Saravanan666/pypi-org huggingface dataset:
Package: lilyweight
lilyweightHypixel SkyBlock Weight CalculatorInformationThis is a reimplementation ofhttps://github.com/Antonio32A/lilyweightin Python.
Which is a reimplementation ofhttps://github.com/LappySheep/hypixel-skyblock-weightWritten without any external libraries other thanaiohttpwhich is used to fetch data from the Hypixel API.This requires a Hypixel API key. You may obtain one by logging ontohypixel.netwith your Minecraft client and typing
/api new.CreditsLappySheep- Original author of the calculatorDesco- Ported the calculator to JavaScript.Antonio32A- Ported the calculator to JavaScript.timnoot- Ported the calculator to Python.UsageimportasynciofromlilyweightimportLilyWeight# replace HYPIXEL_API_KEY with your Hypixel API keylily=LilyWeight("HYPIXEL_API_KEY")asyncdefmain():# using a UUIDprint(awaitlily.get_weight("e710ff36fe334c0e8401bda9d24fa121"))# using a usernameprint(awaitlily.get_weight_from_name("timnoot"))# functions for if you wish to see a certain profile instead of the most recently used profileprint(awaitlily.get_weight_from_name("MooshiMochi","Orange"))print(awaitlily.get_weight("0ce87d5afa5f4619ae78872d9c5e07fe","Mango"))# get raw weight from raw data, read the JSDoc for more information# this does not return the uuid and username fields but it does not make any requestsprint(LilyWeight.get_weight_raw({# Skill levels in a dict'enchanting':60,'taming':60,'alchemy':60,'mining':60,'farming':60,'foraging':52,'combat':60,'fishing':60},{# Skill experience in a dict'enchanting':842351020.815073,'taming':2884548541.3704095,'alchemy':125648244.46351068,'mining':510669860.4613964,'farming':200263881.0307403,'foraging':68274086.12834656,'combat':3590591634.1474257,'fishing':227814154.47671163},{# Dungeon completions in a dict'0':22.0,'1':138.0,'2':967.0,'3':100.0,'4':172.0,'5':323.0,'6':578.0,'7':1201.0},{# Master dungeon completions in a dict'1':907.0,'2':40.0,'3':1100.0,'4':873.0,'5':2729.0,'6':1508.0,'7':974.0},1316600722.1128976,# Total experience in the catacombs**{# Slayer experience as kwargs'zombie':34954055,'spider':64968075,'wolf':1526995,'enderman':3575580,'blaze':259305}))asyncio.run(main())Example output of one of the functions, in JSON:{"total":14439.880600696824,"skill":{"base":10346.795817290036,"overflow":173.30267908613297},"catacombs":{"completion":{"base":1226.2725420124711,"master":532.7492424907152},"experience":1057.0997512507508},"slayer":1103.6605685667157}APIIf you aren't using Python or JavaScript and you need an API, take a look atlilyweight-worker.
Package: lim
Lim is an efficient implementation of Generalized Linear Mixed Models
for genomic analysis.InstallThe recommended way of installing it is viacondacondainstall-cconda-forgelimix-inferencecondainstallh5pypandastabulatepytestand thenpipinstalllimRunning the testsAfter installation, you can test itpython -c "import lim; lim.test()"as long as you havepytest.DocumentationRefer to thedocumentationfor detailed information.AuthorsChristoph Lippert-https://github.com/clippertDanilo Horta-https://github.com/HortaOliver Stegle-https://github.com/osteglePaolo Francesco Casale-https://github.com/fpcasaleLicenseThis project is licensed under the MIT License – see theLICENSEfile for details.
Package: lima
limatakes arbitrary Python objects and converts them into data structures
native to Python. The result can easily be serialized into JSON, XML, and all
sorts of other things. lima is Free Software, lightweight and fast.lima at a Glanceimportdatetimeimportlima# a modelclassBook:def__init__(self,title,date_published):self.title=titleself.date_published=date_published# a marshalling schemaclassBookSchema(lima.Schema):title=lima.fields.String()published=lima.fields.Date(attr='date_published')book=Book('The Old Man and the Sea',datetime.date(1952,9,1))schema=BookSchema()schema.dump(book)# {'published': '1952-09-01', 'title': 'The Old Man and the Sea'}Key FeaturesLightweightlima has only a few hundred SLOC. lima has no external dependencies.Fastlima tries to be as fast as possible while still remaining pure Python 3.Well documentedlima has a comprehensive tutorial and more than one line of docstring per
line of Python code (seedocumentation).Freelima is Free Software, licensed under the terms of the MIT license.RequirementsPython 3.3 or newer. That’s it.Installation$ pip install limaSee thedocumentationfor more comprehensive install instructions.
