content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import time
from datetime import datetime
import re
def _archive_url_parser(
header, url, latest_version=__version__, instance=None, response=None
):
"""Returns the archive after parsing it from the response header.
Parameters
----------
header : str
The response header of WayBack Machine... | 83980923036b27e15d11a3eccaf790d93acf8fcd | 3,617,394 |
import mmap
def fasta_records(filepath):
"""Count the number of records in a FASTA file."""
count = 0
next_pos = 0
angle_bracket = bytes(">", "utf-8")
memory_map = TrimmableMemoryMap(filepath, access=mmap.ACCESS_READ)
with memory_map.map() as mem_map:
size = memory_map.size
nex... | 1262b730f00dff1dbb866b4044b9d22b535de090 | 3,617,395 |
def cifar_iid(dataset, num_users):
"""
Sample I.I.D. client data from CIFAR10 dataset
:param dataset:
:param num_users:
:return: dict of image index
"""
num_items = int(len(dataset) / num_users)
dict_users, all_idxs = {}, [i for i in range(len(dataset))]
for i in range(num_users):
dict_users[i] = ... | 3a79a354b57783ae06297ef870aa4ce4e001394a | 3,617,396 |
def get_area_filter(point_cloud, extents):
"""
Args:
point_cloud: (3, N) point cloud
extents: 3D area in the form [[min_x, max_x], [min_y, max_y], [min_z, max_z]]
Returns:
"""
if not isinstance(point_cloud, np.ndarray) and isinstance(extents, np.ndarray):
raise TypeError('... | 149c520f51f2df0a7d8987247bc4a53126813b26 | 3,617,397 |
def meat2segment(meat):
"""Convert into a list of points."""
asos = load_geodf("sfstns")
tokens = meat.split()
sz = len(tokens)
i = 0
pts = []
gc = "geometry"
while i < sz:
token = tokens[i]
if token.isdigit() and (i + 2) < sz:
miles = float(token)
... | 9908d32f89b009099a17eb99c637885d85eda45b | 3,617,398 |
def ensure_shape(x, y):
"""Util to broadcast on var to another but only when shape is different.
This way we don't convert scalar into array type unnecessarily.
"""
shape_y = np.shape(y)
if np.shape(x) == shape_y:
return x
return np.broadcast_to(x, shape_y) | d389c9cfd553b732ee554267a40cd51771ca0160 | 3,617,399 |
def get_property(name,*args):
"""Convenience function to quickly retrieve any stellar property/properties for a given KepID/KOI numbers
Parameters
----------
name : int, float or str, or array_like
KOI or KIC name (or array of names)
*args : string,
properties to return (must... | 599ea8a604b6c42320dc6212766993585d4b89a9 | 3,617,400 |
def property_immutable_cache(f):
""" This cache should only be used on properties that return an immutable object """
@wraps(f)
def inner(self):
if f.__name__ not in self.cache:
self.cache[f.__name__] = f(self)
return self.cache[f.__name__]
return property(inner) | 89938fe0599dc19d7d93cee0d524d52fe6a7dd24 | 3,617,401 |
def normalize(vec):
"""normalizes an Nd list of vectors or a single vector
to unit length.
The vector is **not** changed in place.
For zero-length vectors, the result will be np.nan.
:param numpy.array vec: an Nd array with the final dimension
being vectors
::
numpy.array... | 93503a9314b1e2982f7afb75009a2d8736fdc621 | 3,617,402 |
def lam(m, f, w):
"""Compute lambda"""
s = 0
for i in range(len(f)):
s += f[i] * w[i]
return float(m)/float(s) | 6a71eb0020a5c2d86d88504f9867150c0fa7e248 | 3,617,403 |
def _get_binary_xentropy(target_values, forecast_probabilities):
"""Computes binary cross-entropy.
This function satisfies the requirements for `cost_function` in the input to
`run_permutation_test`.
E = number of examples
:param: target_values: length-E numpy array of target values (integer clas... | fc40aeeb0151f66f596ddf2c042a254cde1f8522 | 3,617,405 |
def _check_for_collision(sprite1: Sprite, sprite2: Sprite) -> bool:
"""
Check for collision between two sprites.
:param Sprite sprite1: Sprite 1
:param Sprite sprite2: Sprite 2
:returns: Boolean
"""
collision_radius_sum = sprite1.collision_radius + sprite2.collision_radius
diff_x = sp... | 53fd13d622a2b37040c334430c88b7263e9c4bed | 3,617,406 |
def get_value(lst, row_name, idx):
"""
:param lst: data list, each entry is another list with whitespace separated data
:param row_name: name of the row to find data
:param idx: numeric index of desired value
:return: value
"""
val = None
for l in lst:
if not l:
conti... | aa5ba25c4a431fe470921d344887fb02fee68712 | 3,617,407 |
import numpy
def in_domain(X, a_array, c_array):
"""
Check is a given point is inside or outside the design domain
"""
flag = 1
for n in range(numpy.shape(a_array)[0]):
a = a_array[n]
c = c_array[n]
dist = numpy.dot(X-c,a)
if(dist > 0.7):
flag = 0
if(abs(dist)<0.7):
flag = 2
return flag | e39e779b456ea8df4f217d1c2ed5eaddf498881a | 3,617,408 |
def generate_script(job):
"""
Generates a script from a job.
