content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Tuple
from typing import List
from typing import Dict
def find_folders(root: str,
file_client: FileClient) -> Tuple[List[str], Dict[str, int]]:
"""Find classes by folders under a root.
Args:
root (string): root directory of folders
Returns:
Tuple[List[... | d01c41a6fad24c1d869042b1af5980f099510e2d | 3,616,200 |
import torch
def matrix_to_homogeneous(batch: torch.Tensor) -> torch.Tensor:
"""
Transforms a given transformation matrix to a homogeneous
transformation matrix.
Args:
batch: the batch of matrices to convert [N, dim, dim]
Returns:
torch.Tensor: the converted batch of matrices
... | ab3bf1acf1e8fab2d4a4fcdcfd062821bc891b9d | 3,616,201 |
from typing import Callable
def council_member(func: Callable[..., R]) -> CouncilMember[R]:
"""
a function decorator to create an appropriate member
:param func: the function to wrap
:return: a CouncilMember that will call func, ignoring council_state unless a parameter of that name exists
in fu... | 5aad18808b62dba6de1ea586f60e2d33ffe8da9a | 3,616,202 |
def setup(hass, config):
"""Setup the Buspro component. """
host = config[DOMAIN][CONF_HOST]
port = config[DOMAIN][CONF_PORT]
name = config[DOMAIN][CONF_NAME]
hass.data[DOMAIN] = "<buspro>"
#load_platform(hass, 'light', DOMAIN, {'optional': 'arguments'})
#load_platform(hass, 'light', ... | 5c7fcdce7e0b8ee87b598c1fb0ba53e0ef4720ef | 3,616,203 |
import time
import yaml
def LoadScene(filepath: str):
"""
Loads spyke scene from given file and makes the scene current.
param filepath: Scene filepath.
"""
def readline():
nonlocal file
line = file.readline()
if not line:
raise EOFError
return line.replace('\n', '')
start = time.perf_counter()
... | a445a79448480296621975352da544c587b3fe59 | 3,616,204 |
def check_output(output):
"""Check if output contains any error.
Several commands return output which we need to return back to the caller.
But, before we return anything back we want to perform a sanity check on
on the output in order to catch wrong input as it is impossible to
perform any sanitiz... | f1d4e64bd6a98cae23ae6c536dcb85c2d4c38024 | 3,616,205 |
import json
def englishToFrench(EnglishText):
"""Translate english to french."""
translation = language_translator.translate(
text=EnglishText,
model_id='en-fr').get_result()
jsonStr = json.dumps(translation, indent=2, ensure_ascii=False)
data = json.loads(jsonStr)
for i in data['translat... | 626f8e813b6316143afc147395cc4827406b48dd | 3,616,206 |
def nulls_first(column: _ColumnExpressionArgument[_T]) -> UnaryExpression[_T]:
"""Produce the ``NULLS FIRST`` modifier for an ``ORDER BY`` expression.
:func:`.nulls_first` is intended to modify the expression produced
by :func:`.asc` or :func:`.desc`, and indicates how NULL values
should be handled whe... | cd3f9cdcb8120cab3cdf38b4b370d64ba97bf97d | 3,616,207 |
import struct
def readHeader(fd):
"""read protocol header"""
data = fd.recv(6)
value = struct.unpack(">bib", data)
return value | 6f1ac379f9eb1f5862754dd10fced9a817bb664a | 3,616,208 |
def load_default_item(monkeypatch) -> Item:
"""Save a default item object, and return the object"""
default_category = MockCategory.default_category()
save_mock_category(monkeypatch, default_category)
default_item = MockItem.default_item(parent_category=default_category)
save_mock_item(monkeypatch, ... | 0f6085e592b9318418477a6eded7ec65778bbbc7 | 3,616,209 |
import tqdm
def encode_vocab(data, word2ind):
"""Converts string tokens to indices based on given dictionary."""
dialogs = data['data']['dialogs']
print("[%s] Encoding caption tokens..." % data['split'])
for i, dialog in enumerate(tqdm(dialogs)):
dialogs[i]['caption_tokens'] = [word2ind.get(wo... | cc67720badc41d8f46af361e5efad4ea2aed7e2a | 3,616,210 |
def get_RSI(df, column='Close', time_window=14):
"""Function to make the RSI values for a given stock dataframe"""
# Differential between the Column
diff = df[column].diff(1)
# Integrity of the difference values
up_chg = 0 * diff
down_chg = 0 * diff
# We consider the upchange as positive... | dca6f44062b8dbc04444f033d2e3b54075ad2ca6 | 3,616,211 |
def text_classification_mappings():
"""Text classification index mappings"""
return {
"_source": mappings.source(
excludes=[
# "words", # Cannot be exclude since comment text_length metric is computed using this source fields
"predicted",
"pre... | da83494f73308bec0c7e56ff196d44cfa1902e03 | 3,616,212 |
def use_mapping(*args):
"""
use_mapping(ea) -> ea_t
Translate address according to current mappings.
