content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def expand_quantized(sample, feat_idxs):
"""
Get the neighbouring value (shift '1' right and left) in a quantized one-hot feature vector.
:param sample: Initial node.
:type sample: numpy array.
:param feat_idxs: Indexes pointing to transformable features in the sample array.
:type feat_idxs: nu... | 86e95bee22b89be10a5fade8493b051a009abe27 | 3,600,570 |
def is_primary() -> bool:
"""Is Primary Device"""
return get_rank() == 0 | e0c43bf971988407d1392fb457fb3ca0be53c729 | 3,600,571 |
def advect(dens, dx, dy, dt, u):
"""
second-order upwind method for 2d advection
Advects density in some subdomain by velocity u,
given timestep dt and grid spacing (dx, dy).
"""
gradx = np.zeros_like(dens)
grady = np.zeros_like(dens)
if u[0] > 0.:
gradx[2:, :] = (3*dens[2:, :] ... | a90bf3678115246556cf8c63cd603d57007bd182 | 3,600,573 |
def load_labels_as_index(label_filename):
"""
Load a set of labels (with no attached vectors) from a text file, and
represent them in a pandas Index.
"""
labels = [line.rstrip('\n') for line in open(label_filename, encoding='utf-8')]
return pd.Index(labels) | 950cd2dae2f920e4afe894c91328fecaea05c078 | 3,600,575 |
def omp_get_dynamic():
"""Return the value of the dyn-var ICV, which determines whether \
dynamic adjustment of the number of threads is enabled or disabled.
This routine returns 1 if dynamic adjustment of the number of threads
is enabled for the current task; it returns 0, otherwise. If an
i... | 1a93b85905be8397ba6077b031903d36b519f93d | 3,600,576 |
def MACD(ds, count, fastperiod=-2**31, slowperiod=-2**31, signalperiod=-2**31):
"""Moving Average Convergence/Divergence"""
ret = call_talib_with_ds(ds, count, talib.MACD, fastperiod, slowperiod, signalperiod)
if ret is None:
ret = (None, None, None)
return ret | 4a917ffcf070d7cacd831dc7873f2e5d34bdba81 | 3,600,577 |
import math
def plot_all_params(predictions_dict, method='best', weighting='exp'):
"""Plots mean and variance bar graphs for all the sampled params for different (scenario, config).
It is assumed that the user provides a dict with 1st layer of keys as the scenarios and 2nd layer
of keys as the con... | c103e306474d425d89804e26d0c8e91a7050b4b1 | 3,600,578 |
def Main():
"""
:return:
"""
a = 1
b = 2
c = a + b
return c | 6482b416105d1ba23f564d6a47a52b7cba9c38bc | 3,600,579 |
def input_stream(fd, sze, url, params):
"""Opens a StringIO whose data is everything after the url scheme.
For example, `raw://hello_world` would return `hello_world` when read by the task.
"""
return (cStringIO.StringIO(url[6:]), len(url) - 6, url) | d0c0af167669bfd5755de15e6cc091df6637152c | 3,600,580 |
def _describe(in_fc):
"""Simply return the arcpy.da.Describe object.
**desc.keys()** an abbreviated list::
'OIDFieldName'... 'areaFieldName', 'baseName'... 'catalogPath',
'dataType'... 'extent', 'featureType', 'fields', 'file'... 'hasM',
'hasOID', 'hasZ', 'indexes'... 'lengthFieldName'... 'name', ... | 58bcad583514ff82e7ef3e8ce3f678f1081d28fd | 3,600,581 |
def users_login():
""" Login page """
if current_user.is_authenticated():
return redirect(url_for('gallery_home'))
form = LoginForm()
if form.validate_on_submit():
user = User.find_by_name(form.username.data)
if user is None or not user.is_valid_password(form.password.data):
... | 62d2e4a8eedd32e7b57f919bb23acba34cffae8e | 3,600,582 |
def trim_walkers(res, threshold=-1e4):
"""Remove walkers with probability below some threshold. Useful for
removing stuck walkers
"""
good = res['lnprobability'][:, -1] > threshold
trimmed = {}
trimmed['chain'] = res['chain'][good, :, :]
trimmed['lnprobability'] = res['lnprobability'][good,... | ba8e3ebbb2c6948be06d4d1bbd3871011763d71c | 3,600,583 |
def train_step(model, optimizer, batch, n_actions):
"""1 step optimize policy."""
obsers, actions, weights = batch
actions_mask = tf.one_hot(actions, n_actions)
with tf.GradientTape() as tape:
# log(a|s) R(tau)
logits = model(obsers)
log_probs = tf.reduce_sum(actions_mask * tf.... | ce86de0759669460fc6c2acd6f859755b8aac417 | 3,600,584 |
import copy
def printcc (lst,extra=2):
"""
Prints a list of lists in columns, customized by the max size of items
within the columns (max size of items in col, plus 'extra' number of spaces).
