content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import os
def cmd_openfile(pid,abs_filename,line_no=1,column_no=1):
"""ファイルをコンパイルする
abs_filename - ファイル名の絶対パス
(Ex.) c:/project/my_app/src/main.cpp
"""
dte = get_dte_obj(pid)
if not dte:
_vs_msg("Not found process")
return False
abs_filename = _to_unicode(a... | 0ca1e0c26fe12677c3c4b3ff73eafdf6ea11485e | 3,627,700 |
def optimize(s, probability, loBound, hiBound):
""" Optimiere auf die max. mögliche Entnahme bei einer vorgegebenen Fehlerquote
Returns:
widthdrawal: max. mögliche prozentuale Entnahme
"""
n_ret_months = s.simulation['n_ret_years'] * 12
accuracy = 0.01 # Genauigkeit der Optimierung
... | 0c572b885f5803d832b1ff1bde0e4013895b4665 | 3,627,701 |
from typing import Tuple
import torch
def torch_data_loader(
features: np.ndarray,
labels: np.ndarray,
batch_size: int,
shuffle: bool = None,
num_workers: int = 0
) -> Tuple[torch.utils.data.DataLoader]:
"""
Creates the data loader for the train and test dataset.
Parameters
------... | 4891a486f980714d8500c246e022c1412f27e563 | 3,627,702 |
def delete_row_col(np_arr, row, col):
"""Removes the specified row and col from a Numpy array.
A new np array is returned, so this does not affect the input array."""
return np.delete(np.delete(np_arr, row, 0), col, 1) | bcdb55aa78d676861ed820a550a85ad0c24bbe76 | 3,627,703 |
def make_3d(adata,
time_var='Metadata_Time',
tree_var='Metadata_Trace_Tree',
use_rep=None,
n_pcs=50):
"""Return three dimensional representation of data.
Args:
time_var (str): Variable in .obs with timesteps
tree_var (str): Variable in .obs with t... | 01abf024914bcb12a2f02f542cc4c406e461fe79 | 3,627,704 |
import re
def _natural_key(x):
""" Splits a string into characters and digits. This helps in sorting file
names in a 'natural' way.
"""
return [int(c) if c.isdigit() else c.lower() for c in re.split("(\d+)", x)] | 1fab7dffb9765b20f77ab759e43a23325b4441f4 | 3,627,705 |
def get_location_1(box_2d, dimension, rotation_x, rotation_y, rotation_z, proj_matrix):
"""
方法1 2Dbbox中心与3Dbbox中心重合
只存在一个中心点间的对应关系。难以约束。
若是将Z的值替换成真实值，效果还行。Z方向的值与XY相比差距太大。
"""
R = get_R(rotation_x, rotation_y)
# format 2d corners
xmin = box_2d[0]
ymin = box_2d[1]
xmax = box_2d[2]... | 54b575ec603de0c65d57b6e07ef62d56b3f12ee2 | 3,627,706 |
import os
def find_file(directory: str, search_file: str) -> str:
"""Finds relative path of file in given directory and its subdirectories.
Args:
directory (str): Directory to search in.
search_file (str): File to search in directory.
Returns:
str: Path to file.
"""
pat... | 4f272ab643d8b271d01cd38041e1e003e3171224 | 3,627,707 |
def get_smoothing_kernel(x, y, smoothing_length):
""" x = x - xj, y = y - yj"""
r_xy_2 = x + y
q_xy_2 = r_xy_2 / smoothing_length
return get_dimensionless_2D_kernel(q_xy_2) | 183087724d2c3a921e64a65a00f2ec5c783ceb40 | 3,627,708 |
def evolve_population(population: list, generation: int) -> list:
"""
This evolves an existing population by doubling them (binary fission), then introducing random mutation to
each member of the population.
:param generation: Helps determine the starting point of the numbering system so the bacteria ha... | e631864de0c25857ffcb4dff0504dac63ee49aab | 3,627,709 |
def create_siamese_trainer(
model,
optimizer,
loss_fn,
device=None,
non_blocking=False,
prepare_batch=_prepare_batch,
output_transform=output_transform_trainer,
):
"""Factory function for creating an ignite trainer Engine for a siamese architecture.
Args:
model: siamese networ... | 5a81b6d58f824dff857ce662505c3896c4af9a66 | 3,627,710 |
import json
def handle_methods(
request,
GET=None,
POST=None,
PUT=None,
PATCH=None,
DELETE=None,
args=[],
kwargs={},
):
"""
REST Method Handler.
