content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def add_days_worked(start, days):
"""adds date but try to only take days worked into account"""
step = get_step(start)
weeks, plus = divmod(days, 5)
end = start + ((weeks * 7) + plus) * step
if weekday(end) >= 5: # saturday or sunday
end += (2 * step)
end += len([x for x in get_national_... | 03471a792b7da2bd7b0bef24141ad73e19d4a56d | 3,596,865 |
def generate_path(start, end, grid, d_move):
"""
(tuple, tuple, Grid object, bool) -> list of tuple
Return the shortest path from start to end through the grid
"""
path = [start]
point = start
while point != end:
point = next_point(point, grid, d_move)
path.append(point)
... | 340e486e824896ba81412b402d2c84b7dfdb047d | 3,596,866 |
import logging
def db_real_delete_for_testing_purpose(user_id):
"""Physically employee records based on requested parameters. This function will really delete record for teseting
purpose, use with care, as per the requirement, the delete function will not be physically delete the record.
Args:
... | 379b922beffdfd99f742b3f5b3dd2f168e209e4c | 3,596,867 |
def average_fields(names, var):
""" given list of filenames and var, generate average field """
# open each file, and add the field data
# to our averaged array
for name in names:
d = vtk.vtkExodusIIReader()
d.SetFileName(name)
d.UpdateInformation()
d.SetPoi... | c0eefa063cdf07733db411609d5a8c7345ba740f | 3,596,868 |
def constrained_solve_gmres(L,f,R,g,ind,tol=1e-5):
"""Constrained GMRES Solve
======
Uses preconditioned [GMRES](https://en.wikipedia.org/wiki/Generalized_minimal_residual_method) to solve
the equation \\(Lx=f\\) subject to \\(Rx=g\\) on a contraint set.
Parameters
----------
L : (n,n) num... | c5ebfe76df78477197dabfd97a8eb81b9fb8a1fe | 3,596,869 |
def get_digits_data(n: int) -> np.ndarray:
"""
Read the digits dataset from UCI through the scikit-learn helper
function. The dataset contains the flattened grayscale pixel values
from 8x8 images of handwritten digits.
Parameters
----------
n : int
Maximum number of data points
... | 009f2bbac16c2c2c879d13f6a86368eec931f1e3 | 3,596,870 |
def _PreprocessCommentByCond(
cnxn, cond, _project_ids, services, _harmonized_config):
"""Preprocess a commentby=emails cond into commentby_id=IDs, if exact."""
return _PreprocessExactUsers(
cnxn, cond, services.user,
[query2ast.BUILTIN_ISSUE_FIELDS['commentby_id']]) | 57c1a37704ad3145d3549a8c368bfb90b193024a | 3,596,871 |
from typing import Tuple
def split_and_trim(
current_indices: list[int], probs: np.array, split_idx: int, threshold: float
) -> Tuple[list[int], list[int]]:
"""splits the segment at the specified index and trims the resulting segments
Args:
indices (list[int]): the indices of the segment frames i... | c3d4255240d832399a771538328fac512aa477a9 | 3,596,872 |
def get_thread_pool_max_workers():
"""Get the number of thread pool max workers."""
return int(KytosConfig().options["daemon"].thread_pool_max_workers) | 24f1f592a9dc794aaeaa0590c388cf259c27c662 | 3,596,873 |
def ilcm(a, b):
"""Computes integer least common multiple of two numbers. """
if a == 0 and b == 0:
return 0
else:
return a * b // igcd(a, b) | eb8bb8b5cb0f4086a8b640e2e05c08c23dc24e19 | 3,596,874 |
def watch_running(zkclient, cell_state):
"""Watch running instances."""
@exc.exit_on_unhandled
@zkclient.ChildrenWatch(z.path.running())
def _watch_running(running):
"""Watch /running nodes."""
cell_state.running = set(running)
for name, item in cell_state.placement.items():
... | 9984e52b2f26fa5ce0cd373a3ec8260ff94f26e7 | 3,596,875 |
from bs4 import BeautifulSoup
def parse(response, option):
"""
Function to extract data from html schedule
:return: Parsed html in dictionary
"""
soup = BeautifulSoup(response.content, 'html.parser')
title_blue_original = soup.find("font", {"color": "#0000FF"}).text.strip()
if option !=... | 2c0baadc8121ad527171177d06a18fcdb391db03 | 3,596,876 |
def compare_sheet(left_sheet, right_sheet, output_sheet, threshold):
"""
Compare two excel sheet objects. Return output sheet.