Package: lima1983analysis-sebastian-achim-mueller
A python implementation of the equations 9, and 17 proposed by Li and Ma in 1983.@article{lima1983analysis,title={Analysis methods for results in gamma-ray astronomy},author={Li, Ti-Pei and Ma, Yu-Qian},journal={The Astrophysical Journal},volume={272},pages={317--324},year={1983}}The names of the variables are adopted from Li and Ma.Installpipinstalllima1983analysis_sebastian-achim-muellerSignificance SEstimate the significanceSof a detection.estimate_S_eq9(N_on,N_off,alpha)estimate_S_eq17(N_on,N_off,alpha)Signal-Counts N_sEstimate how much signal-countsN_sare required to claim a detection.estimate_N_s_eq9(N_off,alpha,S)estimate_N_s_eq17(N_off,alpha,S,margin=1e-6,max_num_iterations=1000,N_s_start=None)
Package: limalist
MistDoxygenhttps://www.geogebra.org/m/fprxd9wa- Sphinxhttps://www.geogebra.org/m/uyfx4ykb- Breathehttps://www.geogebra.org/m/sdrubcuq- sphinx_rtd_themehttps://www.geogebra.org/m/eakkj9dq- sphsdinx_rtd_themehttps://www.geogebra.org/m/qgdnas6a- Doxygenhttps://www.geogebra.org/m/my5ejjej- Sphinxhttps://www.geogebra.org/m/qr9fvygv- Breathehttps://www.geogebra.org/m/sku5uj9f- sphinx_rtd_themehttps://www.geogebra.org/m/yba3bfmu- sphsdinx_rtd_themehttps://www.geogebra.org/m/avbs3n5r- sphsdinx_rtd_themehttps://www.geogebra.org/m/mrgnxj9pThis Python syntax is virtually identical to the C++ code you would write for a program using the Mist library, as you can see in the examples directory.BuildingmistThese packages are required to build the mist library:Doxygenhttps://www.geogebra.org/m/fprxd9wa- Sphinxhttps://www.geogebra.org/m/uyfx4ykb- Breathehttps://www.geogebra.org/m/sdrubcuq- sphinx_rtd_themehttps://www.geogebra.org/m/eakkj9dq- sphsdinx_rtd_themehttps://www.geogebra.org/m/qgdnas6a- Doxygenhttps://www.geogebra.org/m/my5ejjej- Sphinxhttps://www.geogebra.org/m/qr9fvygv- Breathehttps://www.geogebra.org/m/sku5uj9f- sphinx_rtd_themehttps://www.geogebra.org/m/yba3bfmu- sphsdinx_rtd_themehttps://www.geogebra.org/m/avbs3n5r- sphsdinx_rtd_themehttps://www.geogebra.org/m/mrgnxj9p
Package: limao
Limão calculates projected direct sunlight intensities over time for arbitrary building elevations. See how direct sunlight varies throughout the year for windows at different orientations, and heights above ground level, or compare sunlight intensities for different locations.Primarily this depends on the the building orientation, the latitude, and the time of the year, but also on occluding obstacles. Limão incorporates time and location dependent sunlight intensity projections, with digital surface and terrain maps, to determine an estimated overall intensity as a function of time.ApplicationsFor illumination of a wall (for example, for photovoltaic panel placement), Limão provides predicted sunlight intensities on a user-defined vertical plane in real space:Here the intensities are averaged over the year, and the variation is mostly due to occluding buildings. The intensities over time for a single point(lat, lon, height)can also be queried:This is useful for comparing the intensity from different directions, and the produced plots indicate the maximum and minimum intensity during daylight hours.InstallationEither from the PyPI releasepipinstalllimaoor the bleeding edge via GitHubgitclonegit@github.com:dpohanlon/limao.gitcdlimao
pipinstall.UsageLimão requires digital surface models (DSM) and digital terrain models (DTM) for the region of interest, in addition to the latitude and longitude. For the UK these are available fromDEFRA(direct link), and are derived from satellite LIDAR measurements. Latitude and longitude can be obtained from Google Maps, and must correspond to a point on the DSM and DTMs.Currently this assumes UK DSM/DTMs, as the lat/lon is converted into UK Ordnance Survey map coordinates.Limão ships with a standalone executable and as a library - here we'll just use the command line interface. All use cases require the latitude, longitude, a DSM, and a DTM:limao--lat52.198--lon0.135-sTL45ne_LZ_DSM_2m.tif-tTL45ne_DTM_2m.tifBy default this will just produce the intensity from the north and south on the point defined by the latitude and longitude coordinates. To produce the 2D projection, additionally pass the--projargument, with optional horizontal and vertical extent of the surface in metres:--proj--horizontal20--vertical8
Package: limber
Create a file calledlimber.yamlwith the following configuration:cloud:
    provider: google
    project: "<project name>"
    region: "europe-west1"
    key_file: "<path to key_file>"
