content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import TextIO
from typing import Set
def observe_birds(observations_file: TextIO) -> Set[str]:
"""Return a set of the bird species listed in observations_file, which has one bird species per line.
>>> file = StringIO("bird 1\\nbird 2\\nbird 1\\n")
>>> birds = observe_birds(file)
>>> 'bird... | e3ea90e8da4488121ec1ae75c4aa116646db08f5 | 18,200 |
def convert_sheet(sheet, result_dict, is_enum_mode=False):
"""
转换单个sheet的数据
Args:
sheet: openpyxl.worksheet.worksheet.Worksheet
result_dict: [dict]结果都存在这里, key为data_name，value为sheet_result
is_enum_mode: [bool]是否为enum导表模式
Returns:
bool, 是否成功
"""
if is_enum_mode:
... | 284f470844b6722941d0e4725e4c23b1473b08df | 18,201 |
from __main__ import __file__ as main_script_path
import os
def _nsimage_from_file(filename, dimensions=None, template=None):
"""Take a path to an image file and return an NSImage object."""
try:
_log('attempting to open image at {0}'.format(filename))
with open(filename):
pass
... | 079556d6959c6344feb8fa25db3467d4468124d0 | 18,202 |
def bytes_to_int(b: bytes, order: str = 'big') -> int:
"""Convert bytes 'b' to an int."""
return int.from_bytes(b, order) | c959683787e03cc956b5abffc814f98cf4722397 | 18,203 |
def fit_model(params,param_names,lam_gal,galaxy,noise,gal_temp,
feii_tab,feii_options,
temp_list,temp_fft,npad,line_profile,fwhm_gal,velscale,npix,vsyst,run_dir,
fit_type,output_model):
"""
Constructs galaxy model by convolving templates with a LOSVD given by
a specified set of velocity parameters.
... | 44cd0bc61a4472c6a5c3c7b190ee5be96f4bdb1a | 18,204 |
import random
def generate_numbers():
"""
Function to generate 3 random digits to be guessed.
Generate 3 random in a list in order to be compare to the user's digits.
Return:
str_digits (Array): List with 3 random digits converted to String
"""
# List comprehension to generate num... | 8efd0f579a3a0b3dc5021cd762f9ad2f5774f6be | 18,205 |
def get_media():
"""Retrieves metadata for all of this server's uploaded media. Can use
the following query parameters:
* max: The maximum number of records to return
* page: The page of records
"""
error_on_unauthorized()
media = Upload.query.order_by(Upload.id)
total_num = media.cou... | 754417b47f5b9c28427b04ace88bf9ca5c9a5a47 | 18,206 |
def summate2(phasevec):
"""Calculate values b'(j^vec) for combining 2 phase vectors.
Parameter:
phasevec: tuple of two phasevectors
Example:
On input (([b_1(0),b_1(1),...,b_1(L-1)], L), ([b_2(0),b_2(1),...,b_2(L'-1)], L'))
give output [b_1(0)+b_2(0), b_1(0)+b_2(1),..., b_1(1)+b_2(0),...,b_1(L-... | 5150c2ee29a31438bf16104eaadeb85a01f54502 | 18,207 |
def get_children():
""" Return IDs of LIST which currently is zero-based index
Modelled after treeview for future alphabetic IDs
TODO: Should probably return list of all DICT?
"""
iid_list = []
#read()
#print('location.get_children() LIST count:',len(LIST))
for i, ndx in enumera... | 14e855df5c2b218c68e900709fd548d3431c4a8b | 18,208 |
def makeTracker( path, args = (), kwargs = {} ):
"""retrieve an instantiated tracker and its associated code.
returns a tuple (code, tracker).
"""
obj, module, pathname, cls = makeObject( path, args, kwargs )
code = getCode( cls, pathname )
return code, obj | bc23e21bb53357bcf74e6194656cfbea4b24c218 | 18,209 |
from typing import Tuple
def get_anchor_generator(anchor_size: Tuple[tuple] = None, aspect_ratios: Tuple[tuple] = None):
"""Returns the anchor generator."""
if anchor_size is None:
anchor_size = ((16,), (32,), (64,), (128,))
if aspect_ratios is None:
aspect_ratios = ((0.5, 1.0, 2.0),) * le... | e9eef959c009062d5866558d00674c1afa033260 | 18,210 |
import torch
def tensor_to_longs(tensor: torch.Tensor) -> list:
"""converts an array of numerical values to a tensor of longs"""
assert tensor.dtype == torch.long
return tensor.detach().cpu().numpy() | ba1788be8e353936cfc3d604d940b78a96990fd4 | 18,211 |
def test_fixed(SNRs):
"""
Fixed (infinite T1) qubit.
