content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import OrderedDict
def load_status_info(sfile, fudge=None):
""" Parse the output of pb_run_status.py, either from a file or more likely
from a BASH <() construct - we don't care.
It's quasi-YAML format but I'll not use the YAML parser. Also I want to
preserve the order.
"""... | 13e6b546d93847b0321da2cb659150345b6a8b20 | 3,617,754 |
def calculateSimilarity(img1, img2, similarityType="l2"):
"""
This method calculates the similarity of two images given the type of similarity function (such as l2 distance).
:param img1: np.array
:param img2: np.array
:param similarityType: str default: "l2"
:return: float - distance between th... | 7431f6a15e7d4af649efca55f4a99fb057f5dabb | 3,617,755 |
def _is_false(x):
"""Evaluates false for bool(False) and str("false")/str("False").
The function is vectorized over numpy arrays or pandas Series.
Everything that is NA as defined in `is_na()` evaluates to False.
but also works for single values."""
x = np.array(x).astype(object)
return _is_f... | d5fec5506da6fca9eeb1736dc6b42e5689a7cd3f | 3,617,756 |
def badDebts(_trader: str = None,
_start: int = None, _end: int = None) -> []:
"""
Get bad debts
"""
with utils.dbInterface() as client:
db = client['perp']
criteria = {}
if _trader:
criteria['trader'] = _trader
if _start or _end:
criteria['timestamp'] = {}
i... | a9d275770acfc1cb6fdb4c06bcfcda37e5fa3be1 | 3,617,757 |
from benchbuild.utils import schema
def create_run_group(prj):
"""
Create a new 'run_group' in the database.
This creates a new transaction in the database and creates a new run_group
within this transaction. Afterwards we return both the transaction as well
as the run_group itself. The user is r... | 9112e921038df2544c56daf8aa698511bc509555 | 3,617,758 |
def get_tags_from_message(message):
"""
Given a message string, extracts hashtags and returns a comma-separated list
:param message: a Hipchat message body
"""
tags = {word.strip('#') for word in message.split() if word.startswith('#')}
return ','.join(tags) | 528f7702f43f8f81adf942c79b292f508773d205 | 3,617,759 |
def tf_read_img(tf, filename):
"""Loads a image file as float32 HxWx3 array; tested to work on png and jpg images."""
string = tf.read_file(filename)
image = tf.image.decode_image(string, channels=3)
image = tf.cast(image, tf.float32)
image /= 255
return image | 662fc1c9840e67fb0ff3fae4b12da5179e286e25 | 3,617,762 |
def remove_outliers(X):
"""Function to replace "bad" measurements to zero.
Inputs:
H: Measurement applied field [float, SI units]
Hr: Reversal field [float, SI units]
M: Measured magnetization [float, SI units]
Fk: Index of measured FORC (int)
Fj: Index of given measurement within ... | 6e759a35de66655eb93d01da71b80a30316b7f2d | 3,617,763 |
def mumps_eigsh(matrix, k, sigma, **kwargs):
"""Call sla.eigsh with mumps support.
Please see scipy.sparse.linalg.eigsh for documentation.
"""
class LuInv(sla.LinearOperator):
def __init__(self, matrix):
instance = kwant.linalg.mumps.MUMPSContext()
instance.analyze(matr... | bde217f73ba97510eb8d544ba70fbd13f47f78e8 | 3,617,764 |
def collect_server_info(host, port):
""" Collect general information of server.
"""
info = {}
with pymongo.MongoClient(host, port, connect=True, serverSelectionTimeoutMS=3000) as mc:
info['version'] = mc.server_info()['version']
return info | f8dca0057eb9f358a946b53a93098c39b5c8e799 | 3,617,765 |
def mean(array):
"""Return the mean value of the valid elements of an array.
Parameters
----------
array : `numpy.ndarray`
array of values
Returns
-------
`float`
mean value
"""
non_nan = e.isfinite(array)
return array[non_nan].mean() | baf91db62bc92887ce8055a1c4401ac874cd2014 | 3,617,766 |
def _get_boot_list_for_boot_device(node, device, controller_version):
"""Get the boot list for a given boot device.
The DCIM_BootConfigSetting resource represents each boot list (eg.
IPL/BIOS, BCV, UEFI, vFlash Partition, One Time Boot).
The DCIM_BootSourceSetting resource represents each of the boot l... | a0713051126a6187e8e4e4c30b2331ff3873dd16 | 3,617,767 |
def putseconds(secs):
""" Create a datestring for format 'dd HH:MM:SS'
"""
days = int(secs / SECSPERDAY)
secs = int(secs - days * SECSPERDAY)
hours = int(secs / SECSPERHOUR)
secs = int(secs - hours * SECSPERHOUR)
minutes = int(secs / SECSPERMINUTE)
secs = int(secs - minutes * SECSPERMINU... | 28ed6fe7a6fb00b14e9a89a0609819a5947f2f5f | 3,617,768 |
def get_dataloaders(datasets, batch_size, data_type, preprocessor=None,
name=None, label_transformer=None, **kwargs):
"""
Prepare, define and retrieve appropiate data loaders
for the dataset partitions.
