Saravanan666/pypi-org huggingface dataset:
Package: skelly
Generate a project skeleton so you can start coding right away.Installationpip install skellyUsageOnce installed,skellycan be run as a module:python -m skelly.mainor as a console application:skellyskellyhas a plugin architecture whereskelly.builderscan be registered to
create different types of projects. The default builder builds Python projects.Python ProjectPython projects built by the default builder, using its default template, include:avenvusing the Python installation from whichskellywas invokedpackaging withsetuptoolsconfigured viasetup.cfgtooling configured viasetup.cfgcode formatting withblacklinting withpylintPEP 8 style checking withpycodestylestrict type checking withmypyunit testing withpytestrunning all of the above tools withtoxbuilding and uploading a distribution withbuildandtwinethe project package, itself, ispipinstalled in thevenvin editable modean initialized git repoPrompt the user for values required by the template, then create a project in the
current directory.skellyCreate a project in the directory/tmp/mypkg, without prompting the user because
all of the required template values are provided in the command.skelly \
  -t author "Narvin Singh" \
  -t email "Narvin.A.Singh@gmail.com" \
  -t description "A sample project." \
  -t repo "https://gitlab.com/narvin/mypkg" \
  /tmp/mypkgOnly prompt the user for the repo, which is required by the template, then create
a project in the current directory.skelly \
  -t author "Narvin Singh" \
  -t email "Narvin.A.Singh@gmail.com" \
  -t description "A sample project."This command will raise an error because the repo wasn’t specified, and the-soption was used to prevent prompting the user for missing template values.skelly \
  -s \
  -t author "Narvin Singh" \
  -t email "Narvin.A.Singh@gmail.com" \
  -t description "A sample project."Create a project in/tmp/mypkgwith itsvenvin/tmp/mypkg/.venv310, and
install the packages specified in~/requirements.txtin thevenv. Theenv_dirbuilder option, unless specified as an absolute path, is relative to thetargetdirectory. Thereq_filebuilder option, unless specified as an absolute path,
is relative to the current directory.cd ~
skelly \
  -b env_dir .venv310 \
  -b req_file requirements.txt \
  /tmp/mypkgCreate a project in the current directory using a custom template. The user won’t
be prompted for any template values.skelly \
  -T ~/my_template \
  -t my_template_var fooOther Types of ProjectsComing soon.If there was a hypothetical builder calledjavascript, this command would use it
to build a project in the current directory.skelly -B javascript
Package: skellyforge
postprocessing_guiINSTALLING the GUICreate a 3.9 or 3.10 conda environment environment
-have tested this successfully on 3.9.16 (latest 3.9 version) and 3.10.11 (latest 3.10 version)Navigate to this github repo and use the commandpip install -e.to install the dependencies from the pyproject.tomlSTARTING the GUI:Open uppostprocess_GUI.pyScroll down to theif __name__ == '__main__'section at the bottomChange thepath_to_data_foldervariable to the path to your recording
-NOTE: The GUI is set up to take file paths and file names corresponding to the
FreeMoCap 1.0 release. If you need to change these file paths, you can do so
from theFileManagerclass at the top ofpostprocess_GUI.pyAfter changing the path, run thepostprocess_GUI.pyscript, and the GUI will pop upOPERATING the GUIWhen the GUI pops up you'll see the main menu, from which you can alter and apply
various postprocessing parameters (currently mostly for interpolation/filtering)Press the 'Process Data and View Results' button to run the postprocessing on the skeleton,
and the postprocessed skeleton should appear in the 'post-processed data' skeleton viewerYou can move between the Filtering and Interpolation tabs at the top to more closely look
at how the different processing options affect your dataWhen finished, press the 'Save Out Data' button on the main menu to save the post_processed data
and a .toml file of your processing parameters into the 'output_data' folderDefault processed data file name is 'mediapipe_processed_xyz.npy' and the parameter summary name
is 'postprocessing_settings.toml'
Package: skellytracker
skelly_trackerCurrently, the bare bones of a tracking backend for freemocap.Run skelly_trackerCurrently, it is not pip installable. Download it by runninggit clone https://github.com/freemocap/skelly_tracker.Change directories to skelly_tracker withcd skelly_tracker, and install the requirements withpip install ..Then it can be run withskelly_tracker.Using skelly_trackerA GUI should pop up that looks like this:Choose your image in the file dialog and click run to see the image annotated with the tracking information.
