Saravanan666/pypi-org huggingface dataset:
Package: memecomplete-desktop
Unix:Windows:Metrics:Usage:OverviewDesktop client forhttps://memecomplete.com.SetupRequirementsPython 3.6+SpeechRecognition requirements:https://github.com/Uberi/speech_recognition#requirementsmacOS:$ brew install flac portaudio swigInstallationInstall the client with pip:$pipinstallmemecomplete-desktopor directly from the source code:$gitclonehttps://github.com/jacebrowning/memecomplete-desktop.git$cdmemecomplete-desktop$pythonsetup.pyinstallUsageLaunch the GUI from the command-line:$memecompleteRevision History0.2 (2017/06/12)Updated API to usehttps://memecomplete.com.0.1.1 (2016/09/14)Fixed image loading.0.1 (2016/09/05)Initial release.
Package: memedb
MemeDBMemeDB is a minimal in-memory key-value database that doesn't take itself too seriously. It's designed to be simple, lightweight, and fun to use.InstallationInstall MemeDB from PyPI:pipinstallmemedbQuick StartCreate a new MemeDB instance and add some data:frommemedbimportMemeDBdb=MemeDB()db.set("key1","value1")db.set("key2",{"nested_key":"nested_value"})Retrieve data by key:value=db.get("key1")print(value)# output: "value1"nested_value=db.get("key2")["nested_key"]print(nested_value)# output: "nested_value"Deletedatabykey:db.delete("key1")Use transactions for atomicity:withdb.transaction():db.set("key1","new_value")db.delete("key2")Advanced FeaturesMemeDB also includes some more advanced features to make it more powerful:IndexingYou can create an index for a specific key to speed up lookups:db.index("name")db.set("person:1",{"name":"Alice","age":30})db.set("person:2",{"name":"Bob","age":25})db.set("person:3",{"name":"Charlie","age":35})result=db.lookup("name","Alice")print(result)# output: [{"name": "Alice", "age": 30}]QueryingYou can perform queries on the data using a simple SQL-like syntax:db.set("person:1",{"name":"Alice","age":30})db.set("person:2",{"name":"Bob","age":25})db.set("person:3",{"name":"Charlie","age":35})result=db.query("SELECT * FROM data WHERE age > 30")print(result)# output: [{"name": "Charlie", "age": 35}]CachingYou can add a cache to MemeDB to speed up access to frequently accessed data:frommemedbimportCachecache=Cache(max_size=1000,ttl=3600)# cache up to 1000 items for 1 hourdb=MemeDB(cache=cache)db.set("key1","value1")db.get("key1")# retrieves from cacheContributionsContributions are welcome! If you have a feature request or bug report, please open an issue on GitHub. If you'd like to contribute code, please fork the repository and submit a pull request.
Package: memedict
# memedictKnowyourmeme.com definitions scraper## Installpip install memedict## Usage>>> from memedict import search
>>> search('base belong to us')
u'"All Your Base Are Belong to Us" is a popular engrish catchphrase that grew popular across the internet as early as in 1998. An awkward translation of "all of your bases are now under our control", the quote originally appeared in the opening dialogue of Zero Wing, a 16-bit shoot\'em up game released in 1989. Marked by poor grammar, the "All Your Base" phrase and the dialogue scene went viral on popular discussion forums in 2000, spawning thousands of image macros and flash animations featuring the slogan both on the web and in real life.'
Package: mem-edit
mem_editmem_editis a multi-platform memory editing library written in Python.Homepage:https://mpxd.net/code/jan/mem_editCapabilities:Scan all readable memory used by a process.Optionally restrict searches to regions with read + write permissions.Report on address space allocationRead/write using ctypes objectsBasic types, e.g.ctypes.c_ulong()Arrays, e.g.(ctypes.c_byte * 4)()Instances ofctypes.Structure or ctypes.Unionand subclasses.Run on Windows and LinuxInstallationDependencies:python 3 (written and tested with 3.7)ctypestyping (for type annotations)Install with pip, from PyPI (preferred):pip3installmem_editInstall with pip from git repositorypip3installgit+https://mpxd.net/code/jan/mem_edit.git@releaseDocumentationMost functions and classes are documented inline.
