Saravanan666/pypi-org huggingface dataset:
Package: milton
Algorithmic Composition UtilitiesThe aim of this package is to provide a higher level, musically more expressive and abstract set of routines.
It is meant to make expressing algorithmic music more intuitive and easy, by taking care of low-level MIDI implementation
so that the composer can focus on his composition rather than following midi messages.
This package is awork-in-progressandin active development. Your feedback is very welcome! :-)InstallRun the commandpip install miltonto install Milton from Pypi.Quick Start# import the module>>>importmilton# First initialize the module,>>>milton.realtime.init()# create a single note,>>>c4=milton.make_note("c4")# and put it in a list and send it to the processor to play it.>>>milton.proc([c4])# Let us now define a second note with the key number 72, and specify it's starting time (onset) to be 1 second after the processor starts.>>>c5=milton.make_note(pch=72,onset=1)>>>milton.proc([c4,c5])# You should have heared both notes, played after each other.# The full signature of the note function has the parameters:# pch: the name of the note (a string) or the midi key number# onset: is the ongoing time expressed in seconds, with respect to the process start time 0 (default is 0, which means now)# dur: the duration of the note# chnl: the midi channel to play the note on# vel: the dynamic of the note# Now we want to create an A minor chord with a duration of 2 seconds,# and play it 1/2 second after the processor starts,>>>a_min=milton.make_chord(pchs=(69,72,76),onset=.5,dur=2)# and send it to the processor to play it.>>>milton.proc([a_min])# Let us now give the processor a list of notes/chords to play:# the chromatic scale starting from the middle c upwards.>>>voice1=[milton.make_note(pch=60+i,onset=i,dur=0.5)foriinrange(12)]>>>milton.proc([voice1])# Playing polyphony is as easy as passing multiple lists of note/chords# to the processor:>>>voice2=[milton.make_note(pch=48+i,onset=i+0.5,dur=0.5)foriinrange(12)]>>>voice3=[milton.make_chord(pchs=[60+i,60+i+4,60+i+7],onset=i+0.2,dur=0.5)foriinrange(12)]>>>milton.proc([voice1,voice2,voice3])# If you want to write a midi file to the disk instead of playing it back# pass a path string as the second argument to the processor>>>milton.proc([voice1,voice2,voice3],"/tmp/milton.mid")
Package: milvus-cli
Milvus_CLI🚀OverviewMilvusCommand Line Interface based onMilvus Python SDK.Applicable to most platforms: MS Windows, macOS, UbuntuSupport pip install & offline installation packageSupport single executable fileMilvus Python SDK full function coverageBuilt-in help functionSupport auto completionInstallation methods🔝Install in a Python environmentPrerequisitesPython >= 3.8.5Install from PyPI (Recommended)Runpip install pymilvus==2.3.4Runpip install milvus-cli==0.4.2Install from a tarballDownload thelatest releaseofmilvus_cli-<version>.tar.gz.Runpip install milvus_cli-<version>.tar.gz.Install from source codeRungit clone https://github.com/zilliztech/milvus_cli.git.Runcd milvus_cli.Runpip install --editable .Install from an executable fileWindows .exe fileDownload thelatest releaseofmilvus_cli.exeand run it.Ubuntu executable fileDownload thelatest releaseofmilvus_cli-<version>-Linux.Runsudo chmod +x milvus_cli-<version>-Linuxto allow executingmilvus_cli-<version>-Linuxas program.Run./milvus_cli-<version>-Linux.MacOS executable fileDownload thelatest releaseofmilvus_cli-<version>-macOS.Runsudo chmod +x milvus_cli-<version>-macOSto allow executingmilvus_cli-<version>-macOSas program.Run./milvus_cli-<version>-macOS.Docker image in docker hubdocker run -it zilliz/milvus_cli:latestUsageRunmilvus_cli(in a Python environment) or double clickmilvus_cli-<version>.exefile (in a Windows environment).Run Milvus_CLIIn a Python environment, runmilvus_cli.In a Windows environment, double clickmilvus_cli-<version>.exe.In an Ubuntu environment, run./milvus_cli-<version>-Linux.In a MacOS environment, run./milvus_cli-<version>-macOS.Documenthttps://github.com/zilliztech/milvus_cli/tree/main/docCommunity💬 Community isn’t just about writing code together. Come join the conversation, share your knowledge and get your questions answered onMilvus Slack Channel!
