Saravanan666/pypi-org huggingface dataset:
Package: dissect.xfs
dissect.xfsA Dissect module implementing a parser for the XFS file system, commonly used by RedHat Linux distributions. For more
information, please seethe documentation.RequirementsThis project is part of the Dissect framework and requires Python.Information on the supported Python versions can be found in the Getting Started section ofthe documentation.Installationdissect.xfsis available onPyPI.pipinstalldissect.xfsThis module is also automatically installed if you install thedissectpackage.Build and test instructionsThis project usestoxto build source and wheel distributions. Run the following command from the root folder to build
these:tox-ebuildThe build artifacts can be found in thedist/directory.toxis also used to run linting and unit tests in a self-contained environment. To run both linting and unit tests
using the default installed Python version, run:toxFor a more elaborate explanation on how to build and test the project, please seethe
documentation.ContributingThe Dissect project encourages any contribution to the codebase. To make your contribution fit into the project, please
refer tothe development guide.Copyright and licenseDissect is released as open source by Fox-IT (https://www.fox-it.com) part of NCC Group Plc
(https://www.nccgroup.com).Developed by the Dissect Team (dissect@fox-it.com) and made available athttps://github.com/fox-it/dissect.License terms: AGPL3 (https://www.gnu.org/licenses/agpl-3.0.html). For more information, see the LICENSE file.
Package: dissertate
InstallationpipinstalldissertateBasic UsageInnb_config.py,importdissertatec=get_config()c.Exporter.preprocessors=['dissertate.preprocessors.EmptyCellElider','dissertate.preprocessors.CellElider']c.Exporter.template_file=dissertate.markdown_template_path()then,jupyternbconvert--confignb_config.py--tomarkdownyour_nb.ipynbIf you don’t want a cell in the output, edit the cell metadata to include,{"tags":["private"]}Alternatively, you can use{"tags":["todo"]}or,{"tags":["setup"]}for semantic differences that have no effect ondissertateprocessing.If you want to retain the output of the cell but not the code (e.g.,
for an emittedmatplotlibgraph),{"tags":["output-generator"]}
Package: dissimilar
py-dissimilarPython bindings to Rustdissimilarcrate, a diff library with semantic cleanup.InstallationpipinstalldissimilarUsageThis module provides one function:def diff(a: str, b: str) -> List[Chunk]: ...fromdissimilarimportdiff,Equal,Delete,Inserta='[乀丁abcd一]'b='[一abcd丁]'chunks=diff(a,b)assertchunks==[Equal("["),Delete("乀丁"),Insert("一"),Equal("abcd"),Delete("一"),Insert("丁"),Equal("]"),]LicenseThis work is released under the MIT license. A copy of the license is provided in theLICENSEfile.
Package: dis-snek
What is this?This isDis-Snek, a python API wrapper for Discord.
Snek is intended to be fast, easy to use, and easily modified to suit your needs.Features:âœ… 100% coverage of the application commands APIâœ… Dynamic cache with TTL supportâœ… Modern and Pythonic APIâœ… Proper rate-limit handlingâœ… Feature parity with most other Discord API wrappersIs this just anotherDiscord.pyfork?While this library shares features and some stylistic choices withdiscord.py, it is completely separate from them. We thinkdiscord.pyis a fantastic library, but we disagree with the direction and design decisions that were made by it.How do I use this?Here is a basic example:fromdis_snekimportSnake,Button,ButtonStyles,CommandTypes,context_menu,prefixed_command,listenbot=Snake(sync_interactions=True)@listen()asyncdefon_startup():print("Ready")print(f"This bot is owned by{bot.owner}")@prefixed_command()asyncdeftest_button(ctx):awaitctx.send("Blurple button example!",components=Button(ButtonStyles.BLURPLE,"Click me"))@context_menu(name="User menu",context_type=CommandTypes.USER,scopes=[931832853770149918])asyncdefuser_context(ctx):awaitctx.send("Context menu example!",ephemeral=True)bot.start("TOKEN")For more examples check outour examples repoor thedocs. You also canexplore projects with the dis-snek topic.If you get stuck join ourDiscord server."Can I contribute to this project?"Of course, we welcome all contributions to this library. Just ensure you follow ourrequirements.
If youre stuck for things to contribute, check out ourGitHub Projectsfor inspiration.Links:Support Server:https://discord.gg/dis-snekDocumentation:https://dis-snek.rtfd.io/GitHub Projects:https://github.com/orgs/Discord-Snake-Pit/projects/1
Package: dissolve
dissolveThe dissolve library helps users replaces calls to deprecated library APIs.ExampleE.g. if you had a function “inc” that has been renamed to “increment” in version 0.1.0
of your library:fromdissolveimportreplace_me@replace_me("increment({x})",since="0.1.0")definc(x):returnx+1Running this code will yield a warning:...
