content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def serialize(value):
"""If the value is an BSON ObjectId, cast it to a string."""
if isinstance(value, bson.objectid.ObjectId):
return str(value)
else:
return value | 1fd0e01ebb46e4501b3a82744fcd3b2cdeaaf35b | 37,239 |
def stash_node_versions(node_id):
"""Gets all version of a node.
stash currently only keeps upto 10 versions.
"""
return stash_invoke('node-versions', node_id) | 5b0c32c6f3d45e18233d65255267652eca416d59 | 37,240 |
from datetime import datetime
def predict_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train samples
# train_idxs = np.arange(0, len(TRAIN_DATASET))
# 预测不需要打乱
# np.random.shuffle(train_idxs)
# num_batches = len(TRAIN_DATA... | a48c7a309702f2e9db5a59d6ba50d3d1579d33f9 | 37,244 |
from typing import Tuple
import re
import typing
def hex_to_rgb(hex: str, hsl: bool = False) -> Tuple[int, int, int]:
"""Converts a HEX code into RGB or HSL.
Taken from https://stackoverflow.com/a/62083599/7853533
Args:
hex (str): Takes both short as well as long HEX codes.
hsl (bool): C... | 2c912dacfcf6c52c21c94c5d7bb9b9763279245d | 37,245 |
def load_burn_data(url) -> xr.Dataset:
"""Open a GeoTIFF into an in memory DataArray
with DataArray labelled as given name"""
geotiff_burn = xr.open_rasterio(url)
burn_dataset = geotiff_burn.to_dataset('band')
return burn_dataset | 195ba329ba5e3d07a737a93e73403d76caa8ca61 | 37,247 |
def strip_org(name):
""" Returns the name with ORG_PREFIX stripped from it.
"""
if name.startswith(ORG_PREFIX):
return name[len(ORG_PREFIX):]
else:
return name | 855e1bf886c3a3335901e03eab4bb8d1e0e86ebc | 37,248 |
import json
def _ios_format_data(cmd_outputs: json) -> json:
"""
This function will retrieve data from differents
commands outputs and gegroup them in a structured data format.
Three command are executed on devices
-> show ip ospf neighbor detail
-> show ip ospf interface
-> sh... | cf8cd150b0cdb19f80a962d7c13956347d32f665 | 37,249 |
def get_mode_C_array(mode_C):
"""冷房の運転モードを取得する
Args:
mode_C(str): 冷房方式
Returns:
tuple: 冷房の運転モード
"""
# 運転モード(冷房)
if mode_C == '住戸全体を連続的に冷房する方式':
return tuple(["全館連続"] * 12)
else:
return ('居室間歇', '居室間歇', '居室間歇', '居室間歇', '居室間歇', None, None, None, None, None, None,... | 6b9bce2eccab7698ec78ef3b843b17f3c3c9200d | 37,250 |
def launch_and_move(prog_array, workspace,
get_wid=get_wid_by_pid, new_name=None):
"""Launch application and move the created window to `workspace`.
Returns the window id as used by wmcrtl.
`prog_array` : list forwarded to subprocess.Popen. This should
include the co... | db55e33bc6d9f6c9118e04ed0b1c153f5f66e525 | 37,251 |
def construct_label_array(video_labels):
"""Construction label array."""
label_arr = np.zeros((cfg.MODEL.NUM_CLASSES, ))
# AVA label index starts from 1.
for lbl in video_labels:
if lbl == -1:
continue
assert lbl >= 1 and lbl <= 80
label_arr[lbl - 1] = 1
return l... | 0a49f915324311d24888bfa03be186a16f61cdcf | 37,252 |
from typing import Optional
def any(x: _cpp.Variable,
dim: Optional[str] = None,
*,
out: Optional[_cpp.Variable] = None) -> _cpp.Variable:
"""Element-wise OR over the specified dimension or all dimensions if not
provided.
:param x: Input data.
:param dim: Optional dimension al... | fbe911d588adabe9d1f2dbe885a830dedf73e86d | 37,253 |
from datetime import datetime
def get_date():
""" gets current date """
return datetime.now() | dccf420bc6eb216bf76ee153504696ec1c390b5d | 37,254 |
def singular(plural):
"""
Take a plural English word and turn it into singular
Obviously, this doesn't work in general. It know just enough words to
generate XML tag names for list items. For example, if we have an element
called 'tracks' in the response, it will be serialized as a list without
... | 92ab7e074387d943d5593d759a10b3fafa67deca | 37,255 |
def create_simple_keras_model(learning_rate=0.1):
"""Returns an instance of `tf.Keras.Model` with just one dense layer.
