content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import io
def inscription_summary(request, pk):
"""
Print a PDF summary of inscription
"""
candidat = get_object_or_404(Candidate, pk=pk)
buff = io.BytesIO()
pdf = InscriptionSummaryPDF(buff)
pdf.produce(candidat)
filename = slugify('{0}_{1}'.format(candidat.last_name, candidat.first_n... | aeedaaff96e3d08ca0dc503ac6641951a4fa804a | 29,011 |
def hello(name='persona', lastmane='exposito'):
"""Function that return 'name and last name'.
name: strung,
lastname:string,
return: string,
"""
if name != 'persona':
return f'¿Como estas {name}?'
return f'Hola {name} {lastname}' | 537ffb6beab2ad2c65c4cbf556738c9be1a8467d | 29,012 |
def get_num(prompt: str) -> float:
"""Function to check if users input is a num"""
while True:
try:
num = int(input(prompt))
return num
except Exception as e:
print(e) | 39fdc0588d20cc2268a98db0a729e7c6044e29a0 | 29,013 |
import random
def create_negative_mentions(doc, pos_mention_spans, neg_mention_count,
max_span_size, context_size, overlap_ratio=0.5):
""" Creates negative samples of entity mentions, i.e. spans that do not match a ground truth mention """
neg_dist_mention_spans, neg_dist_mention_... | 570c431a3a02505b3c81c2242ae4d3448a17f744 | 29,014 |
def show_item(item_id):
"""Show individual item."""
try:
item = db_session.query(Item).filter_by(id=item_id).one()
except:
flash("This item does not exist")
return redirect(url_for('index'))
# Make sure that user is authorised to see the item
if item.public or item.user_id ==... | 5502d904fa02af975af6a52f3c3892133d62061d | 29,015 |
def yaw_from_pose(pose):
""" Extract the yaw (orientation) from a pose message. """
quat = np.array([pose.orientation.x, pose.orientation.y,
pose.orientation.z, pose.orientation.w])
euler = tf.transformations.euler_from_quaternion(quat)
return euler[2] | 5112ef95693655af1dce50a2fa51de3d7c42006c | 29,016 |
import logging
def create_influxdb_datasource_config(influxdb_parameters, cert, key) -> dict:
"""
:param influxdb_parameters: The retrieved InfluxDB parameter JSON
:param cert: The InfluxDB cert for HTTPS.
:param key: The InfluxDB key for HTTPS.
:return: data: The datasource JSON to add.
"""
... | e57f26336ddad2c56919e776239edaba0fc2ebe7 | 29,017 |
def render_incrementals(iterable, **kwds):
"""helper function for simple incremental_expansion calls
:param iterable: sequence of items to incrementally stack
:param kwargs: options to pass to incremental_expansion
:return: a set of the rendered results from incremental_expansion
"""
s = set()
... | d545fc237ba6ddaf59c20795cfae1f60197938fc | 29,019 |
import re
def get_queues_labels(queue_labels_data):
"""Returns parsed data for main metrics.
Converts input string with raw data to a dictionary."""
queue_regexp = r'QUEUE\(([^)]+)\)'
curdepth_regexp = r'CURDEPTH\(([^)]+)\)'
maxdepth_regexp = r'MAXDEPTH\(([^)]+)\)'
queue_type_regexp = r'TYPE\(... | 908aa435c95028d8f34fb35a6cb02da2aa7ca706 | 29,020 |
def is_auto(item):
"""
Checks if a parameter should be automatically determined
"""
if isinstance(item, float):
if item == 9999.9:
return True
elif isinstance(item, str):
if 'auto' in item.lower():
return True
return False | fe6320adef43c51cdffd5b5d4a0bf34ac43d9c5a | 29,021 |
from typing import Union
def black_scholes_price(S: float,
K: Union[float, np.ndarray],
is_call: bool,
vol: Union[float, np.ndarray],
disc: float,
T: float,
div_disc: float =... | 888845c47fc7d5c17921f83321b6b966adfaaf33 | 29,022 |
def has_group_perm(user_level, obj, ctnr, action):
"""
Permissions for groups
Groups are assigned a subnet
"""
if not obj.subnet in [ip_range.subnet for ip_range in ctnr.ranges.all()]:
return False
return {
'cyder_admin': True, #?
'ctnr_admin': action == 'view', #?