@param ea: address to translate (C++: ea_t)
@return: translated address
"""
return _ida_bytes.use_mapping(*args) | 1fdab28dbbba25d33a01e1f53a81f46f5ce99cbc | 3,616,213 |
def get_oldest_timestamp(data, additional_groups_to_ignore, additional_tags_to_ignore, print_all_tags=False):
"""data as dictionary from json. Should contain only time stamps except SourceFile"""
# save only the oldest date
date_available = False
oldest_date = None
oldest_keys = []
# save src... | fa2212adebacc2713b76ad436b354fec6e733de3 | 3,616,214 |
def get_pascal_voc_colors():
"""Load the mapping that associates pascal classes with label colors
Returns:
np.ndarray with dimensions (21, 3)
"""
num_cls = 21
colors = [0] * (num_cls * 3)
colors[0:3] = (0, 0, 0) # 0: 'road'
colors[3:6] = (128, 0, 0) # 1 'sidewalk'
colors[6:9] ... | 5638e802587763d77350ad0642cadae54167a6b7 | 3,616,215 |
def orth_complement(A, B):
"""Returns the orthogonal complement of A in B
"""
rank_a = A.shape[1]
C = jnp.hstack([A, B])
Q, R = jnp.linalg.qr(C)
return Q[:, rank_a:] | 53f0317c38d81113f616927d56661de139c8c278 | 3,616,216 |
import json
import sys
def __api_operation_prep(log, arguments):
"""
API Operation Common Functionality
"""
# Parse the user data
example_user_config_json = "{\n 'user': <username>," \
"\n 'apikey': <apikey>\n}\n"
try:
user_data = json.load(argument... | 703ca6ba3b1d2472c8acbbadb41dc18cc1a69e9d | 3,616,217 |
def get_grouped_data(input_data, feature, target_col, bins, cuts=0):
"""
Bins continuous features into equal sample size buckets and
returns the target mean in each bucket. Separates out nulls into
another bucket.
:param input_data: dataframe containg features and target column.
:param fe... | f63bf9680107f1fc6f50d5e79faa64d29ce9db00 | 3,616,218 |
from typing import List
def get_trajectory_from_environment(
environment: tf_py_environment.TFPyEnvironment,
observation: List[List[float]],
predicted_action: int) -> trajectories.Trajectory:
"""Gets trajectory data from `environment` based on observation and action.
Aligns `environment` observation ... | 6dcd4d9b4f2628d560f7cca0234453d5af69d259 | 3,616,219 |
def resize_nd_skimage(img, size, order=1, **kwargs):
""" Resize the input numpy array image to the given size.
Args:
img (numpy array): Image to be resized.
size (tuple): Desired output size.
order (int, optional): Desired interpolation. Default is 1
kwargs: other parameters for... | df34f5717b6699687279b8aef3b111994f6956c2 | 3,616,220 |
def MAXA(*args) -> Function:
"""
Returns the maximum numeric value in a dataset.
Learn more: https//support.google.com/docs/answer/3094016
"""
return Function("MAXA", args) | 5f19796115365c0b7e19fd0c6529d22d28d5998c | 3,616,221 |
def row_data(data):
"""handle tuple (row) data arrival"""
if is_ok(data):
return OK(data)
if is_eof(data):
if len(data) < 9:
return EOF(data)
return OK(data)
return Packet(data) | d34873a082071a51c2dcbf269c1c74a76e2e3be8 | 3,616,222 |
def timelined_cb(data, command, rc, stdout, stderr):
"""Very generic callback in case timelined acts weird."""
global buffers
buf = buffers[data]
name = wc.buffer_get_string(buf, "name")
stream = data + "STREAM"
del(hooks[stream])
wc.buffer_close(buf)
print_error("timelined for %s exited... | 8510f6715773af5290d947963412cd63aa138023 | 3,616,223 |
import os
import io
def cli(kilosort_folder=None, do_parallel=True, do_pc_features=True, do_silhouette=True, do_drift=True, fs=3e4):
""" Calculate metrics for all units on one probe"""
# kilosort_folder = '~/res_ss_full/res_ss/tcloop_train_m022_1553627381_'
if kilosort_folder is None:
kilosort_fol... | 4553a590bc70fddce3fab7df993e28ae132d6b83 | 3,616,224 |
import re
import logging
def get_gpu():
"""Returns video device as listed by WMI.