Package: skelly-viewer
InstallationCreate a conda environment using Python 3.9 (though 3.8/3.10 should work too, if that matters) and activate your conda environmentInstall skelly_viewer usingpip install skelly_viewerUsageAfter installing, in your conda terminal (with the right environment active), type `skelly_viewer'You should see a GUI pop up with an empty graph, a slider, and two buttonsClick theLoad a session folderbutton, and select a session folder that you would like to view the data from using the folder dialog
(NOTE: As of right now, this viewer only works with pre-alpha labeled, post-processed data (it looks theDataArraysfolder with a file
calledmediaPipeSkel_3d_origin_aligned.npyinside of it)You can move the slider, and the 3D graph should show your skeletonIf you hit theLoad Videosbutton on the side, select a folder of videos within your current session. When you move the slider, the videos
should load in and appear on the right hand side
Package: skelvis
Edit a file, create a new file, and clone from Bitbucket in under 2 minutesWhen you're done, you can delete the content in this README and update the file with details for others getting started with your repository.We recommend that you open this README in another tab as you perform the tasks below. You canwatch our videofor a full demo of all the steps in this tutorial. Open the video in a new tab to avoid leaving Bitbucket.Edit a fileYou’ll start by editing this README file to learn how to edit a file in Bitbucket.ClickSourceon the left side.Click the README.md link from the list of files.Click theEditbutton.Delete the following text:Delete this line to make a change to the README from Bitbucket.After making your change, clickCommitand thenCommitagain in the dialog. The commit page will open and you’ll see the change you just made.Go back to theSourcepage.Create a fileNext, you’ll add a new file to this repository.Click theNew filebutton at the top of theSourcepage.Give the file a filename ofcontributors.txt.Enter your name in the empty file space.ClickCommitand thenCommitagain in the dialog.Go back to theSourcepage.Before you move on, go ahead and explore the repository. You've already seen theSourcepage, but check out theCommits,Branches, andSettingspages.Clone a repositoryUse these steps to clone from SourceTree, our client for using the repository command-line free. Cloning allows you to work on your files locally. If you don't yet have SourceTree,download and install first. If you prefer to clone from the command line, seeClone a repository.You’ll see the clone button under theSourceheading. Click that button.Now clickCheck out in SourceTree. You may need to create a SourceTree account or log in.When you see theClone Newdialog in SourceTree, update the destination path and name if you’d like to and then clickClone.Open the directory you just created to see your repository’s files.Now that you're more familiar with your Bitbucket repository, go ahead and add a new file locally. You canpush your change back to Bitbucket with SourceTree, or you canadd, commit,andpush from the command line.
Package: skencli
Sken CLIThe full documentation for skencli can be found here:http://docs.sken.ai/en/articles/4331879-advanced-settings-set-up-sken-cli-and-sken-yamlFor test purposes only, you can run:skencli --testIn this state, the CLI will read fromskentest.yamland communicate with our test servers. Theorgidandappidspecified inskentest.yamlmust come from our test servers.
