Saravanan666/pypi-org huggingface dataset:
Package: mama-survey
mama-survey===========|travis|_ |coveralls|_.. |travis| image:: https://travis-ci.org/praekelt/mama-survey.png?branch=develop.. _travis: https://travis-ci.org/praekelt/mama-survey.. |coveralls| image:: https://coveralls.io/repos/praekelt/mama-survey/badge.png?branch=develop.. _coveralls: https://coveralls.io/r/praekelt/mama-surveymama-survey provides a simple multiple-choice question-only survey capabilityfor the askMAMA mobi site.Administrators can create questionnaires in the admin site, and view and exportanswers sheets.The first available questionnaire will only be showed to users on their 2ndlogin. A link will also be displayed at the bottom of the home page.Users can choose to complete the questionnaire immediately, later, or declineto participate.  Decline will apply to all future questionnaires as well.Users can bail out of a questionnaire halfway, and resume it later.Statistics about completed, aborted and declined questionnaires will be sent tothe apporpriate holodeck metrics tracker on a regular basis.Dependencies------------- django-userprofileUsage-----For production, install the application in the askMAMA site with::python setup.py installFor development, install the application in the askMAMA development site with::python setup.py developSettings++++++++The following settings must be added to settings.py::TEMPLATE_DIRS += (os.path.join(PATH, "survey", "templates", "mobi"),)INSTALLED_APPS += ('survey',)LOGIN_REDIRECT_URL = '/survey/check-for-survey/'HOLODECK_URL = 'http://localhost:8001/'Authors=======Praekelt Foundation-------------------* Jurgen BlignautChangelog=========1.0.0=====- Initial release to pypi
Package: mamba
mamba: the definitive test runner for Pythonmambais the definitive test runner for Python. Born under the banner ofbehavior-driven development.InstallI recommend to use pipenv for managing your dependencies, thus you can install mamba like any other Python package.By example:$ pipenv install mambaBut you also can use pip:$ pip install mambaGetting StartedWrite a very simple example that describes your code behaviour:# tennis_spec.pyfrommambaimportdescription,context,itfromexpectsimportexpect,equalwithdescription('Tennis')asself:withit('starts with 0 - 0 score'):rafa_nadal="Rafa Nadal"roger_federer="Roger Federer"game=Game(rafa_nadal,roger_federer)expect(game.score()).to(equal((0,0)))Run the example, and don't forget to watch it fail!$ pipenv run mamba tennis_spec.py

  F

  1 examples failed of 1 ran in 0.0023 seconds

  Failures:

    1) Tennis it starts with 0 - 0 score
      Failure/Error: tennis_spec.py game = Game(rafa_nadal, roger_federer)
          NameError: global name 'Game' is not defined

      File "tennis_spec.py", line 8, in 00000001__it starts with 0 - 0 score--
          game = Game(rafa_nadal, roger_federer)Now write as little code for making it pass.# tennis_spec.pyfrommambaimportdescription,context,itfromexpectsimportexpect,equalimporttenniswithdescription('Tennis')asself:withit('starts with 0 - 0 score'):rafa_nadal="Rafa Nadal"roger_federer="Roger Federer"game=tennis.Game(rafa_nadal,roger_federer)expect(game.score()).to(equal((0,0)))# tennis.pyclassGame(object):def__init__(self,player1,player2):passdefscore(self):return(0,0)Run the spec file and enjoy that all tests are green!$ pipenv run mamba tennis_spec.py

  .

  1 examples ran in 0.0022 secondsSettingsMamba provides a way to configuration usingspec/spec_helper.pyorspecs/spec_helper.pyThis module function is read after parsing arguments so configure function overrides settingsA sample config file :defconfigure(settings):# settings.slow_test_threshold = 0.075# settings.enable_code_coverage = False# settings.code_coverage_file = '.coverage'settings.format='documentation'# settings.no_color = False# settings.tags = NoneOfficial ManualYou can read more features about mamba in itsofficial manualContributorsHere's alistof all the people who have contributed.I'm really grateful to each and every of them!If you want to be one of them, forkrepositoryand send a pull request.
