content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import dbm
def get_sim_data():
"""
Create the data needed to initialize a simulation
Performs the steps necessary to set up a stratified plume model simulation
and passes the input variables to the `Model` object and
`Model.simulate()` method.
Returns
-------
profile : `ambient.Profi... | e253665f451b167a188c997ff36fc406e0f3a587 | 19,171 |
import numpy as np
def _organize_arch(fils, pth):
"""Allocate data from each specific type of file (keys from the input dict) to a new dict
Arguments:
fils {dict} -- Dictionary containing type of files and list of files
Returns:
[dict] -- [description]
"""
imgdata ... | c62c9b23bf4735c2062090d77278ce5a8acbd668 | 19,172 |
def gather_allele_freqs(record, all_samples, males, females, pop_dict, pops, no_combos = False):
"""
Wrapper to compute allele frequencies for all sex & population pairings
"""
#Get allele frequencies
calc_allele_freq(record, all_samples)
if len(males) > 0:
calc_allele_freq(record, male... | 0f74616fa64ee5b3582467da27161906abf28463 | 19,173 |
def get_selected(n=1):
"""
Return the first n selected object, or None if nothing is selected.
"""
if get_selection_len():
selection = bpy.context.selected_objects
if n == 1:
return selection[0]
elif n == -1:
return selection[:]
else:
r... | 6049900ef069731b1fbe9f40fff184085940c83e | 19,174 |
def label_src_vertno_sel(label, src):
""" Find vertex numbers and indices from label
Parameters
----------
label : Label
Source space label
src : dict
Source space
Returns
-------
vertno : list of length 2
Vertex numbers for lh and rh
src_sel : array of int ... | a1858258b6c789557d6bdeff9428cc7aacbe4655 | 19,175 |
def get_subjects(creative_work):
"""
Returns generated html of subjects associated with the
Creative Work HTML or 0-length string
Parameters:
creative_work -- Creative Work
"""
html_output = ''
#! Using LOC Facet as proxy for subjects
facets = list(
REDIS_DATASTORE.smembers(... | 328aeadb21a22972c0843efdba251b2f6c5f937d | 19,176 |
def sort(request):
"""Boolean sort keyword for concat and DataFrame.append."""
return request.param | 83f35eb41bc0cf7eecea932ae4f14646d9e8732f | 19,178 |
def is_comprehension(leaf):
"""
Return true if the leaf is the beginning of a list/set/dict comprehension.
Returns true for generators as well
"""
if leaf.type != 'operator' or leaf.value not in {'[', '(', '{'}:
return False
sibling = leaf.get_next_sibling()
return (sibling.type in... | 11fff76ff8ed19b3d57359b56db886c003603a86 | 19,179 |
def get_class(x):
"""
x: index
"""
# Example
distribution = [0, 2000, 4000, 6000, 8000, 10000]
x_class = 0
for i in range(len(distribution)):
if x > distribution[i]:
x_class += 1
return x_class | 1ae95f3d9bc6f342169232ab10cd08a42de0f692 | 19,180 |
from re import T
def square(x):
"""Elementwise square of a tensor. """
return T.sqr(x) | c052e31a450b91eb1e6a08843f99afd6e618da9d | 19,181 |
import re
def update_email_body(parsed_email, key):
"""
Finds and updates the "text/html" and "text/plain" email body parts.
Parameters
----------
parsed_email: email.message.Message, required
EmailMessage representation the downloaded email
key: string, required
The object key... | d942a4cf47af9d7c1e36a4a2af5d0239b90464d8 | 19,182 |
import json
def create_collaborators(collaborators, destination_url, destination, credentials):
"""Post collaborators to GitHub
INPUT:
collaborators: python list of dicts containing collaborators info to be POSTED to GitHub
destination_url: the root url for the GitHub API
destination: ... | 2d39a2970d9f52af5209b1f4717c0b4d39e1cb5c | 19,183 |
import scipy
def complexity_recurrence(signal, delay=1, dimension=3, tolerance="default", show=False):
"""Recurrence matrix (Python implementation)
Fast Python implementation of recurrence matrix (tested against pyRQA). Returns a tuple
with the recurrence matrix (made of 0s and 1s) and the distance matri... | cc93a80ff34fffc5774f7b52109fd09d8b0ac69e | 19,184 |
def get_awb_shutter( f ):
"""
Get AWB and shutter speed from file object
This routine extracts the R and B white balance gains and the shutter speed
from a jpeg file made using the Raspberry Pi camera. These are stored as text in
a custom Makernote.
