Saravanan666/pypi-org huggingface dataset:
Package: ballsy
ballsyBallsy is a GitHub release tarball signing tool. It tries to promote
signing of the automatically generated release tarballs on GitHub with
developer’s OpenPGP keys. Usually that would involve:pushing a tagnavigating to the GitHub web page and creating a releasedownloading their tarballsigning the tarballuploading the detached ASCII signature to the release page as an
asset(And then redo this for the ZIP file…) The Debian wiki has a good set
ofinstructions for
this.Most of us probably wouldn’t bother doing this. This software automates
this job by taking care of the necessary steps as outlined above.Additional features:Automatic conversion from tags to releasesSelective signing of ZIPs/tarballsAutomatic target repo selection based on current directoryInstallation$ pip install ballsyUsageYou’ll need to log in to GitHub once:$ ballsy loginwhich will ask for your user credentials, and then obtain a token for
future logins (stored in~/.ballsyrc). 2FA by phone is supported.After logging in, signing is as easy as:$ ballsy sign v2.0to sign the release v2.0 in the GitHub repo pointed to by theoriginremote in the current directory (which is the default). Other targets
can easily be specified:$ ballsy sign --remote home v2.0
$ ballsy sign --repo foobar/otherrepo v2.0By default, the key specified in Git’suser.signingkeyproperty is
used, but this can be overridden using the--keyidoption.If you don’t usually use releases on GitHub, just tags, it is possible
to automatically prepare a release given a tag (--include-tags).
This also works when specifying multiple tags:$ ballsy sign --include-tags v1.0 v1.2 v2.0Please seeballsy--helpandballsy sign--helpfor more
options.TODOAt the moment you have to trust GitHub not to alter the contents of the
tarballs when preparing a release. Future versions of ballsy will verify
the contents of the downloaded tarballs against the local content
corresponding to given tags.LICENSE2-clause BSD, seeLICENSE.txt
Package: balltree
A fast ball tree implementation for three dimensional (weighted) data with an
Euclidean distance norm. The base implementation is inCand there is a
wrapper forPython. The tree is optimised towards spatial correlation function
calculations since it provides fast counting routinge, e.g. by implementing a
dualtree query algorithm.Code:https://github.com/jlvdb/balltree.gitDocs:https://balltree.readthedocs.io/PyPI:https://pypi.org/project/balltree/InstallationAClibrary can be built with the provided make file, the python wrapper is
automatically compiled and installed withpip install balltree.The installation does not require any externalClibraries, the python wrapper
requires thePython.hheader (which should be included in a default python
installation) andnumpy(includingnumpy/arrayobject.h).MaintainersJan Luca van den Busch
(author, Ruhr-Universit??t Bochum, Astronomisches Institut)
Package: ballyregan
Find fetch & validate free proxies fast.Ballyregan is a super fast proxy fetcher.It provides a python package and an easy-to-use CLI to help you fetch <bFree Tested Proxies fast, and keep your privacy.Key features:Fetchfree tested proxies super fast using theProxyFetcherValidateyour own proxies using theProxyValidatorFiltercustom proxy list by protocol & anonymity using theProxyFiltererHow does it work?When you use the ProxyFetcher to fetch a proxy, it performs several steps:Gather all the available proxies from a list of built-in providers (each provider gathers its own and returns it to the fetcher).Provider - any website that serves free proxy lists (e.ghttps://free-proxy-list.net).Filter all the gathered proxies by the given protocols and anonymities (if exist).Validate the filtered proxies and return them.NoteYou can write and append your own custom providers and pass them to the ProxyFetcher class as an attribute.Every custom proxy provider must implement theIProxyProviderbase interface.Behind the scenesFetching a proxy is anIO bound operationthat depends on the network. A common approach for this problem is performing your network requests async.After digging a bit, and testing Threads, Greenlets, and async operations, we decided to go the async way.To perform async HTTP requests, ballyregan usesaiohttpandasyncio,
