Saravanan666/pypi-org huggingface dataset:
Package: sc-oa
Olivier Heurtier
Author-email:ihor@kalnytskyi.comLicense: BSD
Classifier: Topic :: Documentation
Classifier: Topic :: Documentation :: Sphinx
Classifier: License :: OSI Approved :: BSD License
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Framework :: Sphinx
Classifier: Framework :: Sphinx :: Extension
Requires-Python: >=3.8
License-File: LICENSE
Requires-Dist: sphinx<7.0
Requires-Dist: sphinxcontrib-httpdomain
Requires-Dist: PyYAML
Requires-Dist: jsonschema
Requires-Dist: m2r2
Requires-Dist: picobox
Requires-Dist: deepmerge
Package: scoach
scoachA setup for training Tensorflow models on SLURM clustersHow does it work?Inputs needed (see examples directory):A.jsonfile with parameters for trainingA.jsonfile with the model definitionA.pyfile with the training code.There's a CLI app for interacting with scoachRunscoach initfor setting up your configuration file, such as inconfig_example.yamlOn the login machine at the SLURM cluster, runscoach start. This will start a daemon that will then launch jobs as requested.On any machine, you can doscoach run submitto submit jobs.This will upload the Python script to MinIO and submit the configurations to the database.The new runs are consumed by the daemon process, which then uses Jinja2 to render the training script and submit it to the cluster.The training script is then run on the cluster, using Dask workers, that will grow as needed.To doAdd option--localonscoach startfor launching runs locallyAdd support for uploading/managing datasetsNo Python script duplicates
Package: scoary-2
Scoary2 associates orthogenes (e.g. generated usingOrthoFinderorRoaryto traits. It reports a list of genes sorted by strength of
association per trait. The results can be explored interactively with a simple, static HTML/JS app.WikiHomeInstallationUsageInputOutputTutorialAppUnderstanding the p valuesUsage as Python libraryPaperThe Scoary2 preprint is now available on bioRxiv. Clickhere) to see it!Roder, T.et al.Scoary2: Rapid association of phenotypic multi-omics data with microbial pan-genomes.BioRxiv(2023) doi:10.1101/2023.04.19.537353.
Package: sc.object.dissect
sc.object.dissectOverviewRequirementsPlone 3.2.x (http://plone.org/products/plone)InstallationTo enable this product,on a buildout based installation:Edit your buildout.cfg and addsc.object.dissectto the list of eggs to install[buildout]
...
eggs =
    sc.object.dissectTell the plone.recipe.zope2instance recipe to install a ZCML slug:[instance]
...
zcml =
    ...
    sc.object.dissectIf another package depends on the sc.object.dissect egg or
includes its zcml directly you do not need to specify anything in the
buildout configuration: buildout will detect this automatically.After updating the configuration you need to run the ‘’bin/buildout’’,
which will take care of updating your system.Go to the ‘Site Setup’ page in the Plone interface and click on the
‘Add/Remove Products’ link.Choose the product (check its checkbox) and click the ‘Install’ button.Uninstall – This can be done from the same management screen, but only
if you installed it from the quick installer.Note: You may have to empty your browser cache and save your resource registries
in order to see the effects of the product installation.SponsoringDevelopment of this product was sponsored bySimples Consultoria.CreditsSimples Consultoria (products at simplesconsultoria dot com dot br) -
ImplementationChangelogtrunk (2009-11-18)xxx [Simples Consultoria]1.0 (2009-11-18)xxx [Simples Consultoria]1.0 - UnreleasedInitial release
Package: scoda
S-CodaOverviewS-Coda is a Python library for handling MIDI files.
S-Coda supports a plethora of different MIDI manipulation operations, such as:quantisation of notesquantisation of note lengthssplitting sequences into barstransposing of sequencescreating piano-roll visualisations of piecesjudging the difficulty of piecesS-Coda was used in our projectPAUL-2to process MIDI files.
