content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Union
import torch
from typing import Sequence
from typing import Mapping
def collect_tensors(collection: Union[torch.Tensor, Sequence, Mapping]):
"""
Collect all the tensors in the sequence/mapping
"""
if isinstance(collection, torch.Tensor):
return [collection]
if isi... | 162a28d40f1aaa9b742877bd19ca769dbbf3c7c5 | 66,600 |
from unittest.mock import call
import os
import re
def compress_js(directory, source, css_variables, advanced_cc):
""" Compress Javascript content
:param directory: The root directory
:param source: The source Javascript string
:param css_variables: A CSS variables object to shorten css variable name... | 61e066144d7426a21b789f9bcffe5bbfbe2208af | 66,601 |
def separate_cell_nmt(src_input, tgt_input, unroll=True, **kwargs):
"""Returns a NMT network."""
hparams = create_nmt_hparams(**kwargs)
assert unroll
return SeparateCellNMT(hparams).build_graph(src_input, tgt_input) | 1a9611bd23c52ccf7a49f30836df8c36ec793ac0 | 66,602 |
def random_xffffffff(shape=(40,60), dtype=np.uint32, add=0xff000000) :
"""Returns numpy array of requested shape and type
filled with random numbers in the range [0,0xffffff] with bits 0xff000000 for alpha mask.
"""
a = 0xffffff*np.random.random(shape) + add
return np.require(a, dtype) | 40709c00e862adc255ac4f245c0c94af43e5085f | 66,603 |
def printSeq(startChar, endChar, seq, maxdepth):
"""
>>> printSeq('(', ')', (1, 2, 3), 4)
['(', '1', '2', '3', ')']
>>> printSeq('(', ')', (), 4)
['(', ')']
"""
if maxdepth < 1: return '&'
maxdepth -= 1
return [startChar] + [cprint2(x, maxdepth) for x in seq] + [endCh... | 9a5d1fa447928cb5769b238516d41a96c5c0ffef | 66,604 |
import configparser
import os
import getpass
import socket
def load_params(args):
"""
reads the parameter file and creates as list of test objects as well as
the suite object
"""
test_list = []
cp = configparser.ConfigParser() # note, need strict=False for Python3
cp.optionxform = str... | c636ac5665f67e327c026850654c78c549a711e0 | 66,605 |
import six
def across_data_formats(func):
"""Function wrapper to run tests on multiple keras data_format and clean up after TensorFlow tests.
Args:
func: test function to clean up after.
Returns:
A function wrapping the input function.
"""
@six.wraps(func)
def wrapper(*args, ... | 224d5ede2a32518569513ce7dfb27bf2a8ab0f0f | 66,606 |
def process_data_arguments(data=None,
single_plot=False,
use_column_source=False,
flatten_np=False,
forbid_split_up=None,
same_length=False,
copy_data=False,
... | 98c2de34510632d2de17515c63c1ed1b81bc1c47 | 66,607 |
def create_stack(nodes):
"""helper function to create a stack"""
test_stack = Stack()
for el in nodes:
test_stack.push(el)
return test_stack | 5a902b693366346cbfd379b0b9d5ebf22feb0396 | 66,608 |
def mae(data: DatapointList) -> float:
"""
Computes the mean squared error.
:param data:
:param targets: A list of targets.
:param preds: A list of predictions.
:return: The computed mse.
"""
targets = [d.targets for d in data.get_data()]
preds = [d.predictions for d in data.get_dat... | d442ab67cdcf0439844f8a71dc576098c7a14128 | 66,609 |
def expand_on_basis_of_basic_representation(mappings):
""" Expansion using Basic Representation
:param response: Response obtained after hitting the base_url of collection
:param mappings: Mappings of variables and values provided by the user to expand template
:return: Expanded URL on the basis of Bas... | fae0ec69922f3be5f25a561c5bba9f602dc3bba6 | 66,610 |
def compile(mod, target=None, target_host=None, params=None):
"""Compile the module to VM executable. A helper function for VMCompiler.
Parameters
----------
mod : tvm.IRModule
The Relay module to build.
target : any multi-target like object, see Target.canon_multi_target
For homog... | 9f0bfb05339c4f122f36582ab7f922aa4e00ae08 | 66,611 |
import sqlite3
def get_multiple_data(table_name):
"""
get document data by document ID
:return:
"""
conn = sqlite3.connect(DATBASE_LOCATION)
cur = conn.cursor()
sql = '''SELECT rowid,* FROM {0}'''.format(table_name)
cur.execute(sql)
data = cur.fetchall()
conn.commit()
conn.... | 7c12261e016abdb779d2f997674cbad7d54d3126 | 66,612 |
def posterior(kalman, problem):
"""Kalman smoothing posterior."""
