Saravanan666/pypi-org huggingface dataset:
Package: ankimaker
AnkimakerWIPA CLI app to generate anki decks.From csv file, with configurable parameters, filters and media.From epub, finding difficult* words in the book and getting their translations.*I still don't know what 'difficult' will mean. Probably difficult words will be less frequent words that are more frequent in the text than in some corpus, cut above a grade threshold. The grades will map percentiles of frequency.Language LevelNumber of Base Words NeededA1500A21000B12000B24000C18000C216000This project is only possible because of the awesome work of genanki team.
Package: anki-ocr
anki_ocranki_ocr is a python program that converts physical flashcards into digitalAnki(Anki is a flashcard program that sychronizes your flashcards and uses spaced repetition for efficient memorization) decks. It usesPyTesseractandgenankito turn your handwritten flashcards into digital anki ones.There are several use cases, mainly its for you if you have a lot of flashcards and and want to digitize them. Anki does support image flashcards, but it would take a lot of time and you wouldn't be able to search the flashcards. Its also useful if you're not allowed to use a laptop/phone in class or prefer to handwrite your notes.InstallationUse the package managerpipto install anki_ocr.pipinstallanki_ocrUsageTo use anki_ocr, you will need a directory with images of your flashcards. The program will automatically sort the images by date, so you shouldcapture the question followed by its answer(i.e question1>answer1>question2>answer2 and so on), and ensure the number of images is evenanki_ocr[img_directory][output_deck_name]This will output an Anki deck package output_deck_name.apkg. This package can be imported into the Desktop or mobile Anki appsContributingThis project is beginner friendly. The entire module is a small single file, and the only new package you might have to deal with is genanki just to see some other ways to generate notes.Clone the project & you probably want a virtual environmentgitclonehttps://github.com/madelesi/anki_ocr.gitcdanki_ocr
python3-mvenvvenv_anki_ocrsourcevenv_anki_ocr/bin/activateThen install an editable version (updates after every save)pipinstall-e.Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: anki-ocr-gui
anki_ocr_guianki_ocr_gui is a PyQt5 gui for the CLI toolanki_ocranki_ocr is a python program that converts physical flashcards into digitalAnki(Anki is a flashcard program that sychronizes your flashcards and uses spaced repetition for efficient memorization) decks. It usesPyTesseractandgenankito turn your handwritten flashcards into digital anki ones.InstallationUse the package managerpipto install anki_ocr_gui.pipinstallanki_ocr_guiUsageTo use anki_ocr, you will need a directory with images of your flashcards. The program will automatically sort the images by date, so you shouldcapture the question followed by its answer, and ensure the number of images is even(i.e question1>answer1>question2>answer2 and so on)anki_ocr_guiContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: anki-sqlalchemy
Anki SQLAlchemy is an interface for interacting with theAnkisqlite database from python without having to
either hack an Anki install or figure out the database structure and field
serialization from scratch.The goal of this project is not to support every version of Anki entirely. The
current version supports at a signficant amount of the Anki 2.1.38.Here is a small code snippet written first withoutanki_sqlalchemyto show
how unintuivite the data format and columns names are without an wrapper.# plain python without anki-sqlalchemyimportsqlite3conn=sqlite3.connect('backup.db')cursor=conn.execute("SELECT id, tags FROM notes WHERE mod >= ?",[1445394366])note=cursor.fetchone()note[0]# 1428143940996note[1]# ' edit math probability wikipedia 'cursor=conn.execute("SELECT mod, type FROM cards WHERE nid = ?",[nid])card=cursor.fetchone()card[0]# 1445394366card[1]# 2# with anki-sqlalchemyimportdatetimefromsqlalchemyimportcreate_enginefromsqlalchemy.ormimportsessionmakerfromanki_sqlalchemyimportCardengine=create_engine("sqlite:///backup.db",echo=True)Session=sessionmaker(bind=engine)session=Session()note=session.query(Note).filter(Note.modification_time>=datetime.datetime(2017,2,5,21,29,49)).first()note.id# 1428143940996note.modification_time# datetime.datetime(2017, 2, 5, 21, 29, 49)card=note.cards[0]card.modification_time# datetime.datetime(2019, 11, 5, 22, 23, 3)card.type# <CardType.due: 2>Anki SQLAlchemy also plays nicely with types too.card:Card=session.query(Card).first()reveal_type(card.modification_time)# Revealed type is 'datetime.datetime*'reveal_type(card.note.tags)# Revealed type is 'builtins.list*[builtins.str]'BEWARE!This package can be used to make changes to your anki database. Before