For information about how to use S-Coda we refer to chapter 5 of thethesisin which S-Coda was introduced.ChangelogSeeCHANGELOG.mdfor a detailed changelog.UsageWe refer to the aforementioned thesis for a more in-depth guide on how to use S-Coda.
We provide a short listing on how to use basic S-Coda functions:# Load sequence, choose correct track (often first track contains only meta messages)sequence=Sequence.sequences_load(file_path=RESOURCE_BEETHOVEN)[1]# Quantise the sequence to thirty-seconds and thirty-second triplets (standard values)sequence.quantise()# Split the sequence into bars based on the occurring time signaturesbars=Sequence.sequences_split_bars([sequence],meta_track_index=0)[0]# Prepare tokeniser and output tokenstokeniser=NotelikeTokeniser(running_value=True,running_time_sig=True)tokens=[]difficulties=[]# Tokenise all bars in the sequence and calculate their difficultiesforbarinbars:tokens.extend(tokeniser.tokenise(bar.sequence))difficulties.append(bar.sequence.difficulty())# (Conduct ML operations on tokens)tokens=tokens# Create sequence from tokensdetokenised_sequence=tokeniser.detokenise(tokens)# Save sequencedetokenised_sequence.save("out/generated_sequence.mid")
Package: scodb
Para utilizadar o conector, você pode seguir o exemplo:>>> from pprint import pprint
>>> import scodb
>>> api = scodb.ScodbConnector(url, user, password)
>>> data = api.member('103238')
>>> pprint(data)Métodos disponíveis:
- member: Busca um membro com base na CID.
- authenticate: Dado um usuário e senha, valida estes dados no SISDM.
- chapter: Busca informações de um Capítulo com base em seu número.
- convent: Busca informações de um Convento com base em seu número.
- council: Informando o tipo de entidade e seu número, retorna uma coleção com as informações dos membros do Conselho Consultivo.
Package: scode
scodeInstallaion (설치)cmd창을 열어서 하단의 코드를 입력 후 실행.pipinstallscodeExample (사용예시)fromscode.seleniumimport*# scode의 selenium 모듈의 모든 것을 임포트driver=load_driver()orfromscode.seleniumimportload_driver# scode의 selenium 모듈의 load_driver만 임포트driver=load_driver()orimportscode.selenium# scode의 selenium 모듈을 임포트, 이 경우에는 하단처럼 전체 경로를 입력해야 함driver=scode.selenium.load_driver()orimportscode# 이 경우에는 하단처럼 전체 경로를 입력해야 함driver=scode.selenium.load_driver()구조도 (트리구조)scodeis_latest_version() : 버전 체크하는 함수update_scode() : 모듈 업데이트하는 함수refresh() : 모듈 리로드하는 함수dropboxdropbox_upload_file(src_file_path, destination_path) : 드롭박스에 파일을 업로드 하는 함수dropbox_upload_folder(folder_path, destination_path) : 드롭박스에 폴더를 업로드 하는 함수paramikocommand(ssh: paramiko.SSHClient, query: str, timeout = None) : 서버에 명령어를 실행하는 함수execute_sql_query(ssh: paramiko.SSHClient, user_id: str, user_pw: str, db_name: str, query: str, timeout = None) : 파라미코로 mysql 쿼리를 실행하는 함수ssh_connect(hostname: str, username: str, password: str) : 연결된 ssh를 가져오는 함수seleniumget_status(driver: Webdriver) : 드라이버의 상태를 가져오는 함수load_driver(chrome_options=None, mode=None, userId=None, port=9222, **kwargs) -> WebDriver : 드라이버를 생성하는 함수load_driver2(port=9222) : 기존의 크롬드라이버를 사용하지 않고 일반 chrome.exe를 실행하는 함수load_cache_driver(userId, chrome_options = None, **kwargs) : 캐시 드라이버를 생성하는 함수n_login(driver: WebDriver, nid: str, pwd: str, keep_login: bool=True, ip_safe: bool=False, force: bool=False) : 네이버 로그인을 하는 함수t_login(driver, tid, pwd) : 트위터 로그인을 하는 함수line_login(driver: WebDriver, line_email, line_passwd) : 라인 로그인을 하는 