Saravanan666/pypi-org huggingface dataset:
Package: mfcrypt
mfcryptAES encryption and decryption utilities for Musicfox JavaScript + Python applications and services. Plain Jane CBC encryption with HMAC (using sha256) payload authentication for both languages.mfcryptaims to offer the same API regardless of using JavaScript or Python. Save your data at
rest in a database and access it from either language, as long as you know the key.MotivationThe JavaScript and Pythonmfcryptutilities are particularly simple/straightforward implementations of multi-layerd
encryption within Musicfox software. In particular, it is important to be able to doubly
encrypt data during application data SSL transport, at-rest under major-3rd party encryption (Google), and to separate concerns/implementations via completely different systems.Combined with access restrictions underneath fortified enterprise authorization schemes, users'
data are separated, encrypted, and always inaccessible by default. Multiple layers of service
authorization and identification are required prior to any data utilized in plaintext.InstallationIt's easy to install the JavaScript and Python libraries. They're small and offered onnpmandpypi.JavaScriptInstall vianpmoryarn, e.g.npm i --save-dev @musicfox/mfcrypt.⚠️  Remove--save-devif you're unsure!PythonInstall viapypi.with Pip:pip install --upgrade mfcryptwith Pipenv:pipenv install mfcryptwith Poetry:poetry install mfcrypt⚠️  Use--devif this is an upstream dependency of your lib/app, rather than your dev environment.Quick start JavaScriptFor detailed usage examine the code inexamples/javascript/within the repo and the test suitefound insrc/test/.But the gist (we'll generate a simple bytes PDKDF2 key using the library):// myEncryptionScript.jsimport{createBytesKey,encrypt}from'@musicfox/mfcrypt';constmySecretPassphrase='really I should encrypt this too, and generate it randomly. DO NOT use words like this. Tha NSA will break me.';constsalt='randomly generated salt';constencStringData=awaitencrypt('TOP SECRET STRING DATA',mySecretPassphrase,salt);constdecStringData=awaitdecrypt(encStringData,mySecretPassphrase,salt,'string');// give it a type hint at the end, you'll be happy you did ;-)Quick start PythonFor detailed usage examine the code inexamples/python, which contains a Python Flask application you can test out. In addition, you can always examine usage via
the test suite found in thetestdirectory.importmfcryptmy_secret_passphrase='really I should encrypt this too, and generate it randomly. DO NOT use words like this. Tha NSA will break me.'salt='randomly generated salt'enc_string_data=encrypt('TOP SECRET STRING DATA',my_secret_passphrase,salt)dec_string_data=decrypt(enc_string_data,my_secret_passphrase,salt,type_hint='string')Over-the-wireThis particular implementation is meant to work with Python HTTP webservices. As such, you should be able to use your code above to send encrypted data which may be decrypted
via a Python service.SupportFile an issue or ask a question viaGithub.
Package: mfc-scraper
MFC ScraperScrapes the content of one’s collection of figures listed on MyFigureCollectionFree software: MIT licenseDocumentation:https://mfc-scraper.readthedocs.io.FeaturesScrapes the following information from MFCAll figure IDs owned by a particular userFor a particular figureThe characters representedIts originThe companies manufacturing the figureIts classificationsIts versionIts release dateThe main image URLImages associated to a given figureStores the scraped content as a JSON list in a file.Stores the figure images.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2021-06-22)Added origin, version and release date to scraped informationChanged company and classification to return a list instead of a stringHandles cases where multiple companies or classifications are involved0.1.2 (2021-06-21)First release on PyPI.Allows to scrapeAll figure IDs owned by a particular userFor a particular figureThe characters representedThe company manufacturing the figureIts classificationThe main image URLImages associated to a given figureStores the scraped content as a JSON list in a file.Stores the figure images.
Package: mfdata
mfdata: Database for macro-finance research“mfdata” is a Python package designed for economic researchers to conveniently collect and navigate in macroeconomic and financial datasets. The package enables easy access to Federal Reserve Economic Database (FRED) through “fredapi” and can parse the Federal Resere Board H.8 table into a well-formatted database. Users have to obtain their own FRED api key in order to properly use this package. The project is ongoing and please come back for updates!
Package: mfdfloods
