Saravanan666/pypi-org huggingface dataset:
Package: fast-alphashape
fast-alphashapeA Rust backed implementation for generating 2Dalpha shapesfrom points.This project is still a prototype and should not be considered production-ready.Alpha shapes are often used to generalise bounding polygons containing sets of points. The alpha parameter is defined as the valuea, such that an edge of a disk of radius1/acan be drawn between any two edge members of a set of points and still contain all the points. Theconvex hull, a shape resembling what you would see if you wrapped a rubber band around pegs at all the data points, is an alpha shape where the alpha parameter is equal to zero.Installationpipinstallfast-alphashapeQuick startThe package provides analphashapefunction that takes a 2D array of points and an alpha value:importnumpyasnpfromfast_alphashapeimportalphashapepoints=np.array([(0.0,0.0),(0.0,1.0),(1.0,1.0),(1.0,0.0),(0.5,0.25),(0.5,0.75),(0.25,0.5),(0.75,0.5),])alpha_polygon=alphashape(points,alpha=2.0)DevelopmentPrequisites:Python (>=3.7) and makeRust toolchainAfter cloning the repository, the Makefile includes helpful commands for setting up a development environment, linting, formatting, testing, and benchmarking. Get started as follows:# setup a new virtual environmentpython-mvenv.venvsource.venv/bin/activate# install the development dependenciesmakeinstall# check other available commandsmakehelpTooling:Cargo andPytestare used for testing the Rust and Python code respectively (see/tests).Python code is linted usingflake8and formatted usingBlack; rust code withcargo fmtandcargo clippy.pre-commitis used to run these checks locally before files are pushed to gitTheGithub Actions pipelineruns these checks and testsSemantic-releaseis used withconventional commitsfor automated releasing to PyPICreditsThis project is largely a Rust port of theAlpha Shape Toolboxby Ken BellockImage credit: Sigbert,CC BY-SA 3.0, viaWikimedia Commons
Package: fasta-manager
FASTA managerThe FASTA Manager is a versatile command-line tool designed to assist in the management and analysis of DNA or protein sequences stored in FASTA format. This tool provides functionalities for formatting, transforming, and generating statistics or plots from sequence data.InstallationYou can install this program using pip with:pip install fasta-managerCommand Line Arguments--dremove: Remove duplicate sequences.--drename: Rename duplicate sequences.--reverse: Reverse sequences.--complement: Complement sequences.--rc: Reverse complement sequences.--stats: Compute statistics.--casefile: Case transformation (original, upper, lower).--plots: Generate plots.ExamplesExample 1: Remove duplicates and generate statisticsfasta --dremove --statsExample 2: Reverse complement sequences and generate plotsfasta --rc --plotsExample 3: Perform case transformationfasta --casefile upperYou can also combine parameters where order doesn't matter.fasta --stats --plots --casefile lower --drenameResultsThe processed sequences will be stored in theresultsdirectory, and statistics (if computed) will be saved in thestatsdirectory. Plots (if generated) will be saved in theplotsdirectoryInput File FormatThe input files are expected to follow the format:>ID
SequenceExampletest_1.fasta>S1
actgACTG
>S2
ctgaCTGA
>S3
gggaGGGA
Package: fasta-merge
FASTA MergeDescriptionA tool to merge multiple FASTA alignments to the same reference sequence. Given multiple files with sequences
aligned to the same reference sequence, merge reference sequences and aligned sequences such that both alignment
and spacing are preserved. This tool may not be suitable for large files, as it reads the full contents of all
provided files into memory.Note: This tool only does rudimentary alignment on the reference sequences. For best results, one of the reference
sequences should be a superset of all other reference sequences. If this is not the case, this tool will fall
back to aligning all reference sequences pairwise, which requires that all pairs of reference sequences overlap.SetupDependenciespython >= 3.6argoptpykxnrInstallationThis project is available through pip as fasta-merge. To install, run:pip install fasta-mergeORpython -m pip install fasta-mergeExecuting programFor basic usage, pass the tag name of the reference sequence and a list of files as input.
