Saravanan666/pypi-org huggingface dataset:
Package: tawazi
tawaziIntroductionTawazifacilitatesparallelexecution of functions using aDAGdependency structure.ExplanationConsider the functionfthat depends on the functiongandh:defg():print("g")return"g"defh():print("h")return"h"deff(g_var,h_var):print("received",g_var,h_var)print("f")return"f"defmain():f(g(),h())main()TheDAGdescribed inmaincan be accelerated ifgandhare executed in parallel. This is whatTawazidoes by adding a decorator to the functionsg,h,f, andmain:fromtawaziimportdag,xn@xndefg():print("g")return"g"@xndefh():print("h")return"h"@xndeff(g_var,h_var):print("received",g_var,h_var)print("f")return"f"@dag(max_concurrency=2)defmain():f(g(),h())main()The total execution time ofmain()is1 second instead of 2which proves that thegandhhave run in parallel, you can measure the speed up in the previous code:fromtimeimportsleep,timefromtawaziimportdag,xn@xndefg():sleep(1)print("g")return"g"@xndefh():sleep(1)print("h")return"h"@xndeff(g_var,h_var):print("received",g_var,h_var)print("f")return"f"@dag(max_concurrency=2)defmain():f(g(),h())start=time()main()end=time()print("time taken",end-start)# h# g# received g h# f# time taken 1.004307508468628FeaturesThis library satisfies the following:robust, well testedlightweightThread SafeFew dependenciesLegacy Python versions support (in the future)MyPy compatibleMany Python implementations support (in the future)InTawazi, a computation sequence is referred to asDAG. The functions invoked inside the computation sequence are referred to asExecNodes.Current features are:Specifying the number of "Threads" that theDAGusessetupExecNodes: These nodes only run once per DAG instancedebugExecNodes: These are nodes that run only ifRUN_DEBUG_NODESenvironment variable is setrunning a subgraph of theDAGinstanceExcluding anExecNodefrom runningcaching the results of the execution of aDAGfor faster subsequent executionPriority Choice of eachExecNodefor fine control of execution orderPerExecNodechoice of parallelization (i.e. AnExecNodeis allowed to run in parallel with otherExecNodes or not)and more!DocumentationYou can find the documentation here:Tawazi.Inthis blogwe also talk about the purpose of usingTawaziin more detail.Note: The library is still at anadvanced state of development. Breaking changes might happen on the minor version (v0.Minor.Patch). Please pinTawazito theMinor Version. Your contributions are highly welcomed.Name explanationThe libraries name is inspired from the arabic word تَوَازٍ which means parallel.Building the docOnly the latest version's documentation is hosted.If you want to check the documentation of a previous version please checkout the corresponding release, install the required packages and run:mkdocs serveDeveloper modepipinstall--upgradepip
pipinstallflitwheelcdtawazi
flitinstall-sFuture developmentsThis library is still in development. Breaking changes are expected.
Package: tawdry
Tawdryis a Type Annotated Web framework which emphasizes Dont Repeat YourselfMore specifically,Tawdryis a “microframework” similar toflaskor
somewhat less similar to Pyramid or Django. It tries to get out of your way
by requiring the absolute minimal amount of boilerplate or extra code of any
kind.Tawdryalso aims to improve the clarity of the code youdowrite by
centralizing and localizing the information about your server and its routes.Example Applicationimporttawdrydefhello(request,publisher):return'World!'app=tawdry.Tawdry({'hello':hello})if__name__=='__main__':app.serve()Once run, it will create a single route at/hello, on localhost by default.$curllocalhost:5000/helloWorld!InstallationpipinstalltawdryRequires Python 3 (and you’re best off with 3.5)Type AnnotationsThe above example doesn’t really do much to show the purpose ofTawdrythough.