    default_bucket: "<gcp cloud storage bucket name>"In the main directory run:python -m limber initpython -m limber terraform loginpython -m limber planpython -m limber applyGoogle Cloud APIs enabledCloudFunctions APIPubSub APICloudScheduler APICloud Build APISecret manager APIOtherCreate app engine in your project:https://console.cloud.google.com/appengineService account needed roles:Pub/Sub EditorService Account UserCloud Scheduler AdminCloud Functions DeveloperStorage AdminSecret Manager Admin
Package: limberframework
READMENote: This repository contains the core code of the Limber Framework. If you want to build an application using Limber, visit the main Limber repository.Limber Framework is a web application framework with the goal of simplifying the development of web applications. It is based on the FastAPI framework and provides utilities to handle common tasks performed when developing web applications.ContributingThank you for considering contributing to the Limber Framework! The contribution guide can be found in thehere.Code of ConductTo ensure that the Limber community is welcoming to all, please review and abide by theCode of Conduct.LicenseThe Limber Framework is open-sourced software licensed under theMIT license.
Package: limbs
like mime, but simpler - a simple, human-readable file formatSimple I/O to flat text files with a header (composed of fields) and a body (arbitrary bytes).
Ideal for no-hassle storage of some metadata and some large data that doesn’t fit nicely into
things like JSON (which becomes really hard to edit in a text editor if there’s a 2 MB blob of
numbers somewhere in it).While the general format is the same as MIME/”internet messages” and most if not all “internet
messages” can be parsed by this module there are a few distinctions. For starters the header
is always UTF-8 encoded (which doesn’t clash with the 7-bit-ASCII-padded-to-bytes used by
internet messages). Field names are mangled to Python identifiers by stripping surrounding
whitespace and dashes, turning them all lower case, replacing dashes (-) with underscores (_)
and appending another underscore if the result is a reserved Python keyword.
If the result of this is not a valid Python identifier, the field name is invalid.When writing files from objects this translation is reversed (underscores replaced with dashes,
surrounding dashes are removed and the result is title-cased).Values are simple strings.The optional body of a file is, in contrast to the header, not UTF-8, but rather treated as
a byte string. If the body is text-like usage of UTF-8 is strongly advised.Line endings in the header can be UNIX style (n, preferred) or Windows style (rn).
Windows style endings are always converted to UNIX style endings when reading, and only UNIX
style endings are written.Primary APIs aredump,dumps,loadandloads.
Package: limbus-components
Limbus-Components: Set of basic components for LimbusThis package is part ofLimbusand contains a bunch of useful components.InstallationThis package can be automatically installed by Limbus. If you want to install it manually, you can do it with:gitclonehttps://github.com/kornia/limbus-componentscdlimbus-components
pipinstall-e.For development purposes, you can install it with:pipinstall-e.[dev]# also installs limbusDev RequirementsTo add new components that must be done in the folderlimbus_components_devand then in order to release the changes you must run the thereleasescript.All the__init__.pyfiles inside the folderlimbus_components_devmust have the imports following the next patterns:from.import<component_module># to import modulesfrom.<component_module>import<component_class0>,<component_class1>,...# to import components# IMPORTANT NOTE: imports with () are not allowedif they are not following these patterns, thereleasescript will fail. Check examples in the folderlimbus_components_dev.Steps to run thereleasescript:If you changed an existing module, first of all you need to remove the module inlimbus_componentsfolder.Run thereleasescript:python scripts/release.pyCheck that the changes in thelimbus_componentsfolder are the expected ones.commit the changes and push them to the repo.
Package: lim-cli
Thelimapp is a command line interface that provides a common
means of interactively accessing multiple network security tools
and data sources, or allowing you to do so from your own scripts
or from existing security event processing and automation platforms.limuses theopenstack/cliffframework to organize features into related
groups of subcommands with lots of built-in help internally documenting their use.
