content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def is_reviewed(protein_id):
"""Return True if the UniProt ID corresponds to a reviewed entry.
Parameters
----------
protein_id : str
The UniProt ID to check.
Returns
-------
True if it is a reviewed entry, False otherwise.
"""
return _strip_isoform(protein_id) in um.unipro... | 684c22dedc79282fd7a6b58a2c6175079f508ab9 | 60,358 |
def matrix_compile(main: list, sub: list, pos: int):
"""
Compile sub-matrices into a main matrix.
"""
n = len(sub)
total = len(main)
if len(main) != 2 * n:
print("ERROR: Cannot merge matrices of incompatible dimension. Stop.")
return None
results = []
if pos == 0:
... | 0ee10948b1e4597dcf34176942b20d119aa12396 | 60,359 |
import logging
def good_from(station: int, missing_days: int = 4, tabname: str = "readings", data: tuple = None) -> dict:
"""
Determines from which year we have almost consecutive data for each value. "Almost" will be assessed by the number
of days where thae value is not available. A year is almost compl... | 84d618b829efe0bb9d9ea6c82c1bd0230e820b36 | 60,360 |
def getGroups(datestring):
"""Utility function to get the parts of the datestring as a dict."""
if not isinstance(datestring, basestring):
raise ParseError("Expecting a string %r" % datestring)
m = ISO8601_REGEX.match(datestring)
if not m:
raise ParseError("Unable to parse date string %r" % datestring)
... | 6d659ec4ba039b2efdb3b2bdecfb7244e91da5e8 | 60,361 |
import math
def get_news(n: int = 100) -> pd.DataFrame:
"""Scrapes news from "https://www.coingecko.com/en/news?page={}" [Source: CoinGecko]
Parameters
----------
n: int
Number of news, by default n=100, one page has 25 news, so 4 pages are scraped.
Returns
-------
pandas.DataFram... | 541f975ea37f9c92875395043481d567cdf9c041 | 60,362 |
def get_variables(hoc, obj, vartype=int(0)):
"""Get the variables associated with an object.
:param hoc: is a hoc object such as would be obtained
when importing neuron.
:param obj: is the object from which to retrieve
its variable names. This can be a Section, Segm... | 9d304031bb0cf8bc553b12f688435220aaf842d3 | 60,363 |
def wtype():
"""Returns the "window type" of $w.
Important notes:
* "window type" is not a tk concept;
rather, Tk refers to a window's "class";
see "winfo class" for more on this context
* "window type" is made up by me, and consists of a matching symbol;
presently (2021-10-09), I... | 31b97f3247f5e32fca761d14decb94a6ed3e7aeb | 60,364 |
from re import I
def compute_matrix_from_ops_two_qubit(ops, wire_order):
"""Given a list of two-qubit operations, construct its matrix representation."""
mat = qml.math.kron(I, I)
for op in ops:
if len(op.wires) == 1:
if op.wires == qml.wires.Wires(wire_order[0]):
mat... | 3b326d0af015559a0112f31735c8208f632ddc28 | 60,365 |
def get_attribute_by_name(cls, group_name, attr_name, verbose_name=False, case_insensitive=False):
""" Return the attribute of :obj:`Model` class :obj:`cls` with name :obj:`name`
Args:
cls (:obj:`class`): Model class
group_name (:obj:`str`): name of attribute group
attr_name (:obj:`str`... | 9f0addc5ca0019366b934c9b88ba3bca11d3d082 | 60,366 |
def interface_r(polarization, n_i, n_f, th_i, th_f):
"""
reflection amplitude (from Fresnel equations)
polarization is either "s" or "p" for polarization
n_i, n_f are (complex) refractive index for incident and final
th_i, th_f are (complex) propegation angle for incident and final
(in radian... | baeaed3438725ac6492714bfca4221249476d226 | 60,367 |
def alignUnalignedSeqsFromFile(filename,WorkingDir=None,SuppressStderr=None,\
SuppressStdout=None):
"""Aligns unaligned sequences from some file (file should be right format)
filename: string, the filename of the file containing the sequences
to be aligned in a valid format.
"""
app = Clust... | 60b3079a775597638cb47bd70993569f87d8657f | 60,368 |
import math
def C_26Al(depth, erosion, exposure, density, inheritance,
P_0=35.):
"""
A model for profiles of 26Al concentration vs. depth.
Notes
-----
The following parameters are used
(Braucher et al. 2003)
26Al radioactive decay: log(2) / 0.72e6
Contribution... | e15450f301c3e24a66337b87a48fbf90f350dc73 | 60,370 |
import time
def Subdue(params, graph):
"""
Top-level function for Subdue that discovers best pattern in graph.
