content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Sequence
from typing import List
from typing import cast
def try_rules(
context: Sequence[Expression],
goal: Expression,
general_rules: Sequence[Expression],
verbosity: int = 0,
) -> List[Expression]:
"""context and context_rules are disjoint, all in context_rules satisfy
is... | 6bfe3b3e46d5591f3a68ab9d6cf68eeacf4f8ca2 | 3,612,555 |
def dissimilarity_loss(latents, mask):
"""
Minimize the similarity between the different instrument latent representations
Arguments:
latents {torch.tensor} -- latent matrix from the encoder of shape: (B, 1, T', N)
mask {torch.tensor} -- boolean mask: True when the signal is 0.0; shape (B, ... | 988e86a535f70425975f178c3dbcd396b340990e | 3,612,556 |
def _stdin_ready_other():
"""Return True, assuming there's something to read on stdin."""
return True | 934e97ba18f9f60ad8e2d77f227dd98b70891b56 | 3,612,557 |
def text_objects(text, font, color):
"""
Function for creating text and it's surrounding rectangle.
Args:
text (str): Text to be rendered.
font (Font): Type of font to be used.
color ((int, int, int)): Color to be used. Values should be in range 0-255.
Returns:
Text surf... | a9ed3d8a68c80930e2594b4dbef06e828de10513 | 3,612,558 |
from typing import List
def split_list(a: List, chunk_size: int):
"""
split a large list to small chunk with the specified size
:param a:
:param chunk_size:
:return:
"""
chunks = []
list_length = len(a)
start_pos = 0
end_pos = start_pos + chunk_size
while end_pos <= list_l... | 24ec6e5c2a86deabb4abb9fd1c8e6a0f86cfb7ed | 3,612,559 |
def process_results(news_list):
"""
Function that processes the news result and transform them to a list of Objects
Args:
news_list: A list of dictionaries that contain news sources
Returns :
news_results: A list of news objects
"""
news_results = []
for news_item in news_l... | 0d2f5b3ca85d7b6aec770bc71de9f1ef749915d7 | 3,612,561 |
def slice_slice(old_slice, applied_slice, size):
"""Given a slice and the size of the dimension to which it will be applied,
index it with another slice to return a new slice equivalent to applying
the slices sequentially
"""
step = (old_slice.step or 1) * (applied_slice.step or 1)
# For now, u... | 97cbf20100da58e3d6712b7fa9d9a2fb04c3322f | 3,612,562 |
import re
def _parse_compile_log(log):
"""parses the pdflatex compile log"""
if log is None:
return {}
IMAGES = {}
i = 0
image_found = False
for line in log.split('\n'):
if not image_found:
m = re.match("^File: (.*) Graphic file", line)
if m:
... | a3888ed3d2664866d47265f2c06720a8a3e757df | 3,612,564 |
def is_correct(list):
"""
判断一个list中单词是否正确
:param list:待识别的单词列表
:return: 正确的单词列表
"""
temp = []
rightlist = spell.known(list) # {'morning'}
global count
global num
count = count + len(list) - len(rightlist)
num = num + len(list)
for item in rightlist:
temp.append(item)... | 2a0e09b009c13b03810979c903969e2b4719879c | 3,612,565 |
def spellcheck(request):
"""
Spellcheck some POST data.
"""
jsondata = request.POST.get("data")
print "Spellcheck data: %s" % jsondata
if not jsondata:
return HttpResponseServerError(
"No data passed to 'spellcheck' function.")
data = json.loads(jsondata)
aspell =... | 80f50c26f4f777499018af02e8ad2939d0eb1af2 | 3,612,566 |
def contains(value, lst):
""" (object, list of list of object) -> bool
Return whether value is an element of one of the nested lists in lst.
>>> contains('moogah', [[70, 'blue'], [1.24, 90, 'moogah'], [80, 100]])
True
"""
found = False # We have not yet found value in the list.
for i in... | 9d3690943c05b4220afabfa65402b7f12c1cb279 | 3,612,567 |
import re
def filter_component_names(raw_npm_list):
"""Filter the raw NPM list to get list of proper component names."""
pattern = re.compile("^[0-9]+.")
components = []
for line in raw_npm_list.splitlines():
if pattern.match(line):
try:
i1 = line.index("[")
... | 7d8a102781a32ddd263d1c4364f261e6f1191e70 | 3,612,568 |
def dispImg(img):
"""
This does the min-max scaling the images to 0 and 1
"""
try:
h, w, d = img.shape
img_tmp = (img - np.min(img)) / (np.max(img) - np.min(img))
plt.matshow(img_tmp)
plt.show(block=False)
except ValueError:
try:
h, w = img.shape
... | b93bcbd644026cbb51082a2a3314d2340f8db278 | 3,612,569 |
import urllib
def download_metadata_file_for_1minute_data(metadatafile: str) -> BytesIO:
""" A function that simply opens a filepath with help of the urllib library and then writes the content to a BytesIO
object and returns this object. For this case as it opens lots of requests (there are approx 1000 differ... | 9fafc7c72c2b35055bbcc62b60254c01e442233d | 3,612,571 |
def check_format_input_obj(
inp,
allow: str,
recursive=True,
typechecks=False,
) -> list:
"""
Returns a flat list of all wanted objects in input.
