Saravanan666/pypi-org huggingface dataset:
Package: placevent
Placevent - 3D-RISM-based solvent and ion placement software created by Daniel Sindhikara,sindhikara@gmail.comPlacevent
This program is designed to automatically place explicit solvent atoms/ions based
on 3D-RISM data. Placevent is compatible with AMBER .dx files and MDF .h5 files.The details of the algorithm are described in:
Placevent: An algorithm for prediction of explicit solvent atom distribution – Awpplication to HIV-1 protease and F-ATP synthase
Daniel J. Sindhikara, Norio Yoshida, Fumio Hiratahttp://dansindhikara.com/Software/Entries/2012/6/22_Placevent_New.htmlIf you have Numpy, this package should work out of the box. Please contact me if you have problems.
This package requires Numpy.
This package requires grid.py.EXAMPLE:
Please see the tests/1L2Y/ for examples/tests
In that directory, run test.sh to see if your local installation matches the reference output.TUTORIAL:
See my website:http://dansindhikara.com/Tutorials/Entries/2012/1/1_Using_3D-RISM_and_PLACEVENT.htmlfor a tutorial.g(r)_0 is printed in the occupation column
g(r)_i is printed in the beta columnCopyright (C) 2012 Daniel J. SindhikaraThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: pladder-client
pladder-clientA client for the Web API of the Pladder Bot.Installationpip install pladder-clientYou may need to replacepipwithpip3.UsageCreate a token usingcreate-tokenat the bot to get access. Then invokepladder-clientlike this:pladder-client 'echo This is a test command'The first time,pladder-clientwill ask for the API endpoint URL and the API token and then store it in a config file.Astruternalias forpladder-clientis also installed. They can be used interchangeably.
Package: plaft
PlaftSimple tool for automating the correction of programming language exercisesInstallationFirst make sure you have Python 3.6 or higher installed. You can download Pythonhere.To install plaft under Linux / OS X:pip install plaftUnder Windows, pleaseinstall the Linux subsystem. Then install plaft within the subsystem.UsageFor example, the directory that contains all the student code files directories, look like this:+ Tests
|-+ student1
    |-- ex01.py
    |-- ex02.py
|-+ student2
    |-- ex01.py
    |-- ex02.pyTo check of students code files, run plaft like so:plaft -a "/tests" -c "tests.yaml"
Package: plagcheck
PlagCheck ✅Moss Results scraper with powerful insights & analysis 💡InstallationInstall usingpipfrom PyPIpipinstallplagcheckor directly from GitHub if you cannot wait to test new featurespipinstallgit+https://github.com/codeclassroom/PlagCheck.gitUsage"""Usage example"""importosimportpprintfromplagcheckimportplagcheckfromdotenvimportload_dotenvload_dotenv()language="python"userid=os.environ["USER_ID"]moss=plagcheck.check(language,userid)moss.addFilesByWildCard("testfiles/test_python*.py")# or moss.addFile("testfiles/test_python.py")moss.submit()print(moss.getHomePage())pprint.pprint(moss.getResults())# print frequency of each shared solutionpprint.pprint(moss.getShareScores())# print potential distributor-culprit relationshipspprint.pprint(moss.getInsights())DocumentationPlagCheck DocumentationDevelopmentPrerequisitesPython 3.6+virtualenvCreate virtual environment.virtualenv-ppython3venv&&cdvenv&&sourcebin/activateClone the repository.githttps://github.com/codeclassroom/PlagCheck.gitInstall Dependencies.pipinstall-rrequirements-dev.txtRun tests.pytestplagcheckLint the project withflake8plagcheck--max-line-length=88--ignore=F401
black--check--diffplagcheck📝 ChangelogSee theCHANGELOG.mdfile for details.Author👥Bhupesh VarshneyTwitter:@bhupeshimselfDEV:bhupesh📜 LicenseThis project is licensed under the MIT License. See theLICENSEfile for details.👋 ContributingPlease read theCONTRIBUTINGguidelines for the process of submitting pull requests to us.Contributors ✨Thanks goes to these wonderful people (emoji key):Alexey Dubrov💻🐛⚠️This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: plagchecker