함수daum_mail_login(driver, did, pwd) : 다음 로그인을 하는 함수scrollDownUntilPageEnd(driver, SCROLL_PAUSE_SEC = 1) : 페이지 끝까지 스크롤을 내리는 함수solve_reCAPTCHA(driver) : 구글 리캡차를 자동으로 푸는 함수telegramTelegramSender : 클래스send(self, chat_id: str, text: str, **kwargs) : 텔레그램으로 메시지를 전송하는 함수send_photo(self, chat_id: str, img_path: str, **kwargs) : 텔레그램으로 이미지를 전송하는 함수utilfwrite(path: str, text, encoding=None) : 파일 끝에 문자열을 추가해주는 함수distinct(myList: list) : 중복 제거된 리스트를 리턴하는 함수ip_change2() : IP를 바꿔주는 함수http_remove(link: str) : 링크에 http를 없애는 함수http_append(link: str) : 링크에 http를 붙히는 함수get_code_from_image(img_path: str) : 이미지의 글자를 반환하는 함수sound_alert(msg: str) : 메시지를 화면에 뿌리고 엔터를 입력할 때까지 알람을 울리는 함수schedulerun_everyday_between(start_time: str, end_time: str, job, *args, **kwargs) : 매일 지정된 시간 사이에 지정된 함수를 실행하는 함수
Package: scodec
Spatial CodecModified: 2021-06NavigationAboutQuickstartDevLicenseAboutSpatial codec is a spatial encoding and decoding algorithm developed for iteratively mapping any number of bytes to a 3D (N3 space) matrix. The algorithm uses a psuedo variant ofHilbert's Space Filling Curvewhich preserves the relative localization of bits in 3D independant of the matrix dimension which is a convienient property for error correction and scalable network policies.QuickstartInstallscodecfrom the package indexpython3-mpipinstall--upgradepip
...
python3-mpipinstallscodecYou may be required to install someaptpackages on some linux distros:sudoxargs-aapt-packages.txtaptinstall-yAPISpatial codec provides an api for interacting in 2 (N2) and 3 (N3) dimensional space. Below is an example of using theN2space api:fromscodec.n2importN2# configure a 2D spatial codec using a 64 bit block sizesc=N2(block_size=64)# encode utf-8 string and enable matplotlib visualizerspace_encode=sc.stream_encode(bytes("Hello World","utf-8"),mpl=True)# feed spatial encode stream back into stream decodebytestream=sc.stream_decode(space_encode)CLI ToolThe codec provides a cli tool for ease of use. Run the algorithm for a specified block size-b/--block, with a data stream-d/--dataand dimension-n/--dimension(2 or 3). The MPL visualizer can be enabled with the-v=flag.# n2 codec invocationpython3-mcodec-n2-b512-d"Hello World"-v=...# n3 codec invocationpython3-mcodec-n3-b8-d"H"-v=LicenseBSD 2-Clause License availablehere
Package: scodeu
This library provides a Python interface for the Euskadi.eus search engine.InstallationThe preferred way of installing is using pip:pip install scodeuUsageThis is a minimal search operation in OpenData family for metheorological documents in basque:>>> import scodeu
>>> client = scodeu.OpenData()
>>> data = client.codified_search(tipo='ds_meteorologicos',
                                  lang='eu')
>>> items = data.get('items')FamiliesDocuments in euskadi.eus are organized in families. Every family defines its allowed content types and metadata. Metadata is used to filter results inside a content type.Every families in euskadi.eus are available using scodeu. Best place to see available families, common metadata and allowed types  is in the families.py file.ReferencesTheese are the base documents used to create scodeuInformation aboutavailable families, metadata and allowed types.Search engineusers manual(pdf).Pseudo-REST APIinformation.LICENSEThis software is licensed as is under GPL v3 license.CONTRIBUTINGPlease send your PR to the projectsGithubpage.