Package: skeptic
SkepticEarly concept stage, feedback is appreciated, but please note that this is not meant to be "useable" or "correct" yet, not even in an early alpha sort of wayA library for easy usage of predictive-power based statistics.Why use predictive power statistics ?Predictive power based statistics are [arguably] better for modeling reality as well as more intuitive for certain problems.When faced with high-dimensionality non-linear problems (e.g. correlation of a set of FMRIs with the presence and severity of brain cancer), "standard" statistical tests can't be used. However, this doesn't mean we can't say anything statistically significant about the above data, it just means we need to use more advanced methods, currently dubbed under machine learning, to find the correlation and assign a p-value to our finding being non-random.When the validity of scientific claims must be explained to a mathematically layman audience, things like the T-test aren't necessarily intuitive to grasp, and make some heavy-handed assumptions about the data. Arguably, at least for some types of problems, predictive power based conclusions are much more intuitive to grasp.Functionality (why you should use this library)This library attempts to provide said predictive power based statistics and in the process tries to abstract away a few things:The process of finding the "best possible model" for a problem, given the amount of compute the researcher has on hand.The process of efficient k-fold cross validation using said predictive model.The process of finding an calculating meaningful errors/accuracy functions based on which to yield a predictive-power correlation (partially abstracted away)The process of cleaning data (e.g. going from a csv file to a pandas dataframe with the correct types assigned to each column), in part.Computing a "p value" analog based on the data and (optionally) input from the researcher about the statistical significance test they would normally attempt to use with the dataset.Various other things that I might add if I find the time and there's some interest in the project:Embedding based techniques for deconfoundingOperating under assumptions about the global distributionOperating under assumptions about the "expected" shape of the sample distribution???RoadmapRoadmap for the feature from the heading aboveWIP - prototype doneWIP - prototype doneWIP - prototype doneWIP - prototype doneNot started yetWhy make this libraryBecause every alternative for this on the market seems to be:Mixing in too many classic statistic assumptions, thus making the tool wider-reaching but diluting it's usefulness for the cases when predictive power is potentially superior.A complex and convoluted mess.Closed source and sometimes paid-for.Too kosher in the machine learning model being used, yielding less than ideal results.How to use this libraryThe documentation is not ready yet, but please seethe integration testsfor some examples of usage.
Package: skeraton
Skeleton Transformer in KerasKeras implementation of "skeleton transformer module", which is mentioned inSkeleton-based Action Recognition with Convolutional Neural Networks.InstallYou can install this module fromPyPIasskeraton.pipinstallskeratonHow to useModuleskeraton.SkeletonTransformerParameters:All parameters are required.timesteps:Timesteps of input time-series data (equal to number of frames, mentioned as 'T' in the paper)kpts_dim:Dimentions of keypoints (usually 2 (x, y) or 3 (x, y, z))output_dim:Dimentions of output (mentioned as 'M' in the paper)Input:x:3-dimentional tensor of shape (batchsize, timesteps, kpts_dim*N) where N is number of jointsPytorch implementationWe also have Pytorch implementation of this module:https://github.com/Yutsuro/skeleton-transformer-Pytorch
Package: skerrorlearner
skerrorlearneris anError Learning Packagefor Machine Learning use cases. It isavailable for both Regression and Classification problems under Supervised Machine Learning. This helps build models thatlearn the error of the current modelbeing built. This approach is taken towardsMachine Learning Model Performance Improvement.Download StatsDocumentationYou can read the documentationhere.LicenseApache License 2.0InstallationUsing pipYou can use pip to install skerrorlearner. Copy the below command and paste in Command Prompt to install skerrorlearner.pip install skerrorlearnerTo upgrade the package, copy the below command and paste in Command Prompt to upgrade skerrorlearner.pip install skerrorlearner--upgradeUsageAs we highly believe in hands-on rather than reading documentations, we have got usage guides in the form of .ipynb notebooks. Below are the linked usage guides.Regression Use CaseClassification Use CaseFurther, if you fork the Skerrorlearner Use Case Demo directory, you’ll be able to get the data on top of which skerrorlearner was tested. You’ll also be able to get the .ipynb notebook to understand how the library works.Once you have forked the library, we’d highly recommend you to read the dockstring of each method falling under skerrorlearner package to know what parameters are to be passed and what is the use of the method.Support & AdvantagesThe library supports below algorithms to build Error Models.Regression Use CaseScikit LearnNon-Scikit LearnLinear RegressionXGBoostSupport Vector MachineLightGBMDecision TreeRandom ForestK-Nearest NeighborsAdaBoostGradientBoostClassification Use CaseScikit LearnNon-Scikit LearnLogistic RegressionXGBoostSupport Vector MachineLightGBMDecision TreeCatBoostRandom ForestK-Nearest NeighborsAdaBoostGradientBoostGaussianNBAdvantagesSupports Hackathon Data PredictionSupports Production Live Data Prediction
Package: sketchbook
A template engine built with async/await syntax support for asyncio and curio.Intallation$pipinstall-UsketchbookSource Codesketchbookis open sourced underApache License 2.0and its
source code is hosted onGitHub.RequirementsPython 3.8.0+aiofiles>=0.6,<1(Optional, used bysketchbook.AsyncSketchFinder)Alternative Event LoopBeside theasynciomodule from the Python standard library, Sketchbook
can also be used withcurio.DocumentationThe Documentation is hosted onRead the Docs.LicenseCopyright 2021 Kaede HoshikawaLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: sketch-converter
Image to Sketch ConverterFond of **sketches**, but bad at **art**? 🥴Here's a little something for people like you and me.
