Saravanan666/pypi-org huggingface dataset:
Package: papermate
papermateA Python package to help researchers and academics write papers using Markdown.This package was first introduced as a blog post onscientificallysound.org.RequirementsWhilepapermatedoes not have any Python dependencies, it does require some external dependencies.pandoc: RequiredLaTeX: Required for PDF output;TexLivecan be downloaded for all operating systemsgit: Required if want to render previous versions of document, or to render marked-up PDF showing difference between two version of documentInstallationDownload this repository.You can installpapermatesystem-wide or in a virtual environment.If you want to usepapermatein a dedicated virtual environment, make sure you are in thepapermatedirectory and run the following commands to create and activate a virtual environment:$cdpapermate
$python3-mvenvvenv
$sourcevenv/bin/activate(venv)$Making sure you are in the top-mostpapermatedirectory (i.e. the one that containssetup.py), run the following command to installpapermate:$pipinstall.To verify that the package is installed properly, run the following command:$papermate-h
usage:papermate[-h][--inputINPUT][--cslCSL][--bibBIB][-t][-d][--tagsTAGS[TAGS...]]Papermatev0.1CreatedbyMartinHéroux(heroux.martinatgmail.com)RenderpaperwritteninMarkdowntoeitherPDF,DOCXorTEX.CanoutputdifferenceoftwoversionsofthepapertoPDFbasedontheirgittags.Totagyourcurrentversioningit,usethefollowingcommand:$gittag-av1-m'First draft'Adjustversionnumberandmessageasneeded.Totaganearlierversion,usethefollowingcommand:$gittag-av2<commitchecksum>Where'commit checksum'istheentire(orfirstpartof)thecommitchecksum.Thiscanbeobtainedusing:$gitlog--pretty=onelineUSAGE:

