content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_image_ids(idol_id):
"""Returns all image ids an idol has."""
c.execute("SELECT id FROM groupmembers.imagelinks WHERE memberid=%s", (idol_id,))
all_ids = {'ids': [current_id[0] for current_id in c.fetchall()]}
return all_ids | 036dfdc9d757b2a7c70b26653252bbb5e180a5f1 | 22,154 |
def sortarai(datablock, s, Zdiff, **kwargs):
"""
sorts data block in to first_Z, first_I, etc.
Parameters
_________
datablock : Pandas DataFrame with Thellier-Tellier type data
s : specimen name
Zdiff : if True, take difference in Z values instead of vector difference
NB: this... | 5127b293b63a0d67da9e0ec21ff3085beea5a836 | 22,155 |
import json
def _extract_then_dump(hex_string: str) -> str:
"""Extract compressed content json serialized list of paragraphs."""
return json.dumps(
universal_extract_paragraphs(
unpack(bytes.fromhex(hex_string))
)
) | 6be6f045ff86580e5d33239e8b8232f5c343723b | 22,157 |
import base64
import hmac
import hashlib
def sso_redirect_url(nonce, secret, email, external_id, username, name, avatar_url, is_admin , **kwargs):
"""
nonce: returned by sso_validate()
secret: the secret key you entered into Discourse sso secret
user_email: email address of the user who lo... | 7aefb1c67a23f0c1311ce4297760a13ea39769fe | 22,158 |
def normalized_cluster_entropy(cluster_labels, n_clusters=None):
""" Cluster entropy normalized by the log of the number of clusters.
Args:
cluster_labels (list/np.ndarray): Cluster labels
Returns:
float: Shannon entropy / log(n_clusters)
"""
if n_clusters is None:
n_clusters... | aaed21c7cd91e1b61eb8a8336978eded8673960e | 22,159 |
def ingest_data(data, schema=None, date_format=None, field_aliases=None):
"""
data: Array of Dictionary objects
schema: PyArrow schema object or list of column names
date_format: Pandas datetime format string (with schema only)
field_aliases: dict mapping Json field names to desired schema names
... | 94ac4c13a71275485a79404f20c702886b39fb1c | 22,160 |
def build_messages(missing_scene_paths, update_stac):
""" """
message_list = []
error_list = []
for path in missing_scene_paths:
landsat_product_id = str(path.strip("/").split("/")[-1])
if not landsat_product_id:
error_list.append(
f"It was not possible to bui... | 07865a38fad6e3f642d3e55b80fac734dbb7d94b | 22,161 |
def DecrementPatchNumber(version_num, num):
"""Helper function for `GetLatestVersionURI`.
DecrementPatchNumber('68.0.3440.70', 6) => '68.0.3440.64'
Args:
version_num(string): version number to be decremented
num(int): the amount that the patch number need to be reduced
Returns:
string: decremente... | e42585791063d7982675065be7480ae7b5ea637d | 22,162 |
def hi_means(steps, edges):
"""This applies kmeans in a hierarchical fashion.
:param edges:
:param steps:
:returns: a tuple of two arrays, ´´kmeans_history´´ containing a number of
arrays of varying lengths and ´´labels_history´´, an array of length equal
to edges.shape[0]
"""
sub_... | 60d242f1ed9a4009bac706053e56f0d450ca7a7a | 22,163 |
def tag_item(tag_name, link_flag=False):
"""
Returns Items tagged with tag_name
ie. tag-name: django will return items tagged django.
"""
print C3 % ("\n_TAGGED RESULTS_")
PAYLOAD["tag"] = tag_name
res = requests.post(
GET_URL, data=json.dumps(PAYLOAD), headers=HEADERS, verify=False)... | 038b6a81dec1ea7c6fb9c4d83eaa6425d950c2fd | 22,164 |
def movie_info(tmdb_id):
"""Renders salient movie data from external API."""
# Get movie info TMDB database.
print("Fetching movie info based on tmdb id...")
result = TmdbMovie.get_movie_info_by_id(tmdb_id)
# TMDB request failed.
if not result['success']:
print("Error!")