"""
fidelities = []
numShots = 10000
dt = 1e-3
for SNR in SNRs:
fakeData = create_fake_data(SNR, dt, 1, numShots, T1=1e9)
signal = dt*np.sum(fakeData, axis=1)
fidelities.append(fidelity_est(signal))
return fidelities | 70ca68f475beed73a47722c719811544ae1bfccb | 18,212 |
def setup(app):
"""
Add the ``fica`` directive to the Sphinx app.
"""
app.add_directive("fica", FicaDirective)
return {
"version": __version__,
"parallel_read_safe": True,
"parallel_write_safe": True,
} | 996e568ab58634e64a845b34bf38082658b58889 | 18,213 |
from typing import Tuple
import torch
def get_binary_statistics(
outputs: Tensor, targets: Tensor, label: int = 1,
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
"""
Computes the number of true negative, false positive,
false negative, true negative and support
for a binary classification pro... | e0c81b404f6da77f40c1e4f3810d699fdef1e6a4 | 18,214 |
def threshold_and_mask(min_normed_weight, W, Mc, coords): # =np.arange(Wc.shape[0])*stride + start):
"""Normalize the weights W, threshold to min_normed_weight and remove diagonal,
reduce DX and DY to the columns and rows still containing weights.
Returns
-------
coords : array_like
the in... | 78d361cf2125cd0d3ac1a3985933e39b09538b18 | 18,215 |
import csv
def readCGcsv(filename, levels):
""" Read a .csv file of a callgraph into a dictionary keyed by callgraph level. """
cgdict = {}
with open(filename, "r") as cgcsv:
cgreader = csv.DictReader(cgcsv)
for row in cgreader:
lvl = int(row['Level'])
if (lvl < l... | ec5dbc3d064a0cf784bfd764b996eb36677642a9 | 18,216 |
def use_colors(tones, i=None):
"""
Use specific color tones for plotting. If i is specified, this function returns a specific color from the corresponding color cycle
For custom color palettes generation check: http://colorbrewer2.org/#type=sequential&scheme=YlGnBu&n=8
Args:
tones : 'hot' or 'co... | e36cce208c89178af8199662edb336c2455bdc37 | 18,217 |
def fill_form(forms, form):
"""Fills a given form given a set or known forms.
:param forms: A set of known forms.
:param form: The form to fill.
:return: A mapping from form element IDs to suggested values for the form.
"""
forms = list(forms)
new_form = {}
def rec_fill_form(form, lab... | 3e6c1f623facb67602fa5e057080a08d0de9926d | 18,218 |
def read_sky_model_from_csv(path: str) -> SkyModel:
"""
Read a CSV file in to create a SkyModel.
The CSV should have the following columns
- right ascension (deg)
- declination (deg)
- stokes I Flux (Jy)
- stokes Q Flux (Jy): if no information available, set to 0
- stokes U Flux (Jy): i... | b649bd1cfd218a924c573bf90b26fa18f62c3cb4 | 18,219 |
def integer_to_vector(x, options_per_element, n_elements, index_to_element):
"""Return a vector representing an action/state from a given integer.
Args:
x (int): the integer to convert.
n_options_per_element(int): number of options for each element in the vector.
n_elements (int): the n... | 2649359d6a62b047f70bfe72f8403e8343a231ab | 18,220 |
def samples_for_each_class(dataset_labels, task):
"""
Numbers of samples for each class in the task
Args:
dataset_labels Labels to count samples from
task Labels with in a task
Returns
"""
num_samples = np.zeros([len(task)], dtype=np.float32)
i = 0
for label ... | 96bc2c794fd955110864f59ddb96c5df1c33b8ed | 18,221 |
def requiredOneInGroup(col_name, group, dm, df, *args):
"""
If col_name is present in df, the group validation is satisfied.
If not, it still may be satisfied, but not by THIS col_name.
If col_name is missing, return col_name, else return None.
Later, we will validate to see if there is at least one... | de46a4ef2f3e45381644db41d617d8c4c0845877 | 18,222 |
def persist(session, obj, return_id=True):
"""
Use the session to store obj in database, then remove obj from session,
so that on a subsequent load from the database we get a clean instance.
