text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import sys
sys.path.append('..')
import os
import glob
from tqdm import tqdm
import time
import shutil
import json
import numpy as np
from converter.nii_reader import Nii_Reader
from converter.utils import save_as_hdf5
# Different samples are saved in different folder
def nii_to_hdf5(input_path, save_path, annotatio... | {"hexsha": "c06a77801affadd6428b77e55122b87d17ba6344", "size": 1818, "ext": "py", "lang": "Python", "max_stars_repo_path": "converter/nii2npy.py", "max_stars_repo_name": "shijun18/Spine_Seg", "max_stars_repo_head_hexsha": "90c41d8ee08235c43bd3a5236da5a0ee7066fced", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
\section{Program gto\char`_fasta\char`_rand\char`_extra\char`_chars}
The \texttt{gto\char`_fasta\char`_rand\char`_extra\char`_chars} substitutes in the DNA sequence the outside ACGT chars by random ACGT symbols. It works both in FASTA and Multi-FASTA file formats.\\
For help type:
\begin{lstlisting}
./gto_fasta_rand_ex... | {"hexsha": "47bad20f4fcd9b3e5191b270e3f7b513b3ce385f", "size": 3254, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "manual/sections/FASTA_tools/FastaRandExtraChars.tex", "max_stars_repo_name": "olgafajarda/gto", "max_stars_repo_head_hexsha": "c0345c3f902eaab4811f5ceff6b24b569dbdd080", "max_stars_repo_licenses": [... |
import scipy
from py2scad import *
class Capillary_Enclosure(Basic_Enclosure):
def __init__(self,params):
self.params = params
self.add_sensor_cutout()
self.add_capillary_holes()
self.add_guide_tap_holes()
self.add_led_tap_holes()
self.add_led_cable_hole()
... | {"hexsha": "d77eab44ca521ea8a0675fccfd53eb25a8a9255e", "size": 18520, "ext": "py", "lang": "Python", "max_stars_repo_path": "capillary_enclosure.py", "max_stars_repo_name": "iorodeo/capillary_sensor_enclosure", "max_stars_repo_head_hexsha": "31eabacec098ab5600d79cbcdadc03ab42a044d1", "max_stars_repo_licenses": ["Apache... |
[STATEMENT]
lemma sub_inserted2:"\<lbrakk>Y \<subseteq> insert a X; \<not> Y \<subseteq> X\<rbrakk> \<Longrightarrow> Y = (Y - {a}) \<union> {a}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Y \<subseteq> insert a X; \<not> Y \<subseteq> X\<rbrakk> \<Longrightarrow> Y = Y - {a} \<union> {a}
[PROOF STEP]
b... | {"llama_tokens": 133, "file": "Group-Ring-Module_Algebra1", "length": 1} |
#!/bin/python
'''
script for compiling single CMX exposures into coadds and runnign them through
redrock
'''
import os
import glob
import h5py
import fitsio
import numpy as np
#dir_redux = "/global/cfs/cdirs/desi/spectro/redux/daily"
dir_redux = "/global/cfs/cdirs/desi/spectro/redux/andes"
dir_output = "/global... | {"hexsha": "fe457c5b95a7bfdd4b5e57702aa4f31fe03c1241", "size": 7806, "ext": "py", "lang": "Python", "max_stars_repo_path": "run/cmx/cmx_exps.py", "max_stars_repo_name": "changhoonhahn/feasiBGS", "max_stars_repo_head_hexsha": "b5f535f12cf64babc9e25bcec75edd45d8668f74", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/* hello world in r_egg */
write@syscall(4);
exit@syscall(1);
main@global(128) {
.var0 = "hi!\n";
write(1,.var0, 4);
exit(0);
}
| {"hexsha": "21d439f040f4fc1dcf9d21560315d4fdaf4634c4", "size": 132, "ext": "r", "lang": "R", "max_stars_repo_path": "standalone/pruntime/rizin/test/unit/legacy_unit/rz_gg/hi.r", "max_stars_repo_name": "ndkazu/guessNumber-vs-Bot", "max_stars_repo_head_hexsha": "6e756977ce849137c62edb0716df6926583da9b2", "max_stars_repo_... |
"""
File: pylinex/nonlinear/RankDecider.py
Author: Keith Tauscher
Date: 20 Apr 2018
Description: File containing a class which represents an IC-minimizer over a
discrete grid defined by a set of basis vector groups.
"""
import numpy as np
from distpy import Expression, KroneckerDeltaDistribution, Distribu... | {"hexsha": "3993f8fbf7deaaac0aa010e4415e278b016bc3c3", "size": 23147, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylinex/nonlinear/RankDecider.py", "max_stars_repo_name": "CU-NESS/pylinex", "max_stars_repo_head_hexsha": "b6f342595b6a154e129eb303782e5268088f34d5", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from lib.torch.constraints import apply_linear_constraint
import numpy as np
import torch
from torch.autograd import Variable
import pytest
def test_apply_linear_constraint():
def lin(x):
"""sum(x) >= 0"""
return x.sum(-1, keepdim=True)
x = Variable(torch.ones(1, 10))
# test equality co... | {"hexsha": "c3ab156724ba3ad976816d935f4ef6f867d4297b", "size": 1484, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_constraints.py", "max_stars_repo_name": "nbren12/nn_atmos_param", "max_stars_repo_head_hexsha": "cb138f0b211fd5743e56ad659aec38c082d2b3ac", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma rev_slice:
"n + k + LENGTH('a::len) = LENGTH('b::len) \<Longrightarrow>
slice n (word_reverse (w::'b word)) = word_reverse (slice k w :: 'a word)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n + k + LENGTH('a) = LENGTH('b) \<Longrightarrow> slice n (word_reverse w) = word_reverse (slice k ... | {"llama_tokens": 246, "file": null, "length": 2} |
From Test Require Import tactic.
Section FOFProblem.
Variable Universe : Set.
Variable UniverseElement : Universe.
Variable wd_ : Universe -> Universe -> Prop.
Variable col_ : Universe -> Universe -> Universe -> Prop.
Variable col_swap1_1 : (forall A B C : Universe, (col_ A B C -> col_ B A C)).
