Saravanan666/pypi-org huggingface dataset:
Package: booktest
Booktestbooktest is review-driven testing tool that combines Jupyterbook style data science
development with traditional regression testing. Booktest is developed byLumoa.me, the actionable feedback analytics platform.booktest is designed to tackle a common problem with the data science
RnD work flows and regression testing:Data science produces results such as probability estimates, which can be
good or bad, but not really right or wrong as in the traditional software engineering.Because the DS results are not strictly right or wrong, it's very difficult to use assertions
for quality assurance and preventing regression.For example, you cannot really say that accuracy 0.84 is correct, while the
accuracy 0.83 is incorrect, especially if you have other measurements (log likelihood)
giving conflicting results. Neither evaluating a topic model as correct or incorrect
is non-sensical. In practice, most data science applications require an expert
review.This less ambigious quality also creates need for a better visibility of how
the system behaves. One typically wants to print out edge cases and their diagnostics
to see the behavior, see intermediate steps and see the results for different data sets .There is also the problem of the data science data being big and the intermediate
results being computationally expensive.Jupyter notebook deals with this problem by keeping the state in memory between runs, while
traditional unittests tend to lose the program state between runs. This leads to very slow
test runs, slow iteration speed and low productivity.While the Jupyter Notebook provides good visibility to results required by the expert review and
powerful caching functionality: it fails short on a) often requiring copy-pasting production code to
make results visible, b) it doesn't support automated regression testing and c) expert review requires
expensive full review even if nothing changed.booktest solves this problem setting by delivering on 3 main points:Focus on the results and analytic as in Jupyter notebook by allowing user to print
the results as MD files.Keep the intermediate results cached either in memory or in filesystem by
having two level cache.Instead of doing strict assertions, do testing by comparing old results with
new results.As such, booktest does snapshot testing, and it stores the snapshots in filesystem and in Git.
Additional benefit of this approach is that you can trace the result development in Git.Getting started guideYou can find getting started guidehereWorkflows, coverage and CIYou can find guide on common workflows, coverage measurements and
continuous integratiohereExamplesExamples are found in thetest example directory.Example results are visible in thebook index.There is alsoseparate example projectAPI referenceAPI reference is generated underdocsdirectory. Main classes are:TestCaseRun, which provides API for testsTestBook, which provide a base class for test suite objectTestSuite, which provide a base class for test suite objectTests, which manages CLI interfaceDeveloping booktestDevelopment guide is availablehere
Package: booktool
booktooleBook (EPUB and Audiobook) management tool.Installpipinstall-UbooktoolLoggingBy default,booktool ...runs with logging level set toWARNING.booktool -v ...sets the level toINFO;booktool -vv ...sets it toDEBUG.The following table describes the logging level strategy.LevelDescriptionWARNINGAnything outside manageable running conditions.INFOMutations (or would be if not in dry-run mode).DEBUGAnything else of note.LicenseCopyright 2019–2020 Christopher Brown.MIT Licensed.
Package: bookworm
This is a Python client for the Bookworm natural language API, made available by Crawlica.You can register to receive an API key athttps://bookworm.crawlica.com/request_api_key/Example usageimporttabulatefrombookwormimportBookwormAPI_KEY='...'documents=['De två största städerna i Sverige är Stockholm och Göteborg','Donald Trump blir Barack Obamas efterträdare']b=Bookworm(API_KEY,'sv')res=b.entities(documents)forentsinres:data=[[ent['entity'],ent['type'],', '.join(ent['variants'])]forentinents]print(tabulate.tabulate(data,headers=['Entity','Type','Variants']))print('')Entity     Type    Variants
---------  ------  ----------
Sverige    geo     Sverige
Stockholm  geo     Stockholm
Göteborg   geo     Göteborg

Entity        Type    Variants
------------  ------  -------------
Donald Trump  person  Donald Trump
Barack Obama  person  Barack ObamasInstallation$pipinstallbookwormFunctionality currently availableautotag- Automatically tag documents with the best matching tags from our large database of tags.wordsmash- Compare document sets to get the essence of what makes a subset special. Get the defining words and phrases.entities- Extract named entities (people, organisations, and places) from each documentsentiment- Get a numerical representation of how positive or negative each document iswordcount- Get a count of the distinct words in the documents. Declinations are grouped into the base form and stop words can be removed.cluster- Automatically collect documents into groups, based on their topics, and describe those groups.categorize- Automatically put documents in a set of categories that you choose. The categories are defined by topic words, but the documents need not contain any of those words, just “close” words.
