Saravanan666/pypi-org huggingface dataset:
Package: dnastorage
dnastorageOverviewDocumentationSystem RequirementsInstallation GuideLicenseIssuesOverviewCore encoding, decoding, and file manipulation support for modeling DNA-based information storage systems.DocumentationAs documentation for the software becomes available, it will be placed under the docs folder.System RequirementsHardware RequirementsThe dnastorage module requires only a standard computer with enough RAM and compute power to support the needed operations. However, encoding or decoding large files may perform poorly, necessitating a more capable system.Software RequirementsOS RequirementsThis package is supported for macOS and Linux. The package has been tested on the following systems:macOS: Catalina 10.15.3Linux: Ubuntu 18.04.3Note that most OSes will support our software by using Docker.Python DependencesOur code has been tested on python versions 3.6 to 3.8. It has the following dependences:nose
sphinx
editdistance
statistics
biopython
matplotlib
numpyInstallation GuideIf you already have python 3 installed on your system, the simplest thing to do is download or checkout the code from GitHub.  Then, run the following commands:git clone https://github.com/dna-storage/dnastorage
cd dnastorageI recommend making a virtual environment first, but this is optional:python -m venv venv
source venv/bin/activateThen, use pip to install the requirements and packages:pip install -r requirements.txt
pip install .LicenseThis software is released under the LGPLv3 license.AcknowledgmentThis work was supported by the National Science Foundation (Grants CNS-1650148, CNS-1901324, ECCS 2027655) and a North Carolina State University Research and Innovation Seed Funding Award (Grant 1402-2018-2509).
Package: dna-tool
DNA AUDIT TOOLDNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.AWS Hardening Standards & Best Practiceshttps://codaglobal.atlassian.net/l/cp/UP1BxJTrAWS Tagging Standardshttps://codaglobal.atlassian.net/l/cp/oj0NgsdbAWS Naming Standardshttps://codaglobal.atlassian.net/l/cp/s99nYLECAudit tool setupThe project basically uses boto client to connect to the aws services.Update the session credentials in the terminal.Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.Audit Tool ExecutionCreating Virtual Environmentpython3 -m venv venvActivating the virtual environmentsource venv/bin/activateInstalling the dependencies in the virtual environmentpip install -r requirements.txtRun the following commandpython3 src/main.py --region {region_name}Test Cases ExecutionnosetestsFinal ReportThe overall report is provided as a HTML document.sample report
Package: dnd-auction-game
dnd-auction-gameIn this game, your goal is to score as many points as possible by participating in auctions.Every fourth round, each player gets 1000 gold to use in these auctions. At the beginning of each round, you'll see a set number of auctions, and you need to submit your bids in the form of a dictionary (you can choose not to bid as well).The server will reward the auctions to the highest bidders (there can be multiple winners if there's a tie), and the winning player will earn points based on a random dice roll.If you don't win an auction, you'll get back 60% of the gold you bid. The exact number of points you receive from an auction is determined by rolling dice, so it's a bit unpredictable.AuctionsThe auctions are presented in D&D form, for example: 3d6+5 means throw a 6 sided die 3 times and add 5, add it all up
and thats the number of points given by the auction. In other words the exact amount of points given for an auction is
stochastic.InstallationTo install, run:pip install dnd_auction_gameFlowA game is done in the following order:start the serverstart&connect all agentsrun the game runnerObserve the leadboard at '/' on the server (http://localhost:8000if running default & local)The logs of all the games is stored in /logs - Use these to train & improve your agent.ServerTo run the server, use: 'uvicorn dnd_auction_game.server:app' in the directory root directory.Ctrl+C to stop it cleanly.Agents (players)See the folder example_agents (on github) for examples on how to create a agent.
