Saravanan666/pypi-org huggingface dataset:
Package: damm32
Damm32Python implementation of the Damm Algorithm in Base 32By default, it uses an alphabet as specified inRFC 4648which contains 32 alphanumeric characters, with similar looking characters removed. The padding symbol is not included.InstallationThe package is available onPyPIand can be installed using pip:pip install damm32It is also available on theArch User Repositoryaspython-damm32.Alternatively, you can clone the repository and use the module.UsageThe module contains a single class calledDamm32, this class implements the methods for the checksum.from damm32 import Damm32

d32 = Damm32()

digit = d32.calculate("HELLO")

d32.verify("HELLO" + digit)You can also pass an list of length 32 to the constructor for the class to specify your alphabet.from damm32 import Damm32

d32 = Damm32(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '2', '3', '4', '5', '6', '7'])How it worksThis is an implementation of theDamm Algorithmfor use in Base 32 systems.It will detect all occurrences of the two most frequently appearing types of transcription errors, namely altering one single digit, and transposing two adjacent digits (including the transposition of the trailing check digit and the preceding digit).Since prepending leading zeros does not affect the check digit, variable length codes should not be verified together since, e.g., 0, 01, and 001, etc. produce the same check digit. However, all checksum algorithms are vulnerable to this.The implementation uses a bitmask fromTable of Low-Weight Binary Irreducible Polynomialsto enable calculation of the check digit without constructing the quasigroup.
Package: dammcheck
DammcheckImplementation of theDamm Algorithmin various bases/alphabets.Usageimport dammcheck

# List available built-in alphabets
dammcheck.builtin_alphabets()

# Calculate the Damm check digit using a built-in alphabet
dammcheck.damm("ABC", alphabet="base32crockford")

# Or a custom alphabet
dammcheck.damm("TATA", custom_alphabet="ACGT")
Package: damnlang
damndamn(desperately agonizing math noobs) is a language to help you write knowledge assessment test generators.Language will help you in variables constraints declaration and generate unique pairs of data. Then, the generated pair of variables can be evaluated with the formula (via wolframalpha integration).Syntax:1 > x > 10
y in [1, 2, 3]
(a, b) in [(4, 5), (1, 10)]ExampleConstraints:10 > a > 20
(v1, v2) in [(1, 2), (4, 8)]Formula:a = (v2 - v1)/tMaking a test:fromdamnimportDefinitionSet,Calculatorimportasynciocalculator=Calculator(["DEMO"])asyncdefmain():def_set=DefinitionSet.from_expressions(["10 < a < 20","(v1, v2) in [(1, 2), (4, 8)]"])data=def_set.get_data()print("What is delta t if "+", ".join((f"{k}={v.get_common_representation()}"fork,vindata.items())))result=awaitcalculator.calculate(data,"a = (v2 - v1)/t")answer=float(input("your answer > "))ifanswer==result.v:print("great!!")returnprint(f"BAD student. the right answer is{result.v}")asyncio.run(main())
Package: damnpth
WarningUse this at your own risk! It’s a tool I wrote out of annoyance, not a
recommended solution.If you install things with setuptools, you might have found yourself cursing
thedamn pth filesit creates that reordersys.path.damnpthfinds the.pthfiles and removes the crazy path-reordering parts
from them. If it can, it then patches the setuptools code to stop adding this
path-reordering stuff.Usage:pip install damnpth
damnpthIn principle, this shouldn’t break your environment, but all the same,
it’s probably not something to run on your production systems.
Package: dAmnViper
dAmn Viperis a Python package created by photofroggy.This package provides an API for connecting to and interacting with the dAmn
chats. That is, deviantART’s messaging network chats.This branch of dAmn Viper is only known to work with Python 2.7 and up due to
use of the twisted library.Brief exampleCreating a client quickly for dAmn is quite easy using dAmn Viper. Below
is one of the simplest examples of a client that simply connects, and
tries to stay connected:from twisted.internet import reactor
from dAmnViper.base import dAmnClient

dAmn = dAmnClient()

dAmn.user.username = 'username'
dAmn.user.token = 'authtoken'
dAmn.autojoin = ['Botdom']

dAmn.on_connection_start = lambda connector: reactor.start()
dAmn.teardown = lambda: reactor.stop()

