Saravanan666/pypi-org huggingface dataset:
Package: mimesis-factory
mimesis_factoryDescriptionMimesisintegration forfactory_boy.Installation➜pipinstallmimesis_factoryUsageLook at the example below and you’ll understand how it works:classAccount(object):def__init__(self,username,email,name,surname,age):self.username=usernameself.email=emailself.name=nameself.surname=surnameself.age=ageNow, use theMimesisFieldclass frommimesis_factoryto define how fake data is generated:importfactoryfrommimesis_factoryimportMimesisFieldfromaccountimportAccountclassAccountFactory(factory.Factory):classMeta(object):model=Accountusername=MimesisField('username',template='l_d')name=MimesisField('name',gender='female')surname=MimesisField('surname',gender='female')age=MimesisField('age',minimum=18,maximum=90)email=factory.LazyAttribute(lambdainstance:'{0}@example.org'.format(instance.username))access_token=MimesisField('token',entropy=32)pytestWe also recommend to usepytest-factoryboy.
This way it will be possible to integrate your factories intopytestfixtures.Licensemimesis_factoryis released under the MIT License.
Package: mimesniff
Pure python mimesniff implementation ofhttps://mimesniff.spec.whatwg.orgAPI interface is similar withAPI of python standard libraryInstallpipinstallmimesniffUsageimportmimesniffres=mimesniff.what('sample.mp3')print(res)# audio/mpegwithopen('sample.mp3','rb')asfin:res=mimesniff.what(fin)print(res)# audio/mpegwithopen('sample.mp3','rb')asfin:header=fin.read(512)res=mimesniff.what(header)print(res)# audio/mpeg
Package: mimetype-description
MIME Type DescriptionHuman readable MIME type descriptions based onshared-mime-info.Installpipinstallmimetype-descriptionUsagefrommimetype_descriptionimportguess_mime_type,get_mime_type_descriptionmime_type=guess_mime_type('file.txt')description=get_mime_type_description(mime_type)print(description)# plain text documentTestpytest
Package: mimetype-match
A tool to make parsing Accept headers and finding the appropriate mimetype to
return easy.  Simply pass your accept header and the mimetype your server can
return and it will find the best match.There is both a programmatic interface to this package as well as a
command line interface.InstallationIt can easily be installed using pippipinstallmimetype-matchOr if you prefer clone the repo and install it simply run the following
from the root directory of the package.pythonsetup.pydevelopFull DocumentationDocumentationUsing the command line interfacemimetype-match[accept-header][server-mimetype1][server-mimetype2]...The first argument should be the comma delimited
mimetypes as an appropriately formatted accept header.
The additional arguments are the mimetypes from the server
that you wish to match. For example you may have an Accept header
of the following:text/*;q=0.3,text/html;q=0.7, text/html;level=1, /html;level=2;q=0.4,*/*;q=0.5and the server can serve the following content types:["text/html", "application/json", "audio/basic"]In this case we would run the following command:mimetype-match"text/*;q=0.3, text/html;q=0.7, text/html;level=1, /html;level=2;q=0.4, */*;q=0.5""text/html""application/json""audio/basic"This command would tell you that “text/html” is the best choice.Using the packageUsing the package programmatically is very similar to the cli.frommimetype_matchimportAcceptHeaderavailable_types=["text/html","application/json","audio/basic"]accept_header=AcceptHeader("text/*;q=0.3, text/html;q=0.7, text/html;level=1, /html;level=2;q=0.4, */*;q=0.5")best_match=accept_header.get_best_match(available_types)# It returns a tuple.  The first object is a MimeType object# which has the orinal requested type from the client# The second object is the available type from the# server that best matches the clients requested types.print(best_match[0].mimetype)print(best_match[0].weight)print(best_match[1])VersioningPrior to version 1.0.0 versioning followssentimental
versioning.   Releases after 1.0.0 ollow
a standardmajor.minor.patchstyle.patch: forwards and backwards compatibleminor: backwards compatiblemajor: No guaranteesContributingWant to help out? We’d love it! Github will be the hub of development for mimetype-matching.
