Saravanan666/pypi-org huggingface dataset:
Package: pyaftership
pyaftershipAsync Python wrapper for the AfterShip APIInstallationpython3-mpipinstallpyaftershipExample usageHere is an example of what you can do, more examples can be found in thetestsdirectory.importasyncioimportaiohttpfrompyaftershipimportAfterShipAPI_KEY='XXXXXXXX0000000000XXXXXXXX0000000XXXXX000'asyncdefexample():"""Get trackings."""asyncwithaiohttp.ClientSession()assession:aftership=AfterShip(API_KEY,session)trackings=awaitaftership.trackings.list()print("Pending trackings:",trackings["trackings"])asyncio.get_event_loop().run_until_complete(example())ContributeAllcontributions are welcome!Fork the repositoryClone the repository locally and open the devcontainer or use GitHub codespacesDo your changesLint the files withscripts/lintEnsure all tests passes withscripts/testEnsure 100% coverage withscripts/coverageCommit your work, and push it to GitHubCreate a PR against themainbranch
Package: py-aga
Aga is a lightweight native Python library for quickly creating documented command-line interfaces usingargparse. It is designed to be as simple as possible to use.UsageUsing Aga couldn't be easier. Assume the file below is namedtest.py:from aga import *


@aga.add_arg
def add(first: int, second: int = 2) -> int:
    """ A simple function to add two numbers

    Args:
        first: One of the numbers to add
        second: The other number to add

    Returns:
        first added to second
    """

    return x + y


if __name__ == '__main__':
    aga.bake()
    print(aga.args.first, aga.args.second)This simple file will result in thexandyarguments as shown below in the output ofpython test.py -h.❯ python test.py -h
usage: test.py [-h] [-s] [-f]