Args:
learning_rate: The learning rate to use with the SGD optimizer.
Returns:
An instance of `tf.Keras.Model`.
"""
model = tf.keras.models.Sequential([
tf.keras.layers.Flatt... | a1c777004fe253da5b9f1689284ab99cd6d98cd9 | 37,256 |
def elli(x, rot=0, xoffset=0, cond=1e6, actuator_noise=0.0, both=False):
"""Ellipsoid test objective function"""
x = np.asarray(x)
if not isscalar(x[0]): # parallel evaluation
return [elli(xi, rot) for xi in x] # could save 20% overall
if rot:
x = rotate(x)
N = len(x)
if actuat... | 8768114827746e1b45ec98aa373201d0e4f8c5bc | 37,257 |
def plot_coauthor_network(docs: DocumentSet, *, max_authors=None, **kwargs):
"""Plot a co-author network.
This is a shorthand for `plot_network(build_coauthor_network(docs))`."""
b, p = split_kwargs(**kwargs)
return plot_network(
build_coauthor_network(docs, max_authors=max_authors, **b), *... | b98e7ec214b92dd413fac7f27ace9423af9e0921 | 37,258 |
def read_gene_list(fname, id_type):
"""Return references for genes from a file with the given ID type.
Parameters
----------
fname : str
The name of the file containing the list of genes. Each line of the
file corresponds to a single gene.
id_type : str
The type of identifie... | 612e5254c47f3ec189a18c989abd89b8a414d3da | 37,259 |
from datetime import datetime
from typing import Dict
def backtest(
start_date: datetime = None,
end_date: datetime = None,
interval=1,
yield_interval=100,
start_balances: Dict[str, float] = None,
starting_coin: str = None,
config: Config = None,
):
"""
:param config: Configuratio... | 9aa527f6fa11a00ccdbc2c39e3cd0f79fb322e8e | 37,260 |
def define_vgg_pre_processing(tf_input):
"""
This function defines the vgg_pre_processing network using the tensorflow nn
api.
Parameters
----------
tf_input: tensorflow.Tensor
The input tensor to the network. The image is expected to be in RGB
format.
Returns
-------
... | e74f686da3f6ec068c4ee69a0a20f63015bec569 | 37,261 |
import torch
def subreservoir(weight: Tensor, k=3):
"""
size of weight must devide by k
"""
subres_size = weight.size(0) // k
mask = np.zeros_like(weight)
for i in range(k):
mask[i * subres_size: (i + 1) * subres_size, i * subres_size: (i + 1) * subres_size] = torch.ones(subres_size,
... | ede0db464956bbf825b57650ae9198c656b850bc | 37,262 |
def buy_tickets(request):
""" Provides the event form data and passes it to the frontend as json """
form_html = ""
message_html = ""
success = True
# Ensure all required data has been sent
if 'event' not in request.GET or not request.GET['event']:
messages.error(request, 'No event infor... | aa5a2791688c9696d462b5efc33ab7f79a83dc93 | 37,263 |
def crop_and_revenue_to_df(financial_annual_overview, waste_adjusted_yields, total_sales, vadded_sales, education_rev, tourism_rev, hospitality_rev, grants_rev):
"""Adding yields and sales information to financial overview
Notes:
Adds waste-adjusted yields for crops 1, 2, 3 and 4 with t... | 6be32e6f4ff3ae0ed6434313b8bcc2f44192231b | 37,264 |
def download_blob(bucket_name, source_blob_name):
"""Downloads a blob from the bucket."""
client = storage.Client()
storage_client = storage.Client()
bucket = storage_client.get_bucket(bucket_name)
blob = bucket.blob(source_blob_name)
return blob.download_as_string() | 1251b9f1675237306e5f0057fecd136da6d4cad7 | 37,266 |
def val_epoch(state: train_state.TrainState):
"""
perform a validation epoch
:param state:
:return:
"""
val_config = deepcopy(config)
val_config['data']['val_fns'] = "path_to_tvqa/val{:03d}of008.tfrecord"
val_config['data']['num_val_files'] = 8
val_config['data']['do_random_scale'] =... | 023a2b9ec1da79cb7d3b604dcf9ff2b23cb99a2d | 37,267 |
import torch
def getBERTFeatures(model, text, attn_head_idx=-1): # attn_head_idx - index o[]
"""
Get BERT features for the `text`
Args:
model: BERT model of type `BertForPreTrainingCustom`
text: required, get features for this text
attn_head_idx: optional, defaults to last layer
... | 63a05f9457bbe8e7178c931321d48dc60243bfab | 37,268 |
def filter_rows_via_column_matching(data, column, index=0):
"""
Filter data, by keeping rows whose particular field index matches the
column criteria
It takes parameters:
data (data in the form of a list of lists)
column (used as the match criteria for a particular field of the data)
and op... | fcd5548677290a34d94c2eb8d5fefcb2bb50f0b4 | 37,269 |
def get_td_at_index(tr, index):
"""
When calculating the rowspan for a given cell it is required to find all
table cells 'below' the initial cell with a v_merge. This function will
return the td element at the passed in index, taking into account colspans.
