Saravanan666/pypi-org huggingface dataset:
Package: argilaparser
Argila ParserWhat is it?It's a lightweight object oriented lib for Python 3.8+ to help create CLI programs more easily. It has an elegant design taking advantage of built-in modules, and delivering a conducive environment to fast development and readability.QuickstartThis is the basic structure of your program, the classApprepresents the application, and the methods within it are the commands.Thetypingmodule provides a built-in range of type hints, giving the developer a sophisticated way of accepting complex arguments.Any, Union, Tuple, Listare currently supported.importtypingfromargilaimportArgila@Argila.createclassApp:@Argila.command({'name':'add','positional':{'a':'First element of the equation','b':'Second element of the equation'}})defadd(self,a:typing.Union[int,float],b:typing.Union[int,float])->None:'''This command adds two numbers together'''print(a+b)if__name__=='__main__':Argila.run()A method is only considered a command if it has the@Argila.commanddecorator.Below is the configuration dict in its completeness.{'name':'command','positional':{'a':'Obligatory argument'},'optional':{'b':'Optional argument'},'keyword':{'c':'Keyword argument'}}*means the key is obligatory.name*: The name of the command used to call it. (This is the only nomination that matters, the method can have any name you'd like)positional: A dict with all the positional arguments, thekeyshould have the same name as the actual parameter in the method; thevalueis a short description of it. You invoke this argument with--key value.optional: Same as positional, but optional arguments arenotobligatory. You invoke this argument with--key value.keyword: Same as positional, but keyword arguments arenotobligatory and work as flags (bools). You invoke this argument with--key.The order in which you invoke each argument doesnotmatter, as long as you keep the syntax--key value. The examplesadd --a 1 --b 2andadd --b 2 --a 1are both correct.Apart from it, the docstring'''This command adds two numbers together'''is how you set the description of a command. If not set, the description defaults to an empty string.Lastly, callArgila.run(), which is the method that will trigger all the subsequent others.
Package: argilla-haystack
Argilla-HaystackArgilla is an open-source platform for data-centric LLM development. Integrates human and model feedback loops for continuous LLM refinement and oversight.With Argilla's Python SDK and adaptable UI, you can create human and model-in-the-loop workflows for:Supervised fine-tuningPreference tuning (RLHF, DPO, RLAIF, and more)Small, specialized NLP modelsScalable evaluation.Getting StartedYou first need to install argilla and argilla-haystack as follows:pipinstallargillaargilla-haystack["haystack-v1"]You will need to an Argilla Server running to monitor the LLM. You can either install the server locally or have it on HuggingFace Spaces. For a complete guide on how to install and initialize the server, you can refer to theQuickstart Guide.UsageYou can use your Haystack agent with Argilla with just a simple step. After the agent is created, we will need to call the handler to log the data into Argilla.Let us create a simple pipeline with a conversational agent. Also, we will use GPT3.5 from OpenAI as our LLM. For this, you will need a valid API key from OpenAI. You can have more info and get one viathis link.After you get your API key, let us import the key.importosfromgetpassimportgetpassopenai_api_key=os.getenv("OPENAI_API_KEY",None)orgetpass("Enter OpenAI API key:")With the code snippet below, let us create the agent.fromhaystack.nodesimportPromptNodefromhaystack.agents.memoryimportConversationSummaryMemoryfromhaystack.agents.conversationalimportConversationalAgent# Define the node with the modelprompt_node=PromptNode(model_name_or_path="gpt-3.5-turbo-instruct",api_key=openai_api_key,max_length=256,stop_words=["Human"])summary_memory=ConversationSummaryMemory(prompt_node)conversational_agent=ConversationalAgent(prompt_node=prompt_node,memory=summary_memory)Let