content stringlengths 35 416k | sha1 stringlengths 40 40 | id int64 0 710k |
|---|---|---|
def soft_crossentropy(predicted_logprobs, target_probs):
"""
Cross-entropy loss capable of handling soft target probabilities.
"""
return -(target_probs * predicted_logprobs).sum(1).mean(0) | 8f6f0168c67cd0b3f432a5c91c7f4069c54de7c8 | 7,396 |
def tflops_per_second(flops, dt):
""" Computes an effective processing rate in TFLOPS per second.
TFLOP/S = flops * / (dt * 1E12)
Args:
flops: Estimated FLOPS in the computation.
dt: Elapsed time in seconds.
Returns:
The estimate.
"""
return flops / (1E12 * dt) | f244632e1378a69ea55d4a994a9711bd3a2dca2a | 7,399 |
import os
import json
def get_hosts(host_file):
""" Fetches host data from a specified host_file
Args:
host_file: Device host file in JSON format
Returns:
A dict mapping keys to the corresponding host data, as follows:
{u'Group-1':
{u'example-device-2':
... | 1d0e58c5cd7d0e9fbe4ea3db6c4e91690afffbd0 | 7,400 |
import torch
def huber_fn_gradient(x, mu):
""" Huber function gradient """
y = torch.zeros_like(x)
with torch.no_grad():
mask = torch.abs(x) <= mu
y[mask] = x[mask]/mu
y[~mask] = x[~mask] / torch.abs(x[~mask])
return y | 1bcbe697a76c06afd49e7bbf106a3c1be0a47481 | 7,401 |
import os
import binascii
def generate_id(hksess):
"""
Generates a unique session id based on the start_time, process_id,
and hksess description.
Args:
hksess (so3g.HKSessionHelper)
"""
# Maybe this should go directly into HKSessionHelper
elements = [
(int(hksess.start_tim... | 2bf52c38b26e9a65071b0db0a7bb0d3edaba17ca | 7,402 |
import os
import json
import logging
def read_settings(path=os.getenv('MOASTROCONFIG',
os.path.expandvars('$HOME/.moastro.json'))):
"""Read the Mo'Astro JSON configurations file.
Parameters
----------
path : str
Path to the ``.moastro.json`` file.
Returns
-------
se... | ef0d8f0dcb176bcca98be9b604c382e3111ec7e7 | 7,403 |
def is_valid(value, cast_fn, expected_data_type, allow_none=False):
"""
Checks whether a value can be converted using the cast_fn function.
Args:
value: Value to be considered
cast_fn: Function used to determine the validity, should throw an
exception if it cannot
e... | ee1a2aca4ba7d437692f5025901f9bf94031434a | 7,404 |
def run_model(network, nodes, demand_per_person_kw_peak, mg_gen_cost_per_kw, mg_cost_per_m2, cost_wire_per_m, grid_cost_per_m2):
"""
"""
# First calcaulte the off-grid cost for each unconnected settlement
for node in nodes:
if node[5] == 0:
node[7] = node[4]*demand_per_person_kw_pe... | 02b386363ac2b18bde7e5773da91109fd234353b | 7,405 |
def numero_lista(df):
"""
Función destinada a obtener el número de lista de clases
:parameter: dataframe
:return:
numero de lista de clases
"""
# Selecciono la columna clase del dataframe y realizo unas modificaciones
df['clase'] = df['clase'].str.replace(
'[', '').str.replace(']... | 86523b6f9eae835de6dfc960e8930cf7659cb2a7 | 7,406 |
def v_relative(v, met):
"""Estimates the relative air speed which combines the average air speed of
the space plus the relative air speed caused by the body movement. Vag is assumed to
be 0 for metabolic rates equal and lower than 1 met and otherwise equal to
Vag = 0.3 (M – 1) (m/s)
Parameters
... | 6dceae6ec076dc800d2aa3e80d7d491d94830580 | 7,407 |
def fully_qualified_name(entry):
"""
Calculates the fully qualified name for an entry by walking the path
to the root node.
Args:
entry: a BeautifulSoup Tag corresponding to an <entry ...> XML node,
or a <clone ...> XML node.
