content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Sequence
from typing import Optional
import math
def partition_dataset(
data: Sequence,
ratios: Optional[Sequence[float]] = None,
num_partitions: Optional[int] = None,
shuffle: bool = False,
seed: int = 0,
drop_last: bool = False,
even_divisible: bool = False,
):
"""... | a8a306e72d256d511d0a8f9493dd46dcbf6c1d7e | 33,600 |
def canopy_PAR_absorbed(states: States, setpoints: Setpoints, weather: Weather):
"""The PAR absorbed by the canopy
Equation 8.26
:return: The PAR absorbed by the canopy [W m^-2]
"""
return canopy_PAR_absorbed_from_greenhouse_cover(states, setpoints, weather) + canopy_PAR_absorbed_from_greenhouse_flo... | 0ea8103d1087be3d283e4834ec7ec8b436357e28 | 33,601 |
def tiff_to_array(tiff):
"""
Open a TIFF file as an array, normalizing the dimensions.
:param tiff: Filename
:return:
"""
array = (
tiff.asarray(out='memmap') if tiff.pages[0].is_memmappable else tiff.asarray()
)
if array.ndim < 3:
array = array[np.newaxis, ...]
retu... | 157591e2f9980602fc9bca3f713fb512c696821b | 33,602 |
def factor_costs_for_var(factor: Constraint, variable: Variable, recv_costs, mode: str):
"""
Computes the marginals to be send by a factor to a variable
The content of this message is a table d -> mincost where
* d is a value of the domain of the variable v
* mincost is the minimum value of f when... | 6910418aa2ce29ca98ba7bd85bc34a08ea44519d | 33,603 |
def version():
"""donghuangzhong version"""
return "0.0.1" | ad5d9834dddad46c2f4add31f46ea470bf370304 | 33,604 |
def list_dot(a, b):
"""
Returns the Euclidean inner product of two itterable data-structures.
"""
try:
if len(a) == len(b):
temp = 0
for i in range(len(a)):
temp += a[i]*b[i]
return(temp)
else:
raise ValueError("The length o... | 80dcdb22ed76a9cfe9750deb55971548efe4380e | 33,605 |
import struct
def pack_bytes(payload):
"""Optimally pack a byte string according to msgpack format"""
pl = len(payload)
if pl < (2**8):
prefix = struct.pack('BB', 0xC4, pl)
elif pl < (2**16):
prefix = struct.pack('>BH', 0xC5, pl)
else:
prefix = struct.pack('>BI', 0xC6, pl)
... | eaea52c44a766d74d0aa10e1da20e70f49b624f6 | 33,606 |
import torch
def disparity_consistency_src_to_tgt(meshgrid_homo, K_src_inv, disparity_src,
G_tgt_src, K_tgt, disparity_tgt):
"""
:param xyz_src_B3N: Bx3xN
:param G_tgt_src: Bx4x4
:param K_tgt: Bx3x3
:param disparity_tgt: Bx1xHxW
:return:
"""
B, _, ... | c407085bf10b0f7c67152d7d92f55a4984520766 | 33,607 |
import re
def split (properties):
""" Given a property-set of the form
v1/v2/...vN-1/<fN>vN/<fN+1>vN+1/...<fM>vM
Returns
v1 v2 ... vN-1 <fN>vN <fN+1>vN+1 ... <fM>vM
Note that vN...vM may contain slashes. This is resilient to the
substitution of backslashes for slashes, since Jam, unb... | 8b15697f6ae15b2fb634144987893ca04eabcccc | 33,608 |
def abort_behavior(token):
""" Abort behavior identified with the token """
return True, stop_nodenetrunner(behavior_token_map[token]) | faee270933a225ab376fef9b3ede589960e9c594 | 33,609 |
def limit_to_value_max(value_max, value):
"""
:param
1.(int) value_max -- value that should not be exceed
2.(int) value -- actual value
:return
1. return a value in the given range bound with value_max
"""
if value > value_ma... | a568bc1febe9a0cb6115efb4c95c0e1705787bfe | 33,610 |
def reference_col(
tablename, nullable=False, pk_name="id", foreign_key_kwargs=None, column_kwargs=None
):
"""Column that adds primary key foreign key reference.
Usage: ::
category_id = reference_col('category')
category = relationship('Category', backref='categories')
"""
foreign_... | 17da349907c2764b4d4a205a9a5a4e4ff9d02484 | 33,611 |
import numpy as np
def extract_municipality_hashtags(df):
""" This function takes a twitter dataframe as an input then the output is the dataframe with 2 new columns namely a hashtag
column and a municipality column.