"""
session.add(obj)
session.flush()
obj_id = obj.id if return_id else None # save this before obj i... | a308931f418616417d10d3115b0f370352778533 | 18,223 |
from unittest.mock import patch
def test_bittrex_query_asset_movement_int_transaction_id(bittrex):
"""Test that if an integer is returned for bittrex transaction id we handle it properly
Bittrex deposit withdrawals SHOULD NOT return an integer for transaction id
according to their docs https://bittrex.gi... | 83e3ce3d8f82b159191c6b9068b54321d06bfa9a | 18,224 |
from typing import Sequence
import logging
def _fixed_point(
searcher: 'AbstractSearcher',
parsed: parsed_file.ParsedFile,
initial_substitutions: Sequence[substitution.Substitution],
start: int,
end: int,
max_iterations: int,
):
"""Repeatedly apply searcher until there are no more changes.""... | 676102d43f965750d497dbbbbc3e87264de7a6d2 | 18,225 |
from operator import sub
def masker(mask, val):
"""Enforce the defined bits in the <mask> on <val>."""
ones = sub(r"[^1]", "0", mask)
val |= int(ones,2)
zeros = sub(r"[^0]", "1", mask)
val &= int(zeros,2)
return val | 68b3edd542b295ca7aade0eb9829e310e4c0ed2d | 18,226 |
def ct_lt_u32(val_a, val_b):
"""
Returns 1 if val_a < val_b, 0 otherwise. Constant time.
:type val_a: int
:type val_b: int
:param val_a: an unsigned integer representable as a 32 bit value
:param val_b: an unsigned integer representable as a 32 bit value
:rtype: int
"""
val_a &= 0xf... | 6816fd1e9633c0c3035d68ac657f3cb917f24527 | 18,227 |
import typing
async def is_banned(ctx: Context, user: typing.Union[discord.Member, discord.User]) -> bool:
"""Returns true if user is in guild's ban list."""
bans = await ctx.guild.bans()
for entry in bans:
if entry.user.id == user.id:
return True
return False | 2807e2d9a296afb360efe9abf9618e0ebe19e796 | 18,228 |
from typing import List
def _create_transformation_vectors_for_pixel_offsets(
detector_group: h5py.Group, wrapper: nx.NexusWrapper
) -> List[QVector3D]:
"""
Construct a transformation (as a QVector3D) for each pixel offset
"""
x_offsets = wrapper.get_field_value(detector_group, "x_pixel_offset")
... | 1504193d1a7731740a607f77c94a810561142c57 | 18,229 |
import random
def buildIterator(spec_name, param_spec, global_state, random_selection=False):
"""
:param param_spec: argument specification
:param random_selection: produce a continuous stream of random selections
:return: a iterator function to construct an iterator over possible values
"""
i... | d86d2af9499117614a11796c17eeccba16149092 | 18,230 |
import logging
def logged(obj):
"""Add a logger member to a decorated class or function.
:arg obj:
the class or function object being decorated, or an optional
:class:`logging.Logger` object to be used as the parent logger
(instead of the default module-named logger)
:return:
... | 5a7d53257ed7d68c53daf90dcf2d48943a430a4e | 18,231 |
def outlier_removal_mean(dataframe, colname, low_cut, high_cut):
"""Replace outliers with the mean on dataframe[colname]"""
col = dataframe[colname]
col_numerics = col.loc[
col.apply(
lambda x: isinstance(x, (int, float))
and (x >= low_cut and x <= high_cut)
)
]... | 03d40bb8098d4313e468d5b4a929756354a7732c | 18,232 |
def non_repeating(value, counts, q):
"""Finds the first non-repeating string in a stream.
Args:
value (str): Latest string received in the string
counts (dict): Dictionary of strings containing the counts to determine if string is repeated
q (Queue): Container for all strings in stream ... | fc5ec025cffa0d7230d814d3677ae640cd652349 | 18,233 |
def auth_optional(request):
"""
view method for path '/sso/auth_optional'
Return
200 reponse: authenticated and authorized
204 response: not authenticated
403 reponse: authenticated,but not authorized
"""
res = _auth(request)
if res:
#authenticated, but can be aut... | 06416fdce6a652ca0cdc169c48219e685c13cdad | 18,234 |
def is_pip_main_available():
"""Return if the main pip function is available. Call get_pip_main before calling this function."""
return PIP_MAIN_FUNC is not None | 3d4243bb4336fbc9eb9e93b2a1cf9ec4cc129c03 | 18,235 |
import torch
def energy_target(flattened_bbox_targets, pos_bbox_targets,
pos_indices, r, max_energy):
"""Calculate energy targets based on deep watershed paper.
Args:
flattened_bbox_targets (torch.Tensor): The flattened bbox targets.
pos_bbox_targets (torch.Tensor): Bounding... | 84bed4cc1a8bf11be778b7e79524707a49482b39 | 18,236 |
def dashtable(df):
"""
Convert df to appropriate format for dash datatable
PARAMETERS
----------
df: pd.DataFrame,
OUTPUT
----------
dash_cols: list containg columns for dashtable
df: dataframe for dashtable
drop_dict: dict containg dropdown list for dashtable
"""
... | 39897244f81a5c6ac0595aac7cb219f59d6c5739 | 18,237 |
def other_identifiers_to_metax(identifiers_list):
"""Convert other identifiers to comply with Metax schema.
