Saravanan666/pypi-org huggingface dataset:
Package: kser-crypto
kser-cryptoKsermodule allow you to encrypt and decrypt messages in kafka usinglibsodium.QuickstartFirst, install kser-crypto usingpip:$ pip install kser-crypto[pykafka]NoteOnly kafka-python is implemented at the moment.ExamplesMake sure to have the environment variableKSER_SECRETBOX_KEYdefinded.Consumerfromkser_crypto.python_kafka.consumerimportCryptoConsumerconsumer=CryptoConsumer(config=dict(...),topics=[...])consumer.run()Producerimporttimefromuuidimportuuid4fromkser.schemasimportMessagefromkser_crypto.python_kafka.producerimportCryptoProducerproducer=CryptoProducer(config=dict(...))producer.send("test",Message(uuid=str(uuid4()),entrypoint="myTest"))time.sleep(1)RequirementsPython 3.xLibsodiumDocumentationsProject:http://kser.readthedocs.io/Libsodium:https://download.libsodium.org/doc/confluent-kafka-python:http://docs.confluent.io/current/clients/confluent-kafka-pythonkafka-python:http://kafka-python.readthedocs.io/en/master/Other linksPyPI:https://pypi.python.org/pypi/kser-cryptoProject issues:https://github.com/cdumay/kser-crypto/issuesLicenseLicensed under MIT license (LICENSEorhttp://opensource.org/licenses/MIT)
Package: kservehelper
kserve-helperThis is a helper for building docker images for ML models.Hereare some basic examples.
For more examples, please visit thisrepo.Implement a Model Class for ServingTo build a docker image for serving, we only need to implement one class withloadandpredictmethods:classModel:defload(self):# Load the modelpassdefpredict(self,image:str=Input(description="Base64 encoded image",default=""),radius:float=Input(description="Standard deviation of the Gaussian kernel",default=2))->Path:ifimage=="":raiseValueError("The input image is not set")im_binary=base64.b64decode(image)input_image=Image.open(io.BytesIO(im_binary))output_image=input_image.filter(ImageFilter.GaussianBlur(radius))output_path=KServeModel.generate_filepath("image.jpg")output_image.save(output_path)returnPath(output_path)Theloadfunction will be called during the initialization step, which will be only called once.
Thepredictfunction will be called for each request. The input parameter info is specified by
theInputclass. ThisInputclass allows us to set parameter descriptions, default value and
constraints (e.g., 0 <= input value <= 1).The output typing of thepredictfunction is important. If the output type isPathorList[Path], the webhook for uploading will be called afterpredictis finished. In this case,
the input request should also contain an additional key "upload_webhook" to specify the webhook server
address (anexample).
If the output type is notPath, the results will be returned directly without calling the webhook.Write a Config for Building Docker ImageTo build the corresponding docker image for serving, we only need to write a config file:build:python_version:"3.10"cuda:"11.7"# a list of commands (optional)commands:-"aptinstall-ysoftware-properties-common"# a list of ubuntu apt packages to install (optional)system_packages:-"git"-"python3-opencv"# choose requirements.txt (optional)python_requirements:-"requirements.txt"# a list of packages in the format <package-name>==<version>python_packages:-"kservehelper>=1.1.0"-"salesforce_lavis-1.1.0-py3-none-any.whl"-"git+https://github.com/huggingface/diffusers.git"-"controlnet_aux==0.0.7"-"opencv-python==4.8.0.74"-"Pillow"-"tensorboard"-"mediapipe"-"accelerate"-"bitsandbytes"# The name given to built Docker imagesimage:"<DOCKER-IMAGE-NAME:TAG>"# model.py defines the entrypointentrypoint:"model.py"In the config file, we can choose python version, cuda version (and whether to use NGC images),
system packages and python packages. We need to set the docker image name and the entrypoint.
The entrypoint is just the file that defines the model class above.To build the docker image, we can simply run in the folder containing the config file:kservehelperbuild.To push the docker image, run this command:kservehelperpush.For more details, please check the implementations in therepo.
Package: ksher
ksher_sdk_pythonKsher python Library athttp://api.ksher.netHow to installpip3 install ksherfrom ksher.ksher_pay_sdk import KsherPay

appid=mch35005
privatekey=/Users/yourpath/repo/ksher_sdk_python/mch_privkey.pem
pubkey=/Users/yourpath/repo/ksher_sdk_python/ksher_pubkey.pem

payment_handle = KsherPay(appid, privatekey, pubkey)
data = {
    "total_fee": "100",
    "fee_type": "THB",
    "mch_code": "",
    "refer_url": "http://www.baidu.com",
    "mch_redirect_url":"http://www.baidu.com/api/gateway_pay/success",
    "mch_redirect_url_fail":"http://www.baidu.com/api/gateway_pay/fail",
    "mch_notify_url":"http://www.baidu.com/api/gateway_pay/notify_url/",
    "product_name":"",
    "channel_list":"promptpay,linepay,airpay,truemoney,atome,card,ktc_instal,kbank_instal,kcc_instal,kfc_instal,scb_easy,bbl_deeplink,baybank_deeplink,kplus,alipay,wechat,card,ktc_instal,kbank_instal,kcc_instal,kfc_instal"
}
data['mch_order_no'] = generate_order_id()
resp = payment_handle.gateway_pay(data)
Package: ks.hivurtcms
ks.hivurtcms==============ks.hivurtcms package is a meta package that requires dozens of products to easycreate cms.This product is part of HivurtChanges for ks.hivurtcms==========================1.0.1 (first release)-------------------- Initial code commitDetailed Documentation**********************ks.hivurtcms==============ks.hivurtcms package is a meta package that requires dozens of products to easycreate cms.This product is part of Hivurt
Package: ks.hivurtskin.textdocument
ks.hivurtskin.textdocumentks.hivurtskin.textdocument package is a plugin for textdocument administration
ks.hivurtskin for Hivurt.This product is part of HivurtChanges for ks.hivurtskin.textdocument0.1.0 (unreleased)Initial code commitDetailed Documentation
Package: ksif
# ksif
Data download and back testing tools for KSIF
# KSIF Preprocessing
Data preprocessing tool for KSIF library## Usage
Set the root directory as your working directory like ‘C:UserswillbePycharmProjectsksif_preprocessing’.
Pass a parameter indicating a file name in data folder like ‘180730’.
After running ‘core/__init__.py’, the result files are saved in the root directory.## 여러가지 feature 추가 작업 재시작, daily, semi-month frequency 추가
Package: ksiterate
ksiteratePython module for iteration over product of given parameter valuesDescriptionThis library provides a classIteration, which implements iteration over a product of given parameters.Example 1:itspec=[{'A':[1,2,3],'B':[10,11,12],'C':[20,21]},];it=ksiterate.Iteration(itspec);foriinit.iterate():print(i);This code ireates over every possible combination of values defined for parametersA,BancC: (1, 10, 20), (1, 10, 21), (1, 11, 20), ...It is also possible to split the iteration into inner/outer loops:foriinit.iterate(include_params={"C",}):print(i);forjinit.iterate(i,):print(j);This code iterates overCin the outer loop and over the rest of parameters in the inner loop.Multiple parameter specifications may be included:itspec=[{'A':[1,2,3],'B':[10,11,12],'C':[20,21]},{'A':[5,6,],'B':[10,11],'C':[22,23]}];it=ksiterate.Iteration(itspec);foriinit.iterate():print(i);In this case, the iteration would account for sum of two products of combinations from first dictionary and from second dictionary.
Package: ksize
ksizeA Tool for Kafka Topic SizeThe Kafka Topic Size Tool is a command-line utility that provides information about the size of topics in a Kafka cluster. It utilizes the Kafka command-line toolskafka-topicsandkafka-log-dirsto retrieve and analyze the size information.InstallationYou can install the Kafka Topic Size Tool fromPyPIusingpip:pip install ksizeUsageCommand Syntaxksize -b <bootstrap-server> [-c <config>] (-t <topic> | -a) [-o <output>]Options-b,--bootstrap-server: The Kafka cluster to connect to (e.g.,localhost:9092).-c,--config: The path to the Kafka config file.-t,--topic: The topic or list of topics to get the size for.-a,--all: Get the size for all topics.-o,--output: Send output to a file.ExamplesGet the size of a specific topic:ksize -b localhost:9092 -c /path/to/config/file.conf -t my_topic -o output.txtGet the size of all topics:ksize -b localhost:9092 -c /path/to/config/file.conf -a -o output.txtImportant NotesEither--topicor--allis required.Make sure you have the Kafka command-line tools (kafka-topicsandkafka-log-dirs) installed and available in your system's PATH.The tool calculates the size of topics in megabytes (MB).If an error occurs during the process, it will be displayed in the console output.LicenseThis tool is provided under the MIT License. Feel free to use, modify, and distribute it according to the terms of the license.
