Saravanan666/pypi-org huggingface dataset:
Package: bigzenodo
BigZenodoThis is a command-line tool for making large uploads toZenodo. It requires aZenodo API access tokenand uses theRestful API.InstallationThis tool can be installed using PyPi, which will also install the dependencies ofrequestsandmarkdown2.pip install bigzenodoUsageIt is a command-line tool that takes in a JSON file (described below) that lists the information for the submission, and the API token for the Zenodo account (in a separate file). It also takes two flags, whether to use the Zenodo sandbox and whether to complete the submission.usage: Tool to upload large files to Zenodo using the Zenodo RESTFUL API

       [-h] --submission SUBMISSION --accessTokenFile ACCESSTOKEN 

       [--sandbox] [--publish]



optional arguments:

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

  --submission SUBMISSION        JSON file with submission details

  --accessTokenFile ACCESSTOKEN  File with Zenodo API access token

  --sandbox                      Whether to use the sandbox for testing purposes

  --publish                      Whether to actually complete the publication of the data to Zenodo. Irreversible!Submission FileThe tool takes in a JSON file with the fields below.existing_zenodo_idis optional and denotes the identifier for an existing Zenodo submission which this submission will update.{

  "file_list": [

    "colour_of_magic.tsv",

    "light_fantastic.tsv"

  ],

  "title": "The Troubles of Luggage",

  "author": "Rincewind",

  "author_affiliation": "Unseen University",

  "description_file": "output_description.md",

  "existing_zenodo_id": "1156241"

}The description_file should be a file that contains text to be shown on the Zenodo page. If it is a Markdown file (and the filename ends with '.md'), it will be rendered appropriately.
Package: bih
BIHBounded Interval Hierachy(BIH)
ofAxes Aligned Bounding Boxes(AABB).Functions:construction of AABB from list of pointssimple operations with AABBsconstruction of BIH from a list of AABBslookup for AABBs containing a givnen pointlookup for AABBs intersecting with given AABBC++ implementation with Python binding.Python installpip3 install bihSee 'test/test_bih_tree.py' for usage example.C++ buildmkdir build
cd build
cmake ..
makeSee 'test/test_bih_tree.cc' for usage example.
Package: bihc
BIHCBeam Induced Heating Computation (BIHC) tool is a package that allows the estimation of the dissipated power due to the passage of a particle beam inside an accelerator component.The dissipated power value depends on the characteristics of the particle beam (beam spectrum and intensity) and on the characteristics of the consdiered accelerator component (beam-coupling impedance).Check :file_folder: examples/ on how to use!Documentation is avaiable inbihc.readthedocs.ioFist release coming soon(January 2023)InstallationThis section explains how to set up the environment to start using BIHC package for power loss computations.Installation in CERN lxplusConnect to CERN lxplus via ssh. Avoid connecting to lxplus8, the code will induce in Kerberos issues. Kerberos logging will expire 4h after each connection and needs to be renewed.ssh -XY user@lxplus.cern.chIn your /user or /work directory, do:# If miniconda is not installed
# Get, install and activate miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh 
source miniconda3/bin/activate

# Get standard packages 
# (to have all spark functionalities pandas needs to be installed before pytimber)
pip install numpy scipy matplotlib ipython pandas

# Change python package index to CERN index
pip install git+https://gitlab.cern.ch/acc-co/devops/python/acc-py-pip-config.git

