Saravanan666/pypi-org huggingface dataset:
Package: fa-essentials
fa-essentialsA library with essentials needed in every backend python app. e.g. logging, local db connection, filtering, formatting etc.SponsorsFreya Alpha,
The Kára System,
Spark & Hale Robotic IndustriesGeneralRun and compiled for Python 3.9.13.
Expected to run for Python 3+DevelopmentTestingrun tests withpytest -s -vvto see all the details.Installation as Consuming DeveloperSimply run:pip install fa-essentialsImport in modules without the dash (e.g.):from faessentials.globallogger import GlobalLoggerSetup as ContributorCreate the virtul environment:py -m venv .venvStart the Environment:./.venv/Scripts/activate(or allow VS Code to start it). Usedeactivateto stop it.All the required libraries must be listed in requirements.txt and installed bypython -m pip install -r .\requirements.txtFor Dev usepython -m pip install -r .\requirements-dev.txtTo cleanup the environment run:pip3 freeze > to-uninstall.txtand thenpip3 uninstall -y -r to-uninstall.txtorpip3 install pip-autoremoveBuild LibraryPrerequisite: make sure that you give your Operating System user the right to modify files in the python directory. The directory where pyhton is installed.
Usepython setup.py bdist_wheelto create the dist, build and .eggs folder.Reference from a different projectIn order to use your own version of the project - to maybe contribute to the library - simply clone the code from github into new directory. Then add the path of that new directory to the requirements.txt file of your project. Then change in fa-essentials whatever you recommend to improve. Don't forget the Open-Closed Principle: extend only (unless it requires a breaking change)Releasing a new versionDelete any old files in the /dist and build folder of your local environment.
Update your pip:python -m pip install --upgrade pipInstall the tools build, twine and bumpver:python -m pip install build twine bumpverUpgrade the setuptools:pip install --upgrade setuptoolsBump the version in pyproject.toml:bumpver update --patchThis will commit a new version to GitHub.Build the project:python -m buildCheck the distribution:twine check dist/*Upload to test-pypi to validate:twine upload -r testpypi dist/*Login with username: (password should be known)If the test-upload was successful, finally, upload to pypi production:twine upload dist/*Done.(P.S. Do not forget to update the library in your projects:pip install --upgrade fa-essentials)bumpver update --patch
Package: fafbseg
Tools to work with the FlyWire and Google segmentations of the FAFB EM dataset. Fully interoperable with navis.FeaturesHere are just some of the things you can do withfafbseg:map locations or supervoxels to segmentation IDsload neuron meshes and skeletonsgenerate high quality neuron meshes and skeletons from scratchquery connectivity and annotationsparse and generate FlyWire neuroglancer URLstransform neurons from FAFB/FlyWire space to other brains spaces (e.g. hemibrain)DocumentationFAFBseg is onreadthedocs.QuickstartInstall latest stable versionpip3installfafbseg-UInstall from Githubpip3installgit+https://github.com/flyconnectome/fafbseg-py.gitHow to citeIf you usefafbsegfor your publication, please cite the two FlyWire papers:"Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila" Schlegelet al., bioRxiv (2023); doi:https://doi.org/10.1101/2023.06.27.546055"Neuronal wiring diagram of an adult brain" Dorkenwaldet al., bioRxiv (2023); doi:https://doi.org/10.1101/2023.06.27.546656
Package: faff
Faff is a Make build tool substitute written in Python. An input file similar
to aMakefiledefines rules used to update arbitrary targets that can have
file or other rule dependencies.InstallationInstall and/or upgrade using pip.$pipinstall-UfaffQuickstartCompile and execute a C hello world program. Create a directory populated with
files from thegcc_hello_worldexample. The example assumes that thegcccommand is available in the system path and compiles source files for the host
platform.$mkdir-pgcc_hello_world$cdgcc_hello_world$# Copy example files.Run the default rule of the input file. Similarly to Make, runningfaffwill run thealldefault rule.$fafffaff:gccgcc_hello_world/main.c-ogcc_hello_world/build/mainfaff:mainHello,world!faff:`all`updated(1/10.694s)DocumentationUserDeveloperReference
Package: fafi
Fafi (short for Favorites Finder) is a commandline client to search indexed webpages. Fafi extracts the content of the webpage and stores them into a full-text search database.Things it does:Index single urls, text files containing urls, firefox profiles.Incrementally indexing the places database from the Firefox profile folder. (The browser bookmarks) It supports picking a profile from multiple profiles.Extract main text content.Skips .local, localhost and .test domains.DeduplicationSearch results are ranked by relevance and displayed with snippets.Content extraction courtesy ofNewspaper.Userspipx install fafi
fafi --help
fafi index --firefox
fafi index --url=https://mylink
fafi index --list=bookmarks.html
fafi search 'linux'Developers# Install project requirements.
poetry install