It produces output in clean tabular form, or in several other data formats that
can feed into yet other network security tools or automation platforms.Usinglim, you can learn skills a SOC analyst needs to understand how
malicious software communicates on the network, or script the post-processing
of network traffic traffic captures from a honeypot or your own network.License: Apache 2.0 LicenseDocumentation:https://lim-cli.readthedocs.io/en/latest/FeaturesUses theopenstack/cliffcommand line framework.Serves as a CLI for theCTU Malware Capture Facility Datasets.Serves as a CLI for In-Q-Tel Labs’Packet Caféservice.Provides basic reporting onPacket Caféworker output.Provides basicPCAPfile manipulation capabilities.Future features include plotting and graphing of output
from PCAP processing.
Package: limcv
图像识别工具-limcv功能用于自动化测试中，定位icon图片在手机中App截图中的位置，从而进一步针对位置进行点击、输入、滑动操作。因为是基于图片做的控件定位，所以此工具适用于Android、iOS、小程序、H5自动化测试的应用。支持多个目标的识别。安装pip3 install limcv使用说明使用代码importlimcvres=limcv.find(im_source_path,im_search_path)print(res)输出[{'result':(996,584),'rectangle':((835,408),(835,760),(1158,760),(1158,408)),'confidence':1.0},{'result':(998,1654),'rectangle':((837,1478),(837,1830),(1160,1830),(1160,1478)),'confidence':1.0}]参数解释result目标中间点rectangle矩形位置四个点坐标，分别为左上、右上、右下、左下confidence置信度感谢limcv是基于aircv改造，增加了对多分辨率识别的支持。TODO增加图片比对，用于兼容性测试中，检测App的显示效果是否符合预期。
Package: limeade
pip install limeadeLive module editing and development enhancement supporting hot reloading for
Python 3.4+.UsageEquip your app with the ability to calllimeade.refresh()on
command (e.g. via hotkey) and invoke after making edits to your source files!FeaturesSpeeds up development: Don’t restart your app, refresh your Python code!Automatic: Just call tolimeade.refresh(); Limeade does the rest!Thorough: Updates your classes/functions even if instantiated/referenced!Object reuse: Mutable objects are modified in-place; doesn’t breakis!Good source of vitamin ACaveatsLimeade is still experimental. I would be deeply appreciative if you could use
it and find ways in which it breaks!The API is not yet stable. I will keeplimeade.refresh()working, but
that is the extent of the public API for the time being.Some things which it cannot (even theoretically) handle are:Closures: These aren’t defined at module reload time; even so, it’s unsafe
to mutate code within instantiated closures. Closures will be updated the
next time they are instantiated.Changes in metaclass: A class can be mutated if its type (metaclass) isn’t
changing, but Python does not permit changing the type of an existing class.Threads: This is not incompatible with threading, but note that threads
may observe the program in an inconsistent state if they’re running during a
refresh operation. Make sure your threads are out of harm’s way!Some things which it may handle in the future:Renaming functions/classes: Currently, old and new definitions are matched
via the qualified name. Heuristics can be used to match definitions when they
are under different names, but this is not yet done.Changes in __slots__ attribute: New slot descriptors cannot be created;
however, Limeade could invent its own descriptors for new slots and insert
those. They wouldn’t be as efficient but they would get the job done.
Descriptors for deleted slots can be cached in case the slot is brought back
in a future mutation.Changes in base classes: Initial experiments show that Python is much more
picky about this one would expect.Automatic rollback: In case of refresh/mutate failure, it would be great
to rollback everything to the state it was in before, so that the running app
isn’t left in a half-updated state.LicenseAll code licensed under 3-clause BSD.Logo licensed under CC-BY-SA 4.0 with attribution to Elizabeth Reedy.
