Saravanan666/pypi-org huggingface dataset:
Package: dimsense
DimSense: Feature Selection and Extraction LibraryDimSense is a Python library designed to streamline the process of feature selection and extraction in machine learning projects. Whether you're working with large datasets or aiming to enhance model performance, DimSense offers a collection of methods to help you identify crucial features and reduce dimensionality effectively.InstallationYou can install DimSense using pip:pipinstalldimsenseUsageDimSense provides a range of feature selection and extraction methods that can be seamlessly integrated into your machine learning pipelines. Here's a basic example demonstrating how to use DimSense's feature selection:fromdimsenseimportFeatureSelector# Load your datasetX,y=load_dataset()# Initialize the FeatureSelectorselector=FeatureSelector(method='select_k_best',num_features=10)# Fit and transform the dataX_selected=selector.fit_transform(X,y)For more detailed examples, function explanations, and advanced usage scenarios, refer to ourdocumentation.ContributingWe welcome contributions from the community! If you'd like to contribute to DimSense, please refer to ourContributing Guidelines.TestingWe take testing seriously to ensure the reliability of DimSense. You can run the test suite using the following steps:Clone the repository:gitclonehttps://github.com/Tinny-Robot/DimSenseNavigate to the project directory:cdDimSenseInstall the required dependencies:pipinstall-rrequirements.txtRun the tests:pythonrun_tests.pyIf all tests pass, you'll see output indicating the success. If any tests fail, carefully review the error messages and traceback to identify the issue. Feel free to reach out to us if you encounter any problems!Continuous IntegrationWe also have set up continuous integration (CI) to automatically run tests whenever changes are pushed to the repository. You can view the test results and coverage reports directly in the pull request checks or on our CI provider's website.Test CoverageWe aim for good test coverage to ensure the robustness of our code. If you're interested in measuring the test coverage, you can do so by running:coveragerunrun_tests.py
coveragereport-mHappy testing with DimSense!ChangelogFor a complete list of changes and versions, please refer to theChangelog.LicenseDimSense is released under theMIT License.ContactIf you have any questions or feedback, feel free to reach out to us athandanfoun@gmail.com.Happy feature engineering with DimSense!
Package: dimsim
DimSim - A Chinese Soundex Library (Python version)DimSim is a library developed by the Scalable Knowledge Intelligence team at IBM Almaden Research Center as part of theSystemTproject.The PyPi project page can be foundhere. It was created in collaboration with IBM Center for Open-Source Data and AI Technologies (CODAIT).OverviewWe provide a phonetic algorithm for indexing Chinese characters by sound. The technical details can be found in the followingpaper:Min Li, Marina Danilevsky, Sara Noeman and Yunyao Li.DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm based on Learned High Dimensional Encoding. CoNLL 2018.In this library, we provide a pre-trained model that can perform the following functions:Given two Chinese phrases (of the same length), return the phonetic distance of the input phrases. Optionally you can feed in pinyin strings of Chinese phrases too.Given a Chinese phrase, return its top-k similar (phoentically) Chinese phrases.How to installDependencies:pypinyin: used for translating Chinese characters into their correponding pinyins.There are two ways to install this library:Install from PyPipipinstalldimsimDownload the source code by cloning this repo and compile it yourself.gitclonegit@github.com:System-T/DimSim.gitcdDimSim/

