Saravanan666/pypi-org huggingface dataset:
Package: sage-numerical-interactive-mip
This package is an extension of SageMath’s mixed integer linear programming
facilities described athttp://doc.sagemath.org/html/en/reference/numerical/index.htmlIt was written by Peijun Xiao, Zeyi Wang, and Yuan Zhou in 2015-2016 at UC Davis
for integration into SageMath.Parts of their work (improvements tosage.numerical.interactive_simplex_methodand MIP backend methods)
that required changes to existing SageMath modules have already been
integrated into SageMath in various tickets, see meta-tickethttps://trac.sagemath.org/ticket/20302(“Interactions withInteractiveLinearProgramand its dictionaries”).  The present
package should run on any recent release of SageMath.The modulesage_numerical_interactive_mip.interactive_milp_problem,
written by Peijun Xiao, provides the interactive MILP problem classes,
MILP tableau classes, and the cutting plane method. It is based on
commit 5a4e3508d95e95e4491efcb2cf16fbe25be60bec, dated August 24, 2016,
from the SageMath treehttps://github.com/pgxiao/cutting-plane-method;
this work superseded an earlier effort (adding integer variables ininteractive_simplex_method) athttps://trac.sagemath.org/ticket/18805The modulessage_numerical_interactive_mip.clean_dictionaryandsage_numerical_interactive_mip.backends.*_backend_dictionary,
written by Zeyi (Aedi) Wang, provide a textbook view on a simplex basis
in a numerical solver. They are based onhttps://trac.sagemath.org/ticket/18804The branches were rebased by Matthias Koeppe in January 2020 onto
SageMath version 9.1.beta1, then filtered usinggitfilter-repoand
merged.How to useThis needs a working SageMath; install, for example, from conda-forge as
described inhttp://doc.sagemath.org/html/en/installation/conda.htmlThe code comes with extensive documentation and tests; see the
docstrings in the modules.How to run the testsuite and build the HTML documentationInstalltox, make sure thatsageis accessible in yourPATHand then runtox.This also builds the documentation in.tox/docs/tmp/html/index.html.
Package: sage_octave
This project provides an improved version of theSageMathinterface  forOctave:sage/interfaces/octave.pyand obviously depends on Sage.Installationpip install sage-octaveThis works on SMC and creates.local/lib/python2.7/site-packages/octave.pyRunThis can be used in Sage as follows:sage: from octave import octaveIt also works in a SMC Sage worksheet.ChangesRecovery from syntax error without losing syncSyntax errors are marked with>>>which was confused
with the prompt character.  We use the full default Octave
prompt sequence instead (also continuation lines PS2).Disable pagination to allow large matrices to be imported
to SageSupport ^C keyboard interrupt
Package: sageodata-db
sageodata_dbSA Geodata database querying tool/wrapper/library for PythonInstallThe source code is onBitbucket, while Python packages (source and binary) are published on
thePython Package Indexand
on a dedicated channel onAnaconda Cloud.From PyPI (most recent release)Run this in Command Prompt to install from PyPI:pip install sageodata_dbTo upgrade to the latest version:pip install -U sageodata_dbFrom AnacondaYou can also install using conda/mamba if you prefer:mamba install -c dew-waterscience saegodata_dbTo upgrade:mamba upgrade -c dew-waterscience sageodata_dbThe version may not be as up-to-date as the PyPI version.From BitbucketThis is how you can install the latest version of the source code directly from Bitbucket (the version control repository):python -m pip install git+https://kinveraritysagov@bitbucket.org/dewsurfacewater/sageodata_db.gitYou will need to replacekinveraritysagovwith your Bitbucket username, and you will also need access to the source repository on the Bitbucket dewsurfacewater workspace.UsageCheck out complete documentation at:http://bunyip:8191/python-docs/sageodata_db/latest_source/index.htmlLicenseAll rights reserved DEW 2023
Package: sageone-api-client
A simple Python client for interacting with the Sage One API.Installation$pipinstallsageone-api-clientUsageRegister for a Sage One account and request an API key by following the instructions athttps://accounting.sageone.co.za/Marketing/DeveloperProgram.aspxReview the supported Sage One API documentation athttps://accounting.sageone.co.za/api/1.1.1/HelpUse the Python API client to interact with the API:>>>fromsageone.clientimportAPIClient>>>c=APIClient(...'https://accounting.sageone.co.za',...'{XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX}',...'your-sage-one-username',...'your-password')>>>r=c('get','Company','Get')>>>print(r)
Package: sage-package
This experimental package provides utilities for authoring SageMath
packages:sage_package.setuptools: utilities for the setup.py. At this stage,
just a command for running tests.sage_package.sphinx: Sphinx theme and configuration to write and
render documentation as in Sage.For an example of use, seehttp://github.com/sagemath/sage_sample.Recent features0.0.7: Live documentation withThebe
Package: sage-patchbot
PATCHBOTThe patchbot only needs a Sage source install (clone of git repository)
and is started with`python3 -m sage_patchbot.patchbot --sage-root=XXX [other options]`Type--helpfor a list of options, though most configuration is done via an optional JSON config file.For more documentation on running a patchbot, seethis page.SERVERThe server needs a Python with Flask and mongodb installed.