The autoexposure and AWB white balance valu... | cfafdf531809729ae0ec96ab90a60a4961b9437a | 19,186 |
def rgb2lab(rgb_arr):
"""
Convert colur from RGB to CIE 1976 L*a*b*
Parameters
----------
rgb_arr: ndarray
Color in RGB
Returns
-------
lab_arr: ndarray
Color in CIE 1976 L*a*b*
"""
return xyz2lab(rgb2xyz(rgb_arr)) | 3eba11b8017908393e1e238e6b4ae046dd265520 | 19,187 |
def format_rfidcard(rfidcard):
"""
:type rfidcard: apps.billing.models.RfidCard
"""
return {
'atqa': rfidcard.atqa if len(rfidcard.atqa) > 0 else None,
'sak': rfidcard.sak if len(rfidcard.sak) > 0 else None,
'uid': rfidcard.uid,
'registered_at': rfidcard.registered_at.is... | 120ca8e338b01235b2ba12ae3f874fd317ffebe8 | 19,188 |
def make_exposure_shares(exposure_levels, geography="geo_nm", variable="rank"):
"""Aggregate shares of activity at different levels of exposure
Args:
exposure_levels (df): employment by lad and sector and exposure ranking
geography (str): geography to aggregate over
variable (str): varia... | 02d990f2b08e3acb2a2b8ac01e44848770bdea71 | 19,189 |
def boxscores(sports=["basketball/nba"], output="dict", live_only=True, verbose=False):
"""
~ 10 seconds
"""
links = boxlinks(sports=sports, live_only=live_only, verbose=verbose)
boxes = [boxscore(link) for link in links]
return boxes | 9cdc1bd4ec90d8ab9593d49316669dc6b801cf2e | 19,191 |
def runningMedian(seq, M):
"""
Purpose: Find the median for the points in a sliding window (odd number in size)
as it is moved from left to right by one point at a time.
Inputs:
seq -- list containing items for which a running median (in a sliding window)
is t... | b37af61c9f6f62bd6fbd395bc2c423a770ba2797 | 19,192 |
def min_max_normalize(img):
""" Center and normalize the given array.
Parameters:
----------
img: np.ndarray
"""
min_img = img.min()
max_img = img.max()
return (img - min_img) / (max_img - min_img) | faaafbc8e0b36f26f8319b671de326dd6a97e6f9 | 19,193 |
def find_common_features(experiment: FCSExperiment,
samples: list or None = None):
"""
Generate a list of common features present in all given samples of an experiment. By 'feature' we mean
a variable measured for a particular sample e.g. CD4 or FSC-A (forward scatter)
Paramete... | 8022a97721eb9ff26efcba347e4f631aff3ede84 | 19,194 |
import heapq
def generate_blend_weights(positions, new_p, n_neighbors):
""" Use inverse distance and K-Nearest-Neighbors Interpolation to estimate weights
according to [Johansen 2009] Section 6.2.4
"""
distances = []
for n, p in positions.items():
distance = np.linalg.norm(new_p - p)
... | f9db2b47d5847cdb2e7367cebe9b9bf81809b11d | 19,195 |
def check_method(adata):
"""Check that method output fits expected API."""
assert "connectivities" in adata.obsp
assert "distances" in adata.obsp
return True | 0ad772187c6d2960149723df17f6b0cf3fa703d1 | 19,196 |
def load_model(Model, params, checkpoint_path='', device=None):
""" loads a model from a checkpoint or from scratch if checkpoint_path='' """
if checkpoint_path == '':
model = Model(params['model_params'], **params['data_params'])
else:
print("model:", Model)
print(f... | 8f1339b5548024714f731de037ef320535fc3b69 | 19,197 |
import inspect
def get_widget_type_choices():
"""
Generates Django model field choices based on widgets
in holodeck.widgets.
"""
choices = []
for name, member in inspect.getmembers(widgets, inspect.isclass):
if member != widgets.Widget:
choices.append((
"%s.... | 143ff91ee3bc4166e5091e344a38fb2bbb72934e | 19,198 |
import array
def iau2000a(jd_tt):
"""Compute Earth nutation based on the IAU 2000A nutation model.