*_, obs, times, states = problem
return kalman.filtsmooth(obs, times) | 724a80cd69efa499cbc62f79ab611de30c10ac26 | 66,613 |
def get_cache_key_counter(bound_method, *args, **kwargs):
""" Return the cache, key and stat counter for the given call. """
model = bound_method.__self__
ormcache = bound_method.clear_cache.__self__
cache, key0, counter = ormcache.lru(model)
key = key0 + ormcache.key(model, *args, **kwargs)
ret... | 241b13b29b3dce0888f2eeb40361dfa03d5f8389 | 66,614 |
from typing import Callable
def fixed_step(f:Callable)->Callable:
"""
Decorator which fills in the missing time step parameters
:param t0: Initial time
:param t1: Final time
:param n_step: Number of time steps to take
:param dt: Time between steps
:param fps: Number of steps per time unit... | ed72b3fe50bbe70b8a539bcee93fde44be738a0c | 66,615 |
def get_HI_surface_density(
h1_intensity: np.array, major: float, minor: float, inclination: int
) -> np.array:
"""Calculate the HI surface density from the THINGS maps
Args:
h1_intensity (np.array): intensity of the HI emission in [Jy/beam km/s]
major (float): FWHM of the major axis of the... | d336eb66015e70788abc4956016280ec21ac7ac0 | 66,616 |
def shufflenet_block(scope, base_depth, num_units, stride, groups, groups_in=None):
"""Helper function for creating a shufflenet bottleneck block.
Args:
scope: The scope of the block.
base_depth: The depth of the bottleneck layer for each unit.
num_units: The number of units in the block.
... | 97a70194db1b30a7956cf058212ec2f33d7ad5fb | 66,617 |
import os
async def convert_body(request: Request):
"""
It's just _way_ easier to deal with files rather than STDIN.
Take the body of the request, write it to a temporary file, then use
wkhtmltopdf to convert it.
"""
data = await request.body()
if not data:
return Response("ERROR... | 7260847659e1cc67bbc12f828b4317a077913e8f | 66,618 |
import array
def read_feature(filename):
"""Read feature dump by C3D
Parameters
----------
filename : str
Fullpath of file to read
Outputs
-------
x : ndarray
numpy array of features
Note: It accomplishes the same purpose of this code:
C3D/examples/c3d_featur... | 5f79e850b629dac1bc1ced22d85d1be9e0131254 | 66,619 |
from datetime import datetime
def set_qos(s_url, s_qos):
"""
Sets a new QoS to the site if existing
:param s_url: str - URL/name of the site
:param s_qos: float - QoS value
:return: Site - The updated site or None if not existing.
"""
assert isinstance(s_qos, float), 'QoS value must be f... | 4a5cfda19b4e34dabba79a2adab41219e35c1c2a | 66,620 |
def extract(mod, models):
""" Returns models with mod removed. """
if mod == {}:
# No mod to delete, return models as it is.
return models
return [model for model in models if model != mod] | 5aa24ccaa238fe85f4f037c8af318e95a77b7b55 | 66,621 |
def extract_labels(all_samples):
"""
this function extracts the labels and speaker from the dataset
"""
labels = []
speakers = []
print("Extracting Labels")
for i in range(len(all_samples)):
temp = all_samples[i]
temp = (temp[0:-3].split("_"))
labels.append(float(temp[0]))
speakers.append(temp[1])
if i... | 65b2a07a709881f5d3c72931949673ee797ba1f3 | 66,622 |
import requests
def bond_spot_deal() -> pd.DataFrame:
"""
中国外汇交易中心暨全国银行间同业拆借中心-市场数据-债券市场行情-现券市场成交行情
http://www.chinamoney.com.cn/chinese/mkdatabond/
"""
url = "http://www.chinamoney.com.cn/ags/ms/cm-u-md-bond/CbtPri"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) ... | 5f4355d54b476d47d86ae4d69e8aceef82ecf83f | 66,623 |
import os
def _replace_desdata(pth, desdata):
"""Replace the NERSC DESDATA path if needed.
Parameters
----------
pth : str
The path string on which to do replacement.
desdata : str
The desired DESDATA. If None, then the path is simply returned as is.