"""
work_dir = job.path
json_data = hjson.loads(job.json_text)
# The base url to the site.
url_base = f'{settings.PROTOCOL}://{settings.SITE_DOMAIN}{settings.HTTP_PORT}'
# Extra context added to the script.
runtime = dict... | 5fb8074df30f7b394d649f7b7c7bcceff360a64e | 3,617,409 |
from datetime import datetime
def metadata(ts, capture_block_id, report_path, run, st=None):
"""Create a dictionary with required metadata.
Parameters
----------
ts : : class:`katsdptelstate.TelescopeState`
telescope state
capture_block_id : int
capture_block_id
report_path : ... | 35460bab4f945f6ad53dfb59160653a8b35608df | 3,617,410 |
def raw_compare_ge(stage: ImportStage, left: ir.Value,
right: ir.Value) -> ir.Value:
"""Emits an ApplyCompareOp for 'ge'."""
return d.ApplyCompareOp(d.BoolType.get(), ir.StringAttr.get("ge"), left,
right).result | 8eded11bff34003a6df0d482b3478c26e6b3fc00 | 3,617,411 |
def SearchLunarApsis(startTime):
"""Finds the time of the first lunar apogee or perigee after the given time.
Given a date and time to start the search in `startTime`, this function finds
the next date and time that the center of the Moon reaches the closest or
farthest point in its orbit with respect ... | 2f62c2b15c4ed808aaef14f8fbe7ad4359d63c31 | 3,617,412 |
def _get_dataset_from_filename(
instruction: _Instruction,
do_skip: bool,
do_take: bool,
file_format: file_adapters.FileFormat,
add_tfds_id: bool,
) -> tf.data.Dataset:
"""Returns a tf.data.Dataset instance from given instructions."""
ds = file_adapters.ADAPTER_FOR_FORMAT[file_format].make_tf_da... | 0a7a10156cb69583043fb6cb1db8ed472960ecdd | 3,617,413 |
def clamp(x, inf=0, sup=1):
"""Clamps x in the range [inf, sup]."""
return inf if x < inf else sup if x > sup else x | 42c178afc0bdfc02fd31fe3f211f23cc04b40d2e | 3,617,414 |
def open_current_file():
"""
Opens the current maya scene file.
:return: <bool> True for success.
"""
cmds.file(cmds.file(q=1, loc=1), o=1, f=1)
return True | 57e777c9a11b959b19fd25195bfe8ff1c511cf22 | 3,617,415 |
def flatten(x, name=None, reuse=None):
"""Flatten Tensor to 2-dimensions.
Parameters
----------
x : tf.Tensor
Input tensor to flatten.
name : None, optional
Variable scope for flatten operations
Returns
-------
flattened : tf.Tensor
Flattened tensor.
"""
... | f279f78891ab4741ee2b305529f14c7bcfbf61a7 | 3,617,416 |
def attribute_startswith(search_string, limit=20):
"""Query attributes starting search_string
:param search_string: e.g. al to match allow_from, allow_to etc.
:param limit: limit result to n results
:return:
"""
query = Attribute.objects.filter(
attribute_id__startswith=search_string)... | 4fadd9c02f8596324735e342cc3efea20acc9c2c | 3,617,417 |
def jaccard_stability(explainer, x, neighborhood, k=1):
"""Jaccard adaptation Stability function.
Takes as argument an explanation method, a single observation
x of shape (n_features, ), the neighborhood as a matrix of
shape (n_neighbors, n_features), and the size of the subset being
considered k
... | b9db7a391ce7163aee3c4f568adb8db3c30c12b5 | 3,617,418 |
def _get_item_kind(item):
"""Return (kind, isunittest) for the given item."""
try:
itemtype = item.kind
except AttributeError:
itemtype = item.__class__.__name__
if itemtype == 'DoctestItem':
return 'doctest', False
elif itemtype == 'Function':
return 'function', Fal... | c597db3de4447c68f3d8187e2f988e6c81e19d00 | 3,617,419 |
def search_by_classifier():
"""Use PyPI XML-RPC API to get all Lektor-tagged distributions.
https://warehouse.pypa.io/api-reference/xml-rpc.html
"""
client = ServerProxy("https://pypi.org/pypi")
return set(name for name, version in client.browse(["Framework :: Lektor"])) | 9e74644b0edb52f1cd2b423349866a2f4a614001 | 3,617,420 |
from bs4 import BeautifulSoup
import requests
import re
def _get_page(url: str, headers: dict = {}, cookies: dict = {}) -> BeautifulSoup:
"""
Return page as BeautifulSoup object
Parameters
----------
url : str
a useable url
headers : dict, optional
headers, by default {}
c... | fea97883c5aa19af6a3f037f32a73269d6e8cb66 | 3,617,421 |
def champ_win_rate(matches_df, champ, lane='all'):
"""Calculate the win rate of a single champion.
