content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def gumbel_softmax(log_pi, tau=0.1, axis=1):
"""Gumbel-Softmax sampling function.
This function draws samples :math:`y_i` from Gumbel-Softmax distribution,
.. math::
y_i = {\\exp((g_i + \\log\\pi_i)/\\tau)
\\over \\sum_{j}\\exp((g_j + \\log\\pi_j)/\\tau)},
where :math:`\\tau` is a tem... | 91751a5bd8069c71de5dbe9f2cbbc7757daff140 | 31,400 |
def has_active_lease(storage_server, storage_index, now):
"""
:param allmydata.storage.server.StorageServer storage_server: A storage
server to use to look up lease information.
:param bytes storage_index: A storage index to use to look up lease
information.
:param float now: The curre... | 544b17489bc766a15bf2eca5cddab55c1bf473dd | 31,401 |
from typing import List
import os
def _enumerate_files(path: str, parts: List[str], repl_list1: List[str], repl_list2: List[str] = None) -> str:
""" Enumerate all possible file names """
if len(parts) <= 2:
for token in repl_list1:
parts[-1] = token
candidate = os.path.join(pat... | a227d9727efe27038f40b814cce6647cb0c75862 | 31,402 |
def MXfunc(A, At, d1, p1):
"""
Compute P^{-1}X (PCG)
y = P^{-1}*x
"""
def matvec(x):
return p1 * x
N = p1.shape[0]
return LinearOperator((N, N), matvec=matvec) | c2c1d6361756779f9318a9356251c8ba1a610057 | 31,403 |
from ._filter import filter_
from typing import Callable
def filter(predicate: Predicate[_T]) -> Callable[[Observable[_T]], Observable[_T]]:
"""Filters the elements of an observable sequence based on a
predicate.
.. marble::
:alt: filter
----1---2---3---4---|
[ filter(i: i>2) ... | b56f4ed6e770d9b623362cca92a791d7f1ef5fa7 | 31,404 |
def scb_to_unit(scb):
"""Convert codes used by Statistics Sweden to units used by the NAD GIS files."""
scbform = 'SE/' + '{:0<9}'.format(scb)
if scbform in g_units.index:
return g_units.loc[scbform, 'G_unit']
else:
return 0 | 1c878492bb0bb4e8c7b7874097c86fa8bbc93329 | 31,405 |
import os
def revision_pattern_from_build_bucket_path(bucket_path):
"""Get the revision pattern from a build bucket path."""
return '.*?' + os.path.basename(bucket_path) | b7db362eb47531413397f0dc2079f4f7fd931d94 | 31,406 |
def img_to_square(im_pic):
""" 把图片处理成正方形
:param im_pic:
:return:
"""
w, h = im_pic.size
if w >= h:
w_start = (w - h) * 0.618
box = (w_start, 0, w_start + h, h)
region = im_pic.crop(box)
else:
h_start = (h - w) * 0.618
box = (0, h_start, w, h_start + w)... | ae672ea715cb982272eddaff0417d4f64926894c | 31,407 |
from typing import List
def load_sentence(
filename: str,
with_symbol: bool=True
) -> List[str]:
"""コーパスをロードする。"""
if with_symbol:
tokens = [
list(sent.split()) + [config.END_SYMBOL]
for sent in (_.strip() for _ in open(filename))
]
else:
tokens = [
... | 3f494b740a4ed157f163329de8cc0568e5541cdc | 31,408 |
def to_str(bytes_or_str):
"""
The first function takes a bytes or str instance and always returns
a str.
"""
if isinstance(bytes_or_str, bytes):
value = bytes_or_str.decode('utf-8')
else:
value = bytes_or_str
return value | 4a73559039501764a00e697c092d20426949058d | 31,409 |
def configure_ampliseq(request):
"""View for ampliseq.com importing stuff"""
ctx = get_ctx_ampliseq(request)
return render_to_response(
"rundb/configure/ampliseq.html", ctx, context_instance=RequestContext(request)
) | 90cdd14de158efc79b5cad11feaa115878469866 | 31,410 |
import struct
def _copy(s):
"""Creates a new set from another set.
Args:
s: A set, as returned by `sets.make()`.
Returns:
A new set containing the same elements as `s`.
"""
return struct(_values = dict(s._values)) | b505af5037d0aa889aa8ed707eaf69d572e54f84 | 31,411 |
import random
def particle_movement_x(time):
"""
Generates a random movement in the X label
Parameter:
time (int): Time step
Return:
x (int): X position
"""
x = 0
directions = [1, -1]
for i in range(time):
x = x + random.choice(directions)
return x | 0dff68080dbfd56997cffb1e469390a1964a326f | 31,412 |
def find_match_characters(string, pattern):
"""Find match match pattern string.
