Saravanan666/pypi-org huggingface dataset:
Package: scargo
ScargoScargo project was written by Spyrosoft team. Find more information atspyro-soft.com.OverviewThis is the documentation for scargo - a Python-based C/C++ package and software development life cycle manager inspired by RUST cargo idea.scargo can:Create a new project (binary or library)Build the projectRun static code analyzersFix chosen problem automatically based on the checker analysisRun unit testsGenerate documentation from the source codeWork with the predefined docker environment depending on the chosen architectureInstallationScargo is available onpypi, so you can install it with pip:pip install scargoIf system does not find 'scargo' command after installing, add the installation directory to your env paths. On Ubuntu you can find installation directory by running:$ find / -name "scargo"Then add to  PATH:$ export PATH=~/.local/bin:${PATH}Working with scargoYou can find all information on how to work with scargo on official documentation webpage:https://spyro-soft.github.io/scargo/index.htmlProject dependenciesWorking with docker (recommended)dockerdocker-composepippython3Working natively (not recommended, a lot of env setup)Base:python >= 3.8cmake >= 3.24.2cppcheck lib32z1 clang clang-format clang-tidy gcovr doxygen libcmocka0 libcmocka-devlizardDepending on the architecture:compiler (e.g. gcc-arm-none-eabi-9-2020-q2-update gcc-arm-none-eabi)flashing toolsuC HAL and dependent filesmuch more....Work environmentYou can always change work environment between docker or native after project is created.
Just edit the scargo.toml file ([project] -> build-env = "docker" or build-env = "native").Working in dockerIf you create a new project, rundocker compose run scargo-devto run project development image depending on chosen architecture. All dependencies should be already there.
Run scargo commands as you would do natively.If you create a project with --docker flag (scargo new <my_proj> --docker ...) or with any docker flag, by default each scargo command will be triggered in docker.Working nativelyCreate a project with --no-docker flag (scargo new <my_proj> --no-docker ...).Testing custom scargo generated project locallyYou can make changes in scargo and install it locally usingpip install .command when you are in the main project folder.
To test the custom scargo version and have this custom scargo available also inside the docker (crucial for testing), in created project update  docker-compose.yaml:volumes:

  - ..:/workspace
  - /dev:/dev
  - ~/.local/lib/python3.10/site-packages/scargo:/usr/local/lib/python3.8/dist-packages/scargoWhere~/.local/lib/python3.10/site-packages/scargois a path to scargo on your local machine. It the following path is not working, find installation dir usingpip show scargo.To keep this setup betweenscargo updatecommands, in scargo.toml file update alsoupdate-excludeas in following example:update-exclude = [".devcontainer/docker-compose.yaml"]ContributingSee contributing guide onhttps://spyro-soft.github.io/scargo/contributing.html
Package: scarletdb
DocsBasic document database mostly intended for use onreplit.db=ScarletDB([])db.replit("data")# data is replit db the key that the database is stored indb.clear()# empty the db for testing purposesdb.insert_many([{"name":"AJ","pet":"Rabbit"},{"name":"Jack","pet":"Dog"}])# insert some documentsdb.update({"name":"Jack"},{"name":"Tom"})# update some documents, while querying them by a value (does not loop over them, uses a data structure to save them in a way where they can be queried)print(db.get({"pet":"Dog"}))# get a document by another key than beforeDocs are ongithub
Package: scarlet-shark-client
