Saravanan666/pypi-org huggingface dataset:
Package: nagra-network-cloudflare-utils
UtilsCloudflare utilities.
This utilities are meant to be used in CI/CD pipelines.You can use it as a pre-commit.
Nb: No public hook is available since it would need to expose the repository => this must be defined locally-repo:localhooks:-id:cfutils-checkname:cfutils-checkdescription:check csv files containing DNS recordsentry:cfutils checklanguage:pythonpass_filenames:falsefiles:^(.*/)?dns_records\.csv$args:["-f","dns_records.csv"]additional_dependencies:["nagra_network_cloudflare_utils==0.2.1"]-id:cfutils-sortname:cfutils-sortdescription:sort csv files containing DNS recordsentry:cfutils sortlanguage:pythonpass_filenames:falsefiles:^(.*/)?dns_records\.csv$args:["-f","dns_records.csv"]additional_dependencies:["nagra_network_cloudflare_utils==0.2.1"]
Package: nagra-panorama-api
READMEWhy the need for this library ?For simple resource retrieval, the existing API is enough, but:The libraries available are not practical for it, the manual management of urls is easierFor more complex operation, this does not suits wellThe python SDK of PaloAltoNetworks itself relies on a third party wrapper for their API.DesignThe library provides 2 things:A simple client for the APITool to manage the xml configurationWe will pull the whole configuration and search on it locally. We can then use the client to do some operationsWe don't want to combine both the client and the management tool. This would requires a lot of boilerplate code that would be difficult to maintain.Usageclient=XMLApi(HOST,API_KEY)tree=client.get_tree()# Find your object using its name (may not be unique)my_object=config.find_by_name("my_object_name")[0]print(my_object)# /address/entry[@name='my_object_name']# Find the references to it (0 or multiple results are possible)ref=my_object.get_references()[0]print(ref)# /post-rulebase/security/rules/entry[@uuid='...']/destination/member# We must not delete the <member> tag, but we can delete its parentclient.delete(ref.parent.xpath)# Now we must commit the changesclient.commit_changes()# We can also revert it# client.revert_changes()The library currently doesn't provide helper tools to know how to edit the configuration. Theses changes must be done manuallyTODOProvide wrapper classes for the resources.nat=NatPolicy(...)tree.insert(nat)# or nat.insertInto(tree)client.create(nat.insert_xpath,nat.xml)Mix Rest API and XML API ?https://docs.paloaltonetworks.com/pan-os/9-1/pan-os-panorama-api/get-started-with-the-pan-os-rest-api/create-security-policy-rule-rest-api
Package: nahida
NahidaRead this inother translations.This is part ofPudgeland 💖 Open SourceecosystemsAn unofficialWaifu.picsAPI wrapper for Python📦 Packages🐍 PyPIpipinstallnahida🔎 Examplesimportnahidaclient=nahida.Client()print(client.nsfw.search('neko'))print(client.sfw.search('awoo'))print(client.sfw.search('bite'))
Package: nahpackpy
nahpackpy is an HTTP/2RFC 7541
HPACKlibrary. It wraps
the Rustnahpacklibrary.Usage>>>fromnahpackpyimportDecoder>>>decoder=Decoder()>>>headers=decoder.decode_block(b'\x82\x86\x84A\x8c\xf1\xe3\xc2\xe5\xf2:k\xa0\xab\x90\xf4\xff')>>>forheaderinheaders:print(header)Header(name=b':method',value=b'GET')Header(name=b':scheme',value=b'http')Header(name=b':path',value=b'/')Header(name=b':authority',value=b'www.example.com')>>>fromnahpackpyimportEncoder>>>encoder=Encoder()>>>encoder.encode_block(((b':method',b'GET'),(b':scheme',b'http'),(b':path',b'/'),(b':authority',b'www.example.com'),))b'\x82\x86\x84A\x8c\xf1\xe3\xc2\xe5\xf2:k\xa0\xab\x90\xf4\xff'InstallationInstallation from source requires the use of the Rust language for
compilation of extensions. The “Nightly” channel of rust is required.
Download a package fromrust-lang.org.You can then create a wheel, which will contain the compiled shared
library, by runningmake. Or, runpython setup.py installto
install in your current environment.Miscnahpackpy is licensed under theMozilla Public License, Version
2.0.
