content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from localstack.services.es import es_api
def start_elasticsearch_service(port=None, asynchronous=False):
"""
Starts the ElasticSearch management API (not the actual elasticsearch process.
"""
port = port or config.PORT_ES
return start_local_api("ES", port, api="es", method=es_api.serve, asynchro... | 0af83d283735ad1bfdd0684bf1bc1ff36e42d727 | 16,975 |
def regexp_ilike(expr, pattern):
"""
---------------------------------------------------------------------------
Returns true if the string contains a match for the regular expression.
Parameters
----------
expr: object
Expression.
pattern: object
A string containing the regular expression to match against... | fe8e7c9a38b5379d265651d60d80bd8804219842 | 16,976 |
def make_tree(path):
"""Higher level function to be used with cache."""
return _make_tree(path) | 26919144c49f238c78a29ff4c2ce91d5da939484 | 16,978 |
def cmd(f):
"""Decorator to declare class method as a command"""
f.__command__ = True
return f | 3bdc82f0c83b0a4c0a0dd6a9629e7e2af489f0ae | 16,979 |
def small_prior():
"""Give string format of small uniform distribution prior"""
return "uniform(0, 10)" | fb636b564b238e22262b906a8e0626a5dff305d1 | 16,980 |
from typing import List
from typing import Dict
from typing import OrderedDict
def retrieve_panelist_ranks(panelist_id: int,
database_connection: mysql.connector.connect
) -> List[Dict]:
"""Retrieve a list of show dates and the panelist rank for the
reque... | 0560a4f0d2c11f9dbd56d25c63d70fc29ed4292d | 16,981 |
def maxPixel(rpl):
"""maxPixel(rpl)
Computes the max pixel spectrum for the specified ripple/raw spectrum object."""
xs = epq.ExtremumSpectrum()
for r in xrange(0, rpl.getRows()):
dt2.StdOut.append(".")
if dt2.terminated:
break
for c in xrange(0, rpl.getColumns()):
... | 9cb40df8a02e7c861aebedb7d2e13c3fac04d024 | 16,982 |
def skip_device(name):
""" Decorator to mark a test to only run on certain devices
Takes single device name or list of names as argument
"""
def decorator(function):
name_list = name if type(name) == list else [name]
function.__dict__['skip_device'] = name_list
return functio... | 1bacdce5396ada5e2ba7a8ca70a8dfb273016323 | 16,983 |
def conv1d(inputs,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initia... | f3e9dc40d7da6a9bc7a55ec8b13c91a4ac8ba2c3 | 16,984 |
from typing import List
from typing import Union
def check_constraints(
df: pd.DataFrame, schema: dict
) -> List[Union[ConstraintError, ConstraintTypeError]]:
"""
Check table field constraints.
Arguments:
df: Table.
schema: Table schema (https://specs.frictionlessdata.io/table-schema)... | bd6569932b2eb6e4510b7a5e8e6b22e92ddaa1e5 | 16,986 |
from typing import Iterable
from typing import Tuple
from typing import Optional
from typing import Mapping
from typing import Any
import textwrap
def consolidate_fully(
inputs: Iterable[Tuple[core.Key, xarray.Dataset]],
*,
merge_kwargs: Optional[Mapping[str, Any]] = None,
combine_kwargs: Optional[Map... | 240f2579f97ed1b2eaef2d4d7e9e35ed17dbacdf | 16,987 |
from typing import Iterator
from re import T
from typing import Optional
def first(items: Iterator[T]) -> Optional[T]:
"""Return the first item of the iterator."""
return next(items, None) | 5571c8d1541ce2cb3f49da736f92e17fe6326e6d | 16,988 |
from typing import Sequence
def plot_precision_recall_curve(
precisions: Sequence[float], recalls: Sequence[float],
title: str = 'Precision/Recall curve'
) -> matplotlib.figure.Figure:
"""
Plots the precision recall curve given lists of (ordered) precision
and recall values.
A... | d71220b71dfe26aae949676105ba643e249f1c69 | 16,989 |
import json
def Serialize(obj):
"""Return a binary serialized version of object.
Depending on the serialization method, some complex objects or input
formats may not be serializable.
UTF-8 strings (by themselves or in other structures e.g. lists) are always
supported.
