Saravanan666/pypi-org huggingface dataset:
Package: episodic
EpisodicA complete pipeline for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution.AboutEpisodic is a tool for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution. The package is built on top ofSNK, and provides a complete pipeline for fitting and testing models of episodic evolution usingBEAST.Episodic implements the ideas of Tay et al. (2022and2023) and detects episodic evolution through Bayesian inference of molecular clock models.Given a multiple sequence alignment and a list of groups to test for episodic evolution, episodic will:Configure BEAST analyses for strict, relaxed (UCGD) and stem fixed local clock models.Configure marginal likelihood analyses for each clock model.Run all the BEAST and marginal likelihood analyses.Plot and summarise the results.Compute and plot Bayes factors for the marginal likelihood analyses.Produce maximum clade credibility (MCC) trees for each clock model.Compute bayes factor on effect size for the FLC models (foreground vs background).Run rank and quantile tests on the all the models.Handel the execution of the pipeline on a HPC cluster via snakemake profiles.Produce a report of the results (TBD).FeaturesComplete pipeline-episodicprovides a complete pipeline for fitting and testing FLC models of episodic evolution.Flexible-episodicis built on top ofSNK, and provides a flexible framework for fitting and testing FLC models of episodic evolution.Easy to use-episodicis easy to use, and provides a simple interface for fitting and testing FLC models of episodic evolution.robust-episodicis robust, and provides a robust framework for fitting and testing FLC models of episodic evolution.Installationpip install episodicOutputsEpisodic will produce a range on log files, trees and plots. The following is a list of the main outputs.BEAST log files-episodicwill produce a BEAST log file for each clock model. These files can be analysed withBeastiary.BEAST trees-episodicwill produce a BEAST tree file for each clock model.MCC trees-episodicwill produce a MCC tree for each clock model.MCC tree plots-episodicwill produce a MCC tree plot for each clock model.Marginal likelihoods-episodicwill produce a marginal likelihood plot for each clock model.Bayes factors on effect size-episodicwill calculate a Bayes factors on effect size for each local clock model.Rate Columnp_pp_oddspos_ppos_oddsbfBA.2.86.rate0.50349961115431621.01409711344819861.0infinfRank and quantile tests-episodicwill produce a rank and quantile test plot for each clock model.Clock rate plots-episodicwill produce a rate plot for each clock model.DAGLicenseepisodicis distributed under the terms of theMITlicense.
Package: epispot
A tool for creating and testing compartmental epidemiological models faster than ever for the mathematical modeling of infectious
diseases. An idea fromhttps://github.com/henrifroese/infectious_disease_modelling.DOI InformationUse epispot's DOI in your research!:fireworks:Citation:q9i, & QLabs. (2021, April 2). epispot/epispot: (Version 2.1.0). Zenodo.http://doi.org/10.5281/zenodo.4624423Current Build StatusesWorkflowStatusbuild.ymlcoverage.ymlpython-publish.ymlcodeql-analysis.ymlInstallationEpispot can be installed via pip or via Anaconda.
If using pip, install with:pipinstallepispotFor Anaconda, install via conda-forge as:condaconfig--addchannelsconda-forge
condainstall-cconda-forgeepispotAs a shorthand, useimport epispot as epi.You can also install theepispot-nightlypackage from pip:pipinstallepispot-nightlyYou can import it the same asimport epispot as epi. Both packages cannot be used at the same time.Getting StartedStart with the tutorials intests/tutorials(seeGitHub repo). Next, check out the
documentation athttps://epispot.github.io/epispot. You may also find
it helpful to see the examples in thetests/examplesfolder.Release NotesSeeGitHub repofor more info.FeaturesSIR-based modelsSusceptibleInfectedRecoveredExposedDeadCriticalHospitalizedCustom-defined compartmental modelsCreate custom models using theModelclassGraphing CapabilitiesPlot real data from a.csvfilePlot model predictions interactivelyCompare different model predictionsA Note on DocumentationDocumentation can easily be accessed from function, class, and file docstrings.
Docstrings provide additional documentation on a certain function.
They can be accessed by the built-in Pythonhelp()command.
These strings are formatted in Github-flavored markdown.
