text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import torch
import os
import numpy as np
import random
from librosa import load
def read(data, normalize=False, sr=16000):
data, sr = load(data, sr=sr)
if normalize:
data /= np.abs(data).max()
return data, sr
class SpeechDataset(torch.utils.data.Dataset):
def __init__(self, noisy_path, clean... | {"hexsha": "1c2f304c2cdde31005f56751f34965b6155440a8", "size": 1341, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset.py", "max_stars_repo_name": "ChangLee0903/SERIL-Noise-Adaptive-Speech-Enhancement-using-Regularization-based-Incremental-Learning", "max_stars_repo_head_hexsha": "73c5dbd8a272a534cc0af455a... |
using RealNeuralNetworks.SWCs
using Test
@testset "test SWC" begin
# read swc
exampleFile = joinpath(@__DIR__, "../asset/77625.swc")
println("load plain text swc ...")
@time swc = SWCs.load( exampleFile )
str = String(swc)
tempFile = tempname() * ".swc"
println("save plain text swc ...")
... | {"hexsha": "3cb17b408b688d6736b57f4bdd2bc1a22251c872", "size": 751, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/SWCs.jl", "max_stars_repo_name": "UnofficialJuliaMirror/RealNeuralNetworks.jl-4491297b-8966-5840-8cb9-b189d60f3398", "max_stars_repo_head_hexsha": "e6f19dd0515e2105de0ff7c26997d7de64fb3153", "m... |
%% PLANETOID CLASS (planetoid.m) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The Moon class is an iteration of the spheriod obstacle class aimed
% mostly providing a reference for satelite simulation.
% Author: James A. Douthwaite 09/02/2019
classdef planetoid < obstacle_spheroid
properties
inclination; ... | {"author": "douthwja01", "repo": "OpenMAS", "sha": "962f321f82167db78066b2c88c783423ecc3b73a", "save_path": "github-repos/MATLAB/douthwja01-OpenMAS", "path": "github-repos/MATLAB/douthwja01-OpenMAS/OpenMAS-962f321f82167db78066b2c88c783423ecc3b73a/objects/planetoid.m"} |
# TRAIN_ON_SMALL=True python3 -m torch.distributed.launch --nproc_per_node=2 train_nq.py
from transformers import BigBirdForQuestionAnswering, BigBirdTokenizer
from transformers import TrainingArguments, Trainer
from datasets import load_dataset
import torch_xla.distributed.xla_multiprocessing as xmp
import torch
im... | {"hexsha": "b40895dc4fe10856f80236e7ed1407d4511f447a", "size": 5642, "ext": "py", "lang": "Python", "max_stars_repo_path": "natural-questions/train_nq_tpu.py", "max_stars_repo_name": "pasikon/bigbird", "max_stars_repo_head_hexsha": "0a06e5aa6a6f8663afdfd6b3886b911946895c93", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from ... import splatalogue
from ...utils.testing_tools import MockResponse
from astropy import units as u
from astropy.tests.helper import pytest, remote_data
import requests
import os
SPLAT_DATA = 'CO_colons.csv'
def data_path(filename):
data_dir ... | {"hexsha": "0ae2b3810a5b57255dd74483adc68a780669968d", "size": 4357, "ext": "py", "lang": "Python", "max_stars_repo_path": "astroquery/splatalogue/tests/test_splatalogue.py", "max_stars_repo_name": "astrocatalogs/astroquery", "max_stars_repo_head_hexsha": "9919a32cb027febcd73cd743efaae6754061a534", "max_stars_repo_lice... |
import gym
import numpy as np
import torch
from gym.spaces import Box, Discrete
################################
################################
class DiscretizedObservationWrapper(gym.ObservationWrapper):
def __init__(self, env, n_bins=10, low=None, high=None):
super().__init__(env)
assert isins... | {"hexsha": "370ab9404ccc099de6ded1c5f6f62cc588131da7", "size": 7068, "ext": "py", "lang": "Python", "max_stars_repo_path": "asynch_rl/envs/wrappers.py", "max_stars_repo_name": "EnricoReg/asynch-rl", "max_stars_repo_head_hexsha": "acd01a49a7a4b8ff4ff0694d1e24274ba87691ee", "max_stars_repo_licenses": ["CC0-1.0"], "max_st... |
using Hiccup
using Test
@tags br, link
# hiccup div conflicts with main div, so use this as compromise
ediv = Hiccup.div
@test occursin("class=\"class1 class2\"", sprint(Hiccup.render, Node(:img, "#id.class1.class2", Dict(:src=>"http://www.com"))))
classMatching = ((".section-title", "section-title"),
... | {"hexsha": "252edc615c746961f8099793ddff97cbed369793", "size": 1747, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Hiccup.jl-9fb69e20-1954-56bb-a84f-559cc56a8ff7", "max_stars_repo_head_hexsha": "28ea6088fd2ada3e1667d98f8808cee1a4fd70d8", "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 17 18:10:16 2020
@author: Ashna
"""
#Importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_selection import RFE # feature elimination
from sklearn.ensemble import ExtraTreesRegressor #importing estim... | {"hexsha": "8c8c2c77344f91630ef95ee9e34822fd1f44c40f", "size": 7077, "ext": "py", "lang": "Python", "max_stars_repo_path": "propulsion_plant.py", "max_stars_repo_name": "AshnaVirdikar/Prediction_models", "max_stars_repo_head_hexsha": "21a235944f98e97b3e80d02fcaeaee57fd8f8535", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
IBN-ResNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNResNet', 'ibn_resnet50', 'ibn_resnet101', 'ibn_resnet152']
import os
import tensorflow as tf
import t... | {"hexsha": "65f2aa0f53935ce7e6e49b92129ec8708b4d88cc", "size": 14465, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow2/tf2cv/models/ibnresnet.py", "max_stars_repo_name": "naviocean/imgclsmob", "max_stars_repo_head_hexsha": "f2993d3ce73a2f7ddba05da3891defb08547d504", "max_stars_repo_licenses": ["MIT"],... |
subroutine addinp(a0 , dav , cgen , cadd , cmax ,
* cmin , itypc , typbnd, nobnd , namcon,
* lstci , kmax )
!----- GPL ---------------------------------------------------------------------
! ... | {"hexsha": "cc0d4614e5423cd9894c09be4282ee01d60818d8", "size": 5483, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/tools_gpl/nesthd2/packages/nesthd2/src/addinp.f", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5bd0d9a1... |
import numpy as np
import time
from plotnine import *
from sklearn import manifold
import pandas as pd
import scanpy as sc
import pdb
import os
import scipy.sparse as ssp
from .. import help_functions as hf
from matplotlib import pyplot as plt
from .. import settings
from .. import logging as logg
##################... | {"hexsha": "f903da4e0c37915e7f9b1a1c4902070c1d930943", "size": 105822, "ext": "py", "lang": "Python", "max_stars_repo_path": "cospar/plotting/_plotting.py", "max_stars_repo_name": "ascendancy09/CoSpar", "max_stars_repo_head_hexsha": "791b320e4f7722d7fc3a61c5ff7d45f23db7af91", "max_stars_repo_licenses": ["MIT"], "max_st... |
#-*-coding:UTF-8-*-
from numpy import *
import matplotlib.pyplot as plt
x=linspace(0,10,50000)
y=x/2-cos(x)-pi/4
plt.figure()
plt.plot(x,y)
plt.savefig("easyplot.png")#导出图像入图片
plt.show() | {"hexsha": "50ee526d609c6b33e2635910938384dafb4579b8", "size": 188, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/pictures/pic.py", "max_stars_repo_name": "coder109/HighSchoolFiles", "max_stars_repo_head_hexsha": "4a73bfcf1ebd6f74787b2c9e34adbda423664b53", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import torch
import cv2
import numpy as np
import sys
import os
import numpy as np
import torch
class Logger(object):
def __init__(self, logfile):
self.terminal = sys.stdout
self.log = open(logfile, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(mes... | {"hexsha": "f7eb9d71a21da2d85b171aec17f848c6929cffbb", "size": 17427, "ext": "py", "lang": "Python", "max_stars_repo_path": "solutions/2nd-place/bbox_model/utils.py", "max_stars_repo_name": "henriquesimoes/humpback", "max_stars_repo_head_hexsha": "ba687a71f95ef9c9c30426eefae11a69efd6f942", "max_stars_repo_licenses": ["... |
% =============================================================================
In a software-only implementation,
execution of AES
and
the associated application program
is
performed by
a general-purpose processor core, using only instructions in the base ISA.