Package: boolcalc
This is boolean calculator with operate from left to right.#—————————#
Develop by PAO,RAM and YN
#—————————#Exampleimport boolcalcinp = “T ^ T <–> ( T –> F v ( T ^ T ) )”.split(” “);
print(boolcalc.calculate(inp));inp = “( ( ( T –> F –> T v F ) <–> T ) ^ T )”.split(” “);
print(boolcalc.calculate(inp));inp = “( ( ( T –> F –> T v F ) <–> T ) ^ T() “.split(” “);
print(boolcalc.calculate(inp));inp = [“(“,”T”,”<–>”,”F”,”^”,”F”,”)”];
print(boolcalc.calculate(inp));This library use expression tree data structure to operate the postfix of boolean term.To convert infix to postfix, use the stack data stucture.The mentioned datastructure we implement by ourselves.
Package: booleabayes
BooleaBayes is a suite of network inference tools to derive and simulate gene regulatory networks from transcriptomics data. To see how BooleaBayes could be applied to your own data, see our publication in PLOS Computational Biology,Wooten, Groves et al. (2019).InstallationTo installBooleaBayes, please use:pip install booleabayesDependenciesThegraph-toolpython package will need to be installed. This can be installed withConda,homebrew, etc as seenhere. All other dependencies will be installed with this package.BooleaBayes Usage:net= make or modify network structureload= loading dataproc= processingrw= random walkplot= plottingtl= toolsutils= utilities
Package: boolean-cayley-graphs
Boolean-Cayley-graphsInvestigations of Boolean functions, their Cayley graphs, and associated structures.The project enables the experimental mathematics that underpins recent and ongoing investigation into the classification of bent functions by their Cayley graphs.This project originally began with the idea of refactoring thebent-functions-duals-Cayley-graphs-public.sagewsworksheet, because it is too long, poorly structured, and takes so long to run that CoCalc cannot run it successfully.
It has since evolved into a Python packageboolean_cayley_graphs, and associated scripts, Sage code, Jupyter notebooks and code that allows the results
of paper [4] below to be fully reproduced.Project URLsCoCalc: public folder of worksheets, source code and data files.GitHub: Git repository of source code, data and papers.PyPI: Installable Python package.SourceForge: Documentation of Python modules in HTML format.NeCTAR: Virtual laboratory prototype .Installation$ sage -pip install boolean_cayley_graphs --userUsageSeeSourceForge: Documentation of Python modules in HTML format.ReferencesPaul Leopardi,"Constructions for Hadamard matrices, Clifford algebras, and their relation to amicability - anti-amicability graphs",
Australasian Journal of Combinatorics, Volume 58(2) (2014), pp. 214–248.Describes how the pattern of commuting and anticommuting pairs of basis elements of a real Clifford algebra, and their representation theory, can be used in the construction of Hadamard matrices.Paul Leopardi, "Twin bent functions and Clifford algebras", in C. Colbourn (ed.) Algebraic Design Theory and Hadamard Matrices (ADTHM 2014), Springer, 2015, pp. 189-199.Preprint:arXiv:1501.05477 [math.CO].Examines a pair of bent functions on and their relationship to a necessary condition for the existence of an automorphism of an edge-coloured graph, whose colours are defined by the properties of a canonical basis for the real representation of a real Clifford algebra.Paul Leopardi, "Twin bent functions, strongly regular Cayley graphs, and Hurwitz-Radon theory", 2015.Preprint:arXiv:1504.02827 [math.CO].Uses a theorem of Radon to prove that the corresponding graphs in the two sequences of strongly regular graphs considered in [1] and [2] are not isomorphic, except in the first 3 cases.Paul Leopardi, "Classifying bent functions by their Cayley graphs", v6, December 2018.Preprint:arXiv:1705.04507 [math.CO].Explores the connections between bent functions and their Cayley graphs, as well as projective two-weight linear codes, and symmetric designs with the symmetric difference property, through various equivalence classes. Includes exhaustive classifications of bent functions up to 8 dimensions and degree 3, with selected examples of degree 4
Package: boolean-expression
