Saravanan666/pypi-org huggingface dataset:
Package: alerts-msg
alerts_msg (v0.2.1)DESCRIPTION_SHORTAll abilities (mail/telegram) to send alert msgs (threading)DESCRIPTION_LONGdesigned for ...Featuressend alert msgs:emailstelegramthreadingLicenseSee theLICENSEfile for license rights and limitations (MIT).Release historySee theHISTORY.mdfile for release history.Installationpip install alerts-msgImportfromalerts_msgimport*USAGE EXAMPLESSee tests and sourcecode for other examples.1. example1.py# =========================================================================================### 0. BEST PRACTICEfromalerts_msgimport*classAlertADX(AlertSelect.TELEGRAM_DEF):passAlertADX("hello")AlertADX("World")AlertADX.threads_wait_all()# =========================================================================================# =========================================================================================# =========================================================================================### AlertSmtp#### 1. add new server if not existsfromalerts_msgimport*classSmtpServersMOD(SmtpServers):EXAMPLE_RU:SmtpAddress=SmtpAddress("smtp.EXAMPLE.ru",123)classAlertSmtpMOD(AlertSmtp):SERVER_SMTP:SmtpAddress=SmtpServersMOD.EXAMPLE_RU# or direct =SmtpAddress("smtp.EXAMPLE.ru", 123)# =========================================================================================#### 2. change authorisation data (see `private_values` for details)fromalerts_msgimport*classAlertSmtpMOD(AlertSmtp):AUTH:PrivateAuto=PrivateAuto(_section="AUTH_EMAIL_MOD")# =========================================================================================#### 3. change other settings (see source for other not mentioned)fromalerts_msgimport*classAlertSmtpMOD(AlertSmtp):RECONNECT_PAUSE:int=60RECONNECT_LIMIT:int=10TIMEOUT_RATELIMIT:int=600RECIPIENT_SPECIAL:str="my_address_2@mail.ru"# =========================================================================================#### 4. send# if no modsfromalerts_msgimport*AlertSmtp(_subj_name="Hello",body="World!")# with modsfromalerts_msgimport*classAlertSmtpMOD(AlertSmtp):pass# changedAlertSmtpMOD(_subj_name="Hello",body="World!")# =========================================================================================#### 5. using in class with saving alert objectfromalerts_msgimport*classAlertSmtpMOD(AlertSmtp):pass# changedclassMyMonitor:ALERT=AlertSmtpMODmonitor=MyMonitor()monitor.ALERT("Hello")# =========================================================================================# =========================================================================================### AlertTelegram# All idea is similar to AlertSmtp.# add auth data# add pv.json or do smth else (for details see private_values.PrivateJsonTgBotAddress)# json{"TG_ID":{"MyTgID":1234567890},"TGBOT_DEF":{"LINK_ID":"@my_bot_20230916","NAME":"my_bot","TOKEN":"9876543210xxxxxxxxxxxxxxxxxxxxxxxxx"}}# =========================================================================================fromalerts_msgimport*classMyMonitor:ALERT=AlertTelegrammonitor=MyMonitor()monitor.ALERT("Hello")
Package: alexachatbot
Alexa ChatbotGenrale InformationAlexa chatbot is a simple  python pakcage for createing chatbot in python, it's help you to create a basic chatbotgithubInstallationpipinstallalexachatbotFirst you need to install alexa-chatbot from pypi it's open-source and completelly freeFeaturesLightweight and FastSupoort mutiples languageEasy to useUsageHere is a simple Exampel how can you user Alexa-chatbotreply() | functionfromalexachatbot.alexaAIimportreplytext="How are you?"res=reply(text)print(res)# do further things what you want to do with resNote: text pateamter are require and it should be string
It's Supoort mutiples language so you can pass any kind of message and you will get a good replyOther example of reaply functionfromalexachatbot.alexaAIimportreplytext="こん に ち わ"res=reply(text)print(res)# same as do further things what you want to do with resreplylang() | functionfromalexachatbot.alexaAIimportreplylangtext="what are you doing right now?"lang='en'res=replylang(text,lang)print(res)# same as do further things what you want to do with resNote: text and lang both pateamter are require and it should be string