The python package is focused on checking Plagiarism from covered content it is the extension of service offered by prepostseo (https://www.prepostseo.com/plagiarism-checker)InstallFor installation one can proceed tohttps://pypi.org/project/plagchecker/or can usepipinstallplagcheckerUsageThe package focus on usage for checking plagiarised content and amount of plagiarised content present in the data spread over upto 5000 words or less with non-premium version ofhttps://www.prepostseo.com/plagiarism-checkerModulesThere is 1 module namedinit.pyClass currently present in module: Checkerinitialization of class: Checker()The constructor takes in 1 manadatry argument i.e the APIKEY provided bywww.prepostseo.comby signning up for free version.Methods: Total of 7 Methods are present in Class: CheckerMethod 1:init(self,apiKey):Method is default constructor of the class Checker and takes in 1 manadatry argument i.e the APIKEY fromwww.prepostseo.comMethod 2: getDataLen(self,data):Method returns boolean value based on the count of words present in data if count is less than or equal to 5000 words return True else False.Method 3: response(self,data):Method returns 2 (two) values in string format:status code of the querry: i.e 200 for successful execution in string format.json response received upon successful execution or empty stringMethod 4: getPlagPercent(self):The function is used to get the percentage of plagiarised content. It returns the %(percentage) value in string formatMethod 5: getUniquePrecent(self):The function is used to get the precentage of unique content present in the data. It returns the %(percentage) value in string formatMethod 6: getSources(self):The function is used to get the sources from where the content was taken if found plagiarised in the data. It returns sources in the form of a list.Method 7: getDetails(self):The function is used to get the additional details of individual querries in data. It returns the additional details in the form of list.Created & Maintained ByShrey PatelSponsor Me for my educationSponsor Shrey via buymecoffeeSponsor Shrey directly
Package: plagdef
PlagDefA tool which makes life hard for students who try to make theirs simple.InstallationGet it from PyPI:$ pip install plagdefOr build it yourself:$ git clone git://github.com/devWhyqueue/plagdef
$ poetry build
$ pip install dist/plagdef-{VERSION}.tar.gzRequirementsChromePlagDef uses Selenium to automate the translation of documents.
By default, it uses a headless version of Chrome.OCRMyPDFThis library is used for improved PDF text extraction.To install its necessary dependencies for your operating system take a look at:https://ocrmypdf.readthedocs.io/en/latest/installation.htmlAnd don't forget to download the German language pack to yourtessdatafolder from here:https://github.com/tesseract-ocr/tessdataLibmagicAfter(important!) you installed PlagDef, install the libmagic library.PlagDef uses it to detect character encodings.Further instructions can be found here:https://github.com/ahupp/python-magic#installationUsageRun the GUI:$ plagdef-guiOr if you prefer a CLI:$ plagdef -hDevelopmentClone the repo and install dependencies:$ git clone git://github.com/devWhyqueue/plagdef
$ poetry installPublish to PyPIIn your virtual environment build and upload PlagDef:$ poetry publish --build
Package: plagiarism
Library for detecting plagiarism in source code. Userful for online judges,
teachers, developers and maybe lawyers.Plagiarism uses the method described [here](http://…). The basic idea is to
classify each submitted file according to different metrics and perform a
series of k-means based clusterizations to determine which objects are most
similar to each other. This approach has a N log N cost and scales fairly well
to big samples.The algorithm can be applied to natural text, source code and can even be
adapted to run on arbitrary data structures (such as the parse tree of a
computer program, ASM output, even binary executables). It requires some tuning
for each application and accuracy may vary widely depending on application.
You should expect better results grading Python and C source code. Performance
on other programming languages or even in other domains may vary.
