Saravanan666/pypi-org huggingface dataset:
Package: scirpy
Scirpy: single-cell immune receptor analysis in PythonScirpy is a package to analyse T cell receptor (TCR) or B cell receptor (BCR)
repertoires from single-cell RNA sequencing (scRNA-seq) data in Python.
It seamlessly integrates withscanpyandmudataand provides various modules for data import, analysis and visualization.Scirpy is part of the scverse project (website,governance) and is fiscally sponsored byNumFOCUS.
Please consider making a tax-deductibledonationto help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.Getting startedPlease refer to thedocumentation. In particular, theTutorial, and theAPI documentation.InstallationYou need to have Python 3.9 or newer installed on your system. If you don't have
Python installed, we recommend installingMambaforge.There are several alternative options to install scirpy:Install the latest release ofscirpyfromPyPI:pipinstallscirpyGet it fromBioconda:Firstsetup conda channelsas described here. Then install scirpy:condainstallscirpyInstall the latest development version:pipinstallgit+https://github.com/scverse/scirpy.git@mainRun it in a container usingDockerorPodman:dockerpullquay.io/biocontainers/scirpy:<tag>wheretagis one ofthese tags.Release notesSee thechangelog.Support and ContactWe are happy to assist with problems when using scirpy.If you need help with scirpy or have questions regarding single-cell immune-cell receptor analysis in general, please join us in thescverse discourse.For bug report or feature requests, please use theissue tracker.We try to respond within two working days, however fixing bugs or implementing new features
can take substantially longer, depending on the availability of our developers.CitationIf you usescirpyin your work, please cite thescirpypublication as follows:Scirpy: A Scanpy extension for analyzing single-cell T-cell
receptor sequencing dataGregor Sturm, Tamas Szabo, Georgios Fotakis, Marlene Haider, Dietmar
Rieder, Zlatko Trajanoski, Francesca FinotelloBioinformatics2020 Sep 15. doi:10.1093/bioinformatics/btaa611.You can cite the scverse publication as follows:The scverse project provides a computational ecosystem for
single-cell omics data analysisIsaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor
Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Scverse Community,
Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca
Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & Fabian J.
TheisNat Biotechnol.2023 Apr 10. doi:10.1038/s41587-023-01733-8.
Package: scisample
scisamplescisampleis a Python 3 package that implements a number of parameter
sampling methods for scientific computing. Specifications for sampling
are written in  the YAML markup language.Installation with a python virtual environmentcdinto the top level scisample directorypython3 -m venv venv_scisamplesource venv_sci_sample/bin/activatepip install --upgrade pippip install -r requirements.txtpip install -e .Documentationcd docsinto the top level scisample directorymake <documentation type>, where  includes 'html', 'latexpdf', 'text', etc.Testingcdinto the top level scisample directorypytest testspytest --cov=scisample tests/Communityscisampleis an open source project. Questions, discussion, and
contributions are welcome. Contributions can be anything from new
packages to bugfixes, documentation, or even new core features.ContributingContributing toscisampleis relatively easy. Just send us a pull
request. When you send your request, makedevelopthe destination
branch on the scisample repository.Your PR must passscisamples's unit tests and documentation tests, and
must pass mostflake8andpylinttests. We enforce these guidelines
with our CI process. Please seeCONTRIBUTING.mdfor
more information.Code of ConductPlease note thatscisamplehas aCode of Conduct. By
participating in thescisamplecommunity, you agree to abide by its rules.AuthorsCurrent authors ofscisampleinclude Brian Daub, Chris Krenn, Cody Raskin, &
Jessica Semler.Licensescisampleis distributed under the the MIT license.All new contributions must be made under the MIT license.Please seeLICENSEandNOTICEfor details.SPDX-License-Identifier: MITLLNL-CODE-815909
Package: sciscicloud
Science VMis a scicloud-computing platform that integrates into the Python Programming Language. It enables you to leverage the computing power of your datacenter and/or your choice of scicloud providers without having to manage, maintain, or configure virtual servers.When using this Python library known assciscicloud, Science VM will integrate seamlessly into your existing code base. To offload the execution of a function to our servers, all you must do is pass your desired function into thesciscicloudlibrary. ScienceVM will run the function on its high-performance cluster. As you run more functions, our cluster auto-scales to meet your computational needs.Before using this package, you will need to sign up aScience VMaccount.Thesciscicloudlibrary also features a simulator, which can be used without a Science VM account.  The simulator uses themultiprocessinglibrary to create a stripped down version of the Science VM service.  This simulated service can then run jobs locally across all CPU cores.Quick command-line example:>>> import sciscicloud
>>> def square(x):
...     return x*x
...