Args:
params: string
pattern
Returns:
Raises:
"""
matched = []
last_index = 0
if not string or not pattern:
return matched
if string[0] != pattern[0]:
return matc... | 6d3bc3844c20584038e41c22eeead7325031b647 | 31,413 |
import itertools
def cross_product_configs(**configs):
"""
Given configs from users, we want to generate different combinations of
those configs
For example, given M = ((1, 2), N = (4, 5)),
we will generate (({'M': 1}, {'N' : 4}),
({'M': 1}, {'N' : 5}),
... | 7fcf61abcbb850630a5be9468822f567199460bc | 31,414 |
from typing import Union
def columnwise_normalize(X: np.ndarray) -> Union[None, np.ndarray]:
"""normalize per column"""
if X is None:
return None
return (X - np.mean(X, 0)) / np.std(X, 0) | 24b5995a5b36738e1c9eecf427fdb4ed83d43145 | 31,415 |
import os
def fq_classification(fqclass, verbose=False):
"""
Read the fastq classification file
:param fqclass: the classification file that has the file name and then arbitrary classifications separated by tabs
:param verbose: more output
:return: a dict of the classification. Guaranteed that all... | 84f71e91ad9b20c5781377b05f3a72c05a6d28b5 | 31,416 |
def line_intersect(a1, da, b1, db):
"""
compute intersection of infinetly long lines
"""
dba = np.array(a1) - np.array(b1)
da_perpendicular = perp(da)
num = np.dot(da_perpendicular, dba)
denom = np.dot(da_perpendicular, db)
dist_b = (num / denom)
return dist_b*db + b1 | 5be8211d3f31d7984820349dfbbb1e05592287a4 | 31,417 |
def sequence_(t : r(e(a))) -> e(Unit):
"""
sequence_ :: (Foldable r, Monad e) => r (e a) -> e ()
Evaluate each monadic action in the structure from left to right, and
ignore the results. For a version that doesn't ignore the results see
sequence.
As of base 4.8.0.0, sequence_ is just sequenceA... | 85c36cc767f60eaccd8d91cdc4d14ca2370069f7 | 31,418 |
import re
def param_validated(param, val):
"""Return True if matches validation pattern, False otherwise"""
if param in validation_dict:
pattern = validation_dict[param]
if re.match(rf'{pattern}', val) is None:
log.error("Validation failed for param='%s', "
"v... | 4359b20437e8cfd21b82b5952cc1c9026e6dca13 | 31,419 |
def remove_keys_from_array(array, keys):
"""
This function...
:param array:
:param keys:
:return:
"""
for key in keys:
array.remove(key)
return array | 3143b8e42eb1e1b2f5818a254bcec3631c30f5ea | 31,420 |
def create_tree_data(codepage, options, target_node, pos):
"""Create structure needed for Dijit Tree widget """
tree_nodes = []
for opt in options:
code = opt['code']
cp = codepage + code
add_tree_node(tree_nodes, cp, code+"-"+opt['label'], cp)
tree_data = {
... | fededfc4bc3e39860221783459d324a15f16d081 | 31,421 |
def detect_octavia():
"""
Determine whether the underlying OpenStack is using Octavia or not.
Returns True if Octavia is found in the region, and False otherwise.
"""
try:
creds = _load_creds()
region = creds['region']
for catalog in _openstack('catalog', 'list'):
... | 265e01730cfc2b7e1870c0364ddb4cd5d7c4b336 | 31,422 |
def add_cache_control_header(response):
"""Disable caching for non-static endpoints
"""
if "Cache-Control" not in response.headers:
response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
return response | 06f3f4a7259076be535b4ea0ca719a13e9e665a0 | 31,423 |
from typing import List
def clean_new_import_aliases(
import_aliases: List[ImportAlias],
) -> List[ImportAlias]:
"""Clean up a list of import aliases."""
# Sort them
cleaned_import_aliases = sorted(import_aliases, key=lambda n: n.evaluated_name)
# Remove any trailing commas
last_name = cleaned... | 5f0b25798f353c999d325125e80c9ccb67b5afec | 31,424 |
def _format_td(timedelt):
"""Format a timedelta object as hh:mm:ss"""
if timedelt is None:
return ''
s = int(round(timedelt.total_seconds()))
hours = s // 3600
minutes = (s % 3600) // 60
seconds = (s % 60)
return '{:02d}:{:02d}:{:02d}'.format(hours, minutes, seconds) | 071f25c3c8cfc75cacf2fedc7002527897362654 | 31,425 |
def add_quad_reaction_node(graph, rxn):
"""
Adds a "Quad Reaction Node" (QRN) group of nodes to a graph, and connects
them to the correct compound nodes.
