Saravanan666/pypi-org huggingface dataset:
Package: mexca
Multimodal Emotion Expression Capture Amsterdammexca is an open-source Python package which aims to capture human emotion expressions from videos in a single pipeline.How To Use Mexcamexca implements the customizable yet easy-to-use Multimodal Emotion eXpression Capture Amsterdam (MEXCA) pipeline for extracting emotion expression features from videos.
It contains building blocks that can be used to extract features for individual modalities (i.e., facial expressions, voice, and dialogue/spoken text).
The blocks can also be integrated into a single pipeline to extract the features from all modalities at once.
Next to extracting features, mexca can also identify the speakers shown in the video by clustering speaker and face representations.
This allows users to compare emotion expressions across speakers, time, and contexts.Please cite mexca if you use it for scientific or commercial purposes.Installationmexca can be installed on Windows, macOS and Linux. We recommend Windows 10, macOS 12.6.x, or Ubuntu. The base package can be installed from PyPI viapip:pip install mexcaThe dependencies for the additional components can be installed via:pip install mexca[vid,spe,voi,tra,sen]or:pip install mexca[all]The abbreviations indicate:vid: FaceExtractorspe: SpeakerIdentifiervoi: VoiceExtractortra: AudioTranscribersen: SentimentExtractorFor details on the requirements and installation procedure, see theQuick InstallationandInstallation Detailssections of our documentation.Getting StartedIf you would like to learn how to use mexca, take a look at ourdemonotebook and theGetting Startedsection of our documentation.Examples and RecipesIn theexamples/folder, we currently provide two Jupyter notebooks (and a short demo):example_custom_pipeline_componentsshows how the standard MEXCA pipeline can be customized and extendedexample_emotion_feature_extractionshows how to apply the MEXCA pipeline to a video and conduct a basic analysis of the extracted featuresTherecipes/folder contains two Python scripts that can easily be reused in a new project:recipe_postprocess_featuresapplies a standard postprocessing routine to extracted featuresrecipe_standard_pipelineapplies the standard MEXCA pipeline to a list of videosComponentsThe pipeline components are describedhere.DocumentationThe documentation of mexca can be found onRead the Docs.ContributingIf you want to contribute to the development of mexca,
have a look at thecontribution guidelines.LicenseThe code is licensed under the Apache 2.0 License. This means that mexca can be used, modified and redistributed for free, even for commercial purposes.CreditsMexca is being developed by theNetherlands eScience Centerin collaboration with theHot Politics Labat the University of Amsterdam.This package was created withCookiecutterand theNLeSC/python-template.[^1]: We explain the rationale for this setup in theDockersection.
Package: mexc-api
Mexc apiPython wrapper for the Mexc apimexc docsUsageRequirementsPython 3.11 or newerrequestswebsocket-clientinstallationpip install mexc-apiapi keygenerate an api key here:https://www.mexc.com/user/openapiExampleimporttimefrommexc_api.spotimportSpotfrommexc_api.websocketimportSpotWebsocketStreamClientfrommexc_api.common.enumsimportSide,OrderType,StreamInterval,Actionfrommexc_api.common.exceptionsimportMexcAPIErrorKEY="<KEY>"SECRET="<KEY>"spot=Spot(KEY,SECRET)server_time=spot.market.server_time()print(server_time)order_book=spot.market.order_book("MXUSDT")print(order_book)order=spot.account.test_new_order("MXUSDT",Side.BUY,OrderType.LIMIT,'1',price='1')print(order)ws=SpotWebsocketStreamClient(KEY,SECRET,on_message=print)time.sleep(1)ws.klines("MXUSDT",StreamInterval.ONE_MIN)ws.account_orders()time.sleep(5)ws.account_orders(Action.UNSUBSCRIBE)ws.klines("MXUSDT",StreamInterval.ONE_MIN,Action.UNSUBSCRIBE)ws.stop()
Package: mexcapiclient
Client MEXC ExchangeA simple python client for theMEXCexchange.