scarlet-shark-pythonScarlet Shark REST API Python client.Installationpip install scarlet-shark-clientUsage examplefromscarlet_shark_client.clientimportClientFactoryclient=ClientFactory.get_client('<your API key>',api_version='v0.4',print_json=True)result=client.search_domain(domain='scarletshark.com')prints{"age":4043,"associated_urls":[],"classification":"security","disclosure_email":"unknown","domain":"scarletshark.com","domain_description":"","ip":"40.[redacted]","reference_url":"","registered":"2012-04-02","service":{"service_email":"","service_id":0,"service_name":"unknown","service_trust":"unknown","service_type":"unknown","service_url":""},"tags":["security"],"threat_actor_aliases":[],"threat_actor_id":0,"threat_classification":"safe","threat_explanation":"The domain is considered to be safe and it hosts no known malicious content.","tracking_domain":false}Supported actionsAPI version 0.4search_dnssearch_domainsearch_emailsearch_hashsearch_ipsearch_networksearch_threat_actorssearch_threat_toolssearch_url
Package: scarlex
Scarlex Asynchronous API Wrapper[Discord] :https://discord.gg/ZKzmmt4gvq[Site] :https://scarlex.org/apiHow To Usejoin our Discord for any support (link above).register your own profile on our website (link above).then login and you're ready to fetch endpoints and check your stats.Examples# import ApiClient from sharex package.fromscarleximportApiClient# import asyncio if you want to run clint Synchronously else the pacakage is in asynchronous format.importasyncio# create an instance of client with name and passwordclient=ApiClient(name="A V I X I T Y",password="pass.word")data=asyncio.run(client.make_request("progressbar?max=100&value=69&size=100&style=3"))# If using asynchronously [can be used only inside async functions]# data = await client.make_request("progressbar?max=100&value=69&size=100&style=3")print(data.json)print(data.status)print(data.message)Developers / ContributorsA V I X I T Y#0001
Package: scarp
SCARP method for scATAC-seq data analysisIncorporating network diffusion and peak location information for better single-cell ATAC-seq data analysisAuthorsyujiating@amss.ac.cnlywu@amss.ac.cnPipelineInstallationWe recommend to create a new environment with Python 3.10:condacreate-npy310python=3.10
condaactivatepy310Thescarppackage can be installed via pip:pipinstallscarpDependenciesscanpy(>=1.9.5)numpy(>=1.25.2)scipy(>=1.11.3)pandas(>=2.1.1)UsagePlease checkout the tutorials athere.1. Preparing your scATAC-seq data in h5ad formatYou can downloaded a example data fromhere.importscanpyasscdata_name='Leukemia'data=sc.read_h5ad('./Example_data/Leukemia.h5ad')2. Running SCARP easily in one stepfromscarpimportmodelCells_df=model.SCARP(adata=data,data_name=data_name,plot_SD=True,verbose=True)parameter descriptions:parameter namedescriptiontypedefaultadatainput scATAC-seq datah5adNonedata_namename of this datasetstrNonemparameter to control NR diffusion intensityfloat1.5gammaparameter to control the threshold for merging adjacent chromosomesint3000betaparameter to control the extent to which prior edge weight decaysint5000return_shapeshape of the returned matrixstr'CN'peak_locuse peak location prior information or notboolTrueparallelparallel computing or not. 0 means automatically determinedint0plot_SDplot the SDs of PCs or notboolTruefig_sizefigure size of the SD plottuple(4,3)save_fileif plot_std is True, the file path you want to savestrNoneverboseprint the process or notboolTrue3. Or you can run SCARP step by step(1) Obtaining the NR diffused matrixt,diffusion_mat=model.SCARP_diffusion_mat(adata=data)(2) Computing the retained dimensionk=model.SCARP_SD_plot(data=diffusion_mat,peaks_num=Peaks_num,title=data_name,plot_SD=True)(3) Dimensional reductionCell_embedding=model.SCARP_cell_embedding(diffusion_mat=diffusion_mat,kept_comp=k)Reproduce resultsFor reproducibility, we provide all the necessary scripts and datahere.