Parameters
----------
input: can be
- objects
allow: str
Specify which object types are wanted, separate by +,
... | 306075bc72c1f79dc1e47997b8bcee33a3ee240f | 3,612,572 |
def sleeper(sleep_time):
""" Function to execute in parallel.
"""
sleep(sleep_time)
return {"sleep_time": sleep_time} | 4b158988eefc6300d02e2c1f110bfb842b0f3679 | 3,612,573 |
import random
def construct_sent(word, table):
"""Prints a random sentence starting with word, sampling from
table.
>>> table = {'Wow': ['!'], 'Sentences': ['are'], 'are': ['cool'], 'cool': ['.']}
>>> construct_sent('Wow', table)
'Wow!'
>>> construct_sent('Sentences', table)
'Sentences ar... | 238a0391b104d15db50d33904308c827851ffb62 | 3,612,576 |
def process_sources(source_list):
"""
We now want to process the dictionary and
output a list of objects - news_results.
We process results will transform our dictionary into a list of objects.
"""
news_results = []
for source in source_list:
id = source.get('id')
print(id)
... | 47f5acfb5e98ca71b8c2605cb38229ba910d4b36 | 3,612,577 |
def _preprocess(expr, func=None, hint='_Integral'):
"""Prepare expr for solving by making sure that differentiation
is done so that only func remains in unevaluated derivatives and
(if hint doesn't end with _Integral) that doit is applied to all
other derivatives. If hint is None, don't do any different... | 49ff4fdce77f64a9e1f48d46b09452f88a8fcd1f | 3,612,578 |
def read_lisp_filter(path):
"""Reads a lisp filter from a file.
For example:
(> (/ (+ (- (field "00000") 4.4)
(field 23)
(* 2 (field "Class") (field "00004")))
3)
5.5)
"""
return read_description(path) | 411027ca42d6e81bc149ab1186bed5c21212195e | 3,612,579 |
def team_games(results, team='Northeastern'):
"""
Collect all games by given team.
Parameters
----------
results : TYPE
DESCRIPTION.
team : TYPE, optional
DESCRIPTION. The default is 'Northeastern'.
Returns
-------
teamGames : TYPE
DESCRIPTION.
"""
... | 93a7260cdb97cd1ac19348af3908010905953e6c | 3,612,580 |
def get_dataset_phase(prefixf):
"""
Return the execution time dataset
from a folder "prefixf" and return the
execution time for the phases
Also filter out the zero values.
"""
#start_time = timeit.default_timer()
tmp = pd.read_csv(prefixf+"/dataset_ph1.cs... | 759773a4ad4d5438d85526351c0cbaac30d3e89c | 3,612,582 |
def UnbiasPmf(pmf, label=''):
"""Returns the Pmf with oversampling proportional to 1/value.
Args:
pmf: Pmf object.
label: string label for the new Pmf.
Returns:
Pmf object
"""
new_pmf = pmf.Copy(label=label)
for x, p in pmf.Items():
new_pmf.Mult(x, 1.0/x)
... | 1146d952bbac0ef3031e3259d98e0f343103598e | 3,612,584 |
def constant_init(value=0):
""" Constant Initializer
The resulting tensor is populated with values of type dtype, as specified by arguments value
following the desired shape.
The argument value can be a constant value, or a list of values of type dtype. If value is a list, then the length
of the l... | fc9788f68a9fe3a2bd96b52ff2c153a237ce6bb2 | 3,612,585 |
def ConfusionMatrix(ftrue, fpred, nf):
"""
CONFUSION MATRIX
Computes the confusion matrix of a discrete classification.