To read the inline help,importmem_edithelp(mem_edit.Process)ExamplesIncrement a magic number (unsigned long 1234567890) found in 'magic.exe':importctypesfrommem_editimportProcessmagic_number=ctypes.ulong(1234567890)pid=Process.get_pid_by_name('magic.exe')withProcess.open_process(pid)asp:addrs=p.search_all_memory(magic_number)# We don't want to edit if there's more than one result...assert(len(addrs)==1)# We don't actually have to read the value here, but let's do so anyways...num_ulong=p.read_memory(addrs[0],ctypes.c_ulong())num=num_ulong.valuep.write_memory(addrs[0],ctypes.c_ulong(num+1))Narrow down a search after a value changes:importctypesfrommem_editimportProcessinitial_value=40final_value=55pid=Process.get_pid_by_name('monitor_me.exe')withProcess.open_process(pid)asp:addrs=p.search_all_memory(ctypes.c_int(initial_value))input('Press enter when value has changed to '+str(final_value))filtered_addrs=p.search_addresses(addrs,ctypes.c_int(final_value))print('Found addresses:')foraddrinfiltered_addrs:print(hex(addr))Read and alter a structure:importctypesfrommem_editimportProcessclassMyStruct(ctypes.Structure):_fields_=[('first_member',ctypes.c_ulong),('second_member',ctypes.c_void_p),]pid=Process.get_pid_by_name('something.exe')withProcess.open_process(pid)asp:s=MyStruct()s.first_member=1234567890s.second_member=0x1234addrs=p.search_all_memory(s)print(addrs)p.write_memory(0xafbfe0,s)
Package: memegen-desktop
Unix:Windows:Metrics:Usage:OverviewDesktop client forhttps://memegen.link.SetupRequirementsPython 3.3+SpeechRecognition requirements:https://github.com/Uberi/speech_recognition#requirementsmacOS:$ brew install flac portaudio swigInstallationInstall the client with pip:$pipinstallmemegen-desktopor directly from the source code:$gitclonehttps://github.com/jacebrowning/memegen-desktop.git$cdmemegen-desktop$pythonsetup.pyinstallUsageLaunch the GUI from the command-line:$memegenRevision History0.1.1 (2016/09/14)Fixed image loading.0.1 (2016/09/05)Initial release.
Package: meme-generator
meme-generator✨ 表情包生成器，用于制作各种沙雕表情包 ✨Note额外表情仓库：meme-generator-contrib表情列表表情详细信息、表情预览等可以在--> 表情列表 <--查看安装本地安装本地安装、图片下载、字体安装方式等可以在--> 本地安装 <--查看DockerDocker 部署方式可以在--> Docker部署 <--查看使用通过 python 程序调用参考docs/examples/test_meme.py通过命令行使用meme-h/--helpmeme list (ls)列出所有已加载的表情meme info (show) KEY查看某个表情的详细信息，如：meme info petpetmeme preview KEY使用默认（随机）参数生成预览结果，如：meme preview petpetmeme generate (make) KEY --text TEXTS --images IMAGES ...制作表情，如：meme generate petpet --images avatar.jpg部分表情有额外的参数，可通过-h/--help查看，如：meme generate petpet --helpmeme run (start)启动 web server，可通过 api 方式调用meme download下载内置的表情包所需的图片通过 api 方式使用执行meme run可以启动 web 服务器web 框架用的是 FastApi , 可查看自动生成的交互式 API 文档（访问http://127.0.0.1:2233/docs）可以调用 api 接口使用，python 调用方式可参考docs/examples/test_api.py接入聊天机器人使用NoneBotnoneplugin/nonebot-plugin-memesNonebot2 表情包制作插件noneplugin/nonebot-plugin-memes-apinonebot-plugin-memes 调用 api 版本Yunzaiikechan8370/yunzai-memeYunzai机器人的表情包插件Koishilgc2333/koishi-plugin-memes-apiKoishi 复刻版 表情包制作插件调用 API 版配置默认配置文件位置：Windows:C:\Users\<username>\AppData\Roaming\meme_generator\config.tomlLinux:~/.config/meme_generator/config.tomlMac:~/Library/Application Support/meme_generator/config.tomlWarning从 v0.0.6 