Saravanan666/pypi-org huggingface dataset:
Package: skrewmpcpy
skrewmpcpyGenerate smooth motions for a n-DOF robot arm end effector using Dual Quaternions while enforcing velocity-, acceleration- and jerk constraints.
The package includes a base optimization scheme and a base motion generator, which can be easily extended for custom robot arms.
A motion generator for the Franka Emika Panda robot is included.InstallGetting startedExtending the available robotsFor adding your custom robot arm, you easily can extend the base class. Make sure the forward kinematics of your robot are available.Create a new fileyour_robot_mg.pyin foldersrc/skrewmpc.py.Costum robot examplefrom dqrobotics import DQ
from .basemg import BaseMotionGenerator
from .skrewmpc import BOUND


class YourRobotMotionGenerator(BaseMotionGenerator):

    def __init__(
                self,
                n_p: int,
                n_c: int,
                q_mpc: float,
                r_mpc: float,
                lu_bound_vel: BOUND,
                lu_bound_acc: BOUND,
                lu_bound_jerk: BOUND,
                ) -> None:
        super().__init__(
            n_p, n_c, q_mpc, r_mpc, lu_bound_vel, lu_bound_acc, lu_bound_jerk
        )
        # custom attributes here
        self._kin = YourRobotKinematics()

    def step(self, q_robot: np.ndarray, goal: DQ) -> np.ndarray:

        # Initialize Dual Quaternion with 6D pose
        x_current = DQ(self._kin.your_forward_kinematics(q_robot))
        error, smooth_traj = super()._step(x_current, goal)
        # calculate commanded dq here
        ...
        return dq
Package: skriba
main/skribaGetting StartedDownload links:SSH clone URL: ssh://git@git.jetbrains.space/odysseus/main/skriba.gitHTTPS clone URL:https://git.jetbrains.space/odysseus/main/skriba.gitThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.PrerequisitesWhat things you need to install the software and how to install them.ExamplesDeploymentAdd additional notes about how to deploy this on a production system.ResourcesAdd links to external resources for this project, such as CI server, bug tracker, etc.
Package: skrit-r0073rr0r
PSkritInstallpip install skrit-r0073rr0rorpip install -i https://test.pypi.org/simple/ skrit-r0073rr0rFeaturesLitrovackiUtrovachkiSatrovackiExample:from skrit_r0073rr0r import skrit
print(skrit.Skrit().satrovacki('rucka'))Output:ckaru
Package: skrl
SKRL - Reinforcement Learning libraryskrlis an open-source modular library for Reinforcement Learning written in Python (on top ofPyTorchandJAX) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAIGym/ FaramaGymnasiumandDeepMindand other environment interfaces, it allows loading and configuringNVIDIA Isaac Gym,NVIDIA Isaac OrbitandNVIDIA Omniverse Isaac Gymenvironments, enabling agents' simultaneous training by scopes (subsets of environments among all available environments), which may or may not share resources, in the same run.Please, visit the documentation for usage details and exampleshttps://skrl.readthedocs.ioNote:This project is underactive continuous development. Please make sure you always have the latest version. Visit thedevelopbranch or itsdocumentationto access the latest updates to be released.Citing this libraryTo cite this library in publications, please use the following reference:@article{serrano2023skrl,author={Antonio Serrano-Muñoz and Dimitrios Chrysostomou and Simon Bøgh and Nestor Arana-Arexolaleiba},title={skrl: Modular and Flexible Library for Reinforcement Learning},journal={Journal of Machine Learning Research},year={2023},volume={24},number={254},pages={1--9},url={http://jmlr.org/papers/v24/23-0112.html}}
Package: skrobot
skrobotWhat is it about?skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top ofscikit-learnframework.Documentation?The documentation is hosted online toRead the Docs.How do I install it?pipinstallskrobotNext Priorities?Add support in the module for multiclass problemsCan I contribute?Of course, the project isFree Softwareand you can contribute to it!What license do you use?See our license (LICENSE.txt) for more details.Thank you!
Package: skrooge
skroogeA quick and dirty kubernetes cost estimatorEbenezer scrooge standing at the helm of a ship, pencil sketchIdeaI've been manually calculating how much scaling kubernetes deployments up or down will cost (or save!).
It's a bit of grunt work every time pulling together our instance types, figuring out if the deployment is cpu-bound or memory-bound, and working out the cost.
Instead we could have a CLI tool (or potentially automated during CI down the road) that could calculate these numbers for us.Previous example (in english):It would be nice to have a CLI tool which could do these calculations for us (and integrate with GCP pricing / instance APIs to get instance shapes and costs automatically)Pod size: 2 cpu, 3GB RAM
Previous deployment: 32 cpu, 48GB RAM
New deployment: 64 cpu, 96GB RAM
Running on c2-standard-30 which have 30 cpu, 120GiB RAM, and cost $914/month
Up to 2 new instances to support +32 CPU, cost $1828 / month ($21,936 / year)How a CLI tool could work:$kubecost--cpu32--mem48--instancec2-standard-30
c2-standard-30:30cpu,120GiBRAM,$914/month
Limitingfactor:CPU(ceil(32/30)=2)Cost:$1828/month($21,936/year)InstallationInstall this tool usingpip:pip install skroogeUsageFor help, run:skrooge --helpYou can also use:python -m skrooge --helpDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd skrooge
