content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def a2str(a):
"""
formatting for 1 or 2 dimensional numpy arrays of booleans
"""
if len(a.shape) == 1:
return "".join(map(str, a))
elif len(a.shape) == 2:
return "\n".join(map(lambda row: "".join(map(str, row)), a)) | 589cbc72bc1c3379f74a924f14b304d91a517157 | 3,619,732 |
def find_PFd(A, B, Q, R, beta=.95):
"""
Taking the parameters A, B, Q, R as found in the `setup_matrices`,
we find the value function of the optimal linear regulator problem.
This is steps 2 and 3 in the lecture notes.
Parameters
----------
(A, B, Q, R) : Array(Float, ndim=2)
The ma... | 30c4085ba99eac914f718495589e37dace74e639 | 3,619,733 |
def _should_save_cookies(request):
""" Return True if cookies should be saved for a request """
# based on QNetworkReplyImplPrivate::metaDataChanged() C++ code
attr = request.attribute(
QNetworkRequest.CookieSaveControlAttribute,
QNetworkRequest.Automatic
)
return attr == QNetworkReq... | 3b24f363e8dfecf227eb16c8fdbf3138938be800 | 3,619,734 |
import json
def generate(data_path, registry):
"""generates templates based on arguments and configurations."""
if not current_folder_has_venv():
logger.warning(Text.no_virtual_environment_remainder)
with open(data_path / "category_tree.json", encoding="UTF-8") as file:
full_tree = json.... | 6d630513c317869603d23110f72839cd68975ffd | 3,619,735 |
import signal
def get_maxima( series, N, start_date = None, stop_date = None, _sorted = True ) :
"""
Summary:
Function that determines the first N maxima of a pandas time series
Arguments:
series - pandas series
N - number of maxima
start_date - start date as DateT... | 7f006c3ce795b8ad46f2a30d718fbab51cfd7515 | 3,619,737 |
def get_latest_challenge_from_player_id(player, should_be_completed=False):
"""
Tries to find the latest challenge belonging to a player
param int player: The player ID to search the challenges for
param bool should_be_completed: If the challenge should already be completed or not
returns os3_rll.m... | 9408cde081714d3994dbc9286ca699354a5f3bd1 | 3,619,739 |
import torch
def runepisode(env, policy, episodesteps, render, windowlength=4):
"""Runs an episode under the given policy
Returns the episode history: an array of tuples in the form
(observation, processed observation, logprobabilities, action, reward, terminal)
"""
observation = env.reset()
... | ef50ac2b2afd6912d27930ae7b5a1101f3d31776 | 3,619,740 |
def lookup_linear_velocity(
frame,
reference=None,
represent_in=None,
outlier_thresh=None,
cutoff=None,
as_dataarray=False,
return_timestamps=False,
):
""" Estimate linear velocity of a frame wrt a reference.
Parameters
----------
frame: str or ReferenceFrame
The ref... | fb6ab94b4f5dea3a96e08bfc854bacaa3b14a34f | 3,619,741 |
def dispatch_slug_path(*views):
"""
Dispatch full path slug in iterating through a set of views.
Http404 exceptions raised by a view lead to trying the next view
in the list.
This allows to plug different slug systems to the same root URL.
Usages::
# in urls.py
path('<slug:slu... | 570402f4461aaf033bc8abafedbaae15c2ef7efd | 3,619,742 |
def applyFxToVolumes( ts, vols, fx, **kwargs ):
""" Apply a function on selected volumes of a timeseries.
'ts' is a 4d timeseries. It can be a NiftiImage or a ndarray.
In case of a ndarray one has to make sure that the time is on the
first axis. 'ts' can actually be of any dimensionality, but datasets ... | 32b24975292f228a28584f146023e051a5da4453 | 3,619,743 |
def getTrack ( tleFile, t_beg, t_end, dt_secs=60):
"""
Given a Two Line Element (TLE) file name, a time interval, returns
tuple with (lon,lat) coordinates of satellite ground track.
"""
dt = t_end-t_beg
n = 1 + int(dt.total_seconds() / dt_secs)
Dt = timedelta(seconds=dt_secs)
nymd =... | 26f82a409f9f01cc738b93b98b1affc6843332a3 | 3,619,744 |
def __read_dataset_item(path):
"""Reads data set from path returns a movie dict.
Parameters
----------
path : str
Absolute path of the MovieLens data set(u.data).
Returns
-------
rating_dict : dict
Returns a dict of users, movies and ratings.... | a87baecc5b0c28675bc715aab646bf41e4b40f96 | 3,619,745 |
import logging
import time
def wait_for_new_checkpoint(checkpoint_dir,
last_checkpoint=None,
seconds_to_sleep=1,
timeout=None):
"""Waits until a new checkpoint file is found.