版本开始，不再生成默认配置文件；修改配置时需在对应的文件位置自行创建配置文件由于 v0.0.6 版本更改了资源链接的拼接方式，如果装过之前的版本，需要修改配置中的resource_url建议将配置文件中不需要更改的选项删除，以使用默认配置默认配置：[meme]load_builtin_memes=true# 是否加载内置表情包meme_dirs=[]# 加载其他位置的表情包，填写文件夹路径meme_disabled_list=[]# 禁用的表情包列表，填写表情的 `key`[resource]# 下载内置表情包图片时的资源链接，下载时选择最快的站点resource_urls=["https://raw.githubusercontent.com/MeetWq/meme-generator/","https://ghproxy.com/https://raw.githubusercontent.com/MeetWq/meme-generator/","https://fastly.jsdelivr.net/gh/MeetWq/meme-generator@","https://raw.fastgit.org/MeetWq/meme-generator/","https://raw.fgit.ml/MeetWq/meme-generator/","https://raw.gitmirror.com/MeetWq/meme-generator/","https://raw.kgithub.com/MeetWq/meme-generator/",][gif]gif_max_size=10.0# 限制生成的 gif 文件大小，单位为 Mbgif_max_frames=100# 限制生成的 gif 文件帧数[translate]baidu_trans_appid=""# 百度翻译api相关，表情包 `dianzhongdian` 需要使用baidu_trans_apikey=""# 可在 百度翻译开放平台 (http://api.fanyi.baidu.com) 申请[server]host="127.0.0.1"# web server 监听地址port=2233# web server 端口[log]log_level="INFO"# 日志等级加载其他表情如果希望加载非本仓库内置的表情，可以在配置文件中填写表情所在的文件夹路径如以下的文件夹：/path/to/your/meme_dir
└── meme1
    └── __init__.py
└── meme2
    └── __init__.py在配置文件中修改meme_dirs如下：[meme]meme_dirs=["/path/to/your/meme_dir"]开发如果希望编写、贡献新的表情，可以参考--> 新表情编写指北 <--对于一些不适合放在主仓库的表情，可以提交至额外表情仓库声明本仓库的表情素材等均来自网络，如有侵权请联系作者删除鸣谢本仓库的表情整合自原nonebot-plugin-petpet和nonebot-plugin-memes仓库感谢以下开发者作出的贡献：部分表情素材或代码参考了以下项目，感谢这些项目的开发者们Ailitonia/omega-miya基于nonebot2的qq机器人FloatTech/ZeroBot-Plugin基于 ZeroBot 的 OneBot 插件HibiKier/zhenxun_bot基于 Nonebot2 和 go-cqhttp 开发，以 postgresql 作为数据库，非常可爱的绪山真寻botSAGIRI-kawaii/sagiri-bot基于Graia Ariadne和Mirai的QQ机器人 SAGIRI-BOTDituon/petpetMirai插件 生成各种奇怪的图片kexue-z/nonebot-plugin-nokia诺基亚手机图生成RafuiiChan/nonebot_plugin_charpic字符画生成插件
Package: meme-manager
简介Web UI 表情包管理器。功能：浏览图片。添加、删除图片。给图片打标签，并可以按标签搜索。图片分组。安装：要求：Python 3.6 及以上。本软件使用 PyPI 进行分发，所以你可以使用 pip 来安装。但是更建议使用pipx将其安装到一个单独的虚拟环境中，避免污染全局系统依赖，如下：$ pipx install meme-manager验证安装成功：$meme-manager--version# 或$meme-manager--help使用：# 初始化 sqlite 数据库：$meme-managerinitdbfoo.sqlite# 运行：$meme-managerrunfoo.sqlite# URL：http://localhost:5000/index.html开发：说明：
后端使用 Python Flask + waitress，前端使用 React + Antd 开发。后端：$gitclonehttps://github.com/valleygtc/meme-manager.git
$cdmeme-manager# create venv$python3-mvenv.venv
$source.venv/bin/activate# install meme-manager and its dependencies.$pipinstall--editable.# run unittest$python-munittestdiscover# ENV$cpenv.sh.exampleenv.sh
$vienv.sh# 填写好程序运行所需环境变量。$sourceenv.sh# 读入环境变量。# init db memes.sqlite$meme-managerinitdb# run on db memes.sqlite$flaskrun前端：见：https://github.com/valleygtc/meme-manager-frontend构建与发布：prerequsite：$ pip install --user --upgrade setuptools wheel

$ pipx install twinebuild and upload to pypi:# 首先要 build 前端。# 然后把前端 build 出来的 build/ 目录复制到 src/meme-manager 目录下，并改名为 frontend。# build$python3setup.pysdistbdist_wheel# upload to pypi$twineuploaddist/*
Package: memento_client
The memento_client library provides Memento support, as specified in RFC 7089 (http://tools.ietf.org/html/rfc7089)For more information about Memento, seehttp://www.mementoweb.org/about/.This library allows one to find information about archived web pages using the Memento protocol.  It is the goal of this library to make the Memento protocol as accessible as possible to Python developers.