Use 'dashes' or '\\n' in the list-of-lists to print dashes or blank lines,
respectively.
Usage: printcc (lst,extra=2)
R... | c432de7c0213e370c1f565b20579f691b716e4cd | 3,600,585 |
def get_zip_location_map():
"""make dictionaries taking zip code to province and municipality
"""
bmap = pd.read_csv("../Belgium-Geographic-Data/dist/metadata/be-dictionary.csv",error_bad_lines=False)
bmap["PostCode"] = bmap["PostCode"].astype(int,errors='ignore')
pro = {}
mun = {}
fn = { "... | 2f2064dd73d809b0fabf01bf6e8930b5371639a3 | 3,600,586 |
def _str_eval_true(eval, act, ctxt) :
"""Returns false."""
return [False] | eb0fd9b576b55133319752f86750159c9684ba0c | 3,600,587 |
def CalculateMaxAtomTypeEState(mol):
"""
#################################################################
Calculation of maximum of E-State value of specified atom type
res---->dict type
Usage:
result=CalculateMaxAtomTypeEState(mol)
Input: mol is a molecu... | ecdb2eb9db340eef0725941fd22eeba1b6a73933 | 3,600,588 |
def swapaxes(dat, ax1, ax2):
"""Swap axes of a Data object.
This method swaps two axes of a Data object by swapping the
appropriate ``.data``, ``.names``, ``.units``, and ``.axes``.
Parameters
----------
dat : Data
ax1, ax2 : int
the indices of the axes to swap
Returns
---... | 576ec10826c3ab92465052edcadf38cf04952c02 | 3,600,590 |
def update_weight(y, unsupervised_weight, next_weight):
"""update weight of the unsupervised part of loss"""
y[:, -1] = next_weight
unsupervised_weight[:] = next_weight
return y, unsupervised_weight | c961714c248ba4c0f752be568115e444e1d963e2 | 3,600,591 |
def pad(image, ts=0, bs=0, ls=0, rs=0, color=(0, 0, 0)):
"""Pads the image through adding pixels to each side of the image.
params:
image: A numpy ndarray, which has 2 or 3 dimensions
ts: An integer, which is the amount to pad the top side
of the image
bs: An integer, which i... | 6acc2b3f97a4aeaf98a524434d646fa12c2eb6c5 | 3,600,592 |
import re
def find_slug(url, prefix, postfix):
"""Извлекает slug из url по известным границам prefix, postfix"""
pattern = f'{prefix}.+{postfix}'
slug = re.search(pattern, url).group()
return slug.lstrip(prefix).rstrip(postfix) | 7fb0b99989ef05f83b370b0c40c7e82c05e51ab7 | 3,600,593 |
def initial_basin_chain(HB_gdf, cl_gdf, buf_wid=0.1):
"""
Finds the chain of drainage basins from the upstream-most centerline point
to the sink (e.g. ocean).
Parameters
----------
HB_gdf : GeoDataFrame
[description]
cl_gdf : GeoDataFrame
Centerline coordinates
buf_wid :... | 30141717fe7e0ae6218492a1c97271867107ee5a | 3,600,594 |
def send_dronelog(dronelog):
"""Post the drone log to the central server."""
try:
post_dronelog = RES_CS.find_suitable_operation(
SCHEMA.AddAction, CENTRAL_SERVER.DroneLog)
resp, body = post_dronelog(dronelog)
assert resp.status in [200, 201], "%s %s" % (resp.status, resp.re... | 9e3f901bc41182620e8d90809be75b930213ddb9 | 3,600,595 |
def data_augmentation(original_image, original_mask):
"""进行样本和掩膜的随机增强
Args:
original_image: 原始图片
original_mask: 原始掩膜
Return:
image_aug: 增强后的图片
mask_aug: 增强后的掩膜
"""
original_height, original_width = original_image.shape[:2]
augmentations = Compose([
Ho... | b6436b895d5657c75a8e892caea51996225347e8 | 3,600,596 |
import re
def is_url(value):
"""Return whether or not given value is a valid URL."""
regex = re.compile(
r"^"
# startchar <
r"<?"