Package: plagiarism-MCA2022temp
PlagyA Simple Plagiarism detector. It supports .txt, .c, ,cpp, ,py, ,java or any other utf-8 encoded text files.It Provides 3 Scenarios to check the Plagiarism.One2One: Check the plagiarism score between two files. The files must be of same type.One2Many: Checks the Plagiarism score betweem one query file and a list of source files. One typical use case would be a newly published journals vs a list of already published journals, to check if the new journal is plagiarized from other sources or not.Many2Many: Checks the similiarity between every pair of files in a collection of files.InstallationInstall this package from pip directlypip install plagiarism-MCA2022temporpip3 install plagiarism-MCA2022tempor clone this repo in your local environment andpython setup.pyUsesfrom plagy.plagy import *
obj = One2One(path_to_file1, path_to_file2)
obj.preprocess_files()
obj.run()
obj.display()obj = One2Many(path_to_source_folder, path_to_query_file)
obj.preprocess()
obj.run()
obj.display()obj = Many2Many(path_to_collection_folder)
obj.preprocess()
obj.run(threshold = 0.7)
obj.display()
Package: plagiat
PlagiatLibrary untuk memeriksa tingkat Plagiarisme atauSimilaritymenggunakan Bahasa Python. Secara default library ini akan menggunakan Algoritma Rabin Karp sebagai perhitungan utamanya.Pustaka ini dibuat hanya sebagai penunjang untuk membantu saya menguji-coba dan memahami semua konsep algoritma untuk menghitung similaritas.Instalasipip install plagiatCara PenggunaanMenggunakan File .txtfromplagiat.deteksiimportDeteksifile_1='/content/kalimat-1.txt'file_2='/content/kalimat-2.txt'cek=Deteksi(file_1,file_2,url=True).hitung()print('Persentase plagiarisme ={0}%'.format(cek))Menggunakan Textfromplagiat.deteksiimportDeteksistring_1="Aku sedang belajar kecerdasan buatan"string_2="Mahasiswa yang cerdas selalu siap menerima tantangan"cek=Deteksi(string_1,string_2,text=True).hitung()print('Persentase plagiarisme ={0}%'.format(cek))Menggunakan URLfromplagiat.deteksiimportDeteksiteks_1='https://raw.githubusercontent.com/novay/amikom/main/datasets/text/kalimat-1.txt'teks_2='https://raw.githubusercontent.com/novay/amikom/main/datasets/text/kalimat-1.txt'cek=Deteksi(teks_1,teks_2,url=True).hitung()print('Persentase plagiarisme ={0}%'.format(cek))Penggunaan Parameterfromplagiat.deteksiimportDeteksiDeteksi(teks_1,teks_2,text=True,url=True,bahasa='english',method='Cosine').hitung()Penjelasantext=Truedigunakan untuk mendeteksi stringdefault Falseurl=Truedigunakan untuk mendeteksi dokumen melalui URLdefault Falsebahasa='english'digunakan untuk menentukan bahasa yang digunakan dalam proses stopwordsdefault 'indonesian'method='Cosine'digunakan untuk mengubah metode yang ingin digunakandefault 'Rabin Karp', pilihan 'Rabin Karp', 'Cosine', 'Jaccard'ReferensiRabin Karp:Ranti Eka Putri, Andysah Putera Utama Siahaanhttps://www.researchgate.net/publication/319272358_Examination_of_Document_Similarity_Using_Rabin-Karp_AlgorithmAndysah Putera Utama Siahaan, Mesran, Robbi Rahim, Dodi Siregarhttps://www.ijstr.org/final-print/july2017/K-gram-As-A-Determinant-Of-Plagiarism-Level-In-Rabin-karp-Algorithm.pdfJaccard Similarity:https://en.wikipedia.org/wiki/Jaccard_indexCosine Similarity:Jiapeng Wang, Yihong Donghttps://www.researchgate.net/publication/344010599_Measurement_of_Text_Similarity_A_SurveyDisclaimerLibrary ini di buat hanya untuk keperluan pembuatan tugas Data Science.Output mungkin saja bisa berbeda dengan pustaka lain khususnya perhitungan Cosine, karena dalam implementasinya ada variasi dalam cara perhitungan vektor TF-IDF, tokenisasi kata, dan faktor-faktor lainnya. Agar hasil lebih maksimal, lebih baik handle dulu masalah stop word, n-gram, dan normalisasi secara mandiri karena perhitungan yang dilakukan dalam pustaka ini hanya melakukan normalisasi sederhana sebelum dieksekusi.Salam hormat,Novianto Rahmadi (22.55.2293)CreditPaper Ranti Eka Putri, Andysah Putera Utama Siahaan, Mesran, Robbi Rahim & Dodi SiregarNLTK - corpus, tokenize, stemNumpyTheDhejavu - Rabin Karp Modulehttps://gist.github.com/TheDhejavu/39d6cfec2b3f75a1ac111042cb8aebdb#file-rabin_karp-pyJiapeng Wang, Yihong Dong (Cosine Similarity)