>>> inc(x=3)
<stdin>:1: DeprecationWarning: <function inc at 0x7feaf5ead5a0> has been deprecated since 0.1.0; use 'increment(4)' instead
4
Package: disspy
DispyAdvantagesPackage withasync/awaitsupportWrappers and decorators supportSimple code with syntax sugarMinimal python version is 3.8Readable library codeGetting startedDispy is package for creating bots in Discord. This package use discord API and discord Gateway
for handle events or, for example, sending messages. Bot use for different goals; handle information
on your server, creating mini games in discord, auto moderation in your discord server and other.For example, you can reply to message that is sended by any user using this code:fromdisspyimportClient,Message# Import packageimportosTOKEN=os.environ["TOKEN"]client=Client(TOKEN)# Create a client@client.on_message("create")# On message createasyncdefon_messagec(message:Message):# message - sended messageawaitmessage.reply("Hello!")# Reply to a messageclient.run()# Run clientDownload packageDownload latest version (needs pip)# Windows
pip install -U disspy

# MacOS / Linux
python3 -m pip install -U disspyDownload dev version (needs git and pip)# Windows
git clone https://github.com/itttgg/dispy.git
cd dispy
pip install -U .

# MacOs / Linux
git clone https://github.com/itttgg/dispy.git
cd dispy
python3 -m pip install -U .Linkshttps://github.com/itttgg/dispy- GitHub repohttps://pypi.org/project/disspy- Project site on PyPihttps://disspy.readthedocs.io- Site with docs for packagehttps://discord.gg/QsE5DSQrsx- Discord server
Package: disstat
DisstatDisstat is a Python package for computing dissimilarity statistics for wrapping the Disstat API.InstallationUse the package managerpipto install disstat.pipinstalldisstatUsagefromdiscordimportIntents,Interactionfromdiscord.extimportcommandsfromdisstatimportDisstatClientclassBot(commands.Bot):def__init__(self):super().__init__(command_prefix="!",intents=Intents.default())self.disstat=DisstatClient(self,"DISSTAT API KEY HERE")asyncdefon_ready(self):awaitself.disstat.start_auto_post()asyncdefon_command_completion(self,ctx:commands.Context):# For using prefix commandsawaitself.disstat.post_command(ctx.command.name,invoker_id=ctx.user.id,guild_id=(ctx.guild.idifctx.guildelseNone),)asyncdefon_app_command_completion(self,interaction:Interaction):# For using slash commandsawaitself.disstat.post_command(interaction.command.name,invoker_id=interaction.user.id,guild_id=(interaction.guild.idifinteraction.guildelseNone),)asyncdefon_disstat_post(self,payload):print("Posted stats to Disstat.")asyncdefon_disstat_post_command(self,payload):print(f"Posted command{payload['command']}to Disstat.")bot=Bot()bot.run("TOKEN")
Package: disstat.py
DisStat.pyThe unofficial pypi package forDisStat-
DisStat itself isopen sourcebtw!You can find the public HTTP api docs onhttps://disstat.pages.dev/docsif you dont want to use an api wrapper.Installationpipinstalldisstat.pyMain usageimportosimportdiscordfromdiscord.extimportcommandsfromdisstatimportDisstatintents=discord.Intents.default()bot=commands.Bot(command_prefix='!',intents=intents)# initialize DisStatbot.disstat=Disstat(bot,"YOUR_DISSTAT_TOKEN_HERE")@bot.eventasyncdefsetup_hook():# start the background task to post statisticsbot.disstat.start_loop()@bot.eventasyncdefon_ready():print(f'We have logged in as{bot.user}')@bot.eventasyncdefon_command(ctx:commands.Context):# log command has been usedawaitbot.disstat.post_command(ctx)@bot.eventasyncdefon_interaction(interaction:discord.Interaction):# log slash command has been usedifinteraction.typeisnotdiscord.InteractionType.application_command:returnawaitbot.disstat.post_command(interaction)@bot.eventasyncdefon_message(message:discord.Message):# your command logic ...awaitbot.disstat.post_command_raw(command_name,user_id,guild_id)# this function posts a raw command to the API@bot.command()asyncdefstop_bg(ctx:commands.Context):# stop the background taskbot.disstat.stop_loop()awaitctx.send("done")@bot.command()asyncdefping(ctx:commands.Context):awaitctx.send("pong")@bot.tree.command(name="ping")asyncdefs_ping(interaction:discord.Interaction):awaitinteraction.response.send_message("pong")bot.run('YOUR_TOKEN_HERE')
Package: dis-taipan
dis-taipan50%Dis-Snekfeatures that are either too undercooked for merging into the library proper
50%  "application specific" common code I want to use in most of my bots, but don't fit the philosophy of dis-snek itself.A taipan is a dangerous australian snake.  I thought it was mildly amusing.pip install dis-taipanCompatibility promisesWhere possible, I avoid breaking changes.  While internals may change, the public interface will not.Any features that are upstreamed and integrated into snek proper will be aliased, and still be accessible under the dis_taipan namespace.I will bump the major version of taipan if I make breaking changes, or if I have to bump theminimumsupported Snek version.Ready-to-Use Scalesdis_taipan.sentrySets up a sentry.io hooks, intended to be as simple as possible.To install, either set an environment variable called SENTRY_TOKEN (I recommend usingdotenv), or put a string in the sentry_token attribute of your bot class.Then callbot.load_extension('dis_taipan.sentry')to initialize the logger.dis_taipan.updaterAutomatic updater.  Makes the following assumptions:You deploy using git, and the .git folder exists in productionYou have some kind of health check that will reboot the bot when it shuts down.To install, callbot.load_extension('dis_taipan.updater').  It'll do its magic every five minutes.
