Saravanan666/pypi-org huggingface dataset:
Package: pygrove
pygroveWriting the same imports over and over is draining. Some imports are standard enough for automation.pyforestallows this, but it is not frequently updated and oriented towards general data science.
Here, I provide other imports, including some huggingface libraries imports.Installationpip install pygroveUsageimportpygrovemodel=AutoModel.from_pretrained('roberta-base')tokenizer=AutoTokenizer.from_pretrained('roberta-base')Please note that thanks to pyforest, pygrove imports are lazy. Objects do not require time or memory until they are actually called.ImportsHere is the list of pygrove imports# HuggingFacedatasets=LazyImport("import datasets")load_metric=LazyImport("from datasets import load_metric")load_dataset=LazyImport("from datasets import load_dataset")Dataset=LazyImport("from datasets import Dataset")transformers=LazyImport("import transformers")Trainer=LazyImport("from transformers import Trainer")TrainingArguments=LazyImport("from transformers import TrainingArguments")AutoTokenizer=LazyImport("from transformers import AutoTokenizer")AutoConfig=LazyImport("from transformers import AutoConfig")AutoModel=LazyImport("from transformers import AutoModel")AutoModelForSequenceClassification=LazyImport("from transformers import AutoModelForSequenceClassification")AutoModelForTokenClassification=LazyImport("from transformers import AutoModelForTokenClassification")AutoModelForMultipleChoice=LazyImport("from transformers import AutoModelForMultipleChoice")AutoModelForQuestionAnswering=LazyImport("from transformers import AutoModelForQuestionAnswering")# Helpersftfy=LazyImport("import ftfy")fc=LazyImport("import funcy as fc")warnings=LazyImport("import warnings")tqdm=LazyImport("from tqdm.auto import tqdm")random=LazyImport("import random")itertools=LazyImport("import itertools")appdirs=LazyImport("import appdirs")copy=LazyImport("import copy")defaultdict=LazyImport("from collections import defaultdict")Counter=LazyImport("from collections import Counter")edict=LazyImport("from easydict import EasyDict as edict")Xp=LazyImport("from xpflow import Xp")millify=LazyImport("from millify import millify")
Package: pygrowcube
PyGrowCubePython module for interacting with Elecrow GrowCube smart plant watering systemFree software: MIT licenseDocumentation:https://pygrowcube.readthedocs.io.FeaturesBasic CLI and module to read current GrowCube version, temperature, humidity and status of each conencted moisture sensorCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-09-02)First release on PyPI.0.2.0 (2023-09-12)Switched to asyncio for networking
Package: pygrowthmodels
A compilation of nonlinear growth modelsFeaturespygrowthmodelsincludes functions for the calculation of the
following nonlinear growth models and its inverse functions:BlumbergBrodyChapman-RichardsGeneralised LogisticGeneralised RichardGompertzJanoschekLog-logisticLogisticMitcherlichMonomolecularMorgan-Mercer-FlodinNegative exponentialRichard’sRossoSchnuteStannardvon BertalanffyWeibullReferencesD. Fekedulegn, M. Mac Siurtain, and J. Colbert, “Parameter estimation
of nonlinear growth models in forestry,” Silva Fennica, vol. 33, no.
4, pp. 327-336, 1999.Full
textM. M. Kaps, W. O. W. Herring, and W. R. W. Lamberson, “Genetic and
environmental parameters for traits derived from the Brody growth
curve and their relationships with weaning weight in Angus cattle.,”
Journal of Animal Science, vol. 78, no. 6, pp. 1436-1442, May 2000.Full textA. Tsoularis and J. Wallace, “Analysis of logistic growth models.,”
Math Biosci, vol. 179, no. 1, pp. 21–55, Jul. 2002. DOI identifier:10.1016/j.bbr.2011.03.031A. Khamiz, Z. Ismail, and A. T. Muhammad, “Nonlinear growth models
for modeling oil palm yield growth,” Journal of Mathematics and
Statistics, vol. 1, no. 3, p. 225, 2005. DOI identifier:10.3844/jmssp.2005.225.233Michael J. Panik, “Growth Curve Modeling: Theory and Applications”,
John Wiley & Sons, December 2013.T. Ross, “Indices for performance evaluation of predictive models in
food microbiology.” J. Appl. Bacteriol. vol. 81, no. 5, pp. 501–508.