proceeding, please make a backup of your database file. You don't want to lose
all your work with a bad query.The Anki database typically lives in acollection.anki2file.Installpip install anki_sqlalchemy
Package: ankisync
This project is deprecated. Please seeankisync2.ankisyncDoing what AnkiConnect cannot do, includingCreating new*.apkgCreating new note type / modelUpserting notesSetting next reviewSetting card statisticsNote ids to Card idsBut of course, this is very unsafe compared to pure AnkiConnect. I will not hold liability to damage it may cost.UsagePlease close yourAnkiapplication first before doing this!fromankisync.ankiimportAnkiwithAnki()asa:a.add_model(name='foo',fields=['field_a','field_b','field_c'],templates={'Forward':(QUESTION1,ANSWER1),'Reverse':(QUESTION2,ANSWER2)})Most of the other API's are similar to AnkiConnect, but_by_id()'s are preferred.Creating a new*.apkgis also possible.fromankisync.apkgimportApkgwithApkg('bar.apkg')asa:model_id=a.init(first_model=dict(name='foo',fields=['field_a','field_b','field_c'],templates={'Forward':(QUESTION1,ANSWER1),'Reverse':(QUESTION2,ANSWER2)}),first_deck='baz',first_note_data=False)a.add_note({'modelName':'foo','deckId':1,# "Default" deck'fields':{'field_a':'aaaaa','field_b':123# Numbers will be converted to string.}})For the example of how I use it in action, seehttps://github.com/patarapolw/zhlib/blob/master/zhlib/export.pyInstallationpip install ankisyncContributionsWhat features outside AnkiConnect (or inside) do you want? I will try to implement it.Help me understand the documentations,AnkiDroid Wiki, andAnki decks collaboration WikiPlease help me implement theNotImplementedmethods.NoteThis is the successor toAnkiTools. I will not update it anymore.
Package: ankitrazorpay
python3 -m buildpython3 -m twine check dist/*python3 -m twine upload --verbose dist/ankitrazorpay-0.1-py3-none-any.whl dist/ankitrazorpay-0.1.tar.gzpython3 -m twine upload --verbose --repository testpypi dist/ankitrazorpay-0.1-py3-none-any.whl dist/ankitrazorpay-0.1.tar.gz- name: Set up Python 3
    uses: actions/setup-python@v2
    with:
      python-version: "3.10"name: Install tools
run: make venv
- name: Publish packages to PyPy
run: |
set -ex
source venv/bin/activate
export VERSION=$(cat VERSION)
gpg --detach-sign --local-user $GPG_SIGNING_KEYID  --pinentry-mode loopback --passphrase $GPG_SIGNING_PASSPHRASE -a dist/stripe-$VERSION.tar.gz
gpg --detach-sign --local-user $GPG_SIGNING_KEYID  --pinentry-mode loopback --passphrase $GPG_SIGNING_PASSPHRASE -a dist/stripe-$VERSION-py2.py3-none-any.whl
python -m twine upload --verbose dist/stripe-$VERSION.tar.gz  dist/stripe-$VERSION-py2.py3-none-any.whl dist/stripe-$VERSION.tar.gz.asc dist/stripe-$VERSION-py2.py3-none-any.whl.asc
env:
GPG_SIGNING_KEYID: ${{ secrets.GPG_SIGNING_KEYID }}
TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
GPG_SIGNING_PASSPHRASE: ${{ secrets.GPG_SIGNING_PASSPHRASE }}
- uses: stripe/openapi/actions/notify-release@master
if: always()
with:
bot_token: ${{ secrets.SLACK_BOT_TOKEN }}
Package: anki-vector
The Vector SDK gives you direct access to Vector’s unprecedented set of advanced sensors, AI capabilities, and robotics technologies including computer vision, intelligent mapping and navigation, and a groundbreaking collection of expressive animations.It’s powerful but easy to use, complex but not complicated, and versatile enough to be used across a wide range of domains including enterprise, research, and entertainment. Find out more athttps://developer.anki.comVector SDK documentation:https://developer.anki.com/vector/docs/Official developer forum:https://forums.anki.com/Requirements:Python 3.6.1 or later
Package: ankiwiktionary
CLI for generating Anki flashcards from wiktionary.org pages.Works only with russian words.ankiwiktionaryUsage: ankiwiktionary [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  gen-cards  Generate flashcards from passed WORDS
  search     Search for the passed WORDS in wiktionaryFeaturesGenerating cloze cardsExamples processingUsage➜  tmp ankiwiktionary gen-cards самоопределяться луддит дихотомия привинтивный дискурс конструктивный
Word "самоопределяться" processed successfully
Word "луддит" processed successfully
Word "дихотомия" processed successfully
[Error] Word "привинтивный" not found
Word "дискурс" processed successfully
Word "конструктивный" processed successfully
➜  tmp ankiwiktionary search привинтивный
Results for "привинтивный":
➜  tmp ankiwiktionary search люмпед
Results for "люмпед": люмпен, люмпен-пролетариат, люмпенизация, люмпен-пролетарий, люмпен-интеллигенция, люмпенский, люмпенизироваться, люмпенизировать, люмпенствовать, люмпен-интеллигентImportant detailsGenerated file should be imported to Anki using "Import File" button. Set "Fields separated by" to~and activate the "Allow HTML in fields" checkbox.
