Saravanan666/pypi-org huggingface dataset:
Package: shady-island
shady-islandUtilities and constructs for the AWS CDK.FeaturesCreate IPv6 CIDRs and routes for subnets in a VPC with theCidrContextconstruct.Set theAssignIpv6AddressOnCreationproperty of subnets in a VPC with theAssignOnLaunchconstruct.Properly encrypt a CloudWatch Log group with a KMS key and provision IAM permissions with theEncryptedLogGroupconstruct.Represent a deployment tier with theTierclass.Create a subclass of theWorkloadconstruct to contain yourStacks, and optionally load context values from a JSON file you specify.DocumentationTypeScript API ReferenceThe NameIt's a pun. In English, the pronunciation of the acronymCDKsounds a bit like the phraseseedy cay. A seedy cay might also be called ashady island.
Package: shadyquant
shadyquant😎This python package allows you to quantile and plot lines where you have multiple samples, typically for visualizing uncertainty.  Your data should be shaped(N, T), whereNis the number of
samples,Tis the dimension of your lines.InstallpipinstallshadyquantExampleConsider you have 100 lines that you want to compute confidence intervals (quantiles) on:importmatplotlib.pyplotaspltimportnumpyasnpx=np.linspace(0,2,1000)y=np.sin(x)plt.plot(x,y)w=np.random.normal(size=100)**2traj=y+y*w[:,None]plt.plot(traj.T,color='C0')plt.show()You can use shadyquant to plot a 95% confidence interval:importsqsq.traj_quantile(traj)You can also do weighted quantiling if you have weights attached to each linesq.traj_quantile(traj,weights=w)You may want to do fancy shading, which just plots a series of quantiles as polygons with transparency. The quantils overlap, which gives
a nice blending. The outer edges of the polygons still correspond to the 95% confidence intervalsq.traj_quantile(traj,fancy_shading=True)Here are some further options you can modify:plt.figure(figsize=(8,3))ax=plt.gca()sq.traj_quantile(traj,ax=ax,fancy_shading=True,lower_q_bound=1/3,upper_q_bound=2/3,levels=100,color="red",alpha=0.01,)
Package: shady-tr
Shady Text RandomizerTheShady Text Randomizeris a python package that helps to avoid spam-filters by replacing some characters in text with similar characters from other languages.
As a result, the output text is not equal to the input but visually indistinguishable from it.TheShady Text Randomizersupports latin and cyrillic symbols.Install$ pip install shady_trUsageClassShadyTextRandomizerhas two parameters:[string] Source text[int from 0 to 100] Symbol replace chance. If chance is equals 100, then each possible symbol well be converted to similar one.>> from shady_tr import ShadyTextRandomizer
>>
>> text_rnd = ShadyTextRandomizer("Hello! How are you?", 100)
>> text_rnd.random_latin()
'Ꮋеllо! Нοԝ аrе γᴏυ?'

>> text_rnd = ShadyTextRandomizer("Привет! Как дела?", 100)
>> text_rnd.random_cyrillic()
'Πpᴎᏼeт! Κaκ дeлa?'Some characters in the output text have completely different UTF-8 codes.LicenseThis project is licensed under the MIT License
Package: shafara-tool-kit
AboutThis package contains simple tools to satisfy day to day coding needsUsinginstall using the following commandpip install shafara-tool-kitload the package in your python file using any of the following ways# load the entire package
import shafaratoolkit

