Saravanan666/pypi-org huggingface dataset:
Package: minimal-podcasts-player
Minimal Podcasts Playerhttps://github.com/son-link/minimal-podcasts-playerSubscribe, listen and (in the future) download your favorite podcasts, quickly and easily.Available for Linux, Windows and Mac.Licensed under GNU/GPL3 or higher.Note: this project is still under development, so some features are not yet available or complete.InstallFrom source code:Clone the repo, download the zip under Code -> Download ZIP or download the last release.Open a terminal and go to the project folder.Install the dependencies:From PIP(with administration permissions):pip install -r requirements.txtDebian/Ubuntu/Mint/MX Linux:apt:apt install python3-pyqt5, python3-pyqt5.qtmultimedia, libqt5multimedia5, libqt5multimedia5-pluginsPypi:pip3 install podcastparserArch Linux/Manjaro:pacman:python-pip python-podcastparser python-pyqt5 qt5-multimediaPypipip installCopy bin/minimal-podcasts-player.desktop to /usr/share/applicationsCopy bin/minimal-podcasts-player.svg to /usr/share/pixmapsWindows:Install Python, or Conda/Miniconda.Open a terminal (cmd or Poweshell) and executepip install -r requirements.txtNote:if you useConda/Miniconda,DON'Tinstall the packagepyqtfrom conda and conda-forge repositories, install it from Pypi, since the one installed from them does not include the libraries for multimedia playback.From Pypi:pip install minimal-podcasts-playerExecutables:You can download aAppImageforGNU/LinuxandexecutableforWindowsonreleases pageIf you use Arch Linux, Manjaro or other Arch based distros a package is avaliable onAUROther credits:App icon(temporary) make byBedexpStockand licensed underPixabay Licenseno-cover.svg image make by me using aMaterial Design Iconsfrom Google and licensed under Apache license version 2.0.Especial mentions:Engels Tercero from the Telegram group SystemInside, for helping me package the Mac OS X application.Thanks to David Linares, member of SystemInside's Telegram group for report the bug and provide several ideas.
Package: minimal-python-project-skeleton
Minimal Python Project SkeletonAbout The ProjectGetting StartedInstallationContributingLicenseContactAcknowledgmentsAbout The ProjectThis folder structure should act as a simple starting point for your next python project..
├── .github
│   └── workflows
│       ├── docs.yaml
│       ├── pre-commit.yaml
│       ├── release.yaml
│       └── test.yaml
├── .gitignore
├── .pre-commit-config.yaml
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── README.org
├── pyproject.toml
├── src
│   └── minimal_python_project_skeleton
│       └── __init__.py
└── test
    └── test_func.py

6 directories, 14 filesIt contains just the minimum to get you started with a ready configured GitHub actions for automatedlinting,testing,documentingandreleasingon PiPy.Getting StartedUse this template directly tocreate a new GitHub repositoryor just clone the repository to your desired destination and start working on your new project.I have marked all the relevant parts that you might want to adjust..github/workflows/release.yaml:      url: https://pypi.org/p/minimal-python-project-skeleton  # TODO: Replace with your PyPI project name
.github/workflows/release.yaml:      url: https://test.pypi.org/p/minimal-python-project-skeleton  # TODO: Replace with your TestPyPI project name
pyproject.toml:dependencies = []  # TODO: Add dependencies
pyproject.toml:license = {text = "MIT"}  # TODO: Choose license
pyproject.toml:name = "minimal_python_project_skeleton"  # TODO: Change package name
pyproject.toml:  'minimal_python_project_skeleton[test]',  # TODO: Change package name
pyproject.toml:Changelog = "https://github.com/MArpogaus/minimal_python_project_skeleton/blob/dev/CHANGELOG.md"  # TODO: Change project repo
pyproject.toml:Documentation = "https://marpogaus.github.io/minimal_python_project_skeleton"  # TODO: Change project repo
pyproject.toml:Issues = "https://github.com/MArpogaus/minimal_python_project_skeleton/issues"  # TODO: Change project repo
pyproject.toml:Repository = "https://github.com/MArpogaus/minimal_python_project_skeleton"  # TODO: Change project repoPro-Tipp:If you later want to update the template, keep a separate branch (i.e.skeleton) around andcherry-pickthe changes you would like to keep for future projects.