Package: scart
SCARTIntroductionThis repository contains code for the SCART framework, an innovative solution for simulating cyber threats in real-time systems. The SCART framework adds a layer to existing simulators of real-time systems, allowing for the simulation of faults and vulnerabilities. The goal of this framework is to increase the reliability of real-time systems by simulating and testing for possible sensor failures and cyber security attacks. The SCART framework is designed to be modular and extensible, allowing for the addition of new types of faults and vulnerabilities.Here you can find the PoC for MiTM threats.├── AnomalyObjects
│   ├── AbstractAnomalyObjectFile.py
│   ├── Action
│   ├── Condition
│   ├── Listener
├── Scenario
│   ├── BaseScenario.py
│   ├── OneSensorScenario.py
│── history.py
├── __init__.pyInstallationInstructions for installing the SCART package and its dependencies can be found in thescartfolder.# from the base folder (SCART) run:
> python setup.py sdist bdist_wheel
> pip install . #install from current dirUsageInstructions for using the SCART package and its various features can be found in thescartfolder.>>> import scart
>>> from scart import *
>>> dir(scart)
['AnomalyObjects', 'Scenario', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'history']
>>>ContributingIf you wish to contribute to this project, please follow the guidelines outlined in theCONTRIBUTING.mdfile.LicenseThis project is licensed under theLICENSElicense. A copy of the license can be found in theLICENSEfile.CitationIf you use the SCART framework in your research or project, please cite our paper:@article{girstein2023scart,
      title={SCART: Simulation of Cyber Attacks for Real-Time Digital Twins}, 
      author={Kfir Girstein and Eliron Rahimi and Prof. Avi Mendelson},
      year={2023},
      eprint={2304.03657},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}
Package: scartrek
ScarTrekPython package for identifying gene disrupting and restoring indels in whole-genome sequencing data of Mycobacterium tuberculosis samplesDescription"Insertion" or "deletions" (affectionly called "indels") of nucleotides are two types of genetic changes besides
single nucleotide polymorphims (SNPs) that can be detected by analyzing sequencing data. ScarTrek is an application
written in python to detect indels in whole-genome sequencing data mapped to a reference genome, and if gene information
for the referece organism is provided, then determine the genes that have the indels and if the indels influence the
translated product of the gene. This analyses is useful to detect gene inactivations due to indels.InstallationUsing pip$ pip install scartrekFrom GitHubScarTrek project can be downloaded directly from GitHub and python scripts can be run directly from the command line as shown in usage.UsageIf installed using pip, ScarTrek can be run as:$ find-scars [-h] -i INPUT [-m MAPRATE] [-c COVTHRES] [-g GENESEQ] [-p PROTSEQ]If downloaded from GitHub, ScarTrek can be run in the directory ScarTrek/scartrek/ as:$ python find_scars.py [-h] -i INPUT [-m MAPRATE] [-c COVTHRES] [-g GENESEQ] [-p PROTSEQ]where the arguments are:-h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input directory that has mpileup files for each
                        sample. See tests/test1 for example (default: None)
  -m MAPRATE, --maprate MAPRATE
                        Minimum read mapping rate required to consider a
                        sample (default: 20.0)
  -c COVTHRES, --covthres COVTHRES
                        Minimum read coverage required at a position to detect
                        an indel (default: 20)
  -g GENESEQ, --geneseq GENESEQ
                        Gene sequences in the reference genome, default
                        reference: M. tuberculosis (default:
                        ../reference/H37Rv_genes.txt)
  -p PROTSEQ, --protseq PROTSEQ
                        Protein sequences for the reference organism, default:
