content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def SourceRef(filename: str) -> FileSourceRef:
"""
Deprecated callable that provides a compatible stand-in for the old-style
SourceRef class constructor
"""
return FileSourceRef(filename) | af6edbf6dde68bb64f5519d57113df49d3904eae | 56,900 |
def service_interface_point (sip_uuid):
"""Retrieve ServiceInterfacePoint by ID
:param sip_uuid: ID of ServiceInterfacePoint
:type sip_uuid: str
:rtype: ServiceInterfacePoint
"""
for sip in context.service_interface_point:
if sip.uuid == sip_uuid:
return sip | 44ddc6b799b4a619c73d23f90f6c63075cf90b23 | 56,901 |
def addError(e, parser):
"""
assumes errors are lists, and
that the state is a position
"""
return bind(getState,
lambda pos: mapError(lambda es: [(e, pos)] + es, parser)) | eeabe120be337e1b05584348cf62933856d908f6 | 56,902 |
from typing import Optional
def get_group(api_management_name: Optional[str] = None,
name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetGroupResult:
"""
Use this data source to access info... | b57060641ffa913c117187f6dbeae21140e0b8c2 | 56,903 |
async def delete_topic(
topic_id: str, usecase: TopicUsecase = Depends(deps.get_topic_usecase)
) -> JSONResponse:
"""Delete a topic."""
try:
deleted: bool = await usecase.delete(topic_id)
except TopicCanNotBeChanged as err:
return JSONResponse(
status_code=status.HTTP_422_UNP... | d1ec1d32ac9f54d09f60702f70ed0a176980ea72 | 56,904 |
def _disc_disc(dynamod, measmod):
"""Check whether the state space model is discrete-discrete."""
dyna_is_disc = issubclass(type(dynamod), DiscreteModel)
meas_is_disc = issubclass(type(measmod), DiscreteModel)
return dyna_is_disc and meas_is_disc | 8fa546a78abe278d3c435686c8f5e09b5c3c65a0 | 56,905 |
def reduceby(key, binop, seq, init=no_default):
""" Perform a simultaneous groupby and reduction
The computation:
>>> result = reduceby(key, binop, seq, init) # doctest: +SKIP
is equivalent to the following:
>>> def reduction(group): # doctest: +SKIP
... re... | 698abf3587e1d54313bb40193534d0af82010fa6 | 56,906 |
def padaxis(array, new_size, axis, pad_value=0, pad_right=True):
"""
Padds one axis of an array to a new size
This is just a wrapper for np.pad, more usefull when only padding a single axis
Parameters
----------
array: ndarray
the array to pad
new_size: int
the new size of ... | 3f3f09de43e5c810797973cc2a57974ec65294bd | 56,907 |
def chomp_empty(seq):
"""Return slice of sequence seq without trailing empty tuples."""
n = len(seq)
while (n > 0) and seq[n - 1] == ():
n -= 1
return seq[:n] | 1c6f5f58bb2e73d44b2638d796b1b0a38bba414c | 56,908 |
def _logit(x):
"""
Logit function in Theano. Useful for parameterizing alpha.
"""
return np.log(x/(1. - x)) | 1dce705a78d7e4084cbb6f1a417517c5d0a17f74 | 56,909 |
def nvidia_model(input_shape=(160, 320, 3), drop_out = 0.5, drop_out_sp = 0.2):
"""
NVIDIA Architecture
src:[http://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf]
"""
print('\n\n ')
print('>> Building the model (NVIDIA Architecture)...')
... | a7365f69a09143f1c17b382f3ee3495e20ff30df | 56,910 |
def rmv_extra_channels(img):
"""
If a greyscale image is stored as some color image, it will have 3 (RGB) color channels. Since we assume it is really greyscale, all the channels will have equal value so we create a new image of same width and height, but with just 1 color channel.
Parameters
---------... | d4e4d5ca985294219be08dda882d4bed2fcbd0a9 | 56,911 |
from typing import Dict
import logging
from typing import List
def get_recommended_channels(**kwargs):
"""Show some of our Community's favorite channels you can join
see https://api.slack.com/methods/channels.list as well as https://api.slack.com/methods/channels.info for API info
"""
_, text = kwargs... | c61fb77a68d22afe03633f97b02a415d5795b227 | 56,912 |
import os
def builtin_name(path):
"""Return the builtin function named in the path."""
name = os.path.basename(path).strip()
if name.startswith('<') and name.endswith('>'):
return name
return None | 6dc701e193eb721957b538dc5bfae311ab46d4d8 | 56,913 |
def main(args=None):
"""
Actually runs Pedal from the command line.
