Saravanan666/pypi-org huggingface dataset:
Package: pp.core
pp.core - Produce & Publish Foundationpp.coreimplements some core foundation functionalities
of the Produce & Publish framework.Provided functionalityfilesystem abstractionintegration with Dropbox and other cloud file servicesresource baseresources registryfilesystem registryInternalsThis module abstracts Produce & Publish basic functionality in order to make
the core implementation as re-usable as possible.  It uses thepyfilesystemto abstract arbitrary file operations independent of the underlaying storage
layer. Through this approach we are able to support local filesystems, network
filesystems or cloud storages like Dropbox without changing the application.
All storage subsystems can be accessed using the same API.RequirementsPython 2.7For using the Dropbox storage API ofpp.coreyou need to havePhantomjsinstalled (thephantomjsbinary must be available in the$PATH. PhantomJS
is required to fake the OAuth process of Dropbox.Source codehttps://bitbucket.org/ajung/pp.coreBug trackerhttps://bitbucket.org/ajung/pp.core/issuesSupportSupport for Produce & Publish Server is currently only available on a project
basis.Licensepp.coreis published under the GNU Public License V2 (GPL 2).ContactAndreas Jung/ZOPYXHundskapfklinge 33D-72074 Tuebingen, Germanyinfo@zopyx.comwww.zopyx.comCHANGES0.4.2 (2018/12/12)some Python 3 cleanup0.4.1 (2017/10/05)further cleanup0.4.0 (2017/08/26)removed outdated Dropbox implementation0.3.3 (2015/02/08)fix in constructor0.3.2 (2015/01/27)registerTransformation no longer support duplicate
registrationsminor fix in transformation code0.3.0 (2014/11/17)code cleanup, removed obsolete modulesdrone.io support for continuous integration testing0.2.4 (2014/08/21)Transformer: fixed parameter handling0.2.3 (2014/08/05)unicode fix0.2.2 (2014/08/05)TransformerXML API is now unicode-aware0.2.1 (2014/08/04)added TransformerXML API0.2.0 (2014/03/16)enhanced resources_registry APIdisabled Dropbox API due to Dropbox changestest fixes0.1.0 (2013/07/11)initial release
Package: pp.core2
pp.core - Produce & Publish Foundationpp.coreimplements some core foundation functionalities
of the Produce & Publish framework.Provided functionalityfilesystem abstractionintegration with Dropbox and other cloud file servicesresource baseresources registryfilesystem registryInternalsThis module abstracts Produce & Publish basic functionality in order to make
the core implementation as re-usable as possible.  It uses thepyfilesystemto abstract arbitrary file operations independent of the underlaying storage
layer. Through this approach we are able to support local filesystems, network
filesystems or cloud storages like Dropbox without changing the application.
All storage subsystems can be accessed using the same API.RequirementsPython 2.7For using the Dropbox storage API ofpp.coreyou need to havePhantomjsinstalled (thephantomjsbinary must be available in the$PATH. PhantomJS
is required to fake the OAuth process of Dropbox.Source codehttps://bitbucket.org/ajung/pp.coreBug trackerhttps://bitbucket.org/ajung/pp.core/issuesSupportSupport for Produce & Publish Server is currently only available on a project
basis.Licensepp.coreis published under the GNU Public License V2 (GPL 2).ContactAndreas Jung/ZOPYXHundskapfklinge 33D-72074 Tuebingen, Germanyinfo@zopyx.comwww.zopyx.comCHANGES0.5.3 (2018/12/13)improved Python 3 compatibilityvarious fixes for Python 2/3 compatibility0.5.1 (2018/12/12)improved Python 3 compatibility0.5.0 (2018/01/19)forked from pp.coreswitched to fs>2
Package: ppdeep
This is a pure-Python library for computing context triggered piecewise hashes
(CTPH), also called fuzzy hashes, or often ssdeep after the name of a popular
tool. At a very high level, fuzzy hashing is a way to determine whether two
inputs are similar, rather than identical. Fuzzy hashes are widely adopted in
digital forensics and malware detection.This implementation is based on SpamSum by Dr. Andrew Tridgell.UsageTo compute a fuzzy hash, simply usehash()function:` >>> import ppdeep >>> h1 =ppdeep.hash('Theequivalence of mass and energy translates into thewell-knownE = mc²') >>> h1 '3:RC0qYX4LBFA0dxEq4z2LRK+oCKI9VnXn:RvqpLB60dx8ilK+owX' >>> h2 =ppdeep.hash('Theequivalence of mass and energy translates into thewell-knownE = MC2') >>> h2 '3:RC0qYX4LBFA0dxEq4z2LRK+oCKI99:RvqpLB60dx8ilK+oA' `To calculate level of similarity, usecompare()function which returns an
integer value from 0 to 100 (full match):` >>> ppdeep.compare(h1, h2) 29 `Functionhash_from_file()accepts a filename as argument and calculates the
hash of the contents of the file:` >>>ppdeep.hash_from_file('.bash_history')'1536:EXM36dG36x3KW732vOAcg3EP1qKlKozcK0z5G+lEPTssl/7eO7HOBF:tKlKozcWT0' `Installation` $ pip install ppdeep `If you want to use the latest version of the code, you can install it from Git:` $ git clonehttps://github.com/elceef/ppdeep.git$ cd ppdeep $ pip install . `
Package: ppdir
mkignorePretty print a directory structure as a treeInstallationpip install ppdirUsageusage:ppdir[-h][-a,--all][-c,--color][DIR]Prettyprintadirectorystructureasatree

positionalarguments:DIR

optionalarguments:-h,--helpshowthishelpmessageandexit-a,--allIncludehiddenfiles-c,--colorColorizeoutputExampleppdir -a -c .Output:
Package: ppdire
So long,ppdirepackage......and thanks for all the fish!Theppdirepackage has been sunset and will no longer be updated.
