content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Optional
import re
def extraer_numero_iniciativa(numero_evento: str) -> Optional[int]:
"""Algunos numeros de evento traen la iniciativa a la
que pertenecen, esta función intenta extraer el ID
Returns
-------
int or None
ID de iniciative o None si no existe un numero con... | 3c1abffca75a66bfad5c9556a194b904b39422c0 | 39,110 |
def next_key(tuple_of_tuples, key):
"""Processes a tuple of 2-element tuples and returns the key which comes
after the given key.
"""
for i, t in enumerate(tuple_of_tuples):
if t[0] == key:
try:
return tuple_of_tuples[i + 1][0]
except IndexError:
... | cb4cdad805049cf57db21180c390bccfb8483f45 | 39,111 |
def sort_crt(crt):
"""Sort df by CRT"""
return (crt[1],) + get_name_sortkey(crt[0]) | 804d951aacd67184a6558a83812488051f4661bf | 39,112 |
def zd_to_airmass(zd):
""" conversion of zenith airmass into airmass assuming Pickering (2002). """
alt = 90. - zd # Altitude in degrees
return 1. / np.sin((alt + 244. / (165. + 47. * alt**1.1)) / RAD2DEG) | 634b94685c5575530585407241d4da26944f5ff7 | 39,114 |
def create_final_generator_network(
Z, projection_layer, to_rgb_conv_layers, blocks, original_image_size, params):
"""Creates base generator network.
Args:
Z: tensor, latent vectors of shape [cur_batch_size, latent_size].
projection_layer: `Dense` layer for projection of noise into imag... | 8f5a83a018f9d35817de92f156457b26716a4f64 | 39,115 |
def delete_redshift_cluster(config, redshift):
""" Deletes the Redshift cluster specified in config
Args:
config: a ConfigParser object
redshift: a boto3 client object for the AWS Redshift service
"""
try:
print("Deleting Redshift Cluster: ", config['CLUSTER']['IDENTIFIER'])
... | 2267eb4f017354563c9a7cf047a3a84983cd0044 | 39,117 |
def NaturalColor(C, gamma=0.8, pseudoGreen=True, night_IR=False, **kwargs):
"""
Natural Color RGB based on CIMSS method. Thanks Rick Kohrs!
(See `Quick Guide <http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_CIMSSRGB_v2.pdf>`__ for reference)
Check out Rick Kohrs `merged GOES images <htt... | 86acae6d74057771578f25a9f9aac9369a570624 | 39,118 |
def _get_adjacency_list(graph):
"""Convert a list of edges into an adjacency list."""
adj = {}
for i, j in graph:
if i in adj:
ni = adj[i]
else:
ni = adj[i] = set()
if j in adj:
nj = adj[j]
else:
nj = adj[j] = set()
ni.a... | 933ba9aa28a13e145be175120520253ef53c2a88 | 39,119 |
def behavior_of(classname):
"""
Finds and loads the behavior class for C++ (decoded) classname or returns
None if there isn't one.
Behaviors do not have a required base class, and they may be used with
Awkward Array's ``ak.behavior``.
The search strategy for finding behavior classes is:
1... | cd0a104620cb98edacad8d822d3d64a030b0ae99 | 39,120 |
def pareto_rank(df: pd.DataFrame, objectives: list, max_rank: int = 10) -> pd.DataFrame:
"""
Rank solutions based on pareto dominance
:param df:
:param objectives:
:return: DataFrame with additional column 'pareto_rank'
"""
df['pareto_rank'] = np.nan
rank = 0
evaluating = df[objecti... | 60aeaa7c52adbcfb79a71ba754c2ae8fe13a35a1 | 39,121 |
def get_domain_notes(domain):
"""
Combine all domain notes if there are any.
"""
all_notes = domain.http.notes + domain.httpwww.notes + domain.https.notes + domain.httpswww.notes
all_notes = all_notes.replace(',', ';')
return all_notes | f199e909184475f6c84a290ef7ac5ad9ff160a54 | 39,122 |
def add_trailing_load(axle_spacing, axle_wt, space_to_trailing_load,
distributed_load, span1_begin, span2_end, pt_load_spacing=0.5):
"""Approximates the distributed trailing load as closely spaced point loads.
The distributed trailing load is approximated as discretly spaced point
loads. The point ... | 3eac900cff7d5e66c399e7f846d66aeff3e7389c | 39,123 |
def str_append(string, add):
"""Append add in end string. Example: str_append('hou', 'se'); Return='house'"""
return string + str(add) + "\n" | efbc9a085d1e63f290af3e6c447cde13bce5f5d0 | 39,124 |
def parse_trigger_plugin(trigger_path, plugin_config, parse_only=False):
"""Parse plugin file and return the trigger config."""