>>> jid = sciscicloud.call(square,3)  #square(3) evaluated on Science VM
>>> sciscicloud.result(jid)
9Full package documentation is available athttp://docs.scivm.com.  Some dependencies may be required depending on your platform and Python version; see INSTALL for more information.
Package: sciscripttools
SciScriptToolsSmall set of tools and functions for scientific code and scripts.InstallpippipinstallsciscripttoolsBuild from Sourcegit clone https://github.com/lifelemons/sciscripttools.git
cd sciscripttools
python setup.py installExamplesIOAn example to get started with the IO tools.
It can save relatively basic python objects, such a values, strings, arrays, and
dictionaries of those items.importsciscripttoolsasstpower=[4,5,6]st.save_data("power_exp_01",power)# save a value, arrary, or dictionaryexp_info={"id":20200117,"wire_r":0.005,"lengths_1":[0.1,0.5,1.5]}st.save_data("exp_01",exp_info,directory="data/")w1=[0.1,0.5,1.5]w2=[0.1,0.3,1.7]w3=[0.1,0.6,1.2]radii=st.create_dictionary("w1",w1,"w2",w2,"w3",w3)st.save_data("radii",radii)Load the previous saved data withimportsciscripttoolsasstexp_info=st.load_dictionary("exp_01",directory="data/")keys,radii=st.load_data("radii",keys=["w1","w3"])# load straight into arrayspower=st.load_item("power_exp_01")PlotAn example to get started with the plotting tools.
Note:standard_fontclass and the unit functions of thestandard_figureclass require LaTeX!importnumpyasnpimportmatplotlib.pyplotaspltimportsciscripttoolsasst# datax=np.linspace(0,2)y=x+np.random.rand(len(x))y2=x+np.random.rand(len(x))# figurefig_params=st.figure_parameters()# generate parametersst.standard_font(font_size=fig_params.font_size)# standarise the font# normal matplotlib plottingfig,axes=plt.subplots(1,2,sharey=True)# create matplotlib figureax1,ax2=axes# plot dataax1.plot(x,y)ax2.plot(x,y2)foraxinaxes:ax.set_ylim(0,3)fig.subplots_adjust(wspace=fig_params.adjust_subplot_wspace)# set subplot width spacingsf=st.standard_figure(fig,axes,fig_params)# create standard_figuresf.add_subplot_labels()# add unitsylabel="Power";yunit="\\watt"sf.ylabel(ax1,ylabel,yunit)xlabel="Time";xunit="\\second"sf.xlabel(axes,xlabel,xunit)fig.savefig("quick_plot.png")ContributeFound a bug, want to add functionality, or fixed a bug?
Create anissueorpull request.
Any comments or feedback, please write them down in anissue.LinksGitHub
Package: sciservices
=====Sci Services=====Sci Services is a package containing utilities for utilizing DBMI Tech-Core'sauthentication and authorization service infrastructureDetailed documentation is in the "docs" directory.Quick start-----------1. Add "sciservices" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [...'sciservices',]2. To ensure a valid user is signed in, decorate your views like so:from sciservices.auth import sciuser_required@sciuser_requireddef my_view(request):...