Package: lime-comb
Lime-combThis tool allows:Encrypt messages directly from your terminal.Push & pull keys to central registryencryptExample usage:$lime-combenc-t${SOMEONES_EMAIL}-m"db_qa_password: SUPERSECRET1"-m"dq_preprod_password:SUPERSECRET2"-----BEGINPGPMESSAGE-----
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3W9E=+MK9
-----ENDPGPMESSAGE-----
Package: limedev
LimeDevLimeDev is collection tools for Python development. These tools are more or less thin wrappers around other packages.Table of ContentsQuick start guideThe first stepsInstallingImportingQuick start guideHere's how you can startThe first stepsInstallingInstall LimeDev with pippip install limedevImportingImport name is the same as install name,limedev.importlimedevChangelog0.4.1 2023-11-06Fixed configs0.4.0 2023-11-06Updated profiling structurefunction_cliPython version range moved from 3.9 -3.11 to 3.10-3.120.3.0 2023-08-27Change to testing interface0.2.2 2023-08-27Fix to performance test deleting previous performance data0.2.1 2023-08-06Performance results dump sorting fix0.2.0 2023-08-06BugfixesCleaner structureReworked readme build0.1.0 2023-05-04Initial assembly of the tools
Package: lime-nda
NDAThis includes two files nda_version_8_0.py & nda_functions.pynda_version_8_0.pyFile to decode data from .nda file to usable form.nda_functions.pyFile that includes lot of functions that allows users to get data from the decoded data. Below are the function names to call and the arguments to pass.recordsArgs: (nda file path)Returns a Dataframe record-wise for the nda file.Add second arguement as True if you want the coloumns renamed (data units converted) to same as that produced in the NEWARE excel filecycleArgs: (records dataframe or nda file path)When passed an nda file or the records data, it returns Cycle-wise data identical to the cycle sheet in the excel file of the test.stepArgs: (records dataframe or nda file path)When passed an nda file or the records data, it returns Step-wise data identical to the cycle sheet in the excel file of the test.get_process_nameArgs: (nda file path)returns Recipe Name for passed NDA fileget_barcodeArgs: (nda file path)<br
returns Barcode for passed NDA fileget_start_timeArgs: (nda file path)returns Start Time for passed NDA file
Package: limespy
============LIMES Py============LIMES Py provides an interface to LIMES link discovery framework.it most useful for tasks involving linking CSV files to existing ontologies.Typical usage often looks like this::#!/usr/bin/env pythonfrom limespy import CsvLinkerCsvLinker.link_my_csv(mycsv, dbpedia)Contributors=========* Ivan Ermilov (AKSW/BIS University of Leipzig)Notes=========The source code is available from:https://github.com/earthquakesan/limes-py
Package: lime-stability
Stability Indices for LIMEThis project is about measuring the stability of explanations obtained through theLIMEtool.LIME (Local Interpretable Model-agnostic Explanations) is a quite well-established albeit recent technique, which enables to understand Machine Learning model's reasoning.For a deeper understanding of the technique, we suggest to read thepaperby Ribeiro, as well as to watch itsvideo.Even though LIME is a great tool, itsuffers from a lack of stability, namely repeated applications of the method, under the same conditions, may obtain different results.
Even worse, many times the stability issue is not spotted at all by the practitioner, e.g. when just a single call to the method is performed and the result is considered to be okay without further checks.An explanation can be considered reliable only if unambiguous.Guided by this notion, we developed a pair of complementary indices to evaluate LIME stability:Variables Stability Index (VSI)andCoefficients Stability Index (CSI).The method creates repeated LIME explanations for the same data point to be explained.The VSI index checks whether the different LIMEs give back the same variables as explanation.The CSI index controls whether the coefficients of each variable, under the repeated LIME calls, can be considered equal.The indices can be calculated on every trained LIME method. Both of them range from 0 to 100, where higher values mean that the tested LIME is stable. They are designed to be used together, since each one tests a different concept of stability.Using the indices will bring enhanced confidence in LIME's results: the practitioner may find out possible instability in its trained LIME, or he may vouch for its consistency.To get a deeper understanding about the approach, we suggest reading the paperStatistical stability indices for LIME: obtaining reliable explanations
for Machine Learning models [1].[1] Visani, Giorgio, et al. "Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models." arXiv preprint arXiv:2001.11757 (2020).InstallationInstalling through pip package manager:pipinstalllime-stabilityPrerequisitesList of dependencies:limestatsmodelssklearnnumpyTypes of data to use the indices onThe stability indices inlime_stabilityare currently available only for tabular data (LimeTabularExplainer class). Although the theory behind the indices allow for their usage also with other types of data, but the implementation is not available yet.AuthorsGiorgio Visani:institutional pageAcknowldegmentsWe would like to thankCRIF S.p.A.andUniversità degli Studi di Bologna, which financially supported the project.
Package: lime-stable
This library provides a set of tools to fit lines in astronomical spectra. Its design aims for a user-friendly workflow
for both single lines and large data sets. The library provides tools for masking, detecting, profile fitting
and storing the results. . The output measurements are focused on the gas chemical and kinematic analysis.Please check theonline documentation.InstallationThe library can be installed directly from itsPyPiproject page running the command:pip installlime-stableDevelopmentLiMe is currently in a beta release. Please check itsgithubfor the latest version news and tutorials.
Any comment/issue/request can be added as an issue on thegithubpage.