Saravanan666/pypi-org huggingface dataset:
Package: medicalsegment
.. raw:: html<embed><p align="center"><img width="200" src="https://github.com/yngtodd/segment/blob/master/img/segment_crop.png"></p></embed>--------------------------.. image:: https://badge.fury.io/py/segment.png:target: http://badge.fury.io/py/segment.. image:: https://travis-ci.org/yngtodd/segment.png?branch=master:target: https://travis-ci.org/yngtodd/segmentSegment-------Medical imaging segmentationDocumentation-------------The full documentation is at http://segment.rtfd.org.History-------0.1.0 (2018-08-27)++++++++++++++++++* First release on PyPI.
Package: medical-shape
Medical ShapeAtorchioextension for shapes and their processing.Usagemedical_shapeprovides 3 major classes for usage:ShapeShapeis a subclass fromtorchio.data.Image. It stores arbitrary pointclouds together with their descriptions and an associated affine matrix.
The pointclouds are stored as 2Dtorch.Tensorin the formNxDwhereNis the number of points andDis the dimensionality of points (usuallyD=3).ShapeSupportSubjectTheShapeSupportSubjectis an extension oftorchio.data.Subjectto allow the inclusion ofShape-type objects into the subject. It should be used instead oftorchio.data.Subject,
whenever a shape is included and is also safe to use without a shape (will behave exactly liketorchio.data.Subjectin that case) as shapes often require special handling.TransformShapeValidationMixinThis class is a transformation mixin to allow checks whether shape-support is required and to raise warnings if the incorrect Subject-type was used.
All transformations supporting shapes should inherit from it (and as it is derived fromtorchio.transforms.Transform, it is also safe to use this class as a standalone baseclass).InstallationThis project can be installed either from PyPI or by cloning the repository from GitHub.For an install of published packages, use the commandpipinstallmedical-shapeTo install from the (cloned) repository, use the commandpipinstallPATH/TO/medical-shapeYou can also add -e to the command to make an editable install in case you want to modify the code.You can also install the package directly from GitHub by runningpipinstallgit+https://github.com/justusschock/medical-shape.gitDocker ImagesWe provide a docker image for easy usage of the package and as a base image for other projects.The file for this image can be found at dockers/Dockerfile. We provide both, a CPU-only and a CUDA-enabled image based on the NVIDIA NGC PyTorch image. These images can be found onDockerHub.
Package: medical-waveforms
medical-waveformsmedical-waveformsis a Python package for preprocessing and analysis of physiological waveforms.This package currently focuses on:Splitting waveforms into individual cycles (e.g. splitting a respiratory waveform into individual breaths)Extracting features from individual cyclesRule-based signal quality assessmentInstallationInstall with:pip install medical-waveformsThe package is tested on Python 3.8 - 3.11.Getting startedSee thetutorial notebookfor a general introduction to using the package.Thesignal quality assessment notebookdemonstrates customisation of the signal quality assessment process.These tutorials currently focus on arterial blood pressure waveforms, but can be adapted to other physiological waveforms.Contributing to this projectContributions are very welcome! Please seeCONTRIBUTING.mdto get started.AcknowledgementsOur signal quality assessment pipeline is adapted from that used in the excellentPhysioNet Cardiovascular Signal Toolbox. Many thanks to itscontributors.
Package: medicalworkers
medicalworkers一人の医療従事者につき、抱える国民の数を可視化するPython Packageです。
期間は2010年から2020年までの10年間で、最大4カ国まで可視化可能。This Python Package visualizes the number of citizens held per healthcare professional.
The period is 10 years, from 2010 to 2020.