This way you can also pull the latest version from upstream andcheckoutthe new files you would like to use in your project.InstallationThis package is available onPyPI. You install it using pip:pip install minimal_python_project_skeletonContributingAny Contributions are greatly appreciated! If you have a question, an issue or would like to contribute, please read ourcontributing guidelines.LicenseDistributed under theMIT LicenseContactMarcel Arpogaus-znepry.necbtnhf@tznvy.pbz(encrypted withROT13)Project Link:https://github.com/MArpogaus/python-project-skeletonAcknowledgmentsREADME inspired byothneildrew/Best-README-TemplateContribution guidelines inspired byprobabilists/zukoRelease workflow inspired bypackaging.python.org
Package: minimal-snowplow-tracker
OverviewAdd analytics to your Python apps and Python games with theSnowplowevent tracker forPython.With this tracker you can collect event data from your Python-based applications, games or Python web servers/frameworks.This is a fork of the original Snowplow Python tracker v0.8.0, with a number of changes:All emitters except for the basic one removedTests converted to dockerAll tracking event methods except for structured/unstructured removedPython 3.7 supportedContributing quickstartAssuming Git andDocker(with docker-compose) are installed:host$ git clone git@github.com:snowplow/snowplow-python-tracker.git
host$ docker-compose run --rm test toxCopyright and licenseThe Snowplow Python Tracker is copyright 2013-2014 Snowplow Analytics Ltd.Licensed under theApache License, Version 2.0(the “License”);
you may not use this software except in compliance with the License.Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.This software has been modified by Fishtown Analytics. Changes are copyright 2018 Fishtown Analytics LLC.
Package: minimal-stats
minimal-statsThis is a simple python package for statistical distributions. Currently this package calcuates Binomial and Gaussian distribution.Installationpipinstallminimal-statsGaussian DistributionWe can directly provide the mean and standard deviation of data (or read data from a file) and add two gaussain distribution.>>>fromdistributionsimportGaussian>>>g1=Gaussian(180,34)>>>g1g=Gaussian(mean=180,stdev=34)>>str(g1)'mean 180, standard deviation 34'>>>g2=Gaussian(180,34)>>>g1+g2g=Gaussian(mean=360,stdev=48.08326112068523)Here, we read data from a file, calculate mean, standard deviation and probability density function of gaussian distribution and then see graphical output>>>fromdistributionsimportGaussian>>>g=Gaussian()>>>g.read_data_file(r'\tests\input\numbers.txt')>>>g.calculate_mean()78.0909090909091>>>g.calculate_stdev()92.87459776004906>>>g.pdf(5)0.0031515485379333356>>>g.plot_histogram_pdf()Binomial DistributionWe can directly provide the n and p of data (or read from file as before) and calculate mean, standard deviation and probability mass function of binomial distribution.>>>fromdistributionsimportBinomial>>>b=Binomial(0.15,60)>>>bb=Binomial(p=0.15,n=60)>>>b.calculate_mean()9.0>>>b.calculate_stdev()2.765863337187866>>>b.pmf(7)0.11985659270959788>>>a=Binomial(0.15,50)>>>a+bb=Binomial(p=0.15,n=110)Here, we read data from a file, `b.replace_stats_with_data()calculate mean, standard deviation, n and p of Binomial distribution and then we plot bar graph of pmf.>>>fromdistributionsimportBinomial>>>b.read_data_file(r'\tests\input\numbers_binomial.txt')>>>b.replace_stats_with_data()>>>str(b)'mean 8.0, standard deviation 1.7541160386140584, p 0.6153846153846154, n 13'>>>b.plot_bar_pmf()ContributionWe appreciate feedback and contribution to this repo! Before you get started, please see the following:Contribution GuidelinesCode of Conduct GuidelinesLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: minimal-surface
Minimal surfaceminimal-surfaceis a Python package which can be utilized for the segmentation of objects in 3D images using as few as 2 points and a 2D slice! This algorithm uses energy minimization to estimate the surface of the object. To achieve fast execution the method was implemented in C++, but can be called from Python through a thin wrapper.InstallationIt can be installed from PyPI usingpip:python -m pip install minimal-surfaceNote: This package is currently available only for Windows. In the future we plan to release it for Linux and Mac systems.UsageThe easiest way to utilize our tool is by usingAnnotation Toolbox, anapariplugin created for fast 2 and 3D image annotation.Exampleimportminimal_surfacefromskimage.dataimportcells3dfromscipyimportndimageimportnumpyasnpimportmatplotlib.pyplotaspltimportnumpyasnpdefcalc_features(image):delta=0.1max_=np.quantile(image,.95)min_=image.min()image=np.clip((image-min_)/(max_-min_),0,1)# This feature will consider both image gradient and intensity# (giving higher values when the gradient is high and the intensity is low)gradient=ndimage.gaussian_gradient_magnitude(image,(1.,1.,1.))weights=1+(delta-1)*image**2phi=gradient*weights+delta*(1-image**2)phi=(phi-(min_:=phi.min()))/(phi.max()-min_)returnphi# Crop the part to the smallest size possible to prevent long computationdata=cells3d()[:,1,60:130,130:190]# Two points on the surface of the nucleusp1=np.asarray([20.,60.,35.])p2=np.asarray([39.,12.,35.])data=(data-(min_:=data.min()))/(data.max()-min_)data=ndimage.gaussian_filter(data,1.)features=calc_features(data)alpha=3e-3phi=alpha+(1-alpha)*np.exp(-5*features)phi=phi/phi.max()calculator=minimal_surface.MinimalSurfaceCalculator()defsegment_slice(image_slice,distance_map):# This function segments the input image slice.