# Open and exec plugin definitions
with open(trigger_path, 'r') as f:
trigger_code = f.read()
trigger_vars = {}
exec(trigger_code, trigger_vars)
# Get trigger... | 69feab8e6d109f9f5a641c3fb738710e97707e19 | 39,125 |
import json
def signout():
"""Signout. 退出。
"""
session.pop('kb', None)
session.pop('islogin', None)
database.skb = ''
state = {
'success' : 1,
'message' : "退出成功"
}
return json.dumps(state) | dfb38a2aad6f4deaf0e971c3e604ab005759a353 | 39,126 |
from .plot_utils import make_3d_axis, Arrow3D
def plot_axis_angle(ax=None, a=a_id, p=p0, s=1.0, ax_s=1, **kwargs):
"""Plot rotation axis and angle.
Parameters
----------
ax : Matplotlib 3d axis, optional (default: None)
If the axis is None, a new 3d axis will be created
a : array-like, s... | 5a239a26eb070c0f7a4126b22ceff5f40098b0fb | 39,128 |
from typing import Tuple
from typing import List
import numpy
def generate_hash_rates(hash_rate_parameter: int, number_of_honest_miners: int, malicious_hash_ratio: float) -> \
Tuple[List[float], List[float]]:
"""
:param number_of_honest_miners:
:param malicious_hash_ratio:
:return:
"""
... | 5c02a78015c8f0d16922a37e5de163ca287932ba | 39,129 |
def create_function(treatment_system, parameter, values):
"""
Creates a function based on user input
"""
func = str(treatment_system.loc[0, parameter])
func = func.replace(' ', '')
for key, value in values.items():
func = func.replace(key, str(value))
return func | d0afaf42e50aef943b7551c60f19d180bbbeba0b | 39,130 |
def _ResourceGraphvizNode(resource, request, resource_to_index):
"""Returns the node description for a given resource.
Args:
resource: Resource.
request: RequestData associated with the resource.
resource_to_index: {Resource: int}.
Returns:
A string describing the resource in graphviz format.
... | e3a1ba2bbe73992444cd001f44f2ac9546f256af | 39,131 |
def unformat(combination: str) -> str:
"""Unformats a formatted string to it's original state"""
return str(combination).replace("<", "").replace(">", "") | d017903ddaac78adf5085198d25eb508b62a78b4 | 39,132 |
def common_base(cls: type, *clss: type) -> type:
"""
Overview:
Get common base class of the given classes.
Only ``__base__`` is considered.
Arguments:
- cls (:obj:`type`): First class.
- clss (:obj:`type`): Other classes.
Returns:
- base (:obj:`type`): Common ba... | 40d43f9aa71537604d2a48ea7705985653f4ed8b | 39,133 |
def get_all_configs() -> Response:
"""Get and return all configs by name"""
return flask.jsonify(config.get_config_names()) | dda1c4991129e8c5072ab06c60777d8eeece3ffb | 39,134 |
def dewpoint(temperature, humidity, *, model='hardy', isdew=True):
"""Calculate the dew point.
Parameters
----------
temperature : :class:`float`
The temperature [degree C].
humidity : :class:`float`
The humidity [%RH].
model : :class:`str`
The dew point model, valid mod... | f2e2b7512a3f7a4ce5ded2fd9f7e51cb9f743408 | 39,135 |
import re
def parse_list_marker(line, offset):
"""
Parse a list marker and return data on the marker (type,
start, delimiter, bullet character, padding) or null.
"""
rest = line[offset:]
spaces_after_marker = None
data = ListData()
if reHrule.search(rest):
return None
... | b295d46c5c4e3ad4b6ac845f883b949c3e18b75b | 39,136 |
def resize(img, height=800, allways=False):
"""Resize image to given height."""
if (img.shape[0] > height or allways):
rat = height / img.shape[0]
return cv.resize(img, (int(rat * img.shape[1]), height))
return img | 011e42c0ef4fbc1c7e77914673a1d3d61771ff92 | 39,137 |
import re
def _size_to_bytes(size):
"""
Parse a string with a size into a number of bytes. I.e. parses "10m", "10MB", "10 M" and other variations into the
number of bytes in ten megabytes. Floating-point numbers are rounded to the nearest byte.