Written by Dario Grana (August 2020)
Parameters
----------
ftrue : array_like
true model
fpred : array_like
predicted model
nf : int
nu... | 332fa870544a3bbcfc842a78e6840837ed19a0ab | 3,612,586 |
def drop_na_1d(df, axis=0, how='all'):
"""
:param df:
:param axis: int;
:param how:
:return:
"""
if axis == 0:
axis_name = 'column'
else:
axis_name = 'row'
if how == 'any':
nas = df.isnull().any(axis=axis)
elif how == 'all':
nas = df.isnull().al... | c321191150208cddc49a150aa08f5f1e452f74dd | 3,612,587 |
def solve_1(x):
"""Returns the checksum (sum of the difference between the biggest and smallest number in each row)"""
return sum(map(get_diff, format_input(x))) | 90c904789f95318c4e57f3cd9e1d33aff3adc46d | 3,612,588 |
def resnet34(pretrained=False, filter_size=1, pool_only=True, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
if pretrain... | 9653156bbbcd6773e5479ca31f2c95a9bf9edec5 | 3,612,589 |
def selvita_sukupuoli(hetu):
"""Selvittää järjestynumeron perusteella sukupuolen: parillinen -> nainen, pariton -> mies
Args:
hetu (string): Henkilötunnus
Returns:
string: Nainen tai mies
"""
# Otetaan hetusta järjestysnumero-osa
jarjestysnumero_str = hetu[7:10]
# Muuteta... | 243982f89e2e9edf4aa62972275a87e571327550 | 3,612,590 |
import re
def search_unknown(filename, dictionary):
""" Searches the words that are misspelled/not in the dictionary"""
numbers = re.findall('[\d]+', filename)
words = filename.lower().replace('?', ' ').replace('!',' ').replace('.',' ').replace('-',' ').replace(':',' ').replace(';',' ').replace(',',' ').replace('(... | 8e91ea65f7a1f04074e68d061677cfa365c762c2 | 3,612,591 |
def cpp_bindata_subtype_type_name(name):
# type: (unicode) -> unicode
"""Return the C++ type name for a bindata subtype."""
assert is_valid_bindata_subtype(name)
return _BINDATA_SUBTYPE[name]['bindata_enum'] | 3cdc6a5bb7fe2977f39a26d78c6bc6146a31301d | 3,612,592 |
def avg_spectra(im):
"""
avg_spectra(im)
Returns numpy.ndarray of mean spectrum, averaged over the image pixels
Parameters
----------
im : image passed as numpy array
Returns
-------
out : ndarray
An array object satisfying the specified requirements.
"""
... | 013bec790da9e27837fc83859c7b2e73c8845181 | 3,612,593 |
def moving_average(x, n, type='simple'):
"""
compute an n period moving average.
type is 'simple' | 'exponential'
"""
x = np.asarray(x)
if type == 'simple':
weights = np.ones(n)
else:
weights = np.exp(np.linspace(-1., 0., n))
weights /= weights.sum()
# print ("Weigh... | faa14d2ac7c0b08e1cdfaae33d18dd308083b11d | 3,612,594 |
def changeContagion(G, A, i):
"""
change statistic for Contagion (partner attribute)
*--*
"""
delta = 0
for u in G.neighbourIterator(i):
if A[u] == 1:
delta += 1
return delta | e6acd316f9fe618f7ca592c5aeae7b902fb774a4 | 3,612,595 |
def collect(config, pconn):
"""
All the heavy lifting done here
"""
branch_info = get_branch_info(config)
pc = InsightsUploadConf(config)
output = None
collection_rules = pc.get_conf_file()
rm_conf = pc.get_rm_conf()
blacklist_report = pc.create_report()
if rm_conf:
logg... | 222fc49ab5dc49eddeda5bc2e992ca931f9b30f4 | 3,612,596 |
def _check_verbosity(verbosity: str) -> str:
"""
Check function used by verbosity setter as a callback.
:param verbosity: verbosity level to apply to resto_client.
:raises ValueError: when verbosity has a wrong value
:returns: the uppercase verbosity level
"""
if verbosity is not None:
... | 449c3c632b0c9dba92aca5d42e49c750e4f21f02 | 3,612,597 |
import unittest
def local():
"""Run all local tests"""
suite = ServiceTestSuite()
suite.addTest(unittest.makeSuite(HomelandTestCase, 'test_local'))
return suite | ae8f24abf632c5b376e71151ef6ef813f1546d2d | 3,612,598 |
def cycle_check(classes):
"""
Checks for cycle in clazzes, which is a list of (class, superclass)
Based on union find algorithm
"""
sc = {}
for clazz, superclass in classes:
class_set = sc.get(clazz, clazz)
superclass_set = sc.get(superclass, superclass)
if class_set !=... | 8079f5e7044318570424a309b6cd16ce78b6e343 | 3,612,599 |
def bivariate_histogram(xsample, ysample, reference_value_mean=None,\
reference_value_covariance=None, bins=None, matplotlib_function='imshow',\
xlabel='', ylabel='', title='', fontsize=28, ax=None, show=False,\
contour_confidence_levels=0.95, reference_color='r', reference_alpha=1,\
minima=None, maxima... | 01e479b80dca6b87494b323f9fa76a8a4a08d4e1 | 3,612,600 |
def arrayToVec(array, arrType=carmcmcLib.vecD):
"""
Convert the input numpy array to a python wrapper of a C++ std::vector<double> object.