Additionally, all files will have a 'STRUCTURE' label.CreditsThe epispot package is supported by the following contributors:Head of Software & Development:@quantum9innovationHead of Code Maintenance:@Quantalabs
Package: epispread
epi-spread-visualizerTool to visualize disease spread based on input data.OverviewWith this library, I am creating a tool that will assist in visualizing the spread of a disease according to an inputted line listing of populations becoming sick over time, translating this data into a map that will change based on a slider simulating the passage of time. My library will use data visualization tools already in place, such as Plotly DASH, as well as some data-processing tools like Python Pandas, combining these two to make the final result.DetailsThis project is a pure python project using modern tooling. It uses aMakefileas a command registry, with the following commands:make: list available commandsmake develop: install and build this library and its dependencies usingpipmake build: build the library usingsetuptoolsmake lint: perform static analysis of this library withflake8andblackmake format: autoformat this library usingblackmake annotate: run type checking usingmypymake test: run automated tests withpytestmake coverage: run automated tests withpytestand collect coverage informationmake dist: package library for distributionInstallation$pip3installepispreadQuick StartfromepispreadimportEpiSpreadmy_epispread_object=EpiSpread(EpiSpread.FILE)epi_instance.world.to_csv('world.csv',index=False)epi_instance.plot_all()
Package: epistasis
EpistasisPython API for estimating statistical, high-order epistasis in genotype-phenotype maps.All models follow aScikit-learninterface and thus seamlessly plug in to the PyData ecosystem. For more information about the type of models included in this package,
read ourdocs. You can also read more about the theory behind these models in ourpaper.Finally, if you'd like to test out this package without any installing, try these Jupyter notebookshere(thank youBinder!).ExamplesThe Epistasis package works best in combinations with GPMap, an API for managing
genotype-phenotype map data. Construct a GenotypePhenotypeMap object and pass it
directly to an epistasis model.# Import a model and the plotting modulefromgpmapimportGenotypePhenotypeMapfromepistasis.modelsimportEpistasisLinearRegressionfromepistasis.pyplotimportplot_coefs# Genotype-phenotype map data.wildtype="AAA"genotypes=["ATT","AAT","ATA","TAA","ATT","TAT","TTA","TTT"]phenotypes=[0.1,0.2,0.4,0.3,0.3,0.6,0.8,1.0]# Create genotype-phenotype map object.gpm=GenotypePhenotypeMap(wildtype=wildtype,genotypes=genotypes,phenotypes=phenotypes)# Initialize an epistasis model.model=EpistasisLinearRegression(order=3)# Add the genotype phenotype map.model.add_gpm(gpm)# Fit model to given genotype-phenotype map.model.fit()# Plot coefficients (powered by matplotlib).plot_coefs(model,figsize=(3,5))More examples can be found in thesebinder notebooks.InstallationEpistasis works in Python 3+ (we do not guarantee it will work in Python 2.)To install the most recent release on PyPi:pip install epistasisTo install from source, clone this repo and run:pip install -e .DocumentationDocumentation and API reference can be viewedhere.Dependenciesgpmap: Module for constructing powerful genotype-phenotype map python data-structures.Scikit-learn: Simple to use machine-learning algorithmsNumpy: Python's array manipulation packagedScipy: Efficient scientific array manipulations and fitting.lmfit: Non-linear least-squares minimization and curve fitting in Python.Optional dependenciesmatplotlib: Python plotting API.ipython: interactive python kernel.jupyter notebook: interactive notebook application for running python kernels interactively.ipywidgets: interactive widgets in python.DevelopmentWe welcome pull requests! If you find a bug, we'd love to have you fix it. If
there is a feature you'd like to add, feel free to submit a
pull request with a description of the addition. We also ask that you write the
appropriate unit-tests for the new feature and add documentation to our Sphinx docs.To run the tests on this package, make sure you havepytestinstalled and run from the base directory:pytestCitingIf you use this API for research, please cite thispaper.You can also cite the software directly:@misc{zachary_sailer_2017_252927,
  author       = {Zachary Sailer and Mike Harms},
  title        = {harmslab/epistasis: Genetics paper release},
  month        = jan,
  year         = 2017,
  doi          = {10.5281/zenodo.1215853},
  url          = {https://doi.org/10.5281/zenodo.1215853}
}
Package: epistle
Epistleepistle /ɪˈpɪs(ə)l/nounformal, humourous- A letter.Takead hocnotes using Vim. Save them in a remote Git repository.  This supports sharing notes across devices.Can be configured to use a private (or public) gist.RequirementsPython 3.6VimGitBoth Vim and Git must be in yourPATH.InstallationVia PyPI$ pip install epistleOr, clone this repo and run pip install directly:$ git clone git@github.com:msharp/epistle.git
$ pip install epistle/UsageEpistle is a command-line tool.To create a new note:$ epistle new my_noteThis will open Vim, where you can compose the note.  After you exit Vim, the note will be saved, added to the repo and pushed to the remote.The following commands are available:listList available EpistlesviewView the contents of an EpistlenewCreate a new EpistleeditEdit an EpistlerenameRename an EpistledeleteDelete an EpistlegitRunpushorpullto the remote Git repositoryRunepistle --helpto see a list of available commands.When you first run theepsitlecommand, it will discover your Vim and ask for the clone URL of the remote repository you want to use.It will then clone this repository and store the URL in its config.