Example
------
if the tweet contains the @mention '@CityPowerJhb' then the c... | 1c58e3154f57ad82a8129c5ed765a622b12b8d08 | 33,612 |
import math
def vertices_homography(vertices, H):
"""Apply projective transformation (homography) on a sequence of points.
Parameters:
vertices: List of (x, y) tuples.
A list for projective transformation.
H: A homography matrix.
Return:
vertices_homo: List of (... | ad0b1cd397d0b01a0f333d872b4f8a8d2e5f0736 | 33,613 |
def SRCNNv2(input_shape, depth_multiplier=1, multi_output=False):
"""
conv 9-64 puis 7-64 puis 5-32 puis 7-1 -> 1.006 120 epoch
conv 9-128 puis 7-64 puis 5-32 puis 7-16 puis 9-1 -> 1.007 130 epoch
@ multi_output : set to True
"""
inputs = Input(input_shape, name="inputs")
conv1 ... | 752d4e7da9f62a532db326c9eb68d91369a44dd9 | 33,614 |
import json
def parse_labels(string, bb_label_mapping, static_label):
"""Returns array of rectangles geometry and their labels
Arguments:
string {str} -- JSON string
bb_label_mapping {dict} -- Mapping from color to label
static_label {list} -- List of labels valid for the whole im... | 3a671abaac1faa326d0faa7e618249b5f17cd705 | 33,615 |
def spike_profile(*args, **kwargs):
""" Computes the spike-distance profile :math:`S(t)` of the given
spike trains. Returns the profile as a PieceWiseConstLin object. The
SPIKE-values are defined positive :math:`S(t)>=0`.
Valid call structures::
spike_profile(st1, st2) # returns the bi-variate ... | ffaff8b0e1e3f81dcbbf8cb0762224dc3850b2b3 | 33,616 |
import re
import pandas
def wig_to_dataframe(infile, step, format):
"""Read a wig file into a Pandas dataframe
infile(str): Path to file
Returns:
Dataframe
"""
fs = open(infile, 'r')
coverage_data = []
pos = 0
chr = ""
for line in fs.readlines():
try:
... | 07873b340b450ef3d0eb3d7715afb9b204a8277e | 33,617 |
from corehq.apps.users.models import CommCareUser
def get_all_commcare_users_by_domain(domain):
"""Returns all CommCareUsers by domain regardless of their active status"""
def get_ids():
for flag in ['active', 'inactive']:
key = [flag, domain, CommCareUser.__name__]
for user i... | 2b9209ac899b73eb534ba98cd5930bb0dc4749c2 | 33,618 |
def print_cycles_info(data):
"""
Print various information about cycles.
"""
n_cycles = len(data.cycles)
output('number of cycles:', n_cycles)
if not n_cycles:
return data
slengths = sorted(set(data.cycles_lengths))
lhist, lbins = np.histogram(data.cycles_lengths,
... | d22d827d966ff467de232818edd7854c20e12bb9 | 33,619 |
def get_block_size(sigma = 1.5):
"""
Devuelve el tamaño de los vecinos (block_size) que se va a utilizar para
obtener los puntos Harris. El valor se fija al valor correspondiente al uso
de máscaras gaussianas de sigma 1.5. El tamaño de la máscara Gaussiana es
6*1.5+1.
"""
return int(6*sigma... | 52f4aa88580252ab9c0f7a1840ac3097166c3930 | 33,620 |
import os
def load_fixture(filename: str) -> str:
"""Load a fixture."""
path = os.path.join(os.path.dirname(__file__), "fixtures", filename)
with open(path, encoding="utf-8") as fptr:
return fptr.read() | fa37cdd1e9a89df1188a67eb1fafb62882a329ca | 33,621 |
from typing import Callable
from typing import Union
from typing import Tuple
import typing
def approxZeroNewton(f: Callable[[float], float], df: Callable[[float], float], ddf: Callable[[float], float], a: Union[int, float], b: Union[int, float], epsilon: float, iteration: int) -> Tuple[float, int]:
"""
Appro... | f15e37f581f2f040af8b2c7edcec11ebce75c93f | 33,622 |
def chain_data(symbol, info=None):
"""Gets chain data for stock. INSTANT. Includes possible expiration dates for options."""
assert type(symbol) == str
return robin_stocks.options.get_chains(symbol, info) | 04cdb028420fbabdd1a4951762a5a6fea24a821a | 33,623 |
def convert_Cf2manningn(Cf, h):
"""
Convert the friction coefficient Cf to the Manning's n
"""
n = h**(1 / 6) * np.sqrt(Cf / g)
return n | 6552425ed1deea8ea93b226e1cbd19df40d3e5af | 33,624 |
def lstmemory_unit(input,
name=None,
size=None,
param_attr=None,
act=None,
gate_act=None,
state_act=None,
mixed_bias_attr=None,
lstm_bias_attr=None,
... | 5e4ec7203b58d44b7b07c865ea7683994869dd90 | 33,625 |
def _GetPrivateIpv6GoogleAccess(dataproc, private_ipv6_google_access_type):
"""Get PrivateIpv6GoogleAccess enum value.
