Saravanan666/pypi-org huggingface dataset:
Package: miwa-package-esi-sba
package_namepackage_nameis a Python library for ..........InstallationUse the package managerpipto installpackage_name.pipinstallpackage_nameUsageimportpackage_name# returns 'words'package_name.module_name('word')# returns 'geese'package_name.module_name('goose')# returns 'phenomenon'package_name.module_name('phenomena')LicenseMIT
Package: mixal
MIXAL[中文|English]AMIXassembly language simulator.InstallMake sure you have a C++ 11 compiler and execute:pipinstallmixalFor MacOS users, you may need to use:MACOSX_DEPLOYMENT_TARGET=10.9pipinstallmixalSampleFollowing is a piece of sample code that finds the maximum value. There are upper-case letters in the names of functions and attributes as the codes were written with C++.importrandomimportmixal# Initialize an environmentcomputer=mixal.Computer()# The location for register Jend_point=3500# Load the assembly codes.# Note that the location for register J is set to HLT# to make sure the codes halt eventuallycomputer.loadCodes(['X       EQU  1000','        ORIG 3000','MAXIMUM STJ  EXIT','INIT    ENT3 0,1','        JMP  CHANGEM','LOOP    CMPA X,3','        JGE  *+3','CHANGEM ENT2 0,3','        LDA  X,3','        DEC3 1','        J3P  LOOP','EXIT    JMP  *','        ORIG{}'.format(end_point),'        HLT',])num_numbers,max_val=100,0# Register I1 denotes the number of integers in the memory buffercomputer.rI1.set(num_numbers)# Register J stores the returning locationcomputer.rJ.set(end_point)foriinrange(1001,1001+num_numbers):val=random.randint(0,100000)# Set random values to memorycomputer.memoryAt(i).set(val)max_val=max(max_val,val)# Execute until the HLT operationcomputer.executeUntilHalt()print('Expected:',max_val)# Register A stores the final maximum valueprint('Actual:',computer.rA.value())# The units of time for executing the codes, exclude the halt operation.print('Compute Cost:',computer.elapsed())IO DevicesThere are several pre-defined IO devices in the environment. You can set the initial values of the input devices before the execution. Following is the code that reads one word from the input device and writes the same one to the output device.importmixal# Initialize an environmentcomputer=mixal.Computer()# Pre-defined indices for IO devicescard_reader_index=16card_punch_index=17computer.loadCodes("""ORIG 3000IN   100(16)LIN     JBUS LIN(16)OUT  100(17)LOUT    JBUS LOUT(17)""")# Set the initial value of input devicecomputer.getDeviceWordAt(card_reader_index,0).set('PRIME')computer.executeUntilHalt()# Check the output text of the output deviceprint(computer.getDeviceWordAt(card_punch_index,0).getCharacters())
Package: mixbaba
MIXpanel Bayesian AB test Analysis tool (MixBABA)This tool is intended to consume a JSON file containing details about a Mixpanel funnel and output the results of the analysis made whithin a Bayesian framework.You can find details about the data processinghere.InstallationFrom PyPiJust easy as:pip install mixbabaFrom sourcesTo install this package you have to clone the repository:git clone https://github.com/NaturalCycles/MixBABA.gitYou can run the unit tests, to ensure the tool will work:cd MixBABA 
python setup.py testAnd then you can install the tool via PIP:pip install .UsageYou can find the full documentationhere,
but if you want a short guide read the following.To use MixBABA you have need:a JSON file containing a list of the funnels together with the details about them
(an example is in this repository)the "API secret" to connect to Mixpanel; You can find your one in the settings
dialog of the Mixpanel web application.Then you can launch the analysis via command line:mixbaba -f [funnel_file.json] -k [API secret]The tool will extract the data relative to the funnel from Mixpanel,