"""
vec = arrType()
vec.extend(array)
return vec | efdbe2ae33821ff6aadfd699aa535eacf962efe3 | 3,612,601 |
import six
def moma(model, reference=None, cache=None, reactions=None, *args, **kwargs):
"""
Minimization of Metabolic Adjustment[1]
Parameters
----------
model: cobra.Model
reference: FluxDistributionResult, dict
cache: ProblemCache
reactions: list
Returns
-------
FluxDi... | 87397e01b8ddba25c1d92d652aecb2c85d8239df | 3,612,602 |
def api_v2_url(path_str,
params=None,
base_route=settings.API_BASE_URL,
**kwargs):
"""
Convenience function for APIv2 usage: Concatenates parts of the absolute API url based on arguments provided
For example: given path_str = '/nodes/abcd3/contributors/' and par... | 71d7efeb9d6e6279776f3e28257428ee3c09b9f7 | 3,612,603 |
import json
def serialize_timingsession(session_pk, pk_offset=0):
"""Serialize all data associated with a timing session into JSON."""
session = TimingSession.objects.get(pk=session_pk)
data_to_dump = [
[session],
[session.coach],
[session.coach.user],
Split.objects.filter(... | 9de0ccf0a0eb2644fc7cf2289815f5e39d9a5416 | 3,612,604 |
def send_via_mail_to(request):
"""
send email to list of freinds
:param request:
in the square represented by :
west < longitude < east
north < latitude < south
"""
idAlert = int(request.POST['id'])
emails = request.POST['list']
text = request.POST['text']
lis... | 68e17f426ea3ba15f3e95cb9ce3daf1a65c7cbed | 3,612,605 |
def get_tables_from_url(url, url_to_html='requests'):
"""Get's a list of pandas dataframes from tables scraped from a url.
Note that this will only work with static pages. If the html needs to be rendered dynamically,
you'll have to get your needed html otherwise (like with selenium).
>>> url = 'https:... | 767a99ca64ce840c04b993c48d9b861c73211da9 | 3,612,607 |
import random
import math
import tqdm
import time
def matrix_split(interaction_dataset, user_test_ratio=0.2, item_test_ratio=0.2, min_user_interactions=0,
seed=0, max_concurrent_threads=4, **kwds):
"""Dataset split method that uses a matrix split strategy. More specifically, item_test_ratio items... | d983e27c162835616c855dd54f7d9cf89708d458 | 3,612,608 |
import copy
def Loadexc(casefile_dyn):
""" Loads exciter data.
@param casefile_dyn: m-file or struct with dynamic data
@return: exciter parameter matrix
@see: U{http://www.esat.kuleuven.be/electa/teaching/matdyn/}
"""
# Load data
if isinstance(casefile_dyn, dict):
exc = casefile_... | f5fcfb34bd6b9a430aed208173ee633b61d957d8 | 3,612,609 |
def _num_requests_needed(num_repos, factor=2, wiggle_room=100):
"""
Helper function to estimate the minimum number of API requests needed
"""
return num_repos * factor + wiggle_room | 525af1e3aa5e1c0b35195fcd8e64cd32101bb6f2 | 3,612,610 |
import re
def linebreakshtml(value, autoescape=True):
"""
Replaces double line breaks in HTML to a paragraph break (``</p>``).
This tag ignores single line breaks.
"""
autoescape = autoescape and not isinstance(value, SafeData)
value = normalize_newlines(force_text(value))
if autoescape:
... | bdd38b299f297194d9e3ca3c31c20ba2203ed622 | 3,612,611 |
from typing import Generator
def find_files(base_path: str, annotation_path: str or None = None) -> Generator[str, Annotation, None]:
"""Get each file and its proper annotation object.
Parameters
----------
base_path : srt
Base path of input files.
annotation_path : str or None
Pa... | 8ee544a6ec876f395caa0be6dcc2de2ccb2fc541 | 3,612,613 |
def request_course_creator(request):
"""
User has requested course creation access.
