content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def svn_wc_adm_probe_retrieve(*args):
"""svn_wc_adm_probe_retrieve(svn_wc_adm_access_t associated, char path, apr_pool_t pool) -> svn_error_t"""
return _wc.svn_wc_adm_probe_retrieve(*args) | 2716094e31c596212b5ccd7833b9ae10ef52d44e | 3,641,300 |
def flights_preclean(df):
"""
Input: Raw dataframe of Flights table.
Output: Cleaned flights table:
- Remove cancelled rows, made available in new dataframe "df_can"
- Drop columns ['Unnamed: 0', 'branded_code_share',
'mkt_carrier', 'cancelled', 'cancellation_code', 'flights', 'ai... | 61dcfa6afd6ec7dd0abb5525187938d6ab978996 | 3,641,301 |
def convert_spectral_kernel_quint(sequences, list_seq_to_id):
""" Return a list seq of nb of time the seq in list_seq_to_id appear in sequence"""
final = []
for j in range(len(sequences)):
sequence = sequences[j]
dico_appear = {seq: 0 for seq in list_seq_to_id}
for i in range(len(seq... | 49f727dd26822834bad2c9a448136288dc1c426c | 3,641,302 |
def grad_of_marginal_fit(c, h, tau, epsilon):
"""Computes grad of terms linked to marginals in objective.
Computes gradient w.r.t. f ( or g) of terms in
https://arxiv.org/pdf/1910.12958.pdf, left-hand-side of Eq. 15
(terms involving phi_star)
Args:
c: jnp.ndarray, first target marginal (either a or b in... | 38b6b57766c97f8eda72162b6919e48c235cd880 | 3,641,303 |
def SuggestField(**kwargs):
"""
Query 'foo' to get the TextField, or 'foo.raw' to get the KeywordField, or 'foo.suggest' to get the CompletionField.
"""
return fields.TextField(
fields={
'raw': fields.KeywordField(),
'suggest': fields.CompletionField(),
},
... | 57f673bbc310a22432178ee078c8f5eec2355e12 | 3,641,304 |
import math
def distance_on_unit_sphere(FoLat, FoLng, ToLat, ToLng):
""" Convert latitude and longitude to spherical coordinates in radians."""
phi1 = math.radians(90.0 - FoLat)
phi2 = math.radians(90.0 - ToLat)
theta1 = math.radians(FoLng)
theta2 = math.radians(ToLng)
"""Compute spherical d... | 98c9294697e36c5b45cd165ba96529187f2750de | 3,641,305 |
import pandas # noqa
def check_pandas_support(caller_name):
"""Raise ImportError with detailed error message if pandsa is not
installed.
Plot utilities like :func:`fetch_openml` should lazily import
pandas and call this helper before any computation.
Parameters
----------
caller_name : ... | f3d484bb3a5dbca43a81cca83b7343e1fcd7cbcf | 3,641,306 |
import argparse
from datetime import datetime
def parse_cmdline():
"""Parse the command line arguments.
Returns:
argparse.Namespace. The parsed arguments or defaults.
"""
parser = argparse.ArgumentParser(
description="Dataplane Automated Testing System, version " + __version__,
... | a9c95d12fe3a30a3fb130310ea0b9cb57fb20e0b | 3,641,307 |
def summarize_sequence(summary_type, hidden, d_model, n_head, d_head, dropout,
dropatt, input_mask, is_training, initializer,
scope=None, reuse=None):
"""Summarize hidden sequence into a vector."""
tf.logging.info("===== Sequence summary =====")
tf.logging.info(" - ... | 50dd0e72c15adfa522847cb822e897c9892cd1cf | 3,641,308 |
def encode_line(line, vocab):
"""Given a string and a vocab dict, encodes the given string"""
line = line.strip()
sequence = [vocab.get(char, vocab['<UNK>']) for char in line]
sequence_length = len(sequence)
return sequence, sequence_length | feb14d86dd6c219d57cffc4cd9d90d16c4e9c987 | 3,641,309 |
import math
def get_like_from_mats(ky_mat, l_mat, alpha, name):
""" compute the likelihood from the covariance matrix
:param ky_mat: the covariance matrix
:return: float, likelihood
"""
# catch linear algebra errors
labels = _global_training_labels[name]
# calculate likelihood
like ... | 8fb7842547ecee25425bdaf920ff69d3386b920b | 3,641,310 |
import os
import json
def cached(*cache_args, **cache_kwargs):
"""General-purpose.
Allows custom filename, with function fallback.
Load/save cached function data. Also handle data types gracefully.