# Install pytimber
pip install pytimber

# Change python package index back to default
pip uninstall acc-py-pip-configTest the installation with$ ipython
import pytimber
ldb = pytimber.LoggingDB(source="nxcals") 
ldb.search('LHC%BEAM_ENERGY%')
ldb.get(ldb.search('LHC%BEAM_ENERGY%')[0], t1='2022-06-15 15:10:30.0000')Setup Git in lxplusgit config --listLook for user.name and user.email. If it is not yet set, run the following commands (which set the information for all your git repositories on lxplus)git config --global user.name "Your Name"
git config --global user.email "your.name@cern.ch"There are other settings recommended as well:git config --global push.default simple
git config --global http.postBuffer 1048576000
git config --global http.emptyAuth true # Required on CC7The push setting makes some operations more straightforward. The second addresses an issue with large pushes via plain http or krb5. The third addresses an issue with libcurl and krb5.Developers: Download BIHC repositorygit clone https://github.com/LeonardoSito/BIHC.gitUsers: pip install(Available soon)pip install bihc
Package: bihexdomedi
BiHexSimple base convertHow to installpip install bihex-domediHow to usefrom BiHex import *To create an instance, use:foo = Bihex("b10110100"), where the letter behind the number is the base (2 = b, 8 = o, 10 = d, 16 = x)To display the number in:decimal:foo.returnnum("d"), gives"180"binary:foo.returnnum("b"), gives"0b10110100"hexadecimal:foo.returnnum("x"), gives"0xB4"octal:foo.returnnum("o"). gives"0o264"To display the number's original base, usefoo.returnbase()
Package: bihex-domedi
BiHexSimple base convertHow to installpip install bihex-domediHow to usefrom BiHex import *To create an instance, use:foo = Bihex("b10110100"), where the letter behind the number is the base (2 = b, 8 = o, 10 = d, 16 = x)To display the number in:decimal:foo.returnnum("d"), gives"180"binary:foo.returnnum("b"), gives"0b10110100"hexadecimal:foo.returnnum("x"), gives"0xB4"octal:foo.returnnum("o"). gives"0o264"To display the number's original base, usefoo.returnbase()
Package: bijoytounicode
BijoyToUnicodeIt is a utility to convert bijoy encoded text to unicode.InstallationpipinstallbijoytounicodeUsagefrombijoytounicodeimportbijoy2unicodeTEST_TEXT="‡Kv‡bv †Kv‡bv w`b, AvKvk‡Rvov †gN K‡i, Kv‡R gb e‡m bv| †W‡¯‹i mvg‡bi Rvbvjvi c`©v Zz‡j w`B| w¯Œ‡b ZvKvB, †`wL bv, `‚ieZ©x AvKvk I Bgvi‡Z wKQz †LvuR _v‡K| Ggb me w`‡bi bvg †KvgjMvÜvi|"bijoy2unicode(TEST_TEXT)Testing And DevelopmentRun test by issuing this command:python3setup.pytestAny kind of development is welcome. Send Pull Request with proper description.
Package: bijoy-to-unicode-file-converter
Bijoy to Unicode File ConverterIf you want to convert your bijoy file to unicode you can use this package.How to use this packagepip install bijoy-to-unicode-file-converterHow to use in your codefrom converter import convert_bijoy_to_unicode

convert_bijoy_to_unicode('bijoy.txt', 'unicode2.txt')

