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@article{FENG2018,
    author = {Feng, Yangqin and Zhang, Lei and Yi, Zhang},
    doi = {10.1007/s11548-017-1663-9},
    journal = {International Journal of Computer Assisted Radiology and Surgery},
    month = {feb},
    number = {2},
    pages = {179--191},
    title = {{Breast cancer cell nuclei classification in histopathology images using deep neural networks}},
    volume = {13},
    year = {2018}
}

@article{OLIVEIRA2018,
    author = {Oliveira, Luiz Eduardo and Heutte, Laurent and Honeine, Paul and Sudharshan, PJ J. and Spanhol, Fabio and Petitjean, Caroline and Spanhol, Fabio and Oliveira, Luiz Eduardo and Heutte, Laurent and Honeine, Paul},
    doi = {10.1016/j.eswa.2018.09.049},
    journal = {Expert Systems with Applications},
    month = {mar},
    pages = {103--111},
    title = {{Multiple instance learning for histopathological breast cancer image classification}},
    volume = {117},
    year = {2018}
}

@article{GUPTA2018,
    author = {Gupta, Vibha and Bhavsar, Arnav},
    doi = {10.1109/CVPRW.2018.00302},
    journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
    pages = {2335--2342},
    title = {{Sequential modeling of deep features for breast cancer histopathological image classification}},
    volume = {2018-June},
    year = {2018}
}

@article{GANDOMKAR2018a,
    author = {Gandomkar, Ziba and Brennan, Patrick C. and Mello-Thoms, Claudia},
    doi = {10.1016/j.artmed.2018.04.005},
    journal = {Artificial Intelligence in Medicine},
    month = {jun},
    pages = {14--24},
    title = {{MuDeRN: Multi-category classification of breast histopathological image using deep residual networks}},
    volume = {88},
    year = {2018}
}

@article{SAHA2018a,
    author = {Saha, Monjoy and Chakraborty, Chandan and Racoceanu, Daniel},
    doi = {10.1016/j.compmedimag.2017.12.001},
    journal = {Computerized Medical Imaging and Graphics},
    month = {mar},
    pages = {29--40},
    title = {{Efficient deep learning model for mitosis detection using breast histopathology images}},
    volume = {64},
    year = {2018}
}

@article{FONDON2018,
    author = {Fond{\'{o}}n, Irene and Sarmiento, Auxiliadora and Garc{\'{i}}a, Ana Isabel and Silvestre, Mar{\'{i}}a and Eloy, Catarina and Pol{\'{o}}nia, Ant{\'{o}}nio and Aguiar, Paulo},
    doi = {10.1016/j.compbiomed.2018.03.003},
    journal = {Computers in Biology and Medicine},
    month = {may},
    pages = {41--51},
    title = {{Automatic classification of tissue malignancy for breast carcinoma diagnosis}},
    volume = {96},
    year = {2018}
}

@article{GANDOMKAR2018b,
    author = {Gandomkar, Ziba and Brennan, Patrick C. and Mello-Thoms, Claudia},
    doi = {10.1117/12.2318320},
    journal = {14th International Workshop on Breast Imaging},
    pages = {54},
    title = {{A framework for distinguishing benign from malignant breast histopathological images using deep residual networks}},
    year = {2018}
}

@article{BARDOU2018,
    author = {Bardou, Dalal and Zhang, Kun and Ahmad, Sayed Mohammad},
    doi = {10.1109/ACCESS.2018.2831280},
    journal = {IEEE Access},
    month = {jun},
    number = {6},
    pages = {24680--24693},
    title = {{Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks}},
    volume = {6},
    year = {2018}
}

@article{ZHENG2018,
    author = {Zheng, Yushan and Jiang, Zhiguo and Zhang, Haopeng and Xie, Fengying and Ma, Yibing and Shi, Huaqiang and Zhao, Yu},
    doi = {10.1109/TMI.2018.2796130},
    journal = {IEEE Transactions on Medical Imaging},
    number = {7},
    pages = {1641--1652},
    title = {{Histopathological Whole Slide Image Analysis Using Context-Based CBIR}},
    volume = {37},
    year = {2018}
}

@article{LI2018,
    author = {Li, Chao and Wang, Xinggang and Liu, Wenyu and Latecki, Longin Jan},
    doi = {10.1016/j.media.2017.12.002},
    journal = {Medical Image Analysis},
    month = {apr},
    pages = {121--133},
    title = {{DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks}},
    volume = {45},
    year = {2018}
}

@inproceedings{BAKER2018,
    author = {Baker, Qanita Bani and Zaitoun, Toqa Abu and Banat, Sajda and Eaydat, Eman and Alsmirat, Mohammad},
    booktitle = {15th International Conference on Computer Systems and Applications},
    doi = {10.1109/AICCSA.2018.8612799},
    month = {oct},
    pages = {1--5},
    publisher = {IEEE},
    title = {{Automated Detection of Benign and Malignant in Breast Histopathology Images}},
    url = {https://ieeexplore.ieee.org/document/8612799/},
    year = {2018}
}

@article{GECER2018,
    author = {Gecer, Baris and Aksoy, Selim and Mercan, Ezgi and Shapiro, Linda G. and Weaver, Donald L. and Elmore, Joann G.},
    doi = {10.1016/j.patcog.2018.07.022},
    journal = {Pattern Recognition},
    month = {dec},
    pages = {345--356},
    title = {{Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks}},
    volume = {84},
    year = {2018}
}

@article{XIE2019,
    author = {Xie, Juanying and Liu, Ran and Luttrell, Joseph and Zhang, Chaoyang},
    doi = {10.3389/fgene.2019.00080},
    journal = {Frontiers in Genetics},
    month = {feb},
    pages = {80},
    title = {{Deep Learning Based Analysis of Histopathological Images of Breast Cancer}},
    volume = {10},
    year = {2019}
}

@article{ROY2019,
    author = {Roy, Kaushiki and Banik, Debapriya and Bhattacharjee, Debotosh and Nasipuri, Mita},
    doi = {10.1016/j.compmedimag.2018.11.003},
    journal = {Computerized Medical Imaging and Graphics},
    month = {jan},
    pages = {90--103},
    title = {{Patch-based system for Classification of Breast Histology images using deep learning}},
    volume = {71},
    year = {2019}
}

@article{ALOM2019,
    author = {Alom, Md Zahangir and Yakopcic, Chris and Nasrin, Mst. Shamima and Taha, Tarek M. and Asari, Vijayan K.},
    doi = {10.1007/s10278-019-00182-7},
    journal = {Journal of Digital Imaging},
    keywords = {Breast cancer recognition,Computational pathology,DCNN,Deep learning,IRRCNN,Medical imaging},
    month = {feb},
    title = {{Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network}},
    year = {2019}
}

@article{MITTAL2019,
    author = {Mittal, Himanshu and Saraswat, Mukesh},
    doi = {10.1016/j.swevo.2018.12.005},
    journal = {Swarm and Evolutionary Computation},
    pages = {15--32},
    title = {{An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering}},
    volume = {45},
    year = {2019}
}

@article{ALQUDAH2019,
    author = {Alqudah, Ali Mohammad Amin and Alqudah, Ali Mohammad Amin},
    doi = {10.1080/03772063.2019.1583610},
    journal = {IETE Journal of Research},
    month = {mar},
    pages = {1--9},
    title = {{Sliding Window Based Support Vector Machine System for Classification of Breast Cancer Using Histopathological Microscopic Images}},
    year = {2019}
}

@article{YAN2019,
    author = {Yan, Rui and Ren, Fei and Wang, Zihao and Wang, Lihua and Ren, Yubo and Liu, Yudong and Rao, Xiaosong and Zheng, Chunhou and Zhang, Fa},
    doi = {10.1109/BIBM.2018.8621429},
    journal = {IEEE International Conference on Bioinformatics and Biomedicine},
    pages = {957--962},
    title = {{A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification}},
    year = {2019}
}

@article{MURTAZA2019,
    author = {Murtaza, Ghulam and Shuib, Liyana and Mujtaba, Ghulam and Raza, Ghulam},
    doi = {10.1007/s11042-019-7525-4},
    journal = {Multimedia Tools and Applications},
    month = {apr},
    title = {{Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach}},
    year = {2019}
}

@article{DAS2019,
    author = {Das, Dev Kumar and Dutta, Pranab Kumar},
    doi = {10.1016/j.compbiomed.2018.11.001},
    journal = {Computers in Biology and Medicine},
    month = {jan},
    number = {May 2018},
    pages = {29--42},
    title = {{Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches}},
    volume = {104},
    year = {2019}
}

@article{GHAZVINIANZANJANI2019,
    author = {{Ghazvinian Zanjani}, Farhad and Zinger, Svitlana and Piepers, Bastian and Mahmoudpour, Saeed and Schelkens, Peter},
    doi = {10.1117/1.jmi.6.2.027501},
    journal = {Journal of Medical Imaging},
    number = {02},
    pages = {1},
    title = {{Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images}},
    volume = {6},
    year = {2019}
}

@article{VO2019,
    author = {Vo, Duc My and Nguyen, Ngoc Quang and Lee, Sang Woong},
    doi = {10.1016/j.ins.2018.12.089},
    journal = {Information Sciences},
    month = {may},
    pages = {123--138},
    title = {{Classification of breast cancer histology images using incremental boosting convolution networks}},
    volume = {482},
    year = {2019}
}

@article{SABEENABEEVI2019,
    author = {{Sabeena Beevi}, K. and Nair, Madhu S. and Bindu, G. R.},
    doi = {10.1016/j.bbe.2018.10.007},
    journal = {Biocybernetics and Biomedical Engineering},
    number = {1},
    pages = {214--223},
    title = {{Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning}},
    volume = {39},
    year = {2019}
}

