Saravanan666/pypi-org huggingface dataset:
Package: gmcm-django-tracing
TracingTracing is a Django app to trace changes in models.This is a fork for django-tracing. Original package in:https://github.com/dbsiavichay/django-tracingQuick startAdd "tracing" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'tracing',
]Runpython manage.py migrateto create the tracing migrations.Import the BaseModel class and add it to your modelsExamplefromtracing.modelsimportBaseModelclassExampleModel(BaseModel):passFor detail audit, create Audit Rules for your models
Package: gmd
Scikit-learn Greedy Maximum Deviation (GMD) AlgorithmThis project provides ascikit-learncompatible python implementation of the
algorithm presented in [Trittenbach2018] together with some usage examples
and a reproduction of the results from the paper.Recent approaches in outlier detection seperate the subspace search from the
actual outlier detection and run the outlier detection algorithm on a
projection of the original feature space. See [Keller2012]. As a result the
detection algorithm (Local Outlier Factor is used in the paper) does not suffer
from the curse of dimensionality.Refer to thedocumentationto see usage examples.
Package: gme
GMEThe GME package was created by the Gravity Modeling Group at the United States International Trade Commission. The package has been primarily developed by Peter Herman and Saad Ahmad. Additional contributions have been made by Serge Shikher, Tamara Gurevich, Samantha Schreiber, Carlos Payan, Caroline Peters, Grace Kenneally, and Austin Drenski.Contact us at [gravity@usitc.gov](mailto://gravity@usitc.gov?subject=[GME package])Additional information about the development team can be located at the project website atgravity.usitc.gov.DocumentationDocumentation for the package can be found athttps://usitc-gravity-group.github.io/GME/.Release informationGME is free, open-source softwareVersion:1.3
Package: gmem
GMEM===============================Author: Stephen Anthony RoseFork of MERFOverview--------Random Effects models for _any ML model_.This is a generalisation of a Mixed Effects Random Forest modelInstallation / Usage--------------------To install use pip:$ pip install gmemOr clone the repo:$ git clone https://github.com/arose13/gmem.git$ python setup.py installContributing------------Me (Stephen Anthony Rose)Example-------To be announced.
Package: gmemo
Installpip install gmemousagegmemo is command-line application.$ gmemo --help
usage: gmemo [option | text]
Options and arguments:
-a     : Display all lines.
-h N   : Display N lines from the begining.
-t N   : Display N lines from the end.
$ gmemo
>>> --help
Commands:
-q        : Quit gmemo.
-s --save : Save curret memo.
--replace : Replace line.
--reset   : Initialize memo.
>>> global
>>> memo
>>> test
>>> -s
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save succeeded!!
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>>> -q
$ cd somewhere
$ gmemo
global
memo
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>>> add
>>> text
>>> --save
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>>> -q
$ gmemo
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>>> --replace
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input line number for replace.
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>>> 3
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replace target line
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>>> replaced
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memo
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>>> -q
$ gmemo add text from argument
$ gmemo
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add text from argument
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>>> reset
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$ gmemo
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5Memo locationMemo stored in home directory.~/.global_memo_v0_0_1
Package: gmeterpy
Gravity (smallg) measurement data processing framework written in Python.The goal of the gMeterPy is to create an open source software to process
raw data from all types of gravimeters.The project is in the early planning and development stage.AboutgMeterPyis an open source and free Python library for processing
measurement data from gravimeters, which are used for gravity field surveys
in geodesy, geophysics and other branches of Earth and planetary sciences.
The main intention is to go as deep as possible in processingrawdata
from scratch, as well as to support all types of instruments,
corrections, common file formats and processing procedures.InstallYou will needPython 3.5 or greater.To installgMeterPysimply usepipas usual:pipinstallhttps://github.com/opengrav/gmeterpy/archive/master.zipOr clone the git repository locally and run:gitclonehttps://github.com/opengrav/gmeterpy.gitcdgmeterpypipinstall.ContributingThere are many ways you can contribute! Please readCONTRIBUTING.rstto see how you can help and give feedback.Please note also thatgMeterPyis released with a Code of Conduct
(seeCODE_OF_CONDUCT.md).