Not cached as the GPU driver may change underneat.
"""
wbem = _get_wmi_wbem()
if not wbem:
return None, None
client = _get_win32com()
dimensions = set()
state = set()
# https://msdn.microsoft.com/library/aa394512... | 653c99f5539fafda3a7d8a641adc08c14b7b732b | 3,616,225 |
def tfnormalize(X):
"""
Skip connection layer normalization.
"""
sh = np.shape(X)
X_norm = tf.contrib.layers.layer_norm(X, trainable=False)
return X | 5eabf19bc7cd376af6638c092b452a239b5650d3 | 3,616,226 |
def do_filter(parser, token):
"""
Filters the contents of the block through variable filters.
Filters can also be piped through each other, and they can have
arguments -- just like in variable syntax.
Sample usage::
{% filter force_escape|lower %}
This text will be HTML-escape... | edd2b6ee87615a24f7f9124da5eb95b3752d7028 | 3,616,227 |
def find_ch_interest_dict(show_channel_dict : dict, usr_pref_dict : dict):
"""Pass in show_channel_dict {show:channels} and usr_pref_dict {show: rating}. Returns dictionary {channel : total rating}"""
ch_interest_dict = {}
for show in usr_pref_dict:
if show in show_channel_dict:
if show_... | 9928b03c0ceea3ea38c3808a5fd4053553f4e5c4 | 3,616,228 |
async def discover_source_aliases(serial_port=None):
"""
Discover the alias configured for each input
Cycle through the device sources and get the alias from the feedback message
Restore the original source (which we can only do once we know its alias)
Note that:
- the device will be powered o... | e3a8ae0eb07d9ccc53b3150e4ad547398c4b2697 | 3,616,229 |
def compute_average_embedding(input_embeddings, input_lengths):
"""Computes bag-of-words embedding.
Args:
input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
input_lengths: <tf.int64>[bs, 1]
Returns:
bow_embedding: <tf.float32>[bs, emb_dim]
"""
max_seq_len = tf.shape(input_embeddings)[1]
... | 471a56d680ec3194a48300f0d573bc54ab2708b7 | 3,616,230 |
def is_valid_int(s: str) -> bool:
"""
Return true if s can be converted into a valid integer, and false otherwise.
:param s: value to check if can be converted into a valid integer
:return: true if s can be converted into a valid integer, false otherwise
>>> is_valid_int("hello")
False
>>> ... | 9d2c849839f6fdcf729a7c1503a3eac3daa5f000 | 3,616,231 |
def validate_report_input(X, y_true=None, y_pred=None, y_prob=None,
prtc_attr=None, priv_grp:int=1):
""" Raises error if data are of incorrect type or size for processing by
the fairness or performance reporters
Args:
X (array-like): Sample features
prtc_attr... | d159ad9cec76e0030b881545f82f5aea81cb9849 | 3,616,232 |
def gap_statistic(X, Wk, n_components, n_trials=100,
reference='uniform', n_jobs=1, random_state=None):
"""Calculate gap statistic for k-means clustering."""
rng = check_random_state(random_state)
random_seeds = []
for _ in range(n_trials):
has_seed_already = True
wh... | c31bf0ac3ed664a43e49d73b514c62ca76b9f976 | 3,616,233 |
from datetime import datetime
import pytz
def date_to_milliseconds(date_str):
"""Convert UTC date to milliseconds
If using offset strings add "UTC" to date string e.g. "now UTC", "11 hours ago UTC"
See dateparse docs for formats http://dateparser.readthedocs.io/en/latest/
:param date_str: date in r... | 2d9d4142627f10a8691280f10ead852ce5b1d2ee | 3,616,234 |
import argparse
def _build_validator_subparser(
subparser_action: argparse._SubParsersAction,
base_subparser: argparse.ArgumentParser,
) -> argparse.ArgumentParser:
"""Returns the base subparser with arguments required for every validator.
Returns:
argparse.ArgumentParser: validator subparser... | 12a532e260f1ae5fb766c5686988425ddea52b19 | 3,616,235 |
import re
def InstanceNameRegexpValidator():
"""Returns a function that validates an instance name using predefined rules.
Returns:
function: str -> str, usable as an argparse type
"""
# : and . are not valid characters, but we allow them through this regex so
# that we can give a better error message... | 57b8004a4023bb5011f1ecabb8658785c396cead | 3,616,236 |
def segmentation_metrics_blend(
isolated_images, detected_segmentations, matches, noise_threshold, meas_band_num
):
"""Calculates segmentation metrics given information from a single blend.
