content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def transform_instance_annotations(
annotation,
transforms,
image_size,
*,
keypoint_hflip_indices=None,
bbox_key="bbox",
):
"""
NOTE: Adapted from detection_utils.
Apply transforms to box, segmentation, keypoints, etc. of annotations of a single instance.
It will use `transforms... | 19227c58c314be7e388f26ea44d77ba7c6277a51 | 58,800 |
def strip_comments(contents):
"""Strips the comments from coq code in contents.
The strings in contents are only preserved if there are no
comment-like tokens inside of strings. Stripping should be
successful and correct, regardless of whether or not there are
comment-like tokens in strings.
... | 2c7ddc9fb3e2981cf297ff37a4bb0b7019247a3a | 58,801 |
def get_P_l(mol, configs, weights, epos_rot, l_list, e, at):
"""
Returns a nconf x naip x nl array, which is the legendre function values for each l channel.
The factor (2l+1) and the quadrature weights are included.
Parameters:
l_list: [-1,0,1,...] list of given angular momenta
weights: int... | 51f82d9394e94790b93dde24f03ced5e71122850 | 58,802 |
async def async_rpc_set_tmux_window_visible(connection, tmux_connection_id, window_id, visible):
"""Hides/shows a tmux window (which is an iTerm2 tab)"""
_assert_not_in_transaction()
request = _alloc_request()
request.tmux_request.SetInParent()
request.tmux_request.set_window_visible.SetInParent()
... | 69f8d7aa7a91dcf3daa0db8f772948b920739ac4 | 58,803 |
def localize_timestamp(timestamp, tz=None):
"""
Localizes timestamp to a given timezone
If none of those have worked, localizes to settings.TIME_ZONE.
"""
if isinstance(timestamp, str):
timestamp = get_parsed_datetime(timestamp)
time_zone = get_tz(tz)
try:
local_time = timezo... | 12d5cd68a68b3f40fed06874066d6461de2a4687 | 58,804 |
import os
def read_error_ouput(exceldir, workbook, rowS=18, colS=1, colE=5):
"""
"""
wb = openpyxl.load_workbook(os.path.join(exceldir, workbook), data_only=True)
sheet = wb.get_sheet_by_name('summery')
rowS = rowS
colS = colS
colE = colE
mean_abs_error = sheet.rows[rowS][colS:colE]
... | 96915db4421c5088c37f5fe3797135e1eed941ce | 58,805 |
import yaml
import textwrap
def get_validated_configs(config_text: str, config_path: str, preset_target_definitions_text: str) -> "PlotmanConfig":
"""Return a validated instance of PlotmanConfig with data from plotman.yaml
:raises ConfigurationException: Raised when plotman.yaml is either missing or malforme... | 830c69aa24addde96876871eef55f99ffcf03296 | 58,806 |
def wrapsync(func):
"""Turns synchronous function into asynchronous."""
if iscoroutinefunction(func):
return func
async def inner(*args, **kwargs):
return func(*args, **kwargs)
return update_wrapper(inner, func) | 4b8b490facf00f5f632b71f9c440dd90b96dbd70 | 58,807 |
def softmax(x):
"""Calculates the softmax for x(numpy array, matrix, vector, of any shape)"""
return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True) | 059ccba8b3fb69e7d76814778469b457440bb3c4 | 58,808 |
def diff(x):
"""
Computes the approximate local derivative of `x` via finite differencing assuming unit spacing.
Can be viewed as the opposite of trapz().
Args:
x: The vector-like object (1D np.ndarray, cas.MX) to be integrated.
Returns: A vector of length N-1 with each piece corresponding... | e7e21447f2c745ab8d05f05a9c6c50e24a9aec55 | 58,809 |
import random
def random_sentence_swap(sentences, p):
"""Swaps sentences with probability p."""
def swap_sentence(s):
idx_1 = random.randint(0, len(s) - 1)
idx_2 = idx_1
counter = 0
while idx_2 == idx_1:
idx_2 = random.randint(0, len(s) - 1)
counter += 1
if counter > 3:
... | 5a2fa1a7303dc6fe924803fe6fad2ef5da998733 | 58,810 |
import torch
def multiclass_dice_threshold(logits, targets, threshold=0.5, iou=False, eps=1e-8):
"""
Dice coefficient metric for multiclass binary target.
If iou=True, returns iou metric, classic for segmentation problems.
