Saravanan666/pypi-org huggingface dataset:
Package: refutil
Ref Utileffortless weakrefs to objects, methods, and functions with annotationsPreambleThere is a fairly common need to reference some data in the callback of a weakref when the object is being garbage collected. This package provides a simple interface for creating new annotated, slotted, reference types, which subclass weakref.ref in the standard library.Documentation consists of this README and the code.Table of ContentsRef UtilPreambleTable of ContentsTechnologiesExampleCodeOutputTechnologiespython >=2.5ExampleCodeimportcollectionsimportrefutilSubscription=collections.namedtuple('Subscription',['callback','topic','subscription_id'])SubscriptionWeakRef=refutil.reftype('SubscriptionWeakRef',['subscription_id'])classSubscriptions:def__init__(self):self.subscriptions={}def_weak_remove_callback(self,ref):print('weak callback triggered')self.remove(ref.subscription_id)defadd(self,topic,callback,subscription_id):# make a weak reference to the callback, so that it will be garbage# collected, but store the subscription_id in the reference# and then add a method callback, which will be weakly referenced as well,# to handle when the callback is garbage collectedwrcallback=SubscriptionWeakRef(callback,self._weak_remove_callback,subscription_id=subscription_id)subscription=Subscription(topic=topic,callback=wrcallback,subscription_id=subscription_id)self.subscriptions[subscription_id]=subscriptionprint('subscription added',subscription_id)defremove(self,subscription_id):print('subscription removed',subscription_id)delself.subscriptions[subscription_id]subs=Subscriptions()defmy_callback():passsubs.add('topic',my_callback,'sub-1')subs.add('topic',my_callback,'sub-2')print(subs.subscriptions)print('deleting callback')delmy_callbackprint(subs.subscriptions)Outputsubscription added sub-1
subscription added sub-2
{'sub-1': Subscription(callback=<weakref at 0x01874300; to 'function' at 0x01873C48 (my_callback)>, topic='topic', subscription_id='sub-1'), 'sub-2': Subscription(callback=<weakref at 0x018743F0; to 'function' at 0x01873C48 (my_callback)>, topic='topic', subscription_id='sub-2')}        
deleting callback
weak callback triggered
subscription removed sub-2
weak callback triggered
subscription removed sub-1
{}
Package: refy
refyA scientific papers recommendation tool.Overviewrefyleverages Natural Langual Processing (NLP) machine learning tools to find new papers that might be relevant given the ones that you've read already.There's a few software tools out there that facilitate the exploration of scientific literature, including:meta.orgwhich allows users to set up feeds that identify newly published papers that seem relevant given a set ofkeywordsincitefulandscite.ailet you explore the network of citations around a given paper of interestconnected paperslet's you visualize a graph representation of papers related to a given paper of interestMost currently available software is limited in two key ways:Tools likemeta.orgrely on keywords, but keywords (e.g. computational neuroscience, Parkinson's Disease) are often overly general. As a result of that you have to sift through a lot irrelevant literature before you find something interestingOther tools likeconnected papersonly work with one input paper at the time: you give it the title of a paper you've read and they give you suggestions. This is limiting: any software that can analyseall papers you've readcan use a lot more information to find new papers that match more closely your interests.This is whatrefyis for:refyanalyzes the abstracts of several papers of yours and matches them agaist published preprints. By using many input papers at oncerefyhas a lot more information at its disposal which (hopefully) means that it can better recommend relevant papers. By using the abstracts and not the paper titles, authors or keywords,refyfocuses exclusively on the content of an article and has access to a wealth of data.Refy downloads recently published preprints from BiorXiv and ArXiv, we thank BiorXiv and ArXiv for the API services they made freely available.UsageInstallationIf you have an environment withpython >= 3.6, you can installrefywith:pip install refygetting suggested papersimportrefyd=refy.Recomender('library.bib',# path to your .bib filen_days=30,# fetch preprints from the last N dayshtml_path="test.html",# save results to a .html (Optional)N=10# number of recomended papers)
Package: reg2info
Reg2inforeg2infois a Python package for getting meaningful information from a Linear regression model.Table of contentInstallationDependenciesMain FeaturesUsageContributingLicenseWhere to get itBinary installers for the latest released version are available at thePython Package Index (PyPI)Use the package managerpipto install reg2info :pipinstallreg2infoDependenciesNumpyMatplotlibMain FeaturesA linear regression line has an equation of the formY = a + bX, whereXis the explanatory variable andYis the dependent variable. The slope of the line isb, andais the intercept (the value ofywhenx= 0).A regression line is the “best fit” line for your data. You basically draw a line that best represents the data points. It’s like an average of where all the points line up. In linear regression, the regression line is a perfectly straight line.Usageimportreg2infoasr2ilr=LinearRegression().fit(X_train,y_train)# returns equationeq=r2i.reg_equation(lr)# returns line and data plotplot=r2i.reg_plot(lr,X_train[feature],y_train)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: reg4opt
Welcome toreg4optDocs|Installation|Citereg4optis a Python implementation of operator regression and convex regression methods presented in the paper "OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression" by Nicola Bastianello, Andrea Simonetto, Emiliano Dall'Anese.The documentation is availablehere.Installationreg4optworks on Python 3.7 and depends on:tvopt, numpy, scipy. To run the examples, cvxpy may also be needed.pip installationpip install reg4optCiteTODOAuthorreg4optis developed byNicola Bastianellounder the supervision ofAndrea SimonettoandEmiliano Dall'Anese
Package: regal
Regal用于"灰度发布"或 A/B Testing的智能分组引擎Regal能做什么？举个最简单的例子，比如需要针对一个版本进行灰度发布，而这一版本对应的可能是一大堆服务器集群， 如下图:就像图中描述的一样，无论你的服务器是多还是少，尤其很多中小型企业在进行灰度发布时，通常会遇到所制定的分流策略在实际的技术或开发中如何去实现，是机器直接写死？因此让Regal智能分组引擎直接介入，让它来根据你的策略提前进行动态地分组分流。
在这里，我再举一个简单的例子，方便大家能够更清楚的明白Regal的主要工作：假设有一个版本A，需要针对六台机器进行发布现在应该已经了解Regal到底是什么干货了吧，当然了，上面的例子是服务器非常少的情况，实际情况中，所面对的服务器集群是非常多，这个时候可以通过提供的combine和schedule两个API进行策略调整。详情可以见下文的使用介绍Feature：提供发布策略，动态智能分流支持多版本分组和优先级数据格式化同时兼容Python2.5以上和Python3以上的版本（建议使用Python2.7+或者Python3.5以后的版本）安装和使用安装pip install regal使用说明单个版本场景In [1]: from regal import BaseInfo


