Saravanan666/pypi-org huggingface dataset:
Package: samscore
SAMScore: A Semantic Structural Similarity Metric for Image Translation EvaluationYunxiang Li, Meixu Chen, Wenxuan Yang, Kai Wang,Jun Ma,Alan C. Bovik,You Zhang.Quick startRunpip install samscore.pipinstallsamscorepipinstallgit+https://github.com/facebookresearch/segment-anything.gitThe following Python code is all you need.importrequestsimportosimportsamscoredefdownload_image(url,save_path):response=requests.get(url)response.raise_for_status()# Raise an exception if the request was unsuccessfulwithopen(save_path,'wb')asfile:file.write(response.content)os.makedirs('imgs',exist_ok=True)# Example usageimage_url='https://i.ibb.co/yFFg5pn/n02381460-20-real.png'save_location='imgs/real.png'download_image(image_url,save_location)image_url='https://i.ibb.co/GCQ2jQy/n02381460-20-fake.png'save_location='imgs/fake.png'download_image(image_url,save_location)## Initializing the modelSAMScore_Evaluation=samscore.SAMScore(model_type="vit_b")samscore_result=SAMScore_Evaluation.evaluation_from_path(source_image_path='imgs/real.png',generated_image_path='imgs/fake.png')print('SAMScore:%.4f'%samscore_result)CitationIf you find this repository useful for your research, please use the following.@inproceedings{li2023samscore,
  title={SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation},
  author={Yunxiang Li, Meixu Chen, Wenxuan Yang, Kai Wang, Jun Ma, Alan C. Bovik, You Zhang},
  booktitle={arxiv},
  year={2023}
}
Package: sam-scraper
Sam ScraperIntroductionThesam-scraperPython package is designed for seamless interaction with the sam.gov API, enabling efficient search operations, detailed retrieval of records, and resource downloads.FeaturesAdvanced Search Capabilities:Execute detailed searches using criteria such as relevance, status, and result limits.Detailed Information Retrieval:Access in-depth details for records, including exclusion details and resource information.Resource Download:Directly download resource files using specific record IDs.Ease of Use:Offers intuitive methods and clear documentation for an optimal user experience.InstallationTo installsam-scraper, run the following command in your terminal:pipinstallsam-scraperUsageBelow is a step-by-step guide on how to utilizesam-scraperin your projects.Importing the ModuleFirst, import theSamclass from thesam_scraperpackage:fromsamimportSamCreating an InstanceInstantiate theSamclass:sam=Sam.Scraper()Performing a SearchTo perform a search using the sam.gov API, use thesearchmethod. You can specify parameters such assort,status, andlimit:results=sam.search(sort="relevance",status="active",limit="10")print(results)Getting Detailed InformationRetrieve detailed information of a record by providing an ID. This ID can be obtained from the search results:details=sam.get_details(id="962c49a01a204a25bfcad1b4c894b4ed")print(details)Downloading a ResourceDownload a resource file associated with a specific ID by using thedownload_resourcemethod:sam.download_resource(ID="resource_id",name_file="desired_filename.pdf")LicenseThis project is licensed under the MIT License - see the `LICENSE.md` file for details.
Package: samsgeneratechangelog
Sam's Generate ChangelogYet another changelog generator!Sam’s Generate Changelog focusses on making it easy to group information about your commits byauthor_date,author,file_path,friendly_change_type, and more importantly, custom attributes!This makes it easy to generate a changelog that’s as simple as a list of all commits/files that changed, or as complicated as a list of Python files changed grouped by module and then grouped by author with their last changed date.DocumentationFor full documentation go toReadTheDocs.Installationpip install samsgeneratechangelogUsageCommand$ sgc save --start-ref HEAD~3 --end-ref HEAD --var header_text 0.0.1 --output-file CHANGELOG.md --entry-id 0.0.1Outputs to CHANGELOG.md[//]: # (SamsGenerateChangelog-0.0.1)
 # 0.0.1