By default, looks in every lane. Lane can also
be specified (eg. TOP_SOLO)
"""
teams_lanes_roles = dc.get_teams_lanes_roles()
if lane != 'all':
teams_lanes_roles = ['100_' + lane, '200... | 64be101dd939df5b25ef5523651b41b121c7045e | 3,617,422 |
def computeDiffSig (train_X, train_Y, mu, classLabel):
"""
Computes the means of the GDA
"""
m = train_Y.shape[0]
classIndicator = np.array(train_Y == classLabel).flatten()
classCount = np.sum(classIndicator == True)
Sigma = np.matrix([[0, 0], [0, 0]])
for (indicator, x) in zip(classInd... | a8d133f848804f705c9791f51e95e0e9a2a06662 | 3,617,424 |
def _inc_path(path):
""":returns: The path of the next sibling of a given node path."""
newpos = MP_Node._str2int(path[-MP_Node.steplen :]) + 1
key = MP_Node._int2str(newpos)
if len(key) > MP_Node.steplen:
raise Exception("Path Overflow from")
return "{0}{1}{2}".format(
path[: -MP_No... | 80f77ba2499bed3b82705fae96ac88a6bb4f1664 | 3,617,425 |
def delete():
"""
delete() : Delete a document from Firestore collection
"""
try:
# Check for ID in URL query
doc_id = request.args.get('id')
db_ref.document(doc_id).delete()
return jsonify({"success": True}), 200
except Exception as e:
return f"An Error O... | 092464c5dd46fb2225bdf94f0d53dafd06dcb98a | 3,617,426 |
from typing import Optional
def mongo_event(event: str, resource: str, func: Optional[EventFuncType]=None
) -> Event:
"""A function to return an :class:`Event` with aliases set-up for mongo
events.
The following aliases can be used for mongo events::
+----------+--------------+
... | 0d0b89ec0611f4b8ccc4eaac692552352c93fab1 | 3,617,427 |
import json
def xml_to_json(survey: database.Survey):
"""Convert survey from Ankieter xml format to json format
:param survey: The Survey that is edited or created
:type survey: Survey
:return: The survey in json format
:rtype: Dict
"""
def write_element(question, res):
res[... | 4a7c7ee03d47b435f04e6fa0c54153ae37b76868 | 3,617,428 |
from typing import Optional
from typing import Union
from typing import Tuple
def payoff_table_method(
problem: MOProblem,
initial_guess: Optional[np.ndarray] = None,
solver_method: Optional[Union[ScalarMethod, str]] = "scipy_de",
) -> Tuple[np.ndarray, np.ndarray]:
"""Uses the payoff table method to ... | c3917b52e04bf631d9033ac2a99ec2a3e89a262d | 3,617,429 |
def absmag(mag, z, band1='megacam_r', band2='megacam_r',
model='cb07_burst_0.1_z_0.02_salp.model', zf=5):
"""
Takes a set of magnitudes all at the same redshift z
and returns the aboslute magnitudes. This does not need to be
done for each object since the conversion from apparent
to absol... | 7cd52aed7d65fc69721ec0b281554c6d639656f7 | 3,617,430 |
def load_yields(location):
"""Loads up the county corn yields"""
pgconn = get_dbconn('coop')
df = read_sql("""
select year, num_value as yield
from nass_quickstats where
county_ansi = %s and state_alpha = 'IA' and year >= 1980
and commodity_desc = 'CORN' and statisticcat_desc... | f1ae5a06216916fa7358821ebc192ff2ea158359 | 3,617,431 |
def mapattr(value, arg):
"""
Maps an attribute from a list into a new list.
e.g. value = [{'a': 1}, {'a': 2}, {'a': 3}]
arg = 'a'
result = [1, 2, 3]
"""
if len(value) > 0:
res = [getattr(o, arg) for o in value]
return res
else:
return [] | 34e45bcf804d37feb5995b88534cca78679d8cfb | 3,617,432 |
def _rxcheck(model_type, interval, iss_id, number_of_wind_samples):
"""Gives an estimate of the fraction of packets received.