Returns
-------
p... | 082681d1366b7409d11fba13c7d41fa43e4d9d9e | 66,624 |
def get_mega_info_dict(samples):
"""Read mega info of all samples,
return a dict {(o_prefix, pbid) --> MergeGroupOperation(pbid, group1, group2)}
ex:
{('tmp_1009', 'PB.1.1'): MergeGroupOperation('PB.1.1', 'PB.2.1', None)}
"""
mega_info = {} # ex: (tmp_1009, PB.1.1) --> MergeGroupOperation(...)... | 9f9c3283633cdcc65dca4751fe65baa5b988d201 | 66,625 |
def write_xyz(tgt_geo, bath_geo):
""" Write the geom strings
"""
tgt_str = automol.geom.string(tgt_geo)
bath_str = automol.geom.string(bath_geo)
return tgt_str, bath_str | 9503e97dbad2621c5841ce6ad53ed86b8a1aa9e5 | 66,626 |
def get_prop(prop, default=None):
"""First order function which can be used to extract property of nodes"""
def get(n: Node):
return default if prop not in n else n[prop]
return get | 509602b279e6ee47128acaa84e788cab18024289 | 66,627 |
def _is_no_cache(request):
"""Returns True if the request should skip the cache."""
cache_control = request.headers.get('Cache-Control') or ''
return 'no-cache' in cache_control or 'max-age=0' in cache_control | bf8421a3f9a654a877cdf518aa9a4de532098f89 | 66,628 |
def scanner_config():
"""
读取配置文件
:return: dict
"""
with open("config.inf", "r") as f:
lines = f.readlines()
lines = [line for line in lines if cmp(line.strip(), "") != 0]
lines = [line for line in lines if not line.startswith("#")]
kv_dict = {}
for line in lines:
kv =... | 219003bac6d7b4dd7efa498ef58ba003f2f6a01d | 66,629 |
def get_missing(array, first=True):
"""
returns the ids of similar looking images that we haven't collected yet
array is
| ID | row | col | filename | lat | lon | X_1 | ... | X_n |
where
ID is the image id
row is the row pixel index of top left corner of subimage
col is the ... | 323a2b88a02fafe6f06f2e65be1be625a0ace29b | 66,630 |
def generate_voronoi_polygons(points, scale, aoi):
"""
Generates Voronoi polygons
:param points:
:param scale:
:param aoi:
:return:
"""
error = ee.ErrorMargin(1, 'projected')
# proj = ee.Projection('EPSG:3857').atScale(scale)
proj = ee.Projection('EPSG:4326').atScale(scale)
... | bb450a3566db6ecdb3b9342209666f380e3c0fc8 | 66,631 |
def open_text(fname, **kwargs):
"""On Python 3 opens a file in text mode by using fs encoding and
a proper en/decoding errors handler.
On Python 2 this is just an alias for open(name, 'rt').
"""
if PY3:
# See:
# https://github.com/giampaolo/psutil/issues/675
# https://github.... | 56e4c364fa915a3b75eccfbd69fa91180612585b | 66,632 |
def plot_timeseries(server, e, dataset_id):
"""This defines the initial bqplot time series plot"""
dt_x = bq.DateScale()
sc_y = bq.LinearScale()
constraints = {"time>=": server.get("min_time"), "time<=": server.get("max_time")}
df, var = get_timeseries(
e=e,
dataset=dataset_id,
... | e6f12533716a33e2ef84a547d1ed471cd9b9ff31 | 66,633 |
def tf_idf(text):
"""
Compute the TF-IDF scores for each word in each document. The collection
of documents must be in bag-of-words format.
.. math::
\mbox{TF-IDF}(w, d) = tf(w, d) * log(N / f(w))
where :math:`tf(w, d)` is the number of times word :math:`w` appeared in
document :math:`... | e008b666b58206ceb195ef84960eccadcef0b854 | 66,634 |
def get_chassis_serial(self):
""" Returns the serial number of the chassis
"""
output = self._vchannel.cmd('show inventory rack | inc "^ 0"')
line = output.split('\n')[0] # first line
tmp = line.split()
if len(tmp) > 2:
result = tmp[2]
else:
result = ''
BuiltIn().log... | ccbc69baaa0d093f50c6d49c3f68eb7a5a9c82c0 | 66,635 |
def create_resnet_encoder(
backbone_options: config_pb2.ModelOptions.BackboneOptions,
bn_layer: tf.keras.layers.Layer,
conv_kernel_weight_decay: float = 0.0) -> tf.keras.Model:
"""Creates a ResNet encoder specified by name.
Args:
backbone_options: A proto config of type
config_pb2.ModelOption... | f98c1bda75614c284fc8d7255b8e693aa6253a47 | 66,636 |
def group_function(table_columns, table_row, tree):
"""True if resultset should be grouped
:return: Grouping function or None and its position.