Package: scisonify
Turning Scientific Data into Sounds & MusicWhat is it?Sci-Sonify is a Python package that enables simple and straightforward data sonification (a.k.a. representing data as sounds). It converts data stored using common packages in the Scientific Python Ecosystem such as Numpy, Pandas, and Xarray as musical notes.SoundmapsInstead of using colors (i.e. colormaps) for visualuzing data, Sci-Sonify introduces the concept of "soundmaps", which allow for mapping quantitative data to a range of musical notes.AccessorsIn addition to providing standalone functionality for working with lists and arrays, Sci-Sonify introduces accessors into commonly used packages in the Scientific Python Ecosystem.XarraySonification through axr.DataArrayimportxarrayasxrimportscisonify.xarrayda=xr.DataArray([1,2,3])da.sonify()PandasSonification through apd.Seriesimportpandasaspdimportscisonify.pandass=pd.Series([1,2,3])s.sonify()Installation
Package: sci-streak
sci-streakPython GUI for plotting and initial analysis of Streak Camera dataFor use with the streak camera in the Kambhampati Lab at McGill University (should be easy to extend to other systems).For the scripts to correct and plot data directly see:https://github.com/dstrande/Streak-data-plottingCurrently only works for corrected picosecond/photswitch data.Only tested on Windows with Python 3.9/3.10.Includes example data in the form of test.hdf5. This contains only two streak datasets/traces. Please request more if you need.InstallationRecommended from a conda environment:conda create --name streak python=3.9Replace "streak" with whatever you want the environment name to be.pip install sci-streakDependencies: NumPy, Scipy, h5py, PySide6, PyQtGraph, numdifftools, astropyAlso installs lmfit for further useRunningRun from the main data folder. This folder should have a \data folder with the hdf5 file and the experiment.py file.You can then run python -m streakguiVersion notesv005 - initial uploadv006 - Added ability to change between two traces/plots (also changed the data files to allow this).v007 - stylesheet/color changes. Added ROI plot for decay (Now two roi plots are on the bottom). Organize into main.py and modules in /gui/v008 - Separated stylesheet into a different file (stylesheet.qss), Updates to the data processing to correct for uneven axis window sizes. Added option to change between colormaps (other than default colormaps).Skipped multiple versions that were used during pypi upload.v025 - Major reorganization into proper module. Can now run with python -m streakgui (after pip install sci-streak).TODO (in rough order)Include option to "save" ROI plots in the software to compare late/early times or other differencesExtend to nanosecond data.Include option to correct the data directly.
Package: scistringparser
PyScientificStringParserA python module that converts strings in the metric scientific notation to numerical data types.This package is available atPyPI.It is a very simple module that contains one function, made for a specific application, but i think that it can be useful for other people since i couldn't find anywhere a similar package.UsageYou can import the module by calling the functionparse_str.
The input is a string that contains the number in the metric scientific notation, like1u->1e-6.Example:parse_str('1.2k')Outuput:1.2e3It is compatible with all the metric system prefixes.InstallationYou can install this package by callingpip install scistringparser.
Package: scistyles
scistylesMatplotlib stylesheets for scientific publications.InstallationThe package is available on pypi. Simply run:pip install scistylesUsageThe first purpose of this module is to supply matplotlib stylesheets to assist
in the creation of scientific figures. Custom stylesheets can be stored in
relative paths or inmpl_configdir, but both solutions require manual copy
and emailing for reusing and sharing. Storing the stylesheets in a centralized
repository facilitate reuse and sharing.The stylesheets stored undersrc/scistyles/stylelibare loaded automatically
when importingscistyles.Therefore, justusethem:importmatplotlib.pyplotaspltimportscistylesplt.style.use("presentation-sp")# plot away!Under the hood, this module simply extends thematplotlib.styleto accept
a directory as an argument. If you have your own personal styles you don't want
to share^1, you can still load the styles directly:importmatplotlib.pyplotaspltfromscistylesscistyles.update_library(style_dirs=["~/path/to/secret/stylesheets"])plt.style.use("trademarked-style")# ...ContributingContributions are welcome! In case you have strong disagreement with our choice
of tick width or legendhandlelength, then fine, just contribute your own
stylesheet. We won't argue[^2], and just add it to the pile. Simply make sure
to give the stylesheet a name that is not already taken.To contribute a stylesheet, just write an issue. For large contributions, a PR
would be appreciated.See alsoFor everything aboutrcParamsandstylesheets, see the officialmatplotlib
documentation.TODOAdd a test plot and autogenerated gallery.add a few further design tools (colors, palettes...)Notes[^2]: We aren't talking about colormaps.
