Saravanan666/pypi-org huggingface dataset:
Package: parchords-jupyter
parchords-jupyterjupyter widget with parallel coords that enables interactive sonificationInstallationYou can install usingpip:pipinstallparchords_jupyterIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]parchords_jupyterDevelopment InstallationCreate a dev environment:condacreate-nparchords_jupyter-dev-cconda-forgenodejspythonjupyterlab
condaactivateparchords_jupyter-devorpython-mvenvvenvsourcevenv/Scripts/activate
python-mpipinstall-Upipsetuptools
pipinstall'jupyterlab>=3.6.5,<4'jupyter-packaging# nodejsFor a development installation the version of the python packagejupyterlaband the npm package@jupyterlab/builderneed to match.Install the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
npm run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py parchords_jupyter
jupyter nbextension enable --sys-prefix --py parchords_jupyterNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when needednpmrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.Updating the versionTo update the version, install tbump and use it to bump the version.
By default it will also create a tag.pipinstalltbump
tbump<new-version>
Package: parcon
Parconis a parser library. It can be used for parsing both normal
text and binary data. It’s designed to be easy to use and to provide informative
error messages.Pargen, which is provided as a submodule of Parcon, is a formatter
library. It’s much the opposite of Parcon: while Parcon is used to parse text
into various objects, Pargen is used to format objects into text. As an
example, if you wanted to reimplement Python’s json module, you would use
Parcon to implement json.loads and Pargen to implement json.dumps.Static, which is also provided as a submodule of Parcon, is a static typing
library and Python object pattern library. It lets you build patterns that
match Python objects based on their type, their attributes, certain properties
such as whether or not the object is a sequence, the types that make up the
object’s values if the object is a sequence, and so on.All of the classes, and most of the functions, in Parcon are comprehensively
documented. The best place to look for help is in Parcon’s module documentation.
Pargen’s classes and functions and Static’s classes and functions are also
documented, though not quite as well as Parcon’s are at present.Parcon grammars are written as Python expressions, so there’s no need to learn
a separate language for writing grammars or anything like that.Here’s an example of a simple expression evaluator written using Parcon:from parcon import number, Forward, InfixExpr
import operator
expr = Forward()
term = number[float] | "(" + expr + ")"
term = InfixExpr(term, [("*", operator.mul), ("/", operator.truediv)])
term = InfixExpr(term, [("+", operator.add), ("-", operator.sub)])
expr << term(name="expr")This expression evaluator can be used thus:print expr.parse_string("1+2") # prints 3
print expr.parse_string("1+2+3") # prints 6
print expr.parse_string("1+2+3+4") # prints 10
print expr.parse_string("3*4") # prints 12
print expr.parse_string("5+3*4") # prints 17
print expr.parse_string("(5+3)*4") # prints 32
print expr.parse_string("10/4") # prints 2.5A syntax diagram can then be generated from it like this:expr.draw_productions_to_png({}, "syntax-expr.png")which would drawthis imageto syntax-expr.png.More examples can be found onParcon’s blog,
and more information in general can be found atParcon’s website.
But reports should be submitted tothe GitHub issue tracker.Parcon is currently much more comprehensively documented than Pargen and Static
are. Improved documentation for Pargen and Static will come soon.
Package: parcoords
Parallel Coordinates Plotting for PythonparcoordsThis package implements a parallel-coordinate plot, commonly used in machine learning, i.e. for hyperparameter visualization.InstallationTheparcoordspackage can be installed with pip:pip install parcoordsYou can also view the package onPyPI.UsageThe following code produces the sample plot at the beginning of this page.importmatplotlib.pyplotaspltfromparcoordsimportplot_parcoords# let's define some data to plotvalues=[[0.1,10,"smote",0.78],[0.15,9,"none",0.46],[0.18,8,"rand_over",0.67],[0.01,16,"rand_over",0.84],[0.01,15,"smote",0.86],[0.015,12,"smote",0.87],[0.03,12,"smote",0.81],[0.12,13,"none",0.51],[0.07,11,"rand_over",0.7],[0.2,14,"none",0.55],]# plot the values as parallel-coordinate plotplot_parcoords(values,labels=["lr","# epochs","sampling method","accuracy"],color_field="accuracy",scale=[("lr","log")],title="Parallel-Coordinates of the Hyperparameters",)plt.show()LicenseThis repository is licensed under theMIT-License.