By participating in this project you agree that you will follow all its terms.LicenseThis project is licensed under the MIT license.
Please seeLICENSE.txtfor details.
Package: gmg
Green Mountain Grill MiddlewareMiddleware for talking to your Green Mountain GrillThe intent of this project is to create a plug and play module that can be used in any python application.  This will create a standard way for sending and receiving data to and from your grill.SetupThis package will be published in Test.pypi so to include this into your project just do the following:pip install gmgIt's not published yet, so this won't work until I feel like its readyExamplefrom gmg import GreenMountainGrill
myGrill = GreenMountainGrill('0.0.0.0',8080)
status = myGrill.status
print(status)Results{
  temp: 151,
  tempSet: 150,
  probe: [ { temp: 122, tempSet: 145 }, { temp: 35, tempSet: 0 } ],
  state: { power: 'ON', fire: 'RUNNING', warning: 'FAN_OVERLOADED' }
}PrerequisitesA Green Mountain Grill is requiredMethodsThis is a work in progress so not all methods are available yet.When initiating your grill, you will need the IP address of your grill, and the port which is most likely 8080.status();This method will return the data from the grill and make it available within the callback function.serial();This method will return the serial number for your grillpower(on/off) NOT IMPLEMENTED YET;Hopefully this will be the power control for the grill, accepting one parameter indicating either on or off, and a callback to handle what happens next.temp(device, temp) NOT IMPLEMENTED YET;Available devices should be:'grill'- This would set the temp for the grill'probe1'- This would set the desired food temp for probe 1'probe2'- This would set the desired food temp for probe 2CreditMost of my work is because of the decoding work found by these developers.@Aenima4six2 :Aenima4six2/gmg@FeatherKind :FeatherKing/grillsrv
Package: gmic-sphinx
gmic-sphinxASphinxextension for displayingG'MICcommand results as images using thegmic-pyPython binding.UsageThis Sphinx extension adds a new directive namegmicpicthat takes any gmic expression as input and outputs an image and the gmic command below as caption (other could come later). It has been tested with Sphinx'shtmlbuilder only for now.It works only with thereStructuredText (aka ReST) documentation format, not Markdown or others.In any of your.rstfile, add the following:..gmicpic::your gmic commandFor example:..gmicpic::sp earth blur 4 output earthy.pngwill yield a picture file-namedearthy.pngfollowed by the command as caption:sp earth blur 4 output earthy.pngG'MIC command pre-processingthat theoutputparameter is optional.In order to prevent proxy-blocking issues at docs build-time, G'MIC's samples are stored in this extension:..gmicpic::sp leno blur 4will yield a picture file-named with a unique idcce2fce2-e6fc-11ea-9e0e-8cec4b8c0881.pngfollowed by the command as caption:sp leno blur 4...implies that leno.png exists in thegmic_samplesdirectory (we have done it for you for <=2020 image samples already).
The resulting implicitoutputimage will be pre-stored in gmic-images/ with a unique-id generated.pngfilename.Installing & set-upInstall this Python module from pypi.org (in the same virtual environment as Sphinx):pipinstallgmic-sphinxEdit your Sphinx documentation project'sconf.pyfile and ensure you have line like:extensions=['gmic-sphinx']You might need to add gmic-sphinx to your Python path.Projects using thisThis extension is used in the following projects:gmic-py//readthedocs.io documentationPR to add your project here :)TestsAutomated pytest test cases are run in theGithub Actionstab. You can run them yourself as well in a Python >= 3.7 environment.Install the test requirements:pipinstalldev-requirements.txtInstall the package locally:pipinstall.Run the pytest test suite:py.testtests/ReleasingIf you are maintainer and would like to trigger a new release for this project, you do not need any credential since they are stored as Github Secret for this project.
