Saravanan666/pypi-org huggingface dataset:
Package: refget
RefgetThe refget package provides a Python interface to both remote and local use of the refget protocol.This package provides clients and functions for both refget sequences and refget sequence collections (seqcol).Documentation is hosted atrefgenie.org/refget.TestingLocal unit tests of refget packagepytestto testrefgetpackage, local unit testsCompliance testingUnder/test_apiare compliance tests for a service implementing the sequence collections API. This will test your collection and comparison endpoints to make sure the comparison function is working.pytest test_apito tests API compliancepytest test_api --api_root http://127.0.0.1:8100to customize the API root URL to testLoad the fasta files from thetest_fastafolder into your API database.Runpytest test_api --api_root <API_URL>, pointing to your URL to testFor example, this will test my remote server instance:pytest test_api --api_root https://seqcolapi.databio.org
Package: refget-compliance
Refget Compliance SuiteRepository for therefget APICompliance document and test suite.Important URLSRefget specificationCompliance DocumentCompliance utility documentationInstalling the compliance suitepip3installrefget-complianceRunning the compliance suiteThe following will generate a HTML report for your server and serve said HTML. It will also generate a tarball locally of the reportrefget-compliancereport-shttps://refget.server.com/--serveThe following will generate a JSON report of your server:refget-compliancereport-shttps://refget.server.com/--jsonserver.jsonSetting--json -will have the compliance suite write the JSON to STDOUT.Additional componentsBuilding and uploading the package to PyPIFirst do your edits, test and updatesetup.pywith your new version number. Then run the following and provide your user credentials before doing this. This will upload the compliance suite to the test PyPI server.python3setup.pysdistbdist_wheel
twineupload--repository-urlhttps://test.pypi.org/legacy/dist/*Assuming this works then upload to live PyPI and provide your live login details.twineuploaddist/*Compliance DocumentationTo generate this documentation locally, follow these steps:gitclonehttps://github.com/ga4gh/refget-compliance.gitcdrefget-compliance

pip3installmkdocs

mkdocsserveCompliance Test SuiteCompliance test suite is an API testing suite for refget servers.To run the tests, follow these steps:gitclonehttps://github.com/ga4gh/refget-compliance.gitcdrefget-compliance-suite/test_suite

pip3install-rrequirements.txtIf the server to be tested supports circular sequences then runpy.test--server<your-server-base-url-without-http://-prefix>--cirand if it doesn't support circular sequences then runpy.test--server<your-server-base-url-without-http://-prefix>If the server to be tested supports trunc512 algorithm then runpy.test--server<your-server-base-url-without-http://-prefix>--trunc512and if it doesn't support trunc512 algorithm then runpy.test--server<your-server-base-url-without-http://-prefix>If the server to be tested redirects success queries then runpy.test--server<your-server-base-url-without-http://-prefix>--redirAnd if it doesn't redirects then runpy.test--server<your-server-base-url-without-http://-prefix>You can try multiple combinations of these flags as per the server implementation for examplepy.test--server<your-server-base-url-without-http://-prefix>--cir--trunc512