Since we only consider use of the RISC-V scalar base IS... | {"hexsha": "983ea86b01e9a1bdd885d9b06d56716b5153b6a1", "size": 7154, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/tex/bg-aes_impl_sw.tex", "max_stars_repo_name": "mfkiwl/aes-risc-pipeline", "max_stars_repo_head_hexsha": "9ebf788db1465cd5959fb23fbbcefdd60a5e0a34", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using Blocks
using Compat
const datafile = joinpath(dirname(@__FILE__), "test.csv")
const nloops = 10
function testfn(f::Function, s::AbstractString, exp_res)
println("\t$(s)...")
ret = f()
println("\t\tresult: $(ret)")
@assert (ret == exp_res)
println("\t\tresult: $(ret)")
t = @elapsed for i... | {"hexsha": "cd43651868b231dc030df3b6fea91dfbc545ddbe", "size": 2525, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Blocks.jl-7cb6cbaf-3823-58df-a841-732a989b1231", "max_stars_repo_head_hexsha": "36ad82ee703de133045469780319cdd6f7883339", "max... |
# -*- coding: utf-8 -*-
"""
A module to generate simulated 2D time-series SOSS data
Authors: Joe Filippazzo, Kevin Volk, Jonathan Fraine, Michael Wolfe
"""
import datetime
from functools import partial, wraps
from multiprocessing.pool import ThreadPool
from multiprocessing import cpu_count
import os
from pkg_resources... | {"hexsha": "ccb7c52d035270555f2cc4e7d1f1dd788ae7b455", "size": 49664, "ext": "py", "lang": "Python", "max_stars_repo_path": "awesimsoss/awesim.py", "max_stars_repo_name": "jotaylor/awesimsoss", "max_stars_repo_head_hexsha": "e8047cad598d0af8c7b41ddaae1ea7d01d116eaf", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
* Assignment: Numpy Trigonometry
* Complexity: easy
* Lines of code: 8 lines
* Time: 13 min
English:
1. Define function `trigonometry(angle_deg: int|float) -> dict`
2. Return angle in radians and trigonometric function values (sin, cos, tg, ctg)
3. Ctg for angle 180 and Tan for 90 degrees has infinite ... | {"hexsha": "a57f9aa3ca295721754f29d35aa8403332d7f2a3", "size": 1832, "ext": "py", "lang": "Python", "max_stars_repo_path": "_assignments/numpy/math/numpy_trigonometry_a.py", "max_stars_repo_name": "sages-pl/2022-01-pythonsqlalchemy-aptiv", "max_stars_repo_head_hexsha": "1d6d856608e9dbe25b139e8968c48b7f46753b84", "max_s... |
[STATEMENT]
lemma bit_shiftl_word_iff [bit_simps]:
\<open>bit (w << m) n \<longleftrightarrow> m \<le> n \<and> n < LENGTH('a) \<and> bit w (n - m)\<close>
for w :: \<open>'a::len word\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bit (w << m) n = (m \<le> n \<and> n < LENGTH('a) \<and> bit w (n - m))
[... | {"llama_tokens": 161, "file": "Word_Lib_Word_Lemmas", "length": 1} |
import numpy as np
from ..graph_io import TensorProtoIO, OpsProtoIO
from ..operations import OpsParam
def shape_2_ak_shape(shape):
"""
onnx shape to anakin shape
:param shape:
:return:
"""
mini_shape = [i for i in shape if (i is not None and i > 0)]
return map(int, [1] * (4 - len(mini_shape... | {"hexsha": "31c479efed5a3a150666bc4f3f5c9d23bc82381c", "size": 10522, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/external_converter_v2/parser/onnx/med_trans_util.py", "max_stars_repo_name": "baajur/Anakin", "max_stars_repo_head_hexsha": "5fd68a6cc4c4620cd1a30794c1bf06eebd3f4730", "max_stars_repo_licen... |
"""
Provides the functions related to generating documentation stubs.
"""
module Generator
using DocStringExtensions
"""
$(SIGNATURES)
Attempts to save a file at `\$(root)/\$(filename)`. `f` will be called with file
stream (see [`open`](https://docs.julialang.org/en/latest/base/io-network/#Base.open)).
`filename` c... | {"hexsha": "24a7ea37d363991201af036a1b5541fce9576e3c", "size": 3467, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Generator.jl", "max_stars_repo_name": "aminya/DocumenterTools.jl", "max_stars_repo_head_hexsha": "34383ad76869e2a0a21e6b9e1d6701a37ac77de4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
!
! Copyright 2011 Sebastian Heimann
!
! 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 applicable... | {"hexsha": "4707ea30b147ac9b5cb94c8605583a4b171ea372", "size": 19445, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source_circular.f90", "max_stars_repo_name": "emolch/kiwi", "max_stars_repo_head_hexsha": "8aa37d74b578afadb344a34117a99b8d144f4a6b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
#!/usr/bin/env python
# -*- coding: utf-8
# Functions dealing with centerline detection and manipulation
from __future__ import absolute_import, division
import os, datetime, logging
import numpy as np
import sct_utils as sct
from ..image import Image
logger = logging.getLogger(__name__)
def centerline2roi(fname... | {"hexsha": "7c4d9f2c878eba67fec343e6a7c1a2ac4eb72649", "size": 4744, "ext": "py", "lang": "Python", "max_stars_repo_path": "spinalcordtoolbox/centerline/optic.py", "max_stars_repo_name": "valosekj/spinalcordtoolbox", "max_stars_repo_head_hexsha": "266bfc88d6eb6e96a2c2f1ec88c2e185c6f88e09", "max_stars_repo_licenses": ["... |
import torch
import wandb
import numpy as np
import os
from sklearn.model_selection import train_test_split
from attack.unsupervised import generate_chain
from attack.supervised import generate_adv
from thirdparty.pytorch_msssim import msssim
from utils.distributions import gaus_skl
def train(model, dataloader, args... | {"hexsha": "b552eb2f22ff2a50a1cb79512d24920d291c9626", "size": 4258, "ext": "py", "lang": "Python", "max_stars_repo_path": "attack/trainer.py", "max_stars_repo_name": "AKuzina/attack_vae", "max_stars_repo_head_hexsha": "c6930e89bb4c7fc676c2c241b81e05ec71bb3363", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7,... |
#======================================================================
# IMPORTS:
#======================================================================
import csv
import numpy as np
import matplotlib.pyplot as plt
#---------------------------------------------------------
# DEBUGGERS:
#-----------------------... | {"hexsha": "c38c73423a0f0c57b86c283253587184fc742c1a", "size": 5766, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/count.py", "max_stars_repo_name": "Olo112/Avrage-Death-Rate", "max_stars_repo_head_hexsha": "9260da3174ad0b9909ac364f15f7690205a370f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
program demo_ceiling
implicit none
real :: x = 63.29
real :: y = -63.59
print *, ceiling(x)
print *, ceiling(y)
! elemental
print *,ceiling([ &
& -2.7, -2.5, -2.2, -2.0, -1.5, -1.0, -0.5, &
& 0.0, &
& +0.5, +1.0, +1.5, +2.0, +2.2, +2.5, +2.7 ])
... | {"hexsha": "f07ef95b0a59dc967ef439bb3129be5f57182fad", "size": 347, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "example/ceiling.f90", "max_stars_repo_name": "urbanjost/fortran-intrinsic-descriptions", "max_stars_repo_head_hexsha": "59b3618e6c247802cb26f32a1a77e8b718bcc165", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import numpy as np
import skimage.io
import skimage.transform
from image_transform import perturb