options:
  -h, --help      show this help message and exit
  -s , --second   The other number to add
  -f , --first    One of the numbers to addAs you can see, one must simply add the decoratoraga.add_argto a function and Aga will parse the function definition and create a documented command-line interface.Aga will search for PEP484 type annotations, default values and Google-style docstrings to add further information to the argument definition. None of these are necessary however, and if one (or all) are missing it will simply be ignored.
Package: pyagar
pyagaris a python client for the popular online gameagar.io.This package allows you to play the game, see the gameplay as an spectator
and also play automatically with theautofeature.DependenciesIn order to run this software you’ll need:Python 3.4+SDL2 (you may found it in your distribution as libsdl2)sdl2_ttfhttps://www.libsdl.org/projects/SDL_ttf/sdl2_gfxhttp://cms.ferzkopp.net/index.php/software/13-sdl-gfxInstallationIt is recomended to install this package into a virtualenv.Stable version$pipinstallpyagarUsageCommandThis package creates the commandpyagar.$pyagar--helpusage:pyagar[-h][--no-visualize][-nNICK][--auto][--debug][--spectate]optionalarguments:-h,--helpshowthishelpmessageandexit--no-visualize-nNICK,--nickNICK--auto--debug--spectateControlsActionControlMoveMouse (Relative to the center of the window)StartMouse (Left button)EjectWkeySplitSpacekeyFullscreenFkeyZoomMouse wheelExitESCkeyPlay examplesJust play$pyagarplayPress the left mouse button to start.Just watch the game$pyagarspectatePlay automatically using a bot$pyagarbot--type=EatWhenNoPredatorsOther implementationshttps://github.com/Gjum/pyAgar.iohttps://github.com/Raeon/pygar0.0.3Documenting pyagar module.
Package: py-agata
PYAGATAOfficial Python porting of AGATA (Automated Glucose dATa Analysis): a toolbox to analyse glucose data.InstallationTo install it, just runpipin your terminal:pip install py_agataCitingIf you are using AGATA in your research, please cite it:Cappon G, Sparacino G, Facchinetti A. AGATA: A toolbox for automated glucose data analysis.  
J Diabetes Sci Technol. 2023. DOI: 10.1177/19322968221147570Link to the paper -->here
Package: pyagenda
# Py Agenda## Demo
[![asciicast](https://asciinema.org/a/EAggfLAeafonpFy2Jtd6fXwzI.png)](https://asciinema.org/a/EAggfLAeafonpFy2Jtd6fXwzI)Py Agenda es unAdministrador de contactos🌟hecho en pythonsin dependencias👏y es compatible conPython 3🍻.## UsoSolo ejecuta lo siguiente en la terminal:`bash $ pyagenda `
Package: py-agender
Face Age & Gender detection toolSimple opencv & tensorflow solution to estimate Age and Gender in your
next project. Command line or Python DIY style.Based on and forked from:https://github.com/yu4u/age-gender-estimationGood enough for pet-projects and prototypes, but not ready for production
real-life applicationsHowpip3 install py-agender[cpu]  # for the cpu version of TensorFlow
pip3 install py-agender[gpu]  # for the gpu version of TensorFlowWarning: ~190MB download (pretrained network is heavy).CLI:py-agender PATH_TO_IMAGEPython:frompyagenderimportPyAgenderagender=PyAgender()# see available options in __init__() srcfaces=agender.detect_genders_ages(cv2.imread(MY_IMAGE))# [#   {left: 34, top: 11, right: 122, bottom: 232, width:(r-l), height: (b-t), gender: 0.67, age: 23.5},#   ...# ]# Additional options & methods in PyAgender sourceDon't forget to download pretrained weights if using source code DIY style.TODO:add options (like STDIN input, output formatters etc.) for useful commandline
applicationsadd help outputtrain better network with higher image resolutiontrain another (more compact & precise) network architectureTestspython3 -m unittestDependenciesPython 3.5, 3.6, 3.7numpy ~> 1.15Keras ~> 2.2tensorflow or tensorflow-gpu ~> 1.10opencv-python ~> 3.4.2+contribTested on:MacOS 10.13 high Sierra without GPU (you're welcome to update & contribute!)ModelThese weigts are fromhttps://github.com/yu4u/age-gender-estimationon first console version run they are cached in./pyagender.pretrained_modelsfolder:https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5LicenseThis project is released under the MIT license.However,the IMDB-Wiki datasetused for pretrained network above is originally provided under the following conditions:Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.Refer to fresh IMDB-Wiki dataset License and act accordingly.
Package: pyagentx2
pyagentx2 is a fork of pyagentx, which is a pure Python implementation of AgentX protocol (RFC 2741), that
allows you to extend SNMP agent (snmpd) by writing AgentX subagents, without modifying your original SNMP agent.The agent can support the following commands:
- snmpget
- snmpwalk
- snmptable
- snmpset
Package: pyagentx3
pyagentx3 is a pure Python3 implementation of AgentX protocol (RFC 2741), it
will allow you to extend an SNMP agent (e.g. snmpd) by writing AgentX subagents,
without modifying the original SNMP agent.The agent can support the following commands:
- snmpget
- snmpwalk
- snmptable
- snmpsetIt also allows sending notifications/traps.
Package: pyAGI
pyAGIpyAGIis a Python package that functions as an autonomous agent for your Python application. By interfacing with OpenAI models, it utilizes a sequence of prompts to instruct the AI in generating various outputs such as application descriptions, architecture outlines, user experience flows, and even code samples. All these are achieved using thelangchainframework to construct chains of language models.FeaturesSupports several OpenAI models, including a range of GPT-3 and Davinci models.Provides functionality for generating:Application descriptionsSoftware architecture outlinesUser experience flowsCode flow descriptionsCoding stepsApplication codeSetupTo use pyAGI, you need to have anOpenAI accountand obtain your API key. Once you have your OpenAI API key, set it as an environment variable OPENAI_API_KEY in your system.export OPENAI_API_KEY='your-api-key-here'InstallationTo install pyAGI, use pip:pip install pyAGIThe package is available at PyPI pyAGI (https://pypi.org/project/pyAGI/).UsageAfter installing, you can use pyAGI to generate content for your application as follows:from pyAGI.pyagi import PyAGI

Create a PyAGI instance with a selected OpenAI model
py_agi = PyAGI.create_llm_chain(selected_model="text-davinci-003")

