Saravanan666/pypi-org huggingface dataset:
Package: mmgen-node-tools
MMGen Node ToolsTerminal-based utilities for Bitcoin and forkcoin full nodesRequires modules from theMMGen online/offline cryptocurrency wallet.Install:If installing as user (without venv), make sure that~/.local/binis inPATH.Windows/MSYS2:InstallMSYS2 and the MMGen Wallet dependencies, skipping installation of
scrypt, libsecp256k1 and the wallet itself if desired.Install some additional dependencies:$pacman-S\mingw-w64-ucrt-x86_64-python-pandas\mingw-w64-ucrt-x86_64-python-tqdm\mingw-w64-ucrt-x86_64-python-lxml
$python3-mpipinstallrequests-futures
$python3-mpipinstall--no-depsyahooqueryLinux:Install therequired MMGen Wallet packagesfor your Linux distribution.Stable version:$python3-mpipinstall--upgrademmgen-node-toolsDevelopment version:Install the latest development version ofMMGen Walletfor your platform.$gitclonehttps://github.com/mmgen/mmgen-node-tools
$cdmmgen-node-tools
$python3-mbuild--no-isolation
$python3-mpipinstalldist/*.whlTest:NOTE: the tests require that the MMGen Wallet and MMGen Node Tools repositories be
located in the same directory.Windows/MSYS2:Tested only on NTFS – with ReFS your mileage may varyTurn on Developer Mode to enable symlinks:Settings -> Update & Security -> For developers -> Developer Mode: Onand add this to your~/.bashrc:exportMSYS=winsymlinks:nativestrictClose and reopen the MSYS2 terminal to update your environment.Initialize the test framework (must be run at least once after cloning, and
possibly again after a pull if tests have been updated):$ test/init.shBTC-only testing:$ test/test-release.sh -AFull testing:$ test/test-release.shHomepage:Clearnet|I2P|OnionCode repository:Clearnet|I2P|OnionCode repository mirrors:Github|Gitlab|GitflicKeybase|Reddit|BitcointalkPGP Signing Key: 5C84 CB45 AEE2 250F 31A6 A570 3F8B 1861 E32B 7DA2Donate:⊙ BTC:bc1qxmymxf8p5ckvlxkmkwgw8ap5t2xuaffmrpexap⊙ BCH:15TLdmi5NYLdqmtCqczUs5pBPkJDXRs83w⊙ XMR:8B14zb8wgLuKDdse5p8f3aKpFqRdB4i4xj83b7BHYABHMvHifWxiDXeKRELnaxL5FySfeRRS5girgUvgy8fQKsYMEzPUJ8h
Package: mmgmt
Media Management Command Line InterfaceSummaryAn intuitive command line interface wrapper around boto3 to search and manage media assetsInstallingmmgmt& Supported Versionsmmgmtis available on PyPI:python-mpipinstallmmgmtMedia Management Command Line Interface officially supports Python 3.8+.Supported Features & UsageFor help, run:mmgmt--helpYou can also use:python-mmmgmt--helpCommands:Usage:mmgmt[OPTIONS]COMMAND[ARGS]...AsimpleCLItosearchandmanagemediaassetsinS3andlocally.Setupwith`mmgmtconfigure`Options:--versionShowtheversionandexit.-h,--helpShowthismessageandexit.