Return the view handleMethods(request)
Add all allowed methods with their responses. These can either be a Django HttpRespon... | ee87a12959c32a42ca39b231c6e3956050b427b3 | 3,627,711 |
def compute_bounding_box(points, convex_hull=None,
given_angles=None, max_error=None):
"""
Computes the minimum area oriented bounding box of a set of points.
Parameters
----------
points : (Mx2) array
The coordinates of the points.
convex_hull : scipy.spatial.C... | 0c77963ed739431f1e0a02125a3703a76a26866f | 3,627,712 |
import argparse
from typing import Tuple
from typing import List
import re
import sys
def parse_outputs_from_args(args: argparse.Namespace) -> Tuple[List[str], List[int]]:
"""Get a list of outputs specified in the args."""
name_and_port = [output.split(':') for output in re.split(', |,', args.output_layers)]
... | 25fe89ed77b44344bad945bf91a2024f3753cd49 | 3,627,713 |
def n_pitches_used(tensor):
"""Return the number of unique pitches used per bar."""
if tensor.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
return tf.reduce_mean(tf.reduce_sum(tf.count_nonzero(tensor, 3), 2), [0, 1]) | 0ad75015c4333e2a981d3cd344d6a279a98d9392 | 3,627,714 |
from datetime import datetime
def survey():
"""Survey home page."""
N_SIMULATION_PERIODS = get_n_periods()
db = get_db()
user_data = db.execute(
"SELECT * FROM user WHERE id = ?",
(session["user_id"],)
).fetchone()
user_stage = user_data['current_stage']
simulation_period =... | 8a839041bbb4cc8b5e2c0ecb2e5a09395552bf8a | 3,627,715 |
import struct
def ReadXTrace(trace_filename):
"""
Returns the trace for this XTrace dataset.
@param trace_filename: location of file to read into XTrace object
"""
# maximum size for strings
max_bytes = 32
max_function_bytes = 64
# open the file
with open(trace_filename, 'rb') as ... | 9ce8271e9f45463721582939ea54a63a25e2e9ac | 3,627,716 |
import pandas as pd
import numpy as np
import mydatapreprocessing as mdp
def to_vue_plotly(data: np.ndarray | pd.DataFrame, names: list = None) -> dict:
"""Takes data (dataframe or numpy array) and transforms it to form, that vue-plotly understand.
Links to vue-plotly:
https://www.npmjs.com/package/vue-... | e29814b89550ae0247342607bea90ec263dbf72a | 3,627,717 |
def get_model_name(file_path, sheet_name):
"""
Return the model name, which is assumed to be in the first row, second column of the sheet_name.
Args:
file_path: path to file containing the model
sheet_name: sheet_name: name of the excel sheet where the model name is, should be 'general'
... | 30cb673e87a6b71d76049434615a9b4396130125 | 3,627,718 |
def duplicate_ticket_view(request, uuid):
"""
Create duplicate of a given ticket (found by uuid) i
The result is a new ticket, which has the same connection and validity_period
Does not allow duplicating shared tickets.
Does not allow to duplicate if you are not the author of ticket.
"""
t... | 07cfd486d9812227686301f87d9317d65a51d82a | 3,627,719 |
from typing import Tuple
import math
def _projected_velocities_from_cog(beta: float, cog_speed: float) -> Tuple[float, float]:
"""
Computes the projected velocities at the rear axle using the Bicycle kinematic model using COG data
:param beta: [rad] the angle from rear axle to COG at instantaneous center ... | defbfa58d1e67b67ff4a118ebff03e62f4c1042c | 3,627,720 |
from typing import List
import math
def prime_factors(a:int) -> List[int]:
"""
Returns the prime factors of a number.
Parameters:
a (int): the number to return the prime factors of
Returns:
(list[int]): an unsorted list of the prime factors of a
"""
# prime numbers only... | 75b27331c8dbb9fd6ed03c97af841bc76ed6cd8f | 3,627,721 |
def add_ngram(sequences, token_indice, ngram_range=2):
"""
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
>>> add_ngram(sequences, token_in... | 8e339e6b5c3fca6f62fd38804465488297b93ad3 | 3,627,722 |
import json
def get_sea_surface_height_trend_image():
"""generate bathymetry image for a certain timespan (begin_date, end_date) and a dataset {jetski | vaklodingen | kustlidar}"""
r = request.get_json()
image = ee.Image('users/fbaart/ssh-trend-map')
image = image.visualize(**{'bands': ['time'], 'mi... | 89e23fdb458b8fbb3230f19ab8664e102ed215fb | 3,627,723 |
def xy_to_rho(pt1, pt2):
"""convert two points of line into rho, theta form"""
# find inverse of slope of line
m, b = xy_to_mb(pt1, pt2)
minv = -1 / m if m else None if m == 0 else 0
# find intersection point of line with line defined by rho, theta
intersection = line_intersection(m, b, minv, 0... | b9ec3e3af6e734580a9ffd05a022e29e8e36d1eb | 3,627,724 |
from numpy import meshgrid, arange, ones, zeros, sin, cos, sqrt, clip
from scipy.special import jv as bessel
from numpy.random import poisson as poisson
def generate_image(image_parameters):
"""Generate image with particles.