Args:
args (argparse.Namespace): The arguments parsed from the command line.
"""
# Get command line arguments, unless we were explicitly given them.
if args is None:
args = parse_args()
pipeline = MODES.PIPE... | b1082e49e0c6e5aa33ac6cf709ae0717eb29918f | 56,914 |
def tessdata_to_df(tessdata, keep_garbage=False):
"""Ingests a string repr of tesseract output and spits out a dataframe"""
rows = [r.split("\t") for r in tessdata.split("\n")[:-1]]
h = rows[0]
rows = rows[1:]
df = pd.DataFrame(rows)
df.columns = h
# set types
dtypes = [int] * 10 + [fl... | a55320c90fe848b555e44c447fe95d762756b707 | 56,915 |
def find_best_match(gts, pred, pred_idx, threshold=0.5, form='pascal_voc', ious=None) -> int:
"""Returns the index of the 'best match' between the
ground-truth boxes and the prediction. The 'best match'
is the highest IoU. (0.0 IoUs are ignored).
Args:
gts: (List[List[Union[int, float]]]) Coord... | 83ccbb67565e92d6b2420d38ac8e90fb6456cc5c | 56,916 |
def rotation(v1, v2 = None, dim = 3):
"""
Rotation Matrix
rotates one matrix onto another
"""
if not isinstance(v1, Point):
if (isinstance(v1, np.ndarray) or isinstance(v1, list)):
v1 = Point(*v1)
else:
raise ValueError("Point must be Point Ojbect or list")
else:
v1 = Point(*v1.euclidean)
if not ... | 371979d44a916c0d6d5e9d6ac6fa3423bcd3fdcd | 56,917 |
import secrets
async def redirect(request: Request) -> Response:
"""Return plain password if user follows redirect chain."""
url = request.url_for("level:redirect")
secret = request.query_params.get("secret", "")
# If secret is not given, use first secret in redirect chain
if not secret:
... | a46829391aa543669238835aa0f149d8c41348c2 | 56,918 |
def get_time_signature_confidence(h5,songidx=0):
"""
Get signature confidence from a HDF5 song file, by default the first song in it
"""
return h5.root.analysis.songs.cols.time_signature_confidence[songidx] | 578992f3a3261555fc9003166c725bc82eb00d5c | 56,919 |
def remove_duplicates(probs, curr_teams, classes):
""" Iterates through algorithm predictions and if any prediction is the
same as a player's current team, replaces it with the next highest
scoring team.
Parameters:
probs (np
array): array of probabi... | f72932fa00c4ad291a4195d99b1334ffea9cc080 | 56,920 |
from typing import Optional
from typing import Sequence
def _render(
self,
frames: Optional[Sequence[int]] = None,
*,
num_frames: int,
) -> dict[str, np.ndarray]:
"""Mocked render."""
del frames
batch_shape = (num_frames, *self.scene.resolution)
return {
k: make_array_fn(batch_shape=batc... | 6b5a088235fcbb86614998e688c59d3c867e832c | 56,921 |
import os
def get_map(lon_min, lon_max, lat_min, lat_max, zoom=19):
"""
Get an ESRI World Imagery map of the selected region
Args:
lon_min: Minimum longitude (degrees)
lon_max: Maximum longitude (degrees)
lat_min: Minimum latitude (degrees)
lat_max: Maximum latitude (degre... | 93f85ddaa8074ac30d10c5dcb658cff4f6dcfe72 | 56,922 |
def tag_id(user_id, tag, escape=False):
"""tag to feedly ``:tag_id`` format
:param user_id: ``:user_id`` format data
:type user_id: :class:`basestring`
:param tag:
:type tag: :class:`basestring`
:param escape:
:type escape: :class:`bool`
:returns:
:rtype: :class:`basestring`
... | 379320f577e14cfdee892931049481cd6af11514 | 56,923 |
import os
def get_iam_policies(handler):
"""
Instantiate the IAM client and request a full list of Policies.
We then write this list to the filesystem so that the corresponding XUI for this feature
can read previously discovered objects without having to call this API each time the
AWS RH detail ... | 25b60b151735a9687671ab9e03018c8c3cb8be55 | 56,924 |
def read_replica_or_default():
"""
If there is a database called "read_replica",
return "read_replica", otherwise return "default".
This function is similiar to `use_read_replica_if_available`,
but is be more syntactically convenient for method call chaining.
