Saravanan666/pypi-org huggingface dataset:
Package: power-analysis
power-analysis 💪🔍power-analysisis a Python package for performing power analysis and calculating sample sizes for statistical models. The package provides classes for defining statistical models, performing power analysis, calculating sample sizes for two-sample t-tests, and conducting power analysis on observational panel data using a clustered bootstrap method.Installation 📥You can install thepower_analysispackage using pip:pipinstallpower-analysisUsage 🧑‍💻Panel Data Power AnalysisTo use thePanelBootstrapclass for power analysis on observational panel data, you can follow these steps:Import the required packages and classes:importnumpyasnpimportpandasaspdfrompower_analysisimportPanelBootstrapLoad your panel data into a pandas DataFrame:data=pd.read_csv('your_data.csv')Create aPanelBootstrapobject with the required parameters:power_analysis=PanelBootstrap(data,outcome_var='outcome',treatment_var='treatment',individual_var='individual',random_seed=42)Use thefit_modelmethod to fit a linear regression model and obtain the treatment effect and p-value:treatment_effect,p_value=power_analysis.fit_model(data)Perform a clustered bootstrap analysis to obtain the mean effect size, standard deviation of effect sizes, and a list of p-values:mean_effect_size,std_effect_size,p_values=power_analysis.clustered_bootstrap(n_bootstrap=1000)Calculate the statistical power when varying the number of observations (N) or effect sizes:n_values=[50,100,150,200,250,300]alpha=0.05n_bootstrap=1000power_by_n=power_analysis.calculate_power_by_n(n_values,alpha,n_bootstrap)power_by_effect_size=power_analysis.calculate_power_by_effect_size(effect_sizes,alpha,n_bootstrap)Contributing 🤝Contributions topower-analysisare welcome! If you find a bug or would like to suggest a new feature, please open an issue on GitHub.License 📜power-analysisis licensed under the MIT license. SeeLICENSEfor more information.
Package: powerbeatsvr
# PowerBeatsVR_song_writer
Convert a BeatSaber level in to a PowerBeatsVR level.
Walls might be buggy, notes work perfectly.## Usage### Website
There is a website where you can convert downloaded file, its source code is in this repository too so you can request changes:https://powerbeatsvr.gnethomelinux.com/### Commandline` pip3 install powerbeatsvr powerbeatsvr [beat saber zip map] [output folder (created)] `Place the generated json in the PowerBeatsVR layouts folder, and play the ogg file in the game.
Package: powerbi-cli
PowerBI CLI applicationThis applicaton is basically a Click wrapper around thepbipyPython package, following the NodeJS CLI applicationpowerbi-cliconvention and documentation.Its main feature is the Docker image available in parallel which can be used for various task like:Frequently export PowerBI Reportpbixfrom PowerBI Service for historizationFrequently test PowerBI Dataset through a list of DAX query to monitor dataset performance through timeInstallationUsing pipxpipxinstallpowerbi-cliUsing Dockerdockerpullghcr.io/lgrosjean/powerbi-cli:latestUsagePlease refer to thesample documentationfor list of usages.ReferencespbipyPowerBI REST APINodeJSpowerbi-clisource codeNodeJSpowerbi-clidocumentation
Package: powerbiclient
powerbi-jupyterPowerbi-jupyter is a python IPyWidget that enables customers to use Power BI embedding and reporting capabilities in a Jupyter notebook seamlessly.InstallationYou can install usingpip:pipinstallpowerbiclientIf you are using jupyterlab:pipinstallpowerbiclient
jupyterlabextensioninstall@jupyter-widgets/jupyterlab-managerIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]powerbiclientFor the full list of features, refer to theDOCUMENTATION.Demo NotebooksPower BI report demo.ipynb: a Jupyter notebook that embeds a user's report.
It demonstrates the complete flow of embedding and interacting with Power BI report i.e. embedding a Power BI report, setting report event handlers, getting a list of pages and visual, exporting and visualizing visual data and applying filters.Visualize with Power BI demo.ipynb: a Jupyter notebook that creates a quick auto-generated Power BI report over pandas DataFrames. The created reports can be customized and saved to a Power BI workspace.Required python packages:pandasmatplotlib - only forreport_demo.ipynbTo run the demo, first run the following commands:cddemo
jupyternotebookThen, open and runreport_demo.ipynborquick_visualize_demo.ipynb.ContributingThis project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.Please read thecontributing guidebefore starting.We Value and Adhere to the Microsoft Open Source Code of ConductThis project has adopted theMicrosoft Open Source Code of Conduct. For more information see theCode of Conduct FAQor contactopencode@microsoft.comwith any additional questions or comments.
Package: powerBI-EIO-NPP
PowerBI APIs Merge and update | NPPPer executar el programa s'a de tenir instal·lat python v3 o superior.Requeriments arequirements.txt.Executar amb opcio -h per veure mes opcions i funcionalitats.Estructura de la base de dadesMirar a cada subprograma d'apis l'estructura que necessitaInstal·lacióUtilitzant pip:pip install powerBI-EIO-NPPClonar el repositorigh repo clone NilPujolPorta/powerBI-EIO-NPPÚsManeres d'execució del programa (ordenades per recomenades)A la linea de commandesgather [opcions]python -m gather [opcions]./gather-runner [opcions]Executar el fitxergather.pyamb les opcions adients.ÚsManeres d'execució del programa (ordenades per recomenades)A la linea de commandesgather [opcions]python -m gather [opcions]./gather-runner [opcions]Executar el fitxergather.pyamb les opcions adients.Opcionsusage: gather.py [-h] [--portable-chrome-path RUTA] [-q] [-v]



