content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_centerline_tolerance(centerline, n=50):
"""
Finds tolerance based on
average length between first N points
along the input centerline.
Args:
centerline (vtkPolyData): Centerline data.
n (int): Number of points
Returns:
tolerance (float): Tolerance value.
"""... | f670bc529a586591d1db8f9a948e8b4ce6b6ba40 | 50,100 |
import token
import requests
def _api_action(url, req, data=None):
"""Take action based on what kind of request is needed."""
requisite_headers = {'Accept': 'application/json',
'Content-Type': 'application/json'}
auth = (user, token)
if req == "GET":
response = reques... | 42c2e4cbde1c907730e241e68c01b9076a003021 | 50,101 |
def detf(entry, evals):
"""Determines a function value given a number of evaluations.
Let A be the algorithm considered. Let f=f_A(evals) be the smallest
target value such that the average running time of algorithm A was
smaller than or equal to evals.
:keyword DataSet entry: data set
:keyword... | 0cf27a8edf218be762509bf1905d9d580c18409f | 50,102 |
def make_visit_output_schema(visual_attributes):
"""Return the visit output schema."""
schema = [
("lid", pa.int64()),
("pid", pa.int64()),
("inf_prob", pa.float64()),
("n_contacts", pa.int32()),
]
columns = set(k for k, _ in schema)
for attr in visual_attributes:
... | d5ea46c0aa0946917027127a2231ddc9511bb368 | 50,103 |
def _xrank(a, w, b, vol, scale = 0 , reweight = False):
"""
performs a cross-sectional rank
a = np.random.normal(0,1,20)
a[np.random.normal(0,1,20) > 1] = np.nan
w = np.full(20, 1.)
b = np.full(20, 1.)
scale = 0; vol = -1; reweight = False
a
_xrank(a, w, b, vol)
ra... | aefa45d2831bda4d6a00e53821e36b0dbfff5297 | 50,104 |
import copy
import datatable as dt
from datatable import sort, f, by, ifelse, join
import polars as pl
from polars import col
from polars.lazy import col
import numpy as np
import datatable as dt
from datatable import f, by, rbind, join
from catboost import Pool
import xgboost as xgb
import lightgbm as lgbm
def ML0_G... | 181d6d24e2e304ef9b5462e16a32326df87a07cf | 50,105 |
def softmax(x):
"""
Compute softmax values for each sets of scores in x.
Parameters:
x (numpy.ndarray): array containing m samples with n-dimensions (m,n)
Returns:
x_softmax (numpy.ndarray) softmaxed values for initial (m,n) array
"""
e_x = np.exp(x - np.max(x)) # Subtract ... | 3a5ad143126f9adcdd46aecd207439a0e6fe23ad | 50,106 |
import os
from operator import add
def merge():
"""
Merges two branches. Since we currently support only one branch, merge takes the commit from FETCH_HEAD for now
"""
had_conflict = False
repo_root_path = get_repo_root_path()
fetch_head_path = os.path.join(repo_root_path, '.git', 'FETCH_HEAD'... | 91956c4fb90418f95d09f46bd60544e1a4bf9405 | 50,107 |
def vareq(x1, x2):
"""Determine if two vars are equal. This does not check if they're values
are the same, but if they are exactly the same pointer.
Also note that it makes no difference at all if they have the same `name`
value.
@param x1: A Logic Variable
@param x2: Another Logic Variable
... | ba123f9be3cad453399f8e6002ee98d9cb1cf336 | 50,108 |
def compute_induced_segment_family_coverage(path_family):
"""Compute induced segment family and coverage from path family
Notebook: C4/C4S3_AudioThumbnailing.ipynb
Args:
path_family: Path family
Returns
segment_family: Induced segment family
coverage: Coverage of path family
... | 213482828207b41f773ca790e3bee5bbf14e97a1 | 50,109 |
def slugify(text, delim=u'-'):
"""Generates an ASCII-only slug."""
result = []
for word in _punct_re.split(text.lower()):
result.extend(undecode(word).split())
return unicode(delim.join(result)) | 00c17b82070627c7e49cf3ebcf64199be3fee6e4 | 50,110 |
def _find_last_larger_than(target, val_array):
"""
Takes an array and finds the last value larger than the target value.
Returns the index of that value, returns -1 if none exists in array.
"""
ind = -1
for j in range(len(val_array), 0, -1):
if val_array[j - 1] > target:
ind ... | dbdba59ba35b502669082c8416159770843b7312 | 50,111 |
import six
def requires_auth(f):
"""Wraps a request handler with token authentication."""