Package: parcoursupy
parcoursupyPython API wrapper pour ParcoursupIntroductionC'est un wrapper python pour Parcoursup qui permet de récupérer des informations sur les voeux pendant la phase d'admission.A proposDependancespython-dateutilbeautifulsoup4requestsInstallationStableIntallez directement depuis pypi avec la commandepip install parcoursupy(Si vous êtes sous Windows et avez des difficultés avec cette commande, utilisez celle-ci en supposant que vous avez python 3.x.x installé sur votre ordinateur:py -3 -m pip install parcoursupy)LatestVous pouvez installez la dernière version de la bibliothèque directement depuis Githubpip install git+https://github.com/Bapt5/parcoursupyUsageUn programme simple permettant de récupérer tous les voeux et d'en afficher le nom et l'établissementfromparcoursupyimport*client=Parcoursup_Client("Your Username","Your Password")wishes=client.get_wishes()forwinwishes:print(w.name,w.etablissement["nom"])ContributionN'hésitez pas à apporter votre contribution. Toute aide est appréciée. Pour contribuer, veuillez créer un Pull Request avec vos changements.La configuration de l'environnement de développement consiste simplement à cloner le dépôt et à s'assurer que vous avez toutes les dépendances en
en exécutantpip install -r requirements.txt.Ajout de fonctionnalitésParcoursupy couvre les fonctionnalités essentielles, mais si vous avez besoin de quelque chose qui n'est pas encore implémenté, vous pouvezcréer un issueavec votre demande. (ou vous pouvez contribuer en l'ajoutant vous-même)LicenseMozilla Public License, version 2.0
Package: parc-reader
# corenlp-xml-reader
A utility for easily working with CoreNLP-annotated text in Python.## About this package
Other than reading the xml file into a python representation, this utility
does some necessary “fixing up” of the data in CoreNLP.
[to be described]## Quickstart### Install
Install from the Python Package Index:`bash pip installcorenlp-xml-reader`Alternatively, install a version you can hack on:`bash git clonehttps://github.com/enewe101/corenlp-xml-reader.gitcdcorenlp-xml-readerpython setup.py develop `### Use[Add basic usage examples.]
Package: pardata
ParData (homophone ofpartake) is a Python API that enables data consumers and distributors to easily use and share
datasets, and establishes a standard for exchanging data assets. It enables:a data scientist to have a simpler and more unified way to begin working with a wide range of datasets, anda data distributor to have a consistent, safe, and open source way to share datasets with interested communities.Quick Example>>>importpardata>>>pardata.list_all_datasets(){'claim_sentences_search':('1.0.2',),...,'wikitext103':('1.0.1',)}>>>pardata.load_dataset('wikitext103'){...}# Content of the datasetInstall the Package & its DependenciesTo install the latest version of ParData, run$pipinstallpardataAlternatively, if you have downloaded the source, switch to the source directory (same directory as this README file,cd/path/to/pardata-source) and run$pipinstall-U.Quick StartImport the package and load a dataset. ParData will downloadWikiText-103dataset (version1.0.1) if it’s not already
downloaded, and then load it.importpardatawikitext103_data=pardata.load_dataset('wikitext103')View available ParData datasets and their versions.>>>pardata.list_all_datasets(){'claim_sentences_search':('1.0.2',),...,'wikitext103':('1.0.1',)}To view your globally set configs for ParData, such as your default data directory, usepardata.get_config.>>>pardata.get_config()Config(DATADIR=PosixPath('dir/to/download/load/from'),...,DATASET_SCHEMA_FILE_URL='file/to/load/datasets/from')By default,pardata.load_datasetdownloads to and loads from~/.pardata/data/<dataset-name>/<dataset-version>/. To change the default data directory, usepardata.init.pardata.init(DATADIR='new/dir/to/download/load/from')Load a previously downloaded dataset usingpardata.load_dataset. With the new default data dir set, ParData now
searches for theGroningen Meaning Bankdataset (version1.0.2) innew/dir/to/download/load/from/gmb/1.0.2/.gmb_data=load_dataset('gmb',version='1.0.2',download=False)# assuming GMB dataset was already downloadedTo learn more about ParData, check outthe documentationand thetutorial.