Args:
checkpoint_dir: The directory in which check... | ee355f2e55cc8a714c6d4e523615b2508589f1df | 3,619,746 |
def get_data(data_fn, param):
"""Feed data_fn with param
"""
return data_fn(**merge_dicts(param["data"], param.get("shared", {}))) | 605e29d8e45967f3d938dd094c233d5b36ae6aa9 | 3,619,748 |
def get_heat_flow_3(matrix_temp: np.ndarray, param: Parameters) -> float:
"""
各部温度から断熱材+内装材伝導熱量を計算する
:param matrix_temp: 各部温度計算結果 (5,1), degC
:param param: 計算条件パラメータ群
:return: 断熱材+内装材伝導熱量, W/m2
"""
return param.C_2 * (matrix_temp[2] - matrix_temp[3]) | 9af08c218351d51bec890635abcce301cd83b8f1 | 3,619,749 |
def create_training_label(center, size, corners, resolution=0.50, scale=4,
x=(0, 90), y=(-50, 50), z=(-4.5, 5.5)):
"""Create training labels which satisfy the range of experiment"""
min_value = np.array([x[0], y[0], z[0]])
xyz_logical = judge_in_voxel_area(center, x, y, z)
center[:, 2] ... | be6139bc4b5665194afc890fcce474d3d13ce872 | 3,619,750 |
import urllib
import re
def proxy_list_from_free_proxy_list_net():
"""
will connect to https://free-proxy-list.net/
and will get the proxy list from that page
proxy format : ip address, port
"""
link = 'https://free-proxy-list.net/'
regex = '<td>(?P<ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})<... | 6f8721331024a85f5e406e36557ffdddacd7654d | 3,619,751 |
def adv_training(sess, inputs, labels):
"""Train and evaluates a model on adversarial examples
"""
adversarial_model = get_model(FLAGS.model_type, 'train_adv')
fgsm2 = FastGradientMethod(adversarial_model, sess=sess)
def attack(inputs):
return fgsm2.generate(inputs, **ADV_CONSTANTS.get_attack_details(FLA... | 088595fe5a7ab681f25ea7149017eba4555353cd | 3,619,752 |
def unifiable(cls):
""" Register standard unify and reify operations on class
This uses the type and __dict__ or __slots__ attributes to define the
nature of the term
See Also:
>>> class A(object):
... def __init__(self, a, b):
... self.a = a
... self.b = b
>>> un... | 48e4cbc5373dc445923e47c8c8f65e99a035b338 | 3,619,753 |
def kMeansInitCentroids(X, k):
"""
初始化 cluster centroids
:param X:
:param k:
:return:
"""
m = X.shape[0]
centroids = np.zeros((k, X.shape[1]))
m_arr = np.arange(0, m)
np.random.shuffle(m_arr)
rand_indices = m_arr[:k]
centroids = X[rand_indices, :]
return centroids | d6aa56fb1157ff9bdc2cf82bb7864b08ef471dc1 | 3,619,754 |
from typing import AnyStr
from typing import List
from typing import Dict
def get_metric_rating_max(metric: AnyStr,
start: AnyStr,
end: AnyStr,
tenant_id: AnyStr,
namespaces: List[AnyStr]) -> List[Dict]:
"""
... | 33971a0f846a0bcb40b0acf16b32fc62a5bfefb1 | 3,619,755 |
def generate_binary_random_number(size=1):
"""Generates a binary random number or array based on an uniform distribution.
Args:
size (int): Size of array.
Returns:
A binary random number or array.
"""
binary_array = np.round(np.random.uniform(0, 1, size))
return binary_array | 3833115e82b87a9773f53046d6dc6114febdc56f | 3,619,756 |
def make_class(any, base=data.module.YO, *args, **kwargs):
"""check base is correctly resolved to Concrete0"""
class Aaaa(base):
"""dynamic class"""
return Aaaa | d0729d5f50e656a2fd76eda02d7d07ebd4682162 | 3,619,757 |
from typing import Optional
def make_nbh_test(station: str) -> Optional[dict]:
"""Builds NBH test file for station"""
return make_forecast_test(avwx.Nbh, station) | 761db3a8587e852dd9048eb3dd12cc64ecd7e8c1 | 3,619,758 |
def get_manifest_indexes(manifest, channel, column):
"""Get the indices for channel and column in the manifest"""
channel_index = -1
for x in range(len(manifest["channels"])):
if manifest["channels"][x]["channel"] == channel:
channel_index = x
break
if channel_index == -1... | 63d82417b1e106d408866933bce776c272b2e77d | 3,619,759 |
from typing import List
def get_function_call(fn_to_call: ts.FunctionType, argument_names: List[str], fc: context.FunctionContext):
"""
DOES NOT COPY stack symbolic registers!
