Saravanan666/pypi-org huggingface dataset:
Package: pepephone-data
pepephone-dataEnglish BelowCastellanopepephone-dataes una manera rápida para ver el uso de datos si usas Pepephone: sin tener que hacer login en la Web / aplicación móvil desde la línea de comandos.Se necesita crear un fichero en uno de los sitios:$HOME/.pepephone
/etc/pepephoneCon este contenido:[authentication]
email = email_used_to_authenticate_on_pepephone.com
password = password_for_your_pepephone_account
phone = your_pepephone_phone_number

[extra]
extra_GB = 25El estra es opcional: en este caso añade 25 GB a los datos disponibles de Pepephone. La razón: si planeas añadir datos pero no lo has hecho aún.Ejemplo:Time         : 2020-05-10 16:37:02
GB total     : 75.58 GB (Including extra 25 GB)

GB used total: 17.93 GB (EU roaming: 0.00 GB)
GB used/day  : 1.79 GB/day

GB remaining : 57.65 GB
GB remain/day: 2.62 GB/day

% Used       : 23.72%
% Month      : 32.26%ElGB totalincluye los datos acumulados del mes pasados, regalos de Pepephone, etc.Englishpepephone-datais a quick way to check usage data of Pepephone: without Web or mobile application from the command line.Create a file in one of the places:$HOME/.pepephone
/etc/pepephoneWith this contents:[authentication]
email = email_used_to_authenticate_on_pepephone.com
password = password_for_your_pepephone_account
phone = your_pepephone_phone_number

[extra]
extra_GB = 25The extra is optional: in this case it will add 25 GB to the data available on Pepephone. The reason: you might plan to add 25 GB during the month but not have done it yet.Executeshow-data-used.py:Time         : 2020-05-10 16:37:02
GB total     : 75.58 GB (Including extra 25 GB)

GB used total: 17.93 GB (EU roaming: 0.00 GB)
GB used/day  : 1.79 GB/day

GB remaining : 57.65 GB
GB remain/day: 2.62 GB/day

% Used       : 23.72%
% Month      : 32.26%The GB total includes last months remaining data, Pepephone "gifts", etc.
Package: peperoncino
peperoncino: A library for easy data processing for pandasInstall$ pip install peperoncinoHow to useProcessing DataFrameimportpeperoncinoaspppipeline=pp.Pipeline(# query datapp.Query("bar <= 3"),# assign new featurepp.Assign(hoge="foo * bar"),# generate combination featurepp.Combinations(["foo","baz"],["*","/"]),# target encodingpp.TargetEncoding(["baz"],"y",ref=0),# select featurespp.Select(["hoge","*_foo_baz","TARGET_ENC_baz_BY_y","y"],lackable_cols=["y"],))# execute the processingtrain_df,val_df,test_df=\pipeline.process([train_df,val_df,test_df])Predefined processingsnamedescriptionApplyColumnApply a function to a column.AsCategoryAssigncategorydtype to columns.AssignAssign a feature by a formula.CombinationsCreate combination features.DropColumnsDrop columns.DropDuplicatesDrop duplicate rows.PipelineChain processings.QueryQuery rows by a given condition.RenameCOlumnsRename columns.SelectSelect columns.StatsEncodingEncode columns by statistical values of another column.TargetEncodingTarget Encoding with smoothing.Define processingAll processings are subclass ofpp.BaseProcessing.All you need is define the_process(self, dfs: List[pd.DataFrame]) -> List[pd.DataFrame]function.classExampleProcessing(pp.BaseProcessing):def_process(self,dfs:List[pd.DataFrame])->List[pd.DataFrame]:return[df+1fordfindfs]If your processing doesn't depent on each other data frames, then usepp.SeparatedProcessing.classExampleProcessing(pp.SeparatedProcessing):defsep_process(self,df:pd.DataFrame)->pd.DataFrame:returndf*2If you need to merge all dataframes and then apply your processing, usepp.MergedProcessing.classExampleProcessing(pp.SeparatedProcessing):defsimul_process(self,df:pd.DataFrame)->pd.DataFrame:returndf.assign(col1_mean=df['col1'].mean())
Package: pepfrag
pepfragpepfrag is a library for generating possible dissociation fragment ions of peptides
in tandem mass spectrometry experiments.Table of ContentsInstallationUsageLicenseInstallationpepfragcan be installed fromPyPI.Compatibilitypepfragis written using Python 3 and should be compatible with most
operating systems. The package has been tested onWindows 10MacOS 10.15Ubuntu 18.04 LTSBecausepepfragincludes C/C++ extensions, installation requires the
presence of a C++ 11 compatible compiler on your machine.InstructionsInstall Python 3 (>= version 3.6).Install the latest version of thepepfraglibrary usingpip:pipinstallpepfragUsagePeptideConstructionpepfragprovides one key public class:Peptide. This class includes public methods
