PdbStructure:
"""Returns the first model in a Biopython structure."""
return next(structure.get_models()) | fb3431655ef7f83aa50931f96696ffe296bb6501 | 48,371 |
def createDiffLineCV(video, cropx1, cropx2, cropy1, cropy2):
"""
Binarizes video and returns a frame to frame difference trace.
Inputs:
video - list of images
cropx1 - int, crop pixel starting for first dimension
cropx2 - int, crop pixel ending for first dimension
cropy1 - int, crop pixel st... | fb6793f3b44e6b042df97588ceebc178ee8c2bb2 | 48,372 |
def convert_entity_schema(entity_schema):
""" Convert entity schmea to record schema
"""
spots = list()
asocs = list()
spot_asoc_map = dict()
for entity in entity_schema:
spots += [entity]
spot_asoc_map[entity] = list()
return spots, asocs, spot_asoc_map | 6e3cc2bbecbbd88312c1a486142d9e8a50a5e39a | 48,373 |
def get_inchi_key(mol: Molecule) -> str:
"""Get an InChI key from a molecule."""
return inchi.generate(mol).getKey() | 81d4065ee2364ebbdedfa69ebdd450cd372638fc | 48,374 |
def process_raw_input(input, source='html'):
"""
Parameters
----------
input
type
Returns
-------
processed text (str)
"""
if source == 'html':
return '\n'.join(input)
elif source == None:
return input | f1e3b45ed8507b910f18f9a44856e17e530b4119 | 48,375 |
def get_pheno_list(serotype_hits, session):
"""
Function to return phenotype list from list of serotype hits (deduplicated)
:param serotype_hits: list of serotype hits from stage 1 mash analysis
:param session: DB session
:return: list of deduplicated phenotypes or groups
"""
out_res = []
... | 335fafb66dd04be0b20775d086cbafd93900f080 | 48,376 |
def get_review_dates(app_reviews) -> pd.DataFrame:
"""Create a new dataframe, `reviewDates`, with the number of reviews for each app per year"""
review_dates = (
app_reviews.groupby(["appId", "reviewYear"])["appId"]
.count()
.unstack()
.reset_index()
)
app_total_reviews ... | d92f7ed51a9c77f1ada409350d05872920489c3e | 48,377 |
def validate(config):
"""
Validate the beacon configuration
"""
# Configuration for memusage beacon should be a list of dicts
if not isinstance(config, list):
return False, "Configuration for memusage beacon must be a list."
else:
_config = {}
list(map(_config.update, con... | eddeb201e7789fdf3440513ca006813e49930a81 | 48,378 |
def mmd_t(
dist_xx, dist_xy, dist_yy, batch_size, alpha=1.0, beta=2.0, var_target=None, name='mmd',
do_summary=False, scope_prefix=''):
"""This function calculates the maximum mean discrepancy with t-distribution kernel
The code is inspired by the Github page of following paper:
Binkowski M... | 6c5f935ebba86b2ed42b3039c5454795075fe17a | 48,380 |
def reset_from_schedules(scheduler):
""""Reset all scheduler jobs, using information from the JSON object
:param scheduler: the TornadoScheduler instance to modify
:type scheduler: TornadoScheduler
:returns: True in case of success
:rtype: bool"""
ret = False
try:
scheduler.remove_a... | 1bf1bd20bbba7af5fba156c6632d0b86a616feba | 48,382 |
import pandas as pd
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.metrics import precision_recall_curve, average_precision_score
def get_threshold_metrics(y_true, y_pred, drop_intermediate=False,
disease='all'):
"""
Retrieve true/false positive rates and auroc/aup... | be44c49f1d0c8dbb6ef1543a538efb03d3cda3a4 | 48,383 |
from typing import Dict
from typing import Any
import requests
def pull_astronaut_list(url: str ='http://api.open-notify.org/astros.json') -> Dict[str, Any]:
"""
Pull a list of astronauts via API. Defaults to open-notify's API.
Args:
url: the URL to pull data from.
Returns:
A dict co... | d008cd1d62a435086dbd8dc08baaa5323298f11c | 48,384 |
def get_widget_for_attr(traits_ui, attr_name):
""" Return the Qt widget in the UI which displays the attribute specified.
