content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def search_by_pattern(pattern, limit=20):
"""Perform a search for pattern."""
pattern_ = normalize_pattern(pattern)
db = get_db()
results = db.execute(
"""
SELECT json FROM places
WHERE document MATCH ?
ORDER BY rank DESC
LIMIT ?;
""",
(fts_pattern... | 467b85c850bb27ac1ed9a6e7fff6bb969a5f84e0 | 3,650,091 |
def gcd(a, b):
"""Greatest common divisor"""
return _gcd_internal(abs(a), abs(b)) | 886d366893a0215ccf0208af56c9c45037ad9549 | 3,650,092 |
def exp_create_database(db_name, demo, lang, user_password='admin', login='admin', country_code=None, phone=None):
""" Similar to exp_create but blocking."""
_logger.info('Create database `%s`.', db_name)
_create_empty_database(db_name)
_initialize_db(id, db_name, demo, lang, user_password, login, count... | b1d956628d864e0aa3998c00fd6a0b7cfb3ba411 | 3,650,093 |
def fix_cr(data):
"""Cosmic ray fixing function.
Args:
data (:class:`numpy.ndarray`): Input image data.
Returns:
:class:`numpy.dtype`: Fixed image data.
"""
m = data.mean(dtype=np.float64)
s = data.std(dtype=np.float64)
_mask = data > m + 3.*s
if _mask.sum()>0:
... | 40702ccc9400f4ba5f10cad1f376b83eac487876 | 3,650,094 |
def iou(box1, box2, iouType='segm'):
"""Compute the Intersection-Over-Union of two given boxes.
or the Intersection-Over box2.
Args:
box1: array of 4 elements [cx, cy, width, height].
box2: same as above
iouType: The kind of intersection it will compute.
'keypoints' is for intersectio... | 42ef4689c977e4ccbdbb987ff3ae63b265d3c42d | 3,650,095 |
def transform_color(color1, color2, skipR=1, skipG=1, skipB=1):
"""
transform_color(color1, color2, skipR=1, skipG=1, skipB=1)
This function takes 2 color1 and color2 RGB color arguments, and then returns a
list of colors in-between the color1 and color2
eg- tj.transform_color([0,0,0],[10,10,20]) returns a list:... | 5f04daa951c59b0445387b2dc988ab7efb98aff4 | 3,650,096 |
def sandwich(func):
"""Write a decorator that prints UPPER_SLICE and
LOWE_SLICE before and after calling the function (func)
that is passed in (@wraps is to preserve the original
func's docstring)
"""
@wraps(func)
def wrapped(*args, **kwargs):
print(UPPER_SLICE)
fun... | 167e1a753b7ba1f0d42732e12c5b37e0b0670f1b | 3,650,097 |
def as_dict(bdb_path, compact=True):
"""Get the state of a minter BerkeleyDB as a dict. Only the fields used by EZID are
included.
"""
with nog.bdb_wrapper.BdbWrapper(bdb_path, dry_run=False) as w:
return w.as_dict(compact) | dab02d671c099bd726839dc40167632cab812015 | 3,650,098 |
import json
def main() -> int:
"""
Builds/updates aircraft.json codes
"""
craft = {}
for line in AIRCRAFT_PATH.open().readlines():
code, _, name = line.strip().split("\t")
if code not in craft:
craft[code] = name
json.dump(craft, OUTPUT_PATH.open("w"))
return 0 | e35bc5b5f8be2452d60b73b891bc5756931b18aa | 3,650,100 |
def bootstrap_mean(x, alpha=0.05, b=1000):
"""
Calculate bootstrap 1-alpha percentile CI of the mean from a sample x
Parameters
----------
x : 1d array
alpha : float
Confidence interval is defined as the
b : int
The number of bootstrap samples
Returns
-------
lb... | 10b240f97196c7e2922574230a675d7e7c89038e | 3,650,101 |
def _penalize_token(log_probs, token_id, penalty=-1e7):
"""Penalize token probabilities."""
depth = log_probs.shape[-1]
penalty = tf.one_hot([token_id], depth, on_value=tf.cast(penalty, log_probs.dtype))
return log_probs + penalty | af8bf807438ff7ae96be0c5be0ec37fdbf81a5d1 | 3,650,102 |
from typing import Iterable
def minimize(function,
vs,
explicit=True,
num_correction_pairs=10,
tolerance=1e-05,
x_tolerance=0,
f_relative_tolerance=1e7,
initial_inverse_hessian_estimate=None,
max_iterations=1000,
... | 22659e37afa29fde75ba344e0054207582e44fb3 | 3,650,104 |
def variation_reliability(flow, gamma=1):
""" Calculates the flow variation reliability
Parameters
----------
flow: numpy array
flow values
gamma: float, optional
soft threshold
Returns
-------
variation reliability map (0 less reliable, 1 reliable)
"""
#comput... | cbdc4ab49402239e5e0fd484e5bf7bceaca383d4 | 3,650,105 |
def get_means(df: pd.DataFrame, *, matching_sides: bool,
matching_roots: bool) -> pd.Series:
"""
Calculates mean conditional probabilities from a given co-occurrence table
with filters restricting for matching sides and roots.