argument_names should be located in stack symbolic registers [argument_names]
RETURNS a stack symbolic register which th... | dace1b83478e8750518878ac0be77fb24393ef0f | 3,619,760 |
def parse_training_args(args=None, ignore_unknown=False):
"""parser for training script"""
arg_populate_funcs = [training_args, custom_mlp_args]
arg_check_funcs = [process_training_args]
return parse_various_args(
args, arg_populate_funcs, arg_check_funcs, ignore_unknown
) | 93fdd653199873c9cb32b5f6b7f63ae22a21f057 | 3,619,762 |
import torch
def query_ball_point(radius, nsample, xyz, new_xyz):
"""
Input:
radius: local region radius
nsample: max sample number in local region
xyz: all points, [B, N, 3]
new_xyz: query points, [B, S, 3]
Return:
group_idx: grouped points index, [B, S, nsample]
... | e74992747103d11b6618ecf7daf035c4f83e9911 | 3,619,763 |
import math
def get_num_blocks(content_length, block_size=DEFAULT_BLOCK_SIZE):
"""
Split file of specified length into blocks
"""
return int(math.ceil(content_length / block_size)) | e39f20c48f4dbd4f4baaf150bd35122b49ad5b30 | 3,619,764 |
def is_bounded(coord, shape):
"""
Checks if a coord (x,y) is within bounds.
"""
x, y = coord
g, h = shape
lesser = x < 0 or y < 0
greater = x >= g or y >= h
if lesser or greater:
return False
return True | 0a0b6921fcdf285c89b5021d113585ff38255013 | 3,619,765 |
def perp2coast(X1,X2,Y1,Y2,X0=0,Y0=0,hip=10000,deltahip=1000,units='m',side=1):
"""
INPUT:
X1: Initial point in X for compute slope
X2: Final point in X for compute slope
Y1: Initial point in Y for compute slope
Y2: Final point in Y for compute slope
X0: Point to colocate the slope in X
... | 9836150c3aa3d2c5cae9aec62d47401cbd542d02 | 3,619,767 |
def conv2d(input_, W_shape, name, reuse=False):
"""
name - layer name for variable scope W_shape - [height, width, input_layers, output_layers]
"""
# 在名称空间name下添加变量Variable，但最好传递一个reuse参数。
with tf.variable_scope(name, reuse=reuse):
# 对convolution层的权值参数 W 和 b 进行初始化
W_conv = tf.get_var... | e4ac8a798a49ed9ffc14f83b44704119f5f7f0fb | 3,619,768 |
import re
import requests
from bs4 import BeautifulSoup
import time
def get_one_postalcode(ps='00100', onSale=True):
"""
Perform the query on one postal code.
QUERY TESTED: 13.11.2019
:param ps: the postal code to query.
:param onSale: bool, if true do the query sales, if false do the query on ren... | fb0b070121878b5bf5304b9e1712e87e8195a771 | 3,619,769 |
def CV_ARE_SIZES_EQ(*args):
"""CV_ARE_SIZES_EQ(CvMat mat1, CvMat mat2) -> int"""
return _cv.CV_ARE_SIZES_EQ(*args) | b559f7e849c36b9f1683d8c0a2562aa5640a6b21 | 3,619,770 |
def sph2car(theta, phi, radius=1.0):
"""
Transform the spherical coordinate to cartesian 3D point.
:param theta: longitude
:type theta: numpy
:param phi: latitude
:type phi: numpy
:param radius: the radius of projection sphere
:type radius: float
:return: +x right, +y down, +z is fr... | 1397983ca6ae42280603109ad28f49f41361539c | 3,619,771 |
from re import T
def expand_range_spec(
spec: T.Union[int, str], min_value: int, max_value: int
) -> T.Set[int]:
"""Expands strings of the range specification format to RangingSet
objects containing the individual numbers.
Any whitespace is ignored. If an int is given instead of a string,
a set co... | c6a3c2550126cc4ff8d4553ef1471d0edfb9b3b3 | 3,619,772 |
from typing import Dict
import requests
def get_tasks_by_project_id(workspace_id: str, project_id: str,
api_key_header: Dict) -> Dict:
"""Get tasks associated with a given project ID and workspace ID.