Nov. 1996.Wikipedia contributors, “Generalised logistic function”, Wikipedia,
The Free Encyclopedia, 6 April 2018, 16:27 UTC,accessed 6 April
2018DisclaimerCopyright (C) 2018 Daniel Rodríguez PérezThis program is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version.This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.You should have received a copy of the GNU General Public License along
with this program. If not, seehttps://www.gnu.org/licenses/.
Package: py-grpc-profile
py-grpc-profileProfile the grpc server.Provide a grpc interceptor to profile each request in the cProfile module.Installation$pipinstall-Upy-grpc-profileExampleLoad the module and set the interceptors.fromconcurrentimportfuturesimportgrpcfrompy_grpc_profile.server.interceptorimportProfileInterceptor# ...server=grpc.server(futures.ThreadPoolExecutor(max_workers=10),interceptors=[ProfileInterceptor()],)# ...The complete code is available inexample. You can find more details there.License
Package: pygrpfe
pygrpfepygfeprovides helping functions to use group fixed effects.If you want to see the methods in action to reproduce part of the first figure of the paper, you canstart the example notebook on binder:.use the google colab version here:ColabQuick StartTo install from pip, run:pip install pygrpfeCitationPlease use following citation to cite pytwoway in academic publications:Bibtex entry:@techreport{blmgfe2020,
  title={Discretizing Unobserved Heterogeneity},
  author={Bonhomme, St{\'e}phane Bonhomme, Thibaut Lamadon and Elena Manresa},
  year={2020}
}
Package: pygrype
PyGrypeA python wrapper forAnchore GrypeStatusSupported commandscompletiondbcheckdeletediffimportliststatusupdatehelpscanversionGetting startedPrerequisitesPyGrype relies on an existing grype binary.Install grype following the official instructions.Installationinstall usingpippipinstallpygrypeUsageInstantiateGrypeusing the default pathfrompygrypeimportGrypegrype=Grype()or specify the binaryfrompygrypeimportGrypegrype=Grype(path='/opt/grype')Full examplefrompygrypeimportGrypegrype=Grype()version_info=grype.version()print(f'Using grype{version_info.version}')images=['alpine:3.12','ubuntu:18.04','debian:9']forimageinimages:scan=grype.scan(image)criticals=len(list(filter(lambdax:x.vulnerability.severity.lower()=='critical',scan.matches)))print(f'{image}has{len(scan.matches)}vulnerabilities ({criticals}critical)')Example outputUsing grype 0.62.3
alpine:3.12 has 23 vulnerabilities (3 critical)
ubuntu:18.04 has 18 vulnerabilities (0 critical)
debian:9 has 213 vulnerabilities (23 critical)
Package: pygsa
pygsaGlobal Sensitivity AnalysisBadges in Markdown format:For readthedocs, click on the info button next to the badge inhttps://readthedocs.org/projects/whatever/, and copy the markdown codeFor travis, click on the badge inhttps://travis-ci.org/BONSAMURAIS/whatever, and copy the markdown codeFor appveyor, go to the projects settings page, and then click on "badges", and copy the markdown codeInstallationconda install pygsaContributingDetails on how to contribute
Package: pygscholar
pygcholarTool for extracting statistics from Google scholar built uponscholarlyImagine you have a department full of researchers and you want to keep track of the most recent or most cited papers in the department, or that you simply want to track yourself, then this package might be what you need.Documentation:https://finsberg.github.io/pygscholarSource code:https://github.com/finsberg/pygscholarInstallpygscolaris primarily a command line tool which makes it very suitable to install withpipxpipx install pygscholarYou can of course also install it with regularpippython -m pip install pygscholarOnce installed you should get access to thescholarcommand. Seethe tutorialfor a guide on how to use this command.ContributingSeehttps://finsberg.github.io/pygscholar/docs/CONTRIBUTING.htmlLicenseMIT
Package: pygsdata
pygsdataAn intuitive interface for 21cm Global Signal Data.InstallationWe highly recommend usingcondato create a new environment:condacreate-npygsdatapython=3.11Then installpygsdatawithpip:pipinstallpygsdataIf you are intending to developpygsdata, first clone then install in developer mode:gitclonegit@github.com:edges-collab/pygsdata.gitcdpygsdata
pipinstall-e".[dev]"DocumentationRead all the documentation athttps://pygsdata.readthedocs.io/en/latest/
Package: pygsearch
pygsearchpygsearchis a python library that let's you use google search.