Package: ankix
AnkixNew file format for Anki with improved review intervals. PurepeeweeSQLite database, no zipfile. Available to work with on Jupyter Notebook.UsageOn Jupyter Notebook,>>>fromankiximportankix,dbasa>>>ankix.init('test.ankix')# A file named 'test.ankix' will be created.>>>ankix.import_apkg('foo.apkg')# Import the contents from 'foo.apkg'>>>iter_quiz=a.iter_quiz()>>>card=next(iter_quiz)>>>card'A flashcard is show on Jupyter Notebook. You can click to change card side, to answer-side.''It is HTML, CSS, Javascript, Image enabled. Cloze test is also enabled. Audio is not yet tested.'>>>card.right()# Mark the card as right>>>card.wrong()# Mark the card as wrong>>>card.mark()# Add the tag 'marked' to the note.You can directly make use of Peewee capabilities,>>>a.Card.select().join(a.Note).where(a.Note.data['field_a']=='bar')[0]'The front side of the card is shown.'Adding new cardsAdding new cards is now possible. This has been tested inhttps://github.com/patarapolw/zhlib/blob/master/zhlib/export.py#L15fromankiximportankix,dbasaankix.init('test.ankix')a_model=a.Model.add(name='foo',templates=[a.TemplateMaker(name='Forward',question=Q_FORMAT,answer=A_FORMAT),a.TemplateMaker(name='Reverse',question=Q_FORMAT,answer=A_FORMAT)],css=CSS,js=JS)# Or, a_model = a.Model.get(name='foo')forrecordinrecords:a.Note.add(data=record,model=a_model,card_to_decks={'Forward':'Forward deck','Reverse':'Reverse deck'},tags=['bar','baz'])Installation$ pip install ankixPlansTest by using it a lot.
Package: anko
ankoToolkit for performing anomaly detection algorithm on 1D time series based on numpy, scipy.Conventional approaches that based on statistical analysis have been implemented, with mainly two approaches included:Normal DistributionData samples are presumably been generated by normal distribution, and therefore anomalous data points can be targeted by analysing the standard deviation.Fitting AnsatzData samples are fitted by several ansatzs, and in accordance with the residual, anomalous data points can be selected.Regarding model selections, models are adopted dynamically by performing normal test and by computing the (Akaike/Bayesian) information criterion.
By default, the algorithm will first try to fit in the data into normal distribution, if it passednormal test.
If this attempt suffers from the loss of convergence or it did not passnormal testfrom begining,
then the algorithm will pass data into the second methods and try to execute all the available fitting ansatzs simultaneously.