Output can be redirected to a file to save merged alignments.fasta-merge <label> [<file>...] > output.fastaHelpfasta-merge -hAuthorsConnor KeaneLicenseThis project is licensed under the GPL3 License - see the LICENSE.md file for detailsAcknowledgmentsThanks to the CU Boulder Whiteley Lab
Package: fastami
FastAMIA Monte Carlo approximation to the adjusted and standardized mutual information for faster clustering comparisons. You can use this package as a drop-in replacement forsklearn.metrics.adjusted_mutual_info_score, when the exact calculation is too slow, i.e. because of large datasets and large numbers of clusters.Installationfastamirequires Python >=3.8. You can installfastamivia pip from PyPI:pipinstallfastamiUsage ExamplesFastAMIYou can use FastAMI as you would useadjusted_mutual_info_scorefromscikit-learn:fromfastamiimportadjusted_mutual_info_mclabels_true=[0,0,1,1,2]labels_pred=[0,1,1,2,2]ami,ami_error=adjusted_mutual_info_mc(labels_true,labels_pred)# Output: AMI = -0.255 +- 0.008print(f"AMI ={ami:.3f}+-{ami_error:.3f}")Note that the output may vary a little bit, due to the nature of the Monte Carlo approach. If you would like to ensure reproducible results, use theseedargument. By default, the algorithm terminates when it reaches an accuracy of0.01. You can adjust this behavior using theaccuracy_goalargument.FastSMIFastSMI works similarly:fromfastamiimportstandardized_mutual_info_mclabels_true=[0,0,1,1,2]labels_pred=[0,1,1,2,2]smi,smi_error=standardized_mutual_info_mc(labels_true,labels_pred)# Output: SMI = -0.673 +- 0.035print(f"SMI ={smi:.3f}+-{smi_error:.3f}")While FastSMI is usually faster than an exact calculation of the SMI, it is still orders of magnitude slower than FastAMI. Since the SMI is not confined to the interval[-1,1]like the AMI, the SMI by default terminates at a given absolute or relative error of at least0.1, whichever is reached first. You can adjust this behavior using theprecision_goalargument.Citing FastAMIIf you usefastamiin your research work, please cite the corresponding paper (will probably be published by March 2023):Klede et al., (2023). FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics. Proceedings of the AAAI Conference on Artificial Intelligence.
Package: fastani
FastANIThis package has been developed to provide a Python interface to theFastANItool.InstallationNote: You must install the FastANI binaries yourself for this package to work.This package only has standard dependencies and works for Python versions >=3.6.pipinstallfastaniBasic UsageNote: All features except for the matrix and visualization method have been implemented.To use this package, simply call thefastanimethod, all parameters except for thequeryandreferencearguments are optional, and in this case the FastANI default values are used.Thequeryandreferencearguments can either be a string, or collection of strings that point to the fasta file(s).fromfastaniimportfastaniresult=fastani(query='query.fna',reference='reference.fna')dict_results=result.as_dict()# Accessing resultsprint(dict_results['query.fna']['reference.fna'].ani)# 89.1234print(dict_results['query.fna']['reference.fna'].n_frag)# 50print(dict_results['query.fna']['reference.fna'].total_frag)# 100print(dict_results['query.fna']['reference.fna'].align_frac)# 0.5# Writing results to diskresult.to_file('results.txt')The FastANI default parameters can be overridden by passing the following arguments:exe: The path to the FastANI binary.k: The kmer size to use.cpus: The number of CPUs to use.frag_len: Fragment length to use.min_frac: Minimum fraction of genome shared.min_frag: Minimum number of aligned fragments(version < 1.3).Advanced UsageThere are two additional arguments that can be supplied to thefastanimethod:single_execution: If set toTrue(default), FastANI will use the query and reference list parameters. If set
toFalse, then each genome will be analysed individually.bidirectional: If set toFalse(default), FastANI will only perform aquery -> referencecomparison. If set
toTrue, then areference -> querycomparison will also be performed.