@wraps(f)
def decorated(*args, **kwargs):
self = args[0]
request = args[1]
if six.PY3:
token = request.args.get(b'token', [b''])[0].decode("utf8")
else:
token ... | a9e65de80d264838a8fe827ac90c5fd625bf92a4 | 50,112 |
from typing import List
import logging
def determine_valid_intersection_points(
intersection_geoms: gpd.GeoSeries,
) -> List[Point]:
"""
Filter intersection points between trace candidates and geom.
Only allows Point geometries as intersections. LineString intersections
would be possible if geome... | d1f46abf2a0b4eb9dac1790bca3adc4ae88cffa8 | 50,113 |
import struct
def readStruct(fb, structFormat, seek=False, cleanStrings=True):
"""
Return a structured value in an ABF file as a Python object.
If cleanStrings is enabled, ascii-safe strings are returned.
"""
if seek:
fb.seek(seek)
varSize = struct.calcsize(structFormat)
byteStri... | 1920c69f1881698a3898774be95e8f10a462d936 | 50,114 |
def hello_world(event, context):
"""
Test function, calls algorithmia algorithm
:param event: AWS lambdas function event
:param context: AWS lambdas function context
:return:
"""
if "algorithm" in event:
algo_name = event['algorithm']
else:
raise Exception("'algorithm' fi... | 8d12674b30c1964119e90c969e00c882b12009d7 | 50,115 |
def remove_from_query(context, *args, **kwargs):
"""Renders a link with modified current query parameters"""
query_params = []
# go through current query params..
for key, value_list in context["request"].GET.lists():
# skip keys mentioned in the args
if key not in args:
for ... | 0fcdc4b6da91ab64c27e225728567d4034d1f626 | 50,116 |
def trapz_int(x, y):
"""
This function computes the area underneath a curve (y) over time vector (x) including
checking if the time vector is sorted.
Parameters
----------
x : list
Time vector.
y : list
Vector of the integrand.
Returns
-------
float
Area... | c74a56508cb9fae20a29e3e12b5ad1a9ad5c8895 | 50,117 |
def register(func, *args, **kwargs):
"""Register a specialization of a `Function` into the graph.
This won't actually call the function with the inputs, and only put the
function definition into graph. Register function with different input param
will result into multiple version of functions registered in gra... | 1d39c32f45758e1fa9dca14ae666ac395f92fa98 | 50,118 |
def chainable_batches(parent_batch_id, job_level):
"""Returns all batches that have completed and we could possibly chain from"""
if job_level == 1:
raise Exception("can't chain in job_level 1")
if job_level == 2:
return db.get_child_batch_metadata(
parent_batch_id, BatchMetada... | 6aa442d1b6298d94d7434908d2070366508adcb1 | 50,119 |
import os
import click
def clean():
"""キャッシュをゴミ箱に移動します"""
user_cache_dir = os.getenv(_MLKOKUJI_CACHE_DIR)
if user_cache_dir is not None:
if os.path.exists(user_cache_dir):
if not click.confirm('You are using a custom cache directory ({}). Do you want to continue?'.format(user_cache_d... | 121beffc7c392bb72471e32d5067fc9fc3152b47 | 50,120 |
def mask_rgb(rgb, mask):
"""
Apply a binary (T/F, 1/0) mask to a 3-channel RGB image and output the result.
Args:
rgb: RGB image as a NumPy array.
mask: An image mask to determine which pixels in the original image should be displayed.
Returns:
NumPy array representing an RGB image with mask appli... | 1d81d4e0c5ddae69163e969ad24313ac7bc87743 | 50,121 |
import os
import time
def create_tags_for_metadata(src_client, run, export_metadata_tags):
""" Create destination tags from source run """
tags = run.data.tags.copy()
for k in _databricks_skip_tags:
tags.pop(k, None)
if export_metadata_tags:
uri = mlflow.tracking.get_tracking_uri()
... | 5218f217d3cddd269c7140fa585bcbec92486c79 | 50,122 |
def fit_table(lc, expect=1.0):
"""Generate a summary table from a light curve dataframe"""
fits = lc.fit
flux = fits.apply(lambda f: f.flux)
errors = fits.apply(lambda f: (round(f.errors[0]-f.flux,3), rorebinnedund(f.errors[1]-f.flux ,3) ) )
sigma_dev = fits.apply(lambda f: round(f.poiss.sigma_dev(e... | 334b4e07696e796c04a6243566419960f5446da2 | 50,123 |
def createDagNode(nodeType, nodeName, mDagMod):
"""
Create and rename node
:param nodeType: string
:param nodeName: string
:param mDagMod: MDagModifier
:return: MObjectHandle
"""
nodeMObj = mDagMod.createNode(nodeType)
mDagMod.renameNode(nodeMObj, nodeName)
mDagMod.doIt()
no... | d46bbb2076566df36d76127c8a41a77d7e324ecc | 50,124 |
import os
import errno
from operator import invert
def get_ppis(reactome_ppis, threshold=5000.0):
""" Get human co-expressed pairs of proteins. They are not necessarily ppi, but to keep same naming structure.