:param left_sheet: first sheet to compare (left)
:param right_sheet: second sheet to compare (right)
:param output_sheet: resulting sheet object
:param threshold: numer... | 3044a92ebc331db327d4882b6b69fc902b0313a9 | 3,596,877 |
def flatten_bipartite_densitymatrix(bipartite_tensor):
""" Flatten a bipartite state density matrix to a rank-2 matrix.
:param bipartite_tensor: density matrix (rank-4) for the bipartite system
:return: flatten rank-2 density matrix
:type bipartite_tensor: numpy.ndarray
:rtype: numpy.ndarray
""... | b8731996371d1330563b1dc6573a2565233c3363 | 3,596,878 |
import types
import numpy
def hpat_pandas_series_median(self, axis=None, skipna=True, level=None, numeric_only=None):
"""
Pandas Series method :meth:`pandas.Series.median` implementation.
.. only:: developer
Test: python -m sdc.runtests sdc.tests.test_series.TestSeries.test_series_median1
... | 19e40ad7dec4eb17da496e82cf58451fadcec0e0 | 3,596,879 |
def next_time_open(location):
"""
Returns the next possible OpeningHours object, or False
if the location is currently open or if there is no such object.
"""
obj, ts = utils.next_time_open(location)
return obj | db4db3878b07e4b652aec058137724a436b70f31 | 3,596,880 |
import random
def test_random_intervals(interval_len=16):
"""Tests randomly generated interval of length interval_len"""
random.seed(0)
intervals = list(generate_range(interval_len))
return run_intervals(intervals) | 56635eabc639fae954b6bfefd92be4dc181d1b40 | 3,596,881 |
from typing import Dict
from typing import Any
def module() -> Dict[str, Any]:
"""
Returns:
The schema for the `module` block
"""
data = {
"type": "list",
"schema": {
"type": "dict",
"schema": {
"module_type": {
"type"... | 036e9e7b0e719ad90c9da45a002d792415c8e315 | 3,596,882 |
import numpy
def pred_probs(f_pred_prob, prepare_data, data, iterator, verbose=False):
""" If you want to use a trained model, this is useful to compute
the probabilities of new examples.
"""
""" DO NOT USE. NOT MAKE ANY SENSE IN THIS CASE """
n_samples = len(data[0])
probs = numpy.zeros((n_sa... | 4d24d2bf46be84357e63f05939b64dfb07980685 | 3,596,883 |
def get_token(url: str, scopes: str, credentials_dir: str) -> dict:
"""
Get access token info.
"""
tokens.configure(url=url, dir=credentials_dir)
tokens.manage('lizzy', [scopes])
tokens.start()
return tokens.get('lizzy') | 2fa481e01c47de007a6aa844a7bf17ec8d9dd86d | 3,596,886 |
def rgb_conv_stem(inputs,
num_frames,
filters,
temporal_dilation,
bn_decay: float = rf.BATCH_NORM_DECAY,
bn_epsilon: float = rf.BATCH_NORM_EPSILON,
use_sync_bn: bool = False):
"""Layers for a RGB stem.
Args:... | 027205ef1575ed7b647b979d761059a18ab7dfad | 3,596,887 |
def mocked_response_create(*args, **kwargs):
"""Mock MaksuPalvelu responses based on provider url"""
class MockResponse:
def __init__(self, data, status_code=200):
self.json_data = data
self.status_code = status_code
def json(self):
return self.json_data
... | 835c8c7949f7948e560c1709ab11ca61866d07a9 | 3,596,888 |
def randomPricing(N, s_radius):
""" Returns Uniformly random prices from constraint-set S, when S = ball.
Args:
N (int): Number of products.
s_radius: radius of ball S.
"""
rand_mag = np.random.uniform(low=0.0, high=s_radius) # random magnitude
return( rand_mag *... | 1f694719e148d58013236a8399500f4308bbdbdf | 3,596,890 |
import uuid
def remap_cols(ou_to_df):
"""Canonicalize the column names and add unique identrifiers for all the original DataFrames.
Parameters
----------
ou_to_df : dict[str, DataFrame]
Dictionary of raw DataFrames.