py.test--server<your-server-base-url-without-http://-prefix>--cir--redir

py.test--server<your-server-base-url-without-http://-prefix>--redir--trunc512

py.test--server<your-server-base-url-without-http://-prefix>--cir--trunc512--redir
Package: ref-gsd
GSDInstallation|Documentation|Cite usReference implementation of generalised score distribution in pythonThis library provides a reference implementation of gsd probabilities for correctness and efficient implementation of samples and log_probabilities injax.CitationsTheoretical derivation of GSD is described in the following papers.@Article{Cmiel2023,
author={{\'{C}}miel, Bogdan
and Nawa{\l}a, Jakub
and Janowski, Lucjan
and Rusek, Krzysztof},
title={Generalised score distribution: underdispersed continuation of the beta-binomial distribution},
journal={Statistical Papers},
year={2023},
month={Feb},
day={09},
issn={1613-9798},
doi={10.1007/s00362-023-01398-0},
url={https://doi.org/10.1007/s00362-023-01398-0}
}@ARTICLE{gsdnawala,
  author={Nawała, Jakub and Janowski, Lucjan and Ćmiel, Bogdan and Rusek, Krzysztof and Pérez, Pablo},
  journal={IEEE Transactions on Multimedia}, 
  title={Generalized Score Distribution: A Two-Parameter Discrete Distribution Accurately Describing Responses From Quality of Experience Subjective Experiments}, 
  year={2022},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TMM.2022.3205444}
  }If you decide to apply the concepts presented or base on the provided code, please do refer our related paper.InstallationYou can install gsd viapip:pipinstallref_gsdNote that you installref_gsdbut importgsde.g.importgsdgsd.fit_moments([2,8,2,0,0.])DevelopmentTo develop and modify gsd, you need to installhatch, a tool for Python packaging and
dependency management.To  enter a virtual environment for testing or debugging, you can run:hatchshellRunning testsGsd uses unitest for testing. To run the tests, use the following command:hatch run testStandalone estimatorYou can quickly estimate GSD parameters from a command line interfacepython3-mgsd-c12345psi=3.6667 rho=0.6000AcknowledgmentsDevelopment of this software has received funding from the Norwegian Financial Mechanism 2014-2021 under project-
2019/34/H/ST6/00599.
Package: ref_index
Code for calculating refractive index of air, under varying atmospheric
conditions, is provided in this module. Functions for conversion of
wave length of light in vacuum to that in air, and vice-versa are also
defined.The code is based on the documentation for theNIST online refractive
index of air calculator.ExamplesRefractive index can be calculated using two different equations: one
due to Edlén and another due to Ciddor.>>> ref_index.ciddor(wave=633.0, t=20, p=101325, rh=20)
1.0002716285340578
>>> ref_index.edlen(wave=633.0, t=20, p=101325, rh=20)
1.0002716291691649
>>> ref_index.edlen(wave=633.0, t=20, p=101325, rh=80)
1.0002711197635226
>>> ref_index.ciddor(wave=633.0, t=20, p=101325, rh=80)
1.0002711183472626
>>> ref_index.edlen(wave=633.0, t=60, p=101325, rh=80)
1.0002339748542823
>>> ref_index.ciddor(wave=633.0, t=60, p=101325, rh=80)
1.0002340241754055Conversion of wave length of light in vacuum to that in air, and
vice-versa. Both of these functions use the Ciddor equation, as
implemented inciddor_ri()andciddor().>>> ref_index.vac2air(633.0)
632.82500476826874
>>> ref_index.air2vac(632.82500476826874)
633.00000139949032

