content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def service_unavailable(request,template_name='503.html'):
"""
Default service unavailable view
"""
return TemplateResponse(request,template_name,status=503) | e474d5780bff381a6365fe9c8e28416c7a689708 | 52,900 |
from datetime import datetime
def utcnow_str():
""" Return a new datetime string representing UTC day and time. """
return time2str(datetime.datetime.utcnow()) | 1008efc7536880a389913f5dd3a03c157323dc2a | 52,901 |
def accuracy(output, target, topk=(1,)):
"""
Computes the precision@k for the specified values of k
Parameters
----------
output : pytorch tensor
output, e.g., predicted value
target : pytorch tensor
label
topk : tuple
specify top1 and top5
Returns
-------
... | 018d19d70084c9e1f4f5269f4cf6c444a41e016b | 52,902 |
def compute_divide(z, w):
"""
Compute `z` divided by `w`.
:param z: MathsComplexNumber object to be divided.
:param w: MathsComplexNumber object to divide by.
:return: MathsComplexNumber object of `z` divided by `w`.
"""
# z = a + bj
# w = c + dj
a, b = z.re, z.im
c, d = w.re,... | 0740446226dbc52273c7dcdc0e7c1cf1deb8df70 | 52,903 |
import six
def _build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label):
"""Builds a clone of DeepLab.
Args:
inputs_queue: A prefetch queue for images and labels.
outputs_to_num_classes: A map from output type to the number of classes.
For example, for the task of semantic segmentation wi... | 5ff0a26a68355eff7a940534c79a8edcb39fe981 | 52,904 |
from datetime import datetime
def clean_up():
"""Clean up the cache.
Finish introspection for timed out nodes.
:return: list of timed out node UUID's
"""
timeout = CONF.timeout
if timeout <= 0:
return []
threshold = timeutils.utcnow() - datetime.timedelta(seconds=timeout)
uui... | d22423d9e9eb8d06e4f9887b9f4ee0a395e9a51d | 52,905 |
from typing import Type
from typing import Sequence
from textwrap import dedent
def create_triggers_sql(*, audit_logged_model: Type[Model]) -> Sequence[str]:
"""
Create the SQL requried to set up triggers for audit logging to the given
audit log entry model.
"""
# Get the model that we are audit ... | 3b5b5e3e236adb06f6f30961adb7ee4da5ad6d30 | 52,906 |
def _get_neighbors(loc, image, voxels, thresh, dist_params):
"""Find all the neighbors above a threshold near a voxel."""
neighbors = set()
for axis in range(len(loc)):
for i in (-1, 1):
next_loc = np.array(loc)
next_loc[axis] += i
if thresh is not None:
... | e7e1e7e7509609a40803fff0816d30a056824395 | 52,907 |
def is_partition(set_of_sets, alphabet):
"""
Determine whether `set_of_sets` partitions `alphabet`; that is,
is every element of `alphabet` represented exactly once in `set_of_sets`?
Parameters
----------
set_of_sets : a (frozen)set of (frozen)sets
The potential partition.
alphabet ... | dab446b6f9beefe4acc5c400f71749352ab81b46 | 52,908 |
def OMO(
directed = False, preprocess = "auto", load_nodes = True, load_node_types = True,
load_edge_weights = True, auto_enable_tradeoffs = True,
sort_tmp_dir = None, verbose = 2, cache = True, cache_path = None,
cache_sys_var = "GRAPH_CACHE_DIR", version = "2020-06-08", **kwargs
) -> Graph:
"""Ret... | c9e37f025788e8c1a2630e9ba9b7ee8f542f34dc | 52,909 |
def _render_dimensional_metric_cell(row_data: pd.Series, metric: Metric):
"""
Renders a table cell in a metric column for pivoted tables where there are two or more dimensions. This function
is recursive to traverse multi-dimensional indices.
:param row_data:
A series containing the value for t... | 36dce77d07b6581d0bf120f55f4bc3e1813c3deb | 52,910 |
import tqdm
def pca_init_dense(model, mu_dense_layer_name, undense_layer_name, generator,
input_len=None,
do_vae=True,
logvar_dense_layer_name=None,
nb_samples=None,
tqdm=tqdm,
vis=False):
"""
ini... | 4ae2d4248e75d0cee9dd7aa0d9783895d07f31fc | 52,911 |
def allocationsCount(memory):
"""Return the total number of allocations.