"""
n = targets.shape[0] #Batch size of 4
#Flatten logits ... | e6c1565532accde1debfbf2cf92939797d30b020 | 58,811 |
def process_not_implemented(**kwargs):
""" default process function - return not implemented """
ret_dict = { 'function_return': 'not-implemented',
'slack_response': {} }
return ret_dict | c78b7cda6e52471a5839d1231f45ba45d077888b | 58,812 |
def Eval_point(state, data):
""" External validity, where data is a multiset of
points, to be used as point predicates"""
vals = [state(*a) ** data(*a) for a in data.sp.iter_all()]
val = _prod(vals)
return val | 0dc29e2c1d668e4b077efe55e2c3ec3243f77abc | 58,813 |
import collections
import itertools
def WrapTypeDeclUnit(name, items):
"""Given a list (classes, functions, etc.), wrap a pytd around them.
Args:
name: The name attribute of the resulting TypeDeclUnit.
items: A list of items. Can contain pytd.Class, pytd.Function and
pytd.Constant.
Returns:
A... | 49e5e1e8e962b8c9add3ec99fc63b9f5f3442022 | 58,814 |
def plot_decision_boundary(ax, clf, xx, yy, **params):
"""Plot the decision boundaries for a classifier.
Parameters
----------
ax: matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
"""
Z ... | 99f8d9f42b8e9fba9ab6b5936ed5f3c24ab18789 | 58,815 |
def reassign_assignment(worker_id, assignment_id,
staffing_request_inquiry=None):
"""
Return a given assignment after reassigning it to the specified
worker.
Args:
worker_id (int):
The ID of the worker to be assigned.
assignment_id (int):
... | 8be2e860203249ab4001170062db56edb2212806 | 58,816 |
from typing import Tuple
import torch
def draw_img_preds(
img: Tensor,
bboxes: Tensor,
bbox_labels: LongTensor,
img_size: Tuple[int],
color: Tuple[int] = (0, 0, 255)
) -> np.ndarray:
"""
Args:
img:
bboxes: xyxy order, normalized
img_size: (h, w)
"""
img = TF... | b576e151eb6641c76bf1cfb1ae6d1d8afd5537e0 | 58,817 |
def read_depot(df):
"""
reads the instance with the data, return the clietns data only
:param dataframe:
:return: a list of clients with their locations, demand
"""
#clients = df.loc[]
depot = df.loc[df.demand.isna(), ["x_loc", "y_loc"]]
return [depot.values[-1]] | baece330e54db024915f5ab6fd1ec724b687b5a0 | 58,818 |
def parse_human_activity_recog_data(
data_path='https://raw.githubusercontent.com/ChicagoBoothML/MLClassData/master/HumanActivityRecognitionUsingSmartphones',
X_names_file_name='features.txt',
train_subfolder_name='train', X_train_file_name='X_train.txt', y_train_file_name='y_train.txt',
... | 6ecb076308e5121262a07f836562791b7bf6c05e | 58,819 |
def quaternions_to_euler_angles(quats, rotmode=EulerOrder.ZXY):
"""
See https://www.geometrictools.com/Documentation/EulerAngles.pdf
"""
if rotmode == EulerOrder.ZXY:
return quaternions_to_ZXY_euler_angles(quats)
elif rotmode == EulerOrder.YXZ:
return quaternions_to_YXZ_euler_angles(... | 92a0a17180fe451bb7d7968b01b9694e6ca6b7fa | 58,820 |
def _expand_mock_imports(*mock_imports):
"""Returns an expanded set of mock imports.
mock_imports is expected to be a dict which looks like:
{
"absolute.module.name": SomeObject,
}
The objects in this dictionary will be the ones returned in the
eval-compatible env dict. Think of it as a fake sys... | 78941ddde22dc750fac3904a5a0b3d0578f8b91e | 58,821 |
import collections
def duplicate_steps(name, value):
"""Argument validator to check template for duplicate steps"""
# TODO(mgoddard): Determine the consequences of allowing duplicate
# steps.
# * What if one step has zero priority and another non-zero?
# * What if a step that is enabled by default... | 848f2540b8c55a4a851b458718787a948f3e2694 | 58,822 |
def insert_perform_labeling_job_metadata(
parent_batch_id,
batch_id,
batch_status,
batch_metadata_type,
num_children_batches,
):
"""Creates a new record in the batch execution metadata table.
Parameters
----------
batch_id: id to retrieve the associated batch_execution_metadata
... | e01d5c8b4fcea732ce1e9eec00fc352a8e2c5d18 | 58,823 |
def read_rosbag(bagfile, state_topic='/state_output'):
""" Read states from a rosbag file """
states = []
time = []
with Bag(bagfile) as bag:
start = bag.get_start_time()
for topic, msg, t in bag:
if topic == state_topic:
states.append([
ms... | c0eccba042b24fbe6dccbcd7495dc4b316a861f9 | 58,824 |
def lazy_npgettext(context, singular, plural, num, **variables):
"""Like :func:`npgettext` but the string returned is lazy. The translation
happens when it is used as an actual string.
