Saravanan666/pypi-org huggingface dataset:
Package: genSearch
Primitive Genetic Search AlgorithmOverviewGiven a function,decoder, that establishes a bijection between solution-type objects and bitstrings of a given length, and acostfunction, which maps solution-type objects to real numbers, this algorithm optimizes thecostfunction.Connection to EvolutionThis algorithm solves problems by modeling evolutionary competition. Elements in a problem's domain are mapped to bitstrings, analogous to DNA sequences, viadecoder. A random population is generated and winners are chosen via tournaments using each individual'scostas its "fitness" (lower fitness wins). Winners then reproduce: offspring are generated selecting from two winners' bits (genes), and occassionally switching them, analogous to mutation. The algorithm iterates this process a set number of times. When done, it returns the best individual of the final generation.InstallationActivate your virtual environment and run:pip install genSearchUsageThe algorithm is contained in the function:genetic_simulation(decoder, cost)which returns as a tuple an object that minimizes `cost` and `cost` evaluated at this input.Here is an example of the algorithm in action to find the square root of 2 on the interval [0, 10]:import genSearch

# bitstring to value in [0, 10]
def decoder(x):
    # Convert to base 10 equivalent
    xbase10 = int(x, 2)
    # Find the number divisions of the interval
    resolution = 2**len(x)
    # Return value scaled to interval
    return 10 * xbase2 / resolution

# function to be minimized
def cost(num):
    return (num**2 - 2)**2

solution = genSearch.genetic_simulation(decoder, cost)

print(f"Solution: {solution[0]}")
print(f"Cost: {solution[1]}")To use this algorithm, first import the package into the working python project usingimport genSearchThen, define adecoderwhich converts binary strings to objects in the domain ofcost, and acostwhich assigns a real number to objects in its domain. The object type of the domain is to be defined by clients according to the problem at hand.Once these parameters are defined, you can run the function and store its result:import genSearch

def decoder(x):
    ...
    return object

def cost(object):
    ...
    return cost 


result = genSearch.genetic_simulation(decoder, cost)Note thatresult[0]contains the approximate solution to your problem andsolution[1]contains the cost associated with this solution.HyperparametersThe simulation is controlled by the following variables.## Population size
N_POP = 100  
## Bitstring length      
N_LEN = 64         
## Crossover rate     
R_CROSS = 0.5    
## Bit mutation rate       
R_MUT = 1.0 / N_LEN
## Number of generations      
N_GEN = 100Clients may access and modify these via the following:# N_POP
genSearch.getPop()
genSearch.setPop(new_x)

# N_LEN
genSearch.getLen()
genSearch.setLen(new_x)

# R_Cross
genSearch.getCross()
genSearch.setCross(new_x)

# R_Mut
genSearch.getMut()
genSearch.setMut(new_x)

# N_Gen
genSearch.getGen()
genSearch.setGen(new_x)The setter functions returnTrueupon execution.CreditThis package is inspired byJason Brownlee's algorithm. This package reorganizes and simplifies his algorithm to facilitate versatile use.
Package: genseq
Genseq is sequence data structure that lazily consumes any iterable,
including a generator, so that you can enjoy the benefits of both
delayed evaluation, and the slicing and random access of lists.TheGenseqclass implements the collectionsSequenceABC,
so the standard methods of using an immutable sequence are all available.UsageInstall using Pip:pipinstallgenseqThen wrap your generator withgenseq:>>>fromgenseqimportgenseq>>>@genseq...defmyiter(stop):...foriinrange(stop):...yieldi...>>>myiter(5)[2]2>>>Or use theGenseqclass on any iterable:>>>fromgenseqimportGenseq>>>Genseq(range(5))[3]3>>>Happy indexing!
Package: genser
The GenSer package (i.e. Generalised Serialisation) contains the set of functions to perform the dimension transformation of the numerical dataset. To use the package you should have a dataset of non-negative integer values. Having n features in your dataset, you may transform it to m features and, after your work with data, return back to n features. The following functions available:dim_step_down(data, powers):
data is a list of lists;
powers is list of powers of the features, i.e. how many different values can the feature take.dim_step_up(data, powers):
the same description as for dim_step_down.transform_to(data, d):
Transforms the dimension of the given dataset to the d valueArguments:
data (list of lists): the input dataset without labels
d (int): the target dimension

