content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def dict_fetch_all_by_month(cursor):
"""
We need the data in this format
data = [
Month: "jan", "type1": value , "type2": value,
Month: "feb", "type1": value , "type2": value,
......
]
do the below code helps to build the dictionary on that method
"""
collectors = []
... | e1056dd16310915d6acb3ae5570df85920681da3 | 3,602,797 |
import re
def pypi_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
"""Role for linking to PyPI packages."""
env = inliner.document.settings.env
text = utils.unescape(text)
has_explicit, title, target = split_explicit_title(text)
m = re.match(r'(.*)\s+\((.*?)\)', target)
if m... | fa75191fcd4e661c08e3269aeabbb091ec991e33 | 3,602,798 |
from typing import Dict
def dict_from_text_file(fpath: str) -> Dict[str, float]:
"""Parse a two-column CSV file into a dictionary of string keys and float values.
Will fail an assertion if the second column contains values which are not valid floats.
Args:
fpath (str): Path to file to parse.
... | 5baf29832e0a44c8131a97168cde796757c05bfa | 3,602,800 |
from re import T
from re import A
def twitter_outbox():
"""
RESTful CRUD controller for the Twitter Outbox
- all sent Tweets are visible here
"""
if not auth.s3_logged_in():
session.error = T("Requires Login!")
redirect(URL(c="default", f="user",
args ... | 7c26501e4665d255377c7011b093669abc62a984 | 3,602,801 |
def filter_by_organization(object_type, user=current_user):
"""
Filter object by user's organization
:param object_type: Object type to filter
:param user: User('s Organization) to use for the filter
:return: List of object filter by the user's organisation
"""
db = Info.get_db()
return ... | ae94ea6042ad9ee16dbbde36369490f9c6ad0ac1 | 3,602,802 |
def auth_wrapper(f):
"""Decorator ensures User is authorized to perform editing commands."""
def _wrapper(obj, id):
"""
Check that the password is the proper password for User of the given id, then continue to perform function.
Else, return authentication error.
Return not found... | e4320ad32a97ffe2658f13833596f55683e18560 | 3,602,803 |
import inspect
def analyse_action(func):
"""Analyse a function."""
description = inspect.getdoc(func) or 'undocumented action'
arguments = []
args, varargs, kwargs, defaults = inspect.getargspec(func)
if varargs or kwargs:
raise TypeError('variable length arguments for action not allowed.'... | 2639bb58d27927eed38a9b7c5ba96f3f9c260836 | 3,602,804 |
from typing import Dict
def _check_instance(entry: Dict):
"""
Function to check instances in the expand_grid function.
This checks if entry is a dictionary,
checks the instance of value in key:value pairs in entry,
and makes changes to other types as deemed necessary.
Additionally, type-spec... | a6ecd16d5488af5368042316b927299dbed97239 | 3,602,805 |
from typing import Union
from typing import Optional
def get_baseline(
powers: list[mne.time_frequency.AverageTFR],
picks: Union[str, list[str], slice],
baseline: tuple[
Optional[Union[int, float]], Optional[Union[int, float]]
] = (None, None),
) -> list[np.ndarray]:
"""Get baseline array ... | 3df65de8e7f6ef0e4ec7dcd088492b8b936da5e1 | 3,602,806 |
def define_bayesian_model():
"""Define the architecture of the Bayesian network."""
kl_divergence_function = (lambda q, p, _: tfp.distributions.kl_divergence(q, p) / tf.cast(NUM_TRAIN_EXAMPLES, dtype=tf.float32))
model = tf.keras.models.Sequential([
tfp.layers.Convolution2DReparameterization(
... | d97a4fcf22218327068980b4b89643017afba870 | 3,602,807 |
import time
def auto_canny(image, ):
"""
by zj
计算一个合适的canny阈值 thr1 第二个阈值一般是 thr1 的 2 到三倍
性能 0.05s 左右(C_ZZ_JG 和 套管双耳实测是0.05s左右， 图片大小一般是 250 × 250)
图片过大的会增加计算时间计算时间和图像的size（w × h）正相关
:param image: 构件图片
:return: canny_thr
"""
t1 = time.time()
i = 1
canny_contour_num = [] ... | 79d823163cab9e8805093f7a044226dfe3ae4636 | 3,602,808 |
import tqdm
def progress(*args, **kwargs):
"""
Progress bar
Uses ``tqdm.autonotebook`` to automatically use a native Jupyter widget
when executing within a Jupyer Notebook.