Ref: Vantage Serial Protocol doc, V2.1.0, released 25-Jan-05; p42"""
# The formula for the expected # of packets varies with model number.
if model_type == 1:
_expec... | 610fa2c2aa83e6d0c9cf9c93961ba8aa6b496188 | 3,617,433 |
import lxml.etree as ET
def header_to_xml(header):
"""
Converts image header metadata into an XML Tree that can be inserted into
a JP2 file header.
Parameters
----------
header : `MetaDict`
A header dictionary to convert to xml.
Returns
----------
`lxml.etree._Element`
... | 2a9e3391076db3253c99127f7258bec01c01ebeb | 3,617,435 |
def string_in_list(str, substr_list):
"""Returns True if the string appears in the list."""
return any([str.find(x) >= 0 for x in substr_list]) | b6e8ce2f918fec0b9a671f1c557f6f1d005c734e | 3,617,436 |
import asyncio
from datetime import datetime
async def _async_watch(job_id, directory, python_datetime=None, first_time=True, **kwargs):
"""Wait until a list of jobs finishes and get updates."""
watch_one = qwatch.Qwatch(jobs=[job_id], directory=directory, watch=True, users=None, **kwargs)
job_dict = wa... | 12f436301fe5f237e8ff21c70cf2a79bb408456e | 3,617,437 |
def vals_missing_plot_list_cmap(binned_array, listed_cmap):
"""Returns a normalized imshow plot using a 3 value array w vals 2,3,4."""
cmap, norm = listed_cmap
bins, arr = binned_array
arr[arr == 1] = 2
arr[arr == 5] = 4
f, ax = plt.subplots(figsize=(5, 5))
return ax.imshow(arr, cmap=cmap... | ef56fb6e657325645103310bab9f64424acbbdde | 3,617,438 |
def click_by_js(element):
"""Clicks on element by triggering .click() using JavaScript
Arguments:
element -- the Selenium WebDriver Element to click
Returns:
True or False
"""
if verbose:
print 'Clicking on element by js...'
try:
Browser.execute_script('arguments[0].click();', element)
... | bfab7d634029dd96ff8b8103145438e96e12dd4e | 3,617,439 |
def process(tweet, preserve_case=True, preserve_stopwords=False):
"""
Perform tweet preprocessing
:param tweet:
:param preserve_case:
:param preserve_stopwords:
:return:
"""
tweet = basicProcess(tweet) # perform basic preprocessing
if preserve_stopwords is False: # in case we want ... | 7f2e321ef8a4663b983f6425630e25acf18837b4 | 3,617,441 |
def loadRaster(source):
"""
Load a raster dataset from a path to a file on disc
Parameters:
-----------
source : str or gdal.Dataset
* If a string is given, it is assumed as a path to a raster file on disc
* If a gdal.Dataset is given, it is assumed to already be an open raster
... | 4b89d5f4e890f850f18d69cb745023ffa25b23b3 | 3,617,442 |
def process(fname):
"""Process verif output file.
fname: name of file
return: tuple of (exp_name, solver, dx, dt, dome_e, max_e, min_e, mean_e, sd_e)"""
# extract errors
ncf = Scientific.IO.NetCDF.NetCDFFile(fname)
diff = ncf.variables['thke'][-1,:,:] - ncf.variables['thk'][-1,:,:]
centre... | 07cbe5e776c55c92a792b8f3502d59015b15daea | 3,617,444 |
import copy
def build_kfold_config(params_dict, train_path, dev_path):
"""按k-fold拆分好的数据，构造新的json配置，用来启动训练任务
:param params_dict: 原始json配置构造出来的param_dict
:param train_path: k-fold拆分之后的训练集路径，list类型
:param dev_path: k-fold拆分之后的评估集路径，list类型
:return: task_param_list: 生成新的json配置，用来启动run_with_json
"""... | e13a7468a2fa3fd33219abfdf3347579a82de518 | 3,617,445 |
def bft_events_graph(start):
"""Builds graph of events traversing events in breadth-first order
This graph doesnt necessary reflect deployment order, it is used
to show dependencies between resources
"""
dg = nx.DiGraph()
stack = [start]
visited = set()
while stack:
item = stac... | f14af4f6c0e8ac13e2d7d048d05dbddd170d4fc0 | 3,617,446 |
import bisect
def _get_past_names(cur: cx_Oracle.Cursor) -> dict[str, list[str]]:
"""Returns all the names that each InterPro entry ever had.
Names are sorted chronologically.
:param cur: Oracle connection cursor.
:return: A dictionary (key: entry accession, value: list of names)
"""
versions... | d4096eeae756793dbfdfe0f22ac8e61208332402 | 3,617,447 |
def serve_subtitles(request, great_media_id, language):
"""Subtitles are stored along with the core.models.GreatMedia instance
but they need to be served via their own dedicated URL.