Package: fast-ann
:rocket: ANN :facepunch:Installpip install fast-annUsagefromannimportANNimportnumpyasnpdata=np.random.random((1000,128)).astype('float32')ann=ANN()ann.train(data,index_factory='IVF4000, Flat',noramlize=True)dis,idx=ann.search(data[:10])print(dis)print(idx)faiss不同量级对应的训练时间及内存测试压缩方式测试四种组合：默认是查向量返回 distance与indexid => id/vectorvector => id/vectorpush场景需要 docid => title_vector => docid线上服务id2wordid2vector
Package: fasta-one-hot-encoder
Simple python to lazily one-hot encode fasta files using multiple processes, either single bases or considering arbitrary kmers.How do I install this package?As usual, just download it using pip:pipinstallfasta_one_hot_encoderTests CoverageSince some software handling coverages sometime get slightly different results, here’s three of them:ExamplesBasesOne-hot encode to bases.fromfasta_one_hot_encoderimportFastaOneHotEncoderencoder=FastaOneHotEncoder(nucleotides="acgt",lower=True,sparse=False,handle_unknown="ignore")path="test_data/my_test_fasta.fa"encoder.transform_to_df(path,verbose=True).to_csv("my_result.csv")Obtained results should look like:acgt000101010020100KmersOne-hot encode to kmers of given length.fromfasta_one_hot_encoderimportFastaOneHotEncoderencoder=FastaOneHotEncoder(nucleotides="acgt",kmers_length=2,lower=True,sparse=False,handle_unknown="ignore")path="test_data/my_test_fasta.fa"encoder.transform_to_df(path,verbose=True).to_csv("my_result.csv")Obtained results should look like:aaacagatcacccgctgagcgggttatctgtt0000000000100000010000010000000000
Package: fast-aosim
FAST (Fourier domain Adaptive optics Simulation Tool)FAST is a simulation tool that utilises a Fourier domain AO model to enable rapid Monte Carlo characterisation of free space optical links between the Earth and satellites. For more details, see the paperhttps://doi.org/10.1364/OE.458659.Documentation is hosted atojdf.github.io/fast.WARNING: FAST is under active development and the simulation API and configuration is not fixed. If you find a bug, or something is not working as you would expect, please email me or make an issue on GitHub, it's likely I've broken something!RequirementsSeerequirements.txtIt is recommended to installpyFFTWto speed up FFTs.InstallationPyPIpip install fast-aosimFrom SourceFor the latest developments, clone this repository runpip install -r requirements.txtto get the required dependencies. Then, ensure that the FAST directory is on yourPYTHONPATH, or navigate to the directory and runpip install .To confirm correct installation, try runningcd test && python test_script.pyin the FAST directory, which should run a short simulation. You can also run the unit tests withpytest -v test/tests_pytest.pyConfigurationConfig is handled currently by python scripts, an example of which is shown intest_params.py. The config file should define a dictionarypwhich contains all of the configuration information required. If any values are missed, they will be replaced by defaults.To start a simulation either in the python shell or in a script, you can either pass the filename of the script defining the config dictionary, or you can provide the dictionary itself, which can be useful if you are scanning through parameters. Soimport fast
sim = fast.Fast("your_config_file.py")orimport fast
p = {your config}
sim = fast.Fast(p)Running the simulationThe simulation is run by calling therun()function on the sim object. This will compute the phase screens and log-amplitude values, and then compute the detected power or phase/amplitude for coherent detection. The results are stored insim.resultand are also returned fromrun(), i.e.res = sim.run(). Results in either dB or power units can be obtained from e.g.sim.result.dB_rel(for dB relative to the diffraction limit) orsim.result.power. See the documentation for more details.To save the simulation results as a fits file with header information, usesim.save(filename).
Package: fastaparser
FastaParserA Python FASTA file Parser and Writer.The FASTA file format is a standard text-based format for representing nucleotide and aminoacid sequences
(usual file extensions include: .fasta, .fna, .ffn, .faa and .frn).
FastaParser is able to parse such files and extract the biological sequences within into Python objects.
It can also handle and manipulate such sequences as well as write sequences to new or existing FASTA files.InstallationWithpip:$pipinstallfastaparserUsageRead FASTA filesGenerate python objects from FASTA files:>>>importfastaparser>>>withopen("fasta_file.fasta")asfasta_file:parser=fastaparser.Reader(fasta_file)forseqinparser:# seq is a FastaSequence objectprint('ID:',seq.id)print('Description:',seq.description)print('Sequence:',seq.sequence_as_string())print()output:ID: sp|P04439|HLAA_HUMAN
Description: HLA class I histocompatibility antigen, A alpha chain OS=Homo sapi...