It's Supoort mutiples language so you can pass any kind of message and you will get a good replyNote:Alexa chatbot using a api, but sometimes api go down so don't worry and wait a little bitLicenseAlexa chatbot is released under MIT License, See License for more detalisContectSololearnDiscosrd serverTelegramyoutubegit-hubGamil
Package: alexandra-ai
AlexandraAIA Python package for Danish data scienceInstallationTo install the package simply write the following command in your favorite terminal:pip install alexandra-aiQuickstartBenchmarking from the Command LineThe easiest way to benchmark pretrained models is via the command line interface. After
having installed the package, you can benchmark your favorite model like so:evaluate --model-id <model_id> --task <task>Heremodel_idis the HuggingFace model ID, which can be found on theHuggingFace
Hub, andtaskis the task you want to benchmark the
model on, such as "ner" for named entity recognition. See all options by typingevaluate --helpThe specific model version to use can also be added after the suffix '@':evaluate --model_id <model_id>@<commit>It can be a branch name, a tag name, or a commit id. It defaults to 'main' for latest.Multiple models and tasks can be specified by just attaching multiple arguments. Here
is an example with two models:evaluate --model_id <model_id1> --model_id <model_id2> --task nerSee all the arguments and options available for theevaluatecommand by typingevaluate --helpBenchmarking from a ScriptIn a script, the syntax is similar to the command line interface. You simply initialise
an object of theEvaluatorclass, and call this evaluate object with your favorite
models and/or datasets:>>> from alexandra_ai import Evaluator
>>> evaluator = Evaluator()
>>> evaluator('<model_id>', '<task>')ContributorsIf you feel like this package is missing a crucial feature, if you encounter a bug or
if you just want to correct a typo in this readme file, then we urge you to join the
community! Have a look at theCONTRIBUTING.mdfile, where you can
check out all the ways you can contribute to this package. :sparkles:Your name here?:tada:MaintainersThe following are the core maintainers of thealexandra_aipackage:@saattrupdan(Dan Saattrup Nielsen;saattrupdan@alexandra.dk)@AJDERS(Anders Jess Pedersen;anders.j.pedersen@alexandra.dk)The AlexandraAI ecosystemThis package is a wrapper around other AlexandraAI packages, each of which is standalone:AlexandraAI-eval: Evaluation of finetuned models.Project structure.
├── .flake8
├── .github
│   └── workflows
│       ├── ci.yaml
│       └── docs.yaml
├── .gitignore
├── .pre-commit-config.yaml
├── CHANGELOG.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── gfx
├── makefile
├── notebooks
├── poetry.toml
├── pyproject.toml
├── src
│   ├── alexandra_ai
│   │   └── __init__.py
│   └── scripts
│       ├── fix_dot_env_file.py
│       └── versioning.py
└── tests
    └── __init__.py
Package: alexandria
Alexandria is a a DNS management solution that allows you to easily manage your
zone files using a simple to use web interface.InstallationWait for this package to be available on PyPi…DevelopingGetting the application set up locally:Change directory to this projectCreate virtualenv for Project in $venv$venv/bin/pip install -e .$venv/bin/initialize_alexandria_db config/development.ini$venv/bin/pserve config/development.iniFor submitting patches back to the project:Create a new branch for the feature/topic from masterHack awaySubmit a pull request0.0Initial version
Package: alexandria3k
Alexandria3kThealexandria3kpackage supplies a library and a command-line tool
providing efficient relational query access to diverse publication open
data sets.
The largest one is the entireCrossref data set(157 GB compressed, 1 TB uncompressed).
This contains publication metadata from about 134 million publications from
all major international publishers with full citation data for 60 million
of them.
In addition,
the Crossref data set can be linked with
theORCID summary data set(25 GB compressed, 435 GB uncompressed),
containing about 78 million author records,
theUnited States Patent Office issued patents(11 GB compressed, 115 GB uncompressed),
containing about 5.4 million records,
as well as
data sets of
funder bodies,
journal names,
open access journals,
and research organizations.Thealexandria3kpackage installation contains all elements required
to run it.
It does not require the installation, configuration, and maintenance
of a third party relational or graph database.