"""
video = get_object_or_404(GreatMedia, id=great_media_id)
# See if there's a subtitle field for the appropriate ... | b89907bf722ddb49fde24e48794ce4152ba887de | 3,617,448 |
import numpy
def makeMostCommonPatternHeuristic(weights):
"""Return a function that chooses the most common (currently most-used) pattern."""
def weightedPatternHeuristic(wave, total_wave):
print(total_wave.shape)
# [print(e) for e in wave]
wave_sums = numpy.sum(total_wave, (1, 2))
... | 514bd14e6f04896165d2068a3ddaa77e778e19c5 | 3,617,449 |
def ServicesDecorator(func):
""" Make sure cfn-hup is running """
def wrapper(*args, **kwargs):
kwargs['services'] = {
'sysvinit': InitServices(
{
'cfn-hup': InitService(
ensureRunning='true',
enabled='true',... | 2951b4f87d63ecea3de6728024a4175abedb0566 | 3,617,450 |
import uuid
async def _fetch(
flow_id: uuid.UUID,
yaml_only: bool = False
):
"""
Get Flow information using `flow_id`.
Following details are sent:
- Flow YAML
- Gateway host
- Gateway port
"""
try:
with flow_store._session():
host, port_expose, yaml_spec = ... | d6698d17fe47810fd8944a68c726e56b6b5b21ca | 3,617,451 |
from typing import Optional
def get_sample_graph(model_name: Optional[str] = None) -> tf.Graph:
"""Return a sample model as tf.Graph"""
graph_def = get_sample_graph_def(model_name, fmt='proto')
graph = tf.Graph()
with tf.compat.v1.Session(graph=graph):
tf.graph_util.import_graph_def(graph_def,... | 7e00b28a41f83e40cf2da5aa75ab4ce0bae6f0c7 | 3,617,452 |
def selectMetric(name: str):
"""Return the metric defined by name.
Args:
name (str): a string referenced in DeepHyper, one referenced in keras or an attribute name to import.
Returns:
str or callable: a string suppossing it is referenced in the keras framework or a callable taking (y_true,... | 356f35faa40c1f70f0d2f65cb74ac216d029dd9c | 3,617,453 |
def getTagNames(domain):
"""
Returns a list of tag names used by the domain.
:param domain: a domain object
:type domain: `escript.Domain`
:return: a list of tag names used by the domain
:rtype: ``list`` of ``str``
"""
return [n.strip() for n in domain.showTagNames().split(",") ] | f24ccdec61eca07cef283ed4e9d981236b530c62 | 3,617,454 |
def matching(fingerAi, finger_i):
"""
Matching entre dos huellas
"""
it = 0
ta = 0
tb = 0
for i in range(len(fingerAi)):
if fingerAi["f0"].iloc[i]==finger_i["f0"] and \
fingerAi["f1"].iloc[i]==finger_i["f1"] and \
fingerAi["utime"].iloc[i]==finger_i["... | 2ca3c16c5a9ac126314a39598719c8f7f64fa97a | 3,617,455 |
def glob_texture_files(texture_file_pattern):
"""Collect / glob specified image file path.
:arg texture_file_pattern: Image file path need to glob,
file name must have glob pattern '*' (asterix).
:type texture_file_pattern: str or path.Path
:rtype: list of path.Path
"""
texture_file_glob =... | 1a9287331d5ad2007a63eb3ead1a6d697244aec8 | 3,617,456 |
def make_rotation(theta):
"""
makes a rotation matrix
Note: HWP is a mirror matrix, not a rotation!
:param theta:
:return:
"""
a = cos(theta)
b = -sin(theta)
c = sin(theta)
d = cos(theta)
retMat = np.array([[a,b], [c, d]])
# need to remove issues from machine precision
... | e57a90f8c4363fcfa22f9d881bad52191849d8cd | 3,617,457 |
def verify_routing_route_ip_on_interface(device, interface_dict):
""" Verify routes match the configured IP address in running config
Args:
device (`obj`): Device object
interface_dict (`dict`): Interface dict contain ip route info. Get from libs/routing/verify.py::verify_routing_lo... | 30572c447ac0a30dea1cd21d60f00194f1a67bba | 3,617,458 |
def calculate_denominator(unvisited_nodes, pheromone_matrix, distance_matrix, current_node, node, load, max_load, demand, alpha, beta, gamma, lam):
"""
Calculate denominator to compute the probability of a route
"""
sum = 0.0
for node in unvisited_nodes:
tau = calculate_tau(pheromone_matrix... | 65447eec8383cf43d26c9ee7f483f090a0cd2d89 | 3,617,459 |
import ipdb
def get_right_stem_aligned_indices(
string, inner_potential_indices, outer_potential_indices
):
"""Return the indices of the aligned strings
:param str string: the original string
:param tuple inner_potential_indices: [start, end) of inner part
:param tuple outer_potential_indices: [s... | cddf26b5200f05a8aaece22ebe4b659208c63b04 | 3,617,460 |
def stack_tensor_dict_list(tensor_dict_list):
"""
Stack a list of dictionaries of {tensors or dictionary of tensors}.
