Saravanan666/pypi-org huggingface dataset:
Package: au
au: Outlier Detection ToolkitFiltering outliers to find the golden nuggets that standout from the rest.To install:pip install auOutlier detection is a fundamental step in data analysis, particularly relevant in statistics, data mining, and machine learning. This toolkit provides a set of functions and classes in Python for identifying outliers - observations in data that are significantly different from the majority. The toolkit is designed to accommodate various methodologies, ranging from statistical methods to machine learning-based approaches.FeaturesZ-Score Based Outlier DetectionDetects outliers by measuring how many standard deviations an element is from the mean.Suitable for datasets where the distribution is expected to be Gaussian.Interquartile Range (IQR) Based Outlier DetectionUtilizes the IQR, which is the difference between the 75th and 25th percentile of the data.Effective for skewed distributions.Isolation Forest Based Outlier DetectionImplements the Isolation Forest algorithm, a machine learning method for anomaly detection.Ideal for high-dimensional datasets.InstallationEnsure that you have Python installed on your system. This toolkit requiresnumpyandscikit-learn. They can be installed via pip.pip install numpy scikit-learnFeaturesZ-Score Based Outlier DetectionDetects outliers by measuring how many standard deviations an element is from the mean.Suitable for datasets where the distribution is expected to be Gaussian.Interquartile Range (IQR) Based Outlier DetectionUtilizes the IQR, which is the difference between the 75th and 25th percentile of the data.Effective for skewed distributions.Isolation Forest Based Outlier DetectionImplements the Isolation Forest algorithm, a machine learning method for anomaly detection.Ideal for high-dimensional datasets.InstallationEnsure that you have Python installed on your system. This toolkit requiresnumpyandscikit-learn. They can be installed via pip:pip install numpy scikit-learnUsageZ-Score Based Outlier Detectionfromoutlier_detectionimportdetect_outliers_zscoreoutliers=detect_outliers_zscore([10,12,12,13,12,11,40])Interquartile Range (IQR) Based Outlier Detectionfromoutlier_detectionimportdetect_outliers_iqroutliers=detect_outliers_iqr([10,12,12,13,12,11,40])Isolation Forest Based Outlier Detectionfromoutlier_detectionimportIsolationForestOutlierDetectordetector=IsolationForestOutlierDetector()outliers=detector.detect_outliers([10,12,12,13,12,11,40])DocumentationEach function and class in this toolkit comes with a detailed docstring, explaining its purpose, parameters, return values, and examples.ContributingContributions to this project are welcome! Please fork the repository and submit a pull request with your changes.
Package: auacm
# auacm-cliA command line interface to the Auburn ACM web app (auacm.com)## Installation### From pipRun `pip install auacm`### Build from sourceClone this repository`git clone https://github.com/brandonlmorris/auacm-cli`Then in the top-level directroy, run`python3 setup.py install`This will install all the code and create the `auacm` shell command.---## Usage`auacm-cli` installs itself as a shell script. In the terminal, run`$ auacm`to display the help.All of the features of `auacm-cli` are written as subcommands to the main`auacm` script. To log in, run`$ auacm login`You will be prompted for your name and password. Your session will be saved,so you don't need to authenticate with every run of the script. Subcommands`logout` and `whoami` exist to destroy the current session and print dataabout the current user respectively.### ProblemsTo view all the problems on the server, run`$ auacm problem`(Protip: pipe the result into `less` for your reading pleasure:`$ auacm problem | less`)To search for a problem, simply add the query after `problem` (multi-wordqueries must be enclosed in quotes):`$ auacm problem "cash cow"`Adding a `-v` flag will increase the verbosity of the results, providingthe problem id, shortname, difficult, etc.To get more detailed information on a specific problem, including thedescription, input/output, and sample cases, run the subcommand `problem-info`with the problem name or id.`$ auacm problem-info parity`**Note:** Searching by problem name is done by case-insensitive substrings. Ifyou search "cow" but another problem contains "cow" and comes firstalphabetically, that other problem will be used.### SubmissionYou can also submit solutions to problems using `auacm`. To do so, simplyrun the `submit` subcommand, followed by the problem name and the solutionfile:`$ auacm submit "cash cow" solution.py`Note that if you're submitting a Python solution, you can use the `-p` flagto indicate the version of Python. The default is Python 3.`$ auacm submit parity parity.py -p 2`After you submit, `auacm` will query your submission to obtain the results.## CompetitionsThe `competition` subcommand can list competitions, and give a detailed viewof a particular competition. To see all competitions, run`$ auacm competitions`To get more info about a specific competition, run the command with thecompetition name afterword (multi-word queries need to be enclosed in spaces).`$ auacm competitions "october 15th"Similar to problems, you can also search for a competition using it's uniquecompetition id with the `-i` or `--id` flag.---## Bugs? Improvements? Funny jokes?Send them to me at brandon dot morris95 at gmail dot com, or leave a commenton this repo. Contributions are warmly welcomed.
Package: aubay-sam-deploy
