Saravanan666/pypi-org huggingface dataset:
Package: blablado
blabladoblabladois an extensible Assistant, that listens to your voice and can execute custom Python functions you provided.
It can speak as well. It is based onLangChainandOpenAI's API. You need an openai API account to use it.Usage: APIYou can use it from Python (see example notebook) byinitializing anAssistant,fromblabladoimportAssistantassistant=Assistant()providing functions/tools andfromdatetimeimportdatetime@assistant.register_tooldefbook_room(room:str,author:str,start:datetime,end:datetime):"""Book a room for a specific person from start to end time."""print(f"""Booking{room}for{author}from{start}to{end}.""")prompting for tasksassistant.do("Hi I'm Robert, please book room A03.21 for me from 3 to 4 pm tomorrow. Thanks")Output:I have successfully booked room A03.21 for Robert from 3 to 4 pm tomorrow.Usage: MicrophoneYou can also use it via microphone (see example notebook). Therfore, it is recommended to print out the list of available microphones like this:fromblabladoimportlist_microphoneslist_microphones()Example output:['Microsoft Sound Mapper - Input',
 'Microphone (KLICK&SHOW Audio)',
 'AI Noise-cancelling Input (ASUS',
 'Headset (MAJOR IV Hands-Free AG',
 'Microphone Array (Realtek(R) Au']Then, choose the right microphone after initializing the assistant:fromblabladoimportAssistantassistant=Assistant()assistant.microphone_index=3You can then call the assistant to listen to your voice and execute the function you want to call:assistant.listen()Usage: Voice outputTo make the voice output work as well, just activate the voice when initializing the assistant:fromblabladoimportAssistantassistant=Assistant(has_voice=True)Installationblabladois available on pypi and can be installed usingpip. It is recommended to install it in a virtual environment, e.g. set up with mamba.mamba create --name bla python=3.9mamba activate blapip install blabladoTo make the voice-output work (optional), consider installingffmpegusing mamba.mamba install ffmpegIssuesIf you encounter any problems or want to provide feedback or suggestions, please create a thread onimage.scalong with a detailed description and tag [@haesleinhuepf].AcknowledgementsParts of the code reused here were originally written bykevinyamauchifor thebia-bobproject.
Package: blablamower
BlaBlaMowerThis project is for respond to the BlaBlaCar technical test that they have asked me to work on. The specifications are on this filespecs/BlaBlaCar_Technical Test.mdProgram for mowing a rectangular lawn surface with multiple mowers.CHANGELOGInstallationpip and python3.8+ is requiredpypi$>pipinstallblablamowerRelease$>wgethttps://github.com/renardguill/BlaBlaMower/releases/download/v1.0.0/blablamower-1.0.0-py3-none-any.whl
$>pipinstallblablamower-1.0.0-py3-none-any.whlSource codepoetry is needed$>gitclonehttps://github.com/renardguill/BlaBlaMower.git
$>cdBlaBlaMower
$>poetrybuild
$>pipinstalldist/blablamower-1.0.0-py3-none-any.whlUpdatepypi$>pipinstallblablamower--upgradeUsageThe program expect an input file constructed in the following manner: :The first line corresponds to the upper right corner of the lawn. The bottom left corner is
implicitly (0, 0).
The rest of the file describes the multiple mowers that are on the lawn. Each mower is described
on two lines:
The first line contains the mower's starting position and orientation in the format "X Y O". X and
Y are the coordinates and O is the orientation.
The second line contains the instructions for the mower to navigate the lawn. The instructions
are not separated by spaces.exemple :5 5
1 2 N
LFLFLFLFF
3 3 E
FFRFFRFRRF$>blablamower./inputfile.txt
Package: blackadder
black-adderA code formatter for vyper.UsageTo run it manually, first install using pip:pip install blackadderThen run the following in you terminal:blackadder --fast --include '\.vy$' .You can also use pre-commit hooks: put this in your repo's.pre-commit-config.yamland add Python'spre-committo your test requirements.repos:
-   repo: https://github.com/spinoch/blackadder
    rev: "master"
    hooks:
    - id: blackadder
      language_version: python3WarningThis is still in an experimental state. Formatting may be done incorrectly forlogkeywords if the next variable name is less than 3 characters long.
If you run into issues, check out the logs to see what happens.As of now,blackadderwill fail if invoked without--fast.
In general, black options shouldn't be expected to work except for--diffand--include.