`jd_tt` - Terrestrial Time: Julian date float, or NumPy array of floats
Returns a tuple ``(delta_psi, delta_epsilon)`` measured in tenths of
a micro-arcsecond. Each value is either a float, or a NumPy... | beadd6469a85b475dc22ca1e2a967310555140a9 | 19,199 |
import select
import json
async def _async_get_states_and_events_with_filter(
hass: HomeAssistant, sqlalchemy_filter: Filters, entity_ids: set[str]
) -> tuple[list[Row], list[Row]]:
"""Get states from the database based on a filter."""
for entity_id in entity_ids:
hass.states.async_set(entity_id, ... | fa0131a87ac9ac517ffd63bb563600e12bed68de | 19,201 |
def decrypt_password(private_key: PrivateKey, encrypted: str) -> str:
"""Return decrypt the given encrypted password using private_key and the RSA cryptosystem.
Your implementation should be very similar to the one from class, except now
the public key is a data class rather than a tuple.
"""
n = p... | 607b5e33cff940aa999f56b2e39f56673d94ff7f | 19,202 |
from typing import OrderedDict
def load_jed(fn):
"""
JEDEC file generated by 1410/84 from PALCE20V8H-15 06/28/20 22:42:11*
DM AMD*
DD PALCE20V8H-15*
QF2706*
G0*
F0*
L00000 0000000000000000000000000100000000000000*
"""
ret = {}
d = OrderedDict()
with open(fn) as f:
... | 6570bcdaabb495c13e9419a532c85b15efdf957a | 19,203 |
def plaintext(text, keeplinebreaks=True):
"""Extract the text elements from (X)HTML content
>>> plaintext('<b>1 < 2</b>')
u'1 < 2'
>>> plaintext(tag('1 ', tag.b('<'), ' 2'))
u'1 < 2'
>>> plaintext('''<b>1
... <
... 2</b>''', keeplinebreaks=False)
u'1 < 2'
:param text: `... | c4e5e9a9b41fc7e0dc6b50995d7ec9a9bae1296f | 19,204 |
def fetch_rows(product):
"""
Returns the product and a list of timestamp and price for the given product in the current DATE,
ordered by timestamp.
"""
# We query the data lake by passing a SQL query to maystreet_data.query
# Note that when we filter by month/day, they need to be 0-padded strin... | 8b6f3df658ca38054bd49255b0842f40f6d4bffa | 19,206 |
def create_learner(sm_writer, model_helper):
"""Create the learner as specified by FLAGS.learner.
Args:
* sm_writer: TensorFlow's summary writer
* model_helper: model helper with definitions of model & dataset
Returns:
* learner: the specified learner
"""
learner = None
if FLAGS.l... | 76231a6413560ccc1e1d90fb974f90a83b3bb4f4 | 19,207 |
def load_decoder(autoencoder):
"""
Gets the decoders associated with the inputted model
"""
dim = len(autoencoder.get_config()['input_layers'])
mag_phase_flag = False
decoders = []
if dim == 2:
mag_phase_flag = True
decoders.append(autoencoder.get_layer('mag_decoder'))
... | 8e39470e48f5a6c147d93567c0bdb33a588c790d | 19,208 |
def translate_boarding_cards(boarding_cards):
"""Translate list of BoardingCards to readable travel instructions.
This function sorts list of random BoardingCard objects connecting starts
with ends of every stage of the trip then returns readable instructions
that include seat numbers, location names a... | 0986ab2669fa4376aebd28804586a1566544610e | 19,209 |
def detect_side(start: dict, point: dict, degrees):
"""detect to which side robot should rotate"""
if start['lat'] < point['lat'] and start['lng'] < point['lng']:
return f'{degrees} degrees right'
elif start['lat'] < point['lat'] and start['lng'] > point['lng']:
return f'{degrees} degrees... | 124833bbdcdf36c280cdde8e829f15ae5301e323 | 19,210 |
import pathlib
import sh
def iterate_log_lines(file_path:pathlib.Path, n:int = 0, **kwargs):
"""Reads the file in line by line
dev note: One of the best featuers of this functions is we can use efficient
unix style operations. Because we know we are inside of a unix container
there should... | 54684fcc7a41b623321534202ee250e7c46760d2 | 19,212 |
import torch
def moving_sum(x, start_idx: int, end_idx: int):
"""
From MONOTONIC CHUNKWISE ATTENTION
https://arxiv.org/pdf/1712.05382.pdf
Equation (18)
x = [x_1, x_2, ..., x_N]
MovingSum(x, start_idx, end_idx)_n = Sigma_{m=n−(start_idx−1)}^{n+end_idx-1} x_m
for n in {1, 2, 3, ..., N}
... | fa3cb672e23fccad75965da2ca10955134167c7e | 19,213 |
import json
import time
def _wait_for_event(event_name, redis_address, extra_buffer=0):
"""Block until an event has been broadcast.
