Saravanan666/pypi-org huggingface dataset:
Package: lyser
A python package that has classes that processes text and creates a rag chef chat agent that gives cooking tips and recipes.How to installpip install lyserA class to perform text analysis on a given corpus.Attributes:
        corpus (str): The corpus to analyze.
        
    Methods:
        pipeline(): A function to perform text analysis on a given corpus.
        Returns a list of tokens.
        e.g. ['the', 'cat', 'sat', 'on', 'the', 'mat']
        
        pos_tagging(): A function to perform POS tagging on a given corpus.
        Returns a list of tuples containing the word and its POS tag.
        e.g. [('the', 'DT'), ('cat', 'NN'), ('sat', 'VB'), ('on', 'IN'), ('the', 'DT'), ('mat', 'NN')]
        
        visualize_word_cloud(): A function to visualize the word cloud of a given corpus.
        
    Example:
        analyzer = TextAnalyzer(corpus=sample_text)
        analyzer.pipeline()
        analyzer.pos_tagging()
        analyzer.visualize_word_cloud()A class to invoke Chef ChatAttributes:
        folder_path (str, pdf): path to folder_path with recipe files
        query (str): user input
        

        
    Methods:
        data_loader(): A function that loads the pdf books in a folder and returns a list of documents.
        Returns:
            list: A list of documents. Each document is a dictionary with a 'page_content' key and 'metadata' key.
        
        chat_summary():  A function that creates a chat summary 
        Returns:
            chat_memory: A AgentTokenBufferMemory chat summary memory.

        
        rag_agent(): A function that creates a rag agent that returns recipes of meals from cook pdf books 
        Returns:
            agent_result: A dictionary with the output of the rag agent.
        
