Saravanan666/pypi-org huggingface dataset:
Package: rian
RianRianis a machine learning- and data analysis framework, that implementscloud-assisted meta programming(CAMP).The key goal of Rian is to provide a long-term data analysis framework, which
seemingly integrates into existing enterprise data environments and thereby
supports collaborative data science. To achieve this goal Rian orchestrates
established frameworks likeTensorFlowÂ®and
dynamically extends their capabilities by community driven algorithms (e.g. forprobabilistic graphical
modeling,machine
learningandstructured
data-analysis).Thereby Rian allows the client-side usage of abstract algorithms,
that are specified with respect to their category, the used data type and an
evaluation metric that determines their fitness. During runtime
these abstract specifications are resolved server-sided from a code catalog, by
acurrently best fitting(CBF) algorithm.For given category and application, the CBF algorithms are determined by their
used metric. Examples for such metrices would be the prediction accuracies
within a fixed set of gold standard samples of the respective domain of
application (e.g. latin handwriting, spoken words, TCGA gene expression data,
etc.).Rian is open source, based on thePythonprogramming
language and actively developed as part of theLiquid
Codeframework atFrootlab.Current Development StatusRian currently is inPre-Alphadevelopment stage, which immediately follows
thePlanningstage. This means, that at least some essential requirements of
Rian are not yet implemented.InstallationComprehensive information and installation support is provided within theonline manual. If you already have a
Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install rianDocumentationThe documentation of the latest distributed version is available as anonline
manualand for download, given in the
formatsPDF,EPUBandHTML.ContributionsContributors are very welcome! Feel free to report bugs, ideas and feature
requests to theissue tracker,
provided by GitHub. Currently, as our team still is growing, we do not provide
any Contribution Guide Lines. So, if you are interested to help or to join the
team, we would be glad, tohear about you.LicenseRian is open source software and available free for any use under theGPLv3 license:Â© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>
Â© 2013-2019 Patrick Michl
Package: riana
Riana.py - Relative Isotope Abundance AnalyzerRiana (Relative Isotope Abundance Analyzer) takes in standard mass spectrometry spectra and spectral ID files,
and returns mass isotopomer distributions, e.g., for protein turnover analysis.Visithttps://ed-lau.github.io/riana/for Documentations and Usage.Installing RIANAInstall Python 3.7+ and pip. See instructions on Python website for specific instructions for your operating system.Riana can be installed from PyPI via pip or directly from GitHub. We recommend using a virtual environment.$pipinstallrianaLaunch riana as a module (Usage/Help):$python-mrianaSee thedocumentationsfor instructions to process a test data file.DependenciesRiana.py is tested in Python 3.7, 3.8, 3.9 and uses the following packages:matplotlib==3.4.1
pandas==1.2.4
pymzml==2.4.7
tqdm==4.60.0
scikit-learn==0.24.2ContributingPlease contact us if you wish to contribute, and submit pull requests to us.AuthorsEdward Lau, PhD-Code/design-ed-lauSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: ribalta
This is a library for creating RiBa CBI documents starting from Odoo 12 data.Table of contentsInstallationUsageKnown issues / RoadmapBug TrackerLicenseCreditsAuthorsContributorsInstallationThe library is available on PyPI and the suggested way of installing
it is by using pip:pip install ribaltaUsageImport the Document and Receipt classes:fromribaltaimportDocument,ReceiptCreate and instance of the Document class passing the required data:riba_doc=Document(creditor_company=creditor_res_partner_obj,creditor_bank_account=creditor_res_partner_bank_obj)Create a Receipt object for each receipt to be included in the CBI and
add it to the riba_doc:rcpt_1=Receipt(move_line_1,invoice_1,debtor_partner_1,debtor_bank_1)rcpt_2=Receipt(move_line_2,invoice_2,debtor_partner_2,debtor_bank_2)riba_doc.add_receipt(rcpt_1)riba_doc.add_receipt(rcpt_2)Render the CBI document (the result of the rendering is a string):cbi_str=riba_doc.render_cbi()See the docstring for details about the required arguments.Known issues / RoadmapNo known issues at the momentBug TrackerBugs are tracked onGitHub Issues.