Also, it always falls back to "defa... | d0b2391e0fbbe0c32e682b2e43d892a6737c8055 | 56,925 |
def _delta(x, y):
"""Computes |y|/|x|."""
return max(float(len(y))/float(len(x)), 1.0) | 22a55950406daeb3e7653d9a0a232d52a7bd76e4 | 56,926 |
import re
def compile(text, **variables):
"""Compile a table text to a ``Table`` object.
:param text: a table text
:param variables: values passed to the table
:type text: string
:type variables: dict
:return: a table object
:rtype: Table
:raise TableMarkupError: the text format is in... | 7014d8a07051123cb2df7abffced241fc5dcd2c6 | 56,927 |
def emphasized_url(
name: str, rawtext: str, text: str, lineno: int, inliner: docutils.parsers.rst.states.Inliner, *__
) -> tuple[list, list]:
"""
Sphinx role to add hyperlinked literals.
ReST: :literal-url:`Google <https://google.com>`
Markdown equivalent: [`Google`](https://google.com)
Refer... | 50c744063c6b65e02044d0711236e3058cec4ebb | 56,928 |
def choose_named_type(*args):
"""
choose_named_type(out_sym, root_til, title, ntf_flags, predicate=None) -> bool
Choose a type from a type library.
@param out_sym: pointer to be filled with the chosen type (C++:
til_symbol_t *)
@param root_til: pointer to starting til (the function wil... | 75d5bbc1179149d12e03992f4d91e551d9dd8a65 | 56,929 |
from datetime import date
def voto(ano):
"""
-> Função recebe o ano de nascimento do usuário .
:return: A idade e a verificação se o usuário está em idade eleitoral.
"""
atual = date.today().year
idade = atual - anonasc
if idade < 16:
return f'Você tem {idade} e não voto'
elif ... | b392efba24d943f591ca0ba3060bfc06313006e5 | 56,930 |
def quiet_call(cmd, devnull):
"""Calls an external command while suppressing stdout.
Args:
cmd (str): The command to run
devnull (object): File-like object
Returns:
bool: True if the commabd return code was zero, else otherwise
Examples:
>>> with open(os.devnull, 'wb')... | b24a68f7abe7b51feb44290aa15805a93d31e502 | 56,931 |
def _l2_clogistic_gradient_intercept(IL, Y, alpha):
""" Gradient of penalized loglikelihood with respect to the intercept parameters
Args:
IL : array_like. See _l2_clogistic_gradient_IL
Y : array_like. response matrix
alpha : array_like. intercepts. must have shape == one less than the ... | 3e818e7d010a6261a14df2c2b666d55ab86c5257 | 56,932 |
def dict_to_margins(margin_dict):
"""Convert a dictionary with margins informations into a list of distributions.
Parameters
----------
margin_dict : dict
A dictionary of information on the margins
Returns
-------
margins
"""
margins = []
for i in sorted(margin_dict.key... | ad82c5f4748d1e3b9b9128c33632aecca85e338d | 56,933 |
def omp_in_parallel():
"""Return 1 if the active-levels-var ICV is greater than zero; \
otherwise, return 0.
The effect of the omp_in_parallel routine is to return 1 if the current
task is enclosed by an active parallel region, and the parallel region is
enclosed by the outermost initial task... | ab2203bfe4f22b2ece40b469267fd94953ab7637 | 56,934 |
def get_token_time(token_index, sentence, duration):
"""
Linearly interpolate to guess the time a token was utterred
"""
return (token_index + 1) / max(1, len(sentence)) * duration | d227e782083ae6265e1bc4e286269c421e809985 | 56,935 |
def is_ip_addr(candidate):
"""Check a string to see if it is a valid v4 or v6 IP address
:param candidate: string to check
:return boolean
"""
return is_ipv4_addr(candidate) or is_ipv6_addr(candidate) | 31f531fc2279713376c327c360fcdbf61086701a | 56,936 |
import os
import shutil
import atexit
import tempfile
import io
def make(destDir, verbose = 0):
"""
Make an executable zip file of project
"""
zipAppFileName = os.path.join(destDir, ZIPAPP_NAME)
zipPkg = ZipPkg(__name__)
if zipPkg.zipexists:
src, dst = zipPkg.zippath, zipAppFileName
... | a276151d68718be1fee86546f5c7eb71767c72fb | 56,937 |
def get_voxel_file(category, model_id):
"""Get the voxel absolute filepath for the model specified by category
and model_id.