Package: mementoizer
MementoizerA python library toMemento-izevideos.Meaning, cut the video up into scenes, pass the first half through a black-and-white filter, and interleave them.Created for NaMoGenMo 2022. For an example, see:https://www.youtube.com/watch?v=vkgGLhG3ccIThe original film for comparison can be viewed here:https://en.wikipedia.org/wiki/File:The_Memphis_Belle_-_A_Story_of_a_Flying_Fortress.webmInstallationMementoizer can be installed from PyPI:pip install mementoizerUsageCommand-line usage:mementoize <video-filename>Programmatic usage:from mementoizer import mementoize

mementoize(video_filename, ...)Options--skip-startNumber of seconds to ignore for cutting purposes at the beginning of the video file. For example, skipping intro credits. Default: 0--skip-endNumber of seconds to ignore for cutting purposes at the end of the video file. For example, skipping end credits. Default: 0--min-scene-lengthMinimum number of seconds to count as a scene. If a cut is detected less than this many seconds after a previous one, it is disregarded. Default: 120 seconds--thresholdParameter for tuning cut detection. Must be a float between 0 and 1. Typical values for normal shot detection are 0.4-0.6. Since we are trying to detectsceneboundaries rather than plain shots, it's set a little higher by default. Default: 0.7--overlapNumber of seconds to overlap between clips (chronologically). You will see this many seconds repeated to connect the previous scene to the one you are watching now. Default: 4--cutsManually specify cut times, in seconds after the start of the video. If this is provided, cut detection is skipped.--skip-start,--skip-end, and--min-scene-lengthare still applied. Values should be comma-separated integers.
NOTE: the resulting video will start at the first timestamp you provide. So you must start the list with0if you don't want to skip the first scene.--verbosePrint out the cut times detected (or supplied if--cutsis specified).--dry-runPrint detected cut timstamps and exit. Do not write or modify any files.
Package: memento-llm
MementoSimple LLM Memory.Memento automatically manages your conversations with LLMs with just 3 lines of code. It leverages SQLAlchemy and Alembic to store conversations between users and assistants in SQLite3 or in memory.Getting StartedTo install Memento, runpip install memento-llmin your terminal.With Memento, you no longer have to worry about setting up message storage logic in your application, here is how I can be integrated into your code:fromopenaiimportOpenAIfrommementoimportMementoclient=OpenAI()### Stores message history in-memory.memory=Memento()@memory### Memento provides a decorator for your LLM generation function.defgenerate():returnclient.chat.completions.create(model="gpt-3.5-turbo",# messages=[    ### No longer worry about the message parameter.#     {"role": "user", "content": "Extract Jason is 25 years old"},# ],)response_1=generate("My name is Anibal")print(response_1)# Output: Hello Anibal!response_2=generate("What´s my name?")print(response_2)# Output: Your name is Anibal.
Package: mementor
To use (with caution), simply do:$ pip install mementor
$ memento <dirname>Make sure you have a OCR tool liketesseractand certain data value for comparing OCR, egtesseract-data-engalong withPillowandWandfor image conversion and loading to draw along withnumpyandcv2for all Image processing required to crop the image with border values defined which will be fetched during pip install.
Package: memeoverflow
A simple framework for Twitter bots creating memes from Stack Exchange
questions.Take questions posted on a particular Stack Exchange site, generate a meme out
of them and tweet them.Uses the following APIs:Stack ExchangeTwitter(viaTwython)imgflipInstances@pi_stack(Raspberry Pi)@overflow_meme(Stack Overflow)@worldbuildingme(World Building)@askubuntumemes(Ask Ubuntu)@stackamemia(Academia)Run your own instance. SeeGitHubfor more info.