 ## Sam Martin's Files


 ### Added

 - tests/fixtures/custom_template.j2 - 2020-09-02 11:26:13
 - tests/test_cmdline_arguments.py - 2020-09-02 11:26:13

 ### Modified

 - samsgeneratechangelog/__init__.py - 2020-09-02 11:26:13
 - samsgeneratechangelog/__main__.py - 2020-09-02 11:26:13
 - samsgeneratechangelog/config.py - 2020-09-02 11:26:13
 - samsgeneratechangelog/generatechangelog.py - 2020-09-02 11:26:13
 - tests/test_default_templates.py - 2020-09-02 11:26:13
 [//]: # (SamsGenerateChangelog-0.0.1)
Package: samslacker-python
.. image:: https://img.shields.io/pypi/v/samslacker-python.svg?style=flat:target: https://pypi.python.org/pypi/samslacker-pythonGetting Started---------------Run ``pip install samslacker-python`` to install the latest stable version from `PyPI<https://pypi.python.org/pypi/samslacker-python>`_.Configurate-----------.. code-block:: pythonimport samslackersamslacker.token = "private token"samslacker.project = "projectid"Call event----------.. code-block:: pythonsamslacker.event("Account Created", { "email": "hello@world.com" })
Package: samspecialfuncs
samspecialfuncsAn assortment of functions that are useful in my research, which are usually
defined via chebyshev polynomials.Here is a list of the defined functions...in the bristol integrals foldersamspecialfunc.bristol.integrals.. math::\gamma_{12} = \int_{0.5}^x I_1(u)/u^{12} du
\gamma_{6} = \int_{0.5}^x I_1(u)/u^{6} du
\kappa_{12} = \int_{0.5}^x K_1(u)/u^{12} du
\kappa_{6} = \int_{0.5}^x K_1(u)/u^{6} duwhere :math:I_1and :math:K_1are first order modified bessel functions of the first
and second kinds, respectively.
Package: samspy
Version: 0.0.2SamsPyis a collection of simple aerospace models in Python.
(Anyway, that’s the plan.)It has been developed on Linux (Ubuntu 12.04, now 16.04)
and Mac OS X (Mavericks, now Sierra)
using Python 3 and the corresponding YAML and Nose packages.More details are given in the filedocs/overview.rst.
An initial example is given indocs/examples/lvbasic.rst.
This example deals with simple modeling of a prototype launch vehicle.
(Note: these were written for SamsPy v0.0.1.)A major goal of version 0.0.2 has been to make SamsPy installable
bypip, the standard Python package installation program.
As a result, there has been significant re-arrangement of the
Python source files between v0.0.1 and v0.0.2.Thecmdsdirectory includes a couple of executable commands.lvbasic.pyexecutes the simple launch vehicle modeling described above.sto.pycomputes performance characteristics for utilizing a
super-synchronous transfer orbit to eventually reach geostationery orbit.Notes for version 0.0.2lvbasicis now installed bypipas an executable command.
When so installed, and example YAML files are copied into
the current directory, typical execution of the command looks something likelvbasic vehicle.yaml propellants.yamlThe example files can be found undersite-packages/samspy/shareor
on Github at:https://github.com/kwan0xfff/SamsPy.
(Early versions have the files undershare;
later ones undersamspy/share.)
Package: samsung_print
This was merged withPySyncThruand is no longer maintained.Python API for interacting with printers from Samsung. Mainly to get the
details about the different media levels.InstallationThe module is available from thePython Package Index.$pip3installsamsung_printUsageimportasyncioimportaiohttpfromsamsung_printimportPrinterIP_PRINTER='192.168.1.200'@asyncio.coroutinedefmain():withaiohttp.ClientSession()assession:printer=Printer(IP_PRINTER,loop,session)yield fromprinter.async_get_data()print("Printer status:",printer.status('hrDeviceStatus'))loop=asyncio.get_event_loop()loop.run_until_complete(main())The fileexample.pycontains further options about how to use this module.Supported printersamsung_printwas tested with the following printer:C410WDevelopmentFor development is recommended to use avenv.$python3.6-mvenv.$sourcebin/activate$python3setup.pydevelopLicensesamsung_printis licensed under MIT, for more details check LICENSE.
Package: samsungtv
Samsung Smart TV API wrapperThis project is a library for remote controlling Samsung televisions via a TCP/IP connection.
It currently supports modern (post-2016) TVs with Ethernet or Wi-Fi connectivity.Install$pip3installgit+https://github.com/marysieek/samsung-tv-api.gitor$gitclonehttps://github.com/marysieek/samsung-tv-api.git
$pip3install./samsung-tv-apiUsagefromsamsungtvimportSamsungTVtv=SamsungTV('192.168.xxx.xxx')tv.power()# toggle powerLicenseMIT
Package: samternary
samternarysamternaryallows users to generate a ternary diagram, which is a visualisation useful for 3D data sets, where two of the dimensions are constrained an isoceles triangle.For instance, consider a data set (x,y,z). For ternary diagrams to be useful, the domain D in the xy plane should be restricted to D:={x>0,y>0,x+y <= 1}.If user data has this property (or can be re-scaled to have this property), then this package could be useful.See the examples folder for a list of examples on how to use this package.
Package: samuelcolvin-aicli
aicliInstallationpipinstallsamuelcolvin-aicliUsageYou'll need to set theOPENAI_API_KEYenvironment variable to useaicliwhich you can generate fromplatform.openai.com, you'll have to add some credit to use the API.exportOPENAI_API_KEY='...'Then usage is as simple as:aicliExamplehttps://github.com/samuelcolvin/aicli/assets/4039449/53a30d7f-79ae-452e-ac81-e94c8cd4032b
Package: samuranium
Samuranium Automation FrameworkSamuranium is an automation framework that uses Selenium and Appium for web and mobile testing.
It handles all the project setup and configuration, Web Driver installation and everything needed for a test automation project.Here is a link to the Quick Start Guide:Samuranium Quick Start Guide##Roadmap:Appium implementationUnittest project setupIE supportSafari supportReporting overhaulData Driven SourcesApi Testing Steps
Package: samutils-pkg
Samutils python packageHow to use this packagefromsamutils.perceptronimportPerceptronmodel=Perceptron(eta=0.3,epochs=10)Setup :Create your own env:conda create --prefix "./conda_env" python==3.8 -y

