Saravanan666/pypi-org huggingface dataset:
Package: sc-diff-analysis
A simple Python projectThis project provides a simple utility for analyzing difference of retail reports.InstallationIt is possible to install the tool withpip:pip install sc-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: scdiffeq
An analysis framework for modeling dynamical single-cell data withneural differential equations, most notablystochasticdifferential equations  allow us to buildgenerative modelsof single-cell dynamics.Install the development package:gitclonehttps://github.com/mvinyard/sc-neural-diffeqs.git;cd./sc-neural-diffeqs;pipinstall-e.Main APIimportscdiffeqassdqfromneural_diffeqsimportNeuralSDEmodel=sdq.models.scDiffEq(adata,func=NeuralSDE(state_size=50,mu_hidden=[400,400],sigma_hidden=[400,400]))model.fit()Built on:
Package: scDML
scDMLOverviewscDML (Batch Alignment ofsinglecell transcriptomics data usingDeepMetricLearning) is designed for single cell transcriptomics data's clustering, which is a deep learning model based ondeep metric learningto remove batch effect, guided by the initial clusters and MNN information intra and inter batches. In this manuscript, we proposed a strategy to merge initial clusters successively that takes batch effect into consideration, by computing the number of KNN pairs intra batch and MNN pairs inter batches, then calculating the similarity of clusters, and finally constructing a hierarchical tree, in which the root of the tree is the unique cluster obtained after gathering all clusters, and the leaves are the clusters to be merged. Thereafter, we used the above MNNs to guide information for building better low-dimensional embeddings. In this way, this procedure guarantees that scDML outperforms existing methods in terms of merging the same cell types, separating different cell types and preserving cell types unique to some batches. To find code to reproduce the results we generated in that paper, please visit this separate github repository (https://github.com/eleozzr/scDML_reproduce), which provides all code (including that for other methods) necessary to reproduce our results.Merge rulescDML presents a novel merge rule to reassign cluster labels, which is important for scDML. Below figures illustrates that how to merge clusters hierarchicallyUsageThe following notebooks show you how to run scDMLtutorial1gives a detailed description in each step of scDMLtutorial2provides a simple running example.IntallationStep1InstallAnacondaif you do not have it.Create a conda environment with python, and then activate it as follows in terminal. we have test two environment on linuxenvironment 1condacreate-nscDMLpython==3.6.10
condaactivatescDMLenvironment 2condacreate-nscDMLpython==3.8.12
condaactivatescDMLStep2Install directly from Github using pip:pipinstallgit+https://github.com/eleozzr/scDML.gitDownload the package from Github and install it locally:gitclonehttps://github.com/eleozzr/scDMLcdscDML
pythonsetup.pyinstall--user#or pip installorgitclonehttps://github.com/eleozzr/scDMLcdscDML
pythonsetup.pybuild
pythonsetup.pyinstallorgitclonehttps://github.com/eleozzr/scDMLcdscDML
pipinstall.Step3 (optional)Run jupyter notebook underscDMLenvironmentcondaactivatescDML
pipinstalljupyter
python-mipykernelinstall--user--namescDML--display-name"Python_scDML"jupyternotebookSoftware Requirementsnumba==0.51.2numexpr==2.7.1numpy==1.18.1numpy-groupies==0.9.14anndata==0.7.6tables==3.6.1scanpy==1.7.2
umap-learn==0.4.6
python-igraph==0.8.2louvain==0.7.0plotly==5.2.2hnswlib==0.5.2annoy==1.17.0networkx==2.5ipykernel==5.5.6nbformat==5.1.3
pytorch-metric-learning==0.9.95
torch>=1.10.1Citation
Package: scdn
Estimating high dimensional ODE models from convoluted observations with an application to fMRIscdn is a Python-based package implementing sparse causal dynamic network analysis for convolution model, particular for Functional magnetic resonance imaging (fMRI) in our paper. It aims to provide a sparse dynamic network estimation not only for fMRI data but for other possible data that can be represented by convolution model. The introduction and explanation of parameters and ODE models can be found in(1). For more details of convolution model, see(2)Getting StartedThe examples provided in the repo have been tested in Mac os and Linux environment. This package supports both Python 2.7 and Python 3.6.These instructions will get you a copy of the project up running on your local machine for development and testing purposes.This package is also published in pypi.  For a quick installation, trypip install scdnPrerequisitesWhat things you need to install the software and how to install themSee setup.py for details of packages requirements.Installing from GitHubDownload the packages by using git clonehttps://github.com/xuefeicao/scdn.gitpython setup.py installIf you experience problems related to installing the dependency Matplotlib on OSX, please seehttps://matplotlib.org/faq/osx_framework.htmlIntro to our packageAfter installing our package locally, try to import scdn in your python environment and learn about package's function.from scdn.scdn_analysis import scdn_multi_sub
help(scdn_multi_sub)ExamplesThe examples subfolder includes two examples.
