Saravanan666/pypi-org huggingface dataset:
Package: scutlogin
Purpose: login the Unified identity authentication system of South China University of TechnologyWarning: Do not use for illegal purposes!Requirements: execjspip install pyexecjsCall function:(1) Method:from scutlogin.login import *
 GetLoginState().login(un, pd, server_url)(2) Parameter description:un --> student number
 pd --> student password
 server_url --> service url(3) Rules of service url:https://sso.scut.edu.cn/cas/login? --> location of unified identity authentication system
 service=server_url --> the server url you need(4) Examples:IAmOK --> https://sso.scut.edu.cn/cas/login?service=https://iamok.scut.edu.cn/cas/login
 Integrated information service --> https://sso.scut.edu.cn/cas/login?service=https://my.scut.edu.cn/up/
 Student educational administration system (2018) --> https://sso.scut.edu.cn/cas/login?service=http://jw2018.jw.scut.edu.cn/sso/driotlogin(5) If login is successful:Directly use <r.post> or <r.get> to send request
Package: scuts
#SCUTS - Standard shortcutsThis repo intends to holds good programming heuristics for Data Scientist wanting to code smoothly.Based on self-experience, it combines “heuristics” and “tricks” in order to make the Pythonista’s life easier.This repo also holds some scripts in the ressources/scripts section that should make people’s life easier when it comes to installs on Ubuntu !## Remarks :This folder uses pipenv for dependencies handling. Go PIPENV !## Image functions## Scraping functions## Other interesting ressourcesDocumenting :https://github.com/ericholscher/sphinx-tutorial/blob/master/start.rstFRENCH : if you want to deploy amazing python packages and look for a good tuto :http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/notebooks/td1a_unit_test_ci_correction.html
Package: scuttlebuddy
ScuttleBuddy | External Scripting Framework (13.23)This framework provides developers a way to develop applications by reading the external memory of League of Legends. This is meant to be used as a training tool for developers and not intended nor created to be used in online play.Memory reading utilizes the pyMeow library.ScuttleBuddy SetupCreate python environment with the tools of your choosingconda,miniconda,venv, etc.Install the ScuttleBuddy Framework into your virtual environmentpip install scuttlebuddyInstallpyMeow(v1.53.36)Download the zip file from the link above and place into the root directory of your project / virtual environment.Run the following command to install pyMeow:pip install pyMeow-1.53.36.zipSimple UsageThis script prints all enemies who have a higher health value than the local player.fromscuttlebuddyimportLeagueReaderfromscuttlebuddy.ModelsimportPlayerEntityfromtypingimportListif__name__=='__main__':lr:LeagueReader=LeagueReader()local_player:PlayerEntity=lr.local_playerenemy_players:List[PlayerEntity]=lr.enemy_playerslp_health:float=local_player.healthforenemyinenemy_players:e_health:float=enemy.healthife_health>lp_health:print(f'THICCer Player found:{enemy.name}with{e_health}hp')DocumentationThe base class for the entire framework is theLeagueReaderclass. This class gives access to everything in real-time during a LoL Match.# Import the classfromscuttlebuddyimportLeagueReader# Initialize the classlr:LeagueReader=LeagueReader()TheLeagueReaderclass gives access to multiple properties relating to in-game entities.fromtypingimportListfromscuttlebuddy.ModelsimportPlayerEntity,MinionEntitylocal_player:PlayerEntity=lr.local_playerteam_players:List[PlayerEntity]=lr.team_playersenemy_players:List[PlayerEntity]=lr.enemy_playersall_players:List[PlayerEntity]=lr.all_playersteam_minions:List[MinionEntity]=lr.team_minionsenemy_minions:List[MinionEntity]=lr.enemy_minionsall_minions:List[MinionEntity]=lr.all_minionsModelsThePlayerEntityclass represents a player/champion in the game. This class holds numerous properties with real-time information about the player in-game.namelevelteam_idis_targetablehealthmax_healthmanamax_manaapadbonus_attack_speed_percentmagic_resistarmormagic_pen_flatmagic_pen_percent (NOT IMPLEMENTED)armor_pen_percent (NOT IMPLEMENTED)lethalityattack_rangegame_posscreen_posis_visibleon_screenCurrently Used OffsetsThis is here just as a small documentation for developers working on the libraryLocalPlayer
HeroList