"""
user_requested_access(request.user)
return render(request) | ad036a1852e39c7c626f4013c4d8b6e3958b1d2c | 3,612,614 |
def _generate_monthly_price_materialized_view_sql():
"""
Generate the SQL used to generate the materialized view used by the last point view
:return:
"""
return """
CREATE MATERIALIZED VIEW instancedata_monthly_price_mv
ENGINE = SummingMergeTree
PARTITION BY crawlMonth
... | 8cbe81bd07074accf6f621ab68d795282e0e844d | 3,612,615 |
def number_constructors():
"""A decorator for parametrized tests that test particular number operations.
"""
return pytest.mark.parametrize(
"newobj",
[
lambda: weakget(0),
lambda: pep505(0),
lambda: _weakget__nothing, # a singleton
],
ids... | 8b8c1534f1285f03b2219dba58ed9490236d87a3 | 3,612,616 |
import threading
def approve_paper(request):
"""API approve_paper"""
if not request.user.is_authenticated:
return JsonResponse({'status': 'false'}, status=401)
if request.method == 'POST':
id = request.POST['id']
comment = request.POST['comment']
paper = get_object_or_404(... | 0f5c48e747d4b5fea890de5586561da3f13e18ac | 3,612,617 |
def dopamine_eval(runner, patient0, seed=100):
"""Evaluate an agent."""
base_env = runner._environment.environment # pylint: disable=protected-access
initial_health_state = np.zeros_like(
base_env.initial_params.initial_health_state)
initial_health_state[patient0] = 1
base_env.set_initial_health_state... | b5d497923ff62e953bb6a6df717645b886b0138a | 3,612,618 |
import torch
def pad_data(data_list, fill=0):
"""
Pad tensors with fill-value such that list of variable shaped tensors can be stacked
"""
n_samples = len(data_list)
#first determine longest time series:
lengths = [x.shape[0] for x in data_list]
max_len = max(lengths)
output = fil... | d6d978d0247b7481f5eb33a23292addaa10ba792 | 3,612,619 |
import json
def market(request):
""" Consulta a API procurando por informações sobre o mercado """
conn.request("GET", "/market/v2/get-summary?region=BR", headers=headers)
res = conn.getresponse()
data = res.read()
summary = json.loads(data.decode("utf-8"))['marketSummaryAndSparkResponse']
ret... | 1bb8cbc8bd44811a8e0f50b328ec0a2b858a82d3 | 3,612,620 |
def setup_invalid_yaml(tmpdir):
"""Returns a list containing three Vector instances"""
test_config_data = """
test: 1
test2:
1
"""
default_dict = {'test': 'testing', 'test2': 'still testing'}
test_cfg = tmpdir.mkdir("sub").join('test_config.yml')
test_cfg.write(test_config_data)
... | 0f16cc6e8974b077996957b3f26f0d5f3c194ad3 | 3,612,621 |
import torch
def fuse_features(features1, features2, frame_batch_size=64, device='cuda'):
"""feature fusion"""
video_length = features1.shape[0]
frame_start = 0
frame_end = frame_start + frame_batch_size
features = np.empty(shape=[0, 4096], dtype=np.float32)
if video_length <= frame_batch_siz... | 539838c30cb9ac1e01af7f9b5d15df9cf4663275 | 3,612,622 |
def raHMS2deg(angle):
"""
EXAMPLE: Angle("10:42:44",unit=u.hourangle).deg
"""
return astropy.coordinates.Angle(angle,unit=u.hourangle).deg | f0b910131e8802c029f8f7a6758f4fe1184f24e0 | 3,612,623 |
def same_padding_for_kernel(shape, corr, strides_up=None):
"""Determine correct amount of padding for `same` convolution.
To implement `'same'` convolutions, we first pad the image, and then perform a
`'valid'` convolution or correlation. Given the kernel shape, this function
determines the correct amount of p... | 8d956c75a2e0609a04ec56374a4cb9b3b367b90a | 3,612,626 |
import re
def dedeidentify(text):
"""
MIMIC-specific
:param text: The text to de-de-identify
:return:
"""
return re.sub(r"\[\*\*.*\*\*\]", xxxx, text).lower() | 033c04bb61ce26fa2e7af902cc4d3ffbb9c88004 | 3,612,627 |
def embed_net_siam1():
"""
This is a siamese type network to compare two patches.
"""
data1 = mx.sym.Variable("data1")
data2 = mx.sym.Variable("data2")
conv_weight = []
conv_bias = []
for i in range(3):
conv_weight.append(mx.sym.Variable('conv' + str(i) + '_weight'))
conv... | 0817dcead9060991d9fe0bf951b0cb4790823166 | 3,612,628 |
def get_time_ms(frame, position):
"""Get time in ms and string based on the finishing position
:param frame: A frame from AsyncVideoCapture / cv2.VideoCapture
:type frame: numpy.ndarray
:param position: Position 1-4
:type position: int
:return: Time in milliseconds, time as string
:rtype: (... | ffd10436170823a2c3a4b08c0d2d262355a12bb7 | 3,612,630 |
def _extract_license_outliers(license_service_output):
"""Extract license outliers.