Variable col_swap2_... | {"author": "janicicpredrag", "repo": "Larus", "sha": "a095ca588fbb0e4a64a26d92946485bbf85e1e08", "save_path": "github-repos/coq/janicicpredrag-Larus", "path": "github-repos/coq/janicicpredrag-Larus/Larus-a095ca588fbb0e4a64a26d92946485bbf85e1e08/benchmarks/coq-problems/col-trans/col_trans_1281.v"} |
import numpy as np
from astropy.cosmology import FlatLambdaCDM
from abc import ABC, abstractmethod
class BaseCosmoBNNPrior(ABC):
"""Abstract base class for a cosmology-aware BNN prior
"""
def __init__(self, bnn_omega):
self._check_cosmology_config_validity(bnn_omega)
self._define_cosmology... | {"hexsha": "6d0c76f0219f399160746fc95c5ad73085530fd8", "size": 4021, "ext": "py", "lang": "Python", "max_stars_repo_path": "baobab/bnn_priors/base_cosmo_bnn_prior.py", "max_stars_repo_name": "jiwoncpark/baobab", "max_stars_repo_head_hexsha": "2a9a1b3eafbafef925bedab4b3137a3505a9b750", "max_stars_repo_licenses": ["MIT"]... |
#include <string.h>
#include <stdlib.h>
#include <registryFunction.h>
#include <aSubRecord.h>
#include <menuFtype.h>
#include <errlog.h>
#include <epicsString.h>
#include <epicsExport.h>
#include "epicsTypes.h"
#include <string>
#include <iostream>
#include <map>
#include <iterator>
#include <algorithm>
#include "buf... | {"hexsha": "8087a066d4232000d3c699604630d48cb303f8af", "size": 2559, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Keithley2001Sup/src/buffer_parsing.cpp", "max_stars_repo_name": "ISISComputingGroup/EPICS-Keithley_2001", "max_stars_repo_head_hexsha": "5f8edf73001d6e8a4fba82683c282fa241ab34ee", "max_stars_repo_li... |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from math import ceil, floor
import numpy as np
from yaml import safe_load
from maro.backends.frame import FrameBase, SnapshotList
from maro.data_lib.cim import CimDataContainerWrapper, Order, Stop
from maro.event_buffer import AtomE... | {"hexsha": "d3e9ed364f01ca9d15b3a19df892bc05cb919027", "size": 30184, "ext": "py", "lang": "Python", "max_stars_repo_path": "maro/simulator/scenarios/cim/business_engine.py", "max_stars_repo_name": "anukaal/maro", "max_stars_repo_head_hexsha": "21c88f4ef93729d51fc1a5b1a957150c51af2574", "max_stars_repo_licenses": ["MIT... |
/***************************************************************************
* Copyright (C) 2008 by Mikhail Zaslavskiy *
* mikhail.zaslavskiy@ensmp.fr *
* *
* This program is free software; you can redistribute it and/or modify *
*... | {"hexsha": "e866f1779ad99a76531b02b271358ff9d3266df7", "size": 3326, "ext": "h", "lang": "C", "max_stars_repo_path": "code/clustered_setup/fgm-master/LSGMcode-master/algorithms/graphm-0.52/algorithm.h", "max_stars_repo_name": "mk2510/jointGraphMatchingAndClustering", "max_stars_repo_head_hexsha": "52f579a07d106cb241d21... |
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you m... | {"hexsha": "dddce8726fa95307c3d66c4b854eaf9f07afbcd2", "size": 4430, "ext": "py", "lang": "Python", "max_stars_repo_path": "kglib/kgcn/pipeline/encode.py", "max_stars_repo_name": "lolski/kglib", "max_stars_repo_head_hexsha": "2265009bc066454accb88cdaad8769b920d5df39", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import unittest
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors im... | {"hexsha": "3570b65682ebb2ceada5322d64b6df226c37a769", "size": 10066, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter06/unittests/testActivity6_01.py", "max_stars_repo_name": "nijinjose/The-Supervised-Learning-Workshop", "max_stars_repo_head_hexsha": "33a2fec1e202dc1394116ed7a194bd8cabb61d49", "max_stars... |
import numpy as np
import random, itertools
from torchio import Transform, DATA
'''
Data augmentation on-the-fly
For Brats data, use affine transformation. Ref: https://www.frontiersin.org/articles/10.3389/fncom.2019.00083/full
Example code https://github.com/pytorch/vision/blob/master/torchvision/transforms/tr... | {"hexsha": "f69ce960c424fe660cd971000d0e288bccf0c122", "size": 3090, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/brats_transforms.py", "max_stars_repo_name": "weinajin/GloRe_brain", "max_stars_repo_head_hexsha": "ba291206504a4e754fa19811f73aee11d3734b16", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'mixerNew.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtG... | {"hexsha": "80850b236c85d7c47f7800eac4f0d4e81d7b2138", "size": 15719, "ext": "py", "lang": "Python", "max_stars_repo_path": "FFT-ImageMixer/Part-A/mixer.py", "max_stars_repo_name": "Radwa-Saeed/Didital-Signal-Processing-PyQt-GUI", "max_stars_repo_head_hexsha": "8d97e2925a20dd6a74d4bc0613bfceea668f2731", "max_stars_repo... |
# Imports
import random
import numpy as np
# Snake classes
class snake_obj:
def __init__(self, head):
self.head = head
@property
def length(self):
length = 0
part = self.head
while part:
part = part.next
length += 1
return length
class snake... | {"hexsha": "b83bfffa1b22761232639df882f214d8b9f0b710", "size": 4668, "ext": "py", "lang": "Python", "max_stars_repo_path": "game.py", "max_stars_repo_name": "NoahBlack012/snake_game_ml", "max_stars_repo_head_hexsha": "0bc77f199e2759f9b43dd7c4bb07994dc121a521", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
// @@@LICENSE
//
// Copyright (c) 2009-2013 LG Electronics, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless require... | {"hexsha": "ee4cc408f26513b43671e48e26d700fb5ff44f4b", "size": 7159, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "pop/src/commands/PopProtocolCommand.cpp", "max_stars_repo_name": "webOS-ports/mojomail", "max_stars_repo_head_hexsha": "49358ac2878e010f5c6e3bd962f047c476c11fc3", "max_stars_repo_licenses": ["Apache... |
import numpy as np
import sqlite3
from utils import image_ids_to_pair_id, pair_id_to_image_ids, blob_to_array, array_to_blob
from database import COLMAPDatabase
class MatchesList:
def __init__(self, num_images, database=None):
# `[[]] * N` create a list containing the same list object N times!!!... | {"hexsha": "a69e396120288ecde3d34a6fece99dbc64b68767", "size": 2656, "ext": "py", "lang": "Python", "max_stars_repo_path": "yan2017/matches_list.py", "max_stars_repo_name": "lxxue/colmap", "max_stars_repo_head_hexsha": "1e2cbf61160205fd53d9fe7a1a8df359b870cae3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
import numpy as np
import numba as nb
import mcmc.util as util
import mcmc.util_2D as u2
spec = [
('basis_number', nb.int64),
('extended_basis_number', nb.int64),
('t_end', nb.float64),
('t_start', nb.float64),
('dt', nb.float64),
('t'... | {"hexsha": "537a0e2b8d51e529fdf130c54fcc352a41f65c70", "size": 7043, "ext": "py", "lang": "Python", "max_stars_repo_path": "Legacy/mcmc/fourier.py", "max_stars_repo_name": "puat133/MCMC-MultiSPDE", "max_stars_repo_head_hexsha": "2beca39f32c0cdd7664baeacd495b193850d8e7d", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
data = pd.read_csv('201213177_data.csv', engine='python')
# Remove the column named 'department' because it's not part of data analysis.
data_var = data.drop(['municipio'], a... | {"hexsha": "31e060dadfbdbddaaae0cad157687dab55fa4bb4", "size": 2191, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/201213177.py", "max_stars_repo_name": "luisferliza/CoronavirusML", "max_stars_repo_head_hexsha": "5298b6f000ee5597de55f850a963fa15004b44d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from scipy.stats import norm
import numpy as np
alpha = 0.05
z = norm().ppf( 1 - alpha / 2)
p = 0.85
N = 100
Cn = (p - z * np.sqrt(p * (1 - p) / N), p + z * np.sqrt(p * (1 - p) / N))
_ = ["%0.4f" % x for x in Cn]
print(_)
# bootstrap
X = np.zeros(N)
X[:85] = 1
B = []
for _ in range(500):
s = np.random.randint(... | {"hexsha": "a2e443cf2d8523d2e7f91a6b51c3a7436d3beda7", "size": 6305, "ext": "py", "lang": "Python", "max_stars_repo_path": "codigo/comparacion.py", "max_stars_repo_name": "INGEOTEC/AprendizajeComputacional", "max_stars_repo_head_hexsha": "96d2bab8911313d2655cfc05965393c01c4efac9", "max_stars_repo_licenses": ["Apache-2.... |
/**
* @file stabrk3.cc
* @brief NPDE homework StabRK3 code
* @author Unknown, Oliver Rietmann, Philippe Peter
* @date 13.04.2021
* @copyright Developed at ETH Zurich
*/
#include "stabrk3.h"
#include <Eigen/Core>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <vector>
namespace StabRK3 {
/* S... | {"hexsha": "df2df6fa1b8461e2346812fec719a43e30e4c25d", "size": 2525, "ext": "cc", "lang": "C++", "max_stars_repo_path": "developers/StabRK3/mastersolution/stabrk3.cc", "max_stars_repo_name": "yiluchen1066/NPDECODES", "max_stars_repo_head_hexsha": "f7b1d96555bace59aba2b65f3ef1e95fa7a9017c", "max_stars_repo_licenses": ["... |
/*
* Copyright (c) 2013-2014, Filippo Basso <bassofil@dei.unipd.it>
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code must retain the above copyr... | {"hexsha": "06c1022e9387971e64673729580dc8e17f909e61", "size": 5724, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "calibration_pcl/include/impl/calibration_pcl/utilities/point_plane_extraction.hpp", "max_stars_repo_name": "pionex/calibration_toolkit", "max_stars_repo_head_hexsha": "ef9a6f7c6e83ac8c1a052f6f4f4887... |
# Density Fitting
Density fitting is an extremely useful tool to reduce the computational scaling of many quantum chemical methods.
