Saravanan666/pypi-org huggingface dataset:
Package: tajimas-d
Compute the Tajima's-D, Pi-Estimator or Watterson-Estimator for multiple sequences.Tajima's D is a population genetic test statistic that computes the difference between the mean number of pairwise differences and the number of segregating sites. It is used to determine whether a population is expanding or shrinking.Tajima's DTajima's D is defined as follows:If, there are many rare variants, indicating anexpandingpopulation.Whereas, indicates andecliningpopulation as there are many intermediate variants.A result is consideres significant ifor.Pi-EstimatorThe π estimator is the average number of pairwise differences between any two sequences:Watterson-EstimatorThe Watterson estimator is the expected number of segregating sites.InstallationUsing pip  / pip3:pipinstalltajimas_dUsing conda:condainstall-cbiocondatajimas_dOr by source:gitclonegit@github.com:not-a-feature/tajimas_d.gitcdtajimas_d
pipinstall.How to usefromtajimas_dimporttajimas_d,watterson_estimator,pi_estimatorsequences=["AAAA","AAAT","AAGT","AAGT"]theta_tajima=tajimas_d(sequences)theta_pi=pi_estimator(sequences)theta_w=watterson_estimator(sequences)Standalone versionusage: tajimas_d [-h] -f PATH [-p] [-t] [-w]

tajimas_d: Compute Tajima's D, the Pi- or Watterson-Estimator for multiple
sequences.

optional arguments:
  -h, --help            show this help message and exit
  -f PATH, --file PATH  Path to fasta file with all sequences.
  -p, --pi              Compute the Pi-Estimator score.
  -t, --tajima          Compute the Pi-Estimator score. (default)
  -w, --watterson       Compute the Watterson-Estimator score.LicenseCopyright (C) 2022 by Jules Kreuer - @not_a_feature
This piece of software is published unter the GNU General Public License v3.0
TLDR:

| Permissions      | Conditions                   | Limitations |
| ---------------- | ---------------------------- | ----------- |
| ✓ Commercial use | Disclose source              | ✕ Liability |
| ✓ Distribution   | License and copyright notice | ✕ Warranty  |
| ✓ Modification   | Same license                 |             |
| ✓ Patent use     | State changes                |             |
| ✓ Private use    |                              |             |Go toLICENSE.mdto see the full version.DependenciesIn addition to packages included in Python 3, this piece of software uses 3rd-party software packages for development purposes that are not required in the published version.
Go toDEPENDENCIES.mdto see all dependencies and licenses.
Package: takamol-einvoicer-test-kit7
This package is an implementation for e-invoices that comply with ZATCA regulations.Quick startAdd “takamol-einvoicer-test-kit2” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'takamol-einvoicer-test-kit2',
]2. Include the takamol_einvoicer_test_kit2 URLconf in your project urls.py like this::
url(r’^takamol_einvoicer_test_kit2/’, include(‘takamol_einvoicer_test_kit2.urls’)),Runpython manage.py migrateto create the invoices models.Start the development server and visithttp://127.0.0.1:8000/admin/
Package: takanori-cli
takanori-cliTakanori commands to automate daily works.Installpipinstalltakanori-cliUsageCreate pluginsExecute commands below.takaplugincreateAnd answer some questions to generate a pacpage froma template. It's also available to create a pcakge from custom templates by using-tor--templateoptions.takaplugincreate--template<custom_template>
Package: takao
TakaoTakao is a dark theme for theSphinx documentation generator.I created it mainly for my own projects. Feel free to try it and open an issue
if you have any problems.DemoFor a demo of the theme, seehttps://columns-ui-sdk.readthedocs.io.LicenceTakao uses the MIT licence.Distributions of Takao include theInter font,
whichuses the SIL Open Font Licence.InstallationInstall the Python package:pipinstalltakaoUpdatehtml_themein your Sphinx project configuration:html_theme="takao"Create a clean documentation build in your Sphinx project directory:makeclean
makehtmlDevelopmentDevelopment of Takao requires Python 3.11,Poetryand Node.js 18.Set upInstall Python dependencies:poetryinstallInstall Node.js dependencies:npminstallBuild a wheelpoetryrunpythonscripts/build.pyBuild the docspoetryrunpythonscripts/build_docs.pyRun visual regression testsRequiresPodman.poetryrunpythonscripts/run_e2e_tests.pyUpdate changed snapshotspoetryrunpythonscripts/run_e2e_tests_update_snapshots.py
Package: takara
Takara is a secure secrets store. Takara differs from other secret stores in
that it supports progressive usage. Secret stores need to be easy to use and
easy to start with, but able to scale from easy local security all the way up
to enterprise and military level security. If someone starts with something
simple, it is optimal if it can scale all the way up.Simple Usage TutorialGetting started with Takara is easy, it has a lot of options but you don’t need
to know them! In fact you only need to know that takara stores encrypted data in
a hierarchy, so data has a location, just like a file on a filesystem.InstallWe will just need to install the basictakarafor this to work:pipinstalltakaraCreate a Crypt StoreTakara stores encrypted data inunits. This allows you to have separate units
that store separate data, these unit can even be used to store data in different
storage systems with different access systems.But we don’t need to worry about that now! We just need to make our first unit!
If you don’t tell it what to name the unit, then the unit will default to being
calledmain. Lets make themainunit now:takaracreateThis will create a secure store that is password protected. The command will
prompt you for the password that you wish to use to secure the secrets.Set a ValueNow that we have a unit set up we can set a value inside it. Just
telltakarato chose a path for the unit to be stored and the data to be
stored in said unit.takaraset-pmy/secret-s'The speed of an unladen swallow...'Done! Now the encrypted secret is secure inside of takara.Get the ValueGetting the value out is just as easy:takaraget-pmy/secretNow the plain text of the secret is presented back!
Package: takashi
Documentation–GitHubA [simple] [general-purpose] Python template 🐍🚀🎉🦕importpackage# NOTE: URI params must be strings not integersprint("this is just for testing")When the module containing this class is loaded,GitHubUser.urlis
evaluated and so the template is created once. It’s often hard to notice in
Python, but object creation can consume a great deal of time and so can theremodule which uritemplate relies on. Constructing the object once should
reduce the amount of time your code takes to run.Installingpip install takashiLicenseGPLlicense
Package: takayi
# TakayiType hints for python 2.X.`takayi` means `high` in japanese.## Usage```pythonfrom takayi.parser import Parser, typehintsparser = Parser()# ==================================================@typehints(parser)def get_sum(x, y):# type: (int, int) -> intreturn x + y_sum = get_sum(1, 2)  # result should be 3# takayi.exc.ParameterTypeError: Expect => [<type 'int'>, <type 'int'>], Actually => [<type 'int'>, <type 'str'>]_err_sum = get_sum(1, 'hello')# ==================================================@typehints(parser)def test(x, y=1):# type: (int, y: int) -> intreturn x + yassert test(1, y=10) == 11# ==================================================class Node(object): passnode = Node()@typehints(parser, attach_cls=Node)def get_node():# type: () -> Nodereturn node```## TODO- [x] Support kwargs type check- [ ] More types. FYI: [pep484](https://www.python.org/dev/peps/pep-0484/)
Package: take-a-break
Take-a-Breaka small python application that will remind me to take breaks after a period of time (configured).when triggered - this app will display a random image fromunsplashfollowed by a randomjokewhyi've built this app just to learn how to build a python package from scratch, how to version control it, test it and release it topypi.orgusingtravis-ci.orgprerequisitespython 3.8 or greaterinstallationpipinstalltake-a-breakusagepython-mtake_a_breakthe application will remain opened in console and will be displayed a notification after x minutesthank youhanddrawngoodsfor freeicon.icoicndb.comfor funny jokesunsplashfor free images
Package: take-ai-evaluation
TakeAiEvaluationTakeAiEvaluation is a tool toprovide metrics and visualizations for evaluating a chatbot's AI utilization.This currently addresses two types of evaluation: Knowledge Base Quality and Message Base Information.InstallationThetake_ai_evaluationpackage can be installed from PyPI:pipinstalltake_ai_evaluationUsageAs input, either apandas.DataFrameor aCSVfile path can be used.Matrix all vs allimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_all_vs_all_confusion_matrix(title='All vs All')plt.show()Matrix one vs allimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_one_vs_all_confusion_matrix(intent='Intent',title='All vs All')plt.show()Best intentJust the values for the default metric, which is 'accuracy'importmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_best_intent()plt.show()Just the values for 'recall' metricimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_best_intent(metric='recall')plt.show()As graphimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_best_intent(as_graph=True)plt.show()Worst intentJust the values for the default metric, which is 'accuracy'importmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_worst_intent()plt.show()Just the values for 'recall' metricimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_worst_intent(metric='recall')plt.show()As graphimportmatplotlib.pyplotaspltfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_worst_intent(as_graph=True)plt.show()Classification Reportfromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_classification_report()As pandas DataFramefromtake_ai_evaluationimportAiEvaluationai_evaluation=AiEvaluation(analysed_base='knowledge-base.csv',sentence_col='id',intent_col='intent',predict_col='predicted')ai_evaluation.get_classification_report(as_dataframe=True)AuthorTake Blip Data&Analytics Research (ROps)
Package: takeaway
# TakeAwayTakeAway is a command line interface for ordering food via:AT:https://www.lieferservice.atBE:https://www.pizza.beCH:https://www.lieferservice.chFR:https://www.pizza.frLU:https://www.pizza.luNL:https://www.thuisbezorgd.nlPT:https://www.pizza.ptVT:https://www.vietnammm.com## Installationpip install takeaway## Quick starttakeaway country postcodeAvailable countries: AT, BE, CH, FR, LU, NL, PT, VT.For Vietnam see [here](https://github.com/Ovvovy/TakeAway/wiki/Vietnam).### Create the database–db-make### SearchSearch for meals in every restaurants:-m meal1 meal2 …Search for meals in restaurants:-m meal1 meal2 … -r restaurant1 restaurant2 …Add meals to your basket:-a index index …### UserCreate an user:–u-create usernameLoad an user:-u username### Order–checkout –confirm### All in onetakeaway country postcode -m meal … -r restaurant -a index -u username –checkout –confirm## LinksGithub:https://github.com/Ovvovy/TakeAwayPypi:https://pypi.python.org/pypi/takeaway
Package: take-blip-scanner
IntroductionTake Blip Scanner - Finite State Machine implentation for Text ScannerGetting StartedUse suggested conda env inside 'conda_env' folderCreate: conda env create --file conda_env\take_scanner.ymlUpdate: conda env update --file conda_env\take_scanner.ymlRun 'python -m build' in terminal to create a local distributionWill create 'dist' folder with lattest version of TakeBlipScannerInstall itpip install dist\take-blip-scanner-X.Y.Z.tar.gzrun benchmark with 'python benchmark\benchmark_lex.py benchmark\benchmark.txt 10 0''benchmark\benchmark.txt' is the bench file'10' - is the number of runs to generate statistics'0' - is the benchtype: 0 - full bench, 1 - just the Scanner Lexycal, 2 - just the SentenceTokenizerif outputs looks liks bellow, everyting is okSTART: BenchmarkLexycal_2 >> With 3 runs, takes 1.295s * 0.147; Median: 1.212Lexycal_3 >> With 3 runs, takes 1.224s * 0.029; Median: 1.235SentenceTokenizer >> With 3 runs, takes 1.811s * 0.010; Median: 1.81200:00:13END: BenchmarkBuild and TestBUILD- run 'python -m build'TEST- Two steps:install local package ´pip install dist\take-blip-scanner-X.Y.Z.tar.gz´run 'pytest'ContributeTODO: Explain how other users and developers can contribute to make your code better.
Package: take-blipscore
Take Blip ScoreThis project proposes tooffer a rate named Blip Score and represents a general metric for bot.The proposal uses thesatisfaction rateandresolution rateas input to create the BLiP Score rate.
The value of the rate is in interval (0, 1) and it is better as near to 1 (one)InstallationUse [pip] to install:pipinstalltake-blipscoreUsageimporttake_blipscoreastbsresult=tbs.run(satisfaction_rate=0.67,resolution_rate=0.82)print(result["rate"])Which will result in0.7665976833542061.AuthorTake Data&Analytics Research - squad XD.
Package: takedown
TakeDown ScriptA simple python script/package that allows users to search potential
copyright violated information on GitHub(probably more sites in future).
It returns the targets with a set of usernames.Requirementspython >= 3.8
requests~=2.23.0
PyYAML~=5.3.1Arequirements.txtis provided, so simply run:pip install -r requirements.txtSo all dependencies will be added. Of course you should havepipinstalled.InstallSimply clone this repo and runpip install -e .Or install it from PyPipip install takedownUsageThere are a program provided calledtakedownand the entire package calledtakedown.softwareAfter installing the package, runtakedownin command line.packageThere are only GitHub TakeDown implemented now, so here's a piece of sample for you.fromtakedown.client.GitHubimportGitHubClientclient=GitHubClient()# authenticate and search# you can choose not to authenticate, but limitations apply. check docs for detailsresults=client.authenticate("Your personal GitHub token").search("this is awesome","code",)# check fields options that can be used for list generationprint(results.get_fields())results.generate_list(['owner__login','owner__html_url'])For more samples, visitexample_scriptsfolders.NotesFor GitHub client to search, there are certain restrictions:You must provided a personal token to search the entire GitHuh site, or
you must specify a user, org, or repo.The max length limit of search query is 256, so keep it shorter.There is a rate limit of sending GitHub REST requests.Consult thispageif you have more questions.To get a personal token(free), check
thistutorial.The implementation only returns the list of max 100 search results. It is very uncommon
that will be more than 50 repos or codes results that
match a very specific pattern. Your search is either too broad(so narrow the search down), or your
work is entirely leaked. Limits might be raised in the future.
Package: take-forecast
Take ForecastA time series forecasting library that uses Kedro pipelines.For performing the forecast, there are four main steps:Tune:Analyse the time series to find seasonality, unit roots,
memory lags and stochastic process lags.Fit:Fit a SARIMA model with hyperparameters obtained
in the last step and input data.Predict:Make forecast using the fitted model.
Also provide upper and lower boundaries for confidence interval.Evaluate:Assess the model quality by error metrics evaluated
in train and test sets.InstallationUsepipto install:pipinstalltake-forecastUsageA simple example usingtake_forecast.importnumpyasnpimportpandasaspdfromtake_forecastimportProjectContextnp.random.seed(0)samples=60x=100+80*np.linspace(0,1,samples)+20*np.random.randn(samples)y=x[:-3]+x[1:-2]+x[2:-1]+x[3:]date_end=pd.Timestamp.now()date_start=date_end-pd.Timedelta(len(y)-1,'d')index=pd.date_range(date_start,date_end,freq='d')ts=pd.Series(y,index)context=ProjectContext()results=context.run_forecast(ts)Theresultsare informed as a report in a dict with following keys:forecast: Predictionforecast_lower: Lower limitforecast_upper: Upper limitalpha: Significance levelerror_metrics_report: Train and test error metricsmodel: Fitted SARIMA modelAuthorTake Blip - Data & Analytics - Research Tribe - Squad XD
Package: takelessons-scraper
Scrape the chat logs from TakeLessonsWhat is this?If you noticed that TakeLessons doesn't have an API or a way to export your own data you can use this package to get at it.How do I use it?PrerequisitesInstall ChromeFind the correct selenium driver version for your installhereAdd it to PATH or make a note of where it ispip install takelessons-scraperExample Usage:# Assume some db object you can save your data todb=SomeDBStore()fromtakelessons_scraperimportTakeLessonsScraperchromedriver_path='/path/to/chromedriver'username='username'password='password'scraper=TakeLessonsScraper(chromedriver_path)# login to load cookies behind scenesscraper.login(username,password)# get a block of chat data up to datechat_date='2020-01-01'# you may need to sleep a bitchat_log=scraper.get_chat_history(chat_date)# Chat obj, can get raw json backdb.save(chat_log)Notes:Please consider this a hobby project to be used as reference. Since it is a scraper I expect it to fail as regularly as the source is updated... which could be at any time and not within my control.