                        M. tuberculosis (default:
                        ../reference/H37Rv_proteins_from_genbank.txt)
Package: scaruffi
Get some data from scaruffi.com from Python.Piero Scaruffi has written a lot about rock music, jazz, classical, whether it
is reviews or history. It is a valuable ressource for a variety of reasons and
this script aims to make data fetching easier for personal usage.Features:Get a big list of musicians.Get best albums per decade, grouped by rating.This is a work in progress, I would like to add more content to be usable!InstallThis library is available on PyPI:`bash pip install scaruffi `UsageCheck out theScaruffiApifor all available methods.`python from scaruffi.api import ScaruffiApi api = ScaruffiApi() api.get_ratings(1960) # { 9.5: [Release(title='TroutMask Replica', ... `This module can also be used as a command-line tool:`bash scaruffi--helpscaruffi--musicians--offset5555--limit5 # Mooseheart Faith # Morbid Angel # Morcheeba # Morgan Fisher # Morning 40 Federation scaruffi--ratings1960 # 9.5 # - Captain Beefheart - Trout Mask Replica (1969) # 9.0 # - Bob Dylan - Blonde On Blonde (1966) # - Captain Beefheart - Safe As Milk (1967) # ... `
Package: scastpy
ScastPyScastPy is a digital media player written by Python, which can receive and handle screencast requests from mobile phone applications.UsageUsage: scastpy -l [ip] -p [player]

Options:
  -h, --help            show this help message and exit
  -l HOST, --local=HOST
                        the local ip address
  --port=PORT           listening port of HTTP service
  -p PLAYER, --player=PLAYER
                        the player to use
  -c CONFIG, --config=CONFIG
                        config string for player
  --loglevel=LOGLEVEL   set logging level for debuggingExample: use ffmpeg as player and set output directory.$scastpy-l192.168.1.2-pffmpeg-coutput_directory=/tmp--loglevelDEBUGSupported PlayersPlayer is a backend can handle DLNA actions, but also can perform download or video codec.vlc: Provide a user interface to play videosffmpeg: Use ffmpeg to download videosdummy: For testing and developmentSupported ClientsBilibiliBaidu NetDiskiQiyiAirPlay
Package: scatnetgpu
Descriptionscatnetgpuis a module to compute the Scattering Network
representation [1] of an image using the power of a CUDA capable GPU.The output of the computation is compatible with the softwarescatnet.RequirementsPython 2.7 (not tested yet with Python 3.x)A CUDA capable GPU withCUDA
toolkitcorrectly
installedInstallationJust run:$pipinstallscatnetgpuAlternatively, clone the repository and run:$pipinstall.QuickstartLoad or create an image as a Numpy’s ndarray. Here we load an image with
OpenCV:importcv2img=cv2.imread('image.png')Create the ScatNet object:fromscatnetgpuimportScatNetsn=ScatNet(M=2,J=4,L=6)Perform the actual transformationout=sn.transform(img)Nowoutcontains the features of the Scattering Network
representation ofimg.References[1] Bruna, Joan, and Stéphane Mallat. “Invariant scattering convolution
networks.” IEEE transactions on pattern analysis and machine
intelligence 35.8 (2013): 1872-1886.
Package: sca-tools
## Scalability Analysis Tools[![Build Status](https://travis-ci.org/mookerji/sca_tools.svg?branch=master)](https://travis-ci.org/mookerji/sca_tools)[![codecov](https://codecov.io/gh/mookerji/sca_tools/branch/master/graph/badge.svg)](https://codecov.io/gh/mookerji/sca_tools)[![PyPI version](https://badge.fury.io/py/sca-tools.svg)](https://badge.fury.io/py/sca-tools)`sca_tools` analyzes software scalability benchmarking experiments,specifically measurements of throughput as a function of applied loador concurrency. It is a Python library and set of command lineutilities and that will help you:- Model and quantify throughput bottlenecks in your application- Capacity plan- Compare performance benchmarks for regressionsWe rely on Neil Gunther's[Universal Scalability Law](http://www.perfdynamics.com/Manifesto/USLscalability.html)as a model and [lmfit](https://github.com/lmfit/lmfit-py) to performmodel fitting. `sca_tools` differs from existing implementations (see[Related Work](#related-work)) in a few ways:- Emphasis on robust parameter estimation- Support for propagation of parameter uncertanties when computingderived quantities, such as latency, queue size, etc.