Package: epistoch
EpiStochEpidemics Models with Random Infectious PeriodThis software allows you to model epidemics with general random distribution for the infectious period.Traditional epidemiology models, like SIR, do not take into account the distribution for the length of
the infectious period. In this software, we include three functions that compute these type of models
using other distributions.In this graph you can see how different the predictions are for the regular SIR model with respect to SIR-G
that actually uses a more realistic distribution for the infectious period.
In SIR-G case the peak of infection occurs before, and has a bigger intensity.
The number of individuals that eventually get infected, however, remains the same for both modelsModelsSIR: Classical SIR model, with (implied) exponential infectious period.SIR_G: Like the classical SIR model, but with an arbitrary distribution.SIR-PHG: A SIR model with Phase-Type distributions for the infectious period.SEIRD: A SEIRD Model with hase-Type distributions for each stage.NotesThe theoretical foundation of the method is explained in thispaper.Documentation:https://epistoch.readthedocs.io.Source Code:https://github.com/griano/epistoch.Free software: MIT license. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.History1.0 (2020-05-01)First release on PyPI.
Package: episuite
.. image:: docs/source/_static/banner.png
:width: 600
:align: center
:alt: BannerEpisuite|Development Status| |Run Tests| |PyPi Shield| |Coverage Status|Welcome toEpisuite. Episuite is a suite of tools for epidemiology
in Python.Click here for documentation <https://perone.github.io/episuite>_Episuite is currently under development, and accepting contributions... |Development Status| image::https://img.shields.io/badge/Development%20Status-2%20--%20Alpha-yellow.. |Run Tests| image::https://github.com/perone/episuite/workflows/CI/badge.svg:target:https://github.com/perone/episuite/actions?query=workflow%3A%22CI%22+branch%3Amain.. |PyPi Shield| image::https://img.shields.io/pypi/v/episuite.svg:target:https://pypi.python.org/pypi/episuite.. |Coverage Status| image::https://codecov.io/gh/perone/episuite/branch/main/graph/badge.svg?token=XE5J0UX1U4:target:https://codecov.io/gh/perone/episuite
Package: epithet
EpithetManage your labels.Free software: MIT licenseDocumentation:https://epithet.readthedocs.io.FeaturesAllows you to list all the labels on a repo, or all repos on an organization.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.4 (2017-11-12)Add “update” commandAdd ability to set key in environment variableRework interface0.0.3 (2017-06-11)Add “delete” commandAllow specifying an API URL for GitHub EnterpriseMake–keyparam required0.0.2 (2017-05-25)Improved rate limit handling0.0.1 (2017-05-25)First release on PyPI.