# Can'... | ee7188eddcc50d0114ae5b80bc753e803632d557 | 22,165 |
def diabetic(y, t, ui, dhat):
"""
Expanded Bergman Minimal model to include meals and insulin
Parameters for an insulin dependent type-I diabetic
States (6):
In non-diabetic patients, the body maintains the blood glucose
level at a range between about 3.6 and 5.8 mmol/L (64.8 and
104.4 mg/d... | 35949f9a3d6010e89346ebb7f2818230ca6148a0 | 22,166 |
def get_spacy_sentences(doc_text):
"""
Split given document into its sentences
:param doc_text: Text to tokenize
:return: list of spacy sentences
"""
doc = _get_spacy_nlp()(doc_text)
return list(doc.sents) | 30345e04add02fd74e8470ce667fee60ddc7140d | 22,167 |
def get_recommendations(commands_fields, app_pending_changes):
"""
:param commands_fields:
:param app_pending_changes:
:return: List of object describing command to run
>>> cmd_fields = [
... ['cmd1', ['f1', 'f2']],
... ['cmd2', ['prop']],
... ]
>>> app_fields = {
... ... | 03fa583a5d4ea526cfeaa671418488218e1b227f | 22,168 |
import collections
def file_based_convert_examples_to_features(examples,
label_list,
max_seq_length,
tokenizer,
output_file):
"""Convert a... | efb6a0d347ae3a99a5859cbd3e0c1216d09377e6 | 22,169 |
def hello():
"""Return a friendly HTTP greeting."""
return 'Hello World!!!' | ae3528d10f94c92c169f53d5c3897572c9032bc2 | 22,170 |
from typing import Union
from typing import List
from typing import Tuple
def _findStress(
syllables: Union[List[Syllable], List[List[str]]]
) -> Tuple[List[int], List[int]]:
"""Find the syllable and phone indicies for stress annotations"""
tmpSyllables = [_toSyllable(syllable) for syllable in syllables]
... | d4adc4b6156e4d823640a29815d75832c21f68ac | 22,171 |
from hydrus.data.helpers import get_path_from_type
import random
import string
def gen_dummy_object(class_title, doc):
"""
Create a dummy object based on the definitions in the API Doc.
:param class_title: Title of the class whose object is being created.
:param doc: ApiDoc.
:return: A dummy objec... | 88f096a483699b9126496564cfe755386012acce | 22,172 |
def gather_info(arguments) -> Info:
"""Gather info."""
if arguments.integration:
info = {"domain": arguments.integration}
elif arguments.develop:
print("Running in developer mode. Automatically filling in info.")
print()
info = {"domain": "develop"}
else:
info = _... | 4cc527373fe29b36526388716f2402101039cea2 | 22,173 |
def __is_geotagging_input(question_input, _):
"""Validates the specified geotagging input configuration.
A geotagging input configuration contains the following optional fields:
- location: a string that specifies the input's initial location.
Args:
question_input (dict): An input configuratio... | 8fc392f832cc6d5c38eb5ffd2e7e20ff50b4ffd3 | 22,176 |
def _be_num_input(num_type, than, func=_ee_num_input, text='', error_text="Enter number great or equal than ",
error_text_format_bool=True,
error_text_format="Enter number great or equal than {}", pause=True, pause_text_bool=True,
pause_text='Press Enter...', clear=... | 92f8d3c119b0c331c50c4213644003c62534deb9 | 22,177 |
def createParetoFig(_pareto_df,_bestPick):
"""
Initalize figure and axes objects using pyplot for pareto curve
Parameters
----------
_pareto_df : Pandas DataFrame
DataFrame from Yahoo_fin that contains all the relevant options data
_bestPick : Pandas Series
Option data for the b... | dc6d8e6566c8c12d9938bf57a22c569124b65895 | 22,178 |
def rem4(rings, si):
"""finds if the silicon atom is within a 4 membered ring"""
for i in range(len(rings)):
triangles = 0
distances = []
locations = []
for n in range(len(rings[i]) - 1):
for m in range(1, len(rings[i]) - n):
distances.append(distance(... | e38fb7e1349597fa79cd21d39ecb222f39de86b3 | 22,179 |
def ilogit(x):
"""Return the inverse logit"""
return exp(x) / (1.0 + exp(x)) | 064383b65c2e8b011d2a6cd6ce14bf7936dd3178 | 22,180 |
def get_out_of_bounds_func(limits, bounds_check_type="cube"):
"""returns func returning a boolean array, True for param rows that are out of bounds"""
if bounds_check_type == "cube":
def out_of_bounds(params):
""" "cube" bounds_check_type; checks each parameter independently"""
... | 56c82efd63afdb36fc4d60cda7912fd9a4edb1d0 | 22,181 |
from typing import Dict
from typing import Set
def inspectors_for_each_mode(lead_type="lead_inspector") -> Dict[str, Set[str]]:
"""
We want to be able to group lead inspectors by submode.