aubay-sam-deployDeploy & Documentation tool for Aubay SAM specificationsSAM_DOC_GENSam Documentation GeneratorThis module allows the conversion of a file in YAML format to an HTML file.The generated HTML document presents the OpenAPI style, by default, describing each YAML grouping as an OpenAPI endpoint grouping (sections).The connectors approach assumes the existence of the 'specifications' directory, where each of the specifications will be distributed.Script Example:generate-active --source isylight/calle
Package: aubergine
aubergineis a library that helps you effortlessly create REST API using
API-first approach.Creating an API inaubergineis simple and requires only the following steps:Create your OpenAPI 3 specification file, withoperationIdset to the callable of your choice.openapi:"3.0.0"servers:-url:/v1/restinfo:version:1.0.0title:Hello Worlddescription:My first api.paths:/hello:get:operationId:hello_world.helloparameters:-who:whoin:querydescription:who to greatrequired:trueschema:type:stringresponses:'200':description:Greeting dedicated to given name.content:application/json:schema:$ref:'#/components/schemas/Greeting'components:schemas:Greeting:required:-messageproperties:message:type:stringImplement operations that you declared in your specification. Placehello_worldmodule with the following content in an importable directory.defhello(who):"""Handles v1/rest/hello"""return{'message':'Hello{}!'.format(who)}Tell aubergine to create an app for you. Create ahello_app.pywith the following contents:fromaubergineimportAubergineapp=Aubergine.from_file('hello_world.yml')api=app.build_api()Your app is ready to go. You can run it with any WSGI server like so:gunicornhello_app:apiTest it by navigating tohttp://localhost:8000/v1/rest/hello?who=World
Package: aubio
aubio is a collection of tools for music and audio analysis.This package integrates the aubio library withNumPyto provide a set of
efficient tools to process and analyse audio signals, including:read audio from any media file, including videos and remote streamshigh quality phase vocoder, spectral filterbanks, and linear filtersMel-Frequency Cepstrum Coefficients and standard spectral descriptorsdetection of note attacks (onset)pitch tracking (fundamental frequency estimation)beat detection and tempo trackingaubio works with both Python 2 and Python 3.Linksmodule documentationinstallation instructionsaubio manualaubio homepageissue trackerDemosSome examples are available in thepython/demosfolder. Each
script is a command line program which accepts one ore more argument.Notes: installing additional modules is required to run some of the demos.Analysisdemo_source.pyuses aubio to read audio samples from media filesdemo_onset_plot.pydetects attacks in a sound file and plots the results
usingmatplotlibdemo_pitch.pylooks for fundamental frequency in a sound file and plots the
results usingmatplotlibdemo_spectrogram.py,demo_specdesc.py,demo_mfcc.pyfor spectral
analysis.Real-timedemo_pyaudio.pyanddemo_tapthebeat.pyusepyaudiodemo_pysoundcard_play.py,demo_pysoundcard.pyusePySoundCarddemo_alsa.pyusespyalsaaudioOthersdemo_timestretch.pycan change the duration of an input file and write the
new sound to disk,demo_wav2midi.pydetects the notes in a file and usesmidoto write the
results into a MIDI fileExampleUsedemo_timestretch_online.pyto slow downloop.wav, write the results instretched_loop.wav:$ python demo_timestretch_online.py loop.wav stretched_loop.wav 0.92Built withThe core of aubio is written in C for portability and speed. In addition toNumPy, aubio can be optionally built to use one or more of the following
libraries:media file reading:ffmpeg/avcodecto decode and read audio from almost any format,libsndfileto read audio from uncompressed sound files,libsamplerateto re-sample audio signals,CoreAudioto read all media formats supported by macOS, iOS, and tvOS.hardware acceleration:AtlasandBlas, for accelerated vector and matrix computations,fftw3, to compute fast Fourier Transforms of any size,Acceleratefor accelerated FFT and matrix computations (macOS/iOS),Intel IPP, accelerated vector computation and FFT implementation.
Package: aubio-beat-osc
Onset Beat Detection to OSCThis is a simple beat detector built withaubio.
It will detect the beat and BPM on the default audio input.
On every beat, the current BPM is sent to one or more OSC servers.Installationpip install aubio-beat-oscUsageaubio-beat-osc beat [-h] -c IP PORT ADDRESS [-b BUFSIZE] [-v] [-d DEVICE]