Package: ksj
ksj国土数値情報ダウンロードサービスのWeb APIを簡単に扱うためのPythonパッケージです。データ分析での利用を想定しており、本パッケージのメソッドの返り値はpandasやgeopandasのオブジェクトになります。インストール方法pip install ksj⚠️Windowsの場合geopandasの依存パッケージがpipではインストールできないため、先にそちらをインストールする必要があります。ご注意ください。UNIX系OSの場合、pip install ksjだけでgeopandasもインストールされます。ドキュメント使用方法はksj documentationをご覧ください利用上の注意APIの利用やAPIで得られる国土数値情報の利用にあたっては、国土数値情報ダウンロードサービスの利用約款および同Web APIの利用規約をご確認の上ご利用ください。http://nlftp.mlit.go.jp/ksj/other/yakkan.htmlhttp://nlftp.mlit.go.jp/ksj/api/about_api.html
Package: kslhub
NAMEklshub - a datahub based on jupyterhubSYNOPSISkslhub -f config_fileDESCRIPTIONUSEAUTHORWritten by Samuel Kortas (samuel.kortas (at) kaust.edu.sa)REPORTING BUGSReport kslhub bugs tohelp@hpc.kaust.edu.saCOPYRIGHTCopyright (c) 2019, KAUST Supercomputing Laboratory
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.SEE ALSOkslhub official documentation pages:<http://kslhub.readthedocs.io>KAUST Supercomputing Laboratory: <http://hpc.kaust.edu.sa/>
Package: ks-metric
Kolmogorov-Smirnov metric (ks metric) is derived from K-S test. K-S test
measures the distance between two plotted cumulative distribution
functions (CDF). To use it as a metric for classification machine
learning problem we see the distance of plotted CDF of target and
non-target. The model that produces the greatest amount of separability
between target and non-target distribution would be considered the
better model.InstallationThe package requires:pandasandnumpy.To install the package, execute:$pythonsetup.pyinstallorpipinstallks_metricUsageTo get the KS score :fromsklearn.datasetsimportload_breast_cancerfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLogisticRegressionfromks_metricimportks_scoredata=load_breast_cancer()X,y=data['data'],data['target']X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)clf=LogisticRegression(random_state=0,max_iter=10000).fit(X_train,y_train)ks_score(y_train,clf.predict_proba(X_train)[:,1])KS table :fromks_metricimportks_tableks_table(y_train,clf.predict_proba(X_train)[:,1])KS scorer (for hyperparameter search) :fromsklearn.model_selectionimportGridSearchCVfromks_metricimportks_scorerclf=GridSearchCV(estimator=LogisticRegression(),param_grid={'C':[0.01,0.1,1]},scoring=ks_scorer)see the example notebook for detailed usage.