Example:
>>> cached('myfile.json', directory=SOME_DIR)
>>> def my_thing():
>>... | c8f85b8f4ba80cbc991622c220e59bce80683863 | 3,641,311 |
def engulfing(data: pd.DataFrame):
"""
engulfing
Positive numbers are multi-side, negative numbers are short-side
0 is abnormal, meaning that the ratio of the absolute value of the current Candle up or down to the previous one is more than 10 times.
For machine learning convenience, a floating point... | 2974a62afa6b77ae2d8d02b35d7293362dd90927 | 3,641,312 |
import os
def create_dat_files(ipc_tests, test_results, dst_dir):
"""
Creates .dat files for all the test_results.
@param ipc_tests: list of ipc tests which was used to created the data.
@param test_results: dictionary containing all test results.
@param dst_dir: directory where the .dat-files sh... | 363a37046b9ad8c495bed2d92c3a6b2d5adcc505 | 3,641,313 |
def filter_matches(kp1, kp2, matches, ratio = 0.75):
"""
This function applies a ratio test
:param kp1: raw keypoint 1
:param kp2: raw keypoint 2
:param matches: raw matches
:param ratio: filtering ratio
:return: filtered keypoint 1, filtered keypoint 2, keypoint pairs
"""
mkp1, mkp2... | a54d96e092019b9629852b1bf57511f9994aba46 | 3,641,314 |
import itertools
def add_derived_columns(
data: pd.DataFrame,
differences: bool = True,
second_differences: bool = True,
multiplications: bool = True,
rolling_means: int | None = 10,
rolling_stds: int | None = 10,
mean_distances: bool = True,
) -> pd.DataFrame:
"""This will create many... | 080f3853f67ead678c55a3c95bf2a1c19614452c | 3,641,315 |
from typing import List
def parse_query(
query: List[str],
format,
use_youtube,
generate_m3u,
lyrics_provider,
threads,
path_template,
) -> List[SongObject]:
"""
Parse query and return list containing song object
"""
songs_list = []
# Iterate over all search queries a... | a58e5bd6acf2c12de7eb7fae7824aab924188c26 | 3,641,316 |
import os
import shutil
import time
def start_db_server():
"""Spin up a test database server"""
log.debug('start_db_server()')
try:
os.mkdir(DATA_DIR)
except FileExistsError:
shutil.rmtree(DATA_DIR)
sp.check_call(['initdb', '-D', DATA_DIR])
db_proc = sp.Popen(['postgres', '-D',... | 94bddfad0e926dd330672d55598686ae7c2fbb9f | 3,641,317 |
def assert_address_book(address_book):
"""Fixture returning an object providing a custom address book asserts."""
return icemac.addressbook.testing.AddressBookAssertions(address_book) | fd9197472c86a59dd1c52dad14febe1a0b318c85 | 3,641,318 |
def make_shell_context():
"""Open shell."""
db = get_db()
return {"db": db, "Doi": Doi, "Url": Url, "FBRequest": FBRequest} | 996988c06aa8039c7689126360ce0fea886ab392 | 3,641,319 |
import re
def get_version():
"""
Extracts the version number from the version.py file.
"""
VERSION_FILE = 'fleming/version.py'
mo = re.search(r'^__version__ = [\'"]([^\'"]*)[\'"]', open(VERSION_FILE, 'rt').read(), re.M)
if mo:
return mo.group(1)
else:
raise RuntimeError('Un... | b50df998254d83bd48d7a5c0863ba89b29ab529b | 3,641,320 |
import os
def get_cache_path():
""" Return a path suitable to store cached files """
try:
os.mkdir(cache_path)
except FileExistsError:
pass
return cache_path | 2370a3433d3a29603b625a6b5ea8b6afe23d024d | 3,641,321 |
import math
def distance(s1, s2):
""" Euclidean distance between two sequences. Supports different lengths.
If the two series differ in length, compare the last element of the shortest series
to the remaining elements in the longer series. This is compatible with Euclidean
distance being used as an u... | 61c308da89b98b4bbde1bba690c86559fd5e1400 | 3,641,322 |
def remove_test_set_gender_and_age(nodes):
"""Remove the gender feature from a subset of the nodes for estimation"""
# todo: the 40k random can be adjusted if youre working with a subset
test_profiles = np.random.choice(nodes["user_id"].unique(), 40000, replace=False)
nodes["TRAIN_TEST"] = "TRAIN"
... | 285ee3f99b49e52af52f5a03465021154c41c11b | 3,641,323 |
import re
def valid_attribute(attr_filter_key, attr_filter_val, hit):
"""Validates the hit according to a filter attribute."""
if (attr_filter_key != "None") and (attr_filter_val != "None"):
try:
# If key for filtering is not correct or doesn't exist-> error
# should be ignored... | c7480008f24e011f0803d82f1243a5d00c5a4030 | 3,641,324 |
import calendar
def dek_to_greg(dek_day: int, dek_month: int, dek_year: int) -> tuple:
"""Returns a Gregorian date from a Dekatrian date.