"""
current = 0
for td in tr.xpath(... | f9b72d5597714273c527187738b99a69580c0576 | 37,270 |
import re
def _normalize_name(name: str) -> str:
"""
Normalizes the given name.
"""
return re.sub(r"[^a-zA-Z0-9.\-_]", "_", name) | b38a90c05b0a6ec5a26db6d0da85bed2ae802cea | 37,271 |
def time_step(las, historic_las, max_t, transport, data_df,
transport_threshold, transport_time, transport_severity,
transport_release, beta_threshold, beta_time, beta_severity,
lockdown_release, nat=False):
"""
Enacts all actions of the day such as the morning commute,... | 4a95d47304616288dbf8e5ea6f1a9abf63700bec | 37,272 |
def filter_claims_by_date(claims_data, from_date, to_date):
"""Return claims falling in the specified date range."""
return [
claim for claim in claims_data
if (from_date <= claim.clm_from_dt <= to_date)
] | d1568d0fd52382bdb3f1f02414f591d5f4da3596 | 37,274 |
import json
def get_policies_in_category(jamf_url, object_type, object_name, enc_creds, verbosity):
"""return all policies in a category"""
url = "{}/JSSResource/{}/{}".format(
jamf_url, object_types(object_type), object_name
)
r = curl.request("GET", url, enc_creds, verbosity)
if r.stat... | c0621a10c922d6a6cbcaad4d3a1ba68bb70a79b4 | 37,275 |
from userbot.modules.sql_helper.notes_sql import add_note
async def add_note(fltr):
""" .save """
try:
except AttributeError:
await fltr.edit("`Bot Non-SQL modunda işləyir!!`")
return
keyword = fltr.pattern_match.group(1)
string = fltr.text.partition(keyword)[2]
msg = await fl... | 276290fc376671adc8694c6f0e9869e344c12309 | 37,276 |
import tqdm
def get_sorted_nodelist(nodelist, timeout):
"""
check all nodes and poll for latency,
eliminate nodes with no response, then sort
nodes by increasing latency and return as a list
"""
pool_size = mp.cpu_count()*2
n = len(nodelist)
with mp.Pool(processes=pool_size... | c903f8b5bae1aaf5557b4ed60b32aebdf34fc0d7 | 37,277 |
def featurise_mols(smiles_list, representation, bond_radius=3, nBits=2048):
"""
Featurise molecules according to representation
:param smiles_list: list of molecule SMILES
:param representation: str giving the representation. Can be 'fingerprints' or 'fragments'.
:param bond_radius: int giving the b... | bb2a3f1eb50b52beabd2da013ee50cf668f02354 | 37,278 |
def clear_inactive_structural_variant_sets(_self):
"""Task to cleanup variant sets and their variants that are stuck in a non-active status for a long time."""
return models.cleanup_variant_sets() | cbcefe13b6cbadc9cf6636ba6dbe17b6db944a80 | 37,279 |
def unicode2str(content):
"""Convert the unicode element of the content to str recursively."""
if isinstance(content, dict):
result = {}
for key in content.keys():
result[unicode2str(key)] = unicode2str(content[key])
return result
elif isinstance(content, list):
r... | 30618f0305d28646af36bcff488af971643fd142 | 37,280 |
def cell_test(U,count,max_kpd_=None):
"""Tests the ability of the niggli basis to preserve the symmetry of
the parent cell under action of the HNF.