python -m venv venv
source venv/bin/activateNow install the dependencies, test dependencies, and GCP cost scraping dependencies:pip install -e '.[lint,scrape,test]'To run the tests:pytestTo update the instances.json file:python3 skrooge/scraper.py > skrooge/instances.json
Package: skrt
containersSpecialized containers as alternatives to Python’s built-in types and to those
defined in the collections standard module.defaultnamedtuplefactory function for namedtuples with default argumentsforwardingdictdefaultdict subclass that passes missing key to factoryfunctionalHigher order functions like those found in languages like Haskell.composecompose a list of functionsflipcreate a function with reversed arguments of another functionfoldlleft-associative reducefoldrright-associatve reducetextUtilities for manipulating text.coloradd ansi colors and styles to stringsutilsUtility functions for manipulating containers.
ThanksJack Fischer, for the idea forrmap.subdictextract a subset of a dictionarymatchcompare multiple objects based on a list of shared attributesrmaprecursively map a function onto items of nested containersheadget the first element of a sequencetailget all but the first element of a sequencelastget the last element of a sequenceinitget all but the last element of a sequence
Package: skrub
skrub(formerlydirty_cat) is a Python
library that facilitates prepping your tables for machine learning.If you like the package, spread the word and ⭐ this repository!What canskrubdo?skrubprovides data assembling tools (TableVectorizer,fuzzy_join…) and
encoders (GapEncoder,MinHashEncoder…) formorphological similarities,
for which we usually identify three common cases:similarities, typos and variationsSee ourexamples.Whatskrubcannot doSemantic similaritiesare currently not supported.
For example, the similarity betweencarandautomobileis outside the reach
of the methods implemented here.This kind of problem is tackled byNatural Language Processingmethods.skrubcan still help with handling typos and variations in this kind of setting.For a detailed description of the problem of encoding dirty categorical data, seeSimilarity encoding for learning with dirty categorical variables[1]andEncoding high-cardinality string categorical variables[2].Installation (WIP)There are currently no PiPy releases.
You can still install the package from the GitHub repository with:pipinstallgit+https://github.com/skrub-data/skrub.gitDependenciesDependencies and minimal versions are listed in thesetupfile.Related projectsAre listed on theskrub’s websiteContributingThe best way to support the development of skrub is to spread the word!Also, if you already are a skrub user, we would love to hear about your use cases and challenges in theDiscussionssection.To report a bug or suggest enhancements, pleaseopen an issueand/orsubmit a pull request.Additional resourcesIntroductory video (YouTube)JupyterCon 2023 talk (YouTube)EuroSciPy 2023 poster (Dropbox)References[1]Patricio Cerda, Gaël Varoquaux, Balázs Kégl. Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer.[2]Patricio Cerda, Gaël Varoquaux. Encoding high-cardinality string categorical variables. 2020. IEEE Transactions on Knowledge & Data Engineering.
Package: skrutable
skrutableA toolkit and online workbench providing
transliteration, scansion, and meter identification for Sanskrit text,
as well access to powerful sandhi and compound splitting.Web-app interface live online atskrutable.info.Seemanual.mdfor instructions.Feedback welcome!
My name is Tyler
(Academia,LinkedIn)
and my Gmail is tyler.g.neill.And please share and share-alike! Licensed under aCreative Commons Attribution-ShareAlike 4.0 International License.
Package: skryptorium
skryptoriumTools for working with single-cell DNA sequencing dataUsage (CLI)skryptoriumUsage (API)from skryptorium import hello

print(hello.hello())DevelopmentSet up and activate virtual environment# Create `venv` and install IPython and twine
0/make_venv.sh
venv/bin/activate

