Saravanan666/pypi-org huggingface dataset:
Package: scidb4py
Pure python SciDB client library.This library aims to provide access to SciDB server through native network protocol based on protobuf. It still on early
stages of development, so do not expect complete features support and stable work :)Any feedback and patches are welcome:https://github.com/artyom-smirnov/scidb4py/Runtime dependenciespython >= 2.7 or pypy >= 1.8 (python 3 not supported yet)python-protobuf >= 2.4bitstringBuild dependenciesprotobuf-compiler >= 2.4Installationsudo pip install scidb4pyorsudo python setup.py installExamplesIterating through array item-by-itemfrom scidb4py import Connection
conn = Connection('localhost', 1239)
conn.open()
array = conn.execute("select * from array(<a:int32>[x=0:3,2,0], '[0,1,2,3]')")
for pos, val in array:
    print '%d - %d' % (pos['x'], val['a'])
conn.close()Iterating through array chunk-by-chunk, item-by-itemfrom scidb4py import Connection
conn = Connection('localhost', 1239)
conn.open()
array = conn.execute("select * from array(<a:int32 null>[x=0:2,3,0, y=0:2,3,0], '[[1,2,3][4,5,6][7,8,9]]')")
while not array.end:
    while not array.chunk_end:
        print '%s - %s' % (array.get_coordinates(), array.get_item("a"))
        array.next_item()
    array.next_chunk()
conn.close()Changelog0.0.6Fixed bug #15 (Large chunk can not fit single network message)Fixed reading strings greater than 8 bytes from RLE chunksAdded ‘active’ property to Connection which indicate active queryBasic query autocomplete and autocancel. Complete by default.0.0.5Fixed bug #14 (Strange bzipped chunk issue)Fixed bug #5 (Compressed chunk support)scidb4py now provide __version__ string
Package: scidb-bridge
RequirementsPython3.5.x,3.6.x,3.7.x,3.8.x,3.9.x, or3.10.xSciDB19.11or newerSciDB-Py19.11.7or newerApache PyArrow5.0.0up to11.0.0Boto31.14.12for Amazon Simple Storage Service (S3) supportInstallationInstall latest release:pip install scidb-bridgeInstall development version from GitHub:pip install git+http://github.com/paradigm4/bridge.git#subdirectory=py_pkgContributingCheck code style before committing codepipinstallpycodestylepycodestylepy_pkgFor Visual Studio Code seeLinting Python in Visual Studio Code
Package: scidb-py
Version InformationThe major and minor version numbers of SciDB-Py track the major and
minor version of SciDB they are compatible with. For example SciDB-Py16.9.1,16.9.2or16.9.10are all compatible with SciDB16.9.x.During SciDB16.9, Shim (HTTP service for SciDB) transitioned from
query authentication to session authentication. SciDB-Py has been
updated to be compatible with the new Shim. Below is the compatibility
matrix between SciDB-Py and Shim:SciDB-PyShim16.9.1query authentication (old Shim)16.9.2query authentication (old Shim)16.9.10session authentication (new Shim)From16.9.10onwards only Shim with session authentication is
supported.Since SciDB-Py Release16.9.1(released inSeptember 2017) the
library has been rewritten entirely from scratch.16.9.1and newer
versions arenot compatiblewith the previous versions of the
library. The documentation for the previous versions is available atSciDB-Py documentation (legacy). GitHub pull
requests are still accepted for the previous versions, but the code is
not actively maintained.RequirementsSciDB19.11or newer with ShimPython3.6.x,3.7.x,3.8.x,3.9.x, or3.10.xRequired Python packages:backports.weakref
enum34
numpy
pandas (see version requirements in setup.py)
pyarrow (see version requirements in setup.py)
requests
sixCentOS 6 and Red Hat Enterprise Linux 6CentOS6and Red Hat Enterprise Linux6come with Python2.6. SciDB-Py requires Python2.7or newer (see above). The
default Python cannot be upgraded on these operating systems. Instead
a different Python version can be installed in parallel usingSoftware Collections. For
example,hereare
the instructions to install Python2.7using Software Collections.InstallationInstall latest release:pip install scidb-pyInstall development version from GitHub:pip install git+http://github.com/paradigm4/scidb-py.gitDocumentationSeeSciDB-Py Documentation.