Commands:configureprintprojectconfigs&setconfigsmanuallydeletedeletefilefromcloudstorage-TODO-downloaddownloadobjectfromcloudstorageget-statusgetobjectheadfromcloudstoragehellotestendpointlslistfilesinlocation(local,s3,orglobal)searchsearchfilesinlocaldirectoryandcloudstorageuploaduploadfiletocloudstorageWhy not useawscli?You can, and I do, in tandem withmmgmt-- the purpose is to create an additional interface that minimized the lookup/copy/paste process I found myself frequently going through.Another use case is for rapid prototyping applications that require an S3 interface.For example:importpandasaspdimportmmgmtasmmgmtaws=mmgmt.AwsStorageMgmt(project_name="mmgmt")obj_list=aws.get_bucket_objs()res=[]fors3_objinobj_list:res.append([str(s3_obj.key),str(s3_obj.key.split('/')[0]),s3_obj.last_modified,s3_obj.storage_class,s3_obj.size])df=pd.DataFrame(res)df.columns=['key','group','last_modified','storage_class','size']DevelopmentTo contribute to this tool, first checkout the code:gitclonehttps://github.com/will-wright-eng/media-mgmt-cli.gitcdmedia-mgmt-cliThen create a new virtual environment:python-mvenvvenvsourcevenv/bin/activateNow install the dependencies and test dependencies:pipinstall-e'.[test]'To run the tests:pytestInstall pre-commit before submitting a PR:brewinstallpre-commit
pre-commitinstallReferencesPyPI PackageBased on cookiecutter templatewill-wright-eng/click-appRewrite of original projectwill-wright-eng/media_mgmt_cli
Package: mmgpt
🤖 Multi-modal GPTTrain a multi-modal chatbot with visual and language instructions!Based on the open-source multi-modal modelOpenFlamingo, we create variousvisual instructiondata with open datasets, including VQA, Image Captioning, Visual Reasoning, Text OCR, and Visual Dialogue. Additionally, we also train the language model component of OpenFlamingo using onlylanguage-only instructiondata.Thejoint trainingof visual and language instructions effectively improves the performance of the model!FeaturesSupport various vision and language instruction dataParameter efficient fine-tuning with LoRATuning vision and language at the same time, complement each otherInstallaionTo install the package in an existing environment, rungitclonehttps://github.com/open-mmlab/Multimodal-GPT.git
pipinstall-rrequirements.txt
pipinstall-e.-vor create a new conda environmentcondaenvcreate-fenvironment.ymlDemoDownload the pre-trained weights.Usethis scriptfor converting LLaMA weights to HuggingFace format.Download the OpenFlamingo pre-trained model fromopenflamingo/OpenFlamingo-9BDownload our LoRA Weight fromhereThen place these models in checkpoints folders like this:checkpoints
├── llama-7b_hf
│   ├── config.json
│   ├── pytorch_model-00001-of-00002.bin
│   ├── ......
│   └── tokenizer.model
├── OpenFlamingo-9B
│   └──checkpoint.pt
├──mmgpt-lora-v0-release.ptlaunch the gradio demopythonchat_gradio_demo.pyExamplesRecipe:Travel plan:Movie:Famous person:Fine-tuningPrepare datasetsA-OKVQADownload annotation fromthis linkand unzip todata/aokvqa/annotationsIt also requires images from coco dataset which can be downloaded fromhere.COCO CaptionDownload fromthis linkand unzip todata/cocoIt also requires images from coco dataset which can be downloaded fromhere.OCR VQADownload fromthis linkand place indata/OCR_VQA/LlaVADownload fromliuhaotian/LLaVA-Instruct-150Kand place indata/llava/It also requires images from coco dataset which can be downloaded fromhere.Mini-GPT4Download fromVision-CAIR/cc_sbu_alignand place indata/cc_sbu_align/Dolly 15kDownload fromdatabricks/databricks-dolly-15kand place it indata/dolly/databricks-dolly-15k.jsonlAlpaca GPT4Download it fromthis linkand place it indata/alpaca_gpt4/alpaca_gpt4_data.jsonYou can also customize the data path in theconfigs/dataset_config.py.Start trainingtorchrun--nproc_per_node=8mmgpt/train/instruction_finetune.py\--lm_pathcheckpoints/llama-7b_hf\--tokenizer_pathcheckpoints/llama-7b_hf\--pretrained_pathcheckpoints/OpenFlamingo-9B/checkpoint.pt\--run_nametrain-my-gpt4\--learning_rate1e-5\--lr_schedulercosine\--batch_size1\--tuning_configconfigs/lora_config.py\--dataset_configconfigs/dataset_config.py\--report_to_wandb\AcknowledgementsOpenFlamingoLAVISStanford AlpacaMiniGPT-4LLaVAInstruction Tuning with GPT-4
Package: mmgroup
This is the pythonmmgroupproject.It gives the user the capability to compute in the Monster group.
