Saravanan666/pypi-org huggingface dataset:
Package: sketchpy
Welcome to sketchpyIntro to the project and some quick information,followed by an image of the project.DescriptionThis is the beginning level python project to do some awesome drawing animation using theturtlemodule, hope it grows in the future
It is a Python module for animating drawings of images. The sketchpy module is created on top of the turtle module in Python.
To install sketchpy on your computer, you can go to your command prompt (command line) and run the following command.UsageJust install the packagepip install sketchpyImport it to you projectimport sketchpyand use as you want😊Built withTurtleOpen-cvPillownumpySvgpathtoolsGetting startedPrerequisitesPythonBasic text editorcreativity😂Installpip install sketchpyit should probably work, If not then try the following codepip install turtle open-cv wheel sketchpyExampleOpen your code editor and write the example Python code snippets given below. Run your code and see the magic by yourself.Drawing Robert Downey Jr. Using Pythonfrom sketchpy import library as lib
    obj = lib.rdj()
    obj.draw()OUTPUTDrawing Tom Holland Using Pythonfrom sketchpy import library
    myObject = library.tom_holland()
    myObject.draw()OUTPUTMore examplesfrom sketchpy import library as lib

    obj = lib.bts()
    obj.draw()from sketchpy import library as lib

    obj = lib.vijay()
    obj.draw()Drawing Iron Man ASCII Animation Using Pythonfrom sketchpy import library
    myObject = library.ironman_ascii()
    myObject.draw()OUTPUTDrawing fromSVGfileUse the following code to draw a file from svg file, insted of tracing full imageNOTE: use this specific website to convert image to svg, sketchpy is specifically made to work with thiswebsiteonlyfrom sketchpy import canvas
    obj = canvas.sketch_from_svg('FILE PATH')
    obj.draw()Savinga loaded svg fileInsted of waiting for the svg file to load, you can save as .npy file and use that for future usefrom sketchpy import canvas
    obj = canvas.sketch_from_svg('FILE PATH')
    obj.load_svg(filename = 'data.npy')Drawing form.npyfileuse the following code to draw your image from saved data filefrom sketchpy import canvas
    obj = canvas.sketch_from_svg('FILE PATH')
    obj.draw(filename = 'data.npy')Drawing fromraw imageuse the following code to draw any image, it need not to be an svg filefrom sketchpy import canvas
    obj = canvas.sketch_from_image('IMAGE PATH')
    obj.draw(threshold = 127)NOTE: you can change the value of threshold to draw more detailed image, it's range is 0 - 255,use values between 90-190TroubleshootingIf you find any problem, you can pull request, or contact me on eitherinstaordiscordYou can also find video on myyoutube channelAcknowledgementsThanks to all who helped inspire this project.❤See alsoYoutube VideosRelated BlogsContact me on DiscordMy insta IDConsider supporting meupi id sriramanand23@okiciciscan and encourage us to develop more featureseven one rupee make a huge differenceLicenseThis project is licensed under theMIT License.
Package: sketchtml
SketcHTMLHelper library to experiment with HTML fingerprinting.Free software: MIT licenseDocumentation:https://sketchtml.readthedocs.io.FeaturesTODOReferencesLocality Sensitive Hashing for Scalable Structural Classification and Clustering of Web Documents (2013)Enforcing k-anonymity in Web Mail Auditing – Mail-Hash (2016)(patent)Structural Clustering of Machine-Generated Mail (2016)Web-Scale Information Extraction with Vertex (2011)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.2 (2017-06-23)Add lxml.etre.iterparse-based tag sequence iteratorAdd implementation forMailHashAdd implementation forStripped-XPath-lists0.0.1 (2017-06-19)Hachenberg & Gottron HTML tag fingerprint based on LZW
Package: skewdi
Python Web Scrapping ToolThis is a simple module written on the top of BeautifulSoup Module.Simply install it by using :pip install skewdi##How to Use :Do Something like this-->>>from skewdi import skewdi>>>skewdi.fetchAnchor("http://yourDomain.com")It will return all the hyperlinks in that perticular Webpage!>>>skewdi.fetchPara("http://yourDomain.com")It will return all the paragraph tags of the Webpage!>>>skewdi.help()It will show all the avaiable functions with their description.More Functions are comming!>>>skewdi.author()Written By - Samprit (sam123)
Package: skewer
The Skew ElasticSearch RepoSkewer uses skew to enumerate AWS resources and then indexes those resources
into ElasticSearch.InstallationInstall skewer using pip:$ pip install skewerOnce installed, configure your AWS config file as described in the
[skew README](https://github.com/scopely-devops/skew/blob/develop/README.md)
so skew can associate your AWS account IDs with the right profile in your
config file.Finally, run skewer:$ skewer index –host <name of ElasticSearch host> –port 9200This will use skew to enumerate all resources matching the ARN patternarn:aws::::/*and will send the JSON data associated with each resource to the specified
ElasticSearch server for indexing.  A new index will be created namedskewer-<timestamp>and an index alias will also be created calledskewer.Running:$ skewer –helpwill print out help for theskewercli tool.