Package: sage-physics
sage-physicsCategoryToolsDevelopmentPackageDocumentationCommunicationIntroductionA Python package to create and simulate physics models.PrerequisitesAn installation ofSageMathwith version > 10.0 is required.InstallationYou can installsage-physicseither as a normal user or for development purposes.UserFor user installation,sage-physicsis currently available on the PyPi's repository, and you can
install it viapip:sage--pipinstallsage-physicsYou can then start the Sage REPL with the commandsageand usesage-physicsthrough the Sage REPL:importsagephysDevelopmentDevelopment installation requires to clone the repository and change directory to the project's root:gitclonehttps://github.com/georgedouzas/sage-physics.gitcdsage-physicsThen create a Python virtual environment using thesagecommand:sage-python-mvenv--system-site-packages.venvFinally, usePDMto select the virtual environment and install the project as well as the
main and development dependencies:pdmuse.venv
pdminstallYou can then start the Sage REPL with the commandPYTHONPATH=src sageand usesage-physicsthrough the Sage REPL:importsagephysUsageOne of thesage-physicsmain goals is to provide a unified interface for various physics models. For example let's define two
independent harmonic oscillators that oscillate in the axes x and y:fromsage.allimportvar,assumefromsagephys.classical_mechanicsimportNewtonianPointParticlesModelk,x_particle1,y_particle2=var('k x_particle1 y_particle2')assume(k>0)forces={'particle1':{'elastic':[-k*x_particle1,0,0]},'particle2':{'elastic':[0,-k*y_particle2,0]}}model=NewtonianPointParticlesModel(forces)We can get the dynamical equations:model.analyze()model.equations_We can also try to solve them:model.solve()
Package: sagesand
SageMaker SandboxAWS SageMaker has a fantastic set of functional components that can be used in concert to setup production level data processing and machine learning functionality.Training Data:Organized S3 buckets for training dataFeature Store:Store/organize 'curated/known' feature setsModel Registery:Models with known performance stats/Model ScoreboardsModel Endpoints:Easy to use HTTP(S) endpoints for single or batch predictionsWhy SageMaker Sandbox?SageMaker is awesome but fairly complexSpider lets us setup SageMaker Pipelines in a few lines of codePipeline Graphs: Visibility/Transparency into a PipelineWhat S3 data sources are getting pulled?What Features Store(s) is the Model Using?What's theProvenanceof a Model in Model Registry?What SageMaker Endpoints are associated with this model?Installationpip install sagesand
Package: sage-superset
SupersetA modern, enterprise-ready business intelligence web application.Why Superset?|Supported Databases|Installation and Configuration|Release Notes|Get Involved|Contributor Guide|Resources|Organizations Using SupersetWhy Superset?Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams.Superset provides:Ano-code interfacefor building charts quicklyA powerful, web-basedSQL Editorfor advanced queryingAlightweight semantic layerfor quickly defining custom dimensions and metricsOut of the box support fornearly any SQLdatabase or data engineA wide array ofbeautiful visualizationsto showcase your data, ranging from simple bar charts to geospatial visualizationsLightweight, configurablecaching layerto help ease database loadHighly extensiblesecurity roles and authenticationoptionsAnAPIfor programmatic customizationAcloud-native architecturedesigned from the ground up for scaleScreenshots & GifsLarge Gallery of VisualizationsCraft Beautiful, Dynamic DashboardsNo-Code Chart BuilderPowerful SQL EditorSupported DatabasesSuperset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena,and more) that has a Python DB-API driver and a SQLAlchemy dialect.Here are some of the major database solutions that are supported:A more comprehensive list of supported databasesalong with the configuration instructions can be foundhere.Want to add support for your datastore or data engine? Read morehereabout the technical requirements.Installation and ConfigurationExtended documentation for SupersetGet InvolvedAsk and answer questions onStackOverflowusing theapache-supersettagJoin our community's Slackand please read ourSlack Community GuidelinesJoin our dev@superset.apache.org Mailing listContributor GuideInterested in contributing? Check out ourCONTRIBUTING.mdto find resources around contributing along with a detailed guide on
