Saravanan666/pypi-org huggingface dataset:
Package: radioSphere
Welcome to radioSphereJoin the chat room for support here.This project contains a series of tools for the analysis of divergent radiographs containing spherical particles, in particular to measure 3D positions froma single radiographlike this one:The reconstruction technique proposed is a two-step approach:Step 1: is a technique calledtomopackwhich is an FFT-based pattern matching approach.
It uses a template image, or "structuring element" that we call ψ to pick out spheres.
Since the size of ψ needs to be very close to the correct size, this allows us to distinguish different sized projections of spheres.Step 2: Position optimisation: Starting from a guess of particle positions, the projection is computed and compared to the measured projection. Particle positions are modified iteratively in order to minimise the difference between computed projection and measured one.Geometry inradioSphereis everything: the coordinate systems defined are as follows:See theonline documentationto learn about the functions.ContributorsThe technique has been developed by Edward Andò (EPFL),Benjy Marks, and Stéphane Roux (CNRS) and published inMeasurement Science and Technology.The technique has been developed thanks to UGA Tec21 funding further by Olga Stamati (ESRF), resulting in a publication inThe Journal of Multiphase Flow, and by Leonard Turpin (Diamond).The numba projector was contributed byYoussef Haouchat(EPFL),which is faster than the original C code and allows us to distribute the package on pypi, thanks!Notes on the repositorysrc/radioSphere: this folder contains the core functions of radioSphere:detectSpheres: functions related totomopack(Step 1)optimisePositions: functions related to the optimiser (Step 2)projectSphere: The tools to create projections (units mm)tests: contains tests to test the functionality of what is in toolsexamplesandpaper/figureScripts: examples of the use ofradioSphereon both synthetic and experimental datadata: where the presented experimental and synthetic data is stored to run the examplesfigures: figures for paper and this websitepaper: will contain final sources to the paperpresentations: sources for presentations given about this topicInstalling radioSphereFor users:pip install radioSphereFor developers:Please clone this repository once checked out, activate your virtual environment, and then:pip install -e ".[dev]"run the tests to make sure everything is OK:pytest
Package: radio-telescope-delay-model
Radio Telescope Delay ModelBuild instructionscdcalc11/src
makeinstallhttps://github.com/ska-telescope/delay-testinghttps://bitbucket.alma.cl/projects/ASW/repos/control/browse/thirdPartyPackageshttps://github.com/astropy/astropy/pull/6882https://github.com/astropy/astropy/issues/7766https://github.com/astropy/astropy/pull/9869https://github.com/astropy/astropy/pull/10737IERS Convention (2010)  Section 11.1.4 VLBI delay modelhttps://www.iers.org/SharedDocs/Publikationen/EN/IERS/Publications/tn/TechnNote36/tn36.pdf?__blob=publicationFile&v=1EHT UVW calculationhttps://github.com/achael/eht-imaging/blob/master/ehtim/observing/obs_helpers.py#L55Simple Astropy UVW implmentation (Paul Demorest)https://gist.github.com/demorest/8c8bca4ac5860796593ca07006cc3df6SKA SA Delay Calculationshttps://github.com/ska-sa/katpoint/tree/master/katpointhttps://syrte.obspm.fr/jsr/journees2005/pdf/s1_06_Sekido.pdf
Package: radiotool
Important noteSome parts of this codebase have had significant changes since the
last revision of the documentation- so the current documentation is
wrong in some places. I will try to update the documentation as soon
as I can, but for now, feel free to ask me questions if anything is
not clear.DescriptionRadiotool is a python library that aims to make it easy to create
audio by piecing together bits of other audio files. This library was
originally written to enable my research in audio editing user
interfaces, but perhaps someone else might find it useful.Read the fulldocumentation.To perform the actual audio rendering, radiotool relies onscikits.audiolab, a python wrapper forlibsndfile.InstallationEitherpip install radiotoolor clone the repository and runpython setup.py install.Requirementslibsndfile, numpy,scikits.audiolab, andlibrosa.exempifor writing Audition/Premiere-readable labels to audio files.CompositionThe heart of radiotool is theComposition. ACompositionis
built out of Segments, which represent segments of audio
Tracks (or raw PCM data, in the case of RawTracks). You can
also add Dynamics to adjust the volume of segments in certain
ways.Simple examplefromradiotool.composerimport*comp=Composition()# create a track with a pre-existing wav filetrack=Track("test.wav")# create a segment of a track that:# 1. starts at the 0.0 mark of the composition# 2. begins playing at the 0.5 second mark of the track# 3. plays for 1.0 secondssegment=Segment(track,0.0,0.5,1.0)# add segment to the compositioncomp.add_segment(segment)# output your composition as a numpy arrayarr_out=comp.build()# or export your composition as an audio file, composition.wavcomp.export(filename="composition")RetargetingMusic retargeting is the idea of taking a song and remixing itfrom
its own existing beats/structureto fit the music to certain
constraints.Seehttp://ucbvislab.github.io/radiotool/algorithms/retarget.htmlfor
applications of music retargeting, and details about how to retarget
music using raditool.See thedocumentationfor more detail.