agent_print_info.py
agent_linear_prediction.py
agent_linear_prediction.py
agent_tiny_bid.pyNOTE: If playing on a non-local server the agent must set the host in the file.In general you must implement a make_bid() function that takes the following parameters (see agent_print_info.py for how to parse this info):@agent_id:str - a string that is the agent id of the current agent.@states:dict - a dict of all agents, key: agent_id, value is a dict:{"gold": agents_gold, "points": agents points}@auctions:dict - a dict of the auctions in the current round, key: auction_id, value is a dict:{"die": size of the die: 2,3,4,6,8,10,12 or 20,"num": the number of dices to be thrown,
"bonus": a flat sum to be added.}This can be read as D&D style throw, example: 3d6+7would be: {"die": 6, "num": 3, "bonus": 7}.@prev_auctions:dict - a dict of the previous round. similar to
@auctions, but also contains the "reward" field that tells how many
the winner of the auction gained. In addition it contain a "bids" key
that gives a list of the bids for this particular auction, sorted so that the winning bid is always the first in the list.Play the GameRun 'python -m dnd_auction_game.play'
This will run the game for 12 rounds.Use python 'python -m dnd_auction_game.play XX'
to play for XX rounds. Example: 'python -m dnd_auction_game.play 42' will play the game for 42 rounds.Remember: connect all agents BEFORE running play_game.py, the server does not need to be restarted.The logs (complete history)The logs (complate history) will be stored in ./logs use it to
create clever agents.
Package: dnd-dice
DnD DiceShell simulating Dungeons and Dragons dice rollsFree software: MIT licenseDocumentation:https://dnd-dice.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-10-20)First release on PyPI.
Package: dndlprof
# DND: Dnd’s Not Dlprof![![PyPI version](https://badge.fury.io/py/dndlprof.svg)](https://badge.fury.io/py/dndlprof)DND is a simple tool for profiling and tracing PyTorch programs on NVIDIA GPUs.
If uses NVIDIA’s NSIGHT Systems and NSIGHT Compute along-side PyTorch 2.0’s
Dynamo graph capturing system to provide framework operator and CUDA kernel
level breakdowns of a Deep Learning application. Check out the
[Sample App](./dnd/sample_app.py) for an example of an instrumented application.## Install`bash $ pip install dndlprof `## Quick Start`bash $ dnd--python-mdnd.sample_app `
Package: dnds
Calculate dN/dS ratio precisely (Ka/Ks) using a codon-by-codon counting
method. Also calculates the pN/pS ratio precisely (previously referred to as
dN/dS).Usage>>>sequence_1="ATGCTTTTGAAATCG">>>sequence_2="ATGCGTTCGAAGTCG">>>pnps(sequence_1,sequence2)Fraction(38,71)>>>round(float(pnps(sequence_1,sequence2)),3)0.535>>>round(dnds(sequence_1,sequence_2),3)0.467
Package: dnest4
These are experimental Python bindings to the DNest4 sampler allowing the user
to implement their models in Python. See the example(s) inexamplesfor
some demos for how you might do this.BuildingDNest4 relies on recent C++11 features so building with python can be fun. On
a Mac, you might need to do something like:MACOSX_DEPLOYMENT_TARGET=10.9pythonsetup.pyinstallIf you only want the postprocessing scripts and don’t want to worry about
building the bindings, you can run:pythonsetup.pyinstall--without-cextand then use it as follows:import dnest

# NOTE: the path will default to the current working directory if you
#       don't give another path.
dnest.postprocess("/path/to/where/you/ran/your/model")AuthorThese bindings were written byDan Foreman-Mackeyand they are licensed as part of DNest4 with the MIT license.
Package: dnevniklib
Библиотека на базе API ЭЖД города МосквыПочему именно ЭЖД?У дневника города Москвы нет открытого API, они его закрыли в 2017 году из-за наплыва сторонних приложений.