# Help
poetry run fafi --help
Package: fahrplan
Goal: Simple access to the SBB/CFF/FFS timetable service from the commandline with human
readable argument parsing.Relies on the public transport API by opendata.ch:http://transport.opendata.ch/Fahrplan supports Python 3.5+.InstallingTo install the current version using pip, issue:$ sudo pip install fahrplanTo install from this repository, clone it and use:$ python setup.py fahrplanUsagefahrplan--help:usage: fahrplan [--full] [--info] [--debug] [--help] [--version]
                [--proxy PROXY]
                ...

A SBB/CFF/FFS commandline based timetable client.

positional arguments:
  request

optional arguments:
  --full, -f            Show full connection info, including changes
  --info, -i            Verbose output
  --debug, -d           Debug output
  --help, -h            Show this help
  --version, -v         Show version number
  --proxy PROXY, -p PROXY
                        Use proxy for network connections (host:port)

Arguments:
 You can use natural language arguments using the following
 keywords in your desired language:
 en -- from, to, via, departure, arrival
 de -- von, nach, via, ab, an
 fr -- de, à, via, départ, arrivée

 You can also use natural time and date specifications in your language, like
 - "now", "immediately", "at noon", "at midnight",
 - "tomorrow", "monday", "in 2 days", "22/11".

Examples:
 fahrplan from thun to burgdorf
 fahrplan via bern nach basel von zürich, helvetiaplatz ab 15:35
 fahrplan de lausanne à vevey arrivée minuit
 fahrplan from Bern to Zurich departure 13:00 monday
 fahrplan -p proxy.mydomain.ch:8080 de lausanne à vevey arrivée minuitTestingTesting is done using tox and nosetests.To run the tests on supported Python versions:$ ./test.shSourcecodeThe sourcecode is available on Github:https://github.com/dbrgn/fahrplanLicenseThe code is licensed as GPLv3. SeeLICENSEfile for more details.
Package: fai
Library for FAI customization scriptsThis Python lib eases the implementation ofFAIcustomization scripts with a
high level of abstraction and resonable error handling. It is meant to beimport’ed in scripts run byfai-do-scripts(1)during an FAI
installation or softupdate.DocumentationThe compiled documentation can befound on Read the Docs.The documentation is based on Sphinx and can be built usingmake:make doc. Documentation sources are in thedocs/directory.TestsTests reside in thetests/directory and are based onpytestand can be run
easily usingmake:make testFormattingThe code is formatted withyapf. Simply usemaketo format the code in place:make format.Code CheckWith themakecommandmake checkthe code base is tested (including
documentation), analyzed withpylint, and the code formatting verified.Pylint messages should be fixed or directly suppressed in the code if
necessary. A suppression should have a comment explaining briefly why
the construct is ok (e.g., “false positive”).Consider usingmake checkas pre-commit hook.MakefileAMakefileis provided to ease testing and building Python packages. Runmake helpfor usage hints.Packaging and ReleasingThis lib uses PEP 517/PEP 518-compatible packaging based on setuptools. All
project settings should normally go intosetup.cfg, whilesetup.pyis mainly
provided for compatibility. The build system configuration resides inpyproject.toml.Each released version is tagged with a tag of the formrelease/x.y.zwithx,y, andzbeing major, minor, and patch version, respectively, as
defined bySemantic Versioning.
Package: faicons
Font Awesome for ShinyAn interface toFont-Awesomefor use in Shiny for Python.This package currently uses Font-Awesome 6.2.0.InstallationpipinstallfaiconsUsageUseicon_svg()to get an<svg>representation of the icon.fromfaiconsimporticon_svgicon_svg("play")Example usage in Shiny:fromshinyimportuiui.input_action_button("btn","Press me",icon=icon_svg("play")).show()
Package: fail
run a program until it fails and gather statisticsElaborations from a favorite bash one-liner:I=0; while ./do_some_things.py --param $arg; do I=$((I+1)); echo Iteration ${I}; sleep 1; donefun things to tryRussian roulette (with two chambers):fail 'exit $(expr $RANDOM % 2)'Jeff Hammelhttp://k0s.org/
Package: fail2bangeolocation
fail2bangeolocationShows geolocation of failed attempts registered byfail2ban.It's useful to know from which locations you are being attacked the most.You can group locations by country or by country and by city.ScreenshotsGrouped by countryGrouped by country and cityRequirementspython3fail2banpython libraries:setuptoolsrequeststqdmcoloramaInstallationYou can installfail2bangeolocationviapip3:$pip3installfail2bangeolocationYou also can runfail2bangeolocationdirectly without installing it, but you need to install the dependencies manually:$sudopip3install-rrequirements.txtUsageIf you installedfail2bangeolocationwithpip3you can run it directly from the command line interface:$fail2bangeolocation[-h][-c]{fail2ban,log,server}You can runfail2bangeolocationwithout installation running thefail2bangeolocation.pyscript$./fail2bangeolocation.py[-h][-c]{fail2ban,log,server}or$python3fail2bangeolocation.py[-h][-c]{fail2ban,log,server}fail2bangeolocationargumentsusage:fail2bangeolocation.py[-h][-c]{fail2ban,log,server}...

