Saravanan666/pypi-org huggingface dataset:
Package: basic_image_preprocessing
📸 Basic Image Preprocessing 📸Basic Image Preprocessing is a Python package that is focused on handling the various Image enhancement
and Noise removal techniques.The package encompasses most of the basic methods and algorithms used for enhancing the quality
of the image as well as the removal of noise from the image.Quick InstallYou can install the package directly by using the pip command or through the conda command promptpip install basic_image_preprocessingorconda install basic_image_preprocessing -c conda-forgeList of Techniques Available in the PackageTypesMethodSub TypesExample FileTraditionalLinear EquationLinear Equation Usage ExamplesNon Linear MethodsPower transformationExponential transformationLograthimic transformationNon Linear Method Usage ExamplesBasic Mathematical OperationsAdditionSubtractionMultiplicationDivisionBasic Mathematical Operation Usage ExamplesConventionalHistogram EqualizationHistogram Equalization Usage ExamplesCLAHECLAHE Usage ExamplesEdge DetectionLaplacianLaplacian Usage ExamplesCanny Edge DetectionCanny Edge Detection Usage ExamplesEdge Filtering TechniquesSharpenningCustom Edge DetectionEdge Filtering technique Usage ExamplesFrequency Noise FilteringFourier TransformFourier Transform Usage ExamplesSpatial Noise FilteringBilateral FilterBilateral Filter Usage ExamplesWiener FilterWiener Filter Usage ExamplesBasic Noise FilteringMean FilteringMedian FilteringGaussian FilteringBasic Noise Filtering Usage Examples💁 ContributingIf you would like to contribute to this project, create a pull request with your changes and provide
a detailed description of the change being done.:lady_beetle: Report a bugIf you find a bug or unexpected behavior when using any of the methods, kindly raise an Issue.
Please follow the bug templateherewhile raising the bug, so that it will be
easy for us to analyze and provide a fix for the issue.:placard: Request a new Algorithm / FunctionalityIf you find any method or algorithm missing from the package, please create a feature request under
the Issue section by following the feature request template foundhereWe will
go through the request and do the required works to get the feature ready.
Package: basic-iter
Basic Iterator Operators for PythonThis package provides basic functions on sequences (now onlylistis supported).
All these provided functions are defined in functional way and do not update given arguments, but return newly constructed values.Install$pipinstallbasic_iterDevelopInstall dependencies for developingbasic_iter$poetryinstall--no-rootGenerate documentsTo generate documents:basic_iter$makedocThis will generate documentation under./docs/build/html.Format CheckingFor format checking by black:basic_iter$makeformat_checkType CheckingFor type checking by mypy:basic_iter$maketype_checkUnit TestFor executing unit tests:basic_iter$maketest
Package: basic-knn
KNN Algorithm ModuleWhat is KNN?In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation. Since this algorithm relies on distance for classification, normalizing the training data can improve its accuracy dramatically.(Wikipedia Article about KNN,https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)How to Install?It is always better to use "pip" (Package manager for Python).pip install basic_knnSample UsageCreate Model# import knn classifier
from basic_knn import KNNClassifier

# sample data
data_x = [...]
data_y = [...]
labels = [...]

# create model
model = KNNClassifier(xs = xs, ys = ys, labels = labels)Make Predictions# sample input for predictions
sample_input = (..., ...)

# make prediction
model.predict(sample_input)
Package: basicldap3
Basic LDAP3Basic LDAP is a small package that uses the ldap3 library. It can be used to authenticate a user against an LDAP server and can check whether the users is in a specific group. In addition it is possible to get user attributes if the authentication is successful.Basic exampleImport basicldap and define the serverfrombasicldapimportLdapLDAP_HOST='ldap.server.com'LDAP_BASE_DN='DC=example,DC=com'conn=Ldap(LDAP_HOST,LDAP_BASE_DN)# Using SSLssl={"port":636,"use_ssl":True}conn=Ldap(LDAP_HOST,LDAP_BASE_DN,ldap3_args=ssl)Authenticate the userconn.authenticate(user_name,password)Get user attributes (requires successful authentication)# To get all attributesconn.get_attributes()# To get specific attributesconn.get_attributes(['mail','cn])
Package: basiclog
Basic Log (basiclog)Example code:importtracebackimportbasicloglogger=basiclog.log("debug",__file__)# for slack error messageslogger.add_slack_handler(slack_api_toke,slack_channel)logger.info("Script has been started")try:main()exceptKeyboardInterrupt:logger.info("Script has been stopped manually")except:e=traceback.format_exc()logger.error(e)
Package: basic_math_calculator
Basic calculatorThis is a calculator project for basic math functions.Implemented methods:additionsubtractionmultiplicationdivisionnth-root ((n) root of a number)reset memoryInstallationpip install basic-math-calculatorfrom calculator.calculator import CalculatorUsageResult is stored and overwritten in memory until reset() method is ran.Calculation methods can take either one or two arguments (in addition to self).
If only one arguments is given calculations will be performed with memory value (latest result), otherwise calculations will be performed between two arguments and memory value will be overwritten.For example:>>> calc = Calculator()
>>> calc.addition(4, 5)
9
>>> calc.addition(10)
19
>>> calc.multiplication(2)
38
>>> calc.multiplication(2, 5)
10
>>> calc.reset()
0LicenseThis project is licensed under the terms of theMITlicense
Package: basicmathfunctions
This is a library with basic math functions, including adding two numbers, getting the value of pi, converting fahrenheit to celsius, converting celsius to fahrenheit, and much more!Install via pippipinstallbasicmathfunctionsFunctionsadd_two_numbers(number1,number2)Add two numberssubtract_two_numbers(number1,number2)Subtract two numbersmultiply_two_numbers(number1,number2)Multiply two numbersdivide_two_numbers(number1,number2)Divide two numberspi()Get value of pi (to 4 decimal places)the_power_of(number,power)Get a number to the power of another numbersqrt(number)Get the square root of a numberf_to_c(temperature)Convert fahrenheit to celsiusc_to_f(temperature)Convert celsius to fahrenheitChange Log0.0.1 (4/28/2021)First Release
Package: basic-ml
BasicMLBasicML is a Python package containing implementations of basic machine learning algorithms. Below is a list of all the ML models that come with this package:Regression:LinearRegressionClassification:DecisionTreeKNearestNeighborsLogisticRegressionNeuralNetworkClusteringKMeansInstallation:Install using pip:pip install basic-mlUsageimportnumpyasnpfrommlimportLinearRegression# Replace with your own datatrn_X,trn_y,tst_X,tst_y=np.ones(1),np.ones(1),np.ones(1),np.ones(1)lr=LinearRegression()lr.fit(trn_X,trn_y)predictions=lr.predict(tst_X)print('Predicted:{}\nActual:{}'.format(predictions,tst_y))To see example code, open/run any of the 6 main Python files in themlfolder.ContactReach out to me atalan.bi326@gmail.comfor questions and feedback!
Package: basicMLpy
basicMLpybasicMLpy is a package that implements simple machine learning algorithms. It currently contains eight modules that implement multiple machine learning techniques for supervised learning.The basicMLpy.regression module contains the following functionalities:Linear RegressionRidge RegressionThe basicMLpy.classification module contains the following functionalities:Multiclass classification through the IRLS(Iteratively Reweighted Least Squares) algorithmThe basicMLpy.nearest_neighbors module contains the following functionalities:An implementation of the K-Nearest Neighbors algorithm, that can fit both classification and regression problemsThe basicMLpy.model_selection module contains the following functionalities:A Cross-Validation algorithm for the functions presented by the basicMLpy packageThe basicMLpy.ensemble module contains the following functionalities:An implementation of the Random Forests algorithm for regression and classificationAn implementation of the AdaBoost algorithm for classificationAn implementation of the Gradient Boosting algorithm for regressionThe basicMLpy.decomposition module contains the following functionalities:An implementation of the SVD decomposition algorithmAn implementation of the PCA algorithmThe basicMLpy.loss_functions module contains the following functionalities:Multiple functions for error evaluation, e.g. MSE, MAE, exponential loss, etc.The basicMLpy.utils module contains the following functionalities:Useful functions utilized all throughout the other models.InstallationTo install basicMLpy run the following command:pip install basicMLpyPackage's site and documentationhttps://henrysilvacs.github.io/basicMLpy/DependenciesbasicMLpy requires Python >= 3.8, Numpy >= 1.19, Scipy >= 1.5.2, scikit-learn >= 0.23.On Githubhttps://github.com/HenrySilvaCS/basicMLpyOn Pypihttps://pypi.org/project/basicMLpy/Some thoughtsThis is a work in progress project, so more functionalities will be added with time.AuthorHenrique Soares AssumpÃ§Ã£o e Silva
Package: basicModules
