Saravanan666/pypi-org huggingface dataset:
Package: ds4ml
The recent enforcement of data privacy protection regulations, such as GDPR,
has made data sharing more difficult. This tool intends to facilitate data
sharing from a customer by synthesizing a dataset based on the original dataset
for later machine learning.There are two parts to this tool:Data synthesizer
Synthesize a dataset based on the original dataset. It accepts CSV data as
input, and output a synthesized dataset based on Differential Privacy. The
algorithm in the data synthesizer reference to the paper (http://dimacs.rutgers.edu/~graham/pubs/papers/privbayes-tods.pdf).Data utility evaluation
Evaluate the data utility for the synthesized dataset. The original dataset
and the synthesized dataset as the input, one utility evaluation report will
be generated with several indicators.
Package: ds4n6-chrysalis
DS4N6 stands for Data Science Forensics.We also refer to this project as DSDFIR, AI4N6 or AIDFIR, since Data Science (DS) includes Artificial Intelligence (AI), and the project goes beyond the strictly Forensics, covering the whole Digital Forensics & Incident Response (DFIR) discipline (and sometimes even beyond). But hey, we had to give the project a catchy name!The Mission of the DS4N6 project is simple:Bringing Data Science & Artificial Intelligence
to the fingertips of the average Forensicator,
and promote advances in the fieldThe first (modest) alpha version of our ds4n6 python library, together with some easy-to-use python scripts, was originally made public after the presentation at the SANS DFIR Summit US, July 16-17.For detailed information about the Project, the Library, its Functions, its Usage, etc., visit the project page:http://www.ds4n6.io/tools/chrysalis.htmlGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.https://github.com/ds4n6/ds4n6_chrysalis.gitPrerequisitesThe DS4N6 library works on the 3.x versions of the Python programming language. The module has external dependencies related to datascience and extraction of forensic evidence.Install requirements:- python-evtx
- Evtx
- ipyaggrid
- IPython
- ipywidgets
- keras
- matplotlib
- nbformat
- numpy
- pandas
- pyparsing
- qgrid
- ruamel.yaml
- sklearn
- tensorflow
- tqdm
- traitlets
- xmltodictInstallationThe installation can be easily done through pip.pip installationpipinstallpython-evtxEvtxipyaggridIPythonipywidgetskerasmatplotlibnbformatnumpypandaspyparsingqgridruamel.yamlsklearntensorflowtqdmtraitletsxmltodictds4n6-libFinally, import in your python3 program or Jupyter Notebook as "ds".importds4n6_chrysalisasdsContributingIf you think you can provide value to the Community, collaborating with Research, Blog Posts, Cheatsheets, Code, etc., contact us!Please readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.download from githubAll you will need to do is to clone the library, install the test, create a virtual enviroment to use it and active it.gitclonehttps://github.com/ds4n6/ds4n6_chrysalisvirtualenv-ppython3.7.testsource.test/bin/activatepipinstall-rrequirements.txtAuthorsJess Garcia-Initial work-http://ds4n6.io/community/jess_garcia.htmlSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the GNU GPL v3.0 License - see theLICENSEfile for details
Package: ds4n6-lib
DS4N6 stands for Data Science Forensics.We also refer to this project as DSDFIR, AI4N6 or AIDFIR, since Data Science (DS) includes Artificial Intelligence (AI), and the project goes beyond the strictly Forensics, covering the whole Digital Forensics & Incident Response (DFIR) discipline (and sometimes even beyond). But hey, we had to give the project a catchy name!The Mission of the DS4N6 project is simple:Bringing Data Science & Artificial Intelligence
to the fingertips of the average Forensicator,
and promote advances in the fieldThe first (modest) alpha version of our ds4n6 python library, together with some easy-to-use python scripts, was originally made public after the presentation at the SANS DFIR Summit US, July 16-17.For detailed information about the Project, the Library, its Functions, its Usage, etc., visit the project page:http://www.ds4n6.io/tools/ds4n6.py.htmlGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.https://github.com/ds4n6/ds4n6_lib.gitPrerequisitesThe DS4N6 library works on the 3.x versions of the Python programming language. The module has external dependencies related to datascience and extraction of forensic evidence.Install requirements:- python-evtx
- Evtx
- ipyaggrid
- IPython
- ipywidgets
- keras
- matplotlib
- nbformat
- numpy
- pandas
- pyparsing
- qgrid
- ruamel.yaml
- sklearn
- tensorflow
- tqdm
- traitlets
- xmltodict
- networkx
