content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def arglast(arr, convert=True, check=True):
"""Return the index of the last true element of the given array.
"""
if convert:
arr = np.asarray(arr).astype(bool)
if np.ndim(arr) != 1:
raise ValueError("`arglast` not yet supported for ND != 1 arrays!")
sel = arr.size - 1
sel = sel -... | b4c6424523a5a33a926b7530e6a6510fd813a42a | 31,563 |
def number_formatter(number, pos=None):
"""Convert a number into a human readable format."""
magnitude = 0
while abs(number) >= 100:
magnitude += 1
number /= 100.0
return '%.1f%s' % (number, ['', '', '', '', '', ''][magnitude]) | a9cfd3482b3a2187b8d18d6e21268e71b69ae2f2 | 31,564 |
from pathlib import Path
import shutil
def simcore_tree(cookies, tmpdir):
"""
bakes cookie, moves it into a osparc-simcore tree structure with
all the stub in place
"""
result = cookies.bake(
extra_context={"project_slug": PROJECT_SLUG, "github_username": "pcrespov"}
)
work... | f9889c1b530145eb94cc7ca3547d90759218b1dc | 31,565 |
def calc_density(temp, pressure, gas_constant):
"""
Calculate density via gas equation.
Parameters
----------
temp : array_like
temperatur in K
pressure : array_like
(partial) pressure in Pa
gas_constant: array_like
specicif gas constant in m^2/(s^2*K)
Returns
... | 1e492f9fb512b69585035ce2f784d8cf8fd1edb0 | 31,566 |
def address(addr, label=None):
"""Discover the proper class and return instance for a given Oscillate address.
:param addr: the address as a string-like object
:param label: a label for the address (defaults to `None`)
:rtype: :class:`Address`, :class:`SubAddress` or :class:`IntegratedAddress`
"""... | 13b1e24abc7303395ff9bbe82787bc67a4d377d6 | 31,569 |
def retrieve_molecule_number(pdb, resname):
"""
IDENTIFICATION OF MOLECULE NUMBER BASED
ON THE TER'S
"""
count = 0
with open(pdb, 'r') as x:
lines = x.readlines()
for i in lines:
if i.split()[0] == 'TER': count += 1
if i.split()[3] == resname:
... | 8342d1f5164707185eb1995cedd065a4f3824401 | 31,570 |
import ctypes
import ctypes.wintypes
import io
def _windows_write_string(s, out, skip_errors=True):
""" Returns True if the string was written using special methods,
False if it has yet to be written out."""
# Adapted from http://stackoverflow.com/a/3259271/35070
WIN_OUTPUT_IDS = {
1: -11,
... | 471fd456769e5306525bdd44d41158d2a3b024de | 31,571 |
def in_relative_frame(
pos_abs: np.ndarray,
rotation_matrix: np.ndarray,
translation: Point3D,
) -> np.ndarray:
"""
Inverse transform of `in_absolute_frame`.
"""
pos_relative = pos_abs + translation
pos_relative = pos_relative @ rotation_matrix
return pos_relative | 5f7789d7b5ff27047d6bb2df61ba7c841dc05b95 | 31,572 |
def check_url_namespace(app_configs=None, **kwargs):
"""Check NENS_AUTH_URL_NAMESPACE ends with a semicolon"""
namespace = settings.NENS_AUTH_URL_NAMESPACE
if not isinstance(namespace, str):
return [Error("The setting NENS_AUTH_URL_NAMESPACE should be a string")]
if namespace != "" and not names... | e97574a60083cb7a61dbf7a9f9d4c335d68577b5 | 31,573 |
def get_exif_data(fn):
"""Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
exif_data = {}
i = Image.open(fn)
info = i._getexif()
if info:
for tag, value in info.items():
decoded = TAGS.get(tag, tag)
if decoded == "GPSInfo":
gps_data = {}
for t in value:
... | b6a97ed68753bb3e7ccb19a242c66465258ae602 | 31,574 |
def _setup_modules(module_cls, variable_reparameterizing_predicate,
module_reparameterizing_predicate, module_init_kwargs):
"""Return `module_cls` instances for reparameterization and for reference."""
# Module to be tested.
module_to_reparameterize = _init_module(module_cls, module_init_kwarg... | 367ecae71835044055765ace56f6c0540e9a44ba | 31,576 |
def external_compatible(request, id):
""" Increment view counter for a compatible view """
increment_hit_counter_task.delay(id, 'compatible_count')
return json_success_response() | c82536cdebb2cf620394008d3ff1df13a87a9715 | 31,577 |
def lowpass_xr(da,cutoff,**kw):
"""
Like lowpass(), but ds is a data array with a time coordinate,
and cutoff is a timedelta64.
