Saravanan666/pypi-org huggingface dataset:
Package: meta-hb
metahbmetahb部署通用框架1、安装最新版 meta-cvpip install meta-cv2、安装最新版 meta-hbpip install meta-hb3、目标检测示例（参考detection_demo.py代码）import platform, cv2
import metahb as mh

Detection = mh.DetectionHB

y = Detection(model_path='models/yolov8m.onnx' if platform.machine() == 'x86_64' else 'models/yolov8m.bin',
              input_width=640,
              input_height=480,
              confidence_thresh=0.5,
              nms_thresh=0.3,
              class_names=classnames,
              device_id=0)

img = cv2.imread('models/bus.jpg')
_dets, _scores, _labels = y.predict(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), use_preprocess=True)

# 显示
y.show(img, _dets, _scores, _labels)
cv2.imwrite("models/bus.png", img)4、实例分割示例（参考segment_demo.py代码）import platform, cv2
import metahb as hb

Segment = mr.SegmentHB

y = Segment(model_path='models/yolov8m-seg.onnx' if platform.machine() == 'x86_64' else 'models/yolov8m-seg.bin',
            input_width=640,
            input_height=480,
            confidence_thresh=0.5,
            nms_thresh=0.3,
            class_names=classnames,
            device_id=0)

img = cv2.imread('models/bus.jpg')
_dets, _scores, _labels = y.predict(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), use_preprocess=True)