Package: milvus-haystack
milvus-documentstoreInstallationpip install -e milvus-haystackUsageFirst, to start up a Milvus service, follow the'Start Milvus'instructions in the documentation.Then, to use theMilvusDocumentStorein a Haystack pipeline"fromhaystackimportDocumentfrommilvus_haystackimportMilvusDocumentStoreds=MilvusDocumentStore()ds.write_documents([Document("Some Content")])ds.get_all_documents()# prints [<Document: {'content': 'foo', 'content_type': 'text', ...>]Licensemilvus-documentstoreis distributed under the terms of theApache-2.0license.
Package: milvus-kernel
milvus_kernelMilvus Kernel for JupyterMilvus Kernel for Jupyter中文介绍Installationstep1:pip install milvus_kernelTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip install git+git://github.com/Hourout/milvus_kernel.gitstep2:Add kernel to your jupyter:python3 -m milvus_kernel.installALL DONE! 🎉🎉🎉Uninstallstep1:View and remove milvus kerneljupyter kernelspec list
jupyter kernelspec remove milvusstep2:uninstall milvus kernel:pip uninstall milvus-kernelALL DONE! 🎉🎉🎉Usingjupyter notebookstep1: you should set milvus host and portstep2: write your milvus codeQuotekernel logohttps://blog.csdn.net/weixin_44839084/article/details/108070675
Package: milvus-one
Milvus Python SDKPython SDK forMilvus. To contribute code to this project, please read ourcontribution guidelinesfirst.CompatibilityThe following collection shows Milvus versions and recommended PyMilvus versions:Milvus versionRecommended PyMilvus version1.0.x1.0.11.1.x1.1.2InstallationYou can install PyMilvus viapiporpip3for Python 3.6+:$pip3installpymilvusYou can install a specific version of PyMilvus by:$pip3installpymilvus==1.1.2You can upgrade PyMilvus to the latest version by:$pip3install--upgradepymilvusDocumentationDocumentation is available online:https://pymilvus.readthedocs.io/.PackagesReleased packagesThe release of PyMilvus is managed on GitHub, and GitHub Actions will package and upload each version to PyPI.The release version number of PyMilvus follows PEP440, the format is x.y.z, and the corresponding git tag name is vx.y.z (x/y/z are numbers from 0 to 9).For example, after PyMilvus 1.0.1 is released, a tag named v1.0.1 can be found on GitHub, and a package with version 1.0.1 can be downloaded on PyPI.Developing packagesThe commits on the development branch of each version will be packaged and uploaded to Test PyPI.