Package: radiotray-ng-mpris
Radiotray-NG MPRISOverviewRadiotray-NG MPRIS is a wrapper forRadiotray-NGto add anMPRIS2
interfacewhich integrates well with desktop environments
(likeGNOME,KDEorXFCE) or
desktop independent music player control tools likeplayerctl.InstallationFirst, you need a working installation ofRadiotray-NG.Radiotray-NG MPRIS is available on PyPI and can be installed withpip:python3-mpipinstallradiotray-ng-mprisPlease note that at least Python 3.10 is required (because of its dependencympris_server). You can check your installed Python version withpython3--versionIf your Python version is too old, you don't want to usepipfor the installation or you don't care about Python at
all, you can also download self-contained executables (no Python installation required) for Linux x64 from thereleases
page.If you run an Arch-based system, you can also install Radiotray-NG MPRIS from theAUR:yay-Sradiotray-ng-mprisIn this case, Radiotray-NG will be installed as a package dependency automatically.UsageRunradiotray-ng-mprison the command line (or any application finder) to start Radiotray-NG and the MPRIS2 integration. Any MPRIS2 control
interface should start working with Radiotray-NG immediately.Exit the application by quitting Radiotray-NG or the wrapper script (press<Ctrl-c>on the command line).Command line optionsusage: radiotray-ng-mpris [-h] [-p] [-V]
                          [-q | --error | --warn | -v | --debug]

radiotray-ng-mpris is a wrapper script for radiotray-ng to provide an MPRIS2
interface.

options:
  -h, --help     show this help message and exit
  -p, --play     start playback immediately
  -V, --version  print the version number and exit
  -q, --quiet    be quiet (default: "False")
  --error        print error messages (default: "False")
  --warn         print warning and error messages (default: "True")
  -v, --verbose  be verbose (default: "False")
  --debug        print debug messages (default: "False")
Package: radioz
radiozAplicativo para ouvir rádio via Internet.Youtube Video (https://youtu.be/iJ1_qCCF3EI)InstalaçãoLinux e Windows:$pythonsetup.pyinstallou$sudopipinstallradiozExemplo de uso$radiozConfiguração para desenvolvimento$virtualenvvenv
$sourcevenv/bin/activate
$pythonsetup.pydevelop
$radiozHistorico de lançamentos-   Work in progressMetaAndré P. Santos –@ztzandre–andreztz@gmail.comDistributed under the XYZ license. SeeLICENSEfor more information.https://github.com/andreztz/radiozContributingFork it (https://github.com/andreztz/radioz/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: radis-client
RADIS ClientAboutRADIS Client is the official Python client ofRADIS (Radiology Report Archive and Discovery System).UsagePrerequisitesGenerate an API token in your RADIS profile.Make sure you have the permissions to access the RADIS API.Also make sure you have the permissions for the resources you like to access.Codeserver_url="https://radis"# The host URL of the RADIS serverauth_token="my_token"# The authentication token generated in your profileclient=RadisClient(server_url=server_url,auth_token=auth_token)
Package: radish
A set of common tools for testing django projects with lettuce.-------------------------------------This module adds a set of step definitions to lettuce (http://www.lettuce.it)implementing the most common actions that a user can perform through a browser(such as navigating, clicking, filling forms).The goal is to make Django developers integrate lettuce features in their codemore rapidly, since they do not have to write the step definitions, which areincluded in this module.