Package: blackarrow
"Universal" Text FinderIt's basically just grep in python... Nothing fancy, just an easy extensible way
to find things....Yeah, I know, it's "reinventing the wheel" but ehhh, this is easier to extend to
cover any and all weird cases without having to memorize a bunch of obscure
combinations of bash commands.That out of the way, let's talk about what thisactually does...Quick OverviewEssentially Black Arrow (orbafor short) is a way to search through every line in every file and
find matching keywordorregular expressions. It uses smart case, so if the search term is all
lowercase it defaults to case-insensitive (mostly for ease of use).You can also supply it with regular expressions and it handles them natively.Other features include -excluding certain files or paths,replacing any matches that have been found,specifying the max depth to search (good for large directory structures),"pipe" mode for unix piping,open the files in the$EDITORfor manual parsing.Installation┬─[zoe@fillory:~/Dropbox/Projects/black-arrow]─[09:22:12 PM]
╰─>$ pip install --user blackarrowBlack-Arrow Script┬─[zoe@fillory:~/Dropbox/Projects/black-arrow]─[09:33:40PM]╰─>$./black-arrow/blackarrow.py-h
usage:ba[-h][-dDIRECTORIES[DIRECTORIES...]][-iIGNORE[IGNORE...]][-fFILENAME[FILENAME...]][-wWORKERS][-p][-e][-l][-rREPLACE][-DDEPTH][--dev]R

positionalarguments:RSearchterm(regularexpression)optionalarguments:-h,--helpshowthishelpmessageandexit-dDIRECTORIES[DIRECTORIES...],--directoriesDIRECTORIES[DIRECTORIES...]Director(y|ies)torunagainst-iIGNORE[IGNORE...],--ignoreIGNORE[IGNORE...]Thingstoignore(regularexpressions)-fFILENAME[FILENAME...],--filenameFILENAME[FILENAME...]Filenamesearchterm(s)-wWORKERS,--workersWORKERSNumberofworkerstouse(defaultnumcores,withfallback6unlessset)-p,--pipeRunin"pipe"modewithbriefoutput-e,--editEditthefiles?-l,--lowerCheckstrictlowercase?-rREPLACE,--replaceREPLACEReplacetextfoundinplacewithsupplied-DDEPTH,--depthDEPTHDirectorydepthtosearchin--devRunindevelopmentmode(NOOUTPUT)The Name"Arrow! Black arrow! I have saved you to the last. You have never failed me and
I have always recovered you. I had you from my father and he from of old. If
ever you came from the forges of the true king under the Mountain, go now and
speed well!"― J.R.R. Tolkien, The Hobbit
Package: blackbeard2109
Blackbeard Library. A python’s life for me!This library is designed for people who need to optimize time in an agile way with an ease of understanding, as well as dealing with projects under the umbrella of Data Science, including cleaning data frame (including images), visualization and machine learning.OverviewThis library consists of 3 parts that are the fundamental aspects of Data Science:Machine LearningThese functions are for ML-based projects and speed up their execution:
grid_search, models, NN_conv_model, model_train, load_model, save_model, basic_regression_models, regression_errors,print_regress_metrics, print_classif_metrics, split_and_scale, categorical_encoding, automl, captain.VisualizationThese functions allow us to optimize the execution of the graphs in time:
draw_boxplot, draw_donut_chart, draw_maps, draw_three_countplot, show_roc_curve, sort, pairplot_heatmap, draw_missing_ratio, draw_statistic_values, draw_target_transformation, draw_feat_importance, draw_maps, draw_donut_chart, draw_three_countplot, show_roc_curve.Data CleaningUsing the following functions, we are able to increase the efficiency when it comes down to cleaning data and images:
get_root_path, delete_duplicates, missing_values, save_col_names, Unix_to_UTC, download_csv, split_datetime, max_min, null_to_0, null_to_mean, categorical_numeric, df_information, merge_df, drop_NaN, eleate_column, replace_by_numpy, replace_string, percentage, sort_column, outliers, sales_by_category, month_to_name, columns_dtype,  get_products_that_are_sold_together, frames_from_video, images_dataset_properties, read_images, flip_images, resize_images
edit_images, color2gray, negative_colors_images, monocolor_images, images_load_data, save_images.UsageIn the following paragraphs, we are going to describe how you can use Blackbeard:Getting itTo download Blackbeard, either fork this github repo or simply use Pypi via pip.pipinstallblackbeard2109Using itBlackbeard was programmed with ease-of-use in mind. First, import Blackbeard and then call any of the functions based on your needs.If you are a Mac user, consider the following: When you are executing a function for images, by default, it installs the file called .DS_Store on your computer. In order for execute the function correctly, what you have to do is the following: right on click on the images folder that you downloaded, click on a terminal, and execute the following code:  find . -name '.DS_Store' -type f -delete.