:param tensor_dict_list: a list of dictionaries of {tensors or dictionary of tensors}.
:return: a dictionary of {stacked tensors or dictionary of stacked tensors}
"""
keys = list... | 21dbd264571f76c743aecfa7a7d3cd1cd5c65ee5 | 3,617,461 |
def db_insert(conn, query):
"""
Execute the insert the query on the db and return the insert id
@param conn: Connection
@param query: Query string to be executed
"""
logger = bp_logger('dbutils')
try :
conn.query(query)
except _mysql_exceptions.ProgrammingError as (errno, strerr... | bf6d20dfdc4550b6948103dba2ae97d8bf7fa8fc | 3,617,462 |
import socket
def is_gce_instance():
"""Check if it's GCE instance via DNS lookup to metadata server"""
try:
socket.getaddrinfo('metadata.google.internal', 80)
except socket.gaierror:
return False
return True | 74605bca73a9a46b629212c87f154aa9a4920a1d | 3,617,463 |
def get_count_value(context, fieldname):
""" {% get_count_value fieldname %}
"""
return {"value":fieldname} | 9356b602b2685341f402193c830842e656acc6cb | 3,617,464 |
from parrot_api.core.requests import get_request_access_token
def post_json_example(body: dict) -> dict:
"""Sample API handler that accepts a json body
:param body: the json body variable
:return: dictionary containing a single "value" parameter
"""
return dict(value=True, token=get_request_acces... | 3b0a5708042022ea271eb245ff5223b51fdde825 | 3,617,466 |
def registerShortcut(categories, keyseq, context=Qt.WidgetShortcut, actionName=None):
"""Decorate a function to be called when a keyboard shortcut is typed
When the keyboard shortcut `keyseq` is pressed in any widget matching `categories`, the decorated
function will be called, with the widget passed as first param... | 427a8d4e19ff04c999ca4b4d95ad9795ebb3dbae | 3,617,467 |
def as_strided(x, shape, strides, storage_offset=None):
"""Create a new view of array with the given shape, strides, and offset.
Args:
x (tuple of :class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
The array pointing a memory buffer. Its view is totall... | 613f71045f77aa71fe0dbc096c87aff2b04f6b07 | 3,617,469 |
def GranularFormLayout(request):
"""
A simple demonstration of partial rendering of parts of forms.
"""
schema = schemaish.Structure()
schema.add( 'firstName', schemaish.String(title='First Name', \
description='The name before your last one', \
validator=v... | c69725b91e44c0259ce8fd318fb0dca97bf3646a | 3,617,470 |
import functools
def _find_metadata(func):
"""
Support `xarray.DataArray` for find function.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
x, y = args
x_in, y_in, kwargs = _dataarray_strip(x, y, suffix=('', '_track'), **kwargs)
x_out, y_out = func(x_in, y_in, **k... | c0803fc3d443aa807c692bacbf21240c4730aad1 | 3,617,471 |
def _forgy_center_initializer(
X: np.ndarray, n_clusters: int, random_state: np.random.RandomState
) -> np.ndarray:
"""Compute the initial centers using forgy method.
Parameters
----------
X : np.ndarray (3d array of shape (n_instances,n_dimensions,series_length))
Time series instances to c... | bba4bd967297501f73d0e641df421baeb9bf18c8 | 3,617,473 |
from typing import Optional
def generate_richcompare_wrapper(cl: ClassIR, emitter: Emitter) -> Optional[str]:
"""Generates a wrapper for richcompare dunder methods."""