Up to 4 countries can be visualized.Example of usepip install medicalworkers
python medicalworkers.py Japan France India
Package: medicare_utils
medicare_utilsScripts to assist working with Medicare data.FeaturesProvides the classMedicareDF. This class contains some canned scripts to make common tasks easier. It currently contains two functions:get_cohort(), which uses the beneficiary summary file to find a set of medicare beneficiaries according to options given to the function.search_for_codes(), which searches for HCPCS, ICD-9 diagnosis, and/or ICD-9 procedure codes in a given type of file.InstallationInstall the package with:pip install git+https://github.com/kylebarron/medicare_utils --upgradeUsageThe class is initialized withimportmedicare_utilsasmedmdf=med.MedicareDF('05',range(2010,2013))mdf.get_cohort(gender='female',ages=range(65,75))mdf.search_for_codes(2010,'med',icd9_diag='41071')It has attributes that refer to different levels of the data, when applicable:mdf.pl: patient-level data. Here the index of the data isbene_idfor data post-2005, orehicfor data pre-2005.mdf.cl: claim-level data.History0.0.1 (2018-03-01)First release on PyPI.
Package: mediciasafely
MediciaSAFELY Command Line InterfaceThis tool is part of theMediciaSAFELYproject. It is based on the open-sourceOpenSAFELYproject.It gives a CLI to run local studies through themediciasafelycommand.To use it you will need to install:Python(at least version 3.8, but the more recent the better)gitDockerOnce these are installed you can install the tool itself with:pip install mediciasafelyTo see available commands runmediciasafely --helpDeveloper docsPlease seethe additional information.
Package: medidata
Medidata - Medical Image DatasetMediData is a Python library that provides access to medical image datasets. It aims to simplify the process of loading and working with medical image data in various machine learning and data analysis projects.FeaturesLoad and preprocess medical image datasets.
Resize images to the desired dimensions.
Access images and corresponding labels as numpy arrays.
Support for various medical imaging formats.
Easily integrate with machine learning workflows.InstallationYou can install MediData using pip:pip install medidataUsageTo use the Medidata dataset, you can install the Medidata library and import it into your Python project. You can then access the dataset using the provided functions and utilities. Here's an example of how to load the dataset:To use MediData, follow these steps:Import the medidata module:from medidata import datasetLoad the BR35H dataset using the load_br35h function:X, y, labels = dataset.load_br35h()This function loads the BR35H dataset from the default directory path and returns the resized images and corresponding labels as numpy arrays.You can now use the X, y, and labels variables in your data analysis or machine learning workflows.ExampleHere's an example of how to use MediData to load and process the BR35H dataset:from medidata import dataset

X, y, labels = dataset.load_br35h()

# Perform further operations on the loaded dataset
# ...ContributingWe welcome contributions to the Medidata library, including the addition of new medical image datasets. If you have other medical image datasets that you would like to include in Medidata, please reach out to us or submit a pull request.Together, let's build a comprehensive and valuable resource for medical image analysis.CitationIf you use the Medidata dataset in your research or projects, we kindly request that you cite the original source of the dataset. The dataset can be found at:Br35H Dataset on KagglePlease refer to the original dataset and follow any additional citation guidelines provided by the dataset creators.ContactFor any questions or inquiries regarding the Medidata dataset, please contactalperent@mail.com.References[1] Ahmed Hamada. (2023). Br35H: Brain Tumor Detection Dataset. Kaggle Datasets. Retrieved fromhttps://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection
Package: medil
MeDILMeDIL is a Python package for causal modeling, originally focusing on the measurement dependence inducing latent (MeDIL) causal model framework1, but now including more general methods for causal discovery and inference.More information can be found in thedocumentation.Support, Bugs, and ContributingIf you have any questions, suggestions, feedback, or bugs to report, pleaseopen an issue on Gitlaboron Githuborcontact me.
Additionally, if you would like to use this package or any of its code in your research, or to contribute to this package, feel free (but not obliged) tocontact me.LicenseSeeLICENSE, which is the Cooperative Non-Violent Public License v7 or later (CNPLv7+).Brief License Summary (For Lay-People)The Nonviolent Public License aims to ensure basic protections against forms of violence, coercion, and discrimination.