Sequence: MAVMAPRTLLLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRM...

ID: sp|P15822|ZEP1_HUMAN
Description: Zinc finger protein 40 OS=Homo sapiens OX=9606 GN=HIVEP1 PE=1 SV=3...
Sequence: MPRTKQIHPRNLRDKIEEAQKELNGAEVSKKEILQAGVKGTSESLKGVKRKKIVAENHLKKIPKSPLRN...or just parse FASTA headers and sequences, which is much faster but less feature rich:>>>importfastaparser>>>withopen("fasta_file.fasta")asfasta_file:parser=fastaparser.Reader(fasta_file,parse_method='quick')forseqinparser:# seq is a namedtuple('Fasta', ['header', 'sequence'])print('Header:',seq.header)print('Sequence:',seq.sequence)print()output:Header: >sp|P04439|HLAA_HUMAN HLA class I histocompatibility antigen, A alpha c...
Sequence: MAVMAPRTLLLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRM...

Header: >sp|P15822|ZEP1_HUMAN Zinc finger protein 40 OS=Homo sapiens OX=9606 GN...
Sequence: MPRTKQIHPRNLRDKIEEAQKELNGAEVSKKEILQAGVKGTSESLKGVKRKKIVAENHLKKIPKSPLRN...Write FASTA filesCreate FASTA files from FastaSequence objects:>>>importfastaparser>>>withopen("fasta_file.fasta",'w')asfasta_file:writer=fastaparser.Writer(fasta_file)fasta_sequence=fastaparser.FastaSequence(sequence='ACTGCTGCTAGCTAGC',id='id123',description='test sequence')writer.writefasta(fasta_sequence)or single header and sequence strings:>>>importfastaparser>>>withopen("fasta_file.fasta",'w')asfasta_file:writer=fastaparser.Writer(fasta_file)writer.writefasta(('id123 test sequence','ACTGCTGCTAGCTAGC'))DocumentationDocumentation for FastaParser is available here:https://fastaparser.readthedocs.io/en/latestHistory1.1.1 (04-09-2022)Added support for Python 3.9 and 3.101.1 (13-02-2020)Added property setters for:FastaSequence.idFastaSequence.descriptionAdded property deleters for:FastaSequence.idFastaSequence.descriptionFastaSequence.sequence_typeLetterCode.letter_type1.0 (27-01-2020)First release on PyPI and Anaconda CloudReader, Writer, FastaSequence and LetterCode classes
Package: fastapi2postman
A tool that creates aPostmancollection from aFastAPIapplication.Inspired fromflask2postmanInstall$pipinstallfastapi2postmanExampleLet’s say that you have a FastAPI project called “app.py”  and you want to generate a Postman collection out of it.You just need to tellfastapi2postman$fastapi2postman--appapp.pyIf you want to change the name of the output file use –output flag, the default name is ofcousre output.json$fastapi2postman--appapp.py--outputoutput.jsonThis will generate the JSON configuration, and write it into theoutput.jsonfile. You can then import this file into Postman (“Import
Collection” button), and profit:On a side note, you can see that endpoint’s docstrings are automatically
imported as descriptions.Usageusage:fastapi2postman[-h][--outputOUTPUT][--nameNAME][--hostHOST][--portPORT]--appAPPoptional arguments:-h,--helpshow this help message and exit--outputOUTPUTOutput file--nameNAMECollection name--hostHOSTAPI host--portPORTAPI port--appAPPPath to FastAPI application instanceLicenseMIT
Package: fastapi-admin
FastAPI Admin中文文档Introductionfastapi-adminis a fast admin dashboard based onFastAPIandTortoiseORMwithtablerui,
inspired by Django admin.Installation>pipinstallfastapi-adminRequirementsRedisOnline DemoYou can check a online demohere.username:adminpassword:123456Or pro version online demohere.username:adminpassword:123456ScreenshotsRun examples in localClone repo.Create.envfile.DATABASE_URL=mysql://root:123456@127.0.0.1:3306/fastapi-admin
REDIS_URL=redis://localhost:6379/0Rundocker-compose up -d --build.Visithttp://localhost:8000/admin/initto create first admin.DocumentationSee documentation athttps://fastapi-admin-docs.long2ice.io.LicenseThis project is licensed under theApache-2.0License.