You just have:Change the version number ingmic_sphinx/gmicpic.py'ssetup()function andgmic_sphinx/gmicpic.py'ssetup()function.to Git-push a new tag, as described in thisGithub Action Worfklow file.LicenseThis project is under theCeCILL License.
Package: gmimtools
Ground Motion Intensity Measures (GMIM) toolsThis repository provides a suite of ground motion intensity measure tools. These tools include codes for computing the following ground motion intensity measures based on ground motion records:FunctionAbbreviationDescriptionget_PGA_cyPGAPeak ground accelerationget_PGV_cyPGVPeak ground velocityget_Ia_cyIaArias intensityget_Ia_times_cyIa_timesTimes to get to a Ia percentageget_CAV_cyCAVCumulative absolute velocityget_CAVn_cyCAVnCAV on accelerations after applying a cutoffget_CAVgm_cyCAVgmCAV on accelerations after applying a cutoffget_CAVstd_cyCAVstdStandardized CAVget_CAVdp_cyCAVdpDamage-potential CAVExampleA detailed example on Jupyter Notebooks is presentedhere.Relevant referencesArias A (1970) A measure of earthquake intensity, in Seismic Design for Nuclear Power Plants (Hansen R. J., ed.), The MIT Press, Cambridge, MA.Campbell KW and Bozorgnia Y (2011) Prediction equations for the standardized version of cumulative absolute velocity as adapted for use in the shutdown of U.S. nuclear power plants.” Nuclear Engineering and Design 241(2011): 2558–2569.EPRI (1988) A criterion for determining exceedance of the operating basis earthquake. EPRI NP-5930, Palo Alto, CA.Kramer SL and Mitchell RA (2006) Ground motion intensity measures for liquefaction hazard evaluation. Earthquake Spectra 22(2): 413–438.CitationIf you use these codes, please cite:Renmin Pretell. (2023). RPretellD/gmimtools: Initial release (0.1.0). Zenodo.https://doi.org/10.5281/zenodo.10099774
Package: gmisclib
Various Python Libraries, mostly scientific purposes.Seehttp://kochanski.plus.com/code/speechresearch/gmisclibfor documentation
andhttp://sourceforge.net/projects/speechresearchfor downloads,
or look on the Python Cheese shop,http://pypi.python.org.Note that most of the algorithms here are carefully tested, but the stuff that
I don’t use may have suffered from bit rot.
Package: gmi-utils
GetMoney Inc. Utilsgmi_exceptionExceptions caused by GMI must be instances ofGmiException:# using plain GmiExceptionifis_something_bad_happened:raiseGmiException('something bad happened!')# using child of GmiExceptionclassMyGmiException(GmiException):# your code...passifis_something_bad_happened_inside_lib:raiseMyGmiException('something bad happened inside my lib!')intervalcall_repeatedlystop_func = call_repeatedly(timeout, func, *args)starts callingfunc(*args)everytimeoutseconds in different thread. It also
returns func that will stop this behaviour.defbla_bla(last_bla:str):print(f"bla bla{last_bla}")stop_func=call_repeatedly(1.,bla_bla,"BLA!")time.sleep(2.5)stop_func()# will print:# bla bla BLA!# bla bla BLA!
Package: gmix2
Overviewgmix2is a reimplementation ofgmixthat provides functions for working with univariateGaussian Mixture distributionssimilar todmixnormof theKScorrectR package.
Unlike gmix, gmix2 does not include the code and recipes for implementing conditional density implementation using neural networks.
This reduces the number of external dependencies and complexity of the project considerably (e.g., no need for TensorFlow).
gmix2 is entirely written in C++ (and provides python bindings), so it is also slightly faster than gmix in most cases, and considerably faster in a a few cases[1].
It also provides a few bug fixes and the plotting function has been improved.[1]Most of the functions in gmix were written using numpy so it was already relatively efficient.DependenciesC++A recent C++ compiler (gcc 9.3 is known to work)CMake>= 3.16Python development libraries (for Python.h)Eigen3PythonOnly python3 is supported.RequirednumpyNeeded For PlottingscipypandasplotninepycairoMatplotlib may complain if this is not installed separately.PyQt5You may need to install a rending package for interactive plots.InstallNote, this package is only available as a source distribution.
