content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def handle_array_input(args):
"""
Format any command argument that is supposed to be an array from a string to a list.
parameter: (dict) args
The command arguments dictionary
returns:
The arguments dict with field values transformed from strings to lists where necessary
"""
arr... | f072412936e16425d1217423c01689d8c32e3f2e | 71,937 |
def parse_source(source):
"""Parse a source in the format <region>:<db-identifier>
Args:
source (str): The source string
Returns:
:class:`Source`: A Source namedtuple.
"""
try:
region, _id = source.split(":", 1)
except ValueError:
raise ValueError(
"... | a799392e353dedc6e34d2242143ae3212923cae0 | 71,938 |
def mat_load(path, m_dict=None):
"""
Load mat files.
:param path:
:return:
"""
if m_dict is None:
data = sio.loadmat(path)
else:
data = sio.loadmat(path, m_dict)
return data | ebab6c377c0d7d2fc544d5f94b4f002021454677 | 71,939 |
def internal_redirect(url, default_path):
"""
Redirect to the specified URL after checking that
host is in the allowed hosts list
:param url: URL to redirect to
:param default_path: Default path to redirect to if url is unsafe
"""
if not is_safe_url(url, settings.ALLOWED_HOSTS):
retu... | 5ee3e46e46ed9a28e46ae3e741a9ffa255ec3a7d | 71,940 |
def convert_to_letter(grade):
"""Convert a decimal number to letter grade"""
grade = round(grade, 1)
if grade >= 82.5:
return 'A'
elif grade >= 65:
return 'B'
elif grade >= 55:
return 'C'
elif grade >= 50:
return 'D'
else:
return 'F' | 13ce25275750e7a27e0699078a15ba551674a941 | 71,941 |
import numpy
def _reorient_4d(image):
"""
Reorganize the data for a 4d nifti
"""
# print 'converting 4d image'
# Create empty array where x,y,z correspond to LR (sagittal), PA (coronal), IS (axial) directions and the size
# of the array in each direction is the same with the corresponding dire... | 9159e75daef5bc65dab9a7d8c6ff0cfa35fae96c | 71,942 |
def mask_sequence(aln, rstart, rend):
"""Masks quality according to changed softclips in cigarstring
Args:
aln (pysam.AlignedSegment): aligned read
rstart (int): start of non-primer sequence
rend (int): end of non-primer sequence
Returns:
aln (pysam.AlignedSegment): aligned... | ba351335136411e4c0dd1c55a800c9990fe3f217 | 71,943 |
import base64
def get_encid(decid):
"""
Generate an encrypted ID from a decrypted ID
:param decid: Decrypted ID, type int
:return: Encrypted ID, type str
"""
try:
int(decid)
except:
raise InvalidIDException('Decrypted ID must be int-castable')
# Slashes are not URL-fr... | 9fd425bc8f09ade997b12d533386ef2be7c6eaa8 | 71,944 |
def time_coord(cube):
"""
Return the variable attached to time axis.
Examples
--------
>>> import iris
>>> url = ('http://omgsrv1.meas.ncsu.edu:8080/thredds/dodsC/'
... 'fmrc/us_east/US_East_Forecast_Model_Run_Collection_best.ncd')
>>> cube = iris.load_cube(url, 'sea_water_potent... | 12a706f956846e8471d0d7f044367c77210c4486 | 71,946 |
def filter_lowpass(trace: Trace, sampling_frequency: int, cutoff: int) -> Trace:
"""
Apply a lowpass digital filter (Butterworth) to `trace`, given `sampling_frequency` and `cutoff` frequency.
:param trace:
:param sampling_frequency:
:param cutoff:
:return:
"""
return _filter_any(trace,... | 47dc83a5a53f97af562eba45289b9bf52bcfbc9b | 71,947 |
import multiprocessing
def _valid_general_args(cfg, logger):
"""
Validates the "general" parameters of a json configuration file used for inference.
:param cfg: EasyDict, json configuration file imported as dictionary
:param logger: logger instance
:return: EasyDict, dictionary compos... | 0f78e2b905a15289730ed4252d3c7e6568d1b5fb | 71,948 |
import re
def oneliner(s) -> str:
"""Collapse any whitespace in stringified `s` into a single space. """
return re.sub(r"[\n ]+", " ", str(s).strip()) | ed1d419b4fab8cb2deccdbc2944996ef7be28cc5 | 71,949 |
def _get_nominal_integer_dict(nominal_vals):
"""Convert nominal values in integers, starting at 0.
