text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
export isbase64
function isbase64(str::AbstractString, urlsafe::Bool=false)::Bool
notBase64 = r"[^A-Z0-9+\/=]"i
urlSafeBase64 = r"^[A-Z0-9_\-]*$"i
if str === ""
return true
end
if urlsafe
return contains(str, urlSafeBase64)
end
if length(str) % 4 !== 0 || contains(str, no... | {"hexsha": "f214b94de9da385c146c3fd5f8f9e2fe55efe0c9", "size": 629, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/isbase64.jl", "max_stars_repo_name": "iskyd/validators.jl", "max_stars_repo_head_hexsha": "e3aba26ffb583ed48a5453358878c79aecce1df7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "... |
import numpy as np
import logging
from itertools import product
from copy import copy
import random
class RandomSampler:
def __init__(self, num_pops, num_strats, num_players, alpha_rank_func=None):
self.num_pops = num_pops
self.num_strats = num_strats
self.num_players = num_players
... | {"hexsha": "43beaa5b2159f48a9d908b6d79d13d19ebc8ffa9", "size": 1570, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sampling_schemes/random.py", "max_stars_repo_name": "microsoft/InfoGainalpharank", "max_stars_repo_head_hexsha": "8cdab55e4c08f913918b48308c5062184bd8c7a8", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
arr = np.arange(10)
np.random.shuffle(arr)
print(arr)
| {"hexsha": "574e795b40102127dfb4faa95635da3bd03a4900", "size": 74, "ext": "py", "lang": "Python", "max_stars_repo_path": ".notes/test.py", "max_stars_repo_name": "upupming/2019-MCM", "max_stars_repo_head_hexsha": "e86fd9152102d7e2f8f9b86597850cdf48dce917", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max... |
from celery import shared_task,current_task
from numpy import random
from scipy.fftpack import fft
@shared_task
def fft_random(n):
"""
Brainless number crunching just to have a substantial task:
"""
for i in range(n):
x = random.normal(0, 0.1, 2000)
y = fft(x)
if(i%30 == 0):
... | {"hexsha": "99054946983885bb445a0a78b73f6331774e06c8", "size": 633, "ext": "py", "lang": "Python", "max_stars_repo_path": "testapp/tasks.py", "max_stars_repo_name": "rohitchopra32/Django-Celery-With-Progress-Bar", "max_stars_repo_head_hexsha": "208e03c7efbaab95d51cae875c31b1c5dfdab88b", "max_stars_repo_licenses": ["MIT... |
# Distributed under the MIT License.
# See LICENSE.txt for details.
import numpy as np
from scipy.optimize import newton
def compute_alpha(density, radius):
def f(a):
return density * radius**2 - 3. / (2. * np.pi) * a**10 / (1. + a**2)**6
def fprime(a):
return 3. * a**9 * (a**2 - 5.) / (1. +... | {"hexsha": "a29c1b43f627d7d0fc052668a8540a3acac4bc41", "size": 1052, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/Unit/PointwiseFunctions/AnalyticSolutions/Xcts/ConstantDensityStar.py", "max_stars_repo_name": "nilsvu/spectre", "max_stars_repo_head_hexsha": "1455b9a8d7e92db8ad600c66f54795c29c3052ee", "ma... |
# Load various imports
import pandas as pd
import os
from datetime import datetime
import librosa
import numpy as np
from os import environ
environ['TF_CPP_MIN_LOG_LEVEL']='5'
from keras.utils import to_categorical
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers imp... | {"hexsha": "28f1ef0c6e92d465af461384507b131c9f012c7e", "size": 3520, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "fmented/Sound-classifier", "max_stars_repo_head_hexsha": "7a9a6f77f21886729cf896d0f261efc9f5e3489b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import argparse
import os
import torch
import numpy as np
import cv2
import math
import sys
sys.path.append('../')
from torch.utils import data
from torch.autograd import Variable
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def rot_normal(yaw, pitch, roll, normal):
'''
Input:
normal... | {"hexsha": "2e5e0d67ef45069aa96634221ccba15f86b16a9c", "size": 8738, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/normal_estimation/valid.py", "max_stars_repo_name": "qabach/coen-342-project", "max_stars_repo_head_hexsha": "70713ccbda4d53b18d29405b454c06a9dd6946cb", "max_stars_repo_licenses": ["BS... |
[STATEMENT]
lemma pairK_neq [simp]: "Key K \<notin> used evs \<Longrightarrow> pairK(A,B) \<noteq> K"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Key K \<notin> used evs \<Longrightarrow> pairK (A, B) \<noteq> K
[PROOF STEP]
apply clarify
[PROOF STATE]
proof (prove)
goal:
No subgoals!
[PROOF STEP]
done | {"llama_tokens": 128, "file": null, "length": 2} |
# copyright Nils Deppe 2019
# (See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
import matplotlib as mpl
import matplotlib.ticker as mtick
import numpy as np
# Use standard LaTeX font on plots
mpl.rcParams['mathtext.fontset'] = 'cm'
mpl.rcParams['mathtext.rm'] = 'serif'
mpl.rcParams['fon... | {"hexsha": "adf18796c2918e5a8b9025a102164a6c8f4b9520", "size": 8851, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydro/Plotting.py", "max_stars_repo_name": "nilsdeppe/pydro", "max_stars_repo_head_hexsha": "aae4a985d45228301fabd8b725da682a545d9d32", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 3... |
function [hfig,finalim,final_pforefly] = PlotSampleFramesColor(trx,readframe,bkgdim0,predictions,mainfly,otherflies,ts,varargin)
colorpos = [.7,0,0];
colorneg = [0,0,.7];
border = 20;
[colorpos,colorneg,border,hfig,figpos,...
cm,fg_thresh,bg_thresh,sigma_bkgd,wmah,frac_a_back,dist_epsilon,...
ncolors_reference,so... | {"author": "kristinbranson", "repo": "JAABA", "sha": "5d778a23e3e7cf272df9a89a72b1b66d94f535d7", "save_path": "github-repos/MATLAB/kristinbranson-JAABA", "path": "github-repos/MATLAB/kristinbranson-JAABA/JAABA-5d778a23e3e7cf272df9a89a72b1b66d94f535d7/figurecode/PlotSampleFramesColor.m"} |
# https://www.jakeruss.com/cheatsheets/stargazer/
# https://www.rdocumentation.org/packages/stargazer/versions/5.2.2/topics/stargazer
suppressMessages(library(stargazer))
library(xtable)
library(functional)
load_model <- function(path){
model <- readRDS(path)
return(model)
}
format_covariate_labels <- func... | {"hexsha": "f5f427194db7be1cfc6a92836b456edfa58231c1", "size": 893, "ext": "r", "lang": "R", "max_stars_repo_path": "pipeline/templates/stargazer.r", "max_stars_repo_name": "OpenSourceEconomics/pipeline", "max_stars_repo_head_hexsha": "cdd9679c62af32f52a07aba800b752e90af08396", "max_stars_repo_licenses": ["BSD-3-Clause... |
function c = tapas_hgf_ar1_binary_config
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Contains the configuration for the Hierarchical Gaussian Filter (HGF) for AR(1) processes
% for binary inputs in the absence of perceptual uncertainty.
%
% The HGF is the mod... | {"author": "translationalneuromodeling", "repo": "tapas", "sha": "604c56843c15411f5bd80190f81d845ac57d8592", "save_path": "github-repos/MATLAB/translationalneuromodeling-tapas", "path": "github-repos/MATLAB/translationalneuromodeling-tapas/tapas-604c56843c15411f5bd80190f81d845ac57d8592/HGF/tapas_hgf_ar1_binary_config.m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This node calculates deviation and pitch angular velocity estimations both in simulation and real world.
The monitor node has to subscribe to results of this node and form the negative return.