optionalarguments:-h,--helpshowthishelpmessageandexit--inputINPUTMarkdownfilecontainingpaper(e.g.paper.md)--cslCSLcslstylefiletoformatbibliography(e.g.APA.csl)--bibBIB.bibfilecontainingreferences(e.g.refs.bib)-t,--texOutput.texverionofpaper-d,--docxOutput.docxverionofpaper--tagsTAGS[TAGS...]PairofgittagsusedtogeneratePDFdiffdocumentYou should see the help printed to your screen.First stepsWith LaTeX install on your system, you can usepapermateto render the tutorial PDF located in thedemofolder.First, we will change to thedemofolder:$cddemo
$ls-p
bib/header.yamlimg/paper.mdtex/We can see that thedemodirectory contains a Markdown document, a YAML file, and a few directories.Rendering the Markdown document to PDF can be done by running thepapermatecommand on the command-line. If you created a virtual environment and installpapermatewithin that virtual environment, you will have to activate the virtual environment before running the following command.$papermate
$ls-p
bib/header.yamlimg/paper.mdpaper.pdftex/We can see that a new PDF document has been created. When no arguments are provided, papermate will look for a Markdown file in the current directory, and Citation Style Language and BibTeX reference files in abibdirectory.The more explicit way to achieve the same results would be to run:$papermate--inputpaper.md--cslbib/vancouver-author-date.csl--bibbib/refs.bibNext stepsAdditional documentation will be added in the future. For now, please refer to the relevantblog poston scientificallysound.org.
Package: papermc-bibliothek
papermc-bibliothekpythonicbibliothekAPI wrapperInstallationpapermc-bibliothek requires python 3.9 or abovepipinstallpapermc-bibliothekpoetryaddpapermc-bibliothekAPIAll functions and classes are properly type hinted and documented with triple quotes. Please file an issue or pull
request with any corrections if any issues are found.You can refer tohttps://papermc.io/apiif looking for a specific
method, they are named similarly.Basic UsagefrombibliothekimportBibliothek,BibliothekExceptionbibliothek=Bibliothek()# Create an instance of the Bibliothek classtry:projects=bibliothek.get_projects()print(projects)# ['paper', 'travertine', 'waterfall', 'velocity']exceptBibliothekExceptionase:# Catch BibliothekException in case something goes wrongprint(f"Error:{e}")
Package: papermc-updater
papermc-updaterA command-line tool to update a PaperMC server .jar file (in-place) to the latest build. Supports latest and previous MC versions.InstallationRequires Python 3.10+.From PyPI$ python -m pip install papermc-updateror$ pip install papermc-updaterFrom source$ git clone https://github.com/tjkessler/papermc-updater
$ cd papermc-updater
$ python -m pip install .UsageWarning: this tool replaces the existing PaperMC server .jar file!Command line$ update-papermc $PATH_TO_EXISTING_JARFILEThe default MC version is1.20.2- specify a different MC version with:$ update-papermc $PATH_TO_EXISTING_JARFILE --version 1.19.4Python scriptfrompapermc_updaterimportupdate_paper_to_latestupdate_paper_to_latest("$PATH_TO_EXISTING_JARFILE",version="1.20.1")
Package: papermerge
Papermerge - Document Management SystemPapermerge is an open source document management system (DMS) primarily
designed for archiving and retrieving your digital documents. Instead of
having piles of paper documents all over your desk, office or drawers - you
can quickly scan them and configure your scanner to directly upload to
Papermerge DMS.Papermerge DMS on its turn willOCRthe
document and index it. You will be able to quickly find any (scanned!)
document using full text search capabilities.It is built onDjango 3.0.Papermerge is actively developed and Irelease quite frequently.This is web-based software. This means there is no executable file (aka no
.exe files), and it must be run on a web server and accessed through a web
browser.FeaturesDocuments of pdf, jpg, png, tiff formats are supportedPer page OCR of the documentsFull Text Search of the scanned documentsFiles and Folders - users can organize documents in foldersMulti-UserUser permissions managementDocument permissions managementREST API (screencast demo)Page Management - delete, reorder, cut & paste pages (screencast demo)ResourcesVideo DemoDocumentationEnglish WebsiteGerman WebsiteScreenshotsTry it!You can try it with just 3 simple commands (you need git and docker-compose):git clone git@github.com:ciur/papermerge.git
cd docker
docker-compose up -dDocker compose command will pull all necessary docker images and start
papermerge (stable 1.2 version) onhttp://localhost:8000. Access it with