pipinstall-e.How to useOnce you have the package installed you can use it for the two functions as shown below.Computing phonetic distance of two Chinese phrases. The optional argumentpinyin(False by default) can be used to provide a pinyin string list directly. See example usage below.importdimsimdist=dimsim.get_distance("大侠","大虾")0.0002380952380952381dist=dimsim.get_distance("大侠","大人")25.001417183349876dist=dimsim.get_distance(['da4','xia2'],['da4','xia1']],pinyin=True)0.0002380952380952381dist=dimsim.get_distance(['da4','xia2'],['da4','ren2']],pinyin=True)25.001417183349876Return top-k phonetically similar phrases of a given Chinese phrase. Two parameters:modecontrols the character type of the returned Chinese phrases, where 'simplified' represents simplified Chinese and 'traditional' represents traditional Chinese.thetacontrols the size of search space for the candidate phrases.importdimsimcandidates=dimsim.get_candidates("大侠",mode="simplified",theta=1)['打下','大虾','大侠']candidates=dimsim.get_candidates("粉丝",mode="traditinoal",theta=1)['門市','分時','焚屍','粉飾','粉絲']CitationPlease cite the library by referencing the published paper:@InProceedings{K18-1043,
  author = 	{Li, Min and Danilevsky, Marina and Noeman, Sara and Li, Yunyao},
  title = 	{{DIMSIM:} An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding},
  booktitle = 	{Proceedings of the 22nd Conference on Computational Natural Language Learning},
  year = 	{2018},
  publisher = 	{Association for Computational Linguistics},
  pages = 	{444-453},
  location = 	{Brussels, Belgium},
  url = 	{http://aclweb.org/anthology/K18-1043}
}
Package: dimsm
[![build](https://github.com/ihmeuw-msca/dimsm/actions/workflows/build.yml/badge.svg)](https://github.com/ihmeuw-msca/dimsm/actions/workflows/build.yml)
[![pypi](https://badge.fury.io/py/dimsm.svg)](https://badge.fury.io/py/dimsm)# Dimension SmootherKalman based smoother, smoothing data across different dimensions.## InstallTo install thedimsmpackage do` pip install dimsm `If want to install the package with experimental features do` pip installgit+https://github.com/ihmeuw-msca/dimsm.git@develop`## Exampleexamples/1d_smoother.py: Simple 1D smoother example with sinusoidal curve
Package: dimspy
Python package for processing direct-infusion mass spectrometry-based metabolomics and lipidomics dataDocumentation:https://dimspy.readthedocs.io/en/latestSource:https://github.com/computational-metabolomics/dimspyBug reports:https://github.com/computational-metabolomics/dimspy/issuesInstallation (Conda, PyPi and Galaxy)See theInstallation pageof
theonline documentation.Bug reportsPlease report any bugs that you findhere.
Or fork the repository onGitHuband create a pull request (PR). We welcome all contributions, and we
will help you to make the PR if you are new togit.CreditsDIMSpy was originally written by Ralf Weber and Albert Zhou and has been developed with the help of many others.
Thanks to everyone who has improved DIMSpy by contributing code, adding features, bug reports and fixes, and documentation.Developers and contributersRalf J. M. Weber (r.j.weber@bham.ac.uk) -University of Birmingham (UK)Jiarui (Albert) Zhou (j.zhou.3@bham.ac.uk) -University of Birmingham (UK),HIT Shenzhen (China)Thomas N. Lawson (t.n.lawson@bham.ac.uk) -University of Birmingham (UK)Martin R. Jones (martin.jones@eawag.ch) -Eawag  (Switzerland)DIMSpy acknowledges support from the following funders:BBSRC, grant number BB/M019985/1European Commission’s H2020 programme, grant agreement number 654241Wellcome Trust, grant number 202952/Z/16/ZCitationTo cite DIMSpy please use one of the Zenodo references listedhere.LicenseDIMSpy is licensed under the GNU General Public License v3.0 (seeLICENSE filefor licensing information). Copyright © 2017 - 2020 Ralf Weber, Albert ZhouThird-party licenses and copyrightRawFileReader reading tool. Copyright © 2016 by Thermo Fisher Scientific, Inc. All rights reserved. SeeRawFileReaderLicensefor licensing information.
Using DIMSpy software for processing Thermo Fisher Scientific *.raw files implies the acceptance of the RawFileReader license terms.
Anyone receiving RawFileReader as part of a larger software distribution (in the current context, as part of DIMSpy) is considered an “end user” under
section 3.3 of the RawFileReader License, and is not granted rights to redistribute RawFileReader.
