Saravanan666/pypi-org huggingface dataset:
Package: gaussian-step
SEAMM Gaussian Plug-inA SEAMM plug-in for A SEAMM plug-in for GaussianFree software: BSD-3-ClauseDocumentation:https://molssi-seamm.github.io/gaussian_step/index.htmlCode:https://github.com/molssi-seamm/gaussian_stepFeaturesPlease edit this section!AcknowledgementsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award ACI-1547580History2024.1.19: Switched to new way to run Gaussian, added option to just write input fileSwitched to using the new way to run executables, which supports containers.Added an option to just write the input file, without running
Gaussian. This is useful for debugging, and for running Gaussian
on a remote server.2023.10.25 Bug fixes: variable for functional, and parsing FChk fileFixed a problem with handling the functional if it was a variable rather than a
specific functional.Fixed a problem parsing the FChk file. For exponents > 99 the FORTRAN format used
in Gaussian grops the “E”, resulting in numbers like 0.947-104 that caused a
problem when trying to read them.2023.10.22 Bug fixes: orbital plots and outputThe plots of the HOMO and LUMO were shifted by one orbital due to some code
counting from 1 and other, from 0. Sigh.The output to Job.out was inadvertently truncated.2023.10.7 Added structure file for plots of density and orbitals.Always write the current structure as ‘structure.sdf’ in the directory where the
cube files for orbitals and densities are written. The Dashboard picks up this
file to render the structure along with the surfaces.2023.9.27 Added composite and other methods, DFT functionalsNow support HF, DFT, MP4, CCD & CCSD, CBS-x, and Gn methodsAdded PBE, PBE-98, PBE0, and HSE06 functionalsAdded analysis of HOMO/LUMO gap energyAdded plotting of orbitals and densitiesAdded otuput of atomic charges and spins, and placing them on the configuration.Added ability to control the system/configuration update2023.2.26.1 Moved Gaussian output to output.txtCapturing stdout prevent users from seeing the output during a calculation.
This fixes that.2023.2.26 Initial version with energy and optimizationSupport running the energy or optimization with HF, DFT, MP2 and MP3 though
testing has not yet been thorough.The DFT functional supported are at the moment limited.2023.2.24Plug-in created using the SEAMM plug-in cookiecutter.
Package: gaussian-suite
gaussian_suitegaussian_suiteis a python package for working with Gaussian input and output filesInstallation viapipInstallgaussian_suiteusingpip(if using a shared machine, add the--userargument afterinstall)pip install gaussian_suiteUpdatingUpdate the code usingpip(if using a shared machine, add the--userargument afterinstall)pip install gaussian_suite --upgradeInstallation withpipeditable installOnly do this if you are developing (i.e. changing) the code.Clone a copy of this repository, preferably while within a directory called gitmkdir -p git; cd git
git clone https://gitlab.com/chilton-group/gaussian_suiteNavigate to the package directorycd gaussian_suite/packagesand install the package in editable mode (if using a shared machine, add the--userargument afterinstall)pip install -e .When you're done developing (i.e. your changes have been merged to the master),
or if you just want to use the current release version of the package, uninstall usingpippip uninstall gaussian_suiteand follow the Installation viapipinstructions above.Building a.whlfile (Advanced)Only do this if you are told to.To build a copy of thegaussian_suite.whlfile, move to thepackagedirectory.Now run./build_binaries.shThen install the.whlfile withpip(if using a shared machine, add the--userargument afterinstall)pip install dist/*.whlDocumentationThedocumentationfor this package is hosted by gitlab, and is automatically generated whenever new code is committed to themainbranch. The automatic generation of this documentation relies on a common layout for comments and docstrings within the code, seecontributingfor more information.DevelopmentBefore making changes to this repository, please follow the steps outlined in theChilton group wiki.BugsIf you believe you have a bug,please check that you are using the most up to date version of the code.If that does not fix the problem, please create an issue on GitLab detailing the following:The commands you enteredThe error messageRemember to simplify the problem as much as possible in order to provide a minimum working example, e.g. an example for a small molecule rather than one with 100000 atoms.Then, look at the code, try and figure out what you think is wrong if possible, and include this in your issue.