Package: pardeeVisualizationPackage
TITLE
Pardee_ThemeDESCRIPTION
Package used internally at the Pardee Center to create a consistent and recognizable theme for visualizations and data presentationsVERSION
version 0.1
Note: Early Production - Likely Unstable - Frequent Updates ExpectedAUTHOR
Created by Cory Vandenberg for use by the Pardee Center for International FuturesUSAGE
Standardized package for consistency for the following aspects of visualizations:
Fonts
Backgrounds
Data Colors
Tick Markers
Expected to be used with seaborn
Not tested or designed for other visualization packages on PythonINCLUDED FILEScolor_palletes.pystyle_dictionaries.pytest_packages.pyexample.pyCOLOR PALLETES
Accessed through color_palletes.py
Contains functions for calling all primary color palletes for visualizations
all functions act as getters, allows for ease of import of color palletes
Contains functions for calling individual, primary DU colors
all functions act as getters, allows for ease of import of individual colorsSTYLE DICTIONARIES
Accessed through style_dictionaries.py
Contains one style dictionary "standard_style"TEST PACKAGES
Accessed through test_packages.py
Contains functions for testing all color pallets with a standardized sin graphEXAMPLE
Contains usage of all included files in the package
proper import naming
proper argument inputs
useage with a dataset (COLT_data)
typical dependencies and imports when working with Pardee_Theme
Package: pardon
PARDONPardon is a Python library developed as a machine learning (ML) and data transformation accelerator, to rapidly prepare data as well as test and deploy supervised ML models, by massively reducing the amount of code required during development.The library can be used to perform data cleansing and transformation, as well as model and feature selection, data visualisations, and prediction auditing. Data can be quickly prepared for use with ML algorithms, and all data transformations are recorded in a 'script'. This 'script' is then automatically applied to data prior to predictions, which ensures data consistency, transparency, and removes the need for multi-stage data engineering while reducing the chances of error.The intention of this library is not to replace or automate the work of a data scientist, but to enable integration work to begin early by rapidly deploying a working machine learning model. This then allows the data science work to be completed in parallel, with the final model being deployed on completion.The pardon library functionality includes:
•	Data transformation
•	Data cleansing
•	Feature selection
•	Feature engineering
•	Model selection
•	Class balancing
•	Prediction auditing
•	Principal Component Analysis
•	Data scaling
•	Data encoding
•	Sentiment analysis
•	Hyperparameter tuning
•	Model “explainability”
•	Model performance metrics
•	Model training, testing and validation
•	Identifying data outliers
•	Testing model output in a REST API
•	Data visualisation
•	Use unsupervised learning to cluster dataInstallationThe easiest way to install and use pardon is to pip install using pip or the latest pardon package whl file.pipinstallpardonpipinstallpardon-1.0.0-py3-none-any.whlUsageimportpardon# create a new ML Data object. The minimum requirements are a data file or data stream, and the name of the column you're trying to predict.paml=pardon.Pardon(data='\\your\\specified\\directory\\data_file.csv',target='column_name_to_predict')# rapid ml will perform the following steps by default:# -> 1. Perform the minimum required data cleansing and transformation to prepare your data for model training.# -> 2. Trains multiple models using the data and selects the best performing model.# -> 3. Where applicable, display a summary of the model performance.# -> 4. Save the model as a .pkl file to the location specified. You must ensure the filename is a .pkl file.# -> 5. Display the output scores of all the models tested.paml.rapid_ml(model_fullpath='\\your\\specified\\directory\\your_model_name.pkl')# you can run a test on the localhost to see the API outputpardon.TestAPI(model_fullpath='\\your\\specified\\directory\\your_model_name.pkl')ContributingFor changes, please contactpardonaccelerator@gmail.comto discuss your amendments or requirements.Please make sure to update tests as appropriate.LicenseMIT
