Saravanan666/pypi-org huggingface dataset:
Package: scslat
scSLAT: single cell spatial alignment toolsscSLATpackage implements theSLAT(SpatialLinkedAlignmentTool) model to align single cell spatial omics data.Directory structure.
├── scSLAT/                  # Main Python package
├── env/                     # Extra environment
├── data/                    # Data files
├── evaluation/              # SLAT evaluation pipeline
├── benchmark/               # Benchmark pipeline
├── case/                    # Case studies in paper
├── docs/                    # Documentation files
├── resource/                # Other useful resource 
├── pyproject.toml           # Python package metadata
└── README.mdTutorialTutorial ofscSLATishere, if you have any question please open an issue on githubInstallationDockerDockerfile ofscSLATis available atenv/Dockerfile. You can also pull the docker image directly fromhereby:dockerpullhuhansan666666/slat:latestPyPINoteInstallingscSLATwithin a newconda environmentis recommended.First, we create a clean environment and installscSLATfrom PyPI. Then we also need install dependencies forpygmanually. We default install with CUDA 11.7. Please referherefor CPU version or different CUDA versions.Warningold NVIDIA driver may raise error, please update NVIDIA driver to the latest version.condacreate-nscSLATpython=3.8-y&&condaactivatescSLAT
pipinstallscSLAT
pipinstallpyg_libtorch_scattertorch_sparsetorch_clustertorch_spline_conv-fhttps://data.pyg.org/whl/torch-2.0.0+cu117.htmlDevelopment versionFor development purpose, clone this repo and install:gitclonegit@github.com:gao-lab/SLAT.gitcdSLAT
pipinstall-e".[dev,docs]"pipinstallpyg_libtorch_scattertorch_sparsetorch_clustertorch_spline_conv-fhttps://data.pyg.org/whl/torch-2.0.0+cu117.htmlConda (Ongoing)We plan to provide a conda package ofscSLATin the near future.Reproduce manuscript resultsPlease follow theenv/README.mdto install all dependencies. Please checkout the repository to v0.2.1 before installscSLAT.Download and pre-process data follow thedata/README.md.Whole benchmark and evaluation procedure can be found in/benchmarkand/evaluation, respectively.Every case study is recorded in the/casedirectory in the form of jupyter notebook.
Package: scsm
SteamCMD Server Manager ( SCSM )SCSM is a python program used to manage SteamCMD servers. It includes a core library, basic configuration file management, and a command line interface.FeaturesBackup / RestoreInstall / Update / ValidateStart / Stop / Restart / KillMonitor running serversMultiple server supportRequirmentspython (3.9+)pipsteamcmdtmuxIf SteamCMD is not available in your repository you can install it through SCSM itself by using thescsm install steamcmdcommand.InstallInstall using pip.pip install scsmBasic Usagescsm setup
scsm install gmod
scsm start gmod
scsm --help
Package: sc-sme-analysis
A simple Python projectThis project provides a simple utility to analysis SME reports.InstallationIt is possible to install the tool withpip:pip install sc-sme-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-sme-diff-analysis
A simple Python projectThis project provides a simple utility to analysis SME difference.InstallationIt is possible to install the tool withpip:pip install sc-sme-diff-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: scSpatial
scSpatialThis software aims to allow analysis of 2D spatial transcriptomics data by integrating segmentation methods and information transfer methods in a graphical UI. Interactions with your images are allowed by the multi dimentional image viewerNapari. This package provides segmentation  of the cells and mapping of gene expression data. This is controlled by a widget in the Napari viewer.1. SegmentationFor high resolution gene expression analysis, where the exact position of a gene is known (such as inHybISS), segmentation of the cells are critical to allow cell level analysis of gene expression. We include several options for cell segmentation:Segmentation using Cellpose (nuclei)Segmentation using Cellpose (cytoplasm)Loading of 3rd party segmentation (ImageJ, Ilastik, CellProfiler etc.)Cellposecan be used with data including only nuclei staining or images with both nuclei and cytoplasm markers. To achive high gene mapping ratios, segmentation using cytoplasm markers are prefered! When loading 3rd party segmentation images, these should have unique pixel values for each object in the segmentation, with the value 0 reserved for background.2. Information transferWith a segmented image, it is possible to map genes to objects (cells) creating a table with gene expression for each object. This information is similar to the data in scRNA-seq datasets, and information transfer between a reference dataset and the spatial mapping can be performed using theTangrammethod. Here we have implemented theBoneFightmodification from Linnarssons lab which allows for cell cluster based computation.3. VisualizationCurrently, we support visualization of gene expression and cell type prediction. This section will be expanded as new tools are implemented.Getting startedInstallationInstall Miniconda or Anaconda and create a virtual environment with python 3.7 and activate it.condacreate-nscSpatialpython=3.7
condaactivatescSpatialInstall scSpatial using pip:pip install scSpatialNext we install some dependencies that are not available via pip:pipinstallgit+https://github.com/linnarsson-lab/BoneFight.git@8c1ec1f
pipinstallgit+https://github.com/linnarsson-lab/loompy.git@e0963fbRunning napari with the scSpatial widgetTo open Napari with the scSpatial widget, run the scSpatial command in the terminal:scSpatialIf you want to start napari from within your own pipeline/script, import thescSpatial.app.Appclass and instantiate it.fromscSpatial.appimportAppapp=App()Change Logv1.0.2Bumped version of cellpose to > 2.0.4Removed resample during cell pose model evaluation to speed up segmentation.v1.0.1Constrained Cellpose to version 1.0.2 as later versions cased errors.V1.0.0Initial release
Package: scs-re
Modified Regex for Syntactically Constrained LLM SamplingThescs_repackage is a modified version of the pythonremodule that returns a match for any prefix of a string matching the original regex pattern. For example, the pattern^abcdmatches the stringabc. This behavior is used to consider the validity of beams under a particular regex pattern during a language model's output generation.Note: This behavior is only expected for patterns that begin with^and end with$and therefore define a match contstraint over all characters in a sequence.Building from sourceWe use the _sre module from the python 3.11 branch of Python/cpython/Modules/_sre. You can clone the original source by runningsvnexporthttps://github.com/python/cpython/branches/3.11/Modules/_sre_sreThe module requires python 3.11 headers to compile. To install runwgethttps://www.python.org/ftp/python/3.11.3/Python-3.11.3.tar.xz
tarxfPython-3.11.3.tar.xzcdPython-3.11.3
./configure--enable-optimizations--enable-shared
make-j$(nproc)sudomakealtinstallThen you can install the package withsudo python3.11 setup.py installto install.The python version you install to must be the same version whose headers were compiled against.
Package: scss_tools
SCSS ToolsSCSS Tools is a bundle of tools for making web development with SCSS in Python much easier. It monitors your SCSS file directory for file change and recompile automatically for you. It also has Compass and Blueprint CSS framework built-in, To use SCSS Tools, you need to write a YAML configuration file. Following is an example of configuration fileexample/scss.yaml:---

