Saravanan666/pypi-org huggingface dataset:
Package: pyflag
DESCRIPTIONThe FLAG code provides functionality to perform fast and exact Fourier-Laguerre and Fourier-Bessel transforms on the ball. More details may be found in the extensivedocumentation.BASIC USAGEFirst install flag for python by runningpipinstallpyflagThen you can call it from python to perform forward and inverse flag transforms and their adjoints byimportpyflagasflagimportnumpyasnpL=10# Angular bandlimitP=5# Radial bandlimittau=1# Laguerre scaling factorspin=0# Spin of signalreality=0# Real or complex signals# Create a random complex signal (c indexing)f=np.random.rand(P,L,2*L-1)+1j*np.random.rand(P,L,2*L-1)f=f.flatten('C')# Compute e.g. the Forward transformflmp=flag.flag_analysis(f,L,tau,P,spin,reality)ContributorsB. Leistedt,J. D. McEwen, andM. A. PriceREFERENCES@article{price:2021:bayesian,
    author  = {Matthew~A.~Price and Jason~D.~McEwen},
    title   = {Bayesian variational regularization on the ball},
    journal = {ArXiv},
    eprint  = {arXiv:2105.05518},
    year    = 2021
}@article{leistedt:2012:exact,
    author  = {Boris~Leistedt and Jason~D.~McEwen},
    title   = {Exact Wavelets on the Ball},
    journal = {IEEE Trans. Sig. Proc.},
    year    = 2012,
    volume  = {60},
    number  = {12},
    pages   = {6257-6269},
    doi     = {10.1109/TSP.2012.2215030},
}@article{McEwen:2011:novel,
    author  = {Jason~D.~McEwen and Yves~Wiaux},
    title   = {A novel sampling theorem on the
     sphere},
    journal = {IEEE Trans. Sig. Proc.},
    year    = 2011,
    volume  = {59},
    number  = {12},
    pages   = {5876-5887},
    doi     = {10.1109/TSP.2011.2166394},
}LICENSEFLAG package to perform fast wavelet transform on the sphere<br>
Copyright (C) 2012 Boris Leistedt & Jason McEwenThis program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.This program 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 General Public License for more details (LICENSE.txt).You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
MA  02110-1301, USA.
Package: pyflagr
The fusion of multiple ranked lists of elements into a single aggregate list is a well-studied research field with numerous applications in Bioinformatics, recommendation systems, collaborative filtering, election systems and metasearch engines.FLAGR is a high performance, modular, open source library for rank aggregation problems. It implements baseline and recent state-of-the-art aggregation algorithms that accept ranked preference lists and generate a single consensus list of elements. A portion of these methods apply exploratory analysis techniques and belong to the broad family of unsupervised learning techniques.PyFLAGR is a Python library built on top of FLAGR library core. It can be easily installed with pip and used in standard Python programs and Jupyter notebooks.FLAGR Website:https://flagr.site/GitHub repository:https://github.com/lakritidis/FLAGR
Package: pyflags
flagsis a Python flag parsing library for humans, not robots.Why?Argparse is complicated.Placremedies most of its issues. However, sometimes you just need a really simple POSIX-style flag parser. You can use getopt, but it makes you write all the help screens yourself, which can easily go out-of-sync with the program source.flagsaims at filling this void by providing a simple, practical API that’s truly easy to use and doesn’t suck.ExampleSince README examples always go out of sync with the source, included atexamples/grep.pyisflagsparser forPOSIX grep’s arguments.
Package: pyflagser