Но, как же наш дневник работает на телефоне, компьютере и доске? Правильно, закрытый от пользователей API, который все также отправляет некие запросы на некий сервер и получает некие ответы.Цель проектаДостать все URLы, к которым обращается мобильное приложение дневника города Москвы и написать на базе собранных URLов документацию к нему, а на базе этой документации реализовать Python и GoLang библиотеку.При помощи чего я буду доставать API МЭШа?Все довольно не очень просто. Для перехвата всех запросов я буду использовать очень мощный инструмент Burp Suite, в котором есть функционал сниффера, который работает как Proxy-сервер. Для использования мобильного приложения МЭШ я буду использовать 2 инструмента:Эмулятор Android, который входит в среду разработки Android Studio.Мой старенький телефон, который будет находится в одной локальной сети с ноутбуком.По терминамBurp Suite - утилита для пентеста веб приложений на проникновение и эксплуатацию уязвимостей, таких какMiTM(Man in the Middle - уязвимость при которой злоумышленник находится между клиентом и сервером, соответственно, может менять исходящий и входящий трафик),SQL INJECTION(уязвимость, которая в основном работает на сайтах, написанных на PHP, которая позволяет внедрять SQL код, такой какSELECT,DROP,DELETEили просто создавать "полезную" нагрузку на приложение) и подобных.UPD. Переходим на другой сниффер, из-за громосткости Burp'аИз примеров, можем взять MiTMProxy или Charles.
Пока пробую чашку, если ны выйдет, перейду на MitmProxyUDP. Вышла 2 ВЕРСИЯ 🫧🫧🫧Я наконец-то взялся за нее. Рад что смог реализовать идею!
Новые URL, меньше мусора и свои типы у каждого объекта!!!
Ура!
Package: dnevnik-mos-ru
dnevnik-mos-ruPython библиотека, для удобного доступа к ЭЖД.Пример кода# Вывод сегодняшних уроков# Импорт библиотекиimportdnevnik# Данные для авторизацииLOGIN=""PASSWORD=""# Данные для SeleniumDRIVER_PATH=""# Авторизуемсяme=dnevnik.Client(login=LOGIN,password=PASSWORD,selenium_executable_path=DRIVER_PATH)# Получаем список уроков на сегодняlessons=me.get_lessons()# Выводим уроки по порядкуforlessoninlessons:print("{0.lesson_number}.{0.subject_name}".format(lesson))УстановкаУстановка SeleniumПока реализован только вариант с ChromeDriver.Зайти в Google Chrome. Три точки сверху -> Справка -> О браузуре Google ChromeЗапомнить версию.Отсюдаскачать chromedriver для своей версии Chrome и своей OSСкопируйте исполняемый файл в какую-нибудь папку и запомните путь.В конструкторе Client параметр флаг use_selenium и внесите путь в selenium_executable_pathУстановка библиотекиВ Linux/MacOSpip3 install dnevnik-mos-ruВ Windowspip install dnevnik-mos-ruДокументация APIДокументацию APIв формате .RAML можно найти в в папке docs. Можно сгенерировать файл .HTML с помощью
утилитыraml2htmlДокументация библиотекиДокументацию библиотеки можно будет найтиздесь.JoinDiscord Server
Package: dnevnikru
dnevnikruModule for working with the site dnevnik.ru with pythonDnevnik object accepts login and password from the dnevnik.ru accountMethods: homework, marks, searchpeople, birthdays, weekRead the full functionality of the module here:Wiki(Doesn't work in regions where you can enter the Diary only through GosUslugi !)InstallationWindows:Run the command in the terminal:pip install dnevnikruExamples of usefromdnevnikruimportDnevnikdairy=Dnevnik(login='Your login',password='Your password')homework=dairy.homework(studyyear=2020,datefrom='01.12.2020',dateto='30.12.2020')marks=dairy.marks(index=0,period=1)class_11b=dairy.searchpeople(grade='11B')birthdays=dairy.birthdays(day=9,month=5)schedule=dairy.week(info="schedule",weeks=-1)For more examples of uses and parameters in methods, seeWiki.RequiresThe module requiresrequests,lxml,bs4librariesReleases1.0ContactAleksandr вЂ“ tg:@paracosm17вЂ“ email:paracosm17@aol.comLICENSEDistributed under the Apache License 2.0 license. SeeLICENSEfor more information.