Outputs:
A dataset of dimension d, 
A list of transformation hints: a tuple (powers, tdict) for every step.transform_out_down(data, rlist):
Transforms back the dimension of the given dataset
when the dimension had been increased by transform_toArguments:
data (list of lists): the input dataset without labels
rlist (list): the list resulted from transform_to

Outputs:
A restored dataset, 
A powers of the restored dataset (may differ from the 
    initial powers of transform_to argument data).transform_out_up(data, rlist):
Transforms back the dimension of the given dataset
when the dimension had been decreased by transform_toArguments:
data (list of lists): the input dataset without labels
rlist (list): the list resulted from transform_to

Outputs:
A restored dataset, 
A powers of the restored dataset (may differ from the 
    initial powers of transform_to argument data).Additional information available directly from the author by request on emailshoukhov@mail.ru
Package: genshi_compiler
Genshi Compiler allows for compiling simple Genshi XML templates
to pure Python code. You can render the whole template or any of
the template functions by importing generated code. The generated
code is typically ~40x faster than rendering the same via Genshi.
There is a cost of this speedup. Some of Genshi’s dynamic
features are not available, most notably anything that depends
on a template loader (xi:include, xi:fallback), the XML element
tree representation (py:match) or the token stream (filters).
Language translation (i18n) support is currently limited
to simple text inside the translatable elements and attributes
and the i18n:msg directive.
Package: genshin
genshin.pyModern API wrapper for Genshin Impact & Honkai Impact 3rd built on asyncio and pydantic.Documentation:https://thesadru.github.io/genshin.pyAPI Reference:https://thesadru.github.io/genshin.py/pdoc/genshinSource Code:https://github.com/thesadru/genshin.pyThe primary focus of genshin.py is convenience. The entire project is fully type-hinted and abstracts a large amount of the api to be easier to use.Key features:All data is in the form of Pydantic Models which means full autocompletion and linter support.Requests are significantly faster thanks to proper usage of asyncio.Chinese and Engrish names returned by the API are renamed to simpler English fields.Supports the majority of the popular endpoints.Cleanly integrates with frameworks like FastAPI out of the box.Note: This library is a successor togenshinstats- an unofficial wrapper for the Genshin Impact api.RequirementsPython 3.8+aiohttpPydanticpip install genshinExampleA very simple example of how genshin.py would be used:importasyncioimportgenshinasyncdefmain():cookies={"ltuid":119480035,"ltoken":"cnF7TiZqHAAvYqgCBoSPx5EjwezOh1ZHoqSHf7dT"}client=genshin.Client(cookies)data=awaitclient.get_genshin_user(710785423)print(f"User has a total of{data.stats.characters}characters")asyncio.run(main())ContributingAny kind of contribution is welcome.
Please readCONTRIBUTING.mdto see what you need to do to make a contribution.
Package: genshin-account-switcher
genshin-account-switcherSimple account switcher for Genshin Impact on Linux.InstallationYou need to have Python 3.10+ installed.$pipinstallgenshin-account-switcherCLI: UsageRegistering an account:$genshin-account-switcherregister
Successfullyregisteredaccount'999999999'.Switching between accounts:# No argument -> shows list of registered accounts$genshin-account-switcherswitchusage:genshin-account-switcherswitch[-h][uid]positionalarguments:uid

options:-h,--helpshowthishelpmessageandexitAvailableUIDs:
*[0]888888888*[1]999999999✔# Pick one via the UID itself or the shortcut$genshin-account-switcherswitch888888888Successfullyswitchedtoaccount'888888888'$genshin-account-switcherswitch1Successfullyswitchedtoaccount'999999999'Naming accountsSince UIDs are confusing you can name your account:$genshin-account-switcherregister--name"Main Account"Successfullyregisteredaccount'888888888'underthe8name'Main Account'.# You'll now see the name when trying to switch:$genshin-account-switcherswitchusage:genshin-account-switcherswitch[-h][uid]positionalarguments:uid