# protocol identifier
r"(?:(?:https?|ftp)://)"
r"(?:"
r"(localhost)"
r"|"
# host name
r"(?:(?:[a-z\... | 3a50e541a9e62156a89693d15d248080abb3718f | 3,600,598 |
import random
def greedy_file_select(aud_len, aud_files):
"""
Select x number of files greedily till aud_len is satisfied
Args:
Returns:
"""
len_sofar = 0
files_sofar = []
total_aud_ixs = list(range(len(aud_files)))
while True:
rand_ix = random.sample(total_aud_ixs, 1)[... | da947a88e43af576c3aeb6fc23bb9c072f612872 | 3,600,599 |
def get_targets(words, idx, window_size=5):
"""
获取input word(idx下标对应的word)对应的上下文words
:param words: 单词列表
:param idx: input word的下标
:param window_size: 窗口大小
:return:
"""
target_window = np.random.randint(1, window_size + 1)
start_point = idx - target_window if (idx - target_window) > ... | a7b6cf14fc0fffb53cbf7d8fa4cd2e0785468018 | 3,600,600 |
def sanitize_value(p_value):
"""Returns p_value or its part if it represents a sane simple value, else returns 'None'"""
if isinstance(p_value, STR_TYPES):
match = SIMPLE_VALUE_RE.match(p_value)
if match:
return match.groups()[match.lastindex - 1]
else:
return 'No... | 21133a4810209f284da8ed3de1110c92f27dd7e9 | 3,600,602 |
def ysum_ysq_count(ysum, ysq, counts):
""" calculate mean and error given accumulated sum of val and sq
Args:
ysum (np.array): accumulated sum of values, (nentry,)
ysq (np.array): accumulated sum of squares, (nentry,)
counts (np.array): number of hits for each entry, (nentry,)
Return:
(np.array, ... | ecdb04c291b28bb2ad8385be3b09a45a32a72c05 | 3,600,603 |
def array_to_image(array, image_float=False):
""" """
normalized_data = normalize_data(array)
rgba = from_data_to_pixel_value(normalized_data, image_float)
return rgba | 01712bb81cfe15ca03ec99a4e76e0bb3fdc7c781 | 3,600,605 |
import textwrap
def format_function_call(name, pdict):
"""
Formats a function call with named parameters.
@param pdict dictionary
@return string
.. runpython::
:showcode:
from mlinsights.helpers.parameters import format_function_call
d = dict(... | efcadb3a41a6059faf778fc459658a480c98e539 | 3,600,606 |
def _get_profile_image_urls(name, storage, file_extension=PROFILE_IMAGE_FILE_EXTENSION, version=None):
"""
Returns a dict containing the urls for a complete set of profile images,
keyed by "friendly" name (e.g. "full", "large", "medium", "small").
"""
def _make_url(size):
url = storage.url(
... | bd98d22a154a5bb48b3e084c39de1640daf2e9cb | 3,600,608 |
def load_torch_resnet152(scopes, return_fn=_assign):
"""Converted from the [Torch ResNets][5]."""
filename = 'resnet_152_cpu.pth'
weights_path = get_file(
filename, __model_url__ + 'resnet/' + filename,
cache_subdir='models',
file_hash='3339f6aca7f746f8ae7f6ce577efc0c0')
values =... | 80e981ce53540e3c145320a8cb7dcebc2526f849 | 3,600,609 |
from typing import Iterable
def merged_codepoints(cps: Iterable[CodePoint]):
"""return a list of codepoints (start, end) for inclusive ranges"""
cps = sorted(cps, key=lambda cp: cp.codepoint)
if not cps:
return []
ranges = [(cps[0], cps[0])]
for cp in cps[1:]:
last_range = ranges[-... | e28c7f0a0a1298812f1770eb43f55f7573fd4304 | 3,600,610 |
def is_bool_type(type_):
""" Checks if the given type is a bool type.
:param type_: The type to check
:return: True if the type is a bool type, otherwise False
:rtype: bool
"""
return type_ in _get_types(Types.BOOL) | 2c947f1dc4207f85908b8cdb71e16dd86f353a6f | 3,600,611 |
def train (param):
"""
**************************************************************************
This function returns a Handle to the training process
The training process is running in the background after the functions ends.
***********************************************************************... | 7ceb97548a7155632234d7fa1a93b31fc2e24355 | 3,600,612 |
import json
def pretty(ob, lexer=None):
"""
Return a pretty console text representation of 'ob'.
If 'ob' is something else than plain text, specify it in 'lexer'.
If 'ob' is not string, Json lexer is assumed.
Command line switches can be used to control highlighting and style.