"""
return np.count_nonzero(memory['allocations'] > 0) | 171374eb60cc63f56c54b667eaadaa43cc611b38 | 52,912 |
def file_docker_pull_latest():
"""#! /usr/bin/env bash
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
source ${SCRIPT_DIR}/docker-environment-vars.sh
echo "${SENZING_HORIZONTAL_RULE}"
echo "${SENZING_HORIZONTAL_RULE:0:2} Pull ${SENZING_PROJECT_NAME} docker containers for DockerHub.... | b71227f2c95cd4a582528b632450cc60c29c4aa2 | 52,913 |
import os
import tempfile
import requests
import zipfile
import io
def load_data(path=None):
"""Downloads the Mendeley dataset to local storage, if not already downloaded. This will generate 2 csv files (train and test), which contain all the path
information.
Args:
path (str, optional): The ... | 79cf58c2686574e8091add87c355ee93548a18de | 52,914 |
def _get_annotation_type(annotation) -> AnnotationType:
"""
Returns the type of a given annotation.
Annotation can be a BinaryBuffer, TypedBuffer or something else
"""
if isinstance(annotation, list):
if len(annotation) == 2:
return AnnotationType.BINARY_BUFFER
elif len... | 1b499a6137d7378661b521bbc94ed574994a3c9d | 52,915 |
import ast
def is_obvious_builtin(node, value):
# type: (ast.expr, Any) -> bool
"""
Return True if this node looks like a builtin and it really is
(i.e. hasn't been shadowed).
"""
return ((isinstance(node, ast.Name) and
node.id in builtins_dict and
builtins_dict[node.... | 8a404b5cc84a6ea19edf60e3211497002426ede6 | 52,916 |
import requests
import sys
from bs4 import BeautifulSoup
import json
def extract_problems(cfg, login_reply, filename='kattis'):
""" Stores solved prolbems and stats in .json file """
data = {}
solved = []
header_url = get_url(cfg, '', 'problems')
try:
result = submissions(header_url, login... | b3a547cc88503f45e209fd560d783ae7c9eb782e | 52,917 |
def insert_records(conta, data):
"""
Recebe o objeto e inseri a data no banco de dados. A data são os logs de exibições recebidos do manager.
param: conta : Account:
data: list of dicts:
return: records: int: Quantidade de registros inseridos
"""
records = 0
i = len(data) - 1
... | a136fd7bda8d0c32deb45290661574735d9faf2f | 52,918 |
def sample_2d_no_compete(alternatives, choosers, sample_size, capacities=None):
"""
Samples alternatives in a manner that guarantees that capacities will
be respected: the maximum # of times a given alternative can appear
in the sample (across choosers) is equal to its capacity.
Use this for the la... | d96dd35c7c03ddb39784c91bd8efc989c5226f26 | 52,919 |
from typing import Sequence
def notch_filter():
""" implements a notch filter with notches at 1/week and 2/week; assuming input signal sampling rate is 1/week"""
fs, f0, Q = 1, 1/7, 1
b1, a1 = iirnotch(f0, Q, fs)
b2, a2 = iirnotch(2*f0, 2*Q, fs)
# Frequency response
b = convolve(b1, b2)
a ... | 1a7162d24124c177e1ec8b76bb0d424a8a3ccbb4 | 52,920 |
def monthly_file_name(var, model, rcp):
"""Function for creating file connections for different variables
scenarios and models. Preasently, ensemble member r1i1p1 is assumed, as
well as 200601-210012 dime span for the monthly data."""
# e.g. hfls_Amon_CNRM-CM5_rcp85_r1i1p1_200601-210012.nc
f = var + "_" + "Amon_... | 7aeea6d3724c470e076645ddb96877e9fa15843f | 52,921 |
def extract_sum_of_products(cluster, template, *args):
"""
Extract sub-expressions in sum-of-product form, and assign them to temporaries.
"""
make = lambda: Scalar(name=template(), dtype=cluster.dtype).indexify()
rule = q_sum_of_product
costmodel = lambda e: not (q_leaf(e) or q_terminalop(e))
... | b5eab74b889dfdf528677170fe0c5b0c5828e4d1 | 52,922 |
def minimize(_mol, nsteps, opttol, func, minimizer):
""" Minimizes a single molecule
Arguments:
nstesp -- perform a maximum of nsteps steps
opttol -- the maximum rms gradient shall be below this value
func -- energy and gradient function
minimizer -- a minimizer
"""
... | e465c415066c0619a7fbc84656e32eeddf21ac8e | 52,923 |
from re import DEBUG
def _init_logger():
"""Initialize the Bempp logger."""