MFD FloodsA python script to model hidrologic behavior of downstream drainpaths. With a DTM cover of your study area you can define longitude and latitude (in the DTM distance unit) as a start point and an income flow to see how the water will flood the territory.InstallationWith pippip install mfdfloodsFrom sourcepython -m pip install -r /path/to/mfdfloods/directoryDependenciesThe script requires GDAL installed on your system and python-gdal as a python dependency.To install GDAL executeapt install gdal-bin libgdal-dev.ModuleExecute .py from inside the folder to test the algorithm.The test.py is a script that call the class MFD and execute its modelization with
the datasource from thedata/folder. There you have to place your GeoJSON files
with the modelized line geometry.python test.py <path:data> <lng:float> <lat:float>Arguments:datais the pathname of the data directory. There you have to place adtm.tif, amannings.tifand ahydrogram.csvfiles.lngis the longitude in your reference dtm distance units.latis the latitude in your reference dtm distance units.The output will be placed in yourdatafloder as three files with the name(draft|flood|speed)_{lng}-{lat}.tif.UseInclude mfdfloods as a module on your scripts withfrom mfdfloods import MFDthen instantiate the class MFD to execute its drainpaths method.
Package: mfdls
medford-language-serverA language server, implementing the Language Server Protocol, for the MEDFORD metadata markup language.Editors:VS CodeDevelopmentThis project is configured to use black, mypy, and pylint. Please run them and deal with any errors or warnings before commiting to master.runblackwithblack mfdlsrunmypywithmypy mfdlsrunpylintwithpylint mfdls
Package: mfe
mfeYet another automated mass spectrometry imaging data processing package, but forgeological samples.PrerequisiteBefore using the workflow, proprietary mass spectrometry data format (e.g., .D from Bruker) needs to be exported as plain text file (represented asda_exported_txtin the following examples). Only the coordinates and the centroid mass-to-charge ratios along with the peak intensity are needed from each spectrum.Python >= 3.5 is needed, and the required library is listed in requirements.txt.This package is OS-independentInstallationJust run the following command and the package with all dependecies will be installed.pipinstallgit+https://github.com/weimin-liu/msi_feature_extraction.gitInstructionMass calibrationDataset should be calibrated first if it hasn't been calibrated yet. Currently, a quadratic mass error calibration function (mfe.quadratic_calibration) is available in this package. Just provide the list of calibrants, and a quadratic function will be fitted to calibrate the spectrum.Bin-wise KDE for reference peak detection and peak alignmentReference peaks that are used for spectra alignment are detected in the evenly spaced discrete mass bins, with a peak picking threshold valuepth.The following is an example of how to create a feature table from the MSI spectra with ion intensities normalized by the median peak intensity of each individual spectrum.frommfe.from_txtimportmsi_from_txt,get_ref_peaks,create_feature_table# create a msi object from the exported txt filespectra=msi_from_txt(da_exported_txt)# get the reference peaks, with a peak picking threshold of pth, and pth is a listref=get_ref_peaks(spectra,peak_th=pth)# create a feature table with the reference peaksfeature_table=create_feature_table(spectra,ref,normalization='median')A 2D table will be produced in this step, with columns being the name of the reference peaks (m/zratios), and each row representing one laser spot.Pick peaks using grey-level co-occurrences matrix (optional)No peak has been dropped until this step, grey-level co-occurrences matrix (GLCM) are used to detect how structured are those ion images and rank them.The following is an example of comparing the GLCM features in MSI dataset with those in the X-radiophotograph.frommfe.peak_pickingimportGLCMPeakRankingglcm=GLCMPeakRanking(q_val=8)glcm.fit(feature_table,[1,2,3,4,5],[np.pi/6,0,-np.pi/6,np.pi/2,-np.pi/2,np.pi/4,-np.pi/4])results=glcm.resultsresults=pd.DataFrame(results)Feature extraction using non-negative matrix factorizationfrommfe.featureimportrepeated_nmf# first detect the appropriate rank for the data, the list of images are used here instead of the feature table, because the images have already been normalized with quantiles removed.rank_candidates=list(range(2,20))summary={}forrankinrank_candidatessummary[rank]=repeated_nmf(ims,rank,30,init='random',max_iter=3000)TODO:Add a more sophisticated piecewise function for mass calibrationAdd an image registration function (Affine transformation already existed in the code)Creditsfrancisbrochu/msvlmfor theSpectrumClass
Package: mfe_fwaas