Density fitting works by approximating the four-index electron repulsion integral (ERI) tensors from Hartree-Fock
theory, $g_{\mu\nu\lambda\sigma} = (\mu\nu|\lambda\sigma)$, by
$$(\mu... | {"hexsha": "2ab4ddbd66c0090b79cec1d5dd8ba8a25d801103", "size": 16973, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Example/Psi4Numpy/03-HatreeFock/density-fitting.ipynb", "max_stars_repo_name": "yychuang/109-2-compchem-lite", "max_stars_repo_head_hexsha": "cbf17e542f9447e89fb48de1b28759419ffff956... |
[STATEMENT]
lemma suminf_eq_zero_iff:
assumes "summable f" and pos: "\<And>n. 0 \<le> f n"
shows "suminf f = 0 \<longleftrightarrow> (\<forall>n. f n = 0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (suminf f = (0::'a)) = (\<forall>n. f n = (0::'a))
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 sub... | {"llama_tokens": 1282, "file": null, "length": 13} |
module PrivateMultiplicativeWeights
using
Distributions: Laplace, wsample
using
Printf,
Hadamard,
LinearAlgebra,
Random,
IterTools,
Statistics,
Distributed,
export
mwem,
MWParameters,
Tabular,
Histogram,
HistogramQueries,
SeriesRangeQueries,
RangeQueries,
... | {"hexsha": "1ffd4d572ceb94a14c4fea3de62809d707d77020", "size": 1091, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PrivateMultiplicativeWeights.jl", "max_stars_repo_name": "giladroyz/MWEM-project", "max_stars_repo_head_hexsha": "e587b6bae85a2bf47b09e33e12dfac0adfa7e2ca", "max_stars_repo_licenses": ["MIT"], ... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\documentclass[b5paper, 11pt, openany, titlepage]{book}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\usepackage[pdftex]{graphicx,color}
%\usepackage[T1,plmath]{polski}
\usepackage[cp1250]{inputenc}
\usepackage{indentfirst}
\usepackage[numbers,sort&com... | {"hexsha": "7826d4cb98c69cb86136cada4863cdb085b9adb2", "size": 6577, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "reports/monograph/Ijjeh/ChapterTwo/monograph_template.tex", "max_stars_repo_name": "IFFM-PAS-MISD/aidd", "max_stars_repo_head_hexsha": "9fb0ad6d5e6d94531c34778a66127e5913a3830c", "max_stars_repo_lic... |
import sys
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
from fhi_lib.geometry import Point
class ImgCoord():
def __init__(self, info):
self.mask = info[0].astype(np.uint8)
self.roi = info[1]
self.class_id= info[2]
def draw_point_of_interest(self, img):
img = cv2.circle(img,... | {"hexsha": "d68ccc40d7708621f78ee425dd8f36dad1145387", "size": 2565, "ext": "py", "lang": "Python", "max_stars_repo_path": "fhi_lib/img_coordinate.py", "max_stars_repo_name": "yhsueh/FHI_RCNN", "max_stars_repo_head_hexsha": "f12df17049d5c72d1a7cec89e3943013150177a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
COLORS = {'train': 'b', 'test': 'r'}
def plot_validation_curve(train_scores, test_scores, train_sizes, expected_score=None, ax=None, stat_error=True):
ax = _plot_generic_curve(
train_scores=train_scores,
test_scores=test_scor... | {"hexsha": "a4fabe54657fe1ca9150e2b004d354311f08fae8", "size": 3019, "ext": "py", "lang": "Python", "max_stars_repo_path": "SmallSampleClassification/my_lib/plotting.py", "max_stars_repo_name": "RaulRPrado/LearningDataScience", "max_stars_repo_head_hexsha": "0a7d6cfffadd7cdd657be0609b7f088a19f9deb5", "max_stars_repo_li... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | {"hexsha": "66f7b2ff23d1bef698e7da165ea39509a7262d1a", "size": 11435, "ext": "py", "lang": "Python", "max_stars_repo_path": "pahelix/model_zoo/light_gem_model.py", "max_stars_repo_name": "agave233/PaddleHelix", "max_stars_repo_head_hexsha": "e5578f72c2a203a27d9df7da111f1ced826c1429", "max_stars_repo_licenses": ["Apache... |
import torch
import tensorflow as tf
import os
import numpy as np
import cv2
class EpisodeScalerSummary(object):
"""docstring for EpisodeScalerSummary."""
def __init__(self):
self.episode_scalers = {}
self.final_scalers = {}
self.reset()
def at_step(self, step_scalers={}):
... | {"hexsha": "2cf5c6e0373b1c2db8ac4b97b707450568aee7ae", "size": 3194, "ext": "py", "lang": "Python", "max_stars_repo_path": "assets/logger.py", "max_stars_repo_name": "YuhangSong/Arena-Baselines-Depreciated", "max_stars_repo_head_hexsha": "78c33994e67aede7565dda3f68f5cebe0d5ee6e6", "max_stars_repo_licenses": ["Apache-2.... |
from functools import cmp_to_key
import numpy as np
from matplotlib.ticker import FuncFormatter
from scipy import signal
from pydynamo_brain.files import TraceCache
from pydynamo_brain.ui.baseMatplotlibCanvas import BaseMatplotlibCanvas
from pydynamo_brain.ui.common import createAndShowInfo
import pydynamo_brain.uti... | {"hexsha": "6dced6e6fe4966bade06b8a0be01dd040e71e2b8", "size": 3792, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydynamo_brain/pydynamo_brain/ui/traces/allTracesCanvas.py", "max_stars_repo_name": "ubcbraincircuits/pyDynamo", "max_stars_repo_head_hexsha": "006eb6edb5e54670574dbfdf7d249e9037f01ffc", "max_star... |
# Copyright 2021 Huawei Technologies Co., Ltd.All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | {"hexsha": "2b7e6badc80aeb6871af3228e2c624f47a4a53ff", "size": 6808, "ext": "py", "lang": "Python", "max_stars_repo_path": "ecosystem_tools/mindconverter/mindconverter/graph_based_converter/mapper/onnx/ops/select_mapper.py", "max_stars_repo_name": "mindspore-ai/mindinsight", "max_stars_repo_head_hexsha": "8c57fdd62eb7f... |
#!/usr/bin/env python
from __future__ import print_function
import argparse
import glob
import numpy as np
from cycler import cycler
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('runprefixes', type=str, nargs='+',
help='Prefixes of the ordinate and time txt... | {"hexsha": "090e2bbb3b133a859e76c101e9469fa83f295552", "size": 2616, "ext": "py", "lang": "Python", "max_stars_repo_path": "unit_test/burn_cell/burn_cell_compare_ordinate.py", "max_stars_repo_name": "doreenfan/Microphysics", "max_stars_repo_head_hexsha": "bbfabaae0a98af32dbf353a7747a8ca787710ac6", "max_stars_repo_licen... |
import os
import time
import playsound
import speech_recognition as sr
from gtts import gTTS
import cv2
import numpy as np
import webbrowser
print("How can i help you")
def speak(text):
tts=gTTS(text=text,lang="en")
filename="voice.mp3"
tts.save(filename)
playsound.playsound(filename)
... | {"hexsha": "d517b1a9bff0631f01bdd8fa4e1668440a88fd38", "size": 1049, "ext": "py", "lang": "Python", "max_stars_repo_path": "Machine Learning Projects/Speech To Text/code5.py", "max_stars_repo_name": "anshu1905/IoT-and-ML", "max_stars_repo_head_hexsha": "78e8fdb83201564867ac4f07beb922265f516237", "max_stars_repo_license... |
import pandas as pd
import numpy as np
import pickle
from sklearn.multioutput import MultiOutputClassifier, ClassifierChain
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import os
def load_data(difficulty):
"""Loads DataFrames saved in pickle files based o... | {"hexsha": "d8a03e56d77e1446b5a82247499abfd5a9aa061b", "size": 2672, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/RFClassifier.py", "max_stars_repo_name": "wvsharber/BeatMapSynthesizer", "max_stars_repo_head_hexsha": "9497ad8dcb0567eaab6b5102121c982dd0e74d54", "max_stars_repo_licenses": ["MIT"], "max_star... |
# an example of a greta function
# pull out the necessary internal functions from greta
op <- .internals$nodes$constructors$op
#' @importFrom stats var median quantile
#' @import greta
#'