# 初始化信息，请注意一下格式
In [6]: ab = BaseInfo(
version_host={'app-test-version1.0':'10.1.1.1,10.1.1.2,10.1.1.3,10.1.1.4,10.1.1.1.5'},
combine=2    # combine 希望以每组多少台服务器作为一组,进行用户群B的分流
             # 在这个例子中为2台
             # 默认：每组1台
)

# grouping() 进行分组
In [11]: smart_grouping = ab.grouping() 


# result属性 进行分组后的返回结果
In [12]: smart_grouping.result
Out[12]:
[('app-test-version1.0',
  [['10.1.1.1'], ['10.1.1.2', '10.1.1.3'], ['10.1.1.4', '10.1.1.1.5']])]根据你的策略设置，会得到一个数据结构，我们来观察一下：再看一个例子In [7]: ab = BaseInfo(
version_host={'app-test-version1.0':'10.1.1.1,10.1.1.2,10.1.1.3,10.1.1.4,10.1.1.5'},
combine=3,
schedule=2)

In [10]: ab.grouping().result
Out[10]:
[('app-test-version1.0',
  [['10.1.1.1,10.1.1.2'], ['10.1.1.3', '10.1.1.4', '10.1.1.5']])]多版本场景In [17]: ab = BaseInfo(
   ....: version_host={
   ....: 'app-test-version1.0': '10.1.1.1,10.1.1.2,10.1.1.3,10.1.1.1.4,10.1.1.5',
   ....: 'app-test-version2.0': '10.1.1.9,10.1.1.8,10.1.1.7,10.1.1.6'},
   ....: combine=3,
   ....: schedule=2
   ....: )
   
In [20]: ab.grouping().result
Out[20]:
[('app-test-version2.0', [['10.1.1.9,10.1.1.8'], ['10.1.1.7', '10.1.1.6']]),
 ('app-test-version1.0',
  [['10.1.1.1,10.1.1.2'], ['10.1.1.3', '10.1.1.1.4', '10.1.1.5']])]   