:type size: ``str``
:param size: The size to parse, given as a stri... | 833657e51bb2c54b0e86684759e263d2f8b03ffe | 39,138 |
import torch
def transform_to_tensor_per_dataset(feature, label, drug,device, basal_expression_file):
"""
:param feature: features like pertid, dosage, cell id, etc. will be used to transfer to tensor over here
:param label:
:param drug: ??? a drug dictionary mapping drug name into smile strings
... | f238ffa2a042942d5df2cf858435a5b1e5080872 | 39,140 |
def isbn_gendigit (numStr):
"""
(string)-->(string + 1-digit)
Generates the 10th digit in a given 9-digit isbn string. Multiplies the values of individual digits within the given 9-digit string to determine the 10th digit.
Prints the original string with its additional 10th digit.
('123456788')-->12... | 2ceb5cb4f2f8efc2ee9625138666f7cca2c4aefb | 39,141 |
def count_unique_sequences_per_otu(otu_ids, otu_map_file, input_seqs_file):
"""Counts unique sequences per-OTU for a given set of OTUs
otu_ids: a set of OTU IDs
otu_map_file: file-like object in the format of an OTU map
input_seqs_file: FASTA containing sequences that were used to generate
... | 8a50160ac14a6a6960c63a68f8b23e5fe3df043a | 39,142 |
from typing import Tuple
def fetch_add_portmapping_services() -> Tuple[UPnPServiceNames, ...]:
"""
:return: returns the available devices and services for which the action 'AddPortMapping' exists
"""
devices = upnpclient.discover()
if not devices:
raise NoPortMapServiceFound("No UPnP devic... | 006fc914558be3afa9f8c05451c8816d6e7c0cb4 | 39,143 |
def is_valid_group(group_name, nova_creds):
"""
Checks to see if the configuration file contains a SUPERNOVA_GROUP
configuration option.
"""
valid_groups = [value['SUPERNOVA_GROUP'] for key, value in
nova_creds.items() if 'SUPERNOVA_GROUP'
in nova_creds[key].k... | 6f94e88cfcea8775bab3c05a0720ba7df11f68cc | 39,144 |
import math
def Make_scroll_bar(self, scroll_bar):
"""
Make a scroll_bar for a Text object.
For internal use only. This function is therefore also not imported by __init__.py
"""
if isinstance(scroll_bar, Slider):
scroll_bar.right = self.width - self.text_offset[0]
scroll_bar.cente... | 1cf7e94dfddb32a9ac55e72a58b41b6642a96411 | 39,146 |
def value_iteration(env, rewards):
""" Computes a policy using value iteration given a list of rewards (one reward per state) """
n_states = env.observation_space.n
n_actions = env.action_space.n
V_states = np.zeros(n_states)
theta = 1e-8
gamma = .9
maxiter = 1000
policy = np.zeros(n_sta... | 29dcd90de0881986f2b08621dd0fac32c5eee305 | 39,147 |
def mktime(t):
"""mktime(tuple) -> floating point number
Convert a time tuple in local time to seconds since the Epoch."""
tm_year = t[0]
tm_mon = t[1] - 1
tm_mday = t[2]
tm_hour = t[3]
tm_min = t[4]
tm_sec = t[5]
date = JS("new Date(@{{tm_year}}, @{{tm_mon}}, @{{tm_mday}}, @{{tm_hou... | e35b7a4f1367f9951201540c17aa4032113e5113 | 39,148 |
import pandas
def package_matrix(request, pkg=None, arch=None):
"""
Generate a build matrix for one or more specs.
"""
# Unique package names and os options
packages = (
Spec.objects.exclude(build=None).values_list("name", flat=True).distinct()
)
failed_packages = (
Spec.ob... | 2bcfb437062b92bc4c02140b07a9cdf5803f0995 | 39,149 |
def _PlcReady(timeout=1):
"""Returns True if PLC AIO node reports actuator state is 'ready'."""
t_start = time.time()
while time.time() - t_start < timeout:
ready = (listener.status_msg and (listener.status_msg.detwist_state ==
actuator_types.kActuatorStateReady))
if... | 1da6241efdfe8b1bcae5e8894f35a6708a11f963 | 39,151 |
from typing import List
def convert_dataframe(metrics: List[Metric]) -> pd.DataFrame:
"""
convert_dataframe converts list of metric to pandas DataFrame
"""
fields = ['date', 'point']
columns = {'date': 'ds', 'point': 'y'}
return pd.DataFrame([{f: getattr(m, f) for f in fields} for m in metrics... | 2bf41a0089928c6f1842dd56ff86e6ca47a7e079 | 39,152 |
def linear_shap_corr(model, data):
""" Linear SHAP (corr 1000)
"""
return LinearExplainer(model, data, nsamples=1000).shap_values | 9239b8a1083195321a766cd9894db307276ab33d | 39,153 |
import torch
def ind2sub(ind, shape, out=None):
"""Convert linear indices into sub indices (i, j, k).