#' @title compute the Bayesian R square for a greta regression model
#' @export
#'
#' @description Compute a Bayesian version o... | {"hexsha": "74bc225949f5e9ec8c73ab5232d15756e4290c02", "size": 2835, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bayes_R2.r", "max_stars_repo_name": "lionel68/greta.checks", "max_stars_repo_head_hexsha": "bb5cb6b4fca11a79367f1370409a5d17d4a0ccac", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1, "ma... |
# https://en.wikipedia.org/wiki/Tak_(function)
module BenchTarai
using BenchmarkTools
module SeqTarai
tarai(x, y, z) =
if y < x
tarai(tarai(x - 1, y, z), tarai(y - 1, z, x), tarai(z - 1, x, y))
else
y
end
end # module SeqTarai
module BaseTarai
tarai(x, y, z) =
if y < x
a =... | {"hexsha": "a00a3bc9c0ddcee995834ebda34127fe7db72abd", "size": 1775, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/TapirSchedulersBenchmarks/src/bench_tarai.jl", "max_stars_repo_name": "cesmix-mit/TapirSchedulers.jl", "max_stars_repo_head_hexsha": "fd80ca0e78bfacacf6b337cae5f586f45994d945", "max_stars... |
import sys
import copy
import math
import numpy as np
"""This file contains various gradient optimisers"""
# class for simple gradient descent
class SimpleGradientDescent:
def __init__(self, eta, layers, weight_decay=0.0):
# learning rate
self.eta = eta
# number of layers
self.lay... | {"hexsha": "566ff10ef3ddd4840a59ddade7269e5cb7c43026", "size": 16268, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment1/optimiser.py", "max_stars_repo_name": "utsavdey/Fundamentals_Of_Deep_Learning_Assignments", "max_stars_repo_head_hexsha": "c1b2fc49e929ab09760f083aa8b052845afad48f", "max_stars_repo_l... |
using DirectDependents
using Test
@testset "DirectDependents.jl" begin
@test !isempty(direct_dependents("RecipesBase"))
end
| {"hexsha": "46a16d337638b45283e00bfc2fd7f2101f519c95", "size": 129, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "daschw/DirectDependents.jl", "max_stars_repo_head_hexsha": "67b730f852488aedb3544cb461275ece8507ccb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
export PermutationLayer
import Base: eltype
@doc raw"""
The permutation layer specifies an invertible mapping ``{\bf{y}} = g({\bf{x}}) = P{\bf{x}}`` where ``P`` is a permutation matrix.
"""
struct PermutationLayer{ T } <: AbstractLayer
dim :: Int
P :: PermutationMatrix{T}
end
function PermutationLayer(dim:... | {"hexsha": "90c79acafbec2feea5f8dd231278bb41dc945b37", "size": 6094, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nodes/flow/layers/permutation_layer.jl", "max_stars_repo_name": "HoangMHNguyen/ReactiveMP.jl", "max_stars_repo_head_hexsha": "f3e848ab171e0786e3d8eb6a0843dbf6dacc7415", "max_stars_repo_licenses... |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "634b0fccab27c3552b1794373b64b0eabd3f0b1b", "size": 17621, "ext": "py", "lang": "Python", "max_stars_repo_path": "rrlfd/residual/train.py", "max_stars_repo_name": "shaun95/google-research", "max_stars_repo_head_hexsha": "d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 24 2018
@author: Fei Yan
"""
import numpy as np
from scipy.linalg import eig
from qutip import *
import logging
log = logging.getLogger('LabberDriver')
# import scipy.constants as const
# #Constants.
# h = const.h #planck constant
# h_bar = const.hbar #h_bar
# e = cons... | {"hexsha": "0f1e97534f28b86287ed219219c037154981ae7f", "size": 9426, "ext": "py", "lang": "Python", "max_stars_repo_path": "QSolver/QSolver_ForDriver.py", "max_stars_repo_name": "roniwinik/Drivers", "max_stars_repo_head_hexsha": "ba473bc21d1b5321da1e6caadec5b4d624282edc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\clearpage
\subsection{C Parameter Declaration (with Arrays)} % (fold)
\label{sub:c_parameter_declaration_with_arrays_}
\csyntax{csynt:type-decl-parameter-decl}{Parameter Declarations (with Arrays)}{type-decl/parameter-decl-with-types}
% subsection c_parameter_declaration_with_arrays_ (end) | {"hexsha": "6e70c1c82ede0c8756a786106c3f19b35a4d29ab", "size": 293, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "topics/type-decl/pascal/pas-parameter-decl-with-types.tex", "max_stars_repo_name": "thoth-tech/programming-arcana", "max_stars_repo_head_hexsha": "bb5c0d45355bf710eff01947e67b666122901b07", "max_star... |
%%Ex. 8 Extracting an individual element of an array
a = [3 6 7];
b = [1 9 4 5];
c = a(2) + b(4)
%Output: c = 11 | {"author": "TheAlgorithms", "repo": "MATLAB-Octave", "sha": "e150b77ad256de46c1ce3815c3d7945ac4fc28dc", "save_path": "github-repos/MATLAB/TheAlgorithms-MATLAB-Octave", "path": "github-repos/MATLAB/TheAlgorithms-MATLAB-Octave/MATLAB-Octave-e150b77ad256de46c1ce3815c3d7945ac4fc28dc/matlab_for_beginners/part_1(learn_basic_... |
# Using Android IP Webcam video .jpg stream (tested) in Python2 OpenCV3
import urllib
import cv2
import numpy as np
import time
import subprocess
import urllib
import cam_find
import socket
import bluetooth
# Replace the URL with your own IPwebcam shot.jpg IP:port
url='http://192.168.43.1:8080/shot.jpg'
... | {"hexsha": "5e5d9fc4f65093a894acd291aad83ef0e23730b9", "size": 2038, "ext": "py", "lang": "Python", "max_stars_repo_path": "programs-pi/rd.py", "max_stars_repo_name": "AshwinRaikar88/rhok", "max_stars_repo_head_hexsha": "5be70c78605e81e1af191a006460fa48e849c62b", "max_stars_repo_licenses": ["Unlicense"], "max_stars_cou... |
"""Routines and classes related to RPyC package"""
from . import module as module_utils, net, py3, strpack
import numpy as np
import importlib
rpyc=importlib.import_module("rpyc") # Python 2 compatibility (importing module from a module with the same name)
import pickle
import warnings
import socket
_default_packe... | {"hexsha": "62706294c183137aa3216da43365aed110ce0467", "size": 8140, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylablib/core/utils/rpyc.py", "max_stars_repo_name": "AlexShkarin/pyLabLib-v0", "max_stars_repo_head_hexsha": "1c3c59d4bcbea4a16eee916033972ee13a7d1af6", "max_stars_repo_licenses": ["MIT"], "max_s... |
# %%
import warnings
from functools import partial
from itertools import product
import numpy as np
import pandas as pd
from graspologic.utils import symmetrize
from hyppo.ksample import KSample
from joblib import Parallel, delayed
from scipy.stats import ks_2samp, mannwhitneyu, ttest_ind
from tqdm import tqdm
from s... | {"hexsha": "022b923393737a486b3b0c5d2751f2cce924981c", "size": 3930, "ext": "py", "lang": "Python", "max_stars_repo_path": "supplement/S1-edge-simulation/edge_simulation_changing_means.py", "max_stars_repo_name": "neurodata/MCC", "max_stars_repo_head_hexsha": "42c6b5fab09edf10bce2143c2366199531b2626b", "max_stars_repo_... |
using MarketData
facts("Data quality checks") do
cl_incomplete = TimeArray(cl.timestamp[1:5], [cl.values[1:4]; NaN], cl.colnames)
context("Determine uniformity of observations") do
@fact cl[1:5] --> is_equally_spaced
@fact cl[1:10] --> not(is_eq... | {"hexsha": "2cc38c6f0c370901a36d28bb174fb79322d4eaad", "size": 1143, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/data_quality.jl", "max_stars_repo_name": "GordStephen/TimeSeriesTools.jl", "max_stars_repo_head_hexsha": "3b10959392a723e570b99ce4053134781a4d3556", "max_stars_repo_licenses": ["MIT"], "max_st... |
// Copyright 2018-2019 Henry Schreiner and Hans Dembinski
//
// Distributed under the 3-Clause BSD License. See accompanying
// file LICENSE or https://github.com/scikit-hep/boost-histogram for details.