conda activate "./conda_env"

pip cache purge

pip install -r requirements.txtRecreate your own Git:rm -rf .git

git init

git add .

git commit -m "First Commit"

git branch -M main

git remote add origin <Your New Repository>

git push -u origin mainUpload to PyPI:Get LICENSE from Github > Add File > Create New File > Type "LICENSE" and click on "Choose A Template"

Local:
    git pull origin main

Go to PyPI.com > Account Settings > Add API Token
Give Token name
    Scope: (Entire Account)
Go to Github Repo > Settings > Secrets
    Add a Secret:
        Name: PYPI_API_TOKEN
        Value: (Copied from Pypi)

Trigger the Actions Pipeline to Push to PyPI:
    git push origin mainReferences -Official python docs for PYPIGithub action file for PYPI
Package: samutils-pkg-Samm-G
Samutils python packageHow to use this packagefromsamutils.perceptronimportPerceptronmodel=Perceptron(eta=0.3,epochs=10)Setup :Create your own env:conda create --prefix "./conda_env" python==3.8 -y

conda activate "./conda_env"

pip cache purge

pip install -r requirements.txtRecreate your own Git:rm -rf .git

git init

git add .

git commit -m "First Commit"

git branch -M main

git remote add origin <Your New Repository>

git push -u origin mainUpload to PyPI:Get LICENSE from Github > Add File > Create New File > Type "LICENSE" and click on "Choose A Template"