Development branches refer to branches such as 1.0 and 1.1, and version releases are generated from the development branches, such as 1.0.1 and 1.0.2.The package name generated by the development branch is x.y.z.dev, where  is the number of commits that differ from the most recent release.For example, after the release of 1.0.1, two commits were submitted on the 1.0 branch. At this time, the automatic packaging version number of the development branch is 1.0.1.dev2.To install the package on test.pypi.org, you need to append the parameter --index-url after pip, for example:$python3-mpipinstall--index-urlhttps://test.pypi.org/simple/pymilvusLicenseApache License 2.0
Package: milvus-test
Milvus Python SDK -- pymilvusUsing Milvus python sdk for Milvus
DownloadPymilvus only supportspython >= 3.4, is fully tested under 3.4, 3.5, 3.6, 3.7.Pymilvus can be downloaded usingpip. If no use, trypip3$ pip install pymilvusIf you are usingmilvus-0.3.0, last version that supportsmilvus-0.3.0is0.1.13, you can download by:$ pip install pymilvus==0.1.13[Note]It's NOT recommended to upgrade to higher version if you are still usingmilvus-0.3.0If you want to upgradepymilvusto newest version$ pip install --upgrade pymilvusImportfrommilvusimportMilvus,IndexType,StatusGetting startedInitial aMilvusinstance andconnectto the sever>>>milvus=Milvus()>>>milvus.connect(host='SERVER-HOST',port='SERVER-PORT')Status(code=0,message="Success")Once successfully connected, you can get the version of server>>>milvus.server_version()(Status(code=0,message='Success'),0.3.1)# this is example version, the real version may varyAdd a newtableFirst set param>>>param={'table_name':'test01','dimension':256,'index_type':IndexType.FLAT,'store_raw_vector':False}Thencreate table>>>milvus.create_table(param)Status(message='Table test01 created!',code=0)Describe the table we just created>>>milvus.describe_table('test01')(Status(code=0,message='Success!'),TableSchema(table_name='test01',dimension=256,index_type=1,store_raw_vector=False))Add vectors into tabletest01First create 20 vectors of 256-dimension.Note thatrandomandpprintwe used here is for creating fake vectors data and pretty print, you may not need them in your project>>>importrandom>>>frompprintimportpprint>>>dim=256# Dimension of the vector# Initialize 20 vectors of 256-dimension>>>fake_vectors=[[random.random()for_inrange(dim)]for_inrange(20)]Then add vectors into tabletest01>>>status,ids=milvus.add_vectors(table_name='test01',records=vectors)>>>print(status)Status(code=0,message='Success')>>>pprint(ids)# List of ids returned2345532113551123312245748929023489...Search vectors# create 5 vectors of 256-dimension>>>q_records=[[random.random()for_inrange(dim)]for_inrange(5)]Then get results>>>status,results=milvus.search_vectors(table_name='test01',query_records=q_records,top_k=10)>>>print(status)Status(code=0,message='Success')>>>pprint(results)# Searched top_k vectorsDelet the table we just created>>>milvus.delete_table(table_name='test01')Status(code=0,message='Success')Disconnect with the server>>>milvus.disconnect()Status(code=0,message='Success')Example pythonThere are some small examples inexamples/, you can find more guide there.Build docs$ sphinx-build -b html doc/en/ doc/en/buildIf you encounter any problems or bugs, please open new issues
Package: milwaukee-dpw-parser
Milwaukee, Wisconsin Department of Public Works ParserGets the next upcoming trash and recycling date for an address in Milwaukee, WI.Installationpip install milwaukee-dpw-parserRunfrom milwaukee_dpw_parser import get_next_garbage_and_recycling_dates
garbage_date, recycling_date = await get_next_garbage_and_recycling_dates('1011', 'E', 'BRADY', 'ST')
print(garbage_date)
print(recycling_date)2022-01-11
2022-01-19
Package: milwrap
milwrapPython package for multiple instance learning (MIL).
This wraps single instance learning algorithms so that they can be fitted to data for MIL.Featuressupport count-based multiple instance assumptions (seewikipedia)support multi-class settingsupport scikit-learn algorithms (such asRandomForestClassifier,SVC,LogisticRegression)InstallationpipinstallmilwrapUsage# Prepare single-instance supervised-learning algorithm# Note: only supports models with predict_proba() method.fromsklearn.linear_modelimportLogisticRegressionclf=LogisticRegression()# Wrap it with MilCountBasedMultiClassLearnerfrommilwrapimportMilCountBasedMultiClassLearnermil_learner=MilCountBasedMultiClassLearner(clf)# Prepare follwing dataset## - bags ... list of np.ndarray#            (num_instance_in_the_bag * num_features)# - lower_threshold ... np.ndarray (num_bags * num_classes)# - upper_threshold ... np.ndarray (num_bags * num_classes)## bags[i_bag] contains not less than lower_thrshold[i_bag, i_class]# i_class instances.# run multiple instance learningclf_mil,y_mil=learner.fit(bags,lower_threshold,upper_threshold,n_classes,max_iter=10)# after multiple instance learning,# you can predict instance classclf_mil.predict([instance_feature])Seetests/test_countbased.pyfor an example of a fully working test data generation process.Licensemilwrap is available under the MIT License.