import matplotlib.pyplot as plt
from matplotlib import animation
import matplotlib.pyplot as plt
from skimage import io
image = np.clip(io.imread("dickbutt.jpg"),0.0, 1.0)[:,:,0]
print image.shape
res... | {"hexsha": "ffb5506f08d93a1d5ed28584401fa3f3a06002e8", "size": 1842, "ext": "py", "lang": "Python", "max_stars_repo_path": "util_scripts/test_shearing.py", "max_stars_repo_name": "ShuaiW/kaggle-heart", "max_stars_repo_head_hexsha": "022997f27add953c74af2b371c67d9d86cbdccc3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma weakStatImpWeakenStatImp:
fixes \<Psi> :: 'b
and P :: "('a, 'b, 'c) psi"
and Rel :: "('b \<times> ('a, 'b, 'c) psi \<times> ('a, 'b, 'c) psi) set"
and Q :: "('a, 'b, 'c) psi"
assumes cSim: "\<Psi> \<rhd> P \<lessapprox><Rel> Q"
and cStatEq: "\<And>\<Psi>' R S \<Psi>''. \<lb... | {"llama_tokens": 2808, "file": "Psi_Calculi_Weaken_Stat_Imp", "length": 25} |
\documentclass[a4paper,12pt,twoside]{article}
%DIF LATEXDIFF DIFFERENCE FILE
%DIF DEL doc/sed/v1.2/main.tex Mon Mar 12 15:32:28 2018
%DIF ADD doc/sed/v2.0/main.tex Mon May 14 17:10:32 2018
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\renewcommand\familydefault{\sfdefault}
% \usepackage[backref=true,back... | {"hexsha": "4fe547db4dd756a06bda828bdfb5a4b097ebb490", "size": 525086, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "diffs/06-diff-v1.2-v2.0-fixed.tex", "max_stars_repo_name": "georgeslabreche/tubular-bexus-sed", "max_stars_repo_head_hexsha": "c0db957167dfc90c25743af64c514fce837c1405", "max_stars_repo_licenses":... |
import sys,re
from sympy.solvers import solve
from sympy import Symbol
from fractions import gcd #최대공약수
from collections import defaultdict
Ls=list('abcdefghijklmnopqrstuvwxyz')
print("Example = C7H16+O2->CO2+H2O ")
eq= input("화학방응식을 입력하세요 : ")
first_OBJ = defaultdict(list)
second_OBJ = Ls[:]
Get = []
a = 1
i = 1... | {"hexsha": "69eaeb0cdc981fb701b817c0f64be008094d3b36", "size": 1568, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chemical equation/equation.py", "max_stars_repo_name": "kwonbosung02/2019_-_-", "max_stars_repo_head_hexsha": "4ae45f9a4bc6e2088522d61fda72cae48a22e553", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Engagement AI
"""
# Import dependencies
from src.engine import reko,Bucket_name,Folder_in_S3,upload_folder_to_s3,s3,facedetect
import os
import glob
import json
import cv2
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import numpy as np
from PIL import Image
from tqdm im... | {"hexsha": "2f20187066652340cf9f24617d37c2bfc02ce746", "size": 9257, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "ashishkrb7/EngagementAI", "max_stars_repo_head_hexsha": "fe253db082f35643e2c07d5b9c5211247f6496ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
INCLUDE 'VICMAIN_FOR'
subroutine main44
implicit integer(a-z)
real slat,slon,plat,plon,rpol,requ,range,rlat,rlon,tg,cg,ci,ce,
* lumlat,lumlon,rad
DATA rad/57.2957795/
call XVMESSAGE(
. 'this pix vgr:1636832 image space line 500,samp 500',' ')
slat=.55539604/rad
... | {"hexsha": "55c2c79f3da400d54cd4a00ead7645274fcff78d", "size": 2763, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/sub/lumllp/test/tlumllp.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2014-2015, Ghislain Antony Vaillant
# All rights reserved.
#
# This file is distributed under the BSD License, see the LICENSE file or
# checkout the license terms at http://opensource.org/licenses/BSD-2-Clause).
from __future__ import absolute_import, division, print_function... | {"hexsha": "96c7bd87045dc2f867ffcdc6e745091a64ccc919", "size": 894, "ext": "py", "lang": "Python", "max_stars_repo_path": "ismrmrdpy/backend/hdf5.py", "max_stars_repo_name": "ghisvail/ismrmrdpy", "max_stars_repo_head_hexsha": "1e4e5085473cf6fc9731c834b6d62460866c430a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
# -*- coding: utf-8 -*-
import numpy as np
from numpy.linalg import inv
from numba import njit, prange
from ..utils import points_of_layers
__cache = True
@njit(nogil=True, parallel=True, cache=__cache)
def rotation_matrices(angles: np.ndarray):
"""
Returns transformation matrices T_126 and T_45 for each an... | {"hexsha": "9a58605dc6e7136d5ea71989937f85ad0ddcf03c", "size": 8470, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dewloosh/solid/model/mindlin/utils.py", "max_stars_repo_name": "dewloosh/dewloosh-solid", "max_stars_repo_head_hexsha": "dbd6757ddd1373df870ccd99f5ee791c08d342cb", "max_stars_repo_licenses": [... |
from flask import Flask,request,jsonify
from funcs import getClasses,bagOfWords,load_JSON,ProcessData
from pathlib import Path
import numpy as np
from model import get_prediction
import os
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
dir_path = os.path.dirname(os.path.realpath(__file__))
root = Path(d... | {"hexsha": "bd16902cdf4da151734a2ec6ea45496f367ff7b7", "size": 1582, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "deepraj1729/TChatBot-API", "max_stars_repo_head_hexsha": "24fb47c0801ee798d251975e8d2374b0b70dff93", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
module NicePipes
if VERSION < v"1.3.0-rc4"
@warn "Can't use binary artifacts, using your system's `grep` and `sed`."
grep(f) = f("grep")
sed(f) = f("sed")
else
using grep_jll, sed_jll
end
struct ShPipe{T,C}
val::T
cmd::C
args::Cmd
end
# like Base.open, but doesn't throw if exitcode is non... | {"hexsha": "40fd9dd59cdd5a33f4e25304453982f1765d17ac", "size": 2084, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NicePipes.jl", "max_stars_repo_name": "simeonschaub/NicePipes.jl", "max_stars_repo_head_hexsha": "cc0382a7510f997726317b8a872a5b8daa9f55ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import cv2
from skimage.transform import PiecewiseAffineTransform, warp
from skimage import img_as_ubyte
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import FIMM_histo.deconvolution as deconv
f... | {"hexsha": "6f19d73c4a2b86926625b91eafb21071fc6b34b4", "size": 19390, "ext": "py", "lang": "Python", "max_stars_repo_path": "src_RealData/Data/ImageTransform.py", "max_stars_repo_name": "XYZsake/DRFNS", "max_stars_repo_head_hexsha": "73fc5683db5e9f860846e22c8c0daf73b7103082", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Tests for @snn_kw constructors
coretype(T::UnionAll) = coretype(T.body)
coretype(T::DataType) = T
paramnames(T) = coretype(T).parameters
function test_typeparams(Model; args=())
Model = coretype(Model)
n = Model(args...)
for idx in 1:length(fieldnames(Model))
fieldtypes(Model)[idx] isa TypeVar ||... | {"hexsha": "83c75c54ac88ffd7f2b701153eda77abcc0edcea", "size": 1766, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ctors.jl", "max_stars_repo_name": "aquaresima/SpikingNeuralNetworks.jl", "max_stars_repo_head_hexsha": "8135f6c375ca07a07036e9894d2b58910e8e46df", "max_stars_repo_licenses": ["MIT"], "max_star... |
import pytest, os
import numpy as np
import astraeus.hdf5IO as h5io
"""
pytest --cov=.