"""
data=da.values
time_secs=(da.time.values-da.time.values[0])/np.timedelta64(1,'s')
cutoff_secs=cutoff/np.timedelta64(1,'s')
axis=da.get_axis_num('time')
... | 0628d63a94c3614a396791c0b5abd52cb3590e04 | 31,578 |
def _calc_zonal_correlation(dat_tau, dat_pr, dat_tas, dat_lats, fig_config):
"""
Calculate zonal partial correlations for sliding windows.
Argument:
--------
dat_tau - data of global tau
dat_pr - precipitation
dat_tas - air temperature
dat_lats - latitude of the given mo... | f596536bde5ded45da2ef44e388df19d60da2c75 | 31,579 |
def is_unary(string):
"""
Return true if the string is a defined unary mathematical
operator function.
"""
return string in mathwords.UNARY_FUNCTIONS | 914785cb757f155bc13f6e1ddcb4f9b41f2dd1a2 | 31,580 |
def GetBucketAndRemotePath(revision, builder_type=PERF_BUILDER,
target_arch='ia32', target_platform='chromium',
deps_patch_sha=None):
"""Returns the location where a build archive is expected to be.
Args:
revision: Revision string, e.g. a git commit hash or... | 30ced6c37d42d2b531ae6ecafc4066c59fb8f6e4 | 31,581 |
def cutmix_padding(h, w):
"""Returns image mask for CutMix.
Taken from (https://github.com/google/edward2/blob/master/experimental
/marginalization_mixup/data_utils.py#L367)
Args:
h: image height.
w: image width.
"""
r_x = tf.random.uniform([], 0, w, tf.int32)
r_y = tf.random.uniform([], 0, h, tf... | adf627452ebe25b929cd78242cca382f6a62116d | 31,582 |
import math
def compute_star_verts(n_points, out_radius, in_radius):
"""Vertices for a star. `n_points` controls the number of points;
`out_radius` controls distance from points to centre; `in_radius` controls
radius from "depressions" (the things between points) to centre."""
assert n_points >= 3
... | 97919efbb501dd41d5e6ee10e27c942167142b24 | 31,583 |
def create_ordering_dict(iterable):
"""Example: converts ['None', 'ResFiles'] to {'None': 0, 'ResFiles': 1}"""
return dict([(a, b) for (b, a) in dict(enumerate(iterable)).iteritems()]) | 389a0875f1542327e4aa5d038988d45a74b61937 | 31,584 |
def sparse2tuple(mx):
"""Convert sparse matrix to tuple representation.
ref: https://github.com/tkipf/gcn/blob/master/gcn/utils.py
"""
if not sp.isspmatrix_coo(mx):
mx = mx.tocoo()
coords = np.vstack((mx.row, mx.col)).transpose()
values = mx.data
shape = mx.shape
return coords, values, shape | a20b12c3e0c55c2d4739156f731e8db9e2d66feb | 31,585 |
def correct_predicted(y_true, y_pred):
""" Compare the ground truth and predict labels,
Parameters
----------
y_true: an array like for the true labels
y_pred: an array like for the predicted labels
Returns
-------
correct_predicted_idx: a list of index of correct predicted
correct... | 3fae4287cb555b7258adde989ef4ef01cfb949ce | 31,586 |
def coord_image_to_trimesh(coord_img, validity_mask=None, batch_shape=None, image_dims=None, dev_str=None):
"""Create trimesh, with vertices and triangle indices, from co-ordinate image.
Parameters
----------
coord_img
Image of co-ordinates *[batch_shape,h,w,3]*
validity_mask
Boolea... | 8719498ddf24e67ed2ea245d73ac796662b5d08e | 31,587 |
def expand_db_html(html, for_editor=False):
"""
Expand database-representation HTML into proper HTML usable in either
templates or the rich text editor
"""
def replace_a_tag(m):
attrs = extract_attrs(m.group(1))
if 'linktype' not in attrs:
# return unchanged
r... | 2e01f4aff7bc939fac11c031cde760351322d564 | 31,588 |
def hungarian(matrx):
"""Runs the Hungarian Algorithm on a given matrix and returns the optimal matching with potentials. Produces intermediate images while executing."""
frames = []
# Step 1: Prep matrix, get size
matrx = np.array(matrx)
size = matrx.shape[0]
# Step 2: Generate trivi... | dc4dffa819ed836a8e4aaffbe23b49b95101bffe | 31,589 |
def open_spreadsheet_from_args(google_client: gspread.Client, args):
"""
Attempt to open the Google Sheets spreadsheet specified by the given
command line arguments.