ViewProjMatrix

ObjectName
Level
Team
Targetable
Health
MaxHealth
Mana
MaxMana
AbilityPower
BaseAttackDamage
BonusAttackDamage
BonusAttackSpeed
MagicResistance
BonusMagicResistance
Armor
BonusArmor
MagicPenetration
Lethality
AttackRange
XPosition
YPosition
ZPosition
IsVisible
Package: scuwl
💀 Scuwl 💀Simple custom wordlist generatorScuwl (skull) is a Python CLI program that quickly and easily generates a wordlist from a webpage. The idea for Scuwl was inspired by the program Cewl. Scuwl defaults to a crawling depth of zero and most webpages return a wordlist in less than a second. Using a crawling depth of one generally takes a few minutes.Scuwl is fast because it recursively scrapes websites asynchronously.
Scuwl minimizes its memory footprint by processing HTML as it goes and updating the wordlist in memory as a set. By default Scuwl keeps unique words, three characters long and over, and removes all punctuation.Note: Using a crawling depth of over one remains untested.FeaturesFast recursive asynchronous web requests using aiohttpCLI options gives you control over the generated wordlistSimple Python codebase (< 175 lines)Low memory usage (~100MB)Installation$ python -m pip install scuwlUsage$ scuwl -h
usage: scuwl.py [-h] [-a] [-d DEPTH] [-H HEADERS] [-m MIN_LENGTH]
                [-M MAX_LENGTH] [-o OUTFILE] [-P PROXY] [-p] [-t]
                [-u USER_AGENT] [-v]
                url

💀SCuWl💀, Simple custom wordlist generator.

positional arguments:
  url                   url to scrape

options:
  -h, --help            show this help message and exit
  -a, --alpha           extract words with alphabet characters only,
                        default=False
  -d DEPTH, --depth DEPTH
                        depth of search, default=0
  -H HEADERS, --headers HEADERS
                        json headers for client
  -m MIN_LENGTH, --min-length MIN_LENGTH
                        minimum length of words to keep, default=3
  -M MAX_LENGTH, --max-length MAX_LENGTH
                        maximum length of words to keep, default=20
  -o OUTFILE, --outfile OUTFILE
                        outfile for wordlist, default=stdout
  -P PROXY, --proxy PROXY
                        proxy address for client
  -p, --punctuation     retains punctutation in words
  -t, --tables          extract words from tables only, default=False
  -u USER_AGENT, --user-agent USER_AGENT
                        user-agent string for client
  -v, --version         show program's version number and exitExamplesGenerate wordlist and send to stdout

$ scuwl https://github.com/petebuffon/scuwl
1000
122
150
2022
20220930
...Generate wordlist and save as wordlist.txt

$ scuwl -o wordlist.txt https://github.com/petebuffon/scuwl
$ wc -l wordlist.txt
309 wordlist.txtKeep punctuation

$ scuwl -p -o wordlist.txt https://github.com/petebuffon/scuwl
$ head wordlist.txt
(2022-09-30)
(scrapes
(skull)
(~80mb)
--depthUse a crawl depth of one (scrapes all links from input webpage)