Parameters
----------
*args
Passed to the tqdm
**kwargs
Passed to the tqdm
Returns
--... | 16c8f758212dec704060e0e0845a0a20ebba91f0 | 3,602,809 |
def get_PSF_model(psf_models, n_psf, current_idx):
"""Get a single PSF model from the model(s) previously instantiated
Parameters
----------
psf_model : list
list of PSF model(s)
n_psf : int
number of PSF model(s)
current_idx : int
Returns
-------
lenstronomy.PS... | 3848ff065fecea79be7e13ed2ace692f855d3a4d | 3,602,810 |
from typing import List
import zmq
def send_array(socket, A: np.ndarray, t: List[int],
n_samples: int, std: List[float],
flags=0, copy=True, track=False):
"""send a numpy array with metadata
A can be:
xu: (n, n_x + n_u): 2D array containing the nominal points about which
... | 28647122f0fa21f0d065db83a65f5718065497fd | 3,602,811 |
import traceback
def lenovo_umount_virtual_media(ip, login_account, login_password, image, mounttype):
"""Unmount virtual media, supporting both 18D and 19A version of Lenovo XCC.
:params ip: BMC IP address
:type ip: string
:params login_account: BMC user name
:type login_account: string
:para... | 1731800986d35d1843f7d60f573728738ad0dd10 | 3,602,812 |
from . import context
import functools
def non_transactional(allow_existing=True):
"""A decorator that ensures a function is run outside a transaction.
If there is an existing transaction (and allow_existing=True), the existing
transaction is paused while the function is executed.
Args:
allo... | bebbe60c7b478aa74880f3a113a36558e444e450 | 3,602,813 |
def numeric_amount(param, data):
"""Validate that param in data is a representation of a positive decimal number"""
try:
numeric_amount = float(data[param])
except ValueError:
return (None, error("Invalid {} specified: {}. Must be a decimal number, e.g. 123.45.".format(param, data[param]), 4... | b191cdf239495be174d3cf96c7b9a870cdf8e01f | 3,602,814 |
from typing import Optional
def generate_hdwallet(mnemonic: str = MNEMONIC, cryptocurrency=EthereumMainnet, passphrase: Optional[str] = None) -> BIP44HDWallet:
"""[summary]
Args:
cryptocurrency (hdwallet.cryptocurrencies, optional): crypto of the wallet. Defaults to EthereumMainnet.
mnemonic (... | 56a02125b425c67d39fee9be37691ca846c32747 | 3,602,815 |
import zipfile
def _HasMultipleResDirs(zip_path):
"""Checks for magic comment set by prepare_resources.py
Returns: True iff the zipfile has the magic comment that means it contains
multiple res/ dirs inside instead of just contents of a single res/ dir
(without a wrapping res/).
"""
with zipfile.ZipFile(... | 0a8edb987eb927700510d96f796741ae2bcd251e | 3,602,816 |
def get_population_count(data_path:str, roi):
""" Check if we have population count for roi and
add to timeseries df if we do.
Args:
data_path (str): Full path to data directory.
roi (str): Region.
Returns:
population (int): Population count for ROI (if e... | 7cc83773bf25e83258716312604faa453e00a2d2 | 3,602,817 |
def convert_to_schema(schema: List[Dict[str, str]) -> List[bigquery.SchemaField]:
"""Read the schema as a JSON and reformats as an array.
Args:
schema: list of dicts to convert to list of SchemaField
Returns:
List of bigquery.SchemaField objects holding the schema.
"""
input_fields... | 7709fa109db7951743e82a3757a931592497d283 | 3,602,818 |
import posixpath
def get_fuzzer_benchmark_covered_regions_filestore_path(
fuzzer: str, benchmark: str, exp_filestore_path: str) -> str:
"""Returns the path to the covered regions json file in the |filestore| for
|fuzzer| and |benchmark|."""
return posixpath.join(exp_filestore_path, 'coverage', 'da... | f999a81c53d961a2ed38a7941d7dd6912dae9621 | 3,602,819 |
def rescale(info, max_size):
"""
Rescale a heatmap given its info and a maximum size.
Biggest side (width or height) will be rescaled to maximum size, and the smallest
will be rescaled proportionally
:param info: dict, heatmaps info
:param max_size: int
:return: dict, heatmaps info rescaled... | 279ad2bce061439ef3ee588f1f393e0e8b81157a | 3,602,820 |
import base64
def base64_encode(data):
"""Returns base 64 encoded value of data."""