This is used to synchronize drivers for the multi-node tests.
Args:
event_name: The name of the event to wait for.
redis_address: The address of the Redis s... | 0aa10f52e1682dd9d1cdc0949d245da26c26bcd4 | 19,214 |
def _stack_exists(stack_name):
""" Checks if the stack exists.
Returns True if it exists and False if not.
"""
cf = boto3.client('cloudformation')
exists = False
try:
cf.describe_stacks(StackName=stack_name)
exists = True
except botocore.exceptions.ClientError as ex:
... | 5ddc6c17342e3c03317d5da0bf8b4d0a338a8f21 | 19,215 |
def make_sequence_output(detections, classes):
"""
Create the output object for an entire sequence
:param detections: A list of lists of detections. Must contain an entry for each image in the sequence
:param classes: The list of classes in the order they appear in the label probabilities
:return:
... | 019d3b74699af20a9f3cbc43b575e8bae5e15946 | 19,216 |
def json_to_dataframe(json, subset=0):
"""Load data from path. The file needs to be a .csv
Returns:\n
Dataframe
"""
# This is to make sure it has the right format when passed to pandas
if type(json) != list:
json = [json]
try:
df = pd.DataFrame(json, [i for i in range(0, len... | 151914e3e11759ff74283c303912a0b6842cd213 | 19,217 |
def dms_to_angle(dms):
"""
Get the angle from a tuple of numbers or strings giving its sexagesimal
representation in degrees
@param dms: (degrees, minutes, seconds)
"""
sign = 1
angle_string = dms[0]
if angle_string.startswith('-'):
sign = -1
angle_string = angle_string[1... | c56c66093a877aae6474d583da6d1db81ccbc7cd | 19,218 |
def fix(text):
"""Repairs encoding problems."""
# NOTE(Jonas): This seems to be fixed on the PHP side for now.
# import ftfy
# return ftfy.fix_text(text)
return text | 7fd97db345a604131f52b272a7dd13ab4f3f9153 | 19,219 |
def generate_labeled_regions(shape, n_regions, rand_gen=None, labels=None,
affine=np.eye(4), dtype=np.int):
"""Generate a 3D volume with labeled regions.
Parameters
----------
shape: tuple
shape of returned array
n_regions: int
number of regions to gene... | 501c9bab430558fdc0cf45491498c8e3bcc7d3c4 | 19,220 |
def get_source_item_ids(portal, q=None):
"""
Get ids of hosted feature services that have an associated scene service.
Can pass in portal search function query (q).
Returns ids only for valid source items.
"""
source_item_ids = []
scene_item_ids = get_scene_service_item_ids(portal)
... | 448ac2d94fda4dc3c69bd8fe9eb00587a0f0dcb2 | 19,221 |
def rasterize_poly(poly_xy, shape):
"""
Args:
poly_xy: [(x1, y1), (x2, y2), ...]
Returns a bool array containing True for pixels inside the polygon
"""
_poly = poly_xy[:-1]
# PIL wants *EXACTLY* a list of tuple (NOT a numpy array)
_poly = [tuple(p) for p in _poly]
img = Image.ne... | d1abf5cef5a1fb57286ff38d575a575a679a4002 | 19,222 |
def from_url_representation(url_rep: str) -> str:
"""Reconvert url representation of path to actual path"""
return url_rep.replace("__", "/").replace("-_-", "_") | 5cf4e1e8cb284c66449807ea275e4fa6b5a3e3ad | 19,223 |
from unittest.mock import patch
async def test_async_start_from_history_and_switch_to_watching_state_changes_multiple(
hass,
recorder_mock,
):
"""Test we startup from history and switch to watching state changes."""
hass.config.set_time_zone("UTC")
utcnow = dt_util.utcnow()
start_time = utcnow... | 6fb66dde3fad24fbccffb0f8ce74e666e3551e56 | 19,224 |
def runningmean(data, nav):
"""
Compute the running mean of a 1-dimenional array.
Args:
data: Input data of shape (N, )
nav: Number of points over which the data will be averaged
Returns:
Array of shape (N-(nav-1), )
"""
return np.convolve(data, np.ones((nav,)) / nav, m... | 8ba55de399d8789a43624582ac14f2f4804668ef | 19,225 |
def test_space(gym_space, expected_size, expected_min, expected_max):
"""Test that an action or observation space is the correct size and bounds.