Args:
dek_day (int): Day of the month.
dek_month (int): Month of the year.
dek_year (int): Year.
Return:
tuple: A tuple with the day, ... | c190ff52fa104c4c522397eb46a82e263096dc84 | 3,641,325 |
def encrypt_message(kx, ky, message):
"""
Encrypts a message using ECC and AES-256
First generates a random AES key and IV with os.urandom()
Then encrypts the original message with that key
Then encrypts the AES key with the ECC key
NOTE:
This means that plaintext will not have the same cip... | e4bd462e8724a85ed0e183e048203ff23e349f34 | 3,641,326 |
from typing import List
import os
def _create_init_files(original_file_location: FilePath) -> List[str]:
"""Given the original file location of a handler, create the artifact paths for all the __init__.py files that
make the handle a valid python modules
Args:
original_file_location (str): origin... | 442f299e493bd25afd2798922b4ef0652dda0416 | 3,641,327 |
def mirror_notes(key_position: int) -> int:
"""
指定したキーポジションを反転させた値を返します
引数
----
key_position : int
-> キーポジション
戻り値
------
int
-> キーポジションを反転したときのキーポジション
"""
return 512 - key_position | 03ad894eca67405bb79cbf6ea1ecef12b19958ed | 3,641,328 |
def import_odim_hdf5(filename, **kwargs):
"""Import a precipitation field (and optionally the quality field) from a
HDF5 file conforming to the ODIM specification.
Parameters
----------
filename : str
Name of the file to import.
Other Parameters
----------------
qty : {'RATE', ... | 650875bb3d04627f4570507892ee26b42912c39e | 3,641,329 |
def sugerir(update: Update, _: CallbackContext) -> int:
"""Show new choice of buttons"""
query = update.callback_query
query.answer()
keyboard = [
[
InlineKeyboardButton("\U0001F519 Volver", callback_data=str(NINE)),
InlineKeyboardButton("\U0001F44B Salir", callba... | e278c6bdab82e4fdfc38c7a4bb58a5511a003515 | 3,641,330 |
def clone_subgraph(*, outputs, inputs, new_inputs, suffix="cloned"):
"""
Take all of the tensorflow nodes between `outputs` and `inputs` and clone
them but with `inputs` replaced with `new_inputs`.
Args:
outputs (List[tf.Tensor]): list of output tensors
inputs (List[tf.Tensor]): list of... | b61d73d79635551f8277cbc0c2da97d0c5c2908e | 3,641,331 |
async def refresh_replacements(db, sample_id: str) -> list:
"""
Remove sample file `replacement` fields if the linked files have been deleted.
:param db: the application database client
:param sample_id: the id of the sample to refresh
:return: the updated files list
"""
files = await virt... | 43667801bf6bb96edbeb59bf9d538b62c9bf9785 | 3,641,332 |
def torch_model (model_name, device, checkpoint_path = None):
""" select imagenet models by their name and loading weights """
if checkpoint_path:
pretrained = False
else:
pretrained = True
model = models.__dict__ [model_name](pretrained)
if hasattr (model, 'classifier'):
... | 831cf1edd83b76049e7f6d60434961cbd44e4bd9 | 3,641,333 |
from typing import Tuple
from datetime import datetime
def get_timezone() -> Tuple[datetime.tzinfo, str]:
"""Discover the current time zone and it's standard string representation (for source{d})."""
dt = get_datetime_now().astimezone()
tzstr = dt.strftime("%z")
tzstr = tzstr[:-2] + ":" + tzstr[-2:]
... | f73cedb8fb91c75a19104d4d8bef29f73bfb9b1a | 3,641,334 |
from typing import List
from typing import Tuple
def get_model(input_shape: List[int], weight_array: np.array,
batches_per_step: int, replication_factor: int, batch_size: int,
channels: int, data_len: int, synthetic_data: bool,
buffer_streams: bool) -> Tuple:
"""Get a sim... | ab71faae9faeb082c4139a52dddac6116ee0a194 | 3,641,335 |
from collections import OrderedDict
import ast
import json
def shell_safe_json_parse(json_or_dict_string, preserve_order=False):
""" Allows the passing of JSON or Python dictionary strings. This is needed because certain
JSON strings in CMD shell are not received in main's argv. This allows the user to specif... | f812a3c13a4c460fac1569d7ca9fe143312e43b8 | 3,641,336 |
def get_timed_roadmaps_grid_common(
ins: Instance, T: int, size: int,
) -> list[TimedRoadmap]:
"""[deprecated] get grid roadmap shared by all agents
Args:
ins (Instance): instance
T (int): assumed makespan
size (int): size x size grid will be constructed
Returns:
list[n... | 9b8e283ad66db35132393b53af2bfa36fc4aaf83 | 3,641,337 |
def arithmetic_series(a: int, n: int, d: int = 1) -> int:
"""Returns the sum of the arithmetic sequence with parameters a, n, d.