dAmn.start()That is all that is required! It is advised that you use the dAmnClient class
when making applications that connect to dAmn. Chat bots and full clients can
be made by extending the dAmnClient class to add functionality, as shown in the
examples provided.DependenciesA disadvantage of using twisted is the acquisition of dependencies. This means
that applications using dAmn Viper will depend on twisted as well as
dAmn Viper.It is more of an issue for end users, as they will not want to spend
time installing multiple dependencies. This problem can, however, be
eleminated by creating installers for applications using dAmn Viper.As such, this is somewhat a non-point, but it does mean an installer has to be
created to achieve easy setup for end users. Fortunately, installers are
something which users tend to be ok at using, so long as they aren’t too
complicated. Having an installer which downloads and installs multiple
dependencies may complicate things too much. We’ll see.FeedbackFeedback on this branch of dAmn Viper would be very much appreciated.
Please send any feedback to my deviantART account via notes, or submit
something on github, I dunno.Thanks for reading.DISCLAIMERDisclaimer:dAmn Viper is in no way affiliated with or endorsed by deviantART.com.
This is not an official service of deviantART.com. This is an independent
project created by Henry Rapley:
        <http://photofroggy.deviantart.com>

        THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
Package: damo-embedding
Damo-EmbeddingQuick Installpipinstalldamo-embeddingExampleDeepFMimporttorchimporttorch.nnasnnfromdamo_embeddingimportEmbeddingclassDeepFM(torch.nn.Module):def__init__(self,emb_size:int,fea_size:int,hid_dims=[256,128],num_classes=1,dropout=[0.2,0.2],**kwargs,):super(DeepFM,self).__init__()self.emb_size=emb_sizeself.fea_size=fea_sizeinitializer={"name":"truncate_normal","mean":float(kwargs.get("mean",0.0)),"stddev":float(kwargs.get("stddev",0.0001)),}optimizer={"name":"adam","gamma":float(kwargs.get("gamma",0.001)),"beta1":float(kwargs.get("beta1",0.9)),"beta2":float(kwargs.get("beta2",0.999)),"lambda":float(kwargs.get("lambda",0.0)),"epsilon":float(kwargs.get("epsilon",1e-8)),}self.w=Embedding(1,initializer=initializer,optimizer=optimizer,)self.v=Embedding(self.emb_size,initializer=initializer,optimizer=optimizer,)self.w0=torch.zeros(1,dtype=torch.float32,requires_grad=True)self.dims=[fea_size*emb_size]+hid_dimsself.layers=nn.ModuleList()foriinrange(1,len(self.dims)):self.layers.append(nn.Linear(self.dims[i-1],self.dims[i]))self.layers.append(nn.BatchNorm1d(self.dims[i]))self.layers.append(nn.BatchNorm1d(self.dims[i]))self.layers.append(nn.ReLU())self.layers.append(nn.Dropout(dropout[i-1]))self.layers.append(nn.Linear(self.dims[-1],num_classes))self.sigmoid=nn.Sigmoid()defforward(self,input:torch.Tensor)->torch.Tensor:"""forwardArgs:input (torch.Tensor): input tensorReturns:tensor.Tensor: deepfm forward values"""assertinput.shape[1]==self.fea_sizew=self.w.forward(input)v=self.v.forward(input)square_of_sum=torch.pow(torch.sum(v,dim=1),2)sum_of_square=torch.sum(v*v,dim=1)fm_out=(torch.sum((square_of_sum-sum_of_square)*0.5,dim=1,keepdim=True)+torch.sum(w,dim=1)+self.w0)dnn_out=torch.flatten(v,1)forlayerinself.layers:dnn_out=layer(dnn_out)out=fm_out+dnn_outout=self.sigmoid(out)returnoutSave Modelfromdamo_embeddingimportsave_modelmodel=DeepFM(8,39)save_model(model,"./",training=False)DocumentDoc Website
Package: damp11113
damp11113-libraryA Utils library and Easy to use.If feature in wiki haven't in library that is a preview feature. Please wait for new update.If the library has bugs. Please new issue.This is how to turn off check update and load all funcimportosos.environ["damp11113_load_all_module"]="NO"os.environ["damp11113_check_update"]="NO"Installinginstall from PyPipip install damp11113or install from githubpip install git+https://github.com/damp11113/damp11113-library.gitLicenseMIT LicenseCopyright (c) 2021-2022 damp11113Permission 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: dampr