Package: mimebytes
mimebytesMake repr ofbytesbetter in ipythonRich outputs in Jupyter using_repr_png_and_repr_jpeg_In[1]:frommimebytesimportmimeIn[2]:withopen('image.png','rb')asf:...:content=f.read()In[3]:mime(content,'image/png')Out[3]:PNGBytes<376151>(image/png)Ifpython-magicis installed,
you do not need to specify the mimetype as it will be autodetected.In[1]:frommimebytesimportmimeIn[2]:withopen('image.png','rb')asf:...:content=f.read()In[3]:mime(content)Out[3]:PNGBytes<376151>(image/png)Why?Have you ever tried to output a bytes object in ipython and seen a garbled mess
like the following?If so, then this is for you.In [1]: with open('image.png', 'rb') as f:
   ...:     content = f.read()
   ...:

In [2]: content
Out[2]: b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x02\x00\x00\x00\x01\xd1\x08\x06\x00\x00\x00\x1c\xf2\x01\xed\x00\x00 \x00IDATx\x9c\xec\xbdy\x9cfWU\xef\xfd\xddk\xefs\x9e\xb1\xc6\xee\xeaNO\xe9$\x9d9\x84$\x1d\x08\x19\x84@\x80\x10@\x01\x15d\x14D\x05\x15\x05\x05\xe1\xe2\xf0^\xbdr\xf5\xeau\x06\x04E#*<... continues on forever>
Package: mime-common
Mime CommonAn package containing misc helper libraries for projectsconsole - contains Console class for terminal output including som coler supportlogg - A logging wrapper that do some standardisationproperties - A wrapper for properties that support reading different value types like logglevel, integer, string and booleanInstallationRun the following to install:pipinstallmime-commonUsagefromconsoleimportConsolecons=Console(use_colors=True)cons.green("Hello World")Developing Mime CommonTo install Mime Common, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstall-e.[dev]
Package: mimelib
mimelib 🙊A MIME type is a label used to identify a type of data. It is used so software can know how to handle the data.
It serves the same purpose on the Internet that file extensions do on Microsoft Windows.
—Quentinmimelibaims at working with MIME types easier in Python. The standard library comes with themimetypesmodule. This library builds on top of it and adds more niceties to it, so you're generally happier when working with MIME types today.>>>importmimelib>>>mimelib.mimetype('application/json').is_textTrue>>>mimelib.url('https://example.com/avatar.jpg').is_imageTrue>>>mimelib.url('pianoman.mp3').file_typemediaInstallationTo install mimelib, use pipenv (or pip):$ pipenv install mimelibUsagemimelibis intended to be used alongside themimetypesstandard library
module, and builds on top of it under the hood. So, if you are adding additional
MIME types to be recognized,mimelibwill work just as fine.InitializationEither a valid MIME type string, or a URL or path can be used to work withmimelib.>>>m1=mimelib.mimetype("application/json")# pass a valid MIME type>>>m2=mimelib.url("foo/bar/dataset.csv")# or pass a path / urlBoth these methods return aMIMEobject, the various useful properties of which
are listed below.Getting the file typeThe following file types are reported:text,image,mediaandbinary.>>>mimelib.mimetype("application/ecmascript").file_typetext>>>mimelib.mimetype("video/mpeg").file_typemedia>>>mimelib.url("archive.rar").file_typebinaryTheMimeobject also has the following properties for conveniently checking
specific file types:is_textis_imageis_mediais_binary
Package: mimeparse
This module provides basic functions for handling mime-types. It can handle
matching mime-types against a list of media-ranges. See section 14.1 of
the HTTP specification [RFC 2616] for a complete explanation.http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.1Contents:parse_mime_type():   Parses a mime-type into its component parts.parse_media_range(): Media-ranges are mime-types with wild-cards and a ‘q’ quality parameter.quality():           Determines the quality (‘q’) of a mime-type when compared against a list of media-ranges.quality_parsed():    Just like quality() except the second parameter must be pre-parsed.best_match():        Choose the mime-type with the highest quality (‘q’) from a list of candidates.