Parameters:
nominal_vals (pd.Series): A series.
Returns:
d (dict): An dictionary with numeric values.
"""
d = {}
for val in nominal_vals:
if val not in d:
curre... | 1005c00ea5d7033368b589d4954934e85b0e1d6e | 71,950 |
import cgi
def xml_escape(input):
"""Replace special characters "&", "<" and ">" to
HTML-safe sequences.
"""
return cgi.escape(input) | 87f60cdf680372761da23e64c2880ad649d5b0a6 | 71,951 |
from typing import Counter
def diff_counts(values : list[int]) -> dict[int, int]:
"""Count the gaps between ordered elements in a list, by size."""
ordered = [0] + sorted(values) + [max(values) + 3]
return Counter(j - i for i, j in zip(ordered, ordered[1:])) | 897cb7fdfed85b37bd8bd7031290e34199c57574 | 71,952 |
def plot_visibility(snnames, obs_windows, dark_time, priorities=None, labeltype='index', figsize=None):
"""Function for plotting visibility of targets as previously determined in
ztfcosmology_visibility.ipynb
"""
if priorities is None:
priorities = np.zeros(len(snnames), dtype=int)
... | 2f396fb45cc3c9526cb74e5824aca5acbce10c07 | 71,953 |
import re
def find_autoxc_code_skeleton(lines,subr_lines):
"""
Assuming that the subroutine is of the autoxc code skeleton type find all
the if-branches and return a list of all the (start,end) tuples.
"""
(lineno_start,lineno_end) = subr_lines
ifbranches = []
line = lineno_start
ifstart = -1
... | 122e5e082929451019d1689eed9f182085593daa | 71,954 |
def get_one_prior_probability(state, segment_df, total_positions):
"""
Returns the number of genomics positions that are of the state
"""
state_df = segment_df[segment_df[3] == state]
num_pos_this_state = float(sum(state_df['num_occ']))
return num_pos_this_state / float(total_positions) | 157666f84d9ba6b7d236bb0f10e331e9315d9efa | 71,955 |
def ticket_details():
"""
Upon request, generate a TicketDetails object for the user's session if it does not
exist, fetch the details of the requested ticket as well as its associated users by
the given ticket URL using the TicketDetails get_ticket() method. Render the pop-up
modal HTML with the ti... | 2276f2a5b9d57667433c75bec35d1320c6a7cca9 | 71,956 |
from typing import Callable
def stochastic_policy(
fun: Callable[[FloatLike], FloatLike],
fun_params0: randvars.RandomVariable,
rng: np.random.Generator,
) -> float:
"""Policy returning a random action.
Parameters
----------
fun :
One-dimensional objective function.
fun_params... | 57524f09045a21b47eafe553b869e5ad98838a29 | 71,957 |
import torch
def conv1x1(in_planes, out_planes, stride=1, num_experts=1, binary=False,
activation=torch.sigmoid, use_se=False):
"""1x1 convolution"""
if num_experts > 1:
return EBConv2d(in_planes, out_planes, kernel_size=1, stride=stride,
bias=False, num_experts=num... | 8461c4e8be1438aeaf383b0804e4aebfa9720298 | 71,959 |
def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
speech_output = "Welcome to the UCSC Bus Bot. " \
"Please state the bus stop id or location to get starte... | deed05421cf8d19b88f1c1703f3bab15cf6354a2 | 71,960 |
def parse_request_query(request) -> QueryModel:
"""
Like atoolbox.utils.parse_request_query but simplified and with a more human readable error response.
"""
data = {}
for k in QueryModel.__fields__.keys():
v = request.query.get(k)
if v is not None:
data[k] = v
try:
... | 62c59422610d6080d97a465b159e881746abcbec | 71,961 |
def convert_netdict_to_pydict(dict_in):
"""Convert a net dictionary to a Python dictionary.
Parameters
----------
dict_in : dict
Net dictionary to convert.
Returns
-------
dict
Dictionary converted to Python.