If you have any issues, comments, or complaints post them there.  Additionally, we
are definitely accepting pull requests (hint: we almost always love more tests and
documentation).  We do have just a few requests:Every method, function, and class should have a thorough docstringThere should be at least one unit test for each function and methodKeep your pull requests to one issue. (Preferably open an issue on github first for record keeping)
Package: mimicpy
MiMiCPyMiMiCPy is the companion library to the MiMiC framework for streamline preparation of input files. It includes a suite of command line tools and plugins for PyMOL/VMD. Visit theMiMiC homepagefor more details and documentation.InstallationTo install, run the following command:pip install mimicpyThe PrepQM plugins for PyMOL and/or VMD can be optionally installed by running:mimicpy_plugin_installer -pymoldir /path/to/plugin/ -vmddir /path/to/plugin/The path to the plugin is usually either the path to the PyMOL/VMD installation, or the user home directory.Portability IssuesMiMiCPy requires Python >= 3.5, pandas >= 0.24.0 and numpy >= 1.12.0. The plugins have been tested with PyMOL version 2.3.4 and VMD version 1.9.4a38, although other versions are expected to work. If any incompatibilities are found, please post an issuehere.DemoA demo of atom selection for the QM region, and generation of the MiMiC-based CPMD input file using MiMiCPy is shown below.user@system:~$mimicpyprepqm-topacetone.top-coordsacetone.gro***** MiMiCPy *****For more information type mimicpy [subcommand] --help=====> Running prepqm <=====**Reading topology**Cannot find path to Gromacs installation.Read atoms from acetone.itp.No atoms found in acetone.top.Some atom types had no atom numbers information.They were guessed as follows:+---------------------+| Atom Type | Element |+---------------------+|     c     |    C    |+---------------------+|     c3    |    C    |+---------------------+|     o     |    O    |+---------------------+|     hc    |    H    |+---------------------+**Reading coordinates**  |DonePlease enter selection below. For more information type 'help'> add resname is ACT> qUsing default values for maxstep and timestepWrote Gromacs index file to index.ndxWrote new CPMD input script to cpmd.inp=====> Done <=====
Package: mimic-text
💬 MimicMimic mimics the style of a given corpus, for great fun and delight.Developer InstallationWe usepipenvfor dependency management.gitclonehttps://github.com/ubclaunchpad/mimic.gitcdmimic/
pipinstallpipenv
pipenvinstall--devpipenvwill manage avirtualenv,
so interacting with the program or using the development tools has to be done
through pipenv, like so:pipenvrunpycodestyle.This can get inconvenient, so you can instead create a shell that runs in the managed
environment like so:pipenvshelland then commands likepycodestyleandpytestcan be run like normal.Additionally, we useTravis CIas
a CI system. To run the same checks locally, we providescripts/build_check.sh;
this can be run with:./scripts/build_check.shServerTo start the server:$ export FLASK_APP=index.py
$ flask run --without-threads
  Running on http://127.0.0.1:5000/if on windows powershell, export using:PS C:\path\to\app> $env:FLASK_APP = "index.py"
Package: mimic-useragent
mimic_user_agentFuncion que retorna un 'user-agent' falso aleatorio o estático.Uso>>>frommimic_useragentimportmimic_user_agent>>>mimic_user_agent()'Mozilla/5.0 (Windows NT 6.3; Win64; x64; rv:109.0) Gecko/20100101 Firefox/109.0'>>>>>>mimic_user_agent(seed=10)'Mozilla/5.0 (Windows NT 6.3; Win64; x64; rv:112.0) Gecko/20100101 Firefox/112.0'Recibe un parámetrointpara el uso de unaseed, por defecto esNone.Aclaración:Por ahora, solamente genera user-agent Firefox.Testspython-munittest
Package: mimikit
mimikitThe MusIc ModelIng toolKIT (mimikit) is a python package that does Machine Learning with audio data.Currently, it focuses on training auto-regressive neural networks to generate audio.but it does also contain an app to perform basic & experimental clustering of audio data in a notebook.Installationyou can install with pippipinstallmimikit[torch]or withpipinstall--upgrademimikit[torch]if you are looking for the latest versionfor an editable install, you'll needpipinstall-e.--config-settingseditable_mode=compatUsageHead straight to thenotebooksfor example usage ofmimikit, or open them directly in Colab:Output SamplesYou can explore the outputs of different trainings done withmimikitat this demo website:https://ktonal.github.io/mimikit-demo-outputsLicensemimikitis distributed under the terms of theGNU General Public License v3.0