... | aa55bcf846389492734046b8d33c2676ed165763 | 29,023 |
def get_tags_with_sha(repo_dir):
"""
_get_tags_with_sha_
Get list of tags for a repo and return a map of
tag:sha
"""
repo = git.Repo(repo_dir)
return {tag.name: tag.commit.hexsha for tag in repo.tags} | d1443ea8289f5588e29517e42da4564cbcc65177 | 29,024 |
from typing import List
from typing import Tuple
def get_reference_sample_name(sample_list: List[Tuple[str, str]]) -> str:
"""Gets the name from the reference sample, raising an exception if it does not exist in the sample table."""
for sample, sample_type in sample_list:
if sample_type == 'yes':
... | 5b702c1395fe9c19135279cee7deff870f4707c4 | 29,025 |
def form_check(form_field: BoundField, col: str = '') -> dict:
"""
Делаем бутстраповский чекбокс из джанговского поля формы
Фичи:
- если после валидации нашлись косяки, показываем их
- если в поле есть хелп-текст, то показываем его
- опционально передаем в form-group CSS-класс для выстраивания п... | 0fbfe9a57c1e496f0726c0bcb65f8cf14a85000e | 29,026 |
import fnmatch
def IncludeFiles(filters, files):
"""Filter files based on inclusion lists
Return a list of files which match and of the Unix shell-style wildcards
provided, or return all the files if no filter is provided."""
if not filters:
return files
match = set()
for filter in filters:
ma... | 03d9a6359cd1c88e496f8f8a047f08c868c7b976 | 29,027 |
def make_fade_window_n(level_start, level_end, N_total, fade_start_end_idx=None):
"""
Make a fade-in or fade-out window using information on sample amounts and not time.
f_start_end defines between which start and stop indexes the fade happens.
"""
if not fade_start_end_idx:
fade_start_idx,... | 7566cc95eb58ee03e7cb90da5a03b7e24f8ea114 | 29,028 |
def translate_ethosu_tir_call_extern(tir_call_extern):
"""This is a dispatcher function to dispatch
correct translation call depending on the extern call's
first argument"""
supported_call_extern = {
"ethosu_conv2d": translate_ethosu_conv2d,
"ethosu_copy": translate_ethosu_copy,
... | b9cc6ce7b9db7d311695d7d4d1086f9faefca4f8 | 29,029 |
def _getSinglePosValueKey(valueRecord):
"""otBase.ValueRecord --> (2, ("YPlacement": 12))"""
assert isinstance(valueRecord, ValueRecord), valueRecord
valueFormat, result = 0, []
for name, value in valueRecord.__dict__.items():
if isinstance(value, ot.Device):
result.append((name, _ma... | b49e60fdba99f2deb11c002d1632b34bd82226f0 | 29,030 |
def top_half(bbox=full_frame()):
"""Returns a bounding box covering the top half of ``bbox``."""
return make_bbox(bbox['x1'], bbox['y1'],
bbox['x2'], (bbox['y1'] + bbox['y2']) / 2.) | a005568dc4d8ca2a99120ca319e66ebf64371a89 | 29,031 |
def __command(client, command, default_method, use_bytes=False):
""" Private function supporting multiple command formats:
- string
- function
- (function, args, kwargs) """
if is_function(command):
return command(client)
elif is_list(command):
a, kw = (), {}
... | ba6b46820756486c81b757da4b04a0e97c7c7cd7 | 29,032 |
def designate_node(fqdn, type):
"""
"""
execCmd = ' '.join([BDVAGENT, OPTION, 'designate', type, fqdn])
return executeCmd(execCmd) | e1f89a01c182bb1302c6997fb2a5461b09cd5319 | 29,033 |
def get_identity_for_user(user):
"""Get the Identity for the user specified via email or ID."""
identity = None
if user is not None:
# note: this seems like the canonical way to go
# 'as_user' can be either an integer (id) or email address
u = current_accounts.datastore.get_use... | a7cdd089ab25ee67de21d22ab1811dca3fbab0df | 29,035 |
import torch
def MetaOptNetHead_Ridge(query, support, support_labels, n_way, n_shot, lambda_reg=50.0, double_precision=False):
"""
Fits the support set with ridge regression and
returns the classification score on the query set.
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
... | 07e83a8dd597a332c03f9ef01633c1c6007b9fc8 | 29,036 |
def getUserZaduzenja(userClass):
"""
Vraca zaduzenja datog korisnika
"""
_result = []
for k,v in _rented.items():
if int(k) == userClass.GetCardNumber():
_result.append(v)
return _result | 5dc236baaad480c7169724685d8c6035a9839a61 | 29,038 |
def add_testcase_properties(xml_obj, tcconfig=None):
"""add properties to testcases"""
if xml_obj.tag == "testsuites":
expression = "./testsuite/testcase"
else:
expression = "./testcase"
multile_test_ids = {}
for testcase in xml_obj.findall(expression):
tcproperties = et.El... | ca8b59367e4cff027ca3eae81fd740a7bff121cd | 29,039 |
def calc_rho(RhoRef,T,S,alpha=2.0E-4, beta=7.4E-4):
"""-----------------------------------------------------------------------------
calc_rho calculates the density profile using a linear equation of state.