Returns
-------
dict[str, DataFrame]
Dictionary of remap... | 6bfb3142b394c49f90ed63393c5c4138f25c217c | 3,596,891 |
def _instance_name_from_url(instance_url):
"""Extract instance name from url."""
return instance_url.split('/')[-1] | e64bc076be9480a04821e9a9a79f13e25fadad35 | 3,596,892 |
def nots():
"""matches !guard, where guard is either a paranthesis or a Beq"""
yield not_sign
g = yield par ^ bools # type: ignore
return Not(g) | d5f6bcb7594297e022fec600c9230e5d3aae9fc7 | 3,596,893 |
def get_train_test_lst(dataset: pd.DataFrame, init_train_len: int, test_len: int, split_perc: float) -> list:
"""
Get list of train and test sets
:param dataset: dataset to use
:param init_train_len: length of first train set
:param test_len: usual length of test set (could be shorter for last test ... | eb4278dbae53256eafcc129f99d7c3386faebbe4 | 3,596,894 |
def create_method_config(request, responses):
"""
Creates a method config object, stitching together the request and reponses that are passed in
:param request: a request config object.
:param responses: a list of response config objects
:return: a method config object
"""
return ApiGateway... | be1cd3d895af8462926fd60d3e6d705ff11675bb | 3,596,895 |
from typing import Union
def decrypt(receiver_sk: Union[str, bytes], msg: bytes) -> bytes:
"""
Decrypt with receiver's secp256k1 private key
Parameters
----------
receiver_sk: Union[str, bytes]
Receiver's private key (hex str or bytes)
msg: bytes
Data to decrypt
Returns
... | b0deb682cd50201d910ec301e8a07d08f583a01f | 3,596,896 |
def face_verify(args, command_line_parameters, external_dependencies = [], external_fake_job_id = 0):
"""This is the main entry point for computing face verification experiments.
You just have to specify configuration scripts for any of the steps of the toolchain, which are:
-- the database
-- the preprocessing... | ea27510338fba41f6ec05ea082fc0ae32b4c6e6b | 3,596,897 |
def graph_wearout(devices: dict) -> str:
""" This function displays the drives' percent_used metric. """
values = {}
config = dict(
graph_title='NVMe disks wearout',
graph_category='disk',
graph_info='The graph shows nvme disks wearout (percentage_used)',
graph_args='--base 1... | 86b823c2127072012556b9c0ed0d295ea151fd21 | 3,596,898 |
def lnlik_C(yerr):
""" constant term of the log likelihood expression """
lnC = -0.5 * len(yerr) * np.log(2 * np.pi * yerr ** 2)
return lnC | ec8897164482ad304ef6e3c2c0d36e29688ff208 | 3,596,899 |
import logging
def NodeVolumes():
"""List all volumes on this node.
@rtype: list
@return:
A list of dictionaries, each having four keys:
- name: the logical volume name,
- size: the size of the logical volume
- dev: the physical device on which the LV lives
- vg: the volume group to... | 63709f5a394ceb40e3772285dc6051c63b17ec22 | 3,596,901 |
def random(limits, ndoe):
"""
Function define the experiment by randomly picking points in the
designspace
:param limits: Numpy array representing the limit of the desingn-space
:type limits: Numpy array
:param ndoe: Number of points to be sampled in the design-space
:type ndoe: Integer
... | 044a571fb17dd14a6f9ceed5d87ddda04dd35ed3 | 3,596,902 |
def draw_result_on_frame(frame:np.ndarray, gesture_id:int):
"""In-place draw gesture name on frame
Parameters
----------
frame: np.ndarray
dtype uint8, shape (H, W, 3)
gesture_id: int
gesture id
"""
if gesture_id == 0: # no gesture
... | cfe9971f3ce35a7dda1abf2e72feeadd8788d00d | 3,596,903 |
import socket
def get_hostname_from_address(ip_address):
"""
Return the DNS name for an IP address or an empty string if
any error occurred.
"""
socket.setdefaulttimeout(0.5)
try:
return socket.gethostbyaddr(str(ip_address))[0]
except (socket.herror, socket.gaierror):
retur... | 7ef678eb40dbc3c8f765adc633abd7ffcd8cd644 | 3,596,904 |
def all_bert_finetune_squad11(m:Manager)->BertSquad:
""" Finetune BERT on Squad-1.1 dataset """
squadref=all_squad11_tfrecords(m)
return bert_finetune_squad11(m,squadref) | 7562fc3c822928df974cba361e3257bd31387fd5 | 3,596,905 |
def dbsession(request, app) -> Session:
"""Create a test database and database session.
Connect to the test database specified in test.ini. Create and destroy all tables by your Initializer configuration.
Performs SQLAlchemy table initialization for all models connected to ``websauna.system.model.meta.Bas... | cbc35449f3fe7d1d77c24f2c182d0471324acd9b | 3,596,906 |
def prune_event(event):
""" Returns a pruned version of the given event, which removes all keys we
don't know about or think could potentially be dodgy.