Args:
obj: any object
Return... | d9632f0104c69bfb38396f47f5813fd9a87d6361 | 16,990 |
from typing import Union
def getDragObject(parent: QWidget, item: Union['SourceListWidgetItem', 'DestTreeWidgetItem']) -> QDrag:
"""Instantiate QDrag of type application/draggerItem with corresponding QMimeData
Parameters
----------
parent: QWidget
item: Union['SourceListWidgetItem', 'DestTreeWid... | a6990e9f1a95632d25e15d993d0117cdb911cf7b | 16,991 |
import requests
from bs4 import BeautifulSoup
def get_soup(url):
"""
Makes a request to the given url and returns a BeautifulSoup instance of Soup
"""
res = requests.get(url)
if not res.content:
return None
soup = BeautifulSoup(res.content, "lxml")
return soup | bc4e79f4e2313e3c3edc6f6f123b6d13f71c0075 | 16,992 |
def _grow_segment(segment, addition):
"""Combine two segments into one, if possible."""
if _eq(segment[-1], addition[0]): # append addition
return segment + addition[1:]
elif _eq(segment[-1], addition[-1]): # append reversed addition
return segment + list(reversed(addition[:-1]))
elif ... | 12f48ec2efbd74ac09f9277f2769a4b35030a425 | 16,993 |
def dsmoothlist_by_deform_exp(deform_exp, ag_mode):
"""
Automatically extract the selected artificial generations for training and validation set:
'Resp': ['respiratory_motion', 'single_frequency', 'mixed_frequency', 'zero'],
'NoResp': ['single_frequency', 'mixed_frequency', 'zero'],
'Si... | 965ecf7373c313dccd290fb8a7c6c2075645a16a | 16,994 |
from typing import Callable
from typing import BinaryIO
from typing import Tuple
def get_data_reader(header: Header) -> Callable[[BinaryIO], Tuple]:
"""Make a binary reader function for data."""
names = get_data_names(header)
format_ = ""
for name in names:
if "CH" in name:
forma... | c243d5d50ec8738f8f8673fd9bf40b9d26cad69b | 16,995 |
def test_api_mediawiki(monkeypatch):
"""The api_mediawiki test using mocks."""
result = "OpenClassrooms est une école en ligne..."
def mock_summary(*args, **kwargs):
return result
monkeypatch.setattr(
MediawikiApi, 'search', mock_summary)
wikipedia = MediawikiApi()
assert wiki... | 28b22d4acf195dee3d1e7f10688610fea71fea3f | 16,996 |
from typing import Callable
def check_fnr(fnr: str, d_numbers=True, h_numbers=False, logger: Callable = lambda _x: None) -> bool:
"""
Check if a number is a valid fødselsnumber.
Args:
fnr: A string containing the fodselsnummer to check
h_numbers: False (the default) if h-numbers should be ... | 2d5af194f1a69a093c6bf69b2cd42537d2dd32b0 | 16,997 |
import unittest
def get_case_list_from_cls(test_cls_list):
"""
将测试类转化为测试用例
:return:
"""
test_list = []
for test_cls in test_cls_list:
test_cases = unittest.TestLoader().loadTestsFromTestCase(test_cls)
test_list.append(test_cases)
return test_list | 3f7ed0c7ed0b9110a9cb11579087712321ec868e | 16,998 |
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
def align_times(sync_behavioral, sync_neural, score_thresh=0.9999,
ignore_poor_alignment=False, return_model=False, verbose=False):
"""Align times across diff... | 8bc8ad2a92267a0c1c5e8a4c6a71494910df8b7f | 16,999 |
def check_media(url):
"""Check if something is available or has a new hash
Checks if url is available, uf yes, download and hash it, then see if it has changed
Args:
url: A complete url to something
Returns:
0 if available and no change.
1 if not available.
2 if it... | f7237207a7ff6e555533cebe4dc83fa77538886c | 17,000 |
from typing import Iterable
from typing import Any
from typing import Tuple
def tuple_from_iterable(val: Iterable[Any]) -> Tuple[Any, ...]:
"""Builds a tuple from an iterable.
Workaround for https://github.com/python-attrs/attrs/issues/519
"""
return tuple(val) | 7880b1395f14aa690f967b9548456105b544d337 | 17,002 |
from typing import Counter
def merge_vocabs(vocabs, vocab_size=None):
"""
Merge individual vocabularies (assumed to be generated from disjoint
documents) into a larger vocabulary.
Args:
vocabs: `torchtext.vocab.Vocab` vocabularies to be merged
vocab_size: `int` the final vocabulary siz... | db83e858c1a8910b382bcd485923ef6ba9a1466e | 17,003 |
def count_nonzero(X, axis=None, sample_weight=None):
"""A variant of X.getnnz() with extension to weighting on axis 0
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : sparse matrix of shape (n_samples, n_labels)
Input data. It should be of CSR format.
ax... | e6754cca480d626dd7ba2c96426e5eebf17a1fcb | 17,005 |
def isolate_shape_axis(base, target, axis_list = ['X','Y','Z']):
"""
Given a base mesh, only take axis movement on the target that is specified in axis_list.