Raises:
ValueError: if entry does not correspond to one of the ... | 68119b640509cd972770f810b80ba1a2ad54f688 | 7,408 |
def sort_tasks_by_exec_time(tasks):
"""
Sort tasks in descending order by the execution time
Args:
tasks (list(Node)
Returns:
list(Node)
"""
n = len(tasks)
for i in range(n):
for j in range(0, n - i - 1):
if tasks[j].get_exec_time() < tasks[j+1].get_exe... | 44f24408803c851ae7f1dd021ec19f99efc3feda | 7,409 |
import re
def shorten_int_name(interface_name):
"""
Returns the Cisco shortened interface name from a full one.
If the full interface name is invalid, this will return None
"""
short = None
regex = "(\w{2}).*?(\d+(?:/\d+)?(?:/\d+)?)"
match = re.match(regex, interface_name)
... | 48a6f730c8d3d2f0abaec299385b5d558cf06a00 | 7,410 |
import re
def read_version():
"""Read version from the first line starting with digit
"""
regex = re.compile('^(?P<number>\d.*?) .*$')
with open('../CHANGELOG.rst') as f:
for line in f:
match = regex.match(line)
if match:
return match.group('number') | 7188470ab1a794b6e72a1fe8bcd804f7290be4a4 | 7,411 |
def RefDefaults():
"""
Returns dictionary of default values for all properties. These are used
to provide defaults to fields that we do not want to automatically
calculate dimensions for
"""
return {
'phi': {
'min':-180,
'max':180... | e6e87fd7c3b3a05f83808bebb410a6d5c687d3d3 | 7,412 |
import json
def load_json(path: str):
"""
Load the contents of a json file into a python dictionary
"""
with open(path) as f:
content = json.load(f)
return content | b35ae26ca303347a98ea3dd3ca42370279d19a2a | 7,413 |
def enc_backbuffer(backbuffer):
"""Helper function for RLE compression, encodes a string of uncompressable data."""
compdata = []
if len(backbuffer) == 0:
return compdata
while len(backbuffer) > 128:
compdata.append(127)
compdata.extend(backbuffer[0:128])
backbuffer = bac... | 75e9860cd0a8563f3e5655b998b4d0dfa1658e9c | 7,416 |
import os
def output_path(inid=None, ftype='data', format='json', site_dir='_site',
must_work=False):
"""Convert an ID into a data, edge, headline, json, or metadata path
Args:
inid: str. Indicator ID with no extensions of paths, eg '1-1-1'.
Can also be "all" for all data... | 39a0bd9dab42eae7c2ff9fa6fb407d291b5ecdbf | 7,417 |
def film_availability_keys():
"""
FilmAvailability definition
Optional keys: "id"
"""
return ["service", "displayName", "country", "url"] | 829ec5ea5492b58c19639f0f83245a88aedb6cc8 | 7,418 |
def V_tank_Reflux(Reflux_mass, tau, rho_Reflux_20, dzeta_reserve):
"""
Calculates the tank for waste.
Parameters
----------
Reflux_mass : float
The mass flowrate of Reflux, [kg/s]
tau : float
The time, [s]
rho_Reflux_20 : float
The destiny of waste for 20 degrees celc... | 3e1adc446bbe2dd936663af895c59222cd000a48 | 7,419 |
def angular_travel(angle_array):
"""
Takes in an array of angular change and returns the angles travelled. This is sensitive to changes in
direction, The angles travelled are returned as an array of stepwise values
"""
travelled = [0]
for a, angle in enumerate(angle_array):
travelled.app... | 0c9eca97614a38c7400796ae3930f18707bc948d | 7,420 |
import os
def check_output_dir(output_dir):
"""
Checks the output directory for files generated in previous runs, these can be skipped later by detect_trs()
Checking is done quite naively, only looking for files ending in '.pickle' (so no support for .pcl, .pkl ...)
Parameters:
output_dir (str): ... | 30e9945cabced00a65d4d5dc9d4194656d0083dc | 7,421 |
import torch
def attention_aggregator(embedding_lists, weights, embed_dim=0) -> torch.Tensor:
"""
Returns a weighted sum of embeddings
:param embedding_lists: list of n tensors of shape (l, K) embedding tensors (l can vary)
:param weights: list of n tensors of shape (l,) weights (l can... | 88fe01d8baea23321593bf88fd522eb0ef379be9 | 7,422 |
def _calculate_application_risk(module):
"""
Function to calculate Software risk due to application type. This
function uses a similar approach as RL-TR-92-52 for baseline fault
density estimates. The baseline application is Process Control
software. Every other application is ranked relative to ... | 703aaf086aecf717be5c13694a8f1dae9f70a86c | 7,423 |
import os
def get_annotations(directory):
""" Returns rel path for all anvil files in a directory"""
return (
os.path.join(directory, f)
for f in os.listdir(directory)
if f.endswith(".anvil")
) | 606fce3064f7fca95c0860d07829dc9f3e121be8 | 7,425 |
def bytesToBits(numBytes):
"""
Converts number of bytes to bits.