Args:
df: The co-occurrences.
matching_sides: ... | e6318fe4d284b134a8b655003b074ec554abca82 | 3,650,108 |
from typing import Dict
import math
def main(
pathname: str,
sheetname: str='Sheet1',
min_row: int=None,
max_row: int=None,
min_col: int=None,
max_col: int=None,
openpyxl_kwargs: Dict=None
):
"""
main is the main function. It accepts details about a excel sheet and returns an HTML ... | b44884bf84909b3bb76553aff247df6a961f3289 | 3,650,109 |
def get_go_module_path(package):
"""assumption: package name starts with <host>/org/repo"""
return "/".join(package.split("/")[3:]) | 1443d59391a36c7b9ba1d72ade9fd51f11cc1cc3 | 3,650,110 |
def get_hports(obj, *args, **kwargs):
"""
get_hports(obj, ...)
Get hierarchical references to ports *within* an object.
Parameters
----------
obj : object, Iterable - required
The object or objects associated with this query. Queries return a collection of objects associated with the
... | f4a18a43018c10ef5860e974ed1d3eaf0ab73ac3 | 3,650,111 |
def binary_cross_entropy(preds, targets, name=None):
"""Computes binary cross entropy given `preds`.
For brevity, let `x = `, `z = targets`. The logistic loss is
loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))
Args:
preds: A `Tensor` of type `float32` or `float64`.
... | b56e8bbaf16c688ebeb8be05b15a8c63745def3d | 3,650,113 |
from typing import Optional
def get_organization(name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetOrganizationResult:
"""
Use this data source to retrieve basic information about a GitHub Organization.
## Example Usage
```python
import ... | f534525be6bc7bc7e6e3c80be9b94543b327a8e7 | 3,650,114 |
import array
def numerator_LGG(mfld_dim: int,
ambient_dim: array,
vol: array,
epsilon: array,
prob: float) -> array: # our theory
"""
Theoretical M * epsilon^2 / K, our formula
Parameters
----------
mfld_dim
K, dimen... | b8f22f269cfb5a42ebb60118e6715c812d18ef73 | 3,650,115 |
from typing import OrderedDict
def group_nodes(node_list, tree_height):
"""
Groups a list of nodes.
"""
dict = OrderedDict()
for node in node_list:
nodelist = _make_node_list(GroupNode(node), tree_height)
if nodelist.get_name() not in dict:
dict[nodelist.get_name()] = n... | 50dc5f0356bed740e8b88e8bc05df01057fab29d | 3,650,116 |
def power_over_line_rule_pos_rule(m, i, j, t):
"""
If decision variable m.var_x[i, j, t] is set to TRUE, the positive power over the line var_power_over_line is
limited by power_line_limit
:param m: complete pyomo model
:type m: pyomo model
:param i: startnode index of set_edge
:type i: int... | c4d5a70f4cc2a67a45f11b3072fcb98c9f54e4e2 | 3,650,117 |
def join_tiles(tiles):
"""Reconstructs the image from tiles."""
return np.concatenate(np.concatenate(tiles, 1), 1) | 559b88d8bdd42662961669050d29b6583dfbc706 | 3,650,118 |
def solve2(lines, max_total):
"""Solve the problem for Part 2."""
points = parse_points(lines)
xmin = min([p[0] for p in points])
xmax = max([p[0] for p in points])
ymin = min([p[1] for p in points])
ymax = max([p[1] for p in points])
size = 0
for x in range(xmin, xmax+1):
for y ... | 1e743f736f92f60df960d4946f89ae222054d4aa | 3,650,120 |
import random
import json
def main():
"""
関数の実行を行う関数。
Return:
"""
def shuffle_dict(d):
"""
辞書（のキー）の順番をランダムにする
Args:
d: 順番をランダムにしたい辞書。
Return:
dの順番をランダムにしたもの
"""
keys = list(d.keys())
random.shuffle(keys)
re... | eca402495d47c8d856568c2271f3de92b1ca2d4f | 3,650,121 |
def scheme_load(*args):
"""Load a Scheme source file. ARGS should be of the form (SYM, ENV) or (SYM,
QUIET, ENV). The file named SYM is loaded in environment ENV, with verbosity
determined by QUIET (default true)."""
