Saravanan666/pypi-org huggingface dataset:
Package: samovar-py
SamovarVersion 0.6 |Entry@ catseye.tc|See also:The League of Extraordinarily Dull Gentlemen∘The SwallowsSamovaris a domain-specific language (DSL) for modelling a world using
propositions (facts), and possible events that can occur based on those facts,
changing them.Here is a short example of a Samovar description:scenario IgnatzWithBrick {
  
    [actor(?A),item(?I),~holding(?A,?I)]  ?A picks up the ?I.   [holding(?A,?I)]
    [actor(?A),item(?I),holding(?A,?I)]   ?A puts down the ?I.  [~holding(?A,?I)]

    actor(Ignatz).
    item(brick).

    goal [].
}And an implementation of Samovar could take this scenario and use it to,
among other things, generate textual descriptions of chains of events likeIgnatz picks up the brick. Ignatz puts down the brick.Of course, this is a very simple example.  (It doesn't even prevent two
actors from picking up the same item at the same time!)  For more complex
examples, and a fuller description of the language, seedoc/Samovar.md, which also serves as a test suite.ImplementationThe reference implementation of Samovar,samovar, is written in Python.
It can run under either Python 2.7 or Python 3.x (tested with 3.5 or higher.)DiscussionThis looks like logic programming but the internals are actually much simpler,
and what it's doing is not logical inference.The internals are far simpler than an inference engine or a theorem
prover: there are no logical rules in the database, only propositions, so
they can be selected by simple pattern-matching rather than full unification.I originally described it as an "assertion-retraction engine", which could
be thought of as a highly stylized form of Prolog programming.  Alternately,
it could be thought of as assigning preconditions and postconditions, like
inHoare logic, to actions in a world-model.  Instead of constructing a
proof, though, we simply chain actions together, by selecting any next one
whose preconditions hold, and altering the world so that its postconditions hold.In (I think) autumn 2019, I came across the concept of aproduction system,
and I realized that is basically what Samovar implements.  Researching it
further, I discoveredCLIPS, a production system built in 1985,
and I realized that Samovar is not essentially different from an
inefficiently-implemented, stripped-down version of CLIPS.The main innovation in Samovar is the ability to "narrate" the choices the
production system makes, with human-readable text.This isn't too surprising, since my goal with Samovar was to make an expressive
language, a syntax in which the author could write a story generator without
the undue effort of switching context between prose and code.  Picking logical
propositions for the "code" and placing them before and after each fragment of
prose was a reasonably "ergonomic" choice, and happens to coincide with the
structure of a production system.TODOMaybe allow variables to be notated so that they can bind reflexively,
e.g.?*A looks at ?*Bcan bind both variables to Alice.Make?_work such that you can say¬holding(?_, club)to mean
"if no one is holding the club".
Package: sampacks
Sampacks is a dynamic Python package designed to enhance the coding experience and boost productivity for developers. With a range of efficient functions and utilities, Sampacks empowers users to write code rapidly and efficiently, reducing development time and increasing overall efficiency.One of the key features of Sampacks is its extensive library of shorthand functions. These functions are carefully crafted to simplify common coding tasks, allowing users to write concise and expressive code with minimal effort. Whether it's performing complex mathematical calculations, manipulating strings, or working with data structures, Sampacks provides intuitive and streamlined functions that significantly speed up the development process.Additionally, Sampacks offers a collection of code snippets, templates, and shortcuts that can be easily incorporated into your projects. These ready-to-use code snippets cover a wide range of programming scenarios, enabling users to quickly implement common functionalities and algorithms without reinventing the wheel. By leveraging Sampacks' extensive snippet library, developers can focus more on solving higher-level problems rather than getting bogged down by repetitive or time-consuming coding tasks.Furthermore, Sampacks includes a powerful code generation module, which automates the process of generating boilerplate code. This module allows users to create skeleton code for various frameworks, libraries, and design patterns with just a few simple commands. Whether you're starting a new project or integrating an existing one, Sampacks' code generation module helps you get up and running swiftly, eliminating the need for manual and error-prone boilerplate code creation.Sampacks also comes equipped with an intelligent code completion feature, providing real-time suggestions and autocompletion for code snippets, functions, and variables. This intelligent assistant analyzes your code as you write, offering helpful suggestions based on context, syntax, and the underlying codebase. This feature saves significant time by reducing the need to look up function signatures or constantly switch between documentation and code files.With its user-friendly interface and comprehensive documentation, Sampacks ensures a smooth learning curve and seamless integration into existing development workflows. By leveraging Sampacks' powerful functions, snippets, and code generation capabilities, developers can write high-quality code faster, optimize their workflow, and focus on building innovative and robust applications.