"""
return _lazy_translate('npgettext',
context, singular, plural, num, **variables) | 94991ca1fc2a5050027d9120cb7b6429f4078b45 | 58,825 |
def is_complete_sentence(root_node):
"""
We'll use "complete sentence" to mean we've found a node tagged 'S'
directly below a node tagged 'ROOT', i.e.: (ROOT (S ...))
"""
return root_node.label() == 'ROOT' and \
len(root_node) == 1 and \
root_node[0].label() == 'S' | 4bac130f2264b7dfabd66371031b5caa472017a7 | 58,826 |
from re import T
def clamp(v: T, min_v: T, max_v: T) -> T:
"""
clamp関数
入力値を`[min_v, max_v]`の範囲に収める
:param T v: 入力値
:param T min_v: 最小値
:param T max_v: 最大値
:return: `[min_v, max_v]`内に収めた値
"""
return min(max_v, max(v, min_v)) | 4d90a11e7201cf449e82befa1a05d7a3cfb9ba3c | 58,827 |
import base64
def base64url_encode(data):
"""
Url safe version of urlsafe_b64encode with stripped `=` sign at the end.
:param data: input data
:return: Base64 URL safe encoded string
"""
return to_string(base64.urlsafe_b64encode(to_bytes(data))) | 002c975239fce8b64915ea4296d83e2f0be4b8e5 | 58,828 |
def _get_hub_by_entity_id(hass: HomeAssistant, entity_id: str) -> HmHub | None:
"""
Get ControlUnit by device address
"""
for control_unit in hass.data[DOMAIN].values():
if control_unit.hub.entity_id == entity_id:
return control_unit.hub
return None | b6503c060b54701dccf8ab2243c8a8cc8fa4fcd4 | 58,829 |
def pressure2dBSPL (p, pRef=20e-6):
"""
Convert sound pressure (p [Pa]) to sound pressure level (L [dB]).
Parameters
----------
p : ndarray-like or scalar
Array or scalar containing the sound pressure in Pa
pRef : Sound pressure reference in the medium (air:20e-6 ... | 3bdf675db331aab5e4b502a07b44238243f0ebd7 | 58,830 |
def info():
"""Returns the challenge name and site."""
init()
challenge = get_challenge()
return "Challenge name : %s @ %s" % (challenge.name,
challenge.domain) | d598fe046ab9de0d86f8188b9df0ddfb46a23377 | 58,831 |
import json
def create_mapping_dict(total_parsed_results, type_field):
"""
Create a {'field_name': 'fields_properties'} dict to be used as mapping schemas.
Args:
total_parsed_results: list. the results from the splunk search query
type_field: str. the field that represents the type of the ... | 3dd29abbd986a42a2c3a2cab8cd5e40b0b6a65ca | 58,832 |
def metric_fn(config, metrics, eval_fn_inputs):
"""Computes the loss and accuracy of the model."""
d = eval_fn_inputs
metrics["masked_lm_accuracy"].update_state(
y_true=tf.reshape(d["masked_lm_ids"], [-1]),
y_pred=tf.reshape(d["masked_lm_preds"], [-1]),
sample_weight=tf.reshape(d["ma... | 450bb814c93b3eb260e81e0c10135cc5e3ed37fd | 58,833 |
import csv
import numpy
def read_file(input_filename, parm):
"""
Read the given parameter from the given file and return array of data
"""
# print('Reading', input_filename)
infile = open(input_filename)
reader = csv.DictReader(infile)
vals = []
for row in reader:
vals.append(f... | af9417ae42a582aae69b5209d6bf73f7e890dc4a | 58,834 |
def not_safe_gerone(a, b, c):
"""
Compute triangle area from valid sides lengths
:param float a: first side length
:param float b: second side length
:param float c: third side length
:return: float -- triangle area
>>> not_safe_gerone(3, 4, 5)
6.0
"""
p = (a+b+c)/2
return ... | 499cf398158d170363ea22096b44ea16e402a23a | 58,835 |
def shelf_pwlin(r):
"""
Ocean followed by continental slope, continental shelf, and beach.
The ocean is flat, the slope, shelf, and beach are linear.
"""
z = z0_shore + slope_of_beach * r # beach
z = where(r<x0_beach, z0_shelf + slope_of_shelf*(r-x0_shelf), z)
z = where(r<x0_shelf, z0_ocea... | 99bfff14129bd634376395bbe346b52eb169ec1f | 58,836 |
def generate_symmetric_group(configuration, symmetry):
"""
For a particular configuration and list of sublattices with symmetry,
generate all the symmetrically equivalent configurations.