Package: mimeparse-ng
This module provides basic functions for handling mime-types. It can handle
matching mime-types against a list of media-ranges. See section 14.1 of
the HTTP specification [RFC 2616] for a complete explanation.http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.1Contents:parse_mime_type():   Parses a mime-type into its component parts.parse_media_range(): Media-ranges are mime-types with wild-cards and a ‘q’ quality parameter.quality():           Determines the quality (‘q’) of a mime-type when compared against a list of media-ranges.quality_parsed():    Just like quality() except the second parameter must be pre-parsed.best_match():        Choose the mime-type with the highest quality (‘q’) from a list of candidates.
Package: mime-parser
mime-parserEncodes and decodes MIME dataFeaturesSupported in Python 3.8 and later.MIME text parsingMIME based encode and decodePlugin supportIANA media typesNo dependenciesList of pre-implemented codecsapplication/jsonapplication/gzipInstallationpipinstallmime-parserIf you want to addorjsonsupport:pipinstallmime-parser[full]UsageMIME text parsingfrommime_parserimportparse_mimemime=parse_mime("image/jpeg;q=0.8")print(mime.family)# imageprint(mime.subtype)# jpegprint(mime.parameter)# q=0.8print(mime.original)# image/jpeg;q=0.8print(mime.parameter_tuple)# ('q', '0.8')MIME based encode and decodefrommime_parserimportdecode,encodeoriginal_data=b"..."encoded_data=encode("application/gzip",original_data)decoded_data=decode("application/gzip",encoded_data)assertoriginal_data==decoded_dataRegister MIME codecHow to registerpyyaml:importyamlfrommime_parserimportdecode,registerdefyaml_encoder(data):returnyaml.dump(data).encode("utf-8")defyaml_decoder(data):returnyaml.full_load(data)register("application/yaml",encoder=yaml_encoder,decoder=yaml_decoder)test_yaml="""test:value: 0"""decoded_yaml=decode("application/yaml",test_yaml.encode(encoding="utf-8"))print(decoded_yaml)Plugin supportYour package name must start withmime-parser-.Add the__mime_parser__attribute to your package's root__init__.pyfile.__mime_parser__must beIterable.The Element of__mime_parser__must contain:mimestringencoder(Any) -> bytescallabledecoder(bytes) -> AnycallableThe plugin must be installed with pip or included insys.pathExamples of plugins that supportpyyaml:importyamldefyaml_encoder(data):returnyaml.dump(data).encode("utf-8")defyaml_decoder(data):returnyaml.full_load(data)__mime_parser__=[{"mime":"application/yaml","encoder":yaml_encoder,"decoder":yaml_decoder,},]Command line usageEncoding:echo"..."|python-mmime_parserencodeapplication/gzip>dataDecoding:catdata|python-mmime_parserdecodeapplication/gzip>originalPrints a list of installed codecs:python-mmime_parserlistPrints the MIME types registered withIANA:python-mmime_parserianaMore options:python-mmime_parser--helpEnvironment variablesMIME_PARSER_PLUGIN_PREFIX: Package name prefix to search for plugins.MIME_PARSER_PLUGIN_DENIES: Deny list of plugins separated by:.MIME_PARSER_PLUGIN_ALLOWS: Allow list of plugins separated by:.MIME_PARSER_DISABLE_ORJSON_INSTALL: without using orjson, Use the default json library.LicenseSee theLICENSEfile for details. In summary,mime-parseris licensed under theMIT license.