Package: pardot-api-client
A Python client for the Pardot API.It provides object notation for interacting with resources on the API in clean and simple idiomatic statements like:>>>client.prospect.read('someone@example.com')>>>client.prospect.update('someone@example.com',first_name='John')FeaturesSupports Python 2 and 3Supports API version 3Supports all API entities: Account, Campaign, Custom Field, Custom Redirect, Dynamic Content, Email, Email Clicks, Form, Identified Company, Lifecycle History, Lifecycle Stage, List, List Membership, Opportunity, Profile, Profile Criteria, Prospect, Prospect Account, Tag, Tag Object, User, Visit, Visitor, Visitor Activity, Visitor Page View, Visitor ReferrerSupports most API operations: query, assign, unassign, create, read, update, upsert, deleteSupports un-setting of field valuesHandles API session timeouts implicitlyTransparent API error feedbackOptional request retries with incremental back-offPlanned featuresSupport for API version 4Support for API batch operations: batchCreate, batchUpdate, batchUpsertSupports result set manipulation, eg specifying fields, limit, offset, sort_by, sort_orderImplicit results paginationImplicit chunking for batch operationssInstallation$pipinstallpardot-api-clientUsageObtain API authentication credentials from Pardot by following instructions athttp://developer.pardot.com/#authentication- you’ll need the following: email, password, user_keyReview the supported Pardot API documentation athttp://developer.pardot.com/#official-pardot-api-documentationUse the Python API client to interact with the API:>>>frompardot.clientimportAPIClient>>>client=APIClient(...'pardot-email',...'pardot-password',...'pardot-user_key')...>>># read>>>r=client.prospect.read('someone@example.com')>>>print(r)>>># update>>>r=client.prospect.update('someone@example.com',first_name='John')>>>print(r)>>># query>>>forrinclient.prospect.query(new=True):...print(r)...Arguments vs keyword argumentsTo keep the client’s API simple it makes educated guesses about the identifiers passed in resource operation calls.To make this work all required parameters (except user_key and api_key, which are automatically added) must be passed as positional arguments and any optional parameters passed as keyword arguments.For instance, the API documentation states that “assign” operations on “prospect” resources may use either “email” or “id” as identifiers and must supply one of the following parameters:  “user_email” or “user_id” or “group_id”.  Any of the forms below could be used to make this call from the client:>>># identify by email>>>client.prospect.update('someone@example.com',user_email='someone-else@example.com')>>>client.prospect.update('someone@example.com',user_id=2544897)>>>client.prospect.update('someone@example.com',group_id=5499876)>>># identify by id>>>client.prospect.update(7142577,user_email='someone-else@example.com')>>>client.prospect.update(7142577,user_id=2544897)>>>client.prospect.update(7142577,group_id=5499876)
Package: parea
Mostliftanddragcalculations require the use of a frontal/projected area. This is normally not that big of a deal, especially when dealing with nice clean CAD files. Unfortunately, in fast-paced design environments (motorsport especially), you'll often get a collection ofSTLgeometry files instead. This is annoying for a number of reasons, but mainly because there are several methods for computing the frontal area of an STL and all of them involve a fairly tedious/lengthy process. pArea aims to solve this with a single command.Installationpip install pareaUsageAs a simple example, thecubeSTL located within thetestsdirectory has a projected area of 4.0 along all three coordinate axes. To validate this, simply run the following (assuming you've downloaded cube_ascii.stl into your current working directory):parea -stl cube_ascii.stl -xFor models comprised of multiple STL files, simply separate the file names with a space:parea -stl file_1.stl file_2.stl file_3.stl -xOr use shell-style wildcards:parea -stl file_*.stl  -xWhen simulating ground vehicles with non-rigid