Arguments:
identifiers_list (list): List of other identifiers from frontend.
Returns:
list: List of other identifiers that comply to Metax schema.
"""
other_identifiers = []... | 986c98d5a557fb4fb75ed940d3f39a9a0ec93527 | 18,238 |
def enforce_excel_cell_string_limit(long_string, limit):
"""
Trims a long string. This function aims to address a limitation of CSV
files, where very long strings which exceed the char cell limit of Excel
cause weird artifacts to happen when saving to CSV.
"""
trimmed_string = ''
... | 9b8bcf4590dc73425c304c8d778ae51d3e3f0bf3 | 18,239 |
def gaussian_blur(image: np.ndarray, sigma_min: float, sigma_max: float) -> np.ndarray:
"""
Blurs an image using a Gaussian filter.
Args:
image: Input image array.
sigma_min: Lower bound of Gaussian kernel standard deviation range.
sigma_max: Upper bound of Gaussian kernel standard ... | 2fd31d016e4961c6980770e8dd113ae7ad45a6ed | 18,240 |
def get_number_of_pcs_in_pool(pool):
"""
Retrun number of pcs in a pool
"""
pc_count = Computer.objects.filter(pool=pool).count()
return pc_count | 812de24ad2cbc738a10258f8252ca531ef72e904 | 18,241 |
import tqdm
import math
def save_images(scene_list, video_manager, num_images=3, frame_margin=1,
image_extension='jpg', encoder_param=95,
image_name_template='$VIDEO_NAME-Scene-$SCENE_NUMBER-$IMAGE_NUMBER',
output_dir=None, downscale_factor=1, show_progress=False,
... | 37088294395539acd3b88543d1bdd4d05ef82ce5 | 18,242 |
from typing import List
def get_used_http_ports() -> List[int]:
"""Returns list of ports, used by http servers in existing configs."""
return [rc.http_port for rc in get_run_configs().values()] | 12982ff4d5b2327c06fef1cf874b871e2eee08c1 | 18,243 |
import io
def get_img_from_fig(fig, dpi=180, color_cvt_flag=cv2.COLOR_BGR2RGB) -> np.ndarray:
"""Make numpy array from mpl fig
Parameters
----------
fig : plt.Figure
Matplotlib figure, usually the result of plt.imshow()
dpi : int, optional
Dots per inches of the image to save. Note... | dde9f35b78df436b30d4f9452b9964c93f924252 | 18,244 |
def split_data_by_target(data, target, num_data_per_target):
"""
Args:
data: np.array [num_data, *data_dims]
target: np.array [num_data, num_targets]
target[i] is a one hot
num_data_per_target: int
Returns:
result_data: np.array [num_data_per_target * num_targets... | d4425753b4d9892d2c593ec8e58e75bae0005c3d | 18,245 |
def top_mutations(mutated_scores, initial_score, top_results=10):
"""Generate list of n mutations that improve localization probability
Takes in the pd.DataFrame of predictions for mutated sequences and the
probability of the initial sequence. After substracting the initial value
from the values of the... | f574bf7f7569e3024a42866873c5bb589ff02095 | 18,246 |
def npmat4_to_pdmat4(npmat4):
"""
# updated from cvtMat4
convert numpy.2darray to LMatrix4 defined in Panda3d
:param npmat3: a 3x3 numpy ndarray
:param npvec3: a 1x3 numpy ndarray
:return: a LMatrix3f object, see panda3d
author: weiwei
date: 20170322
"""
return Mat4(npmat4[0, 0],... | 7b58014d5d354aefac84786212b6ca190a983e48 | 18,247 |
import requests
def is_at_NWRC(url):
"""
Checks that were on the NWRC network
"""
try:
r = requests.get(url)
code = r.status_code
except Exception as e:
code = 404
return code==200 | b909a9087940eb70b569ea6c686ff394e84a6ed9 | 18,248 |
import torch
def lmo(x,radius):
"""Returns v with norm(v, self.p) <= r minimizing v*x"""
shape = x.shape
if len(shape) == 4:
v = torch.zeros_like(x)
for first_dim in range(shape[0]):
for second_dim in range(shape[1]):
inner_x = x[first_dim][second_dim]
... | 24bda333cdd64df9a0b4fa603211036bbdad7200 | 18,249 |
def _transform_index(index, func):
"""
Apply function to all values found in index.