Mcafee NGFW Firewall as a Service Integration====About----This is not an official product of McAfee but a PoC implementation of the NGFWFWaaS integration into OpenStack. Unreleased NGFW features are required to makefull use of this. Contact McAfee sales for a demonstration.The code is still riddled with TODOs and will be refactored and cleaned up inthe near future.Documentation----* Architecture and configuration:https://wiki.openstack.org/wiki/Neutron/mcafee-ngfw-firewall* For information on Intel NGFW CI, refer to:https://wiki.openstack.org/wiki/ThirdPartySystems/Intel_NGFW_CI* Intel NGFW CI contact:- yalei.wang@intel.com- rui.zang@intel.com* Currently maintained by- lauri.vihervuori@intel.com
Package: mfeprimer-py3
mfeprimer-py3Python3 version initiative for command line scripts ofMFEprimer-2.0InstallationUsing pip:pipinstallmfeprimer-py3Usinggit(optional):gitclonehttps://github.com/Ulises-Rosas/mfeprimer-py3.gitcdmfeprimer-py3
python3setup.pyinstallUsage# Index the databaseIndexDb.pytest/test.rna# Run MFEprimerMFEprimer.py-itest/p.fa-dtest/test.rnaCheck original repo (https://github.com/quwubin/MFEprimer-2.0) for further details about features, outcomes and license agreement.CitationsWubin Qu, Yang Zhou, Yanchun Zhang, Yiming Lu, Xiaolei Wang, Dongsheng Zhao, Yi Yang and Chenggang Zhang*. MFEprimer-2.0: A fast thermodynamics-based program for checking PCR primer specificity. Nucleic Acids Res. 2012 (accepted).Wubin Qu, Zhiyong Shen, Dongsheng Zhao, Yi Yang and Chenggang Zhang. (2009) MFEprimer: multiple factor evaluation of the specificity of PCR primers, Bioinformatics, 25(2), 276-278.
Package: mfetcher
Mangas fetcherAn python script based on mangas scan providers likescantrad.frorjapscan.
(* only japscan is supported by now)InstallationIt's availaible onpypiso just install by doing:# pip install mfetcherwhy ?For reading the scan on the providers web site, they are plenty adds, even adblock is not enougth, so I decide to write this.TodoExtend the providers tojaiminisbox,Setup sqlite db to save the favorite downloadswhy not, schedule downloadLICENCEThis script is provided under MIT Licence.
Package: mff
Machine learning nonparametric force fields (MFF)An example tutorial jupyter notebook can be found in thetutorialsfolder.Table of ContentsBackground on MFFsInstallExamplesMaintainersReferencesBackground on MFFThe MFF package uses Gaussian process regression to extract non-parametric 2- and 3- body force fields from ab-initio calculations.
For a detailed description of the theory behind Gaussian process regression to predict forces and/or energies, and an explanation of the mapping technique used, please refer to [1].For an example use of the MFF package to build 3-body force fields for Ni nanoclusters, please see [2].Pip InstallationTo install MFF with pip, simply run the following in a Python 3.6 or 3.7 environment:pip install mffSource InstallationIf the pip installation fails, try the following:
Clone from source and enter the folder:git clone https://github.com/kcl-tscm/mff.git
cd mffIf you don't have it, install virtualenv:pip install virtualenvCreate a virtual environment using a python 3.6 installationvirtualenv --python=/usr/bin/python3.6 <path/to/new/virtualenv/>Activate the new virtual environmentsource <path/to/new/virtualenv/bin/activate>To install from source run the following command:python setup.py installOr, to build in place for development, run:python setup.py developExamplesRefer to the two files in the Tutorial folder for working jupyter notebooks showing most of the functionalities of this package.MaintainersClaudio Zeni (claudio.zeni@kcl.ac.uk),Aldo Glielmo (aldo.glielmo@kcl.ac.uk),Ádám Fekete (adam.fekete@kcl.ac.uk).References[1] A. Glielmo, C. Zeni, A. De Vita,Efficient non-parametric n-body force fields from machine learning(https://arxiv.org/abs/1801.04823)[2] C .Zeni, K. Rossi, A. Glielmo, A. Fekete, N. Gaston, F. Baletto, A. De VitaBuilding machine learning force fields for nanoclusters(https://arxiv.org/abs/1802.01417)
Package: mff-pretty-cli
Pretty-CLI: Pretty Printing for the CLIThis package providesPrettyCli, a class providing utilities for structured printing in the CLI. Simply use itsprint()and helper methods instead of the defaultprint()and you're good to go!Here is a full example:frompretty_cliimportPrettyClicli=PrettyCli()defmain():cli.main_title("my example file:\nAmazing")cli.print("Hello, world!")cli.print("你好！")cli.big_divisor()# Divisors, titles, etc. add blank space above/under as needed.cli.print("Let's print a dict:")cli.blank()# Add a blank if the previous line is not blank already.cli.blank()cli.blank()cli.print({# Enforces nice alignment of dict contents."foo":"bar","nested":{"hi":"there"},"another one":{"how":"are you?","fine":"thanks"},})cli.small_divisor()cli.print("Some header styles:")cli.chapter("a chapter")cli.subchapter("a sub-chapter")cli.section("a section")cli.print("That's all, folks!")if__name__=="__main__":main()And the produced output:==================================================================
======================== MY EXAMPLE FILE: ========================
============================ AMAZING =============================
==================================================================

Hello, world!
你好！

================================

Let's print a dict:

foo:         bar
nested:
    hi:      there
another one:
    how:     are you?
    fine:    thanks

----------------

Some header styles:

================ A Chapter ================

-------- A Sub-Chapter --------

[A Section]

That's all, folks!
Package: mfg
Message ForegroundThis library allows you to create a message that is displayed in the foreground and above all other elements.Simple Demo# Import the libraryfrommfgimportforeground# First create a instance with the following parameters# 	Text: 'Hello World'# 	Color: White# 	No backgroundf=foreground('Hello World','#fffffe')# Displayed the messagef.display()Requirementtkinterwin32apiwin32conthreadingpywintypesDevelopmentThis library can be improved and if you want to be part of it, contact me.
Package: mf-gitvars
GitVarsUse the Gitlab API to extract CI/CD variables and output them in a format useable for running projects locally.Installationpip install mf-gitvars --upgradeSetupCreate an API key on Gitlabhereand grant itread_api, read_repositorypermissionsCreate a local configuration file and replace your tokencat ~/.python-gitlab.cfg[global]default=momentfeedssl_verify=truetimeout=5api_version=4[momentfeed]url=https://gitlab.comprivate_token=[YOUR API TOKEN]UsageFind the gitlab project id on the project page here:And then run with$mf-gitvars[project-id]You will get an output for each environment and one for the global environment.There is a special string forIntelliJand you can paste that directly into your run configuration like so:
Package: mfglib
MFGLib: A Library for Mean-Field GamesOverviewMFGLib is an open-source Python library dedicated to solving Nash equilibria (NEs) for generic mean-field games (MFGs) with a user-friendly and customizable interface, aiming at promoting both applications and research of MFGs. On one hand, it facilitates the creation and analysis of arbitrary user-defined MFG environments with minimal prior knowledge on MFGs. On the other hand, it serves as a modular and extensible code base
for the community to easily prototype and implement new algorithms and environments of MFGs as well as their variants and generalizations.The official documentation for MFGLib is available athttps://mfglib.readthedocs.io/en/latest/.InstallationMFGLib supports all major platforms and can be installed withpip:$ pip install mfglibCitingIf you wish to citeMFGLib, please use the following:@misc{mfglib_code,
    author       = {Guo, X. and Hu, A. and Santamaria, M. and Tajrobehkar, M. and Zhang, J.},
    title        = {{MFGLib}: A Library for Mean Field Games, version 0.1.0},
    howpublished = {\url{https://github.com/radar-research-lab/MFGLib}},
    year         = {2023}
}
Package: mfhm
MFHM - MangFu HTTP Microservices简介基于HTTPX和FastAPI的HTTP微服务框架，侧重于Web领域注意：该项目只是本人用于研究微服务而创建的探索项目, 项目代码只注重功能的实现, 没有进行过严格的测试, 所以请将本项目用于个人/研究为目的/无关紧要的项目中, 切勿在生产项目中使用文档：在线文档:https://mangfu26.github.io/mfhm/site/
Package: mficlient
The APIs in this library (and tool) should not be considered stable!To install:$ pip install mficlientTo get started, set your connection information like this:$ export MFI="https://user:password@192.168.1.123:6443"Then you can run the client, doing something like:$ mfi dump_sensors
               Model |                Label |        Tag  |      Value | Extra
-------------------------------------------------------------------------------
          Output 12v |        Relay Control |     output  |          0 | 0.0
          Output 24v |                Relay |     output  |          0 | 0.0
    Ubiquiti mFi-THS |                 Temp | temperature |       14.3 | None
       Input Digital |          Garage Door |      input  |          1 | None
              Outlet |       Heater Control | active_pwr  |   5.810242 | 1.0
     Ubiquiti mFi-CS |              Furnace |       amps  |     1.0539 | None
     Ubiquiti mFi-CS |         Water Heater |       amps  |        0.0 | None