>>> ref_index.vac2air(np.array([633.0, 550.0, 400.0]))
array([ 632.82500477,  549.84723175,  399.88692724])
>>> x = ref_index.vac2air(np.array([633.0, 550.0, 400.0]))
>>> ref_index.air2vac(x)
array([ 633.0000014 ,  550.00000164,  400.00000243])Note that the reversibility ofair2vac()is ~1e-5nm.Default temperature is 15∘C, pressure is 101325Pa, relative humidity is
0, and CO2 concentration is 450µmole/mole. All these can be changed.>>> ref_index.vac2air(633.0, t=20, p=100000.0, rh=50)
632.83051710791892
>>> ref_index.air2vac(632.83051710791892, t=20, p=100000.0, rh=50)
633.00000131884678For more details please see the docstring for the module. Detailed
description of the equations can be found in theNIST documentation.InstallationThe module can be installed usingpipandeasy_install.$ pip install ref_indexor,$ easy_install ref_indexCreditsAll equations used in this module come from the documentation for theNIST online refractive index calculator, written by Jack A. Stone
and Jay H. Zimmerman.Please send comments and suggestions to the email idprasanthhnin
thegmail.comdomain.
Package: refine-client
The Google Refine Python Client Library provides an interface to
communicating with a Google Refine server.Currently, the following API is supported:project creation/import, deletion, exportfacet computationtexttext filternumericblankstarred & flagged… extensible class‘engine’: managing multiple facets and their computation resultssorting & reorderingclusteringtransformstransposessingle and mass editsannotation (star/flag)columnmoveaddsplitrenamereorderremovereconciliationreconciliation judgment facetguessing column typequerying reconciliation services preferencesperform reconciliationConfigurationBy default the Google Refine server URL ishttp://127.0.0.1:3333The environment variablesGOOGLE_REFINE_HOSTandGOOGLE_REFINE_PORTenable overriding the host & port.In order to run all tests, a live Refine server is needed. No existing projects
are affected.Installation(Someone with more familiarity with python’s byzantine collection of installation
frameworks is very welcome to improve/”best practice” all this.)Install dependencies, which currently isurllib2_file:sudo pip install-rrequirements.txtEnsure you have a Refine server running somewhere and, if necessary, set
the envvars as above.Run tests, build, and install:python setup.py test # to do a subset, e.g.,--test-suitetests.test_facetpython setup.py buildpython setup.py installThere is a Makefile that will do this too, and more.TODOThe API so far has been filled out from building a test suite to carry out the
actions inDavid Huynh’s Refine tutorialwhich while certainly showing off a
wide range of Refine features doesn’t cover the entire suite. Notable exceptions
currently include:reconciliation support is useful but not completeundo/redoFreebasejoin columnscolumns from URLContributePatches welcome! Source is athttps://github.com/PaulMakepeace/refine-client-pyUseful ToolsOne aspect of development is watching HTTP transactions. To that end, I foundFiddleron Windows andHTTPScoopinvaluable. The latter won’t URL-decode nor nicely
format JSON but theOnline JavaScript Beautifierwill.CreditsPaul Makepeace, author, <paulm@paulm.com>David Huynh,initial cutArtfinder, inspirationSome data used in the test suite has been used from publicly available sources,louisiana-elected-officials.csv: fromhttp://www.sos.louisiana.gov/tabid/136/Default.aspxus_economic_assistance.csv:“The Green Book”eli-lilly.csv:ProPublica’s “Docs for Dollars”leading to aLilly Faculty PDFprocessed byDavid Huynh’s ScraperWiki script
Package: refined
refined: refinement type hints in Pythonrefinedis a Python 3 library that leverages the gradual type system in Python to constrain types by one or multiple
predicates. In short, you can ensure that the following script won't raise anIndexError:fromrefinedimportrefined,NonEmptyListfromtypingimportGenerator@refineddefhead_with_tail_generator(ls:NonEmptyList[int])->(int,Generator[int]):returnls[0],(_for_inls[1:])if__name__=='__main__':head,tail=head_with_tail_generator([])# this call raises a RefinementTypeExceptionprint(head)[print(_)for_intail]HelpSeedocumentationfor more details.Installationrefinedis available for Python 3.7+:pipinstall-UrefinedContributingFor guidance on setting up a development environment and how to make a contribution torefined, seeContributing to refined.AcknowledgementsThis library is a port offthomas'refinedScala library, which, in turn, is a port ofNikita Volkov'srefinedHaskell library.
Package: refineGEMs
refineGEMsrefineGEMsis a python package intended to help with the curation of genome-scale metabolic models (GEMS).The documentation can be foundhere.Table of contentsOverviewInstallationHow to citeRepositories using refineGEMsOverviewCurrentlyrefineGEMscan be used for the investigation of a GEM, it can complete the following tasks:loading GEMs withCOBRApyandlibSBMLreport number of metabolites, reactions and genesreport orphaned, deadends and disconnected metabolitesreport mass and charge unbalanced reactionsreportMemotescorecompare the genes present in the model to the genes found in:theKEGGDatabase (Note: This requires the GFF file and the KEGG identifier of your organism.)Or theBioCycDatabase (Note: This requires that a database entry for your organism exists in BioCyc.)compare the charges and masses of the metabolites present in the model to the charges and masses denoted in theModelSEEDDatabase.Other applications ofrefineGEMsto curate a given model include:The correction of a model created withCarveMev1.5.1 or v1.5.2 (for example moving all relevant information from the notes to the annotation field or automatically annotating the GeneProduct section of the model with the respective NCBI gene/protein identifiers from the GeneProduct identifiers),The addition ofKEGGPathways as Groups (using thelibSBMLGroups Plugin),Updating the SBO-Term annotations based onSBOannotator,Updating the annotation of metabolites and extending the model with reactions (for the purpose of filling gaps) based on a table filled by the userdata/manual_annotations.xlsx(Note: This only works when the structure of theexample Excel fileis used.),And extending the model with all information surrounding reactions including the corresponding GeneProducts and metabolites by filling in the tabledata/modelName_gapfill_analysis_date_example.xlsx(Note: This also only works when the structure of theexample Excel fileis used).InstallationYou can installrefineGEMsvia pip:pipinstallrefineGEMsor to a local conda environment whererefineGEMsis distributed via this GitHub repository and all dependencies are denoted in thesetup.pyfile:# clone or pull the latest source codegitclonehttps://github.com/draeger-lab/refinegems.gitcdrefinegems

