Saravanan666/pypi-org huggingface dataset:
Package: maggit
IntroductionMaggit is a implementation of git in Python.
Maggit doesn’t use subproccess call to git or external C libraries.
There is no more dependancy than python standard library.Maggit need Python 3.4 at least.The actual version (0.1) is pretty rough:The low level allow read and write git objects but the high level API provide
only reading.There is remote, no push/pull, no merge/rebase, no working tree.API is not perfect and expect it changes.Documentation is not complete.Patches are welcomes. (Here are theissues trackerand themerge requests)ExampleHere a simple example code equivalent togit log -n 10to see how to use maggit:importmaggit# Create a repositoryrepo=maggit.Repo()branches=repo.branchesmaster=branches['master']commit=master.commitforiinrange(10):print(commit.message)commit=commit.parents[0]See thedocfor full information and how to use it.
Package: maggport
maggportA mongodb aggregate export CLI toolOverviewmaggport is a python CLI tool designed to bridge the gap of exporting aggregate pipeline(s) in mongodb. What used to be a feat accomplished by writing your own shell script or exporting a custom mongodb collection to csv, txt, or JSON, generated by running an aggregate pipeline, can now be achieved by this tool.InstallationpipinstallmaggportUsageArgumentsDescription-c/--collectionCollection of database-d/--dbDatabase to connect--headerExport with header-h/--hostHostname of mongodb instance--no-allow-disk-useDon't allow disk use-o/--outOutput file-p/--portPort of mongodb instance-q/--pipelinePipeline to run-f/--pipeline-pathPath of pipeline if saved in filehost: sample_hostport: 8080db: foodcollection: fruits[{"name":"apple","origin":"usa","price":5},{"name":"apple","origin":"italy","price":4},{"name":"apple","origin":"philippines","price":3},{"name":"mango","origin":"malaysia","price":3}]Example 1Passing pipeline as a parameter and output results in terminal.maggport--hostsample_host--port8080--dbfood--collectionfruits--pipeline'[{"$group": {"_id": "$name", "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 1}}}]'Output:[{"_id":"apple","count":3}]Example 2Passing pipeline as a file and output results in terminal.pipeline.txt[
    {"$group": {"_id": "$name", "count": {"$sum": 1}}},
    {"$match": {"count": {"$gt": 1}}}
]maggport--hostsample_host--port8080--dbfood--collectionfruits--pipeline-pathpipeline.txtOutput:[{"_id":"apple","count":3}]Example 3Export results as csv file.pipeline.txt[
    {"$group": {"_id": "$name", "count": {"$sum": 1}}},
    {"$match": {"count": {"$gt": 1}}}
]maggport--hostsample_host--port8080--dbfood--collectionfruits--pipeline-pathpipeline.txt--outtest_results.csvOutput:test_results.csvBugs/RequestPlease submit an issue inGitHubContributingWhen contributing to this repository, please first discuss the change you wish to make via issue in themaggport dashboard, or any other method with the owner(s)(info at the bottom of this README) of this repository before making a change.Please note we have a code of conduct, please follow it in all your interactions with the project.For more information about how to contribute, take a look atCONTRIBUTE.mdOur ResponsibilitiesProject maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.AttributionThis Code of Conduct is adapted from theContributor Covenant, version 1.4,
available athttp://contributor-covenant.org/version/1/4Maintainers@ccavales3
Package: maggy
Maggy is a framework fordistribution transparentmachine learning experiments onApache Spark.
In this post, we introduce a new unified framework for writing core ML training logic asoblivious training functions.
Maggy enables you to reuse the same training code whether training small models on your laptop or reusing the same code to scale out hyperparameter tuning or distributed deep learning on a cluster.