Serveix per actualitzar dashboard de PowerBI desktop localment.



optional arguments:

  -h, --help            show this help message and exit

  --portable-chrome-path RUTA

                        La ruta del executable de chrome

  -q, --quiet           Nomes mostra els errors i el missatge de acabada per pantalla.

  -v, --versio          Mostra la versio
Package: powerbi-parser
Python Power BI ParserParse Power BI files to browse dataset and report data, either from desktop or versionned workspace on GITPurposeParse Power BI files to extract useful contentHow to UseEasy steps:pip install powerbi-parserAdd in the file you wantfrom powerBIParser import PowerBIParserparser = PowerBIParser("filepath of the PBI sources")
    parser.parse()
    for dataset in parser.datasets:
        print (dataset.name)
    for report in parser.reports:
        print (report.name)LicenseThis library is licensed under Apache 2.0. Full license text is available inLICENSE.
Package: powerbi-push-datasets
powerbi_push_datasets - Power BI Push Datasets MgmtThe main classPushDatasetsMgmtencapsulates the Power BI REST operations onPush Datasets(which allows programmatic access for pushing data into PowerBI) into a few simple methods:deploy_dataset: Create a pushable dataset (or update the metadata and schema for existing tables) in Power BI Service by aTabular Model(.bim file);push_tables: Push allResultSets of a Stored Procedure- data for multiple tables into a Power BI Push Dataset;truncate_tables: Removes all rows from specified (or all) tables in a Power BI Push Dataset;This module also provides two conversion tools for development-time:convert_bim_to_push_dataset: Convert aTabular Model(.bim file) into a Push Dataset Model supported by Power BI Service;derive_bim_from_resultsets: Generate aTabular Model(.bim file) based onResultSets of a Stored Procedure (ResultSets)- data for multiple tables;For detailed usage, see the practice sample code:https://github.com/DataBooster/PyWebApi/blob/master/Sample/UserApps/PowerBIPusher/db_to_pbi.pyand its product documentation:https://github.com/DataBooster/PyWebApi#powerbi-data-pusherHomepage and documentation:https://github.com/DataBooster/PyWebApiCopyright (c) 2020 Abel ChengLicense: MIT
Package: powerbox
Make arbitrarily structured, arbitrary-dimension boxes and log-normal mocks.powerboxis a pure-python code for creating density grids (or boxes) that have an
arbitrary two-point distribution (i.e. power spectrum). Primary motivations for creating
the code were the simple creation of log-normal mock galaxy distributions, but the
methodology can be used for other applications.FeaturesWorks in any number of dimensions.Really simple.Arbitrary isotropic power-spectra.Create Gaussian or Log-Normal fieldsCreate discrete samples following the field, assuming it describes an over-density.Measure power spectra of output fields to ensure consistency.Seamlessly uses pyFFTW if available for ~double the speed.InstallationSimplypip install powerbox. If you want ~2x speedup for large boxes, you can also
installpyfftwby doingpip install powerbox[all]. If you are a conda user, you
may want to installnumpywith conda first. If you want to developpowerbox,
clone the repo and install withpython-mpip install-e".[dev]".AcknowledgmentIf you findpowerboxuseful in your research, please cite the Journal of Open Source Software paper athttps://doi.org/10.21105/joss.00850.QuickLinksDocs:https://powerbox.readthedocs.ioQuickstart:http://powerbox.readthedocs.io/en/latest/demos/getting_started.html
Package: powerboxes
PowerBoxesPowerboxes is a package containing utility functions for transforming bounding boxes and computing metrics. It is implemented in both Python and Rust.
It shows a significant speedup over the equivalent numpy implementations in Python, or other libraries such asshapelyortorchvision.Checkout out the documentation !🐍 PythondocumentationInstallationpipinstallpowerboxesPython Usageimportpowerboxesaspbimportnumpyasnp# Create a bounding boxbox=np.array([[0,0,1,1]])# Compute the area of the boxarea=pb.box_areas(box)# Compute the intersection of the box with itselfintersection=pb.iou_distance(box,box)
Package: powerchat
PowerchatPowerchat is a Python command-line tool that allows you to list and view the contents of all.pyand.mdfiles in a directory and its subdirectories. It also provides other useful features, such as generating pull request descriptions and suggesting updates to a README.md file based on Git diff or full file contents.InstallationTo install Powerchat, clone the repository and install the dependencies using Poetry:gitclonehttps://github.com/yourusername/powerchat.gitcdpowerchat
poetryinstallThis will install all the required dependencies and create a virtual environment for Powerchat.To activate the virtual environment, run:poetry shellNow you can run Powerchat from the command line with thepython src/main.pycommand.UsagePowerchat can be run from the command line with thepython src/main.pycommand. Powerchat accepts several command-line arguments:directory: The directory to search for .py and .md files. Required.
--refactor: Prompt for refactoring suggestions.
--documentation: Suggest README.md updates based on Git diff or full contents.
--features: Prompt for new feature suggestions.
--pull_request: Prompt for a pull request description based on the changed code.
--copy_code: Copy the code of the directory and its subdirectories to the clipboard.
--include: Pattern to filter included files (e.g. *.py or *_test.py).
--exclude: Pattern to filter excluded files (e.g. *_test.py).
--recursive: Search for files recursively in subdirectories.For more information on how to use Powerchat, runpython main.py --help.LicensePowerchat is released under the MIT License. See LICENSE for more information.
Package: powerchord
Powerchord: Concurrent CLI task runnerInstallationpip install powerchordUsageRun a number of tasks:$ powerchord -t "ruff check ." pytest mypy
✔ ruff check .  21.075 ms
✔ mypy          166.433 ms
✔ pytest        187.096 msTasks can be labeled by passing them as NAME=COMMAND values:$ powerchord -t lint="ruff check ." test=pytest typing=mypy
To do:
• lint    ruff check .
• test    pytest
• typing  mypy