Package: epitome
EpitomePipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility.Epitome leverages chromatin accessibility (either DNase-seq or ATAC-seq) to predict epigenetic events in a novel cell type of interest. Such epigenetic events include transcription factor binding sites and histone modifications. Epitome computes chromatin accessibility similarity between ENCODE cell types and the novel cell type, and uses this information to transfer known epigentic signal to the novel cell type of interest.DocumentationEpitome documentation is hosted atreadthedocs. Documentation for Epitome includes tutorials for creating Epitome datasets, training, testing, and evaluated models.Requirementscondapython >= 3.6Setup and InstallationCreate and activate a conda environment:conda create --name EpitomeEnv python=3.6 pip
source activate EpitomeEnvInstall Epitome:pip install epitomeTraining a ModelFirst, create an Epitome dataset that defines the cell types and ChIP-seq
targets you want to train on,fromepitome.datasetimport*targets=['CTCF','RAD21','SMC3']celltypes=['K562','A549','GM12878']dataset=EpitomeDataset(targets=targets,cells=celltypes)Now, you can create and train your model:fromepitome.modelsimport*model=EpitomeModel(dataset,test_celltypes=["K562"])model.train(5000)# train for 5000 batchesEvaluate a Model:model.test(1000)# evaluate how well the model performs on a validation chromosomeUsing Epitome on your own dataset:Epitome can perform genome wide predictions or region specific predictions on
a sample that has either DNase-seq or ATAC-seq.To score specific regions:chromatin_peak_file=...# path to peak called ATAC-seq or DNase-seq in bed formatregions_file=...# path to bed file of regions to scoreresults=model.score_peak_file([chromatin_peak_file],regions_file)To score on the whole genome:chromatin_peak_file=...# path to peak called ATAC-seq or DNase-seq in bed formatfile_prefix=...# file to save compressed numpy predictions to.model.score_whole_genome([chromatin_peak_file],file_prefix)Install Epitome for developmentTo build Epitome for development, run:make developRunning unit testsmake test
Package: epitools
Welcome to EpiTools!EpiTools is a Python package and associatednapariplugin to extract the membrane signal from epithelial tissues and analyze it with the aid of computer vision.The development of EpiTools was inspired by the challenges in analyzing time-lapses of growing Drosophila imaginal discs.The folded morphology, the very small apical cell surfaces and the long time series required a new automated cell recognition to accurately study growth dynamics.InstallationFirst, installnapari.The recommended way to installEpiToolsis viapippython-mpipinstallepitoolsTo install the latest development version ofEpiToolsclone this repository
and runpython-mpipinstall-e.If working on Apple Silicon make sure to also install the following package fromconda-forge.condainstall-cconda-forgepyqtRecommended Companion Napari PluginsTo also install the recommended plugins for theEpiToolsworkflow runpython-mpipinstallepitools[wf]andpython-mpipinstall-e.[wf]When installing with Apple Mac OS X terminal, you might need to add '"' to [wf] as in:python-mpipinstall-e."[wf]"If working on Apple Silicon make sure to also install the following package fromconda-forgecondainstall-cconda-forgecvxoptwhich is required forbtrack.IssuesIf you encounter any problems, pleasefile an issuealong with a
detailed description.ContributingContributions are very welcome. Tests can be run withtox,
please ensure the coverage at least stays the same before you submit a pull request.
Package: epitopepredict
Backgroundepitopepredict provides a standardized programmatic interface and command line tool for
executing multiple epitope prediction methods. Currently this largely consists of interfaces
to several MHC binding prediction, the results of which can then be processed and visualized
in a consistent manner. The Tepitope module implements the TEPITOPEPan method is provided as
a ‘built in’ method. The IEDB tools and netMHCIIpan and mhcflurry are also supported.
All of these tools are free for academic use. This software runs on most linux systems.
Users are recommended to use the snap package for convenience. This software is under
active development particularly with a view to improve the command line and web tools.Documentation is athttp://epitopepredict.readthedocs.ioSupported methodsTEPITOPEPan
NetMHCpan
IEDB MHC tools
MHCflurryDependenciespandas
biopython
tornado
bokeh
mhcflurry (optional)Installationpip install epitopepredictLinkshttps://github.com/dmnfarrell/epitopepredict
Package: epivizFileParser
Parse and Query Genomic File FormatsDescriptionEFS parser provides parsers and utility methods to read and query commonly used genomic file formats.
The results of the query are transformed into a Pandas DataFrame. This allows users to take
advantage of many of the visual and analysis packages in the pandas/numpy ecosystem.Explore the docsClass informationA new parser for a file format can be added to the package. The minimum skeleton
is available in theskeletondirectoryDeveloper NotesThis project has been set up using PyScaffold 4. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.use a virtualenv for testing & development.
To setup run the following commands from the project directoryvirtualenvenv--python=python3sourceenv/bin/activate# (activate.fish if using the fish-shell)pipinstall-rrequirements.txt# to deactivate virtualenvdeactivateTest -`python setup.py test`Docs -`python setup.py docs`Buildsource distribution`python setup.py sdist`binary distribution`python setup.py bdist`wheel  distribution`python setup.py bdist_wheel`
Package: epivizFileServer
Compute and Query Parser for Genomic FilesDescriptionEpiviz file Server is a Python library, to query genomic files,
not only for visualization but also for transformation.