        
    Example:
        # chef_chat = ChefChat(folder_path="data", query="How can i make pineapple chicken")
        print(chef_chat.rag_agent())
Package: lyseum-hack-ege-client
DescriptionДобавляет возможность сохранять файлы из хранилищаInstallation(PyPi)pipinstalllyseum_hack_ege_clientUsageПолучим все баркоды существующие в базеfromlyseum_hack_ege_clientimportClientcodes=Client.all_barcodes()print(codes)>>>>['1234567890123','1234567890124']Сохраним файлы документов по одному баркодуfromlyseum_hack_ege_clientimportClientfiles=Client.barcode('1234567890123')>>>>["<YaSimpleFile filename='4.jpg' path='resource/detect/1234567890123/4.jpg'>"]forfileinfiles:print(file.name)>>>>4.jpgprint(file.fullpath)>>>>resource/detect/1234567890123/4.jpgfile.save()# Здесь фактическое сохранение файла.Другой способ сохранения файловfromlyseum_hack_ege_clientimportClientfiles=Client.barcode('1234567890123')Client.save_files(files)# Последовательно скачаются файлыСохранение всех картинок из директории allfromlyseum_hack_ege_clientimportClientfiles=Client.barcode('1234567890123')paths=Client.synchronize_all()print(paths)>>>>['resource/all/1.jpg','resource/all/2.jpg','resource/all/3.jpg']
Package: lysis-curve
Lysis-curveThis package generates automated lysis curves (bacterial growth curves) for
biological research viaPlotlyand utilizes code for automated grouping, titles, annotations and subplotting within a single custom graphing function.Simply changing the function arguments can generate a variety of bacterial growth curves.The graphs are dynamicwhen generated within a webpage (i.e.Jupyter) which can be useful when teasing apart data.For a very similar package which generates non-dynamic but prettier bacterial growth curves / lysis curves usingRrather thanPython, seeCody Martin's lysis_curves package.Install Package UsingPyPiAt the command line, runpipinstalllysis-curveRunning (in Jupyter or at command line)First, make sure your x-axis (time) data is yourzeroth (first) column(this script always plots the first column in the csv file as the x-axis). Next,make sure you save your data in the .csv file format.Next, navigate to the directory containing your .csv file in Jupyter.importosos.chdir('your_path_here')Next, import the lysis_curve.py file usingfromlysis_curveimportlysis_curveAlternatively, copy/paste the file into a jupyter cell from github
and you can modify the code yourself.Generate basic plotfromlysis_curveimportlysis_curvelysis_curve('yourcsvfile.csv')Generate plot with groupingThis argument is useful if you wish to visually group your data by color.
It automatically sets each line in each group the same color,
but assigns them different markers.Does not work with subplots.Pass the argument togroupas a list of strings, with each column in a group separated by vertical bars.lysis_curve('122120JSC.csv',title='Title Goes Here',group=['1','4','3|5'],annotate=True)Generate plot with annotationsUse the argumentannotations=Trueand follow the prompts.Generate plot with subplotsUse the argumentsubplots=Trueto split your data into subplots.lysis_curve('051321JSC.csv',title='Title Goes Here',subplots=True)Generate plot with custom titleUse the argumenttitle='Your Custom Title Here'By default, the title will be taken from your csv file name - thus 'yourcsvfile' if 'yourcsvfile.csv' is passed.Pass custom colorslysis_curve('yourcsvfile.csv',colors=['red','blue','blah'])Save as .pngSet the argumentpng=Trueand the function will generate a .png file of the graph in your current directory.Save as .svgSet the argumentsvg=Trueand the function will generate a .svg file of the graph in your current directory.
Requires Kaleido or OrcaSave .png, .svg and legendless .svgsave=TrueSaves three versions of the graph: (1) a .png version with a legend (2) a .svg version with a legend (3) a .svg version without a legend and square dimensionsDependenciesPython 3.5+Pandaspip install pandasPlotlypip install plotlyRequestspip install requestsKaleidopip install kaleido(Kaleido is recommended over Orca according to Plotly)
Package: lys-python