optional arguments:
  -h, --help            show this help message and exit
  -c IP PORT ADDRESS, --client IP PORT ADDRESS
                        OSC Client address (multiple can be provided)
  -b BUFSIZE, --bufsize BUFSIZE
                        Size of audio buffer for beat detection (default: 128)
  -v, --verbose         Print BPM on beat
  -d DEVICE, --device DEVICE
                        Input device index (use list command to see available devices)-c/--clientAdd anIP,PORTand OSCADDRESSto which the BPM beat signal will be sent to. Example:-c 127.0.0.1 31337 /foo/beat-b/--bufsizeSelect the size of the buffer used for beat detection.
A larger buffer is more accurate, but also more sluggish.
Refer to theaubiodocumentation of the tempo module for more details.
Example:-b 128-v/--verboseOutput a handy beat indicator and the current BPM to stdout.-d/--deviceSpecify the index of input device to be used.
If not provided, the default system input is used.Runaubio-beat-osc listto get all available devices.Example$ aubio-beat-osc beat -c 127.0.0.1 31337 /foo/bar -c 10.10.13.37 12345 /test/baz -vThis will send beat messages to the OSC address/foo/baron127.0.0.1:31337and/test/bazon10.10.13.37:12345.
Additionally the current BPM will be printed to stdout.
Package: aucampia.rdflib-xtl
Extra command line tools for rdflibThis package provides some additional command line tools for rdflib:SPARQL Query execution with optional reasoning usingOWL-RL:rdflib-xtlsparql--helpOWL Reasoning usingOWL-RLrdflib-xtlreason--helpUsage examples:# Install pipxpip3install--user--upgradepipx# (optionally) clear pipx cache if you want the latest version ...\rm-vr~/.local/pipx/.cache/# check versionpipxrun--specaucampia.rdflib-xtlrdflib-xtl--version# Run SPARQL without reasoning ...pipxrun--specaucampia.rdflib-xtl\rdflib-xtlsparql\-q'SELECT * WHERE { ?s rdfs:subClassOf owl:Thing. }'\http://xmlns.com/foaf/spec/index.rdf# Dump input graph with SPARQL query ...pipxrun--specaucampia.rdflib-xtl\rdflib-xtlsparql\-q'CONSTRUCT { ?s ?p ?o } WHERE { ?s ?p ?o }'\http://xmlns.com/foaf/spec/index.rdf# Run SPARQL with reasoning ...pipxrun--specaucampia.rdflib-xtl\rdflib-xtlsparql--reason\-q'SELECT * WHERE { ?s rdfs:subClassOf owl:Thing. }'\http://xmlns.com/foaf/spec/index.rdf# Dump reasoned graph with SPARQL query ...pipxrun--specaucampia.rdflib-xtl\rdflib-xtlsparql--reason\-q'CONSTRUCT { ?s ?p ?o } WHERE { ?s ?p ?o }'\http://xmlns.com/foaf/spec/index.rdf# Run reasoning ...pipxrun--specaucampia.rdflib-xtl\rdflib-xtlreasonhttp://xmlns.com/foaf/spec/index.rdf
Package: aucmedi
The open-source software AUCMEDI allows fast setup of medical image classification pipelines with state-of-the-art methods via an intuitive, high-level Python API or via an AutoML deployment through Docker/CLI.ResourcesWebsite:AUCMEDI Website - HomeGit Repository:GitHub - frankkramer-lab/aucmediDocumentation:AUCMEDI Wiki - API ReferenceGetting Started:AUCMEDI Website - Getting StartedExamples:AUCMEDI Wiki - ExamplesTutorials:AUCMEDI Wiki - TutorialsApplications:AUCMEDI Wiki - ApplicationsPyPI Package:PyPI - aucmediDocker Image:GitHub - ghcr.io/frankkramer-lab/aucmediZenodo Repository:Zenodo - AUCMEDIHow to citeAUCMEDI is currently unpublished. But coming soon!In the meantime:Please cite our base framework MIScnn as well as the AUCMEDI GitHub repository:Müller, D., Kramer, F. MIScnn: a framework for medical image segmentation with
convolutional neural networks and deep learning. BMC Med Imaging 21, 12 (2021).
https://doi.org/10.1186/s12880-020-00543-7Müller, D., Mayer, S., Hartmann, D., Meyer, P., Schneider, P., Soto-Rey, I., & Kramer, F. (2022).
AUCMEDI: a framework for Automated Classification of Medical Images (Version X.Y.Z) [Computer software].
GitHub repository. https://github.com/frankkramer-lab/aucmediThank you for citing our work.Lead AuthorDominik MüllerEmail:dominik.mueller@informatik.uni-augsburg.deIT-Infrastructure for Translational Medical ResearchUniversity AugsburgBavaria, GermanyLicenseThis project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.See the LICENSE.md file for license rights and limitations.
Package: auction-crawler
Auction crawler projectDescriptionAuction crawler project is a Python package to crawl a website with forest auctions. This project utilizes Python
3.12, along with requests, selenium libraries. It focuses on data extraction from certain website.InstallationUsing a package managerYou can install forest auction crawler as a package: Using pip:pipinstallauction_crawlerOr using 'poetry':poetryaddauction_crawlerCloning the repositoryAlso, you can clone this repository and install dependencies using 'poetry':gitclonehttps://github.com/vytenisdam/auction_crawlercdauction_crawler
poetryinstallUsageAs a modulefromauction_crawler.mainimportcrawl_siteprint(crawl_site('csv',scroll_time=40))StructureThe project is structured as follows:auction_crawler/: Main package directory.__init__.py: Package initialization file.main.py: Main script for the auction crawler package.csv_write.py: Script for writing crawled data to csv file.selenium_crawl.py: Separate functions that make the crawler work.tests/: Tests directory.__init__.py: Initialization file for tests.tests_main.py: Test scripts for the package
Package: aud
Support for Python 3.8, 3.7, & 3.6Support for Windows & UbuntuRequires ffmpeg is already installed and updatedInstalling FFMPEG:>> On Linux:
apt-get install ffmpeg libavcodec-extra