The rightmost dimension is the most rapidly changing one
-> if shape == [D, H, W], the strides are therefore [H*W, W, 1]
Parameters
----------
ind : tensor_like
Linear indices
sh... | 001a88850721f8946c48a395454e6818e78a7887 | 39,154 |
import requests
def get_repo_prs(r, state, base, session):
"""
Get all pull requests of a given repository
:param r: repository name 'author/repo-name'
:param state: state of the PR
:param base: base branch
:param session: open and authenticated session
:type r: string
... | 898f1937978114df673e0291a2a18f7e5f3f2d12 | 39,155 |
def register():
"""Register user"""
# Forget any user_id
session.clear()
# User reached route via POST (as by submitting a form via POST)
if request.method == "POST":
# Ensure username was submitted
if not request.form.get("firstname"):
return apology("must provide FIR... | 7799a1a7284fb311bd58fbb1f6882bf84d49b2e7 | 39,156 |
def format_sse(data: str, event=None) -> str:
"""Formats a string and an event name in order to follow the event stream convention.
>>> format_sse(data=json.dumps({'abc': 123}), event='Jackson 5')
'event: Jackson 5\\ndata: {"abc": 123}\\n\\n'
"""
msg = f'data: {data}\n\n'
if event is not None:... | a97cbd392b3ed087c5663fc1e874c30c20d61039 | 39,157 |
from typing import Tuple
async def _unpack_message(session: Session, enc_message: bytes) -> Tuple[str, str, str]:
"""Decode a message using the DIDComm v1 'unpack' algorithm."""
try:
wrapper = JweEnvelope.from_json(enc_message)
except ValidationError:
raise WalletError("Invalid packed mess... | 84c96ac11a19f59a2e205c3f3a6ec8597c22e519 | 39,158 |
def minweight_match_realmxeigs(a, b, metricfn=None,
pass_indices_to_metricfn=False, eps=1e-9):
"""
Matches the elements of `a` and `b`, whose elements are assumed to either real or one-half of a conjugate pair.
Matching is performed by minimizing the weight between elements,
... | e98d7f4f5f41e9672a2863fc5d7bc2c2c8bf0c10 | 39,159 |
def _invert_aliases():
"""Reverse the alias dictionary to be a lookup from command to aliases."""
out = _defaultdict(list)
for alias, name in ALIASES.items():
out[name].append(alias)
return out | d4b88ee5434fe989cd71ae730573cd4b6bab1562 | 39,160 |
import math
def get_positive_expectation(p_samples, average=True):
"""Computes the positive part of a JS Divergence.
Args:
p_samples: Positive samples.
average: Average the result over samples.
Returns:
th.Tensor
"""
log_2 = math.log(2.)
Ep = log_2 - tlx.softplus(- p_sa... | db8d991ebe2bf46c58d150cb7d0fdd5122e0ccaa | 39,161 |
def setbit(byte, offset, value):
"""
Set a bit in a byte to 1 if value is truthy, 0 if not.
"""
if value:
return byte | (1 << offset)
else:
return byte & ~(1 << offset) | 037fa6e6a14c502554a3e5ee94e2f9ed63e2f717 | 39,162 |
def insertDoubleQuote(string, index):
""" Insert a double quote in the specified string at the specified index and return the string."""
return string[:index] + '\"' + string[index:] | 00d16f3bc619765895408f9fcdd3a7a6e428b153 | 39,163 |
import io
import base64
def title_cloud(cat, rate, year):
"""
Makes a word cloud of movie and TV show titles.
Parameters
----------
cat: list
List of genres we want to filter out from the dataframe.
rate: list
List of ratings we want to filter out from the dataframe.
y... | af42db09486a60fab6e7da5df0dc53b5f24deefc | 39,164 |
import torch
def run_one_epoch_aae(model, x, y, num_critic=1, clip_value=0.01, train=True, optimizer=None,
batch_size=None, return_loss=True, loss_weight=[1., 1., 1.],
loss_fn_cls=nn.CrossEntropyLoss(), loss_fn_reg=nn.MSELoss(),
loss_fn_critic=nn.L1Lo... | 125d6d34eb50b5f20b6d726a90cc85040f9c571c | 39,165 |
def And(*seq):
""" Return boolean expression ``x1 and x2 and ...``.