wheels, you will need to account for the tire deformation and subsequent ride height drop. This can be facilitated using the-floorflag followed by a floor height float value. Note that floor height is in reference to the vertical axis of the specified projection plane.parea -stl file_*.stl  -x -floor 0.0125Since the projection vector isx, our projection plane is thereforeyzyielding a projected area based off all geometry above z=0.0125.Optionsvectors:-x,-y,-zplanes:-yz/-zy,-xz/-zx,-xy/-yxReferencesComputational Geometry: Algorithms and Applications (3rd Edition) by Mike de Borg, Otfried Cheong, Mark van Kreveld, and Mark OvermarsComputational Geometry in C (2nd Edition) by Joseph O'RourkeFinite Element Mesh Generation by Daniel S.H. Lo
Package: parediffusers
PareDiffusersThe librarypareddown the features ofdiffusersimplemented the minimum function to generate images without usinghuggingface/diffusersto understand the inner workings of the library.Why PareDiffusers?PareDiffusers was born out of a curiosity and a desire to demystify the processes of generating images by diffusion models and the workings of the diffusers library.I will write blog-stylenotebooksunderstanding how works using a top-down approach. First, generate images using diffusers to understand the overall flow, then gradually replace code with Pytorch code. In the end, we will write the code for thePareDiffusers codethat does not include diffusers code.I hope that it helps others who share a similar interest in the inner workings of image generation.Versionsv0.0.0: After Ch0.0.0, inprement StableDiffusionPipeline.v0.1.2: After Ch0.1.0, imprement DDIMScheduler.v0.2.0: After Ch0.2.0, imprement UNet2DConditionModel.Table of ContentsCh0.0.0 PareDiffusersPipelineversion: v0.0.0Generate images using diffusersImprement StableDiffusionPipelineImprement DDIMSchedulerImprement UNet2DConditionModelImprement AutoencoderKLCh0.0.1 Test parediffusersTest PareDiffusersPipeline by pip install parediffusers.Ch0.0.2 Play prompt_embedsPlay prompt_embeds, make gradation images by using two prompts.Ch0.1.0: PareDDIMSchedulerversion: v0.1.3Imprement images using diffusersImprement StableDiffusionPipelineImprement DDIMSchedulerImprement UNet2DConditionModelImprement AutoencoderKLCh0.1.1: Test parediffusersTest PareDiffusersPipeline by pip install parediffusers.Ch0.2.0: PareUNet2DConditionModelversion: v0.2.0Generate images using diffusersImprement StableDiffusionPipelineImprement DDIMSchedulerImprement UNet2DConditionModelImprement AutoencoderKLCh0.2.1: Test parediffusersTest PareDiffusersPipeline by pip install parediffusers.UsageimporttorchfromparediffusersimportPareDiffusionPipelinedevice=torch.device("cuda")dtype=torch.float16model_name="stabilityai/stable-diffusion-2"pipe=PareDiffusionPipeline.from_pretrained(model_name,device=device,dtype=dtype)prompt="painting depicting the sea, sunrise, ship, artstation, 4k, concept art"image=pipe(prompt)display(image)ContributionI am starting this project to help me understand the code in order to participate in diffusers' OSS. So, I think there may be some mistakes in my explanation, so if you find any, please feel free to correct them via an issue or pull request.
Package: paref
documentation//notebooks//demo//paperParef - problem tailored MOO for expensive black-box functionsA multi-objective optimization (MOO) problem comes with an idea of what properties the identified
(Pareto) points must satisfy.
The fact that these properties are satisfied is what makes a MOO successful in the first place.
Why not construct MOO algorithms that search for exactly these properties and,
by their very nature, use only a minimum number of evaluations?
With the language of PAreto REFlections this is now possible.
This package contains...a series of ready-to-useMOO algorithmscorresponding to frequently targeted propertiesa framework for you to implement your problem tailored MOO algorithmgeneric and intuitiveinterfacesfor MOO
algorithms, black-box functions and more, so solving a MOO problem with user-defined properties with Paref requires
only minimal effortSee the officialdocumentationfor more information.The official release is available at PyPi:pip install paref