"""
if args.spreadsheet_id:
logger.info("Opening spreadsheet by ID '{}'".format(args.spreadsheet_id))
return google_cl... | 355545a00de77039250269c3c8ddf05b2f72ec48 | 31,590 |
def perturb_BB(image_shape, bb, max_pertub_pixel,
rng=None, max_aspect_ratio_diff=0.3,
max_try=100):
"""
Perturb a bounding box.
:param image_shape: [h, w]
:param bb: a `Rect` instance
:param max_pertub_pixel: pertubation on each coordinate
:param max_aspect_ratio_diff: result ca... | 4044291bdcdf1639e9af86857cac158a67db5229 | 31,591 |
def neural_network(inputs, weights):
"""
Takes an input vector and runs it through a 1-layer neural network
with a given weight matrix and returns the output.
Arg:
inputs - 2 x 1 NumPy array
weights - 2 x 1 NumPy array
Returns (in this order):
out - a 1 x 1 NumPy array, rep... | dc2d5cccf0cf0591c030b5dba2cd905f4583821c | 31,593 |
def complex_randn(shape):
"""
Returns a complex-valued numpy array of random values with shape `shape`
Args:
shape: (tuple) tuple of ints that will be the shape of the resultant complex numpy array
Returns: (:obj:`np.ndarray`): a complex-valued numpy array of random values with shape `shape`
... | 6379fb2fb481392dce7fb4eab0e85ea85651b290 | 31,594 |
def sin(x: REAL) -> float:
"""Sine."""
x %= 2 * pi
res = 0
k = 0
while True:
mem_res = res
res += (-1) ** k * x ** (2 * k + 1) / fac(2 * k + 1)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
return res
k += 1 | 0ae009139bc640944ad1a90386e6c66a6b874108 | 31,596 |
import tokenize
from operator import getitem
def _getitem_row_chan(avg, idx, dtype):
""" Extract (row,chan,corr) arrays from dask array of tuples """
name = ("row-chan-average-getitem-%d-" % idx) + tokenize(avg, idx)
dim = ("row", "chan", "corr")
layers = db.blockwise(getitem, name, dim,
... | ff3da6b935cd4c3e909008fefea7a9c91d51d399 | 31,597 |
import gzip
def make_gzip(tar_file, destination):
"""
Takes a tar_file and destination. Compressess the tar file and creates
a .tar.gzip
"""
tar_contents = open(tar_file, 'rb')
gzipfile = gzip.open(destination + '.tar.gz', 'wb')
gzipfile.writelines(tar_contents)
gzipfile.close()
ta... | 38d9e3de38cb204cc3912091099439b7e0825608 | 31,598 |
def symmetrize_confusion_matrix(CM, take='all'):
"""
Sums over population, symmetrizes, then return upper triangular portion
:param CM: numpy.ndarray confusion matrix in standard format
"""
if CM.ndim > 2:
CM = CM.sum(2)
assert len(CM.shape) == 2, 'This function is meant for single subje... | 91964cc4fd08f869330413e7485f765696b92614 | 31,599 |
def get_entry_values():
"""Get entry values"""
entry = {}
for key, question in ENTRY_QUESTIONS.items():
input_type = int if key == "time" else str
while True:
print_title(MAIN_MENU[1].__doc__)
print(question)
user_input = validate(get_input(), input_type)... | 6736ac24bbbe83a0dcbd7a43cd12a1c1b1acbdab | 31,600 |
def _create_snapshot(provider_id, machine_uuid, skip_store, wait_spawning):
"""Create a snapshot.
"""
_retrieve_machine(provider_id, machine_uuid, skip_store)
manager = _retrieve_manager(provider_id)
return manager.create_snapshot(machine_uuid, wait_spawning) | 0d35309341dd27cc41e713c4fd950fee735c866d | 31,601 |
def get_masked_lm_output(bert_config, input_tensor, positions,
label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation be... | 7668ff4c4bd18cb14ff625dc0de593250cedb794 | 31,602 |
import torch
def binary_classification_loss(logits, targets, reduction='mean'):
"""
Loss.
:param logits: predicted classes
:type logits: torch.autograd.Variable
:param targets: target classes
:type targets: torch.autograd.Variable
:param reduction: reduction type
:type reduction: str
... | 507f3b076f6b59a8629bf02aa69ece05f5063f45 | 31,603 |
def transform_with(sample, transformers):
"""Transform a list of values using a list of functions.