:rtype: tuple(EtcdbFunction, int)"""
try:
group_position = 0
for select_item in tree.expressions:
expr = select_item[0]
... | 68d6d27bb61499f191ed74b6e6e586e227aaff48 | 66,637 |
import numpy
import itertools
def stratify_irregular_dimension(storage_units, dimension):
"""
Creates a new series of storage units for every index along an irregular dimension that must be merged together
:param storage_units:
:param dimension:
:return: storage_units: list of storage_unit-like ob... | 331a0acbe670ce71341cc6fa3dd3ac4ae51e29b5 | 66,638 |
def _var_get_id(self, vname):
""" Lookup var value by name or ID
returns ISY Id or None
"""
if not self._vardict:
self.load_vars()
if isinstance(vname, IsyVar):
return vname["id"]
else:
v = str(vname)
if (v).upper() in self._vardict:
# print("_get_var_id : " ... | 17b3a47524cba6ff338ee830a820173ab942c34a | 66,639 |
from re import T
def sgd(cost, params, lr=1.0, alpha=0.1):
"""SGD with Momentum (and Langevin Dynamics)"""
grads = T.grad(cost=cost, wrt=params)
updates = []
for p, g in zip(params, grads):
v = shared(p.get_value() * 0.)
v_new = v * (1.0 - alpha) - alpha * lr * g
updates.append... | a4eae61be3ca192553adda9ea829bd5997d4e900 | 66,640 |
def get_or_else(data, key, default_value = None):
"""
Tries to get a value from data with key. Returns default_value in case of key
not found.
"""
if not data:
return default_value
try:
return data[key]
except:
return default_value | 53bc769a3331684c46127b08976fd62ca1ab47df | 66,641 |
def update_rl_veh(env,
rl_queue,
rl_veh,
removed_veh,
control_range,
num_rl,
rl_ids):
"""Update the RL lists of controllable, entering, and exiting vehicles.
Used by the open environments.
Parameter... | 616be730420795755d5b628284517572b0c2248f | 66,642 |
def tree_BFS(root_node):
"""
My Python implementation of BFS for a tree
Traverses a binary tree from its root and returns an array of the nodes in traversal order
Time complexity: O(n), n = Number of nodes
Space complexity: O(n)
"""
# Track BFS traversal
bfs_traversal = []
# Queue for next nodes to explore... | 349f4df805dee0c13bc3c58865b2fc3543528a53 | 66,643 |
import numpy
import os
def combine_habitat_contraints(args):
"""Build maps that will be used to constrain where habitats can be placed:
Every constraint can be expressed as a mask that follows the same
pattern:
1- Compute masks with limits dictated by the threshold's sign
... | 094b35ed2d4b0b28f31ac6a4353e4e7abb539957 | 66,644 |
import functools
def DeletionTest(f):
"""This indicates a test that uses deletion."""
@functools.wraps(f)
def Decorator(testinstance):
if testinstance.TEST_DELETION:
return f(testinstance)
else:
return testinstance.skipTest("Tests that use deletion are disabled "
... | 2f27803408a4a92e3f29def203845f27dfd0a113 | 66,645 |
def get_hyponyms(synset):
"""
Get the WordNet hyponyms that are in the model
Params
synset: nltk WordNet synset
"""
hyponyms = synset.hyponyms()
# hyponym_terms = [hyponym.lemmas()[0].name() for hyponym in hyponyms]
# hyponym_terms = [term for term in hyponym_terms if term in diction... | a0e28825869240868e0d48a02481d93b57aeee6a | 66,646 |
def calc_frac(x, func, total=None):
"""
calculate the fraction of entries in *x* that satisfy *func*
"""
total = total or len(x)
return np.count_nonzero(func(x)) / total | fba8c6b2eba855740adb8ebb2d5585af5ea644a9 | 66,647 |
from datetime import datetime
def get_monthly_file_content_by_date(date: datetime) -> str:
"""
Get the content of the monthly trajectory summary file for a given date.
:param date: The date to get the monthly file for.
:return: The content of the monthly file.
:raises: requests.HTTPError if the ... | 35a1f57f952e94285ddea50bb623db2d25e71136 | 66,648 |
def get_frame(df, t, fps=20):
"""Gets the player data from the right frame
Arguments:
df: pd.DataFrame with player tracking data
t: timestamp of the play
fps: frame per second
Returns:
dfFrame: pd.DataFrame with player tracking data from timestamp t
Raises:
"""
dfFrame = ... | 51bad21aa17978bcfee37fe31edb79ddb45e9191 | 66,649 |
def MOE_batch_means(samples, log=False):
"""Assumes unkown mean, unknown variance, unknown distribution.