Args:
base (str): The base mesh that has no targets applied.
target (str): The target mesh vertices moved to a different position... | a097442c2c379338890e5571d0e3516553fe70f3 | 17,006 |
from typing import List
def _ge(t1: 'Tensor', t2: 'Tensor', isnew: bool) -> 'Tensor':
"""
Also see
--------
:param t1:
:param t2:
:param isnew:
:return:
"""
data = t1.data >= t2.data
requires_grad = t1.requires_grad or t2.requires_grad
depends_on: List[Dependency] = []
... | 02b0407c3b2bc3ed6bf65555ab62257e3f041d0e | 17,007 |
from typing import Dict
from typing import Any
import logging
import numpy
def convert_homogeneous_graph(graph: Dict[str, Any],
num_graphs: int,
output_dir: str):
"""Process a homogeneous graph."""
# NOTE(blais): We could in theory stash the data in the... | 4aa0751437861af58159228c018a3f6d94b8613a | 17,008 |
def second_deriv_log_pdf(phi, alpha, beta, eps=1e-4):
"""Second derivative of `log_pdf` with respect to latitude."""
return (
log_pdf(phi + eps, alpha, beta)
- 2 * log_pdf(phi, alpha, beta)
+ log_pdf(phi - eps, alpha, beta)
) / eps ** 2 | 5df140d62466481997a472e260241961e872cbe3 | 17,009 |
from typing import OrderedDict
def normalize_data(data, zp=25., zpsys='ab'):
"""Return a copy of the data with all flux and fluxerr values normalized
to the given zeropoint. Assumes data has already been standardized.
Parameters
----------
data : `~numpy.ndarray`
Structured array.
zp ... | 9aa3c4faf6f9a9f98afd9e11d2bafaf1b026519c | 17,011 |
import base64
def CreateMessage(sender, to, subject, message_text):
"""
Creates an object containing a base64url encoded email object.
"""
message = MIMEText(message_text)
message['to'] = to
message['from'] = sender
message['subject'] = subject
raw_message = base64.urlsafe_b64encode(message.as_bytes())
raw_m... | 8d55b64ebf4801781126f244441f619201d51190 | 17,012 |
def bg_lookup(bg_name: str) -> str:
"""Look up ANSI escape codes based on background color name.
:param bg_name: background color name to look up ANSI escape code(s) for
:return: ANSI escape code(s) associated with this color
:raises ValueError if the color cannot be found
"""
try:
ansi... | 8c520f599bc41ce847e5c602ddf8500fe366f24d | 17,013 |
def readData(f):
"""
Parse taxon count table (from count-taxon.py)
Parameters:
-----------
f : str
file name of taxon count table
Returns:
--------
tuple
a list of taxons and a list of their counts
"""
taxa_lis = []
num_lis = []
for n, line in enumerate... | fcd10e1d7dc1db0b871c7a4802f012eec43c08a9 | 17,014 |
def detect_onsets_offsets(data, threshold, min_distance):
"""
detects when a when a signal jumps above zero, and when it goes back to zero
"""
on = (data > threshold) # when the data is greater than zero
left_on = np.concatenate(([0], on), axis=0)[0:-1]
onset = np.squeeze(np.where(on & (left_on... | faa81445828b72bc7d7433a4c2c8740bb36050bb | 17,016 |
def STEPConstruct_PointHasher_IsEqual(*args):
"""
* Returns True when the two keys are the same. Two same keys must have the same hashcode, the contrary is not necessary.
:param Point1:
:type Point1: gp_Pnt
:param Point2:
:type Point2: gp_Pnt
:rtype: bool
"""
return _STEPConstruct.STEP... | b3aa095d723203b05ea29ec4f5b34a70bc4c5276 | 17,017 |
def step_smooth(x) :
""" Smooth polinomial rising step from 0(x=0) to 1(x=1)
"""
return np.select([x>1, x>0], [1, 3*np.square(x)-2*np.power(x,3)], default=0) | ccf53e2561e256d2114510598ebb7a2ec1ce7cbd | 17,018 |
def getBitSizeOfVarInt64(value):
"""
Gets bit size of variable 64-bit signed integer value.
:param value: Value to use for bit size calculation.
:returns: Bit size of the value.
"""
return _getBitSizeOfVarIntImpl(value, VARINT64_MAX_VALUES, signed=True) | 20c52df5ec9a00680e771f206319a02e3ba3de66 | 17,019 |
import functools
def nan_if_exception(func):
"""Wrap func such that np.nan is returned if func raises an exception.