"""
def test_writeH5():
"""
Test writing HDF5 file.
"""
flux = np.ones((5,5))
time = np.arange(5)
filename = "foo.hdf5"
success = h5io.writeH5(filename, flux=flux, time=time)
assert success == 1
... | {"hexsha": "605ea6f6da1ba831082fdc33113ae3af6ef3e2d4", "size": 974, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_h5.py", "max_stars_repo_name": "kevin218/Astreus", "max_stars_repo_head_hexsha": "b0e346193e22e2bef5d6103ee85022c89ba2eb04", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
# Copyright 2020 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | {"hexsha": "811075c5573ba0dddd94d0b6f6085af05f2cb270", "size": 7724, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_graphics/rendering/reflectance/tests/lambertian_test.py", "max_stars_repo_name": "jackd/graphics", "max_stars_repo_head_hexsha": "736b99a3306e302674a9b7599e3e2857b85fdb74", "max_stars_r... |
[STATEMENT]
theorem (in Congruence_Rule) Ang_split_unique :
assumes
"Def (Ang (An h1 o1 k1))" "Def (Ang (An h2 o2 k2))"
"Cong (Geos (Ang (An h1 o1 k1)) add Emp) (Geos (Ang (An h2 o2 k2)) add Emp)"
"Ang_inside (An h1 o1 k1) l1"
"Ang_inside (An h2 o2 k2) l21"
"Cong (Geos (Ang (An h1 o1 l1)) add Emp... | {"llama_tokens": 11362, "file": "Foundation_of_geometry_Congruence", "length": 76} |
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from gamestonk_terminal.helper_funcs import (
check_positive,
get_next_stock_market_days,
parse_known... | {"hexsha": "9f15c595217dd7b3192b9053e6d65802a6f6d162", "size": 10427, "ext": "py", "lang": "Python", "max_stars_repo_path": "gamestonk_terminal/prediction_techniques/ets.py", "max_stars_repo_name": "sandsturm/GamestonkTerminal", "max_stars_repo_head_hexsha": "1969ff3b251711099a448024ec71e5b4e50413f7", "max_stars_repo_l... |
C %W% %G%
function get_value (ib, ia)
integer ib, ia
include 'tspinc/params.inc'
include 'tspinc/wstequ.inc'
include 'tspinc/room.inc'
include 'tspinc/wfeq.inc'
include 'tspinc/vfhistory.inc'
include 'tspinc/filter.inc'
if (ia .eq. 1) then
volt = sqrt (e... | {"hexsha": "3b7eca9563bd8e931459f51a6146341b302410c5", "size": 820, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "libtsp/get_value.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Returns parameters of a two phase hyper-exponential fitting a mean and an SCV.
"""
function hyper_exp_init(mean_desired::Float64, scv_desired::Float64)::PHDist
scv_desired < 1.0 && error("SCV must be greater than 1")
μ1 = 1/(scv_desired+1) #mean parameter
p = (scv_desired-1)/(scv_desired+1+2/(μ1^2)-4... | {"hexsha": "25e889f3213d9df79a6aacc8985d0a1346e41584", "size": 1099, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/structured_ph.jl", "max_stars_repo_name": "yoninazarathy/PhaseTypeDistributions.jl", "max_stars_repo_head_hexsha": "d709fc6f763baa6eeec50d10452482cd295606a0", "max_stars_repo_licenses": ["MIT"]... |
#include <tiny.h>
#include <convex.h>
#define BOOST_AUTO_TEST_MAIN
#include <boost/test/auto_unit_test.hpp>
#include <boost/test/unit_test_suite.hpp>
#include <boost/test/floating_point_comparison.hpp>
#include <boost/test/test_tools.hpp>
BOOST_AUTO_TEST_SUITE(convex_simplex);
BOOST_AUTO_TEST_CASE(simplex_testing)
{... | {"hexsha": "3af7cb84a22b3e933d0c6d538f54669a790bcf41", "size": 19988, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "PROX/FOUNDATION/CONVEX/unit_tests/convex_simplex/convex_simplex.cpp", "max_stars_repo_name": "diku-dk/PROX", "max_stars_repo_head_hexsha": "c6be72cc253ff75589a1cac28e4e91e788376900", "max_stars_rep... |
import pandas as pd
import numpy
import re
file = pd.read_csv("works.csv", header=0, index_col=False)
administration = r'(.*менедж.*)|(.*директ.*)|(.*секретар.*)|(.*управ.*)'
studies = r'(.*педагог.*)|(.*преподават.*)|(.*учител.*)|(.*воспита.*)'
law = r'(.*юри.*)|(.*адвокат.*)'
finance = r'(.*эконом.*)|(.*банк.*)|(.*... | {"hexsha": "b8a84cf4e496388614e5b7ab21dbf0a84d121e41", "size": 1822, "ext": "py", "lang": "Python", "max_stars_repo_path": "jobs.py", "max_stars_repo_name": "KirillDmit/da", "max_stars_repo_head_hexsha": "67afbc46f478f5591724ece065a5d8de1a235767", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
import time
import string
import math
import random
import csv
from functools import reduce
from openpyxl import load_workbook
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import itertools
import selenium
from selenium impor... | {"hexsha": "53edcce747f9f88cfb0490c35337f92dd19156c0", "size": 19056, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/source/GA_Analysis.py", "max_stars_repo_name": "jg719/test_documentation", "max_stars_repo_head_hexsha": "0b466ada5b7de7b22dcc1135a15ed72aa68f7966", "max_stars_repo_licenses": ["MIT"], "max_... |
import pytest
import pandas as pd
import numpy as np
from orbit.diagnostics.backtest import TimeSeriesSplitter, BackTester
from orbit.diagnostics.metrics import smape, wmape, mape, mse, mae, rmsse
from orbit.models import LGT, KTRLite
@pytest.mark.parametrize(
"scheduler_args",
[
{
'min_tr... | {"hexsha": "6d1ead99b3081605e3d741e4f6029a7198a3c8fc", "size": 6553, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/orbit/diagnostics/test_backtest.py", "max_stars_repo_name": "uber/orb", "max_stars_repo_head_hexsha": "c1d65082728e627d1bd048978a3a1abb85cbbee6", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import torch.nn.functional as F
from torch.autograd import Variable
import torch
import torch.nn as nn
import numpy as np
class cdssmtb(nn.Module):
def __init__(self, opt):
super(cdssmtb, self).__init__()
self.vocab_size = opt.vocab_size
self.d_word_vec = opt.term_size
self.use_cuda... | {"hexsha": "b65f42137400bb6a569832d2408b36f4e7aa3067", "size": 2426, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/cdssmtb.py", "max_stars_repo_name": "thunlp/NeuDEF", "max_stars_repo_head_hexsha": "8e8b4cc1da2d51783d23729a8563c6186230dff8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
"""
Script for serving.
"""
import os
import random
import numpy as np
import torch
from PIL import Image
from flask import Flask, request
from utils.datasets import letterbox
from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywh, plot_one_box
from utils.serve import encode_image, dec... | {"hexsha": "97c6704fe4c75d73303416732a38bb57019a4df1", "size": 3468, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolov5_shellfish/serve_http.py", "max_stars_repo_name": "kesamet/examples", "max_stars_repo_head_hexsha": "6baef94cc7db99abf17ca3df7dae35fcc3b08d25", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
'''Backtest Moving Average (MA) crossover strategies
'''
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from backtester import Backtester
class HigherPriceBacktester(Backtester):
'''Backtest a strategy that goes long when the price today is higher than loo... | {"hexsha": "a6784116dfef46138c0d9d009334ddd2bbdad3bd", "size": 1382, "ext": "py", "lang": "Python", "max_stars_repo_path": "backtesters/higher.py", "max_stars_repo_name": "learn-crypto-trading/crypto-price-analysis", "max_stars_repo_head_hexsha": "70618ecf296e40404f3ebaa2e640c90097c227cb", "max_stars_repo_licenses": ["... |
import numpy as np
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, precision_recall_fscore_support
def compute_accuracy(y_true, y_pred, n_class):
""" compute accuracy for each class and the "macro"&"micro" average accuracies.