Right now you can't search for images. Image support might be added later. PRs are welcome.InstallingPython 3.6.0 or higher is requiredTo install the library you can run the following command:# Linux/MacOSpython3-mpipinstall--upgradepygsearch# Windowspy-3-mpipinstall--upgradepygsearchQuickstartYou can make a simple search query like this:frompygsearchimportgsearchsearch=gsearch("github")print(search.results)Or you can iterate over the results:frompygsearchimportgsearchresults=gsearch("github")forresultinresults:print(result)You can also change how many results you want, pass your own headers, use proxies or even change the language:frompygsearchimportgsearchproxies={"http":"proxy_http","https":"proxy_https","ftp":"proxy_ftp"}headers={"key1":"val1","key2":"val2","key3":"val3",}language="en"search=gsearch("github",20,language,headers,proxies)print(search.results)
Package: pygsl-lite
This package provides a very minimal wrapper forGSL, based on thepygsl library.
This is done to drastically reduce complexity. In particular,  the following features are supported as they provide performance
advantages useful for physics simulations.ODE integrationCubic spline interpolation1D root findingDue to the much reduced scope, this package can eschew a lot of
complexity in wrapping all ofGSL. Furthermore, only latestGSLversions(>=2.7) are supported.
As a result, the installation process is significantly streamlined.For a complete GSL experience, it is recommended to usepygslAll credit for GSL functionality is due to original pygsl authors/maintainers. Seethanks,maintainersand inCREDITS.rst.Installation instructionsInstallation requiresgsl>=2.7and optionallyswig>=4.0, if one wants to rebuild the wrappers.
Onlypython>=3.8is supported.If installing from source, runpipinstall.Thepygsldocumentation can be found athere
Package: pygslodeiv2
pygslodeiv2provides aPythonbinding to theOrdinary Differential Equationintegration routines exposed by theodeiv2 interfaceofGSL - GNU Scientific Library.