"""
if lead_type not in ["lead_inspector", "deputy_lead_inspector"]:
raise ValueError("Can only query for ... | b712e82a14ddc8153c0ce61ebd3e95faca5993ae | 22,182 |
import tables
def is_hdf_file(f):
"""Checks if the given file object is recognized as a HDF file.
:type f: str | tables.File
:param f: The file object. Either a str object holding the file name or
a HDF file instance.
"""
if((isinstance(f, str) and (f[-4:] == '.hdf' or f[-3:] == '.h5')... | 55d89b0d1afdf2acf705d5266e2c44f6d3901c2e | 22,184 |
def dummy_receivers(request, dummy_streamers):
"""Provides `acquire.Receiver` objects for dummy devices.
Either constructs by giving source ID, or by mocking user input.
"""
receivers = {}
for idx, (_, _, source_id, _) in enumerate(dummy_streamers):
with mock.patch('builtins.input', side_ef... | 70d8e0f7c4f84f949bdc73a4fd240684d86baaed | 22,185 |
def construct_reverse_protocol(splitting="OVRVO"):
"""Run the steps in the reverse order, and for each step, use the time-reverse of that kernel."""
step_length = make_step_length_dict(splitting)
protocol = []
for step in splitting[::-1]:
transition_density = partial(reverse_kernel(step_mapping[... | 7526e1cab43eeb3ef58a1ac4a673bf9a9992e287 | 22,187 |
def tabinv(xarr, x):
"""
Find the effective index in xarr of each element in x.
The effective index for each element j in x is the value i such that
:math:`xarr[i] <= x[j] <= xarr[i+1]`, to which is added an interpolation fraction
based on the size of the intervals in xarr.
Parameter... | c020222355e4d7671e7117c30e3babf0fa1d1f46 | 22,188 |
import configparser
def getldapconfig() :
""" Renvoie la configuration ldap actuelle"""
cfg = configparser.ConfigParser()
cfg.read(srv_path)
try :
return (cfg.get('Ldap', 'ldap_address'),
cfg.get('Ldap', 'ldap_username'),
cfg.get('Ldap', 'ldap_password').replace("$perce... | 505c7d3728b986811a86841003cd763912c82a93 | 22,189 |
def rename_dict_key(_old_key, _new_key, _dict):
"""
renames a key in a dict without losing the order
"""
return { key if key != _old_key else _new_key: value for key, value in _dict.items()} | ddc497796e0e52677afdf09b7f4995cf3a534cbc | 22,190 |
def api_browse_use_case() -> use_cases.APIBrowseUseCase:
"""Get use case instance."""
return use_cases.APIBrowseUseCase(items_repository) | 653525c9338bf8f520014a530228512fae1ed03d | 22,191 |
def get_descriptive_verbs(tree, gender):
"""
Returns a list of verbs describing pronouns of the given gender in the given dependency tree.
:param tree: dependency tree for a document, output of **generate_dependency_tree**
:param gender: `Gender` to search for usages of
:return: List of verbs as st... | ec021c89cb59da2ff8abb0169ea2567cb2e3a13c | 22,193 |
def client():
"""Return a client instance"""
return Client('192.168.1.1') | 19cda306e37e7a34395b86010fb4331a238a6cbc | 22,194 |
import termios, fcntl, sys, os
def read_single_keypress():
"""Waits for a single keypress on stdin.
This is a silly function to call if you need to do it a lot because it has
to store stdin's current setup, setup stdin for reading single keystrokes
then read the single keystroke then revert stdin bac... | 646c04bc5441557064714716087a9893c7fa66dc | 22,196 |
def set_table(table, fold_test, inner_number_folds, index_table, y_name):
""" Set the table containing the data information
Set the table by adding to each entry (patient) its start and end indexes in the concatenated data object.
In fact each patients i is composed by `n_i` tiles so that for example patie... | d46c0601b59f27a60ec99a5305113d94893ba748 | 22,197 |
def parse_args():
"""
引数パース
"""
argparser = ArgumentParser()
argparser.add_argument(
"-b",
"--bucket-name",
help="S3 bucket name",
)
argparser.add_argument(
"-d",
"--days",
type=int,
help="Number of days",
)
return argparser.par... | 36302c14466a2c1a1791217566c49687fc55b567 | 22,198 |
def predict(file):
"""
Returns values predicted
"""
x = load_img(file, target_size=(WIDTH, HEIGHT))
x = img_to_array(x)
x = np.expand_dims(x, axis=0)
array = NET.predict(x)
result = array[0]
answer = np.argmax(result)
return CLASSES[answer], result | 98ce2d770d7bffc47e6fe84521fd6992ab2a53fa | 22,199 |
def sample_categorical(pmf):
"""Sample from a categorical distribution.