Args:
U (numpy array): The parent lattice as columns of vector.
count (dict): A dictionary to count how many times each niggli
cel... | e9e488470f19275a42101f1e3224f59acc0f841c | 37,281 |
def get_engine(path: str) -> create_engine:
"""
Function for connecting to the database-file
:param path: path to the file
:return: sqlmodel's engine
"""
return create_engine(path, echo=False) | 0a7eb924934ed9b0ea276daa46943ae626ed3073 | 37,282 |
def mri_signal_inversion_recovery_function(input_data: dict) -> np.ndarray:
""" Function that calculates the inversion recovery signal """
t1: np.ndarray = input_data["t1"].image
t2: np.ndarray = input_data["t2"].image
m0: np.ndarray = input_data["m0"].image
mag_enc: np.ndarray = input_data["mag_en... | ef0819be79a1d575caf4defdedcf2e1d6bb32d49 | 37,283 |
def clip_grads(grad_tree, max_norm):
"""Clip gradients stored as a pytree of arrays to maximum norm `max_norm`."""
norm = l2_norm(grad_tree)
normalize = lambda g: np.where(norm < max_norm, g, g * (max_norm / norm))
return layers.nested_map(grad_tree, normalize) | 0f6790f92716939263c61a732121e67579916162 | 37,284 |
def get_all_facility_users(admin: Client, facility_id: int) -> dict: # noqa: unused client
"""Query for users related to the given facility."""
disallow_parameters(request)
return {
'user': [
user.to_json()
for user in Facility.from_id(facility_id).users()
]
} | 1d72d1e506d052ca67c204035d3d32078833cded | 37,285 |
def rpkm(count, length, nb_read):
""" Return the rpkm value """
length /= 10**3
return rpm(count, length, nb_read) / length | 8e0c8a557b8bd6a10eead706e5176811d7706dec | 37,286 |
import copy
def usm_make(sequence, A=None, seed='centroid'):
"""
Calculates USM coordinates of a categorical sequence of arbitrary alphabet size.
Parameters
----------
sequence : LIST OR ARRAY TYPE;
CATEGORICAL SEQUENCE OF DATA
d : LIST, optional;
LIST CONTAINING ALL POSSIBLE ... | c689a3fba96b0bc995103b9d2c16ef6ab98030aa | 37,287 |
def single_dataset_variableselect(c=cmdc.Client()):
"""
This example loads a subset of the demographic data by selecting
a few variables and a few fips codes
"""
c.demographics(
variable=[
"Total population",
"Fraction of population over 65",
"Median age",... | 4a64844a434e06d188a142f2a5658112104f4caa | 37,288 |
from operator import gt
def _get_total_sizes(graph_tensor: gt.GraphTensor) -> SizeConstraints:
"""Returns the total number of items in the `graph_tensor`."""
return SizeConstraints(
total_num_components=graph_tensor.total_num_components,
total_num_nodes={
name: node_set.total_size
... | a9c802f5d2b1280132f0016c2b30cfede2692e5e | 37,290 |
def _lookup_credentials_data_workspace_access(access_key_id):
"""
Raises HawkFail if the access key ID is not of Data workspace
"""
if access_key_id != settings.HAWK_LITE_DATA_WORKSPACE_CREDENTIALS:
raise HawkFail(f"Incorrect Hawk ID ({access_key_id}) for Data workspace")
return _lookup_cre... | 7f2adcd5576d03a5d41ab7af2aa273fb8a2e13e7 | 37,291 |
def generate_2d_data(func, size=100, data_range=(-2., 2.),
validation=False, visualize=False, seed=100):
"""Generates 2d data according to function.
Args:
func: (function) function that takes (x, y) and return a scalar
size: (int) size of training sample to generate
... | a78116489d151624a997fb1b464b86b068cd1df6 | 37,293 |
def sphere_line_intercept(l, o, r):
"""Calculate intercept point between line y = l.x + o
and a sphere around the center of origin of the
coordinate system: c=0
Parameters:
------------
l ... vector of line of sight
o ... observer position
r ... vector of dis... | 83f3d22a0af6f2a7b9042622525dde7d1984842d | 37,294 |
from typing import List
def compress_pulses(schedules: List[Schedule]) -> List[Schedule]:
"""Optimization pass to replace identical pulses.
Args:
schedules: Schedules to compress.
Returns:
Compressed schedules.
"""
existing_pulses = []
new_schedules = []
for schedule in... | 5782b725fa08b26125722a9b695ca4ff92e229d3 | 37,295 |
def get_localhost_info() -> dict:
"""Get information about the specifications of localhost.
Returns:
dict: Current dict keys: 'os', 'cpu_cores', 'memory', 'python_version', 'workspace_version', 'gpus'.