Parameters
----------
configuration : tuple
Tuple of a sublattice configuration.
symmetry : list... | 12883715b424347468c638fe58d2b52f571ce48f | 58,837 |
import sympy
def r_per_amplitude(m, theta_i):
"""
Calculate the reflected amplitude for perpendicular polarized light.
Args:
m : complex index of refraction [-]
theta_i : incidence angle from normal [radians]
Returns:
reflected fraction of perpendicular field [-]... | 96a278c908282e16b29e724c2bfae954395f83b6 | 58,838 |
def is_iterable(an_object, include_strings=True):
""" Returns True if an object is iterable, False otherwise. Iterable
types include lists, tuples, dicts, strings, numpy arrays, etc.
If include_strings is False, then strings are not considered iterable.
This is useful because often we use is_iterable()... | 7b9f531e3cf69d7fa90d8b9d17188c07f8d7e2da | 58,839 |
from keras.applications.vgg19 import preprocess_input
def preprocess_VGG19(img):
"""
Preprocess image for use with VGG19
Parameters
----------
img : ndarray
Raw image as a numpy array
Returns
-------
img : ndarray
Preprocessed image as a numpy array
"""
return... | aa18c5f92b82e8d7059b7060a67ca40a4326794d | 58,840 |
def quote_text(text):
"""Quote text and normalize it in sonic protocol context.
Arguments:
text str -- text to quote/escape
Returns:
str -- quoted text
"""
if text is None:
return ""
return '"' + text.replace('"', '\\"').replace('\r\n', ' ').replace('\n', ' ') + '"' | 530042874e8d7531ed14ef8d348211d8d5907902 | 58,841 |
def noiseReductionUsingGaussian(image, Kx = 5, Ky = 5) :
"""
Performs Gaussian Blurring
Parameters
----------
image : (M,N) ndarray
Input image
Kx : Int
Kernel size in X direction
Ky : Int
Kernel size in Y direction
Returns
-------
image : (M, N) ... | fde36d4ba0b1df67f9ae75fea630c04f0fbfc8bf | 58,842 |
def compName(n1, n2):
""" Compare names: n1 n2 strings, blankspace separated names
return value between -1 (mismatch) and 1 (match)
return None if any of n1, n2 is empty
can be used on names, normalised names
"""
if (not n1) or (not n2): return None
nn1 = n1.strip().split()
n... | 7a37261779f38040e0baa3bf99bbaf66569d6962 | 58,843 |
def add(cm_response, **data):
"""
Creates template of VM. Template has a name and some description.
It defines VM's hardware parameters: CPU and memory. It also defines
number of points utilized by VM created of it (per hour and overly).
@clmview_admin_cm
@cm_request_transparent{user.add()}
... | ff8b29ccb0fa288c018275d09208a0b3cfddfce0 | 58,844 |
def z_tf(u, H):
"""
discrete-time transfer function
u - the input signal
H - the transfer function
"""
def get_zm1_coeff(L):
numcf = L.num[0][0]
dencf = L.den[0][0]
N = len(dencf)-1
M = len(numcf)-1
# convert to normalized z^... | 8a87bce5f3968916d67706e1ac4ce4d137ab7927 | 58,845 |
def find_fixed_point(arr):
"""
Returns i such that arr[i] = i (or -1, if no such i exists).
"""
#---------------------------------------------------------------------------
# Easier Version: Assume that all integers are distinct (binary search).
#-------------------------------------------------... | 6697a83cb923e228bbf46c9da49f5f4da1732b4c | 58,846 |
import os
import time
from datetime import datetime
import tqdm
import json
def download_vn_data(
url='http://enviinfo.cem.gov.vn/',
save_folder='../data/vn_epa/'):
"""Download hourly data for all monitoring stations from Vietnamese EPA.
All data are saved into the save files
Args:
... | 0d78a61b7c5bf2ce1657e6017d54fb3a03c8b822 | 58,847 |
def devices_action(module):
"""
Manage all actions related to devices.