INPUT:
state: xarray dataframe
RhoRef : reference density at the same z as T and S slices. C... | 844a324481fccf2e695caac282a6f8711546c38f | 29,040 |
import gettext
def get_request_details(request_id=None, srp_request=None):
"""Handles responding to all of the :py:class:`~.models.Request` detail
functions.
The various modifier functions all depend on this function to create the
actual response content.
Only one of the arguments is required. Th... | e47197af84c88f59b866e0476da32055091dbb2b | 29,041 |
import numpy
from typing import Tuple
from typing import Optional
from typing import Dict
from typing import Any
import copy
import itertools
def packSpecialData(
data: numpy.ndarray, paramName: str
) -> Tuple[Optional[numpy.ndarray], Dict[str, Any]]:
"""
Reduce data that wouldn't otherwise play nicely wi... | ec07057f4c38b8169cd1bc04ee19dd79a585a050 | 29,042 |
def swapaxes(x, axis1, axis2):
"""Swap two axes of a variable.
Args:
x (~chainer.Variable): Input variable.
axis1 (int): The first axis to swap.
axis2 (int): The second axis to swap.
Returns:
~chainer.Variable: Variable whose axes are swapped.
"""
return Swapaxes(a... | 274f6960fb463e7660b0bf6e963f51957ab3b728 | 29,043 |
def add_header(img, labels, mark_midpoints=True, header_height=20):
"""Adds labels to the image, evenly distributed across the top.
This is primarily useful for showing the names of channels.
Args:
img: A PIL Image.
labels: list of strs. Labels for segments to write across the top.
mark_midpoints: b... | f3d8d69009dd72f7e7e9c0305b248492cdc076fc | 29,044 |
def predict_by_lr_model(test_feature, lr_model):
"""
predict by lr_model （调用 sklearn 实例方法）
"""
result_list = [] #存储每个样本label为1的概率
prob_list = lr_model.predict_proba(test_feature)
for index in range(len(prob_list)):
result_list.append(prob_list[index][1]) #下标为0的对应label为0的概率，下标为1的对应label为1... | 03ea185aa4398e8ccb7449d9e32006dd391e9c13 | 29,045 |
def _default_schedule(outs):
"""Default schedule for gpu."""
outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
s = tvm.create_schedule([x.op for x in outs])
scheduled_ops = []
def traverse(op):
if tag.is_broadcast(op.tag) or op.tag in ['bbox_score', 'sorted_bbox']:
s... | b2e328e04e2ad522580e8966f66eb52d4f4b0d4d | 29,046 |
def get_track_reviewer_abstract_counts(event, user):
"""Get the numbers of abstracts per track for a specific user.
Note that this does not take into account if the user is a
reviewer for a track; it just checks whether the user has
reviewed an abstract in a track or not.
:return: A dict mapping t... | c73b00bd641e20c6b6f3c1e51173b6162e8f76ee | 29,047 |
def fit_logistic(X, w, var_prior, X_test, initial_phi):
"""MAP logistic regression.
Input: X - (D + 1) * I training data matrix, where D is the dimensionality
and I is the number of training examples.
w - I * 1 vector containing world states for each e... | ba425ca35cbb1fe5359019ed46d4756daa62779b | 29,048 |
from typing import Optional
from enum import Enum
def middle_drag_and_drop(
element_path1: UI_Element,
element_path2: UI_Element,
duration: Optional[float] = None,
mode: Enum = MoveMode.linear,
timeout: Optional[float] = None) -> UI_Element:
"""
Drags and drop with midd... | f7325683af4b0891e68add86a0efc6abc9b5044d | 29,049 |
def get_pi0(pv, lambdas):
"""
Compute Storey's C{pi0} from p-values C{pv} and C{lambda}.