So, you must have a working C++ compiler and the required libraries listed above installed in a location visible to CMake.From Git:>pipinstallgit+https://bitbucket.org/reidswanson/gmix2.gitOr on PyPI:>pipinstallgmix2C++ LibraryThe base C++ code is also available as aheader only library, but no additional documentation is provided for using it.DocumentationThe full documentation is available onRead the Docs.LicenseAny file without a specific copyright notification is released using the Apache v2 License by me (Reid Swanson).
See theLICENSEfile for more details.
Some code has been copied or adapted from the web (e.g., StackOverflow) that does not have clear license indications.
In those cases the code is flagged with comments and links to where the code was found.
Package: gmlc-tdc-sphinxcontrib-redoc
Fork (temporary?) of sphinxcontrib-redoc that adds support for Swagger
specs that use include files with relative paths.TheSphinxextension that rendersOpenAPI(fka Swagger) specs with love
(❤️) using amazingReDoc. Don’t believe it? Here’sthe proof. Now stop
frustrating and start moving your projects to OpenAPI and ReDoc with this
small first step:$pipinstallgmlc-tdc-sphinxcontrib-redocFree software: BSD LicenseDocumentation:https://sphinxcontrib-redoc.readthedocs.ioSource:https://github.com/gmlc-tdc/redocBugs:https://github.com/gmlc-tdc/redoc/issuesAny feedback, any contribution is really appreciated!
Package: gm-libs
掘金量化的简化函数包（第三方）个人自用包，出问题了欢迎提 Issue。功能列表A 股获取信息获取所有指数（股票市场、当前有效）: get_all_index获取所有股票（股票市场、当前有效）: get_all_security获取龙虎榜股票列表: get_dragon_tiger_list执行交易买指定数量的股票:buy_count(stock: str, count: int, price: float)卖指定数量的股票:sell_count(stock: str, count: int, price: float)调整仓位（到特定数量）:order_target_count(stock: str, volume: int, price: float)调整仓位（到特定价值）:order_target_money(stock: str, worth: int, price: float)统计模块【必须】进行初始化：l_static_init(context)执行某个统计：将schedule(statistic_func, date_rule="1d", time_rule="07:00:00")放在初始化区域，其中statistic_func是你期望进行的统计项。月度统计：近期净值变动:l_static_month_rate(context)日度统计：回撤区间统计：l_static_day_back(context)，到达历史最高净值后回落，开始计算回撤区间，下次突破最高净值时，记“起点、终点、区间最大回撤比例”为一次回撤区间记录日志日志输出且保存:log_all(level: str, info: str, source: str = "", filetype: str = "")日志不输出仅保存:log_save(level: str, info: str, source: str = "", filetype: str = "")日志输出不保存，gm.print即可获取日志地址:l_get_log_path()打包更新pyproject.toml文件执行./build.sh输入 API token旧的打包方式：更新pyproject.toml文件执行python -m build执行python -m twine upload dist/*对输入框，输入账号:__token__并回车最后输入 API token 即可
Package: gmlp
Genetic Machine Learning for PythonGMLP orGenetic Machine Learning for Python, is a user friendly python machine learning package. GMLP is intuitive and can be used for lots of Machine Learning Projects.What is GMLP?GMLP helps you with your genetic programming! You can turn90lines of code into18 lines of code!ExamplesGMLP can be used for evolutionary neural networks, and genetic programming!