:param reactome_ppis: dictionary one --> set
:param threshold: Maximum correlation score to be c... | b2e81617f4eac3ae6aaf1f06d3a430d406202978 | 50,125 |
def is_denied_because_of_spider(ua_str):
"""检查user-agent是否因为是蜘蛛或机器人而需要ban掉"""
ua_str = ua_str.lower()
if 'spider' in ua_str or 'bot' in ua_str:
if is_ua_in_whitelist(ua_str):
infoprint("A Spider/Bot's access was granted", ua_str)
return False
infoprint('A Spider/Bot w... | 8e186ab286bab50b4f5368c812d4477e27d46c6b | 50,126 |
import torch
def predict(model, dataset, out_channels, device, x=None, y=None, z=None):
"""
Return prediction masks by applying the model on the given dataset
Args:
model (Unet3D): trained 3D UNet model used for prediction
dataset (torch.utils.data.Dataset): input dataset
out_chan... | 718acda917f05cb3e567471884e666ea3dd9cdf6 | 50,127 |
def arc_argmax(parse_probs, length, tokens_to_keep, ensure_tree=True):
"""
Adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/models/nn.py
Parameters
----------
parse_probs : NDArray
seq_len x seq_len, the probability of arcs
length : NDArray
real senten... | 2d0488660b2b95e2af8c9c6b6964497bdc8bda03 | 50,128 |
def addr(registers, a, b, c):
"""(add register) stores into register C the result of adding register A and register B."""
registers[c] = registers[a] + registers[b]
return registers | db88d8e4faeb69660a279f8a893130e83053176a | 50,129 |
def get_data_frame():
"""A function to read the csv files and return dataframe objects"""
roaster = pd.read_csv ("roaster.csv")
activity = pd.read_csv ("activity.csv")
headimpact = pd.read_csv ("headimpacts.csv")
return roaster, activity,headimpact | 07b272832d0656a34e5ae394fccbff2401385bfa | 50,130 |
def latin1_to_ascii (unicrap):
"""This replaces UNICODE Latin-1 characters with
something equivalent in 7-bit ASCII. All characters in the standard
7-bit ASCII range are preserved. In the 8th bit range all the Latin-1
accented letters are stripped of their accents. Most symbol characters
are convert... | 3ca0631c4cbb0e3fc9c18a09785b4be40fde8a48 | 50,131 |
async def queue_clear_handler():
"""
Clear the plan queue.
"""
msg = await zmq_to_manager.send_message(method="queue_clear")
return msg | 2a53c4ba4be5751a6e09abf4449e01bcce68305a | 50,132 |
def create_rain_gauge_reading(r, loc_obj, sys_type, dev_obj):
"""Creat rain gauge object in database from array row"""
datetime = r.get('datetime', None)
rain = r.get('rain', None)
accum_rain = r.get('AccumulatedRain', None)
rgr = Raingaugereading.objects.create(
LocationID=loc_obj,
... | f200da8ebc99f9a1adf723f7d929df52926b619b | 50,133 |
def _pre_validate_int(value, name):
"""
Converts the given `value` to `int`.
Parameters
----------
value : `Any`
The value to convert.
name : `str`
The name of the value.
Returns
-------
value : `int`
Raises
------
TypeError
If `value` w... | 29291514faf3c77fd37a88a7b55a3159a65a6b5d | 50,134 |
import re
import math
def load_hessian(shess: str, dtype: str) -> np.ndarray:
"""Construct a Hessian array from any recognized string format.
Parameters
----------
shess
Multiline string specification of Hessian in a recognized format.
dtype
{"fcmfinal", "cfour", "gamess"}
... | d5011f2381fbf1d23b4914112d444a6d385f7167 | 50,135 |
import torch
def as_rgb_visual(x, vallina=False, colors=None):
""" Make tensor into colorful image.
Args:
x (torch.Tensor): shape in (C, H, W) or (N, C, H, W).
vallina (bool) : if True, then use the `stack_visuals`.
"""
def batched_colorize(batched_x):
n, c, h, w = batched_x.... | cfce54774841d948544f5308e464a19e82f6cce4 | 50,136 |
def detach_dict(dict_tensor, to_numpy=False):
"""
Helper function to recursively detach a dictionary of tensors
This should be used when storing elements that could accumulate gradients.