This is used when we "redact" an event. We want to remove all fields that
the user has specified, but we do want to keep necessary information like
... | 9ee35802efe2e82e4df3897bea9eb80984c79007 | 3,596,907 |
def calculateGC(seq):
"""Take a sequence as input and calculate the GC %"""
gc = round((seq.count("G")+seq.count("C")) / len(seq) * 100, 2)
return gc | ed0c863c31214de970d0d0efd81206084b4bfbcf | 3,596,908 |
def get_route_name(request): # type: (Request) -> str
"""Get a route name from URL Dispatch or Traversal."""
if request.matched_route:
return str(request.matched_route.name)
return 'no-matched-route' | d7603501cb45a822ede18b93b63cfa902c48ee05 | 3,596,909 |
def df_p(x: str, group: str, df: pd.DataFrame, hue: str = None, agg_func: str = 'mean', agg: bool = False,
n_quantiles: int = 10):
"""
returns a DataFrame with the p value. See hypothesis testing.
:param x: name of column to evaluate
:param group: name of grouping column
:param df: pandas D... | 6cb4ffc4f3b7c8d638d4cf98a7729d8df5a55cb7 | 3,596,911 |
def build_machine(system_info,
core_resource=Cores,
sdram_resource=SDRAM,
sram_resource=SRAM):
"""Build a :py:class:`~rig.place_and_route.Machine` object from a
:py:class:`~rig.machine_control.machine_controller.SystemInfo` object.
.. note::
Lin... | 5dea236c8048f2bf301fa427afbfef2b850df827 | 3,596,913 |
def flatten_selector_hierarchy(selector_hierarchy: []) -> []:
"""
For a given list of items we unpack them to an extended
list of objects that can be manipulated by BaseSelector.flatten_hierarchy(..)
Parameters
==========
selector_hierarchy: [
BaseSelector,
LabeledSelector,
... | bedc9fa7756c14b4eae1b6304d17a2a7e3ad39e7 | 3,596,915 |
def forget_set(bot, update, session, chat, user):
"""Forget every usage of the set of the previously posted sticker."""
if chat.current_sticker is None:
return "You need to send me a sticker first."
usage_file_ids = session.query(StickerUsage.sticker_file_id) \
.join(Sticker) \
.fil... | aba5650921c57d78fe61e6a35f230b797ac296ce | 3,596,916 |
import re
def strip_html_comments(text):
"""Strip html comments from text (which doesn't need to be valid HTML)"""
return re.sub(r"<!--(.|\s|\n)*?-->", "", text) | 4ac4c2061520a8ecdafe77884a1bae9196bc4e21 | 3,596,917 |
import json
def entity_data(request):
""" """
site = request.matchdict['code']
claims, site = verify_access(request, site=site)
e = Entity(request)
summnote, fullnote = e.data()
responseBody = "{ \"summnote\":" + json.dumps(summnote) + ", \"fullnote\": " + json.dumps(fullnote) + "}"
respo... | d6349d91629f05dcefc679978882a25c296a86a4 | 3,596,918 |
def find(f, seq):
"""Return first item in sequence where f(item) == True"""
for item in seq:
if f(item):
return item | ed81de35efd2144b45d404ba6e0c8b42cfa337fe | 3,596,919 |
def normal_to_uniform(x):
"""normal_to_uniform converts a given vector x with normal
distribution (0, I) into a vector with uniform distribution.
x: [float] * len(x)
returns: [float] * len(x)
"""
length = len(x)
output = scipy_norm.cdf(x)
for i in range(length):
limi... | f48582bf78ff62d10c33685c6dbd32c4aeb10776 | 3,596,920 |
import click
def cli_option_quiet(func):
"""
Decorator for adding a reusable CLI option `--quiet`/'-q'.
"""
# noinspection PyUnusedLocal
def _callback(ctx: click.Context, param: click.Option, value: bool):
ctx_obj = ctx.ensure_object(dict)
ctx_obj["quiet"] = value
return v... | 4249c71b38d24693b0064157dc313d5798d1529b | 3,596,923 |
def setup_tdl_kernel(tdl_mode, kwargs):
"""Temporal kernel interface."""
if tdl_mode == 'OSD':
tdline = OneStepDelayKernel()
elif tdl_mode == 'EWS':
tdline = ExponentiallyWeightedSmoothingKernel(kwargs['tdl_alpha'])
return tdline | 303be491b5eeffa2b45b2c72f6d392f2f97b66b1 | 3,596,925 |
def nodeIsDmgr( nodename ):
"""Return true if the node is the deployment manager"""
return nodeHasServerOfType( nodename, 'DEPLOYMENT_MANAGER' ) | db7cbc3c78e0bcbaa00659d3555540a4ee1257da | 3,596,926 |
import numpy
import multiprocessing
import pandas
def parallelize_df(df, func, n_cores=4):
"""Run a function parallely on a pandas dataframe.