Input:
image_parameters: list with the values of the image parameters in a d... | 1ebb0b5fa200b5590769d5e09fdeef9d57bfcdcb | 3,627,725 |
import numpy
def gaussian_filter(input, sigma, order=0, output=None, mode="reflect", cval=0.0, truncate=4.0):
"""Multidimensional Gaussian filter.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
Standard deviation for Gaussian kernel. The standard
deviations ... | f9fdac5e8c3c38936db8f44731dd975ed8d78c12 | 3,627,726 |
def run_test(target_call,
num_steps,
strategy,
batch_size=None,
log_steps=100,
num_steps_per_batch=1,
iterator=None):
"""Run benchmark and return TimeHistory object with stats.
Args:
target_call: Call to execute for each step.
nu... | f9e996198a0dee309f7a2e08505d0cc0e5778023 | 3,627,727 |
import tqdm
import logging
def remove_punctuation_from_text(data):
""" Enriches a dataframe or Anytree structure containing "text" field with "clean text" field
See utils.clean_text for more information
Returns:
[pd.DataFrame or dictionary] -- conversations with new clean text field
"""
if isinst... | 1c76585b07c8865913aa3e0660b3bc59b9edf6a3 | 3,627,728 |
def querystring_parse(parameter_data):
"""Parse dictionary to querystring"""
data = parameter_data
return urlencode(data).replace("%2F","/") | 41035637b5af123b6102c27df24f95bbfd030de2 | 3,627,729 |
def _deployment_rollback(deployment_id):
"""
:param deployment_id: the application id
:type deployment_di: str
:returns: process return code
:rtype: int
"""
client = marathon.create_client()
deployment = client.rollback_deployment(deployment_id)
emitter.publish(deployment)
retu... | 37faad940a7f5a20b5e8d17c62920a9c9f781cd8 | 3,627,730 |
def find_regular_bin_edges_from_centers(centers):
"""
Finds bin (grid cell) edges from center positions. Assumes a regular grid.
Inputs:
centers = bin/grid center position vector of current grid, shape [nb]
Returns:
edges = edge positions of bins (grid), shape [nb+1]
"""
edges =... | c5a957209222b9b1d63ce1b6efa72b4abb3591b9 | 3,627,731 |
def postordereval(parseTree):
"""Compute the result inline with postorder"""
ops = {'+': op.add, '-': op.sub, '*': op.mul, '/': op.truediv}
if parseTree:
evalLeft = postordereval(parseTree.getLeftChild())
evalRight = postordereval(parseTree.getRightChild())
if evalLeft and evalRight... | 0192ddfb601192ac5ca37527e28da9655fde10ea | 3,627,732 |
def non_max_suppression(boxes, scores, threshold, max_num):
"""Performs non-maximum suppression and returns indices of kept boxes.
boxes: [N, (z1, y1, x1, z2, y2, x2)]. Notice that (z2, y2, x2) lays outside the box.
scores: 1-D array of box scores.
threshold: Float. IoU threshold to use for filtering.
... | 5e0d166667f3f82f622ac4607b4235a4db06aab3 | 3,627,733 |
def mask2rle(img):
"""
- https://www.kaggle.com/paulorzp/rle-functions-run-lenght-encode-decode
img: numpy array, 1 -> mask, 0 -> background
Returns run length as string formated
"""
pixels= img.T.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[... | e2f06ac4767e3af1a88cee0ac1ae7686487dece6 | 3,627,734 |
import os
def fp(path):
"""Prepends SEIR_HOME to path and returns full path."""