Package: skewt-scipy
Skew student $t$ distributionSkewt-Scipyis a Python package for skew student $t$ distribution.Skew student $t$ distributionWe employ the definition of the skew-t distribution from Azzalini (2014, Section 4.3.1). Firstly, we need two density functions: $t$ distribution and skew-normal distribution respectively. The $t$ distribution is defined as:$$
f_{t}(x|\nu) = \frac{\Gamma(\frac{\nu+1}{2})}{\sqrt{\nu\pi}\Gamma(\frac{\nu}{2})}\left(1+\frac{x^{2}}{\nu}\right)^{-\frac{\nu+1}{2}}, \quad x\in \mathbb{R},\quad \nu>0,
$$where $\nu$ denotes the degree of freedom. The skew-normal distribution is defined as:$$
f_{sn}(x|\xi,\omega,\alpha) = \frac{2}{\omega}\phi\left(\frac{x-\xi}{\omega}\right)\Phi\left(\alpha \left( \frac{x-\xi}{\omega} \right)\right), \quad x\in \mathbb{R},\quad \omega>0,\quad \alpha\in \mathbb{R},
$$where $\xi, \omega$ are the location and scale parameters respectively. $\alpha$ is the skewness parameter. $\phi(\cdot)$ and $\Phi(\cdot)$ are the standard normal density function and cumulative distribution function respectively.The skew-t variable can be defined as:$$
Z= \frac{Z_{0}}{\sqrt{V}},
$$where $Z_{0}\sim f_{sn}(x|0,1,\alpha)$
and $V\sim \chi^{2}_{\nu}/\nu$ are independent. Then, the density function of $Z$ is$$
f_{st}(\alpha,\nu)= 2f_{t}(x|\nu)F_{t}\left(\alpha x\sqrt{\frac{\nu+1}{\nu+x^{2}}}\Big|\nu+1\right),
$$where $F_{t}(\cdot|\nu+1)$ represent the $t$ cumulative density function with degree of freedom $\nu+1$. As $\alpha\in \mathbb{R}$ and $\nu>0$, then skew-t distribution can degenerate several special distributions.InstallationInstall via pip withpython3-mpipinstallskewt_scipyUsageAs the classskewtinherits from the classrv_continuousofScipy, many methods are available. The shape parametersaanddfrepresent $\alpha$ and $\nu$ (the degree of freedom).MethodDescriptionrvs(a, df, loc=0, scale=1, size=1, random_state=None)Random variates.pdf(x, a, df, loc=0, scale=1)Probability density function.logpdf(x, a, df, loc=0, scale=1)Log of the probability density function.cdf(x, a, df, loc=0, scale=1)Cumulative distribution function.logcdf(x, a, df, loc=0, scale=1)Log of the cumulative distribution function.ppf(q, a, df, loc=0, scale=1)Percent point function (inverse ofcdf).stats(a, df, loc=0, scale=1, moments='mvsk')Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').fit(data)Parameter estimates for generic data.Note that the parameters $\alpha =\pm\infty$ and $\nu=+\infty$ are valid for the above methods.Examplesimportnumpyasnpfromskewt_scipy.skewtimportskewt# random number generatorskewt.rvs(a=10,df=6,loc=3,scale=2,size=10)# probability distributionx=(np.linspace(-50,50,100),)skewt.pdf(x=x,a=-10,df=6,loc=3,scale=2)# log of probability distributionskewt.logpdf(x=x,a=-10,df=6,loc=3,scale=2)# cumulative distributionskewt.cdf(x=x,a=8,df=10,loc=3,scale=2)# log of cumulative distributionskewt.logcdf(x=x,a=8,df=10,loc=3,scale=2)# percent point functionskewt.ppf(np.array([0.5,0.9,0.99]),a=3,df=6,loc=3,scale=2)a=3df=5loc=3scale=2data=skewt.rvs(a=a,scale=scale,df=df,loc=loc,size=10000,random_state=123)skewt.fit(data)skewt.fit(data,fdf=df)# fixed dfskewt.fit(data,fa=a,fdf=df)# fixed a and df
Package: skfeature-gli