#include <bh_python/pybind11.hpp>
#include <bh_python/axis.hpp>
#include <bh_python/kwargs.hpp>
#include <bh_pytho... | {"hexsha": "3268708741b771890a960d1686e07ac210ff10ee", "size": 3274, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/register_axis.cpp", "max_stars_repo_name": "andrzejnovak/boost-histogram", "max_stars_repo_head_hexsha": "cdbfabb1c22f5545bf3900be01f2025411e699f1", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#pdm.py
#
#Copyright (c) 2018, Oracle and/or its affiliates. All rights reserved.
#The Universal Permissive License (UPL), Version 1.0
#
#by Joe Hahn, joe.hahn@oracle.come, 11 September 2018
#this executes the pdm demo
#get commandline argument
try:
import sys
inputs_path = sys.argv[1]
except:
inputs_path... | {"hexsha": "826e91a9d867446b45a60317a4f82496e748fac3", "size": 5115, "ext": "py", "lang": "Python", "max_stars_repo_path": "pdm.py", "max_stars_repo_name": "JosephSe/predictive-maintenance-sim", "max_stars_repo_head_hexsha": "49c28f75b30fe7c7668f94388fc48acf247e5f4e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
#!/usr/bin/python3
# number of output figures = 129
import multiprocessing
import matplotlib.patches
import numpy as np
import helper.basis
from helper.figure import Figure
import helper.plot
def drawImage(imageNumber):
fig = Figure.create(figsize=(5, 5 * aspect), scale=0.5)
ax = fig.gca()
xUnits = imageNu... | {"hexsha": "42039adad89ca0335f4ab29072579b31da888a1f", "size": 2556, "ext": "py", "lang": "Python", "max_stars_repo_path": "gfx/py/flipBookBSpline.py", "max_stars_repo_name": "valentjn/thesis", "max_stars_repo_head_hexsha": "65a0eb7d5f7488aac93882959e81ac6b115a9ea8", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_c... |
! RUN: %S/test_errors.sh %s %t %flang_fc1
! REQUIRES: shell
! Error tests for recursive use of derived types.
! C744 If neither the POINTER nor the ALLOCATABLE attribute is specified, the
! declaration-type-spec in the component-def-stmt shall specify an intrinsic
! type or a previously defined derived type.
program m... | {"hexsha": "34c4d815cbcdff1bf8942e9c8074744a04d967da", "size": 2192, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "flang/test/Semantics/resolve44.f90", "max_stars_repo_name": "acidburn0zzz/llvm-project", "max_stars_repo_head_hexsha": "7ca7a2547f00e34f5ec91be776a1d0bbca74b7a9", "max_stars_repo_licenses": ["Ap... |
#!/usr/bin/env python
""" Calculates numbers for use in writeup. """
from collections import defaultdict, Counter
import csv
import json
from math import log
import os
import pathlib
import random
import sys
import matplotlib.pyplot as plt
import requests
from scipy import stats
from statsmodels.stats.proportion impo... | {"hexsha": "193ad37d15be8b8e5e40694cffa0c7cb9869e5e3", "size": 3139, "ext": "py", "lang": "Python", "max_stars_repo_path": "elo/calculations.py", "max_stars_repo_name": "porcpine1967/aoe2_comparisons", "max_stars_repo_head_hexsha": "7c5e5e5223127325269b558d59ea4bdb5949345e", "max_stars_repo_licenses": ["CC0-1.0"], "max... |
import os.path as osp
import os
import numpy as np
import json
def get_index_by_label(used_labels, label):
return list(used_labels.keys())[list(used_labels.values()).index(label)]
def cache_label_name(labels_dict, label_name):
cached_keys = sorted(list(labels_dict.keys()))
if len(cached_keys) == 0:
... | {"hexsha": "154818c7049c0865d87a5ec8135c5b1e5ab3a4b1", "size": 2883, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gen_labels.py", "max_stars_repo_name": "supervisely-ecosystem/FairMOT", "max_stars_repo_head_hexsha": "493aa8b626e0eac1a71d27af8eefb680210b8a11", "max_stars_repo_licenses": ["MIT"], "max_stars... |
export opt_linear_fit!
"""
opt_linear_fit!(
graph,
objfun,
discr,
linear_cref;
input = :A,
errtype = :abserr,
linlsqr = :backslash,
droptol = 0,
)
Linear fitting of a `graph` of the form
c_1 g_1(x) + c_2 g_2(x) + … + c_n g_n(x)
to the value... | {"hexsha": "c2e836f88842f92ac841388ed6998eafb4e317e5", "size": 1398, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/optimization/linear_fit.jl", "max_stars_repo_name": "matrixfunctions/GraphMatFun.jl", "max_stars_repo_head_hexsha": "1fac14aa849e7f050ae5281bf6414b4356807199", "max_stars_repo_licenses": ["MIT"... |
# Truncated and folded distributions
This tutorial will cover how to work with truncated and folded
distributions in NumPyro.
It is assumed that you're already familiar with the basics of NumPyro.
To get the most out of this tutorial you'll need some background in probability.
### Table of contents
* [0. Setup](#0)... | {"hexsha": "27cc248fb1a13d61d8c9cb619f6ee8619447d493", "size": 162336, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/source/truncated_distributions.ipynb", "max_stars_repo_name": "karm-patel/numpyro", "max_stars_repo_head_hexsha": "34e0cdf4fa0ab9a0300a0d894d6758419fb46f40", "max_stars_re... |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from __future__ import division
import sys
import networkx as nx
import numpy as np
reload(sys)
sys.setdefaultencoding('utf8')
path4 = '/Users/amy/Desktop/rls_14/0729/'
nodes = np.loadtxt(path4 + 'rls14_nodes0729.csv', skiprows=1, delimiter=",", dtype=str)
edges = np.loadt... | {"hexsha": "8391cd40867b9bffd1340c7e466f61795a1a7db6", "size": 910, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/form_gexf_data.py", "max_stars_repo_name": "yuxia-zhang/company_collaboration_in_OSS", "max_stars_repo_head_hexsha": "2106d45df75d89873c0364eb70fded8541c157b6", "max_stars_repo_licenses": ["Ap... |
# single coil reconstruction class
import numpy as np
import tensorflow as tf
import dnnlib
import dnnlib.tflib as tflib
from training import misc
class Reconstructor:
def __init__(self):
# inference settings
self.num_steps = 1000 # number of optimization / infer... | {"hexsha": "e57e046a70235128910704cebf19fb722e4b4469", "size": 11465, "ext": "py", "lang": "Python", "max_stars_repo_path": "reconstruction_single_coil.py", "max_stars_repo_name": "icon-lab/SLATER", "max_stars_repo_head_hexsha": "4ec15a16f9cc204150c2cc48c4c3e60de2b7cbfb", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import time
from pathlib import Path
from collections import deque
from typing import Optional
import numpy as np
from lib.opengl.core.base import *
from lib.opengl import *
from lib.opengl.postproc import PostProcNode
from lib.gen.automaton import ClassicAutomaton
ASSET_PATH = Path(__file__).resolve().parent.parent... | {"hexsha": "196dfe73051a92968f5d3394c4ee2d5dacc655cd", "size": 8105, "ext": "py", "lang": "Python", "max_stars_repo_path": "sketches/graphs/tiled.py", "max_stars_repo_name": "defgsus/thegame", "max_stars_repo_head_hexsha": "38a627d9108f1418b94b08831fd640dd87fbba83", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from numpy.random import uniform, seed
from sys import argv, stdout, stderr
# Process arguments
if len(argv) < 3:
stderr.write("Usage: {} Q_LOW Q_HIGH [SEED_FILE]\n"
"Sample from a uniform distribution with "
"lower bound Q_LOW and upper bound Q_HIGH\n"
"If given SEED_FILE, read... | {"hexsha": "5595ac75a65cf3fda17be80a3ecd79206f599ce3", "size": 1022, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/seed/stateful-prior-sampler.py", "max_stars_repo_name": "ThomasPak/pakman", "max_stars_repo_head_hexsha": "8d74f3b7162d04a1c72fbda9517ed19063f1a246", "max_stars_repo_licenses": ["BSD-3-Claus... |
'''
Module with functions for converting audio files across several formats
'''
# imports of built-in packages
import os
import sys
import csv
import re
# imports from package modules
from .common_file_ops import path_splitter, run_exec, img_fmt_converter
from .config import read_config
## get paths to required execu... | {"hexsha": "100e1fffa9f80482d02d912a4ceaa7e259bf9d1b", "size": 42286, "ext": "py", "lang": "Python", "max_stars_repo_path": "file_manipulators/audio_conv_ops.py", "max_stars_repo_name": "Retr0Metal98/file_manipulators", "max_stars_repo_head_hexsha": "fb9992c5e34910a3ddbb97ea88c3fe2e86477818", "max_stars_repo_licenses":... |
"""
Integration operations.