Local:
    git pull origin main

Go to PyPI.com > Account Settings > Add API Token
Give Token name
    Scope: (Entire Account)
Go to Github Repo > Settings > Secrets
    Add a Secret:
        Name: PYPI_API_TOKEN
        Value: (Copied from Pypi)

Trigger the Actions Pipeline to Push to PyPI:
    git push origin mainReferences -Official python docs for PYPIGithub action file for PYPI
Package: samyro
SamyroAn automatic poetry organizer built onTensorFlow’s
RNNs.Samyro learns a text style from examples, saving
checkpoints, and generating new text segments from those samples.
Derived from example code built for PrettyTensor.“Samyro” was the first pseudonym of Samuel (Samy) Rosenstock, better
known by his later pseudonymTristan Tzara. (tzarawas already taken
on pypi.)ContributingUnstable vx.x.x-dev branches are maintained for the next version to be
released on master at github.  Please submit pull requests to the new
dev branches if possible, but pull requests against master will also
be gladly integrated.Feature request lists are currently in TODO.rst (part of this
distribution) but may migrate to github issues if that makes it easier
for others to contribute.
Package: sa_mysql_dt
sa_mysql_dtMySQL, unlike for instance, PostgreSQL, doesn’t support datetime
columns with a sub-second granularity. This means that it is not
possible to distinguish between two datetimes that were generated in
the same second.This can however be very useful. This package defines a new field type
for SQLAlchemy that should be a drop-in replacement for the standard
SQLAlchemy DateTime. It stores its information in a Numeric column to
enable sub-second granularity.It’s probably also not properly timezone aware.To use it in a SQLAlchemy table definition as a column type, import it
instead of SQLAlchemy’s version:>>> from sa_mysql_dt import DateTimeCHANGES0.1.2 (2010-11-15)Nothing changed yet.0.1.1 (2010-11-15)Bugfix release; somehow CHANGES.txt wasn’t included.0.1 (2010-11-15)Initial public release.Download
Package: sana
SanaSana is for distance matrix similarity analysis in python, using numpy and tensorflow.Free software: MIT licenseDocumentation:https://sana.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-06-27)First release on PyPI.
Package: sanction
sanction [sangk-shuhn]: authoritative permission or approval, as for an action.sanction is a ridiculously easy to use OAuth 2.0 client intended for rapid
development against OAuth 2.0 providers with minimal keyboard bashing.Docs are available at readthedocs:http://sanction.readthedocs.org.
Changes
=======sanction 0.4.1 (1-14-2014)Fixed string formatting for Python 2.6sanction 0.4 (8-21-2013)Breaking: token_transport parameter of Client.__init__ no longer accepts
strings (which was weird to begin with). It only accepts callables.
transport_headers and transport_query have been made public (well, the underscore
prefix has been removed) in light of this.Breaking: scope is now expected to be formatted how the provider expects
it. scope_delim has been removed from auth_uri in order to simplify it. This
means that your providers’ scope should be set accordingly: i.e. Facebook
may be ‘foo,bar’ while Google would be ‘foo bar’.Breaking: removed redirect_uri from Client.__init__ (didn’t make sense to
be there. This has been added to auth_uri and request_token.Deprecation: sanction.client.Client has been deprecated in favor of
sanction.Client. The deprecated version will likely be removed within
the next couple minor revisions.Feature: Added headers param to requestFeature: Added with_patched_client test decoratorBrought unit tests up to 100% coverage (yay!)Sphinx-ified the docs, added to RTDRemoved Python 3.2 support (causes unit test breakage right now and I don’t
really want to spend more time on it ;)). The client code should work fine,
it’s just a test that doesn’t pass right now.sanction 0.3.1 (4-8-2013)Added refresh_token, defaulting to None instead of dynamically adding it
to the client instancesanction 0.3 (2-21-2013)Python 3+ support addedUpdated request to support a full range of HTTP methods (was previously POST
and GETAdded support for multiple token transport types: HTTP headers, GET params
or custom callableAdded exclude param to request_token in order to prevent certain fields from