boolean_expressionThis is a simple library for creating nested boolean expressions and rendering them in a variety of dialects. It is provided as a single file so that it can be vendored into other projects where an external dependency is not possible or desirable. It is not assumed to be universally useful, but might be handy to someone out there.Getting startedYou can use this library out-of-the-box as a miniature DSL to construct boolean expressions in your library, which can later be converted to a search expression, query language, or the like. For example:fromboolean_expressionimportAND,OR,EQ,NOTcondition=AND(EQ("id",record_id),OR(NOT(EQ("status","private")),EQ("status","private")&EQ("owner",searching_user),))There are a few built-in renderers, like one that creates LDAP expressions. Given the above, the following code...fromboolean_expressionimportLdapRendererprint(LdapRenderer().to_str(condition))...produces the following output:(&(id=theRecordId)(|(!(status=private))(&(status=private)(owner=theUser))))In most cases, however, it is likely that if you're using this library you have your own particular needs and will need to implement your own renderer.UsageDevelopers considering this library are encouraged to read the docstring tests, because they demonstrate 100% of the functionality of the library. Basics are reproduced below.EqualityComparisons express an lval and an rval which are not interpreted at all during construction (but which you could choose to interpret during rendering, if appropriate to your use case).To specify a comparison where some name or expression is expected to match a value:EQ("some_name",expected_value)Less Than / Greater ThanThere are convenience methods for these common types of comparisons:LT("some_name",0)GT("some_name",0)LTE("some_name",0)GTE("some_name",0)And / OrThese can be constructed in one of a few ways. The most straightforward is the "Excel style":AND(GT("total",0),OR(EQ("alpha","A"),EQ("bravo","B")))...which can be made more tersely by using&and|operators:GT("total",0)&(EQ("alpha","A")|EQ("bravo","B"))TheAND()andOR()functions also accept keyword arguments (which are all interpreted asEQ):GT("total",0)&OR(alpha="A",bravo="B")NegationThere are two ways of negating an expression. One is an atomic comparison, equivalent toX != Y:NE("some_name","some_value")The other way to construct "not equal" is with a compound condition. The following Python expressions all produce the same data structure:~EQ("some_name","some_value")NOT(EQ("some_name","some_value"))Compound("NOT",[EQ("some_name","some_value")])Raw ExpressionsShould your use case require it, the library also allows for inserting raw expressions into the data structure:AND(NOT(OR(Role="Admin",Role="Owner")),Expression("AttemptedDelete"))
Package: booleanfix
booleanfixThe most useless pip package so farThe problemIf you come from another programming language, you may have noticed that Python's boolean variables are a bit different. This module aims to fix that, by giving you boolean variables like you're used to.The solutionThis very simple project gives you boolean variables like you're used to. It's as simple as that.Note: Since v1.1.0, booleanfix also allows you to usenullandundefinedasNone.Behind the scenestrue=Truefalse=Falsenull=Noneundefined=NoneUsageInstall the package in your repopipinstallbooleanfix==1.1.0If you use a requirements file, add this line to it :booleanfix==1.1.0Use it in your codea. The classic wayimportbooleanfixasbfprint(isinstance(bf.true,bool))print(bf.false==False)b. The easy wayfrombooleanfiximporttrue,falseprint(isinstance(true,bool))print(false==False)Examplefrombooleanfiximporttrue,false,null,undefinedarray=[1,2,3,4,5]foriinrange(len((array))):ifarray[i]%2==0:array[i]=trueelse:array[i]=falseprint(array)foriinarray:print(type(i),isinstance(i,bool),i)ifnull==undefined:print("null == undefined")ifarray[5]==null:print("array[5] == null")ContributingFeel free to open anissueor apull requestif you want to contribute to this projectHow to build ?py-mpipinstall--upgradepipbuildtwinesetuptoolswheel
py-mbuild
py-mtwinecheckdist/*# Optional : publish to test.pypi.orgpy-mtwineupload--repositorytestpypidist/*# Or to pypi.orgpy-mtwineuploaddist/*How to test ?py-mpipinstall--upgradepytest
py-mpytestLicenseThis project is licensed under the MIT License - see theLICENSEfile for detailsAuthorsEDM115-Initial work-EDM115
Package: boolean-jaccard
boolean_jaccardCalculate Jaccard metrics for boolean values.The source code lives ongithub.The documentation lives atReadTheDocs.The project can be installed fromPyPI.AbstractThe code here represents a python implementation of the Jaccard package hostedhereby N. Chung. Its citation follows.CitationChung, N., Miasojedow, B., Startek, M., and Gambin, A. "Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data"BMC Bioinformatics(2019)20(Suppl 15): 644.https://doi.org/10.1186/s12859-019-3118-5InstallationIt's aPyPIpackage,
so the pocess is pretty straightforward:pipinstall-Uboolean_jaccard# for most recent versionpipinstall-Uboolean_jaccard==0.0.1# for a specific versionA list of all released versions can be found at ourtags.A Note on Version Numbersboolean_jaccarduses strict automatedsemantic versioning.