This helper function extracts license outliers from the given output of
license analysis REST service.
:param license_service_output: output of license analysis REST service
:return: list of license outlier package... | 101a916fec08a3a5db1a09a2817e82314ca19f6b | 3,612,631 |
def maxout(x, pool_size, axis=1):
"""Maxout activation function.
It accepts an input tensor ``x``, reshapes the ``axis`` dimension
(say the size being ``M * pool_size``) into two dimensions
``(M, pool_size)``, and takes maximum along the ``axis`` dimension.
Args:
x (:class:`~chainer.Variab... | 5e30761e4abefb5d823690c4c92c9a8666e2aded | 3,612,632 |
from tests.fpga_drivers import FpgaDriverBase
from ctypes import c_uint32
def test_fpga_drivers_base():
"""
Test accelize_drm.fpga_drivers.FpgaDriverBase.
"""
library = 'fpga_library.so'
fpga_slot_id = 5
base_address = 0x10
fpga_image = 'fpga_image'
class Fpga:
"""Fake FPGA""... | c7d6d59e00a1167ded304170a81b960ac17f5fb6 | 3,612,633 |
from typing import Set
def GetNumDistinctChromeVersions(db, build_config, start_date, end_date):
"""Get the number of distinct chrome versions.
This represents the number of successful chrome uprevs.
Args:
db: cidb.CIDBConnection object.
build_config: Name of build config of master builder.
start_... | 83a7fd123a3154990629406b2c3d5e2ccb3b1e6f | 3,612,634 |
def install_pip():
"""
Install pip using apt-get install.
"""
#we need to upgrade pip package using pip itself
return apt_get_install("python-pip") + pip_install("pip") | 16df3a5d123735f1d3b1ba85eb55849ddcbf1e26 | 3,612,635 |
def center(map, object):
"""
Center an ee.Image or ee.Feature on the map.
Args:
map: The map to center the object on.
object: The ee.Image or ee.Feature to center.
Returns:
The provided map.
"""
coordinates = object.geometry().bounds().coordinates().getInfo()[0]
... | 60a2baa1c4f83b0e9b1221bcc474f109c35cbd7a | 3,612,638 |
from datetime import datetime
def login_user():
"""
Login user in api
:return: token
"""
print('Login')
auth = request.authorization
# print(request.authorization)
if not auth or not auth.username or not auth.password:
return make_response('could not verify', 401, {'WWW.Authent... | 3e371f9dde691a19ccb72b1f1255e0569034ac24 | 3,612,639 |
import warnings
def normalized_diff(b1, b2):
"""Take two numpy arrays and calculate the normalized difference.
Math will be calculated (b1-b2) / (b1+b2).
Parameters
----------
b1, b2 : arrays with the same shape
Math will be calculated (b1-b2) / (b1+b2).
Returns
----------
n_... | e905b76c7dced3b8194b98bf83339be6a6e16d46 | 3,612,640 |
import tkinter as tk
from tkinter import filedialog
def get_json_file_path():
"""Opens dialog box for user to choose where to save the measurements.
Uses default directory in case of failure.
Requires very hacky work-around of installing
tk and tcl in NX directories"""
try:
root = tk.Tk(... | ce234384a6d2c98693a7a52b0d22ec8716ad7a76 | 3,612,642 |
import shlex
def parse_config_string(config_string, issue_warnings=True):
"""
Parses a config string (comma-separated key=value components) into a dict.
"""
config_dict = {}
my_splitter = shlex.shlex(config_string, posix=True)
my_splitter.whitespace = ","
my_splitter.whitespace_split = Tru... | 1decd3b203b779099111e949b2757a3f52b8c25d | 3,612,643 |
def load_data_clf(ds_name):
"""
Load the data for classification by the specified dataset name.
Parameters
----------
ds_name : str
Returns
-------
data : DataFrame
"""
ds_load_func = None
if ds_name in DS_LOAD_FUNC_CLF.keys():
ds_load_func = DS_LOAD_FUNC_CLF[ds_na... | 159a3a7b347400416f17cedf0dc04037b133431c | 3,612,645 |
def parse_file_name(file_name):
"""
<Purpose>
Parses a file name to identify its module and its descriptor
<Arguments>
file_name: the name of the test file
<Exceptions>
InvalidTestFileError: if you provide a test file which does not follow the
naming convention of 'ut_<module>_<descriptor>... | 55698a5934b639ce0ddb40cb6eb2edf1614ec04f | 3,612,646 |
def _proto_builder_test_impl(ctx):
"""The implementation of the 'proto_builder_test' rule.