Open, unofficial project, intended to consume all endpoints provided by Exchangedocumentation.SummaryPypiDependencieshow to usefunctions implemented so farInstalationPython versionPypisee the project on Pypi by accessing thelink.DependenciesThis project uses some libraries for its operation as well as tests and linter for code organization. Its dependencies are:RequestsBlackPytesthow to useA simple code example for client use is:from MexcClient.client import MexcClient

client = MexcClient("API_KEY", "API_SECRET")
client.server_time()functions implemented so farFuncMethodEndpointParamsSectioncheck_connectionGET/api/v3/pingNoneMarket Data Endpointsserver_timeGET/api/v3/timeNoneMarket Data Endpointsexchange_infoGET/api/v3/exchangeInfoNoneMarket Data Endpointsorder_book_of_symbolGET/api/v3/depthsymbol: str, limit: intMarket Data Endpointsrecent_trades_listGET/api/v3/tradessymbol: str, limit: intMarket Data Endpointsold_trade_lookupGET/api/v3/historicalTradessymbol: str, limit: intMarket Data Endpointskline_dataGET/api/v3/klinessymbol: str, interval: EnumKlineInterval, start_time: int, end_time: int, limit: int = 500Market Data Endpointscurrent_average_priceGET/api/v3/avgPricesymbol: strMarket Data Endpointscreate_order_testPOST/api/v3/order/testsymbol: str, side: EnumOrderSide, _type: EnumOrderType, timestamp: int, quantity: str, quote_order_quantity: str, price: str, new_client_order_id: str, recv_window: intSpot Account/Tradecreate_new_orderPOST/api/v3/ordersymbol: str, side: EnumOrderSide, _type: EnumOrderType, timestamp: int, quantity: str, quote_order_quantity: str, price: str, new_client_order_id: str, recv_window: intSpot Account/Tradeload_balancesGET/api/v3/accountNoneSpot Account/Tradeload_balance_by_symbolGET/api/v3/accountsymbol: strSpot Account/Tradecancel_orderDELETE/api/v3/ordersymbol: str, order_id: str, timestamp: intSpot Account/Tradecancel_all_open_orders_on_a_symbolDELETE/api/v3/openOrderssymbols: list, timestamp: intSpot Account/TradeinstallationTo install just run the following command:pip install mexcapiclientPython versionpython version used in this project was:Python3.9
Package: mexcpy
mexcpyUnofficial Mexc exchange API wrapper. Officialdocumentation.FeaturesMarket, Spot, Wallet endpoints implementationEasy authentication using only api & secret keysStartAccount registrationCreate new API keyInstallation:pip install mexcpyExamplefrom mexcpy import MexcAPI

# Public endpoints example
# All methods return an instance of ResponseAPI class, to get clear data, the json() method must be called
data = MexcAPI.get_server_time().json()
print(data)

# Instead of usind the json() method you can refer to attributes of ResponseAPI class instance
# Attributes like symbol, priceChange, priceChangePercent are adding dynamically after method execution.
# You can find all possible attributes in documentation
data = MexcAPI.get_current_average_price()
print(data.symbol, data.priceChange, data.priceChangePercent)


# Private endpoints example
# These methods require api and secret keys
# You need to create instance of MexcAPI class
mexc = MexcAPI(API_KEY, SECRET_KEY)
result = mexc.get_account_information().json()
print(result)Public methodsget_server_time()
get_exchange_information()
get_order_book()
get_recent_trades()
get_current_average_price()
get_price_change_statistics()
get_symbol_price_ticker()
get_order_book_ticker()Private methodsget_account_information()
create_new_order()
cancel_order()
cancel_orders_on_symbol()
check_order_status()
get_current_open_orders()
get_all_orders()
get_account_trade_list()

get_currency_information()
withdraw()
get_deposit_history()
get_withdraw_history()
generate_deposit_address()
get_deposit_address()
universal_transfer()
Package: mex-gene-archive
Introductionmex_gene_archive is a minimal file format designed to meet the needs
of archiving sparse gene matrices in a format compatible with the
ENCODE 4 Data Coordination Center.We had the requirement that a data type result needed to be a single
file and unfortunately the common output format for alignment programs
of thematrix market exchangeuse three files. One to store the
coordinates and values of the non-zero sparse matrix elements, one for
the row labels, and one for the column labels.UsageReading an archiveThe archive format is fairly simple and started with just archiving
the key matrix market files from a STAR Solo.out directory, with a
simple manifest.tsv file included to help tell different files
apart.Probably the more useful function is the one that will read an archive
into an anndata structure with the gene features going across the
columns and the cell barcode observations going down across the rows.frommex_gene_archive.readerimportread_mex_archive_as_anndataadata=read_mex_archive_as_anndata("archive.tar.gz")req=requests.get("https://www.encodeproject.org/files/ENCFFexample/@@download/ENCFFexample.fastq.gz",stream=True)adata=read_mex_archive_as_anndata(fileobj=req.raw)The reader module can also convert archives to anndata directly from
the command linepython-mmex_gene_archive.reader-oarchive.h5adarchive.tar.gzpython-mmex_gene_archive.reader-oarchive.h5ad\--urlhttps://www.encodeproject.org/files/ENCFFexample/@@download/ENCFFexample.fastq.gzGenerating an STAR archivePossibly you might want to generate an archive file currently only
STAR is directly supported. See archive_star_solo for the full list of
arguments.frommex_gene_archive.starsoloimportarchive_star_soloconfig={"experiment_accession":"ENCSR724KET","description":"snRNA on human adrenal gland.","library_accession":"ENCLB002DZK",}archive_star_solo("experiment",config)
Package: mexican-sign-language-toolkit
Mexican sign language toolkitScientific computing toolkit designed to support visual languages such as mexican sign detection and analysis.Installation from pypipipinstallmexican_sign_language_toolkitCLIDetect segment in imagemsl-lv[file_path]Launch web servermsl-sCreate language spacemsl-csPythonConsult our notebooks to learn more. We recommend you starting with Getting started.NoteThis project has been set up using PyScaffold 4.4. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: mexp
You can use mexp to help in filtering a list of objects using find and find_any of elements that mactch properties conditionsExemple:filetest_mexp.py:from car_class import Car
from mexp import *


if __name__ == "__main__":
    listTesti = []
    listTesti.append(Car('Jack', 'benlalla', True, 1))
    listTesti.append(Car('Jack2', 'elarrs', False, 5))
    listTesti.append(Car('Marc', 'roro', False, 2))
    result = find(listTesti, lname = 'roro')
    print(result)Filecar_class.pyclass Car(object):
    def __init__(self, name, lname, active, rank):
        self.name = name
        self.lname = lname
        self.active = active
        self.rank = rankfind_any(list_of_object, **kwargs)Args:
       list_of_object: provide a list of objects you want to filter
       kwargs: properties with values you want to use as filter
   Returns:
       A filtered list of objects that match any of the filter propertiesdef find(list_of_object, **kwargs):Args:
        list_of_object: provide a list of objects you want to filter
        kwargs: properties with values you want to use as filter
    Returns:
        A filtered list of objects that match the filter propertiesmexp repo
Package: mexpress
MexpressMath parser and evaluator in Python capable of computing gradients and Hessians. Mexpress uses the Rust crateExmex.Installationpip install mexpressUsageimportmexpressimportnumpyasnp# parse function with parse_f64 or parse_f32f=mexpress.parse_f64("(x - 1) ** 2 - y * x + 3 * y ** 2")# evaluate function at (2, 4)y=f(2.0,4.0)assertabs(y-41)<1e-12# evaluate gradient at (2, 4)grad_2_4=f.grad(2.0,4.0)assertnp.linalg.norm(grad_2_4-[-2,22])<1e-12# evaluate Hessian at (2, 4)hess_2_4=f.hess(2.0,4.0)assertnp.linalg.norm(hess_2_4-[[2,-1],[-1,6]])<1e-12Besides**you can also use^for exponentiation. Currently, a list of supported mathematical operators can be found in the documentation ofExmex.Optimization ExampleWith gradients and Hessians one can at least locally optimize differentiable functions passed as strings, e.g., withscipy.optimize.fromscipy.optimizeimportminimizeimportmexpressfunc_str=f"(1 - x) ** 2 + 100 * (y - x ** 2) ** 2 + (z - 7) ** 2 + (ρ + 5) ** 2"f=mexpress.parse_f64(func_str)res=minimize(f,x0=[0.0,0.0,0.0,0.0],method="trust-ncg",jac=f.grad,hess=f.hess)We have played around with different methods to optimizefunc_str, see the following output ofpy/demo/opt.py. In the following table,#failsis the number of fails out of 100 attempts with randomx0s. The iterations and elapsed seconds are medians.CG             #fails   0   #it  44   0.0049996 sec   jac True    hess False
CG             #fails  23   #it  44   0.0179558 sec   jac False   hess False
Newton-CG      #fails   0   #it  38   0.0049839 sec   jac True    hess True
Newton-CG      #fails   5   #it  37   0.0059988 sec   jac True    hess False
trust-krylov   #fails   0   #it  31   0.0255845 sec   jac True    hess True
trust-ncg      #fails   0   #it  32   0.0030000 sec   jac True    hess True
trust-exact    #fails   0   #it  30   0.0060000 sec   jac True    hess True
BFGS           #fails   0   #it  72   0.0059998 sec   jac True    hess False
BFGS           #fails  21   #it  74   0.0169995 sec   jac False   hess False
L-BFGS-B       #fails   0   #it  43   0.0019979 sec   jac True    hess False
L-BFGS-B       #fails   0   #it  42   0.0069985 sec   jac False   hess False
Nelder-Mead    #fails   0   #it 441   0.0131288 sec   jac False   hess False
SLSQP          #fails   0   #it  34   0.0029492 sec   jac True    hess False
dogleg         #fails  17   #it  27   0.0027690 sec   jac True    hess True
TNC            #fails   0   #it  29   0.0029995 sec   jac True    hess False
TNC            #fails   0   #it  27   0.0110002 sec   jac False   hess False
COBYLA         #fails  46   #it  -1   0.0163412 sec   jac False   hess False
POWELL         #fails   0   #it  22   0.0139999 sec   jac False   hess False
Package: mext
MEXT - Manga/Manhua/Manhwa ExtractorA simple manga extractor. Extracts any comic info, chapter list and chapter pages. This is an app that strictly provides API.This package uses multiple provider codes to extract manga/manhua/manhwa data from online sites.The providers and it's usage is listed below.It's easy to use from python code.Still in development.This has been tested only on Windows 11 64-bit.RequirementsNode.jsInstallationspipinstallmextProxyTo use proxy IP'sexportHTTP_PROXY='http://10.10.10.10:8000'exportHTTPS_PROXY='http://10.10.10.10:1212'Steps:ImportMextlike thisfrommextimportMextGet the URL of manga or chapter and pass it toMextsite_url='xxx0'manga_url='xxx1'chapter_url='xxx2'# Initializeme=Mext()# To get mangamanga=me.get_manga(manga_url)# To get manga listmanga_list=me.get_manga_list(site_url)# To get one particular chapterchapter=me.get_chapter(chapter_url)# Pass manga url to get chapter list belonging to mangachapter_list=me.get_manga_chapters(manga_url)ProvidersMangaDexAsuraScansMangaUpdates (In development)ReaperScans (In development)ManhuaPlus (In development)...others coming soonUsageGet Manga using Manga URLfrommextimportMextmanga_url='https://mangadex.org/title/d1c0d3f9-f359-467c-8474-0b2ea8e06f3d/bocchi-sensei-teach-me-mangadex'manga=me.get_manga(manga_url)# You'll get back a instance of Mext with Manga dataprint(manga.to_dict())Get Chapter using Chapter URLfrommextimportMextchapter_url='https://mangadex.org/chapter/e183d3f4-fde0-4288-a1ed-8547490f84b3'chapter=me.get_chapter(chapter_url)# You'll get back a instance of Mext with Chapter dataprint(chapter.to_dict())Get Chapters List belonging to a manga using Manga URLfrommextimportMextmanga_url='https://mangadex.org/title/f7972eed-0040-4aac-b8de-fc99c522c25a/anti-kissmanga-anthology'chapter_list=me.get_manga_chapters(manga_url)# You'll get back a list of instances of Mext with a list of Chapter datachapter_list=me.chapter_listprint(chapter_list.to_dict())CreditsMade usingMangadex API DocumentationMangadex APIManga-pymanga-pyMangadex API -MangaDex.pyContributionsReady for any genuine support and valid pull requests.
Package: mextractor
mextractor: media metadata extractorVideos and images can be large.InstallationDownload and install from PyPi withpip:pipinstallmextractorIf you are extracting metadata from videos, install additional dependencies:pipinstallmextractor[video-extract]UsagePlease back up your files before using them with the package, things might break during runtime causing corruption.Command line interface (CLI)Copy directory to a new directory while extracting media info and a single frame from videos in subdirectories:mextractorvideo-subdirs<path_to_root>ProgrammaticallyThese functions are useful when integrating mextractor to your own package. You can also use it for quick scripts, see themextractor.workflowssubmodule for inspiration.Extract and dump metadataVideofrommextractor.workflowimportextract_and_dump_videometadata=extract_and_dump_video(dump_dir,path_to_video,include_image,greyscale,lossy_compress_image)Imagefrommextractor.workflowimportextract_and_dump_imagemetadata=extract_and_dump_image(dump_dir,path_to_image,include_image,greyscale,lossy_compress_image)Load mediaVideoimportmextractorvideo_metadata=mextractor.load(mextractor_dir)print(video_metadata.average_fps)print(video_metadata.frames)print(video_metadata.resolution)print(video_metadata.video_length_in_seconds)Imageimportmextractorimage_metadata=mextractor.load(mextractor_dir)print(image_metadata.resolution)
Package: meylenplan
meylenplanFetches and displays the SchlemmerMeyle meal plan for today.Installation and Updatepipinstall--user--upgrademeylenplanUsageInvokemeylenplanin a terminal.Notice of Non-Affiliation and DisclaimerWe are not affiliated, associated, authorized, endorsed by, or in any way
officially connected with SchlemmerMeyle, or any of its subsidiaries or its
affiliates. The official SchlemmerMeyle website can be found athttp://schlemmermeyle.de/. The name “SchlemmerMeyle” as well as related names,
marks, emblems and images are registered trademarks of SchlemmerMeyle.
Package: meyworldutils
A list of personal functionalities used throughout my career.Each functionality will be short and have a single purpose.Firstly, I will list each functionality with a one sentence description. Following each functionality will be demonstrated along with an example and output.Enjoy.Description:split_x_into_n: Splits two variables into a list of n parts from [0, ..., x]count_occurence:Examples:split_x_into_n:split(10, 5)output: [0, 2, ,4, 6, 8, 10]split(10, 5, include_zero=False)output: [2, 4, 6, 8, 10]
Package: mezcla
Note: This is an upgrade of the misc-utility repository with Python 2 support being phased out.Miscellaneous Python scripts developed over the course of several independent consulting projects. This also includes some code samples I adapted from publicly available source. (The code is not proprietary in nature. For example, it was not "borrowed" from proprietary source files, nor based on proprietary processes.)Spoiler alter: this is not "Pythonic python": I'm more into R&D than production programming. Nonetheless, there's a some useful scripts here, so I made the repository available. It is public in the spirit of open source software.This is a companion script to shell-scripts from Github:https://github.com/tomasohara/shell-scriptsThis repository is licensed under the GNU Lesser General Public Version 3 (LGPLv3). See LICENSE.txt.Tom O'HaraMay 2021
Package: mezmorize
mezmorizeA python function memoization library heavily inspired by Flask-Cache.This is a fork of theFlask-Cacheextension.Setupmezmorize is available on PyPI and can be installed with:pip install mezmorizeUsagefromrandomimportrandrangefrommezmorizeimportCachecache=Cache(CACHE_TYPE='simple')@cache.memoize(60)defadd(a,b):returna+b+randrange(0,1000)# Initialadd(2,5)# Memoizedadd(2,5)add(2,5)# Delete cachecache.delete_memoized(add)# Initialadd(2,5)For more configuration options, check out the theexamplesorFlask-Caching documentation.Compatibility with Flask-Cache and Flask-CachingThere are no known incompatibilities or breaking changes between either the latestFlask-Cache v0.13orFlask-Caching v1.8.0and the current version of mezmorize.Python versionsStarting with version 0.26.0, mezmorize dropped Python 2 support. The library is tested against Python 3.6, 3.7, 3.8, and PyPy 3.6.Environment VariablesCACHE_DIR: the directory your cache will be stored in. The default is thecachedir in the current folder.LinksFlask-Caching DocumentationSource CodeIssuesCurrent Flask-Caching ExtensionOriginal Flask-Cache Extension
Package: mezo
MeZO: Fine-Tuning Language Models with Just Forward PassesThis is the implementation for the paperFine-Tuning Language Models with Just Forward Passes.
In this paper we propose a memory-efficient zeroth-order optimizer (MeZO),
adapting the classical zeroth-order SGD method to operate in-place, thereby fine-tuning language models (LMs) with the same memory footprint as inference.With a single A100 80GB GPU, MeZO can train a 30-billion parameter OPT model, whereas fine-tuning with Adam can train only a 2.7B LM.
MeZO demonstrates comparable performance to fine-tuning with backpropagation across multiple tasks, with up to 12× memory reduction. MeZO is also compatible with both full-parameter and parameter-efficient tuning techniques such as LoRA and prefix tuning. We also show that MeZO can effectively optimize non-differentiable objectives (e.g., maximizing accuracy or F1).GPU memory usage comparison between zero-shot, in-context learning (ICL), Adam fine-tuning (FT), and our proposed MeZO.OPT-13B results with zero-shot, in-context learning (ICL), MeZO (we report the best among MeZO/MeZO (LoRA)/MeZO (prefix)), and fine-tuning with Adam (FT). MeZO demonstrates superior results over zero-shot and ICL and performs on par with FT (within 1%) on 7 out of 11 tasks, despite using only 1/12 memory.Installationpipinstallgit+https://www.github.com/lebrice/MeZOReproduce our paper resultsFor reproducing RoBERTa-large experiments, please refer to themedium_modelsfolder. For autoregressive LM (OPT) experiments, please refer to thelarge_modelsfolder. If you want to learn more about how MeZO works and how we implement it, we recommend you to read thelarge_modelsfolder as the implementation is clearer and more extensible. If you want to explore more variants of MeZO, we recommend trying outmedium_modelsas it's faster and has more variants implemented.How to add MeZO to my own code?Our implementation of MeZO is based onHuggingFace's Trainer. We add MeZO to the official implementation of trainer with minimum editing. Please refer to "How to add MeZO to my own code?" section inlarge_modelsREADME for more details.Bugs or questions?If you have any questions related to the code or the paper, feel free to email Sadhika (smalladi@princeton.edu) or Tianyu (tianyug@princeton.edu). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!Citation@article{malladi2023mezo,title={Fine-Tuning Large Language Models with Just Forward Passes},author={Malladi, Sadhika and Gao, Tianyu and Nichani, Eshaan and Damian, Alex and Lee, Jason D and Chen, Danqi and Arora, Sanjeev},year={2023}}