Package: dnf
pypi-dnfDummy package reserved for any future possibility of having DNF Python API available through PyPI. Installing this package will not be completed as it is intended to be empty therefore installation failure will be thrown.A number of DNF's dependencies (notable libdnf and libsolv) are currently
only available as part of Linux distro packages.Right now, this package just reserves thednfname on PyPI to avoid the
potential for a name conflict with thepython3-dnfpackages on Linux distros
using DNF for RPM dependency management.However, I eventually hope to replace it with a CMake-dependent PEP 517 backend
that runs the full libdnf build process, and then emits a statically linked
completely self-contained extension module as part of a wheel file (together
with the pure Python components of the DNF Python API).
Package: dnf-converter
An example package. Generated with cookiecutter-pylibrary.Free software: MIT licenseInstallationpip install dnf-converterYou can also install the in-development version with:pip install git+ssh://git@https://github.com/christe90/dnf_converter.git/christe90/python-dnf_converter.git@masterDocumentationhttps://python-dnf_converter.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2021-02-23)First release on PyPI.0.0.1 (2021-02-23)Github verbunden0.0.2 (2021-02-23)readthedocs verbunden
Package: dnf-humbug
dnf-humbugdnf-humbugtool lists packages that
are marked as "user installed" and gives a list of such, allowing a user to
adjust this."user installed" are packages the system thinks you have directly installed,
and are thus always kept and not removed automatically.So, as my list of "user installed" packages included a ton of old dev packages,
lots of libraries I could not remember installing, I wanted something to make a
list out of those and see what was where.One such way was this:comm -2 -3    <(dnf repoquery --userinstalled --qf '%{name}-%{version}-%{release}.%{arch}'|sort)  <(dnf leaves| sed 's/^[- ] //'|sort)Clearly not optimal, so I wrote this utility.To install in a venvpython3 -m venv --system-site-packages humbug-venv
source humbug-venv/bin/activate
pip install dnf-humbugLaunchingEither use:python3 -m dnf_humbugOr:dnf-humbugBugs, Features, Issues?Feel free to hack on it, I'll probably do too. I don't have a plan to port it
to dnf5 yet, but who knows, dnf5 might also annoy me and I'll have to do it
there as well.LicenseI snagged some code from the "dnf leaves" code when I started this, so we follow suit with GPLv2 license.DependenciesThis is not a DNF plugin, but a separate tool that uses python3-dnf on Fedora
(or other systems).So make sure to install python3-dnf  and python3-textual,
or you can just have python3-dnf installed and use pypi to install textual.
Package: dngsmgmt
dngsmgmtdngsmgmt is a Django app to manage NGS projectsQuick startAdd “dngsmgmt” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'dngsmgmt',
]Include the dgenome URLconf in your project urls.py like this:url(r'^dngsmgmt/', include((dngsmgmt.urls, 'dngsmgmt'), namespace='dngsmgmt')),Runpython manage.py makemigrations dngsmgmtto create the dngsmgmt tables.Runpython manage.py migrateto create the dngsmgmt models.
Package: dng-to-cube
DNG to Cube Converter
This is a Python package that provides a command-line interface for converting DNG files to cube files. The package uses the rawpy and numpy libraries to read and manipulate the image data.Installation
To install the package, use pip:pip install dng-to-cube
Usage
To convert a DNG file to a cube file, use the dng-to-cube command:dng-to-cube (input.dng output.cube)By default, the package generates a 33x33x33 LUT cube file. You can customize the size of the cube using the –size option:dng-to-cube (input.dng output.cube –size 17)This generates a 17x17x17 LUT cube file.License
This package is distributed under the MIT License. See the LICENSE file for details.Feel free to modify and distribute this package as you see fit. If you find any issues or have suggestions for improvement, please submit an issue or pull request on GitHub.