and the output will be put in the same directory as CSV files,
as many as the funnels specified in the JSON file given in input.By default no output will be sent to the console. If you want a CSV file as output you can ask it withmixbaba -f [funnel_file.json] -k [API secret] -o csvExample resultThis is the standard output format for the analysis of a funnelGroupControl ImpressionsControl Conversionstest Impressionstest Conversionstest CR improvementtest ProbabilityAll.All34164253311052840.2323870.992551goal.PREVENT6175256016370.5001530.947624goal.PLAN15615141150.1061570.568093$country_code.US16631224154382420.1634480.95048$country_code.SE8024237275350.6543910.974175Or, if you specify the option-of longat the command launch:DiscriminantCohortCommentControl ImpressionsControl Conversionstest Impressionstest Conversionstest CR improvementtest ProbabilityNoneAllResult for test is OK!34164253311052840.2323870.992551user.goalPREVENTResult for test is uncertain.6175256016370.5001530.947624user.goalPLANResult for test is uncertain.15615141150.1061570.568093user.$country_codeUSResult for test is OK!16631224154382420.1634480.95048user.$country_codeSEResult for test is OK!8024237275350.6543910.974175
Package: mixbag
MixBagLocal DevelopmentPyenvIt's recommended that you usepyenvpyenv-installercurl-Lhttps://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer|bashInstall PoetryThis project usespoetry. Install it using the following command.curl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py|python-More instructionshere.Install DependenciespoetryinstallInstall pre-commit hookspoetryrunpre-commitinstall--hook-typecommit-msg

poetryrunpre-commitinstallRunning TestspoetryrunpyscripttestsBuild Docspoetryrunpyscriptdocs_build
Package: mixbalanced
Mixes the elements of multiple lists in a balanced manner based on their proportional lengths.pip install mixbalancedTested against Windows 10 / Python 3.10 / AnacondaThisfunctiontakesmultiplelistsasinputandreturnsanewlistthatcontainstheelementsoftheinputlists.Theelementsarecombinedinabalancedmannerbasedontheirproportionallengths.Longerlistscontributemoreelementstotheresultinglist.Parameters:*args:Variable-lengthargumentlist,containingmultiplelists/tuples.Returns:list:Anewlistcontainingtheelementsfromtheinputlists/tuplesmixedinabalancedmanner.Example:frommixbalancedimportmix_balancedl1=["Antonio"]*10l2=["Paulo"]*5l3=["Anna"]*15l4=["Maria"]*3mix=mix_balanced(l1,l2,l3,l4)print(mix)Output:['Anna','Antonio','Anna','Antonio','Paulo','Anna','Anna','Antonio','Anna','Maria','Anna','Antonio','Paulo','Anna','Antonio','Anna','Antonio','Paulo','Anna','Anna','Maria','Antonio','Anna','Antonio','Paulo','Anna','Anna','Antonio','Anna','Antonio','Paulo','Anna','Maria']
Package: mixbox
A library of common code leveraged by python-cybox, python-maec, and python-stix.DevelopingTo set up an environment to developmixbox:# Create a new virtualenv$mkvirtualenvmixbox# Change to this directory and install requirements$cd/path/to/mixbox$pipinstall-rrequirements.txt# Install python-cybox, python-maec, and python-stix in "develop" mode.$pipinstall-e/path/to/python-cybox$pipinstall-e/path/to/python-maec$pipinstall-e/path/to/python-stixThen you can make changes to themixboxlibrary and ensure the test cases for
the corresponding projects continue to pass (usingtoxornosetests).Releasing$bumpversionpatch$gitpushoriginmaster--tags$rmdist/*$pythonsetup.pysdistbdist_wheel$twineuploaddist/*
Package: mixbytes-tank
MixBytes TankMixBytes Tank is a console tool which can set up a blockchain cluster in minutes in a cloud and bench it using various transaction loads.
