Saravanan666/pypi-org huggingface dataset:
Package: lihzahrd
lihzahrdA Terraria 1.4.4.9 world parser in Python.You can use this package to get programmer-friendly data from a Terraria world!InstallationUse your favourite Python package manager to installlihzahrd:Venv + Pip$source.venv/bin/activate$pipinstalllihzahrdPoetry$poetryaddlihzahrdAnd more!UsageYou can open a world file and get aWorldobject by calling:importlihzahrdworld=lihzahrd.World.create_from_file("filename.wld")Itwilltake a while to process: a small Terraria world contains more than 5 million tiles!Once you have aWorldobject, you can use all data present in the save file by accessingits attributes.WarningLoading maliciously designed Terraria worlds can drain system resources, crash the interpreter, or possibly do more evil things!Make sure you trust the worlds you are parsing!DocumentationThe documentation is availablehere.If you know something that is missing in the documentation, please let me knowwith an issue!PyPylihzahrdis compatible withPyPy, an alternative implementation on Python!If you think that parsing a world takes too much time, you can use PyPy to reduce the required time by a factor of ~3!BenchmarksTime to parse the same large world:CPython took 11.45 s.Pypy took 3.57 s!DevelopmentTo contribute tolihzahrd, you need to havePoetryinstalled on your PC.After you've installed Poetry, clone the git repo with the command:$gitclonehttps://github.com/Steffo99/lihzahrdThen enter the new directory:$cdlihzahrdAnd finally install all dependencies and the package:$poetryinstallThis will create a new virtualenv for the development of the library; you can activate it by typing:$poetryshellPlease note that for compatibility with PyPy, the project needs to target Python 3.6.Building docsYou can build the docs by entering thedocs_sourcefolder and runningmake html, then committing the wholedocsfolder.References usedTheTEdit World Parser, the most accurate source currently available.ThetModLoader wiki, containing lists of all possible IDs.TheTerrafirma world documentation, accurate for old worlds (version <69)The1.3.x.x world documentation, a bit incomplete, but an useful source nevertheless.AJS World Parseron GitHub.ABackground Guideon Steam that displays all possible world backgrounds.Licenselihzahrdis licensed under theAGPL 3.0.
That means you have to publish under the same license the source code of any program you create that useslihzahrd.See alsoflyingsnake, a map renderer using this package
Package: liionpack
Overview of liionpackliionpacktakes a 1D PyBaMM model and makes it into a pack. You can either specify
the configuration e.g. 16 cells in parallel and 2 in series (16p2s) or load a
netlist.InstallationFollow the steps given below to installliionpack. The package must be installed to run the included examples. It is recommended to create a virtual environment for the installation, seethe documentation.To installliionpackusingpip, run the following command:pipinstallliionpackCondaThe following terminal commands are for setting up a conda development environment for liionpack. This requires theAnacondaorMinicondaPython distribution. This environment installs liionpack in editable mode which is useful for development of the liionpack source code. General users should install liionpack with pip.# Create a conda environment named lipackcdliionpack
condaenvcreate--fileenvironment.yml# Activate the environmentcondaactivatelipack# Exit the environmentcondadeactivate# Delete the environmentcondaenvremove--namelipackLaTeXIn order to use thedraw_circuitfunctionality a version of Latex must be installed on your machine. We use an underlying Python packageLcapyfor making the drawing and direct you to its installation instructionsherefor operating system specifics.Example UsageThe following code block illustrates how to use liionpack to perform a simulation:importliionpackaslpimportnumpyasnpimportpybamm# Generate the netlistnetlist=lp.setup_circuit(Np=16,Ns=2,Rb=1e-4,Rc=1e-2,Ri=5e-2,V=3.2,I=80.0)output_variables=['X-averaged total heating [W.m-3]','Volume-averaged cell temperature [K]','X-averaged negative particle surface concentration [mol.m-3]','X-averaged positive particle surface concentration [mol.m-3]',]# Heat transfer coefficientshtc=np.ones(32)*10# Cycling experiment, using PyBaMMexperiment=pybamm.Experiment(["Charge at 20 A for 30 minutes","Rest for 15 minutes","Discharge at 20 A for 30 minutes","Rest for 30 minutes"],period="10 seconds")# PyBaMM parametersparameter_values=pybamm.ParameterValues("Chen2020")# Solve packoutput=lp.solve(netlist=netlist,parameter_values=parameter_values,experiment=experiment,output_variables=output_variables,htc=htc)DocumentationThere is a full API documentation, hosted on Read The Docs that can be foundhere.Contributing to liionpackIf you'd like to help us develop liionpack by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at theseguidelinesfirst.Get in touchFor any questions, comments, suggestions or bug reports, please see thecontact page.AcknowledgmentsPyBaMM-team acknowledges the funding and support of the Faraday Institution's multi-scale modelling project and Innovate UK.The development work carried out by members at Oak Ridge National Laboratory was partially sponsored by the Office of Electricity under the United States Department of Energy (DOE).Licenseliionpack is fully open source. For more information about its license, seeLICENSE.