>> On Mac:
brew install ffmpeg --with-libvorbis --with-sdl2 --with-theora

>> On Windows:
>> visit https://www.ffmpeg.org/download.html
>> download and install the appropriate package
>> ensure it is added to your PATH and you can call it from cmdTo start contributing:>> git clone https://github.com/zdhoward/aud.git
>> cd aud
>> virtualenv venv
>> source venv/bin/activate
>> python3 -m pip install -r requirements.txtQuickstart:a = aud.Dir("folder", ['wav', 'mp3'], logfile="main.log")

a.log("Backing up")
a.backup("backups/todays_date/")

a.log("Changing filenames to all uppercase")
a.name_upper()

a.log("Creating exports for Amuse")
a.export_for("amuse", "exports/amuse")

a.log("add initials to all files")
a.name_append("_ZH")

a.log("Zip files up")
a.zip("my_files.zip")
Package: audacianumberrecognition
MIT LicenseCopyright (c) 2022 AudaciaPermission 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: audacityDiscogsExporter
commandLineMusicVideoThis is a Python3pip packagethat connencts to Audacity'smod-script-pipe for Python scripting.This package will batch render each selection of an Audacity track to mp3 files with metadata tags based on a Discogs URL. vinyl2digital makes digitizing vinyl records much easier and faster.QuickstartInstall the package with this command:pip install vinyl2digital==0.0.3Launch Audacity from your terminal (Windows 10:start audacity.exe)Record your audio, and splice each song as its own selection on the track (place the cursor and press Ctrl+i)Run the installed package withpython3 -m vinyl2digital -h -tto view the help page and test your package's connecting to Audacity.While Audacity is open, run the pip package from the command line.Audacity ConnectionFollow the step's listed onthispage: Go to "Audacity -> Edit -> Preferences -> Modules" and set the "mod-script-pipe" parameter to "Enabled", restart Audacity and confirm that "mod-script-pipe" is set to "Enabled" by default.Example commands:python3 -m vinyl2digital -discogs 1525832 -img front.jpg "..\..\..\..\martinradio\uploads\my output folder"This command:Will get the metadata from theDiscogs release page with id=1525832Will tag the output mp3 files with albumart from the location:..\..\..\..\martinradio\uploads\my output folder\front.jpgWill render each selection from the open Audacity program as an individual mp3 file in order from left to right, with tags for title, album name, artist, year, track number, and title. The files will be rendered to..\..\..\..\martinradio\uploads\my output folderFlags-tTest audacity pipe "Help" commands.--hView the help page--discogs 2342323Discogs release ID from URL to base tags off of.--img front.jpg(optional) Filename of image located inside your output folder to use as albumart for mp3 files.-hDisplay help.
Package: audacity-scripting
audacity-scriptingA Python package for automating Audacity tasks based onAudacity's Scripting Reference.https://github.com/unfor19/audacity-scripting/assets/15122452/080e772c-3e40-424b-87ce-c2ac0dd75570Music by Sergio Prosvirini from PixabayRequirementsInstallPython 3.9+InstallAudacity 3.4.2+Audacity - Enablemod-script-pipeRun AudacityGo into Edit > Preferences > ModulesChoose mod-script-pipe (which should show New) and change that to Enabled.Restart AudacityCheck that it now does show Enabled.This establishes that Audacity is finding mod-script pipe and that the version is compatible.InstallationSupported OS: Windows, Linux, and macOSpython -m pip install audacity-scriptingFeaturesRemove spaces between