Converts private_ipv6_google_access_type argument value to
PrivateIpv6GoogleAccess API enum value.
Args:
dataproc: Dataproc API definition
private_ipv6_google_access_type: argument va... | 56c830257ce996716a6dea7d205dca00e06ab6a9 | 33,626 |
def retry_(f, ex, times=3, interval=1, on_error=lambda e, x: None, *args, **kwargs):
"""
Call a function and try again if it throws a specified exception.
:param funciton f: The function to retry
:param ex: The class of the exception to catch, or an iterable of classes
:type ex: class or iterable
... | e28d39dfee43c9c651b174f87acdd077920f3ed9 | 33,627 |
from sklearn.decomposition import PCA
from sklearn import linear_model
def pca_analysis(model, data):
"""Run PCA analysis on model to visualize hidden layer activity.
To get the values of the intermediate layer, a new model needs to be
created. This model takes the normal input from the RNN, and returns... | 00ec016e4c47cd15b2570457b835990ae9aef2e2 | 33,628 |
def get_plugin_history(name):
"""
Get history of results for single plugin
:param name: name of the plugin
:type name: string
"""
plugin = smokerd.pluginmgr.get_plugin(name)
results = []
for res in plugin.result:
res = standardized_api_list(res)
results.append({'result'... | 64225c04d13ee228c0a375c78ff805c0dcd56bb4 | 33,629 |
def _parse_instance_info(node):
"""Gets the instance and driver specific Node deployment info.
This method validates whether the 'instance_info' and 'driver_info'
property of the supplied node contains the required information for
this driver to deploy images to the node.
:param node: a single Nod... | 65e687704ad5fa70f8fc23eaf73f3c48eec9e17c | 33,630 |
import sys
def pipe(db_new, db_old, table):
"""新表中默认数据的insert语句"""
res = db_new.query('select * from %s' % table)
if len(res) <= 0:
return []
values = ''
keys = None
_sqls = []
for i, _item in enumerate(res):
# TODO 导入默认数据
if keys is None:
_keys = '`, `'... | ce4d42438ed50f463228e3a4d0cce92c0b72984a | 33,631 |
def str2polynomial(string):
""" Get a string, return a polynomial """
try:
parts = advanced_split(string, '+', '-', contain=True)
terms = [str2term(each) for each in parts]
return Polynomial(*terms)
except:
raise Exception('Example input: -5x_1^2*y_1^3+6x_2^2*y_2^4-x_3^1*y_3^... | a52641bcbbc67159f5b73bbbc91ba84ea38cb223 | 33,632 |
def transpose_2d(array):
"""Transpose an array represented as an iterable of iterables."""
return list(map(list, zip_equal(*array))) | 48249de78d7d7c591f6d9fc8d79e184d3f291b49 | 33,633 |
def get_test_examples(args):
"""See base class."""
src = file2list(args.src_data)
trg = file2list(args.trg_data)
return _create_examples(src, trg, "test") | b1353a7b2bb87379c71c025f7abeb6142c55cf30 | 33,634 |
import json
def parse_site_config(config_site):
"""
Parse Site level configuration
:param config_site: Site config dict
:return: Tuple of WAN Interface config, LAN Network config, Element Config, DHCP Server config, and
Site Extension config
"""
local_debug("SITE CONFIG: " + str(j... | 9835469789e7c14f8abca0cfa641bc5f37e51b93 | 33,635 |
def precrec_unvoted(preds, gts, radius, pred_rphi=False, gt_rphi=False):
"""
The "unvoted" precision/recall, meaning that multiple predictions for the same ground-truth are NOT penalized.
- `preds` an iterable (scans) of iterables (per scan) containing predicted x/y or r/phi pairs.