Package: gaussian-theorems
Gaussian TheoremGaussian Theorem: A Python Package for Gaussian and Binomial DistributionsGAUSSIAN THEOREMTable of ContentsIntroductionFeaturesInstallationFrom PyPIFrom SourceUsageProject StructureExamplesTestingContributingLicenseAcknowledgmentsContactIntroductionGaussian Theorem is a Python package that provides classes for working with Gaussian and Binomial distributions. This package is designed to make it easy for developers and data scientists to perform calculations, visualize distributions, and integrate statistical functionality into their projects.FeaturesGaussian Distribution:Calculate mean and standard deviation.Generate probability density function (PDF) values.Perform distribution addition.Binomial Distribution:Calculate mean and standard deviation from given probabilities and sample sizes.Visualize distributions with histograms.Plot PDFs for Binomial distributions.InstallationFrom PyPIYou can install the package directly from PyPI usingpip. Make sure you have Python 3.x installed on your machine.pipinstallgaussian_theoremsFrom SourceIf you prefer installing from the source, follow these steps:1. Clone the repository:gitclonehttps://github.com/Dharren09/Gaussian_Theorem.gitcdGaussian_Theorem2. Install the required dependencies:pipinstall-rrequirements.txt3. Install the Package:pythonsetup.pyinstallUsage# Example code demonstrating how to use the Gaussian classfromGaussian_Theorem.gaussiandistributionimportGaussian# Create a Gaussian distribution instancegaussian=Gaussian(25,4)# Read data from a file (replace 'data.txt' with your data file)gaussian.read_data_file('datasets/numbers.txt')# Calculate mean and standard deviationmean=gaussian.calculate_mean()stdev=gaussian.calculate_stdev()# Print the Gaussian distribution characteristicsprint(gaussian)# Plot histogram and PDFgaussian.plot_histogram_pdf()Project StructureThe project is structured as follows:GAUSSIAN_THEOREM/|--Gaussian_Theorem/||--__init__.py||--gaussiandistribution.py||--generaldistribution.py|--Binomialdistributions/||--__init__.py||--binomialdistributions.py|--tests/||--__init__.py||--tests.py|--setup.py|--License|--CODE_OF_CONDUCT.md|--contribution_guidelines.md|--requirements.txt|--datasets/||--numbers.txt||--numbers_binomial.txt|--buildTestingTo run tests, execute the following command:python-munittesttests.testsContributingWe welcome contributions! If you find a bug or have an enhancement idea, please open an issue or submit a pull request. Please check ourContribution Guidelinesfor details.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.AcknowledgmentsThe project is inspired by the need for a simple yet powerful Python package for working with statistical distributions. Special thanks to the Matplotlib, Scikit-learn, Scipy teams for their excellent libraries Plus Udacity Data Science instructors for their immersive contributions towards my knowlegde base.ContactFor any questions or feedback, feel free to reach out:Email:dharrenpius@outlook.comTwitter: @iamdevdharrenzugGitHub: Dharren09
Package: gaussquality
This is a tool that fitsGaussian Mixture Modelsto grey value distributions from X-ray micro-computed tomography images. This tool was developed as part of a PhD project at theUniversity of Southamptonin the3-D X-ray histology team.How it worksGaussQuality fits a 1-D Gaussian Mixture Model (GMM) to the grey value distribution of a 2D X-ray micro-computed tomography (μCT) image. The GMM fits a specified number of Gaussian components to the grey value distribution, where each component represents the distribution of grey values for a single material in the specimen. The mean (μ), standard deviation (σ), and weight (φ) of each Gaussian component are estimated.μ is the location of the Gaussian along the grey value axis, dependent on X-ray interactions with the materialσ is its spread which is dependent on heterogeneity of the material and image noiseφ is the proportion of the image taken up by this materialμ and σ can be used to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).μ, σ, and φ can be estimated for several 2D images in a 3D stack, showing how the image varies in 3D. Only one 2D image is held in memory at once, so images larger than memory can be processed. 3D images must be saved as sequences of 2D images.InstallationGUIDownload the .exe for Windows or .app for MacOS.Simply double click the executable and the GUI should start. If you have any trouble, try opening the executable in a terminal to debug any errors.Python libraryGaussQuality is available onconda. In the terminal:conda activate <myenv>
conda install gaussqualityExample usageSeeGaussQuality_Example.ipynbfor a demo, and explanation of what each parameter controls.CitationAPI DocsTheAPI docs are available here.