Package: radish-bdd
radish - the root from red to greenAwesome python BDD toolhttps://radish-bdd.github.ioWhat is radish?radish is a Behavior Driven Development tool completely written in python.
It supports allgherkinlanguage features.
In addition to that, radish implements a fewunconventionalBDD features such asPreconditions,Scenario Loops,ConstantsandExpressions.Supported platformsradish is supported on Windows, Mac and LinuxInstallationradish is available aspippackage:pipinstallradish-bddNote: Make sure you have enough privileges to install radish on your system.Getting startedradish is very easy to use. Take the 5 minutes and follow theGetting startedinstruction on the radish website:https://radish-bdd.github.io/#gettingstartedDocumentationThe whole documentation of radish is available onRead The Docs.ContributionYes, we are looking for some contributors and people who spread out a word about radish.
Help us to improve these piece of software. You don't know what to do?
Just have a look at the Issues or create a new one.
Please have a look at theContributing Guidelines, too.Releasevim CHANGELOG.md docs/conf.py radish/__init__.py
git commit -am "release: vX.X.X" && git tag vX.X.X && git push --tags
rm -rf dist
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*Special ThanksA special thanks goes toHassan Ashraffrom the companyCyber Avanzafor creating an awesome logo for free! I really appreciate their work and time!This project is published underMIT.ATimo Furrerproject.- :tada: -
Package: radish-run
Radish is a task runner that understands version control.You define a command that applies to multiple subprojects in your
repository and thenradish will figure out which projects have changed
and run the command for just those projects. Radish isn’t a replacement
for make, gulp, gradle, rake, or any other task runner. It’s a
supplement for orchestrating other runners and scripts with some smarts
from your version control repository.If you’re using aCI/CDserver likeConcourseorGoCDwhich supports pipelines as first-class
citizens you probably have no need for radish on your CI/CD server. It
might still be useful on your dev machine, though.InstallationRadish is available on PyPi asradish-runand can be
installed on most systems with pip:$pipinstallradish-runHow to useAn example invocation of radish:$radishcommandtests--from19abc023--to2514ecb1Changedpaths:-extensions/cool-extension/-frontend/js/Runningtestsforextensions/cool-extension/:...........OKRunningtestsforfrontend/js/:..........................OKAllcommandsendedsuccessfullyandranin9.75s.Configurationradish configuration is a yaml file namedRadishfile, because I can.paths:-extensions/*/# Mark each subdirectory in extensions as a path-frontend/js/commands:# Runs from the directory denoted by paths abovetests:default:bin/rspec specfrontend/js/:npm testAn example use caseTake that you’re building a single page web app, it consists of two
parts: - The backend that delivers JSON - The frontend that holds all
the clicky bits that end-users seeBecause cross-functional teams this project is in one repository, which
is a great win for productivity. But it has a downside: when there are
only changes to the backend, then all the tests for the frontend is
still run. Finally, after running all tests it can get deployed, but
then both sites get deployed despite nothing changing on the frontend.So this is a crazy situation. It shouldn’t be. So this is where radish
comes in. You tell radish abouttestanddeploy, and what that
means for both the backend and the frontend, then on your CI server you
run the commands with the last green commit. Radish then figures out
what has changed from the current commit and the last one, and only runs
the command for those projects.ContributingRoadmapDefine a path/project to always run, no matter whether there are
changes or notRun a different command if there are changes to the projectAllow for changed files to be passed in to commands. Primarily a
feature for local dev boxes, so you only run the command against
changed files.Dependencies between projects. If thefrontend-paymentproject
changes then run the command in thefrontendproject as well.Custom differs for Ci systems so they can smartly figure out what the
last green commit was, instead of relying onHEAD~1as the poor
mans “what was the last change?”Local developmentTo get started make with your current global version of Python do:$gitclonehttps://github.com/gaqzi/radish.git$cdradish/$makedevelop$maketestThis will install all dependencies, check out the test repo, and then
run all the tests.LicenseBeerware License
Package: radishsalad
Radishsalad is simple lib for work with redis datastore in more pythonic way.BaseIt include String, Hash, List and Set classes, that represent redis datatypes.
You need to initialize datatype with key:>>> dt.List('main stream').append('Blogger Navalniy discovered new corruption scheme in Russia!')