clips - Removes spaces in all tracks for a given project absolute path/path/to/my_project.aup3Add labels to clipsClean silenceNormalize audioEnd Goal - Prepare videos for voice-clone-finetuning-vitsGetting StartedAll the commands assume that theAudacityapplication is up and running; That is mandatory as we communicate withAudacity's pipeto execute all the commands.NOTE: See theGitHub Actions Tests Workflowto check it in action.Available CommandsAuto-generated byunfor19/replacer-action; seereadme.ymlUsage: audacity-scripting [OPTIONS] COMMAND [ARGS]...

  No confirmation prompts

Options:
  -ci, --ci  Use this flag to avoid confirmation prompts
  --help     Show this message and exit.

Commands:
  add-labels     Alias: al Add labels to clips in a given project
  clean-spaces   Alias: cs Clean spaces between clips in a given project
  do-command     Alias: dc - Execute a raw command in Audacity
  testing        This is for testing purposes only
  version-print  Get the version from the version file.Remove spaces between clipsThis command copies the original file and removes spaces (gaps) between audio clips from the copied file.audacity_scriptingclean-spaces--file_path"/path/to/my_project.aup3"Send a command to AudacitySend a command to Audacity according toAudacity's Scripting Reference.audacity_scriptingdo-command--command"Select: Tracks=0.0 Start=0.0 End=0.0"Known IssuesmacOS prints Audacity's server logs (Server sending,Read failed on fifo, quitting, etc.) - documented inJOURNEY.mdReferencesGitHub Issue - Audacity Remove spaces between clipsSourceForge Issue - Audacity pipesGitHub Actions Windows Pipes DiscussionAudacity pipes issue on WindowsPython and Windows named pipes on StackoverflowAuthorsCreated and maintained byMeir GabayLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: audapa
To remove configuration files launch the program with one argument.rpm -i --nodepsandyum remove --noautoremoveto deal at pyaudioo...rpm, audapa...rpm and python3-pyaudio.PortAudio can ask for portaudio.h (the file is in portaudio19-dev).PyAudio and python 3.10 can report playback errors (install fromhereand add libportaudio2 package).The audio records are saved whereappdirs.user_data_dir("audapa")points at (example: ~/.local/share/audapa/1650089398.wav).The points are saved in the_audapacache_folder which is at the file folder or in the home folder if the option is selected. (example: /home/x/audapa/_audapacache_/example.wav.json or /home/x/_audapacache_/home/x/audapa/example.wav.json)Knowing where the points are saved, in the root folder at source, write "example.wav" and click the build button from top-right.Git Page
Package: audb
audbmanages your annotated media files.Databases used in machine learning
should be easily exchangeable
and versioned for reproducibility.audballows both
as it stores all databases in acommon formatand manages different versions of a database.
Databases are stored in repositories
on local file systems
orArtifactoryservers.You can request resampling or remixing of audio content
and filter the downloaded data,
e.g. if you just want to download the test set.Have a look at theinstallationandquickstartinstructions.CitationIf you want to citeaudb, you can refer to ourpaper:@article{wierstorf2023audb,title={audb -- Sharing and Versioning of Audio and Annotation Data in Python},author={Hagen Wierstorf
    and Johannes Wagner
    and Florian Eyben
    and Felix Burkhardt
    and Bj{\"o}rn W. Schuller},journal={arXiv preprint arXiv:2303.00645},year={2023},}