:param sample: list of values
:param transformers: list of functions
"""
assert not isinstance(sample, dict)
assert isinstance(sample, (tuple, list))
if transformers is None or len(transforme... | 9a1d7741070b670e7bf8dbf88e8a23361521265f | 31,605 |
def concat_eval(x, y):
"""
Helper function to calculate multiple evaluation metrics at once
"""
return {
"recall": recall_score(x, y, average="macro", zero_division=0),
"precision": precision_score(x, y, average="macro", zero_division=0),
"f1_score": f1_score(x, y, average="macro... | 5a0732ac5926173f12e3f0bd6d6e0ace653c7494 | 31,606 |
from typing import List
def split_into_regions(arr: np.ndarray, mode=0) -> List[np.ndarray]:
"""
Splits an array into its coherent regions.
:param mode: 0 for orthogonal connection, 1 for full connection
:param arr: Numpy array with shape [W, H]
:return: A list with length #NumberOfRegions of arr... | 59e46f5877f3f4fd12a918e9aa26a67a92eb4d5b | 31,607 |
def register_model(model_uri, name):
"""
Create a new model version in model registry for the model files specified by ``model_uri``.
Note that this method assumes the model registry backend URI is the same as that of the
tracking backend.
:param model_uri: URI referring to the MLmodel directory. Us... | 7dcdaa54717e6e0ea45390a5af48b1e350574d12 | 31,608 |
def noreplace(f):
"""Method decorator to indicate that a method definition shall
silently be ignored if it already exists in the full class."""
f.__noreplace = True
return f | 88b6e8fdf7064ed04d9a0c310bcf1717e05e7fa8 | 31,609 |
def position_encoding(length, depth,
min_timescale=1,
max_timescale=1e4):
"""
Create Tensor of sinusoids of different frequencies.
Args:
length (int): Length of the Tensor to create, i.e. Number of steps.
depth (int): Dimensions of embedding.
... | 9d8c9082d82fd41ea6b6655a50b3e802a12f6694 | 31,610 |
def perform_exchange(ctx):
"""
Attempt to exchange attached NEO for tokens
:param ctx:GetContext() used to access contract storage
:return:bool Whether the exchange was successful
"""
attachments = get_asset_attachments() # [receiver, sender, neo, gas]
address = attachments[1]
neo_amo... | 6c2f01a27b40a284e89da1e84de696baa1464e1d | 31,611 |
def Pose_2_Staubli_v2(H):
"""Converts a pose to a Staubli target target"""
x = H[0,3]
y = H[1,3]
z = H[2,3]
a = H[0,0]
b = H[0,1]
c = H[0,2]
d = H[1,2]
e = H[2,2]
if c > (1.0 - 1e-10):
ry1 = pi/2
rx1 = 0
rz1 = atan2(H[1,0],H[1,1])
elif c < (-1.0 + 1e-1... | 9fae83e10df544b7d2c096c7a59aca60567de538 | 31,612 |
def create_gru_model(fingerprint_input, model_settings, model_size_info,
is_training):
"""Builds a model with multi-layer GRUs
model_size_info: [number of GRU layers, number of GRU cells per layer]
Optionally, the bi-directional GRUs and/or GRU with layer-normalization
can be explored... | 222581216edaf6225fabe850d977d14955c66c6e | 31,613 |
def convergence_rates(N, solver_function, num_periods=8):
"""
Returns N-1 empirical estimates of the convergence rate
based on N simulations, where the time step is halved
for each simulation.
solver_function(I, V, F, c, m, dt, T, damping) solves
each problem, where T is based on simulation for
... | e66b4395557e0a254636546555d87716e4b0cc50 | 31,614 |
import cProfile
import io
import pstats
def profile(fnc):
"""A decorator that uses cProfile to profile a function"""
def inner(*args, **kwargs):
pr = cProfile.Profile()
pr.enable()
retval = fnc(*args, **kwargs)
pr.disable()
s = io.StringIO()
s... | 9b5d248e2bd13d792e7c3cce646aa4c0432af8db | 31,615 |
def _decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
"""Decodes the output of a softmax.
Can use either greedy search (also known as best path)
or a constrained dictionary search.
# Arguments
y_pred: tensor `(samples, time_steps, num_categories)`
containing th... | 7a73aa329245136ae560e92ebe67d997e57557f9 | 31,616 |
def rand_xyz_box(image_arrays, label, n, depth, img_size):
"""Returns n number of randomly chosen box.
Args:
image_arrays: 3D np array of images.
label: label of images. normally is A or V
n: number of random boxes generated from this function.
depth : number of slices in Z... | 3127522a7d08b5694fc92ab058736db1d7471676 | 31,617 |
def pageviews_by_document(start_date, end_date, verbose=False):
"""Return the number of pageviews by document in a given date range.
* Only returns en-US documents for now since that's what we did with
webtrends.