Package: gauss-rank-scaler
Gauss Rank ScalerA scikit-learn style transformer that scales numeric variables to normal distributions.Input normalization for neural networks is very important. Gauss Rank is an effective algorithm for converting numeric variable distributions to normals. It is based on rank transformation. The first step is to assign a spacing between -1 and 1 to the sorted features, then apply the inverse of error functionerfinvto make it look like a Gaussian.This generally works much better than Standard or Min Max Scaler.Important LinksInterview of the Kaggle competition winner (Michael Jahrer)Blog post introducing Gauss Rank's concept and simple implementation (Zygmunt Zając)UsageGauss Rank Scaler is a fully compatible sklearn transformer that can be used in pipelines or existing scripts. Supported input formats include numpy arrays and pandas dataframes. All columns passed to the transformer are properly scaled.Examplefromgauss_rank_scalerimportGaussRankScalerimportpandasaspdfromsklearn.datasetsimportload_boston%matplotlibinline# prepare some databunch=load_boston()df_X_train=pd.DataFrame(bunch.data[:250],columns=bunch.feature_names)df_X_test=pd.DataFrame(bunch.data[250:],columns=bunch.feature_names)# plot histograms of two numeric variables_=df_X_train[['CRIM','DIS']].hist()# scale the numeric variables with Gauss Rank Scalerscaler=GaussRankScaler()df_X_new_train=scaler.fit_transform(df_X_train[['CRIM','DIS']])# plot histograms of the scaled variables_=pd.DataFrame(df_X_new_train,columns=['CRIM','DIS']).hist()# scale test dataset with the fitted scalerdf_X_new_test=scaler.transform(df_X_test[['CRIM','DIS']])This is a direct copy of the repositopry by Aldente06030 packaged for pip(https://github.com/aldente0630/gauss-rank-scaler)
Package: gau-tensorflow
GAU - TensorFlowGated Attention Unit (TensorFlow implementation)Originally from the paperTransformer Quality in Linear TimeREPO UNDER DEVELOPMENTCitations@article{Hua2022TransformerQI,title={Transformer Quality in Linear Time},author={Weizhe Hua and Zihang Dai and Hanxiao Liu and Quoc V. Le},journal={ArXiv},year={2022},volume={abs/2202.10447}}
Package: gauth
GAuthCommand Line tool to migrate Google Authenticator from phone to desktop.How to use itpip install gauthand check the help usinggauth -h.Installation of optional shared system librariesFor reading QR codes withZBARQR reader, the zbar library must be installed.
If you do not use theZBARQR reader, you do not need to install the zbar shared library. Note: TheZBARQR reader is the showed for me the best results and is thus default QR Reader.For a detailed installation documentation ofpyzbar.Linux (Debian, Ubuntu, …)sudo apt-get install libzbar0Linux (OpenSUSE)sudo zypper install libzbar0Linux (Fedora)sudo dnf install zbarLinux (Arch Linux)pacman -S zbarMac OS Xbrew install zbarWindowszbarThe zbar DLLs are included with the Windows Python wheels. However, you might need additionally to installVisual C++ Redistributable Packages for Visual Studio 2013. Installvcredist_x64.exeif using 64-bit Python,vcredist_x86.exeif using 32-bit Python. For more information seepyzbarReleasepip install --upgrade twine
python setup.py sdist
twine upload dist/*YAML example---
keys:
  - Name:    TEST1
    Secret:  1234567
    Issuer:  ABC
    Type:    totp

  - Name:    TEST2
    Secret:  1234455
    Issuer:  CDE
    Type:    totpReferenceextract_otp_secrets v2.4.3
Package: gauth-python
GAuth-Python단 하나의 계정으로 모든 GSM의 서비스를 이용할 수 있게.___           ___           ___                       ___     
     /  /\         /  /\         /__/\          ___        /__/\    
    /  /:/_       /  /::\        \  \:\        /  /\       \  \:\   