# bijoy.txt is in input file
# unicode2.txt is the converted unicode file

# Happy Coding!Creditbijoy2unicodelangdetect
Package: bikash-calculator
Bikash CalculatorUnder construction! No tready for use yet! Currently experimenting and planning!Developed by Bikash Chowdhury from DIA (c) 2023Example of How TO USe (Bikash Calculator)Creating A caalculatordefadd_number(a,b):returna+bdefsub_number(a,b):returna-bdefmul_number(a,b):returna*bdefdiv_number(a,b):returna/bUSES:frommycalculatorimportcalculatorcalculator.add_number(7,8)
Package: bike
bikeA lightweight model validator for modern projects.InstalationpipinstallbikeFirst PedalsLets define a simple model to represent a person.importbike@bike.model()classPerson:name:strheight:floatweight:float...p1=Person(name='Patrick Love',height=75,weight=180)A Person instance can be created passing the attributes.
Also can be instantiated by a dict....data={'name':'Patrick Love','height':75,'weight':180}p1=Person(**data)Nested ModelsWe can create and mananger more complex structs by nested
models. It's simply by using a model as field type of other model.# implement.pyimportbike@bike.model()classAddress:address_1:straddress_2:str=''city:strstate:strcode:strcountry:str@bike.model()classPhone:country_code:strlocal_code:strnumber:str@bike.model()classPerson:name:straddress:Addressphones:list[Phone]...payload={'name':'Aline Mark','address':{'address_1':'239 W 45th St','address_2':'','city':'New York','state':'NY','code':'10036','country':'EUA'},'phones':[{'country_code':'+1','local_code':'010','number':'134354325'},{'country_code':'+2','local_code':'011','number':'134565436'}]}p1=Person(**payload)print(p1.phones[0].country_code)# +1print(p1.phones[1].local_code)# 011print(p1.address.state)# NYParsing to Json and DictWe can parse data to dict or json by using
.dict() or json() methods respectively.importbike@bike.model()classMake:name:strcountry:str@bike.model()classCar:name:strmake:Makem1=Make(name='Nissan',country='JP')c1=Car(name='Leaf',make=m1)c1_dict=c1.dict()print(c1_dict['make']['country'])# JPc1_json=c1.json()print(c1_json)# {"name": "Leaf", "make": {"name": "Nissan", "country": "JP"}}Validating FieldsFields can be validated adding the validator annotation.importbike@bike.model()classCharacter:name:strhealth:float=1.0@bike.validator('name')defname_validator(cls,val):returnval.title()@bike.validator('health')defhealth_validator(cls,val):ifval<0:return0.0elifval>1:return1.0returnvalc1=Character(name='ninki walker',health=2.0)print(c1.name)# Ninki Walkerprint(c1.health)# 1.0
Package: bike2csv
bike2csv - Convert FIT, PWX and TCX files from a bike computer to CSV formatParse any file obtained from a bike computer to a csv file.
This packages supports the following file extensions:.fit,.FIT,.pwxand.tcx.InstallationThe package is available under pypi:pip install bike2csvUsageThis code unzips.gzfiles, then converts this unzipped (.fit,.FIT,.pwxor.tcx) file to a.csvfile. The code is designed such that all different file extensions are converted to similar csv files (e.g. the same column names). This makes it easier for analyzing and merging the files later.If your exported files have a.gzformat, you can run the sample script underneath tounzipyour files and convert them to csv.importosfrombike2csv.converterimportConverterroot='data/'person='Albert Einstein'path=dict(_path_zip=os.path.join(root,'export',person),# where your .fit.gz files are saved_path_fit=os.path.join(root,'fit',person),# where your .fit files will be saved_path_csv=os.path.join(root,'csv',person))# where the .csv files will be savedconverter=Converter(**path)forfileinconverter.files:converter.convert(file)If your exported files arenot zippedanymore, you can run the following sample script to convert your files to csv. Note that the only difference is whether you give_path_zipto theConverterclass. If you do not give it a_path_zip, it simply assumes your files are already unzipped.importosfrombike2csv.converterimportConverterroot='data/'person='Albert Einstein'path=dict(_path_fit=os.path.join(root,'fit',person),# where your .fit files are be saved_path_csv=os.path.join(root,'csv',person))# where the .csv files will be savedconverter=Converter(**path)forfileinconverter.files:converter.convert(file)You can of course adjust the script as you please.
A sample script can also be found underbin/run.py.If you are running into problems, feel welcome to contact the author (evanweenen@ethz.ch).AttributionTo readfitfiles in python, this package makes use of thefitparsepackage © David Cooper, Carey Metcalfe, 2021.LicenseThis code is © E. van Weenen, 2022, and it is made available under the MIT license enclosed with the software.Over and above the legal restrictions imposed by this license, if you use this software for an academic publication then you are obliged to provide proper attribution.E. van Weenen. bike2csv: Convert files from a bike computer to CSV, v0.1 (2022). github.com/evavanweenen/bike2csv.
Package: bikesrv
Aboutbikesrv is a server to measure the output of an exercising bike and make
it available via a web API.Wait, what?Ok, you know exercising bikes. That thing that looks like a bike, but
doesn’t move. You use it when you want to get in shape.Some of these bikes have an output signal that is fed to a small
computer with a large display across a cable and a mini-jack. The
display then shows your speed, virtual distance etc.Turns out that this mini-jack just fits into the audio in of a stock
computer. This is where bikesrv comes into play. It analyses the signal
from the bike and computes the bike’s speed.The final step is a built-in web server in bikesrv. It makes the current
speed is available through a simple HTTP API.Ok, but why?You could do all sorts of analysing and feedback that an ordinary bike
computer does.But it is much more fun to turn the exercising bike into a game
controller.What is the HTTP API?The server will listen on port 1805. If you callhttp://HOST:1805/bikefreqyou will receive atext/plainresponse that contains the current
number of revolutions per second.PrerequisitesPython 2.xhttp://www.python.orgpyohttps://code.google.com/p/pyo/issues/listInstallationUnzip the file, then at the command line runpython setup.py installUsageOn the command line, runpython2 bikesrv.py [options]Options:--version             show program's version number and exit

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

-v, --verbose         Display debugging information while analysing.

