Saravanan666/pypi-org huggingface dataset:
Package: smclarify
smclarifyAmazon Sagemaker ClarifyBias detection and mitigation for datasets and models.InstallationTo install the package from PIP you can simply do:pip install smclarifyYou can see examples on running the Bias metrics on the notebooks in theexamples folder.TerminologyFacetA facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive".LabelThe label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome.Bias measureA bias measure is a function that returns a bias metric.Bias metricA bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure.Bias reportA collection of bias metrics for a given dataset or a combination of a dataset and model.DevelopmentIt's recommended that you setup a virtualenv.virtualenv -p(which python3) venv
source venv/bin/activate.fish
pip install -e .[test]
cd src/
../devtool allFor running unit tests, dopytest --pspec. If you are using PyCharm, and cannot see the green run button next to the tests, openPreferences->Tools->Python Integrated tools, and set default test runner topytest.For Internal contributors, run../devtool integ_testsafter creating virtualenv with the above steps to run the integration tests.
Package: smc.lucene
lucene module wrapperThe lucene module wrapper takes care of the initialization and threading
pitfalls in the JCC API. It automatically initializes the VM when the first
object is retrieved from the facade module. It also attaches the current
Python thread to the JNI VM environment when it hasn’t been attached before.The wrapper also solves the problem with JVM installing its own signal
handlers. By default the JVM overwrites Python’s signal handlers in initVM(),
thus causing some hard to debug problems. For example SIGINT is no longer
translated into a KeyboradInterrupt exception. The wrapper code restores all
signal handlers to its former state.
Only JVM’s SIGSEGV handler is left in place because it logs and prints useful
information when the JVM segfaults.The classpath argument is optional. When no classpath is set, smc.lucene
automatically adds classpath=lucene.CLASSPATH for you.Example 1 - Initialize Lucene on demandReplace code like:import lucene
lucene.initVM(classpath=lucene.CLASSPATH, vmwargs='...')

def query_lucene(...):
    lucene.attachCurrentThread()
    query = lucene.BooleanQuery()
    ...with:from smc.lucene import lucene
lucene.set_initargs(vmargs='...')

def query_lucene(...):
    query = lucene.BooleanQuery()Lucene is automatically initialized when the BooleanQuery attribute is retrieved
from the facade module. If PyLucene was already initialized the facade module
makes sure that the current thread is attached.You must not assign Lucene attributes to objects which a shared across threads.
If you have to share an object across threads you must call lucene.attach()
before you can use an object.Example 2 - Explicit initialization of LuceneExample:from smc.lucene import lucene as lucene_wrapper
from lucene import BooleanQuery

def query_lucene(...):
    lucene_wrapper.attach()
    query = BooleanQuery()The attach method either initializes lucene or or attaches the current thread.AuthorsChristian HeimesCopyrightCopyright (C) 2008 semantics GmbH. All Rights Reserved.:semantics
Kommunikationsmanagement GmbH
Viktoriaallee 45
D-52066 Aachen
Germany