Here are some Examples:# GMLP Example 1. - Phrase Guesser.# This Evolutionary Neural Network will have you put in a phrase and the E.N.N will guess it.importrandomimportgmlpasgp# Importing our modules.# Our characters that we will be using.characters='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ,.?!: '# We make our characters into a list.characters=list(characters)# The phrase.phrase="Hello World!"# Making the phrase into numbers by using ord() so working with the phrase will be easier.ord_phrase=[ord(target)fortargetinphrase]# Having the population generate random letters and turn them into numbers using the ord() function.starting_population=[[ord(random.choice(characters))forgeneinrange(len(ord_phrase))]forpop_sizeinrange(10000)]print("Our goal phrase is:",phrase)# Setting up the enviroment.env=gp.Enviroment(ord_phrase,.9)# Our population.population=env.generate_population(settings=starting_population)# Our fitnesses.scores=gp.calculate_fitness(population,env.goal)# Our maximum number of generations.max_generations=500forgenerationinrange(max_generations):# Calculating the scores for the population.scores=gp.calculate_fitness(population,env.goal)# The best score is the minimum of the scores because the scores are how far you are away from your goal.best=min(scores)# getting the score index of the best score.best_score=scores[scores.index(best)]# the population organism of the best score index.Output=population[scores.index(best)]print("Generation:%1s, Best Score:%2s, Output->%3s"%(generation,str(best_score),''.join([chr(c)forcinOutput])))# Our new population is the mutated population of the crossover of the fittest population .population=gp.ValueEncodingMut(env.crossover(env.tournament_selection(population,scores,3),ord_phrase),.15)# If we are 0% away from our goal we print the output and phrase and break the loop.ifmin(scores)==0:print(f'Our Output ->{"".join([chr(c)forcinOutput])}')print(f'Our Phrase ->{phrase}')breakGameThis Feature is new to GMLP and is still in progress
Package: gmlsnets-pytorch
PyTorch implementation of GMLS-Nets.If you find these codes or methods helpful for your project, please cite:@article{trask_patel_gross_atzberger_GMLS_Nets_2019,
    title={GMLS-Nets: A framework for learning from unstructured data},
    author={Nathaniel Trask, Ravi G. Patel, Ben J. Gross, Paul J. Atzberger},
    journal={arXiv:1909.05371},  
    month={September}
    year={2019}
    url={https://arxiv.org/abs/1909.05371}
  }Source code and additional information for this package available athttps://github.com/atzberg/gmls-nets.
Package: gmltools
# gmltoolsMachine Learning library aiming for a higher level programming, organizing best tools`bash $ pip install gmltools `## UsageTODO## ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.## Licensegmltutilswas created by Diego Sanz-Gadea Sánchez. It is licensed under the terms of the GNU General Public License v3.0 license.## Creditsgmltutilswas created with [cookiecutter](https://cookiecutter.readthedocs.io/en/latest/) and thepy-pkgs-cookiecutter[template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
Package: gmms
Ground Motion Models (GMMs)This repository provides a ground motion models (GMMs) and supporting tools. The models are listed below, and the tools include codes for computing the distances required in GMMs.Ground motion modelGround motion intensity measureCampbell & Bozorgnia (2010)Geometric mean horizontal standardized cumulative absolute velocity (CAVgm)Campbell & Bozorgnia (2011)Damage-potential cumulative absolute velocity (CAVdp)Campbell & Bozorgnia (2019)Arias intensity (Ia)Campbell & Bozorgnia (2019)Cumulative absolute velocity (CAV)Foulser-Piggott & Goda (2015)Arias Intensity (Ia)Foulser-Piggott & Goda (2015)Cumulative absolute velocity (CAV)ExampleThree examples on Jupyter Notebooks are presented:Use of Campbell and Bozorgnia models for the 1989 Loma Prieta Earthquake (single-segment fault)here. En españolaquí.Use of Campbell and Bozorgnia models for the 2023 Pazarcik Earthquake (multi-segment fault)here. En españolaquí.Use of the Foulser-Piggott and Goda models for the 2003 Tokachi Earthquakehere. En españolaquí.AcknowledgementsThe cython