Package: takeme-mailgun
takeme-mailgunThis module provides wrapper forMailgun APIs.Installationpipinstalltakeme-mailgunUsageimporttakeme_mailgunAPI_KEY='xxxxxxxxxxxx'DOMAIN='xxx.abc.jp'EMAIL='hr@abc.jp'MAIL_FROM='from@abc.jp'MAIL_SUBJECT='Test Mail'MAIL_BODY='Hello World'mailgun=takeme_mailgun.Mailgun(DOMAIN,API_KEY)mailgun.mail_from=MAIL_FROMifmailgun.is_valid(EMAIL):mailgun.messages(EMAIL,MAIL_SUBJECT,'',MAIL_BODY)else:print('[{}] is an ivalid email.'.format(EMAIL))ifmailgun.is_delivered(EMAIL):print('[{}] is delivered successfully.'.format(EMAIL))
Package: takeme-openpyxl
openpyxlThis module provides wrapper foropenpyxl.Installationpipinstalltakeme-openpyxlUsage (Reader)importtakeme_openpyxlINPUT_FILE_NAME='input.xlsx'INPUT_COLUMNS={'column1':{'label':'Label 1','column':'A'},'column2':{'label':'Label 2','column':'B'}}reader=takeme_openpyxl.ExcelReader(INPUT_FILE_NAME,INPUT_COLUMNS)ifreader.validate_labels()===True:row1_column1=reader.get_value('column1')row1_column2=reader.get_value('column2')print('Column1:{}, Column2:{}'.format(row1_column1,row1_column2))reader.next_row()row2_column1=reader.get_value('column1')row2_column2=reader.get_value('column2')print('Column1:{}, Column2:{}'.format(row1_column1,row1_column2))reader.close()Usage (Writer)OUTPUT_FILE_NAME='output.xlsx'OUTPUT_COLUMNS={'column1':{'label':'Label 1','column':'A'},'column2':{'label':'Label 2','column':'B'}}writer=takeme_openpyxl.ExcelWriter(OUTPUT_COLUMNS)writer.write('column1','Row1: Column1')writer.write('column2','Row1: Column2')writer.next_row()writer.write('column1','Row2: Column1')writer.write('column2','Row2: Column2')writer.flush(OUTPUT_FILE_NAME)
Package: takeme-pymsteams
pymsteamsThis module provides wrapper forpymsteams.Installationpipinstalltakeme-pymsteamsUsageimporttakeme_pymsteamsSP_SITE='https://xxxx.sharepoint.com'SITE_NAME='XXXX'SP_ID='user'SP_PWD='abcd'TEAMS_CHANNEL='https://outlook.office.com/webhook/xxxxx/IncomingWebhook/xxx/xxxx/xxxx'teams=takeme_pymsteams.Teams()try:teams.connect_to_share_point(share_point_site=SP_SITE,site_name=SITE_NAME,user_name=SP_ID,password=SP_PWD)teams.send(channel=TEAMS_CHANNEL,text='Hello Microsoft Teams!',file='Hello.xlsx')exceptExceptionasex:print(str(ex))
Package: take-ngram
Take NGramTakeNGram is a tool to provide analysis of n-grams in a dataset of messages.The recommendation usage is with the InsightExtractor Cloud CSV output.The analysis consists in creation of a dictionary with the n-grams of all messages and their respective frequency. Besides the creation of word cloud of the n-grams.All analysis can be made in a group of sentences of a subject (most useful with the Insight Extractor output).OverviewInstallationUsageInstallationThetake_ngrampackage cab be installed from PyPI.pipinstalltake_ngramUsageFor usage the file must have to be aCSVfile.All the examples are based on the Insight Extractor output.Creating a BiGram of the sentences and get the WordCloud.fromtake_ngramimportNGrambigram=NGram('file.csv','Structured Message')bigram.get_word_cloud()Creating a BiGram of the sentences and saving the WordCloud.fromtake_ngramimportNGrambigram=NGram('teste.csv','Structured Message')bigram.get_word_cloud(file_path='image.png')Adding stop wordsfromtake_ngramimportNGrambigram=NGram('file.csv','Structured Message',stop_words=['segunda'])bigram.get_word_cloud(file_path='image.png')Removing prepositions from stop wordsBy default prepositions are added to the stop wordsfromtake_ngramimportNGrambigram=NGram('file.csv','Structured Message',remove_prepositions=False)bigram.get_word_cloud(file_path='image.png')Making n-grams for some specific subjects.fromtake_ngramimportNGrambigram=NGram('file.csv','Structured Message',subject_column='Groups',subject_list=['fatura','plano'])bigram.get_word_cloud(file_path='image.png')AuthorTake Blip Data&Analytics Research
Package: takeoff
Takeoff is a service orchestration framework for python. It provides a python
“DSL” for declaring complicated cloud infrastructures and provisioning those
blueprints in an idempotent way.You can find us in #yaybu on irc.oftc.net.Here is an exampleTakeofffile:aws = workspace.add_aws(
    region='eu-west-1',
)

vpc = aws.add_virtual_private_cloud(name='example')
vpc.add_internet_gateway(name="internet")

example = vpc.add_subnet(
    name='application',
    cidr_block='192.168.0.0/24',
)

asg = aws.add_autoscaling_group(
    name='example',
    launch_configuration=aws.add_launch_configuration(
        name="example",
        ami='ami-62366',
        subnets=[example],
    ),
)You can then apply this configuration with:takeoff apply