The QRN consists of two nodes constituting the intended forward direction
of the reaction and two nodes constituting the reverse direction. Each ... | 360e7c4e74ed58da9b85548c4d217e3d1f40150b | 31,426 |
import os
def api_dataset(dataset):
"""
Return a list of available years
"""
path = os.path.join(geodata_dir, dataset)
if os.path.exists(path):
return jsonify(find_maps(path))
else:
return 'error: cannot find dataset: %s' % dataset | 92dd50dfcb4b8d4ec2bb87f1e098ebc06ac3bec9 | 31,427 |
def matrixFilter(np_image_2D, np_mask):
"""
Processing filtering with given matrix
Keyword argument:
np_image_2D -- two dimensional image(grayscale or single color channel)
np_mask -- mask matrix as numpy array
Return:
np_image_fil -- image as numpy 2D array, after specified filtering
... | 422de6c4c539ceb154827d511127f1b120519a2a | 31,428 |
def batch_split_axis(batch, n_split):
"""Reshapes batch to have first axes size equal n_split."""
x, y = batch
n = x.shape[0]
n_new = n / n_split
assert n_new == int(n_new), (
"First axis cannot be split: batch dimension was {} when "
"n_split was {}.".format(x.shape[0], n_split))
n_new = int(n_... | 0f413e40961b15b64bf118b2daa012e853dbc294 | 31,429 |
def right_align(value, length):
"""
:param value: string to right align
:param length: the number of characters to output (spaces added to left)
:return:
"""
if length <= 0:
return u""
value = text(value)
if len(value) < length:
return (" " * (length - len(value))) + va... | de8c42734b094514ebd45c2cb5517da806ec74b8 | 31,430 |
import yaml
from pathlib import Path
def test_disable_functions_as_notebooks(backup_spec_with_functions):
"""
Tests a typical workflow with a pieline where some tasks are functions
"""
with open('pipeline.yaml') as f:
spec = yaml.safe_load(f)
spec['meta']['jupyter_functions_as_notebooks']... | f5e5c8cc687a64d4c7593ef571e181d6cf4d27ce | 31,431 |
import builtins
def proxy_gettext(*args, **kwargs):
"""Proxy calls this function to the real built-in gettext() function.
This is not required for normal operation of the application, could be not imported at all,
but will help development.
"""
### this would load gettext in case it wasn't done b... | 870e5c3d7c6bceec438f042c875e313e4877d9b4 | 31,432 |
def generate_users_data(users):
"""
Generate users' rows (assuming the user's password is the defualt one)
:param users:
:return:
"""
headers = ['שם משתמש', 'סיסמה']
rows = [[user.username, DEFAULT_TEAM_USER_PASSWORD] for user in users]
rows.insert(0, headers)
return rows | c6d9ef03b3b28c31f59627be574c4d20328b5d82 | 31,433 |
def ListToMatrix(lv):
""" Convert a list of 3 or 4 ``c4d.Vector`` to ``c4d.Matrix``. """
if not isinstance(lv, list):
raise TypeError("E: expected list of vectors, got %r" % type(lv))
m = len(lv)
if not isinstance(lv[0], c4d.Vector):
raise TypeError("E: expected list elements of type c4... | b60e1f62d250ce1f7c8ef2772755c3a3ce878395 | 31,434 |
def loadEpithelium(name):
"""Returns an epithelium from a CSV file with the given name.
Precondition: name exists and it's in CSV format"""
assert type(name) == str, "name isn't a string"
recs = []
text = open(name)
i = 0
for line in text:
if i > 0:
recs.append(loadRecept... | 01e0c613faa71dbc77be650c3eba1f5006966f9e | 31,435 |
def focal_attention(query, context, use_sigmoid=False, scope=None):
"""Focal attention layer.