-r RATE, --rate=RATE  The number of measurements per second. Default: 500

-d DROPOUTS, --dropouts=DROPOUTS
                      The number of signal breaks per full wheel turn.
                      Default: 3DocumentationTo read the API documentation, open a shell / DOS window, navigate to
the bikesrv directory, and runpydoc bikesrvYou can create a HTML version usingpydoc -w bikesrvLicensebikesrv is licensed under the GPL. See the file COPYING for details.AuthorFlorian Berger
Package: bikram
Python Bikram SamwatUtilities to work with Bikram/Vikram Samwat dates. Documentation:https://bikram.readthedocs.io.Getting startedInstall thebikrampackage:https://bikram.readthedocs.io/installation.html.Read the usage guide:https://bikram.readthedocs.io/usage.html.Read the module reference guide:https://bikram.readthedocs.io/bikram.html.FeaturesConvert Bikram Samwat dates to AD and vice versa.
Intended to be useful for Nepali software developers.Well tested and readable source code.Date operations, i.e. addition/subtraction,
supported withdatetime.dateanddatetime.timedeltawithin range.Supports comparison withdatetime.dateanddatetime.timedeltaobjects.Supports string formatting ofsamwatdates.CaveatsIs not very helpful if the date falls outside the map of BS years to days in month.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2018-03-15)Added usage guide and module docs.Addedpipenvsupport.Removedtox.Removed support for python versions older than 3.6.0.1.2 (2016-11-13)Minor test fixes.0.1.0 (2016-11-13)First release on PyPI.
Package: bilateralfilter_torch
This repository contains our GPU-accelerated trainable bilateral filter
layer (three spatial and one range filter dimension) that can be
directly included in any Pytorch graph, just as any conventional layer
(FCL, CNN, …). By calculating the analytical derivative of the bilateral
filter with respect to its parameters and its input, the (so far)
hyperparameters can be automatically optimized via backpropagation for a
calculated loss.Our corresponding paperUltra low-parameter denoising: Trainable
bilateral filter layers in computed
tomographycan be found onMedical Physics(open access) andarXiv(pre-print).Citation:If you find our code useful, please cite our work@article{wagner2022ultra,
  title={Ultra low-parameter denoising: Trainable bilateral filter layers in computed tomography},
  author={Wagner, Fabian and Thies, Mareike and Gu, Mingxuan and Huang, Yixing and Pechmann, Sabrina and Patwari, Mayank and Ploner, Stefan and Aust, Oliver and Uderhardt, Stefan and Schett, Georg and Christiansen, Silke and Maier, Andreas},
  journal={Medical Physics},
  year={2022},
  doi={https://doi.org/10.1002/mp.15718}
}Setup:The C++/CUDA implemented forward and backward functions are compiled via
the setup.py script using setuptools:Create and activate a python environment (python>=3.7).InstallTorch(tested versions: 1.7.1, 1.9.0).Install the bilateral filter layer via pip:pipinstallbilateralfilter_torchIn case you encounter problems with 3. install the layer directly from ourGitHub repository:Download the repository.Navigate into the extracted repo.Compile/install the bilateral filter layer by callingpythonsetup.pyinstallExample scripts:Can be found in ourGitHub repositoryTry out the forward pass by running the example_filter.py (requiresMatplotlibandscikit-image).Run the gradcheck.py script to verify the correct gradient
implementation.Run example_optimization.py to optimize the parameters of a bilateral
filter layer to automatically denoise an image.Optimized bilateral filter prediction:Implementation:The general structure of the implementation follows the PyTorch
documentation forcreating custom C++ and CUDA
extensions.
The forward pass implementation of the layer is based on code from theProject MONAIframework, originally published under theApache License, Version
2.0. The correct
implementation of the analytical forward and backward pass can be