:param numBytes: The n number of bytes to convert.
:returns: Number of bits.
"""
return numBytes * 8 | 6dc14c9d9f5829337e826c63a7772ea8d3c6962c | 7,426 |
def get_model_field(model, name):
"""
Gets a field from a Django model.
:param model: A Django model, this should be the class itself.
:param name: A Django model's field.
:return: The field from the model, a subclass of django.db.models.Model
"""
return model._meta.get_field(name) | e0f692aff82c20c7817d7de5d1fbeec1b69d3a3d | 7,427 |
import os
def _get_answer_files(request):
"""
Gets the path to where the hashed and raw answers are saved.
"""
answer_file = f"{request.cls.__name__}_{request.cls.answer_version}.yaml"
raw_answer_file = f"{request.cls.__name__}_{request.cls.answer_version}.h5"
# Add the local-dir aspect of the... | 4d1faed155090f329c4d5efb78a606e25e9aca0f | 7,428 |
def inert_masses(m_1, H, z_m, E_1):
"""First stage inert masses.
Arguments:
m_1 (scalar): First stage wet mass [units: kilogram].
H (scalar): Fraction of the recovery vehicle dry mass which is added recovery
hardware [units: dimensionless].
z_m (scalar): Fraction of baseline... | 5698fcb36ef1f532cc8bc1dc0c86a25adc5bcab8 | 7,429 |
def commnets(milestone):
"""Filtrira milestone tako da nadje samo broj komentara za svaki pojedinacni"""
comments = milestone.event_set.filter(event_kind="K")
size = comments.count()
return size | 9c3654911fe993c359bc593433b6fde1c467a504 | 7,430 |
def buildList(pdList, matrix):
"""Takes a list of primary datasets (PDs) and the AlCaRecoMatrix (a dictinary) and returns a string with all the AlCaRecos for the selected PDs separated by the '+' character without duplicates."""
alCaRecoList = []
for pd in pdList:
alCaRecoList.extend(matrix[pd].spli... | 7e9351f115aac1064068e16f12276ed5506217e4 | 7,431 |
import os
def has_image_extension(uri) -> bool:
"""Check that file has image extension.
Args:
uri (Union[str, pathlib.Path]): the resource to load the file from
Returns:
bool: True if file has image extension, False otherwise
"""
_, ext = os.path.splitext(uri)
return ext.lowe... | e9f338ecda0fa5842fd1aeb15362718d6f026fe6 | 7,432 |
import re
def _get_ip_addr_num(file_path):
"""Get the next IPADDR index num to use for adding an ip addr to an
ifcfg file.
"""
num = ''
with open(file_path, 'r') as f:
data = f.read()
data = data.splitlines()
for line in data:
found = re.search(r'IPADDR(\d?)=', line)
... | 09dfd6bc8a9da240d3044bd6f5b974c69cbebf76 | 7,433 |
from numpy import nan
def tobool(value):
"""Convert value to boolean or Not a Number if not possible"""
if value is None: value = nan
else: value = bool(value)
return value | 9517d817381111c55e73e03256d516ccbbd940a2 | 7,434 |
def tokenize(obj, tokenizer, max_seq_len):
"""Recursively convert to tokens."""
if isinstance(obj, str):
toks = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj)[:max_seq_len])
assert all(
[t < len(tokenizer.encoder) for t in toks]
) # all(toks < len(tokenizer.encoder)... | 9e8ae8a4c1af0df29d95afcef183851792af2b43 | 7,437 |
def manifest_to_file_list(manifest_fn):
"""
Open a manifest file and read it into a list.
Entries in the list are relative, i.e. no leading
slash.
manifest_fn -- the manifest file to read
"""
image_manifest_list = []
with open(manifest_fn) as image:
image_manifest_list = [x[1:] ... | 982f02e0b00fad20af8d50d44673d65d9bba5a37 | 7,438 |
def intersect (sequence_a, sequence_b):
"""Return true if the two sequences contain items in common
If sequence_a is a non-sequence then return false.