1L
>>> list(dt.List('main stream'))
['Blogger Navalniy discovered new corruption scheme in Russia!']>>> up = dt.Hash('0:user profile')
>>> up['name'] = 'deepwalker'
>>> dict(up)
{'name': 'deepwalker'}
>>> 'name' in up
True>>> friends = dt.Set('friends')
>>> friends.add(1929340)
>>> set(friends)
set(['1929340'])
>>> 1929340 in friends
TrueModelsRadishsalad hasmodelsmodule that is not positioned as Django ORM thing. Its just helper
for keys name generation.
So you create an model:>>> from radishsalad import models as m
>>> class User(m.Model):
...      name = m.String()
...      subscribers = m.Set()
...      profile = m.Hash()
...      messages = m.List()
...      read = m.List()
...And for using you need initialize it with key:>>> user = User(1000)Now you haveuserinstance, and you can get keys for it members:>>> user.name.get_key()
'user:1000:name'Library does not return you a string instead ofStringobjects for using in redis.mget:>>> from radishsalad.connection import get_redis
>>> r = get_redis()
>>> r.mset(dict((User(i).name.get_key(), i) for i in xrange(40)))
>>> r.mget(User(i).name.get_key() for i in xrange(40))
['0', '1', '2', '3', '4', '5', '6', '7', ... '38', '39']
Package: radius
py-radiusRADIUS authentication module for Python 2.7.13+(c) 1999 Stuart Bishop <stuart@stuartbishop.net>This module provides basic RADIUS client capabilities, allowing your Python
code to authenticate against any RFC2138 compliant RADIUS server.Installation$ pip install py-radiusUsageThe radius.py module can be run from the command line, providing a minimal
RADIUS client to test out RADIUS servers:$ python -m radius
Host [default: 'radius']: radius
Port [default: 1812]: 1812
Enter RADIUS Secret: s3cr3t
Enter your username: foobar
Enter your password: qux
...
Authentication SuccessfulExampleHere is an example of using the library.importradiusradius.authenticate(secret,username,password,host='radius',port=1812)# - OR -r=radius.Radius(secret,host='radius',port=1812)print('success'ifr.authenticate(username,password)else'failure')If your RADIUS server requires challenge/response, the usage is a bit more
complex.importradiusr=radius.Radius(secret,host='radius')try:print('success'ifr.authenticate(username,password)else'failure')sys.exit(0)exceptradius.ChallengeResponsease:pass# The ChallengeResponse exception has `messages` and `state` attributes# `messages` can be displayed to the user to prompt them for their# challenge response. `state` must be echoed back as a RADIUS attribute.# Send state as an attribute _IF_ provided.attrs={'State':e.state}ife.stateelse{}# Finally authenticate again using the challenge response from the user# in place of the password.print('success'ifr.authenticate(username,response,attributes=attrs)else'failure')This module has extensive logging, enable it using the Python logging framework.
Package: radius-eap-mschapv2-client
RADIUS EAP-MSCHAPv2 and MSCHAPv2 ClientA pure Python 3 RADIUS EAP-MSCHAPv2 and MSCHAPv2 client implementation.ExplanationThis project was developped because no RADIUS client library supports EAP-MSCHAPv2 (A ticket is open for the pyrad
library, seehere).This libraryonly supportsEAP-MSCHAPv2 and (legacy) MSCHAPv2. This code has been tested with Microsoft Windows
Server 2016 Network Policy Server and FreeRADIUS 3.0.25.Installationpip3 install radius-eap-mschapv2-clientUsagefrom radius_eap_mschapv2 import RADIUS

radius_host = '10.1.2.3'
radius_secret = 'r4d!us_$3cr3t'
radius_nas_ip = '10.3.2.1'
radius_nas_id = 'mynas'
username = 'myuser'
password = 'mypassword!'

r = RADIUS(radius_host, radius_secret, radius_nas_ip, radius_nas_id)

# legacy MSCHAPv2 (no EAP)
r = RADIUS(radius_host, radius_secret, radius_nas_ip, radius_nas_id, eap=False)
print(r.is_credential_valid(username, password))RequirementsThis script has been written for Python 3.5 and newer.You must install librairies withpip3 install -r requirements.txt.CreditsTo make this implementation, I used thedaphp/radius PHP implementation.The articleUnderstanding PEAP In-Depthalso helped me
a lot.Thanks to them.