Returns a dict with pageviews for each document:
{<document_id>: <pageviews>,
... | c1a2c4ba2711803ca4b5e0cb8959a99b36f928ec | 31,618 |
from re import T
def format_time_string(seconds):
""" Return a formatted and translated time string """
def unit(single, n):
# Seconds and minutes are special due to historical reasons
if single == "minute" or (single == "second" and n == 1):
single = single[:3]
if n == 1:... | 27e0a084165605aa4b1a2b42c87840439686c255 | 31,619 |
import torch
def warp_grid(flow: Tensor) -> Tensor:
"""Creates a warping grid from a given optical flow map.
The warping grid determines the coordinates of the source pixels from which to take the color when inverse warping.
Args:
flow: optical flow tensor of shape (B, H, W, 2). The flow values ... | 21f5765603f8fb42d5fe70668ab6d52b60c16bfe | 31,620 |
def FORMULATEXT(*args) -> Function:
"""
Returns the formula as a string.
Learn more: https//support.google.com/docs/answer/9365792.
"""
return Function("FORMULATEXT", args) | 17cb21ee8b36439395b64fd410006ff03db7fedc | 31,621 |
def num_prim_vertices(prim: hou.Prim) -> int:
"""Get the number of vertices belonging to the primitive.
:param prim: The primitive to get the vertex count of.
:return: The vertex count.
"""
return prim.intrinsicValue("vertexcount") | 298a4a67133fc857c129b922f7f5a0f21d6d0b40 | 31,623 |
def read_geoparquet(path: str) -> GeoDataFrame:
"""
Given the path to a parquet file, construct a geopandas GeoDataFrame by:
- loading the file as a pyarrow table
- reading the geometry column name and CRS from the metadata
- deserialising WKB into shapely geometries
"""
# read parquet file ... | 0fddb5452010e5d4546b3b34e7afae93698cd953 | 31,624 |
def cmd_run_json_block_file(file):
"""`file` is a file containing a FullBlock in JSON format"""
return run_json_block_file(file) | 594e10a7ef4e20b130a5b39c22a834208df846a6 | 31,625 |
def collide_mask(left, right):
"""collision detection between two sprites, using masks.
pygame.sprite.collide_mask(SpriteLeft, SpriteRight): bool
Tests for collision between two sprites by testing if their bitmasks
overlap. If the sprites have a "mask" attribute, that is used as the mask;
otherwis... | fcb309e0c5ca7bc59e5b39b8fd67a45a5281d262 | 31,626 |
import requests
def fetch_production(zone_key='IN-GJ', session=None, target_datetime=None,
logger=getLogger('IN-GJ')) -> list:
"""Requests the last known production mix (in MW) of a given country."""
session = session or requests.session()
if target_datetime:
raise NotImplemen... | e23e409d24349e998eb9c261805a050de12ed30c | 31,627 |
def xyz_order(coordsys, name2xyz=None):
""" Vector of orders for sorting coordsys axes in xyz first order
Parameters
----------
coordsys : ``CoordinateSystem`` instance
name2xyz : None or mapping
Object such that ``name2xyz[ax_name]`` returns 'x', or 'y' or 'z' or
raises a KeyError ... | 983c7adc5df8f54ecc92423eed0cd744971d4ec3 | 31,628 |
def parse_item(year, draft_type, row):
"""Parses the given row out into a DraftPick item."""
draft_round = parse_int(row, 'th[data-stat="draft_round"]::text', -1)
draft_pick = parse_int(row, 'td[data-stat="draft_pick"]::text', -1)
franchise = '/'.join(
row.css('td[data-stat="team"] a::attr(href... | 822a596e0c3e381658a853899920347b95a7ff59 | 31,629 |
def buildJointChain(prefix, suffix, startPos, endPos, jointNum, orientJoint="xyz", saoType="yup"):
"""
Build a straight joint chain defined by start and end position.
:param prefix: `string` prefix string in joint name
:param suffix: `string` suffix string in joint name
:param startPos: `list` [x,y,... | fda63b96d2e5a1316fab9d2f9dc268ae0ff270d2 | 31,630 |
import time
import torch
def predict(model, img_load, resizeNum, is_silent, gpu=0):
"""
input:
model: model
img_load: A dict of image, which has two keys: 'img_ori' and 'img_data'
the value of the key 'img_ori' means the original numpy array
the value of the key 'img_data' is the list of five ... | 04da68453aab79f732deb153cdcbed9ea267355c | 31,631 |
def reverse_preorder(root):
"""
@ input: root of lcrs tree
@ output: integer list of id's reverse preorder
"""
node_list = []
temp_stack = [root]
while len(temp_stack) != 0:
curr = temp_stack.pop()
node_list.append(curr.value)
if curr.child is not None:
... | 06a53756db0f5c990537d02de4fcaa57cc93169d | 31,632 |
import scipy
def calc_binned_percentile(bin_edge,xaxis,data,per=75):
"""Calculate the percentile value of an array in some bins.