"""
c = Logic.convert
return Logic.And(*map(c, seq)) | f6229ca92a52263d26476741c2f3ce9e495678c1 | 39,166 |
def powerlaw(x, a, b, c):
"""Powerlaw function used by fitting software to characterise uncertainty."""
return a * x**b + c | e67a0be2f5faaff7867b713b43caec48910bad87 | 39,167 |
def quatFromXYZW(xyzw, seq):
"""Convert quaternion from XYZW (pybullet convention) to arbitrary sequence."""
assert (
len(seq) == 4 and "x" in seq and "y" in seq and "z" in seq and "w" in seq
), "Quaternion sequence {} is not valid, please double check.".format(seq)
inds = ["xyzw".index(axis) fo... | 87d8a907fd5fc95c320df03feeffe8c185ad78e9 | 39,168 |
def readPeakList(peak_file):
"""
Read in list of peaks to delete from peaks_file. Comment lines (#) and
blank lines are ignored.
"""
f = open(peak_file,'r')
peak_list = f.readlines()
f.close()
peak_list = [l for l in peak_list if l[0] != "#" and l.strip() != ""]
peak_list = [... | 7c99f9fb18b36b658fe142a43adf18db7c42c7bd | 39,169 |
import ast
def parse_coordinates(raw_coordinates):
"""Parses cell coordinates from text."""
return {ast.literal_eval(x) for x in ast.literal_eval(raw_coordinates)} | b550709c29ba56b1dec73a94bdccda1af743c2c9 | 39,170 |
import itk
def array_view_from_vector_container(
container: "itkt.VectorContainer", ttype=None
) -> np.ndarray:
"""Get an Array view with the content of the vector container"""
container_template = itk.template(container)
IndexType = container_template[1][0]
# Find container type
if ttype is... | 0f2824993eb3f5504d6985e593a4b05ee49e9271 | 39,171 |
def convert_linear_problem_to_dual(model, sloppy=False, infinity=None, maintain_standard_form=True, prefix="dual_", dual_model=None): # NOQA
"""
A mathematical optimization problem can be viewed as a primal and a dual problem. If the primal problem is
a minimization problem the dual is a maximization probl... | 196ff6f48cdadce1f248586485adc772f889d0a7 | 39,172 |
def snake_case(text):
"""Converts `text` to snake case.
Args:
text (str): String to convert.
Returns:
str: String converted to snake case.
Example:
>>> snake_case('This is Snake Case!')
'this_is_snake_case'
.. versionadded:: 1.1.0
.. versionchanged:: 4.0.0
... | 0836bc291e01daa74287af7e3e2e52b80628344a | 39,173 |
def chunkify(tsd, seq_len):
"""
Splits a TimeSeriesDataset into chunks of length seq_len
Args:
tsd: TimeSeriesDataset object
seq_len: length of the subsequences to return
Returns: numpy arrays with chunks of size seq_len
"""
x, y = [], []
for s in tsd:
for i in ra... | a3ad94bed06cfaad05663d4898fdeb9e3e6200e3 | 39,174 |
from typing import Dict
import secrets
def create_survey(
title: str,
hide_votes: bool,
is_anonymous: bool,
description: str,
question_author_name_field_visible: bool,
limit_question_characters_enabled: bool,
limit_question_characters: int,
author: User)... | 9ae8271c4b2f228264555bc2daa887222b270a47 | 39,175 |
def remove_batch_dimension(shape: tuple) -> tuple:
"""Set the batch dimension to None as it does not matter for the
Network interface."""
shape = list(shape)
shape[0] = None
shape = tuple(shape)
return shape | 92b7cd7a7165338cc397e530b0b81513910b46b0 | 39,176 |
import string
def load(path):
"""Load an image from given path. Automatically de-compresses."""
file, reason = filesystem.open(path, 'rb')
if file:
## print(f'Length: {file.proxy.size(path)[0]}')
readSignature = string.char(file.readString(len(OCIFSignature)))
if readSignature == OC... | 32843e05059a468df8c6e5e2ef282e159da6caeb | 39,177 |
def compute_windows(numpy_image, patch_size, patch_overlap):
"""Create a sliding window object from a raster tile.
Args:
numpy_image (array): Raster object as numpy array to cut into crops
Returns:
windows (list): a sliding windows object
"""
if patch_overlap > 1:
raise Valu... | d85c91ad7684c03eea25763b9fbbcf87ddf78df4 | 39,178 |
from datetime import datetime
def date_to_format(value, target_format):
"""Convert date to specified format"""
if target_format == str:
if isinstance(value, datetime.date):
ret = value.strftime("%d/%m/%y")
elif isinstance(value, datetime.datetime):
ret = value.strftime(... | dba55f49ea4c2c016803d80b6c80809629062df5 | 39,179 |
def fetch_file(file_id: str, chunk: int = None, verify: bool = False) -> FileStatus:
"""Fetches file information
Args:
file_id: The id of the file to fetch
chunk: (Optional) If included, fetches a single chunk instead of the entire file.
verify: (Optional) If included, fetches file vali... | 67a4a67cd70c97c0f50fd13924e293650b4a198a | 39,181 |
import string
def __get_settings(file_name):
"""
Opens the settings file associated to a csv file and returns it.