if not (2 <= len(args) <= 3):
expressions = args[:-1]
raise SchemeError... | d41bb38e60d5e82022c0857629937774c6b180d5 | 3,650,122 |
import torch
from typing import Optional
from typing import Tuple
def mask2idx(
mask: torch.Tensor,
max_length: Optional[int] = None,
padding_value: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
E.g. input a tensor [[T T F F], [T T T F], [F F F T]] with padding value -1,
ret... | b0900aadd14c9eff8af4fcf8e9043127d2a3562c | 3,650,123 |
def generate_paths(data, path=''):
"""Iterate the json schema file and generate a list of all of the
XPath-like expression for each primitive value. An asterisk * represents
an array of items."""
paths = []
if isinstance(data, dict):
if len(data) == 0:
paths.append(f'{path}')
... | 367f244b44c254b077907ff8b219186bd820fccd | 3,650,124 |
def enable_heater_shaker_python_api() -> bool:
"""Get whether to use the Heater-Shaker python API."""
return advs.get_setting_with_env_overload("enableHeaterShakerPAPI") | f849413c072034a15f0242e4b65dd36753a8d6f1 | 3,650,128 |
def solidityKeccak(abi_types, values):
"""
Executes keccak256 exactly as Solidity does.
Takes list of abi_types as inputs -- `[uint24, int8[], bool]`
and list of corresponding values -- `[20, [-1, 5, 0], True]`
"""
if len(abi_types) != len(values):
raise ValueError(
"Length ... | 2e31d05404b204a233694f14e808c78e96e67aed | 3,650,129 |
def run_fib_recursive_mathy_cached(n):
"""Return Fibonacci sequence with length "n" using "fib_recursive_mathy_cached".
Args:
n: The length of the sequence to return.
Returns:
A list containing the Fibonacci sequence.
"""
return [fib_recursive_mathy_cached(i + 1) for i in range... | 7cdd01ab747bccbd95dbb886e680c9214c2f883d | 3,650,131 |
def binning(LLRs_per_window,info,num_of_bins):
""" Genomic windows are distributed into bins. The LLRs in a genomic windows
are regarded as samples of a random variable. Within each bin, we calculate
the mean and population standard deviation of the mean of random variables.
The boundaries of the bins ... | bc872aa9d32545a91d9854d83ba2efb238cbfc02 | 3,650,132 |
from typing import Optional
from typing import List
def conj(node: BaseNode,
name: Optional[Text] = None,
axis_names: Optional[List[Text]] = None) -> BaseNode:
"""Conjugate a `node`.
Args:
node: A `BaseNode`.
name: Optional name to give the new node.
axis_names: Optional list of nam... | 9baffa5ad00289b0f44ad4953dc12931eccb8ed9 | 3,650,133 |
from typing import Union
import torch
def f1(
predictions: Union[list, np.array, torch.Tensor],
labels: Union[list, np.array, torch.Tensor],
):
"""Calculate F1 score for binary classification."""
return f1_score(y_true=labels, y_pred=predictions) | e35fa02281351a2c2003982fc6450aa4d8e5561b | 3,650,134 |
def length(v, squared=False, out=None, dtype=None):
"""Get the length of a vector.
Parameters
----------
v : array_like
Vector to normalize, can be Nx2, Nx3, or Nx4. If a 2D array is
specified, rows are treated as separate vectors.
squared : bool, optional
If ``True`` the sq... | 16caea4730b7dfd7cfcc71d253ee1fc7691fe05d | 3,650,135 |
def generateUniqueId():
"""
Generates a unique ID each time it is invoked.
Returns
-------
string
uniqueId
Examples
--------
>>> from arch.api import session
>>> session.generateUniqueId()
"""
return RuntimeInstance.SESSION.generateUniqueId() | f5dd066c1f9e6670b1e0949c890d5abc9931142a | 3,650,136 |
def order_rep(dumper, data):
""" YAML Dumper to represent OrderedDict """
return dumper.represent_mapping(u'tag:yaml.org,2002:map', data.items(),
flow_style=False) | 6b455d49cd5702324f4b1e825dabb4af90734730 | 3,650,137 |
def match_format(
format_this: spec.BinaryInteger,
like_this: spec.BinaryInteger,
match_pad: bool = False,
) -> spec.BinaryInteger:
"""
will only match left pads, because pad size cannot be reliably determined
"""
output_format = get_binary_format(like_this)
output = convert(data=forma... | 0a9638cbe5e522b1c7dc9ee316e559f9238b9abe | 3,650,138 |
def parse_nonterm_6_elems(expr_list, idx):
"""
Try to parse a non-terminal node from six elements of {expr_list}, starting
from {idx}.