This is a simple python script that converts yourcaptured imagesinto asketchin real-time with just one line of command, fascinating? 😎This script is written with the help of the OpenCV library in python.All the sketches will be saved in the parent directory itself.Quick Setup# Clone the repositorygitclonehttps://github.com/samyak-jn/sketch-converter.git
poetryshell
poetryinstall
sketch_converter--helpCamera Usage# video_src = 1 , grayscale mode = 1sketch_convertervideo-capture11Contributing ✔️Please contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.All the issues/features are welcome. Open a PR and let's have a discussion.Licensesketch-converter is licensed underMIT, hence it is open source for all.Copyright © 2023 Onuralp SEZER, Samyak Jain
Package: sketch-dask-extension
Sketch Dask ExtensionThis is a very simple extension to extend the support ofSketchto Dask Dataframes instead of only Pandas.
This isexperimentaland not meant to be used in critical environments.How to installpython-mpipinstallsketch-dask-extensionHow to usefromdask.dataframeimportread_csvimportsketch-dask-extensiondataframe=read_csv('...')dataframe.sketch.ask('tell me something interesting')ContributingWe are open to collaboration! If you want to start contributing you can in different ways:Search for an issue in which you would like to work and open a PR with the resolution.Create an issue with something that you would like to see and work on it opening a PR with the resolution if you feel confident for.Create a PR with something that you would like to seeIn any case we will only provide our feedback and either approve it or ask for some change/revision.SupportFor support in using this library, please join the #help Slack channel. The Discord community is very friendly and great about quickly answering questions about the use and development of the extension.Click here to join our Discord community!About 👯‍♂️With ❤️ fromYDataDevelopment teamLicenseMIT License
Package: sketch-document-py
.sketchdocument for pythonSketchstores documents in.sketchformat, a zipped
archive of JSON formatted data and binary data such as images.Inspired bysketch-hq/sketch-documentBuilt package is avaliable inPypi, install with Pippipinstallsketch-document-pySketch file format schemas and APIs.This project contains the APIs to work with Sketch
documents and document elements in Python dataclass.sketch-file-format-py: Python dataclass type hint to strongly type objects
representing Sketch documents, or fragments of Sketch documents in TypeScript
projects.sketch-file: Python APIs to read and write.sketchfiles.DevelopmentTo build this project, you need install Python build dependency management toolPoetry, to install Poetry , followPoetry installation guideTo install nessasary deps and CLI tools, including a task runnerPoe the Poet(CLI executable namedpoe) that work with Poetry, run command:This will also install current package to your environment rootFor further usages of Poetry Install, checkPoetry InstallpoetryinstallTo generate Sketch Dataclass type file, which is nessasary for build or install development, run command:For further usages of Poe the Poet, checkPoe the Poet Homepagepoegen_typesTo check project typing, run command:For further usages of Mypy, checkMypy DocumentationpoemypyTo run project test and coverage, run command:For further usages of Coverage, checkCoverage.py DocumentationpoetestTo build project to wheel and tar, run command:For further usages of Poetry build, checkPoetry BuildpoebuildTo publish project, run command:For further usages of Poetry, checkPoetry PublishpoepublishFor further usages of Poetry, checkPoetry Documentation
Package: sketch-ds
InstallationRun./build.sh, which should callpython3 -m build.We currently supportpython3 setup.py install, but this may be deprecated.Usepython3 setup.py installor pip to build and install.This should build easily on x86-64.On Apple silicon, you may need to addARCHFLAGS="-arch arm64"to your environment or on the function call.
Package: sketcher
WarningThis project is deprecated.
Due to bugs and incoherency, as well as a relatively poorly though interface I decided to deprecate this project.