scikit-featureis an open-source (GNU General Public License v2.0) feature selection repository in Python developed by Data Mining and Machine Learning Lab at Arizona State University.It serves as a platform for facilitating feature selection application, research and comparative study. It is designed to share widely used feature selection algorithms developed in the feature selection research, and offer convenience for researchers and practitioners to perform empirical evaluation in developing new feature selection algorithms.This is may or may not be a temporary fork of the original repository as development seems to have stalled and various modules have be depreciated due to updates toscikit-learn. I will see if should get reintegrated back into the original project if it ever gets revived again.Forked project informationProject site -https://github.com/lguangyu/scikit-featureProject site -https://github.com/chappers/scikit-featureOriginalscikit-featureproject informationProject site -https://github.com/jundongl/scikit-featureDocumentation -http://featureselection.asu.edu/InstallationFrom SourcesUnpack the source package somewhereRunpip install -e .from the source distribution's top level folderFrom pippipinstallskfeature-gli
Package: skflow
WARNING: We are not maintaining PyPi package anymore. Please install TensorFlow and install Scikit Flow from there.This is a simplified interface for TensorFlow, to get people started onpredictive analytics and data mining.Library covers variety of needs from linear models to Deep Learningapplications like text and image understanding.Why TensorFlow?- TensorFlow provides a good backbone for building different shapes of machine learning applications.- It will continue to evolve both in the distributed direction and as general pipelinining machinery.Why Scikit Flow?- To smooth the transition from the Scikit Learnworld of one-liner machine learning into the more open world of buildingdifferent shapes of ML models. You can start by using fit/predict andslide into TensorFlow APIs as you are getting comfortable.- To provide a set of reference models that would be easy to integrate with existing code.
Package: skga
SKGAWelcome to the SKGA repository! This repository contains theskgapackage, which is a Python library for performing machine learning hyperparameters optimization.InstallationTo install theskgapackage, follow these steps:Make sure you have Python 3.9 installed on your system.Open a terminal or command prompt.Run the following command to install the package using pip:pipinstallskgaThis will automatically download and install theskgapackage along with its dependencies.Basic UsageOnce you have installed theskgapackage, you can start using it in your Python projects. Here's a basic example to get you started:fromsklearnimportsvm,datasetsfromsklearn.model_selectionimporttrain_test_splitfromskgaimportHyperBRKGASearchCV# Load the Iris datasetirisX,irisY=datasets.load_iris(return_X_y=True)# Split the dataset into training and testing setsX_train,X_test,y_train,y_test=train_test_split(irisX,irisY,test_size=0.5,random_state=0)# Define the parameters for the SVM classifierparameters={'kernel':('linear','rbf'),'C':[1,10]}# Create an SVM classifiersvc=svm.SVC()# Create a HyperBRKGASearchCV instanceclf=HyperBRKGASearchCV(svc,parameters=parameters,data=X_train,target=y_train)print("# Optimizing hyperparameters for accuracy\n")# Fit the classifier to the training dataclf.fit(X_train,y_train)print("Best combination of parameters found in the training set:\n")print(clf.cv_results_['best_param_decoded'])print()print("Scores of HyperBRKGA in the training set:\n")print(clf.cv_results_)This example demonstrates how to use theHyperBRKGASearchCVclass from theskgapackage to perform hyperparameter optimization for an SVM classifier.For more detailed examples and use cases, please refer to theexamplesdirectory in the SKGA repository.ContributingIf you find any issues or have suggestions for improvement, please feel free to contribute to this repository. You can submit bug reports, feature requests, or pull requests through theGitHub repository. We appreciate your contributions!LicenseThis project is licensed under theBSD 3-Clause License.