Package: liitos
LiitosSplice (Finnish: liitos) contributions.DocumentationUser and developerdocumentation of liitos.Bug TrackerAny feature requests or bug reports shall go to thetodos of liitos.Primary Source repositoryThe main source ofliitosis on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clone ofliitosis:at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.SupportPlease kindly submit issues athttps://todo.sr.ht/~sthagen/liitosor write plain text email to ~sthagen/liitos@lists.sr.htto submit patches and request support. Thanks.StatusExperimental.Note: The default branch isdefault.
Package: lika
lika✨ 简易 Python ASGI Web 框架 ✨💿 安装使用 pip 安装已发布的最新版本pipinstalllika可以克隆 Git 仓库后手动安装gitclonehttps://github.com/KarisAya/lika.git或者选择任意你喜欢的方式🎉 使用使用 uvicorn 运行服务importuvicornfromlika.serverimportServerif__name__=="__main__":server=Server()uvicorn.run(server,host="127.0.0.1",port=8080)这样你就已经运行了一个 Web 服务器添加路由当然你的 web 服务器里面要有内容
下面是一个示例。添加一个路由返回随机图。importuvicornfrompathlibimportPathfromlika.serverimportServerfromlika.responseimportResponse,Headersserver=Server()root=server.router_map# root是你的web服务器根目录路由图image_src=list(Path("./src/image").iterdir())@root.router("/image")asyncdef_(scope,receive):image=random.choice(image_src)withopen(image,"rb")asf:returnResponse(200,Headers.from_ext(image.suffix),[f.read()])# /重定向到/imageroot.redirect(301,"/","/image/")使用子目录# 添加子目录hello=root.set_map("/hello")@hello.router("/world")asyncdef_(scope,receive):returnResponse(200,[(b"Content-type",b"text/plain")],[b"hello world"])等效于@root.router("/hello/world")asyncdef_(scope,receive):returnResponse(200,[(b"Content-type",b"text/plain")],[b"hello world"])hello=root.get_map("/hello")# 如果在@root.router之前执行这行代码会导致 hello == None使用本地资源你可以添加一些本地资源root.directory("./src",True)src_pathPath,本地资源路径htmlbool,访问文件夹路径是否视为访问文件夹下 index.html 文件for_routerset,此内扩展名的文件在每次访问时读取，其他文件放进内存for_responseset,此内扩展名的文件存放进内存，其他在每次访问时读取for_router与for_response二选一在根目录添加你在本地./src目录的资源假设这是你的文件结构│favicon.ico
├─image
│07c438ee01fc3bbeb21a116f2ad1e440.png
│10de76b884dde180b52b20bc198f9851.jpeg
├─index
│bundle.js
│index.html
└─homebundle.jsindex.html访问/image/07c438ee01fc3bbeb21a116f2ad1e440.png你将会看到这张图片。（即便你已经把/image/做成了随机图。）ps:访问http://127.0.0.1:8080/index/你将会看到/index/index.html除非你用root.mount("./src", False)地址占位符形如"{id}"的地址占位符@root.router("/test/{code}/{other}")asyncdef_(scope,receive,code:str,other:str):returnResponse(int(code),[(b"Content-type",b"text/plain")],[other.encode()],)现在你可以正常访问/test/418/hello或者/test/200/world请不要访问/test/hello/world，因为你不能int("hello")路由图server=Server()root=server.router_maptest=root.set_map("/test")root 实际上是服务器根路径 "/" 的路由图（RouterMap）test 实际上也是服务器路径 "/test" 的路由图路由图（RouterMap）并不依托于服务器，你也可以先有 RouterMap，再把 RouterMap 添加到服务器上importuvicornfromlika.serverimportServerfromlika.routerimportRouterMapfromlika.responseimportResponserouter_map=RouterMap()# 创建一个路由图router_map.directory("./src",True)# 向路由图里面添加本地资源# 向路由图里面添加响应@router_map.router("/test")asyncdef_(scope,receive):# Do ...returnResponse(200,[(b"Content-type",b"text/plain")],[b"hello world"])# 使用路由图的子路由图hello=router_map.set_map("/hello")@hello.router("/world")asyncdef_(scope,receive):# Do ...returnResponse(200,[(b"Content-type",b"text/plain")],[b"hello world"])new_rtmp=RouterMap()# 创建一个新的路由图new_rtmp.set_map("/index",router_map)# 把整个router_map放new_rtmp的/index/目录下if__name__=="__main__":server=Server()server.router_map.set_map("/index",new_rtmp)# 把整个new_rtmp放进服务器根路由图的/index/目录下print(server.router_map)# 了解一下发生了什么（uvicorn.run(server,host="127.0.0.1",port=8080)📖 介绍📝 更新日志