Package: mimikko-util
mimikko_util简介兽耳助手非官方模块，用于用户签到、能量值兑换、查询等操作由于考虑到主要受众为中国用户，故模块注释和文档均采用中文项目托管也使用国内的Gitee使用方法使用方法请参考项目Wiki链接：https://gitee.com/MrDeveloper123/mimikko_util/wikis项目链接https://gitee.com/MrDeveloper123/mimikko_utilpypi链接https://pypi.org/project/mimikko-util/
Package: mimimongo
minimongo=========:Info: Minimal database Model management for MongoDB.:Author: Steve Lacy, Dmitrii Vlasov <yablokoff.tlt@gmail.com>About-----``minimongo`` is a lightweight, schemaless, Pythonic Object-Orientedinterface to MongoDB.It provides a very thin, dynamicly typed (schema-less) object managementlayer with simple consistency for any data stored in any MongoDB collection.``minimongo`` directly calls the existing pymongo_ query syntax.``minimongo`` can easily layer on top of existing MongoDB collections, andwill work properly with almost any existing schema, even from third partyapplications.Installation------------If you have `setuptools <http://peak.telecommunity.com/DevCenter/setuptools>`_you can use ``easy_install -U minimongo``. Otherwise, you can download thesource from `GitHub <http://github.com/yablokoff/minimongo>`_ and run ``pythonsetup.py install``.Dependencies============- pymongo_ 2.1+- `sphinx <http://sphinx.pocoo.org>`_ (optional -- for documentation generation)Example-------Here's a very brief example of creating an object, querying for it, modifyinga field, and then saving it back again::from minimongo import Model, Indexclass Foo(Model):class Meta:# Here, we specify the database and collection names.# A connection to your DB is automatically created.database = "minimongo"collection = "rocks"# Now, we programatically declare what indices we want.# The arguments to the Index constructor are identical to# the args to pymongo"s ensure_index function.indices = (Index("a"),)if __name__ == "__main__":# Create & save an object, and return a local in-memory copy of it:foo = Foo({"x": 1, "y": 2}).save()# Find that object again, loading it into memory:foo = Foo.collection.find_one({"x": 1})# Change a field value, and save it back to the DB.foo.other = "some data"foo.save()
Package: mim-ocr
mim_ocrproject_goalThe goal of this project is to create a robust and reliable Python library that will be able handle OCR tasks. Several capabilities and features are envisioned:Running OCR with different tools such asTesseract (local)Google Cloud Vision (cloud)AWS OCR (cloud)EasyOCR (local)Image Preprocessing, such as:rotationreorientationReturn OCR results in common data structuresFinding features in OCR-ed images usingregular expressionskeyword listsNLP modelsOCR Result visulizationRunning OCR on large dataparallelizationusage of GPUDetecting various features in OCR resultsThe project was started in the context of manipulating medical data, but is planned to be used in other fields as well.Rules for developerscreate automatic tests for your featuresWhen providing example images for your tests please strip them from personal data. Please also verify that you have permission of the image ownerUsageRequirementsRequired python version 3.9 or 3.10.
Additional required system packages (tested on ubuntu 20.04):libgl1libglib2.0-0tesseract-ocrpoppler-utilsprotobuf-compiler
Useful requirements:tesseract-ocr-pol (As Tesseract is set by default to Polish language)The complete setup pipeline starting from raw ubuntu docker image is described inbuild/distribution/test_distribution.DockerfileInstallationpython -m pip install mim-ocrRunningTo run Google OCR locally (both for running and tests) You need to store in local (not commited to git) files a key
to Google service account in JSON format. The path to this file should be set as a
GOOGLE_APPLICATION_CREDENTIALS variable.To run AWS Textract locally (both for running and tests) You need to have properly configured AWS credentials prefferably using environment variables:AWS_ACCESS_KEY_ID- The access key for your AWS account.AWS_SECRET_ACCESS_KEY- The secret key for your AWS account.AWS_DEFAULT_REGION- Specifies the AWS Region to send the request to.
For more information seehttps://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html.Some features might require creation of a config YAML file with local paths or parameters. Examples of such features:Keyword Features finder (requires path to directory where local hyperscan databases are/should be located).
