Saravanan666/pypi-org huggingface dataset:
Package: darbiadev-businesscentral
darbiadev-businesscentralA wrapper for Business Central's API.Example usagefromdarbiadev_businesscentralimportBusinessCentralServicesclient=BusinessCentralServices(base_url="https://api.businesscentral.dynamics.com/v2.0/",tenant_id="00000000-0000-0000-0000-000000000000",environment="Production",company_name="My company",client_id="00000000-0000-0000-0000-000000000000",client_secret="secret",)print(client.make_request("get","salesDocuments"))
Package: darbiadev-shipping
darbiadev-shippingA package wrapping multiple shipping carrier API wrapping packages, providing a higher level multi carrier package.Documentation is hostedhereExample usage:fromdarbiadev_shippingimportShippingServicesclient=ShippingServices(ups_auth={'base_url':'','username':'','password':'','access_license_number':'',},fedex_auth={'web_service_url':'','key':'','password':'','account_number':'','meter_number':'',},usps_auth={'base_url':'','user_id':'','password':'',})print(client.track('1Z5338FF0107231059'))
Package: darbiadev-ups
darbiadev-upsThis package provides a wrapper for UPS' API.Example usagefromdarbiadev_upsimportUPSServicesclient=UPSServices(base_url="https://onlinetools.ups.com/",username="username",password="password",access_license_number="access_license_number",)print(client.track("1Z12345E1305277940"))
Package: darcsver
I need a new maintainer!Darcsver needs a new maintainer. The original author, Zooko Wilcox-O’Hearn,
is not planning to make further changes to it unless there is some unexpected
need to do so.What Does It DoCreate files containing version numbers, based upon the latest darcs
release tag.If your source tree is coming from darcs (i.e. it is in a darcs
repository), this tool will determine the most recent release tag,
count the patches that have been applied since then, and compute a
version number to be written into _version.py (and optionally other
version files). This version number will be available by doing:from your_package_name import __version__Source trees that do not come from darcs (e.g. release tarballs, nightly
tarballs) and are not within a darcs repository should instead, come with a
_version.py that was generated before the tarball was produced. In this case,
this tool will quietly exit without modifying the existing _version.py .‘release tags’ are tags in the source repository that match the following
regexp:^your_package_name-(d+)(.(d+)(.(d+))?)?((a|b|c|rc)(d+))?InstallationWith easy_install:easy_install darcsverAlternative manual installation:tar -zxvf darcsver-X.Y.Z.tar.gz
cd darcsver-X.Y.Z
python setup.py installWhere X.Y.Z is a version number.Alternative to make a specific package use darcsver without installing
darcsver into the system:Put “setup_requires=[‘darcsver’]” in the call to setup() in the
package’s setup.py file.UsageThere are two ways to use this: the command-line tool and the
setuptools plugin.To use the command-line tool, execute it as:darcsver $PACKAGE_NAME $PATH_TO_VERSION_PYTo use the setuptools plugin (which enables you to write “./setup.py
darcsver” and which cleverly figures out where the _version.py file
ought to go), you must first package your python module withsetup.pyand use setuptools.The former is well documented in the distutils manual:http://docs.python.org/dist/dist.htmlTo use setuptools instead of distutils, just editsetup.pyand
changefrom distutils.core import setuptofrom setuptools import setupReferencesHow to distribute Python modules with Distutils:http://docs.python.org/dist/dist.htmlSetuptools complete manual:http://peak.telecommunity.com/DevCenter/setuptoolsThanks to Yannick Gingras for providing the prototype for this
README.rst.