"""
try:
for item in sequence_a:
if item in sequence_b:
return 1
except TypeError:
return 0
return 0 | f28c3b6258584fdc1821c65867b4d89a1c33373a | 7,439 |
def get_location_dictionary(deployment):
""" Construct location dictionary from ui deployment information.
"""
try:
have_location_dict = False
latitude = None
longitude = None
location = None
depth = None
orbitRadius = None
if 'depth' in deployment:
... | 3c2b00c97c681d89628ca8c6708545b73bf84649 | 7,440 |
from typing import List
def select_dominoes(dominoes: List[str], order: int, num_players: int) -> str:
"""Randonly generate a set of dominoes for a player
- If the number of players is 3, each player gets 9 dominoes,
plus a 10th dominoe that is to be played first (all players will get it)
- If the num... | c96d1a5b54e3623807ca57a3684f63fa3ff6737c | 7,442 |
import dis
def _opcode(name):
"""Return the opcode by name from the dis module."""
return dis.opmap[name] | d2c8612138c94da68adcc1b8979395987090157c | 7,444 |
import re
def remove_citation(paragraph: str) -> str:
"""Remove all citations (numbers in side square brackets) in paragraph"""
return re.sub(r'\[\d+\]', '', paragraph) | dc88606e69187143d767215ddc098affdbd185d5 | 7,445 |
def get_model_field(model):
""" get the verbose_name of all fields in the model """
field_dict = dict()
for field in model._fields:
attr = getattr(model, field)
if hasattr(attr, 'verbose_name'):
verbose_name = attr.verbose_name
if verbose_name:
field_d... | 8ec9a4f9b571483c13d5a23330e98691279c12ad | 7,446 |
def job_metadata_filename(metadata):
"""Construct relative filename to job metadata."""
return "data/{metadata}".format(metadata=metadata) | bb5e8dc6c0ec50fed6801b9c67f8234d9115372a | 7,447 |
def get_db_cols(cur, table_name, schema='public', type_map=True):
"""
Gets the column names of a given table
if type_map is true, returns also a dictionary
mapping each column name to the corresponding
postgres column type
"""
db_cols_sql = """SELECT column_name, data_type
FROM in... | 936952ea0bbc0c165f089e700828ea876d30ec16 | 7,448 |
def make_modbusmap_channel(i, chan, device_type_name):
"""Make a channel object for a row in the CSV."""
json_obj = {
"ah": "",
"bytary": None,
"al": "",
"vn": chan['subTitle'], # Name
"ct": "number", # ChangeType
"le": "16", # Length(16 or 32)
"grp": ... | cd53f32f47653e791893b90bbbf27e540d741268 | 7,451 |
def CleanData_CB(X,drop_feature):
"""
This function is to clean the data for training and testing with CatBoost.
X : dataframe type for train
y : dataframe type for test
drop_feature : feature name that is wanted to be dropped , e.g., ['Time']
"""
x_new = X.drop... | b6c77e29147dfbdf012f787e29ba4ac364b37fa7 | 7,453 |
def split_indexes(indexes):
"""Split indexes list like 1 2 5 in 1 2 and 5."""
left, right = [indexes[0], ], []
left_now = True
for i in range(1, len(indexes)):
prev = indexes[i - 1]
curr = indexes[i]
if curr > prev + 1 and left_now:
left_now = False
if left_no... | 1bdb3b57226737280b83dbdfa3226dc344eb47c0 | 7,455 |
def get_exact_match(user_input, groups):
"""Return an exact match from the groups
"""
lower_groups = [group.lower() for group in groups]
if user_input.lower() in lower_groups:
return groups[lower_groups.index(user_input.lower())] | 8d28c05106f308bc3f65e07b011003e968cee99d | 7,456 |
def compute_perc_id(aln):
""" Compute percent identity of aligned region on read """
length = len(aln.query_alignment_sequence)
edit = dict(aln.tags)['NM']
return 100 * (length - edit)/float(length) | 7bc172649a452fc0c26d4e40d3240d709fb76534 | 7,457 |
def pipeline_dict() -> dict:
"""Pipeline config dict. Updating the labels is needed"""
pipeline_dict = {
"name": "german_business_names",
"features": {
"word": {
"embedding_dim": 16,
},
},
"head": {
"type": "TextClassification"... | 9505692f13759f392b930dff33ecf7ff781dcd9c | 7,458 |
def get_name_from_filename(filename):
"""Gets the partition and name from a filename"""
partition = filename.split('_', 1)[0]
name = filename.split('_', 1)[1][:-4]
return partition, name | 606cfcc998c4a8405c9ea84b95b2c63f683dd114 | 7,459 |
def process_html(html_page, this_parser):
""" extract links from an html page """
this_parser.feed(html_page)
return {
"int_links": this_parser.int_links,
"ext_links": this_parser.ext_links,
"static_links": this_parser.static_links
} | abd380ae2738bb98fdab4b5026d5cb9bdaa76efa | 7,461 |
import torch
def _set_device(disable_cuda=False):
"""Set device to CPU or GPU.