Package: mambabyte
MambaByteImplementation of MambaByte in "MambaByte: Token-free Selective State Space Model" in Pytorch and Zeta. Note this will be a higher performance implementation of Mamba with parallel scanInstallationpipinstallmambabyteUsageimporttorchfrommambabyteimportMambaConfig,Mambax=torch.randn(2,3,4)config=MambaConfig(dim=4,depth=3,dt_rank=2,d_state=2,expand_factor=2,d_conv=3,dt_min=0.001,dt_max=0.1,dt_init="random",dt_scale=1.0,bias=False,conv_bias=True,pscan=True)model=Mamba(config)out=model(x)print(out)LicenseMIT
Package: mamba-django
mamba-djangoA Django test runner formamba.How to installpip install mamba-djangoHow to useGo to your Django test settings and add this lineTEST_RUNNER = 'mamba_django.MambaRunner'and then execute the Django tests./manage.py test --settings=project.settings.test --keepdbor you can execute the tests passing it as parameter./manage.py test --testrunner mamba_django.MambaRunner --settings=project.settings.test --keepdbAlso, you can pass all the mamba parameters to the Django test command.For example:./manage.py test --settings=project.settings.test --keepdb -f documentation -s 1How to execute mamba tests with Django database transactionsfromexpectsimport(equal,expect)frommamba_djangoimport(start_django_transactions,rollback_django_transactions)frommyapp.modelsimportAnimalwithdescription("Mamba with Django")asself:withbefore.each:self.transactions=start_django_transactions()withcontext("Running atomic tests"):withit("creates an animal"):Animal.objects.create(name="lion",sound="roar")expect(Animal.objects.all().count()).to(equal(1))withit("creates another animal"):Animal.objects.create(name="lion",sound="roar")expect(Animal.objects.all().count()).to(equal(1))withafter.each:rollback_django_transactions(self.transactions)How to load Django fixturesYou can use the methodload_fixturesto loadDjango fixtures.fromexpectsimport(equal,expect)frommamba_djangoimport(load_fixtures,start_django_transactions,rollback_django_transactions)fromdjango.contrib.auth.modelsimportGroupwithdescription("Mamba with Django")asself:withbefore.each:self.transactions=start_django_transactions()load_fixtures(['group.json'])withcontext("Fixtures"):withit("can retrieve loaded fixtures"):expect(Group.objects.all().count()).to(equal(1))withafter.each:rollback_django_transactions(self.transactions)
Package: mamba-paw
Base project for thepawpackage. Very much a work in progress.To create tasks:# tasks.pyfrompawimporttask@task(description='Stupid simple example')deftask_one(print_me):print(print_me)returnTrueTo start a worker:# start_workers.pyfrompawimportMainPawWorkerimporttasks# importing tasks from tasks.py in local projectworkers=MainPawWorker(azure_storage_name='storage account name',azure_storage_private_key='storage account private key',azure_queue_name='name of the queue',azure_table_name='name of the table',tasks_module=tasks,workers=4)if__name__=='__main__':workers.start_workers()To queue a task:# queue_tasks.pyfrompawimportqueue_taskqueue_task(task_name='task_one',account_name='storage account name',account_key='storage account private key',queue_name='name of the queue',args=['List','of','arguments'],kwargs={'Key_word':'arguments'})
Package: mambatransformer
Mamba TransformerIntegrating Mamba/SSMs with Transformer for Enhanced Long Context and High-Quality Sequence Modeling.This is 100% novel architecture that I have designed to combine the strengths and weaknesses out of SSMs and Attention for an all-new advanced architecture with the purpose of surpassing our old limits. Faster processing speed, longer context lengths, lower perplexity over long sequences, enhanced and superior reasoning while remaining small and compact.The architecture is essentially:x -> norm -> mamba -> norm -> transformer -> norm -> ffn -> norm -> out.I added in many normalizations as I believe by default training stability would be severly degraded due to 2 foreign architecture's integrating with one another.Installpip3 install mambatransformerUsageimporttorchfrommamba_transformerimportMambaTransformer# Generate a random tensor of shape (1, 10) with values between 0 and 99x=torch.randint(0,100,(1,10))# Create an instance of the MambaTransformer