_logging.addLevelName(11, "TIMING")
logger = _logging.getLogger("bempp")
logger.setLevel(DEBUG)
logger.addHandler(_logging.NullHandler())
return logger | 9cb78a26c88d08b1dd7d7103791769aaf6ed8dfc | 52,924 |
import requests
def request(method, url, **kwargs):
"""
Central place to make any v1 api request.
:param (str) method: The request method ('get', 'put', 'delete', ...).
:param (str) url: The full api url to make the request to.
:param kwargs: These are passed along to requests.
:return: (requ... | ca3bc5e5607d1b3dbebcfa92c8532dee9ab2b673 | 52,925 |
import tqdm
import torch
def single_epoch_train(model, data_loader, criteria, optimizer, device, pad_idx, args, epoch=None):
"""
:param model:
:param data_loader:
:param criteria: (dict) holding all modules for computing the losses.
:param optimizer:
:param device:
:param pad_idx: (int)
... | 8c387fc4ab0bb980b60df2a324d45c6a524c98e1 | 52,926 |
from typing import Callable
import cloudpickle
def default_key_provider(func: Callable, *args, **kwargs) -> str:
"""
Default cache key function. This uses cloudpickle to hash the function and all arguments.
"""
return cloudpickle.dumps({"function": func, "args": args, "kwargs": kwargs}) | 693129568a45270d48c1cedb6995d07abd875715 | 52,927 |
import warnings
def fips_to_location_id(fips: str) -> str:
"""Converts a FIPS code to a location_id"""
try:
return dataset_utils.get_fips_to_location().at[fips]
except KeyError:
# This happens mostly (entirely?) in unittest data where the first two digits
# are not a valid state FI... | d1478f36a39534a22860b8382fc4808cd0deedff | 52,928 |
from datetime import datetime
import pytz
def check_asset_availability(start=None, end=None):
"""
Am I within the Asset's availablity window?
If "start" is defined, now >= start must be True
If "end" is defined, now <= end must be True.
Otherwise each condition is presumed TRUE
(e.g., n... | 12c2480c51c8656f46b905f454620c73c826a6f5 | 52,929 |
def calc_distance(l):
"""Calculates distance between list items in two lists"""
# l needs to start from zero, get lowest number and substract it from all numbers
min_l = min([x[1] for x in l if x[1] != ''], default=0)
l = [[x[0], (x[1] - min_l)] for x in l if x[1] != '']
distance = 0
for id... | 57d52eb4bb4b772024d68cb397f2e315825a25df | 52,930 |
def load_archive(task_name: str, hparam_set_name: str):
"""Load a precomputed archive file for `task_name` and `hparam_set_name`."""
path = get_baseline_archive_path(task_name, hparam_set_name)
return read_npz(path) | 69cf98f9e7193bb60aa8a5c17e9574289c73e7c4 | 52,931 |
import sys
import fileinput
def use_learned_model(model_file: str, remove_pattern: str = "", replace_pattern_file: str = "") -> pd.DataFrame:
"""標準入力から読み込んだ文字列に対して学習済みモデルによる推論を実施
:param model_file:
:param remove_pattern:
:param replace_pattern_file:
:returns:
:rtype:
"""
print(... | a9b65f7529ef901688ea9d00037c0e7cb0380c3c | 52,932 |
import torch
from typing import Tuple
def get_explanation_accuracy(model:torch.nn.Module,loader:torch.utils.data.DataLoader,mem_loader:torch.utils.data.DataLoader,device:torch.device)-> Tuple[float, float, float]:
""" Method to compute the explanation accuracy for different settings, as
described in the paper... | 2c90a5df55925016c85b6c494cee0279cddfdb41 | 52,933 |
import locale
import warnings
def locale_convert(key):
"""Creates a converter for a locale key."""
def lc_converter(val):
try:
locale.setlocale(LOCALE_CATS[key], val)
val = locale.setlocale(LOCALE_CATS[key])
except (locale.Error, KeyError):
msg = 'Failed to ... | 54f45d2290c47e9773a269f9cd3e65ebce8a233d | 52,934 |
from datetime import datetime
def get_offer_info(soup, offer_id):
"""get information about a specific offer according to its id
information: title, company name, published date, link
Parameters
----------
soup: BeautifulSoup
html code
offer_id: str
offer identifier
Retur... | 516c555a86e40e2b9e45e05a7994e39c5ae9ae23 | 52,935 |
def to_csv(raw_data):
"""Converts a serialized TfStatsDatabase string to CSV."""
tf_stats_db = tf_stats_pb2.TfStatsDatabase()
tf_stats_db.ParseFromString(raw_data)
return generate_chart_table(tf_stats_db.with_idle,
tf_stats_db.device_type).ToCsv() | eecd980878849ec5853662fe647cb9dbe3ec98a3 | 52,936 |
def lam(x, lam0, alpha=4.0):
"""Return classic alpha model lambda value(s) for input value(s)."""
return lam0 * ( 1.0 - x**alpha ) | 37a10d890acc2b983c09300b865e58fe9dc2f7fe | 52,937 |
def Add(left: Expr, right: Expr) -> BinaryExpr:
"""Add two numbers.