lyslys is an interactive multi-dimensional data analysis and visualization platform based on several popular libraries such as numpy, scipy, dask, and matplotlib. lys enables users to intuitive, flexible, parallelized, low-code, and extendable data analysis.Check out for thedocumentationfor more information.InstallationSee theinstall documentation. System requirements are also found in this page.ContributingPlease readdocumentationfor details on our code of conduct, and the process for submitting pull requests to us.LicenseThis project is licensed under the GPLv3 License - see theLICENSE.mdfile for details
Package: lyt
# Lyt : simple literate programming in Markdown.Lyt is a literate programming tool, written in Python, that extracts fenced code blocks from Markdown documents. It is meant to be used as a preprocess tool before a conventional build.It is licensed under the GPLv3. See the LICENSE.txt file in this repo.## Installation## Usage
Open your favorite command line:$ lyt file.mdFor more information, use–help:$ lyt –help## GoalsStay python 2 and 3 compatible.Stay simple.In particular, handling several files at a time isn’t a goalNor is handling the dependency between generated files and the source.## FutureAdd support for more languages.Allow autodetection of languages.Add better tests.Add static types and validate with mypy.Maybe allow hierarchical embedding?
Package: lytekit
Flytekit PythonFlytekit Python is the Python SDK built on top of FlytePlugins·Contribution GuideFlytekit Python is the Python Library for easily authoring, testing, deploying, and interacting with Flyte tasks, workflows, and launch plans.If you haven't explored Flyte yet, please refer to:Flyte homepageFlyte core repository🚀 Quick StartFlytekit is the core extensible library to author Flyte workflows and tasks and interact with Flyte backend services.InstallationpipinstallflytekitA Simple Examplefromflytekitimporttask,workflow@task(cache=True,cache_version="1",retries=3)defsum(x:int,y:int)->int:returnx+y@task(cache=True,cache_version="1",retries=3)defsquare(z:int)->int:returnz*z@workflowdefmy_workflow(x:int,y:int)->int:returnsum(x=square(z=x),y=square(z=y))📦 ResourcesLearn Flytekit by examplesFlytekit API documentation📖 How to Contribute to FlytekitYou can find the detailed contribution guidehere. Plugins' contribution guide is included as well.Code StructurePlease see thecontributor's guidefor a quick summary of how this code is structured.🐞 File an IssueRefer to theissuessection in the contribution guide if you'd like to file an issue.🔌 Flytekit PluginsRefer toplugins/README.mdfor a list of available plugins.
There may be plugins outside of this list, but the core maintainers maintain this list.
Package: lytemaps
lytemapsThis repository contains an implementation of a minimal fork ofneuromapsfunctionality. If you're an end user, this is likely not for you -- this implementation exists mostly forhypercoildeveloper purposes. As developers, we'd like to use certainneuromapsoperations without burdening our software with the bulky dependencies (nilearn,sklearnand Connectome Workbench) that the full install ofneuromapsrequires. This implementation is intended to support the subset of functionality that requires only core Scientific Python packages together with the essentialnibabelandpoochfor handling data fetch operations. (But we're not there yet -- in particular, many transforms still require Workbench at this time.) Obviously, functionality here is limited -- inter alia, this means that null models aren't included for now. As development progresses, we'll index theneuromapsfunctions that are implemented below.If for some reason you still decide to use this repository, please follow the citation prescriptions fromNeuromapsandpooch. In particular, specify that you usedlytemapsin your methods section, specify thatlytemapscomprises code fromneuromapswith apooch-based downloader backend, and cite theneuromapsandpoochpapers.LicenseMost code in this repository is taken directly from theneuromapsrepository, and is therefore licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License. The exception to this is several small utility functions insrc/lytemaps/utils.pyandsrc/lytemaps/datasets/utils.py, which are taken from thenilearnandsklearnrepositories and are therefore licensed under the 3-clause BSD license. As we redevelop code into our own implementations, we will relicense. See theLICENSEfile for more details.RoadmapImplementpooch-based data fetchers forneuromapsdatasetsAdd support for token-based authentication for OSF datasetsAdd support for querying thetemplateflowAPIUsenitransformswherever possible for surface-to-surface, volume-to-surface, and surface-to-volume transformsUse neuroimaging tensor library backend for operations originally implemented withnilearnandsklearn