As such,
we guarantee bugfixes in path releases,
backwards compatible features in minor releases,
and breaking changes in major releases.
We will endeavour to avoid breaking changes where possible,
but,
should they occur,
they willonlybe in major releases.Installing from SourceMost users **will not need** these instructions.If you need to customise the code in some manner,
you'll need to install from source.
To do that,
either clone the repository from github,
or download one of our releases.
For full instructions,
please see our guide oncontributing.ContributingOpen-source software is only open-source becaues of the excellent community,
so we welcome any and all contributions!
If you think you have found a bug,
please log a report in ourissues.
If you think you can fix a bug,
or have an idea for a new feature,
please see our guide oncontributingfor more information on how to get started!
While here,
we request that you follow ourcode of conductto help maintain a welcoming,
respectful environment.Future DevelopmentsFully vectorise to improve performance.CitationsIf you useboolean_jaccardin your work,
please cite the following manuscripts:Chung, N., Miasojedow, B., Startek, M., and Gambin, A. "Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data"BMC Bioinformatics(2019)20(Suppl 15): 644.https://doi.org/10.1186/s12859-019-3118-5
Package: booleano
Booleanois an interpreter ofboolean expressions, a library todefine
and run filtersavailable as text (e.g., in a natural language) or inPythoncode.stable branchedevelopment statusIn order to handle text-based filters, Booleano ships with a fully-featured
parser whose grammar isadaptive: Its properties
can be overridden using simple configuration directives.On the other hand, the library exposes a pythonic API for filters written
in pure Python. These filters are particularly useful to build reusable
conditions from objects provided by a third party library.TLDR;a string + some variable = safe boolean evaluation# is this character a minor guy with a "0" in his name and born after 1983 ?eval_boolean('age < const:majority & "o" in name & birthdate > "1983-02-02"',{"name":"sokka","age":15,"birthdate":datetime.date(1984,1,1)},{'majority':18},grammar_tokens={'belongs_to':'in'})=>TrueThe Fun Use CaseBooleano allow to safely evaluate an expression into something usable.user:name is "john" and user:surname in {"doe", "shepard"}+``{“user”: {“name”: “katara”}}`` => False
+``{“user”: {“name”: “john”, “surname”: “doe”}}`` => Truewith some code, you can provide any type you want, and the expression can still be in text:+``user:birthdate > “03-07-1987”``
+``duration > 1m30s``check the sample dirrectory to view more running examples !DocumentationThe full documentation is athttp://booleano.readthedocs.io/en/latest/.Contributethis project was not created by the current maintainer. in fact, the knowlege of this project from us is fare from
perfect, but with 100% of test coverages, it’s not hard to keep it running.if you find a bug, or want some feature, feel free to create a issues, or a Pull Request, but keep in mind that
it can be hard for us to work on it. the best way to have it fixed, it’s to write a Pull Request with passing tests,
and we will merge it if it’s a good piece of code.see CONTRIBUTING.rst to know how work with ease on this project.Upgrade Notethe old alpha release 1.0a1 is not compatible with newer version (starting with 1.1a1) because of heavy
import path changes. all import from the modules (__init__.py) is discouraged.Creditforkedfrom  Gustavo Narea’s booleano project onlaunchpad.net.maintenedby yupeekLinksWeb site.Bug reports.
