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//
// $Id$
//
// -------------------------------------------------------------------------
// This file is part of ZeroBugs, Copyright (c) 2010 Cristian L. Vlasceanu
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.... | {"hexsha": "a6f784204472b8f0d2335fa1a5d03844eec19d0a", "size": 22981, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "plugin/gui/variables_view.cpp", "max_stars_repo_name": "cristivlas/zerobugs", "max_stars_repo_head_hexsha": "5f080c8645b123d7887fd8a64f60e8d226e3b1d5", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
/*
Copyright 2007-2008 Christian Henning, Andreas Pokorny, Lubomir Bourdev
Use, modification and distribution are subject to the Boost Software License,
Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt).
*/
/********************************************... | {"hexsha": "0dc0155515f0d9c97947458e145bcee56a09264e", "size": 17003, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "extern/boost/gil/extension/io_new/formats/png/write.hpp", "max_stars_repo_name": "qc2105/librjmcmc", "max_stars_repo_head_hexsha": "6e031a9f6f3612394f8918c745700ae41d2aa586", "max_stars_repo_licens... |
[STATEMENT]
lemma P_inner_t0[simp]: "P_inner g t0 = x0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P_inner g t0 = x0
[PROOF STEP]
by (simp add: P_inner_def) | {"llama_tokens": 79, "file": "Ordinary_Differential_Equations_IVP_Initial_Value_Problem", "length": 1} |
import os
import argparse
from argparse import Namespace
import logging
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import copy
from decimal import Decimal
import wandb
import sys
sys.path.append('../../../../')
from sopa.src.solvers.utils import create_solver
from s... | {"hexsha": "df471018a4c3f81400f758c32be191fc187de7c8", "size": 11905, "ext": "py", "lang": "Python", "max_stars_repo_path": "sopa/src/models/odenet_mnist/runner_new.py", "max_stars_repo_name": "juliagusak/neural-ode-metasolver", "max_stars_repo_head_hexsha": "a5ca6ae0c00d2a8da3a5f4b77ee20fb151674d22", "max_stars_repo_l... |
# File which contains helper method to visualize, process, predict for CNN models
# Dt- 02.08.21
import os
import random
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tensorflow as tf
#####3
## Method 1
def get_class_names(path):
'''
getting all the... | {"hexsha": "58792e47b3c073354a955cba253dc3a797932943", "size": 3341, "ext": "py", "lang": "Python", "max_stars_repo_path": "Computer-Vision/helper.py", "max_stars_repo_name": "teddcp2/Tensorflow-Deep-Learning-notes", "max_stars_repo_head_hexsha": "6b52cc32338695052256852879dc383c581fbf7f", "max_stars_repo_licenses": ["... |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
#
# 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 law or agre... | {"hexsha": "0781606810839912910f846b554bb464e94b31ca", "size": 8589, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cuml/dask/cluster/kmeans.py", "max_stars_repo_name": "harrism/cuml", "max_stars_repo_head_hexsha": "060dcd94138deed2ac692031cfe70a674b15c6f0", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 2 13:14:32 2021
@author: ali_d
"""
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-10,9,20)
#-9 ile 10 arasında esıt aralıklarda 20 tane deger olusturdum
y = x ** 3
z = x**2
figure = plt.figure()
#bos bir figur olusturuyorum
axes_cube = figure.... | {"hexsha": "ab891fee163e0e78db9cae560e8118a1517c7340", "size": 1495, "ext": "py", "lang": "Python", "max_stars_repo_path": "Data Visualization/Matplotlib/3 Matplotlib.py", "max_stars_repo_name": "ALDOR99/Python", "max_stars_repo_head_hexsha": "a76f37bb3e573cd3fdcfc19f4f73494cafa9140e", "max_stars_repo_licenses": ["MIT... |
\chapter{Chain Database}
\label{chaindb}
TODO\todo{TODO}: This is currently a disjoint collection of snippets.
\section{Union of the Volatile DB and the Immutable DB}
\label{chaindb:union}
As discussed in \cref{storage:components}, the blocks in the Chain DB are
divided between the Volatile DB (\cref{volatile}) and ... | {"hexsha": "8d794bc13600d5fbabb730659650cedd457b9447", "size": 18976, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ouroboros-consensus/docs/report/chapters/storage/chaindb.tex", "max_stars_repo_name": "RyanGlScott/ouroboros-network", "max_stars_repo_head_hexsha": "85b06a74c7b895c5412ba2ac8a43b9c264ad7957", "max... |
import pretty_midi
import numpy as np
def extract_label(label_path, m_beat_arr):
"""Extract drum label notes.
Process ground-truth midi into numpy array representation.
Parameters
----------
label_path: Path
Path to the midi file.
m_beat_arr:
Extracted mini-beat array of the ... | {"hexsha": "ce6049dc2958bd2bbe76d473f2f8c9036a8c64ad", "size": 3522, "ext": "py", "lang": "Python", "max_stars_repo_path": "omnizart/drum/labels.py", "max_stars_repo_name": "nicolasanjoran/omnizart", "max_stars_repo_head_hexsha": "b0e74af39b2e3a312ef32dbf0837626b2e043cb6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#imports
import tensorflow as tf
from tensorflow import layers
import numpy as np
import cv2
from os import listdir
from os.path import join, isfile
#standard of numpy randomness
np.random.seed(7)
#use matplotlib to read and process images
def getImages(directory, name):
images = []
tfImages = []
allImage... | {"hexsha": "b183257d048aabb8721410fabaf5fd47589c138b", "size": 2014, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Obsolete-UsedDCGAN/ops.py", "max_stars_repo_name": "esslushy/AbstractArtGenerator", "max_stars_repo_head_hexsha": "48ebfee04673bc109fc202c3368dbbc80f0e7021", "max_stars_repo_licenses": ["MIT"]... |
# Test of reduction on scalar backend
# Unit testing for element-wise expressions on scalar backend
import Devectorize
import Devectorize.@devec
import Devectorize.@inspect_devec
import Devectorize.dump_devec
import Devectorize.sqr
using Base.Test
# data
a = [3., 4., 5., 6., 8., 7., 6., 5.]
b = [9., 8., 7., 6., 4... | {"hexsha": "58d53dddd3e458435a9793784817f803fdb7d107", "size": 2100, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_scalar_reduc.jl", "max_stars_repo_name": "jakebolewski/Devectorize.jl", "max_stars_repo_head_hexsha": "6e28d48cfdb70c0fe9622ac5a79942decdc73460", "max_stars_repo_licenses": ["MIT"], "max_... |
# -*- coding: utf-8 -*-
# *****************************************************************************
# ufit, a universal scattering fitting suite
#
# Copyright (c) 2013-2019, Georg Brandl and contributors. All rights reserved.
# Licensed under a 2-clause BSD license, see LICENSE.
# ********************************... | {"hexsha": "4c2577a98a7ce9d52c332b8742023397d1b7cd65", "size": 2028, "ext": "py", "lang": "Python", "max_stars_repo_path": "ufit/backends/scipy.py", "max_stars_repo_name": "McStasMcXtrace/ufit", "max_stars_repo_head_hexsha": "02640e2b802bf6d42ae6829a1c1852b21c6fa9f7", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf8
from __future__ import division, print_function
import numpy as np
import os
import pandas as pd
import plac
import tables
def get_above(time_series):
pops = time_series[:, 0]
dups = time_series[:, 1]
audi = time_series[:, 2]
dups = dups[pops >= 20]
a... | {"hexsha": "d2ecd7d1a16d94cd59990aa3ca8f08b7afecd5c7", "size": 2256, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/revisits_over_time.py", "max_stars_repo_name": "flaviovdf/phoenix", "max_stars_repo_head_hexsha": "59177657f13337b14d1fe27527a9b09c2c1c1419", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
#!/usr/bin/env python
"""SimPEG: Simulation and Parameter Estimation in Geophysics
SimPEG is a python package for simulation and gradient based
parameter estimation in the context of geophysical applications.
