Saravanan666/pypi-org huggingface dataset:
Package: mful-dev
Masterful is the training platform for computer vision models.Pass in your model architecture and data. Masterful figures out the right hyperparameters for its SOTA regularization, optimization, and semi-supervised learning engines. And then trains your model weights to the peak accuracy.FeaturesBuilt on Tensorflow 2.SOTA regularization implementation to improve accuracy from your existing labeled training data while pushing all computations to GPU.SOTA semi-supervised learning implementation to improve accuracy from your raw, unlabeled images.Automatic, high speed hyperparameter discovery algorithms, based on custom search algorithms and analytical / closed-form solutions, to reduce manual experimentation.Maximum GPU utilization and minimium GPU-hours used.Support for most computer vision tasks:Binary classificationSingle-label classificationMulti-label classifcationDetectionSemantic SegmentationInstance Segmentation (coming soon)Keypoint Detection (coming soon)Key LinksWebsiteDocsInstallationQuickstartInstallationFor detailed instructions with a troubleshooting guide, see theInstallation Guide.The tldr instructions:sudo apt install python3-pip
pip install --upgrade pip
pip install masterful
python -c "import masterful; print(masterful.__version__)"Getting HelpVisit the documentation to see quick tutorials and in depth guides atwww.masterfulai.com/docs.Join the Slack community to speak to fellow users of Masterful and with Masterful AI's engineering team. You'll find a link to Slack fromwww.masterfulai.com.
Package: mfunc
mfuncThis library was made so that you can get a list of points of a function by list slicing.How to UseFirst, putfrom mfunc.mfunc import mfuncat the top of your code.
Then, if you have already defined a functionf(x), you can initiate an instance of anmfuncby assigningmfunc(f)to a variable.Supported FeaturesHere is the list of supported features for anmfunc:Item retrieval and slicing (e.g.mfunc(lambda x:x)[:5]returns the values of the function from 0 to 4).The arithmetic operators+ - * / // **.A string representation (returns actual function).BugsIf you hae discovered a bug or have an idea, post ithere.
Package: mfvis
Membership-function-visualizationPlotting various membership functions with matplotlibInstallationMfvis requires an installation of Python 3.6 or greater, as well as pip. (Pip is typically bundled with Pythoninstallations.)To install from PyPI with pip:$python-mpipinstallmfvisInstall the required packages:$python-mpipinstall-rrequirements.txtUsing mfvisTo start the script through the cli:$python-mmfvisExample: S-function:
Package: mfw-template
Cookiecutter templates for Morp FrameworkChangelog0.3 (2022-03-31)Release0.3b3 (2021-01-27)fix %(here)s replacement in rpm buildrelease bump to morpfw-0.4.0b40.3b2 (2021-01-10)added missing buildout-rpm.cfgstop using bootstrap-buildout.py0.3b1 (2020-12-29)Initial release with changelog
Package: mfyhttpreport
mfyreport介绍1、什么是mfyhttpreport?
mfyhttpreport是基于unittest开发的的一个功能扩展库，关于mfyhttpreport最初在开发的时候。后续还会持续的扩展和开发一些新的功能，目前实现了以下功能：HTML测试报告生成unittest数据驱动发送测试结果及报告到邮箱2、安装mfyhttpreportmfyhttpreport是基于python3.7开发的，安装前请确认你的python版本>3.73、安装命令3.1、pip install mfyhttpreport3.2、通过工具pycharm下载
Package: mfypublic
mfypublic介绍1、什么是mfypublic?mfypublic是基于webdriver开发的一个功能扩展库，关于mfypublic最初在开发的时候，是根据webdriver进行的二次封装，简化代码。后续还会持续的扩展和开发一些新的功能，目前实现了以下功能：对webdriver底层代码进行二次封装，改变了元素定位的方式获取日志读取excel数据读取json文件读取yaml文件2、安装mfypublicmfypublic是基于python3.7开发的，安装前请确认你的python版本>3.7安装命令pip install mfypublic
Package: mfyreport
mfyreport介绍1、什么是mfyreport?
