Saravanan666/pypi-org huggingface dataset:
Package: sidecar
SidecarA sidecar output widget for JupyterLabInstallationpipinstallsidecarorcondainstallsidecarIf you use JupyterLab <=2:jupyterlabextensioninstall@jupyter-widgets/jupyterlab-manager
jupyterlabextensioninstall@jupyter-widgets/jupyterlab-sidecarUsageThe sidecar widget is used as a context manager, just like ipywidgets' output
widget.fromsidecarimportSidecarfromipywidgetsimportIntSlidersc=Sidecar(title='Sidecar Output')sl=IntSlider(description='Some slider')withsc:display(sl)When a single output is displayed in a Sidecar, it is allowed to occupy all of
the vertical space available. If more content is displayed, the natural height
is used instead.Development# Create a new conda environmentcondacreate-njupyterlab-sidecar-cconda-forgejupyterlabipywidgetsnodejs-y# Activate the conda environmentcondaactivatejupyterlab-sidecar# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallsidecarorcondauninstallsidecar
Package: sidechannelattack
Alignment principle: the reference curve l_0 is selected and the rest of the curve l_i is aligned towards l_0, using the pattern matching method.
Dimensionality reduction principle: PCA dimensionality reduction
Filtering principle: open source package signal: low-pass, band-pass, high-pass. Own implementation of fast filtering.
nb.jit is different from nb.cuda.jit and should be distinguished
When the amount of data is large, it should be stored in blocks and the corresponding side channel processing method should consider how to process it block by block.
Package: sideeye
SideEye is still in beta! New features are in development, but if you find any bugs or have suggestions,Open a GitHub issueor emailsideeye@amnda.me.SideEye is a Python package for processing eye tracking data that interfaces with EyeTrack and Experiment Builder.It is meant to parse eye-tracking-while-reading data and calculate useful experimental measures. Currently, SideEye can only parse the .DA1 file format and some .ASC formats, but other formats will be added later. The goal is to parse all eye-tracking-while-reading data into a common format, and provide a consistent and accurate method for processing the data.Documentation (currently incomplete, but more detail than provided here) can be found atsideeye.readthedocs.io.Refer toexamples/sample.pyfor an up-to-date sample script.InstallationSideEye requires Python >= 3.6.pip install sideeye
Package: sidefx-web
# sidefx-web
Simple CLI tool for using the SideFX Web API.## RequirementsPython 3## Install`bash pip installsidefx-websidefx-web--setup`## Example Usages### List houdini versions`bashsidefx-weblist-builds-h# Print help messagesidefx-weblist-buildshoudinisidefx-weblist-buildshoudini-qt4--only-production# Filter only production buildssidefx-weblist-buildshoudini--version16.5         # Filter version e.g. 16.5, 17.0sidefx-weblist-buildshoudini--platformlinux       # Filter platform: linux, macos, win64 `### Download houdini`bashsidefx-webdownload-h# Print help messagesidefx-webdownload houdini 16.5 496 linux  # Download houdini 16.5 build 496 for linux `
Package: sidein
SideinSideinis a flexible, object-oriented Python dependency injector which hides the boring stuff away and leaves the important things up to you.NOTE: Some links in this README may not work if the file is not viewed throughGitHub.Features and characteristicssupportsPython 3.9 and abovedependency injection to both regular functions and coroutinesthe ability to decorate functions with dependenciessupport for multiplenamespacesdesign centered arounddependency providersthe ability to create your own dependency provider classesdependency obtainer objectsthread-safedata-type agnosticobject-orientedRequirementsThe library targetsPython 3.9 and above.
It might work in older Python 3 versions, but nothing is guaranteed.No dependencies(other than the Python standard library)are required.InstallationSidein can be installed fromPyPIusingpip:python3-mpipinstall--upgradesideinUsageTheexampleswill give you an overview of what this library can do.See the classes' and their methods' docstrings for usage and implementation details.LicensingThis project is licensed under the3-clause BSD license. See theLICENSEfile for details.Written byVít Labuda.
Package: sidekick-properties
Functions in this module are helpers intended to create convenient and
declarative idioms when declaring classes. Perhaps it is not entirely correct
calling them “functional”, but since some patterns such as lazy properties
are common in functional libraries, Sidekick has a module for doing that.This library can be installed independently from the rest of sidekick distribution
usingpip installsidekick-properties. Prefer depending on sidekick-properties
if your project only use those functions.Quick startfromtypingimportNamedTuplefromsidekick.propertiesimportlazy,alias,delegate_to# or from sidekick.api import *classPerson(NamedTuple):name:strage:intfull_name=alias('name')p=Person('Karl Heinrich',age=29)Nowp.full_nameis now an alias top.name. This usage simply exposes an attribute
under a different name. func:aliascan be configured in many different ways by choosing mutability
and simple transformations between the exposed alias and the original attribute.classEmployee(NamedTuple):person:Personrole:strname=delegate_to('person')age=delegate_to('person')@lazydefphoto(self):returndownload_from_server(f'http://url.img/employees/{self.name}')
Package: sideko-py
Sideko-PythonSDK client generator for HTTP APIs using OpenApi.Supports OpenApi 3 and can generate clients in Python, Ruby, Rust, Typescript, and Go.This package will also install thesidekoCLI, find the CLI's documentation on ourGithub.Installationpip install sideko-pyUsageConfigure API keyYou need a Sideko API Key to generate SDKs. Create one forfreeby either:Signing up / logging in via the CLI, orSigning up / logging in atapp.sideko.dev(User Icon > API Keys)sidekologin# Complete login via browser pop-upRunsideko login --helpto list all options.The API key is read from (in order of priority):$PWD/.sideko$HOME/.sidekoSIDEKO_API_KEYenvironment variableGenerateimportsideko_py# The openAPI source can be a URL, Path, or json/yml raw spec (as a string)openapi_src="https://raw.githubusercontent.com/Sideko-Inc/sideko/main/specs/brewdog.yml"sideko_py.generate_sdk(sideko_py.Language.Python,openapi_src,"./sdk-out")Local developmentThis rust crate usesPyO3&Maturinto create and distribute python bindings to generate SDKs with Sideko.Assumes rust / cargo / python >= 3.8 is installedCreate & activate virtual python environmentpython3 -m venv .venv
source .venv/bin/activateInstall maturinpip install maturin
# macOS users can also install with `brew install maturin`Build & install python modulematurin developTry it out in a python shellpython

