Saravanan666/pypi-org huggingface dataset:
Package: med-diagnostics
Framework for interactive monitoring and diagnostic analyses of the ACCESS model suite.The ACCESS-NRI Model Live Diagnostics framework is designed to provide useful and practical Jupyter-based tools for interactive monitoring and diagnostic
analyses of currently running (aka ‘live’) ACCESS climate models on theAustralian NCI supercomputer Gadi.The primary purpose of the ACCESS-NRI Model Live Diagnostics package is to provide a simple, easy to use and accessible framework for the
ACCESS modelling community to check, monitor, visualise and evaluate model behaviour and progress on currently running or ‘live’ ACCESS
models on Gadi. In addition to monitoring a live model, the package provides the functionality to load,
visualise and compare legacy ACCESS model data with the selected live user model.What does this package do?This package is currently in active development within the Model Evaluation Team at theAustralian Earth System Simulator (ACCESS-NRI)so watch this space for version updates containing new features, model diagnostics tools and visualisation options!We value your feedback, especially in the form of reporting issues/bugs or suggesting ways to improve the framework. To do so, please open anissue.Usage / installation instructionsThe med-diagnostics package is pre-installed in the GadiACCESS-NRI MED conda environment. To use this environment for your ARE JupyterLab session
simply set the following parameters in the ARE JupyterLab‘Advanced options’:Module directories/g/data/xp65/public/modulesModulesconda/access-medAlternatively, the med-diagnostics package can be installed directly into your chosen conda environment on Gadi either from theaccess-nri conda channelorPyPI.condaconda install-caccessnrimed-diagnosticsPyPIpip installmed-diagnosticsGetting startedTo use the med-diagnostics package use:import med_diagnosticsAn example Jupyter notebook describing usage options can be found in thedocs/notebooksdirectory.The full documentation is available fromreadthedocs.DocumentationPackageLicense
Package: meddlr
meddlrGetting StartedMeddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems.⚡ QuickStart# Install Meddlr with basic dependenciespipinstallmeddlr# Install Meddlr with all dependencies (e.g. pretrained models, benchmarking)pipinstall'meddlr[all]'Installing locally: For local development, fork and clone the repo and runpip install -e ".[alldev]"Installing from main: For most up-to-date code without a local install, runpip install "meddlr @ git+https://github.com/ad12/meddlr@main"Configure your paths and get going!importmeddlrasmrimportos# (Optional) Configure and save machine/cluster preferences.# This only has to be done once and will persist across sessions.cluster=mr.Cluster()cluster.set(results_dir="/path/to/save/results",data_dir="/path/to/datasets")cluster.save()# OR set these as environment variables.os.environ["MEDDLR_RESULTS_DIR"]="/path/to/save/results"os.environ["MEDDLR_DATASETS_DIR"]="/path/to/datasets"Detailed instructions are available inGetting Started.VisualizationsUseMeddlrVizto visualize your medical imaging datasets, ML models, and more!pipinstallmeddlr-viz🐘 Model ZooEasily serve and download pretrained models from the model zoo. A (evolving) list of pre-trained models can be foundhere, onHuggingFace 🤗, and inproject folders.To use them, pass the URLs for the config and weights (model) files tomr.get_model_from_zoo:importmeddlrasmrmodel=mr.get_model_from_zoo(cfg_or_file="https://huggingface.co/arjundd/vortex-release/resolve/main/mridata_knee_3dfse/Supervised/config.yaml",weights_path="https://huggingface.co/arjundd/vortex-release/resolve/main/mridata_knee_3dfse/Supervised/model.ckpt",)📓 ProjectsCheck out someprojectsbuilt with meddlr!✏️ ContributingWant to  add new features, fix a bug, or add your project? We'd love to include them! SeeCONTRIBUTING.mdfor more information.AcknowledgementsMeddlr's design for rapid experimentation and benchmarking is inspired bydetectron2.AboutIf you use Meddlr for your work, please consider citing the following work:@article{desai2021noise2recon,