Assumes 50 samples. Batches into 5 groups of 10 samples.
Computes the 95 percent margin of error."""
if len(samples) == 0:
return float('nan')
batchSize = 10
batches = [samples[i:i+batchSize] for i in range(... | d7e9fc2cc2396e3072cc947bede43b0b8cde751c | 66,650 |
import math
def pformat_bar(
value, width=40, prefix="", vmin=0., vmax=1., border=True, fill=' ',
reverse=False):
"""Return a progressbar-like str representation of value.
Parameters
==========
value : float
Value to be represented.
width: int
Bar width (in charac... | 6b12a5bb2bd4d051224b2f5960a0378b0593b2f7 | 66,651 |
import functools
def Elrn_chan(state, chan, data):
""" External learning along a channel with data,
as mulitset of predicates """
def lshift(a, b): return a << b
freqs = [data(*data.sp.get(*a))
for a in np.ndindex(*data.sp.shape)]
freq = sum(freqs)
stats = [(data(*a) / freq) *... | 62846791f085ccf2d03a11a7f6a1bdc69a3a3605 | 66,652 |
import select
async def get_all_tenants():
"""
Return all tenants
"""
query = select(Tenant)
results = await DBSession.execute(query)
return {"action": "get", "tenants": results.scalars().all()} | 03de24fa79c8565d8409fa664f52e4b65ae84a78 | 66,653 |
import cmath
import math
def exp(x):
"""
Return the exponential value of x
"""
if isinstance(x,ADF):
ad_funcs = list(map(to_auto_diff,[x]))
x = ad_funcs[0].x
########################################
# Nominal value of the constructed ADF:
f = exp(x)
... | 3bbb7a29ae187f9450366e730cb13aec6da04bbc | 66,654 |
def lab2xyz(lab_arr):
"""
Convert colur from CIE 1976 L*a*b* to CIE 1931 XYZ
Parameters
----------
lab_arr: ndarray
Color in CIE 1976 L*a*b*
Returns
------
xyz_arr: ndarray
Color in CIE 1931 XYZ
"""
L, a, b = lab_arr[:, 0], lab_arr[:, 1], lab_arr[:, 2]
... | 08edc4ce78837369a87135e3cf4b35e2965f356d | 66,655 |
def test_generator(func_graph, stack_roots):
"""Return the function that will start the fault-tolerance testing for a graph"""
def test(self):
"""Test fault-tolerance of the topology"""
self.set_up(func_graph, stack_roots)
self.network_function()
return test | 1a607086ef68833894f8d031589c61dd8557f090 | 66,656 |
def gaussian2D(omega1, cent1, delta1, omega2, cent2, delta2, corr=0.0):
"""Two-dimensional complex Gaussian lineshape
"""
gamma1 = 0.0
gamma2 = 0.0
return voigt2D(omega1, cent1, delta1, gamma1,
omega2, cent2, delta2, gamma2, corr=corr) | 104e692401e87608cf235b21c8e79d5b8cb35f2c | 66,657 |
from typing import List
def create_msk_from_classes(
mc_msk: np.ndarray,
classes_to_use: List[int],
out_msk_pix_value: int = 255,
out_msk_dtype: np.dtype = np.uint16,
) -> np.ndarray:
"""
Create a mask of type out_msk_dtype set to the out_msk_pix_value for pixels
belonging to the required ... | d1818935a9948090882431454dc04dc0d34b076f | 66,658 |
def kmeans(
s: pd.Series,
n_clusters=5,
init="k-means++",
n_init=10,
max_iter=300,
tol=0.0001,
precompute_distances="auto",
verbose=0,
random_state=None,
copy_x=True,
n_jobs=-1,
algorithm="auto",
):
"""
Perform K-means clustering algorithm.
"""
vectors = l... | c28dcca61c637923a69dece2cc9b1b61d66526f0 | 66,659 |
def tile_read_utm(source, bounds, tilesize, indexes=[1], nodata=None,
alpha=None, dst_crs='EPSG:3857', verbose=False,
boundless=False):
"""Read data and mask.
Arguments
---------
source : str or :py:class:`rasterio.io.DatasetReader`
input file path or :py:cla... | c16c89cc2d4c62c530f8f9ecafe73fb858a7f664 | 66,660 |
def __float2pcm(npy_array, _type='int16'):
"""Convert <npy_array> from float to pcm.
Default conversion type is 'int16'.