options:-h,--helpshowthishelpmessageandexitAvailableUIDs:
*[0]MainAccount(888888888)*[1]999999999✔# You can also use the set-name command instead of doing this while registering$genshin-account-switcherset-name999999999"Alt Account"SuccessfullysavedAltAccount(999999999)GUI: UsageYou can open a graphical user interface by executing the "gui" sub command:$genshin-account-switcherguiLicenseGNU General Public License v3
Package: genshin-data
Genshin DataPython version ofhttps://github.com/dvaJi/genshin-data.Installationpipinstallgit+https://github.com/Victorsitou/genshin-dataUsagefromgenshin_dataimportGenshinData,Languageimportasyncioasyncdefmain():data=GenshinData(Language.spanish)characters=awaitdata.characters()fori,characterinenumerate(characters):print(f"{i+1}:{character.name}")if__name__=="__main__":asyncio.run(main())
Package: genshinhelper
genshinhelperA Python library for miHoYo bbs and HoYoLAB Community.InstallationVia pip:pip install genshinhelperOr via source code:git clone https://github.com/y1ndan/genshinhelper2.git
cd genshinhelper2
python setup.py installBasic Usageimportgenshinhelperasghcookie='account_id=16393939; cookie_token=jPjdK4yd7oeIifkdYhkFhkkjde00hdUgh'g=gh.Genshin(cookie)roles=g.roles_infoprint(roles)
Package: genshin-mummy
GenshinMummy简介(ﾉﾟ ▽ ﾟ)ﾉ   : 原神妈咪是一个基于计算机视觉和 RPA 的原神游戏辅助项目，它旨在...(#｀皿 ´)   ： 说人话！(；´д ｀)ゞ ：好吧，它是一个中道崩殂的项目。崩殂的原因很简单，游戏的快乐果然还是在于参与，如果什么事情都自动化了，就失去了游戏的初心。(#｀皿 ´) ：说人话！d(´ω ｀*)  ：好吧，就是去年国庆写了好多天脚本。突然发现自己的开发时间，已经超过了手动去整理的时间。QAQ 我宝贵的假期有限！(#｀皿 ´)  ：那这个项目还有什么意义吗？(ﾉﾟ ▽ ﾟ)ﾉ  ：实用的脚本还是有的！把那些垃圾圣遗物从堆积如山的仓库里分离出来，通过加解锁分离想要的和不想要的圣遗物。使用方式教程是已经假定你已经安装了 Python3 的情况下。安装工具包pipinstallgenshin-mummy使用管理员权限，打开你的终端。管理员、管理员、管理员！！！重要事情说三遍。~原神，启动。打开圣遗物界面，筛选你想加解锁的类别，然后选中最左上角的圣遗物。切出你的终端，执行下面的命令。# ヽ(`Д´)ﾉ  敲击此命令时候，要带上你的委屈与不甘！！！fuck-shit-artifact切换回原神。双手脱离鼠标键盘，请不要和它抢鼠标。打开 B 站，刷视频去吧。当它全部完成的后，你可以在桌面上看到一个 GenshinMummy 的文件夹，里面存放着日志文件。其中有一个 csv 的表格文件，里面记录了你的圣遗物信息。也许你会稀罕它，如果不稀罕，就放心的删除吧。圣遗物加解锁逻辑默认的加解锁策略是非常保守的，它会锁定绝多数的圣遗物，只把垃圾到家的圣遗物解锁，它的逻辑是这样的。等级大于 0=>锁非五星=>不锁沙、杯、帽主词条为类别独有词条=>锁双暴词条=>锁初始四词条 且不要存在所有小攻防命都有=>锁小攻击、小防御、小生命大于等于两个=>不锁其余情况保险起见=>锁算法参考《原学》必修四：圣遗物筛选与强化，主流套装分析，萌新收藏必看！什么圣遗物值得强化？怎么快速整理出狗粮？原神圣遗物秒懂攻略自定义加解锁逻辑在桌面创建一个叫做 GenshinMummy 的文件夹。在里面创建一个 Excel 文件，xlsx 尾缀哦~在 Excel 里面配置你的加解锁规则，如下：圣遗物类型条件等级条件星级条件主词条条件副词条条件期望结果>0锁<5不锁有防御力，有防御力百分比不锁有暴击率，有暴击伤害锁时之沙，理之冠，空之杯是元素精通锁时之沙不是水元素伤害加成不锁锁示例规则解释等级大于 0 的圣遗物=>锁定非五星的圣遗物=>不锁定有防御力，有防御力百分比的圣遗物=>不锁定有暴击率，有暴击伤害的圣遗物=>锁定时之沙，理之冠，空之杯，是元素精通的圣遗物=>锁定时之沙，不是水元素伤害加成的圣遗物=>不锁定其余情况=>锁定Excel 配置说明表头模板是固定的请务必按照上方表头。圣遗物类型条件 | 等级条件 | 星级条件 | 主词条条件 | 副词条条件 | 期望结果每一行称为一条策略，越靠前的策略越先执行，一旦满足当前策略，后续策略不再执行。每一条策略必须有一个期望结果，期望结果只有两种，锁和不锁。如果条件为空，则代表该条件没有限制。比如圣遗物类型条件为空，则代表五种类型都可以。圣遗物类型条件：时之沙，理之冠，空之杯，生之花，死之羽。可以填写多条，用逗号，隔开。等级条件和星级条件，只支持，>，<，=，三种判断符号，判断符号后必须紧跟整数值。主词条的判断条件，只支持，是，不是，两种判断符号。副词条的判断条件，只支持，有，没有，两种判断符号。词条包括：'生命值', '生命值百分比', '攻击力', '攻击力百分比', '防御力', '防御力百分比',