"""
if lex... | 216c1457161c0b10ff98c1227408ce2238bf3f53 | 3,600,614 |
from typing import Optional
from typing import List
def browse_results(
data: pd.DataFrame, severities: Optional[List[str]] = None, **kwargs
) -> SelectItem:
"""
Return TI Results list browser.
Parameters
----------
data : pd.DataFrame
TI Results data from TIProviders
severities :... | c7d8bc6fe5bca86de81a186ff4d25a58e2d8c9d6 | 3,600,615 |
def infection_rate_symptomatic_10x80():
"""
Real Name: b'infection rate symptomatic 10x80'
Original Eqn: b'Susceptible 80*Infected symptomatic 10x80*contact infectivity symptomatic 10x80*(self quarantine policy SWITCH self 80\\\\ *self quarantine policy 80+(1-self quarantine policy SWITCH self 80))/non cont... | 76522534c07d798608ed24da15b15054a832ae8b | 3,600,616 |
import functools
def _make_modules(is_train):
"""Returns modules, with split based on the boolean `is_train`."""
return {
'swr_p_sequence': functools.partial(
swr_prob_sequence, is_train=is_train, sample_range=_SWR_SAMPLE_COUNT),
'swr_p_level_set': functools.partial(
swr_prob_level... | 675b30495080df5e8d2b57cf9f9dcb1f155b9c70 | 3,600,617 |
import ctypes
def DMAR(val):
"""Create class based on decode of an DMAR table from address or filename."""
base_length = ctypes.sizeof(dmar_factory(list()))
addr = val
if isinstance(val, str):
data = open(val).read()
buf = ctypes.create_string_buffer(data, len(data))
addr = cty... | 9c3cedf4b52d078e15488c683f39b594e4e6313d | 3,600,618 |
def validate_device_name_text(device_name):
"""Extends the validate_device_name function with a return text if the validation fails.
Can be used with the validate option of the questionary package.
Args:
device_name: Device name to validate as string.
Returns:
True if the given string ... | 9957e9c8ebd91bf6d1dcce43a4b4d2fab3b2edeb | 3,600,619 |
from datetime import datetime
def get_chat_metadata_for_header(transcript, duration, wait):
"""Will generate the section that comes before the transcript on the HTML page.
It contains metadata information such as Duration and Wait Time of the chat
Args:
transcript ([type]): [description]
... | 6206e7a54d32b61a96d3615ef0475f946cb0c85a | 3,600,620 |
def index():
"""Main page."""
return render_template("index.html") | d776731cc585dd3006ca7aeb0f1dd2565106049c | 3,600,622 |
import collections
import random
def gen_undirected_graph_rand(nodes = 1000, edge_factor = 3, costs = (1,1)):
"""
generates an undicrected graph with `nodes` nodes and up to `edge_factor` edges per node
@param nodes amount of nodes
@param edge_factor up to `edge_factor` edges per node
@param ... | 96947e153d235cad9b900492693cfb316eaff32d | 3,600,623 |
import json
def append_users(channel: str, *users_to_add: str):
"""
Appends users to the specified channel's user list.
"""
c = Channel.query.get(channel)
if not c:
return no_channel
users = json.loads(c.users)
for user in users_to_add:
if user not in users:
use... | 2425f1abb0e621389b8db5d41de5c584a4cf7603 | 3,600,624 |
def symmetrized_csiszar_function(logu, csiszar_function, name=None):
"""Symmetrizes a Csiszar-function in log-space.
A Csiszar-function is a member of,
```none
F = { f:R_+ to R : f convex }.
```
The symmetrized Csiszar-function is defined as:
```none
f_g(u) = 0.5 g(u) + 0.5 u g (1 / u)
```
wher... | 931094ace56953e31cf65a78c1015dd1d1de59bd | 3,600,626 |
def _int2farray(ftype, num, length=None):
"""Convert a signed integer to an farray."""
if num < 0:
req_length = clog2(abs(num)) + 1
objs = _uint2objs(ftype, 2**req_length + num)
else:
req_length = clog2(num + 1) + 1
objs = _uint2objs(ftype, num, req_length)
if length:
... | 9be8cf28a3f379235d07718df8db9dff7e223d2f | 3,600,627 |
def pick_coupons(user):
"""
Choose the coupons which would be used in redemptions by the user. There should be at most one coupon
per program in the output.
