Saravanan666/pypi-org huggingface dataset:
Package: sktools
sktoolssktools provides tools to extend sklearn, like several feature engineering based transformers.InstallationTo install sktools, run this command in your terminal:$pipinstallsktoolsDocumentationCan be found inhttps://sktools.readthedocs.ioUsagefromsktoolsimportIsEmptyExtractorfromsklearn.linear_modelimportLogisticRegressionfromsklearn.pipelineimportPipeline...mod=Pipeline([("impute-features",IsEmptyExtractor()),("model",LogisticRegression())])...FeaturesHere’s a list of features that sktools currently offers:sktools.encoders.NestedTargetEncoderperforms target encoding suited for variables with nesting.sktools.encoders.QuantileEncoderperforms target aggregation using a quantile instead of the mean.sktools.preprocessing.CyclicFeaturizerconverts numeric to cyclical features via sine and cosine transformations.sktools.impute.IsEmptyExtractorcreates binary variables indicating if there are missing values.sktools.matrix_denser.MatrixDensertransformer that converts sparse matrices to dense.sktools.quantilegroups.GroupedQuantileTransformercreates quantiles of a feature by group.sktools.quantilegroups.PercentileGroupFeaturizercreates features regarding how an instance compares with a quantile of its group.sktools.quantilegroups.MeanGroupFeaturizercreates features regarding how an instance compares with the mean of its group.sktools.selectors.TypeSelectorgets variables matching a type.sktools.selectors.ItemsSelectorallows to manually choose some variables.sktools.ensemble.MedianForestRegressorapplies the median instead of the mean when aggregating trees predictions.sktools.linear_model.QuantileRegressionsklearn style wrapper for quantile regression.sktools.model_selection.BootstrapFoldbootstrap cross-validator.sktools.GradientBoostingFeatureGeneratorAutomated feature generation through gradient boosting.LicenseMIT licenseCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.4 (2021-03-20)Gradient boosting feature regressor0.1.3 (2020-07-13)Bootstrap cross-validationCyclic featurizer0.1.2 (2020-06-24)L1 linear model and random forestQuantile encoder refactor0.1.1 (2020-06-10)Refactor code, add group featurizers0.1.0 (2020-04-19)First release on PyPI.
Package: skt-war-result
SKT War Resultversion number: 1.0
author: yumenoshimaOverviewLists war results for Japanese servers in Lineage Mobile game.Installation / UsageTo install use pip:$ pip install skt-war-resultOr clone the repo:$ git clone https://github.com/yumenoshima/skt-war-result.git
$ python setup.py installContributingTBDExampleTBD
Package: skua
SkuaA pythonic static site generator.A static site generator written in Python.DocumentationSkua's documentation is build usingMkDocsand is hosted atskua.netlify.com.Command-line interfaceSkua can be used from the command line (still under development). It takes every template it finds and places an HTML template in the chosen output folder.Python scriptingSkua can be imported and used to create Python scripts, which allows for greater flexibility and custom site generation pipelines.VersioningSkua uses romantic versioning.MAJOR: A conceptually major change with (possible) breaking changes.MINOR: A conceptually minor change with (possible) breaking changesPATCH: Guaranteed not to cause any breaking changes. Used for minor changes, such as:Bug fixes in the current versionUpdates to algorithms which don't add extra dependencies.