This license covers several formats of creative work but has extra terms for software given the power it has as a tool outside of its creative capacities.
The Cooperative Nonviolent Public License goes further to only allow commercial use of the copyrighted work for individuals and worker-owned organizations.ChangelogSeeCHANGELOGfor a history of the already implemented features, works in progress, and future feature ideas.References1.Markham, Alex & Grosse-Wentrup, Moritz. (2020). Measurement Dependence Inducing Latent Causal Models. InConference on Uncertainty in Artificial Intelligence (UAI)PMLR 124:590–599. URL:http://proceedings.mlr.press/v124/markham20a/markham20a.pdf
Package: medimages4tests
Generates dummy medical images, with junk image data but realistic headers,
to test imaging handling pipelinesInstallationMedimage4tests is available on PyPI so to install, simply use pip$pip3installmedimages4testsor include in your package’stest_requires.UsageCreate a pytest fixture consisting of a dummy image with field-map metadata in DICOM format# Import medimages4tests generator functionsfrommedimages4tests.dummy.dicom.mri.fmap.ge.discovery_mr888.dv26_0_r05_2008aimportget_image# Return generated images in pytest fixtures (or alternative test framework)@pytest.fixture()defge_dicom_fmap():returnget_image()Create a dummy NIfTI imageimportnumpy# Import `get_image` functionfrommedimages4tests.dummy.niftiimportget_image# Create dummy nifti image of 10x10x10 containing all ones@pytest.fixture()defones_nifti():returnget_image(data=numpy.ones((10,10,10)))Access real T1-weighted from OpenNeuro.orgfrommedimages4tests.mri.neuro.t1wimportget_image@pytest.fixture()deft1w_nifti():returnget_image(sample="ds004130-ON01016")AcknowledgementsThe authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability.
Package: medimtools
medimtoolsA python package containing all the tools you might ever need (allegedly) for medical imagingFree software: MIT licenseDocumentation:https://medimtools.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-12-09)First release on PyPI.
Package: medipy
medipyCode Collection for Medical Data Processing and Analysis in PythonThis Python package provides a broad collection of code for processing and analyzing medical data (like ECG, EEG, ACC, EMG, GYRO, ..). Every modul reflects a model, algorithm or concept published in scientific journal (or similiar) and is named after its main author. The publications are cited in the docstring of each modul.Implemented CodeThese are the current modules which are implemented in this package:R-Peak-Detection for 1D ECG Signals byHamiltonRR-Interval-Error-Detection for 1D ECG Signals byLipponenInstallationYou can install the package using pip:pipinstallmedipyUsageYou can use any function of this package by either importing the complete packageimportmedipyasmpr_peaks=mp.hamilton.detect_r_peaks(ecg,sampling_rate)or single modulesfrommedipyimporthamiltonr_peaks=hamilton.detect_r_peaks(ecg,sampling_rate)Exampleimportmathfrommedipyimporthamiltonfrommedipyimportlipponenfrommatplotlibimportpyplotaspltfromscipy.miscimportelectrocardiogram# Load ECG Data using scipyecg=electrocardiogram()sample_rate=360# Detect R-Peaksecg_preprocessed=hamilton.preprocessing(ecg,sample_rate)r_peaks=hamilton.detect_r_peaks(ecg,ecg_preprocessed,sample_rate,least_distance=0.2,th_coefficient=0.189,th_search_back=0.3)r_peaks_refined=hamilton.refine_r_peaks(ecg,r_peaks,refine_samples=30)# Calculate RR-Intervalsrr_intervals=[math.ceil(r_peaks[k]-r_peaks[k-1])forkinrange(1,len(r_peaks))]# Detect RR-Errorsrr_errors,ectopic_intervals,missed_intervals,extra_intervals,long_short_intervals=lipponen.rr_interval_error_detection(rr_intervals)# Print Resultsprint("R-Peaks: ",len(r_peaks))print("RR-Intervals: ",len(rr_intervals))print("RR-Errors: ",sum(rr_errors))print("Ectopic Intervals: ",sum(ectopic_intervals))print("Missed Intervals: ",sum(missed_intervals))print("Extra Intervals: ",sum(extra_intervals))print("Long Short Intervals: ",sum(long_short_intervals))# Plotfig=plt.figure()plt.plot(ecg)plt.plot(r_peaks,ecg[r_peaks],'rx')plt.plot(r_peaks_refined,ecg[r_peaks_refined],'go')plt.show()ContributionIf you want to support the project or contribute to the code, feel free to contact.LicenseThis project is licensed under the MIT License - see theLicensefile for details.