Package: memepack-builder
memepack-builder简体中文A tool for packaging Memefied Chinese resource pack for Minecraft.This is the source code repository of PyPI packagememepack-builder. It's not intended for production use. Please usepip install memepack-builderto install the package.This package has no dependencies.This package requires Python 3.9 or above.UsageThis package can be invoked by other Python programs, or run from command line interface.Used as libraryThe package provides four classes:PlatformChecker,ModuleChecker,JEPackBuilderandBEPackBuilder. In a simple build (which is the most case), just import the module checker and one of the two builders, and callbuild(). The build log is stored as a list in the builder'sbuild_logproperty. Use'\n'.join()if you prefer string.Used in CLIWhen called in CLI (i.e.python -m memepack_builder), the package will ask for proper arguments, and build the resource pack as needed. For full usage, seethis page.ContributionWe would be grateful if you can contribute to this project. See thecontribution guidefor more information.LicenseCopyright 2021 MysticNebula70 & Teahouse StudiosLicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: memesis
Mnemosyne is the custodian and the live editor of your LLM's memoryThis comes with a few consequences:the LLM's next answer in your chat window will depend on what you make the LLMbelievethat it answered beforethus, you cantime travelthrough the history of the chat, change what you have asked about and what was answered to
youif you are a fan of movies likeTotal Recall,MementoorInceptionyou can see how the LLM's view ofrealityis
distorted by lettingMnemosyneplay with its memoryOther features that simply manage your chat context:if you asked the same question again, the answer will be retrieved from the short term or long term memory at no API
cost to youwhen you get close to the token limit, the oldest item in the short term memory is moved to the long-term memorywhen changing the subject of your chat you can spill all the content of the short-term memory into the long-term
memoryif you run an LLM using the same API as OpenAI (e.g.,Vicuna) listening on a local port, Mnemosyne can also manage
your interaction with itInstallationFirst, you will need to acquire your OpenAI key fromhere.If you have cloned this repo, you can install the packagememesisfrom its folder with:pip3 install -e .and then run it with:./run.shYou can also install it frompypiwith:pip3 install memesisand then run it with:python -m memesis.runSoon, you can also try out at:https://mnemosyne.streamlit.app/Enjoy,Paul Tarau
Package: memflow
memflow-pyPythonsupport forMemflow, a physical memory introspection framework written inRust.InstallationInstall python package:pip install memflowInstall appropriate memflow components (seememflowup).Examplefrommemflowimport*classCOFFHeader(Structure):_fields_=[("_pad0x0",c_byte*6),("sections",c_short),("timestamp",c_uint32),]inventory=Inventory()os=inventory.create_os("native")process=os.process_from_name("CalculatorApp.exe")module=process.module_by_name("CalculatorApp.dll")header=process.read(module.base+0x40,COFFHeader)print(header)Building from sourcePrerequisitesRust (Compilation support)Python (3.7 and up)python virtual environmentStepsFetch repository:git clone https://github.com/memflow/memflow-pyInstall maturin package:pip install maturinInstall dev packages:pip install '.[dev]'Create virtualenvvirtualenv .Active virtualenvsource ./bin/activateBuild wheels and install package:maturin developRepeat step 4 when you make changes to source.Install pytestpip install pytestTest your changes:python -m pytestFor more information on building please seeMaturin.
Package: memfo
Quick StartOn python 3.11+, install with:pipx upgraded memfo || pipx install memfoOn python 3.8 to 3.10, install with:pip install --upgrade --user memfoAfter install, run:memfoand enter?for help.memfo -- /proc/meminfo Viewer for Linuxmemfois a viewer for/proc/meminfothat shows meminfo:as a continuously updated display of current and past values,with numbers represented in chosen units,with selected fields at the top in the frozen section of the display,with the ability to hide certain fields, andmore.Example memfo Outputu:MiB d:show-values z=show-if-zero e:enter-edit ?=help
        0s        10s        20s        28s        37s        47s 01/28 12:51:27
      +0.0       +0.0       +0.0       +0.0       +0.0   32,060.8 MemTotal