for computing the mono/average mass of the peptide, including any configured modifications
(ModSites), and the peptide fragment ions, with configurable neutral losses.APeptidecan be constructed from its amino acid sequence, charge state and modifications,
for example:frompepfragimportMassType,ModSite,Peptidepeptide=Peptide("ABCMPK",2,(ModSite(15.994915,4,"Oxidation"),ModSite(304.20536,"nterm","iTRAQ8plex")),mass_type=MassType.mono)Peptidemodifications are defined using a sequence ofModSiteinstances, which
arenamedtuplesdefined by the mass of the modification (float), the site of the
modification (string for terminal modifications or 1-indexed integer otherwise) and
the name of the modification.ThePeptideconstructor has two keyword parameters:mass_type:Description: The type of mass to calculate.Type:MassTypeenumeration.Default:MassType.mono.radical:Description: Flag indicating whether radical cation fragments should be
generated.Type: bool.Default:False.Fragment GenerationFragment ions can be generated using thefragmentmethod; for efficiency when the
samePeptideinstance is used repeatedly, the resulting fragments are cached on the
instance. This cache is invalidated if the instance attributes are changed.Thefragmentmethod has two keyword parameters:ion_types:Description: The types of fragment ion species to generate. The default
setup would generate precursor, immonium, b, y, a, c and z ions.Type: dictionary mappingIonTypeenumeration instances to possible
neutral loss species, represented as strings for the most common neutral losses
(i.e. those configured inpepfrag, namely'H2O','NH3','CO2'and'CO').
For additional losses, these can be input as tuples of(name (str), mass (float)).Default:frompepfragimportIonTypeDEFAULT_IONS={IonType.precursor:['H2O','NH3','CO2'],IonType.imm:[],IonType.b:['H2O','NH3','CO'],IonType.y:['NH3','H2O'],IonType.a:[],IonType.c:[],IonType.z:[]}Example:frompepfragimportIonType,Peptidepeptide=Peptide('APYSMLK',2,[])peptide.fragment(ion_types={IonType.b:['NH3',('customLoss',17.04)]})force:Description: Flag indicating whether fragments should be forcibly regenerated,i.e.bypassing the cached ions.Type: bool.Default:False.Licensepepfragis released under the terms of theMIT license.
Package: pepita
Prediction of Exoplanet Precisions using Information in Transit Analysis (PEPITA)IntroductionPEPITA is a Python package that allows making predictions for the precision of exoplanet parameters using transit light-curves. Behind scenes, it makes use of the Information Analysis techniques to predict the best precision you will get by fitting a light-curve without actually needing to perform the fit.MotivationBeing able to predict the precision of parameters without needing to perform fits to data allows a more efficient planning of observations or re-observations. For example, if you find that an exoplanet of your interest which has been observed with a cadence of 1800s cadence will get an improved measurement of its radius ratio of 30% if reobserved with 120s cadence, you know that re-observations will be worth it. Or you may find out that the improvement is 1% and re-observing is not worth it.For more details about the motivation and results using this package see theassociated paper(ArXiv version)Get startedInstall the package usingpipinstallpepitaRead thedocsand follow through the example notebooks.Data and othersSome notebooks are provided either as examples of how to use our information analysis class to make predictions of parameter precisions or to showcase how some of the analyses used in the paper were performed. These can be found under the data and notebooks directories
Package: pepnet
pepnetNeural networks for amino acid sequencesPredictor APISequence and model construction can both be handled for you by pepnet’sPredictor:frompepnetimportPredictor,SequenceInput,NumericInput,Outputpredictor=Predictor(inputs=[SequenceInput(length=4,name="x1",variable_length=True),NumericInput(dim=30,name="x2")],outputs=[Output(name="y",dim=1,activation="sigmoid")],dense_layer_sizes=[30],dense_activation="relu")sequences=["ACAD","ACAA","ACA"]vectors=np.random.normal(10,100,(3,30))y=numpy.array([0,1,0])predictor.fit({"x1":sequences,"x2":vectors},y)y_pred=predictor.predict({"x1":sequences,"x2":vectors})["y"]Convolutional sequence filteringThis model takes an amino acid sequence (of up to length 50) and applies