This includes transforming multiindex entries separately.
"""
if isinstance(index, MultiIndex):
items = [tuple(func(y) for y in x) for x in index]
return MultiIndex.from_tuples(items, names=index.na... | c642dd9330032ed784224b7ede6ee299b6d3ed67 | 18,250 |
def extractQualiTeaTranslations(item):
"""
# 'QualiTeaTranslations'
"""
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol or frag) or 'preview' in item['title'].lower():
return None
if 'Harry Potter and the Rise of the Ordinary Person' in item['tags']:
return None
i... | 446b7f7598e118222c033bbfce074fa02340fd8e | 18,251 |
def feature_norm_ldc(df):
"""
Process the features to obtain the standard metrics in LDC mode.
"""
df['HNAP'] = df['HNAC']/df['ICC_abs']*100
df['TCC'] = (df['ICC_abs']+df['DCC_abs'])/df['VOL']
df['ICC'] = df['ICC_abs']/df['VOL']
df['DCC'] = df['DCC_abs']/df['VOL']
return df | 60e3ef31c0be07179854de3191c2c75f4ec2cb4d | 18,252 |
def dice_jaccard(y_true, y_pred, y_scores, shape, smooth=1, thr=None):
"""
Computes Dice and Jaccard coefficients.
Args:
y_true (ndarray): (N,4)-shaped array of groundtruth bounding boxes coordinates in xyxy format
y_pred (ndarray): (N,4)-shaped array of predicted bounding boxes coordinates... | ed3a043b53d843e05ff3e32954eb9dbc2939b6ca | 18,253 |
def forward_pass(output_node, sorted_nodes):
"""
Performs a forward pass through a list of sorted nodes.
Arguments:
`output_node`: A node in the graph, should be the output node (have no outgoing edges).
`sorted_nodes`: A topologically sorted list of nodes.
Returns the output Node's v... | a91c5b7ebef98815a47b26d58a680b36098969d5 | 18,254 |
def qr_decomposition(q, r, iter, n):
"""
Return Q and R matrices for iter number of iterations.
"""
v = column_convertor(r[iter:, iter])
Hbar = hh_reflection(v)
H = np.identity(n)
H[iter:, iter:] = Hbar
r = np.matmul(H, r)
q = np.matmul(q, H)
return q, r | 94aa433e31e93dc36f67f579cb03f67930cfabc4 | 18,255 |
def main(args=None):
"""Main entry point for `donatello`'s command-line interface.
Args:
args (List[str]): Custom arguments if you wish to override sys.argv.
Returns:
int: The exit code of the program.
"""
try:
init_colorama()
opts = get_parsed_args(args)
... | 1977a2bf8e537664a4eab2fb05d6300998a59977 | 18,256 |
import logging
import torch
import operator
def build_detection_train_loader(cfg, mapper=None):
"""
A data loader is created by the following steps:
1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts.
2. Coordinate a random shuffle order shared among all p... | 007b09ce00814264b3264798d4a0afd05c23d6eb | 18,257 |
def discRect(radius,w,l,pos,gap,layerRect,layerCircle,layer):
"""
This function creates a disc that is recessed inside of a rectangle. The
amount that the disc is recessed is determined by a gap that surrounds the
perimeter of the disc. This much hangs out past the rectangle to couple to
a bus waveguide.Calls sub... | 1cb5f505fb868f31771fe6e48faa6399d8b051ad | 18,258 |
def sub_factory():
"""Subscript text: <pre>H[sub]2[/sub]O</pre><br />
Example:<br />
H[sub]2[/sub]O
"""
return make_simple_formatter("sub", "<sub>%(value)s</sub>"), {} | 4f721d0713c1a2be496a45c1bf7abe8766572135 | 18,259 |
from typing import Tuple
def train_test_split(
structures: list, targets: list, train_frac: float = 0.8
) -> Tuple[Tuple[list, list], Tuple[list, list]]:
"""Split structures and targets into training and testing subsets."""
num_train = floor(len(structures) * train_frac)
return (
(structures[:... | 279fbe353bf07aa9b9654f4be4c21cf248f2c8bb | 18,260 |
def reset_password(token):
"""
Handles the reset password process.
"""
if not current_user.is_anonymous():
return redirect(url_for("forum.index"))
form = ResetPasswordForm()
if form.validate_on_submit():
user = User.query.filter_by(email=form.email.data).first()
expired... | c34d090b09a236eecfe101d66ec0daaf3c08eb87 | 18,261 |
def delete(vol_path):
"""
Delete a kv store object for this volume identified by vol_path.