Args:
datasets: dict, {key:partition, val:dict of X, y lists}
batch_size: int, batch size
data_type: st... | d695a0caf9b86cbfa88ec726374019aa00efb1e0 | 3,617,769 |
def Gillespie_FRM4X(generics_func, func_param):
"""
FUNCTION: Gillespie_FRM4X(a{}, b str)
a: dictionary containing function's generic values
b: XML string file name
- Function generic's handler for "Gillespie_FRM4X".
"""
# Python's variable... | 20b40610607b2923877c65fe5fdbbc846a28adf7 | 3,617,770 |
def test_1_subject(sess,MR_image,CT_GT,MR_patch_sz,CT_patch_sz,step):
"""
receives an MR image and returns an estimated CT image of the same size
"""
matFA=MR_image
matSeg=CT_GT
dFA=MR_patch_sz
dSeg=CT_patch_sz
eps=1e-5
[row,col,leng]=matFA.shape
margin1=int((dFA[0]-dSeg[0])/2)
margin2=int((dFA[1]-dSeg[... | 918877392d3d4a69c626a8cc175739d0750c0d46 | 3,617,771 |
def jpeg_cmyk_to_rgb(image_bytes, quality=100):
"""Converts JPEG CMYK image (bytes) to RGB JPEG (bytes)."""
runner = _get_runner()
image = runner.run(tf.image.decode_jpeg, image_bytes)
fn = lambda img: tf.image.encode_jpeg(img, format="rgb", quality=quality)
return runner.run(fn, image) | dc09430d20d09bd44d82672ac779b557ee369346 | 3,617,773 |
def indices_for_pixel(pix_x, pix_y, x, y):
"""
Return the indices of the sources for which the coordinates lie in
the x, y pixel
"""
(indxs,) = np.where(
np.logical_and.reduce([pix_x > x, pix_x <= x + 1, pix_y > y, pix_y <= y + 1])
)
return indxs | 0f8427927bea388e8bb23e0cc615b02ac1821fda | 3,617,774 |
def is_valid_config_entry(opp, logger, origin, destination, region):
"""Return whether the config entry data is valid."""
origin = resolve_location(opp, logger, origin)
destination = resolve_location(opp, logger, destination)
try:
WazeRouteCalculator(origin, destination, region).calc_all_routes_... | a052e508931cb1538b042c60ec205e54324d068b | 3,617,775 |
import numpy as np # noqa
def is_numpy_array(obj: Any) -> bool: # type: ignore
"""
Checks if the given object is a numpy array
Args:
obj (Any): The object to check
Returns:
bool: True if the object is a numpy array otherwise False
"""
try:
except Exception:
retu... | f32644e4dd9dfbb2513dc50a85fa3ee79973dfef | 3,617,776 |
from pathlib import Path
def construct_storage_path() -> Path:
"""Construct a Path to draw the standard "storage" flowchart shape."""
# NOTE: After a MOVETO, we need to put the pen down for CLOSEPOLY to
# complete a filled shape.
_path_data = [
# main shape
(Path.MOVETO, [-1.000, -0.80... | 5b4c263c9412071b93ef9db395392a8a8db8193d | 3,617,778 |
def create_pb_from_mathtext(text, align='center', weight='heavy', color='b', style='normal'):
"""
Create a Gdk.Pixbuf from a mathtext string
"""
global pbmt_cache
global dpi
if not text in pbmt_cache:
parts, fontsize = _handle_customs(text)
pbs = []
width = 0
... | a368e7a2ab1ac543ad511fcea0a7c00a1b70b047 | 3,617,779 |
def compare_rrs_types(exp_val, got_val, skip_rrsigs):
"""sets of RR types in both sections must match"""
def rr_ordering_key(rrset):
if rrset.covers:
return rrset.covers, 1 # RRSIGs go to the end of RRtype list
else:
return rrset.rdtype, 0
def key_to_text(rrtype, rr... | 85bd9f3cffaccaf70ccbb1020ad611fd450dc709 | 3,617,780 |
def __do_config_section(parser):
"""Populate the config section"""