"""
info = {
"os": _get_os_on_localhost(),
"cpu_cores": _get_cpu_count_on_loc... | ce6959f9a5cc95d643c99c0180da53e661226c4f | 37,296 |
def predictions(logits):
"""Class prediction from logits."""
inner_dim = logits.get_shape().as_list()[-1]
with tf.name_scope('predictions'):
# For binary classification
if inner_dim == 1:
pred = tf.cast(tf.greater(tf.squeeze(logits, -1), 0.), tf.int64)
# For multi-class classification
else:
... | 11b08eca2c8f7a65bb778651374bdee605968a30 | 37,299 |
def clean_string_dots(str_to_clean=None):
"""
this function try to clean a string like "Media. . . . . . . . . . . "
it will eliminate all the dots and blanck spaces
:param str_to_clean: <string>
:return: <string>
"""
if str_to_clean is None:
raise("Error, We need a string")
... | 6d64bc10a26af5cbd11ccfcbfb3cbd7b2775f6be | 37,300 |
def delete_multiple( request ):
"""
Delete multiple posts.
Variables required in the POST request:
- api_key : User identifier.
- id[] : A list with the posts identifiers.
"""
posts = _get_post_list( request )
if isinstance( posts, HttpResponse ):
ret... | d7fe7f973b3809285382f6489b64933cc264b889 | 37,301 |
def plot_round(logs):
"""
Plot round growth
"""
dfs = {k : v.round for k,v in logs.items() if not v.round.empty}
#
fig,ax = figure_with_legend()
plt.title("Round vs T")
plt.grid()
plt.xlabel("Time")
plt.ylabel("Round")
for k,v in dfs.items():
plt.plot(v['at'], v['R'],... | df1aa45cf7e208dbc0ad07f04f4a9b93b1c915c4 | 37,304 |
def r_example(y0=None, ymax=None, xi=0.01):
"""https://rdrr.io/rforge/bvpSolve/f/inst/doc/bvpTests.pdf"""
L = np.eye(1, 2)
R = np.eye(1, 2)
y0 = np.array([1.0]) if y0 is None else y0
ymax = np.array([1.5]) if ymax is None else ymax
t0 = 0.0
tmax = 1.0
return BoundaryValueProblem(
... | b725f2a76b2291ed7a6f3fd1ec87a98253434bcf | 37,305 |
def get_languages() -> dict[str, str]:
"""
This method is used to get all the languages available
for translation with googletrans
:return: dict of languages
"""
return LANGUAGES | 3a1ff4c00edaaac350dd2a8317ad3460df788a24 | 37,306 |
import pickle
def train(sound_dir, noise_dir=None, neural_network=None, n_files=None, val_split=0.15, ext='mp4',
**train_kwargs):
"""
Train model using a simpler pipeline that is mostly automated for the user.
The user simply has to point to the directory with pure sound videos and directory
... | bf4cc7db313f3f75f4cf461d30dcddc79ccdbd70 | 37,307 |
from typing import List
from typing import Tuple
def get_random(building_list: List[str]) -> Tuple[str, str]:
"""
Get 2 random element from the given list.
Parameters
----------
building_list: List[str]
A list containing all the remaining building that haven't been pla... | b91b6560f68e33a6d59abd0ed8af7514f36128d6 | 37,308 |
import collections
def average_over_transforms(func, geometric_transforms, state):
"""Average a function over transformations to achive rotation invariance."""
result_list = collections.defaultdict(list)
for transform in geometric_transforms:
output = transform.inverse(func(transform.forward(state)))
f... | 4f1a6ec9f660bcc6470ac7afe41bf45f1b7b6a8f | 37,312 |
import json
def encode_json(struct):
"""Encode a structure as JSON bytes."""
return bytes(json.dumps(struct), "utf-8") | 6724c0a687a98230a32fef81b3a4447c12d164fc | 37,314 |
def categories_to_errors_dict():
"""Categories to error dictionary function maps common error substrings to categories.
The categories are keys in the dictionary and the values are arrays of strings
or arrays of list.
Some error categories, such as User Errors, has many subcategories. Thus,
... | 4b59fc9775d3d5d808ecedee7756dad5d302848a | 37,315 |
def extract_comments(filename, mime=None):
"""Extracts and returns the comments from the given source file.
Args:
filename: String name of the file to extract comments from.
mime: Optional MIME type for file (str). Note some MIME types accepted
don't comply with RFC2045. If not give... | d0f2de5b4935b5fad6b525d5e6bd83efe453d073 | 37,316 |
def cov(arr : np.ndarray):
"""Compute the sample covariance matrix for
a matrix arr, assumed to be given in (samples x features)
form for data fitting applications.