Package: skulpt_python
Skulpt Python Kernelis the merging of [Skulpt](http://www.skulpt.org/) (a Python implementation that runs in the browser) with [Project Jupyter](http://jupyter.org/) (aka IPython). In addtion, it has an interface to [ProcessingJS](http://processingjs.org/). Skulpt scripts are entered into a cell, where they run independently from other cells.Because Skulpt Kernel uses [MetaKernel](https://github.com/Calysto/metakernel/blob/master/README.rst), it has a fully-supported set of “magics”—meta-commands for additional functionality. A list of magics can be seen at [MetaKernel Magics](https://github.com/Calysto/metakernel/blob/master/metakernel/magics/README.md).Skulpt Kernel in use:[Video](https://www.youtube.com/watch?v=iSGXOU5C3sQ)[Example notebook from video](http://jupyter.cs.brynmawr.edu/hub/dblank/public/Examples/Skulpt%20Python%20Examples.ipynb)You can install Skulpt Kernel with:` pip install--upgradeskulpt_python `or in the system kernels with:` sudo pip install--upgradeskulpt_python `Use it in the notebook with:` ipython notebook `and selectSkulpt Pythonas the kernel for a new notebook.Requires:ipython-3.0+Python2 or Python3metakernel (installed with pip)calysto (installed with pip)Skulpt Kernel supports:MetaKernel Magicsprocessingturtle
Package: skultrafast
What is skultrafast?Skultrafast stands for scikit.ultrafast and is an python package which aims to
include everything needed to analyze data from time-resolved spectroscopy
experiments in the femtosecond domain. Its current features are listed further
down below.The latest version of the package is available ongithub. A build of the documentation can be found atRead
the docs. The documentation
includesInstalltion notes.FundingThe package was created and is maintained byTill Stensitzki. The package was
created while being employed in theHeyne groupand was therefore founded by the DFG viaSFB
1078andSFB 1114. Recent development
focussed on 2D-spectroscopy is part my stay inUltrafast Structual Dynamics
Groupin Potsdam under MÃ¼ller-Werkmeister.Scope of the projectI like to include any kind of algorithm or data structure which comes up in
ultrafast spectropy. I am also open to add a graphical interface to the package,
but as experience shows, a GUI brings in a lot of maintenance burden. Hence, the
first target is a interactive data-explorer for the jupyter notebook.This package also tries its best to follow modern software practices. This
includes version control usinggit, continues integration testing via
github action and a decent documentation hosted onRead the docs.FeaturesThe current releases centers around working with time-resolved spectra:Publication ready plots with few lines.Global fitting of transient Data: DAS, SAS and compartment modelling.Support for polarization resovled datasets.Easy data processing: Selection, filtering, recalibration of data.Automatic dispersion correction of chriped spectra.Modern error estimates of the fitting results vialmfit.Lifetime-density analyses using regularized regression.2D spectroscopy: CLS-decay, digonal extraction, pump-slice-amplidude
spectrum, integration.UsersAt the moment it is mostly me and other people in my groups. I would be happy
if anyone would like to join the project!LicenseStandard BSD-License. See the LICENSE file for more details.
Package: skunk
skunkInsert SVG images into matplotlib elements. Can be used to also compose matplotlib plots by nesting them.pipinstallskunkJupyter NotebooksTo show generated SVGs in Jupyter Notebooks:skunk.display(svg)Overwrite Subplotimportskunkimportnumpyasnpimportosimportmatplotlib.pyplotaspltfig,axs=plt.subplots(ncols=2)x=np.linspace(0,2*np.pi)axs[0].plot(x,np.sin(x))# important line where we set IDskunk.connect(axs[1],'sk')plt.tight_layout()# Overwrite using file path to my svg# Can also use a string that contains the SVGsvg=skunk.insert({'sk':'skunk.svg'})# write to filewithopen('replaced.svg','w')asf:f.write(svg)# or in jupyter notebookskunk.display(svg)OutputSVG in AnnotationRead aboutannotation boxes firstimportskunkfrommatplotlib.offsetboximportAnnotationBboximportnumpyasnpimportmatplotlib.pyplotaspltfig,ax=plt.subplots()x=np.linspace(0,2*np.pi)ax.plot(x,np.sin(x))# new code: using skunk box with id sk1box=skunk.Box(50,50,'sk1')ab=AnnotationBbox(box,(np.pi/2,1),xybox=(-5,-100),xycoords='data',boxcoords='offset points',arrowprops=dict(arrowstyle="->"))ax.add_artist(ab)# sknunk box with id sk2box=skunk.Box(50,50,'sk2')ab=AnnotationBbox(box,(3*np.pi/2,-1),xybox=(-5,100),xycoords='data',boxcoords='offset points',arrowprops=dict(arrowstyle="->"))ax.add_artist(ab)# insert current figure into itself at sk1# insert svg file in sk2svg=skunk.insert({'sk1':skunk.pltsvg(),'sk2':'skunk.svg'})# write to filewithopen('replaced2.svg','w')asf:f.write(svg)# or in jupyter notebookskunk.display(svg)OutputSVG to Replace ImageSometimes you may want a raster image to appear if not using an SVG. This can be done with anImageBox.