Package: sconce-scms
SCONCE-SCMSSpherical and Conic Cosmic Web Finders with the Extended SCMS AlgorithmsSCONCE-SCMS(Spherical andCONicCosmic wEb finder with the extendedSCMSalgorithms[1]is a Python library for detecting the cosmic web structures (primarily cosmic filaments and the associated cosmic nodes) from a collection of discrete observations with the extended subspace constrained mean shift (SCMS) algorithms ([2],[5],[6]) on the unit (hyper)sphere (in most cases, the 2D (RA,DEC) celestial sphere), and the directional-linear product space (most commonly, the 3D (RA,DEC,redshift) light cone).(Notes: RA -- Right Ascension, i.e., the celestial longitude; DEC -- Declination, i.e., the celestial latitude.)Free software: MIT licenseDocumentation:https://sconce-scms.readthedocs.io.Installation guidesconce-scmsrequires Python 3.6+ (earlier version might be applicable),NumPy,SciPy, andRay(optional and only used for parallel computing). To install the latest version ofsconce-scmsfrom this repository, run:python setup.py installTo pip install a stable release, run:pip install sconce-scmsReferences[1]Y. Zhang, R. S. de Souza, and Y.-C. Chen (2022). SCONCE: A cosmic web finder for spherical and conic geometriesarXiv preprint arXiv:2207.07001.[2]U. Ozertem and D. Erdogmus (2011). Locally Defined Principal Curves and Surfaces.Journal of Machine Learning Research,12, 1249-1286.[3] Y.-C. Chen, S. Ho, P. E. Freeman, C. R. Genovese, and L. Wasserman (2015). Cosmic web reconstruction through density ridges: method and algorithm.Monthly Notices of the Royal Astronomical Society,454(1), 1140-1156.[4] Y. Zhang and Y.-C. Chen (2021). Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data.Journal of Machine Learning Research,22(154), 1-92.[5]Y. Zhang and Y.-C. Chen (2022). Linear convergence of the subspace constrained mean shift algorithm: from Euclidean to directional data.Information and Inference: A Journal of the IMA, iaac005,https://doi.org/10.1093/imaiai/iaac005.[6]Y. Zhang and Y.-C. Chen (2021). Mode and ridge estimation in euclidean and directional product spaces: A mean shift approach.arXiv preprint arXiv:2110.08505.
Package: scone
scone: Server CONfiguration EngineScone is a tool for configuring servers (or even other computers).The idea is that you write a declarative configuration using Scone Menu Language (.scoml) files and runsconeon it.Scone will, ideally speaking, do whatever is necessary to bring your declarative configuration into reality.You can track your configuration ingit, for example, and should be able to re-apply this to your server whenever you need to recreate it (or update it!).Scone tries to be quick, owing to the fact that iterative development is more engaging and productive.
To do this, Scone runs multiple recipes (jobs) in parallel (Scone has a fairly rich dependency tracking system to allow ordering recipes fairly intuitively to allow this). Scone also caches outcomes of recipes, so a small change to the configuration does NOT need to re-execute all recipes afterwards.Scone is currently alpha-grade software; the author uses it to configure their servers (fully), mobile phone (mostly fully) and laptop/desktop computers (partially).