"""
pydict = {}
for key in dict_in.Keys:
pyd... | 6aa9bb5ac00ff92d23ff5a449d096caad0d01c9c | 71,962 |
def sigmoid (x, parameters):
"""Sigmoid function
POI = A2 + (A1 - A2) / (1. + exp((x - x0) / dx))
Parameters
----------
x: float or array of floats
variable
parameters: dict
dictionary containing 'sigmoid_A1','sigmoid_A2','sigmoid_x0',
and 'sigmoid_dx'
R... | bcf77e2e508800f6c4c37ec4c8c79c4bda2a6934 | 71,963 |
def heatmap_chart(df, title=""):
"""Plot custom confusion matrix chart."""
source = df.copy()
source = source.reset_index()
source = pd.melt(source, id_vars="index", value_vars=df.columns)
source.columns = ["m1", "m2", "value"]
base = alt.Chart(source).encode(
alt.X('m1:O', title="New M... | 6e6405afcbffff7159446c96ad1c87757708ac09 | 71,964 |
def server(request):
"""Return the server based upon the server class in the request."""
if 'production' == request.param:
return ReviewBoardServer('https', 'reviews.reviewboard.org')
if 'demonstration' == request.param:
return DemonstrationServer()
raise ValueError("invalid internal tes... | d4a485706258a032e6a069ce9cdad4671f13c141 | 71,965 |
def poolformer_s24(pretrained=False, **kwargs):
""" PoolFormer-S24 model, Params: 21M """
model = _create_poolformer('poolformer_s24', pretrained=pretrained, layers=(4, 4, 12, 4), **kwargs)
return model | 84f255eff21b158e4826830e74e3cb8dcdfe4ae0 | 71,966 |
def laptop_history(request, id):
"""
View a history of password retrievals and rotations for a given laptop
:param id: Primary key of laptop
"""
laptop = get_object_or_404(Laptop, retired=False, pk=id)
password_retrievals = laptop.password_retrievals.all()
password_rotations = laptop.passwo... | 20908500aa5946e3e4dce7004ef57ae15efae753 | 71,967 |
import logging
def make_restore_checkpoint_fn(checkpoint_path, prefix='', skip_regex=None):
"""Returns scaffold function to restore parameters from v1 checkpoint.
Args:
checkpoint_path: path of the checkpoint folder or file.
Example 1: '/path/to/model_dir/'
Example 2: '/path/to/model.ckpt-22500'
... | 3af8013b460cd9e7f3ef0396c144723600943a73 | 71,968 |
from typing import List
def binary_search(input_array: List[int], target: int):
"""
Given a sorted input array of integers, the function looks for a target or returns None
Returns:
Target index or None
"""
if not input_array or not target:
return None
left_pointer = 0
ri... | b9f389d1b31e5b95bb885bd638549f7775db207e | 71,969 |
from datetime import datetime
def _sort_by_date(time_coord):
"""
Private sorting function used by _file
_sort_by_earliest_date() and sort_by_earl
iest_date().
Args:
time_coord: Cube time coordinate for each cube
to be sorted by.
Returns:
time_origin: The time origin t... | 89c52c40dd8dfa03cbe431a4db6f107b42b22e81 | 71,970 |
def preprocess_img(img):
"""
Preprocesses screenshot of AH for OCR.
Removes details which would be needed for image matching.
"""
# grayscale
newimg = get_grayscale(img)
#cv2.imwrite("screenshots/img_9g.png",newimg)
# crop
newimg = newimg[175:660, 0:1920]
# cv2.imwrite("screensh... | ff3348580e84e2d01069c25b46c327dd98177817 | 71,971 |
from typing import Callable
import functools
def aws_provider(api: str = None, name="default", should_load: Callable[[], bool] = None):
"""
Decorator for marking methods that create a Service instance as a ServicePlugin. Methods marked with this
decorator are discoverable as a PluginSpec within the namesp... | 8db109647f82fbf2fe44ccd1f4140e704f1247b6 | 71,972 |
import galsim
def rotate(image, rot_center, interpolate=False, force_interpolate=False):
""" Rotate an image about a point. Defaults to using numpy array operations, which means that
the rotation center is rounded to the nearest half-pixel grid point. Optionally, use GalSim to
rotate about arbitrary pos... | 86bc0e6a651ac997cd044daac07597cc5896c066 | 71,973 |
def _parse_labels_map(labels_path):
"""Loads a labels map via the `label_map_util` from the TF-Models library.
Args:
labels_path: path to a labels map in `.pbtxt` format
Returns:
(category_index, class_labels), where `category_index` is a dict
mapping IDs to names, and `class_l... | d737831d57f27c983ff54bb7154c26136f3f6a24 | 71,974 |
def is_superuser(view):
"""Allow access to the view if the requesting user is a superuser."""
return view.request.user.is_superuser | 5a15433200634ca326c36bdc17acbc1ada4e6426 | 71,975 |
def _restrict_asap(func=None, backend=None, issuers=None,
required=True, subject_should_match_issuer=None):
"""Decorator to allow endpoint-specific ASAP authorization, assuming ASAP
authentication has already occurred.