"""
x_editor = traits_ui.get_editors(attr_name)[0]
qt_widget = x_editor.control
return qt_widget | 2bb2959963734bee48d067f41425808412bd2421 | 48,385 |
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Unload Bold config entry."""
if unload_ok := await hass.config_entries.async_unload_platforms(entry, PLATFORMS):
del hass.data[DOMAIN][entry.entry_id]
return unload_ok | cfab6c4d2e7ff1fdb261b95641128dda0153be7e | 48,386 |
def strip_url(url, origin_only=False):
"""
参考 https://www.w3.org/TR/referrer-policy/#strip-url
去除敏感信息如username
:param url:
:param origin_only:
:return:
"""
if not url:
return None
parsed_url = urlparse(url)
netloc = parsed_url.netloc
if origin_only and (parsed_url.use... | b424909e70d5078292c700a4605b9a493d22cb0a | 48,387 |
def eval_single_file(simulation_output_dir):
"""
number of cells
number of cells detected
"""
sim_dat_dir = simulation_output_dir[0:simulation_output_dir.rindex('/')]
true_barcodes = get_barcodes_set(
'%s/true_barcodes.txt' % (sim_dat_dir))
pred_barcodes = get_barcodes_set(
'%s/threshold_paths.txt' % simul... | 816aed4808fe21abb91b483ea7fc5aedf1c49d2a | 48,388 |
def all(*args, span=None):
"""Create a new expression of the intersection of all conditions in the
arguments
Parameters
----------
args : list
List of symbolic boolean expressions
span : Optional[Span]
The location of this operator in the source code.
Returns
-------... | f0cebfb241c10c2d53c58a8b4fb186e9d65a1b7a | 48,389 |
def plot_all_answer_traces(inputtrace: np.ndarray, colors: list) -> list:
"""
This function plots the answer traces of all tests; one plot per test.
:param inputtrace: Dataframe with the answer trace. Attributes of the dataframe: test, approach, answer, time.
:param colors: List of colors to use for th... | 26e5bcf8378a4b2ad77b202f2c11f8347bfffb31 | 48,390 |
from datetime import datetime
def initialise_library(members, items, item_copies, library):
"""Takes in items that needs to be populated into the library,
and conduct a series of pre-defined events by members (loan, renewal, return)
The Library object after conducting the events is used to initialise... | 0f5021358dd701790be75140673ede4634de1a41 | 48,391 |
def update_cache_bykey(cache_list, new_list, key='id'):
"""
Given a cache list of dicts, update the cache with a 2nd list of dicts by a specific key in the dict.
:param cache_list: List of dicts
:param new_list: New list of dicts to update by
:param key: Optional, key to use as the identifier to upd... | b077a1c40cbf0a8848ff9e017a644c20e1d25199 | 48,392 |
def calc_theor_avg_mass(dictionary, cfg, prec=6, reducing_end=None) -> float:
"""Returns theoretical average mass for glycan in dictionary form"""
reducing_end_tag_mass = 0.0
if reducing_end is not None:
if reducing_end in cfg["reducing_end_tag_avg"].keys():
reducing_end_tag_mass = cfg["... | 32a5aab08463366e8b43d13f2891489d94ab075a | 48,393 |
def get_node_info(session, node_id):
"""Wrapper for HAPI_GetNodeInfo
Fill an NodeInfo struct.
Args:
session (int): The session of Houdini you are interacting with.
node_id (int): The node to get.