# Asking for user input is also possible for accurate segmentation.returnimage_slice>=0.24calculator.set_initial_plane_calculator(segment_slice)# If you have multiple objects, you can run the first part separately. which requires user input,# then finish the computationally expensive part together for the whole dataset.# calculator.calc_eikonal_and_transport_init(phi, data, p1, p2, True)transport_function=calculator.calculate(phi,data,p1,p2)print("calculated")mask=transport_function>=0.plt.imshow(transport_function[35],cmap="plasma")plt.show()
Package: minimal_validator
Minimal Validatorminimal_validatoris a very simple validation framework for python.InstallationInstallation into avirtualenvis recommended. Also seepipenv.Install frompipyas external dependency:$ pip install minimal-validatorInstall source locally:$ pip install -e .Run unit tests:$ python setup.py testOverviewMinimal validator is a very simple validation framework. The framework expects to run through a list of validation functions. Each validation function must have at least two parameters:attributeanddata. Theattributeis a string representing the attribute in thedataobject to be validated. Each validation function returns aResultobject. Several validation functions for the same attribute can be chained together with thecombine_validatorsfunction. Thecombine_validation_resultsfunction is used to perform all of the validations against a givendataobject.EachResulthas akeep_checkingboolean that defaults toTrue. For a given attribute, ifkeep_checkingisTrue, thevalidate_sequentiallyfunction ofcombine_validatorswill continue gathering results. If it'sFalse,validate_sequentiallywill not run any subsequent validations in the list. The idea is that some errors make it impossible to continue validating. For example, if an attribute is not set, then no further validation logic can be applied to it. On the other hand, for something like a password, a number of validations such as "minimum length" and "presence of special characters", etc. can be applied indepdendently. In that case the final result of the validation will include all such errors rather than stopping at the first error encountered.ExamplesThe following is an examplevalidate_username_and_passwordfunction (along with a few helper functions) taken from the unit tests:defvalue_has_min_length_6(attribute,data):returnvalue_has_min_length(attribute,data,6)defvalue_has_max_length_12(attribute,data):returnvalue_has_max_length(attribute,data,12)defvalue_has_at_least_one_uppercase_char(attribute,data):returnvalue_matches_at_least(attribute,data,list(string.ascii_uppercase),1)defvalue_has_at_least_one_special_symbol(attribute,data):returnvalue_matches_at_least(attribute,data,list('!@#$%^&*'),1)@pytest.fixturedefvalidate_username_and_password():defvalidate(data):username_validators=combine_validators('username',data,[attribute_exists,value_is_set,value_is_valid_email])password_validators=combine_validators('password',data,[attribute_exists,value_is_set,value_has_min_length_6,value_has_max_length_12,value_has_at_least_one_uppercase_char,value_has_at_least_one_special_symbol])results=combine_validation_results(username_validators,password_validators)return[result.to_dict()forresultinresults]returnvalidateWhile the framework includes some validation functions, it will accept any function that takesattributeanddataparameters and returns a validResultobject (orNoneif the validation passes and no validation messages are needed).If you just need a single validation function for a given attribute, you can just wrap that function in alambdainstead of usingcombine_validators.
Package: miniman
MINminimal manpagesproviding you with the 80/20 of how many unix commands are used, for whenever you've forgotten how to usefindorgrepfor the 3rd time this weekInstallationavailable from pypi viapip install minimanUsagecontains minpages for unix commands, as well as more general explainer pages for important topics in the unix workflow (e.g. file, filesystem)Unix commandsrunminto get a list of all available minpages for bash commandsrunmin commandto get the minpage for bash commandcommande.g.min grepto get the minpage forgrepUnix Topicsrunmin -tto get a list of all available minpages for unix topicsrunmin -t topicto get the minpage for unix topictopice.g.min -t filesystemto the minpage forfilesystemContributeObviously, this is a work in progress.