# add an alias
import shafaratoolkit as stk

# load only props
from shafaratoolkit import props

# load a specific function
from shafaratoolkit import props.colored

# give a the loaded function an alias
from shafaratoolkit import props.colored as colorize

# another way to load a specific function
from shafaratoolkit.props import import colored
Package: shag
Spatial Hashing Approximate Gaussian ProcessesImplementation of spatial hashing approximate Gaussian processesDocumentation:https://san-soucie.github.io/shagGitHub:https://github.com/san-soucie/shagPyPI:https://pypi.org/project/shag/Free software: MITFeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: shaggy
ShaggyDiscover and connect with social media accounts across multiple platforms using just a username with SocialTrackr, the ultimate account hunting software.As you know sherlock's devs don't really care about theimportantwebsites, so we came and cut thebsto make your lifes easier. May theshaggysupremacy beggin.~Sherlock, but better░░░░░░░░░░░░░░░░▄▄█▀▀██▄▄░░░░░░░
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Package: shahr2.pymailsender
Py Mail-SenderA small Email sending package. You can use this package on your project.ReleasesRequirementsInstallationInstall shahr2.pymailsender with pippipinstallshahr2.pymailsenderUsage/Examplesfromshahr2importpymailsenderSENDER='sender gmail'PASSWORD='sender app password/gmail password'RECEIVER='receiver email'SUBJECT='subject of mail'BODY='body of mail'mail=pymailsender.input(SENDER,PASSWORD,RECEIVER,SUBJECT,BODY)mail.send()Authors@shahadathshahriarakash
Package: shahr2.pymultimailer
Py Multi-MailerSend mail to multiple user at once.ReleasesRequirementsInstallationInstall shahr2.pymultimailer with pippipinstallshahr2.pymultimailerUsage/Examplesfromshahr2importpymultimailerSENDER='sender gmail'PASSWORD='sender app password/gmail password'SUBJECT='subject of mail'BODY='body of mail with{}{}2 empty braces for first and last name.'FILE='csv file location'FIRST_NAME='first name parameter in csv file'LAST_NAME='last name parameter in csv file'EMAIL='email parameter in csv file'mail=pymultimailer.input(SENDER,PASSWORD,SUBJECT,BODY)mail.csvData(FILE,FIRST_NAME,LAST_NAME,EMAIL)mail.send()Authors@shahadathshahriarakash
Package: shaka-streamer
Shaka Streamer offers a simple config-file based approach to preparing streaming
media. It greatly simplifies the process of using FFmpeg and Shaka Packager for
both VOD and live content.Live documentation can be found athttps://google.github.io/shaka-streamer/and is generated from thedocs/source/folder, as well as the source code
itself.Sample configs can be found in theconfig_files/folder in the repo.Release versions of Shaka Streamer can be installed or upgraded throughpip3with:# To install globally (drop the "sudo" for Windows):sudopip3install--upgradeshaka-streamer# To install per-user:pip3install--user--upgradeshaka-streamer
Package: shakeabuse
ShakeabuseA simple command line app to display a random shakespearean abuse on the console.Installationpip install shakeabuseor, since you're probably usingpython3pip3 install shakeabuseUsage$ shkabuse