Source: https://towardsdatascience.com/make-your-own-super-pandas-using-multiproc-1c04f41944a1
"""
df_split = numpy.array_split(df, n_cores)
with multi... | 99fcd50553465645bcbcb01b5f05b3c2062141f8 | 3,596,927 |
def alarm(duration=250):
"""
Red alarm; flashing bright red to dark red.
:param int duration: The duration between hi/lo brightness,in milliseconds.
:returns: A list of transitions.
:rtype: list
"""
transitions = [
HSVTransition(0, 100, duration=duration, brightness=100),
H... | b7785eff64b4e83d66fb72f6655dcb1d93baab93 | 3,596,928 |
def add_sparsity_gap(sparse_key, non_sparse_key, now_epoch_info, **kwargs):
"""Get sparse loss / non_sparse_loss."""
try:
sparse_loss = find_value(now_epoch_info, sparse_key)
non_sparse_loss = find_value(now_epoch_info, non_sparse_key)
except AssertionError as e:
# logging.warning(f"... | d50a4f6961239ec72e5ddc12bb3cb9b75cb50675 | 3,596,930 |
def supports_download(ixload: IxlApp) -> bool:
""" Returns True of server supports download, else False.
:param ixload: IxLoad server.
"""
return not ixload.is_remote or ixload.api.connection.ixload_version >= '8.50.115.333' | 0aff232dd930daeb9cd7ad891101597f7b870272 | 3,596,931 |
from pydicom.dataelem import empty_value_for_VR
from typing import Any
from typing import Optional
from typing import Union
from typing import Type
def convert_to_python_number(value: Any, vr: str) -> Any:
"""When possible convert numeric-like values to either ints or floats
based on their value representatio... | fa0444a60945fca0f411aec3d6dcdd31ffcb81e3 | 3,596,932 |
def upper(inter):
"""Return the upper bound of an interval."""
return __pos(inter, 1) | 2a8c38df876592154ca642523f93ffa3b7d837e8 | 3,596,933 |
import torch
def _compute_alphas(x, c_world):
"""
Computes barycentric coordinates of x in the frame c_world.
Args:
x: Batch of 3-dimensional points of shape `(minibatch, num_points, 3)`.
c_world: control points in world coordinates.
"""
x = F.pad(x, (0, 1), value=1.0)
c = F.pa... | b17dddcae016deca783a627df2ad5f931ac9ff8c | 3,596,934 |
def bartlett(n, normalize=1):
"""Bartlett window
Parameters
----------
n : int
Window length
normalize : int, optional
Normalization flag
(0) don't normalize
(1) normalize by power
(2) normalize by magnitude
Default is 1.
Returns
... | e3c3788656b91dce555d5609a84f3c94d2adcf53 | 3,596,935 |
def locations(request):
"""Show all locations."""
locations = Location.objects.order_by('date_added')
locations = locations.exclude(desc='world')
context = {'locations': locations}
return render(request, 'locations.html', context) | f1fa31f370bbfa19138ef9dfa6da0d71a28c4572 | 3,596,936 |
import torch
def predict_mask_loss(net, img_pillow, num_patch, patch_size, bin_thresh=0.2, thresh_num_pixel=1,
flag_mask_edge=False, flag_mask_inner=False, flag_equalizer=False):
""" Predict if the pillow image have the defect under investigation
Args:
------
net: pytorch net ... | c0bb621f8075e133135b0033be3cdba601a0e081 | 3,596,937 |
import re
def indent_string(s, indent=12):
"""
Add the given number of space characters to the beginning every
non-blank line in `s`, and return the result.