Package: sci-dl
sci-dl: help you download SciHub PDF fasterFeaturesconfiguration file support.search by Google Scholar(coming soon).download using DOI.custom SciHub mirror url.proxy support.failure retry.captacha support(coming soon).a Python library that can be embedded in your program.Installationuse as command line softwarepipinstall'sci-dl[cmd]'use as Python librarypipinstallsci-dlUsageuse as command line softwareinitialization configuration filesci-dlinit-configfollow the prompt to create the configuration file.download using DOIsci-dldl-d'10.1016/j.neuron.2012.02.004'# 10.1016/j.neuron.2012.02.004 is the article DOI you want to downloaduse as Python librarysci_dl.SciDlError raises when exception happens.if you don't have a proxyfromsci_dlimportdl_by_doiconfig={'base_url':'https://sci-hub.se',# sci-hub URL'retries':5,# number of failure retries'use_proxy':False# means you don't want to use a proxy}response=dl_by_doi('10.1016/j.neuron.2012.02.004',config)if you use a proxyfromsci_dlimportdl_by_doiconfig={'base_url':'https://sci-hub.se',# sci-hub URL'retries':5,# number of failure retries'use_proxy':True,# means you don't want to use a proxy'proxy_protocol':'socks5',# available protocols: http https socks5'proxy_user':None,# proxy user, if your proxy don't need one, you can pass None'proxy_password':None,# proxy password, if your proxy don't need one, you can pass None'proxy_host':'127.0.0.1',# proxy host'proxy_port':1080# proxy port}response=dl_by_doi('10.1016/j.neuron.2012.02.004',config)how to save response?get all content one timewithopen('xxx.pdf','wb')asfp:fp.write(response.content)chunk by chunkwithopen('xxx.pdf','wb')asfp:forchunkinresponse.iter_content(1024):# 1024 is the chunk sizefp.write(chunk)
Package: scie2g
sci-Epi2GeneLink to docsWarning!!If you have non normal chr's please remove them it will make the program extremely slow.Another warning: If you have duplicates (i.e. multiple things with the same start and end it will be extremely slow!Sci-epi2gene maps events annotated to a genome location to nearby genes - i.e. peaks from histone modification data
ChIP-seq experiemnts stored as bed data, or DNA methylation data in csv format (e.g. output from DMRseq, methylKit or methylSig).The user provides a SORTED gene annotation file with start, end, and direction for each gene (we recommend usingsci-biomart, see examples for detail.The user then selects how to annotate, i.e. whether it is in the promoter region, or overlaps the gene body. Finally,
the parameters for overlap on each side are chosen.It is available under theGNU General Public License (Version 3).This package is a wrapper that allows various epigenetic data types to be annotated to genes.Examples are in the docsI also wanted to have different upper flanking and lower flanking distances that took into account the directionality of the strand
and also an easy output csv file that can be filtered and used in downstream analyses. This is why I keep all features
that fall within the annotation region of a gene (example below):The overlapping methods are as follows:
1) overlaps: this means does ANY part of the peak/feature overlap the gene body + some buffer before the TSS and some buffer on the non-TSS side
2) promoter: does ANY part of the peak/feature overlap with the TSS of the gene taking into account buffers on either side of the TSS... image:: _static/example_overlaps.png
:width: 600As you can see from the above screenshot using IGV, the input peaks are in purple, and the green are the output
peaks as annotated to genes. The functionconvert_to_bedconverts the output csv to bed files for viewing. This example
shows that a peak/feature can be annotated to multiple genes. Peaks/features outside of the regions of genes (e.g.
the first peak) are dropped from the output.We show this example in the notebook (see examples folder), where we useIGVto view the tracks (see image below)... image:: _static/igv_jupyter.png
:width: 600Lastly, there are sometimes differences between annotations (i.e. the TSS on your annotation in IGV may differ to the
annotation you input to sciepi2gene), naturally, how your genes/features are annotated depends on the input file so if you see differences check this first!Please post questions and issues related to sci-epi2gene on theIssues <https://github.com/ArianeMora/sciepi2gene/issues>_  section of the GitHub repository.