The best fitting ansatz will be selected by information criterion, and finally the algorithm will pick up anomalous points in accordance with the residual.click here to see all available methods.Future development will also include methods that are based on deep learning techniques, such as isolation forest, support vector machine, etc.Requirementspython >= 3.6.0numpy >= 1.16.4scipy >= 1.2.1Installationpip install ankoFor current release version please refer toPyPI - anko homepage.DocumentationFor details about anko API, see thereference documentation.Jupyter Notebook Tutorial (in dev)Runanko_tutorial.ipynbon your local Jupyter Notebook or host ongoogle colab.Basic UsageCall AnomalyDetectorfrom anko.anomaly_detector import AnomalyDetector  
agent = AnomalyDetector(t, series)Define policies and threshold values (optional)agent.thres_params["linregress_res"] = 1.5  
agent.apply_policies["z_normalization"] = True  
agent.apply_policies["info_criterion"] = 'AIC'for the use ofAnomalyDetector.thres_paramsandAnomalyDetector.apply_policies,
please refer to the documentation.Run checkcheck_result = agent.check()The type of outputcheck_resultisCheckResult, which is basically a dictionary that contains the following attributes:model: 'increase_step_func'popt: [220.3243250055105, 249.03846355234577, 74.00000107457113]perr: [0.4247789247961187, 0.7166253174634686, 0.0]anomalous_data: [(59, 209)]residual: [10.050378152592119]extra_info: ['Info: AnomalyDetector is using z normalization.', 'Info: There are more than 1 discontinuous points detected.']model (str): The best fit model been selected by algorithm.popt (list): Estimated fitting parameters.perr (list): Corresponding errors of popt.anomalous_data (list[tuple(float, float)]): Return a list of anomalous data points (t, series(t)), or an empty list if all data points are in order.residual (list): Residual of anomalous data.extra_info (list): All convergence errors, warnings, informations during the execution are stored here.Run Testpython -m unittest discover -s test -p '*_test.py'or simplymake test
Package: anko-sdk
Anko Investor Python SDKThis module provides a simple Anko Investor Forecasts gRPC Service client for python.This module does little more wrapgrpcwith retry logic and authorization wrappers.$ pip install anko-sdkUsageGiven a valid token fromhttps://anko-investor.com(see:Getting Startedfor more information), the following example will start consuming ForecastsimportosimportsocketfromankoimportClientc=Client(os.environ.get('ANKO_TOKEN'),socket.gethostname())forforecastinc:ifforecast:print(forecast)(Here we use the current machine's hostname as a client identifier- this can be anything, really; it's useful to set in case you need to open a support ticket to help debug connections. It can even be an empty string).
Package: anl
ANAC Automated Negotiations League PlatformOverviewThis repository is the official platform for running ANAC Automated Negotiation Leagues (starting 2024). It will contain a package
calledanlXXXXfor the competition run in year XXXX. For example anl2024 will contain all files related to the
2024’s version of the competition.Installationpip install anlYou can also install the in-development version with:pip install https://github.com/autoneg/anl/archive/master.zipDocumentationhttps://yasserfarouk.github.io/anl/Changelog0.1.9 (2024.02.14)Adding divide-the-pies scenariosAdding workflow to test on negmas masterTutorial and docs updateUpdate faq0.1.8 (2023.12.31)bugfix in visualizer initial tournament listCorrecting auto pushing to PyPi0.1.7 (2023.12.31)Adding simple dockerfileAdding –port, –address to anlv show. You can now set the port and address of the visualizerVisualizer parses folders recursivelyminor: faster saving of figsAdding mkdocs to dev requirementsRemoving NaiveTitForTat from the default set of competitorsImproving tutorial0.1.6 (2023.12.27)Improved visualizerAdding filtering by scenario or strategy to the main view.Adding new options to show scenario statistics, scenario x strategy statistics, and cases with no agreements at all.You can show multiple negotiations togetherYou can show the descriptive statistics of any metric according to strategy or scenarioMore plotting options for metricsImproved CLIAdding the ability to pass parameters to competitors in the CLI.Removing NaiveTitForTat from the default set of competitorsMaking small tournaments even smallerNew and improved strategiesAdding RVFitter strategy which showcases simple implementation of curve fitting for reserved value estimation and using logging.Adding more comments to NashSeeker strategySimplified implementation of MiCROAdding a simple test for MiCROAvoid failure when Nash cannot be found in NashSeekerMigrating to NegMAS 0.10.11. Needed for logging (and 0.10.10 is needed for self.oppponent_ufun)0.1.5 (2023.12.24)Changing default order of agentsAdding a basic visualizerAdding make-scenarios to the CLIPassing opponent ufun in the private infoSeparating implementation of builtin agentsrequiring NegMAS 0.10.90.1.4 (2023.12.24)Retrying scenario generation if it failedDefaulting to no plotting in windows0.1.3 (2023.12.23)Defaulting to no-plotting on windows to avoid an error caused by tkinterRetry scenario generation on failure. This is useful for piece-wise linear which will fail (by design) if n_pareto happened to be less than n_segments + 10.1.2 (2023.12.18)Adding better scenario generation and supporting mixtures of zero-sum, monotonic and general scenarios.Requiring negmas 0.10.80.1.2 (2023.12.11)Controlling log path in anl2024_tournament() through the added base_path argument0.1.1 (2023.12.09)Added anl cli for running tournaments.Added the ability to hide or show type names during negotiationsCorrected a bug in importing unique_nameNow requires negmas 0.10.60.1.0 (2023.11.30)Adding ANL 2024 placeholder
Package: anlearn
anlearn - Anomaly learnInGauss Algorithmic, we're working on many anomaly/fraud detection projects using open-source tools. We decided to put our two cents in and  "tidy up" some of our code snippets, add documentation, examples, and release them as an open-source package. So let me introduceanlearn. It aims to offer multiple interesting anomaly detection methods in familiarscikit-learnAPI so you could quickly try some anomaly detection experiments yourself.So far, this package is an alpha state and ready for your experiments.Do you have any questions, suggestions, or want to chat? Feel free to contact us viaGithub,Gitter, or email.Installationanlearn depends onscikit-learnand it's dependenciesscipyandnumpy.Requirements:python >=3.6scikit-learnscipynumpyRequirements for every supported python version with version and hashes could be found inrequirementsfolder.