Package: blackbench
BlackbenchA benchmarking suite for Black, the Python code formatter. It’s intended to help
quantify changes in performance between versions of Black in a robust and repeatable
manner. Especially useful for verifying a patch doesn’t introduce performance
regressions.Helpful links:User documentationContributing documentationChangelogChat on DiscordLicenseBlackbench: MIT.Targets based off real code maintain their original license. Please check the directory
containing the target in question for a license file.
Package: blackberry-workspaces
# WatchDox
This repository provides functions to interact with the Blackberry WatchDox REST API<img src=”https://www.blackberry.com/content/dam/blackberry-com/media-gallery/images/blackberry-logo/BlackBerry_Logo_Black.png” alt=”BlackBerry” width=”200”/>## What is BlackBerry Workspaces?
>BlackBerryÂ® Workspaces is the leading secure EFSS solution, enabling users to share, edit and control their files on every device. Only BlackBerry Workspaces can provide the level of security organizations need â€” wherever files are, wherever they need to go, and whoever needs to access them.
[Workspaces Homepage](https://www.blackberry.com/us/en/products/blackberry-workspaces-efss)## What is this repository for?
This repository contains updated production code that made it possible to interact with Watchdox Workspaces, Folders and User Permissions via the BlackBerry Workspaces REST API. This wrapper provides extensible functionality to create, delete and edit folders.## Other Resources:
BlackBerry Workspaces REST API
[Workspaces REST API Reference Homepage](https://developers.blackberry.com/content/dam/developer-blackberry-com/api-reference/workspaces/workspaces-rest/index.html)
Package: blackbirds
BlackBIRDSis a Python package consisting of generically applicable, black-box inference methods for differentiable simulation models. It facilitates both (a) the differentiable implementation of simulation models by providing a common object-oriented framework for their implementation in PyTorch, and (b) the use of a variety of gradient-assisted inference procedures for these simulation models, allowing researchers to easily exploit the differentiable nature of their simulator in parameter estimation tasks. The package consists of both Bayesian and non-Bayesian inference methods, and relies on well-supported software libraries (e.g. normflows, Stimper et al. (2023)) to provide this broad functionality.1. InstallationThe easiest way to install Birds is to install it from the PyPI repositorypip install blackbirdsTo get the latest development version, you can install it directly from gitpip install git+https://github.com/arnauqb/blackbirds2. UsageRefer to thedocsfor examples and specific API usage. Here is a basic example:importtorchfromblackbirds.models.random_walkimportRandomWalkfromblackbirds.infer.viimportVIfromblackbirds.posterior_estimatorsimportTrainableGaussianfromblackbirds.simulateimportsimulate_and_observe_model# random walk modelrw=RandomWalk(n_timesteps=100)# generate synthetic data to fit totrue_p=torch.logit(torch.tensor(0.25))true_data=rw.observe(rw.run(torch.tensor([true_p])))# define loss to minimizeclassL2Loss:def__init__(self,model):self.model=modelself.loss_fn=torch.nn.MSELoss()def__call__(self,params,data):observed_outputs=simulate_and_observe_model(self.model,params)returnself.loss_fn(observed_outputs[0],data[0])# initialize generalized variational inferenceposterior_estimator=TrainableGaussian([0.],1.0)prior=torch.distributions.Normal(true_p+0.2,1)optimizer=torch.optim.Adam(posterior_estimator.parameters(),1e-2)loss=L2Loss(rw)vi=VI(loss,posterior_estimator=posterior_estimator,prior=prior,optimizer=optimizer,w=0)# no regularization# train the estimatorvi.run(true_data,1000,max_epochs_without_improvement=100)3. TestsTo run the unit tests of the code, you need to have pytest installed,pipinstallpytestpytest-covand run the commandpytesttest4. ContributingSeeCONTRIBUTING.mdfor the contribution guidelines.5. Citation@article{Quera-Bofarull2023, 
    doi = {10.21105/joss.05776}, 
    url = {https://doi.org/10.21105/joss.05776}, 
    year = {2023}, 
    publisher = {The Open Journal}, 
    volume = {8}, 
    number = {89}, 
    pages = {5776}, 
    author = {Arnau Quera-Bofarull and Joel Dyer and Anisoara Calinescu and J. Doyne Farmer and Michael Wooldridge}, 
    title = {BlackBIRDS: Black-Box Inference foR Differentiable Simulators}, 
    journal = {Journal of Open Source Software} }
Package: blackboard_analysis_tools