return os.path.join(SEIR_HOME, path) | e085a9fdc54b891ffcfedb5ab614edd1148426c9 | 3,627,735 |
def rgb_to_hsv(color: np.ndarray) -> np.ndarray:
"""
Convert a color from the RGB colorspace to the HSV colorspace
>>> rgb_to_hsv(np.array([10, 20, 30], np.uint8))
array([105, 170, 30])
Args:
color: Color as numpy array. Can either have shape (X, Y, 3)
if it is a whole image, (... | b44e443a215c080f9fa7a107686a400aa8ac3ea7 | 3,627,736 |
def plot_params(model):
"""Print parameters
"""
x0 = 0.05
y0 = 0.95
dy = 0.03
fig = plt.figure(1, figsize=(10, 10))
plt.subplots_adjust(left=0.1, top=0.95, bottom=0.05, right=0.95)
ax_lab = fig.add_subplot(111)
ax_lab.xaxis.set_visible(False)
ax_lab.yaxis.set_visible(False)
... | 58ae599ff0073c6c51ef2c4058f54646186b5d87 | 3,627,737 |
import torch
def class_avg_chainthaw(model, nb_classes, loss_op, train, val, test, batch_size,
epoch_size, nb_epochs, checkpoint_weight_path,
f1_init_weight_path, patience=5,
initial_lr=0.001, next_lr=0.0001, verbose=True):
""" Finetunes give... | 2aeb2f442fb648ac7bb9536b989e3a0ea2f77087 | 3,627,738 |
def quality_scrub(df, target_cols = ['quality_1', 'quality_2', 'quality_3']):
"""
Definition:
Filters a dataframe where each target_col does not contain 'no_cough'
Args:
df: Required. A dataframe containing the target columns
target_cols: default = ['quality_1', 'quality_2', 'quality_3'].
Returns:
Re... | 1187278e008f1e4ec4688d3cf9a3d7a0c1a82dc0 | 3,627,739 |
def create_arrival_timer(model, name, descr = None):
"""Return a new timer that allows measuring the processing time of transacts."""
y = ArrivalTimerPort(model, name = name, descr = descr)
code = 'newArrivalTimer'
y.write(code)
return y | 276a439fb0152f66cf6cc420cb7599b83d1718f6 | 3,627,740 |
from typing import Dict
from re import T
import torch
def get_default_transforms() -> Dict[T.Compose, T.Compose]:
"""augmentationを取得
Returns:
Dict[T.Compose, T.Compose]: 学習用，検証用のaugmentation
"""
transform = {
"train": T.Compose(
[
T.RandomHorizontalFlip(),
... | 5cbea521348a2bed215a2692eb9be7dea2af1b7e | 3,627,741 |
import subprocess
import os
def simple_shell(args, stdout=False):
""" Simple Subprocess Shell Helper Function """
if stdout:
rc = subprocess.call(args, shell=False)
else:
rc = subprocess.call(args, shell=False, stdout=open(os.devnull, "w"), stderr=subprocess.STDOUT)
return rc | b922e35565a5da58cec153415b9112e560de6c73 | 3,627,742 |
def print_decorator(fct):
"""dento dekorator pouzivam, na to aby som dokazal testovat aj vypisy na standardny vystup"""
original_fct = fct
output = []
def wrapper(*args):
output.append((args))
return original_fct(*args)
return wrapper | 4fa74cf9bf3653f89114cbdd6503ef13630a17e7 | 3,627,743 |
import logging
def parse_testresults(xml, test_id, domain):
""" Parse the given XML file and build mappings """
global_lookup = {}
global_testresults = {}
for event, element in etree.iterparse(xml, events=("start", "end")):
try:
global_id, global_title, global_fixtext = \
... | f36923e8a17f0c9646fcde570a008b030eda464f | 3,627,744 |
def remodel_matrix(matrix, new_fire_cells, moisture_matrix):
"""
matrix: Array of the fire spread area
new_fire_cells: list of tuples, each tuple representing the x,y coordinates
of a new cell that has been affected by the fire spread.
"""
for cell in new_fire_cells:
x = int(cell[0]... | c24c8fae19e0a8bb884103191906e76af625430f | 3,627,745 |
import re
def getOffers(session, city):
"""
Parameters
----------
session : ikabot.web.session.Session
city : dict
Returns
-------
offers : list[dict]
"""
html = getMarketHtml(session, city)
hits = re.findall(r'short_text80">(.*?) *<br/>\((.*?)\)\s *</td>\s *<td>(\d+)</td>\s *<td>(.*?)/td>\s *<td><img src=... | bed8332cd501da9871ec2a9a576783bdc87340de | 3,627,746 |
def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
"""
给训练添加一个新的softmax和全连接层，
我们需要重新训练顶层来识别我们的新类，所以这个函数为graph添加了正确的操作
:param class_count: 多类的事物总数
:param final_tensor_name: 生成结果的新的最终节点的名称字符串。
:param bottleneck_tensor: 主CNN图的输出。
:return: The tensors for the training... | 96d80fe0aded67684a4ed8534a9634995c4cad4e | 3,627,747 |
def generate_northern_ireland_data(directory, file_date, records):
"""
generate northern ireland file.