- `gts` an iterable ... | eff8aef552999db2377c9d053c548a705f07bf3a | 33,636 |
def get_weapon_objects(json_load):
"""creates weapon objects by iterating over the json load
and making an object of each dictionary, then returns
a list of all the objects
"""
weapon_object_list = []
for weapon_dict in json_load:
# weapon_dict is a dictionary which has data for one weap... | e3b7309b4267ce4f237db3e7d6e17c69b187a1fc | 33,637 |
def _t_P(P):
"""Define the boundary between Region 2 and 3, T=f(P)
>>> "%.2f" % _t_P(16.52916425)
'623.15'
"""
n=[0, 0.34805185628969e3, -0.11671859879975e1, 0.10192970039326e-2,0.57254459862746e3, 0.1391883977870e2]
return n[4]+((P-n[5])/n[3])**0.5 | 196f4fae80d9425b0f3a06213c21f77d3049e401 | 33,638 |
import inspect
def add_as_function(cls):
""" Decorator for classes. Automatically adds functional interface for `call` method of class.
For example, `ConvBlock` class is transformed into `conv_block` function, while
`Conv1DTranspose` class is transformed into `conv1d_transpose` function.
"""
name... | 38f2e604e03e5a356450569bbfe7d0764bd784cb | 33,639 |
def remove_from_cart(request):
"""
Remove product from cart
"""
product_id = int(request.POST['product_id'])
# Checking if user session has cart or session may already flushed
# Cart an empty cart for user
if 'cart_id' in request.session:
cart_id = int(request.session['cart_id'])
... | 7a5fe35bce0d8ad7adb00c6b8a5677099c728c14 | 33,640 |
def preresnet164bn_svhn(classes=10, **kwargs):
"""
PreResNet-164(BN) model for SVHN from 'Identity Mappings in Deep Residual Networks,'
https://arxiv.org/abs/1603.05027.
Parameters:
----------
classes : int, default 10
Number of classification classes.
pretrained : bool, default Fal... | ebad863e846fd865772e93daf1225dd71654fc6a | 33,641 |
import os
import hashlib
def compute_hash_info(fd, unit_size=None):
"""Get MediaFireHashInfo structure from the fd, unit_size
fd -- file descriptor - expects exclusive access because of seeking
unit_size -- size of a single unit
Returns MediaFireHashInfo:
hi.file -- sha256 of the whole file
... | 6672b0c4245998401199265a00afe24edd174223 | 33,642 |
from typing import List
def value_map_distribution(value_map: dict, bounds: List[float] = None):
"""Percent of values that fall in ranges.
Args:
value_map: dict, value map
bound: list of float, boundaries to count values within
Returns:
dist: dict, distribution values... | 09988024007327ee26f27f43d9dc647e78a78915 | 33,643 |
def expand_basic(state):
"""
Simple function which returns child states by appending an available move to
current state.
"""
assert(len(state) < 9)
# Calculte set difference to get remaining moves.
n = tuple(set(range(9)) - set(state))
# Create tuple of available new states and return... | 0889a21b043f6f675d133fed6e3c825eb69f4a82 | 33,644 |
def schedule_conv2d_winograd_nnpack_weight_transform(attrs, outs, target):
"""Schedule conv2d_winograd_nnpack_weight_transform"""
with target:
return topi.generic.schedule_conv2d_winograd_nnpack_weight_transform(outs) | 24882fd6b578fd34f806970c44991dec023e150e | 33,645 |
def bce_loss(input, target):
"""
Numerically stable version of the binary cross-entropy loss function.
As per https://github.com/pytorch/pytorch/issues/751
See the TensorFlow docs for a derivation of this formula:
https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits
... | 9f9c722fbc8a9be4ed436084097af40241d2a7ee | 33,646 |
import os
from pathlib import Path
import re
def get_best_files(folder, filters):
"""
Compare all files in a folder that differ only by tags (and extension) and return only the best one according to their tags.
If filters is None, return all files.
folder: folder containing the files to check
... | a36c681838ccc739b286ff3e0400e503dfe17610 | 33,647 |
from typing import List
def query_normalised_list(x: str or None,
ref: List[NormalisedName]) -> str:
"""
Internal method for querying a channel/marker against a reference list of
NormalisedName's
Parameters
----------
x: str or None
channel/marker to query
... | a52e0aba0c67be4d82ba2b9d42e5415d0c738263 | 33,648 |
def get_reachable_observed_variables_for_inferred_variables(model, observed=set()):
"""
After performing inference on a BayesianModel, get the labels of observed variables
("reachable observed variables") that influenced the beliefs of variables inferred
to be in a definite state.
Args
mode... | a693d6c57969b38b357a4a57fe2e868650b514b6 | 33,649 |
from typing import Dict
import logging
def load_bias(dataset_name, filtered=False) -> Dict[str, np.ndarray]:
"""Loads the output of our bias-only model
Note that since this produces per-token output, it is only valid on data with the
same tokenization as our annotated data.