Package: medit
MEdit - Previewing [M]arkup [Editor]A simple text editor based on PyQt6/QScintilla with the following goals in mind:Auto-saves and tracks filesystem in order to make loading/saving obsoleteSyntax highlighting for typical file formatsPreviewing markdown or representations in other file formatsSupport for local and remote images in Markdown/reSTDecent nice looking interface for easily distractable people like meAs almost all of my projects,meditis in dangerous alpha state - use it with
care and don't believe anything written here.Installation[<PYTHON>-m]pip[3]install[--upgrade]meditUsagemedit[<PATH>]# will open medit in given directory or on given fileDevelopment & Contributionpip3install-Upoetrypre-commit
gitclone--recurse-submoduleshttps://projects.om-office.de/frans/pocketrockit.gitcdpocketrockit
pre-commitinstall# if you need a specific version of Python inside your dev environmentpoetryenvuse~/.pyenv/versions/3.10.4/bin/python3
poetryinstallAfter modifications, this way a newly built wheel can be checked and installed:poetrybuild
poetryruntwinecheckdist/medit-0.0.5-py3-none-any.whl
python3-mpipinstall--user--upgradedist/medit-0.0.5-py3-none-any.whlMy personal MLP (v1.0) goalsFile to titleFile viewer for Plain, Python, YAML, JSON, ..AutosaveManage word wrap in editorinclude files[?] sort files by modificationAutoloadSearch in filesspell checkadd / remove / rename filessupport sequence diagrams mermaidWorkdir/file history / Recent filesMulti-File Undo/RedoChange into / step up current directoryZen modeSearch/open filesPreview for previewable onlyHightlight todo.txtNotify external file changes(Re-)store zoom and fullscreenFile ignore filterIcon / .desktop fileProper Qt styleShow local imagesShow remote imagesSlim file / folder create / renameProper View CSS selectorView follows editorLinks clickableFix Links to support(text)[url]syntaxFeature ideasExport to Pdf / Html / Docx ..Copy / paste imagesDrag & drop imagesSpell checkerScript console with previewPreview rules (markdown->HTML, xml/json/yaml -> xml/json/yaml ..)ReadMermaidhttps://unpkg.com/browse/mermaid@8.1.0/dist/https://pypi.org/project/md-mermaid/https://github.com/oruelle/md_mermaid/issues/2Qt6https://github.com/5yutan5/PyQtDarkThemeQScintillahttps://web.archive.org/web/20190604145031/https://qscintilla.com/prepare-image-hack/Markdownhttps://pypi.org/project/markdown-include/(Markdown Editor)https://github.com/jamiemcg/remarkableStyles / CSShttps://thomasf.github.io/solarized-css/https://thomasf.github.io/solarized-css/solarized-light.csshttps://github.com/altercation/solarizedhttps://github.com/sindresorhus/github-markdown-csshttps://python-markdown.github.io/extensions/code_hilite/#step-1-download-and-install-pygmentshttps://github.com/richleland/pygments-csshttps://github.com/OzakIOne/markdown-github-darkhttps://github.com/sindresorhus/github-markdown-csshttps://markdowncss.github.io/https://github.com/markdowncss/retrohttps://mixu.net/markdown-styles/https://www.jsdelivr.com/package/npm/@naokim03/markdown-theme-solarized
Package: meditate
A command line meditation clock.Installation:user install:pipinstall--usermeditatesystem wide (why would you do such a thing?):sudopipinstallmeditateCopyrightThemeditate packageis released under theAGPL-3.0-or-later.Thebell sound, taken fromfreesound.org, is licensed underCC0.MirrorsBitBucketGitGudGitHub
Package: meditor
Markup Editor is a editor for reStructuredText and Markdown.TipFor bad performance, close “preview on input”.Featuresupport Markdown and reStructuredTextPygments to support Syntax Highlightsupport MathJaxFormat tableVIM modeHTML PreviewSynchronize scroll with preview windowdrag and dropMultiple workspacesCustomized templatePeview CSS ThemesMultiple editor windowsVIMvim command:h, j, k, l          # move left, down, up, right
~, gu, gU           # change uppper or lower case