Package: fastapi-admin-2
FastAPI Admin中文文档Introductionfastapi-adminis a fast admin dashboard based onFastAPIandTortoiseORMwithtablerui,
inspired by Django admin.Installation>pipinstallfastapi-adminRequirementsRedisOnline DemoYou can check a online demohere.username:adminpassword:123456Or pro version online demohere.username:adminpassword:123456ScreenshotsRun examples in localClone repo.Create.envfile.DATABASE_URL=mysql://root:123456@127.0.0.1:3306/fastapi-admin
REDIS_URL=redis://localhost:6379/0Rundocker-compose up -d --build.Visithttp://localhost:8000/admin/initto create first admin.DocumentationSee documentation athttps://fastapi-admin.github.io.LicenseThis project is licensed under theApache-2.0License.
Package: fastapialchemycollector
Metis FastAPI SQLAlchemy collectorDemo ProjectFastAPI (Sync). SQLAlchemy(sync) and PostgreSQLFastAPI (Sync). SQLAlchemy(asyncpg) and PostgreSQLAboutThis library logs the HTTP requests created by FastAPI and SQLAlchemy with the SQL commands they generate. The library
can also collect the execution plan for deeper analysis usingMetis.
Read more in our:DocumentationTechnicalThis library usesOpenTelemetryto instrument both FastAPI and
SQLAlchemy.Tested on python 3.8.9, FastAPI 0.78.0, SQLAlchemy 1.4.33, PostgreSQL 12 or higher.
Package: fastapiali-classes
FastAPI ClassisClassi per la richiesta di interogazioni a dbInstallazionepip install -e git+ssh://git@github.com/giaca95/Fastapiali-classes.git#egg=fastapiali-classesAggiornamento su pypipython setup.py bdist_wheel
twine upload --skip-existing dist/*Classi presentiAcquisizioni buoni covid territorio

Richieste volanti

Utilities:
    punto vendita
    WrapperException
    RecordException
Package: fastapi-allauth
Welcome to K-Villain FastAPI Allauth!K-villain FastAPI Allauth is based on SQLAlchemy, and designed to be cutomizable.It enables you to bring various oauth social login to your services.Docs:https://k-villain.github.io/fastapi-allauthGithub:https://github.com/K-villain/fastapi-allauthFeaturesExtensible base user modelSQLAlchemy ORM basedCutomiazable User tableReady-to-use social oauth login flowContributorssangham🚧choisaywhy🚧
Package: fastapi_amis_admin_cli
FastAPI-Amis-Admin-Cli项目介绍FastAPI-Amis-Admin-Cli是一个用于快速构建FastAPI-Amis-Admin项目的拓展库.安装pipinstallfastapi-amis-admin-cli快速使用# 查看帮助faa--help# 初始化一个`FastAPI-Amis-Admin`项目faanewproject_name--init# 初始化一个`FastAPI-Amis-Admin`应用faanewapp_name# 快速运行项目faarun相关项目FastAPI-Amis-Admin: 一个拥有高性能，高效率，易拓展的FastAPI管理后台框架.FastAPI-User-Auth: 一个简单而强大的FastAPI用户RBAC认证与授权库.FastAPI-Scheduler: 一个基于APScheduler的简单定时任务管理FastAPI拓展库.FastAPI-Amis-Admin-Demo:  一个FastAPI-Amis-Admin应用程序示例.FastAPI-User-Auth-Demo: 一个FastAPI-User-Auth应用程序示例.许可协议该项目遵循 Apache2.0 许可协议。
Package: fastapi_amis_admin_nav
项目介绍FastAPI-Amis-Admin-NavFastAPI-Amis-Admin-Nav是一个基于FastAPI-Amis-Admin并且为FastAPI-Amis-Admin提供可视化导航页面管理的拓展库.源码·在线演示·文档·文档打不开？fastapi-amis-admin-nav是一个基于FastAPI-Amis-Admin并且为FastAPI-Amis-Admin提供可视化导航页面管理的拓展库.安装pipinstallfastapi-amis-admin-nav简单示例fromfastapiimportFastAPIfromfastapi_amis_admin.admin.settingsimportSettingsfromfastapi_amis_admin.admin.siteimportAdminSitefromfastapi_amis_admin_nav.adminimportNavPageAdminfromsqlmodelimportSQLModel# 创建FastAPI应用app=FastAPI()# 创建AdminSite实例site=AdminSite(settings=Settings(database_url_async='sqlite+aiosqlite:///amisadmin.db'))# 注册导航页面管理site.register_admin(NavPageAdmin)# 挂载后台管理系统site.mount_app(app)# 创建初始化数据库表@app.on_event("startup")asyncdefstartup():# 创建数据库表awaitsite.db.async_run_sync(SQLModel.metadata.create_all,is_session=False)# 运行后台管理系统启动事件awaitsite.fastapi.router.startup()if__name__=='__main__':importuvicornuvicorn.run(app,debug=True)界面预览Openhttp://127.0.0.1:8000/admin/in your browser:许可协议fastapi-amis-admin基于Apache2.0开源免费使用，可以免费用于商业用途，但请在展示界面中明确显示关于FastAPI-Amis-Admin的版权信息.