Package: dimtim-utils
dimtim-utilsPython utils for Dim-Tim projectsChangelogv0.1.11[23.02.2024]fix command runner info outputv0.1.10[23.02.2024]fix command runner prepare hook orderv0.1.9[23.02.2024]fix command runner prepare hookupdate dependenciesv0.1.8[25.12.2023]addutils.proxy.DictProxyclassv0.1.7[21.12.2023]optimize command runneraddutils.importing.load_file_as_module(str, str?, bool?)v0.1.6[14.12.2023]minor fixesv0.1.5[14.12.2023]addutils.importing.import_string(str)methodaddPydanticV2compatibility to json serializerv0.1.4[13.09.2023]update requirementsaddtzdataas dependencyv0.1.3[24.03.2023]utils.timezonepackage fixesdeprecation ofpytzlibrary in favor ofzoneinfov0.1.2[06.10.2022]some fixes and update requirementsv0.1.1[15.06.2022]fixedhelpers.management.cron_command.CronCommandcron tasks setupv0.1.0[14.06.2022]addedhelpers.management.runner.CommandRunnerclassaddeddtrunconsole commandv0.0.18[07.06.2022]fixutils.json.JsonEncoderset and frozenset serializationv0.0.17[26.05.2022]fixutils.json.JsonEncoderdatetime serializationupdate requirementsv0.0.16[31.01.2022]fixutils.terminal.show_progress(int, int, str?, IOBase?)functionv0.0.15[31.01.2022]addedutils.terminal.show_progress(int, int, str?, IOBase?)functionv0.0.14[23.01.2022]fixhelpers.managementimport without installedpython-crontablibraryv0.0.13[22.01.2022]addedoutargument toutils.debug.timeit(str?, Logger?)to specify logger outputsmall fix in importing moduleaddedutils.text.to_kebab_case(str)functionadded some testsv0.0.12[17.01.2022]small json serializer fixesv0.0.11[17.01.2022]small fixesv0.0.10[17.01.2022]small fixesv0.0.9[17.01.2022]addedutils.decorators.freezeargs()decoratorv0.0.8[10.12.2021]updateutils.debug.timeit(str?)functionv0.0.7[10.12.2021]addedutils.text.inline(str)function
Package: dimtown-spider
dimtown-spider⭐基于httpx的为次元小镇的异步爬虫⭐⭐ 介绍本项目采用Apache License 2.0开源协议。在使用这个项目之前，请确保你已经仔细阅读并理解了协议的相关条款。本项目严禁用于任何商业用途，并且只能用于学习和研究。如果在使用本项目的过程中产生任何问题或结果，我们不承担任何责任。同时，我们鼓励用户为这个项目的改进和发展提供反馈和建议。🦈 安装安装pip 安装pip install dimtown-spider -Upoetry 安装poetry add dimtown-spider🐟 使用可见以下示例：# 测试程序fromdimtownimportSearch,ReturnImageimportasynciosc=Search("原神")loop=asyncio.get_event_loop()list_=loop.run_until_complete(sc.get_img_list())img_list=loop.run_until_complete(sc.get_img_url(list_[0]))asyncdefshow_image(img_list:ReturnImage):forimginimg_list:print(img)# 异步迭代器，显示图片asyncforimginimg_list:img.show()awaitasyncio.sleep(1)loop.run_until_complete(show_image(img_list))🐖 注意事项别爬太多适度使用健康生活鸣谢httpx->一款同步异步兼容的网络请求库
Package: dimutex
dimutexPython library implementingasyncio-based distributed mutex on top of different providers.Mutexis a synchronization primitive used to ensure that only one worker can do the given job. It can be used for safe access to a shared resource or for distributing tasks among multiple workers.Currently, the only implemented provider is GCS (Google Cloud Storage). The implementation is based on the algorithm described in articleA robust distributed locking algorithm based on Google Cloud Storage(see alsoRuby implementation).FeaturesAsynchronous.Type-safe.Atomic.Expiration mechanism to ensure that a single worker won't hold the lock forever.Supports emulators.Installationpython3-mpipinstalldimutexUsageimportdimutexasyncdefdo_something():lock=dimutex.GCS(bucket='bucket-name',name='lock-name')asyncwithlock:try:awaitlock.acquire()exceptdimutex.AlreadyAcquiredError:return'already acquired'try:...# do something with the shared resuourcefinally:awaitlock.release()
Package: dimweb-persona-bot
Datasetoriginal_datasethypothesis 1использовал CausalLM и Seq2Seq. Seq2Seq показал себя лучше.