verified by running the gradcheck.py script, comparing numerical
gradients with the derived analytical gradient using the PyTorch
built-ingradcheck
function.Troubleshootingnvcc-related errors:Compiling the filter layers requires the Nvidia CUDA toolkit. Check
its versionnvcc--versionor install it via, e.g.,sudoaptupdatesudoaptinstallnvidia-cuda-toolkitThe NVIDIA CUDA toolkit 11.6 made some problems on a Windows machine
in combination with pybind. Downgrading the toolkit to version 11.3
fixed the problem (seethisdiscussion). #### Windows-related problems: Make sure thecl.exeenvironment variable is correctly set.
Package: bilbo
bilboCommands to help build and maintain a gollum wiki.Usagebilbotemplate[-s<pathToSettingsFile>]bilbotasks[-s<pathToSettingsFile>]bilbotidy[-s<pathToSettingsFile>]bilboscaffold[-s<pathToSettingsFile>]bilboblog[-s<pathToSettingsFile>]COMMANDS========templatepopulatethewikiwithtemplatecontentandcleanupfilenamestasksaddprojectsfromwikistotaskpaperfilestokeeptrackofprojecttaskstidytidyupthegollumwiki,updatinglists,sidebars,footer,headers,cleaningfilenamesandmdmetadatascaffoldaddandmaintainsidebars,footers,headersandvariouslistingpagesinagollumwikiblogtakepagesthathavebeenflaggedtobepostedasaJekyllblogpost,formatthemcorrectlyandpostthemOPTIONS=======-h,--helpshowthishelpmessage-s,--settingsthesettingsfileDocumentationDocumentation for bilbo is hosted byRead the Docs(laststable versionandlatest version).InstallationThe easiest way to install bilbo is to usepip:pipinstallbilboOr you can clone thegithub repoand install from a local version of the code:gitclonegit@github.com:thespacedoctor/bilbo.gitcdbilbopythonsetup.pyinstallTo upgrade to the latest version of bilbo use the command:pipinstallbilbo--upgradeDevelopmentIf you want to tinker with the code, then install in development mode.
This means you can modify the code from your cloned repo:gitclonegit@github.com:thespacedoctor/bilbo.gitcdbilbopythonsetup.pydevelopPull requestsare welcomed!IssuesPlease report any issueshere.LicenseCopyright (c) 2016 David YoungPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: bilby
BilbyA user-friendly Bayesian inference library.
Fulfilling all your Bayesian dreams.Online material to help you get started:Installation instructionsDocumentationIf you need help, find an issue, or just have a question/suggestion you canJoin ourSlack workspace(you may need to email the support desk to request an invite)Email our support desk:contact+lscsoft-bilby-1846-issue-@support.ligo.orgAsk questions (or search through other users questions and answers) onStackOverflowusing the bilby tagFor www.git.ligo.org users, submit issues directly throughthe issue trackerFor www.chat.ligo.org users, join the#bilby-helpor#bilby-develchannelsWe encourage you to contribute to the development of bilby. This is done via a merge request.  For
help in creating a merge request, seethis pageor contact
us directly. For advice on contributing, seethe contributing guide.Citation guideIf you usebilbyin a scientific publication, please citeBilby: A user-friendly Bayesian inference library for gravitational-wave
astronomyBayesian inference for compact binary coalescences with BILBY: validation and application to the first LIGO-Virgo gravitational-wave transient catalogueThe first of these papers introduces the software, while the second introduces advances in the sampling approaches and validation of the software.
If you use thebilby_mcmcsampler, please additionally citeBILBY-MCMC: an MCMC sampler for gravitational-wave inferenceAdditionally,bilbybuilds on a number of open-source packages. If you
make use of this functionality in your publications, we recommend you cite them
as requested in their associated documentation.SamplersdynestynestlepymultinestcpnestemceenessaiptemceeptmcmcsamplerpypolychordPyMC3Gravitational-wave toolsgwpylalsuiteastropyPlottingcornerfor generating corner plotmatplotlibfor general plotting routines