how to set up a development environment.ResourcesGetting Started with SupersetSuperset in 2 Minutes using Docker ComposeInstalling Database DriversBuilding New Database ConnectorsCreate Your First DashboardComprehensive Tutorial for Contributing Code to Apache SupersetDocumentation for Superset End-Users (by Preset)Deploying SupersetOfficial Docker imageHelm ChartRecordings of PastSuperset Community EventsLive Demo: Interactive Time-series Analysis with Druid and SupersetLive Demo: Visualizing MongoDB and Pinot Data using TrinoSuperset Contributor BootcampIntroduction to the Superset APIApache Superset 1.3 MeetupBuilding a Database Connector for SupersetVisualizationsBuilding Custom Viz PluginsManaging and Deploying Custom Viz PluginsWhy Apache Superset is Betting on Apache EChartsSuperset API
Package: sage-sws2rst
Provides a scriptsage-sws2rst, which translates a Sage worksheet file (.sws) into a reStructuredText (.rst) file.Sage worksheet files (.sws) are a file format that was used by the now-obsolete Sage notebook (https://github.com/sagemath/sagenb), superseded by the Jupyter notebook.  SageNB was dropped in the course of the transition of SageMath to Python 3.This package was extracted from the SageNB sources inhttps://github.com/sagemath/sage/issues/28838to provide a way to convert pedagogical material written available in Sage worksheet format.
Package: sagetasks
Sage Prefect Tasks⚠️Warning: This repository is a work in progress.⚠️Python package of useful Prefect tasks for common use cases at Sage Bionetworks.Some thoughts are included below the Demo Flow and Usage.Inspired byPocket/data-flows.Demo FlowDemo UsageGetting accessTo run this demo, you'll need the following access:You need to ask Bruno for edit-access on theINCLUDE SandboxSynapse project.You need to ask Bruno for edit-access on theinclude-sandboxCavatica project.Getting set up# Create a virtual environment with the Python dependenciespipenvinstall# Copy the example `.env` file and update the auth tokenscp.env.example.envRun the flow at the command lineYou'll need toget set upfirst.# Run the demo (pipenv will automatically load the `.env` file)pipenvrunpythondemo.pyInspect the flow using the Prefect Server UIYou'll need toget set upfirst.# Deploy Prefect Server (Orion)prefectorionstart# Explore the flow runs in Prefect Server# Usually hosted at http://127.0.0.1:4200/# Stop the running server with Ctrl-CThoughtsTheCavaticaBaseTaskdemonstrates a use case for classes (i.e.extendingTask) as opposed to functions (i.e.decorated by@task). On the other hand,SynapseBaseTaskdoesn't really benefit from the class structure.The SevenBridges Python client embeds the client instance into every resource object, which preventscloudpickleto serialize these objects due toTypeError: cannot pickle '_thread.lock' object.importosimportcloudpickleimportsevenbridgesassbgapi=sbg.Api(url="https://cavatica-api.sbgenomics.com/v2",token=os.environ["SB_AUTH_TOKEN"])proj=api.projects.query(name="include-sandbox")[0]proj._API=Noneproj._api=Noneproj._data.api=Nonepickle=cloudpickle.dumps(proj)NoteThis project has been set up using PyScaffold 4.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: sage-testsuite
A useful script to running FATE’s testsuites.quick start(optional) create virtual envpython-mvenvvenvsourcevenv/bin/activateinstall fate_testsuitepipinstallfate_testsuitefate_testsuite--helpnew and edit the testsuite_config.yaml# create a testsuite_config.yaml in current dirtestsuiteconfignew# edit priority config file with system default editor