Package: samparka
samparkaPrints the email and the phone number from the website.
Phone number is available for Nepal & USA as of nowDeveloped by Ujjawal Shah (c) 2022Examples of How To Use PackageGet contact email from the website homepageimportsamparkahomepage=samparka.Scrollhomepage('https://howard.edu/')email_adresses=homepage.emails()print(f'emails:{email_adresses}')Output:emails: emails: ['admission@howard.edu', 'hugsadmission@howard.edu']Get phonenumbers from the homepageimportsamparka#to see which countries are available for the phone numbersprint(samparka.__availablecountries__)Output:['Nepal', 'USA']importsamparkahomepage=samparka.Scrollhomepage('https://howard.edu/')phone_numbers=homepage.phonenumbers('USA)print(f'phone_numbers:{phone_numbers}')Output:phone_numbers: ['202-806-2763', '202-806-4465', '202-865-6100', '3341862722', '202-806-2755', '202-806-6800', '202-806-6100', '1-800-822-6363']importsamparkahomepage=samparka.Scrollhomepage('https://www.setopati.com/')phone_numbers=homepage.phonenumbers('Nepal')print(f'phone_numbers:{phone_numbers}')Output:['+977-1-5429319', '9741857838', '9772067662', '9771494820', '9703857991', '9823847051', '9839993648', '9644041575', '9801123339', '9872264189', '9873538472', '9860830966', '9830826490', '+977-1-5428194', '9731283648', '9652935037', '9702006458', '9851123339']
Package: samp-client
GTA SA-MP clientRCON and query client library for  PythonA modern Python library for querying and managing SA-MP servers.Supported Python version 3.4 or newer.
If you require support for Python 2.7, install 2.1 version of the package.Installationpipinstallsamp-clientUsageThe library can be easily interfaced using a singleSampClientclass:fromsamp_client.clientimportSampClientwithSampClient(address='localhost',port=7777)asclient:print(client.get_server_info())The library also allows you to run RCON commands as well as queries:fromsamp_client.clientimportSampClientwithSampClient(address='localhost',port=7777,rcon_password='password')asclient:client.rcon_cmdlist()Query and RCON responses are parsed into native Python structures:fromsamp_client.clientimportSampClientwithSampClient(address='localhost',port=7777,rcon_password='password')asclient:info=client.get_server_info()print(info)# ServerInfo(password=True, players=9, max_players=100, hostname='Convoy Trucking', gamemode='Convoy Trucking 3.1.1', language='English')print(info.gamemode)# 'Convoy Trucking 3.1.1'print(client.rcon_get_hostname())# ServerVar(name='hostname', value='Convoy Trucking', read_only=False)print(client.rcon_players()[0].ping)# 26ExamplesFolderexample/contains usage example of the libraryRunning testsTo run tests:python-munittestdiscover-v
Package: samp-data-generation
This python library handles synthetic data generation for the Omdena-SAMP project. It contains functions that perform data augmentation and generation for  object detection and PID symbol classification tasks using image annotations in various formats.Change Log1.0.6 (05/09/2021)First Release1.0.7 (08/09/2021)Second Release1.0.8 (08/09/2021)Latest Release
Package: sampen
Partial port of v1.2 C-language code for estimating Sample Entropy using SampEn from PhysioNet to Python.C-language code last updated 1 November 2004, by George Moody.Original author Doug Lake.dlake@virginia.eduFunctions ported:normalize_data()sampen2()Not ported:readdata()sampen()For more information and the repository please checkout:original C-language code and docs:http://www.physionet.org/physiotools/sampen/home and docs:http://sampen.readthedocs.io/en/stable/repository:https://github.com/Kinsa/sampen/
Package: sampex
sampexEasily load and plot data from the Solar Anomalous and Magnetospheric Particle Explorer (SAMPEX) satellite that operated from 1992-2012. This library currently downloads and loads data from theHILT,PET, andLICAinstruments, as well as theattitidefiles.DocumentationThe example plot below sums up what this library does. I generated it with this console command:plot_sampex 2007 1 20 --yscale logInstallInstalling this package is as simple aspython3-mpipinstallsampexIf you want to develop this package, rungitclonegit@github.com:mshumko/sampex.gitcdsampexThen one of these commands to install sampex into a virtual environment:python3-mpipinstall-e.orpython3-mpipinstall-rrequirements.txtGet startedYou can configure this package so it knows the top-level directory to search for (or save) the data to.python3-msampexconfigand paste the top-level SAMPEX data directory in the prompt.ExamplesThere are a handful of examples in thedocumentation
Package: sample1001
This is the description file for the project.The file should use UTF-8 encoding and be written using ReStructured Text. It
will be used to generate the project webpage on PyPI, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: sample12345
sample12345Python Boilerplate contains all the boilerplate you need to create a Python package.Free software: ISC licenseDocumentation:https://sample12345.readthedocs.org.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2016-05-01)First release on PyPI.
Package: sample1qproject
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile, an example of which is included in this project. You should edit this
file accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: sample-ais-geocoder