Package: sciebo-rds-cli
Status: not for production, yetSciebo RDS CLIThis is a helper tool to install sciebo RDS to your owncloud instances. It supports ssh and kubectl.UsageYou need python3 (>= 3.10) and pip to use this tool.pipinstallsciebo-rds-cli
sciebords--helpIf you prefer the sourcecode way:gitclonehttps://github.com/Heiss/Sciebo-RDS-Install.git&&cdSciebo-RDS-Install
pipinstall-rrequirements.txt
chmod+xsciebo_rds_install/main.py
sciebo_rds_install/main.py--helpIf you have poetry installed, you can use it, too. So the installation will not rubbish your local python environment, because it uses virtualenv on its own.gitclonehttps://github.com/Heiss/Sciebo-RDS-Install.git&&cdSciebo-RDS-Install
poetryinstall
poetryshell
sciebords--helpThe application will look for avalues.yaml, which is needed for the sciebo RDS helm supported installation process. So you only have to maintain a single yaml file. Just append the content ofconfig.yaml.exampleto yourvalues.yaml. But you can also set your config stuff for this tool in a separatedconfig.yamlwith--configflag. For options for the configuration, please take a look into theconfig.yaml.example, because it holds everything with documentation you can configure for this app. Also you should take a look into the help parameter, because it shows, what the tool can do for you.Developer installationThis project usespoetryfor dependencies. Install it with the described methods over there in the official poetry documentation.Then you need to install the developer environment.poetryinstall--withdev
poetryshellAfter this you can run the application in this environment.sciebords--helpIf you add or update the dependencies, you have to generate a new requirementst.txt for easier user installations.poetryexport-frequirements.txt--outputrequirements.txt
Package: science2-amcgail
This repository contains
the code and data I used to analyze the deaths of a million citations in Sociology articles.
The repository provides code and documentation for producing all analyses and figures in the final paper.InstallationInstall Python 3.7Runpip install science2-amcgailQuick startThe following command starts jupyterlab in the base directory of this repository. This is a good place to start.python -m science2 start
Package: sciencebeam-parser
ScienceBeam Parser Python LibraryScienceBeam Parser allows you to parse scientific documents. It provides a REST API Service, as well as a Python API.Installationpipinstallsciencebeam-parserCLICLI: Start Serverpython-msciencebeam_parser.service.server--port=8080The server will start to listen on port8080.Thedefault config.ymldefines what models to load.Python APIPython API: Start Serverfromsciencebeam_parser.config.configimportAppConfigfromsciencebeam_parser.resources.default_configimportDEFAULT_CONFIG_FILEfromsciencebeam_parser.service.serverimportcreate_appconfig=AppConfig.load_yaml(DEFAULT_CONFIG_FILE)app=create_app(config)app.run(port=8080,host='127.0.0.1',threaded=True)The server will start to listen on port8080.Python API: Parse Multiple Filesfromsciencebeam_parser.resources.default_configimportDEFAULT_CONFIG_FILEfromsciencebeam_parser.config.configimportAppConfigfromsciencebeam_parser.utils.media_typesimportMediaTypesfromsciencebeam_parser.app.parserimportScienceBeamParserconfig=AppConfig.load_yaml(DEFAULT_CONFIG_FILE)# the parser contains all of the modelssciencebeam_parser=ScienceBeamParser.from_config(config)# a session provides a scope and temporary directory for intermediate files# it is recommended to create a separate session for every documentwithsciencebeam_parser.get_new_session()assession:session_source=session.get_source('test-data/minimal-example.pdf',MediaTypes.PDF)converted_file=session_source.get_local_file_for_response_media_type(MediaTypes.TEI_XML)# Note: the converted file will be in the temporary directory of the sessionprint('converted file:',converted_file)More Usage ExamplesFor more usage examples seesciencebeam-usage-examples.
