Saravanan666/pypi-org huggingface dataset:
Package: scmdata
scmdatascmdataprovides some useful data handling routines for dealing with data related to simple climate models
(SCMs aka reduced complexity climate models, RCMs). In particular, it provides a high-performance way of
handling and serialising (including to netCDF) timeseries data along with attached metadata.scmdatawas inspired bypyamand was originally part of theopenscmpackage.PyPI :Other info :Full documentation can be found at:scmdata.readthedocs.io.
We recommend reading the docs there because the internal documentation links
don't render correctly on GitHub's viewer.Installationscmdata can be installed with conda or pip:pipinstallscmdata
condainstall-cconda-forgescmdataAdditional dependencies can be installed using# To add plotting dependenciespipinstallscmdata[plots]# To add notebook dependenciespipinstallscmdata[notebooks]# If you are installing with conda, we recommend# installing the extras by hand because there is no stable# solution yet (issue here: https://github.com/conda/conda/issues/7502)For developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(instructions here,
we found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, runmake virtual-envir˚onment.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker˚.For the rest of our developer docs, please see.
Package: scMDCF
scMDCFscMDCFis a python package containing tools for clustering single cell multi-omics data based on cross-modality contrastive learning to learn the common latent representation and assign clustering.OverviewSystem RequirementsInstallation GuideUsageData AvailabilityLicenseOverviewSingle-cell multi-omics (scMulti-omics) technologies enable simultaneous measurements of diverse modalities within individual cells and have the potential to comprehensively unravel cellular functions and interactions. Despite this promise, the complexity, high-dimensionality, and heterogeneity of the datasets pose significant challenges to integrative analysis. Here, we develop a single-cell multi-omics deep learning model (scMDCF) based on contrastive learning that is designed to efficiently characterize different types of omics data and capture potential features in the data through deep embedding for a complete and integrated analysis. In scMDCF, we propose a cross-modality contrastive learning module to unify representations from various omics types to ensure consistency, while the cross-modality feature fusion module utilizes conditional entropy to preserve the heterogeneity information. Extensive empirical studies have confirmed that scMDCF outperforms other state-of-the-art scMulti-omics models across various types of scMulti-omics data. In particular, scMDCF demonstrates advanced capability in determining the vital cell-type specific peak-gene associations and cis-regulatory elements from SNARE-seq data as well as surmizing immune regulation within cellular differentiation pathways from CITE-seq data. In addition, we demonstrate that in the post-SARS-CoV-2 vaccination dataset, scMDCF successfully provides annotations on the specific vaccine-induced B cell subpopulations in each modality and reveals the dynamic interactions and regulatory mechanisms within the post-vaccination immune system after vaccination.System RequirementsHardware requirementsscMDCFpackage requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS requirementsThis package is supported forLinux. The package has been tested on the following systems:Linux: Ubuntu 18.04Python DependenciesscMDCFmainly depends on the Python scientific stack.
numpy
pytorch
scanpy
pandas
scikit-learn
For specific setting, please seerequirements.Installation GuideInstall from PyPiconda create -n scMDCF_env python=3.9.16
conda activate scMDCF_env
pip install scMDCF==0.1.2UsagescMDCFis a deep embedding learning method for single-cell multi-omics data clustering, which can be used to:CITE-seq dataset clustering. The example can be seen in themain_CITE.pySNARE-seq (paired RNA-seq and ATAC-seq) dataset clustering. The example can be seen in themain_SNARE.pyData AvailabilityThe datasets we used can be download indatasetLicenseThis project is covered under theMIT License.
Package: scmer
SCMER - Manifold Preserving Feature SelectionSCMER is a feature selection methods designed for single-cell data analysis.
It selects a compact sets of markers that preserve the manifold in the original data.
It can also be used for data integration by using features in one modality to match the manifold of another modality.TutorialsTutorials are available athttps://scmer.readthedocs.io/en/latest/examples.htmlYou may start with theMelanoma data (Tiorsh et al.).To install the latest version here, you can dopip install git+https://github.com/KChen-lab/SCMER/Long Term SupportWe try to keep the package work with new versions of Python and other dependencies.
Lastest tested version: python 3.11 + torch 2.1.1 + SCANPY 1.9.6
OS: Windows 10.
Hardware: AMD Ryzen R5 3600 + Nvidia RTX 3080
For other tested version, please check theltsfolder in this repository.
The four scripts in the folder can also give you an idea of how to run SCMER for a given scenario.Running in RPython is more established in machine learning tasks, but many people prefer R as their primary language in data science. Luckily, a thin wrapper is what you need to run SCMER in R. Please see this shorttutorial.Full DocumentationDetailed documentation is available athttps://scmer.readthedocs.io/en/latest/The mechanism and capabilities of SCMER is detailed in our pre-printSingle-Cell Manifold Preserving Feature Selection (SCMER)Additional package infoUsing GPU can be tricky sometimes. Here is alist of package versionswe successfully used with GPU.PublicationSingle-cell manifold-preserving feature selection for detecting rare cell populationsNature Computational Science(2021)Paid access:https://www.nature.com/articles/s43588-021-00070-7Free access (no download):https://rdcu.be/ckZGTBioRxiv preprint:https://www.biorxiv.org/content/10.1101/2020.12.01.407262v1.fullContactI do monitor the "Issues" and aim to clear any issues in a few weeks.
If you have an urgent request, please emailshaohengliang@gmail.com.Version log0.1.2 (12/7/2023) SCMER now compatible with Python 3.11 + torch 2.1.1 + SCANPY 1.9.6; pending uploading to PyPI; please usepip install git+https://github.com/KChen-lab/SCMER/to install.0.1.1 (6/8/2023) Fixed an issue that caused an error whennp.matrixis used instead ofnp.array.0.1.0a4 (2/12/2023) Fixed an issue that caused an error when batch correction is enabled on GPU runs. CPU runs were not affected.0.1.0a3 (2/17/2021) Initial version.
Package: scMGCA
scMGCAscMGCAis a Python package containing tools for clustering single-cell data based on a graph-embedding autoencoder that simultaneously learns cell–cell topology representation and cluster assignments.OverviewSystem RequirementsInstallation GuideUsageData AvailabilityLicenseOverviewSingle-cell RNA sequencing (scRNA-seq) provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from the curse of dimensionality, and the prevalence of dropout events. To address these concerns, we developed a single-cell clustering method (scMGCA) based on a graph-embedding autoencoder that simultaneously learns cell–cell topology representation and cluster assignments. In scMGCA, we propose a graph convolutional autoencoder to preserve the topological information of cells from the embedded space in multinomial distribution, and employs the positive pointwise mutual information (PPMI) matrix for cell graph augmentation. Experiments show that scMGCA is accurate and effective for cell segregation and superior to other state-of-the-art models across multiple platforms, and is also able to correct for the batch effect from multiple scRNA-seq protocols. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. In a pancreatic ductal adenocarcinoma (PDAC) dataset, with 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, scMGCA successfully provided annotations on the specific cell types and revealed differential gene expression levels across multiple tumor-associated and cell signalling pathways in PDAC progression through single-cell trajectory and gene set enrichment analysis.System RequirementsHardware requirementsscMGCApackage requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS RequirementsThis package is supported forLinux. The package has been tested on the following systems:Linux: Ubuntu 18.04Python DependenciesscMGCAmainly depends on the Python scientific stack.numpy
scipy
tensorflow
scikit-learn
pandas
sklearnrequirements inhttps://github.com/Philyzh8/scMGCAInstallation Guide:Install from PyPi$ conda create -n scMGCA_env python=3.6.8
$ conda activate scMGCA_env
$ pip install -r requirements.txt
$ pip install scMGCAUsagescMGCAis a deep graph embedding learning method for single-cell clustering, which can be used to:Single-cell data clustering. The example can be seen in thedemo.py.Correct the batch effect of data from different scRNA-seq protocols. The example can be seen in thedemo_batch.py.Analysis of the mouse brain data with 1.3 million cells. The example can be seen in thedemo_scale.py.Provide a automatic supershell search algorithm. The example can be seen in thedemo_para.py.We give users some suggestions for running in thetutorial.md.Data AvailabilityThe real data sets we used can be download indata.LicenseThis project is covered under theMIT License.
Package: scm-helper
SCM-HelperHelper tool forSwim Club Manager.Download latestWindows Version.FeaturesWorks onWindows,LinuxorAppledevicesBackup and Restoreof SCM dataVerification of member detailsCheck session and group consistencyCheck consistency ofSwim EnglandregistrationsCheckFacebookgroup members are club membersAutomaticfixingof some errorsGeneration ofswimming recordsGeneration ofemail listsbased on group/class/gender/ageReport on DBS and Safeguarding issueReport on swimmers not attendingReport on swimmers whose confirmation of details has expiredFullyconfigurable(without programming skills)DocumentationSeedocumentationon GitHub for installation and usage instructions.Change LogSeechangelogAuthorColin Robbins
Package: sc.microsite
Table of ContentsLife, the Universe, and EverythingMostly HarmlessDon’t PanicInstallationUsageShare and EnjoyChangelog1.1b1 (2017-10-25)1.0b4 (2014-09-12)1.0b3 (2014-08-25)1.0b2 (2013-08-23)1.0b1 (2013-08-15)Life, the Universe, and EverythingA very basic Dexterity-based container to be used as a microsite.Mostly HarmlessGot an idea? Found a bug? Let us know byopening a support ticket.Don’t PanicInstallationTo enable this product in a buildout-based installation:Edit your buildout.cfg and addsc.micrositeto the list of eggs to install:[buildout]...eggs=sc.micrositeAfter updating the configuration you need to run ‘’bin/buildout’’, which will take care of updating your system.Go to the ‘Site Setup’ page in a Plone site and click on the ‘Add-ons’ link.Check the box next tosc.micrositeand click the ‘Activate’ button.UsageTBA.Share and EnjoyMicrositewould not have been possible without the contribution of the following people:Héctor VelardeJuan Pablo GiménezÉrico AndreiXenatt(icon)Development sponsored bySimples Consultoria.ChangelogThere’s a frood who really knows where his towel is.1.1b1 (2017-10-25)Remove dependency on five.grok.
[rodfersou]Fix package dependencies.
[hvelarde]Drop support for Python 2.6 and Plone 4.2.
[hvelarde]1.0b4 (2014-09-12)An new permissionsc.microsite: Add Micrositeand an event subscriber are now provided to avoid the creation of a microsite inside another microsite.
An upgrade step to fix existing microsites is also available.
[ericof]1.0b3 (2014-08-25)Improve setup testing
[ericof]EnableIPublicationbehavior by default to add effective date and expiration date fields to the Microsite content type.
[hvelarde]1.0b2 (2013-08-23)Basic ISelectableConstrainTypes support. [jpgimenez]Update view methods, sc.microsite content has the same methods than a
folder. [jpgimenez]Default view changed to folder_listing. [jpgimenez]A microsite must have INavigationRoot behavior enabled.
[agnogueira, cleberjsantos]1.0b1 (2013-08-15)Initial release.
Package: scmmd
There is a classical problem in statistics known as thetwo-sample problem.In this setting, you are given discrete observations of two different distributions and asked to determine if the distributions are significantly different.
A special univariate case of this problem is familiar to many biologists as a “difference in means” comparison – as performed using Student’st-test.This problem becomes somewhat more complex in high-dimensions, as differences between distributions may manifest not only in the mean location, but also in the covariance structure and modality of the data.
One approach to comparing distributions in this setting leverages kernel similarity metrics to find the maximum mean discrepancy (Gretton et. al. 2012,JMLR) – the largest difference in the means of the distributions under a flexible transformation.Here, we adapt the MMD method to compare cell populations in single cell measurement data.
In the frame of the two-sample problem, each cell population of interest is considered as a distribution and each cell is a single observation from the source distribution.
We use the MMD to compute (1) a metric of the magnitude of difference between two cell populations, and (2) a p-value for the significance of this difference.
Package: scMMT
scMMTscMMT (single-cellmulti-modal data andmulti-task learningtool) is a powerful deep learning computational tool designed for the analysis of CITE-seq and scRNA-seq data. It offers various functionalities such as cell annotation, protein expression prediction, and low-dimensional embedding. With scMMT, researchers can efficiently explore and interpret complex single-cell datasets, enabling deeper insights into cellular heterogeneity and intercellular interactions.Create environmentcondacreate-nscMMTpython=3.10
condaactivatescMMTInstallationpipinstallscMMTAlternatively, you can also install the package directly from GitHub viapipinstallgit+https://github.com/SongqiZhou/scMMT.gitExample Demo:Guided TutorialData(1) Seurat 4 human peripheral blood mononuclear cells (GEO: GSE164378).(2) H1N1 influenza PBMC dataset (https://doi.org/10.35092/yhjc.c.4753772).(3) COVID dataset(https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10026/).The University of Pennsylvania has put these data sets together for the convenience of downloading.Download Here. The reference github link is:https://github.com/jlakkis/sciPENN_codesSoftware RequirementsPython >= 3.10torch >= 2.0.0scanpy >= 1.9.3scikit-learn >= 1.2.2scikit-learn-intelex >= 2023.1.1pandas >= 2.0.1numpy >= 1.24.3scipy >= 1.10.1tqdm >= 4.65.0anndata >= 0.9.1
Package: scmpy
Set Covering MachineThe Set Covering Machine is a learning algorithm for binary classification. The classifier is a solution of the set cover problem minimizing the empirical risk.This implementation follow the idea presented in the original paper:So thesetsareballsgenerated from the training set and the set cover problem is solved using greedy algorithm (Chvátal, 1979), accordingly to the paper. Sets can be combined in conjunction or disjunction and this can be specified using the parametermachine_type.How to installYou can install the package using pip:pip install scmpyExamplefrom sklearn.model_selection import train_test_split
from sklearn.datasets import make_circles
from scmpy import SCM

# make some artificial data
X, y = make_circles(n_samples=200, noise=0.2, factor=0.5, random_state=666)

# split data in train and test
X_train, X_test, y_train, y_test = \
    train_test_split(X, y, test_size=.3, random_state=7)

scm = SCM(machine_type="disjunction")

# fit the model
scm.fit(X_train, y_train)

# now the model is fitted, we can predict values

# print accuracy
print(scm.score(X_test, y_test))
Package: scmrepo
SCM wrapper and fsspec filesystem for Git for use in DVC.FeaturesWorks with multiple backends:pygit2,dulwichandgitpython.Provides fsspec filesystem over Git:GitFileSystem.Seefsspec docsfor full list of available fs methods.RequirementsInstallationYou can installscmrepoviapipfromPyPI:$pipinstallscmrepoUsageGit File Systemscmrepo providesfsspecbased gitfs that provides fs-like API for your git
repositories without having togit checkoutthem first. For example:fromscmrepo.fsimportGitFileSystemfs=GitFileSystem("path/to/my/repo",rev="mybranch")forroot,dnames,fnamesinfs.walk("path/in/repo"):fordnameindnames:print(fs.path.join(root,dname))forfnameinfnames:print(fs.path.join(root,fname))ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,scmrepois free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.
Package: scm-source
A simple command line tool to generatescm-source.jsonfiles according to theSTUPS documentation.Installation$sudopip3install--upgradescm-sourceUsage$scm-source# generate scm-source.json in current directory$scm-source-ftarget/scm-source.json$scm-source--author"John Doe"$scm-sourcemy/path/to/git/repo-foutput/scm-source.jsonYou can also use it from your Python scripts:fromscm_sourceimportgenerate_scm_sourcegenerate_scm_source('foo/bar/scm-source.json','John Doe')ReleasingUploading a new version to PyPI:$./release.sh<NEW-VERSION>
Package: scmvae
The paired measurement of multiple modalities, known as multimodal analysis, is an exciting frontier for connecting single-cell genomics with epitopes and functions. Mapping of transcriptomes in single-cells and the integration with cell phenotypes enable a better understanding of cellular states. However, assembling these paired omics into a unified representation of the cellular state remains challenging with the unique technical characteristics of each measurement. In this work, we present a python package for single-cell multimodal analysis based on a mixing variational autoencoder that not only joins single-cell transcriptomes and epitopes but also enables simultaneous execution of independent modal analysis.
Package: sc-mysqlhelper
MySQL helper for PythonA helper for working with mysql written in python, with
connection-pooling featureInstallationIt is possible to install the tool withpip:pip install sc-mysqlhelperFeaturesConnection poolingConfigurationThe script itself is currently configuration free.DependenciesPyMySQL 1.0.2DBUtils 2.0UsageSample usage:from mysqlhelper import MySQLHelper

helper = MySQLHelper(host="localhost", port=3306, user="test", password="test", database="test")
rs = helper.select_one(sql="select count(*) from users")
print(rs[0])LicenseThe 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: scnn
SCNN====An implementation of search-convolutional neural networks [1], a new model for graph-structured data.Installation------------Using pip:pip install scnnUsage-----import numpy as npfrom scnn import SCNN, datafrom sklearn.metrics import f1_score# Parse the cora dataset and return an adjacency matrix, a design matrix, and a 1-hot label matrixA, X, Y = data.parse_cora()# Construct array indices for the training, validation, and test setsn_nodes = A.shape[0]indices = np.arange(n_nodes)train_indices = indices[:n_nodes // 3]valid_indices = indices[n_nodes // 3:(2* n_nodes) // 3]test_indices  = indices[(2* n_nodes) // 3:]# Instantiate an SCNN and fit it to corascnn = SCNN()scnn.fit(A, X, Y, train_indices=train_indices, valid_indices=valid_indices)# Predict labels for the test setpreds = scnn.predict(X, test_indices)actuals = np.argmax(Y[test_indices,:], axis=1)# Display performanceprint 'F score: %.4f' % (f1_score(actuals, preds))References----------[1] http://arxiv.org/abs/1511.02136
Package: sco1-misc
miscA collection of miscellaneous helpers.The Helpsco1_misc.promptsHelper wrappers forTkinter's selection dialogsprompt_for_file(title: str, start_dir: pathlib.Path, multiple: bool, filetypes: list[tuple[str, str]])prompt_for_dir(title: str, start_dir: pathlib.Path)csvdatetrimA CLI tool for date windowing CSV log filesNOTE:The following assumptions are made about the input CSV file:The CSV file contains a column named"Time", with timestamps formatted asMM/DD/YYYY HH:MM:SSThe CSV file ends on the same date as the specified date filterInput ParametersParameterDescriptionTypeDefault--log-filepathPath to log file to trim.Path|NoneGUI Prompt--test_dateTrim date selection, asYYYY-MM-DD.strToday's date--out-filenameOutput filename.1,2,3str|None<in_filename>_<test_date>.csvOutput file is saved to the parent directory of--log-filepathTrimming will be aborted if--out-filenamematches--log-filepathAny existing data will be discardedContributingDevelopment EnvironmentThis project usesPoetryto manage dependencies. With your fork cloned to your local machine, you can install the project and its dependencies to create a development environment using:$poetryinstallApre-commitconfiguration is also provided to create a pre-commit hook so linting errors aren't committed:$pre-commitinstall
Package: sco1-pylox
pyloxIntroductionThis is my Python implementation of an interpreter for the Lox programming language from Robert Nystrom'sCrafting Interpreters.Python?While the text is implemented in Java and C as its high & low-level implementations, I have no idea how to write either of them! Instead, I'll be using Python for the high-level implementation & eventually Rust for the low-level imeplementation.Differences From TextFor the sake of fitting within a decently sized text, the fully implemented Lox spec omits features that users of other programming languages may miss. Often these are discussed as notes within a chapter, or presented as challenges at the end of a chapter. Significant difference in this implementation from the text reference are noted below.Defined by Challenges(Chapter 4): Arbitrarily nested block comments (/* ... */)(Chapter 9):breakstatements are available forforandwhileloopsUser ChoiceDivision by zero returnsNaN(Python'sfloat('nan'))Strings may be defined using either"or'Modulo operator (%)Power operator (^)Integer division operator (\)Both floats and integers are representedReturn type from operations follows Python3's semanticsContainersarray()A basicincludeheader systemSupports "stdlib" imports (<header_name>) and path imports ("path/to/file")Recursiveincludenot supportedImported source assumed to be valid codeAdditional Built-ins:Unless otherwise noted, behavior mirrors the similarly named Python function.Generalinputlenordread_text(viapathlib.Path.read_text)str2numstring_arrayGives aLoxArraywhose contents are equivalent tocollections.deque(<some string>)MathabsceildivmodfloormaxminRegexFor methods whose Python equivalent returnsMatch objects, aLoxArrayis returned. The first value in the array will always correspond tomatch.group(0); if the pattern contains one or more groups then the array will match the output ofmatch.groups()re_findallre_matchre_searchre_subStatsmeanmedianmodestdPure lox headers<array_sum><hello_world><map><split_on>