per is the percentile at which to extract it. """
percen = np.zeros(np.size(bin_edge)-1)
for i in xrange(0,np.size(bin_edge)-1):
ind = np.where((xaxis > bin_edge[i])... | 798cd1e4f1070b27766f2390442fa81dfad15aaa | 31,633 |
def run_services(container_factory, config, make_cometd_server, waiter):
""" Returns services runner
"""
def _run(service_class, responses):
"""
Run testing cometd server and example service with tested entrypoints
Before run, the testing cometd server is preloaded with passed
... | df7d1c3fdf7e99ebf054cfc6881c8073c2cf4dee | 31,634 |
import requests
def cleaned_request(request_type, *args, **kwargs):
""" Perform a cleaned requests request """
s = requests.Session()
# this removes netrc checking
s.trust_env = False
return s.request(request_type, *args, **kwargs) | b6c99c85a64e5fd78cf10cc986c9a4b1542f47d3 | 31,635 |
from typing import List
from typing import Set
def construct_speech_to_text_phrases_context(event: EventIngestionModel) -> List[str]:
"""
Construct a list of phrases to use for Google Speech-to-Text speech adaption.
See: https://cloud.google.com/speech-to-text/docs/speech-adaptation
Parameters
-... | e8834afd4e53d446f2dda1fd79383a0266010e5b | 31,636 |
def data_science_community(articles, authors):
"""
Input: Articles and authors collections. You may use only one of them
Output: 3-tuple reporting on subgraph of authors of data science articles
and their co-authors: (number of connected components,size of largest
connected component, size of smalle... | 3a81fc7674a2d421ff4649759e61797a743b7aae | 31,637 |
def breadth_first_search(G, seed):
"""Breadth First search of a graph.
Parameters
----------
G : csr_matrix, csc_matrix
A sparse NxN matrix where each nonzero entry G[i,j] is the distance
between nodes i and j.
seed : int
Index of the seed location
Returns
-------
... | 047596e378f0496189f2e164e2b7ede4a6212f19 | 31,638 |
def main_page():
"""
Pass table of latest sensor readings as context for main_page
"""
LOG.info("Main Page triggered")
context = dict(
sub_title="Latest readings:",
table=recent_readings_as_html()
)
return render_template('main_page.html', **context) | 6c9ac7c3306eb10d03269ca4e0cbca9c68a19644 | 31,639 |
import yaml
def load_config_file(filename):
"""Load configuration from YAML file."""
docs = yaml.load_all(open(filename, 'r'), Loader=yaml.SafeLoader)
config_dict = dict()
for doc in docs:
for k, v in doc.items():
config_dict[k] = v
return config_dict | d61bb86e605a1e744ce3f4cc03e866c61137835d | 31,640 |
def CausalConv(x, dilation_rate, filters, kernel_size=2, scope = ""):
"""Performs causal dilated 1D convolutions.
Args:
x : Tensor of shape (batch_size, steps, input_dim).
dilation_rate: Dilation rate of convolution.
filters: Number of convolution filters.
kernel_size: Width of convolution kernel. ... | 08ffde5e4a9ae9ebdbb6ed83a22ee1987bf02b1e | 31,641 |
import functools
def makeTable(grid):
"""Create a REST table."""
def makeSeparator(num_cols, col_width, header_flag):
if header_flag == 1:
return num_cols * ("+" + (col_width) * "=") + "+\n"
else:
return num_cols * ("+" + (col_width) * "-") + "+\n"
def normalizeCe... | c889a4cf505b5f0b3ef75656acb38f621c7fff31 | 31,642 |
def coords_to_bin(
x: npt.NDArray,
y: npt.NDArray,
x_bin_width: float,
y_bin_width: float,
) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]:
"""
x: list of positive east-west coordinates of some sort
y: list of positive north-south coordinates of some sort
x_bin_width: bin width fo... | 874950836d6d03e1dc0f39bdb53653789fe64605 | 31,644 |
from typing import Callable
def _gcs_request(func: Callable):
"""
Wrapper function for gcs requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url: str, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except NotFound:
... | a57867df668eb9b139ee8e07a405868676c9e0f2 | 31,645 |
def nllsqfunc(params: np.ndarray, qm: HessianOutput, qm_hessian: np.ndarray, mol: Molecule,
loss: list[float]=None) -> np.ndarray:
"""Residual function for non-linear least-squares optimization based on the difference of MD
and QM hessians.