Package: darcyforchheimer
darcy-forchheimer calculatorThe goal of the package is to help obtaining the Darcy and Forchheimer coefficients, very useful for porous material fluid simulations.InstallationRun the following to install:pipinstalldarcyforchheimerUsefromdarcyforchheimerimportcalculate_coeficientscalculate_coefficients([velocity],[pressure],thickness,density,viscosity)If you want to calculate absolute coefficients, not depending on thickness, include the argumentcalculate_coefficients(...,absolute=True)
Package: dardanelles
dardanellesTable of ContentsBackgroundInstallationClientServerContributingMaintainersLicenseBackgroundConceptsInstallationClientServerdardanellesis also a web service for LCI data exchange. The default URL islci.brightway.dev, but you can run your own server. The server usesflask.Running the web serviceRun theflaskapplication any way you want. For example, to run the test server (not in production!), do:.. code-block:: bashexport FLASK_APP=/path/to/dardanelles/__init__.py
flask runAPI endpointsThe following API endpoints are supported:/Ping the server. Returns something likedardanelles life cycle inventory web data exchange, version (0, 1).HTTP method:GET/catalogGet the list of datasets availableHTTP method:GETResponse200: Return a JSON payload of the form:[('file name','database name','hex-encoded sha256 hash of file'),]/uploadUpload a datapackage.HTTP method:POSTParametersPost the following required form data:name: File namesha256: SHA 256 hash of fileThe file should be in the fieldfile.Responses201: The file was uploaded and registered. Returns a JSON payload:{'filename':'some file name','database name':'reported name of the database','sha256':'hex-encoded sha256 hash of file contents'}400: The request form was missing a required field406: The input data was invalid (either the hash wasn't correct or the file isn't readable)409: File already exists413: The uploaded file was too large (current limit is 250 MB)/downloadRequest the download of the file.HTTP method:POSTParametersPost the following form data:hash: SHA 256 hash of the fileResponses200: The requested file will be returned400: The request form was missing a required field404: A file for this hash was not foundContributingYour contribution is welcome! Please follow thepull request workflow, even for minor changes.When contributing to this repository with a major change, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository.Please note we have acode of conduct, please follow it in all your interactions with the project.Documentation and coding standardsBlack formattingisort formattingSemantic versioningMaintainersChris MutelLicenseBSD-3-Clause.
Package: dardrive
DardriveWarning!needs beta testing!Dardrive is acommand line backup toolwhich keeps track of backup jobs and
automates backup rotation.It uses theDisc ARchiveprogram to store
backups and has many features such as differential backup via catalogued data,
par2 integration, per job configuration, backup rotation, email reporting, and
mysql integration among other things.The idea behind dardrive is to store catalogs in fast media while storing the
backup content in large and slow cloud storage (it was written withs3qlin
mind). Creating differential backups against the cataloged data saves network
traffic and IOPs.Dardrive uses a full+incremental backup scheme which saves the last full backup
from previous months as “historic” backups, you’ll end up with a (time)
configurable amount of incremental archives between full backups, plus
“monthly” and “yearly” historic backups.Read the docshere.NewsRelease date: UNRELEASED0.2.11b13Fixed orphan catalogs bug.Ask the user to confirm passwords (thanks @jayme-github)0.2.11b1Fix: The import command now works as expectedFix: recover -f used to fail when recovering large amounts of filesrecover command adds a new -x switch0.2.10b3Bugfix to the purge routine.0.2.10b2Stats are preserved from rotationThere’s a command to reset stats“stats” command was moved to “show stats”Fix: It is now possible to show stats while a backup is running.0.2.10b1The “show archive” code now correctly displays the time.archive_store and catalog_store are now validated, and report an error.Catch and report errors when saving extended attributes.Added an option which disables xattr writing.Now dardrive detects and reports when archive_store and catalog_store
point to the same location.Added a “show ver” subcommand that shows the dardrive version.Show archives now reports running jobs.Check for path existence where rebuilding the catalogs.Fix dmd rebuilding locking bug0.2.9b2Initial GH import! (http://github.com/jfardello/dardrive)
Package: dare-datasets
Darelab Datasets DocsA quick access library of datasets used inDarelab.InstallationInstall:pip install dare-datasetsDocumentation:https://darelab.athenarc.gr/datasets-docs/add_dataset/Datasets included:QR2T BenchmarkfromMikeXydasIrisfromMikeXydasSpiderfromGeorge Katsogiannis&Anna MitsopoulouToTTofromMikeXydasWikitablefromMikeXydasUsagefromdare_datasetsimportQR2TBenchmarkqr2t_benchmark=QR2TBenchmark()qr2t_data=qr2t_benchmark.get()For each dataset, additional methods might exist. Check the documentation of each dataset for
more details.Dev InstallationFor development purposes, additional libraries must be installed such aspytestandmkdocs.Prerequisites:Python >=3.8PoetryPreCommit(Optional)1.Clonetherepository2.poetryinstall3.pre-commitinstall(Optional)4.gitbranchnew_dataset_nameAfter any contribution you should open a pull request.Testingpytestis the testing framework used for this project. Commands:pytest- Run all tests.Warning: This will download all datasets in a temp directory (~4GB).pytest -m "not download"- Run all tests except the ones that download datasets (suggested during development).