default username/password: admin/admin.InstallationThere are couple options:From docker containerUsing systemdManual way - detailed step by step instructionsDeploy it with ansible... coming soon!Comparison With Other DMSFrom my own reasearch I found another two very similar projects -PaperlessandMayanEDMS. Similar to the way parents tend to love their own children more - I am biased towards my own project - Papermerge. So, below is a comparison table of the three based on my research.MetricPapermergeMayanEDMSPaperlessOCRYesYesYesMetadataYes (> 1.3.0)YesNoFolders/CabinetsYes (Folders)Yes (Cabinets)NoAdvanced Permissions ManagementYesYesNoDocuments VersioningNoYesNoDigital SignaturesNoYesNoWorkflowsNoYesNoTagsNoYesYesREST APIYesYesYesLicenseApache-2.0Apache 2.0GPLv3StatusActive DevelopmentActive DevelopmentMaintenance / No DevelopmentStable Release1.2.2 / May 16, 20203.4.7 / April 28, 20202.7.0 / January 27, 2019Initial ReleaseFebruary 9, 2020February 3, 2011~ 2015Python3?YesYesYesDjango Version3.02.22.0Related ProjectsMgMail- Imports document attachments from SMTP account/email into Papermerge DMS via REST API keyMgClipboard- Django Middleware used in Papermerge DMS to power cut/paste of the document/folder/page (IDs) into user's sessionDjango Lessons- All knowledge I gain during development of Papermerge nicely presented as screencasts. Screencasts are short and to the point lessons about Django ecosystemContactTwitter AccountYouTube ChannelEMail
Package: papermerge-meta-plugin-ard-zdf-deutschlandradio
ARD ZDF Deutschlandradio - Zahlung der RundfunkbeiträgePapermerge DMSmetadata extractor plugin for ARD ZDF Rundfunkbeiträge service.This plugin returns 5 metadata labels/keys named as follows:context - always set to 'ARD ZDF Deutschlandradio'betrag - amount need to be paidbeitragsnummer - contract numberfaellig_am - pay until datedatum - date when snail letter was sentbetrag is of type money (as per main app metadata types) with format \d+,\d+betragsnummer is an ID (string) composed of 9 digitsboth faellig_am and darum are of type date. Format dd.mm.yyyyInstallationpip install papermerge-meta-plugin-ard-zdf-deutschlandradioNotice that for configuration you will use plugin name WITHOUT prefix (i.e.
without papermerge-meta-plugin- part).
Also, for importing this module you need to use name WITHOUT prefix:import deutschlandradioUsually you don't need to import plugin directly, main app does it for you.In papermerge.conf add "deutschlandradio.Deutschlandradio" entry to METADATA_PLUGINS:METADATA_PLUGINS = [
    "deutschlandradio.Deutschlandradio", ...
]Prepare Development Environment & Run Testsvirtualenv .venv -p /usr/bin/python3.8  # provide virtualenv path to 3.8 interpretersource .venv/bin/activate  # activate .venv virtual environmentpip install -r requirements.txt # install dependenciespython setup.py develop  # provide a link to dev version of hocronpython test/run.py
Package: papermerge-meta-plugin-lidl-receipts-de
Lidl Receipts (Germany)Papermerge metadata plugin for lidl receipts  used in Germany.This plugin returns 3 metadata labels/keys named as follows:shoppricedateOn the main app you need to keep in mind that:shop - has free text formatprice - is of type money (as per main app), with
format "dd,cc". Example: 38,95date - is of type date (as per main app), with
format "dd.mm.yy". Example: 21.12.19 - 21st of December 2019Installationpip install papermerge-meta-plugin-lidl-receipts-deNotice that for configuration you will use plugin name WITHOUT prefix (i.e.
without papermerge-meta-plugin- part).
Also, for importing this module you need to use name WITHOUT prefix:import lidl_receipts_deUsually you don't need to import plugin directly, main app does it for you.In papermerge.conf add "lidl_receipts_de.Lidl" entry to METADATA_PLUGINS:METADATA_PLUGINS = [
    "lidl_receipts_de.Lidl", ...
]In case you want to map returned metadata to differently named keys:METADATA_PLUGIN_MAPS = {
    "lidl_receipts_de.Lidl": {
        "shop": "Firma",
        "price": "Betrag",
        "date": "Datum",
    },
}Prepare Development Environment & Run Testsvirtualenv .venv -p /usr/bin/python3.8  # provide virtualenv path to 3.8 interpretersource .venv/bin/activate  # activate .venv virtual environmentpip install -r requirements.txt # install dependenciespython setup.py develop  # provide a link to dev version of hocronpython test/run.py
Package: papermerge-s3
S3 StorageThis module enables Papermerge to store documents in S3 object storage.InstallationAdd cloud storage repository as dependency in requirements file:pip install papermerge-s3How to use it?In papermerge.conf.py file setDEFAULT_FILE_STORAGEandFILE_STORAGE_KWARGSvariables:DEFAULT_FILE_STORAGE = 'papermerge.storage.S3Storage'
FILE_STORAGE_KWARGS = {
    'bucketname': '<bucketname>',
    'namespace': '<namespace>'
}Alternatively setPAPERMERGE_DEFAULT_FILE_STORAGEandPAPERMERGE_FILE_STORAGE_KWARGSin django settings file:PAPERMERGE_DEFAULT_FILE_STORAGE = 'papermerge.storage.S3Storage'
PAPERMERGE_FILE_STORAGE_KWARGS = {
    'bucketname': '<bucketname>',
    'namespace': '<namespace>'
}Set following evironment variables:S3_ACCESS_KEY_ID = 'your aws access key id'
S3_SECRET_ACCESS_KEY = 'your aws secret access key id'
S3_ENDPOINT_URL = '<endpoint url>'
S3_REGION_NAME = '<region name>'
Package: papermill-jupytext