The odeiv2 interface allows a user to numerically integrate (systems of) differential equations.The followingstepping functionsare available:rk2rk4rkf45rkckrk8pdrk1imprk2imprk4impbsimpmsadamsmsbdfNote that all implicit steppers (those ending with “imp”) and msbdf require a user supplied
callback for calculating the jacobian.You may also want to know that you can usepygslodeiv2frompyodesyswhich can e.g. derive the Jacobian analytically (using SymPy).pyodesysalso provides
plotting functions, C++ code-generation and more.DocumentationAutogenerated API documentation for latest stable release is found here:https://bjodah.github.io/pygslodeiv2/latest(and the development version for the current master branch are found here:http://hera.physchem.kth.se/~pygslodeiv2/branches/master/html).InstallationSimplest way to install is to use theconda package manager:$ conda install -c bjodah pygslodeiv2 pytest
$ python -m pytest --pyargs pygslodeiv2tests should pass.Binary distribution is available here:https://anaconda.org/bjodah/pygslodeiv2, conda recipes for stable releases are available here:http://hera.physchem.kth.se/~pygslodeiv2/conda-recipes.Source distribution is available here (requires GSL v1.16 or v2.1 shared lib with headers):https://pypi.python.org/pypi/pygslodeiv2(with mirrored files kept here:http://hera.physchem.kth.se/~pygslodeiv2/releases)ExamplesThe classic van der Pol oscillator (seeexamples/van_der_pol.py)>>>importnumpyasnp>>>frompygslodeiv2importintegrate_predefined# also: integrate_adaptive>>>mu=1.0>>>deff(t,y,dydt):...dydt[0]=y[1]...dydt[1]=-y[0]+mu*y[1]*(1-y[0]**2)...>>>defj(t,y,Jmat,dfdt):...Jmat[0,0]=0...Jmat[0,1]=1...Jmat[1,0]=-1-mu*2*y[1]*y[0]...Jmat[1,1]=mu*(1-y[0]**2)...dfdt[0]=0...dfdt[1]=0...>>>y0=[1,0];dt0=1e-8;t0=0.0;atol=1e-8;rtol=1e-8>>>tout=np.linspace(0,10.0,200)>>>yout,info=integrate_predefined(f,j,y0,tout,dt0,atol,rtol,...method='bsimp')# Implicit Bulirsch-Stoer>>>importmatplotlib.pyplotasplt>>>series=plt.plot(tout,yout)>>>plt.show()# doctest: +SKIPFor more examples seeexamples/, and rendered jupyter notebooks here:http://hera.physchem.kth.se/~pygslodeiv2/branches/master/examplesLicenseThe source code is Open Source and is released under GNU GPL v3. SeeLICENSEfor further details.
Contributors are welcome to suggest improvements athttps://github.com/bjodah/pygslodeiv2AuthorBjörn I. Dahlgren, contact:gmail address: bjodah
Package: pygsm-gaussian
pyGSMpyGSM (Python + GSM) is a reaction path and photochemistry tool. It combines the powerful tools of python with the Growing String Method to allow for rapid prototyping and improved readability. This forked repo is fromhttps://github.com/ZimmermanGroup/pyGSM. This repo was modified so it can run with Gaussian software. This package has been published in PyPi so you can easily download it.DocumentationSeehttps://zimmermangroup.github.io/pyGSM/Install instructionsOption 1: Installing from PyPiconda create -n gsm_env python=3.8conda activate gsm_envconda install -c conda-forge xtb-pythonconda install -c conda-forge cclib=1.7.1conda install -c conda-forge ase=3.22.1conda install xtb=6.4.1pip install pygsm-gaussianOption 2: Installing from sourceInstall the code usingpython setup.py install.You can also install locally without sudo permissions like:python setup.py install --prefix ~/.localYou might need to create the folder~/.local/lib/if setup.py complains about the folder not existing.It's also recommended to do the installation within a conda environment e.g.:conda create -n gsm_envsource activate gsm_envpython setup.py install --prefix ~/.localLICENCE NotificationsThis project contains source material from the geomeTRIC package.Copyright 2016-2019 Regents of the University of California and the AuthorsAuthors: Lee-Ping Wang, Chenchen Song\https://github.com/leeping/geomeTRIC/blob/master/LICENSECredits:Cody Aldaz (lead author)Prof. Paul ZimmermanProf. Lee-Ping WangProf. Todd Martinez
Package: pygsmmodule
# pygsmmodule![](https://travis-ci.org/JFF-Bohdan/pygsmmodule.svg?branch=master)
[![Coverage Status](https://coveralls.io/repos/github/JFF-Bohdan/pygsmmodule/badge.svg?branch=master)](https://coveralls.io/github/JFF-Bohdan/pygsmmodule?branch=master)GSM modem control library for SIM-800/SIM-900 modules.## OverviewLibrary able to control GSM modems SIM800/SIM900 connected via serial interface.## Install### Automatic installation from PyPiYou can automatically install pygsmmodule from PyPi just by typing:pip install pygsmmodule### Manual installationIn case if you want install from archive you can download latest version from [PyPi](https://pypi.python.org/pypi/pygsmmodule) after you download and extract, you can installpygsmmodulejust by typingpython setup.py installThis method also can be used for source files cloned from [github](http://github.com/JFF-Bohdan/pygsmmodule) repository.Warning! You also need install dependancies listed inrequirements.txt## UsageYou can find usage examples inexamplesfolder. This folder also contains [README.md](./examples/README.md) file with detailed description of each example.## TestingFirst of all you need install all development dependancies listed inrequirements-dev.txt. Next you can run tests by executing:python -m pytest tests -vvYou can run tests with coverage information collecting bypython -m pytest tests -vv –cov=pygsmmodule
Package: pygsom
pygsom- python GSOM algorithmpygsomis anopen sourcepython-based implementation ofGSOMalgorithm. GSOM is unsupervised  dimensionality reduction and clustering  algorithmTable of ContentsInstallationMinimal exampleGetting started[Citing gsom](#citing pygsom)InstallationTo pip installpygsomfrom github:pipinstallpygsompygsom  supports Python 3.6+.Minimal exampleimportnumpyasnpimportpandasaspdimportgsomdata_filename="data/zoo.txt".replace('\\','/')if__name__=='__main__':np.random.seed(1)df=pd.read_csv(data_filename)print(df.shape)data_training=df.iloc[:,1:17]gsom_map=gsom.GSOM(.83,16,max_radius=4)gsom_map.fit(data_training.to_numpy(),100,50)map_points=gsom_map.predict(df,"Name","label")gsom.plot(map_points,"Name",gsom_map=gsom_map)map_points.to_csv("gsom.csv",index=False)Getting startedTrain the GSOM algorithm : need to give input data in numpy array with training iterations and smoothing iterationsgsom_map.fit(data_training.to_numpy(),<trainingiterations>,<smoothiterations>)Predict cluster nodes : need to give input data in pandas dataframe with names and labelsmap_points=gsom_map.predict(df,<namecolumnname>,<labelcolumnname>)Plot the 2D map: need to give the output of predict function with label column (name column or label column)gsom.plot(map_points,<namecolumnname/labelcolumnname>,gsom_map=<gsom_map>)Citing pygsomIf you usepygsom, please cite the following paper:@article{alahakoon2000dynamic,
  title={Dynamic self-organizing maps with controlled growth for knowledge discovery},
  author={Alahakoon, Damminda and Halgamuge, Saman K and Srinivasan, Bala},
  journal={IEEE Transactions on neural networks},
  volume={11},
  number={3},
  pages={601--614},
  year={2000},
  publisher={IEEE}
}
Package: pygsr
Simple way to access google api for speech recognition with pythonDependenciessox     -    apt-get install soxpyaudio  -  apt-get install python-pyaudioLinks:home:https://bitbucket.org/drneox/pygsr/UsageExample:from pygsr import Pygsr
speech = Pygsr()
speech.record(3) # duration in seconds (3)
phrase, complete_response = speech.speech_to_text('es_ES') # select the language
print phrase
Package: pygst
pygst (Python global stock time)This lib will tell you what is current market status of your stock.Holiday is coming soon.Examplefrom pygst import get_market_status, MarketStatus

status = get_market_status("AAPL")
if status == MarketStatus.PRE or status == MarketStatus.POST:
    print("Extended time")
else:
    print(f"{status.name.lower()} time")
Package: pygster_logparsers
# pygster_logparsers
Parsers for pygsterhttps://pypi.python.org/pypi/pygsterUsage:pygster [options] pygster_logparsers.PARSER logfileFor use Sentry add dsn in parser-options:pygster [options] –parser-options ‘–sentry_dsn {SENTRY_DSN}’ pygster_logparsers.PARSER logfilePARSER is only HAProxyPygster on this moment…pygster options like aslogsterhttps://github.com/etsy/logster
Package: pyGSTi