# grouping()方法还提供了priority_name参数，当需要在多版本发布的时候，设置优先级，指定你需要优先发布的'版本名'
 In [22]: smart_grouping = ab.grouping(priority_name='app-test-version1.0')

In [23]: smart_grouping.result
Out[23]:
[('app-test-version1.0',
  [['10.1.1.1,10.1.1.2'], ['10.1.1.3', '10.1.1.1.4', '10.1.1.5']]),
 ('app-test-version2.0', [['10.1.1.9,10.1.1.8'], ['10.1.1.7', '10.1.1.6']])]

# 提供一个简易的API，可以让结果返回的更简洁  
In [16]: for i in smart_grouping.iter_dict():   
    print i
   ....:
{'app-test-version1.0': ['10.1.1.1', '10.1.1.2,10.1.1.3', '10.1.1.4,10.1.1.1.5']}Demo你也可以通过git clone https://github.com/boylegu/regal/cd regal/参考example.py分流分组之后？Regal本身只是一个分组引擎，因此它并不承担直接发布的作用，但是通过Regal分组之后，你所得到数据，是非常容易和其他可以用来发布的组件进行配合；下面是我的一些建议和指导。versionA:

  （第一组）    groupA   ip......     用户群A    
  （第二组）    groupB1  ip...... __ 
  （第三组）    groupB2  ip......   |
  （第四组）    groupB3  ip......   | --   用户群B   
   ......                       --|关于发布分组之后，每一组的所有机器可以看作一个整体，扔进发布组件，进行'组内并发'你可以把每一组直接放在ansible、saltstack、pssh或异步IO框架等等进行发布；甚至你也可以和前端nginx＋lua进行组合；关于停止发布每组进行发布，一旦出现异常，你可以利用发布组件，或者你自己写一套异常抓取工具来停止发布，这个时候就不会再针对剩下的组进行发布操作了。关于回滚把回滚也看作一种发布,就不多说了作者顾鲍尔 (Boyle Gu)技术交流与支持有任何问题、建议可以通过Github；也可以直接加入讨论群 QQ：315308272 与我进行交流Darwin's finches第一次在Mac上绘图，这就当做本项目的吉祥物吧～人类的创造从来没有离开大自然带给我们的启发，而无论是灰度发布，还是A/B Testing，早在千年以前，大自然早有绝佳的解决方案。因此我以‘Darwin's finches’作为原型，手工绘制了这张图，向伟大的大自然和达尔文《物种起源》致敬。Author: 顾鲍尔Date： 2015.12.23 绘
Package: regalias
# Regalias: [Elona](http://ylvania.org/elona)-like alias generator for Python, with CLI.[![PyPI](https://img.shields.io/pypi/wheel/regalias.svg?style=flat-square)](https://pypi.python.org/pypi/regalias)[![PyPI](https://img.shields.io/pypi/pyversions/regalias.svg?style=flat-square)](https://pypi.python.org/pypi/regalias)[![PyPI](https://img.shields.io/pypi/l/regalias.svg?style=flat-square)](https://github.com/letla/regalias/blob/master/LICENSE.txt)[![PyPI](https://img.shields.io/pypi/implementation/regalias.svg?style=flat-square)](https://pypi.python.org/pypi/regalias)[![PyPI](https://img.shields.io/pypi/v/regalias.svg?style=flat-square)](https://pypi.python.org/pypi/regalias)[![Twitter](https://img.shields.io/badge/twitter-@letlafox-55acee.svg?style=flat-square)](https://twitter.com/letlafox):jp: Currently, it only supports to generate Japanese aliases.**Example**```sh$ regalias  # 引数を渡さずに実行すると...王国の記憶  # 異名をランダムに生成します$ regalias結末の水平線  # != 王国の記憶$ regaliasやさしすぎた略奪者``````sh$ regalias @  # 引数に名前を渡して実行すると...奇跡を呼ぶ幻術使い @  # その名前に対応する異名を生成します$ regalias マニダイヤモンドの妹 マニ  # !?$ regalias エヘカトルザ・ヒットマン エヘカトル  # うみみゃぁ！$ regalias エヘカトルザ・ヒットマン エヘカトル  # 名前付き異名の生成結果は常に同一です！```### :gem: InstallationInstallation is easy, with pip:```sh$ pip3 install regalias```### :gem: Usage```shusage: regalias [-h] [--version] [name]Elona-like alias generator for Python, with CLI.positional arguments:name        seed of aliasoptional arguments:-h, --help  show this help message and exit--version   show program's version number and exit```### :gem: ThanksSpecial thanks to [Noa (aka Rfish)](http://ylvania.org) for giving license to use Elona's ndata in this project.