  title={Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising},
  author={Desai, Arjun D and Ozturkler, Batu M and Sandino, Christopher M and Vasanawala, Shreyas and Hargreaves, Brian A and Re, Christopher M and Pauly, John M and Chaudhari, Akshay S},
  journal={arXiv preprint arXiv:2110.00075},
  year={2021}
}
Package: meddlr-viz
MeddlrVizMeddlrViz is a visualization suite for medical imaging datasets, machine learning models, and algorithms built withMeddlrandMeerkat.⚡ QuickStartWe recommend downloading MeddlrViz in a virtual environment,# Install Meddlr with basic dependencies# This will also install meddlr and meerkat-mlpipinstallmeddlr-vizWhy MeddlrViz?MeddlrViz offers interfaces for visualizing and interacting with data, machine learning models, and algorithm outputs. These interfaces are built withMeerkat, an open-source Python library, designed to help technical teams interactively wrangle images, videos, text documents and more with foundation models.(1) Complex medical dataset visualization.Medical images and associated data and model outputs (e.g. segmentations, raw data, etc.) are high-dimensional and difficult to visualize. MeddlrViz provides useful tools for visualizing, formatting, and interacting with complex data types in your medical imaging dataset.(2) Interactive demos in native Python.Building interactive demos can be challenging. MeddlrViz provides built-in interfaces for easily demoing models and algorithms on your data and conducting high-level analyses, like reader studies.✉️ AboutPlease reach out toarjundd [at] stanford [dot] eduif you would like to use MeddlrViz for a project, at your company or if you have any questions.
Package: medenv
Environmental measures from mediterranean seeThis package provides several utilities for requesting environmental measures in the Mediterrannean see.InstallationIn order to install the package, you can install the latest release with :pip install medenvYou can also install, using pip, the current version on the main branch by either :python3 -m pip install git+https://github.com/jeremyfix/medenv.gitOr, you can also install it by cloning the repositorygit clone https://github.com/jeremyfix/medenv.git
python3 -m venv venv
source venv/bin/activate
python -m pip install medenvUsageCheck the examples in theexamples/directory, but basically, grabbing environmental variables require aFetcherto which you specify which features you want to grab and then provides(latitude, longitude, time, depth)requests:features=["sea-surface-temperature","sea-surface-salinity","bathymetry","temperature","salinity","chlorophyl-a","nitrate","phosphate","ammonium","phytoplankton-carbon-biomass","oxygen","net-primary-production","ph","alkalinity","dissolved-inorganic-carbon","northward-water-velocity","eastward-water-velocity",# 2D features"mixed-layer-thickness","sea-surface-above-geoid","surface-partial-pressure-co2","surface-co2-flux",]fetcher=medenv.Fetcher(features,reduction="mean")date=datetime.datetime(year=2012,month=9,day=22,hour=14)long,lat=13.63,43.55tol_spatial=0.2long0=(long-tol_spatial/2,long+tol_spatial/2)lat0=(lat-tol_spatial/2,lat+tol_spatial/2)depth=6values,info_values=fetcher.get_values(date,(long0,lat0),depth)The request accepts a range for the longitude/latitude and a single value for the date and depth. For both the date and depth, the fetcher will grab the closest values.