Tests the files generated by the 'proto_builder' rule. The test performs a
textual comparison of the generated source and header with the expected
source and header respectively.
Args:
ctx: The current... | 705ffebeb126ca04c7f4ea35aa82e81f44210b8f | 3,612,647 |
def _make_nxm_w (*args, **kw):
"""
Make a simple wildcarded NXM entry class
"""
t = _fix_types(kw.pop('type', _nxm_maskable_numeric_entry))
ok = False
for tt in t:
if _issubclass(tt, _nxm_maskable):
ok = True
break
if not ok:
t.insert(0, _nxm_maskable)
return _make_nxm(*args, type=t... | 5a4f3b020a3cc91b908f8a8892716865dd7decf9 | 3,612,648 |
def process_datasets(dataset_class, dataset_args, unc_model, device, test_fn=test_unc_model, N=1000, **forward_kwargs):
"""
calls test_fn(unc_model, dataset) for every dataset in datasets
"""
results = {}
for name in dataset_args:
print("Testing", name)
print(N)
dataset = dat... | 8a049e099715b5315524fea15f168964bb49f500 | 3,612,649 |
def accuracy_metric(actual, predicted, correct=0) -> float:
"""calculate accuracy of the dataset comparing the actual test label vs the predicted label"""
for i in range(len(actual)):
if actual[i] == predicted[i]:
correct += 1
return correct / len(actual) * 100.0 | d7f19ccd1da426e00043ee3887261c85d963b47d | 3,612,650 |
from typing import Any
import copy
def get_test_case(
data: pd.DataFrame,
model: Any,
key: str,
add: ARITHEMETIC_OPERATIONS = None,
multiply: ARITHEMETIC_OPERATIONS = None,
) -> SCORES:
"""
Helper method to create the different test cases that alter the keys in a
dictionary
"""
... | 6996c1ef020ae12c65eebbe9275ff0ad3d0c155f | 3,612,651 |
def realign_mol(mol,conf,coord_Map, alg_Map, mol_template, maxsteps): # RAUL: This function requires a clear separation between minimization and alignment.
"""
Minimizes and aligns the molecule provided freezing the atoms that match
the mol_template.
Parameters
----------
mol : rdkit.Chem.Mol... | 325d324693c5b0e064b56209ba2029a357b7fe91 | 3,612,652 |
def measure_shadow_work(simulation, n_steps):
"""Run the simulation for n_steps and return a vector of the shadow work accumulated
during integration.
* Check whether simulation.integrator has bookkeeping variables W_shad and/or heat.
* If only W_shad is available, measure shadow work as W_shad
* I... | c3ef18a0caf040f5d34316bf0d7b5b51b6a6cd43 | 3,612,653 |
def cumulative_cross_corr(I1, I2):
"""
doing normalized cross correlation on distance imagery
:param I1: NP.ARRAY (_,_)
binary array
:param I2: NP.ARRAY (_,_)
binary array
:return result: NP.ARRAY (_,_)
similarity surface
... | 082ee285ba75c72ff1d9b1551b2b882212bde291 | 3,612,654 |
def getGbdbTables(database, tableset):
""" Remove tables that aren't pointers to Gbdb files."""
sep = "','"
tablestr = sep.join(tableset)
tablestr = "'" + tablestr + "'"
hgsqlOut = qaUtils.callHgsql(database, "select table_name from information_schema.columns where\
... | 31c591104698eb50b8ecc63c08609ebf08dd9a10 | 3,612,655 |
def four_bar_plane_truss(num_samples: int = 100, distribution: str = "lhs") -> list:
"""Four bar plane truss problem.
As found in Multiobjective structural optimization using a microgenetic algorithm.
Parameters
----------
x : list or ndarray
Should have 4 elements
Returns
-------
... | 952d57cf9cbc89dca61046e7a06f6eee1a93a953 | 3,612,656 |
def parseFile (pomFile):
""" Parses a pom.xml file and returns a Maven object or None
"""
with open (pomFile, 'rb') as f:
return parseString (f.read())
return None | cd6baa8bfd141a0778dca5bd0cfe2b2d2b86a9ae | 3,612,657 |
def evaluations_to_columns(evaluation):
"""Convert the results of :meth:`metrics.ScoringMixIn.evaluate` to a pandas DataFrame-ready format
Parameters
----------
evaluation: dict of OrderedDicts
The result of consecutive calls to :meth:`metrics.ScoringMixIn.evaluate` for all given dataset types
... | 26c5f4b2a9830f7547f6b59d9653fea25345b43d | 3,612,659 |
import abc
import functools
def get_metric(name: str, **kwargs) -> abc.Callable:
"""Returns metric function for given metric name
Parameters
----------
name : str
Metric name
kwargs : any
Additional keyword-arguments for metric
Returns
-------
metric : callable
... | ddd94ad7d39538f7308dc5bdf6b57f238a6173b4 | 3,612,660 |
def resource_exists(env, resource):
"""Checks for resource existence without actually instantiating a model.