"""
northern_ireland_data_description = lambda: { # noqa: E731
"UIC": _("random.custom_code", mask="############", digit="#"),
"Sample": _("random.custom_code", mask="#&&&", digit="#",... | b74bb5d25b401a3695276f49bbddfb08ed025d26 | 3,627,748 |
def create_message(address, subject, message_text, html=True, attachments=None):
"""Create a message for an email, using the low-level API.
Arguments:
address (str): Email address(es) of the receiver.
subject (str): The subject of the email message.
message_text (str): The text of the e... | 9e1638e2940ef133dfb185e2bf54002cfb25f9e4 | 3,627,749 |
import logging
def whole_appendix(xml, cfr_part, letter):
"""Attempt to parse an appendix. Used when the entire appendix has been
replaced/added or when we can use the section headers to determine our
place. If the format isn't what we expect, display a warning."""
xml = deepcopy(xml)
hds = xml.xp... | b1e757ae292d299096abde80c74354093c1d6684 | 3,627,750 |
import collections
def read_image_files(image_files,image_shape=None, crop=None, label_indices=None):
"""
:param image_files:
:param image_shape:
:param crop:
:param use_nearest_for_last_file: If True, will use nearest neighbor interpolation for the last file. This is used
because the last ... | 0e01d5a47786154cde030b7cd252154a183c7358 | 3,627,751 |
import traceback
def wrap_unexpected_exceptions(f, execute_if_error=None):
"""A decorator that catches all exceptions from the function f and alerts the user about them.
Self can be any object with a "logger" attribute and a "ipython_display" attribute.
All exceptions are logged as "unexpected" exceptions... | d2f37ff0c8a1dac6cbab1fe8e8c6598de0ab059a | 3,627,752 |
def store_topology(topology_file_string: str, fileformat="pdbx"):
"""Store a file (containing topology, such as pdbx) in a topology XML block."""
root = etree.fromstring(f'<TopologyFile format="{fileformat}"/>')
root.text = topology_file_string
return root | ed9b2b27cda4a31fcd04a920ce4d344b268bf012 | 3,627,753 |
def zeros_like(tab):
"""
Wrapper to numpy.zeros_like, force order to hysop.constants.ORDER
"""
return np.zeros_like(tab, dtype=tab.dtype, order=ORDER) | ab089b5003ce07ef8bc9a6ac4b027a974833a04b | 3,627,754 |
def get_bert_embeddings(input_ids,
bert_config,
input_mask=None,
token_type_ids=None,
is_training=False,
use_one_hot_embeddings=False,
scope=None):
"""Returns embeddings for ... | 8dc60142ded4951e9ae69f02f8eb928c3f6c0b2a | 3,627,755 |
import time
def blind_deconvolution_multiple_subjects(
X, t_r, hrf_rois, hrf_model='scaled_hrf', shared_spatial_maps=False,
deactivate_v_learning=False, deactivate_z_learning=False,
deactivate_u_learning=False, n_atoms=10, n_times_atom=60, prox_z='tv',
lbda_strategy='ratio', lbda=0.1, ... | a4fa4eabe035c792fdd1a3b75600a443e9f73058 | 3,627,756 |
def _TestRemovePhotos(tester, user_cookie, request_dict):
"""Called by the ServiceTester in order to test remove_photos service API call."""
validator = tester.validator
user_id, device_id = tester.GetIdsFromCookie(user_cookie)
request_dict = deepcopy(request_dict)
user = validator.GetModelObject(User, user_i... | d71619608435bc763d8902856ce39585f62dd320 | 3,627,757 |
import csv
def get_author_book_publisher_data(filepath):
"""
This function gets the data from the csv file
"""
with open(filepath) as csvfile:
csv_reader = csv.DictReader(csvfile)
data = [row for row in csv_reader]
return data | 5d095b20e2e32aacbe4d85efd80461abfa175127 | 3,627,758 |
from typing import Optional
async def update_workflow_revision(
# pylint: disable=W0622
id: UUID,
updated_workflow_dto: WorkflowRevisionFrontendDto,
) -> WorkflowRevisionFrontendDto:
"""Update or store a transformation revision of type workflow in the data base.
If no DB entry with the provided i... | 888fa1edd72232c0300cf02b194330e4d9dbfaea | 3,627,759 |
def tf_repeat(tensor, repeats):
"""
Args:
input: A Tensor. 1-D or higher.
repeats: A list. Number of repeat for each dimension, length must be the same as the number of dimensions in input
Returns:
A Tensor. Has the same type as input. Has the shape of tensor.shape * repeats
"""
expande... | 5a9022d427caed7ad645c7ede8142851c1d0af88 | 3,627,760 |
def apply_slim_collections(cost):
"""
Add the cost with the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
Args:
cost: a scalar tensor
Return:
a scalar tensor, the cost after applying the collections.