The true segmentation is obtained from the isolated images by setting to True
the pixels above the noise_threshol... | 8993780ab0d1c170bfc369d96cdc38f0b14ac087 | 3,616,237 |
def convert_RGB_to_OD(I):
"""
Convert from RGB to optical density (OD_RGB) space.
RGB = 255 * exp(-1*OD_RGB).
:param I: Image RGB uint8.
:return: Optical denisty RGB image.
"""
mask = (I == 0)
I[mask] = 1
return np.maximum(-1 * np.log(I / 255), 1e-6) | 5e22600f1985aebe10a2472fb98f664facbd0f0a | 3,616,238 |
def delete_profession(profession_id):
"""delete_profession
Delete profession # noqa: E501
:rtype: Response
"""
# Remove evidences of this profession
evidences = DBEvidence.query.filter(DBEvidence.evidence_profession == profession_id).all()
for evidence in evidences:
delete_evidenc... | 9195220969d759d57f5f4412b1772b51f205b7ee | 3,616,239 |
def process_remaining_mult_cases(chrom, positions, strand, edge_IDs, vertex_IDs,
transcript_dict, gene_starts, gene_ends, edge_dict,
locations, vertex_2_gene, run_info, cursor, tmp_gene):
""" This function is a catch-all for multiexonic transcripts t... | b1afa70df1f3e1836c6a05bd6b85d97c21f656fb | 3,616,240 |
def read_xlsx(filename: str, rows_count: int):
"""Читает документ xlsx и записывает данные в глобальную переменную названия телеграм каналов, локации и ссылки"""
tg = []
worksheet = xlrd.open_workbook(filename, on_demand=True).sheet_by_index(0)
for i in range(rows_count):
tg.append({'name': work... | 2ff498b9f942d0f91c8a4d4a679dcdfa9e4c0e3c | 3,616,241 |
def HammerProj( lonlat, lon0=0.0 ):
"""Takes a point at the (longitude, latitude) point in lonlat (arg 1,
in radians) and returns the (x,y) coordinates of the Hammer
projection of that point. lon0 is the central longitude for the
projection process (in radians). The x coordinate is in the range
[-2*... | 262623305e0ee794259446ecc5d470c39efd01ec | 3,616,242 |
def confusion_matrix_to_accuracy(conf_mat):
"""
Convert confusion matrix to accuracy
Parameters
conf_mat: np.ndarray | pd.DataFrame: confusion matrix, length of columns and that of rows must be same.
Returns
accuracy: float64: accuracy
"""
if type(conf_mat) is pd.DataFrame:
... | 2acb8bd8e2301b5f56c3371671a1ccb7c36848eb | 3,616,243 |
import shutil
def make_valid_param_card(path, restrictpath, outputpath=None):
""" modify the current param_card such that it agrees with the restriction"""
if not outputpath:
outputpath = path
cardrule = ParamCardRule()
cardrule.load_rule(restrictpath)
try :
cardrule.... | 1c2111eacaa8afb1748581e7ceb3d25724c849b2 | 3,616,244 |
import socket
from contextlib import closing
from typing import Generator
from typing import Any
import time
def multidevice_test_context(server_configuration: dict) -> Generator[MultiDeviceTestContext, None, None]:
"""Get generator for MultiDeviceTestContext."""
def _get_port() -> int:
with closing... | c6b1eb644133b8ed927729269fab45851495ab94 | 3,616,245 |
from typing import Optional
from typing import Any
def deeplabv3_mobilenet_v2(
pretrained: bool = False,
progress: bool = True,
num_classes: int = 21,
aux_loss: Optional[bool] = None,
**kwargs: Any
) -> nn.Module:
"""Constructs a DeepLabV3 model with a MobileNetV2 backbone.
Args:
... | 5a1dc413c3754d7b80aff584a7442ebd4af49564 | 3,616,246 |
def evaluate_compliance(configuration_item):
"""Check if given IAM user has a MFA device and return a string to be used for AWS config put_evaluations."""
if configuration_item['resourceType'] not in ['AWS::IAM::User']:
return 'NOT_APPLICABLE', 'Not applicable.'
if DEBUG_MODE is True:
print(... | 9cbf90090f0e2c0bbde1dae263a346bf730cc874 | 3,616,247 |
import os
def process_sub_image(i, input_filepath, output_location, date_string, coords_string):
"""
Read file and run network centrality
"""
# open BWNDVI image
sub_image = Image.open(input_filepath)
image_array = pillow_to_numpy(sub_image)
# run network centrality
feature_vec, _ = ... | aac7ecd0330e597b64ce7b8707391e20c7ec4a07 | 3,616,248 |
from REDACTED.minigo.dual_net_edge_tpu import DualNetworkEdgeTpu
from REDACTED.minigo.dual_net import DualNetwork
import os
def make_gtp_instance(load_file, cgos_mode=False, kgs_mode=False,
minigui_mode=False):
"""Takes a path to model files and set up a GTP engine instance."""