Package: skg-connector
System Knowledge Graphs (SKGs) to AutomataDescriptionModule developed within theAuto-TwinHorizon EU project
to extract data from Neo4j SKGs for automata learning purposes and to store
learned automata into the SKG.RequirementsFollow instructions for thepromg-coreproject to create the SKG.ConfigurationThe default configuration file is available in the./resources/config/folder.The module currently assumes the Neo4j instance to be running onlocalhost:7687:
if this is not the case, update theconfig.inifile accordingly.How to useAvailable queries are implemented as methods of theSkg_Readerclass:get_events(): Returns all Event nodes.get_events_by_date(start_t, end_t): Return Event nodes filtered by timestamp. If both start_t and end_t are given, it returns events such that the timestamp is within range [start_t, end_t]. If only one parameter is specified, it returns events such that the timestamp is greater than start_t or smaller than end_t. If no parameter is specified, it returns all events.get_entities(): Returns all Entity nodes.get_sensors(): Returns all Class nodes.
Package: skgpytorch
skgpytorchGPyTorchModels inScikit-learnwrapper.Exampleimporttorchfromskgpytorch.modelsimportExactGPRegressorfromskgpytorch.metricsimportmean_squared_error,negative_log_predictive_densityfromgpytorch.kernelsimportRBFKernel,ScaleKernel# Define a modeltrain_x=torch.rand(10,1)train_y=torch.rand(10)test_x=torch.rand(10,1)test_y=torch.rand(10)kernel=ScaleKernel(RBFKernel(ard_num_dims=train_x.shape[1]))gp=ExactGPRegressor(train_x,train_y,kernel)# Fit the model (This supports batch training of GP models as well)gp.fit(n_epochs=2,verbose=True,n_restarts=1,verbose_gap=2,batch_size=10,lr=0.1,random_state=0)# Get the predictionspred_dist=gp.predict(test_x)# Access properties of predictive distributionpred_mean=pred_dist.mean# Meanpred_var=pred_dist.variance# Variancepred_stddev=pred_dist.stddev# Standard deviationlower,upper=pred_dist.confidence_region()# 95% confidence region# Calculate metrics (Soon this will be implemented in gpytorch itself)print("MSE:",mean_squared_error(pred_dist,test_y))print("NLPD:",negative_log_predictive_density(pred_dist,test_y))Restart:0,Iter:0,Loss:1.0135,BestLoss:inf
Restart:0,Iter:2,Loss:0.9371,BestLoss:inf
Restart:0,Iter:4,Loss:0.8644,BestLoss:inf
Restart:0,Iter:6,Loss:0.7978,BestLoss:inf
Restart:0,Iter:8,Loss:0.7382,BestLoss:inf
Restart:1,Iter:0,Loss:0.9626,BestLoss:0.6819
Restart:1,Iter:2,Loss:0.8948,BestLoss:0.6819
Restart:1,Iter:4,Loss:0.8239,BestLoss:0.6819
Restart:1,Iter:6,Loss:0.7537,BestLoss:0.6819
Restart:1,Iter:8,Loss:0.6880,BestLoss:0.6819
MSE:0.08736331760883331
NLPD:0.49492106437683103
Package: skgrf
skgrfprovidesscikit-learncompatible Python bindings to the C++ random forest implementation,grf, usingCython.The latest release ofskgrfuses version2.0.0ofgrf.skgrfis still in development. Please create issues for any discrepancies or errors. PRs welcome.DocumentationstablelatestInstallationskgrfis available onpypiand can be installed via pip:pipinstallskgrfEstimatorsGRFForestCausalRegressorGRFForestInstrumentalRegressorGRFForestLocalLinearRegressorGRFForestQuantileRegressorGRFForestRegressorGRFBoostedForestRegressorGRFForestSurvivalUsageGRFForestRegressorTheGRFForestRegressorpredictor