# Sort for determinism on Python 3.5
matches = sorted([name for name in RICHCOMPARE_OPS if cl.has_method(name)])
if not matches:
... | cbbc66a22ee61ed869331fbecd59eb615588fd48 | 3,617,475 |
import imageio
import numpy as np
def assign_embedding_colors(points, texPath, flipTexUD=False, flipTexLR=False, rot90=0):
"""
:param points:
:param texPath:
:return:
"""
# read texture
tex = np.array(imageio.imread(texPath))
if flipTexUD:
tex = np.flipud(tex)
if flipTex... | 9fec12cdf8c6a6429a2d250a0c28b885a4833ca0 | 3,617,476 |
def parse_table_html(s, first_row_th=True, first_col_th=False, cls=None):
"""
parser for csv table, creates html
:s: the input string to parse, which should be of the format
v11, v12, v13, v14
v21, v22, v23, v24
v31, v32, v... | 86b375b2e0785512325ad6e44f48f4a4e6d9d71a | 3,617,477 |
import decimal
def get_savings_rate(exp_records: list, inc_records: list) -> decimal.Decimal:
"""
:param List[far_core.db.ExpenseRecord] exp_records: window of expenses
:param List[far_core.db.IncomeRecord] inc_records: window of incomes
:return: the savings rate over the window, which is defined as:
... | 905000fa95fecdace5a01e84fbb98e412d07b605 | 3,617,478 |
def random_row(gdf,x,y):
"""
sampling the GNSS data randomly by entering the specific number of samples
Parameters
----------
gdf: geodataframe
geodataframe of all gnss data(population)
x : int
x is the number of samples
y: int
y is the random seed, different ran... | eb8c5ab2c577045cf21b9292f1c92cfb5dea5ab5 | 3,617,479 |
import pathlib
import typing
def _get_data_for_write(
path: pathlib.Path,
file_format: str,
prefix_keys: typing.Iterable[str],
) -> typing.Tuple[typing.Dict[str, typing.Any], typing.Dict[str, typing.Any]]:
"""
Retrieve the data from the file if it exists or create a new data object if not.
:p... | 22ecaaad57f025431db484e1d07db63e95832a70 | 3,617,480 |
import functools
import scipy
def scipy_powell(
criterion_and_derivative,
x,
lower_bounds,
upper_bounds,
*,
convergence_relative_params_tolerance=CONVERGENCE_RELATIVE_PARAMS_TOLERANCE,
convergence_relative_criterion_tolerance=CONVERGENCE_RELATIVE_CRITERION_TOLERANCE,
stopping_max_crite... | e3a535669eb9ceed6398d2e9795934be5b1a4f84 | 3,617,482 |
def prettyPrintDataFrame(df: pd.DataFrame, url_columns=["url"], max_column=50):
"""Prints pandas dataframes with custom max-width for column and potentially
transforms URL into clickable content by using to_html(escape=False) method.
WARNING!: Can potentially execute external code if not properly used.
... | 534a90aae1497945ea5c73895629aa7818490812 | 3,617,483 |
import unittest
def require_retrieval(test_case):
"""
Decorator marking a test that requires a set of dependencies necessary for pefrorm retrieval with
[`RagRetriever`].
These tests are skipped when respective libraries are not installed.
"""
if not (is_tf_available() and is_datasets_availab... | a1c3c81cccbcedf7f82519eecf05789433a238c4 | 3,617,484 |
from typing import List
def missing_impact_1v1( # pylint: disable=too-many-locals
df: dd.DataFrame, x: str, y: str, bins: int, ndist_sample: int
) -> Intermediate:
"""
Calculate the distribution change on another column y when
the missing values in x is dropped.
"""
df0 = df[[x, y]]
df1 ... | e74cc7ceed3017422566b95660306ad34c4eaf53 | 3,617,486 |
def rle_kyc(seq: str) -> str:
""" Run-length encoding """
counts = []
count = 0
prev = ''
for char in seq:
# We are at the start
if prev == '':
prev = char
count = 1
# This letter is the same as before
elif char == prev:
count += 1... | c617ca2bfe62baed5e141c65ec923297f27ac488 | 3,617,488 |
def is_legal_parameter(name):
"""
Function that returns true if the given name can be used as a
parameter in the c programming language.
"""
if name == "":
return False
if " " in name:
return False
if "." in name:
return False
if not name[0].isalpha():
... | de4426f5fc14740439c8f278d19e8ec9944e1a2f | 3,617,489 |
import requests
def getDefaultGateway(host, args, session):
"""
Called by the network function. Prints out the DefaultGateway.
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the ldap subcommand
args.json: bool... | eaae94a1b9c92d359fcd6ba786e05c6738023be9 | 3,617,490 |
def unity():
"""Return a unity function"""
return lambda x:x | 5e7aa6bfe20f0534244ed0932b3bf87bf7505230 | 3,617,491 |
from typing import List
from typing import Tuple
def tf_idf(search_keys:str, data:List) -> Tuple:
"""calculate the tf-idf matrices for the vocabulary
and keyword matrix"""
tfidf_vectorizer = TfidfVectorizer()
tfidf_weights_matrix = tfidf_vectorizer.fit_transform(data)
search_query_weights = tfidf_... | dd63e2496a05936670e52bce6aa02ec03cc99213 | 3,617,492 |
def scale(prob):
"""Scales a problem to lie in :math:`[-1, +1]^D`.
:param prob: a :ref:`problem <problem>`.
:return: a scaled :ref:`problem <problem>` with the ``limits`` and ``scale`` as additional keys.