Parametrize and run scripts as notebooks with jupytext and papermillThis is on-going research on how to run scripts as notebooks using Jupytext and Papermill.The corresponding GitHub issues areJupytext #231andPapermill #365.Note that the below is a Bash Jupyter notebook that istested on our CI. If you wish, you can also open and run it interactively onBinder.cddemoJupytext and PapermillIt is possible to convert a script to a notebook using Jupytext, and then to run it using Papermill. Here we use--set-kernel -to use the kernel that matches the current Python environment.jupytextscript.py-onotebook.ipynb--set-kernel-papermillnotebook.ipynbexecuted_notebook.ipynb-pinteger2-ptext'updated text'python-c"import nbformatwith open('executed_notebook.ipynb') as fp:nb = nbformat.read(fp, as_version=4)print(nb.cells[-1]['outputs'][0]['text'])"Inject parameters in a script using PapermillPapermill needs the language information in the kernel to inject the parameters in the notebooks, so in this case we also need to set a kernel for the notebook.jupytextscript.py-onotebook.ipynb--set-kernel-papermillnotebook.ipynbnotebook_with_parameters.ipynb--prepare-only-pinteger3-ptext'updated text, v3'Once the parameters have been injected, we can convert back the notebook to a script, and drop the kernel information:jupytextnotebook_with_parameters.ipynb-oscript_with_parameters.py--update-metadata'{"kernelspec":null, "jupytext":null}'catscript_with_parameters.pyAnd finally, we run the script using the Python interpreter:pythonscript_with_parameters.pyTowards Papermill + Jupytext?With thepapermill_jupytextpackage, we can open Jupytext scripts with thetxt://address. A kernel pointing to the current Python environment is injected in the documents that have no kernel.papermilltxt://script.pyexecuted_notebook.ipynb-pinteger4-ptext'updated text, v4'python-c"import nbformatwith open('executed_notebook.ipynb') as fp:nb = nbformat.read(fp, as_version=4)print(nb.cells[-1]['outputs'][0]['text'])"
Package: papermill-origami
papermill-origamiA papermill engine for running Noteable notebooksInstall|Getting Started|Documentation|License|Code of Conduct|ContributingIntro to Papermill-OrigamiPapermill-Origami is the bridge library between theOrigami Noteable SDKandPapermill. The papermill engine can talk to Noteable APIs to run Notebooks.RequirementsPython 3.8+InstallationPoetrypoetryaddpapermill-origamiPippipinstallpapermill-origamiGetting StartedAPI TokenGet your access token from your User Settings -> API Tokensor alternatively you can generate a post request to generate a new tokencurl -X 'POST' \
  'https://app.noteable.io/gate/api/v1/tokens' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "ttl": 31536000,
  "name": "my_token"
}'Engine RegistrationThenoteableengine keyword will use the following environment variables by default:NOTEABLE_DOMAIN=app.noteable.ioNOTEABLE_TOKEN=MY_TOKEN_VALUE_HEREThen the engine is enabled by running papermill as normal. But now you have access to
thenoteable://scheme as well as the ability to tell papermill to use Noteable as
the execution location for your notebook.importpapermillaspmfile_id='...'pm.execute_notebook(f'noteable://{file_id}',None,# Set no particular output notebook, but a log of the resulting exeuction link still prints# This turns on the Noteable API interfaceengine_name='noteable',# exclude this kwarg to run the Notebook locally)Advanced SetupFor more advanced control or reuse of a NoteableClient SDK object you can use
the async await pattern around a client constructor. This reuses the connection
throughout the life cycle of the context block.importpapermillaspmfrompapermill.iorwimportpapermill_iofrompapermill_origamiimportClientConfig,NoteableClient,NoteableHandlerdomain='app.noteable.io'token=MY_TOKEN_VALUE_HEREfile_id='...'asyncwithNoteableClient(token,config=ClientConfig(domain=domain))asclient:file=awaitclient.get_notebook(file_id)papermill_io.register("noteable://",NoteableHandler(client))pm.execute_notebook(f'noteable://{file_id}',None,engine_name='noteable',# Noteable-specific kwargsfile=file,client=client,)ContributingSeeCONTRIBUTING.md.Open sourced with ❤️ byNoteablefor the community.
Package: papermill-service
This package provides a service-line runner for jupyter notebooks, combining FastAPI and papermill.PyPIpip install papermill_serviceSource codehttps://github.com/garryod/papermill_serviceDocumentationhttps://garryod.github.io/papermill_serviceReleaseshttps://github.com/garryod/papermill_service/releasesTo start the service, simply run:$ NOTEBOOK_PATH=/path/to/notebook.ipynb python -m papermill_serviceTo view the API docs navigate tohttp://localhost:8000/docsSeehttps://garryod.github.io/papermill_servicefor more detailed documentation.
Package: papernetwork
Collect and analyze scientific literature from Semantic ScholarExamplesBasic example of loading data fromSemantic Scholarvia theAPI, be sure to read thedataset license agreement:from papernetwork import PaperNetwork, Paper, PaperList