Action ordonancer and output bui
Structure output:
>>> devices_action(module)
{
"changed": "True",
"data": {
"tasks": [
{
"workOrderId": "108",
"name": "",
... | 4bf09077987f449e4011338434207485bb07b947 | 58,848 |
def EventGrain(src_id_or_meta=None,
flow_id_or_data=None,
origin_timestamp=None,
creation_timestamp=None,
sync_timestamp=None,
rate=Fraction(25, 1),
duration=Fraction(1, 25),
event_type='',
topic='',
... | 04207a6fb517e608450a0d40e31d9dcf60cf0c8a | 58,849 |
from typing import Optional
from typing import List
def tall_info_card(
box: str,
name: str,
title: str,
caption: str,
label: Optional[str] = None,
icon: Optional[str] = None,
image: Optional[str] = None,
image_height: Optional[str] = None,
categ... | 2596b739df78b87b20d7cf236b41f087e6cf38d0 | 58,850 |
def _func(coeffs, *labels):
""" Takes the dot product of coefficients vec & labels vector
Parameters
----------
coeffs: np.ndarray
the coefficients on each element of the label vector
*labels: np.ndarray
label vector
Returns
-------
dot product of coeffs vec and lab... | b445df24f1137f0c43de469cf9270f43115710c9 | 58,851 |
def make_object(n):
"""Take a list of pairs and create a dict
NOTE: run through make_array to transform the results to an array
"""
return dict(make_array(n)) | a96403a493809d3be6ed5391f41e2d431769d9cf | 58,852 |
def load_image(test_data, shape):
"""Load calibration images."""
test_np = np.fromfile(test_data, dtype=np.float32)
test_shape = (-1,) + tuple(shape[1:])
test_np = np.reshape(test_np, test_shape)
calib_num = 32
if test_np.shape[0] > calib_num:
return test_np[:calib_num]
else:
... | c91ff785c8b466e573f990fd51a56665742db152 | 58,853 |
def ssl_loss_pi_model(labels_x, logits_x, logits_teacher, logits_student):
"""
Computes two cross entropy losses based on the labeled and unlabeled data.
loss_x is referring to the labeled CE loss and loss_u to the unlabeled CE loss.
Args:
labels_x: tensor, contains labels corresponding t... | e1d013366d3e13ae13ec4fc5e7362ff36faa0f95 | 58,854 |
def ellipseCircleIntersections(s_circle, a, b, mx, my, x, y, minSep, maxSep, lminSep, lmaxSep, yrealAllRealInds, yrealImagInds, sma=None, e=None, W=None, w=None, inc=None):
""" Calculates the intersections between a circle centered at x,y with radius s_circle and an ellipse centered at x=0, y=0 with semi-major axis... | b8f183e2892f1dfac52564f01aebf679857e34ec | 58,855 |
from typing import Tuple
from typing import Optional
import urllib
def parse_netloc(netloc: str) -> Tuple[str, Optional[int]]:
"""
Return the host-port pair from a netloc.
"""
url = build_url_from_netloc(netloc)
parsed = urllib.parse.urlparse(url)
return parsed.hostname, parsed.port | 4f24482b60f38e880d9748d4e42361265d0b7964 | 58,856 |
def glcm_features(image, distances, orientations, properties):
""" Compute properties of the Grey-Level Co-occurance Matrix for an image
:param image: image to compute the GLCM for
:param distances: distances to compute the GLCM across
:param orientations: angles to computes the orientations over
:... | 63196a13f257aab56f43deb1156a2391b42f3089 | 58,857 |
def darknet53(**kwargs):
"""
DarkNet-53 'Reference' model from 'YOLOv3: An Incremental Improvement,' https://arxiv.org/abs/1804.02767.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
... | 8d88394ec127ac376df02d69ffb51af74649bc60 | 58,858 |
def triangulate_vertices(vertices):
"""
Built a triangular mesh from 3d points
Uses the scipy Delaunay algorithm in a u,v parametrization
see for more info about Delaunay:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Delaunay.html
:param vertices: flatten 3D points used fo... | 6371ccb1aca2f2d725d2240559e91f0c6c18c146 | 58,859 |
import numpy
def draw_mandelbrot(width, height,
left, right, top, bottom,
iterations,
z0):
"""Returns a PIL.Image representing the given portion of the set."""
x, y = numpy.meshgrid(numpy.linspace(left, right, width),
numpy.... | af01deddb80917bf577b757b6a5d552e148109aa | 58,860 |
from typing import List
def make_field_dimensions_cartesian(mask: List[int] = None) -> FieldDimensions:
""" Create FieldDimensions of cartesian type
:param mask: mask to identify which cartesian dimensions are legal (default is [1, 1, 1])
"""
if mask is None:
mask = [1, 1, 1]
assert len... | 618e158ac4e6052de42be76b916f51ff2a9006a4 | 58,861 |
def change_config(set_attr):
"""
因为每次更改属性都要刷新托盘菜单，并且写到配置文件中，
所以定义一个装饰器，以简化代码
:param set_attr: 更改属性的函数
:return: 装饰过的函数
"""
def wrapper(plugin, option_text):
f = set_attr(plugin, option_text)
plugin.save_config()
plugin.root.refresh_menu()
return f
return w... | 0f497fb83007dbcd353f44a684c5be995821b424 | 58,862 |
def matrix_determinant(M, check=True):
"""Calculates the determinant of a square matrix M.