Package: dare-rf
DaRE RF: Data Removal-Enabled Random Forestsdare-rfis a python library that implementsmachine unlearningfor random forests, enabling theefficientremoval of training data without having to retrain from scratch. It is built using Cython and is designed to be scalable to large datasets.Installationpipinstalldare-rfUsageSimple example of removing a single training instance:importdareimportnumpyasnp# training dataX_train=np.array([[0,1],[0,1],[0,1],[1,0],[1,0]])y_train=np.array([1,1,1,0,1])X_test=np.array([[1,0]])# test instance# train a DaRE RF modelrf=dare.Forest(n_estimators=100,max_depth=3,k=5,# no. thresholds to consider per attributetopd=0,# no. random node layersrandom_state=1)rf.fit(X_train,y_train)rf.predict_proba(X_test)# prediction before deletion => [0.5, 0.5]rf.delete(3)# delete training example at index 3 ([1, 0], 0)rf.predict_proba(X_test)# prediction after deletion => [0.0, 1.0]LicenseApache License 2.0.ReferenceBrophy and Lowd.Machine Unlearning for Random Forests. ICML 2021.@inproceedings{brophy2021machine,
  title={Machine Unlearning for Random Forests},
  author={Brophy, Jonathan and Lowd, Daniel},
  booktitle={International Conference on Machine Learning},
  pages={1092--1104},
  year={2021},
  organization={PMLR}
}
Package: darfortie
Darfortie is a front-end for the dar (Disk ARchive) utility
(http://dar.linux.free.fr/). It adds functionality to the dar utility
and makes it simpler to use for creating incremental backups. It is
meant to facilitate a more convienent backup strategy, typically one run
periodically by a cron task.The utility can be run repeatedly with the same parameters, and each run
will generate a new incremental archive based on the last one created.
It will search the directory where the current archive is being created
for past reference archives, or a directory can be specified to search
in (-I).The first backup is created without the incremental option (-i).Further incremental backups are created using the -i option and thesame(base) name. Darfortie adds the date/time to the final name to
make it unique. It relies on this naming convention to find the most
recent reference backup it should use.It is suggested that for the base name you adopt a convention such as:machine_dailywhere “machine” is the machine name being backed up, and “daily”
is for daily incremental backups. The end result would result in names
like:machine_daily_20160228T0352UTC.1.dar
machine_daily_20160228T0403UTC_based_on_20160228T0352UTC.1.darA configuration file can be passed through to dar by using the -c
switch. If not specified, dar will search for and use any dar
configuration file it normally would (ie. /etc/darrc, ~/.darrc). See the
dar man page for details on its structure (“CONDITIONAL SYNTAX”).It also allows you to pass thru dar prune paths.Install darfortie by running:Installing Python (either v2 or v3) if not already installed.Download a configuration file to use with the -c option athttps://github.com/kagalle/darfortie/blob/master/darfortie.conf.Runpip install-ihttps://pypi.python.org/pypidarfortieIf this is done as a normal user, then the install puts the package in/home/ken/.local/lib/python2.7/site-packages/darfortieand creates an execuatable such that the application can be run by:$ ./local/bin/darfortieIf installed asroot, then the install is done into/usr/local/lib/python2.7/dist-packages/darfortieand the executable is created as:/usr/local/bin/darfortiewhich is normally on the system PATH.For a complete list of options, run:python darfortie --helpThis is BETA software. Use at your own risk. Please:Test for suitability before using for “real” data.During testing, use “dar -d …” to compare the created backup against
the filesystem.For testing, do a complete restore to a separate filesystem and
compare results.There is an older bash-based version in the “bash_version” directory,
kept ONLY for historical reasons.There is currently no restore option - use the dar utility directly to
do restores.
Package: dargs
Argument checking for python programsThis is a minimum version for checking the input argument dict.
It would examine argument's type,  as well as keys and types of its sub-arguments.A special case calledvariantis also handled,
where you can determine the items of a dict based the value of on one of itsflag_namekey.There are three main methods ofArgumentclass:checkmethod that takes a dict and see if its type follows the definition in the classnormalizemethod that takes a dict and convert alias and add default value into itgendocmethod that outputs the defined argument structure and corresponding docsThere are alsocheck_valueandnormalize_valuethat
ignore the leading key comparing to the base version.When targeting to html rendering, additional anchor can be made for cross reference.
Setmake_anchor=Truewhen callinggendocfunction and use standard ref syntax in rst.
The id is the same as the argument path. Variant types would be in square brackets.Please refer to test files for detailed usage.