$ scuwl -d 1 -o wordlist.txt https://github.com/petebuffon/scuwl
$ wc -l wordlist.txt
6675 wordlist.txt
Package: scvae
scVAE: Single-cell variational auto-encodersscVAE is a command-line tool for modelling single-cell transcript counts using variational auto-encoders.Install scVAE using pip for Python 3.6 and 3.7:$ python3 -m pip install scvaescVAE can then be used to train a variational auto-encoder on a data set of single-cell transcript counts:$ scvae train transcript_counts.tsvAnd the resulting model can be evaluated on the same data set:$ scvae evaluate transcript_counts.tsvFor more details, see thedocumentation, which include a user guide and a short tutorial.Release History2.1.4 (2020-06-30)Better handling of indefinite losses during training.2.1.3 (2020-06-29)Fix loading cell and gene names for H5 data sets.Report expected model directory path when scVAE cannot find a model during evaluation for easier troubleshooting.2.1.2 (2020-04-07)Export of decomposition of data sets and latent values as compressed TSV files.Export of predictions as compressed TSV files.Fix potential crash duringt-SNE decomposition.2.1.1 (2020-02-24)Requires TensorFlow 1.15.2 because of a security vulnerability.Export of latent values as compressed TSV files.Make folder names and filenames more safe on Windows.Regrouped analyses, so fewer analyses are performed by default. All available analyses can be performed using--included-analyses all.Fix loading of KL divergences when evaluating VAE models.Fix crash during model analyses, if the model did not exist.2.1.0 (2019-11-12)Requires Python 3.6 or 3.7 as well as TensorFlow 1.15.Documentation with user guide and tutorial.Support for sparse matrices in HDF5 format.Improved support for Loom files by following conventions.Scatter plots of classes against the primary latent feature as well as the two primary latent features against each other when evaluating a model.Fix crash related toargparsewhen using Python 3.6.2.0.0 (2019-05-18)Complete refactor and clean-up including structuring as Python package.Easier loading of custom data sets.Batch correction included in models for data sets with batch indices.Learnable mixture coefficients for the GMVAE model.Full covariance matrix for the GMVAE model.Sampling from models.1.0 (2018-04-25)Initial release.
Package: sc-var
SC-VARAn approach to interpreting disease-associated human variants using single-cell epigenomicsWhat can sc-var do?Identify risk genes, gene sets, and cells related to different stages and diseases.Infer cell types involved in complex traits and diseases using single-cell epigenomes AND does not rely on any other annotations and other Omics data.How to install?pip install sc-varcheckhttps://pypi.org/project/sc-var/AboutUsageCheck Usage.ipynb for detailsData request:Single cell ATAC-seq data &
Peak co-accessibility Data &
GWAS data
Package: scvelo
scVelo - RNA velocity generalized through dynamical modelingscVelois a scalable toolkit for RNA velocity analysis in single cells; RNA velocity
enables the recovery of directed dynamic information by leveraging splicing kinetics1. scVelo collects different
methods for inferring RNA velocity using an expectation-maximization framework2, deep generative modeling3,
or metabolically labeled transcripts4.scVelo's key applicationsestimate RNA velocity to study cellular dynamics.identify putative driver genes and regimes of regulatory changes.infer a latent time to reconstruct the temporal sequence of transcriptomic events.estimate reaction rates of transcription, splicing and degradation.use statistical tests, e.g., to detect different kinetics regimes.Citing scVeloIf you include or rely on scVelo when publishing research, please adhere to the
following citation guide:EM and steady-state modelIf you use theEM(dynamical) orsteady-state model, cite@article{Bergen2020,title={Generalizing RNA velocity to transient cell states through dynamical modeling},volume={38},ISSN={1546-1696},url={http://dx.doi.org/10.1038/s41587-020-0591-3},DOI={10.1038/s41587-020-0591-3},number={12},journal={Nature Biotechnology},publisher={Springer Science and Business Media LLC},author={Bergen, Volker and Lange, Marius and Peidli, Stefan and Wolf, F. Alexander and Theis, Fabian J.},year={2020},month=aug,pages={1408–1414}}veloVIIf you useveloVI(VI model), cite@article{Gayoso2023,title={Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells},ISSN={1548-7105},url={http://dx.doi.org/10.1038/s41592-023-01994-w},DOI={10.1038/s41592-023-01994-w},journal={Nature Methods},publisher={Springer Science and Business Media LLC},author={Gayoso, Adam and Weiler, Philipp and Lotfollahi, Mohammad and Klein, Dominik and Hong, Justin and Streets, Aaron and Theis, Fabian J. and Yosef, Nir},year={2023},month=sep}RNA velocity inference through metabolic labeling informationIf you use the implemented method for estimating RNA velocity from metabolic labeling