# Purge section before adding.
parser.remove_section(dtfglobals.CONFIG_SECTION_CONFIG)
parser.add_section(dtfglobals.CONFIG_SECTION_CONFIG)
# Color is enabled by default
parser.set(dtfglobals.CONFIG_SECTION_CONFIG, 'use_color... | 90d6d07aefb8057f5c05ab8e8ca61deb0e4febce | 3,617,781 |
def act_gsymbol_reference(context, nodes):
"""Repetition operators (`*`, `+`, `?`) will create additional productions in
the grammar with name generated from original symbol name and suffixes:
- `_0` - for `*`
- `_1` - for `+`
- `_opt` - for `?`
Zero or more produces `one or more` productions a... | 07ca0e0454b7b6e9b9c127d29afea6faf0f500e5 | 3,617,782 |
def is_higher_permission(level1, level2):
"""
Return True if the level1 is higher than level2
"""
return (is_publish_permission(level1) and
not is_publish_permission(level2) or
(is_edit_permission(level1) and
not is_publish_permission(level2) and
not is_... | 2ec9ee12804c37aeaa64d2fcad9842f05d434760 | 3,617,783 |
def vectorize_array(y, steps=1, return_type='df'):
"""
Take in an array like sequence of values.
Vectorize the array and add steps from further in the series to each item.
The values in the input array must be of shape [n, 1].
Rows with nan values in the result array will be dropped.
If return_... | 60be622200aa5000d936d13f3da8a4f7445dfe70 | 3,617,784 |
from typing import List
from typing import Dict
from typing import Callable
from typing import Tuple
import copy
def do_upper_envelope_step(
policy: List[np.ndarray],
value: List[np.ndarray],
*,
expected_value: np.ndarray,
params: pd.DataFrame,
options: Dict[str, int],
compute_utility: Cal... | 9a9051751bb43211421012ca662c7fe8ef6248e2 | 3,617,786 |
def normalize_yaml(path):
"""Normalize a YAML file, and return whether the file changed."""
data = read_yaml(path)
changed = write_yaml(data, path)
return changed | f74c1b9a5a59bd728ac86c1aa92a70e182be2809 | 3,617,787 |
def rotateQuaternionByRPYInUnrotatedFrame(roll, pitch, yaw, in_quat):
"""
Apply RPY rotation in the reference frame of the quaternion.
Input: geometry_msgs.msg.Quaternion
Output: geometry_msgs.msg.Quaternion rotated by roll, pitch, yaw in its frame
"""
q_in = [in_quat.x, in_quat.y, in_quat.z, i... | 55e2f10e9a2af76c590a5a580c898fcfbdb66422 | 3,617,789 |
def log_mat(U):
"""Matrix logarithm, only use for normal matrices, i.e., square matrices U with U * U^T = U^T * UU"""
vals, vecs = la.eig(U)
vals = np.log(vals)
return np.real(np.einsum('...ij,...j,...kj', vecs, vals, vecs)) | e73c610e5584a379e7b4eb40ad8085e943a30511 | 3,617,790 |
from typing import Optional
def date_str_to_datetime(date_str: Optional[str]) -> timezone.datetime:
"""
Converts a date string to a datetime object.
:param date_str: Date string (iso8601)
:return: A datetime object.
"""
if date_str is None:
return None
return parse_datetime(date_... | 70e8fa578a7a713d014f9d2e927abf8b3196c912 | 3,617,792 |
def add_field(radar_dest, radar_orig):
"""
adds the fields from orig radar into dest radar. If they are not in the
same grid, interpolates them to dest grid
Parameters
----------
radar_dest : radar object
the destination radar
radar_orig : radar object
the radar object conta... | df6e62f2e5fb5bbddad77e037f196e0dbc3c5c97 | 3,617,793 |
def _convert_str_to_html(string):
"""Helper function to insert <br> at line endings etc."""
if not string: return ""
lines = string.splitlines()
for index, line in enumerate(lines):
for char in line:
if char == '\t':
lines[index] = line.replace(char, " &nbs... | b357d04f28a08f6b65d98ee381dcaef8969f6ff0 | 3,617,795 |
def build_icd(cp, instruction):
"""
Build the integer representation of the input channels to dequeue.
:param cp: CoreParameters instance for the target architecture
:param instruction: Instruction instance
:return: integer representation of icd
"""
# OR in the literal array of bits.
i... | 99c1a7799a51ec53136cd7fee4104093921e3f61 | 3,617,796 |
def _parseLinks(response, rel):
"""
Parses an HTTP response's ``Link`` headers of a given relation, according
to the Corelight API specification.
response (requests.Response): The response to parse the ``Link`` headers
out of.
rel (str): The link relation type to parse; all other relations are... | 73a4f2a7e981b335aa511e5e311ef7290a7695e3 | 3,617,798 |
def compute_crop_box(bound_box: dict):
"""Computes the coordinates to crop an image based on the provided
bound_box and the SCALE variable. The input only describes a box around
the mask (or area where the mask would be), and the width and height could
could be different. The output box will be a square... | 66bee6c87b5cb21ed1091f62bab1b9dd940e43d9 | 3,617,799 |
def find_min_max(data):
"""Solution to exercise C-4.9.