:param y_true: 1-D array or 2-D array.
:p... | {"hexsha": "a42a1bee9a2ffde6e0a424a9c831af2881d422f9", "size": 6186, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/utils/metric_utils.py", "max_stars_repo_name": "liuph0119/Semantic_Segmentation_Keras", "max_stars_repo_head_hexsha": "b595f0e2d62c471256dcc800f9539dbdf354d391", "max_stars_repo_licenses": ["... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Imputer
dataset = pd.read_csv('DataSample/train.csv')
test_data = pd.read_csv('DataSample/test.csv')
# Histogram
dataset['Age'].h... | {"hexsha": "45ab629a06abf62f9ea99a41bdd943866db25987", "size": 3755, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sundar/Titanic-Analysis/ChatVisualization.py", "max_stars_repo_name": "techunison-software/data-science-trials", "max_stars_repo_head_hexsha": "46c9e7bfcda8270573e49a7be8cee3b9c445c2cf", "max_star... |
From Coq Require Import Arith.Arith.
From Coq Require Import Arith.EqNat.
From Coq Require Import Arith.PeanoNat. Import Nat.
From Coq Require Import micromega.Lia.
From Coq Require Import micromega.Zify.
From Coq Require Import Lists.List.
From Coq Require Import Reals.Reals.
From Coq Require Import Logic.FunctionalEx... | {"author": "ChezJrk", "repo": "verified-scheduling", "sha": "e9876602147114e4378f10ac1402bd5705c0cef0", "save_path": "github-repos/coq/ChezJrk-verified-scheduling", "path": "github-repos/coq/ChezJrk-verified-scheduling/verified-scheduling-e9876602147114e4378f10ac1402bd5705c0cef0/src/LetLifting.v"} |
from functools import reduce
import numpy as np
from manim2.constants import *
from manim2.animation.animation import OldAnimation
from manim2.old_animations.old_movement import OldHomotopy
from manim2.animation.composition import OldAnimationGroup
from manim2.animation.composition import OldSuccession
from manim2.ol... | {"hexsha": "bbdaad93a2e533c7274186945c9457d74cb4771b", "size": 9428, "ext": "py", "lang": "Python", "max_stars_repo_path": "manim2/old_animations/old_indication.py", "max_stars_repo_name": "tigerking/manim2", "max_stars_repo_head_hexsha": "93e8957e433b8e59acb5a5213a4074ee0125b823", "max_stars_repo_licenses": ["MIT"], "... |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 9 01:50:32 2020
@author: varun
"""
import numpy as np
import math
class adaBoost(object):
def __init__(self, weakClf=5):
self.weakClf = weakClf
self.thresh = None
self.alpha = None
self.polarity = 1
self.fea... | {"hexsha": "79f3c3b9928f684239ee61adf69b8acee2613c13", "size": 3739, "ext": "py", "lang": "Python", "max_stars_repo_path": "Face-Detection/Face detection using probabilistic-modelling/Adaboost/ada_boost.py", "max_stars_repo_name": "swapnilgarg7/Face-X", "max_stars_repo_head_hexsha": "fab21bf667fa7387b8e73e5a1d72fcba4fb... |
# Let's Do Some Algebra
In a Jupyter notebook, with sympy.
```python
# First we import sympy:
from sympy import *
# Init pretty printing
init_printing()
```
```python
# Then we define some symbols:
x, a, b, c, d = symbols('x, a, b, c, d')
```
```python
# Write some expressions with our symbols:
expr1 = a + x * c... | {"hexsha": "8d5f17f0ec3a52a32e742593a6ba572c7a7d46f1", "size": 9326, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "JupyterAlgebraTest.ipynb", "max_stars_repo_name": "ErnstHot/Jupyter", "max_stars_repo_head_hexsha": "bcaf5dde1411e8024f41802bc78f1bf6f7cb73e5", "max_stars_repo_licenses": ["MIT"], "ma... |
import requests
import time
from time import sleep
from distutils.version import LooseVersion
from functools import partial
from codetiming import Timer
from astropy.io.votable import parse as votableparse
from pyvo.dal.tap import AsyncTAPJob, TAPService, TAPQuery, TAPResults
from pyvo.dal.exceptions import DALQueryE... | {"hexsha": "78339111f77208f3bc9f9218f1ed1730e5de8363", "size": 9458, "ext": "py", "lang": "Python", "max_stars_repo_path": "servicemon/pyvo_wrappers.py", "max_stars_repo_name": "JohnGood/servicemon", "max_stars_repo_head_hexsha": "a320033904ee86c710248e68f12de7da7959853c", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# -*- coding: utf-8 -*-
"""
@author: Pu Du
@email: pdu2@lsu.edu
anaconda is required: https://www.continuum.io/downloads
modified xyz file is required:
****************************************************
*number of atom *
*Boxx Boxy Boxz *
*Atom... | {"hexsha": "437eb1f9814f41c2a1f7b4a5cb29d1894952d4f5", "size": 13644, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/cf.py", "max_stars_repo_name": "ipudu/correlation_functions", "max_stars_repo_head_hexsha": "0778d857d30c8b9c4132ff93de6debc313b9a8e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import data.num.lemmas data.list.basic .tactics .int
open tactic
def nat.to_num (n : nat) : num := n
def int.to_znum (z : int) : znum := z
def nat.to_znum : nat → znum := int.to_znum ∘ nat.to_int
def num.to_nat (n : num) : nat := n
def znum.to_int (z : znum) : int := z
def znum.to_nat : znum → nat := int.to_nat ∘ znu... | {"author": "skbaek", "repo": "cooper", "sha": "812afc6b158821f2e7dac9c91d3b6123c7a19faf", "save_path": "github-repos/lean/skbaek-cooper", "path": "github-repos/lean/skbaek-cooper/cooper-812afc6b158821f2e7dac9c91d3b6123c7a19faf/num.lean"} |
import random
from typing import List, Set, Tuple, Union
from pathlib import Path
import gym
import networkx as nx
import numpy as np
from gym.spaces import Discrete, MultiBinary
from gym_PBN.types import GYM_STEP_RETURN, REWARD, STATE, TERMINATED
from .bittner import base, utils
class PBNTargetEnv(gym.Env):
me... | {"hexsha": "67c31b606a39588e47ce3475169da4bae679870d", "size": 11031, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_PBN/envs/pbn_target.py", "max_stars_repo_name": "UoS-PLCCN/gym-PBN", "max_stars_repo_head_hexsha": "b5d22cf5892cc004d24f66b236cd2d5edcbddddc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import os
import sys
import numpy as np
import pprint
import time
import _init_paths
import torch
import pickle
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.rpn.bbox_t... | {"hexsha": "5ea0fc77b67f1ba3baa34f30cb359ad0bb5a1b16", "size": 11053, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_annos.py", "max_stars_repo_name": "strongwolf/CDG", "max_stars_repo_head_hexsha": "a78864ca3519de77deb60a11f68059b76e076b5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "... |
module aws_cloudfront
include("AWSCorePrototypeServices.jl")
using .AWSCorePrototypeServices: cloudfront
"""
List CloudFront distributions.
"""
ListDistributions2019_03_26() = cloudfront("GET", "/2019-03-26/distribution")
"""
Add tags to a CloudFront resource.