Package: dni
dniSimple DNI class and operations to make your life easier with Spanish Ids.Release: 0.2.0>>>importdni>>>dni.is_valid("27592354J")True>>># Not a DNI>>>dni.is_valid("ABC123XYZ")False>>>dni.compute_check_letter("27592354")"J">>>some_dni=dni.DNI("27592354J")>>>some_dni.number"27592354">>>some_dni.check_letter"J">>>some_dni.format(case="upper",separator="-")"27592354-J">>>dni.DNI("27592354J")==dni.DNI("27592354-j")True>>>dni.text_contains_dni("El señor Forges, con DNI 12345678Z, sactamente.")True>>>dni.extract_dnis_from_text("Mi DNI no es 12543456-S, es el 65412354-D.")[DNI('12543456S'),DNI('65412354D')]>>>dni.DNI.random()DNI("02448431N")Install$pipinstalldni`FeaturesApplies for Spanish DNI IDs.Check validity of DNIs.Get check letter for a DNI number.Avoid primitive obsession with the DNI class. Get the components of the DNI,
format it in different ways, check for equality.Find and extract multiple DNIs from text.Get detailed exceptions when a string has issues.Generate random, valid DNIs.DocsFor an extensive compilation of usage examples, check thequickstartin the docs.You can also check the fullAPI reference.MiscIf you spot a bug or want to request a feature, feel free to open an issue in
this repository.
Package: dnif
This is the official Python DNIF client library. This allows you to
directly write log statements into your application code and integrate
it with DNIF Adapters.InstallationYou can download the library using pip, as:$pipinstalldnifGetting StartedThe DNIF library currently supports two ingestion mechanisms: 1. TCP
protocol uploads using the HTTP(S) endpoint 2. Uploads using the UDP
protocolTypical usages for both of these are given below.TCPfromdnif.consumerimportAsyncHttpConsumerfromdnif.loggerimportDnifLoggerdlog=DnifLogger(AsyncHttpConsumer('http://TARGET_IP:PORT/json/receive'))dlog.log({'key':'value'})The AsyncHttpConsumer is thread-safe, so you can use the same instance
across threads (recommended).UDPfromdnif.consumerimportAsyncUDPConsumerfromdnif.loggerimportDnifLoggerdlog=DnifLogger(AsyncUDPConsumer('UDP_IP',UDP_PORT))dlog.log('Hello World')The AsyncUDPConsumer is thread-safe, so you can use the same instance
across threads (recommended).
Package: dnif-logger
This is the official Python DNIF client library. This allows you to
directly write log statements into your application code and integrate
it with DNIF Adapters.InstallationYou can download the library using pip, as:$pipinstalldnif-loggerGetting StartedThe DNIF library currently supports two ingestion mechanisms: 1. TCP
protocol uploads using the HTTP(S) endpoint 2. Uploads using the UDP
protocolTypical usages for both of these are given below.TCPfromdnif.consumerimportAsyncHttpConsumerfromdnif.loggerimportDnifLoggerdlog=DnifLogger(AsyncHttpConsumer('http://TARGET_IP:PORT/json/receive'))dlog.log({'key':'value'})The AsyncHttpConsumer is thread-safe, so you can use the same instance
across threads (recommended).UDPfromdnif.consumerimportAsyncUDPConsumerfromdnif.loggerimportDnifLoggerdlog=DnifLogger(AsyncUDPConsumer('UDP_IP',UDP_PORT))dlog.log('Hello World')The AsyncUDPConsumer is thread-safe, so you can use the same instance
across threads (recommended).
Package: dnikit
Data and Network Introspection Kit (DNIKit)Data and Network Introspection Kit (DNIKit) uses the activations at hidden layers
of a neural network to understand the network’s behavior and how it is learning,
with respect to a given dataset. DNIKit algorithms can be applied to many problems,
including network compression, failure and bias analysis, dataset QA, and more.To learn more, please see thedocs.Installationpip install dnikitSee available installation extensions also in thedocs.
