content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def S_inverse_values(_data_list, _infinity_value='inf'):
"""
Returns data samples where values are inversely proporsional.
"""
i_data = []
ds = len(_data_list)
for i in range(ds):
if _data_list[i] == 0:
i_data.append(_infinity_value)
if _data_list[i] == _infinity... | 97a140b1cb3649c7036758eaaeb7c21bcc8da06d | 81,571 |
def forward_propagate(x, w1, b1, w2, b2):
""" 构建三层神经网络 -- 前向传播 """
""" x.shape = (m,n1)
y.shape = (m, n3)
w1.shape = (n, n2)
b1.shape = (1, n2)
w2.shape = (n2, n3)
b2.shape = (1, n3)
"""
z_1 = np.dot(x, w1) + b1
a_1 = sigmoid(z_1)
z_2 = np.dot(a_1, w2) + b... | bc5ce85fa622021a2026c5426c6a27735133125b | 81,572 |
def dsp(typenames=None,name1=None,name2=None,templates=None):
"""Delete special points.
Type FUNC(x,list) to delete the special points in list from
the Python object x, which must be a solution list or a bifurcation diagram.
Type FUNC(list,'xxx') to delete from the data-files b.xxx, and s.xxx.
Type... | e48196952f257b297d6914bfb12d5f3ac917242f | 81,573 |
def map_flat_chunked(f, values, *, limit=1<<29):
"""
Apply the function f to chunked, flattened, concatenated values, then split and reshape back to original shapes.
"""
values = tuple(values)
for v in values:
assert not isinstance(v, tf.IndexedSlices)
values = [tf.convert_to_tensor(v) f... | cfb27fa8a337966fc384ec45bf49ef0c730c973e | 81,574 |
def execute_steps(data_type, runtype, data_repository, step_list, tc_junit_object, iter_ts_sys):
"""executes steps based on given data_type and runtype"""
tc_status = True
if data_type.upper() == 'CUSTOM' and \
runtype.upper() == 'SEQUENTIAL_KEYWORDS':
tc_status = execute_custom(data_typ... | dc5cd2493584bc5c96467f24699ffec4d43afc93 | 81,575 |
def findPayload(message, type):
"""
Find a payload/part that matches a type as closely as possible and decode it
properly.
Parameters
----------
message : email.message.Message
The message to search.
type : str
A MIME type.
Returns
-------
str
The payload as a string.
"""
charset =... | 265159546f1b0d2a6cc065e2c467528ea74048ef | 81,576 |
def get_from_convergence(J, domain, nof_coefficients, disc_type='sp_quad',
interval_type='log', mapping_type='sp_hes', permute=None, residual=True,
low_memory=True, stable=False, min_ncap=10, max_ncap=2000, step_ncap=1,
stop_rel=1e-10, stop_abs=... | 1c72eac980700b0c263ba631b4dbf4ac03c332ca | 81,578 |
import csv
def calculate_number_of_synthetic_data_to_mix(original_data_file,
target_ratio):
"""Calculate the number of negative samples that need to be added to
achieve the target ratio of negative samples.
Args:
original_data_file: path to the origi... | eb83cdbb9af39715f16e412f8ae799222a2a232f | 81,580 |
def label_genres(row):
"""For each segment in `genres_mean`,
labels the apps that cost less than its segment's mean with `1`
and the others with `0`."""
aff = row["affordability"]
gc = row["genre_count"]
price = row["Price"]
if price < genres_mean.loc[(aff, gc)][0]:
return 1
el... | afedf4c591370ab4cb105a0b7bbdaef57e27b930 | 81,581 |
import html
def card(target, responses, score):
"""Summary card component"""
kpi_color = get_color_from_score(score)
return dbc.Card(
[
dbc.CardBody(
[
html.Img(src=f"assets/img/{target.image}", className="card-img"),
html.H4(targ... | 8d77115ab396d48afe34183bf601d57ab56596c3 | 81,582 |
import tqdm
from typing import Iterable
def eval_bootstrap_rdm(model, data, theta=None, method='cosine', N=1000,
rdm_descriptor='index', boot_noise_ceil=True):
"""evaluates a model on data
performs bootstrapping to get a sampling distribution
Args:
model(pyrsa.model.Model):... | b46c71188dea0416fec5a376188c438eb6432230 | 81,583 |
def is_empty(self) -> ObservableBase:
"""Determines whether an observable sequence is empty.
Returns an observable {Observable} sequence containing a single element
determining whether the source sequence is empty.
"""
return self.some().map(lambda b: not b) | 20c18aec3a4020a17e759aa733fe5e1b6aba9a10 | 81,584 |
def crawler_dou_ban():
"""
豆瓣讨论精选
:return:
"""
url = 'https://www.douban.com/group/explore'
headers = {
'Host': 'www.douban.com',
'Referer': 'https://www.douban.com/group/explore'
}
response_html = get_text(url, options=headers)
content_list = []
if response_html:... | e49c5190b12a8b7beaa6b0979b9a2ec12c9e88b5 | 81,585 |
def _spark_col_apply(col_or_df, sfun):
"""
Performs a function to all cells on a dataframe, the function being a known sql function.
"""
if isinstance(col_or_df, Column):
return sfun(col_or_df)
assert isinstance(col_or_df, DataFrame)
df = col_or_df
df = df._spark_select([sfun(df[col]... | 317587fff888af117b4426fea656a886294fed0b | 81,586 |
from typing import Any
from typing import List
from typing import Dict
import requests
def get_data(url: str, /, **kwargs: Any) -> List[Dict[str, Any]]:
"""
Extract data from URL.
Parameters
----------
url : str
URL where to send the request to.
**kwargs : Any
... | 3200e020c05df822183011c116e8f25a7643e9f3 | 81,587 |
def _get_valid_number(section, option, provided):
"""Validates an int type configuration option.
Returns None by default if the option is unset.
"""
if provided is None:
return None
try:
return int(provided)
except ValueError:
error = "Value provided for '{0}: {1}' is... | 533934aa48693ec53b30c4069804431095c4d717 | 81,588 |
from re import T
def is_line_comment(token):
"""
ラインコメント判定
"""
if is_comment(token):
comment = token.token_next_by_type(0, T.Comment)
return comment.value not in ["/*", "*/"]
return False | 8786ec5cd1f881509ed8a065f164c683c6dfdc04 | 81,589 |
def get_view_name(stack):
"""Determine view name from leftover stack of path segments
:param stack: a list of path segments left over after consuming
the path.
:return: view name string or ``None`` if no view name can be determined.
"""
unconsumed_amount = len(stack)
if unconsumed_amount ... | 65e0878689a08a07d14db1976c460f5b598f2919 | 81,590 |
def granulation(time, sigma, tau):
"""
Create granulation time series
Usage:
time - array: time stamps
sigma - float: rms amplitude (ppm)
tau - float: characteristic timescale (s)
Author: grd349
Edited by jsk389
"""
# Compute cadence from time series - assuming regular sampl... | 8f9a12679e70bb5c1c340eb6a7f8f8a303a880b1 | 81,591 |
import time
def get_public_ip(instance_id:str=None) -> str:
"""
A function to get public IP for an instance
:param instance_id: instance id
"""
ec2_client = boto3.client("ec2", region_name="ap-south-1")
reservations = ec2_client.describe_instances(InstanceIds=[instance_id]).get("Reservations")... | a58d4a8291c403baab8566ecb3dcdec6de129b5b | 81,592 |
def e_x_lda_unpolarized(rho):
"""Evaluates LDA exchange energy density for spin unpolarized case.
Parr & Yang Density-functional theory of atoms and molecules Eq. 6.1.20.
Args:
rho: Float numpy array with shape (num_grids,), the electron density.
Returns:
Float numpy array with shape (num_grids,), th... | f95f0f711dc334f66400c7b676b0c77f9e2bdb9a | 81,594 |
def get_policy_document(override_json=None, policy_id=None, source_json=None, statements=None):
"""
Generates an IAM policy document in JSON format.
This is a data source which can be used to construct a JSON representation of
an IAM policy document, for use with resources which expect policy docum... | e6001db84deeabbb7868a7a0b627f454e84962fc | 81,595 |
import torch
from typing import Union
def allclose(mat1: torch.Tensor, mat2: Union[torch.Tensor, float], tol=_EPS):
"""Check if all elements of two tensors are close within some tolerance.
Either tensor can be replaced by a scalar.
Note:
This is similar to torch.allclose(), but considers just th... | 208367c6f38d1c4a923bfa4d324e7de0eff4a786 | 81,596 |
def my_lcs(string, sub):
"""
Calculates longest common subsequence for a pair of tokenized strings
:param string : list of str : tokens from a string split using whitespace
:param sub : list of str : shorter string, also split using whitespace
:returns: length (list of int): length of the longest co... | f81ad39b7eb5e80847d22f1f6643021c1bcd3dd2 | 81,597 |
def gausslin_logL(xi, A=0.5, sigma=1.0, mu=5.0, L=10.0):
"""Equation 5.80: gaussian likelihood with uniform background"""
xi = np.asarray(xi)
shape = np.broadcast(sigma, A, mu, L).shape
xi = xi.reshape(xi.shape + tuple([1 for s in shape]))
return np.sum(np.log(A * np.exp(-0.5 * ((xi - mu) / sigma... | 269b31c2a664f22e6a68eaec4b1c85db4d1190f4 | 81,598 |
def get_batch_account(
batch_mgmt_client, config, account_name=None, resource_group=None,
raw_override=False, get_keys=False):
# type: (azure.mgmt.batch.BatchManagementClient, dict, str, str, bool) ->
# Tuple[azure.mgmt.batch.models.BatchAccount,
# azure.mgmt.batch.models... | d7abcd1bd162a44a050f01689dd6922e204d8934 | 81,599 |
def GenerateId_base10(hash_sha512, input_file):
"""
Implement asset id of input file for base10 format
@param hash_sha512: string to encode
@param input_file: input file to encode
"""
string_id = int(hash_sha512.encode("hex_codec"), 16)
return string_id | 82512598623ca4cefe1abf535b556d175fe1549e | 81,600 |
def calc_M_arc_line(arc: ArcAbstract, line: Line):
"""Comupute the mutual between an arc and a line."""
VEC_0 = np.array([0., 0., 0.])
Rp = arc.radius
x_u = arc.vec_x
y_u = arc.vec_y
θ1 = arc.theta
s_u = line.vec_n
Ls = line.ell
s0 = np.sqrt((line.vec_r0 - arc.vec_r0) @ (line.vec_... | 4a67336f315792a52e4736cc3f26f3a99c71b82a | 81,601 |
def static_fallback_open(static_path, mode="r"):
"""
attempts to open static_path first within collected
staticfiles storage.
if that doesn't work then next does a file system search
for the file and if located then attempts opening using
standard file i/o.
"""
writing = "w" in mode or ... | 3d0764b44155cb148c0d06fc53c0f6b301d2bc44 | 81,602 |
def get_metadata(dataset_id, fname=None, force=False):
"""
Downloads a CSV of document metadata for all
documents in the dataset.
"""
return _download_file(dataset_id, "sampled-metadata", file_name=fname, force=force) | 7597bc31363f14a100cf5463080fd67de8ab815b | 81,603 |
def awaiting_submission_app():
"""Fixture for testing application submission types"""
application = BootcampApplicationFactory.create(
state=AppStates.AWAITING_USER_SUBMISSIONS.value
)
installment = InstallmentFactory.create(bootcamp_run=application.bootcamp_run)
app_steps = [
Applic... | 889ced1e238b635402326afa625fce3ab0e581b5 | 81,604 |
def get_profile_info():
"""
Gets the profile information of the last executed query
:param db: db connection
:return: time in milli seconds
"""
cursor = db.system.profile.find({"op": 'query'}, {"millis": 1}).sort("ts", pymongo.DESCENDING).limit(1)
return cursor.next()['millis'] | b62fb52eec3e767435e223901642d1238e01f8e3 | 81,605 |
import logging
def _do_call_do_repository(repository, command):
"""Run the command's _do_repository on the given repository."""
# pylint: disable=protected-access
def determine_outcome_labels():
"""Helper function to inject rpeository name into command labels."""
result = command._do_determine_outcome_m... | 484dc5a46b8495992b099fc08988fd48f105e206 | 81,606 |
def get_user_input(prompt_message):
""" Get input from the user
This function uses the console to get user input for the main menu. This also
checks that the user entered 1, 2, or X.
There is no unittest coverage for this function.
Args:
prompt_message The default message when asking fo... | 30ec084dd2e877e07ee91a54292fc68ed67d521b | 81,607 |
from typing import Tuple
def pluralize(forms: Tuple[str, str, str], amount: int) -> str:
"""
Возвращает правильную форму слова в зависимости от количества.
Формы слова:
#. Единственное число, именительный падеж (штука).
#. Единственное число, родительный падеж (штуки).
#. Множественное число... | 3e356b8c7f4c34a5aed5bd95fa0d88f5a918c0b0 | 81,608 |
def update_products(product_id):
"""Updates a specific product's details"""
email = get_jwt_identity()
user = users_obj.get_user_by_email(email)
role = user["role"]
if role != "admin":
return jsonify({"Message":
"You must be an admin to perform this action"}), 401
... | d62c48587d00ffa23a492c79a2840c113f1a52c7 | 81,609 |
import json
def global_weight(criterion, report):
"""
Formula:
Global Weight = Criterion W value / Criterion Count
For example:
W Value = 1
Criterion Count = 5
Global Weight = 1 / 5 = 0.2
"""
criterion_count = criterion['parent']['count']
data = report.criterion_compare[str(cr... | 02ce4873fa93556e5d7f86037819c0962b0ad207 | 81,610 |
def modelToShape(model, dynamic=False):
"""
:type model: panda3d.core.NodePath
:type dynamic: bool
:return: tuple(panda3d.bullet.BulletTriangleMeshShape, transform)
"""
geomnodes = model.findAllMatches('**/+GeomNode')
transform = model.getTransform()
gn = geomnodes.getPath(0).node()
... | 81fd00464e5d189c55f923b467f03ef818a938f2 | 81,613 |
import warnings
def kruskal(dataset: pd.DataFrame, test_col: str, target_col: str, nan_policy: str = 'propagate') -> np.float:
"""Applies Kruskal-Wallis H-test to a single column
Applies Kruskal-Wallis H-test to (test col, target_col) in order to
test whether the medians in each of the classes in test_c... | c8be3e55c51310cadef6b2aac6d88c5431b5bb87 | 81,614 |
def add_nancodes(df):
"""Add nancodes to a signal dataframe."""
# Default missingness codes
df["missing_val"] = Nans.NOT_MISSING
df["missing_se"] = Nans.NOT_APPLICABLE
df["missing_sample_size"] = Nans.NOT_APPLICABLE
# Mark any remaining nans with unknown
remaining_nans_mask = df["val"].isnu... | 8e451656a7025e0363c4616e403853c4051543f2 | 81,615 |
def dictfetchall(cursor):
""" Returns all rows from a cursor as a dict """
columns = [column[0] for column in cursor.description]
return [dict(zip(columns, row)) for row in cursor.fetchall()] | 710d0f31fe931bb953a536070a1b50f23203e5a8 | 81,616 |
def list_usages(client, resource_group_name, account_name):
"""
List usages for Azure Cognitive Services account.
"""
return client.list_usages(resource_group_name, account_name).value | 83c03cef1e887f31eb15da85f63b095a15460693 | 81,617 |
def pickChromosomes(selection='A'):
"""
Selects the chromosomes you want to overlap.
:param selection: A(autosomes), AS (Auto+Sex), or ASM (Auto+Sex+M)
:return: chrList
"""
chrList = [
'1', '2', '3', '4', '5',
'6', '7', '8', '9', '10',
'11', '12', '13', '14', '15',
... | 40b1922a6995ad0d5ead8c282b945c152ecbf0be | 81,618 |
def getTestStream(size: int):
"""
Produces a stream of repeating prime sequences to avoid accidental repetition
"""
arr = bytearray()
while True:
for prime in [4759, 4783, 4787, 4789, 4793, 4799, 4801, 4813, 4817, 4831, 4861, 4871, 4877, 4889, 4903, 4909, 4919, 4931, 4933, 4937]:
... | 0a0d8d8a8eead10ed15b12ff74aaa85145947a5f | 81,619 |
def get_contact_info_keys(status_update):
"""Returns the contact info method keys (email, sms)
used to send a notification for a status update if the notification
exists. Returns [] if there is no notification
"""
if hasattr(status_update, 'notification'):
return list(status_update.notificat... | 020a9742df99cd65be1433165823c4f364009d85 | 81,620 |
def _jobs_cleanup_hard_timed_out():
"""Find all jobs with hard_timeout values which have passed
and delete them, logging the timeout / failure as appropriate"""
now = timeutils.utcnow()
session = get_session()
num_del = session.query(models.Job)\
.filter(models.Job.hard_timeout <= now)\
... | eff12a79b259eba1ce3f5ef0165ab0d065130089 | 81,622 |
from typing import Iterable
def count_if(iterable: Iterable, unary_predicate: UnaryPredicate) -> int:
"""Count for how many elements in a iterable an unary predicate returns True.
Args:
iterable: An iterable for which to count for how many elements unary_predicate returns True
unary_predicate... | 3fd773ca3d6f7f611a5695cd4ce77edd8fda0a9f | 81,623 |
import requests
def insert(g, named_graph_uri=None):
""" Securely insert a named graph into the DB"""
if named_graph_uri:
data = {'update': 'INSERT DATA { GRAPH <' + named_graph_uri + '> { ' + g.serialize(format='nt').decode("utf-8") + ' } }'}
else: # insert into default graph
data = {'up... | 5c635b1c2e3dbd17dfd8dcc4ab1a99264961d316 | 81,624 |
async def _wtforms_base_form_validate(self, extra_validators=None):
"""
Validates the form by calling `validate` on each field.
:param extra_validators:
If provided, is a dict mapping field names to a sequence of
callables which will be passed as extra validators to the field's
`val... | 378366444278c4409bc5164b143ee8971af53bf1 | 81,625 |
def remove_empty_elements(d):
"""
Remove empty elements from dict or list
"""
if isinstance(d, dict):
return dict((k, remove_empty_elements(v)) for k, v in d.items() if v and remove_empty_elements(v))
elif isinstance(d, list):
return [remove_empty_elements(v) for v in d if v and remo... | e440c6d6e478ed02bbd99071edf4de05146392c2 | 81,626 |
def plot_optimization_history(study: Study) -> "go.Figure":
"""Plot optimization history of all trials in a study.
Example:
The following code snippet shows how to plot optimization history.
.. testcode::
import optuna
def objective(trial):
x = trial.... | 7487a17ad312d6b07c1dfb98cd4c3e0c5a9b9f18 | 81,627 |
from typing import Optional
def test_selection_in_resolver(query: str, runtime_type: Optional[str], expected_result: dict):
""" Test selected_field_names() when used in a resolver function """
# GraphQL resolver
def resolve_object(obj, info: graphql.GraphQLResolveInfo):
names = selected_field_name... | d33f305613e37d9f60dcc3efc4d27f906726a7be | 81,628 |
def get_vpc_running_config(device):
"""Gets vpc running config
Args:
device (Device): This is the device object of an NX-API enabled device
using the Device class within device.py
Returns:
str: 'show run section vpc'
"""
command = 'show running section vpc'
try:
... | 9b71d62cc07ae5a4f314956f0e0adf7d6db2d196 | 81,629 |
def rel_ent_key_list(topic_model, n_top_keywords, relevant_topics):
"""Returns a list of the top n keywords based on relative entropy score
Arguments:
topic_model (TopicModel): a topic by vocabulary word matrix where each entry
is the total word count for that word in that topic
n_top_word... | c650875e3f3287b9f1ccf4b6d6637c4014cf0e06 | 81,630 |
def isgooddeltainfo(revlog, deltainfo, revinfo):
"""Returns True if the given delta is good. Good means that it is within
the disk span, disk size, and chain length bounds that we know to be
performant."""
if deltainfo is None:
return False
# - 'deltainfo.distance' is the distance from the ... | 15b4a094a9ae11123cbccc544508835e7d6ecba7 | 81,631 |
def count_running_ns():
"""Find the number of the instantiated NSs in OSM r4
"""
running_ns = 0
token = bearer_token(OSM_ADMIN_CREDENTIALS.get('username'),
OSM_ADMIN_CREDENTIALS.get('username'))
ns = Ns(token)
request = ns.get_list()
ns_list = request.json()
fo... | c78088dd6e4b5ae78f4b1763a350f51217b21dd7 | 81,632 |
def pack_to_uint64(locale):
"""Pack a full locale into a 64-bit unsigned integer."""
_, script, _ = get_locale_parts(locale)
return ((pack_to_uint32(locale) << 32) |
(ord(script[0]) << 24) |
(ord(script[1]) << 16) |
(ord(script[2]) << 8) |
ord(script[3])) | 73a73e46ee47087d832205f4918b97bb19d62949 | 81,633 |
def _convert_generator_to_delayed_seq_block(generator, chunksize=5000):
"""
Converts from a generator to a block of sequences
"""
seq_block = [dask.delayed(_to_seq_array)(seqs)
for seqs in _chunks(generator, chunksize)]
return seq_block | e626509eabe801b7a2aa66fe8b1dd8523b91c484 | 81,634 |
def hardnet68(**kwargs):
"""
HarDNet-68 model from 'HarDNet: A Low Memory Traffic Network,' https://arxiv.org/abs/1909.00948.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location ... | 28d4e98e43a7dfad3a4658ccf7211e8ad5a0a3aa | 81,635 |
from io import StringIO
def _array_schema_html(schema: tiledb.ArraySchema) -> str:
"""Returns a HTML representation of a TileDB array."""
output = StringIO()
output.write("<ul>\n")
output.write("<li>\n")
output.write("Domain\n")
output.write("<table>\n")
for i in range(schema.domain.ndim):... | bb06bceb0382f680464067b8833a1104eb2ab252 | 81,636 |
def binary_search(input_list, number, min_idx, max_idx):
"""
Find the index for a given value (number) by searching in a sorted array
Time complexity: O(log2(n))
Space Complexity: O(1)
Args:
- input_list(array): sorted array of numbers to be searched in
- number(int)... | 20358c096c529937d57503285150a90a37f62dd1 | 81,637 |
def get_goal_status(goal):
"""
1 = Success
2 = Rejected
3 = Locked
"""
print(goal.is_closed)
if goal.is_closed:
if goal.was_accomplished:
return 1
else:
return 2
return 3 | 64e742899a3a6484855f0543a7184db24c0c396f | 81,638 |
def omega_s(distance_source, mass, xi_var, x_ratios, x_lin_dist):
"""Returns the evaluated kernel OMEGA"""
gamma0_const = gamma0_ds(distance_source, x_lin_dist)
return gamma0_const*xi_var*np.sqrt(x_ratios*(1.-x_ratios))*(mass)**(-1./2.) | 427ce345b159356b2c0f9ad5d04d4e8049f6b29d | 81,639 |
def _IntersectTwoHandlers(first_handler, second_handler):
"""Returns intersections of first_handler and second_handler patterns."""