us import the ArgillaCallback and run it. Note that the dataset with the given name will be pulled from Argilla server. If the dataset does not exist, it will be created with the given name.fromargilla_haystackimportArgillaCallbackapi_key="argilla.apikey"api_url="http://localhost:6900/"dataset_name="conversational_ai"ArgillaCallback(agent=conversational_agent,dataset_name=dataset_name,api_url=api_url,api_key=api_key)Now, let us run the agent to obtain a response. The prompt given and the response obtained will be logged in to Argilla server.conversational_agent.run("Tell me three most interesting things about Istanbul, Turkey")Other Use CasesPlease refer to thisnotebookfor a more detailed example.
Package: argilla-llama-index
Argilla-Llama-IndexArgilla is an open-source platform for data-centric LLM development. Integrates human and model feedback loops for continuous LLM refinement and oversight.With Argilla's Python SDK and adaptable UI, you can create human and model-in-the-loop workflows for:Supervised fine-tuningPreference tuning (RLHF, DPO, RLAIF, and more)Small, specialized NLP modelsScalable evaluation.Getting StartedYou first need to install argilla and argilla-llama-index as follows:pipinstallargilla-llama-indexYou will need to an Argilla Server running to monitor the LLM. You can either install the server locally or have it on HuggingFace Spaces. For a complete guide on how to install and initialize the server, you can refer to theQuickstart Guide.UsageIt requires just a simple step to log your data into Argilla within your LlamaIndex workflow. We just need to call the handler before starting production with your LLM.We will use GPT3.5 from OpenAI as our LLM. For this, you will need a valid API key from OpenAI. You can have more info and get one viathis link.After you get your API key, let us import the key.importosfromgetpassimportgetpassopenai_api_key=os.getenv("OPENAI_API_KEY",None)orgetpass("Enter OpenAI API key:")Let us make the necessary imports.fromargilla_llama_indeximportArgillaCallbackHandlerfromllama_indeximportVectorStoreIndex,ServiceContext,SimpleDirectoryReaderfromllama_index.llmsimportOpenAIfromllama_indeximportset_global_handlerWhat we need to do is to set Argilla as the global handler as below. Within the handler, we need to provide the dataset name that we will use. If the dataset does not exist, it will be created with the given name. You can also set the API KEY, API URL, and the Workspace name. If you do not provide these, the default values will be used.set_global_handler("argilla",dataset_name="query_model")Let us create the LLM.llm=OpenAI(model="gpt-3.5-turbo",temperature=0.8)With the code snippet below, you can create a basic workflow with Llama Index. You will also need a txt file as the data source within a folder named "data". For a sample data file and more info regarding the use of Llama Index, you can refer to theLlama Index documentation.service_context=ServiceContext.from_defaults(llm=llm)docs=SimpleDirectoryReader("data").load_data()index=VectorStoreIndex.from_documents(docs,service_context=service_context)query_engine=index.as_query_engine()Now, let us run thequery_engineto have a response from the model.response=query_engine.query("What did the author do growing up?")Theauthorworkedontwomainthingsoutsideofschoolbeforecollege:writingandprogramming.TheywroteshortstoriesandtriedwritingprogramsonanIBM1401.Theylatergotamicrocomputer,builtitthemselves,andstartedprogrammingonit.The prompt given and the response obtained will be logged in to Argilla server. You can check the data on the Argilla UI.And we also logged the metadata properties into Argilla. You can check them via the Filters on the upper left and filter your data according to any of them.
Package: argilla-server
argilla-serverThe repository for backend argilla server.Clone repositoryargilla-serveris usingargillarepository as submodule to build frontend statics so when cloning use the following command:gitclone--recurse-submodulesgit@github.com:argilla-io/argilla-server.gitIf you already cloned the repository without using--recurse-submodulesyou can init and update the submodules with:gitsubmoduleupdate--remote--recursive--init[!IMPORTANT]