There is no configuration.Code Quality things:ProtocolsProtocolsare essentially the duck-typing equivelent of ABCs.  Think of them like Interfaces from C-like languages.SendableContextis a protocol that mimics a Context with SendMixin.  Useful when typing error handlers.
Package: distance3d
distance3dDistance computation and collision detection in 3D.Robot collision detectionCapsule collision detectionClosest pointsHydroelastic contactFeaturesCollision detection and distance computation with GJK.Calculation of penetration depth with EPA.Collision detection and calculation of penetration depth with MPR.Various specific distance calculations for points, lines, line segments,
planes, triangles, rectangles, circles, disks, boxes, cylinders, ellipsoids,
...Broad phase collision detection with bounding volume hierarchy (AABB tree).Self-collision detection for robots.Contact wrench computation with hydroelastic contact model (pressure field
model).Dependenciesdistance3d relies on numba to speed up computations. numba in its latest
version requires at least Python 3.7 and NumPy 1.18. Seeherefor current requirements. Required Python libraries will automatically be
installed during installation of distance3d.InstallationInstall the package withpip install -e .[all]or from PyPI withpip install distance3d[all]Unit TestsInstall dependencies withpip install -e .[test]Run unit tests withNUMBA_DISABLE_JIT=1 pytestYou will find the coverage report inhtmlcov/index.html.API DocumentationInstall dependencies withpip install -e .[doc]Build API documentation withcd doc
make htmlYou will find the documentation indoc/build/html/index.html.LicensesThese implementations are mostly based onChrister Ericson: Real-Time Collision Detection, CRC Press, 2004.David H. Eberly: 3D Game Engine Design, CRC Press, 2006.and accompanying implementations. These are marked as such.The distance computation between a line and a circle is based on David Eberly's
implementation, Copyright (c) 1998-2022 David Eberly, Geometric Tools,
Redmond WA 98052, distributed under the Boost Software License, Version 1.0.The original GJK algorithm is a translation to Python of the translation to C of
the original Fortran implementation. The C implementation is from Diego Ruspini.
It is available fromhttp://realtimecollisiondetection.net/files/gilbert.cSome features related to the GJK algorithm have been inspired byBullet(zlib license) and are
marked as such in the source code.The EPA algorithm is adapted fromKevin Moran's GJK implementation(MIT License or Unlicense).A GJK intersection test and the MPR algorithm are based on libccd (for details,
seehttps://github.com/danfis/libccd). For the original code the copyright is
of Daniel Fiserdanfis@danfis.cz. It has been released under 3-clause BSD
license.The main GJK implementation is based on Jolt Physics, Copyright 2021 Jorrit
Rouwe, MIT license.The translation to Python has been done by Alexander Fabisch and the glue
code around it is licensed under the 3-clause BSD license.
Package: distancecalc
Distance CalculatorIntroductionThe distance calculator is a way to find the distance between two points!UsageFirst, you will need to create four variables,p1x,p2x,p1y, andp2y.
Next, you will need to assign them to coordinates. It will look like this:#The coordinates shown here are just an example.importdistancecalcp1x=4p2x=3p1y=5p2y=6After you have done this, you can calculate the distance by simply runningprint(distancecalc.distance(p1x, p1y, p2x, p2y)).(Obviously you don't have to print it, you can save as variable.)