information, cite@article{Weiler2023,title={Unified fate mapping in multiview single-cell data},url={http://dx.doi.org/10.1101/2023.07.19.549685},DOI={10.1101/2023.07.19.549685},publisher={Cold Spring Harbor Laboratory},author={Weiler, Philipp and Lange, Marius and Klein, Michal and Pe’er, Dana and Theis, Fabian J.},year={2023},month=jul}SupportFound a bug or would like to see a feature implemented? Feel free to submit anissue.
Have a question or would like to start a new discussion? Head over toGitHub discussions.
Your help to improve scVelo is highly appreciated.
For further information visitscvelo.org.
Package: scvf
Spikes ComputerVision FrameworkSpikes ComputerVision Framework, or scvf for short,
is a framework that is made to make programming CV for the FRC easierThis framework wrapsopencvand pipelines generated bygripin a way that is efficient and elegant.Installation Instructions:$ python3 -m pip install scvfAPIcommunication through network tablesuse thepipeline_namekey to send the pipeline_nameuse thecamera_idkey to send the camera iduse theexposurekey to send the exposure for the camerapipeline compatibilityThough we recommend grip as the main tool to generate cv2 pipelines
using grip is not strictly requiredYou can provide any object to server as a pipeline as long as it contains the next two methods:process()- a method that processes a given imageget_output()- a method that returns the output of the processingIO functionsscvf receives two functions that are responsible for communications with external data sources.settings_supplier(callback)this function receives settings and supplies them to callback provided to it.output_consumer(output)this function sends the output of the image processing to it's next destinationNotesGRIP and SCVF compatibilityif you are usinggripto generate your pipelines keep in mind that the pipelines generated from it
aren't compatible with scvf out of the box due to differences between python3 and python2as of now, these changes are required:change the enum for theblurtype to apython3-enumchange the findContours function as instructedhereadd aget_output()method that is compatible with the API specified aboveOpencv 4Switch between theim2and contourcontoursvariables in thefilter_contoursfunction of your pipelineIO implementationsmake sure that your customoutput_consumeris compatible with the output provided by your pipelines.
Package: scvi-criticism
scvi-criticismEvaluation metrics for scvi-tools modelsGetting startedPlease refer to thedocumentation.InstallationYou need to have Python 3.8 or newer installed on your system. If you don't have
Python installed, we recommend installingMiniconda.To install the latest development version of scvi-criticism:pipinstallgit+https://github.com/YosefLab/scvi-criticism.git@mainRelease notesSee thechangelog.ContactFor questions and help requests, you can reach out in thescverse discourse.
If you found a bug, please use theissue tracker.Citationt.b.a
Package: scvi-tools
scvi-tools(single-cell variational inference
tools) is a package for probabilistic modeling and analysis of single-cell omics
data, built on top ofPyTorchandAnnData.scvi-tools is part of the scverse project (website,governance) and is fiscally sponsored byNumFOCUS.
Please consider making a tax-deductibledonationto help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.Analysis of single-cell omics datascvi-tools is composed of models that perform many analysis tasks across single- or multi-omics:Dimensionality reductionData integrationAutomated annotationFactor analysisDoublet detectionSpatial deconvolutionand more!In theuser guide, we provide an overview of each model.
All model implementations have a high-level API that interacts withscanpyand includes standard save/load functions, GPU acceleration, etc.Rapid development of novel probabilistic modelsscvi-tools contains the building blocks to develop and deploy novel probablistic
models. These building blocks are powered by popular probabilistic and
machine learning frameworks such asPyTorch
LightningandPyro. For an overview of how the scvi-tools package
is structured, you may refer tothispage.We recommend checking out theskeleton
repositoryas a
starting point for developing and deploying new models with scvi-tools.Basic installationFor conda,conda install scvi-tools -c conda-forgeand for pip,pip install scvi-toolsPlease be sure to install a version ofPyTorchthat is compatible with your GPU (if applicable).ResourcesTutorials, API reference, and installation guides are available in