- Support for experimental measurement uncertainty- Written using Scientific Python librariesThis is alpha software: use it at your **own risk** (e.g., don't useit to make business decisions). It's very much a **work in progress**,but currently includes:- Fitting routines to the USL and basic reporting aroundgoodness-of-fit for USL's convention and coherence parameters.- Graph outputs for best fit model, best fit model confidenceintervals, fit data, residuals, etc.- Command line tools to produce these models from CSV data, as wellas manipulate and aggregate CSV data from computer experiments.## UsageThe `fixtures/` directory contains the SPEC SDM91 load-througputbenchmark ported from Stefan Möding's[R implementation](https://github.com/smoeding/usl) of USL.```shell> python sca_tools/sca_fit.py --model_type usl fixtures/specsdm91.csv----- Summary -----[[Model]]Model(_usl_func)[[Fit Statistics]]# function evals   = 41# data points      = 7# variables        = 3chi-square         = 27453.720reduced chi-square = 6863.430Akaike info crit   = 63.920Bayesian info crit = 63.758[[Variables]]lambda_:   89.9954927 +/- 14.21296 (15.79%) (init= 1000)sigma_:    0.02772863 +/- 0.009121 (32.90%) (init= 0.1)kappa:     0.00010437 +/- 1.99e-05 (19.04%) (init= 0.001)[[Correlations]] (unreported correlations are <  0.100)C(lambda_, sigma_)           =  0.964C(sigma_, kappa)             = -0.467C(lambda_, kappa)            = -0.243```Graphically:![Throughput model](docs/specsdm91-throughput_model.png)## Related Work- https://github.com/smoeding/usl- https://github.com/codahale/usl4j## Citations- Neil J. Gunther. *Guerrilla Capacity Planning: A Tactical Approachto Planning for Highly Scalable Applications andServices*. Springer, Heidelberg, Germany, 1st edition, 2007.- Baron Schwartz. *Practical Scalability Analysis with the UniversalScalability Law*. VividCortex, November 2015.## LicenseCopyright © 2017 Bhaskar MookerjiDistributed under the Apache License 2.0
Package: scatrex
Map single-cell transcriptomes to copy number evolutionary trees. Check out thetutorialfor more information.Installation$ pip install scatrexSCATrEx usesJAXto perform automatic differentiation. By default, SCATrEx installs the CPU-only version of JAX, but we strongly recommend the use of GPU acceleration. Please follow the instructions inhttps://github.com/google/jax#pip-installation-gpu-cudato install the GPU version of JAX.Preprinthttps://doi.org/10.1101/2021.11.04.467244
Package: scatter3d
##示例代码importnumpyasnpfromscatter3d.scatter3dimportscatter3dnum=1000x=np.random.random(500)*num-num/2y=np.random.random(500)*num-num/2z=np.random.random(500)*num-num/2scatter3d.set_data(x,y,z)# scatter3d.host="127.0.0.1"# scatter3d.port=80# scatter3d.start_color = "#ffffff" # 开始渐变色# scatter3d.end_color = "#ffffff"  # 结束渐变色# scatter3d.autorotation=0.0005  # 自动旋转 数字表示旋转速度， 默认不旋转scatter3d.run()然后在浏览器中输入http://127.0.0.1:80即可查看3d散点图
Package: scatterd
scatterdscatterdis a python package to creating scatter plots in a easy and fast manner.⭐️ Star this repo if you like it ⭐️Install scatterd from PyPIpipinstallscatterdImport scatterd packagefromscatterdimportscatterdDocumentation pagesOn thedocumentation pagesyou can find detailed information about the working of thescatterdwith many examples.ExamplesExample: Quick scatterplotExample: Coloring DotsExample: Overlay with Kernel DensityExample: Dark scatterplotSupportThis project needs some love! ❤️ You can help in various ways.

* Become a Sponsor!
* Star this repo at the github page.
* Other contributions can be in the form of feature requests, idea discussions, reporting bugs, opening pull requests.
* Read more why becoming an sponsor is important on the Sponsor Github Page.

Cheers Mate.