monitor:
    # path to Scss directory for monitoring
    scss_dir:
        scss

compiler:
    # verbosity of message output
    verbosity: 2

    # compress
    compress: True

    # debug info
    debug_info: False

    # root of static files
    static_root:
        static

    # directory to output sprite images
    asset_root:
        static/asset

    # paths to import
    load_paths: [
        static/scss
    ]

    # path of input files and path of file to output
    scss_files: [
        [scss/style.scss, static/style.css],
        [scss/style2.scss, static/style2.css],
    ]

...To start monitoring and compiling SCSS, just type:scss_monitor -c scss.yamlThe path to configuration is scss.yaml, so you can also type:scss_monitorIt has built-in Compass/Blueprint SCSS framework, therefore, you can leverage
them directlyexample/scss/style.scss:@import "compass/reset";

@include blueprint-global-reset;And you will getexample/static/style.css:a, abbr, acronym, address, applet, article, aside, audio, b, big,
blockquote, body, canvas, caption, center, cite, code, dd, del,
details, dfn, div, dl, dt, em, embed, fieldset, figcaption, figure,
footer, form, h1, h2, h3, h4, h5, h6, header, hgroup, html, i, iframe,
img, ins, kbd, label, legend, li, mark, menu, nav, object, ol, output,
p, pre, q, ruby, s, samp, section, small, span, strike, strong, sub,
summary, sup, table, tbody, td, tfoot, th, thead, time, tr, tt, u, ul,
var, video {
  margin: 0;
  padding: 0;
  border: 0;
  font-size: 100%;
  font: inherit;
  vertical-align: baseline;
}
body {
  line-height: 1;
}
ol, ul {
  list-style: none;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
caption, td, th {
  text-align: left;
  font-weight: normal;
  vertical-align: middle;
}
blockquote, q {
  quotes: none;
}
blockquote:after, blockquote:before, q:after, q:before {
  content: "";
  content: none;
}
a img {
  border: none;
}
article, aside, details, figcaption, figure, footer, header, hgroup,
menu, nav, section, summary {
  display: block;
}InstallationTo install SCSS Tools, you can type:pip install scss_toolsIf you perfer easy_install, then type:easy_install scss_toolsCommandsscss_compileCompile SCSS into CSS files.scss_monitorMonitor and compile SCSS into CSS files.Source codeSource code is available atBitbucket.0.1.1Fix pyscss config API change issue0.1.0Initial version
Package: scStateDynamics
scStateDynamicsIntroductionscStateDynamicsis a computational method to thoroughly decipher the tumor cell state dynamics in response to drug treatment by modeling single-cell level expression changes.System RequirementsPython version: >= 3.7InstallationThescStateDynamicspython package can be installed directly via pip:pip install scStateDynamicsTutorialThe example data used in this tutorial can be downloaded fromhere.The step-by-step tutorials are available in thetutorial directory.Citation
Package: sctdl
DeepTL_devVersion: 0.0.6_dev0.0.1_dev update: integrate deeptl with anndata0.0.2_dev update: support pyscenic regulons0.0.3_dev update: support large scale data analysis0.0.4_dev update: support random state0.0.5_dev update: support trajectory draw graph0.0.6_dev update: integrate scanpy pca
Package: scTenifoldpy
scTenifoldpyThis package is a Python version ofscTenifoldNetandscTenifoldKnk. If you are a R/MATLAB user,
please install them to use their functions.
Also, pleasecitethe original paper properly
