Saravanan666/pypi-org huggingface dataset:
Package: mkite-vasp
mkite_vaspmkite_conformeris a plugin that helps running DFT calculations usingVASP.
The plugin usespymatgenandcustodianto create the VASP inputs using Python, as well as running and monitoring the VASP calculation.
As an example, this plugin implements recipes such as relaxation and single-point calculations.DocumentationGeneral tutorial formkiteand its plugins are available in themain documentation.
Complete API documentation is pending.InstallationTo installmkite_vasp, use pip:pipinstallmkite_vaspAlternatively, for a development version, clone this repo and install it in editable form:pipinstall-Ugit+https://github.com/mkite-group/mkite_vaspContributionsContributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to theCode of Conductin the mkite suite.The mkite package was created by Daniel Schwalbe-Kodadskoda@llnl.gov.Citing mkiteIf you use mkite in a publication, please cite the following paper:@article{mkite2023,title={mkite: A distributed computing platform for high-throughput materials simulations},author={Schwalbe-Koda, Daniel},year={2023},journal={arXiv:2301.08841},doi={10.48550/arXiv.2301.08841},url={https://doi.org/10.48550/arXiv.2301.08841},arxiv={2301.08841},}LicenseThe mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.All new contributions must be made under this license.SPDX: Apache-2.0, LLVM-exceptionLLNL-CODE-848161
Package: mkjsfiddle
MkDocs JSFiddle PluginA plugin forMkDocsthat lets you
augment your code fences with an "Try it in JSFiddle" button.InstallationThis package can be installed via pip.pip install mkjsfiddleThen, addjsfiddleto thepluginssection of yourmkdocs.ymlfile.plugins:-jsfiddleUsageCode fences will be left alone by default, as many code blocks may not
form a coherent block for a user to edit. To opt in for a given code fence,
addjsfiddle-to your fence language declaration, like so.```jsfiddle-html<!DOCTYPE html><!-- Some content here -->```Thehtmlabove will be preserved for syntax highlighting, and thejsfiddle-will be dropped. Other languages are theoretically
supported, but will have no impact besides code highlighting.The default behavior is for a simple HTML parser to extract the inline JS
and CSS elements and put their contents in the appropriate JSFiddle panels.
More specifically, if you have ascripttag with the attributetype=text/javascriptAND not having thesrcattribute, or if you have astyletag with the attributetype=text/css, it will be extracted to the appropriate panel. If you have multiple
of these, they will all be extracted and concatenated with some newlines thrown in as
breaks.If you prefer to opt out of this behavior and just dump everything as-is into
the HTML box, you can add-htmlonlyto the language string (for a full string that
looks something likejsfiddle-html-htmlonly).Known limitationsWe attempt to keep formatting as-is, but make no guarantees in the default mode. The-htmlonlyoption however should preserve formatting (if it does not, please submit
a bug report). This has been developed and tested withMaterial for MkDocs,
and has not been tested extensively with additional styles. Pull requests to improve styling
are welcome.
Package: mkkm-mr
Multiple-Kernel-k-Means-Clustering-with-Matrix-Induced-Regularization Python ImplementationNon-official python implementation for AAAI16：Multiple Kernel k-Means Clustering with Matrix-Induced RegularizationUsageInstallationYou can install the package through pip with:pipinstallmkkm_mrUsing the moduleYou can see an example usage underdemoDevelopmentThe project is usingpoetryfor reliable development.See poetry documentation on how to install the latest version for your system:https://python-poetry.org/docsSetupAfter installing poetry, start an environment:poetryinstallIf you are using PyCharm you can usethis pluginfor setting up interpreter.TestingTests are using standardpytestformat. You can run them after the setup with:pytest
Package: mkl
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mkl-devel
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mkl-devel-dpcpp