:param file_name: The path to settings file.
:type file_name: str
:return: The settings.
:rtype: dict[str, str]
"""
settings = {}
if not file_name.endswith('.setting')... | 4da37f3f2581196446515e06a3d4a6b93d0e15f2 | 39,182 |
import torch
def set_device(args, distributed=False, rank=0):
""" set parameter to gpu or cpu """
if torch.cuda.is_available():
if isinstance(args, list):
return (set_gpu(item, distributed, rank) for item in args)
elif isinstance(args, dict):
return {key:set_gpu(args[ke... | 99f5c1a1214d5c52003aa87dcb44845e2452594d | 39,183 |
def oned_const_vel_bump(g=3.5,total=10000):
"""
make figure for traveling bump
"""
dat = oned_simple.SimDat(g=g,q=0,zshift=.1,T=total)
# get four bumps at four equal time intervals. use second half of sim,
# use velocity to determine time intervals
# Peaks of phase plot over time are at ... | 880eab55b73b71874da52d453c991150c3959d81 | 39,184 |
def article_published_today(link):
""" Compares the date the article was written with today's date."""
soup = lvm.soup_session(link)
todays_date = (date.today() - timedelta(0)).strftime("%B %#d, %Y") # The # is platform specific
date_to_check = soup.find('time', attrs={'class': 'date'})
return date_to_check ==... | bbc9b235e5cbe156e83dc103eafb1052ce99ccbb | 39,186 |
def nearest_neighbor(left_gdf, right_gdf, k_neighbors=1, return_left_columns=True, return_right_columns=True):
"""
For each point in left_gdf, find closest point in right GeoDataFrame and return them.
Adapted from https://stackoverflow.com/questions/62198199/k-nearest-points-from-two-dataframes-with-geopand... | 52f50bf54c9b12ef1ab11eaf3c83325b27f585a8 | 39,188 |
import string
import json
import torch
def load_sequences_from_file(path, voc_path):
"""
Used for reading sentences or sequences of slots.
Splits input lines into lists of words and performs word to id transformation.
:param path: path to txt file with data
:param voc_path: path to json file with ... | f23a55f550b5ae9cd852ee1e16532c0595f21380 | 39,189 |
def lambda_handler(event, context):
""" main function """
try:
for region in get_regions():
stop_instances(get_untagged_instances(region), region)
return {
"statusCode": 200,
"body": "successfully shutted down all untagged instances",
}
except:
... | db7e683f6e98d546b3c77749e59b1baff52e4142 | 39,190 |
from sphinx.util.inventory import InventoryFile as IFile
def sphinx_load_test():
"""Return function to perform 'live' Sphinx inventory load test."""
def func(path):
"""Perform the 'live' inventory load test."""
with path.open("rb") as f:
try:
IFile.load(f, "", osp.... | 6ab148dd821119cf8d299cc6bbecfe82a6c2966f | 39,191 |
from operator import pos
def position() -> s.SearchStrategy[pos.Position]:
"""Generates a random Position.
Returns:
A new search strategy.
"""
return s.builds(pos.Position) | bd85a07677a5fa34842561549a318a4a138f742f | 39,192 |
import base64
def get_gpg_keys(conn, dns):
"""
Grab pgpKeyInfo records (base64 encoded gpg keys) for the key attribute
entries in LDAP, returning a dict {key_attr: pgpKeyInfo}
"""
r = {}
for dn in dns:
data = get_dn_attribute(conn, dn, '(objectClass=pgpKeyInfo)',
'pgpKey')
... | 5fbc0dc8e7e7c45d500f5834ca6f1431532271ff | 39,193 |
import numpy
def get_pandas_field_metadata(pandas_col_metadata, field_name):
"""
Fetch information for a given column. The column statistics returned
will be a bit different depending on if the types in the column are a
number or a string. 'NAN' values are stripped from statistics and don't
even s... | cbf1a740a202c36fa7b008451d44582e195d71f8 | 39,194 |
def predict_single_image(model, image):
"""
Predict the bounding box quads for a single image
:param model:
:param image:
:return:
"""
im_resized, (ratio_h, ratio_w) = east_utils.resize_image(image)
im_resized = cv2.cvtColor( im_resized, cv2.COLOR_BGR2RGB)
image_batch = np.expand_di... | f22e7788d4be372b710ac5ead9188e5bb22b97d0 | 39,195 |
def get_reduced_string(
text: list[int], suffix_arr: list[int], lms_suffixes: list[int]
) -> tuple[list[int], bool]:
"""
>>> suffix_arr = [10, 0, 1, 2, 3, 4, 6, 8, 5, 7, 9]
>>> lms_suffixes = [6, 8, 10]
>>> get_reduced_string("AAAAACACAG", suffix_arr, lms_suffixes)
([1, 2, 0], False)
"""
... | b6b5078ec160799912eb99a02009d65751a1347f | 39,196 |
def gridappsdvolttron(config_path, **kwargs):
"""Parses the Agent configuration and returns an instance of
the agent created using that configuration.