"""
regulization_losses = set(tf.get_collection(tf.GraphKeys.REGULAR... | 7182995a31c3daa6b33bb8be28ec3a1b2a98e7d6 | 3,627,761 |
import re
def isGoodResult(name, show, log=True, season=-1):
"""
Use an automatically-created regex to make sure the result actually is the show it claims to be
"""
all_show_names = allPossibleShowNames(show,season=season)
showNames = map(sanitizeSceneName, all_show_names) + all_show_names
f... | c29ff6dbb829553d546d9f80975b3eb6c355fa6d | 3,627,762 |
import six
import itertools
def get_configuration(configuration_schema,
command_line_options=None,
environment_variables=None,
config_content=None,
django_settings=None):
"""Get configuration from all sources.
Notes:
... | 88a363d474ba3eebace2ebb2085572850345240b | 3,627,763 |
from typing import Tuple
def convert_descriptor_to_type(desc: str) -> Tuple[str, int]:
""" Converts a java descriptor to the java type, in the inverse of convert_descriptor_to_type()
Returns the type, and the number of array levels (e.g. [[Z would return ('boolean', 2), not 'boolean[][]'
Optionally will r... | 6f5aaa164636c0e99472c56b212b3a586939f62a | 3,627,764 |
import pandas
def _normalize_similarity(df: pandas.DataFrame) -> None:
"""Normalizes similarity by combining cls and transformation."""
df["params.similarity"] = (df["params.similarity.cls"] + "_" + df["params.similarity.transformation"])
# transformation only active if similarity in {l1, 2}
unused_t... | 048c1a7107cf61c20ebc7d2e10214c434898466e | 3,627,765 |
def density_bounds(density, wi,
vo=.49,
ve=.5,
dt=.1,
exact=False):
"""THIS IS A BOUND, NOT THE ACTUAL VELOCITY.
Min density bound for nnovation front as derived from MFT and compared with
simulation results.
Depends on ob... | 548e06e8b4a148c5ca1ffd95ce1cfcfb51183773 | 3,627,766 |
def process_instructions(instructions):
"""Process instructions in order, starting from line 0"""
line = 0
instructions_executed = set()
accumulator = 0
while line not in instructions_executed:
try:
instruction = instructions[line]
except IndexError:
print(f'E... | 4c2278b03db2ddb0ca3292f591509d82b2e8f361 | 3,627,767 |
from typing import List
def check_absence_of_skip_series(
movement: int,
past_movements: List[int],
max_n_skips: int = 2,
**kwargs
) -> bool:
"""
Check that there are no long series of skips.
:param movement:
melodic interval (in scale degrees) for line continuatio... | 94ff2f3e03956d5bea1173182e389a3e6bb4b487 | 3,627,768 |
import glob
import os
def get_preview_images_by_rootname(rootname):
"""Return a list of preview images available in the filesystem for
the given ``rootname``.
Parameters
----------
rootname : str
The rootname of interest (e.g. ``jw86600008001_02101_00007_guider2``).
Returns
-----... | cf615abde2f09251e0b9e2ab5e89347994f9c29f | 3,627,769 |
def get_discharge_measurements(sites=None, start=None, end=None, **kwargs):
"""
Get discharge measurements from the waterdata service.
Parameters (Additional parameters, if supplied, will be used as query parameters)
----------
sites: array of strings
If the qwdata parameter site_no is supp... | 855b875dda057108129da5f742a4257b73a20510 | 3,627,770 |
def get_full_frame_size(body_size):
"""
Returns size of full frame for provided frame body size
:param body_size: frame body size
:return: size of full frame
"""
return eth_common_constants.FRAME_HDR_TOTAL_LEN + \
get_padded_len_16(body_size) + \
eth_common_constants.FRAME... | 23986f5d3ffda84fe8eebff8ab6159f96ab5f2f4 | 3,627,771 |
def is_iterable(obj):
"""
Returns *True* when an object *obj* is iterable and *False* otherwise.
"""
try:
iter(obj)
except Exception:
return False
return True | cb4b383780ac6f257c734aef2ccd8f00ecd9af77 | 3,627,772 |
def vels2waves(vels, restwav, hdr, usewcs=None, observatory="SPM"):
"""Heliocentric radial velocity (in km/s) to observed wavelength (in
m, or whatever units restwav is in)
"""
# Heliocentric correction
vels = np.array(vels) + helio_topo_from_header(
hdr, usewcs=usewcs, observatory=observat... | 7153d751793a7e8370fa206c591d73d5245ff955 | 3,627,773 |
def make_blueprint(db_connection_string=None, configuration={}): # noqa
"""Create blueprint.