Write a short recursive Python function that finds the minimum and
maximum values in a sequence without using any loops.
"""
n = len(data)
min_val = data[0]
max_val = data[0]
def recurse_minmax(idx):
nonlocal min_val, max_v... | b8f50d1dafa0f66ab61db8d4974c8d201cd4dc3c | 3,617,800 |
def generate_T3_uvh5(name, pt_dec, tstart, ntint, nfint, filelist, params=T3PARAMS, start_offset=None, end_offset=None):
"""Generates a measurement set from the T3 correlations.
Parameters
----------
name : str
The name of the measurement set.
pt_dec : quantity
The pointing declinat... | c0714aa0ec61c8c5762424f8a5d3f3434237c978 | 3,617,801 |
import torch
from functools import reduce
def mask_with_tokens(t, token_ids):
"""
用记号遮掩
"""
init_no_mask = torch.full_like(t, False, dtype=torch.bool)
mask = reduce(lambda acc, el: acc | (t == el), token_ids, init_no_mask)
# print("mask",mask)
return mask | f233f2e2111e02919ff3795820f024b0e5b850a1 | 3,617,802 |
def isStandard(descriptorType):
"""
>>> isStandard(0x0a)
True
>>> isStandard(0x22)
False
>>> isStandard(0x61)
False
>>> isStandard(0x1a)
True
"""
# See USB Common Class Specification s. 3.11 for this field's structure
# Bit 7: reserved
# Bits 6..5: descriptor type
... | a4e002e58c07638cb14d1a13a27ac7232af140cf | 3,617,803 |
def split_test(df, test_method='fo', test_size=.2):
"""
method of splitting data into training data and test data
Parameters
----------
df : pd.DataFrame raw data waiting for test set splitting
test_method : str, way to split test set
'fo': split by ratio
... | 8256a16f3f73f42e06c7b28f34295a39dc8e2091 | 3,617,804 |
def store_bot_config(host, content):
"""Stores a new version of bot_config.py.
Returns the ndb.Key of the new stored entity.
"""
if not _validate_python(content):
raise ValueError('Invalid python')
# The memcache entry will be cleared out automatically after 60s. Try a best
# effort.
signature = _get... | 2930b24b764e9b931f0c36fa921d6c4c2d1a3772 | 3,617,805 |
def equals(val1, val2):
"""
Returns True if the two strings are equal, False otherwise.
The time taken is independent of the number of characters that match.
For the sake of simplicity, this function executes in constant time only
when the two strings have the same length. It short-circuits when t... | 9a9951ee08251cebfda90d52e4e1e04bb2510a5b | 3,617,806 |
def binary_close(fig, size=5):
""" Joins unconnected pixel by dilation and erosion"""
selem = disk(size)
img = pad(fig.img, size, mode='constant')
img = binary_closing(img, selem)
img = crop_skimage(img, size)
return Figure(img, raw_img=fig.raw_img) | 98a4c5393a7010eef7ff7e6f94a08fe359600d08 | 3,617,807 |
def build_delete_item(obj):
"""Build div tag for delete details"""
parent = "None"
if obj.parent is not None:
parent = obj.parent.name
return (
"<ul><li>Serial: %s</li><li>Subject: %s</li><li>Parent: %s</li><li>Description: %s</li>"
"<li>x509 Extension: %s</li><li>Created: %s</... | f1bb11f2bec57035abea6667a05127d856086b61 | 3,617,808 |
def get_user_token(user, purpose, minutes_valid):
"""Return login token info for given user."""
token = ''.join(
dumps([
user.get_username(),
get_auth_hash(user, purpose),
]).encode('base64').split('\n')
)
return {
'id': get_meteor_id(user),
'token... | a2706da9fe38b10c85815c8a6d2a821e27004b3d | 3,617,809 |
def cell_content_to_str(v):
"""
Convert the value of a cell to string
:param v: Value of a cell
:return:
"""
if v:
if isinstance(v, float) or isinstance(v, int):
return str(int(v))
else:
return str(v).strip()
else:
return None | b73746d735ed155d387512704b91fc92c0c93560 | 3,617,811 |
def create_haar_state(num_qubits: int):
"""Create a random Haar quantum state
Args:
num_qubits (int): number of qubits
Returns:
qiskit.QuantumCircuit
"""
psi = 2*np.random.rand(2**num_qubits)-1
psi = psi / np.linalg.norm(psi)
qc = qiskit.QuantumCircuit(num_qubits, num_qubit... | aa9be2e086f20daaf6597bf726e148fecbf4b9ae | 3,617,812 |
def partition_layers(layers, split=2):
""" partitions the each layer of a list according to the layer # and number of elements """
layers_partitioned = []
for i in range(len(layers)):
num_parts = split ** i
layer_partitioned = partition_list(layers[i], num_parts)
layers_partitioned.... | be76def26a41488203540b257e1ded0d279bbdb8 | 3,617,813 |
def __hamming_distance_with_hash(dhash1, dhash2):
"""
*Private method*
根据dHash值计算hamming distance
:param dhash1: str
:param dhash2: str
:return: 汉明距离(int)
"""
difference = (int(dhash1, 16)) ^ (int(dhash2, 16))
return bin(difference).count("1") | c8a28a3f20a037fe9e96bfe82cb522caf6600337 | 3,617,815 |
def _van_es_entropy(X, m):
"""Compute the van Es estimator as described in [6]."""