this function is equivalent to::
m = len(pv)
return [sum(p >= l for p in pv)/((1.0-l) * m) for l in lambdas]
but the above is C{O(m*n)}, while it needs only be C{O(m+n)
(n = len(la... | 4937aaa13ce3f5dda18fa3669e535c4d88be3ad6 | 29,050 |
def read_accelerometer(serial, calibration):
"""
Reads the raw values from the Arduino, parses them into separate variables
and uses the calibration data to normalize the data
Args:
serial: a reference to the serial connection with the Arduino
calibration: a reference to the calibration... | 3c5537e2a017f57dca8dccd24c2ba083a9c47345 | 29,051 |
def get_access(name):
"""Get access based on name
In Python __var__ refers to a private access
_var refers to protected access
and var would refer to public access
"""
assert isinstance(name, str), "Expecting name to be a string"
if len(name) > 4 and "__" == name[:2] and "__" == name[-2:]:
... | ffe072ed1820ce0536533a5882af1e1270780744 | 29,052 |
def run_query_series(queries, conn):
"""
Iterates through a list of queries and runs them through the connection
Args:
-----
queries: list of strings or tuples containing (query_string, kwargs)
conn: the triplestore connection to use
"""
results = []
for item in queries:
... | 7a3e920663222b57233e9a01d1b3cacb039a02eb | 29,053 |
def _get_key(block_id, block_dict, extra_args):
"""
Given a dictionary, return an element by ``key``.
block_id:
Block id
block_dict:
key: (Mandatory)
Key value to get
starting_dict: (Optional)
Starting dictionary param
... | 1c966f9d4966ff9eaafde60fce13f88a176def3d | 29,054 |
def AP(predictions, scores):
"""
Computes the average precision per class, the average precision and the interpolated average precision at 11 points
:param predictions: list of lists of every class with tp, fp and fn. fps are zeros, the others one, indicating this is a ground truth
:param scores: confi... | bc8b98d91153487261a76c07befe4cce6211d0d3 | 29,055 |
def _sample_optimization_test_problems(
rng):
"""Sample an optimization test function problem."""
is_noise = utils.sample_bool(rng, 0.5)
return {
"problem":
rng.choice(sorted(_opt_test_problems.keys())),
"noise_stdev":
utils.sample_log_float(rng, 0.01, 10.0) if is_noise else 0.... | 47edaa72d9d0f43e3c9c67684b686f5abbeb74d8 | 29,056 |
def get_domain_id_field(domain_table):
"""
A helper function to create the id field
:param domain_table: the cdm domain table
:return: the id field
"""
return domain_table + '_id' | 5805da82b4e57d14d4105d92a62cf4b5cc4bc3f2 | 29,057 |
def construct(symbol, strategy, chains, **kwargs):
"""
This is a convenience method to allow for creation of option spreads
from predefined sources.
:param symbol: The symbol of the option chains
:param strategy: The option strategy filter to use
:param chains: Option chains data to use. This d... | 99ff1d3e9cb6a4718fc06e767904a95340ca9b6f | 29,058 |
def get_delete_query(table_name: str) -> str:
"""Build a SQL query to delete a RDF triple from a MVCC-PostgreSQL table.
Argument: Name of the SQL table from which the triple will be deleted.
Returns: A prepared SQL query that can be executed with a tuple (subject, predicate, object).
"""
return f"... | 9ff605f77caa7e8d2e6a7ea1e2b82c22821c86dd | 29,059 |
def _parse_ipv6(a):
"""
Parse IPv6 address. Ideally we would use the ipaddress module in
Python3.3 but can't rely on having this.
Does not handle dotted-quad addresses or subnet prefix
>>> _parse_ipv6("::") == (0,) * 16
True
>>> _parse_ipv6("1234:5678::abcd:0:ff00")
(18, 52, 86, 120, 0... | 9a999df1cd352fe175801cbe4476f2080cc5d37d | 29,060 |
def weighted(weights,metric='categorical_accuracy'):
""" weighted metric
Args:
- weights<list>:
* a weight for each value in y_true
- metric<str|metric>:
* snake-case strings will be turned to camel-case
* if metric is not a string the passed metric will be r... | ffb356a178c50c517485d126c90724b5bc408ad2 | 29,061 |
from typing import Tuple
def compute_reg_strs(product_graph: TwoPlayerGraph,
coop_str: bool = False,
epsilon: float = -1) -> Tuple[list, dict, TwoPlayerGraph]:
"""
A method to compute strategies. We control the env's behavior by making it purely cooperative, pure adve... | 99d3632ed6d692c1531a4420f794cd84df5c3264 | 29,062 |
import torch
from typing import Tuple
import functools
def make_tanh_warp_grid(matrix: torch.Tensor, warp_factor: float,
warped_shape: Tuple[int, int],
orig_shape: Tuple[int, int]):
"""
Args:
matrix: bx4x4 matrix.
warp_factor: The warping factor.... | 3f1226ea19006c5cbf683deff15a120ffb7c7af6 | 29,063 |
def insertion_sort(arr):
"""
Returns the list 'arr' sorted in nondecreasing order in O(n^2) time.