Return the new expression list on success, None on error.
"""
(it_a, it_b, it_c, it_d, it_e, it_f) = expr_list[idx : idx + 6]
# Match against and_n.
if ... | 1c82d6b0654cf15c5aafec2c08e92c86cb5a4543 | 3,650,139 |
def make1d(u, v, num_cols=224):
"""Make a 2D image index linear.
"""
return (u * num_cols + v).astype("int") | 1f37c7ae06071ce641561eadc1d0a42a0b74508d | 3,650,140 |
def xkcd(scale=1, length=100, randomness=2):
"""
Turn on `xkcd <https://xkcd.com/>`_ sketch-style drawing mode. This will
only have effect on things drawn after this function is called.
For best results, the "Humor Sans" font should be installed: it is
not included with Matplotlib.
Parameters... | 1ce7aed60b2b67febb1658e98f14434005f3434a | 3,650,141 |
def team_size(data):
"""
Computes team size of each paper by taking the number of authors in 'authors'
Input:
- df: dataframe (dataset); or just the 'authors' column [pandas dataframe]
Output:
- team: vector of team_size for each paper of the given... | 76a5aafe90cf63fb0506525e566ca7759d0e27ce | 3,650,142 |
from datetime import datetime
def header_to_date(header):
""" return the initial date based on the header of an ascii file"""
try:
starttime = datetime.strptime(header[2], '%Y%m%d_%H%M')
except ValueError:
try:
starttime = datetime.strptime(
header[2] + '_' + he... | 3e2757ae39a2a9008a5f0fb8cd8fe031770c83ad | 3,650,143 |
def PhenylAlanineCenterNormal(residue):
""" Phenylalanine """
PHE_ATOMS = ["CG", "CD1", "CD2", "CE1", "CE2", "CZ"]
return RingCenterNormal(residue, PHE_ATOMS) | 4d96c7fe14bd411749136cf1569f32bd1c01679d | 3,650,144 |
def diamBiasK(diam, B, Kexcess, RshellRstar=2.5):
"""
diameter bias (>1) due to the presence of a shell
only works for scalar diam, B and Kexcess
validity: Kexcess>0 and Kexcess<0.1 and B*diam <~ 500
return 1 if Kexcess <= 0
"""
global __biasData, KLUDGE
d = np.abs(__biasData['Rshell/... | 6754ee70c64b2028cd5b72d3331e65bb64ea53b4 | 3,650,148 |
def interpolate_poses_from_samples(time_stamped_poses, samples):
"""
Interpolate time stamped poses at the time stamps provided in samples.
The poses are expected in the following format:
[timestamp [s], x [m], y [m], z [m], qx, qy, qz, qw]
We apply linear interpolation to the position and use SLERP for
... | 3131fe895a53b7d6c262930c635ce8cfa1c277f2 | 3,650,149 |
def test_add_client(case, client_name, client=None, client_id=None, duplicate_client=None, check_errors=False,
log_checker=None):
"""
UC MEMBER_47 main test method. Tries to add a new client to a security server and check logs if
ssh_host is set.
:param case: MainController object
... | 0b15ee41c8a61c0602e89e59a6343e67c6dc712f | 3,650,150 |
from typing import List
def get_data_unit_labels(data_unit: DataUnit) -> List[Attributes]:
"""
Extract important information from data_unit. That is, get only bounding_boxes and
associated classifications.
Args:
data_unit: The data unit to extract information from.
Returns: list of pairs ... | 36577b13713258fe1542c9bfc377a469ed5d6fd6 | 3,650,151 |
def calc_4points_bezier_path(svec, syaw, spitch, evec, eyaw, epitch, offset, n_points=100):
"""
Compute control points and path given start and end position.
:param sx: (float) x-coordinate of the starting point
:param sy: (float) y-coordinate of the starting point
:param syaw: (float) yaw angle at... | 455225b10c034895c32329bc14ce6dc384f5e0b3 | 3,650,152 |
import copy
def permutationwithparity(n):
"""Returns a list of all permutation of n integers, with its first element being the parity"""
if (n == 1):
result = [[1,1]]
return result
else:
result = permutationwithparity(n-1)
newresult = []
for shorterpermutation in result:
... | 218b728c2118a8cca98c019dff036e0ae2593974 | 3,650,153 |
from typing import List
import torch
import logging
def _train_model(model: BertForValueExtraction, optimizer, scheduler, train_data_loader, val_data_loader) -> List[int]:
"""
Main method to train & evaluate model.