"""
for key, val in dict_tensor.items():
if isinstance(val, dict):
dict_tensor[key] = detach_... | 27b217eb44cb00012460bc1b954d89165a26545c | 50,137 |
def model_query(session, model):
"""model query."""
if not issubclass(model, models.Base):
raise exception.DatabaseException("model should be a subclass of BASE!")
return session.query(model) | aa72c20fa2f13b49f0dead5cd886bf351f9e9132 | 50,138 |
import gzip
import pickle
def load_batch_gcnn(sample_files):
"""
Loads and concatenates a bunch of samples into one mini-batch.
"""
c_features = []
e_indices = []
e_features = []
v_features = []
candss = []
cand_choices = []
cand_scoress = []
# load samples
for filenam... | 49afc38949128009682a3b828a0cd2a8fc14ce46 | 50,139 |
def mutate_uniform(individual, lb, ub):
"""
Mutate one variable from the individual
Uses uniform random within the range
"""
indi = np.copy(individual)
mut_idx = np.random.choice(len(indi))
indi[mut_idx] = np.random.uniform(low=lb[mut_idx],
high=ub[... | 4fe62bb1aee666a0f075ac350f8f7a864f2a2b05 | 50,140 |
def make_qfq(data, xdxr, fq_type='01'):
"""使用数据库数据进行复权"""
# 过滤其他，只留除权信息
xdxr = xdxr.query('category==1')
# data = data.assign(if_trade=1)
if len(xdxr) > 0:
# 有除权信息, 合并原数据 + 除权数据
# data = pd.concat([data, xdxr.loc[data.index[0]:data.index[-1], ['category']]], axis=1)
# data[... | eba76a3a2a9c23a9d0430ade3dcabc281d562929 | 50,141 |
def compute_average_precision_detection(ground_truth, prediction, tiou_thresholds=np.linspace(0.5, 0.95, 10)):
"""Compute average precision (detection task) between ground truth and
predictions data frames. If multiple predictions occurs for the same
predicted segment, only the one with highest score is mat... | c1e84350bde0f998d86c30be46edd69ec49b94ef | 50,142 |
def data_augmenter(image, label, shift, rotate, scale, intensity, flip):
"""
Online data augmentation
Perform affine transformation on image and label,
which are 4D tensor of shape (N, H, W, C) and 3D tensor of shape (N, H, W).
"""
image2 = np.zeros(image.shape, dtype=np.float32)
... | 4d6115f72c5bbd3b57f265aa8630b80831b02b02 | 50,143 |
def RidPreviouslyProcessed(dtCurrentHosts,sPreviousProcessedFileName):
"""Remove host-guests processed for NEB calculations previously.
[dtHosts]: dict, key: host name; value: int, binary flags for presence of each guest for this host.
[sPreviousProcessedFileName]: string, name of file listing previously processed f... | deca4c0510542ca36c0f390badf8a26ce581de48 | 50,144 |
def abspath(newpath, curpath):
"""Return the absolute path to the given 'newpath'.
The current directory string must be given by 'curpath' as an absolute
path.
"""
assert newpath
assert curpath
assert curpath.startswith('/')
subdirs = newpath.split('/')
if not subdirs[0] or curpath... | 0b1416492891121f433ce3bfbf934601bfc96f06 | 50,145 |
import os
import logging
def loadSiteLocalConfig():
"""
_loadSiteLocalConfig_
Runtime Accessor for the site local config.
Requires that CMS_PATH is defined as an environment variable
"""
overVarName = "WMAGENT_SITE_CONFIG_OVERRIDE"
if os.getenv(overVarName, None):
overridePath =... | 3489c073ea69d173d5ca97bcfacee7957eed0725 | 50,146 |
import os
def icbm_v1_nifti():
""" Returns the ICBM v1 test nifti data """
return NiftiImageContainer(
nifti_img=nib.load(
os.path.join(definitions.ROOT_DIR, "data", "hrgt_ICBM_2009a_NLS_v1.nii.gz")
)
) | 67e3539d5d5262c30491398bcc0b1f6392fc2019 | 50,147 |
def _create_vector_Iqxy(model_info):
"""
Define Iqxy as a vector function if it exists, or default it from Iq().