'暴击伤害', '暴击率', '元素精通', '元素充能效率', '火元素伤害加成', '水元素伤害加成',
'冰元素伤害加成', '雷元素伤害加成', '草元素伤害加成', '风元素伤害加成', '岩元素伤害加成',
'物理伤害加成', '治疗加成'GPU 加速看到这，你大概也是个程序原，所以我就长话短说~。你只需要重装对应的 Paddle 版本即刻。只是需要有些额外工作是，你需要自己解决 CUDA 的安装与兼容。# 卸载CPU版本的PADDLEpipunintallpaddlepaddle# 安装GPU版本的PADDLEpipinstallpaddlepaddle-gpuGPU 版本依赖：CUDA10.1 / CUDA10.2 + CUDNN 7.6PaddleOCR 参考官方文档常见问题Q：加解锁途中出现了，加解锁提示页怎么办？A：勾选不再提示，然后重新执行脚本即可。
Package: genshinpyrail
GenshinPyRailA module that allows you to create cards for your projects based on the games Honkai Star Rail and Genshin ImpactInstall:pip install genshinpyrailUsage example:fromgenshinpyrailimportgenshinpyrailimportasynciocookie={"ltuid_v2":YOU_ltuid_v2,"ltoken_v2":"YOU_ltoken_v2","ltmid_v2":"YOU_ltmid_v2"}module=genshinpyrail.GenPyRail(cookie,"en")asyncdefmain():client=awaitmodule.set_by_uid(700649319)print(client.game)print(client.uid)print(client.hoyolab_id)help=awaitclient.get_ascension(character_id=1306)help.card.show()asyncio.run(main())[!WARNING]
The module is still in development and beta testing, there is no normal documentation, so if you want to find out what this module can do, open this file:HELPWell, or open the code:genshinpyrail.py
Package: genshinrecipes
genshin-recipe-data/genshinrecipesA repository to keep a current copy of Genshin Impact's cooking recipe data. Updated on a best effort basis.Why?I'm making tools for myself as someone trying to be a completionist in a gacha game, despite how stupid it sounds.This was originally inenkanomiyabut since I am making another project using this data, it makes more sense to share a repository between the 2 projects.For the record, all of this was typed out manually for the most part. There are future plans to make the data much better to contribute to, see#2.Using it in PythonInstallgenshinrecipesfrom PyPI to use it in your (Python) project. There are currently 2 functions available.genshinrecipes.return_data()Returns the full JSON object.genshinrecipes.filter_by_rarity(rarity: int)Returns all recipes that are of a certain rarity (1-5).Using it in not PythonSimply use the data ingenshinrecipes/recipes.json(for example, you could use a git submodule). It's a big JSON object basically looking like this:{"food-name":{"name":"Food Name","rarity":1,"location":"Where you can find it"}// and it goes on... and on}LicenseUnlicense, public domain, whatever you want to call it. You are free to use this data however you see fit, because I didn't make the game anyway.
Package: genshi_view