# No equation number, but referred to as HVE_mn.
# Typo: there should be a log within the summation.
n = X.shape[-1]
difference = X[..., m:] - X[..., :-m]
term1 = 1/(n-m) * np.sum(np.log((n+1)/m * difference), axi... | 90143d9395bf3851a848ac6f0e0349a5131349fb | 3,617,816 |
def insert_coke(message):
"""Function to add ingested coke.
Parameters
----------
message : telebot.types.Message
The message object.
Returns
-------
msg : success or failure message - to be displayed at Telegram's chat.
"""
message_text = message.text.lower().split(' ')
... | 95e97edd600351a1f1e828d01bad17b37cd94263 | 3,617,817 |
async def async_browse_media(
hass: HomeAssistant,
media_content_type: str,
media_content_id: str,
cast_type: str,
) -> BrowseMedia | None:
"""Browse media."""
if media_content_type != DOMAIN:
return None
try:
get_url(hass, require_ssl=True, prefer_external=True)
except ... | 333ee495d5a0be9f3ce1957473aa0f6e5ded09ad | 3,617,819 |
def is_internal_ip(ip):
"""
判断是否为内网ip
:param ip:
:return:
"""
if ip in ('127.0.0.1', '0.0.0.0', 'localhost'):
return True
ip = ip_into_int(ip)
net_a = ip_into_int('10.255.255.255') >> 24
net_b = ip_into_int('172.31.255.255') >> 20
net_c = ip_into_int('192.168.255.255') >>... | f68ccf10d7aff3f6b0bb853077ddc90489d48420 | 3,617,820 |
def part_3b_output_equilb_NVT_started(job):
"""Check to see if the equilb_NVT (set temperature) gomc simulation is started."""
return gomc_simulation_started(job, equilb_NVT_control_file_name_str) | e86a95a13438c9da4a4214e4e82ee90e22b79dc9 | 3,617,821 |
def readAllSopv(sopvFilepath, logger = None):
"""Return information of all registered scanposes in VOCS."""
sopvs = []
first_line = True
with open(sopvFilepath, 'r') as f:
for line in f:
if first_line:
first_line = False
continue
sopvs.appe... | 531e14ad2684b9c4a7afe9164090fe6e3a09c0b1 | 3,617,822 |
def _min_to_sec(minutes):
"""converts minutes to seconds,
assuming that input is a number representing minutes"""
return minutes*60 | dff3330038c7e8cd1abda2c8a0a4433979fedf58 | 3,617,823 |
def get_ip(request) -> str:
"""
获取当前请求的ip地址
:param request:
:return:
"""
if request.META.get('HTTP_X_FORWARDED_FOR', None):
ip = request.META['HTTP_X_FORWARDED_FOR']
else:
ip = request.META['REMOTE_ADDR']
return ip | eb084135920231aa176e6099d70ed3954b407070 | 3,617,824 |
def aggregate_argmax(z_mean, z_logvar, new_mean, new_log_var, labels,
kl_per_point):
"""Argmax aggregation with adaptive k.
The bottom k dimensions in terms of distance are not averaged. K is
estimated adaptively by binning the distance into two bins of equal width.
Args:
z_mean: Mean... | 6c262a3b6356ad4a6f5493b1174db3d120f828dd | 3,617,825 |
def get_file_data_old(archivo):
"""
Separa del nombre del archivo y extrae nombre de Centroide (ctrd)
y cultivo (clt)
"""
diccionario = {}
listado = archivo.split('-')
diccionario['ctrd'] = listado[0]
condicion = 'TS(S2)' in listado[1] or\
'TS(TC)' in listado[1] or\
... | 4d02b5b77aaf0abfc768c898534493d3723ef54e | 3,617,826 |
def mask_rcnn_loss(mask_outputs, mask_targets, select_class_targets, params):
"""Computes the mask loss of Mask-RCNN.