usesgrf’s RegressionPredictionStrategy class.fromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromskgrf.ensembleimportGRFForestRegressorX,y=load_boston(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y)forest=GRFForestRegressor()forest.fit(X_train,y_train)predictions=forest.predict(X_test)print(predictions)# [31.81349144 32.2734354  16.51560285 11.90284392 39.69744341 21.30367911#  19.52732937 15.82126562 26.49528961 11.27220097 16.02447197 20.01224404#  ...#  20.70674263 17.09041289 12.89671205 20.79787926 21.18317924 25.45553279#  20.82455595]GRFForestQuantileRegressorTheGRFForestQuantileRegressorpredictor usesgrf’s QuantilePredictionStrategy class.fromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromskgrf.ensembleimportGRFForestQuantileRegressorX,y=load_boston(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y)forest=GRFForestQuantileRegressor(quantiles=[0.1,0.9])forest.fit(X_train,y_train)predictions=forest.predict(X_test)print(predictions)# [[21.9 50. ]# [ 8.5 24.5]# ...# [ 8.4 18.6]# [ 8.1 20. ]]Licenseskgrfis licensed underGPLv3.DevelopmentTo develop locally, it is recommended to haveasdf,makeand a C++ compiler already installed. After cloning, runmake setup. This will setup the grf submodule, install python and poetry from.tool-versions, install dependencies using poetry, copy the grf source code into skgrf, and then build and install skgrf in the local virtualenv.To format code, runmake fmt. This will run isort and black against the .py files.To run tests and inspect coverage, runmake testormake xtestfor testing in parallel.To rebuild in place after making changes, runmake build.To create python package artifacts, runmake dist.To build and view documentation, runmake docs.
Package: skia-pathops
Python bindings for theGoogle Skialibrary'sPath
Opsmodule, performing boolean
operations on paths (intersection, union, difference, xor).InstallTo install or update to the latest released package, run:pip3 install --upgrade skia-pathopsBuildA recent version ofCythonis
required to build the package (see thepyproject.tomlfile for
the minimum required version).For developers we recommend installing in editable mode, and
compiling the extension module in the same source directory:git clone --recursive https://github.com/fonttools/skia-pathops.git
cd skia-pathops
pip install -e .If this fails, try upgrading pip to v18 or later, and try again:pip3 install --upgrade pip
Package: skia-python
Skia python bindingPython binding toSkia Graphics Library.Binding based onpybind11.InstallBinary package is available on PyPI:pipinstallskia-pythonSupported platforms:Linux x86_64, aarch64macOS x86_64, arm64Windows x86_64For Linux platforms, there must be OpenGL and fontconfig installed:apt-getinstalllibfontconfig1libgl1-mesa-glxlibgl1-mesa-driOr:yuminstallfontconfigmesa-libGLmesa-dri-driversFor unsupported environment, check thebuild instruction.ExamplesShowcaseCanvas OverviewCanvas CreationPath OverviewPaint OverviewPython Image I/ODrawing TextsDocumentationhttps://kyamagu.github.io/skia-pythonTutorialReferenceContributingFeel free topost an issueorPR.