Args:
category: Category of the model as a string (eg. '03001627')
model_id: Model ID of the model as a string
(eg. '587ee5822bb56bd07b11ae648ea92233')
... | 7a2934267ff43df56e553b92b94e2b4658ff4d2d | 56,938 |
def ccn(curr_div, curr_mod, curr_list, prev_level):
""" Caclulate current neighbours """
value = 0
largest_mod = max(2 * prev_level - 1, 0)
#TODO error: larges mod gets calculated wrong
if (curr_div == 3) and curr_mod > largest_mod-2:
value += curr_list[0]
if curr_mod > 0:
# Defa... | 6761bc2748ab09bf3d1214555e82c7d894f97691 | 56,939 |
from ..scene import SceneCanvas
from vispy.app import use_app
def TestingCanvas(bgcolor='black', size=(100, 100), dpi=None, decorate=None,
**kwargs):
"""Avoid importing scene until necessary."""
# On Windows decorations can force windows to be an incorrect size
# (e.g., instead of 100x10... | a41e399adcdff4bb68385ccb824a966b0ff65855 | 56,940 |
def bold(string, *funcs, **additional):
"""Text effect - bold. (see sgr_combiner())."""
return sgr_combiner(string, ansi.BOLD, *funcs, **additional) | 45804a5ea85b0bc0cc213054f37403a0429122b9 | 56,941 |
def forward_method_kwargs(**kwargs) -> dict:
"""Return all the keyword-arguments of a method, excluding the 'self' argument"""
retval = {}
for key, value in kwargs.items():
if key == 'self' or key.startswith('_'):
continue
elif key == 'kwargs':
retval.update(value)
... | 571ad0c61f33e608ce253c16f452e488257cbf31 | 56,942 |
def esc_ansicolor (color):
"""convert a named color definition to an escaped ANSI color"""
control = ''
if ";" in color:
control, color = color.split(";", 1)
control = AnsiControl.get(control, '')+";"
cnum = AnsiColor.get(color, '0')
return AnsiEsc % (control+cnum) | 71737fbd83a39b220e7e283da2978a1f12c6cd65 | 56,943 |
def load_inputs_twitter_(input_file, word_id_file, sentence_len, type_='', is_r=True, target_len=10, encoding='utf8'):
"""
Method obtained from Trusca et al. (2020). NOTE: not used in this project
:param input_file:
:param word_id_file:
:param sentence_len:
:param type_:
:param is_r:
:p... | 386b769b5fb3467ecb1164d309fe9b479e23445d | 56,944 |
def log_softmax(x):
"""
Compute log-domain softmax of logits
Args:
x: tensor, here logits
Returns:
tensor, log-domain softmax of input
"""
x_dev = x - tf.reduce_max(x, 1, keepdims=True)
logsoftmax = x_dev - tf.math.log(tf.reduce_sum(tf.exp(x_dev), 1, keepdims=True))
r... | 469c2fb6999c370e6926531480ddad8c106f70f9 | 56,945 |
import numpy
def standardize_and_check_cloud_variable( var ):
"""Checks whether var is a legitimate cloud variable for histogram plotting.
Presently only the axes are checked. They need to be prs and tau axes where each value
falls within the standard bounds defined above."""
var = cloud_regrid_to_st... | 5d39626739edccf6218f1249ceefa350522ab278 | 56,946 |
def percent_identity(perc_indentities):
"""Plots read percent identity as a distribution/histogram plot.
Args:
perc_indentities: A list of the percentage identity figures.
Returns:
A matplotlib figure object containing the plot.
"""
bins = 100
xlabel = 'Read percent identity'
... | 43f8c0f49ce1ad8e5f3bdf8eebbf4d47a9412fdd | 56,947 |
def vel_disp_function_CPV2007(vel_disp_grid):
"""Evaluate the velocity dispersion function from the fit on SDSS DR6
by [1]_ on a provided grid and normalizes the result to unity, so it can
be used as a PMF from which to draw the velocity dispersion.
Parameters
----------
vel_disp_grid : array-... | c5e82a485a169ccb9c47ece39e4d1ab07e5f3b6a | 56,948 |
def match_json(json, mongo_filter):
"""
Returns the json that matches the given filter
:param json: json dict or list of json dicts
:param mongo_filter: json dict in mongo query syntax
:return: list of json dicts that match
"""
if type(json) is list:
fset = make_filter_chain(json, mo... | 5115d88c3b68e00a40599d3c6ab7a708a8bc13ce | 56,949 |
def mask_to_3ch(mask, r=True, g=True, b=True):
"""
Convert binary mask to 3 channel image. (for visualization purposes)
:param mask: binary 1-channel mask
:param r: fill red channel with 255 where mask==1
:param g: fill green channel with 255 where mask==1
:param b: fill blue channel with 255 w... | e0a3bd992fc2be291b8e372597113e8021cb6dd5 | 56,950 |
def load_cs(
src_file: str,
dhis2_instance: str,
dhis2_username: str,
dhis2_password: str,
dhis2_groups: str,
) -> gpd.GeoDataFrame:
"""Load cases de santé geometries from source file or DHIS2."""