KeyboardInterrupt and SystemExit are still raised.
Examples:
>>> @nan_if_exception
... def f(x, y):
... assert x + y >= 5
>>> f(1, 2)
nan
>>> def f(x, y):
... | f03c314741c47805d767fc62fbce49cda9be35fe | 17,020 |
def get_client_public_key(patient_id, study_id):
"""Grabs a user's public key file from s3."""
key_pair_paths = construct_s3_key_paths(study_id, patient_id)
key = s3_retrieve(key_pair_paths['public'], study_id, raw_path=True)
return encryption.import_RSA_key( key ) | d6e6560c49f925a8f87a84829d632f67957f3c79 | 17,021 |
from typing import List
from typing import Tuple
def plot_offset_direction(
dsaimage: Image, coords: SkyCoord, ra_offsets: List[float], dec_offsets: List[float]
) -> Tuple["matplotlib.fig", "matplotlib.axes.Axes"]:
"""Plot measured offsets on an image."""
fig, ax = dsaimage.show()
dsaimage.add_arr... | 24416863b795538ee8c86fa1949f6d701b66f28d | 17,022 |
def sensitivity_metric(event_id_1, event_id_2):
"""Determine similarity between two epochs, given their event ids."""
if event_id_1 == 1 and event_id_2 == 1:
return 0 # Completely similar
if event_id_1 == 2 and event_id_2 == 2:
return 0.5 # Somewhat similar
elif event_id_1 == 1 and eve... | b04c5fa27ef655dd3f371c3ce6ef0410c55dd05b | 17,024 |
def duracion_promedio_peliculas(p1: dict, p2: dict, p3: dict, p4: dict, p5: dict) -> str:
"""Calcula la duracion promedio de las peliculas que entran por parametro.
Esto es, la duración total de todas las peliculas dividida sobre el numero de peliculas.
Retorna la duracion promedio en una cadena de ... | a8cfcc96a43480ee6830cc212343a33148036c5d | 17,025 |
def _to_test_data(text):
"""
Lines should be of this format: <word> <normal_form> <tag>.
Lines that starts with "#" and blank lines are skipped.
"""
return [l.split(None, 2) for l in text.splitlines()
if l.strip() and not l.startswith("#")] | 8f0bae9f81d2d14b5654622f1493b23abd88424d | 17,026 |
import copy
def append(motion1, motion2):
"""
Combines two motion sequences into one. motion2 is appended to motion1.
The operation is not done in place.
Note that the operation places the sequences next to each other without
attempting to blend between the poses. To interpolate between the end o... | dc51812f450a072ad283173a15fb2c07ae978e5b | 17,027 |
from datetime import datetime
from typing import List
def service(
fmt: SupportedFormats,
begints: datetime = Query(
..., description="Inclusive UTC timestamp window start for issuance."
),
endts: datetime = Query(
..., description="Exclusive UTC timestamp window end for issuance."
... | eeb0a8b1187ff2386401440b6ddd812b81cd0fdd | 17,028 |
def cols_shuffled(expr_df, dist_df=None, algo="agno", seed=0):
""" Return a copy of the expr_df DataFrame with columns shuffled randomly.
:param pandas.DataFrame expr_df: the DataFrame to copy and shuffle
:param pandas.DataFrame dist_df: the distance DataFrame to inform us about distances between columns
... | 37773c5219ecc92925c155e9d911c42ddbebc8ea | 17,029 |
from typing import Dict
from typing import Any
from re import L
def build_model(task_description: Dict[str, Any]) -> Dict[str, Any]:
"""Build the predinet model."""
# ---------------------------
# Setup and process inputs
processors = {"image": process_image, "task_id": process_task_id}
mlp_inputs... | 03c7951d3fb0fddbfb1e4bad3b4e5ce54253f994 | 17,031 |
def create_unet_model(N_classes, input_shape=(None, None, 1), dropout_rate=0.24, learning_rate=1e-5):
"""
Implementation of Unet mode for multiclass semantic segmentation
:param N_classes: Number of classes of segmentation map
:param input_shape: input image shape
:param dropout_rate: dropout rate
... | 93c15376eed0c5cf1abe689ef1daca6c8877e61a | 17,032 |
from uutils.torch_uu.models.learner_from_opt_as_few_shot_paper import get_default_learner
from typing import Optional
def get_5cnn_model(image_size: int = 84,
bn_eps: float = 1e-3,
bn_momentum: float = 0.95,
n_classes: int = 5,
filter_size: i... | 6b3e21e33433102b16b70c88dd5d033e1f069b86 | 17,034 |
def valid_extract_input_specification(instance_of_property, depth, language_code, named_entity_label):
""" Checks if the input for the extraction is valid. Both to help
the user get correct input and to sanitize it to avoid
attacks as the values are used to generate filenames.