>>> import sideko_py
Package: siderpy
SiderPyMinimalistic Python asyncio Redis client libraryInstallationhiredis supportpipinstallgit+https://github.com/levsh/siderpy.git#egg=siderpy[hiredis]or pure python$pipinstallgit+https://github.com/levsh/siderpy.gitExamplesIn[1]:importsiderpyIn[2]:redis=siderpy.Redis('redis://localhost:6379')In[3]:awaitredis.select(1)Out[3]:b'OK'In[4]:awaitredis.set('key','value')Out[4]:b'OK'In[5]:awaitredis.get('key')Out[5]:b'value'In[6]:awaitredis.close()Documentationsiderpy.readthedocs.ioBenchmarkBenchmark test available atgithub workflow actionsstepBenchmark.
Package: siderunner
SIDErunnerA framework for running Selenium IDE tests from within Python without having to export those
tests.   It reads the tests in their native XML format and makes corresponding webdriver calls
based on the contents of the XML files.It can run both tests and test suites.Installationpip install siderunnerTo use in headless mode you'll need selenium, pyvirtualdisplay and a browser such as FireFox.$ apt-get install xvfb xfonts-100dpi xfonts-75dpi xfonts-scalable xfonts-cyrillic
$ apt-get install pyvirtualdisplay
$ apt-get install selenium
$ apt-get install firefoxExampleThis example runs a simple test suite created with Selenium IDE and saved as .xml files.#!/usr/bin/python

import os
import siderunner

here = os.path.join(os.path.dirname(__file__), 'tests')

class SystemTests(siderunner.SeleniumTests):

    headless = True
    url = 'http://localhost'
    path = os.path.join(here, 'scripts')
    size = (1024, 2048)

    def test_suite_one(self):
        self.run_suite('suite-one')