thedocumentation.For discussion of usage, check out ourforum.Please use theissuesto submit bug reports.If you'd like to contribute, check out ourcontributing
guide.If you find a model useful for your research, please consider citing
the corresponding publication (linked above).ReferenceIf you usescvi-toolsin your work, please citeA Python library for probabilistic analysis of single-cell omics dataAdam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran, Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto, Mohammad Lotfollahi, Valentine Svensson, Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov, Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan, Jeffrey Regier & Nir YosefNature Biotechnology2022 Feb 07. doi:10.1038/s41587-021-01206-w.along with the publicaton describing the model used.You can cite the scverse publication as follows:The scverse project provides a computational ecosystem for single-cell omics data analysisIsaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & Fabian J. TheisNature Biotechnology2023 Apr 10. doi:10.1038/s41587-023-01733-8.
Package: sc-vortex-2d
sc-vortex-2dThis library provide numerical results of s-wave Caroli-de Gennes-Matricon mode (CdGM mode) at
$T/T_c=0.3, 0.5, 0.8$.
You can get the eigenenergy, eigenfunction and pair potential at each temperature.DescriptionIt is known that there are low-energy excitation levels inside the s-wave vortex core in the superconductor.
These states are got by solving following Bogoliubov-de Gennes equation(BdG eq) self-consistently.\left[-\frac{1}{2k_{F}\xi_{0}}\nabla^{2}-\mu\right]\mathcal{U}_{q}(\boldsymbol{r})+\Delta(\boldsymbol{r})\mathcal{V}_{q}(\boldsymbol{r}) = E_{q}\mathcal{U}_{q}(\boldsymbol{r})\left[\frac{1}{2k_{F}\xi_{0}}\nabla^{2}+\mu\right]\mathcal{V}_{q}(\boldsymbol{r})+\Delta^{*}(\boldsymbol{r})\mathcal{U}_{q}(\boldsymbol{r}) = E_{q}\mathcal{V}_{q}(\boldsymbol{r})\Delta(\boldsymbol{r})=g\sum_{E_{q}\leq E_{\mathrm{c}}} \mathcal{U}_{q}(r)\mathcal{V}_{q}^{*}(r)[1-2f(E_{q})]Here, BdG eq is rewritten in dimensionless form using Pippard length $\xi_{0} = \hbar v_{F}/\Delta_{0}$ and zero-temperature bulk gap $\Delta_{0}$.
$f(E_{q})$ is Fermi distribution function. Solutions in an isolated vortex, especially CdGM mode is given by following form.\begin{bmatrix}
\mathcal{U}_{n}(r, \theta) \\
\mathcal{V}_{n}(r, \theta)
\end{bmatrix}
=\frac{1}{\sqrt{2\pi}}
\begin{bmatrix}
u_{n}(r)e^{in\theta} \\
v_{n}(r)e^{i(n + 1)\theta}
\end{bmatrix}Here, $n$ corresponds to angular momentum quantum number, i.e. CdGM mode is characterized by this number. In this library, the range of $n$ is integers of in $[-100, 99]$. Note that the part of $u_{n}(r), v_{n}(r)$ in the right side of above formula is one of the target of this library, not the left side of it.InstallYou can install viaPyPI. For example,$ pip install  sc-vortex-2dUsage"""Sample python code"""fromsc_vortex_2d.vorteximportVortexInstanceT03fromscipyimportinterpolateinstance:VortexInstanceT03=VortexInstanceT03()delta:interpolate.CubicSpline=instance.get_pair_potential()e0:float=instance.get_ith_eigen_energy(0)# lowest energy level in the region of e > 0.u0,v0=instance.get_ith_eigen_func(0)# get wave functionsparams=instance.Parameters# Parameters of the system. Enum.Linkshttps://webpark1378.sakura.ne.jp/F. Gygi and M. Schlüter, Phys. Rev. B, 41, 822 (1990)
Package: scvoting-vjbaskar
scvotingThis is a voting method to obtain the density (smoothed votes) of a 10 sc-RNASeq dataset on a common reference sc-RNASeq data's landscape which can be viewed using umap, fdg etcThe method works with scanpy annData objects only and contains two parts.Projecting onto reference landscapeThis package follows the Xiaonan Wang's method of projecting a 10x sc-RNASeq dataset onto a reference sc-RNASeq data.
After projectionVoting and smoothingFor every cell in the dataset its NN in the reference dataset gets 1 vote.Smooth out the votes for each cell in the reference dataset by sharing them equally among 100 NN in it. So if its vote is 40 you share 40/100 among each of them.Compute this for WT and mutant datasets.Plot their difference or logFC etc on the reference landscape (UMAP)Example runfromscvotingimportProjector# Initantiationvotp=Projector(adata=adata,ref_adata=adata_niki,ref_hvg=niki_hvg,npcs=25)# Proj onto refvotp.project_on_ref()# Plot PCAs (works only with X11, use jupyter on remote servers)votp.plotPCA()# Compute distances between cells in two datasets in PCA spacevotp.pwdist()# Voting startsvotp.voting(tot_votes=500000)# Compute distances between cells in reference datasetsvotp.pwdist(rtype='ref')# Smooth the voting for better visualisationvotp.vote_smoothing()# Smoothed votesresults=votp.votes_smooth