During rollouts in the real world, this node evaluates safety measures of t... | {"hexsha": "5da1d0fffb5492b179c8b99f1163fae4e7fb2140", "size": 6823, "ext": "py", "lang": "Python", "max_stars_repo_path": "monitor/scripts/safety/safety.py", "max_stars_repo_name": "gwaxG/robot_ws", "max_stars_repo_head_hexsha": "ec8c85cc3b5e7262bba881bf96f4213f5403b8b5", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from util import data_io
def build_features(data):
features = []
# remove NaNs
data.fillna(0, inplace=True)
data.loc[data.Open.isnull(), "Open"] = 1
# Use some properties directly
features.ext... | {"hexsha": "c54c63e2789f6485ebbceb0250be6d37ba087b78", "size": 5313, "ext": "py", "lang": "Python", "max_stars_repo_path": "rossmann_data.py", "max_stars_repo_name": "dertilo/regression", "max_stars_repo_head_hexsha": "eb0cad7d47db610720ebc7aea7dacbbfc2e3af0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
using PlotlyJS
export drawMolGMM, drawMolGMMs, drawmol, drawPharmacophoreGMMs, plotdrawing
const atom_colors =
Dict(:C => "#383838", # dark grey
:H => "#b5b5b5", # light grey
:O => "#d62728", # red
:N => "#1f77b4", # blue
:S => "#cbd123", # yellow
:... | {"hexsha": "b44598117eba14bfea64fcba8c4af18d223e7372", "size": 6237, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/draw.jl", "max_stars_repo_name": "tmcgrath325/MolecularGaussians.jl", "max_stars_repo_head_hexsha": "298ccb0f01b004c00a405a06bfe656de5b97f4a0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
\section{Materials}
\index{materials}
\MMonCa\ assumes that the space is divided in {\tt materials}. A material can be a unary\index{unary material} material (i.e. Silicon, Iron), or a binary material\index{binary material} (i.e., SiC, GaAs). All materials are defined as directories in the \param{config} \MMonCa\ fold... | {"hexsha": "797961326f7b2917ed8accc932fd4854d640fd01", "size": 9813, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/defects/syntaxis.tex", "max_stars_repo_name": "imartinbragado/MMonCa", "max_stars_repo_head_hexsha": "126744a90253d7d7884c6dc7ec100db00a106a66", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
[STATEMENT]
lemma hom_graph_inf:
"hom_graph f S x a \<Longrightarrow> hom_graph f S y b \<Longrightarrow>
hom_graph f S (x \<sqinter> y) (a \<sqinter> b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>hom_graph f S x a; hom_graph f S y b\<rbrakk> \<Longrightarrow> hom_graph f S (x \<sqinter> y) (a \<s... | {"llama_tokens": 1218, "file": "Free-Boolean-Algebra_Free_Boolean_Algebra", "length": 6} |
__precompile__(true)
module MathParse
import Base.string
import Base.show
export string
export show
function replacewithfunc(s::String, m::Regex, f)
while (i = match(m, s)) != nothing
s = replace(s, m => f(i.captures), count=1)
end
return s
end
function mparse(s::String)
s = replace(s, r"\s+" => "")
s = r... | {"hexsha": "fc7323564df66fca5e0cde11af952eb5d50c1fec", "size": 2136, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "MathParse/src/MathParse.jl", "max_stars_repo_name": "MarcusTL12/JuliaModules", "max_stars_repo_head_hexsha": "3ddb3f05548d25f74a6f9d7e3499be7f01510cd7", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma order_mult:
assumes "p * q \<noteq> 0" shows "order a (p * q) = order a p + order a q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. order a (p * q) = order a p + order a q
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. order a (p * q) = order a p + order a q
[PROOF STEP]... | {"llama_tokens": 1869, "file": null, "length": 21} |
/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2016-2017 Davide Faconti
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
... | {"hexsha": "ff5521871a1b52bfc34c54ebbcd835c68bcec976", "size": 6074, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/ros_type_introspection/substitution_rule.hpp", "max_stars_repo_name": "seanyen/ros_type_introspection", "max_stars_repo_head_hexsha": "f1f7ef5f8e3f1fe71fb3aa8682b59dc0c3920324", "max_stars_r... |
[STATEMENT]
lemma (in HMM) likelihood_init:
"likelihood s os = T (s, obs) {\<omega> \<in> space S. L os \<omega>}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. likelihood s os = emeasure (T (s, obs)) {\<omega> \<in> space (stream_space (count_space UNIV)). \<exists>xs \<omega>'. \<omega> = xs @- \<omega>' \<and>... | {"llama_tokens": 2754, "file": "Hidden_Markov_Models_Hidden_Markov_Model", "length": 14} |
import copy
import os
import tempfile
import numpy as np
import pytest
import json
from kaggle_environments.envs.halite.helpers import Board
from halite_rl.utils import HaliteStateActionPair
@pytest.fixture
def halite_sap():
base_path = os.path.dirname(os.path.realpath(__file__))
sample_file = os.path.join... | {"hexsha": "918804128b5c107317fbc8c94cabb7128b4f2fb5", "size": 5256, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/utils/test_halite_state_action_pair.py", "max_stars_repo_name": "RyanJDick/halite_rl", "max_stars_repo_head_hexsha": "e6309a24d3d613171ceb6522ddf07fece3815e62", "max_stars_repo_licenses": ["... |
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
def plot_data():
(load_timestamp, avg_loads, exp_loads, raw_loads,
runtime_timestamps, runtimes) = (np.load("load_timestamp.npy"),
np.load("avg_loads.npy"), np.load("exp_loads.npy"),
np.load("raw_loads... | {"hexsha": "3ddfe9dd35913af250a6cc13202397de20206cee", "size": 5375, "ext": "py", "lang": "Python", "max_stars_repo_path": "utility/data_plotter.py", "max_stars_repo_name": "EfficientAI/efficient_cv", "max_stars_repo_head_hexsha": "e308f229e4d99da86ad56f87f3a78b2c81f27ca5", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Video 01: Gradient Descent
video: https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
source code: https://github.com/llSourcell/Intro_to_the_Math_of_intelligence/blob/master/demo.py
gradient descent for line fitting:
## Sum of Squares Error Function
$$sse(m,b) = \frac{1}{n} ... | {"hexsha": "1008dbc5cf7df476f9a24bd9d5135f48211a6075", "size": 7523, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "py/jupyter/Course-Math_of_Intelligence-Siraj_Raval.ipynb", "max_stars_repo_name": "jsdelivrbot/study-1", "max_stars_repo_head_hexsha": "3357cf1c3112e40018fee84b7eb62d14ce2258a1", "max... |
import pandas as pd
import numpy as np
from heapq import nsmallest
import matplotlib.pyplot as plt
# Pull in data
intrinio_df = pd.read_csv("intrinio_pull_total.csv")
intrinio_df = intrinio_df[['enterprise_value', 'sector', 'ticker']]
quandl_df = pd.read_csv("quandl_pull_total.csv")
# Merge, Reorder, Drop NaNs, Drop ... | {"hexsha": "b69f3a19c2e580264a82db2704a4506a72e5e9f2", "size": 4261, "ext": "py", "lang": "Python", "max_stars_repo_path": "valuation_models/comp.py", "max_stars_repo_name": "Sean-Koval/PyVal", "max_stars_repo_head_hexsha": "c465cafed95d2375d088f3749b6d0b2f30bb3609", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import cv2
import numpy as np
# Read image
img = cv2.imread("imori_noise.jpg")
H, W, C = img.shape
# Gaussian Filter
K_size = 3
sigma = 1.3
## Zero padding
pad = K_size // 2
out = np.zeros((H + pad*2, W + pad*2, C), dtype=np.float)
out[pad:pad+H, pad:pad+W] = img.copy().astype(np.float)
## Kernel
K = np.zeros((K_s... | {"hexsha": "7319c7384f88ad293fb14d1d0d0dbd91b924e86d", "size": 868, "ext": "py", "lang": "Python", "max_stars_repo_path": "Question_01_10/answers/answer_9.py", "max_stars_repo_name": "nishidayoshikatsu/Gasyori100knock", "max_stars_repo_head_hexsha": "f7fe35bca772eda2961a0790274c4934119b3fc2", "max_stars_repo_licenses":... |
//
// test-stat-collector.cc
//
// Created by Peter Gusev on 10 March 2016.