# filling some field according to commentsfate_testsuiteconfigeditrun some testsuitesfate_testsuitesuite-i<pathcontains*testsuite.json>useful logs or exception will be saved to logs dir with namespace showed in last steptestsuite_config.yaml examplesno need ssh tunnel:9999, service: service_a10000, service: service_band both service_a, service_b can be requested directly:work_mode:1# 0 for standalone, 1 for clusterdata_base_dir:<path_to_data>parties:guest:[10000]host:[9999,10000]arbiter:[9999]services:-flow_services:-{address:service_a,parties:[9999]}{address:service_b,parties:[10000]}need ssh tunnel:9999, service: service_a10000, service: service_bservice_a, can be requested directly while service_b don’t,
but you can request service_b in other node, say B:work_mode:0# 0 for standalone, 1 for clusterdata_base_dir:<path_to_data>parties:guest:[10000]host:[9999,10000]arbiter:[9999]services:-flow_services:-{address:service_a,parties:[9999]}-flow_services:-{address:service_b,parties:[10000]}ssh_tunnel:# optionalenable:truessh_address:<ssh_ip_to_B>:<ssh_port_to_B>ssh_username:<ssh_username_to B>ssh_password:# optionalssh_priv_key:"~/.ssh/id_rsa"command optionsexclude:testsuitesuite-i<path1contains*testsuite.json>-e<path2toexclude>-e<path3toexclude>...will run testsuites inpath1but not inpath2andpath3replace:testsuitesuite-i<path1contains*testsuite.json>-r'{"maxIter": 5}'will find all key-value pair with key “maxIter” indata conforconfordsland replace the value with 5glob:testsuitesuite-i<path1contains*testsuite.json>-g"hetero*"will run testsuites in sub directory start withheteroofpath1
Package: sagetk
The sagetk Python modulesagetk is a Python module for creating GUI applications using a HTML and CSS like syntax . It is built on top of classical tkinter library and provides a high-level interface for defining widgets, handling events, and managing application state.INSTALLATIONS:sagetk can be installed using pip:pip install sagetkIt depends on few other packages, including,  pillow and html.parser, which will also be installed automatically.USAGEThe basic usage of sagetk involves defining a document object that describes the structure and content of the GUI using a markup language similar to HTML. This document can include various widgets, such as buttons, labels, text areas, menus, and tables, as well as event handlers that respond to user interactions with the widgets.Once the document is defined, it can be displayed in a window by calling the document() function from the sagetk module that serves the document and handles user interactions, such as clicks, key presses, and mouse movements.The run() function can then be called to start the event loop and handle these interactions. This function blocks until the user closes the window or the program exits.DOCUMENTATIONSThe sagetk module includes comprehensive documentation that covers the various widgets, events, functions, and classes available in the library. The documentation is available online athttp://sagetk.atspace.cc, and can also be accessed programmatically using the help() function in Python.EXAMPLESThe sagetk module includes several examples that demonstrate the usage and capabilities of the library. These examples cover a wide range of GUI applications, from simple calculators and text editors to more complex data analysis tools and scientific simulations.The examples are located in the examples directory of the sagetk source distribution, and can be run by executing the corresponding Python scripts. They can also be used as templates or starting points for building custom GUI applications using sagetk.//CREATING BUTTON
import sagetk as tk
tk.g=globals()
doc=”””
<root>
<title>my app</title>
<button onclick=”alert(‘hello world’)”>Click here</button>
</root>
“””
tk.document(doc)
tk.run()//CREATING INPUTS
import sagetk as tk
tk.g=globals()
doc=”””
<root>
<title>my app</title>
<input type=”text”></input>
</root>
“””
tk.document(doc)
tk.run()//CREATING TABLE
import sagetk as tk
tk.g=globals()
doc=”””
<root>
<title>my app</title>
<table>
<tr>
<th>name</th>
<th>age</th>
</tr>
<tr>
<td>Mark</td>
<td>77</td>
</tr>
</table>
</root>
“””
tk.document(doc)
tk.run()//CREATING Menu
import sagetk as tk
tk.g=globals()
doc=”””
<root>
<title>my app</title>
<menu>
<mi label=file>
<md onclick=”func”>open</md>
<sp></sp>
<md>save as</md>
</mi>
<mi label=edit>
<md alt=”ctrl + x”>cut</md>
<sp></sp>
<md alt=”ctrl + c”>copy</md>
</mi>
</menu>
</root>
“””
tk.document(doc)
tk.run()Change log0.0.1 (02/04/2023)Third release
Package: sageup
SageUpInteractive CLI tool for creating a SageMaker notebook instance from terminal, getting signed URL and opening it in browser.Installation:pipinstallsageupUsageRunStarts an interactive session to create a SageMaker notebook instance and launch it in browsersageuprunNotes:Default IAM role has full access to SageMaker and S3. Use a custom IAM role if need granular access, more security and protection against accidental change of resources by the notebook.More features coming soon!Author: Iman KamyabiFeedback:contact@imankamyabi.com