This function returns a dictionary of tasks corresponding to a given
project ID and workspace ID... | 3141b04424c87408e1c155e31c81c8f87a5ee0d1 | 3,619,773 |
def checksum_file(path: str) -> str:
"""
Gets the checksum of a file
Parameters
----------
path : str
The path to checksum
Returns
-------
string
Checksum
"""
message = PrettyStatusPrinter("Getting MD5 hash of " + path).print_start()
result = run_piped_comma... | 0a5747e1094348be042cec165509710661c3be84 | 3,619,775 |
from typing import Optional
def bool_to_int(bool_value: Optional[bool]) -> Optional[int]:
"""Cast bool to int value.
:param bool_value: some bool value
:return: int represenation
"""
if bool_value is None:
return bool_value
return int(bool_value) | fde6cda3dc8909638bb315451a09938224f2f306 | 3,619,776 |
def create_pool2d(pooling_params, ifm_expr):
"""Create a relay pooling operation"""
assert pooling_params.ifm.layout == "NHWC"
params = {
"pool_size": (pooling_params.size[0], pooling_params.size[1]),
"strides": (pooling_params.strides[0], pooling_params.strides[1]),
"padding": [0, 0... | 6f163a737993630f7af1264f609a74aee286c4d5 | 3,619,777 |
import socket
def send_msg(msg: bytes) -> bool:
"""Send a msg. Returns True on success, False otherwise"""
succeed = True
try:
# 1 second timeout
sock = socket.create_connection((c.HOSTNAME, c.PORT), timeout=1)
except Exception:
return False
try:
sock.send(msg)
... | b76698cf946b645825272c32771dfac792491840 | 3,619,778 |
def check_dwd_observations_parameter_set(
parameter_set: DWDObservationParameterSet,
resolution: DWDObservationResolution,
period: DWDObservationPeriod,
) -> bool:
"""
Function to check for element (alternative name) and if existing return it
Differs from foldername e.g. air_temperature -> tu
... | edc5b9ab3ae6c10db113e6a01d73467bd3d8ff0e | 3,619,779 |
def pobj_ident(l, r):
"""
Check if value of PObject `l`
is the same Python Object
as value of PObject `r`
"""
return mkbool(r.hasvalue and l.value is r.value) | f8c3d7b1867f9869083a41d211971674ed99f703 | 3,619,780 |
from typing import Any
def arghash(args: Any, kwargs: Any) -> int:
"""Simple argument hash with kwargs sorted."""
sorted_args = tuple(
x if hasattr(x, "__repr__") else x for x in [*args, *sorted(kwargs.items())]
)
return hash(sorted_args) | c3e95c63831c958bb2a52cabad9f2ce576a4fed8 | 3,619,781 |
from typing import Union
from typing import Optional
from typing import Sequence
def visualize_accuracy_grouped_by_probability(y_test: np.ndarray, labeled_class: Union[str, int],
probabilities: np.ndarray,
threshold: float = 0... | e9be1be8e5e650b2808fa31db80cfc9fb61e43bd | 3,619,782 |
def run_workflow_stddft(config: DictConfig) -> PromisedObject:
"""Compute the excited states using simplified TDDFT using `config`."""
# Single Point calculations settings using CP2K
mo_paths_hdf5, energy_paths_hdf5 = unpack(calculate_mos(config), 2)
# Read structures
molecules_au = [change_mol_uni... | ac2f3c6f39765d555530329948aa21719436216c | 3,619,784 |
def build_resnet_fpn_gnbn_lowlevel_cbp10_backbone(cfg, input_shape: ShapeSpec):
"""
Args:
cfg: a detectron2 CfgNode
Returns:
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
"""
bottom_up = build_resnet_gnbn_lowlevel_model_backbone(cfg, input_shape, gra... | 81ee7d331ab17bf47f8e252cfebd7a07045c62c9 | 3,619,786 |
import pickle
def load_db(fname, binp=True, keys=None ):
"""
Submodule to read the station database from file
Parameters
----------
fname : str
File name
binp : bool
Whether or not to use binary input
keys : List
Default None
If a list, then load database a... | 5c83b4c4d69ac7f196acc0803593cca87b484499 | 3,619,788 |
def _get_pcluster_version_from_stack(stack):
"""
Get the version of the stack if tagged.
:param stack: stack object
:return: version or empty string
"""
return next((tag.get("Value") for tag in stack.get("Tags") if tag.get("Key") == "Version"), "") | 86106e52ea6ef8780c8aa8f514e0708dd53fb8e3 | 3,619,789 |
import time
def condor_object(net):
"""Initialization of the condor object. The function gets a network in edgelist format encoded in a pandas dataframe.
Returns a dictionary with an igraph network, names of the targets and regulators, list of edges, modularity, and vertex memberships.