// Copyright 2013-2016 Regents of the University of California
//
#include <stdlib.h>
#include <boost/asio.hpp>
#include <boost/assign.hpp>
#include <boost/make_shared.hpp>
#include <boost/algorithm/string/classification.hpp>
#include <boos... | {"hexsha": "b604f3e5c1be23b7c6f37d67c14503f73f7cf906", "size": 14013, "ext": "cc", "lang": "C++", "max_stars_repo_path": "cpp/tests/test-stat-collector.cc", "max_stars_repo_name": "peurpdapeurp/ndnrtc", "max_stars_repo_head_hexsha": "59552bff9398ee2e49636f32cac020cc8027ae04", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2014 Peter Caspers
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/o... | {"hexsha": "370c18414a610160494ed3a017e5119b96056223", "size": 10134, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ql/experimental/volatility/sviinterpolation.hpp", "max_stars_repo_name": "thejourneyofman/QuantLib", "max_stars_repo_head_hexsha": "98467eaf6d1a20885f05ea1aa602bb8380c39390", "max_stars_repo_licens... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: tshzzz
"""
import torch
import numpy as np
def load_conv(buf, start, conv_model):
num_w = conv_model.weight.numel()
num_b = conv_model.bias.numel()
conv_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]).view_as(conv_model.bias));... | {"hexsha": "0b95c5ebd443b389431ef4a5762c735191c1c530", "size": 2874, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "b1xian/jinnan_yolo_baseline", "max_stars_repo_head_hexsha": "539d748d7aa60ab0e3c964eab333af46b806e1db", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
//#include <cstddef>
#include <boost/preprocessor.hpp>
#ifndef POLICY_LIMIT
#define POLICY_LIMIT 4
#endif
namespace detail {
struct nada { };
template<typename Before, typename T>
struct dummy_concept : public Before {
};
}
#define TEMPL_PARAM(z, N, data) \
BOOST_PP_COMMA_IF(N)... | {"hexsha": "96b59eb27da29b021226d55aec6f9cca14d48620", "size": 2182, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sketches/old/greatcontainer2.cpp", "max_stars_repo_name": "aristidb/fragments", "max_stars_repo_head_hexsha": "5bcf566b02a77f97d9726a0ba91425ca15a1f946", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
# coding: utf-8
# Copyright (c) SJTU Pymatgen Development Team.
from __future__ import division, unicode_literals, print_function
import logging
from collections import defaultdict
import numpy as np
from monty.io import zopen
from pymatgen.core.structure import Structure
from pymatgen.core.lattice import Lattice
f... | {"hexsha": "11fe4ccb0c258f639688210d96557f26fcfe2e34", "size": 9316, "ext": "py", "lang": "Python", "max_stars_repo_path": "lmpdump.py", "max_stars_repo_name": "lingtikong/sjtumgi", "max_stars_repo_head_hexsha": "15c94d853b89eb6fa44fefe257925f285052bcc4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
SUBROUTINE TRLG
C
C THIS IS THE MODULE DRIVER FOR TRLG(TRANSIENT LOAD GENERATOR)
C
C INPUTS(14)
C CASEXX CASECONTROL
C USETD
C DLT DYNAMIC LOAD TABLE
C SLT STATIC LOAD TABLE
C BGPDT BASIC GRID POINT DEFINITION TABLE
C SIL S... | {"hexsha": "5b93fd1eba7d92db21e3c849a7a2e6cd5c9975cb", "size": 2355, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/trlg.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": 14... |
""":mod:`mirgecom.flux` provides inter-facial flux routines.
Numerical Flux Routines
^^^^^^^^^^^^^^^^^^^^^^^
.. autofunction:: gradient_flux_central
.. autofunction:: divergence_flux_central
.. autofunction:: flux_lfr
.. autofunction:: divergence_flux_lfr
"""
__copyright__ = """
Copyright (C) 2021 University of Illi... | {"hexsha": "92d5ff838e7635f1507f763f981cafc6cb19f1ac", "size": 7429, "ext": "py", "lang": "Python", "max_stars_repo_path": "mirgecom/flux.py", "max_stars_repo_name": "dreamer2368/mirgecom", "max_stars_repo_head_hexsha": "dc79645af040510a7e2b11d3f93db4c34ad39228", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
/*
Copyright 2005-2007 Adobe Systems Incorporated
Distributed under the MIT License (see accompanying file LICENSE_1_0_0.txt
or a copy at http://stlab.adobe.com/licenses.html)
*/
/*************************************************************************************************/
#include <adobe/config.hpp>
#includ... | {"hexsha": "75fcf31ab79471d889e5974b1908e4d97b9d6592", "size": 1389, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit_tests/name/name_test.cpp", "max_stars_repo_name": "brycelelbach/asl", "max_stars_repo_head_hexsha": "df0d271f6c67fbb944039a9455c4eb69ae6df141", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from types import SimpleNamespace
from typing import Dict, Tuple, Union
import gym
import numpy as np
class RewardModelMeanWrapper(gym.RewardWrapper):
def __init__(self, env: gym.Env, reward_model, debug=False, normalize=False):
self.reward_model = reward_model
self.debug = debug
self.nor... | {"hexsha": "35a2121067a6bf1dea06a214f4b3b56acc0f21ba", "size": 2208, "ext": "py", "lang": "Python", "max_stars_repo_path": "active_reward_learning/envs/reward_model_mean_wrapper.py", "max_stars_repo_name": "david-lindner/idrl", "max_stars_repo_head_hexsha": "54cfad330b0598ad4f6621796f2411644e50a6ba", "max_stars_repo_li... |
print("Testing Parameters...")
took_seconds = @elapsed include("ParametersTests.jl")
println("done (took ", took_seconds, " seconds)")
print("Testing Trial/TrialEstimate/TrialRatio/TrialJudgement...")
took_seconds = @elapsed include("TrialsTests.jl")
println("done (took ", took_seconds, " seconds)")
print("Testing Be... | {"hexsha": "e725948232c89d6a3d5e7cc240c9178a3c174a8c", "size": 714, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "sthagen/JuliaCI-BenchmarkTools.jl", "max_stars_repo_head_hexsha": "adaa9e8f6e06bf625a85973d159884810920e470", "max_stars_repo_licenses": ["MIT"], "max_star... |
# "NOQA" to suppress flake8 warning
from cupy.functional import piecewise # NOQA
| {"hexsha": "866e855103435a3c6e361d6af64598e0fef2b343", "size": 82, "ext": "py", "lang": "Python", "max_stars_repo_path": "cupy/functional/__init__.py", "max_stars_repo_name": "mnicely/cupy", "max_stars_repo_head_hexsha": "9ed1d0db1404e9baa3c139032515d6028078bc91", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import math
import os
import random
from typing import List, Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from torch import Tensor
__all__ = ["plot", "set_random_seed"]
sns.set_theme(style="... | {"hexsha": "f184c211050a611cbd73805a8f9b24b3d92c73ce", "size": 4524, "ext": "py", "lang": "Python", "max_stars_repo_path": "tsts/utils.py", "max_stars_repo_name": "TakuyaShintate/tsts", "max_stars_repo_head_hexsha": "483db1edc2f765a5449137446a77acaf17684ce9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "... |
"""
Likelihood module to evaluate the joint likelihood of a set of tomographic 3x2pt power spectra, binned into bandpowers,
on the cut sky using a multivariate Gaussian likelihood.