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mkl-dpcpp
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mklearn
mlkit-learnmlkit-learn is a lightweight machine learning library designed to beinteractive,easy-to-understand, andeducational. It implements all of the classic machine learning algorithms from regression to gradient boosting trees.With only two lines of code, users can witness popular Kaggle datasets being preprocessed and predicted in action.ContentsNearest NeighborMultivariate Linear RegressionNaive BayesDecision TreeLogistic RegressionInstallpip install mklearnDemoNearest NeighborDemodownloadtrain.csvand put in the directory of your choosing.run the following code.frommklearnimportknnknn.demo(5)# demo(k [the number of nearest neighbour], dir [default: current directory], row [default: first 5000 rows]UseKNN Classifiermodel=KNNClassifier(k)model.fit(train_x,train_y)size=test_x.shape[0]predictions=[]foriinrange(size):result=classifier.predict(test_x[i])predictions.append(result)KNN Regressormodel=KNNRegressor(k)model.fit(train_x,train_y)size=test_x.shape[0]predictions=[]foriinrange(size):result=classifier.predict(test_x[i])predictions.append(result)Multivariate Linear RegressionDemodownloadkc_house_data.csvand put in the directory of your choosing.Naive BayesDecision TreeLogistic Regression
Package: mkl-fft
mkl_fft--  a NumPy-based Python interface to Intel (R) MKL FFT functionalitymkl_fftstarted as a part of Intel (R) Distribution for Python* optimizations to NumPy, and is now being released
as a stand-alone package. It can be installed into conda environment usingconda install -c intel mkl_fftTo install mkl_fft Pypi package please use following command:python -m pip install --index-url https://pypi.anaconda.org/intel/simple  mkl_fftSince MKL FFT supports performing discrete Fourier transforms over non-contiguously laid out arrays, MKL can be directly
used on any well-behaved floating point array with no internal overlaps for both in-place and not in-place transforms of
arrays in single and double floating point precision.This eliminates the need to copy input array contiguously into an intermediate buffer.mkl_fftdirectly supports N-dimensional Fourier transforms.More details can be found in SciPy 2017 conference proceedings:https://github.com/scipy-conference/scipy_proceedings/tree/2017/papers/oleksandr_pavlykIt implements the following functions:Complex transforms, similar to those inscipy.fftpack:fft(x, n=None, axis=-1, overwrite_x=False)ifft(x, n=None, axis=-1, overwrite_x=False)fft2(x, shape=None, axes=(-2,-1), overwrite_x=False)ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False)fftn(x, n=None, axes=None, overwrite_x=False)ifftn(x, n=None, axes=None, overwrite_x=False)Real transformsrfft(x, n=None, axis=-1, overwrite_x=False)- real 1D Fourier transform, likescipy.fftpack.rfftrfft_numpy(x, n=None, axis=-1)- real 1D Fourier transform, likenumpy.fft.rfftrfft2_numpy(x, s=None, axes=(-2,-1))- real 2D Fourier transform, likenumpy.fft.rfft2rfftn_numpy(x, s=None, axes=None)- real 2D Fourier transform, likenumpy.fft.rfftn... and similarirfft*functions.The package also providesmkl_fft._numpy_fftandmkl_fft._scipy_fftinterfaces which provide drop-in replacements for equivalent functions in NumPy and SciPy respectively.To buildmkl_fftfrom sources on Linux:install a recent version of MKL, if necessary;executesource /path/to/mklroot/bin/mklvars.sh intel64;executepip install .
Package: mkl-include
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mklog
mklogis a python program that converts standard input, content of files, or
output of a command in a log-like format, i.e. current date and time is