Args:
pmf: Probablity mass function. Output of a softmax over categories.
Array of shape [batch_size, number of categories]. Rows sum to 1.
Returns:
idxs: Array of size [batch_size, 1]. Integer of category sa... | 5b270e63bb5e290a97cacede9bd0f8bf34fc0ecf | 22,200 |
def make_Dex_3D(dL, shape, bloch_x=0.0):
""" Forward derivative in x """
Nx, Ny , Nz= shape
phasor_x = np.exp(1j * bloch_x)
Dex = sp.diags([-1, 1, phasor_x], [0, Nz*Ny, -Nx*Ny*Nz+Nz*Ny], shape=(Nx*Ny*Nz, Nx*Ny*Nz))
Dex = 1 / dL * sp.kron(sp.eye(1),Dex)
return Dex | 1d3a47624f180d672f43fb65082f29727b42f720 | 22,201 |
def feature_decoder(proto_bytes):
"""Deserializes the ``ProtoFeature`` bytes into Python.
Args:
proto_bytes (bytes): The ProtoBuf encoded bytes of the ProtoBuf class.
Returns:
:class:`~geopyspark.vector_pipe.Feature`
"""
pb_feature = ProtoFeature.FromString(proto_bytes)
retur... | ff7cdc6c0d7f056c69576af2a5b5eb98f57266af | 22,202 |
async def calculate_board_fitness_report(
board: list, zone_height: int, zone_length: int
) -> tuple:
"""Calculate Board Fitness Report
This function uses the general solver functions api to calculate and return all the different collisions on a given board array
representation.
Args:
boa... | a770863c044a4c4452860f9fccf99428dbfb5013 | 22,203 |
def quote_fqident(s):
"""Quote fully qualified SQL identifier.
The '.' is taken as namespace separator and
all parts are quoted separately
Example:
>>> quote_fqident('tbl')
'public.tbl'
>>> quote_fqident('Baz.Foo.Bar')
'"Baz"."Foo.Bar"'
"""
tmp = s.split('.', 1)
if len(tmp)... | 26cf409a09d2e8614ac4aba04db1eee6cac75f08 | 22,204 |
def row_generator(x, H, W, C):
"""Returns a single entry in the generated dataset.
Return a bunch of random values as an example."""
return {'frame_id': x,
'frame_data': np.random.randint(0, 10,
dtype=np.uint8, size=(H, W, C))} | e99c6e8b1557890b6d20ea299bc54c0773ea8ade | 22,205 |
def accuracy(output, target, topk=(1,)):
"""Computes the precor@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
... | 80e73c907e57b9666a8f399b8ed655c919d79abb | 22,206 |
def define_model(input_shape, output_shape, FLAGS):
"""
Define the model along with the TensorBoard summaries
"""
data_format = "channels_last"
concat_axis = -1
n_cl_out = 1 # Number of output classes
dropout = 0.2 # Percentage of dropout for network layers
num_datapoints = input_sh... | 4d6bea9444935af1b95e9b209eee2df7d455e90c | 22,207 |
import re
import collections
def group_files(config_files, group_regex, group_alias="\\1"):
"""group input files by regular expression"""
rx = re.compile(group_regex)
for key, files in list(config_files.items()):
if isinstance(files, list):
groups = collections.defaultdict(list)
... | 7f0c14387a9a63d03e8fdcb2297502a4ebf31e80 | 22,208 |
def get_current_icmp_seq():
"""See help(scapy.arch.windows.native) for more information.
Returns the current ICMP seq number."""
return GetIcmpStatistics()['stats']['icmpOutStats']['dwEchos'] | 4e5798a6187cd8da55b54698deab4f00aec19144 | 22,209 |
def text_mocked_request(data: str, **kwargs) -> web.Request:
"""For testng purposes."""
return mocked_request(data.encode(), content_type="text/plain", **kwargs) | fe9acfd2d7801a387f6497bdd72becd94da57ea9 | 22,210 |
def get_imu_data():
"""Returns a 2d array containing the following
* ``senses[0] = accel[x, y, z]`` for accelerometer data
* ``senses[1] = gyro[x, y, z]`` for gyroscope data
* ``senses[2] = mag[x, y, z]`` for magnetometer data
.. note:: Not all data may be aggregated depending on the IMU device co... | 24f24316a051a4ac9f1d8d7cbab00be05ff11c25 | 22,211 |
def parse_proc_diskstats(proc_diskstats_contents):
# type: (six.text_type) -> List[Sample]
"""
Parse /proc/net/dev contents into a list of samples.
