content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def list_steps(df, step_type=None):
"""
Function that finds and lists the step number(s) corresponding to a step type.
Or lists them if you specify nothing as second argument
ARGS :
- df [PANDAS.DATAFRAME] : your global rheology data
- step_type
* [STRING], has to be one of : 'amplitude... | 2bcb60c6adb5e757f51a9f7d3fee2b1a03506b8c | 3,603,269 |
def read_mh_z19():
""" Read the CO2 PPM concenration from a MH-Z19 sensor"""
result = read_mh_z19_with_temperature()
if result is None:
return None
ppm, temp, s = result
return ppm, temp, s | ca746b879f28eac9677c4420c4fb4a5c30873fca | 3,603,270 |
from typing import Optional
def get_cluster(cluster_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetClusterResult:
"""
Cluster details.
Latest API Version: 2020-10-01.
:param str cluste... | b78644aa140cb03b4b37aac3a1adeb9794faf919 | 3,603,271 |
def ignore(*exceptions, retval=None):
"""Execute function ignoring any one of the given exceptions if raised."""
def wrapper(func):
@wraps(func)
def wrapped(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
if not ... | 8e5729e4ec7f9a5a9d5c974c021bfca6151a391a | 3,603,272 |
def remove_recorders(osi):
"""Removes all recorders"""
return osi.to_process('remove', ['recorders']) | c63f23a031a5c957fd8667fc5be87fbd96b26a1e | 3,603,273 |
def removeCutsFromFootprint(footprint):
"""
Find all graphical items in the footprint, remove them and return them as a
list
"""
edges = []
for edge in footprint.GraphicalItems():
if edge.GetLayerName() != "Edge.Cuts":
continue
footprint.Remove(edge)
edges.app... | ddb047e00f98d73037abb5d9dc0a2e00de457f56 | 3,603,274 |
def addPhoto(link, fullPath,object):
""" add new photo
perm: link = the link to the photo
perm: object = the object this photo Belongs to as Model object
return the new photo object if added or false if failed"""
try:
photo = Photos(link=link, full_path=fullPath ,object=object)
db.session.add(photo)
db.s... | b6606a1c6c264c08c4c7e6f569783c3fc156050a | 3,603,275 |
from typing import OrderedDict
def to_options(split_single_char_options=True, **kwargs):
"""Transform keyword arguments into a list of cmdline options
Imported from GitPython.
Original copyright:
Copyright (C) 2008, 2009 Michael Trier and contributors
Original license:
BSD 3-Clause "... | b5835d8d9f52fed1ba34799c319be06e9913a1e3 | 3,603,276 |
def main_ctx():
"""Return a reference to the global Z3 context.
>>> x = Real('x')
>>> x.ctx == main_ctx()
True
>>> c = Context()
>>> c == main_ctx()
False
>>> x2 = Real('x', c)
>>> x2.ctx == c
True
>>> eq(x, x2)
False
"""
global _main_ctx
if _main_ctx is None... | b4a85d6badbbdb239dde158f5406fc48209ccf3b | 3,603,277 |
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues, axis_font_size=12):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=Tru... | 7c6bdf68cf0f82826b947ba7572e8449254525ed | 3,603,278 |
def make_primitive_smoothed(cas_coords, smoothing_level=2):
""" Generates smoothed primitive from a list of coordinates.
Parameters
----------
cas_coords : list(numpy.array or float or tuple)
Each element of the list must have length 3.
smoothing_level : int, optional
Number of time... | bcb94241eb9a82482b523a07bb276f48b9ca91db | 3,603,279 |
def manager():
"""Retrieves the default top-level manager"""
global _defaultManager
return _defaultManager | 0b917f263010553565d45f527531f3f6bcb7c631 | 3,603,280 |
import inspect
def getclasses():
"""return a list of classes defined in `__all__`"""
classes = []
for name, member in getmembers():
if inspect.isclass(member):
classes.append(member)
return classes | ab734ac5578ba4b8fed342d2c9d41a294937b5e7 | 3,603,281 |
def load_lang_data(corpus_name, topk=10000):
"""
Load {topk} sentences from the corpus named by {corpus_name}.