"""
controllers = Controllers(configuration=configuration,
connection_string=db_connection_string)
# Create instance
blueprint = Blueprint('pipelines', 'pipelines')
@che... | df5871436fd8768224a342daffe2afffb262e402 | 3,627,774 |
from typing import Any
from pathlib import Path
def jsonable(obj: Any):
"""Convert obj to a JSON-ready container or object.
Args:
obj ([type]):
"""
if isinstance(obj, (str, float, int, complex)):
return obj
elif isinstance(obj, Path):
return str(obj.resolve())
elif isi... | 494bd41dc0b3ef4cc81e4daf5b1bc24b618ea7f8 | 3,627,775 |
def read_data_from(file_: str) -> dict:
"""Load image tiles from file."""
tiles = {}
tile = []
for line in open(file_, "r").read().splitlines():
if "Tile" in line:
idx = int(line[5:-1])
elif line == "":
tiles[idx] = np.array(tile)
tile = []
els... | 4b4a072cf9c2a28fa64b18adff1354ac171ad18c | 3,627,776 |
def create_graph(A, create_using=None, remove_self_loops=True):
"""
Function for flexibly creating a networkx graph from a numpy array.
Params
------
A (np.ndarray): A numpy array.
create_using (nx.Graph or None): Create the graph using a specific networkx graph.
Can be used for forcing an ... | 3baa2be7cbf3f0e2c18273aabe7ae7864853c59f | 3,627,777 |
def delete(*tables):
"""
Returns :py:class:`~.Delete` instance and passed arguments are used for list
of tables from which really data should be deleted. But probably you want
to use :py:func:`~.delete_from` instead.
"""
return Delete(*tables) | 9cd4099655d1e8f4393fcad07b453a9597aaf5d7 | 3,627,778 |
def create_string_for_failing_metrics(hpo_objects):
"""
Function is used to create a string for the failing
metrics that can ultimately be inserted into the email output.
Parameters
----------
hpo_objects (list): contains all of the HPO objects. the
DataQualityMetric objects will now be ... | 4128685d058e5fba4efbcbe30aa6e45bf5aeef2a | 3,627,779 |
import argparse
def _main(argv, standard_out, standard_error, standard_in):
"""Run internal main entry point."""
flargs = {}
if "--config" in argv:
flargs = find_config_file(argv)
parser = argparse.ArgumentParser(description=__doc__, prog='docformatter')
changes = parser.add_mutually_exc... | 3a87528c2680eea464cd3b2f1f911f7010d7d018 | 3,627,780 |
from typing import Coroutine
from typing import Any
def current_effective_deadline() -> Coroutine[Any, Any, float]:
"""
Return the nearest deadline among all the cancel scopes effective for the current task.
:return: a clock value from the event loop's internal clock (``float('inf')`` if there is no
... | b3230c8aeb240d0a02fabdfabbb2adadb57062a8 | 3,627,781 |
import time
def from_openid_response(openid_response):
""" return openid object from response """
issued = int(time.time())
sreg_resp = sreg.SRegResponse.fromSuccessResponse(openid_response) \
or []
ax_resp = ax.FetchResponse.fromSuccessResponse(openid_response)
ax_args = {}
if ax_... | d98ce4587f2ac380c77ec92f948f355c051f4184 | 3,627,782 |
from typing import Callable
import click
def dcos_login_pw_option(command: Callable[..., None]) -> Callable[..., None]:
"""
A decorator for choosing the password to set the ``DCOS_LOGIN_PW``
environment variable to.
"""
function = click.option(
'--dcos-login-pw',
type=str,
... | fe4b4d9dac90536046bcebf56fa2fe5144aaa665 | 3,627,783 |
from typing import Dict
from typing import Tuple
from typing import Any
def get_default_triggers() -> Dict[Tuple[Tuple[str, Any]], Dict[str, Any]]:
"""Make _triggers read only"""
return _default_triggers | a467de3534e58701f26d9d8d57f105743dcf283a | 3,627,784 |
def yesno_choice(title, callback_yes=None, callback_no=None):
"""
Display a choice to the user. The corresponding callback will be called in case of
affermative or negative answers.
:param title: text to display (e.g.: 'Do you want to go to Copenaghen?' )
:param callback_yes: callback function to be... | 93b76a3c7740b90dd01bd46ed429411991f3f34d | 3,627,785 |
def is_tensor_object(x):
"""
Test whether or not `x` is a tensor object.
:class:`tf.Tensor`, :class:`tf.Variable` and :class:`TensorWrapper`
are considered to be tensor objects.
Args:
x: The object to be tested.