The example above is identical, except we replace theskunk.Boxwith askunk.ImageBoxthat has the same
arguments (after first) asOffsetImage# use image box, so can have PNG when not in SVGwithopen('skunk.png','rb')asfile:skunk_img=plt.imread(file)box=skunk.ImageBox('sk2',skunk_img,zoom=0.1)OutputYou can see that the inner image contains the raster now instead of the blue rectangle. This example is overly fancy, normally you won't berecursivelynesting plots so the raster image will only appear if you're not using SVG.Save to PDFI prefercairosvg:importcairosvgcairosvg.svg2pdf(bytestring=svg,write_to='image.pdf')Layout a set of SVGsSometimes you just want to slap a bunch of SVGs together into a grid. You can do that with this method:svg=skunk.layout_svgs(svgs)
Package: skunkbooth
SkunkboothWhySee it in actionUsageInstallationRunMedia locationContributingInstall PoetryClone the repoActivate poetry shellInstall dev depsRun the applicationLogsApp WalkthroughHomeShootRecordEffectsGalleryPreviewQuitSkunkboothA camera app in terminal. One more reason to stay inside the box you call terminal.WhyWith our revolutionary application, you don’t have to leave your terminal and climb a mountain,
just to get your next perfect Instagram picture.
Open terminal, runskunkboothand capture funkiest imagesinsidethe box (aka terminal).We support all the modern operating systems. All you need ispython3.See it in actionUsageInstallationpipinstallskunkboothRunAfter installation, useskunkboothcommand to launch camera.skunkboothMedia locationThe photos and videos that you take are present in the following locationMacOS and Linuxls~/skunkbooth/picturesWindowsdirC:\Users\<username>\skunkbooth\picturesContributingPoetryis used for package management.
For setting up your environment, follow along.Install PoetryMacOS, Linux or WSLcurl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py|python-Windows Powershell(Invoke-WebRequest-Urihttps://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py-UseBasicParsing).Content|python-Clone the repogitclonehttps://github.com/Davidy22/scholarlySkunkJam.gitcdscholarlySkunkJamActivate poetry shellpoetryshellInstall dev depspoetryinstallRun the applicationpython3-mskunkbooth.mainLogsLogs are located inskunkboothfolder.macOS and Linuxtail-f~/skunkbooth/.logs/skunkbooth.logWindows (powershell)Get-ContentC:\Users\<username>\skunkbooth\.logs\skunkbooth.log-WaitApp Walkthrough🏠 HomeHome screen is the first point of contact for the application. It displays the camera output in the screen by default,
when the application is started and enables access/navigation to all features of the application.📷 ShootImage(s) can be taken instantly by clicking on Shoot button in home screen.⏯ RecordRecord button in the home screen toggles video recording and it can be used to start and stop recording.Image and video files generated will be stored inMedia locationas .jpg and .avi respectively.🖌 EffectsThe Effects screen features filters that can be applied to the camera output.To capture images and video by applying filters follow the below steps:Enable the checkbox associated with the desired filter(s).Navigate to the home screen using <👈 Back to 📷> button, which will now display the camera output
post application of the selected filters.Use Shoot or Record options in the home screen as explained before.Play around with multiple filters to capture somegrooviestimages and videos.🖼 GalleryGallery screen is a file browser which displays list of images and videos captured using the application,
as shown in the image below.👁 PreviewPreview screen renders the media file that is opened via gallery screen. This screen currently
supports display of files only in .jpg format, with more formats to be added in the future versions.🛑 QuitThe application can be safely exited using Quit button in home screen.