# Here so we ... | c250992c0ef9f48412355d12f6beb3dfa1f6a940 | 3,616,249 |
import requests
def fetch_minion_pool() -> dict:
""" Fetch a new minion pool """
minions = [
minion
for minion in requests.get(settings.CARD_DB_ENDPOINT).json()
if minion.get("battlegroundsPremiumDbfId")
]
pool = defaultdict(list)
for minion in minions:
pool[minion.... | 92539fc27f3c55058be72c00e4b37498fd306d64 | 3,616,250 |
def supprCouloir(grapheAmodif, noeud):
"""renvoie le graphe sans le couloir dans lequel se trouvait noeud"""
#un noeud-couloir est un noeud qui a seulement 2 voisins, qui ne contient pas de pièce.
#et aussi qui ne soit ni la position de départ du joueur, ni la position de départ de l'adversaire
#supprim... | b9f36776b1afaeea4721d23968843d72e0bc1569 | 3,616,251 |
import os
import errno
import time
from datetime import datetime
def make_notebook(data,
train_type='conv',
field_size=61,
ndim=2,
transform='pixelwise',
epochs=10,
optimizer='sgd',
skips=0,
... | 78ab93871760d9e3b6c29c2a13a6c2b629434b94 | 3,616,252 |
def API_StackDriver(config, auth, iterate=False):
"""StackDriver helper configuration for Google API.
Defines agreed upon version.
"""
api = {
'api': 'logging',
'version': 'v2',
'auth': auth,
'iterate': iterate
}
return API(config, api) | 1c7125dddf7e822b60082f755e0af6cfea3486bd | 3,616,253 |
import re
import os
import ast
def from_file(f):
""" Reads a namelist/configuration file and generates a
Namelist object. Anything enclosed in ${...} will be expanded
to a environment variable, while anything enclosed in
%(...) will be expanded to a previously defined variable within
the file.... | 9fc209c70fe81176b818525d793ca89e8c8c10b0 | 3,616,254 |
def con_K(x, y, beta, method='cdist', return_d=False):
"""con_K constructs the kernel K, where K(i, j) = k(x, y) = exp(-beta * ||x - y||^2).
Arguments
---------
x: :class:`~numpy.ndarray`
Original training data points.
y: :class:`~numpy.ndarray`
Control points used t... | 6f608bb7958b5756456d3c6cf3d4b3223ec1aea5 | 3,616,255 |
import torch
def gin_predictor_nasbench_nlp(search_space,
num_init=10,
k=10,
total_queries=150,
acq_opt_type='mutation',
allow_isomorphisms=False,
... | 8025f8ec847531ef75213233d3f0df89c0db572f | 3,616,256 |
def main(request: Request):
"""
Responds to an HTTP request.
Parameters:
----------
request (flask.Request):
HTTP request object.
"""
a.generate()
return jsonify({"sucess": True}) | eeb2317faeafcb141844c2a0ef2e0d20963bdc73 | 3,616,257 |
def manage(request):
""" All-encompassing view for management stuff """
assert isinstance(request, HttpRequest)
return render(
request,
'app/manage.html',
{
'title':'Dashboard',
}
) | b5bf4b86c58d21b29ea6c81d105dcc05f0a0c9e5 | 3,616,258 |
def accuracy_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""Accuracy classification score.
Args:
y_true (np.ndarray): correct target values.
y_pred (np.ndarray): estimated targets as returned by a classifier.
Returns:
float: proportion of correctly classified samples.