It can therefore be used out-of-the-box for performing reproducible
publication research on the desktop.DocumentationThe complete reference and use documentation foralexandria3kcan be foundhere.PublicationDetails about the rationale, design, implementation, and use of this software
can be found in the following paper.Diomidis Spinellis. Open reproducible scientometric research with Alexandria3k.PLoS ONE18(11): e0294946. November 2023.doi: 10.1371/journal.pone.0294946
Package: alexandria-python
AlexandriaAlexandriais a Python package for Bayesian time-series econometrics applications. This is the first official release of the software. For its first release, Alexandria includes only the most basic model: the linear regression. However, it proposes a wide range of Bayesian linear regressions:maximum likelihood / OLS regression (non-Bayesian)simple Bayesian regressionhierarchical (natural conjugate) Bayesian regressionindependent Bayesian regression with Gibbs samplingheteroscedastic Bayesian regressionautocorrelated Bayesian regressionAlexandria is user-friendly and can be used from a simple Graphical User Inteface (GUI). More experienced users can also run the models directly from the Python console by using the model classes and methods.===============================Installing AlexandriaAlexandria can be installed from pip:pip install alexandria-pythonA local installation can also obtain by copy-pasting the folder containing the toolbox programmes. The folder can be downloaded from the project website or Github repo:https://alexandria-toolbox.github.iohttps://github.com/alexandria-toolbox===============================Getting startedSimple Python example:# imports
from alexandria.linear_regression import IndependentBayesianRegression
from alexandria.datasets import data_sets as ds
import numpy as np

# load Taylor dataset, split as train/test
taylor_data = ds.load_taylor()
y_train, X_train = taylor_data[:198,0], taylor_data[:198,1:]
y_test, X_test = taylor_data[198:,0], taylor_data[198:,1:]

# set prior mean and prior variance for the model
b = np.array([1.5, 0.5])
b_const = 1
V = np.array([0.01, 0.0025])
V_const = 0.01

# create and train regression
br = IndependentBayesianRegression(endogenous=y_train, exogenous=X_train,
constant=True, b_exogenous=b, V_exogenous=V, b_constant=b_const, V_constant=V_const)
br.estimate()

# get predictions on test sample, run forecast evaluation, display log score
estimates_forecasts = br.forecast(X_test, 0.95)
br.forecast_evaluation(y_test)
print('log score on test sample : ' + str(round(br.forecast_evaluation_criteria['log_score'], 2)))===============================DocumentationComplete manuals and user guides can be found on the project website and Github repo:https://alexandria-toolbox.github.iohttps://github.com/alexandria-toolbox===============================Contactalexandria.toolbox@gmail.com
Package: alexa-reply
alexa-reply : 0.1.2About: An ai python package to respond to any message suitably.PyPI repo:https://pypi.org/project/alexa-reply/Installation:# from github : unstable

pip install git+https://pypi.org/project/alexa-reply/# from github : stable

pip install alexa-replyExample Usage:fromalexa_replyimportreplyowner="your name"bot="bot's name"message="the message u wanna reply to"resp=reply(message,bot,owner)print(resp)```
Package: alexa-siterank
SiteRank-AlexaThis is vanilla Python library for gathering data about website ranks from Alexa!It is ultra customizable.InstallationYou can usepip install alexa-siterankorpip install git+https://github.com/mytja/SiteRank-Alexa⚡️ Wanna try outnew OTR function! It is up to 5 times faster than current functions⚡️ Wanna try outnew asynchronous function! It requires httpxUsageGet PageRankfromalexa_siterankimport*print(getRank("google.com"))Output:{"rank":{"global":1,"us":1},"rating":false}Get Top keywordsfromalexa_siterankimport*print(getTopKeywords("google.com"))Output:{"titles":["keyword","metric_one","metric_two"],"google.com":[[{"title":"keyword","value":"gmail"},{"title":"metric_one","value":"5.11%"},{"title":"metric_two","value":"83.27%"}],[{"title":"keyword","value":"google translate"},{"title":"metric_one","value":"3.84%"},{"title":"metric_two","value":"59.46%"}],[{"title":"keyword","value":"google maps"},{"title":"metric_one","value":"1.93%"},{"title":"metric_two","value":"55.67%"}],[{"title":"keyword","value":"translate"},{"title":"metric_one","value":"1.72%"},{"title":"metric_two","value":"51.89%"}],...Get visitorsfromalexa_siterankimport*print(getVisitors("google.com"))Output:[{"pageviews_per_user":"25.22","code":"US","visitors_percent":"19.5","name":"United