Parameters
----------
arr : np.ndarray
matrix in (samples x features) form
"""
n, _ = arr.shape
return... | 9727922a984d4e27b4de0f92d9ec94328231a34b | 37,317 |
def is_rdn_equal(rdn1, rdn2):
"""Returns True if and only if the RDNs are equal.
* RDNs must have the same number of AVAs.
* Each AVA of the RDNs must be the equal for the same attribute type. The
order isn't significant. Note that an attribute type will only be in one
AVA in an RDN, otherwise ... | 8f2710aa33f251020841b0f86b2f2f61cbf64cc6 | 37,318 |
def EMetaclass(cls):
"""Class decorator for creating PyEcore metaclass."""
superclass = cls.__bases__
if not issubclass(cls, EObject):
sclasslist = list(superclass)
if object in superclass:
index = sclasslist.index(object)
sclasslist.insert(index, EObject)
... | 7e84de2daa4b8bc08843e63295aa540de0a907d6 | 37,321 |
def _first_defined(*args):
"""Return the first non-null argument (PRIVATE)."""
for arg in args:
if arg is not None:
return arg
return None | 0e0fc10e5ef14d9b445dae5b5dbb5ce467e1cb1b | 37,322 |
import re
def rhyme_analyser(str, rhyme_db):
"""
Rhyme Analyzer - Print out the rhyme scheme of a poem.
Given: A string - like
If you want to smell a rose
You must put it to your nose.
If you want to eat some bread
You must not eat it in bed
Or I will eat your toes.
Output: Rhyme s... | 0fb166c92b2e60d1739638b23a3de5dae18ce26b | 37,324 |
def show_log() -> list:
"""
Отоброжает содержимое log_tab.
:return: список кортежей со всеми данными из таблицы.
"""
try:
with DatabaseConnection(app.config['log_db_config']) as cursor:
_SQL = """SELECT * FROM log_tab ORDER BY ts DESC LIMIT 1000;"""
cursor.execute(... | 1d47b4adf178656a7c63ff23a91337a6eb6ed171 | 37,325 |
def gate_neighbours(current, grid, path):
"""
Generate a heatmap for gates so that A star can avoid gates. This consists of all direct neighbours,
diagonal neighbours, and every coordinate straight above the gate in question
"""
neighbours = list()
# Generate all other coordinates around the ... | a3055ee81756c147a44335b219da194471b4fe4c | 37,326 |
def is_chosen(bbox, area_threshold=None) -> bool:
"""
Calculate area of bounding boxes and return True if area >= threshold
Args:
bbox: (x1, y1, width, heigh)
area_threshold:
Returns:
True/False
"""
are = bbox[2] * bbox[3]
if area_threshold is not None:
if ... | 091fda7a389a74e92703c6eeca05aec413bc65a5 | 37,329 |
import pymess
def pymess_dae2_cnt_riccati(mmat=None, amat=None, jmat=None,
bmat=None, wmat=None, z0=None, mtxoldb=None,
transposed=False, aditol=5e-10, nwtn_res2_tol=5e-8,
maxit=20, verbose=False, linesearch=False, **kw):
""" solv... | 7de34ce6106ba5c0cb1f13e2ad9c2f79327ce829 | 37,330 |
def is_unary_operator(oper):
"""returns True, if operator is unary operator, otherwise False"""
# definition:
# memeber in class
# ret-type operator symbol()
# ret-type operator [++ --](int)
# globally
# ret-type operator symbol( arg )
# ret-type operator [++ --](X&, int)
symbols = [... | fa563cb7bc24462779ddcb0a61dbf6f93903a1d8 | 37,331 |
def map_advanced_param(param, value):
"""
Maps an advanced parameter value to
the value expected on the krdict website.