"""
for i in range(1,len(arr)):
key = arr[i]
j = i-1
while j >= 0 and arr[j] > key:
arr[j+1] = arr[j]
j = j-1
arr[j+1] = key
return arr | cafd83cd31cbadcbc0a5c3aaff7d21f3ae907083 | 29,064 |
def tokens_refresh_post(body=None, project_name=None, scope=None): # noqa: E501
"""Refresh tokens for an user
Request to refresh OAuth tokens for an user # noqa: E501
:param body:
:type body: dict | bytes
:param project_name: Project Name
:type project_name: str
:param scope: Scope for ... | 58e59e54cf53ecf23ca2e45f62b00094adfb49ab | 29,065 |
def load_xml_data(path, start_node="header", search_node="name"):
"""
load the XML data
"""
retval = []
fetched_xml = minidom.parse(path)
item_list = fetched_xml.getElementsByTagName(start_node)
for value in item_list:
retval.append(value.attributes[search_node].value)
return ret... | 6a281b2b9ce3fac22ec32afa151438c8259b4f94 | 29,066 |
def saturationcheck(thermal_data,startframe,sat_threshold=0.9):
""" Determine the fraction of thermal_data that is saturated
on or after startframe (0 based). It is assumed the highest
temperature recorded in thermal_data for a particular pixel
is the saturation value and that the thermal data has alre... | d706c14c9ce59ad738cf6817240c5095664ad866 | 29,067 |
def make_image_justification(g, doc_id, boundingbox, system, confidence,
uri_ref=None):
"""
Marks a justification for something appearing in an image.
:param rdflib.graph.Graph g: The underlying RDF model
:param str doc_id: A string containing the document element (child) I... | 8561c8b890cd53318fa3e76ce66fd3ec7e204858 | 29,068 |
def update_u_gates(drag_params, pi2_pulse_schedules=None,
qubits=None, cmd_def=None, drives=None):
"""
Update the cmd_def with new single qubit gate values
Will update U2, U3
Args:
drag_params: list of drag params
pi2_pulse_schedules: list of new pi/2 gate as a pulse s... | f5719f9d14c0f37d347629207484e200b0934b1e | 29,069 |
import select
def make_acc_fun(network_apply_fun, num_outputs=1):
""" Given a network function and number of outputs, returns an accuracy
function """
if num_outputs == 1:
prediction_function = lambda x: (x >= 0.).astype(jnp.int32)
else:
prediction_function = lambda x: x.argmax(axis=-1).astype(jnp.in... | 977d18c7b3309abee5ab3233d5355a72712f00dd | 29,071 |
from typing import Union
from typing import Dict
from typing import Any
from typing import List
from typing import Tuple
def upsert_all(
engine: Engine,
table: Table,
data: Union[Dict[str, Any], List[Dict[str, Any]]],
) -> Tuple[int, int]:
"""
Update data by primary key columns. If not able to upd... | 070c0091bdbef01459c0d8a46340e5997bdf0a34 | 29,073 |
def index():
"""Root route test"""
return "Weights route" | c2a609f067a8155f16bd2a638a7a5c9f399a1575 | 29,074 |
from typing import Optional
def get_backend_health(backend_name: Optional[str] = None,
backend_set_name: Optional[str] = None,
network_load_balancer_id: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetBackendHealthR... | 6bd328878a45fa128e13bbe2fd6cd9d2c3f80129 | 29,076 |
def calculate_velocity(start_longitude, start_latitude, start_time,
end_longitude, end_latitude, end_time):
"""
Finds the magnitude of the velicty, in kilometers per hour.
"""
distance_traveled = calculate_distance(start_longitude, start_latitude,
... | 1a36fce9340de4fc6271a9d3bc3515b3b8806293 | 29,078 |
def get_raw_html_pbp(season, game):
"""
Loads the html file containing this game's play by play from disk.
:param season: int, the season
:param game: int, the game
:return: str, the html pbp
"""
with open(get_game_pbplog_filename(season, game), 'r') as reader:
page = reader.read()... | b2bee91be984e8d6782ee0bfabb32fc9e43dc4f4 | 29,079 |
def prepare_inputs_by_partition(
df,
partition_col,
split_date,
categorical_cols=None,
output_col=0,
lookback=12,
num_predictions=12,
):
"""
Lags, splits and normalizes a dataframe based around a partition.
"""
partitions = df[partition_col].unique()
scalers = {}
trai... | 95bdd21be019d7ed9f27797d8268c43c64d2c22e | 29,080 |
def read_fits_data(filename, dtype="float32", **kwargs):
""" Read fits image into numpy array.
Args:
filename (str): The name of ther file to read.
dtype (str, optional): The data type for the array. Default: float32.