"""
info = np.iinfo(_type)
amp = 2**(info.bits - 1)
offset = info.min + amp
npy_array = npy_array * amp + offset
return npy_array.clip(info.min, info.max).astype... | 1d7e125740e77cce01ea15081d17bea0e397c82e | 66,661 |
def force_str(s, encoding="ascii", errors="strict"):
"""Converts s to str (which is bytes on Python 2, and unicode on Python 3), using
the provided encoding if necessary. If s is already str, it is returned as is.
If errors="strict", str is bytes, and s is str, its encoding is verified by decoding
it; ... | 0b8b78f715923680bd56e025bded36fba98ee332 | 66,662 |
import csv
def process_csv(csv_file):
"""Turn the contents of the CSV file into a list of lists"""
print("Processing {}".format(csv_file))
with open(csv_file, "r") as datafile:
data = list(csv.reader(datafile))
return data | 87603c21d09332cc1e1d25a3de6074f984f95426 | 66,663 |
def _change_vname(match) -> str:
"""Change variable name using the match object from re.
:param match:
:return: String with the variable name translated
"""
return (
match.group('mup_pre')
+ _translate(match.group('vname'))
+ match.group('mup_post')
) | e1c8a5c250a5bf0d67906e54d12a97067403fdb8 | 66,664 |
def weighted_average(values, weights=None):
"""Calculate the weighted mean average between two iterables of `values`
and matching `weights`. If weights is ``None`` they will be autogenerated
Args:
values(iterable): Values to average, either `int` or `float`
weights(iterable): Matching weigh... | 465b63f02e3a7cf92737938e4352c154abbae911 | 66,665 |
import math
def random_cartesian_from_polar(r_low, r_high):
""" function that returns a random cartesian coordinates gives within the radius limits from polar coordinates """
# generate the polar angle between 0 <= x < 360 degree
polar_angle = np.random.uniform(0, 360, 1)[0]
# generate the radius... | 0dc136ab0e56902625cba8a70ba76b782b7bab04 | 66,666 |
def unwrap_f(arg):
"""Multiprocessing pool.map requires a top-level function."""
return Corex.calculate_p_xi_given_y(*arg) | f3440ca4927eff696b95ed00bbf92b0e1a58f8a3 | 66,667 |
from typing import Union
def precision_rate(true: np.ndarray,
predicted: np.ndarray,
condition: Union[bool, int, str, list, tuple, set]) -> float:
"""
compute precision rate, refer module description for calculation;
:param true: numpy.ndarray(shape=(m), ), an array o... | 671c00b4a3c5886a22d34a822564f7fd4e5bde17 | 66,668 |
def u3(self, theta, phi, lam, q):
"""Apply u3 to q."""
return self.append(U3Gate(theta, phi, lam), [q], []) | 85f9da6a911e7ef8fbfb47771331843511d1b8fb | 66,669 |
import re
def new_exposures(out):
"""Scan rsync output for exposures to be transferred.
Parameters
----------
out : :class:`str`
Output from :command:`rsync`.
Returns
-------
:class:`set`
The unique exposure numbers detected in `out`.
"""
e = set()
e_re = re.c... | 8fc8817fe0ad79c177473ec676853569d733ec65 | 66,670 |
def conv2d_reduce_sum(x, input_height, input_width, padding, strides):
"""Sums along the output dimensions in line with a convolution.
For example, with a kernel size of 3, padding=`VALID`, and stride of 1, then
with respect to each spatial dimension of the convolution (and disregarding
class, batch and channe... | d7d345cf0b0c03144b173404d258a5d745793dd7 | 66,671 |
from typing import OrderedDict
import os
import logging
import glob
def getInputTasks(input_dir, stranded=False, has_control=False,
require_peaks=True, mode='train'):
"""Dictionary specifying the various tasks for the profile model
Args:
input_dir (str): the path to dire... | 7dfd1eacb7c959b12af94bd5446d58df59ea0534 | 66,672 |
def _find_relevant_examples(dataset, rule):
"""Find examples in `dataset` where `rule` could be used in a derivation."""