:param config_path: Path to a configuration file.
:type config_path: str
:returns: Gridappsdvolttron
:rtype: GridAPPSDVolttron
"""
try:... | 1656b8d877a64a8ec746196fca162165a5fb67a0 | 39,198 |
def is_valid_if_yes_definition(definition):
"""
Returns true if the if_yes definition is valid.
str -> bool
"""
if definition.count(constants.QUESTION_DENOTE) < 2 or definition[0] != constants.QUESTION_DENOTE:
return False
# First char is white space so it needs to be removed.
if_tr... | a68e8e4bce14802cf8693bd2803669743298be4f | 39,199 |
def get_route_count(device, table, protocol, active=True, output=None):
"""
Get total route count for each table via 'show route target_route extensive'
Args:
device (`obj`): Device object
table (`str`): Table name such as `inet.0`, `inet6.0`
protocol (`str`): Protocol name such as ... | b152088e5d6d0c9e4e2b68e9b141181b9577b9ad | 39,200 |
def deep_replace(arr, x, y):
"""
Help function for extended_euclid
"""
for i in range(len(arr)):
element = arr[i]
if type(element) == list:
arr[i] = deep_replace(element, x, y)
else:
if element == x:
arr[i] = y
return arr | 55ef1c7efe04d436f9ce96bda0f565a092131400 | 39,202 |
def get_imgaug_transform(cfg: DictConfig) -> iaa.Sequential:
"""Create simple data transform pipeline that resizes images."""
data_transform = iaa.Resize(
{
"height": cfg.data.image_resize_dims.height,
"width": cfg.data.image_resize_dims.width,
}
)
return iaa.Sequ... | 6a18dc04fca1d18e40a3f28ae22d6761a96dd53f | 39,203 |
def inerIntra(groupes, centres):
"""Calcul de l'inertie intra-groupe d'une liste de groupes"""
inerGroupeList = []
i = 0
for centre in centres:
inerGroupeList.append(inerGroupe(groupes[i], centre))
i = i + 1
inersum = 0
for iner in inerGroupeList:
inersum += iner
retu... | 0d2f39eefdcfd3f02191111e318820e1c767234f | 39,204 |
def k_prototypes(X, n_clusters, gamma, init, n_init, max_iter, verbose):
"""k-prototypes algorithm"""
assert len(X) == 2, "X should be a list of Xnum and Xcat arrays"
# List where [0] = numerical part of centroid and
# [1] = categorical part. Same for centroids.
Xnum, Xcat = X
# Convert to nump... | 5ee8cb28ef84862fe980b1e6b70437b6893a675a | 39,205 |
def update_password(password, value, position=None):
"""Update password with value
:param str password: The password string
:param str value: The character(s) to update with
:param int position: The position in which to insert the character(s)
:return: The updated password
"""
result = list... | 19287d5fd65141de6edbd806b18419e54492e201 | 39,206 |
import yaml
def run_distribute(filename, distribution, graph=None, algo=None):
"""
Run the distribute cli command with the given parameters
"""
filename = instance_path(filename)
algo_opt = '' if algo is None else '-a ' + algo
graph_opt = '' if graph is None else '-g ' + graph
cmd = 'pydco... | 395d956f922ffedf4bfbe7cbcfe6f200017543f6 | 39,207 |
def find_pmp(df):
"""simple function to find Pmp on an IV curve"""
return df.product(axis=1).max() | 5ed7c14bc58a62f6168308ccd1dfa17e56e2db89 | 39,208 |
def chebyshev_distance(v1, v2, norm=False):
"""
return ||v1 - v2||_oo
"""
v1, v2 = check_pairwise_vector(v1, v2, norm)
diff = v1 - v2
K = np.abs(diff).max()
return K | b65fbdfe3dee68f360ef66e0fd3c4ee5085be062 | 39,209 |
def get_distances_from_other_points(point_index: int, num_samples: int, distance_matrix: ndarray):
"""Get distances from point i to other points in the dataset.