Package: nahuatl-tools
nahuatl_tools: a selection of tools for Nahuatl NLP, designed to be fast, simple, and easy to use. It has no library dependencies other than those included in base Python. All code files have fully documented functions for ease of use. Download withpip:pip install nahuatl_tools.For all features ofnahuatl_tools, it is expected that the text be in an orthography designed for a one-to-one phoneme to grapheme conversion. For automatic orthography conversion, seenahuatl_tools/orthography_converter.py. The wordlists are also written with this orthography.Features:Morphological segmentation (seenahuatl_tools/parse.py)Stemming (seenahuatl_tools/parse.py)Part-of-speech tagging (seenahuatl_tools/pos_tagger.py)Full text tokenization/morpheme segmentation pipeline, including optional orthography conversion (seenahuatl_tools/tokenizer.py)Wordlists and lists of stems (seewordlists/)Morpheme glosser (seenahuatl_tools/gloss.py)For questions, suggestions, or anything else, emailnicocloutier1@gmail.com.
Package: naif
$\texttt{naif}$ is a pure-python package for numerical analysis of
frequencies for orbits. It is based on the NAFF algorithm originally
proposed by Laskar (1990, 1993), and further developed by Valluri &
Merritt (1998). It is specially based on, and improves on, the Fortran
code developed by Valluri & Merritt (1998). Besides extracting the
frequencies with largest amplitudes in the spectrum, the package also
has tools to print the spectra before extraction of each frequency and
to "debug" the frequency identification around a specific peak.
Package: naif-de440
naif_de440: NAIF Development Ephemeris (DE) 440 for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's DE440kernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.InstallationThe latest version of the file can be install via pip:pip install naif-de440Usageimportspiceypyasspfromnaif_de440importde440sp.furnsh(de440)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-earth-itrf93
naif_earth_itrf93: NAIF Earth Body-fixed Reference Frame/Body Association Kernel for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's Earth Body-fixed Reference Frame/Body Associationkernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.InstallationThe latest version of the file can be install via pip:pip install naif-earth-itrf93Usageimportspiceypyasspfromnaif_earth_itrf93importearth_itrf93sp.furnsh(earth_itrf93)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-eop-high-prec
naif_eop_high_prec: NAIF High Precision Earth Orientation Parameters Kernel for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's high precision Earth orientation parameters (EOP)kernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.Every night at around 2:15 AM UTC, the NAIF high precision Earth orientation parameters (EOP) kernel is downloaded and compared (via md5 checksum) to the current version of this package. If the checksums are different, a new package will be published.InstallationThe latest version of the file can be install via pip:pip install naif-eop-high-precUsageimportspiceypyasspfromnaif_eop_high_precimporteop_high_precsp.furnsh(eop_high_prec)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-eop-historical
naif_eop_historical: NAIF Historical Earth Orientation Parameters Kernel for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's high accuracy, historical Earth orientation parameters (EOP)kernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.The current version of the file released on 2007 APR 27 spans the following times: 1972 JAN 01 00:00:42.183 - 2023 JUN 01 00:01:09.184InstallationThe latest version of the file can be installed via pip:pip install naif-eop-historicalUsageimportspiceypyasspfromnaif_eop_historicalimporteop_historicalsp.furnsh(eop_historical)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-eop-predict
naif_eop_predict: NAIF Longterm Prediction Earth Orientation Parameters Kernel for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's low accuracy, longterm prediction Earth orientation parameters (EOP)kernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.The current version of the file released on 2023 JUN 03 spans the following times: 2020 JAN 01 00:01:09.183 TDB - 2099 AUG 25 00:01:09.182 TDBInstallationThe latest version of the file can be install via pip:pip install naif-eop-predictUsageimportspiceypyasspfromnaif_eop_predictimporteop_predictsp.furnsh(eop_predict)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-leapseconds
naif_leapseconds: NAIF Leapseconds Kernel for SPICEA Python package by the Asteroid Institute, a program of the B612 FoundationThis package ships the Navigation and Ancillary Information Facility's leapsecondskernel.This is not an official NAIF package. It is an automatically generated mirror of the file so that it is
installable viapip.Every night at around 2:15 AM UTC, the NAIF leapseconds kernel is downloaded and compared (via md5 checksum) to the current version of this package. If the checksums are different, a new package will be published.InstallationThe latest version of the file can be install via pip:pip install naif-leapsecondsUsageimportspiceypyasspfromnaif_leapsecondsimportleapsecondssp.furnsh(leapseconds)AcknowledgmentThis project makes use of data provided and maintained by the Navigation and Ancillary Information Facility (NAIF).References[1] Acton, C.H.; "Ancillary Data Services of NASA's Navigation and Ancillary Information Facility;" Planetary and Space Science, Vol. 44, No. 1, pp. 65-70, 1996.