"""
Iq, Iqxy = model_info.Iq, model_info.Iqxy
if callable(Iqxy):
if not getattr(Iqxy, 'vectorized', False):
#print("vectorizing Iqxy")
def vector_Iqxy(qx, ... | 05751cf921279f305cdf68c49955f5dc23046f8e | 50,148 |
def load_geo_adwords(filename='AdWords API Location Criteria 2017-06-26.csv.gz'):
""" WARN: Not a good source of city names. This table has many errors, even after cleaning"""
df = pd.read_csv(filename, header=0, index_col=0, low_memory=False)
df.columns = [c.replace(' ', '_').lower() for c in df.columns]
... | b0c69b74c61d18d677b35ef994d5a4446ad9e645 | 50,149 |
from typing import List
from typing import Dict
from typing import Any
def from_protos(
proto_list: List[american_option_pb2.AmericanEquityOption],
config: "AmericanOptionConfig" = None
) -> Dict[str, Any]:
"""Creates a dictionary of preprocessed swap data."""
prepare_fras = {}
for am_option_proto i... | 59680f34e45125e619d353f1d0744fb0132730ca | 50,150 |
def optimal_step_weights():
"""Return the optimal weights for the neural network with a step activation function.
This function will not be graded if there are no optimal weights.
See the PDF for instructions on what each weight represents.
The hidden layer weights are notated by [1] on the pr... | 0a38b9f7fc017a6a90dbb2fb51a394386f0fafd0 | 50,151 |
import time
import requests
def get_remote_file_content(
url,
as_text=True,
headers_only=False,
headers=None,
_delay=0,
):
"""
Fetch and return a tuple of (headers, content) at `url`. Return content as a
text string if `as_text` is True. Otherwise return the content as bytes.
If `... | f96f0f841873c318584305665b66be37d198aa97 | 50,152 |
from typing import Sequence
from typing import Optional
def crop_img_padded(
img: np.ndarray,
cropping_range: Sequence[int],
dtype: Optional["DTypeLike"] = None
) -> np.ndarray:
"""Crop image or mask with padding.
Parameters
----------
img : np.ndarray
Input image/mask as 2D (heig... | 353a209decb7a0f0cc2ab0d947fcb9e42e709900 | 50,153 |
import os
def extractImageCorners(directory, filename):
"""
Extract the image corners from an image that is assumed to be
a DICOM image.
Corners are returned as:
[bl, br, tl, tr]
:param directory: the directory where the file given with filename exists.
:param filename: the filename of ... | b104ea6ec8a6fed5f089621514abd67f4ec24699 | 50,154 |
from typing import cast
import copy
def merge(this: SchemaCompatibilityResult, that: SchemaCompatibilityResult) -> SchemaCompatibilityResult:
"""
Merges two {@code SchemaCompatibilityResult} into a new instance, combining the list of Incompatibilities
and regressing to the SchemaCompatibilityType.incompat... | 0e404c80c0dd074ab65780da4bc086d2a214e3b9 | 50,155 |
def flatten(attname):
"""Fixup helper for serialize.
Given an attribute name, returns a fixup function suitable for serialize()
that will pull all items from the sub-dict and into the main dict. If
any of the keys from the sub-dict already exist in the main dict, they'll
be overwritten.
"""
... | 242726a7dcd6c57458e6276527f08b62cb4ef941 | 50,156 |
def P_DCG(rank, b=2): # pylint: disable=invalid-name
"""
Discounted Cumulative gain based stopping probability
Args:
rank (int): rank
b (int, optional): Defaults to 1. log base.
Returns:
float: stopping probability at the given rank
"""
def __log_n(x, n): # pylint: dis... | 616fb6440bd4462e397bd4ce4dfe3f5a220bf217 | 50,157 |
def _get_player_regions(player_id):
""" Return a list of regions for whom 'player_id' has reported latency values. """
regions_key = _make_player_regions_key(player_id)
if g.redis.conn.exists(regions_key):
return g.redis.conn.smembers(_make_player_regions_key(player_id))
return set() | 8f3e371bd58111109480eaad48e17f570d2be655 | 50,158 |
def getImageFromTexture2d(texture2d, flip=True) -> Image:
"""
将给定的纹理转换为PIL.Image
:param texture2d: 要转换的纹理
:type texture2d: Texture2D
:param flip: 将图像翻转回原始（默认情况下，所有Uniiity纹理都翻转）
:type flip: bool
:return: PIL.Image object
:rtype: Image
"""
imageData = texture2d.imageData
textur... | b08492424dc951eddc5e4144c581c63e103ba95e | 50,159 |
def _finalize_build(stage_results):
"""
stage_results (list of (stage_exit_code, stage_name, stage_status)) as returned by
_perform_stage()
logs a final report and returns an overall exit code for whether the
build succeeded.
"""
overall_success = True
final_report = ""
for sta... | f5096ffd4f14db9694e535e57c125aa347df0e1c | 50,160 |
import os
def indentr(model, wdir, infile, input_data, aux_file, toab_gmean):
"""Compute the material entropy production with the indirect method.