In mathematics, the Monster group is the largest finite sporadic
simple group.Project Documentation seehttps://mmgroup.readthedocs.io/en/latest/Quick installation and testFor Windows 64:pip install mmgroup
pip install pytest
python -m pytest --pyargs mmgroup -m "not slow"For Linux and macOS with a 64-bit x64 CPU:pip3 install mmgroup
pip3 install pytest
python3 -m pytest --pyargs mmgroup -m "not slow"Python wheels for some other platforms may be built with the
cibuildwheel tool, seehttps://mmgroup.readthedocs.io/en/latest/guide.html#cibuildwheel-labelLicenseCopyright Martin Seysen, 2020.Distributed under the terms of the MIT license, mmgroup is free and
open source software.https://github.com/Martin-Seysen/test_repository/blob/master/LICENSE
Package: mmguero
mmguerommguerois my personal collection of miscellaneous helper functions for Python.ContentsAggressiveUrlEncode- urlencode each character of a stringAskForPassword- get interactive password (without echoing)AskForString- get interactive user responseAtomicInt- atomic integer class and context managerBase64DecodeIfPrefixed- decode a string as base64 only if it starts withbase64:, otherwise just returnCaselessDictionary- dictionary that enables case insensitive searching while preserving case sensitivity when keys are listedCheckOutputInput- run command with arguments and return its exit code, stdout, and stderrChooseMultiple- select multiple options from a list of optionsChooseOne- select a single option from a list of optionsClearScreen- attempt to clear the screen withclear(*NIX) orcls(Windows)ContainsWhitespace- check if any character in a string is whitespaceCustomMakeTranslationDeepGet- safe deep get for a dictionaryDeepSet- convenience routine for setting-getting a value into a dictionaryDictSearch- recursive dictionary key searchDisplayMessage- display a message to the userDisplayProgramBox- "stream" the contents of a file descriptor into a program boxDoDynamicImport- attempt dynamic imports, prompting for install via pip if possibleDownloadToFile- download a URL to a local fileeprint- print to stderrEscapeAnsi- remove ANSI escape sequencesEVP_BytesToKey- create key compatible with openssl encFileContents- read the contents of a file, optionally falling back to binaryFlatten- flatten a collection, but don't split stringsGetIterable- return an iterable for an iterable (as-is) or a scalar (as a single element in a tuple)GetPrimaryIP- return the primary IP (the one with a default route) on the local boxGitClone- create a local git cloneLoadFileIfJson- does this file contain valid json? if so, load and return itLoadStrIfJson- is this string valid json? if so, load and return itpushd- chdir to directory as context manager, returning automaticallyRemoveEmptyFolders- recursively remove empty subfoldersRemovePrefix- remove a prefix from a stringRemoveSuffix- remove a suffix from a stringRunProcess- run command with arguments and return its exit code, stdout, and stderrSameFileOrDir- return true if os.path.samefile, also False on exceptionsha256sum- calculate a sha256 hash of a fileSizeHumanFormat- nice human-readable file sizesstr2bool- convenient boolean argument parsingTablify- print a list of lists into a nice tableTemporaryFilename- a context manager returning a temporary filename which is deleted upon leaving the contextTestSocket- test if a remote port is openTouch- open a file and close it, updating its access timeval2bool- convenient boolean argument parsingWhich- determine if a program/script exists and is executable in the system pathYesOrNo- get interactive user response to Y/N questionInstallationUsingpip, to install the latestrelease from PyPI:python3 -m pip install -U mmgueroOr to install directly from GitHub:python3 -m pip install -U 'git+https://github.com/mmguero/python-mmguero'AuthorsSeth Grover-Initial work-mmgueroLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: mmhash
murmurhash is a fast hash function ::>>> import mmhash>>> print mmhash.get_unsigned_hash("stdyun.com zsp007@gmail.com")
11740402118952761905>>> print mmhash.get_hash("stdyun.com zsp007@gmail.com")
-6706341954756789711MurmurHash2http://murmurhash.googlepages.com/MurmurHash2, 64-bit versions, by Austin ApplebyModified bychangsheng@douban.comzsp windows,linux python lib
Package: mmhash2
murmurhash is a fast hash function ::>>> import mmhash2>>> print(mmhash2.murmurhash64a("zhenhao", 0x1234))
16299279845682521441>>> print(mmhash2.dechex(mmhash2.murmurhash64a("zhenhao", 0x1234)))
'e232aca990972561'>>> print(mmhash2.base62encode(mmhash2.murmurhash64a("zhenhao", 0x1234)))
'jq2Se1vfXaN'>>> print(mmhash2.murmurhash64b('zhenhao', 0x1234))
4178523214822422458MurmurHash2http://murmurhash.googlepages.com/MurmurHash2, 64-bit versions, by Austin ApplebyModified byhit_zhenhao@163.comlinux python lib
Package: mmhelloworld
mmhelloworldTrying uploading to pypi a hello world package.InstallationRun the following to install:pipinstallmmhelloworldUsagefrommmhelloworldimportsay_hello# Prefer to paste logs from ipython.# Prints "Hello! World"say_hello()# Prints "Hello! Everybody"say_hello('Everybody')Developing Hello WorldTo install hello world along with tools you need to develop and run tests, run the following in your virtualenv.$pipinstall-e.[dev]
Package: mmic