Package: skunk.web
skunk.web is SkunkWeb 4, a set of of libraries and applications for
web development, including:a caching system for memoizing callables, with in-memory, disk, or
memcached backends;a standalone implementation of SkunkWeb’s venerable STML (Skunk
Template Markup Language, not to be remembered to be confused with
Short Term Memory Loss), a powerful componentized templating
language;a lightweight but effective web controller framework.
Package: skutil
Utilities for scikit-learn.fromskutil.estimatorsimportColumnIgnoringClassifier# use a classifier that can't handle string data as# an inner classifier in some stacked model, for exampleContents1Installation2Basic Use2.1estimators2.2model_selection3Contributing3.1Installing for development3.2Running the tests3.3Adding documentation4Credits1Installationpipinstallskutil2Basic Useskutilis divided into several sub-modules by functionality:2.1estimatorsColumnIgnoringClassifier- An sklearn classifier wrapper that ignores input columns by index.ObjColIgnoringClassifier- An sklearn classifier wrapper that ignores object columns in dataframes.classifier_cls_by_name- Get an sklearn classifier class by name. Also supports lowercasing and some shorthands (e.g. svm for SVC, logreg and lr for LogisticRegression).2.2model_selectionConstrainedParameterGrid- Grid of discrete-valued parameters with constraints.3ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.3.1Installing for developmentClone:gitclonegit@github.com:shaypal5/skutil.gitInstall in development mode, and with test dependencies:cdskutilpipinstall-e".[test]"3.2Running the testsTo run the tests use:cdskutilpytest3.3Adding documentationThe project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, followthese conventions.4CreditsCreated by Shay Palachy (shay.palachy@gmail.com).
Package: sk-variable-handler
Variable HandlerThis is a Python package that provides aVariableHandlerclass for extracting variable values from a string of variable declarations.InstallationTo use theVariableHandlerpackage, you need to install it first. You can install it using pip:pipinstallsk_variable_handlerUsageTo extract variable values from a string of variable declarations, follow these steps:Import theVariableHandlerclass from thevariable_handlermodule:fromsk_variable_handlerimportVariableHandlerCreate an instance of theVariableHandlerclass:variable_handler=VariableHandler()Call theget_valuesmethod of theVariableHandlerinstance, passing the string of variable declarations as an argument. It returns a dictionary containing the variable names as keys and their corresponding values as values.declarations="Your variable declarations"values=variable_handler.get_values(declarations)ExampleHere is an example that demonstrates the usage of theVariableHandler:fromvariable_handlerimportVariableHandlervariable_handler=VariableHandler()declarations="$x=1+2;$y=2+1;$var=12+223+(222+2)+sin(90);$var2=$x+$y;$xy=($var2+$x+$y);$yx=$xy+$var2"expected_result={'$x':'3.0','$y':'3.0','$var':'460.0','$var2':'6.0','$xy':'12.0','$yx':'18.0'}result=variable_handler.get_values(declarations)assertresult==expected_resultTest CasesThe package includes a set of unit tests to verify the functionality of theVariableHandlerclass. The tests cover various scenarios and edge cases to ensure the correct extraction of variable values.To run the tests, execute the script as a standalone Python program:python<filename>.pyThe output will indicate whether all the tests have passed or if there are any failures.Please note that the tests are implemented using theunittestframework. Each test case is independent and tests a specific aspect of theVariableHandlerfunctionality.ContributingContributions to theVariableHandlerpackage are welcome! If you encounter any issues, have suggestions for improvements, or would like to add new features, please create a pull request or open an issue on the GitHub repository.LicenseTheVariableHandlerpackage is distributed under theMIT License. See theLICENSEfile for more information.