Package: ksmi
kairos-smiMulti-server gpu monitoring programSee change Log:change logusage: ksmi [-h] [-l] [-c CONFIG]

optional arguments:
  -h, --help            show this help message and exit
  -l, --loop            loop forever
  -c CONFIG, --config CONFIG
                        set config file locationquick start1. installInstall with pip$pipinstallksmi
or
$pip3installksmi2. Setup config fileEditconfig.json. Add your gpu server address inconfig.json.{"hosts":["<username>@<host>[:port]","<username>@<host>[:port]"]}3. Add rsa_id to servercreate new rea_id and add to your server$python3-mksmi.copy_id-cconfig.json-n4. Run It!$python3-mksmi-cconfig.json-lkairos-smiusage: kairos_smi.py [-h] [-l] [-c CONFIG]

optional arguments:
  -h, --help            show this help message and exit
  -l, --loop            loop forever
  -c CONFIG, --config CONFIG
                        set config file locationSimple usageRun once$ python3 -m ksmi -c config.jsonRun forever$ python3 -m ksmi -c config.json -lutilscopy_idusage: copy_id.py [-h] [-n] [-c CONFIG] [-s SERVER]

optional arguments:
  -h, --help            show this help message and exit
  -n, --new_id          generate new id_rsa key
  -c CONFIG, --config CONFIG
                        set config file to use host list
  -s SERVER, --server SERVER
                        set a server to copy idcopy id(rsa) to serverUse withconfig.json$ python3 -m ksmi.copy_id -c config.json
or
$ python3 -m ksmi.copy_id --config config.jsonUse with individual Server address$ python3 -m ksmi.copy_id -s [username@]<IP>[:port]
or
$ python3 -m ksmi.copy_id --server [username@]<IP>[:port]Make a new id_rsa$ python3 -m ksmi.copy_id -n
or
$ python3 -m ksmi.copy_id --new_id
Package: ksmtp
ksmtp=====**ksmtp** - Simple Python SMTP relay replacement for sendmail with SSL authenticationUseful for relaying all email through an account like Gmail, without themessy configurations of Postfix / Sendmail.Source======GitHub - https://github.com/oeey/ksmtpPyPI====PyPI - https://pypi.python.org/pypi/ksmtppip install ksmtpUsage=====1. pip install ksmtp2. edit /etc/ksmtp.conf with your login credentials3. (optional) create symlink to ksmtp to replace sendmail```ln -s `which ksmtp` /usr/sbin/sendmail```4. send test mail```ksmtp test@test.comksmtp test@test.com -s "some subject"```Code Usage==========Sample Python Code:import ksmtpksmtp.send(to="test@test.com",subject="subject",body="some message")Sample Config=============Warning: make sure to backup this configuraiton file!  Python's pip will overwrite it on updates to this package./etc/ksmtp.conf:```[ksmtp]## from email addressfrom = username@gmail.com## useruser = username@gmail.com## passwordpass = password## serverserver = smtp.gmail.com## portport = 465#port = 587## securesecure = ssl#secure = tls## default to email address (if none specified)#to = user@domain.com## default catch-all (always CC)#catch-all = user@domain.com## default subject#subject = default test subject```Issues======Gmail:If you get an "smtplib.SMTPAuthenticationError" and your credentials arecorrect, you may need to "allow less secure apps access" to your account.See https://support.google.com/accounts/answer/6010255
Package: ksns
ksnsKubernetes CLI wrapper in Python for switching and listing namespaces.
It does not require kubectl to work.Developed by Ratul (c) 2021InstallationRequires Python 3.6+.pip3installksnsUsageYou need to Set theKUBECONFIGenvironment variable or~/.kube/configwill be considered.ratul➤ksns--helpUsage:python-mksns[OPTIONS]COMMAND[ARGS]...NOTE:SettheKUBECONFIGenvironmentvariableor~/.kube/configwillbeconsideredUSAGE:1.listnamespaces:ksnslist2.switchnamespaces:ksnsns<namespace_name>Options:--helpShowthismessageandexit.Commands:listListofnamespacesincontextnsSwitchtoanothernamespace:<namespace_name>The current namespace color you are in will beyellowin the output of list namespace.List namespacesratul➤ksnslistdefaultdemokube-node-leasekube-publickube-systemkubernetes-dashboardmonitoringnote:usingconfig:/home/ubuntu/configSwitch Namespaceratul➤ksnsnsmonitoringswitchedtomonitoringnamespacenote:usingconfig:/home/ubuntu/configLocal ChangesIt’s recommended to have a virtualenv for the project.Go to the project dir and install dependenciescd<project-path>pip3install-rrequirements.txtpip3install--editable.