if you are using this in a scientific publication. Thank you!Installationpip install scTenifoldpyUsagesscTenifold can be imported as a normal Python package:scTenifoldNetfromscTenifold.dataimportget_test_dffromscTenifoldimportscTenifoldNetdf_1,df_2=get_test_df(n_cells=1000),get_test_df(n_cells=1000)sc=scTenifoldNet(df_1,df_2,"X","Y",qc_kws={"min_lib_size":10})result=sc.build()scTenifoldKnkfromscTenifold.dataimportget_test_dffromscTenifoldimportscTenifoldKnkdf=get_test_df(n_cells=1000)sc=scTenifoldKnk(data=df,ko_method="default",ko_genes=["NG-1"],# the gene you wants to knock outqc_kws={"min_lib_size":10,"min_percent":0.001},)result=sc.build()Command Line toolOnce the package is installed, users can use commandline tool to generate all the resultsUse this command to create a config.yml file,python-mscTenifoldconfig-t1-p./net_config.ymlNext, open the config file, add data path, and edit the parameters.Then use the command below to produce the scTenifoldNet results:python-mscTenifoldnet-c./net_config.yml-o./output_folderOr use the command below to produce the knockout results:python-mscTenifoldknk-c./knk_config.yml-o./output_folder
Package: sctk
sctkSingleCell analysisToolKit: A large collection of functions built around the scanpy ecosystem that faciliates preprocessing, clustering, annotation, integration and some down-stream analysis of scRNA-seq data.InstallationpipinstallsctkIf you encounter dependency clashes, create a fresh python 3.9 virtual environment (e.g. via conda) and install SCTK there. It may also help to update pip:pipinstall--upgradepipUsage and DocumentationSCTK's documentation is availablehere, and features a tutorial and API reference for scAutoQC (an automated QC workflow).
Package: scTM
scTM is a package for spatial transcriptomics for single cell that uses topic modelling, solved with stochastic variational infernce. The interesting
part is with the formulation of topic models, we can get interpretable embedding which are useful for downstream analysis.Currently available modules: STAMPFree software: MIT licenseDocumentation:https://JinmiaoChenLab.github.io/scTM/.FeaturesSTAMP: A spatially-aware dimensional reduction designed for spatial data.Minimal InstallationpipinstallscTMorcondacreate--namesctmpython=3.8gitclonehttps://github.com/JinmiaoChenLab/scTM.gitcondaactivatesctmcdscTMpipinstall.Basic UsageCheck out our usage of STAMP in the documentation athttps://jinmiaochenlab.github.io/scTM/notebooks/stamp/example1/CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: sctokenizer
sctokenizerA Source Code TokenizerSupports those languages:C, C++, Java, Python, PHPHow to installpip install sctokenizerHow to useUsesctokenizer:importsctokenizertokens=sctokenizer.tokenize_file(filepath='tests/data/hello_world.cpp',lang='cpp')fortokenintokens:print(token)Or create newCppTokenizer:fromsctokenizerimportCppTokenizertokenizer=CppTokenizer()# this object can be used for multiple source fileswithopen('tests/data/hello_world.cpp')asf:source=f.read()tokens=tokenizer.tokenize(source)fortokenintokens:print(token)Or better solution:fromsctokenizerimportSourcesrc=Source.from_file('tests/data/hello_world.cpp',lang='cpp')tokens=src.tokenize()fortokenintokens:print(token)Result is alistofToken. EachTokenhas four attributes includingtoken_value, token_type, line, column:(#, TokenType.SPECIAL_SYMBOL, (1, 1))
(include, TokenType.KEYWORD, (1, 2))
(<, TokenType.OPERATOR, (1, 10))
(bits/stdc++.h, TokenType.IDENTIFIER, (1, 11))
(>, TokenType.OPERATOR, (1, 24))
(using, TokenType.KEYWORD, (3, 1))
(namespace, TokenType.KEYWORD, (3, 7))
(std, TokenType.IDENTIFIER, (3, 17))
(;, TokenType.SPECIAL_SYMBOL, (3, 20))
(int, TokenType.KEYWORD, (5, 1))
(main, TokenType.IDENTIFIER, (5, 5))
((, TokenType.SPECIAL_SYMBOL, (5, 9))
(), TokenType.SPECIAL_SYMBOL, (5, 10))
({, TokenType.SPECIAL_SYMBOL, (6, 1))
(cout, TokenType.IDENTIFIER, (7, 5))
(<<, TokenType.OPERATOR, (7, 11))
(", TokenType.SPECIAL_SYMBOL, (7, 13))
(Hello World, TokenType.STRING, (7, 14))
(", TokenType.SPECIAL_SYMBOL, (7, 25))
(;, TokenType.SPECIAL_SYMBOL, (7, 26))
(return, TokenType.KEYWORD, (8, 5))
(0, TokenType.CONSTANT, (8, 12))
(;, TokenType.SPECIAL_SYMBOL, (8, 13))
(}, TokenType.SPECIAL_SYMBOL, (9, 1))TODOSupport other languages:Matlab, Javascript, Typescript,...Auto detect languageParse source to a tree of tokens???