Maggy enables the replacement of the current waterfall development process for distributed ML applications, where code is rewritten at every stage to account for the different distribution context.Maggy uses the same distribution transparent training function in all steps of the machine learning development process.Quick StartMaggy uses PySpark as an engine to distribute the training processes. To get started, install Maggy in the Python environment used by your Spark Cluster, or install Maggy in your local Python environment with the'spark'extra, to run on Spark in local mode:pipinstallmaggyThe programming model consists of wrapping the code containing the model training
inside a function. Inside that wrapper function provide all imports and
parts that make up your experiment.Single run experiment:deftrain_fn():# This is your training iteration loopforiinrange(number_iterations):...# add the maggy reporter to report the metric to be optimizedreporter.broadcast(metric=accuracy)...# Return metric to be optimized or any metric to be loggedreturnaccuracyfrommaggyimportexperimentresult=experiment.lagom(train_fn=train_fn,name='MNIST')lagomis a Swedish word meaning "just the right amount". This is how MAggy
uses your resources.DocumentationFull documentation is available atmaggy.aiContributingThere are various ways to contribute, and any contribution is welcome, please follow the
CONTRIBUTING guide to get started.IssuesIssues can be reported on the officialGitHub repoof Maggy.CitationPlease see our publications onmaggy.aito find out how to cite our work.
Package: maghilchi
MaghilchiMaghilchi is a interpreted language with a natural language-like syntax with tamil words.Why Maghilchi?In Tamil Nadu(India), There are millions of students who studies in tamil medium schools or schools who use tamil as medium of teaching subjects. Many of them wants to learn coding since most of the proggraming language is in English they see that as barrier. So I decided to make proggraming language that use tamil in syntax instead of English which will eventually breaks that language barrier. This will help many students from tamil mediums to become a good programmer in the future, Even some of them might use proggraming skills to create product to solve real life problems.Goals for Maghilchi:Simple and natural language based Programming languageHassle Free Installation and configuration so that anyone can install easily and use!Need to add various functionality such as GUI Creation, networking support etc which will encourage kids to create games etcMemory Safe programming language for security of applications.Future Plans for MaghilchiCreate a package manager to install packages and remove packages for maghilchiHow to Access Source Code of Maghilchi.Until first version of stable release source code of Maghilchi will be closed source as I need to do lots of code cleanup as code is clumsy as of now.Usage/ExamplesCopmiler Usage:mg++ Hello_world.mgppCode :அச்சிடு "Hello-World"Simple Arithmetic Using maghilchi Code :பொறுத்து a செய்ய 1
பொறுத்து b செய்ய 1
கூட்டு a செய்ய b
கூட்டு 1 செய்ய b
அச்சிடு b
அச்சிடு aUsers can also Use Maghilchi IDE instead for commandline
Which you can download from following repository and Maghichi IDE requires you to install mg++ via PypiInstallationpip3installmaghilchiWill add standalone installation binary in stable release.Authors@HarishAcknowledgementsI like to thank following people for their books and coursesDmitry Soshnikov for this coursehttps://www.udemy.com/course/essentials-of-interpretation/David for his parser design for Natural language.C++ and G++ (Name of Maghilchi interpreter and file format inspired from C++)
Package: maghilchiplusplus