The heuristic is currently:
- choose attached coupons over automatic coupons
- choose the coupon which has been most recently attached, ... | 886f3224f7925dfdbc7caace65903bf9ecbec741 | 3,600,628 |
def closer_ang(x,a,dir=0):
"""
find angle y (==x mod 2*PI) that is close to a
dir == 0: minimize absolute value of difference
dir == 1: y > x
dir == 2: y < x
"""
if dir == 0:
return a + smaller_ang(x-a)
elif dir == 1:
return a + (x-a)%(2*PI)
elif dir =... | c87bf0976e87d195e10c4886be62ca9ee473702f | 3,600,629 |
def make_name(name):
"""
Converts a given name to a lower snake case
:param name: Name to convert
:return: Lower snake case
"""
return to_snake_case(name) | 1950402f193b1b8c4d14fa5e0e5e332b72b83891 | 3,600,630 |
import re
def update():
"""Find up to 10 places within view."""
# ensure parameters are present
if not request.args.get("sw"):
raise RuntimeError("missing sw")
if not request.args.get("ne"):
raise RuntimeError("missing ne")
# ensure parameters are in lat,lng format
if not re.... | 18967e68dbea1b5d3c274005c9dfd92ae6c1295a | 3,600,632 |
def _riccati_bessel_y(degree, argument):
""" Riccati-Bessel function of first kind and derivative """
return (np.array(special.riccati_jn(degree, float(argument)))
- 1j * np.array(special.riccati_yn(degree, float(argument)))) | 65162b85a0c2058702377fd209207b2d80e9c739 | 3,600,633 |
def fit_vfa_nonlinear(s, fa, tr):
"""Return T1 based on VFA signals using NLLS fitting.
Parameters
----------
s: ndarray
1D array of signals.
fa: ndarray
1D array of flip angles (deg).
tr: float
Repetition time (s).
Returns
-------
... | 667c4ac86a844bc110bcfcd0c89d51ec106e42a4 | 3,600,634 |
def newsletters(request, context):
"""back to balafon newsletters"""
if request and request.user.is_authenticated and request.user.is_staff:
view_name = 'admin:{0}_{1}_changelist'.format(get_model_app(Emailing), get_model_name(Emailing))
return make_link(
reverse(view_name), _('Email... | 5d220d1b5c5856c68a56354eb7d7f1e85a7f7389 | 3,600,635 |
def commands_overlap(first, second):
"""
Returns True if the two given commands overlap in their output targets (regardless of color)
"""
if isinstance(first, SetAll) or isinstance(second, SetAll):
# SetAll will conflict with whatever
return True
if isinstance(first, SetStrand) or i... | b292d8b5f3bceb4e55a2a6e5b49448ecf662f73f | 3,600,636 |
from functools import reduce
def problem():
""" Solve the problem.
2520 is the smallest number that can be divided by each of the numbers
from 1 to 10 without any remainder.
What is the smallest positive number that is evenly divisible by all of
the numbers from 1 to 20?
Answer: 232792560
... | 442be80c10b9317c2fc4223ec55123a39c633da9 | 3,600,637 |
def overhang(wall):
"""Calculate the overhang parameter as a function of y along the wall. See _overhang() for details.
Parameters
----------
wall : pandas.DataFrame or datautil.DomainWall
Location of the zero crossing of Mz.
Returns
-------
np.ndarray or list of np.ndarray
... | 25605af6628a237eae64724e473539c03ab873ff | 3,600,638 |
def ssh_tailc(address:str, filename:str,
**kwargs)->Popen:
"""tail cat and follow via ssh"""
proc = Popen(f'ssh {address} "tail -Fc+0 {filename}"',
stdout=PIPE, shell=True, **kwargs)
return proc | 051b244ca17f5b56ebdea30a7f05b82754f08309 | 3,600,639 |
def raj_logfact(n):
"""Ramanujan approximation of log(n!)"""
f1 = n*np.log(n)
f2 = -n
f3 = np.log(n*(1+4*n*(1+2*n)))/6
f4 = np.log(np.pi)/2
return f1 + f2 + f3 + f4 | ca5c93f83bb8b4956eb616d89f5697fa6be439c1 | 3,600,641 |
def post_permission_webhook_view(request):
"""
Permission webhook used for created or removed permission events.
"""
event = ar.get_multiformat_body(request, "event")
ax.verify_param(event, param_name="event",
param_compare=ValidOperations.values(),
is_in=True... | c20b1f93ba8e973a608c4405300b870f75c46ce1 | 3,600,642 |
def convmtx2_valid(H, M, N):
"""
2d convolution matrix with the boundary condition 'valid', i.e., only filter
within the given data block.