mfyreport是基于unittest开发的的一个功能扩展库，关于mfyreport最初在开发的时候，在开发的过程中参考了木先生的思路。后续还会持续的扩展和开发一些新的功能，目前实现了以下功能：HTML测试报告生成unittest数据驱动发送测试结果及报告到邮箱2、安装mfyreportmfyreport是基于python3.7开发的，安装前请确认你的python版本>3.73、安装命令3.1、pip install mfyreport3.2、通过工具pycharm下载
Package: mg
MG is an extension package for Machine Learning. Currently wrapsvfianddaggingpackages.How to use MGThe mg package inherits from sklearn classes, and thus drops in neatly
next to other sklearn classifiers with an identical calling API. Similarly it
supports input in a variety of formats: an array (or pandas dataframe) of shape(num_samples x num_features).import mg
from sklearn.datasets import load_iris

data, target = load_iris(return_X_y=True)

model = mg.VFI()
model.fit(data, target)InstallingPyPI install, presuming you have an up to date pip:pip install mgIf pip is having difficulties pulling the dependencies then we'd suggest to first upgrade
pip to at least version 10 and try again:pip install --upgrade pip
pip install mgOtherwise install the dependencies manually using anaconda followed by pulling mg from pip:conda install numpy scipy
conda install scikit-learn
pip install mgRunning the TestsThe package tests can be run after installation using the command:pytest mg --covPython VersionThe mg package supports only Python 3.ContributingWe welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome.LicensingThe mg package is MIT licensed. Enjoy.
Package: mgarch
mgarchmgarch is a python package for predicting volatility of daily returns in financial markets.DCC-GARCH(1,1) for multivariate normal and student t distribution.Use case:For Multivariate Normal Distribution# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assetsimportmgarchvol=mgarch.mgarch()vol.fit(rt)ndays=10# volatility of nth daycov_nextday=vol.predict(ndays)For Multivariate Student-t Distribution# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assetsimportmgarchdist='t'vol=mgarch.mgarch(dist)vol.fit(rt)ndays=10# volatility of nth daycov_nextday=vol.predict(ndays)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseAcademic Free License v3.0
Package: mgarch-setup-fix
mgarchmgarch is a python package for predicting volatility of daily returns in financial markets.DCC-GARCH(1,1) for multivariate normal and student t distribution.Use case:For Multivariate Normal Distribution# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assetsimportmgarchvol=mgarch.mgarch()vol.fit(rt)ndays=10# volatility of nth daycov_nextday=vol.predict(ndays)For Multivariate Student-t Distribution# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assetsimportmgarchdist='t'vol=mgarch.mgarch(dist)vol.fit(rt)ndays=10# volatility of nth daycov_nextday=vol.predict(ndays)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseAcademic Free License v3.0
Package: mgc
mgcmgc(pronounced "Magic") is an open-source software package for independence and k-sample testing.Installation Guide:https://mgc.neurodata.io/install.htmlDocumentation:https://mgc.neurodata.io/Tutorials:Source Code:https://github.com/neurodata/mgc/tree/master/mgcIssues:https://github.com/neurodata/mgc/issuesContribution Guide:mgcintends to be a comprehensive independence and k-sample testing package and runs on all major versions of operating systems. It also includes novel tests not found in other packages that operate on multivariate data. It is quick to install and free of charge. If you need to use high dimensional independence or k-sample testing, be sure to givemgca try!
Package: mgclipboard
MgClipboardClipboard middleware forPapermerge DMS.
Designed as Django reusable app.InstallationInstall it using pip::pip install mgclipboardAdd app to INSTALLED_APPS in settings.py:INSTALLED_APP = (
...
'mgclipboard',
...
)Add it to MIDDLEWARE list:MIDDLEWARE = [
    ...
    # AFTER
    # * django.contrib.sessions.middleware
    # * django.contrib.auth.middleware
    'mgclipboard.middleware.ClipboardMiddleware'
    ...