"""
# This regexp matches the start of non-blank lines:
return re.sub('(?m)^(?!$)', indent*' ', s) | cf9603be231749f55e4a29c92c90b9139515894c | 3,596,938 |
def generate_password(self):
"""
a function that generates password randomly
"""
auto_password = Credentials.generate_password(self)
return auto_password | 9c3065668c04b2d9fcf06b5318642759f44df0be | 3,596,939 |
def detection_performance(regressor, X, Y, outf):
"""
Measure the detection performance
return: detection metrics
"""
num_samples = X.shape[0]
l1 = open('%s/confidence_TMP_In.txt' % outf, 'w')
l2 = open('%s/confidence_TMP_Out.txt' % outf, 'w')
y_pred = regressor.predict_proba(X)[:, 1]
... | a7d81dde423e10a4b4d8c8bfec99ac79718ea40a | 3,596,940 |
def Get_Mono_Char_Strain(
source,
method="SPA",
freq=None,
f_gw_frame="observer",
pn_frame="observer",
out_frame="observer",
inc=None,
):
"""Calculates the characteristic strain from a slowly evolving (monochromatic) binary black hole
Parameters
----------
source: object
... | 91230a193311fa93f7fd2824e3ac4e2b0a601745 | 3,596,941 |
def is_fused_matmul(node: NodeDef, activation: MaybeText = None) -> bool:
"""Return whether a node is a fused matmul operation with given activation
"""
return is_fused_op(node, 'MatMul', activation) | 02b2114bf4c1abcd7d48b1eb855fc83c50bdfb5f | 3,596,942 |
def get_movie_in_db_by_id(sapo_id):
"""Gets movie from database given its ID"""
return db.movie.find_one({'sapo_id': sapo_id}) | 1c00e5701400ea511376fb189973ff2c45faaf05 | 3,596,943 |
def rename(**kwargs):
"""Rename one or more columns, leaving other columns unchanged
Example usage:
diamonds >> rename(new_name=old_name)
"""
def rename_columns(df):
column_assignments = {old_name_later._name: new_name
for new_name, old_name_later in kwargs.items()}
return... | 0f4c87795f663a9cab8867c4b856ef75e59e2102 | 3,596,944 |
import tempfile
import shutil
def zip_folder(folder: Text) -> Text:
"""Create an archive from a folder."""
zipped_path = tempfile.NamedTemporaryFile(delete=False)
zipped_path.close()
# WARN: not thread-safe!
return shutil.make_archive(zipped_path.name, "zip", folder) | b6de66be401926e584b5e68c12247222fcbda1a3 | 3,596,945 |
def CV_ELEM_SIZE1(*args):
"""CV_ELEM_SIZE1(int type) -> int"""
return _cv.CV_ELEM_SIZE1(*args) | d8adee3bf707a2525463497352eb69a0cc41d6e4 | 3,596,946 |
def inpaint_mean(x: TensorLike, mask: TensorLike) -> TensorType:
"""Fill the image x at the locations where mask is 0 with the mean value of the image."""
mean = tf.ones(tf.shape(mask)) * tf.reduce_mean(x)
return x * mask + mean * (1 - mask) | 58492c239038316cd99ced0526f337975cc4433b | 3,596,947 |
def part_2(steps):
"""Function which calculates the solution to part 2
Arguments
---------
Returns
-------
"""
max_dist = 0
for ii in range(len(steps)):
dist = part_1(steps[:ii])
if dist > max_dist:
max_dist = dist
return max_dist | 1f1dbe46fd573e0d6984f6170bbb190929e48e18 | 3,596,948 |
from typing import Tuple
def _get_values_or_raise(series_a: TimeSeries,
series_b: TimeSeries,
intersect: bool) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns the numpy values of two time series. If intersect is true, considers only their time intersection.
... | 24eccb62db2927dce311c8df211a0287cc73e23c | 3,596,949 |
def status_of_ec2_instance(config, instance_id):
"""
Get the status of an ec2 instance
Parameters
----------
config : dict
configuration
instance_id : str
instance id
Returns
-------
dict with instance status or None
if can return None if the instance has jus... | 0a2d2f92c1ad90bfb1ee79abdcc0f1ac7bb7bfcf | 3,596,950 |
def gen_sta(a):
"""Generate three statistics.
Arg:
a: feature matrix
Return:
t = [mu, sd, tail], where dim(t) = 18*3 = 54
"""
t = list(range(len(a) * 3))
for i in range(len(a)):
t[i] = np.mean(a[i])
t[i + len(a)] = np.std(a[i])
t[i + len(a) * 2] = np.size(... | 47568270f2ebb133221a5f86cac1e8e7e13ad420 | 3,596,951 |
from re import S
from typing import Counter
def aggregate_noises_by_edge(sample_gdf: gpd.GeoDataFrame, log: Logger) -> pd.DataFrame:
"""Calculates edge-level noise attributes (noises & noise_sources) from sampling points.
e.g. noises = { 45: 13.2, 50: 22.1 }, noise_source = 'train' & noise_sources = { 'road':... | b6b7d87bde01866b4203d730c3578f9cf324fea3 | 3,596,952 |
def get_label_n(y, y_pred, n=None):
"""Function to turn raw outlier scores into binary labels by assign 1
to top n outlier scores.