Return true if successful, false otherwise
"""
return kvESX.delete(vol_path) | 5d120b6a509119587df5f2dc9f1436115b01a257 | 18,262 |
import uuid
def get_tablespace_data(tablespace_path, db_owner):
"""This function returns the tablespace data"""
data = {
"name": "test_%s" % str(uuid.uuid4())[1:8],
"seclabels": [],
"spcacl": [
{
"grantee": db_owner,
"grantor": db_owner,
... | 3272e9b941d6bfb426ed754eed7f956c4c0933f4 | 18,263 |
def join_chunks(chunks):
"""empty all chunks out of their sub-lists to be split apart again by split_chunks(). this is because chunks now
looks like this [[t,t,t],[t,t],[f,f,f,][t]]"""
return [item for sublist in chunks for item in sublist] | a5daf41ba3fa6e7dafc4f05b29cc5aeaa397d5a5 | 18,264 |
def urls_equal(url1, url2):
"""
Compare two URLObjects, without regard to the order of their query strings.
"""
return (
url1.without_query() == url2.without_query()
and url1.query_dict == url2.query_dict
) | f2cbcf111cd5d02fa053fbd373d24b2dab047dfc | 18,265 |
def bytes_to_ints(bs):
"""
Convert a list of bytes to a list of integers.
>>> bytes_to_ints([1, 0, 2, 1])
[256, 513]
>>> bytes_to_ints([1, 0, 1])
Traceback (most recent call last):
...
ValueError: Odd number of bytes.
>>> bytes_to_ints([])
[]
"""
if len(bs) % 2 != 0:... | e8ac9ec973ff58973703e3e109da5b45d3f9d802 | 18,266 |
import logging
import click
import yaml
def create_default_yaml(config_file):
"""This function creates and saves the default configuration file."""
config_file_path = config_file
imgdb_config_dir = Config.IMGDB_CONFIG_HOME
if not imgdb_config_dir.is_dir():
try:
imgdb_config_dir.m... | 786b3488b4400a66f44900811171df396b3ab3a9 | 18,267 |
import site
def canRun(page):
""" Returns True if the given check page is still set to "Run";
otherwise, returns false. Accepts one required argument, "page."
"""
print("Checking checkpage.")
page = site.Pages[page]
text = page.text()
if text == "Run":
print("We're good!")
retur... | 3cb1276d82ffeadb1a730bb2eb1c1f3427905e94 | 18,268 |
import os
def parse_configs_for_multis(conf_list):
"""
parse list of condor config files searching for multi line configurations
Args:
conf_list: string, output of condor_config_val -config
Returns:
multi: dictionary. keys are first line of multi line config
val... | faf9ea4a5ce40c31797a4d570f79826902bc05da | 18,269 |
def _bgp_predict_wrapper(model, *args, **kwargs):
"""
Just to ensure that the outgoing shapes are right (i.e. 2D).
"""
mean, cov = model.predict_y(*args, **kwargs)
if len(mean.shape) == 1:
mean = mean[:, None]
if len(cov.shape) == 1:
cov = cov[:, None]
return mean, cov | 23bb62927e767057df94ef8b95b57874fc078d7f | 18,270 |
import copy
import json
def create_waninterface(config_waninterface, waninterfaces_n2id, site_id):
"""
Create a WAN Interface
:param config_waninterface: WAN Interface config dict
:param waninterfaces_n2id: WAN Interface Name to ID dict
:param site_id: Site ID to use
:return: New WAN Interface... | f4de347a1e8120e1da8c38d16ed0054e13f13ae5 | 18,271 |
import numpy
def max_pool(images, imgshp, maxpoolshp):
"""
Implements a max pooling layer
Takes as input a 2D tensor of shape batch_size x img_size and performs max pooling.
Max pooling downsamples by taking the max value in a given area, here defined by
maxpoolshp. Outputs a 2D tensor of shape b... | acbbfb686f77dc6e05f385b2addc8f49e7f344d3 | 18,272 |
def rmean(A):
""" Removes time-mean of llc_4320 3d fields; axis=2 is time"""
ix,jx,kx = A.shape
Am = np.repeat(A.mean(axis=2),kx)
Am = Am.reshape(ix,jx,kx)
return A-Am | 39edcdca0cc4d411c579991086bf555d65686020 | 18,273 |
def default_pruning_settings():
"""
:return: the default pruning settings for optimizing a model
"""
mask_type = "unstructured" # TODO: update based on quantization
sparsity = 0.85 # TODO: dynamically choose sparsity level
balance_perf_loss = 1.0
filter_min_sparsity = 0.4
filter_min_pe... | a81f153872a20eaaa5e654957ddf7b4a79ff42a9 | 18,274 |
def build_request_url(base_url, sub_url, query_type, api_key, value):
"""
Function that creates the url and parameters
:param base_url: The base URL from the app.config
:param sub_url: The sub URL from the app.config file. If not defined it will be: "v1/pay-as-you-go/"
:param query_type: The query ... | ecf3ef0a3d7d5591b1f6aa9787f4f2984688f9f2 | 18,275 |
import re
def snake_to_camel(action_str):
"""
for all actions and all objects unsnake case and camel case.