to it two layers of 9mer convolution with 3x maxpooling and 2x
downsampling in between. The second layer’s activations are then pooled
across all sequence positions (using both mean and max pooling) and
passed to a single dense output node called “y”.peptide=predictor=Predictor(inputs=[SequenceInput(length=50,name="peptide",encoding="index",variable_length=True,conv_filter_sizes=[9],conv_output_dim=8,n_conv_layers=2,global_pooling=True)],outputs=[Output(name="y",dim=1,activation="sigmoid")])Manual index encoding of peptidesRepresent every amino acid with a number between 1-21 (0 is reserved for
padding)frompepnet.encoderimportEncoderencoder=Encoder()X_index=encoder.encode_index_array(["SYF","GLYCI"],max_peptide_length=9)Manual one-hot encoding of peptidesRepresent every amino acid with a binary vector where only one entry is
1 and the rest are 0.frompepnet.encoderimportEncoderencoder=Encoder()X_binary=encoder.encode_onehot(["SYF","GLYCI"],max_peptide_length=9)FOFE encoding of peptidesImplementation of FOFE encoding fromA Fixed-Size Encoding Method for
Variable-Length Sequences with its Application to Neural Network
Language Modelsfrompepnet.encoderimportEncoderencoder=Encoder()X_binary=encoder.encode_FOFE(["SYF","GLYCI"],bidirectional=True)Example networkSchematic of a convolutional model:
Package: pepotron
pepotronCLI to open PEPs in your browser.InstallationFrom PyPIpython3-mpipinstall--upgradepepotronWithpipxpipxinstallpepotronFrom sourcegitclonehttps://github.com/hugovk/pepotroncdpepotron
python3-mpipinstall.UsageOpen a PEP$pep8https://peps.python.org/pep-0008/Open release schedule PEP for a Python version$pep3.11https://peps.python.org/pep-0664/Open a PEP by searching for words in the title$pepdeadbatteriesScore   Result90      PEP 594: Removing dead batteries from the standard library58      PEP 415: Implement context suppression with exception attributes55      PEP 476: Enabling certificate verification by default for stdlib http clients55      PEP 500: A protocol for delegating datetime methods to their tzinfo implementations55      PEP 696: Type defaults for TypeVarLikeshttps://peps.python.org/pep-0594/Open a PEP topic$pepgovernancehttps://peps.python.org/topic/governance/$peppackaginghttps://peps.python.org/topic/packaging/$pepreleasehttps://peps.python.org/topic/release/$peptypinghttps://peps.python.org/topic/typing/$peptopicshttps://peps.python.org/topic/Open a build preview of a python/peps PR$pep594--pr2440https://pep-previews--2440.org.readthedocs.build/pep-0594/Open the PEPs website$pephttps://peps.python.org$pep--pr2440https://pep-previews--2440.org.readthedocs.buildFind the next available PEP numberCheck published PEPs andopen PRsto find the
next available PEP number.$pepnextNext available PEP: 730Open a BPO issue in the browserIssues frombugs.python.orghave been migrated toGitHub issuesand have new numbers. This
command will open the redirect page to take you to the new issue.$bpo46208https://bugs.python.org/issue?@action=redirect&bpo=46208This redirects tohttps://github.com/python/cpython/issues/90366Help$pep--helpusage: pep [-h] [-u URL] [-p PR] [--clear-cache] [-n] [-v] [-V] [search ...]pepotron: CLI to open PEPs in your browserpositional arguments:search             PEP number, or Python version for its schedule, or words from titleoptions:-h, --help         show this help message and exit-u URL, --url URL  Base URL for PEPs (default: https://peps.python.org)-p PR, --pr PR     Open preview for python/peps PR--clear-cache      Clear cache before running-n, --dry-run      Don't open in browser-v, --verbose      Verbose logging-V, --version      show program's version number and exit$bpo--helpusage: bpo [-h] [-n] [-v] [-V] bpoOpen this BPO in the browserpositional arguments:bpo            BPO numberoptions:-h, --help     show this help message and exit-n, --dry-run  Don't open in browser-v, --verbose  Verbose logging-V, --version  show program's version number and exit
Package: pepper8
Transform pep8 or flake8 output to HTMLTo install pepper8, simply use pip.pipinstallpepper8Or you can clone the latest master branch and build using setuptoolsgitclonegit@github.com:myth/pepper8.git&&cdpepper8&&pythonsetup.pyinstallUsagepepper8 operates in two different input modes and two different output modes.