Parameters
----------
y : list or numpy array of shape (n_samples,)
The ground truth. Binary (0: inliers, 1: outliers).
y_pred : list or numpy array of sha... | d84fb9d7841b27c265e83c64c5229959e9000520 | 3,596,953 |
def parcels_arg_tuple(dimensions):
"""Return Parcels triplet for given dimensions dictionary"""
dims = {
"xt": np.array([25, 75, 125]),
"xu": np.array([50, 100, 150]),
"yt": np.array([30, 40, 50, 60]),
"yu": np.array([35, 45, 55, 65]),
}
shape = (2, 4, 3)
parcels_v... | 0d12ba95534122c135cf8b67554ab6182423398b | 3,596,954 |
from datetime import datetime
def datetime_to_string(value, format=None):
""" Transform datetime object in a string with input format
:returns: formatted datetime
:rtype: str
"""
if format is None:
format = "%Y-%m-%dT%H:%M:%S.%f%z" if django_settings.USE_TZ else "%Y-%m-%dT%H:%M:%S.%f"
if not isin... | 9f1a76b3cdec312e19ab7532aaa60969b6851ee4 | 3,596,955 |
def is_partial(self: JSONClassObject) -> bool:
"""This property indicates this object is not a full version of the item it
represents in the database. Only part of fields are fetched from the
database.
"""
return self._is_partial | 071bba571621353410658b005ebb295cd55a7ebd | 3,596,956 |
def check_nanos(original_question):
"""Check if original_question from new Nanos survey."""
if original_question in NANOS_QUESTIONS:
# Return int as will be stored as BOOL (alias for TINYINT(1))in MySQL
return 1
return 0 | 2d4c0dd5ff18a11b06dd45a73d325bdd31156f82 | 3,596,957 |
def add_facility_id_unit_id_epa(df):
"""
Harmonize columns that are added later.
The datapackage validation checks for consistent column names, and these
two columns aren't present before August 2008, so this adds them in.
Args:
df (pandas.DataFrame): A CEMS dataframe
Returns:
... | 408d3e6d548834c8b1ceadd13f2711dbbecfcf87 | 3,596,958 |
def get_type_res_list(org_id, all=True):
"""
Liste der in org_id verfuegbaren Typen (Kategorien) und Ressourcen
[ [typ, [res1, res2, ...], nres] ... ]
Wenn all!=True: nur die Typen, in denen Ressourcen definiert sind
"""
ret = []
for t in get_type_list(org_id):
r_t = []
for r in get_resource_list(... | 16407e314e0c4e220ec9ca710ac7003ca665a9ae | 3,596,959 |
import math
def R_ac(R_dc, grouping=1, d_c=None, s=None, material='aluminum', shape='round',
arrangement='stranded', treatment='untreated', f=60, prox_method='IEC_287',
pipe_correction=1):
"""
Compute AC Resistance of a conductor cable based on DC Resistance, accounting for
skin and proximity effects.
... | 243002f7371358f3f674f97ab3efad8d50b16792 | 3,596,960 |
import scipy
def get_ZXY_data(Data,
zf,
xf,
yf,
FractionOfSampleFreq=1,
zwidth=10000,
xwidth=5000,
ywidth=5000,
filterImplementation="filtfilt",
timeStart=None,
... | 683ab5edab27943e532581df002e26ff48c8aef1 | 3,596,961 |
def pytest_namespace():
"""
Add the following functions to the pytest namespace:
- :func:`get_index_doctree`
- :func:`assert_issue_xref`
"""
return dict((f.__name__, f) for f in
(assert_issue_xref, assert_issue_pending_xref)) | c3822858b329a36320dd21db0f106c6e9a6ae730 | 3,596,962 |
def qtrfit(numpoints, defcoords, refcoords, nrot):
"""Find the best-fit quaternion.
Find the quaternion, q, [and left rotation matrix, u] that minimizes
.. math::
| qTXq - Y | ^ 2 [|uX - Y| ^ 2]
This is equivalent to maximizing
.. math::
Re(q^T X^T q Y)
The left rotation matri... | 91285de8e293fa71b0c111effa21f9bfcc8d52ba | 3,596,963 |
def serialize_messages(messages):
"""JRPC serialization
Method that serializes an array of messages as read from the database
through a query.