"""
return_me = [] # type: List[Sample]
for line in proc_diskstats_contents.splitlines():
match = PROC_DISKSTATS_RE.match(line)
if ... | af24bc01d7e31dc43cf07057fae672ba62b20e53 | 22,212 |
def normalize(x):
"""Normalize a vector or a set of vectors.
Arguments:
* x: a 1D array (vector) or a 2D array, where each row is a vector.
Returns:
* y: normalized copies of the original vector(s).
"""
if x.ndim == 1:
return x / np.sqrt(np.sum(x ** 2))
eli... | f4e813b22a9088c3a9a209e94963b33c24fab88e | 22,213 |
def compute_perrakis_estimate(marginal_sample, lnlikefunc, lnpriorfunc,
lnlikeargs=(), lnpriorargs=(),
densityestimation='histogram', **kwargs):
"""
Computes the Perrakis estimate of the bayesian evidence.
The estimation is based on n marginal pos... | 70a287e3ed8391ecef1e48d7db846593fe240823 | 22,214 |
def postNewProfile(profile : Profile):
"""Gets all profile details of user with given profile_email
Parameters:
str: profile_email
Returns:
Json with Profile details """
profile_email = profile.email
profile_query = collection.find({"email":profile_email})
profile_query = [it... | be81eac071e89a9ff8d44ac8e2cd479e911763b6 | 22,215 |
from typing import List
def get_templates() -> List[dict]:
"""
Gets a list of Templates that the active client can access
"""
client = get_active_notification_client()
if not client:
raise NotificationClientNotFound()
r = _get_templates(client=client)
return r | b7603ba33e1628eb6dad91b861bf17ecb914c1eb | 22,217 |
def svn_mergeinfo_intersect2(*args):
"""
svn_mergeinfo_intersect2(svn_mergeinfo_t mergeinfo1, svn_mergeinfo_t mergeinfo2,
svn_boolean_t consider_inheritance, apr_pool_t result_pool,
apr_pool_t scratch_pool) -> svn_error_t
"""
return _core.svn_mergeinfo_intersect2(*args) | 5c8176eec56fb1a95b306af41c2d98caa75459ec | 22,218 |
def conv_backward(dZ, A_prev, W, b, padding="same", stride=(1, 1)):
"""
Performs back propagation over a convolutional layer of a neural network
dZ is a numpy.ndarray of shape (m, h_new, w_new, c_new) containing the
partial derivatives with respect to the unactivated output of the
convolutional lay... | d55eab80411efa903e03b584464ff50196468d7d | 22,220 |
import collections
def get_final_text(pred_text, orig_text, do_lower_case):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_... | 5ca989a4ae5ce00cd9c09c4d9480dbeb935d6ca8 | 22,222 |
def createContext(data, id=None, keyTransform=None, removeNull=False):
"""Receives a dict with flattened key values, and converts them into nested dicts
:type data: ``dict`` or ``list``
:param data: The data to be added to the context (required)
:type id: ``str``
:keyword id: The I... | a97c599689932cbfea7063fdae32702d413352ac | 22,223 |
import math
def haversine(phi1, lambda1, phi2, lambda2):
"""
calculate angular great circle distance with haversine formula
see parameters in spherical_law_of_cosines
"""
d_phi = phi2 - phi1
d_lambda = lambda2 - lambda1
a = math.pow(math.sin(d_phi / 2), 2) + \
math.cos(phi1) * math... | acb25fc8d305dde7b18059a770bdcd9b135b295a | 22,225 |
import yaml
from datetime import datetime
def get_backup_start_timestamp(bag_name):
"""
Input: Fisrt bag name
Output: datatime object
"""
info_dict = yaml.load(Bag(bag_name, 'r')._get_yaml_info())
start_timestamp = info_dict.get("start", None)
start_datetime = None
if start_timestamp i... | b8ee55c3028fb6f6e1137d614e836724c3e00bd6 | 22,226 |
def get_mask_areas(masks: np.ndarray) -> np.ndarray:
"""Get mask areas from the compressed mask map."""
# 0 for background
ann_ids = np.sort(np.unique(masks))[1:]
areas = np.zeros((len(ann_ids)))
for i, ann_id in enumerate(ann_ids):
areas[i] = np.count_nonzero(ann_id == masks)
return are... | bf584c9529118d9946e461b4df22cf64efbeb251 | 22,228 |
def cursor_from_image(image):
"""
Take a valid cursor image and create a mouse cursor.