The function computes the material entropy production with the indirect
method, isolating a vertical and a horizontal component
(after Lucarini et al., 20... | 6380b957d94846fac81084f56a50c27e948dacc1 | 50,161 |
import base64
def derive_key(secret: bytes, salt: bytes):
"""Derives the key from specified secret and salt using KDF function"""
kdf = get_kdf(salt)
key = base64.urlsafe_b64encode(kdf.derive(secret))
return key | f0edb615261f282dedc313793a0865be98efbf0e | 50,162 |
def load_image(infilename):
"""
Function to load the image
"""
data = mpimg.imread(infilename)
return data | 4f8be3212caa56669f9d785f1af5478a18ae397b | 50,163 |
from pathlib import Path
def outdir():
"""Determine if test artifacts should be stored somewhere or deleted."""
return None if test_output_dir is None else Path(test_output_dir) | b9191a6bb1e8bf916070d4ca99a0f307ef05bad5 | 50,164 |
import os
def check_mask_patches():
"""
Returns this list of mask patches that we have from the images.
There are patches taken from the masks.
:return: python list of strings
"""
path_to_mask_patches = os.path.join(SEGMENTATION_DATA_PATH, "results", "mask_patches_"+ str(PATCH_COUNT_PER_IMAG... | f5f60a1ffe8a4a38be9a7ebde65f80f1227fc4ef | 50,165 |
def status(user):
"""
:param user: User whose status we want to look at
:return: dict of boolean status codes
"""
return AttrDict({
'member': user.member_of(config.member_group),
'traffic_exceeded': user.current_credit < 0,
'network_access': user.has_property('network_access'... | 64accc7a55ddfa93d274f3629a440d2e6b83b4b0 | 50,166 |
def doublePendulumDerivativesSolver(t, x):
"""
Returns time derivative of double pendulum phase space vector, solve_ivp
compatible
"""
g = 9.8
t1, w1, t2, w2 = x
return [w1,
(-w1**2*np.sin(t1-t2)*np.cos(t1-t2) + g*np.sin(t2)*np.cos(t1-t2)
- w2**2*np.sin(t1-t2) - 2*g*n... | cfe855ae644ff8206a2b24fb100ebe008caf1a9b | 50,167 |
def feedback_fm(gain1=1, gain2=1, carrier1=900, modulator1=300, carrier2=900, modulator2=300,
index1=5, index2=0, attack=0.01, release=1, sr=44100):
"""A convenience to render as function call."""
return FeedbackFM1(gain1=gain1, gain2=gain2, carrier1=carrier1, modulator1=modulator1,
... | 4717a64ce554b043d842cab822d0c865168ede5b | 50,168 |
def polya():
"""Fixture returning a Polya instance."""
perm_a = np.array([[8, 9, 10, 11], [9, 10, 8, 11], [10, 8, 9, 11],])
perm_b = np.array([[3, 4, 5], [5, 4, 3], [3, 4, 5],])
perms_list = [perm_a, perm_b]
return Polya(perms_list) | 905102e4183460e88a1f2fdea7d139532d05abf4 | 50,169 |
def get_by_string(source_dict, search_string, default_if_not_found=None):
"""
Search a dictionary using keys provided by the search string.
The search string is made up of keywords separated by a '.'
Example: 'fee.fie.foe.fum'
:param source_dict: the dictionary to search
:param search_string: se... | 59386f5777805f2e7c5a7c7204c56d3d5792c190 | 50,170 |
def get_history_dropdown(obj, project=None):
"""Return link to object timeline events within project"""
timeline = get_backend_api('timeline_backend')
if not timeline:
return ''
url = timeline.get_object_url(obj, project)
return (
'<a class="dropdown-item" href="{}">\n'
'<i c... | 5e19a7f57189819db77f7b7840a461505c606345 | 50,171 |
def get_na_row_values(columns):
"""
returns na values for all columns starting from gender
"""
row_values = [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
False,
-1
]
actions_idx = columns.index('actions')
row_values += [0] * (len(columns) - actions_idx)
return row_val... | 11c45f3a5a7bb9a42458c6f4c4b3d73a5e75fc3c | 50,172 |
def avg_waves(*waves):
"""
Create the average wave.
:param waves: an iterator of waves
:return: a wave
"""
return map(lambda x: x / len(waves), add_waves(*waves)) | 8720f90a1e5a105e6b752ada106245f62cddecdc | 50,173 |
import logging
import hashlib
def md5(fpath="", data=""):
"""
Calculates the the MD5 hash of a file.
You can only provide a file path OR a data string, not both.
:param fpath: Path of the file for which the MD5 hash is required.