try:
data = base64.b64encode(data.encode())
except TypeError as e:
LOG.error("Couldn't encode data to base64: %s", e)
return data | 7c22438c57a9901b50472079c610c1dadbe0acbb | 3,602,821 |
import json
def customer_add_order(request):
"""
params:
access_token
restaurant_id
address
order_details (json format), example:
[{"meal_id": 1, "quantity": 2},{"meal_id": 2, "quantity": 3}]
stripe_token
return:
... | 8302dbb91dd7a30b6fb1f26b2225a211483cf240 | 3,602,822 |
def resnext50_32x4d(pretrained=False, **kwargs):
"""Constructs a ResNeXt50-32x4d model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
return _create_resnet('resnext50_32x4d', pretrained, **model_args) | 92c7e7185d08fd423d08e3dbaeea6c4090c02622 | 3,602,823 |
def get_language_array(lang, textlist=None):
"""gets language array with encoding and texts for all possible models as an EmbeddingSet
Args:
lang (whatlies, tfhub or huggingface model): language model to encode texts
textlist (list, optional): list of texts to encode. Defaults to None.
Ret... | 363a63caaf9fde9048fef5cf9b5d8869ca133df3 | 3,602,824 |
def make_feature(record):
""" Get a complete GeoJSON feature object for a record.
"""
return dict(
type="Feature",
id=record.get("Key"),
geometry=wkt.loads(record.get("WktGeometry")),
properties=extract_properties(record),
) | ba61f8f0c014fa0a96e201d29a2f3db49f6ffd96 | 3,602,825 |
def check_compliance(business: Business) -> list:
"""Check for non-compliant business data."""
result = []
legal_type = business.legal_type
result.extend(check_office(business))
result.extend(check_parties(legal_type, business))
return result | e37bc36a2dc21100aefc2eb6ecf9b14e5e0f47a1 | 3,602,826 |
def histvalue(image, channel="red"):
""" Create Histogram values of an image
Inputs:
image (np.array) : Image input
channel(string) : red (or) blue (or) green (or) all channel
on which histvalue to be calculated
Output:
hist (np.array) : (255, 1) array wi... | 89d425c86963830de74c2cb6e634451c854c5ef5 | 3,602,827 |
def del_dref(*args):
"""
del_dref(frm, to)
Delete a data cross-reference.
@param to: linear address of referenced data (C++: ea_t)
"""
return _ida_xref.del_dref(*args) | 7b5ec52243fce3b85a38b308e16d7ae772caf397 | 3,602,828 |
def breakdown(data):
"""Break down each row into context-utterance pairs.
Each pair is labeled to indicate truth (1.0) vs distraction (0.0).
Output is a native array with format : [context, utterance, label]"""
output = []
for row in data:
context = row[0]
ground_truth_utterance = ro... | 613cc8ca3d65d8eb22631114a88a934f67f37867 | 3,602,829 |
from datetime import datetime
import pytz
def mailgun_timestamp_to_datetime(timestamp):
"""
Parses a timestamp value from a Mailgun API response as a datetime
Args:
timestamp (float): A timestamp value from a Mailgun API response
Returns:
datetime.datetime: The parsed timestamp
"... | bc48c0eb74c2cebe887098ef62389198cd020ad6 | 3,602,830 |
def string_escape(text: str) -> str:
"""Escape values special to javascript in strings.
With this we should be able to use something like:
elem.evaluateJavaScript("this.value='{}'".format(string_escape(...)))
And all values should work.
"""
# This is a list of tuples because order matters, an... | f85d02527f3a9fdb9373f2359c2fce86b86a5a89 | 3,602,831 |
import doctest
def testmod_flt(mod,verbose=False):
"""Doctest a module using float comparisons.
"""
finder = doctest.DocTestFinder()
dtlist = finder.find(mod)
checker = FltOutputChecker()
runner = doctest.DocTestRunner(checker)
for dt in dtlist:
runner.run(dt)
results = runner.... | 0ddaee0f4785cf045c4f3ad3ffd4ddf78c1e5bd4 | 3,602,832 |
import torch
def cross_points_set_solve_3d(L, points, a, b, step=1, solver=1):
# points_set: (N, 3), # (c, y, x)
"""
L[cj, yj-a, xj]
L[cj, yj, xj-a] L[cj, yj, xj] L[cj, yj, xj + b]
L[cj, yj+b, xj]
"""
cj, yj, xj = points[:, 0], ... | 64c675a678debca8f85e8092d588c8216170bbe2 | 3,602,833 |
import scipy
def ci(data, confidence=0.95):
"""
Calculates the confidence intervals for a given 1D dataset. Calculated by multiplying standard
error of the mean and percent point function (inverse CDF) for a given confidence and sample
size. Then +/- mean.
Args:
data (array-like): 1D arra... | 0918ce249892ff4b4a47989c443a54915bea60d7 | 3,602,835 |
def check_permutation(str1, str2):
""" 1.2 Check Permutation: Given two strings, write a method to decide if
one is a permutation of the other.