w, b, e             # move forward or backward by word
0, ^, $             # move begin of line, first word of line, end of line
gg, G               # move document top and bottom
ctrl+f, ctrl+b      # page down and up
d, db, dw, D, dd    # delete back word, word, to end of line, line
y, yy, x, X, p      # copy, cut, paste
i, a, A, o, O       # insert, append, newline
<, >                # indent
J, I                # line join, and vertical insert
u, .                # undo and redo
/, :/, /<search>, s/<search>/<replace/  # search and replace
c, w, r             # selection change, read and write
n [file]            # new window

,f                  # format tablevim mode:normalinsertvisualvisual blockcommand::w,:qInstallin Linux:pip3 install meditor --userinstall with download from github:# download source
git clone https://github.com/liuyug/meditor.git
cd meditor

# for Virtualenv
virtualenv ../virtualenv
# on Linux
source ../virtualenv/bin/activate
# on Window cmd
../virtualenv/scripts/activate
# on Window PowerShell
Set-ExecutionPolicy -Scope CurrentUser  RemoteSigned
../virtualenv/scripts/activate

# check version, above 3.5
python --version
pip --version

# install 3rd packages
pip install -r requirements.txt
# prepre data files
# ui window
bash ui.sh
# fetch reStructuredText documents
bash help.sh
# for Simple MathJax
bash math.sh
# for preview themes
bash themes.sh
# for icon theme that is used under win32
bash icon_theme.sh list
bash icon_theme.sh <theme name>
# install meditor
python3 setup.py install . --userNoteIf you run as user ‘root’ it need add environment variable “export QTWEBENGINE_DISABLE_SANDBOX=1” to disable sandboxing.Screen Shotcheck screenshot directory to view more ThemesOtherIconset:NuoveXT 2Icons by Saki
Package: medium2md
Convert your favorite Medium articles into markdown files usingMedium2md, a powerful and user-friendly command-line tool. Designed in Python, it extends beyond simple conversion to download all images within the article, saving them locally and integrating them into your markdown file seamlessly.Table of ContentsFeaturesGetting StartedUsageProject StructureContributingLicenseFeaturesEfficient Conversion: Download Medium articles as HTML and convert them into clean, readable Markdown files.Image Handling: Download images from the article, save them locally, and update image URLs in the markdown files to point to local paths.Post-Processing: Automatically clean up unnecessary data after conversion.User-Friendly: Interactive CLI interface with progress bars and prompts for an enhanced user experience.Getting StartedPrerequisitesPython 3.8 or laterCondaInstallationClone the repository:gitclonehttps://github.com/siddharthksah/medium2md.gitNavigate into the project directory:cdmedium2mdCreate a new conda environment and activate it:condacreate--namemedium2mdpython=3.8-y
condaactivatemedium2mdInstall the package and its dependencies usingsetup.py:python3setup.pyinstall
playwrightinstallThis will automatically install all required packages listed insetup.py'sinstall_requiressection.UsageRunmedium2mdwith:python3src/main.py-u<URL>Replace  with the URL of the Medium article you wish to download. If no URL is provided, the script will prompt you to input one.When the process is complete, the script will ask if you wish to open the downloaded markdown file in your default editor.OutputThe converted articles are stored in the output directory, which is created in the project's root directory.UnittestsRunning UnittestsTo run the unittests for theMedium2mdproject, navigate to the root directory of the project and use the following command:python-munittestdiscoverunittestsThis will automatically discover and run all the unittests in the unittests directory.Please ensure that you have installed the required packages for the project by following the installation instructions in theGetting Startedsection.📚 Project Structure.