union_poi_bank,
union_poi_bus_station,
union_poi_bar_restaurant,
union_poi_parks,
union_poi_police,
join_collective_areas,
join_with_pois,
join_with_pois_by_category,
join_with_events,
join_with_event_by_dist_and_time,
join_with_home_by_id,
merge_home_with_poi
"""
from __future__ import an... | {"hexsha": "90e240765c74d31b1b1f00293fc8c190218314c0", "size": 54509, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymove/utils/integration.py", "max_stars_repo_name": "JuniorNunes15/PyMove", "max_stars_repo_head_hexsha": "ee5b68282502bfcb9cf38b52dcdefed5bd927a90", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python
# /****************************************************************************
# * Copyright (c) 2019 Parker Lusk and Jesus Tordesillas Torres. All rights reserved.
# *
# * Redistribution and use in source and binary forms, with or without
# * modification, are permitted provided that the ... | {"hexsha": "fd617c56f88f56202f0ae0b6808f14eb8da889f7", "size": 9708, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/flipper.py", "max_stars_repo_name": "jtorde/uav_trajectory_optimizer_gurobi", "max_stars_repo_head_hexsha": "ed50a357ee6e6bea40d6e54656f8c223fb551b3c", "max_stars_repo_licenses": ["BSD-3-C... |
import numpy as np
def stoi(reference, estimation, sample_rate):
"""Wrapper to allow independent axis for STOI.
Args:
reference: Shape [..., num_samples]
estimation: Shape [..., num_samples]
sample_rate:
Returns:
"""
from pystoi.stoi import stoi as pystoi_stoi
estim... | {"hexsha": "ca6126cd4d7deb25bf6297fa7cb373aadc866499", "size": 651, "ext": "py", "lang": "Python", "max_stars_repo_path": "pb_bss/evaluation/module_stoi.py", "max_stars_repo_name": "mdeegen/pb_bss", "max_stars_repo_head_hexsha": "e8c380e27d82707e8d2b2d83c5c918d47ea5d89f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
"""
This script was used to check whether every word from the gold standard
is in the ukwac_100m corpus. The check was successful.
"""
wacfile = "../ukwac_100m/ukwac_100m_oneline.txt"
menfile = "data/MEN_dataset_natural_form_full"
wordlist_WAC = []
checklist = []
unshared_words = []
with open(wa... | {"hexsha": "41f92766b1f1c3ce16204bbf31a819ec27ab870e", "size": 1147, "ext": "py", "lang": "Python", "max_stars_repo_path": "checker.py", "max_stars_repo_name": "SimonPreissner/fruitfly", "max_stars_repo_head_hexsha": "99dffa7c1ed31da39513eda1ddacc4f9b968a6df", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
import argparse
import sys
import torch
import yaml
import numpy as np
import wandb
from train import load_model
from text import text_to_sequence
# Needed to unpickle waveglow model
sys.path.append("./waveglow/")
# Needed to avoid warnings
torch.nn.Module.dump_patches = True
with open("hparams.yaml") as yamlfile... | {"hexsha": "520ded8782057bccb26df1887102bb7a18dddc3d", "size": 3644, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference.py", "max_stars_repo_name": "bcsherma/tacotron2", "max_stars_repo_head_hexsha": "827e4d59c14da3d41cabd54864bc08fd71eef3fa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
import math
import numpy as np
import numpy.random as random
import torch
import torch.nn as nn
import copy
import torch.nn.functional as F
import torch.optim as optim
from scipy.stats import levy_stable
# This script is only for propagating randomly initializaed networks with square connectivity matrices (NOT FOR ... | {"hexsha": "78cd7198785d4a0dfaa74846b8335d2d7440295d", "size": 7443, "ext": "py", "lang": "Python", "max_stars_repo_path": "nporch/Randnet.py", "max_stars_repo_name": "CKQu1/extended-criticality-dnn", "max_stars_repo_head_hexsha": "e19efd34f84dc684b31b3ba0b1e41432dfc1bb59", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python3
###################################################################################
# Copyright 2021 National Technology & Engineering Solutions of Sandia, #
# LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the #
# U.S. Government retains certain rights in t... | {"hexsha": "9e4e4bb414aad9b43d098730b765b1b99c84c4d4", "size": 8448, "ext": "py", "lang": "Python", "max_stars_repo_path": "drivers/runNonlocal.py", "max_stars_repo_name": "sandialabs/PyNucleus", "max_stars_repo_head_hexsha": "98b87cf779c2c1853ce16d47998b692f594a55a4", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
from typing import Callable
import numpy as np
class Signature:
def __init__(self, args, ret):
self.args = args
self.ret = ret
class OpRegistry:
signatures = {}
def __init__(self):
pass
... | {"hexsha": "0a8df0f82580d9c09e6be0ed40eb65a2aa97c017", "size": 553, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/pyis-onnx/pyis/onnx/transpiler/ops/op_registry.py", "max_stars_repo_name": "microsoft/python-inference-script", "max_stars_repo_head_hexsha": "cbbbe9d16be0839e4df357b1bd9e8274ca44f1f7", "max_sta... |
import numpy as np
def simulation(data, params):
'''
Cema-Neige snow model
Input:
1. Data - pandas dataframe with correspondent timeseries:
'Temp'- mean daily temperature (Celsium degrees)
'Prec'- mean daily precipitation (mm/day)
2. Params - list of model parameters:
'CTG'... | {"hexsha": "ce58ad875e450f01b3d4d15762f6449550bfff43", "size": 2836, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cema_neige.py", "max_stars_repo_name": "hydrogo/2018_VinoRead_wrkshp", "max_stars_repo_head_hexsha": "1ebcaacedc9eb96e0ed749ae93225b8cf3246d2b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy
from audionmf.transforms.nmf import NMF
def nmf_matrix(matrix, max_iter=100, rank=30):
# increment the matrix to make sure it's positive
matrix_inc, min_val = increment_by_min(matrix)
# TODO save
# use Kullback-Leibler divergence
# nmf = nimfa.Nmf(matrix_inc, max_iter=max_iter, rank... | {"hexsha": "c4e884d6a217216411210ac48f2cb0f0b305fe63", "size": 873, "ext": "py", "lang": "Python", "max_stars_repo_path": "audionmf/util/nmf_util.py", "max_stars_repo_name": "argoneuscze/AudioNMF", "max_stars_repo_head_hexsha": "04f360653bb0df3d5ed19bf2eb1459bff16a944c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import os
import torch
import elf
import numpy as np
import wandb
from elf.segmentation.features import compute_rag
from torch.nn import functional as F
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
from collections import namedtuple
import matplotlib.pyplot as plt
from ... | {"hexsha": "035be3535f18b8a922684f7a01e6917e86723756", "size": 20020, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/sac_obj_lvl_rew.py", "max_stars_repo_name": "edosedgar/RLForSeg", "max_stars_repo_head_hexsha": "fc748d8e7d2f2a1e7ac0dddb3f268ec3025d40ca", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# This file is part of spot_motion_monitor.