Complexity: O(n) time, O(n) space
"""
h = {}
for c in str1:
if c not in h:
h[c] = 0
h[c] += 1
for c in str2:
if ... | 4da48752e963c05115b3255b03749e8579a9f8ff | 3,602,836 |
def calculate_metrics_single_ticker_via_celery(tuple, tca_request, dummy_market):
"""Calls auxillary methods to get market/trade data for a single ticker. If necessary splits up the request into
smaller date chunks to collect market and trade data in parallel (using Celery)
Parameters
----------
tc... | 31b86cd5e6cd9c0135ae3e41b352a5c7f6aaa251 | 3,602,837 |
from typing import Union
import torch
from typing import Optional
from typing import Any
import tqdm
def extract_features(
model: nn.Module,
dataset: Union[data.Dataset, torch.Tensor],
transform: Optional[Any] = None,
device: Optional[torch.device] = None,
verbose: bool = True,
**kwargs
) -> t... | 3c52769c22399543f4d36bead0b50f65133155ed | 3,602,838 |
def sli_usage(dir):
""" Calculate the Storyline-items (sli) statistics given the sitting-data calculated
from the Robomind Academy. The sli statistics consist of storyline/person/count/cumtime
vectors. Count is the number of time the person executed a program for the storyline
and cumtime is the accumul... | b8e8a237055b6a6c52ae50af0f3f74cdde9aa8a0 | 3,602,840 |
def climb_stairs(n: int) -> int:
"""
Args:
n: number of steps of staircase
Returns:
Distinct ways to climb a n step staircase
Raises:
AssertionError: n not positive integer
"""
fmt = "n needs to be positive integer, your input {}"
assert isinstance(n, int) and n > 0, ... | 71ac88a1f475e8fe2da8e525f7924e289dbf28e4 | 3,602,841 |
def accel_ES(q: np.ndarray):
"""
Compute the gravitational accelerations in the earth-sun system.
q in row vector of 6 elements: sun (x, y, z), earth (x, y, z)
"""
# Number of celestial bodies
num_bodies: int = 2
# Number of dimensions in arrays; 3 spatial dimensions times the number of bod... | 4084e0502cd8fa64066f3c6926ffd961661f0e65 | 3,602,844 |
import time
import json
def get_json(url, params):
"""
Attempt to fetch JSON for the given URL and query parameters. Since ArcGIS occasionally
returns empty HTTP 200 responses, we also retry on those empty responses.
"""
empty_retries = 5
sleep_time = 0.05
while True:
# Throttle our requests to red... | 75108c26e3949d0571a39df6ef4d2577c7f2eec6 | 3,602,845 |
def _process(proc_data):
"""
Final processing to conform to the schema.
Parameters:
proc_data: (Dictionary) raw structured data to process
Returns:
Dictionary. Structured to conform to the schema.
"""
int_list = ['hop', 'asn']
float_list = ['rtt']
if 'hops' in proc... | 172ff750af67d777b15e5a0370c7e7c41cba6c5a | 3,602,846 |
from typing import overload
from typing import Union
import types
from typing import Optional
from typing import Callable
def _blanket_implementation(
group: Blanket,
):
"""Generator of blanket types.
Unless blanket requires something fancy, this function should be
used in order to obtain new blanket... | c4c982677dbe944a9a7c09163a7aa9e37e3c0dd6 | 3,602,847 |
def calculateProbableRootOfGeneTree(speciesTree, geneTree, processID=lambda x : x):
"""
Goes through each root possible branch making it the root.
Returns tree that requires the minimum number of duplications.
"""
#get all rooted trees
#run dup calc on each tree
#return tree with fewest num... | 551dced0a943ca309c91b3572c700bccea0c8af0 | 3,602,849 |
def _linear_solution(wcs_dict):
"""Constructs a Linear1D model based on the WCS information obtained
from the header.
"""
intercept = wcs_dict['crval'] - (wcs_dict['crpix'] - 1) * wcs_dict['cdelt']
model = models.Linear1D(slope=wcs_dict['cdelt'], intercept=intercept)
return model | 29d104eb4fc8d72d78d21810329411cf592aa939 | 3,602,850 |
from pathlib import Path
from typing import Optional
def load_dataset(corpus_path: Path) -> Optional[pd.DataFrame]:
"""Load dataset from given xlsx or csv files, as dataframe
Parameters
----------
corpus_path : pathlib.Path
dataset file's path
Returns
-------
loaded dataframe
... | c0d483d63caf56bbb6c8f10c6e9fe250a0181a99 | 3,602,851 |
def determine_targets(gts, anchors, bg_threshold=0.3, fg_threshold=0.4):
"""
inputs:
gts: [n, 4] [x1, y1, x2, y2]
anchors: [k, 4] [x1, y1, x2, y2]
bg_threshold: float, 0.3~0.4
fg_threshold: float, 0.4~0.5
outputs:
anchor_gts_index: [k, ] # ... | e40e6277cf5b9974ab5bb63a481babb9cc1da71b | 3,602,852 |
import torch
def clusterize(α, x, scale=None, labels=None):
"""
Performs a simple 'voxelgrid' clustering on the input measure,
putting points into cubic bins of size 'scale' = σ_c.