# Run the PyAGI instance with the objective
py_agi({"objective": "Your Objective", "selected_model": "text-davinci-003"})Usage without installing the pip packagepython main.py "Objective of your app" "OpenAI Model"For example:python main.py "Build a weather app" "text-davinci-003"Available ModelsCurrently supported models:"text-davinci-002","text-davinci-003","gpt-3.5-turbo","gpt-3.5-turbo-0301","text-curie-001","text-babbage-001","text-ada-001"Upcoming models (https://openai.com/waitlist/gpt-4-api):"gpt-4","gpt-4-0314","gpt-4-32k","gpt-4-32k-0314"Please note that upcoming models will not be supported until released.ContributingContributions are welcome! Please open an issue if you encounter a bug, or a pull request if you have improvements to the existing code.LicenseThis project is licensed under the MIT License.
Package: pyaging
🐍pyaging: a Python-based compendium of GPU-optimized aging clockspyagingis a cutting-edge Python package designed for the longevity research community, offering a comprehensive suite of GPU-optimized biological aging clocks.Installation-Clock gallery-Search, cite, get metadata and clock parameters-Illumina Human Methylation Arrays-Illumina Mammalian Methylation Arrays-RRBS DNA methylation-Bulk histone mark ChIP-Seq-Bulk ATAC-Seq-Bulk RNA-Seq-Blood chemistry-API ReferenceWith a growing number of aging clocks and biomarkers of aging, comparing and analyzing them can be challenging.pyagingsimplifies this process, allowing researchers to input various molecular layers (DNA methylation, histone ChIP-Seq, ATAC-seq, transcriptomics, etc.) and quickly analyze them using multiple aging clocks, thanks to its GPU-backed infrastructure. This makes it an ideal tool for large datasets and multi-layered analysis.❓ Can't find an aging clock?If you have recently developed an aging clock and would like it to be integrated intopyaging, pleaseemail us. We aim to incorporate it within two weeks! We are also happy to adapt to any licensing terms for commercial entities.💬 Community DiscussionFor coding-related queries, feedback, and discussions, please visit ourGitHub Issuespage.📖 CitationTo citepyaging, please use the following:@article {de_Lima_Camillo_pyaging,
	author = {Lucas Paulo de Lima Camillo},
	title = {pyaging: a Python-based compendium of GPU-optimized aging clocks},
	elocation-id = {2023.11.28.569069},
	year = {2023},
	doi = {10.1101/2023.11.28.569069},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/11/29/2023.11.28.569069},
	journal = {bioRxiv}
}📝 To-Do ListIncorporate more murine DNA methylation clocksAdd torch data loader for age prediction of large datasetsMove preprocessing/postprocessing step to the AnnLoaderAdd other blood chemistry biological age clocks (KD age)Incorporate proteomic clocks (and datasets)Integrate scAge clocks (this is proving to be difficult)Integrate scRNAseq clocks (and datasets)
Package: pyagree
pyagreeA simple Python package to compute some inter-rater agreement measures. Among the other measures this package provides an implementation for both theInformation Agreementand theextension-by-continuity of Information Agreementcoefficients (see[1]).DocumentationHereyou can find thepyagreedocumentation. It contains:the instruction to install the packagethe user manual and some examplesthe API manualExamplesIn order to usepyagree, it is sufficient to import it, for instance,
by using the statementimportpyagreeAfter that, all the functions reported can be invoked as follows:pyagree.<functionname>(<parameter1>,...)Alternatively, the desired functions can be individually imported
and avoiding the package name prefix as in:frompyagreeimport<functionname><functionname>(<parameter1>,...)For instance, the Python program:frompyagreeimportyule_y,cohen_kappaA=[[10,1],[5,10]]Y=yule_y(A)K=cohen_kappa(A)print("Yule's Y ={}".format(Y))print("Cohen's Kappa ={}".format(K))evaluates both Yule's Y and Cohen's kappa of  the agreement matrix A and print them in output.References[1]Casagrande, A. and Fabris, F. and Girometti R. (2020).  Beyond Kappa: An Informational Index for