   /  /:/ /\     /  /:/\:\        \  \:\      /  /:/        \__\:\  
  /  /:/_/::\   /  /:/ /::\   ___  \  \:\    /  /:/     ___ /  /::\ 
 /__/:/__\/\:\ /__/:/ /:/\:\ /__/\  \__\:\  /  /::\    /__/\  /:/\:\
 \  \:\    /:/ \  \:\/:/__\/ \  \:\ /  /:/ /__/:/\:\   \  \:\/:/__\/
  \  \:\  /:/   \  \::/       \  \:\  /:/  \__\/  \:\   \  \::/     
   \  \:\/:/     \  \:\        \  \:\/:/        \  \:\   \  \:\     
    \  \::/       \  \:\        \  \::/          \__\/    \  \:\    
     \__\/         \__\/         \__\/                     \__\/GAuth는 여러 프로젝트에서 편하게 로그인을 구현할 수 있고 사용자들이 새로 계정을 만들 필요가 없어 접근성을 더욱 쉽게 만들어줄 수 있는 서비스 입니다.Installation// pip3 install gauth-python
// pip install gauth-pythonIssueGit :GitHub - GSM-MSG/GAuth-SDK-PythonDiscord : dev_yohan05How to useCode 발급fromgauth_python.requestsimportcode_issuancecode_issuance(email=YOUR_EMAIL,password=YOUR_PASSWORD)token 발급fromgauth_python.requestsimporttoken_issuancetoken_issuance(code=YOUR_CODE,clientId=YOUR_CLIENT_ID,clientSecret=YOUR_CLIENT_SECRET,redirectUri=YOUR_REDIRECT_URL)token 재발급fromgauth_python.requestsimporttoken_reissuancetoken_reissuance(refreshToken=YOUR_REFRESH_TOKEN)유저 정보 가져오기fromgauth_python.requestsimportuser_infouser_info(accessToken=YOUR_ACCESS_TOKEN)
Package: ga-utils
通过GA协议获取数据，用于调试GA控件树，示例：ga-utilsget 300--port2004##安装$ pip installga-utils##使用ga-utilsget CMD--portPORT##示例获取控件树：ga-utilsget 300--port2004获取控件的位置信息：ga-utilsget 103--port2004##说明port为GA服务的本地端口号，可用adb forward命令到本地。示例：adb forward tcp:2004 tcp:27019。请求的日志会存在当前目录的ga-utils.yyyy-MM-DD.log。
Package: gautodoc
gautodocstupid simple autodoc.how do I use your stupid tool?gautodoc operates by looking at the 'roots' of your python project and
recursively searching for imported modules that are part of your project
directory.if you just want to document a script with some imports, you can just:gautodocinityour-script.pythis will create .gautodoc in your project folder.gautodoc initcan also be
used to reconfigure your project if you need it to.once.gautodocexists, you can run:gautodocbuildand adoc/folder containing a simple static web app will appear!
Package: gautomator2
GA2 (Android/iOS)Why GAutomator2便于发布和维护。作为库发布，有真正意义上的版本。用户不需要带着一堆源码上传平台测试。跨平台。接口统一iOS与android，也方便扩展其他平台。可扩展。用户能够扩展常用功能的自定义实现而不用修改库源码。增加UI定位方法。在原先GA引擎接口的基础上，增加平台原生控件以及模板图像定位支持。支持UI信息与逻辑分离。通过将UI元素定义在配置文件，脚本开发人员不需要关注UI元素的定位方式框架结构使用说明支持平台Android（Unity+UE4)iOS(Unity for now)脚本语言：python2.7/3.4+安装（公司内网可能需要设置代理）基础包： pip install gautomator2包含辅助包：pip install gautomator2-contrib环境准备android自动化需要PC安装adb并配置在环境变量中。iOS自动化需要Mac OS(10.13.6+),安装libimobiledevice，并且先在目标iOS设备上启动WebDriverAgent，并通过iproxy xxxx 8100做PC到设备的端口转发。使用引擎交互需要游戏集成GA SDK辅助工具针对android的游戏引擎控件获取工图像具GAutomatorView针对iOS的引擎控件录制工具GA Recorder接口文档详见docs
Package: gautomator2-contrib
GA2 (Android/iOS)Why GAutomator2便于发布和维护。作为库发布，有真正意义上的版本。用户不需要带着一堆源码上传平台测试。跨平台。接口统一iOS与android，也方便扩展其他平台。可扩展。用户能够扩展常用功能的自定义实现而不用修改库源码。增加UI定位方法。在原先GA引擎接口的基础上，增加平台原生控件以及模板图像定位支持。支持UI信息与逻辑分离。通过将UI元素定义在配置文件，脚本开发人员不需要关注UI元素的定位方式框架结构使用说明支持平台Android（Unity+UE4)iOS(Unity for now)脚本语言：python2.7/3.4+安装（公司内网可能需要设置代理）基础包： pip install gautomator2包含辅助包：pip install gautomator2-contrib环境准备android自动化需要PC安装adb并配置在环境变量中。iOS自动化需要Mac OS(10.13.6+),安装libimobiledevice，并且先在目标iOS设备上启动WebDriverAgent，并通过iproxy xxxx 8100做PC到设备的端口转发。使用引擎交互需要游戏集成GA SDK辅助工具针对android的游戏引擎控件获取工图像具GAutomatorView针对iOS的引擎控件录制工具GA Recorder接口文档详见docs
Package: gauto-profiler