- gensimInstallationThe installation can be easily done through pip.pip installationpipinstallpython-evtxEvtxipyaggridIPythonipywidgetskerasmatplotlibnbformatnumpypandaspyparsingqgridruamel.yamlsklearntensorflowtqdmtraitletsxmltodictds4n6-libFinally, import in your python3 program or Jupyter Notebook as "ds".importds4n6_libasdsContributingIf you think you can provide value to the Community, collaborating with Research, Blog Posts, Cheatsheets, Code, etc., contact us!Please readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.download from githubAll you will need to do is to clone the library, install the test, create a virtual enviroment to use it and active it.gitclonehttps://github.com/ds4n6/ds4n6_libvirtualenv-ppython3.7.testsource.test/bin/activatepipinstall-rrequirements.txtAuthorsJess Garcia-Initial work-http://ds4n6.io/community/jess_garcia.htmlSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the GNU GPL v3.0 License - see theLICENSEfile for details
Package: ds5ctl
ds5ctlA GUI tool for configuring a DualSense 5 controller (currently only supports direct USB connection)Installationpipinstallds5ctlUsageTo run the GUI:ds5ctl# Orpython-mds5ctlTo send commands to the controller, press the Send button at the bottom. This will emit all currently-configured controls. Though all sliders and controls are shown (such as Haptics Left/Right or Lightbar Colour), emitting them will have no effect on the controller unless the appropriate Control flags are also checked.To send haptics to the device, ensureDS5_MODEOperating Mode is checked, and modify the Haptics sliders. The controller appears to spin the motors for 5 seconds before desisting automatically.To change the adaptive trigger effects, ensureDS5_MODEOperating Mode is checked, as well asTRIGGER_EFFECTS_RIGHTand/orTRIGGER_EFFECTS_LEFT(depending on which sides you wish to modify), then using the trigger effects panes to modify the effect.
Package: ds78
ds78Здесь наверное должно быть какое-то краткое описание программыUsage:$ds78[OPTIONS]COMMAND[ARGS]...Options:--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.Commands:print-something: Напечатает заранее захардкоженый текстsay-hello: Типа говорит привет и приглашает попить кофеvd: Показывает суммарную длительность видео в...ds78 print-somethingНапечатает заранее захардкоженый текстUsage:$ds78print-something[OPTIONS]Options:--help: Show this message and exit.ds78 say-helloТипа говорит привет и приглашает попить кофеUsage:$ds78say-hello[OPTIONS]NAMEArguments:NAME: [required]Options:--city TEXT--help: Show this message and exit.ds78 vdПоказывает суммарную длительность видео в текущей папке и её подпапкахUsage:$ds78vd[OPTIONS]Options:--help: Show this message and exit.
Package: ds9norm
ds9norm=======This file implements a matplotlib Normalize object which mimics thefunctionality of image scaling functions in`ds9 <http://ds9.si.edu/site/Home.html>`__. It provides 5 ways ofconfiguring image scaling:-  Clipping the low and high intensities at specified percentiles(``clip_lo``, ``clip_hi``)-  Specifying an intensity transfer function for ramping from black towhite (``stretch``)-  Setting the mid-point of this transfer function (``bias``, akin todragging the right mouse button left/right in ds9)-  Setting how sharp the transition from black-white is (``contrast``,akin to dragging the right mouse button up/down in ds9)Examples--------See the `examplenotebook <http://nbviewer.ipython.org/github/glue-viz/ds9norm/blob/master/Examples.ipynb>`__.. code:: pythondata = fits.getdata('M51.fits')norm = DS9Normalize(bias=0.2)figure, axs = plt.subplots(ncols=3, nrows=3, squeeze=False, tight_layout=True)for ax, bias in zip(axs[2], [.2, .5, .8]):ax.imshow(data, norm=DS9Normalize(bias=bias))ax.set_title('Bias = %0.1f' % bias)for ax, contrast in zip(axs[1], [0.5, 1, 2]):ax.imshow(data, norm=DS9Normalize(contrast=contrast))ax.set_title('Contrast = %0.1f' % contrast)for ax, stretch in zip(axs[0], ['linear', 'sqrt', 'arcsinh']):ax.imshow(data, norm=DS9Normalize(stretch=stretch))ax.set_title(stretch).. figure:: gallery.png:alt: ds9norm demods9norm demoBuild Status------------[|Build Status|\ ](https://travis-ci.org/glue-viz/ds9norm?branch=master) [|CoverageStatus|\ ] (https://coveralls.io/r/glue-viz/ds9norm).. |Build Status| image:: https://travis-ci.org/glue-viz/ds9norm.png.. |Coverage Status| image:: https://coveralls.io/repos/glue-viz/ds9norm/badge.png
Package: dsa-buddy
DSA buddy client @Copyright 2021-22 [Botoservices]Steps:Please Check your Python version. Python3 is required for this packageInstall it using below command:pip install dsa-buddyYou can submit your code using below decorator.  [Check below Sample code]When you will run your code, it will ask you to enter dsa-buddy credentials.  [Note: You must be registered with dsa-buddy Telegram bot]If you have any query regrading registeration or want to share any feedback, Please contact us via "botoservices@gmail.com"Sample Code:#################### Sample code #####################