as"asyncio is often a perfect fit for IO-bound and high-level structured network code."(from asyncio docs).By using the power of async HTTP requests, ballyregan can validate thousands of proxies really fast.it to the ProxyFetcher class as an attribute.Every custom proxy provider must implement theIProxyProviderbase interface.InstallAll you need to do is install the package from pypi, and it will automatically install the CLI for you.pipinstallballyreganUsage📦 PackageCreate a fetcher instancefromballyreganimportProxyFetcher# Setting the debug mode to True, defaults to Falsefetcher=ProxyFetcher(debug=True)Get one proxyproxy=fetcher.get_one()print(proxy)Get multiple proxiesproxies=fetcher.get(limit=4)print(proxies)Get proxies by filtersfromballyregan.modelsimportProtocols,Anonymitiesproxies=fetcher.get(limit=4,protocols=[Protocols.HTTPS,Protocols.SOCKS5],anonymities=[Anonymities.ELITE])print(proxies)💻 CLINeed some help?ballyreganget--helpGet one proxyballyregangetGet all proxiesballyreganget--allUse debug modeballyregan--debugget[OPTIONS]Format output to jsonballyreganget-ojsonGet proxies by limitballyreganget-l4Get proxies by filtersballyreganget-l4-phttps-psocks5-aelite📝 LicenseCopyright © 2022Idan Daniel.This project isApache License Version 2.0licensed.
Package: baloo
BalooImplementing thebare necessitiesofPandaswith thelazyevaluating
and optimizingWeldframework.DocumentationhereInstallpip install balooNote that currently it has only been tested on Python 3.5.2, though any Python 3 version should be fine.BenchmarksBenchmark results over seeded randomized data are shown below.
The data consists of 4 NumPy array columns: 2 of float64, 1 of int64, and 1 of int32.
First 2 plots run the following operations over 56MB and 420MB total data, respectively:df = df[(df['col1'] > 0) & (df['col2'] >= 10) & (df['col3'] < 30)]              # filter                                                   
df = df.agg(['min', 'prod', 'mean', 'std'])                                     # 4x agg
df['col4'] = df['col1'] * 2 + 1 - 23                                            # 3x op
df['col5'] = df['col1'].apply(np.exp)                                           # udf
df = df.groupby(['col2', 'col4']).var()                                         # groupby*
df = df[['col3', 'col1']].join(df[['col3', 'col2']], on='col3', rsuffix='_r')   # join** Note that the groupby and join implementations are simplified in Baloo. For instance, the groupby result is not sorted
in Baloo as is in Pandas. The join implementation in Baloo currently relies on theondata being sorted and distinct;
sortednes is expected to be patched soon.This last graph shows the execution time of3x opover varying dataset sizes:Weld is, indeed, expected to scale well due to features such as vectorization, however the compilation time outweighs
the improved computation time for small datasets. Nevertheless, Baloo currently only supports a limited subset of
Pandas. More work coming soon!The scripts used to run the benchmarks are available in the relevant folder.
Package: balsa
balsa (logging utility)Simple to use package that sets up Python logging.  With just a few lines of code get well formatted logging to the
console, log file, popup windows and exception services.Here is a shortPresentation on Balsa.Installationpip install balsaMajor FeaturesSimple to use.  Add full-featured Python logging in just a few lines of code.Sane default log levels.  Singleverboseflag.  (All levels can be overridden if desired.)Both console (stdout) and GUI (popup window) support.Log file support. Usesappdirsfor log file paths.Structured logging viayasf.sf()(optional - you can still use simple strings).Sentrysupport. Just provide yourSentry DSN.Informative log message formatting (or you can change it if you like).ISO 8601 timestamp format (with fractional seconds).Cross platform (Windows, Linux, MacOS).  Pure Python.Multiprocessing support.AWS CloudWatch logssupport.
Structured logs enableCloudWatch Logs Insights.Simple Examplefrombalsaimportget_logger,Balsaapplication_name='example'log=get_logger(application_name)defmain():balsa=Balsa(application_name,'james abel')balsa.init_logger()log.error('my error example')This will yield output of this form:2021-10-24T10:49:04.150790-07:00 - example - balsa_simple_example.py - 12 - main - ERROR - my error exampleWhere did the name come from?Balsa lumber is very soft and light, with a coarse, open grain.