pyflagserpyflagseris a python API for the flagser C++ library by Daniel Lütgehetmann which computes the homology of directed flag complexes. Please check out the originalluetge/flagserGitHub repository for more information.Project genesispyflagseris the result of a collaborative effort betweenL2F SA, theLaboratory for Topology and Neuroscienceat EPFL, and theInstitute of Reconfigurable & Embedded Digital Systems (REDS)of HEIG-VD.InstallationDependenciespyflagserrequires:Python (>= 3.7)NumPy (>= 1.17.0)SciPy (>= 0.17.0)User installationIf you already have a working installation of numpy and scipy, the easiest way to install pyflagser is usingpippython -m pip install -U pyflagserDocumentationAPI reference (stable release):https://docs-pyflagser.giotto.aiContributingWe welcome new contributors of all experience levels. The Giotto community goals are to be helpful, welcoming, and effective. To learn more about making a contribution topyflagser, please see theCONTRIBUTING.rstfile.Developer installationC++ dependencies:C++14 compatible compilerCMake >= 3.9Source codeYou can check the latest sources with the command:git clone https://github.com/giotto-ai/pyflagser.gitTo install:From the cloned repository’s root directory, runpython-mpipinstall-e".[tests]"This way, you can pull the library’s latest changes and make them immediately available on your machine.TestingAfter installation, you can launch the test suite from outside the source directory:pytest pyflagserChangelogSee theRELEASE.rstfile
for a history of notable changes to pyflagser.Important linksOfficial source code repo:https://github.com/giotto-ai/pyflagserDownload releases:https://pypi.org/project/pyflagser/Issue tracker:https://github.com/giotto-ai/pyflagser/issuesContacts:maintainers@giotto.ai
Package: pyflagstats
pyflagstatsGiven a stream of k-bit words, we seek to sum the bit values at indexes 0, 1, 2,
..., k-1 across multiple words by computing k distinct sums. If the k-bit words
are one-hot encoded then the sums corresponds to their frequencies.This multiple-sum problem is a generalization of the population-count problem
where we count the total number of set bits in independent machine words. We
refer to this new problem as the positional population-count problem.Using SIMD (Single Instruction, Multiple Data) instructions from recent Intel
processors, we describe algorithms for computing the 16-bit position population
count using about one eighth (0.125) of a CPU cycle per 16-bit word. Our best
approach is about 140-fold faster than competitive code using only non-SIMD
instructions in terms of CPU cycles.This package contains native Python bindings for the applying the efficient
positional population count operator to computing summary statistics for the SAM
FLAG fieldIntallationInstall withpip3install.or locally withpython3setup.pybuild_ext--inplaceUninstall withpip3uninstallpyflagstatsExampleimportnumpyasnpimportpyflagstatsasfs# Compute summary statistics for 100 million random FLAG fields.# Completes in around 1 second.fs.flagstats(np.random.randint(0,8192,100000000,dtype="uint16"))returns (for example){'passed': array([ 624787,  312748, 2500089,  312384,  312314,  312678,  312045,
        311845, 2499502, 4999279, 2497500, 1248979,  389744,  156194,
        156029,       0], dtype=uint32), 'failed': array([ 625143,  312906, 2498840,  312818,  312129,  312802,  311869,
        312105, 2501477, 5000721, 2499178, 1249105,  390962,  155828,
        156018,       0], dtype=uint32)}
Package: pyflakes
A simple program which checks Python source files for errors.Pyflakes analyzes programs and detects various errors.  It works by
parsing the source file, not importing it, so it is safe to use on
modules with side effects.  It’s also much faster.It isavailable on PyPIand it supports all active versions of Python: 3.6+.InstallationIt can be installed with:$ pip install --upgrade pyflakesUseful tips:Be sure to install it for a version of Python which is compatible
with your codebase:python#.#-mpip install pyflakes(for example,python3.10-mpip install pyflakes)You can also invoke Pyflakes withpython#.#-mpyflakes .if you want
to run it for a specific python version.If you require more options and more flexibility, you could give a
look toFlake8too.Design PrinciplesPyflakes makes a simple promise: it will never complain about style,
and it will try very, very hard to never emit false positives.Pyflakes is also faster thanPylint. This is
largely because Pyflakes only examines the syntax tree of each file
individually. As a consequence, Pyflakes is more limited in the
types of things it can check.If you like Pyflakes but also want stylistic checks, you wantflake8, which combines