import dsa_buddy

@dsa_buddy.submit()
def factorial(num):
	ans = 1
    while num > 0:
        ans = ans * num
        num = num - 1

    return ans


factorial(5)


######################################################
Package: dsa-downloader
DSA DownloaderDSA Downloader is a Disney Sorcerer's Arena resources downloader,
which allow developers to download localization files and assets.How to useShow supported commands$ dsa-downloader --help
Usage: dsa-downloader [OPTIONS] COMMAND [ARGS]...

Options:
  --debug / --no-debug
  --help                Show this message and exit.

Commands:
  download-assets   Download assets.
  download-langs    Download localization files.
  download-gamedata Download gamedata.
  extract-config    Extract bootstrap config from APK file.Install the library using pip$ python -m pip install dsa-downloaderExtract bootstrap config fromDisney Sorcerer's Arena APKfile$ dsa-downloader extract-config com.glu.disneygame.apk
bootstrap_config file is written to out/bootstrap_config.json.Download gamedata$ dsa-downloader download-gamedata
gamedata-static.bin is written to out/gamedata/9c49b3b7-1479-4c28-8894-d1da716e36ce_gamedata.bin

# Convert gamedata binary to json
$ curl -O https://github.com/phstudy/dsa_proto_dumper/blob/main/gamedata/gamedata.fbs
$ flatc --json --defaults-json --strict-json --raw-binary gamedata.fbs -- out/gamedata/9c49b3b7-1479-4c28-8894-d1da716e36ce_gamedata.binDownload localization files$ dsa-downloader download-langs --langs ChineseTraditional --langs English
ChineseTraditional localization file is written to out/langs/Loc_ChineseTraditional.txt.
ChineseTraditional localization file is written to out/langs/Loc_ChineseTraditional.json.
English localization file is written to out/langs/Loc_English.txt.
English localization file is written to out/langs/Loc_English.json.Download assets$ dsa-downloader download-assets
  2%|██▋                                         | 81/3896 [00:03<03:00, 21.08it/s]DockerUse docker container to extract bootstrap config fromAPKfile$ docker run --rm -v "$PWD":/dsa study/dsa-downloader dsa-downloader \
        extract-config com.glu.disneygame.apk
bootstrap_config file is written to out/bootstrap_config.json.
Package: dsagraph
This is a graph library which allows user to make a graph, plot the graph and also make a adjacency list and matrix of the graph.A graph is a non-linear data structure consisting of vertices (V) and edges (E). The most commonly used representations of a graph are adjacency matrix (a 2D array of size V x V where V is the number of vertices in a graph) and adjacency list (an array of lists represents the list of vertices adjacent to each vertex).An adjacency list is a collection of unordered lists used to represent a finite graph.An adjacency matrix is a way of representing a graph as a matrix of booleans (0's and 1's). A finite graph can be represented in the form of a square matrix on a computer, where the boolean value of the matrix indicates if there is a direct path between two vertices.This library is made for the college project and is not made for any industrial project. If this library is related to any of the hosted library it is completely coincidental.
Package: dsakit
DSA Kit (Data Structures and Algorithms Kit)OverviewDSA Kit is a comprehensive collection of Python modules that implement various data structures and algorithms from scratch. The primary goal of this project is to provide efficient and well-optimized implementations of fundamental data structures and algorithms.FeaturesDeveloped all data structures as Python modules using best-known algorithms.Ensured efficient Time and Space complexities for all implemented algorithms.Comprehensive modules for Linked Lists, Binary Trees, and Tries with essential functions not available in Python's standard library.Implemented default data structures like HashSet, HashMap, Circular Deque, and Min/Max Heap.Index of ModulesClick on the following link for their respective documentationsLinked ListBinary TreesTriesUnionFindInstallationYou can install DSA Kit using pip:pipinstalldsakitORpip3installdsakitUsageAfter installation, you can use any of the 4 modules of this package:LinkedListBinaryTreeTrieUnionFind,
using the following import on pythonfromLinkedListimport*fromBinaryTreeimport*fromTrieimport*fromUnionFindimport*LicenseDSA Kit is open-source software released under theMIT License. Feel free to use it in your projects.
Package: dsalgo