**kwargs: Parsed to fits.getdata.
Returns:
np.array: The i... | d8e5fa22c636cfa263b0acfdabfa642e87ef486a | 29,081 |
def streamplot(UV, ax=None, map=None, geodata=None, drawlonlatlines=False,
basemap_resolution='l', cartopy_scale="50m", lw=0.5,
cartopy_subplot=(1,1,1), axis="on", **kwargs):
"""Function to plot a motion field as streamlines.
Parameters
----------
UV : array-like
... | bd634a5602ae0ec2a95a0e9d918a872d91691296 | 29,082 |
import random
def random_add_text(new_canvas: np.ndarray):
"""
:param new_canvas: RGBA image.
:return RGBA image.
"""
# font
font_list = [
cv2.FONT_HERSHEY_SIMPLEX,
cv2.FONT_HERSHEY_PLAIN,
cv2.FONT_HERSHEY_DUPLEX,
cv2.FONT_HERSHEY_COMPLEX,
cv2.FONT_HERSH... | dc5f0a63ed8ccfd6cd2aee798f8b70706c6019d0 | 29,083 |
import torch
def len_to_mask(len_seq, max_len=None):
"""len to mask"""
if max_len is None:
max_len = torch.max(len_seq)
mask = torch.zeros((len_seq.size(0), max_len))
for i, l in enumerate(len_seq):
mask[i, :l] = 1
return mask | 58cf7b3de6e28a541531ce5a6f516671493b5e08 | 29,084 |
def get_games_for_steamid(steamid: str) -> set[tuple[int, str]]:
"""Gets the games owned by a Steam user
Parameters
----------
steamid : str
The user's 64-bit Steam ID
Returns
-------
set[tuple[int, str]]
The set of games this user owns, in tuples of appid and game name
"""
body = steam_request('IPlayer... | 7edbf85fcb5a337681c1aa21bb54948be9122fa2 | 29,085 |
from typing import Callable
from typing import Any
import functools
def normalize(how: str) -> Callable[..., Any]:
"""Apply a row or column normalization to a pandas DataFrame argument.
Parameters
----------
how : str
The normalization method to apply. Can be one of {'row', 'colum', 'minmax',... | 5e837d0e8754ef4d2fc42d0b9484f1f699fb3b67 | 29,087 |
from typing import Union
def resize_image(
image: np.ndarray,
target_shape: Union[list, tuple],
ground_truth: np.ndarray = None,
):
"""
@param `image`: Dim(height, width, channels)
@param `target_shape`: (height, width, ...)
@param `ground_truth`: [[center_x, center_y, w, h, class_... | 8a3474728546605a462f4ddbab551af0afa675f5 | 29,090 |
def _get_security_item(security_type, exchanges, code=None):
"""
get the security item.
Parameters
----------
code : str
the security code,default: None
security_type : str
the security type
exchanges : list
the exchanges
Returns
-------
DataFrame
... | a0c8b555dbdf3c5fa1b5f7b69fceeb7bea4f9c8f | 29,091 |
def generate_launch_description():
"""Launch file for training node to training network."""
return LaunchDescription([
DeclareLaunchArgument(
'yaml_file',
default_value=[get_default_file_path('template.yaml')],
description='Parameter file for experiment.'
),
... | ca388302307fcef3e57d672ef3375751a297c9a3 | 29,092 |
def plot_density(
data,
data_labels=None,
var_names=None,
credible_interval=0.94,
point_estimate="mean",
colors="cycle",
outline=True,
hpd_markers="",
shade=0.0,
bw=4.5,
figsize=None,
textsize=None,
):
"""Generate KDE plots for continuous variables and histograms for ... | 1d0ef2ea506a6c6013844aa7d43ca867332903ca | 29,093 |
def _vivid_light(a, b):
"""
:type a: ImageMath._Operand
:type b: ImageMath._Operand
:rtype: ImageMath._Operand
"""
color_burn = _color_burn(a, b * 2)
color_dodge = _color_dodge(a, 2 * (b - 128))
return color_burn * (b < 128) + color_dodge * (b >= 128) | 7465a4441dd05b0e6d83acaee6a9c387654899fb | 29,094 |
def normalize_cookies(cookies):
"""Takes cookies from Selenium or from Python Requests and
converts them to dict.
This throws away information that Selenium otherwise has (like the host and
such), but a dict is essentially all we need.
"""
requests_cookies = {}
if type(cookies) == list:
... | 5a57fc15e7545427e8797439264245f1b7bebe7c | 29,095 |
def from_file(filename):
"""Create a list structure from a special format of file.