Thou paunchy fly-bitten giglet!
$ shkabuse
Thou vain ill-nurtured bugbear!
$ shkabuse
Thou artless hasty-witted flap-dragon!
$ shkabuse
Methink'st thou art a general offence and every man should beat thee.Idea stolen from my friendShuvojit Sarkar. He once wrote an android app that did the same thing.
Package: shakecast-aebm
[![Build Status](https://travis-ci.org/usgs/shakecast-aebm.svg?branch=master)](https://travis-ci.org/usgs/shakecast-aebm)
[![codecov](https://codecov.io/gh/usgs/shakecast-aebm/branch/master/graph/badge.svg)](https://codecov.io/gh/usgs/shakecast-aebm)A Python package for estimating potential impact to a structure using HAZUS’s Advanced Engineering Building Module. Potentially useful for engineering research, but developed for integration with [ShakeCast](www.github.com/usgs/shakecast)
Package: shakelab
ShakeLab is an opensource project with the goal of providing a set of free and opensource tools for seismologists dealing with most common engineering problems.Warning: This is stillWORK IN PROGRESS!InstallationShakeLab can be installed directly from the GitHub repository using pip:python3 -m pip install git+https://github.com/shakelab/shakelab.gitTo upgrade an existing installation:python3 -m pip install --upgrade git+https://github.com/shakelab/shakelab.gitIf you are a developer, you can simply install the cloned GIT repository with:python3 -m pip install --upgrade --editable shakelabDependenciesShakeLab requires the following dependencies:NumPy/ScipyMatplotlibShapelyLicenseCopyright (c) 2019, ShakeLab DevelopersShakeLab is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.You should have received a copy of the GNU General Public License with this download. If not, seehttp://www.gnu.org/licenses/DisclaimerShakeLab is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.The authors of the software assume no liability for use of the software.
Package: shakemap-modules
shakemap-modulesIntroductionThis repository holds the core modules of the ShakeMap processing software. The modules may be used either as
a library of classes that can be included in other programs (as they are with ShakeMap), or as standalone
programs. See theShakeMap Documentationfor more on ShakeMap
and its software architecture.InstallationTo install the library modules, use pip:pip install shakemap-modulesTo install as standalone modules, clone the repository, and execute:bash install.shwhich will create a conda environment with the various dependencies.
Package: shaker
Shaker is BSD-New Licensed command-line application to launch Salt
minions as Amazon EC2 instances.Saltis a powerful remote execution
manager that can be used to administer servers in a fast and
efficient way.Running an Amazon EC2 instance as a Salt minion is fairly simple.
Also tedious, if you need to launch minions often.  Shaker bridges
the gap between launching an EC2 instance and bootstrapping it as
a Salt minion.ExampleShaker is usually run from the command line.  To start a Salt minion,
have it connect upon boot to Salt mastersalt.example.com:$ shaker --ami ami-057bcf6c --master salt.example.com