Its contents have migrated into thedirepackpackage:PyPIGitHubTheppdirepackage in this final version will still stay live for a while for backwards compatibility.How to installThe package is distributed through PyPI, so install through:pip install ppdireDocumentationDetailed documentation on how to use the classes is provided in theDocumentation file.
Package: ppDownloader
A python download accelerator that uses parallel requests to increase download speed.Free software: MIT licenseInstallationpip install ppDownloaderYou can also install the in-development version with:pip install https://github.com/AriaBagheri/ppDownloader/archive/master.zipDocumentationhttps://ppDownloader.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 toxChangelog0.0.1 (2020-06-06)First release on PyPI.0.0.2 (2020-06-06)Documentation added for method download.
Package: ppdp-anonops
IntroductionThis project implemets basic anonymization operations for event data which are used by process mining techniques. The anonymization operations are formally explained in the following paper:https://www.researchgate.net/publication/342048551_Privacy-Preserving_Data_Publishing_in_Process_MiningRef: implemeted by "Alexander 'DevSchnitzel' Schnitzler" as part of his bachelor thesis at PADS group.Python PackageThe implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:pipinstallppdp-anonopsUsageLook at the following directory in the Github project to see the samples of usage: "ppdp-anonops/tests"
Package: ppds
PPDS:
Andrew Donshik, 2017The Private-Peer-Domain-Syndication Manifesto:PPDS is an open-source and semi-centralized replacement to traditional domain registration and distribution methods. PPDS is meant to be a drop-in challenger to traditional domain serving and registration. It seeks to completely remove the commercial and governmental reliance of domain spaces, and make domain resolution and registration a user controlled and user regulated process. Domain name servers are replaced by RSS-esque domain aggregators that can be created and maintained by anyone. It is their responsibility to map domain names to their respective IP address, but they can choose to map any domain to any address they wish. Users of PPDS can also subscribe to other peoples’ domain aggregators, and these aggregations are downloaded in full or in part to their computer. These domain aggregators come in the form of JSON mappings between the ip address and the new domain. Requests for these domains are resolved locally whenever possible, minimizing the amount of outgoing information related to their browsing activity. Users can enable multiple aggregators at once if they want, and users decide how domains are deduplicated between multiple aggregators. The goal of the project is to make domain names an openly managed and accessible resource.Repo formatting and protocol to follow.The current idea of implementation is to create a hosts-file patch manager that downloads from a central repository (user-repos will be possible of course) that is verified through some kind of cert service.TODO:Implement download requests from the repositories.RealConflict ManagementSome kind of public key private key verification for repositories.GUIA spec so that people can code implementations that aren’t written by meBuilding/Running:I’ve only tested this against Python 3.6, but there’s no reason it wouldn’t work on earlier versions.DOES NOT WORK ON PYTHON 2run setup.py install or setup.py build bdist_wheel and install with pip
Package: ppextensions
PPExtensions is a suite of ipython and jupyter extensions built to improve user experience and reduce time to market inJupyternotebooks.FeaturesPPMagics- Set of magics to simplify access to different storage systems and tableau.Github Integration- A jupyter extension to integrate notebooks with github. This extension simplifies version controlling, sharing and resolving merge conflicts of notebooks.Notebooks Scheduling- A jupyter extension to productionalize the notebooks development environment. This extension enables scheduling notebooks with help ofairflow.Config UI- A simple UI built to change the configurations of different extensions like PPMagic,sparkmagic..etc.Installationpip install ppextensionsCurrent StateFeatureAvailableStatePPMagicsAvailableBetaSchedulerAvailableBetaGithubAvailableBetDocumentation & Getting StartedClick here to read the docsQuestionsSlackUser ForumDeveloper Forum