"""
fpath = corpus_name + '.' + tokenizer_name
tokens = []
with open(fpath) as f:
for i, line in enumerate(f):
tokens.append(list(map(int, line.split(' '))))
... | af26319304d364f18d40fca645f7e28cc382bf10 | 3,603,282 |
from typing import Match
def gen_ruby_html(match: Match) -> str:
"""Convert matched ruby tex code into plain text for bbs usage
\ruby{A}{B} -> <ruby><rb>A</rb><rp>（</rp><rt>B</rt><rp>）</rp></ruby>
Also support | split ruby
\ruby{椎名|真昼}{しいな|まひる} ->
<ruby><rb>椎名</rb><rp>（</rp><rt>しいな</rt><rp>）</rp>... | bd603b67a61812152f7714f3a13f48cac41db277 | 3,603,284 |
import numpy
def FingerprintMol(mol):
""" generates the EState fingerprints for the molecule
Concept from the paper: Hall and Kier JCICS _35_ 1039-1045 (1995)
two numeric arrays are returned:
The first (of ints) contains the number of times each possible atom type is hit
The second (of floats) contain... | 034b78422c4add407457fdf88c3178d529229b16 | 3,603,285 |
import time
def _generate_request_id():
"""
生成一个请求 id，暂时用 (时间戳 % 60 分钟)
:return:
"""
return int(time.time() * 1000 % (60 * 60 * 1000)) | b334265d41b68fc3455297bbc21065e7cb064920 | 3,603,286 |
def SkipIf(should_skip):
"""Decorator which allows skipping individual test cases."""
if should_skip:
return lambda func: None
return lambda func: func | 135486c94640231328089115d61ba2d1eb87e46a | 3,603,287 |
def generic_image_dataset_summary():
"""
Return a short HTML summary of a DatasetJob
"""
job = job_from_request()
return flask.render_template('datasets/images/generic/summary.html', dataset=job) | 26919ae503d0a6decb0d13143aeb7dcd617d5fdf | 3,603,288 |
def Choice(*choices):
""" note that the class of the choices becomes a base class, e.g. issubclass(Choice(1,2,3), int) == True """
if not len(choices):
raise ValueError(f"cannot define Choice without any choices")
if any(type(c) is not type(choices[0]) for c in choices):
raise ValueError(f"a... | dcbb638d2e062562e7692572d60c13f80acce033 | 3,603,289 |
from typing import List
import torch
def add_sine_features(
data_list: List[Data],
n_jobs: int = 1,
only_physical_cores: bool = False,
verbose: bool = False,
) -> List[Data]:
"""Add the Sine matrix for the given data of a homogeneous graph.
Args:
data_list (list of :class:`torch_geomet... | 7571ac175d79dc41405d380ad130100b760b30be | 3,603,290 |
def cache_execute(fn, cache, pass_kwargs=False, return_tuple=True, expand_fn=None):
"""Decorator that adds caching to a function that executes
multiple tapes on a device.
This decorator makes use of :attr:`.QuantumTape.hash` to identify
unique tapes.
- If a tape does not match a hash in the cache,... | 51e8ea9f8cf6596df88e219268627b11bd42ee68 | 3,603,291 |
def get_hmslincs_annotations(cell_lines):
""" Returns metadata annotations for cell lines available
on LINCS DB
Parameters
----------
cell_lines: list of str
Name of cell lines
Return
------
dfs: pandas dataframe
dataframe of cell line annotations from HMS LINCS DB
for g... | 80ff21797e429a6dcdc5b44bae284caec68cb9dd | 3,603,292 |
def plot_trajectory_2color (data, movie_ID):
"""Separately plot two channel trajectories of a given movie side by side.
Args:
data (pandas dataframe): dataframe containing parsed data. At the very
least, must contain the columns 'color', 'movie_ID', and 'parsed'.
movie_ID (str): uni... | 56eba8559e3fd2a6bd126b6ef5502924c5e13cb6 | 3,603,293 |
def yaml_cloudformation_constructor(loader, node):
""" Implements the yaml tag !Cloudformation
The tag takes a dict {stack: $stack_name, output: output_key}
as node (argument).
Example:
VpcId: !Cloudformation {stack: ${vpc_stack}, output: VPC}
VpcId: !Cloudformation {stack: ${vpc_stack}, r... | 5375081cdeaf1fa5a125c08c5cbbc71605439984 | 3,603,294 |
from datetime import datetime
def convert_row(column_name_type, row):
"""
Make cell data more user friendly.
Args:
column_name_type (List[(str, str)]): A list of pairs of column names and types
row (dict): The row data as a dict
Returns:
A list containing row data transformed... | 2d5d29ea57f0fe22dd8c3c74b38a34ed16bf637d | 3,603,296 |
def norm(x, train_stats):
"""
Normalize the data.