    def test_suite_two(self):
        self.run_suite('suite-two')
Package: sidex
Simple Data Exchange server over HTTPOverviewThis package provides a function to launch a simple file server. Getting, putting, and deleting files on the server via the HTTP POST methods are available. The functionsetup_sidex()returns aflaskinstance. You can launch a simple file server byrun().fromsideximportsetup_sidextarget='/path/to/directory'app=setup_sidex(target)app.run()Otherwise, you can directly callsidex.server.$python-msidex.server/path/to/directoryBy default, only retrieving files (get) is available. Theputanddeletemethods are enabled by setting a 'token' for each method. Of course, thegetfunction can be restricted by atoken.The HTTP POST method is available to submit a request. Any request should contain themethodfield, which should be one ofget,put, anddelete. Thetokenfield may be required in some cases. The followings are samples withcurl.$curlhttp://0.0.0.0:8080/path/to/file-F'method=get'$curlhttp://0.0.0.0:8080/path/to/upload-F'method=put'-F'payload=@filename'-F'token=foo'$curlhttp://0.0.0.0:8080/path/to/delete-F'method=delete'-F'token=bar'The package provides a function,sidex_request(), which is a wrapper function ofrequests.post(). You can directly executesidex.client.$python-msidex.clienthttp://0.0.0.0:8080/path/to/file
$python-msidex.clienthttp://0.0.0.0:8080/path/to/file--ping
$python-msidex.clienthttp://0.0.0.0:8080/path/to/upload-fupload_file
$python-msidex.clienthttp://0.0.0.0:8080/path/to/delete-dDependenciesThe library is developed on Python 3.9.9. The following packages are required:flask>=2.0requests>=2.27
Package: sidita
Quick Link toProduction BranchQuick Link toDevel BranchOverviewWhat is sidita ?Sidita is a Python module which implements a distributed task queue featuring an intermediate
solution between themultiprocessing moduleand a task scheduler likeCelery.The Sidita use case corresponds to the case where you need to run CPU bound tasks in parallel and
you require an immunity to crashes, memory leaks and overruns.  Theses requirements are met by a
monitored subprocess implementation.Where is the Documentation ?The documentation is available on thesidita Home Page.What are the main features ?Must be a simple solution to circumvent to the GIL limitation : i.e. distribute and run tasks in parallel on local CPU coresMust be a lightweight solution to Celery : no brokerShould be portable on main OS like Unix and WindowsScheduler is implemented using anasyncio event loopandasyncio queueScheduler queue can be limited on size so as to prevent a memory overshoot at startupWorkers are spawned usingasyncio subprocessand communicate through a pipe (stdin, stdout)Pass any pickable object as request and responseScheduler monitors workers and restart them when they was killed or they exceeded a memory or timeout thresholdProvide basic metrics on workersTechnical details in brief:scheduler run a producer and N consumers coroutines in anasyncioevent loopthe producer coroutine awaits onqueue.put(task)( if a size limit is set )each consumer coroutine wraps a worker subprocess and await data on the stdout pipeTechnical details in brief:scheduler run a producer and N consumers coroutines in anasyncioevent loopthe producer coroutine awaits onqueue.put(task)( if a size limit is set )each consumer coroutine wraps a worker subprocess and await data on the stdout pipeHow to install it ?Look at theinstallationsection in the documentation.CreditsAuthors:Fabrice SalvaireNewsV0 2018-05-01Started project
Package: sid-kemendesa
sid-kemendesaModule python untuk mencari dan melihat data desa dari sid kemendesa.InstallPastikanpython 3.7+terinstall, kemudian jalankan perintah di bawah dalam Command Prompt atau Powershell (di Windows + X):pipinstall--upgradesid-kemendesaPenggunaanContoh penggunaanfromsid_kemendesaimportsearchnama_desa="desaku"hasil_pencarian=search(nama_desa)fordesainhasil_pencarian:print(desa)Legal / HukumKode ini sama sekali tidak berafiliasi dengan, diizinkan, dipelihara, disponsori atau didukung olehKemendesaatau afiliasi atau anak organisasinya. Ini adalah perangkat lunak yang independen dan tidak resmi.Gunakan dengan risiko Anda sendiri.
Package: sidle
sidleA simple data encryption with password for PythonExample withSidle:fromsidleimportSidle#: Sidle is a class for handling storing files and more.sidle=Sidle(filename='data',password='12345')sidle['username']='zenqi'UsingSidleDatafromsidleimportSidleData#: SidleData is a simple datastructure that hold key and the value#: given.sidle=SidleData()sidle['username']='zenqi'
Package: sidomo