What is MMIC?The Molecular Mechanics Interoperable Components (MMIC) project provides a standard for input and output of MM programs by defining the scientific and computational stages of classical MM pipelines, but leaving the implementation up to the developer/user. MMIC attempts to define the "what" of scientific stages without restricting the "how" i.e. MMIC defines only the input and output the implementation must conform to so that end-users can swap out different implementations with minimal effort in their existing pipelines, or workflow tools of their preference. The components themselves allow users to speed up most of their pipelines by integrating the expertise from component developers, letting them focus on the parts of their research they are experts in.This allows reproducibility from statically defined and shareable components, experimentation to find a quality series of components for specific problems and systems, and a mechanism for quality methodological assessment without the need for deep scientific expertise.We construct an abstract Base Component that is inherited by all MMComponent blueprints. For each scientific problem, a blueprint is defined that specifies what the component seeks to achieve, along with the necessary inputs and outputs. This component is implemented to satisfy the scientific problem by any number of users/developers.Components being developedSee theMM portalfor the latest updates.
Package: mmif-docloc-baapb
### mmif-docloc-baapbThis plugin finds the location of AAPB files in the CLAMS-Brandeis Datahousing server for the purpose of resolving the ‘baapb’ scheme in MMIF document URIs.#### requirementsThis python package is written as a plugin to [mmif-python](https://github.com/clamsproject/mmif-python) and won’t work as a standalone package. (requiresmmif-python>= 1.0.2)This is basically a client implementation for the server applicationhttps://github.com/clamsproject/aapb-brandeis-datahousing.The environment variableBAAPB_RESOLVER_ADDRESSshould store the address (including port number) where the server is running.
Package: mmigrator
MongoDb MigratorMigration engine for MongoDBSupports CLIInstallation$python-mpipinstallmmigratorConfigurationinitcommand (see below) will create ammigrator.config.jsonfile
with following structure:{"dist":"migrations","connection":{"host":"","port":"","database":"","user":"","password":""}}Connection variables could be set from .env|.json files.e.g. from .env file:"host":".env[MONGO_HOST]","port":".env[MONGO_PORT]","database":".env[MONGO_DB]","user":".env[MONGO_USER]","password":".env[MONGO_PASSWORD]"UsageHelp$mmigratorhelpInit configsoptional, will run automatically on any migration command*CLI$mmigratorinitfrom code:frommmigratorimportMigrationManager...MigrationManager.init()Create migrationCLI$mmigratorgSomeName# or$mmigratornewSomeNamefrom code:MigrationManager.generate("SomeName")Run migrationsCLI$mmigratormigrate# or add --silent flag for dry-run (skip exceptions)$mmigratormigrate--silentfrom code:MigrationManager.migrate()# or add silent=True flag for dry-run (skip exceptions)MigrationManager.migrate(silent=True)Revert migrationsCLI$mmigratorrevert# or add --silent flag for dry-run (skip exceptions)$mmigratorrevert--silentfrom code:MigrationManager.revert()# or add silent=True flag for dry-run (skip exceptions)MigrationManager.revert(silent=True)
Package: mmit
mmitis a python library to build any encoder matched with any decoder for any Computer Vision model.For a quick overview ofmmit, check out thedocumentation.Let's take a look at what we have here!Main FeaturesInstallationQuick StartTo Do ListMain Featuresmmitis engineered with the objective of streamlining the construction of Computer Vision models. It offers a consistent interface for all encoders and decoders, thus enabling effortless integration of any desired combination.Here are just a few of the things that mmit does well:Any encoder works with any decoder at any input sizeUnified interfacefor all decodersSupport for all pretrainedencoders from timmPretrained encoder+decoders modules 🚧PEP8 compliant (unified code style)Tests, high code coverage and type hintsClean codeInstallationTo install mmit:pip install mmitQuick StartLet's look at a super simple example of how to use mmit:importtorchimportmmitencoder=mmit.create_encoder('resnet18')decoder=mmit.create_decoder('unetplusplus')# automatically matches encoder output shape!x=torch.randn(2,3,256,256)features=encoder(x)out=decoder(*features)To Do ListIn the future, we plan to add support for:timm encoderssome of timm transformers encoders with feature extractiontorchvision / torchub modelsmore decoderslightning script to train modelsmultiple headspopular loss functionpopular datasetspopular metricsAwesome SourcesThis project is inspired by, and would not be possible without, the following amazing librariesPyTorchPyTorch LightningTimmSMP
Package: mm-json
AboutA small collection of tools to help converting to Marshmallow schemas.Installation$python-mpipinstallmm_jsonDocumentationDocumentation is availablevia GitHub.TestingTo run all tests runnox. No venv is needed; nox makes all of them for us.$python-mpipinstall--usernox$gitclonehttps://peilonrayz.github.io/mm_json/$cdmm_jsonmm_json$noxLicenseMM JSON is available under the MIT license.