prepended to each line.Runmklog –helpfor the list of available options; see examples below.ExampleDownload using wget:$mklog-cwgethttps://archive.org/download/Popeye_Nearlyweds/Popeye_Nearlyweds.ogv2015-02-0513:13:41--2015-02-0513:13:41--http://t/2015-02-0513:13:41Résolutiondet(t)…échec:Nomouserviceinconnu.2015-02-0513:13:41wget:impossiblederésoudrel'adresse de l'hôte«t»2015-02-0513:13:41--2015-02-0513:13:41--https://archive.org/download/Popeye_Nearlyweds/Popeye_Nearlyweds.ogv2015-02-0513:13:41Résolutiondearchive.org(archive.org)…207.241.224.22015-02-0513:13:41Connexionàarchive.org(archive.org)|207.241.224.2|:443…connecté.2015-02-0513:13:42requêteHTTPtransmise,enattentedelaréponse…302MovedTemporarily2015-02-0513:13:42Emplacement:https://ia700502.us.archive.org/6/items/Popeye_Nearlyweds/Popeye_Nearlyweds.ogv[suivant]2015-02-0513:13:42--2015-02-0513:13:42--https://ia700502.us.archive.org/6/items/Popeye_Nearlyweds/Popeye_Nearlyweds.ogv2015-02-0513:13:42Résolutiondeia700502.us.archive.org(ia700502.us.archive.org)…207.241.237.1222015-02-0513:13:42Connexionàia700502.us.archive.org(ia700502.us.archive.org)|207.241.237.122|:443…connecté.2015-02-0513:13:43requêteHTTPtransmise,enattentedelaréponse…200OK2015-02-0513:13:43Taille:26698780(25M)[video/ogg]2015-02-0513:13:43Sauvegardeen:«Popeye_Nearlyweds.ogv»2015-02-0513:13:432015-02-0513:13:440K..................................................0%126K3m26s[...]2015-02-0513:14:1826000K..................................................99%541K0s2015-02-0513:14:1826050K.......................100%5,80M=34s2015-02-0513:14:182015-02-0513:14:182015-02-0513:14:18(762KB/s)—«Popeye_Nearlyweds.ogv»sauvegardé[26698780/26698780]2015-02-0513:14:182015-02-0513:14:18Terminé—2015-02-0513:14:18—2015-02-0513:14:18Tempstotaleffectif:37s2015-02-0513:14:18Téléchargés:1fichiers,25Men34s(762KB/s)Monitor logs (which are not dated):$ tail -f /var/log/gdm3/\:0.log | mklogWhat’s new?Seechangelog.Download and installSee the end of list for a (quick and dirty) Debian package.From sources:Download:https://pypi.python.org/pypi/mklogInstall (in avirtualenv, not to mess with your distribution installation system):python3 setup.py installWith pip:pip install mklogQuick and dirty Debian (and Ubuntu?) packageThis requiresstdebto be installed:python3 setup.py --command-packages=stdeb.command bdist_deb
sudo dpkg -i deb_dist/mklog-<VERSION>_all.debSee alsoSee also (other program with the same purpose):ts
Package: mkl-random
mkl_random-- a NumPy-based Python interface to Intel (R) MKL Random Number Generation functionalitymkl_randomhas started as Intel (R) Distribution for Python optimizations for NumPy.Per NumPy's community suggestions, voiced inhttps://github.com/numpy/numpy/pull/8209, it is being released as a
stand-alone package.Prebuiltmkl_randomcan be installed into conda environment from Intel's channel on Anaconda cloud:conda install -c intel mkl_randommkl_randomis not fixed-seed backward compatible drop-in replacement fornumpy.random, meaning that it implements sampling from the same distributions asnumpy.random.For distributions directly supported in Intel (R) Math Kernel Library (MKL),methodkeyword is supported:mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')Additionally,mkl_randomexposes different basic random number generation algorithms available in MKL. For example to useSFMT19937usemkl_random.RandomState(77777, brng='SFMT19937')For generator families, such thatMT2203and Wichmann-Hill, a particular member of the family can be chosen by specifyingbrng=('WH', 3), etc.See MKL reference guide for more details:https://software.intel.com/en-us/mkl-developer-reference-c-random-number-generatorsThe list of supported bymkl_random.RandomStateconstructorbrngkeywords is as follows:'MT19937''SFMT19937''WH' or ('WH', id)'MT2203' or ('MT2203', id)'MCG31''R250''MRG32K3A''MCG59''PHILOX4X32X10''NONDETERM''ARS5'
Package: mkl-service
mkl-service- Python package for run-time control of Intel(R) Math Kernel Library.See theblogannouncing the release.To install the package, useconda install -c intel mkl-service, orconda install -c conda-forge mkl-service.A short example, illustrating it use:importtomopyimportmklmkl.domain_set_num_threads(1,domain='fft')# Intel(R) MKL FFT functions to run sequentially
Package: mkl-static
Intel® oneAPI Math Kernel Library (Intel® oneMKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. This package provides C and Data Parallel C++ (DPC++) programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface. Use it to optimize code for current and future generations of Intel® CPUs and GPUs.