#
# Developed for LSST System Integration, Test and Commissioning.
#
# See the LICENSE file at the top-level directory of this distribution
# for details of code ownership.
#
# Use of this source code is governed by a 3-clause BSD-style
# license that can be found in the LICE... | {"hexsha": "a6169d9bc4002058b12328278a319c47f7906338", "size": 8516, "ext": "py", "lang": "Python", "max_stars_repo_path": "spot_motion_monitor/camera/gaussian_camera.py", "max_stars_repo_name": "lsst-sitcom/spot_motion_monitor", "max_stars_repo_head_hexsha": "3d0242276198126240667ba13e95b7bdf901d053", "max_stars_repo_... |
/**
* Testival.cpp
*
* Test interval class
*
* Created by Yinan Li on July 20, 2016.
*
* Hybrid Systems Group, University of Waterloo.
*/
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE IntervalClass
#include <boost/test/unit_test.hpp>
//#include <boost/test/unit_test_log.hpp>
#include <cmath>
#in... | {"hexsha": "ff739ed4ee3dbb8c35893b5436b63aa07b5b465b", "size": 10436, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/testIval.cpp", "max_stars_repo_name": "yinanl/rocs", "max_stars_repo_head_hexsha": "bf2483903e39f4c0ea254a9ef56720a1259955ad", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": n... |
using Test
td = pwd()
for dir in ["basic","hamiltonian_zoo"]
print("Including Test Dir:",dir,"\n")
for file in readdir(td*"/"*dir,join=true)
print("\tIncluding Test File:",file,"\n")
include(file)
end
end | {"hexsha": "90e79db04e7200dc9e6414a8c792a730cc54b9cd", "size": 233, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "qiyang-ustc/EasyHamiltonian.jl", "max_stars_repo_head_hexsha": "2a56e7de20d93714848ed90ae0e0cb6287070df8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class AtariNetwork(nn.Module):
n_features = 512
def __init__(self, input_shape, _, n_actions_per_head, use_cuda, n_games,
features, dropout):
super().__init__()
self._n_input = input_shape... | {"hexsha": "57de75decc2524ccfcf879c9b0e7cfcbd68a761b", "size": 7684, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/shared/dqn/networks.py", "max_stars_repo_name": "MushroomRL/mushroom-rl-meta", "max_stars_repo_head_hexsha": "08c13bd8115c81aba083ec62672956026e7ddd8a", "max_stars_repo_licenses": ["MIT"]... |
# Author: Andrey Boytsov <andrey.boytsov@uni.lu> <andrey.m.boytsov@gmail.com>
# License: BSD 3 clause (C) 2017
# Fitting iris dataset (from sklearn) Embedding the same data using transform function, see how close new Ys are
# to original data. Feel free to play with transformation parameters.
# Transformation is done ... | {"hexsha": "8ee6af5a35d593660fa434c8604e47641d60fc17", "size": 1749, "ext": "py", "lang": "Python", "max_stars_repo_path": "Experiments/TransformationLightweight/iris_double_transform.py", "max_stars_repo_name": "andreyboytsov/DynamicTSNE", "max_stars_repo_head_hexsha": "9ccd18d80c7a0bae31defde2f2336f9ea3d1965a", "max_... |
#include <Access/DiskAccessStorage.h>
#include <IO/WriteHelpers.h>
#include <IO/ReadHelpers.h>
#include <IO/ReadBufferFromFile.h>
#include <IO/WriteBufferFromFile.h>
#include <IO/ReadBufferFromString.h>
#include <Access/User.h>
#include <Access/Role.h>
#include <Access/RowPolicy.h>
#include <Access/Quota.h>
#include <A... | {"hexsha": "8b249813f7cfac6945257d9b194a3bdd05533c0e", "size": 27404, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Access/DiskAccessStorage.cpp", "max_stars_repo_name": "chengy8934/ClickHouse", "max_stars_repo_head_hexsha": "c1d2d2d7f759536cdac991077110ea40023030c2", "max_stars_repo_licenses": ["Apache-2.0"... |
import argparse
import logging
import imageio
import numpy as np
import yaml
from IPython import embed
from common import get_image_array, get_probability_for_class, get_perturbed_images
from differential_evolution import init_population, gen_children
from models.base import get_model_from_name
CONFIG = None
logging... | {"hexsha": "cddf82db8bc8bedf25a1d7e7867a9c1d25e29c23", "size": 3829, "ext": "py", "lang": "Python", "max_stars_repo_path": "targeted.py", "max_stars_repo_name": "StefanieStoppel/one-pixel-attack", "max_stars_repo_head_hexsha": "e1a984b508453f6a20adbdb2e522b4b33cc6ed90", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import pandas as pd
import numpy as np
import talib as tb
from indicators.indicator_utils import *
def reg_envelopes(rates, price = 'Close', deviation = 0.008, reg_window=250, reg_mean=75):
rates["new_pol"] = (rates["Close"].rolling(reg_window).apply(regression(rates,price), raw=False)).rolling(reg_mean).mean()
... | {"hexsha": "df9d95d159fb721a8ed922e2fcecc82d29eea520", "size": 2703, "ext": "py", "lang": "Python", "max_stars_repo_path": "indicators/custom_indicators.py", "max_stars_repo_name": "zqngetsu96/PyForex", "max_stars_repo_head_hexsha": "09783c7c9bc4bf0cfefea1ebca8c0328a58b176c", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import tqdm
import pickle
from tfc.utils import MakePlot
from matplotlib.ticker import PercentFormatter
## TEST PARAMETERS: ***************************************************
tfc = pickle.load(open('data/EOL_TFC.pickle','rb'))
spe = pickle.load(open('data/EOL_Spec.pickle','rb'))
## Plot: *****... | {"hexsha": "c6be9fa9aed366640725d0369ddac3418c14042a", "size": 3259, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/Hunter_Johnston_Dissertation/Chapter_6/Example_6_2/plotData.py", "max_stars_repo_name": "leakec/tfc", "max_stars_repo_head_hexsha": "f814be4643270498a68bb0859720191ff7216012", "max_stars_... |
[STATEMENT]
lemma inf_co_total:
"co_total x \<Longrightarrow> co_total y \<Longrightarrow> co_total (x \<sqinter> y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>co_total x; co_total y\<rbrakk> \<Longrightarrow> co_total (x \<sqinter> y)
[PROOF STEP]
by (metis co_total_def order.antisym bot_least mult_... | {"llama_tokens": 136, "file": "Correctness_Algebras_Lattice_Ordered_Semirings", "length": 1} |
import sys
import os
import multiprocessing
import subprocess
import scipy.stats as sci
from scipy.stats.mstats import mquantiles
from methylpy.utilities import print_checkpoint, print_error, print_warning
from methylpy.utilities import split_fastq_file
from methylpy.utilities import split_fastq_file_pbat
from methylpy... | {"hexsha": "931d5c095069d59518016ff5526efeb7c5415a33", "size": 105768, "ext": "py", "lang": "Python", "max_stars_repo_path": "methylpy/call_mc_se.py", "max_stars_repo_name": "yupenghe/methylpy", "max_stars_repo_head_hexsha": "0dbf4ef30d6c4d1b98f4a53c3b08b721114c9aaa", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import data.nat.prime
import data.list
-- import data.bool
-- set_option trace.simplify true
set_option trace.simplify.rewrite true
set_option trace.simplify.failure false
set_option trace.simplify.rewrite_failure false
namespace first
constants a b : ℤ
constant f : ℤ → ℤ
constant g : ℤ → ℤ → ℤ
#check λ x : ℤ, g (f ... | {"author": "mathprocessing", "repo": "lean_mathlib_examples", "sha": "743c6456c0a3219dd1722efdd31ee6f3a113818a", "save_path": "github-repos/lean/mathprocessing-lean_mathlib_examples", "path": "github-repos/lean/mathprocessing-lean_mathlib_examples/lean_mathlib_examples-743c6456c0a3219dd1722efdd31ee6f3a113818a/src/hitch... |