Dampr - Pure Python Data ProcessingDampr is intended for use as single machine data processing: it's natively out of core, supports map and reduce side joins, associative reduce combiners, and provides a high level interface for constructing Dataflow DAGs.It's reasonably fast, easy to get started, and scales linearly by core.  It has no external dependencies, making it extremely lightweight and easy to install.  It has reasonable REPL support for data analysis, though there are better tools for the job for it.FeaturesSelf-Contained: No external dependencies and simple to installHigh-Level API: Easy computationOut-Of-Core: Scales up to 100s of GB to TBs of data.  No need to worry about Out of Memory errors!Reasonably Fast: Linearly scales to number of cores on the machinePowerful: Provides a number of advanced joins and other functions for complex workflowsSetuppip install damprorpython setup.py installAPIdocs/dampr/index.htmlExamplesLook at theexamplesdirectory for more complete examples.Similarly, the tests are intended to be fairly readable as well.  You can view them in thetestsdirectory.Example - WCimportsysfromdamprimportDamprdefmain(fname):wc=Dampr.text(fname)\.map(lambdav:len(v.split()))\.a_group_by(lambdax:1)\.sum()results=wc.run("word-count")fork,vinresults:print("Word Count:",v)results.delete()if__name__=='__main__':main(sys.argv[1])Why not Dask for data processing?Dask is great!  I'd highly recommend it for fast analytics and datasets which don't need complex joins!However.Dask is really intended for in-memory computation and more analytics processing via interfaces like DataFrames.  While it does have a reasonablebagimplementation for data processing, it's missing some important features such as joins across large datasets.  I have routinely run into OOM errors when processing datasets larger than memory when trying more complicated processes.In that sense, Dampr is attempting to bridge that gap of complex data processing on a single machine and heavy-weight systems, geared toward ease of use.Why not PySpark for data processing?PySpark is great!  I'd highly recommend it for extremely large datasets and cluster computation!However.PySpark requires large amounts of setup to really get going.  It's the antithesis of "light-weight" and really geared for large scale production deployments.  I personally don't like it for proof of concepts or one-offs; it requires just a bit too much tuning to get what you need.
Package: damsenviet.kle
kle-pyA Python library for interacting with KLE data structures and KLE JSON files.Originally ported fromkeyboard-layout-editorwith improvements to make the source code increasingly portable across
different language platforms.Table of ContentsDocumentationInstallationQuick StartContributingDocumentationTo view documentation, examples, visit thedocumentation site.InstallationTo install and use the library, use the installation method listed below.pip3installdamsenviet.kleQuick StartThis quick start demo demonstrates parsing a KLE JSON file.importosimportjsonfromdamsenviet.kleimportKeyboard# relative to this filejson_relative_file_path="./keyboard.json"json_absolute_file_path=os.path.abspath(os.path.join(os.path.dirname(__file__),json_relative_file_path,))keyboard=Keyboard.from_json(json.load(json_absolute_file_path))forkeyinkeyboard.keys:forlabelinkey.labels:passThe schemas for KLE JSON can be found atkle-json.ContributingThere are many ways to contribute to this project.Creating IssuesContributing CodeSponsoring DevelopersFor more information please see thecontributing guidelines.
Package: damson
Damson: A Concise Python Validation Library™Damson is a concise python library which aims to simplify the field validation.Hold your breath! The power of Damson:import damson
from damson.constraint import (Required, DataType, Between)


@damson.verify(a=[DataType(int,float)], b=[DataType(int), Between(1, 10)])
def add(a, b, ):
    return a + b


print(add(2, 3))
try:
    add(2, 3.0)
except Exception as e:
    print(e)

try:
    add(2, 10)
except Exception as e:
    print(e)


@damson.verify(**{'name': [Required(), DataType(str)], '0': [Required(), DataType(int)]})
def mix_args(name, *args):
    return '%s=%s' % (name, args)


mix_args('stone', 10, 20, 30)


@damson.verify(**{'0': [Required(), DataType(str)], '1': [Required(), DataType(int)]})
def create_kv(*args):
    return {args[0]: args[1]}


kv = create_kv('name', 10)
assert kv['name'] == 10Installation# Use pip
pip install damson

# Use ananconda
conda install damson
Package: damvitool
IntroductionAs stated above this project was inspired by Sandman by Jeff Knupp. At the same time damvitool improves on the original in a few important areas:Support for compound queries (both for REST API and UI)Extremely flexible and powerful UI (query builder wizard) thanks to SmartClient library by IsomorphicSupport for column summariesUnlimited resulting grid (sorted&filtered) export to CSV fileResourcesRun/edit damvitool in cloud IDEDocumentationBug trackerDemoCodeFrameworks and libraries usedSQLAlchemyv0.9.8+Morepathv0.9+AngularJSv1.2.1+Isomorphic SmartClientv9.1+ng_iscv0.2+VersioningSemantic Versioning 2.0.0TodoAbility to save queriesExtended authorisation support with fine grained control of access to queries/tablesEditing of recordsCharting engine for data visualizationQuick startInstallationUse pip to install damvitool:$ pip install damvitoolRun damvitool from command line:$ damvitoolWhen you run damvitool from command line without parameters it connects by default to the demo Chinook Database for SQLite.To connect to your legacy database run damvitool with your database URL as parameter, like so:$ damvitool --database sqlite:///damvitool/data/Chinook_Sqlite.sqlitewheresqlite:///damvitool/data/Chinook_Sqlite.sqliteis database URL in SQLAlchemy format (http://docs.sqlalchemy.org/en/rel_0_9/core/engines.html#database-urls).Admin panel accessDefault admin panel URL ishttp://localhost:8080Build new database requestClickWizardbutton.Login with the following credentials:user1/password1.Choose root entity for your data query. If tables needed for your query don’t have relations between them you can add another root entity to your query.Choose relevant entities fields.Set filter criteria.View results.ChangelogVersion 0.2.0Move RESTful API from /proxy/* to /api/*Improved documentationFix backend and frontend e2e tests
Package: dan
danIn what followspythonis an alias forpython3.5or any later
version (python3.6and so on),pypyis an alias forpypy3.5or any later
version (pypy3.6and so on).InstallationInstall the latestpip&setuptoolspackages versions:withCPythonpython-mpipinstall--upgradepipsetuptoolswithPyPypypy-mpipinstall--upgradepipsetuptoolsUserDownload and install the latest stable version fromPyPIrepository:withCPythonpython-mpipinstall--upgradedanwithPyPypypy-mpipinstall--upgradedanDeveloperDownload the latest version fromGitHubrepositorygitclonehttps://github.com/lycantropos/dan.gitcddanInstall:withCPythonpythonsetup.pyinstallwithPyPypypysetup.pyinstallDevelopmentBumping versionPreparationInstallbump2version.Pre-releaseChoose which version number category to bump followingsemver
specification.Test bumping versionbump2version--dry-run--verbose$CATEGORYwhere$CATEGORYis the target version number category name, possible
values arepatch/minor/major.Bump versionbump2version--verbose$CATEGORYThis will set version tomajor.minor.patch-alpha.ReleaseTest bumping versionbump2version--dry-run--verbosereleaseBump versionbump2version--verbosereleaseThis will set version tomajor.minor.patch.NotesTo avoid inconsistency between branches and pull requests,
bumping version should be merged intomasterbranch as separate pull
request.Running testsPlain:withCPythonpythonsetup.pytestwithPyPypypysetup.pytestInsideDockercontainer:withCPythondocker-compose--filedocker-compose.cpython.ymlupwithPyPydocker-compose--filedocker-compose.pypy.ymlupBashscript (e.g. can be used inGithooks):withCPython./run-tests.shor./run-tests.shcpythonwithPyPy./run-tests.shpypyPowerShellscript (e.g. can be used inGithooks):withCPython.\run-tests.ps1or.\run-tests.ps1cpythonwithPyPy.\run-tests.ps1pypy
Package: dana
DANA (Distributed (asynchronous) Numerical and Adaptive computing) is a python
computing framework based on numpy and scipy libraries whose primary goals
relate to computational neuroscience and artificial neural networks. However,
this framework can be used in several different areas like physic simulations,
cellular automata or image processing.The computational paradigm supporting the DANA framework is grounded on the
notion of a unit that is a set of arbitrary values that can vary along time
under the influence of other units and learning. Each unit can be linked to any
other unit (including itself) using a weighted link and a group is a structured
set of such homogeneous units.