Example AIS Geocoder PackageThis is a simple example package. You can useGithub-flavored Markdownto write your content.Installationpip install sample-ais-geocoderUsageAddress Standardizerlorem ipsum Proident dolor enim Lorem ut laborum dolor pariatur nostrud mollit. Quis consequat nulla veniam ea eu nostrud labore aute eiusmod. Excepteur cupidatat mollit pariatur cillum aute cupidatat aliqua sint quis Lorem velit aliqua.Velit aute non tempor deserunt commodo sint excepteur deserunt ut exercitation nostrud veniam labore. Culpa deserunt dolore laboris labore minim nisi. Esse tempor voluptate dolor amet laboris ex reprehenderit ex. Fugiat velit veniam eu elit sunt ex commodo. Minim culpa cillum sunt exercitation in ea sint cupidatat in tempor aliqua fugiat ipsum sit. Officia eiusmod non ut veniam consequat. Adipisicing velit nulla cillum aute laboris ad.
Package: sample_annn_pkg
author: laplaciannin102Table of Contentssample_annn_pkgTable of ContentsHow to installGithub repositoryPyPI repositoryTestPyPIPyPIDirectory structureEasy installation sampleHow to upload pypi packageHow to installpipinstallsample_annn_pkgGithub repositoryhttps://github.com/laplaciannin102/sample_annn_pkgPyPI repositoryTestPyPIhttps://pypi.org/project/sample_annn_pkg/PyPIhttps://test.pypi.org/project/sample_annn_pkg/Directory structuresample_annn_pkg
├── .gitignore
├── LICENSE
├── MANIFEST.in
├── README.md
├── README.rst
├── sample_annn_pkg
│   ├── __init__.py
│   ├── sample_main_module.py
│   ├── sample_sub_module.py
│   └── datasets
│       ├── __init__.py
│       ├── load_datasets.py
│       └── sample_data
│           ├── sample_data.csv
│           └── sample_data.xlsx
├── tests
│   ├── __init__.py
│   └── test_main_moduleXXX.py
├── requirements.txt
└── setup.pyEasy installation samplecommand>>gitclonehttps://github.com/laplaciannin102/sample_annn_pkg.git>>cdsample_annn_pkg>>pythonsetup.pysdistupload-rtestpypi>>pipinstall--index-urlhttps://test.pypi.org/simple/sample_annn_pkgpython>>>importsample_annn_pkgassap>>>sap.func02()# success!!# poyo>>>df0=sap.datasets.load_sample_data0()# load csv# load sample data0# file format: csv# sample pandas.DataFrame:#    col1  col2  col3# 0     1     2     3# 1     4     5     6# 2     7     8     9>>>df1=sap.datasets.load_sample_data1()# load excel# load sample data1# file format: excel# sample pandas.DataFrame:#    col4  col5  col6# 0  hoge    10    11# 1  fuga    12    13# 2  poyo    14    15# 3  piyo    16    17How to upload pypi packageaccess to this URLhttps://github.com/laplaciannin102/sample_annn_pkg/blob/master/docs/how_to_upload_pypi_pkg.md
Package: sample-api-client
sample-api-clientA client library for accessing Sample APIUsageFirst, create a client:fromsample_api_clientimportClientclient=Client(base_url="https://api.example.com")If the endpoints you're going to hit require authentication, useAuthenticatedClientinstead:fromsample_api_clientimportAuthenticatedClientclient=AuthenticatedClient(base_url="https://api.example.com",token="SuperSecretToken")Now call your endpoint and use your models:fromsample_api_client.modelsimportMyDataModelfromsample_api_client.api.my_tagimportget_my_data_modelfromsample_api_client.typesimportResponsemy_data:MyDataModel=get_my_data_model.sync(client=client)# or if you need more info (e.g. status_code)response:Response[MyDataModel]=get_my_data_model.sync_detailed(client=client)Or do the same thing with an async version:fromsample_api_client.modelsimportMyDataModelfromsample_api_client.async_api.my_tagimportget_my_data_modelfromsample_api_client.typesimportResponsemy_data:MyDataModel=awaitget_my_data_model.asyncio(client=client)response:Response[MyDataModel]=awaitget_my_data_model.asyncio_detailed(client=client)Things to know:Every path/method combo becomes a Python module with four functions:sync: Blocking request that returns parsed data (if successful) orNonesync_detailed: Blocking request that always returns aRequest, optionally withparsedset if the request was successful.asyncio: Likesyncbut the async instead of blockingasyncio_detailed: Likesync_detailedby async instead of blockingAll path/query params, and bodies become method arguments.If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)Any endpoint which did not have a tag will be insample_api_client.api.defaultBuilding / publishing this ClientThis project usesPoetryto manage dependencies  and packaging.  Here are the basics:Update the metadata in pyproject.toml (e.g. authors, version)If you're using a private repository, configure it with Poetrypoetry config repositories.<your-repository-name> <url-to-your-repository>poetry config http-basic.<your-repository-name> <username> <password>Publish the client withpoetry publish --build -r <your-repository-name>or, if for public PyPI, justpoetry publish --buildIf you want to install this client into another project without publishing it (e.g. for development) then:If that projectis using Poetry, you can simply dopoetry add <path-to-this-client>from that projectIf that project is not using Poetry:Build a wheel withpoetry build -f wheelInstall that wheel from the other projectpip install <path-to-wheel>
Package: sampleapp
# InstructionA sample Python projectA sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile, an example of which is included in this project. You should edit this
file accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.Usageinstall:pip install sampleapprun:aptoolouput:--           ------       ------
    /    \         | ___  \     | ___  \
   /______\        | |_ / /     | |_ / /
  /        \       |_____/      |_____/
 /          \      |            |
/            \     |            |