"""
colors = {(0,0,0,255) : "X",
(255,255,255,255) : "."}
rect = image.get_rect()
icon_string = []
for j in range(rect.height):
this_row = []
for i in range(rect.width):
... | 173c3fc6bfcc6bb45c9e1e6072d7c68244750da9 | 22,229 |
def h_matrix(jac, p, lamb, method='kotre', W=None):
"""
JAC method of dynamic EIT solver:
H = (J.T*J + lamb*R)^(-1) * J.T
Parameters
----------
jac: NDArray
Jacobian
p, lamb: float
regularization parameters
method: str, optional
regularization method
Ret... | fc4d225bb2d98ee067b03c10f14ad23db6fad1a9 | 22,230 |
def _get_flavors_metadata_ui_converters_from_configuration():
"""Get flavor metadata ui converters from flavor mapping config dir."""
flavors_metadata_ui_converters = {}
configs = util.load_configs(setting.FLAVOR_MAPPING_DIR)
for config in configs:
adapter_name = config['ADAPTER']
flavor... | 4cb8dc1737579cd76dd696ec08f984015e3ef77b | 22,231 |
def outermost_scope_from_subgraph(graph, subgraph, scope_dict=None):
"""
Returns the outermost scope of a subgraph.
If the subgraph is not connected, there might be several
scopes that are locally outermost. In this case, it
throws an Exception.
"""
if scope_dict is None:
scope_dict... | 0bd649d00b745065e75e2dcfc37d9b16eaa0c3db | 22,232 |
def calc_entropy_ew(molecule, temp):
"""
Expoential well entropy
:param molecule:
:param temp:
:param a:
:param k:
:return:
"""
mass = molecule.mass / Constants.amu_to_kg * Constants.amu_to_au
a = molecule.ew_a_inv_ang * Constants.inverse_ang_inverse_au
k = molecule.ew_k_kca... | 7d4d2c13ce5b081e4b209169a3cf996dd0b34a44 | 22,233 |
def csm(A, B):
"""
Calculate Cosine similarity measure of distance between two vectors `A` and `B`.
Parameters
-----------
A : ndarray
First vector containing values
B : ndarray
Second vector containing values
Returns
--------
float
d... | cebca4a53ed3200d4820041fca7886df57f4a40c | 22,235 |
import random
import string
def rand_email():
"""Random email.
Usage Example::
>>> rand_email()
Z4Lljcbdw7m@npa.net
"""
name = random.choice(string.ascii_letters) + \
rand_str(string.ascii_letters + string.digits, random.randint(4, 14))
domain = rand_str(string.ascii... | 9898669f59511d5b8fd403de0ab7174e7710d898 | 22,237 |
import asyncio
async def value_to_deep_structure(value, hash_pattern):
"""build deep structure from value"""
try:
objects = {}
deep_structure0 = _value_to_objects(
value, hash_pattern, objects
)
except (TypeError, ValueError):
raise DeepStructureError(hash_patte... | 05df4e4cec2a39006631f96a84cd6268a6550b68 | 22,239 |
def get_users_run(jobs, d_from, target, d_to='', use_unit='cpu',
serialize_running=''):
"""Takes a DataFrame full of job information and
returns usage for each "user"
uniquely based on specified unit.
This function operates as a stepping stone for plotting usage figures
and return... | ab40605468e40b7e76a35d4bc2c1344be9050d5f | 22,240 |
import collections
def get_classes_constants(paths):
"""
Extract the vtk class names and constants from the path.
:param paths: The path(s) to the Python file(s).
:return: The file name, the VTK classes and any VTK constants.
"""
res = collections.defaultdict(set)
for path in paths:
... | e58531e99c37c1c23abb46b18f4b2af0b95c5db9 | 22,241 |
def predict_unfolding_at_temperature(temp, data, PDB_files):
"""
Function to predict lables for all trajectoires at a given temperature
Note: The assumption is that at a given temperature, all snapshots are at the same times
Filter should be 'First commit' or 'Last commit' or 'Filter osc' as descri... | 9fec4ee407bf41692c57899e96ff16ad2acdf4ea | 22,242 |
def _frac_scorer(matched_hs_ions_df, all_hyp_ions_df, N_spectra):
"""Fraction ion observed scorer.
Provides a score based off of the fraction of hypothetical ions that were observed
for a given hypothetical structure.