modelmodel=MambaTransformer(num_tokens=100,# Number of tokens in the input sequencedim=512,# Dimension of the modelheads=8,# Number of attention headsdepth=4,# Number of transformer layersdim_head=64,# Dimension of each attention headd_state=512,# Dimension of the statedropout=0.1,# Dropout rateff_mult=4,# Multiplier for the feed-forward layer dimensionreturn_embeddings=False,# Whether to return the embeddings,transformer_depth=2,# Number of transformer blocksmamba_depth=10,# Number of Mamba blocks,use_linear_attn=True,# Whether to use linear attention)# Pass the input tensor through the model and print the output shapeout=model(x)print(out.shape)# to trainmodel.eval()# Would you like to train this model? Zeta Corporation offers unmatchable GPU clusters at unbeatable prices, let's partner!# Tokenizermodel.generate(text)LicenseMIT
Package: mambo-minhhai
READMEThis README would normally document whatever steps are necessary to get your application up and running.What is this repository for?Quick summaryVersionLearn MarkdownHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesAdd source code to /srcRun the following command for building the package:bash ./bash/build.shRun the following command for uploading the package to the Pypi hub:bash ./bash/upload.shEnter the following user name and password:
- User name:token- Password:Who do I talk to?Repo owner or adminOther community or team contact
Package: maml
maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML
for materials science as easy as possible.The goal of maml is not to duplicate functionality already available in other packages. maml relies on well-established
packages such as scikit-learn and tensorflow for implementations of ML algorithms, as well as other materials science
packages such aspymatgenandmatminerfor
crystal/molecule manipulation and feature generation.Official documentation athttps://materialsvirtuallab.github.io/mamlFeaturesConvert materials (crystals and molecules) into features. In addition to common compositional, site and structural
features, we provide the following fine-grain local environment features.Bispectrum coefficientsBehler Parrinello symmetry functionsSmooth Overlap of Atom Position (SOAP)Graph network features (composition, site and structure)Use ML to learn relationship between features and targets. Currently, themamlsupportssklearnandkerasmodels.Applications:pesfor modelling the potential energy surface, constructing surrogate models for property prediction.Neural Network Potential (NNP)Gaussian approximation potential (GAP) with SOAP featuresSpectral neighbor analysis potential (SNAP)Moment Tensor Potential (MTP)rfxasfor random forest models in predicting atomic local environments from X-ray absorption spectroscopy.InstallationPip install via PyPI:pip install mamlTo run the potential energy surface (pes), lammps installation is required you can install from source or fromconda:conda install -c conda-forge/label/cf202003 lammpsThe SNAP potential comes with this lammps installation. The GAP package for GAP and MLIP package for MTP are needed to
run the corresponding potentials. For fitting NNP potential, then2p2package is needed.Install all the libraries from requirement.txt file:pip install -r requirements.txtFor all the requirements above:pip install -r requirements-ci.txt
pip install -r requirements-optional.txt
pip install -r requirements-dl.txt
pip install -r requirements.txtCiting@misc{maml,
    author = {Chen, Chi and Zuo, Yunxing and Ye, Weike and Ong, Shyue Ping},
    title = {{Maml - materials machine learning package}},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {https://github.com/materialsvirtuallab/maml},
}For the ML-IAP package (maml.pes), please cite:Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.;
Wood, M. A.; Ong, S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials.
J. Phys. Chem. A 2020, 124 (4), 731-745. https://doi.org/10.1021/acs.jpca.9b08723.
Package: maml-apps-rfxas
This is the package associated with our recent publication:Zheng, C., Chen, C., Chen, Y.M. & Ong, S.P. (2020), Random Forest Models for Accurate Identification of Coordination
Environments from X-ray Absorption Near-Edge Structure, Patterns, accepted.
