Saravanan666/pypi-org huggingface dataset:
Package: lithium-enrichment-cost-calculator
A Python package that is able to estimate the cost of lithium enrichment using a separative work approach.Installationpipinstalllithium-enrichment-cost-calculatorEstimating the cost.Required informationCost of input feed stock (cost_feed)Enrichment percent of input feed stock (product_enrichment_fraction) which defaults to be lithium natural abundance of isotopes (7.59% Li6 and 92.41% Li7).Amount of enriched product required (product_amount).Enrichment percent of lithium 6 required in the product (product_enrichment_fraction).Resale value of the depleted tails (cost_tails).Cost per separative work unit (swu_cost)Once these inputs are specified then the cost of enrichment will be calculated and returned along with the optimal Li6 depletion level in the tails using thefind_minimal_cost_of_enrichment()function.The depletion level in the tails is automatically optimized to reduce the total overall costs of the enrichment process. However the cost at a specific tails depletion percent can also be calculated using thefind_cost_of_enrichment()function.Usage examplesFind the minimal cost of lithium enrichment and the corresponding tails depletion.importlithium_enrichment_cost_calculatorasleccminimal_enrichment_cost,tails_percent=lecc.find_minimal_cost_of_enrichment(product_amount=1000,# 1000kg of enriched lithiumproduct_enrichment_fraction=0.6,# 60% is a typical HCPB blanket enrichment amountfeed_cost=29,#Cost in $ per kg from https://www.lme.com/Metals/EV/Lithium-pricesswu_cost=50,# Cost on $ per kg based on typical Uranium SWU coststails_cost=900,# Assuming there is a market for depleted lithium where 10% discount has been applied.)print(f'minimal_enrichment_cost{minimal_enrichment_cost}in $')print('lithium 6 enrichment in tails at this optimal cost is{tails_percent}%')The tails can be depleted to a lesser or greater amount to optimise the cost of the process. It is possible to achieve the required amount of product at the required enrichment in numerous ways. At the two extremes one could:highly depleted the tails and produce a small amount of tailsmildly depleted the tails and produce a large amount of tailsThe most cost effective solution depends on the relative cost of Separate Work Units compared to the cost of the feed (feed_cost) and any resale value for the tails (tails_cost). A basic cost optimiser has been built into this package to
demonstrate the difference that the depletion level in the tails can make to the overall cost.importlithium_enrichment_cost_calculatorasleccminimal_cost,optimal_tails=lecc.find_minimal_cost_of_enrichment(product_amount=100,product_enrichment_fraction=0.6,swu_cost=56,tails_cost=28,feed_cost=33,feed_enrichment_fraction=0.0759,)print(f"minimal cost of enrichment{minimal_cost}")print(f"enrichment of tails at this cost{optimal_tails}")Useful resourcesSeparative work it well documented and explained elsewhere1,2,3
Package: litho1pt0
Litho1pt0litho1pt0is a python interface to thecrust 1.0dataset and the lithospheric part of thelitho 1.0dataset (Laske et al, 2013 and Pasyanos et al, 2014) which both requires and demonstrates the triangulation / searching and interpolation on the sphere that is provided bystripy.Navigation / Notebookspython-c'import litho1pt0; litho1pt0.documentation.install_documentation(path="Notebooks")'The first three notebooks are an introduction tolitho1pt0the next two worked examples show how to search, interpolate and plot with the help ofstripyroutines.stripy is a dependencyEx1-Litho1Layers.ipynbEx2-Litho1Properties.ipynbEx3-CrustalRegionalisation.ipynbWorkEx1-CratonAverageProperties.ipynbWorkEx2-OceanDepthAge.ipynbInstallationDependenciesYou will needPython 3.5 or greater.