Package: mmkg-mm-entity-linking
视觉-文本实体链接使用说明1. 环境依赖CUDA版本: 11.7
其他依赖库的安装命令如下：condainstallpytorchtorchvisiontorchaudiopytorch-cuda=11.7-cpytorch-cnvidia2. 下载安装可使用如下命令下载安装算法包：pipinstall-Ummkg-mm-entity-linking3. 使用示例及运行参数说明fromPILimportImagefrommmkg_mm_entity_linkingimportMMEntityLinkingimage=Image.open("path/to/image")probs=MMEntityLinking().inference(image,["a dog","a cat"])
Package: mmkg-mm-hoi-detection
人-物交互关系检测使用说明1. 算法描述该算法用于人-物交互关系检测，检测图像内部细粒度人-物三元组列表。该算法基于PyTorch框架开发，输入文本与图像，输出人-物三元组关系列表及概率。2. 依赖及安装CUDA版本: 11.3
其他依赖库的安装命令如下：condainstallpytorch==1.9.0torchvision==0.10.0cudatoolkit=11.3可使用如下命令下载安装算法包：pipinstall-Ummkg-mm-hoi-detection3. 使用示例及运行参数说明frommmkg_mm_hoi_detectionimportMMHOIDetectionfromPILimportImageckpt_path='***.pth'model=MMHOIDetection(ckpt_path)img_path='***.jpg'img=Image.open(img_path)res,_=model.inference(img,output_path='')4. 论文@article{liao2022gen,
  title={GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection},
  author={Liao, Yue and Zhang, Aixi and Lu, Miao and Wang, Yongliang and Li, Xiaobo and Liu, Si},
  journal={arXiv preprint arXiv:2203.13954},
  year={2022}
}
Package: mmkg-sgg
图像场景图生成使用说明1. 算法描述该算法用于图像场景图生成(SGG)。该算法基于PyTorch框架开发，输入一张图片，算法会检测图片中人和物的交互关系，输出图像场景图。2. 依赖及安装CUDA版本: 11.3
其他依赖库的安装命令如下：condainstallpytorch==1.10.1torchvision==0.11.2torchaudio==0.10.1cudatoolkit=11.3-cpytorch-cconda-forge可使用如下命令下载安装算法包：pipinstall-Ummkg-sgg3. 使用示例及运行参数说明需要准备 pretrained_ckpt, labelmap_file, freq_prior 三个文件，可以通过mmkg-sgg上所提供的链接进行下载frommmkg_vrdimportSGGsgg=SGG(pretrained_ckpt='custom_io/ckpt/RX152FPN_reldn_oi_best.pth',labelmap_file='custom_io/ji_vrd_labelmap.json',freq_prior='custom_io/vrd_frequency_prior_include_background.npy',)sgg.det_and_vis(img_file='custom_io/imgs/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.jpg',save_file='custom_io/out/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.reldn_relation.jpg')print('all done')
Package: mmkg-stvg-bert
STVGBert使用说明1. 环境依赖CUDA版本: 11.7
其他依赖库的安装命令如下：condainstallpytorchtorchvisiontorchaudiopytorch-cuda=11.7-cpytorch-cnvidia2. 下载安装可使用如下命令下载安装算法包：pipinstall-Ummkg-stvg-bert3. 使用示例及运行参数说明frommmkg_stvg_bertimportdetector_factory,optsimage_path="path/to/image"opt=opts().init()opt.load_model="path/to/model"Detector=detector_factory[opt.task]detector=Detector(opt)ret=detector.run(image_path)
Package: mmkg-visual-entity-recognition
视觉实体识别使用说明1. 环境依赖CUDA版本: 11.7
其他依赖库的安装命令如下：condainstallpytorchtorchvisiontorchaudiopytorch-cuda=11.7-cpytorch-cnvidia2. 下载安装可使用如下命令下载安装算法包：pipinstall-Ummkg-visual-entity-recognition3. 使用示例及运行参数说明fromPILimportImagefrommmkg_visual_entity_recognitionimportImageEntityRecognitionimage=Image.open("path/to/image")label_id,label,prob=ImageEntityRecognition().inference(image)
Package: mmkg-vrd
视觉关系检测使用说明1. 算法描述该算法用于图像中的视觉关系检测(VRD)。该算法基于PyTorch框架开发，输入一张图片，算法会检测图片中的视觉关系对。2. 依赖及安装CUDA版本: 11.3