Package: scatter-downsample
Downsampling high resolution images using the scattering transformAtlasing high resolution data,Scattering transform, Visualization of imges, Downsampling with multiple methodsUsagepython3 -m scatter_downsample -i IMAGE -n NUMBER OF DOWNSAMPLE [-s] [filtered standard deviation channels] [-d] [directions for gaussian] [-m] [methods]orimport scatter_downsamplein python3 environmentScatterDown(img,ndown2,filtered_std=2,method=1,direction=2):Input:- img: input image
- ndown2: how many times to downsample by a factor of 2
- filtered_std: how many standard deviations maximum should not get filtered out (default 2)
- direction numbers: for gaussian downsampling (default 2)
- method: (default 1)
    - 2*2 mean and standard deviation (1)
    - 2*2 mean and standard deviation with directions (2)
    - Gaussian filtering (3)Output:- high dimensional image in ndarray form
- labels for each channel
- an example visualization that you save as a png fileTestsinstalled by:python -m unittest tests/testcase.pyan example image to test for the shape of output image and labels for method 1 (mean and std)Versions0.2.4Add options for Gaussian DownsamplingAdd Gaussian Flipping and Rotations methodsSetup requirements.txt for auto download required modules0.2.0Add in updated methods (mean/std with directions and gaussian filtering), add command line interfaces with argparse
Package: scattering-compositional-learner
Scattering Compositional LearnerPyTorch implementation of Scattering Compositional Learner [1] for solving Raven's Progressive Matrices.Setup$pipinstallscattering_compositional_learnerUsageimporttorchfromscattering_compositional_learnerimportScatteringCompositionalLearnerx=torch.rand(4,16,160,160)scl=ScatteringCompositionalLearner(image_size=160)logits=scl(x)y_hat=logits.log_softmax(dim=-1)y_hat# torch.Tensor with shape (4, 8)Unit tests$python-mpytesttestsAlternative implementationsThe same model was additionally implemented by:paper authorslucidrainsBibliography[1] Wu, Yuhuai, et al. "The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning." arXiv preprint arXiv:2007.04212 (2020).Citations@article{wu2020scattering,title={The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning},author={Wu, Yuhuai and Dong, Honghua and Grosse, Roger and Ba, Jimmy},journal={arXiv preprint arXiv:2007.04212},year={2020}}
Package: scattermind
scattermindScattermind is a decentralized and distributed horizontally scalable model
execution framework. Worried your model won't fit in memory? Scattermind
splits the model into small independently executable sub-models to accomodate
even small machines (*).(*) Currently only available through plugins. Once the plugins have matured
they will be included in the main distribution.TODOsA rough outline of planned features:node based cachingrecord / replay inputsrecord / replay activitylog granular activitysplit nodesbenchmarkdiffusion modelpush notification on resultcomputation timing estimatesself configurationconfig hotloadingmodel training
Package: scatterplot
Here is an example README file for your scatterplot package:ScatterplotA customizable scatter plot package for Python.InstallationpipinstallscatterplotUsagefromscatterplotimportscatterx=[1,2,3,4]y=[2,4,1,3]# Basic plotscatter(x,y)# Custom colorscolors=['red','green','blue','black']scatter(x,y,c=colors)# Size by y-valuesizes=[10,20,5,30]scatter(x,y,s=sizes)# Colormapscatter(x,y,s=y,cmap='viridis')FeaturesSimple interface with sensible defaultsCustomize colors, sizes, alpha, marker styleSize markers by value for bubble chartsColormaps for continuous sizes/colorsPass through any matplotlib scatter kwargsDevelopmentClone the repo and install in editable mode:gitclonehttps://github.com/yourusername/scatterplot.gitcdscatterplot
pipinstall-e.Run tests with:python-mtests.test_scatterplotLicenseThe MIT License (MIT)Copyright (c) 2023 Sameh Salah
Package: scatterpy
Python module to calculate scattering amplitude and phase matrices of
rotationally symmetric particles based on the T-matrix method.Based on the original Fortran T-matrix code by M.I. Mishchenko. (https://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html). Includes orientation averaging schemes adapted from J. Leinonen (https://github.com/jleinonen/pytmatrix).This is a pure Python reimplementation of the original routines using NumPy, not a Python wrapper around the Fortran code.Currently spheroids, Chebyshev shapes and generalized chebyshev shapes are
implemented. Also includes several empirical raindrop shapes.Download and install with pip:pip install scatterpyTo use:import scatterpy