Seq2Seq:
входная последовательность: сконкатенированная персона + chat: + последняя реплика от пользователя
таргет: ответ от пользователя
CausalLM:
входная последовательность: сконкатенированная персона + последняя реплика от пользователя+ответ от пользователя
таргет: входная последовательность сдвинутая на 1 вправоhypothesis 2Seq2Seq:
входная последовательность:
<bos> <persona> persona_fact[0]<p_sep>persona_fact[1]<p_sep>persona_fact[2]<p_sep>persona_fact[3]<p_sep>persona_fact[4]<p_sep> <chat> реплика[-6]<с_sep>реплика[-5]<с_sep>реплика[-4]<с_sep>реплика[-3]<с_sep>реплика[-2]<response>
таргет: реплика[-1] <eos>

CausalLM:
входная последовательность:
<bos> <persona> persona_fact[0]<p_sep>persona_fact[1]<p_sep>persona_fact[2]<p_sep>persona_fact[3]<p_sep>persona_fact[4]<p_sep> <chat> реплика[-6]<с_sep>реплика[-5]<с_sep>реплика[-4]<с_sep>реплика[-3]<с_sep>реплика[-2]<response>реплика[-1]<eos_token>
таргет: входная последовательность сдвинутая на 1 вправо

<с_sep> - специальный токен, который разделяет реплики.
<p_sep> - специальный токен, который разделяет персону.
<chat> - специальный токен, который разделяет реплики от персоны.
<persona> - специальный токен, который разделяет персону от реплик.
<response> - специальный токен, который разделяет реплики от ответа.hypothesis 3попробовать случайно перемешать порядок предложений в персоне. в остальном все остальное также как и в hypothesis 2hypothesis 4Seq2Seq:
входная последовательность:
<bos> <persona> persona_fact[0]persona_fact[1]persona_fact[2]persona_fact[3]persona_fact[4]<sep>реплика[-6] реплика[-5] ... <query>реплика[-2]<query/><eos>
таргет:<bos><response>реплика[-1]<response/><eos>

<sep> - специальный токен, раздедяющий токен
<query> - специальный токен, который оборачивает последнюю реплику пользователя
<query/> - 
<response> - специальный токен, оборачивает ответ пользователя
<response/>hypothesis 5тоже самое что и в hypothesis 4, но теперь исполььзую датасет FoCuspackage projectinstall project from gitpython3-mbuildtwineuploaddist/*
Package: dimz
dimzCLI tool for counting the number of lines of code in any given directory.$ dimz
File Name                Lines    Tokens    Tokens / Line  Language
---------------------  -------  --------  ---------------  ----------
dimz/__main__.py          67       509             7.60  Python
dimz/test.py              39       255             6.54  Python
setup.py                    35       134             3.83  Python
tools/autopep8_all.py       15        83             5.53  Python
dimz/__init__.py           3         9             3.00  Python
push_pypi.sh                 2        11             5.50  Shell
test.sh                      2         4             2.00  Shell

File count: 7
Total Line Count: 163
Total Token Count: 1005[!NOTE]
dimz only counts actual lines of code. Not whitespace. This means that if it tells you that you have a 100,000 line C project, you really did write 100,000 lines of C.LicenseCopyright (c) Brandon PacewicSPDX-License-Identifier: MIT
Package: dinamopy
DinamopyDinamopy is a python helper library fordynamodb. You can define youraccess patternsin a json file and can use dynamic method names to make operations.InstallationUse the package managerpipto install dinamopy.pipinstalldinamopyUsageapp.pyfromdecimalimportDecimalimportdinamopyclassMyHooks(dinamopy.DinamoHooks):defafter_get(self,response):fork,vinresponse.items():ifisinstance(v,Decimal):response[k]=int(v)returnresponsedb=dinamopy.DinamoPy('dinamopy.json',MyHooks())db.put(item={'customerId':'xyz','sk':'CUST#xyz','email':'rasimandiran@outlook.com','fullName':'Rasim Andiran','userPreferences':{'language':'en','sort':'date','sortDirection':'ascending'}})db.overwrite(item={'customerId':'xyz','sk':'CUST#xyz','email':'rasimandiran@outlook.com','fullName':'Rasim Andiran','userPreferences':{'language':'en','sort':'date','sortDirection':'ascending'}})db.get_customer_profile_by_customer_id(customerId='xyz')db.get_bookmarks_by_customer_id(customerId='123')db.get_customer_profile_by_email(email='shirley@example.net')db.get_bookmarks_by_url(url='https://aws.amazon.com',customerId='123')db.get_customer_folder(customerId='123',folder='Cloud')db.update_customer_profile_by_customer_id(customerId='321',sk='CUST#321',new_fields={'email':'rasimandiran@gmail.com'})db.update_bookmarks_by_customer_id(customerId='123',sk='https://aws.amazon.com',new_fields={'folder':'Tools'})db.update_customer_profile_by_email(email='rasimandiran@gmail.com',new_fields={'fullName':'Rasim