In case of trouble, please check there if your issue has already been reported.Do not contact contributors directly about support or help with technical issues.LicenseLicense:LGPL-3CreditsAuthorsDidotech s.r.l.SHS-AV s.r.l.ContributorsMarco Tosato <marco.tosato@didotech.com>
Package: ribasim
Ribasim PythonThe Ribasim Python package (namedribasim) aims to make it easy to build, update and analyze Ribasim models
programmatically.The Ribasim QGIS plugin allows users to construct a model from scratch without programming.
For specific tasks, like adding observed rainfall timeseries, it can be faster to use
Python instead.One can also use Ribasim Python to build entire models from base data, such that your model
setup is fully reproducible.The package isregistered in PyPIand can therefore
be installed withpip install ribasim.For documentation please see theexamplesandAPI reference.ContributingFor the developer docs please have a look athttps://deltares.github.io/Ribasim/python/developer.html
Package: ribasim-api
Ribasim APIribasim_apiis an extension toxmipyfor the Ribasim API.Theribasim_apican be used to access functionality in the eXtended Model Interface (XMI) wrapper (XmiWrapper)
and additional functionality specific to the Ribasim API.ribasim_apican be installed by runningpip install ribasim-apiorconda install -c conda-forge ribasim-apiContributingFor the developer docs please have a look athttps://deltares.github.io/Ribasim/python/developer.html
Package: ribbity
ribbityHop, frog, hop!Turn a bunch of GitHub issues into amkdocssite.For example, turnthe sourmash-bio/sourmash-examples issue trackerintothis site.Project informationYou can see the introductory blog post here:Announcing ribbity - a hacky project to build Web sites from GitHub issue trackers.tl;dr Ribbity turns GitHub issue trackers into mkdocs sites that can be
hosted via GitHub pages.If you want to see ribbity in action, we have a sandbox repo where you can play with it a bit!first, add an issueover at ribbity-sandbox!then, go tothe rendered sandbox siteand search for your issue text!See ribbity documentationhereand visit the source codeon GitHub.Ribbity is released under theBSD 3-Clause license.Please file issueson GitHub.
Package: ribbon
simple color print library for console.Version HistoryVERSION 1.0 - 2014/07/30First release upon the worldWhyJust inspired byfabric.colorsmodule, and want to try how to release a python package to PyPi, so create this simple library.The ribbon library can help you to print colorful text in console more easily, and even you can printrainbow-liketext. Just for fun!!DemoUsagefrom ribbon import red
print(red('This text is red'))

from ribbon import red, green
print(red('This is red and ' + green('that is green')))

from ribbon import rainbow
print(rainbow('This is a rainbow-like text!'))Installpip install ribbonContributionWaiting for your pull requestLisenceMIT Lisence
Package: ribdif
RibDifRibDif evaluates the usefulness of a given amplicon at differentiating within a genus or speciesInstallationRibdif is most easily installed with pip inside a virtual environment. Make sure your pip version is up to datecondapip install git+https://github.com/Rob-murphys/ribdif.gitNext we need to install some dependencies:conda install -c bioconda vsearch fasttree muscle barrnap -yRunningribdif/ribdif -g <some genus name>Use-hfor full options list.
Package: ribes
Ribes RPCAsync framework for JSON-RPC via RabbitMQKey featureImplements JSON-RPC via RabbitMQ RPCFull support to asyncioSimple configurationIntegrates to existing framework like FastAPI or SanicFull support to Pydantic objects as parametersInstallationpipinstallribesGetting startedCreate server handler:app=Ribes("application")@app.register(name="namespace.method")asyncdefmethod(a,b):...Call method from clientapp=Ribes("application")method=app.caller("namespace.method")result=awaitmethod(1,2)To DoDocumentation and examplesRequirementsPython >= 3.8RabbitMQLicenseribesis offered under the Apache 2 license.Source codeThe latest developer version is available in a GitHub repository:https://github.com/aiselis/ribes
Package: ribogadgets
ribogadgets_alphaThis repository is a predecessor of ribogadgets.