Pyflakes with style checks againstPEP 8and adds
per-project configuration ability.Mailing-listShare your feedback and ideas:subscribe to the mailing-listContributingIssues are tracked onGitHub.Patches may be submitted via aGitHub pull request.
If you are comfortable doing so, pleaserebase your changesso they may be applied to main with a fast-forward merge, and each commit is
a coherent unit of work with a well-written log message.  If you are not
comfortable with this rebase workflow, the project maintainers will be happy to
rebase your commits for you.All changes should include tests and passflake8.ChangelogPlease seeNEWS.rst.
Package: pyflames
pyflamesPython library to check two names and return their relationship status as per "FLAMES"FlamesHow it works?What is FlamesGame?FlamesGame is a relationship calculating algorithm famous between the youngsters. At the time of graduation everyone might heard about this and many of them tried out this secretly. Some took this as very serious also. So what FLAMES stance for?F - FriendshipL - LoveA - AffectionM - MarriageE - EnemyS - Sister (Sibling)How we can calculate the FLAMES?It is very easy to explain with some example:Your name:asdPartner name:abcdMainly two steps are there:Get the flames countTake the two names ('asd' and 'abcd')Remove the common characters (two common characters 'a', 'd')Get the count of the characters that are left (Removed a,d and the rest are s,b,c. So total 3.)Get the flames resultWe take FLAMES letters ('F', 'L', 'A', 'M', 'E', 'S')And start removing letters using the flames count we got.And the letter which last the process is the result.In our example we gotflames count = 3. So first we takes FLAMES.FLAMESThen we start count from left up to flames count 3. Then remove the letter which is in the position 3. In this case it is 'A'. So the letters become:FLMESThen we start count again from the letter which is removed ie, from 'M'. So the next character to remove is 'S'. So our letters become:FLMEAfter next step:FLEThen:FELast:FSo the result is 'Friend'.So it's as simple as that!USAGE:From packagename import package nameCreate an instance of the classCreate variable and call the flames method referencing the instance that we createdSYNTAX:import pyflames
variable_name = pyflames.pyflames()
another_variable = variable_name.flames("name1","name2")
print(another_variable)EXAMPLE CODE:import pyflames
word = pyflames.pyflames()
status = word.flames("John Wick","Jennifer Aniston")
print(status)
Package: pyflann-ibeis
This is a Fork of the FLANN repo, under a different name for use in the IBEIS
project. The main difference is that it has a few more helper function calls
and it should be easier build wheels and to pip install.FLANN - Fast Library for Approximate Nearest NeighborsFLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.
FLANN is written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby.DocumentationCheck FLANN web page [here](http://www.cs.ubc.ca/research/flann).Documentation on how to use the library can be found in the doc/manual.pdf file included in the release archives.More information and experimental results can be found in the following paper:Marius Muja and David G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”, in International Conference on Computer Vision Theory and Applications (VISAPP’09), 2009 [(PDF)](http://people.cs.ubc.ca/~mariusm/uploads/FLANN/flann_visapp09.pdf) [(BibTex)](http://people.cs.ubc.ca/~mariusm/index.php/FLANN/BibTex)Getting FLANNIf you want to try out the latest changes or contribute to FLANN, then it’s recommended that you checkout the git source repository:git clone git://github.com/mariusmuja/flann.gitIf you just want to browse the repository, you can do so by going [here](https://github.com/mariusmuja/flann).Conditions of useFLANN is distributed under the terms of the [BSD License](https://github.com/mariusmuja/flann/blob/master/COPYING).Bug reportingPlease report bugs or feature requests using [github’s issue tracker](http://github.com/mariusmuja/flann/issues).
Package: pyflapjack