Args:
query : [N, dim1]
context: [N, num_channel, T, dim2]
use_sigmoid: use sigmoid instead of softmax
scope: variable scope
Returns:
Tensor
"""
with tf.variable_scope(scope or "attention", reus... | 27f480b8911b3ff1a4367af6b7d5b9a549d24653 | 31,436 |
def format_float(x):
""" Pretty formatting for floats
"""
if pd.isnull(x):
return " ."
else:
return "{0:10.2f}".format(x) | 9f3cdc0ab41bc69807d178e1c4a56abec0ac6fab | 31,437 |
def random_points_and_attrs(count, srs_id):
"""
Generate Random Points and attrs (Use some UTM Zone)
"""
points = generate_utm_points(count, srs_id)
rows = []
for p in points:
rand_str = ''.join(choice(ascii_uppercase + digits) for _ in range(10))
rand_bool = bool(randint(0, 1))
... | 1dcdbb376f91d75c33baf40275a047393bf13fd5 | 31,438 |
from typing import Union
from typing import List
def trimap(adata, **kwargs) -> Union[Axes, List[Axes], None]:
"""\
Scatter plot in TriMap basis.
Parameters
----------
{adata_color_etc}
{edges_arrows}
{scatter_bulk}
{show_save_ax}
Returns
-------
If `show==False` a :class... | e3294c689e9081813c6e1defecdf20918dc02b8b | 31,439 |
import collections
def quantile(arg, quantile, interpolation='linear'):
"""
Return value at the given quantile, a la numpy.percentile.
Parameters
----------
quantile : float/int or array-like
0 <= quantile <= 1, the quantile(s) to compute
interpolation : {'linear', 'lower', 'higher', ... | 42e53b43d7ea580616d82fea4bdc08260de3b661 | 31,440 |
def random_new(algo=RNG_CMWC):
"""Return a new Random instance. Using ``algo``.
Args:
algo (int): The random number algorithm to use.
Returns:
Random: A new Random instance using the given algorithm.
"""
return tcod.random.Random(algo) | f6eef62d3eb483dbcb85d420262cf411d6934790 | 31,441 |
def get_kth_value(unsorted, k, axis=-1):
"""
Args:
unsorted: numpy.ndarray of any dimensionality.
k: int
Returns:
kth values along the designated axis.
"""
indices = np.argpartition(unsorted, k, axis=axis)[..., :k]
k_smallests = np.take_along_axis(unsorted, indices, axis=... | ab787a89c04d390424749916a6ec7958efcc931e | 31,442 |
def rdc_transformer(
local_data,
meta_types,
domains,
k=None,
s=1.0 / 6.0,
non_linearity=np.sin,
return_matrix=False,
ohe=True,
rand_gen=None,
):
# logger.info('rdc transformer', k, s, non_linearity)
"""
Given a data_slice,
return a transformation of the features data... | 420e0ca3dfedf23434cb1f2ee500a2d0f8969f52 | 31,443 |
import sys
def trim(docstring):
"""Trims the leading spaces from docstring comments.
From http://www.python.org/dev/peps/pep-0257/
"""
if not docstring:
return ''
# Convert tabs to spaces (following the normal Python rules)
# and split into a list of lines:
lines = docstring.expa... | f1b96cebf76df60491324a9f8a20477b3bd6cc11 | 31,444 |
async def get_layer_version(compatible_runtime=None,layer_name=None,version=None,opts=None):
"""
Provides information about a Lambda Layer Version.
"""
__args__ = dict()
__args__['compatibleRuntime'] = compatible_runtime
__args__['layerName'] = layer_name
__args__['version'] = version
_... | 57319a257b4e15e2d7ab2d14bfc43718967ea7e2 | 31,445 |
from typing import Optional
from typing import List
def get_header_names(header_annotation_names: Optional[List[str]],
doc: Optional[str] = None,
docs: Optional[List[str]] = None):
"""Get a list of header annotations and a dictionary for renamed annotations."""
# Get ... | e011af009e350e0ddbd5b18d19eb45816a7e8d6c | 31,446 |
def data_index(data, key):
"""Indexing data for key or a list of keys."""
def idx(data, i):
if isinstance(i, int):
return data[i]
assert isinstance(data, dict)
if i in data:
return data[i]
for k, v in data.items():
if str(k) == str(i):
... | f2b6d18bcd83eb0ffd9b355643e79b40459d8d6a | 31,447 |
def calculate_misfit(da):
""" For each force orientation, extracts minimum misfit
"""
misfit = da.min(dim=('origin_idx', 'F0'))
return misfit.assign_attrs({
'best_force': _min_force(da)
}) | 3dcba853b2e30d9fb5d3cbf96cfa6a00760335a6 | 31,448 |
def f_score_one_hot(labels,predictions,beta=1.0,average=None):
"""compute f score, =(1+beta*beta)precision*recall/(beta*beta*precision+recall)
the labels must be one_hot.
the predictions is prediction results.