"""
patte... | c71f744fef82e54ca2fad0ea64c8637692256299 | 17,035 |
def get_company_data(mid):
"""Looks up stock ticker information for a company via its Freebase ID."""
query = MID_TO_TICKER_QUERY % mid
bindings = make_wikidata_request(query)
if not bindings:
if mid:
print("%s No company data found for MID: %s" % (WARNING, mid))
return Non... | 464b9ef795938b2d83fd6a629b9af09ff165a922 | 17,036 |
def embed_data_into_square_lattice(data):
"""Insert MR image into square 2D array."""
dims = np.array(data.shape)
offset_x = int((dims.max() - dims[0]) / 2.)
offset_y = int((dims.max() - dims[1]) / 2.)
temp = np.zeros((dims.max(), dims.max()))
temp[offset_x:offset_x+dims[0], offset_y:offset_y+... | e701e871b4df9f4085b2548ad1e10f93ce33bf38 | 17,037 |
def is_partial_link_text_selector(selector):
"""
A basic method to determine if a selector is a partial link text selector.
"""
if (
selector.startswith("partial_link=")
or selector.startswith("partial_link_text=")
or selector.startswith("partial_text=")
or selector.start... | 4f21143173e46ed273ca719ea1aac8489afa2395 | 17,038 |
def scell(obj, dims, method=1, **kwds):
"""Build supercell based on `dims`.
Uses coords_frac and cell.
Parameters
----------
obj : Structure or Trajectory
dims : tuple (nx, ny, nz) for a N = nx * ny * nz supercell
method : int, optional
Switch between numpy-ish (1) or loop (2) impl... | e0cf7e03323c5994d0c56ba171d168aed105cfda | 17,039 |
def create_config(device: str = 'CPU', *,
per_process_gpu_memory_fraction: float = 0.0,
log_device_placement: bool = False) -> tf.ConfigProto:
"""Creates tf.ConfigProto for specifi device"""
config = tf.ConfigProto(log_device_placement=log_device_placement)
if is_gpu(devi... | 0b6f351bcad2d816d6c03896ed60223cf2bb90c9 | 17,040 |
from typing import Union
from typing import Dict
def format_childproc(cp: Union[Event, Dict]):
"""Format childproc event into single line."""
return f" @{as_configured_timezone(cp.get('event_timestamp'))}: {cp.get('childproc_cmdline')} - {cp.get('childproc_process_guid')}" | f233a0fca52fdbef8d7ed0177772c9a8d196ec0d | 17,041 |
def format_maven_jar_dep_name(group_id, artifact_id, repository = DEFAULT_REPOSITORY_NAME):
"""
group_id: str
artifact_id: str
repository: str = "maven"
"""
return "@%s//:%s" % (repository, format_maven_jar_name(group_id, artifact_id)) | a331ce788a510c09c32a1d2c7d1f8d4fbeaba975 | 17,043 |
import ctypes
def PCO_GetCameraName(handle):
"""
This function retrieves the name of the camera.
"""
f = pixelfly_dll.PCO_GetCameraName
f.argtypes = (ctypes.wintypes.HANDLE, ctypes.c_char_p, ctypes.wintypes.WORD)
f.restype = ctypes.c_int
cameraName = ctypes.create_string_buffer(41)
re... | f704f2a875f29f0876553c631de032d25b5166f4 | 17,044 |
def issingleton(var):
""" If isunitset(var) is True, this function returns True,
otherwise isscalar(var) is returned.
"""
# Here we define singleton as a unit set or scalar
if isunitset(var):
return True
return isscalar(var) | cd1808ad99647486e81e0f903047db9327b77fb8 | 17,046 |
def satisfiesF(L):
"""
Assumes L is a list of strings
Assume function f is already defined for you and it maps a string to a Boolean
Mutates L such that it contains all of the strings, s, originally in L such
that f(s) returns True, and no other elements. Remaining elements in L
... | 429c385f51ba254fff7170f4e69725cc98c8b337 | 17,047 |
def calc_circle_radius(area: float) -> float:
"""
Calculate radius from area.