Package: dnikit_tensorflow
Data and Network Introspection Kit (DNIKit) - TensorFlowThis package contains the TensorFlow extensions ofDNIKit.To learn more about DNIKit, please see thedocs.InstallationTo install TensorFlow 2 (CPU or GPU), use:pip install "dnikit[tensorflow]"To install TensorFlow 1 CPU, use:pip install "dnikit[tensorflow1]"For TensorFlow 1 GPU:pip install  "dnikit[tensorflow1-gpu]"
Package: dni-pypi-test-1
dniSimple DNI class and operations to make your life easier with Spanish Ids.Release: 0.1.0>>>importdni>>>dni.is_valid("27592354J")True>>># Not a DNI>>>dni.is_valid("ABC123XYZ")False>>>dni.compute_check_letter("27592354")"J">>>some_dni=dni.DNI("27592354J")>>>some_dni.number"27592354">>>some_dni.check_letter"J">>>some_dni.format(case="upper",separator="-")"27592354-J">>>dni.DNI("27592354J")==dni.DNI("27592354-j")True>>>dni.text_contains_dni("El señor Forges, con DNI 12345678Z, sactamente.")True>>>dni.extract_dnis_from_text("Mi DNI no es 12543456-S, es el 65412354-D.")[DNI('12543456S'),DNI('65412354D')]Install$pipinstalldni`FeaturesApplies for Spanish DNI IDs.Check validity of DNIs.Get check letter for a DNI number.Avoid primitive obsession with the DNI class. Get the components of the DNI,
format it in different ways, check for equality.Find and extract multiple DNIs from text.Get detailed exceptions when a string has issues.DocsFor an extensive compilation of usage examples, check thequickstartin the docs.You can also check the fullAPI reference.MiscIf you spot a bug or want to request a feature, feel free to open an issue in
this repository.
Package: dnmf-python
dnmf-pythonUnofficial Python implementation of the Discrete Non-negative Matrix Factorization (DNMF) overlapping community
detection algorithmPaperYe, Fanghua, Chuan Chen, Zibin Zheng, Rong-Hua Li, and Jeffrey Xu Yu. 2019. “Discrete Overlapping Community Detection
with Pseudo Supervision.” In 2019 IEEE International Conference on Data Mining (ICDM),
708–17.https://doi.org/10.1109/ICDM.2019.00081.Official implementation in MATLAB athttps://github.com/smartyfh/DNMF.Requirementspython>=3.7.1torch>=1.9.1Quick startTo install the package run one of the two commands:python -m pip install dnmf-python(installation from PyPI)python setup.py install(compile from source, if cloned the repository)To run the algorithm, load the graph adjacency matrix into atorch.FloatTensor(for ex.A), then call:from dnmf.DNMF import DNMF
dnmf = DNMF()
F = dnmf(A)To run a quick test of the algorithm with an example graph, runpython test.pyfrom inside thesrc/dnmf/directoryConfigThe DNMF module supports the following hyperparameters as arguments:alpha: tradeoff parameter for the U-subproblembeta: tradeoff parameter for the F-subproblemgamma: regularization parameterk: desired number of overlapping communitiesnum_outer_iter: number of iterations for the outer loop (SDP iterations)num_inner_iter: number of iterations for the inner loops (U and F subproblems)How to citeIf you useddnmf-pythonfor work on your paper please use the following BibTeX entry to cite this software:@misc{janchevski_dnmf_2021,
  title        = "dnmf-python",
  author       = "{Janchevski, Andrej}",
  howpublished = "\url{https://github.com/Bani57/dnmf-python}",
  year         = 2021,
  note         = "Unofficial Python implementation of the Discrete Non-negative Matrix Factorization (DNMF) overlapping community detection algorithm"
}AuthorAndrej Janchevskiandrej.janchevski@epfl.chEPFL STI IEM LIONSLausanne, Switzerland
Package: dnn
0.6 (2020.6.29)add dnn.setup_gpus (memory_limit = ‘growth’, gpu_devices = [])add dnn.layers for proxying tf.keras.layersadd dnn.callbacks for tf.kerasminor fixes fortfserverupdate for tensorflow 2.x0.4 (2020.6.24)integrate tfserver into dnn.tfserverdata processing utils were moved to rs4.mldp0.3:remove trainale ()add set_learning_rate ()add argument to set_train_dir () for saving chcekpoitmake compatible with tf 1.12.00.2add tensorflow lite conversion and interpreting0.1: project initialized