# 显示
y.show(img, _dets, _scores, _labels)
cv2.imwrite("models/bus.png", img)
Package: metaheuristic-clustering
Metaheuristic ClusteringAs the name suggests, this is a repository for metaheuristic clustering algorithms, implemented in Python 3, that I could not find implemented elsewhere.Implementations are designed to work with or without the sklearn implementation style.Currently the algorithms implemented are:Artifical Bee Colony (ABC)Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied soft computingShuffled Frog Leaping Algorithm (SFLA)Amiri, B., Fathian, M., & Maroosi, A. (2009). Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology, 45(1), 199-209.DependenciesNumpyPyClusteringscikit-learn- only needed for interop with scikit-learnExampleSklearn/Object styledata=X# your data# SFLA Clusteringfrommetaheuristic_clustering.sflaimportSFLAClusteringsfla_model=SFLAClustering()sfla_labels=sfla_model.fit_predict(data)# ABC Clusteringfrommetaheuristic_clustering.abcimportABCClusteringabc_model=ABCClustering()abc_labels=abc_model.fit_predict(data)Function styleimportmetaheuristic_clustering.utilasutildata=X# your data# SFLA Clusteringimportmetaheuristic_clustering.sflaassflabest_frog=sfla.sfla(data)sfla_labels=util.get_labels(data,best_frog)# ABC Clusteringimportmetaheuristic_clustering.abcasabcbest_bee=abc.abc(data)abc_labels=util.get_labels(data,best_bee)Sample ResultsABCSFLA
Package: metahyper
MetahyperParallelisation and distribution framework for neural pipeline search algorithms.Features:Asynchronous parallelization and distributionFault tolerance for crashes and job time limitsAgnostic to search space, objective functions, ...InstallationUsing pippipinstallmetahyperUsagePlease see our examples inmetahyper_examples.ContributingPlease see our guidelines and guides for contributors atCONTRIBUTING.md.
Package: metaibricks
MetAIBricks : Meteorological AI BricksStatus (develop branch)MetAIBricks is a package that allows you to easily train a neural network, by providing the basic development bricks you may need.For now MetAIBricks allows you to download files through FTP protocol.InstallationDependenciesPython (>= 3.9.7)pydantic (>=1.9.0)User installationTo install MetAIBricks, simply :$pipinstallmetaibricksTo install the latest development release from MF Nexus:$pipinstall--index-urlhttp://nexm01-sidev.meteo.fr/repository/pypi-snapshots-metaibricks/simple--trusted-hostnexm01-sidev.meteo.frmetaibricksDocumentationMetAIBricks documentationcontains :aUser documentationaDevelopper documentationChangelogSee thechangelogfor a history of notable changes to MetAIBricks.DevelopmentImportant linksOfficial source code repo:http://gitlab.meteo.fr/deep_learning/ai-lab-tools/met-ai-bricksDownload releases:https://pypi.org/project/metaibricks/Issue tracker:http://gitlab.meteo.fr/deep_learning/ai-lab-tools/met-ai-bricks/-/issuesDevelopper documentationSource codeYou can check the latest sources with the command:gitclonehttp://gitlab.meteo.fr/deep_learning/ai-lab-tools/met-ai-bricks.gitTo install the MetAIBricks in dev-mode, checkout thedevelopper installation guide.
Package: metaimageio
MetaImageIOSupport for reading and writing images inMetaIOfile format.Getting started in PythonInstall usingpip install metaimageioorconda install -c conda-forge metaimageio.importmetaimageioimage,meta=metaimageio.read('/path/to/input.mha')metaimageio.write('/path/to/output.mha',image,ElementSpacing=meta['ElementSpacing'])Add toimageioplugins.importimageiometaimageio.imageio()image=imageio.imread('/path/to/input.mha')meta=image.metaGetting started in MATLABInstall using theAdd-On Manager.[image,meta]=metaimageio.read('/path/to/input.mha');metaimageio.write('/path/to/output.mha',image,'ElementSpacing',meta.ElementSpacing);Add toimage file format registry.metaimageio.imformats();image=imread('/path/to/input.mha');meta=imfinfo('/path/to/input.mha');
Package: metaindexmanager
metaindexmanagerA filemanager-like program to inspect your indexed documents.InstallationEasiest way is to go through PyPi:pip install metaindexmanagerIt depends onmetaindexandcursedspace. You probably want to installmetaindexmanually though, to be able to select all the delicious extra
features that are not enabled by default, because they depend on more packets.You could also clone the git repository and pip install from there:git clone <location to be decided>
cd metaindexmanager
pip install .UsageJust start it withmetaindexmanagerOpen a file manager panel withgf, open another metadata panel withgm. To
search for documents in a metadata panel, try:searchfollowed by any valid
metaindex search terms, e.g.:search mimetype:image.To immediately have a search panel and a file panel, you could start like
this:metaindexmanager "mimetype:image" ~Which will open with two panels: one the search results ofmimetype:imageand the other a file manager panel in your home directory.
You could also start with two file manager panels, like this:metaindexmanager ~ ~/DocumentsAnother good use case is to run infile select mode:metaindexmanager --file-select-modeIt’ll provide the exact same experience as before, but when you press Enter,
the path of the selected document or file will be written to stdout.
That way you can use metaindexmanager as a file selection utility in other
applications.The--file-select-outputallows you to write the path to the selected file
to a file instead of stdout.
Package: meta-info-tools
Meta Info ToolsValidates objects described with the MetaInfo format.
Meta info is a format initially developed in theNOMAD Center of Excellenceto describe data.
SeeNOMAD Archiveandhttps://kitabi.eu/nomad/metainfo/for more information.
Meta info describes hierarchical data for both humans and computer.
The main aim is to have a flexible ad extensible definition.nomad-meta-infocontains the definition of the meta info used in NOMAD to describe atomistic simulations, in particular ab-initio simulations.Install latest release:pipinstallmeta-info-toolsRun the command line utilitypython-mmeta_info_tools.meta_tool--help(or python3 if your python still refers to python 2.x). It describes the various option of the meta tools.
It can be used to generates an exploded or reformatted version of a dictionary, check a dictionary, or generate documentation for it.
The cascade command does all those things if the files are put in a standard directory format (does the exploded dictionaries first, reformats them, then moves to the single dictionaries, regenerating them from the exploded version, reformatting them otherwise, finally performs checks and generates documentation for all available dictionaries.Developmentfiles are formatted withpython black/
Install a local development version from this repository:cd<repositoryDir>
pipinstall-e.
Package: meta-json
meta_jsonGiven a JSON response as a dictionary, extract the metadata such as its structure and data model.IntroductionThis package is intended to help with JSON analysis by extracting its metadata and ease the data modeling tasks regularly used in design of databases, data catalogs, data warehouses, APIs, etc.InstallationThis package is available in PyPI and GitHub. Just run:pipinstallmeta-jsonOr clone the repository:git clone https://github.com/juangcr/meta_json.gitcd meta_jsonpython setup.py installUsagefrommeta_jsonimportMetaJsonyour_json_data_as_dict={"name":"John Doe","contact":"john_doe@mail.net","status":{"start_date":"1970-01-01","active":"true","credits":{"due":10,"remaining":90}}}meta=MetaJson(your_json_data_as_dict)meta.types# Returns every data type available.{"name": "str","contact": "str","status": {"start_date": "datetime","active": "str","credits": {"due": "int","remaining": "int"}}}Keep in mind that the datetime recognition supports the following patterns:YYYY-MM-DD (Single supported pattern in v0.0.2)YYYY/MM/DDDD-MM-YYYYDD/MM/YYYYMM-DD-YYYYMM/DD/YYYYmeta.attributes# Returns a list with two elements: the grouped main keys# and the rest of the subkeys alltogether.[["name","contact","status"],["start_date","active","credits","due","remaining"]]meta.layers# Returns all keys grouped by layer depth.{"layer_0" :["name","contact","status"],"layer_1": ["start_date","active","credits"],"layer_2": ["due","remaining"]]
Package: metaknowledge
metaknowledgemetaknowledgeis a Python3 package that simplifies bibliometric research using data from various sources. It reads a directory of plain text files containing meta-data on publications and citations, and writes to a variety of data structures that are suitable for quantitative, network, and text analyses. It handles large datasets (e.g. several million records) efficiently. You can find thedocumentation.InstallingTo install runpython3 setup.py install
Package: metalake-file-management
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: metal-cli
# Metal 🤘Docker wrapper: Forget about your bare-metal and get started right away.## InstallationInstall [docker](https://docs.docker.com/install/) and [docker-compose](https://docs.docker.com/compose/install/) before using metal. Once that’s setup, install the metal cli running the following command:pip install metal-cli## UsageGet detailed information runningmetal –help.## DevelopmentRun tests by usingpython testscommand.TODO: update /etc/hosts### Add mysql user`sh metal shell--service="mysql"mysql-uroot--password="secret"-e"CREATE USER'{user}'@'%'IDENTIFIED BY'';"mysql-uroot--password="secret"-e"GRANT ALL PRIVILEGES ON *.* TO'{user}'@'%';"mysql-uroot--password="secret"-e"FLUSH PRIVILEGES;" `### Add mysql database`sh metal shell--service="mysql"mysql-uroot--password="secret"-e"CREATE DATABASE{database};"`
Package: metald
Metald (meta linked data; pronounced “metal d”) is a Python library for gathering metadata about LSST’s software and documentation projects and synthesizing it into JSON-LD (linked data) resources.This project is related to LSST DocHub, an experimental project to gather LSST information artifacts from across the web, and make them discoverable through a single API.
DocHub adapters can use this library to create LSST DocHub JSON-LD resources.Metald is developed byLSST, the Large Synoptic Survey Telescope.
Learn more atwww.lsst.org.LicenseMetald is MIT licensed. SeeLICENSE.
Package: metalgpy
Meta Algorithm in Python (MetalgPy)What if we could write a program that generates programs? Inspired by Automated Machine Learning research.:warning:Experimental: Contributions are welcome!Installpip install metalgpyExampleA simple but detailed example:importwarningswarnings.filterwarnings("ignore")importmetalgpyasmpy# the @mpy.meta decorator transform an original python code# into a meta-program. f is now symbolizing the original python code@mpy.metadeff(x):returnx**2# program is a symbol representing the call to f (original python code)# where the input is a symbol representing a variable List (categorical decision variable)program=f(mpy.Float(0,10))print("Program: ",program,end="\n\n")# the choice method returns the variable symbols of the symbolized programchoices=program.choices()print("Variable Space: ",choices)# optimize the programfori,evalinmpy.sample(program,size=100):sample_program=program.clone().freeze(eval.x)y=sample_program.evaluate()print(f"{i:02d}->{sample_program}={y}")eval.report(y)gives the following output:Program:  f(Float(id=0, low=0, high=10))Variable Space:  {'0': Float(id=0, low=0, high=10)}01 -> f(1.4883186068135734) = 2.21509227538749602 -> f(9.731099196329486) = 94.6942915688043703 -> f(2.8835900819936366) = 8.31509176097206804 -> f(6.684879549955022) = 44.6876145974068605 -> f(6.369117254195896) = 40.5656545976958606 -> f(8.311599275340795) = 69.0826825138456307 -> f(3.9495544683795036) = 15.59898049869650408 -> f(2.719439725535402) = 7.39535242082006209 -> f(5.076587322264285) = 25.77173884057446810 -> f(6.509647409342488) = 42.3755093939593711 -> f(0.0) = 0.012 -> f(0.07807885269930037) = 0.00609630723883904513 -> f(0.004326455132792617) = 1.8718214016067583e-0514 -> f(0.03447243207111301) = 0.001188348572897500815 -> f(0.018114237444373238) = 0.000328125598191133516 -> f(0.0020049360585783216) = 4.019768598987575e-0617 -> f(0.6959012004878518) = 0.4842784808404332318 -> f(0.006902913600794758) = 4.765021618003725e-0519 -> f(0.04812048929037971) = 0.002315581489545548320 -> f(0.015496977861506611) = 0.0002401563228400260321 -> f(0.03973738943234384) = 0.001579060118897751922 -> f(0.14944732113771342) = 0.02233450179523884323 -> f(0.1538705525239814) = 0.0236761469340353224 -> f(0.02714364492250043) = 0.000736777459678783525 -> f(0.013367771420287281) = 0.0001786973127450494426 -> f(0.07504851702564763) = 0.005632279907748922527 -> f(0.061488350499158975) = 0.003780817247107423628 -> f(0.010089082470558145) = 0.0001017895850977236629 -> f(0.1785305521706959) = 0.03187315805837357530 -> f(0.07218850699949093) = 0.00521118054281555131 -> f(0.08273460533704255) = 0.00684501492027618932 -> f(0.004884441886340666) = 2.3857772541039158e-05
Package: metalist.py
Warning: metalist.py has been deprecatedPlease consider usingdiscordlists.pyinstead which is also available onPyPi.metalist.pyA simple API wrapper to post Discord bot stats to all known bot lists using metalist.xyz data.InstallationInstall via pip (recommended)pip install metalist.pyFeaturesPOST server countAUTOMATIC server count updatingALL bot lists' APIs includedExample Discord.py Rewrite cogimportmetalistclassStats:def__init__(self,bot):self.bot=botself.metalist=metalist.Client(self.bot)# Create a Client instanceself.metalist.set_auth("botsfordiscord.com","cfd28b742fd7ddfab1a211934c88f3d483431e639f6564193")# Set authorisation token for a bot listself.metalist.start_loop()# Posts the server count automatically every 30 minutesdefsetup(bot):bot.add_cog(Stats(bot))Discussion, Support and IssuesFor general support and discussion of this project, please join the Discord server:https://discord.gg/qyXqA7yTo check known bugs and see planned changes and features for this project, please see the GitHub issues.Found a bug we don't already have an issue for? Please report it in a new GitHub issue with as much detail as you can!
Package: metallic
MetallicMetallic is a clean library for meta-learning research based onPyTorch.Different from other meta-learning libraries, Metallic aims at providing a lightweight modularized toolbox for setting up meta-learning experiments quickly with the least amount of code.The library iswork in progress.Installationgitclonehttps://github.com/Renovamen/metallic.gitcdmetallic
pythonsetup.pyinstallorpipinstallgit+https://github.com/Renovamen/metallic.git--upgradeDocumentationsCheck the API documentation here:metallic-docs.vercel.appAcknowledgementsMetallic is highly inspired by the following awesome libraries:learn2learnTorchmetahigherLicenseMetallic is MIT licensed, see theLICENSEfile for more details.
Package: metalmetrics
metalmetricsIntroductionmetalmetricsis a worker ofmetalflowwritten in Python.PrerequisitesPython >= 3.7RunLocal modegitclonehttps://github.com/craftslab/metalmetrics.gitcdmetalmetrics
pipinstall-Urrequirements.txt
pythonmetrics.py--config-file="config.yml"--inxi-file="inxi"--output-file="output.json"Service modegitclonehttps://github.com/craftslab/metalmetrics.gitcdmetalmetrics
pipinstall-Urrequirements.txt
pythonmetrics.py--config-file="config.yml"--inxi-file="inxi"--listen-url="127.0.0.1:9090"DockerLocal modegitclonehttps://github.com/craftslab/metalmetrics.gitcdmetalmetrics
dockerbuild--no-cache-fDockerfile-tcraftslab/metalmetrics:latest.
dockerrun-it-v/tmp:/tmpcraftslab/metalmetrics:latest./metalmetrics--config-file="config.yml"--output-file="/tmp/output.json"Service modegitclonehttps://github.com/craftslab/metalmetrics.gitcdmetalmetrics
dockerbuild--no-cache-fDockerfile-tcraftslab/metalmetrics:latest.
dockerrun-it-p9090:9090craftslab/metalmetrics:latest./metalmetrics--config-file="config.yml"--listen-url="127.0.0.1:9090"Usageusage: metrics.py [-h] --config-file CONFIG_FILE [--inxi-file INXI_FILE]
                  [--listen-url LISTEN_URL | --output-file OUTPUT_FILE] [-v]