@article{GUO2019,
    author = {Guo, Zichao and Liu, Hong and Ni, Haomiao and Wang, Xiangdong and Su, Mingming and Guo, Wei and Wang, Kuansong and Jiang, Taijiao and Qian, Yueliang},
    doi = {10.1038/s41598-018-37492-9},
    journal = {Scientific Reports},
    month = {dec},
    number = {1},
    pages = {882},
    title = {{A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images}},
    volume = {9},
    year = {2019}
}

@article{LI2019,
    author = {Li, Yuqian and Wu, Junmin and Wu, Qisong},
    doi = {10.1109/ACCESS.2019.2898044},
    journal = {IEEE Access},
    pages = {1--1},
    title = {{Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning}},
    volume = {7},
    year = {2019}
}

repository: eiBrunaSilva/LaTeX

repo_name: eiBrunaSilva/LaTeX


text:
package main

import (
	"fmt"
	"math/rand"
	"time"
)

const width, height, elevation = 16, 16, 9

// we set the random chance of a peak occuring to 5%
const peakProbability = 5

var fullMap [height][width]int

func main() {
	// rand needs to be seeded, so we set the current
	// nanosecond timestamp as the seed
	rand.Seed(time.Now().UnixNano())

	// iterate down from max elevation, assigning vals
	for e := elevation; e > 0; e-- {
		for h := 0; h < height; h++ {
			for w := 0; w < width; w++ {
				// if the element has already been
				// assigned, skip it
				if fullMap[h][w] > 0 {
					continue
				}

				// if the random value meets our criteria,
				// it's a peak
				if rand.Intn(100) < peakProbability {
					fullMap[h][w] = e
				}
			}
		}
	}

	// print out map
	for h := 0; h < height; h++ {
		fmt.Println(fullMap[h])
	}
}

license: MIT

repository: janithl/go-terrain-gen

license_type: permissive

repo_name: janithl/go-terrain-gen

branch_name: refs/heads/master

extension: go


text:
@InProceedings{heller09a,
  title = {Infinite Hierarchical Hidden Markov Models},
  author = {Katherine Heller and Yee Whye Teh and Dilan Gorur},
  pages = {224--231},
  abstract = {In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowing a potentially unbounded number of levels in the hierarchy, instead of requiring the specification of a fixed hierarchy depth.  Inference and learning are performed efficiently using Gibbs sampling and a modified forward-backtrack algorithm. We show encouraging demonstrations of the workings of the IHHMM.},
  pdf = {http://jmlr.org/proceedings/papers/v5/heller09a/heller09a.pdf},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_license_id: MIT

gha_language: TeX

extension: bib


text:
package main

import (
  "fmt"
)

func main() {
  fmt.Println("Welcome to the GO!")
  // variables
  // c := add(5, 6)
  // fmt.Println(c)

  //for
  // loop1()
  // loop2()
  // loop3()
  // whatday(4)
  // arr1()
  // arr2()
  // matrix()
  // imatrix(4)
  // slice1()
  // slice3()
  // varidaic()
  // sortDemo()
  // StructDemo()
  // readAFile()
  // read3()
  // write2()
  // mapdemo()
  // methoddemo()
  // interfacedemo()
  // goroutinedemo()
  channeldemo()
}


text:
% Encoding: UTF-8
@Article{Manski.2019,
  author    = {Manski, Charles F.},
  title     = {Communicating uncertainty in policy analysis},
  journal   = {Proceedings of the National Academy of Sciences},
  year      = {2019},
  volume    = {116},
  number    = {16},
  pages     = {7634--7641},
}

@Article{Kucherenko.2012,
  author    = {Kucherenko, Sergei and Tarantola, Stefano and Annoni, Paola},
  title     = {Estimation of global sensitivity indices for models with dependent variables},
  journal   = {Computer Physics Communications},
  year      = {2012},
  volume    = {183},
  number    = {4},
  pages     = {937--946},
}

@Book{Oberkampf.2010,
  author    = {Oberkampf, William L. and Roy, Christopher J.},
  title     = {Verification and validation in scientific computing},
  year      = {2010},
  publisher = {Cambridge University Press},
  address   = {Cambridge, UK},
}

@Book{Adams.2012,
  editor    = {Adams, Marvin L.},
  title     = {Assessing the reliability of complex models: {M}athematical and statistical
  foundations of verification, validation, and uncertainty quantification},
  year      = {2012},
  publisher = {National Academies Press},
  address   = {Washington, D.C.},
}

@Book{Wolpin.2013,
  author    = {Wolpin, Kenneth I.},
  title     = {The limits to inference without theory},
  year      = {2013},
  publisher = {MIT University Press},
  address   = {Cambridge, MA},
}

@Article{Sobol.1993,
  author  = {I. Sobol},
  title   = {On sensitivity estimation for nonlinear mathematical models},
  journal = {Math. Modelling \& Comp. Exp},
  year    = {1993},
}

@Article{Owen.2014,
  author  = {Owen, Art B.},
  title   = {Sobol' indices and Shapley value},
  journal = {SIAM/ASA Journal on Uncertainty Quantification},
  year    = {2014},
  volume  = {2},
  number    = {1},
  pages   = {245--251},
}

@Article{Hansen.1996,
  author  = {Hansen, Lars Peter and Heckman, James J.},
  title   = {The empirical foundations of calibration},
  journal = {Journal of economic perspectives},
  year    = {1996},
  volume  = {10},
  number  = {1},
  pages   = {87--104},
}

@Article{Canova.1994,
  author    = {Canova, Fabio},
  title     = {Statistical inference in calibrated models},
  journal   = {Journal of Applied Econometrics},
  year      = {1994},
  volume    = {9},
  number    = {1},
  pages     = {123--144},
}

@Article{Kydland.1992,
  author    = {Kydland, Finn E.},
  title     = {On the econometrics of world business cycles},
  journal   = {European Economic Review},
  year      = {1992},
  volume    = {36},
  number    = {2-3},
  pages     = {476--482},
}

@article{Harris.1990,
  author = {Harris, Ford W.},
  title = {How Many Parts to Make at Once},
  journal = {Operations Research},
  year = {1990},
  volume = {38},
  issn = {0030-364X},
  number = {6},
  pages = {947--950},
}

@article{Borgonovo.2016,
  author = {Borgonovo, Emanuele and Plischke, Elmar},
  title = {Sensitivity analysis: {A} review of recent advances},
  journal = {European Journal of Operational Research},
  year = {2016},
  volume = {248},
  issn = {0377-2217},
  number = {3},
  pages = {869--887},
}

@Article{Ge.2014,
  author    = {Ge, Qiao and Menendez, Monica},
  title     = {An efficient sensitivity analysis approach for computationally expensive microscopic traffic simulation models},
  journal   = {International Journal of Transportation},
  year      = {2014},
  volume    = {2},
  number    = {2},
  pages     = {49--64},
}
@Comment{jabref-meta: databaseType:bibtex;}


text:
package main

import "fmt"

// -------------
type Eagle1 struct{}

func (e Eagle1) Fly() {
	fmt.Println("Eagle flies over the cloud")
}

// ------------
type Crow1 struct{}

func (c Crow1) Fly() {
	fmt.Println("Crow flies at normal height")
}

// ---------
type Hen1 struct{}

func (h Hen1) Fly() {
	fmt.Println("Hen can not fly")
}

// ===========================
// Functions with interface type
// Empty interface can just be used only

func canYouFly1(a interface{}) {
	fmt.Println("a =", a)
	// a.Fly()  // a.Fly undefined (type interface {} is interface with no methods)
}

func main() {
	canYouFly1(Eagle1{})
	canYouFly1(Crow1{})
	canYouFly1(Hen1{})
}

repository: udhayprakash/GoLangMaterial

repo_name: udhayprakash/GoLangMaterial

gha_language: Go


text:
// Copyright 2011 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package yacr

import (
	"compress/bzip2"
	"compress/gzip"
	"io"
	"io/ioutil"
	"os"
	"path"
)

type zReadCloser struct {
	f  *os.File
	rd io.ReadCloser
}

// Zopen transparently opens gzip/bzip files (based on their extension).
func Zopen(filepath string) (io.ReadCloser, error) {
	f, err := os.Open(filepath)
	if err != nil {
		return nil, err
	}
	var rd io.ReadCloser
	// TODO zip
	ext := path.Ext(f.Name())
	if ext == ".gz" {
		rd, err = gzip.NewReader(f)
		if err != nil {
			_ = f.Close()
			return nil, err
		}
	} else if ext == ".bz2" {
		rd = ioutil.NopCloser(bzip2.NewReader(f))
	} else {
		rd = f
	}
	return &zReadCloser{f, rd}, nil
}
func (z *zReadCloser) Read(b []byte) (n int, err error) {
	return z.rd.Read(b)
}
func (z *zReadCloser) Close() (err error) {
	err = z.rd.Close()
	if err != nil {
		_ = z.f.Close()
		return
	}
	return z.f.Close()
}

repository: gwenn/yacr

repo_name: gwenn/yacr


text:
package main

import (
	// "fmt"
	"github.com/fzzy/radix/extra/pool"
	"github.com/fzzy/radix/redis"
	"github.com/stretchr/testify/assert"
	"github.com/stretchr/testify/suite"
	"testing"
)