Tel.: +49 241 89 49 89 29
eMail: info(at)semantics.de
http://www.semantics.de/
Package: smc-movement-models
Estimating behavioral parameters in animal movement models using a state-augmented particle filterProjet pour le coursHidden Markov models and Sequential Monte-Carlo methodsde l'ENSAE.Gabriel WatkinsonGabriel GuaquiereJérémie Stym-PopperBenjamin MaurelIntroductionDans ce projet, nous implementons le modèle décrit dans le papierEstimating behavioral parameters in animal movement models using a state-augmented particle filter, et l'approfondissons avec des méthodes plus récentes.Notre rapport est disponibleicià la racine du projet.Le packagesmc_movement_modelsLes fonctions sont définies dans le packagesmc_movement_models.Les modulesmodels.pyetmodels_SMC2.pycontiennent les modèles de mouvement et l'algorithme du papier et une version SMC2 respectivement.Le moduleplot.pycontient des fonctions utilitaires pour génerer des graphes.Le moduleprocess_data.pycontient des fonctions utilitaires pour traiter les données brutes et une CLI.Les notebooksLes notebooks sont dans le dossiernotebookset permettent de lancer les expériences et de visualiser les résultats.Le notebooknotebooks/simulation_papier.ipynbcontient les expériences avec l'algorithme du papier.Le notebooknotebooks/implementation_smc2.ipynbcontient les expériences avec la version SMC2.Le resteLe dossierdatacontient les données brutes et les données traitées.Le dossierimagescontient les images utilisées dans le rapport.Le dossierpapercontient les papiers liés au projet.Le dossierr_codecontient le code R original du papier (pas utilisé par notre groupe).Les fichierspoetry.locketpyproject.tomlsont utilisés parpoetrypour gérer les dépendances.Le fichierrequirements.txtest utilisé parpippour gérer les dépendances et est généré par poetry.Le dossierlatexcontient le LaTeX utilisé pour le rapport.InstallationNous utilisons Python pour simuler les données et mettre en place les modèles, notamment la librairieparticles.Avant toutes choses, il faut se déplacer dans le dossiersmc_movement_models:cd/path/to/smc_movement_modelsAvecpoetry# Creation d'un environement virtuel et installation des packagespoetryinstall# Activation de l'environementpoetryshell# sub shell# OU source $(poetry env info --path)/bin/activate  # activer l'environement dans le shell actuelAvecpip# Creation d'un environement virtuelpython-mvenv.venv# Activation de l'environement.venv/Script/activate# pour Windows# OU source .venv/bin/activate  # pour Linux / MacOS# Installation des packagespipinstall-rrequirements.txtProcessing des donnéesPour générer les données à partir du fichier brut, il faut lancer le scriptprocess_data.py:pythonsmc_movement_models/process_data.py# Pour plus d'informations# python smc_movement_models/process_data.py --helppre-commitPour activer les pre-commit qui formattent le code avant chaque commit :pre-commitinstall
pre-commitrun--all-files# Pour installer les modules et lancer les testsPour forcer un commit sans vérifier :gitcommit-m"..."--no-verify
Package: smcpp-pipeline
SMCPP pipelineA python pipeline for implementing smcpp to decipher demographic history of populations of interestAll the functions assume that the user has generated population population-specific chromosome file and stored it in a directory popname_vcf present in the current working directory. all the chromosome files should be bgzipped and tabix indexed.If the user has a single genotype file, then user can make use of bcftools to generate population-specific bgzipped and tabix index VCFs.The original package can be found herehttps://github.com/popgenmethods/smcppThe original article can be found hereTerhorst, J., Kamm, J. & Song, Y. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat Genet 49, 303–309 (2017).https://doi.org/10.1038/ng.3748The package can be installed through Pypi (https://pypi.org/project/smcpp-pipeline/)A tutorial has been provided in a blog (https://medium.com/@devashishtripathi697/population-history-inference-using-whole-genome-sequence-data-9cf99821cd1f) using the simulated data hosted at figshare (https://figshare.com/articles/dataset/msprime_single_population_simulated_dataset/25234849)pipinstallsmcpp-pipelinefromsmcpp_pipeline.smcpp_pipelineimportvcf2smc,estimate,plot1)Functiontoconvertvcffiletosmcformatdefvcf2smc(popfile,chromosome_info,gaps_bed,num_workers):2)Functiontoestimatethedemographichistoryofasinglepopulationdefestimate(popfile,mu=1.25e-8,spline="piecewise",start_time=100,end_time=100000,knots=8):3)Functiontogenerateplotsfromthemodel.jsonfileobtainedusingestimatefunctiondefplot(popfile,time_start=100,time_end=15000):popfileshouldbeatabseparatedfilecontainingtwocolumnssample.idpopulationi1pop1i2pop1i3pop2