Args:
labels: A np.array whose shape matches `predictions` and must be one_hot.
... | 091143244858dee1e001042931f625db30c58195 | 31,449 |
def articles():
"""Show a list of article titles"""
the_titles = [[a[0], a[1]] for a in articles]
return render_template('articles.html', titles = the_titles) | bb8f9af9cedb30f89fa950f60c7710ac840f026c | 31,450 |
import json
from pathlib import Path
import uuid
def create_montage_for_background(montage_folder_path: str, im_b_path: str, f_path: str, only_face: bool) -> str:
"""
Creates and saves the montage from a designed background. If a folder is provided for faces, it will create a file
'faces.json' inside the ... | b0c229d16e0ffdf2a8ea63cbc5785be918c09d46 | 31,451 |
def deserialize_question(
question: QuestionDict
) -> Question:
"""Convert a dict into Question object."""
return Question(
title=question['title'],
content=question.get('content'),
choices=[
Choice(
title=title,
goto=goto
)
... | c6f5dd962cdc7a0ef273d4397472de572f92c1f8 | 31,452 |
from datetime import datetime
def ensure_utc_datetime(value):
"""
Given a datetime, date, or Wayback-style timestamp string, return an
equivalent datetime in UTC.
Parameters
----------
value : str or datetime.datetime or datetime.date
Returns
-------
datetime.datetime
"""
... | 0d5c631d2736094f5a60c2eb4ca7c83fcb1e3e6a | 31,453 |
def get_pod_status(pod_name: str) -> GetPodEntry:
"""Returns the current pod status for a given pod name"""
oc_get_pods_args = ["get", "pods"]
oc_get_pods_result = execute_oc_command(oc_get_pods_args, capture_output=True).stdout
line = ""
for line in oc_get_pods_result.splitlines():
if po... | 1353fb4f457a4818ffcfda188ca4d3db55ce5cc9 | 31,454 |
def helix_evaluate(t, a, b):
"""Evalutes an helix at a parameter.
Parameters
----------
t: float
Parameter
a: float
Constant
b: float
Constant
c: float
Constant
Returns
-------
list
The (x, y, z) coordinates.
Notes
-----
An i... | 2d62cae57dac72cd244d66df8de2d0a5d3b70c38 | 31,455 |
import scipy
def get_pfb_window(num_taps, num_branches, window_fn='hamming'):
"""
Get windowing function to multiply to time series data
according to a finite impulse response (FIR) filter.
Parameters
----------
num_taps : int
Number of PFB taps
num_branches : int
Number o... | 1193a29ab754e2c8f30e1a58f34c9efcf58513af | 31,456 |
from typing import Dict
from typing import OrderedDict
def retrieve_bluffs_by_id(panelist_id: int,
database_connection: mysql.connector.connect,
pre_validated_id: bool = False) -> Dict:
"""Returns an OrderedDict containing Bluff the Listener information
for ... | f3f5d3e86423db20aa4ffe22410cc491ec5e80ed | 31,457 |
def extract_protein_from_record(record):
"""
Grab the protein sequence as a string from a SwissProt record
:param record: A Bio.SwissProt.SeqRecord instance
:return:
"""
return str(record.sequence) | a556bd4316f145bf23697d8582f66f7dcb589087 | 31,458 |
import cftime
def _diff_coord(coord):
"""Returns the difference as a `xarray.DataArray`."""
v0 = coord.values[0]
calendar = getattr(v0, "calendar", None)
if calendar:
ref_units = "seconds since 1800-01-01 00:00:00"
decoded_time = cftime.date2num(coord, ref_units, calendar)
co... | e430d7f22f0c4b9ac125768b5c69a045e44046a5 | 31,459 |
import _tkinter
def checkDependencies():
"""
Sees which outside dependencies are missing.
"""
missing = []
try:
del _tkinter
except:
missing.append("WARNING: _tkinter is necessary for NetworKit.\n"
"Please install _tkinter \n"
"Root privileges are necessary for this. \n"
"If you have these, t... | 05aad218f3df84ddb5656d0206d50ec32aa02dcb | 31,460 |
from typing import Dict
from typing import Any
def get_user_groups_sta_command(client: Client, args: Dict[str, Any]) -> CommandResults:
""" Function for sta-get-user-groups command. Get all the groups associated with a specific user. """
response, output_data = client.user_groups_data(userName=args.get('user... | b21b087e4e931e33111720bbc987b1bb6749fee8 | 31,461 |
def get_capital_flow(order_book_ids, start_date=None, end_date=None, frequency="1d", market="cn"):
"""获取资金流入流出数据
:param order_book_ids: 股票代码or股票代码列表, 如'000001.XSHE'
:param start_date: 开始日期
:param end_date: 结束日期
:param frequency: 默认为日线。日线使用 '1d', 分钟线 '1m' 快照 'tick' (Default value = "1d"),
:param... | f7c3f94fd012672b75d960ef1c4d749959a7e6cc | 31,462 |
def split_quoted(s):
"""Split a string with quotes, some possibly escaped, into a list of
alternating quoted and unquoted segments. Raises a ValueError if there are
unmatched quotes.