Package: skxcs
skxcsskxcs is a SciKit learn wrapper for implementation of XCS algorithmxcs.InstallationUse the package managerpipto install skxcs. You need to haveCythoninstalled.pipinstallskxcsUsageNumeric Valuesfromskxcs.classifiersimportXcsClassifierimportpandasaspdfromsklearn.model_selectionimporttrain_test_split# Numeric valuesnumerical_frame=pd.read_csv('https://raw.githubusercontent.com/kliegr/arcBench/master/data/datasets/iris.csv')numerical_frame.dropna(inplace=True)y=numerical_frame['class']numerical_frame.drop('class',axis=1,inplace=True)X_train,X_test,y_train,y_test=train_test_split(numerical_frame,y,test_size=0.33)classifier=XcsClassifier()# If data input is non binary, classifier automatically uses MLDP discretizer for numeric values# and one hot encoding for categorical values to transform data in both fit and predict methods.classifier.fit(X_train,y_train)# Get prediction arrayy_pred=classifier.predict(X_test)# Get pretty rulesforruleinclassifier.get_pretty_rules():print(rule)# To use get_pretty_rules or pretty_print_prediction methods,# classifier has to transform train and test data first.Categorical valuesimportpandasaspdfromskxcs.classifiersimportXcsClassifierfromsklearn.model_selectionimporttrain_test_split# Categorical valuescategorical_frame=pd.read_csv('https://raw.githubusercontent.com/kliegr/arcBench/master/data/datasets/autos.csv')categorical_frame.dropna(inplace=True)y=categorical_frame['XClass']categorical_frame=categorical_frame.select_dtypes(include=[object])categorical_frame.drop('XClass',axis=1,inplace=True)X_train,X_test,y_train,y_test=train_test_split(categorical_frame,y,test_size=0.25)classifier=XcsClassifier()# You can transform data yourself. You should either transform both training# and testing data, or none of them. It is necessary to ensure correct values are passed to classifier.X_train_bin=classifier.transform_df(X_train,y=y_train)classifier.fit(X_train_bin,y_train)# Note that we don't pass 'y' to transform method when we transform test dataX_test_bin=classifier.transform_df(X_test)# pretty print predictionresult=classifier.pretty_print_prediction(X_test_bin)print(result)Contributing...LicenseMIT
Package: sky130
sky130 gdsfactory PDK 0.9.0gdsfactory pdk based onskywater130documentationInstallationFor users:pip install sky130For developers:git clone https://github.com/gdsfactory/skywater130.git
cd skywater130
make installThen you need to restart Klayout to make sure the new technology installed appears.TODOadd layoutlink spice modelsadd sample netlist and circuit simulation
Package: sky130ph
sky130ph v0.0.6Skywater photonics PDK for the SKY130 process node. The documentation is athttps://byucamacholab.github.io/sky130ph/Installationpip install sky130phTODOAdd component libraryAdd testsTest componentsAuthorsSkandan ChandrasekarSequoia PloegElise BangerterJoel Kartchner
Package: skyagi
SkyAGI: Emerging human-behavior simulation capability in LLMhttps://github.com/litanlitudan/skyagi/assets/4970420/a584b364-e659-476d-a884-932b3c04df61SkyAGIis a python package that demonstrates LLM's emerging capability in simulating believable human behaviors.
Specifically,SkyAGIimplements the idea ofGenerative Agentsand delivers a role-playing game that creates a very interesting user experience.Different from previous AI based NPC systems,SkyAGI's NPC generates very believable human responses.
The interesting observations in this demo show a huge potential for rethinking game development in many aspects, such as NPC script writing.To demonstrate this,SkyAGIprovides example characters fromThe Big Bang TheoryandThe Avengersas a starting point.