Gate set tomography (GST) is a quantum tomography protocol that provides full characterization of a quantum logic device
(e.g. a qubit).  GST estimates a set of quantum logic gates and (simultaneously) the associated state preparation and
measurement (SPAM) operations.  GST is self-calibrating.  This eliminates a key limitation of traditional quantum state
and process tomography, which characterize either states (assuming perfect processes) or processes (assuming perfect
state preparation and measurement), but not both together.  Compared with benchmarking protocols such as randomized
benchmarking, GST provides much more detailed and accurate information about the gates, but demands more data.  The
primary downside of GST has been its complexity.  Whereas benchmarking and state/process tomography data can be analyzed
with relatively simple algorithms, GST requires more complex algorithms and more fine-tuning (linear GST is an exception
that can be implemented easily).  pyGSTi addresses and eliminates this obstacle by providing a fully-featured, publicly
available implementation of GST in the Python programming language.The primary goals of the pyGSTi project are to:provide efficient and robust implementations of Gate Set Tomography algorithms;allow straightforward interoperability with other software;provide a powerful high-level interface suited to inexperienced programmers, so that
common GST tasks can be performed using just one or two lines of code;use modular design to make it easy for users to modify, customize, and extend GST functionality.
Package: pygsuite
pygsuitePygsuite provides utilities for working with g-suite products such as docs, forms, sheets, and slides
in an intuitive, pythonic, object-oriented way. TLDR; it makes the google APIs more fun to use.Base level goals is 100% support for all API objects, with an emphasis on type completion and IDE+autocomplete support.
Where helpful, additional higher level abstractions are provided to improve developer velocity.Typical use cases include personal or business process automation.Pygsuite supports the following:SheetsSlidesDocsFormsas well as providing a base level of Drive support for these 4 and beyond, such as for sharing and managing objects.Installpygsuiteishosted on PyPI, and is installable withpip:$pipinstallpygsuiteDocumentationRead the DocsContributingpygsuiteis an open source project, and community contributions are very welcome. Before contributing, please check out ourcontribution pageas part of our documentation.
Package: py-gsuite-apis
Py-GSuite-APIsThis package is meant to standardize and simplify working with Google Sheets, Slides, and Drive APIs through utility classes and functions. It also seeks to provide an interactive environment to inspect and valdiate the shape/structure of requests by using Pydantic models that mirror the Classes implemented by Google APIs.NOTE:This repo is under active development and is not yet intended for production.Python >=3.7 requiredDevelopmentCreate a new conda environment:condaenvcreate-fenvironment.yml&&condaactivatepyGsuiteApisAnd install the appropriate packages:pythonsetup.pydevelopUploadBuild wheels usingsetup.pyand python.python3setup.pysdistbdist_wheelThen upload it with twine.python3-mtwineupload--skip-existingdist/*
Package: pygsw
PyGSW
===Python bindings for the TEOS-10 V3.0 GSW Oceanographic Toolbox in CAuthor
—Luke Campbell &lt;luke.s.campbell@gmail.com&gt;Copyingpygsw- Python bindings for the TEOS-10 V3.0 GSW Oceanographic Toolbox in CCopyright 2012 Lucas CampbellLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: pygt3x
pygt3xPython module for reading GT3X/AGDC file format data generated by ActiGraph devices.Example UsageTo read calibrated accelerometer data, you can use the code snippet below:frompygt3x.readerimportFileReader# Read raw data and calibrate, then export to pandas data framewithFileReader("FILENAME")asreader:was_idle_sleep_mode_used=reader.idle_sleep_mode_activateddf=reader.to_pandas()print(df.head(5))If your AGDC file contains temperature data, you can read it using:frompygt3x.readerimportFileReaderwithFileReader("FILENAME")asreader:df=reader.temperature_to_pandas()print(df.head(5))