Package: sc-toolbox
sc-toolboxWarningThis package is not properly maintained. It was primarily used for in-house analyses at theTheislabandSchillerlab. Do not expect it to be well tested or documented.
However, contributions are welcome and we will provide guidance if required.FeaturesCreate minimal containers for single-cell data analysis with ScanpyAPI for advanced Scanpy based plots and analysesInstallationYou can installsc-toolboxviapipfromPyPI:$pipinstallsc-toolboxUsageimportsc_toolbox.apiassctPlease see theUsage documentation.CreditsThis package was created withcookietempleusingcookiecutterbased onHypermodern_Python_Cookiecutter.
Most scripts were developed byMeshal Ansariand the package was designed byLukas Heumos.
Package: scTOP
=========================================================================================
scTOP: Single-cell Type Order ParametersA Python package for finding projections onto known cell phenotyes, given matrices of raw RNA count data.
The theoretical background for this project can be found inEpigenetic Landscapes Explain Partially Reprogrammed Cells and Identify Key Reprogramming Genes <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003734>_ by Alex H. Lang, Hu Li, James J. Collins, and Pankaj Mehta.A manuscript containing the technical details of applying our method to single-cell RNA-seq will be available in January 2023.InstallationTo install using pip:pip install sctopTo install from github: clone or download this repo and import the module fromsctop/sctop.py. Seetutorial/tutorial.ipynbfor an example.(In progress!) Documentation available via Read the Docs <https://sctop.readthedocs.io/>_Please report bugs or request new features in the Github "Issues" tab. You can also contact me directly atmariay@bu.edu.DependenciesNumPyPandasSciPySources for reference databasesMouse Cell Atlas <http://bis.zju.edu.cn/MCA/>_
Package: sctour
scTourscTour is an innovative and comprehensive method for dissecting cellular dynamics by analysing datasets derived from single-cell genomics.It provides a unifying framework to depict the full picture of developmental processes from multiple angles including the developmental pseudotime, vector field and latent space.It further generalises these functionalities to a multi-task architecture for within-dataset inference and cross-dataset prediction of cellular dynamics in a batch-insensitive manner.Key featurescell pseudotime estimation with no need for specifying starting cells.transcriptomic vector field inference with no discrimination between spliced and unspliced mRNAs.latent space mapping by combining intrinsic transcriptomic structure with extrinsic pseudotime ordering.model-based prediction of pseudotime, vector field, and latent space for query cells/datasets/time intervals.insensitive to batch effects; robust to cell subsampling; scalable to large datasets.Installationpip install sctourDocumentationFull documentation can be foundhere.ReferenceQian Li, scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics. Genome Biology, 2023
Package: sctr
Supervisor ControllerIntroductionThe sctr is a supervisor controller that gives non-privileged users access to
start stop their applications. The secondary goal is to create a workflow where
a privileged user create new applications.Probably this project will grow to be a full supervisord controller, but we'll
stick to the initial and secondary goals right now.InstallationTODO: create installation documentationUsageTODO: create usage documentationSupportThe sctr project is one ofCandango Open Source
Groupinitiatives. It is available underApache License,
Version 2.0.This website and all documentation are licensed underCreative
Commons 3.0.
Package: sctrader
easy to use to trade in China StockInstallationpip install sctraderUpgradepip install sctrader –upgradeQuick Startimport sctrader