MaghilchiMaghilchi is a interpreted language with a natural language-like syntax with tamil words.Why Maghilchi?In Tamil Nadu(India), There are millions of students who studies in tamil medium schools or schools who use tamil as medium of teaching subjects. Many of them wants to learn coding since most of the proggraming language is in English they see that as barrier. So I decided to make proggraming language that use tamil in syntax instead of English which will eventually breaks that language barrier. This will help many students from tamil mediums to become a good programmer in the future, Even some of them might use proggraming skills to create product to solve real life problems.Goals for Maghilchi:Simple and natural language based Programming languageHassle Free Installation and configuration so that anyone can install easily and use!Need to add various functionality such as GUI Creation, networking support etc which will encourage kids to create games etcMemory Safe programming language for security of applications.Future Plans for MaghilchiCreate a package manager to install packages and remove packages for maghilchiHow to Access Source Code of Maghilchi.Until first version of stable release source code of Maghilchi will be closed source as I need to do lots of code cleanup as code is clumsy as of now.Usage/ExamplesCopmiler Usage:mg++ Hello_world.mgppCode :அச்சிடு "Hello-World"Simple Arithmetic Using maghilchi Code :பொறுத்து a செய்ய 1
பொறுத்து b செய்ய 1
கூட்டு a செய்ய b
கூட்டு 1 செய்ய b
அச்சிடு b
அச்சிடு aUsers can also Use Maghilchi IDE instead for commandline
Which you can download from following repository and Maghichi IDE requires you to install mg++ via PypiInstallationpip3installmaghilchiplusplusWill add standalone installation binary in stable release.Authors@HarishAcknowledgementsI like to thank following people for their books and coursesDmitry Soshnikov for this coursehttps://www.udemy.com/course/essentials-of-interpretation/David for his parser design for Natural language.C++ and G++ (Name of Maghilchi interpreter and file format inspired from C++)
Package: magi
Overviewmagiis a high level python wrapper around other time series forecasting libraries to allow easily parallelized univariate time series forecasting in python by using dask delayed wrapper functions under the hood. In particular, the library currently supports wrappers to Rforecastlibrary and facebook’sprophetpackageUsageThis is how easy it is to clean, forecast, and then plot accuracy metrics for 100 time seres using the auto arima model from R forecast packageImporting libraries, generate dataframe of series for example, and start local dask clusterfrommagi.coreimportforecastfrommagi.plottingimportfc_plot,acc_plotfrommagi.utilsimportgen_tsfrommagi.accuracyimportaccuracyfromdask.distributedimportClient,LocalClusterimportdaskcluster=LocalCluster()client=Client(cluster)df=gen_ts(ncols=100)cleaning and forecasting for 100 series in parallel, then calculate and plot accuracy metrics by seriesfc_obj=forecast(time_series=df,forecast_periods=18,frequency=12)forecast_df=fc_obj.tsclean().R(model='auto.arima(rdata,D=1,stationary=TRUE)',fit=True)acc_df=accuracy(df,forecast_df,separate_series=True)acc_plot(acc_df)Use CasesWhat this package should be used forforecasting for 1 or more Univariate Time Seriesforecasting using many different time series models in parallel with minimal effortwrapper for R forecast library to implement those models in python workflowwrapper around Prophet library to provide easier data framework to work withsingle source of access for many different time series forecasting modelsWhat this package should NOT be used forMultivariate Time Series data. If you have multiple x variables that are correlated with your response variable, I’d suggest simply using regression with lags and seasonal variable to account for autocorrelation in your errorData exploration - The time series analysis step is much more suited to using the R forecast package directlyDependenciesdaskdistributedplotlycufflinksrpy2 (& forecast package >=8.3 installed in R)fbprophetInstallation$pipinstallmagiDocumentationDocumentation is hosted onRead the Docs.DisclaimerThis package is still very early in development and should not be relied upon in production. Everything is still subject to change
Package: magia-flow
Magia FlowBriefDesign flow integration and automation with Magia.Project RoadmapTBDInstallationpipinstallmagia-flowExamplesTBDDocumentationTBDContributingWe also have aContribution GuidelineandCode of Conduct.
Please take a look before you contribute.In case you are interested in this project, contact me via:https://github.com/khwong-cReferenceTBD
Package: magia-hdl