To read pep8 / flake8 status reports from file usepepper8-oreport.html<filename>or you can pipe output from another file or processcatflake8.out|pepper8>report.htmlIf no output file is specified with-o, the HTML report is written to stdout, allowing
output redirection or pipingpepper8<filename>|lessBuild statisticsWhen pepper8 is run using an output file-o<filename>, it will check its run environment
and print out available build statistics to stdout.Currently, only TeamCity build statistics are supported with keyspepper8warningsandpepper8errors.If you wish to enable full parsing of all PEP 8 / Flake8 errors as failures in TeamCity,
there already exists a package calledTeamCity-messageswhich will be treated as build errors in TeamCity.The TeamCity build statistics provided by pepper8 is only intended to provide statistical data for use in
custom build report graphs in addition to the HTML report.Screenshots from TeamCity integrationPepper8 PEP 8 HTML ReportTeamCity custom chart data using custom buildStatistics messages if run under TeamCityChangelog1.1.0Expand pepper8 to account for all possible alphanumeric
violation codes on a [A-Z][0-9]{3,4} format.
Updated broken link to flake8-readthedocs.1.0.4Fixed a Python 2 and 3 compatibility issue.
Added changelog to README.1.0.3Fixed a bug causing missed statistics for last file.
Split the title summary stats into the Flake8 plugins.1.0.2Changed links in HTML report to default to new window.
This prevents TeamCity to display blank page due to
loading of “unsafe scripts” inside the iframe.1.0.1A few bugfixes1.0.0Initial release
Package: pepper8tc
Transform pep8 or flake8 output to HTMLTo install pepper8, simply use pip.pipinstallpepper8Or you can clone the latest master branch and build using setuptoolsgitclonegit@github.com:myth/pepper8.git&&cdpepper8&&pythonsetup.pyinstallUsagepepper8 operates in two different input modes and two different output modes.
To read pep8 / flake8 status reports from file usepepper8-oreport.html<filename>or you can pipe output from another file or processcatflake8.out|pepper8>report.htmlIf no output file is specified with-o, the HTML report is written to stdout, allowing
output redirection or pipingpepper8<filename>|lessBuild statisticsWhen pepper8 is run using an output file-o<filename>, it will check its run environment
and print out available build statistics to stdout.Currently, only TeamCity build statistics are supported with keyspepper8warningsandpepper8errors.If you wish to enable full parsing of all PEP 8 / Flake8 errors as failures in TeamCity,
there already exists a package calledTeamCity-messageswhich will be treated as build errors in TeamCity.The TeamCity build statistics provided by pepper8 is only intended to provide statistical data for use in
custom build report graphs in addition to the HTML report.Screenshots from TeamCity integrationPepper8 PEP 8 HTML ReportTeamCity custom chart data using custom buildStatistics messages if run under TeamCityChangelog1.0.3Fixed a bug causing missed statistics for last file.
Split the title summary stats into the Flake8 plugins.1.0.2Changed links in HTML report to default to new window.