The Balsa package is light weight, malleable, and open source.
Package: balsam-flow
BSD 3-Clause LicenseCopyright (c) 2019, UChicago Argonne LLC
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of UChicago Argonne LLC nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL  BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: baltic
balticbaltic is a Python library for parsing phylogenetic trees. It takes newick, Nexus or nextstrain JSON trees and allows you to manipulate, explore and visualise them. baltic stands for Backronymed Adaptable Lightweight Tree Import Code if you like that sort of thing.InstallationUse package managerpipto install baltic:pip install balticUsageimportbalticasbt# When called with a tree string the `make_tree()` function return a baltic tree object:treeString='((A:1.0,B:2.0):1.0,C:3.0);'myTree=bt.make_tree(treeString)# Otherwise you can import trees from newick, nexus or nextstrain JSON filesnewickPath='/Users/myUsername/tree.newick'myTree=bt.loadNewick(newickPath)nexusPath='/Users/myUsername/tree.nex'myTree=bt.loadNexus(nexusPath,absoluteTime=False)nextstrainPath='https://nextstrain.org/charon/getDataset?prefix=/dengue/denv1'myTree,myMeta=bt.loadJSON(nextstrainPath)Copyright 2016Gytis Dudas. Licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Package: balticlsc
Computation Module Template PythonPackagebalticlscis supposed to help with creating modules for the
BalticLSC system in the python environment.Upload new version of the packagepython setup.py sdist
twine upload dist/balticlsc-{version}.tar.gzDownload the packageYou can simply download the package usingPyPI:python -m pip install balticlscor clone the repo and use the code directly.Build your moduleCreate an implementation of theProcessingInterface*.
It should handle tokens according to the documentation.Init in your code the BalticLSC api using yours implementation of processing*:from balticlsc.scheme.api import init_baltic_api

app, rest_client = init_baltic_api(Processing)Build the proper Dockerfile according to the documentation*.*Here you got some example modules on which you can base yours:Face recogniser
Package: balticlsc-test
Computation Module Template PythonPackagebalticlscis supposed to help with creating modules for the
BalticLSC system in the python environment.Upload new version of the packagepython setup.py sdist
twine upload dist/balticlsc_test-{version}.tar.gzDownload the packageYou can simply download the package usingPyPI:python -m pip install balticlscor clone the repo and use the code directly.Build your moduleCreate an implementation of theProcessingInterface*.
It should handle tokens according to the documentation.Init in your code the BalticLSC api using yours implementation of processing*:from balticlsc.scheme.api import init_baltic_api

app, rest_client = init_baltic_api(Processing)Build the proper Dockerfile according to the documentation*.*Here you got some example modules on which you can base yours:Face recogniser
Package: balto
BALTOBAlto is a Language independent Test Orchestratoris an unique tool to drive
all your test-runners with one common interface.InstallationInstall balto withpipx:pipxinstallbaltoYou should see at the end of the command:These binaries are now globally available
    - balto
    - balto-curses
    - balto-server
done! ✨ 🌟 ✨It is highly recommended to avoid installing Balto in either your global Python environment or a virtual environment as it might causes conflicts with some dependencies.UsageTo use it, point balto to a directory containing a.balto.tomlfile:baltotests/The.balto.tomlfile should look like:name="Acceptance Test Suite Subprocess"tool="pytest"If you just want to give Balto a try, you can use the--toolto indicate which tool you want to use. For example:balto --tool pytest testsThe tool must be one of the supported one, you can see the list here:https://github.com/lothiraldan/litf#compatible-emittersYou can test balto against examples for supported test runners. Clone this repository and launchbaltoagainst one of the examples directories. Forpytest, launch:baltoexamples/pytest/For more help:balto--helpDevelopmentBalto is composed of two components: the server and the web interface.Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. Please report unacceptable behavior tolothiraldan@gmail.com.Balto-serverBalto-server is a Python 3.7 project using Asyncio. To build the development version, first create a virtualenv (or equivalent):virtualenv.venvsource.venv/bin/activateInstall the project in development mode:pipinstall-e.Then start the server:balto-server--debugexamples/pytest/The server will start on port 8889.Web interfaceThe web interface is a React project communicating with the server using WebSockets. You can start developing on it with these instructions:cdbalto/web_interfaces/balto_react