condacreate-n<EnvName>python=3.9

condaactivate<EnvName># check that pip comes from <EnvName>whichpip

pipinstall.How to citeWhen usingrefineGEMs, please cite the latest publication:Famke Bäuerle, Gwendolyn O. Döbel, Laura Camus, Simon Heilbronner, and Andreas Dräger.
Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium
striatum. Front. Bioinform., oct 2023.doi:10.3389/fbinf.2023.1214074.Repositories using refineGEMsC_striatum_GEMsdraeger-lab/Shaemolyticus -privatedraeger-lab/Ssanguinis -private
Package: refiner
refiner-pythonTheRefinerPython package can be used to convert and store text and metadata as vector embeddings. Embeddings are generated usingOpenAIand stored as vectors inPinecone. Stored embeddings can then be "queried" using thesearchmethod. Matched embeddings contain contextually relevant metadata that can be used for AI chatbots, semantic text search APIs, etc.InstallationpipinstallrefinerOpenAI and Pinecone API Keys.You'll need API keys for OpenAI and Pinecone.Once you have your API keys, you can either set local ENV variables in a shell:exportPINECONE_API_KEY="API_KEY"exportPINECONE_ENVIRONMENT_NAME="ENV_NAME"exportOPENAI_API_KEY="API_KEY"or you can create a.env(dotenv) config file and pass in the file path when initializing the Embeddings class:fromrefiner.embeddingsimportEmbeddingsembeddings_client=Embeddings(config_file="/path/to/.env")Your .env file should follow key/value format:PINECONE_API_KEY="API_KEY"PINECONE_ENVIRONMENT_NAME="ENV_NAME"OPENAI_API_KEY="API_KEY"Create Embeddingsfromrefiner.embeddingsimportEmbeddingsembeddings_client=Embeddings(config_file="/path/to/.env")payload=[("index-id",# an id for your embedding"Example text to embed",# some text to embed{"key":"value"}# add any metadata you want here)]embeddings_client.create(payload,"example-index")# {'upserted_count': 1}Semantic Searchembeddings_client=Embeddings(config_file="/path/to/.env")limit=10embeddings_client.search('search text','index-id',limit)# {'matches': [...]}LoadersfromloadersimportLoadersurl="https://news.yahoo.com/"loaders=Loaders()data=loaders.get_document_from_url(url)# [#  Document {#    pageContent:TransformersfromeimportEmbeddingsfromtransformersimportTransformersembeddings_client=Embeddings(config_file="/path/to/.env")transformers=Transformers()data=transformers.split_text(data,chunk_size=500,chunk_overlap=0)vectors=[]fori,indexindata:embeddings=openai_client.create_embeddings(i[1])vector=(str(index),embeddings,{"page_content":i[1],"source":url},)vectors.append(vector)created=embeddings_client.create(vectors,"test-index");# { upsertedCount: 251 }Document Chatbot Examplequestion="what are the top news stories today?"results=e.search(question,"test-index",10)document=''forresultinresults["matches"]:document+=result['metadata']['page_content']prompt='''Q:{}\nUsing this document answer the question as a friendly chatbot that knows about the details in the document.You can answer questions only with the information in the documents you've been trained on.{}\nA:'''.format(question,document)payload={"model":"text-davinci-003","prompt":prompt,"max_tokens":50,"temperature":0,"stream":True}response=openai_client.create_completion(payload)forrespinresponse:print(resp.choices[0].text)CLIYou can install theCLItocreateandsearchyour vectors.pipinstallrefiner-cliThe --help option can be used to learn about the create and search commands.refiner--help
refinercreate--help
refinersearch--help
Package: refiner-cli
refiner-cliCLIfor theRefinerpython package convert and store text and metadata as vector embeddings. Embeddings are generated usingOpenAIand stored as vectors inPinecone. Stored embeddings can then be "queried" using thesearchcommand. Matched embeddings contain contextually relavant metadata that can be used for AI chatbots, semnatic search APIs, and can also be used for training and tuning large language models.Installationpipinstallrefiner-cliOpenAI and Pinecone API Keys.You'll need API keys for OpenAI and Pinecone.Once you have your API keys, you can either set local ENV variables in a shell:exportPINECONE_API_KEY="API_KEY"exportPINECONE_ENVIRONMENT_NAME="ENV_NAME"exportOPENAI_API_KEY="API_KEY"or you can create a.env(dotenv) config file and pass it in with the--config-fileoption.Your .env file should follow key/value format:PINECONE_API_KEY="API_KEY"PINECONE_ENVIRONMENT_NAME="ENV_NAME"OPENAI_API_KEY="API_KEY"HelpThe--helpoption can be used to learn about thecreateandsearchcommands.refiner--help
refinercreate--help
refinersearch--help
Package: refiners
The simplest way to train and run adapters on top of foundation modelsManifesto|Docs|Guides|Discussions|DiscordLatest News 🔥AddedSDXL-LightningAddedLatent Consistency ModelsandLCM-LoRAfor Stable Diffusion XLAddedStyle Aligned adapterto Stable Diffusion modelsAddedControlLoRA (v2) adapterto Stable Diffusion XLAddedEuler's methodto solvers (contributed by@israfelsr)AddedDINOv2for high-performance visual features (contributed by@Laurent2916)AddedFreeUfor improved quality at no cost (contributed by@isamu-isozaki)AddedRestart Samplingfor improved image generation (example)AddedSelf-Attention Guidanceto avoid e.g. too smooth images (example)AddedT2I-Adapterfor extra guidance (example)AddedMultiDiffusionfor e.g. panorama imagesAddedIP-Adapter, aka image prompt (example)AddedSegment Anythingto foundation modelsAddedSDXL 1.0to foundation modelsMade possible to add new concepts to the CLIP text encoder, e.g. viaTextual InversionInstallationThe current recommended way to install Refiners is from source usingRye:gitclone"git@github.com:finegrain-ai/refiners.git"cdrefiners
ryesync--all-featuresDocumentationRefiners comes with a MkDocs-based documentation website available athttps://refine.rs. You will find there aquick start guide, a description of thekey concepts, as well as in-depth foundation model adaptationguides.Awesome Adaptation PapersIf you're interested in understanding the diversity of use cases for foundation model adaptation (potentially beyond the specific adapters supported by Refiners), we suggest you take a look at these outstanding papers:ControlNetT2I-AdapterIP-AdapterMedical SAM Adapter3DSAM-adapterSAM-adapterCross Modality Attention AdapterUniAdapterProjects using Refinershttps://github.com/brycedrennan/imaginAIryCreditsWe took inspiration from these great projects:tinygrad- For something between PyTorch andkarpathy/microgradComposer- A PyTorch Library for Efficient Neural Network TrainingKeras- Deep Learning for humansCitation@misc{the-finegrain-team-2023-refiners,author={Benjamin Trom and Pierre Chapuis and Cédric Deltheil},title={Refiners: The simplest way to train and run adapters on top of foundation models},year={2023},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/finegrain-ai/refiners}}}
Package: refinery
Refinery is a map extractor for games utilizing Bungie's Blam engine.Here's a list of what Refinery can currently do:
* Load and extract tags and data from Halo 1 Xbox, PC, CE, PC Demo, and Yelo maps
* Load and extract tags and data from Stubbs the Zombie maps on PC and Xbox
* Load and extract tags and data from CE(not PC) resource maps(loc.map, bitmaps.map,
and sounds.map)
* Load and extract data from Halo 2 Vista maps* Display all pertinent information contained in the map's header, tag index, 
    scenario bsp headers, and Open sauce header(if it exists).
* Has 3 ways to view the map contents: hierarchy, class, and hybrid
    Hierarchy sorts the tags by folder, like a regular directory structure.
    Class sorts all tags by their class(bitmap, gbxmodel, weapon, etc)
    Hybrid combines the two. It sorts everything by class, then inside
    each class is a directory structure of all tags of that class.