We're usingpip-toolsfor generating requirements files.Intallation optionsPyPI installationpip install anlearnInstallation from sourcegit clone https://github.com/gaussalgo/anlearn
cd anlearnInstalilanlearn.pip install .or by usingpoetrypoetry installDocumentationYou can find documentation at Read the Docs:docs.Contat usDo you have any questions, suggestions, or want to chat? Feel free to contact us viaGithub,Gitter, or email.LicenseGNU Lesser General Public License v3 or later (LGPLv3+)anlearn  Copyright (C) 2020  Gauss Algorithmic a.s.This package is in alpha state and comes with ABSOLUTELY NO WARRANTY.
This is free software, and you are welcome to use, redistribute it, and contribute under certain conditions of its license.Code of ConductCode of Conduct
Package: anlis
ANLIS - Analysis for PythonANLIS is a Python package foranalysisbuilt on top ofnumpyandsympy. ANLIS provides a set of functions to perform analysis tasks. ANLIS is a work in progress and currently supports the following tasks:SeriesPlotting any seriesArithmetic SeriesFinding $a_n$ from two elementsGeometric SeriesFinding $a_n$ from two elementsFinding the sum of an infinite series (based on ratio and first elementortwo elements)ConvergenceDetermining if a sequence is convergent (or divergent)Convergence TestsDerivativesFinding the critical points of a functionFinding the extrema of a functionVectorsFinding the magnitude of a vectorFinding the unit vector of a vectorFinding the dot product of two vectorsTaylor SeriesFinding the Taylor Series of a functionFinding the Taylor Polynomial of a functionFinding the Lagrange Remainder of a functionIntegralsLeft/Right Riemann SumsTrapezoidal RuleSimpson's RuleDifferentials (e.g. for error analysis)Absolute differentialRelative differentialMultidimensional CalculusCritical pointsFinding critical pointsFinding extremaDerivativesFinding the (or all) partial derivatives of a functionFinding partial derivatives of composite functionsFinding the gradient of a functionFinding the Jacobian of a functionFinding the determinant of a functionFinding the linearisation of a functionSolving with Newton-RaphsonFinding the directional derivative of a functionContour LinesFinding the contour lines of a functionFinding the tangent lines of contour linesPlotting the contour lines of a function (2D or 3D)Differentials (e.g. for error analysis)Absolute differentialRelative differentialTotal differentialThewikicontains more information on the functions along with examples and when to use them.
Package: anlogger
anloggerA python3 module to assist in setting up logging.Installpython3-mpipinstallanloggerUsagefromanloggerimportLoggerlogger_obj=Logger(name="appname",default_loglevel="INFO",fmt=None,syslog=None,syslog_facility=None,log_to_console=True,console_stream='stderr')logger=logger_obj.get()logger.info("Message on info-level")nameis application name used in logging (REQUIRED).default_loglevelis the logging level which is used unlessLOGLEVELenvironment variable is set.fmtis the format used for formatting the logger. Se python's logging module documentation for formattion options.syslogis the syslog configuration. Set toTrueto use local syslog, or a tuple of("ipaddress-string", port-int)for remote logging.syslog_facilityis one of well-known syslog facilities. If syslog is used butsyslog_facilityis not set, theuserfacility is used by default.log_to_consoledefines whether the logging is also outputted to the console. Default isTrue.console_streamdefines which output stream to use for console logging. Accepted values arestderr(default) andstdout.Seelogger.Loggerclass code for additional details.