This function implements the mask loss of Mask-RCNN. As the `mask_outputs`
produces `num_classes` masks for each RoI, the reference model expands
`mask_targets` to match the shape of `ma... | 7cbf78de040fba38d66e0e669ab8fecfea06e3ae | 3,617,827 |
from typing import Optional
def get_error_handler(
request: web.Request, config: Optional[Config]
) -> Optional[_Handler]:
"""Find error handler matching current request path if any."""
if not config:
return None
path = request.rel_url.path
for item, handler in config.items():
if ... | a49351ab5479b653f45eff3bc6b069b354615154 | 3,617,828 |
def subject_stats_comparison(combined_df):
"""
Calculates the percentage of subjects with an exclusion and the rate of exclusions per
patient.
Parameters:
combined_df: A DataFrame in the format provided by prepare_for_comparison
Returns:
A DataFrame with run names as the index and the colu... | ebf3ec1771861565130d926af1b59405ca74e1f9 | 3,617,829 |
def flip_randomly_left_right_image_with_annotation(image_tensor, annotation_tensor):
"""Accepts image tensor and annotation tensor and returns randomly flipped tensors of both.
The function performs random flip of image and annotation tensors with probability of 1/2
The flip is performed or not performed fo... | bfad843de51500fb83dd453680983cbc983af8f1 | 3,617,830 |
from typing import Dict
def dict_squares(n: int) -> Dict[int, int]:
"""Generates a dictionary with numbers from 0 to n as keys
which are mapped to their squares using dictionary comprehension.
doctests:
>>> dict_squares(2)
{0: 0, 1: 1, 2: 4}
>>> dict_squares(5)
{0: 0, 1: 1, 2: 4, 3: 9,... | 4101d0256069c07da6c3d5a8d1e40783a4b26eea | 3,617,831 |
def factor_cartesian(table):
"""Factor a cartesian products of lists
The function unrolls a cartesian product of lists.
The table argument is a list of sequences and the output is a
tuple of lists
Example:
>>> factor_cartesian([('a1','b1'),('a1','b2'),('a2','b1'),('a2','b2')])... | 8b283944644d89c863e2bc7f5dc3824fa26ca27e | 3,617,834 |
def _palettize_seq(seq):
""""
seq must be a sequence of 3-d numpy arrays with dtype np.uint8,
all with the same depth (i.e. the same length of the third dimension).
"""
# Call np.unique for each array in seq. Each array is viewed as a
# 2-d structured array of colors.
depth = seq[0].shape[-... | 491831139adf23a13403e68a30ea0b33ec636e21 | 3,617,836 |
import mimetypes
def _fetch_bundle_contents_blob(uuid, path=''):
"""
API to download the contents of a bundle or a subpath within a bundle.
For directories, this method always returns a tarred and gzipped archive of
the directory.
For files, if the request has an Accept-Encoding header containin... | d709378344a9a7916bbdb96f2102ddc02f430293 | 3,617,837 |
def get_parents(model):
"""
Return the list of instances refered
as "parents" of a given model instance.
"""
result = list()
options = getattr(model, 'RoleOptions', None)
if options:
parents_list = getattr(options, 'permission_parents', None)
if parents_list:
for ... | cd8fd84bfc9fc87e9a7374d505b14fb3c1ff6034 | 3,617,838 |
import socket
def _setDNSCache():
""" DNS缓存 """
def _getaddrinfo(*args, **kwargs):
if args in _dnscache:
# print str(args) + " in cache"
return _dnscache[args]
else:
# print str(args) + " not in cache"
_dnscache[args] = socket._getaddrinfo(*args... | b4d46a204e66113e81bc4ade3768a35edf6d4bba | 3,617,839 |
def create_word2vec(all_questions, embeddings_dim, window, workers, min_count):
""" Create the word2vec model"""
model = gensim.models.Word2Vec(sentences=all_questions, size=embeddings_dim, window=window, workers= workers, min_count=min_count)
# vocab size
words = list(model.wv.vocab)
print("vocab... | 808c42339bb4596427fa4c784cd58d7b89d1a171 | 3,617,840 |
import math
def velocity_line_trajectory(start, end, velocity, sampling_rate=0.01):
"""
start and end being n dimentional points, velocity
a float value (meter per seconds) and the sampling
rate between two points, returns a list of instance
of States corresponding to a point going from
start... | dabe203380cbcdc4ce3ff82ca7372b99caf4c68e | 3,617,841 |
from .Equal import Equal
def isEqual(*, value):
"""
Check that a numeric value is equal to {value}
"""
return Equal(value) | fa7c9c26c092a8a2cae1d2ad6a5e4255d93abdaf | 3,617,842 |
def wrap_coro(coro, unpack, *args, **kwargs):
""" building a coroutine receiving one argument and call it curried
with *args and **kwargs and unpack it (if unpack is set)
"""
if unpack:
async def _coro(value):
return await coro(*args, *value, **kwargs)
else:
async def _co... | 2e1916f5f34be5878a3af64ea28b5ffb56f6b350 | 3,617,843 |
def rasterizeSourcePositionList(shape,
led_pattern,
illumination_source_position_list_na=[],
objective_numerical_aperture=0.25,
objective_magnification=10,
syst... | ddde89a347f32d9e3aa3ee1530f041e0bf639e71 | 3,617,844 |
from typing import List
def list_to_dict(list: List, name_key: str = "name", value_key: str = "value"):
"""
Returns a key-value pair dictionary with given list of dictionary having
`name` and `value` keys or custom keys.