Both the first and last entry are unquoted, but might be empty, and
therefore the length of the resulting list must... | 0790e7b2fecfd6c2aa1ca04c8cb5f1faebb3722b | 31,463 |
import torch
def calc_IOU(seg_omg1: torch.BoolTensor, seg_omg2: torch.BoolTensor, eps: float = 1.e-6) -> float:
"""
calculate intersection over union between 2 boolean segmentation masks
:param seg_omg1: first segmentation mask
:param seg_omg2: second segmentation mask
:param eps: eps for numerica... | 6586b1f9995858be9ab7e40edd1c3433cd1cd6f4 | 31,464 |
import time
def _call_list(urls, method, payload=None, headers=None, auth=None,
proxies=None, timeout=None, stream=None, verify=True,
payload_to_json=True, allow_redirects=True):
"""Call list of supplied URLs, return on first success."""
_LOGGER.debug('Call %s on %r', method, url... | f172e726991235fc9b6bfc63c9071994ab33e7c0 | 31,465 |
def td_path_join(*argv):
"""Construct TD path from args."""
assert len(argv) >= 2, "Requires at least 2 tdpath arguments"
return "/".join([str(arg_) for arg_ in argv]) | 491f1d50767a50bfbd7d3a2e79745e0446f5204c | 31,466 |
import torch
def calculate_segmentation_statistics(outputs: torch.Tensor, targets: torch.Tensor, class_dim: int = 1, threshold=None):
"""Compute calculate segmentation statistics.
Args:
outputs: torch.Tensor.
targets: torch.Tensor.
threshold: threshold for binarization of predictions.... | ccc017dd5c7197565e54c62cd83eb5cdc02d7d17 | 31,467 |
def sample_from_cov(mean_list, cov_list, Nsamples):
"""
Sample from the multivariate Gaussian of Gaia astrometric data.
Args:
mean_list (list): A list of arrays of astrometric data.
[ra, dec, plx, pmra, pmdec]
cov_list (array): A list of all the uncertainties and covariances:
... | 353c08bfd8951610fdcf1511107888c1153d3eed | 31,468 |
def get_finger_distal_angle(x,m):
"""Gets the finger angle th3 from a hybrid state"""
return x[2] | f93b1931f3e4a9284ccac3731dfeea21526ea07c | 31,469 |
def get_karma(**kwargs):
"""Get your current karma score"""
user_id = kwargs.get("user_id").strip("<>@")
session = db_session.create_session()
kama_user = session.query(KarmaUser).get(user_id)
try:
if not kama_user:
return "User not found"
if kama_user.karma_points == ... | 29f2622e65c45e642285014bbfa6dc33abb0e326 | 31,470 |
from pathlib import Path
import os
import logging
def medical_charges_nominal(dataset_dir: Path) -> bool:
"""
medical_charges_nominal x train dataset (130452, 11)
medical_charges_nominal y train dataset (130452, 1)
medical_charges_nominal x test dataset (32613, 11)
medical_charges_nominal y trai... | 719ae4cef8fdae440fb601d18dc5ae8d88bbacca | 31,471 |
def plotly_shap_violin_plot(X, shap_values, col_name, color_col=None, points=False, interaction=False):
"""
Returns a violin plot for categorical values.
if points=True or color_col is not None, a scatterplot of points is plotted next to the violin plots.
If color_col is given, scatter is colored by c... | 03754df82272826965e73266075f3a0334620e93 | 31,472 |
def animation():
"""
This function gives access to the animation tools factory - allowing you
to access all the tools available.
Note: This will not re-instance the factory on each call, the factory
is instanced only on the first called and cached thereafter.