Usage example of the package can be found in the notebooks folder and experiment.csv contains processed experimental
data used in the paper.InstallationPip install via PyPI:pip install maml-apps-rfxas
Package: mammath
-Mammath on GithubMammath is the python library with all the functions you need to do complicated mathematics, anywhere from 2D and 3D graphing calculators to linear algebra, calculus and even physics. We even have a constant and function for practically everythingRelease notes 0.1.9 - What's newFixed bugs with graphingChanged primary function notation to underscore notation from camel caseFixed the calculus functions and added custom derivative and integral formula functions.Fixed all general functions bugs that we were aware ofAdded docstrings to all functionsAdded 120 new constantsAdded unit conversion functions with distance, area and volumeMany new maths functions, including equation solvers and linear algebra functions, populated each category with many more functionsAdded a new physics section with 40+ functions already. This includes utilities like a Solar System class to access planets, implementations of many popular and useful physics formulas, such as a SUVAT equation solver, a Schwarzschild radius calculator, and many others. This section is still expanding.Install viapip install mammathIt is built upon several other modules including sys, tkinter, string, time, math, cmath, fractions, mpmath, keyboard, numpy, sympy, tabulate, re, matplotlib, and modules within them.All categoriesAll categoriesShunting Yard AlgorithmMath Teachers (beta ascii courses)Operations (logs, factorials, sums, hcf, lcm, etc.)Prime NumbersNumber / Sequence ChecksFibonacciDivisibility checks + learn tricks (beta)FractionsGeometrySequencesFormulasBase conversionsGraphingAlgebraLinear AlgebraCalculusComplex (complex ln, etc.)PhysicsUnit conversionsMiscellaneous (Equation parser, special numbers)We hope Mammath is a useful and reliable asset. Install viapip install mammathThank you!
Package: mammograms
Test MammogramsThe purpose of this repo is to provide a handful of mammograms for unit testing, not for executing a performance test of statistical significance.Usefrommammogramsimportgen_casesforcaseingen_cases():forview,dicom_pathincase.items():# `view` may be "rcc", "lmlo", etc.# `dicom_path` will be Path(.../FILENAME.dcm)CasesThis repo includessfm-benign-0andsfm-malign-0which are taken from DDSM.DDSM was originally provided by the University of South Florida and can be downloadedhere, and a curated version known as CBIS-DDSM can be downloadedhere. Please also take a look at the license for CBIS-DDSMhere.The DICOMs provided in this repo were taken from CBIS-DDSM and compressed withRLE.
Package: mammopy
MammoPyA Comprehensive Deep Learning Library for Mammogram AssessmentUseful Links[Documentation]|[Paper]|[Notebook examples]|[Web applications]IntroductionWelcome toMammoPyRepository!MammoPyis a python-based library designed to facilitate the creation of mammogram image analysis pipelines . The library includes plug-and-play modules to perform:Standard mammogram image pre-processing (e.g.,normalization,bounding box cropping, andDICOM to jpeg conversion)Mammogram assessment pipelines (e.g.,breast area segmentation,dense tissue segmentation, andpercentage density estimation)Modeling deep learning architectures for various downstream tasks  (e.g.,micro-calcificationandmass detection)Feature attribution-based interpretability techniques (e.g.,GradCAM,GradCAM++, andLRP)VisualizationAll the functionalities are grouped under a user-friendly API.If you encounter any issue or have questions regarding the library, feel free toopen a GitHub issue. We'll do our best to address it.
Package: mammoth-analytics-sdk
Mammoth is a data management platform that allows you to take your data from its raw state to insights with all the steps in between. More specifically Mammoth allows you to:Upload data or fetch data from various data sourcesWarehouse this dataTransform data through an iterative process of discovery, preparation, blending, and insightsSave process steps to a task pipeline so that any data changes in the future can go through the same steps automaticallyUse the prepared data for dashboarding or reporting either within Mammoth or in a dashboarding system outside of MammothYou use Mammoth to implement a data flow pipeline where your data traverses through successive transformations to produce output. The process is feedback driven where you get to do data discovery along the way.Website:https://mammoth.ioDocumentation:https://mammoth.io/docsSample usage:https://gist.github.com/ankitkpandey1/8dc12fa00b89a5ab0b0ed0e385237657
Package: mammut-py
Mammut-PyInstallationUse pip to install the package:pip install mammut-pyDevelopmentThe following tools are used in this project:Poetryis used as package manager.Noxis used as automation tool, mainly for testing.Blackis the mandatory formatter tool.PyEnvis recommended as a tool to handle multiple python versions in your machine.The library is intended to be compatible with python ~3.6.9, ~3.7.4 and ~3.8.2. But the primary version to support is ~3.8.2.The general structure of the project is trying to follow the recommendations
inCookiecutter Data Science.