// Set up the suite for running tests with Redis
type Suite struct {
	suite.Suite
}

func (suite *Suite) SetupTest() {
	var err error
	df := func(network, addr string) (*redis.Client, error) {
		client, err := redis.Dial(network, addr)
		// fmt.Println("DIaling")
		if err != nil {
			return nil, err
		}
		err = client.Cmd("SELECT", 8).Err
		if err != nil {
			return nil, err
		}
		err = client.Cmd("FLUSHDB").Err
		if err != nil {
			return nil, err
		}
		return client, nil
	}
	redisPool, err = pool.NewCustomPool("tcp", redisURL, 1, df)
	if err != nil {
		panic(err)
	}
}

func TestSuite(t *testing.T) {
	suite.Run(t, new(Suite))
}

func (suite *Suite) TestGetDomainSomething() {
	// authKeys = new(AuthKeys)
	// authKeys.Init()

	c, err := redisPool.Get()
	errorHandler(err)
	defer redisPool.Put(c)
	err = c.Cmd("HSET", "$domainkeys", "xyz1234567890", "peterbe.com").Err
	errorHandler(err)

	domain, err := GetDomain("xyz1234567890", c)
	errorHandler(err)
	assert.Equal(
		suite.T(),
		domain,
		"peterbe.com",
	)
}

func (suite *Suite) TestGetDomainNothing() {
	// authKeys = new(AuthKeys)
	// authKeys.Init()

	c, err := redisPool.Get()
	errorHandler(err)

	domain, err := GetDomain("xyz1234567890", c)
	errorHandler(err)
	assert.Equal(
		suite.T(),
		domain,
		"",
	)
}

repository: shopsmart/autocompeter

repo_name: shopsmart/autocompeter


text:
package auth

import (
	"context"
	"crypto/sha256"
	"encoding/hex"
	"math/rand"
	"net/http"
	"net/url"
	"time"

	"github.com/gorilla/securecookie"
	"github.com/lyft/flyteadmin/pkg/auth/interfaces"
	"github.com/lyft/flytestdlib/errors"
	"github.com/lyft/flytestdlib/logger"
)

const (
	// #nosec
	accessTokenCookieName = "flyte_jwt"
	// #nosec
	refreshTokenCookieName = "flyte_refresh"
	// #nosec
	csrfStateCookieName = "flyte_csrf_state"
	// #nosec
	redirectURLCookieName = "flyte_redirect_location"
)

const (
	ErrSecureCookie errors.ErrorCode = "SECURE_COOKIE_ERROR"
	// #nosec
	ErrInvalidCsrfToken errors.ErrorCode = "CSRF_TOKEN_VALIDATION_FAILED"
)

var AllowedChars = []rune("abcdefghijklmnopqrstuvwxyz1234567890")

func HashCsrfState(csrf string) string {
	shaBytes := sha256.Sum256([]byte(csrf))
	hash := hex.EncodeToString(shaBytes[:])
	return hash
}

func NewSecureCookie(cookieName, value string, hashKey, blockKey []byte) (http.Cookie, error) {
	var s = securecookie.New(hashKey, blockKey)
	encoded, err := s.Encode(cookieName, value)

	if err == nil {
		return http.Cookie{
			Name:  cookieName,
			Value: encoded,
		}, nil
	}

	return http.Cookie{}, errors.Wrapf(ErrSecureCookie, err, "Error creating secure cookie")
}

func ReadSecureCookie(ctx context.Context, cookie http.Cookie, hashKey, blockKey []byte) (string, error) {
	var s = securecookie.New(hashKey, blockKey)
	var value string
	var err error
	if err = s.Decode(cookie.Name, cookie.Value, &value); err == nil {
		return value, nil
	}
	logger.Errorf(ctx, "Error reading secure cookie %s %s", cookie.Name, err)
	return "", errors.Wrapf(ErrSecureCookie, err, "Error reading secure cookie %s", cookie.Name)
}

func NewCsrfToken(seed int64) string {
	rand.Seed(seed)
	csrfToken := [10]rune{}
	for i := 0; i < len(csrfToken); i++ {
		csrfToken[i] = AllowedChars[rand.Intn(len(AllowedChars))]
	}
	return string(csrfToken[:])
}

func NewCsrfCookie() http.Cookie {
	csrfStateToken := NewCsrfToken(time.Now().UnixNano())
	return http.Cookie{
		Name:     csrfStateCookieName,
		Value:    csrfStateToken,
		SameSite: http.SameSiteLaxMode,
		HttpOnly: true,
	}
}

func VerifyCsrfCookie(ctx context.Context, request *http.Request) error {
	csrfState := request.FormValue(CsrfFormKey)
	if csrfState == "" {
		return errors.Errorf(ErrInvalidCsrfToken, "Empty state in callback, %s", request.Form)
	}
	csrfCookie, err := request.Cookie(csrfStateCookieName)
	if csrfCookie == nil || err != nil {
		return errors.Errorf(ErrInvalidCsrfToken, "Could not find csrf cookie %s", err)
	}
	if HashCsrfState(csrfCookie.Value) != csrfState {
		return errors.Errorf(ErrInvalidCsrfToken, "CSRF token does not match state %s, %s vs %s", csrfCookie.Value,
			HashCsrfState(csrfCookie.Value), csrfState)
	}
	return nil
}

// This function takes in a string and returns a cookie that's used to keep track of where to send the user after
// the OAuth2 login flow is complete.
func NewRedirectCookie(ctx context.Context, redirectURL string) *http.Cookie {
	urlObj, err := url.Parse(redirectURL)
	if err != nil || urlObj == nil {
		logger.Errorf(ctx, "Error creating redirect cookie %s %s", urlObj, err)
		return nil
	}

	if urlObj.EscapedPath() == "" {
		logger.Errorf(ctx, "Error parsing URL, redirect %s resolved to empty string", redirectURL)
		return nil
	}

	return &http.Cookie{
		Name:     redirectURLCookieName,
		Value:    urlObj.String(),
		SameSite: http.SameSiteLaxMode,
		HttpOnly: true,
	}
}

// At the end of the OAuth flow, the server needs to send the user somewhere. This should have been stored as a cookie
// during the initial /login call. If that cookie is missing from the request, it will default to the one configured
// in this package's Config object.
func getAuthFlowEndRedirect(ctx context.Context, authContext interfaces.AuthenticationContext, request *http.Request) string {
	queryParams := request.URL.Query()
	// Use the redirect URL specified in the request if one is available.
	if redirectURL := queryParams.Get(RedirectURLParameter); len(redirectURL) > 0 {
		return redirectURL
	}
	cookie, err := request.Cookie(redirectURLCookieName)
	if err != nil {
		logger.Debugf(ctx, "Could not detect end-of-flow redirect url cookie")
		return authContext.Options().RedirectURL
	}
	return cookie.Value
}

repository: nuclyde-io/flyteadmin

repo_name: nuclyde-io/flyteadmin


text:
SECTION = "base"
LICENSE = "GPL"
SRC_URI = "cvs://anonymous@cvs.berlios.de/cvsroot/at76c503a;module=at76c503a \
	   file://makefile.cc.patch;patch=1"
S = "${WORKDIR}/at76c503a"
PR = "r2"
PV = "0.0cvs${CVSDATE}"

inherit module

MODULES = "at76c503 at76_usbdfu at76c503-i3861 at76c503-rfmd at76c503-rfmd-acc \
	at76c505-rfmd at76c503-i3863 at76c505-rfmd2958"
MODULES_EXTENSION = "o"
MODULES_EXTENSION_kernel26 = "ko"

pkg_postinst() {
#!/bin/sh
if [ "x$D" != "x" ]; then
  exit 1
fi
update-modules || true
}

pkg_postrm() {
#!/bin/sh
update-modules || true
}

do_install() {
	install -d ${D}${base_libdir}/modules/${KERNEL_VERSION}/kernel/drivers/usb/
	for i in ${MODULES}; do
		install -m 0644 $i.${MODULES_EXTENSION} ${D}${base_libdir}/modules/${KERNEL_VERSION}/kernel/drivers/usb/
	done
	if [ "${MACHINE}" = "h3900" ]; then
		install -d ${D}${sysconfdir}/modutils
		echo "at76c503-rfmd" > ${D}${sysconfdir}/modutils/at76c503-rfmd
	fi
}

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn

extension: bb


text:
// Copyright 2016-2019, Pulumi 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 agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package states

import (
	"fmt"
	"strings"

	"github.com/pulumi/pulumi-kubernetes/provider/v2/pkg/logging"
	"github.com/pulumi/pulumi/sdk/v2/go/common/util/cmdutil"
	"k8s.io/api/core/v1"
	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)

func NewPodChecker() *StateChecker {
	return &StateChecker{
		conditions: []Condition{podScheduled, podInitialized, podReady},
		readyMsg:   cmdutil.EmojiOr("✅ Pod ready", "Pod ready"),
	}
}