Parameters
----------
gym_space : gym.spaces.Box
gym space object to be tested
expected_size : int
expected size
expected_min : float... | e43e2e4d064bec033e6cef6f9c1c905b13541cc7 | 19,226 |
from typing import Optional
from typing import List
def multiindex_strategy(
pandera_dtype: Optional[DataType] = None,
strategy: Optional[SearchStrategy] = None,
*,
indexes: Optional[List] = None,
size: Optional[int] = None,
):
"""Strategy to generate a pandas MultiIndex object.
:param pa... | 580a312790d7ff5d9c5f5309f3100e4ebd490f7e | 19,227 |
def pitch_from_centers(X, Y):
"""Spot pitch in X and Y direction estimated from spot centers (X, Y).
"""
assert X.shape == Y.shape
assert X.size > 1
nspots_y, nspots_x = X.shape
if nspots_x > 1 and nspots_y == 1:
pitch_x = pitch_y = np.mean(np.diff(X, axis=1))
elif nspots_y > 1 and n... | c9816d3bee4d658a3b00769f26f22b8c0cd0fd10 | 19,228 |
def _create_lists(config, results, current, stack, inside_cartesian=None):
"""
An ugly recursive method to transform config dict
into a tree of AbstractNestedList.
"""
# Have we done it already?
try:
return results[current]
except KeyError:
pass
# Check recursion depth an... | ef9a51023a44ae1cdbfbadbc762a0ffcd1959562 | 19,229 |
def encode(value):
"""
Encode strings in UTF-8.
:param value: value to be encoded in UTF-8
:return: encoded value
"""
return str(u''.join(value).encode('utf-8')) | 697f99f028d4b978b591d006273b9d5f688711f3 | 19,230 |
def get_season(months, str_='{}'):
"""
Creates a season string.
Parameters:
- months (list of int)
- str_ (str, optional): Formatter string, should contain exactly one {}
at the position where the season substring is included.
Returns:
str
"""
if months is None:
retur... | 73b4e8169f08ef286a0b57779d22c3436538fc30 | 19,231 |
def data_availability(tags):
"""
get availability based on the validation tags
Args:
tags (pandas.DataFrame): errors tagged as true (see function data_validation)
Returns:
pandas.Series: availability
"""
return ~tags.any(axis=1) | 240bed8f169d23610f11c214d3644f02e5435412 | 19,232 |
async def fetch_image_by_id(
image_uid: str
):
"""
API request to return a single image by uid
"""
image_uid = int(image_uid)
image = utils_com.get_com_image_by_uid(image_uid)
return image | 153d24fd35ce18ae9c94d1c7ecf797154bc32c0f | 19,233 |
from datetime import datetime
def get_spring_break(soup_lst, year):
"""
Purpose:
* returns a list of the weekdays during spring break
* only relevant for spring semesters
"""
spring_break_week = set()
# search for the "Spring Break begins after last class." text
for i in range... | cfd80d12da8a22a26d66f4f64f6f8511ed7238a4 | 19,234 |
def GetProQ3Option(query_para):#{{{
"""Return the proq3opt in list
"""
yes_or_no_opt = {}
for item in ['isDeepLearning', 'isRepack', 'isKeepFiles']:
if query_para[item]:
yes_or_no_opt[item] = "yes"
else:
yes_or_no_opt[item] = "no"
proq3opt = [
"-r... | e2fe6ba97aa96d01a19a191aabcc3e793a63c490 | 19,235 |
def is_empty_config(host):
"""
Check if any services should to be configured to run on the given host.
"""
return host.AS is None | c4ec3861c497ac49ed69ecd1d6da31ab8fe2829c | 19,236 |
def total_value(metric):
"""Given a time series of values, sum the values"""
total = 0
for i in metric:
total += i
return total | 4454bfaeb0797bc03b14819bde48dc8f5accc4d3 | 19,237 |
import re
def validate_email_add(email_str):
"""Validates the email string"""
email = extract_email_id(email_str)
return re.match("[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?", email.lower()) | 0f77f223b208471a960e2829efb12a85f82b1381 | 19,239 |
def get_seed_nodes_json(json_node: dict, seed_nodes_control: dict or list) -> dict:
""" We need to seed some json sections for extract_fields.