### :panda_face: Author| [![Letla Fox](https://github.com/letla.png?size=96)](https://www.letla.net) ||:---:|| [Letla Fox](https://www.letla.net) |### :gem: LicenseApache License 2.0
Package: regallager
KartothekKartothek is a Python library to manage (create, read, update, delete) large
amounts of tabular data in a blob store. It stores data as datasets, which
it presents as pandas DataFrames to the user. Datasets are a collection of
files with the same schema that reside in a blob store. Kartothek uses a metadata
definition to handle these datasets efficiently. For distributed access and
manipulation of datasets Kartothek offers aDaskinterface.Storing data distributed over multiple files in a blob store (S3, ABS, GCS,
etc.) allows for a fast, cost-efficient and highly scalable data infrastructure.
A downside of storing data solely in an object store is that the storages
themselves give little to no guarantees beyond the consistency of a single file.
In particular, they cannot guarantee the consistency of your dataset. If we
demand a consistent state of our dataset at all times, we need to track the
state of the dataset. Kartothek frees us from having to do this manually.Thekartothek.iomodule provides building blocks to create and modify these
datasets in data pipelines. Kartothek handles I/O, tracks dataset partitions
and selects subsets of data transparently.InstallationInstallers for the latest released version are availabe at thePython
package indexand on conda.# Install with pippipinstallkartothek# Install with condacondainstall-cconda-forgekartothekWhat is a (real) Kartothek?A Kartothek (or more modern: Zettelkasten/Katalogkasten) is a tool to organize
(high-level) information extracted from a source of information.
Package: regapp-tools
regapp-toolsThese is a collection of (python for the moment) tools for regapp.Main goal of this package is to have them easily installable via:pip install regapp-toolsThe initial tools are:subiss-to-unix that allows finding a user that was registered via OIDC
in regapp. Examples:subiss-to-unix <sub>@<iss>subiss-to-unix test-idsubiss-to-unix 6c611e2a-2c1c-487f-9948-c058a36c8f0e@https://login.helmholtz-data-federation.de/oauth2ssh-key-retriever (there is a go version in an rpm package available
elsewhere). This one is for retrieving ssh-keys that users have
registered in reg-app. Examples:ssh-key-retriever <username>Installationpip install regapp-toolsConfigurationBoth tools read config files from
- ./regapp-tools.conf
- $HOME/regapp-tools.conf
- /etc/regapp-tools.conf
in that orderSample Config:#vim: ft=conf