It'll highlight blockchain problems and give insights into performance and stability of the technology.At the moment, supported blockchains areHayaandPolkadot.Setup - bench - dispose workflow is very similar to a test case, that is why configuration of such run is described in a declarative YAML file called "testcase".More info can be found at:GuideCookbookQuick guide belowContributions are welcomed!Discuss in our chat:https://t.me/MixBytes.Quick guideRequirementsPython3Installationpip3installmixbytes-tankUse the--preoption to get the latest release candidate:pip3installmixbytes-tank--preUsage1. Configure the user configConfigure~/.tank.yml. The example can be found atdocs/config.yml.example.Please configure at least one cloud provider. The essential steps are:providing (and possibly creating) a key pairregistering a public key with your cloud provider (if needed)specifying a cloud provider access token or credentials2. Create or get a tank testcaseThe example can be found atdocs/testcase_example.yml.3. Start a tank runtankclusterdeploy<testcasefile>As a result, the cluster instance listing will be printed along with the run id and the monitoring link.4. Log in to the monitoringOpen the monitoring link in your browser, type in 'tank' in the username and password fields.
You will see cluster metrics in the predefined dashboards.5. List current active runsThere can be multiple tank runs at the same time. The runs list and brief information about each run can be seen via:tankclusterlist6. Create synthetic loadtankclusterbench<runid><loadprofilejs>[--tpsN][--total-txN]<run id>- run ID<load profile js>- a js file with a load profile: custom logic which creates transactions to be sent to the cluster--tps- total number of generated transactions per second,--total-tx- total number of transactions to be sent.7. Shutdown and remove the clustertankclusterdestroy<runid>DevelopmentBranchingfeatures ->develop->master.masteris production-ready code.developis expected-to-be production-ready code which is merged into master after thorough testing
(so, no faulty code indeveloppls).developandmasterbranches are permanent.
Package: mixcloud
Mixcloud.com API access from PythonThis provides a Python API for thehttp://mixcloud.comwebsite.ExamplefrommixcloudimportMixcloudm=Mixcloud()u=m.user('michelplatiniste')forcinu.cloudcasts():printc.nameAuthorizationYou must have an application registered with Mixcloud and a corresponding
client ID and secret.Authorization is a multi-step process:Generate the authorization URLRedirect the user to the URL to authorize your applicationCollect thecodequery string parameter from the redirectExchange the code for an access tokeno=mixcloud.MixcloudOauth(client_id=client_id,client_secret=client_secret,redirect_uri=redirect_uri)url=o.authorize_url()# Next redirect the user to `url`. They will be redirected to your# redirect uri with a `code` query string parameter. This must be# exchanged with Mixcloud for an access token.access_token=o.exchange_token(code)# This can then be used for calls that required authorization.m=mixcloud.Mixcloud(access_token=access_token)print(m.me())Optionally you can store an access token in your.netrcfile and it
will be used automatically. The machine name must bemixcloud-api.E.g.:machine mixcloud-api
password my_access_tokenUploadingIt is possible to use this module to upload cloudcasts. In order to do
that you need to be authenticated. To do that, provide an API token to
the constructor.m=mixcloud.Mixcloud(access_token=access_token)cc=Cloudcast(...)withopen(mp3_path)asmp3:r=m.upload(cc,mp3)YML file supportIt is possible to represent cloudcasts as YAML files. Seeexample.yml.The relevant keys are:KeyTypetitleStringdescStringtagsList of stringstracksList of tracksEach track is a dict with the following keys:KeyTypestartIntegerartistStringtrackStringYou can leverage YAML syntax for the “start” field:2:54will be
parsed directly as 174 (then number of seconds).It is possible to parse such a file with:withopen(yml_path)asyml:cc=mixcloud.Cloudcast.from_yml(yml,None)MockingA mock server is provided for testing purposes inmixcloud.mock.Changelog0.4.0release date:2015-10-27Switch to semantic versioning.Include access token when calling /me (#10)Add a helper class for OAuth (#11)Support access token stored in netrc (#9, #12)0.0.3release date:2015-03-15Pictures can be uploaded and retrieved (#6)Include LICENSE in tarball (#8)0.0.2release date:2015-01-28Switch from requirements.txt to install_requires (#7)Add HISTORY.rst to MANIFEST.in0.0.1release date:2015-01-28Initial release
Package: mixedbread-ai
mixedbread ai Python SDKIntroduction to mixedbread aimixedbread ai is a cutting-edge research and development company specializing in Natural Language Processing (NLP). At our core, we focus on advancing the field of NLP through innovative research, offering powerful tools for embeddings, retrieval, and other NLP functionalities. Our mission is to make NLP accessible to everyone on every device. To learn more about mixedbread ai, visit ourwebsite.Requirements.Python 3.7+Installation & UsageInstallationIf the python package is hosted on a repository, you can install directly using:pipinstallmixedbread-aiUsagefrommixedbread_aiimportMixedbreadAiimportosos.environ["MIXEDBREAD_API_KEY"]="{YOUR_API_KEY}"mxbai=MixedbreadAi()embeddings=mxbai.embeddings(model="e5-large-v2",input=["I like to eat apples.","I like to eat bananas."])print(embeddings)Alternatively, you can set the api key via configuration:frommixedbread_aiimportMixedbreadAimxbai=MixedbreadAi(api_key="{YOUR_API_KEY}",base_url="https://api.mixedbread.ai",timeout=30,headers={"X-Custom-Header":"foobar"},...)embeddings=mxbai.embeddings(model="e5-large-v2",input=["I like to eat apples.","I like to eat bananas."],normalized=False)print(embeddings)Don't forget to replace"{YOUR_API_KEY}"with your actual API key. If you don't have an API key, you can get one for free by signing up for an account atmixedbread.ai.