"""
TagResource2019_03_26(Resource, Tags) = cloudfront("P... | {"hexsha": "6cce14ffb99f4987718d57a8e387332641863fa0", "size": 14954, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/services/cloudfront.jl", "max_stars_repo_name": "nicoleepp/JuliaCloud-AWS-State", "max_stars_repo_head_hexsha": "a1568b0e3f8ef48e5d8ed5a737a9361a38d51591", "max_stars_repo_licenses": ["MIT"], ... |
/**
* Copyright (c) 2017 Melown Technologies SE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the f... | {"hexsha": "79efe2474ac26e9e19e42ce600e3a0972dbdb5e4", "size": 3323, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "mapproxy/src/mapproxy/generator/geodatavectorbase.hpp", "max_stars_repo_name": "maka-io/vts-mapproxy", "max_stars_repo_head_hexsha": "13c70b1bd2013d76b387900fae839e3948e741c3", "max_stars_repo_licen... |
import vision.datasets.mtsd_default_classes as dflt
from sys import argv
import pathlib
import json
import os
import numpy as np
from tqdm import tqdm
if len(argv) < 4:
print("Usage: prune_mtsd_dataset.py <dataset> <orig split> <output> [-y]")
exit(1)
dataset_path = pathlib.Path(argv[1])
orig_split = pathlib.... | {"hexsha": "70b7076f97f6c7d0840819fb165ca8007fcb3af3", "size": 2560, "ext": "py", "lang": "Python", "max_stars_repo_path": "balance_mtsd_dataset.py", "max_stars_repo_name": "hdamron17/pytorch-ssd", "max_stars_repo_head_hexsha": "04c00243d40c1f9d2d3d6e17f711d7da5e7177f6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma leftmost_unique: "leftmost i a \<Longrightarrow> leftmost j a \<Longrightarrow> i = j"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>leftmost i a; leftmost j a\<rbrakk> \<Longrightarrow> i = j
[PROOF STEP]
by (metis leftmost_def leftmost_notword linorder_neqE_nat) | {"llama_tokens": 109, "file": "LocalLexing_Derivations", "length": 1} |
#######################################################################
# Copyright (C) #
# 2020 solitone (https://github.com/solitone) #
# 2018 Carsten Friedrich (Carsten.Friedrich@gmail.com). #
# ... | {"hexsha": "8f79b50c5cecc563100d9c6d4eea6738f643e5d8", "size": 5051, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "solitone/tic-tac-toe", "max_stars_repo_head_hexsha": "a6795c42700a333e829649116f41ef2cfbf43c3a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
theory Closure
imports "../03Debruijn/Debruijn"
begin
datatype closure =
CConst nat
| CLam ty "closure list" dexpr
datatype cframe =
CApp1 "closure list" dexpr
| CApp2 closure
| CReturn "closure list"
datatype closure_state =
CSE "cframe list" "closure list" dexpr
| CSC "cframe list" closure
fun... | {"author": "xtreme-james-cooper", "repo": "Lambda-RAM-Compiler", "sha": "24125435949fa71dfc5faafdb236d28a098beefc", "save_path": "github-repos/isabelle/xtreme-james-cooper-Lambda-RAM-Compiler", "path": "github-repos/isabelle/xtreme-james-cooper-Lambda-RAM-Compiler/Lambda-RAM-Compiler-24125435949fa71dfc5faafdb236d28a098... |
program t
print *,'don''t'
end program t
| {"hexsha": "04691b68f00bf6062d898229ce283fdd92e23e84", "size": 59, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0044x/t.f", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "m... |
[STATEMENT]
lemma DirProds_one_cong: "(DirProds f {G}) \<cong> (f G)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. DirProds f {G} \<cong> f G
[PROOF STEP]
using DirProds_one_iso is_isoI
[PROOF STATE]
proof (prove)
using this:
(\<lambda>x. x ?G) \<in> Group.iso (DirProds ?f {?G}) (?f ?G)
?h \<in> Group.iso ?G ?H \<... | {"llama_tokens": 193, "file": "Finitely_Generated_Abelian_Groups_DirProds", "length": 2} |
import numpy as np
from numdifftools import Gradient, Hessian, Jacobian
import itertools
import seaborn as sns
# import pandas as pd
from cycler import cycler
import palettable
import logging.config
import matplotlib.pyplot as plt
from plots.plot_helper_functions import set_size
import os
import matplotlib a... | {"hexsha": "a22119a02ca742e1de5f0003468c8cff0dfc6029", "size": 15618, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hybrid_rosenbrock.py", "max_stars_repo_name": "leviyevalex/hybrid_rosenbrock", "max_stars_repo_head_hexsha": "798a5442db78e1676dafd8a90f4801a337cc17b8", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def nlschools(path):
"""Eighth-Grade Pupils in the Netherlands
Snijder... | {"hexsha": "e7e0c85a3d0f433476689b60385028c6f8351404", "size": 2109, "ext": "py", "lang": "Python", "max_stars_repo_path": "observations/r/nlschools.py", "max_stars_repo_name": "hajime9652/observations", "max_stars_repo_head_hexsha": "2c8b1ac31025938cb17762e540f2f592e302d5de", "max_stars_repo_licenses": ["Apache-2.0"],... |
[STATEMENT]
lemma isin_prefix :
assumes "isin t (xs@xs')"
shows "isin t xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. isin t xs
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. isin t xs
[PROOF STEP]
obtain m where "t = PT m"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>m. ... | {"llama_tokens": 2216, "file": "FSM_Tests_Prefix_Tree", "length": 26} |
#!/usr/bin/env python
import numpy as np
import math
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def SchwarzD(x):
"""
:param x: a vector of coordinates (x1, x2, x3)
:return: An approximation of the Schwarz D "Diamond" infinite periodic minimal surface
"""
a = np.sin(x... | {"hexsha": "eb18b0de5117179dd3480122289b3b3b4a16142b", "size": 1984, "ext": "py", "lang": "Python", "max_stars_repo_path": "LLC_Membranes/BCC/Scripts/pn3m.py", "max_stars_repo_name": "shirtsgroup/LLC_Membranes", "max_stars_repo_head_hexsha": "e94694f298909352d7e9d912625314a1e46aa5b6", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import pandas as pd
import datashader as ds
from pyproj import Proj, transform
from datashader.utils import lnglat_to_meters as webm
from functools import partial
from datashader.uti... | {"hexsha": "54737436359a1f68eeaf6b2139932f8782ec3c8d", "size": 6435, "ext": "py", "lang": "Python", "max_stars_repo_path": "Baumkataster_Visualisierung/Viz/Baumanalyse_Frankfurt.py", "max_stars_repo_name": "smorrow1/baumkataster_viz", "max_stars_repo_head_hexsha": "611085a4b5e4f334f7016e4d3df0efbf34b56f72", "max_stars_... |
#!/usr/bin/env julia
pnglist = filter!(r"\.png", readdir())
function expand(locci)
list = []
pos = split(locci, ':')
chr = pos[1]
region = split(pos[2], '-')
n = parse(Int64, region[1])
while n <= parse(Int64, region[end])
out = "$chr" * ":" * "$n"
push... | {"hexsha": "f277b3fbcb614c1a75134b8f4d6a1474ad2b4acd", "size": 815, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "misc/filter_igvIMG.jl", "max_stars_repo_name": "mgvel/NGS.jl", "max_stars_repo_head_hexsha": "56f5cabacdb47bcabe930541afdf7a74680106a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
#ifndef MEDIA_SERVER_H
#define MEDIA_SERVER_H
class rtmp2rtc_writer;
class hls_writer;
class httpflv_server;
class websocket_server;
class rtmp_server;
class httpapi_server;
class rtmp_relay_manager;
#include <stdint.h>
#include <stddef.h>
#include <iostream>
#include <boost/asio.hpp>
class MediaServer
{
public:
... | {"hexsha": "6ec975f3951cb4a9d4ee9f784c0b3919bc6cbe7c", "size": 1235, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/media_server.hpp", "max_stars_repo_name": "mtdxc/cpp_media_server", "max_stars_repo_head_hexsha": "98e267e712690c2fe36796ce752fcc1b969afa24", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
c-----------------------------------------------------------------------
subroutine bjinta(ier)
c-----------------------------------------------------------------------
c fin. state interactions and decays
c-----------------------------------------------------------------------
include 'epos.inc'
dou... | {"hexsha": "9814d3f5dd9023b3b2f4806b75a20731276571a7", "size": 199391, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "GeneratorInterface/ReggeGribovPartonMCInterface/src/epos-int.f", "max_stars_repo_name": "ckamtsikis/cmssw", "max_stars_repo_head_hexsha": "ea19fe642bb7537cbf58451dcf73aa5fd1b66250", "max_stars_r... |
# maintenance.jl
println("Updating all packages\n")
@time Pkg.update()
println("Done!\n")
| {"hexsha": "06defa5fe4424d52340a40872b1deaaa28fa1a6e", "size": 92, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "maintenance.jl", "max_stars_repo_name": "anthony-wang/julia-scripts", "max_stars_repo_head_hexsha": "01401a57873103c3b12f0da8e0551932834a2ef6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#=
Examples of tuples.