Returns:
NodeInfo: NodeInfo of querying node
"""
node_info = HDATA.NodeInf... | 16b6200ec083da4cc15f57a02c53e0431cdc56a7 | 48,395 |
def Eliminar_Columnas(df, Dic):
""" Recibe el dataframe y el listado de las columnas que se quieren eliminar
"""
df = df.drop(columns=Dic)
return df | 96c049508f196406807cc9102b87cb4aa4884650 | 48,396 |
def getCurrentUser():
"""
Get the current user associated with whatever email is stored in session
"""
if 'email' not in session:
return None
email = mailsane.normalize(session['email'])
if email.error:
return None
return getUser(str(email)) | 9709c927c3d4fb7e74911859962c521eeaf3e2de | 48,397 |
def wgs84_to_web_mercator(df, lon="LON", lat="LAT"):
"""convert mat long to web mercartor"""
k = 6378137
df.loc[:,"x"] = df[lon] * (k * np.pi/180.0)
df.loc[:,"y"] = np.log(np.tan((90 + df[lat]) * np.pi/360.0)) * k
return df | 94ec56293ecc9510d4f6687b941253be359274ee | 48,398 |
def process_medusa(line):
"""
Process a medusa line and return a dictionary
:param line: A line from a medusa output file
:returns dict: A dictionary based upon the content
{ 'ip' : ip address
'port': port info - can be port # or module name
'user': username,
'pass': password,
... | 406fdaeee3ae91399095a9ae86feb5c3f9d66e60 | 48,399 |
def parse_tpl_file(tpl_file):
""" parse a pest template file to get the parameter names
Parameters
----------
tpl_file : str
template file name
Returns
-------
par_names : list
list of parameter names
"""
par_names = []
with open(tpl_file,'r') as f:
try... | 0797cfedbef07dcd118e13440691c287f952a740 | 48,400 |
def find_factors(n):
"""
Finds a list of factors of a number
"""
factList = {1, n}
for i in range(2, int(n ** 0.5) + 1):
if (n % i == 0):
factList.add(i)
factList.add(n // i)
return sorted(factList) | 0b8992bfe81bfd49c738b49380ceb0c8e7155b3f | 48,401 |
def unique(a):
""" Return the list with duplicate elements removed.
Args:
a (list): A list.
Returns (list):
The list with duplicate elements removed.
"""
# NOTES:
# 1. Built-in function 'set()' can convert a list (ordered) into a set (unordered).
# 2. B... | 1aeac608e53ebc91cb0709b69fc8731f0ad39562 | 48,402 |
from typing import Tuple
def sim_seird_decay(
s: float, e:float, i: float, r: float, d: float, beta: float, gamma: float, alpha: float, n_days: int,
decay1:float, decay2:float, decay3: float, decay4: float, step1_delta: int, fatal: float
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarra... | 4217488020bfcf28020fc31aee898655a6cf191e | 48,403 |
def do_quote_form(expressions, env):
"""Evaluate a quote form.
"""
check_form(expressions, 1, 1)
# BEGIN Question 6B
return expressions.first
# END Question 6B | 989df694053dfeeb774a9062aea3a27cb0ddcc38 | 48,404 |
def make_flatmap_image(braindata, height=1024, recache=False, **kwargs):
"""Generate flatmap image from volumetric brain data
This
Parameters
----------
braindata : one of: {cortex.Volume, cortex.Vertex, cortex.Dataview)
Object containing containing data to be plotted, subject (surface id... | fc36acb5701e6b9346fe676b18cb109dad314d88 | 48,405 |
from typing import List
def compute_i_k_index(citations: List[int], k: int = 10):
"""Given a list of citations (integers) compute the i-k-index (default i10)."""
citations = np.asarray(citations)
i_k_index = (citations > k).sum()
return i_k_index | 16ba1e7be56d10d2e5ff9e20d97df86ee482a899 | 48,406 |
from datetime import datetime
def get_session_details_helper(client):
"""
Retrieve details regarding the current session within `client`
:param client: ICAT client containing an authenticated user
:type client: :class:`icat.client.Client`
:return: Details of the user's session, ready to be conver... | 2c1d714f6e131f7639a44a41ed6e0d45cb54b5b3 | 48,407 |
def resnet18(use_rp=False, width=1, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if use_rp:
print('model using random projection')
model = ResNetRP(width, bb.BasicBlockRP, [2, 2, 2, 2], **kwargs)
els... | cd51bc95741b3e5ea78145af71091658c8c8e25f | 48,408 |
def assign_seller(request, campaign_id):
"""
Shows a list of sellers to assign contacts to.
"""
campaign = Campaign.objects.get(pk=campaign_id)
campaign.count = ContactCampaignStatus.objects.filter(campaign=campaign, seller=None).count()
message = ""
if request.POST:
seller_list = [... | 1945384df939306fc5379ae2b071446116b85997 | 48,409 |
def _swissroll_dataset():
"""Interwined spirals."""
sz = 100
Y = np.arange(sz) % 2
t = np.linspace(0, 4 * np.pi, sz)
X = t[:, None] * np.vstack([np.cos(t + Y * np.pi),
np.sin(t + Y * np.pi)]).T
X += 0.2 * np.random.randn(*X.shape)
return X, Y | 388969317aff95be12fed6545f7f6eb8102bbce7 | 48,410 |
def load_mat_data(dataset_str):
""" dataset_str: protein, metabolic, conflict, powergrid """
dataset_path = 'data/' + dataset_str + '.mat'
mat = loadmat(dataset_path)
if dataset_str == 'powergrid':
adj = sp.lil_matrix(mat['G'], dtype=np.float32)
feats = None
return adj, feats
... | 73cfc51aeeb9fdc08cd9a727402791c1bfc5e970 | 48,411 |
def filter_row_by_data_type(col_name, data_type=None, get_type=False):
"""
A Pandas UDF function that returns bool if the value match with the data_type param passed to the function.
Also can return the data type
:param col_name: Column to be process
:param data_type: The data_type to be compared wi... | 552e930bd3b56c71efd89be051f4faf7f783adac | 48,412 |
def get_ajax_job_status_msg(jobid):
"""return the job status msg (as a string)"""
# user's browser requesting job status msg
global STAT_CODE_RUNNING
if not validate_jobid(jobid):
return Response("Invalid Job ID: %s" % jobid, mimetype='text/plain', headers = {'X-Dalton-Webapp':'OK'})
stat_co... | 69d6eaa5753a69b714b03d93299ab3423d607921 | 48,413 |
import shelve
def run(args, config, prog_args):
"""Run an experiment."""
name = args.name
repo = pygit2.Repository('.')
with shelve.open('.em', writeback=True) as emdb:
exp_info = emdb.get(name)
if exp_info:
if exp_info['status'] == 'running':
return _die(E... | 259a27861b149daff3948f27883f672c64bb3ff3 | 48,414 |
from datetime import datetime
def EarliestActiveTimestamp():
"""Calculates the earliest timestamp of an active channel.
Returns:
A DateTime representing the earliest possible timestamp of an active
channel.
"""
return datetime.now() - timedelta(hours=CHANNEL_LIFETIME_HOURS) | 848eb41ebac30778ec3fac7bc3afd71b65d931b4 | 48,415 |
def get_avg_male_ellipse(exp):
"""Gets the average major and minor axis lengths of the ellipse
fitted to the male for all males across all groups in an experiment.
Parameters
----------
exp : FixedCourtshipTrackingExperiment
Experiment to get average ellipses from.
Returns
-------
... | c65bdd7883cea9007b233a46fa75d15c95fcedb9 | 48,416 |
def on_request(f, name=None):
"""
An interceptor which updates the context value of `REQUEST` during the
enter stage.
:param f: Callable to update the request.
:param unicode name: Interceptor name.
:rtype: Interceptor
"""
return middleware(f, None, name=name) | 9dfe6bd77693d63ff60a9edc82700ae26a86a14d | 48,417 |
import tokenize
def build_model():
"""Build the model
Returns
-------
sklearn.pipeline.Pipeline
The model
"""
pipeline = Pipeline([
(const.FEATURES, FeatureUnion([
(const.TEXT_PIPELINE, Pipeline([
(const.VECT, CountVectorizer(tokenizer=tokenize)),
... | 305642beb9250d12e3f1019b461eeb151e253fbb | 48,418 |
import copy
def extend_and_specialize(items, loader):
# type: (List[Dict[Text, Any]], Loader) -> List[Dict[Text, Any]]
"""Apply 'extend' and 'specialize' to fully materialize derived record
types."""
items = deepcopy_strip(items)
types = {t["name"]: t for t in items} # type: Dict[Text, Any]
... | 0480c3dd1dd8dfef4c460e592717f9d5c9eede72 | 48,419 |
import json
def curl(url, tokens=None, headers=None, request_type="GET", data=None, parse=False,
validate=False, soft_validation=False):
"""
:rtype type
"""
_headers = {}
handler_chain = []
post_req = ["POST", "PUT"]
get_req = ["GET", "DELETE"]
print_url = Options.CURL_PRINT_ONLY is not None... | 9ef6d5d2e18acb9d4037ce0faed0ddc185f56a67 | 48,420 |
def reorder_atoms(mol):
"""change index of the atoms to ensure atoms are ordered by ascending residue number
"""
order = [(i.GetPDBResidueInfo().GetName().strip(), i.GetPDBResidueInfo().GetResidueNumber()) for i in mol.GetAtoms()] # currently the atom name is not used in sorting
order = [i[0] for i in s... | dc87436974482b1c815ff43b2c3030a31ad49e95 | 48,421 |
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