Metal Metrics

optional arguments:
  -h, --help            show this help message and exit
  --config-file CONFIG_FILE
                        config file (.yml)
  --inxi-file INXI_FILE
                        inxi file (/path/to/inxi)
  --listen-url LISTEN_URL
                        listen url (host:port)
  --output-file OUTPUT_FILE
                        output file (.json|.txt|.xlsx)
  -v, --version         show program's version number and exitSettingsmetalmetricsparameters can be set in the directoryconfig.An example of configuration inconfig.yml:apiVersion:v1kind:workermetadata:name:metalmetricsspec:metrics:-cpu-disk-io-ip-kernel-mac-network-os-ram-system-usersDesignLicenseProject License can be foundhere.ReferencegRPChealth-check-scriptinxipython-diamondsysperfsysstat
Package: metalo
MetaLois a framework for the Metabolic analysis (FBA) of Logical models extracted automatically from detailed mechanistic maps.
See thispublished articlefor more details.InstallMetalo is provided as a Python3 package, you can install it from thePython package indexwithpip,condaor your Python package manager of choice:$python3-mpipinstallmetaloIf you want to use the GUI you will need to explicitly request that feature, but it depends onwxPythonwhich sometimes fails to build on some platformes.$python3-mpipinstallmetalo[gui]Command-line usageA video tutorial is availablehere.Or follow the instructions:$ metalo --help

usage: metalo [-h] [-v] [-D] [-f] [-i INIT] [-c CASQ] MAP METABOLISM

Metabolic analysis of Logical models extracted from maps. Copyright (C) 2023 Sahar.Aghakhani@inria.fr and Sylvain.Soliman@inria.fr
GPLv3

positional arguments:
MAP                   CellDesigner file containing the mechanistic map
METABOLISM            MitoCore style metabolic model

options:
-h, --help            show this help message and exit
-v, --version         show program's version number and exit
-D, --debug           display some debug information
-f, --fva             run FVA to get interval of values
-i INIT, --init INIT  CSV file with forced initial values for the Logic model
-c CASQ, --casq CASQ  Additional arguments for CaSQ like -u, -d or -rOr runmetalowith no arguments to launch the GUI, if it is installed.
Package: metaloader
Metaloader: A Configuration File Loader and PreprocessorThis package offers a highly configurable pipeline for loading complex configuration file hierarchies.It provides abstractions for several stages in the configuration loading pipeline:Filesystems: Files could be stored on any kind of filesystem - Local, Amazon S3, FTP etc.Serialisations: Files could be in any number of formats - JSON, YAML etc.Directives: Files can contain special "preprocessor directives" - Imports, Macros etc.Stanza Handlers: Different top-level stanzas in the files may need to be handled and merged in a special way, or validated.
Package: metaloop-python-sdk
Python SDK for Metaloop platformMetaloop Python SDK is a python library to accessMetaloopand manage your datasets.
It provides:A pythonic way to access your Metaloop resources by Metaloop OpenAPIapi.Installationpip install metaloop-python-sdkDocumentationMore information can be found on theexamplesUsage生成API Token在登录平台后，点击页面右上角的用户名，在个人信息页面中，即可获取本用户的API Token
  正式环境将http://192.168.100.71:30301 替换为 http://data.deepglint.com操作数据集importtimefrommetaloop.clientimportMDSif__name__=='__main__':# use MDS("0c02ca70e142b75a75ca4118ce33dbb0", "http://192.168.100.71:30301") when connecting test-servermds_client=MDS("0c02ca70e142b75a75ca4118ce33dbb0")date_time=time.strftime("%Y%m%d%H%M%S",time.localtime())dataset_name=f"test_dataset_{date_time}"# create new datasetdataset=mds_client.create_dataset(dataset_name,"image",["screw"],comment="this is a test dataset for mds")# print dataset infodataset.summary()# get existed datasetdataset=mds_client.get_dataset(dataset_name)dataset.summary()# create versiondataset.create_version(comment="this is a test version for mds")dataset.summary()# switch versiondataset.checkout(0)dataset.summary()# import local data to datasetdataset.import_data("import_test/sample_test")# accelerated when running on tencent clouddataset.import_data("import_test/sample_test",storage_type='cos')# export data to local filesystemdataset.export_data("export_test")# delete versiondataset.delete_version(0)dataset.summary()# iter objectsforitemindataset:print(item)response=requests.get(item['obj_url'])image=Image.open(BytesIO(response.content)).convert("RGB")image.show()# delete datasetmds_client.delete_dataset(dataset_name)dataset.summary()DevelopmentCI pipeline