Also, the following packages are required:stripygfortran(or any Fortran compiler)numpyInstalling using pipYou can installlitho1pt0and thestripydependency using thepip package manager:pipinstall[--user]litho1pt0stripyThe dependencies will be automatically installed bypip, except forgfortran(or any Fortran compiler). It must be installed in your system before installingstripywithpip.DocumentationAutomatically populated tounderworldcode.github.io/litho1pt0
Package: lithon
Note:Many endpoints have not been implemented yet, but will be soon.lithonLichess is a free, libre, adless, open-source chess server, and lithon (li[chess in py]thon) is a Python API wrapper for the Lichess API.Key FeaturesModern Pythonic interface with an object-oriented approach.Proper JSON, NDJSON and exception handling.Optimised in both, speed and memory.InstallationPython 3.8 or higher is required.To install the stable version, do the following:# Unix / macOSpython3-mpipinstall"lithon"# Windowspy-mpipinstall"lithon"To install the development version, do the following:$gitclonehttps://github.com/Infiniticity/lithonLinksLichessDocumentation
Package: lithophane
Python Lithophane GeneratorThis module generates a 3D model of a lithophane from an given image. The output file is a STL file. It can then be printed on a 3D printer."A lithophane is a thin plaque of translucent material, [...] which has been moulded to varying thickness, such that when lit from behind the different thicknesses show as different shades, forming an image." -WikipediaThe code is originally written by Dirk Colbry (original repository) and edited by Robert Hülsmann. Core of this module uses matlab-stl to write stl files written by Rick van Hattem.How to InstallpipinstalllithophaneHow to Useimportlithophaneasli# 1. Generate xyz point cloud from imagex,y,z=li.jpg_to_stl("path/to/image.jpeg")# 2. Generate stl model from point cloudmodel=li.make_mesh(x,y,z)# 3. Save model as stl filemodel.save("path/to/model.stl")Command Line Interfacelithophanethe_best_siblings.jpeg--width100--frame3Optional Arguments--widthor-wto set the width of the lithophane. Default is image width with a resolution of 10 pixels per millimeter. The height is calculated automatically.--depthor-dto set the depth of the lithophane. Default is 3mm. More depth means more contrast but also longer print time and less translucency.--offsetor-oto set the offset of the lithophane. Default is 0.5mm. It's like the back of the lithophane. More offset means more stability but also less translucency. The thickness of the lithophane is the sum ofdepthandoffset.--frameor-fto add a frame around the lithophane. Default is 0mm (no frame). The frame is added to the width and height of the lithophane.--showto show the lithophane in a 3D plot.After Model GenerationAfter the model is generated, open the STL file in your favorite slicer software to generate the GCODE.Use 100% infill!Then, print it with a (slightliy) translucent filament and delight yourself and your beloved ones with a personalized memento.BackgroundIf you want to understand how the code works, please take a look into theoriginal repositoryof Dirk Colbry. There are two jupyter notebooks you can try out.
Package: litncov