"""
t ... | a5b07ee8f8886baff9ae6b25f335d9c42343b3c0 | 3,619,790 |
import json
def extract(spark, source):
"""Return an RDD[String]."""
rdd = spark.sparkContext.textFile(source)
return rdd.map(lambda x: json.loads(x)) | fef96d6cae65551a6879396ea699acfd2e4cda4b | 3,619,792 |
def _remove_leading_zeroes_in_field(string):
"""If blank-separated fields are integer, remove the leading zero."""
split = string.split(" ")
for i, field in enumerate(split):
if field.isdigit():
split[i] = f"{int(field)}"
return " ".join(split) | 48ba659b25809f19a0c3ed722d6201ffef73c409 | 3,619,793 |
def pstdev(data):
"""Calculates the population standard deviation."""
#: http://stackoverflow.com/a/27758326
n = len(data)
if n < 2:
raise ValueError('variance requires at least two data points')
ss = _ss(data)
pvar = ss/n # the population variance
return pvar**0.5 | fd67040adf793c5ea4da4125fe007050af40bd1a | 3,619,794 |
def make_sharded_index(index_prefix: str, release_date: Date) -> str:
"""Make a sharded Elasticsearch index given an index prefix and a date.
:param index_prefix: the index prefix.
:param release_date: the date.
:return: the sharded index.
"""
return f'{index_prefix}-{release_date.strftime("%Y... | e9e8fa05efcff9868326d8e453f1379f5b134322 | 3,619,795 |
def kmp(pattern, text):
"""Knuth-Morris-Pratt substring matching"""
pattern_length = len(pattern)
matched, subpattern = 0, [0] * pattern_length
for i, glyph in enumerate(pattern):
if i:
while matched and (pattern[matched] != glyph):
matched = subpattern[matched - 1]
... | 4f2a30a7a92d2e5890d9c257626c37201b281fec | 3,619,797 |
from datetime import datetime
def ssl_valid_time_remaining(hostname):
"""Get the number of days left in a cert's lifetime."""
expires = ssl_expiry_datetime(hostname)
return expires - datetime.datetime.utcnow() | 7820dde512fa505b82875fe4a88cf9e5915212c7 | 3,619,799 |
def system(cmd):
"""Win32 version of os.system() that works with network shares.
Note that this implementation returns None, as meant for use in IPython.
Parameters
----------
cmd : str
A command to be executed in the system shell.
Returns
-------
None : we explicitly do NOT ret... | 591c03e6555a271ae3152890888963928bb66d2e | 3,619,800 |
def make_dataset(name, args):
""" Creates the dataset with the given name.
Parameters
----------
name: the name of the dataset to create.
args: hyper-paremeters for the dataset.
Returns
-------
dataset_fn: a function which creates the specified dataset.
"""
if name not in _data... | 13a06cba585f673a83fcfdbdf1ab32874ac51d65 | 3,619,801 |
def create_df(client, candles):
"""
Создаеv датафрейм для списка свечей
:param dict candles: List of candles from API
:return: DataFrame with candles
"""
df = DataFrame([{
'time': c.time,
'volume': c.volume,
'open': cast_money(client, c.open),
'cl... | 4d84cd1c5368586926fcbfd4104aac74c6f807ef | 3,619,802 |
def istext(obj):
"""
Deprecated. Use::
>>> isinstance(obj, str)
after this import:
>>> from future.builtins import str
"""
return isinstance(obj, type(u'')) | 60f3d751f22ad4d120ce7624dd9a07d2c841e206 | 3,619,804 |
def track_to_p_matrix(track, char_map=CHAR_MAP, x_vel_limits=None, y_vel_limits=None,
x_accel_limits=None, y_accel_limits=None, max_total_accel=np.inf):
"""
Converts a map described by a list of strings to P-matrix format for an OpenAI Gym Discrete environment.
Maps are specified usin... | 68cddbbd0b8bd9bd1b0a0cf33c0d5eb4b9a22a31 | 3,619,805 |
def crc16(string, value=0):
"""CRC-16 poly: p(x) = x**16 + x**15 + x**2 + 1
@param string: Data over which to calculate crc.
@param value: Initial CRC value.