├── LICENSE.txt
├── README.md
├── docs
├── output
├── requirements.txt
└── src
    ├──__init__.py
    ├── clean_md.py
    ├── clean_unnecessary_data.py
    ├── download_with_media.py
    ├── html_to_md.py
    ├── main.py
    └── url_to_local.py🤝 ContributingWe welcome contributions! If you would like to make changes, please submit a pull request. For substantial updates, we request that you open an issue first to discuss the proposed changes.📃 LicenseMedium2mdis licensed under the terms of theMIT License. For the full text, see theLICENSEfile.
Package: medium-api
Python Wrapper on top ofUnofficial Medium APIto quickly extract data from Medium’s website (https://medium.com).InstallationInstall fromPyPI$pipinstallmedium-apiFor more information, seeDetailed InstallationExampleGetting a Medium user’s information and fetching his articles.frommedium_apiimportMediummedium=Medium('YOUR_RAPIDAPI_KEY')user=medium.user(username="nishu-jain")print(f'{user.fullname}has{user.followers_count}followers.')user.fetch_articles()forarticleinuser.articles:print(article.title)For more examples, seeUsageHow to get your RapidAPI KeySteps:Sign up onRapidAPI PlatformSubscribe to ourUnofficial Medium-APIGo to the API’sEndpointstab on theRapidAPI Hub listingand select the API key from theX-RapidAPI-Keydropdown underHeader Parameterssection.For more details, see the following links:https://rapidapi.com/blog/api-glossary/api-key/https://docs.rapidapi.com/docs/keysFeaturesExtract/Scrape/Fetch/Get:Medium User information and User-written ArticlesMedium User’s Followers and FollowingMedium Articles informationMedium Article’s Textual Content and MarkdownMedium Article’s Responses/CommentsMedium Publications informationMedium Publication’s ArticlesMedium Publication’s Newsletter InfoMedium’s Top WritersMedium’s Topfeeds (Trending, Latest, All time best, best of year/month/week)Medium’s LatestPosts (distributed articles)DocumentationFull Documentation athttps://medium-api.readthedocs.ioOther MaterialsMedium REST API:Swagger Documentation:https://docs.mediumapi.comLicenseFree software:MIT licenseEULA:Terms of UseCode of ConductIn the interest of fostering an open and welcoming environment, all contributors, maintainers
and users are expected to abide by the Python code of conduct:https://www.python.org/psf/codeofconduct/
Package: mediumbackup
mediumbackup - A Backup Utility for MediumSaves all your Medium stories locally as html or markdown files.Installationpip install mediumbackupUsageAs a script from the command linepython -m mediumbackup "<your username>"As a moduleimport mediumbackup as mb

username = "<your username>"
mb.backup_stories(username)OptionsSpecify a folder.python -m mediumbackup "<username>" --backup_dir "backup 2020-11-01"Save as markdown.python -m mediumbackup "<username>" --format "md"Download images.python -m mediumbackup "<username>" --format -iDownload images to a different folder than the stories.python -m mediumbackup "<username>" --backup_dir "posts" --download_images --images_dir "assets/images"Include a front matter for jekyll.python -m mediumbackup "<username>" --format "md" --jekyll_front_matterTestsTo run the tests, execute:python -m tests