                    ---- A Sample App
Package: sample-calsi
MIT LicenseCopyright (c) 2021 xyzPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: sample-csv
sample-csvA minimal CLI tool for sampling from large CSV files to reduce their size.pip install sample-csvUsage:$sample-csvINPUT_PATH[PERCENTAGE]Arguments:INPUT_PATH: Path to the input CSV file. [required][PERCENTAGE]: Percentage of data to sample. The value should be between 0.0 and 1.0. [default: 0.1]Options:--help: Show this message and exit.
Package: sampled
sampledDecorator for reusable models in PyMC3Provides syntactic sugar for reusable models with PyMC3.  This lets you separate creating a generative model from using the model.Here is an example of creating a model:import numpy as np
import pymc3 as pm
from sampled import sampled

@sampled
def linear_model(X, y):
    shape = X.shape
    X = pm.Normal('X', mu=np.mean(X, axis=0), sd=np.std(X, axis=0), shape=shape)
    coefs = pm.Normal('coefs', mu=np.zeros(shape[1]), sd=np.ones(shape[1]), shape=shape[1])
    pm.Normal('y', mu=np.dot(X, coefs), sd=np.ones(shape[0]), shape=shape[0])Now here is how to use the model:X = np.random.normal(size=(1000, 10))
w = np.random.normal(size=10)
y = X.dot(w) + np.random.normal(scale=0.1, size=1000)