:param messages: The message objects as read from the database
:return: JSON serialiazible array
"""
serial_messages = []
for m in messages:
... | 02f8574f6599d2f89095a81173d6daa9c983cc2d | 3,596,964 |
def _get_normalized_data(case):
"""helper method for ``_get_fringe_data``"""
#def _get_normalized_data(result_case):
#(obj, (i, name)) = result_case
#case = obj.get_result(i, name)
if case is None:
return None
if len(case.shape) == 1:
normi = case
else:
assert isinstance... | eaa0fa0df35970e2e11c643b2ec2f9e628d777f7 | 3,596,965 |
import torch
def safe_cumprod(x, *args, **kwargs):
"""Computes cumprod of x in logspace using cumsum to avoid underflow.
The cumprod function and its gradient can result in numerical instabilities
when its argument has very small and/or zero values. As long as the argument
is all positive, we can ins... | 4cdaecee6c1cbbe8424893fc132a2b748a314a44 | 3,596,966 |
def trcnam(index):
"""trcnam(SpiceInt index)"""
return _cspyce0.trcnam(index) | 166b79d16ca04e0c23a27cc07fff1bb880ce206c | 3,596,967 |
def predictability(S,N, inital_guess = 0.5):
"""Finds the value of the predictability for a process with an entropy rate S and a vocabulary size N."""
# explodes for small values of N or large values of S :(
try:
f = lambda Pi : S + Pi*np.log2(Pi) + (1 - Pi)*np.log2(1 - Pi) - (1 - Pi)*np.log2(N-1)
... | 358f347587e8e6bf3f41243050de72015421a527 | 3,596,968 |
import scipy
def get_signal(wavpath, emphasize=True, pre_emphasis=0.97):
"""
Gets signal and sample rate from wav file.
If emphasize is True https://www.quora.com/Why-is-pre-emphasis-i-e-passing-the-speech-signal-through-a-first-order-high-pass-filter-required-in-speech-processing-and-how-does-it-work/ans... | d9dbfb678a41fdf4bf8db274ee849c27a3bf51cc | 3,596,969 |
def auto_flushing_queued_client(request, blank_transport):
"""A Riemann client using the StringIO transport and
AutoFlushingQueuedClient with max_delay=300 and
max_batch_size=5000"""
client = riemann_client.client.AutoFlushingQueuedClient(
transport=blank_transport,
max_delay=300,
... | 21b601dcfceff9a2381a1b5d27abfffed43ae3a0 | 3,596,970 |
import optparse
def comma_separated_callback(*, is_valid_func=lambda v: True,
error_msg="{invalid} is not an allowed value"):
""" Return an optparse callback for comma-separated args.
Default value is not processed.
Usage::
my_callback = comma_separated_callback(
... | 3b7c1de63a7a29f0fc2f4a6ae7445d6e05075d32 | 3,596,972 |
import tqdm
from datetime import datetime
def k_symmetric(Nbin):
"""A function that calculates the symmetric part of the diffusivity tensor for every time gap up to 60 days"""
pi=np.pi
#Data paths:
Path_data = '/Users/tychobovenschen/Documents/MasterJaar2/Thesis/data/'
File_data = 'interpolated_gl... | 370c00289d495cfbf5ee333623d1ed2662827b1e | 3,596,973 |
def max_pool(x, kernel_size, stride, padding, name):
"""
:param x:
:param kernel_size:
:param stride:
:param padding:
:param name:
:return:
"""
return tf.nn.max_pool(x, ksize=[1, kernel_size, kernel_size, 1],
strides=[1, stride, stride, 1], padding=padding, ... | 7c60a1167287d3daf398c208bc4bc8e1425a2980 | 3,596,974 |
def get_huc12_results(id, analysis_acres, total_acres):
"""Get results for NatureScape dataset for a given HUC12.
Parameters
----------
id : str
HUC12 ID
analysis_acres : float
area of HUC12 summary unit less any area outside SE Blueprint
total_acres : float
area of HUC1... | 75bb025af9387b66e6bfdb739100dbdbfc51653f | 3,596,975 |
def vector_rejection(a, b):
"""
Return the vector rejection from vector a onto vector b
See:
* https://en.wikipedia.org/wiki/Vector_projection
Args:
:a: vector to be projected
:b: vector onto which a is projected
Returns:
:a2: vector rejection
"""
a1 = vec... | 558ac1ef3ba0f8d95096f3aae87f77cc8a0c1d89 | 3,596,976 |
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