The whole point here is to use type annotations, as they are the mechanism
through whichTawdrydoes its stuff! So lets look at a slightly more complex
example.importtawdrydefexclaim(request,exclamation:str)->Response:returnexclamation+'!'sitemap={'exclaim':{'{exclamation}':exclaim,},}app=tawdry.Tawdry(sitemap)if__name__=='__main__':app.serve()$curllocalhost:5000/exclaim/wahhoooowahhoooo!Of note in the above example:An argument type annotation will convert the argument to the annotated type
before calling the function.A return type, type annotation will convert the function’s return value
after it returns.Thesitemapis a dict given in the form where:The values are the functions which will be called in the event of a match.The keys match a url segment with the same string, so nested dictionaries
result in matches to subsequent nested segments.Any key enclosed in curly braces is given as a parameter to the function
given as a value, as well as any parameters leading up to it.So in the above example:the sitemap defines a single manifested route in the form/exclaim/{paramerer}.parameterwill be converted (in this case unnecessarily) to a string before
being passed toexclaim.The return value ofexclaimwill be converted to aResponseobject
(noteResponseis applied by default, but other return types exist, such
asJsonResponseandXmlResponse)TheResponseobject is wsgi compatible and will return the result to
the caller
Package: tawnyapi
Python-TAWNYAPIDocumentationFind documentation underdocs.tawny.aiMore information about tawny.ai underinfo@tawny.aiSimple Example with OpenCVimportcv2fromtawnyapi.vision.clientimportTawnyVisionApiClientfromtawnyapi.visionimportapitypesVIDEO_PATH="<PATH TO YOUR VIDEO FILE>"API_KEY="<YOUR TAWNY API KEY>"client=TawnyVisionApiClient(api_key=API_KEY)cap=cv2.VideoCapture(VIDEO_PATH)whileTrue:ret,frame=cap.read()ifnotret:break# Analyzing images already in memory:result=client.analyze_images(images=[frame],max_results=1,resize=None,features=[apitypes.ImageAnnotationFeatures.FACE_DETECTION,apitypes.ImageAnnotationFeatures.FACE_EMOTION,apitypes.ImageAnnotationFeatures.FACE_DESCRIPTOR,apitypes.ImageAnnotationFeatures.HEAD_POSE,apitypes.ImageAnnotationFeatures.FACE_LANDMARKS,apitypes.ImageAnnotationFeatures.ATTENTION,apitypes.ImageAnnotationFeatures.POSE])print(result)
Package: tawnycalc
tawnycalcprovides tools for the orchestration ofthermocalcsimulations
from a Python environment.This package operates as a high level wrapper tothermocalc, allowing
users to configure, execute and analyse their models. Generally models will
be configured in one of two ways:Utilise existingthermocalcsetup scripts. In this casetawnycalcwill read in the existing input files and present them as Python objects
for further modification. This is useful for where you have an existing
model that you would like to control from Python.Start from scratch. Here the user will generate the entire configuration
required forthermocalcfrom within Python. This workflow consolidates
your entire model configuration to the Python environment which may
provide a more streamlined experience.Once configured satisfactorily, execution is performed as follows:tawnycalcutilises the currentContextconfiguration to generatethermocalcinput files. These files are generated in either a temporary
or user specified locationA sub-process is spawned which executesthermocalcagainst the generated
input files.The results of thethermocalcexecution are then parsed bytawnycalcand
provided as Python objects for analysis. The user may use the results to
determine the input configuration for furtherthermocalcexecutions.
Package: tax
A taxy Tox to tax your current site-packages. This is a variant of Tox that doesn’t use virtualenvs at all - just
installs everything in the current environment. Use at your own peril.Free software: BSD licenseInstallationpip install taxDocumentationhttps://python-tax.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog1.0.0 (2017-03-15)Add support for latest Tox and test with all the Tox versions since 2.2.0.1.0 (2015-10-06)First release on PyPI.
Package: tax2graph
tax2graphGoaltax2graphprovide a simple way to convert i4Life tabled taxonomy to Neo4J graph representation.Example dataA example data are available on data folder. It contains the Sordariomycetes taxonomy downloaded from "i4Life WP4 Download Service of the Catalogue of Life:
Darwin Core Archive Export" (seehttp://www.catalogueoflife.org/DCA_Export/).UsageInitialize a connection dict containing at last the password key to perform queries to Neo4J database:importosfromtax2graphimportGraphBuilder,ConnectionTypeconnection_variables:ConnectionType={"password":str(os.getenv('NEO_PASSWORD'))}builder=GraphBuilder(connection_variables)And build the graph from Sordariomycetes tab-separated file:builder.read('tax2graph/data/sordariomycetes/taxa.txt',low_memory=False)builder.build_col_graph()The second parameter (low_memoty) is optional and is the same ofpandas.read_csv()(see details inhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).After load the base taxonomy, the Manager class can be used to perform queries. To perform simple queries use:fromtax2graphimportManagermanager=Manager(connection_variables)manager.get_node('Glomerellales')manager.get_parent('Glomerellales')In the above code the constructor of Manager class require the 'connection_variables' to be started, like GraphBuilder class. After started both methodsget_nodeandget_parentare called. Both receives a simgle parameter as string indicating the name of the Node to search. The former return an dict containing the target (e.g. order Glomerellales) node and the last return the parent node (e.g. class Sordariomycetes).The manager class also contain a single method to create custom nodes. To this, simpleously useset_custom_node, as example:fromtax2graphimportCustomNodeTypecustom_node:CustomNodeType={'taxonRank':'species','description':'A custom clade'}manager.set_custom_node(custom_node,'Colletotrichum')This method receive two parameters: the first is a dict of CustomNodeType type that contains two keys, astaxonRankanddescription; and the second is a string indicating the parent node in with thecustom_nodewill be connected.You can also include properties to relationships. To do, simpleously create an instance of CustomRelPropertiesType and include an additional parameter as the third argument ofset_custom_nodemethod.fromdatetimeimportdatetimefromtax2graphimportCustomRelPropertiesTyperelationship_properties:CustomRelPropertiesType={'created':datetime.now()}manager.set_custom_node(custom_node,'Colletotrichum',relationship_properties)Feel free to add new features and contribute through pull requests. Be happy!!