:param data: Data string for which the MD5 hash should be calculated.... | bf065e682cf243271a48a2a84717121c696c7f72 | 50,174 |
def _drop_ignored(gold, pred, ignore_in_gold, ignore_in_pred):
"""Remove from gold and pred all items with labels designated to ignore."""
keepers = np.ones_like(gold).astype(bool)
for x in ignore_in_gold:
keepers *= np.where(gold != x, 1, 0).astype(bool)
for x in ignore_in_pred:
keepers... | e76a14cfa1a8e37bbbac9026235f4ec4d3010a2b | 50,175 |
def nonspecific(rna_id, sequence, min_length, max_length):
"""
Compute all the fragment sequences in case of nonspecific cleavage, based on the info selected by the user
on minimum and maximum length for the sequences generated from nonspecific cleavage
"""
output_sequences, seq_list = [], list(sequ... | c507d5ffdf5dad5ad6c30e7aab095c9db59cc16c | 50,176 |
from sympy.tensor.array import NDimArray
def flatten(iterable, levels=None, cls=None): # noqa: F811
"""
Recursively denest iterable containers.
>>> from sympy import flatten
>>> flatten([1, 2, 3])
[1, 2, 3]
>>> flatten([1, 2, [3]])
[1, 2, 3]
>>> flatten([1, [2, 3], [4, 5]])
[1, ... | 9c7960a962e271698a475bd40202f7656a688cd0 | 50,177 |
def black_thresh(img):
"""
Extract the black part based on RGB color space
"""
thresh_R = threshold(thresh(img_channel(img, 'RGB', 0), thresh=[0, 40]))
thresh_G = threshold(thresh(img_channel(img, 'RGB', 1), thresh=[0, 40]))
thresh_B = threshold(thresh(img_channel(img, 'RGB', 2), thresh=[0, 40])... | af4765ae8088b8862efce4acbd33a064327817b1 | 50,178 |
def covmat2direction(covmat):
"""Return eccentricity and principal direction given a covariance matrix.
The eccentricity is the ratio of the largest eigenvalue to the second
largest. In a 3D problem, there is probably some more descriptive shape
metric that uses all three eigenvalues.
"""
evals... | 89027b13bb118599afbb75453f2e5a117fef78dc | 50,179 |
def get_summary(master_bricks, mastervol, slavehost, slavevol):
"""
Wrapper function around Geo-rep Status and Gluster Volume Info
This combines the output from Bricks list and Geo-rep Status.
If a Master Brick node is down or Status is faulty then increments
the faulty counter. It also collects the... | 5a89206bb380cc9f4b5f0440c4c94b14da83bae6 | 50,180 |
def _get_request_url(message: str, chat_id: int):
"""Compose url for telegram."""
bot_token = settings.TELEGRAM_BOT_TOKEN
url = f'{TELEGRAM_API_URL}{bot_token}/sendMessage?chat_id={chat_id}&text={message}'
return url | b8b3f367186259cb7c3259925d5f27633a740700 | 50,181 |
def print_projection_cv2(points, color, image, black_background=False):
""" project converted velodyne points into camera image """
if black_background:
image = np.zeros_like(image)
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
for i in range(points.shape[1]):
cv2.circle(
hsv_image, (np.in... | c43281911f869b2bfe6ecb030fe990cbcb241dfa | 50,182 |
import socket
import struct
def int_to_ip(addr):
"""
Converts a the numeric representation of an ip address
to an ip address strin
Example:
>>> int_to_ip(2130706433)
'127.0.0.1'
"""
return socket.inet_ntoa(struct.pack("!I", addr)) | 6db99b4cb7e7274eb1ac2b7783a4b606d845c4a5 | 50,183 |
def general_telescope(empty_lamda, grid_size, PASSVALUE):
"""
#TODO pass complex datacube for photon phases
propagates instantaneous complex E-field through the optical system in loop over wavelength range
this function is called as a 'prescription' by proper
uses PyPROPER3 to generate the comple... | e3a635de3a6718cada6b1082c2f79aeabbfa8d6f | 50,184 |
def has_hue_shifted(
before: np.ndarray, after: np.ndarray, hue: float, tolerance: float = 0.02
) -> bool:
"""
Detect whether pixels of a specified 'hue' have shifted away from where pixels were in a
baseline image. Used to detect whether rasters of pages with colorized images had
contain accidental... | 440a29587eb1c89c75d93b26e2113280711ef37a | 50,185 |
def plotdim(npanel, maxcol=12, mode="square"):
"""work out a rectangular plot grid that achieves a total of npanel. maxcol
limits how many columns we allow. mode can be 'square' (keep approximately
equal nrow and ncol), 'cols' (as few rows as possible), 'rows' (as few
columns as possible)."""
# here... | 10fe99e6dd9b0fb56694b7c1c6fe49041e25e5e5 | 50,186 |
def a_lambda_cardelli_fast(W,R=3.08):
"""Description: inspired by Eran's function of the same name, faster version using numba.