Package: sconf
sconf: Simple config system supporting CLI modificationsconf is yaml-based simple config library.FeaturesSupports merging multiple configsSupports CLI modification by argparse-like interfaceSupports coloring modified key-valuesSupports global access to config objectsInstall$ pip install sconfUsageQuickstartA Minimal ExamplefromsconfimportConfigcfg=Config(default="configs/defaults.yaml")cfg.argv_update()# apply CLI modificationYou can modifycfgby CLI in the runtime, by argparse-like interface.Init with argparse and multiple configsimportargparsefromsconfimportConfigparser=argparse.ArgumentParser()parser.add_argument("name")parser.add_argument("config_paths",nargs="*")parser.add_argument("--show",action="store_true",default=False)args,left_argv=parser.parse_known_args()# merging multiple configs if givencfg=Config(*args.config_paths,default="configs/defaults.yaml")cfg.argv_update(left_argv)Run:python train.py example configs/exp.yaml --lr 0.1The resultingcfgis based onconfigs/defaults.yaml, merged withconfigs/exp.yaml, and updated by--lr 0.1.Dumpssconf dumps contents with coloring modified items.print(cfg.dumps())# If you do not want to colorize:print(cfg.dumps(modified_color=None))AccessItem access with dictionary-like interfaces:# accessprint(cfg['key'])print(cfg['key1']['key2'])# getprint(cfg.get('non-key','default-value'))# unpackingfunction(**cfg['model'])Attribute access:print(cfg.key)print(cfg.key1.key2)Notethat the attribute access returns method object for the duplicated key, unlike the item access.cfg=Config({'get':2})print(cfg['get'])# 2print(cfg.get)# method objectCLI modificationsconf supports CLI modification like argparse. Also you can access to the child key using dot.# yaml example
batch_size: 64
model:
    encoder:
        n_channels: 64
    decoder:
        n_channels: 64CLI modification:> python train.py --batch_size 128 --model.encoder.n_channels 32Accessing via partial key is also available:> python train.py --encoder.n_channels 32Use triple dashs---if you want to modify multiple keys:# modifying encoder.n_channels and decoder.n_channels both.
> python train.py ---n_channels 32Global access to config objectGlobal access is useful in ML project, even though it can be anti-pattern in SW engineering.# main.pycfg=Config({'weight_decay':0.001})# first config is automatically registered to 'default' key# train.pycfg=Config.get_default()# get 'default' configprint(cfg.weight_decay)# 0.001Notefrom_registryhelps global access to multiple configs.Notesconf use utf-8 as a default encoding. If you want different encoding, use file pointer (openfunction) instead of file path as a key.
Package: scons517
Scons517Project StatusThis project is unmaintained. It is the result of exploring some ideas for an
interface to SCons for building Python wheel distributions.If you're interested in using SCons to build Python Wheels, seeenscons.If you're interested in picking up this project or in using this name for a
different project, contact me.AboutPep 517 compliant Python Distribution Wheel BuilderThis project's primary goal is to provide a simple and extensible set of tools for building
Python wheelswithoutusing setuptools. Setuptools has served the Python community well,
but I've often been frustrated when trying to extend it in uncommon ways.For example, extending setuptools to also run build steps such as building a webpack bundle or
running Django's collectstatic routine are non-trivial to add.This led me to theensconsproject which provides a
set of wheel building tools on top of theSconsbuild framework.
Enscons works well (and you should use it if you want a more stable and mature project) but
for compiling C extension modules, it still calls into setuptools under the hood.So this project started out as an experiment to see if I could eliminate setuptoolsentirelyand still provide C-extension support and the full power of Scons. I also started to re-imagine
the interface to make things (in my opinion) a bit more straightforward.Using Scons517Enough history. How do you use it?There are two files you need in the root of your project directory:pyproject.tomlThis should contain at a minimum a[build-system]section declaring Scons517 as the build
backend, and a[project]section with at a minimum anameand aversionkey.For example:[build-system]build-backend="scons517.api"requires=["scons517",][project]name="example-project"version="0.0.1"dependencies=["dep1","dep2",]readme="README.md"The formal list of keys allowed in the[project]table are defined by the PyPA specification
page onDeclaring project metadatasconstruct.pyThis file defines your build targets and how to build them. Here's the minimal boilerplate for a
pure-python distribution with no platform-specific dependencies:importscons517env=Environment(tools=["default",scons517.tool])tag=scons517.get_pure_tag()sources=["add_your_source_files_here.py"]wheel=env.Wheel(tag=tag)wheel.add_sources(sources)sdist=env.SDist(["pyproject.toml","sconstruct.py"]+sources)editable=env.Editable(tag,".")env.Alias("wheel",wheel.target)env.Alias("sdist",sdist)env.Alias("editable",editable)The sconstruct filemustdefine the two target aliaseswheel,sdist.