... | 24d5d6a113896d5c243ae492323c4d425179a8ec | 3,616,259 |
def setup_logging_server():
""" Gets a testing server ready to go
and returns the port being used"""
port_to_try = 5000
# Try to get an open port 32 times
for _ in range(0, 32):
try:
server = LoggingHTTPServer(('', port_to_try), UpdatingWebsite)
return server
... | 225a436ab95bb1ec4706a59020e67d8807a77d21 | 3,616,260 |
from typing import List
from typing import Tuple
def make_source_and_target_plates(target_df_list: List[pd.DataFrame],
max_volume_ul: float = MAX_WORKING_VOLUME_UL
) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""Generates optimized source and target pla... | d123584e9f4737809c77beb355c2dcd16f444b04 | 3,616,261 |
def path_along_circles(
arc_angle: float, circles_centers: np.ndarray, axis: np.ndarray = OUT
) -> PATH_FUNC_TYPE:
"""This function transforms each point by moving it roughly along a circle, each with its own specified center.
The path may be seen as each point smoothly changing its orbit from its starting... | 885e2e3770f9bc2e53d2062a43fe404cb192b529 | 3,616,262 |
def svn_wc_create_notify_url(*args):
"""svn_wc_create_notify_url(char url, svn_wc_notify_action_t action, apr_pool_t pool) -> svn_wc_notify_t"""
return apply(_wc.svn_wc_create_notify_url, args) | 36a42320c94f29516cdc401a9f0e0e35d477cd60 | 3,616,263 |
def make_model(n_size, n_output):
"""Create Keras model."""
model = Sequential()
model.add(LSTM(n_size))
model.add(Dense(n_output))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
ret... | 5c1c4bbc71f5b3fee81e7b3150c6277eb56c08c8 | 3,616,264 |
def humanize_seconds(total_seconds,
frmt='{hours:02d}:{minutes:02d}:{seconds:02d}',
negative_frmt=None):
"""Given time in int(seconds), return a string representing the time.
If negative_frmt is not given, a negative sign is prepended to frmt
and the result is wrap... | 86b58fe361382671f744529c676cc6cd16c877a6 | 3,616,265 |
def joint_residual(params, args):
"""
Residual for joint_fit
Input:
param_dict - suggested parameters of the functions Sigma and eta to be fit
args - arguments which remain fixed (e.g. r values)
Output:
residual - residual of the fit for the given parameters
"""
rA, Sigma... | 12b4ba12dad0ab8f97e721b73dede80987c2caab | 3,616,266 |
def _batch_gather(params, indices, axis, batch_dims):
"""Helper that implements the body for ragged gather() when batch_dims>0.
Args:
params: The tensor from which to gather values.
indices: The indices of values to gather.
axis: The axis in `params` to gather `indices` from.
batch_dims: The number... | a7a73514ce7382fd67e0471792a27bc6e4502819 | 3,616,267 |
import typing
import re
def search(pattern: typing.Union[str, int],
**kwargs):
"""Compare value to pattern using search."""
value = kwargs.pop('value')
# type adaptation only if value is int
if isinstance(value, int):
value = type(pattern)(value)
if config.TYPE_CHECK_LEVEL > 0... | e6989b053da4960f9cfb29fd4b0d21e8c2d31855 | 3,616,268 |
def get_base_info_for_selector(selection_dict, selector_type):
"""
Sets up the basic dictionary for data filters to be added on the data
:param selection_dict:
:param selector_type:
:return:
"""
base_info_dict_for_selector = {
OPTION_TYPE: selector_type,
COLUMN_NAME: selectio... | c08fc9a95517c3388da69e3f9935c255ce0d73c6 | 3,616,269 |
def make_datasets(dataset, train_batch=None, test_batch=None, train_transforms=None, test_transforms=None, num_workers=1, **custom_args):
""" Helper to make new instances of training and testing datasets.
Args:
dataset Case-sensitive dataset name
train_batch Training batch size, None or 0 for ... | 5c2090b9cbfb52c4cdb42f632dfe883c21465e9b | 3,616,270 |
def get_sentiment_by_id(sentiment_id):
"""
Returns the sentiment with given id
:param: sentiment_id
ID of the sentiment
:returns:
Sentiment with given ID
"""
sentiments = []
cursor = current_app.mongo.keywords.find({"request_id": sentiment_id})
for record in list(cursor... | 38fab08784267498fa09e1de35cd286c4a03f786 | 3,616,271 |
def clean_software_config(config):
"""Take an individual `config` data structure (as specified by
config_validation.SoftwareSchema) and return a 'clean' version suitable for
internal use. This allows for a simplified schema to be available to users
whilst preserving consistent internal data structures b... | c89ad5a4b61e4214d4b79ce6782e4fe5a86311bf | 3,616,272 |
def distance_A(A, D):
"""Takes distance matrix D and material attribute/category
matrix A and finds the sum of the pairwise distances of
each entry compared to D. This function should be used
in an optimizer to generate an optimal A matrix.