//
// Conditions
//

func podScheduled(obj metav1.Object) Result {
	pod := toPod(obj)
	result := Result{Description: fmt.Sprintf("Waiting for Pod %q to be scheduled", fqName(pod))}

	if condition, found := filterConditions(pod.Status.Conditions, v1.PodScheduled); found {
		switch condition.Status {
		case v1.ConditionTrue:
			result.Ok = true
		default:
			msg := statusFromCondition(condition)
			if len(msg) > 0 {
				result.Message = logging.StatusMessage(msg)
			}
		}
	}

	return result
}

func podInitialized(obj metav1.Object) Result {
	pod := toPod(obj)
	result := Result{Description: fmt.Sprintf("Waiting for Pod %q to be initialized", fqName(pod))}

	if condition, found := filterConditions(pod.Status.Conditions, v1.PodInitialized); found {
		switch condition.Status {
		case v1.ConditionTrue:
			result.Ok = true
		default:
			var errs []string
			for _, status := range pod.Status.ContainerStatuses {
				if ok, containerErrs := hasContainerStatusErrors(status); !ok {
					errs = append(errs, containerErrs...)
				}
			}
			result.Message = logging.WarningMessage(podError(condition, errs, fqName(pod)))
		}
	}

	return result
}

func podReady(obj metav1.Object) Result {
	pod := toPod(obj)
	result := Result{Description: fmt.Sprintf("Waiting for Pod %q to be ready", fqName(pod))}

	if condition, found := filterConditions(pod.Status.Conditions, v1.PodReady); found {
		switch condition.Status {
		case v1.ConditionTrue:
			result.Ok = true
		default:
			switch pod.Status.Phase {
			case v1.PodSucceeded: // If the Pod has terminated, but .status.phase is "Succeeded", consider it Ready.
				result.Ok = true
			default:
				errs := collectContainerStatusErrors(pod.Status.ContainerStatuses)
				result.Message = logging.WarningMessage(podError(condition, errs, fqName(pod)))
			}
		}
	}

	return result
}

//
// Helpers
//

func toPod(obj metav1.Object) *v1.Pod {
	return obj.(*v1.Pod)
}

func collectContainerStatusErrors(statuses []v1.ContainerStatus) []string {
	var errs []string
	for _, status := range statuses {
		if hasErr, containerErrs := hasContainerStatusErrors(status); hasErr {
			errs = append(errs, containerErrs...)
		}
	}

	return errs
}

func hasContainerStatusErrors(status v1.ContainerStatus) (bool, []string) {
	if status.Ready {
		return false, nil
	}

	var errs []string
	if hasErr, err := hasContainerWaitingError(status); hasErr {
		errs = append(errs, err)
	}
	if hasErr, err := hasContainerTerminatedError(status); hasErr {
		errs = append(errs, err)
	}

	return len(errs) > 0, errs
}

func hasContainerWaitingError(status v1.ContainerStatus) (bool, string) {
	state := status.State.Waiting
	if state == nil {
		return false, ""
	}

	// Return false if the container is creating.
	if state.Reason == "ContainerCreating" {
		return false, ""
	}

	msg := fmt.Sprintf("[%s] %s", state.Reason, trimImagePullMsg(state.Message))
	return true, msg
}

func hasContainerTerminatedError(status v1.ContainerStatus) (bool, string) {
	state := status.State.Terminated
	if state == nil {
		return false, ""
	}

	// Return false if no reason given.
	if len(state.Reason) == 0 {
		return false, ""
	}

	if len(state.Message) > 0 {
		msg := fmt.Sprintf("[%s] %s", state.Reason, trimImagePullMsg(state.Message))
		return true, msg
	}
	return true, fmt.Sprintf("Container %q completed with exit code %d", status.Name, state.ExitCode)
}

// trimImagePullMsg trims unhelpful error from ImagePullError status messages.
func trimImagePullMsg(msg string) string {
	msg = strings.TrimPrefix(msg, "rpc error: code = Unknown desc = Error response from daemon: ")
	msg = strings.TrimSuffix(msg, ": manifest unknown")

	return msg
}

func statusFromCondition(condition *v1.PodCondition) string {
	if condition.Reason != "" && condition.Message != "" {
		return condition.Message
	}

	return ""
}

func filterConditions(conditions []v1.PodCondition, desired v1.PodConditionType) (*v1.PodCondition, bool) {
	for _, condition := range conditions {
		if condition.Type == desired {
			return &condition, true
		}
	}

	return nil, false
}

func podError(condition *v1.PodCondition, errs []string, name string) string {
	errMsg := fmt.Sprintf("[Pod %s]: ", name)
	if len(condition.Reason) > 0 && len(condition.Message) > 0 {
		errMsg += condition.Message
	}

	for _, err := range errs {
		errMsg += fmt.Sprintf(" -- %s", err)
	}

	return errMsg
}

repository: Carlangueitor/pulumi-kubernetes

repo_name: Carlangueitor/pulumi-kubernetes


text:
@article{ISI:000491236600008,
 author = {Stirling, Wynn C. and Tummolini, Luca},
 doi = {10.3917/redp.283.0469},
 issn = {0373-2630},
 journal = {REVUE D ECONOMIE POLITIQUE},
 month = {MAY-JUN},
 number = {3},
 orcid-numbers = {Tummolini, Luca/0000-0001-7791-0457},
 pages = {469-492},
 researcherid-numbers = {Tummolini, Luca/A-5086-2010},
 title = {Coordinated Reasoning and Augmented Individualism},
 unique-id = {ISI:000491236600008},
 volume = {128},
 year = {2018}
}

repository: ideeverdi/website-academic

repo_name: ideeverdi/website-academic


text:
@article{Sankar2017Predicting,
 added-at = {2018-12-02T16:09:07.000+0100},
 author = {Sankar, Aravind and Ranu, Sayan and Raman, Karthik},
 biburl = {https://www.bibsonomy.org/bibtex/2a7ac388a8414223f3a952639ccc904ee/karthikraman},
 citeulike-article-id = {14401087},
 citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btx481},
 day = {15},
 doi = {10.1093/bioinformatics/btx481},
 interhash = {3421686ce8c1133bb8fe492e19f0185e},
 intrahash = {a7ac388a8414223f3a952639ccc904ee},
 issn = {1460-2059},
 journal = {Bioinformatics},
 keywords = {myown retrosynthesis},
 month = {December},
 number = {24},
 pages = {3955--3963},
 posted-at = {2017-07-28 06:26:31},
 priority = {2},
 timestamp = {2019-02-10T16:15:28.000+0100},
 title = {Predicting novel metabolic pathways through subgraph mining},
 url = {http://dx.doi.org/10.1093/bioinformatics/btx481},
 volume = {33},
 year = {2017}
}

repository: karthikraman/ramanlab-home

repo_name: karthikraman/ramanlab-home


text:
//
// FrankyGo - UCI chess engine in GO for learning purposes
//
// MIT License
//
// Copyright (c) 2018-2020 Frank Kopp
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
//

package types

import "fmt"

// Orientation is a set of constants for directions from a squares
type Orientation uint8

// Orientation is a set of constants for moving squares within a Bb
//noinspection GoVarAndConstTypeMayBeOmitted
const (
	NW Orientation = 0
	N  Orientation = 1
	NE Orientation = 2
	E  Orientation = 3
	SE Orientation = 4
	S  Orientation = 5
	SW Orientation = 6
	W  Orientation = 7
)

// IsValid tests if o is a valid Orientation value
func (o Orientation) IsValid() bool {
	return o < 8
}

// String returns a string representation of a Orientation (e.g. N, E, ...,NW,...)
func (d Orientation) String() string {
	switch d {
	case N:
		return "N"
	case E:
		return "E"
	case S:
		return "S"
	case W:
		return "W"
	case NE:
		return "NE"
	case SE:
		return "SE"
	case SW:
		return "SW"
	case NW:
		return "NW"
	default:
		panic(fmt.Sprintf("Invalid orientation %d", d))
	}
}

repository: frankkopp/FrankyGo

repo_name: frankkopp/FrankyGo

gha_license_id: NOASSERTION


text:
@online{noauthor_introduction_nodate,
	title = {Introduction to Mediation, Moderation, and Conditional Process Analysis},
	url = {http://afhayes.com/introduction-to-mediation-moderation-and-conditional-process-analysis.html},
	abstract = {This book, released in early May 2013, describes the foundation of mediation and moderation analysis as well as their analytical integration in the form of "conditional process analysis", with a...},
	titleaddon = {Andrew F. Hayes, Ph.D.},
	urldate = {2013-07-16}
}

repository: pmean/build-website

repo_name: pmean/build-website


text:
%---- Mila articles -----
@article{evans2015,
  title={Methane metabolism in the archaeal phylum {B}athyarchaeota revealed by genome-centric metagenomics},
  author={Evans, Paul N and Parks, Donovan H and Chadwick, Grayson L and Robbins, Steven J and Orphan, Victoria J and Golding, Suzanne D and Tyson, Gene W},
  journal={Science},
  volume={350},
  number={6259},
  pages={434--438},
  year={2015},
  publisher={American Association for the Advancement of Science}
}

@article{daly2016,
  title={Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales},
  author={Daly, Rebecca A and Borton, Mikayla A and Wilkins, Michael J and Hoyt, David W and Kountz, Duncan J and Wolfe, Richard A and Welch, Susan A and Marcus, Daniel N and Trexler, Ryan V and MacRae, Jean D and others},
  journal={Nature Microbiology},
  volume={1},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{kantor2017,
  title={Genome-resolved meta-omics ties microbial dynamics to process performance in biotechnology for thiocyanate degradation},
  author={Kantor, Rose S and Huddy, Robert J and Iyer, Ramsunder M and Thomas, Brian C and Brown, Christopher T and Anantharaman, Karthik and Tringe, Susannah Green and Hettich, Robert L and Harrison, Susan TL and Banfield, Jillian F},
  journal={Environmental Science \& Technology},
  year={2017},
  publisher={ACS Publications}
}

%---- Ancinet Lake Washington methane studies
@article{lidstrom1984,
  title={Seasonal study of methane oxidation in {Lake Washington}},
  author={Lidstrom, Mary E and Somers, Leslie},
  journal={Applied and environmental microbiology},
  volume={47},
  number={6},
  pages={1255--1260},
  year={1984},
  publisher={Am Soc Microbiol}
}

% also: kuivilal1988 (in other bib file)

%----- molecular studies: Lake Wash

@article{auman2002,
  title={Analysis of {sMMO}-containing type {I} methanotrophs in {Lake Washington} sediment},
  author={Auman, Ann J and Lidstrom, Mary E},
  journal={Environmental microbiology},
  volume={4},
  number={9},
  pages={517--524},
  year={2002},
  publisher={Wiley Online Library}
}