Although I use many of these commands in my daily workflow, I am nowhere near understanding all the ins and outs.
So please open up an issue if I've gotten anything wrong. Thanks in advance.Also there are more things I'd like to do with this project:add a search function where you can just search for a string pattern among all minpages, and get returned relevant results (i.e. grepping among all minpages)add additional useful commandsadd additional useful topics (e.g. 80/20 regexp, interactive bash [things like <(command), !:n/!^/!$, piping, difference between '' and "", ...], vim, ...)unify the sections in the commands/topics so that everyone can always have clear expectations of what to expect from aminpagemaybe go for some more advanced markup, to do highlights, bolding, etc.If you could see yourself contributing to any of that, just open up an issue athttps://github.com/mleue/min/issues. I'd be delighted.
Package: minimapi
MinimAPIMinimAPI is a simple dynamic API backend.
Just define your model.json, then your API is readySee usage exampleshereInstallWith pip :pip3installminimapior build with :python-mbuild
pip3installdist/minimapi-*.whlModel formatMinimalistic data structure definition format for dynamic API backend and interface frontfilename: model.jsonExample :{"user":{"name":{"type":"text"},"birthday":{"type":"date"},"password":{"type":"password","tags":["unlistable"]},"city":{"type":"foreign","show":"name"}},"city":{"name":{"type":"text","tags":["required"]},"code":{"type":"number"}}}Data parameters availables :- type : data value type [required]
- show : culumn to show client side for foreign key
- tags : array of optionsCurrently supported types (based on inputs types):- text
- number
- url
- password
- date
- email
- url
- sha265
- pbkdf2
- totp
- foreign (property name must match another table name, value is foreign data id)Currently supported tags :- unlistable : cannot be returned on bulk, replaced by dash in this case
- encrypted : for client side encrypted data, type checking disabled server side
- required : mandatory field
Package: minimata
Minimata: minimalist state-machine in PythonMiniata is a very small library to manage state-machines in Python.Because it doesn't bundle a lot of features, it's pretty flexible.InstallationUse the package managerpipto installminimata.pipinstallminimataWhyI needed a really simple implementations and didn't wanted to reach to heavier
alternatives liketransitions.UsageHere is an example:fromminimataimportStateMachine,skip_transitionmodel_onboarding_state_machine=StateMachine("onboarding_state")@model_onboarding_state_machine.on("event",{"source_state":"destination_state"})defcallback(model:Model,param:bool=False,**kwargs):ifparam:print(model)@dataclassclassUserModel:onboarding_state:struser_model=UserModel(onboarding_state="source_state")model_onboarding_state_machine.trigger(model=user_model,event="event",param=True,)# Executes callback (prints user_model) *THEN* update its state.ContributingWith a hundred line of code, it's possible to get there and customize this. It'll
probably make sense for you to copy-paste that code rather than to add it as a
dependency.That being said, pull-requests are welcome. It would be nice to polish the library,
please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.CreditsInspiration was heavily taken from the following projects.micro-machinetransitionsMany thanks to their authors, maintainers, and contributors.LicenseMIT
Package: minimd
MinimdMinimal markdown parser.https://pypi.org/project/minimd/Tokenize some markdown tags from text files.InstallationInstallminimdlibrary by runningpip install minimd.UsageExtract token from a markdown file.>>>importminimd>>>withopen('docs/doc1.md')asf:...fortokeninminimd.tokenize_file(f):...print(token)...Token.TITLE('# First title\n', level=1)Token.AFTER_TITLE(level=1)Token.LINE('\n')Token.LINE('* Bullet point\n')Token.LINE('\n')Token.START_CODE('```python\n', language='python', skip=False)Token.LINE('def example_function():\n')Token.LINE("    print('foo')\n")Token.END_CODE('```\n')Or for an iterable of files (tokens are added to separate files).>>>importminimd>>>withopen('docs/doc1.md')asf1,open('docs/doc2.md')asf2:...fortokeninminimd.tokenize_files([f1,f2]):...print(token)...Token.FILE()Token.TITLE('# First title\n', level=1)Token.AFTER_TITLE(level=1)Token.LINE('\n')Token.LINE('* Bullet point\n')Token.LINE('\n')Token.START_CODE('```python\n', language='python', skip=False)Token.LINE('def example_function():\n')Token.LINE("    print('foo')\n")Token.END_CODE('```\n')Token.AFTER_FILE()Token.FILE()Token.TITLE('# Second title\n', level=1)Token.AFTER_TITLE(level=1)Token.LINE('\n')Token.TITLE('## Subtitle\n', level=2)Token.AFTER_TITLE(level=2)Token.LINE('\n')Token.LINE('Hello World!\n')Token.AFTER_FILE()Test input files are saved indocsdirectory.There is also aminimdentrypoint to see how is tokenized a set of files.%minimddocs/*.md