Once we have the basic functionalities, we will move this project to beta phase.ribogadgetsribogadgets is a set of tools to create, process, dump, analyze and visualize .ribo files. It provides a CLI (Command L:ine Interface) and Application Programming Interface (API).InstallationNOTEThis is a temporary way of installing ribogadgets. Soon, we plan to have pip and / or conda repositories for ribogadgets.For now, you can install using.
Although pip resolves dependencies and install required packages, it is highly recommended that you install the dependencies using conda prior to issuing the pip installation command.pip install git+https://github.com/hakanozadam/ribogadgets_alpha.gitTo create a conda environment "ribogadgets" and install dependencies using oconda:conda env create -f environment environment.ymlAfter the above command you can install ribogadgets via pip, as described above.
Package: ribonorma
RibonormaAs part of the paperInstallationInstallation can be done with Pip. Python 3.6+.pip install ribonormapip3 install ribonormaHow to useCommand line usageSuppose you have two files, one file with the raw RNASeq count data (example) and one file with the phenotype file data (example).Runribonorma-normalisePython code usageimportcsvfromribonormaimportribonormareads=list(csv.read(...))# Readsgene_length=[]# List of gene lengthsconditions=["Standard media","Standard media","Standard media","Super media","Super media","Super media"]# Example conditionsnormalised_counts=ribonorma.tpmr(reads,gene_length,conditions,percent_housekeep=10)ribonorma.tpm(reads, gene_length)reads: 1D list of read countsgene_length: 1D list of individual gene lengthsribonorma.tpmm(samples, gene_length)samples: 2D list of sample read counts - [sample x count]gene_length: 1D list of individual gene lengthsribonorma.tpmr(samples, gene_length, experimental_conditions, percent_housekeep=10)samples: 2D list of sample read counts - [sample x count]gene_length: 1D list of individual gene lengthsexperimental_conditions: 1D list of individual experimental conditions, same size assamplesalpha: floating point value of percent housekeep as stated in the paperribonorma.tpmr_2(samples, gene_length, experimental_conditions, percent_housekeep=10)samples: 2D list of sample read counts - [sample x count]gene_length: 1D list of individual gene lengthsexperimental_conditions: 1D list of individual experimental conditions, same size assamplesalpha: floating point value of percent housekeep as stated in the paper
Package: riboplot
RiboPlot includes programs to plot and output Ribo-Seq read counts from an alignment file (BAM format).There are two programs in the package:riboplotPlot and output read counts (csv) for a single transcript.ribocountOutput read counts for all transcripts in an alignment.Free software: GPL license.Documentation:http://pythonhosted.org/riboplot.PyPI:https://pypi.python.org/pypi/riboplot.Github Repository:https://github.com/vimalkvn/riboplot.Online:http://ribogalaxy.ucc.ie(under RiboSeq Analysis/RiboPlot).History0.3.1 (2017-03-04)Bugfix: ribocount: allow creation of zip files larger than 2GB.0.3.0 (2015-12-10)Support for multiple read lengths and corresponding offsets.Optional arguments check is now split into multiple steps.Remove the calculation of read lengths present in the BAM alignment.0.2.5 (2015-11-30)Bugfix: Use known versions of libraries as dependencies in setup.py.