Started Instance: i-9175d8f4

To access: ssh ubuntu@ec2-107-20-93-179.compute-1.amazonaws.com
To terminate: shaker-terminate i-9175d8f4Documentation and LinksReadTheDocsGitHub RepositorySalt
Package: shakespeare
IntroductionThe Open Shakespeare package provides a full open set of shakespeare’s works
(often in multiple versions) along with ancillary material, a variety of tools
and a python API.Specifically in addition to the works themselves (often in multiple versions)
there is an introduction, a chronology, explanatory notes, a concordance and
search facilities.All material is open source/open knowledge so that anyone can use, redistribute
and reuse these materials freely. For exact details of the license under which
this package is made available please see COPYING.txt.Open Shakespeare has been developed under the aegis of the Open Knowledge
Foundation (http://www.okfn.org/).Contact the ProjectPlease mailinfo@okfn.orgor join the okfn-discuss mailing list:http://lists.okfn.org/listinfo/okfn-discussInstallation and Setup1. Install the code1.1: (EITHER) Install using setup.py (preferred)Installshakespeareusing easy_install:easy_install shakespeareNB: If you don’t have easy_install you can get from here:<http://peak.telecommunity.com/DevCenter/EasyInstall#installation-instructions>1.2 (OR) Get the code straight from subversionCheck out the subversion trunk:svn co https://knowledgeforge.net/shakespeare/svn/trunkDo:sudo python setup.py developGetting StartedAs a user:1. Basic setupTo access most of the main features of Open Shakespeare you need a database.
For this an other bits and bobs of configuration you will need a configuration
file.You can make a config file as follows:paster make-config shakespeare {your-config.ini}Tweak the config file as appropriate and then setup the application:paster setup-app config.ini[TODO: this should be part of setup-app]Run:$ shakespeare-admin db init2. ExtrasSearch index. [TODO]2. You can start a web server to provide a easy-to-use web interface to the
shakespeare material and facilities by doing:$ paster serve {your-config.ini}NB: {your-config.ini} should be replaced with the name of the config file you
created earlier.As a developer:0. SetupFollow the basic steps above put with an ini file named: development.iniNB: you’ll probably want to change log levels to debug.1. Check out the administrative commands$ bin/shakespeare-admin help.2. Run the tests using either py.test of nosetests::$ nosetests shakespeare
Package: shakespearelang
A friendly interpreter for the Shakespeare Programming Language, implemented in
Python.What is the Shakespeare Programming Language?The Shakespeare Programming Language (SPL) is a programming language
with source code that looks like Shakespeare’s plays. The language is
Turing complete, so theoretically just as powerful as any other
language. It’s a lot of fun to write but not very practical. More info can be
foundon Wikipedia.Note: Shakespeare’s actual plays are not valid SPL. SPL does not aim to
provide backwards compatibility with legacy code written ~400 years ago.Installationpip install shakespearelang
# Or however else you install things. You do you.DocumentationFor more on how to use shakespearelang, seethe docs.ContributingYour contributions would be much appreciated! SeeCONTRIBUTING.md.
Package: shakhs
This is a fake  Iranina person generator that can generate:
random full name,
random boy name,
random girl name,
random last name,
random full boy name,
rando, full girl name,
national id(code meli),
...CHANGE LOG0.0.1 (14/02/2022)First Release#shakhs library creted by Samrand Majnooni##install:pipinstallshakhs```pythonimport:```pythonimportshakhs```python##use:togeneratemanfirstname:```pythonx=shakhs.randomBoyName()print(x)```python---------------------togenerategirlfirstname:```pythonx=shakhs.randomGirlName()print(x)```python----------------------togeneratelastname:```pythonx=shakhs.randomLastName()print(x)```python----------------------togeneratefirstname+lastname(boy):```pythonx=shakhs.randomFullBoyName()print(x)```python----------------------togeneratefirstname+lastname(girl)```pythonx=shakhs.randomFullGirlName()print(x)```python----------------------togeneraterandomname(girlorboy):```pythonx=shakhs.randomName()print(x)```python----------------------togeneraterandomfakeiranianId:# Validate a real codemelli>>>shakhs.validator(2833411839)True# Validate a fake codemelli>>>shakhs.validator(3235632189)False# Generate a random valid codemelli>>>shakhs.generator()6615365987# Generate a random codemelli with a defined city code>>>shakhs.generator(223)2239832630# Lookup city and state of a valid codemelli>>>shakhs.lookup(2239832630){'city':'Ø¨Ù†Ø¯Ø±ØªØ±Ú©Ù…Ù†','state':'Ú¯Ù„Ø³ØªØ§Ù†'}# return a list of codemelli with length "a":shakhs.codemelliGenerator(a)# return a list of boy first name with length "a":shakhs.boyNameGenerator(a)# return a list of girl first name with length "a":shakhs.girlNameGenerator(a)# return a list of lastname with length "a":shakhs.lastnameGenerator(a)# return a list of full boy name with length "a":shakhs.fuulBoyNameGenerator(a)# return a list of full girl  name with length "a":shakhs.fullGirlNameGenerator(a)# return a list of random full  name with length "a":shakhs.randomNameGenerator(a)##Samrand Majnooni
Package: shakyo
Shakyoshakyo calligraphyAboutShakyo is a tool to learn about something just by typing it.Install$ pip install shakyoUsageKeep typing letters on a cursor. For more information, runshakyo--help.Example$ cat source_code.py | shakyo$ shakyo source_code.py$ shakyo https://raw.github.com/raviqqe/shakyo/master/shakyo.py$ for you in more less; do cp `which shakyo` `which $you`; doneLicenseAll the contents are released into the public domain. For more
information, seethe unlicense.
Package: shaleprotocol
shaleprotocolUser-facing Python libraryInstallpip install shaleprotocol