If you want to use such a config file, the path to this file should be set in MIM_OCR_CONFIG_PATH environmental variable.Example working values can be found at config/test_mim_ocr_conf.yaml.Additional FeaturesAdditional features are tested only on python 3.9NER_FEATUREseeNER Feature ReadmeMore InformationLicenceMIT License. SeeLICENSE.txtfor details.For MaintainersBuilding new version of the package: runpython3.9 -m buildfrom the main folder.Uploading new version to pypi:twine upload dist/*(two newly created files).
Package: mi-module-zoo
Machine Intelligence PyTorch Module ZooThis package contains implementations standalone, commonly reusable PyTorchnn.Modules. To
install it runpip install mi-module-zooDocumentation of the library is found athttps://microsoft.github.io/mi-module-zoo/.This library is maintained by theMachine Intelligence groupin Microsoft Research.ModulesA list of the modules follows, for detailed documentation, please check the docstring
of each module.mi_model_zoo.mlp.construct_mlp()A function that generates annn.Sequentialfor a
multilinear perceptron.mi_model_zoo.settransformer.SetTransformerTheSet Transformer models.mi_model_zoo.settransformer.ISABAn Inducing-point Self-Attention Block from theSet Transformer paper.mi_model_zoo.RelationalMultiheadAttentionThe relational multi-head attention variants,
supporting both sparse and dense relationships,
includingShaw et. al. (2019),RAT-SQL,
andGREATvariants.mi_model_zoo.relationaltransformerlayers.RelationalTransformerEncoderLayerA relational
transformer encoder layer that supports both dense and sparse relations among elements. Supports
ReZero and a variety of normalization modes.mi_model_zoo.relationaltransformerlayers.RelationalTransformerDecoderLayerA relational
transformer decoder layer that supports both dense and sparse relations among encoded-decoded
and decoded-decoded elements. Supports ReZero and a variety of normalization modes.Utilitiesmi_model_zoo.utils.randomutils.set_seed()Set the seed across Python, NumPy, and PyTorch (CPU+CUDA).mi_model_zoo.utils.activationutils.get_activation_fn()Get an activation function by name.DevelopingTo develop in this repository, clone the repository, installpre-commit, and runpre-commitinstallReleasing to pipTo deploy a package to PyPI, create a release on GitHub with a git tag of the formvX.Y.Z.
A GitHub Action will automatically build and push the package to PyPI.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: mimodulito
mimodulitoMake themoduluspart of your life style!MiModulitois a package that allouds you to perform the modulus operation between two numbers like you've never seen before. It's efficency will leave you astonished.Download and installIf you are usingPyPIinstallation it's as simple as:pip install mimodulitoYou can also test the unstable version of the package with:pip install -i https://test.pypi.org/simple/ mimodulitoQuick startTo import the package, run the next command:import mimodulitoCode examplesLets calculate the operation 3%2 usind themodfunction:import mimodulito
print(mimodulito.mod(3,2))What's newVersion 0.4:Corrections in main function.Version 0.3:Corrections in stability.Version 0.2:Functionmodwas implemented.Version 0.1:First version of the package.This package has been designed and written by Daniel Niño Villegas (C) 2023
Package: mimosa-monomer
mimosaDatabase management CLI forStilt 2.InstallationRunpip install mimosa_monomer-0.0.1-py3-none-any.whlto install the package
into your chosen python environment.Be sure to update to the current wheel filename.UsageRunmimosain the terminal. Select the service account key file for the
desired Firebase project to connect to. Follow the prompts.DevelopmentRun all tests withtoxcommand.Run tests and recreate virtual environments withtox --recreate.Docker Development EnvironmentYou'll need Docker installed on your machine, obviously 😁.Clone This Repositorygit clone https://github.com/hhelmric/mimosa.git
cd mimosaBuild The ImageFrom cloned directory (where Dockerfile is located):docker build . -t mimosa:latestIf you have updatedpyproject.toml, and are experiencing odd behavior,
you may need to rebuild the image to get the correct dependencies.Run The ContainerFrom cloned directory.docker-compose run appThis will run the container and begin a Bash terminal within.The local source code directory will be synced with the container's/codedirectory.Changes you make to source code in your IDE should be reflected in the container.From here you can execute commands:toxortox -e py38to run tests against the installed version of the package.poetry add some_packageorpoetry add --dev some_packageto add dependencies to pyproject.toml.python -m mimosa.mainfrom withinsrcfolder to run the program against the non-installed source file.poetry buildbuilds the package for distribution.poetry publishpublishes package on pypi. You'll need a pypi account and to be added to the project as a collaborator.