Showsgeolocationoffailedattemptsregisteredbyfail2ban

positionalarguments:{fail2ban,log,server}Theseoptionsaremutuallyexclusivefail2bananalyzeallbannedIPsbyfail2banloganalyzeafail2banlogfile.Use"log -h"toseemoreoptionsserveranalyzeallbannedIPsbyfail2ban(e.g."server sshd")optionalarguments:-h,--helpshowthishelpmessageandexit-c,--show-citygroupIPsbycountryandcityAnalyze all IPs registered by fail2ban:warning: Requires root privilegesRunfail2bangeolocationusing thefail2banargument:$sudofail2bangeolocationfail2banAnalyze all IPs registered by fail2ban for a given jailed server/service, e.g. sshd:warning: Requires root privilegesRunfail2bangeolocationwith theserverargument and the jailed server name:$sudofail2bangeolocationserversshdAnalyze a log file:warning: May require root privileges depending on the file to be analyzedRunfail2bangeolocationwith thelogargument and the path to the log file:$fail2bangeolocationlog/var/log/fai2ban.logYou can also geolocate the unbanned IPs contained in the log adding the-uargument:$fail2bangeolocationlog-u/var/log/fai2ban.logGroup the output by country and cityRunfail2bangeolocationwith "-c" as first argument:$fail2bangeolocation-c{fail2ban,log,server}TroubleshootingIn case of any problem create anissueDiscussionsIf you want ask (or answer) a question, leave an opinion or have an open-ended conversation you can create (or join) adiscussionSupportIf you find this application useful you can star this repo.
Package: fail2ban-importer
fail2ban-from-s3Grabs a JSON-encoded list of things to ban and bans them usingfail2ban.Installationpython -m pip install --upgrade fail2ban-importerUsagefail2ban-importer [--oneshot|--dryrun]ConfigurationThe following paths will be tested (in order) and the first one loaded:./fail2ban-importer.json/etc/fail2ban-importer.json~/.config/fail2ban-importer.jsonFieldsNote thefail2ban_jailfield. If you're going to pick up your logs from fail2ban, and use them for the source of automation, make sure to filter out the actions by this system - otherwise you'll end up in a loop!Field NameValue TypeDefault ValueRequiredDescriptiondownload_modulestrhttpNoThe download module to use (eitherhttpors3)fail2ban_jailstrunsetYesThe jail to use for banning - DO NOT REUSE AN EXISTING JAILsourcestrblankYesWhere to pull the file from, can be ahttp(s)://ors3://URL.fail2ban_clientstrfail2ban_clientNoThe path to thefail2ban-clientexecutable, in case it's not in the user's$PATHschedule_minsint15NoHow often to run the action.s3_endpointstrNoThe endpoint URL if you need to force it for s3, eg if you're using minio or another S3-compatible store.s3_v4boolfalseNoWhether to forces3_v4requests (useful for minio)s3_minioboolfalseNoEnable minio mode, forces3_v4requestsHTTP(S) Sourcex{"source":"https://example.com/fail2ban.json","fail2ban_client":"/usr/bin/fail2ban-client","fail2ban_jail":"automated","schedule_mins":15}S3-compatible SourceYou can use the usualboto3 AWS configuration, or put the options in the config file.{"source":"s3://my-magic-fail2ban-bucket/fail2ban.json","AWS_ACCESS_KEY_ID":"exampleuser","AWS_SECRET_ACCESS_KEY":"hunter2","schedule_mins":1}If you're using minio as your backend, you should add the following additional options to the config file:{"s3_v4":true,"s3_endpoint":"https://example.com",}Example source data file[{"jail":"sshd","ip":"196.30.15.254"},{"jail":"sshd","ip":"119.13.89.28"}]Thanksfail2banboto3requestsschedule
Package: fail2slack
fail2slack lets you send fail2ban jail status updates to Slack with ease.Compatibilityfail2slack is developed for and tested with recent versions of Python, including:3.7, 3.7.7, 3.8, 3.9, 3.10Browse our build history at GitHub.Installationpip install fail2slackInstall fail2slack using the Python package installer.Dependencies for using fail2slack are available in the requirements file.Usageusage: __main__.py [-h] [-w WEBHOOK] [-d DELIVERY] [-j JAILS [JAILS ...]] [-i IDENTIFIER]