"""
if not is_list(list):
raise AttributeError(
"Argument... | 81c4b0597b8012b51f9d6cb619948d6afe972c6e | 3,617,845 |
from typing import Tuple
def _one_grid_to_points(
axes: GridPointsLike,
*,
dim_domain: int,
) -> Tuple[np.ndarray, Tuple[int, ...]]:
"""
Convert a list of ndarrays, one per domain dimension, in the points.
Returns also the shape containing the information of how each point
is formed.
... | ac830c070ff2f456f75ac7f079e7d19987942bc8 | 3,617,846 |
def mAP(results, k=200, AP=True):
"""
Arguments :
results = related images list for sorted file list
k = mAP@k
AP = whether return AP list for each images
return : mAP value
"""
aps = []
for truth, result in enumerate(results):
result = result[:k]
... | 17da5fc85fdee24e24771e785298dfc71e2948a5 | 3,617,847 |
from typing import Any
import typing
def hint_is_specialized(hint: Any, target: Any) -> bool:
"""Checks if a type hint is a specialized version of target.
E.g., hint_is_specialized(ClassVar[int], ClassVar) is True.
isinstance will invoke type-checking, which this methods sidesteps.
Behavior is undef... | b651fc05290de82ab5a5833d10ca68d6a96f2d7a | 3,617,848 |
from typing import Dict
from typing import Union
def compute_selection_criteria(
blast: Dict[str, Union[int, float]]
) -> Dict[str, Union[int, float]]:
"""Calculate a series of parameters used to
discriminate which entries should be kept to
search for a core genome from a series of
blast files (*.... | cc12be84aad55de090b0e6ba26eaeb5c77706073 | 3,617,849 |
def build_spc_queue(rxn_lst):
""" Build spc queue from the reaction lst for the drivers
:return spc_queue: all the species and corresponding models in rxn
:rtype: list[(species, model),...]
"""
if 'all' in rxn_lst:
# First check if rxn_lst is a bunch of species
spc_queue = r... | 0dbe4e2bc3db16dc5dc83a55f0f802d4dfae853f | 3,617,851 |
def patch_telomeres(bands_by_chr):
"""Account for special case with Drosophila melanogaster
"""
for chr in bands_by_chr:
first_band = bands_by_chr[chr][0]
start = first_band[1]
if start != '1':
stop = str(int(start) - 1)
pter_band = ['pter', '1', stop, '1', st... | 23057227c526bb3837fd0c02233ddac4671c6956 | 3,617,852 |
import scipy.cluster as spcluster
def cluster_sites(mol, tol, give_only_index=False):
"""
Cluster sites based on distance and species type.
Args:
mol (Molecule): Molecule **with origin at center of mass**.
tol (float): Tolerance to use.
Returns:
(origin_site, clustered_sites)... | 37c913a484c29420fefb72c41cf9f71b2bd8cbb3 | 3,617,853 |
def get_comment_notification_targets(comment):
"""
Gets a list of comments that should get a notification
about this new comment
:param PostComment|AnswerComment comment:
:param set user_ids: set of user_ids to exclude
:return: returns a tuple, first item is comments to
notify, sec... | d2b0ff99705117b7642366d7080a3a29c48dc22a | 3,617,854 |
def build_model(params: dict, build_options: dict = None) -> CompartmentalModel:
"""
Build the compartmental model from the provided parameters.