# Define a list of DOI's you would like to download
list_of_dois = ['10.1093/nar/gkw1328', '10.1128/CMR.00016-17', '10.1038/s41564-019-0626-z']

# Pull the papers from semanticscholar.org via the API and parse them
my_network = PaperNetwork(doi_list=list_of_dois)

# Show the titles in the PaperNetwork object
print(my_network)  # Rapid resistome mapping using nanopore sequencing, Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis., Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens

# The papers are internally stored in a OrderedSet-like object, accessible via the collection
my_collection = my_network.collection

# The papers themselves are individually stored in a Dict-like object
my_paper = my_collection[0]  # Take the first paper in the list


print(my_paper['title'])

# They keys of the object are defined by the Semantic Scholar API, see https://api.semanticscholar.org/
# To get an overview of the available keys
for key, value in my_collection[0].items():
    print(key)

# Currently the following keys are supplied by semanticscholar.org
# arxivId
# authors
# citationVelocity
# citations
# corpusId
# doi
# fieldsOfStudy
# influentialCitationCount
# is_open_access
# is_publisher_licensed
# paperId
# references
# title
# topics
# url
# venue
# yearA more elaborate example can be found in theexamples directoryto get started.More detailed usage can be found in thetest directoryas well.Run using:python -m examples.simple_exampleInstallationUse pip toinstall papernetwork from
PyPI(recommend doing this
inside avirtual
environment):pip install papernetworkOr from source:git clone --recursive https://github.com/evdh0/papernetwork.git
cd papernetwork
python setup.py installLicenceThe MIT License (MIT)Authorspapernetworkwas written byEric van der Helm.
Package: paper-network
PAPERImplementation of Gibbs sampler for computing posterior root
probabilities under the PAPER (preferential attachment plus
Erdos--Renyi) model for random networks.See details in the arXiv paper:https://arxiv.org/abs/2107.00153DocumentationInstallation$ pip install PAPERUsage>>> from PAPER.gibbsSampling import gibbsToConv
>>> from PAPER.tree_tools import createNoisyGraph
>>> graf = createNoisyGraph(n=100, m=200, alpha=0, beta=1, K=1)[0]
>>> mcmc_out = gibbsSampling.gibbsToConv(graf, DP=False, method="full",
                   K=1, tol=0.1)See example.py for interpreting the inference output. Some sample
network datasets are provided.NotesNo preprocessing required on input graph. If the
input graph is disconnected, the largest connected component is
used.The algorithm
performs roughly 1 outer Gibbs iteration in 1 second on a graph with
10,000 edges. The number of iterations to convergence depends on the
input graph.
Package: paperparser
PaperParserParses academic paper information from URLs.This is a project byWinder Research, a Cloud-Native Data Science consultancy.InstallationpipinstallpaperparserUsageCLI$paperparser--helpUsage:paperparser[OPTIONS]URLSTRATEGYParsethebibtexfromaURLusingtheSTRATEGY.

Options:--versionShowtheversionandexit.--helpShowthismessageandexit.