Results:
✔ lint    48.452 ms
✔ typing  202.403 ms
✔ test    286.231 msVerbosity can be specified for all output, for successful tasks and for failed tasks by setting log levels:$ powerchord -t "ruff chekc ." pytest mypy -l all=info success=info fail=error
✘ ruff chekc .  126.852 ms
chekc:1:1: E902 No such file or directory (os error 2)
Found 1 error.

✔ pytest        255.197 ms
..                                                                       [100%]
2 passed in 0.03s

✔ mypy          542.490 ms
Success: no issues found in 11 source files


✘ Failed tasks: ['ruff chekc .']For all options see the help:powerchord -hConfig can also be specified inpyproject.toml:Tasks:[tool.powerchord]tasks=["command --arg","...","..."]Labeled tasks:[tool.powerchord.tasks]task="command --foo bar /path/to/happiness"other-task="..."you-get-the-idea="..."Log levels:[tool.powerchord.log_levels]all="info"# "debug" | "info" | "warning" | "error" | "critical" | ""success=""# log level of successful task outputfail="error"# log level of failed task output
Package: powercmd
powercmdAn extension of the standard cmd module
(https://docs.python.org/3.5/library/cmd.html) that uses type annotations
to ensure type-safety of defined commands.Requires Python 3.5+.Featuresautomatic conversion of user input into types specified in annotationsintelligent (and customizable!) argument tab-completionprefix, snake-case-prefix and fuzzy command matching
Package: power-cogs
power_cogs.. image::https://img.shields.io/pypi/v/power_cogs.svg:target:https://pypi.python.org/pypi/power_cogs:alt: Latest PyPI version.. image::https://travis-ci.org/kragniz/cookiecutter-pypackage-minimal.png:target:https://travis-ci.org/kragniz/cookiecutter-pypackage-minimal:alt: Latest Travis CI build statusA template for deep learning projects utilizing hydra for configuration, ray for execution, mlflow, tensorboard, and w&b for metrics + artifact collectionUsagerunpython setup_project.pyand enter new project name to change pathsInstallationRequirements
^^^^^^^^^^^^CompatibilityLicenceAuthorspower_cogswas written byShyam Sudhakaran <shyamsnair97@gmail.com>_.
Package: powerdict
Power Station DictionaryA power station dictionary that enables mapping between various naming conventions and associated plant metadataThe core output of this project is a clean power plant dataset that includes relevant attributes such as location and capacity, as well as id mappings that can be used to connect them to other datasets. Currently the focus of this work are plants within the GB power system. Thepowerdictmodule provides a programmatic way in which to combine the raw datasets and apply any additional updates specified in the files contained within the three data directories:raw- source datasets as csvsupdates- JSON mappings from an osuked_id to a new attribute valuedefinitions- JSON specification of how to process the raw sources and what the output should look likeCI/CD is set-up so that any changes in this repository will trigger a reconstruction of the output dataset, additionally a new GitHub release will automatically trigger a new release on Zenodo and produce an updated DOI. The output dataset can be foundhere. As with projects such as theGlobal Power Plant Database,powerdictprovides a clear data lineage from the raw sources/updates to the output. Furthermore, integrating this with GitHub enables version control of the underlying datasets and updates/corrections.InstallationTo install thepowerdictlibrary please run:pipinstallpowerdictDevelopment Set-UpTo set-up a new environment you can run the following from the batch_scripts directorypypi_publishAlternatively you can run these commandscallcondaenvcreate-fenvironment.yml
callcondaactivatePowerDict