dsalgoA package for Datastructures and Algorithms written in Python.Installationpython3-mpipinstall-Udsalgo# to use numba functionality for python <3.11python3-mpipinstall-Udsalgo[nb]# for latest unstable versionpython3-mpipinstall-Ugit+git://github.com/kagemeka/dsalgo-python.gitDevelopmentdockercomposeup-d(enter the container)./setup.shsource~/.bashrcCI before commit./ci.shdocumentingpoetryrunpdocdsalgo--show-source--math-odocspublishpoetryconfigpypi-token.pypi<yourtoken># only oncepoetrybuild
poetrypublish
Package: dsalib
dsalibA clean and minimal Data Structure And Algorithms Library Implemented in Python.Currently in development process.InstallIf you have installed multiple versions of python then use:pip3installdsaliband if you have only installed Python3 then use:pipinstalldsalibUsagefromdsalibimportsortarr=[21,2,3,31,45]sort.merge_sort(arr)print(arr)Output:[2,3,21,31,45]Become a contributorForkCreate new branch, representing the changesMake changes, ensure it's workingPush your own repoCreate PR, describe your changesThank You
Package: dsa-reg
dsa_regThis package approximately computes the offsets needed to register DSA (Digital Slide Archive) item thumbnails.InstallationsThrough pypi package$pipinstalldsa-regFrom Source$gitclonegit@github.com:mmasoud1/dsa_reg.gitUsageAfter the package is installed, main operation can be performed as follows:Register by Thumbnailsfromdsa_regimportrigidRegByThumb#param refItemId (string): ref image Id#param targetItemId (string): target image Id#param xBaseUrl (string): DSA Server URL e.g. https://styx.neurology.emory.edu/girder/api/v1#param xAuthentication: boolean (0,1)#param xEnhancement: boolean (0,1)#return (Dict): homography metrix, psnr, x offset, y offset, horizontal scaleX, vertical  scaleYrigidRegByThumb(refItemId,targetItemId,xBaseUrl)# e.g.rigidRegByThumb(refItemId="5e361da534679044bda81b16",targetItemId="5e361-SOME-OTHER-ID",xBaseUrl="https://styx.neurology.emory.edu/girder/api/v1")# if using a private DSA collection,  set xAuthentication = 1 to provide login credentials:rigidRegByThumb(refItemId="5e361da534679044bda81b16",targetItemId="5e361-SOME-OTHER-ID",xBaseUrl="https://styx.neurology.emory.edu/girder/api/v1",xAuthentication=1,xEnhancement=0)# If a preprocessing is needed such that the tile needs enhancement before registration, set xEnhancement boolean value to 1Register by Magnificationfromdsa_regimportrigidRegByMagnificationrigidRegByMagnification(refItemId,targetItemId,xBaseUrl)#e.g.rigidRegByMagnification(refItemId="5e361da534679044bda81b16",targetItemId="5e361-SOME-OTHER-ID",xBaseUrl="https://styx.neurology.emory.edu/girder/api/v1")# magnification default value is 1rigidRegByMagnification(refItemId,targetItemId,xBaseUrl,magnification,xAuthentication)# magnification can be smaller value < = 1 for fast processing and wise resources userigidRegByMagnification(refItemId,targetItemId,xBaseUrl,magnification,xAuthentication,xEnhancement)#e.g.rigidRegByMagnification(refItemId="5e361da534679044bda81b16",targetItemId="5e361-SOME-OTHER-ID",xBaseUrl="https://styx.neurology.emory.edu/girder/api/v1",magnification=0.5,xAuthentication=1,xEnhancement=1)For contributing, issues and suggestionsYour contribution to enhance the registration technique is welcomed, please start by new issue or  pull a request.Next:This is a demo of initial and approximated rigid registration results, the target is to extend the functionality to whole slide images registration in fast, accurate mode.
Package: dsatools
Digital Signal Analysis (DSA) library for python.
The library includes such methods of signal analysis and
signal parameter estimation as arma-based techniques;
subspace-based techniques; matrix-pencil-based methods;
singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD);
empirical mode decomposition; variational mode decomposition;
empirical wavelet transform; Hilbert vibration decomposition
and many others.
Package: d-save-last
docker-save-last Command Line UtilityThis is a command line utility effectively replicatingdocker saveexcept that it will only save the LAST layer of the image in the output archive.This is especially useful when combined with the experimentaldocker build --squashoption, because it allows you to export only your changes on top of the base image without the entire base image file system in the output archive.This means HUGE savings in file size.General discussion on the issue can be found here:https://github.com/moby/moby/issues/8039InstallationUse pip to install:pip install d-save-lastRequired Dependenciesdockerinstalled, on the PATH, and usable without sudo.UsageTypical usage will look liked-save-last -o ./path/to/output.tar YOUR_IMAGE_IDArguments and options are identical todocker saveseedocker save --help.$ docker save -h