The main functions are setup, which should be called once per analysis, and execute, which is called for every new
point in parameter spac... | {"hexsha": "ecf4bce5787f147212738ea4c98b4e908fabec60", "size": 11043, "ext": "py", "lang": "Python", "max_stars_repo_path": "angular_binning/like_bp_gauss_mix.py", "max_stars_repo_name": "robinupham/angular_binning", "max_stars_repo_head_hexsha": "da3f6bf32efd8bad1a7f61a9a457f521ed8ebe87", "max_stars_repo_licenses": ["... |
import csv
import iomb.util as util
import iomb.refmap as ref
import logging as log
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
class Entry(object):
""" Contains the information of an entry in a satellite table. """
def __init__(self, value: float):
self.value = value
... | {"hexsha": "8cc125c7a901472e7be15f75cb74a5aec226a915", "size": 13573, "ext": "py", "lang": "Python", "max_stars_repo_path": "iomb/sat.py", "max_stars_repo_name": "USEPA/IO-Model-Builder", "max_stars_repo_head_hexsha": "8ddd95a40034fb4080e627e2ca75dc41355f946e", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count":... |
import datetime
from typing import Dict, Optional, Union
import numpy as np
import pandas as pd
from src.core.base_model import ExtraModel
from src.core.common.singletons import RESHAPE
VegAttribute = Union[float, list, tuple, np.ndarray]
class LifeStages(ExtraModel):
def __init__(self, ls, constants):
... | {"hexsha": "5cd90479755be2d2af0f93f912f52053510e9da6", "size": 7062, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/biota_models/vegetation/model/veg_lifestages.py", "max_stars_repo_name": "Deltares/NBSDynamics", "max_stars_repo_head_hexsha": "4710da529d85b588ea249f6e2b4f4cac132bb34f", "max_stars_repo_licen... |
# TODO
# prendi un topo
# prendi 8 parametri! T, lapse, r11, r12, r13, r21, r22, r23
# nll_data(dati, T, lapse, r11, r12, r13, r21, r22, r23)
# come si fa?
# splitta in df11, ..., df23 (protocollo e barrier)
# calcola nll_data(df11, T, r11, lapse), etc..
# fai la somma
# ottimizza per T, r11, r12, r13, r21, r22, r23, l... | {"hexsha": "47792b793cb06814714b35d8082328b44add274f", "size": 4985, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sequence/E_FixedTreshandLapse.jl", "max_stars_repo_name": "DarioSarra/FlippingModel.jl", "max_stars_repo_head_hexsha": "5fa22cfe17506010d3f91e0ab6ff64e5b4610c90", "max_stars_repo_licenses": ["MIT"]... |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative wo... | {"hexsha": "72a7fbe410ac083aa6cb7e338ce793c142d2af25", "size": 6982, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit_nature/problems/second_quantization/vibrational/vibrational_structure_problem.py", "max_stars_repo_name": "davindratulsi/qiskit-nature", "max_stars_repo_head_hexsha": "d5209bbfe6b83496dc2be... |
# @Date: 2020-04-24T10:19:05+02:00
# @Last modified time: 2020-04-25T11:59:59+02:00
import model
import numpy as np
import h5py
import scipy.io as scio
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics imp... | {"hexsha": "ec150ca38cd755feb479409ab5f72403266fc352", "size": 2027, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation.py", "max_stars_repo_name": "ChunpingQiu/benchmark-on-So2SatLCZ42-dataset-a-simple-tour", "max_stars_repo_head_hexsha": "4d28ac48c7b226f79ea792cda6c6a6a58f79c04e", "max_stars_repo_licen... |
#!/usr/bin/env python3
from jinja2 import Environment, FileSystemLoader
import yaml
import os
import datetime
import numpy as np
# HARDCODE
LUMPTEMPLATE = 'LuMP_recorder.j2'
LCUTEMPLATE = 'beamctl.j2'
LUMPPROCESS = 'LuMP_processor.j2'
DATADIRROOT = '/local_data/ARTEMIS/'
SCRIPTDIR = '/data/Commissioning/PSRMonitor/Ar... | {"hexsha": "71eefc09fcc6e718e67615cdb9812684ec786bc9", "size": 6307, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/generateObsScripts.py", "max_stars_repo_name": "ArisKarastergiou/artemis3", "max_stars_repo_head_hexsha": "7c76eedcdd7252113a041d55c185a7e1e8488ba6", "max_stars_repo_licenses": ["MIT"], "m... |
from numpy import empty
import pandas as pd
import re
# This program is used to parse the pokedex.txt file and the gen8ou-1825.txt file.
# From there it adds them to more organized DataFrames
def usage_dataframe():
# File path for usage stats
file = r'C:/Users/kingj/OneDrive/Desktop/python/pokemon/gen8ou-1825... | {"hexsha": "10203b6fa192c84347658d863cb6ba82ad06215d", "size": 1543, "ext": "py", "lang": "Python", "max_stars_repo_path": "pokemon_analysis.py", "max_stars_repo_name": "james051898/showdown_code", "max_stars_repo_head_hexsha": "c3eebbe1540218155102ef667f7b1569c270e283", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
#takes a torch matrix, and applies calculations to create single value aggregations
def matrixCalculations(mat,prefix='',dictionary={}):
if mat is None:
for item in ['sum','avg','avgmax','prod','max']:
dictionary[prefix+item] = '?'
return dictionary
dictionary[prefix+'sum'] =... | {"hexsha": "3ed8bffb556a7911b9b6367a2ef7d8fbc88377e2", "size": 4811, "ext": "py", "lang": "Python", "max_stars_repo_path": "pairwisePreprocess/PairwisePreprocessUtils.py", "max_stars_repo_name": "bmd2007/benchmark_eval", "max_stars_repo_head_hexsha": "aa42bb3369e79db4cb63e1963afcc8af6d8f5696", "max_stars_repo_licenses"... |
[STATEMENT]
lemma P23_invariant:
shows "invariant (composition) P23"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invariant composition P23
[PROOF STEP]
proof (auto simp only:invariant_def)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>a b. reachable composition (a, b) \<Longrightarrow> P23 (a, b)
[PROOF... | {"llama_tokens": 4123, "file": "Abortable_Linearizable_Modules_Idempotence", "length": 50} |
Require Import Merges.Tactics.
Require Import Merges.Map.
Require Import Merges.Machine.
Require Import Merges.Fusion.
Require Import Merges.Example.Base.
Require Import Merges.Example.Combinators.
Require Import Coq.Lists.List.
Import ListNotations.
Set Implicit Arguments.
Require Import Coq.Logic.FunctionalExtensi... | {"author": "amosr", "repo": "merges", "sha": "bf8cb7bca2d859977d6fb8bf4a9d07ac780b7edd", "save_path": "github-repos/coq/amosr-merges", "path": "github-repos/coq/amosr-merges/merges-bf8cb7bca2d859977d6fb8bf4a9d07ac780b7edd/proof/Merges/Example/Eval/MapMap.v"} |
import numpy as np
import time
from math import floor
import multiprocessing as mp
import scipy.special
#Initialize parameters
Num_of_sim_per_proc = 1
start_time = time.time()
e = 5.