a: The first term in the sequence
n: The total number of terms in the sequence
d: The difference between any two terms in the sequence
"""
return n * (2 * a + (n - 1... | 168f0b07cbe6275ddb54c1a1390b41a0f340b0a6 | 3,641,338 |
import re
def get_arc_proxy_user(proxy_file=None):
"""
Returns the owner of the arc proxy. When *proxy_file* is *None*, it defaults to the result of
:py:func:`get_arc_proxy_file`. Otherwise, when it evaluates to *False*, ``arcproxy`` is queried
without a custom proxy file.
"""
out = _arc_proxy... | 01f1040cd1217d7722a691a78b5884125865cf39 | 3,641,339 |
def pass_hot_potato(names, num):
"""Pass hot potato.
A hot potato is sequentially passed to ones in a queue line.
After a number of passes, the one who got the hot potato is out.
Then the passing hot potato game is launched againg,
until the last person is remaining one.
"""
name_queue = Queue()
for name in n... | f78a635bdf3138809329ef8ad97934b125b9335a | 3,641,340 |
import copy
def convert_timeseries_dataframe_to_supervised(df: pd.DataFrame, namevars, target, n_in=1, n_out=0, dropT=True):
"""
Transform a time series in dataframe format into a supervised learning dataset while
keeping dataframe intact.
Returns the transformed pandas DataFrame, the name of the targ... | b62296680f6a871f20078e55eefa20f09392b012 | 3,641,341 |
def build_graph(adj_mat):
"""build sparse diffusion graph. The adjacency matrix need to preserves divergence."""
# sources, targets = adj_mat.nonzero()
# edgelist = list(zip(sources.tolist(), targets.tolist()))
# g = Graph(edgelist, edge_attrs={"weight": adj_mat.data.tolist()}, directed=True)
g = Gr... | bdc8dc5d1c107086c4c548b500f6958bdbe48103 | 3,641,342 |
def retrieve_context_path_comp_service_end_point_end_point(uuid): # noqa: E501
"""Retrieve end-point
Retrieve operation of resource: end-point # noqa: E501
:param uuid: ID of uuid
:type uuid: str
:rtype: List[str]
"""
return 'do some magic!' | e3169e139b5992daf00411b694cf77436fb17fba | 3,641,343 |
def get_external_repos(gh):
"""
Get all external repositories from the `repos.config` file
"""
external_repos = []
with open("repos.config") as f:
content = f.readlines()
content = [x.strip() for x in content]
for entry in content:
org_name, repo_name = entry.sp... | a83515acd77c7ef9e30bf05d8d4478fa833ab5bc | 3,641,344 |
def handle_standard_table(pgconn, table_name, columns, record):
"""
:param pgconn:
:param table_name:
:param columns:
:param record:
:return:
"""
data = dict(record)
log.debug("Standard handler: {}".format(data))
if 'id' in columns:
data_exists = pgconn.execute(
... | c6414b2f60cf90ed6a671c0b7affcb2d6b9e75c9 | 3,641,345 |
import json
def load_fit_profile():
"""
This methods return the FIT profile types based on the Profile.xslx that is included in the Garmin FIT SDK (https://developer.garmin.com/fit/download/).
The returned profile can be used to translate e.g. Garmin product names to their corresponding integer product id... | 13108546c2d88d77d090b222c1b3ff2b59208310 | 3,641,346 |
def mmethod(path, *args, **kwargs):
"""
Returns a mapper function that runs the path method for each instance of
the iterable collection.
>>> mmethod('start')
is equivalent to
>>> lambda thread: thread.start()
>>> mmethod('book_set.filter', number_of_pages__gte=100)
is equivalent to
... | 6ded620d190d338d981c433514018a4182b7e207 | 3,641,347 |
def generate_test_demand_design_image() -> TestDataSet:
"""
Returns
-------
test_data : TestDataSet
2800 points of test data, uniformly sampled from (price, time, emotion). Emotion is transformed into img.
"""
org_test: TestDataSet = generate_test_demand_design(False)
treatment = org... | 238cf11480e0d23f30b426ed19877126edc010fa | 3,641,348 |
def value_iteration(game, depth_limit, threshold):
"""Solves for the optimal value function of a game.