Package: dariah
DARIAH Topics is an easy-to-use Python library for topic modeling and visualization. Getting started isreally easy. All you have to do is import the library – you can train a model straightaway from raw textfiles.It supports two implementations oflatent Dirichlet allocation:The lightweight, Cython-based packageldaThe more robust, Java-based packageMALLETInstallation$ pip install dariahExample>>> import dariah
>>> model, vis = dariah.topics(directory="british-fiction-corpus",
...                            stopwords=100,
...                            num_topics=10,
...                            num_iterations=1000)
>>> model.topics.iloc[:5, :5]
          word0   word1    word2    word3     word4
topic0  phineas    lord    laura   course     house
topic1    don't  mother     came       go    looked
topic2    jones   adams       am   indeed  answered
topic3      tom    adam   maggie     it's  tulliver
topic4  crawley  george  osborne  rebecca    ameliaWith thevisobject, you can visualize the model’s probability distributions, e.g. withvis.topic_document():DevelopingPoetryautomatically creates a virtual environment, builds and publishes the project toPyPI. Install dependencies with:$ poetry installrun tests:$ poetry run pytestformat code:$ poetry run black dariahbuild the project:$ poetry buildand publish it onPyPI:$ poetry publishAbout DARIAH-DEDARIAH-DEsupports research in the humanities and cultural sciences with digital methods and procedures. The research infrastructure of DARIAH-DE consists of four pillars: teaching, research, research data and technical components. As a partner inDARIAH-EU, DARIAH-DE helps to bundle and network state-of-the-art activities of the digital humanities. Scientists use DARIAH, for example, to make research data available across Europe. The exchange of knowledge and expertise is thus promoted across disciplines and the possibility of discovering new scientific discourses is encouraged.This software library has been developed with support from the DARIAH-DE initiative, the German branch of DARIAH-EU, the European Digital Research Infrastructure for the Arts and Humanities consortium. Funding has been provided by the German Federal Ministry for Research and Education (BMBF) under the identifier 01UG1610J.
Package: dariap-cine
dariap_cineNos piden hacer un programa sobre un cine (solo de una sala) tiene un conjunto de asientos (8 filas por 9 columnas, por ejemplo).
Del cine nos interesa conocer la película que se está reproduciendo y el precio de la entrada en el cine.De las películas nos interesa saber el título, duración, edad mínima y director.Del espectador, nos interesa saber su nombre, edad y el dinero que tiene.Los asientos son etiquetados por una letra (columna) y un número (fila), la fila 1 empieza al final de la matriz como se muestra en la tabla. También deberemos saber si está ocupado o no el asiento.8 A 8 B 8 C 8 D 8 E 8 F 8 G 8 H 8 I
7 A 7 B 7 C 7 D 7 E 7 F 7 G 7 H 7 I
6 A 6 B 6 C 6 D 6 E 6 F 6 G 6 H 6 I
5 A 5 B 5 C 5 D 5 E 5 F 5 G 5 H 5 I
4 A 4 B 4 C 4 D 4 E 4 F 4 G 4 H 4 I
3 A 3 B 3 C 3 D 3 E 3 F 3 G 3 H 3 I
2 A 2 B 2 C 2 D 2 E 2 F 2 G 2 H 2 I
1 A 1 B 1 C 1 D 1 E 1 F 1 G 1 H 1 IRealizaremos una pequeña simulación, en el que generaremos muchos espectadores y los sentaremos aleatoriamente (no podemos donde ya este ocupado).
En esta versión sentaremos a los espectadores de uno en uno.Solo se podrá sentar si tienen el suficiente dinero, hay espacio libre y tiene edad para ver la película, en caso de que el asiento este ocupado le buscamos uno libre.
Los datos del espectador y la película pueden ser totalmente aleatorios.