]mgclipboard.middleware is dependent on django.contrib.sessions and django.contrib.auth middleware. Thus, dependencies must be included first in MIDDLEWARE list.UsageMgClipboard middleware adds 3 attributes to the request object:request.clipboardrequest.nodes (shortcut for request.clipboard.nodes)request.pages (shortcut for request.clipboard.pages)To add list of node ids to clipboard use:request.nodes.add(['id1', 'id2', ...])To retrieve all node ids (folder or documents) currently in the clipboard call:request.nodes.all()To clear all nodes data from the clipboard:request.nodes.clear()To add pages, all belonging to same document, use:request.pages.add(
    doc_id=doc_id,
    page_nums=[1, 2, 3]
)Important! page_nums is a list of page numbers within document doc_id. Page numbering starts with 1.All pages currently in clipboard are returned by:request.pages.all()To clear all pages data from the clipboard:request.pages.clear()
Package: mgcomm
Usually when writing some CLI apps I end up writing the same stuff over and over
again. Like searching for configuration files according to Freedesktop’s
standards. Or handling user input from either which can be collected from stdin,
read from a file or piped in. This kind of stuff. So I decided to collect it in
a small (probably personal) library which actually has some tests, sane
interface and most important: can be easily used just by adding it to
requirements.txt.
Package: mge-lpips
LPIPS for MegEngineMegEngine port forLPIPSmetric.Get StartedRunpip3 install mge-lpipsto install this package. The providedmge_lpips.LPIPSis a normal MegEngineM.Modulewhich can be used as a metric or a loss function.importnumpyasnpimportmegengineasmgefrommge_lpipsimportLPIPSmetric_lpips=LPIPS(net="alex")in1=np.random.uniform(-1,1,size=(2,3,256,256)).astype(np.float32)in2=np.random.uniform(-1,1,size=(2,3,256,256)).astype(np.float32)d=metric_lpips(mge.Tensor(in1),mge.Tensor(in2))AcknowledgementsThe code of this project is modified from the original LPIPS project. The backbone model code is modified from torchvision.
The weights are from the origian LPIPS project and torchvision.
Package: mgen
MGen: Convenient matrix generation functionsPython and its most popular packages do not offer out-of-the-box convenient
functions to generate rotation matrices. While there are other projects
that offer rotation and vector classes, or offer rotations via the use of quaternions,
if you simply want a rotation matrix, for example if other packages require them
as an input, or you do not wish to change your current data structure to use
special rotation classes, the common suggestion is to implement them yourself
(see for example this discussion on SE:https://stackoverflow.com/questions/6802577/rotation-of-3d-vector). However,
everybody implementing their own version of the same thing can hardly be seen as
ideal.Therefore, this package provides simple functions to generate rotation matrices
in 2d for a given angle or in 3d for a given axis and angle, or for three given
angles (proper Euler angles or Tait-Bryan angles).Additionally, n-dimensional rotations can be generated using an angle and two
orthogonal vectors that span the plane of rotation.Trivial example usageBelow you see examples of how to use mgen to generate rotation matrices. For further
documentation please have a look here:https://mgen.readthedocs.ioimportnumpyasnpnp.set_printoptions(suppress=True)frommgenimportrotation_around_axisfrommgenimportrotation_from_anglesfrommgenimportrotation_around_xfrommgenimportrotation_from_angle_and_planefrommgenimportrotation_from_anglefrommgenimportrandom_matrix# 2D examplematrix=rotation_from_angle(np.pi/2)matrix.dot([1,0])# array([0., 1.])#3D examplesmatrix=rotation_from_angles([np.pi/2,0,0],'XYX')matrix.dot([0,1,0])# array([0., 0., 1.])matrix=rotation_around_axis([1,0,0],np.pi/2)matrix.dot([0,1,0])# array([0., 0., 1.])matrix=rotation_around_x(np.pi/2)matrix.dot([0,1,0])# array([0., 0., 1.])# n-dimensional examplematrix=rotation_from_angle_and_plane(np.pi/2,(0,1,0,0),(0,0,1,0))matrix.dot([0,1,0,0])# array([0., 0., 1., 0.])# n-dimensional random matrix O(n), e.g. n=27matrix=random_matrix(27)
Package: mgenerator