"""
import numpy as np
import os
import sys
import subprocess
from distutils.core import setup
from setupto... | {"hexsha": "bcb5b8e364009b2572c9670aa5e4674d6ba1b521", "size": 3218, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "KyuboNoh/HY", "max_stars_repo_head_hexsha": "8ba9815137c2cff2f1931a1940e1b762e8df0b02", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo... |
import os
import glob
import random
import time
import h5py
import numpy as np
import torch
import torch.nn.functional as F
from torchvision import transforms as T
from torch.utils.data import Dataset
import torchvision.transforms.functional as tf
def data_augmentation(images):
mode = np.random.randint(0, 5)
... | {"hexsha": "7782a17bb51979df4e358a6ef69ef34b68eaa65a", "size": 8370, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_mpi/datasets/realestate.py", "max_stars_repo_name": "ken2576/deep-3dmask", "max_stars_repo_head_hexsha": "00c12af81ee48b5d0e612fa0f17395284d23fcc2", "max_stars_repo_licenses": ["MIT"], "max_... |
module Test.FmtTest
import IdrTest.Test
import IdrTest.Expectation
import Fmt
simpleTest : Test
simpleTest =
test "Simple test" (\_ => assertEq
(fmt "Hello")
"Hello"
)
stringTest : Test
stringTest =
test "String test" (\_ => assertEq
(fmt "Hello %s" "world")
"Hello world"
)
intTest : Test
i... | {"hexsha": "05a4c811ad71de6b765bd43adc9713a122c64d08", "size": 668, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Base/Fmt/Test/FmtTest.idr", "max_stars_repo_name": "Z-snails/inigo", "max_stars_repo_head_hexsha": "57f5b5c051222d8c630010a0a3cf7d7138910127", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import copy
import json
import numpy
import cepton_sdk.common.transform
from cepton_sdk.common import *
_all_builder = AllBuilder(__name__)
def _convert_keys_to_int(d, ignore_invalid=False):
d_int = {}
for key, value in d.items():
try:
key = int(key)
except:
if ignor... | {"hexsha": "ffb1b066119712f4df04846c2b225ad40eebb3a7", "size": 7420, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cepton_sdk/settings.py", "max_stars_repo_name": "Ly0n/cepton_sdk_redist", "max_stars_repo_head_hexsha": "5b4bf24edadb4fdaf9b8149a70c60d207922a1ad", "max_stars_repo_licenses": ["BSD-3-Clause... |
from __future__ import absolute_import
from copy import deepcopy
import torch
import numpy as np
import pandas as pd
from .utils import get_transform
from .random_noise import label_noise, image_noise
from .datasets import CIFAR10, CIFAR100, Nexperia, Nexperia_eval
from myImageFolder import MyImageFolder
from concat... | {"hexsha": "5c52fdc3378dbf2fca2ae7d5ad17ccd715579251", "size": 11192, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/loaders.py", "max_stars_repo_name": "huangkaiyikatherine/nexperia_new", "max_stars_repo_head_hexsha": "b9b5c35d989883f8b29280726bfbb0fc84e62b10", "max_stars_repo_licenses": ["MIT"], "max... |
import unittest
import pickle
import time
import sys
import numpy as np
import mrestimator as mre
from mrestimator.utility import log
def test_similarity(value1, value2, ratio_different=1e-10):
print('ratio difference: {:.3e}'.format(np.max(np.fabs(value1 - value2)/((value1 + value2)/2))))
return np.all(np.... | {"hexsha": "b34133bf4c7666e8424ac698638430f11f3042d8", "size": 6745, "ext": "py", "lang": "Python", "max_stars_repo_path": "mrestimator/test_suite/test_coefficients.py", "max_stars_repo_name": "balajisriram/mrestimator", "max_stars_repo_head_hexsha": "62a17ed8b101362862364850bb93991dd7e3893a", "max_stars_repo_licenses"... |
# ############################################################################
# cpgd.py
# =======
# Authors : Adrien Besson [adribesson@gmail.com] and Matthieu Simeoni [matthieu.simeoni@gmail.com]
# ############################################################################
"""
Class for the CPGD algorithm. Descripti... | {"hexsha": "5a1b5fdb3355525cedc0d5c99ceb79ba4ddd0b82", "size": 12364, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyoneer/algorithms/cpgd.py", "max_stars_repo_name": "matthieumeo/pyoneer", "max_stars_repo_head_hexsha": "fb7ee05c5319248f9180c10068235ff311b844b0", "max_stars_repo_licenses": ["MIT"], "max_stars... |
## Transforms take values at Chebyshev points of the first and second kinds and produce Chebyshev coefficients
abstract type ChebyshevPlan{T} <: Plan{T} end
size(P::ChebyshevPlan) = isdefined(P, :plan) ? size(P.plan) : (0,)
length(P::ChebyshevPlan) = isdefined(P, :plan) ? length(P.plan) : 0
const FIRSTKIND = FFTW.R... | {"hexsha": "0563f26bed5b28df98b6ff8a57f841dcca8afcc7", "size": 24831, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/chebyshevtransform.jl", "max_stars_repo_name": "JuliaApproximation/FastTransforms.jl", "max_stars_repo_head_hexsha": "4199ae9ac7970390da2546aef7f290f4cf675c65", "max_stars_repo_licenses": ["MI... |
Set Implicit Arguments.
Require Import Coq.Setoids.Setoid.
Require Import Coq.Arith.EqNat.
Local Open Scope bool_scope.
Definition pk : Set := (nat * nat * nat * nat) %type.
Inductive trace : Set :=
| tr_single : pk -> trace
| tr_cons : pk -> trace -> trace.
Inductive hdr :=
| sw : hdr
| pt : hdr
| src : hdr
| dst... | {"author": "frenetic-lang", "repo": "featherweight-openflow", "sha": "4470518794e3ed867919d30500be2d0128b1de1c", "save_path": "github-repos/coq/frenetic-lang-featherweight-openflow", "path": "github-repos/coq/frenetic-lang-featherweight-openflow/featherweight-openflow-4470518794e3ed867919d30500be2d0128b1de1c/coq/Netkat... |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: database_migration_service
using AWS.Compat
using AWS.UUIDs
"""
AddTagsToResource()
Adds metadata tags to an AWS DMS resource, including replication instance, endpoint, security group, and migration task. These tags can also be used w... | {"hexsha": "5d703f036ed123e1882ef2e9f8cc9695ac2de86d", "size": 91969, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/services/database_migration_service.jl", "max_stars_repo_name": "ExpandingMan/AWS.jl", "max_stars_repo_head_hexsha": "8e6e61eb9dcd84fb1e148ff2afe093b3010d9edb", "max_stars_repo_licenses": ["MI... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
**/
//==========================... | {"hexsha": "11b2a290a787beb6f90e28febd5050ae2105a0e8", "size": 4926, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/algorithm/reduce.hpp", "max_stars_repo_name": "xmar/pythran", "max_stars_repo_head_hexsha": "dbf2e8b70ed1e4d4ac6b5f26ead4add940a72592", "max_stars_repo_licenses": ["BSD-3-Clau... |
using SuiteSparseMatrixCollection
using MatrixMarket
using SuiteSparseGraphBLAS
using BenchmarkTools
using SparseArrays
include("tc.jl")
include("pr.jl")
graphs = [
"karate",
"com-Youtube",
"as-Skitter",
"com-LiveJournal",
"com-Orkut",
"com-Friendster",
]
ssmc = ssmc_db()
matrices = filter(row ... | {"hexsha": "9cc2389465d6373d2929db0426efe63021943d12", "size": 962, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/script.jl", "max_stars_repo_name": "Wimmerer/HPEC21-TriangleCentrality", "max_stars_repo_head_hexsha": "dd65a7d1571670b70e444bf46e8c280ae35b754f", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
module UNet
using Flux, Images
using Flux: @treelike
# model
export unet
# utilities
export img2array, array2img, unet_tiling
include("model.jl")
include("utils.jl")
end # module
| {"hexsha": "3f2408761cac59243cdf935547dba7e0eeafa512", "size": 184, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/UNet.jl", "max_stars_repo_name": "CDonnerer/UNet.jl", "max_stars_repo_head_hexsha": "8d34190944f52f7d0beb44140fc32e9259b52db6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
[STATEMENT]
lemma Trgs_are_ide:
shows "Trgs T \<subseteq> Collect R.ide"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Trgs T \<subseteq> Collect R.ide
[PROOF STEP]
apply (induct T)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. Trgs [] \<subseteq> Collect R.ide
2. \<And>a T. Trgs T \<subseteq> Collect R.i... | {"llama_tokens": 288, "file": "ResiduatedTransitionSystem_ResiduatedTransitionSystem", "length": 3} |
import numpy as np
import matplotlib.pyplot as plt
import shutil
import argparse
import os
import json
import random
import warnings
from termcolor import colored
import pandas as pd
from sklearn.metrics import confusion_matrix
import cv2
import importlib
import torch
import torch.nn as nn
import torch.nn.functional ... | {"hexsha": "202ec67999b3abd915bf1dd48c8d109a1cd0c26d", "size": 14439, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer.py", "max_stars_repo_name": "timmyvg/MSBP_Net", "max_stars_repo_head_hexsha": "e2ee6d57ccfc17cd5e5c64e399cbc245281e878d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_st... |
function serverStarted = mrmCheckServer(host)
%Test whether the server has been started.