Package: ksoftapi
KSoftAPI.pyThe official Python WrapperInstallInstalling via pip:pip install ksoftapiExample Usage:importksoftapikclient=ksoftapi.Client('Your API key here')asyncdeffind_lyrics(query:str):try:results=awaitkclient.music.lyrics(query)exceptksoftapi.NoResults:print('No lyrics found for '+query)else:first=results[0]print(first.lyrics)Obtain an API key|Join the Discord server
Package: kson-jbrazeal
KSON: JSON with SQL and NetworkingOf course it's a good idea: why would you ask?KSON is a superset of JSON with the following features:Remote document references (so you can embed a JSON, KSON, or CSV file available at a public URL or file address)Embedded SQL: Write queries against other objects in your JSON file (including references and deeply nested objects)
with the full power of SQLite and have the queries evaluate to JSONUse comments (/* ... */) and global named references ("foo": "bar" as myRef).Compiles to JSON: Runkson file.kson(see installation instructions below) and boom! you have JSON.KSON combines the portability of the top data exchange formats (JSON, CSV) with the expressiveness of the leading data
querying language
(SQL), and the flexibility of dynamic embedded references.InstallationRunpython3-mpipinstall-gksonThis will create a global executableksonwhich you can run on.ksonfiles to produce.jsonfiles:ksonfile.kson# Sends JSON to stdoutorksonfile.kson>file.json# Pipe the output to a fileExamplesYou can find examples in theexamples/directory.examples/gdp.kson: Demonstrates how you can query an external data source (in this case, CSV file on GitHub.)examples/join-gdp-and-population.kson: Fetch data fromtwodata sources (GDP by country
and population by country) and perform a join to see GDP per capita.FAQHow does this work?It's pretty simple, actually: First we parse the KSON file viarecursive descent.
Where JSON has arrays and
dictionaries, we throw in a few extra types - refs, aliases, and SQL queries.To compile the file, we traverse the tree, making network requests, building appropriately-named SQLite tables, and
performing SQL queries as we go, eventually collapsing the whole business to JSON.Some constraints of this approach are that we make network requests in serial, and that you must define an alias before
any SQL queries which use it. On the other hand, we achieve a great deal of flexibility in indexing into deeply nested
remote documents: suppose a remote document has a structure like so:{"foo":{"bar":{"baz":[1,2,3,4]}}}If we alias the document asdoc, then our SQL queries can acess the contents of the array by queryingselect * from "$doc$foo$bar$baz".Is this a good idea, and where did you get it?Haha, well, um. More appropriate adjectives might include "interesting", "tempting", and "risky".For reasons which are best elided, I had to write an enormous number of JSON parsers in a short period of time, and then
got some additional ideas about the format. It's called "KSON" because k comes after j, get it? :-)
Package: ksos
KSOSThe Kelvin Sustainable Operations Software (KSOS) package provides tools that enable users to interact with the KSOS Platform through the command line interface.  KSOS is the AI software platform for building and managing control applications.  Control applications combine insights, events and actions into powerful automation workflows.   KSOS provides tools and interfaces to ease the effort of data science, app development, testing, validation, management, and finally orchestration and deployment.For additional information, visithttps://docs.kelvininc.comor contactsupport@kelvininc.com.LicenseSeeLicensefor more information.
Package: kso-utils
KSO - UtilsThe Koster Seafloor Observatory is an open-source, citizen science and machine learning approach to analyse subsea movies.Module OverviewThis utils module contains scripts and resources that are called by the tutorials used in thedata management moduleandObject Detection module.CitationIf you use this code or its models in your research, please cite:Anton V, Germishuys J, Bergström P, Lindegarth M, Obst M (2021) An open-source, citizen science and machine learning approach to analyse subsea movies. Biodiversity Data Journal 9: e60548.https://doi.org/10.3897/BDJ.9.e60548Collaborations/questionsYou can find out more about the project athttps://www.zooniverse.org/projects/victorav/the-koster-seafloor-observatory.We are always excited to collaborate and help other marine scientists. Please feel free tocontact uswith your questions.