pyFlapjackpyFlapjack is our Python package that allows you to interface theFlapjackwith Pandas and Numpy stack, so that you can easily implement it in your projects.InstalationInstalling pyFlapjack is quite simple. Please refer to our Wiki for installation instructions, available here:Installation.DocumentationPlease visit our documentation with API reference at Read the Docspyflapjack.rtfd.ioTutorialsNow that you have pyFlapjack installed you can familiarize yourself with the tool using theJupyter notebook tutorialsdesigned for this purpose.Related repositoriesThe repository for Flapjack API is available here:Flapjack API; and the one for the Flapjack Frontend is available here:Flapjack Frontend.Reference this paperPlease reference our Flapjack's paper–availablehere, using the following reference:Guillermo Yáñez Feliú, Benjamín Earle Gómez, Verner Codoceo Berrocal, Macarena Muñoz Silva, Isaac N. Nuñez, Tamara F. Matute, Anibal Arce Medina, Gonzalo Vidal, Carlos Vidal Céspedes, Jonathan Dahlin, Fernán Federici, and Timothy J. RudgeACS Synthetic Biology2021 10(1), 183-191
DOI: 10.1021/acssynbio.0c00554
Package: pyflapjackevents
pyflapjackevents is a small library that serializesflapjackevents as described in it’s ownData structures. It’s main intention is to allow sending monitor events to flapjack from python programs.An example:server=redis.Redis(host='localhost',port='6380')sink=FlapjackEventSink(server,'events')event=FlapjackEvent('test-entity','test-check',FlapjackEvent.EVENT_SERVICE,FlapjackEvent.STATE_OK,summary='test-summary',details='test-details',)sink.send(event)This would serialize the event and send it to the appropriate Redis channel for flapjack consumption.InstallationJust do:pip install pyflapjackeventsor installing it from source:git clone https://github.com/tuenti/pyflapjackevents.git
cd pyflapjackevents
python setup.py installRequirementsThere’re no special requirements for this library.Credits & Contactpyflapjackevents was created by Tuenti Technologies S.L.. You can follow Tuenti engineering team on Twitter@tuentieng.Licensepyflapjackevents is available under the Apache License, Version 2.0. See LICENSE file for more info.
Package: pyflare
AboutPyflare is a Python adapter forCloudFlare’s Client APIandCloudFlare’s Hosting provider API.Installation$pipinstallpyflareUsagefrompyflareimportPyflareClientcf=PyflareClient('address@example.com','your_api_key')# Create a new A recordresponse=cf.rec_new('example.com','A','sub','1.2.3.4')frompyflareimportPyflareHostinghf=PyflareHosting('your_host_api_key')# Authenticate a userresponse=hf.user_auth('one@example.com','user_password')
Package: pyflasc
FLASC: Flare-Sensitive ClusteringFLASC - Flare-Sensitive Clustering, adds an efficient post-processing step to theHDBSCAN*density-based clustering algorithm to detect branching structures within
clusters.The algorithm adds two parameters that may need tuning with respect to HDBSCAN*,
but both are intuitive to tune: minimum branch size and branch selection strategy.How to use FLASCThe FLASC package is closely based on the HDBSCAN* package and supports
the same API, except sparse inputs, which are not supported yet.fromflascimportFLASCimportnumpyasnpimportseabornassnsimportmatplotlib.pyplotaspltdata=np.load('./notebooks/data/flared_clusterable_data.npy')clusterer=FLASC(min_cluster_size=15)clusterer.fit(data)colors=sns.color_palette('tab10',10)point_colors=[sns.desaturate(colors[l],p)forl,pinzip(clusterer.labels_,clusterer.probabilities_)]plt.scatter(data[:,0],data[:,1],2,point_colors,alpha=0.5)plt.axis('off')plt.show()Example NotebooksA notebook demonstrating how the algorithm works is available atHow FLASC Works. The other notebooks demonstrate the
algorithm on several data sets and contain the analyses presented in our paper.InstallingBinary wheels are available on PyPI. Presuming you have an up-to-date
pip:pipinstallpyflascFor a manual install of the latest code directly from GitHub:pipinstall--upgradegit+https://github.com/vda-lab/pyflasc.git#egg=pyflascAlternatively download the package, install requirements, and manually run the installer:wgethttps://github.com/vda-lab/pyflasc/archive/main.zip
unzipmain.zip
rmmain.zipcdflasc-main