re-add numbers
"""
if action_str == "toggle object on":
return "ToggleObjectOn"
elif action_str == "toggle object off":
return "ToggleObjectOff"
def camel(match):
... | c71745c02fc712e2b463e7bcb022bfca41c2efd4 | 18,276 |
from datetime import datetime
def todayDate() -> datetime.date:
"""
:return: ex: datetime.date(2020, 6, 28)
"""
return datetime.date.today() | dc9dae8bbeaabf5c8d7d9e3509d1e331e2c609ff | 18,277 |
def lookup_facade(name, version):
"""
Given a facade name and version, attempt to pull that facade out
of the correct client<version>.py file.
"""
for _version in range(int(version), 0, -1):
try:
facade = getattr(CLIENTS[str(_version)], name)
return facade
ex... | eb76df1f7f3a9991c3e283643a52784c9d65f4f1 | 18,278 |
import time
def create_service(netUrl, gwUrl, attributes, token):
"""
Create NFN Service in MOP Environment.
:param netUrl: REST Url endpoint for network
:param gwUrl: REST Url endpoint for gateway
:param serviceAttributes: service paramaters, e.g. service type or name, etc
:param token: see... | 848f8375273ec4583a6c5d361c8a319ff43ba2a8 | 18,279 |
def _drawBlandAltman(mean, diff, md, sd, percentage, limitOfAgreement, confidenceIntervals, detrend, title, ax, figureSize, dpi, savePath, figureFormat, meanColour, loaColour, pointColour):
"""
Sub function to draw the plot.
"""
if ax is None:
fig, ax = plt.subplots(1,1, figsize=figureSize, dpi=dpi)
plt.rcParam... | 43bf53cd4594c1ed58860a6127f40f6345bea6ba | 18,280 |
def rename_columns(df):
"""This function renames certain columns of the DataFrame
:param df: DataFrame
:type df: pandas DataFrame
:return: DataFrame
:rtype: pandas DataFrame
"""
renamed_cols = {"Man1": "Manufacturer (PE)",
"Pro1": "Model (PE)",
"Man2"... | 9c22747d7c6da20cab1593388db5575a38aa313f | 18,281 |
import requests
import json
def get_github_emoji(): # pragma: no cover
"""Get Github's usable emoji."""
try:
resp = requests.get(
'https://api.github.com/emojis',
timeout=30
)
except Exception:
return None
return json.loads(resp.text) | 533a56e2e59b039cbc45ab5acb7ab4e8487e4ad9 | 18,282 |
def transport_stable(p, q, C, lambda1, lambda2, epsilon, scaling_iter, g):
"""
Compute the optimal transport with stabilized numerics.
Args:
p: uniform distribution on input cells
q: uniform distribution on output cells
C: cost matrix to transport cell i to cell j
lambda1: re... | 584607e57b4d216633ef0a03c2cb06726b0f423f | 18,283 |
def add(A: Coord, B: Coord, s: float = 1.0, t: float = 1.0) -> Coord:
"""Return the point sA + tB."""
return (s * A[0] + t * B[0], s * A[1] + t * B[1]) | 53c2f750199d785140154881fdc0ace31b9e2472 | 18,284 |
def from_binary(bin_data: str, delimiter: str = " ") -> bytes:
"""Converts binary string into bytes object"""
if delimiter == "":
data = [bin_data[i:i+8] for i in range(0, len(bin_data), 8)]
else:
data = bin_data.split(delimiter)
data = [int(byte, 2) for byte in data]
return bytes(da... | f16706da2d5b9ae5984a35a13ebd02ae94581153 | 18,285 |
def send_raw(task, raw_bytes):
"""Send raw bytes to the BMC. Bytes should be a string of bytes.
:param task: a TaskManager instance.
:param raw_bytes: a string of raw bytes to send, e.g. '0x00 0x01'
:returns: a tuple with stdout and stderr.
:raises: IPMIFailure on an error from ipmitool.
:raise... | 1f903f1942c5d1b673c9019f9023b1ddf7d2c07a | 18,286 |
def one_on_f_weight(f, normalize=True):
""" Literally 1/f weight. Useful for fitting linspace data in logspace.