States","pageviews_percent":"27.7"},{"pageviews_per_user":"28.07","code":"IN","visitors_percent":"10.4","name":"India","pageviews_percent":"16.5"},{"pageviews_per_user":"26.3","code":"JP","visitors_percent":"5.2","name":"Japan","pageviews_percent":"7.8"}]Get competitorsfromalexa_siterankimport*print(getCompetitors("google.com"))Output:{"site":"google.com","competitors":["youtube.com","wikipedia.org","facebook.com","vk.com"]}Get SiteRank 3 month historyfromalexa_siterankimport*print(getRankHistory("google.com"))Output:{"3mrank":{"20201116":"1","20201117":"1","20201118":"1","20201119":"1","20201120":"1","20201121":"1","20201122":"1","20201123":"1","20201124":"1","20201125":"1","20201126":"1",...}}DisclaimerDeveloperThe developer(s) of this project aren't responsible for any code usage in non-intended ways!Community / People using this projectWith downloading any modified and/or original code from this repository, from any source, you agree, that you will use it only for non-production, non-commercial, private uses and for educational purposes, and in monthly limits! If not, you are responsible for non-legal usage of this project!If you want to use commercial version, then you have to get API token from AWIS.This projectThis project can only be used for private, non-commercial, non-production uses, and for educational uses.
Package: alexa-skill
alexa-skillalexa-skillis flexible, easy to use and extend package for creating Alexa skill applications.This package is based onalexa documentation.InstallingInstall and update using pip:pipinstall-Ualexa-skillExamplesDefine intent classfromalexa_skill.intentsimportBaseIntentsclassExampleIntents(BaseIntents):@propertydefmapper(self):return{'EXAMPLE.hello':self.hello,}defhello(self):returnself.response('Hello. Nice to meet you.'),TrueDefine intent class with slotsfromalexa_skillimportdatesfromalexa_skill.intentsimportBaseIntentsclassDateIntents(BaseIntents):@propertydefmapper(self):return{'EXAMPLE.date_intent':self.date_intent,}defdate_intent(self,slots):date,date_type=dates.AmazonDateParser.to_date(slots['dateslot']['value'])text="Your date is <say-as interpret-as='date'>{}</say-as> and it is a{}".format(date.strftime('%Y%m%d'),date_type)returnself.response(text),TrueDefine buildin intentsfromalexa_skill.intentsimportBuildInIntentsbuildin_intents=BuildInIntents(help_message='Say "HI" to us',not_handled_message="Sorry, I don't understand you. Could you repeat?",stop_message='stop',cancel_message='cancel')FalconInitiate intents in fulfiller webhook for Alexaimportloggingimportalexa_skillimportfalconclassFulfiller(object):defon_post(self,req,resp):get_response=alexa_skill.Processor(req.media,buildin_intents,'Welcome to Alexa skill bot','Good bye',ExampleIntents(),# Insert created Intents as argumentsDateIntents(),)json_response,handled=get_response()logging.info('Response was handled by system:{}'.format(handled))resp.media=json_responseapp=falcon.API(media_type=falcon.MEDIA_JSON)app.add_route('/v1/alexa/fulfiller',Fulfiller())Flaskimportloggingimportalexa_skillfromflaskimportFlask,request,jsonifyapp=Flask(__name__)@app.route("/v1/alexa/fulfiller",methods=['POST'])deffulfiller():get_response=alexa_skill.Processor(request.json,buildin_intents,'Welcome to Alexa skill bot','Good bye',ExampleIntents(),DateIntents(),)json_response,handled=get_response()logging.info('Response was handled by system:{}'.format(handled))returnjsonify(json_response)DocumentationAuto generate documentationcddocs/

sphinx-apidoc-o./source/_modules/../alexa_skill/

makehtml
Package: alexeygameframework
A library that allows to create games easier with built in tile system, screen handler, animation and asset importing and handling, and a basic window.Change Log0.0.1 (2023-09-16)First Release0.0.11 (2023-09-16)Added init python files0.0.12 (2023-09-17)Fixed package issues0.0.13 (2023-09-17)Added imports to init file to make it easier to use the package0.0.14 (2023-09-17)Changed project classifiers to reflect the package better
Package: alexflipnote.py
AlexFlipnote.pyAn easy-to-use Python Wrapper for the AlexFlipnote APIRequirementsPython 3.8 or aboveaiohttp (python -m pip install -U aiohttp)InstallationInstall the package by doing one of the following commands:Using pip (recommended):pip install alexflipnote.py -Upython -m pip install alexflipnote.py -UDocumentationSee the full and detaileddocs hereLinksAPI|Changelogs|Examples|Github|PyPiMade byThis wrapper is made bySoheab_#6240(150665783268212746), DM me on Discord or join my serverherefor anything related to this wrapper.Join AlexFlipnote's serverhereto suggest or report anything on the API.