:param H: 2d filter
:param M: input signal dimension is M x N
:param N: input signal dimension is M x N
:return:
"""
T = convmtx2(H, M, N)
s_... | 522beb76a5744534c5a5a4bfce707fcd680d8a93 | 3,600,643 |
def api_get_theme_display_data():
"""
GET:
1.获取主题展示用的多媒体数据
conditions:<array:dict>, Such as:[{'type':<str>, 'names':<array>, 'name_regex':''}]
说明:
type-可以是"text", "image", "video", "audio"
names-数组,指定要获取数据的name
name_regex-字符串,获取匹配此正则的me... | b0c0b75f4294e6d06d45bac250c419b10a7c9521 | 3,600,644 |
def parse_netscape_bookmarks(bookmarkHTMFile):
"""Extract bookmarks and return them in a list of dictionaries formatted in the following way:
[ {"url":"http://...", "title":"the title", "private":"0"/"1", "tags":"tag1,tag2,...", "posix_timestamp"="<unix time>", "note":"description"}]
Raise a ValueError if the for... | 03fbf11ea59bff898a25c9d1198a93a60cc447b3 | 3,600,646 |
def remote_call(method):
"""Decorator to set a method as callable remotely (remote call)."""
method.remote_call = True
return method | ab276cbdb190185c7e46c5205df25f022a56a097 | 3,600,647 |
from typing import Union
from typing import Iterable
def np_str_len(str_arr: Union[np.ndarray, Iterable[str]]) -> np.ndarray:
"""
Fast way to get string length in a numpy array with datatype string.
Args:
str_arr: Numpy array of strings with arbitrary shape.
Returns:
Numpy array of s... | f74a08d707431a56578a4d2eb84d4b7531d02a97 | 3,600,648 |
import functools
def nodeop(target=None, **kwargs):
"""
Decorator for creating nodes from functions.
"""
# This is called when the decorator is used with arguments
if target is None:
return functools.partial(nodeop, **kwargs)
# This is called when the decorator is used without argumen... | c66544db52749e306384495a42217d07eef28c80 | 3,600,650 |
def get_last(utdt, longitude):
"""
Local Apparent Sidereal Time (LAST)
"""
gast = get_gast(utdt)
last = gast + longitude/15.
last %= 24.
return last | 8ba1304a32b7eb9785da02b43450d9f81b30d1ff | 3,600,651 |
import re
def get_user_by_email(user_to_search: str) -> dict:
"""
Searches for a user when given the user's email.
Args:
user_to_search: the user's email we are searching for.
Returns:
Formatted user results if a user is found.
"""
if not re.match(emailRegex, user_to_search):
... | c4e6240c20e9d9481ca89d36157b80262de7788a | 3,600,653 |
def prep_petroleum_water_intensity():
""" Takes county level petroleum-production values and determines the water intensity for the given county for
both unconventional and conventional petroleum production.
:return: Dataframe of county-level petroleum water intensity... | cbb5d458af89765374854a8156f0acc60a90b68d | 3,600,654 |
from pathlib import Path
def node(ph5, path, class_name):
"""
Get the node handle of a given path and class name
:param ph5: an open ph5 object
:type ph5: ph5 object
:param path: path to node
:type path: string
:param class_name: name of class to get
:type class_name: st... | 2a8090952fdec3dda7490844990f41c8e154c3af | 3,600,655 |
def delete_payload():
"""
Invokes the ControlPlane APIs with a DELETE request. Supports IAM Authentication.
Returns:
Controlplane API result.
"""
return invoke_destination_api("DELETE", app.current_request.uri_params['proxy']) | 16ce295d3b9acf30db24d405be25e1f86fb00412 | 3,600,657 |
def indian_word_currency(value):
"""
Converts a large integer number into a friendly text representation.
Denominations used are: Hundred, Thousand, Lakh, Crore
Examples:
1000 becomes 1 Thousand
15000 becomes 15 Thousands
15600 becomes 15.60 Thousands
100000 becomes 1 Lak... | d162725f588d96a7dc3ead80b5278d71187bf4c1 | 3,600,658 |
def cutout(image, size=16, color=None, name=None):
"""This is an implementation of Cutout as described in "Improved
Regularization of Convolutional Neural Networks with Cutout" by
DeVries & Taylor (https://arxiv.org/abs/1708.04552).
It applies a random square patch of specified `size` over an `image`
... | 1653f9df3bd91579c00868e7659fd06425a37895 | 3,600,659 |
from typing import Union
from typing import Tuple
import requests
def fetch_nab(ts: str,
return_X_y: bool = False
) -> Union[Bunch, Tuple[pd.DataFrame, pd.DataFrame]]:
"""
Get time series in a DataFrame from the Numenta Anomaly Benchmark: https://github.com/numenta/NAB.