"""
def restrict_asap_decorator(func):
@wraps(func)
... | 6e9496d41a5aab3c0e3aef0662c8eb3e91019a2f | 71,977 |
def tag_row(tag):
"""Format a tag table row"""
return [tag.get('id'), tag.get('name'), tag.get('referenceCount', 0)] | 4cd1d0991eae996a52288c6d1d9bb3bd1a368ea6 | 71,979 |
import types
import numpy
import pandas
def hpat_pandas_series_sort_values(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'):
"""
Intel Scalable Dataframe Compiler User Guide
********************************************
Pandas API: pandas.Series.sort_values
Limit... | 31b0f49f2a8e75b0cec27a33b5969893924c96e7 | 71,980 |
def get_owner_apikey_id():
"""
Get owner api key id from the config file.
"""
config_key = 'owner_api_key_id'
try:
owner_apikey_id = CONFIG.get(AUTH_SECTION, config_key)
if not validators.uuid(owner_apikey_id):
print_config_error_and_exit(AUTH_SECTION, 'Owner API key ID(... | 9c7cac14a60b38788ef49d2103e6dd331673ef1b | 71,981 |
import math
import time
def parse_cookies(target_cookie):
"""Deserializes single Target cookie into several individual cookies"""
if not target_cookie:
return {}
raw_internal_cookies = get_internal_cookies(target_cookie)
now_in_seconds = math.ceil(time.time())
valid_cookies = [deserialize... | 33591141624a06bf37f1bc879521b5ac45e8692e | 71,982 |
from typing import Any
def get_state_from_observation(observation: Any) -> State:
"""
Function which given an observation returns a state
:param observation:
:return:
"""
return State(
robots_data=list(observation[:len(observation) // 2]),
time=observation[len(observation) // ... | 95e33e884df25a6c4f00ee91dd764d886eafe05e | 71,983 |
from typing import Iterable
import ctypes
def vaddg(v1: Iterable[float], v2: Iterable[float], ndim: int) -> ndarray:
""" Add two n-dimensional vectors
https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vaddg_c.html
:param v1: First vector to be added.
:param v2: Second vector to be added.
:... | d87c7171e0296587d02d66e04522ff3f1c5d02e7 | 71,984 |
from typing import Any
import torch
import numbers
def python_scalar_to_tensor(data: Any, device: torch.device = torch.device("cpu")) -> Any:
""" Converts a Python scalar number to a torch tensor and places it on the given device. """
if isinstance(data, numbers.Number):
data = torch.tensor([data], de... | 83e4ac093a40225f9c5fe121d9b67424f258e039 | 71,985 |
def allpath(path):
"""
return paths
"""
paths = path.strip("/").split("/")
for i in range(1,len(paths)):
paths[i] = paths[i-1] + "/" + paths[i]
return paths | 8b76f70dbae41a1ae46462d0755dbb5c5c0eb4c1 | 71,986 |
def zoom(input, zoom, mode="nearest", blocks=(16, 16, 4), **kwargs):
"""scale array with nearest neighbors or linear interpolation
If a float, `zoom` is the same for each axis. If a sequence,
`zoom` should contain one value for each axis.
"""
if isinstance(zoom, (int, float)):
zoom = tuple(... | 299689876f03776d121f9d36256ba90adf6f404c | 71,989 |
def calculate_null_kl(experimental_condition_list=None, filename=None):
"""
Calculates the smoothed null KL distribution. One of the two parameters must be non-null
:param experimental_condition_list: [list] The list of treatment condition from which the null kl is to be calculated.
:param filename: If ... | 55ff2d73827e2a5b0140a0460ea9a4e1a9c97efe | 71,990 |
def read_tsv(file_path):
"""
General method for converting a tsv into a dictionary of lists, assuming each key has an associated time series
:param file_path:
:return:
"""
headers = None
header_indexes = None
# Each data type will have a list
data = defaultdict(list)
static_data... | 28f2c53c9302269fc3d3e9d34867e6845fa3c13d | 71,991 |
def validate_actions(data):
"""
Validate an Action configuration dictionary, as imported from actions.yml,
for example.
The method returns a validated and sanitized configuration dictionary.