>>> calc_circle_radius(10.0)
1.7841241161527712
"""
assert not area < 0
radius = numpy_to_python_type(np.sqrt(area / np.pi))
assert isinstance(radius, float)
return radius | 06086e1b130bef960fad200b350cba01b647466e | 17,048 |
import tqdm
def load_imgs(paths, target_size):
"""Load images from `paths`."""
pairs = np.empty((len(paths), 2, *target_size), dtype=np.float32)
for i, row in tqdm(paths.iterrows(), total=len(pairs)):
img1 = img_to_array(load_img(row.p1, target_size=target_size)) / 255
img2 = img_to_array(... | b91b86bcae29a6bf2d1227a25a2b8297c6be1734 | 17,049 |
def load_dict_from_hdf5(h5_filepath):
"""
Load h5 file as a dict
"""
def recursively_load_dict_contents_from_group(h5_obj, path):
"""
Recursively load a dict from h5 file
"""
ans = {}
for key, item in h5_obj[path].items():
if isinstance(item, h5py._hl.... | 2339cc6edb83ed59fb43ec49503d86758d37d83e | 17,050 |
def interactive_grid_shape(grid, max_n=200, plotfxn=None, **kwargs):
""" Interactive ipywidgets for select the shape of a grid
Parameters
----------
grid : pygridgen.Gridgen
The base grid from which the grids of new shapes (resolutions) will be
generated.
max_n : int (default = 200)... | ef126f39f8433a65deb22e09ea825f342a38bea1 | 17,051 |
from lgsvl.utils import transform_to_forward
from typing import Optional
def generate_initial_state(initial_pos: Transform, initial_speed: Optional[float] = None) -> AgentState:
"""
:param initial_speed: Initial speed in km/h
"""
movement = AgentState()
movement.transform = initial_pos
if init... | 30906410d3fe92b84f3d2c93a49db24f90a8ec8b | 17,052 |
def resolve_ami(ami=None, arch="x86_64", tags=frozenset(), tag_keys=frozenset()):
"""
Find an AMI by ID, name, or tags.
- If an ID is given, it is returned with no validation; otherwise, selects the most recent AMI from:
- All available AMIs in this account with the Owner tag equal to this user's IAM us... | 32495fb78a611f57b0e025b0ff68b51a190c7297 | 17,053 |
def _filter_colors(hcl, ihue, nhues, minsat):
"""
Filter colors into categories.
Parameters
----------
hcl : tuple
The data.
ihue : int
The hue column.
nhues : int
The total number of hues.
minsat : float
The minimum saturation used for the "grays" column... | f7ca00bdd17766c859b5262c1b9ae12187c23222 | 17,054 |
def SWO(
directed = False, preprocess = "auto", load_nodes = True, load_node_types = True,
load_edge_weights = True, auto_enable_tradeoffs = True,
sort_tmp_dir = None, verbose = 2, cache = True, cache_path = None,
cache_sys_var = "GRAPH_CACHE_DIR", version = "swo.owl", **kwargs
) -> Graph:
"""Return... | 39936ffcd272e0d6c6c7e5510699bac68a465eb9 | 17,055 |
def upload_needed_files (handle, bucket, prefix, dir_path, kind, iter):
"""
upload the needed local files of a particular kind
"""
extension = f".{kind}"
count = 0
for uuid in iter:
file_name = uuid + extension
local_path = dir_path / file_name
grid_path = prefix + "/pub... | 9357e991eb14eaf9de54beda3ec86defc3e1ecaf | 17,056 |
def detect_tag(filename):
"""Return type and position of ID3v2 tag in filename.
Returns (tag_class, offset, length), where tag_class
is either Tag22, Tag23, or Tag24, and (offset, length)
is the position of the tag in the file.
"""
with fileutil.opened(filename, "rb") as file:
file.seek(... | 5b32c122d804aa5def21c59e73e4369c64b7cbbe | 17,057 |
def merge_peaks(peaks, start_merge_at, end_merge_at,
max_buffer=int(1e5)):
"""Merge specified peaks with their neighbors, return merged peaks
:param peaks: Record array of strax peak dtype.
:param start_merge_at: Indices to start merge at
:param end_merge_at: EXCLUSIVE indices to end me... | 75f86b0c27cb2cac145234cfd9254105048be9a8 | 17,058 |
def batchedpatternsgenerator(generatorfunction):
"""Decorator that assumes patterns (X,y) and stacks them in batches
This can be thought of a specialized version of the batchedgenerator
that assumes the base generator returns instances of data patterns,
as tuples of numpy arrays (X,y). When groupin... | 19a8e8d5c2872c38d469c41e163a947f208fc806 | 17,059 |
def reduce_min(raw_tensor, axis, keepdims=False):
"""
calculate reduce_min of raw_tensor, only support float16
Args:
raw_tensor (tvm.tensor.Tensor): input tensor
axis (Union[int, list]): reduce axis (range : [-len(raw_tensor.shape), len(raw_tensor.shape) - 1])
keepdims (bool): if tr... | b4473ca577a939f3c149758fd73a59c79b1f0db0 | 17,060 |
def align2local(seq):
"""
Returns list such that
'ATG---CTG-CG' ==> [0,1,2,2,2,3,4,5,5,6,7]
Used to go from align -> local space
"""
i = -1
lookup = []
for c in seq:
if c != "-":
i += 1
lookup.append(i)
return lookup | aa914a60d5db7801a3cf1f40e713e95c98cd647e | 17,061 |
def load_nodegraph(filename):
"""Load a nodegraph object from the given filename and return it.