Diagnostic Agreement in Dichotomous and Multivalue Ordered-Categorical Ratings,Submitted for the publication.Copyright and licensepyagreeCopyright (C) 2020 Alberto Casagrandeacasagrande@units.itCopyright (c) 2020 Alberto CasagrandePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: pyagresso
This module helps developers work with the Agresso Unit4 (ver. Milestone 4) Query Engine
SOAP webservice easily without worry about the transport layer/protocol and getting data in easible
ingestible formats: JSON, CSVExample:`python from pyagresso.queryengineservice import QueryEngineService username =os.getenv('AGRESSO_USERNAME')password =os.getenv('AGRESSO_PASSWORD')client =os.getenv('AGRESSO_CLIENT')instance_url =os.getenv('AGRESSO_INSTANCE_URL')ag = QueryEngineService(username, password,client,instance_url) about_response_as_xml = ag.about() `Note:Not using bdd in this library due to small size of module undertest
Package: pyAgrum
pyAgrumpyAgrumis a scientific C++ and Python library dedicated to Bayesian Networks and other Probabilistic Graphical Models. It provides a high-level interface to the part of aGrUM allowing to create, model, learn, use, calculate with and embed Bayesian Networks and other graphical models. Some specific (python and C++) codes are added in order to simplify and extend theaGrUMAPI.ExampleimportpyAgrumasgum# Creating BayesNet with 4 variablesbn=gum.BayesNet('WaterSprinkler')print(bn)# Adding nodes the long wayc=bn.add(gum.LabelizedVariable('c','cloudy ?',["Yes","No"]))print(c)# Adding nodes the short ways,r,w=[bn.add(name,2)fornamein"srw"]print(s,r,w)print(bn)# Addings arcs c -> s, c -> r, s -> w, r -> wbn.addArc(c,s)forlinkin[(c,r),(s,w),(r,w)]:bn.addArc(*link)print(bn)# or, equivalenlty, creating the BN with 4 variables, and the arcs in one linebn=gum.fastBN("w<-r<-c{Yes|No}->s->w")# Filling CPTsbn.cpt("c").fillWith([0.5,0.5])bn.cpt("s")[0,:]=0.5# equivalent to [0.5,0.5]bn.cpt("s")[{"c":1}]=[0.9,0.1]bn.cpt("w")[0,0,:]=[1,0]# r=0,s=0bn.cpt("w")[0,1,:]=[0.1,0.9]# r=0,s=1bn.cpt("w")[{"r":1,"s":0}]=[0.1,0.9]# r=1,s=0bn.cpt("w")[1,1,:]=[0.01,0.99]# r=1,s=1bn.cpt("r")[{"c":0}]=[0.8,0.2]bn.cpt("r")[{"c":1}]=[0.2,0.8]# Saving BN as a BIF filegum.saveBN(bn,"WaterSprinkler.bif")# Loading BN from a BIF filebn2=gum.loadBN("WaterSprinkler.bif")# Inferenceie=gum.LazyPropagation(bn)ie.makeInference()print(ie.posterior("w"))# Adding hard evidenceie.setEvidence({"s":1,"c":0})ie.makeInference()print(ie.posterior("w"))# Adding soft and hard evidenceie.setEvidence({"s":[0.5,1],"c":0})ie.makeInference()print(ie.posterior("w"))LICENSECopyright (C) 2005,2023 by Pierre-Henri WUILLEMIN et Christophe GONZALES
{prenom.nom}_at_lip6.frThe aGrUM/pyAgrum library and all its derivatives are distributed under the LGPL3 license, seehttps://www.gnu.org/licenses/lgpl-3.0.en.html.AuthorsPierre-Henri WuilleminChristophe GonzalesMaintainersLionel TortiGaspard Ducamp
Package: pyAgrum-nightly
pyAgrumpyAgrumis a scientific C++ and Python library dedicated to Bayesian Networks and other Probabilistic Graphical Models. It provides a high-level interface to the part of aGrUM allowing to create, model, learn, use, calculate with and embed Bayesian Networks and other graphical models. Some specific (python and C++) codes are added in order to simplify and extend theaGrUMAPI.ExampleimportpyAgrumasgum# Creating BayesNet with 4 variablesbn=gum.BayesNet('WaterSprinkler')print(bn)# Adding nodes the long wayc=bn.add(gum.LabelizedVariable('c','cloudy ?',["Yes","No"]))print(c)# Adding nodes the short ways,r,w=[bn.add(name,2)fornamein"srw"]print(s,r,w)print(bn)# Addings arcs c -> s, c -> r, s -> w, r -> wbn.addArc(c,s)forlinkin[(c,r),(s,w),(r,w)]:bn.addArc(*link)print(bn)# or, equivalenlty, creating the BN with 4 variables, and the arcs in one linebn=gum.fastBN("w<-r<-c{Yes|No}->s->w")# Filling CPTsbn.cpt("c").fillWith([0.5,0.5])bn.cpt("s")[0,:]=0.5# equivalent to [0.5,0.5]bn.cpt("s")[{"c":1}]=[0.9,0.1]bn.cpt("w")[0,0,:]=[1,0]# r=0,s=0bn.cpt("w")[0,1,:]=[0.1,0.9]# r=0,s=1bn.cpt("w")[{"r":1,"s":0}]=[0.1,0.9]# r=1,s=0bn.cpt("w")[1,1,:]=[0.01,0.99]# r=1,s=1bn.cpt("r")[{"c":0}]=[0.8,0.2]bn.cpt("r")[{"c":1}]=[0.2,0.8]# Saving BN as a BIF filegum.saveBN(bn,"WaterSprinkler.bif")# Loading BN from a BIF filebn2=gum.loadBN("WaterSprinkler.bif")# Inferenceie=gum.LazyPropagation(bn)ie.makeInference()print(ie.posterior("w"))# Adding hard evidenceie.setEvidence({"s":1,"c":0})ie.makeInference()print(ie.posterior("w"))# Adding soft and hard evidenceie.setEvidence({"s":[0.5,1],"c":0})ie.makeInference()print(ie.posterior("w"))LICENSECopyright (C) 2005,2023 by Pierre-Henri WUILLEMIN et Christophe GONZALES