basicModulesRelease history:--- V1.0.4 ---- Added StackOverFlow Functions -- Added Ip Functions -- Removed Roblox Functions due to it being broken -- Added Integer Functions -- Added Sudo Kill App -- Added String Functions -- Added Package Functions -- Made all Functions to Scripts folder ---- V1.0.3 ---- Added Roblox Functions -- Added Shell Functions -- Removed Emails due to insecurity ---- V1.0.2 ---- Added Web Functions -- Fixed issues with Emails ---- V1.0.1 ---- V1.0.1 was recalled. -
Package: basicnanoclient
basicnanoclientA nano (XNO) cryptocurrency RPC python clientInstallationDocumentationBuild docs locallyTestsBefore running tests:Installationpip install basicnanoclientDocumentationhttps://basicnanoclient.readthedocs.io/Build docs locallymkdocs serveTestsTo only run tests:pytest --cov=basicnanoclient --cov-fail-under=80To run style checks:flake8 basicnanoclient --docstring-convention google --ignore=D100
flake8 tests --docstring-convention google --ignore=D100To run all checks:noxBefore running tests:install a local nano test noderun it locally
Package: basico
= Basico: SAP Notes Manager for SAP Consultants== Description
Basico helps you to track those SAP Notes that you use as SAP Consultant.== Features
* Download SAP Notes
* Import SAP Notes from other people
* Export your SAP Notes to several formats
* Bookmark SAP Notes really interesting.
* Browse SAP Notes in Launchpad
* Search and filtering based on SAP Notes metadata
* Build collections (by grouping SAP Notes on one topic)
* Make annotations (linked to SAP Notes or independent)## Installation
Please, follow the [instructions](http://blog.t00mlabs.net/projects/basico/basico-installation) from this page## Authors
Tomás Vírseda <tomasvirseda@gmail.com>## Contributors
Many thanks to the following people, who have kindly help in this project:Iliya Kuznetsov: ideas to deal with OData protocol and other useful info to start this project
Package: basic_oauth
Basic OAuth v2==============What is it?-----------The Oauth v2 spec defines several authorization grant. This library implementsthe "Resource Owner Password Credentials Grant" as described in<http://tools.ietf.org/html/draft-ietf-oauth-v2-31#section-4.3>Requirements:* [Flask](http://flask.pocoo.org/)* [Redis](http://redis.io/)Why using it?-------------The goal of this Grant is to replace the classic "HTTP Basic over SSL" widelyused. With Oauth, you exchange your crendentials against a token.This mechanism has several advantages:* The client does not pass the full credentials for each request.* The server does not check the username and password each time, it willonly check the access token, this will reduce the database lookups.Basic Oauth uses Redis to store the sessions.Is it secure?-------------__It would be stupid to use this mechanism without SSL__. Even if the token ispassed instead of the credentials, the credentials needs to be passed in cleartext during the Authentication phase. Also, it can be problematic to lose thetoken.To limit the risk of losing the token, every single token generated is signedusing the User-Agent and the client IP address. If an attacker tries to re-usea stolen token, he will have to connect to the same IP and using the sameUser-Agent (browser version, OS, architecture) to get access. A wrong try willresult in destroying the session.How to use it?--------------Install basic_oauth from PYPI:```pip install basic_oauth```Create a sample WSGI app with [Flask](http://flask.pocoo.org/):```pythonimport flaskimport basic_oauthapp = flask.Flask(__name__)oauth = basic_oauth.BasicOauth(app)oauth.mount_endpoint('login', '/login')oauth.mount_endpoint('script', '/js/oauth_client.js')oauth.credentials.append(('johndoe', 'foobar42'))# You can declare "oauth.authenticate_handler" to plug your own# database instead of using the in-memory credentials@app.route('/')@oauth.requiredef hello(user_id):return 'Hello World!'if __name__ == '__main__':app.debug = Trueapp.run()```Checkout the ["example" directory](https://github.com/samalba/basic_oauth/tree/master/example) for a complete server/client example.
Package: basic-operations
basic_operationsDescription.
The package basic_operations is used to:
- Addition operation
- Subtraction operation
- Multiplication operation
- Division operationInstallationUse the package managerpipto install basic_operationspipinstallbasic_operationsUsagefrombasic_operationsimportoperationoperation.addition(x,y)operation.subtraction(x,y)operation.multiplication(x,y)operation.division(x,y)AuthorMatheus RoqueLicenseMIT
Package: basic-ops-calculator
package_nameDescription.
The package basic ops Calculator is used to:
Operations
-Basic
-sum, subtractions, division, multiplication, raise a number and make n square of a number
-Advanced
-logarithm, natural logarithm exponential e baseInstallationUse the package managerpipto install basic_ops_calculatorpipinstallbasic_ops_calculatorUsagefrombasic_ops_calculator.operationsimportbasicbasic.make_sum(a,b)AuthorMarcelo L ValerioLicenseMIT
Package: basic-options
Basic OptionsThis package provides a basic way to create configuration files, especially for tkinter GUI applictions.# EXAMPLE USAGE# Create an options class inheriting BasicOptions, and override the set_defaults functionclassExampleOptions(BasicOptions):defset_defaults(self)->None:self.value_a=1self.valueB="2"self.c=[3,4,5]self.delta=6.0self.Eee={"value":7}# Create an instance of the optionsexample=ExampleOptions()# Change a value in the options using .set_option() or item notationexample["delta"]+=0.6666# You can do `example.delta += 0.6666`, however that would not activate the wrappers from .set_option_wrappers()# Retreiving options can be done with .get_option(), or item notationprint(example.get_option("delta"))print(example["c"])# Loading and saving options can be done easily with .save_file(), .try_load_file() and .load_file()# example2 = ExampleOptions().load_file("old_save.json").# example2.save_file("new_save.json")# Retreiving dictionaries and json strings with .to_json() and .to_dict()
Package: basic-pid
BasicPIDis a classic PID controller that is easy to use, works and does the jobThe PID controller is designed to be used in discrete-time regulators and
implements timestep integration that supports two modes of operation:IntegrativeandIterativeInIntegrative Mode, the timestep integrations are calculated inside the controller
and the output from the PID controller for the current timestep is used directly or with
modifications and sent to the device or process plant as the current input signal without
further iterative integration.InIterative Mode, the PID timestep integrations are manually calculated and updated
outside the controller in the algorithm that calls the PID controller for the output of
the PID at the current timestep.BasicPIDhas proven to be a tested, and reliable PID controller. It has been used, for example,
with mobile robotic systems for LVC (Linear Velocity Control) for regulating wheel velocities,
LVDR (Lateral Velocity Differential Regulator) for keeping the wheel velocities in sync for
differential drive mobile robots when traversing a straight line path and
ADVR (Angular Differential Velocity Regulator) for tracking a heading angle using typical
motion control input signals (v,w) for linear and angular velocities.Installation:$pipinstall basic-pidFor documentation seehttps://basic-pid.readthedocs.io/en/latest/
Package: basicpkg-sardorbek
basicpkgThebasicpkgis a simple testing example to understand the basics of developing your first Python package.from multiply.by_three import multiply_by_three
from divide.by_three import divide_by_threemultiply_by_three(9)
divide_by_three(21)
MIT LicenseCopyright (c) [year] [fullname]Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: basicprogressbar