Package: alexflow
alexflowALEXFlow is a python workflow library built for reproducible complex workflow, mainly for machine learning training.Get StartedFor the installation from pypi, simply install via pip.pip install alexflowRemarksSupport of type hints withdataclassesluigi does not work well with type hints, which makes it difficult to build workflow when it is complex. With use of dataclasses, we'd like to gain benefit of type hints.Build workflow by composition, rather than parameter bucket relies.Parameter bucket rely finally build a huge global state at the entrypoint of workflow, which is pretty difficult to maintain in general as it is works similarly with global variables... Instead, we've decided to compose workflow with compositions. With this architecture we can gain the benefit of divide and conquer strategy.Focus of reproducibility with immutability tasksTask class is designed to be a immutable dataclass object, for distributed execution, strong consistency, and reproducibility. And also thoseTaskobjects can be serialized as json object, and you can easily trace the exact parameters used to generate theOutput.Dependency via Outputs, rather than TasksDescription of workflow dependency byOutputmakes it easy to run partially graph.A exmaple of Task constructionAlso you can see the example workflow atexamples/workflow.py.fromtypingimportTuplefromsklearnimportlinear_modelfromdataclassesimportdataclass,fieldfromalexflowimportTask,no_default,NoDefaultVar,Output,BinaryOutput@dataclass(frozen=True)classTrain(Task):# Here you can write parameter of task as dataclass fields. Task's unique id will be# generated from given parameters' and each task is executed at once while the entire# graph computation.X:NoDefaultVar[Output]=no_defaulty:NoDefaultVar[Output]=no_defaultmodel_type:NoDefaultVar[str]=no_default# Here you can describe in-significant parameter with compare=False, with following# dataclass' object equality. Even you changed those variables, Task's unique id is# consistent.verbose:bool=field(default=True,compare=False)definput(self):"""Here describes the dependent output of your task"""returnself.X,self.ydefoutput(self):"""Here describes the dependent output of your task"""returnself.build_output(BinaryOutput,key="model.pkl")defrun(self,input:Tuple[BinaryOutput,BinaryOutput],output:BinaryOutput):# Dependent output you defined in `input()` method is available as input variable.X=input[0].load()y=input[1].load()model_class=getattr(linear_model,self.model_type)cls=model_class().fit(X,y)# And you can store what you want to output in following manner.output.store(cls)
Package: alex-music
AlexAlex is a cli youtube music playerHow to installHave pip installedRequirements: mpv, yt-dlpInstall Alex:pip install alex-musicUsageLaunching:python -m alex_musicAlso,
You can add an alias for alex_music in your .bashrc fileecho "alias alex='python -m alex_music'" >> ~/.bashrcThen, alex_music can be launched by:alexPlay some musicplay despacitoAdd to playlistadd baby shark tu tu ruandplayFor more commandshelp
Package: alex-nn
Stay tuned!More examples and documentation to comeAlex - a neural network analyzerAlex is a Domain Specific Language (DSL) for declaring, representing, analyzing and generating neural networks.
This DSL can be considered as a configuration file to define a neural network.How to install:pip install alex-nnHow to runCommand Line Interfacealex-nnPythonMotivationAlex the little chefDeep learning is like cooking. You have a bunch of ingredients and you put them in a pot. You have some intuitions and somewhat limited theoretical understanding of why this soup tastes better than that one, but you don't really know what's going on. Alex is a little chef that helps you distill and refine your networks. Alex is committed to helping you find better recipes for your use case.What does Alex do?