# from source file
if src_file:
cs = gpd.read_file(src_file)
n_geoms = sum(... | e3cfa9795543fbeac9f4a54e048c208cd3d15577 | 56,951 |
def get_fs_info():
"""return a list containing the info returned by 'df -k', i.e,
file systems size and occupation, in Mb. And the filesystem type:
[dev, size, used, available, use%, mount point, fs type]"""
try:
buf = os.popen('df -k').readlines()
except (IOError, os.error), (errno, strerr... | 16952d7bf76639fb92c6b01b5a714dbea6cbc3ec | 56,952 |
import json
def get_details(details_string):
"""Return details content.
* If JSON as a formatted string
* Otherwise as a string as-is.
"""
content = details_string
rv = ''
try:
details = json.loads(content)
rv = ''.join(['<li>{k}: {v}</li>'.format(k=k, v=get_str_value(v))... | 6b2737b2f5c90b564842760ec55ecade5f49576f | 56,953 |
import os
def load_letter(folder, min_num_images):
"""Load the data for a single letter label."""
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
dtype=np.float32)
print(folder)
num_images = 0
for image in image_files:
im... | 3704657bc6a7f2660755b5881bf6120f70bb7bc2 | 56,954 |
from re import DEBUG
def train_with_ludwig(model_definition, train_file_csv, target_model_path):
"""
Wrap around Ludwig training routine.
:param model_definition: dictionary defining deep learning architecture with Ludwig conventions
:param train_file_csv: path to csv file for training
:param tar... | 53537cedbf3aa52352b095118cfcf488de3e7d31 | 56,955 |
def calc_load_matrix(M,Minv=None,isSparse=False):
"""
Assembles _load matrix_ as used in state-space representation of
equation of motion
$$ B = [[0],[M^{-1}]] $$
where **M** is the system mass matrix.
***
Required:
* `M`, mass matrix, shape [n x n]. Unused i... | 2f2274691c44edb993c810fecc0cc51e4a6402ce | 56,956 |
def _get_keywords_with_score(extracted_lemmas, lemma_to_word):
"""Get words of `extracted_lemmas` and its scores, words contains in `lemma_to_word`.
Parameters
----------
extracted_lemmas : list of (float, str)
Given lemmas with scores
lemma_to_word : dict
Lemmas and corresponding w... | 385a54878e508a56ab772fec31f4ff9838048492 | 56,957 |
def url_scan_sync(url, timeout):
"""
Execute SlashNext's url/scansync API against the requested URL scan sync with the given parameters
:param url: URL to be scanned
:param timeout: Timeout value in seconds
:return: Response of the SlashNext url/scansync API
"""
# Create the required data di... | feb60c21124ca61cd4776f0fc2c067310b37cafe | 56,958 |
def create_app(config_object='webfront_service.settings.__init__'):
"""An application factory, as explained here: http://flask.pocoo.org/docs/patterns/appfactories/.
:param config_object: The configuration object to use.
"""
app = Flask(__name__.split('.')[0])
app.config.from_object(config_object)
... | ce01d13da269063d3886f4614426ec69d14802c8 | 56,959 |
def get_user_friend_profiles(proxies, auth, limit=None, user_id='', screen_name='', skip_status='', include_user_entities=''):
"""
Returns a list of user dictionaries for every user the specified user is following (otherwise known as their
'friends'). Either either user ID or screen name, if both are specif... | 0dd00c095b366088c4c17d9b7cab44266bfb09d6 | 56,960 |
def deci_class_logger():
"""
This decorator wraps every class method with deci_func_logger decorator.
It works by checking if class method is callable and if so it will set a new decorated method as the same method name.