Args:
filename: in which the formated string is located.
Returns:
A 2d list object.
"""
with open(filename, 'r') as f:
return from_text(f.read()) | 1035da5c6709be1a0429b955a6ef88d6877a2101 | 29,096 |
def plot_data(data):
""" Returns a scatter plot that visualizes the passed dataframe. Currently, tailored to merely
encapsulate very specific visualization. """
# lets play with namedtuples for fun. kinda like a struct-ish
PlotArgs = namedtuple('PlotArgs', ['color', 'label', 'marker'])
plottin... | 98f4f517894a9e5b16c9b471cf28bbaae72007ff | 29,097 |
def rotate(img):
"""
Rotation:
OpenCV provides scaled rotation with adjustable rotation center so that you can rotate at any location you prefer.
To find this modified transformation matrix, OpenCV provides a function, cv2.getRotationMatrix2D.
Check below example which rotates the image by 9... | 89bb652c43d61896db74be1a97bffd492d7ab95f | 29,098 |
import hashlib
def get_sign(data_dict, key):
"""
签名函数
:param data_dict: 需要签名的参数，格式为字典
:param key: 密钥 ，即上面的API_KEY
:return: 字符串
"""
params_list = sorted(data_dict.items(), key=lambda e: e[0], reverse=False) # 参数字典倒排序为列表
params_str = "&".join(u"{}={}".format(k, v) for k, v in params_lis... | ea7ee65cd3ae72e19293dc851255bc0f3ad4b321 | 29,100 |
def string():
"""String representation."""
return "{:s}".format('something') | d13ae4fe229f767c515b0f0d6439ac61c6bfdbe8 | 29,101 |
def find_films_in_location(films: pd.DataFrame) -> pd.DataFrame:
"""finds films filmed in certain location
Args:
films (pd.DataFrame): films with their locations
Returns:
pd.DataFrame: films which were filmed in certain location
"""
films.dropna(inplace=True)
# change for more ... | 91801357aab19e2a263951907fe74f114b833f68 | 29,102 |
def _if_installed(pname):
"""Run if the given program name is installed.
"""
def argcatcher(func):
def decorator(*args, **kwargs):
envs = [x for x in args if hasattr(x, "system_install")]
env = envs[0] if envs else None
if shared.which(pname, env):
... | 63eb3a0a3c2b2b6c7370ee4db449e6e3e1d2c84e | 29,104 |
def check_gym_environments(env: gym.Env) -> None:
"""Checking for common errors in gym environments.
Args:
env: Environment to be checked.
Warning:
If env has no attribute spec with a sub attribute,
max_episode_steps.
Raises:
AttributeError: If env has no observati... | 95d3a3b7804981cb8308269580359111b257eefe | 29,105 |
def WI(bands: dict) -> xr.DataArray:
"""
Water Index (2015): Fisher et al. (2016)
Args:
bands (dict): Bands as {band_name: xr.DataArray}
Returns:
xr.DataArray: Computed index
"""
return (
1.7204
+ 171 * bands[obn.GREEN]
+ 3 * bands[obn.RED]
- 70 ... | 400c7277d5d7cca07df7953b0db957f3d4fdfd0a | 29,106 |
import io
def readZipData(filePath):
"""
Opening the zip file in READ mode and transform scalars.csv to data frame
:param filePath: path to zip-file
:return: data frame with scalars.csv content
"""
with ZipFile(filePath.as_posix(), 'r') as zip:
scalars = None
for i in zip.namel... | 2efd90426366754454f13a8c5e9e61ed2a1c150d | 29,107 |
def calc_relative_scale(skeleton, ref_bone_lengths, joint_tree) -> (float, float):
"""Calculate the factor by which the reference is larger than the query skeleton.
Args:
skeleton (torch.DoubleTensor): The query skeleton.
ref_bone_lengths (torch.DoubleTensor): The reference skeleton bone length... | cf1bbf2692666e393eb50eeb4ae9d0724af78c7f | 29,108 |
def vertical_move(t, v_speed=2/320):
"""Probe moves vertically at v_speed [cm/s]"""
return 0.*t, 0*t, v_speed*t | eb6a066bf6b6659728647c78dd7673a3d45b250d | 29,109 |
def get_favored_peaks(rama_key):
"""
returns exact favored peaks with their score value
"""
assert rama_key in range(6)
if rama_key == RAMA_GENERAL:
return [((-115.0, 131.0), 0.57068),
((-63.0, -43.0), 1.0),
((53.0, 43.0), 0.323004),
((53.0, -127.0), 0.0246619)]
if r... | 79bf814becbbf36796e229f69d0a99cd8ef1716e | 29,110 |
def get_all_tablespace_acls(conn):
"""
Returns:
List of :class:`~.types.RelationInfo` objects.