Produces left + right.
Args:
left: Must evaluate to uint64.
right: Must evaluate to uint64.
"""
return BinaryExpr(Op.add, TealType.uint64, TealType.uint64, left, right) | 27ba874d66b57669f6ec332e86530f40b022a637 | 52,938 |
def reduceCoefficients(polynomial : Polynomial) -> Polynomial:
"""
Initialize a Polynomial for the Kerner method:
Make the leading coefficient 1
"""
eq = polynomial.eq()
h_coeff = eq.get(max(eq))
return Polynomial([
coefficient / h_coeff for coefficient in polynomial.arr()
... | d31c538801c3a7472eb6e52b17421b783f239a0f | 52,939 |
def count():
"""
查询记录总数
"""
return Record.objects.owner().count() | 0e4466da6c3a4c1f549bcdbafa0b76233daa4a40 | 52,940 |
import os
def campbell(caseFile, mainFst, tStart, nPerPeriod, workDir, toolboxDir, matlabExe, fastExe,
baseDict=None, generateInputs=True, runFast=True, runMBC=True, prefix='',sortedSuffix=None, ylim=None):
"""
Wrapper function to perform a Campbell diagram study
see: writeFASTLinInputs... | ed6c2779c2d2cc0fd715e817d9f8a075d1be4179 | 52,941 |
import os
def get_files_time_period(prefix, yr_s, yr_e):
"""
Get nc files within the time period, with the prefix
:param prefix: prefix of file names
:param yr_s: start year
:param yr_e: end year
:return: files in folder, max and min year
"""
# Get path and folder
path = directori... | 35e0bb6e223882d11a63b21f6c4302cad7054b2e | 52,942 |
def subset(part, whole):
"""Test whether `part` is a subset of `whole`.
Both must be iterable. Note consumable iterables will be consumed
by the test!
This is a convenience function.
Examples::
assert subset([1, 2, 3], [1, 2, 3, 4, 5])
assert subset({"cat"}, {"cat", "lynx"})
... | 4200ea95e0c9ff03d0b7589812d0313c5d13cfac | 52,943 |
def kl_categorical_uniform(preds):
"""KL divergence between categorical distribution and uniform prior."""
kl_div = preds * jnp.log(preds + EPS) # Constant term omitted.
return kl_div.sum(1) | 311cae987a9e43742d036fe9b23f37654987264f | 52,944 |
def remove_proxy_vcs(session, quantity, return_type=None, **kwargs):
"""
Removes proxy vcs.
:type session: zadarapy.session.Session
:param session: A valid zadarapy.session.Session object. Required.
:type quantity: int
:param quantity: The number of vcs to be removed. Required.
:type re... | 2d5e05f338e0414e0ed5fe6c5eb567aaf8f01f9f | 52,945 |
from pathlib import Path
def stem(fname, include_suffix=False):
"""/blah/my_file.json.gz --> my_file"""
path = Path(fname)
stem = path.stem
# If a filename has multiple suffixes, take them all off.
stem = stem[: stem.index(".")] if "." in stem else stem
if include_suffix:
stem = stem... | 9e4c557dff8583f9129215f91f8e123f062ccf52 | 52,946 |
def compile(fmt, names=None):
"""Compile given format string `fmt` and return a compiled format
object that can be used to pack and/or unpack data multiple times.