"""
def wrapper(cls):
# TODO: Not Working - Breaks the code, tests doe... | 69a4fa9c70ee37ec20d0cfd0aba2ad2f1bbd7dac | 56,961 |
from typing import List
from typing import Dict
from typing import Tuple
from typing import Optional
async def apply_actions(
conns: Connections,
conv_id: int,
actions: List[Action],
*,
spam: bool = False,
warnings: Dict[str, str] = None,
allow_multiple_actors: bool = False,
) -> List[int]... | 172650023ebc39045f85b7cbe2c837bc8bd9bc3c | 56,962 |
def _file_metadata_by_replacing_path(f, new_path, new_is_dir=None):
"""Returns a copy of the f _file_metadata struct with the given path."""
root_path = _get_opt_attr(f, 'rootPath')
if new_is_dir == None:
new_is_dir = f.is_dir
return _struct_omitting_none(
path=new_path,
src=f.src,
root=r... | c38930f17664843525cad6afe0eb580282abac49 | 56,963 |
import re
def tokenize(text):
""""
Tokenization function for use in TfidfVectorizer in build_model()
1. Normalizes text (converts to lowercase)
2. Removes punctuation
3. Removes stop_words
4. Splits sentence into words (tokens)
5. Performs POS tagging
Parameters:
text: string to b... | f1903b3151a3a42622ec8ee3f9cf31ff51d64b63 | 56,964 |
def transform_gcp_firewall(fw_response):
"""
Adjust the firewall response objects into a format that is easy to write to Neo4j.
Also see _transform_fw_entry and _parse_port_string_to_rule().
:param fw_response: Firewall response object from the GCP API
:return: List of transformed firewall rule obje... | d9d6d1a9dfcbd0e7eb2b51a67c0d35ab55768384 | 56,965 |
import requests
def export_highlights_to_readwise(readwise_token, highlight_objects):
""" Submits highlights to Readwise via their API and return the API response.
The function takes 2 parameters:
- readwise_token: the token used for accessing the user's Readwise highlights through the Readwise API.
... | c6b2928aa488fb62d40470e441232ed67d61a020 | 56,966 |
def test_reddit():
"""The ShareOnReddit link enables sharing a link to an url on Reddit. Here we test that
- The link can be instantiated with a link to https://awesome-panel.org
- It works when clicked
"""
return TestApp(
test_reddit,
ShareOnReddit(url="https://awesome-panel.org").... | 5035521ae933ad1b433fc91348f587e730dd1ed7 | 56,967 |
from typing import List
def filter_relative_import_names(import_names: List[str]) -> List[str]:
"""Given a list of import names, filters out the relative import names."""
return list(name for name in import_names if is_relative_import_name(name)) | d4d4c43584f496d9d3392861dbb5cbf559a97f1e | 56,968 |
import pickle
def make_pred_dataframe(key, mask, image):
"""Takes an image id and a mask
and returns a dataframe with all cells"""
classes = ["tumor", "immune cells"]
with open(c.MODEL_DIR / "cell_classifier.pickle", "rb") as f:
features, model = pickle.load(f)
df_pred = pd.DataFrame(
... | 9148ecd9f7428aebe77e29b309d3398b92cc3e4a | 56,969 |
def initialize_omlet_config(cfg: DictConfig):
"""
See _omlet_config_schema()
"""
assert isinstance(cfg, DictConfig)
OmegaConf.set_struct(cfg, False)
om = _omlet_config_schema()
om.update(cfg.get('omlet', {}))
cfg.omlet = om # this is a copy operation
om = cfg.omlet
om._internal... | d041d844aca394bf43825cd1ab37820f1d868c8e | 56,970 |
import os
def path(name=""):
"""
Retrieve the relative filepath to a supplied name from the BrightEdge
root directory. If the name begins with a "/", then consider the supplied
name to be an absolute path.
Example:
If BrightEdge Root is: /home/vagrant/modbus_simu_cli
path() will return "/... | d4ccd6edd34c4d0576e0b1e9d6322d9ca708a4cb | 56,971 |
def h(p1, p2):
"""Heuristic function"""
x1, y1 = p1
x2, y2 = p2
return abs(x1 - x2) + abs(y1 - y2) | 0d7522a3f92ae851bfa9b1a316a1fa97baccf98f | 56,972 |
import torch
def pdist(X, Y):
""" Computes all the pairwise distances
Parameters
----------
X : torch.tensor
shape [n, d]
Y : torch.tensor
shape [m, d]
Returns
-------
torch.tensor
shape [n, m] of all pairwise distances
"""
n, m = X.shape[0], Y.shape[0... | 10fce38b390211999344bd1b0daf0a4484aee2a8 | 56,973 |
def detab(self, elem="", lab="", v1="", v2="", v3="", v4="", v5="", v6="",
**kwargs):
"""Modifies element table results in the database.