$paperparserhttps://arxiv.org/abs/1812.02900arxiv{'title':'Off-Policy Deep Reinforcement Learning without Exploration','journal':'CoRR','volume':'abs/1812.02900','year':'2018','url':'http://arxiv.org/abs/1812.02900','archivePrefix':'arXiv','eprint':'1812.02900','timestamp':'Tue, 01 Jan 2019 15:01:25 +0100','biburl':'https://dblp.org/rec/journals/corr/abs-1812-02900.bib','bibsource':'dblp computer science bibliography, https://dblp.org'}Pythonfrompaperparserimportpagep=page.BibTeXPage(url=url,strategy=strategy)print(p.as_dict())print(p.abstract())Strategies"arxiv"Parses bibtex fromdblpand abstracts directly."nips"Parses bibtex and abstracts directly."acm"Parses bibtex via the doi fromhttps://dx.doi.org/and abstracts directly."ieee"Parses bibtex via the doi fromhttps://dx.doi.org/and abstracts directly."sciencedirect"Parses bibtex via the doi fromhttps://dx.doi.org/and abstracts directly."wiley"Parses bibtex via the doi fromhttps://dx.doi.org/and abstracts directly."semanticscholar"Prefer other parsers where possible. Semantic scholar tends to index other websites and therefore results are sketchy. Parses bibtex via the DOI if possible, manual creation if not. Abstracts are direct.
Package: paperpile-notion
Paperpile Notion IntegrationNOTE:This is a not an official Paperpile integration.This is a Python CLI to manually sync your articles in Paperpile to a Notion database.
Optionally, you may sync an authors database as well.NOTE:This will only be maintained if Paperpile doesn't integrate directly
with Notion. They have expressed interest,here.This is a :construction: work in progress. This isn't production-ready software, so it
may be contain edge-cases not present in the BibTeX's we have tested.Please feel
free to open issues if you encounter any bugs.UsageThe documentation site contains a thorough walk-through to setup a GitHub-based
sync service which just requires some initial configuration.InstallationYou canpipinstallpaperpile-notion,preferably in a virtual environment.pipinstallpaperpile-notionRequirementsTo usepaperpile-notion, you'll need a few things:AJSONexport from Paperpile. You can retrieve this by going to "Settings >
Export > Export to JSON".A configuration file, similar to what you'll find indocs/config.yml.Currently, we do not support venues, but it is
planned.YourArticledatabase URL, which you can copy directly from your browser.(optional) YourAuthordatabase URL, copied in a similar way as above.Yourtoken_v2(detailed below)ORyour email/password (never stored bypaperpile-notion).NOTE:YourArticledatabasemust have the following columns:NameTypeDescriptionIDtextAn ID issued by Paperpile which can be used to uniquely identify papers, feel free to hide the column in Notion once created.StatusselectYour reading status. Can be fully customized in yourconfig.yml.Authorsmulti_selectORrelationThe paper's authors. If you have anAuthordatabase, use therelationtype otherwise amulti_select.URLurlA link to the paper in Paperpile.Fieldsmulti_selectThe [sub-]fields the paper belongs to.Methodsmulti_selectThe methods/tools used in the paper.UsageAs we usenotion-py, we are limited by their support for either an
email/password login OR yourTOKEN_V2. Yourtoken_v2may be retrieved from
your [notion.so][notion] cookies.Using yourtoken_v2(recommended)Using your email/passwordUsing yourtoken_v2(recommended)You have two ways to supply yourtoken_v2topaperpile-notion:(preferred) You may store it in an environment variable calledNOTION_TOKEN_V2, which will be read bypaperpile-notion.ORyou may pass your token in using the--token <token_v2>flag.# Using NOTION_TOKEN_V2$paperpile-notionupdate-db--refs<YOUR_JSON>.json# Using --token ...$paperpile-notion--token<token_v2>update-db--refs<YOUR_JSON>.jsonUsing your email/passwordYou will be prompted each time for your Notion email/password login.paperpile-notionupdate-db--refs<YOUR_JSON>.jsonExample outputWhen adding, adding a new paper to the database:Example resulting database on Notion: