Saravanan666/pypi-org huggingface dataset:
Package: sashimi.py
sashimi.pyTutorialswhat is sashimi.pysashimi.py is a tool for visualizing various next-generation sequencing (NGS) data, including DNA-seq, RNA-seq, single-cell RNA-seq and full-length sequencing datasets.Features of sashimi.pySupport various file formats as inputSupport strand-aware coverage plotVisualize coverage by heatmap, including HiC diagramVisualize protein domain based the given gene idDemultiplex the single-cell RNA/ATAC-seq which used cell barcode into cell populationSupport visualizing individual full-length reads in read-by-read styleSupport visualize circRNA sequencing dataInputsashimi.py supports almost NGS data format, includingBAMBedbigBedbigWigDepth file generated bysamtools depthnaive Hi-C formatOutputThe output will be a pdf and other image file formats which satisfy the requirement of the major journals,
and each track on output corresponds these datasets from config file.UsageThe sashimi.py is written in Python, and user could install it in a variety of ways as followsNote:ifsegment faultwith multiple processing, please try to use docker image, or just run with-p 1.install from PiPypipinstallsashimi.pyinstall from biocondacondainstall-cbiocondasashimi-py# orcondaenvcreate-nsashimi-fenvironment.yaml# orcondaenvcreate-nsashimi-cconda-forge-cbioconda-frequirements.txt
condaactivatesashimi&&pythonsetup.pyinstallusing docker imagedockerpullygidtu/sashimi
dockerrun--rmygidtu/sashimi--help# orgitclonefromhttps://github.com/ygidtu/sashimi.pysashimicdsashimi
dockerbuild-tygidtu/docker.
dockerrun--rmygidtu/sashimi--helpinstall from source codegitclonehttps://github.com/ygidtu/sashimi.pysashimicdsashimi
pythonsetup.pyinstall

sashimipy--help# prpythonmain.py--helprunning from a local webservergitclonehttps://github.com/ygidtu/sashimi.pysashimicdsashimi/web# build the frontend static filesnpminstall-gvue-clivite&&npminstallvitebuild# prepare the backend serverpipinstallfastapipydanticjinja2uvicornpythonserver.py--helpforpipenvusersgitclonehttps://github.com/ygidtu/sashimi.pycdsashimi.py
pipenvinstall# create virtualenv and install required packagespipenvshell# switch to virtualenvsashimipy--help# prpythonmain.py--helpExamplepythonmain.py\-echr1:1270656-1284730:+\-rexample/example.sorted.gtf.gz\--intervalexample/interval_list.tsv\--densityexample/density_list.tsv\--show-side\--igvexample/igv.tsv\--heatmapexample/heatmap_list.tsv\--focus1272656-1272656:1275656-1277656\--stroke1275656-1277656:1277856-1278656@blue\--sites1271656,1271656,1272656\--lineexample/line_list.tsv\-oexample/example.png\--dpi300\--width10\--height1\--barcodeexample/barcode_list.tsv\--domain--remove-duplicate-umihere is theoutput file.QuestionsVisitissuesor
contactYiming ZhangandRan ZhouCitationIf you use Sashimi.py in your publication, please cite Sashimi.py byZhang et al. Sashimi.py: a flexible toolkit for combinatorial analysis of genomic data. bioRxiv 2022.11.02.514803.
Package: sasktran2
SASKTRANThe SASKTRAN radiative transfer framework is a radiative transfer tool developed at the University of Saskatchewan. Originally designed for use with the OSIRIS instrument (https://research-groups.usask.ca/osiris/) it has since evolved to be applicable to a large variety of applications. SASKTRAN is a full framework and not just a radiative transfer model, as such it contains databases or interfaces to standard climatologies and species optical properties.SASKTRAN2 is a full re-implementation of the original SASKTRAN framework with large computational efficiency
improvements, full linearizations of atmospheric input properties, and an improved Python interface.InstallationThe preferred method to install SASKTRAN2 is through the pre-compiled Conda packageconda install -c conda-forge sasktran2these packages are made available for Python versions 3.10, 3.11, 3.12 on Windows/Linux/Mac platforms.
For Mac, both x86_64 and Arm packages are available.Wheels are also built for the same platforms and can be installed through,pip install sasktran2SASKTRAN2 can also be built directly from source,pip install .When building from source it is required that a Blas/LAPACK implementation is findable by CMake.We also provide an examplecondaenvironment that can be used to build the code.  For Mac/Linux
we recommend,conda env create -f conda/dev_env.ymlFor Windows you need to first install Visual Studio 2022, and thenconda env create -f conda/dev_env_windows.ymlThen the package can be installed with.conda activate sasktran2-dev-env
pip install .UsageDocumentation can be found athttps://sasktran2.readthedocs.io/LicenseSASKTRAN2 is made available under the MIT license.AcknowledgementWe request that users of the model contact the authors before publishing results using SASKTRAN, and that the following publications are acknowledged:Zawada, D. J., Dueck, S. R., Rieger, L. A., Bourassa, A. E., Lloyd, N. D., and Degenstein, D. A.: High-resolution and Monte Carlo additions to the SASKTRAN radiative transfer model, Atmos. Meas. Tech., 8, 2609-2623,https://doi.org/10.5194/amt-8-2609-2015, 2015.Bourassa, A. E., Degenstein, D. A., and Llewellyn, E. J.: SASKTRAN: A Spherical Geometry Radiative Transfer Code for Efficient Estimation of Limb Scattered Sunlight, J Quant Spectrosc Radiat Trans, Volume 109, Issue 1, 52-73,https://doi.org/10.1016/j.jqsrt.2007.07.007, 2008.
Package: sasl3
python-saslPython wrapper for Cyrus SASLhowsasl3is different fromsaslpackage?This is a fork from original work from Cloudera's packagesaslpackage.Original package wasn't maintained and had issue wth latest Python 3 releases, and also had runtime issues running
on RHEL6. Pythonsasl3package solves these issues.External dependenciesYou need following external packages installed on the system to pip-installsasl3:C compiler (e.g.gcc)cyrus-sasl-develpackage for Cyrus header files.Both can be installed with OS' package manager, e.g.yuminstallgcccyrus-sasl-devel.x86_64to generate sasl/saslwrapper.cpp from the pyx file:run./recython.sh
Package: sas-log-viewer
sas_log_viewerJupyter lab extension to see SAS log while using the SAS Kernel.RequirementsJupyterLab >= 3.0Installpipinstallsas_log_viewerContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the sas_log_viewer directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallsas_log_viewer
Package: sasmodels
Theory models for small angle scattering.The models provided are usable directly in the bumps fitting package and
in the sasview analysis package.  If OpenCL is available, the models will
run much faster.  If not, then precompiled versions will be included with
the distributed package.  New models can be added if OpenCL or a C compiler
is available.InstallThe easiest way to use sasmodels is fromSasView.You can also install sasmodels as a standalone package in python. Useminicondaoranacondato create a python environment with the sasmodels dependencies:$ conda create -n sasmodels -c conda-forge numpy scipy matplotlib pyopenclThe option-nsasmodelsnames the environment sasmodels, and the option-cconda-forgeselects the conda-forge package channel because pyopencl
is not part of the base anaconda distribution.Activate the environment and install sasmodels:$ conda activate sasmodels
(sasmodels) $ pip install sasmodelsInstallbumpsif you want to use it to fit
your data:(sasmodels) $ pip install bumpsUsageCheck that the works:(sasmodels) $ python -m sasmodels.compare cylinderTo show the orientation explorer:(sasmodels) $ python -m sasmodels.jitterDocumentation is available online as part of the SasViewfitting perspectiveas well as separate pages forindividual models.
Programming details for sasmodels are available in thedeveloper documentation.Fitting ExampleThe example directory contains a radial+tangential data set for an oriented
rod-like shape.To load the example data, you will need the SAS data loader from the sasview
package. This is not yet available on PyPI, so you will need a copy of the
SasView source code to run it.  Create a directory somewhere to hold the
sasview and sasmodels source code, which we will refer to as $SOURCE.Use the following to install sasview, and the sasmodels examples:(sasmodels) $ cd $SOURCE
(sasmodels) $ conda install git
(sasmodels) $ git clone https://github.com/sasview/sasview.git
(sasmodels) $ git clone https://github.com/sasview/sasmodels.gitSet the path to the sasview source on your python path within the sasmodels
environment.  On Windows, this will be:(sasmodels)> set PYTHONPATH="$SOURCE\sasview\src"
(sasmodels)> cd $SOURCE/sasmodels/example
(sasmodels)> python -m bumps.cli fit.py cylinder --previewOn Mac/Linux with the standard shell this will be:(sasmodels) $ export PYTHONPATH="$SOURCE/sasview/src"
(sasmodels) $ cd $SOURCE/sasmodels/example
(sasmodels) $ bumps fit.py cylinder --previewThe fit.py model accepts up to two arguments.  The first argument is the
model type, which has been defined for cylinder, capped_cylinder,
core_shell_cylinder, ellipsoid, triaxial_ellipsoid and lamellar.  The
second argument is view, which can be radial or tangential.  To fit
both radial and tangential simultaneously, use the word “both”.Seebumps documentationfor detailed
instructions on running the fit.
Package: sasoptpy
sasoptpy: SAS Optimization Interface for PythonRepository:https://github.com/sassoftware/sasoptpyIssues:https://github.com/sassoftware/sasoptpy/issuesReleases:https://github.com/sassoftware/sasoptpy/releasesCommunity:https://communities.sas.com/t5/Mathematical-Optimization/bd-p/operations_researchCopyright (C) SAS Institute Inc.
Package: saspt
sasptsasptis a tool for analyzing single particle tracking (SPT) experiments. It relies onstate arrays, a class of variational Bayesian models that identifies
diffusing subpopulations in the presence of large measurement error.
It is particularly useful for complex samples with many subpopulations with distinct
dynamics.See theDocumentationfor more info.See theexamplesdirectory for usage examples.InstallMethod 1 (recommended): install from PyPI:pip install sasptMethod 2: install from sourcegit clone https://github.com/alecheckert/saspt
  cd saspt
  pip install -e .
Package: sas-pylogger-handler
SAS Python Logger Handler


                                Jan 2023
                        Alfredo Lorie Bernardo				

                             version 0.2.0IntroductionThe SASHandler allows to send python logging streams to SAS System logger when using theSAS PROC PYTHON.TheSAS PROC PYTHONprocedure enables you to run statements from the Python programming language within SAS code.
You can submit Python statements from an external Python script or enter Python statements directly.DownloadGitHub:https://github.com/a24lorie/sas-pylogger-handlerUsing SASHandlerimportloggingfromloggingimportHandlerfromsas_handlerimportSASHandlerhandler=SASHandler(sas=SAS)handler.setLevel(logging.ERROR)logger:logging.Logger=logging.getLogger()logger.setLevel(logging.WARN)logger.addHandler(handler)logger.error("This is an error message")# This log message will be shownlogger.debug("This is a debug message")# This log message won't be shownContributingThis library welcomes contributors from all developers, regardless of your experience or programming background.
If you find a bug, send apull requestand we'll discuss things. If you are not familiar with "pull request" term I recommend reading the followingarticlefor better understanding
We value kind communication and building a productive, friendly environment for maximum collaboration and fun.
Package: sas_rigid
sas_rigid is a modelling package designed to enable
efficient rigid body modelling based on small angle scattering (SAS) data.
The package provides fast and easy access to rigid body modeling with
molecules of arbitrary symmetry. In order to speed up modeling the package
enables the user to perform a Monte Carlo based ‘rough’ modeling followed by
a systematic search in a confined parameter range.There are also basic facilities for manipulating or preparing PDB-files
for better handling for the purpose of rigid body modelling.
Package: sasrl-env
sasrl-envOverviewThis package is used for communicating with a remote RL python environment of SAS customers. It uses the google remote procedure call (gRPC) to provide a fast communication.InstallationThe sasrl-env package needs to be installed using the following command:pip install sasrl-envStarting an environment serverIn the python environment, the installed package needs to be imported and started using the following lines of codes:from sasrl_env import runServerrunServer.start(#PORT_NUMBER)Notes:If you do not specify a port when running the server, the environment controller will assign an available port automatically.If the specified port is not free, it will increment the port numbers until finding the first available port.ContributingWe welcome your contributions! Please readCONTRIBUTING.mdfor details on how to submit contributions to this project.LicenseThis project is licensed under theApache 2.0 License.Supported version:We support Python >= 3.7Dependencies:grpcio>=1.50.0protobuf>=4.21.9gym>=0.26.2typing_extensions>=4.4.0Additional ResourcesDocumentation links:https://go.documentation.sas.com/doc/en/vdmmlcdc/v_017/vdmmladvug/n0odbu962pwfy0n11w7tfed90hku.htmBlog posts:https://blogs.sas.com/content/subconsciousmusings/2022/02/25/reinforcement-learning-to-master-games/SAS Communities
Package: sass
Usage>>>importsass>>>sass.compile_string("div { a { color: black}}")'div a {\ncolor: black; }\n'InstallationpipinstallsassCompatibilityTested with Python 2.7 and Python 3.2/3.3DevelopmentSteps to compile and install:sudopipinstallCythongitsubmoduleinitgitsubmoduleupdatecdlibsassgitsubmoduleinitgitsubmoduleupdatecd..makesudopythonsetup.pydevelopIf you want to use an alternatepython, you’ll have to edit theMakefileor execute those commands from activated virtualenv.How to contributeIt is easy. Fork repo on GitHub, fix stuff and send me nice looking pull-request.
Package: sass-cli
I don’t want to install Ruby, but I want to compile*.scssfrom the
command line.Same here.If you use the Ruby CLI, you will find some things missing. This command
accepts two arguments, which may either be a pair of filenames or a pair of
directory names.ExamplesTwo files:$sassassets/scss/file.scssbuild/output.cssTwo dirs:$sassassets/scssbuild/css
Package: sasstastic
sasstasticFantastic SASS and SCSS compilation for pythonInstallationpipinstallsasstasticrunsasstastic--helpTo check sasstastic is install and get help info.UsageDefine a config filesasstastic.yml:download:# downloaded files will be saved in this directorydir:styles/.libssources:# download a font css file from google fonts and save it to goog-fonts.css-url:>https://fonts.googleapis.com/css?family=Merriweather:400,400i,700,700i|Titillium+Web|Ubuntu+Mono&display=swapto:google-fonts.css# download a style sheet from select2, this will be saved to "select2.css" as# the name can be inferred from the url-url:'https://raw.githubusercontent.com/select2/select2/4.0.13/dist/css/select2.css'# download the full bootstrap 4 bundle and extract the scss files to the bootstrap/ directory-url:https://github.com/twbs/bootstrap/archive/v4.4.1.zipextract:'bootstrap-4.4.1/scss/(.+)$':bootstrap/# SCSS and SASS files will be build from this directorybuild_dir:styles/# and saved to this directoryoutput_dir:css/# the output directory "css/" will be deleted before all buildswipe_output_dir:trueThen runsasstasticto build your sass files.note:if yousasstastic.ymlfile isn't in the current working directory you can pass the path to that file
as an argument to sasstastic, e.g.sasstastic path/to/sasstastic.ymlor justsasstastic path/to/by default the paths defined insasstastic.yml:download.dir,build_dirandoutput_dirare
**relative to the thesasstastic.ymlfileyou can override the output directoryouput_dirusing the-oargument to the CLI, seesasstastic --helpfor more infosasstastic can build in "development" or "production" mode:indevelopmentmode css is not compressed, a map file is created and all files frombuild_diranddownload.dirare copied intooutput_dirso map files work correctlyinproductionmode css is compressed, no other files are added tooutput_dir
Package: sastadev
Sastadevpypi sastadevMethod definitions for use in SASTAInstallationYou can install SASTADEV using pip:pip install sastadevUsageCommand line interfaceThe installation provides an entrypointsastadevwhich invokessastadev.__main__.main()To lists arguments and options:sastadev -horpython -m sastadev -hUsing as a libraryfromsastadev.deregulariseimportcorrectinflectionresult=correctinflection('slaapten')print(result)# [('sliepen', 'Overgeneralisation')]ConfigurationThe package contains a configuration modulesastadev.confthat produces aSastadevConfigobject at runtime, calledsettings.Using settings valuesExample 1 (correct):fromsastadev.confimportsettingsdefget_dataroot():print(settings.DATAROOT)Example 2 (wrong!):fromsastadev.confimportsettingsdataroot=settings.DATAROOTdefget_dataroot():print(dataroot)The key difference is that the code in example 2 evaluatessettings.DATAROOTat the moment the code is executed. Ifsettings.DATAROOTchanges between the first time the module is loaded and the time it is ran, the first value will be used. This disables configurable settings.Changing settingssastadev.conf.settingscan be changed at runtime.:warning:Changingsettingsproperties changesallcode that is executed after the change. Therefore, make sure you set the settingsonce, and at the beginning of the runtime cycle.DevelopmentTo install the requirements:pip install -r requirements.txtInstalling locallyTo install the package in editable state:pip install -e .TestingTests should be written and run usingpytest.
To test, make sure the package is installed in editable mode.
Then, each time you wish to run the tests:pytestLintingLinting configuration is provided forflake8.
To lint, run:flake8 ./src/sastadev/Upload to PyPiSpecify the files which should be included in the package inpypi/include.txt.pipinstalltwine
pythonsetup.pysdist
twineuploaddist/*.tar.gzContributingEnhancements, bugfixes, and new features are welcome. For major changes, please follow these steps:open an issue to discuss what you would like to changecreate a branchfeature/<your-branchname>, based ondevelopthat contains your changesensure the code is well testedcreate a pull request for merging the changes intodevelopthe maintainers will take care of reviewing the code, offering suggested changes, and merging the codeat the discretion of the maintainers, thedevelopbranch will be merged intomaster, and a new release will be made
Package: sasx
Data manipulation in Python for SAS usersSASXData manipulation in Python for SAS users, with the %%sasx magic command.SASX (Simple dAta SyntaX) has the best of both worlds:
- Classic python syntax with access to python, numpy, pandas (like Python)
- A few extra keywords to allow easy row-by-row operations (like SAS)Install the lastest release with:
pip install sasxMore info:https://github.com/python-sasx/sasx
Package: sAsync
An enhancement to theSQLAlchemypackage that provides asynchronous,
deferred-result access via theTwistedframework and an access broker
that conveniently manages database access, table setup, and
transactions. Included are modules for implementing persistent
dictionaries and three-dimensional arrays.sAsync lets you use the use the database transaction core of the
SQLAlchemy Python SQL toolkit in an asynchronous fashion. In addition
to an access broker for conveniently managing database access, table
setup, and transactions under the Twisted framework, it provides
persistent item-value stores and arrays.The sAsync package uses a threaded task queue from theAsynQueuepackage to wrap your SQLAlchemy database access code inside
asynchronous transactions. At the lowest level, it provides a@transactdecorator for your database-access methods that makes them
immediately return a Twisted Deferred object.Python 3 compatiblity is in the works, but not yet supported.
Package: sat5ptools
BackgroundThese tools manage “conversations” for use with a chat-style
web interface created as part of the“Improving the transition
and retention of regional students from low−socioeconomic
backgrounds: A ‘5Ps’ approach”project.The chat interface receives the conversation script in a JSON
data file. Each question follows one of the two following
patterns:Multi answer questionText answer questionA question idText, images, and links, in one or more paragraphs.One or more responses, each
containing:a short response labelan optional information
block, displayed only
when this response is
selected by the userthe id of a destination
questionA space for the user to type
text (usually in response to
prompts in the text section).The id of a destination
question.A question idAuthoring the conversation script is usually done in an Excel
spreadsheet with the following columns:Question IDQuestion TextResponses (either a list of responses, or “{text}” indicating that this is a text answer question).Response 1 Result detailing the info and destination question of the first responseResponse 2 Result detailing the info and destination question of the second responsesimilar responses for 3, 4, 5, 6, 7, 8, and 9Tools available``excel2qns``is a command line tool for producing a conversation JSON file from an Excel document.more..``excel2graph``is a command line tool for producing a GraphViz graph indotformat from an Excel document.more..``excel2all``is a command line tool for producing both a conversation JSON file and a GraphViz graph from an Excel document.Formatting available in Excel
Package: satamanosturi
SatamanosturiSatamanosturi (Finnish for "harbor crane") is a tool to copy Docker images from one place to another.Initially, ECR-to-ECR copies are supported, and unfortunately only with the help of a local Docker daemon.Usage exampleFor example,$ python -m satamanosturi copy-image --source-repo ecr:first-repo --dest-repo ecr:other-repo \
  --source-tag latest-master --copy-tags --skip-tag latest-master --add-tag latestwould copy the latest image in the ECRfirst-reporepository tagged withlatest-mastertoother-repo:latest, and also copy all tags from the source image to the destination image,
except thelatest-mastertag.You can add--dry-runto skip the actual pushing.s