"""
if filtered:
bias_ids = ... | 4cbfa171ac998c7d114f6df04a1ffe14457a98b0 | 33,650 |
def isbuildin(name: str) -> bool:
"""[summary]
Checks if name is a keyword or build-in function
[description]
Arguments:
name {str} -- name to be checked
Returns:
bool -- true if it is a build-in
"""
blacklist = ["abs", "delattr", "hash", "memoryview", "set", "all", "dict"... | 42ac527e1bbc2a50f0fe065fa27c9560eb062448 | 33,651 |
def find_dip(pulls):
"""Find the longest sequence of significant observations in the data"""
significant = pulls > 3.
if np.sum(significant) == 0:
return 0, 0
# Find indices of start and end of each significant sequence
changes = np.diff(np.hstack(([False], significant, [False])))
sign... | 0f6a7bd33092605cf851b8b10fbb151a9854cb02 | 33,652 |
def clip_weights(model, weight_constraint):
"""
Clip weights of a keras model to be bounded by given constraints.
Parameters
----------
model: keras model object
model for which weights need to be clipped
weight_constraint:
Returns
-------
model: keras model object
... | 9b6fd73b0f04a9889c96a6d823260ce008cab50d | 33,653 |
def scatter_wrapper(
self, func, *args,
s=None, size=None, markersize=None,
c=None, color=None, markercolor=None,
smin=None, smax=None,
cmap=None, cmap_kw=None, vmin=None, vmax=None, norm=None, norm_kw=None,
lw=None, linewidth=None, linewidths=None,
markeredgewidth=None, markeredgewidths=Non... | 093172b28492864c4462cfc5a8a1e90c89abe1b1 | 33,654 |
from datetime import datetime
def _coerce_loc_index(divisions, o):
"""Transform values to be comparable against divisions
This is particularly valuable to use with pandas datetimes
"""
if divisions and isinstance(divisions[0], datetime):
return pd.Timestamp(o)
if divisions and isinstance(... | 818504516d60c3822ac8f2c0e8fda38a2664ea2e | 33,655 |
def triplet_loss(anchor_vector, positive_vector, negative_vector, metric='cosine_dist', margin=0.009):
"""Computes the triplet loss with semi-hard negative mining.
The loss encourages the positive distances (between a pair of embeddings with
the same labels) to be smaller than the minimum negative distance ... | 6120f3b2ddd581b6dbde427c66643a8d0cc3f6e4 | 33,656 |
import hashlib
def get_file_hash(filepath: str, blocksize: int = 2**20) -> str:
"""Return the hash of the given file, with a default blocksize of 1MiB."""
_hash = hashlib.md5()
if not isfile(filepath):
return _hash
with open(filepath, "rb") as f:
while True:
buffer = f.re... | d1ed17e5d1eb1c38b44b313b245a9244a787014d | 33,657 |
def get_stopwords():
"""common stopwords to skip when checking for article names (derived from nltk)
"""
return [
"i",
"me",
"my",
"myself",
"we",
"our",
"out",
"ours",
"ourselves",
"you",
"your",
"he",
"... | 861037bad40204961f205f03399b4b6bbe0e6b2d | 33,658 |
def test_io_dataset_to_in_dataset(fixture_lookup, io_dataset_fixture):
"""test_io_dataset_to_in_dataset"""
args, func, data_func = fixture_lookup(io_dataset_fixture)
def f(v):
dataset = tf.data.Dataset.range(1000)
dataset = dataset.batch(15)
dataset = dataset.map(tf.strings.as_string)
dataset = d... | f15bfe02e1c89c73c45b36a54800d941c8317172 | 33,659 |
def convert_operation_to_task(operation):
"""Converts an Operation to a legacy Task."""
result = _convert_dict(
operation['metadata'], {
'createTime': ('creation_timestamp_ms', _convert_timestamp_to_msec),
'updateTime': ('update_timestamp_ms', _convert_timestamp_to_msec),
'startT... | 886cb37a29b8d4de5fc15e9a98946d0a7c3ddebb | 33,660 |
def augment_with_derivatives(V=None, theta=None, M=None, tol=1E-8, symm=True, deflate=True):
"""
Make a linear basis containing the subspace spanned by V and a third order tensor theta
e.g. from modal derivatives by deflation.
Parameters
----------
V : ndarray
linear basis
theta : n... | caa1639f7c0b8c3ae24d633ff3d89bceffccfc2e | 33,661 |
from typing import Union
def fomc_statement(
dates: Union[str, list[str], None] = None,
asDict: bool = False,
) -> Union[pd.DataFrame, dict]:
"""
Get FOMC statements for given date or dates.
`dates`: YYYY-MM-DD, or 'current', or 'previous'.