Package: science-data-structure
Science data structureThis library makes it straight forward to make a tree folder structure for large data-sets. For now it supports numpy arrays only, but I have plans to implement pandas, csv, tab-separated and excel soon.The idea behind the library is to make a data-set browse-able with a normal file browser. The components can be rearranged with the use of Python, the terminal or a simple file-browser.InstallInstall through pippip install science-data-structureManual installationpython setup.py installCommand line toolsThis library is bundled with command line tools to create a system wide authorscience_data_structureglobalcreateauthor"<name>"orscience_data_structureglobalcreateauthorand you will be prompted for the name of the author. You only have to run the above commands a single time, the data is stored in a configuration file (the location is dependent of your OS). From the command line you can create a dataset:science_data_structurecreatedataset"<name>""<description>"The author you have created for you system is added to this dataset. Go into the folder of the dataset and execute:science_data_structurelistauthorto view all the authors in this dataset. Alternatively you can list the entire meta filescience_data_structurelistmetaExamplesSimple data-setIn this simple example a data-set is created, with a single branchparabola. In this branch two "leafs" are addedxandy. At the end of the example the data_set is written to disk.Before we can create a dataset we need to create a meta file containing an author, you can do this with the earlier mentioned command line example above.importscience_data_structure.structuresasstructuresfrompathlibimportPathimportnumpy# initialize the empty data-setdataset=structures.StructuredDataSet.create_dataset(Path("./."),"test_set")# add data to the data-setdata_set["parabola"]["x"]=numpy.linspace(-2,2,100)data_set["parabola"]["y"]=data_set["parabola"]["x"].data**2# write the data to diskdata_set.write()Branch overridingWhat will happen when a branch or a leaf is overwritten with another leaf or branch? This example extends the previous exampledata_set["parabola"]["x"]=NoneIn this case the variablexstored in the branchparabolawill be deleted upon the first write.
Package: sciencedates
Science Dates & TimesDate & time conversions used in the sciences.
The assumption is that datetimes aretimezone-naive, as this is required in Numpyet alfornumpy.datetime64.Installpython-mpipinstallsciencedatesUsageDatetime => Year, DayOfYearimportsciencedatesassdT='2013-07-02T12'yeardoy,utsec=sd.datetime2yd(T)Results in year,DayOfYear; UTC fraction of day [seconds](2013102, 72000.0)JuliaJuliaexamplesare providedMatlab / GNU OctaveMatlab / GNU Octaveexamplesare providedFortranFortranexamplesare provided.
For Python-like modern Fortran datetime, seeDatetime-Fortran.
Package: scienceindata-dates
Science in Data DatesA simple library for generating dates in common scenarios for data analysis.Usagefrom scienceindata_dates import scienceindata_datesFunctionscreate_date_from_str: Create a Datetime object from a string with specific date_format.get_today: Create a Date Object or String of Today.get_yesterday: Create a Date Object or String of Yesterday.get_current_month: Return current month.get_last_month: Return last month date.get_previous_nmonth: Return last n-month.get_same_day_last_week: Return the same day of the week of last week.get_same_day_last_month: Return the same day of last month.
Package: scienceio
ScienceIOThe official ScienceIO Python SDK for the ScienceIO API.This package is available viaPypi:scienceIO. View full documentation atScienceIO Docs.Usage and examplesCreate a ScienceIO API key. Both the API key ID and the key secret will be
needed in order to set up the ScienceIO Python SDK for use.Install the ScienceIO Python SDK.pipinstallscienceioCreate a directory.scienceioin your home directory (Mac users, your home directory is/Users/{username}. Linux users, your home directory is/home/{username}. Windows users, your home directory isC:\\Users\{username})Inside this directory, create a text file calledconfigcontaining your credentials:[SETTINGS]
KEY_ID={your ScienceIO API key ID}
KEY_SECRET={your ScienceIO API key secret}Use the ScienceIO Python SDK to annotate text:scio=ScienceIO()input_text=('The COVID-19 pandemic has shown a markedly low proportion of ''cases among children 1–4. Age disparities in observed cases could be ''explained by children having lower susceptibility to infection, lower ''propensity to show clinical symptoms or both.')results=scio.structure(input_text)print(results)
Package: sciencelogic
ScienceLogic - a Python library for the ScienceLogic EM7 APIClient library for sciencelogic EM7Free software: MIT licenseDocumentation:https://sciencelogic.readthedocs.org.UsageTo use Python EM7 in a projectfromsciencelogic.clientimportClientc=Client('jazz','hands!','https://au-monitoring.mcp-services.net/')# API detailsprint(c.sysinfo)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.Thanks to Georgi Dimitrov (@georgijd) for his contributions and testingHistory0.6.0 (2020-04-05)Update get devices methodUpdate requirementsMake SSL verification default0.5.0 (2016-07-12)Add filtering options for theget_datamethod0.1.0 (2016-04-04)First release on PyPI.