"""
crc16_table = []
for byte in range(256):
crc = 0
for _ in range(8):
if (byte ^ crc) & 1:
... | e31ddafc6216b682d9cd0ac24a9fb634f1be1fb8 | 3,619,806 |
def get_fpn_featureMaps(features,num_filt=256):
"""
features: list of different maps produced by a backbone
**Resnet50** : for resnet50 it contains all the 5 feature maps from each block
Return: list of pyramid features
"""
C1,C2,C3,C4,C5 = features
C5d1 = keras.layers.Conv2D(num_filt,(... | 3b265bb62163cc075e8aa608bbbf21b654df4b98 | 3,619,807 |
import time
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
S... | bd225cd4ecd5af4a9ee9f29137e46807bf534ee9 | 3,619,808 |
def Interface_Static_Standards(*args):
"""
* Initializes all standard static parameters, which can be used by every function. statics specific of a norm or a function must be defined around it
:rtype: void
"""
return _Interface.Interface_Static_Standards(*args) | 85dfea02a10d62203eaf7afe814b7ee25b925cfe | 3,619,809 |
from typing import Sequence
from typing import Dict
from typing import List
def batch_inputs(input_records: Sequence[Dict[K, V]]) -> Dict[K, List[V]]:
"""Batch inputs from list-of-dicts to dict-of-lists."""
assert input_records, 'Must have non-empty batch!'
ret = {}
for k in input_records[0]:
ret[k] = [r[... | c7e5a13258b131f93d11886f114d49b8804c3bd1 | 3,619,810 |
def chunks(seq, size):
"""Breaks a sequence of bytes into chunks of provided size
:param seq: sequence of bytes
:param size: chunk size
:return: generator that yields tuples of sequence chunk and boolean that indicates if chunk is
the last one
"""
length = len(seq)
return ((seq... | 25abe8afdb032af0e2b10dbd3c03b369d862813f | 3,619,811 |
def compare(
left, right, left_label="left", right_label="right", drop_close=True, **kwargs
):
"""Compare the data in two IamDataFrames and return a pandas.DataFrame
Parameters
----------
left, right : IamDataFrames
two :class:`IamDataFrame` instances to be compared
left_label, right_la... | 0820c606a93d3e12c333d43018f55f53407ff530 | 3,619,812 |
def combine_shuffle(parcellation, scale, spatnull, alpha):
"""
Combines outputs of all simulations into single files for provided inputs
Parameters
----------
parcellation : str
Name of parcellation to be used
scale : str
Scale of `parcellation` to be used
spatnull : str
... | 6e56b3d57e0c14b7a624f406e66c3a902590a2db | 3,619,814 |
def tcycle(iterable, n):
"""
>>> tcycle([1, 2, 3], 2)
(1, 2)
>>> tcycle([1, 2, 3], 4)
(1, 2, 3, 1)
"""
return tuple(islice(cycle(iterable), n)) | d836316de1dd91770d662aca9fb024c702499317 | 3,619,815 |
from typing import List
from typing import Callable
from typing import Any
from typing import Union
from typing import Tuple
def compute_best_permutation(x: List,
y: List,
sim: Callable[[Any, Any], Union[bool, int, float]],
agg: Ca... | 4d0c9a4d1a719c6b67f585e13fb803f8ff81bb6e | 3,619,816 |
import torch
def change_box_order(boxes, order):
"""Change box order between (xmin,ymin,xmax,ymax) and (xcenter,ycenter,width,height).
Args:
boxes: (tensor) bounding boxes, sized [N,4].
order: (str) either 'xyxy2xywh' or 'xywh2xyxy'.
Returns:
(tensor) converted bounding boxes, sized [N... | 6dd1ef6d2370c79de7ec321ab16592316b7b75da | 3,619,817 |
def GetLidarSimulation(environment,position,orientation):
"""
This function simulates the operation of the LIDAR by returning a list with the polar coordinates of
the location of the obstacles
environment -> List with all objects that are in the circuit as well as the edges of the circuit
posi... | 18b82528945a541411f21f36015c6518cd0e81fd | 3,619,818 |
import ast
def For_range(t, x):
"""Special conversion for ``for name in range(n)``, which detects
``range()`` calls and converts the statement to:
.. code:: javascript
for (var name = 0, bound = bound; name < bound; name++) {
// ...
}
"""
if (isinstance(x.target, ast.Name)... | e1d71e1c76798e7282c67de01dd7ff281ef28f9e | 3,619,819 |
def calculate_G3(
n_numbers,
neighborsymbols,
neighborpositions,
G_elements,
gamma,
zeta,
eta,
cutoff,
cutofffxn,
Ri,
normalized=True,
image_molecule=None,
n_indices=None,
weighted=False,
):
"""Calculate G3 symmetry function.