Package: pp-ez
ppcan be installed with Python 2 or Python 3 usingpiporeasy_install:$ pip install pprintpp pp-ez
- OR -
$ easy_install pprintpp pp-ezThepppackage is just an alias for thepprintpppackage (and falls back
to thepprintpackage ifpprintppisn’t available), plus a bit of magic
so it can be called directly:>>> import pp
>>> pp(["Hello", "world"])
["Hello", "world"]
>>> pp.fmt([1, 2, 3])
'[1, 2, 3]'
>>> pp.pprint([1, 2, 3])
[1, 2, 3]
>>> pp.pformat([1, 2, 3])
'[1, 2, 3]'By default,pptries to use thepprintpppackage, but if that is not
available it will fall back to usingpprint:>>> import pp
>>> pp
<module 'pp' (pp_magic_module with pprint_mod='pprint')>
>>> pp.pprint_mod
<module 'pprint' from '.../lib/python2.7/pprint.pyc'>See also:pprint++:https://pypi.python.org/pypi/pprintpp
Package: ppf-datamatrix
ppf.datamatrixppf.datamatrix is a pure-python package to generate datamatrix codes in SVG.
Also, it integrates nicely in IPython.ppf.datamatrix has been ported fromdatalog's
datamatrix-svg, which is written in
javascript.  If you like to see what you'll get before installation, check out
theirnice web demo.Creating a datamatrix with ppf.datamatrix is as easy asfrom ppf.datamatrix import DataMatrix

myDataMatrix = DataMatrix('Test!')A graphically enabled IPython terminal will even represent myDataMatrix as a
picture.Check out the project website to find out  more!
Package: ppf-jabref
ppf.jabrefppf.jabref provides apythoninterface toJabRefSQL databases. It maps database relations
to python classes usingSQLAlchemy.
Also, ppf.jabref provides tools to parse the data stored inside the
database tables.Using ppf.jabrefppf.jabref relies on SQLAlchemy for database access. All that ppf.jabref
adds to this is a data model which makes sqlalchemy understand how a
JabRef database is structured (by providing classes Entry and Field).A simple example that queries all entries and prints a selection of
the fields looks like this:fromsqlalchemyimportcreate_enginefromsqlalchemy.ormimportsessionmakerfromppf.jabrefimportEntry,Fileengine=create_engine('<your connection string here>',echo=False)Session=sessionmaker(bind=engine)session=Session()q=session.query(Entry)forentryinq:print(entry.fields['author'],'\t',entry.fields['title'],'\t',entry.fields['year'],'\t',end='')files=File.from_string(entry.fields['file'])foriinrange(len(files)):f=files[i]print(f.path,'\t',end='')The first 6 lines are setup code to import required packages and to set up
the database connection. The query then uses ppf.jabref's Entry class to
obtain all Entries (=references) in the JabRef database. The for-loop
shows how to access fields and uses the File class to find out where the
documents linked to this entry are stored.
Package: ppfl
PPF - Python Package Finder LibraryThis project is home to a command line interface used
to search PyPi for various projects.Using this CLI is really simple, just type "pypi" and then
the name of your project.pypi<project>The command will be processed and will return the following fields:NameVersionDescriptionAuthorLicensePython RequirementsProject URLProject URLsClassifiersIf the project doesn't qualify or provide for one of these fields,
they will be omitted. License names are only shown if they are under 100 characters,
as some maintainers like to include the LICENSE file content in the field, when
theoretically it should be just the license name. This limit is imposed to take better
care over the UI and aligned representation of data.Example Usagepypi pathlib

>>>

pathlib | 1.0.1
~~~~~~~~~~~~~~~
Object-oriented filesystem paths

Author: Antoine Pitrou
License: MIT License
Project URL: https://pypi.org/project/pathlib/

Project URLs:
- Download: https://pypi.python.org/pypi/pathlib/
- Homepage: https://pathlib.readthedocs.org/

Classifiers:
- Topic :: Software Development :: Libraries
- Topic :: System :: Filesystems
- License :: OSI Approved :: MIT License
- Operating System :: OS Independent
- Intended Audience :: Developers
- Development Status :: 5 - Production/Stable
- Programming Language :: Python :: 2.6
- Programming Language :: Python :: 2.7
- Programming Language :: Python :: 3
- Programming Language :: Python :: 3.2
- Programming Language :: Python :: 3.3
- Programming Language :: Python :: 3.4InstallationInstall through pip:pipinstallppflRequirementsTherequestsmodule is required for this CLI.