Users could also define customized characters by creating config json files likecustomized_character.jsonFor details about the interesting observations, refer to theobservations section.Quick StartInstallationpipinstall--upgradeskyagiOrmakeinstallHow to runexportOPENAI_API_KEY="..."skyagi# orOPENAI_API_KEY="..."skyagiFor example if the OpenAI key issk-VXl2bPhNEeTaGBavUKRtT3BlbkFJjXm7ZCd8XUCMGsdlcqWP, then the exact command would be the following# make sure no quote around the tokenexportOPENAI_API_KEY=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
skyagi# orOPENAI_API_KEY=sk-VXl2bPhNEeTaGBavUKRtT3BlbkFJjXm7ZCd8XUCMGsdlcqWPskyagiTo use example agent configs, download it from here:https://github.com/litanlitudan/skyagi/tree/main/examples(pip install doesn't contain the agent configuration)An example agent configuration (Sheldon) looks something like this:{"name":"Sheldon","age":27,"personality":"Intelligent, rigid, socially challenged, quirky, and arrogant.","memories":["Sheldon is a theoretical physicist who works at Caltech.","Sheldon has an eidetic memory and is highly intelligent, but struggles with social skills and sarcasm.",..."Knock, knock, knock, Penny - This is the specific knock that Sheldon uses when he visits Penny's apartment, which he repeats three times.","Bazinga! - This is Sheldon's catchphrase that he uses to indicate he was joking or playing a prank on someone."],"current_status":"Sheldon is at the Cheesecake Factory"}Interesting observations in this demoHere is a screenshot of a live demo using The Big Bang Theory example.From the conversation, we can observe three interesting points that have not been widely seen in previous systems:Leonard remembered that Penny had asked him to persuade Sheldon to go for a hike, which shows the capability of some kind of memory.Leonard changed his mind after Sheldon whispered to him and even tried to convince Penny to join the scientific effort, which shows that the agents had meaningful progress in the story even without human intervention.All the responses are quite human-like. As a user, it's quite hard to tell whether it's actually an AI behind the responses.Referenceshttps://arxiv.org/abs/2304.03442https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html#create-a-generative-character
Package: sky-api-client
Sky Api ClientPython client for RENXT APIs.Developed by Uddesh atAlmabaseInstallationpip install sky_api_clientExamplesInitialize the clientfromsky_api_clientimportSkyApisky_api=SkyApi('subscription_key','access_token')Get list of all constituentlist=sky_api.constituent.list()Available methodsList all constituentslist=sky_api.constituent.list()Get a specific constituentconstituent=sky_api.constituent.get('constituent_id')Create a new constituentnew_constituent=sky_api.constituent.create({'first':'','last':''})Update an existing constituentupdated_constituent=sky_api.constituent.update('constituent_id',{'first':'','last':''})Delete a constituentsky_api.constituent.delete('constituent_id')List all entity constituentssky_api.address.list('constituent_id')Available Entities and Methodsaddresslistgetcreateupdatedeletetypescode_tablelistgetcreateupdatedeleteconstituentlistgetcreateupdatedeletesearchcustom_field_categorylistcustom_fieldslistcreateupdatedeleteeducationlistgetcreateupdatedeleteemail_addresseslistcreateupdatedeletetypesphonelistgetcreateupdatedeletetypesrelationshiplistgetcreateupdatedeletetable_entrylistgetcreateupdatedeletesubscription_webhooklistgetcreatedeleteonline_presencelistcreategetupdatedeletetypesconstituent_addresslistconstituent_custom_fieldlistconstituent_educationlistconstituent_email_addresslistconstituent_online_presencelistconstituent_phonelistconstituent_relationshiplistThese entities can be used same as above example forconstituent.email_address_list=sky_api.email_addresses.list()Note:- Current version doesn't have refresh token functionality.