> import shaleprotocol as shaleFor maintainerDev installationpip install -e .Publishpython3 -m pip install --upgrade build
python3 -m pip install --upgrade twine

python3 -m build
twine check dist/*
twine upload dist/*

python3 -m twine upload --repository shaleprotocol dist/*
Package: shallowgibbs-doublebackpropagation
shallowgibbs-doublebackpropagationDouble Backpropagation Algorithm -- Implementation with the Shallow Gibbs ModelDouble Backpropagation with the Shallow Gibbs ModelInstallationInstall using pippip install shallowgibbs-doublebackpropagationRequirementsPython 3.6 or greaterscipytensorflowpandasnumpyjoblibscikit-learnUsageImport the Shallow Gibbs Double Backpropagation moduleimport shallowgibbs.doublebackpropagation as SGDBSYou need to load some initial predictions from the Shallow Gibbs Model, or any alike-Structured Model.
The model requires as parameters: the weigths matrix (W), the biases (b), and the response covariance matrix (Sigma).
The model framework backpropagation is updated per observation using:MSE \left(y_{i}-\hat{y}_{i}\right)=\|y_{i}-\hat{y}_{e s t, i}\|^{2})starting from\hat{\psi}_{0}with the equations:\begin{gathered}
\hat{\psi}_{1, i} \longleftarrow \hat{\psi}_{0}-\epsilon_{\psi, 0} \frac{\partial M S E\left(y_{i}-\hat{y}_{e s t, i}\right)}{\partial \psi} \\
\hat{\psi}_{t, i} \longleftarrow \hat{\psi}_{t-1, i}-\epsilon_{\psi, t-1} \frac{\partial M S E\left(y_{i}-\hat{y}_{e s t}, i\right)}{\partial \psi}
\end{gathered}where\psiis the set of parameters (w,b,Sigma) in our case. They are two additional equations that complete those above
explained in reference [2] and well introduced in [1]. There are about the Training data, and test data predictions update. Please read
reference [2] and the Jupyter Notebook for a guide note of usage and application.Pypi Project Pagehttps://pypi.org/project/shallowgibbs-doublebackpropagation/1.0.3/Notebook Pagehttps://github.com/kgalahassa/shallowgibbs-doublebackpropagation-notebookReferences[1] Murua, Alejandro, Alahassa, Nonvikan Karl-Augustt. The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning, ScienceOpen Preprints (2021).https://www.scienceopen.com/document?vid=9aab283e-130f-4922-accb-20bef8faff9f[2] Alejandro Murua, Nonvikan Karl-Augustt Alahassa. Double Back-Propagation and Differential Machine Learning. The Ninth Annual Canadian Statistics Student Conference (CSSC), Jun 2021, Ottawa, Canada. (hal-03265399)https://hal.archives-ouvertes.fr/hal-03265399
Package: shalooprust
Shaloop 🚣‍Rust optimization to run SHA256 over Numpy arrays.InstallationpipinstallshalooprustDevelopment instructionsBuild, install and test locallyActivate a new virtual env, install Maturin and a Rust toolchain. Run:maturindevelop--release-bcffiThe Python package is now installed. Note: clean your PYTHONPATH to remove the package if necessary.Test and benchmarkpythontest/test_shaloop.pyIf you have the old C version of Shaloop installed, you can run a benchmark with:pythontest/benchmark.pyBuild and publishdockerrun--rm-v$(pwd):/iokonstin2/maturinpublish-bcffi--manylinux2010-u__token__-ppypi-your-token
Package: shaman
This is the documentation for the Shaman. Multiprocessing application to combine different singular handlers against one message.The initial purpose was to create a tool, that:- would make possible to download and analyze a content of an html pages.- simple enough to add a new functionality in it.