lit-ncov-report洛阳理工学院 "健康状况管控平台" 的一个非官方Python封装库兼CLI工具与拓展实现如需旧版请前往v1Serverless(如云函数)请前往scf安装Python# Python3.6+ with pippipinstalllitncov--upgradeDockerdockerrun-it--rmicepie/litncov封装库范例#  导入模块fromlitncov.userimportlitUesr# 新建实例testme=litUesr("username","password")# 判断是否登陆成功iftestme.is_logged():# 打印用户信息print(testme.info)# 打印上次上报信息print(testme.get_last_record())# 查询 2021-01-04 至今的上报信息print(testme.query_record('2021-01-04'))# 查询 2021-01-04 至 2021-01-18 的上报信息print(testme.query_record('2021-01-04','2021-01-18'))# 打印学生学籍信息print(testme.get_instructor())# 打印用户家庭信息print(testme.get_familys())# 打印用户外出信息print(testme.get_trips())# 打印疫情严重地区print(testme.get_important_city())# 判断今天是否上报过ifnottestme.is_record_today():# 进行当日第一次体温上报## 模式：使用上一次上报信息， 次数： 只上报第一次print(testme.first_record(mode='last',rtimes=1))# 判断今天是否第二次上报过ifnottestme.is_record_today(2):# 进行当日第二次体温上报## 模式：手动填入， 温度： 36.6 摄氏度print(testme.second_record(mode='manual',temperature=36.6))# 判断今天是否第三次上报过ifnottestme.is_record_today(rtime=3):# 进行当日第三次体温上报## 模式：随机生成正常体温(36.0~37.2 摄氏度)print(testme.third_record(mode='random'))CLI工具上报功能例子使用上一条上报记录进行今天三次上报$litncov-uUSERNAME-pPASSWORDreport-a# 不带 -a 参数则只进行今天第一次上报使用随机温度进行今天第二次上报$litncov-uUSERNAME-pPASSWORDreport-r2-mrandom手动输入温度进行今天第三次上报$litncov-uUSERNAME-pPASSWORDreport-r3-t36.7帮助$ litncov report -h
usage: litncov report [-h] [-a] [-f] [-r RTIME] [-m MODE] [-t TEMP]

optional arguments:
  -h, --help            show this help message and exit
  -a, --all             do the all report tasks today
  -f, --force           forcely report
  -r RTIME, --rtime RTIME
                        the report time {1,2,3}
  -m MODE, --mode MODE  the report mode {last,random,manual}, default last
  -t TEMP, --temp TEMP  the body temperature (float), only use in manual查询用户信息例子主要个人信息$litncov-uUSERNAME-pUSERNAMEinfo--user外出信息$litncov-uUSERNAME-pUSERNAMEinfo--trip上次上报记录$litncov-uUSERNAME-pUSERNAMEinfo-l帮助$ litncov info -h
usage: litncov info [-h] [-u] [-l] [-f] [-i] [-t]

optional arguments:
  -h, --help         show this help message and exit
  -u, --user         the user main info
  -l, --last_record  the user last record info
  -f, --family       the user family info
  -i, --instructor   the user instructor info
  -t, --tirp         the user tirp info查询上报记录例子查询某日至今$litncov-uUSERNAME-pPASSWORDquery-s2020-01-01查询某日至某日$litncov-uUSERNAME-pPASSWORDquery-s2020-01-01-e2020-01-15帮助usage: litncov query [-h] -s START_TIME [-e END_TIME]

optional arguments:
  -h, --help            show this help message and exit
  -s START_TIME, --start_time START_TIME
                        the start time of the report history (Year-Month-Day)
  -e END_TIME, --end_time END_TIME
                        the end time of the report history, default Today (Year-Month-Day)
Package: litNlp
litNlp 简介litNlp 是基于 Tensorflow 2.0 实现的一个轻量级的深度情感极性推理模型，使用字符级代替词语级进一步提升训练和推理速度，可以实现细粒度的多级别情感极性训练和预测。TF2 下 GPU 和 CPU 平台都能直接安装运行，是搭建 NLP 分类模型类 baseline 的快速方案。

1. 内置情感分析模型-利用深度模型优化语义建模，使用字符级减少 tokenizer 的大小。
2. 直接提供模型训练，默认 Text-CNN 字符级卷积网络作为 baseline ，自带早停操作，使用少的参数即可开始训练多分类模型。直接使用 emample/sa_ui.py 进行前端 ui 展示效果# 安装 streamlit 之后直接运行脚本streamlitrunsa_ui.py使用方法pip install  litNlp模型需要先通过训练，保存在 sa_model 里面，然后就可以批预测，具体的使用见 example 文件内容fromlitNlp.predictimportSA_Model_Predict# 批处理文本predict_text=['这个我不喜欢','这个我喜欢不']# 初始化并加载模型tokenizer_path='sa_model/tokenizer.pickle'sa_model_path='sa_model/c_cnn_m.h5'model=SA_Model_Predict(tokenizer_path,sa_model_path_m=sa_model_path)sa_score=model.predict(predict_text)print([i[1]foriinsa_score])参数解释# 最大句子长度maxlen=100# 最大的tokenizer字典长度max_words=1000# 设置embedding大小embedding_dim=300# 模型的保存位置，后续用于推理sa_model_path_m='sa_model/c_cnn_m.h5'# 离线保存tokenizertokenize_path='sa_model/tokenizer.pickle'# 分类的类别数num_classes=2# train_method : 模型训练方式，默认textcnn，可选：bilstm, grutrain_method='textcnn'2 个 epoch 的二分类性能jupyter 加载情感分析，优化语义的情感推理pipy code
Package: li-traceroute