"""
params = Parameters(**params)
# Get country/region details
country = params.country
pop = params.population
# Create the model object
... | 7738f8e31536822a50856186ffff38c70d807ca6 | 3,617,855 |
import six
def gen_spewer_method(name, args):
"""Generates spewer code for a single opcode."""
method_name = "spew" + name
# Generate code like this:
#
# void spewGuardShape(CacheIRReader& reader) {
# spewOp(CacheOp::GuardShape);
# spewOperandId("objId", reader.objOperandId());
... | a8e546df4a9cfd0562611d8f456f8e109c331062 | 3,617,856 |
from typing import Optional
def cir_LRsRQRQ(w, Rs, L,
R1: Optional[float] = None, Q1: Optional[float] = None,
n1: Optional[float] = None, fs1: Optional[float] = None,
R2: Optional[float] = None, Q2: Optional[float] = None,
n2: Optional[float] = N... | 596951396a68140347cf06844041fcdf87a46974 | 3,617,857 |
from typing import List
def smallest_positive_integer_not_in_array(arr: List[int]) -> int:
"""
[1..N] can cover everything from 1 to (N * (N+1) / 2)
"""
res = 1
for num in arr:
if num > res:
return res
else:
res += num
return res | 9d01e051c278beab40aefa6618a4a5f4934e451a | 3,617,858 |
def module_enclosing_func(offset):
""" Test function to see if module-level enclosures are detected """
def module_closure_func(self):
"""
Actual closure function, should be reported as:
putil.tests.my_module.module_enclosing_func.module_closure_func
"""
self._exobj.add_e... | 399212d5cc04479639cdb5cacb50b167327f2445 | 3,617,859 |
def out_of_bounds_replace(llhs, params, out_of_bounds):
""" replace out of bounds llh evals with large, valid values"""
llhs[out_of_bounds(params)] = NAN_REPLACE_VAL
return llhs | d774ba545cdd8ac99932966495f581a2436c8aab | 3,617,860 |
import numpy
def fourier_sum ( func , N , xmin , xmax , fejer = False ) :
"""Make a function/histiogram representation in terms of Fourier series
>>> func = lambda x : x * x
>>> fsum = fourier_sum ( func , 4 , -1 , 1 )
>>> print fsum
>>> x = ...
>>> print 'FUN(%s) = %s ' % ( x , fsum ( x ) )
... | 566a1640fa1d3bc4c69658bce7e5847fe1103edf | 3,617,861 |
def get_retrieval_model(params):
"""
This method returns the Retrieval class requested in the parameter dict.
Args:
params(dict): A dict of parameters. In this method, the parameters 'logger' and 'query_generation', and
'search_engine' are required. Based on the requested retrievel model, so... | f90596d7bd280a6a000e59b6fe05d05afbdc957c | 3,617,862 |
def trigrams(docs):
"""create trigrams"""
return [trigram_model[bigram_model[doc]] for doc in docs] | c5ecd514e233858abc875fc3a8026a1b0494fd9f | 3,617,863 |
import nilearn.image as niimg
def concat_imgs(in_files, out_file=None):
""" Use nilearn.image.concat_imgs to concat images of up to 4 dimensions.
Returns
-------
out_file: str
The absolute path to the output file.
"""
return niimg.concat_imgs(in_files) | 04b5e376b3d20623ed66e08ffe704875568cebbc | 3,617,864 |
def conv_2d(in_nodes, nb_filter, filter_size, strides=1, padding='same',
activation='relu', bias=True, weights_init='truncated_normal',
bias_init='truncated_normal', regularizer=None, weight_decay=0.001,
trainable=True, restore=True, reuse=False,
name="conv_2d"):
""" ... | 73b8e104cf398e0d34b77c36c1d53a083c2b52df | 3,617,865 |
def tensor_repr(fra_data, dtype=None):
"""Creates a tensor representation of the FRA."""
dtype = dtype or tf.float64
res = dict()
res["fixing_date"] = tf.convert_to_tensor(
fra_data["fixing_date"], dtype=tf.int32)
res["fixed_rate"] = tf.convert_to_tensor(
fra_data["fixed_rate"], dtype=dtype)
con... | 415c6b40e17015d074a0cdb387184468b26a2143 | 3,617,867 |
def find_section_clamping_values(zlevel, lowerfract, upperfract):
"""Find int8 values that correspond to lowerfract & upperfract of zlevel histogram
From igneous (https://github.com/seung-lab/igneous/blob/master/igneous/tasks/tasks.py#L547)
"""
filtered = np.copy(zlevel)
# remove pure black fr... | fc0ebcbca1f10d2028d2ddd829092532544c44d9 | 3,617,868 |
def get_levels(content, language=None):
"""
Returns a list of integers whose value is for the content of the same index.
:param content: Either a list of strings, or a list of dictionaries that contain 'content' and 'language' keys.
:param language: Optional, only needed if only passing in a list of str... | f38acf80394c95ca395a1fd1dc755b1338275ad0 | 3,617,869 |
import logging
def losses(args):
"""Set up ``losses`` dict which stores model losses per epoch as well as evaluation metrics"""
losses = {}
keys = ["D", "Dr", "Df", "G"]
if args.gp:
keys.append("gp")
eval_keys = ["w1p", "w1m", "w1efp", "fpnd", "coverage", "mmd"]
if not args.fpnd:
... | b4da1c280968c510b59d598d699c331194196100 | 3,617,870 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.