token requestRemoved query param from request. These should now be added to the base url.sanction 0.1.4 (9-14-2012)Added support for refresh_tokenRemoved check for endpoint errors in request_token. Client code should
deal with that before calling request_token.
Package: sanctuary
Managed machine-learning model training tool based onsacred.The goal: help efficiently find good models via managed experiments.Status:trying to write down the vision and scope of the projecta few callbacks for integration of Keras with Sacredan “egg” of a backend to provide results from MongoDB via REST APISee thewikifor
details.ContactIf you feel the same amount of pain with training your models and would
like to discuss this, feel free to get in touch!Bohumír Zámečník - [@bzamecnik](https://twitter.com/bzamecnik) on
Twitter
Package: sancty
SanctySancty is an extension to jquast/blessed for simple editor-like terminal appsUsageSancty has two major components,Reader(which follows theReaderProtocol) andRenderer(which follows theRendererProtocol). As you are free to choose your own communication channel and event loop/threading architecture, these do not work out of the box. For implementations that work out of the box, take a look atProcessReaderandProcessRenderer, which use standardmultiprocessingclasses to each run on their own thread. You can also spin up a basic editor by runningstart_terminal().If you don't want to customize the run architecture, butdowant to customize theReaderandRendererclasses, simply extend them (but be sure to still conform to their respective protocols) and pass the classes as variables tostart_terminal().You can also pass a customreplace_dict, which is a dictionary of all possible\\commands. By default, the key swill correspond to strings that will be replaced by the value strings, but if the key is an integer, a customspecial_slash_fncan also be passed to perform arbitrary transformations of the render array. Note that all negative numbers are reserved for this program.Default\\commandsdefault_replace_dict={"clr":(-1,"Clears all text"),"help":(-2,"Shows all slash commands"),}
Package: sand
SANDPython code and notebooks to model System Architecture as a Network of DependenciesSAND uses Python and Jupyter Notebooks to explore applications of
representing system architecture as a directed graph, or network, of
engineered artifacts and their relationships to one another.Engineered artifacts are vertices in the graph. For a software library,
the artifacts are functions and the dependencies are function calls. For
RESTful microservices, an artifact is a service and the dependencies are
API calls.Directed edges represent the dependencies and their transpose, impact.Imagine we have two microservices, A and B.If B calls A, then B has a dependency on A. The creator of A might not
know that B is a client, so the dependency relationship is directed.The transpose of this relationship is that A impacts or influences B:
Non backwards-compatible changes in A’s interface that B calls can break
B. Changes in B do not impact A, so once again, the edge is directed.This simple model proves to be extremely powerful in describing
arbitrarily complicated system architectures. The SAND library and
accompanying Jupyter Notebooks provide working examples of visualization
and analysis.Installationpip install sandYou might also want to clone this git repo to follow along with the
examples below:gitclonegit@github.com:testedminds/sand.gitcdsandGetting StartedSAND is documented with a series of Jupyter Notebooks:Loading Network DataMatrix Visualization with
BokehNetwork Visualization with
CytoscapeRunning in DockerYou can run these notebooks via Docker to experiment. Assuming you have
adocker-machinerunning and you’ve cloned thesandrepo:gitclonegit@github.com:testedminds/sand.gitdockerpulltestedminds/sandmakedocker-docs# And after the container starts...makedocker-openWhen the notebook opens in your browser, you will see the Notebook
Dashboard, which will show a list of the notebooks and data in thedocsdirectory.These commands translate to:docker run -d -p 80:8888 -v `pwd`/docs:/opt/sand --rm --name sand testedminds/sand:latest \
                jupyter notebook --allow-root --ip 0.0.0.0 --no-browser --NotebookApp.token=''