The library provides various modules to perform various tasks -
- Parser to read various genomic file formats,
- Query to access only necessary bytes of file,
- Compute to apply transformations on data,
- Server to instantly convert the datasets into an API and
- Visualization.A quick overview of the library and its features, are described in an IPython notebook
available at -https://epiviz.github.io/post/2019-02-04-epiviz-fileserver/NoteThe library requires the server hosting the data files to support HTTP range requests so that the file server’s parser module can only request the necessary byte-ranges needed to process the queryThe library currently supports indexed genomic file formats like BigWig, BigBed, Bam (with bai), Sam (with sai) or any genomic data file that can be indexed using tabix.Developer NotesThis project has been set up using PyScaffold 3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.use a virtualenv for testing & development.
To setup run the following commands from the project directoryvirtualenvenv--python=python3sourceenv/bin/activate# (activate.fish if using the fish-shell)pipinstall-rrequirements.txt# to deactivate virtualenvdeactivateTest -`python setup.py test`Docs -`python setup.py docs`Buildsource distribution`python setup.py sdist`binary distribution`python setup.py bdist`wheel  distribution`python setup.py bdist_wheel`
Package: epiweeks
EpiWeeksA Python package to calculate epidemiological weeks using the US CDC (MMWR) and
ISO week numbering systems.FeaturesSupport for both the US CDC (MMWR) and ISO week numbering systems.Accurate and tested calculations.Intuitive, clean, and easy-to-use interface.Calculation of the start and end dates of weeks.Iteration of year's weeks or week's dates.Rich comparison between weeks.Logical operations for weeks (addition, subtraction and containment).Validation of input data.Works on Python 3.8+ with zero dependencies.Thoroughly tested with 100% test coverage.InstallationTo install usingpip, run:pipinstallepiweeksTo install usingconda, run:condainstall-cbiocondaepiweeksBasic UsagefromepiweeksimportWeek,Yearweek=Week(2019,1)print(week.enddate())# 2019-01-05forweekinYear(2019).iterweeks():print(week.enddate())# 2019-01-05# 2019-01-12# ...# 2019-12-21# 2019-12-28DocumentationPlease seehttps://epiweeks.readthedocs.iofor full documentation of this
package, including background, more usage examples and API reference.LicenseThis project is licensed under the terms of the MIT license.
Package: epiweeks-barc
A Python package to calculate epidemiological weeks using the CDC (MMWR),
ISO and BARC week numbering systems.FeaturesSupport for both the CDC (MMWR) and ISO week numbering systems.Accurate and tested calculations.Intuitive, clean, and easy-to-use interface.Calculation of the start and end dates of weeks.Iteration of year’s weeks or week’s dates.Rich comparison between weeks.Logical operations for weeks (addition, subtraction and containment).Validation of input data.Works on Python 3.6+ with zero dependencies.Thoroughly tested on all supported Python versions.Add support for Barc (Week start day from Saturday).Installation$pipinstall-Uepiweeks-barcBasic UsagefromepiweeksimportWeek,Yearweek=Week(2019,1)print(week.enddate())# 2019-01-05forweekinYear(2019).iterweeks():print(week.enddate())# 2019-01-05# 2019-01-12# ...# 2019-12-21# 2019-12-28Online ToolThe following is a simple online tool that was developed to calculate
epidemiological weeks (CDC system only) using the latest version of
this package:https://www.dralshehri.com/epiweeks/DocumentationPlease seehttps://epiweeks.readthedocs.io/for full documentation of
this package, including overview, more usage examples and API reference.ContributingContributions are welcome! SeeCONTRIBUTING.rstfor more info.AuthorsThe main author is Mohammed Alshehri —@dralshehri.LicenseThis package is distributed under an MIT license.
SeeLICENSE.Change Log2.1.1Fixed some typos.Improved documentation.2.1.0Changed the parametermethod, which sets how the weeks are
numbered, tosystemfor better clarity and intuitive usage.Updated documentation and unit tests.Other minor fixes and enhancements.2.0.0Dropped support for Python 3.5.Addedfromstring()classmethod to allow constructing theWeekobject
from a formatted string, for example ‘2019W08’ or ‘201908’.Addedcdcformat()method to return a formatted string like the one used
by US CDC for epi weeks, for example for week 8 of 2019 it returns ‘201908’.Addeddaydate()method to return the date of specific weekday for a week.Changed the optional value ‘WHO’ of the parametermethodto ‘ISO’.Changed thefromdate()classmethod ofWeekobject to accept a date
object as an argument instead of year, month, and day.Made theWeekandYearobjects hashable.Improved rich comparison methods.Improved input data validation.Updated and improved documentation examples.Updated unit tests.Other minor fixes and enhancements.1.0.0First release.