This seeds those nodes as needed. """
seed_json_output = {}
if isinstance(seed_nodes_control, dict) or isinstance(seed_nodes_control, list):
for node... | f3672ee019ff4bb72f25582daf5c83fa7c8f72d0 | 19,240 |
def load_object(import_path):
"""
Loads an object from an 'import_path', like in MIDDLEWARE_CLASSES and the
likes.
Import paths should be: "mypackage.mymodule.MyObject". It then imports the
module up until the last dot and tries to get the attribute after that dot
from the imported module.
... | 5fd45ee31a440cbdd4c90e875e04f4f8f1856b3a | 19,241 |
def _inufft(kspace,
trajectory,
sensitivities=None,
image_shape=None,
tol=1e-5,
max_iter=10,
return_cg_state=False,
multicoil=None,
combine_coils=True):
"""MR image reconstruction using iterative inverse NUFFT.
For the ... | 892350c74ca0b7163b4aec9278af30dd770b5e1e | 19,242 |
def adjust_cart(request, item_id):
"""Adjust the quantity of the specified product to the specified amount"""
album = get_object_or_404(Album, pk=item_id)
# Returns 404 if an invalid quantity is entered
try:
quantity = int(request.POST.get("quantity"))
except Exception as e:
return... | ed455747341cf581725d2fae326292155a3b77a8 | 19,243 |
def calculate_delta_v(scouseobject, momone, momnine):
"""
Calculate the difference between the moment one and the velocity of the
channel containing the peak flux
Parameters
----------
scouseobject : instance of the scousepy class
momone : ndarray
moment one (intensity-weighted aver... | a894eac64f5b88fd6230eb060583fd15552bc8d8 | 19,244 |
def _validate_image(values):
"""
Validates the incoming data and raises a Invalid exception
if anything is out of order.
:param values: Mapping of image metadata to check
"""
status = values.get('status', None)
if not status:
msg = "Image status is required."
raise exceptio... | d0ebb8ecbde452c3128e93e917482cff13e47947 | 19,245 |
def revcmp(x, y):
"""Does the reverse of cmp():
Return negative if y<x, zero if y==x, positive if y>x"""
return cmp(y, x) | 52e5382211379d09703996b0da89821a9521de73 | 19,246 |
def linear_regression(data: pd.DataFrame):
"""
https://www.statsmodels.org/
:param data: 数据集中要包含收盘价Close
:return: 拟合的y，k，b以及k转化的角度
"""
y_arr = data.Close.values
x_arr = np.arange(0, len(y_arr))
b_arr = sm.add_constant(x_arr)
model = regression.linear_model.OLS(y_arr, b_arr).fit()
... | 9b30a6d90ed1e0131e12b2f7944eb58a90676ad3 | 19,247 |
from datetime import datetime
from operator import and_
def get_expiry():
"""
Returns the membership IDs of memberships expiring within 'time_frame' amount of MONTHS
"""
time_frame = request.args.get('time_frame')
try:
time_frame = int(time_frame)
except ValueError as e:
print... | 4fd13a5e2de1feb4b797c225c349031a903f2673 | 19,248 |
def get_functions(input_file):
"""Alias for load_data bellow."""
return load_data(input_file) | 7f286809a3c27db32e0aeb3f08d41989a7b3fad2 | 19,249 |
def is_elem_ref(elem_ref):
"""
Returns true if the elem_ref is an element reference
:param elem_ref:
:return:
"""
return (
elem_ref
and isinstance(elem_ref, tuple)
and len(elem_ref) == 3
and (elem_ref[0] == ElemRefObj or elem_ref[0] == ElemRefArr)
) | 282a5ba04b2cafedd5a043bf83b4ccbd6196ae44 | 19,250 |
from typing import Tuple
from typing import List
def analyse_subcommand(
analyser: Analyser,
param: Subcommand
) -> Tuple[str, SubcommandResult]:
"""
分析 Subcommand 部分
Args:
analyser: 使用的分析器
param: 目标Subcommand
"""
if param.requires:
if analyser.sentences !=... | d3be0a7709ae2ebfab414d30494fe7baeba5de8d | 19,251 |
def fetch_pg_types(columns_info, trans_obj):
"""
This method is used to fetch the pg types, which is required
to map the data type comes as a result of the query.
Args:
columns_info:
"""
# get the default connection as current connection attached to trans id
# holds the cursor whic... | 87bdc81134ee4d83ffbce05a77abec555b55a661 | 19,252 |
def open_popup(text) -> bool:
""" Opens popup when it's text is updated """
if text is not None:
return True
return False | 8ced6b6e73531f97df8ac7fe38723438077ca6d1 | 19,253 |
def se_beta_formatter(value: str) -> str:
"""
SE Beta formatter.