user = sctrader.use('ht')

user.prepare('account.json')

user.balancereturn:[{ 'asset_balance': '资产总值',
   'current_balance': '当前余额',
   'enable_balance': '可用金额',
   'market_value': '证券市值',
   'money_type': '币种',
   'pre_interest': '预计利息' ]}

user.positionreturn:[{'cost_price': '摊薄成本价',
   'current_amount': '当前数量',
   'enable_amount': '可卖数量',
   'income_balance': '摊薄浮动盈亏',
   'keep_cost_price': '保本价',
   'last_price': '最新价',
   'market_value': '证券市值',
   'position_str': '定位串',
   'stock_code': '证券代码',
   'stock_name': '证券名称'}]

user.entrustreturn:[{'business_amount': '成交数量',
  'business_price': '成交价格',
  'entrust_amount': '委托数量',
  'entrust_bs': '买卖方向',
  'entrust_no': '委托编号',
  'entrust_price': '委托价格',
  'entrust_status': '委托状态',  # 废单 / 已报
  'report_time': '申报时间',
  'stock_code': '证券代码',
  'stock_name': '证券名称'}]

user.buy('162411', price=5.55)

user.sell('16411', price=5.65)
Package: sctransfer
The supporting Python package of SAVER-XThis package is designed to be used by the R code ofSAVER-X.
It contains simplified code from the pythondcapackage and the new Python code for transfer learning.One can also use this Python package for pre-training using public data. Instructions on pre-training will come out soon.Installation:pip install sctransferThe package only supports Python (>=3.5).Update: the package now works with tensorflow 2.Update: the package requires tensorflow version <=2.2.0 and keras version <=2.3.0.Update: the package has been updated to work with the newer version of scanpy.To test whether the installation is successful or not, you can try with the following code. The mtx file for testing can be downloadedhereimport sctransfer.api as api
api.autoencode(mtx_file = "shekhar_downsampled.mtx")
Package: scTSS
InstallationYou can install from this GitHub repository for latest (often development)
version by following command linepipinstall-Ugit+https://github.com/StatBiomed/scTSSIn either case, add--userif you don’t have the write permission for your
Python environment.Quick startscTSS-countSTEP1:   ProcessingscTSS mainly deal with the output from cellranger (a common alignment tool for 10x data).The preprocessing procedure based on the output file of cellranger.1.cd/cellranger_out/outs2.samtoolsviewpossorted_genome_bam.bam|LC_ALL=Cgrep"xf:i:25">body_filtered_sam3.samtoolsview-Hpossorted_genome_bam.bam>header_filted_sam4.catheader_filted_sambody_filtered_sam>possorted_genome_bam_filterd.sam5.samtoolsview-bpossorted_genome_bam_filterd.sam>possorted_genome_bam_filterd.bam6.samtoolsindexpossorted_genome_bam_filterd.bampossorted_genome_bam_filterd.bam.baiSTEP2:   Run scTSS-countscTSS-count--gtf$gtfFile--refFastq$fastFile--bam$possorted_genome_bam_filterd.bam-c$cluster_toscTSS.tsv-o$output_fileFold--modeUnannotationWant to learn about more parameter, you can usescTSS-count--helpto check.You can find out the example file in the test folder. Please make sure you also have the same column name.Here, you can select one of the mode from annotation and unannotation.Unannotation means that you can detect novel TSS. The distance between different TSS may be wide.Annotation means that you can detect TSS based on the annotation. The distance between different TSS may be narrow.You can check our paper to learn more detail.scTSS-quantscTSS-quant-g$filtered_feature_bc_matrix-c$cluster_toscTSS.tsv--countOut$scTSS_count_folder-mcluster-o$scTSS_quant_folderPlease usescTSS-quant--helpto check more parameter.Here, you can select one of the mode from cluster and disease.Cluster means that you can detect cell type-specific TSS. Any multiple groups detection can use this mode.Disease mode help you select disease-specific TSS. Any two groups detection can use this mode.