%
% serverStarted = mrmCheckServer(host)
%
% There should be a way to test without creating a new window. But I am
% not sure how.
%
% (c) Stanford Vista Team, 2008
if ieNotDefined('host'), host = 'localhost'; end
% Try op... | {"author": "vistalab", "repo": "vistasoft", "sha": "7f0102c696c091c858233340cc7e1ab02f064d4c", "save_path": "github-repos/MATLAB/vistalab-vistasoft", "path": "github-repos/MATLAB/vistalab-vistasoft/vistasoft-7f0102c696c091c858233340cc7e1ab02f064d4c/mrMesh/mrm/mrmCheckServer.m"} |
import Base: ==, copy, size, convert
import SparseArrays: sparse
#
# bm: SymmetricBandedMatrix
# bmat: Banded matrix (the field in a SymmetricBandedMatrix object)
# m: Regular matix
# sbm: Semi-banded matrix, e.g.
# sbm = [0 0 1; 0 2 3; 4 5 6]
# hbw: Half bandwidth, h... | {"hexsha": "eee57f033278c3219e15b086d9e713391ba46133", "size": 2882, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nmlib/SymmetricBandedMatrices.jl", "max_stars_repo_name": "PtFEM/NumericalMethodsforEngineers.jl", "max_stars_repo_head_hexsha": "e4a997a14adbb86b7efe1586962df39eb9285ebb", "max_stars_repo_lice... |
# Maze Navigation. Originally proposed in
# Backpropamine: differentiable neuromdulated plasticity.
#
# This code implements the "Grid Maze" task. See Section 4.5 in Miconi et al.
# ICML 2018 ( https://arxiv.org/abs/1804.02464 ), or Section 4.2 in
# Miconi et al. ICLR 2019 ( https://openreview.net/pdf?id=r1lrAiA5Ym )
#... | {"hexsha": "cc711b85d615a221b71e1a017fccdba933f54b98", "size": 6648, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/maze_navigation/maze.py", "max_stars_repo_name": "rcmalli/warpgrad", "max_stars_repo_head_hexsha": "d9ef72af10eec62ae92bc24595cb1a4a0207e319", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import abc
from typing import List
import numpy as np
import pandas as pd
# abstract base class
class TransformationStrategy():
@abc.abstractclassmethod
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
pass
@abc.abstractclassmethod
def get_code(self, df_name: str) -> str:
pass
... | {"hexsha": "ca249b61f93d9968a1155ead8cd906d293685255", "size": 11214, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/BPMN/TransformationStrategy.py", "max_stars_repo_name": "oilyshelf/AutomaModela", "max_stars_repo_head_hexsha": "690bbc51bb21cccf07457d9e5f7bce504800db19", "max_stars_repo_licenses": ["MIT"],... |
// node
#include <node.h> // for NODE_SET_PROTOTYPE_METHOD, etc
#include <node_object_wrap.h> // for ObjectWrap
#include <v8.h>
#include <uv.h>
#include <node_buffer.h>
#include <node_version.h>
// mapnik
#include <mapnik/color.hpp> // for color
#include <mapnik/image_view.... | {"hexsha": "e6825324d8d7d46406bdfc8512a91fe01621666b", "size": 16434, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mapnik_image_view.cpp", "max_stars_repo_name": "calvinmetcalf/node-mapnik", "max_stars_repo_head_hexsha": "3d26f2089dee3cfc901965f6646d50004a0e0e56", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import time
import numpy as np
from datetime import datetime
from Recon import SensorReader, Recon
class Env(object):
def cap(self,x, down, up, ninter):
if x<=down:
x=down
if up<=x:
x=up-1
step=(up-down)/ninter
#print x
return (x-down)//step
d... | {"hexsha": "19f609685e2a2649c0b28a27da87f2da6d1f6327", "size": 3993, "ext": "py", "lang": "Python", "max_stars_repo_path": "Strawry/control/Env.py", "max_stars_repo_name": "Bossabossy/Strawry", "max_stars_repo_head_hexsha": "8ee28138599d258eaa48a625ea929a8b4ccbd639", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
import os
import george
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import gaussian_process
data_dir = '/home/ilya/Dropbox/petya'
data_file = 'Total_rate_vs_Years_v2.txt'
df = pd.read_table(os.path.join(data_dir, data_file), delim_whitespace=True,
names=['exp... | {"hexsha": "2c8b27754088c20c01704ed1c0359c9151a39094", "size": 2698, "ext": "py", "lang": "Python", "max_stars_repo_path": "gp.py", "max_stars_repo_name": "ipashchenko/ra_orbit", "max_stars_repo_head_hexsha": "d58a920b6b185450012770cc4468be9399f2ea5f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
# Linear Algebra, Handling of Arrays and more Python Features
## Introduction
The aim of this set of lectures is to review some central linear algebra algorithms that we will need in our
data analysis part and in the construction of Machine Learning algorithms (ML).
This will allow us to introduce some central prog... | {"hexsha": "729fa16cd7b47b4d580bd65a3830aee8bed70d39", "size": 26602, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/LectureNotes/_build/jupyter_execute/linalg.py", "max_stars_repo_name": "marlgryd/MachineLearning", "max_stars_repo_head_hexsha": "e07439cee1f9e3042aec765754116dccdf8bcf01", "max_stars_repo_li... |
import numpy as np
import argparse
import json
def get_argument_parser():
parser = argparse.ArgumentParser();
parser.add_argument('--data_type', type=str, default='iid',
help='the type of data that needs to be generated')
parser.add_argument('--num_samples', type=int, default=100000... | {"hexsha": "07d393d2f2df9baae4997c1526ca3d956166c2f3", "size": 3042, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data_generation_scripts/generate_sequence_data.py", "max_stars_repo_name": "rajatdiptabiswas/NN_compression", "max_stars_repo_head_hexsha": "1a2650ad897bcc1f32f3b63d0a6477b8f6be6e29", "max_sta... |
#!/usr/bin/env python
# coding: utf-8
# In[4]:
from numpy import sin, pi, sqrt, arccos, log
from pandas import read_excel
e = 1.602e-19 # [C] electron charge
r_p = 0.15e-3 # [m] probe radius
l_p = 1e-3 # [m] probe length
h = 0.5e-3 # [m] Hole radius
s = 0.7e-3 # [m] Rotation center to Hole edge
R = 0.6e-3 # [m] Rot... | {"hexsha": "f65a5260d81742e10ab591a0c83d700dd4d7de7a", "size": 1595, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_Projects/Mach_Probe/Untitled.py", "max_stars_repo_name": "GUNU-GO/SNUPI", "max_stars_repo_head_hexsha": "a73137699d9fc6ae8fa3d1522f341c04d8d43052", "max_stars_repo_licenses": ["MIT"], "max_... |
import time
import numpy as np
start = time.perf_counter()
def get_input():
with open('inputs/test7.txt') as f:
temp = f.readlines()
init = temp[0].split(",")
init[-1] = init[-1].strip("\n")
for i, val in enumerate(init):
init[i] = int(val)
return init
def day7part1(init):
med = np.m... | {"hexsha": "def65e2b4f9c62fd01f370d19512e46394132215", "size": 901, "ext": "py", "lang": "Python", "max_stars_repo_path": "day7.py", "max_stars_repo_name": "gitkoogie/AdventOfCode2021", "max_stars_repo_head_hexsha": "416408c22bc704abc95ed46105d086d08e116e1d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
from typing import Dict, Tuple
import numpy as np