"""
return [RelationInfo(**row) for row in conn.execute(_pg_tablespace_stmt)] | 561514b7986d374ba1dc7a4addf4d0588b53e59b | 29,111 |
import regex
def chunk_pars(content):
"""Given the context contained between `\\beginnumbering` and
`\\endnumbering`, return list of paragraphs.
This is able to handle paragraphs demarcated by `\\pstart` and `\\pend` as
well as when `\\autopar` is used (see §5.2.2 of the reledmac
documentation). ... | 958890791c67c90a9ed3264e82caca9bfebb5885 | 29,112 |
def bound():
""" Generate boundary for testing"""
bound = data.Boundary()
bound.degree = 3
bound.start = np.array([0.0, 0.0, 0.0])
bound.end = np.array([1.0, 0.0, 0.0])
bound.num_ctrlpts = 5
return bound | 210636e3e0618ff8b5ffd48b48f4aa035a38e928 | 29,113 |
from typing import Union
def to_tensor(pic: Union[Image, np.ndarray]) -> Tensor:
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor."""
if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
raise TypeError(f"input pic should be PIL image or numpy.ndarray, Got {type(pic)}")
if _is_numpy(pic) ... | 26040d594cee200200945d7561bc9e6bcda95f01 | 29,114 |
import numpy
def dummy_image():
"""Create a dummy image"""
x = numpy.linspace(-1.5, 1.5, 1024)
xv, yv = numpy.meshgrid(x, x)
signal = numpy.exp(- (xv ** 2 / 0.15 ** 2 + yv ** 2 / 0.25 ** 2))
# add noise
signal += 0.3 * numpy.random.random(size=signal.shape)
return signal | 8cbf5f31cde69b8ac775114277cee8f88d6dd932 | 29,115 |
def update_user(uid, **kwargs):
"""Updates an existing user account with the specified properties.
Args:
uid: A user ID string.
kwargs: A series of keyword arguments (optional).
Keyword Args:
display_name: The user's display name (optional). Can be removed by explicitly passing
... | 0b52b7e42f286861b43e6e2e25a9547b1cd354d7 | 29,118 |
def add_center_dist(nusc: NuScenes,
eval_boxes: EvalBoxes):
"""
Adds the cylindrical (xy) center distance from ego vehicle to each box.
:param nusc: The NuScenes instance.
:param eval_boxes: A set of boxes, either GT or predictions.
:return: eval_boxes augmented with center dista... | 5a0c09f9de689efe294a6ce500ba4dbf09885149 | 29,119 |
def check_address(btc_addr, network='test'):
""" Checks if a given string is a Bitcoin address for a given network (or at least if it is formatted as if it is).
:param btc_addr: Bitcoin address to be checked.
:rtype: hex str
:param network: Network to be checked (either mainnet or testnet).
:type n... | 9f236f5d6ccf2f28944c577e2ce8fbfb2c2a58b8 | 29,120 |
import time
def format_time(record):
"""Format time to ISO 8601.
https://en.wikipedia.org/wiki/ISO_8601
"""
utc_time = time.gmtime(record.created)
time_string = time.strftime('%Y-%m-%d %H:%M:%S', utc_time)
return '%s.%03dZ' % (time_string, record.msecs) | ea07736965711a214a738f5443f68cf02e20fcb2 | 29,121 |
def _cb_decode(s, maxsize=8192):
"""Decode a list of IDs from storage in a cookie.
``s`` is text as encoded by ``_cb_encode``.
``maxsize`` is the maximum size of uncompressed data. ``0`` means no limit.
Return a list of text IDs.
"""
dec = decompressobj()
squashed = unquote(s).encode('lati... | bf1239cf33bf83b1163d96641a20e4adc3e83221 | 29,122 |
def validate_model(df, fix=False):
"""
Validates the form of a model dataframe. A model dataframe must look something like this:
pos val_A val_C val_G val_T
3 1.1 4.3 -6.19 5.2
4 0.01 3.40 -10.5 5.3
5 0 1.4 10.9 231.0
A 'po... | 81c8663934c2ae33318635dd68939cff5652912b | 29,123 |
def create_training_instances(input_files, tokenizer, max_seq_length,
dupe_factor, short_seq_prob, masked_lm_prob,
max_predictions_per_seq, rng):
"""Create `TrainingInstance`s from raw text."""
all_documents = [[]]
# Input file format:
# (1) One sente... | 0274db246e701ac1da78564707c851c9e295a21e | 29,124 |
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