This formats SE beta values. A valid SE beta values
is a positive float.
@param value:
@return:
"""
try:
se_beta = float(value)
if se_beta >= 0:
result = str(se_beta)
else:
... | 30dde489e1a8a70c0f1093caa1ce289c759b26d6 | 19,254 |
from typing import Optional
def replace_missing_data(
data: pd.DataFrame,
target_col: str,
source_col: str,
dropna: Optional[bool] = False,
inplace: Optional[bool] = False,
) -> Optional[pd.DataFrame]:
"""Replace missing data in one column by data from another column.
Parameters
-----... | a94e41cb88bcf502192855276ed1f11f73b1c3a1 | 19,255 |
def jsonpath_parse(data, jsonpath, match_all=False):
"""Parse value in the data for the given ``jsonpath``.
Retrieve the nested entry corresponding to ``data[jsonpath]``. For
example, a ``jsonpath`` of ".foo.bar.baz" means that the data section
should conform to:
.. code-block:: yaml
---
... | 3b5ab89d8315e36f8412e874f393c414c76b8587 | 19,256 |
def extract_urlparam(name, urlparam):
"""
Attempts to extract a url parameter embedded in another URL
parameter.
"""
if urlparam is None:
return None
query = name+'='
if query in urlparam:
split_args = urlparam[urlparam.index(query):].replace(query, '').split('&')
ret... | 198771d40eeddc3b7dbf2924d9d49fe7a7f0a51d | 19,257 |
from typing import Dict
from typing import Any
def _get_required_var(key: str, data: Dict[str, Any]) -> str:
"""Get a value from a dict coerced to str.
raise RequiredVariableNotPresentException if it does not exist"""
value = data.get(key)
if value is None:
raise RequiredVariableNotPresentExce... | b94db42048779df532a55c2604c7c1b5d02a4f7f | 19,259 |
def phones():
"""Return a list of phones used in the main dict."""
cmu_phones = []
for line in phones_stream():
parts = line.decode("utf-8").strip().split()
cmu_phones.append((parts[0], parts[1:]))
return cmu_phones | 972c4c0739cd3c823f98eb6314d25f07b9f720f6 | 19,260 |
def load_specific_forecast(city, provider, date, forecasts):
"""reads in the city, provider, date and forecast_path and returns the data queried from the forecast path
:param city: city for which the weather forecast is for
:type string
:param provider: provider for which the weather forecast is for
... | 95f00fd07d218f1e19eb6d771898453d2495cb1d | 19,261 |
from numpy import array, isnan
from mne.channels import Montage
def eeg_to_montage(eeg):
"""Returns an instance of montage from an eeg file"""
pos = array([eeg.info['chs'][i]['loc'][:3]
for i in range(eeg.info['nchan'])])
if not isnan(pos).all():
selection = [i for i in range(eeg... | 9d0823bc9633ead4081b4b068717c8f9385c3e69 | 19,262 |
def mul_inv2(x:int, k:int) -> int:
""" Computes x*2^{-1} in (Z/3^kZ)*."""
return (inv2(k)*x)%(3**k) | 5789b4b9837f5b3bf6093aa586fca8f133ff8c51 | 19,263 |
def Be(Subject = P.CA(), Contract=FALSE):
"""Synonym for Agree("be")."""
return Agree("be", Subject, Contract) | e6b1f07d17c34157b9b1ca216f4c0e99c5b25c00 | 19,264 |
def line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=1e-4, alpha0=0.99):
"""
Armijo linesearch function that works with matrices
find an approximate minimum of f(xk+alpha*pk) that satifies the
armijo conditions.
Parameters
----------
f : function
loss function
xk : np.nda... | aefbe34ad1b28317e4fc21b1d80beda430183660 | 19,265 |
def lprob2sigma(lprob):
""" translates a log_e(probability) to units of Gaussian sigmas """
if (lprob>-36.):
sigma = norm.ppf(1.-0.5*exp(1.*lprob))
else:
sigma = sqrt( log(2./pi) - 2.*log(8.2) - 2.*lprob )
return float(sigma) | b224e9b50fc2a171cbb849965946ccae804648d7 | 19,266 |
def convert_from_fortran_bool(stringbool):
"""
Converts a string in this case ('T', 'F', or 't', 'f') to True or False
:param stringbool: a string ('t', 'f', 'F', 'T')
:return: boolean (either True or False)
"""
true_items = ['True', 't', 'T']
false_items = ['False', 'f', 'F']
if isi... | b9840c41a978003e8dcc5191bd7f859fc5b0ecb7 | 19,267 |
def gaussian_device(n_subsystems):
"""Number of qubits or modes."""
return DummyDevice(wires=n_subsystems) | c2779958009ebe2dd7907a0a5f418535d782f4a0 | 19,268 |
def create_playlist(current_user, user_id):
"""
Creates a playlist.