=#
# Tuple of strings
people = ("Marge", "Homer", "Bart", "Lisa")
println(people)
println(people[2])
# Tuple with one item
item = (2,)
println(item[1])
# Named tuple
vehicle = (make="Ford", model="Bronco", year=2021)
println(vehicle.make)
println(vehicle[1])
println(vehicle.model)
println(vehi... | {"hexsha": "a75f63e897d9a191f4668b7ef21af75c5f02bfef", "size": 330, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/tuples.jl", "max_stars_repo_name": "wigging/julia-computing", "max_stars_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/// @file
///
/// @brief Thread which does correlation
/// @details This class holds shared pointers to the filler and the buffer
/// manager. The parallel thread extracts data corresponding to all three
/// baselines, some spectral channel and beam, correlates them and passes
/// to the filler for writing. The fille... | {"hexsha": "81a52e4d212062045ef709e53c47316b4d942a2f", "size": 7687, "ext": "cc", "lang": "C++", "max_stars_repo_path": "Code/Components/swcorrelator/current/swcorrelator/CorrWorker.cc", "max_stars_repo_name": "rtobar/askapsoft", "max_stars_repo_head_hexsha": "6bae06071d7d24f41abe3f2b7f9ee06cb0a9445e", "max_stars_repo_... |
# Automatically generated using Clang.jl
const LIBSSH2_H = 1
const LIBSSH2_COPYRIGHT = "2004-2019 The libssh2 project and its contributors."
const LIBSSH2_VERSION = "1.9.0"
const LIBSSH2_VERSION_MAJOR = 1
const LIBSSH2_VERSION_MINOR = 9
const LIBSSH2_VERSION_PATCH = 0
const LIBSSH2_VERSION_NUM = 0x00010900
const LIBS... | {"hexsha": "55ac70b020fa8576ce7684bba4a100edf1a61459", "size": 24962, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/libssh2/libssh2_api.jl", "max_stars_repo_name": "PhilipVinc/LibSSH2.jl", "max_stars_repo_head_hexsha": "269f0da16a2e730e3356e0b24432ad3ffb79768f", "max_stars_repo_licenses": ["MIT"], "max_star... |
#pragma once
#include "order_executor.hpp"
#include <ccapi_cpp/ccapi_logger.h>
#include <ccapi_cpp/ccapi_macro.h>
#include <ccapi_cpp/ccapi_session.h>
#include <boost/describe/enum.hpp>
#include <boost/log/trivial.hpp>
#include <atomic>
#include <map>
#include <tuple>
#include <unordered_map>
#include <vector>
nam... | {"hexsha": "2bc4130e65b27357297d179fff02434ec75484d6", "size": 6087, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/ccex_order_executor.hpp", "max_stars_repo_name": "Twon/cpp_crypto_algos", "max_stars_repo_head_hexsha": "e785f6c25ef50dc3c2f593b08b6857dffcd32eca", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
import torch
from PIL import Image
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
class RoofImages(Dataset):
def __init__(self, csv_file, transforms, npid=False,test_mode=False, classes = None):
self.transforms = transforms
self.data_df = pd.read_csv... | {"hexsha": "434f9ec7fa4cee7d86152e4d2847b7ab41c3def9", "size": 3399, "ext": "py", "lang": "Python", "max_stars_repo_path": "brails/modules/Roof_Material_Classification/utils/datasets.py", "max_stars_repo_name": "fmckenna/BRAILS", "max_stars_repo_head_hexsha": "cdd4ca4bbc50cbf58f03877b46f1b3d10a281204", "max_stars_repo_... |
[STATEMENT]
lemma partition_by_Nil [simp]:
"partition_by [] ys = replicate (length ys) []"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. partition_by [] ys = replicate (length ys) []
[PROOF STEP]
by (induct ys) simp_all | {"llama_tokens": 91, "file": "FO_Theory_Rewriting_Util_Multihole_Context", "length": 1} |
from astropy.io import fits
import sys
def parse_expression(expr):
pass
def create_parser(subparsers):
parser_map = subparsers.add_parser("map", help="map help")
parser_map.add_argument("-o", "--output", metavar="OUTFILE", default=sys.stdout.buffer)
parser_map.add_argument("-m", "--map-function", requ... | {"hexsha": "f9ca8e5821c2816c862065331c0c22a19d113ceb", "size": 459, "ext": "py", "lang": "Python", "max_stars_repo_path": "miniraf/map.py", "max_stars_repo_name": "vulpicastor/miniraf", "max_stars_repo_head_hexsha": "05653a8138098ac49e19fb43cc4fca890102b2ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" \example X4M200_X4M300_printout_pulsedoppler_data.py
#Target module: X4M200,X4M300
#Introduction: This is an example of how to set up a module for streaming pulse-Doppler
telegrams, and how to assemble them to whole range-Doppler matrices.
#Comm... | {"hexsha": "198d9fedbdafdb655f6f7d4b66a9c6a120d40176", "size": 6688, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup/pymoduleconnector/Linux/pymoduleconnector/examples/X4M200_X4M300_printout_pulsedoppler_data.py", "max_stars_repo_name": "kulia/x4m300-parser", "max_stars_repo_head_hexsha": "f37451774daf9d35... |
import numpy as np
from .policy import ParametricPolicy
from scipy.stats import multivariate_normal
class AbstractGaussianPolicy(ParametricPolicy):
"""
Abstract class of Gaussian policies.