These are 3 body or angular i... | 41b8e163da879ca9e0339f8ac79f3f3d1abfb6fe | 3,619,820 |
import fractions
def decimals():
"""Generates instances of decimals.Decimal."""
return (
floats().map(float_to_decimal) |
fractions().map(
lambda f: Decimal(f.numerator) / f.denominator
)
) | b8e376543fc6182e5a9b6434865410f0950d622b | 3,619,821 |
def is_index(obj):
"""Verifies whether an object is a table index."""
return isinstance(obj, _supported_types().index_types) | e658951433b63e67ddbc4a98439569b1fb5b98fc | 3,619,822 |
def stack_controls(controls: list[dict], key: str = "tau"):
"""
Stack the controls in one vector
Parameters
----------
controls : list[dict]
List of dictionaries containing the controls
key : str
Key of the controls to stack such as "tau" or "qddot"
"""
the_tuple = (c[ke... | 8936bfbb1e4074cc79157fbdffaeb9e75de93f3a | 3,619,823 |
def secondYAxis(requestContext, seriesList):
"""
Graph the series on the secondary Y axis.
"""
for series in seriesList:
series.options['secondYAxis'] = True
series.name= 'secondYAxis(%s)' % series.name
return seriesList | d1c34f16da1f2142021c845afffb63b595e2ce69 | 3,619,824 |
def _next_incompatible_version(version):
"""
Find the next non-compatible version.
This is for use with the ~= compatible syntax. It will provide
the first version that this version must be less than in order
to be compatible.
:param str version: PEP 440 compliant version number
:return: T... | 7a9bdddf27cdd06e975236b0e7a64ae38d08bc98 | 3,619,825 |
def SNR_tot(G, BP, RP, J, H, K, lum, mass, teff, rad, numax, s=1., deltaT=1550, Amax_sun=2.5, obs='kepler-sc', D=1):
"""
predicted S/N for a given set of parameters
INPUT:
mag - relevant magnitude e.g. V or Kp for kepler...
lum - luminosity in Lsun
mass - stellar mass in Msun
... | 14c1f2d1b612fa5a4c1a3b820b14d7d49cab563e | 3,619,826 |
def checkFormat(DATAFIL):
"""
check the file format
"""
# Open file, read first 4 bytes as string
f = open(DATAFIL, 'rb')
dataBytes = np.fromfile(f, dtype=np.uint8, count=4)
data = "".join(map(chr, dataBytes))
f.close()
# If string matches a particular format, return format
if d... | 8e82a9f3a62092dbe75d7d948756eecc026b4b45 | 3,619,827 |
from datetime import datetime
import csv
def parse_csv(csv_path, ftype='csv', dt_col="date_time",
dt_format="%Y%m%d %H:%M:%S", dni_col='DNI',
dhi_col='DHI', stime=None, etime=None):
"""Parse a csv file containing direct normal
and diffuse horizontal data, ignoring NULL
and zero... | e29c7dbbb71b0353ceec0522ecab382f429d3c6a | 3,619,828 |
import logging
def CreateChrootSnapshot(snapshot_name, chroot_vg, chroot_lv):
"""Create a snapshot for the specified chroot VG/LV.
Args:
snapshot_name: The name of the new snapshot.
chroot_vg: The name of the VG containing the origin LV.
chroot_lv: The name of the origin LV.
Returns:
True if t... | ef804e01da6143b2ff4393a27a6db16c66fc5ff4 | 3,619,830 |
import typing
def decode_u256(as_bytes: typing.List[int]) -> int:
"""Decodes an unsigned 256 bit integer.
"""
size = as_bytes[0]
if size <= NUMERIC_CONSTRAINTS[CLTypeKey.U8].LENGTH:
return decode_u8(as_bytes[1:])
elif size <= NUMERIC_CONSTRAINTS[CLTypeKey.U32].LENGTH:
return d... | 0fd8e9a47a805d9d56c6d019c470e22a06e0c606 | 3,619,831 |
def image_code():
"""
图片验证码的实现逻辑
1. 获取参数 图片验证码的随机值
！ 切记！ 一定要判断
2. 生成图片验证码
3. 将图片文字和随机值保存到redis中
4. 将图片验证码 返回
:return:
"""
image_code_id = request.args.get("imageCodeId", None)
# 判断参数是否有值
if not image_code_id:
abort(403)
# 生成图片验证码
name, text, image = capt... | cb154182071efd367d1229e058cf90166b7bea44 | 3,619,833 |
from typing import Union
from pathlib import Path
from typing import Optional
def clone_renku_repository(
url: str,
path: Union[Path, str],
gitlab_token=None,
deployment_hostname=None,
depth: Optional[int] = None,
install_githooks=False,
install_lfs=True,
skip_smudge=True,
recursiv... | 1fbc1941b670caab64619705c666d636f0f6bcf1 | 3,619,834 |
def display_content(value):
"""This callback is used on the User Guide page and returns the selected User Guide when a user selects
a certain tab. User Guides can be added to HIVE in the userguide.py page.