:return: factories.Factor... | 682cfe96f5682296ae721519be03aeb98b918e20 | 31,473 |
def merge(pinyin_d_list):
"""
:rtype: dict
"""
final_d = {}
for overwrite_d in pinyin_d_list:
final_d.update(overwrite_d)
return final_d | 512f551620ccedae8fb53f0c60f7caf931aae249 | 31,474 |
import subprocess
def HttpPostRequest(url, post_dict):
"""Proceed an HTTP POST request, and returns an HTTP response body.
Args:
url: a URL string of an HTTP server.
post_dict: a dictionary of a body to be posted.
Returns:
a response from the server.
"""
body = urlencode(post_dict)
cmd = [GO... | 59322ac0c7beff4ba71c2ec6ee3661c18ac0fb28 | 31,475 |
from typing import Dict
from typing import Callable
def nn_avg_pool2d(expr: Expr,
params: Dict[str, np.ndarray],
schedule: Schedule,
net: Dict[Expr, Expr],
op_idx: Dict[str, int],
RELAY_2_XLAYER: Dict[str, Callable],
... | 6da43b7927049af26be40f3c153dab2f319312e1 | 31,476 |
def dprnn_tasnet(name_url_or_file=None, *args, **kwargs):
""" Load (pretrained) DPRNNTasNet model
Args:
name_url_or_file (str): Model name (we'll find the URL),
model URL to download model, path to model file.
If None (default), DPRNNTasNet is instantiated but no pretrained
... | cf3190656d9c24730d9bab1554987d684ec33712 | 31,477 |
def utcnow():
"""Better version of utcnow() that returns utcnow with a correct TZ."""
return timeutils.utcnow(True) | a23cc98eca8e291f6e9aff5c0e78494930476f78 | 31,478 |
def build_profile(base_image, se_size=4, se_size_increment=2, num_openings_closings=4):
"""
Build the extended morphological profiles for a given set of images.
Parameters:
base_image: 3d matrix, each 'channel' is considered for applying the morphological profile. It is the spectral inf... | 7f7cd0e1259cdd52cd4ce73c3d6eee9c9f87b474 | 31,479 |
def mujoco_env(env_id, nenvs=None, seed=None, summarize=True,
normalize_obs=True, normalize_ret=True):
""" Creates and wraps MuJoCo env. """
assert is_mujoco_id(env_id)
seed = get_seed(nenvs, seed)
if nenvs is not None:
env = ParallelEnvBatch([
lambda s=s: mujoco_env(env_id, seed=s, s... | 5bad4500be5261f33a19e49612ce62e1db8c66dd | 31,480 |
import torch
def polar2cart(r, theta):
"""
Transform polar coordinates to Cartesian.
Parameters
----------
r, theta : floats or arrays
Polar coordinates
Returns
-------
[x, y] : floats or arrays
Cartesian coordinates
"""
return torch.stack((r * theta.cos(), ... | c13225a49d6435736bf326f70af5f6d4039091d8 | 31,481 |
def belongs_to(user, group_name):
"""
Check if the user belongs to the given group.
:param user:
:param group_name:
:return:
"""
return user.groups.filter(name__iexact=group_name).exists() | e1b70b4771dfec45218078ca16335ddc3c6214e2 | 31,482 |
import torch
def sum_log_loss(logits, mask, reduction='sum'):
"""
:param logits: reranking logits(B x C) or span loss(B x C x L)
:param mask: reranking mask(B x C) or span mask(B x C x L)
:return: sum log p_positive i over all candidates
"""
num_pos = mask.sum(-1) # B... | 88a312f74e7d4dce95d8dcadaeeaa1a136fceca6 | 31,483 |
from acor import acor
from .autocorrelation import ipce
from .autocorrelation import icce
def _get_iat_method(iatmethod):
"""Control routine for selecting the method used to calculate integrated
autocorrelation times (iat)
Parameters
----------
iat_method : string, optional
Routine to use... | a5bbe3a4f4bad486f9bab6ca4b367040ce516478 | 31,484 |
def run():
"""Main entry point."""
return cli(obj={}, auto_envvar_prefix='IMPLANT') # noqa | ae9e96478dbf081469052ff29d31873263060bff | 31,485 |
def qtl_test_interaction_GxG(pheno, snps1, snps2=None, K=None, covs=None, test="lrt"):
"""
Epistasis test between two sets of SNPs
Args:
pheno: [N x 1] np.array of 1 phenotype for N individuals
snps1: [N x S1] np.array of S1 SNPs for N individuals
snps2: [N x S2] np.array of S2 S... | 77eebc7c1c673562b1b793e9e5513b9a50aa6f1b | 31,486 |
import copy
import io
def log_parser(log):
"""
This takes the EA task log file generated by e-prime and converts it into a
set of numpy-friendly arrays (with mixed numeric and text fields.)