DOI 10.1016/0032-0633(95)00107-7[2] Charles Acton, Nathaniel Bachman, Boris Semenov, Edward Wright; A look toward the future in the handling of space science mission geometry; Planetary and Space Science (2017);
DOI 10.1016/j.pss.2017.02.013
Package: naif-pds4-bundler
🪐 NAIF PDS4 BundlerSoftware package byNASA's Navigation and Ancillary Information Facilitythat enables SPICE kernels archive producers to get familiar with,
design, and generatePlanetary Data SystemSPICE
archives from end-to-end using the applicable PDS4 standards.🛠️️ PrerequisitesA computer based on a 64-bit Unix operating system: Linux or MacPython 3.8 (or higher)A NAIF supported C compiler(see link)⎆ InstallationInstall with:pip install naif-pds4-bundlerTo execute just to show the help message, run:naif-pds4-bundler --helpSee the online documentation forInstallationinstructions.👉Note:The above commands demonstrate typical usage with a command-line prompt, such as that provided by the popularbashshell; your own prompt may appear differently and may vary depending on operating system, shell choice, and so forth.📄 DocumentationInstallation and Usage information can be found in the documentation online
athttps://nasa-pds.github.io/naif-pds4-bundler/or the latest version is
maintained under thedocsdirectory.The documentation describes the process to prepare SPICE archives and describes the NAIF
approach to using PDS4 standards in great detail.👏 ContributeAll users and developers of the NASA-PDS software are expected to abide by ourCode of Conduct. Please read this to ensure you understand the expectations of our community. For information on how to contribute to NASA-PDS codebases please take a look at ourContributing guidelines.Issue Tracker:https://github.com/NASA-PDS/naif-pds4-bundler/issuesSource Code:https://github.com/NASA-PDS/naif-pds4-bundler💁‍♀️ SupportIf you are having issues file a bug report in GitHub:https://github.com/NASA-PDS/naif-pds4-bundler/issuesOr you can reach us athttps://pds.nasa.gov/?feedback=true💳 LicenseThe project is licensed under the Apache License, version 2. See theLICENSE.txtfile for details.
Package: naifu
Naifunaifu-diffusion (or naifu) is designed for training generative models with various configurations and features. The code in the main branch of this repository is under development and subject to change as new features are added.Other branches in the repository include:sgm - Uses thesgmto train SDXL models.main-archived - Contains the original naifu-diffusion code for training Stable Diffusion 1.x models.InstallationTo install the necessary dependencies, simply run:gitclonehttps://github.com/mikubill/naifu-diffusion
pipinstall-rrequirements.txtUsageYou can train the image model using different configurations by running thetrainer.pyscript with the appropriate configuration file.pythontrainer.py--configconfig/<config_file>
pythontrainer.pyconfig/<config_file>Replace<config_file>with one of the available configuration files listed below.Available ConfigurationsChoose the appropriate configuration file based on training objectives and environment.Train SDXL (Stable Diffusion XL) model# stabilityai/stable-diffusion-xl-base-1.0pythontrainer.pyconfig/train.yamlTrain SDXL refiner (Stable Diffusion XL refiner) model# stabilityai/stable-diffusion-xl-refiner-1.0pythontrainer.pyconfig/train_refiner.yamlTrain original Stable Diffusion 1.4 or 1.5 model# runwayml/stable-diffusion-v1-5# Note: will save in diffusers formatpythontrainer.pyconfig/train_sd15.yamlTrain SDXL model with diffusers backbone# stabilityai/stable-diffusion-xl-base-1.0# Note: will save in diffusers formatpythontrainer.pyconfig/train_diffusers.yamlTrain SDXL model with LyCORIS.# Based on the work available at KohakuBlueleaf/LyCORISpipinstalllycoris_loratoml
pythontrainer.pyconfig/train_lycoris.yamlUse fairscale strategy for distributed data parallel sharded trainingpipinstallfairscale
pythontrainer.pyconfig/train_fairscale.yamlTrain SDXL model with Diffusion DPOPaper: Diffusion Model Alignment Using Direct Preference Optimization (arxiv:2311.12908)# dataset: yuvalkirstain/pickapic_v2# Be careful tuning the resolution and dpo_betas!# will save in diffusers formatpythontrainer.pyconfig/train_dpo_hfdataset.yamlTrain Pixart-Alpha modelPaper: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis (arxiv:2310.00426)# PixArt-alpha/PixArt-XL-2-1024-MSpythontrainer.pyconfig/train_pixart.yamlTrain SDXL-LCM modelPaper: Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference (arxiv:2310.04378)# PixArt-alpha/PixArt-XL-2-1024-MSpythontrainer.pyconfig/train_lcm.yamlPreparing DatasetsEach configuration file may have different dataset requirements. Make sure to check the specific configuration file for any dataset specifications or requirements.You can use your dataset directly for training. Simply point the configuration file to the location of your dataset. If you want to reduce the VRAM usage during training, you can encode your dataset to latents using theencode_latents.pyscript.# prepare images in input_pathpythonencode_latents.py-i<input_path>-o<encoded_path>
Package: nailpack-flask