The main difference lies in the source code itself which is not constraint to data science code.
Package: mamo
MamoYour friendly neighborhood persistent memoization library.Getting startedpipinstallmamoDesignCode changes drive data changes. Especially, with big data, it is highly likely that different calls return different data.Mamo fingerprints data by hashing it when unavoidable and by fingerprinting the computational graph (as far as known to Mamo) otherwise.As future extension, Mamo will support different fingerprints for the same value, but in common use-cases detecting code changes is more impactful.It assumes functions are pure, which allows for ignoring stochasticity. Otherwise anything using a random number generator would constantly be marked as stale.More detailsMamo has concepts: value identity and fingerprints. Fingerprints are used to determine whether a stored computed value is stale:
If an argument value to the function that computes a value is different (different fingerprints) from the one that was used originally, we mark the value as stale.Value identity is about when two values have the same identity. (If every value was unique, there would never be stale values.) This is only an important concept for computed values: the result of a function with the same arguments (argument identity) has the same value identity as the stored result for a previous call.AssumptionsThe biggest assumption for the current design is:Values are unlikely to ever be the same.This means that we can use hashing for checking equality checks, and that different computational graphs imply unequal values.Thus, Mamo does not implement perfect memoization at the moment but only a heuristic that does not try to actually match arguments fully.
Package: mamonca
mamonca - interactive Magnetic Monte CarloThis code allows you to launch Metropolis Monte Carlo simulations via Heisenberg Landau models (with various polynomial degrees) from a jupyter notebook.How to compileDownload all files and runpython setup.py build_ext --inplace.First steps:In the following simple (but complete) example, we create a bcc Fe system usingpyironand launch a Metropolis Monte Carlo simulation with a Heisenberg coefficientJ=0.1(eV) for the first nearest neighbor pairs:frompyironimportProjectfrommcimportMCstructure=Project('.').create.structure.bulk(name='Fe',cubic=True)structure.set_repeat(10)J=0.1# eVneighbors=structure.get_neighbors()first_shell_tensor=neighbors.get_shell_matrix()[0]mc=MC(len(structure))mc.set_heisenberg_coeff(J*first_shell_tensor.toarray())mc.run(temperature=300,number_of_iterations=1000)How to set inputs and get outputsAs a rule of thumb, you can set all input parameters via functions starting withset_. Similarly, output values can be obtained via functions whose names start withget_. Take a look at the list of auto-complete and see their docstrings
Package: mamon-utils
Mamon UtilitiesA set of tools and utilities for Python DevelopmentInstallationpipinstallmamon-utilsUsageTo use any of these modules, just import frommamonutils.DatabaseThis module makes it easy to connect to our PostgreSQL databases. To use it, you have to add a.envfile in the root of your project specifying the following environment variables:DB_HOST=
DB_PORT=
DB_USER=
DB_PASSWORD=Example UsagefrommamonutilsimportDatabase,dotenv# Load environment variablesdotenv()# Connect to clients databasedb=Database('clients')cursor=db.cursorcursor.execute('SELECT * from clients')result=cursor.fetchall()print(result)# Disconnectdb.disconnect()get_dimensionsA utility that allows you to retrieve the dimensions (width and height) of an image from a given URL. The utility uses the requests module to fetch the image data and the PIL (Python Imaging Library) module to extract the image metadata. The utility is designed to be flexible, allowing users to handle exceptions and errors according to their specific needs.Example UsageThe utility takes a URL string as input and returns a dictionary containing the image's width and height.frommamonutilsimportget_dimensionstry:image_url="https://example.com/image.jpg"dimensions=get_dimensions(image_url)print("Image Dimensions:")print(f"Width:{dimensions['width']}")print(f"Height:{dimensions['height']}")exceptValueErrorase:print(f"Error:{str(e)}")
Package: mampoer
MampoerDynamic implementation of Alembic and SQLAlchemyFree software: MIT licenseDocumentation:https://mampoer.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-05-26)First release on PyPI.