Parame... | c20c13c3836ad4391ba8cb4461e95141e6be6eb6 | 3,600,660 |
def get_equips(equipments):
"""
Join all properties needed
"""
itens = []
for equip in equipments:
equip_dict = prepares_equips(equip)
itens.append(equip_dict)
return itens | c642dee1582da1cd0d108aa3a3d991bc833c826a | 3,600,662 |
def annual_NAIP(year, region):
"""Create an NAIP mosaic of a specified year for a specified region.
Args:
year (int): The specified year to create the mosaic for.
region (object): ee.Geometry
Returns:
object: ee.Image
"""
start_date = ee.Date.fromYMD(year, 1, 1)
end_... | ff9f1c54ce56869846baa8c59d08dd2bb1d370f2 | 3,600,663 |
import torch
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in cen... | f523fb0d905aba612cf67816d6aa0ec549dbebce | 3,600,664 |
def make_protocol(protocol_name, device):
"""Make a protocol object factory function."""
return protocol2object[protocol_name](device) | 0d830962c47dcb4fe926a4d37d4503f76ad67749 | 3,600,665 |
def create_parser() -> OptionParser:
"""Initialize parser with options."""
formatter = IndentedHelpFormatter(
indent_increment=2,
max_help_position=56,
width=120,
short_first=1
)
parser = OptionParser(usage=USAGE, epilog=EPILOG, version=f'%prog {VERSION}', formatter=forma... | 17fa20cb3c2d98696440991f646c6f7343d06ac2 | 3,600,666 |
def txfDate(date):
"""Returns a date string in the TXF format, which is MM/DD/YYYY."""
return date.strftime('%m/%d/%Y') | fd1f19b4080447379ec3ec57be0f2af047099673 | 3,600,667 |
from typing import Tuple
async def check_audio_support(try_install: bool = False) -> Tuple[bool, bool, bool]:
"""Check if sox and/or ffmpeg are present."""
cache_key = "audio_support_cache"
if cache := globals().get(cache_key):
return cache
# check for SoX presence
returncode, output = awa... | 5c8defaa5a7e20d9b8934532516388c879a1916a | 3,600,668 |
from typing import Optional
def clamp(
data: NodeInput, min_value: ScalarData, max_value: ScalarData, name: Optional[str] = None
) -> Node:
"""Perform clamp element-wise on data from input node.
@param data: Input tensor. One of: input node, array or scalar.
@param min_value: The lower bound of the <... | 4f45e1f2c2560ff60669f80e76bb969f88a03d97 | 3,600,669 |
def get_arcis_makeai_params(path_to_file):
"""
Returns dict {'param':value}
"""
params = np.genfromtxt(path_to_file, usecols=np.arange(0,3), skip_header=1, dtype='str')
params[:,2] = params[:,2]
params = params[:, [0,2]]
params = dict(params)
for key in params:
params[k... | 22a14c1d10311cf27b5eef89026a5c7634a74f62 | 3,600,670 |
import datetime
def sobject_to_dict(obj, key_to_lower=False, json_serialize=False):
"""
Converts a suds object to a dict. Includes advanced features.
:param json_serialize: If set, changes date and time types to iso string.
:param key_to_lower: If set, changes index key name to lower case.
:param ... | 5915fdd52bee26a0f4bfac67fc1140ab3d6a49a3 | 3,600,671 |
def check_assist(exp):
"""
:param exp: expression string
:return: True if assist operator
"""
return exp in Types.AssistOp | 2d2a943d577e0a106839a2ae5dd952e0b9c19b25 | 3,600,672 |
from datetime import datetime
def get_account_oldest_balance(account: Account) -> ty.Tuple[float, datetime]:
"""Compute and return the oldest possible account balance."""
current_balance: float = account.get_solde()
now = datetime.now()
long_before = now - timedelta(weeks=100 * 52)
operations = l... | a0b853ff180ac3378cdb0e0e0a4753725f70f406 | 3,600,673 |
def _make_msa_df(chain_features: pipeline.FeatureDict) -> pd.DataFrame:
"""Makes dataframe with msa features needed for msa pairing."""
chain_msa = chain_features['msa_all_seq']
query_seq = chain_msa[0]
per_seq_similarity = np.sum(
query_seq[None] == chain_msa, axis=-1) / float(len(query_seq))
per_seq_g... | f2ec656d211adb4174596ae86a563b5fadc69726 | 3,600,674 |
def get_snapshot(connection, snapshot_id):
"""Returns a snapshot with *snapshot_id*."""