// (C) Copyright 2005 Matthias Troyer and Dave Abrahams
// Copyright (c) 2015 Anton Bikineev
// Copyright (c) 2015 Andreas Schaefer
// Copyright (c) 2022 Hartmut Kaiser
//
// SPDX-License-Identifier: BSL-1.0
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt o... | {"hexsha": "d5f30829a9c6dbe17895dd0ed57d476bb7c92a1f", "size": 5886, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/core/serialization/include/hpx/serialization/array.hpp", "max_stars_repo_name": "bhumitattarde/hpx", "max_stars_repo_head_hexsha": "5b34d8d77b1664fa552445d44cd98e51dc69a74a", "max_stars_repo_li... |
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
message(c::GitCommit, raw::Bool=false)
Return the commit message describing the changes made in commit `c`. If
`raw` is `false`, return a slightly "cleaned up" message (which has any
leading newlines removed). If `raw` is `true`, th... | {"hexsha": "0cf8ca641076d53949a7de238b6cbb5968326325", "size": 4056, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "base/libgit2/commit.jl", "max_stars_repo_name": "Mikewl/julia", "max_stars_repo_head_hexsha": "4c5cc04156ba074a8baa028c2a8a41b9e70d56ee", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": 3, ... |
from metrics.metric import Metric
from metrics.minutely_returns import MinutelyReturns
from scipy.stats import kurtosis
import numpy as np
class ReturnsVolatilityCorrelation(Metric):
def __init__(self, intervals=4):
self.mr = MinutelyReturns()
def compute(self, df):
returns = np.array(self.mr... | {"hexsha": "6796b38f03373e8f2be3961f695dd26a4d3c2f13", "size": 621, "ext": "py", "lang": "Python", "max_stars_repo_path": "realism/metrics/returns_volatility_correlation.py", "max_stars_repo_name": "chris-jh-cho/abides", "max_stars_repo_head_hexsha": "0917e099b901a864751233b19eecf50a451085df", "max_stars_repo_licenses"... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TorrentofShame Resume
% LuaLaTeX Template
% Version 1.0.0 (1/3/2021)
%
% Authors:
% Simon Weizman (contact@simon.weizman.us)
%
% License:
% MIT License (see included LICENSE file)
%
% !TEX encoding = utf8
% !TEX program = lualatex
% NOTE: This template mu... | {"hexsha": "d8ac0cb57f889a0b2dadb70a8af4c2da15f35050", "size": 2990, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "simonweizman.tex", "max_stars_repo_name": "TorrentofShame/resume", "max_stars_repo_head_hexsha": "3b86e4af0603557a33e80f32cd51f32cdb112e82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
\chapter{hjgad}
\begin{abox}
Problem set-1
\end{abox}
\begin{enumerate}[label=\color{ocre}\textbf{\arabic*.}]
\item Consider the matrix $M=\left(\begin{array}{lll}1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1\end{array}\right)$\\
\textbf{A.} The eigenvalues of $M$ are
{\exyear{NET/JRF(JUNE-2011)}}
\begin{tasks}(4)
\task[\... | {"hexsha": "bd318aade5e56de9b7311765187ab3ec90f4e98c", "size": 32623, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "CSIR- Mathematical Physics/chapter/Matrix Problem set Solutions.tex", "max_stars_repo_name": "archives-futuring/CSIR-Physics-Study-Material", "max_stars_repo_head_hexsha": "689cff91895fec36b4bb0add... |
"""
Generate synthetic resistivity models randomly.
References
----------
https://stackoverflow.com/questions/44865023/circular-masking-an-image-in-python-using-numpy-arrays
https://docs.scipy.org/doc/numpy-1.16.0/reference/routines.random.html
https://numpy.org/doc/1.18/reference/random/index.html
https://doc... | {"hexsha": "369c43541b0752a58b3316ee7ac329755b902421", "size": 15845, "ext": "py", "lang": "Python", "max_stars_repo_path": "erinn/simpeg_extended/random_model.py", "max_stars_repo_name": "ravisha229/elec", "max_stars_repo_head_hexsha": "8297db51f63d5ef961672ae7ccb01c5ef18c70a3", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
#
import sys, os
sys.path.append('./utils/')
import tools
import datatools as dtools
from time import time
os.environ["CUDA_VISIBLE_DEVICES"]="0"
#
import tensorflow as tf
import tensorflow_hub as hub
#########################... | {"hexsha": "69a76af11f0d745da6a152a2a97e769b34410ee2", "size": 3536, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/samplenet.py", "max_stars_repo_name": "modichirag/cosmic-rim", "max_stars_repo_head_hexsha": "3b621e676fe9aaa6cdc84adc92bb9b9d8bbe5f25", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
@testset "Laplace" begin
test_interface(LaplaceLikelihood(3.0), Laplace)
@test LaplaceLikelihood().β == 1
test_auglik(LaplaceLikelihood(1.0); rng=MersenneTwister(42))
# Test the custom kl divergence
λ = rand()
μ = rand()
@test kldivergence(InverseGaussian(μ, 2λ), InverseGamma(1//2, λ)) ≈
... | {"hexsha": "67af19eb45c2521548ab9ef24e843bbc2c4ae531", "size": 395, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/likelihoods/laplace.jl", "max_stars_repo_name": "simsurace/AugmentedGPLikelihoods.jl", "max_stars_repo_head_hexsha": "79cde8aa87f4c3791b1d3b02a5ae048928bc6fd1", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import io
from gensim.models import KeyedVectors
from gensim.test.utils import datapath, get_tmpfile
from gensim.scripts.glove2word2vec import glove2word2vec
class WordEmbeddingsModel:
def __init__(self, embeddings_file_name, embeddings_dimensions):
self.embeddings_file = embeddings_file... | {"hexsha": "49ba5efbb74481d58a91e6579c8babdaf01aa790", "size": 4045, "ext": "py", "lang": "Python", "max_stars_repo_path": "8_graph_embeddings/modelUtils/LanguageModels.py", "max_stars_repo_name": "ravikiran0606/Game-and-Requirements-KG", "max_stars_repo_head_hexsha": "ede2c176c83e33b401b879461a9312660049cf10", "max_st... |
GUI=1; | {"author": "european-central-bank", "repo": "BEAR-toolbox", "sha": "f33aae80c40f7a2e78a54de99b2ce3663f59aa75", "save_path": "github-repos/MATLAB/european-central-bank-BEAR-toolbox", "path": "github-repos/MATLAB/european-central-bank-BEAR-toolbox/BEAR-toolbox-f33aae80c40f7a2e78a54de99b2ce3663f59aa75/tbx/bear/unreachable... |
@doc raw"""
`PODE`: Partitioned Ordinary Differential Equation
Defines a partitioned initial value problem
```math
\begin{align*}
\dot{q} (t) &= v(t, q(t), p(t)) , &
q(t_{0}) &= q_{0} , \\
\dot{p} (t) &= f(t, q(t), p(t)) , &
p(t_{0}) &= p_{0} ,
\end{align*}
```
with vector fields ``v`` and ``f``, initial conditions ``... | {"hexsha": "a4cb151b114dda873620929f11a64c314b9f72d6", "size": 3682, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/equations/pode.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/GeometricIntegrators.jl-dcce2d33-59f6-5b8d-9047-0defad88ae06", "max_stars_repo_head_hexsha": "5ffdd27e87719a998492287d... |
[STATEMENT]
lemma onl_invariant_sterms:
assumes wf: "wellformed \<Gamma>"
and il: "A \<TTurnstile> (I \<rightarrow>) onl \<Gamma> P"
and rp: "(\<xi>, p) \<in> reachable A I"
and "p'\<in>sterms \<Gamma> p"
and "l\<in>labels \<Gamma> p'"
shows "P (\<xi>, l)"
[PROOF STATE]
proof (prove)
goal ... | {"llama_tokens": 649, "file": "AWN_AWN_Invariants", "length": 8} |
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