[backend.bwidm]
# The base URL of the BWIDM API
url = https://bwidm-test.scc.kit.edu/rest

# The ID of the organisation
org_id = hdf

[backend.bwidm.auth]
# HTTP basic auth to connect to BWIDM API
http_user = xxxx
http_pass = xxxx

[backend.bwidm.service]
# The name of the service the user should be added to on BWIDM:
name = sshtest
Package: regcomsrv
Regcomsrv documentationSee athttps://regcomsrv.readthedocs.ioOverwiewPackage helps you to register or unregister COM-servers that defined in python packages.Register from Pythonfrom regcomsrv import reg
import sys

# set command line attributes
sys.argv.clear()
sys.argv.append(__file__)
sys.argv.append("win32com.servers.dictionary")
sys.argv.append("DictionaryPolicy")

reg()Unregister from Pythonfrom regcomsrv import reg
import sys

# set command line attributes
sys.argv.clear()
sys.argv.append(__file__)
sys.argv.append("win32com.servers.dictionary")
sys.argv.append("DictionaryPolicy")
sys.argv.append("--unregister")

reg()Register from command linepython -m regcomsrv win32com.servers.dictionary DictionaryPolicyUnregister from command linepython -m regcomsrv -u win32com.servers.dictionary DictionaryPolicyShow helppython -m regcomsrv -h
Package: regdata
RegDataA collection of regression datasets.InstallpipinstallregdataQuick exampleimportregdataasrdrd.set_backend('torch')# numpy, tf (numpy is default)X,y,X_test=rd.Step().get_data()# Loads step function datasetFeaturesSimple API for quick benchmarking on various datasets.Get data in any framework:torch,tensorflowornumpyby setting a global backend.ScaleXand/orydata withMinMaxScalerorStandardScaler.Getyin squeezed(n,)or unsqueezed(n,1)format.Perform only mean normalization ony.Add custom noise to the observations (y).Get consistent data with fixed random seed.Plot datasets to have a quick glanceimportregdataasrdrd.Olympic().plot()Checkout all plotshere.Datasetsfromregdataimport(DellaGattaGene,Heinonen4,Jump1D,MotorcycleHelmet,NonStat2D,Olympic,SineJump1D,SineNoisy,Smooth1D,Step)ReferencesDellaGattaGeneHeinonen4Jump1DMotorcycleHelmetNonStat2DOlympicSineJump1DSineNoisySmooth1D- Example 2Step
Package: regdiffusion
RegDiffusion: Probabilistic Diffusion-Based Neural Inference of Gene Regulatory NetworksWelcome to the official repository for RegDiffusion, an innovative and high-performance approach for inferring Gene Regulatory Networks (GRNs) from single-cell RNA sequencing data. RegDiffusion leverages the principles of Denoising Diffusion Probabilistic Models to predict regulatory interactions with lifted performance and efficiency.The details of RegDiffusion is described in the following paper.From Noise to Knowledge: Probabilistic Diffusion-Based Neural Inference of Gene Regulatory Networks
Hao Zhu, Donna K. Slonim
bioRxiv 2023.11.05.565675; doi: https://doi.org/10.1101/2023.11.05.565675InstallationGetting StartedCitation
Package: regdown
RegdownRegdown is aPython-Markdownextension for interactive regulation text.DependenciesInstallationDocumentationGetting helpGetting involvedLicensingCredits and referencesDependenciesPython 3.6, 3.8Python-Markdown3.2InstallationFirst, install Regdown:pipinstallregdownThen you can either:Specify Regdown as an extension in calls tomarkdown:importmarkdownfromregdownimportRegulationsExtensionmarkdown.markdown(text,extensions=[RegulationsExtension()],)Use convenienceregdownfunction to render Markdown with theRegulationsExtension:fromregdownimportregdownregdown(text)DocumentationRegdown adds three major features to Markdown to support making federal regulations easier to navigate and read.Labeled Paragraphs{label} Paragraph textEach paragraph can have a defined label, using{label}syntax at the start of the paragraph. This is translated into anidattribute on the resulting HTML paragraph element. If no label is given, the contents of the paragraph are hashed to generate a uniqueidfor that paragraph. This makes any paragraph in the text directly linkable.Pseudo FormsForm field: ____Form Fieldinline__fields__Example print forms, where the\_\_indicate a space for hand-written input. Can be any number of underscores between 2 and 50.Section symbols§ 1024.5(d)§1024.5(d)Section symbols will always have a non-breaking space ( ) inserted between them and whatever follows to avoid hanging a symbol at the end of a line.Block referencessee(label)Insert the contents of labeled paragraphs in other Regdown documents inline into the current document.References can be placed before or after paragraphs. These references are to labeled paragraphs in other Markdown documents. When acontents_resolvercallback andurl_resolvercallback are provided, the text of those other paragraphs can be looked up and inserted inline into the document making the reference. Ifrender_block_referencecallback is provided, custom rendering of the referenced text to HTML can be performed.Callbacks:contents_resolver(label): resolve the paragraph label and return the Markdown contents of that paragraph if the paragraph exists.url_resolver(label): resolve the paragraph label and return a URL to that paragraph if the paragraph exists.render_block_reference(contents, url=None): render the contents of a block reference to HTML. The url to the reference may be give as a keyword argument ifurl_resolveris provided.fromregdownimportregdowndefmy_contents_resolver(label):# Lookup the document that contains the given label …returncorresponding_markdown_textdefmy_block_renderer(block_markdown_contents,url=None):# Render the block to HTMLreturnblock_htmlregdown(text,contents_resolver=my_contents_resolver,render_block_reference=my_block_renderer)Getting helpPlease add issues to theissue tracker.Getting involvedGeneral instructions onhowto contribute can be found inCONTRIBUTING.LicensingTERMSLICENSECFPB Source Code PolicyCredits and referencesregdown was forked fromWagtail-Flags, which was itself forked fromconsumerfinance.gov.
Package: regendoc
regendocre-render examples in documentationregendoc copies examples from documentation into custom folders and renders it..code::python# content of example.pydeftest_fun():pass..code::text$ pytest example.py
   ============================= test session starts ==============================
   platform linux -- Python 3.9.7, pytest-6.3.0.dev685+g581b021aa.d20210918, py-1.10.0, pluggy-1.0.0
   rootdir: /path/to/example
   plugins: hypothesis-6.14.8
   collected 1 item

   example.py .                                                             [100%]

   ============================== 1 passed in 0.01s ===============================
Package: regener
Regener - Resume GeneratorSimple one page generator.System requirements:Linux (I'm using Debian 11)Python > 3.6reportlab (3.6.5)Installationpip install regener==22.7.17UsageINPUT_PATH: Path to folder with files (json, images, fonts)OUTPUT_PATH: Path to the pdf file or to directory where that pdf file will be generatedUsage:regener -p <INPUT_PATH>
regener -p /home/${USER}/Desktop/My_CV/Or:regener -p <INPUT_PATH> -o <OUTPUT_PATH>
regener -p /home/${USER}/Desktop/My_CV/ -o resume_generator -p /home/${USER}/Desktop/My_CV/
regener -p /home/${USER}/Desktop/My_CV/ -o resume_generator -p /home/${USER}/Desktop/My_CV/My_CV_2022.pdfJson fileScript will search for filecv.jsonin INPUT_PATH.Example of simple CV json file can be found inexamplesdirectory.
Package: re-generate
Re-GenerateA simple and light package for QA development which can generate random strings via RegExr.Not all RegExr mechanics are available now. But you can use all base methods and generate any string.New features1.0.8:Finally, added () groups syntax supportGlobal changes in generation start function (sorry, it's alpha)Getting startedThis package is tested with Python 3.9-3.11 and Pypy 3.
There are two ways to install the library:Installation using pip (a Python package manager):pip install re_generateInstallation from source (requires git):git clone https://github.com/Warrfie/re-generate
cd re-generate
python setup.py installor:pip install git+https://github.com/Warrfie/re-generateIt is generally recommended to use the first option.Package is still under development, and it has regular updates, do not forget to update it regularly by callingpip install re_generate --upgradeSummaryWhat RegExr syntax supportedtypeis supported[] square brackets√[^] ! square brackets√{} braces quantifiers√{i,j} range braces quantifiers√|  or√() groups√\n,\s,\d...etc reserved samples✖*?,+?,?? greedy quantifiers✖*,+,? lazy quantifiers✖Main functionalityHow generatefromre_generateimportre_generateprint(re_generate(r"69[0-9]abc[a-zA-Z]228"))'693abcb228'How generate several strings ot oncefromre_generateimportre_generate_listprint(re_generate_list(r"69[0-9]abc[a-zA-Z]228"),10)['694abcO228', '693abcG228', '692abcV228', '696abcy228', '693abca228', '690abcb228', '694abcD228', '696abck228', '696abcJ228', '692abca228']What you can configueIf you want to use your own libraryfromre_generateimportre_generatorre_generator.char_lib="你f好п我Э的$中(国*朋Ъ友1234+=\/,."Sometimes you need generate string without spaces at begin and end, like after .strip() function.fromre_generateimportre_generatorre_generator.will_be_stripped=TrueTips and tricksIn large project you can link that lib throw cfg py file like thatcfg.pyfromre_generateimportre_generatorre_generator.char_lib="你f好п我Э的$中(国*朋Ъ友1234+=\/,."re_generator.will_be_stripped=Trueyour_func=re_generator.re_generatorand use it in your projectyour_code.pyfromcfgimportyour_funcprint(your_func(r"69[^0-9]abc[a-zA-Z]22[ 8]"))'69我abcb228'
Package: regens
REGENS (REcombinatory Genome ENumeration of Subpopulations) is an open source Python package that simulates whole genomes from real genomic segments. REGENS recombines these segments in a way that simulates completely new individuals while simultaneously preserving the input genomes' linkage disequilibrium (LD) pattern with extremely high fedility. REGENS can also simulate mono-allelic and epistatic single nucleotide variant (SNV) effects on a continuous or binary phenotype without perturbing the simulated LD pattern.please visit ourgithub pagefor more information