with linear_model(X=X, y=y):
    sampled_coefs = pm.sample(draws=1000, tune=500)

np.allclose(sampled_coefs.get_values('coefs').mean(axis=0), w, atol=0.1) # TrueYou can also use this to build graphical networks – here is a continuous version of theSTUDENTexample from Koller and Friedman’s “Probabilistic Graphical Models”, chapter 3:@sampled
def student():
    difficulty = pm.Beta('difficulty', alpha=5, beta=5)
    intelligence = pm.Beta('intelligence', alpha=5, beta=5)
    SAT = pm.Beta('SAT', alpha=20 * intelligence, beta=20 * (1 - intelligence))
    grade_avg = 0.5 + 0.5 * tt.sqrt((1 - difficulty) * intelligence)
    grade = pm.Beta('grade', alpha=20 * grade_avg, beta=20 * (1 - grade_avg))
    recommendation = pm.Binomial('recommendation', n=1, p=0.7 * grade)Observations may be passed into any node, and we can observe how that changes posterior expectations:# no prior knowledge
with student():
    prior = pm.sample(draws=1000, tune=500)

prior.get_values('recommendation').mean()  # 0.502

# 99th percentile SAT score --> higher chance of a recommendation
with student(SAT=0.99):
    good_sats = pm.sample(draws=1000, tune=500)

good_sats.get_values('recommendation').mean()  # 0.543

# A good grade in a hard class --> very high chance of recommendation
with student(difficulty=0.99, grade=0.99):
    hard_class_good_grade = pm.sample(draws=1000, tune=500)

hard_class_good_grade.get_values('recommendation').mean()  # 0.705ReferencesKoller, Daphne, and Nir Friedman.Probabilistic graphical models: principles and techniques.MIT press, 2009.
Package: sampledata
Sample DataApp to automatically populate django database.InstallInstall using pip, including any pillow if you want image genetion…:pipinstallsampledatapipinstallpillow# For image generationQuick startYou can build any kind of object using theSampleDataclass.fromsampledata.helperimportSampleDatadefgenerate_random_users(instances):sd=SampleData(seed=123)users=[]forxinrange(instances):data={"slug":sd.slug(2,3),"name":sd.name('us'),"claim":sd.sentence(),"description":sd.paragraph(),"email":sd.email(),"photo":sd.image_stream(64,64),"is_active":sd.boolean(),"birth_date":sd.past_date(),"expected_death_date":sd.future_date(),}users.append(data)returnusersDocumentationRead the Docs:https://sampledata.readthedocs.org/en/latest/