The first is a simulation generated from our data and another is from DCM.Running the testsThe test is going to be added in the future.Built WithPython 2.7CompatibilityPython 2.7Python 3.6AuthorsXuefei Cao-Maintainer- (https://github.com/xuefeicao)Xi Luo(http://bigcomplexdata.com/)Björn Sandstede(http://www.dam.brown.edu/people/sandsted/)LicenseThis project is licensed under the MIT License - see the LICENSE file for details
Package: scDREAMER
scDREAMEROverviewscDREAMERis a single-cell data integration framework that employs a novel adversarial variational autoencoder for learning lower-dimensional cellular embeddings and a batch classifier neural network for the removal of batch effects. The jupyter notebooks for reproducibility of results in the manuscript are available athttps://github.com/Zafar-Lab/scDREAMER-reproducibility. See our paper below for more details. DOI:https://doi.org/10.1038/s41467-023-43590-8InstallationA stablepipinstallation release for scDREAMER package will be made available shortly. For now, we recommend users to directly clone our stablemainbranch and setscDREAMERas the working directory. Creating conda environment using ./ENVIRONMENTS/scDREAMER.yml will install all the dependent packages and libraries. scDREAMER can be set up as followsgit clone https://github.com/Zafar-Lab/scDREAMER.git
cd scDREAMER/Environments
conda env create -f scDREAMER.yml
conda activate scdreamerWhat Computational tasks can scDREAMER be used for?scDREAMERsuite can be used for:scDREAMER for an unsupervised integration of multiple batchesscDREAMER-SUP for a supervised integration across multiple batchesscDREAMER-SUP can also be when cell type annotations are missing in the datasets i.e., 10%, 20%, 50%Atlas level and cross-species integrationLarge datasets with ~1 million cellsTutorialsCheck out the following Colab notebook to get a flavor of a typical workflow for data integration using scDREAMER and scDREAMER-SUP (Link to Datasets) below.scDREAMER applied to human immune integration taskscDREMER-SUP applied to human immune cells integration taskscDREAMER-SUP applied to human immune cells integration task under 50% missing cell labels settingDocumentationRead the docs:https://scdreamer.readthedocs.io/en/latest/ContributingIn case of any bug reports, enhancement requests, general questions, and other contributions, please create an issue. For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message.Cite this articleAjita Shree*, Musale Krushna Pavan*, Hamim Zafar. scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier. bioRxiv 2022.07.12.499846; doi:https://doi.org/10.1038/s41467-023-43590-8* equally contributed
Package: sceance
Sceance is an application for moviegoers. If you provide it with a list of your favorite cinemas and your movie watchlist, it suggests showtimes, letting you watch your favorite movies in theaters.It was designed with Paris, a city full of small and independent cinemas, in mind.InstallationTo start running sceance, install the project and enable the Google calendar API.DependenciesMost dependencies are optional:A Google Account: for sceance to create and suggest events to your calendar.Firefox or Chrome: to run the selenium headless browser to find film showings.A Letterboxd account: sceance currently provides a way of downloading your watchlist from letterboxd provided you store your letterboxd credentials in a .envrc file. This is done with theupdate_watchlist.pyfile. Otherwise you can manually create a watchlist as a list of newline seperated film titles. Some default watchlists are also available based on the criterion collection and the oscarn winning films.Download the appYou can install sceance with pip.pipinstallsceanceTo run sceance, simply call:sceanceTo get information on parameters run:sceance-hAt this point, sceance can suggest films, which, if approved are automatically added to your google calendar.Customize theaters.txtBy