matrix-generatorThis is a python program that helps you to make a sympy matrix easily. This module usessympy, python library for simbolic mathmatics.By using this generator, you can create a new sympy matrix with only entering the dimension size and elements.DependenciesThe following dependencies are requiredsympyDownload$pipinstallmgeneratorExamplefrommgeneratorimport*foo=new()# makes new sympy Matrix object. You will enter elementsfoo=new(1,2)# makes new sympy Matrix with 1 raw and 2 columnef(foo)# makes foo as Echelon formrf(foo)# makes foo as Reduced Echelon formnum=3ran(num)#finds image num elements vector range#used in finding eigenvalueschar(foo,3)#makes characteristic polynomial equation, and finds soluton with 3*3 matrixorthon(foo)#makes orthogonal basis matrixvec(2,1)#makes the vector (2,1)Instructionsnew( ):When you make a mistake, just enter 'Enter'.It will make new matrixYou can use any type of componenent(int,float,string), even a fraction like 1/2ran( ):First you have to find echelon form of linear combination's agmented coefficient matrixPut coefficients of the range. Ex) If the range is ax-by-cz = 0 -> enter a,-b,-cchar( ):Put a Matrix and number of the matrix dimension ( if 2 by 2 matrix, enter 2 )
Package: mGesf
MicroGesture SensorFusion (mGesf)Check out ourvideoThis repo extends the work in this paper:ThuMouse: A Micro-gesture Cursor Input through mmWave Radar-based Interaction.This application supports a variety of sensors, including:Texas Instrument mmWave Sensors: IWR6843ISK, IWR6843AoPLeapMotion (coming soon)DecaWave UWB sensors (coming soon)To run this application, first make sure you have the GUI backend PyQT5 installed. Depending on your O.S., use the following commands:ubuntu:sudo apt-get install python3-pyqt5MacOs:brew install pyqtThen install the package in your python interpreter:pip install pyqt5first run this command to install the prerequisites.pythonsetup.pyinstallNow run this app with this commandpythonmain.pyMake sure you have the audio library installed:
Mac:brewinstallffmpegUbuntu:sudoapt-getinstallffmpegFor Windows, it's a bit more involved as the downloaded library needs to be added to the environment variables.
This is a goodtuorial.If you are getting 'permission denied' errors (likely to occur on Windows), install the simpleaudio package by running:pipinstallsimpleaudio
Package: mgetool
MGE toolSome useful base tools for other mge packages, but without specific task.InstallpipinstallmgetoolContainsexportStoreStore files in batches.(无差别存储数据)importCallImport files in batches.(无差别导入数据)showBasePlotDraw picture quickly.(快速画图)corr_plotDraw correlation coefficient graph.(相关系数图)toolttfrommgetool.toolimporttttt.t...tt.ttt.pRecord the time of this site, and print uniformly.(测试代码块时间)time_this_functionTime the function(测试函数运行时间，@)parallelizeParallelize theforloop (并行化)loggGet the name of function(输出函数信息，@)newclassfrommgetool.newclassimportcreateimportnumpyasnpdefff(x,y):print(y*1000)returnxFoo=create("Foo",np.ndarray,lenn=dict,spam=1,fu=ff)foo=Foo([1,2,3])a=foo.fu(2,4)Build a simple class quickly.(No initialization parameters)（快速创建新类）packboxfrommgetool.packboximportToolboxdeffunc(a,b=1,c=2,name=4,**kwargs):print(a,b,c,name,kwargs)passto=Toolbox()to.register("a",func,1,5,abss=3)to.refresh("a",3,4,6,name=3,abss=4)to.a()Build a toolbox and you can add function to it.(函数集合)draftfrommgetool.draftimportDraftPyxbd=DraftPyx("hello.pyx")bd.write()a=bd.quick_import(build=True,with_html=True)# bd.remove()Build for .cpp or .pyx temporary .(编译cython和cpp ，自动创建setup.py)
Package: mgit
Fetch collections of git projectsOverviewWithmgit, you can pull/fetch several projects at once,
and also auto-cleanup dangling branches (from past pull requests).A colored output is provided if possible,mgitshould come in handy in general for:quickly getting an overview of what’s up with N git projectsfetch/pull N git objects at onceclone missing projects (useful if you tend to clone projects from same remote in one common folder)Example usagemgitcan show you what’s the status of all your git projects in a folder, for example my repos:~/dev/github: mgit
~/dev/github: 4 github/zsimic
     mgit: [master] up to date
  pickley: [master] 1 diff, up to date*  last fetch 1w 4d ago
    runez: [master] up to date*  last fetch 1w 4d ago
setupmeta: [master] up to date*  last fetch 3d 23h agoHere we can see that:There are 4 git repos in folder~/dev/githubAll 4 come fromgithub/zsimic3 of them haven’t been fetched in a whileWe can fetch them all at once with--fetch(or-f):~/dev/github: mgit --fetch
~/dev/github: 4 github/zsimic
     mgit: [master] up to date
  pickley: [master] 1 diff, up to date
    runez: [master] behind 2
setupmeta: [master] up to dateNow all projects have been refreshed, and we can see there’s nothing new in 2 of them,
but one is 2 commits behind (ie: 2 commits are on the remote, not pulled yet).