Setting Up A New Project========================from start to editing code0. Install virtualenv [Optional but highly recommended]::easy_install virtualenv1. Make a virtualenv::virtualenv myproject2. Install genshi_view::cd myproject. bin/activatemkdir srccd srchg clone http://k0s.org/hg/genshi_view # or `easy_install genshi_view`cd genshi_viewpython setup.py developcd ..3. Instantitate a template.  genshi_view is a pastescript templatethat will get you a hello world application OOTB::paster create -t genshi_view your_project_nameAnswer the questions it asks you regarding the template variables.4. Serve your application to see that it works::cd your_project_namepaster serve your_project_name.iniNavigate to the URL that paster displays5. You're done!  Helloworld works.  Now you just have to edit thesource code:- templates are in your_project_name/templates; there is one by default- static resources are in your_project_name/static- the request dispatcher is at your_project_name/dispatcher- the request handlers are at your_project_name/handlers;  if you addmore, don't forget to add them to dipatcher.py's imports and toself.handlers in Dispatcher's __init__ function
Package: gensim-evaluations
gensim-evaluationsThis library provides methods for evaluating word embedding models loaded withgensim. Currently, it implements two methods designed specifically for the evaluation of low-resource models. The code allows users to automatically create custom test sets in any of the 581 languages supported byWikidataand then to evaluate on them using theOddOneOutandTopkmethods introduced in thispaper.For more details visit thegithub repository
Package: gensimplus
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!To DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.✨🍰✨
Package: gensim-plus
.. gensim-plus documentation master file, created bysphinx-quickstart on Wed Jul 25 13:41:41 2018.You can adapt this file completely to your liking, but it should at leastcontain the root `toctree` directive.Welcome to gensim-plus's documentation!=======================================Contents:.. toctree:::maxdepth: 2Indices and tables==================* :ref:`genindex`* :ref:`modindex`* :ref:`search`
Package: gensio-modems
gensio-modemspython-based ham-radio modem utilities (ax25, kiss, vara) built usinggensio.Currently enough functionality is provided to host a Winlink RMS
gateway, and connect to it (and others) viapattelnet sessions.Installgensio-modemspython3 -m venv ./.venv
. .venv/bin/activate
pip install gensio-modemsalternatively, if you don't want a virtualenv,pip install --user gensio-modemsvaraRMS/Winlink capable gateway for VARA and VARA FMpython gensio_modems.vara \
    -l KF7HVM-10 \
    --banner "gensio+vara experimental rms gateway" \
    --gateway "rms,tcp,cms.winlink.org,8772"ax25a simple ax25 listener-to-gensio proxy, useful for passing off to BBS or
gateway software.Winlink RMSpython -m gensio_modems.ax25 \
    -l KF7HVM-10 \
    -k tcp,localhost,8011 \
    --banner "gensio+ax25 experimental rms gateway" \
    --gateway "rms,tcp,cms.winlink.org,8772"as a serviceDescription="Service for gensio rmsgw"
After=direwolf@dw1.service
BindsTo=direwolf@dw1.service