Package: mimosms
# mimo-sms Python[![](https://img.shields.io/badge/mimosms-OpenSource-blue.svg)](”https://www.mimo.it.ao/pt”)Python library to integration with MIMO SMS Service.
The library is a simple integration for software developers
to use in their projects and they need to quickly connect to an SMS service.# Installation` pip install mimosms `# Configuration
For configuration it is necessary to add the system environment variables, the following variables with the respective valuesMIMO_SMS_TOKENandMIMO_SMS_HOSTOpen terminal and do this
### Linux or Mac` $ exportMIMO_SMS_TOKEN=your-token-key$ export MIMO_SMS_HOST=hostname `### Windows` setMIMO_SMS_TOKEN=your-token-keyset MIMO_SMS_HOST=hostname `# Use### New messageSend sms message` from mimo_sms.resources import Message message_resource = Message()message_resource.send('mysender',['933843893'],'Olá, Sejabem-vindo(a)')`### Consulting credits
View your balance` from mimo_sms.resources import Message message_resource = Message() balance = message_resource.view_credits() print(balance) `### Add Credits
Add credits` from mimo_sms.resources import Message message_resource = Message() voucher_code ="your-voucher-code"message_resource.charge_credits(voucher_code) `### Create sender
Create a new sender` from mimo_sms.resources import Sender payload = {'sender':'your-sender-name','reason':'your-reason'}sender_resource = Sender()sender_resource.create(**payload)`### List your senders
List senders requested` from mimo_sms.resources import Sender payload = {'sender':'your-sender-name','reason':'your-reason'}sender_resource = Sender() senders_requested = sender_resource.list(requested=True) `List all senders available` from mimo_sms.resources import Sender sender_resource = Sender() senders = sender_resource.list() `# Note
All other resources are accessed in the same way by importing a resource class and calling only the class’s methods to interact with the resource’s data.# LicenseThe library is available as open source under the terms of the MIT License.
Package: mimpy
=====mimpy=====.. image:: https://api.travis-ci.org/ohinai/mimpy.png?branch=master:target: https://travis-ci.org/ohinai/mimpyMimpy is a Python based library for solvingthe diffusion equation. Mimpy uses the Mimetic Finite Differencemethod, which as the advantage of solving using general polyhedral elements.Because diffusion is a "kernel" for many physical phenomena,mimpy can also solve problems like the heat equation and multi-phase flowthrough porous media.A simple example can be found in hexmesh_example_1.py_.Run:.. code-block:: bash$ python hexmesh_example_1.pyCode requirements:* NumPy* SciPy* Cython (if you want to update the c code).History-------0.1.1 (2016-3-31)---------------------* Updated README to better fit pypi page.* Added spe10 example0.1.0 (2016-3-29)---------------------* Test release of code.
Package: mimufs
mimufsmimufsis a python library with tools for data processing and analysis inPortuguese Prymary Health Care.
It has a set of functions to facilitate data processing fromMIM@UFandBI-CSPplataforms. It also has a set of functions to facilitate data analysis and visualization.InstallationTo install mimufs, run this command in your project's terminal:pipinstallmimufsor in your jupyter notebook:!pipinstallmimufsUsagemedico()importpandasaspdfrommimufs.processingimportmedicodf=medico(df,column="Médico Familia")
Package: mimultiplicacion
mimultiplicacionmake a multiplicationAlways start the README explaining clearly what your package do.  If
you can include here some beautiful image to call the attention of the
potential user do it!This is an example:Download and installDescribe here how the package can be downloaded and install it in
different arquitectures.If you are usingPyPIinstallation it's as simple as:pip install mimultiplicacionYou can also test the unstable version of the package with:pip install -i https://test.pypi.org/simple/ mimultiplicacionQuick startIn this section you should provide the most simple instructions to use
your package.For instance:import mimultiplicacion
print(mimultiplicacion.saludo_inicial())Code examplesProvide some detailed examples for more advanced users.For instance:import mimultiplicacion
print(pymiau.multuplica_dos_numeros(a=3, b=4))What's newIf your package will be frequently updated with new features include a
section describing the new features added to it:Version 0.3:Functionsaludo_inicialwas implemented.Functionmultiplica_dos_numeroswas implemented.Version 0.3:Third version of the package.This package has been designed and written by Fulanit@ de Tal (C) 2023