Package: mixed-methods
mixed-methodsMixed methods.InstallingPython 3.8 or above is required.pipInstalling the library withpipis quite simple:$pipinstallmixed-methodsAlternatively, the library can be installed from source:$gitclonehttps://github.com/nekitdev/mixed-methods.git$cdmixed-methods$python-mpipinstall.poetryYou can addmixed-methodsas a dependency with the following command:$poetryaddmixed-methodsOr by directly specifying it in the configuration like so:[tool.poetry.dependencies]mixed-methods="^1.1.1"Alternatively, you can add it directly from the source:[tool.poetry.dependencies.mixed-methods]git="https://github.com/nekitdev/mixed-methods.git"Examples# main.pyfrommixed_methodsimportmixed_methodclassType:@classmethoddeftype_method(cls)->int:return13definstance_method(self)->int:return42method=mixed_method(type_method,instance_method)>>>frommainimportType>>>Type.method()13>>>instance=Type()>>>instance.method()42DocumentationYou can find the documentationhere.SupportIf you need support with the library, you can send anemailor refer to the officialDiscord server.ChangelogYou can find the changeloghere.Security PolicyYou can find the Security Policy ofmixed-methodshere.ContributingIf you are interested in contributing tomixed-methods, make sure to take a look at theContributing Guide, as well as theCode of Conduct.Licensemixed-methodsis licensed under the MIT License terms. SeeLicensefor details.
Package: mixem
mixem is a pure-python implementation of the Expectation-Maximization (EM) algorithm for fitting mixtures of probability distributions. It works in Python 2 and Python 3 (tested with 2.7 and 3.5.1) and uses few dependencies (only NumPy and SciPy).FeaturesEasy-to-use and fully-documented APIBuilt-in support for several probability distributionsEasily define custom probability distributions by implementing their probability density function and weighted log-likelihoodDocumentationFind the mix’EM documentation onReadTheDocs.Installationpip install mixem
Package: mixemup
mixemupA string combinerThis program takes as input a file of strings, and produces a list of combinations of these strings as output.InstallationRequires Python 3.7 or greater.pip install mixemupUsageUsage: mixemup.py [OPTIONS] FILE_PATH

  This program takes in a file with strings, and produces delimiter-
  separated combinations of these strings.

Options:
  -d, --delimiter TEXT     Set the delimiter.
  -r, --prefix TEXT        Add the given string to the beginning of each line.
  -o, --postfix TEXT       Add the given string to the end of each line.
  -n, --min-parts INTEGER  Set the minimum number of parts in output. This
                           count does not include prefixes and postfixes.
  -m, --max-parts INTEGER  Set the maximum number of parts in output. This
                           count does not include prefixes and postfixes.
  --version                Show the version and exit.