Package: plagy-mnk
PlagyA Simple Plagiarism detector. It supports .txt, .c, ,cpp, ,py, ,java or any other utf-8 encoded text files.It Provides 3 Scenarios to check the Plagiarism.One2One: Check the plagiarism score between two files. The files must be of same type.One2Many: Checks the Plagiarism score betweem one query file and a list of source files. One typical use case would be a newly published journals vs a list of already published journals, to check if the new journal is plagiarized from other sources or not.Many2Many: Checks the similiarity between every pair of files in a collection of files.InstallationInstall this package from pip directlypip install plagy-mnkorpip3 install plagy-mnkor clone this repo in your local environment andpython setup.pyUsesfrom plagy.plagy import *
obj = One2One(path_to_file1, path_to_file2)
obj.preprocess_files()
obj.run()
obj.display()obj = One2Many(path_to_source_folder, path_to_query_file)
obj.preprocess()
obj.run()
obj.display()obj = Many2Many(path_to_collection_folder)
obj.preprocess()
obj.run(threshold = 0.7)
obj.display()
Package: plai
PlaiPlai is a domain specific programming language(DSL) to create data manipulation pipelines with focus on data treatment, validation and easier syntax. It usespandasas data manipulation engine so it is meant to work with small data.ExamplesExample of pipeline with basic data manipulation using Plai:df = read_file('issues.csv')

pipeline(df) as 'gh_pct_issues_by_language.csv':
    $.groupby(.name, as_index=False).sum()
    (.count/.count.sum()) * 100 as pct
    {.name, .count, .pct}To create validations for the dataframes being manipulated you can define dictionaries mapping each column to a specific type, and apply
that to a dataframe or pipeline. When applied to the dataframe it will validate its schema accordingly to the defined on the dictionary, that is, it will check data type and column presence. For the pipeline, the result dataframe will be validated. The following snippet is an example of implementation:input_type = {
    'name': 'str',
    'year': 'int',
    'quarter': 'int',
    'count': 'int'
}

output_type = {
    'name': 'str',
    'count': 'int',
    'pct': 'float'
}

input_type::df = read_file('issues.csv')

output_type::pipeline(df) as 'gh_pct_issues_by_language.csv':
    $.groupby(.name, as_index=False).sum()
    (.count/.count.sum()) * 100 as pct
    {.name, .count, .pct}DevelopmentInstall the dependencies by running the command on the root folder of the project:pip install -r requirements-dev.txtTo run all the tests execute:pytest testsTo run a specific test execute:# For a specific test file
pytest tests/test_grammar.py

# For a specific test class
pytest tests/test_grammar.py::TestBasicTokens

# For a specific tests method
pytest tests/test_grammar.py::TestBasicTokens::test_token_numberTo run the interactive terminal execute on the root folder:python -m plaiTo execute the code from a file:python -m plai file.plai
Package: plaid
PlaidPython Ansible role checkerFree software: MIT licenseDocumentation:https://plaid.readthedocs.io.FeaturesGeneric ansible role checker. Parses yaml, jinja2 templates and checks for
pep8/flake 8 python errors. Also check for files we expect and those we don’t.