Parameters
----------
f: array
Frequency
normalize: boolean, optional
Normalized the weight to [0, 1].
Defaults to True.
Returns
-------
weight: array... | 54301aa7480e6f3520cbfcccfa463a2a02d34b9c | 18,287 |
def PCSPRE1M2SOC(p0, meas_pcs, meas_pre, x_pcs ,y_pcs, z_pcs, \
x_pre ,y_pre, z_pre, wt_pcs=1.0, wt_pre=1.0, \
tol_pcs=None, tol_pre=None):
"""
Optimize two X-tensors and two PRE centres to two common sites
@param p0: List containing initial guesses for (17 unknowns):
... | ec0f266ed1a8b1a45504c13057486bd26e3cc4a5 | 18,288 |
def load_randomdata(dataset_str, iter):
"""Load data."""
names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
objects = []
for i in range(len(names)):
with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f:
if sys.version_info > (3, 0):
objects.append... | 476a54078680bb711a77fc9e3900192a1ef3b811 | 18,289 |
def plot(figsize=None, formats=None, limit=100, titlelen=10, **kwargs):
"""Display an image [in a Jupyter Notebook] from a Quilt fragment path.
Intended for use with `%matplotlib inline`.
Convenience method that loops over supblots that call
`plt.imshow(image.imread(FRAG_PATH))`.
Keyword arguments... | f1b72c952d1c517ba4f09e03af8463a73d2c8759 | 18,290 |
def tresize(tombfile, keyfile, passphrase, newsize):
"""
Resize a tomb.
Keyfile, passphrase and new size are needed.
"""
cmd = ['tomb',
'resize',
tombfile,
'-k',
keyfile,
'--unsafe',
'--tomb-pwd',
sanitize_passphrase(passphrase),
'-s',
... | 334a722b79aec80bc4a95c67a0b155653e29eb10 | 18,291 |
def auto_z_levels(fid, x, y, variable, t_idx, n_cont, n_dec):
"""
list(float) = auto_z_levels(fid, variable, t_idx, n_cont, n_dec)
... # contour lines
... # post .
"""
fig, ax = plt.subplo... | f80020c01a661412fb79d23f6081bdb94a471102 | 18,292 |
def canonicalize(curie: str):
"""Return the best CURIE."""
# TODO maybe normalize the curie first?
norm_prefix, norm_identifier = normalize_curie(curie)
if norm_prefix is None or norm_identifier is None:
return jsonify(
query=curie,
normalizable=False,
)
norm... | 510b2d10170c674cc24090bf2bcd900912678acf | 18,293 |
def _DefaultValueConstructorForField(field):
"""Returns a function which returns a default value for a field.
Args:
field: FieldDescriptor object for this field.
The returned function has one argument:
message: Message instance containing this field, or a weakref proxy
of same.
That function in... | 3a468e2850aaf9707ee1229eeb009ef5c013f1b6 | 18,294 |
def clean_text(dirty_text):
"""
Given a string, this function tokenizes the words of that string.
:param dirty_text: string
:return: list
input = "American artist accomplishments american"
output = ['accomplishments', 'american', 'artist']
"""
lower_dirty_text = dirt... | 1df63ea0c9be5a518d2fd1f931772080962f878f | 18,295 |
def GetCurrentUserController(AuthJSONController):
""" Return the CurrentUserController in the proper scope """
class CurrentUserController(AuthJSONController):
""" Controller to return the currently signed in user """
def __init__(self, toJson):
""" Initialize with the Json ... | ee710cd4d65982cf01d17fba130b7bb83dffd617 | 18,296 |
import numpy
def fft_in_range(audiomatrix, startindex, endindex, channel):
"""
Do an FFT in the specified range of indices
The audiomatrix should have the first index as its time domain and
second index as the channel number. The startindex and endinex
select the time range to use, and the cha... | 30ce104795d0809f054439ba32f47d33528ecbff | 18,297 |
def drop_arrays_by_name(gt_names, used_classes):
"""Drop irrelevant ground truths by name.
Args:
gt_names (list[str]): Names of ground truths.
used_classes (list[str]): Classes of interest.
Returns:
np.ndarray: Indices of ground truths that will be dropped.
"""
inds = [i fo... | 67d711ae61f3c833fa9e8b33d4bf4bf6d99a34ad | 18,298 |
def get_data_table_metas(data_table_name, data_table_namespace):
"""
Gets metas from meta table associated with table named `data_table_name` and namespaced `data_table_namespace`.
Parameters
---------
data_table_name : string
table name of this data table
data_table_namespace : string
... | 8b4ee249112d399c429a33fed82d9cb01404d441 | 18,299 |
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