The output also shows that one of the projects has uncommitted files.Modified files are shown (by default) if only one project is in scope, for example:~/dev/github: mgit pickley
pickley: [master] 1 diff, up to date
   M tox.iniAbove, we can see that the modified file in question istox.iniin that project.
We can get the same effect using the--verbose(or-v) flag,
like for example with 2 projects with modified files:~/dev/github: mgit -v
~/dev/github: 4 github/zsimic
mgit: [master] 1 diff, up to date
   M README.rst
pickley: [master] 1 diff, up to date
   M tox.ini
runez: [master] up to date
setupmeta: [master] up to dateSynopsis:~/dev/github: mgit --help
Usage: mgit [OPTIONS] [TARGET]

  Fetch collections of git projects

Options:
  --version                       Show the version and exit.
  --debug                         Show debugging information.
  --color / --no-color            Use colors (on by default on ttys)
  --log PATH                      Override log file location.
  --clean [show|local|remote|all|reset]
                                  Auto-clean branches
  -f, --fetch                     Fetch from all remotes
  -p, --pull                      Pull from tracking remote
  -s, --short / -v, --verbose     Short/verbose output
  -cs                             Handy shortcut for '--clean show'
  -cl                             Handy shortcut for '--clean local'
  -cr                             Handy shortcut for '--clean remote'
  -ca                             Handy shortcut for '--clean all'
  -h, --help                      Show this message and exit.InstallationEasiest way to get mgit is viapickleyorpipx:pickley install mgitor:pipx install mgitYou can also compile from source:git clone https://github.com/zsimic/mgit.git
cd mgit
tox -e venv

.venv/bin/mgit --help

source .venv/bin/activate
mgit --help
Package: mgjson
python-mgjsonA nifty library for encoding mgjson documents from python.Made possibly by the the works ofJuanIrache. This library is a new partial implementation in python for generating Motion Graphics Json documents.Static data properties of type int, boolean and ascii-string are supported.