alpha = 0.33
ntot = 100
na = int(ntot*alpha)
nh = ntot - na
height = 5 #height of the attack
p=float(e)/float(1*ntot)
unrealistic = 0 #d... | {"hexsha": "fa3b56e4a1b754b6421ee0737d4108a2bb149d0a", "size": 3856, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/vrf-chain-sim/find_pattern.py", "max_stars_repo_name": "filecoin-project/consensus", "max_stars_repo_head_hexsha": "8824ad5fb8948706995805692d594f6ccf199176", "max_stars_repo_licenses": ["Apa... |
[STATEMENT]
lemma just_cash_plus [simp]: "just_cash a + just_cash b = just_cash (a + b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. just_cash a + just_cash b = just_cash (a + b)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. just_cash a + just_cash b = just_cash (a + b)
[PROOF STEP]
{
[PR... | {"llama_tokens": 1908, "file": "Risk_Free_Lending_Risk_Free_Lending", "length": 22} |
/*
* @file
* @author University of Warwick
* @version 1.0
*
* @section LICENSE
*
* @section DESCRIPTION
*
*/
#define BOOST_TEST_MODULE AllToAllMPI
#include <boost/test/unit_test.hpp>
#include <boost/test/output_test_stream.hpp>
#include <stdexcept>
#include "AllToAllMPI.h"
#include "mpi.h"
#include <iostream... | {"hexsha": "7c464ba96a013ec19c26fbc510db44aa224a7725", "size": 7487, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/comm/implementation/component/AllToAllMPITests.cpp", "max_stars_repo_name": "thorbenlouw/CUP-CFD", "max_stars_repo_head_hexsha": "d06f7673a1ed12bef24de4f1b828ef864fa45958", "max_stars_repo_lic... |
import numba as nb
import numpy as np
from dmdlib.core.ALP import AlpDmd
from dmdlib.randpatterns import utils
from dmdlib.randpatterns import ephys_comms
import os
from dmdlib.randpatterns import saving
from dmdlib.randpatterns.presenter import Presenter
if os.name == 'nt':
appdataroot = os.environ['APPDATA']
... | {"hexsha": "0fd959f4faa7b6df08c47fc62f59e399a5749052", "size": 5209, "ext": "py", "lang": "Python", "max_stars_repo_path": "dmdlib/randpatterns/multisparse_obj.py", "max_stars_repo_name": "murselkaradas/DmdLib", "max_stars_repo_head_hexsha": "d1b46ffa54b9170c248e0c557b7080384aa8cf8a", "max_stars_repo_licenses": ["MIT"]... |
import unittest
import mock
import numpy
import cupy
try:
import cupy.cuda.cudnn as libcudnn
cudnn_enabled = True
modes = [
libcudnn.CUDNN_ACTIVATION_SIGMOID,
libcudnn.CUDNN_ACTIVATION_RELU,
libcudnn.CUDNN_ACTIVATION_TANH,
]
import cupy.cudnn
except ImportError:
cudnn_e... | {"hexsha": "caecf77c7027af88bf7492f3d8eb08cfa27c37bf", "size": 3516, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/cupy_tests/test_cudnn.py", "max_stars_repo_name": "yukiito2/cupy", "max_stars_repo_head_hexsha": "7e707b8ccea13d79b9026c91c27143cb78014d4b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
using Clang.cindex
using Base.Test
top = cindex.parse_header("cxx/cxxbasic.h"; cplusplus = true)
funcs = cindex.search(top, "func")
@test length(funcs) == 1
f = funcs[1]
#@test map(spelling, cindex.function_args(f)) == ASCIIString["Int", "Int"]
@test map(spelling, cindex.function_args(f)) == ASCIIString["x", "y"]
@t... | {"hexsha": "b59928b735f848b1c0797e35af19b24b469aba8d", "size": 572, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/functions.jl", "max_stars_repo_name": "jakebolewski/Clang.jl", "max_stars_repo_head_hexsha": "bb48537bf1e86edb94635704e84df4a1ea8b5bc5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
! RUN: %S/test_errors.sh %s %t %f18
! Tests for the ASSOCIATED() and NULL() intrinsics
subroutine assoc()
abstract interface
subroutine subrInt(i)
integer :: i
end subroutine subrInt
integer function abstractIntFunc(x)
integer, intent(in) :: x
end function
end interface
contains
i... | {"hexsha": "641b7d97d78af61c9668a3d094896fa2300d688e", "size": 7745, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "flang/test/Semantics/associated.f90", "max_stars_repo_name": "Machiry/checkedc-clang", "max_stars_repo_head_hexsha": "ab9360d8be0a737cb5e09051f3a0051adc4b3e47", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma dvd_PM_iff: "p dvd q \<longleftrightarrow> divides_ff (content_ff_ff p) (content_ff_ff q) \<and>
map_poly to_fract p dvd map_poly to_fract q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (p dvd q) = (divides_ff (content_ff (map_poly to_fract p)) (content_ff (map_poly to_fract q)) \<and> map_po... | {"llama_tokens": 7446, "file": "Berlekamp_Zassenhaus_Unique_Factorization_Poly", "length": 75} |
C @(#)ext_gedcc.f 20.8 2/28/00
C****************************************************************
C
C File: ext_gedcc.f
C
C Purpose: Routine to extract d-c converter data in GE format
C
C Input parameters:
C
C savfil - the logical unit opened
C version - "23" or "24"
C
C Aut... | {"hexsha": "5ce50e80e5b6f5b7503d57db5912ca8560d9fbef", "size": 12938, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/ext_gedcc.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
module dapr
const _ProtoBuf_Top_ = @static isdefined(parentmodule(@__MODULE__), :_ProtoBuf_Top_) ? (parentmodule(@__MODULE__))._ProtoBuf_Top_ : parentmodule(@__MODULE__)
module proto
const _ProtoBuf_Top_ = @static isdefined(parentmodule(@__MODULE__), :_ProtoBuf_Top_) ? (parentmodule(@__MODULE__))._ProtoBuf_Top_... | {"hexsha": "6ea8bcfdc66f01f1c3ffc0990fb914e8fd419b73", "size": 1270, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/proto/src/generated/service_invocation/dapr.jl", "max_stars_repo_name": "oolong-dev/Dapr.jl", "max_stars_repo_head_hexsha": "133460f4ccf7c86313089c65e1efa7155afa4c91", "max_stars_repo_licenses"... |
import numpy as np
import os
from glob import glob
from skimage.io import imread, imsave
from skimage.transform import resize
from keras.preprocessing.image import ImageDataGenerator
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='./banque_tableaux_par_artiste_resi... | {"hexsha": "864bcd2ed527371512584ce93156f1443d8a2072", "size": 2267, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/data_augmentation.py", "max_stars_repo_name": "SebastianPartarrieu/ThroughArtistsEyes", "max_stars_repo_head_hexsha": "8e195119cbb1d0edf054819f3d8e1d6ffd185abc", "max_stars_repo_licenses": [... |
#include <boost/compute/interop/vtk.hpp>
| {"hexsha": "f4cd6ac4d83082ed47c0f294071de6314eb60c3f", "size": 41, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_compute_interop_vtk.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"]... |
(********************************************************************
@name Coq declarations for metamodel: <ATOM>
@date 2021/11/02 10:10:52
@description Automatically generated by Ecore2Coq transformation.
********************************************************************)
(* Coq libraries *)
Require Import S... | {"author": "atlanmod", "repo": "coqtl", "sha": "5daf5d915b66328ae5ec48f55c44731372563c87", "save_path": "github-repos/coq/atlanmod-coqtl", "path": "github-repos/coq/atlanmod-coqtl/coqtl-5daf5d915b66328ae5ec48f55c44731372563c87/transformations/RSS2ATOM/ATOMFlattened.v"} |
# Simple Node
import sys
import random
import numpy as np
import pygame
from data import *
from gbls import *
sys.path.append("../common/")
from NodeBase import NodeBase
class Node(NodeBase):
def __init__(self, x, y, ss):
# pass parameters to parent
NodeBase.__init__(self, x, y, ss)
# h... | {"hexsha": "82659292799c4c23a4dff14282a7f25a2e149104", "size": 4061, "ext": "py", "lang": "Python", "max_stars_repo_path": "aps/aps_dvh_node.py", "max_stars_repo_name": "Manewing/APS", "max_stars_repo_head_hexsha": "834c16555f52461c65fc39132653bb9d68f97cca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import torch
from src.helper_functions.helper_functions import parse_args
from src.loss_functions.losses import AsymmetricLoss, AsymmetricLossOptimized
from src.models import create_model
import argparse
from PIL import Image
import numpy as np
from glob import glob
from time import time
parser = argparse.ArgumentPar... | {"hexsha": "cd07f9bfdf686209d4ed491c65cbac74487647c7", "size": 2211, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "MinliangLin/ASL", "max_stars_repo_head_hexsha": "beda0989a8e30ac51a7ce9f9e247a12bbe84ec96", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