Package: darius
Verysimpleway to interact with any http and Websocket requestsfromdarius.serverimportserver,routesfromdarius.client_sideimportstatusclassHome(routes.View):defGET(self,request,**kwargs):returnstatus.Http200().__call__("<h1>Home Page</h1>")defPOST(self,request,**kwargs):returnstatus.HttpBinary().__call__("dog.png",display_in_browser=True)def__init__(self,*args,**kwargs):"""Optional : Add your own custom HTTP method requests"""super(Home,self).__init__(*args,**kwargs)self.cases['CREATE']=self.POST# Custom HTTP methodclassChat(routes.SocketView):defonConnect(self,client,**kwargs):key=kwargs.get("key")username=kwargs.get("headers")['Cookies']['username']self.accept(client,key)return{"username":username,"id":ord(username[0])%51}# Stored as state with clientdefonMessage(self,client,**kwargs):data=kwargs.get('data')# Message sentpath_info=kwargs.get("path_info")# Containing information about clientssend=kwargs.get('send_function')# Functions to send data# Send the message to every clientforclientinpath_info['clients']:if(client['username'].startswith("a"))orclient['id']%2==0:send(client,data)defonExit(self,client,**kwargs):print("Client exited!")And then to actually list the paths and start the ServerWS_URLS={r'/chat(\/)?':Chat()}HTTP_URLS={r'/home(\/)?':Home()}server.Server(WS_URLS,HTTP_URLS,port=8000).start()More can be found atGithub
Package: dark
Darkis an acronym for “Data Analysis and Reporting Kit”. It helps find
patterns in a set of irregular (mixed) data, sort various documents and pile
them in stacks by certain properties, define schemata for existing data, build
pivot tables and calculate various aggregates. Great for CSV, JSON, etc.Moreover, it has cookies.Installation$ pip install darkDocumentationSee thecomplete documentationfor details.AuthorInitially written by Andrey Mikhailenko in 2009 with invaluable feedback from
Ilya Musabirov. See the file AUTHORS for a complete authors list of this
application.Please feel free to submit patches, report bugs or request features:http://bitbucket.org/neithere/dark/LicensingDark is free software; you can redistribute it and/or modify it under the terms
of the GNU Lesser General Public License as published by the Free Software
Foundation; either version 3 of the License, or (at your option) any later
version.Dark is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License along
with this program; see the file COPYING.LESSER.
If not, see <http://www.gnu.org/licenses/>.
Package: dark-and-darker
Dark and DarkerFeaturesTODORequirementsTODOInstallationYou can installDark and DarkerviapipfromPyPI:$pipinstalldark-and-darkerUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Dark and Darkeris free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: darkapi
Установка:pip install darkapiПримеры:Информация о темеfrom pydarkapi import darkapi

api = darkapi.Api(cookie, useragent)

print(api.getThreadInfo(thread_id))Список методовgetThreadInfo(thread_id: int)-Информация о темеgetPosts(thread_id: int, page: int)-Получить сообщения в темеcreatePost(thread_id, message_html)-Написать в темуeditPost(post_id: int, new_html)-Редактировать сообщение в темеcreateThread(forum_id: int, title: str, message_html)-Создать темуeditThread(thread_id: int, title: str, message_html)-Редактировать тему
Package: darkbox
darkboxThis is a work-in-progress, all-in-one, portable, cross-platform, command-line toolkit. It's similar in nature to BusyBox, but is able to run on Windows, Linux, macOS, etc. darkbox is built for hackers, penetration testers, incident responders, and security researchers. It not only comes with many of the standard utilities that you use on a daily basis, but also tools that computer security practitioners know and love.darkbox is written by your friendly neighborhood AbnormalSec <3How to get darkboxPre-built, portable binaries can be downloaded for Linux, Windows, and macOS in thereleases section.If you'd like to compile your own binary, darkbox is compiled usingNuitka. See theNuitka documentationto learn how to get up and running, and then darkbox can be built like so:nuitka3 --recurse-all darkbox/darkbox.pydarkbox is also available onPyPIand can be installed using pip:pip install darkboxIf you'd like to run darkbox from the git repo source code you can:$gitclonehttps://github.com/abnormalsec/darkbox
$# use it directly as a module like so$python-mdarkbox.darkbox-v
darkbox0.1.0
$# or manually install it$pythonsetup.pyinstall
$darkbox-v
darkbox0.1.0UsageOnce you have darkbox it can be used like so:$ darkbox --help
Usage: darkbox <tool> [OPTIONS]
Tools: base64, cat, cp, curl, exip, head, hostname, ls, md5sum, mv, nmap, pwd, rm, sha224sum, sha256sum, sha384sum, sha512sum, unzip, xxd, zip

$ darkbox cat hello.txt
Hello, world!

$ darkbox nmap -p22 localhost
Starting darkbox Nmap v0.0.1 at 2018-14-22 16:14 EST
Nmap scan report for localhost (127.0.0.1)
PORT      STATE
22/tcp    open
Nmap done: 1 IP addresses scanned in 0.02 seconds

$ darkbox md5sum /bin/ls
d77c1dd5bb8e39c2dd27c96c3fd2263e /bin/ls