Package: lyth
A (monolithic) compiled languageFree software: BSD 2-Clause LicenseInstallationpip install lythYou can also install the in-development version with:pip install https://github.com/gmantelet/python-lyth/archive/master.zipDocumentationhttps://python-lyth.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2020-02-15)First release on PyPI.
Package: lytics-smm-demo
lytics-smm-demoA Python package for interacting with theLytics.lolSMM API.InstallationInstall the package using pip:pipinstalllytics-smm-demo* currently demo *UsageTo use the functions in this package, import the Lytics class from lytics, and create an instance with your API key.fromlyticsimportLyticsapi_key='API_Key_Here'lytics=Lytics(api_key)you can then use any of the functions below using the 'lytics' object.FunctionsFor a list of availible services:services=lytics.services()print(services)To check your balance:balance=lytics.balance()print(balance)To create a new order, there is multiple options:# Defaultorder=lytics.order(service_id=1,link='http://example.com/username',quantity=100,runs=2,interval=5)print(order)# Custom Commentsorder=lytics.order(service_id=1,link='http://example.com/username',comments="good pic\ngreat photo\n:)\n;)")print(order)# Mentions with Hashtagsorder=lytics.order(service_id=1,link='http://example.com/username',quantity=100,usernames="test, testing",hashtags="#goodphoto")print(order)# Mentions User Followersorder=lytics.order(service_id=1,link='http://example.com/username',quantity=1000,username="test")print(order)# Packageorder=lytics.order(service_id=1,link='http://example.com/username')print(order)# Subscriptions (Old posts only)order=lytics.order(service_id=1,username='username',min=100,max=110,posts=0,delay=30,expiry='11/11/2022')print(order)# Subscriptions (Unlimited new posts and 5 old posts)order=lytics.order(service_id=1,username='username',min=100,max=110,old_posts=5,delay=30,expiry='11/11/2022')print(order)# Pollorder=lytics.order(service_id=1,link='http://example.com/test',quantity=100,answer_number='7')print(order)# Invites from Groupsorder=lytics.order(service_id=1,link='http://example.com/test',quantity=100,groups="group1\ngroup2")print(order)To check the status of a order / multiple orders:status=lytics.status(order_id=11234)print(status)# check multiple order status'smulti_status=lytics.multi_status(order_ids=[12342,34634,12301,34453])print(multi_status)To create a new refill / multiple refills:refill=lytics.refill(order_id=12345)print(refill)# create multiple refillsmulti_refill=lytics.multi_refill(order_ids=[12342,34634,12983,23443])print(multi_refill)To check the status of a refill / multiple refills:status=lytics.refill_status(refill_id=23423)print(status)# check multiple refill status'smulti_status=lytics.multi_refill_status(refill_ids=[23423,12344,91818,12311])print(status)
Package: lytomo-watershed
LyTomo-WatershedCharacterizing proto-clusters/groups in Lyman-alpha TomographyThe repository for reproducing the results inQezlou et al. 2021Cookbook :There is a step-by-step cookbook inCookBook.ipynbRequirements :Each part of the production has a different package requirements.
Please, review the imported packages in each section. A complete list is :numpyscipyscikit-imagefake_spectrampi4pyInstalling the package:via pip:pip install lytomo_watershedor cloning this repo and :python3 setup.py installGenerated DataThe generated data are available here.Refer to thenotebooks/CookBook.ipynband other notebooks to see which data you need at each step.A clear descitption of each file is provided on the Zenodo website.You can useget_data.pyscript to download the files in your notebook:Get your Zenodo access token fromhereTo downlaod any of the compressed files, run :import lytomo_watershed
from lytomo_watershed import get_data
get_data.download(token="YOUR_ACESS_TOKEN", data_dir='./', file='descendant.zip')
# Set file=None, to downlaod everythingDon't forget to decompress the downloaded files.If you have any questions please send me an email :mahdi.qezlou@email.ucr.eduor raise an issue here!Note : The observed LATIS dataNewman et al. 2020is not publicly released yet.
Please stay tuned for a near future paper!
Package: lytrax-afm