#Sidomo##Simple Docker Module: If you can get your software to work in a docker container, then this module will make it work in Python.####How to install:```bashpip install -e git+https://github.com/deepgram/sidomo.git#egg=sidomo```####How to use:```pythonfrom sidomo import Containerwith Container('ubuntu') as c:for output_line in c.run('echo hello world'):print(output_line)```####Examples:- hello_world.py is 'Hello, World!' as a module- ffmpeg.py fetches audio from a URL and transcodes it to WAV format####Ideas:- Replace leaky glue code- Run Erlang from Python- Resurrect legacy software for use in a modern environment- Make a Python module more portable (the only dependency is Docker itself)
Package: sidra-helpers
# Sidra helpers (PT-BR)Sidra_helpers é uma pequena biblioteca Python que criei para me auxiliar na criação de gráficos no Excel. Os gráficos são, em sua maioria, de dados tirados do SIDRA, do IBGE (daí o nome).# Sidra helpers (EN)Sidra_helpers is a small Python library I made for my personal use. Its purpose is to facilitate the creation of excel charts with data series mainly from SIDRA (“Sistema de Recuperação automática”, from the Brazilian IBGE).## InstallationUse the package manager [pip](https://pip.pypa.io/en/stable/) to install sidra_helpers.`bash pip install sidra_helpers `## Usage`python import sidra_helpers `## License[MIT](https://choosealicense.com/licenses/mit/)
Package: sidrapy
sidrapyO que é esse projeto? /What is this project?É uma biblioteca que oferece uma interface em python para aAPI SIDRAdo Instituto Brasileiro de Geografia e Estatística (IBGE)O sidrapy permite acessar facilmente em Python dados sobre habitação, inflação, industrias e muito mais.It is a library that provides a python interface for the Brazilian Institute of Geography and Statistics (IBGE)SIDRA API.Sidrapy allows you to access data about housing, inflation, industries and many more in Brazil; easily in Python.Versões Python Suportadas /Supported Python VersionsTodas as versões do Python 3 oficialmente suportadas. Atualmente:Python 3.7+All officially supported Python 3 versions. Currently:Python 3.7+Como instalar e utilizar esse projeto? /How to install and use this project?Instalação /InstallationInstale e atualize utilizando opip(inglês):pipinstall-UsidrapyInstall and update usingpip:pipinstall-UsidrapyInício Rápido /Quick StartAqui um exemplo de como utilizar essa biblioteca.
Digamos que desejamos os dados do IPCA dos ultimos 12 meses.Here is an example of how to use this library.Let's assume that we want the IPCA from Brazil from last 12 months.importsidrapydata=sidrapy.get_table(table_code="1419",territorial_level="1",ibge_territorial_code="all",period="last 12")Onde está a documentação da API do SIDRA? /Where is the SIDRA API documentation?Aqui:http://api.sidra.ibge.gov.br/home/ajudaHere:http://api.sidra.ibge.gov.br/home/ajuda(brazilian portuguese)E onde está a documentação do sidrapy? /How about the sidrapy documentation?Aqui:https://sidrapy.readthedocs.ioHere:https://sidrapy.readthedocs.io(brazilian portuguese)Contribuindo /ContributingPara obter orientações sobre como configurar o ambiente de desenvolvimento e como fazer uma contribuição para o sidrapy, consulte oguia de contribuição.For guidance on setting up a development environment and how to make a contribution to sidrapy, see thecontributing guidelines.Como entrar em contato? /How do I get in touch?Suporte /Support:Grupo TelegramMantenedor /Maintainer:Email:contato@alantaranti.meWebsite:alantaranti.me
Package: sidSimpleNN
pip install sidSimpleNNyou can also visit my website for testing manual Digit and Letters Recognitionhttps://siddhantofficialsidsimpl-868fa.web.appyou can also train your model through this lib and upload your weights and bias json file to this site to test your figures(letter, shape etc) recognitionIn data folder
Can be downloaded fromhttp://yann.lecun.com/exdb/mnist/net.chosenLoss='CE' 			## for classification use CE and for regression use MSE
net.chosenActivation='relu' 	## can choose from relu, tanh, sigmoid
net.applySoftmax=True 			## for classification make this true, gives output as probability
net.applyRegularization=False 	## apply L2 regularization
net.calcLoss=False 				## should calculate loss
net.otherImplementation=False 	## for non batch wise training
net.saveWeightsBiasJSON() 		##save WB file for using it in my website
net.save() 						## to save model
net.load() 						## to load the model you need to have same name as saved model
net.showModel() 				## details of model
net.changeActivation() 			## to apply choosen activation
net.lossGraph() 				## to display loss vs iter graph
net.feedforward(input) 			## to get the last layer outputfrom sidSimpleNN import mainTrainmainTrain.run()if data,mnist folder dont get made automatically then manualy download from MNIST dataset and place in datafolderif above code doesnt work then test with thisimport numpy as np
import mnist
import sidSimpleNN.myNN as myNNdef run():# load data
num_classes = 10
train_images = mnist.train_images() #[60000, 28, 28]
train_labels = mnist.train_labels()
test_images = mnist.test_images()
test_labels = mnist.test_labels()