Keyword arguments
-----------------
para... | 0debdca80de9e7ea136683de04bc838ceb2f42e2 | 31,646 |
import time
def wait_for_mongod_shutdown(mongod_control, timeout=2 * ONE_HOUR_SECS):
"""Wait for for mongod to shutdown; return 0 if shutdown occurs within 'timeout', else 1."""
start = time.time()
status = mongod_control.status()
while status != "stopped":
if time.time() - start >= timeout:
... | 837271069f8aa672372aec944abedbd44664a3d3 | 31,647 |
from typing import List
import re
def get_installed_antivirus_software() -> List[dict]:
"""
Not happy with it either. But yet here we are... Thanks Microsoft for not having SecurityCenter2 on WinServers
So we need to detect used AV engines by checking what is installed and do "best guesses"
This test ... | b122960b48edfb0e193c354293b28bc1ead0a936 | 31,648 |
def mi(x,y,k=3,base=2):
""" Mutual information of x and y
x,y should be a list of vectors, e.g. x = [[1.3],[3.7],[5.1],[2.4]]
if x is a one-dimensional scalar and we have four samples
"""
x = [[entry] for entry in x]
y = [[entry] for entry in y]
assert len(x)==len(y), "Lists should have ... | 960501be5134dcfe99ca29b50622dbfc0b403b78 | 31,649 |
def _xls_cc_ir_impl_wrapper(ctx):
"""The implementation of the 'xls_cc_ir' rule.
Wrapper for xls_cc_ir_impl. See: xls_cc_ir_impl.
Args:
ctx: The current rule's context object.
Returns:
ConvIRInfo provider
DefaultInfo provider
"""
ir_conv_info, built_files, runfiles = _xls_cc... | c76bddc8b05322b2df4af67415f783aa1f2635bb | 31,650 |
from typing import List
from typing import Tuple
from typing import DefaultDict
def create_dataset(message_sizes: List[int], labels: List[int], window_size: int, num_samples: int, rand: np.random.RandomState) -> Tuple[np.ndarray, np.ndarray]:
"""
Creates the attack dataset by randomly sampling message sizes o... | 081e0c6ddc18988d8e24a08ec4a4e565f318d23a | 31,651 |
def infer_Tmap_from_clonal_info_alone_private(
adata_orig, method="naive", clonal_time_points=None, selected_fates=None
):
"""
Compute transition map using only the lineage information.
Here, we compute the transition map between neighboring time points.
We simply average transitions across all cl... | 9926e2a6faf50bed2d1668de031a600e0f65c1af | 31,652 |
import math
def percentile(seq: t.Iterable[float], percent: float) -> float:
"""
Find the percentile of a list of values.
prometheus-client 0.6.0 doesn't support percentiles, so we use this implementation
Stolen from https://github.com/heaviss/percentiles that was stolen
from http://code.activesta... | 640f132366bad8bf0c58aa318b5be60136925ab9 | 31,653 |
from typing import Union
def select_view_by_cursors(**kwargs):
"""
Selects the Text View ( visible selection ) for the given cursors
Keyword Args:
sel (Tuple[XTextRange, XTextRange], XTextRange): selection as tuple of left and right range or as text range.
o_doc (GenericTextDocument, opti... | 42c42c4b60d802a66e942ac8fa8efe97a8253ea3 | 31,654 |
from typing import List
from typing import Dict
def load_types(
directories: List[str],
loads: LoadedFiles = DEFAULT_LOADS,
) -> Dict[str, dict]:
"""Load schema types and optionally register them."""
schema_data: Dict[str, dict] = {}
# load raw data
for directory in directories:
load... | 8dc1f3625c03451eb9ac28804715ccf260400536 | 31,655 |
def fit_circle(img, show_rect_or_cut='show'):
"""
fit an ellipse to the contour in the image and find the overlaying square.
Either cut the center square or just plot the resulting square
Code partly taken from here:
https://stackoverflow.com/questions/55621959/opencv-fitting-a-single-circle-to-an-... | fdeb8f9a24159236609eac271016624f95f62504 | 31,656 |
from typing import Mapping
from typing import Any
from typing import MutableMapping
def unflatten_dict(
d: Mapping[str, Any],
separator: str = '.',
unflatten_list: bool = False,
sort: bool = False
) -> MutableMapping[str, Any]:
"""
Example:
In []: unflatten_dict({'count.chans.HU_SN': 10})
Out[]: {'count': {'... | 40662a4884171c444ed40c654497f6a0e17a132d | 31,657 |
def _xinf_1D(xdot,x0,args=(),xddot=None,xtol=1.49012e-8):
"""Private function for wrapping the solving for x_infinity
for a variable x in 1 dimension"""
try:
if xddot is None:
xinf_val = float(fsolve(xdot,x0,args,xtol=xtol))
else:
xinf_val = float(newton_meth(xdot,x0,... | e69c08b914395d93a94544d9ba085a440951a03c | 31,658 |
import types
from typing import Dict
import operator
def to_bag_of_words(
doclike: types.DocLike,
*,
by: TokenGroupByType = "lemma_",
weighting: WeightingType = "count",
**kwargs,
) -> Dict[int, int | float] | Dict[str, int | float]:
"""
Transform a ``Doc`` or ``Span`` into a bag-of-words:... | 0065eba8ff7f74b420efc8c65688ab293dee1dda | 31,659 |
def get_trip_info(origin, destination, date):
"""
Provides basic template for response, you can change as many things as you like.