yarnstartThe web interface is then available onhttp://localhost:3000/and will connect to the server started before.Warning: the WebSocket doesn't auto-reconnect yet, sometimes your React modification requires you to reload your browser tab.ContributorsThanks goes to these wonderful people (emoji key):Boris Feld💻🎨📖🤔📢Elias Dorneles💻🐛Paul Morelle💻This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: baluchon
[WIP] Clickhouse MigrationCurrently, in active developmentA Clickhouse migration tools written in Python.A tool to manage migrations on Clickhouse using clickhouse-driverInspirationsgolang-migrateFeaturesSupport data replicationTODOTestsSupport prefetching migrations for heavy sourcesAdd S3 SourceAdd docComplete exceptionsReplace cerberus by pydanticImplement the graceful stop (finish operations if cancel is requested)
Package: bam
A Python port of Zach Holman’s boom tool (https://github.com/holman/boom).Wut?Boom is a tool that lets you store and access text snippets from the command line. Bam is a Python port.For example:$ bam gifs trektennis http://i.imgur.com/xuzRj.gif
Bam! trektennis in gifs is http://i.imgur.com/xuzRj.gif. Got it.

$ bam trektennis
We just copied http://i.imgur.com/xuzRj.gif to your clipboard.How do get my hands on that?Well, you could install Zach’s Ruby gem.But if you want this Python port run:$ pip install bamor:$ pip install git+git://github.com/mrben/bam.gitCommandsFull list of commands:bam                          display high-level overview
bam all                      show all items in all lists
bam edit                     edit the bam JSON file in $EDITOR
bam help                     this help text

bam <list>                   create a new list
bam <list>                   show items for a list
bam <list> --delete          deletes a list

bam <list> <name> <value>    create a new list item
bam <name>                   copy item's value to clipboard
bam <list> <name>            copy item's value to clipboard
bam open <name>              open item's url in browser
bam open <list> <name>       open all item's url in browser for a list
bam random                   open a random item's url in browser
bam random <list>            open a random item's url for a list in browser
bam echo <name>              echo the item's value without copying
bam echo <list> <name>       echo the item's value without copying
bam <list> <name> --delete   deletes an item
Package: bam2fpkc
... 888                              .d8888b.  .d888          888. 888                             d88P  Y88 d88P"           888. 888                                    88 888             888. 88888b.   8888b.  88888b.d88b.       .d88 888888 88888b.  888  888  .d8888b. 888 "88b     "88b 888 "888 "88b  .od888P" 888    888 "88b 888 .88P d88P". 888  888 .d888888 888  888  888 d88P"     888    888  888 888888K  888. 888 d88P 888  888 888  888  888 888"      888    888 d88P 888 "88b Y88b.. 88888P"  "Y888888 888  888  888 88888888  888    88888P"  888  888  "Y8888P.                                                  888.                                                  888.                                                  888.Overview=========This script works out how many reads are mapping to each contig OR if you ask itnicely, it will find the median number of reads mapping to all contigs in your bins.The number of fragments mapping to any contig is normalised for contig length to givethe average number of fragments mapping per 1000 bases of contig or.. fpkcInstallation=========Should be as simple aspip install bam2fpkcData preparation and running bam2fpkc=========You'll always need a set of contigs and a corresponding set of sorted indexed bam files.Then you just need to decide if you want to get fpkc values for a set of contigs or athe median fpkc for all contigs in your bins.Contigs:Options are in flux, use:bam2fpkc contig -hfor more info...Bins:For this you'll need a 'bins file'. This is a TAB separated file of the formcid -> bidCurrently, the cid MUST exactly match the contig headers in the fasta file ANDall cids must be accounted for. IE. unbinned contigs should be assigned to bin 0or some other sensible assignment.Otherwise use:bam2fpkc bin -hfor more info...Licence and referencing=========Project home page, info on the source tree, documentation, issues and how to contribute, see http://github.com/minillinim/bam2fpkcThis software is currently unpublished. Please contact me at m_dot_imelfort_at_uq_dot_edu_dot_au for more information about referencing this software.Copyright © 2013 Michael Imelfort. See LICENSE.txt for further details.