APDL Command: DETAB
Parameters
----------
elem
Element for which results are to be modified. If ALL, modify all
selected elements [ESE... | 01f13f7e971c2c24e291712a7e634838e2e2ac5a | 56,974 |
from typing import Optional
def retriever_factory(
opt: Opt, dictionary: DictionaryAgent, shared=None
) -> Optional[RagRetriever]:
"""
Build retriever.
Override to build special BB2 Search Retrievers, if necessary
:param opt:
ParlAI Opt
:param dictionary:
dictionary agent
... | aa37babfffabf5491c542fa173352080b702f180 | 56,975 |
import argparse
from typing import Dict
from typing import Tuple
from typing import List
def add_target_parser(
target: Target,
subparsers,
common: argparse.ArgumentParser,
existing: Dict[str, Tuple[argparse.ArgumentParser, List[str]]],
) -> str:
"""Add a subparser for a given build target
Fo... | 941c7e9e1e7fefed148ad93cff5e28e36bf2a816 | 56,976 |
import pathlib
import json
def import_skin_data(mesh_name: str, path: pathlib.Path, import_type: ImportType = ImportType.order):
"""
Load skin data from disk and apply it to the given mesh.
"""
with open(path, "wb") as path_data:
skin_data = json.load(path_data)
return set_skin_weights(mesh_name, skin_data)
... | ef34bbe580000ead33a427c1f45c22243cad4398 | 56,977 |
from typing import List
from typing import Dict
def combine_fields(
fields: List[xr.DataArray],
field_record: Dict,
dim: str="valid_time",
) -> xr.DataArray:
"""
按维度 step 合并
"""
field = xr.concat(fields, dim=dim)
return field | df1973443eadd18730b0cb195bc41539ba53da72 | 56,978 |
import warnings
def derivest(fun, x, par = None, **kwargs):
"""
Estimate the derivative of a function of one variable.
Arguments:
fun : Callable object with signature fun(x, *args) -> float, with x the
(scalar) argument, and args an optional list of parameters.
... | 7a66b5499ccb5e72125a2b323929c2196d747c48 | 56,979 |
def norm(vec):
""" Calculate norm of a vector or list thereof
Args:
vec (Vector or List[Vector]): vector(s) to compute norm of
Returns:
Scalar or List[Scalar]: norms
"""
if len(vec.shape) == 1: # it's just a single column vector
return np.sqrt(vec.dot(vec))
else: # tr... | c225467905ccd30b826d8ef5e0112bb23f106ec9 | 56,980 |
import PIL
def get_image_size(img_filename,img_directory_path):
"""
Return the size of the specified image
img_filename : the name of the image file
img_directory_path : the path to the directory where the image is
Return : (width, height)
width : the width in pixel of the image
height : t... | b46934940f1c62f9110efa219e042a39026db76c | 56,981 |
import torch
def train_deep_encoder(torch_dataset, model, optimizer, criterion, lr, n_epochs,
batchsize, verbose=False):
"""Train label encoder for categorical features
Parameters
----------
torch_dataset : Torch_Dataset
Dataset to feed the NN containing categorical fea... | c89aa6be474233d7521361190f7fdfc0b1cc29a3 | 56,982 |
def convert(s):
"""
Convert a probability string to a float number.
:param s: probability string.
:return: a float probability.
"""
try:
return float(s)
except ValueError:
num, denom = s.split('/')
return float(num) / float(denom) | 84333f21edfdcb2f3c917f23c23b9d399c5f6e56 | 56,983 |
def tree_ones_like(tree: Pytree) -> Pytree:
"""
Return a new tree with the same structure as t, but with all values set to
1.
"""
return jax.tree_util.tree_map(lambda x: jnp.ones_like(x), tree) | 3fa06e05111741eda6a5af8f558e2e6dce29755e | 56,984 |
def writeOctetStream(oStr, minValue = 0):
"""
@summary: write string as octet stream with per header
@param oStr: octet stream to convert
@param minValue: min length value
@return: per header follow by tuple of UInt8
"""
length = len(oStr)
mlength = minValue
if length - minValue... | 0eeb4578f51b4488e762d3f49b1d5c5accdd5b4d | 56,985 |
from typing import Sequence
def _find_fuzzy_line(
py_line_no: int,
py_by_line_no: Sequence[str],
cheetah_by_line_no: Sequence[str],
prefer_first: bool,
) -> int:
"""Attempt to fuzzily find matching lines."""
stripped_line = _fuzz_py_line(py_by_line_no[py_line_no])
cheetah_l... | 07e088df7ca73d52d47d37ff0971c43c496bc68a | 56,986 |
def ensemble_adjustment_kalman_filter_update(
state,
observation,
observation_fn,
minimum_observation_prior_variance=None,
name=None):
"""Ensemble Adjustment Kalman Filter Update.