Package: like-a-srt
Like a SRTCLI to generate SRT subtitles automatically from audio files, usingAzure Speech.pipinstalllike-a-srtGetting startedRequirementsPython 3.9An Azure Speech serviceHow to use:configure environmental variables (recommended: create an.envfile as in
the example below)install the CLI (e.g. installing the package usingpip, in a Python virtual environment)generate subtitles in.srtformat using the commandlas gen -s example.wavExample.envfile, to configure the necessary environmental variables:SPEECH_SUBSCRIPTION="<YOUR_AZURE_SPEECH_SUBSCRIPTION>"SPEECH_ENDPOINT="<YOUR_AZURE_SPEECH_ENDPOINT>"Example endpoint value:https://westeurope.api.cognitive.microsoft.com/sts/v1.0/issuetoken.Example: generate a subtitles fileexample.srtfrom a source fileexample.wav:lasgen-sexample.wavTo create a .wav file from a source video, it's possible to useffmpeg:ffmpeg-isource.mp4destination.wav
Package: likee-downloader
NoteDue to Likee's changes on their website, likee-downloader only works in some European countries. And, it also works in the United States.Likee DownloaderA program for downloading videos from Likee, given a usernameInstallationInstall from PyPIpip install likee-downloaderNoteIn order to run the program, You will need to have the FireFox browser installed on your pcThe program is dependent on selenium, so in order to run it, you will have to download and properly setup geckodriver (setup instructions available below)Dockerdocker pull rly0nheart/likee-downloaderNoteSetting up selenium/geckodriver won't be required for the docker imageGeckodriver setupLinux1. Go to the geckodriverreleases page. Find the latest version of the driver for your platform and download it2. Extract the downloaded filetar -xvzf geckodriver*3. Make it executablechmod +x geckodriver4. Add geckodriver to your system pathexport PATH=$PATH:/path/to/downloaded/geckodriverNoteIf you encounter issues with the above commands, then you should run them as root (with sudo)Windows1. Go to the geckodriverreleases page. Find the geckodriver.exe binary for your platform and download it2. Move the downloaded executable toC:\Users\yourusername\AppData\Local\Programs\Python\Python310NoteThe numbers on the directory 'Python310' will depend on the version of Python you haveMac OSSet up Selenium & GeckoDriver (Mac)UsagePyPI packagelikee_downloader --helpDocker imagedocker run -it -v $PWD/downloads:/app/downloads rly0nheart/likee-downloader --helpusage: likee_downloader [-h] [-s] [-l LIMIT] [-d {csv,json}] [-dl] username

likee-downloader — by Richard Mwewa | https://about.me/rly0nheart

positional arguments:
  username              username

options:
  -h, --help            show this help message and exit
  -s, --screenshot      capture a screenshot of the target's profile
  -l LIMIT, --limit LIMIT
                        number of videos to get (default: 10)
  -d {csv,json}, --dump {csv,json}
                        dump video data to a specified file type
  -dl, --download       Download all returned videos

A program for downloading videos from Likee, given a usernameDonationsIf you would like to donate, you could Buy A Coffee for the developer using the button belowYour support will be much appreciated!