:return: `True` if the resource exists, `False` if it doesn't
and `None` in case no conclusion could be made
(i.e. when `IResourceManager.resource_exists` is not
... | 270d55de9b6fa29628ea996af16af2971a9487de | 3,612,661 |
def run_dqmc(p, unequal_time=True, callback=None, progress=False, *args, **kwargs):
"""Runs a DQMC simulation.
Parameters
----------
p : Parameters
The input parameters of the DQMC simulation.
unequal_time : bool
If `True`, unequal-time measurements will be performed.
callback :... | 661f725d21e819fc1c656fec8c8c395cc23f3c42 | 3,612,662 |
def merge(left: list, right: list) -> list:
"""Merges 2 sorted lists (left and right) in 1 single list, which is
returned at the end.
Time complexity: O(m), where m = len(left) + len(right)."""
mid = []
i = 0 # Used to index the left list.
j = 0 # Used to index the right list.
while i < ... | 9f6c501469e79a9f5ecfd8e23ee3384fc56c5a48 | 3,612,663 |
def replace(i_list: list, target: int,new_value: int)-> list:
"""
Replace all the target value with a new value
:param i_list: the list to be analyzed
:param value: the value to replace
:param target: the value to be replaced
:return: the new list
"""
return list(map(lambda value: new_va... | d0d2bc928c915d3456a25cc4ff341b23a66c4098 | 3,612,664 |
def single(request, id, template_name="microblogging/single.html"):
"""
A single tweet.
"""
tweet = get_object_or_404(Tweet, id=id)
return render_to_response(template_name, {
"tweet": tweet,
}, context_instance=RequestContext(request)) | 117ab58d3626740f91929f8c4e9dd2bfd9d07f21 | 3,612,665 |
import warnings
def astropy_tabular_data(*args, **kwargs):
"""
Build a data set from a table. We restrict ourselves to tables
with 1D columns.
All arguments are passed to
astropy.table.Table.read(...).
"""
result = Data()
with warnings.catch_warnings():
warnings.simplefi... | bcaa8fe58be57ff93618ec0a89ec7070e39c49f9 | 3,612,667 |
def words_cmp(a, b):
"""
word比较器
:param a: (word, 词频)
:param b: (word, 词频)
:return: [-1|0|1]
"""
a_word, a_freq = a
b_word, b_freq = b
if a_freq != b_freq:
return b_freq - a_freq
elif a_word != b_word:
return -1 if a_word < b_word else 1
else:
return 0 | b758a5a685b81f5fbca5840529272727e5fcf5d7 | 3,612,668 |
def _load_data(args):
"""Load data from path."""
if args.data_format == 'CULANE':
return vega.dataset("AutoLaneDataset", dataset_format="CULane", data_path=args.data_path, mode="test",
batch_size=args.batch_size).loader
elif args.data_format == 'COCO':
return ... | 908cab2892fb0661e31cb65c2273ba811ef0427b | 3,612,669 |
def extract_u_nk(xvg, T):
"""Return reduced potentials `u_nk` from a Hamiltonian differences XVG file.
Parameters
----------
xvg : str
Path to XVG file to extract data from.
T : float
Temperature in Kelvin the simulations sampled.
Returns
-------
u_nk : DataFrame
... | 81f24762f0bd637ef46e0d7e1236aaa657853050 | 3,612,670 |
def group(query, fields=None, step=None):
"""Group results by `fields` and/or `step`."""
if fields is None:
fields = []
if isinstance(fields, string_types):
fields = [fields]
fields = ['"%s"' % f for f in fields]
if step:
fields.insert(0, 'time(%s)' % step)
return '%s GRO... | 819e14714bc3e376cca8c7610466d37c0d599511 | 3,612,671 |
def first_level_directory(path):
"""Return the first level directory of a path.
>>> first_level_directory('home/syt/work')
'home'
>>> first_level_directory('/home/syt/work')
'/'
>>> first_level_directory('work')
'work'
>>>
:type path: str
:param path: the path for which we want... | 3e151912f7ed43c8d3ec9593179ea3016ff1ce2c | 3,612,672 |
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