@article{costello2002,
  title={Estimation of methanotroph abundance in a freshwater lake sediment},
  author={Costello, Andria M and Auman, Ann J and Macalady, Jennifer L and Scow, Kate M and Lidstrom, Mary E},
  journal={Environmental Microbiology},
  volume={4},
  number={8},
  pages={443--450},
  year={2002},
  publisher={Wiley Online Library}
}

@article{kalyuzhnaya2004,
  title={Utility of environmental primers targeting ancient enzymes: methylotroph detection in {Lake Washington}},
  author={Kalyuzhnaya, MG and Lidstrom, ME and Chistoserdova, L},
  journal={Microbial ecology},
  volume={48},
  number={4},
  pages={463--472},
  year={2004},
  publisher={Springer}
}

@article{nercessian2005,
  title={Bacterial populations active in metabolism of {C1} compounds in the sediment of {Lake Washington}, a freshwater lake},
  author={Nercessian, Olivier and Noyes, Emma and Kalyuzhnaya, Marina G and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={Applied and environmental microbiology},
  volume={71},
  number={11},
  pages={6885--6899},
  year={2005},
  publisher={Am Soc Microbiol}
}

%---- Isolating Lake Wash

@article{kalyuzhnaya2005Methylosarcina,
  title={Methylosarcina lacus sp. nov., a methanotroph from {Lake Washington}, {Seattle, USA}, and emended description of the genus \textit{Methylosarcina}},
  author={Kalyuzhnaya, Marina G and Stolyar, Sergey M and Auman, Ann J and Lara, Jimmie C and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={International journal of systematic and evolutionary microbiology},
  volume={55},
  number={6},
  pages={2345--2350},
  year={2005},
  notes={NOT SEQUENCED.  Only 16S}
}

% Italics are used for bacterial and viral taxa at the level of family and below.  https://wwwnc.cdc.gov/eid/page/scientific-nomenclature
@article{kalyuzhnaya2006methylotenera,
  title={\textit{Methylotenera mobilis} gen. nov., sp. nov., an obligately methylamine-utilizing bacterium within the family \textit{Methylophilaceae}},
  author={Kalyuzhnaya, Marina G and Bowerman, Sarah and Lara, Jimmie C and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={International Journal of Systematic and Evolutionary Microbiology},
  volume={56},
  number={12},
  pages={2819--2823},
  year={2006},
  publisher={Microbiology Society}
}

@article{kalyuzhnaya2011isolates,
  title={Novel methylotrophic isolates from {Lake Washington} sediment and description of a new species in the genus \textit{Methylotenera}, \textit{Methylotenera versatilis} sp. nov.},
  author={Kalyuzhnaya, Marina G and Beck, David AC and Vorobev, Alexey and Smalley, Nicole and Kunkel, Denis D and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={International Journal of Systematic and Evolutionary Microbiology},
  year={2011},
  publisher={Soc General Microbiol},
  notes={Whole genome sequencing, genome assembly and gene annotation were carried out by the Department of Energy Joint Genome Institute...
    http://ijs.microbiologyresearch.org/content/journal/ijsem/10.1099/ijs.0.029165-0#tab2
    }
}

@article{mctaggart2015,
  title={Draft genome sequences of five new strains of \textit{Methylophilaceae} isolated from {Lake Washington} sediment},
  author={McTaggart, Tami L and Benuska, Gabrielle and Shapiro, Nicole and Woyke, Tanja and Chistoserdova, Ludmila},
  journal={Genome announcements},
  volume={3},
  number={1},
  year={2015},
  publisher={Am Soc Microbiol}
}

@article{beck2015isolates,
  title={Multiphyletic origins of methylotrophy in {A}lphaproteobacteria, exemplified by comparative genomics of {Lake Washington} isolates},
  author={Beck, David AC and McTaggart, Tami L and Setboonsarng, Usanisa and Vorobev, Alexey and Goodwin, Lynne and Shapiro, Nicole and Woyke, Tanja and Kalyuzhnaya, Marina G and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={Environmental microbiology},
  volume={17},
  number={3},
  pages={547--554},
  year={2015},
  publisher={Wiley Online Library}
}

@article{kalyuzhnaya2015,
  title={Draft genome sequences of gammaproteobacterial methanotrophs isolated from {Lake Washington} sediment},
  author={Kalyuzhnaya, Marina G and Lamb, Andrew E and McTaggart, Tami L and Oshkin, Igor Y and Shapiro, Nicole and Woyke, Tanja and Chistoserdova, Ludmila},
  journal={Genome announcements},
  volume={3},
  number={2},
  year={2015},
  publisher={Am Soc Microbiol},
  notes={NOTE: genomes of Methylosarcina lacus LW14, Methylobacter sp. strain 21/22, Methylobacter sp. strain 31/32,
        Methylomonas sp. strain LW13, Methylomonas sp. strain MK1, and Methylomonas sp. strain 11b were sequenced }
}

%----  Burkholderiales

% Italics are used for bacterial and viral taxa at the level of family and below.  https://wwwnc.cdc.gov/eid/page/scientific-nomenclature
@article{kalyuzhnaya2008Burkholderiales,
  title={Characterization of a novel methanol dehydrogenase in representatives of {Burkholderiales}: implications for environmental detection of methylotrophy and evidence for convergent evolution},
  author={Kalyuzhnaya, Marina G and Hristova, Krassimira R and Lidstrom, Mary E and Chistoserdova, Ludmila},
  journal={Journal of bacteriology},
  volume={190},
  number={11},
  pages={3817--3823},
  year={2008},
  publisher={Am Soc Microbiol},
  notes={http://jb.asm.org/content/190/11/3817.full}
}

%---- Synthetic ecology
@article{yu2016synthetic,
  title={A synthetic ecology perspective: {H}ow well does behavior of model organisms in the laboratory predict microbial activities in natural habitats?},
  author={Yu, Zheng and Krause, Sascha MB and Beck, David AC and Chistoserdova, Ludmila},
  journal={Frontiers in Microbiology},
  volume={7},
  year={2016},
  publisher={Frontiers Media SA}
}

%---- Lanthanum
@article{krause2017,
  title={Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions},
  author={Krause, Sascha MB and Johnson, Timothy and Karunaratne, Yasodara Samadhi and Fu, Yanfen and Beck, David AC and Chistoserdova, Ludmila and Lidstrom, Mary E},
  journal={Proceedings of the National Academy of Sciences},
  volume={114},
  number={2},
  pages={358--363},
  year={2017},
  publisher={National Acad Sciences}
}

%---- NMS medium
@article{dedysh2014,
  title={Cultivation of methanotrophs},
  author={Dedysh, Svetlana N and Dunfield, Peter F},
  journal={Hydrocarbon and lipid microbiology protocols, Springer protocols handbooks. Berlin: Springer-Ferlag},
  year={2014},
  publisher={Springer},
  note={Sascha's paper used this}
}

%----- Assembly

@article{li2015,
  title={{MEGAHIT}: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de {B}ruijn graph},
  author={Li, Dinghua and Liu, Chi-Man and Luo, Ruibang and Sadakane, Kunihiko and Lam, Tak-Wah},
  journal={Bioinformatics},
  year={2015},
  publisher={Oxford Univ Press}
}

%---- Annotation
@article{seemann2014,
  title={Prokka: rapid prokaryotic genome annotation},
  author={Seemann, Torsten},
  journal={Bioinformatics},
  pages={153},
  year={2014},
  publisher={Oxford Univ Press}
}

%---- Binning

@article{sedlar2017,
  title={Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics},
  author={Sedlar, Karel and Kupkova, Kristyna and Provaznik, Ivo},
  journal={Computational and Structural Biotechnology Journal},
  volume={15},
  pages={48--55},
  year={2017},
  publisher={Elsevier}
}

@article{concoct2014,
  title={Binning metagenomic contigs by coverage and composition},
  author={Alneberg, Johannes and Bjarnason, Brynjar Sm{\'a}ri and De Bruijn, Ino and Schirmer, Melanie and Quick, Joshua and Ijaz, Umer Z and Lahti, Leo and Loman, Nicholas J and Andersson, Anders F and Quince, Christopher},
  journal={Nature methods},
  volume={11},
  number={11},
  pages={1144--1146},
  year={2014},
  publisher={Nature Research}
}

@article{metabat2015,
  title={{MetaBAT}, an efficient tool for accurately reconstructing single genomes from complex microbial communities},
  author={Kang, Dongwan D and Froula, Jeff and Egan, Rob and Wang, Zhong},
  journal={PeerJ},
  volume={3},
  year={2015},
  publisher={PeerJ Inc.}
}

@article{mycc2016,
  title={Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes},
  author={Lin, Hsin-Hung and Liao, Yu-Chieh},
  journal={Scientific reports},
  volume={6},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{wu2015,
  title={{MaxBin} 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets},
  author={Wu, Yu-Wei and Simmons, Blake A and Singer, Steven W},
  journal={Bioinformatics},
  year={2015},
  publisher={Oxford Univ Press}
}

@article{dick2009,
  title={Community-wide analysis of microbial genome sequence signatures},
  author={Dick, Gregory J and Andersson, Anders F and Baker, Brett J and Simmons, Sheri L and Thomas, Brian C and Yelton, A Pepper and Banfield, Jillian F},
  journal={Genome biology},
  volume={10},
  number={8},
  year={2009},
  publisher={BioMed Central}
}

%---- Bin Evaluation
@article{parks2015,
  title={{CheckM}: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes},
  author={Parks, Donovan H and Imelfort, Michael and Skennerton, Connor T and Hugenholtz, Philip and Tyson, Gene W},
  journal={Genome research},
  volume={25},
  number={7},
  pages={1043--1055},
  year={2015},
  publisher={Cold Spring Harbor Lab}
}