“sudo li-trace -g www.yandex.ru 30” - рисует график“sudo li-trace -I www.yandex.ru 30” - использует ICMP протокол“sudo li-trace -I -g www.yandex.ru 30” - использует ICMP протокол и рисует график“sudo li-trace –help” - вызывает помощь“sudo li-trace -p 33434 www.yandex.ru 30” - указать интересующий портДополнительные возможности:Создание графика на основе полученных данных
Package: litra-driver
Python Utility for Logitech Litra Glow and BeamIntroductionAfter purchasing aLogitech Litra GlowI was unable to find any support for linux. This project reverse-engineers the basic functionality of the litra glow so that we can control it via USB without using the physical buttons on the device. It also now supports theLogitech Litra Beam.Quick StartLinux# If necessary, create a udev role to grant permission to access the lightsudotee/etc/udev/rules.d/82-litra-glow.rules<<<'SUBSYSTEM=="usb", ATTR{idVendor}=="046d", ATTR{idProduct}=="c900",MODE="0666"'sudotee/etc/udev/rules.d/82-litra-beam.rules<<<'SUBSYSTEM=="usb", ATTR{idVendor}=="046d", ATTR{idProduct}=="c901",MODE="0666"'sudoreboot# Uninstall if previously installedpipuninstalllitra-driver
pipinstalllitra-driver# Command Line Interfacelc--help# User InterfacelcuiThe UIA basic UI can be launched to allow control of the light:lcuiThe CLINAME
    lc

SYNOPSIS
    lc COMMAND

COMMANDS
    COMMAND is one of the following:

     on
       Turns on the Litra Glow

     off
       Turns off the Litra Glow

     temp
       Sets the temperature level of the Litra Glow

     bright
       Sets the brightness level of the Litra GlowSample Usagelcon
lcbright10lctemp6500lcoff# Chain multiple commandslconbright100temp2700The log level can be adjusted by setting the environment variableLITRA_LOGLEVELto one of the following:CRITICALERRORWARNINGINFODEBUGDevelopmentCreating / installing the distributionpipinstallbuild# Create distributionpython-mbuild# Local Testingpipinstall--editable.# Lintertox-elint# Unit Test - "Coming Soon"™tox-etest
Package: litstrings-cli
LitStrings Command-Line InterfaceGetting startedDescriptionThe LitStrings CLI enables you to manage your translations within a project without the need of an elaborate UI system.You can use the command line tool to create new resources, map locale files to translations, and synchronize your LitStrings project with your local repository. Translators and localization managers can use it to handle large volumes of translation files.  The LitStrings CLI can help to enable continuous integration workflows and can be run from CI servers likeRapidRabbit, Jenkins and Bamboo.Click  herefor complete documentation on the LitStrings CLI via our documentation site.InstallationYou can install the latest version of litstrings-cli runningpip install litstrings-clioreasy_install litstrings-cli.Getting HelpYou can always get additional help viaGitLab issuesorLitStrings supportLicenseLitStrings CLI is primarily distributed under the terms of the GPL License (Version 2.0).SeeLICENSEfor details.
Package: litterbox
A set of tools to manage all the crap for the CAT Board.Free software: MIT licenseDocumentation:http://devbisme.github.io/litterbox.FeaturesConfigure the FPGA on the CAT Board with a bitstream file stored in the Raspberry Pi.Program the CAT Board serial flash with a bitstream that will be loadedinto the FPGA upon power-up or reset.Erase the serial flash.History0.2.2 (2021-07-12)Moved ownership from xesscorp to devbisme.0.2.1 (2016-09-07)Removed dependency on spi module (python-spi).0.2.0 (2016-09-07)python-spi package was incorporated into litterbox and modified to increase transfer rates.Documentation moved to Github pages.0.1.0 (2016-05-07)First release on PyPI.