Package: sm-crypto
sm-cryptochina gm cryptography such as sm2,sm3,sm4Free software: MIT licenseDocumentation:https://sm-crypto.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-08-03)First release on PyPI.
Package: smd
A tool to search and download manga from web sites, with support for several sites and languages.FeaturesRead manga offline without ads.Save bandwidth downloading only the necessary.Mangas are available in several languages.Multilingual interface.Continues canceled downloads from the same point it was interrupted.Downloaded ongoing mangas can be updated.Configurable.Documented.Always yours! Free as in freedom.Currently supported sitesGerman (de):NineManga-deEnglish (en):NineManga-enMangaReaderMangaAllMangaNeloMangaHereSpanish (es):NineManga-esHeavenMangaMangaDoorItalian (it):NineManga-itPortuguese (pt):NineManga-brRussian (ru):NineManga-ruInstallationThe easiest way to installsmdis usingpip, open your terminal and execute the following command:$pipinstallsmdNow the commandsmdshould be available in your terminal.NoteFor more help installingsmdsee the installation instructions in thesmd manual.ExamplesTo select site and manga to download interactively just run:$smdTo search mangaall.net for mangas containing the word ‘first love’ in its name, and select which one to download interactively:$smd-smangaall'first love'To download from chapter 10 to 20 (ignoring chapter 15) of ‘Naruto’:$smd--chapters'10:20,!15'NarutoTo continue a previously canceled download:$smd--continueNoteFor more examples see the ‘Quick start’ subsection in thesmd manual.SupportTo request new sites or if you are having issues, you canopen an issue. For more information and tutorial read thesmd manual.If you want to supportsmd, please donate tohttp://liberapay.com/adbenitez, any little help is really appreciated, thanks for your support!LicenseThis project isfree software, licensed under the GPL3+ License - see theLICENSEfile for more details.
Package: smdb-logger
Server monitoring Discord bot LoggerPossible log formats:[COUNTER] [LEVEL]: DATA{END}[COUNTER] [FILE_NAME->METHOD_1->...->CALLER]: DATA{END}[COUNTER] [METHOD_1->...->CALLER]: DATA{END}Available log levels:DEBUGINFOWARNINGERRORHEADEROptions:Variable nameDescriptionDefault valuelog_fileThe name of the log file-log_folderThe path of the folder to save the log fileCurrent DirectoryclearClears the log file every time it's initializedFalselevelSets the minimum level for the logger to showINFOlog_to_consoleSets to log to console tooFalsestorage_life_extender_modeLimits the writes to the file by caching the dataFalsemax_logfile_sizeLimits the size of one log file in MB-1 (No limit)max_logfile_lifetimeLimits the time a log file can live (except the currently used one)-1 (No limit)__printCallable for consol loggingstdout.writeuse_caller_nameUse the caller's name in consol logging instead of the levelFalseuse_file_namesUse the file name whe using the caller name or notTruelevel_only_valid_for_consoleSets to log the unwanted levels but don't display them in consoleFalseAdditional infoThe consol logging uses colors for the different levels to be more recognisable at a glance. It also supports headers, after which it indents the logs to add some structure.DEBUG#E600E5INFO#0BFF00WARNING#FEFF00ERROR#FF0000HEADER#4182B7Available methodsget_buffer()Return sthe buffer's content in a list, if the logger is set to use buffers.flush_buffer()Forces a writes of the buffer to the log file.set_level(level)Sets the loggers leve to be used from this point onwards.set_folder(folder)Sets the loggers folder to be used from thi point. It also runs the basic validation on the inputed path.log(level, data, counter = str(datetime.now()), end = "\n")Creates a log entry with the given level.header(data, counter = str(datetime.now()), end = "\n")Creates a HEADER level log entry.debug(data, counter = str(datetime.now()), end = "\n")Creates a DEBUG level log entry.warning(data, counter = str(datetime.now()), end = "\n")Creates a WARNING level log entry.info(data, counter = str(datetime.now()), end = "\n")Creates a INFO level log entry.error(data, counter = str(datetime.now()), end = "\n")Creates a ERROR level log entry.