Parameters
----------
M : list[list[float]]
A square matrix of any dimension.
check : bool
If True, checks if the matrix is square.
Raises
------
ValueError
If the matrix ... | fc9aa8222274cecb3e32c008c664348392351423 | 58,863 |
def get_kubeconfig_current_cluster_name(_kc_model=None):
"""
:param _kc_model: Internally used to cache kubeconfig info.
:return: Returns the cluster associated with the current context.
"""
kc = _get_kubeconfig_model(_kc_model=_kc_model)
current_context_name = get_kubeconfig_current_context_nam... | 8198b0ed906fefc0ffaf1f0b67042f0d390accba | 58,864 |
from pathlib import Path
def find_artefacts(base_dir:str, type="models", target_file="model.py", target_string="mantraml"):
"""
Find a Mantra artefacts directory
:param base_dir: Base directory to look at
:param type: Can be `models`, `data`, `tasks`
:param target_file: File use to identify the a... | 520e2b18e27c20e4f1aa81a471ea940a9e2b6d8b | 58,865 |
def make_allocation(patient,**kwargs):
""" Allocation based on facility preference """
print('allocating',patient)
if(patient.group.category == Group.FAMILY):
family = patient.group.person_set.all()
else:
family = [patient]
allocated = True
for i in range(len(fami... | 18bb76f802a79dc533f3d98cdb7320f55413ecc3 | 58,866 |
import argparse
def _get_parser():
"""
Returns an ArgumentParser for TensorFlow Lite Converter.
"""
parser = argparse.ArgumentParser(
description=("Command line tool to run TensorFlow Lite Converter."))
# Converter version.
converter_version = parser.add_mutually_exclusive_group(required=... | c4f30733852066362a9549856abaf367e1f0ca8d | 58,867 |
def is_container_flag(
df,
watersource_col="mhm_WaterSourceType",
bit_col="mhm_IsWaterSourceContainer",
inplace=False,
):
"""
Creates a bit flag: `mhm_IsWaterSourceContainer` where 1 denotes if a watersource is a container (e.g. ovitrap, pots, tires, etc.) and 0 denotes the contrary.
Parame... | bce47480feb0e3bbc2562d246c4796bc8a822b9b | 58,868 |
from prettytable import PrettyTable
def format_term_rankings( term_rankings, labels = None, top = 10 ):
"""
Format a list of multiple term rankings using PrettyTable.
"""
# add header
header = ["Rank"]
if labels is None:
for i in range( len(term_rankings) ):
header.append("C%02d" % (i+1) )
else:
for la... | 37682b3c8d8535e73181da4c8daaf4fb1ee79617 | 58,869 |
def get_request_user(request):
"""
获取请求user
(1)如果request里的user没有认证,那么则手动认证一次
:param request:
:return:
"""
user: AbstractBaseUser = getattr(request, 'user', None)
if user and user.is_authenticated:
return user
try:
user, tokrn = JWTAuthentication().authenticate(request... | 8dcb6d61821d8a546c0b36a1baa0829425d84cdc | 58,870 |
def test_paper_page_end(root, printerror=False):
"""Verifies if the papers declared in the Lattes CV parsed in the
argument root contain a declared end page.
Args:
root: the Lattes CV that has been parsed with the ElementTree
language.
flag: if 0, then all papers in the Lattes CV hav... | 40b083fa8b2b38211e74e590033c4029cc57b09d | 58,871 |
from sage.matrix.constructor import matrix
def lfun_eta_quotient(scalings, exponents):
"""
Return the L-function of an eta-quotient.
INPUT:
- scalings -- a list of integers, the scaling factors
- exponents -- a list of integers, the exponents
EXAMPLES::
sage: from sage.lfunctions.... | d77a37f0259d82f25263914a187f373bed6e4057 | 58,872 |
from typing import List
def add_option(poll: Poll, text: str, added_options: List[str], is_date: bool):
"""Parse the incoming text and create a single option from it.
We allow option descriptions after an `--` or `—` delimiter.
`added_options` is a list of names of options that have already
been a... | fbdbf35696c45938c2fc8e85bb27ec04a53f1c00 | 58,873 |
def test_gen_moving_timeseries_forecast_with_regressors():
"""Test for `gen_moving_timeseries_forecast` with regressors"""
data = generate_df_for_tests(freq="1H", periods=1000)
df = data["df"]
df["x"] = 40
# A simple train-forecast function which always uses last available
# value, then adds th... | bb22353aa67e97c06d73160773af03f28895202b | 58,874 |
import re
from bs4 import BeautifulSoup
import requests
def mediafire(url: str) -> str:
""" MediaFire direct link generator """
try:
link = re.findall(r'\bhttps?://.*mediafire\.com\S+', url)[0]
except IndexError:
raise DirectDownloadLinkException("No MediaFire links found\n")
page = Be... | 3a79fc7c42a0531b1e3acc56252873b1d3ec7f21 | 58,875 |
from typing import List
def _add_outputs_of_onnx_model(model, output_nodes: List[str]):
"""
Add output nodes of onnx model.