Package: mkl-umath
mkl_umathTo build it:# ensure that MKL is installed, icc is activatedMKLROOT=$CONDA_PREFIXpythonsetup.pyconfig_cc--compiler=intelembuild_ext--inplacemkl_umath._ufuncsexposes MKL-enabled version of loops used in patched NumPy,
that used to be included in Intel(R) Distribution for Python
Package: mkmavrykchain
Install mkmavrykchainGenerate Helm Valuesmkmavrykchain parametersCreate Mavryk ChainmkmavrykchainHelper program to generate values for the Mavryk chain Helm chartPrerequisitesTo runmkmavrykchain, you must either have docker or pymavryk installed.Installing DockerTo be consistent with other documentation in this project, we describe
how to install docker with minikube. We need to install:- [docker](https://docs.docker.com/get-docker/),
- [minikube](https://minikube.sigs.k8s.io/docs/),
- [helm](https://helm.sh/),
- and `python3`.Start minikube and configure your shell environment to use minikube's
Docker daemon:minikubestarteval$(minikubedocker-env)Installing pymavrykThis is documented at:https://pymavryk.mavryk.org/quick_start.html#requirementsInstall mkmavrykchainmkdirmkmavrykchain&&cdmkmavrykchain
python3-mvenv.venv
..venv/bin/activate
pipinstallwheel&&pipinstallmkmavrykchainSetunbuffered IOfor python:exportPYTHONUNBUFFERED=xGenerate Helm ValuesSet as an environment variable the name you would like to give to your chain:exportCHAIN_NAME=my-chainYour chain is uniquely defined by a set of values such as bootstrap account keys, chain id, timestamp...To generate these values run:mkmavrykchain$CHAIN_NAMEThis command will create a yaml file in your current working directory:<$CHAIN_NAME>_values.yamlIf you configure your chain to run over a Zerotier VPN, mkmavrykchain will create an additional file for invitees to join your network:<$CHAIN_NAME>_invite_values.yaml.mkmavrykchain parametersYou can explicitly specify some values by:passing CLI parameters tomkmavrykchaindirectly modifying the values yaml file generated bymkmavrykchainpassing arguments tohelm installorhelm upgradecommands using the--setflag, which will selectively override the current yaml valuesYAML Keymkmavrykchain argumentDescriptionDefault-hmkmavrykchain help message-vmkmavrykchain version--number-of-bakersNumber of baking nodes in the cluster1--number-of-nodesNumber of non-baking nodes in the cluster0bootstrap_peers--bootstrap-peersPeer ips to connect to[]expected_proof_of_work--expected-proof-of-workNode identity generation difficulty0images.octez--octez-docker-imageVersion of the Octez docker image to runmavrykdynamics/mavryk:v17.3--use-docker (--no...)Use (or don't use) docker to generate keys rather than pymavrykautodetectzerotier_config.zerotier_network--zerotier-networkZerotier network id for external chain accesszerotier_config.zerotier_token--zerotier-tokenZerotier token for external chain accessCreate Mavryk ChainMake sure you have the Mavryk Helm chart repo:helmrepoaddmavryk-networkhttps://mavryk-network.github.io/mavryk-helm-chartsThen install the Mavryk Helm chart:helminstall$CHAIN_NAMEmavryk-network/mavryk-chain\--values./${CHAIN_NAME}_values.yaml\--namespacemavryk-network--create-namespace
Package: mkmd
mkmdMakeMarkdown Documents in Python.FeaturesHeadingParagraphCode BlockHorizontal RuleReferenceImageUnordered ListOrdered ListInstallationpipinstallmkmdBasic Usageimportmkmdfrommkmd.utilsimport*md=mkmd.Markdown()(md.add_heading(f"Hello{bold_and_italic('MKMD')}",).add_paragraph(f"""This markdown document was generated by using{refer('mkmd','1')}. This is a quick example showing how touse it:""",wrapped=True).add_codeblock("""import mkmdmd = mkmd.Markdown()md.add_heading("Hello World")md.add_paragraph("Lorem ipsum dolor sit amet.")""",language="python").add_ordered_list(*"So many things are possible!".split()).add_reference("1","http://example.org","Look at this").add_image("Linux Mascot","https://mdg.imgix.net/assets/images/tux.png?auto=format&fit=clip&q=40&w=100"))if__name__=="__main__":md.save("example_result.md")LinksDocumentationPyPIRepository
Package: mkml