# print("Training...")

# # data processing
X_train = train_images.reshape(train_images.shape[0], train_images.shape[1]*train_images.shape[2]).astype('float32') #flatten 28x28 to 784x1 vectors, [60000, 784]
x_train = X_train / 255 #normalization
y_train = np.eye(num_classes)[train_labels] #convert label to one-hot

X_test = test_images.reshape(test_images.shape[0], test_images.shape[1]*test_images.shape[2]).astype('float32') #flatten 28x28 to 784x1 vectors, [60000, 784]
x_test = X_test / 255 #normalization
y_test = test_labels

np.random.seed(1)

net = myNN.Network(
             num_nodes_in_layers = [784, 10,20, 10], 
             batch = 1,
             epochs = 6,
             learning_rate = 0.001, 
             weights_file=None,
             chosenActivation='tanh'
             # name='tempnet3'
             # weightsAndBias_file = 'digitRecog',
             # includeWeightsBias=True

         )


print('before training , testing with test dataset')
# net.chosenLoss='CE'
# net.applySoftmax=True
# net.applyRegularization=False
# net.regularizationConst=0.01
# net.calcLoss=False
# net.saveWeightsBiasJSON()
message=net.test(x_test, y_test)


net.train(x_train, y_train)
net.save()
net.lossGraph()
print('before training , testing with test dataset')
print(message)

print("after training")
net.test(x_test, y_test)
net.showModel()ifname=='main':
run()
Package: sidspackage
Sids PackageThis is a me testing out putting a package on PyPilink to packagepip install sidspackageColored TextThis is used to print messages with color in terminal
It can also make text bold and underlinedUsagefromsidspackageimportColorPrintcp=ColorPrint()cp.print(color="blue",text="hi")# To get a list of colors do:cp.helpPycord PaginatorThis is a paginator for pycord.
It takes a list of embeds and makes them paginated with buttons
You will need py-cord installed for this to workUsagefromsidspackageimportPaginatorasyncdeffunc_name(ctx):embed1=discord.Embed(title="Click the arrows",description="To look through the embeds")embed2=discord.Embed(title="Test",description="Lol")embed3=discord.Embed(title="Test",description="Lol")ems=[embed1,embed2,embed3]view=Paginator(ctx=ctx,ems=ems)message=awaitctx.send(embed=embed1,view=view)# This part is optional but it makes it so that once the button timeouts it will be disabled so you wont get any INTERACTION FAILED errors.res=awaitview.wait()ifres:foriinview.children:i.disabled=Truereturnawaitmessage.edit(view=view)Change Log0.0.1 (08/02/2022)First Release0.0.2 (08/02/2022)Bug Fixes0.0.3 (08/02/2022)Turns out importing the package wasn't working now it should0.0.4 (09/02/2022)Added custom exception, for better handling. Made colors a global variable to work with custom exceptions, rewrote help and made the color argument in print optional.Changes all made by TheOnlyWayUp
Package: siegeapi
siegeapiHow to installpip install siegeapiQuick examplefromsiegeapiimportAuthimportasyncioasyncdefsample():auth=Auth("UBI_EMAIL","UBI_PASSWORD")player=awaitauth.get_player(uid="7e0f63df-a39b-44c5-8de0-d39a05926e77")print(f"Name:{player.name}")print(f"Profile pic URL:{player.profile_pic_url}")awaitplayer.load_playtime()print(f"Total Time Played:{player.total_time_played:,}seconds")print(f"Level:{player.level}")awaitplayer.load_ranked_v2()print(f"Ranked Points:{player.ranked_profile.rank_points}")print(f"Rank:{player.ranked_profile.rank}")print(f"Max Rank Points:{player.ranked_profile.max_rank_points}")print(f"Max Rank:{player.ranked_profile.max_rank}")awaitplayer.load_progress()print(f"XP:{player.xp:,}")print(f"Total XP:{player.total_xp:,}")print(f"XP to level up:{player.xp_to_level_up:,}")awaitauth.close()asyncio.get_event_loop().run_until_complete(sample())# Or `asyncio.run(sample())`OutputName: CNDRD
Profile pic URL: https://ubisoft-avatars.akamaized.net/7e0f63df-a39b-44c5-8de0-d39a05926e77/default_256_256.png
Total Time Played: 7,910,265 seconds
Level: 285
Ranked Points: 1000
Rank: Unranked
Max Rank Points: 1000
Max Rank: Unranked
XP: 56,362
Total XP: 17,973,862
XP to level up: 77,138Siege InventoryIf you want to see every skin, headgear, uniform and more, head over toskins.cndrd.xyzDocsFor docs go tocndrd.github.io/siegeapiCreditsOperator Icons fromr6operatorsbymarcopixelBuilt (and re-built) on top of whatbilly-yoyostartedProblemsIf you experience any problems, reach out to me, or submit a PRYou can reach out here on GitHub or on Discord (cndrd)