The weights are summed, and the centroid position is that of the bin's center of mass.
Most importantly, the "fine" l... | 75b8baaee6f443fed1ecaeb01718a05c11caeaa2 | 3,602,854 |
from typing import List
import smtplib
def send_email(msg: str, to_: List[str] or str or set, headers: str = None):
"""
发送运维信息，该函数仅用于发送简单文本信息
:param msg: 正文内容
:param to_: 发送对象
1. str
to_ == 'self'，发送给“自己”
2. List[str]
传入邮箱列表，群发邮件（... | 5d45326c2c8a9aa5b3efc995ba631b48507d4ca0 | 3,602,855 |
from datetime import datetime
def cases_in_last(domain, days):
"""
Returns the number of open cases that have been modified in the last <days> days
"""
now = datetime.utcnow()
then = json_format_datetime(now - timedelta(days=int(days)))
now = json_format_datetime(now)
q = {"query": {
... | 89d49e9e9a70ed285d17a020df68e5046aa8d73f | 3,602,856 |
import warnings
def GridRaysampler(
min_x: float,
max_x: float,
min_y: float,
max_y: float,
image_width: int,
image_height: int,
n_pts_per_ray: int,
min_depth: float,
max_depth: float,
) -> "MultinomialRaysampler":
"""
GridRaysampler has been DEPRECATED. Use MultinomialRays... | a19d09d0bc93e860b38785bae13efd913cd235f4 | 3,602,857 |
async def _connect_redis(self):
"""
Connect to a Redis URL stored in config.
Parameters:
self - Aiko instance which contains configuration variables
"""
pool = await aioredis.create_redis_pool(self.config["redis_url"])
return pool | 5a72e70885ef6cbfda91a05b738c4cb9009e7cad | 3,602,858 |
def _convert_creators(value):
"""Convert creators."""
if isinstance(value, dict): # compatibility with previous versions
return [Person.from_jsonld(value)]
if isinstance(value, list):
return [Person.from_jsonld(v) for v in value] | 96fe6fd55fa7933728097ed492391a7c23d163d0 | 3,602,859 |
def apply_autocorrelation(signal):
"""
Calculate autocorrelation of a signal.
Parameters
----------
signal : ndarray
Signal to be autocorrelated.
Returns
-------
autocorr : ndarray
Autocorrelation
lags : ndarray
Corresponding lags for each autocorrelation va... | 2699c2a1099c6bde022b11c6def5ecfba8fe4e9e | 3,602,860 |
import codecs
def raw_buffered_line_counter(path, encoding="utf-8", buffer_size=1024 * 1024):
"""
Fast way to count the number of lines in a file.
:param Path path: Path to file.
:param str encoding: Encoding used in file.
:param int buffer_size: Size of buffer for loading.
:return: int
""... | 700135dc1a5b5bf4a807469e34f56213d7b1265a | 3,602,861 |
def drawcoorpoints(vis,points,col_out=black,col_in=red,radius=2):
"""
Funtion to draw points.
:param vis: image array.
:param points: list of points.
:param col_out: outer color of point.
:param col_in: inner color of point.
:param radius: radius of drawn points.