# TODO(petershaw): This could potentially be more effecient by pre-indexing
# the dataset sources in a data structure such as a Trie.
examples = []
for source_str, target_str in dataset:
... | 6f0a1a00753e63295779e40f6515e8210c1b65f4 | 66,673 |
def violin_plot(df, class_masks, class_names, column_name, class_colors=None, alpha=1.0, transformation_function=None,
pseudocount=0, y_label=None, figname=None, vert=True, yticks=None, figax=None, **kwargs):
"""Make a violin plot with len(class_masks) violins for the specified column from a DataFra... | 76445f7d7bed5e503c5978c1aba602893ad389bd | 66,674 |
from datetime import datetime
def now():
"""Current UTC datetime string suitable for use as a Solr dt field."""
return datetime.utcnow().isoformat(timespec='seconds') + 'Z' | 390877fc1b7592df54300cb70b9b62590f1bd4c9 | 66,675 |
def get_classification(X, theta, temp_parameter):
"""
Makes predictions by classifying a given dataset
Args:
X - (n, d - 1) NumPy array (n data points, each with d - 1 features)
theta - (k, d) NumPy array where row j represents the parameters of our model for
label j
... | cce812d6511a197767c7c1c7cf6ad26f09c306c1 | 66,676 |
import base64
def create_message(sender, to, subject, message_text):
"""Create a message for an email.
Args:
sender: Email address of the sender.
to: Email address of the receiver.
subject: The subject of the email message.
message_text: The text of the email message.
Returns:
An object ... | 89f9ddde8d5a1161461078ab614c718d8ef6dd12 | 66,677 |
def get_int(buffer: bytes, offset: int) -> int:
"""
Returns an int at the specified offset.
:param buffer: The buffer to get from.
:param offset: The offset to use.
:return: The int
"""
return _int_serialiser.deserialise_from_bytes(buffer[offset:offset + 4]) | 2f4830cf4693178e3992a861edc1bc234f3b85d4 | 66,678 |
def load_source_table():
""" Load table of arcline sources
Returns
-------
sources : Table
"""
src_file = arclines.__path__[0]+'/data/sources/arcline_sources.ascii'
# Load
sources = Table.read(src_file, format='ascii.fixed_width', comment='#')
# Return
return sources | ec1ccbeee783f3a47d76d1546d77772d38cc385f | 66,679 |
def extract_batches_per_worker(federated_train_loader: sy.FederatedDataLoader):
"""Extracts the batches from the federated_train_loader and stores them
in a dictionary (keys = data.location).
Args:
federated_train_loader: the connection object we use to send responses.
back to th... | 1a727d80c355d0cb5215c2c1b35416a36226984d | 66,680 |
def clang_layer_packages():
"""Packages for the clang layer."""
clang_tools = [
tool_names.binutils,
tool_names.lib_cpp,
]
packages = []
for tool in clang_tools:
packages.extend(get_jessie_packages(jessie_tools()[tool]))
return depset(packages).to_list() | 4473a3038b52327d252cae4723c08d2b997b0b57 | 66,681 |
from typing import Generator
def read_until(
data: bytes, *, return_tail: bool = True
) -> Generator[tuple, bytes, bytes]:
"""
read until some bytes appear
"""
return (yield (Traps._read_until, data, return_tail)) | 277108459577218fd679b4ef44ae10ec9b009293 | 66,682 |
import re
import subprocess
def wait_for_operators(oc: str, kubeconfig_path: str) -> bool:
"""
Waits for enough Cluster Operators to report back healthy. The number of reporting Operators must be greater
than the constant `MINIMUM_HEALTHY_OPERATORS`
:param oc: The OpenShift Client binary
:param k... | 322dbc5ecf3c0587cf0152723f1c9c30ad893e0e | 66,683 |
def clean_data(df):
"""Cleans the passed dataframe.
Actions done while cleaning:
- dropping all rows with missing location ids
- dropping all rows where dropoff time is before pickup time
- consider all location ids that map to the same zone as equivalent and replace them with a single value
... | f29a1d3784914c2dfcae1611c8860dbaa7f2a93c | 66,684 |
def sort_by_scores(scores, features_list, topn=None): #实际在输入的时候scores=labels,features_list=[labels,logits],所以是按照labels进行排序
"""Sorts example features according to per-example scores.
Args:
scores: A `Tensor` of shape [batch_size, list_size] representing the
per-example scores.
features_list: A list of... | 3483225e319f083b23262aead39c76e6f15103fd | 66,685 |
def _distance_sequence_from_scores(scores_1, scores_2):
"""
We want to compute the distance between two -local perturbation- models.
To do this, we simply take the usual distance between two hmm models; the pretrained predictive model
stays constant.
The desired distance is the limiting value of t... | 9a4488f157e8beaa9ad594467585063884800594 | 66,686 |
def polygon_clip(subjectPolygon, clipPolygon):
""" Clip a polygon with another polygon.