Package: sciences
Sciencespipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlab
Package: sciencewriter
sciencewriterA package to support writing of scientific articles by maintaining argumentation consistency combined with external reference management.The purpose of this component is to provide a library that will support the authoring of academic papers based on the argumentation development model explained in the book by Booth, Coloms and Williams (abbrev. BCM), The Craft of Research.Like many I found this book indispensible to writing solid papers but always thought that it would be great if there was an application that; like a bibliography management application, would maintain the consistency of the argumentation model for a particular paper.The objective I have for this library component is that it will help (aspiring) academic authors write better and internally consistent papers. The purpose for this component is that it will take care of the internal consistency management and (external evidence references) so that other can write application based on the BCM argumentation model.Free software: GNU General Public License v3Documentation:https://rayniervanegmond.github.io/sciencewriterFeaturesDevelop the argumentation information model on which the application will be based.Develop the consistency validation feature that ensure the argmentation of the paper in sound.Develop the analysis feature that reports on missing or incomplete pieces in the argumentation.Develop the suggestion feature that uses the analysis to suggest improvements.CreditsThis package was created withCookiecutterand therayniervanegmond/pypackageproject template.
Package: scienco
SciencoCalculate the readability of text using one of a variety of computed indexes including:Flesch-Kincaid scoreAutomated readability indexColeman-Liau indexRequirementsPython 3.8+Installation$ pip install sciencoUsage>>>importscienco>>>metrics=scienco.compute_metrics("Lorem ipsum dolor sit amet ...")>>>metricsMetrics(sentences=32,words=250,letters=1329,syllables=489,is_russian=False)>>>indexes=scienco.compute_indexes(sentences=32,words=250,letters=1329,syllables=489,is_russian=False)>>>indexesIndexes(flesch_reading_ease_score=33.43,automated_readability_index=7.51,coleman_liau_index=11.67)DistributionThis project is licensed under the terms of theMIT License.LinksDocumentation:https://amalchuk.gitlab.io/sciencoCode:https://gitlab.com/amalchuk/sciencoGitHub mirror:https://github.com/amalchuk/scienco
Package: scienlib
scienlibScienlib is a scientific Python library that adds mathematical functions and adds other functions like physics, but also has cybersecurity and other functions.Pre-requirementsThe requirements to use the system is to have the following python modules installed:wheel
requests
python-nmap
speedtest-cliInstallationTo install scienlib on linux run these commands on your Linux Terminal.pip3installscienlibOnce done, it begins to install.ReadyAuthorsDylan Meca-Initial Work-dylan14567You can also look at the list of allcontributorswho have participated in this project.ContributingPlease readCONTRIBUTING.mdfor details of our code of conduct, and the process for submitting pull requests.LicenseThe license for this project isMIT
Package: scientific-paper-matplotlib
Gaussian-Binomial--DistributionsTable of ContentsAbout The ProjectInstallationDependenciesLicenseContactAcknowledgementsAbout The ProjectThis package is for publishers who wants a ready use plot configuration like label font size, datetime config, figure size and most important details needed in which the plotted figure can be directly included in the paper.DependenciesPython3.6matplotlibNumpyPandasInstallationpip install .ContributingFork the ProjectCommit your ChangesPush to the BranchOpen a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactKhaled Alamin -@LinkedInAcknowledgementsUdacity data science nanodegreeGitHub Emoji Cheat SheetImg ShieldsChoose an Open Source LicenseGitHub PagesAnimate.cssLoaders.cssSlick CarouselSmooth ScrollSticky KitJVectorMapFont Awesome