Parameters
----------
disable_cuda : bool (default=False)
Whether to use CPU instead of GPU.
Returns
-------
device : torch.device object
Device to use (CPU or GPU).
"""
# XXX we might ... | 1d7d448dd4e4a844201b73c8da4939009e70eb5f | 7,462 |
from pathlib import Path
import yaml
import json
def print_results_from_evaluation_dirs(work_dir_path: Path, run_numbers: list,
print_results_only: bool = False) -> None:
"""Print the aggregated results from multiple evaluation runs."""
def float_representer(dumper, val... | 4be2d893da5f321390c4b49cd4283c0b6f98b4d5 | 7,463 |
import os
def safe_quote_string(text):
"""
safe_quote_string(text)
returns the text in quotes, with escapes for any quotes in the text itself
text - input text to quote
returns: text in quotes with escapes
"""
if os.sep != '\\':
text2 = text.replace('\\', '\\\\')
text3 = text2.replace('"', '\\"')
el... | bcbf74e8b27ab9a76564c82fbb64110c400f5493 | 7,464 |
def find_max_burst(burst_list: list, offset_start, offset_end):
"""[summary]
Args:
burst_list (list): [description]
offset_start ([type]): [description]
offset_end ([type]): [description]
Returns:
[type]: [description]
"""
burst_levels = set()
burst_lev... | 75a15acf96324cafc806a1664e89054f6ade74d2 | 7,465 |
def center_text(baseline, text):
"""Return a string with the centered text over a baseline"""
gap = len(baseline) - (len(text) + 2)
a1 = int(gap / 2)
a2 = gap - a1
return '{} {} {}'.format(baseline[:a1], text, baseline[-a2:]) | c5683198cf1f28a38d307555943253bd71fe76de | 7,466 |
def _compute_teleport_distribution_from_ratings(user_rating, all_movies):
"""
returns the teleporting distribution as explained in the homework
if a movie M has been rated, its probability is: RATE_M / SUM_OF_ALL_RATINGS
else, its probability is: 0
:param user_rating: a dict of (movie_id, rating)
:param all_movie... | 7a88cf8a69c9fafc70e14d9337f0af25829bfb20 | 7,471 |
import ntpath
def path_base_and_leaf(path):
"""
Splits path to a base part and a file or directory name, as in the following example:
path: '/a/b'; base: '/a'; leaf: 'b'
"""
head, tail = ntpath.split(path)
if not tail: # in case there is trailing slash at the end of path
return {'base... | 956daa06f87cc60c8e304fa129fb86e49c4776ce | 7,472 |
import re
import zipfile
def instance_name_from_zip(path):
"""Determines the instance filename within a SEC EDGAR zip archive."""