- hast to be scalable (multiprocessing).Actual usage can be different from it. There are some spontaneous ideas:- scanning a mongo collection and parsing documents in parallel- parsing a lot of lines from multiple huge files, saving the results to any database (depending on the results)There are three parts in the shaman library::* stages (actual processors, which do represent some functionality)* consumer (worker, that run them all in a particular order)* daemon (run as many as needed workers. Also used as a CLI unstrument.)All stages are run in a particular order and use the same message object (inside one worker).INSTALLATION:----------------------Run the command::pip install shamanIf everything is ok, you should be able to run::shaman --helpIt has to display::usage: shaman [-h] [-i | -d] -c CONFIGURATION [--drop_first DROP_FIRST][-p PRINT_FIELDS [PRINT_FIELDS ...]][-r REMOVE_FIELDS [REMOVE_FIELDS ...]][--ignore_after IGNORE_AFTER][{stop,start,restart,} [{stop,start,restart,} ...]]Main shaman module. Use it to start|stop|restart daemon or start non-daemonmodes of shamanpositional arguments:{stop,start,restart,}Command to daemon (default: )optional arguments:-h, --help            show this help message and exit-i                    Use stdin input as main input (default: False)-d                    Daemonize main process (default: False)-c CONFIGURATION      Path to configuration file (default: None)--drop_first DROP_FIRSTdrop first lines (default: 0)-p PRINT_FIELDS [PRINT_FIELDS ...], --print_fields PRINT_FIELDS [PRINT_FIELDS ...]-r REMOVE_FIELDS [REMOVE_FIELDS ...], --remove_fields REMOVE_FIELDS [REMOVE_FIELDS ...]--ignore_after IGNORE_AFTERCONFIGURATION:---------------------------You may find an example configuration file in <path_to_python_lib>/site-packages/shaman/etc/crawler.configIt includes 4 stages::reading from stdindownloading pagedetecting charsetprint url, charsetBy default, all stages reside in <path_to_python_lib>/site-packages/shaman/src/analyzers/ folder.You may create your custom stage and put it into the custom folder.There is a parameter in a configuration file::custom_stage_dir = <custom_folder>If you put some stages into this folder, shaman will also "see" them.To check if anything is working, please, run::echo "http://google.ru" | shaman -c <path_to_config> -iMore information about the package::http://shaman.readthedocs.io/en/latest/Github::https://github.com/Landish145/shaman
Package: shamanld
Shaman - Programming Language DetectorWhen you inputcode, Shaman detect itslanguage.Languages supported:ASP,Bash,C,C#,CSS,HTML,Java,JavaScript,Objective-c,PHP,Python,Ruby,SQL,Swift, andXML.Shaman is implemented base on Naïve Bayes Classification and pattern matching technique.
Pre-trained model is included in the library, where the size of the model is only214KB.The accuracy of the included model is78%with the test set and 83% with the training set.
See accuracy section for detail.Getting StartedHow to install$pipinstallshamanldHow to usefromshamanldimportShamancode="""#include <stdio.h>int main() {printf("Hello world");}"""r=Shaman.default().detect(code)print(r)# [('c', 42.60959840702781), ('objective-c', 8.535893087527496), ('java', 7.237626324587697), ...]Test and train with your custom datasetShaman supports training the model with your custom dataset easily.
The only thing you have to prepare is to make your dataset with CSV format.
CSV file should includes "language,code" pairs.Test with custom dataset$shaman-testerpath/to/test_set.csvTraining a new model with custom dataset$shaman-trainerpath/to/training_set.csv--model-pathpath/to/your_model.json.gzTesting custom model$shaman-trainerpath/to/test_set.csv--model-pathpath/to/your_model.json.gzUsing custom model on the codefromshamanldimportShamandetector=Shaman('path/to/your_model.json.gz')detector.detect('/* some code */')Test accuracyIncluded model is trained with 120K codes and tested with 42K codes.
Only the codes whose lengths are more than 100 are used in both training & testing. As the codes are collected without verification, there might be some data with wrong labels.LanguageAccuracyTotal78.40% (36428 / 46464)c70.41% (11479 / 16304)java90.24% (8094 / 8969)python92.85% (5230 / 5633)javascript63.08% (2782 / 4410)sql80.92% (2519 / 3113)html83.99% (2156 / 2567)c#84.08% (1753 / 2085)xml80.18% (635 / 792)bash83.58% (560 / 670)swift83.25% (522 / 627)php73.09% (315 / 431)css68.12% (203 / 298)objective-c32.88% (121 / 368)asp36.75% (43 / 117)ruby20.00% (16 / 80)JavaScript versionJavaScriptinferencingimplementation is available atPrev/shamanjs.