"""
return (x - train_stats['mean']) / train_stats['std'] | c7791c39b64a5041e537bb7554c0c4a7267d180c | 3,603,298 |
import random
import string
def randomstring(size=20):
"""Create a random string.
Args:
None
Returns:
result
"""
# Return
result = ''.join(
random.SystemRandom().choice(
string.ascii_uppercase + string.digits) for _ in range(size))
return result | 64ecd935130f6308367d02a5d699ea05c906cbb2 | 3,603,299 |
def get_avg_temp(data):
"""this function gets the average temperatures for each point in the fetched data"""
temp_history = []
for point in data:
this_point_temp = []
for year_reading in point:
this_point_temp.append(int(year_reading['weather'][0]['avgtempC']))
temp_histo... | fe5774ca79e417f75cea2f79832ad8338824e3bf | 3,603,301 |
import torchvision
def _transform_block(init_block):
"""Transform the resnet block to Vega block."""
if isinstance(init_block, torchvision.models.resnet.Bottleneck):
inplanes = init_block.conv1.in_channels
planes = init_block.bn1.num_features
stride = init_block.stride
downsamp... | 4fe764d71a31f1159eb0988d0787439d7b30c7be | 3,603,302 |
def getAction(num, distance, bird_height, hole_height):
"""
Gets the action from a network by feeding forward the inputs.
"""
if strategy == 0:
global population
distance = distance / 450 - 0.5
# distance = distance / 450
bird_height = min(bird_height, SCREENHEIGHT) / SCR... | 139202aa18cdc248db60636c5a47321357bd9582 | 3,603,303 |
def reported_br_add(path1, path2=None):
"""Return expected lines for addition of a (sub)branch.
Params are (SUBBRANCH_RPATH) or (OUTER_BRANCH_FULLPATH, SUBBRANCH_RPATH).
"""
subbranch_rpath, subbranch_fullpath = reported_br_params(path1, path2)
return [reported_element_add_line(subbranch_rpath, r' \(branc... | b2cc6427f062e3f116fe922f2e41c1bb49f001f9 | 3,603,304 |
def span_accuracy(predictions, positions, n_way=5):
"""
Exact span match.
:param predictions: [batch_size, 2]
:param positions: [batch_size, 5, 2]
:return: [batch_size]
"""
predictions = tf.stack(n_way * [predictions], axis=1)
_equal = tf.cast(math_ops.equal(predictions, positions), tf.i... | a80e0fb2a71ecac05309df550c74c85d591b2c44 | 3,603,305 |
def ldns_is_rrset(*args):
"""LDNS buffer."""
return _ldns.ldns_is_rrset(*args) | 208b08cc99db1dfc41b1ede2629f7b7846b16204 | 3,603,306 |
def socketio_service(request):
""" The view that will launch the socketio listener """
socketio_manage(request.environ, namespaces=nsmap, request=request)
return {} | fb8acf76e82b59b492cd9194499b8e96536b7a55 | 3,603,307 |
import psutil
def pid():
"""Find the PID of the qemu usermode binary which we are
talking to.
"""
# Find all inodes in our process which are connections.
targets = set(c.raddr for c in psutil.Process().connections())
# No targets? :(
if not targets:
return 0
for process in ps... | 30f19e80eab375e82499b0d2654ffdde9ded19bd | 3,603,309 |
def has_seven(k):
"""Returns True if at least one of the digits of k is a 7, False otherwise.
>>> has_seven(3)
False
>>> has_seven(7)
True
>>> has_seven(2734)
True
>>> has_seven(2634)
False
>>> has_seven(734)
True
>>> has_seven(7777)
True
"""
if k % 10 == 7:
... | 8c0b7b02e36247b3685a6a0142ebd81dd63e220c | 3,603,310 |
from datetime import datetime
def attempt_auto_login(view):
"""Attempt to login an unauthenticated user using the SSO server.
This decorator does several things:
1. Attempt to log in a locally unauthenticated user using the SSO server.
If this succeeds we are redirected back to the original page due... | 930c9a036f3fce6f3b2dfe7d894cf5db3eae58f7 | 3,603,312 |
import torch
def train_or_val(args, loader, simclr, model, criterion, optimizer=None, scheduler=None, train=False):
"""
Train Linear model
"""
loss_epoch = 0
accuracy_epoch = 0
simclr.eval()
if train:
model.train()
else:
model.eval()
model.zero_grad()
for s... | da4e8b3aa1dc5f8fa47af55e3b2cc291a4f6c1ea | 3,603,313 |
from typing import Callable
def _make_cpp_orderbook(
instrument: Instrument,
exchange_name: str = "",
callback: Callable = lambda x: print(x),
) -> OrderBookCpp:
"""helper method to ensure all arguments are setup"""
return OrderBookCpp(instrument, exchange_name or ExchangeType(""), callback) | 0dd28d83defffb1668af919d4b6a5ae733683bdf | 3,603,314 |
import string
import re
def installed_package_device(ctx):
"""
:param: ctx
:return: device_type with rsp version ie asr900rsp2
"""
cmd = 'show version running | include File:'
# File: bootflash:/Image/asr900rsp2-rpbase.03.13.03.S.154-3.S3-ext.pkg, on: RP0
# File: bootflash:/Image/asr900rsp... | 3e05051f8974aee274bcf28b248b8c1758c9993f | 3,603,315 |
def datetime_from_woql(dt_str, woql_type):
"""Convert woql datetime objects (str format) to datetime object.
Raise ValueError if cannot be converted."""
if woql_type == "xsd:duration" or "P" in dt_str:
if dt_str[0] == "-":
pidx = 1 # is_negative
else:
pidx = 0
... | 2e4e58602486728ab032b15e46d71a74f0125ea3 | 3,603,316 |
def bd09togcj02(bd_lon, bd_lat):
"""
Convert coordinates from BD09 to GCJ02
Parameters
-------
lng : Series or number
Longitude
lat : Series or number
Latitude
return
-------
lng : Series or number
Longitude (Converted)
lat : Series or number
Lat... | dd2c74f37cc0f7635e0be0f7519470fc38eabb92 | 3,603,317 |
def bilinear_additive_upsampling(layer_input, scale_factor, channel_out=None, data_format='channels_last'):
""" This function defines a bilinear additive upsampling operation, proposed in following paper:
Wojna, Z., Ferrari, V., Guadarrama, S., Silberman, N., Chen, L.-C., Fathi, A., & Uijlings, J. (2017).
... | ace1ff54467e48878291e53d73986b9391d20b34 | 3,603,318 |
def reflection_padding(net: tf.Tensor, padding_size: int) -> tf.Tensor:
"""
Create reflation padding.
:param net: layer inputs.
:param padding_size: padding size.
:return: padded inputs.
"""
padding_shape = [
[0, 0],
[padding_size, padding_size],
[padding_size, paddi... | 3a2137b90408f17c84db8854ba7e01d1f6877034 | 3,603,319 |
import torch
def diffusion_coeff(t, sigma, device='cuda'):
"""Compute the diffusion coefficient of our SDE.
Args:
t: A vector of time steps.
sigma: The $\sigma$ in our SDE.
Returns:
The vector of diffusion coefficients.
"""
return torch.tensor(sigma ** t, device=device) | fb57ef3af98e7ad2d6a41a298ec7541f70f9556c | 3,603,320 |
def get_subreport(binary_list, least_most_common_bit, bit_position):
"""Gets the report subset that has the least/most common bit within the specified bit position.
Args:
binary_list ():
least_most_common_bit ():
bit_position ():
Returns:
subreport (list): Subset that fulfil... | ca69762a416ead2dd3ba177150dc6a7ae6a6f494 | 3,603,321 |
def r2(net, distribution, y, *args, n_samples=16, batch_size=32, **kwargs):
""" R2 """
y_hat = distribution.mean.detach().cpu().reshape(-1, 1)
return _r2(y, y_hat) | 420664059af70570780c4f3dc31c2237f20c426d | 3,603,322 |
from typing import Type
from typing import Tuple
from typing import List
import numpy
def pytype_to_myiatype(pytype, instance=None):
"""Convert a Python type into a Myia type.
Arguments:
pytype: The Python type to convert.
instance: Optionally, an instance of the Python type to use
... | 02e0d56a4cd25523978de8eb8cec4f0b519267ef | 3,603,323 |
def GetAnglesPolyline(polyline,closed=False):
"""
see: http://stackoverflow.com/questions/3486172/angle-between-3-points
ported to numpy
returns n-2 signed angles
"""
points = polyline[:,0]
if closed:
a = np.roll(points,1,axis=0)
b = points
c = np.roll(points,-1,axi... | 04bd72d74b42438b17ec0802f0371b1554d02ed5 | 3,603,325 |
def get_settings() -> "RobotServerSettings":
"""Get the settings"""
load_dotenv(get_dotenv_path())
return RobotServerSettings() | ee92591a0819b6a3d82b092206c9545599be08f0 | 3,603,326 |
import torch
def train_epoch(Z, W, net, opt, criterion, batch_size=500):
"""
:param Z: predictors
:param W: labels
:param net: nn
:param opt: torch.optim
:param criterion: BCEloss
:param batch_size: batch size
:return: losses
"""
Z = torch.tensor(Z, dtype=torch.float)
W = t... | 468e9a1ab43b803b2e60394d5e3eedca80820557 | 3,603,327 |
import torch
def fft_zero_low_magnitudes(input, high, low=0, onesided=True,
is_next_power2=True):
"""
:param input: the input image
:param high: the highest value to be zeroed out (up to)
:param low: the lowest value to be zeroed out (down to)
:param val: the value (to ... | cfe83900c7f862a05ba02092190ffbfa2139021d | 3,603,328 |
def extract_core_mesh(xyzlim, mesh, mesh_type="tensor"):
"""
Extracts Core Mesh from Global mesh
Parameters
----------
xyzlim: numpy.ndarray
2D array [ndim x 2]
mesh: BaseMesh
The mesh
mesh_type : str, optional
Unused currently
Returns
-------
tuple
... | a504e6d46b0ef62a87281cb5f038f58af6f958cf | 3,603,329 |
import itertools
def value_iteration(P, nS, nA, gamma=0.9, max_iteration=20, tol=1e-3):
"""
Learn value function and policy by using value iteration method for a given
gamma and environment.
Parameters:
----------
P: dictionary
It is from gym.core.Environment
P[state][action] ... | 346691dd9f2ac3dc7bc44ad2ae49fce99e69203d | 3,603,330 |
import array
def ofdm_tx(x, nfft, nsc, cp_length):
""" OFDM Transmit signal generation """
nfft = float(nfft)
nsc = float(nsc)
cp_length = float(cp_length)
ofdm_tx_signal = array([])
for i in xrange(0, shape(x)[1]):
symbols = x[:,i]
ofdm_sym_freq = zeros(nfft, dtype=complex)
... | a7ce729fc2b3e886e1e9855689fcab6b83ec9a97 | 3,603,331 |
def synthetic_item_response_theory():
"""One-parameter logistic item-response theory (IRT) model.
This uses a dataset sampled from the prior. This dataset is a simulation of
400 students each answering a subset of 100 unique questions, with a total of
30012 questions answered.
Returns:
target: StanModel... | 4748723e5e42961b0299f7e94de0a13898d4a214 | 3,603,332 |
def parse_argument(stream: TokenStream) -> AstNode:
"""Parse argument."""
spec = get_stream_spec(stream)
scope = get_stream_scope(stream)
tree = spec.tree.get(scope)
if tree and tree.parser:
with stream.provide(properties=tree.properties or {}):
return delegate(f"command:argume... | e11e6e2c979ec0e2fedbb07e5fe068210abab2fe | 3,603,333 |
from typing import List
def ordered_list(
children: List[List[BlockToken]], loose: bool = False
) -> block_token.List:
"""Create a List token with ordered items."""
return list_token(children, ordered=True, loose=loose) | bda0922a619b94fbe828d46b0263e11c0625c6d3 | 3,603,334 |
def get_grid_size(grid):
"""Return grid edge size."""
if not grid:
return 0
pos = grid.find('\n')
if pos < 0:
return 1
return pos | 9ff60184bb0eed7e197d015d8b330dcf615b4008 | 3,603,335 |
def count_terms_abundance_corrected_KEGG(ui, assoc_dict):
"""
#!!! modify to use protein groups --> handled in Userinput.py or not?
produce abundance corrected counts of GO-terms of background frequency
round floats to nearest integer
Userinput-object includes ANs of sample, and background as well a... | 87ec2757bc40081dd670011019dca6777e1176d9 | 3,603,336 |
def randomExchange(pos):
"""
Randomly shuffle exchange parameters for rnadom labeling
"""
arraytoShuffle = pos[2, :]
_np.random.shuffle(arraytoShuffle)
newpos = _np.array([pos[0, :], pos[1, :], arraytoShuffle, pos[3, :]])
return newpos | 89b86d851785e165b21db9cb59d46b6aafa0ae95 | 3,603,337 |
def value(s, V, full_state_to_index, nA, P, gamma = 1, theta=0.05):
"""Helper function that calculates value for all actions for a given state.
Parameters:
s: state index
V: Value
full_state_to_index: dictionary of state to index Values
nA: number of actions (3)
P: transi... | 6e0feaa932bb22a40c60bbfb042509d314a3e4b4 | 3,603,338 |
from typing import Tuple
import torch
import tqdm
def compute_predictions_34(model:nn.Module, device:str,
dataloader:DataLoader,
srl_vocab:SemanticRolesVocabulary) -> Tuple[list, list]:
"""
Computes and returns all the true and predicted labels of a model on t... | 8e62510e1c801b420b4dbec4beeba07e824ee51a | 3,603,339 |
from typing import List
from typing import Optional
from typing import Dict
def topic_model(
text_docs: List[str],
model_type: str = "LDA",
num_topics: Optional[int] = None,
min_topics: int = 2,
max_topics: int = 10,
no_below: int = 10,
no_above: float = 0.2,
tfidf: bool = True,
mo... | 15632e6a575bce4fd8197bcae05289b334d7f61c | 3,603,340 |
def ts_code(code: str) -> str:
"""
转换证券代码为 tushare 标准格式
:param code:
:return:
"""
if len(code) != 9:
raise Exception('无效的证券代码: 长度不符')
stock_code = code.upper()
if stock_code.endswith('.SZ') or stock_code.endswith('.SH'):
return stock_code
elif stock_code.startswith('S... | 86f568553955ab66bcea7f86ecc341329f8931e3 | 3,603,341 |
import _warnings
def array(sequence=None, typecode=None, copy=True, savespace=False,
type=None, shape=None, dtype=None):
"""Attempt to convert SEQUENCE to an array of specified TYPE and SHAPE.
SEQUENCE:
Data sequence. Can be None, an object with an __array__ method,
a NumArray, a buffe... | c38e419e20d7f3956732cadba7df5fadeb25d422 | 3,603,342 |
import importlib
import inspect
def export_models(mods, main_color, bg_color, export_file, export_format, view,
peewee_module, display, display_modules_name):
"""
Export peewee models to JPG, PDF, etc.
:param str,list mods: modules, named as imported
:param str main_color: main colo... | 37c09d193b754e89da27197eb1e01081bfee5784 | 3,603,343 |
def find_names_from_num_syms(num_syms):
"""Have kallsyms_num_syms, find kallsyms_names"""
if num_syms is None:
return None
names = num_syms.add(4)
while True:
if getByte(names) != 0:
break
names = names.next()
assert num_syms == find_num_syms_from_names(names), "C... | f73d795e7c65996abdb3c759ded61f9d6d4e42df | 3,603,344 |
def no_cache(func):
"""
Decorator made to modify http-response to make it no_cache
"""
def disable_caching(request_handler, *args, **kwargs):
request_handler.set_header('Cache-Control', 'no-cache, no-store, must-revalidate')
request_handler.set_header('Pragma', 'no-cache')
reques... | f5f5b9c8e78a883395ad6cbf263c1c82b1aafdc9 | 3,603,345 |
def index() -> str:
"""
Static string home page
"""
return "Hello, Python!!!" | 7bf6b684e8404d0b2d52bb7578e1f44e37bdf565 | 3,603,346 |
def efs_energy_vector(
name,
efs_energy_kernel,
x,
hyps,
cutoffs=None,
hyps_mask=None,
n_cpus=1,
n_sample=100,
):
"""
Returns covariances between the local eneregy, force components, and
partial stresses of a test environment and the total energy labels in the
training se... | 1337a360d060fe0680dfe6bcd66a51ce2cceb946 | 3,603,347 |
def get_unique_coords(df, lat_field, lon_field):
"""
Takes in a dataframe and extract unique fields for plotting.
Uses dataframe group by to reduce the number of rows that need to
be plotted, returns the lat and lon fields
"""
unique = df.groupby([lat_field, lon_field]).size().reset_index()
... | 3914e59a9263e07a18833135b0d30a3e5b1d4ce6 | 3,603,348 |
def adv_loss(x_train, x_test, y_train, y_test, kernel_fn, loss='mse', t=None, targeted=False, diag_reg=1e-4):
"""
:param x_train: input tensor (training data).
:param x_test: input tensor (test data; used for evaluation).
:param y_train: Tensor with one-hot true labels of training data.
:param y_tes... | cf941ca3e5a4750b04d69d52dca3e9994ab09c0f | 3,603,349 |
import collections
def update(d, u):
"""Function to update a dictionary with values from another.
Arguments:
d {dict} -- Base dictionary.
u {dict} -- Update dictionary.
Returns:
dict -- Updated dictionary.
"""
for k, v in u.items():
if isinstance(v, colle... | 58ef1268e84d193f1550c5a482b8fbdd5910a6a0 | 3,603,351 |
from typing import Optional
def infer_sex(
mt: hl.MatrixTable,
tmp_bucket: str,
out_ht_path: Optional[str] = None,
overwrite: bool = False,
target_regions: Optional[hl.Table] = None,
) -> hl.Table:
"""
Runs gnomad.sample_qc.annotate_sex() to infer sex
:param mt: input MatrixTable
:... | 2d818160068291f2dd599a8de40d3b71b6a3c36c | 3,603,352 |
def jets(event, R = 1.0, p = -1, minpt=20):
"""Returns a list of jets given an event.
If the argument `n_hadrones`, `R`, `p` and `minpt` arent't passed in,
the default values are used.
Parameters
----------
event : Serie
Hadrons of the event
R : int
Size of the radius f... | de1afaf5301d426e7efeed57f96444c6bc25afc9 | 3,603,354 |
async def get_vpc_peering_connection(cidr_block=None,filters=None,id=None,owner_id=None,peer_cidr_block=None,peer_owner_id=None,peer_region=None,peer_vpc_id=None,region=None,status=None,tags=None,vpc_id=None,opts=None):
"""
The VPC Peering Connection data source provides details about
a specific VPC peering... | 73463aa6e5e10a9faed8cf441c89f7e05a24b73e | 3,603,355 |
def get_subclasses(model, include_abstract=False):
"""
Returns a list of unique models that inherit from the specified model. If
include_abstract is True, abstract inheriting models will also be returned.
"""
return list(set([klass for klass in _get_subclasses(model) \
if hasattr(klass, '_me... | 3440930591e3ed347f640896adffef501c449124 | 3,603,356 |
def _filter_optouts_from_recipients(to_list, course_id):
"""
Filters a recipient list based on student opt-outs for a given course.
Returns the filtered recipient list, as well as the number of optouts
removed from the list.
"""
optouts = Optout.objects.filter(
course_id=course_id,
... | 275d0fba66d41b6c2ca7f5df3dcb557af1d4cf72 | 3,603,357 |
def listify(obj):
"""Return a flat list out of the argument."""
if not obj:
obj = list()
elif is_iter(obj):
obj = list(flatten(obj))
else:
obj = [obj]
return deepcopy(obj) | c7c6d5b26337f51129e3d8468fa55ae77066d1d2 | 3,603,360 |
def load_dataset_A(path=None,train=True,norm=False,selected_chans = None,sfreq = 128,fmin=4,fmax=36,tmin=0,tmax=3,start_id=1,end_id=3):
"""dataset A is in voltage foramt"""
# process Dataset A (S1, S2)
# get train target data
if path is None:
path = '/Users/wduong/mne_data/MNE-beetlmileaderboard... | 2bef2119f8e15ec81b25ec21ac38f392dab64481 | 3,603,361 |
def get():
"""
Returns global API instance.
Returns:
API instance
"""
return INSTANCE | 797e6958c0cbdd0c4ca3decb49de47fa8e4ec072 | 3,603,362 |
def get_context_pairs(graphs, num_time_steps):
""" Load/generate context pairs for each snapshot through random walk sampling."""
# load_path = "data/{}/train_pairs_n2v_{}.pkl".format(FLAGS.dataset, str(num_time_steps - 2))
# try:
# context_pairs_train = dill.load(open(load_path, 'rb'))
# pr... | de72ed953d94a74881a183393311c7b64ecc9e8c | 3,603,363 |
import cmd
def rpcDeleteAll():
""" deletes all objects """
res = cmd.delete('all')
if res is not None:
return res
else:
return '' | 68ea34814417f5fd060a378e7be6cc482cce396d | 3,603,366 |
def getdevices(name):
"""
Return the devices associated with the given names.
Args:
name: The name of the profile being accessed.
"""
cursor = db.find({"_id":name})
for result_object in cursor:
if result_object['devices'] == [u'None']:
return "None"
else:
... | fc30af16b3c919fe741ee3147674eebc41ee2efe | 3,603,367 |
import json
def retrieve_transaction_index_template():
"""This function is used for test configuration"""
with open(str(FILES["template"])) as f:
mapping_dict = json.load(f)
template = json.dumps(mapping_dict)
return template | 7a489079bf8a1e8f7b1c95463621a1a914087051 | 3,603,368 |
def unhex(value):
"""Converts a string representation of a hexadecimal number to its equivalent integer value."""
return int(value, 16) | 0a8c297a93f484c8a1143511ef8511521c887a78 | 3,603,369 |
import heapq
from typing import Counter
def k_nn_q(
k: int, dist: DistanceFunc, training_data: TrainingList, unknown: AnySample
) -> str:
"""
Use heapq to maintain a list of the neighbors, in order, avoiding a sort
after all computations are completed.
1. For each item:
1. Compute the di... | 62f2cdf42042166d7c311687f5f3902fa5a09b9c | 3,603,370 |
def make_00a6(sucess):
"""キャラクター削除結果"""
return ("\x00" if sucess else "\x9c") | 2c89d1ba457a3d550742721ddfa9c6f6e7f391c7 | 3,603,371 |
def _get_all_filters():
"""
Find all Filter classes in modules specified in FILTER_PLUGINS.
Returns
-------
filters: list
The filter classes
"""
filters = list()
for module_name in FILTER_PLUGINS:
logger.info(f"[green bold]Importing filters from [yellow]{module_name}[/] ... | 663a0692aaa556e1f2948dfa22b697746620c5e8 | 3,603,372 |
def puzzles():
""" Import a list of Sudoku puzzles from text file
and return it as a list
Puzzles credit:
http://norvig.com/top95.txt
http://norvig.com/hardest.txt
"""
with open('sudoku_puzzles.txt') as file:
data = file.readlines()
#remove all end ... | e4f876c52b21756a9a97c6f1bc3a0cd2c00fca61 | 3,603,373 |
def getXy_insulation(f_ins_pos, f_ins_neg, f_readcts, f_span, outpath, ctonly=False):
"""
:param f_ins_pos: downstream data from wigvals_from_cutsite()
:param f_ins_neg: upstream data from wigvals_from_cutsite()
:param f_readcts: read_counts() data (i.e. from mre11)
"""
data_pos = m.load_nparray... | 175bda9b199775dc47d10d14af54a7f8e25dbfe9 | 3,603,375 |
def hernquist_profile(mtot,a,r,nfw=0):
"""Defines a herqnuist profile with mass mtot
and scale radius a. has the option of
being nfw instead using a flag."""
return a*mtot/(2*np.pi)*(r*(a+r)**(3-nfw))**-1. | e68dabad1bd51399db5c510700c692b91036d565 | 3,603,376 |
import json
def groups_delete_user_callback(request):
"""A callback to mock the `/groups/{group_id}/delete_user.json` endpoint."""
resp_body = {
'request': TEST_REQUEST_ID
}
headers = {'X-Request-Id': TEST_REQUEST_ID}
req_body = getattr(request, 'body', None)
qs = parse_qs(req_body)
... | 6eefedec6f306a2706efb03d35150a30638c61ae | 3,603,377 |
def ew(node):
"""
Return a dictionary of the nodes and pipes that are connected to node
"""
cY = {}
gM = node.dependent(b.INPUTS | b.HIDDEN_INPUTS)
for p in gM:
cY[p] = []
for c in range(p.inputs()):
if p.input(c) == node:
cY[p].append(c)
return c... | 5012e9dadfe3fd77d9fe1027ddfe51efc60088df | 3,603,378 |
def get_similar_fork(fork_list, fork1):
"""
Args:
fork_list
fork1
Return:
A list contains the similar forks with fork1 (Top 5)
"""
if (fork_list is None) or (fork1 is None):
return None
sort_list = {}
param_list = {}
for fork2 ... | 3100ed7a1585496b3cd71e1e81629a919a34b9df | 3,603,380 |
from typing import Optional
from typing import List
from datetime import datetime
def make_ciphertext_ballot(
object_id: str,
style_id: str,
manifest_hash: ElementModQ,
code_seed: Optional[ElementModQ],
contests: List[CiphertextBallotContest],
nonce: Optional[ElementModQ] = None,
timestamp... | 7d20ae35cb9b433163a27658c27ef3e507cd0274 | 3,603,381 |
import io
def discretize_stationary_from_disk(parameter_file):
"""Generates stationary discretization only based on system relevant data given as .mat files on the hard disc.
The path and further specifications to these objects are given in an '.ini' parameter file (see example below).
Suitable for discre... | 68196452bf1ae00fb4bb26a46abc919f3489884a | 3,603,382 |
from typing import Callable
def load_json_file_with_name(module_path: str) -> Callable[[str], dict]:
""" Return a function that can load a json from a filename and return a dict, but also the filename """
def _json_loader(filename: str):
return _load_json_file_with_name(module_path, filename)
retu... | cd01c04d85e294abaa1c6547a5ef96d091b5d94e | 3,603,383 |
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