:arg data: The configuration dictionary
:rtype: dict
"""
# data is the ENTIRE schema...
cle... | 9e027ccf74d9ec2c610e237fd0b9dca9f7ac9e8b | 71,992 |
def LIGHTNING(conf):
"""Get Lightning Color code from config"""
return conf.get_color("colors", "color_lghtn") | 08781e469c7262228904e883594e475be634816b | 71,993 |
def process_ordinal(X):
"""ordinal numeric value preprocessing."""
# missing value imputation
X = median_impute(X, strategy='median')
# remove any low info columns (high variance)
X = remove_low_info(X)
# remove low variance variables
X = remove_low_variance(X)
# scaling
X = scale... | 9e144dc6e1e2e145feda80a94ac493acdf44522a | 71,996 |
def devname_from_index(if_index):
"""Return Windows adapter name for given Windows interface index"""
return ifaces.devname_from_index(if_index) | 69b5ef2a33451ca755a8388fad5de3fcac0e60d2 | 71,997 |
import io
import locale
def purchase_order_pdf(request, pk):
""" Función para imprimir la orden de ventas
"""
response = HttpResponse(content_type='application/pdf')
pdf_name = _("purchase_order.pdf")
response['Content-Disposition'] = 'attachment; filename=%s' % pdf_name
# Los productos de la... | 6d5be05cfbc32aecb904c724fa406371e822e5c2 | 71,998 |
def chrxor(st1, st2, strf1, ar1, strf2, ar2, fun1, fun2):
"""Takes two strings and applies a function to each charater in the string.
Function is some function that takes characters and turns them into integers.
This could be something to turn them into ascii order, or simply map them to another range of integers.... | d463e21e55bf41ce9dee612d77810c2000210477 | 71,999 |
from typing import List
def set_firewall_rule_ids_to_computer_command(client: Client, computer_id: int, rule_ids: List[int],
overrides: bool) -> CommandResults:
"""
Assign rule IDs to a certain computer.
Args:
client (Client): The Trend Micro API clie... | 58ac799738c8ce510cc30f1f6d5de26ad5e124d2 | 72,000 |
from datetime import datetime
import torch
def train(model, epochs, data, optimizer, criterion, device, logger):
""" Train NN """
epochs = epochs
steps = 0
running_loss = 0
print_every = 40
for e in range(epochs):
start_ts = datetime.now()
model.train()
for images, labe... | 92f5e8b9a7388de26a3369338c5ceed37ffea4ee | 72,001 |
def single_column_kernel(i, scale_sqrt=True):
"""Returns a kernel function with weights that correspond to a single column of the veps numpy.ndarray.
:param int i: column of which weights should be selected
:return: kernel function that linearly weights the genotypes with a single column of veps (e.g. corr... | 9fb5bbbf8bc5f86709a78924869030ac5398c749 | 72,002 |
def is_exists(t):
"""Whether t is of the form ?x. P x."""
return t.is_comb() and t.fun.is_const() and \
t.fun.name == "exists" and t.arg.is_abs() | 7f7c5f32685df636ec2181efd5a1b7dbc8313d4c | 72,004 |
from typing import Dict
from typing import Any
def get_amendments_for_year(year: int, bill_number: int) -> Dict[str, Any]:
"""See: http://wslwebservices.leg.wa.gov/legislationservice.asmx?op=GetAmendmentsForYear"""
argdict: Dict[str, Any] = dict(year=year, billNumber=bill_number)
keydict: Dict[str, Any] =... | 8866bd8bf00fee8431bb4eed9beb2716d7dc1d99 | 72,005 |
def generate_help():
"""Generates help text with alphabetically sorted recipes."""
help_text = '\nAvailable recipes:\n\n'
recipes = config.Config.get_registered_recipes()
for contents, _, _ in sorted(recipes, key=lambda k: k[0]['name']):
help_text += ' {0:<35s}{1:s}\n'.format(
contents['name'], cont... | 92451c52feb7610b8ceb4bbb562d04cec3415b4c | 72,006 |
def welch_statistic(sample_a, sample_b):
"""Calculates Welch's t statistic.
Takes a 2D data matrix, a column to classify data by, and the labels
corresponding to the data of interest. Assumes that the largest (-1)
column in the data matrix is the dependent variable.
Parameters
----------
d... | a6f937aebaa52709a9763e0dbb3d5b6946e69dfe | 72,007 |
def lookup(type, language, key):
"""
Get an object of a certain *type*, specified by the code for what
*language* it is in and its *key*. The types currently supported are:
`assertion`
Use the `id` as the key.
`concept`
Use the concept's raw name as the key.