Package: medford
medford ParserThe Medford ParserThis repository contains all source code for the Medford parser, written in Python (3.8) using thePydanticlibrary. The purpose of this parser is to either validate or compile all MEDFORD files to ensure they follow appropriate MEDFORD syntax (described below) such that they can accurately be converted into other output formats for transfer or submission to coral databases.Official SpecificationPlease see the official specification for further details, hosted on githubhere.Example MEDFORD FilesA repository of sample MEDFORD files is hostedhere. (Note: Out of date/Not compatible with current spec as of 04/13/22. Will be updated soon.)MEDFORD AbstractThe conference publication about the alpha version of MEDFORD is hosted on Polina Shpilker's personal site,here. The abstract is also copied below.Reproducibility of research is essential for science. However, in the way modern computational biology research is done, it is easy to lose track of small, but extremely critical, details. Key details, such as the specific version of a software used or iteration of a genome can easily be lost in the shuffle, or perhaps not noted at all. Much work is being done on the database and storage side of things, ensuring that there exists a space to store experiment-specific details, but current mechanisms for recording details are cumbersome for scientists to use. We propose a new metadata description language, named MEDFORD, in which scientists can record [or encode!] all details relevant to their research. Human-readable, easily-editable, and templatable, MEDFORD serves as a collection point for all notes that a researcher could find relevant to their research, be it for internal use or for future replication. MEDFORD has been applied to coral research, documenting research from RNA-seq analyses to photo collectionsHuman-readible metadata file format to consolidate research information such that it can be stored, updated, and submitted to databases without introducing a huge time investment overhead.A preprint of a journal version of the MEDFORD paper is on the arXiv athttps://arxiv.org/abs/2204.09610.Contributions & ThanksContributors to the development of MEDFORD are as follows:
Polina Shpilker (polina.shpilker@tufts.edu), John Freeman, Hailey McKelvie, Jill Ashey, Jay-Miguel Fonticella, Hollie Putnam, Jane Greenberg, Lenore Cowen (cowen@cs.tufts.edu), Alva Couch (couch@cs.tufts.edu) and Noah M. Daniels (noah_daniels@uri.edu)Please contact Polina, Lenore, Alva, or Noah with any questions.Thank you to initial seed funding from: NSF grant OAC-1939263.
Package: medgeconv
An efficient tensorflow 2 implementation of the edge-convolution layer
EdgeConv used in e.g. ParticleNet.The structure of the layer is as described in ‘ParticleNet: Jet Tagging
via Particle Clouds’https://arxiv.org/abs/1902.08570.InstructionsInstall via:pip install medgeconvUse e.g. like this:importmedgeconvnodes=medgeconv.DisjointEdgeConvBlock(units=[64,64,64],next_neighbors=16,)((nodes,coordinates))Inputs to EdgeConv are 2 ragged tensors: nodes and coordinatesnodes, shape (batchsize, None, n_features)Node features of the graph. Secound dimension is the number of nodes,
which can vary from graph to graph.coordinates, shape (batchsize, None, n_coords)Features of each node used for calculating nearest neighbors.Example: Input for a graph with 2 features per node, and all node features
used as coordinates.importtensorflowastfnodes=tf.ragged.constant([# graph 1: 2 nodes[[2.,4.],[2.,6.]],# graph 2: 4 nodes[[0.,2.],[3.,7.],[4.,0.],[1.,2.]],],ragged_rank=1)print(nodes.shape)# output: (2, None, 2)# using all node features as coordinatescoordinates=nodesExampleThe full ParticleNet for n_features = n_coords = 2, and a dense layer
with 2 neurons as the output can be built like this:importtensorflowastfimportmedgeconvinp=(tf.keras.Input((None,2),ragged=True),tf.keras.Input((None,2),ragged=True),)x=medgeconv.DisjointEdgeConvBlock(units=[64,64,64],batchnorm_for_nodes=True,next_neighbors=16,)(inp)x=medgeconv.DisjointEdgeConvBlock(units=[128,128,128],next_neighbors=16,)(x)x=medgeconv.DisjointEdgeConvBlock(units=[256,256,256],next_neighbors=16,pooling=True,)(x)output=tf.keras.layers.Dense(2)(x)model=tf.keras.Model(inp,output)The last EdgeConv layer haspooling = True.