:param user_id: the ID of the user.
:return: 200, playlist created successfully.
"""
x = user_id
user = session.query(User).filter_by(id=user_id).one()
data = request.get_json()
new_playlist = Playlist(name=data['nam... | 6116949956bbc077205adb66bf51b160b7a0d812 | 19,269 |
def gram_matrix(y):
"""
Input shape: b,c,h,w
Output shape: b,c,c
"""
(b, ch, h, w) = y.size()
features = y.view(b, ch, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (ch * h * w)
return gram | 9ea7595870dccc1375626c374fb9db1436523e40 | 19,270 |
import torch
def process_pair_v2(data, global_labels):
"""
:param path: graph pair data.
:return data: Dictionary with data, also containing processed DGL graphs.
"""
# print('Using v2 process_pair')
edges_1 = data["graph_1"] #diff from v1
edges_2 = data["graph_2"] #diff from v1
edges... | 61e0194f521132cfa4e96db925f566abf6b3b427 | 19,271 |
from typing import Tuple
def calculate_line_changes(diff: Diff) -> Tuple[int, int]:
"""Return a two-tuple (additions, deletions) of a diff."""
additions = 0
deletions = 0
raw_diff = "\n".join(diff.raw_unified_diff())
for line in raw_diff.splitlines():
if line.startswith("+ "):
... | 437859735c904a3c7754091c6cb97ba528dc7e72 | 19,272 |
def get_synonyms(token):
""" get synonyms of word using wordnet
args:
token: string
returns:
synonyms: list containing synonyms as strings
"""
synonyms = []
if len(wordnet.synsets(token)) == 0:
return None
for synset in wordnet.synsets(token):
for lemma in syn... | ec26875e694f860c38b709979dfc8328eff17f0f | 19,273 |
def concat_experiments_on_channel(experiments, channel_name):
"""Combines channel values from experiments into one dataframe.
This function helps to compare channel values from a list of experiments
by combining them in a dataframe. E.g: Say we want to extract the `log_loss`
channel values for a list o... | 04c8004ccb1a2b5ec2906bb1183e685b8c8ff763 | 19,274 |
def sghmc_naive_mh_noresample_uni(u_hat_func, du_hat_func, epsilon, nt, m, M, V, theta_init, r_init, formula):
"""
This is a function to realize Naive Stochastic Gradient Hamiltonian Monte Carlo
with Metropolis-Hastings correction in unidimensional cases without resampling
procedure.
"""
B = 1... | 4f330bf3025506bc2bafca0891025ac8b9a4f280 | 19,275 |
def detect_voices(aud, sr=44100):
"""
Detect the presence and absence of voices in an array of audio
Args:
Returns:
"""
pcm_16 = np.round(
(np.iinfo(np.int16).max * aud)).astype(np.int16).tobytes()
voices = [
VAD.is_speech(pcm_16[2 * ix:2 * (ix + SMOOTHING_WSIZE)],
... | ec987cf5e3384cb20d52d07684f7afb5f38f0e98 | 19,276 |
def process_to_binary_otsu_image(img_path, inverse=False, max_threshold=255):
"""
Purpose:
Process an image to binary colours using binary otsu thresholding.
Args:
img_path - path to the image to process
inverse - if true an inverted binary thresholding will be applied (optional).
... | f450d29540679f2fa7736e7cd0257a56b58c8a8d | 19,277 |
import hashlib
import random
def _fold_in_str(rng, data):
"""Folds a string into a jax.random.PRNGKey using its SHA-1 hash."""
m = hashlib.sha1()
m.update(data.encode('utf-8'))
d = m.digest()
hash_int = int.from_bytes(d[:4], byteorder='big', signed=True)
return random.fold_in(rng, hash_int) | e0b3d135a9573892cf7f4cfdcea1bc29bbc3e8c0 | 19,278 |
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