import pandas as pd
import numpy as np
from os import listdir
from os.path import isfile, join
import nltk
from nltk.stem.porter import PorterStemmer
# from krovetzstemmer import Stemmer as KrovetzStemmer #TODO: uncomment after installing the krovetz setmmer.
import re
import os, shutil
def encode_id(topic_id, facet_... | {"hexsha": "70049f310e6e19f153ff3ea7347286e322d828b8", "size": 2292, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/utils.py", "max_stars_repo_name": "aliannejadi/qulac", "max_stars_repo_head_hexsha": "d175f0179cb4084067f82a5b27d423bc2d9d8656", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 60... |
c code modifications based on: 04/02/93 - analy.f
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
subroutine sort(expid,verbose,luverb,nnobs,
1 rlats,rlons,rlevs,kx,kt,del,sigU,sigO,sigF,tstamp,
1 maxreg,ktmax,iregbeg,ireglen,ityplen)
c.... Sort data b... | {"hexsha": "56d8aa24ea70096c47b9fac69a0d832a905f97e8", "size": 7108, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/Shared/GMAO_Shared/GMAO_psas/sort.f", "max_stars_repo_name": "GEOS-ESM/AeroApps", "max_stars_repo_head_hexsha": "874dad6f34420c014d98eccbe81a061bdc0110cf", "max_stars_repo_licenses": ["NASA-1.... |
%Suggested order of slides
% slides-cod.tex
% slides-cod-examples.tex
\subsection{Curse of Dimensionality}
\includepdf[pages=-]{../slides-pdf/slides-cod.pdf}
\subsection{Curse of Dimensionality - Examples}
\includepdf[pages=-]{../slides-pdf/slides-cod-examples.pdf}
| {"hexsha": "d902385a291e211c6d503e4c12d5104fe75cf2a0", "size": 270, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "slides/cod/chapter-order.tex", "max_stars_repo_name": "jukaje/lecture_i2ml", "max_stars_repo_head_hexsha": "cd4900f5190e9d319867b4c0eb9d8e19f659fb62", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_s... |
/*
* Operator.cpp
* evaluate the expression graph
*
=======================================================*/
#include "Operator.hpp"
#include "utils/utils.hpp"
#include "utils/calcTime.hpp"
#include "Kernel.hpp"
#include "Jit_Driver.hpp"
#include "Grid.hpp"
#include <fstream>
#include <sstream>
#include <stdio.h>
... | {"hexsha": "43d1cb1e3fcc269cf6c86d4a66bbfb392ff534bf", "size": 40659, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Operator.cpp", "max_stars_repo_name": "hxmhuang/OpenArray_Dev", "max_stars_repo_head_hexsha": "863866a6b7accf21fa253567b0e66143c7506cdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0,... |
#define BOOST_TEST_MODULE
#include <boost/test/unit_test.hpp>
#include <util/test_macros.hpp>
#include <string>
#include <random>
#include <set>
#include <sstream>
#include <vector>
#include <algorithm>
#include <util/cityhash_tc.hpp>
// Eigen
#include <Eigen/Core>
#include <Eigen/SparseCore>
// Constraints
#include ... | {"hexsha": "c6546c4e7f15adb374605c043d2a95849ea0218a", "size": 3284, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "test/optimization/constraint_interface.cxx", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_licenses": ["B... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
#################################
# Models for federated learning #
#################################
# McMahan et al., 2016; 199,210 parameters
class TwoNN(nn.Module):
def __init__(self, name, in_features, num_hiddens, num_clas... | {"hexsha": "dc6cf7ee5b9b145dd81a401628e0407528b7c256", "size": 3321, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models.py", "max_stars_repo_name": "vaseline555/Federated-Averaging-PyTorch", "max_stars_repo_head_hexsha": "eef9a4d2cfe7866bc0aec27a28d97486dbada4da", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma (in semiring_0) last_linear_mul_lemma:
"last ((a %* p) +++ (x # (b %* p))) = (if p = [] then x else b * last p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. last (a %* p +++ (x # b %* p)) = (if p = [] then x else b * last p)
[PROOF STEP]
apply (induct p arbitrary: a x b)
[PROOF STATE]
proof (p... | {"llama_tokens": 1302, "file": null, "length": 9} |
# coding=utf-8
# Copyright 2018 The Tensor2Tensor 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 applicable... | {"hexsha": "86d8a3b7cbbc8558e3eb51493c9348017966632f", "size": 8178, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensor2tensor/utils/optimize.py", "max_stars_repo_name": "sivaramakrishna7/tensor2tensor", "max_stars_repo_head_hexsha": "eb0118d3f459913133e3d68a96944480a928bff1", "max_stars_repo_licenses": ["Ap... |
// $Id$
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by th... | {"hexsha": "7b370ff36195e906c6edb3bd4a9a9c980c7c2cc2", "size": 3412, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/decoder-phrasebased/src/native/decoder/moses/FactorCollection.cpp", "max_stars_repo_name": "ugermann/MMT", "max_stars_repo_head_hexsha": "715ad16b4467f44c8103e16165261d1391a69fb8", "max_stars_re... |
import unittest
import numpy as np
import pandas as pd
from mlots.utilities import from_pandas_dataframe
class TestFPD(unittest.TestCase):
def setUp(self) -> None:
print("Starting a test in TestFPD..")
data = {
1: [1, 2, 3, 4, 5],
2: [6, 7, 8, 9, 10],
3: [11, 1... | {"hexsha": "2cdb6c9638abb0671412b59c41d67e491025b5db", "size": 2931, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_FPD.py", "max_stars_repo_name": "I-Form/mlots", "max_stars_repo_head_hexsha": "afb1530e6cc3ad7c41f86ac66c3496106681334c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
import numpy as np
import cdflib
from .DectoHHMM import DectoHHMM
from .DateSplit import DateSplit
def CDFEpoch(Date,ut):
'''
Converts date and time to CDF Epoch - which is the number of
milliseconds since 00000101 00:00
Inputs
======
Date : int
Array of dates int he format yyyymmdd
ut : float
Array of t... | {"hexsha": "28e3fb19c32944d269da4f6cc2e7ee5ffeed59d2", "size": 674, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/DateTimeTools/CDFEpoch.py", "max_stars_repo_name": "pshustov/DateTimeTools", "max_stars_repo_head_hexsha": "e542fd3f0e3c5290faad09b7cf8a2751132d4dd3", "max_stars_repo_licenses": ["MIT"], ... |
import os
import numpy as np
from demo_utils import plot_image
import svmbir
"""
This file demonstrates the use of the proximal map function in svmbir.
The phantom, sinogram, and reconstruction are then displayed.
"""
# Simulated image parameters
num_rows_cols = 256 # Assumes a square image
num_slices = 33
display... | {"hexsha": "e6eea09abdd8d1f4b20d681637a3cf1b92f6b65e", "size": 2110, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/demo_prox.py", "max_stars_repo_name": "Mohammad-Chowdhury-31/svmbir", "max_stars_repo_head_hexsha": "05665eb2a65b7aa951e26dd3691955e737f16c06", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
""" Utility functions """
import numpy as np
import pandas as pd
def path_boundaries(links: np.array) -> np.array:
''' boolean mask of links which have 0 as either upstream or downstream '''
# Links to 0 Bin indicate the beginning or end of a path. 0 Bin has no sequence
return np.any(links == 0, axis=1)... | {"hexsha": "736adfd3eeeba351ffdfcf7c6a288c1ba7a7d47c", "size": 3198, "ext": "py", "lang": "Python", "max_stars_repo_path": "matrixcomponent/utils.py", "max_stars_repo_name": "dimatr/component_segmentation", "max_stars_repo_head_hexsha": "26e89579300f7653cd9e8a531f684cbc64a0cd17", "max_stars_repo_licenses": ["Apache-2.0... |
###########################################
# Concrete implementations of AbstractWord:
# Word and SubWord
#
"""
Word{T} <: AbstractWord{T}
Word as written in an alphabet storing only pointers to letters of an Alphabet.