This is configured in the .plaidrc file:---

include_files:
  - README.md
  - molecule.yml

exclude_files:
  - .travis.ymlAlso in the case a user creates a base role using ansible-galaxy, the tool
checks for ‘empty’ main.yml files in ‘tasks’, ‘defaults’, ‘handlers’ and ‘vars’,
and checks for empty ‘files’ and ‘templates’ directories. In the future there
are plans to check the default information in the ‘README.md’, ‘meta’ and
‘tests’.History0.1.0 (2017-02-09)First release on PyPI.
Package: plaid2
Plaid client, generated from the OpenAPI spec.Usageimportosfromplaid2importAsyncPlaidClientfromplaid2importPlaidClientdefmain():client=PlaidClient.from_env()response=client.item_application_list()print(f"{response!r}")asyncdefasync_main():client=AsyncPlaidClient.from_env()response=awaitclient.item_application_list()print(f"{response!r}")if__name__=="__main__":ifos.environ.get("ASYNC"):importasyncioasyncio.run(async_main())else:main()This example loads configuration from environment variables, specifically:PLAID_ENVPLAID_CLIENT_IDPLAID_SECRETPLAID_VERSIONDocumentationYou can see working examples of every API call in theexamples/directory.ContributingContributions are welcome!Library created withLibninja.
Package: plaidcloud-rpc
plaidcloud-rpcCommon utilities for connecting to PlaidCloud's rpc interfaceDocumentation available athttps://plaidcloud.com/docs/plaidcloud-rpc/InstallationUNIX (Linux/Mac OSX)To install, run:sudo pip install plaidcloud-utilitiesWindowsOpen Anaconda command prompt as Administrator (this is equiv to sudo access)Navigate to the repoEnter the following command:pip install plaidcloud-utilities
Package: plaid-lukepafford
plaid_lukepaffordUses Plaid to instantiate a Chase connection and save transaction data
locally.Graphs and summaries will be built that I find interesting.Main object is atplaid_lukepafford.transactions.ChaseTransactionsInstallInstall packagepip install plaid_lukepaffordFetch transactions from Plaid API. (This will fail if you don't provide correct environment variables)chase-plaid fetch-latest-transactionsDisplay a table summarizing resultschase-plaid money-spent-on-food
Package: plain-abc
plain-abcAnotherABCimplementation withoutmetaclass.It is a little bit annoying to havemetaclassconflict,
especially when trying to use ABC along with other libraries.plain-abcprovides a simpleABCimplementation withoutmetaclass.Installationpipinstallplain-abcUsageBut you can also useplain-abcto solve the problem:fromabcimportabstractmethodfromplain_abcimportPlainABCclass_SomeHiddenMetaclass(type):passclassBase(metaclass=_SomeHiddenMetaclass):passclassIFoo(PlainABC):@abstractmethoddeffoo(self):...classFoo(Base,IFoo):deffoo(self):...To extend an abstract classas another abstract class,PlainABCis required to be one of the bases:fromabcimportabstractmethodfromplain_abcimportPlainABCclassIEntity(PlainABC):@abstractmethoddefget_id(self)->str:...classIProjectile(IEntity,PlainABC):@abstractmethoddefget_speed(self)->float:...classArrow(IProjectile):defget_id(self)->str:...defget_speed(self)->float:...To skip signature checking,
you can add the member names in__abc_concrete_members__of a subclass:fromabcimportabstractmethodfromenumimportEnumfromplain_abcimportPlainABCclassIEnum(PlainABC):@property@abstractmethoddeffoo(self)->str:...classFoo(IEnum,Enum):# for python 3.10 or lower__abc_concrete_members__=('foo',)foo='foo'assertFoo.foo.value=='foo'To solvemetaclassconflict withoutplain-abcHere is an example ofmetaclassconflict
and how to mixABCMetawith othermetaclasses.fromabcimportABC,ABCMeta,abstractmethodclass_SomeHiddenMetaclass(type):...classBase(metaclass=_SomeHiddenMetaclass):...classIFoo(ABC):@abstractmethoddeffoo(self):...# oh no, metaclass conflict!# class Foo(Base, IFoo):#     def foo(self): ...# create a new metaclass to solve the conflictclassNewMetaclass(_SomeHiddenMetaclass,ABCMeta):...classFoo(Base,IFoo,metaclass=NewMetaclass):deffoo(self):...