This prevents TeamCity to display blank page due to
loading of “unsafe scripts” inside the iframe.1.0.1A few bugfixes1.0.0Initial release
Package: pepperbot
PepperBot一个符合直觉的跨社交平台机器人框架，轻松地在平台间传递消息，支持QQ、微信、Telegram文档·QQ交流群生而跨平台QQ 基于Onebot，Mirai(待实现)、OPQ(待实现)微信基于可爱猫Telegram基于Pyrogram提供了跨平台、易用的消息类型(片段)通用事件，比如群消息、私聊消息等，提供了统一的接口，只需要写一次代码，就可以无缝应用到各个平台上，跨平台的消息传递从未如此轻松随意可以跨群、跨平台共享指令(的状态)，或者全局锁定用户(不管用户是通过群聊还是私聊)性能也够用底层基于异步的 Sanic 框架，目前有一定生态的 python web 框架中，性能最好的轻量，主要处理消息，对于其他功能，暴露底层接口，方便实现深度定制几行代码，即可实现多进程负载均衡自带全家桶支持Django或starlette风格的集中化路由，或者flask或fastAPI风格的装饰器式路由非常灵活的权限系统，支持路由、class、method级别的权限控制Django 类视图风格或Vue 生命周期风格的指令系统定时任务日志支持基于ormar的异步数据库支持(ormar基于sqlalchemy+pydantic)自带插件市场，可以安装社区指令提供cli工具，快速创建项目类似Selenium的事件等待fastAPI风格的依赖注入接口合语义API 符合直觉，直观，流畅地把想法映射到代码上基于 python3.6 之后的类型注解，提供了完全的类型提示大部分可以自动获取的参数，都会自动获取，不需要每次手动提供了只会动态注入用到的参数，没必要每次写一长串用不到的参数了！文档中，均有可直接复制使用的代码片段，快速开发测试文档全自带linter通过元编程(AST 之类)手段，对编写的各种代码进行规则检查，并输出易于理解的错误信息，将问题消灭在框架启动之前文档比较全测试覆盖比较全面大量官方示例/指令来自作者的 QA(加群以获取)安装pipinstallpepperbot使用具体使用见文档示例六行代码实现消息互转classWhateverNameYouWant:asyncdefgroup_message(self,bot:UniversalGroupBot,chain:MessageChain):ifbot.onebot:# 转发qq消息至微信、TGawaitbot.arbitrary.keaimao.group_message("19521241254@chatroom",*chain.segments)awaitbot.arbitrary.telegram.group_message("bot_father",*chain.segments)ifbot.keaimao:# 转发微信消息至qq、TGawaitbot.arbitrary.onebot.group_message("1041902989",*chain.segments)awaitbot.arbitrary.telegram.group_message("bot_father",*chain.segments)只需要非常少的代码，就可以实现跨平台的群消息的响应@register()classWhateverNameYouWant:# 注册跨平台群事件asyncdefgroup_message(self,bot:GroupBot,chain:MessageChain,sender:Sender):# chain即为消息链，pure_text是消息中的纯文本，不包含表情、图片等if"撤回我"==chain.pure_text:awaitchain.withdraw()# 可以直接“撤回消息”，符合直觉if"踢出我"==chain.pure_text:awaitsender.kickout()# 可以直接踢出发言群员# 也可以对消息链进行in操作，相当于in chain.pure_textif"禁言我"inchain:awaitsender.ban(10)# 可以直接禁言发言群员ifchain.regex("有人(在|吗|嘛|在吗).?"):# 发送一条群消息# 接受任意个参数，必须是合法的消息片段，比如Text，Face，Imageawaitbot.group_message(Text("没人"),Image("http://123.jpg"),)生态插件市场可以记忆上下文的GPT群聊机器人pepperbot-ai-query通过GPT，自动检测群聊中的广告信息
Package: pepper-cache
pepper-cacheA simple, persistent key/value cache with no dependencies.Installation$pipinstallpepper-cacheAPIcreate_cache- Creates a Cache instance.name: str|Path- The name of the cache instance, also used for the directory relative to$HOME/.cacheif nopathis suppliedpath: str = None- The directory in which to store cache files relative to$HOME/.cacheserializer: "pickle"|"json" = "pickle"- The serializer to use when writing values to the disk, defaults to"pickle"my_cache=create_cache("my cache",path="my_app/my_cache",serializer="json")my_cache=create_cache("my cache")# path arg omitted, path would be $HOME/.cache/my_cacheget_cache- Retrieves a Cache instance.name: str- The name of the cache instance to retrievecreate: bool- Whether to create the cache if it does not existmy_cache=get_cache("my cache")my_cache=get_cache("nonexistent cache")# Nonemy_cache=get_cache("nonexistent cache",create=True)# Now exists!Cache.set- Sets an item in the cache. Existing items will be overwritten.key: str- The name of the cached valuevalue: Any- The value to storettl: int = 0- The time in milliseconds that the value should remain in the cache or 0  for indefinitely, defaults to0cache.set("my key",my_value)# stored indefinitelycache.set("my key",my_value,ttl=1000)# stored for one secondCache.get- Retrieves a value from the cache if it exists and has not expired.key: str- The key of the cache item to retrievedefault: Any- The default value to return if the value is not storedmy_value=cache.get("my key 2")# returns None if the value is not stored or has expired. Consider checking if the item exists belowmy_value=cache.get("my key 2",default="now has a value")Cache.has- ReturnsTrueif an item exists in the cache by its key.key: str- The key of the cache item to check the existence ofifnotcache.has("my key 2"):cache.set("my key 2","has a value!")Cache.delete- Deletes an item from the cache if it exists.key: str- The key of the cache item to delete from the cachecache.delete("my key 2"):cache.get("my key 2")# None"pepper" comes from the code name of a project of mine and instead of writing packages like this specifically for the project, I decided to instead make them full on Python packages that anyone can use.