res_set = get_snapshots(connection, [snapshot_id])
if not res_set or len(res_set) != 1:
raise Exception('unexpected result from get_snapshots')
snapshot = res_set.pop()
return snapshot | 09df168af900ed16b593fe476f02dea70a3bc9cb | 3,600,675 |
def rgba(r, g, b, a = 255) :
"""Create Rgba instance based on r,g,b,a values"""
return Rgba( [r, g, b, a] ) | 4e4c05054836124acc6858aa4b4bb5472a07b7ab | 3,600,676 |
import numpy
def getgridparams():
""" return grid parameters in a python friendly way
Output:
[ Xul, Yul, xsize, ysize, rows, cols]
- xul - x upper left centre
- yul - y upper left centre
- xsize - size of a cell in x direction
- ysize - size of a cell in ... | efd3506699984ce5ad140f7f0a0a1ea6a8a938f7 | 3,600,677 |
def get_hospitals_current():
"""
Return all Hospitals Developments
"""
hospitals = db.session.query(HospitalDevelopment).all()
return jsonify(__as_feature_collection(hospitals)), 200 | 89c37beb196a70ada8934e33fba88e459efb8378 | 3,600,678 |
from typing import Iterable
from pathlib import Path
def to_multi_path(paths: Iterable[Path]) -> Path:
"""Returns a multi-path object from all given paths and their sub-paths.
.. versionadded:: 0.17
"""
multi_path = Path()
for p in paths:
multi_path.extend_multi_path(p)
return multi_... | f247379e4d36502909b3593d7ea116bb77366e90 | 3,600,680 |
def aligned_malloc(shape, dtype, alignment=16):
"""allocates numpy.array of specified shape, dtype
and memory alignment such that array.ctypes.data
is an aligned memory pointer
shape is numpy.array shape
dtype is numpy.array element type
alignment is required memory alignment in bytes
"""
... | 9d5734f78a69e914279b21b8a0889fe26fa2c9b5 | 3,600,681 |
import cv2
from PIL import Image
import io
import base64
def embed_image_html(imgBGR, target_width=TARGET_WIDTH, target_height=TARGET_HEIGHT):
""" Creates an image embedded in HTML base64 format. """
if target_width is not None:
imgBGR = _resize(imgBGR, t_width=target_width)
elif target_height is... | c117bb180f832872386965dde420ebda691e5309 | 3,600,682 |
def create_pitch(dff, strategy=None):
"""Creates the whisky returns by pitch chart"""
data = []
if strategy == 2:
xcol = 'annual_return'
xtitle = 'Annual % return'
ycol = 'APR_5%'
ytitle = 'Compound Annual Return, %'
else:
xcol = 'days_to_close_spread'
xti... | 2ece1f812ecb3a7c3b3e1f127b88a5d8eb1abbaa | 3,600,683 |
def get_institutes(variable):
"""Return the institutes given the dataset name in CMIP6."""
dataset = variable['dataset']
project = variable['project']
try:
return CMOR_TABLES[project].institutes[dataset]
except (KeyError, AttributeError):
return [] | 1dc946496ff4b0ff6830adc7d2452378c1154b82 | 3,600,684 |
def unet(height, width, channels, classes, features=64, depth=4,
temperature=1.0, padding='valid', batchnorm=False, dropout=0.0):
"""Generate U-Net model introduced in
"U-Net: Convolutional Networks for Biomedical Image Segmentation"
O. Ronneberger, P. Fischer, T. Brox (2015)
Arbitrary numb... | b2b3c1a8ba5213b34ba8e1b7dc40a378a23cea9b | 3,600,685 |
def construct_attrs_table(sub_layers_list, node_name2sub_layers=None, module_name=None):
""" 构造不同属性的表格。
"""
def get_node_name(sub_layers):
for k, v in node_name2sub_layers.items():
if v == sub_layers:
node_name = k
break
return node_name
sub_la... | 224ae069b013cafa51d2547825020e584f1bcb69 | 3,600,687 |
def diffuse_colors(n, l, dark, light):
"""
Shader calculation for a normal and a light vector and light and dark
colors.
Args:
n(numpy.array): Unit normal vector
l(numpy.array): Unit vector in the direction to the light
dark(numpy.array): RGB dark color
light(numpy.array)... | 1644022d302299bb3288bd7bd113cf381bb35645 | 3,600,688 |
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