# Blackboard Analysis ToolAn analysis automation tool to avoid repetitive tasks while grading student assignments that have been handed in through the Blackboard learning system.For example:* Extract hundreds of .zip files* Sort the files per assignment, student* Create statistics: number of students, late assignments,...## Usage:* Download assignment files from Blackboard (e.g. gradebook_ART_EA-38302_Assignment1_2013-06-11-20-50-44.zip).* Place these .zip files in the "input" folder.* Run the program: ``python -m blackboard_analysis_tools``.* Wait... (about 10 seconds when processing around 100MB of assignments) (time will vary).* Open the "output" folder to see the results (all files sorted per student, a summary, logfile, ...).## Installation:* Install using pip: ``pip install blackboard_analysis_tools``. (Linux & Windows)* Python Package available in the Python Package Index at: http://pypi.python.org/pypi/blackboard_analysis_tools/.* Download the source and run ``python setup.py install``.## Limitations:* Currently only tested on Linux & Windows7* The program was created with other OS users in mind, so it will eventually get full cross-platform support.## License:If not stated otherwise blackboard_analysis_tools is distributed in terms of the MIT license.See LICENSE in the distribution for details.## Bug reports:* Post issues to GitHub http://github.com/jeroendoggen/blackboard-analysis-tools/issues.## What is happening behind the scenes:1. Scan for .zip files2. Extract the .zip files3. Scan for .txt files (these contain the metadata that describes the student reports/assignments)4. Analyse the .txt files5. Create a folder for each student6. Move all the files to the correct folder (including those with filenames that have been 'mangled' by Blackboard)7. Write some statistics: a list of all students that have handed something in8. Write a summary of the process: number of students, number of assignments, number of 'mangled' files,...## Changelog:0.0.4:* Windows compatible* Modified the default analysis options to 'AP University College'0.0.3:* Cleanup temp folders* Debug code for timing analysis* Parallel processing (no speedup)* Python 3.3 compatible* Partially Windows compatible* Sorting output folders* Print build summary* Detect late files0.0.2:* Detect 'mangled' files* Statistics: logfile, summary, student list* using input & output folders* Code cleanup: pep8, pylint0.0.1: Basic features* Extract .zip files* Create student folders* Move files to folder
Package: blackboardsync
BlackboardSyncAutomatic Syncing Of Your Blackboard ContentBlackboardSyncperforms a periodic, incremental download of all your Blackboard content, such as lecture slides, lab sheets, and other attachments.AboutBeing a student in this day and age means constantly having to keep up to date with the files that are uploaded to the student portal. I needed a tool that would take care of retrieving these files for me, allowing me to focus on the work to be done. Something I could set up and forget about.What I was looking for in such an application was:Automatic syncing with minimal intervention after the initial setupGraphical interfaceCross-platform compatibilityIt would make use of theBlackboard REST API30+ Universities Supported Around the WorldWhy is my university not supported?Simply put, some information is necessary to make the login process compatible with any given university. If you would like to help to add support for your university, or would like to see which universities are currently supported,start here.Built with:Python 3.10PyQt5FeaturesSupported content:Attachments of any type (e.g. .docx, .pptx, .pdf, etc.)Internet linksContent descriptions (saved as html files)Cross-platformLinux, Windows, and macOS readyInstallationBinariesYou can find all releases onGitHub.
Only MacOS (.dmg) and Windows (.exe) are supported at the moment.Note: These releases are automatically built on GitHub Actions from source.⚠️ ATTENTION MACOS USERS On MacOS, you will face an issue when trying to open the application, since it has not
been notarised by Apple. A workaround can be foundhere.From PyPIpython3-mpipinstallblackboardsync
python3-mblackboard_sync# notice the underscoreFrom sourceRequirementsPython 3.10 & pipPipenvGitFrom your command line:Clone the repositorygitclonehttps://github.com/jacobszpz/BlackboardSync.gitcdBlackboardSyncTo fetch the dependencies, runpipenvinstallTo start the programpipenvrunpython-mblackboard_syncContributionsContributions are welcome.More details available atCONTRIBUTING.mdBugs, issues or feature requests?Open a GitHub issuehere.Show Your SupportKo-fi ☕LiberaPayBitcoin ₿bc1qhvcs07y3jnf09kahefscs05gxlmvhu542wzvgpLicenseThis software is distributed under theGeneral Public License v2.0, more information available at theFree Software Foundation.AcknowledgementsBlackboard API documentationPyInstallerREADME templates/guide bytonycrosby-tech,neildrew, andRita Łyczywek
Package: blackbook
blackbookBlackfor Jupyter notebooks.How?$pipinstallblackbook
$blackbook.2019-01-2817:15:10.857|INFO|blackbook.__main__:main:31-Alldone!📖2019-01-2817:15:10.857|INFO|blackbook.__main__:main:33-1notebooksreformatted.1leftunchanged.You can also runblackbookover a single notebook$blackbookunformatted.ipynb2019-01-2817:15:12.663|INFO|blackbook.__main__:main:31-Alldone!📖2019-01-2817:15:12.663|INFO|blackbook.__main__:main:33-1notebooksreformatted.0leftunchanged.Why?Fromblack:Black is the uncompromising Python code formatter. By using it, you agree to
cede control over minutiae of hand-formatting. In return, Black gives you
speed, determinism, and freedom from pycodestyle nagging about formatting. You
will save time and mental energy for more important matters.There are twoblackimplementations for Jupyter notebooks:https://github.com/csurfer/blackcellmagichttps://github.com/tobinjones/jupyterlab_formatblackhttps://github.com/betatim/joliThese both work in a given notebook session,blackbookwill search a directory
tree and reformat the notebooks in an uncompromising way.