Returns a :class:`~bitstruct.CompiledFormat` object if `names` is
``None``, and otherwise a :class:`~bitstruct.CompiledFormatDict`
objec... | 00c4596739e65295b579002917ddfbaee6a7390b | 52,947 |
import sqlite3
def username_lookup(userName):
""" Lookup user from availability.db by unique username """
# connect to db
conn = sqlite3.connect("availability.db")
# create cursor
c = conn.cursor()
# check if lookup initiated for specific user or for all users
if userName:
# quer... | 36c0927a79791eca8d69f574799490c716f2fe05 | 52,948 |
def allInfoTests(P, inIter=10, outIter=10):
"""
performs information theoretic tests on a tripartite behaviour
"""
results = []
# get marginal for X,Y
l = len(P.shape)
t = tuple(np.arange(2,l))
m = pr.marginal(P, t)
# calculate mutual information of the marginal X,Y
mutInf =... | ecf3f2a2e2fd2ba29847e6ae5d883aced5a1f8df | 52,949 |
def Insert(head, data):
"""
>>> head = None
>>> node = Insert(head, 4)
>>> assert(node.data == 4)
>>> head = Node(6)
>>> node = Insert(head, 4)
>>> assert(node.next.data == 4)
>>> head = Node(6, Node(7))
>>> node = Insert(head, 4)
>>> assert(node.next.next.data == 4)
"""
... | 3372a20ba7a9b6e80b07fadedce9bfb12baed4e9 | 52,950 |
def important_features(model):
""" Get the most important features from some model """
invalid_features = ["count_","tf_idf_","word2vec_"]
feat_importances = pd.Series(model.feature_importances_, index=features).to_string()
feat_importances = [i for i in feat_importances if not any(feat in i for feat i... | 22880b50b83f08c224f1e1bd0cc7032f2b6ccd0b | 52,951 |
def get_backend(config):
"""
:param app: celery instance
:type app: celery.Celery
"""
klass = config.backend_class
kwargs = config.backend_kwargs
url = config.backend_url
return klass(url, **kwargs) | 57d6c82f99888ae992503492a8edfa802315a3cc | 52,952 |
def affine(f, T):
"""Applies the affine transformation on a given
image.
' g(r, c) = f(T^{-1}(r,c)) '
Direct mapping:
From image (f) map the values to image (g)
- g(T(r,c)) = f((r,c)) ,
(r,c) in [0,H-1]x[0, W-1]
- con: T(r,c) might not fill every pixel of G
Indirect Mapping
... | 593617f5296361ac7d797e2f804eb12dea347e1c | 52,953 |
def atol(s, base=None): # real signature unknown; restored from __doc__
"""
atol(s [,base]) -> long
Return the long integer represented by the string s in the
given base, which defaults to 10. The string s must consist
of one or more digits, possibly preceded by a sign. If base
is 0, it i... | f39aa403cbad98c448a35305c97c595bc3a77c02 | 52,954 |
def open_or_std(file, std, *args, **kwargs):
"""
Opens the file in the specified mode unless file is STD_IDENTIFIER,
in which case it returns std.
"""
if file == STD_IDENTIFIER:
return std
return open(file, *args, **kwargs) | dd8b08569ff6ee702864fe453e13d3e8f7497267 | 52,955 |
from typing import Tuple
from typing import Optional
def translate(
parsed_symbol_table: parse.SymbolTable,
atok: asttokens.ASTTokens,
) -> Tuple[Optional[SymbolTable], Optional[Error]]:
"""Translate the parsed symbols into intermediate symbols."""
underlying_errors = [] # type: List[Error]
def ... | 7e293389b51fdc9f05ef047c82bd5a0362cb3c01 | 52,956 |
def tokens_to_sovatoms(tokens: float) -> int:
"""Convert tokens to sovatoms."""
return int(tokens * 100000000) | 23f4bc3a1afc4520b5e2416332702d0a594c5039 | 52,957 |
def _frame_only(frame, skeleton_data):
"""
Save only one frame from skeleton data
"""
result = []
for raw_descriptor in skeleton_data:
if raw_descriptor[0] == frame:
result.append(raw_descriptor)
return result | 1581ddc52eedc81dbe2cc71ed66524b6af5f4b3d | 52,958 |
def sort_node_id(a, b):
"""
@param a, b: node pair to be sorted by id
@type treenode, treenode
"""
if a.id > b.id:
return 1
else:
return -1 | 2db33f63f009d4d50c922b4daf45dbac56a60312 | 52,959 |
def generate_steps(model_name, new_steps, generator='random(edge_coverage(100))'):
"""
This is the top level builder for making test steps.
Args:
model_name (str): The name of the model file - no extension needed.
new_steps (bool): Whether to recalculate the model steps.
generator (... | c825ea6fe81a9bdfe436a7382b564729fae585d7 | 52,960 |
def collect_trajectories(env,
policy_fun,
num_trajectories=1,
policy=env_problem_utils.CATEGORICAL_SAMPLING,
max_timestep=None,
boundary=20,
epsilon=0.1,
... | 77af43962dec2ba236ceb7a33fbcbc51b410a9dc | 52,961 |
from datetime import datetime
def request_directions(start_location, end_location):
"""Request directions from start_location to end_location.