... | 0bb71860882cb7f653a274ea083f56bdaa8b7438 | 72,008 |
import requests
import json
def login(request):
"""
Do OAUTH dance with GitHub (for sign in) and PyPI (for releasing
packages)
"""
# # Redir https
# if request.headers.get('X-Forwarded-Proto') is not None:
# if request.headers['X-Forwarded-Proto'] != 'https':
# return H... | bf12dc64dcf286fc3ff4bcef252d12c9eab05140 | 72,009 |
import numpy
def xy_field_grid_points_to_edges(field_matrix=None, x_min_metres=None,
y_min_metres=None, x_spacing_metres=None,
y_spacing_metres=None):
"""Re-references x-y field from grid points to edges.
M = number of rows (unique grid-poin... | 075977ced527458a84823e64d64ebbe05a8f2e14 | 72,010 |
def predict(samplerid):
"""
provides a prediction of the forward model at a set of given query
parameters
It expects a list of lists with n elements in the main list
corresponding to the number of queries and each element has d elements
corresponding to the number of parameters in the forward m... | cc65e566e3bb5bca754d1b5b47c7739cd371ec4a | 72,011 |
from typing import Optional
def all_history_date(
tickers: tuple[str, ...],
*,
start: Optional[pd.Timestamp] = None,
end: Optional[pd.Timestamp] = None,
bus: bootstrap.TableBus = bootstrap.BUS,
) -> pd.Index:
"""Перечень дат для которых есть котировки после проверки на наличие новых данных.
... | 21a84b7451a50db691a885d94e6b09efa8a28461 | 72,012 |
import zfused_api
import maya.cmds as cmds
def software_id():
"""
return zfused software id
"""
_softwares = zfused_api.zFused.get("software", filter = {"Name":"Autodesk Maya", "Code":"maya", "Version": int(cmds.about(version = True))})
if _softwares:
return _softwares[0]["Id"]
return... | 7ead0b78c54d122c6212dfb2ff9d519294ad4efe | 72,013 |
def is_in_poly(p, poly):
"""
:param p: [x, y]
:param poly: [[], [], [], [], ...]
:return: is_in: boolean. True = p in poly
"""
px, py = p
is_in = False
for i, corner in enumerate(poly):
next_i = i + 1 if i + 1 < len(poly) else 0
x1, y1 = corner
x2, y2 = poly[next_... | 00d46af2dc04c6d3c1d86db887d904abe17e3bdb | 72,014 |
from typing import Tuple
def InterpolateValue(x: float, xy0: Tuple[float, float], xy1: Tuple[float, float]) -> float:
"""Get the position of x on the line between xy0 and xy1.
:type x: float
:type xy0: Tuple[float, float]
:type xy1: Tuple[float, float]
:return: y
:rtype: float
"""
if ... | 9de4372b593fae65f772c19a8ac369315a8489d0 | 72,015 |
def max_bounded(max_func, bounds, *args, **options):
"""Finds the input value that maximizes `max_func`.
min_func: computes the function to be maximized
bounds: sequence of two values, lower and upper bounds of the
range to be searched
args: any additional positional arguments are passe... | 5056bd9b19acb2ea145cfa2ce6107ca9176eacae | 72,016 |
from typing import Tuple
from typing import Any
def v3_pos_to_position(v3_rep: Tuple[Any], hex_width: float, hex_depth: float) -> Position:
""" Maps from the Unity-given tuple representing the position to a Position.
Inputs:
v3_rep (Tuple[str]): The Unity representation of the position (x, y, z).
... | 6045afbb17e6e93fd06bb46f728194bceff6339f | 72,017 |
from typing import Dict
def bwt_transform(s: str) -> Dict:
"""
:param s: The string that will be used at bwt algorithm
:return: the string composed of the last char of each row of the ordered
rotations and the index of the original string at ordered rotations list
:raises TypeError: If the s param... | a206074307f0857cbe0e6e75d2101fc9b50d21e9 | 72,018 |
def facebook_share(context, label=None, css_class=None, icon_class=None,
**kwargs):
"""
Renders a 'Share' button to share content on Facebook.
Parameters:
- label: the text to show as button label.
- css_class: the class to apply to the button.
Keyword arguments:
... | 23be9f936c0f5a993b228bf1f0847cbb22791d3c | 72,019 |
def get_sliding_band(sym, lam_int, lam_ext):
"""Returns a list of surface in the airgap including the sliding band surface
Parameters
----------
sym: int
Symmetry factor (1= full machine, 2= half of the machine...)