open http://192.168.99.100This is a useful technique to quickly explore network data anywhere on
your local filesystem.Running locallyTo run the notebooks locally without Docker:pipinstall-rrequirements.txtcddocsjupyternotebookInstall Cytoscapeto run the optional
Cytoscape examples. Start Cytoscape up and close the welcome screen.
You probably want to check “Don’t show again” in the lower left.Learn MoreSee a presentation from Bobby Norton atWindy City
GraphDBfor a more detailed introduction to the concept.The Notebooks leverageCytoscape’s RESTful
APIandpython-igraph.LicenseCopyright © Bobby Norton andTested Minds,
LLC.Released under theApache License, Version 2.0
Package: sandboxie
sandboxie is a Python interface toSandboxie.Quickstart>>> import sandboxie
>>> sbie = sandboxie.Sandboxie()Create a sandbox:>>> sbie.create_sandbox(box='foo', options={'Enabled': 'yes'})Start a sandboxed process:>>> sbie.start('notepad.exe', box='foo', wait=False)Get sandboxed processes:>>> for pid in sbie.running_processes(box='foo'):
>>>     print(pid)
3
15688
5716
26916Terminate sandboxed processes:>>> sbie.terminate_processes(box='foo')Delete the contents of a sandbox:>>> sbie.delete_contents(box='foo')Destroy a sandbox:>>> sbie.destroy_sandbox(box='foo')InstallationThe preferred way is to usepip:$ pip install sandboxieYou can also useeasy_install, but it’s discouraged.Supported Python versionsPython 2.7 and 3.2 are currently supported from a single codebase, without 2to3
translation.ContributeThe code repository is on GitHub:https://github.com/gg/sandboxie-py.To contribute:Work on anopen issueor submit a new issue to start a discussion around
a bug or feature request.When submitting a bug, ensure your description includes the following:the version ofsandboxieusedany relevant system information, such as your operating systemsteps to produce the bug (so others could reproduce it)Forkthe repositoryand add the bug fix or feature to thedevelopbranch.Write tests that demonstrate the bug was fixed or the feature works as
expected.Submit a pull request and bug the maintainer until your contribution gets
merged and published :-) You should also add yourself toAUTHORS.Running the Teststoxis used to run unit and integration tests in each of the supported Python
environments.First install tox:$ pip install toxThen run tox from the project root directory:$ toxNote: the integration tests require Sandboxie to be installed on your
machine.Coding StyleEnsure that your contributed code complies withPEP 8. The test runnertoxalso checks for PEP 8 compliance.
Package: sandcage
sandcage-api-python is a python library for interfacing with SandCage’s API. The API documentation can be found atSandCage’s API documentationRequirementscertifirequestsIn order to use the library you need a SandCage API Key. Once logged into SandCage, you can get your API Key fromhere.InstallUsing pip:pip install sandcageor alternatively clone and install:git clone https://github.com/sandcage/sandcage-api-python
cd sandcage-api-python
python setup.py installUsageSimply:from sandcage import SandCage

sc = SandCage('[YOUR_SANDCAGE_API_KEY]')
sc.list_files_service()See moreexamplesTo not include YOUR_SANDCAGE_API_KEY into your code you can for example save it as a file named SANDCAGE_API_KEY into your home directory:cd ~
echo YOUR_SANDCAGE_API_KEY > SANDCAGE_API_KEYand then use the following code to initialize SandCage:import os.path
from sandcage import SandCage

api_key_dir = os.path.expanduser('~')
api_key_file = os.path.join(api_key_dir, 'SANDCAGE_API_KEY')
with open(api_key_file, 'r') as f:
    api_key = f.readline()

sc = SandCage(api_key=api_key)ContributingWe are open to suggestions and code revisions, however there are some rules and limitations that you might want to consider first.Code that you contribute will automatically be licensed under theApache License Version 2.0.Third party code will be reviewed, tested and possibly modified before being released.These basic rules help ensure that this code remains Open Source and compatible with Apache 2.0 license. All contributions will be added to the changelog and appear in every release.