`asDict`: True or False, will return as dictionary i... | 356416dee9e68eaed036fcbdeb971f22518e70b0 | 33,662 |
def lambda_handler(event, context):
""" This is the entry point for the lambda. It will call the main handler, which is within a
try/catch so that we can efficiently log any unhandled exceptions to Cloudwatch/Splunk.
Args:
event (dictionary): contains event data passed in by AWS Lambda ... | dc03440c4bc54a9142cff726d26ae802ed659c2e | 33,663 |
from textwrap import dedent
def get_device_number(connection_str):
"""Return the integer device number from the connection string or raise ValueError
if the connection string is not in the format "device <n>" with positive n."""
try:
prefix, num = connection_str.split(' ')
num = int(num)
... | 396a13d4449166e0d63e830b17b07b3b22a208e7 | 33,664 |
from click.testing import CliRunner
def runner():
"""Returns a ```click.testing.CliRunner()`` instance."""
return CliRunner() | 8bd6dcdfef85e5afa30ea412a7b7c85b243aac17 | 33,665 |
import os
import sys
def generate_ascii(image_path):
""" Generate New Config
Parameters
----------
image_path : str
Path to image file
"""
if not os.path.isfile(image_path):
print("Invalid image path!")
sys.exit(1)
#
art = ascii_magic.from_image_file(
i... | 8a95c7251894ce4ed90e5b36e249560ecd65681c | 33,666 |
def bent_plume_ic(profile, particles, Qj, A, D, X, phi_0, theta_0, Tj, Sj,
Pj, rho_j, cj, chem_names, tracers, p):
"""
Build the Lagragian plume state space given the initial conditions
Constructs the initial state space for a Lagrangian plume element from
the initial values for... | 24fdf411c69f06c766fe5a7cc59fb64d83dd7992 | 33,667 |
def GetMatrixBase(dim, val = 0):
"""Return matrix base, with a single sand grain in the middle"""
m = np.ones(dim) * val
SandFalling(m, 1)
return m | f22c025d64c532a86c88e0604847f2c1ca79645b | 33,668 |
def are_resize_confirm_logs_created(logs, instance, guest_hb=False):
"""
Check if resize-confirm logs have been created
"""
expected_logs = [{'event_log_id': fm_constants.FM_LOG_ID_VM_RESIZE_CONFIRM,
'severity': fm_constants.FM_ALARM_SEVERITY_CRITICAL},
{'event... | 4254ae1175efefa4244544961a638b3d11fe822a | 33,669 |
def find_second_largest2(root_node):
"""
Time: O(h)
Space: O(h)
h: height of the tree (O(lg n)); n: # of nodes
"""
def find_largest(node):
if node is None:
raise ValueError('Tree must have at least 1 node')
if node.right is not None:
return find_largest(... | 6d012a7fce306cc89f63603462991ac9441d0e57 | 33,670 |
from bs4 import BeautifulSoup
import html
def parse_team_totals(page):
"""
gets only the totals for a team from the box score of a game
(i.e. the last row)
"""
soup = BeautifulSoup(page, features='lxml')
scorebox = soup.find('div', {'class':'scorebox'})
teams = [TEAM_NAME_TO_TEAM(item.text... | 311eb570a74dc628e504a92b3282be8e6fbec613 | 33,671 |
def ball(p, radius, mass=-1):
"""Creates a ball that reacts to gravity.
:param p: The center point of the ball
:type p: (int, int)
:param radius: The radius of the ball
:type radius: int
:param mass: The mass of the shape (defaults to 1)
:type mass: int
:rtype: shape
"""
return... | 8f0da0982ea6f5622a857e96d47c1ba45731437d | 33,672 |
def cas_proxyCallback(request):
"""
This is a placeholder for a proxyCallback service
needed for CAS authentication
"""
logger.debug("Incoming request to CASPROXY (Proxy Callback):")
return HttpResponse("I am at a RSA-2 or VeriSigned SSL Cert. website.") | f26901adf67475c1697c81da66143ef3afe2c637 | 33,673 |
import os
def get_secret_id(source="~/.vault-id"):
""" Reads a vault user-id (UUID) from a file."""
source = os.path.abspath(os.path.expanduser(source))
user_id = None
# pylint: disable=invalid-name
if os.path.isfile(source):
fd = open(source, "r")
user_id = fd.read().strip()
... | 6d584be71cbc52fe43b826690348441d4f54c5fd | 33,674 |
import torch
from typing import List
from typing import Any
def decode_actions(
node_logits: torch.Tensor,
parent_label_logits: torch.Tensor,
new_label_logits: torch.Tensor,
label_vocab: List[Label],
) -> List[Any]:
"""
Decode the most likely actions from action logits for the attach-juxtapose... | 5ef9f875c3e283c935bf50dfec96211737d5e75d | 33,675 |
def timetz_pack(timetup_tz, dl_pack = dl_pack, mktime = mktime):
"""
Pack a time; offset from beginning of the day and timezone offset.