AndroidGameProfiler中文文档A python library that captures the three basic capabilities of a game on Android -- Total PSS (physical memory used by applications, including proportionally Shared library memory), CPU usage, and FPS (frame rate).The above data are all obtained by adb tool.Installpipinstallgauto-profilerUsageimportosfromgauto_profilerimportprofilerfromgauto_profilerimportconfigos.chdir(os.path.dirname(os.path.abspath(__file__)))config.setup(serial="5366a3d3",output_path="output",html_save_path="output/render.html")profiler.run(300)
Package: gavel
A toolset for prover independent premise selection. Template generated with cookiecutter-pylibrary.Installationpip install gavelDocumentationhttps://gavel.readthedocs.io/DevelopmentTo run the all 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 (2019-02-19)First release on PyPI.
Package: ga-vqc
GA for VQC Ansatz SearchThis is a module to support Variational Quantum Circuits by optimizing the ansatz. The ansatz optimization is performed using a Genetic Algorithm, which can be parallelized with GPUs.For a detailed example of using this package see thehttps://github.com/tcoulvert/QAE_4_HEPrepository.InstallationRun the following to install:$pipinstallga-vqcContributorsThis module was developed through the Caltech SURF program. Special thanks
to my mentor at Caltech.Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA)Usageimportga_vqcasgav## Config (hyperparameters) for GA, see full list in example ##vqc_main=<'Function that handles running your VQC optimization'># Example of allowed optimization gates, see Genepool.py for documentationgates_dict={"I":(1,0),"RX":(1,1),"CNOT":(2,0)}gates_probs=[0.35,0.35,0.3]genepool=gav.Genepool(gates_dict,gates_probs)vqc_config={'num_qubits':3,'etc':<'whatever config params your VQC model requires'>}ga_output_path=FILEPATH_FOR_GA_OUTPUTconfig=gav.Config(vqc_main,vqc_config,genepool,ga_output_path)# Create the GA with the given hyperparametersga=gav.setup(config)# Evolve the GA and search for the best ansatzga.evolve()
Package: gavtk
GAVTK - Graph Analysis & Visualization ToolKitGAVTK is an Open Source python library for financial data / graphs Analysis and thier VisualizationFilesGeneraldistribution.py- General Boiler Plate Class File for distributions (includes Parent Class)Binomialdistribution.py- Binomial Distributions Class File (includes all the methods used for Binomially distributed data)Gaussiandistribution.py- Gaussian Distributions Class Files (includes all the methods used for Normally distributed data)init.py- Helper File for name conversionRequirementspython >= "3.6"tkinter >= "8.6"matplotlib >= "3.2.1"InstallationCreate Virtual EnvironmentInstall all the required Files first using pipInstall GAVTK using pippip3installgavtkTry It OutSAMPLE>>>fromgavtkimportGaussian,Binomial>>>g1=Gaussian(10,7)>>>g1.mean10>>>g1.stdev7>>>g1mean10,standarddeviation7>>>b1=Binomial(.4,25)>>>b1mean10.0,standarddeviation2.449489742783178,p0.4,n25
Package: gawseed-processing
OverviewThe GAWSEED processing system allows easy statistical analysis of
time-series data via simple YAML configuration files.  This
architecture:Collect information from data sourcesTransform it to a standardized tab-separated format (called [FSDB])Temporally bin, count and tabulate features within the dataset, producing a triple indexed outputManipulate individual results using aggregation functionsUse extensible mathematical functions to combine multi-column datasets into final temporal waveform that are suitable for feature detectionPerform simple, distributed edge-detection to identify signals of interest.ArchitectureIncoming Data Source and FilterData FormatterFeature CounterTheFeature Countercomponent is responsible for converting all
timeseries records into ``time bins'' and simultaneously counting or