The Ensemble Adjustment Kalman Filter (EAKF) [1], is a deterministic variant
of the [Ensemble Kalman Filter](
h... | 19738e67bfafa3866ea7dfbca6adbf7181636384 | 56,987 |
def S_vib(r,r0,B0,M,grun,T):
"""Vibrational entropy."""
x = debye_temp(r,r0,B0,M,grun) / T
return 3*kb*(4.0/3*debye_func(x) - np.log(1-np.exp(-x))) | 4fbbde4a33219947d523e2ab3d190a441a77333d | 56,988 |
def countExtn(fimg, extname='SCI'):
"""
Return the number of 'extname' extensions, defaulting to counting the
number of SCI extensions.
"""
closefits = False
if isinstance(fimg, string_types):
fimg = fits.open(fimg)
closefits = True
n = 0
for e in fimg:
if 'extn... | 44051dcef1bef901d4905d7b4b805dc9140a32c0 | 56,989 |
def tensor(dimensions):
"""
Create a tensor
:param dimensions: The list of dimensions
:return: A tensor
"""
global csp
check_engine()
csp.variables.append(csp.int(size=None, deep=dimensions))
return csp.variables[-1] | e96338ed9df016bb425642f4fd05de4775aa2d92 | 56,990 |
import random
def makeData(test=False):
""" Make the data and return a dict that contains the data and goal."""
# open the files, I already made them smaller; they contain 3000 pics each
Ball = np.load("../data/sBasketball.npy")
LightBulb = np.load("../data/sLight_bulb.npy")
Sun = np.load("../dat... | 427fca4cb41b776f4281dadda2971930214a2864 | 56,991 |
from typing import Union
from pathlib import Path
import re
def model_from_checkpoint(
filename: Union[Path, str],
model_name=None,
backbone_name=None,
classes=None,
is_coco=False,
img_size=None,
map_location=None,
strict=False,
revise_keys=[
(r"^module\.", ""),
],
... | 4296da4efc9499a8d10d09a5c0a2329bbc8cb54d | 56,992 |
def get_problem(pid=None, name=None, tid=None, show_disabled=True):
"""
Gets a single problem.
Args:
pid: The problem id
name: The name of the problem
show_disabled: Boolean indicating whether or not to show disabled problems. Defaults to True
Returns:
The problem dictio... | 5e781b867bd4ebc26d451e522262a954dd2ea27c | 56,993 |
def mock_boto_client_upload_success(*args, **kwargs):
"""
Mock boto client, so uploading always returns success
"""
return MagicMock() | 3fec23fa670a9066ae9fc881d0cf1d874f9cf480 | 56,994 |
def git_log_file(file_name):
"""要求文件路径是相对于git仓库更目录到文件完整路径"""
cmd = "git log -1 %s |grep Date" % file_name
return cmd | e7d0481d4ee7e1a68492df07ed9c35e95a87ab5c | 56,995 |
import json
def sfjson_loads(s):
"""Exception safe json.loads()"""
try:
return rlk_jsonloads(s)
except json.decoder.JSONDecodeError:
return {} | cbc7bc01d895cc0cce46b9c8d865eeee31442e3d | 56,996 |
import math
def calc_tols(polys_truth, tick_dist=5, max_d=250, rel_tol=0.25):
""" Calculate tolerance values for every GT baseline according to https://arxiv.org/pdf/1705.03311.pdf.
:param polys_truth: groundtruth baseline polygons (normalized)
:param tick_dist: desired distance of points of the baseline... | c26cecf5a8bdd2fbca85aa1d3bc6fce4593869e2 | 56,997 |
def to_terminal(group: list, outcomes: list) -> float:
"""
Create a terminal node value
:param group: group of samples with target as last element
:return: most representative class value for group
"""
outcomes = [row[-1] for row in group]
return [max(set(outcomes), key=outcomes.count), outc... | 886c397aaef82d1e38c371436c69b4152f6ce760 | 56,998 |
def _log_multivariate_normal_density_spherical(X, means, covars):
"""Compute Gaussian log-density at X for a spherical model."""
cv = covars.copy()
if covars.ndim == 1:
cv = cv[:, np.newaxis]
if cv.shape[1] == 1:
cv = np.tile(cv, (1, X.shape[-1]))
return _log_multivariate_normal_dens... | f74f18c76103bed534c1d98c4931dab9a2f12a72 | 56,999 |
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