Package: taxamatcher
# taxamatchertaxamatcher is an easy to use tool for assigning taxon IDs of reference taxonomy to the desired list of taxa or consensus lineages. This tool is useful when there is a need to assign taxon identifiers of reference taxonomy map file to a list of available taxa (i.e. list of consensus lineages from desired OTU table). Reference taxonomy can be 16S ribosomal RNA database like Greengenes and SILVA with corresponding map files like "97_otu_taxonomy.txt" and "tax_slv_ssu_132.txt". Latest versions of these files are readily available and can be downloaded from respective online websites. Moreover, after assignment of reference taxon identifiers, it is possible to easily obtain truncated trees from newick tree files of processed reference database taxonomy# Currently available taxonomic classifications- GreenGenes- SILVA# Installation$ pip install taxamatcher# Usage$ taxamatcher -r [gg or silva] -m [ref-map-file] -t [my-lineages].csv -o [output].csv- [ref-map-file] - path to map file. Example is "97_otu_taxonomy.txt" for greengenes taxonomy- [my-lineages] - path to target taxonomy. Target file must be CSV file with taxon IDs in first column and consensus lineages in second column- [output].csv - path to output file. Output will be generated as CSV file# Author- Farid MUSA (farid.musa.h@gmail.com)
Package: taxa-sdk
Taxa Network Python SDKLibrary used to aid developers in building applications on the Taxa Network.
It works with both Python 2.7 and Python 3.5+.InstallationDownload the zip file from Github. Extract into a folder anywhere.On the command line, navigate to that folder and run the install script:python setup.py installTo enable the SGX's remote attestation feature on your node, follow these instructions to generate a service provider ID (SPID) and primary key:Register with Intelhere.Go tohttps://api.portal.trustedservices.intel.com/EPID-attestationIn the Development access section, click the Subscribe (unlickable) button. Follow the steps to generate an SPID and Primary Key.Then run the following command, including your newly generated SPID and primary key:python -m taxa_sdk.install_intel_keys [SPID] [primary key]If you are on OSX, you need to install the Apple signed libraries:sudo python -m taxa_sdk.install_osx_libs(Note the requirement of sudo)DocumentationThe documentation for this SDK can be foundhere.Running testsTo run the tests for this module, run the following commands:Testing through the WebUITo test just the attestation process, using the WebUI:python -m taxa_sdk.tests TestAttestationWebUITo test the full millionaire test using the WebUI, run this command:python -m taxa_sdk.tests TestMillionaireByIdentityNote: If you want to limit your tests to just a WebUI running of a specific server,
then modify theFORCEIPvariable at the top oftaxa_sdk/tests.py. IfFORCEIPis set toNone(The default), then the node distributer will pick a server
for you.Also note that all tests ran though the WebUI will use the version oftaxa_clientthat comes bundled with the SDK.Running tests through the command line (bypassing WebUI)To run the millionaire test viataxa_servervia command line, bypassing the
WebUI, then use the following command:python -m taxa_sdk.tests TestBypassWebUITo just run the attestation via thetaxa_servervia command line, use this
command:python -m taxa_sdk.tests TestBypassWebUI.test_attestationNote: you must modify the tests.py file at the top to include the paths to the
command linetaxa_clientandtaxa_serverto do theBypassWebUItests.Testing parametersThere are a few things you can change via the command line. These extraparameters must be placedbeforethe name of the test classes.--forceippython -m taxa_sdk.tests --forceip=13.92.194.125 TestMillionaireByIdentityThis will force the test to run on the node at the passed in IP. If this option
is not used, the ip will be gotten from the P2P network.--nop2pThis will force the IP to be gotten from the node distributor web service instead
of the P2P network.--keepkeysBy default all tests delete their keys after finishing. With this option included,
the keys will not be deleted. This means you can run the tests over and over, and it
will skip attestation. hence speeding up the tests.