  --help                   Show this message and exit.Example$ cat names.txt
bob
alice
john
jack
sally$ mixemup names.txt

bob
alice
john
jack
sally
bob alice
bob john
bob jack
bob sally
alice john
alice jack
alice sally
john jack
john sally
jack sally
bob alice john
bob alice jack
bob alice sally
bob john jack
bob john sally
bob jack sally
alice john jack
alice john sally
alice jack sally
john jack sally
bob alice john jack
bob alice john sally
bob alice jack sally
bob john jack sally
alice john jack sally
bob alice john jack sally
Package: mix-gamma-vi
Variational Inference for Mixtures of Gamma DistributionsThis package implements variational inference for mixtures of gamma distributions. For more information, seeComparing Markov Chain Monte Carlo and Variational Methods for Bayesian Inference on Mixtures of Gamma Distributions.Two parameterisations of the gamma distribution are available: the shape-rate parameterisation and the shape-mean parameterisation. The shape-mean parameterisation is recommended since it generally produces superior posterior approximations and predictive distributions.Getting StartedPrerequisitespython>=3.6
tensorflow>=2.0
tensorflow_probability>=0.8InstallingI recommend installing using pip as it will also install the prerequisites. Runpip install mix_gamma_viExampleThis is an example of posterior inference on a mixture of two gamma distributions under the shape-mean parameterisation. Suppose we have a one-dimensional tensorxof data.# import mix_gamma_vi functionfrommix_gamma_viimportmix_gamma_vi# Fit a modelfit=mix_gamma_vi(x,K=2)# Get the fitted distributiondistribution=fit.distribution()# Print the means of the parameters under the fitted distributiondistribution.mean(){'pi': <tf.Tensor: id=4201, shape=(1, 2), dtype=float32, numpy=array([[0.50948393, 0.49051604]], dtype=float32)>,
 'beta': <tf.Tensor: id=4208, shape=(1, 2), dtype=float32, numpy=array([[1.0013412, 1.9965338]], dtype=float32)>,
 'alpha': <tf.Tensor: id=4212, shape=(1, 2), dtype=float32, numpy=array([[20.712543, 82.77388 ]], dtype=float32)>}We can sample from this distribution by callingdistribution.sample().For a more in-depth example, seeexample.py.Performance TipTo avoid retracing the tensor graph every time you change the parameters, pass them as TensorFlow constants. e.g. instead of the above, dofit=mix_gamma_vi(x,K=tf.constant(2))AuthorsThis work is submitted by Isaac Breen in partial fulfillment of the requirements for the Bachelor of Science degree with Honours at the University of Western Australia. Supervised by John Lau and Edward Cripps.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: mixingbear
MixingBearAutomatic beat-mixing of music files in Python, usingAudioOwl🎚Jump to:QuickstartInstallationUsageQuickstartMix two WAV files -importmixingbearmixingbear.mix('track01.wav','track02.wav','output.wav')InstallationTested on Python 3.6 or later⚠️ AudioOwl needsffmpegto be installed on your machine.
The easiest way to install ffmpeg (at least on a Mac) is using homebrew.See instructions here.The latest stable release is available on PyPI.Install it using the following command -$pipinstallmixingbearUsagemixingbear.mix()Saves a mixed WAV file locally tooutput_file_pathSupported keyword arguments foraudioowl.get_waveform():top_file- Path to a WAV file you want to mix ontobottom_file. e.g.top_file=wav_file.wavbottom_file- Path to a WAV file you want to mixtop_fileonto. e.g.bottom_file=wav_file.wavoutput_file_path- Path for the mixed output WAV file you want to mixoutput_file_pathonto. e.g.bottom_file=output.wavmix_mode[optional, default == 'random']- String:random- MixingBear will find the best mixing points, and will mix the tracks starting on a random one out of them.first- MixingBear will find the best mixing points, and will mix the tracks on thefirstone.sr[optional, default == 22050]- Integer. Sample rate.offset[optional, default == 880, equal to ~20 milliseconds on a track with 44100 sample rate]- Integer. Number of samples to use as padding on beats, to choose sync points. e.g. With offset=880, beats will be considered as 'matching' is they are positioned away from each other in 880 samples or less.trim_silence[optional, default == True]- Boolean. If True, MixingBear will trim leading silence ontop_file.