Otherwise, pip installs latest versions which haven’t been tested.Remove Codons label, change START to AUG [riboplot].0.2.4 (2015-11-25)Bugfix: ribocount now returns correct read counts if an offset is provided.Bugfix: Don’t include read counts in the longest ORF start or stop positions
i.e., only include reads upstream or downstream of the start or stop positions.0.2.3 (2015-11-24)Bugfix: Recalculate read frame positions after applying offset.Add sequence to CSV output.0.2.2 (2015-10-29)Use default linewidth (riboplot), Minor changes to default plot style.Use ‘Agg’ as the default matplotlib backend (prevent $DISPLAY errors).Use smaller images for the help section.Fix typo in HISTORY.rst.Update Github repository URL.0.2.1 (2015-10-16)Fix: Add mock again to setup requirements. Matplolib install under Galaxy fails otherwise.0.2.0 (2015-10-15)riboplotSupport for color schemes - options: default, colorbrewer, rgb, greyorfs.Plot changes - colored, light background for ORF architecture, legend moved down.Image DPI change 300 → 600.Sample scripts to plot multiple transcripts and multiple color schemes.ribocountAbility to output read counts in 5’(-v) and 3’(-r) leader region.BugfixesReport error if a bam index could not be generated (ex. bam sorted using
reference names instead of chromosome coordinates).No start/stop values when transcript sequences are in lower case.TestsSplit into three different files.Updated test configuration.MinorXML wrapper changes - remove ‘leader’, add help text and output label change [ribocount].Remove unused pysam import [riboplot].Replace doc includes with sym links (breaks Galaxy toolshed otherwise).Updated CSS/Table styles (removed number column from table).y limit is now 1.25x [riboplot].Removed line width on bars [riboplot].0.1.1 (2015-09-08)Remove invalid Python 3 version (whl).Remove unused test data files.Remove strict dependence on matplotlib and pysam versions.Cleanup MANIFEST.0.1.0 (2015-08-24)First release on PyPI.
Package: ribopy
RiboPyRiboPy is a set of tools to create, read and visualize .ribo files. It provides a Command Line Interface and Application Programming Interface to .ribo files.InstallationIt is highly recommended to create and use a separate environment
using conda before installing RiboPy.pip install ribopyTo install the latest version, use the code in Githubpip install git+https://github.com/ribosomeprofiling/ribopy.gitAlthough pip resolves dependencies and installs required packages automatically,
it is highly recommended that you install the dependencies using conda prior to issuing the pip installation command.conda env create -f environment.ymlAfter the above command you can install RiboPy via pip, as shown above.DocumentationRiboPy documentation, for both command-line interface (CLI) and application programming interface (API), is available onRead the Docs. Also, specification ofribo file formatis included in this documentation.Release Notes
Package: riboraptor
riboraptor : a pipeline for analysing ribosome profiling dataPython package to analyse ribosome profiling dataFree software: BSD licenseDocumentation:http://saketkc.github.io/riboraptor/InstallationSetting up condaInstallconda, the best way to install it is with theMinicondapackage.The Python 3 version is recommended.Set up channels,It is important to add them in this order.condaconfig--addchannelsrcondaconfig--addchannelsdefaultscondaconfig--addchannelsconda-forgecondaconfig--addchannelsbiocondaInstalling dependenciescondacreate--nameriboraptorpython=3.6matplotlibnumpypandaspybedtools\pyBigWigpyfaidxpysamscipyseabornstatsmodelssixclickclick-help-colorshtseqbiopython\snakemakesra-toolsstarfastqctrim-galoreucsc-bedgraphtobigwigucsc-bedsortInstalling riboraptorsourceactivateriboraptorgitclonegit@github.com:saketkc/riboraptor.gitcdriboraptorpythonsetup.pyinstall--single-version-externally-managed--record=/tmp/record.txtDownloading datasets from SRAInstallaspera connectInstall additional dependenciessourceactivateriboraptorcondainstallgcccondainstall-crr=3.4.1condainstall-cbiocondabioconductor-annotationdbibioconductor-geometadbcondainstall-crr-devtoolsSince there is currently bug with bioconductor-sradb, we will install it from githubgitclonehttps://github.com/seandavi/SRAdbcdSRAdbRunR, and install SRAdb withinRusedevtoolslibrary(devtools)devtools::install(".")FeaturesSee:http://saketkc.github.io/riboraptor/cmd-manual.htmlHistory0.2.0 (04-23-2018)First release on PyPI.