Package: danatools
danatoolsA bunch of convenient tools to analyze data. It provides some handy functions on top of NumPy, SciPy and Matplotlib to perform some frequent tasks.Installpython-mpipinstalldanatoolsFeaturesCalculate the root of the mean of the squares of an arrayCompute a profile histogramSave a figure to multiple formats and print their namesCalculate the fit errors by propagating the parameter errorsCalculate the coordinates of a confidence ellipseEstimate the bias of a parameter estimatorEstimate the coverage of the confidence intervalsEstimate the p-value of a chi-squared testHow to useimportdanatoolsContributingIf you'd like to contribute, please fork the repository and use a feature
branch. Pull requests are warmly welcome.LinksRepository:https://github.com/ravignad/danatools/LicensingThe code in this project is licensed under MIT license.
Package: danbooru
DanbooruVersion:0.0.9License under:MIT LicenseDependenciesPython: >= 3.9.7beautifulsoup4cloudscraperExample of useDanboorufromdanbooruimportDanboorubooru=Danbooru()orDanbooru(host="safebooru")post_random=booru.post_random()print(post_random.file_url)results12Login examplefromdanbooruimportDanboorubooru=Danbooru("YOUR-USERNAME","YOUR-API_KEY")print(booru.searchs(tags="loli"))fromdanbooruimportDanboorubooru=Danbooru("YOUR-USERNAME","YOUR-PASSWORD")print(booru.searchs(tags="1girl 1boy"))fromdanbooruimportDanboorubooru=Danbooru()account=booru.create_account()USERNAME=account["username"]PASSWORD=account["password"]print(USERNAME,PASSWORD)print(booru.searchs(tags="long_hair"))
Package: danbooru-py
Danbooru단보루 크롤링 라이브러리Installation$ pip install danbooru-pyHow to use최신작품fromdanbooruimportclientclient=client.Danbooru()print(client.get_posts())인기작품fromdanbooruimportclientclient=client.Danbooru()print(client.get_hot())태그검색fromdanbooruimportclientclient=client.Danbooru()print(client.search_tag("genshin_impact"))이미지검색(ID)fromdanbooruimportclientclient=client.Danbooru()print(client.get_image("5681761"))
Package: danbot-hosting
dbh-bots-wrapper - DanBot Hosting Bots API WrapperOverviewThedbh-bots-wrapperis a Python module designed to simplify interactions with the DanBot Hosting API for adding Discord bots. It provides abotclass with methods for adding bots, retrieving bot information, and obtaining information of all bots.InstallationTo usedbh-bots-wrapper, you must install it first. You can do this using the following command:pipinstalldbh-bots-wrapperUsageImporting the Modulefromdbh-bots-wrapperimportbotInitializing the Botmy_bot=bot(discord_id="YOUR_DISCORD_BOT_ID",owner_id="YOUR_DISCORD_USER_ID",api_key="YOUR_DBH_API_KEY",name="BotName",avatar="AvatarURL",users=1000,guilds=10,shards=1)API InformationTo retrieve information about the bot:info=my_bot.info()print(info)Bot StatusTo retrieve the status of the API from the DanBot Hosting API:status=my_bot.status()print(status)Adding a BotTo add the bot to DanBot Hosting:add_result=my_bot.add_bot()print(add_result)Retrieving a BotTo retrieve information about a specific bot:bot_info=my_bot.get_bot()print(bot_info)Retrieving Bot ListTo retrieve a information of all bots:bots_list=my_bot.get_bots()print(bots_list)Examplefromdbhwrapperimportbot# Initialize the botmy_bot=bot(discord_id="123456789012345678",owner_id="987654321098765432",api_key="YOUR_DBH_API_KEY",name="MyAwesomeBot",avatar="https://example.com/avatar.png",users=1000,guilds=10,shards=1)# Retrieve bot informationinfo=my_bot.info()print("Bot Information:",info)# Retrieve bot statusstatus=my_bot.status()print("Bot Status:",status)# Add the bot to DanBot Hostingadd_result=my_bot.add_bot()print("Add Bot Result:",add_result)# Retrieve information about the added botbot_info=my_bot.get_bot()print("Bot Info:",bot_info)# Retrieve a list of bots associated with the userbots_list=my_bot.get_bots()print("User's Bots:",bots_list)Replace the placeholder values with your actual Discord bot and user information.DanBot[!NOTE]
Found a bug? Open aGitHub Issue. Found a vulnerability? Open a ticket in ourDiscord Server(Soon enough on a support website!)Made with ❤️ byDanBot Hosting.
Package: danbot-hosting-py
danbot-hosting-pyA Python API Wrapper for the DanBot Hosting APIInstallationFor Windows:pip install danbot-hosting-pyFor Linux/MacOSpip3 install danbot-hosting-pyQuick Examplefromdiscord.extimportcommandsfromdanbot_apiimportDanBotClientbot=commands.Bot(command_prefix='>',description="This is a Helper Bot")@bot.eventasyncdefon_ready():DanBotClient(bot,key="your_dbh_key",autopost=True)print('Bot is Online')@bot.eventasyncdefon_dbh_post(data):print("Posted Bot Data to DBH. Data Posted:")print(data)@bot.command()asyncdefping(ctx):awaitctx.send('pong!')bot.run("your bot's token here")FAQHow do you get an API key?Go to thediscord serverand use theDBH!ApiKeycommand in a respective text channel there!I'm having trouble with the moduleYou may post at theissues section in Githubor message me on Discord! (Makiyu^#4707)LinksDanBot Hosting Discord ServerDanBot Hosting WebsiteOriginal Wrapper (for discord.js)Changelog