Package: radix-ops
radix-opsConvert numbers - both integer and floating-point - from one base to another.
Perform arithmetic operations in a given base.Installationpipinstallradix-opsUsagefromradiximportNuma=Num(20,16)# The number 20 in base 16print(a.to(2))# Convert to base 2 (100000) - inplace conversiona=Num(20,16)b=Num('ff',16)# FF or 255 in base 16print(a+b)# Result in base 16 (11F)c=a.conv(2)# To create a new instance upon conversion (to base 2)print(a)# 20print(c)# 100000An easier way to evaluate expressions:fromradiximportexpr# All numbers in base 16. result is a `Num` instance.result=expr('a + b * c',16)print(result)# prints 8Eprint(result.to(10))# prints 142(Signed) binary numbers>>>fromradiximportBin# `Bin` is a subclass of `Num`>>>num=Bin(-7)>>>>>># -ve number in 2's complement>>>num1001>>>num.twos_compl()# 2's complement'1001'>>>num.ones_compl()# 1's complement'1000'>>>num.format(size=8,blanks_every=4)# format 2's complement'1111 1001'>>>num.sign_mag()# sign magnitude representation'1111'>>>>>># -10 in 2's complement>>>Bin(-5)+Bin(3)-Bin(8)# 2's complement arithmetic10110Examples>>>fromradiximportNum,expr>>>Num(value='FE',base=16).to(base=10)254>>>Num(1100,2).to(10)12>>>Num(10.75).to(16)# When base is 10, it can be omitted.A.C>>>Num(10.75).to(2)1010.11>>>pi=3.141592653589793>>>Num(pi).to(16)3.243F6A8885>>>Num(-1001,2).to(10)-9>>>(Num('1a',16)-Num('ff',16))*Num(2,16)# (26 - 255) * 2 = -458 = -0x1ca-1CA>>>expr('-fe',16,show=True).to(10)-Num('fe',16)-254>>>expr('b / (a + 1.5) * 2',12)1.B15A50B68B>>>fromradiximportdim_radix_compl,radix_compl>>>dim_radix_compl(Num('012398'))# 9's complement (diminished radix complement)'987601'>>>radix_compl(Num('012398'))# 10's complement (radix complement)'987602'NoteBase should be between 2 and 36.The.tomethod mutates the number, i.e. changes its value and base.Very large or very small floating-point numbers should be in quotes.
Package: radon-defect-predictor
radon-defect-predictionThe RADON command-line client for Infrastructure-as-Code Defect Prediction.How to InstallFromPyPI:pip install radon-defect-predictorFrom source code:git clone https://github.com/radon-h2020/radon-defect-prediction.git
cd radon-defect-predictor
pip install -r requirements.txt
pip install .Quick Startusage: radon-defect-predictor [-h] [-v] {train,predict,model} ...

A Python library to train machine learning models for defect prediction of infrastructure code

positional arguments:
  {train,predict,model}
    train               train a brand new model from scratch
    model               get a pre-trained model to test unseen instances
    predict             predict unseen instances

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exitHow to build Docker containerdocker build --tag radon-dp:latest .How to run Docker containerFirst, create a host volume to share data and results between the host machine and the Docker container:mkdir /tmp/radon-dp-volume/TrainCreate a training datasetmetrics.csvand copy/move it to/tmp/radon-dp-volume/.
See how to generate the training data for defect predictionhere.Run:docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor train metrics.csv ...See thedocsfor more details about this command.The built model can be accessed at/tmp/radon-dp-volume/radondp_model.joblib.ModelRun:docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor download-model ...See thedocsfor more details about this command.The downloaded model can be accessed at/tmp/radon-dp-volume/radondp_model.joblib.PredictMove the model and the files to predict in the shared volume.
For example, if you want to run the prediction on a .csar, thencp patah/to/file.csar /tmp/radon-dp-volume.Alternatively, you can create a volume from the folder containing the .csar (in that case, make sure to move the model within it).Run:docker run -v /tmp/radon-dp-volume:/app radon-dp:latest radon-defect-predictor predict ...See thedocsfor more details about this command.The predictions can be accessed at/tmp/radon-dp-volume/radondp_predictions.json.