# Install skryptorium package in editable mode
0/run_pip_installe.shRun IPython for testing withautoreloadipython
%load_ext autoreload
%autoreload 2
Package: sksearchspace
sksearchspaceScikit-learn Search Space Configurations with curated search spaces forscikit-learn estimators.UsagefromsksearchspaceimportSearchSpacefromsklearn.treeimportDecisionTreeClassifierestimator_space=SearchSpace.for_sklearn_estimator(DecisionTreeClassifier,seed=42)estimator_space.sample()# {'criterion': 'entropy','min_samples_leaf': 15, 'min_samples_split': 11}estimator_space.sample()# {'criterion': 'entropy', 'min_samples_leaf': 12, 'min_samples_split': 4}sksearchspaceusesConfigSpacefor sampling. TheConfigSpaceconfiguration can be accessed through an attribute:estimator_space.configuration# Configuration space object:# Hyperparameters:#   criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini#   min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1#   min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2A json file can be loaded as follows:withopen("search_space.json","r")asf:estimator_space=SearchSpace(f.read())LicenseCopyright (c) 2020 Thomas J. FanDistributed under the terms of the MIT license, pytest is free and open source software.
Package: skself
UNDER CONSTRUCTIONSelf-supervised learning sklearn-styleTo directly jump into the code look at the sample notebookFeaturesTBDProvides a Model inheriting sklearn.base.BaseEstimator  forMethodPaperAPI………InstallCreate a new python=3.9 env and installskselffrom pippipinstallskselfExamplesimportskself...TBDUsageAll parmetersimportskself...TBDDocsFOR API Reference seehttps://sklef.readthedocs.io/en/latest/autoapi/skself/index.htmlCiteIf this project helped you during your work:
Until a publication is available, please cite asTobias Schiele et al. (2023). skself - Self-supervised learning sklearn-style.https://github.com/thetoby9944/skself.@misc{Schiele2019,
    author ={Tobias Schiele, Daria Kern, Prof. Dr. Ulrich Klauck},
    title ={Skself - Self-supervised learning sklearn-style},
    year ={2022},
    publisher ={GitHub},
    journal ={GitHub repository},
    howpublished ={\url{https://github.com/thetoby9944/skself}},}
Package: sksparse-minimal
sksparse_minimalThis project is forked fromhttps://github.com/rgl-epfl/cholespyChanges made:Add support for all solving modes (CHOLMOD_A, CHOLMOD_L, CHOLMOD_Lt, CHOLMOD_P, etc)Remove support for GPU solving (because I didn't want to bother implementing it for all modes and I didn't need it)Emulate sksparse API for ease of useChange CHOLMOD configuration to match sksparseUpdate build to match recommendations fromhttps://nanobind.readthedocs.io/en/latest/building.htmlInstallingWith PyPI (recommended)pipinstallsksparse_minimalFrom sourcegitclone--recursivehttps://github.com/tansey-lab/sksparse_minimal.git
pipinstall.Example usageimportnumpyasnpfromsksparse_minimalimportSparseCholeskyfromscipy.sparseimportcsc_matrixM=np.array([[4,12,-16],[12,37,-43],[-16,-43,98]],dtype=np.float64)M=csc_matrix(M)sparse_cholesky=SparseCholesky(M)b=np.array([1,2,3],dtype=np.float64)sparse_cholesky.solve_A(b)
Package: sk-sugar
SUGARPython version of [sugar][sugar]Installpip install sk-sugarSetupTo get all the pagackes required, run the following command:$condaenvcreate-fenvironment.ymlThis will create a new conda environment sklab-sugar, which can be activated via:$condaactivatesklab-sugarAdd kernel to Jupyter Notebookautomatic conda kernelsFor greater detail see the official docs for nb_conda_kernels. In short, install nb_conda_kernels in the environment from which you launch JupyterLab / Jupyter Notebooks from (e.g. base) via:$condainstall-n<notebook_env>nb_conda_kernelsto add a new or exist conda environment to Jupyter simply install ipykernel into that conda environment e.g.$condainstall-n<python_env>ipykernelmanual ipykerneladd to your Jupyter Notebook kernels via$python-mipykernelinstall--user--namesklab-sugarIt can be removed via:$jupyterkernelspecuninstallsklab-sugarlist kernels found by Jupyterkernels recognized by conda$ python -m nb_conda_kernels listcheck which kernels are discovered by Jupyter:$jupyterkernelspeclist
Package: sktan-cdk.single-page-app
Sktan AWS CDK Base Stack - Single Page Application BaseThis base stack is designed to provide the bare minimum requirements required to support a serverless single page application styled website on AWS.Resources CreatedCloudfront DistributionWebsite Assets S3 BucketStaging Deployment S3 BucketLambda Function (to copy assets from the staging s3 bucket to the website assets bucket)PrerequisitesPython 3.6+ and pipsktan_cdk.single_page_apppip install sktan-cdk.single-page-appDeploying as isIf you want to use the stack as is, you will be able to use this like any other CDK stack and then runcdk synthto view your results:Folder Structureexample-spa-website
|-- app.py
|-- cdk.json
|-- website_html
|   |-- index.html
|   |-- css
|   |   |-- example.css
|   |-- images
|   |   |-- logo.jpgapp.pyfrom aws_cdk import core
import sktan_cdk.single_page_app