[Service]
RestartSec=5
Restart=always
User=rmsgw
ExecStart=/home/rmsgw/.gensio/bin/python -m gensio_modems.ax25 \
    -l N7DEM-12 \
    --banner "gensio+ax25 experimental rms gateway" \
    --gateway "rms,tcp,cms.winlink.org,8772"

[Install]
WantedBy=direwolf@dw1.service multi-user.targetproxy.pyExpose an ax25 client connection via a telnet listener.python -m gensio_modems.proxy \
    --mycall KF7HVM-1 \
    -k tcp,localhost,8010 \
    --listen tcp,localhost,8772In pat, connect via telnet topat:KF7HVM-10@localhost:8772.The "password" is taken as the gateway to connect to!
Package: genslug
Slug GeneratorGenerate a Slugified stringScreenshotInstall$ pip install genslugUsageimportgenslugprint(genslug.slug("Unicorns are awesome"))#=> unicorns-are-awesomeprint(genslug.slug("Unicorns-are awesome"))#=> unicorns-are-awesomeprint(genslug.slug("Unicorns :are: awesome"))#=> unicorns-are-awesomeGet HelpThere are few ways to get help:Pleasepost questions on Stack Overflow. You can open issues with questions, as long you add a link to your Stack Overflow question.For bug reports and feature requests, open issues.For direct and quick help, you canemail me.How to contributeHave an idea? Found a bug? Seehow to contribute.Thanks!LicenseMIT
Package: gensp
gensp比对postgres数据库备份的数据，得出新增的存储过程，目前不支持更新和删除，所有数据库备份区别都以新增的格式输出成存储过程。Installationpip install genspUsage examplegensp help          # 帮助
gensp login         # 配置数据库链接信息
gensp backup        # 备份数据库
gensp compare       # 比较两次数据库备份，并生成存储过程
gensp clear         # 清理本地数据库备份Release Historyv0.0.1ADD: Initial version
Package: gen-stm8
STM8 project skeleton generatorgen_stm8is toolset for generation STM8 project skeleton for
developmet of embedded applications.Developed inpythoncode.The README is used to introduce the modules and provide instructions on
how to install the modules, any machine dependencies it may have and any
other information that should be provided before the modules are installed.Table of ContentsInstallationInstall using pipInstall using buildInstall using py setupInstall using dockerDependenciesTool structureDocsContributingCopyright and licenceInstallationUsed next development environmentCurrently there are three ways to install packageInstall process based on using pip mechanismInstall process based on build mechanismInstall process based on setup.py mechanismInstall process based on docker mechanismInstall using pipPython package is located atpypi.org.You can install by using pip#python3pip3installgen_stm8Install using buildNavigate to releasepagedownload and extract release archive.To installgen_stm8type the followingtarxvzfgen_stm8-x.y.z.tar.gzcdgen_stm8-x.y.z/# python3wgethttps://bootstrap.pypa.io/get-pip.py
python3get-pip.pypython3-mpipinstall--upgradesetuptools
python3-mpipinstall--upgradepip
python3-mpipinstall--upgradebuild
pip3install-rrequirements.txt
python3-mbuild--no-isolation--wheel
pip3install./dist/gen_stm8-*-py3-none-any.whl
rm-fget-pip.py
chmod755/usr/local/lib/python3.9/dist-packages/usr/local/bin/gen_stm8_run.py
ln-s/usr/local/lib/python3.9/dist-packages/usr/local/bin/gen_stm8_run.py/usr/local/bin/gen_stm8_run.pyInstall using py setupNavigate torelease pagedownload and extract release archive.To installgen_stm8locate and run setup.py with argumentstarxvzfgen_stm8-x.y.z.tar.gzcdgen_stm8-x.y.z# python3pip3install-rrequirements.txt
python3setup.pyinstall_lib
python3setup.pyinstall_egg_infoInstall using dockerYou can use Dockerfile to create image/container.Dependenciesgen_stm8requires next modules and librariesats-utilities - Python App/Tool/Script UtilitiesTool structuregen_stm8is based on OOP.Generator structuregen_stm8/├──conf/│├──gen_stm8.logo│├──gen_stm8.cfg│├──gen_stm8_util.cfg│├──project.yaml│└──template/│├──Makefile.template│├──module.template│└──stm8s.template├──__init__.py├──log/│└──gen_stm8.log├──pro/│├──__init__.py│├──read_template.py│└──write_template.py└──run/├──factory_reset.sh└──gen_stm8_run.py6directories,14filesDocsMore documentation and info atgen_stm8.readthedocs.iowww.python.orgCopyright and licenceCopyright (C) 2018 - 2024 byvroncevic.github.io/gen_stm8gen_stm8is free software; you can redistribute it and/or modify
it under the same terms as Python itself, either Python version 3.x or,
at your option, any later version of Python 3 you may have available.Lets help and support PSF.