Package: smdb-web-interface
Web-CLIEasy to use Web CLI for python applications.Table of contentUsageSettingsWebCLIServerUsageImport and make a instantiate it.settings=Settings(name="Example page")server=WebCLIServer(settings,backend)funbackend(user_command:UserCommand)->None:# do stuff hereAfter, start the server with server.start().server.start()The example provded will result in a webserver opened on 127.0.0.1:8080 (localhost:8080). Opening this page will result in a command line looking webpage that can communicate with the server.Settingshost: Host's IP.port: Port for the IP address.name: Name of the web server.functionsto_filestring_path (str or None): A string path for the settings file.Creates a config file with the path provided or in the current folder witht he default name.from_filestring_path (str or None): A string path for the settings file.Reads the file provided, and creates a settings class.WebCLIServersettings: Settings data to set up the server.backend: A func that can handle UserCommand data class as input, and do something with it, with a return value that's ignored.logger: An smdb_logger logger. It can be None or ignored, so it won't produce output on stdrout.functionspush_dataresponse (str or List[str]): The output for a user command.command (UserCommand or None): The user command to reply to. If None, the last command will be used.Adds a response for a user command. It can be used to oush data to the web cli, with command being not set, it will result in the data being pushed to the last command in the history.startStarts the server.stopStops the server.
Package: smdebug
This library powers Amazon SageMaker Debugger, and helps you develop better, faster and cheaper models by catching common errors quickly.
It allows you to save tensors from training jobs and makes these tensors available for analysis, all through a flexible and powerful API.
It supports TensorFlow, PyTorch, MXNet, and XGBoost on Python 3.6+.Zero Script Change experience on SageMaker when using supported versions of SageMaker Framework containers or AWS Deep Learning containersFull visibility into any tensor which is part of the training processReal-time training job monitoring through RulesAutomated anomaly detection and state assertionsInteractive exploration of saved tensorsDistributed training supportTensorBoard support
Package: smdebug-rulesconfig
This library, intended to be used with [Amazon SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk), helps you specify builtin rules without worrying about any details or tweak the configuration of builtin rules. These builtin rules are available in Amazon SageMaker.Amazon SageMaker Debugger Rulesconfig package can be used with Amazon SageMaker Debugger or asstand-alone rule config retriever. In addition to retrieving builtin rules, configuration for common collections can be retrieved as well.
Package: smdg
Simple (pythonic) MarkDown GeneratorModules to programmatically generate MarkDown source code in a pythonic waypip install smdgInstallation in a virtual environment is strongly recommended.Example usageUse the classes in thesmdg.elementsmodule
to create MarkDown elements.Some elements may be nested,
and thesmdg.elements.render()function
can be used to return a MarkDown document
(consisting of the provided elements)
as a single string.>>> from smdg import elements as me
>>>
>>> h1 = me.Header(1, "Main header")
>>> h2 = me.Header(2, "Printing colors:")
>>> p = me.Paragraph("Lorem ipsum dolor sit amet,\nconsectetuer adipiscing elit.")
>>> pre = me.CodeBlock('def main() -> int:\n    """main function"""\n    return 0')
>>> ul = me.UnorderedList("Cyan", "Magenta", "Yellow", "Key")
>>>
>>> print(h1)
# Main header
>>> print(h2)
## Printing colors:
>>> print(p)
Lorem ipsum dolor sit amet,
consectetuer adipiscing elit.
>>>
>>> print(me.render(h1, p, pre, h2, ul))
# Main header