Dynamic data as time series are supported. These will be translated to timezone encoded time series.installationpipinstallmgjsonUsagefrommgjsonimportMgJSONmgjson=MgJSON()# static propertiesmgjson.add_property("numberOfCats",3)mgjson.add_property("isItTrue",False)mgjson.add_property("title","A new adventure awaits!")# dynamic data (time series)mgjson.add_stream("temperature",[[0.0,1.000],[2.59292,0.777]])print(mgjson.json)LicenseMIT
Package: mgkegg
Kegg color pathway object for metagenomeCommand line version forKEGG Pathway obejctandKEGG rest apiinstallationpipinstallmgkeggUsageDisplay database release information and linked db informationmgkegginfo<dbentries># displays the current statistics of the KEGG databasemgkegginfokegg# displays the number of pathway entries including both the reference and organism-specific pathwaysmgkegginfopathway# displays the number of gene entries for the KEGG organism Homo sapiensmgkegginfohsaObtain a list of entry identifiers and associated definitionmgkeggls<dbentries># returns the list of reference pathwaysmgkegglspathway# returns the list of human pathwaysmgkegglspathwayhsa# returns the list of KEGG organisms with taxonomic classificationmgkegglsorganism# returns the entire list of human genesmgkegglshsa# returns the list of a human gene and an E.coli O157 genemgkegglshsa:10458+ece:Z5100# returns the list of a compound entry and a glycan entrymgkegglscpd:C01290+gl:G00092Find entries with matching query keyword or other query datamgkeggfind<databases><query># for keywords "shiga" and "toxin" (use nop option to disable this processing)mgkeggfindgenesshiga+toxin# for keywords "shiga toxin"mgkeggfindgenesshigatoxin# for chemical formula "C7H10O5"mgkeggfindcompoundC7H10O5formula# for chemical formula containing "O5" and "C7"mgkeggfindcompoundO5C7formula# for 174.045 =< exact mass < 174.055mgkeggfind174.05exact_mass# for 300 =< molecular weight =< 310mgkeggfind300-310mol_weightRetrieve given database entriesmgkeggget<dbentries># retrieves a compound entry and a glycan entrymgkegggetcpd:C01290+gl:G00092# retrieves a human gene entry and an E.coli O157 gene entrymgkegggethsa:10458+ece:Z5100# retrieves amino acid sequences of a human gene and an E.coli O157 genemgkegggethsa:10458+ece:Z5100aaseq# retrieves the gif image file of a compoundmgkegggetC00002image# retrieves the png image file of a pathway mapmgkegggethsa05130image# retrieves the conf file of a pathway mapmgkegggethsa05130conf# retrieves the kgml file of a pathway mapmgkegggethsa05130kgml# retrieves the htext file of a brite hierarchymgkegggetbr:br08301# retrieves the json file of a brite hierarchymgkegggetbr:br08301jsonDownload the latest kegg reference map.mgkeggdownloadUpdate to the latest kegg reference map if not already the lastet.mgkeggupdateHighlight the given kegg object with the given color offline.mgkegg highlight --glist <gene.list> --color <color> -o <outdir>
Package: mgkit
The aim of this library[1]is to provide a series of useful modules and packages to make it easier to build custom pipelines for metagenomics or any kind of bioinformatics analysis. It integrates other well known python libraries in bioinformatics, likeHTSeq,pysam,numpyandscipy.Documentation is available athttps://frubino.github.ioData and scripts used in the documentations are available at[2].A discussion mailing list is available atmgkit-users.Papers using MGKitRubino, F., Carberry, C., M Waters, S., Kenny, D., McCabe, M. S., & Creevey, C. J. (2017). Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome. The ISME Journal, 1–13.https://doi.org/10.1038/ismej.2016.172Lambert, B. S., Raina, J. B., Fernandez, V. I., Rinke, C., Siboni, N., Rubino, F., … Stocker, R. (2017). A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nature Microbiology, 2(10), 1344–1349.https://doi.org/10.1038/s41564-017-0010-9Hitch, T. C. A., Thomas, B. J., Friedersdorff, J. C. A., Ougham, H., & Creevey, C. J. (2018). Deep sequence analysis reveals the ovine rumen as a reservoir of antibiotic resistance genes. Environmental Pollution, 235, 571–575.https://doi.org/10.1016/j.envpol.2017.12.067Computational haplotype recovery and long-read validation identifies novel isoforms of industrially relevant enzymes from natural microbial communities
Samuel M Nicholls, Wayne Aubrey, Arwyn Edwards, Kurt de Grave, Sharon Huws, Leander Schietgat, André Soares, Christopher J Creevey, Amanda Clare
bioRxiv 223404; doi:https://doi.org/10.1101/223404CitingRubino, F. and Creevey, C.J. 2014. MGkit: Metagenomic Framework For The Study Of Microbial Communities. . Available at:figshare[doi:10.6084/m9.figshare.1269288].`a citation is also available using themgkit.cite()function or using the–citeoption on all scripts included. This will ouput the followingLinks[1]https://github.com/frubino/mgkit[2]http://dx.doi.org/10.6084/m9.figshare.1588384