Args:
subjectPolygon: a list of (x,y) 2d points, any polygon.
clipPolygon: a list of (x,y) 2d points, has to be *convex*
Note:
**points have to be counter-clockwise ordered**
Return:
a list of (x,y) ... | 1064e3f20c4145ac42484c8b4fc772e396032be2 | 66,687 |
import re
import os
def handle_spectrum( flmfile, fitsfile, objname, objid=None, runid=None, just_ask=False ):
"""
flmfile and fitsfile should be absolute paths, objname should be the name of the
object. can be in folder format instead of DB format.
Can optionally include objid or runid (for objects... | c702af733ffc3c3165b4b2dc9a52391c4d6291c3 | 66,688 |
def is_openbabel_molecule(obj):
"""Check whether an object is an `oddt.toolkits.ob.Molecule` instance.
.. versionadded:: 0.6
"""
return (hasattr(oddt.toolkits, 'ob') and
isinstance(obj, oddt.toolkits.ob.Molecule)) | 1bf1cd6da46aab746f7d63d8f962c97cd5911ee0 | 66,689 |
def confirm(prompt=None, resp=False):
"""prompts for yes or no response from the user. Returns True for yes and
False for no.
'resp' should be set to the default value assumed by the caller when
user simply types ENTER.
>>> confirm(prompt='Create Directory?', resp=True)
Create Directory? [y]|n... | a5a6dcb89434aa7dbeac85ae1164b3912100bd4a | 66,690 |
def streamline(mesh, field, startCoord, dLengthSteps, dataMesh=None,
maxSteps=1000, verbose=False, coords=(0, 1)):
"""
Create a streamline from startCoord and following a vector field in up
and down direction.
"""
xd, yd, vd = streamlineDir(mesh, field, startCoord, dLengthStep... | d01339b965c2e99c5fecd3f8df93e6025904b769 | 66,691 |
def wall_filter(gray):
"""
Filter walls
Filter out walls from a grayscale image
@Param image
@Return image of walls
"""
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thres... | 31d71c640aa229efc4384140b1bd6d502544827e | 66,692 |
from typing import Tuple
def FloatRangeTupleDataclassField(N=2, default: Tuple = (0.9, 0.999), min=0, max=1):
"""Returns a dataclass field with marshmallow metadata enforcing a `N`-dim. tuple with all values in given
range.
In particular, inputs must be N-dimensional tuples of purely numeric values withi... | 2166b6aad243df5e9bc9d4b1ceb071cf00e7e5e0 | 66,693 |
import htmlentitydefs
import re
def xss_strip_all_tags(s):
"""
Strips out all HTML.
"""
return s
def fixup(m):
text = m.group(0)
if text[:1] == "<":
return "" # ignore tags
if text[:2] == "&#":
try:
if text[:3] == "&#x":
... | f78dc85368c63e4ed0484add6755bb9645186d02 | 66,694 |
def safe_divide(numerator, denominator, eps=1e-7):
"""Avoid dividing by zero by adding a small epsilon."""
safe_denominator = tf.where(denominator == 0.0, eps, denominator)
return numerator / safe_denominator | 551b74f9c9404882585831c316980d36e3bb3359 | 66,695 |
def sentinel2_sr_cloud_mask(input_img):
"""Extract cloud mask from the Sentinel 2 SR QA60 band
Parameters
----------
input_img : ee.Image
Image from the COPERNICUS/S2_SR collection with a QA60 band.
Returns
-------
ee.Image
Notes
-----
Output image is structured to be ... | 9b456f9b130d057cb7b2a811458e1cf426365037 | 66,696 |
def get_spacegroup(cell, positions, numbers, symprec=None):
"""Returns Pearson symbol and intl number corresponding to the given structure"""
cell = (cell, positions, numbers)
sg = spglib.get_spacegroup(cell, symprec=symprec or SYMPREC).split()
return sg[0], int(sg[1][1:-1]) | 19d6e98cfe9fe2119bbd053a81622971f018ba90 | 66,697 |
def preprocess_idl_type_and_value(idl_type, cpp_value, extended_attributes):
"""Returns IDL type and value, with preliminary type conversions applied."""
idl_type = idl_type.preprocessed_type
if idl_type.name == 'Promise':
idl_type = IdlType('ScriptValue')
if idl_type.base_type in ['long long', ... | cc270ba1b811fef6992c582cdd3e28ee04554434 | 66,698 |
def start_heterogeneous_tile(
tile_name,
pin_assignments,
sites,
equivalent_sites,
):
""" Returns a new tile xml definition.
Input parameters are:
- tile_name: name to assign to the tile
- pin_assignments: location of the pins to correctly build pin locations
... | f670e3deedd396b09a2f6a497f2f223e909413fb | 66,699 |
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