shared_prefix = _SharedPrefix(first_handler.pattern, second_handler.pattern)
if shared_prefix:
return _HandleCommonPrefix(first_handler, second_handler, shared_prefix)
share... | af143ec8940a076ab961aeb19237d5ed845739c8 | 81,640 |
def string_to_root(string_in):
"""Arguments: string_in [string]: input in XML format in a string
Return: [element]: root element of parsed etree
"""
return etree.fromstring(string_in) | b768c03e434e3fc8d9d6cf187ff9887930c7ad6c | 81,641 |
def get_ranking17():
"""
Return the ranking with ID 17.
"""
return [
("a3", 0.500000),
("a2", 0.433013),
("a4", 0.433013),
("a1", 0.000000),
("a5", 0.000000),
] | 5b3e3083a86d3a484f6932d6cb6430bef6a3383e | 81,642 |
import scipy
def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
Parameters
----------
data: ... | 593709a3a8ce6333b441a03b91e033c689dff9c9 | 81,643 |
def uefa_dims():
""" Create 'uefa dimensions."""
return FixedDims(left=0., right=105., top=68., bottom=0., aspect=1.,
width=68., length=105., pitch_width=68., pitch_length=105.,
goal_width=7.32, goal_length=2., goal_bottom=30.34, goal_top=37.66,
six... | de14239cf10443b1c5683e477d287813325f31a5 | 81,644 |
def compute_date_level_metrics(df_detail,
bin_labels,
date_col_name,
return_col_name,
feature,
corr_method):
"""
Description: This function computes the back... | 87faef203a21dbeba1bc3980cbd2bba9116f76ef | 81,645 |
def row_basis(matrix):
"""
Outputs a basis for the rows of the matrix.
Parameters
----------
matrix: numpy.ndarray
The input matrix
Returns
-------
numpy.ndarray
A numpy.ndarray matrix where each row is a basis element.
Examples
--------
>>> H=np.arra... | ede9e83cdeddae71df61316590ea274213e702bf | 81,646 |
def show_zoning_enabled_configuration_rpc(self, zone_name_pattern=None, last_rcvd_zone_name=None, api_timeout=''):
"""
This is an auto-generated method for the PySwitchLib.
**Supported Versions**:
* NOS: 6.0.2f, 7.2.0
* SLXOS: 17r.1.01a, 17s.1.02
**Instance Argument(s)**:
:ty... | 58e98846a9fb25508bd61037d0d0ddcf4d064dc3 | 81,647 |
import torch
def _calculate_ece(logits, labels, n_bins=10):
"""
Calculates the Expected Calibration Error of a model.
(This isn't necessary for temperature scaling, just a cool metric).
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs int... | af311a47b7558b07838a38d736386147804ea109 | 81,648 |
def buildMod(Adj, thresh):
"""Builds the modularity matrix from the Adjacency matrix.
Given an adj matrix, it constructs the modularity matrix and its graph.
"""
Dim = Adj.shape[1]
#print ("\n Dim = ", Dim)
#print ("\n Computing modularity matrix ...")
Deg = ... | d8528e1acf5f8a410612928689d147e57c1f420d | 81,649 |
import random
def add_ships_to_board(board, ships):
"""
Function goes through a list of ships and add them randomly into a board.
board -> list of board tiles
ships -> list of ships to place on board
returns list of board tiles with ships placed on certain tiles
"""
new_board = board[... | 13fd365c2249f41ce4147729a97286b7cb58a828 | 81,650 |
def closure(kb, closing_relation_id, cat_id):
"""Return set of ids for logical closure of a category/region.