Args:
model (ModelProto): ONNX model.
output_nodes (list[str]): Output nodes list.
Returns:
ModelProto, edited ONNX model.
"""
onnx = import_mod... | 4170d36d9d18ee023a550a72209d769ca182c64d | 58,876 |
def get_pane_working_directory(session: str, window: int, pane: int):
"""
Get the working directory of a given pane
Parameters
----------
session : str
Name of the session
window : int
Window number of pane
pane : int
Pane index in the window
"""
return get_t... | f5b469ddbd5efc483be944dc09ac539256cc9f93 | 58,877 |
def export_report(id: str, report: str, filename: str) -> bool:
"""Exports a report summary to a file.
Args:
id: An extension identifier string.
report: A string of the report summary.
filename: The chosen filename as a string.
Returns:
A boolean result for exporting the re... | 682694ce9eb0bae2e7f56b4afcdad0d13f6d54ff | 58,878 |
import pickle
def simulation(*vars):
"""
Evaluate a dam hazard potential 'simulation' with a given set of spatial parameters and random forest
hyperparameters
:param vars: tuple
A set of spatial and nonspatial parameters
:return: tuple
Objective values
"... | e949876d60e7cc1fada727a7279ccff562b29d3b | 58,879 |
def GetInfoForCountry(country_name_or_code):
"""Get code, name, lat and lon for a given country name or code."""
match = False
partial_match = False
if not country_name_or_code:
return None
if len(country_name_or_code) == 2:
country_code = country_name_or_code.upper()
country_name = False
els... | db773274d73e210bac2ed237ff0704d923973562 | 58,880 |
def sqs_lookup_url(session, queue_name):
"""Lookup up SQS url given a name.
Args:
session (Session) : Boto3 session used to lookup information in AWS.
queue_name (string) : Name of the queue to lookup.
Returns:
(string) : URL for the queue.
Raises:
(boto3.ClientError):... | 4f7bfb98d10a372b3b07f270e2a25e3227732f41 | 58,881 |
def calcstat(iv, ev, base, level, nature_stat):
"""Calculate the battle stat of a Pokémon.
Note that some stats may be off by one compared to the
"official" PKM data. I've only found this to be true on a
specific FAL2010 Mew file, but it's worth mentioning. If you
would like to be safe, deposit the... | facaeeb2f054a1b091f7383b6342dc624317fed2 | 58,882 |
import typing
def interpret_target(raw_target: str) -> tuple[typing.Callable, str]:
"""Determine saving function and target string from input target"""
HELP = "Target must be u/username or r/subreddit"
SAVERS = {"u": save_user, "r": save_subreddit}
assert "/" in raw_target, HELP
raw_target = raw... | 370680104db88f1cecf66001ea31797efd32d146 | 58,883 |
import os
import json
import codecs
def write_files(journal, path, format):
"""Turns your journal into separate files for each entry.
Format should be either json, md or txt."""