Input : wavelength in microns
Output : numpy array of A(lambda), the size of wavelengths.
Tested : ?
By : Erez (Dec 2019), on top of Ido Irani (Nov 2019)
URL ... | 21d6655482f4e4f8b77151ea99bb1abf6a4f0aed | 50,187 |
from pathlib import Path
from typing import Tuple
from typing import Collection
def preprocess(path: Path) -> Tuple[Collection[Context], Collection[ContextTagging]]:
"""
Preprocess data file
:param path: Path to data file
:return: Contexts with their corresponding tags (if there are any)
"""
w... | ad104c1544d10c3bb84f8c13b1b2841169874b99 | 50,188 |
import argparse
def parse():
"""Parse system arguments."""
parser = argparse.ArgumentParser(description="Main entry point into \
the MoonTracker application")
parser.add_argument('-p', dest='port', action='store', default=5000,
type=int, help='h... | 4feafae8a598eee96e049c4830e17987d5476dad | 50,189 |
def SectionsMenu(base_title="Sections", section_items_key="all", ignore_options=True):
"""
displays the menu for all sections
:return:
"""
items = get_all_items("sections")
return dig_tree(SubFolderObjectContainer(title2="Sections", no_cache=True, no_history=True), items, None,
... | c59f7340c9dc79bed51756ebea4798cc82f6aef9 | 50,190 |
from astroquery.vizier import Vizier
import numpy as np
from astropy.coordinates import SkyCoord
def query_cat(catalog, min_ra, max_ra, min_dec, max_dec, columns=None,
column_filters=None):
"""
Use vizquery to get a reference catalog from vizier
"""
# Build vizquery statement
width = int(n... | 2983412579e8c5b2cef597aded712d3998b73e26 | 50,191 |
import types
import pandas
def hpat_pandas_series_ge(self, other, level=None, fill_value=None, axis=0):
"""
Intel Scalable Dataframe Compiler User Guide
********************************************
Pandas API: pandas.Series.ge
Limitations
-----------
- Parameters level, fill_value are cu... | 65c1e874b65c5624639c0b36e3b20b19602faa29 | 50,192 |
def aa_seq_doc(aa_sequence):
"""This function takes in an amino acid sequence (aa sequence) and adds spaces between each amino acid."""
return ' '.join([aa_sequence[i:i+1]
for i in range(0, len(aa_sequence))]) | cb497c340d5ecc29184dc079ea9530ebddc43fbd | 50,193 |
def load(project_path):
"""
Loads the given project and returns a Project Instance
:param project_path: Path to the location you want to save the project
path.
:type project_path: str
:return: fracture.Project instance
"""
return _project.Project(project_path) | aec53a3da8d053853029d1f5f526ba1226463414 | 50,194 |
import re
def _find_units(powers_cleaned, dimensions, strict):
"""
Finds the powers associated with each of the units
:param list((chemdataextractor.quantities.Unit, str, str, str)) powers_cleaned: The units found, in the format (units found, string in which this occured, power associated with the unit, s... | 13851922fffa7cf9e373e2d3308f0c075c00a433 | 50,195 |
from typing import Dict
from typing import Union
from typing import Callable
from typing import Any
def get_result_data(result: Dict, value: Union[str, Callable]) -> Any:
"""Returns data from a GCP API call result dict.
Args:
result: Dict containing the result or a GCP API execute() call.
val... | 15c1eb46fe78e1df48d3068f30e2cba8a86f8e49 | 50,196 |
def expr_plot(data,
exp,
dim=[1, 2],
cmap=plt.cm.magma,
marker='o',
s=MARKER_SIZE,
alpha=1,
edgecolors=EDGE_COLOR,
linewidth=EDGE_WIDTH,
w=libplot.DEFAULT_WIDTH,
h=libplot.DEFAULT_... | 8eabbd722a4a8fea0a9ef9fe0a096007701a1216 | 50,197 |
import json
def get_blacklists(blacklist_config, filename):
""" Updates blacklist file.
:param blacklist_config: A dict object containing Name/Url key value pairs.
:param filename: File name to write the lists.
"""
blacklists = get_blacklist_items(blacklist_config)
with open(filename, 'w') a... | 591cfceb76478dede34315550a6e1387439f4c28 | 50,198 |
def neighborhood_density_all_words(corpus_context, tierdict, tier_type = None, sequence_type = None,
algorithm = 'edit_distance', max_distance = 1, output_format = 'spelling',
num_cores = -1, settable_attr = None, collapse_homophones = False,
stop_check = None, call_back = None):
... | 28d32eb05af2cde8ad405326c55625630a59145b | 50,199 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.