"""
return userguide.user_guide(value) | f47dd22f395b8b249d2d2bfc1d14a09f6004325b | 3,619,836 |
def adjust_anomaly_scores(scores, dataset, is_train, lookback):
"""
Method for MSL and SMAP where channels have been concatenated as part of the preprocessing
:param scores: anomaly_scores
:param dataset: name of dataset
:param is_train: if scores is from train set
:param lookback: lookback (win... | 2f6adcdcae841573b362f1629138fdc70a568a24 | 3,619,837 |
import mpmath
def sf(k, nc, ntotal, ngood, nsample):
"""
Survival function of Fisher's noncentral hypergeometric distribution.
"""
_hg._validate(ntotal, ngood, nsample)
sup, p = support(nc, ntotal, ngood, nsample)
if k < sup[0]:
return mpmath.mp.one
elif k >= sup[-1]:
retur... | 4b4915b3bc2a9201bb66fca3e630cf59cd9afc8e | 3,619,838 |
def part_2_solution(graph, search_key):
"""
keeps a persistant counter that is recursively passed into a helper function
for ever depth of the helper function, the branch product gets updated, when a recursive function
exits it updates the counter with the product of counts for each sub-bag
the co... | 16ac48370de6d94397747b6b16791e0615f390aa | 3,619,839 |
def get_func_qty(*args):
"""get_func_qty() -> size_t"""
return _idaapi.get_func_qty(*args) | affa7952d604fd20674cadbe76cd9f55db669c14 | 3,619,841 |
def is_model_quantized(sym_model):
"""Checks whether the model is quantized.
Args:
sym_model (tuple): symbol model (symnet, args, auxs).
Returns:
boolean: True if model is quantized, else False.
"""
assert isinstance(sym_model, tuple) and isinstance(sym_model[0], mx.... | 32e8fd62bbc3280c0edd158194e8e7b2ffee53bf | 3,619,842 |
def evaluate(bounds, func):
"""
Evaluates simpsons rules on an array of values and a function pointer.
.. math::
\int_{a}^{b} = \sum_i ...
Parameters
----------
bounds: array_like
An array with a dimension of two that contains the starting and
ending points for the int... | af5102d37c1943420a0ff3fff980342ee8c9dad9 | 3,619,843 |
def process_image(image, target_shape): # downsampling to desired shape
"""Given an image, process it (downsample if needed) and return a numpy array."""
h, w, c = target_shape
image = load_img(image, target_size=(h, w)) # Load the image. Downsampling included! e.g. to (32,32,3)
img_arr = img_to_array(... | 968daaf8da198fc4ce129ea07c57c84942dc86ab | 3,619,844 |
import hashlib
def get_dataset_id(settings):
""" Creates and returns an unique ID (for practical purposes), given the data parameters.
The main use of this ID is to make sure we are using the correct data source,
and that the data parameters weren't changed halfway through the simulation sequence.
"""... | a696f0f85c8ee9c3cab975838db520cc061dfaf4 | 3,619,845 |
def get_sampler_diags(fit):
"""Returns useful sampler diagnostics for a particular MCMC fit with pystan"""
rhat=fit.summary()['summary'][:,-1]
rhat_worst=rhat[np.abs(1-rhat)==max(np.abs(1-rhat))][0]
n_eff_int_site=int(fit.summary()['summary'][0,-2])
return rhat_worst,n_eff_int_site | acee6147024ae32116e8b905eae18d8e47125148 | 3,619,846 |
def get_category(url=None, category='nieruchomosci', transaction_type='wszystkie', voivodeship=None, city=None,
street=None, filters=None):
""" Parses available offer urls from given category search page
:param url: Url to search web page
:param category: Type of property of interest (Mies... | 60aa51056bb209de8b777a7977504ab04847b6a2 | 3,619,847 |
def wmt_preprocess(dataset, training, max_length=-1, max_eval_length=-1):
"""Preprocessing for LM1B: filter out targets exceeding maximum length."""
def train_right_length(example, target):
l = tf.maximum(tf.shape(example["inputs"])[0], tf.shape(target)[0])
return tf.less(l, max_length + 1)
def eval_rig... | 870d1c1a7b6ee8e2a9c424884558355d4164a6d6 | 3,619,848 |
import torch
def instance_masks_to_semseg_mask(instance_masks, category_labels):
"""
Converts a tensor containing instance masks to a semantic segmentation mask.
:param instance_masks: tensor(N, T, H, W) (N = number of instances)
:param category_labels: tensor(N) containing semantic category label fo... | 127c9b9ff3d1044b1c5e1e8650ed493f8a4bc6de | 3,619,849 |
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