"""
return _get_advanced_param(_ADVANCED_PARAM_MAP[param], str(value), True) | 39d174e897ecde71d6185db815caa2c272e292ea | 37,332 |
def basin_to_gridcell_dict(df_reference):
"""Generate a dictionary of gridcell id to basin id {grid_id: basin_id}
:param df_reference: Input data reference dataframe containing grid to basin info
:type df_reference: dataframe
:return: dict. {grid_id: basin... | 730823f7c2f1da2bbf5a114c5b270ab741dfdb24 | 37,333 |
def fitness_coeff_constant(N_site,N_state,h_0,J_0):
"""
creating the mutational fitness coefficients for the simulated sequences
in the case of constant fields and constant couplings
Parameters:
N_site: int
sequence length
N_state: int
number of states per site
... | 811c248de631d91c8bafd0617f05f7b02a07289c | 37,334 |
def add_to_user_path(new_path: str, ):
"""
Add **one** path into PATH of current user
"""
assert ';' not in new_path
old_fixed_paths = get_user_path()
for p in old_fixed_paths:
if is_same_file(p, new_path):
return
old_paths = get_user_path()
return set_user_path([new_... | 51dec120301296847dbfbc4da17f20581dfa6c2b | 37,335 |
def generate_1d_scatter(data, variables, class_data=None,
class_names=None, nrows=None, ncols=None,
sharex=False, sharey=False, show_legend=True,
outliers=False, cmap_class=None, **kwargs):
"""Generate 1D scatter plots from the given data and v... | 9b09b2fbc92bd888b060cd7d3b751783030bc8f7 | 37,336 |
def create_translator_instance():
"""Create IBM Translator Instance."""
authenticator = IAMAuthenticator(apikey)
language_translator = LanguageTranslatorV3(
version='2018-05-01',
authenticator=authenticator
)
language_translator.set_service_url(url)
return language_translator | 0e6124be33d8a783937a09980b25bcdfe09e3c2b | 37,338 |
def shot(**kwargs):
"""
Adds a shot entity under the shots table in the `wgid` database.
Args:
kwargs(dict): Reference alfred configs
Returns:
True if successful
"""
key = "shot"
cmd = _create_from_config(key)
cmd += _create_entity(key, **kwargs)
# Submit SQL ... | 747cb2623a3ea197ea1f6b1a96d5104d26cfbe23 | 37,339 |
def symmAndRefl(t, ipt=False):
""" Return the transducer t | t.inverse, if ipt is True;
return the transducer t | t.inverse | id, otherwise
:type t: SFT
:type ipt: bool
:rtype: SFT"""
t1 = t | t.inverse()
if ipt is True:
return t1
t2 = SFT()
s0 = t2.addState()
... | 81f088bd27aa25a6d8104912ace56306873aa8ac | 37,340 |
import logging
def show(
document: vp.Document,
classic: bool,
force: bool,
show_pen_up: bool,
show_points: bool,
outline: bool,
colorful: bool,
show_axes: bool,
show_grid: bool,
hide_legend: bool,
unit: str,
):
"""Display the geometry in an graphical user interface.
... | a0e1e1819c5ed3e1d73bdaa427afefe1ca8e4300 | 37,341 |
def _float(value):
"""Return env var cast as float."""
return float(value) | 254b1e3a542c5a74153cd58d3f43e86dab964028 | 37,342 |
import copy
import warnings
from datetime import datetime
def correctMSdataset(data, window=11, method='LOWESS', align='median', parallelise=True, excludeFailures=True):
"""
Conduct run-order correction and batch alignment on the :py:class:`~nPYc.objects.MSDataset` instance *data*, returning a new instance with cor... | b7f7cd1ffc8c01520b8a0231f83db5cbdf7f35b6 | 37,343 |
def create_assignment_registry():
"""Create the registry for course properties."""
reg = FieldRegistry(
base.OfflineAssignmentBase.NAME,
description=base.OfflineAssignmentBase.DESCRIPTION)
# Course level settings.
reg.add_property(SchemaField(
'key', 'ID', 'string', editable=Fa... | f26fd224133c059e734029cfd8add042ac16b718 | 37,344 |
def do_s3_media_url(parser, token, static=False):
"""
This is for use with ``CloudFrontURLs`` and will return the appropriate url
if a match is found.
Usage::
{% s3_media_url path %}
For ``HTTPS``, the ``cuddlybuddly.storage.s3.middleware.ThreadLocals``
middleware must also be used.
... | db8d740a7c3b8406694a53494c9835c3986d2536 | 37,345 |
def wavlm(refresh=False, *args, **kwargs):
"""
The default model - Base-Plus
refresh (bool): whether to download ckpt/config again if existed
"""
return wavlm_base_plus(refresh=refresh, *args, **kwargs) | 371ccdf7f955142b2f8343d59998323fbe4f02e9 | 37,346 |
def add_area_from_serialized_data(
annotation_model, area_id, serialized_area_data, lv0_height
):
"""Add point annotation data to annotation model from serialized_data"""
serialized_area_data = AreaAnnotationSerializedData(
*serialized_area_data
)
flag, area_record = create_area_annotati... | 2d03640a7674e0a7e5af5cf6a05ea77424c05f97 | 37,347 |
def select_largest_3prime_footprint(transcripts):
"""Select transcript(s) with largest 3' footprint"""
ret = [transcripts[0]]
furthest = transcripts[0].utr3_end
for i in range(1, len(transcripts)):
t = transcripts[i]
utr3footprint = t.utr3_end
if utr3footprint == furthest:
... | 35a437351a886c00c431b2adf61fad42118e94f8 | 37,348 |
def read_mutation_matrix(file_in):
"""Create a table from a mutation matrix provided by SNPad
Returns
-------
tbl : Pandas.table
A table containing the mutation probability for base at each position of a read.