Package: fastapi-amis-admin-offline
static assets for fastapi_amis_adminmake fastapi & amis-admin work offline without CDN.Featuresthis package provide following static assets used by fastapi & amis_admin to make them work without CDN:swaggerredocamisInstallpip install fastapi-amis-admin-offlineUsage examplesfromfastapiimportFastAPIfromfastapi_amis_admin_offlineimportpatch_offlineapp=FastAPI(debug=settings.debug)patch_offline(app)if fastapi_amis_admin is installed, it will also patch the Page and App components to use local static assets.
Package: fastapi-and-babel
FastAPI And BabelFastAPIAndBabel allows you to easily use babel in your FastAPI projects and offers some features to improve and ease things.InstallYou can either download the source code of this repository or install it via pip:pip install fastapi-and-babelUsageFirst, we create an instance of FastAPIAndBabel in our program as shown below
‍‍‍from fastapi import FastAPI
from fastapi_and_babel.translator import FastAPIAndBabel


app = FastAPI()
translator = FastAPIAndBabel(__file__, app, "en")We create the following mapping file
babel.cfg[python: **.py]Save the file and then run the following commandpybabel extract -F babel.cfg -o messages.pot .babel uses this file for mapping and stores the patterns in messages.potpybabel init -i messages.pot -d translations -l enHere -d says to save translations in translations, you can change it in configAfter adding your messages, use the following command to compilepybabel compile -d translationsNow we can translate our texts using the gettext methodfrom fastapi_and_babel import gettext as _

@app.get("/")
def index():
    return {"text": _("Hello World!")}
Package: fastapi-another-jwt-auth
FastAPI Another JWT AuthThe project is based onFastapi-jwt-auththat is no longer maintained.Documentation:https://GlitchCorp.github.io/fastapi-another-jwt-authSource Code:https://github.com/GlitchCorp/fastapi-another-jwt-authFeaturesFastAPI extension that provides JWT Auth support (secure, easy to use and lightweight), if you were familiar with flask-jwt-extended this extension suitable for you, cause this extension inspired by flask-jwt-extended 😀Access tokens and refresh tokensFreshness TokensRevoking TokensSupport for WebSocket authorizationSupport for adding custom claims to JSON Web TokensStoring tokens in cookies and CSRF protectionInstallationThe easiest way to start working with this extension with pippipinstallfastapi-another-jwt-authIf you want to use asymmetric (public/private) key signing algorithms, include theasymmetricextra requirements.pipinstall'fastapi-another-jwt-auth[asymmetric]'LicenseThis project is licensed under the terms of the MIT license.
Package: fastapi-apex
apex_fastapi FrameworkThis repo contains some extra steup for an FastAPI projectfastapi_plus : create fast app with some default settings and handlerslogger_plus : logger setup for fastapi, tortoise , aws and etc.schema_plus : Base module for schemasettings : Env base settingsTestRundocker-compose up -din the test folder.Copy thetest.envfile to.envand fill in or change the values.Runexport $(cat .env | xargs)to set the environment variables.Runpytestin the root folder.ShellInstantiate class objectShellCommandRun the instance# utils/cli.py
shell = ShellCommand(
    db_url=settings.DATABASE_URL,
    models=settings.APPLICATION_MODELS,
    )
# run shell()