Implements first term of Equation 4
Parameters:
D... | dd896e36565a8daf5951fac3ceb29594c70707bb | 3,616,273 |
def get_word_by_name(name):
"""Get word detail by name
As title
---
tags: [word]
parameters:
- name: name
description: 单词名字
in: path
type: string
required: true
responses:
200:
description: Word matched.
schema:
$ref: '#/defin... | d76eacc085d24f6c71ffe8ced3fe55f0933ff153 | 3,616,274 |
def group_peaks_naive_1pass_centroid(df, ppmtol=1.0):
"""Compute groups of peaks, naive, one-pass, control distance to centroid ."""
# compute group indexes
glabel = 0
start = 0
glabels = [0]
reltol = ppmtol * 1.0e-6
m1 = df.loc[0, 'm/z']
samples = [df.loc[0, '_sample']]
masse... | 055f69644dc12d28fc6c477d497f2f32b6a8a593 | 3,616,275 |
import time
def run_firfilter(no_trick, duration=0.000001):
"""[summary]
Arguments:
no_trick ([type]): [description]
Keyword Arguments:
duration (float): [description] (default: {0.000001})
Raises:
Exception: [description]
"""
h0 = np.zeros(n) # initial x0
E = e... | fc2a223ce85113f8f8514cbae7e6688744b30214 | 3,616,276 |
def cdfinv_r(fName=None,pdf=None):
"""
Calculates the inverse of the cumulative distribution function for
probability as a function of r.
*** Arguments ***
* fName * File name for retrieving sigma(r). If None, the user must
define a function to calculate the inverse CDF. Otherw... | 498a2960cbede769eac396130ec5e306a0978bf3 | 3,616,277 |
def _base_down(x, base=10):
"""
Floor `x` to the nearest lower ``base^n``, where ``n`` is an integer.
Parameters
----------
x : float
Number to calculate the floor from.
base : float, optional
Base used to calculate the floor.
Return
------
float
The nearest... | 0489809838c9377b4114c751db34a4a81941ae57 | 3,616,278 |
def get_periodic_frame():
"""
Construct a simple periodic table dataframe organised by group and row. Note that
the lanthanides and actinides are each found in a single cell.
Returns
-------
:class:`pandas.DataFrame`
"""
table = pd.DataFrame(columns=np.arange(1, 19), index=np.arange(1,... | b5db8f2a0373c19fcea7ab0fe78cb8a4b7f0ade8 | 3,616,279 |
def miller_rabin_d(n: int) -> bool:
"""Check if n is a prime number via deterministic Miller-Rabin test.
Miller showed that it is possible to make the algorithm deterministic by
only checking all bases ≤ O(lg(n)^2). Bach later gave a concrete bound, it
is only necessary to test all bases a ≤ 2lg(n)^... | ac668cb55e417a6784ba52d1c5da1dc26d3693ad | 3,616,280 |
def get_path_upload_avatar(instance, file):
"""Построение пути к файлу, format: (media)/avatar/user_id/photo.jpg"""
return f"avatar/user_{instance.id}/{file}" | d3169d68e4abee31ee9649548892c2dc960717cc | 3,616,281 |
def is_category_url(url, ignore_exceptions=False):
"""This function identifies if a provided URL is for a category in the environment.
.. versionadded:: 2.1.0
:param url: The URL to be evaluated
:type url: str
:param ignore_exceptions: Determines if exceptions should not be raised
:type ignore... | 53127f4eaa23d5c8029ab0e6783be026d6a7328e | 3,616,282 |
import re
def maximum_time(time: str) -> str:
"""
Takes in a time with a single digit of either the hour part or minute part hidden with ?. The format of the time
is hh:mm. This returns the maximum possible time from the given input.
@param time: time of format hh:mm with a single digit of either the ... | 9bef83c27e7911c67172dbe41ca616c5adda1b17 | 3,616,283 |
def get_fd(file_or_fd, default=None):
"""Helper function for getting a file descriptor."""
fd = file_or_fd
if fd is None:
fd = default
if hasattr(fd, "fileno"):
fd = fd.fileno()
return fd | 7e8a5b91d010878bcc091fa9ef3b281431e34e19 | 3,616,284 |
from typing import List
from typing import Callable
def evaluate_predictions(preds: List[List[float]],
targets: List[List[float]],
metric_func: Callable) -> List[float]:
"""
Evaluates predictions using a metric function and filtering out invalid targets.