This will attach a node-wise global
average pooling layer in the end, producing normal not-ragged tensors again.The model can then be used on ragged Tensors:nodes=tf.RaggedTensor.from_tensor(tf.ones((3,17,2)))model.predict((nodes,nodes))Loading modelsTo load models, use the custom_objects:importmedgeconvmodel=load_model(path,custom_objects=medgeconv.custom_objects)knn_graph kernelThis package includes a cuda kernel for calculating the k nearest neighbors
on a batch of graphs. It comes with a precompiled kernel for the version of
tensorflow specified in requirements.txt.To compile it locally, e.g. for a different version of
tensorflow, go tomedgeconv/tf_opsand adjust thecompile.shbash script.
Running it will download the specified tf dev docker image and produce the
filemedgeconv/tf_ops/python/ops/_knn_graph_ops.so.PublicationsResults using this model architecture in the context of particle
physics were presented at the ICRC 2021 conferencehttps://doi.org/10.22323/1.395.1048, as well as the
VLVnT 2021https://arxiv.org/abs/2107.13375.
Package: media
MediaA Media Toolkit.Text-to-speech is currently available.Other features will be developed in the future.Thanks for Microsoft Azure!Release Note0.1.4 - 0.1.6: Fixed some issues.0.1.3:Added phonemes to improve pronunciation.Updated the documentation.0.1.2:Added a link to the official site (Azure) to get the key.Fixed some issues.0.1.1: Text-to-speech is currently available.Quick StartGet the keysSee "Get the keys for your resource - Azure"Text-to-speechfrommediaimportVoicetext="One Piece! Dragon Ball! Doraemon! Naruto!"voice=Voice("YourSubscriptionKey","YourServiceRegion")# use Englishvoice.speak(text)# use Japanesevoice.speak(text,lang=Voice.LANG.JA_JP,voice_name=Voice.NAME.FEMALE.JA_JP_NANAMI)# Save the voice file to the localvoice.save(text)Use phonemes to improve pronunciationfrommediaimportVoice,SSMLvoice=Voice("YourSubscriptionKey","YourServiceRegion")ssml=SSML()# If you want to modify the phoneme of a word, add '[]' in textssml.voice={"text":"His name is Mike [Zhou]","phonemes":[{"word":"Zhou","alphabet":"ups",# default: sapi"ph":"JH AU",}]}voice.speak(ssml)The values ​​ofalphabetandphcan refer to here, see: "Use phonemes to improve pronunciation - Azure"Error detectionsuccess=voice.speak()ifnotsuccess:# do something...print(voice.error)# do something...View the generated XML for SSMLfrommediaimportSSMLssml=SSML()# for humanprint(ssml)# for programssml.dump()Full ExampleText-to-speech, in a different tone.frommediaimportVoice,SSMLvoice=Voice("YourSubscriptionKey","YourServiceRegion")ssml=SSML(lang=SSML.LANG.ZH_CN,voice_name=SSML.NAME.FEMALE.ZH_CN_XIAO_XUAN)ssml.voice="啊？"ssml.voice={"text":"这是可以说的吗？","role":SSML.ROLE.YOUNG_ADULT_FEMALE,"style":SSML.STYLE.CHEERFUL,"rate":SSML.RATE.MEDIUM,}ssml.voice={"text":"啊，可以可以","name":SSML.NAME.FEMALE.ZH_CN_XIAO_MO,"style":SSML.STYLE.FEARFUL,"role":SSML.ROLE.OLDER_ADULT_FEMALE,"degree":"2",}ssml.voice={"text":"没事没事","name":SSML.NAME.FEMALE.ZH_CN_XIAO_MO,"style":SSML.STYLE.SAD,"role":SSML.ROLE.OLDER_ADULT_FEMALE,"degree":"2","rate":SSML.RATE.FAST,}# It will play the generated speechvoice.speak(ssml)If you want to save:voice.save(ssml)If you want to save and customize the name or location:voice.save(ssml,path="这是可以说的吗.mp3")
Package: mediaaccount
MediaAccount Python ClientDocumentation for the api hier:http://api.media-account.de/Package found here:https://pypi.org/project/mediaaccount/UsageThe client supports the python logging-package for detailed information.Request articlesimportdatetimefrommediaaccountimportMediaAccountClientapiKey='123456789'mediaAccount=MediaAccountClient(key=apiKey)# raw clientarticles,nextlink,count=client.articles('ImportDatum',von='01.05.2021',bis='05.08.2021',maxItems=150)articles,nextPageLink,count=mediaAccount.articleNext(nextPageLink)# full requestscroll=client.scroll('ImportDatum',von='04.08.2021',bis='05.08.2021',maxItems=1000)articlesAll=[i[0]foriinscroll]DevelopmentPublish./publish.shtest# publish to test./publish.shprod# publish to prodRoadmapBoard:https://github.com/CKrowiorsch/mediaaccount-py/projects/1Integration Api-V3