Greek TIN/AFM Validator and GeneratorValidate and generate Greek TIN (Tax Identification Number) / AFM (Αριθμός Φορολογικού Μητρώου). Generation function can create valid or invalid numbers including parameters for old format, individuals, legal entities and repet tolerance digits control.Online demo and presentationhttps://lytrax.io/blog/projects/greek-tin-validator-generatorInstallationpip install lytrax-afmUsageImport functions:fromlytrax_afmimportvalidateAFM,\generateAFM,\generateValidAFM,\generateInvalidAFMValidate a number:>>>validate_afm("090000045")True>>>validate_afm("123456789")FalseGenerate a valid number:>>>generate_valid_afm()'731385437'Generate an invalid number:>>>generate_invalid_afm()'853003357'APIvalidate_afmafm: str- A string to be check if it's a valid AFMextended_result: bool, optional- Return extended object result if True, single boolean otherwise (default is False)Returns:strordict(Dictionary with'valid': booleanand'error': str ('length' or 'nan' or 'zero' or 'invalid'))Example:>>>validate_afm("ab1234",extended_result=True){'valid':False,'error':'length'}generate_afmforce_first_digit: int, optional- If specified, overrides all pre99, legalEntity and individual (default is None)pre99: bool, optional- Για ΑΦΜ πριν από 1/1/1999 (ξεκινάει με 0),
(if True, overrides both legal_entity and individual)
(default is False)individual: bool, optional- Φυσικά πρόσωπα, (ξεκινάει με 1-4) (default is False)legal_entity: bool, optional- Νομικές οντότητες (ξεκινάει με 7-9) (default is False)repeat_tolerance : int, optional- Number for max repeat tolerance
(0 for no repeats, unspecified for no check)
(default is None)valid: bool, optional- Generate valid or invalid AFM (default is True)Returns:str- A valid or invalid 9 digit AFM numberExample:>>>generate_afm(force_first_digit=3,repeat_tolerance=1,valid=True)'335151580'generate_valid_afm- Same asgenerate_afmwithvalidparameter force and override toTrueReturns:str- A valid 9 digit AFM numberExample:>>>generate_valid_afm(pre99=True)'013583460'generate_invalid_afm- Same asgenerate_afmwithvalidparameter force and override toFalseReturns:str- An invalid 9 digit AFM numberExample:>>>generate_invalid_afm(legal_entity=True)'780300643'TestClone this repository, run test:git clone https://github.com/clytras/afm-python.git && cd afm-python
python -m unittest discover -s './test' -vChangelogSeeCHANGELOGLicenseMIT License - see theLICENSEfile for details
Package: lywsd03mmc
LYWSD03MMCA Python library for working with Xiaomi Mijia LYWSD03MMC bluetooth temperature and humidity sensors.Updating the firmware on the devices isnotrequired.This package is built on top of the lywsd02 package, which may include additional useful information.InstallationThis relies on bluepy installed via python pip, which itself needs libglib2 to install:sudo apt-get install python3-pip libglib2.0-devThe LYWSD03MMC package can then be installed fromPyPi, using the following command:pip3 install lywsd03mmcFinding the MAC address of the devicesFrom the Xiaomi Home app:Go into the details of the deviceClick on the three dots to get into the settingsClick "About" (near the top of the list)And make a note of the MAC address shown.It is also possible to find the addresses of all devices by runningsudo hcitool lescanand looking for devices labelled "LYWSD03MMC"ToolsTwo helper commands are distributed here:lywsd03mmc- Current dataThis shows the current temperature, humidity and battery level of the device.Example usage:lywsd03mmc A4:C1:38:12:34:56lywsd03mmc2csv- Export historyThis exports the history to a CSV file, containing the maximum and minimum temperature and humidity for each hour there is data for.This can be very slow, and may take up to about 10 minutes to download all the data from the device.Example usage:lywsd03mmc2csv A4:C1:38:12:34:56 --output data.csvLibrary UsageThe library interface closely matches theLYWSD02package, with the following changes:Setting the time has been removedBattery data is available from the main data exportAn extra option has been included to estimate the time the device was startedHistory data times are calculated based on the start time of the deviceConnecting and retrieving informationHere's an example of getting the basic information out of the device:from lywsd03mmc import Lywsd03mmcClient
client = Lywsd03mmcClient("A4:C1:38:12:34:56")