Package: tax-bpjs
tax_bpjsEasy Tax & BPJS Calculation according to Indonesia Govermennt Regulation using Python!:)Features:
- BPJS Calculation
- Tax CalculationInstallationUse the package managerpipto install tax_bpjs.pipinstalltax_bpjsUsagefromtax_bpjs.bpjsimportBpjsfromtax_bpjs.taximportTaxQuick Startcheck example.pyContributingPull 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: taxcalc
OrgPackageTestingTax-CalculatorTax-Calculator is an open-source microsimulation model for static
analysis of USA federal income and payroll taxes.We are seeking contributors and maintainers. If you are interested in joining the project as a contributor or maintainer,
open a newissueand ping@MattHJensenor@jdebacker-- or just jump right in.Complete documentation is availablehere.
Package: tax-calculator
# IntroductionAllows a regime forfettario partita IVA to compute the taxes it needs to pay to the Italian Government.# User## Installation` pip installtax-calculator`For a help, use:`tax-calculator--help`To compute taxes:`tax-calculatorcompute-forfettario--ateco="62.02.00"--ricavi=15000`For additional options, consult the help of the subparser:`tax-calculatorcompute-forfettario--help`## FeaturesAllows you to compute regime forfettario taxes;# Developer## TestJust tox it:tox## UploadingFirst create in your HOME folder the file.pypirc, with the following content:` [pypirc] servers = pypi[server-login]username:<yourpypi username>password:<yourpypi password> `Then perform your contribution. To test, update the version automatically, upload to pypi and create
a new tag on the git repo do the following:` python setup.py test update_version_patch bdist_wheel upload push_tag `There are alsoupdate_version_minorandupdate_version_majorwhich increase the minor and major version
number, respectively.
Package: taxfinder
TaxFinderThe TaxFinder module is a library to help with NCBI taxonomy ids.Install it usingpip, preferable into a virtual environment:$pipinstalltaxfinder### TODO: This is not working for now!### TODO: Use for now:$cdpath/to/taxfinder
$pipinstall-e.To do this, TaxFinder needs to download and create a databse with taxonomy information. This database needs in the order of 300 MB disc space. To create the database, run$taxfinder_updateBy default, TaxFinder tries to create the database in the installation directory. If you experience problems or you want to save the database to another path, set the environment variableTFPATHto the desired path. This path must be readable and writable for TaxFinder.To use TaxFinder, you need to import it and instanciate the main class. This will open the database and also cache your queries so repeated queries are answered faster.fromtaxfinderimportTaxFinderTF=TaxFinder()print(TF.getLineage(9606))In an interactive session, you can also get help on the TaxFinder methods:>>>fromtaxfinderimportTaxFinder>>>help(TaxFinder)
Package: taxhub
TaxHubApplication web de gestion centralisée des taxons basée sur le référentiel TAXREF (http://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref) du MNHN.Elle permet de gérer la liste des taxons présents dans chaque structure, d’y greffer des informations spécifiques, de définir des listes de taxons et des filtres en fonction des besoins.Elle est utilisée pour la structuration des taxons dans GeoNature (https://github.com/PnX-SI/GeoNature) à partir de sa version 1.4.0.Elle permet aussi de gérer les descriptions et les médias des taxons pour leur affichage dans GeoNature-atlas.DocumentationLa documentation d’installation de TaxHub est disponible surhttp://taxhub.readthedocs.io.TaxHub peut aussi être installé à partir du script d’installation globale de GeoNature :http://docs.geonature.fr/installation-all.html.PrincipesVoici le modèle conceptuel de la base de données de TaxHub (à mettre à jour) :Une partie correspond au TAXREF complet tel que fourni par le MNHN. Son contenu ne doit pas être modifié.Une partie correspond à la partie spécifique à chaque structure. Il faut commencer par renseigner la tablebib_nomsen selectionnant les taxons qui nous intéressent dans le TAXREF.Il faut ensuite y greffer des informations spécifiques grace àbib_attributs(patrimonialité, marqueurs, autres selon les besoins) et définir des listes de taxons (espèces d’un protocole, ….) dansbib_listesen fonction des besoins.TechnologiesLangages : Python, HTML, JS, CSSBDD : PostgreSQL, PostGISServeur : Debian ou UbuntuFramework python : FlaskFramework JS : AngularJSFramework CSS : BootstrapGestion des droitsElle est centralisée dans l’applicationUsersHub. Il faut donc disposer de l’applicationTaxHubdans UsersHub et y intégrer des groupes et/ou utilisateurs.Niveaux de droits :2 = Gestion des médias uniquement3 = Idem 2 + Gestion des attributs deGeoNature-atlas4 = Idem 3 + Possibilité d’ajouter des taxons dansbib_noms, de les mettre dans des listes et de renseigner tous leurs attributs (notamment ceux utilisés parGeoNature)6 = AdministrateursAuteursAmandine SahlGil DeluermozDamien FrazzoniClaire LagayeChristophe ChilletSamuel PriouThomas LebardCamille MonchicourtQuang PhamJean-Baptiste DesbasJean-Pascal MilcentElie BouttierDonovan MaillardKevin SamuelLicenceOpenSource - GPL V3Copyright (c) 2014-2023 - Parc National des Écrins - Parc national des Cévennes
Package: taxi
What is Taxi ?Taxi is a timesheeting tool that focuses on simplicity to help you write your
timesheets without wasting time. All you’ll do is edit a text file and write
down what you’ve worked on and how long, like so:23/01/2014

pingpong 09:00-10:00 Play ping-pong
infra         -11:00 Repair coffee machineYou can then get a summary of your timesheet:Staging changes :

# Thursday 23 january #
pingpong (123/456)             1.00  Play ping-pong
infra (123/42)                 1.00  Repair coffee machine
                               2.00

Total                          2.00

Use `taxi ci` to commit staging changes to the serverThrough the use of backends, Taxi allows you to push your timesheets to
different systems.Getting startedRefer to the“Installation” sectionin the docs.Supported backendszebra: Liip’s zebra backendtempo: Atlassian JIRA’sTempo Timesheetsbackendtipee: Gammadia’stipeebackendbexio: BexioTimesheetsbackendmulti: a special backend to push entries over multiple other backendsclockify: backend for the free timesheeting toolclockify.meContrib packagesThese resources, not part of Taxi core, provide an enhanced experience for certain use cases.Cabdriver(generate taxi entries based on Google Calendar, Slack, etc)Syntax highlighting for VSCodeVim plugin(features syntax highlighting, auto completion)
Package: taxi_clockify
This is the Clockify backend forTaxi. It
exposes theclockifyprotocol to push entries and fetch projects and
activities from Clockify.Installationtaxi plugin install clockifyUsageIn your.taxircfile, use theclockifyprotocol for your backend and set
thetokenin the query string. For example:[backends]
clockify = clockify://?token=abcdefghijklmnopqrIf you have multiple workspaces, add the workspace in the URL:[backends]
clockify = clockify://?token=abcdefghijklmnopqr&workspace=ff00acab01ContributingTo setup a development environment, create a virtual environment and run the
following command in it:pip install -e .To use a specific version of Taxi, eg. if you need to also make changes to Taxi,
install it in the virtual environment in editable mode:pip install -e /path/to/taxi
Package: taxidTools
TaxidTools - A Python Toolkit for TaxonomyOverviewProvides a set of classes and tools to work with taxonomy data.
Although built to work with the NCBI Taxdump files it can also work with other taxonomy definitions.
Currently impelemented:Easily load the NCBI taxdump filesRetrieve name, rank, parent or full ancestry from a unique taxonomic identifierTest if a node is parent or descendant of an otherFind last common ancestor or consensus node from a list of idsCalculate the distance between two nodesList all children of a given nodeRe-root TaxonomyFormat to given ranksRequirementsPython >= 3.9
Optionally some taxonomy definiton files usch as theTaxdump definition filesfrom the NCBI server.Installationpython3-mpipinstalltaxidToolsClone or copy the github repository to your project for the developement version.UsageCheck ourhomepage!ContributingI add new functionnalities as I need them, if you think of a cool new thing you would like to see implemented, post an issue
or a pull request!LicenseThis project is licensed under a BSD 3-Clauses License, see the LICENSE file for details.AuthorFor questions, problems, suggestions or requests, feel free to contact:Grégoire Denay, Chemisches- und Veterinär-Untersuchungsamt Rhein-Ruhr-Wuppergregoire.denay@cvua-rrw.de