By defaultargillasubmodule is usingdevelopbranch so the previous command will get the latest commit from that branch.Specify a tag for argilla submoduleWhen doing a release we should changeargillasubmodule to use an specific tag. In the following example we are setting tagv1.22.0:cdargilla
gitfetch--tags
gitcheckoutv1.22.0[!NOTE]
You should see some changes on theargilla-serverroot folder where the subproject commit is now changed to the one from the tag version. Feel free to commit these changes.Development environmentBy default all commands executed withpdm runwill get environment variables from.env.devexcept commandpdm testthat will overwrite some of them using values coming from.env.testfile.These environment variables can be overrided if necessary so feel free to defined your own ones locally.Run clipdmcliRun database migrationsBy default a SQLite located at~/.argilla/argilla.dbwill be used. You can create the database and run migrations with the following custom PDM command:pdmmigrateRun testsA SQLite database located at~/.argilla/argilla-test.dbwill be automatically created to run tests. You can run the entire test suite using the following custom PDM command:pdmtestRun development serverBuild frontend static filesBefore running Argilla development server we need to build the frontend static files. Node version 18 is required for this action:brewinstallnode@18After that you can build the frontend static files:./scripts/build_frontend.shAfter running the previous script you should have a folder atsrc/argilla_server/staticwith all the frontend static files successfully generated.Run uvicorn development serverpdmserver
Package: arglabels
arglabelsA simple decorator to enable Swift-like argument labels for Python functions.It re-labels certain keyword arguments, so that your function parameters can have an external and an internal name likeargument labels and parameter namesin Swift.InstallationInstall from PyPI with:pip install arglabelsUsageIf you have a function like the following:definvite(name,activity):returnf"Hey{name}! Would you like to go{activity}?"When calling that function it would be nice to have a syntax that almost reads like plain english, for example:invite("Alex",to_go="fishing")You can achieve this with the arglabels decorator, by using it on the function definition like so:fromarglabelsimportarglabels@arglabels(activity="to_go")definvite(name,activity):returnf"Hey{name}! Would you like to go{activity}?"
Package: arglite
A lightweight, dynamic argument parsing library for Python programs with klugy support for typing variables.I made this for a teaching machine project I’m working on (I needed a custom argument parser for _reasons_),
and I’m always too impatient to useargparse.NotesFlags with no value are automatically converted toTrueboolean
The module usesast.literal_eval, so“{‘a’:’b’}”will convert to adict(all quotes required)
Programs will produce error messages when flags are missing
Package: arglog
arglogAdd logging options to argparse automaticallyDemo# main.pyimportargparseimportloggingimportarglogif__name__=='__main__':parser=argparse.ArgumentParser()arglog.patch(parser)args=parser.parse_args()This will trigger the same effect as running:logging.basicConfig(level='<level name>')However, you now also have a new CLI option for your program:$pythonmain.py--loglevel=WARNINGAll of the logging levels are supported,INFO,DEBUGand so on.
Package: argmagic
ArgmagicBased on a idea by@lbb.Automatically generate argparse based env-var/CLI-interface from a given function.Example:Given a function with a docstring and type hints.defhello(name:str):'''Say hello to name.Args:name: Your name.Raises:Nothing.Returns:Nothing.'''print('Hello',name)Create a CLI interface:argmagic(hello)Argmagic will call the function with all parameters filled from CLI arguments.$./hello.py-h
usage:hello[-h][--nameNAME]Sayhellotoname.