Args:
kb: a sling kb
closing_relation_id: SUBCLASS_OF_ID or REGION_OF_ID
cat_id: id of the category to find ancestors of
Returns:
the set of all things in the KB of which the category ... | f116064131db68b1288c276614c6271c5656f0c6 | 81,651 |
def GetReader(thisalgorithm, dimension, pixelType):
""" Get Reader Object to read the input file """
# Check dimension
# Check if input stack or single image
# If input stack
if dimension == "3" and thisalgorithm.GetInputImageStack():
READER = "itk.ImageSeriesReader"
READER_PIXELTYPE = "F"
# Im... | 74f0c361172e0da835fbd41404fadae3cc13f666 | 81,652 |
from typing import Tuple
def _perform_dmd(video_matrix: np.ndarray, frame_rate:
float, filename: str) -> Tuple[np.ndarray, np.ndarray,
np.ndarray]:
"""Performs DMD computations. Returns the DMD eigenvalues, eigenvectors
and initial coefficients.
"""
print('Performing DMD computations...')
... | 425fb10cb202b447c004088b0fb0a03a0e4ad7af | 81,654 |
def build_kwargs(docs: dict, text: str = "Inherited keyword arguments\n", **kwargs) -> str:
"""
Builds a full keyword-arguments string, ready for use in a class
docstring.
Args:
docs: Documentation dictionary
text: A short heading for the keyword arguments
... | 7087bb91bae8292db83b718d25f8f2a2528f6a37 | 81,655 |
def load_data_wrapper():
"""Return a tuple containing ``(training_data, validation_data,
test_data)``. Based on ``load_data``, but the format is more
convenient for use in our implementation of neural networks.
In particular, ``training_data`` is a list containing 50,000
2-tuples ``(x, y)``. ``x`` ... | ac78df90e4d1673c4685e48f6a9c9e55823b5efe | 81,656 |
import itertools
def findValidWyckoffCombos(stoich, spacegroup, wy_data=loadWyckoffData()):
"""
Given a composition's stoichiometry (such as [4,4,12] for Mg4Si4O12) and
a single spacegroup (1-230), this function will find all valid wyckoff
combinations that can produce that stoichiometry.
stoich... | 7a86584450f87e3b329d51631495c37410045b21 | 81,657 |
from typing import Optional
from typing import List
def plot_cvrp_solution(
solution: CVRPSolution, route_indices_to_plot: Optional[List[int]] = None
) -> None:
"""Plot solution deliveries in a map
This is a simplified version showing only the edges between each delivery.
It does not require an OSRM s... | 6d00d5fda19d02539ba5602e4dfea13e36d0e59f | 81,658 |
from typing import Union
from typing import Optional
def getVodPlayInfo(
video_seq: Union[str, int],
vod_id: str = None,
session: UserSession = None,
silent: bool = False
) -> Optional[dict]:
"""Get detailed play info of VOD.
Arguments:
video_seq (:class:`str`) : Uniqu... | 0ebb86a3067311f86a45049f950a98c18a3a8699 | 81,659 |
def _thin_iter_2_lut():
"""
Thinning morphological operation; lookup table for iteration 2
:return: lookup table
"""
lut = identity_lut()
cond = _thin_cond_g1() & _thin_cond_g2() & _thin_cond_g3_prime()
lut[cond] = False
return lut | 2be73a9d8bd3861cd5b05d47267116306c4a4eb1 | 81,660 |
import sympy
def expe_minus_1(lhs, ctx):
"""Element ∆E
(num) -> (e ** a) - 1
(str) -> expand expression a
"""
ts = vy_type(lhs)
return {
NUMBER_TYPE: lambda: sympy.exp(lhs) - 1,
str: lambda: str(sympy.expand(make_expression(lhs))),
}.get(ts, lambda: vectorise(expe_minus_1, ... | 1567c0141aef6305d12cea38d193aeafd4492f48 | 81,661 |
from datetime import datetime
def get_last_cdr(phone_number, date_time, cdr_type):
"""
Gets the last CDR of a phone number
"""
cdr_type = str.casefold(cdr_type)
today = date.today()
billing_cycle = today.strftime("%Y%m")
new_date = datetime.strptime(date_time, '%Y-%m-%d %H:%M:%S')
re... | 0673b6a52de23357fa3f975195ec52160b37c8d4 | 81,662 |
def floor_div_mul(value, factor):
"""Fuction to get floor number."""
return (((value) // (factor))) * (factor) | 05c9b01c297f4ec68d3c5755db550482c520dac8 | 81,663 |
def get_docstring(filename, fragment_loader, verbose=False, ignore_errors=False):
"""
DOCUMENTATION can be extended using documentation fragments loaded by the PluginLoader from the module_docs_fragments directory.
"""
data = read_docstring(filename, verbose=verbose, ignore_errors=ignore_errors)
#... | ff64279918d2af78b1182cd7083bc5b90882658b | 81,664 |
def get_adjacency_list(nodes, exit_index, include_back_edges=False):
"""Computes the adjacency list for the control flow graph.