Package: sageutils
sageutilsCommon utility functionshttps://github.com/ttong-ai/sageutils/blob/main/README.mdSupported Python version: 3.8.0+Install dependencies:Make sure you install to the correct Python environment.pip install -r requirements.txtorpip3 install -r requirements.txtpassutils.pypermutateandreverse_permutateuse SHA256 to cryptographically add puseudorandom permutation to the mnemonic pass phrases commonly seen in the crypto world.If the mnemonic pass phrase isone two three four five six seven eight nine ten, you can randomize it by adding an easy-to-remember personalkey.  In the following example, if thekeyisdisney, the randomized pass phrase would beone four ten five eight three six seven two nine. You can store this permutated version of passphrase in your cloud-based password managers. Even if a bad actor somehow hacked your password manager and stole your randomized private key phrase due to security breach, it would be almost impossible for the bad actor to figure out the correct sequence to your private pass phrase. You will have precious time to move your funds to newly secured locations. (It is still important to keep best security practices for your password managers!)When you need to use your private pass phrase, just use thereverse_permutatecommand with the same personalkey. This can be generated only when you need to use the private pass phrase. But do not store it anywhere!!You do need to always remember your personalkey, this is your responsibility. Use a simple word that you and trusted ones would always remember. If you forget your personalkey, the original sequence of the pass phrase may be permenantly lost.Command line usage:passutilpermutate"one two three four five six seven eight nine ten"--key="disney"Outputwithencryptedpermutation:eightsevenfourninethreefivetwotenonesixpassutilsreverse-permutate"eight seven four nine three five two ten one six"--key="disney"Outputoforiginalsequence:onetwothreefourfivesixseveneightninetenDisclaimerThe password utiltiy functions are provided as is. We are not responsible for any losses due to misuses or forgotten personal key or any bugs. You will need to test it and feel comfortable with the process before using it.
Package: sage-utils
SDK for Open Matchmaking microservices in PythonFeaturesFramework agnostic solutionEasy to write a new extension and use it later with your codeBase class for implementing AMQP workersRegister a new microservice in Open Matchmaking platformInstallationThis package should be installed using pip:pip install sage-utilsExamplefromsanicimportSanicfromsage_utils.extensionimportBaseExtensionclassCustomExtension(BaseExtension):extension_name=app_attribute='custom'defhello(self,user):print("Hello,{}!".format(user))app=Sanic(__name__)CustomExtension()# available via `app.custom` or `app.extensions['custom']`app.custom.hello('world')# Hello, world!LicenseThe sage-utils-python is published under BSD license. For more details readLICENSEfile.
Package: sageworks
Welcome to SageWorksThe SageWorks framework makes AWS® both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Job, Athena, Feature Store, Models, and Endpoints, SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.Full AWS ML OverViewHealth Monitoring 🟢Dynamic UpdatesHigh Level SummaryDrill-Down ViewsIncoming DataGlue JobsDataSourcesFeatureSetsModelsEndpointsInstallationOne time AWS Onboarding:AWS SetupAfter that your development team can simplypip install sageworksSageWorks DocumentationSageWorks Documentation: The documentation contains examples from the SageWorks source code in this repository under theexamples/directory. For a full code listing of any example please visit ourSageWorks ExamplesSageWorks Beta ProgramUsing SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Beta Tester, contact us atsageworks@supercowpowers.com.ContributionsIf you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing projectlicense. If you are interested in contributing or have questions please feel free to contact us atsageworks@supercowpowers.com.® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates.