content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def allInstances(cls):
"""Return cls instances for each of this class's set"""
items = [
(choice.value, cls(name=choice.name))
for choice in cls.set.values()
]
items.sort()
items = [v[1] for v in items]
return items | 702a77b9c247ec7976201cd0829a728c6c5fdd1f | 3,600,689 |
def linspace(start: float = 0.0, stop: float = 1.0, num: int = 50):
"""
Returns evenly spaced numbers over a specified interval.
See syntax here: https://numpy.org/doc/stable/reference/generated/numpy.linspace.html
"""
if not is_casadi_type([start, stop, num], recursive=True):
return _onp.l... | aa6a2f003e43e399b7cb4e2eef4cf811860a1691 | 3,600,690 |
def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02,
gpu_ids=[]):
"""Create a generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
... | 17e268540b344131130b022e2433a13d9e0d1330 | 3,600,691 |
def classify(train=None, test=None, data=None, res_dir="res/", disp=True, outfilename=None):
"""Description of compare
compare multiple classifier and display the best one
"""
utils.print_success("Comparison of differents classifiers")
if data is not None:
train_features = data["train_featur... | 4b8cfec3db44a5477fea766b2e676155121df95e | 3,600,692 |
def _map_sbs_sigs(
df: pd.DataFrame,
ref_df: pd.DataFrame,
ref_type: str,
sub_index: str = 'Substitution Type',
) -> pd.Series:
"""
Map Single-Base Substitution Signatures.
-----------------------
Args:
* df: pandas.core.frame.DataFrame with index to be mapped
* ref_d... | c93f170717b97981f27c642a7b64957d36d822c0 | 3,600,693 |
def _dvalid_checkfocus(
focus=None,
focus_half_width=None,
lines_keys=None,
lines_lamb=None,
lamb=None,
):
""" Check the provided focus is properly formatted and convert it
focus specifies the wavelength range of interest in which S/N is evaluated
It can be provided as:
- a spec... | b4a49ae53b128efb6f93c8783cfa25685d7a7b78 | 3,600,695 |
def mu_calc(Wv, delta):
"""
Calculates the mu function used by Thm. 1 (Katz and Reggia 2017).
Wv should be the (N by 1) numpy.array W.dot(v) for some point v.
delta should be an (N by K) array of deltas to Wv.
returns (N by K) numpy.array mu, where
mu[i,j] is the maximum of tanh'' over (Wv[i] ... | 9b891ca74c0f2b12972d7137d82c3227c97b7a89 | 3,600,696 |
from typing import Union
def get_json_url(year: Union[str, int], kind: str = "photos") -> str:
"""Returns url for the json data from news-navigator for given `year` and `kind`"""
return f"https://news-navigator.labs.loc.gov/prepackaged/{year}_{kind}.json" | 0c7650f667cb1fceebbd73e7a6eaf00145061d38 | 3,600,697 |
def d_linalg_norm__d_x(x, return_norm=False):
"""
Last dimension is normalized.
Calculate Jacobian
xn = x * (x^2 + y^2 + z^2)^(-1/2)
d xn / d x = (y^2 + z^2) * (x^2 + y^2 + z^2)^(-3/2)
d yn / d y = (x^2 + y^2) * (x^2 + y^2 + z^2)^(-3/2)
d zn / d z= (x^2 + z^2) * (x^2 + y^2... | a4b2eefc93daa0959668b2905beaddbb76403520 | 3,600,698 |
def SEQUENCE(*args) -> Function:
"""
Returns an array of sequential numbers, such as 1, 2, 3, 4.
Learn more: https//support.google.com/docs/answer/9368244.
"""
return Function("SEQUENCE", args) | 37ff63df9df9ebf3b2294aaeb3d6126b00641949 | 3,600,699 |
import re
def _transform_endpoint(endpoint: URL) -> URL:
"""Transforms a URL endpoint containing variable with realistic values.
Args:
endpoint (URL): The URL to transform.
Returns:
URL: The transformed URL.
"""
endpoint = URL(re.sub(r"<uuid:[^>]*>", str(_create_uuid()), endpoint... | ce08f433bdda02cf51e0fdebed57e0f4b6bed37a | 3,600,700 |
def problem_to_solve(radius_m):
"""
안전률을 고려한 허용 응력
:param radius_m:
:return:
"""
# 안전률
safety_factor = 2.0
# 허용 최대 응력
stress_max_Pa = 207e6
# 하중
force_N = 100
result = circular_section_stress(radius_m, force_N) - stress_max_Pa / safety_factor
return result | 7d06f9fd392074b7a00e9cbc2f30f1b1d75e7389 | 3,600,701 |
import collections
import functools
def get_config():
"""Generates the config for the experiment."""
name = 'finite_simple_sanity'
n_arm = 3
step_size = 0.01
step_count = 100
agents = collections.OrderedDict(
[('Laplace TS', functools.partial(FiniteBernoulliBanditLaplace, n_arm)),
('Langevin ... | c871263f115b815d954a4d73b592caa676c19e2e | 3,600,702 |
def list_grouper(iterable, n, fillvalue=None):
"""
Collect data into fixed-length chunks or blocks.
From: https://docs.python.org/2.7/library/itertools.html#recipes
Example: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx
zip_longest: Make an iterator that aggregates elements from each of the
iterab... | b9e82907d4a0ff6d4d5b994e12e79590710a29d0 | 3,600,703 |
import itertools
def map_DEgenes_to_pathways(sign_paths_to_cluster_df: pd.DataFrame,
mathys_dict: dict,
all_genes_in_pathways: list,
path_dict: dict,
clusters: list,
path_name_to... | 75843ab7446dbc4dcff61c02d87a891440e771f3 | 3,600,704 |
def capture_progress(config: ProgressConfig) -> t.Tuple[ProgressConfig, t.List[AbstractProgressMonitor]]:
"""
Creates a ``ProgressConfig`` which captures references to the progress monitor instances created
with it.
This is intended to be used for testing functions which create progress monitor instances
internal... | e1673c7cacc21c8f9968152f0a8e9840618424d5 | 3,600,705 |
def move_right(l: list) -> list:
"""リストの各要素を1つ右に移動させる"""
return [l[len(l) - 1]] + l[:len(l) - 1] | c645595532fa2bc89b58cc38eb79feaeb43cf9b2 | 3,600,706 |
def upsampling_python(data, scale, layout='NCHW'):
""" Python version of scaling using nearest neighbour """
ishape = data.shape
if layout == 'NCHW':
oshape = (ishape[0], ishape[1], ishape[2]*scale, ishape[3]*scale)
output_np = np.zeros(oshape, dtype=data.dtype)
for b in range(oshap... | aea0b41998a4da5c33c8624df083d2f4a2cd0847 | 3,600,708 |
import json
def jsonify(request, *args, **kwargs):
"""
jsonify with support for MongoDB ObjectId
"""
dumps = partial(json.dumps, cls=MongoJsonEncoder, indent=True)
return json_response(dict(*args, **kwargs), dumps=dumps) | 6817c2093aa0e938cdfd05e57ae66b8adbe3e747 | 3,600,709 |
def quantile_function(y_pdf, y_true, bins, bin_axis=1):
"""
Evaluates the quantile function at given y values.
"""
if len(y_pdf.shape) == 1:
bin_axis = 0
n_y = y_pdf.shape[bin_axis]
n_b = len(bins)
n_dims = len(y_pdf.shape)
_check_dimensions(n_y, n_b)
xp = get_array_module(y_... | 04b7bd1abea52fe75de9e6c7387a452be507c955 | 3,600,710 |
def _apple_framework_relative_headers_impl(ctx):
"""Implementation for apple_framework_relative_headers rule."""
output_dir = ctx.attr.framework_name + "_apple_framework_relative_headers"
outputs = []
for f in ctx.files.hdrs:
framework_path = "/".join([output_dir, ctx.attr.framework_name, f.basename])
... | a3198c1054204b58624bbef06532bd8cb30136b5 | 3,600,711 |
from typing import Literal
def prov_api_record_example():
"""
This is a more complex record example
:return:
"""
doc = ProvDocument()
doc.add_namespace("ex", "http://example.com")
doc.add_namespace("custom", "http://custom.com")
attributes = attributes_dict_example()
del attribut... | c76c34235a9769533a0b16b4d8de90c2062e64ad | 3,600,712 |
def create_banded(mat, up=2, low=2, col_wise=True, dtype=None):
"""Create a banded matrix from a given quadratic Matrix.
The Matrix will to be returned as a flattend matrix.
Either in a column-wise flattend form::
[[0 0 Dup2[2] ... Dup2[N-2] Dup2[N-1] Dup2[N] ]
[0 Dup1... | 2dd1f2c25eb122a770de8cb62856baec5020c220 | 3,600,713 |
def _constant_value(ragged_factory, inner_factory, pylist, dtype, ragged_rank,
inner_shape):
"""Constructs a constant RaggedTensor or RaggedTensorValue.
Args:
ragged_factory: A factory function with the signature:
`ragged_factory(values, row_splits)`
inner_factory: A factory funct... | 9785af9dc48db70525365884ed45a3923e95ec3f | 3,600,714 |
def gauss_jordan_elimination(arr_: np.ndarray) -> np.ndarray:
"""
This function is based on Gauss-Jordan elimination algorithm:
k = 0, 1, 2, ..., n - 1
> a[k][j] <- a[k][j] / a[k][k] (j = k, k + 1, ..., n)
> i != k
> a[i][j] <- a[i][j] - a[i][k] * a[k][j]
:param arr_: A np.n... | d0f71ce971add957a707bf6d289e6dc85b96cc88 | 3,600,716 |
def read_dataset(positive, negative, vocab):
"""
Create a pytorch SportsDataset for the train and test data.
:param positive: Positive examples file pointer
:param negative: Negative examples file pointer
:param vocab: Vocabulary words file pointer
"""
# Below, we'll give you some code to ... | f5a826b2646c0b4b8a05e837559d4e9d60c5cb5a | 3,600,717 |
def quantile(x, q, **kwargs):
"""
Return the values of the quantile cut points specified by fractions `q` of
a sequence of data given by `x`.
"""
x = np.asarray(x)
p = np.asarray(q) * 100
return np.nanpercentile(x, p, **kwargs) | 2961d62e5834a5f1a402565741bf319f288a2636 | 3,600,718 |
def get_champion_by_id(id):
"""Retrieves a Champion object from the Riot database with the given ID
:param id: the ID of the champion.
:return: a :class:`pydrake.ddragon.Champion` object with the given ID
.. warning:: This should only be called through
:meth:`pydrake.PyDrake.get_champion_by_id... | 8b058273fa73c5d71bcabed199860978a3416b31 | 3,600,720 |
def _mbondi3_radii(topology, all_bonds = None):
""" Sets the mbondi3 radii """
if all_bonds is None:
all_bonds = _get_bonded_atom_list(topology)
radii = _mbondi2_radii(topology, all_bonds=all_bonds)
for i, atom in enumerate(topology.atoms()):
# carboxylate and HH/HE (ARG)
if _is_... | eb2b45e7e5459492c9875d4b083f11c2dbe763ec | 3,600,721 |
def load_and_clean_table(input_data, return_as_df=False):
"""load and clean table where the input format is a table record """
try:
df = load_and_clean_dataframe(pd.DataFrame.from_dict(input_data))
if return_as_df:
return df
else:
return df.to_dict(orient="records")
except:
print("# [warning] error c... | 96bd865f17084a71313b91c00a3580f4cf8dbf1d | 3,600,722 |
def _condition_3_13(A_1_norm, n0, m_max, ell):
"""
A helper function for the _expm_multiply_* functions.
Parameters
----------
A_1_norm : float
The precomputed 1-norm of A.
n0 : int
Number of columns in the _expm_multiply_* B matrix.
m_max : int
A value related to a ... | 92bd4b2521571a1632051c3702b402e7ac8e3e48 | 3,600,723 |
import numpy
def group(n, fft, grouping=lambda i: i):
"""
Put fft data into n bins by adding them.
grouping function defines how things are grouped
lambda i: i --> linear grouping
lambda i: 2 ** i --> logarithmic
"""
if isinstance(n, (list,tuple)):
splitPoi... | d7f69e3f575051c00023a5a0d6bb1d4c3ea8c428 | 3,600,724 |
import torch
def kl_energy(potential_fn, rates_fn, passive_rates_fn):
""" Calculates the mean log-likelihood ell of a trajectory.
Compares Feynman-Kac measure with parametrized measure.
Normalizes mean log-likelihood by dividing by total time and number of sites.
"""
def validate_fn(traj, dts, tr... | 0d081d08e379c7cb5021f565e949dc7e7a48a7d5 | 3,600,725 |
def wrap_binary(executable, dotnet, extra = None):
"""Wraps provided executable with appropriate runfiles and providers.
Args:
executable: [DotnetLibraryInfo](api.md#DotnetLibraryInfo) to provide launcher for.
dotnet: [DotnetContextInfo(api.md#DotnetContextInfo)] for current rule.
extra: deps... | e94d29242e0e6d0d2a314e64879670ded440c90a | 3,600,727 |
def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):
""" Predict next state (prior) using the Kalman filter state propagation
equations.
Parameters
----------
x : numpy.array
State estimate vector
P : numpy.array
Covariance matrix
F : numpy.array()
State Transition ma... | ca1adfb97087e96e6fe07bb56bf55ebad71d75ce | 3,600,728 |
from typing import Dict
def non_renewables_production_mix(zone_key: str, session) -> Dict[str, dict]:
"""Retrieves production mix for non renewables using CAMMESA's API"""
params = {"id_region": 1002}
api_cammesa_response = session.get(CAMMESA_DEMANDA_ENDPOINT, params=params)
assert api_cammesa_respo... | dfde41b00930f4590ce6a38ef6c617bef262cf29 | 3,600,729 |
def id_for_dst(value):
"""generate a test id for dest"""
return f"dst->{value.comment}" | 8db759514d720fd15f437a45ad320edaf3b51c93 | 3,600,730 |
def a_from_ssig(ssig, ncounts):
"""Amplitude of a sinusoid corresponding to a given Z/PDS value
From Leahy et al. 1983, given a pulse profile
p = lambda * (1 + a * sin(phase)),
The theoretical value of Z^2_n is Ncounts / 2 * a^2
Note that if there are multiple sinusoidal components, one can use
... | 1485f78a6a8a9da1f29083d8793843b080849c2b | 3,600,732 |
def does_not_contain(token, value):
"""
Validate that `token` is not a substring of `value`
:param: token: string e.g. : | .