:return:
"""
po... | 7be3d7e0c9dd429115bc3fdc1adad080a9ef202e | 3,602,862 |
def neighborhood_quality(x):
"""
Strip field 'WOHNLAGE' to be an interval representing only the neighborhood quality
If it's a rural area, fill the value with NaN
Re-order values from poor to good
Original values of 'WOHNLAGE':
0: no score calculated (should be converted to NaN already... | 3dcae226a5035357939eb5fc25df8d3db2ebea43 | 3,602,863 |
def check_aggregate(
df, variable, components=None, exclude_on_fail=False, multiplier=1, **kwargs
):
"""Check whether the timeseries values match the aggregation
of sub-categories
Parameters
----------
df : IamDataFrame
args : passed to :meth:`IamDataFrame.check_aggregate`
kwargs : used... | 969dbd8e221c89e172fcdde0d6a91469bfead1d8 | 3,602,864 |
def consider_trip_number(trip_strategy, total_trips, trip_num):
"""Determines if the vehicle should charge given trip strategy and current trip
:param int trip_strategy: a toggle that determines if should charge on any trip or only after last trip (1-anytrip number, 2-last trip)
:param int total_trips:... | b2fe3a43b95fead4ebf65981cfba39857d1bee3c | 3,602,866 |
import pathlib
def as_pathlib(path):
"""
Converts the supplied path to an pathlib.Path object
:param path: The path to convert
:return: The path converted to pathlib.Path
"""
return pathlib.Path(path) | 88129771be8c3277320c813502efbcfee6abdc84 | 3,602,867 |
def load_test_data(transformed_corpus, ques_len, ans_len):
"""
load train data
"""
pointwise_corpus = []
for sample in transformed_corpus:
q, a = sample
q_pad, q_len = padding(q, ques_len)
a_pad, a_len = padding(a, ans_len)
pointwise_corpus.append((q_pad, a_pa... | d8dbb1079602abd3865b7cb48da0f10b0fedfeec | 3,602,869 |
import re
def remove_non_alpha_numeric(input_string):
"""
return string by removing non alpha numric characters from it
"""
return re.sub(r'\W+', '', input_string) | fc7b203e4937a7a5a80f0d3dc9a2d7bca5de6b45 | 3,602,870 |
import torch
def cov(x, rowvar=False, bias=False, ddof=None, aweights=None):
"""Estimates covariance matrix like numpy.cov"""
# ensure at least 2D
if x.dim() == 1:
x = x.view(-1, 1)
# treat each column as a data point, each row as a variable
if rowvar and x.shape[0] != 1:
x = x.t(... | 97650dd602fc51bd0b61cf4a401a53870d4d41c6 | 3,602,871 |
def setup(lifecycle_hooks):
"""Setup fixture which adds FakeOIDC lifecycle hook"""
class FakeOIDC:
"""Fake oidc class which updates services to oidc config with example.com:81 url"""
# pylint: disable=unused-argument
@staticmethod
def before_proxy(service: Service, proxy_params: ... | a2266001e99ee34a2978a791cde24a7dbae94a4d | 3,602,872 |
def run_gmm_test(sc, sqlCtx, table_name, fields, model, where_clause=''):
"""
Test a pretrained model on a table of test data
Args:
sc (pyspark.SparkContext): Spark Context to use for execution
sqlCtx (pyspark.sql.SQLContext): Spark SQL Context to use for sql queries
table_name (str... | 245d73bedada563030e460297a2305e5c7a6c433 | 3,602,873 |
def runQuery(HTSQLquery,fetch=None):
"""Query MRIC database, return query result"""
if not fetch:
fetch=_login()
return fetch(HTSQLquery) | 6cdb0f3eee2d853cc72044053078b747ce9dc941 | 3,602,874 |
from pathlib import Path
def index_gff3(path: Path): # gff3/{species}
"""Create bgzipped and indexed genome.gff3."""
genome = htslib.create_genome_bgzip(path)
print(genome)
print(htslib.tabix(genome))
return genome | 7c4d97cc6123dd8f9d08618e8495659d386d949e | 3,602,875 |
def _gaussian(x, x0, dx, A, N=False):
"""Simple Gaussian function with oversampling."""
if not N:
return A * np.exp(-np.power((x - x0) / dx, 2))
xx = np.linspace(x[0], x[-1], x.size * int(N))
yy = A * np.exp(-np.power((xx - x0) / dx, 2))
return np.array([np.average(yy[i*N:(i+1)*N]) for i in ... | 878246d642269640bc35c7ac8cd1206aca94f450 | 3,602,876 |
def fit_sigmoid_diffusion(
l_value,
x_data,
y_data,
start_parameters,
number_of_iterations=10000
):
"""Fit sigmoid curve based on two points on the diffusion curve
Arguments
----------
l_value : float
The sigmoids curve maximum value (max consumption)... | 409b9b1926d29300b4ca76d12cd52fbc50e7ad1c | 3,602,877 |
import string, urllib
def url2pathname(url):
"""OS-specific conversion from a relative URL of the 'file' scheme
to a file system path; not recommended for general use."""
# e.g.
# ///C|/foo/bar/spam.foo
# and
# ///C:/foo/bar/spam.foo
# become
# C:\foo\bar\spam.foo
# Windows i... | 7a1359fc0422127a1ca27b39a68c933d3642603f | 3,602,878 |
async def coordinator(hass, zigpy_device_mock, zha_device_joined):
"""Test zha light platform."""
zigpy_device = zigpy_device_mock(
{
1: {
"in_clusters": [],
"out_clusters": [],
"device_type": zha.DeviceType.COLOR_DIMMABLE_LIGHT,
}... | e53cf36b59d069ef9d870c37b19d7ecd3c286deb | 3,602,879 |
import random
def median(values):
"""Return the middle value, when the values are sorted.