$ mfi control_device --device 'Heater Control' --state onClient API example:>>> c = mficlient.MFiClient('192.168.1.123', 'admin', 'password')
>>> p = c.get_port(label='Water Heater Control')
>>> p.model
'Outlet'
>>> p.value
5.746462
>>> p.tag
'active_pwr'
>>> p.data
{'rpt_time': 1456258144771, 'mac': 'redacted', 'locked': False, 'label': 'Water Heater Control', 'wattHours': 8001.875, 'y': 222.18320610687024, 'map_id': 'redacted', 'output': 1.0, 'fovrotation': 0, 'active_pwr': 5.746462, 'pf': 0.678653, 'reported_val': 1.0, '_id': 'redacted', 'i_rms': 0.069931, 'fovradius': 10, 'v_rms': 121.082737, 'val_time': 1456258144000, 'port': '1', 'energy_sum': 8001.875, 'wattHoursBase': 0.0, 'tag': 'active_pwr', 'val': 5.746462, 'wh_rpt_time': 1456258144761, 'model': 'Outlet', 'x': 564.8839694656489, 'fovangle': 1.5707963267948966, 'output_val': 1.0}
 >>> p.control(False)
Package: mfig
mfig: A tool for merging multiple figures into one🛠️ InstallationDownload and install the latest package from therelease sectionor directly by pippip install mfig✔ PrerequisiteWorking LaTeX environment withpdflatexand necessary packages. Installtexlive-latex-extra.pdfcrop. Installtexlive-extra-utils.🚀UsageRun the installedmfigutility to use this tool. Description of different arguments, can also be checked withmfig -hoption.⚡ Check theexample sectionfor details usage.ArgumentDescription-iList of input figures-oOutput file name-itPosition of the subfigure index, possible values are:i(inner),b(bottom),t(top-right corner),n(no index)-irNumber of figures in one row-wWidth of each figures.-vVertical space between each rows-sShift as x,y coordinate in positionfor inner index type option
Package: m-filetypes
Thư viện kiểm tra file type.Cài đặt:$pip3installm-filetypeLink chữ ký:https://en.wikipedia.org/wiki/List_of_file_signaturesSử dụngfrommobio.sdks.filetype.fileimportFilefrommobio.sdks.filetype.commonimportExtensionImageFile.check_filetype_by_file_extensions(file_binary=file,# Check filetype của định dạng file binary.Mặc định Nonefile_path=file_path,# Check filetype của path fileextensions=[ExtensionImage.PNG]# Danh sách extension cần check.)Lấy extensions supportExtension Image:frommobio.sdks.filetype.commonimportExtensionImageExtensionImage.LIST_EXTENSION_SUPPORTEDExtension Document:frommobio.sdks.filetype.commonimportExtensionDocumentExtensionDocument.LIST_EXTENSION_SUPPORTEDExtension Audio:frommobio.sdks.filetype.commonimportExtensionAudioExtensionAudio.LIST_EXTENSION_SUPPORTED
Package: m-filetypes-test