def pad(
img: np.ndarray, target_size: Tuple, pad_value: float = 0.0, targets: Dict = None
):
targets = dict() if targets is None else targets
h, w, c = img.shape
tw, th = target_size
pad_left = int((tw - w) // 2) + (tw - w) % 2
pad_right = i... | {"hexsha": "f54ed3ef55454769cc593cbda83988a6dd9babad", "size": 1017, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastface/transforms/functional/pad.py", "max_stars_repo_name": "mdornseif/fastface", "max_stars_repo_head_hexsha": "72772db1fae4af17e829cd5479c4848fe5eb8948", "max_stars_repo_licenses": ["MIT"], "... |
source("../power_priors_aux.r")
source("../data_Gaussian.r")
gs.data <- list(
N0 = N_0,
y0 = y_0,
mu0 = mu_0,
kappa0 = kappa_0,
alpha0 = alpha_0,
beta0 = beta_0,
a_0 = 1
)
###
get_l_a0_gaussian <- function(y0, n0, alpha0, beta0, m0, k0, a_0){
nstar <- a_0 * n0
ybar <- mean(y0)
s <- mean( (y0-ybar)... | {"hexsha": "f4c74be7be65d7f8bfce867e02e3bf2e7af1f968", "size": 2092, "ext": "r", "lang": "R", "max_stars_repo_path": "code/extra/test_Gaussian_derivative.r", "max_stars_repo_name": "maxbiostat/propriety_power_priors", "max_stars_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_stars_repo_licenses": [... |
#include <yaml-cpp/yaml.h>
#include <boost/filesystem.hpp>
#include "imgui.h"
#include "imgui-SFML.h"
#include <SFML/Graphics.hpp>
#include "tinyfiledialogs.h"
#include "scene/Scene.hpp"
scene::Scene* parseSceneFromFile(const std::string &path) {
try {
return new scene::Scene(YAML::LoadFile(path));
}... | {"hexsha": "b411064f8dc5b1f2b04e2cad84917882141d40d8", "size": 11399, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/main.cpp", "max_stars_repo_name": "r-o-b-o-t-o/cpp-raytracer", "max_stars_repo_head_hexsha": "23f7e483a786f760c8663d9fcaf0ec1627902e38", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
program lonlat_dist
implicit none
c
character*240 cfilea,cfileb,cline
integer ios,l
logical lsum,skip_new,skip_old
real lat1,lat2,lon1,lon2,dist,distmax
real*8 dist_sum
real*4 spherdist
c
c lonlat_dist - Usage: lonlat_dist in.txt out.t... | {"hexsha": "d6295ca0ad776c257e6d8373ab823b8737622cff", "size": 6647, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "bin/lonlat_dist.f", "max_stars_repo_name": "TillRasmussen/HYCOM-tools", "max_stars_repo_head_hexsha": "7d26b60ce65ac9d785e0e36add36aca05c0f496d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Require Import StateType SmallStepRelations Divergence.
Require Import Classical_Prop Coq.Logic.Epsilon.
Set Implicit Arguments.
Lemma three_possibilities S `{StateType S} (σ:S)
: (exists σ', star2 step σ nil σ' /\ normal2 step σ')
\/ (exists σ', star2 step σ nil σ' /\ activated σ')
\/ diverges σ.
Proof.
destruc... | {"author": "sigurdschneider", "repo": "lvc", "sha": "be41194f16495d283fe7bbc982c3393ac554dd5b", "save_path": "github-repos/coq/sigurdschneider-lvc", "path": "github-repos/coq/sigurdschneider-lvc/lvc-be41194f16495d283fe7bbc982c3393ac554dd5b/theories/Equiv/StateTypeProperties.v"} |
#classes and subclasses to import
import cv2
import numpy as np
def blend_transparent(face_img, overlay_t_img):
# Split out the transparency mask from the colour info
overlay_img = overlay_t_img[:,:,:3] # Grab the BRG planes
overlay_mask = overlay_t_img[:,:,3:] # And the alpha plane
# Again calculat... | {"hexsha": "362f2c9978b3b02f595499ad518b85d9529d463a", "size": 3366, "ext": "py", "lang": "Python", "max_stars_repo_path": "snappy_lips.py", "max_stars_repo_name": "ninjakx/Snappy", "max_stars_repo_head_hexsha": "b0289ae7d79d86c875c010972744e06d75f5575d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
# coding=utf-8
import numpy as np
import scipy.stats
from .cobsampler import ChangeOfBasisSampler
class Test(object):
"""
Super class implementing tests for CoBSampler. Sub-classes should specify
target distribution.
"""
def __init__(self, ndim, target, nsteps, cobparams={}):
self.ndim = ... | {"hexsha": "6ff64929cc4dd7af2b28f398df524a10cf507044", "size": 4799, "ext": "py", "lang": "Python", "max_stars_repo_path": "cobmcmc/tests.py", "max_stars_repo_name": "exord/cobmcmc", "max_stars_repo_head_hexsha": "162ba8d4fab35aa44bc8a4828eb51e25df13c4e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
\section{UTxO}
\label{sec:utxo}
\begin{figure*}[htb]
\emph{Derived types}
%
\begin{equation*}
\begin{array}{r@{~\in~}l@{\qquad=\qquad}lr}
\var{uin}
& \UTxOIn
& \TxId \times \Ix
% & \text{transaction output preference}
\\
\var{uout}
& \UTxOOut
& (\TxOutND \union... | {"hexsha": "5d45206e919e0e19320f9c4607ad6dc34e4daadd", "size": 40872, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "goguen/formal-spec/utxo.tex", "max_stars_repo_name": "michaelpj/cardano-ledger-specs", "max_stars_repo_head_hexsha": "d371ad2ebf5d1ddff93776fac2dfd1045aa6b06c", "max_stars_repo_licenses": ["Apache-... |
Require Import PV.Types.
Require Import PV.Nat.
Require Import Coq.Lists.List.
Require Import Coq.Arith.PeanoNat.
Lemma remove_not_in :
forall (a : Type) (xs : list a) (x : a),
forall (dec : DecidableEq a),
~In x xs -> xs = remove dec x xs.
Proof.
intros.
induction xs.
compute. auto.
unfold remove.
... | {"author": "MichaelBurge", "repo": "pornview", "sha": "b4aefdc0e49504aa88345b96710bd86645ab2477", "save_path": "github-repos/coq/MichaelBurge-pornview", "path": "github-repos/coq/MichaelBurge-pornview/pornview-b4aefdc0e49504aa88345b96710bd86645ab2477/PV/Lists.v"} |
from ektelo.dataset import DatasetFromRelation
import numpy as np
from ektelo import support
from ektelo.operators import TransformationOperator
class Vectorize(TransformationOperator):
stability = 1
def __init__(self, name, normed=False, weights=None, reduced_domain=None):
self.name = name
... | {"hexsha": "8caf1406bf79f7d51f58d9a56e3adf62757f78ea", "size": 2598, "ext": "py", "lang": "Python", "max_stars_repo_path": "ektelo/private/transformation.py", "max_stars_repo_name": "dpcomp-org/ektelo", "max_stars_repo_head_hexsha": "7629fbf106f9b9568c66a0b97f6005280022c3d8", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import numpy as np
logistic = lambda z: 1.0 / (1.0 + np.exp(-z))
tanh = lambda z: (np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))
rectifier = lambda z: np.maximum(0.0, z) | {"hexsha": "c6ffd7fba07289bb52f9d4f59a68af4d9adbf44e", "size": 175, "ext": "py", "lang": "Python", "max_stars_repo_path": "titanium/activation.py", "max_stars_repo_name": "MaxNoe/titanium", "max_stars_repo_head_hexsha": "ace635604d29a5607b5005653ef486b5f2fb6b9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
import numpy as np
from math import pi
import os
from src import RDModes, Config, list_tl_files
import matplotlib.pyplot as plt
plt.style.use('elr')
plt.ion()
fc = 400
z_int = 150.