sn : list of dict
The list contains the highest probability for which we ... | 28a397d25ce6afb2d9b06534c2e8780a44253508 | 37,349 |
def ctc_cost(acts, labels, input_lengths = None):
"""
Given sequences of output layer activations and labels, compute the softmax output at each timestep,
and then compute the CTC cost of each sequence with respect to its corresponding label sequence.
:param acts: Tensor of pre-softmax activations, with shape=... | e12183a91ab146e123be58e82ad54c9284db67d5 | 37,350 |
import torch
def center_size(boxes):
""" Convert prior_boxes to (cx, cy, w, h)
representation for comparison to center-size form ground truth data.
Args:
boxes: (tensor) point_form boxes
Return:
boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
"""
wh = boxes[:, 2... | 8e2cc32aa59aeae2e8ef7ffd9489420b94a105a2 | 37,351 |
def train_network(data, supervisor):
"""
This function abstracts out the learning process for the Fuzzy ARTMAP algorithm.
:param data: The data of the training set
:param supervisor: The supervisor data, to train against
:return: new_network, the trained ARTMAP network utilised in classification and... | 29d71e51590c8407c994db067cd25f8ee16422a4 | 37,352 |
def add(user_obj):
"""Add a User"""
user_obj.set_password()
return crud.add(user_obj) | 433e8fe8ecc44e66008a403ba26a666de6870b60 | 37,353 |
def perspective_transform(img):
"""
This class takes in the Gradient and Color Space thresholded binary image,
and perspective transforms the Binary image to halp make view the lanes to be
viewed from the top (as like in Bird's eye view). We take 4 source points that
are assumed to form a Quadrilat... | b6c18fff72d403b13ec8dd392237b88102ebf0d7 | 37,354 |
def numericise(value, empty_value=''):
"""Returns a value that depends on the input string:
- Float if input can be converted to Float
- Integer if input can be converted to integer
- Zero if the input string is empty and empty2zero flag is set
- The same input string, empty or not, ... | 4faff17b9434cfa9c57556c335b8fbf2463842ea | 37,355 |
def test_likelihood_value():
"""Just a sanity check to show we get the right likelihood values"""
config = test_conf_reparam(events_per_day=1)
conv_config = deepcopy(BASE_CONV_CONFIG)
# initialize the old likelihood first
# this is an input for the reparameterized likelihood
lf_old = UnbinnedLog... | b6f2d369846a5eb36760e9bffbe660d4747be590 | 37,356 |
def show_booking_page():
"""Show booking page."""
return render_template('booking.html')
# will need another route to process the booking form | 930726776fb226e9d066e89e21e96d32bd1aeb43 | 37,358 |
def axis(*v, **kwargs):
"""
Convenience method to get or set axis properties.
Calling with no arguments::
>>> axis()
returns the current axes limits ``[xmin, xmax, ymin, ymax]``.::
>>> axis(v)
sets the min and max of the x and y axes, with
``v = [xmin, xmax, ymin, ymax]``.::
... | 7b28f933be0d60a708ae9fe7fc817dcbe92a86d0 | 37,359 |
def split_data(data, num_folds, fold):
"""
Partitions and returns selected partition
:param data: data
:param num_folds: num folds to make
:param fold: selected fold
:return: data_fold, data_rest
"""
X = data['X']
t = data['t']
xJoinT = np.c_[X, t]
rows = np.size(xJoinT, 0)
... | f79094ca5065c79e11514fbb912c8ce30df685d8 | 37,360 |
import typing
import requests
def buy(
symbol: str,
quantity: int,
duration: str = "gtc",
order_type: str = "market",
price: typing.Any = None,
stop: typing.Any = None,
brokerage: typing.Any = USER_BROKERAGE,
access_token: str = USER_ACCESS_TOKEN,
... | e04fe21778634af8c24a83a1e694a976c2ac96cf | 37,361 |
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