Note that the negative values in `genptrs` field represent the inverse of letter.
If type i... | {"hexsha": "19d414b63d6241e367225be73752b414b40d12f7", "size": 2290, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/words.jl", "max_stars_repo_name": "kalmarek/KnuthBendix.jl", "max_stars_repo_head_hexsha": "74f6bdbb804723f88cb8ed1b60bb27b3e839576b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, ... |
#---------------------------------------------------------------------#
#This code computes the element mass and differentiation matrices
#Written by F.X. Giraldo on April 24, 2019
# Department of Applied Mathematics
# Naval Postgraduate School
# Monterey; CA 93943-5216
#------------------... | {"hexsha": "7ae998f05fb4229ebfca46b020a18bff787e2d3b", "size": 1444, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Projects/Project_06_2D_Wave/For_Instructors/julia/element_matrices.jl", "max_stars_repo_name": "fxgiraldo/Element-based-Galerkin-Methods", "max_stars_repo_head_hexsha": "3e7cd28b5d6000a95b00710f15a... |
import zlib
import numpy as np
import tensorflow.compat.v2 as tf
from pyquaternion import Quaternion
from waymo_open_dataset import dataset_pb2
from waymo_open_dataset.utils import range_image_utils
from waymo_open_dataset.utils import transform_utils
tf.enable_v2_behavior()
def decode_frame(frame, frame_id):
... | {"hexsha": "0fcb1f7016845cdbb417522bdf501b605cd5cb6e", "size": 8259, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/preprocess/waymo_decoder.py", "max_stars_repo_name": "eladb3/BoxeR", "max_stars_repo_head_hexsha": "995a38b67e3f84b5d6ea6fedbcb16896c4b1d020", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
module TestCat
using Base.Test
using DataTables
#
# hcat
#
nvint = NullableArray(Nullable{Int}[1, 2, Nullable(), 4])
nvstr = NullableArray(Nullable{String}["one", "two", Nullable(), "four"])
dt2 = DataTable(Any[nvint, nvstr])
dt3 = DataTable(Any[nvint])
dt4 = convert(DataTable... | {"hexsha": "52c230a86192beefd1cdc2c148148694803f531b", "size": 12557, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/cat.jl", "max_stars_repo_name": "UnofficialJuliaMirror/DataTables.jl-933ac57d-ae2c-5684-854b-0ea345a8088a", "max_stars_repo_head_hexsha": "5199293f36e13d29819f7bd0259ca6be0f4195e2", "max_star... |
*dk,vorpoint
subroutine vorpoint(n1,n2,n3,n4,xv,yv,zv,distsq)
implicit real*8 (a-h,o-z)
C
C #####################################################################
C
C PURPOSE -
C
C This routine calculates the 3-D voronoi point
C
C INPUT ARGUMENTS -
C
C n1-n4 - the points in the tetrah... | {"hexsha": "c12dc0dfe34c968f9d9eca01f1107d2a349be9fd", "size": 3106, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/vorpoint.f", "max_stars_repo_name": "millerta/LaGriT-1", "max_stars_repo_head_hexsha": "511ef22f3b7e839c7e0484604cd7f6a2278ae6b9", "max_stars_repo_licenses": ["CNRI-Python"], "max_stars_count"... |
import discord
from pymongo import MongoClient
from discord.ext import commands
import matplotlib.pyplot as plt
import numpy as np
import pprint
collection = MongoClient('localhost', 27017).maindb.VCJoins
pipeline = [{'$match': {'$and': [{'guildId': 802298523214938153}, {'time': {'gt': 1638996791.4233649}}]}}, {'$gr... | {"hexsha": "f6a136d889b4ffc0584b2d1325441f48d15c50dc", "size": 603, "ext": "py", "lang": "Python", "max_stars_repo_path": "documentation/statsAggregate.py", "max_stars_repo_name": "XFazze/discordbot", "max_stars_repo_head_hexsha": "6b4201a6d6ff1bed5f65de4b4d30738b4d51e223", "max_stars_repo_licenses": ["MIT"], "max_star... |
const ϵ = 1.0e-6
function decibel(value::Real)
return 10 * log10(value)
end
# Test odd-length signal
@testset "summarize PSD" begin
N = 90000
fs = 100.
num_segments = 13
smoothing_width_factor = 2.
T = Float64
data = Array{T, 2}(undef, num_segments, N)
for i in 1:num_segments
f... | {"hexsha": "72703da9b26b5b2cb613529a624fe9e44c87eda1", "size": 1480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_summarize_psd.jl", "max_stars_repo_name": "Cuda-Chen/SeisPDF.jl", "max_stars_repo_head_hexsha": "fd8a865c19ea1b9caef71ef7bae900d3cc99b070", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
"""
Medoto di newton modificato
"""
import numpy as np
# L'unica differenza che c'e rispetto al metodo di newton e che
# l'incremento d viene moltiplicato per m
def newton_modificato(func, dfunc, x0, m, tolx, tolf, max_iterazioni):
xks = []
it = 0
if not (abs(dfunc(x0) > np.spacing(... | {"hexsha": "1d45efa8aa92a40a6bb9b00a6e9770f53f0a0518", "size": 983, "ext": "py", "lang": "Python", "max_stars_repo_path": "zeri_di_funzioni/newton_modificato.py", "max_stars_repo_name": "Ro0t-set/metodi-numerici", "max_stars_repo_head_hexsha": "d586bce78f94f1aa6736218c9123064a57cb87ec", "max_stars_repo_licenses": ["MIT... |
if (1 == 0){
probs10 <- read.table("D:/DATASETS/UKTraffic/probs/recprobs10.txt", header = F)
plot(probs10$V1, type = 'b', cex = 0.4, xlim = c(0, 2000))
abline(h = 0.003)
abline(h = 0.004)
}
# Prepare figures for the paper
####
### 1) Plot improvement vs delay fractions
two_plots_traffic <- function(perf1, perf2, o... | {"hexsha": "eef7ad0ffc2d54008a657710f6d56a1478d8892a", "size": 4256, "ext": "r", "lang": "R", "max_stars_repo_path": "v.1.0_clojure/src/r-code/4.1PublicDataPlots.r", "max_stars_repo_name": "av-maslov/Recurrency", "max_stars_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_stars_repo_licenses": ["MIT"... |
from gsitk.datasets.datasets import DatasetManager
from nltk.corpus import opinion_lexicon
from collections import Counter
def prepare_lexicon(process=True, dim=250, save=False):
if process:
dm = DatasetManager()
data = dm.prepare_datasets()
nega = set(opinion_lexicon.negative())
p... | {"hexsha": "e20c8f68dea990bc4c32367fa6b8c1733c2c1da3", "size": 9726, "ext": "py", "lang": "Python", "max_stars_repo_path": "sematch/classify.py", "max_stars_repo_name": "dhimmel/sematch", "max_stars_repo_head_hexsha": "7e92b171c27a8b25e844a467554fe4bb2adfb883", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import numpy as np
import pytest
from nlp_profiler.constants import NaN
from nlp_profiler.granular_features.letters \
import gather_repeated_letters, count_repeated_letters # noqa
text_with_repeated_letters1 = '2833047 people live in this aaaaa area'
text_with_repeated_letters2 = '2833047 people live in this aaa... | {"hexsha": "820bc8cc06a7447de95919c4819aab1cebf571e7", "size": 2493, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/granular/test_repeated_letters.py", "max_stars_repo_name": "neomatrix369/nlp_profiler", "max_stars_repo_head_hexsha": "db094b9a9fe02b9840d266f0d522978df6ca0c51", "max_stars_repo_licenses": [... |
! { dg-do compile }
! PR fortran/66725
!