For small games only! Solves the game using value iteration,
with the maximum error for the value function less than threshold.
This algorithm works for sequential 1-player games or 2-player zero-sum
games,... | 2a9ae3903666ee16e86fe30a0458707394fe4695 | 3,641,349 |
import typing
def printable_dataframe(data: typing.List[typing.Mapping], ignore_phase: bool = True) -> pd.DataFrame:
""" Print performance results using pandas data frames.
TODO: Re-write me.
"""
columns = {'name': 'Model', 'input_shape': 'Input shape', 'num_parameters': '#Parameters',
... | c76841d06891da4019e32194347f8d87c11dbea1 | 3,641,350 |
def _import_and_infer(save_dir, inputs):
"""Import a SavedModel into a TF 1.x-style graph and run `signature_key`."""
graph = ops.Graph()
with graph.as_default(), session_lib.Session() as session:
model = loader.load(session, [tag_constants.SERVING], save_dir)
signature = model.signature_def[
sign... | 1610c4d52fa8d18a770f1f347b9cd30b4652ab8b | 3,641,351 |
import os
def new_module(fname, main=False, parser_vmx=None):
"""
`fname` is Python str (or None for internal Module)
`main` is Python True for main program (from command line)
`argv` is Python list of str (if main is True)
`parser_vmx` is Python str for parser VMX file to use
returns (CModule... | c5732841b213d41e8bb466fb755804dd3f0902f2 | 3,641,352 |
def url_by_properties(
config_properties,
db_type,
submit_dir=None,
top_dir=None,
rundir_properties=None,
cl_properties=None,
props=None,
):
""" Get URL from the property file """
# Validate parameters
if not db_type:
raise ConnectionError(
"A type should be p... | 3f742af88320eccdacd81603ceeaf94116852798 | 3,641,353 |
def nth(seq, idx):
"""Return the nth item of a sequence. Constant time if list, tuple, or str;
linear time if a generator"""
return get(seq, idx) | cca44dca33d19a2e0db355be525009dce752445c | 3,641,354 |
import psutil
def get_internal_plot_drive_to_use():
"""
Same as above but returns the next drive. This is the drive we will use for internal plots. We do
this to make sure we are not over saturating a single drive with multiple plot copies. When you run
out of drives, these scripts will fa... | de86c7a6bb61ba9ebbf7555dae2d07576f8ccb3e | 3,641,355 |
def _build_discretize_fn(value_type, stochastic, beta):
"""Builds a `tff.tf_computation` for discretization."""
@computations.tf_computation(value_type, tf.float32, tf.float32)
def discretize_fn(value, scale_factor, prior_norm_bound):
return _discretize_struct(value, scale_factor, stochastic, beta,
... | 75f9f50ec376b1a10b5fcb629527a873b8768235 | 3,641,356 |
def expand_mapping_target(namespaces, val):
"""Expand a mapping target, expressed as a comma-separated list of
CURIE-like strings potentially prefixed with ^ to express inverse
properties, into a list of (uri, inverse) tuples, where uri is a URIRef
and inverse is a boolean."""
vals = [v.strip() for... | b4a4f08d39728c8f61b7b373a521890f88d6f912 | 3,641,357 |
def home(request):
"""Handle the default request, for when no endpoint is specified."""
return Response('This is Michael\'s REST API!') | a37a2eaa68366de4d8542357c043c4e29ac7a9f9 | 3,641,358 |
def create_message(sender, to, subject, message_text, is_html=False):
"""Create a message for an email.
Args:
sender: Email address of the sender.
to: Email address of the receiver.
subject: The subject of the email message.
message_text: The text of the email message.
Returns:
... | 2b5dc225df5786df9f2650631d209c53e3e8145b | 3,641,359 |
def get_agent(runmode, name): # noqa: E501
"""get_agent
# noqa: E501
:param runmode:
:type runmode: str
:param name:
:type name: str
:rtype: None
"""
return 'do some magic!' | 065302bb7793eff12973208db5f35f3494a83930 | 3,641,360 |
def find_splits(array1: list, array2: list) -> list:
"""Find the split points of the given array of events"""
keys = set()
for event in array1:
keys.add(event["temporalRange"][0])
keys.add(event["temporalRange"][1])
for event in array2:
keys.add(event["temporalRange"][0])
... | c52f696caddf35fa050621e7668eec06686cee14 | 3,641,361 |
def to_subtask_dict(subtask):
"""
:rtype: ``dict``
"""
result = {
'id': subtask.id,
'key': subtask.key,
'summary': subtask.fields.summary
}
return result | 5171d055cc693b1aa00976c063188a907a7390dc | 3,641,362 |
from typing import Tuple
from typing import Optional
def _partition_labeled_span(
contents: Text, labeled_span: substitution.LabeledSpan
) -> Tuple[substitution.LabeledSpan, Optional[substitution.LabeledSpan],
Optional[substitution.LabeledSpan]]:
"""Splits a labeled span into first line, intermediate... | 6f22341d32c03ba0057fbfd6f08c88ac8736220f | 3,641,363 |
def is_active(relation_id: RelationID) -> bool:
"""Retrieve an activation record from a relation ID."""