Given a pair, ((seconds, microseconds), timezone_offset), pack it into its
serialized form: "!dl".
"""
(timetup, tz_offset) = timetup_tz
return dl_pack((mktime(timetup), tz_off... | 5dc027656c8b5f474ac48c9391fcdcce981c1228 | 33,676 |
import inspect
def authentication_exempt(handler):
"""Mark the endpoint handler as not requiring authentication.
Note:
This only applies when the authentication_required_middleware is
being used.
"""
# Can't set attributes directly on a bound method so we need to
# wrap it in a fu... | 2e817d2adcf4ae1a08c21de4588c796155851470 | 33,677 |
def make_scan(headers, light_ROI=[0, np.inf, 0, np.inf],
curvature=np.array([0., 0., 0.]), bins=1,
ADU_per_photon=1, detector='rixscam_centroids',
min_threshold=-np.inf, max_threshold=np.inf,
background=None):
"""
Make 4D array of RIXS spectra with structu... | 0a855a8cfe4102d2299e02d0233f07230ba8d6b6 | 33,678 |
import functools
def print_args(function):
"""Decorate the given function to print out it's arguments and return val if not None
"""
@functools.wraps(function)
def wrapper(*args, **kwargs):
bound_arguments = bind_args(function, *args, **kwargs)
print("{name}({call})".format(
... | ffccb7d3fe73167927b8328bf56f150321ef288c | 33,679 |
def load_data(cutoff: float) -> DataFrame:
"""
Loads descriptors and binding data for given cutoff.
"""
# Load ECIF
ecif = pd.read_csv(f'Descriptors/ECIF_{cutoff}.csv')
# Load ligand descriptors
ligand_descriptors = pd.read_csv("Descriptors/RDKit_Descriptors.csv")
# Load binding affinity... | 80a9709f1034abe7f1b3af1f41bdab06a840e68f | 33,680 |
def phys2digital(mvolts):
"""
Obtains the digital difference value in the signal that corresponds to
a certain physical magnitude variation.
"""
return mvolts * ADCGain | 523c06eea2ce23d4ba18e709e185ebb8bae09428 | 33,681 |
from typing import Any
def resolve_mock_target(target: Any) -> str:
"""
`mock.patch` uses a str-representation of an object to find it, but this doesn't play well with
refactors and renames. This method extracts the str-representation of an object.
This method will not handle _all_ kinds of objects, ... | 4c7520d2b17daaf79d1de2d9eca4f615e401fb12 | 33,682 |
import math
import heapq
def a_star_dist(start_state: frozenset, end_state: frozenset):
"""A* algorithm to calculate minimum energy needed to traverse the given states."""
start_node = Node(0, start_state)
open_pq = [start_node]
g_scores = defaultdict(lambda: math.inf)
g_scores[start_state] = 0
... | b55160bd50e245a0d8236f7c7c402933c8bc665a | 33,683 |
def get_calc_data(stock_code, s, e, fq='qfq',
drop_columns=['code', 'preclose', 'adj'],
scaler=['amount', 'volume'],
scaler_func=sklearn.preprocessing.MinMaxScaler):
"""获取计算用数据源
Args:
fq: 是否采用复权数据。默认使用前复权。如果不需要复权则传''即可。
stock_code:
s... | d8d382ff1523cfdc95753dcc2f73c758301a416c | 33,684 |
def encode_add_validator_and_reconfigure_script(
sliding_nonce: st.uint64, validator_name: bytes, validator_address: AccountAddress
) -> Script:
"""# Summary
Adds a validator account to the validator set, and triggers a
reconfiguration of the system to admit the account to the validator set for the syst... | 6a19907eaa0b1e94ec8339f783540f01555bd599 | 33,685 |
import numpy
def get_microphone():
"""Return raw data from microphone as Numpy array
Default format will be 16-bit signed mono. Format will match
audio playback. You must call tick() every frame to update the
results from this function.
"""
glock.acquire()
d = gmicdata
glock.releas... | 755072e394603f22282328b553a3ada5c101bd1e | 33,686 |
from typing import OrderedDict
from typing import ChainMap
def _expand_arrays(raw_variables, old_variables={}, compat='identical'):
"""Expand a dictionary of variables.
Returns a dictionary of Variable objects suitable for inserting into a
Dataset._arrays dictionary.