Keyword argument:
filename -- the name of the nodegraph file
"""
nodegraph = _Nodegraph(1, [1])
nodegraph.load(filename)
return nodegraph | cd552fda874f1e8667bd09e95bdf43e6c5bd75c1 | 17,062 |
def get_bprop_sqrt(self):
"""Grad definition for `Sqrt` operation."""
mul_func = P.Mul()
fill_func = P.Fill()
div_op = P.RealDiv()
sqrt = P.Sqrt()
dtype = P.DType()
def bprop(x, out, dout):
temp = div_op(fill_func(dtype(x), shape_op(x), 0.5), sqrt(x))
dx = mul_func(dout, tem... | b297695effd9d063384b3343337d1647050b5f1a | 17,063 |
def classify_top1_batch(image):
"""Define method `classify_top1` for servable `resnet50`.
The input is `image` and the output is `lable`."""
x = register.add_stage(preprocess_batch, image, outputs_count=1, batch_size=1024)
x = register.add_stage(resnet_model, x, outputs_count=1)
x = register.add_st... | ff4ae67619f29e0e22e275845709ab73daabe2f0 | 17,064 |
def ngram_word(max_features=2_000):
"""Word count vectorizer.
Args:
max_features: number of features to consider.
"""
return CountVectorizer(
ngram_range=(1, 3),
analyzer='word',
max_features=max_features,
) | 2b8935b72a836ff6ab3cdb0b17939806d9f7ce02 | 17,065 |
def func_dispatcher(intent):
"""
Simple effect dispatcher that takes callables taking a box,
and calls them with the given box.
"""
def performer(dispatcher, intent, box):
intent(box)
return performer | 48dc23a8124569d5537c38b8f704fdea282853e8 | 17,066 |
import multiprocessing
import tqdm
def encode(x, bps_arrangement='random', n_bps_points=512, radius=1.5, bps_cell_type='dists',
verbose=1, random_seed=13, x_features=None, custom_basis=None, n_jobs=-1):
"""Converts point clouds to basis point set (BPS) representation, multi-processing version
Para... | 66edc2dd5d42fe53e55f2e5b95e2069123510006 | 17,068 |
def parsec_params_list_to_dict(var):
"""
convert parsec parameter array to dictionary
:param var:
:return:
"""
parsec_params = dict()
parsec_params["rle"] = var[0]
parsec_params["x_pre"] = var[1]
parsec_params["y_pre"] = var[2]
parsec_params["d2ydx2_pre"] = var[3]
parsec_para... | 4ea4b4d2c0cbcb8fb49619e103b09f354c80de6a | 17,069 |
def parse_msiinfo_suminfo_output(output_string):
"""
Return a dictionary containing information from the output of `msiinfo suminfo`
"""
# Split lines by newline and place lines into a list
output_list = output_string.splitlines()
results = {}
# Partition lines by the leftmost ":", use the s... | 6883e8fba9a37b9f877bdf879ebd14d1120eb88a | 17,070 |
from typing import Dict
from typing import Any
import json
from datetime import datetime
def create_indicators_fields(tag_details: Dict[str, Any]) -> Dict[str, Any]:
"""
Returns the indicator fields
Args:
tag_details: a dictionary containing the tag details.
Returns:
A dictionary repre... | 349ab542d2c25cb24fe40aeb98c16a9bfccc871f | 17,071 |
def spatial_difference(gdf1: GeoDataFrame, gdf2: GeoDataFrame) -> GeoDataFrame:
"""Removes polygons from the first GeoDataFrame that intersect with polygons from the second GeoDataFrame
:param gdf1: First input data frame
:param gdf2: Second input data frame
:return: Resulting data frame
"""
gd... | 2713376f45ed574399f9f406a06a60a47f002579 | 17,072 |
def frustumShellIxx(rb, rt, t, h, diamFlag=False):
"""This function returns a frustum's mass-moment of inertia (divided by density) about the
transverse x/y-axis passing through the center of mass with radii or diameter inputs.