@article{segata2013,
  title={{PhyloPhlAn} is a new method for improved phylogenetic and taxonomic placement of microbes},
  author={Segata, Nicola and B{\"o}rnigen, Daniela and Morgan, Xochitl C and Huttenhower, Curtis},
  journal={Nature communications},
  volume={4},
  year={2013},
  publisher={Nature Publishing Group}
}

%----- Misc

% many can't be isolated
@article{kaeberlein2002,
  title={Isolating" uncultivable" microorganisms in pure culture in a simulated natural environment},
  author={Kaeberlein, Tammi and Lewis, Kim and Epstein, Slava S},
  journal={Science},
  volume={296},
  number={5570},
  pages={1127--1129},
  year={2002},
  publisher={American Association for the Advancement of Science}
}

% many can't be isolated
@article{stewart2012,
  title={Growing unculturable bacteria},
  author={Stewart, Eric J},
  journal={Journal of bacteriology},
  volume={194},
  number={16},
  pages={4151--4160},
  year={2012},
  publisher={Am Soc Microbiol}
}

% differential binning is better than composition
@article{albertsen2013,
  title={Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes},
  author={Albertsen, Mads and Hugenholtz, Philip and Skarshewski, Adam and Nielsen, K{\aa}re L and Tyson, Gene W and Nielsen, Per H},
  journal={Nature biotechnology},
  volume={31},
  number={6},
  pages={533--538},
  year={2013},
  publisher={Nature Research}
}

% Elviz
@article{cantor2015,
  title={Elviz--exploration of metagenome assemblies with an interactive visualization tool},
  author={Cantor, Michael and Nordberg, Henrik and Smirnova, Tatyana and Hess, Matthias and Tringe, Susannah and Dubchak, Inna},
  journal={BMC bioinformatics},
  volume={16},
  number={1},
  pages={130},
  year={2015},
  publisher={BioMed Central}
}

% TPM
@article{wagner2012,
  title={Measurement of {mRNA} abundance using {RNA}-seq data: {RPKM} measure is inconsistent among samples},
  author={Wagner, G{\"u}nter P and Kin, Koryu and Lynch, Vincent J},
  journal={Theory in Biosciences},
  volume={131},
  number={4},
  pages={281--285},
  year={2012},
  publisher={Springer}
}

@article{sieber2017,
  title={Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy},
  author={Sieber, Christian MK and Probst, Alexander J and Sharrar, Allison and Thomas, Brian C and Hess, Matthias and Tringe, Susannah G and Banfield, Jillian F},
  journal={bioRxiv},
  year={2017},
  publisher={Cold Spring Harbor Labs Journals}
}

%sharon gut
@article{sharon2013,
  title={Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization},
  author={Sharon, Itai and Morowitz, Michael J and Thomas, Brian C and Costello, Elizabeth K and Relman, David A and Banfield, Jillian F},
  journal={Genome research},
  volume={23},
  number={1},
  pages={111--120},
  year={2013},
  publisher={Cold Spring Harbor Lab}
}

ANIcalculator
@article{varghese2015,
  title={Microbial species delineation using whole genome sequences},
  author={Varghese, Neha J and Mukherjee, Supratim and Ivanova, Natalia and Konstantinidis, Konstantinos T and Mavrommatis, Kostas and Kyrpides, Nikos C and Pati, Amrita},
  journal={Nucleic acids research},
  year={2015},
  publisher={Oxford Univ Press}
}

%--- Xox
@article{chu2016,
  title={Xox{F} acts as the predominant methanol dehydrogenase in the type {I} methanotroph \textit{Methylomicrobium buryatense}},
  author={Chu, Frances and Lidstrom, Mary E},
  journal={Journal of bacteriology},
  volume={198},
  number={8},
  pages={1317--1325},
  year={2016},
  publisher={Am Soc Microbiol}
}

repository: JanetMatsen/thesis

repo_name: JanetMatsen/thesis


text:
package grpc

import "go.uber.org/fx"

// InitGrpcBeforeServingFx : constructor for InitGrpcBeforeServing method.
var InitGrpcBeforeServingFx = fx.Options(
	fx.Provide(
		InitGrpcBeforeServing,
	),
)

repository: radeqode/GoArcc

repo_name: radeqode/GoArcc

branch_name: refs/heads/main


text:
DESCRIPTION = "Simple helloworld application"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"

SRC_URI = "git://mon-scm-repos.hme.com/scm/git/YoctoTest.git;protol=https"
SRCREV = "d4cd00352838073182599d6250f8d939"
S = "${WORKDIR}/git"

do_compile() {
	${CC} hello.c ${LDFLAGS} -o hello
}

do_install() {
	install -d ${D}${bindir}
	install -m 0755 hello ${D}${bindir}
}

repository: linuxemb/yoctotest

repo_name: linuxemb/yoctotest


text:
@inproceedings{dicapua:2017ferrite,
  title={Ferrite inductor models for switch-mode power supplies analysis and design},
  author={Di Capua, Giulia and Femia, Nicola and Stoyka, Kateryna and Lodi, Matteo and Oliveri, Alberto and Storace, Marco},
  booktitle={2017 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)},
  pages={1--4},
  year={2017}
}

@misc{daisy,
  author = {De Moor,  B.L.R},
  title = {{DaISy: Database for the Identification of Systems}},
  howpublished = "\url{https://homes.esat.kuleuven.be/~tokka/daisydata.html/}",
  year = {2008},
  note = "[Online; accessed 19-July-2019]"
}

@book{ljung:1999system,
 editor = {Ljung, Lennart},
 title = {System Identification: Theory for the User},
 edition  = 2,
 year = {1999},
 isbn = {0-13-656695-2},
 publisher = {Prentice Hall PTR},
 address = {Upper Saddle River, NJ, USA},
}

@inproceedings{paszke:2017automatic,
  title={Automatic Differentiation in {PyTorch}},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  booktitle={NIPS Autodiff Workshop},
  year={2017}
}

@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}

@article{gevers:2009identification,
  title={Identification and the information matrix: how to get just sufficiently rich?},
  author={Gevers, Michel and Bazanella, Alexandre S and Bombois, Xavier and Miskovic, Ljubisa},
  journal={IEEE Transactions on Automatic Control},
  volume={54},
  number={ARTICLE},
  pages={2828--2840},
  year={2009}
}

@article{bombois2006least,
  title={Least costly identification experiment for control},
  author={Bombois, Xavier and Scorletti, G{\'e}rard and Gevers, Michel and Van den Hof, Paul MJ and Hildebrand, Roland},
  journal={Automatica},
  volume={42},
  number={10},
  pages={1651--1662},
  year={2006},
  publisher={Elsevier}
}

@article{hjalmarsson:2009system,
  title={System identification of complex and structured systems},
  author={Hjalmarsson, H{\aa}kan},
  journal={European journal of control},
  volume={15},
  number={3-4},
  pages={275--310},
  year={2009},
  publisher={Elsevier}
}

@article{hornik1989multilayer,
  title={Multilayer feedforward networks are universal approximators},
  author={Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  journal={Neural networks},
  volume={2},
  number={5},
  pages={359--366},
  year={1989},
  publisher={Elsevier}
}

@article{rumelhart1988learning,
  title={Learning representations by back-propagating errors},
  author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J and others},
  journal={Cognitive modeling},
  volume={5},
  number={3},
  pages={1},
  year={1988}
}

@inbook{williams1995gradient,
  author={Williams, Ronald J and Zipser, David},
  year= 1995,
  chapter={Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity},
  title= {Oxford Handbook of Innovation},
  publisher = {L. Erlbaum Associates Inc.},
  address = {Hillsdale, NJ, USA},
}

@book{chauvin2013backpropagation,
  title={Backpropagation: theory, architectures, and applications},
  author={Chauvin, Yves and Rumelhart, David E},
 title = {Backpropagation: Theory, Architectures, and Applications},
 year = {1995},
 isbn = {0-8058-1259-8},
 publisher = {L. Erlbaum Associates Inc.},
 address = {Hillsdale, NJ, USA},
}

@article{schmidhuber2015deep,
  title={Deep learning in neural networks: An overview},
  author={Schmidhuber, J{\"u}rgen},
  journal={Neural networks},
  volume={61},
  pages={85--117},
  year={2015},
  publisher={Elsevier}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{ljung:2015system,
  title={System identification toolbox},
  author={Ljung, Lennart},
  journal={The Matlab user’s guide},
  year={1988}
}

@inproceedings{werbos1989neural,
  title={Neural networks for control and system identification},
  author={Werbos, Paul J},
  booktitle={Proceedings of the 28th IEEE Conference on Decision and Control,},
  pages={260--265},
  year={1989}
}

@inproceedings{masti2018learning,
  title={Learning nonlinear state-space models using deep autoencoders},
  author={Masti, Daniele and Bemporad, Alberto},
  booktitle={2018 IEEE Conference on Decision and Control (CDC)},
  pages={3862--3867},
  year={2018}
}

@article{gonzalez2018non,
  title={Non-linear system modeling using LSTM neural networks},
  author={Gonzalez, Jes{\'u}s and Yu, Wen},
  journal={IFAC-PapersOnLine},
  volume={51},
  number={13},
  pages={485--489},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{wang2017new,
  title={A new concept using lstm neural networks for dynamic system identification},
  author={Wang, Yu},
  booktitle={2017 American Control Conference (ACC)},
  pages={5324--5329},
  year={2017}
}

@inproceedings{chen2018neural,
  title={Neural ordinary differential equations},
  author={Chen, Tian Qi and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David K},
  booktitle={Advances in neural information processing systems},
  pages={6571--6583},
  year={2018}
}

@article{raissi2019physics,
  title={Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations},
  author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George E},
  journal={Journal of Computational Physics},
  volume={378},
  pages={686--707},
  year={2019},
  publisher={Elsevier}
}