This includes converting tuples ... | 04ce236b447d26e7d7c748dd155c16f83d2b526e | 33,687 |
import os
import subprocess
def build_and_push_docker_image(args):
"""docker-py doesn't seem to work, so use subprocess to call Docker"""
# This could be configurable, but there isn't much point.
HTTP_PORT = 8081
image_name = f"{args.dockerhub_repo}/scpca_portal_api"
# Change dir so docker can s... | 3878599b53323dcb6dcddced6eda0ec421ba7781 | 33,688 |
import logging
def redundant_peaks(usrdata):
"""Remove redundant, often ambiguous peaks by keeping the peak
with the highest ion score"""
peaks = usrdata.sort_values(by="IonScore", ascending=False).drop_duplicates(
subset=["SpectrumFile", "SequenceModi", "Charge", "PrecursorArea"]
)
peaks[... | da3413e4239c68168e1c4cd08feafd0320fcb8d5 | 33,689 |
import importlib
def getattr_in_module(module_name: str, func_name: str):
""" 在某个模块中获取属性
Args:
module_name: 模块名
func_name: 属性名
Returns:
属性
"""
m = importlib.import_module(module_name)
return getattr(m, func_name) | e0ceec50c063cea8350c04a4f048ca53d75ab5f6 | 33,690 |
def codeblock(request):
"""Parametrized fixture of each convention codeblock."""
return request.param | 52209ca4c84c873a33d9b6b3dc4ba044ebcd9513 | 33,691 |
import numpy
def cp_ls_cholesky_factor_objective(beta_gamma, norb, nthc, cholesky_factor, calcgrad=False):
"""cholesky_factor is reshaped into (norb, norb, num_cholesky)
Cholesky factor B_{ab,x}
Least squares fit objective ||B_{ab,x} - \sum_{r}beta_{a,x}beta_{b,x}gamma_{ab,x}||
This function provid... | cfa02ca214c0d0638243f916afdbfa052dbc9efe | 33,692 |
def volumes(jukebox_name, slot_id=[], as_object=False, p5_connection=None):
"""
Syntax: Jukebox <name> volumes
Description: Returns a list of all volumes currently loaded in the <name>
jukebox. To update the list of the volumes in the jukebox, use the
inventory method.
Return Values:
-On Suc... | 8c43a598a8d3ec55bcc3fd07b4b0f0ad1c585bcb | 33,693 |
def calculate_psi(cube, cfg):
"""Calculate temperature variability metric psi for a given cube."""
window_length = cfg.get('window_length', 55)
lag = cfg.get('lag', 1)
psi_years = []
psis = []
# Moving average
for yr_idx in range(cube.shape[0] - window_length):
slc = slice(yr_idx, y... | 641b55232dc4c845332aac5f28aef78c26783c43 | 33,694 |
import torch
def _get_product_features(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Get outer product of 2 tensors along the last dimension.
All dimensions except last are preserved. The last dimension is replaced
with flattened outer products of last-dimension-vectors from input tensors... | cf438a799b749563ea9509184cf117f4075730ab | 33,695 |
def resume_job(args, wording):
"""
used by fg and bg to resume a job either in the foreground or in the background.
"""
_clear_dead_jobs()
if len(tasks) == 0:
return "", "There are currently no suspended jobs"
if len(args) == 0:
tid = tasks[0] # take the last manipulated task b... | 235db36f3d4c59071b53f61777c908eccd86aa5c | 33,696 |
def drop_me(message):
"""This function removes user/chat id into a database.
Parameters
----------
message : telebot.types.Message
The message object.
Returns
-------
msg : str
User/Chat alert list addition/removal.
"""
helpers.start_connection().query("""
DELETE FROM
`mooncake-304003.misc.ps5-broa... | dd6c44b42e1809ff887ca6a99d8d3d4ecc63e882 | 33,697 |
def cmpd_to_pt(cmpd, els):
"""
Args:
cmpd (str) - chemical formula
els (list) - ordered list of elements (str) in triangle (right, top, left)
Returns:
(x, y) for compound
"""
tri = [CompAnalyzer(cmpd).fractional_amt_of_el(el) for el in els]
return triangle_to_square(... | aafc4d1a1821ee2a5e41f1540f114f8899a8485a | 33,698 |
def si_unit_lookup_table(units=UNITS):
"""
Creates a lookup table from all possible input unit names and symbols to
their corresponding SI unit.
"""
return {
**{ u.name: (u.si_equivalent, u.coefficient) for u in units
if u.name is not None},
**{ u.symbol: (u.si_equivalent... | a859dc78dca4cd31728fdda226609d67fccff7b1 | 33,699 |
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