# query to DB
try:
sups = db.session.query(RelationDB) \
.filter(RelationDB.supercedes_or_suppresses == int(relation_id)) \
.first()
except Exception as e:
rais... | 352f44e2f025ac0918519d0fe8e513b3871be7b9 | 3,641,364 |
def vectorize_with_similarities(text, vocab_tokens, vocab_token_to_index, vocab_matrix):
"""
Generate a vector representation of a text string based on a word similarity matrix. The resulting vector has
n positions, where n is the number of words or tokens in the full vocabulary. The value at each position indicate... | 5b843ffbfdefbf691fb5766bbe6772459568cf78 | 3,641,365 |
def get_puppet_node_cert_from_server(node_name):
"""
Init environment to connect to Puppet Master and retrieve the certificate for that node in the server (if exists)
:param node_name: Name of target node
:return: Certificate for that node in Puppet Master or None if this information has not been found
... | 7f7fa2164bf7f289ce9dbc1b35f2d8aea546bb60 | 3,641,366 |
from typing import Optional
def get_notebook_workspace(account_name: Optional[str] = None,
notebook_workspace_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> Awaitabl... | d9020323c0ea520951730a31b2f457ab80fcc931 | 3,641,367 |
def get_current_player(player_one_turn: bool) -> str:
"""Return 'player one' iff player_one_turn is True; otherwise, return
'player two'.
>>> get_current_player(True)
'player one'
>>> get_current_player(False)
'player two'
"""
if player_one_turn:
return P1
else:
retu... | 6bade089054513943aef7656972cadd2d242807c | 3,641,368 |
import os
import glob
def CityscapesGTFine(path: str) -> Dataset:
"""`CityscapesGTFine <https://www.cityscapes-dataset.com/>`_ dataset.
The file structure should be like::
<path>
leftImg8bit/
test/
berlin/
berlin_000000_000019_l... | 70e1adb939519fe6641f1a390ed6b011b27fc1ec | 3,641,369 |
def is_word(s):
""" String `s` counts as a word if it has at least one letter. """
for c in s:
if c.isalpha(): return True
return False | 524ed5cc506769bd8634a46d346617344485e5f7 | 3,641,370 |
def index_all_messages(empty_index):
"""
Expected index of `initial_data` fixture when model.narrow = []
"""
return dict(empty_index, **{'all_msg_ids': {537286, 537287, 537288}}) | ea2c59a4de8e62d2293f87e26ead1b4c15f15a11 | 3,641,371 |
def compute_affine_matrix(in_shape,
out_shape,
crop=None,
degrees=0.0,
translate=(0.0, 0.0),
flip_h=False,
flip_v=False,
resize=False,
... | 0c3786c44d35341e5e85d3756e50eb59dd473d64 | 3,641,372 |
def Bern_to_Fierz_nunu(C,ddll):
"""From semileptonic Bern basis to Fierz semileptonic basis for Class V.
C should be the corresponding leptonic Fierz basis and
`ddll` should be of the form 'sbl_enu_tau', 'dbl_munu_e' etc."""
ind = ddll.replace('l_','').replace('nu_','')
return {
'F' + in... | 4f08f79d6614c8929c3f42096fac71b04bfe7b4b | 3,641,373 |
def enforce_boot_from_volume(client):
"""Add boot from volume args in create server method call
"""
class ServerManagerBFV(servers.ServerManager):
def __init__(self, client):
super(ServerManagerBFV, self).__init__(client)
self.bfv_image_client = images.ImageManager(client)
... | 4ae4d2624f216c96722e811d9d44cb04caa46e1d | 3,641,374 |
def img_to_yuv(frame, mode, grayscale=False):
"""Change color space of `frame` from any supported `mode` to YUV
Args:
frame: 3-D tensor in either [H, W, C] or [C, H, W]
mode: A string, must be one of [YV12, YV21, NV12, NV21, RGB, BGR]
grayscale: discard uv planes
return:
... | 002506b3a46fa6b601f4ca65255c8f06b990992d | 3,641,375 |
def assemblenet_kinetics600() -> cfg.ExperimentConfig:
"""Video classification on Videonet with assemblenet."""
exp = video_classification.video_classification_kinetics600()
feature_shape = (32, 224, 224, 3)
exp.task.train_data.global_batch_size = 1024
exp.task.validation_data.global_batch_size = 32
exp.ta... | 3356b6ea758baf04cc98421d700f25e342884d5a | 3,641,376 |
import math
import torch
def channel_selection(inputs, module, sparsity=0.5, method='greedy'):
"""
현재 모듈의 입력 채널중, 중요도가 높은 채널을 선택합니다.