Package: black-box
blackbox: A Python module for parallel optimization of expensive black-box functionsWhat is this?A minimalistic and easy-to-use Python module that efficiently searches for a global minimum of an expensive black-box function (e.g. optimal hyperparameters of simulation, neural network or anything that takes significant time to run). User needs to provide a function, a search domain (ranges of each input parameter) and a total number of function calls available. A code scales well on multicore CPUs and clusters: all function calls are divided into batches and each batch is evaluated in parallel.A mathematical method behind the code is described in this arXiv note (there were few updates to the method recently):https://arxiv.org/pdf/1605.00998.pdfDon't forget to cite this note if you are using method/code.Demo(a) - demo function (unknown to a method).(b) - running a procedure using 15 evaluations.(c) - running a procedure using 30 evaluations.Installationpip3 install black-boxObjective functionSimply needs to be wrapped into a Python function.deffun(par):...returnoutputparis a vector of input parameters (a Python list),outputis a scalar value to be minimized.Running the procedureimportblack_boxasbbdeffun(par):returnpar[0]**2+par[1]**2# dummy examplebest_params=bb.search_min(f=fun,# given functiondomain=[# ranges of each parameter[-10.,10.],[-10.,10.]],budget=40,# total number of function calls availablebatch=4,# number of calls that will be evaluated in parallelresfile='output.csv')# text file where results will be savedImportant:All function calls are divided into batches and each batch is evaluated in parallel. Total number of batches isbudget/batch. The value ofbatchshould correspond to the number of available computational units.An optional parameterexecutor = ...should be specified withinbb.search_min()in case when custom parallel engine is used (ipyparallel, dask.distributed, pathos etc).executorshould be an object that has amapmethod.Intermediate resultsIn addition tosearch_min()returning list of optimal parameters, all trials are sorted by function value (best ones at the top) and saved in a text file with the following structure:Parameter #1Parameter #2...Parameter #nFunction value+1.6355e+01-4.7364e+03...+6.4012e+00+1.1937e-04...............AuthorPaul Knysh (paul.knysh@gmail.com)Feel free to email me if you have any questions or comments.
Package: blackbox-adversarial-toolbox
Black-box Adversarial Toolbox (BAT)A Python Library for Deep Learning Security that focuses on Distributed Black-box attacks.Installationpipinstallblackbox-adversarial-toolboxUsage (CLI)Usage: bat [OPTIONS] COMMAND [ARGS]...

  The CLI tool for Black-box Adversarial Toolbox (BAT).

Options:
  --help  Show this message and exit.

Commands:
  api      Manage Cloud APIs
  attack   Manage Attacks
  example  Manage ExamplesUseful commands:# List supported Cloud APIs
$ bat api list

# List supported Attacks
$ bat attack list

# Test Cloud APIs
$ bat api run deepapi
$ bat api run google
$ bat api run imagga

# Run exmaples
$ bat example run simba_deepapi
$ bat example run bandits_deepapi
$ bat example run square_deepapiUsage (Python)importnumpyasnpfromPILimportImagefrombat.attacksimportSimBAfrombat.apis.deepapiimportDeepAPI_VGG16_Cifar10# Load Imagex=np.asarray(Image.open("dog.jpg").convert('RGB'))x=np.array([x])# Initialize the Cloud API ModelDEEP_API_URL='http://localhost:8080'model=DeepAPI_VGG16_Cifar10(DEEP_API_URL)# Get Preditctiony_pred=model.predict(x)[0]# Distributed SimBA Attacksimba=SimBA(model)x_adv=simba.attack(x,np.argmax(y_pred),epsilon=0.05,max_it=10)