cf = Config(fc=fc)
tl_files = list_tl_files(fc)
tl_data = np.load(tl_files[23])
r_a = tl_data['rplot']
rd_modes = RDModes(tl_data['c_... | {"hexsha": "8836dee62a94a1afd038d3a528c224f6d078537c", "size": 2194, "ext": "py", "lang": "Python", "max_stars_repo_path": "reports/jasa/mode_shapes.py", "max_stars_repo_name": "nedlrichards/tau_decomp", "max_stars_repo_head_hexsha": "77560307836f67ae68f3571fb6cd0fd9d831398d", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 12 12:35:01 2018
@author: abhijay
"""
import numpy as np
import pandas as pd
#import os
#import pickle
import copy
from sklearn import preprocessing
from sklearn import tree
#os.chdir('/home/abhijay/Documents/ML/hw_2/Q10')
class tree_node():
... | {"hexsha": "05f2e7331cb9a34641aecd991068062164844130", "size": 16024, "ext": "py", "lang": "Python", "max_stars_repo_path": "id3.py", "max_stars_repo_name": "abhijayghildyal/id3DecisionTree", "max_stars_repo_head_hexsha": "e6e126b91db6b086af748c34a058a937b74f4b72", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
/*=============================================================================
Copyright (c) 2001-2011 Joel de Guzman
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
====================================... | {"hexsha": "a3b29dabffaa5eb52c77182bf41655c5cb8f7104", "size": 2014, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ios/Pods/boost-for-react-native/boost/fusion/container/list/convert.hpp", "max_stars_repo_name": "rudylee/expo", "max_stars_repo_head_hexsha": "b3e65a7a5b205f14a3eb6cd6fa8d13c8d663b1cc", "max_stars_... |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "06d4a3ce2dd778daad5c8fbb19588bd35c1713b6", "size": 5510, "ext": "py", "lang": "Python", "max_stars_repo_path": "epistasis_selection.py", "max_stars_repo_name": "captaincapsaicin/slip", "max_stars_repo_head_hexsha": "3c112f51cd11118f1e11c0c6fdd8c3d31d304d9b", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""
Extended kalman filter (EKF) localization sample
author: Atsushi Sakai (@Atsushi_twi)
"""
import math
import matplotlib.pyplot as plt
import numpy as np
# Covariance for EKF simulation
Q = np.diag([
0.01, # variance of location on x-axis
0.01, # variance of location on y-axis
np.deg2rad(1.0), #... | {"hexsha": "fc599e63fb4a8ff1c1c5578d6d44ae5325de4344", "size": 5405, "ext": "py", "lang": "Python", "max_stars_repo_path": "Localization/extended_kalman_filter_6_state/extended_kalman_filter.py", "max_stars_repo_name": "MC9529/PythonRobotics", "max_stars_repo_head_hexsha": "e8aef156ccb32186e502576bdf6875475181742b", "m... |
import numpy as np
import pywcs
# **** check that rotang we are using agrees with telescope definition! ****
# -- set geometry for RSS (and write region file)
# should probably have some smarter way of storing these global parameters
pxscale=0.2507/2. # unbinned
dcr=4./60. # radius of field (deg)
#dcr=3.9/60. # ... | {"hexsha": "e50b1ad0c4697a95841d589cd990d358b9b899a9", "size": 9819, "ext": "py", "lang": "Python", "max_stars_repo_path": "proptools/RSS_geom.py", "max_stars_repo_name": "Richard-Tarbell/pysalt", "max_stars_repo_head_hexsha": "2815d5533c7e60b7042f2bc3cf46cecdd38fc609", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
#pragma once
#include <Python.h>
#include <boost/python.hpp>
#include <sys/inotify.h>
#include <blackboard/Adapter.hpp>
#include <types/BehaviourRequest.hpp>
#include <utils/Timer.hpp>
class Blackboard;
#define INBUF_LEN 32 * (sizeof(struct inotify_event) + 16)
class PythonSkill : Adapter {
public:
static... | {"hexsha": "d78f0df8f00e4a25909e3ea49d7777c164469dde", "size": 2015, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Core/External/unsw/unsw/perception/behaviour/python/PythonSkill.hpp", "max_stars_repo_name": "pedrohsreis/boulos", "max_stars_repo_head_hexsha": "a5b68a32cad8cc1fb9f6fbf47fc487ef99d3166e", "max_... |
include("$(pwd())/startup.jl")
#fp = "./ExampleFiles/STOFDATA/" # All files in this path will be processed
fp = "/media/wiebke/Extreme SSD/PSM_vs_PTR3/Data/apiTOFdata/CLOUD10/run1734_02/"
filefilterRegexp = r"\.h5$"
#rf = "./ExampleFiles/STOFDATA/2017-05-24_12h50m39_NH4.h5" # The mass scale from this file defines ... | {"hexsha": "4ca2e1eb5863c711f7f03f21e2b0984d3f33a43e", "size": 1815, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "processingProjects/processingProject-example_STOF.jl", "max_stars_repo_name": "weikou/TOF-Tracer2", "max_stars_repo_head_hexsha": "78406cc829d9903aece2d848960344aa09a263f9", "max_stars_repo_license... |
% !TeX root = article.tex
\section{Description of plasticity in the framework of physics engines}
In this section, key concepts related to the introduced model are explained. The main differences between
traditional structural analysis and physics engines based approaches are reviewed and discussed.
Velocity-based f... | {"hexsha": "5d238740e4b5a484233187b66593d0ee9b5fd7cc", "size": 17549, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "pdocs/thesis/article-section-2.tex", "max_stars_repo_name": "simo-11/bullet3", "max_stars_repo_head_hexsha": "af7753f5d7fbc0030a3abbe43356d9a9ea784a62", "max_stars_repo_licenses": ["Zlib"], "max_st... |
"""
Utility functions for COOT model
"""
import ctypes
import datetime
import logging
import multiprocessing as mp
import os
from pathlib import Path
import random
import sys
from typing import Tuple, Dict
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch import cuda
import torch.nn.funct... | {"hexsha": "5d0095cfb0e4086db7918fa3f2a637440e53c9a7", "size": 11745, "ext": "py", "lang": "Python", "max_stars_repo_path": "gluoncv/torch/utils/coot_utils.py", "max_stars_repo_name": "RafLit/gluon-cv", "max_stars_repo_head_hexsha": "dae504a4de8fff1421fd4fe398accbe396c504cc", "max_stars_repo_licenses": ["Apache-2.0"], ... |
library(data.table)
library(hyperSpec)
source("~/Repositories/PHESANT/summarise_phenotypes.r")
root_file <- "pharma_exomes_parsed_output_100k_chunk"
number_of_chunks <- 10
# Old
phenotype_info_file <- "../variable-info/outcome_info_final_round2.tsv"
# Latest pharma firm variable info file
phenotype_info_file <- "..... | {"hexsha": "1bc8d91f055ad87fd8cfdf6d8cdc406f3e113863", "size": 15751, "ext": "r", "lang": "R", "max_stars_repo_path": "post_PHESANT_exome_pipeline/03_run_summarise_exomes_phenotypes_cloud.r", "max_stars_repo_name": "lganel/PHESANT", "max_stars_repo_head_hexsha": "0f94a3683986b18ca90e20bff0d8bf723bd80211", "max_stars_re... |
(* Copyright (c) 2011. Greg Morrisett, Gang Tan, Joseph Tassarotti,
Jean-Baptiste Tristan, and Edward Gan.
This file is part of RockSalt.
This file is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation;... | {"author": "mpettersson", "repo": "reins-verifier-proof", "sha": "44d0b8e0c29b07eb71b1d6d44b020648783409fb", "save_path": "github-repos/coq/mpettersson-reins-verifier-proof", "path": "github-repos/coq/mpettersson-reins-verifier-proof/reins-verifier-proof-44d0b8e0c29b07eb71b1d6d44b020648783409fb/Model/Decode.v"} |
subroutine system_setup
use systemparams
implicit none
open(3,file = parameter_path, status='unknown')
read(3,*) nx
read(3,*) star_mass
read(3,*) star_lum
read(3,*) a
read(3,*) e
read(3,*) phi_peri
read(3,*) angular_position
read(3,*) period ... | {"hexsha": "317a9414ceb338c950660889c9cd4c336c714951", "size": 1836, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/system_setup.f90", "max_stars_repo_name": "mjdbahram/exoClimate", "max_stars_repo_head_hexsha": "48ae1ea333d8ca117dd736da575fd1f2a55012f4", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
"""
This script works for foam phantom.