Package: mediaampy
This is a fairly low-level wrapper around the MediaAmp/MPXendpointsprovided by theplatform.com. It primarily handles token authentication and
provides HTTP methods with configurable default parameters (e.g. schema) for the
service endpoints. Because of the multitude of available parameters available at
each endpoint this package is intended to be used by your application to build
your own abstractions on top of the request and response data. Currently it only
supports the JSON (and cJSON) forms.UsageTo get started, supply your credentials: username, password, account id (This is
a URL and can be found on the “About” screen in the MPX console). You can
optionally supply an authentication token if you have one.importmediaampsession=mediaamp.Session('example@example.com','very_very_secret','http://access.auth.theplatform.com/data/Account/{{YOURID}}',auth_token='YOUR_TOKEN',)Once initialized, you can obtain services by key lookup:media_data=session['Media Data Service']The endpoints on the service have get(), put(), post(), and delete()
methods you can use depending on the actions you are taking.media_item=media_data.Media.get('{{MEDIA_ID}}')media_item['description']You can specify your own defaults per-endpoint by calling the endpoint
object.media=media_data.Media(schema='1.8',form='cjson')InstallationpipinstallmediaampyOr you can clone the source and run:makeinit
Package: mediaApiClient
mediaApiClientПубличная библиотека для работы c MediAPI ContentApiGate, написанная на PYTHON.MediAPIMediAPI - контент-платформа Медиатек, предназначенная для доставки контента от правообладателей к операторам.Для получения токена доступа production и staging-среды обратитесь к вашему менеджеру или свяжитесь с нами:sales@mediatech.byУстановкаУстановка последней версии mediaApiClient с ресурсаPyPI:pipinstallmediaApiClientДокументация по отдельным контроллерамКонтроллерыРабота с Content ControllerРабота с Stream ControllerРабота с Service ControllerРабота с Account ControllerРабота с Compilation ControllerТак же,MediAPI Swaggerпоможет вам в изучении структуры API.Основные концепции MediAPIClient: клиент MediAPI, как правило - OTT/IPTV-оператор.Service: библиотека контента, как правило сформированная одним поставщиком контента.Account: аккаунт конечного пользователя услуги, как правило подписчик OTT/IPTV-оператора.Content: сам контент, фильмы, сериалы и др.Stream: уникальная ссылка для каждой еденицыContent, сгенерированная дляAccountCompilation: коллекция контента, может объеденять в себе контент из разных сервисов, доступных клиенту. Зачастую это тематические подборки.ЛицензияBSD 3-Clause License
Package: media-archon
Media ArchonTable of ContentsInstallationLicenseInstallationpip install media-archonCopy the config filemedia-archon.tomlfrom therepositoryinto the top level of the directory tree of media you wish to convert and edit to meet your needs.TheoryMedia archon is an aggressively multithreaded Linux/Unix/MacOSX tool designed to mirror a tree of source media files to a destination tree, converting matching media using any command line tool selected by the user. It has been designed and tested around using the excellentfre:acopensource audio software to convert existing MP3 audiobooks etc. into Opus files for unbeatable size vs. quality.When executed withmedia-archon /directory/of/media/files/, it will:Look for and read the/directory/of/media/files/media-archon.toml.Attempt to discover how many threads can execute in parallel on the host (THREADS).Create two threadpools, onelightwith10*THREADSthreads for exploring directories and copying, and oneheavyof1*THREADSthreads for conversions or other CPU heavy tasks. (The numbers of threads can be overridden in the configuration file.)After