optionalarguments:-h,--helpshowthishelpmessageandexit--nameNAMEYourname.Additionally all specified parameters can also be defined via environment
variables.$NAME=testhello.py
HellotestThese can then again be overriden by CLI arguments.$NAME=testhello.py--namesomething
HellosomethingSupport of composite typesCurrent parsing of lists, tuples and unions are supported.For example a typehint containingList[str], will parse input[a, b, c]to
a python list containing["a", "b", "c"].Also these types can be arbitrarily nested, such asDict[str, List[int]]will
correctly parse strings of structure{a: [1, 2, 3], b: [5, 3]}.Syntax overview:Tuple syntax:
(a, b, c)

List syntax:
[a, b, c]

Dict syntax:
{a: i, b: j, k: l}Alternativesclickuses decorators
Package: argmark
argmarkConvert argparse based executable scripts to markdown documents. It is based onargdownbut has a simpler interface and a cleaner code.InstallationpipinstallargmarkUsageUsingargmarkis very simple. For a sample python filesample_argparse.py:importargparseparser=argparse.ArgumentParser(prog="sample_argparse.py",description="Just a test",formatter_class=argparse.ArgumentDefaultsHelpFormatter,)parser.add_argument("-f","--files",help="Files to read.",required=True,nargs="+",)values=parser.parse_args()Runargmark -f sample_argparse.pyand it would generate:sample_argparse.py
    ==================

    # Description


    Just a test
    # Usage:```bashusage:sample_argparse.py[-h]-fFILES[FILES...]```# Arguments

    |short|long|default|help|
    | :---: | :---: | :---: | :---: |
    |`-h`|`--help`||show this help message and exit|
    |`-f`|`--files`|`None`|Files to read.|License
Package: argmin
Argmin client for PythonInstallationpip install argminYou can also install the in-development version with:pip install https://github.com/arg-min/argmin/archive/master.zipDocumentationhttps://argmin.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2020-10-30)First release on PyPI.
Package: arg-needle
arg-needleThis repository contains arg-needle, which implements the ARG inference algorithms ARG-Needle and ASMC-clust.QuickstartInstall the Python module from PyPIThe Python module can be installed with:pipinstallarg-needleThis Python module is currently available on Linux and macOS.DocumentationPlease see theARG-Needle manualfor all usage instructions and documentation.Licensearg-needle is distributed under the GNU General Public License v3.0 (GPLv3). For any questions or comments on arg-needle, please contact Pier Palamara using<lastname>@stats.ox.ac.uk.Acknowledgementsarg-needle is developed by Brian C. Zhang with support from Fergus Cooper, Árni Freyr Gunnarsson, and Pier Francesco Palamara.ReferenceIf you use this software, please cite:Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits. B. C. Zhang, A. Biddanda, P. F. Palamara.bioRxiv. November 2021.arg-needle Release Notesv1.0.3 (2023-08-30)ASMC decoders can now take a genetic map file with a specified non-default location.ASMC decoders no longer require genetic positions to be strictly increasing: adjacent identical positions are now acceptable.v1.0.2 (2023-05-25)Fix default values for sequencing in arg_needle script.v1.0.1 (2023-03-17)Bug fix for running build_arg_simulation with ASMC-clust.v1.0.0 (2023-03-10)Initial PyPI release of arg-needle.