기존의 output을 가장 근접하게 만들어낼 수 있는 입력 채널을 찾아냅니댜.
:param inputs: torch.Tensor, input features map
:param module: torch.nn.module, layer
:param sparsity: float, 0 ~ 1 h... | 957cbcc799185fd6c2547662bfe79205389d44da | 3,641,377 |
import six
def format_host(host_tuple):
"""
Format a host tuple to a string
"""
if isinstance(host_tuple, (list, tuple)):
if len(host_tuple) != 2:
raise ValueError('host_tuple has unexpeted length: %s' % host_tuple)
return ':'.join([six.text_type(s) for s in host_t... | f4822aec5143a99ccc52bb2657e1f42477c65400 | 3,641,378 |
import psutil
def get_cpu_stats():
"""
Obtains the system's CPU status.
:returns: System CPU static.
"""
return psutil.cpu_stats() | f538977db72083f42c710faa987a97511959c973 | 3,641,379 |
from typing import Dict
from typing import Union
import os
import re
def load_gene_metadata(gtf_file : str) -> Dict[str, Dict[str, Union[int, str]]]:
"""
Read gene metadata from a GTF file.
Args:
gtf_file (str): path to GTF file
Returns:
A Dict with ea... | bc5f096f7a14579fcdee5a862f3f800d21012244 | 3,641,380 |
import sys
import glob
import os
import pathlib
import importlib
def get_plugins() -> dict[str, Plugin]:
"""
This function is really time consuming...
"""
# get entry point plugins
# Users can use Python's entry point system to create rich plugins, see
# example here: https://github.com/Piore... | 234a86b1ee142b9ccf1cfc39a6bc3947bd103488 | 3,641,381 |
import json
def _deserialise_list_of(collection_type, kind, owning_cls, field, value):
"""
Deserialise a list of items into a collection objects of class `kind`. Note
that if the value is None, we return None here so that we get a more
meaningful error message later on.
Args:
kind: Type t... | 8dc7dfa88966d90bb29714c887a2457d9aeb1e8f | 3,641,382 |
def get_minmax_array(X):
"""Utility method that returns the boundaries for each feature of the input array.
Args:
X (np.float array of shape (num_instances, num_features)): The input array.
Returns:
min (np.float array of shape (num_features,)): Minimum values for each feature in array.
... | 5453371759af5bf6d876aa8fe5d2caf88ee6eb08 | 3,641,383 |
def getAllHeaders(includeText=False):
"""
Get a dictionary of dream numbers and headers. If includeText=true, also
add the text of the dream to the dictionary as 'text' (note that this key
is all lowercase so it will not conflict with the usual convention for
header names, even if "Text" would be an... | 2bbd78d9c9cbfaa50a62e99c25148844d7c5e330 | 3,641,384 |
def zscore(arr, period):
"""
ZScore transformation of `arr` for rolling `period.` ZScore = (X - MEAN(X)) / STDEV(X)
:param arr:
:param period:
:return:
"""
if period <= 0:
raise YaUberAlgoArgumentError("'{}' must be positive number".format(period))
# Do quick sanity checks of a... | 8a49afe3ecefc326b3bd889279085cccd1d19a61 | 3,641,385 |
import glob
import pandas
def _load_event_data(prefix, name):
"""Load per-event data for one single type, e.g. hits, or particles.
"""
expr = '{!s}-{}.csv*'.format(prefix, name)
files = glob.glob(expr)
dtype = DTYPES[name]
if len(files) == 1:
return pandas.read_csv(files[0], header=0, ... | 04b2e4a7483ba56fdd282dc6355e9acb2d6da7b1 | 3,641,386 |
from datetime import datetime
def check_file(file_id: str, upsert: bool = False) -> File:
"""Checks that the file with file_id exists in the DB
Args:
file_id: The id for the requested file.
upsert: If the file doesn't exist create a placeholder file
Returns:
The file object
... | 2f4e94a064d0bdfea8f001855eb39675f78ab6e5 | 3,641,387 |
def parse(volume_str):
"""Parse combined k8s volume string into a dict.