codes for the estimation of Joyner-Boore and rupture distances are based on Pengfei Wang'sR implementations.ReferencesCampbell KW and Bozorgnia Y (2010) A ground motion prediction equation for the horizontal component of cumulative absolute velocity (CAV) based on the PEER-NGA strong motion database. Earthquake Spectra 26(3): 635–650.Campbell KW and Bozorgnia Y (2011) Prediction equations for the standardized version of cumulative absolute velocity as adapted for use in the shutdown of U.S. nuclear power plants. Nuclear Engineering and Design 241(2011): 2558–2569.Campbell KW and Bozorgnia Y (2019) Ground motion models for the horizontal components of Arias intensity (AI) and cumulative absolute velocity (CAV) using the NGA-West2 Database. Earthquake Spectra 35(3): 1289–1310.Foulser‐Piggott R and Goda K (2015) Ground‐motion prediction models for Arias intensity and cumulative absolute velocity for Japanese earthquakes considering single‐station sigma and within‐event spatial correlation. Bulletin of the Seismological Society of America 105 (4): 1903–1918.CitationIf you use these codes, please cite:Renmin Pretell. (2023). RPretellD/gmms (0.1.1). Zenodo.http://doi.org/10.5281/zenodo.10397816
Package: gmn
Generative Manifold Networks (GMN)Generative Manifold Networks is a generalization of nonlinear dynamical systems from a single state-space with a manifold operator, to an interconnected network of operators on the state-space(s) introduced byPao et al.GMN is developed at theBiological Nonlinear Dynamics Data Science Unit, OISTInstallationPython Package Index (PyPI)gmn.pip install gmnDocumentationGMN documentation.UsageExample usage at the python prompt in directorygmn:>>>importgmn>>>G=gmn.GMN(configFile='./config/default.cfg')>>>G.Generate()>>>G.DataOut.tail()TimeABCDOut295996-2.487000e-010.927389-0.50180.383759-0.902106296997-1.874000e-010.973968-0.47080.471114-0.961839297998-1.253000e-010.989932-0.42480.540129-0.989022298999-6.280002e-020.984369-0.36710.591274-0.9866312991000-2.438686e-080.957464-0.30160.624011-0.951023ReferencesExperimentally testable whole brain manifolds that recapitulate behavior
Package: gmn-python-api
gmn-python-apiThis library provides a Python API for accessing openGlobal Meteor Network(GMN) meteor trajectorydata. Global meteor data is generated using a
network of low-light cameras pointed towards the night sky. Meteor properties (radiants,
orbits, magnitudes and masses) are produced by the GMN and are available through this
library.FeaturesListing available daily and monthly meteor trajectory files from theGMN Data Directory.Downloading specific meteor trajectory data from the GMN Data Directory or GMN REST
API.Functions for loading meteor trajectory data intoPandasDataFrames.Functions for retrieving availableIAUregistered meteor
showers.RequirementsPython 3.8, 3.9, or 3.10InstallationYou can installgmn-python-apiviapipfromPyPI:pipinstallgmn-python-apiOr install the latest development code, throughTestPyPIor directly fromGitHubviapip:pipinstall-ihttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simplegmn-python-api==<version>Orpipinstallgit+https://github.com/rickybassom/gmn-python-apiRefer to theTroubleshootingguide if you encounter any issues.UsageSimple meteor analysis example:fromgmn_python_apiimportdata_directoryasddfromgmn_python_apiimportmeteor_trajectory_reader# Analyse recorded meteor data for the 24th of July 2019traj_file_content=dd.get_daily_file_content_by_date("2019-07-24")# Read data as a Pandas DataFrametraj_df=meteor_trajectory_reader.read_data(traj_file_content)print(f"{traj_df['Vgeo (km/s)'].max()}km/s was the fastest geostationary velocity")# Output: 65.38499 km/s was the fastest geostationary velocityprint(f"{traj_df.loc[traj_df['IAU (code)']=='PER'].shape[0]}Perseid meteors")# Output: 3 Perseid meteorsprint(f"Station #{traj_df['Num (stat)'].mode().values[0]}recorded the most meteors")# Output: Station #2 recorded the most meteorsPlease see theUsageandAPI Referencesections for more details.ContributingContributions are very welcome. To learn more, see theContributing guide.LicenseDistributed under the terms of theMITlicense,gmn-python-apiis free and open source software.