Package: plainbox
PlainBoxPlainBox is a toolkit consisting of python3 library, development tools,
documentation and examples. It is targeted at developers working on testing or
certification applications and authors creating tests for such applications.PlainBox can be used to both create simple and comprehensive test tools as well
as to develop and execute test jobs and test scenarios. It was created as a
refined and rewritten core of the CheckBox project. It has a well tested and
documented core, small but active development community and a collection of
associated projects that use it as a lower-level engine/back-end library.PlainBox has a novel approach to discovering (and probing) hardware and
software that is extensible and not hardwired into the system. It allows test
developers to express association between a particular test and the hardware,
software and configuration constraints that must be met for the test to execute
meaningfully. This feature, along with pluggable test definitions, makes
PlainBox flexible and applicable to many diverse testing situations, ranging
from mobile phones, traditional desktop computers, servers and up to testing
“cloud” installations.External Documentation LinksUsing PlainBoxHacking on PlainBoxTesting PlainBoxKnown Issueshttps://bugs.launchpad.net/plainbox
Package: plainchart
PlainChartA simple plain-text, no-dependencies,pip-installable, open-source charting utility in Python.Usage:>>>importplainchart>>>chart=plainchart.PlainChart([3,1,4,1,5,9,2,6,5,3,5,9])# 🥧>>>print(chart.render())▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌InstallationTo install PlainChart, you can usepipenvor pip:$pipenvinstallplainchartFeaturesWith PlainChart, you can:render an array of values in a plain text chartlimit the height of the chart and have the values rendered accordinglyrender a different style of chart, e.g.,plainchart.PlainChart.barorplainchart.PlainChart.scatterimplement your own style of chart, e.g.,mean_html(see example below)Examples>>>importplainchart>>>importrandom>>>>>>values=[random.randint(0,10)for_inrange(100)]>>>chart=plainchart.PlainChart(values)>>>>>>print(chart.render())▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌>>>importplainchart>>>importmath>>>importnumpyasnp>>>>>>values=[1.3+math.sin(x)forxinnp.linspace(0,4*math.pi,num=100)]>>>chart=plainchart.PlainChart(values,style=plainchart.PlainChart.scatter)>>>>>>print(chart.render())××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××You can also implement your own style of chart. Below is an example of a HTML chart (mean_html.py) with different colors for values below and above the mean.importplainchartimportrandomimportstatisticsdefmean_html(chart,value,y):mean=statistics.mean(chart.values)mean_y=chart.y(mean)value_y=chart.y(value)ifvalue_y<=mean_y:ify<=value_y:return'<span style="color:green">▌</span>'return'<span style="color:white">▌</span>'else:ify<=mean_y:return'<span style="color:green">▌</span>'elify<=value_y:return'<span style="color:red">▌</span>'return'<span style="color:white">▌</span>'values=[random.randint(0,10)for_inrange(100)]chart=plainchart.PlainChart(values,style=mean_html)print(chart.render(new_line='<br>'))$pythonmean_html.py>mean.htmlContributePlease feel free to open an issue to propose a new feature or point out a bug. You can also fork the PlainChart repository and submit a pull request.SupportPlainChart is free and under theMIT License. To support its development, you can make a donation tocash.me/$gduverger.