Token.FILE()Token.TITLE('# First title\n',level=1)Token.AFTER_TITLE(level=1)Token.LINE('\n')Token.LINE('* Bullet point\n')Token.LINE('\n')Token.START_CODE('```python\n',language='python',skip=False)Token.LINE('def example_function():\n')Token.LINE("    print('foo')\n")Token.END_CODE('```\n')Token.AFTER_FILE()Token.FILE()Token.TITLE('# Second title\n',level=1)Token.AFTER_TITLE(level=1)Token.LINE('\n')Token.TITLE('## Subtitle\n',level=2)Token.AFTER_TITLE(level=2)Token.LINE('\n')Token.LINE('Hello World!\n')Token.AFTER_FILE()DevelopmentEnvironmentUsepip install -e '.[dev]'to installminimdwith development dependencies (tests & lint).ContributingCode of the project is managed onhttps://github.com/entwanne/minimd/git repository.Building & deploying a new versionYou need to installtwinepackage (pip install twine) to be able to deploy a version of the library.You can usepython setup.py sdistto build the current version of the package.Then you can deploy this version to PyPI by runningtwine upload dist/*.
Package: mini-memgraph
Mini-MemgraphA basic wrapper to simplify interacting with a localMemgraphinstance using Python.Produced predominantly to serve as a common backend class for my other projects, but should be suitable for general use. Docstrings incoming and pull requests welcomed.InstallationInstall fromPyPIpip install mini-memgraphDisclaimerThis software was produced as a backend interface for an unsecured locally hosted Memgraph database for data analysis
and research. I am not qualified to write secure code that would be suitable for a production environment.
Do not use this code for production. All code is utilised at user's own risk etc.
Package: miniMinio
miniMinioA thin python helper get either 1) local files or 2) Minio objects. 1) Heavily relies on the default os library while 2) is built offminio-py. The package is designed to provide a seamless switch between testing file operations locally/over a file system and interacting with a minio server when in deployment.InstallationUse the package managerpipto install miniMinio.pipinstallminiMinioQuick StartfromminiMinioimportMinio# For connecting to minio s3mc=Minio(type="minio").config(hostname='host',access_key='minio',secret_key='minio',secure=False)mc.ls('bucket/dir/')# For connecting to local filesystemmc=Minio(type="local").config()mc.ls('.')ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: minimint
Minimint (MIni Mist INTerpolation)Software to do simple interpolation of MIST isochrones.Author: Sergey Koposov (2020-2021) skoposovATedDOTacDOTukInstructionsInstall minimint. You can either pip install the released version or install from githubpip install minimintDownload and prepare isochrone filesminimint.download_and_prepare()That will download everything and process the evolutionary tracks and bolometric corrections by creating the necessary files for the package.minimint.download_and_prepare()by default creates bolometric corrections for these filters
'DECam', 'GALEX', 'PanSTARRS', 'SDSSugriz', 'SkyMapper','UBVRIplus', 'WISE'
If you need additional filters, you can specify them using the filters parameterminimint.download_and_prepare(filters=['JWST','WISE','DECam', 'GALEX', 'PanSTARRS', 'SDSSugriz', 'SkyMapper','UBVRIplus'])Check which filters are available on the MIST websitehttp://waps.cfa.harvard.edu/MIST/model_grids.htmlThis will take some time (20-30 min) and will use some space (10-30 Gb).If you want to put those processed isochrone files in a location different from the site-packages folder of minimint, you can use the outp_prefix parameter ofdownload_and_prepare. You then will need to either specify the location each time when you construct the interpolators or with the MINIMINT_DATA_PATH environment variableUsageIn order to create an interpolator object for two filters (your can provide a list of any numbers of filters)ii = minimint.Interpolator(['DECam_g','DECam_r'])The interpolator is a callable, so you can call it on mass, log10(age), fehii(mass, logage,feh)This returns a dictionary with photometry, logg, logteff and logl.You also can use the interpolator to find the maximum valid mass on the isochrone.ii.getMaxMass(logage, feh)ExamplesSee thenotebookin the examples folderSynthetic stellar populationsIf you are interested in synthetic stellar populations you will need
the implementation of the IMF. For this you may want to usehttps://github.com/keflavich/imfAcknowledgementIf you are using this package please cite it through zenodohttps://doi.org/10.5281/zenodo.4002971