"""
def __call__(self, state, action):
mu, sigma = self._compute_multivariate_gaussian(state)[:2]
... | {"hexsha": "e8d7e7ce3a6112419d626e686e6b58334a4f4ac9", "size": 11182, "ext": "py", "lang": "Python", "max_stars_repo_path": "x_mushroom_rl/policy/gaussian_policy.py", "max_stars_repo_name": "ml-research/X-mushroom-rl", "max_stars_repo_head_hexsha": "ef5f131d3cfa9c229a614c044d8c001afe8812d2", "max_stars_repo_licenses": ... |
import logging
import time
from typing import Optional
import cv2
import numpy as np
from numpy import ndarray
from config import CarStatus, Config
logger = logging.getLogger(__name__)
class Track:
def __init__(self) -> None:
self._jpeg = None
self._lines = Config.PROCESS_LINES
def __call_... | {"hexsha": "687c0c0c98297f12f0a7de5ad5e4dccfa66d2f5b", "size": 2902, "ext": "py", "lang": "Python", "max_stars_repo_path": "plugins/track.py", "max_stars_repo_name": "guitaoliu/raspi-autonomous-vehicle", "max_stars_repo_head_hexsha": "1cf41038b4b03a66b09f4194710109b00ce2ba2d", "max_stars_repo_licenses": ["MIT"], "max_s... |
// -*- C++ -*-
//
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
//
// Jiao Lin
// California Institute of Technology
// (C) 2007 All Rights Reserved
//
// {LicenseText}
//
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... | {"hexsha": "3c8fb467af3d3f21bbcb03d838d06ec738fea306", "size": 1677, "ext": "cc", "lang": "C++", "max_stars_repo_path": "packages/mccomponents/mccomponentsbpmodule/wrap_He3TubeKernel.cc", "max_stars_repo_name": "mcvine/mcvine", "max_stars_repo_head_hexsha": "42232534b0c6af729628009bed165cd7d833789d", "max_stars_repo_li... |
from __future__ import print_function
import numpy as np
import cv2
import sys
import os
def print_process_bar(percent):
cnt = 50
print('['+('>'*int(percent*cnt+0.5)).ljust(cnt)+']%2d%s'%(int(percent*100),'%'), end='\r')
def video2img(video_file,save_dir,stride=1,resize=1,suffix='.png'):
if not os.path.ex... | {"hexsha": "f5a8e6b1272c4091b31e602f26968651febd5dc3", "size": 1901, "ext": "py", "lang": "Python", "max_stars_repo_path": "video2img.py", "max_stars_repo_name": "LEE-SEON-WOO/grabcut_to_labelme", "max_stars_repo_head_hexsha": "107c6bf7332e8cabd1a7b13b2bdd14d818468dfc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
c---------------------------------------------------------------------
double precision function randlc (x, a)
c---------------------------------------------------------------------
c---------------------------------------------------------------------
c
c This routine returns a uniform pseudorandom double pre... | {"hexsha": "c7080717ceb0260973f3860a56db6d6c4a4dabbc", "size": 7104, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/NPB3.1/common/randdpvec.f", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_licenses": ["MIT... |
MODULE trdglo
USE oce
USE dom_oce
USE sbc_oce
USE trd_oce
USE phycst
USE ldftra
USE ldfdyn
USE zdf_oce
USE zdfddm
USE eosbn2
USE phycst
USE lib_mpp
USE in_out_manager
USE iom
IMPLICIT NONE
PRIVATE
PUBLIC :: trd_glo
PUBLIC :: trd_glo_init
REAL(KIND = wp) :: tvolt
REAL(KIND = wp) :... | {"hexsha": "efd60c9eb8e09a0ab9bd7fff291859c9439c87c7", "size": 19171, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "trdglo.f90", "max_stars_repo_name": "deardenchris/psycloned_nemo_CDe", "max_stars_repo_head_hexsha": "d0040fb20daa5775575b8220cb5f186857973fdb", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras import layers
from tensorflow.keras import backend
from tensorflow.keras import regularizers
import lib.tensor_helper as tensor_helper
class ConvAutoencoder(Model):
'''
Appropriate hyperparameter combos:
learning rate:... | {"hexsha": "db9b1f2efcbbb7e174dab21ae1f74ca31aabefd8", "size": 2117, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/classes/model_classes/ConvAutoencoder.py", "max_stars_repo_name": "jwspaeth/FAA-Project", "max_stars_repo_head_hexsha": "afa9d3bec10deead48c4b17dff69df2e02691e41", "max_stars_repo_licenses": [... |
from maze import BlockingMaze
import numpy as np
class DynaAgentPlus:
def __init__(self, epsilon=0.3, lr=0.9, n_steps=5, episodes=1, kappa=1e-4, with_model=True, enable_change_env=False, enable_after=0):
self.maze = BlockingMaze()
self.state = self.maze.state
self.actions = self.maze.actions_index
self.sta... | {"hexsha": "fa2047af0685a685c223ebcb2878800d6f797d90", "size": 3396, "ext": "py", "lang": "Python", "max_stars_repo_path": "dyna/dynaqplus.py", "max_stars_repo_name": "code-asc/rl", "max_stars_repo_head_hexsha": "8e217f695d4219327fcec58061c213e534578a4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
using CoilFunctions
using CoilFunctions: ideal_fill, AWG_Chart
using Unitful
using Test
@testset "CoilGeometry" begin
@test CoilGeometry(10u"mm",20u"mm",30u"mm") == CoilGeometry(10.0u"mm",20.0u"mm",30.0u"mm")
@test CoilGeometry(10u"mm",20u"mm",30u"mm", 1.5u"mm") == CoilGeometry(13u"mm",17.0u"mm",27u"mm")
@... | {"hexsha": "c9ad60a7fb438c70bc0b30fd49d101d598f72a46", "size": 3083, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "DanBerge/CoilFunctions.jl", "max_stars_repo_head_hexsha": "4d836be16005e1cb41410da8fb6eeacfc4cd2075", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Computes statistics for dense isosurface superresolution.
"""
import math
import os
import os.path
import time
import json
import h5py
import collections
import numpy as np
import scipy.misc
import cv2 as cv
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from... | {"hexsha": "66bfc94b6fe77faf24fb664425bc78d9cb5e91ca", "size": 18129, "ext": "py", "lang": "Python", "max_stars_repo_path": "network/statistics/mainDenseIsoStats.py", "max_stars_repo_name": "shamanDevel/AdaptiveSampling", "max_stars_repo_head_hexsha": "2d3014a61fc8128b2b5c578a3fc6bade4cb3fe4e", "max_stars_repo_licenses... |
[STATEMENT]
theorem sturm_above:
assumes "poly p a \<noteq> 0"
shows "card {x. poly p x = 0 \<and> a < x} = changes_gt_smods a p (pderiv p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. int (card {x. poly p x = 0 \<and> a < x}) = changes_gt_smods a p (pderiv p)
[PROOF STEP]
using sturm_tarski_above[OF assms, ... | {"llama_tokens": 276, "file": "Sturm_Tarski_Sturm_Tarski", "length": 2} |
"""
Test module for :py:class:`emloop.hooks.Check`.
"""
import numpy as np
import collections
import pytest
from emloop.hooks.check import Check
from emloop.hooks.abstract_hook import TrainingTerminated
_VAR = "accuracy"
_MIN_ACCURACY = 0.95
_MAX_EPOCH = 10
_CURRENT_EPOCH = 5
def _get_epoch_data():
epoch_data... | {"hexsha": "b927d08ba5f444e34e56506f14d6ef5451527ade", "size": 1387, "ext": "py", "lang": "Python", "max_stars_repo_path": "emloop/tests/hooks/check_test.py", "max_stars_repo_name": "iterait/cxflow", "max_stars_repo_head_hexsha": "4dfccf2b49186261ab3fe151bc4f7d0de454ac03", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 23 20:48:10 2015
@author: jmeza
"""
# ===========================================================================
# Packages ==================================================================
# ===========================================================================
... | {"hexsha": "617bdff970740e0eb384340f02bb0f4655a0035b", "size": 30823, "ext": "py", "lang": "Python", "max_stars_repo_path": "ReduceSpec_tools.py", "max_stars_repo_name": "joshfuchs/photometry", "max_stars_repo_head_hexsha": "b12e7c1d54dcc7a0ddc02a43917c341206c034b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
*
* Routine: PZMOUT - Parallel Version of ARPACK utility routine ZMOUT
*
* Purpose: Complex*16 matrix output routine.
*
* Usage: CALL PZMOUT (COMM, LOUT, M, N, A, LDA, IDIGIT, IFMT)
*
* Arguments
* COMM - MPI Communicator for the processor grid
* M - Number of rows of A. (Input)
* N ... | {"hexsha": "727b20b5550fb7833cad6b38d4be96a298772f6d", "size": 9180, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "roms_Sep2018/roms/Lib/ARPACK/PARPACK/UTIL/MPI/pzmout.f", "max_stars_repo_name": "JamiePringle/ROMScode-PringleBBLpaper", "max_stars_repo_head_hexsha": "87166b65c0d926ea04e5f17ea96d16e694bac1ea", "... |
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