{prenom.nom}_at_lip6.frThe aGrUM/pyAgrum library and all its derivatives are distributed under the LGPL3 license, seehttps://www.gnu.org/licenses/lgpl-3.0.en.html.AuthorsPierre-Henri WuilleminChristophe GonzalesMaintainersLionel TortiGaspard Ducamp
Package: py_aho_corasick
===============================py-aho-corasick===============================.. image:: https://img.shields.io/pypi/v/py_aho_corasick.svg:target: https://pypi.python.org/pypi/py_aho_corasick.. image:: https://img.shields.io/travis/JanFan/py_aho_corasick.svg:target: https://travis-ci.org/JanFan/py_aho_corasick.. image:: https://readthedocs.org/projects/py-aho-corasick/badge/?version=latest:target: https://py-aho-corasick.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/JanFan/py_aho_corasick/shield.svg:target: https://pyup.io/repos/github/JanFan/py_aho_corasick/:alt: Updatespy-aho-corasick* Free software: MIT license* The prototype is inspired by and borrowed from `Carolyn Shen <http://carshen.github.io/data-structures/algorithms/2014/04/07/aho-corasick-implementation-in-python.html>`_Features--------* Pure Python implementation* Python2 && Python3 support* Unicode && UTF-8 encoding support* Pickle-able serializationUsage--------Install::pip install py_aho_corasickUsage::from py_aho_corasick import py_aho_corasick# keywords onlyA = py_aho_corasick.Automaton(['cash', 'shew', 'ew'])text = "cashew"for idx,k,v in A.get_keywords_found(text):assert text[idx:idx+len(k)] == k# keywords and valueskv = [('cash',1), ('shew',2), ('ew',3)]A = py_aho_corasick.Automaton(kv)text = "cashew"for idx,k,v in A.get_keywords_found(text):assert text[idx:idx+len(k)] == kassert v == dict(kv)[k]Performance--------Compared with `pyahocorasick (C extention) <https://github.com/WojciechMula/pyahocorasick>`_You can run the testing script to get this::# Requirements:# pip install pyahocorasickpython cmp.py* pyahocorasick: text of 1000000 length, 1000 keywords, building time 0.026426076889038086 and searching time cost 0.047805070877075195* py_aho_corasick: text of 1000000 length, 1000 keywords, building time 0.47435593605041504 and searching time cost 4.24287486076355Sorry about the poor performance :-(Development--------Run tests::# testing against py2 and py3toxTODO--------* Performance optimization=======History=======0.1.0 (2017-04-17)------------------* First release on PyPI.
Package: pyahooapis
Setup$ easy_install pyahooapisExamplefrom pyahooapis import jlp

appid = 'your-application-id'
api = jlp.JLPAPIs(appid)
sentence = 'sentence'

chunks = api.da.get_chunks(sentence)

for chunk in chunks:
    print '%s -> %s' % (chunk, chunks[chunk.dependency] if chunk.dependency != -1 else None)DocumentationGo toPyahooapis’s documentation
Package: pyAIC
pyAICThis package define a series of function to manipulate, convert and (syntactically) validate AIC codes as described in the italian "Gazzetta Ufficiale della Repubblica Italiana"Serie Generale n.165 del 18-07-2014,attachment A.
The document describes two valid representation of an AIC code.FunctionsThe functions to validate a code areis_base32_AICandis_base32_AIC. Using strict=True will validate only real AIC codes (they must start with "0"), while strict=False will validate PARAF codes.The functions to convert between base32 and base10 representation arefrom10to32andfrom32to10.The last char of a base10 code is a checksum, to check it usecheck_AIC_base10_checksum, to compute it usecalc_AIC_base10_checksum.