Package: booleanOperations
BooleanOperationsBoolean operations on paths which uses a super fastpolygon clipper
library by Angus Johnson.You can download the latest version from PyPI:https://pypi.org/project/booleanOperations.InstallPipis the recommended tool to
install booleanOperations.To install the latest version:pipinstallbooleanOperationsBooleanOperations depends on the following packages:pyclipper: Cython wrapper for
the C++ Clipper libraryfonttoolsAll dependencies are available on PyPI, so they will be resolved
automatically upon installing booleanOperations.BooleanOperationManagerContaining aBooleanOperationManagerhandling all boolean operations
on paths. Paths must be similar todefcon,robofabcontours. A
manager draws the result in apointPen.frombooleanOperationsimportBooleanOperationManagermanager=BooleanOperationManager()BooleanOperationManager()Create aBooleanOperationManager.manager.union(contours, pointPen)Performs a union on allcontoursand draw it in thepointPen.
(this is a what a remove overlaps does)manager.difference(contours, clipContours, pointPen)Knock out theclipContoursfrom thecontoursand draw it in thepointPen.manager.intersection(contours, clipContours, pointPen)Draw only the overlaps from thecontourswith theclipContoursand draw it in thepointPen.manager.xor(contours, clipContours, pointPen)Draw only the parts that not overlaps from thecontourswith theclipContoursand draw it in thepointPen.manager.getIntersections(contours)Returning all intersection for the given contoursBooleanGlyphA glyph like object with boolean powers.frombooleanOperations.booleanGlyphimportBooleanGlyphbooleanGlyph=BooleanGlyph(sourceGlyph)BooleanGlyph(sourceGlyph)Create aBooleanGlyphobject fromsourceGlyph. This is a very
shallow glyph object with basic support.booleanGlyph.union(other)Perform aunionwith theother. Other must be a glyph orBooleanGlyphobject.result=BooleanGlyph(glyph).union(BooleanGlyph(glyph2))result=BooleanGlyph(glyph)|BooleanGlyph(glyph2)booleanGlyph.difference(other)Perform adifferencewith theother. Other must be a glyph orBooleanGlyphobject.result=BooleanGlyph(glyph).difference(BooleanGlyph(glyph2))result=BooleanGlyph(glyph)%BooleanGlyph(glyph2)booleanGlyph.intersection(other)Perform aintersectionwith theother. Other must be a glyph orBooleanGlyphobject.result=BooleanGlyph(glyph).intersection(BooleanGlyph(glyph2))result=BooleanGlyph(glyph)&BooleanGlyph(glyph2)booleanGlyph.xor(other)Perform axorwith theother. Other must be a glyph orBooleanGlyphobject.result=BooleanGlyph(glyph).xor(BooleanGlyph(glyph2))result=BooleanGlyph(glyph)^BooleanGlyph(glyph2)booleanGlyph.removeOverlap()Perform aunionon it self. This will remove all overlapping
contours and self intersecting contours.result=BooleanGlyph(glyph).removeOverlap()booleanGlyph.nameThenameof thesourceGlyph.booleanGlyph.unicodesTheunicodesof thesourceGlyph.booleanGlyph.widthThewidthof thesourceGlyph.booleanGlyph.libThelibof thesourceGlyph.booleanGlyph.noteThenoteof thesourceGlyph.booleanGlyph.contoursList thecontoursof the glyph.booleanGlyph.componentsList thecomponentsof the glyph.booleanGlyph.anchorsList theanchorsof the glyph.
Package: boolean-parser
boolean_parserPython package for parsing a string with conditional expressions joined with boolean logic.  Uses thepyparsingpackage to construct grammatical clauses representing conditional expression, e.g. "x > 1 and y < 2".  String conditional expressions can then be parsed into object representation to be handled downstream.  Can
convert string boolean expressions into SQLAlchemy filter conditions.Documentation:https://boolean-parser.readthedocs.io/en/latest/Parsers:Parser: core parser for handling parsing complex boolean conditional expressionsSQLParser: parser that enables converting a string conditional into a SQLAlchemy filter clause
Package: boolean.py
This library helps you deal with boolean expressions and algebra with variables
and the boolean functions AND, OR, NOT.You can parse expressions from strings and simplify and compare expressions.
You can also easily create your custom algreba and mini DSL and create custom
tokenizers to handle custom expressions.For extensive documentation look either into the docs directory or view it online, athttps://booleanpy.readthedocs.org/en/latest/https://github.com/bastikr/boolean.pyCopyright (c) 2009-2020 Sebastian Kraemer,basti.kr@gmail.comand others
SPDX-License-Identifier: BSD-2-Clause