NOTE: This is for a frustum SHELL, not a solid
INPUTS:
Parameters
... | 3d23805d4f7ed952b50752ac4ab8158c2826137f | 17,073 |
def default_shaders():
"""
Returns a list with all thte default shadres of the current DCC
:return: str
"""
return shader_utils.get_default_shaders() | 795ca337c9ba163bb70ce4ff226c04d1034ec542 | 17,074 |
def get_domains_and_slugs():
"""
returns all the domain names and slugs as dictionary
{domain_name: slug}
"""
return_data = {}
domain_slugs = Domain.objects.filter(active=1).order_by('name')
if domain_slugs:
for domain in domain_slugs:
return_data[domain.name] = domain.sl... | d19af879fe96895808f1c1815d3cc563499d358d | 17,075 |
def has_video_ads() -> bool:
"""has_video_ads() -> bool
(internal)
"""
return bool() | 6b4822bb18171df5bfc5b4f3797e574557cd65dd | 17,076 |
from typing import Iterable
def compile_sites(inp: NetInput,
y_true: Iterable[np.ndarray],
y_pred: Iterable[np.ndarray],
masks: Iterable[np.ndarray]):
"""
Prepares sites to be dumped in tsv file
:param inp: NetInput
:param y_true: True known classe... | 14f655e18b5651c22373d4c23b51f55704cd63c8 | 17,078 |
def femda_estimator(X, labels, eps = 1e-5, max_iter = 20):
""" Estimates the matrix of means and the tensor of scatter matrix of the dataset using MLE estimator.
To tackle singular matrix issues, we use regularization.
Parameters
----------
X : 2-d array of size n*m
... | 639532f9307e023561d6193730473533b240fb28 | 17,079 |
def get_collections():
"""read .db file, return raw collection"""
col = {}
f = open(collection_db, "rb")
version = nextint(f)
ncol = nextint(f)
for i in range(ncol):
colname = nextstr(f)
col[colname] = []
for j in range(nextint(f)):
f.read(2)
col[c... | b134e7e970fa7f5486226d2c2cab3c63ab9f67c3 | 17,080 |
def ot_has_small_bandgap(cp2k_input, cp2k_output, bandgap_thr_ev):
""" Returns True if the calculation used OT and had a smaller bandgap then the guess needed for the OT.
(NOTE: It has been observed also negative bandgap with OT in CP2K!)
cp2k_input: dict
cp2k_output: dict
bandgap_thr_ev: float [eV]... | fbc63c373d052111932ea0fd2cd458d59b486d10 | 17,081 |
def profile():
"""Checking if user is already logged_in"""
if 'logged_in' in session:
'''getting all the account info for the user for displaying it on the profile page'''
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
cursor.execute('SELECT * FROM accounts WHERE username =... | 4796058d3bbc911cc0610b7a5458be80fa330d67 | 17,082 |
import time
import json
async def ping(ws):
"""Send a ping request on an established websocket connection.
:param ws: an established websocket connection
:return: the ping response
"""
ping_request = {
'emit': "ping",
'payload': {
'timestamp': int(time.time())
... | 587d2a72cbc5f50f0ffb0bda63668a0ddaf4c9c3 | 17,084 |
def split_files_each_proc(file_arr,nprocs):
""" Returns array that distributes samples across all processors. """
ntot = len(file_arr)
post_proc_file_arr = []
for i in range(0,nprocs):
each_proc_arr = []
ib,ie = split_array_old(ntot,nprocs,i)
if i... | 0c5c481d1b9a9e0d5c6efdfb7abf6669f5a05ecf | 17,085 |
def get_data_generators_for_output(output):
""" Get the data generators involved in an output
Args:
output (:obj:`Output`): report or plot
Returns:
:obj:`set` of :obj:`DataGenerator`: data generators involved in the output
"""
data_generators = set()
if isinstance(output, Repo... | d05fde5b5ce25504b53d8ca4491235d3ab3b8680 | 17,086 |
from re import M
def clustering_report(y_true, y_pred) -> pd.DataFrame:
"""
Generate cluster evaluation metrics.
Args:
y_true: Array of actual labels
y_pred: Array of predicted clusters
Returns:
Pandas DataFrame with metrics.
"""
return pd.DataFrame(
{
... | dc124dc4f248a2acedfd6201a205f285adc6ec1c | 17,088 |
from typing import List
from typing import Sequence
from typing import Set
from typing import Tuple
def _create_sampler_data(
datastores: List[Datastore], variables: Sequence[Variable],
preconditions: Set[LiftedAtom], add_effects: Set[LiftedAtom],
delete_effects: Set[LiftedAtom], param_option: Parameteriz... | ecf7ed06183264722df5d6e2d645bb899cb8358b | 17,089 |
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