options:
  -h, --help            show this help message and exit
  -w WEBHOOK, --webhook WEBHOOK
                        Slack webhook URL. Required if delivery method is 1.
  -d DELIVERY, --delivery DELIVERY
                        Delivery method: 0 = print, 1 = Slack webhook.
  -j JAILS [JAILS ...], --jails JAILS [JAILS ...]
                        Jails to include in status report. Required.
  -i IDENTIFIER, --identifier IDENTIFIER
                        Update identifier. STRING of identifier to use. Default is hostname.ResultTestsFor instructions on running test check out theREADME.ContributingFeedback, bug reports, feature requests, and pull requests are welcome!If you'd like to contribute to fail2slack please reference ourcode of conductandcontributingguides.
Package: fail-importing
A decorator designed to make testing optional dependencies painless.It’s common for libraries to have optional dependencies where the code has to detect if they are installed, using a
pattern like this:try:importsomethingelse:importsomething_elseassomethingThat waysomethingis always defined and the rest of the codebase does not need to care. But how do we test both
branches under tests?Withfail_importing, you can just slap a decorator onto your test and the code being tested will act as if
the dependency isn’t installed.@fail_importing("something")deftest_something_else():...When the test finishes executing, the name can be imported as normal. It can be applied to normal functions, nested
functions, relative imports, and even generators!full_matchis used to determine if the path matches or not, so you can use any regex matching you need. Note that
relative imports are resolved to absolute imports before the check is made.
Package: failrunner-django
failrunner-djangoA command line tool to run django tests that failed during a travis jobInstallationInstall with pippip install failrunner-djangoYou should now have thefailrunnercommandBasic UsageTo run a jobfailrunner -j jobnumber(The job number is shown in the url when viewing a job)To run a job on travis-ci.orgfailrunner -j jobnumber --orgTo specify manage.py pathfailrunner -j jobmnumber -p /path/to/manage/For all cli optionsfailrunner --helpContributingContributions welcome. Usepython -m unittestto run tests.
Package: failsafe-kill
Kill your script with a hotkey$pipinstallfailsafe-killfromfailsafe_killimportstart_failsafestart_failsafe(hotkey='ctrl+e').....int()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseint()can't convert non-string with explicit baseProcessfinishedwithexitcode1
Package: failstodeliver
failstodeliverfailstodeliveris a python library to downloadFails-to-DeliverData from U.S. SECURITIES AND EXCHANGE COMMISSION database.The data downloaded is in csv file format.The file contains the date, CUSIP numbers, ticker symbols, issuer name, price, and total number of fails-to-deliver (i.e., the balance level outstanding) recorded in the National Securities Clearing Corporation's ("NSCC") Continuous Net Settlement (CNS) system aggregated over all NSCC members.failstodeliver data can also be used to getCUSIP-TICKER mapping.The record layout and maximum field sizes are shown below :Field NameField DescriptionMaximum SizeSETTLEMENT DATESETTLEMENT DATENumber - 8 digitsCUSIPCUSIP9 charactersSYMBOLTICKER SYMBOL10 charactersQUANTITY (FAILS)TOTAL FAILURE-TO-DELIVER SHARESNumber - unlimitedDESCRIPTIONCOMPANY NAME30 charactersPRICECLOSING PRICE ON PREVIOUS DAYNumber - unlimitedInstallationUse the package managerpipto install failstodeliver.pip install failstodeliverUsagefromfailstodeliverimportftdDownloaderftdDownloader.download_csv()Sourcehttps://www.sec.gov/data/foiadocsfailsdatahtmContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: failureaction
This package is intended to provide decorators which execute custom actions
in case of exceptions. Let’s see an example:First we need an object with some methods. These methods we decorate with
thePrintOnFailure-decorator. This simple example decorator prints a given
message in case of an exception.>>> from failureaction import ConflictError
>>> from failureaction import PrintOnFailure
>>> class TestOb(object):
...
...    @PrintOnFailure(msg='Some numeric calculation went wrong!')
...    def divide(self, a, b):
...        return a/b
...
...    @PrintOnFailure()
...    def doraise(self):
...        raise ConflictErrorWe have two methods. One (divide) does numerical division of two numbers
and another raises a custom defined ConflictError. Now let’s see the methods in
action:>>> ob = TestOb()
>>> ob.divide(4, 2)
2>>> ob.divide(42, 0)
Some numeric calculation went wrong!>>> ob.doraise()
Traceback (most recent call last):
...
ConflictErrorTheActionOnFailuredecorator provided by the module is intended to be
overriden by a custom class. Like this:>>> from failureaction import ActionOnFailure
>>> class MailOnFailure(ActionOnFailure):
...
...     def __init__(self, subject):
...         self.subject = subject
...
...     def _doaction(self, context, e):
...         """ send a mail, if an exception was raised """
...         print "Subject:", self.subject
...         print e>>> class TestOb2(object):
...
...     @MailOnFailure(subject='An error occured')
...     def critical(self):
...         import _not_existent_hopefully_>>> ob2 = TestOb2()
>>> ob2.critical()
Subject: An error occured
No module named _not_existent_hopefully_
Package: failure-directions
Distilling Model Failures as Directions in Latent SpacePython package for automatically finding failure modes in your dataset.The paper:Distilling Model Failures as Directions in Latent Spaceand correspondingblog postSee thegithub repositoryfor documentation and examples!Getting StartedInstall using pip, or clone our repository.pip install failure-directionsBasic usage. Here,svm_decision_valuesare the alignment of the images to the identified failure modes.import failure_directions
# let hparams contain mean and std for dataset
# let loaders contain a dict of train, test, val loaders.
# let split_gts contain the ground truth labels for each split
# let split_preds contain the predictions for each split