re_instance_name = re.compile(r'.+-\d{8}\.xml')
for name in zipfile.ZipFile(path).namelist():
if re_instance_name.fullmatch(name):
return name
raise Run... | 59b2154d433e500e9b0cdf39ee70d4c058da1d06 | 7,475 |
import argparse
def get_arguments():
"""Obtains command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--inputs',
required=True,
nargs='+',
metavar='INPUT',
help='read Q2 inputs from Feather files %(metavar)ss')
parser.add_argumen... | 118e76d633bf5ea37b85a53dcd5ba07cf7e46e04 | 7,476 |
import re
def headers_ok(meta):
"""check that headers are 'name' or end with c/d/ll"""
meta_fh = open(meta)
headers = meta_fh.readline().rstrip().split('\t')
return headers[0] == 'name' and \
all(map(lambda s: re.search(r'\.(c|d|ll)$', s), headers[1:])) | 408975c795de8bf22529cf917ca881ca98ede4f9 | 7,477 |
import re
def has_number(name):
"""判断名name内是否出现了数字（包括中文的数字）"""
if bool(re.search(r'\d',name)):
return True
num_str = ['一','二','三','四','五','六','七','八','九','十']
for s in num_str:
if s in name:
return True
return False | 56dec9664e945d852cbfee4791f386aaab15f215 | 7,478 |
def cohort_to_int(year, season, base=16):
"""cohort_to_int(year, season[, base])
Converts cohort tuple to a unique sequential ID.
Positional arguments:
year (int) - 2-digit year
season (int) - season ID
Keyword arguments:
base (int) - base year to treat as 0
Returns:
... | 1f1981eb6c43ab6f77abf6d04ba3b92d9053953d | 7,479 |
def dict_of_transition_matrix(mat):
""" Convert a transition matrix (list of list or numpy array) to a dictionary mapping (state, state) to probabilities (as used by :class:`pykov.Chain`)."""
if isinstance(mat, list):
return {(i, j): mat[i][j] for i in range(len(mat)) for j in range(len(mat[i]))}
el... | b823ff496a751f4ffe305a31f1c1d019f7a25d33 | 7,481 |
def save_file(filename, contents):
"""Save a file from the editor"""
if not filename:
return 0, 0
with open(filename, 'w') as f:
f.write(contents)
return len(contents), hash(contents) | 8e973f67a22a2e7b0836f8db25090c65238492e3 | 7,482 |
from typing import Tuple
import hashlib
def hashfile(path: str, blocksize: int = 65536) -> Tuple[str, str]:
"""Calculate the MD5 hash of a given file
Args:
path ()str, os.path): Path to the file to generate a hash for
blocksize (int, optional): Memory size to read in the file
... | e38e6622534f27bed109a2e2b71373503ca4e7b0 | 7,483 |
import pathlib
def package_data() -> pathlib.Path:
""" Returns the absolute path to the circe/data directory. """
return pathlib.Path(__file__).parents[1].joinpath("data") | 19d8fa28ba872f8633e6efddb310d30264d831e6 | 7,484 |
import re
def changeFileNoInFilePath(path: str, fileNo: int) -> str:
"""replaces the number in the path with the given number."""
separator = r"[0-9]+\."
splitted_path = re.split(separator, path, 1)
new_path = splitted_path[0] + str(fileNo) + "." + splitted_path[1]
return new_path | 070fbe30d2937b57ef601fb764cf68ec219b9c95 | 7,485 |
def secret_view(request):
"""Dummy view with redirect to login."""
return {} | 9b0c3a6d2fe0b6aef2328a97d6407b72f8bc3c16 | 7,487 |
def construct_organisation_role_dict(organisation_roles):
"""Return a dict with 3 keys: organisations, roles, and organisation_roles.
Args:
organisation_roles: an iterable of OrganisationRoles.
"""
data = {}
# Defensive programming: make sure we have a unique set of
# organisation_rol... | b1105832eab9ee89dfa5d1e43b51c05fb569b953 | 7,489 |
async def is_dark_theme(monitor=None, app=None):
"""Return whether or not iTerm2 theme is dark"""
theme=None
if monitor:
theme = await monitor.async_get()
elif app:
theme = await app.async_get_variable("effectiveTheme")
else:
raise ValueError('Need a monitor or app instance t... | 8c356514d19219af83a36f422d78e688351c9f09 | 7,490 |
import json
def load_json(path):
"""Load json from file"""
json_object = json.load(open(path))
return json_object | 17db7327b6dac16aaeaff2354f828646eff695b2 | 7,491 |
import os
def module_path():
"""Figures out the full path of the directory containing this file.