program foo
open(unit=1,access = 999) ! { dg-error "ACCESS requires" }
open(unit=1,action = 999) ! { dg-error "ACTION requires" }
open(unit=1,asynchronous = 999) ! { dg-error "ASYNCHRONOUS requires" }
open(unit=1,blank = 999) ! { dg-error "BLA... | {"hexsha": "8ad97f7e18de3881f3479180f491bfeb416f3ceb", "size": 1523, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/pr66725.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_license... |
function [f,g] = autoGrad(x,useComplex,funObj,varargin)
% [f,g] = autoGrad(x,useComplex,funObj,varargin)
%
% Numerically compute gradient of objective function from function values
p = length(x);
mu = 1e-150;
if useComplex % Use Complex Differentials
diff = zeros(p,1);
for j = 1:p
e_j = zeros(p,1);
... | {"author": "rbgirshick", "repo": "voc-dpm", "sha": "c0b88564bd668bcc6216bbffe96cb061613be768", "save_path": "github-repos/MATLAB/rbgirshick-voc-dpm", "path": "github-repos/MATLAB/rbgirshick-voc-dpm/voc-dpm-c0b88564bd668bcc6216bbffe96cb061613be768/external/minConf/minFunc/autoGrad.m"} |
import vaex
import numpy as np
from common import *
# def test_count_multiple_selections():
def test_sum(df, ds_trimmed):
df.select("x < 5")
np.testing.assert_array_almost_equal(df.sum("x", selection=None), np.nansum(ds_trimmed.data.x))
np.testing.assert_array_almost_equal(df.sum("x", selection=True), np.... | {"hexsha": "2a1e37c4d2fa20219ea664a657e21e7979cb8c43", "size": 9229, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/agg_test.py", "max_stars_repo_name": "skywalk163/vaex", "max_stars_repo_head_hexsha": "f5e38f1fa448ef78936e2cb6b3d026fda1f2a1c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
/*
* Copyright (c) 2017, Ben Smith
* All rights reserved.
*
*/
#include <vector>
#include <boost/format.hpp>
#include "flow_collector.hpp"
#include "logger.hpp"
using namespace std;
using namespace Tins;
using namespace boost;
void FlowsCollector::collect(time_t current_time, const PDU &pdu) {
const EthernetI... | {"hexsha": "f435e21f6355231a179e10962d0dd36c99e8ae79", "size": 4603, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "flow_collector.cpp", "max_stars_repo_name": "benhsmith/netwhere", "max_stars_repo_head_hexsha": "94ae228fd38b2f0c8b00a29af67f72e47bc9f188", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0... |
import logging
#logging.basicConfig(level=logging.DEBUG)
from pymothoa.jit import default_module, function
from pymothoa.types import *
from pymothoa.dialect import *
@function(ret=Int)
def test_constant():
var (
A = Int,
B = Int,
C = Int,
)
A = (123 + 321)*2
B = 9
C = A... | {"hexsha": "e4776effac134c539dba6d807d7325cb19c1dee1", "size": 4523, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/testbasic.py", "max_stars_repo_name": "sklam/pymothoa", "max_stars_repo_head_hexsha": "330bd70666ccf761f39c75f3acb70aa7e0a92ac6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
#!/usr/bin/env python3
"""
An implementation of the Libet clock as a QtWidget. This is a heavily
modified version of the Qt5 example forked from:
# forked from https://github.com/baoboa/pyqt5/blob/master/examples/widgets/analogclock.py
The original license is copied below
"""
#######################################... | {"hexsha": "bb5e0a5b7a8663bfc08906562d82935a5c739d75", "size": 11647, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/spike_recorder/experiments/libet/clock.py", "max_stars_repo_name": "gitter-badger/py-spike-recorder", "max_stars_repo_head_hexsha": "3dad322ef753570293e42968a435453cfa389ac3", "max_stars_repo... |
import os
import argparse
import glob
import h5py
import numpy as np
from PIL import Image
from dgl.data.utils import load_graphs
from histocartography.visualization import OverlayGraphVisualization, InstanceImageVisualization
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
... | {"hexsha": "b98410027da16e372107ffb9fd842487714616a1", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/visualize_explanations.py", "max_stars_repo_name": "histocartography/patho-quant-explainer", "max_stars_repo_head_hexsha": "010533cf231c4394645111c3356b9b41636233fe", "max_stars_repo_licenses... |
import numpy as np
from lsb import LSB
from PIL import Image
class PNG(LSB):
"""
Реализация алгоритма LSB для файлов формата PNG.
"""
def __init__(self, file_name: str, message: bytes = None) -> None:
"""
Возвращает простой PNG кодер,
принимает на вход имя файла и сообщение.
... | {"hexsha": "701439c3ba26447ea3f1ab92344cc601d85547d8", "size": 1895, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/png.py", "max_stars_repo_name": "neoksei/Thesis", "max_stars_repo_head_hexsha": "efb41eee8482da9d7bd192e447a14c26161d6c11", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, ... |
#!/usr/bin/env python
"""Get nearest alerts"""
# *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
# ** Copyright UCAR (c) 1992 - 2015
# ** University Corporation for Atmospheric Research(UCAR)
# ** National Center for Atmospheric Research(NCAR)
# ** Research Applications Laboratory(RAL)
... | {"hexsha": "48f6728243478ab13987195f5a00161e518e9291", "size": 5886, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/python/backend_server/wsgi/maw_alert_test.py", "max_stars_repo_name": "OSADP/Pikalert-Vehicle-Data-Translator-", "max_stars_repo_head_hexsha": "295da604408f6f13af0301b55476a81311459386", "... |
"""Plotting methods"""
from __future__ import unicode_literals
import matplotlib.pyplot as plt
import numpy as np
from .plot_sweep import assign_axes
from .. import GPCOLORS
def compare(models, sweeps, posys, tol=0.001):
"""Compares the values of posys over a sweep of several models.
If posys is of the same ... | {"hexsha": "677e3bb82c27e798ef97aeb7b59fa708b41dc921", "size": 2115, "ext": "py", "lang": "Python", "max_stars_repo_path": "gpkit/interactive/plotting.py", "max_stars_repo_name": "giserh/gpkit", "max_stars_repo_head_hexsha": "71b953fcac8f67f148b67b54b6e8cd4182dc0b3b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""Scatter layer controls"""
from typing import TYPE_CHECKING
import numpy as np
from napari._qt.utils import disable_with_opacity, qt_signals_blocked
from napari._qt.widgets.qt_color_swatch import QColorSwatch
from napari.layers.points._points_constants import SYMBOL_TRANSLATION
from napari.utils.events import discon... | {"hexsha": "a9f328d4a5eec332b51fa99acfd8fb94374112bf", "size": 10159, "ext": "py", "lang": "Python", "max_stars_repo_path": "napari_plot/_qt/layer_controls/qt_scatter_controls.py", "max_stars_repo_name": "lukasz-migas/napari-1d", "max_stars_repo_head_hexsha": "b0f081a8711ae941b3e4b5c58c3aea56bd0e3277", "max_stars_repo_... |
import numpy as np
import pandas as pd
#Check a dataframe for nulls, print/report them in a nice "pretty" format
def check_nulls(my_df):
new_df = my_df.copy()
return new_df.isna()
if __name__ == "__main__":
#Creating a dictionary
dict = {'Name': ['John', 'Peter', 'Sam', np.nan],
... | {"hexsha": "4baa1adca9544110b54ece5dac525e18d200bd59", "size": 573, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_lambdata/nulls_func.py", "max_stars_repo_name": "Khislatz/lambdata-khislatz", "max_stars_repo_head_hexsha": "8df039e2f3ed1309e7b9ac4b53cb0a770ccf0420", "max_stars_repo_licenses": ["MIT"], "max_s... |
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