Package: little-api
Little-API: The little web framework that couldIt's a WSGI framework and can be used with any WSGI application server such as Gunicorn.Installationpipinstalllittle-apiUsagefromlittle_api.apiimportAPIapp=API()# Json Response Example@app.route("/home")defhome(request,response):response.json={"name":"little-api"}# Class Base Route Example@app.route("/book")classBookResource:defget(self,req:Request,resp:Response):resp.text="Get Books Page"defpost(self,req:Request,resp:Response):resp.text="Create Books Page"# Rendering Template Example@app.route("/template")deftemplate_render(req:Request,resp:Response):resp.body=app.template("index.html",context={"name":"Little-Api","title":"Best Framework"})Running with DebugServerlittle-api has a wrapper around the gunicorn server to allow for easier debuggingif__name__=="__main__":fromlittle_api.debug_serverimportDebugServerDebugServer(application=app,port=8080).run()
Package: little-bank
little-bankLittle Bank is a framework for analyzing financial transactions.Design goalsAll analysis should be a matter of summarizing credit, debit or balance of an account
in a system. The intention is to reduce bugs by making all analysis use the same
numerical implementation.Allow defining rules for a system in a declarative way.Disallow mutation. The design encourages immutable, append-only transactions.All operations that update the system are very similar: appending transactions which
automatically verifies all defined rules, and persisting the new transactions. This
allows centralizing code that saves transactions to a single place.
Package: little-bio-parser
A minimalistic parser for sequence alignments
(fasta or stockholm formats).The parser returns an iterator over the alignment records as
(identifier, sequence) tuples.Usage example:>>> import lilbio
>>> parsed_records = lilbio.parse('alignment.fa', 'fasta')Built over modified low-level parsers from Biopython
(SimpleFastaParser function and StockholmIterator class).Changes0.8 (2019-05-27)Python3 only (>= 3.4)
Package: littlebird
LittleBird is a Python 3.6+ asynchronous library for accessing twitter utilizing new python features: async generators, type hinting, etc.UsageCurrently only the oauth1 methods are supported (application only authentication, user account access).importasyncioimportcontextlibfromlittlebirdimportLittleBirdfromlittlebird.webimportOAuth1HttpClientlittle_bird=LittleBird(OAuth1HttpClient(# required for oauth1 signing:consumer_key:str,consumer_secret:str,# optionally necessary for endpoints requiring a user's scope:access_token:Optional[str]=None,access_token_secret:Optional[str]=None))asyncdefmain(little_bird):# watch the random sampling of tweets chosen by twitterasyncfortweetinlittle_bird.sample():print(tweet)if__name__=='__main__':loop=asyncio.get_event_loop()withcontextlib.suppress(KeyboardInterrupt):loop.run_until_complete(main(little_bird))
Package: littlebluefox-python
LittleBlueFox pip package (python)Push your security events on the LittleBlueFox.io API.Installation$pip3installlittlebluefox-pythonUsagefromlittlebluefoximportLittleBlueFoxClientaccess_token='...'lbf_client=LittleBlueFoxClient(access_token)event={'event_type':'authentication_success','uref':'42','email':'demo@demo.com','remote_ip':'...','http_headers':{'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.0.3 Safari/605.1.15',},}try:lbf_client.push(event)exceptExceptionaserr:print('Error:{}'.format(err))ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/LittleBlueFox/littlebluefox-python.$ python3 setup.py sdist bdist_wheel
$ python3 -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Package: little-boxes
# Little Boxes<a href="https://travis-ci.org/tsileo/little-boxes"><img src="https://travis-ci.org/tsileo/little-boxes.svg?branch=master" alt="Build Status"></a><a href="https://codecov.io/gh/tsileo/little-boxes"><img src="https://codecov.io/gh/tsileo/little-boxes/branch/master/graph/badge.svg" /></a><a href="https://github.com/tsileo/little-boxes/blob/master/LICENSE"><img src="https://img.shields.io/badge/license-ISC-blue.svg?style=flat" alt="License"></a>Tiny [ActivityPub](https://activitypub.rocks/) framework written in Python, both database and server agnostic.**Still in early development, and not published on PyPI yet.**## Getting Started```pythonfrom little_boxes import activitypub as apfrom mydb import db_clientclass MyBackend(BaseBackend):def __init__(self, db_connection):self.db_connection = db_connectiondef inbox_new(self, as_actor, activity):# Save activity as "as_actor"# [...]def post_to_remote_inbox(self, as_actor, payload, recipient):# Send the activity to the remote actor# [...]db_con = db_client()my_backend = MyBackend(db_con)ap.use_backend(my_backend)me = ap.Person({})  # Init an actoroutbox = ap.Outbox(me)follow = ap.Follow(actor=me, object='http://iri-i-want-follow')outbox.post(follow)```## Projects using Little Boxes- [microblog.pub](http://github.com/tsileo/microblog.pub) (using MongoDB as a backend)## ContributionsTODO: document Mypy, flake8 and black.PRs are welcome, please open an issue to start a discussion before your start any work.