app = core.App()

spa_app = single_page_app(app, "example-spa-website", website_identifier='www-example-com')
spa_app.create_website_bucket('website_html')
spa_app.create_cloudfront_distribution(cloudfront_alias={
  'acm_cert_ref': 'arn:aws:acm:us-east-1:123456789012:certificate/example-certificate-arn-format',
  'names': [
    'www.example.com'
  ]
})
app.synth()Extending the stackIf you'd like to use this stack as a basis for your application, then you are free to extend it to implement additional functionality.import sktan_cdk.single_page_app
from aws_cdk import (
  aws_s3 as s3
)

class existing_s3_bucket_website(single_page_app):
  def create_website_bucket(self, deployment_path: typing.Optional[str]):
    s3.fromBucketArn(self, self.__website_identifier, 'arn:aws:s3:::www-example-com')
Package: sktclcli-minbin
Socket cluster client withCodecMinBinA client in Python to connect tosocket cluster server.Forked fromsacOO7/socketcluster-client-python.ChangesApply codecMinBin.Add requirements.txtInstall the packagepip install sktclcli-minbinUsageExample for publishing and subscribing data inmain_test.pyCommands for devsUbuntu common python packages:sudo apt install -qy python3-dev python3-distutils python3-pip python3-setuptools python3-venvCreate and active virtual environment:python3 -m venv venvsource venv/bin/activateGet latest pip:pip install --index-url https://pypi.python.org/simple/ --upgrade pipInstall dependencies:pip install -r requirements.txtSave dependencies list:python -m pip freeze --local > requirements.txtPackaging tools (should be installed on packaging machine, not venv):sudo python3 -m pip install --upgrade setuptools wheel twine keyrings.altCreate built distribution:python3 setup.py sdist bdist_wheelUpload the distribution:python3 -m twine upload --repository pypi ./dist/*
Package: sktda-docs-config
sktda-docs-configSettings common across all scikit-tda doc sites(soon) Install withpip install sktda_docs_configand then add the following line to your docs'conf.pyfile:from sktda_docs_config import *You might also want to add the parameter to thesetupfunction in yoursetup.py:extras_require={
    'docs': [ # `pip install -e ".[docs]"`
        'sktda_docs_config'
    ]
},this will make it so all required docs are installable withpip install -e ".[docs]"
Package: skt-hello-world
Print SKT Hello World!Python script that prints skt hello world on terminalInstall using source codeTo download the app generator, you can clone this repository to your local machine.$ git clone https://github.com/yumenoshima/skt-hello-world
$ cd skt-hello-worldInstall using pip3$ pip3 install skt-hello-worldPackage @ PyPI repository:https://pypi.org/project/skt-hello-world/Usage$ skt-hello-worldTutorial for packagehere!
Package: sktime-dl
sktime-dlAn extension package for deep learning with Keras forsktime, ascikit-learncompatible Python toolbox for learning with time series and panel data.The package is under active development. Currently, classification models based off the the networks indl-4-tschave been implemented, as well as an example of a tuned network for future development.InstallationThis package uses the base sktime as a dependency. Follow theoriginal instructionsto install this.For the deep-learning part of sktime-dl, you need:Keraskeras-contriband a compatible backend for Keras, one oftensorflow(confirmed working, v1.8.0)theano(untested)CNTK(untested)If you want to run the networks on a GPU,CUDNNis also required to be able to utilise your GPU.For windows users, we recommend followingthis(unaffiliated) guide.For linux users, all of these points should hopefully be relatively straight forward via simple pip-commands and conversions from the previous link.For mac users, I am unfortunately unsure of the best processes for installing these. If you have links to a tested and up-to-date guide, let us know (@James-Large).OverviewA repository for off-the-shelf networksThe aim is to define Keras networks able to be directly used within sktime and its pipelining and strategy tools, and by extension scikit-learn, for use in applications and research. Overtime, we wish to interface or reimplement networks from the literature in the context of time series analysis.Currently, we interface with a number of networks for time series classification in particular.dl-4-tsc interfacingThis toolset currently serves as an interface todl-4-tsc, and implements the following network archtiectures:Time convolutional neural network (CNN)Encoder (Encoder)Fully convolutional neural network (FCNN)Multi channel deep convolutional neural network (MCDCNN)Multi-scale convolutional neural network (MCNN)Multi layer perceptron (MLP)Residual network (resnet)Time Le-Net (tlenet)Time warping invariant echo state network (twiesn)DocumentationThe full API documentation to the base sktime and an introduction can be foundhere.
Tutorial notebooks for currently stable functionality are in theexamplesfolder.Documentation for sktime-dl shall be produced in due course.ContributorsFormer and current active contributors are as follows.sktime-dlJames Large (@James-Large), Aaron Bostrom (@ABostrom), Hassan Ismail Fawaz (@hfawaz), Markus Löning (@mloning)sktimeProject management: Jason Lines (@jasonlines), Franz Király (@fkiraly)Design: Anthony Bagnall(@TonyBagnall), Sajaysurya Ganesh (@sajaysurya), Jason Lines (@jasonlines), Viktor Kazakov (@viktorkaz), Franz Király (@fkiraly), Markus Löning (@mloning)Coding: Sajaysurya Ganesh (@sajaysurya), Bagnall(@TonyBagnall), Jason Lines (@jasonlines), George Oastler (@goastler), Viktor Kazakov (@viktorkaz), Markus Löning (@mloning)We are actively looking for contributors. Please contact @fkiraly or @jasonlines for volunteering or information on paid opportunities, or simply raise an issue in the tracker.
Package: sktmls
mls-model-registry (sktmls)ContentsDescriptionHow to useDevelopmentRequirements for developmentLocal model registryPython environmentDocuments generationVersionDescriptionA Python package for MLS model registry.This python package includesCustomized prediction pipelines inheriting MLSModelModel uploader to AWS S3 for meta management and online predictionInstallationInstallation is automatically done by training containers in YE. If you want to install manually for local machines,# developpipinstall--index-urlhttps://test.pypi.org/simple/--no-depssktmls# productionpipinstallsktmlsHow to useMLS Docs:https://ab.sktmls.com/docs/model-registrysktmls Docs:https://sktaiflow.github.io/mls-sdk/sktmlsDevelopmentRequirements for developmentPython 3.6requirements.txtrequirements-dev.txtLocal model registryTo enable all model related features in local environment, you need to create a directorymodelsin your home directory.$cd~/
$mkdirmodelsPython environmentFirst you need to do the followings$python-V# Check if the version is 3.6.$python-mvenvenv# Create a virtualenv.$.env/bin/activate# Activate the env.$pipinstall--upgradepip
$pipinstall--upgradesetuptools
$pipinstall--upgradewheel
$pipinstall-rrequirements.txt# Install required packages.$pipinstall-rrequirements-dev.txt# Install required dev packages.Documents generationBefore a commit, generate documents if any docstring has been changedrm-rfdocs
pdoc--html--configshow_source_code=False-f-o./docssktmlsVersionsktmlspackage version is automatically genereated followd by a production release on formatYY.MM.DDWe useCalendar Versioning. For version available, see thetags on this repository.