MagiaAsta e magia ea a căntat.What is Magia?Magia generates Synthesizable SystemVerilog in pythonic syntax.The goal of Magia isTo provide a simple and intuitive way to generate HDL code.Take advantage of the Python to produce reconfigurable, reusable HDL code.What is not Magia?MagiaIS NOTa High Level Synthesis (HLS) framework, which compile arbitrary Python code into HDL.MagiaDOES NOTsupport simulation.Major reason is we have reliable and mature simulation tools likecocotbverilatorWe want to focus on the Design Capturing / HDL generation part, instead of rebuilding the whole workflow.Project RoadmapPlease refer to theMagia Roadmap.Installationpipinstallmagia-hdl# Install with full dependencies if advanced features are requiredpipinstallmagia-hdl[full]ExamplesMagia generates Synthesizable SystemVerilog code with the following command:Refer theSyntax Documentationfor more details.frommagiaimportElaborator,Module,Input,Output# Define a moduleclassTopLevel(Module):def__init__(self,width,**kwargs):super().__init__(**kwargs)# Define IO portsself.io+=[Input("clk",1),Input("a",width),Input("b",width),Output("dout",width),]# Implement the logicclk=self.io.clkself.io.dout<<=(self.io.a+self.io.b).reg(clk)# Specialize the moduletop=TopLevel(width=16,name="TopModule")# Elaborate SystemVerilog coderesult=Elaborator.to_string(top)# Obtain SystemVerilog code of the top modulesv_code_of_top=Elaborator.to_dict(top)["TopModule"]# Write SystemVerilog code to a directoryElaborator.to_files("/tmp/output_dir",top)Simulation with cocotbAlthough Magia does not support simulation, we can usecocotbto simulate the generated code.Make sure you have installed cocotb and simulator required (e.g.verilator).importcocotbfromcocotb.runnerimportget_runnerfrommagiaimportElaborator,ModulefrompathlibimportPathTOP_LEVEL_NAME="TopLevel"OUTPUT_FILE="TopLevel.sv"# Define a moduleclassTopLevel(Module):...# Define your test@cocotb.test()asyncdeftest_smoke(dut):...if__name__=="__main__":# Elaborate SystemVerilog code to a fileElaborator.to_file(OUTPUT_FILE,TopLevel(width=16,name=TOP_LEVEL_NAME))runner=get_runner("verilator")runner.build(verilog_sources=[OUTPUT_FILE],hdl_toplevel=TOP_LEVEL_NAME,always=True,)runner.test(hdl_toplevel=TOP_LEVEL_NAME,testcase="test_smoke",# Let cocotb locates this filetest_dir=Path(__file__).parent.absolute(),test_module=Path(__file__).stem,)DocumentationSyntax DocumentationElaborate DesignsBundle SyntaxMemory SyntaxExternal ModuleRelated ProjectsMagia Flow: Design flow integration and automation with Magia.Magia IP: IP libraries designed with Magia.ContributingThe project is still a personal project, in a very early stage.
Feel free to open an issue for any bug / feature wishlist.We also have aContribution GuidelineandCode of Conduct.
Please take a look before you contribute.In case you are interested in this project, contact me via:https://github.com/khwong-cReferenceThere are many attempts to generate HDL code in Python.
Similar projects are listed below:Amaranth/nMigenPyRTLMyHDLPyverilog
Package: magia-ip
Magia IPBriefIP libraries designed with MagiaProject RoadmapTBDInstallationpipinstallmagia-ipExamplesTBDDocumentationTBDContributingWe also have aContribution GuidelineandCode of Conduct.
Please take a look before you contribute.In case you are interested in this project, contact me via:https://github.com/khwong-cReferenceTBD
Package: magic-admin
Magic Admin Python SDKThe Magic Admin Python SDK provides convenient ways for developers to interact with Magic API endpoints and an array of utilities to handleDID Token.Table of ContentsDocumentationQuick StartDevelopmentChangelogLicenseDocumentationSee theMagic doc!InstallationYou can directly install the SDK with:pip:pip install magic-adminconda:conda install magic-adminPrerequisitesPython 3.6Note: This package has only been tested withPython 3.6.Python 3.7andPython 3.8have not been tested yet. We will get to it very soon. Support forPython 2.7+will not be actively worked on. If you are interested using this package with earlier versions of Python, please create a ticket and let us know :)Quick StartBefore you start, you will need an API secret key. You can get one from theMagic Dashboard. Once you have the API secret key, you can instantiate a Magic object.from magic_admin import Magic

magic = Magic(api_secret_key='<YOUR_API_SECRET_KEY>')

magic.Token.validate('DID_TOKEN')

# Read the docs to learn more! 🚀Optionally if you would like, you can load the API secret key from the environment variable,MAGIC_API_SECRET_KEY.# Set the env variable `MAGIC_API_SECRET_KEY`.