Package: ppftps
ppftpsPush Push directories over a secured FTP connection.ForKeePassaccounts you need to set valid KDBX='/path/to/db.kdbx',
KDBXPW="$(cmd_get_kdbx_master_pw)" and KDBXUUID='server-uuid' vars
in your shell environment or wrapper script.Installpipinstallppftp
pipinstallpykeepass# for KeePass supportand create a ftp wrapper script with your settings in your project directory#!/bin/shKDBX="${HOME}/.keepass/ftp.kdbx"\KDBXPW="$(x11-ssh-askpass"ftp.kdbx master password)"\KDBXUUID="KDBX-UUID"\ppftps$@Usage./ftppush./local-folder/absolut/path/remote/folder
Package: ppfun
ppfunppfun allows you to use the PixelPlanet.Fun's (hence the name) API in your Python code. I personally didn't come up with any use case besides auto-drawing bots. But you may. Please e-mail me if you come up with any idea, I will truly be interested.Example code:# import the libraryimportppfun# create an API objectpp=ppfun.PPFun_api()# print the list of available canvasesprint('---------')print('Canvases:')forcinpp.list_canv():print(c['title']+'\t\t'+c['description']+'\t\t'+str(c['size'])+'x'+str(c['size'])+'\t\t'+'('+c['identifier']+')')print('---------')# get the main canvas (Earth)canv=pp.get_canv('d')# print the approximation of the lime color for this canvasprint('Lime color ID: '+str(canv.approx_color((0,255,0))))# set a pixel at (-1532, -550) and print the cooldown timecanv.set_pixel((-1532,-550),canv.approx_color((0,255,0)))print('Cooldown after setting the pixel: '+str(canv.remaining_cooldown()))installationpip install ppfundocumentationPPFun_api classThis is where the journey starts. You create a client by creating an instance of this class. The library then fetches some data from the server.
It is available through these functions:list_canv()- returns a list of canvas descriptors. Each descriptor is a dictionary that contains thetitle,description,sizeandidentifierkeysget_canv(ident)- returns a canvas object of the canvas with a specific identifierPPFun_canv classThis class contains all the functions you need to create your masterpiece. Here they are:approx_color(rgb_tuple)- returns a color index that approximates the given color the mostset_pixel(pos_tuple, color_index)- places a pixel on the map. Throws an exception on error, returns True on successremaining_cooldown()- returns the remaining cooldown in seconds
Package: ppf.webref
ppf.webrefppf.webref is a web app providing an interface to aJabRef SQL
database.
Access your references from anywhere in the world and from any device with a
web browser. You do not need to install Java, you do not need to install an
app. Any non-archaic phone, tablet, PC, Mac, or Raspberry Pi will do.Create a JabRef database (using your normal JabRef) and point ppf.webref to
this database. Voila: Your references just became accessible worldwide.Note: ppf.webref providesread-onlyaccess to your library. To add, edit, or
delete entries from your library, you still need a standard JabRef installation
somewhere.InstallationPrerequisite: You need JabRef to create, edit, and extend your library.Installppf.webref(or have a look atdocker.webrefthat provides
a docker container runningppf.webref):pipinstallppf.webrefThen, create the config file~/.config/ppf.webref/ppf.webref.conf:[flask]
secret_key = <your secret key here>

[database]
server = <server>:<port>
databasename = <name of your jabref database>
username = <username ppf.webref should use to access db>
password = <password ppf.webref should use to access db>secret_keyis needed to encrypt cookies. Set it to a random string, e.g. by
running this snippet:python-c'import secrets; print(secrets.token_hex())'You can now start the web server byflask--appppf.webrefrunand point your webbrowser tohttp://localhost:5000.[This will start ppf.webref on your local machine which is nice for testing.