mkmlmkml implements a Microkernel Architecture for Machine Learning Librarytry it outInstall from PyPipipinstallmkmlBy default, 3 Plug-in modules have been loadedMKMLMetaclass.register('standardization',StandardizationPlugin)MKMLMetaclass.register('monitoring',MonitoringPlugin)MKMLMetaclass.register('local_datasource',LocalDataSourcePlugin)StandardizationPluginenforces the method signatures of the model class. In our case, the model class must havefit,predictandscoremethodsMonitoringPluginmonitors all the functions of the model class. In our case, it logs the input parameters, exception as well as the duration of each function in the model classLocalDataSourcePluginhelps loading the data locally. It dynamically ingest data-loading functions into the model class to help data scientist retrieve the data without worrying how to retrieve itCreate your own modelfrommkmlimportBaseModelclassUserModel(BaseModel):def__init__(self):self._model=LinearRegression()deffit(self,features,labels):self._model.fit(X_train,y_train)defpredict(self,features):self._model.predict(features)defscore(self,features,labels):returnself._model.score(features,labels)Instantiate the model class instantnce and load features and labels for training and predictionum=UserModel()features=um.get_local_data(feature_mart_location='data',group_id='train_features')labels=um.get_local_data(feature_mart_location='data',group_id='train_labels')um.fit(features,labels)test_features=um.get_local_data(feature_mart_location='data',group_id='test_features')test_labels=um.predict(test_features)Create and register your own Plug-in Module (ie. Remote DataSource)frommkmlimportBasePluginclassRemoteDataSourcePlugin(BasePlugin):def__init__(self,name):self._name=namedefapply(self,attrs,**kwargs):logger.debug('Entering data source plugin')attrs['get_remote_data']=self._get_remote_datadef_get_remote_data(self,feature_mart_location,group_id):# To be implementedpass## You can add additional instantiation parameters to the Plug-in class as wellMKMLMetaclass.register('remote_datasource',RemoteDataSourcePlugin,'remote_ds_plugin')Use the new Remote DataSource Plug-in to retrieve features and labelsum=UserModel()features=um.get_remote_data(feature_mart_location='http://fm',group_id='train_features')labels=um.get_remote_data(feature_mart_location='http://fm',group_id='train_labels')
Package: mk-mpu6050-tools
Mk MPU6050 ToolsMkNxGn MPU - 6050 6-Axis Gyroscope ToolsInstall with pip-pip3 install mk_mpu6050_toolsMake sure to have py smbus installed on your device.Use this Module to use the:- MPU-6050Code Exampleimport mk_mpu6050_tools
import sleep

Chipset = mk_mpu6050_tools.MPU_6050()

while True:
    time.sleep(1)
    print(Chipset.Gyro.x, Chipset.Gyro.y)VirtualizingMk MPU Tools comes with Panda3D code to make visualization easy.Make sure to Install Panda3D toimport mk_mpu6050_tools
import sleep

Chipset = mk_mpu6050_tools.MPU_6050()

from mk_mpu6050_tools import Visulize

Visulize.Run_visiulization_on_Gyro(Chipset.Gyro)More on VirtualizingVisualization comes with multiple models to use for visualization.#    POSSIBLE MODELS
#    Visulize.MODELS.PLANE
#    Visulize.MODELS.TRUCK
#    Visulize.MODELS.DRONE
#    Visulize.MODELS.GRYO_MULTIPLEX - Default

SELECTED_MODEL = Visulize.MODELS.DRONE

Visulize.Run_visiulization_on_Gyro(Chipset.Gyro, model=SELECTED_MODEL)Made By Mark.Endorsed By MkNxGnProgressing Technology.
Package: mkmqr
mkmqr (MaKe Micro QR code)This is a package to make micro QR codes.日本語版のREADMEはこちらFeaturesEfficient encoding of text, use the most of the capacity of the symbolAutomatically selects the best model from the textMaximizes the error correction level with the selected model numberDEMOcommandgenerated imagepython -m mkmqr 12345python -m mkmqr -p kanji.png 漢字python -m mkmqr -p Q.png -e q LevelQpython -m mkmqr -p github.png HTTPS://GITHUB.COMCaution: When reading the micro QR code, please pay attention to security, for example, make sure the URL is correct.USAGECommandCreate a micro QR code from error correction level (optional) and text.description of argumentsargumentchoicedefaultRequired error correction level-ex(error detection only),l(L),m(M),q(Q)xPath to save ths image-p-./(text).pngShow the image-s-snoneSpecify encoding--encoding-shift-jisShow debugging information-d-d,-dd(more information)noneProgramfrommkmqrimport(ErrorCorrectionLevel,create_symbol_image,InvalidCharacterError,InvalidPairError,OverCapacityError)try:image=create_symbol_image('demo',ErrorCorrectionLevel.M)image.show()exceptOverCapacityError:print('Capacity is exceeded.')exceptInvalidCharacterError:# By default, the final encoding is Shift-JIS.print('Unencodable characters are used.')exceptInvalidPairError:# May be thrown if model number or other information is specified.print('The combination of the model or error correction level is incorrect.')Installationfrom GitHub:pip install git+https://github.com/tkamiya22/mkmqr.gitfrom PyPI:pip install mkmqrRequirementnumpy>= 1.20.0Pillow>= 9.1.0NotesAn overview of Micro QR Code can be found atQR code.com.The detailed specifications can be found atJIX X0510.The operation is confirmed byQRQR.QR Code is a registered trademark of DENSO WAVE INCORPORATED.AuthorToshiki KamiyaLicenseMIT license