pic -- 'Picture' lines, which contain the participant's ratings.
res -- 'Response' lines, which contain the... | 7793cb1b53100961aca5011655211b0da47af856 | 31,487 |
from typing import List
def line_assign_z_to_vertexes(line_2d: ogr.Geometry,
dem: DEM,
allowed_input_types: List[int] = None) -> ogr.Geometry:
"""
Assign Z dimension to vertices of line based on raster value of `dem`. The values from `dem` are interp... | ae3e6c496cd10848e35830c1122a77589f322aad | 31,488 |
def backoff_linear(n):
"""
backoff_linear(n) -> float
Linear backoff implementation. This returns n.
See ReconnectingWebSocket for details.
"""
return n | a3a3b3fc0c4a56943b1d603bf7634ec50404bfb3 | 31,489 |
import pkg_resources
def _doc():
"""
:rtype: str
"""
return pkg_resources.resource_string(
'dcoscli',
'data/help/config.txt').decode('utf-8') | e83f8a70b9d6c9cff38f91b980cd3f9031d84fd7 | 31,490 |
def sk_algo(U, gates, n):
"""Solovay-Kitaev Algorithm."""
if n == 0:
return find_closest_u(gates, U)
else:
U_next = sk_algo(U, gates, n-1)
V, W = gc_decomp(U @ U_next.adjoint())
V_next = sk_algo(V, gates, n-1)
W_next = sk_algo(W, gates, n-1)
return V_next @ W_next @ V_next.adjoint() @ W... | e8251d7a41899584f92c808af1d4fdee10757349 | 31,491 |
def get_movie_list():
"""
Returns:
A list of populated media.Movie objects
"""
print("Generating movie list...")
movie_list = []
movie_list.append(media.Movie(
title='Four Brothers',
summary='Mark Wahlberg takes on a crime syndicate with his brothers.',
trailer_yo... | e00f67b55a47bf13075a4b2065b94feec4138bcd | 31,492 |
def check_dna_sequence(sequence):
"""Check if a given sequence contains only the allowed letters A, C, T, G."""
return len(sequence) != 0 and all(base.upper() in ['A', 'C', 'T', 'G'] for base in sequence) | 2f561c83773ddaaad2fff71a6b2e5d48c5a35f87 | 31,493 |
def test_inner_scalar_mod_args_length():
"""
Feature: Check the length of input of inner scalar mod.
Description: The length of input of inner scalar mod should not less than 2.
Expectation: The length of input of inner scalar mod should not less than 2.
"""
class Net(Cell):
def __init__... | 06bc7530106c5bf2f586e08ee2b941bd964228f1 | 31,494 |
import requests
def zip_list_files(url):
"""
cd = central directory
eocd = end of central directory
refer to zip rfcs for further information :sob:
-Erica
"""
# get blog representing the maximum size of a EOBD
# that is 22 bytes of fixed-sized EOCD fields
# plus t... | 694f6340145d509e7a18aa7b427b75f521c389df | 31,495 |
import torch
import numpy
import math
def project_ball(tensor, epsilon=1, ord=2):
"""
Compute the orthogonal projection of the input tensor (as vector) onto the L_ord epsilon-ball.
**Assumes the first dimension to be batch dimension, which is preserved.**
:param tensor: variable or tensor
:type ... | 188eda46ede2b6ac08bc6fc4cfa72efb56e2918e | 31,496 |
import os
import base64
def decode_json(filepath="stocks.json"):
"""
Description: Generates a pathname to the service account json file
needed to access the google calendar
"""
# Check for stocks file
if os.path.exists(filepath):
return filepath
creds = os.environ.get("GOOGLE_SER... | 095dabf2a397576289bf1754f4eae4406e6648c1 | 31,497 |
def load_model(filename):
"""
Loads the specified Keras model from a file.
Parameters
----------
filename : string
The name of the file to read from
Returns
-------
Keras model
The Keras model loaded from a file
"""
return load_keras_model(__construct_path(file... | 89656f682f1e754a08c756f0db49fc3138171384 | 31,498 |
import os
def get_ffmpeg_executable_path(ffmpeg_folder_path):
"""
Get's ffmpeg's executable path for current system, given the folder.
:param ffmpeg_folder_path: Folder path for the ffmpeg and ffprobe executable.
:return: ffmpeg executable path as absolute path.
"""
return os.path.join(ffmpeg... | a309030dce3adc5e6252b915f6a48d540d64dee3 | 31,499 |
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