    +222.7     -106.9     -106.4     -103.4      -71.3   22,325.0 MemAvailable
──────────────────────────────────────────────────────────────────────────────────────
    +222.6     -106.6     -103.8     -101.1      -68.9   15,776.4 MemFree
      +0.0       +0.0       +0.0       +0.0       +0.0       93.6 Buffers
     -24.0       -0.3       -2.6       -2.2       -2.4    7,726.8 Cached
    -322.8      +14.5      +24.9      +22.9       -4.2    7,989.8 Active
      +0.1       -0.3       -2.6       -2.3       -2.4    5,464.2 Inactive
    -322.8      +14.5      +24.9      +22.9       -4.2    5,833.6 Active(anon)NOTES:the columns are absolute or delta values for the given statistic for that interval; in this case, deltas are shown.the stats above the line can are chosen by the "edit" menu.Command Line OptionsYou selection of statistics to put in the non-scrolled region and hidden is saved a config file. If you choose another config file on start up, you can have set of statistics per for each use case.$ memfo -h
usage: memfo [-h] [-u {KiB,MB,MiB,GB,GiB,human}] [-c CONFIG]
                [-i INTERVAL_SEC] [--vmalloc-total] [-z] [-d] [--DB]
options:
  -h, --help            show this help message and exit
  -u {KiB,MB,MiB,GB,GiB,human}, --units {KiB,MB,MiB,GB,GiB,human}
                        units of memory [dflt=MiB]
  -c CONFIG, --config CONFIG
                        use "{config}.ini" for configuration
  -i INTERVAL_SEC, --interval-sec INTERVAL_SEC
                        loop interval in seconds [dflt=1.0]
  --vmalloc-total       Show "VmallocTotal" row (which is mostly useless)
  -z, --zeros           Show lines with all zeros
  -d, --dump            "print" the data only once rather than "display" it
  --DB                  add some debugging output
Package: memformers
MemformerImplementations for Memformer and MemBARTCitationsMemformer: A Memory-Augmented Transformer for Sequence Modeling@inproceedings{DBLP:conf/ijcnlp/WuLQGGY22,author={Qingyang Wu andZhenzhong Lan andKun Qian andJing Gu andAlborz Geramifard andZhou Yu},title={Memformer: {A} Memory-Augmented Transformer for Sequence Modeling},booktitle={Findings of the Association for Computational Linguistics: {AACL-IJCNLP}2022, Online only, November 20-23, 2022},pages={308--318},publisher={Association for Computational Linguistics},year={2022},url={https://aclanthology.org/2022.findings-aacl.29},timestamp={Tue, 29 Nov 2022 14:53:03 +0100},biburl={https://dblp.org/rec/conf/ijcnlp/WuLQGGY22.bib},bibsource={dblp computer science bibliography, https://dblp.org}}Stateful Memory-Augmented Transformers for Dialogue Modeling@article{DBLP:journals/corr/abs-2209-07634,author={Qingyang Wu andZhou Yu},title={Stateful Memory-Augmented Transformers for Dialogue Modeling},journal={CoRR},volume={abs/2209.07634},year={2022},url={https://doi.org/10.48550/arXiv.2209.07634},doi={10.48550/arXiv.2209.07634},eprinttype={arXiv},eprint={2209.07634},timestamp={Tue, 27 Sep 2022 16:29:43 +0200},biburl={https://dblp.org/rec/journals/corr/abs-2209-07634.bib},bibsource={dblp computer science bibliography, https://dblp.org}}
Package: memfunc
Thank you for using memfunc.Here are some tutorials to help you get started quickly.linear membership functionMembership functions can be divided into linear and nonlinear. There are two types of linear equations: Triangular and Trapezoidal.
To use them you can refer to the following code.
First create the combination to be converted, and set the upper and lower limits of the conversion, then create a group of membership function combinations "head" and "end" parameters are the ratio of the leftmost and rightmost ends of the membership function.frommemfunc.memfunc.linearimportContainer,Triangular,Trapezoidalx=[1,3,6,5,2,4,3.5,4.5,1.2]minx=[0for_inrange(len(x))]maxx=[6for_inrange(len(x))]mf=Container(head=1/6,end=1/6,maxlst=maxx,minlst=minx)mf.add(Trapezoidal(p=1/6,beta=1/6))mf.add(Triangular(p=1/6,end=True))print(mf.transform(x))nonlinear membershipfunctionNonlinear equations are more commonly used, and pymf supports the use of Gaussian, Generalizedbell, Sigmoid, Left, and Right equations.frommemfunc.memfunc.nonlinearimportFuzzy,Sigmoid_mf,Gaussian_mfx=[1,3,6,5,2,3]min0=[0for_inrange(len(x)-1)]max0=[10for_inrange(len(x)-1)]mf=Fuzzy(minlst=min0,maxlst=max0)mf.add(Sigmoid_mf(a=2,p=2/3))mf.add(Gaussian_mf(p=1/2,sigma=1))mf.add(Sigmoid_mf(a=-2,p=1/3))x=mf.transform(x)print(x)mf.plot()