Args:
point_index: The index of the point.
num_samples: The number of points.
distance_matrix: Condensed distance matrix.
... | e9610355ddc37fd58261bfb9233cc5a20ca4c2ac | 39,210 |
def get_help_option(input_args):
"""Get help about option or values authorized for option."""
global cmdhelp_settings, cmdhelp_option_infolist
global cmdhelp_option_infolist_fields
pos = input_args.find(' ')
if pos > 0:
option = input_args[0:pos]
else:
option = input_args
opt... | 5436480c38a32de189ab1c2418519d5216502a46 | 39,212 |
import math
from datetime import datetime
def aggregate_report():
"""Generate a report with total of resources in used and reserved for
each aggregate."""
context = {}
aggregates = Aggregates.objects.all()
for aggregate in aggregates:
# Add aggregate on context dict
if aggregate... | 134d59d1c58cb7160b88bbf596fe9f51b39e0cdb | 39,213 |
def create_app(object_name):
"""
:argument object_name: the python path of the config object
:return: app
"""
app = Flask(__name__)
app.config.from_object(object_name)
db.init_app(app)
app.register_blueprint(main_blueprint)
return app | 3596a4c887326af72692189654e936bc4816308e | 39,214 |
def fix_timestamp(dataframe):
"""
Convert timestamp from UTC into engine timezone and from ISO to format expected by graphs
:param1 dataframe: data frame to process
return: dataframe with converted timestamp column
"""
dataframe["timestamp"] = dataframe["timestamp"].apply(convert_from_utc, args=(__eng... | 8ed075fb6afb5c5269ebf5def7c75a3632ccc7a4 | 39,215 |
def scale(data, remove_outliers=False):
"""
Scales the input and removes outliers
"""
if remove_outliers:
perc_2 = np.zeros(data.shape[1])
perc_98 = np.zeros(data.shape[1])
for i in range(data.shape[1]):
perc_2[i] = np.percentile(data[:, i], 2)
perc_98[i] ... | 1eec5537e3762f36e46eef37b36297c7b83359a8 | 39,217 |
def subpixel_values(img, pts):
"""
References:
stackoverflow.com/uestions/12729228/simple-efficient-binlinear-interpolation-of-images-in-numpy-and-python
SeeAlso:
cv2.getRectSubPix(image, patchSize, center[, patch[, patchType]])
"""
# Image info
nChannels = get_num_channels(img)... | 7778c91a320a61caecef42dec98d83861d4798a4 | 39,218 |
def loadZenMakeMetaFile(filePath):
"""
Load ZenMake common ConfigSet file. Return None if failed
"""
dbfile = db.PyDBFile(filePath, extension = '')
try:
data = dbfile.load(asConfigSet = True)
except EnvironmentError:
return None
return data | 5ab84701e2f18ffb397f4d22528a87c627e84041 | 39,219 |
def readable_timedelta(days):
"""Print the number of weeks and days in a number of days."""
#to get the number of weeks we use integer division
weeks = days // 7
#to get the number of days that remain we use %, the modulus operator
remainder = days % 7
return "{} week(s) and {} day(s).".format(w... | 120f517939842b4e0686a57a3117221e3db63004 | 39,220 |
def create_net(in_sx, in_sy, out_sx):
"""
Creates a tflearn neural network with the correct
architecture for learning to hear the keyword
"""
net = tflearn.input_data([None, in_sx, in_sy])
net = tflearn.lstm(net, lstm_size, dropout=lstm_dropout)
net = tflearn.fully_connected(net, out_sx, activation='softmax')
n... | 686df9637496c1595fc0bed55e03a9eec550e0b9 | 39,221 |
def compute_iou(boxes1, boxes2):
"""
compute_iou() 函数用来计算 IOU 值,即真实检测框与预测检测框(
当然也可以是任意两个检测框)的交集面积比上
它们的并集面积,这个值越大,代表这个预测框与真实框的位置越接近
如果说得到的 IOU 值大于设置的正阈值,那么我们称这个预测框为正预测框(
positive anchor)其中包含着检测目标;
如果说得到的 IOU 值小于于设置的负阈值,那么我们称这个预测框为负预测框(
)negative anchor),其中包含着背景
"""
le... | ba6ae63a8d15233fd18a100c6e50c2176559ecaa | 39,222 |
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