data = client.data
print('Temperature: ' + str(data.temperature))
print('Humidity: ' + str(data.humidity))
print('Battery: ' + str(data.battery))
print('Display units: ' + client.units)HistoryTimes given in the history output are for the end of the hour in which data was recorded.Downloading the history data can take a significant amount of time (up to about 10 minutes).A property is available on the client to output data from each record retrieved, to allow you to see the progress:# Create the client
from lywsd03mmc import Lywsd03mmcClient
client = Lywsd03mmcClient("A4:C1:38:12:34:56")

# Enable history output
client.enable_history_progress = True

# Retrieve the history data
history = client.history_dataTroubleshootingFailed to connect to peripheralCheck you are connecting to the correct MAC address, and are in range of the device.If those are correct, this can normally be fixed by retrying the connection.
Package: lyzr
lyzrLyzr AI is a set of super abstracted LLM SDKs for rapid generative AI application development (RAD) that comes with Lyzr Enterprise Hub – a control center with an AI-only Data Lake and IAM for administering the AI applications built with Lyzr SDKs. Available both as open-source and enterprise SDKs.Lyzr SDKs helps you build all your favorite GenAI SaaS products as enterprise applications in minutes. It is the enterprise alternative to popular in-demand Generative AI SaaS products like Mendable, PDF.ai, Chatbase.co, SiteGPT.ai, Julius.ai and more.Table of ContentsInstallationBuilding from SourceLicenseInstallationYou can install thelyzrpackage directly from PyPI:pip install lyzrBuilding from SourceIf you prefer to build thelyzrpackage from source, you'll need to have Python installed along withsetuptoolsandwheel.Steps to Build:Clone the repository or download the source code.Navigate to the root directory of the project (wheresetup.pyis located).Run the following commands:#Ensuresetuptoolsandwheelareinstalledpip install setuptools wheel#Buildthepackagepython setup.py sdist bdist_wheelThis will generate adistdirectory containing the built package files.Installing the Built Package:Once you've built the package, you can install it using pip:cd dist/pip install lyzr-[version]-py3-none-any.whlReplace[version]with the actual version of the package you have built.Licenselyzris distributed under the terms of theMITlicense.
Package: lz4
StatusIntroductionThis package provides python bindings for theLZ4 compression library.The production ready bindings provided in this package cover theframe format, and theblock formatspecifications. The frame format bindings are the recommended ones to use, as
this guarantees interoperability with other implementations and language
bindings.Experimental bindings for the thestreaming formatspecification are also included, but further work on those is required.The API provided by the frame format bindings follows that of the LZMA, zlib,
gzip and bzip2 compression libraries which are provided with the Python standard
library. As such, these LZ4 bindings should provide a drop-in alternative to the
compression libraries shipped with Python. The package provides context managers
and file handler support.The bindings drop the GIL when calling in to the underlying LZ4 library, and is
thread safe. An extensive test suite is included.DocumentationFull documentation is included with the project. The documentation is
generated using Sphinx. Documentation is also hosted on readthedocs.master:http://python-lz4.readthedocs.io/en/stable/development:http://python-lz4.readthedocs.io/en/latest/HomepageTheproject homepageis hosted
on Github. Please report any issues you find using theissue tracker.LicensingCode specific to this project is covered by theBSD 3-Clause License
Package: lz4a
OverviewThis package provides bindings for thelz4 compression libraryby Yann Collet.Code specific to this project is covered by theBSD 3-Clause LicenseInstallThe package is hosted onPyPI:$ pip install lz4
$ easy_install lz4UsageThe library is pretty simple to use:>>> import lz4
>>> compressed_data = lz4.dumps(data)
>>> data == lz4.loads(compressed_data)
True
>>>Methods and ConstantsThe bindings provides some aliases too:>>> import lz4
>>> lz4.LZ4_compress == lz4.compress == lz4.dumps
True
>>> lz4.LZ4_uncompress == lz4.uncompress == z4.decompress == lz4.loads
True
>>> lz4.VERSION == lz4.__version__  # e.g. "0.7.0"
True
>>>Is it fast ?Yes. Here are the results on my 2011 Macbook Pro i7 with lz4.c as input data:$ python tests/bench.py
Data Size:
  Input: 24779
  LZ4: 10152 (0.41)
  Snappy: 9902 (0.40)
  LZ4 / Snappy: 1.025247
Benchmark: 200000 calls
  LZ4 Compression: 9.737272s
  Snappy Compression: 18.012336s
  LZ4 Decompression: 2.686854s
  Snappy Decompression : 5.146867sImportant noteBecause LZ4 doesn’t define a container format, the python bindings will insert the original data size as an integer at the start of the compressed payload, like most bindings do anyway (Java…)