If there are an odd number of elements, try to average the middle two.
If they can't be averaged (e.g. they are strings), choose one at random.
>>> median([10, 100, 11])
11
>>> median([1, 2, 3, 4])
2.... | cedbf00a96f99914c9b3ceafda0275f968d4327e | 3,602,880 |
def storage_backend(storage_dir) -> LocalStorage:
"""Provide a local storage backend for all local tests"""
return LocalStorage(path=storage_dir) | 2fb83dcadc1aac09b4776a112e0a3022e1d573a0 | 3,602,881 |
def nmc_LGM50_diffusivity_Chen2020(sto, T):
"""
NMC diffusivity as a function of stoichiometry, in this case the
diffusivity is taken to be a constant. The value is taken from [1].
References
----------
.. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
Dh... | 5b7cbd03376ad34cf0522283ac945c7eaeaaac87 | 3,602,882 |
def gen_borders(border=40,H=800,W=800):
"""
Generate linear random borders (50% straight, 50% with random points on the border frame)
"""
border = (float)(border)
if np.random.uniform()<0.5:
border_points = (gen_p(border,W-border),H-gen_p(border,H-border),W-gen_p(border,W-border),gen_p(borde... | 3c5e7eefb5fa0854afe06bd95f91ba66a20f6a82 | 3,602,883 |
def set_par(self, dummy=None, setpar=None, npar=None, verbose=False, roundto=5, **args):
"""Set the current parameter values.
In essence, this is a wrapper around `get_par` which also compiles the transition function with the desired parameters.
Parameters
----------
dummy : str or array, optional... | f88f3a88fcbe6fdf7d2b63bb8f0e2e4e47be6b0d | 3,602,884 |
def mask_box_ellip(image, ellip):
"""Creates boolean mask of the ellipse and finds minimal-area rectangle.
Parameters
------------
image : 2D array
region of interest as grayscale image
ellip : tuple
Ellipse defined by its centroid, axes lengths and angle.
Returns
-... | ddd4966c6c5f067b69bb14e6add82737d555f156 | 3,602,885 |
def is_raster_intersecting(src0: DatasetReader, src1: DatasetReader) -> bool:
"""Test two rasters for overlap
Parameters:
src0: rasterio read source
src1: rasterio read source
Returns:
True if the raster sources intersect
"""
bbox0 = window_bounds(((0, 0), src0.shape), src... | 2ccad9ff8876bc7d03f47cd6f53cf134581e2095 | 3,602,886 |
from typing import List
from typing import Tuple
def find_all_comment_segments(src: str) -> List[Tuple[int, int]]:
"""
Find all comment segments in a database starting from a position
return a list of pairs [ (comment start pos, comment end pos) ]
where start pos is inclusive and end pos is exclusive
... | eec33aae79caa029186d3b320f90682b5a9bebd6 | 3,602,891 |
def get_key(file_path):
"""
获取一个文件的key，七牛的算法是获取文件的hash值，使用的是 qiniu.etag()
:param file_path:
:return: key (str)
"""
key = etag(file_path)
return key | 17391d45bb47b8c7f948243e08bba08fc6c265e7 | 3,602,892 |
import copy
def get_choices(cli, prog_name, args, incomplete):
"""
:param cli: command definition
:param prog_name: the program that is running
:param args: full list of args
:param incomplete: the incomplete text to autocomplete
:return: all the possible completions for the incomplete
"""... | ce40e8dba98416f59c8d3de12ca40c995ea4c151 | 3,602,895 |
def R_value_U(X, y, k=None, theta=None):
"""Compute the R-value. Return high values for overlapping classes, low values for well separated classes"""
if k == None:
k = int(0.1 * len(X))
R_U_ = 0.
for d_lbl in unique_elements(y):
msk_ = np.asarray([lbl == d_lbl for lbl in y])
th... | 7b897298a023bc0442d7d173e22fbbe24d8b1d2e | 3,602,896 |
def toGeneMatrix(segments, gene_mapping, style='weighted', missingchrom=['Y'], gene_names_col='gene_name'):
"""
makes a geneXsample matrix from segment level copy number (works with multiple sample file)
Args:
----
style: str one of "weighted","mean","closest"
segments: dataframe of segments containing... | 91518ad547a3e72e3d03a6160216890901b43445 | 3,602,897 |
def mean_squared_error(y_true, y_pred):
""" loss function computing MSE of non-blank(!=0) in y_true
Args:
y_true(tftensor): true label
y_pred(tftensor): predicted label
return:
MSE reconstruction error for loss computing
"""
loss = K.switch(K.equal(y_true, tf.constant(0.)),tf.zeros(K.shape(y_true)),K.sq... | 7f03ff02f433c9ff935b526c5ce55f0b209e4691 | 3,602,898 |
def all_tasks_to_do(client=default):
"""
Returns:
list: Tasks assigned to current user which are not complete.