Args:
nodes: A list of control_flow.ControlFlowNodes.
exit_index: The index of the exit node.
include_back_edges: Whether to include back edges in the list.
Returns:
A P... | e48e5b47de7a9832581778692bada7b6626eaeaa | 81,665 |
def ExpandIntData(compint):
""" return expanded integer list
i.e., 1,3 -5, 10 -12, 18,... -> 1, 3,4,5, 10,11,12, 18,...
:param lst compint: commrest integer list
:return: expnint(lst) - expanded integers list
:seealso: CompressIntData()
"""
expnint=[]
if len(compint) < 0: return exp... | 9006bbd3476798f2113b74a74b29a247e18a932f | 81,666 |
def get_activation_layer(activation: str) -> Layer:
"""Return a activation layer by input activation function.
Parameters
----------
activation : str
Activation function name.
Returns
-------
Layer
An activation layer.
"""
if activation == "dice":
return Dic... | 984ae992e663a1fa8459e6493214088e1770f0f2 | 81,667 |
from typing import Callable
from typing import Any
import functools
def with_select(function_object: Callable[["TaborChannelTuple", Any], Any]) -> Callable[["TaborChannelTuple"], Any]:
"""Asserts the channel pair is selcted when the wrapped function is called"""
@functools.wraps(function_object)
def sele... | b500dafbde7293b058de2cb4807a1c760b583be4 | 81,669 |
def history_parser(arg):
"""
@param: arg is a string that contains the words seperated by spaces
@return: Returns two strings. The first word removed from arg and everything after the space
"""
v = -1
try:
v = arg.index(' ')
except ValueError:
return None, None
first_word... | 267f0cd8ddfc0bfa9106d18341421d5d4d48ed1f | 81,670 |
import json
import base64
def temp_assignmentToggleShow():
"""
POST Request
This API can be used in bulk and single manner. In bulk mode, pass multiple ids of assignements to be marked opposite
This API reverse the show property of all the assignments whose Id will be provided.
I'm relying on Mood... | 308e3521d7bfa231dcf1e46b6a1e828094dc83bb | 81,671 |
def _to_scanner(varr):
"""Transform data to scanner coordinate space by properly swapping axes
Standard vnmrj orientation - meaning rotations are 0,0,0 - is axial, with
x,y,z axes (as global gradient axes) corresponding to phase, readout, slice.
vnmrjpy defaults to handling numpy arrays of:
... | f8a8506e49722e7f5f66199ca15f0a441fb5e180 | 81,672 |
def select_clause(table_name, *fields, distinct=False):
""" Create a select from clause string for SQL.
Args:
table_name: The table to select from as string.
*fields: The fields to select.
Returns:
A string with the crafted clause.
"""
select = ... | 3e373d6b493f60d94afe5d0372e2b1e752f98284 | 81,673 |
import torch
def attr_grad(tensor, h, w, window=8, reduce='sum'):
"""
:param tensor: B, C, H, W tensor
:param h: h position
:param w: w position
:param window: size of window
:param reduce: reduce method, ['mean', 'sum', 'max', 'min']
:return:
"""
h_x = tensor.size()[2]
w_x = t... | 727d348dcb360c8d73956d89211a958c7fae354b | 81,674 |
import psutil
def get_machine_info():
"""
Gets information on the memory, cpu and processes in the server
:returns: tuple of strings containing cpu percentage, used memory, free memory and number of active processes
"""
if not SUPPORTED_OS:
return (None, None, None, None)
used_cpu = st... | e4352a9e794585a172661ea893a65aea387c17d2 | 81,676 |
import pandas as pd
import math
def removeMissing(filename):
"""Takes a file that contains missing scans and removes those rows, while providing the subject name and reason for removal."""
loaded_file = pd.read_csv(filename)
cleaned_list = []
missing_counter = 0
for row in loaded_file.index:
... | 497af6cc2b15da59d0bbad5785368ad8e1cbb5d9 | 81,677 |
import msgpack
def dumps(msg):
""" Transform Python message to bytestream suitable for communication """
try:
data = {}
# Only lists and dicts can contain serialized values
if isinstance(msg, (list, dict)):
msg, data, bytestrings = extract_serialize(msg)
small_heade... | c74fc086213531eeff1339b1df8bd3b0adb69dcc | 81,678 |
from typing import Dict
from typing import Type
def get_serialisers(method) -> Dict[Type, Type[RegressionSerialiser]]:
"""
Gets the serialisers for the given test method.
:param method: The test method.
:return: The mapping from result type to serialiser.
"""
return getattr(method, _c... | cb1c524eeccc3abe69545be68e17ac81c00eaa97 | 81,679 |
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