:param: value: dictionary, list, or str
"""
if isinstance(value, str):
if token in value:
raise ValueError(f"{value} cannot contain {token}")
if ... | 06acdf6643a84f2991180c54cb116d5450bf0a9c | 3,600,733 |
def sample_random_configs(random_config, samples=1, seed=None):
"""
Sample random configurations from the specified search space.
Parameters
----------
random_config: dict
dict where each key is a parameter and the value defines how the random sample is drawn. The samples will be
dr... | 0bde490d77b349a466de3e55230b579eabf265c9 | 3,600,734 |
def get_welcome_response(mycity_request):
"""
Welcomes the user and sets initial session attributes. Is triggered on
initial launch and on AMAZON.HelpIntent.
If we wanted to initialize the session to have some attributes we could
add those here.
:param mycity_request: MyCityRequestDataModel ob... | fab9645e49d5eb5f1a16cbb04a78faeef00538c6 | 3,600,735 |
def get_dummy_metadata():
"""
Incase of a temporal gap, the following metadata is used
"""
return dict({"product_file": "None"}) | 71a8a053579a153b9f78fffefe935ed4e9e40d01 | 3,600,736 |
def _crc_sign_full_packet_com_key(data: str) -> str:
"""Calculate the crc for packets before send.
Args:
data: packet data to sign.
Return:
The calculated and signed packet data.
Raises:
aioswitcher.erros.EncodingError: when failed to sign the packet.
Note:
This is a priv... | ad72b7e9d6b0a1683181874e1f0f128c1db0bca1 | 3,600,737 |
def annotate_phased_variants(
unphased_isovar_results,
min_shared_fragments_for_phasing=MIN_SHARED_FRAGMENTS_FOR_PHASING):
"""
Annotate IsovarResult objects with phasing information. Phasing
is determined by looking at RNA fragments used for assembled protein
sequences and counting the n... | ca355bd0c35fd84cbcffc5e84d3929cc9ab8e51b | 3,600,739 |
def get_visualization(name, **kwargs):
""" Returns visualization_fn(endnodes, image_info)
Args:
name: The name of the task.
Returns:
visualization_fn: A function that visualize the results.
Raises:
ValueError: If visualization `name` is not recognized.
... | 2f257e014cb932cf8f19896f1e78a1e548eb005e | 3,600,740 |
def get_callback_result(callback, res_str):
"""
根据callback后的jsonp语句
:param callback:
:param res_str:
:return:
"""
if callback is None:
return res_str
else:
return '%s(%s)' % (callback.encode("utf-8"), res_str) | c78d7ff07b9fa07499f9dad41805c67308aa8646 | 3,600,741 |
def helpers(request):
"""return a dict of app helpers"""
app = request.app
return dict(
app_title=app.title,
app_url=request.app.url,
app_menu=request.app.menu,
app_name=request.app.name,
app_class=zoom.utils.id_for(request.app.name.strip()),
app_description=r... | 0bba6085b2e4103813395e76bea7b42dda783cac | 3,600,742 |
def round_nearest(dt, delta):
"""Round the datetime to the nearest timedelta (e.g. 5 minutes, 2 hours, etc.)"""
c = ceil_dt(dt, delta)
f = floor_dt(dt, delta)
dc = c - dt
df = dt - f
return c if dc < df else f | 67172665f53547d693d2d3814d559af459837614 | 3,600,743 |
def find_nearest(array, value):
"""
Thanks StackOverflow!
Parameters
----------
array : np.array of ints or floats
1-D array of numbers to search through. Should already be sorted from
low values to high values.
value : int or float
The value you want to find the closes... | 0c490569c52567c498110953b776eba722f27ec5 | 3,600,744 |
def compute_output(dims, kernel_size, stride, padding=0):
""" Computes the output shape given an input shape, kernel size, stride and padding. """
dims = vectorize(dims)
padding = vectorize(padding)
kernel_size = vectorize(kernel_size)
stride = vectorize(stride)
# Vectorized computation
... | 37c131263822f54579566e6db2289082cb7b3b58 | 3,600,745 |
def _trac_to_github_wiki_links(text: str):
"""
Takes content with Trac wiki link directives and coverts
the directives to inline GitHub wiki links.
"""
link_matchers = [
# RST markup:
':trac:`wiki:(.+?)`',
'`wiki:(.+?)`:trac:',
# TracWiki markup:
r'`\[wiki:"... | 4d8dbcd96540b0dbef3603e9ecb81c6978ef087b | 3,600,746 |
def stouffer_p_value(z_scores, k, rho=0.):
"""
Parameters
----------
z_scores: array [n, b]
Return
------
p_value of the double sided gaussian
"""
if z_scores.ndim == 2:
z_scores = z_scores.sum(axis=1)
z_agr = z_scores / np.sqrt(k * (1 + (k - 1) * rho))
return 2 * (1... | 893a6413ed12253855b0a21c992b9bdf06ce69bd | 3,600,747 |
def expand_mask(masks, bboxes, im_shape, scores=None, thresh=0.5, scale=1.0, sortby=None):
"""Expand instance segmentation mask to full image size.
Parameters
----------
masks : numpy.ndarray
Binary images with shape `N, M, M`
bboxes : numpy.ndarray
Bounding boxes with shape `N, 4`. ... | 7d964ef7e4d24f9128ddfd26e4e9e8d69d9e9c9f | 3,600,748 |
def solve_bernoulli(A, E, B, trans=False, maxiter=100, after_maxiter=3, tol=1e-8):
"""Compute a solution factor of a Bernoulli equation.
Returns a matrix :math:`Y` with identical dimensions to the matrix :math:`A` such that
:math:`X = Y Y^H` is an approximate solution of a (generalized) algebraic Bernoulli... | d8dd4483be3208d23b4ecbe22f35d0116dd83d1f | 3,600,749 |
import time
def error_2d(nx_power_min, nx_power_max, nx_power_step, mode='O',
dielectric=dt.ColdElectronColdIon, max_harmonic=2, max_power=2,
has_main_phase=False):
"""
nx_step: the step size in nx power
"""
nx_power_array = np.arange(nx_power_min, nx_power_max+1, nx_power_step) ... | ab1d3f90f6352f61e85340fa0ec4d9e653c068e0 | 3,600,750 |
def import_dialout_primers(filename):
"""Returns an array of (forward, reverse) primer pairs.
"""
return pd.read_csv('kosuri_dialout_primers.csv').values | a1af85aa3805c09b33dfcc8a4fdce3eaf36a9025 | 3,600,751 |
def white_noise_block(vary=False, inc_ecorr=False, gp_ecorr=False,
efac1=False, select='backend', name=None):
"""
Returns the white noise block of the model:
1. EFAC per backend/receiver system
2. EQUAD per backend/receiver system
3. ECORR per backend/receiver syst... | b15553f18dc44ed916492af6f8d80e124a804743 | 3,600,752 |
def fully_connected_inference(lhs, rhs, nthreads=1):
"""Create an extern op that compute fully connected of 1D tensor lhs and
2D tensor rhs with nnpack.
Parameters
----------
lhs : Tensor
lhs 1D array input[input_channels] of FP32 elements
rhs : Tensor
lhs 2D matrix kernel[outpu... | 89770b75704a5c0c93aa715ae84f7c1f49e02083 | 3,600,753 |
def MPC_VcGetLastErrorCode() -> int:
"""Gets the last VICC error code
Returns
-------
int
Last error code field
"""
err_code = c_uint8()
CTS3Exception._check_error(_MPuLib.MPC_VcGetLastErrorCode(
c_uint8(0),
byref(err_code)))
return err_code.value | af16de29df6744342c44662e1e1b43f11e4e291f | 3,600,754 |
def load_into_tf_Dataset(tokenizer, train_texts, val_texts, y_train, y_valid, test_df, batch_size=BATCH_SIZE, prefetch_buffer_size=AUTO):
"""
Load splitted dataset of training, validation, and test into Tensorflow Dataset API for a more efficient input pipeline, especially for parallelism.
Parameters:
... | fb1a8256fdd32bb04fc9fbd06cf94beb1a69908e | 3,600,755 |
def vdecomp(v, m=None, minlen=None, maxlen=None):
"""
Decompose a vector into components. an nD stack of m-element vectors will return a tuple with up to m elements,
each of which will be an nD stack of scalars
:param v: nD stack of m-element vectors, a numpy (n+1)D array with shape
(n_stack0,n... | cbc846be6a07082711e5c9faa191181c8cdc75f8 | 3,600,756 |
def get_credentials():
"""Returns the credentials that will be sent to plotly."""
credentials = tools.get_credentials_file()
session_credentials = get_session_credentials()
for credentials_key in credentials:
# checking for not false, but truthy value here is the desired behavior
sessio... | db40c16cd024fac8f7a613b629b83ddab4ca8cc0 | 3,600,757 |
def extract_group2(filename, refpix):
"""Read in the PEDESTAL values from a *fitopt.fits file
Parameters
----------
filename : str
Name of uncalibrated file. This should be a *uncal.fits file.
refpix : tup
4-element tuple listing the number of outer rows and columns that
ar... | 0bf87eea8559d3a8bcef7302e54c71c2d50fa6b3 | 3,600,758 |
def plus97_modulus26_minus97(number):
"""
Replica algunas operaciones realizadas por el DGA a cada caracter.
Suma 97, realiza el modulo 26 y vuelve a sumar 97
:param number: entero que representa el carácter en base a la tabla ASCII
:return: el carácter resultante
"""
return chr(((number - 9... | ddf68bb67775fd033d20a6662602a2c0ef34a628 | 3,600,759 |
def fixture(names=None, scope="test", per_thread=False):
"""
Decorator, declare a function as a fixture.
:param names: an optional list of names that can be used to access the fixture value,
if no names are provided the decorated function name will be used
:param scope: the fixture scope, avai... | 080cea531f7c4e75b3f54a7aa1f87b5f13be2e66 | 3,600,760 |
def browser_to_use(webdriver, browser):
"""Recover the browser to use with the given webdriver instance.
The browser string is case insensitive and needs to be one of the values
from BROWSERS_CFG.
"""
browser = browser.strip().upper()
# Have a look the following to see list of supported brows... | 0e6bca7bad48c7c2934b81fce080f848e9bcdb22 | 3,600,761 |
import gzip
import bz2
def open(filename, *args):
""" Return a file-like object for various compressed format. """
if filename.endswith('.gz'):
opener = gzip.open
elif filename.endswith('.bz2'):
opener = bz2.BZ2File
else:
opener = file
return opener(filename, *args) | 3da3517a6381510a4e901083a3b6cbbde0157873 | 3,600,762 |
def update_appliance_hostname(
self,
ne_pk: str,
hostname: str,
) -> bool:
"""Add or update hostname to Edge Connect appliance. This operation
will take a few seconds to run.
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* -... | c08af64efe2e9c1e31faec0845e6bf61606b527b | 3,600,763 |
import functools
def get_callable_id(c):
""" Get an reasonably good id of any commonly used callable. """
if c is None:
return None
elif isinstance(c, functools.partial):
return get_callable_id(c.func)
elif hasattr(c, '__wrapped__'): # @functools.wraps()
return get_callable_id... | 1e61dbba5a9feace6c2b7f8658c6ef389424cda6 | 3,600,764 |
def make_thermostat(
device: hm_device.HmDevice, address: str, group_base_channels: list[int]
) -> list[hm_entity.BaseEntity]:
"""Creates RfThermostat entities."""
return make_custom_entity(
device,
address,
RfThermostat,
EntityDefinition.RF_THERMOSTAT,
group_base_cha... | 3f61caed764578ce36a9d416ef2aa5f10927ef22 | 3,600,765 |
def _matrix_multiply(left, right):
"""Perform symbolic matrix multiply of two 1D or 2D arrays."""
if not (left.ndim in (1, 2) and right.ndim in (1, 2)):
raise NotImplementedError(
'Matrix multiplication only implemented for 1D and 2D operands.')
elif not left.shape[-1] == right.shape[0]:... | 6cf9ebd1a5e0b355c85e0c1d854d555441d4f297 | 3,600,766 |
def get_config(path=None):
# type: (str) -> RawConfigParser
"""
Returns a ConfigParser with our current configuration.
"""
if path is None:
path = default_config_path()
defaults = {
'base_url': DEFAULT_BASE_URL,
}
config = RawConfigParser(defaults)
config.read([path... | 2b965a0bb4a949bd8a852ecd32b4cd18acbe3621 | 3,600,767 |
def A_forecast(submodel, horizon, seasonality):
"""Computing the forecast for the trend=A models
ŷ_t+h|t = l_t + h * b_t
ŷ_t+h|t = l_t + h * b_t + s_f(s, h) (if seasonality = "A")
ŷ_t+h|t = (l_t + h * b_t) * s_f(s,h) (if seasonality = "M")
"""
points = []
final_state = submodel.get("final_st... | e3511608085b6345e7bfc4a42389fc1ce858c5a8 | 3,600,768 |
def get_inspector():
"""Reuse inspector"""
global _INSPECTOR
if _INSPECTOR:
return _INSPECTOR
bind = op.get_bind()
_INSPECTOR = reflection.Inspector.from_engine(bind)
return _INSPECTOR | ab4c0459f87c1843cc006e3d44f54199bf43f732 | 3,600,769 |
def get_max_table_size(final_tables):
"""
Compute the maximum number of elements that appear in one of the table generated inside the main process.
Parameters
----------
final_tables : list
list of the tables generated inside the loop for each bucket.
Returns:
--------
... | a562d6f944a03785034028e8b2e83bdb19a8d9aa | 3,600,770 |
def IIR1(q_inst, x, a):
"""
Rekursives Fixpoint-Filter mit y[i] = Q< x[i-1] + a y[i-1] >
Da y immer von der Vorgeschichte abhängt, kann die Rechnung nicht vekto-
risiert werden : Langsam!
"""
y = zeros(len(x))
for i in range(0,len(x)-1):
# y[i+1] = x[i] + a * y[i] # no quantization ... | f8f751f3e04d8ef56ffe7d721514d5f1df101934 | 3,600,771 |
def StudentTCDF(degree_of_freedom, X):
"""Student's T distribution CDF. Returns probability that a value x < X.
Ported from Java: http://jsci.sourceforge.net/"""
A = 0.5 * incompleteBeta(degree_of_freedom / (degree_of_freedom + X * X), 0.5 *degree_of_freedom, 0.5)
if X > 0:
ret... | dd7773728301b2ea0be77a6167e7e57c301f99b3 | 3,600,772 |
def load_or_create_troubleshoot():
"""To troubleshoot, replace the load_or_create function in main
This forces recreation of dataframes from scratch"""
path = pathS3DF.take(1)
pathDF = spark.sparkContext.parallelize(path).toDF()
cityDF, nearbyDF, cityDayDF, weatherDF, forecastDF = path_to_dfs(path)
... | f91fa6aab0ff45af41a5c635b59ac56333b2a56f | 3,600,773 |
def queuing(training_subjects, validation_subjects, patch_size, n, samples_per_volume=10,
max_length=200, num_workers=2):
"""
Queue used for stochastic patch-based training.
See :class:`tio.data.Queue`.
:param n: # structures,
:type n: int
:param training_subjects: train dataset.
... | 2712a0ac1ff9cd95ca21113678de53e7a3067175 | 3,600,774 |
def init_nfov_projector(imsize, fov):
"""Creates an equirectangular to perspective projector.
Args:
imsize: The width and height of the square perspective image.
fov: Field of view of the square perspective image.
Returns:
nfov.NFOV projector.
"""
horizontal_fov_frac = float(fov) / 360
vertical... | bf9398b5b396ecf86e121f87b2492510a24cffc7 | 3,600,775 |
import gzip
def read_vocab(input_file):
"""Read vocabulary file and return a dict"""
if input_file is None:
return None
vocab = {}
if input_file.endswith('.gz'):
f = tf.gfile.Open(input_file, 'r')
fin = gzip.GzipFile(fileobj=f)
else:
fin = tf.gfile.Open(input_file,... | 54f7f55e2b3d66b3e887c9e07757294c4f664f25 | 3,600,776 |
def s_block(name=None, group=None, encoder=None, dep=None, dep_value=None, dep_values=None, dep_compare="=="):
"""
Open a new block under the current request. The returned instance supports the "with" interface so it will
be automatically closed for you::
with s_block("header"):
s_stati... | eef58ab77168f7ccd48a72a9e0f8726fbf48ad53 | 3,600,778 |
from typing import Optional
def get_timedelta_from_time_frame(time_frame: Optional[str]) -> timedelta:
"""Get the timedelta for the given time frame option."""
if time_frame == "year":
return timedelta(days=356)
if time_frame == "month":
return timedelta(days=30)
if time_frame == "week... | ee23ad8446a12e3ecd5466a13f2814622b13baf3 | 3,600,779 |
def resize_and_threshold(img, thresh, high):
"""Resizes the image to (216, 64) and does Otsu's thresholding on it.
Args:
img (np.array[np.uint8]): Image to be processed.
mid (int|float): Initial threshold for Otsu's method.
high (int|float): Max value of the image for Otsu's thresholdin... | 0cfd2fea247661e0d6bc7fde5d35db160c58163c | 3,600,781 |
def build_list(*args):
"""
Converts an arbitrary list of resources into a dict modeling a kube List.
:param args: The incoming arguments can be json/yaml strings, dicts, or apiobjects.
:return: A dict modeling a kube dict
"""
return _to_dict_list(args) | c7b164addb2dd4652267bbd8e7dbe10d8837be47 | 3,600,782 |
def _extract_bars(data, metric, exp_num_ticks_init=100000, num_prev_bars=3, num_ticks_ewma_window=20,
cache=None, flag=False, num_ticks_bar=None):
"""
For loop which compiles the various imbalance bars: dollar, volume, or tick.
:param data: Contains 3 columns - date_time, price, and volum... | 6ea9b39002392ff6d08c362eb5b0fabe4cd26cd8 | 3,600,783 |
import itertools
def next_bigger(n: int) -> int:
"""
A function that takes a positive integer number
and returns the next bigger number formed by the same digits.
If no bigger number can be composed using those digits, return -1
"""
numbers = sorted(set(sorted(int(''.join(i)) for i in iterto... | 8800040f3e88054bcacdb432131f8a9e0a872277 | 3,600,784 |
async def async_setup(hass, config):
"""Set up the KEF platform."""
if DOMAIN not in hass.data:
hass.data[DOMAIN] = {}
for conf in config[DOMAIN]:
host = conf[CONF_HOST]
if host not in hass.data[DOMAIN]:
hass.data[DOMAIN][host] = {}
mode = get_ip_mode(host)
... | 53926dee4084c2ef6dc2be8d0d8a84dff055af48 | 3,600,785 |
def decode_ec_public_key(der, encode_ec_point=True):
"""
Decode a DER-encoded EC public key as stored by OpenSSL into a dictionary
of attributes able to be passed to :meth:`pkcs11.Session.create_object`.
.. note:: **encode_ec_point**
For use as an attribute `EC_POINT` should be DER-encoded (Tr... | 5bec5a93ee811b1559cc8ee43d092472be8c1e49 | 3,600,786 |
def getItemPos(item, inList):
""" get item position from the list """
for i, j in enumerate(inList):
if j == item:
return i
return -1 | a77bc384641881582625d6c48187de8d23e7154b | 3,600,787 |
import requests
import socket
def _get_response_tuple(url):
"""Return a tuple which contains a result of url test
Arguments:
url -- a string containing url for testing
Result tuple content:
result[0] -- boolean value, True if the url is deemed failed
result[1] -- unchange url argumen... | 786f7ff4e731b968fc502847d1b96b9f3a8274bc | 3,600,788 |
from datetime import datetime
from contextlib import suppress
def _from_string(value: str) -> tuple[str, TemporalType, datetime]:
"""Guess the temporal date type from a given string.
This function should not be called by the end user. It is used internally by the `parse()` function.
Parameters
-----... | f1d3205ebdcf486078eb6f521037970006449918 | 3,600,789 |
import re
def ReplaceResourceZoneWithRegion(ref, args, request):
"""Replaces the request.name 'locations/{zone}' with 'locations/{region}'."""
del ref, args # Unused.
request.name = re.sub(
r'(projects/[-a-z0-9]+/locations/[a-z]+-[a-z]+[0-9])[-a-z0-9]*((?:/.*)?)',
r'\1\2', request.name)
return re... | 97f416e77799c28699b251ccd45a89f29cd34c65 | 3,600,790 |
from typing import Optional
from typing import List
def build_transform_gen(
cfg: CfgNode, is_train: bool, tfm_gen_repr_list: Optional[List[str]] = None
) -> List[d2T.Transform]:
"""
This function builds a list of TransformGen or Transform objects using a list of
strings (`tfm_gen_repr_list). If list ... | b53e599edc892c4b571a3fc05f03154ec33c3a4a | 3,600,791 |
import re
def _squash_column_spec(mif):
"""Modify MIF column specification.
Makes it lok like there is only one column.
Returns modified contents and list of original column names.
"""
with open(mif) as fp:
col_start = _find_column_start(fp)
data_point = _find_data_line(fp)
... | c40a22ab4344f341ec4184672d77ad9697a9219f | 3,600,792 |
def get_mac_addr_from_datapath(datapath):
"""
Return the MAC address from the datapath ID.
According to OpenFlow switch specification, the lower 48 bits of the datapath
ID contains the MAC address.
"""
mac_addr_int = datapath.id & 0x0000ffffffffffff
mac_addr = format(mac_addr_int, '02x')
return... | 539c0b0a92f3eead947aed65ed62a1e630dcb30f | 3,600,793 |
from typing import Optional
def read_devices(
skip: int = 0,
limit: int = 100,
group: Optional[str] = None,
db: Session = Depends(get_db),
):
"""
Get Devices
"""
if group:
db_devices = crud.get_devices_by_group(
db=db, group=group, skip=skip, limit=limit
)
... | 2e59eb3cee3e31238422d263b5190c7d9f08017d | 3,600,794 |
import math
def psnr(img1, img2, max_val=255.):
"""
computer by batch
image: shape (N,H,W,C), or (N,C,H,W)
compute in dB psnr = 10log
"""
mse = F.mse_loss(img1, img2, reduction="none").mean((1,2,3))
return 10. * (2*math.log10(max_val) - mse.log()/LOG10) | 80d4ccbfe37c9b884aa489f18bfa8d6eb1a772ae | 3,600,796 |
from typing import Tuple
from operator import pos
def return_linear_scalarisation(
source_coord: Tuple[float, float], target_coord: Tuple[float, float], weight: float
):
"""Get shortest path where each edge cost is weight * distance + (1-weight) * pollution"""
# find vertices in the graph that are close e... | 541889dc2bfc34883449a41a71f03323211794a4 | 3,600,797 |
def store_to_DB(DB_pano_base, DB_panos, DB_connectors, DB_roads):
"""
store road data to DB
"""
config_local = {"con": ENGINE, 'if_exists':'replace'}
DB_pano_base_bak = DB_pano_base.copy()
try:
# transfer the `list` to `str`, for it not compable for saving in pandas dataframe
fo... | e0ff92141e26770c7085e21f16ceefbb8fd4a7e1 | 3,600,798 |
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