Args:
start_location: tuple of floats (lat, lon) for starting point
end_location: tuple of floats (lat, lon) for ending point
Returns:
a list of directions ... | a4e2208f18fb885d083f28aafcb2e48529b9e3fe | 52,962 |
def __Boundary_outside__(self):
"""Is the boundary is on the outside of the mesh."""
return self.leftCell() is not None and self.rightCell() is None | cce3ffadc5e2598beae8b320b4d2f5122d6de904 | 52,963 |
def create_dataset_lookup(py_obj):
""" What type of object are we trying to pickle? This is a python
dictionary based equivalent of a case statement. It returns the correct
helper function for a given data type.
Args:
py_obj: python object to look-up what function to use to dump to disk
... | 0a36c178c6ceccafe35f1023cbfe6eb293ad0ba3 | 52,964 |
def find_anagrams(s):
"""
:param s: search str
:return: a list of anagrams
"""
print('Searching...')
return find_anagrams_helper(s, '', []) | f636200d611d0a38a8cdfc8802f611a0fb6073b1 | 52,965 |
from typing import List
def follow_directions(start: Cell, directions: List[Direction]) -> Cell:
"""
Walk the ``directions`` from the ``start``.
:return: The final :py:class:`Cell` of the journey
"""
cursor = start
for direction in directions:
cursor = next_cell(cell=cursor, direction... | 4409a7ba6a2edf53e0f9aaeb2cbf90e96e72c5e5 | 52,966 |
def mongo_dump(server, args_array, **kwargs):
"""Method: mongo_dump
Description: Function stub holder for mongo_db_dump.mongo_dump.
Arguments:
(input) server -> Server instance.
(input) args_array -> Dictionary of arguments.
"""
status = False
err_msg = None
if server... | 895ad2d77282d1d5c65c54940a4f83b64023f92d | 52,967 |
def majority(taxa, th=0.8):
"""Select taxon from list by majority rule.
Parameters
----------
taxa : list of str
Input taxon list.
th : float, optional
Threshold of majority, range = (0.5, 1.0].
Returns
-------
str or None
Selected taxon.
"""
for taxon, ... | 0828b8704aeb1f7ced1446b563ba4f1f98634ffe | 52,968 |
def version(serial=None):
"""
Returns version information for MicroPython running on the connected
device.
If such information is not available or the device is not running
MicroPython, raise a ValueError.
If any other exception is thrown, the device was running MicroPython but
there was a... | b082467669fe00321f50406b1a94cd013d971469 | 52,969 |
from .matplotlib import plot_roc_curve as _plot_roc_curve
from .plotly import plot_roc_curve as _plot_roc_curve
def plot_roc_curve(fpr, tpr, roc_auc=None, backend="plotly", **kwargs):
"""Plots a receiver operating characteristic (ROC) curve.
Args:
fpr: an array of false postive rates
tpr: an ... | d4bcfe3d37478602a9a80435c5aa979fcb2143cc | 52,970 |
def send(draw, min_value=0, max_value=None):
"""Generates the send for Blast+6 file format.
Arguments:
- `min_value`: Minimum value of send to generate.
- `max_value`: Maximum value of send to generate.
"""
return draw(integers(min_value=min_value, max_value=max_value)) | 6b6cd9d5965cb3e114c61aeff76dca1bc3149d64 | 52,971 |
def flow_cell_mode(info_reads):
"""Return flow cell sequencing mode."""
res = ''
if info_reads:
read_lens = [
a['num_cycles'] for a in info_reads if not a['is_indexed_read']]
if len(read_lens) == 1:
res = '1x{}'.format(read_lens[0])
elif len(set(read_lens)) ==... | 0563d0c163c2bd42b05d07fb30940669a8ef3513 | 52,972 |
def dynamic_module_wrapper(wrapped_module: nn.Module):
"""
Wrapped module must locate on one single device, but could be moved around.
Input device and output device are automatically detected.
"""
wrapper = NeuralNetworkModule()
wrapper.add_module("wrapped_module", wrapped_module)
wrapper.... | 963200ae5abf305deb08b2e3a2b78e58d068ff75 | 52,973 |
def fixture_conf2():
"""Instance of loaded config_test2.ini."""
return md.FrontMatter("./tests/config_test2.ini") | 34b78a4a23fc253dea014152a3f20ec3a1c535ce | 52,974 |
import pycountry
import urllib
import requests
import json
def find_best_match(row, scopus_url):
"""Splits the name and surnames and tries to find some combination
of them, that still matches the country."""