"""
import numpy as np
import glob
import dxchange
import matplotlib.pyplot as plt
import scipy.interpolate
import tomopy
from scipy.interpolate import Rbf
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm
from project import *
fro... | {"hexsha": "286679f84960e6ae0f9492ba15fa6e49685d30ea", "size": 916, "ext": "py", "lang": "Python", "max_stars_repo_path": "sampling_demo.py", "max_stars_repo_name": "mdw771/tomosim", "max_stars_repo_head_hexsha": "7736031aee861cd0ac995d83c2231a7df4fc3365", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1... |
import numpy as np
import torch
from lamp.optimization import LearningScheduleWrapper
from bottleneck.components import PredictionEnsemble, Analysis
from bottleneck.VirtualObservables import QuerryPointEnsemble, QuerryEnsemble, VirtualObservablesEnsemble, EnergyVirtualObservablesEnsemble
from torch.utils.tensorboard im... | {"hexsha": "cd53c7598ac16f9139d44000fd288d304f6644fa", "size": 22506, "ext": "py", "lang": "Python", "max_stars_repo_path": "training.py", "max_stars_repo_name": "bdevl/PGMCPC", "max_stars_repo_head_hexsha": "cac2fe4304ae42ef2a0d94219b4349d51e86ab2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars... |
from collections import OrderedDict
import numpy as np
import megengine.functional as F
import megengine.module as M
from megengine import Tensor
from megengine.core._imperative_rt.core2 import apply
from megengine.core.ops import builtin
from megengine.module import Module
from megengine.traced_module import TracedM... | {"hexsha": "43d3f492eb4782364ae0a8cdf347342b5543c20b", "size": 4653, "ext": "py", "lang": "Python", "max_stars_repo_path": "imperative/python/test/unit/traced_module/test_trace_module.py", "max_stars_repo_name": "bealwang/MegEngine", "max_stars_repo_head_hexsha": "df4153dc718b4544e720c58e439a0623c018cee2", "max_stars_r... |
include("../src/QPnorm.jl")
include("../examples/subproblems.jl")
using Main.QPnorm
using Random, Test
function optimality_metrics(P, q, A, b, r_min, r_max, x, λ)
m, n = size(A)
f = dot(x, P*x)/2 + dot(x, q)
grad_residual = norm(P*x + q + A'*λ[1:end-1] + λ[end]*x, Inf)
infeasibility = max(maximum(A*x... | {"hexsha": "5b0669c2647172c63d75f9af23a8f79f4db960ed", "size": 1675, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_solver.jl", "max_stars_repo_name": "oxfordcontrol/QPnorm.jl", "max_stars_repo_head_hexsha": "746eaed3901b47622c0337cde615e82321707eba", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from numpy import exp, array, random, dot
class NeuronLayer():
def __init__(self, number_of_neurons, number_of_inputs_per_neuron):
self.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1
class NeuralNetwork():
def __init__(self, neural_layers):
self.ne... | {"hexsha": "3a489554b2dbcd153d7bbca7b3912d6fc109646b", "size": 4859, "ext": "py", "lang": "Python", "max_stars_repo_path": "talks-articles/machine-learning/toolbox/numpy/multi-layer-neural-network.py", "max_stars_repo_name": "abhishekkr/tutorials_as_code", "max_stars_repo_head_hexsha": "f355dc62a5025b710ac6d4a6ac2f9610... |
[STATEMENT]
lemma b_least2_less_impl_eq: "b_least2 f x y < y \<Longrightarrow> (b_least2 f x y) = (Least (%z. (f x z) \<noteq> 0))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. b_least2 f x y < y \<Longrightarrow> b_least2 f x y = (LEAST z. f x z \<noteq> 0)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1... | {"llama_tokens": 1675, "file": "Recursion-Theory-I_PRecFun", "length": 19} |
C File: test_module_11.f
C Purpose: Illustrates the use of multiple Fortran modules to define values
C for several variables that are used in the program. This program
C differs from test_module_03.f in having modules that have "USE MODULE"s
C inside them , as well as some variables that are declared to be ... | {"hexsha": "cd3d130a57bde8cbb1fb55d9c4b0b0b3bb1473de", "size": 2771, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/data/program_analysis/modules/test_module_11.f", "max_stars_repo_name": "mikiec84/delphi", "max_stars_repo_head_hexsha": "2e517f21e76e334c7dfb14325d25879ddf26d10d", "max_stars_repo_licenses"... |
using Clang
GAGE_INCLUDE = raw"C:\Program Files (x86)\Gage\CompuScope\include"
clang_includes = String[]
push!(clang_includes, GAGE_INCLUDE)
push!(clang_includes, raw"C:\Program Files\LLVM\include\clang-c",raw"C:\Program Files\LLVM\include\llvm-c" )
clang_extraargs = ["-v"]
clang_extraargs = ["-D", "__STDC_CONSTANT_M... | {"hexsha": "b2cb3670ab17ada559cbc64525999e42315e8e36", "size": 966, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gage_clang_gen.jl", "max_stars_repo_name": "jarrison/Gage.jl", "max_stars_repo_head_hexsha": "b8f05666721210e272aea9737136066a59c4d65d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
/*** Copyright (c), The Regents of the University of California ***
*** For more information please refer to files in the COPYRIGHT directory ***/
/*
ICAT test program.
*/
#include "rodsClient.h"
#include "parseCommandLine.h"
#include "readServerConfig.hpp"
#include "irods_server_properties.hpp"
#includ... | {"hexsha": "9c8f8046b8d59ac6019029487b7fef8c8a40fd18", "size": 35651, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "iRODS/server/test/src/test_chl.cpp", "max_stars_repo_name": "iychoi/cyverse-irods", "max_stars_repo_head_hexsha": "0070b8677a82e763f1d940ae6537b1c8839a628a", "max_stars_repo_licenses": ["BSD-3-Clau... |
[STATEMENT]
lemma connected_trans:
assumes u_v: "u \<rightarrow>\<^sup>* v" and v_w: "v \<rightarrow>\<^sup>* w"
shows "u \<rightarrow>\<^sup>* w"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. u \<rightarrow>\<^sup>* w
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. u \<rightarrow>\<^sup>*... | {"llama_tokens": 1799, "file": "Tree_Decomposition_Graph", "length": 22} |
-makelib ies_lib/xilinx_vip -sv \
"B:/Xilinx/Vivado/2018.3/data/xilinx_vip/hdl/axi4stream_vip_axi4streampc.sv" \
"B:/Xilinx/Vivado/2018.3/data/xilinx_vip/hdl/axi_vip_axi4pc.sv" \
"B:/Xilinx/Vivado/2018.3/data/xilinx_vip/hdl/xil_common_vip_pkg.sv" \
"B:/Xilinx/Vivado/2018.3/data/xilinx_vip/hdl/axi4stream_vip_pkg... | {"hexsha": "28944f8b0b48a6b06fe06baa23491a991a8ae68e", "size": 3470, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "CNN_HW.ip_user_files/sim_scripts/CNN_top_module/ies/run.f", "max_stars_repo_name": "awe777/CNN_HW", "max_stars_repo_head_hexsha": "c677e5969c235aa8f5f9cf34da71a5fbe61dcd90", "max_stars_repo_licens... |
from __future__ import print_function
from asdl.transition_system import GenTokenAction, TransitionSystem, ApplyRuleAction, ReduceAction,score_acts
import sys, traceback
import numpy as np
from common.registerable import Registrable
import tqdm
cachepredict=[]
cachetrue=[]
from dependency import nlp
from nltk.tree impo... | {"hexsha": "08a6035c2219b4f1db6b7180acba5e960978d7f4", "size": 5862, "ext": "py", "lang": "Python", "max_stars_repo_path": "components/evaluator.py", "max_stars_repo_name": "tomsonsgs/TRAN-MMA-master", "max_stars_repo_head_hexsha": "91bf927c64a8d813ba60ae12e61e8f44830a82cc", "max_stars_repo_licenses": ["Apache-2.0"], "... |
## Import the required modules
# Check time required
import time
time_start = time.time()
import sys
import os
import argparse as ap
import math
import imageio
from moviepy.editor import *
import numpy as np
sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread, imsave, imresize
from s... | {"hexsha": "3ddc11ef37d843205813df5fc7a5913a0a68982e", "size": 12910, "ext": "py", "lang": "Python", "max_stars_repo_path": "video_tracking_sort.py", "max_stars_repo_name": "hiepnth/people-counting-pose", "max_stars_repo_head_hexsha": "8cdaab5281847c296b305643842053d496e2e4e8", "max_stars_repo_licenses": ["Apache-2.0"]... |
#!/usr/bin/env python3
""" 2D Ising simulator using Metropolis algorithm
Author: Akhlak Mahmood
License: MIT
Last update: April 18, 2019
"""
## Import modules
# -------------------------------------------
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from... | {"hexsha": "a51625211ec86631f5232753a03b2bbd22be3764", "size": 9026, "ext": "py", "lang": "Python", "max_stars_repo_path": "ising/ising.py", "max_stars_repo_name": "akhlak-mahmood/ising-model-2d", "max_stars_repo_head_hexsha": "69739ef250e5719a6d3f8dccf49e4d81828aaa80", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import json
import time
import random
import warnings
from typing import Union, Callable, Tuple, Any
from types import MethodType
try:
import h5py
except ModuleNotFoundError:
h5py = None
import joblib
import matplotlib # for version info
import numpy as np
import pandas as pd
try:
from scipy.s... | {"hexsha": "a1e0fb4188617c3fd337d5335d7314541708898f", "size": 67256, "ext": "py", "lang": "Python", "max_stars_repo_path": "ai4water/_main.py", "max_stars_repo_name": "csiro-hydroinformatics/AI4Water", "max_stars_repo_head_hexsha": "cdb18bd4bf298f77b381f1829045a1e790146985", "max_stars_repo_licenses": ["MIT"], "max_st... |
# File to check that the two different action-value functions (MC estimate and the action-value function in the
# estimated MDP) are actually different functions, see Section 3.2.2 in "Evaluation of Safe Policy Improvement with
# Soft Baseline Bootstrapping" by Philipp Scholl.
import os
import sys
import numpy as np
im... | {"hexsha": "6ac2e55f4721af50cd9966b7c2a9c848e1a0bd45", "size": 3356, "ext": "py", "lang": "Python", "max_stars_repo_path": "auxiliary_tests/difference_in_action_value_functions.py", "max_stars_repo_name": "Philipp238/Safe-Policy-Improvement-Approaches-on-Discrete-Markov-Decision-Processes", "max_stars_repo_head_hexsha"... |
#include <boost/asio.hpp>
#include <string>
#include <sstream>
using namespace std;
namespace net {
namespace {
boost::asio::ip::tcp::iostream net;
}
void connect(const string& addr, int port) {
ostringstream oss;
oss<<addr<<':'<<port;
net.connect(oss.str());
}
void sendRaw(const string& data) {
net<<data;
net.... | {"hexsha": "f42aaf5f70a02e4ba32e302b1e82b7076ea17d8e", "size": 413, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "net.cpp", "max_stars_repo_name": "sisu/lib24", "max_stars_repo_head_hexsha": "b1aefdfa37a2e3d9b0b7adf9154a86afbde5f461", "max_stars_repo_licenses": ["WTFPL"], "max_stars_count": 1.0, "max_stars_repo_... |
from datetime import datetime
import os
from urllib.request import urlopen
from PIL import Image
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
filename = 'model.pb'
labels_filename = 'labels.txt'
output_layer = 'loss:0'
input_node = 'Placeholder:0'
graph_def = tf.GraphDef()
labels = []... | {"hexsha": "bbd53ce61e904fcbf979a216317965a1cb54b52b", "size": 4353, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict_helpers.py", "max_stars_repo_name": "anthonychu/hackthenorth-ml-workshop", "max_stars_repo_head_hexsha": "849cbbac34cd9e9c0ec1b14d39ca8e598c8b14ce", "max_stars_repo_licenses": ["MIT"], "ma... |
! Copyright (c) 2015-2021, the ELSI team.
! All rights reserved.
!
! This file is part of ELSI and is distributed under the BSD 3-clause license,
! which may be found in the LICENSE file in the ELSI root directory.
!>
!! Determine occupation numbers, chemical potential, and electronic entropy.
!!
module ELSI_OCC
u... | {"hexsha": "2b61c6dcb37dd9a03879a64da0ead195980bd231", "size": 23275, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/elsi_occ.f90", "max_stars_repo_name": "ElectronicStructureLibrary/elsi-interface", "max_stars_repo_head_hexsha": "95d2b02ca627e08eea52eea8358fdb44ab0e67e3", "max_stars_repo_licenses": ["BSD... |
from ctypes import *
import numpy as np
import numpy.ctypeslib as npct
import os
import sys
import glob
version = str(sys.version_info.major)+str(sys.version_info.minor)
class Pos(Structure):
_fields_ = [('x', c_float), ('y', c_float), ('z', c_float)]
# load the library, using numpy mechanisms
_libdir = os.path.... | {"hexsha": "acb441f05e46f267025bcaef2daa36d67bb93a07", "size": 2697, "ext": "py", "lang": "Python", "max_stars_repo_path": "CosmAna/Ext_C/libgrid/Loadlibgrid.py", "max_stars_repo_name": "POFK/CosmAna", "max_stars_repo_head_hexsha": "153af155d243e38f64b8bdf79abc496163269219", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import numpy.linalg as LA
import control
class KalmanFilter:
"""
Kalman Filter for a linear system
Parameters
----------
F: State Transition (A matrix)
H: Observation Model (B matrix)
Q: Process Covariance
R: Observation Covariance
B: Input Model (Optional)
... | {"hexsha": "180f289c032f22659f65e17153c51c4359a2fe1f", "size": 2202, "ext": "py", "lang": "Python", "max_stars_repo_path": "estimation/KalmanFilter.py", "max_stars_repo_name": "andrelimzs/state-estimation", "max_stars_repo_head_hexsha": "57fa0682bb0933200510ed5dc27d09851d39f3b1", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <rem_tree/rem_tree.h>
#include <iostream>
#include <chrono>
#include <NTL/ZZ.h>
#include <NTL/vector.h>
using namespace std;
using namespace NTL;
ZZ startFunc(ZZ modProd);
int main(){
int startBound = 10;
int endBound = 20;
Vec<ZZ> A;
A.setLength(endBound);
Vec<ZZ> m;
m.setLength(startBound);
for(i... | {"hexsha": "48b80de7463a3bfa6e64ac2fec9159cffda3b339", "size": 779, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "archives/cppfiles/rem_tree/main.cpp", "max_stars_repo_name": "adienes/remainder-tree", "max_stars_repo_head_hexsha": "0aa76214ab6f2a4389ec45a239ea660749989a90", "max_stars_repo_licenses": ["MIT"], "m... |
# coding: utf-8
# # Time Optimal Velocity Profiles
#
# When the maze solver commands that the robot go forward, it can say that it must go forward one or more squares depending on what it knows about the maze. When we don't know what is after the square we pass through, we must be going slow enough to handle any sce... | {"hexsha": "72a46ee4fc3d7611f74be024ef511b2bdc07056c", "size": 7998, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/ipynb_gen_py/time optimal velocity profiles.py", "max_stars_repo_name": "keionbis/SmartMouse_2018", "max_stars_repo_head_hexsha": "26d548a93c282bca8d550b58609f22ea1910fc78", "max_stars_repo_l... |
! RUN: bbc -emit-fir %s -o - | FileCheck %s
! CHECK-LABEL: func @_QPtest1(
! CHECK-SAME: %[[VAL_0:.*]]: !fir.ref<!fir.array<100xf32>>{{.*}}, %[[VAL_1:.*]]: !fir.ref<i32>{{.*}}, %[[VAL_2:.*]]: !fir.ref<i32>{{.*}}, %[[VAL_3:.*]]: !fir.ref<i32>{{.*}}) {
! CHECK: %[[VAL_4:.*]] = arith.constant 100 : index
! CH... | {"hexsha": "3fb34b39ea35367d2bf00409ddb248976bf813f4", "size": 2274, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "flang/test/Lower/associate-construct-2.f90", "max_stars_repo_name": "akashhansda/llvm-project", "max_stars_repo_head_hexsha": "32f146010968ded160f54af464673451ad574135", "max_stars_repo_licenses... |
# NMF for dense matrices
using NMF
function run(algname)
# prepare data
p = 8
k = 5
n = 100
Wg = abs(randn(p, k))
Hg = abs(randn(k, n))
X = Wg * Hg + 0.1 * randn(p, n)
# run NNMF
println("Algorithm: $(algname)")
println("---------------------------------")
r = nnmf(X, k... | {"hexsha": "ac6b1824c0e48a5f910bc15fa0867a617259cb2b", "size": 1461, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/densenmf.jl", "max_stars_repo_name": "UnofficialJuliaMirror/NMF.jl-6ef6ca0d-6ad7-5ff6-b225-e928bfa0a386", "max_stars_repo_head_hexsha": "d753312f4686578879fdeae009ba1250102b1e1f", "max_sta... |
"""
Code for "Invertible Residual Networks"
http://proceedings.mlr.press/v97/behrmann19a.html
ICML, 2019
"""
import threading
import logging
import contextlib
import numpy
import torch
import termcolor
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils... | {"hexsha": "37c1174626f8417520492303350131821f7a3921", "size": 22819, "ext": "py", "lang": "Python", "max_stars_repo_path": "CIFAR_main.py", "max_stars_repo_name": "ohadoh-math/invertible-resnet", "max_stars_repo_head_hexsha": "4f05b9d1761c2d46cc05d9748ef3e690f8b9c0b2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import cv2
import numpy as np
import pandas as pd
from torchvision.transforms import transforms
from torch.utils.data import Dataset
from datasets.base_dataset import BaseDataset
from utils.augmenters.augment import seg
import xml.etree.ElementTree as ET
from PIL import Image
import matplotlib.pyplot as plt
... | {"hexsha": "5ddc9b6b9f37f411ee3ce8dae34f97bf191bb248", "size": 3065, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/CARLA_dataset.py", "max_stars_repo_name": "And1210/AutoencoderTransformer", "max_stars_repo_head_hexsha": "9c6142421d311d34f6a00cb90dd49388e5f1cdff", "max_stars_repo_licenses": ["MIT"], "... |
from decimal import Decimal
import argparse
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import graph_utils
import numpy as np
import graph_utils
import process_csv
import sys
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('--input-file', dest='input_files', ... | {"hexsha": "786d97d2ef18ac0971363c4909806a1a5ff8fb51", "size": 7252, "ext": "py", "lang": "Python", "max_stars_repo_path": "ipg_distribution_graph.py", "max_stars_repo_name": "j-c-w/EXPCAP_Process", "max_stars_repo_head_hexsha": "33ec7f6fdc8e794261a293d7bba0225b76606012", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import numpy as np
from pymongo.errors import DuplicateKeyError
from huntsman.drp.utils import mongo
from huntsman.drp.utils.ingest import METRIC_SUCCESS_FLAG
from huntsman.drp.utils.date import parse_date
from huntsman.drp.utils.fits import read_fits_data, read_fits_header, parse_fits_header
from huntsman.drp.collec... | {"hexsha": "37869bfc04280b9092a0054512078913663a0db5", "size": 10335, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/huntsman/drp/collection/exposure.py", "max_stars_repo_name": "AstroHuntsman/huntsman-drp", "max_stars_repo_head_hexsha": "00f045ccccc1f7545da491457a2b17b9aabea89a", "max_stars_repo_licenses":... |
##################################################################
#--------------- Error stats for saved data ---------------
# (T. Kent: amttk@leeds.ac.uk)
##################################################################
## generic modules
import os
import errno
import numpy as np
import matplot... | {"hexsha": "9c9c2abd255d8b07967ea55699497eaf29ff141b", "size": 10395, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_diag_stats.py", "max_stars_repo_name": "tkent198/modRSW_EnKF", "max_stars_repo_head_hexsha": "fc9f0bcc6f753a05fed245d4d2987cd3a34078ad", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from lassolver.matrices.base import Base
class iidGaussian(Base):
def __init__(self, M, N, m=0, v=1):
super().__init__(M, N)
self.A = self.set_matrix(M, N, m, v)
def set_matrix(self, row, column, mean, var):
"""
Return i.i.d(independent and identically distri... | {"hexsha": "d7289c2aa7f58733a0aed36a4f86d9c75f3e89ba", "size": 418, "ext": "py", "lang": "Python", "max_stars_repo_path": "lassolver/matrices/iid_gauss.py", "max_stars_repo_name": "Ken529n/Lassolver", "max_stars_repo_head_hexsha": "f9f6997bf065622fe462b329c5cc99bd20f7d68b", "max_stars_repo_licenses": ["MIT"], "max_star... |
'''
Script for training DFN on self-driving data...
TODO: iterative pruning method proposed by Han 2015
'''
import os
import numpy as np
import argparse
from keras.models import Model, load_model
from keras import optimizers
from keras.callbacks import EarlyStopping, ModelCheckpoint, Callback
from DataGenerator import ... | {"hexsha": "824201cbbe0665b08f16d90041853fcb63b4790f", "size": 3845, "ext": "py", "lang": "Python", "max_stars_repo_path": "transfer/train-dfn.py", "max_stars_repo_name": "paulaksm/feature-transfer", "max_stars_repo_head_hexsha": "0814d1e38691f67d6aa1637235df0b152d0a4b05", "max_stars_repo_licenses": ["MIT"], "max_stars... |
using Libdl, Test, OpenBLAS_jll, OpenBLAS32_jll, MKL_jll
include("utils.jl")
function unpack_loaded_libraries(config::lbt_config_t)
libs = LBTLibraryInfo[]
idx = 1
lib_ptr = unsafe_load(config.loaded_libs, idx)
while lib_ptr != C_NULL
push!(libs, LBTLibraryInfo(unsafe_load(lib_ptr), config.num... | {"hexsha": "401a3273e50363095db2c5db5f7248bf2dc9fcb8", "size": 11629, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/direct.jl", "max_stars_repo_name": "JuliaLinearAlgebra/libblastrampoline", "max_stars_repo_head_hexsha": "145bb64256c441d11b0a742e38f9ef3f08921e8e", "max_stars_repo_licenses": ["MIT"], "max_s... |
#######################################################################
import JSON, Conda
using Compat
using Compat.Unicode: lowercase
jupyter=""
# remove deps.jl at exit if it exists, in case build.jl fails
try
#######################################################################
# Make sure Python uses UTF-8 ou... | {"hexsha": "4769e8e506ec1b0f8ec453a9ba309c8260cfc7bb", "size": 4835, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/build.jl", "max_stars_repo_name": "jordancluts/IJulia.jl", "max_stars_repo_head_hexsha": "2211a9c9b6821429254e88c60c6bb1d3c357e863", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
"""
KorQuAD open 형 데이터 processor
본 스크립트는 다음의 파일을 바탕으로 작성 됨
https://github.com/huggingface/transformers/blob/master/src/transformers/data/processors/squad.py
"""
import json
import logging
import os
import sys
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm impo... | {"hexsha": "8a20e48b98a3915cafdd612d4439d5db6d41d3fc", "size": 31335, "ext": "py", "lang": "Python", "max_stars_repo_path": "open_squad_multihead.py", "max_stars_repo_name": "JongSuk1/KorQuad", "max_stars_repo_head_hexsha": "757dccb3cfee887692ec9ee7eec5b9d91b0af5d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import numpy as np
import unittest
import networkx as nx
from numpy.testing import assert_allclose
from graphik.graphs import ProblemGraphRevolute
from graphik.robots.robot_base import Robot
from graphik.robots import RobotRevolute
from graphik.solvers.constraints import get_full_revolute_nearest_point
from graphik.so... | {"hexsha": "655b6260d48553e89ec4487bf28f6010af183ba7", "size": 9444, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_sdp_snl.py", "max_stars_repo_name": "utiasSTARS/GraphIK", "max_stars_repo_head_hexsha": "c2d05386bf9f9baf8ad146125bfebc3b73fccd14", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module TestTUI
#=
Remaining problems:
- bug in TerminalUserInterfaces.terminal_size()
- printing of outputs is cut off. See `paragraph.jl` in TerminalUserInterfaces
ToDo:
- also track files in `test` directory
=#
using Glob
using Revise
using Parameters
using Pkg
using Suppressor
using TerminalUserInterfaces
const TU... | {"hexsha": "0cafe92d9939861400c561f003951f99aa957c3b", "size": 2232, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TestTUI.jl", "max_stars_repo_name": "lorenzoh/TestTUI.jl", "max_stars_repo_head_hexsha": "071577764a32d5dad0160dc13307d39582c123e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
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