Package: mimerender-pr36
Fork forhttps://github.com/martinblech/mimerender/pull/36.
This module provides a decorator that wraps a HTTP
request handler to select the correct render function for a given HTTP
Accept header. It uses mimeparse to parse the accept string and select the
best available representation. Supports Flask, Bottle, web.py and webapp2
out of the box, and it’s easy to add support for other frameworks.
Package: mimerpy
MimerPy is an adapter for Mimer SQL version 11 inPythonwhich implements thePEP 249specification.  It allows the user to access Mimer SQL through Python. MimerPy is
implemented on top of a Cpython wrapper of the native Mimer API interface.Requirements:A Mimer SQL version 11 or later installation on the client sideA Mimer SQL version 11 or later database serverMimerPy is currently supported on Linux, and Windows.Python 3.5 or laterThe source code for MimerPy resides onGitHub. Installable packages
can be found onPyPi. To install, use the command:python3 -m pip install mimerpyThe source for the MimerPy manual is stored together with the source
in the doc/ directory. We recommend reading it on the
Mimer Information Technologydocumentation site.Thehome pagefor the project can be found on the Mimer Information Technology developer site.
Package: mimes
An internet MIME-message is usually accompanied by a content-type string liketext/plainorapplication/collection+json. The mimes library allows
parsing these and can if types are subsets of another – a parser that handlesapplication/json, for example, will be perfectly fine handing the
collection+json type with less semantics.It also parses more complex strings including parameters (e.g.application/vnd.company.someapi; version=3) and can construct these as
well.See thedocsfor details.
Package: mimesis
Mimesis: The Fake Data GeneratorDescriptionMimesis (/mɪˈmiːsɪs) is a robust data generator for Python that can produce a wide range of fake data in various languages. This tool is useful for populating testing databases, creating fake API endpoints, filling pandasDataFrames, generating JSON and XML files with custom structures, and anonymizing production data, among other purposes.InstallationTo install mimesis, simply use pip:pipinstallmimesisTo work with Mimesis on Python versions 3.8 and 3.9, the final compatible version is Mimesis 11.1.0.
Install this specific version to ensure compatibility.FeaturesMultilingual: Supports 35 different locales.Extensibility: Supports custom data providers and custom field handlers.Ease of use: Features a simple design and clear documentation for straightforward data generation.Performance: Widely recognized as the fastest data generator among Python solutions.Data variety: Includes various data providers designed for different use cases.Schema-based generators: Offers schema-based data generators to effortlessly produce data of any complexity.Intuitive: Great editor support. Fully-typed, thus autocompletion almost everywhere.DocumentationYou can find the complete documentation on theRead the Docs.It is divided into several sections:About MimesisQuickstartLocalesData ProvidersStructured Data GenerationRandom and SeedTricks and TipsAPI ReferenceAdditional InformationChangelogYou can improve it by sending pull requests to this repository.UsageThe library is exceptionally user-friendly, and it only requires you to import aData Providerobject that corresponds to the desired data type.For instance, thePersonprovider can be imported to access personal information, including name, surname, email, and other related fields:>>>frommimesisimportPerson>>>frommimesis.localesimportLocale>>>person=Person(Locale.EN)>>>person.full_name()'Brande Sears'>>>person.email(domains=['example.com'])'roccelline1878@example.com'>>>person.email(domains=['mimesis.name'],unique=True)'f272a05d39ec46fdac5be4ac7be45f3f@mimesis.name'>>>person.telephone(mask='1-4##-8##-5##3')'1-436-896-5213'You can learn more about other providers and locales in ourdocumentation.How to ContributeTake a look atcontributing guidelines.Check for open issues or open a fresh issue to start a discussion
around a feature idea or a bug.Fork the repository on GitHub to start making your changes to theyour_branchbranch.Add yourself to the list ofcontributors.Send a pull request and bug the maintainer until it gets merged and
published.LicenseMimesis is licensed under the MIT License. SeeLICENSEfor more
information.