Package: scisuit
scisuitA computing and plotting library designed with engineers
in mind..PlatformWindows and Python 3.10, 3.11 and 3.12.Available LibrariesPlotting,EngineeringStatistical Distributions and Tests,Numerics: Roots, Integration, FittingPlot LibraryCompletely interactive charts (Bar, Box-Whisker, Bubble, Histogram, Line, Pie, Psychrometry,
QQnorm, QQplot, Quiver, Scatter).A simple scatter chart example:importnumpyasnpimportscisuit.plotaspltx=np.arange(1,6)y=x**2-2*x+5plt.scatter(x=x,y=y)plt.show()Once the chart is displayed, let's say a trendline is wished to be added:Click on one of the data points to select the series,Right-click and select "Add trendline",By default a linear trendline will be added.Just right-click again and select "Format Trendline" and following options will be shown:Engineering LibraryDesigned mostly for process engineers.Examples1. Psychrometry:Computation of properties of humid-air.fromscisuit.engimportpsychrometryr=psychrometry(P=101,Tdb=30,Twb=20)#all of the propertiesprint(r)P=101.0 kPa,
Tdb=30.0 C
Twb=20.0 C
Tdp=14.17 C
H=57.06 kJ/kg da
RH=39.82 %
W=0.0106 kg/kg da
V=0.876 m3/kg da2. Food:A rich class for not only computation of food properties but also
to perform food arithmetic.importscisuit.engasengmilk=eng.Food(water=88.13,protein=3.15,cho=4.80,lipid=3.25,ash=0.67)water=eng.Food(water=100)#removal of 87% water from milkpowder=milk-0.87*waterprint(powder)Type = Food
Weight (unit weight) = 0.13
Temperature (C) = 20.0
water (%) = 8.69
cho (%) = 36.92
protein (%) = 24.23
lipid (%) = 25.0
ash (%) = 5.15
aw = 0.194Statistics LibraryFollows R notation especially for statistical distributions.importscisuit.statsasst#Normal distributionst.dnorm(0.1,mean=1,sd=2)st.pnorm(0.1,mean=1,sd=2)#Binomial distributionst.dbinom(x=[7,8,9],size=9,prob=0.94))#Weibull distributionst.dweibull(x=3,shape=2,scale=4)#log-normal distributionst.dlnorm(0.1,meanlog=1,sdlog=2)st.plnorm(0.1,meanlog=1,sdlog=2)Numerics LibraryProcedures for root finding, fitting, integration...fromscisuit.rootsimportbisect,brentq,Infodeffunc(x):returnx**2-5root,info=bisect(f=func,a=0,b=5)print("**** Bisection method ****")print(root," ",info)root,info=brentq(f=func,a=0,b=5)print("\n**** Brent's method ****")print(root," ",info)**** Bisection method ****
2.2360706329345703   Info(err=9.5367431640625e-06, iter=19, conv=True, msg='')

 **** Brent's method ****
2.2360684081902256   Info(err=None, iter=8, conv=True, msg='')
Package: scitools-iris
Iris is a powerful, format-agnostic, community-driven Python package for
    analysing and visualising Earth science data⚙️ CI💬 Community📖 Documentation📈 Health✨ Meta📦 Package🧰 RepoFor documentation see thelatestdeveloper version or the most recent releasedstableversion.#ShowYourStripesGraphics and Lead Scientist:Ed Hawkins, National Centre for Atmospheric Science, University of Reading.Data: Berkeley Earth, NOAA, UK Met Office, MeteoSwiss, DWD, SMHI, UoR, Meteo France & ZAMG.#ShowYourStripesis distributed under aCreative Commons Attribution 4.0 International License
Package: scitree
Scitree - Liketree, but optimized for scienceIn computing, tree is a recursive directory listing command or program that
produces a depth-indented listing of files.Wikipedia, 2022.Scitree is like tree, but different. Instead of sorting content on its name,
scitree sorts and highlights given opiniated scientific principles. In this
way, you can inspect directory listings more easily.Scitree is based onscisort, a Python
package to sort file paths based on scientific principles.PhilosophyPhilosophy ofscisortand scitree:Read the README first, therefore I'm on topBefore I install or use the content, I open theLICENSE.Files first, folders secondNumbered files arenaturally sortedI loveintuitive and reproducible project structuresFollow the order of execution where possibleI ignore, what git ignores** Only forscitree.For more information about the structure, seescisort/scisort/keygen.py.InstallationScitree requires Python 3.6 or later.pipinstallscitreeGetting startedscitreecommandScittree command on the command line outputs a tree.scitree./
├──README.md
├──LICENSE.txt
├──jobs.sh
├──data/
│└──Bos_2018.csv
├──scripts/
│├──get_plot.py
│├──merge_descriptives.py
│└──merge_metrics.py
└──output/├──simulation/│└──Bos_2018/│├──metrics_sim_Bos_2018_0.json│├──metrics_sim_Bos_2018_6.json│├──metrics_sim_Bos_2018_559.json│├──metrics_sim_Bos_2018_1640.json│├──metrics_sim_Bos_2018_3154.json│├──metrics_sim_Bos_2018_3518.json│├──metrics_sim_Bos_2018_3519.json│├──metrics_sim_Bos_2018_3721.json│├──metrics_sim_Bos_2018_4612.json│├──metrics_sim_Bos_2018_4699.json│├──metrics_sim_Bos_2018_5673.json│├──plot_recall_sim_Bos_2018.png│├──descriptives/││├──wordcloud_Bos_2018.png││├──wordcloud_irrelevant_Bos_2018.png││├──wordcloud_relevant_Bos_2018.png││└──data_stats_Bos_2018.json│└──state_files/│├──sim_Bos_2018_0.asreview│├──sim_Bos_2018_6.asreview│├──sim_Bos_2018_559.asreview│├──sim_Bos_2018_1640.asreview│├──sim_Bos_2018_3154.asreview│├──sim_Bos_2018_3518.asreview│├──sim_Bos_2018_3519.asreview│├──sim_Bos_2018_3721.asreview│├──sim_Bos_2018_4612.asreview│├──sim_Bos_2018_4699.asreview│└──sim_Bos_2018_5673.asreview└──tables/├──data_descriptives.csv├──data_descriptives.xlsx├──data_metrics.csv└──data_metrics.xlsx8directories,38filesscitreefunction for PythonThescitreefunction in Python prints the tree for the current folder (".").fromscitreeimportscitreescitree()LicenseMITContactFeel free to reach out with questions, remarks, and suggestions. Theissue trackeris a good starting point. You can also email me atjonathandebruinos@gmail.com.
Package: sciunit
SciUnit: A Test-Driven Framework for Formally Validating Scientific Models Against DataConceptThe conference paperDocumentationJupyter TutorialsAPI DocumentationInstallationpipinstallsciunitorcondainstall-cconda-forgesciunitBasic Usagemy_model=MyModel(**my_args)# Instantiate a class that wraps your model of interest.my_test=MyTest(**my_params)# Instantiate a test that you write.score=my_test.judge()# Runs the test and return a rich score containing test results and more.Domain-specific libraries and informationNeuronUnitfor neuron and ion channel physiologySee othershereMailing ListThere is amailing listfor announcements and discussion.
Please join it if you are at all interested!ContributorsRick Gerkin, Arizona State University (School of Life Science)Cyrus Omar, Carnegie Mellon University (Dept. of Computer Science)Reproducible Research IDRRID:SCR_014528LicenseSciUnit is released under the permissiveMIT license, requiring only attribution in derivative works. See the LICENSE file for terms.