# Prepare the parameters
csv_name = row['firstName'].replace(",","").replace(" or ", " ")
... | 42d9b2cd4a9cc2544337cad3e211a1d9bd3ab6d7 | 52,975 |
def _quote_dict_to_embed(quote: dict) -> discord.Embed:
"""Changes a given quote dict to an embed format
Args:
quote (dict): The quote in `dict` format
Returns:
discord.Embed: The embed itself.
"""
message_link = quote["Message Link"]
title = f"***Quote #{quote['Number']}***"
... | 18d4cfb36c20dcc016c9692c73bfe5b57928b5a5 | 52,976 |
def git_get_tags_by_referred_object(context: RepoContext, obj_name: str) -> list:
"""
:param context:
:param obj_name:
:rtype: list of Ref
"""
commit_tags = git_get_tag_map(context).get(obj_name)
return commit_tags if commit_tags is not None else [] | 59211ed55db6b446caae2c5ecade7bc956c2cb92 | 52,977 |
def sortKey(e):
"""
This sorts the chores based on their start time.
e[0] is the start time for all the chores in my array of chores.
"""
return int(e[0]) | 00d37751d23fe6210b406485cbd76906075b2578 | 52,978 |
def merge_into_dict(original, secondary):
"""Merge two dictionaries into the first and return it.
This is simply a conveinence wrapper around the dictionary update method. In
addition to the update it returns the original dict to allow for chaining.
Args:
original: The dict which will be updated.
seco... | 899d40396885f0775f2cbaa865702ed0e5706dba | 52,979 |
def drop_cols(dataset: pd.DataFrame,
cols: tuple = ('ConnID', 'Repseudonym',
'siteid', 'visdat', 'IDs', "prmdiag")) -> pd.DataFrame:
"""
Drops the columns which are not needed for further modelling
Args:
dataset: dataset on which the cols should be dropped... | f3a96d16d3da9c30980f72879142eb9c50736e2f | 52,980 |
def matchXandYbyIndex(clr_x, model_proc_df):
"""
This fxn drops rows in the design matrix & response vector
according to index equivalency.
"""
# drop all rows in x that don't match to y
x_bool = clr_x.index.isin(model_proc_df.index)
x_1 = clr_x[x_bool]
# drop all values in y that don't... | cf9b78e7e7ffb2ba4a18eca9ee74cca39b44946b | 52,981 |
def conic_sag(c, kappa, rhosq, phi=None):
"""Sag of a spherical surface.
Parameters
----------
c : float
surface curvature
kappa : float
conic constant
rhosq : numpy.ndarray
radial coordinate squared
e.g. for a 15 mm half-diameter optic,
rho = 0 .. 15
... | 4de6955ba1f1633ede0008743f95321c38649c93 | 52,982 |
def get_nba_player_name(div_name: list, tag_name: str, class_name: str) -> list:
"""For getting the names of NBA player and return the names as a list
Arguments:
div_name {list} -- [The div that contain all the players name]
tag_name {str} -- [The tag name which contain the name of the Play... | 39e4afd86eb8a2d27589953bf5614a4bdb8837bf | 52,983 |
def verify_author(author, connection):
"""Given an author id, check it."""
database = connection['test']
collection = database['users']
try:
post = collection.find_one({"_id" : ObjectId(author)})
except InvalidId:
post = None
if post is None:
return False
return T... | 35ad43b2c32c90787051766cfe9af02e034084ad | 52,984 |
import math
def quatfrom_m43(m):
""" Convert the top of an m33 matrix into a quaternion."""
(m0, m1, m2, m3, m4, m5, m6, m7, m8, _, _, _) = m
trace = m0 + m4 + m8 + 1
if trace > PRECISION:
w = math.sqrt(trace) / 2
x = (m5 - m7) / (4*w)
y = (m6 - m2) / (4*w)
z = (m1 - m3... | c8eb0d07c9e82c1ad32eb4c036c310f5ab1ce73f | 52,985 |
def simulated_get_actions(seed_value=0, number_created_agents=1):
""" Simulates the function which
"""
#Returns a random actions list for each robot
np.random.seed(seed_value)
action_list = list(np.random.randint(low=0, high=5, size=number_created_agents))
print(action_list)
return action... | a8d46342e1bb807fb51c7c4467b0ae1d572bcfd7 | 52,986 |
def readAlignments(filename, name):
"""
reads alignment from alignment file
"""
FOUND = False
alignment = ""
alignments = []
text = []
file = open(filename)
# searching for correct protein entry
while True:
line = file.readline()
if line == "":
break
... | 0e306e9d9009f82e564258e5894e973c72536826 | 52,987 |
def get_v_left(g_or_n, v_list):
"""Return vertices from graph not in v_list"""
V, n = get_set_vertices(g_or_n)
v_left = [x for x in V if x not in v_list]
return v_left | 175f47c3d0734d917604b68bfa102a08f947933a | 52,988 |
def normalize_obs(obs_dict, obs_normalization_stats):
"""
Normalize observations using the provided "mean" and "std" entries
for each observation key. The observation dictionary will be
modified in-place.