Package: skidder
OverviewSkidder is a python logging library. Note that this is veryalphalevel software and things may change dramatically as the project evolves.In real life askidderis any type of heavy vehicle used in a logging operation for pulling cut trees out of a forest.Getting Startedpip install skidderRunning TestsTBDLicenseThe project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: skidings
About this lib:this library was primarly made for people that are getting into networking and sql databases, also requests library ect. this is just a shortcut for learning these librarys and makes you’re code more simple and easier to use, this is a open source project so this means you are able to upload community versions of this applications and add and improve the current source code.Module Functionsget_hwid() // Getting a clients Hwidget_ip() // Getting a clients Ipcheck_user(pastebin_url,username_input) // pastebin_url is where the usernames are kept (PASTEBIN URL RAW), and username is a users input.check_password(pastebin_url,password_input) // pastebin_url is where the password are kept (PASTEBIN URL RAW), and password is a password input.check_ip(pastebin_url) // This function will get the clients current ip and check for it in the pastebin url.send_request(url) // This Function will allow you to send requests to sites/apis.get_sql_data(host_name,user_name,pass_word,db_name,query) // This function will allow you to send a sql query to you’re db.fetch_sql_data(host_name,user_name,pass_word,db_name,query,index1,index2) // This function will allow you to send a sql query with indexis to you’re db.encrypt_key(contents) // This function will allow you to encrypt any text/data you enter.geo_location(ip) // This function will ip lookup any ip entered.reverse_hostname(hostname) // This function will reverse lookup any hostname you enter.phone_lookup(phone_number) //This function will lookup any international number (without the +).webhook_delete(webhook_url) // This function will alow you to delete discord urls.dns_lookup(ip) // This function looks up any dns server.portscan(ip) // This function is a Tcp Portscannercurrent_time() // This function gets the current time for you.webhook_spammer(url,message,delay) // This function is for spamming discord webhooks.
Package: skidmarks
Skid Marks: Check for runs in sequencesQ: how do you check for runs?A: look for skidmarks.This module implements some functions to check a sequence for randomness.
in some cases, it is assumed to be a binary sequence (not only 1’s and 0’s
but containing only 2 distinct values).
Any feedback, improvements, additions are welcomed.>>> from skidmarks import gap_test, wald_wolfowitz, auto_correlation, serial_testWald-Wolfowitzhttp://en.wikipedia.org/wiki/Wald-Wolfowitz_runs_testhttp://support.sas.com/kb/33/092.html>>> r = wald_wolfowitz('1000001')
>>> r['n_runs'] # should be 3, because 1, 0, 1
3>>> r['p'] < 0.05 # not < 0.05 evidence to reject Ho of random sequence
Falsethis should show significance for non-randomness>>> li = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
>>> wald_wolfowitz(li)['p'] < 0.05
TrueAutocorrelation>>> result = auto_correlation('00000001111111111100000000')
>>> result['p'] < 0.05
True>>> result['auto_correlation']
0.83766233766233755Serial Testhttp://books.google.com/books?id=EIbxfCGfzgcC&lpg=PA141&ots=o-8ymmqbs9&pg=PA142#v=onepage&q=&f=false>>> serial_test('101010101111000')
{'chi': 1.4285714285714286, 'p': 0.69885130769248427}>>> serial_test('110000000000000111111111111')
{'chi': 18.615384615384617, 'p': 0.00032831021826061683}Gap Testhttp://books.google.com/books?id=EIbxfCGfzgcC&lpg=PA141&ots=o-8ymmqbs9&pg=PA142#v=onepage&q=&f=false>>> gap_test('100020001200000')
{'chi': 756406.99909855379, 'item': '1', 'p': 0.0}>>> gap_test('101010111101000')
{'chi': 11.684911193438811, 'item': '1', 'p': 0.23166089118674466}gap_test() will default to looking for gaps between the first value in
the sequence (in this case ‘1’) and each later occurrence. use theitemkwarg to specify another value.>>> gap_test('101010111101000', item='0')
{'chi': 11.028667632612191, 'item': '0', 'p': 0.27374903509732523}
Package: skidtools
A collection of utilities and tools to rapidly develop CLI programs. It aims to get rid of boilerplate and aid skiddies.PrerequisitesSkidtools works with Python 3.6 or higher. Support for earlier versions of Python is not provided. Python 2.7 or lower is not supported.InstallationWhile there are several ways of installing Skidtools, the recommended method is by usingpip- the Python package manager.with pip recommendedSkidtools can be installed withpip:pipinstallskidtoolsDocumentationhttps://docs.clast.dev/skidtools/LicenseAbd0s/skidtools is licensed under theGNU General Public License v3.0