# Load CLIP features
clip_processor = failure_directions.CLIPProcessor(ds_mean=hparams['mean'], ds_std=hparams['std'])
clip_features = {}
for split, loader in loaders.items():
    clip_features[split] = clip_processor.evaluate_clip_images(loader)
    
# Fit SVM
svm_fitter = failure_directions.SVMFitter()
svm_fitter.set_preprocess(clip_features['train'])
cv_scores = svm_fitter.fit(preds=split_preds['val'], ys=split_gts['val'], latents=clip_features['val'])

# Get SVM decision values
svm_predictions = {}
svm_decision_values = {}
for split, loader in loaders.items():
    mask, dv = svm_fitter.predict(ys=split_gts[split], latents=clip_features[split], compute_metrics=False)
    svm_predictions[split] = mask
    svm_decision_values[split] = dvCitationIf use this package, please cite the corresponding paper@inproceedings{jain2022distilling,
   title = {Distilling Model Failures as Directions in Latent Space},
   author = {Saachi Jain and Hannah Lawrence and Ankur Moitra and Aleksander Madry}, 
   booktitle = {ArXiv preprint arXiv:2206.14754},
   year = {2022}
}MaintainersSaachi JainHannah Lawrence
Package: failures-analysis
Failure analysisTests failure analysis package provides fast and reliable way to find and group similar failures in your CI/CD
pipeline. When failure grouping and similarity scoring is done automatically by a machine, it will free
resources from development team member to fix the most important failures in their CI/CD pipeline. It is tedious
work for a human to download, open and read all the test failures and analyze which failures belong to the same group.
The failure-analysis package solves this problem by processing xunit xml files and failures found within by calculating the similarity score of failures using cosine similarity.Results of this approach are to be published in Springer Book Series: Advances in Intelligent Systems and Computing
later (link to be provided once published).Installation instructionsOnly Python 3.8 or newer is supported.Update pippip install -U pipto ensure latest version is usedInstall from the commandline:pip install failures-analysisUsageTo be able to find similar failures, users need to download xunit result(s) in to folder. How and where the download of
the xunit files is done, is not part of this project, but exampleflaky-test CIhas an example
how download from GitHub can be performed. Tool can be used from command line and it needs only one argument:
path to folder where xunit xml files are located, example:failures-analysis path/to/xunit/filesSupported xunit formatsPackage has been tested with Pytest and Robot Framework xunit output files. Other format might be supported,
but because we do not have visibility on those formats, those are not listed.