Args:
obs_dict (dict): dictionary mapping observation key to np.array or
... | c866b6d18df13b9e6903b7d96024d48cde99d2ff | 52,989 |
def get_closest_language(differences):
"""[summary]
Args:
differences ([list]): [contains tuples with language and difference score]
Returns:
[tuple]: [containing 3 closest possible languages to given input]
"""
# print(differences)
differences_sorted = sorted(differences, key=... | 55e231e249c8bae5116be0b2feac96d184a6cbee | 52,990 |
from typing import List
from typing import Optional
from typing import Dict
from typing import Union
from typing import Any
def apply_trace_attribute(log: EventLog, values: List[str], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> EventLog:
"""
Filter a log on the trace attribute values
... | e876620f0b4f94521a656f392647b6269c76bee5 | 52,991 |
def cross_product(X1, X2):
""" Cross product of features in matrices X1 and X2
"""
n = np.shape(X1)[0]
assert n == np.shape(X2)[0]
return (X1.reshape(n,1,-1) * X2.reshape(n,-1,1)).reshape(n,-1) | 56590019253d90beb97038b41dc901a5eb56cd40 | 52,992 |
def getAtomType(atom, bonds):
"""
Determine the appropriate atom type for an :class:`Atom` object `atom`
with local bond structure `bonds`, a ``dict`` containing atom-bond pairs.
"""
cython.declare(atomSymbol=str)
cython.declare(molFeatureList=cython.list, atomTypeFeatureList=cython.list)
... | fbe5a477d1c1cca11e3011107b08028267b03285 | 52,993 |
def impute_rare_titles(df: pd.DataFrame, tresh: int = 100) -> pd.DataFrame:
"""Substitutes rare titles with closest ones by Elo
Parameters:
thresh: threshold for title to be considered as rare
"""
for color in ['White', 'Black']:
title_column_name = f'{color}Title'
elo_column_name =... | c5c2ee6105b56e0a883f03328f388f00fbe5f64f | 52,994 |
from typing import Dict
import pandas
from typing import Counter
def calculate_most_mistaken_heatmap(label_classifiers: Dict[str, pandas.DataFrame], labels) -> pandas.DataFrame:
"""
Generates a 2d table describing how many times each column is mistaken for a given index.
:param label_classifiers: A numb... | 409d089945ebc9150f06152fbce60aa3b907cc2c | 52,995 |
from typing import Tuple
from typing import Dict
from typing import Any
def get_label_metadata(
app: App,
label: LabelRef,
state_machine: StateMachine,
) -> Tuple[Dict[str, Any], bool]:
"""Get the metadata and whether the label has been deleted."""
return app.session.query(Label.metadata, Label.de... | 8fe26e55240b0d8394f7e1d1a3a3041235c8b789 | 52,996 |
def copy_of_xml_element(elem):
"""
This method returns a shallow copy of a XML-Element.
This method is for compatibility with Python 2.6 or earlier..
In Python 2.7 you can use 'copyElem = elem.copy()' instead.
"""
copyElem = ET.Element(elem.tag, elem.attrib)
for child in elem:
cop... | 685c3ba214179a8958db7d1292642dd28796dbff | 52,997 |
def get_arrivals(knots: dict, func=None, *func_args, **func_kwargs) -> list :
"""
Generate a sequence from a nonhomogeneous Poisson process
specified by a piecewise linear function determined from
the knots parameter. The knots should specify the (domain, range)
of each segment's knot.
knots: dictionary where ke... | 627079cb9cb984e163c7fb0a5cb1c91147fac393 | 52,998 |
import copy
def align_trajectory(traj, traj_ref, correct_scale=False,
correct_only_scale=False, n=-1, return_parameters=False):
"""
align a trajectory to a reference using Umeyama alignment
:param traj: the trajectory to align
:param traj_ref: reference trajectory
:param corre... | 9e892d9336f8c7c2be9de9e29f2e22352e3f4740 | 52,999 |
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