callipythonkernelinstall--user--name=PowerDictPublishing to PyPiTo publish thepowerdictmodule to PyPi simply run the following from the batch_scripts directorypypi_publishor follow these commandscallcondaactivatePowerDict
callpythonsetup.pysdistbdist_wheel
calltwineupload--skip-existingdist/*When prompted you should enter your PyPi username and passwordAfter this you will be able to install the latest version of powerdict usingpip install powerdict
Package: powerdnsapi
PowerDNSAPIPython Package to make API calls to PowerDNS Authoritative Server.This does not implement functions for all PowerDNS Authoritative API calls, rather common ones that I want automated.InstallingTBDUsagefrompowerdnsapiimportPowerDNSAPIpdns=PowerDNSAPI("http://my_server:8081","123ABC")pdns.get_servers()Q&AWhy another PowerDNS API python library?Looking at the existing projects, none suited what I wanted. I wanted to have some helpers to simplify common tasks and also get the data back as a dict/list instead of nested objects.Why no tests?This is a side/after hours project and I haven't set aside time for that yet.RoadmapAdd documentationAdd PowerDNS Container for local developmentAdd unit testsAdd integration tests against different versions of PowerDNS
Package: powerdns-secondary-cleaner
powerdns-secondary-cleanerUse powerdns-secondary-cleaner to delete zones on secondary PowerDNS server that were deleted on primary PowerDNS server.The PowerDNS API is used for both the primary and secondary, so the program can run anywhere. Be it on the primary, secondary or elsewhere.InstallGenericRun the following command to create a source distribution:python3 setup.py sdistPyPIRun the following command to install the package from PyPI:pip3 install powerdns-secondary-cleanerConfigureNo configuration is supported.UsageSyntax:powerdns-secondary-cleaner --primary-api-url=<primary-api-url> --primary-api-key=<primary-api-key> --secondary-api-url=<secondary-api-url> --secondary-api-key=<secondary-api-key> [--dry-run]Example when running on primary:powerdns-secondary-cleaner --primary-api-url=http://localhost:8081 --primary-api-key=example --secondary-api-url=http://secondary.test:8081 --secondary-api-key=exampleExample when running on secondary:powerdns-secondary-cleaner --secondary-api-url=http://localhost:8081 --secondary-api-key=example --primary-api-url=http://primary.test:8081 --primary-api-key=exampleOnly show which zones would be deleted on secondary:powerdns-secondary-cleaner ... --dry-runTestsRun tests with pytest:pytest tests/
Package: powerengine
PowerEngine: A library which calculates signals and electricity.Installationpython-mpipinstallpowerengine# ORpython-mpipinstallgit+https://github.com/AaravMalani/powerengineUsagefrompowerengineimportEngineengine=Engine()engine.add_blocks([Power(engine,{},(0,0,0)),Wire(engine,{'facing':None},(0,0,1)),Wire(engine,{'facing':None},(0,0,2)),Delayer(engine,{'facing':'east','delay':5},(0,0,3)),Wire(engine,{'facing':None},(0,0,4)),])engine.run(3,ignore_speed=True,output=False)Plugin StructureEach plugin is a directory in theinstallation/path/pluginsdirectory.It contains amanifest.jsonJSON file with the following fields.FieldTypeDescriptionnamestringPlugin nameversionstringPlugin versionauthor (optional)stringorarray[string]The author of the pluginblockTypesobject[string, array[string]]An object of the screaming snake case version of the name to the block types defined (each block type being an array of[namespace, id])A plugin is a Python module (it has an__init__.py)An example plugin would bemain.pyimportpowerengineclassPizzaBlock(powerengine.Block):id=('pizzamod','pizzablock')defupdate(self,engine:powerengine.Engine):self.state['facing']={'north':'south','east':'west','south':'north','west':'east'}[self.state['facing']]__init__.pyfrom.mainimportPizzaBlockmanifest.json{"name":"Pizza Mod","version":"1.0.0","blockTypes":{"PIZZA_BLOCK":["pizzamod","pizzablock"]}}