Rgap_mec_int: float
Internal lamination mechanic radius
Rgap_me... | 65b67d8a22492d94cdd6d49fcc2a120a225be4d1 | 72,020 |
def get_subset_with_sum(a, n):
"""Returns a subset of a with sum equal to n (if exists)."""
dp = [[]] * (n + 1)
for i in range(len(a)):
for j in range(n, a[i] - 1, -1):
if j == a[i]:
dp[j] = [a[i]]
elif dp[j]:
continue
elif dp[j - a... | c9e2ba7b6ea3b33d839cdad3586ccd8717089ebf | 72,021 |
def blackbody_lambda(in_x, temperature):
"""Like :func:`blackbody_nu` but for :math:`B_{\\lambda}(T)`.
Parameters
----------
in_x : number, array-like, or `~astropy.units.Quantity`
Frequency, wavelength, or wave number.
If not a Quantity, it is assumed to be in Angstrom.
temperatur... | 8db191c843836a7bef4e8a82d1218120ba075f9c | 72,022 |
def op_wait(pc,lc,seconds,dummy,dummy2,rtime):
"""
waits for a number of seconds or on a previously set condition
seconds : wait time, or timeout if waiting for condition
"""
global condition,wtime
res = pc
if (condition == None):
# unconditional wait based on time
if wtime == 0:
wtime = rtime
if... | f72705ea0a6a229c8a247d6323f3712407f6ec62 | 72,023 |
def _safe_latex(to_fix):
"""Escape characters that make LaTeX unhappy.
"""
chars = ["%", "_", "&", "#"]
for char in chars:
to_fix = to_fix.replace(char, "\\%s" % char)
return to_fix | 166de7858b319d9e07f7e0ad67e293ca96da63ab | 72,024 |
import six
import json
def create_doc_obj(body):
"""Turn a message str into a |DocumentObj|.
Parses a message body, adds an ``_id`` field based on the document's
``@uuid`` field, and creates a |DocumentObj| from the result.
Parameters
----------
body : str
Returns
-------
|Docum... | 37ee7ac927909e51fd1fdd3de175364cfaaacd69 | 72,025 |
def _abs(u):
"""np.abs for AD array."""
if isinstance(u, ADI):
return u.abs()
else:
return np.abs(u) | f8820046cde6a999b64e2ac8c7817269ba88f35a | 72,026 |
def boolean(prompt=None, yes='y', no='n', default=None, sensitive=False,
partial=True):
"""Prompt for a yes/no response.
Parameters
----------
prompt : str, optional
Use an alternative prompt.
yes : str, optional
Response corresponding to 'yes'.
no : str, optional
... | 159c8be6004e4e9f2c5a271e36facd3610561b61 | 72,027 |
def get_tags_from_names(session,taglist):
"""Returns all tag objects with Tag.name in taglist.
"""
return session.query(Tag).filter(Tag.name.in_(taglist)).all() | 52a1db2eef1bb7ebf769d606698f107fbdfbf4e4 | 72,028 |
def top_token_set_ratio(values):
"""Return the best token set ratio match from fuzzywuzzy module."""
scores = []
for combo in combinations(values, 2):
score = fuzz.token_set_ratio(combo[0], combo[1])
tokens_0 = len(combo[0].split())
tokens_1 = len(combo[1].split())
if tokens_... | 084b86af527ca2832530c066883a49d7a69bf7ad | 72,029 |
def get_non_refinement_chain():
""" Create 3-level chain without class_decompose node """
node_scaling = PrimaryNode('scaling')
node_rf = SecondaryNode('rf', nodes_from=[node_scaling])
node_logit = SecondaryNode('logit', nodes_from=[node_scaling])
node_xgboost = SecondaryNode('xgboost', nodes_from=[... | 0504218d27d7c49146022cca57c5e5c9602c624d | 72,030 |
def get_auc(m, auc_limits):
"""Gustavo's method for generating uniformly distributed auc values.
Args:
m: (int) > 2, representing the number of base classifiers
auc_limits: (len(2) list, tuple, or ndarray, i.e. Sequence)
representing the (minimum AUC, maximum AUC) among the
... | b658fc028f69cc0c4f934e7176a6cfbd7738c661 | 72,031 |
def create_model(architecture, num_features):
"""
create uncompiled model with given architecture
NOTE: currently only the DeepSpeech model is supported. Other models can be added here
:param architecture: name of the architecture (see descriptions in argparse)
:param num_features: number of feature... | f33959efa568f898df3927e314fb3a8eac8a5ccb | 72,032 |
from pathlib import Path
def get_image_path(card_number):
"""
Return the full path of the image file on the server. This may be
used by printable templates.