magic = Magic()Note: The argument passed to theMagic(...)object takes precedence over the environment variable.Configure Network StrategyTheMagicobject also takes inretries,timeoutandbackoff_factoras optional arguments at the object instantiation time so you can override those values for your application setup.magic = Magic(retries=5, timeout=10, backoff_factor=0.03)DevelopmentWe would love to have you contributing to this SDK. To get started, you can clone this repository and create a virtualenv.make developmentThis will create a virtualenv for all the local development dependencies that the SDK will needs.Once it is done, you cansourcethe virtualenv. It makes your local development easier!source virtualenv_run/bin/activateTo make sure your new code works with the existing SDK, run the test against the current supported Python versions.make testTo clean up existing virtualenv, tox log and pytest cache, do amake cleanThis repository is installed withpre-commit. All of the pre-commit hooks are run automatically with every new commit. This is to keep the codebase styling and format consistent.You can also run the pre-commit manually. You can find all the pre-commit hookshere.pre-commit runPlease also see ourCONTRIBUTINGguide for other information.ChangelogSeeChangelogLicenseSeeLicense
Package: magical
This is a nifty script I whipped up which when called will run whatever
sequence you use to update your application(s).For example a very typical sequence many of us use is:./configure && make &&
sudo make install, so instead of having to type that over and over again you
could just typemagic.magic will check to see if a sequence has been defined for the current working
directory, if yes then it will run that sequence and carry on, if no it will
then ask for the sequence, save that sequence and then run it and carry on.
Package: magicalimport
Importing a module from physical file path.examplesthese files are existed, then..$tree.├──a│└──b│└──c│└──foo.py└──main.py4directories,3filesa/b/c/foo.pyname="foo"_age="*secret*"frommagicalimportimportimport_from_physical_path# importing foo.py as the module named foo2foo=import_from_physical_path("./a/b/c/foo.py",as_="foo2")print(foo.name)# cached in sys.modules, so it is ok.importfoo2print(foo2.name)here optionfrommagicalimportimportimport_from_physical_pathimport_from_physical_path("bar.py",here="/tmp/foo",as_="bar")star importfrommagicalimportimportimport_from_physical_pathfrommagicalimportimportexpose_all_members# something of like a `from foo import *`expose_all_members(import_from_physical_path("./a/b/c/foo.py"))print(name)# "foo"# print(_age)  # NameError.. because expose_all_members() doesn't expose the symbols started by "_"# orfrommagicalimportimportexpose_membersexpose_members(import_from_physical_path("./a/b/c/foo.py"),members=["_age"])print(_age)# "*secret*"0.9.1import_module() and import_symbol()’s cwd is True0.9.0revert default behaviour (detailhttps://github.com/podhmo/magicalimport/pull/18)0.8.1cwd option0.8.0default behaviour is changed, from now, if here option is omitted, treated ashere=__file__on caller module.( previous behaviour ishere=os.getcwd())0.7.3skip needless __init__.py creation, in some cases0.7.2fix directly import module handling is not enough, now, treated as a same module0.7.1fix permission error, and same file, treated as a same module0.7.0fix same file, treated as a same module0.6.0fix module_id is not stored in sys.modules, on exec_module()0.5.0raised exception adjustment0.3.0ns option in import_symbol0.2.1here option is supported0.2python2 support0.1first release
Package: magical-magic
magical-magicDead simple reusableIPythonmagics.Installationpip installmagical-magicBasic Usageimportmagicalenv=magical.register_jinja2_magic()# returns a jinja environmentmagical.register_mistune_magic(escape=False)magical.register_yaml_magic()<IPython.core.display.Javascript object><IPython.core.display.Javascript object><IPython.core.display.Javascript object>data={'🐮':'moo','🐑':'bah','🔥':'burn'}%%jinja2# Jinja Cell Magics`%%jinja2`insertsdatafromthenotebookintothetemplate.## An example of a list{%forkeyindata%}*{{key}}-__{{data[key]}}__{%endfor%}Jinja Cell Magics%%jinja2inserts data from the notebook into the template.An example of a list🐮 -moo🔥 -burn🐑 -bah%%mistune-d## Mistune Cell Magics`%%mistune`usesapurePythonmarkdownparser.Forconvenience,jinjatemplatescanbeusedtotellusaboutthelengthof`data`is{{data.__len__()}}.DevelopmentRunning the Build and Testspipinstall-rrequirements-dev.txtpythonsetup.pydevelopwatchmedotrickstricks.yamlThewatchmedoscript will convert your notebooks to scripts and html
files.py.test-ipynbwill test all notebooks matchingtest-*.ipynb.Running the docsjekyllservedocs-witDocs are hosted athttp://localhost:4000/magical-magic/.Licensemagical-magicis released as free software under theBSD 3-Clause
license.