Package: powerfactorypy
powerfactorypyThis package is a wrapper around the Python API of PowerFactory© (power system simulation software by DIgSILENT) with improved syntax compared to the native API. Please have a look at thedocsfor further information.This module is under active development and mainly maintained by Fraunhofer IEE. You are welcome to contribute or feel free to get in touch (contact:simon.eberlein@iee.fraunofer.de).
Package: powerful-benchmarker
Powerful BenchmarkerDocumentationView the documentation hereGoogle Colab ExamplesSee theexamples folderfor notebooks that show a bit of this library's functionality.A Metric Learning Reality CheckSeesupplementary materialfor theECCV 2020 paper.Benchmark results:4-fold cross validation, test on 2nd-half of classesBenefits of this libraryHighly configurable:Yaml filesfor organized configurationApowerful command line syntaxthat allows you to merge, override, swap, apply, and delete config options.Customizable:Benchmark your own losses, miners, datasets etc.with a simple function call.Easy hyperparameter optimization:Append the ~BAYESIAN~ flagto the names of hyperparameters you want to optimize.Extensive logging:View experiment data intensorboard, CSV and SQLite format.Reproducible:Config files are saved with each experiment and areeasily reproduced.Trackable changes:Keep track of changesto an experiment's configuration.Installationpip install powerful-benchmarkerCiting the benchmark resultsIf you'd like to cite the benchmark results, please cite this paper:@misc{musgrave2020metric,
    title={A Metric Learning Reality Check},
    author={Kevin Musgrave and Serge Belongie and Ser-Nam Lim},
    year={2020},
    eprint={2003.08505},
    archivePrefix={arXiv},
    primaryClass={cs.CV}}Citing the codeIf you'd like to cite the powerful-benchmarker code, you can use this bibtex:@misc{Musgrave2019,
  author ={Musgrave, Kevin and Lim, Ser-Nam and Belongie, Serge},
  title ={Powerful Benchmarker},
  year ={2019},
  publisher ={GitHub},
  journal ={GitHub repository},
  howpublished ={\url{https://github.com/KevinMusgrave/powerful-benchmarker}},}AcknowledgementsThank you to Ser-Nam Lim at Facebook AI, and my research advisor, Professor Serge Belongie. This project began during my internship at Facebook AI where I received valuable feedback from Ser-Nam, and his team of computer vision and machine learning engineers and research scientists.
Package: powerful-calculator
Installationpipinstallnumpy
pipinstallpowerfull-calculatorUsageWith use of this tool you can:add numbers - via add() metodsubtract numbers - via subtract() metodmultiply numbers - via multiply() metoddivide numbers - via divide() metodroot numbers - root add() metodBefore start usingThere is always number in memory of calculator.
Operations are perfomed on this number.
You can get it via get_state() method.>>>frompowerful_calculator.powerful_calculatorimportCalculator>>>calc=Calculator()>>>calc.add(0.1)0.1>>>calc.add(0.2)0.3>>>calc.subtract(10.3)-10.0>>>calc.divide(3)-3.333333333333>>>calc.multiply(3)-10.0>>>calc.root(1/3)-1000.0>>>calc.reset()0.0>>>calc.get_state()0.0ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Make sure to add or update tests as appropriate.LicenseMIT
Package: powerfuldeveloper-json-walker
Powerfuldeveloper Json WalkerWalk on jsons like it's nothing ðŸ˜ŽUsageJust giveJsonWalkerclass the json which you want
to pars and this class is able to give you feet which
need to walk on json !frompowerfuldeveloper.json_walkerimportJsonWalkersome_json={"key_1":'test',"key_2":1,}json_walker=JsonWalker(some_json)print(json_walker.key_1)# testprint(type(json_walker.key_1))# JsonWalkerprint(json_walker.key_1.inner_key_1)# Noneprint(type(json_walker.key_1.inner_key_1))# JsonWalkerifjson_walker.key_1.inner_key_1:print('inner key 1 exists')