make_filename = lambda entry: e.date.strftime("%Y-%m-%d_{0}.{1}".format(slugify(u(e.title)), format))
for e in journal.entri... | 26ad732c0a087147578a0a9703ca312ef2f3673f | 58,884 |
def split_dataset(data, keys, p=[ .6, .2, .2 ]):
""" This procedure splits a dataset, with split_percentage, and filters it keeping only specified keys,
:param data: dictionary with all datat in (key, sentences) format
:param keys: list of required keys
:param p: percentage of the test_set set, what is ... | d2775be2563d97f11d1f7f1a2e7466365e433ed5 | 58,885 |
def get_image_array(self, image_id=None, ann_id=None):
""" Retrieves the array associated with the image_id or ann_id """
image_pil = self.get_image_pil(image_id=image_id, ann_id=ann_id)
return np.asarray(image_pil) | ca7a6202b2c690790bfd94683ab64d8a13486301 | 58,886 |
import os
import logging
import hmac
import hashlib
import threading
def generate_xferspec_download(
blob_service, file_service, args, storage_in_queue, localfile,
remoteresource, addfd, blobprop):
"""Generate an xferspec for download
Parameters:
blob_service - blob service
fil... | e2ebda0aa9c5679d3a24d336055e230f67313d19 | 58,887 |
from datetime import datetime
import traceback
async def login_post(request):
"""
Server login page for the browser
:param request:
:return:
"""
try:
try:
post_data = await request.json()
except Exception as _e:
print(f'Cannot extract json() from req... | c873796744b944fdf033e94e3696db97da724470 | 58,888 |
def getMSDP(obdm):
"""
This function takes the time dependent obdm matrix and finds the time evolution
of the momentum space density profile (MSDP)
OUTPUTS:
msdp[time idx][p idx]
"""
msdp = np.zeros((len(obdm), numpts), dtype=np.complex_)
# to evaluate this integral we need... | 35d01cd77dbca2cc6a36dbf39cb310a20c81ef68 | 58,889 |
def get_government_factions(electoral_term):
"""Get the government factions for the given electoral_term"""
government_electoral_term = {
1: ["CDU/CSU", "FDP", "DP"],
2: ["CDU/CSU", "FDP", "DP"],
3: ["CDU/CSU", "DP"],
4: ["CDU/CSU", "FDP"],
5: ["CDU/CSU", "SPD"],
... | cfd502f81ebb0536432da4974ab9f25724dc83df | 58,890 |
import os
def load_cifar10(batches_data_folder):
# language=rst
"""
Load a single batch of the cifar dataset
:param filename: Where the batch is located
"""
# Load the cifar training data batches
xs, ys = [], []
for batch_idx in range(1,6):
filename = os.path.join(batches_data... | 09d76b4bedd76d6dc2dea005dcc5f3b8d44d9226 | 58,891 |
def lognormal(mean=0.1, std=0.5, shape=1, z=0):
"""Draw burst from lognormal distribution.
Args:
shape (tuple): Required array shape
z (array): Redshift of pulses
Returns:
type: Description of returned object.
"""
w_int = gd.lognormal(mean, std, shape).astype(np.float32)
... | 26c1d6d06da01699fb6afa44824d98670d4ef86f | 58,892 |
def krisciunas_schaefer_free(obs_zenith, moon_zenith, separation_angle, moon_phase,
vband_extinction, C_R, C_M0, C_M1, M0, M1, M2):
"""Calculate the scattered moonlight surface brightness in V band.
Based on Krisciunas and Schaefer, "A model of the brightness of moonlight",
PASP, vo... | 867b4b148e4e76b73aa02bfcc88f1b61bf7d87ae | 58,893 |
from typing import List
def _ipv6_address_validator(address: str, strict: bool = True) -> bool:
"""Validates the address part of an IPv6 address"""
valid = True
segments = []
empty_segment = f'{IPV6_MIN_SEGMENT_VALUE}' * IPV6_NUMBER_BIT_COUNT
# Split on zero-skip, if any
try:
left, *... | 91f6035b98e35fefd594af4b0e6d83b78f6da83c | 58,894 |
def bam_read_count(bamfile, samtools_path="samtools"):
""" Return a tuple of the number of mapped and unmapped reads in a BAM file """
p = Popen([samtools_path, 'idxstats', bamfile], stdout=PIPE)
mapped = 0
unmapped = 0
for line in p.stdout:
rname, rlen, nm, nu = line.rstrip().split()
... | 914bf5ff14c7e267937f33b14cfaec563f7519b4 | 58,895 |
import logging
import tqdm
import gc
def train_survci_info(model,
x_train, y_train, e_train,W_train,
x_valid, y_valid, e_valid,W_valid,
n_iter=1, lr=1e-3, elbo=True,
bs=100): #bs=100
"""Function to train the torch instance of the model."""
#bs = 100 (ACTG-SYNT... | c0be3c0678c6671f187488c2070c13833007d287 | 58,896 |
def run_model(mod, params=None, opts=None):
"""Run model.
Paramters
---------
mod: Module
The relay module.
params: Parameters
The model parameters.
opts: Dict
The compiler options.
Returns
-------
out: Data
The output data.
"""
with transf... | f173fe3b2388177d53c25e6016b2f89e8cf382aa | 58,897 |
def VectorVector_scalar(vectorA, vectorB):
""" N diemnsional. Return a float as a scalar resulting from vectorA * vectorB"""
result = 0
for i in range(0, len(vectorA)):
result += vectorA[i] * vectorB[i]
return result | c7f0448a05573b8ddba63ed7a8262072ffcda38f | 58,898 |
def artifact_removal(X, n_components=None, check_result=True):
"""Remove artifacts from data.
The artifacts are detected via Independent Component Analysis (ICA) and
subsequently removed. To plot the results, use
:func:`gumpy.plot.artifact_removal`
Args:
X (array_like): Data to remove arti... | 59d15348ad3eb4744f3c364331d6d6e5f453f66e | 58,899 |
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