Package: distanceclosure
Distance Closure on Complex Networks (Network Backbone)DescriptionThis package implements methods to calculate the Distance Closure of Complex Networks including its Metric and UltraMetric Backbone.InstallationLatest development release on GitHub (v0.5):pip install git+https://github.com/CASCI-lab/distanceclosureLatest PyPI stable release (v0.4.1):$pip install distanceclosureSimple usageHow to calculate Closure of a weighted distance graph:importnetworkxasnximportdistanceclosureasdc# Instanciate a (weighted) graphedgelist={('s','a'):8,('s','c'):6,('s','d'):5,('a','d'):2,('a','e'):1,('b','e'):6,('c','d'):3,('c','f'):9,('d','f'):4,('e','g'):4,('f','g'):0,}G=nx.from_edgelist(edgelist)# Make sure every edge has an attribute with the distance valuenx.set_edge_attributes(G,name='distance',values=edgelist)# Compute closure (note this will be a fully connected graph for an undirected connected component. It can be slow for large graphs)C=dc.distance_closure(G,kind='metric',weight='distance')# You can now access the new `metric_distance` value and whether the edge is part of the metric backbone.C['s']['c']>{'distance':6,'metric_distance':6,'is_metric':True}If you are only interested in the metric backbone itself:B=dc.metric_backbone(G,weight='distance')B.number_of_edges()>9C.number_of_edges()>28G.number_of_edges()>11PapersF.X. Costa, R.B. Correia, L.M. Rocha [2023]. "The distance backbone of directed networks". In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds)Complex Networks and Their Applications XI. COMPLEX NETWORKS 2022.Studies in Computational Intelligence, vol 1078. Springer, Cham. doi: 10.1007/978-3-031-21131-7_11T. Simas, R.B. Correia, L.M. Rocha [2021]. "The distance backbone of complex networks".Journal of Complex Networks, 9 (6):cnab021. doi: 10.1093/comnet/cnab021T. Simas and L.M. Rocha [2015]."Distance Closures on Complex Networks".Network Science, 3(2):227-268. doi:10.1017/nws.2015.11Creditsdistanceclosurewas originally written by Rion Brattig Correia with input from many others. Thanks to everyone who has improveddistanceclosureby contributing code, bug reports (and fixes), documentation, and input on design, and features.SupportThose who have contributed todistanceclosurehave received support throughout the years from a variety of sources.  We list them below.CASCI, Binghamton University, Binghamton, NY; PI: Luis M. RochaCAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil; Rion B. Correia.DevelopmentPull requests are welcome :) Please get in touch beforehand:rionbr(at)gmail(dot)comorfcosta(at)binghamton(dot)edu.Changelogv0.5Iterative backbone computation (faster for graphs with smaller backbones)v0.4Code simplification and compliance to NetworkXv0.3.6Dijkstra Cythonizedv0.3.2S and B measure added to closurev0.3.0Dijkstra APSP algorithmSupport for sparse matricesv0.2First docs releasedv0.1First release nad dense matrix APSP
Package: distance-computation
distance-computationDocumentation:https://MFStark.github.io/distance-computationSource Code:https://github.com/MFStark/distance-computationPyPI:https://pypi.org/project/distance-computation/Distance dataInstallationpipinstalldistance-computationDevelopmentClone this repositoryRequirements:PoetryPython 3.7+Create a virtual environment and install the dependenciespoetryinstallActivate the virtual environmentpoetryshellTestingpytestDocumentationThe documentation is automatically generated from the content of thedocs directoryand from the docstrings
of the public signatures of the source code. The documentation is updated and published as aGithub project pageautomatically as part each release.ReleasingTrigger theDraft release workflow(pressRun workflow). This will update the changelog & version and create a GitHub release which is inDraftstate.Find the draft release from theGitHub releasesand publish it. When
a release is published, it'll triggerreleaseworkflow which creates PyPI
release and deploys updated documentation.Pre-commitPre-commit hooks run all the auto-formatters (e.g.black,isort), linters (e.g.mypy,flake8), and other quality
checks to make sure the changeset is in good shape before a commit/push happens.You can install the hooks with (runs for each commit):pre-commitinstallOr if you want them to run only for each push:pre-commitinstall-tpre-pushOr if you want e.g. want to run all checks manually for all files:pre-commitrun--all-files
Package: distancecontrol
A library used to control programs(mainly games) without focusing them.This isn't guaranteed to work and will sometimes require to be very creative with how you perform actions.Example:importDistanceControlasdchwnd=dc.Windows_by_title('GamesTitle',strict=True,limit=1)[0]# Open up menu(It doesn't close it)dc.KeyPress_Down(hwnd,dc.con.VK_ESCAPE)dc.KeyPress_Up(hwnd,dc.con.VK_ESCAPE)# Press the right keydc.KeyPress_Down(hwnd,dc.con.VK_RIGHT)dc.KeyPress_StopAll(hwnd)# KeyPress_Up() won't work. Why? I don't know, for every program it's different!Module's functions are easy to understand.ChangelogDistanceControl 0.0.2.5:Made usage much cleaner.Fixed a few tiny bugs.DistanceControl 0.0.2:Fixed unable to download package.DistanceControl 0.0.1:Initial release.
Package: distance-locker
linux_distance_lockerThis Open source is in beta mode.Every contribution is welcome!The Bluetooth RSSI densor bash script was copied from this blog:https://www.raspberrypi.org/forums/viewtopic.php?t=47466(direct bash script link:)https://www.raspberrypi.org/forums/viewtopic.php?t=47466#p417970Requirements:Ubuntu Base Linux (tested on Ubuntu 18.04)Python 3.6 or upGNOME Screensaver - (you can overwrite with any command line with environment variables)Usage:$ pip install distance-locker
  $ distance_locker -a <Bluetooth driver mac address>Enjoy
Package: distance-map
DistanceMapPython Distance Map libraryFor a N-dimension (2 ≤ N ≤ 3) binary matrix, return the corresponding distance map.
For each pixel, the value is equal to the minimum distance to a "positive" pixel.
Due to the way I plan to use this library, the implementation is in reality articulate
over a list of positive points positions and not a binary matrix.Δ = distance to nearest positive point
α = a user defined function (ℝ+ → ℝ+) (default : x ↦ x)
Ω = the maximum value

P_result(x, y) = min(α(Δ), Ω)ExamplesUsageInput is a list of positive pointsdistance_map(input.shape, points)Input is binary (boolean) matrixdistance_map_from_binary_matrix(input)Input is a list of positive points and you want to use custom argumentsdistance_map(input.shape, points, distance="manhattan", beta=15.0, alpha="square")ParametersDistanceeuclideanmanhattanAlphaidentity (x ↦ x)square (x ↦ x*x)linear (x ↦ A*x + B)use set_a(a) and def set_b(b) to change A and B valuesBenchmarkThe following tests has been made on a laptop with an Intel Core i7-7700HQ and 24Go of RAM.TestTime distance_mapTime distance_map_from_binary_matrix2D 1024 x 1024 image with 10 positive points8ms11ms2D 1024 x 1024 image with  5 positive points5ms9ms3D 128 x 128 x 128 image with 10 positive points61ms148msAcknowledgementThis library is inspired for use as in the following paper.N. Xu, B. Price, S. Cohen, J. Yang and T. Huang
"Deep Interactive Object Selection"
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Las Vegas, NV, 2016, pp. 373-381
doi: 10.1109/CVPR.2016.47
Package: distancematrix
pydistancematrix will partition a list of edges based on a hard limit of
nodes per graph.Essentially, this module will take a graph and partition it into subgraphs
of a hard limit size, returning both the lists of nodes in each sub-graph,
and the list of edges that are ‘cut’ by the partitioning.For example:>>> import distancematrix

>>> distancematrix.matrix_calls([
    (1, 2),
    (2, 3),
    (7, 8),
    (9, 10)], limit=3)
[[1, 2, 3], [7, 8], [9, 10]], []LicenseBSDAuthorMike Axiak
Package: distance-metrics
MetricsPython implementation of some more uncommon metrics. Currently only longest common subsequence LCS metrics are implemented.DependenciesThis package requires the following python libraries:numpy(automatically installed when package is installed via pip)InstallationThe metrics package can be installed directly frompip.pip3 install distance-metricsMetricsThe metrics library currently has support for the following modules.Longest common subsequence metricsdistance_metrics.lcsLongest common subsequence metricsThe LCS module currently implements 2 distances:Length of longest common subsequence (distance_metrics.lcs.llcs(u, v)).Bakkelund distance[1] (metrics.lcs.bakkelund(u, v))Usage# Importsfromdistance_metricsimportlcsimportnumpyasnp# Create example input arraysu=np.random.choice(list('ABCD'),size=20)v=np.random.choice(list('BCDE'),size=20)# Compute metricsllcs=lcs.llcs(u,v)bakkelund=lcs.bakkelund(u,v)# Print valuesprint("LLCS     :{}".format(llcs))print("Bakkelund:{}".format(bakkelund))References1Bakkelund, D. (2009). An LCS-based string metric. Olso, Norway: University of Oslo.