nailpack-flask *Beta*================A python MVC framework built with FlaskPlease ★ this repo if you found it useful ★ ★ ★Features---------  BlueprintsInstallation------------Dunno yetDependencies-------------  `Python`_Support-------Submit an `issue`_Buy Me Coffee-------------A ridiculous amount of coffee was consumed in the process of buildingthis project.`Add some fuel`_ if you’d like to keep me going!Contributing------------1. Fork it!2. Create your feature branch: ``git checkout -b my-new-feature``3. Commit your changes: ``git commit -m 'Add some feature'``4. Push to the branch: ``git push origin my-new-feature``5. Submit a pull request :DLicense-------`MIT License`_`Jam Risser`_ © 2017Credits--------  `Jam Risser`_ - AuthorChangelog---------0.1.0 (2017-10-09) \* Beta release.. _Python: https://www.python.org/.. _issue: https://github.com/jamrizzi/nails/issues/new.. _Add some fuel: https://pay.jamrizzi.com.. _MIT License: https://github.com/jamrizzi/nails/blob/master/LICENSE.. _Jam Risser: https://jamrizzi.com
Package: nails
A python MVC framework built with FlaskPlease ★ this repo if you found it useful ★ ★ ★FeaturesBlueprintsInstallationDunno yetDependenciesPythonSupportSubmit anissueBuy Me CoffeeA ridiculous amount of coffee was consumed in the process of building
this project.Add some fuelif you’d like to keep me going!ContributingFork it!Create your feature branch:git checkout-bmy-new-featureCommit your changes:git commit-m'Add some feature'Push to the branch:git push originmy-new-featureSubmit a pull request :DLicenseMIT LicenseJam Risser© 2017CreditsJam Risser- AuthorChangelog0.1.0 (2017-08-13) * Beta release0.0.1 (2017-06-25) * Alpha release
Package: naima
naimais a Python package for computation of non-thermal radiation from
relativistic particle populations. It includes tools to perform MCMC fitting of
radiative models to X-ray, GeV, and TeV spectra usingemcee, an affine-invariant ensemble sampler for Markov
Chain Monte Carlo.naimais named after aballad composed by John Coltrane in 1959which appeared in the albumsGiant Steps (1959)andLive at the Village Vanguard (1961).DocumentationDocumentation is atnaima.readthedocs.io.AttributionIf you findnaimauseful in your research, you can citeZabalza (2015)to acknowledge its use. The BibTeX entry for
the paper is:@ARTICLE{naima,author={{Zabalza}, V.},title={naima: a Python package for inference of relativistic particle
             energy distributions from observed nonthermal spectra},year=2015,journal={Proc.~of International Cosmic Ray Conference 2015},pages="922",eprint={1509.03319},adsurl={http://adsabs.harvard.edu/abs/2015arXiv150903319Z},}LicenseNaima is released under a 3-clause BSD style license - see theLICENSE.rstfile.Code status
Package: naimai
NaimAI is a Python package that (1)searches effeciently in papersand (2)generates an automatic review. It does do by structures each scientific paper using their abstract into 3 categories : objectives methods and results.
Hence, when searching, the results will be showed by category. The results can then be reviewed and a review text will be
automatically generated along with the references.All the features are deployed on theNaimAI's website, where millions of paper are processed.AMedium articlegoes more in depth with naimai's features of theweb app.Search in your own papersYou can either give a directory of the folder with articles in PDF format or a csv file with abstracts and other meta data as showedhere.The processing, the results and searching for relevent papers are explained inthis colab.Search in millions of papersTo search in the millions of papers already processed, you can use thenaimai website.
I might open source this part too if needed.Structure your abstractIf you already have an abstract and want to test the segmentor (naimai's algorithm that structures abstract into Background, 
Objectives, Methods and Results),this colabwalks you through the necessary steps.Example of structured abstract :Features to improveReview GenerationThe review generation needs more enhancement. The actual method consists of only rephrasing the objective phrase of each paper. 
I've some idea to go further and improve the review generation part. Let me know if you're interested and we'll do it
together!Besides the generated text, the references generation still can be brushed up to meet with many references style,
 and also to export it to other formats (BibTeX..).Semantic searchThe search is mainly based on a v0 semantic algorithm (using TfIdf model mainly). In a previous version, 
I've finetuned bert model for each field and the results were pretty interesting. The problem is that, with 10 fields 
on the web app, I ended up having 10 fine-tuned model. So the usage was pretty slow and the models were heavy.
If you have any idea and/or want to contribute in this part, I'll be happy to talk to you!Data papersI've used about 10 millions open access abstracts I found here and there on the internet. If you've any source that could be useful, or even better, if we can process much more papers together to get more informations for the users, that'd be cool!ReferencesFor abbreviations purposes, I usedthis code.For PDF processing, I usedGrobid.This work is licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.