Package: arg-needle-lib
arg-needle-libThis repository contains arg-needle-lib, which implements an ARG data structure and ARG-based analyses such as genealogy-wide association.QuickstartInstall the Python module from PyPIMost functionality is available through a Python module which can be installed with:pipinstallarg-needle-libThis Python module is currently available on Linux and macOS.DocumentationPlease see theARG-Needle manualfor all usage instructions and documentation.Licensearg-needle-lib is distributed under the GNU General Public License v3.0 (GPLv3). For any questions or comments on arg-needle-lib, please contact Pier Palamara using<lastname>@stats.ox.ac.uk.Acknowledgementsarg-needle-lib is developed by (in alphabetical order) Arjun Biddanda, Fergus Cooper, Árni Freyr Gunnarsson, Pier Francesco Palamara, Sinan Shi, Brian C. Zhang, and Jiazheng Zhu.ReferenceIf you use this software, please cite:B. C. Zhang, A. Biddanda, Á. F. Gunnarsson, F. Cooper, P. F. Palamara, Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits.Nature Genetics, 2023.arg-needle-lib Release Notesv1.0.2 (2023-09-29)Breaking changesNoneOther changesImprove documentation.Build Python wheels for macOS arm64 (Apple Silicon)Build wheels for Python 3.12v1.0.1 (2023-07-14)Breaking changesNoneOther changesClean-up and release of the source code.Improved mutation class.Added from-to parameters to some functions.v1.0.0 (2023-03-07)Initial PyPI release of arg-needle-lib.
Package: argnorm
argNormAntibiotic resistance gene (ARG) normalization by mapping to theantibiotic resistance ontology (ARO)by CARD.Right now, many tools exist for annotating ARGs in genomes and metagenomes.
However, each tool will output in its own format.
ThehAMRonizationpackage can normalize file formats, but each tool will use different names/identifiers (e.g.,TetAorTETAortet(A)ortet-Aare all different ways to spell the same gene name).
For a small number of isolate genomes, a human user can still quickly evaluate the outputs.
However, for metagenomics, especially for large-scale projects, this becomes infeasible.
Thus,argNormnormalizes theoutput vocabularyof these tools by mapping all tools to the same ontology (ARO).This is a beta-quality implementation (subject to changes and some bugs may remain), but you're welcomed to try it and provide feedback.We welcome your feedback on theIssues Page.Supported toolsNote that CARD RGI already uses ARO, thus there is no need to use argNorm.DeepARG(v1.0.2)ARGs-OAP(v2.3)ABRicate(v1.0.1) with NCBI (v3.6), ResFinder (v4.1.11), MEGARes (v2.0), ARG-ANNOT (v5)ResFinder(v4.0)AMRFinderPlus(v3.10.30)InstallationpipinstallargnormBasic usageUseargnorm -hto see available options.argnorm[tool]-i[original_annotation.tsv]-o[annotation_result_with_aro.tsv]Examplesargnormargsoap--modereads-iexamples/raw/args-oap.sarg.reads.tsv-otmp
argnormargsoap--modereads-iexamples/hamronized/args-oap.sarg.reads.tsv-otmp--hamronized
argnormargsoap--modeorfs-iexamples/raw/args-oap.sarg.orfs.tsv-otmp
argnormargsoap--modeorfs-iexamples/hamronized/args-oap.sarg.orfs.tsv-otmp--hamronized