Lorem ipsum dolor sit amet,
consectetuer adipiscing elit.

    def main() -> int:
        """main function"""
        return 0

## Printing colors:

*   Cyan
*   Magenta
*   Yellow
*   Key
>>>
>>> print(me.BlockQuote(h1, p, pre, h2, ul))
> # Main header
>
> Lorem ipsum dolor sit amet,
> consectetuer adipiscing elit.
>
>     def main() -> int:
>         """main function"""
>         return 0
>
> ## Printing colors:
>
> *   Cyan
> *   Magenta
> *   Yellow
> *   Key
>>>Further readingPlease see the documentation athttps://blackstream-x.gitlab.io/smdgfor detailed usage information.If you found a bug or have a feature suggestion,
please open an issuehere
Package: smdv
smdvasimplemarkdownviewer for linux.DependenciesRequiredpython3pointing to Python 3.6+.Pandoc[pip3 install pandoc|apt install pandoc|pacman -S pandoc| ... ]Flask[pip3 install flask|apt install python3-flask|pacman -S python-flask| ... ]Websockets[pip3 install websockets|apt install python3-websockets|pacman -S python-websockets| ... ]OptionalJupyter(to view jupyter notebooks) [pip3 install jupyter|apt install jupyter|pacman -S jupyter| ... ]Neovim Remote[pip3 install neovim neovim-remote]Installationpip3 install smdvConfigurationsmdv listens to a single environment variable:SMDV_DEFAULT_ARGS. As an example, below
you can see how smdv can be configured to always open in firefox on port 9999 by placing
the following line in your.bashrc:SMDV_DEFAULT_ARGS="--browser firefox --port 9999"Consultsmdv --helpto see which flags can be used.Compatibility with neovimThis viewer was made with neovim compatibility in mind. With the use ofneovim-remote,
this script is able to open files in the current neovim window (or spawn a new neovim
window if there is no window available).However, to make it fully compatible with neovim and to make neovim able to sync
its current file to the viewer,neovim-remoteshould be installed and the following line should be added to yourinit.vim:" sync on save
    autocmd BufWritePost *.md silent execute '!smdv 'expand('%:p')' -v "'.v:servername'"'This setting will sync the file to the viewer after every save.Compatibility with vim-instant-markdownAlternatively, if syncing after every save is not enough, smdv can also be used
in conjuction withvim-instant-markdownfor
instant markdown previews. Install the vim-plugin and add the following line to
your vimrc:let g:instant_markdown_python = 1This line disables the default javascript daemon handling instant previews in favor of
smdv. Consider removing thesync-on-saveline defined above when using this
option; both options are not completely compatible.Screenshotsmarkdown previewfolder view
Package: sme
Spatial Model EditorA GUI editor to create and edit spatial SBML models of bio-chemical reactions and simulate them using thedune-copasisolver for reaction-diffusion systems.To get started, download and run the GUI for your operating systemLinuxMacOSWindowsOr take a look at ourwebsiteor thedocumentationPre-release preview binaries are also available which are built from the main branch and can be used for testing new features before the next release:Linux pre-releaseMacOS Intel pre-releaseMacOS Apple Silicon pre-releaseWindows pre-releaseNote: on linux some additional system libraries are required that may not be installed by default. To install them:Fedora/RHEL/CentOS:sudo yum install xcb-util-image xcb-util-keysyms xcb-util-renderutil xcb-util-wmContributingBug reportsandfeature requestsare very welcome, as are fixes or improvements to thedocumentation(to edit a page in the documentation, click the 'Edit on GitHub' button in the top right).If you are interested in contributing code, please see thecontributing guidelines.DependenciesSpatial Model Editor makes use of the following open source libraries:dune-copasi- license:GPL2 + runtime exceptionlibSBML- license:LGPLQt- license:LGPLQCustomPlot- license:GPLspdlog- license:MITfmt- license:MITSymEngine- license:MITLLVM Core- license:Apache 2.0 License with LLVM exceptionsGNU Multiple Precision Arithmetic Library- license:LGPLGNU MPFR- license:LGPLComputational Geometry Algorithms Library- license:GPLBoost- license:boostlibTIFF- license:MITExpat- license:MIToneTBB- license:Apache-2.0Open Source Computer Vision Library- license:Apache-2.0pybind11- license:BSD-styleCatch2- license:BSL-1.0Cereal- license:BSDzlib- license:zlibbzip2- license:BSD-stylepagmo- license:GPLzipper- license:MITLibCombine- license:BSDLicensing NoteThe source code in this repository is released under the MIT license, which is a permissiveGPL-compatiblelicense.The open source libraries that it uses are either also released under a permissive GPL-compatible license, or
under a GPL license. As described in thegpl-faq,
this means that the work as a whole is then licensed under the GPL.
Package: smear
Ejana smearThe package is a part of Electron Ion Collider ESCalate project(located here)Simple command line interface to eic-smear and jleic-smear particle smearing engines.Make smearing simple:Smear the file with handbook detectorejana_smearbeagle_eD.txtSmears the file using BeAST detector, use only 10k events, name output file as my_file.rootejana_smear-dbeast-n10000beagle_eD.txt-omy_file.rootList of all available detector parametrisations:ejana_smear-l# --listRead the full documentation
Package: smear-api-tools
SMEAR API ToolsThis project contains functions that help interacting with the SmartSMEAR API.Installationpipinstallsmear-api-toolsDocumentationUsageExample 1Download DMPS data from Hyytiälä field station for each day in May, 2018importpandasaspdfromsmear_api_toolsimportgetDmpsDatamay2018=pd.date_range(start='2018-05-01',end='2018-05-31')v=getDmpsData(station='HYY',dates=may2018)Example 2List all variables that contain the term "SO2" in the database and write them to a file.importpandasaspdfromsmear_api_toolsimportlistAllDatalistAllData("SO2").to_csv("all_so2_data.csv")Example 3Download number concentration between 3 nm and 200 nm from the urban measurement station in Kumpula for the year 2021.importpandasaspdfromsmear_api_toolsimportgetConcDatac=getConcData(station='KUM',dp1=3e-9,dp2=200e-9,start='2021-01-31',end='2021-12-31')Example 4Download condensation sink from Värriö research station between2013-07-01and2013-07-05importpandasaspdfromsmear_api_toolsimportgetCScs=getCS(station='VAR',start="2013-07-01",end="2013-07-05")ResourcesJunninen, H., Lauri, A., Keronen, P., Aalto, P., Hiltunen, V., Hari, P., Kulmala, M. 2009. Smart-SMEAR: on-line data exploration and visualization tool for SMEAR stations. Boreal Environment Research 14, 447–457.https://smear.avaa.csc.fi/
Package: smeasures
smeasures - Python package for measuring resource utilization and execution timeThesmeasurespackage provides a simple way to capture resource utilization and time taken for a given code block or method. It uses thepsutillibrary to collect system resource usage data, and provides a decorator that can be used to wrap a function or code block to measure its execution time and resource usage.Installation:To installsmeasures, simply run:pip install smeasuresUsage:To usesmeasures, import themeasurefrom thesmeasurespackage, and surround you code to measure by Measure start and end calls like below:from smeasures import measure

m = measure.Measure(pid, useCpuMonitor=True, cpuMonitorInterval=0.1)
m.start()

# code to measure here...

m.end()This will print out the execution time and resource usage of the code between measure start and end calls.Dependencies:smeasuresrequires the following dependencies:psutilthreadinggcLicense:smeasuresis released under the MIT License.