Package: peppercorn
PeppercornA library for converting a token stream into a data structure comprised of
sequences, mappings, and scalars, developed primarily for converting HTTP form
post data into a richer data structure.  It runs on Python 2.7, 3.4, 3.5, 3.6
and 3.7.Please seehttps://docs.pylonsproject.org/projects/peppercorn/en/latest/for the documentation.Seehttps://github.com/Pylons/peppercornfor in-development version.Changes0.6 (2018-08-24)Add support for Python 3.4, 3.5, 3.6 and 3.7.Drop support for Python 2.6, 3.2 and 3.3.Add contributing.md and update docs. Seehttps://github.com/Pylons/peppercorn/issues/130.5 (2014-09-29)Switch to an iterative parser rather than a recursive parser to avoid
DoS attacks.Add theignoreoperation. The subsequent data elements in the stream
will be ignored until the corresponding__end__marker.  This feature
is useful for form elements designed for client-side scripting, such as a
“select all” checkbox in the middle of a list of other kinds of fields.Add support for Python 3.3.Drop support for Python 2.5 and Jython.0.4 (2012-02-14)Drop support for Python 2.4.Add support for Python 3.2.Move project to GitHub.Switch to Pylons Project Sphinx theme.Add tox configuration.0.3 (2010-09-02)0.2 was a brownbag release. Handle the case whererenameoperation types
may not have children.0.2 (2010-09-02)Add new operation type:rename.renamebegins a special mode.
The value of the first subsequent data element in the stream will be
used within its parent sequence or mapping. Any remaining data
elements until the corresponding__end__marker are ignored.
This is mostly in support of radio buttons.  See therenamedocs
withinhttps://docs.pylonsproject.org/projects/peppercorn/en/latest/for more
information.0.1 (2010-03-23)Initial release.
Package: pepper-fusion
API Testing Package
Repository Name: pepper-fusion-api
Description: A comprehensive package for API testing, including tools for automation, validation, and integration.Data Compare Testing Package
Repository Name: pepper-fusion-data-compare
Description: Tools and libraries for comparing data across various sources, with customizable rules and reporting features.Machine Learning & Data Science Testing Package
Repository Name: pepper-fusion-ml-ds
Description: A robust package for validating and testing machine learning models, data preprocessing, and experimentation.Predictive Analytics Package
Repository Name: pepper-fusion-predictive
Description: Advanced tools for predictive analytics, including forecasting, visualization, and integration with BI tools.
Package: pepperize.cdk-apigateway-swagger-ui
CDK Apigateway SwaggerUIAdd SwaggerUI to your AWS Apigateway RestApiInstallTypeScriptnpminstall@pepperize/cdk-apigateway-swagger-uioryarnadd@pepperize/cdk-apigateway-swagger-uiPythonpipinstallpepperize.cdk-apigateway-swagger-uiC# / .Netdotnet add package Pepperize.CDK.ApigatewaySwaggerUiJava<dependency><groupId>com.pepperize</groupId><artifactId>cdk-apigateway-swagger-ui</artifactId><version>${cdkApigatewaySwaggerUi.version}</version></dependency>Usageimport{Stack}from"aws-cdk-lib";import*asapigatewayfrom"aws-cdk-lib/aws-apigateway";import{SwaggerUi}from"@pepperize/cdk-apigateway-swagger-ui";conststack=newStack();constrestApi=newapigateway.RestApi();newSwaggerUi(stack,"SwaggerUI",{resource:restApi.root});Open your SwaggerUI:https://<rest api id>.execute-api.<aws region>.amazonaws.com/<stage>/api-docs/swagger-ui.htmlView your API docs:https://<rest api id>.execute-api.<aws region>.amazonaws.com/<stage>/api-docs.json