argnormdeeparg-iexamples/raw/deeparg.deeparg.orfs.tsv-otmp
argnormdeeparg-iexamples/hamronized/deeparg.deeparg.orfs.tsv-otmp--hamronized

argnormabricate--dbncbi-iexamples/raw/abricate.ncbi.tsv-otmp
argnormabricate--dbmegares-iexamples/raw/abricate.megares.tsv-otmp
argnormabricate--dbargannot-iexamples/raw/abricate.argannot.tsv-otmp

argnormabricate--dbncbi-iexamples/hamronized/abricate.ncbi.tsv-otmp--hamronized
argnormabricate--dbmegares-iexamples/hamronized/abricate.megares.tsv-otmp--hamronized
argnormabricate--dbargannot-iexamples/hamronized/abricate.argannot.tsv-otmp--hamronized
argnormabricate--dbresfinder-iexamples/hamronized/abricate.resfinder.tsv-otmp--hamronized

argnormresfinder-iexamples/raw/resfinder.resfinder.orfs.tsv-ooutputs/raw/resfinder.resfinder.orfs.tsv
argnormresfinder-iexamples/raw/resfinder.resfinder.reads.tsv-ooutputs/raw/resfinder.resfinder.reads.tsv

argnormamrfinderplus-iexamples/raw/amrfinderplus.ncbi.orfs.tsv-ooutputs/raw/amrfinderplus.ncbi.orfs.tsvAuthorsVedanth Ramjivedanth.ramji@gmail.com*Hui Chonghuichong.me@gmail.comSvetlana Ugarcina Perovicsvetlana.ugarcina@gmail.comFinlay Maguirefinlaymaguire@gmail.comLuis Pedro Coelholuis@luispedro.org*: current maintainer
Package: argofamiglia
Argo Famiglia APIAn API wrapper created to interface Python with Argo ScuolaNext.
You can use this library to automate some actions like:Check for new announcements;Check homework for the next and past days;And more...How the idea was bornThe idea was born because the official app doesn't notify you if an announcements arrives, or if you receive a new vote, and also because the app isREALLYslow.
I then implemented this API in aDiscord bot(not working for now) that notifies me and my class about new announcements from the school.There are other libraries similar to this one, such as the library created byHearot, but it doesn't work anymore. For now this is the only working library.How to useYou can install this library with:python3 -m pip install -U git+https://github.com/salvatore-abello/argofamiglia.gitYou can also install this from the Python Package Index with:python3 -m pip install argofamigliaHere's an example of how to obtain a dict containing the homework.
The keys of the dict will be equal to the due dates of the homework.from argofamiglia import ArgoFamiglia

session = ArgoFamiglia("SCHOOL_CODE", "myusername", "mypassword")
print(session.getCompitiByDate())
# prints:
#  {'2022-09-22': {'compiti': ['compiti materia 1',
#                            'compiti materia 2',
#                            'compiti materia 3'],
#                'materie': ['MATERIA 1',
#                            'MATERIA 2',
#                            'MATERIA 3']
# }, ...DisclaimerThis library is not finished, some methods aren't working in the file calledauth.py
Package: argolid
ArgolidBuild RequirementsArgolidusesTensorstorefor reading and writing pixel data. SoTensorstorebuild requirements are needed to be satisfied.
For Linux, these are the requirements:GCC10 or laterClang8 or laterPython3.8 or laterCMake3.24 or laterPerl, for buildinglibaomfrom source (default). Must be inPATH. Not required if-DTENSORSTORE_USE_SYSTEM_LIBAOM=ONis specified.NASM, for buildinglibjpeg-turbo,libaom, anddav1dfrom source (default). Must be inPATH.Not required if-DTENSORSTORE_USE_SYSTEM_{JPEG,LIBAOM,DAV1D}=ONis specified.GNU Patchor equivalent. Must be inPATH.Building and InstallingHere is an example of building and installingArgolidin a Python virtual environment.python -m virtualenv venv
source venv/bin/activate
pip install cmake
git clone --recurse-submodules https://github.com/sameeul/argolid.git 
cd argolid
python setup.py installUsageArgolid can generate Pyramids from a single image or an image collection with a stitching vector provided. It can generate three different kind of pyramids:Neuroglancer compatible Zarr (NG_Zarr)Precomputed Neuroglancer (PCNG)Viv compatible Zarr (Viv)Currently, three downsampling methods (mean,mode_maxandmode_min) are supported. A dictionary with channel id (integer) as key and downsampling method as value can be passed to specify downsampling method for specific channel. If a channel does not exist as a key in the
dictionary,meanwill be used as the default downsampling methodHere is an example of generating a pyramid from a single image.from argolid import PyramidGenerartor
input_file = "/home/samee/axle/data/test_image.ome.tif"
output_dir = "/home/samee/axle/data/test_image_ome_zarr"
min_dim = 1024
pyr_gen = PyramidGenerartor()
pyr_gen.generate_from_single_image(input_file, output_dir, min_dim, "NG_Zarr", {0:"mode_max"})Here is an example of generating a pyramid from a collection of images and a stitching vector.from argolid import PyramidGenerartor
input_dir = "/home/samee/axle/data/intensity1"
file_pattern = "x{x:d}_y{y:d}_c{c:d}.ome.tiff"
output_dir = "/home/samee/axle/data/test_assembly_out"
image_name = "test_image"
min_dim = 1024
pyr_gen = PyramidGenerartor()
pyr_gen.generate_from_image_collection(input_dir, file_pattern, image_name, 
                                        output_dir, min_dim, "Viv", {1:"mean"})