Optionally, Subdue can then compress the graph with the best pattern, and iterate.
:param graph: instance of Subdue.Graph
:param params: instance of Subdue.Parameters
:return: patter... | 635be5cab9fcccaec040b268b0a21042dd07ca52 | 60,372 |
def func_proDATA(inlist):
"""
Pre-process input DATA list
Parameter:
inlist: 2D nested list
sublist in an array
[ symbol deltaTbM deltaTM deltaPM deltaVM nickName(as many as you can) ]
Return:
3D nested list
in a format,
[
... | 2221a5399db0d6896ef8f7fb303834f91f654aed | 60,373 |
import ibeis
def find_installed_tomcat(check_unpacked=True, strict=True):
"""
Asserts that tomcat was properly installed
Args:
check_unpacked (bool): (default = True)
Returns:
str: tomcat_dpath
CommandLine:
python -m ibeis find_installed_tomcat
Example:
>>> ... | 1c5488bc528207641826dc14f277ea0604357cbd | 60,374 |
def get_tokens_of_doc(doc):
"""分词获得token"""
sent_list = split_doc(doc)
tokens = []
for sent in sent_list:
tmp_tokens = tokenizer.segment(sent.strip())
for token in tmp_tokens:
tokens.append(str(token).split('/')[0].strip())
return tokens | 82b854411f56435634241fec3b76bb9e124ad58a | 60,375 |
def normalize(img: np.ndarray, div255=True, mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) -> np.ndarray:
"""
normalize using standard pytorch model normalization parameters
expects RGB image with 0-255 pixels as input
returns float type
"""
img = img.astype(float)
if d... | 6a835950494b824b918cb4b9bd4bf85e2f9723d9 | 60,376 |
def upper_case(string, **kwargs):
"""
大写命名法
eg:USER_NAME
"""
return case_convert.upper_case(string, **kwargs) | 1539031c0626d11b42ebc5583e84cf6e18f108f5 | 60,377 |
def response_for_courses_list(all_courses):
"""
Return the response for when courses are requested by the user.
:param courses:
:return:
"""
return make_response(jsonify({
'status': 'success',
'courses': all_courses
})) | a5af95a709dfe8dd3e7b19dfddbcf3aa99ecb3de | 60,378 |
def move_ara_files(source_dir, dest_dir):
""" old name of the collect_text_files function;
renamed because the name does not reflect what function does;
old name kept for backward compatibility
"""
return collect_text_files(source_dir, dest_dir) | 7730715d1d77d1ed93b574f131bd5e114a09dad8 | 60,379 |
def normalize_and_dedupe_with_counts(dirty_urls):
""" Takes a list of URLs, takes a count of them, sanitises them and
returns a count of the sanitised URLs. Output is namedtuple of count
pre-cleaning, count post-cleaning, and then the list of URLs.
"""
urls_entered = len(dirty_urls)
cl... | 02ab7c2365f8f3e5329baa8f4f9628003665b60f | 60,380 |
def get_country_code(country_name):
"""根据指定的国家，返回pygal使用的两个字母的国别码"""
for code, name in COUNTRIES.items():
if name == country_name:
return code
if country_name == "Hong Kong SAR, China":
return 'hk'
if country_name == "Korea, Dem. People’s Rep.":
retur... | 4f0b5c28027f409638004464b8af3a1c304c7a66 | 60,381 |
from fsspec import get_filesystem_class
def can_be_local(path):
"""Can the given URL be used wih open_local?"""
try:
return getattr(get_filesystem_class(get_protocol(path)), "local_file", False)
except (ValueError, ImportError):
# not in registry or import failed
return False | 918646e65907d59bad1d40e8b5238865356e9fdf | 60,383 |
def create_hdmedians_multiple_band_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
... | 3d9697325169693cebdc1a7c8f15d0d9e2080c3b | 60,384 |
def get_ti16_config():
"""Returns the ViT-S/16 configuration."""
config = get_base_config()
config.update(dict(
patch_size=(16, 16),
hidden_size=192,
intermediate_size=768,
num_attention_heads=3,
representation_size=192
))
return config | 9ba8c89d858e4960944ef06cba1e81dfe04e3f8a | 60,385 |
def lookup_unique_task_from_func(task_func, default_pipeline_name="main"):
"""
Go through all pipelines and match task_func to find a unique task
Throw exception if ambiguous
"""
def unique_task_from_func_in_pipeline(task_func, pipeline):
if task_func in pipeline.lookup:
if len(... | 39b70af87e019fc948563fcda20597b57e3bb292 | 60,386 |
def data_to_nameCat(LOC, quota, rank, CCA):
"""
Returns a dictionary of the category of a CCA
"""
final_dic = {}
dic_quota = quota.dic #dictionary
cat = ""
for category, dic_CCAs in dic_quota.items(): #for each category
for cca, quota in dic_CCAs.items(): #for each cca
... | 96de92f435f8257425887d21c42d6b2a1359d747 | 60,388 |
def GenerateRSAKey(passphrase=None, key_length=2048):
"""Generate an RSA key and return tuple of pem strings for (priv,pub) keys."""
if passphrase is not None:
passphrase_cb = lambda: passphrase
else:
passphrase_cb = None
key = RSA.gen_key(key_length, 65537)
priv_key = key.as_pem(passphrase_cb)
bio ... | c40b0879f998875e3bea17af864daa37f98c27d5 | 60,390 |
def get_parsed_url(test_item):
# type: (AnyMagpieTestItemType) -> ParseResult
"""
Obtains the parsed URL result from the test class web application implementation.
"""
app_or_url = get_app_or_url(test_item)
if isinstance(app_or_url, TestApp):
app_or_url = get_magpie_url(app_or_url.app.re... | ea57174e89349b43b66bebd0068f8e13e1173d82 | 60,391 |
def transformer_revnet_encoder(encoder_input,
encoder_self_attention_bias,
hparams,
name="encoder"):
"""A stack of transformer layers.
Args:
encoder_input: a Tensor
encoder_self_attention_bias: bias Tensor for self... | b049e40bf3cdd08e476663e7494f426fa3339ea0 | 60,392 |
import utool as ut
def inverable_unique_two_lists(item1_list, item2_list):
"""
item1_list = aid1_list
item2_list = aid2_list
"""
unique_list1, inverse1 = np.unique(item1_list, return_inverse=True)
unique_list2, inverse2 = np.unique(item2_list, return_inverse=True)
flat_stacked, cumsum = ... | 85b032e927be2615e3927f6f14b2bfa1ea5842f5 | 60,393 |
def sanitize_host(host):
"""Return the hostname or ip address out of a URL"""
for prefix in ['https://', 'http://']:
host = host.replace(prefix, '')
host = host.split('/')[0]
host = host.split(':')[0]
return host | f00a79804624076c75e9b90c0da5b03888067a42 | 60,394 |
def compute_CCC(matrix, CCC_params):
"""
Compute causal complexity estimates for all pairs of rows of input matrix
Estimates are derived as per 4 models: ETCP, ETCE, LZP and CCC
Parameters
----------
matrix : np.ndarray, 2d, uint32
MxN matrix, C(M,2) rowpairs, with each row of length N... | 28f3c3135cd354007e6d12979277528d6257fdcf | 60,395 |
def team_user_names_query(after_cursor=None, team_name=None) -> gql:
"""A GraphQL query to get the list of user names within each organisation team.
Args:
after_cursor ([type], optional): Is the pagination offset value gathered from the previous API request. Defaults to None.
team_name ([type],... | ab9a02b498c9f194002b03c28f45cf3f0545414b | 60,396 |
def cut_rod_extend(p, n):
"""重构解"""
r = [0]
s = [0]
for i in range(1, n + 1):
res_r = 0 # 收益的最大值
res_s = 0 # 收益最大时对应方案的左边不切割部分的长度
for j in range(1, i + 1):
if p[j] + r[i - j] > res_r:
res_r = p[j] + r[i - j]
res_s = j
r.append... | e37d3300d2b79b1542dac46a723b0bff9668de09 | 60,397 |
def create_regions(algorithms: str, hash_file: HashFile):
"""
:param algorithms: the type of the algorithm to create the region
:param hash_file: the hash file object
"""
if algorithms == "FIXED-SIZE":
return create_fixed_regions(hash_file, 4)
if algorithms == "CONTENT-DEFINED":
... | ae942c80c4fd22e9bb1addd49525542c41831f83 | 60,398 |
def _get_one_dimensional_median_and_error_bar(
posterior, key, fmt=".2f", quantiles=(0.16, 0.84)
):
"""Calculate the median and error bar for a given key
Parameters
----------
key: str
The parameter key for which to calculate the median and error bar
fmt: str, ('.2f')
A format s... | 11a2630720c0968e16e484388ebb48c9b8c0ec35 | 60,399 |
from multi_mesh.components.plotter import create_projection
def find_good_projection(
name: str = "default",
central_longitude: float = 0.0,
central_latitude: float = 0.0,
satellite_height: float = 10000000.0,
lat_extent=(-90.0, 90.0),
lon_extent=(-180.0, 180.0),
):
"""
Function which ... | 88b57ed8910d7dad5f0c2b2f641353def3f7bbc4 | 60,400 |
import torch
def vectorize(ex, model):
"""Torchify a single example."""
word_dict = model.word_dict
# Index words
word_idx = [word_dict[w] for w in ex['sent']]
if len(word_idx) == 0:
print(ex)
if model.args.model_type == 'cnn':
pad = max(model.args.kernel_sizes) - 1
w... | 7ec144048a4dd2cd61edab2b9c5de8697cbabc5b | 60,401 |
def edit_sub(request, proj='', srt1='', id1='', srt2='', id2=''):
"""Edit subitem - let op: theoretisch kan dit bij edit_item() in
"""
# try: # do we have form data?
data = request.POST
# except AttributeError:
# data = {}
obj1 = funcs.get_object(srt1, id1) # my.rectypes[srt1].objects.... | cae5ed830326f6ffd7ec83bcba549721da859a6e | 60,402 |
def main(output, effective_tld_filename):
"""
effective_tld_filename is the effective TLD file to parse.
A C++ array of { domain, exception, wild } entries representing the
eTLD file is then printed to output.
"""
def boolStr(b):
if b:
return "true"
return "false"
for etld in getEffectiveT... | 189449f07847d3121c46a5cf19795dba3f20bb98 | 60,403 |
def byte_convert_(number, original, new, printed=False):
"""Convert from one suffix to another suffix."""
if original == '':
if new == '':
None
elif new == 'kb':
number = number/1_000
elif new == 'mb':
number = number/1_000_000
elif new == 'gb'... | ca5a12308e13fc46dd9bd1de91a7cde8207749e1 | 60,404 |
def filter_none(data, split_by_client=False):
"""This function filters out ``None`` values from the given list (or list of lists, when ``split_by_client`` is enabled)."""
if split_by_client:
# filter out missing files and empty clients
existing_data = [
[d for d in client_data if d ... | 7cc88ecdf7aba245f56598ee1094fed7c1f9f4f7 | 60,405 |
def cal_dist(weights, codes, keys_list=[-1, 0, 1]):
""" Compute Distribution of CODES and generate results for PyPlot
Args:
weights (list):
codes (list):
keys_list (list): optional
Returns:
dist_dict (dict): a list of weights for each key
"""
dist_dict = dict.fromke... | 52eb2bd39e0e403ecf130f486c34c51f7b71cd26 | 60,407 |
def plot_contours(clf, xx, yy, **params):
"""Plot the inverese-transformed decision boundaries for a classifier.
Parameters
----------
ax: matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
""... | 615b453323a30b01bd7b3531c1915c201006e313 | 60,409 |
def test_funding_reorg_remote_lags(node_factory, bitcoind):
"""Nodes may disagree about short_channel_id before channel announcement
"""
# may_reconnect so channeld will restart; bad gossip can happen due to reorg
opts = {'funding-confirms': 1, 'may_reconnect': True, 'allow_bad_gossip': True}
l1, l2... | 5be31fc732cc2ec6fab59ca2586c93a9094dffd0 | 60,410 |
def print_num(n):
""" there must be a package that does this better. oh well"""
if n >= 1000000000:
return "{}{}".format(round(float(n) / 1000000000, 2), "B")
elif n >= 1000000:
return "{}{}".format(round(float(n) / 1000000, 2), "M")
elif n >= 1000:
return "{}{}".format(round... | 7a6e241a60d81515b03e29de595cd192afde7d9a | 60,411 |
def process_row(row):
"""Split a row into the original sentence, its corresponding summary and its rating.
Args:
row: a row in the MS dataset tsv file.
Returns:
current_original_sentence: the original sentence of the row
current_shortened_sentences_list: a list of summaries corresponding to... | bff7a139e34e150210da73928a1dce1c51cfa8c2 | 60,412 |
def update_system(system):
"""
:param system: Dictionary with all the information of the system
Dictionary system already has the indexes of the excited molecules
1. Looks for the neighbourhood of every centre.
2. Chooses a process for every exciton (KMC). This path variables include: dict(centre, p... | 1981423d878e1ba5490f4d68ebc2f57989e31d4a | 60,413 |
def relu(Z):
"""Applies relu function to an array/value
Arguments
---------
Z: float/int/array_like
Original Value
Returns
-------
A: same shape as input
Value after applying relu function
"""
return np.maximum(Z, 0) | 6319f41a73b61911c5c72051cd80f2832b679932 | 60,414 |
import inspect
def calculate_median(dataframe, *args, display=True, to_csv=False, to_html=False) -> DataFrame:
"""
This function calculates the median of all *numeric* columns within a Pandas dataframe. Alternately, users can
specify a list of columns for which they would want to calculate the median. If ... | abccfc2cafd8ae241031f78dba30d4dd53c75c87 | 60,415 |
def have_double_impl(math_name):
"""
Check whether we have an implementation of the math function in libc
for type double (e.g. logf or logl may not be available, but log may be).
"""
return math_suffix(math_name, double) in libc_math_funcs | 76bf591fbe64f4a04805bbd485ff85c91de5fd83 | 60,416 |
def translate_name(name):
"""
因为老数据的缘故，导致有些人名错了，需要翻译一下
:param name: 成员或组合名
:return:
"""
name_dict = {
'ruru_dambara': 'ruru_danbara',
'memi_tamura': 'meimi_tamura',
'chisaki_morito_m': 'chisaki_morito',
'musubu_funaki_a': 'musubu_funaki'
}
return name_dic... | 52b852e9ecd2b1ac4b02288198f2c84986a1ed17 | 60,417 |
from typing import Union
from typing import Iterable
from datetime import datetime
def query_unexpired_records(*,
users: Union[User, Iterable[User]]=None,
types: Union[RecordType, Iterable[RecordType]]=None
):
"""
:param users... | 0c32b9a90e5f0f3b2892102db7484963cb2d4b0b | 60,419 |
from bs4 import BeautifulSoup
import re
import logging
def skip_team(soup: BeautifulSoup) -> bool:
"""Skip team if no players meet the minimum
:param soup: BeautifulSoup object for a team
:returns: True if the team should be skipped, False otherwise
"""
pattern = re.compile("No players meet the m... | 023a0a0076f8751f5447acd30e09ac239736e751 | 60,421 |
def _battin_second_equation(u, h1, h2):
"""Battin's second equation.
Parameters
----------
u: float
The dependent variable.
h1: float
The first of the h coefficients.
h2: float
The second of the h coefficients.
Returns
-------
y: float
The dependent ... | a00a6fe572b22cc4e27330667189325223d208fd | 60,422 |
def compute_pca(data2d):
"""
Compute PCA using sklearn
:param data2d: 2d array. PCA will be computed on non-zeros values.
:return: coordsrc: 2d array: centered non-zero coordinates\
pca: object: PCA result.\
centermass: 2x1 array: 2d coordinates of the center of mass
"""
# round... | 1bdbe46c4b709e1a72755ce1b9bfc630b52f886b | 60,424 |
def le(n, alpha, beta=0):
"""
Draw from the Levy-stable ensemble
:param n: size of random matrix
:param alpha: parameter controlling the asymptotic power law of the distribution tails, between 0 and 2
:param beta: skew of the distribution, between -1 and 1
:return: random n by n matrix, drawn f... | 90ae6fc4437acd29ca7125458a8923dce904da1a | 60,425 |
def run_database_command(client, command):
"""
Run a database command using the "mysql -e" command
:param client: String
:param command: String database command
:return:
"""
return mode.run_command(
helper.get_command(client, 'mysql') + ' ' + generate_mysql_credentials(
c... | c256dbcc5a1fe5a8fbdb41eef373570052bc5ede | 60,426 |
def get_kw_active(df):
"""
returns the df with only enabled keyword status
"""
return select_col_equals(df,'Keyword state','enabled') | 38a4bfa4c46a443e2b12c73b6d18055c771ad99b | 60,427 |
import urllib
def _urlencode(items):
"""A Unicode-safe URLencoder."""
try:
return urllib.urlencode(items)
except UnicodeEncodeError:
return urllib.urlencode([(k, smart_str(v)) for k, v in items]) | 5a5e0d90e695433ba3774221d835e44b6e8ad749 | 60,428 |
def LLV(x: pd.Series, n: int):
"""
talib.MIN()
"""
LLV = [np.NAN] * (n - 1)
for index in range(len(x)):
if index < n - 1:
continue
else:
window = x[index - n + 1:index + 1]
LLV.append(min(window))
return pd.Series(LLV) | 9a4591d841c5dbd8cbe704f695e65a2cf5b1a429 | 60,430 |
from typing import List
def find_max_consecutive_ones(numbers: List[int]) -> int:
"""https://leetcode.com/problems/max-consecutive-ones/"""
current_sum, max_sum = 0, 0
for value in numbers:
current_sum += value
if not value:
max_sum = max(max_sum, current_sum)
curr... | 4b2f343553ff4756e21fe13d0b569e78add5b95d | 60,431 |
def _amplitudeFromPeak(peak, x, y, radius, x_0=10, y_0=10):
"""
This function can be used to estimate an Airy disc amplitude from the peak pixel, centroid and radius.
"""
rz = jn_zeros(1, 1)[0] / np.pi
r = np.sqrt((x - x_0) ** 2 + (y - y_0) ** 2) / (radius / rz)
if r == 0.:
return peak
... | a8af3d20fa069e5289feeede93c9969edbb1e88f | 60,432 |
def get_user(token):
"""Find user authenticated by token or return None."""
return USERS.get(token) | c24deed25ec58c3c4495da46815a3e81e18a09d7 | 60,433 |
import urllib
import json
from datetime import datetime
def by_location_id(id):
""" retrieve next appointment by location id
"""
raw = urllib.request.urlopen(SCHED_URL + str(id)).read()
appt = json.loads(raw.decode('utf-8'))
ts = None
if len(appt) > 0:
ts = datetime.datetime.strptime(a... | 7a8a90c11c75ca98529bd266c283fd2cb2b85b66 | 60,434 |
def sieve(limit):
"""
Returns a list of prime numbers up to but not including the given number.
"""
array = [True] * limit
array[0] = array[1] = False
primes = []
for (number, prime) in enumerate(array):
if prime:
primes.append(number)
... | 2b31d824cf8058044fa58de62d88cadf2f473c17 | 60,435 |
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up LoadShedding as config entry."""
suburb = Suburb(
id=entry.data.get("suburb_id"),
name=entry.data.get("suburb"),
municipality=entry.data.get("municipality"),
province=entry.data.get("province... | e6efcd2f3eadb8b893ea9e9f616323f7d4caa0c0 | 60,436 |
from typing import List
from typing import Tuple
def key_exists(key: str, list_keys: List) -> Tuple[int, bool]:
"""
Finds a dict which has `key` defined from a list `list_keys`
Args:
key: the key to find
list_keys: list of dicts
Returns:
index of key if found, whether the key... | 65d0dbb35be5773a6435d639ab218ebe05638c7b | 60,437 |
def combined_threshold_4(color_img, show_dbg=False):
"""Applies different methods to find the thresholds in the image."""
hls = cv2.cvtColor(color_img, cv2.COLOR_RGB2HLS)
s_channel = hls[:, :, 2]
gray = cv2.cvtColor(color_img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take th... | c8554a9c7fc4123bdd4e39ca57dab2600632c762 | 60,438 |
def mod_rand_invertible(p):
"""Return a random invertible element modulo p for p > 1"""
assert p > 1
while True:
a = randint(1, p - 1)
# Check with the Euclidean algorithm if a is invertible
a2, a1 = p, a
while a1 > 0:
a2, a1 = a1, a2 % a1
# Now a is in... | a0fdf33721a26bbca6517e98318e72d963a686f8 | 60,439 |
def normed_duration(catalog):
"""
One-line description
Full description
Parameters
----------
Returns
-------
Examples
--------
"""
return (catalog['T']/day_hrs)/(catalog['period'])**(1/3.0) | ccdecf9aa4d90f05b8d00ca67bbea621ad604c0b | 60,440 |
def total_ordering_from_cmp(cls):
"""Class decorator that fills in missing ordering methods from __cmp__.
This lets us take a class defined for Python 2's ordering system (using
__cmp__) and use it with minimal changes -- just a decorator -- in Python 3.
This implementation is adapted from the Python 2 versio... | 41d40e6175185a6314ef02fa7bdc58b416957706 | 60,441 |
from typing import Any
from typing import Optional
from typing import Callable
def unmarshal_dict(
obj: dict,
cls: Any,
allow_extra_keys: bool=True,
ctor: Optional[Callable]=None,
):
""" Unmarshal @obj into @cls
Args:
obj: The dict to unmarshal into @cls
cls: ... | fde5db1f612f10266a5ec091e9052f7810f56713 | 60,442 |
def make_label_df(labels, behavior_subj, psychometric_params):
"""Turns labels into a parsed df"""
label_df = pd.DataFrame(data={"stim_id": labels})
morphs.data.parse.stim_id(label_df)
label_df["behave_data"] = False
for dim, dim_group in label_df.groupby("morph_dim"):
if dim in psychometri... | d7e9cc36c81570d22cc19132fc0213cf828757c8 | 60,446 |
def dwolla_get_url(url):
"""
A helper method for the DwollaClient to retreive any url
Args:
url (str): the url to retreive
Returns:
response
"""
try:
result = DwollaClient.instance().token.get(url)
return result.body
except Exception as e:
logger.err... | 44a4ba68389a3d9017766f05bacb953d0a639947 | 60,447 |
from typing import Optional
from typing import List
from typing import Tuple
def cat1_gt_cat2(
df: pd.DataFrame, cat1: str, cat2: str, drop_levels: Optional[List[str]] = None
) -> Tuple[bool, pd.MultiIndex]:
"""
Checks that values from subset where `category == cat1` are
greater than the values from s... | b3b105e2d2664575c6ea76e7897d8de97a1f570e | 60,448 |
def extract_all(session_path, save=False, bpod_trials=False, settings=False):
"""Extract all datasets from habituationChoiceWorld
Note: only the datasets from the HabituationTrials extractor will be saved to disc.
:param session_path: The session path where the raw data are saved
:param save: If True, ... | 67a6ad5b5f4587b82176d33da9414d12c933fac2 | 60,449 |
def compute_norm_metrics(gradient):
"""Compute the metrics"""
l1 = np.linalg.norm(gradient, ord=1)
l2 = np.linalg.norm(gradient)
Min = np.linalg.norm(gradient, ord=-np.inf) ## min(abs(x))
Max = np.linalg.norm(gradient, ord=np.inf) ## max(abs(x))
Mean = np.average(gradient)
Skewness = skew(... | 7adf9a26eddd56373ddbadf0688411bed11a1749 | 60,450 |
def perform_wavelet_transf(f, Jmin, dir, filter = "9-7",separable = 0, ti = 0):
"""""
perform_wavelet_transf - peform fast lifting transform
y = perform_wavelet_transf(x, Jmin, dir, filter = "9-7",separable = 0, ti = 0);
Implement 1D and 2D symmetric wavelets with symmetric boundary treatements, using
... | fbcfd39bd9564f9040a72436923133beeebac77d | 60,451 |
def create_channel(n_or_buffer=1):
"""Create a new channel.
As a conveinence, if n_or_buffer is a positive integer then creates a
blocking buffer for the new channel.
"""
buf = n_or_buffer
if isinstance(n_or_buffer, int):
buf = create_blocking_buffer(n_or_buffer)
elif buf.maxsize < ... | 5ebb051159d46c4c5f81e9822b83c470ddeb7ca7 | 60,454 |
def filter(src):
"""Filter a comments and blank lines out of heredoc-style source string"""
lines = src.strip().split("\n")
lines = [L.split("#")[0].strip() for L in lines] # filter comments
lines = [L for L in lines if len(L) > 0] # filter empty lines
return lines | b77ee55f27835a4ba1210b08274969ee060c6c35 | 60,455 |
def lower_convex_hull ( bs ) :
"""Get lower convex hull for BSpline
>>> bspline = ...
>>> lower = bspline.lower_convex_hull ()
>>> lower = lower_convex_hull ( bspline )
- for all values of x: bspline(x) >= lower ( x )
"""
return Ostap.Math.lower_convex_hull ( bs.bspline() ) | 55f6271ff34ad7c8c518ca708b0b9a8778126558 | 60,457 |
import random
def death_with_chance(p_death: float) -> bool:
"""
Takes a float between 0 and 1 and returns a boolean
if the player has survived (based on random chance)
Returns True if death, False if survived
"""
return p_death > random.random() | ad2a88727369e703cee6c345882c873db2104827 | 60,458 |
from datetime import datetime
def make_timestamp():
"""
Generate a timestamp of the current time.
returns:
datetime.datetime: The current time.
"""
return datetime.datetime.now() | 77999688b1af5982a3d4293b92b27549f6e6b1d2 | 60,459 |
def _radix_length(num: int) -> int:
"""Finds the number of digits for a number"""
if num == 0:
return 1
digits: int = 0
while num != 0:
digits += 1
num = int(num / 10)
return digits | 3df84020aae22c7287596103c3615bb039d3e59f | 60,460 |
import imp
def _pip_install():
"""If `pip` isn't installed, install it"""
try: # Check if `pip` installed
imp.find_module('pip')
return True
except ImportError: # If not installed, install it
get_pip = """do shell script "python '{0}/get-pip.py'" """.format(
... | 2083507180c3e8e28397bc83c5d642512f756b68 | 60,461 |
def service():
"""
Returns an instance of the service
"""
return Clickstream() | eb3a28f47f0604778c193968cb4cf86c391bf732 | 60,462 |
def enabled(mapper, args) -> bool:
"""Determine if hooks are enabled for the current method."""