Package: likefit
LikeFitLikeFit is a library to fit data for science and engineering.
It provides a user-friendly and comprehensive interface for linear and non-linear least squares, and likelihood fits.Installpython-mpipinstalllikefitFeaturesLinear and nonlinear least squares fit with a consistent interfaceFit of histograms with a Poisson likelihoodCalculation of estimators, errors, and correlationsEvaluation of goodness-of-fit with chi-squared testPlot of error bands, confidence regions, and likelihood functionsHow to useCheck the tutorials at the end of this README for examples on how to use LikeFit.ContributingIf you want to contribute, please fork the repository and use a feature
branch. Pull requests are warmly welcome.LinksRepository:https://github.com/ravignad/likefit/Tutorials:https://bit.ly/3HFlFJ1LicensingThe code in this project is licensed under MIT license.
Package: likelihood
This repository contains the code to build thelikelihood packagewhich contains tools for typical tasks inMLOpsand training of custom models.PrerequisitiesBefore you begin, ensure you have met the following requirements:You have aWindows/Linux/Macmachine runningPython 3.10+.You have installed the latest versions ofpipandvirtualenvorconda(Anaconda).InstallationThis package can be easily installed with pip:pipinstalllikelihoodExamplesYou can check theexamplesfolder.More examples will be added soon.
Package: likelycause2
# Likelycause2Likelycause is an utility package that uses several functions to attribute causes to variations. Using a combination of arithmetical decompositions and bayesian techniques, this was built to facilitate the workflow of a data-analyst working for the private sector.## What the package contains
This package has everything built under the likelycause2 module, so all the functions should be called using “likelycause2.”. Currently, we have 1 auxiliary function and 1 causal function.### Auxiliary functions
- likelycause2.last_period: The last period function is a utility function that builds variation variables in a dataframe._### Causal functions
- likelycause2.bayes_suspects: The bayes_suspects function calculates the conditional probabilities of the event and each suspicious causes or a combination of those causes.  It also suggests an attribution to each individual cause, by adjusting the intersections of causes## Likelycause2.last_period### Description:
The last period function is a utility function that builds variation variables in a dataframe.
Variations are defined between moment t and a moment in the past.### Arguments:df (pd.DataFrame): the dataframeunique_id (string): unique identifier of each line. Must be unique, and can only be 1 columninterval (string): what is the interval you want to calculate variations for. Accepts days, weeks and hoursperiods (int): number of periods you want to look back on that interval. For last variations, for example, the argument period would be 1date_column (string): the date column in your dateframe. Must be a datetime. To convert, use pandas.to_datetime functionto_past (list): list of columns you want to calculate the variations for### Returns:
Returns the dataframe that was inputed with additinal columns named v+name of the columns in the to_past argument. Those columns represent the variation of that variable between moment t and t-periods. This variation is calculated as (Variable in moment t)/(Variable in moment t-periods).## Likelycause2.bayes_suspects### Description:
The bayes_suspects function calculates the conditional probabilities of the event and each suspicious causes or a combination of those causes.