Parameters
----------
matched_hs_ions_df : pd.DataFrame
Dataframe of... | a341b02b7ba64eb3b29032b4fe681267c5d36a00 | 22,243 |
def role_in(roles_allowed):
"""
A permission checker that checks that a role possessed by the user matches one of the role_in list
"""
def _check_with_authuser(authuser):
return any(r in authuser.roles for r in roles_allowed)
return _check_with_authuser | 24ff0423dc50187f3607329342af6c8930596a36 | 22,244 |
from typing import List
def elements_for_model(model: Model) -> List[str]:
"""Creates a list of elements to expect to register.
Args:
model: The model to create a list for.
"""
def increment(index: List[int], dims: List[int]) -> None:
# assumes index and dims are the same length > 0
... | a0769d762fc31ac128ad077e1601b3ba3bcd6a27 | 22,245 |
def form_IntegerNoneDefault(request):
"""
An integer field defaulting to None
"""
schema = schemaish.Structure()
schema.add('myIntegerField', schemaish.Integer())
form = formish.Form(schema, 'form')
form.defaults = {'myIntegerField':None}
return form | 322671035e232cfd99c7500fe0995d652a4fbe7a | 22,246 |
import string
def tokenize(text, stopwords):
"""Tokenizes and removes stopwords from the document"""
without_punctuations = text.translate(str.maketrans('', '', string.punctuation))
tokens = word_tokenize(without_punctuations)
filtered = [w.lower() for w in tokens if not w in stopwords]
return fil... | 7a231d124e89c97b53779fee00874fb2cb40155e | 22,247 |
def to_dict(prim: Primitive) -> ObjectData:
"""Convert a primitive to a dictionary for serialization."""
val: BasePrimitive = prim.value
data: ObjectData = {
"name": val.name,
"size": val.size,
"signed": val.signed,
"integer": prim in INTEGER_PRIMITIVES,
}
if val.min... | 32d57b89e6740239b55b7f491e16de7f9b31a186 | 22,248 |
import requests
def get_raw_img(url):
"""
Download input image from url.
"""
pic = False
response = requests.get(url, stream=True)
with open('./imgs/img.png', 'wb') as file:
for chunk in response.iter_content():
file.write(chunk)
pic = True
response.close()
... | 67b2cf9f2c89c26fca865ea93be8f6e32cfa2de5 | 22,249 |
def get_and_validate_study_id(chunked_download=False):
"""
Checks for a valid study object id or primary key.
If neither is given, a 400 (bad request) error is raised.
Study object id malformed (not 24 characters) causes 400 error.
Study object id otherwise invalid causes 400 error.
Study does n... | 405420481c343afcaacbcfc14bc75fc7acf5aae9 | 22,250 |
import re
def tokenize_char(pinyin: str) -> tuple[str, str, int] | None:
"""
Given a string containing the pinyin representation of a Chinese character, return a 3-tuple containing its
initial (``str``), final (``str``), and tone (``int; [0-4]``), or ``None`` if it cannot be properly tokenized.
"""
... | e4bfb4712857d9201daff187ab63c9846be17764 | 22,251 |
def is_in_cell(point:list, corners:list) -> bool:
"""
Checks if a point is within a cell.
:param point: Tuple of lat/Y,lon/X-coordinates
:param corners: List of corner coordinates
:returns: Boolean whether point is within cell
:Example:
"""
y1, y2, x1, x2 = corners[2][0], corn... | 5f8f13a65ea4da1909a6b701a04e391ebed413dc | 22,252 |
def json_response(function):
"""
This decorator can be used to catch :class:`~django.http.Http404` exceptions and convert them to a :class:`~django.http.JsonResponse`.
Without this decorator, the exceptions would be converted to :class:`~django.http.HttpResponse`.
:param function: The view function whi... | 0b13ff38d932c64fd5afbb017601e34c1c26648b | 22,253 |
import re
def generate_junit_report_from_cfn_guard(report):
"""Generate Test Case from cloudformation guard report"""
test_cases = []
count_id = 0
for file_findings in report:
finding = file_findings["message"]
# extract resource id from finsind line
resource_regex = re.search... | cdf747c535042bf93c204fe8d2b647b3045f7ed7 | 22,254 |
def new_custom_alias():
"""
Create a new custom alias
Input:
alias_prefix, for ex "www_groupon_com"
alias_suffix, either .random_letters@simplelogin.co or @my-domain.com
optional "hostname" in args
Output:
201 if success
409 if the alias already exists
"""
... | 552812711eefd182d7671e3ac72776bbf908ff33 | 22,255 |
def seq(seq_aps):
"""Sequence of parsers `seq_aps`."""