"""
return raw.fetch_all("user/tasks", client=client) | 92a5faf6e323d2ff85c6a8cb468c517050c91976 | 3,602,899 |
import itertools
def filter(iterator, field, value):
"""
Takes an iterator and returns only the docs that have a field == value.
The field can be a nested field like a.b.c and it will descend into the
embedded documents.
"""
return itertools.ifilter(lambda x: _deep_get(x, field) == value, it... | c9f7a1a60c2add295e547144b63146c8d957d2b4 | 3,602,900 |
def progress(iter, **kwargs):
"""
프로그래스 bar
for i in progress(10):
print i
for i in progress(iter):
print i
"""
if isinstance(iter, int):
iter = xrange(iter)
if hasattr(iter, '__len__') or 'target' in kwargs:
cls = Progress
else:
cls = ProgressBas... | 296f9f2574b939d57e583742f7833817eebefdd7 | 3,602,901 |
import sklearn
def auc_roc(yhat, ytrue):
"""
Class wise AUC ROC.
:param yhat:
:param ytrue:
:return:
"""
auc_roc = np.array([
sklearn.metrics.roc_auc_score(y_true=ytrue[:, i], y_score=yhat[:, i])
for i in range(ytrue.shape[0])
])
return auc_roc | 44a30350aa9aa0eff5cc6d947932dedd901da3e1 | 3,602,902 |
def keV_to_kelvin(temperature):
"""Convert from temperature expressed in keV to a temperature
expressed in Kelvin"""
return temperature * (con.e / con.k * 1000.0) | b7888babed588be952308a3c2821d57eba43f778 | 3,602,903 |
def extract_text(tweet):
"""Gets the full text from a tweet if it's short or long (extended)."""
def get_available_text(t):
if t['truncated'] and 'extended_tweet' in t:
# if a tweet is retreived in 'compatible' mode, it may be
# truncated _without_ the associated extended_tweet
... | 41660b71e3b020bc25309821b46471e88d79aa49 | 3,602,904 |
def search_linearf(a, x):
"""
Returns the index of x in a if present, None elsewhere.
"""
for i in range(len(a)):
if a[i] == x:
return i
return None | 949cb93218381a1a45002be03c6b8ca4d00092bf | 3,602,905 |
def update_posterior(n: np.ndarray, alpha: np.ndarray) -> np.ndarray:
"""
Updates posterior distribution.
Parameters
----------
n : np.ndarray
Amount to update posterior distribution parameter by.
alpha : np.ndarray
Posterior distribution parameter.
Returns
-------
... | fa9f69c0dddb8c819982485d411cf76b88b7f53e | 3,602,906 |
def from_xml(xml):
"""Deserialize XML to a dictionary."""
if xml is None:
return None
deserializer = XmlDeserializer()
return deserializer(xml) | 61ee1ce93e427ca405542d5b46853466b9db3a00 | 3,602,907 |
import networkx
import itertools
def primes2stg(Primes, Update, InitialStates=lambda x: True):
"""
Creates the state transition graph (STG) of a network defined by *Primes* and *Update*.
The *InitialStates* are either a list of states (in *dict* or *str* representation),
a function that flags states t... | caba25a6b11fa6e5a80c217de7d8592d9697fb32 | 3,602,908 |
def autoprops_override_decorate(func, # type: Callable
attribute=None, # type: str
is_getter=True # type: bool
):
# type: (...) -> Callable
"""
Used to decorate a function as an overridden getter or... | 80ac64ead677d0b84d1a86f6cff38839d564d3bb | 3,602,909 |
import logging
def fetch_from_landing_page(landing_url):
"""Returns a tuple of (pdf_link, summary_text)."""
add_pdf = False
skip = False
page = utils.beautifulsoup_from_url(landing_url)
report_tables = page.select('table[summary~="reports"]')
# in the rare case that doesn't work, have faith
if len(rep... | 9e02f0525921596909af00fdf5b08e08c7b22eb4 | 3,602,910 |
import requests
def get_user_contribs(username):
""" Fetch user contributions via MediaWiki API's Usercontribs module """
params = {
"action": "query",
"format": "json",
"list": "usercontribs",
"ucprop": "user|ids|title|sizediff",
"ucuser": username,
"uclimit": ... | d59810b6ac87e0dba28c9b21be9a21de6f878e80 | 3,602,911 |
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