Basic Progress BarProgress bar with no dependencies.installation:frombasicprogressbarimportBasicProgressBarArguments:current:float=0# current progresstotal:float=-1# total progress '-1' for unlimitedposttext:str=""# text behind the barpretext:str="Progress:"# text before the barlength:int=60# length of the barendtext:str=""# text after the bar when doneendline:str='\r'# endline character to rewite same lineBasic Examples:prog=BasicProgressBar(1,10,pretext="Before bar:")foriinrange(11):time.sleep(0.1)prog.current=iprog.endtext=(f"I ended on{i}")prog.bar(True)foriinrange(11):time.sleep(0.1)BasicProgressBar(i,10).bar(True)prog=BasicProgressBar(1,10)foriinrange(11):time.sleep(0.1)prog.current=iprog.posttext=f"processing{i}"print(prog.bar(),end=prog.endline)prog=BasicProgressBar()foriinrange(10):time.sleep(0.1)print(prog.next(),end="\r")print()Discord Progress Bar:Progress bar for discordDependencies: requests, timeinstallation:frombasicprogressbarimportDiscordProgressBarArguments:# All the arguments of from BasicProgressBar firstidtoken:str=""# discord id tokendisuser:str="Progress Bar"# name of discord userthrottle:float=0.5# time between messages# shouldnt have to edit the ones belowmesstime:float=0.0# time used for waiting between messagesmessid:str=""# message id to edit linetimeout:float=10.0# discord timeoutExamples:# all examples from BasicProgressBar applytoken="23135245523/f43faDSAF-FEAfe24f3qfq-2yfbB-agdagADGA-g334t34gqarGS"prog=DiscordProgressBar(1,100,idtoken=token)foriinrange(1,101):time.sleep(0.1)prog.current=iprog.send()prog=DiscordProgressBar(idtoken=token)foriinrange(1,101):time.sleep(0.1)prog.current=iprog.send()prog=DiscordProgressBar(total=100,idtoken=token)foriinrange(1,101):time.sleep(0.1)prog.next()
Package: basicprop
# Basicprop (Synthetic Dataset)## InstructionsThere are the following datasets:1. Line (10 classes)2. Rects (100 classes)Each dataset has the same API:- get_image(y) -> returns an image of class y.- get_batch(batch_size) -> returns a batch of images with random classes.Here is an example:```from basicprop.datasets import Line, Rectsline = Line()rects = Rects()line_images = [line.get_image(y) for y in range(10)]rects_images = [rects.get_image(y) for y in range(100)]```## LicenseMIT
Package: basicpy2captcha
Welcome to basicpy2captcha python module👋A basic python 2captcha.com moduleInstallpipinstallbasicpy2captchaUsageimportbase64importbasicpy2captchapy2c=captcha.py2c("YOUR_2CAPTCHA_API_KEY")#Text Image:withopen("captcha.png","rb")asf:img_b64=base64.b64encode(f.read())solved=py2c.solveCaptcha(img_b64)print(solved)#ReCaptcha:key=py2c.get_sitekey(driver)#driver - selenium webbrowser objecttoken=solveReCaptcha(key,driver.current_url)iftoken:driver.execute_script("document.getElementById('g-recaptcha-response').innerHTML='{}';".format(token))#getBalance:balance=py2c.getBalance()print(balance)#report Correct resolve:py2c.reportCorrect()#report Incorrect resolve:py2c.reportIncorrect()Show your supportGive a ⭐️ if this project helped you!
Package: basicpycalc
— README —This is a clock extension which creates a basic clock using a few modules.
If you have any questions, mail me at -ananyo.bhattacharya2010@gmail.comMake sure to keep up with the changes!— END OF README —— CHANGELOG —- Version 1.0 (21 June, 2022)- Version 1.1 (21 June, 2022)- Version 1.2 (21 June, 2022)— END OF CHANGELOG —Copyright 2022 Ananyo BhattacharyaPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: basic-repo-template
Basic Repo TemplateThis is a basic template repository for a python project.Change thepackage_namein:pyproject.tomlto the name of your package (doing a global search and repolace for the default stringbasic_repo_template.You'll also want to change thename,emailetc inpyproject.tomland{repository_name}.__init__.pyto your own details.Publishing to PyPi (to make it pip installable)NOTE - These instructions haven't been tested yet, this is placeholder text (written 2023-01-19)You'll need to create a PyPi account and then create an API token for the Github Action configured in the file:.github/workflows/publish_to_pypi_when_new_tag_is_pushed_to_main.ymlThese instructions may help you set that up!https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/