@article{chen1990non,
  title={Non-linear system identification using neural networks},
  author={Chen, Sheng and Billings, SA and Grant, PM},
  journal={International journal of control},
  volume={51},
  number={6},
  pages={1191--1214},
  year={1990},
  publisher={Taylor \& Francis}
}

@inproceedings{forgione2020model,
  title={Model structures and fitting criteria for system identification
  with neural networks},
  author={Forgione, Marco and Piga, Dario},
  booktitle={Proc. of the 14th IEEE International Conference Application
  of Information and Communication Technologies, Tashkent, Uzbekistan},
  year={2020}
}


text:
LICENSE = "GPL"
PR = "r4"

inherit gpe

DESCRIPTION = "GPE session startup scripts"
SECTION = "gpe"
PRIORITY = "optional"
MAINTAINER = "Philip Blundell <pb@handhelds.org>"
RDEPENDS_${PN} = "matchbox matchbox-panel (>= 0.9.2-r12) matchbox-desktop (>= 0.9.1-r1) matchbox-common (>= 0.9.1-r2) gpe-session-starter gpe-bluetooth xstroke xtscal gpe-question gpe-clock matchbox-applet-inputmanager xrandr xmodmap xdpyinfo xserver-common"
# more rdepends: keylaunch ipaq-sleep apmd blueprobe
DEPENDS = "matchbox-wm matchbox-panel gpe-bluetooth xstroke xtscal gpe-question matchbox-applet-inputmanager gpe-clock xrandr xmodmap xdpyinfo xserver-common"

SRC_URI += "file://matchbox-session \
	file://0.66-reinstate-volume-applet-and-fix-order.patch;patch=1 \
	file://disable-composite.xsettings"

do_install_append() {
	install -d ${D}${sysconfdir}/gpe/xsettings-default.d
	if [ "${GUI_MACHINE_CLASS}" != "bigscreen" ]; then
		echo "Gtk/ToolbarStyle:S:icons" > ${D}${sysconfdir}/gpe/xsettings-default.d/toolbar
	fi
	install -d ${D}${sysconfdir}/matchbox
	install ${WORKDIR}/matchbox-session ${D}${sysconfdir}/matchbox/session

	install -d ${D}${sysconfdir}/gpe/xsettings-default.d
	install -m 0644 ${WORKDIR}/disable-composite.xsettings ${D}${sysconfdir}/gpe/xsettings-default.d/disable-composite
}

# This makes use of GUI_MACHINE_CLASS, so set PACKAGE_ARCH appropriately
PACKAGE_ARCH = "${MACHINE_ARCH}"

repository: Terentyev/familiar-linux

repo_name: Terentyev/familiar-linux


text:
@article{RN1261,
 author = {Ong, Huei Ruey and Khan, Md Maksudur Rahman and Yousuf, Abu and Hussain, Nor Amalina and Cheng, Chin Kui},
 journal = {RSC Advances},
 number = {121},
 pages = {100362-100368},
 title = {Synthesis and characterization of a CaFe 2 O 4 catalyst for oleic acid esterification},
 type = {Journal Article},
 volume = {5},
 year = {2015}
}

repository: drnazmul/ayusuf

repo_name: drnazmul/ayusuf


text:
package main

import "fmt"

func main() {
	var house_start, house_end int
	fmt.Scanf("%d %d", &house_start, &house_end)

	var apple_tree, orange_tree int
	fmt.Scanf("%d %d", &apple_tree, &orange_tree)

	var count_apples, count_oranges int
	fmt.Scanf("%d %d", &count_apples, &count_oranges)

	var apple_location int
	apples_on_house := 0
	for i := 0; i < count_apples; i++ {
		fmt.Scanf("%d", &apple_location)

		apple_location += apple_tree
		if apple_location >= house_start && apple_location <= house_end {
			apples_on_house++
		}
	}

	var orange_location int
	oranges_on_house := 0
	for i := 0; i < count_oranges; i++ {
		fmt.Scanf("%d", &orange_location)

		orange_location += orange_tree
		if orange_location >= house_start && orange_location <= house_end {
			oranges_on_house++
		}
	}

	fmt.Println(apples_on_house)
	fmt.Println(oranges_on_house)
}

repository: zqureshi/hackerrank

repo_name: zqureshi/hackerrank


text:
package registries

import "errors"

var registeredFactories = make(map[string]AdapterFactory)

// AdapterFactory specifies a constructor for RegistryAdapter factories.
type AdapterFactory interface {
	// New builds a RegistryAdapter, which should be a client of a registry listening on the given
	// address.
	New(address string) (RegistryAdapter, error)
}

// RegistryAdapter specifies the contract a registry adapter (consul, etcd) should follow.
type RegistryAdapter interface {
	Register(service *Service) error
	Deregister(service *Service) error
	Services() ([]*Service, error)
}

// Service represents a service.
type Service struct {
	ID   string
	Name string
	IP   string
	Port uint
}

// Register registers an AdapterFactory for use.
func Register(rf AdapterFactory, name string) error {
	if _, ok := registeredFactories[name]; ok {
		// Should be unique (either "consul", "etcd", etc.)
		return errors.New("A registry with the name \"" + name + "\" was already registered.")
	}
	registeredFactories[name] = rf
	return nil
}

// Deregister deregisters an existent factory. (Mostly here for testing.)
func Deregister(name string) bool {
	_, ok := registeredFactories[name]
	delete(registeredFactories, name)
	return ok
}

// LookUp returns an AdapterFactory registered with a given name.
func LookUp(name string) (AdapterFactory, bool) {
	registry, ok := registeredFactories[name]
	return registry, ok
}


text:
package clog

import (
	"bytes"
	"encoding/json"
	"errors"
	"fmt"
	"io"
	"io/ioutil"
	"net/http"
	"strings"
	"time"
)

type (
	discordEmbed struct {
		Title       string `json:"title"`
		Description string `json:"description"`
		Timestamp   string `json:"timestamp"`
		Color       int    `json:"color"`
	}

	discordPayload struct {
		Username string          `json:"username,omitempty"`
		Embeds   []*discordEmbed `json:"embeds"`
	}
)

var (
	discordTitles = []string{
		"Trace",
		"Information",
		"Warning",
		"Error",
		"Fatal",
	}

	discordColors = []int{
		0,        // Trace
		3843043,  // Info
		16761600, // Warn
		13041721, // Error
		9440319,  // Fatal
	}
)

// DiscordConfig is the config object for the Discord logger.
type DiscordConfig struct {
	// Minimum logging level of messages to be processed.
	Level Level
	// Discord webhook URL.
	URL string
	// Username to be shown in the message.
	// Leave empty to use default as set in the Discord.
	Username string
	// Title for different levels, must have exact 5 elements in the order of
	// Trace, Info, Warn, Error, and Fatal.
	Titles []string
	// Colors for different levels, must have exact 5 elements in the order of
	// Trace, Info, Warn, Error, and Fatal.
	Colors []int
}

var _ Logger = (*discordLogger)(nil)

type discordLogger struct {
	*noopLogger

	url      string
	username string
	titles   []string
	colors   []int

	client *http.Client
}

func (l *discordLogger) buildPayload(m Messager) (string, error) {
	descPrefixLen := strings.Index(m.String(), "] ")
	if descPrefixLen == -1 {
		descPrefixLen = 0
	} else {
		descPrefixLen += 2
	}

	payload := discordPayload{
		Username: l.username,
		Embeds: []*discordEmbed{
			{
				Title:       l.titles[m.Level()],
				Description: m.String()[descPrefixLen:],
				Timestamp:   time.Now().Format(time.RFC3339),
				Color:       l.colors[m.Level()],
			},
		},
	}
	p, err := json.Marshal(&payload)
	if err != nil {
		return "", err
	}
	return string(p), nil
}

func (l *discordLogger) postMessage(r io.Reader) (int64, error) {
	resp, err := l.client.Post(l.url, "application/json", r)
	if err != nil {
		return -1, fmt.Errorf("HTTP request: %v", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode == http.StatusTooManyRequests {
		rateLimitMsg := struct {
			RetryAfter int64 `json:"retry_after"`
		}{}
		if err = json.NewDecoder(resp.Body).Decode(&rateLimitMsg); err != nil {
			return -1, fmt.Errorf("decode rate limit message: %v", err)
		}

		return rateLimitMsg.RetryAfter, nil
	} else if resp.StatusCode/100 != 2 {
		data, err := ioutil.ReadAll(resp.Body)
		if err != nil {
			return -1, fmt.Errorf("read HTTP response body: %v", err)
		}
		return -1, fmt.Errorf("non-success response status code %d with body: %s", resp.StatusCode, data)
	}

	return -1, nil
}

func (l *discordLogger) Write(m Messager) error {
	payload, err := l.buildPayload(m)
	if err != nil {
		return fmt.Errorf("build payload: %v", err)
	}

	const retryTimes = 3

	// Try at most X times with respect to "retry_after" parameter.
	for i := 1; i <= retryTimes; i++ {
		retryAfter, err := l.postMessage(bytes.NewReader([]byte(payload)))
		if err != nil {
			return fmt.Errorf("post message: %v", err)
		}

		if retryAfter > 0 {
			time.Sleep(time.Duration(retryAfter) * time.Millisecond)
			continue
		}

		return nil
	}

	return fmt.Errorf("gave up after %d retries", retryTimes)
}

// DefaultDiscordName is the default name for the Discord logger.
const DefaultDiscordName = "discord"

// NewDiscord initializes and appends a new Discord logger with default name
// to the managed list.
func NewDiscord(vs ...interface{}) error {
	return NewDiscordWithName(DefaultDiscordName, vs...)
}