Package: bam2html
Generate Highlighted HTML of Bamhighlight the specific sites of bam withsamtools tviewDependenciesLinux/UnixPython2/Python3samtoolstar/gzip/zip [optional for compressing]Installationpipinstallbam2htmlUsagebam2html-b<input.bam>chrom:pos# single positionbam2html-b<input.bam>chrom:pos1,pos2...# multiple positionsbam2html-b<input.bam>chrom:start-end# a region of positionsbam2html-b<input.bam>chrom:pos-r/path/to/reference.fa# with referencebam2html-b<input.bam>chrom:pos-c300# set the width of windowbam2html-b<input.bam>chrom:pos-colorpink-bggreen# set colorsbam2html-blbam_listpos_list# batch modebam2html-blbam_listpos_list-s# generate summary.htmlbam2html-blbam_listpos_list-s-xzip# compress the result with zipbam2html-blbam_listpos_list-s-xtar.gz# compress the result with tar/gzipExample Resultssingle position:demo.1_985460.htmlmultiple positions:demo.1_985460,985463,985469.htmlregion positions:demo.1_985461-985465.htmlsummary result:summary.htmlMeaning of Base's ColorsColorMapping QualitySelectorBlue0-9.tviewcu1Green10-19.tviewc2Yellow20-29.tviewc3Black>=30.tviewc4Underline: Secondary or orphanDocumentshttps://bam2html.readthedocs.io
Package: bam2plot
BAM2Plot Package DocumentationThe BAM2Plot package provides a comprehensive toolset for processing and visualizing coverage data from BAM files. This package simplifies the analysis process and enables the generation of coverage plots for multiple reference sequences within a single BAM file.bam2plot generates coverage plots:... and cumulative coverage plots for each reference (e.g.chromosomes) for each sample:Dependenciesbam2plotdepends onperbase, which you can install via:cargoinstallperbase# orcondainstall-cbiocondaperbaseInstallationYou can install BAM2Plot using the following pip command:pipinstallbam2plotUsageOnce installed, you can use thebam2plotcommand to perform coverage analysis on BAM files and generate coverage plots. Here's how to use it:bam2plot--bam<path_to_bam_file>[--sample_name<sample_name>][--outpath<output_path>][--rolling_window<window_size>][--threshold<coverage_threshold>][--zoom<"start end">]Replace the placeholders with the following information:<path_to_bam_file>: Path to the input BAM file (required).<outpath>: Path to save the output plots (optional, default:<bam2plot>).<whitelist>: List of chromosomes or reference sequences to keep. A comma separated list. Example:--whitelist=seq1,seq2<window_size>: Rolling window size for calculating the depth average (optional, default: 100).<coverage_threshold>: Coverage threshold for plotting (optional, default: 3).<zoom>: Region to zoom into. Example: "100 200" zooms into position 100-200. (optional, default: False)OutputsThe BAM2Plot package generates coverage plots for each reference sequence in the BAM file. The plots are saved as SVG and PNG files in the specified output path or the default location.API DocumentationThe BAM2Plot package provides several modules and functions for performing coverage analysis. Here's an overview of the main components:bam2plot.sortingThis module handles sorting BAM files using thepysamlibrary.bam2plot.mpileupThis module performs thepysam.mpileupoperation and processes the output.bam2plot.analysisThis module creates a Pandas DataFrame from thempileupoutput and calculates the rolling average depth.bam2plot.plottingThis module generates coverage plots usingseabornandmatplotliblibraries.ExamplesHere's an example of how to use the BAM2Plot package:bam2plot--baminput.bam--sample_namemy_sample--outpathoutput_folder--rolling_window50--threshold5