Thư viện kiểm tra file type.Cài đặt:$pip3installm-filetypeLink chữ ký:https://en.wikipedia.org/wiki/List_of_file_signaturesSử dụngfrommobio.sdks.filetype.fileimportFilefrommobio.sdks.filetype.commonimportExtensionImageFile.check_filetype_by_file_extensions(file_binary=file,# Check filetype của định dạng file binary.Mặc định Nonefile_path=file_path,# Check filetype của path fileextensions=[ExtensionImage.PNG]# Danh sách extension cần check.)Lấy extensions supportExtension Image:frommobio.sdks.filetype.commonimportExtensionImageExtensionImage.LIST_EXTENSION_SUPPORTEDExtension Document:frommobio.sdks.filetype.commonimportExtensionDocumentExtensionDocument.LIST_EXTENSION_SUPPORTEDExtension Audio:frommobio.sdks.filetype.commonimportExtensionAudioExtensionAudio.LIST_EXTENSION_SUPPORTED
Package: mfinancials
mfinancialsSimple module for downloading financial statements and estimates from financials.morningstar.comSetupInstall using pip:pip install mfinancialsor install from source:git clone https://github.com/tobigs/mfinancials.git
cd mfinancials
python setup.py installUsageBasic usageimportmfinancialsasmf# Apple, Inc.aapl=mf.Ticker("aapl")# financials from http://financials.morningstar.com/ratios/r.html?t=aaplaapl.financials# key ratios from from http://financials.morningstar.com/ratios/r.html?t=aaplaapl.keyRatios# estimates from financials.morningstar.com/valuation/earnings-estimates.html?t=aaplaapl.estimates# display like morningstar websiteaapl.financials.Taapl.keyRatios.Taapl.estimates.T# get urls to view data on morningstar websiteaapl.url_estimatesaapl.url_financialsNon-US stocksStocks not listed in the USA require either country orMarket Identifier Code (MIC).It is generally advised to use country, mic only for not supported countries.# LVMH Moët Hennessy – Louis Vuitton SElvmh=mf.Ticker("mc",mic="XPAR")# Samsung Electronics Co., Ltd.smsng=mf.Ticker("005930",country="South Korea")# list supported countriessmsng.countriesSome estimate data is in a different currency than the financials currency. To convert estimates use:# Alibaba Group Holding Limitedbaba=mf.Ticker("baba")baba.financialsbaba.estimatesbaba.estimatesConv# access currenciesbaba.currency_estimatesbaba.currency_financials
Package: mfind
mfindThe mfind library allows to scan a directory for file an apply callbacks on file matchesHow to installpipinstallmfindHow to usefrommfindimportMultiFinderdefinternal_handler(filename:str):returnFalse# Just about all other handler checksdefhtml_handler(filename:str):print(f"Processing HTML file{filename}")defmardkown_handler(filename:str):print(f"Processing markdown file{filename}")file_processing_map={"_*":internal_handler,"*.html":html_handler,"*.md":mardkown_handler}mf=MultiFinder(file_processing_map)mf.scan("test_dir",filename_only_match=True)