Package: sciviso
scivisoA wrapper to format all plots the sameSci-viso is a wrapper around matplotlib and seaborn. These are already great libraries, but as I needed to generate
figures for publication, I found that I had specific formats that I needed, and continually adding these in was
making my code clunky and repetative. I also wanted my charts within a publication to have the same "look and feel",
like palette, font, sizing etc. So I made sciviso so that my figures could come out of python "publication ready" and
asthetic.I have only made wrappers for charts that I use (obvs) but keen for people to add others or make suggestions (feel free
to do that via github issues).Others:https://github.com/lukelbd/proplothttps://matplotlib.org/stable/index.html#contributingCheck out the docsLink to docs
Package: sci-ztools
Science ToolsZhai Silong's Greate tools for science research.UsageutilsCorepepPep toolschemchem tools上传先注册一个 PyPi 的测试帐号获取帐号的api token写入$HOME/.pypirc# 安装或更新 setuotools 和 wheelpython3-mpipinstall--upgradesetuptoolswheel# cd 到 setup.py 所在目录python3setup.pysdistbdist_wheel# 产生一个 dist 文件夹，用于本地安装# 更新 twinepython3-mpipinstall--user--upgradetwine# 使用 twine 上传python3-mtwineupload--repositorytestpypidist/*# 正式上传python3-mtwineuploaddist/*# 测试pipinstallexample-pkg-YOUR-USERNAMEReferencepython--如何将自己的包上传到PyPi并可通过pip安装
Package: sckg
sckgA Software Concept Knowledge GraphFree software: MIT licenseDocumentation:https://sckg.readthedocs.io.PrerequisitesSpacy 3.0 以及对应的模型。其中模型有两种选择，
- 一种是更加大而慢的，en_core_web_trf` pip install-Upip setuptools wheel pip install-Uspacy-nightly--prepython-mspacy download en_core_web_trf `- 一种是小而快的，en_core_web_sm，目前我们先用小而快的` pip install-Upip setuptools wheel pip install-Uspacy python-mspacy download en_core_web_sm `FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-11-01)First release on PyPI.0.1.1 (2021-11-05)0.1.2 (2021-11-05)0.1.3 (2021-11-05)0.1.4 (2021-11-09)0.1.5 (2021-11-09)0.1.6 (2021-11-12)0.1.7 (2021-11-16)0.1.8 (2021-11-23)add get common upper concepts method0.1.9 (2021-11-24)add get upper concepts method
modify error0.1.10 (2021-11-24)add find_longest_concept_from_sentence method0.1.11 (2021-12-28)add get_random_node method0.1.12 (2021-12-29)modify get_random_node method0.1.13 (2021-12-31)modify get_random_node method0.1.14 (2022-1-2)add find_out_facet_of_relation_concept_by_id method0.1.15 (2022-1-4)add find_out_is_a_relation_concept_by_id method0.1.16 (2022-1-5)add get_relation_mehod0.1.17 (2022-1-6)[modify]get_random_lasted_node0.1.18 (2022-1-11)[modify]get_hot_concept0.1.19 (2022-3-10)[modify]get_concept_from_sentence(add label)0.1.20 (2022-3-11)[modify] concept_from_sentence(isvalid)0.1.23 (2022-3-31)[add] most related nodes0.1.24 (2022-3-31)[update] most related nodes0.1.25 (2022-4-15)[add] add neo4j searcher0.1.26 (2022-4-18)[add] find_longest_valid_concept_non_stop_words_from_sentence method0.1.27 (2022-4-18)[modify] calling interface0.1.28 (2022-4-18)[modify] calling parameters0.1.29 (2022-4-19)[modify] get concepts from sentence0.1.30 (2022-4-21)[modify] modify some sckg neo4j0.1.31 (2022-4-22)[modify] modify softwareKG search0.1.32 (2022-4-22)[add] file expiration0.1.33 (2022-4-29)[modify] distinguish between remote and local softwareKG services0.1.34 (2022-5-12)[modify] update softwareKG search0.1.35 (2022-5-19)[modify] modify trie load in sckg search0.1.36 (2022-5-25)[modify] update softwareKG search0.1.37 (2022-5-25)[modify] sorry update softwareKG search again0.1.38 (2022-6-23)[modify] update softwareKG search0.1.39 (2022-7-8)[modify] fix some bugs in softwareKG search