pipinstall-t.CitingA scientific publication of this algorithm and codebase is in progress. Please
refer back to this section to see how you can cite this work in the future.This FLASC algorithm and software package is very closely related to McInnes et al.'s HDBSCAN* software package. If you wish to cite the HDBSCAN* package in a
scientific publication, please use theirJournal of Open Source Software article.L. McInnes, J. Healy, S. Astels, *hdbscan: Hierarchical density based clustering*
In: Journal of Open Source Software, The Open Journal, volume 2, number 11.
2017@article{mcinnes2017hdbscan,title={hdbscan: Hierarchical density based clustering},author={McInnes, Leland and Healy, John and Astels, Steve},journal={The Journal of Open Source Software},volume={2},number={11},pages={205},year={2017}}To reference their high performance algorithm please cite their paper
in ICDMW 2017 proceedings.McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering*
In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42.
2017@inproceedings{mcinnes2017accelerated,title={Accelerated Hierarchical Density Based Clustering},author={McInnes, Leland and Healy, John},booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},pages={33--42},year={2017},organization={IEEE}}LicensingThe FLASC package has a 3-Clause BSD license.
Package: pyflash
pyflashGet flash like super powers by automating everyday tasks!Install$pipinstallpyflashQuickstart$pyflash--help$python-mpyflash--helpCommandspython-version-readinessThis command is useful to detect the python version readiness when compared to top 5000 most downloaded PyPI packages.# Command to download and install top 300 packages for the given python version and generate a log file$flashpython-version-readiness--python/usr/local/bin/python3.10--number300# Show top 10 lines of the log file$head-n11pvr.python3.10.log[{"download_count":267740673,"project":"boto3","is_ready":true},{"download_count":203850147,"project":"botocore","is_ready":true},# Show packages that are failed to install$grepfalse-C1pvr.python3.10.log"project":"pyodbc","is_ready":false},--"project":"tensorflow","is_ready":false},--"project":"backports-zoneinfo","is_ready":false},--otpThis command is used to generate OTP for 2 factor authentication.Setup a file called~/.pyflash.iniand then runotpcommand.$cat~/.pyflash.ini[otp]gmail=secret_key_of_gmailvault=secret_key_of_vault$python-mpyflashotpgmail:282910vault:420529Noteflash needs several third party packages depending on the command being used. Packages which are available on PyPi will get installed as a requirements.sortphotos,ocropyare not available on PyPi. You have to download them and add them to your python path.HelpUsage:python-mpyflash[OPTIONS]COMMAND[ARGS]...Options:--helpShowthismessageandexit.Commands:adb-connectScannetworkandconnecttoadbvianetwork.download-bookSearchanddownloadbookbynamedownload-imd-dataDownloadIMDdataforgivenrange.download-subtitlesDownloadsubtitlesforvideosinadirectory.fix-buildFixafailingCIbuild.fix-importsFiximportsinapythonproject.ipa-installResign&installiOSapps.nsedbCreate/SyncNSEstocksOHLCdata.ocrRungivenOCRengineongivenimage.organize-booksOrganizebooksinaspecifieddirectory.organize-downloadsOrganizedownloadedfiles.organize-photosOrganizephotosbydate.otpShowOTPpg-statsShowstatsforpostgresdatabase.procfileStartprocessesinProcfilepython-version-readinessShowversionreadinessoftoppythonpackages.rate-moviesShowIMDb/RTratingsformoviesandseries.rent-receiptsGeneratemonthlyrentreceiptsforagivenFY.send-to-kindleSendbookstokindleviaDropbox/IFTTT.split-pdfSplitpdfhorizontally/vertically.validate-aadhaarCheckifgivenAADHAARnumberisvalidornot.
Package: pyFlaskBootstrap4
pyFlaskBootstrap4Flask + Bootstrap 4 static components and templates for webprojectsWhat it isFlask + Bootstrap 4 static components and templates for webprojectsDemosseehttp://fb4demo.bitplan.com/https://confident.dbis.rwth-aachen.de/dblpconf/InstallationpipinstallpyFlaskBootstrap4Get Sourcesgitclonehttps://github.com/WolfgangFahl/pyFlaskBootstrap4cdpyFlaskBootstrap4
scripts/installTestingscripts/testUsageseetest casesDocumentationWikiAuthorsWolfgang Fahl
Package: pyflaskcreator
pyFlaskCreatorGenerate simple Flask project templateInstallationpip install pyflaskcreatorUsagepyflaskcreator [-h] -p PATH [-e ENV]



options:

  -h, --help            show this help message and exit

  -p PATH, --path PATH  Path of the app

  -e ENV, --env ENV     Python enviroment nameExampleRunning :pyflaskcreator -p myAppOutputProject Structure/myApp

├── static/

│   ├── flask_icon.png

│   └── welcome.css

├── templates/

│   └── welcome.html

├── venv/

└── app.pyExecuting Appcd myApp

python app.pyLanding Page
Package: pyflat
pyflatThe pyflat package aims to provide easy serialization of Python objects into positional fixed-width strings.
These kind of structures are useful when integrating with old system which depends on positional flat files.Quick StartInstalationTo install Pyflat, use pip or pipenv:$pipinstallpyflatExample UsagefrompyflatimportBase,Field,RIGHTclassUser(Base):name=Field(size=20)income=Field(size=10,sep='0',justify=RIGHT)user=User()user.name='John Doe'user.income=4500.35user.dump()# => John Doe            0000450035