Args:
volume_str: The string representation for k8s volume,
e.g. "claim_name=c1,mount_path=/path1".
Return:
A Python dictionary parsed from the given volume string.
"""
kvs = volume_str.split(",")
... | f6984faf90081eb8ca3fbbb8ffaf636b040c7ffc | 3,641,388 |
def longest_common_substring(text1, text2):
"""最长公共子字符串，区分大小写"""
n = len(text1)
m = len(text2)
maxlen = 0
span1 = (0, 0)
span2 = (0, 0)
if n * m == 0:
return span1, span2, maxlen
dp = np.zeros((n+1, m+1), dtype=np.int32)
for i in range(1, n+1):
for j in range(1, m+1)... | ed892739d22ee0763a2fe5dd44b48b8d1902605e | 3,641,389 |
def make_subclasses_dict(cls):
"""
Return a dictionary of the subclasses inheriting from the argument class.
Keys are String names of the classes, values the actual classes.
:param cls:
:return:
"""
the_dict = {x.__name__:x for x in get_all_subclasses(cls)}
the_dict[cls.__name__] = cls
... | 36eb7c9242b83a84fcd6ee18b4ca9297038f9ee6 | 3,641,390 |
import time
def _parse_realtime_data(xmlstr):
"""
Takes xml a string and returns a list of dicts containing realtime data.
"""
doc = minidom.parseString(xmlstr)
ret = []
elem_map = {"LineID": "id", "DirectionID": "direction",
"DestinationStop": "destination" }
ack = _singl... | 90958c7f66072ecfd6c57b0da95293e35196354c | 3,641,391 |
def tocopo_accuracy_fn(tocopo_logits: dt.BatchedTocopoLogits,
target_data: dt.BatchedTrainTocopoTargetData,
oov_token_id: int,
pad_token_id: int,
is_distributed: bool = True) -> AccuracyMetrics:
"""Computes accuracy metrics.
... | 828b7d3db40d488a7e05bbfe1f3d2d94f58d8efa | 3,641,392 |
def cols_from_html_tbl(tbl):
""" Extracts columns from html-table tbl and puts columns in a list.
tbl must be a results-object from BeautifulSoup)"""
rows = tbl.tbody.find_all('tr')
if rows:
for row in rows:
cols = row.find_all('td')
for i,cell in enumerate(cols):
... | 94bef05b782073955738cf7b774af34d64520499 | 3,641,393 |
from typing import List
from typing import Tuple
def get_score_park(board: List[List[str]]) -> Tuple[int]:
"""
Calculate the score for the building - park (PRK).
Score 1: If ONLY 1 park.
Score 3: If the park size is 2.
Score 8: If the park size is 3.
Score 16: If the par... | 2bf1629aeb9937dfd871aa118e675cd9358b65ef | 3,641,394 |
def kernel_epanechnikov(inst: np.ndarray) -> np.ndarray:
"""Epanechnikov kernel."""
if inst.ndim != 1:
raise ValueError("'inst' vector must be one-dimensional!")
return 0.75 * (1.0 - np.square(inst)) * (np.abs(inst) < 1.0) | 7426e068c3a939595b77c129af4f8d30bbfc89fb | 3,641,395 |
def submission_parser(reddit_submission_object):
"""Parses a submission and returns selected parameters"""
post_timestamp = reddit_submission_object.created_utc
post_id = reddit_submission_object.id
score = reddit_submission_object.score
ups = reddit_submission_object.ups
downs = reddit_submiss... | d2b406f38e799230474e918df91d55e48d27f385 | 3,641,396 |
def dashboard():
"""Displays dashboard to logged in user"""
user_type = session.get('user_type')
user_id = session.get('user_id')
if user_type == None:
return redirect ('/login')
if user_type == 'band':
band = crud.get_band_by_id(user_id)
display_name = band.display... | 1cec9fcd17a963921f23f03478a8c3195db9a18e | 3,641,397 |
from bs4 import BeautifulSoup
def parse_site(site_content, gesture_id):
""" Parses the following attributes:
title, image, verbs and other_gesture_ids
:param site_content: a html string
:param gesture_id: the current id
:return: {
title: str,
img: str,
id: number,
compares... | b9719dbbd2ca7883257c53410423de5e3df3fe93 | 3,641,398 |
from multiprocessing import Pool
import multiprocessing
def test_multiprocessing_function () :
"""Test parallel processnig with multiprocessing
"""
logger = getLogger ("ostap.test_multiprocessing_function")
logger.info ('Test job submission with module %s' % multiprocessing )
ncpus = mu... | a59635b844b4ff80a090a1ec8e3661e340903269 | 3,641,399 |
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