:param model: BertForValueExtraction object
:param optimizer: optimizer
:param sch... | fbf5a588d9da24f72c0955c767f454297d91e74d | 3,650,154 |
def mass_at_two_by_counting_mod_power(self, k):
"""
Computes the local mass at `p=2` assuming that it's stable `(mod 2^k)`.
Note: This is **way** too slow to be useful, even when k=1!!!
TO DO: Remove this routine, or try to compile it!
INPUT:
k -- an integer >= 1
OUTPUT:
a r... | 35f85dd32e3c81a4921c5d5a87ab4cdc13e8ae46 | 3,650,155 |
import logging
def get_paginiation_data(first_pagination_url, family, cazy_home, args, session):
"""Parse the first paginiation page and retrieve URLs to all pagination page for the Family.
:param first_pagination_url: str, URL to the fist page of the family
:param family: Family class instance, represen... | 89cdd6e0d333cc69f0b92c202b6f0d4257849382 | 3,650,156 |
def gravitationalPotentialEnergy(mass, gravity, y):
"""1 J = 1 N*m = 1 Kg*m**2/s**2
Variables: m=mass g=gravity constant y=height
Usage: Energy stored by springs"""
U = mass*gravity*y
return U | f4fcfc9e7ddac8b246b2200e3886b79f6706936e | 3,650,157 |
def to_list(obj):
"""List Converter
Takes any object and converts it to a `list`.
If the object is already a `list` it is just returned,
If the object is None an empty `list` is returned,
Else a `list` is created with the object as it's first element.
Args:
obj (any object): the object... | 3ca373867ea3c30edcf7267bba69ef2ee3c7722e | 3,650,159 |
from molsysmt.basic import select, extract
def remove(molecular_system, selection=None, frame_indices=None, to_form=None, syntaxis='MolSysMT'):
"""remove(item, selection=None, frame_indices=None, syntaxis='MolSysMT')
Remove atoms or frames from the molecular model.
Paragraph with detailed explanation.
... | a2232e6226f76df6760eef59aab0f31edf7a75ec | 3,650,160 |
def macd_cross_func_pd(data):
"""
神一样的指标：MACD
"""
if (ST.VERBOSE in data.columns):
print('Phase macd_cross_func', QA_util_timestamp_to_str())
MACD = QA.QA_indicator_MACD(data)
MACD_CROSS = pd.DataFrame(columns=[ST.MACD_CROSS,
FLD.MACD_CROSS_JX... | d7c970efc931a3f1f2c25e51cf8e55e630eb37ad | 3,650,161 |
import json
def jsonp_response(data, callback="f", status=200, serializer=None):
"""
Returns an HttpResponse object containing JSON serialized data,
wrapped in a JSONP callback.
The mime-type is set to application/x-javascript, and the charset to UTF-8.
"""
val = json.dumps(data, default=seri... | 3ac71358043184b84b2f1f610a852b0b587d158d | 3,650,162 |
import collections
def enhance_bonds(bond_dataframe, structure_dict):
"""Enhance the bonds dataframe by including derived information.
Args:
bond_dataframe: Pandas dataframe read from train.csv or test.csv.
structure_dict: Output of :func:`make_structure_dict`, after running :func:`enhance_st... | 46836576b6bec8e1ca9f5685185d0f379b9e63f6 | 3,650,163 |
import numbers
def _tofloat(value):
"""Convert a parameter to float or float array"""
if isiterable(value):
try:
value = np.asanyarray(value, dtype=np.float)
except (TypeError, ValueError):
# catch arrays with strings or user errors like different
# types o... | 796b03699cb3b1e201436b6eb61df0636318de14 | 3,650,164 |
import time
import functools
def sp2mgc(sp, order=20, alpha=0.35, gamma=-0.41, miniter=2,
maxiter=30, criteria=0.001, otype=0, verbose=False):
"""
Accepts 1D or 2D one-sided spectrum (complex or real valued).
If 2D, assumes time is axis 0.
Returns mel generalized cepstral coefficients.
... | 01e380ddf10c56c3c4b9d09726ac98aef58715ca | 3,650,165 |
def has_prefix(sub_s):
"""
:param sub_s: (str) A substring that is constructed by neighboring letters on a 4x4 square grid
:return: (bool) If there is any words with prefix stored in sub_s
"""
for key, val in word_dic.items():
if key.startswith(sub_s):
return True
else:
pass
return False | b52937578d62d464df3131374a5dc0bdca735807 | 3,650,166 |
def _package_data(vec_f_image, vec_f_imageBscan3d, downscale_size, downscale_size_bscan, crop_size, num_octa,
str_angiography, str_structure, str_bscan, vec_str_layer, vec_str_layer_bscan3d, str_bscan_layer,
dict_layer_order, dict_layer_order_bscan3d):
"""
Organizes the angio... | dfe9ed58bd25715948cfb09f069c76d2f844df3c | 3,650,167 |
def apply_padding_by_last(list_of_lists):
""" The same as applying pad_into_lists followed by carry_previous_over_none
but takes a list of lists instead of events
Args:
lists_of_lists: list of lists with possibly different lengths
Returns:
lists of lists padded to the same length by ... | 83316fedb46230665fc543d4a3961cb72692023b | 3,650,168 |
def combine_patterns(
*patterns: str, seperator: Expression = None, combine_all=False
) -> str:
"""
Intelligently combine following input patterns.
Parameters
----------
patterns :
The patterns to combine.
seperator :
The seperator to use. If None, the default seperator :dat... | 1bcd703a183b72d88a8bfa7f1680754e0e3ee35e | 3,650,169 |
from datetime import datetime
async def utc_timediff(t1, t2):
"""
Calculate the absolute difference between two UTC time strings
Parameters
----------
t1, t2 : str
"""
time1 = datetime.datetime.strptime(t1, timefmt)
time2 = datetime.datetime.strptime(t2, timefmt)
timedelt = time1... | ba0b406048467029f6d05d72898b534dd6309e45 | 3,650,170 |
def function():
"""
>>> function()
'decorated function'
"""
return 'function' | 46b892fb70b5672909d87efcf76ffd3f96f9cf7f | 3,650,171 |
def load_stopwords(file_path):
"""
:param file_path: Stop word file path
:return: Stop word list
"""
stopwords = [line.strip() for line in open(file_path, 'r', encoding='utf-8').readlines()]
return stopwords | 9cb6578b5cbc608bc72da7c4f363b4f84d0adbb7 | 3,650,172 |
def hello():
"""Return a friendly HTTP greeting."""
return 'abc' | 9acda65833bec5976c3e2f0ffa77df8a2a7537bf | 3,650,173 |
def show_ip_ospf_route(
enode,
_shell='vtysh',
_shell_args={
'matches': None,
'newline': True,
'timeout': None,
'connection': None
}
):
"""
Show ospf detail.
This function runs the following vtysh command:
::
# show ip ospf route
:param dic... | 0f80ef7a46211002141ea489459df6be78aeeb28 | 3,650,174 |
from typing import get_origin
def attrs_classes(
verb,
typ,
ctx,
pre_hook="__json_pre_decode__",
post_hook="__json_post_encode__",
check="__json_check__",
):
"""
Handle an ``@attr.s`` or ``@dataclass`` decorated class.
This rule also implements several hooks to handle complex case... | 9db1f0a9ddefe1fb32d52331e158e8e2565b2697 | 3,650,175 |
import torch
def delta(pricer, *, create_graph: bool = False, **kwargs) -> torch.Tensor:
"""Computes and returns the delta of a derivative.
Args:
pricer (callable): Pricing formula of a derivative.
create_graph (bool): If True, graph of the derivative will be
constructed, allowing... | 88216117ba58afd68c88515210ad927a581eaf54 | 3,650,176 |
def formula_search(min_texts, max_texts, min_entries, max_entries):
"""Filter search results"""
result = Cf.query.filter(
Cf.n_entries >= (min_entries or MIN),
Cf.n_entries <= (max_entries or MAX),
Cf.unique_text >= (min_texts or MIN),
Cf.unique_text <= (max_texts or MAX)
).... | 938cc7ea25c2fe1bcd240ad4b60e517295eebe7b | 3,650,177 |
from datetime import datetime
import uuid
def versioneer():
"""
Function used to generate a new version string when saving a new Service
bundle. User can also override this function to get a customized version format
"""
date_string = datetime.now().strftime("%Y%m%d")
random_hash = uuid.uuid4(... | 7c5123d28e3bee45f2c9f7d519e830cf80e9fea8 | 3,650,178 |
import http
def request_url(method, url):
"""Request the specific url and return data"""
try:
r = http.request(method, url)
if r.status == 200:
return r.data.decode('utf-8')
else:
raise Exception("Fail to {} data from {}".format(method, url))
except Exceptio... | 9d25df49c9996364a9cb0195b90454378aefa5fd | 3,650,179 |
def _ifail(repo, mynode, orig, fcd, fco, fca, toolconf, labels=None):
"""
Rather than attempting to merge files that were modified on both
branches, it marks them as unresolved. The resolve command must be
used to resolve these conflicts."""
# for change/delete conflicts write out the changed versio... | 4d90ff7296fa9042392c7ffe28034fbbf804614f | 3,650,180 |
from typing import Callable
def __noise_with_pdf(im_arr: np.ndarray, pdf: Callable, **kwargs) -> np.ndarray:
"""Apply noise to given image array using pdf function that generates random values."""
util.check_input(im_arr)
im_arr = im_arr/255.0
noise = pdf(**kwargs, size=im_arr.shape)
out_im = im_a... | d1b9f612f4490ac526c2a952c54ee03cbedc2139 | 3,650,181 |
def train_test_split(df, frac):
"""
Create a Train/Test split function for a dataframe and return both
the Training and Testing sets.
Frac refers to the percent of data you would like to set aside
for training.
"""
frac = round(len(df)*frac)
train = df[:frac]
test = df[frac:]
r... | 8e233e017a261141f57f7b2bff9a527e275d2ed9 | 3,650,183 |
def load_special_config(config_filename, special_type, image_type='extent'):
"""Loads the google earth ("google"), science on a sphere ("sos"), or any other
special type of image config.
"""
cfg = load_config(config_filename)
# Promote the image type's keys
cfg = _merge_keys(cfg, cfg[image_typ... | b3a38d4ea9e39e42685604b4f01c7dcfa8ee2cdd | 3,650,184 |
def softmax_strategy_cim(attrs, inputs, out_type, target):
"""softmax cim strategy"""
strategy = _op.OpStrategy()
strategy.add_implementation(
wrap_compute_softmax(topi.nn.softmax),
wrap_topi_schedule(topi.cim.schedule_softmax),
name="softmax.cim",
)
return strategy | 11c5e581d9ad2814068558bc9faf24f57f5acba3 | 3,650,185 |
def get_statements_by_hash(hash_list, ev_limit=100, best_first=True, tries=2):
"""Get fully formed statements from a list of hashes.
Parameters
----------
hash_list : list[int or str]
A list of statement hashes.
ev_limit : int or None
Limit the amount of evidence returned per Statem... | 26924f7d05b35b6655eb69989a32677edf1eedbf | 3,650,186 |
def _is_in(val_set):
"""Check if a value is included in a set of values"""
def inner(val, val_set):
if val not in val_set:
if isinstance(val_set, xrange):
acceptable = "[%d-%d]" % (val_set[0], val_set[-1])
else:
acceptable = "{%s}" % ", ".join(val_... | 462afc33c73ae78bd62fa446f652a30492150643 | 3,650,187 |
def onebyone(transform, loglikelihood, parameter_names, prior, start = 0.5, ftol=0.1, disp=0, nsteps=40000,
parallel=False, find_uncertainties=False, **args):
"""
**Convex optimization based on Brent's method**
A strict assumption of one optimum between the parameter limits is used.
The bounds are narrowed until... | 86a12d7d7b738cc8ab7d8e65e3765e6b81f825b4 | 3,650,189 |
def test_data():
"""Test data object for the main PlantCV module."""
return TestData() | f8b2dc49d460ddadcd74c89da1159274660ecdfb | 3,650,191 |
def make_cmap(infile):
"""Call correct cmap function depending on file."""
cornames = ["coherence-cog.tif", "phsig.cor.geo.vrt", "topophase.cor.geo.vrt"]
phsnames = ["unwrapped-phase-cog.tif", "filt_topophase.unw.geo.vrt"]
if infile in cornames:
cpt = make_coherence_cmap()
elif infile in ph... | cd6408e1f8718b7073d1c5baa4e5a20ab8553720 | 3,650,193 |
def tail_correction(r, V, r_switch):
"""Apply a tail correction to a potential making it go to zero smoothly.
Parameters
----------
r : np.ndarray, shape=(n_points,), dtype=float
The radius values at which the potential is given.