default, the theaters file is based on a set of my favorite Parisian movie theaters. If you live somewhere else (or want to add your own set of favorite theaters), you can create your own theaters file in thedata/theaterssubfolder of the sceance package. Currently, the only options are Paris, San Francisco, and New York. If you create a good list of theaters for your city, feel free to make a pull request! You can use the-c --set-theater flagto change your default theaters file.Customize watchlist.txtBy default, the watchlist.txt contains every film from the criterion collection. However, you can create a personal watchlist or pick from some of the defaults. You can set a watchilst using the-s --set-watchlist flag.If you have a letterboxd account, sceance provides a way to automatically download your watchlist. To do this, use theupdate_watchlist.pyfile (you will need to store your letterboxd credentials in environment variables).Make sure your watchlist is in English (which is currently the only language that sceance supports). If you want to run sceance without providing a watchlist, you can use the-aflag, but this might provide you with a lot of movies.You can find the default watchlist files in thedata/watchlistssubfolder of the sceance package.How sceance worksSceance uses selenium to gather showtime information for every theater in your theater.txt file, from Google. These films are then intersected with your watchlist. Details on the resulting films and their showtimes are then filtered out based on 9am-5pm monday-friday work hours (both workdays and hours are customizable).For all the films that remain, you are prompted with a list of showtimes and can select one showtime per film.This showtime is then automatically added to your calendar using the google calendar api.Contribution GuidelinesNo contribution guidelines yet.
Package: scEasyMode
ScEasyMode: Wrappers for automating single cell workflows in PythonFeaturesMultiseq correction in python using a within-barcode zscore correctionPlotting for stacked barplots in your datasetMouse cell filtering/separation from mixed datasetScanpy wrapper that simplifies the workflowInstallationInstall using Pippip3installscEasyModeInstall using a Conda EnvironmentYou may also use Conda to start an environment with ScEasyMode installed inside it. You can install conda fromhere.Firstly, clone the repository and create the environment as shown below. Then, activate the environment.gitclonehttps://github.com/johnnyUCSF/scEasyModecdscEasyMode
condaenvcreate-fenvironment.yml
condaactivatesceasymode_envNow start your Jupyter Notebook or Python shell inside the conda environmentUsageLoad the modulesfromscEasyModeimportmousefilterfromscEasyModeimportclusterplotfromscEasyModeimportpymultifromscEasyModeimportsceasyDemultiplex your samplesimportpandasaspdfromscEasyModeimportpymulti# Define parameterslen_10x=16# Number of bases in cell barcodeslen_umi=12# Length of UMIlen_multi=15,# Number of bases in the HTO barcodes / HashTag Ofastq_r1='path/to/file'fastq_r2='path/to/file'sample_name='test_demultiplexing'cell_BC_file='path/to/cell_barcodes'# Counts Matrix after alignment and pre-processingcell_bcs=pd.read_csv(cell_BC_file,sep='\t',header=None)[0].tolist()multi_BC_file='path/to/barcodes'# Barcodes TSV file from 10x or Illuminabcsmulti=pd.read_csv(multi_BC_file,sep=',',index_col=1,header=None)bcsmulti.columns=['multi']bcsmulti=bcsmulti['multi'].tolist()pymulti.pymulti(fastq_r1,fastq_r2,bcsmulti=bcsmulti,bcs10x=cell_bcs,len_10x=len_10x,len_multi=len_multi,len_umi=len_umi,split=True,hamming=True,median_only=True,sampname=sample_name,filter_unmapped_reads=True)# This function will output multiple graphs# It will also store a matrix of the assigned barcodes in the 'pymulti' directory inside the working directory.# Note that some reads are unmapped. If you want to retain them, you can do so by specifying filter_unmapped_reads=False.
Package: scegot
scEGOTsingle cell trajectory inference framework based on Entropic Gaussian mixture Optimal TransportInstallation$pipinstallscegotRequirementsPython3pandasanndataGraphVizClickhereto learn how to install GraphViz (official documentation).DocumentationThe tutorial is availablehere.PreprintPlease see the following paper:T. Yachimura et al. scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport. bioRxiv
Package: scelephant
SC-Elephant (Single-Cell Extremely Large Data Analysis Platform)SC-ElephantutilizesRamData, a novel single-cell data storage format, to support a wide range of single-cell bioinformatics applications in a highly scalable manner, while providing a convenient interface to export any subset of the single-cell data inSCANPY'sAnnDataformat, enabling efficient downstream analysis of the cells of interest. The analysis result can then be made available to other researchers by updating the originalRamData, which can be stored in cloud storage likeAWS(or any AWS-like object storage).SC-ElephantandRamDataenable real-time sharing of extremely large single-cell data using a browser-based analysis platform as it is being modified on the cloud by multiple other researchers, convenient integration of a local single-cell dataset with multiple large remote datasets (RamDataobjects uploaded by other researchers), and remote (private) collaboration on an extremely large-scale single-cell genomics dataset.Tutorials can be found atdoc/jn/Tutorial 1) Processing and analysis of the 3k PBMCs dataset using SC-ElephantTutorial 2) Alignment of PBMC3k to the ELDB (320,000 cells subset) and cell type prediction using SC-ElephantTutorial 3) Combine 10x MEX count matrices memory-efficiently using SC-ElephantTutorial 4) Convert existing AnnData into RamData for collaborative data sharingBriefly, aRamDataobject is composed of twoRamDataAxis(Axis) objects and multipleRamDataLayer(Layer) objects.The two RamDataAxis objects,'Barcode'and'Feature'objects, use'filter'to select cells (barcodes) and genes (features) before retrieving data from theRamDataobject, respectively.RamDataemploysRAMtx(Random-accessible matrix) objects to store count matrix in sparse or dense formats.RamDatagreatly simplify sharing of very large single-cell datasets on the Web. Once processed by SC-Elephant,RamDatacan be uploaded to GitHub, Amazon S3 Cloud, or any static file servers to share your single-cell datasets publicly with the research community or privately with your collaborators. The machine learning models, kNN graphs, cell-type annotations, and random-accessible expression count matrices (to name a few) of your single-cell datasets on the Web can be easily explored in Python environments and web browsers using SC-Elephant andSC-Elephant.js, respectively.To exploreRamDataobjects publicly available on the Web using a web browser, please visit ourSC-Elephant DB Viewer.
Package: scell
Scell is a MIT-licensed selector library, written in Python.At the heart of event-driven platforms is the selector, the
humbleselect.selectfunction. Using it is very simple,
but requires you to pass in lists of file handles again and
again. Keeping track of these file handles are tricky. Let
Scell handle it for you and focus on building an awesome
library/server:>>> selector = scell.Selector()
>>> monitor = selector.register(open('file.txt'), mode='r')
>>> monitor.callback = lambda: 1
>>> [event.callback() for event in selector.ready]
[1]Scell allows implementors to effortlessly build libraries
atop of the minimal abstraction provided, while also having
good performance. The API is very small and works across
many platforms, including Windows and OSX.FeaturesStateful wrapper aroundselect.selectExtremely small API with small footprint and 100% coverageCore abstractions and utilities can be used directlyEfficient implementation of callbacksHighlydocumentedcodebaseInstallationTo install Scell, simply:$ pip install scell
Package: scelvis
SCelVis: Easy Single-Cell VisualizationDocumentationThe SCelVis documentation atReadTheDocs.orgcontains comprehensive information including aTutorial.MovieInstallationYou can install with Pip and Bioconda or run directly via Docker.$pipinstallscelvis# OR$condainstallscelvis# OR$dockerrunghcr.io/bihealth/scelvis:0.8.8-0scelvis--helpLook up the latest version (instead of0.8.8-0to use athere)Building Docker ImagePush changes to Github (we need a [git tree-ish](https://stackoverflow.com/questions/4044368/what-does-tree-ish-mean-in-git), so a tag works as well as a feature branch).CallGIT_TAG=tag-or-branchbashdocker/build-docker.sh.Run withdocker runghcr.io/bihealth/scelvis:[version]-0.Push withdocker pushghcr.io/bihealth/scelvis:[version]-0.Historyv0.8.9avoid loading datasets just to get meta-dataupdate dash importsv0.8.8fixing problem with categoricaldata:wqv0.8.7fixing problem with ports in HTTP URLsv0.8.6bug fixes for cell filteringv0.8.5allow text import with mtx filesdisplay only most frequent categoriesusead.rawif presentenable downsampling during conversionlink to publicationv0.8.4fix bug in custom static folder routefix iRODS authentication defaultsv0.8.3allow custom markdowns for home screenenable gene-gene plots and change colormap for gene scatterv0.8.2fix iRODS authentication issuesupdate docs for .h5ad inputv0.8.1fixing iframe sources (important if running behind reverse proxy with HTTPS)link documentation on home screenv0.8.0adding sphinx documentation for ReadTheDocsupload and convert with IFrames; increase size limitadd box plotsv0.7.3fix cache timeout errorv0.7.2fix ReferenceErrorupdated README and tutorialmoviev0.7.1improved cache handlingimproved user feedback for filtering & differential expressionv0.7.0added conversion from .loom filescell filtering also supports downsamplingadded PBMC dataset hosted on figshareadded demo moviev0.6.0cell filteringdifferential expressionv0.5.0upgrades to Dash v1fixes to UI, upload and conversionavoid creation of dense matricesv0.4.1Fixing bug with specifying single.h5adfile as data source.AddingDockerfilefor building Docker images from intermediate versions.v0.4.0Adding support for HTTP(S) data sources.Embeddingabout.mdinformation in Anndata file.Adding support for passingv0.3.0Adding example data set.Adding nice introduction to start page.Adding functionality for creating simple fake data set.Making import ofruamel_yamlmore robust still.Adding tests.Adding Travis CI–based continuous integration tests.v0.2.1Fixing SFTP support.Fixing import ofruamel_yaml.v0.2.0More refactorization.Fixing dependency onruamel-yamltoruamel.yaml.Adding conversion feature.Adding upload feature.Adding support to load from SSHFS, FTP through pyfilesystem (no FTPS support).Adding support to load from iRODS, also works via tickets (pass?ticket=TICKETto the query parameters).v0.1.0Initial release.Everything is new!
Package: scenario
ScenarIOSCENe interfAces forRobotInput /Output.DescriptionScenarIOis a C++ abstraction layer to interact with simulated and real robots.It mainly provides the followingC++ interfaces:scenario::core::Worldscenario::core::Modelscenario::core::Linkscenario::core::JointThese interfaces can be implemented to operate on different scenarios,
including robots operating on either simulated worlds or in real-time.ScenarIO currently fully implementsGazebo ScenarIO,
a simulated back-end that interacts withIgnition Gazebo.
The result allows stepping the simulator programmatically, ensuring a fully reproducible behaviour.
It relates closely to other projects likepybulletandmujoco-py.A real-time backend that interacts with theYARPmiddleware is under development.ScenarIO can be used either from C++ (APIs)
or from Python (APIs).If you're interested to know the reasons why we started developing ScenarIO and why we selected Ignition Gazebo
for our simulations, visit theMotivationssection of thewebsite.InstallationScenarIO only supports a single distribution of the Ignition suite.
Visit ourSupport Policyto check the distribution currently supported.Then, install the supported Ignition suite following theofficial instructions.PythonExecute, preferably in avirtual environment:pipinstallscenarioC++You can either clone and install the standalone project:gitclonehttps://github.com/robotology/gym-ignitioncdgym-ignition/scenariocmake-S.-Bbuild/cmake--buildbuild/--targetinstallor include it in your CMake project withFetchContent.UsageYou can find some examples that show the usage of ScenarIO in theGetting Startedsection of thewebsite.ContributingPlease visit theLimitationssection of thewebsiteand check thegood first issueandhelp wantedissues.You can visit our community forum hosted inGitHub Discussions.
Even without coding skills, replying user's questions is a great way of contributing.
If you use ScenarIO in your application and want to show it off, visit theShow and tellsection!Pull requests are welcome.For major changes, please open adiscussionfirst to propose what you would like to change.Citation@INPROCEEDINGS{ferigo2020gymignition,title={Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning},author={D. {Ferigo} and S. {Traversaro} and G. {Metta} and D. {Pucci}},booktitle={2020 IEEE/SICE International Symposium on System Integration (SII)},year={2020},pages={885-890},doi={10.1109/SII46433.2020.9025951}}LicenseLGPL v2.1or any later version.We vendor some resources from the Ignition code base.
For this reason, Gazebo ScenarIO is double-licensed with theApache License.