"""
lims = limits(prob)
mid = (lims[0] + lims[1])/2
scale = (lims[1] - lims[0])/2
... | 5e6cd771a9603dbdb50aecf9b5af9390cd6d1c98 | 3,617,493 |
import io
def run_opt(mol, prog, meth, bas, mol_is_smiles=True):
"""
Runs a g09 or molpro job for
INPUT:
stoich - stoichiometry of molecule
theory - theory energy should be listed or computed with
basisset - basis set energy should be listed or computed with
prog - program an energ... | 613ec51ec002388a7fbb33c9d40810e5a63f686f | 3,617,494 |
from typing import Union
def rf_make_zeros_tile(num_cols: int, num_rows: int, cell_type: Union[str, CellType] = CellType.float64()) -> Column:
"""Create column of constant tiles of zero"""
jfcn = RFContext.active().lookup('rf_make_zeros_tile')
return Column(jfcn(num_cols, num_rows, _parse_cell_type(cell_t... | a0c6ca9ddaa78c4618115277f6193f851f94f753 | 3,617,496 |
def generateAndTrain(generationParameters, trainingParameters,
resultsDir):
"""
Creates a random tree, creates its tuples
and trains a tree from these tuples
Return
generatedTree, trainedTree, purity
"""
generatedObjectTree = simpleRandomBifurcatio... | 2b3e292b84a096016fb5fe92d4505d0b49e19136 | 3,617,497 |
import requests
def amac_person_bond_org_list() -> pd.DataFrame:
"""
中国证券投资基金业协会-信息公示-从业人员信息-债券投资交易相关人员公示
https://human.amac.org.cn/web/org/personPublicity.html
:return: 债券投资交易相关人员公示
:rtype: pandas.DataFrame
"""
url = "https://human.amac.org.cn/web/api/publicityAddress"
params = {"rand... | 6e1630e66cb640f853e6ed828301f081ed529a00 | 3,617,498 |
def check_line_comp_options(lam_gal,line_list,comp_options,edge_pad=10,verbose=True):
"""
Checks each entry in the complete (narrow, broad, outflow, absorption, and user) line list
and ensures all necessary keywords are input. It also checks every line entry against the
front-end component options (co... | eb7a1f003b119846e7aac8801cf745e9f7e01071 | 3,617,499 |
def parse(address):
"""Parses an address into its components"""
for _, addresser in ADDRESSERS.items():
result = addresser.parse(address=address)
if result:
return result
raise ValueError("parse error, no addresser found for address {}".format(address)) | 0ee6ca4c14727e9e29da4afe4cd7d4aa9c737e64 | 3,617,500 |
def num_added_features(include_am, include_lm):
""" Determine the number of added word-level features (specifically AM and LM) """
added_feature_count = 0
if include_am:
added_feature_count += 1
if include_lm:
added_feature_count += 1
return added_feature_count | 83f344ad693f846f7a6dda0bcef429565d96870b | 3,617,502 |
from typing import Union
import torch
from typing import Set
from typing import List
from typing import Dict
import operator
def split_const_subgraphs(
module: Union[torch.nn.Module, torch.fx.GraphModule]
) -> FoldedGraphModule:
"""
Looks through `module` for any nodes that have all constant attribute inp... | 6a4e88df5319e66b27ba83be8e346495c4f2d242 | 3,617,503 |
import re
def validate_CNPJ(value):
"""
Value can be either a string in the format XX.XXX.XXX/XXXX-XX or a
group of 14 characters.
:type value: object
"""
value = str(value)
if value in EMPTY_VALUES:
return u''
if not value.isdigit():
value = re.sub("[-/\.]", "", value)... | 5ee18981bbaba30bdf68cbe63658a27a23a3a284 | 3,617,504 |
def _get_mudata_autodetect_options_and_encoding_modes(
identifier: str, autodetect: dict, encodings: dict[str, dict[str, list[str]]]
) -> tuple[bool, dict | None]:
"""
Extract the index column (if any) and the columns, for obs only (if any) from the given user input.
This function is only called when d... | 1b763bba23957e71897bc40acaf020088f75feb3 | 3,617,505 |
def multishells():
"""Resolve the path of the "rst_operators/multishells.rst" result file."""
return resolve_test_file("model_with_ns.rst", "", "multishells_rst") | 470cb8874712493f6e33723723d77235bcdb5529 | 3,617,507 |
from .prompts import (
prompt_str,
prompt_int,
prompt_float,
prompt_bool,
prompt_datetime,
prompt_enum,
)
from typing import Type
from typing import Dict
from re import T
from datetime import datetime
from enum import Enum
def _get_validator(
cls: Type,
attr_nam... | ae093e35030afac4574fb3ce2081bcaf96df3ad3 | 3,617,508 |
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