其他依赖库的安装命令如下：condainstallpytorch==1.10.1torchvision==0.11.2torchaudio==0.10.1cudatoolkit=11.3-cpytorch-cconda-forge可使用如下命令下载安装算法包：pipinstall-Ummkg-vrd3. 使用示例及运行参数说明需要准备 pretrained_ckpt, labelmap_file, freq_prior 三个文件，可以通过mmkg-vrd上所提供的链接进行下载frommmkg_vrdimportVRDvrd=VRD(pretrained_ckpt='custom_io/ckpt/RX152FPN_reldn_oi_best.pth',labelmap_file='custom_io/ji_vrd_labelmap.json',freq_prior='custom_io/vrd_frequency_prior_include_background.npy',)vrd.det_and_vis(img_file='custom_io/imgs/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.jpg',save_file='custom_io/out/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.reldn_relation.jpg')print('all done')
Package: mmkit
MMKit: Multimodal KitA toolkit for multimodal information processingInstallationpip install mmkitAn exampleA Neural Network in PyTorch for Tabular Data with Categorical Embeddings.Herefrommmk.predictionimportMultimodalPredictionModelinput_features=[...]categorical_features=[...]output_feature="..."output_error=0all_features=input_features+[output_feature]mmpm=MultimodalPredictionModel("data/multimodal_data.csv",all_features,categorical_features,output_feature,output_error)mmpm.train()acc=mmpm.get_last_accuracy()print(acc)LicenseThemmkitproject is provided byDonghua Chen.
Package: mmlf
Maja Machine Learning FrameworkThe Maja Machine Learning Framework (MMLF) is a general framework for
problems in the domain of Reinforcement Learning (RL). It provides a
set of RL related algorithms and a set of benchmark domains.
Furthermore it is easily extensible and allows to automate
benchmarking of different agents.Among the RL algorithms are TD(lambda), DYNA-TD, CMA-ES,
Fitted R-Max, and Monte-Carlo learning. MMLF contains different
variants of the maze-world and pole-balancing problem class as
well as the mountain-car testbed.Further documentation is available underhttp://mmlf.sourceforge.net/Contact the mailing listMMLF-support@lists.sourceforge.netif you have any questions.
Package: mmlparser
MML Parser and Player for PythonPython-based MML (Music Macro Language) parser and player.Installationpip install mmlparserExamplesExamples, as well as simple songs written in MML, can be found in the
directorymmlparser/examples, which can be played directly from a command
line.python -m mmlparser.examples.simple_playerTo actually play the song, installpyrtmidiand make sure at least one MIDI
output is available.  The notes will be sent to the MIDI out device.pip install rtmidi
python -m mmlparser.examples.simple_playerThe code inexamples/simple_player.pyplays musical notes withMMLParser.play()method, which relies on the blockingtime.sleep()function.  The async version,MMLParser.aplay(), may be used to play music
concurrently with other asyncio tasks.  Seeexamples/async_player.pyfor
some examples.python -m mmlparser.examples.async_playerLimitationsTriplets are not yet supported.User event and repeat commands are not supported.