`PACKAGE_DIR` becomes the parent of that directory, which is the root
of the solvertools package."""
return os.path.dirname(__file__) | f4576fbdcca394f525b9419a7e9d8a81ed1ff223 | 7,492 |
def _bin_labels_to_segments(bin_labels: list) -> list[tuple]:
"""
Convert bin labels (time-axis list data) to segment data
>>> _bin_labels_to_segments(['female'] * 5 + ['male'] * 10 + ['noise'] * 5)
[('f', 0, 5), ('bbb', 5, 15), ('v', 15, 20)]
"""
if len(bin_labels) == 0:
return []
... | 6b0eafdaf6affee33a3b655ba8ae7aebf2b38746 | 7,493 |
def build_efficiencies(efficiencies, species_names, default_efficiency=1.0):
"""Creates line with list of third-body species efficiencies.
Parameters
----------
efficiencies : dict
Dictionary of species efficiencies
species_names : dict of str
List of all species names
default_e... | a8f8912cd290b86697c67465b4aed18220a8c889 | 7,494 |
def generate_county_dcids(countyfips):
"""
Args:
countyfips: a county FIPS code
Returns:
the matching dcid for the FIPS code
"""
if countyfips != 59:
dcid = "dcid:geoId/" + str(countyfips).zfill(5)
else:
dcid = "dcid:country/USA"
return dcid | ae294e5467b9c735e175d4a69ff30f8ca189c71f | 7,495 |
def _inverse_lookup(dictionary, value):
"""Does an inverse lookup of key from value"""
return [key for key in dictionary if dictionary[key] == value] | 4ad34b27fbc35b3bae95bcb8442d1a2f7df94e9f | 7,496 |
import os
def get_expected_returncode(filename):
"""
Reads expectrc file to determine what the expected
return code is
"""
expected_rc = 0
expected_rc_file = filename + '.expectrc'
if os.path.isfile(expected_rc_file):
with open(expected_rc_file) as f:
expected_rc = int(... | ba52ccaa3e34ed823a9a5f55c3afdcb9c59abc6f | 7,497 |
def isentropic_beta(tab, spec, *XYf):
"""Isentropic bulk modulus"""
return XYf[0]*tab.q['Cs2', spec](*XYf) | 3592e31612c94e0000d80f8be13369338fb6b329 | 7,498 |
import os
def urllist():
"""加载图片链接
"""
list_file = os.path.join('piclist/baidu.txt')
url_list = []
with open(list_file, 'r') as f:
url_list = [line.strip() for line in f]
return url_list[:50] | 38c87fb70fc303bf49f09dac2e5f37b9c7ca03ce | 7,499 |
def checkdeplaid(incidence):
"""
Given an incidence angle, select the appropriate deplaid method.
Parameters
----------
incidence : float
incidence angle extracted from the campt results.
"""
if incidence >= 95 and incidence <= 180:
return 'night'
elif incidence... | 806ef360e7b5b3d7138d88be2f83267e7668d71e | 7,501 |
def error_test(true, predict):
"""
Function for classifcation of errors.
"""
if true == predict:
return 1
else:
return 0 | 87c1a56ffb52e1cec61a9ce3cab870b7c70fa059 | 7,502 |
def determine_high_cor_pair(correlation_row, sorted_correlation_pairs):
"""Select highest correlated variable given a correlation row with columns:
["pair_a", "pair_b", "correlation"]. For use in a pandas.apply().
Parameters
----------
correlation_row : pandas.core.series.series
Pandas seri... | 36eccfe0ffb0ac43caf49fe4db8c35c58d0fa29c | 7,503 |
def get_file_content(file_name):
"""获取文件的内容"""
try:
f = open(file_name, 'rb')
file_content = f.read()
f.close()
except Exception as e:
print("打开文件异常")
else:
return file_content | 329eb747a6513899b7ccfaf70754fad721f8d88d | 7,504 |
def create_nonlocal_gateway_cluster_name(namespace: str) -> str:
"""Create the cluster name for the non-local namespace that uses a gateway."""
return "remote-{0}-gateway".format(namespace) | 9ca9758a7ee68ede6e57a7f50f2d772b45ee844b | 7,507 |
def get_go2parents_isa(go2obj):
"""Get set of immediate parents GO IDs"""
go2parents = {}
for goid_main, goterm in go2obj.items():
parents_goids = set(o.id for o in goterm.parents)
if parents_goids:
go2parents[goid_main] = parents_goids
return go2parents | 1a7d79e1233e497dce109690d3e2105f442bc3b9 | 7,508 |
from typing import Union
from typing import Dict
from typing import List
def flatten_errors(error_message: Union[Dict, List, str]) -> str:
"""Flatten Cerberus' error messages."""