"""
if card_number:
parts = get_image_parts(card_number)
# return os.path.join(settings.MEDIA_ROOT, *parts)
return Pa... | 7bc97c2272447aaea2dd0b36e78aa8a1e4d8241c | 72,033 |
def export_result_image(song: Song, chords_vocabulary: ChordVocabulary, midi: bool = True, tab: bool = True,
audio: str = 'CHF_2017', df: bool = True):
"""
Export visualisation to a png file.
:param song: Song for which we want to export the visualisation
:param chords_vocabular... | d0e404779eeaae4a963d37f24b6615796bdf1375 | 72,034 |
def arg_num_in_range(args, low, up):
""" check if low <= len(args) <= up """
return check_in_range(len(args), low, up) | b478d0b3aee1373e6a45b2f6339af005bf8a093c | 72,035 |
import time
def retry(exceptions=Exception, tries=4, delay=3, backoff=2, logger=None, silence: bool = False):
"""
Retry calling the decorated function using an exponential backoff.
Args:
exceptions: The exception to check. may be a tuple of
exceptions to check.
tries: Number o... | 24b61bd7634a75ad38ec35a878f1faf39bbbbd1a | 72,036 |
import json
def dumps(obj, **kwargs):
""" calls json.dumps using DateTimeEncoder """
return json.dumps(obj, cls=DateTimeEncoder, **kwargs) | 397b2efd2145a894582532fd20f9e0631caa34d5 | 72,037 |
def parse_image_meta(meta):
"""Parses an image info Numpy array to its components.
See compose_image_meta() for more details.
"""
image_id = meta[:, 0]
image_shape = meta[:, 1:4]
window = meta[:, 4:8] # (y1, x1, y2, x2) window of image in in pixels
active_class_ids = meta[:, 8:]
return... | 9c5099351b6a63be3a2e7b62464b846db8fe3836 | 72,038 |
import unicodedata
import re
def slugify(value):
"""
URL to filename
Normalizes string, converts to lowercase, removes non-alpha characters,
and converts spaces to hyphens.
"""
val = unicodedata.normalize('NFKD', value).encode('utf-8', 'ignore')
val = val.decode()
val = re.sub(r'[^\w\s... | 028d6800c87932c61c18fc2ec09d5d97ad3fd399 | 72,039 |
def make_html(email_dict: dict) -> MIMEText:
"""
Function that makes html text portion of message, including placeholders for inline images
@param email_dict: output of make_email_dict, a dict with the email body text as a key, the images to attach as values
@returns: HTML encoded email text
""... | 4ea2885d0381419ebec5d05c144445ce2de1cb2e | 72,040 |
def kernel(t):
""" 畳み込みカーネル """
return np.exp(-t / 20) - np.exp(-t / 10) | b5b4085681b6e84f25072863e04f09e6cdf72ed0 | 72,041 |
def scoring_function(context_features, example_features, mode):
"""A feed-forward network to score query-document pairs."""
with tf.compat.v1.name_scope("input_layer"):
context_input = [
tf.compat.v1.layers.flatten(context_features[name])
for name in sorted(context_feature_columns())
]
e... | edae314eb09e896028d9c8334fdaa1fb92733c0f | 72,042 |
def _not_(x: symbol) -> symbol:
"""
>>> isinstance(not_(symbol(True)), symbol)
True
>>> not_.instance(True)
False
"""
return not x | 6961757837cfbfeb134e7b932dd48d7fcc9d070a | 72,043 |
import torch
def compute_local_cost(pi, a, dx, b, dy, eps, rho, rho2, complete_cost=True):
"""Compute the local cost by averaging the distortion with the current
transport plan.
Parameters
----------
pi: torch.Tensor of size [Batch, size_X, size_Y]
transport plan used to compute local cost
... | 3d29e8ae5ef14ab30cd676eebeb6507e9cbfafca | 72,044 |
def discretenorm(K,mu,sig):
"""
Discrete approximation of normal density with N bins, mean mu and
standard deviation sigma.
"""
pts = np.linspace(mu-3*sig, mu+3*sig, K)
space = pts[1]-pts[0]
probs = st.norm.cdf(pts+space/2, loc=mu, scale=sig) - st.norm.cdf(pts-space/2, loc=mu, scale=sig)
... | f3bd2178ed8c7e171c54426726e100c8869b07f0 | 72,045 |
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