if not settings.HOOKS_ENABLED:
return False
if mapper is None:
return False
if mapper.manual:
return False
if args and isinstance(args[0], str):
if args[0] in {"Meta", "... | 520651b3d1109cc2044b5af001d36b0b63eabbc2 | 60,463 |
from typing import List
from typing import Tuple
from typing import Dict
from typing import Union
from typing import Callable
def optimize_huggingface_model(
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
input_texts: List[str],
batch_size: int,
max_input_sizes: List[Tuple[int, ...]],
... | 1cf643afb0b5710f87a7faa9a834061ec01a4bdb | 60,464 |
def cli(ctx, result_id, file_path):
"""Download a result to a file
Output:
None
"""
return ctx.gi.result.download(result_id, file_path) | 09a4a3d6ceab783e248ae53274c0bff8ad1fd249 | 60,465 |
def dummy_encode(df):
"""
Encode any dataframe column of type category or object.
---
df: pandas dataframe
"""
columnsToEncode = list(
df.select_dtypes(include=['category', 'object']))
df1 = df.copy()
for feature in columnsToEncode:
le = LabelEncoder()
try:
... | 6c65383f78727d62175a76aafbaf0140cfee6d8d | 60,466 |
def main(reactor, jid, secret):
"""
Connect to the given Jabber ID and return a L{Deferred} which will be
called back when the connection is over.
@param reactor: The reactor to use for the connection.
@param jid: A L{JID} to connect to.
@param secret: A C{str}
"""
return Client(reactor... | 59544915c9b83ae8cdee3d30a5ce1916f0978bcc | 60,467 |
from pathlib import Path
from typing import List
def get_schema_metadata_error_messages(file_path: Path) -> List[str]:
"""Return a list of error messages for the table schema at file_path
Undefined behaviour if the table schema is valid
"""
s = Schema(descriptor=file_path)
return [err.message for... | 9fe78512e188c14253fc86a60d45ff488bbef519 | 60,468 |
def create_holiday_calendar(
weekend_mask=None,
holidays=None,
start_year=None,
end_year=None):
"""Creates a holiday calendar.
Note: providing bounds for the calendar, i.e. `holidays` and/or `start_year`,
`end_year` yields a better-performing calendar.
Args:
weekend_mask: Boolean `Tensor` ... | fc76a842a293da3e03bad1d95813d1c49a317bc7 | 60,469 |
import copy
def get_object_vector_to(cls, search_cls, subtype, avoid=None):
"""
Returns a list of all possible paths to the given class.
Only searches classes that are subtype of the given class.
"""
# Does the name point to this class? Then we are done.
if search_cls == cls:
return [(... | a1b56bd59e96b4765cb0e75e496dc77eea94fa17 | 60,470 |
from typing import Any
from typing import Optional
import json
import hashlib
def gethash(
value: "Any",
hashtype: str = "sha256",
encoding: str = "utf-8",
json_encoder: "Optional[json.JSONEncoder]" = None,
) -> str:
"""Return a hash of `value`.
Can hash most python objects. Bytes and bytearr... | cdec65a96b068ded153f86af29af53d68fe7b822 | 60,471 |
def subplot(nrows, ncols, plot_number, **kwargs):
"""
Returen a subplot axes positioned by the given grid definition.
:param nrows, nrows: (*int*) Whree *nrows* and *ncols* are used to notionally spli the
figure into ``nrows * ncols`` sub-axes.
:param plot_number: (*int) Is used to identif... | 8d60d315aab7ad77513225848e592fc0f96faae6 | 60,472 |
def sub(a: PipeNumeric, b: PipeNumeric) -> PipeNumeric:
"""
Pipeline node which subtracts b from a.
:param a: parameter a
:param b: parameter b
:return: PipeNumeric
"""
if a.get_type() == b.get_type():
number_type = a.get_type()
if isinstance(number_type, num.FloatingNumberTy... | 6ce937bfad1ac96e7ebd5412f6707070996e0241 | 60,473 |
def get_raw_hcor(thing: dict) -> dict:
"""For an RAW hsilo-like dict, get only the non hmeta part.
This function requires at leat one key that seems an hmeta, but if more
than one exists, will also return None.
Args:
thing (dict): An HSilo-like dictionary
Returns:
dict: An dict wi... | 8247758ef8c223158c6daca69334034bc3a7bc5b | 60,474 |
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