Andiran'})db.update_bookmarks_by_url(url='https://aws.amazon.com',customerId='123',new_fields={'description':'Deneme'})db.update_customer_folder(customerId='123',folder='Cloud',new_fields={'folder':'CloudFolder'})db.delete_customer_profile_by_customer_id(customerId='123')db.delete_bookmarks_by_customer_id(customerId='123',sk='https://aws.amazon.com',new_fields={'folder':'Tools'})db.delete_customer_profile_by_email(email='rasimandiran@gmail.com',new_fields={'fullName':'Rasim Andiran'})db.delete_bookmarks_by_url(url='https://aws.amazon.com',customerId='123',new_fields={'description':'Deneme'})db.delete_customer_folder(customerId='123',folder='Cloud',new_fields={'folder':'CloudFolder'})dinamopy.json{"region":"localhost","port":8000,"tableName":"CustomerBookmark","partitionKey":"customerId","sortKey":"sk","timestamps":true,"paranoid":true,"returnRawResponse":false,"logLevel":"debug","accessPatterns":{"customerProfileByCustomerId":{"table":"table","partitionKey":"customerId","sortKey":"sk","sortKeyOperator":"begins_with","sortKeyValue":"CUST#"},"bookmarksByCustomerId":{"table":"table","partitionKey":"customerId","sortKey":"sk","sortKeyOperator":"begins_with","sortKeyValue":"http"},"customerProfileByEmail":{"table":"ByEmail","partitionKey":"email"},"bookmarksByUrl":{"table":"ByUrl","partitionKey":"url","sortKey":"customerId"},"customerFolder":{"table":"ByCustomerFolder","partitionKey":"customerId","sortKey":"folder"}}}To-DoMore generic hooksLoggingTestsDocumentationContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: dina-test
poetrystep 1: install poetry usingcurl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py|pythonbefore using the above code in Ubuntu system, you must set the default python to the version you want. Because by default python is set to version 2.7 in Ubuntu.step 2: Create a new projectpoetrynew[project-name]The output will create a structured project. It will create a ‘pyproect.toml’ file also . This ‘.toml’ file will have all the configuration for project.step 3: Some useful cli commands for poetry# add packages to projectpoetryaddnumpy,pandas# checkingpoetrycheck# build the packagepoetrybuild# publish to pypipoetrypublish# venv of poetrypoetryshellPoetry + Vscodestep 1: open the project with vscode.step 2: Set the environment:# add the poetry venv directory path to vscode setting"python.venvPath":"~/.cache/pypoetry/virtualenvs"# now add the python path from down below bar of vscode
# to project env.step 3: Set the testing in vscode:# open command palette'ctrl'+'shift'+'p'# search for 'test' in command palette# choose the python test from drop down# Then select pytest from drop down# In the down below of vscode a lightning
# 'Run test' will appear# In the left side of vscode a chemistry
# lab flask will appear for testing your tests.Poetry + Sphinxstep 1: Add the package in development modepoetryadd-Dsphinxstep 2: Create a new doc directory and inside it runsphinx-quickstart. Then fill out all the things it asks, incase of not knowing just press enter, it will take the default setting.step 3: The ‘docs’ folder now will contain_build,conf.pyandindex.rst. We need to edit them.step 4: Openconf.py# Uncomment themimportosimportsyssys.path.insert(0,os.path.abspath(".."))# Add sphinx packages and themeextensions=["sphinx.ext.todo","sphinx.ext.viewcode","sphinx.ext.autodoc"]# html_theme = "alabaster"html_theme="nature"step 5: Usemake htmlcommand for building html pages.step 6: open the html fromdocs/_build/html/index.html
Package: dinero