if not seq_aps:
return succeed(list())
else:
ap = seq_aps[0]
aps = seq_aps[1:]
return ap << cons >> seq(aps) | ab94d3372f229e13a83387b256f3daa3ab2357a5 | 22,257 |
def Growth_factor_Heath(omega_m, z):
"""
Computes the unnormalised growth factor at redshift z given the present day value of omega_m. Uses the expression
from Heath1977
Assumes Flat LCDM cosmology, which is fine given this is also assumed in CambGenerator. Possible improvement
could be to tabulate... | c14e93a871f57c0566b13adb9005c54e68fbfa0f | 22,258 |
def freq2bark(freq_axis):
""" Frequency conversion from Hertz to Bark
See E. Zwicker, H. Fastl: Psychoacoustics. Springer,Berlin, Heidelberg, 1990.
The coefficients are linearly interpolated from the values given in table 6.1.
Parameter
---------
freq_axis : numpy.array
... | f6bd27c54debe8cd8b79099f106e1bf7d4350010 | 22,259 |
def close_connection(conn: Connection):
"""
Closes current connection.
:param conn Connection: Connection to close.
"""
if conn:
conn.close()
return True
return False | bca91687677860a7937875335701afb923ba49cc | 22,261 |
import warnings
def tile_memory_free(y, shape):
"""
XXX Will be deprecated
Tile vector along multiple dimension without allocating new memory.
Parameters
----------
y : np.array, shape (n,)
data
shape : np.array, shape (m),
Returns
-------
Y : np.array, shape (n, *sha... | f800c44ddd2a66553619157d8c8374a4c33dde18 | 22,262 |
def load_ref_system():
""" Returns d-talose as found in the IQMol fragment library.
All credit to https://github.com/nutjunkie/IQmol
"""
return psr.make_system("""
C -0.6934 -0.4440 -0.1550
C -2.0590 0.1297 0.3312
C -3.1553 -0.9249 0.167... | 7b41df916cb06dccaa53f13461f2bb7c6bfd882a | 22,263 |
def format_user_id(user_id):
"""
Format user id so Slack tags it
Args:
user_id (str): A slack user id
Returns:
str: A user id in a Slack tag
"""
return f"<@{user_id}>" | 2b3a66739c3c9c52c5beb7161e4380a78c5e2664 | 22,264 |
import socket
import ssl
def test_module(params: dict):
"""
Returning 'ok' indicates that the integration works like it is supposed to.
This test works by running the listening server to see if it will run.
Args:
params (dict): The integration parameters
Returns:
'ok' if test pass... | 0f49bff09fcb84fa810ee2c6d32a52089f2f0147 | 22,265 |
def class_loss_regr(num_classes, num_cam):
"""Loss function for rpn regression
Args:
num_anchors: number of anchors (9 in here)
num_cam : number of cam (3 in here)
Returns:
Smooth L1 loss function
0.5*x*x (if x_abs < 1)
x_abx - 0... | cad962f1af1a1acb2013063c6803261535652c18 | 22,266 |
import smtplib
import ssl
def smtplib_connector(hostname, port, username=None, password=None, use_ssl=False):
""" A utility class that generates an SMTP connection factory.
:param str hostname: The SMTP server's hostname
:param int port: The SMTP server's connection port
:param str username: The SMTP... | 511fe48b1f3f2d5d3b9ef3a803166be1519a1b7f | 22,267 |
def _to_one_hot_sequence(indexed_sequence_tensors):
"""Convert ints in sequence to one-hots.
Turns indices (in the sequence) into one-hot vectors.
Args:
indexed_sequence_tensors: dict containing SEQUENCE_KEY field.
For example: {
'sequence': '[1, 3, 3, 4, 12, 6]' # This is the amino acid ... | 32ff14139b53f181d6f032e4e372357cf54c1d62 | 22,268 |
def kaiser_smooth(x,beta):
""" kaiser window smoothing """
window_len=41 #Needs to be odd for proper response
# extending the data at beginning and at the end
# to apply the window at the borders
s = np.r_[x[window_len-1:0:-1],x,x[-1... | 2b766edd85927766330c8cddded3af639d5f16f3 | 22,269 |
def get_indel_dicts(bamfile, target):
"""Get all insertion in alignments within target. Return dict."""
samfile = pysam.AlignmentFile(bamfile, "rb")
indel_coverage = defaultdict(int)
indel_length = defaultdict(list)
indel_length_coverage = dict()
for c, s, e in parse_bed(target):
s = i... | e8f6883f1cf1d653fe0825b4f10518daa2801178 | 22,270 |
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