* Extract all Halo 1 tags
* Extract all Open sauce tags
* Extract the cheape.map from Yelo maps
* Extract most Stubbs the Zombie tags(excludes vege, imef, terr, bipd, vehi, and
    obje tag classes)
* Fix mangled class names(aka Zteam protection)

* Decompress and save Xbox maps
* Save any loaded non-resource map(rearranges structures and renames protected
    tags so the map can be opened by other map tools)

* Allows you to rename tags and folders of tags in any loaded non-resource map(tags
    have to at least partially exist in the map to rename them, i.e. they cannot be
    in a resource map.)
* Allowspreviewing any tag in the map as if loaded in Mozzarilla. Changes made
    to the tag will NOT be saved to the map. Exporting works in the window, but rawdata
    will be in little-endian and other meta-specific details will not have been tweaked,
    so don't expect to be able to properly import ANY exported block into a tag.
* Allows extraction by tag class as well as by folder or individual tags.Here are all the known bugs that I can think of right now(I plan to fix them):
* Tag explorers might not be sorted if you edit them(if you rename an entire folder
and it disappears from the tree, it will be added to the bottom instead of alphabetically)So now that you've read all that, here's a few tips on using Refinery:
* You can double click an item in the map explorer(or select multiple and hit enter) to
either rename it/them, add it/them to the queue, or preview the tag(only if selecting
individual tags).
* Renaming a folder to one that already exists puts all tags in that folder into the new one.
* Renaming is case insensitive, and everything gets converted to lowercase. It's just
easier this way.
* If you just want stuff from the bitmaps.map, sounds.map, or loc.map just load that map
up instead of an actual map.
* If you only want one type of tag(all bitmaps) then change the explorer to "class" view,
select the tag class(bitm in this case), and add it to the queue.
* You can edit the details of something in the queue by double clicking it.