V : np.ndarray, shape=r.shape, dtype=float
The potent... | 50934031776cfd4d92ef7a05ca2e63c215518352 | 3,650,194 |
def process_input(input_string):
"""
>>> for i in range (0, 5):
... parent_node = Node(None)
... parent_node.random_tree(4)
... new_node = process_input(parent_node.get_test_string())
... parent_node.compute_meta_value() - new_node.compute_meta_value()
0
0
0
0
... | 3cea94806034ebd3d95fdc7b5c8844d79698a684 | 3,650,195 |
def equiv_alpha(x,y):
"""check if two closed terms are equivalent module alpha
conversion. for now, we assume the terms are closed
"""
if x == y:
return True
if il.is_lambda(x) and il.is_lambda(y):
return x.body == il.substitute(y.body,zip(x.variables,y.variables))
return False
... | 27cd246e217403320bd16580029eb1d7c0122e33 | 3,650,197 |
def delete(isamAppliance, file_id, check_mode=False, force=False):
"""
Clearing a common log file
"""
ret_obj = {'warnings': ''}
try:
ret_obj = get(isamAppliance, file_id, start=1, size=1)
delete_required = True # Exception thrown if the file is empty
# Check for Docker
... | 7f27555eb33e489a59e1d9d1a7127713afd25d2f | 3,650,198 |
def filter_imgs(df, properties = [], values = []):
"""Filters pandas dataframe according to properties and a range of values
Input:
df - Pandas dataframe
properties - Array of column names to be filtered
values - Array of tuples containing bounds for each filter
Output:
df - Filter... | cdc5c8bfef10fae60f48cee743df049581a0df04 | 3,650,199 |
def conjugate_term(term: tuple) -> tuple:
"""Returns the sorted hermitian conjugate of the term."""
conj_term = [conjugate_field(field) for field in term]
return tuple(sorted(conj_term)) | d21834ff5c2abe5ff6ec85304db962d809b07637 | 3,650,200 |
def create_notify_policy_if_not_exists(project, user, level=NotifyLevel.involved):
"""
Given a project and user, create notification policy for it.
"""
model_cls = apps.get_model("notifications", "NotifyPolicy")
try:
result = model_cls.objects.get_or_create(project=project,
... | 3d2eec3e3a5f12a4cbba3c3c111608f38133cf94 | 3,650,201 |
def parse_table(soup, start_gen, end_gen):
"""
- Finds the PKMN names in the soup object and puts them into a list.
- Establishes a gen range.
- Gets rid of repeated entries (formes, e.g. Deoxys) using an OrderedSet.
- Joins the list with commas.
- Handles both Nidorans having 'unmappable' chara... | 1bb1ce6135f162e532b02e2d95eabd675540878d | 3,650,202 |
import torch
def get_expert_parallel_src_rank():
"""Calculate the global rank corresponding to a local rank zero
in the expert parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_expert_parallel_world_size()
return (global_rank // local_world_size) * local_world_si... | 0022f953707f26f9a3b3b021422ebc16e1d14213 | 3,650,203 |
from typing import Dict
from typing import Any
from typing import Optional
def _add_extra_kwargs(
kwargs: Dict[str, Any], extra_kwargs: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Safely add additional keyword arguments to an existing dictionary
Parameters
----------
kwargs : dic... | cfc4c17f608c0b7fe1ae3046dc220d385c890caa | 3,650,204 |
import random
import math
def Hiker(n,xLst,yLst,dist):
"""
Hiker is a function to generate lists of x and y
coordinates of n steps for a random walk of n steps
along with distance between the first and last point
"""
x0=0
y0=0
x=x0
y=y0
xLst[1] = x0
yLst[1] = y0
... | abe341c8ecdc579de2b72f5af1ace3f07dd40dc3 | 3,650,205 |
def extractdata(csvName='US_SP_Restructured.csv'):
"""
Parameters
----------
:string csvName: Name of csv file. e.g. 'US_SP_Restructured.csv'
"""
df = pd.read_csv(csvName)
df['index'] = df.index
# extract alternative specific variables
cost = pd.melt(df, id_vars=['quest', 'index'],
value_vars=['Ca... | e0a3f405da0e31e252b6110f84f81363c692d66a | 3,650,206 |
def has_field(entry: EntryType, field: str) -> bool:
"""Check if a given entry has non empty field"""
return has_data(get_field(entry, field)) | e13d973fde62e36764871fd3b565552ff46b359b | 3,650,207 |
def open_file(app_id, file_name, mode):
# type: (int, str, int) -> str
""" Call to open_file.
:param app_id: Application identifier.
:param file_name: File name reference.
:param mode: Open mode.
:return: The real file name.
"""
return _COMPSs.open_file(app_id, file_name, mode) | 7c38d219d4a867e72d90b873412ec7d5e5aad78a | 3,650,209 |
def correct_repeat_line():
""" Matches repeat spec above """
return "2|1|2|3|4|5|6|7" | b9c1e48c5043a042b9f6a6253cba6ae8ce1ca32c | 3,650,211 |
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