Package: ribotools
RibotoolsRibotools, anadd-ontoRp-Bp, is a toolbox for the analysis of matched ribosome profiling (Ribo-seq) and RNA sequencing (RNA-seq) data.Ribotoolscan be used to generate count tables, starting from raw, trimmed reads, or existing alignments, taking into consideration Ribo-seq periodicity estimates and RNA-seq fragment length.Ribotoolsinclude scripts to perform translation efficiency (TE) analysis, accommodating for sample-to-sample variance, complex experimental designs, or confounding factors, and classifies features into different regulatory layers.DocumentationConsult theuser guidefor instructions on how to install the package and detailed usage instructions.How to report issuesFor bugs, issues, or feature requests, use thebug tracker. Follow the instructions and guidelines given in the templates.LicenseThe MIT License (MIT). Copyright (c) 2019 Etienne Boileau.
Package: ric
Converts Reuters RIC suffix to ISO10383 MIC code and currency codesInstallpip3 install XXXUsageimport xxxxWelcome toyourrepository for your GitHub Learning Lab course. This repository will be used during the different activities that I will be guiding you through. See a word you don't understand? We've included an emoji 📖 next to some key terms. Click on it to see its definition.Oh! I haven't introduced myself...I'm the GitHub Learning Lab bot and I'm here to help guide you in your journey to learn and master the various topics covered in this course. I will be using Issue and Pull Request comments to communicate with you. In fact, I already added an issue for you to check out.I'll meet you over there, can't wait to get started!This course is using the :sparkles: open source projectreveal.js. In some cases we’ve made changes to the history so it would behave during class, so head to the original project repo to learn more about the cool people behind this project.
Package: riccati
riccatiA package implementing the adaptive Riccati defect correction (ARDC) methodAboutriccatiis aPythonpackage for solving ODEs of the form$$ u''(t) + 2\gamma(t)u'(t) + \omega^2(t)u(t) = 0,$$on some solution interval $t \in [t_0, t_1]$, and with initial conditions $u(t_0) = u_0$, $u'(t_0) = u'_0$.riccatiuses the adaptive Riccati defect correction method -- it switches
between using nonoscillatory (spectral Chebyshev) and a specialised oscillatory
solver (Riccati defect correction) to propagate the numerical solution based on
its behaviour. For more details on the algorithm, please seeAttribution.DocumentationRead the documentation atriccati.readthedocs.io.AttributionIf you find this code useful in your research, please citeAgocs & Barnett (2022). Its BibTeX entry is@ARTICLE{ardc,
       author = {{Agocs}, Fruzsina J. and {Barnett}, Alex H.},
        title = "{An adaptive spectral method for oscillatory second-order
        linear ODEs with frequency-independent cost}",
      journal = {arXiv e-prints},
     keywords = {Mathematics - Numerical Analysis},
         year = 2022,
        month = dec,
          eid = {arXiv:2212.06924},
        pages = {arXiv:2212.06924},
          doi = {10.48550/arXiv.2212.06924},
archivePrefix = {arXiv},
       eprint = {2212.06924},
 primaryClass = {math.NA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221206924A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}LicenseCopyright 2022-2023 The Simons Foundation, Inc.riccatiis free software available under the Apache License 2.0, for
details see theLICENSE.