To get the most out of ppf.webref, you will probably want to run ppf.webref on
a server.]ppf.webref will present a login form. However, as we have not created any users
yet, we can't login. To create a user, run:flask--appppf.webrefuseradd<username>This will:create a table 'user' in your db if it does not exist yetregister user  in user tableTo set a password for this new user or to change the password of an existing
user, doflask--appppf.webrefpasswd<username>which will ask for and store (a salted hash of) the password in the
user table.Now we are able to login, but the entry table will not provide clickable links
so you can easily open your documents. Forppf.webrefto be able to serve the
documents themselves, we have to put them under<app.root_path>/references(just place a symlink to your JabRef library there). The app's root path is
something like/usr/local/lib/python3.11/site-packages/ppf/webref/.Still reading?If you read this far, you're probably not here for the first time. If you use
and like this project, would you consider giving it a Github Star? (The button
is at the top of this website.) If not, maybe you're interested in one of mymy other
projects?ContributingDid you find a bug and would like to report it? Or maybe you've fixed it
already or want to help fixing it? That's great! Please readCONTRIBUTINGto learn how to proceed.To help ascertain that contributing to this project is a pleasant experience,
we have established acode of conduct. You can expect
everyone to adhere to it, just make sure you do as well.Changelog0.1.1: Fix problem with path handling. Improve docs.0.1: Basic read-only functionality
Package: ppgm
PGMProbabilistic graphs: Representation, Learning, and InferenceFeaturesRepresentationBayesian Network RepresentationLinked List BN RepresentationLinked List MN RepresentationConditional EstimationMarginal EstimationJoint EstimationInferenceMetropolis-Hastings algorithmGibbs Sampling on 2d gridGeneralized Gibbs SamplingMessage Parsing and BPLoopy BPVECausal Interventionssearch methodsDFSBFSAdditionalFinding Active TrailsMax clique size and clique nodeCalculate tree-widthLearningMiscellaneousRandom BN and MN generationInstallationpip install ppgmContactAvinash Kori (koriavinash1@gmail.com)
Package: ppg-package
ppg_package这是一个用于处理和分析脉搏波形(PPG)信号的Python包。该包提供了一系列工具，用于信号的滤波、分析和可视化。功能ppg_package包括以下主要功能：快速傅里叶变换（FFT）分析功率谱密度（PSD）分析短时傅里叶变换（STFT）分析连续小波变换（CWT）分析Poincaré图分析计算PPG信号的多种统计量和特征安装您可以通过以下命令安装ppg_package：pip install ppg_package
Package: ppg-pre
ppgppg preprocessing (e.g., ppg> spectogram, bandpass, moving average)ALL in One: Simple PPG preprocessingUsageInstallationpip install ppg-prepython usagefrom ppg_pre import get_ppg_sample
x = get_ppg_sample() # get sample ppg data

from ppg_pre import ppg2specgram
x = ppg2specgram(x) # u can get the spectrogram dataFunctionppg2specgram function has the following processing.interpolatebandpass (0.5~10 Hz)moving average (30 taps)z-score normalization (mean:47, std:15)spectrogramVisualizationfrom matplotlib import pyplot as plt
from ppg_pre import get_ppg_sample

x = get_ppg_sample()
plt.plot(x[12000:15000])
Package: pph
pphpph generates a minimal order-preserving hash function for a list of keys.Reference:Practical perfect hashing
GV Cormack, RNS Horspool, M Kaiserswerth - The Computer Journal, 1985Table of ContentsLicenseBuildingUsingLicenseThis project is licensed under theApache License, Version 2.0.BuildingThis library uses the Boost library.export LDFLAGS="$LDFLAGS -L/path/to/boost/lib"
export CPPFLAGS="$CPPFLAGS -I/path/to/boost/include"pph uses CMake for its build system.mkdir build
cd build
cmake ..
makeUsingThe basic command line to generate a hash function from a file containing a list of strings (one per line) is:pph -i ./file.txt -o ./file.hashThe command line to verify an existing hash function is:pph --verify ./file.hashThe other command line options can be seen by typing:pph --helpThe default timeout for creating a hash function is 60000 milliseconds (1 minute).If a hash function is not generated, you can try sorting the input file:pph -i file.txt --index > file_index.txt
sort --numeric-sort --key=2 file_index.txt > file_sorted_index.txt
awk -F' ' '{print $1}'  file_sorted_index.txt > file_sorted.txtPythonThis library uses the Boost library. Install the Boost library and setLDFLAGSandCPPFLAGSbefore installing the Python module.export LDFLAGS="$LDFLAGS -L/path/to/boost/lib"
export CPPFLAGS="$CPPFLAGS -I/path/to/boost/include"Install the module.pip3 install pphImport the module.from pph import PphHashTable, PphRandomNumber, PphKeyFunctionsSee the tests for how to generate a hash function using the Python interface.