It also suggests an attribution to each individual cause, by adjusting the intersections of causes### Arguments:df (pd.DataFrame): the dataframeevent (string): name of the column that contains the event that we want to explainsuspects (list): list with name of the columns that contains the potential causes for what we want to explainpoint (dictionary): dictionary with the point for which we want to calculate the probability. Must be a combination of the cause and all the individual points of suspects### Returns:
Returns a dataframe with all the possible probabilities combinations, and the conditional probabilities:name: name of that conditional combination. If it has one event, it represents P(event|a). If it has 2 events it represents P(event1 & event2|a)prob_ba: P(cause | event)prob_a: P(cause)prob_b: P(event)pbayes: confitional probabilitypbayes_attribution: suggested probability attribution if we want to attribute to individual causes
Package: liker
Python packagelikerThe book(R과 비교하며 배우는) 파이썬 전처리와 시각화introduces Python to R Users.
This packagelikerhas functions that mimic R in Python.Here are examplesc(2,3,5,7,11)# creates list or array with elements 2,3,5,7,11ac(2,3,5,7,11)# creates a numpy arraylc(2,3,5,7,11)# creates a listc=lcpdDataFrame(x=[1,3,2],y=['a','b','c'])# equivalent to R data.frame(x=c(1,3,2), y=c('a', 'b', 'c'))# you can also dopdDataFrame(x=c(1,3,2),y=c('a','b','c'))More examples to come!
Package: like-spider
like spider: a crawler framework that allows developers to easily extract web page information and quickly store data.author:wyjhaha@foxmail.comgithub:https://github.com/wuyingjie1002/like_spiderNote: please read the development documentation and sample code in detail during development.github wiki:https://github.com/wuyingjie1002/like_spider/wikiIf you have any questions or suggestions, please contact me by email.
Package: liknorm
liknorm-pyLiknorm Python wrapper.InstallIt can be done viapiporconda.PippipinstallliknormCondacondainstall-cconda-forgeliknorm-pyRunning the testsAfter installation, you can test itpython-c"import liknorm; liknorm.test()"as long as you havepytest.Example>>>fromnumpyimportempty>>>fromnumpy.randomimportRandomState>>>fromliknormimportLikNormMachine>>>>>>machine=LikNormMachine('bernoulli')>>>random=RandomState(0)>>>outcome=random.randint(0,2,5)>>>tau=random.rand(5)>>>eta=random.randn(5)*tau>>>>>>log_zeroth=empty(5)>>>mean=empty(5)>>>variance=empty(5)>>>>>>moments={'log_zeroth':log_zeroth,'mean':mean,'variance':variance}>>>machine.moments(outcome,eta,tau,moments)>>>>>>print('%.3f%.3f%.3f'%(log_zeroth[0],mean[0],variance[0]))-0.671-0.5150.946AuthorsDanilo HortaLicenseThis project is licensed under theMIT License.
Package: lila
lilaLila is a Python library to work withSirenprotocol. It aims to encapsulate most of the requirements of the protocol, so that one can utilize EAFP principle instead of validating incoming and outgoing data.Installation$ python -m pip install lilaDocumentationDocumentation for the package can be found on theWiki.QuickstartAssume we expect to receive or need to send the following json data:entity_data={"class":["order"],"properties":{"orderNumber":42,"itemCount":3,"status":"pending"},"entities":[{"class":["items","collection"],"rel":["http://x.io/rels/order-items"],"href":"http://api.x.io/orders/42/items"},{"class":["info","customer"],"rel":["http://x.io/rels/customer"],"properties":{"customerId":"pj123","name":"Peter Joseph"},"links":[{"rel":["self"],"href":"http://api.x.io/customers/pj123"}]}],"actions":[{"name":"add-item","title":"Add Item","method":"POST","href":"http://api.x.io/orders/42/items","type":"application/x-www-form-urlencoded","fields":[{"name":"orderNumber","type":"hidden","value":"42"},{"name":"productCode","type":"text"},{"name":"quantity","type":"number"}]}],"links":[{"rel":["self"],"href":"http://api.x.io/orders/42"},{"rel":["previous"],"href":"http://api.x.io/orders/41"},{"rel":["next"],"href":"http://api.x.io/orders/43"}]}One can parse these data into a python object and access parts of it (client-side):fromlila.serialization.json.parserimportJSONParserentity=JSONParser().parse_entity(entity_data)assertentity.actions[0].fields[0].value=="42"Or start with a python object and build this json (server-side):fromlila.core.fieldimportInputType,Fieldfromlila.core.actionimportMethod,Actionfromlila.core.linkimportLink,EmbeddedLinkfromlila.core.entityimportEntity,EmbeddedRepresentationfromlila.serialization.json.marshalerimportJSONMarshalerentity=Entity(classes=["order"],properties={"orderNumber":42,"itemCount":3,"status":"pending",},entities=[EmbeddedLink(classes=["items","collection"],relations=["http://x.io/rels/order-items"],target="http://api.x.io/orders/42/items",),EmbeddedRepresentation(classes=["info","customer"],relations=["http://x.io/rels/customer"],properties={"customerId":"pj123","name":"Peter Joseph",},links=[Link(relations=["self"],target="http://api.x.io/customers/pj123",),],),],actions=[Action(name="add-item",title="Add Item",method=Method.POST,target="http://api.x.io/orders/42/items",media_type="application/x-www-form-urlencoded",fields=[Field(name="orderNumber",input_type=InputType.HIDDEN,value="42"),Field(name="productCode",input_type=InputType.TEXT,),Field(name="quantity",input_type=InputType.NUMBER,),])],links=[Link(relations=["self"],target="http://api.x.io/orders/42",),Link(relations=["previous"],target="http://api.x.io/orders/41",),Link(relations=["next"],target="http://api.x.io/orders/43",),])entity_data=JSONMarshaler().marshal_entity(entity)assertentity_data["actions"][0]["fields"][0]["value"]=="42"
Package: lilacai
🌸 LilacNEW: Try theLilac hosted demo with pre-loaded datasets👋 WelcomeLilacis an open-source product that helps youanalyze,structure, andcleanunstructured data with AI.Lilac can be used from our UI or from Python.https://github.com/lilacai/lilac/assets/2294279/cb1378f8-92c1-4f2a-9524-ce5ddd8e0c53💻 InstallTo install Lilac on your machine:pipinstalllilacaiYou can also use Lilac with no installation byforking our public HuggingFace Spaces demo.🔥 Getting startedStart the Lilac webserver from the CLI:lilacstartOr start the Lilac webserver from Python:importlilacasllll.start_server()This will open start a webserver athttp://localhost:5432/.📁 DocumentationVisit our website:lilacml.com💻 Why Lilac?Lilac is a visual tool and a Python API that helps you:Exploredatasets with natural language (e.g. documents)Enrichyour dataset with metadata (e.g. PII detection, profanity, text statistics, etc.)Conceptuallysearchand tag your data (e.g. find paragraphs about injury)Removeunwanted or problematic data based on your own criteriaAnalyzepatterns in your dataLilac runs completelyon deviceusing powerful open-source LLM technologies.💬 ContactFor bugs and feature requests, pleasefile an issue on GitHub.For general questions, pleasevisit our Discord.
Package: lil-aretomo
li'l AreTomoA lightweight Python API forAreTomo.InstallationIt is recommended to install into a fresh virtual environment.pipinstalllil_aretomoUsageimportnumpyasnpimportmrcfilefromlil_aretomoimportalign_tilt_seriestilt_series=mrcfile.read('my_tilt_series.mrc')align_tilt_series(tilt_series=tilt_series,tilt_angles=np.linspace(-60,60,41),pixel_size=1.35,# angstroms per pixelsample_thickness_nanometers=2000,# angstromscorrect_tilt_angle_offset=True,basename='TS_01',# basename for files passed to AreTomooutput_directory='TS_01_AreTomo',skip_if_completed=False# set to True to skip if results from a previous alignment are present)For developersWe use pre-commit to ensure code style and formatting remains consistent.To install the package in editable mode development dependenciespipinstall-e.[dev]pre-commitinstallFlake8, black and isort will then run automatically on each commit.
Package: lilbulb
Dave's lil' bulb - small function lib for creating ilidata.xml files and other utilities...pip install lilbulb>>> import lilbulb

>>> lilbulb.make_ilidata( path='.', project_name='daves_kbs15', owner='signedav@lilbulb.ws', dataset_version='2022-05-11', publishing_date='2022-05-31', linking_models=['KbS_V1_5'])