quantifying the data.  The output records will consist of namedindexkeywords extracted from the YAML configuration, and akeyandsubkeyvalue that is extracted from the data
itself (if the second subkey column is not needed, an empty string is
typically used).Example feature counter yaml config:binSize: 5
timeColumn: time
featureCounter:
  outputs:
    http_host_names:
      function: gawseed.algorithm.generic.re_match_one
      arguments: ['col(dnsname)', '.*https?://(^[/]+)/.*']
    http_type:
      function: gawseed.algorithm.generic.re_match_one
      arguments: ['col(dnsname)', '(https:|http:)']AggregatorThe aggregator component of the GAWSEED architecture is responsible
for performing any calculations that require a complete dataset for a
given combination of the set<timebin, index, key>.  Summing
values from distributed operations and counting of unique subkeys are
the most common computations that occur at this step.An example YAML configuration for this step in the data architecture
is shown in below.  This outputs both the total counts of the things
counted in the featureCounter step(s), and counts the number of uniquehttp_host_namesvalues seen as well.Example YAML configuration for the aggregator:aggregator:
  aggregators:
    - function: summer
    - function: unique
      arguments: ['http_host_names', 'unique_http_host_names']Relationship AnalyzerThe Relationship Analyzer is where any heavy final math is performed.
For example, in the example data in this document, we may want to
calculate the ``popularity'' of each discoveredhttp_host_names.
The YAML configuration to do this is shown below:relationshipAnalysis:
  outputs:
    host_name_popularity_fraction:
      function: fraction_otherindex_keyval
      arguments:
        - unique_http_host_names
        - http_host_namesNote that the Relationship Analyzer also pivots
the table back to a more normal looking table (at least for humans),
as the distributed processing aspects of the calculations are now
over; specifically, the previous index values are converted back into
table columns.Edge Detection and Result AnalysisTBDCopyright
Package: gawseed-threat-feed-tools
OverviewThe gawseed-threat-feed-tools package provides a mechanism that binds
together:A threat feed source that returns a list of "threats"A data source, that returns rows of data to search through for the threatsA searcher that can bind the two together, looking for threats/data
that meet particular criteriaA list of "enrichers" that can take the results of any matches and
gather additional context to pass to the ....A report generator that can take the results of everything and
print/save the resultsUsageTypical usage would be runningthreat-feed.pyand loading a YAML
configuration file (passed to the-yswitch) to bind the above
modules together.  Seetheat-feed.py --config-templatesfor a
selection of YAML configuration templates to use when creating config
files.Example configurationComing soon...
Package: gawsoft-api-client
OverallRest API client for PythonSimple REST API client for PythonInstallationUse the package manager pip to install our client in Python.pipinstallgawsoft-api-clientORpip3installgawsoft-api-clientUsageWrite your client apifromgawsoft.api_clientimportRequest,ResponseclassClient(Request):def__init__(self,api_key:str,api_version:str='',api_host:str='http://httpbin.org',user_agent:str='Example Api Python client'):super().__init__(api_key,api_version,api_host,user_agent)definfo(self,link:str,params:dict={})->Response:returnself.request(link,'POST',params)c=Client("abc")response=c.info("delay/1")print(response.status_code)print(response.data())TestsmaketestmakemypyRelease# Release in test envmakerelease-test# Real releasemakereleaseLicenseMIT