Dinero: Make exact monetary calculationsThis project is inspired by the excellentdinero.jslibrary.Python Decimal instances are enough for basic monetary calculations, but when you face more complex use-cases they often show limitations and are not so intuitive to work with. Dinero provides a cleaner and easier to use API while still relying on the standard library. So it's still Decimal, but easier.Read the DocumentationInstallpipinstalldineroThe problemUsing floats to do exact calculations in Python can be dangerous. When you try to find out how much 2.32 x 3 is, Python tells you it's 6.959999999999999. For some calculations, that’s fine. But if you are calculating a transaction involving money, that’s not what you want to see. Sure, you could round it off, but that's a little hacky.>>>2.32*3==6.96False>>>2.32*36.959999999999999You can readHow to Count Money Exactly in Pythonto get a better idea.Why Dinero?ADineroobject is an immutable data structure representing a specific monetary value. It comes with methods for creating, parsing, manipulating, testing and formatting.>>>fromdineroimportDinero>>>fromdinero.currenciesimportUSD>>>>>>Dinero(2.32,USD)*3==Dinero(6.96.USD)TrueCurrenciesDinero give you access to more than 100 different currencies:>>>fromdinero.currenciesimportUSD,EUR,GBP,INR,CLP>>>Dinero(2.32,EUR)Dinero(amount=2.32,currency={'code':'EUR','base':10,'exponent':2,'symbol':'€'})>>>Dinero(2.32,EUR).format(symbol=True,currency=True)'€2.32 EUR'>>>Dinero(2.32,EUR).raw_amountDecimal('2.32')More aboutcurrencies.OperationsOperations can be performed between Dinero objects or between Dinero objects and numbers by using Python operators or its own methods:>>>total=Dinero(456.343567,USD)+345.32*3>>>print(total)# 1,492.30>>>total=(Dinero(345.32,USD).multiply(3)).add(456.343567)>>>print(total)# 1,492.30More aboutoperations.ComparisonsDinero objects can be compared to each other by using Python comparison operators or by using its own methods:>>>Dinero(100,EUR)==Dinero(100,EUR)True>>>Dinero(100,EUR).equals_to(Dinero(100,EUR))TrueMore aboutcomparisons.ToolsDinero give you access to some useful tools that allow you to perform common monetary calculations, like percentages, VAT, simple and compound interests, etc.fromdineroimportDinerofromdinero.currenciesimportUSDfromdinero.toolsimportcalculate_compound_interestprincipal=Dinero("2000",USD)total_interest=calculate_compound_interest(principal=principal,interest_rate=5,duration=10,compound_frequency=12,)total_interest.format(symbol=True,currency=True)'$1,294.02 USD'See all the available tools in thetoolssection.Custom currenciesYou can easily create custom currencies:fromdineroimportDineroBTC={"code":"BTC","base":10,"exponent":2,"symbol":"₿",}Dinero(1000.5,BTC)Dinero(amount=1000.5,currency={'code':'BTC','base':10,'exponent':2,'symbol':'₿'})More aboutcustom currencies.
Package: dinette
Dinette Forum---------------The name==============Dinette is a composition by Django Reinhardt. I haven't personally heard it, but I believe that like everythingbuilt with or by Django it is going to be superb.Overview.===========Dinette is a forum built with Django. It uses the PunBB philosophy of being lightweight, but functional. Additionalyit takesthe HTML wholesale from PunBB. Like PunBB, it is GPL licensed.Why Another?================There are a lot of existing forum applications for Django, so why another.In our belief, there is no forum application, (Apart from that bundled with Ellington)of quality comparable to that available to PHP. Djorum is not there yet, but Usware plansto provide long term support for it,and continue to develop this depending on community support.Features===============1. Fully reuable, plug and play in your existing site.2. Plays well with other Django components, like auth and django-registration, admin.3. Support BBCode4. Supports File attachement.5. Supports image attachment6. Supports supercategories, categories, and topic. (Upto 3 levels of nesting).7. Support groups and limited visibility.8. Supports Karma9. Supports Ranks10.Supports announcements.11. Supports RSSAnd many, many others.Why use this==================Why use this instead of say PubBB?This integrates tightly with other Django subsystems, including Admin, User and Groups.How to use it.================1. Put it into your existing Django project.2. Syncdb3. Make your registration system create a DinetteUserProfile object for each user who is going to use Dinette.4. Create supercategories, categories and categories.5. Play.