configuring itself alightwalker thread will be scheduled on thelightthreadpool to walk/directory/of/media/files/. The thread will:Iterate through the objects in the directory looking for a configuration override file (defaultmedia-archon-override.toml). If found it will update its converter parameters for this directory and all its subdirectories.Loop through the objects again and depending on whether it is a directory or a file with a particular extension (e.g..mp3):If a directory, schedule a new walkerlightthread to search the subdirectory in parallel.If a file on the ignore list (.*) ignore.If a file with an extension in the copy list, and one or more of the following conditions are met schedule alightthread to copy it to the target:Themedia-archon.tomlmtime is newer than the target mtimeAnymedia-archon-override.tomlmtime in the current or parent directory is newer than the targetThe source file mtime is newer than the target mtimeIf a file with an extension in the convert list, and one or more of the following conditions are met schedule aheavythread to convert it from the source to the target using the supplied command line in the config.Themedia-archon.tomlmtime is newer than the target mtimeAnymedia-archon-override.tomlmtime in the current or parent directory is newer than the targetThe source file mtime is newer than the target mtimeSchedulelightthreads to delete files and directories in the target that are not in the source.Licensemedia-archonis distributed under the terms of theAGPL-3.0-or-laterlicense.
Package: media-catalogue
media-catalogueA Wagtail app for media items that are presented in a unified view in the admin interface. Please bear with us while we prepare more detailed documentation.Compatibilitymedia-catalogue' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.xInstallationInstall usingpip:pipinstallmedia-catalogueAddmedia_catalogueto yourINSTALLED_APPSsetting:INSTALLED_APPS=[# ...'media_catalogue'# ...]
Package: mediacatch-s2t
MediaCatch Speech-To-Text Uploadermediacatch-s2t is theMediaCatchservice for uploading a file in python and get the transcription result in a link. This module requires python3.9 or above.You can use it on your CLIpipinstallmediacatch_s2t

python-mmediacatch_s2t<api_key><path/to/your/media/file>--fallback_languagedaOr import it as a modulefrommediacatch_s2t.uploaderimportupload_and_get_transcription'''The result will be a JSON object:{"url": "url-to-your-transcription","status": "uploaded","estimated_processing_time": "your-estimated-time-to-get-your-transcription-done","message": "The file has been uploaded."}'''result=upload_and_get_transcription('path/to/your/media/file','api_key',fallback_language='da')
Package: mediachecker
DescriptionThis library usesffmpegto check if a file (such as an mkv video) is corrupt.  Any errors generated byffmpegwhen reading the file are assumed to be caused by file corruption.Installationpip3 install mediacheckerTheffmpegcommand must also be available on your system (andPATH).Usagefrom mediachecker import AVFile

f = AVFile('some_file_to_check.mp4')
file_is_good = f.is_good()By default, we only scan the first audio track of the file.  This should be relatively fast.  Alternatively, you can do:file_is_good = f.is_good(method='full')which will probably be slower.If you want to see the output that was generated byffmpeg, you can usewrite_log:file_is_good = f.is_good(write_log=True)This will save any errors tosome_file_to_check.mp4.log.CLIInstalling themediacheckerpackage also creates a command-line tool calledcheck_media.Runcheck_media -hfor help using it.  Example usage:$ check_media -r -l /path/to/my/media/That will recursively search for media in/path/to/my/media/, scan
each file (using the default method), and write logs for all of them.TODOAdd more command line optionsAdd more testsEnable HW acceleration, e.g.ffmpeg -hwaccel cuda ...Enable debug statements regarding progress, etc.