Package: ricco
安装与更新pipinstallricco
pipinstallricco-U# 或pipinstallricco--upgrade文档Document便捷方法介绍数据读写自动识别文件类型进行读写万能读取数据文件：rdf()支持.xls/.xlsx/.csv/.parquet/.shp/.ovkml等多种文件格式fromriccoimportrdfdf=rdf('/path/test.csv')读取文件夹中的数据：rdf_by_dir()适用于读取的文件夹中数据结构相同的多个数据，可指定扩展名，如不指定则读取所有文件；支持的文件格式与rdf()相同fromriccoimportrdf_by_dirdf=rdf_by_dir('/path_dir/',exts=['.csv'])文件保存：to_file()根据扩展名可自动报错多种格式的文件，支持的文件格式与rdf()相同可自动新建目录，避免了文件保存时需要手动创建目录的麻烦importpandasaspdfromriccoimportto_filedf=pd.DataFrame()to_file(df,'/path/test.csv')文件处理文件拆分：split2x()将文件拆分为多个文件，放置在与文件同名目录下支持的文件格式与rdf()相同，默认'.csv'fromriccoimportsplit2xsplit2x('/path/test.csv',chunksize=1000)split2x('/path/test.csv',parts=3)文件批量拆分：reshape_files()将文件夹的文件大小进行统一拆分，并可传入自定义处理函数fromriccoimportreshape_filesdefprocess(df):returndf# 将'/path_dir/'中的文件，拆分为大小为1000的 csv 文件，并保存在'/path_dir_to/'目录下，处理过程中调用 'process' 方法reshape_files(from_dir='/path_dir/',to_dir='/path_dir_to/',to_ext='.csv',chunksize=1000,func=process,)万能格式转换：file_to_x()批量转换可使用dir_file_to_x()fromriccoimportfile_to_x# 将csv转为Excel文件，保存在相同目录下file_to_x('/path/test.csv',to_ext='.xlsx')地理处理自动识别地理格式并进行转换，节省时间自动转为shapely格式：auto2shapely()将带有地理信息的数据，自动转为 shapely 格式的GeoDataframe，地理格式可以为wkb/wkt/geojson等fromriccoimportauto2shapelyfromriccoimportrdfdf=rdf('./test.csv')auto2shapely(df)# Return: GeoDataframeshapely转为任意格式：shapely2x()fromriccoimportrdffromriccoimportshapely2xdf=rdf('./test.csv')# 转为wkb格式的Dataframeshapely2x(df,geometry_format='wkb')自动转为任务任意地理格式：auto2x()fromriccoimportrdffromriccoimportauto2xdf=rdf('./test.csv')# 转为wkb格式的Dataframeauto2x(df,geometry_format='wkb')空间计算投影变换：projection()根据经纬度自动获取 epsg code 进行投影，也可以指定城市和 epsg code 进行投影；
该方法是对geometry列进行投影，
如需要对经纬度进行投影，请使用projection_lnglat()方法fromricco.geometry.dfimportprojection面积计算：get_area()自动地理转换、投影、计算面积，新增面积列area，不改变原数据fromricco.geometry.dfimportget_area近邻分析：nearest_kdtree()和nearest_neighbor()计算一个数据集中的元素到另一个数据集中全部元素的最短距离（单位：米）,
同时可进行其他统计fromricco.geometry.dfimportnearest_kdtreefromricco.geometry.dfimportnearest_neighborbuffer计算：buffer()生成指定半径的buffer的geometry列fromricco.geometry.dfimportbuffer空间统计：spatial_agg()对面数据覆盖范围内的点数据进行空间统计fromricco.geometry.dfimportspatial_agg空间连接打标签：mark_tags_v2()fromriccoimportrdffromriccoimportmark_tags_v2df_poi=rdf('./poi.csv')df_plate=rdf('./plate.csv')# 给POI数据打上plate_name的标签mark_tags_v2(df_poi,df_plate,col_list='plate_name')
Package: ricebowl
What is this package and what does it do?This package is meant to ease the lives of the analysts and data scientists by providing generalised functions for preprocessing of dataframes and images which are commonly used in Machine Learning.
Additionally, using this package, one can also call various machine learning models as base models with customised data, target, and parameters to check how different models are performing.Please check the docs:ricebowl documentationHow to use this package?Simply do:pip install ricebowland import and use after having a look at the documentation