Returns:
bool: A boolean indicating whether `x` is a tensor objec... | 8b2610d6d26bc3bb1ae72a11416507110a2d2bef | 3,627,786 |
import json
def slack(text: str, webhookAddress: str) -> str:
"""Send a slack message"""
data = bytes(json.dumps({"text": text}), "utf-8")
handler = urlopen(webhookAddress, data)
return handler.read().decode('utf-8') | 5570ba3c11f907e0b96f4878fc915c711b01ef3b | 3,627,787 |
def wrap_col(string, str_length=11):
"""
String wrap
"""
if [x for x in string.split(' ') if len(x) > 25]:
parts = [string[i:i + str_length].strip() for i in range(0, len(string), str_length)]
return ('\n'.join(parts) + '\n')
else:
return (string) | 7b5cdf37cb84a2d2ebbc421ea917fc563026927e | 3,627,788 |
import re
def tokenize_text_with_special(text):
"""
Tokenizes a string. Does not filter any characters.
:param text: The String to be tokenized.
:return: Tokens
"""
token = []
running_word = ""
for c in text:
if re.match(alphanumeric, c):
running_word += c
e... | e6fbc0067dddf749f1d969646f2544352c36a380 | 3,627,789 |
def rms_slope_from_profile(topography, short_wavelength_cutoff=None, window=None,
direction=None):
"""
Compute the root mean square amplitude of the height derivative of a
topography or line scan stored on a uniform grid. If the topography is two
dimensional (i.e. a topography... | fc6a1b1ce653c16b34bc42151dd62a05be6fc36f | 3,627,790 |
def _get_exec_driver():
"""
Get the method to be used in shell commands
"""
contextkey = "docker.exec_driver"
if contextkey not in __context__:
from_config = __salt__["config.option"](contextkey, None)
# This if block can be removed once we make docker-exec a default
# option... | 33a9b06543af74c91bf0720caaad7c9ddd793ccd | 3,627,791 |
def combine_results_jsons(drtdp_json, psrtdp_json, vi_json):
"""
takes overall results jsons and combines them to one json
:param drtdp_json a json for drtdp overall results
:param psrtdp_json a json for ps-rtdp overall results
:param vi_json a json for value iteration overall results
:return co... | a2bedf628e2af91af2c16111cd33600ade7e435e | 3,627,792 |
def preprocess_data(data_path, embeds_path, lang='fr'):
"""
Loads pre-embedded dataset and labels, in a random (but consistent) order.
:param data_path: (str) filepath to csv
:param embeds_path: (str) filepath to json
:return: X (list of list of list), y (len(train) x 2) np array
"""
X = loa... | 6b516bacc7084b1df892fdbadecea78ff7e65121 | 3,627,793 |
def text_comp19_to_df():
"""
Returns a pandas Dataframe object with
the data of the TextComplexityDE19 dataset
"""
# Path to relevant csv file
csv_path = join(
dirname(dirname(dirname(abspath(__file__)))),
"data",
"TextComplexityDE19/ratings.csv",
)
# read in c... | d12500ace3fe92bc0e39cb86d58a8077fcc28635 | 3,627,794 |
def from_relay(func: relay.Function) -> IRModule:
"""Convert a Relay function into a Relax program.
Parameters
----------
func : relay.Function
Relay function to be converted
Returns
-------
mod : tvm.IRModule
The Relax IRModule for compilation
"""
# A map to store ... | fc1031fff3098e7c53321c2f7ca4ecfbdb456255 | 3,627,795 |
import os
def get_list_of_all_data_file_names(datadirectory):
"""
Return list of all data files (.txt) in specified directory
"""
print('get_list_of_all_data_file_names', datadirectory)
list_of_files = []
for file in os.listdir(datadirectory):
if file.endswith('txt'):
list_... | 35dd02acdc492d1d38e9cedbe154f2754706e25b | 3,627,796 |
def function_profiler(naming='qualname'):
"""
decorator that uses FunctionLogger as a context manager to
log information about this call of the function.
"""
def layer(function):
def wrapper(*args, **kwargs):
with FunctionLogger(function, naming):
return function(... | 001977a23788a6e897b0882b894a5cdecc6b881f | 3,627,797 |
from unittest.mock import Mock
def mock_data_manager(components):
"""Return a mock data manager of a general model."""
dm = Mock()
dm.components = components
dm.fixed_components = []
return dm | e796dbe73e2ec7df650ceab450a3a5449a6af9ed | 3,627,798 |
def load_data_fashion_mnist(batch_size, resize=None): #@save
"""下载Fashion-MNIST数据集，然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = paddle.vision.datasets.FashionMNIST(mode="train", transform=... | d946c33f7bff29a3278f5dcdc85195de772e85f7 | 3,627,799 |
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