A better scoped, better designed version may come later.What is this ?sketcheris a python module which may be used to quickly create littles
program (sketches) that produce graphical output.  It is obviously deeply
inspired by processing and try to get the same feeling in the python world. It
is different from processing.py in that it has no dependencies to Java
environnement and it use a more Pythonic approach of a sketch. By the waysketcherdoes not try to stick to the processing API but a lot of methods
names are the same.How to install it ?The simpliest way to install this is to usepip.
Run$ pip3 install sketcherand that is all.You could also copy the sketcher folder from this repository and put it in your
sketches directory.How to use it ?A basic sketch consist in a class of arbitrary name having two methodssetupandloop. The class have to be decorated by the decoratorsketchfrom
thesketchermodule. Here is the default empty sketch :fromsketcherimportsketch@sketchclassSk:defsetup(self):pass# Here come the initialisationdefloop(self):pass# Here come the per-frame actionsIn theloopandsetupmethods you have access to all the useful methods
from theSketchclass (thanks to the decorator) to produce graphical output
and more.Then you can run a sketch like this :$ python3 path/to/your/sketch.pyFurther explanations are availablehere.About graphical backendTo produce graphical outputsketchercan use different backends.
For now tkinter and pyglet are implemented. The two backends expose roughly
the same features however some differences remains. If you notice any let me
know so I can improve the compatibility.ContributeAny kind of contribution is welcomed. You could propose a new graphical backend
or extend the functionnalities of sketches. Please contact me if you are
intereseted.
Package: sketchify
SketchifyA simple sketching software to convert an image to a realistic sketch!Installationpip install sketchifyInstallation notessketchify depends onopencv-contrib-python.Usagefrom sketchify import sketch
img = sketch.normalsketch(â€˜path/to/image.extensionâ€™, 'path/to/save/image', 'saveimagename')ExamplesGo to tests/test_images for more examples.Obama OriginalObama SketchifiedColloseum OriginalColloseum SketchifiedArticlehttps://medium.com/analytics-vidhya/image-to-pencil-sketch-in-just-2-lines-of-code-292de57be483
Package: sketch-image
Sketch ImageConvert Images to SketchesInstallationpipinstallsketch-imageGet Startedsketch-image--image<image_path>--output<output_path>Note: The--imageand--outputflags are optional.The CLI will prompt for input image path until supplied.The output will be placed in the Pictures directory within the user's home directory by default.For help, run:sketch-image--help
Package: sketchKH
SketchKHDistribution-Informed Sketching with Kernel HerdingOverviewWe provide a set of functions for distribution-aware sketching of multiple profiled single-cell samples via Kernel Herding. Our sketches select a small, representative set of cells from each profiled sample so that all major immune cell-types and their relative frequencies are well-represented.Please see our paper for more information (ACM-BCB 2022) :https://arxiv.org/abs/2207.00584Updated : June 27, 2023InstallationDependenciesPython >= 3.6, anndata >= 0.7.6, numpy >= 1.22.4, scipy >= 1.7.1, tqdmYou can install the package withpipby,pip install sketchKHAlternatively, you can clone the git repository by,git clone https://github.com/CompCy-lab/SketchKH.gitExample usageTo perform sketching, first read in a preprocessed.h5adadata object. This dataset contains multiple profiled single-cell samples. Hence, sketches will select a limited set of cells from each profiled sample. We refer to each profiled sample as asample-set.importanndataimportosadata=anndata.read_h5ad(os.path.join('data','nk_cell_preprocessed.h5ad'))Then simply sketch your data with 500 cells per sample-set by,# Inputs# adata: annotated data object (dimensions = cells x features)# sample_set_key: string referring to the key within adata.obs that contains the sample-sets to subsample# sample_set_inds: (alternative to specifying sample_set_key) list of arrays containing the indices of the sample-sets to subsample# gamma: scale parameter for the normal distribution standard deviation in random Fourier frequency feature computation# frequency_seed: random state# num_subsamples: number of cells to subsample per sample-set# n_jobs: number of tasks# ----------------------------# Returns:# kh_indices: list of indices referencing the subsampled cells per sample-set# adata_subsample: downsampled annotated data object (dimensions = num_subsamples*sample-sets x features)# ----------------------------fromsketchKHimport*kh_indices,adata_subsample=sketch(adata,sample_set_key='FCS_File',gamma=1,num_subsamples=500,frequency_seed=0,n_jobs=-1)