Usage:	docker save [OPTIONS] IMAGE [IMAGE...]

Save one or more images to a tar archive (streamed to STDOUT by default)

Options:
  -o, --output string   Write to a file, instead of STDOUTHow does it work?This utility uses the special docker-in-docker image fromhttps://github.com/brthor/dind-saveThat docker image contains a version of the docker daemon with patches to docker save built fromhttps://github.com/brthor/engineThe utility launches thedind-saveimage in a container, and connects to its docker daemon over TCP.CaveatsThis is currently only tested with host machines that use the overlay2 storage driver. Any other storage driver will almost certainly not work.Currently only supports docker version 18.09. Adding new versions requires changes in the docker daemon anddind-saveimage repository. Open an issue if you'd like a new version added for your purposes.Feel free to contribute here if any of these are an issue and you'd like to use this utility. Pull Requests will be reviewed quickly.
Package: dsawl
[![Build Status](https://travis-ci.org/Nikolay-Lysenko/dsawl.svg?branch=master)](https://travis-ci.org/Nikolay-Lysenko/dsawl)
[![codecov](https://codecov.io/gh/Nikolay-Lysenko/dsawl/branch/master/graph/badge.svg)](https://codecov.io/gh/Nikolay-Lysenko/dsawl)
[![Maintainability](https://api.codeclimate.com/v1/badges/98fc23b8b51fb20f2920/maintainability)](https://codeclimate.com/github/Nikolay-Lysenko/dsawl/maintainability)
[![PyPI version](https://badge.fury.io/py/dsawl.svg)](https://badge.fury.io/py/dsawl)# dsawl## What is it?This is a set of tools for machine learning. Provided by the package utilities are described in the below table:Subject | Description | Docs
:—–: | :———: | :–:
Active Learning | Highly-modular system that recommends which previously unlabelled examples should be labelled in order to increase model quality quickly and significantly. Special features: various options for both exploitation and exploration. | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/active_learning_demo.ipynb)
Stacking | A method that applies machine learning algorithm to out-of-fold predictions or transformations made by other machine learning models. Special features: support of anysklearn-compatible estimators (in particular, pipelines). | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/stacking_demo.ipynb)
Target Encoding | An alternative to one-hot encoding and hashing trick that attempts to have both memory efficiency and incorporation of all useful information from initial features. Special features:sklearn-compatible wrapper that can transform data out-of-fold and apply an estimator to the result.| [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/target_encoding_demo.ipynb)Repository name is a combination of three words: DS, saw, and awl. DS is as an abbreviation for Data Science and the latter two words represent useful tools.## How to install the package?The package is compatible with Python 3.5 or newer. A virtual environment where it is guaranteed that the package works can be created based on [the file](https://github.com/Nikolay-Lysenko/dsawl/blob/master/requirements.txt) namedrequirements.txt.To install a stable release of the package, run this command:` pip install dsawl `To install the latest version from sources, execute this from your terminal:` cd path/to/your/destination git clonehttps://github.com/Nikolay-Lysenko/dsawlcd dsawl pip install-e. `If you have any troubles with installation, your questions are welcome.