Aneditabletarget enables editable installs (pip install -e)For examples compiling extension modules and cython modules, see thetests/examples/directory.Building a projectOnce you have those two files in place, build your wheel using a compatible build frontend
such asbuildInstall it withpipinstallbuildThen build your wheel withpython-mbuildYour wheel files will be placed in thedist/directory by default.
Package: scons-tool-bennugd
scons-tool-bennugdSConstool to buildBennuGDprojects.InstallationThere are a few ways to install this tool for your SCons project.FrompypiThis method may be preferable if you build your project under a virtualenv. To
add ascons-tool-bennugdfrom pypi, type (within your virtualenv):pip install scons-tool-bennugdOr, if your project usespipenv:pipenv install --dev scons-tool-bennugdAlternatively, you may add this to yourPipfile:[dev-packages]
scons-tool-bennugd = "*"The tool will be installed as a namespaced packagesconstool.bennugdin the
project's virtual environment.As a git submoduleIn your git repository, add thescons-tool-bennugdas a submodule:git submodule add git://bitbucket.org/dacucar/scons-tool-bennugd.gitManually downloadingdcbcmoduleDownload thedcbctool module into thesite_scons/site_tools.mkdir-psite_scons/site_tools
curlhttps://bitbucket.org/dacucar/scons-tool-bennugd/raw/HEAD/sconstool/bennugd/dcbc.py-osite_scons/site_tools/dcbc.pyUsage exampleFirst, create your game sources:game/
│
└───lib/
│   │   lib.prg
|   |   lib.h
│
└───game/
│   │   game.prg
│   │   game_part_1.prg
|   |   ...
│
└───tool/
    │   tool.prg
    │   tool_part_1.prg
    |   ...In this example,toolandgamedepend onlib. Alsogame_part_1.prgis included ingame.prgandtool_part_1.prgis included intool.prg.Then, write SConstruct file.# SConstructenv=Environment(# We need to tell scons how to find the tool package...# ...if you installed it with pip/pipenv usetoolpath=[PyPackageDir('sconstool.bennugd')],# ... OR if you installed it as a git submodule# toolpath = ['scons-tool-bennugd/sconstool/bennugd']# ... OR if you downloaded the dcbc.py into site_scons/site_tools, then there is no need to specify toolpathtools=['default','dcbc'])# Declare three targetsenv.Dcl('lib.dcl','lib/lib.prg')env.Dcb('game.dcb','game/game.prg',DCBCLIBS='lib/lib.dcl')env.Dcb('tool.dcb','game/tool.prg',DCBCLIBS='lib/lib.dcl')TIPSThe extension of the targets and source files can be omitted.Finally, try it out.To build all targets:sconsTo clean:scons -cTo build onlygametarget:scons gameYou may want to check theExample.SCons is a very feature rich build-system and highly customizable.It can also be used withPixTudio!The variableDCBCallows defining the DCB compiler, which can be set topxtb.env=Environment(toolpath=[PyPackageDir('sconstool.bennugd')],tools=['default','dcbc'],DCBC='pxtb')Supported OptionsThe following options are accepted by theDcland theDcbbuilders:VariableDefaultDescriptionDCBC"bgdc"DCB compiler.DCBCFLAGS""Additional compilation flags.DCBCMACROS{}Additional compilation macros, expressed as a dictionary.DCBCPATH[]Additional paths where the compiler shall search for files.DCBCFILES[]A list of files that shall be added to the dcb.DCBCLIBS[]A list of libraries that shall be included by the compiler.DCBCLIBEXTENSION".dcl"The extension used when compiling inlibmode.