:param origin: from which airport your trip beings
:param destination: where are you flying to
:param date: when
:return:
"""
template = {
"kind": "q... | d1dfd35f41538e800b5c6f5986faac7fcd30ebf3 | 31,660 |
def serialise(data, data_type=None):
"""
Serialises the specified data.
The result is a ``bytes`` object. The ``deserialise`` operation turns it
back into a copy of the original object.
:param data: The data that must be serialised.
:param data_type: The type of data that will be provided. If no data type is
pr... | 6c4e7b144e3e938d30cceee5503290f8cf31ca27 | 31,661 |
def topopebreptool_RegularizeShells(*args):
"""
* Returns <False> if the shell is valid (the solid is a set of faces connexed by edges with connexity 2). Else, splits faces of the shell; <OldFacesnewFaces> describes (face, splits of face).
:param aSolid:
:type aSolid: TopoDS_Solid &
:param OldSheNewS... | 8aa44c5b79f98f06596a5e6d9db8a4cf18f7dad3 | 31,663 |
def empty_filter(item, *args, **kwargs):
"""
Placeholder function to pass along instead of filters
"""
return True | d72ac5a0f787557b78644bcedd75e71f92c38a0b | 31,665 |
def Get_User_Tags(df, json_response, i, github_user):
"""
Calculate the tags for a user.
"""
all_repos_tags = pd.DataFrame(0, columns=df.columns, index=pyjq.all(".[] | .name", json_response))
num_repos = len(pyjq.all(".[] | .name", json_response))
#
new_element = pd.DataFrame(0, np.zeros(... | 80955e2794e9f9d4f65a3f048bc7dc0d450ebb3d | 31,666 |
import ctypes
def ssize(newsize, cell):
"""
Set the size (maximum cardinality) of a CSPICE cell of any data type.
http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ssize_c.html
:param newsize: Size (maximum cardinality) of the cell.
:type newsize: int
:param cell: The cell.
:type c... | 52eb884e7477ddb98dc905ab848c61b83ac16123 | 31,667 |
def extend_data(data, length, offset):
"""Extend data using a length and an offset."""
if length >= offset:
new_data = data[-offset:] * (alignValue(length, offset) // offset)
return data + new_data[:length]
else:
return data + data[-offset:-offset+length] | 923372c1fde14335331eb38b40e118b426cc9219 | 31,669 |
def RAND_egd(path): # real signature unknown; restored from __doc__
"""
RAND_egd(path) -> bytes
Queries the entropy gather daemon (EGD) on the socket named by 'path'.
Returns number of bytes read. Raises SSLError if connection to EGD
fails or if it does not provide enough data to seed PRNG.
... | 5ef4e3e065c44058996c1793541cd9f2a599b106 | 31,670 |
from typing import List
from typing import Dict
from typing import Any
def get_types_map(types_array: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
"""Get the type name of a metadata or a functionality."""
return {type_["name"]: type_ for type_ in types_array} | 9354eff434b589a19360ee13d8bf7d9ab9e1002d | 31,671 |
def update_flavor(request, **kwargs):
"""Update a flavor.
"""
data = request.DATA
flavor_id = data['flavor']['id']
conn = _get_sdk_connection(request)
flavor = conn.load_balancer.update_flavor(
flavor_id,
name=data['flavor'].get('name'),
description=data['flavor'].get('... | 9f165df73f3c557956d466e3fec6d720a1ee76cb | 31,672 |
from typing import List
import re
async def get_all_product_features_from_cluster() -> List[str]:
"""
Returns a list of all product.feature in the cluster.
"""
show_lic_output = await scontrol_show_lic()
PRODUCT_FEATURE = r"LicenseName=(?P<product>[a-zA-Z0-9_]+)[_\-.](?P<feature>\w+)"
RX_PROD... | 9822c952654b3e2516e0ec3b5cf397ced8b3eaaf | 31,673 |
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