Package: littlechat
littlechatAboutA little chatroom run in console, write in python base
onurwidandUDP,
only support Linux, OSX, Cygwin or other unix-like OS, not support WindowsChat exampleChinese character exampleemoji exampleQuick StartInstallFrom repositoryFrom githubpipinstallgit+https://github.com/yujun2647/littlechat.gitFrom giteepipinstallgit+https://gitee.com/walkerjun/littlechat.gitFrom PypipipinstalllittlechatUsageStart serverstart server at port: 5000lchat-tserver-sp5000Connect with clientconnect server abovelchat-sp5000Hotkeys supportsNameUseAlt+Enterforce to start a new line at input boxAlt+eopen emoji boxAlt+lkeep focus in the edit box (Author needs)
Package: little-cheesemonger
little-cheesemongerlittle-cheesemongeris a package that can set up a manylinux build environment to compile a
correctly linked manylinux wheel. Configuration data is loaded from apyproject.tomlfile, or
a custom data loader can be implemented.Installationpipinstalllittle-cheesemonger==0.2.1QuickstartTo trigger environment setup, either runlittle-cheesemongerfrom the root directory of a package, or pass the path to the package you want compiled. The package must contain configuration data in itspyproject.tomlfile for environment setup to work.little-cheesemongerpath/to/packageDefault ConfigurationThe manylinux image version is controlled by last section of the configuration heading. Thelatesttag is always used.[tool.little-cheesemonger.manylinux2014_x86_64]environment_variables=["FOO=BAR"]system_dependencies=["atlas"]python_dependencies=["nyancat==0.1.2"]python_versions=["cp36-cp36m""cp38-cp38"]steps=["touch /foobar.txt"]environment_variablesis a list of environment variables to set in the container
prior to building the wheel. They are expected to be inKEY=VALUEformat.system_dependenciesis a list of CentOS system dependencies to install viaYUM.
They are expected to be inpackage-versionformat.python_dependenciesis a list of Python dependencies to install viapip. They
are expected to be inpackage==versionformat. They are currently installed for
all available versions of Python in a given manylinux image.python_versionsis a list of Python versions to install Python dependencies for
in a given manylinux image. They are expected to follow the Python installation directory
naming convention from in the specified manyliniux image, ex.cp36-cp36m. Python version names
can be found by runningls /opt/pythonin a manylinux image.stepsis a list of steps to execute via bash. Package building and uploading could
be implemented here, orlittle-cheesemongercould be integrated into a larger system!Custom Data LoaderYou can pass the path to an importable function, as well as positional and keyword arguments tolittle-cheesemongerto customize how configuration data is loaded.little-cheesemonger--data-loaderpath.to.function--loader-argfoo--loader-kwargfoo=barCustom data loader functions must acceptdirectoryas the first argument with positional and keyword arguments passed to the loader function after. The function must return a dictionary of configuration data matching the fields above.defmy_custom_loader(directory:Path,...)->Dict:pass
Package: littlechef
With LittleChef you will be able to get started more quickly cooking withChef, the excellent Configuration Management System.You will just need your local (preferably version controled) kitchen with all
your cookbooks, roles data bags and nodes, which will get rsynced to a node
each time you start a Chef Solo configuration run with the bundled ‘fix’
command.It also adds a library for chef_environment compatibility with Chef 10.