Package: kspam-filtering
piss_off_spam_kwordㅅr 고ㅏ E l ㅂ i 물렀거라📌 프로젝트를 만든 이유유투브를 보면서 가장 많이 보는 화면이 있습니다. 물론 영상 목록이겠지만, 영상 별 달린 댓글을 쭉 읽게 되는데 한때 xx티비 광고가 댓글을 점령했던 영상을 본 적도 있었고, 해당 유투버가 차단 키워드를 설정했는지 xxEl비 등 다양한 변화로 키워드를 피해가는 것을 볼 수 있었습니다. 그래서 재밌겠다 싶어서 '사과티비'라는 키워드만 입력해서 모든 변화구를 다 잡아보는 프로젝트를 진행하게 되었습니다📂 모듈 구조📁 piss_off_spam_kword📁 model : 해당 서비스에 사용되는 ML/DL 등의 모델 관련 소스들이 들어 있습니다__ init __.py추후 수정📁 module : 해당 서비스에 사용되는 python 모듈이 있습니다__ init __.py📁 complete_fake_df(완료) : 완전형 특수문자(ex. ㉼)를 원래 문자로 바꿔주는 모듈__ init __.pymaking_complete_fake_df.py📁 predict_sentence(미완) : 문자 예측에 관련된 모듈📁 base📁 files📁 font__ init __.pypredict.py📁 text_preprocessing(완료) : 텍스트 처리에 관련된 모듈📁 default__ init __.pytext_preprocessing.py📁 utils : 모듈에 사용되는 각 종 함수들이 있습니다__ init __.pyfunctions_for_math.py : 수학계산에 사용되는 함수들functions_for_predict.py : 예측 모듈에 사용되는 함수들functions_for_text.py : 텍스트 처리에 사용되는 함수들
Package: kspconfig
kspconfig-pythonloadl(list) : 줄 list를 받아 dict를 출력한다loads(str) : 엔터로 묶인 줄 뭉치를 받아 dict를 출력한다dumpl(dict): 위의 출력을 받아 예쁘게 들여써진 줄 list를 출력한다dumps(dict) : 위의 출력을 받아 예쁘게 들여써진 엔터로 묶인 줄 뭉치를 출력한다_rdump(dict) : 위의 출력을 받아 들여써지지 않은 줄 list를 출력한다
Package: kspconfig-penta
kspconfig-pythonloadl(list) : 줄 list를 받아 dict를 출력한다loads(str) : 엔터로 묶인 줄 뭉치를 받아 dict를 출력한다dumpl(dict): 위의 출력을 받아 예쁘게 들여써진 줄 list를 출력한다dumps(dict) : 위의 출력을 받아 예쁘게 들여써진 엔터로 묶인 줄 뭉치를 출력한다_rdump(dict) : 위의 출력을 받아 들여써지지 않은 줄 list를 출력한다
Package: kSpider
@dib-lab/kSpider📖 Table of Contents➤ Table of Contents➤ Table of Contents➤ Introduction➤ Quick Installation (pip)➤ Build from source➤ Authors➤ License➤ IntroductionkSpideris a user-friendly command line interface program to perform sequence clustering. First, it creates an index using kProcessor for the source sequences. Second, it constructs a pairwise containment matrix through a single iteration over the index. Finally, it builds a graph from the pairwise matrix and applies a connected-components graph algorithm to extract the clusters with a user-defined containment threshold.Documentations are hosted athttps://dib-lab.github.io/kSpider➤ Quick Installation (pip)pipinstallkSpider➤ Manual build / DevelopmentInstall dependenciessudoapt-getinstallg++swigcmakepython3-devzlib1g-devlibghc-bzlib-devpython3-distutilslibboost-all-devgitclonehttps://github.com/dib-lab/kSpider.gitcdkSpider
gitsubmoduleupdate--init--recursive
cmake-Bbuild
cmake--buildbuild
bashbuild_wrapper.sh➤ AuthorsMohamed AbuelaninTamer Manosur➤ LicenseLicensed underMIT License.
Package: kSpider2
kSpider2 | C++ verison of the kSpider software tool.InstallationClonegitclonehttps://github.com/mr-eyes/kSpider2.gitcdkSpider2
gitsubmoduleupdate--init--recursiveInstall# pwd = kSpider2PROJECT=$(pwd)cdlib/kProcessor
mkdirbuild&&cdbuild&&cmake..&&makecd$PROJECTmkdirbuild&&cdbuild/
cmake..&&makecd..UsageINDEX_PREFIX=idx_seq

./kSpider_pairwise${INDEX_PREFIX}