def flatten_dict(error_dict: Dict) -> str:
"""Return a string version of the dict."""
return ", ".join([f"{key}: {f... | ab6f359cc214a5a1929254e6066fdc6740938f09 | 7,509 |
import os
import pickle
def get_best_estimators(classification):
"""
Loads the estimators that are pickled in `grid` folder
Note that if you want to use different or more estimators,
you can fine tune the parameters in `grid_search.py` script
and run it again ( may take hours )
"""
grid_di... | ac35183c83ce90c0f13c5237634735f08a5d644f | 7,510 |
def get_subset(container, subset_bounds):
"""Returns a subset of the given list with respect to the list of bounds"""
subset = []
for bound in subset_bounds:
subset += container[bound[0]: bound[1]]
return subset | 4932ecba987c4936f9f467f270c6c07fd8681840 | 7,511 |
def _encode_decimal128(name, value, dummy0, dummy1):
"""Encode bson.decimal128.Decimal128."""
return b"\x13" + name + value.bid | f0d72f1fdef51559eb66dd2ac65ba6a43a91bf85 | 7,512 |
from typing import OrderedDict
def _get_exp_uri():
"""Return expected basic result for OpenUri action."""
return OrderedDict(
(
("@type", "OpenUri"),
("name", "Open URL"),
(
"targets",
[OrderedDict((("os", "default"), ("uri", "http://... | a3e527a52769083fd7adb8cf04a27ebcfc272922 | 7,514 |
import getpass
def get_html_connector_kwargs_options_from_args(args):
"""Take a parsed ArgumentParser and return a dict of argument."""
if not args.password:
args.password = getpass.getpass(
"Please enter the password for {} with login {}:\n".format(args.url, args.login)
)
retu... | d96f070363c6db33632974028b6363cbad739537 | 7,516 |
def yesnoquery(message):
"""
Displays `message` and waits for user Y/N input.
Returns Boolean where true means Y.
"""
useryn = None
while useryn is None:
if not isinstance(message, str):
raise ValueError("Must pass a valid string to query")
useryn =... | 87ec3cb01e4a2e52ce1cd900e5446cbab9a05373 | 7,518 |
def formatProccessingTime(ss, verbose: int = 1, estimate: bool = True, keep_seconds=False):
""" Format processing time to string
Args:
ss: Time in seconds or a string
"""
if isinstance(ss, (str, bytes)):
res = ss
else:
if ss < 0:
res = '-1'
elif ss < 60:
... | a2bea60365530169013322f8a767c9da3cc44c31 | 7,519 |
def lorenz(xyz, t, sigma, beta, rho):
"""The most famous of the strange attractors."""
x, y, z = xyz
dx = sigma * (y - x) # dt
dy = x * (rho - z) - y # dt
dz = x * y - beta * z # dt
return dx, dy, dz | 4241c36b8d4b924289edaa522a49855949208327 | 7,520 |
import copy
def add_classes_to_geojson(geojson, class_map):
"""Add missing class_names and class_ids from label GeoJSON."""
geojson = copy.deepcopy(geojson)
features = geojson['features']
for feature in features:
properties = feature.get('properties', {})
if 'class_id' not in properti... | 9aadf15fbe64995e7e52b2f6182e76ab722f06b5 | 7,521 |
from pathlib import Path
import errno
import argparse
def is_file_ro(filename: Path) -> Path:
"""Verifies file exists and can be open for read-only
Args:
filename (Path): path/filename to check
"""
try:
with open(filename) as f:
f.read()
f.close()
r... | 48082137363ac60b8c6666b0236d5f4314daf47d | 7,522 |
import re
def replace_urls(text, filler='<url>'):
"""Replaces URLs in text with `f' {filler}'`.
Potentially induces duplicate whitespaces.
Includes punctuation in websites
(which is not really a problem, because URLs on Twitter are rendered
as https://t.co/randomnum).
The regex doesn't account... | 0556120c0b8ab8a888acad550cb4cad24c5961ae | 7,523 |
def solution(X, A):
"""Find the earliest time that a frog can jump to position X.
In order to reach X, a leaf must be present at every position from 1 to X.
Args:
X (int): The position that the frog must reach.
A (list): A list of integers from 1 to X, where A[k] represents a leaf
... | d1fec5a3ec4c6dc06cd0feab295c90cb4c920ced | 7,524 |
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