... | 7b7f550a0983cbb8af90f13b214a195cdb8cbfe3 | 3,616,285 |
def recommend_size(args, img):
""" Recommend size (in pixels) for populated image to reach 300 dpi"""
current_ppi = 72.0/img.scale
target_ppi = args.normal_ppi
scale = target_ppi/current_ppi
return (int(img.image.width*scale), int(img.image.height*scale)) | b32a0520586984124ba2134dc99ecc0b71cd01df | 3,616,286 |
def retain_top_pct(vals, pct_keep):
"""
Retain as many components as you need to capture `pct_keep` proportion
of the overall value
"""
if pct_keep > 1 or pct_keep <= 0:
raise ValueError(
"Must set pct_keep between 0 and 1 be when using "
"'retain_top_pct' retention ... | 6530550c8c31a708806e035196a59cb76010221b | 3,616,287 |
def test_curve_plot():
"""
Tests mpl image of curve.
"""
well = Well.from_las(FNAME)
fig = well.data['GR'].plot(return_fig=True)
return fig | 077ad46ea7012f032d85ebe92b9e9633b7228535 | 3,616,288 |
from pgdumplib import dump
def new(dbname: str = 'pgdumplib', encoding: str = 'UTF8',
converter=None):
"""Create a new :py:class:`pgdumplib.dump.Dump` instance
:param str dbname: The database name for the dump (Default: ``pgdumplib``)
:param str encoding: The data encoding (Default: ``UTF8``)
... | c41cffcc961b7a273bf9b7402af7a22401a738db | 3,616,289 |
def audio(sec=3):
""" Record audio for a given duration """
display(Javascript(RECORD_AUDIO))
s = output.eval_js('record(%d)' % (sec*1000))
b = b64decode(s.split(',')[1])
return Audio(b) | c4b6c43aa75a44a785b9f920e5e276a3d318847a | 3,616,290 |
import sys
def rename(location: str, file: str, new_name: str) -> str:
"""
Change file name via copy old file to file with new_name and delete(old_file)
:param location:
:param file:
:param new_name:
:return: str - new file path in s3 storage
"""
try:
location = remove_start(st... | 2b89fb16dfa7b4c36c30ebc18c998d8f102729fe | 3,616,291 |
import os
def get_latency(email, photo_name, processType):
"""This function returns latency for a processed image
Args:
email (string): user email for ID
photo_name (string): which photo to load latency for
processType (string): which process type to load
Returns:
latency... | a34a8c02dc0645e4d947c198c9b54c8d8c4f7d86 | 3,616,292 |
from .core import SegmentedImage
from recipes import pprint
import logging
def detect_loop(image, mask=None, snr=3, npixels=3,
deblend=True, dilate=0, edge_cutoff=None,
max_iter=np.inf, group_name_format='sources{count}',
model=None, opt_kws=None, report=None):
"""
... | 9eaa8e7cab463d7c7831565e6d662de796f0f98c | 3,616,293 |
def isinstalled(name: str,
lib_loc=None):
"""
Find whether an R package is installed
:param name: name of an R package
:param lib_loc: specific location for the R library (default: None)
:rtype: a :class:`bool`
"""
instapack = InstalledPackages(lib_loc)
return instapack... | a05dce14c5280aa96113933bf9dfd0141ffd2d5b | 3,616,294 |
import math
def create_experiment(run_config,
hparams,
model_name,
problem_name,
data_dir,
train_steps,
eval_steps,
min_eval_frequency=2000,
s... | 31cbb1ba9948af5a3e84e864271e0888f4fdf448 | 3,616,295 |
def quit_hermit():
"""usage: quit
Exit Hermit.
"""
clear_screen()
return True | 1b27fdcb6a1fe69541f5f388e05e70b3731a5239 | 3,616,296 |
from typing import Union
def flatten(x: Union[list, tuple]) -> list:
"""
Flattening function for nested lists and tuples
Args:
x: List or tuple
Returns:
object (list): Flat list
"""
if not isinstance(x, list) and isinstance(x, tuple):
raise TypeError("input must be a ... | 36c35dfbef4214ccf0f6d355f36865996fd6d88e | 3,616,297 |
from typing import Mapping
def update_nested(original_dict, update_dict):
"""Update a nested dictionary with another nested dictionary.
Has equivalent behaviour to :obj:`dict.update(self, update_dict)`.
Args:
original_dict (dict): The original dictionary to update.
update_dict (dict): The... | a1a372ac4d26066c3fe32cd4ee1a49fff6972cd9 | 3,616,298 |
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
num_epochs = 1500, minibatch_size = 32, print_cost = True):
"""
Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
Arguments:
X_train -- training set, of shape (input size = 1... | c45624090d044dcafaa254f8fb3c5a5340c3efaf | 3,616,299 |
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