Package: mixin-python
Python Bindings forMixinQuick StartInstall mixin-pythonpipinstallmixin-pythonQuick Startimportasynciofrompymixin.mixin_apiimportMixinApiapi=MixinApi('http://mixin-node0.exinpool.com:8239')addr=api.create_address()print(addr)asyncdefget_info():info=awaitapi.get_info()print(info)asyncio.run(get_info()){'address': 'XIN9M9T32UhraHpJ9Do4s7FVFeTpery49JB1u6bAcgLe2wY4As918roNTVmbh3GXuuoRLx5FyeuhvUQUmvtWtUthGdgBCdMG',
     'view_key': '6396fd4201bbec6f495ded697428003dfd227578174f97e034c94e1abb420d0f',
     'spend_key': '02f0ea8504740a1c2916e1b9965c23c242aeeb02d093f3f1ed0e5e0d494bc603'}Run Mixin from Pythonpython3-mpymixin.mainkernel--dirconfig--port9000Run a Local Mixin Testnetpython3tests/start_testnet.pyConnect to Local Testnetimportasynciofrompymixin.mixin_apiimportMixinApiapi=MixinApi('http://127.0.0.1:8001')asyncdefget_info():info=awaitapi.get_info()print(info)asyncio.run(get_info())ReleasesDocsInstall Build Dependenciesclang & go 1.16 & cmakeUbuntusudo apt install python3-dev
sudo apt install python3-pip
sudo apt install clang
sudo apt install cmakeIntall golangBuildingDownload Source Codegit clone https://github.com/learnforpractice/mixin-python --recursive
cd mixin-python
python3 -m pip install -r requirements-dev.txtUpdate mixin-python Source Codegitpull
gitsubmoduleupdate--init--recursiveBuild on Linux./build-linux.shBuild on macOS X./build-mac.shRun Tests in Jupyter Notebookpython3-mpipinstallnotebookcdnotebook
python3-mnotebookOpen helloworld.ipynb, hit Ctrl+Enter to run the test code in cellDo not forget to run testnet.stop() to stop the testnet, otherwise the testnet processes will still running in the backgroud.Referencehttps://github.com/wenewzhang/mixin-python3-sdkLicenseGPL3.0
Package: mixinsdk
Mixin SDK for Python (Unofficial)OverviewThis is theMixin NetworkSDK for Python.Project objectives: ease of use and readability for developers.StatusThis SDK is still considered a work in progress, therefore things might (and will) break with every update.Getting startedFor sdk user:pipinstallmixinsdkFor sdk dev:Runtime support: Python 3.9+Clone the repository, or download code.Set up and activate virtual environment, like this:cdmixin-sdk-python-main
python3-mvenv.env
..env/bin/activate# for windows# .\.env\Scripts\activateInstall dependencies:python3 -m pip install -r requirements.txtThan see "examples" folder, and run it.RelationsMixin Messenger Bot Boilerplate:nodewee/mm-bot-boilerplateContributingWe'd love to accept your patches and contributions to this project.How to contribute, see fileCONTRIBUTING.mdChangelog see fileCHANGELOG.mdTODOSupport for send encrypted messages?Address APICircles APIUpload AlbumWithdrawal APIMulti-Signature APICollectibles APIMainnet RPC API and ClientReferenceshttps://developers.mixin.one/docs/apihttps://github.com/MixinNetwork/bot-api-go-client/https://github.com/includeleec/mixin-python3-sdkhttps://github.com/learnforpractice/mixin-pythonLicenseThe project is licensed under the MIT License - see theLICENSEfile for details.
Package: mix-mavis
MAVIS 交互式可视化分析工具依赖包ipythonmatplotlibseabornipywidgetsboto3numpypandasscipyscikit-learntorch云服务器安装pip install mix-mavis平台AWS S3JUPYTER NOTEBOOK数据分析分析数据数据交互设置数据桶访问凭据获取实时MIXIOT数据导出MIXIOT历史数据数据上传至数据桶从数据桶下载数据