// NewDiscordWithName initializes and appends a new Discord logger with given
// name to the managed list.
func NewDiscordWithName(name string, vs ...interface{}) error {
	return New(name, DiscordIniter(), vs...)
}

// DiscordIniter returns the initer for the Discord logger.
func DiscordIniter() Initer {
	return func(name string, vs ...interface{}) (Logger, error) {
		var cfg *DiscordConfig
		for i := range vs {
			switch v := vs[i].(type) {
			case DiscordConfig:
				cfg = &v
			}
		}

		if cfg == nil {
			return nil, fmt.Errorf("config object with the type '%T' not found", DiscordConfig{})
		} else if cfg.URL == "" {
			return nil, errors.New("empty URL")
		}

		titles := discordTitles
		if cfg.Titles != nil {
			if len(cfg.Titles) != 5 {
				return nil, fmt.Errorf("titles must have exact 5 elements, but got %d", len(cfg.Titles))
			}
			titles = cfg.Titles
		}

		colors := discordColors
		if cfg.Colors != nil {
			if len(cfg.Colors) != 5 {
				return nil, fmt.Errorf("colors must have exact 5 elements, but got %d", len(cfg.Colors))
			}
			colors = cfg.Colors
		}

		return &discordLogger{
			noopLogger: &noopLogger{
				name:  name,
				level: cfg.Level,
			},
			username: cfg.Username,
			titles:   titles,
			colors:   colors,
			client:   http.DefaultClient,
		}, nil
	}
}

repository: cuibaiyi/clog

repo_name: cuibaiyi/clog


text:
package inputsrc

//go:generate go run github.com/maxbrunsfeld/counterfeiter/v6 -generate

//counterfeiter:generate -o fakes/inputSrc.go . InputSrc
type InputSrc interface {
	// ReadString returns the value (if any) and true/false to indicate whether the read was successful
	ReadString(
		inputName string,
	) (*string, bool)
}

repository: apexskier/opctl

repo_name: apexskier/opctl


text:
require recipes-support/mpfr/mpfr_3.1.2.bb
FILESEXTRAPATHS_prepend = "${COREBASE}/meta/recipes-support/mpfr/mpfr-3.1.2:"

DPN = "mpfr4"
inherit debian-package

DEBIAN_SECTION = "math"
DPR = "0"

LICENSE = "GPLv3+ & LGPLv3+"
LIC_FILES_CHKSUM = " \
file://COPYING;md5=d32239bcb673463ab874e80d47fae504 \
file://COPYING.LESSER;md5=6a6a8e020838b23406c81b19c1d46df6 \
"

SRC_URI += " \
file://long-long-thumb.patch \
"

repository: takuya-t/meta-debian

repo_name: takuya-t/meta-debian

branch_name: refs/heads/daisy


text:
// Package corona provides data from the Novel Coronavirus (COVID-19) Cases; data collected from here: https://github.com/open-covid-19/data
package corona

const (
	// Version is the library version.
	Version = "0.7.0"
)

repository: Oshuma/corona

repo_name: Oshuma/corona


text:
file(REMOVE_RECURSE
  "CMakeFiles/clangTidyAndroidModule.dir/AndroidTidyModule.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecAccept4Check.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecAcceptCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecCreatCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecDupCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecEpollCreate1Check.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecEpollCreateCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecFopenCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecInotifyInit1Check.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecInotifyInitCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecMemfdCreateCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecOpenCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/CloexecSocketCheck.cpp.o"
  "CMakeFiles/clangTidyAndroidModule.dir/ComparisonInTempFailureRetryCheck.cpp.o"
  "../../../../../../lib/libclangTidyAndroidModule.pdb"
  "../../../../../../lib/libclangTidyAndroidModule.a"
)

# Per-language clean rules from dependency scanning.
foreach(lang CXX)
  include(CMakeFiles/clangTidyAndroidModule.dir/cmake_clean_${lang}.cmake OPTIONAL)
endforeach()

license: NCSA

repository: venkyjntu-git/leg

repo_name: venkyjntu-git/leg

extension: cmake


text:
@Article{katayama:ar:2023,
  author    = {Katayama, Sotaro and Murooka, Masaki and Tazaki, Yuichi},
  title     = {Model predictive control of legged and humanoid robots: models and algorithms},
  journal   = {Advanced Robotics},
  year      = {2023},
  volume    = {37},
  number    = {5},
  pages     = {298-315},
  month     = {February},
  doi       = {10.1080/01691864.2023.2168134},
  url       = {https://www.tandfonline.com/doi/full/10.1080/01691864.2023.2168134},
  publisher = {TAYLOR \& FRANCIS LTD},
  address   = {2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND}
}


text:
package v1alpha1

import (
	corev1 "k8s.io/api/core/v1"
	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)

const (
	// ResourceKindFederatedResourceQuota is kind name of FederatedResourceQuota.
	ResourceKindFederatedResourceQuota = "FederatedResourceQuota"
	// ResourceSingularFederatedResourceQuota is singular name of FederatedResourceQuota.
	ResourceSingularFederatedResourceQuota = "federatedresourcequota"
	// ResourcePluralFederatedResourceQuota is plural name of FederatedResourceQuota.
	ResourcePluralFederatedResourceQuota = "federatedresourcequotas"
	// ResourceNamespaceScopedFederatedResourceQuota indicates if FederatedResourceQuota is NamespaceScoped.
	ResourceNamespaceScopedFederatedResourceQuota = true
)

// +genclient
// +k8s:deepcopy-gen:interfaces=k8s.io/apimachinery/pkg/runtime.Object
// +kubebuilder:resource:categories={karmada-io}
// +kubebuilder:subresource:status
// +kubebuilder:storageversion

// FederatedResourceQuota sets aggregate quota restrictions enforced per namespace across all clusters.
type FederatedResourceQuota struct {
	metav1.TypeMeta   `json:",inline"`
	metav1.ObjectMeta `json:"metadata,omitempty"`

	// Spec defines the desired quota.
	// +required
	Spec FederatedResourceQuotaSpec `json:"spec"`

	// Status defines the actual enforced quota and its current usage.
	// +optional
	Status FederatedResourceQuotaStatus `json:"status,omitempty"`
}

// FederatedResourceQuotaSpec defines the desired hard limits to enforce for Quota.
type FederatedResourceQuotaSpec struct {
	// Overall is the set of desired hard limits for each named resource.
	// +required
	Overall corev1.ResourceList `json:"overall"`

	// StaticAssignments represents the subset of desired hard limits for each cluster.
	// Note: for clusters not present in this list, Karmada will set an empty ResourceQuota to them, which means these
	// clusters will have no quotas in the referencing namespace.
	// +optional
	StaticAssignments []StaticClusterAssignment `json:"staticAssignments,omitempty"`

	// DynamicAssignments represents the rule about how to assign and ensure the quota for each cluster.
	// E.g.
	//   - The rule about how to assign the total amount of quotas(specified by Overall) to Clusters.
	//   - The rule about how to balance the quota between clusters in a dynamic way.
	//   - The rule about how to prevent workload from scheduling to cluster without quota.
	//   - The rule about how to prevent workload from creating to Karmada control plane.
	//   - The rule about how to cooperate with FederatedHPA to scale workloads.
	//
	// Note: We are in the progress of gathering user cases, if you are interested in
	//  this feature/capacity, please feel free to let us know.
	//
	// +optional
	// DynamicAssignments []DynamicClusterAssignment `json:"dynamicAssignments,omitempty"`
}

// StaticClusterAssignment represents the set of desired hard limits for a specific cluster.
type StaticClusterAssignment struct {
	// ClusterName is the name of the cluster the limits enforce to.
	// +required
	ClusterName string `json:"clusterName"`

	// Hard is the set of desired hard limits for each named resource.
	// +required
	Hard corev1.ResourceList `json:"hard"`
}

// FederatedResourceQuotaStatus defines the enforced hard limits and observed use.
type FederatedResourceQuotaStatus struct {
	// Overall is the set of enforced hard limits for each named resource.
	// +optional
	Overall corev1.ResourceList `json:"overall,omitempty"`

	// OverallUsed is the current observed total usage of the resource in the namespace.
	// +optional
	OverallUsed corev1.ResourceList `json:"overallUsed,omitempty"`

	// AggregatedStatus is the observed quota usage of each cluster.
	// +optional
	AggregatedStatus []ClusterQuotaStatus `json:"aggregatedStatus,omitempty"`
}

// ClusterQuotaStatus represents the set of desired limits and observed usage for a specific cluster.
type ClusterQuotaStatus struct {
	// ClusterName is the name of the cluster the limits enforce to.
	// +required
	ClusterName string `json:"clusterName"`

	corev1.ResourceQuotaStatus `json:",inline"`
}

// +k8s:deepcopy-gen:interfaces=k8s.io/apimachinery/pkg/runtime.Object

// FederatedResourceQuotaList contains a list of FederatedResourceQuota.
type FederatedResourceQuotaList struct {
	metav1.TypeMeta `json:",inline"`
	metav1.ListMeta `json:"metadata,omitempty"`
	Items           []FederatedResourceQuota `json:"items"`
}

repository: karmada-io/karmada

repo_name: karmada-io/karmada


text:
package database

// StorageBackends stores all the storage backends.
type StorageBackends struct {
	Space					*SpaceStorage
	WorkItem 			*WorkItemStorage
	WorkItemType	*WorkItemTypeStorage
	Area					*AreaStorage
	Comment				*CommentStorage
	Iteration			*IterationStorage
	LinkCategory	*LinkCategoryStorage
	Link					*LinkStorage
	LinkType			*LinkTypeStorage
	User					*UserStorage
}

repository: rtnpro/themis

repo_name: rtnpro/themis