Saravanan666/pypi-org huggingface dataset:
Package: glit
Manages sets of git repositories.Clone all setsClone specific setFast forward all repositoriesClone and add to set in a single commandConfiguration can be stored onlineWhy is Glit is different from other git managers?Glit can handle more than one set of repositoriesLet’s say that you work on more than one project at once.
With Glit you can share the file with the set of repositories
associated with a specific project with those involved in that project.
Most other git managers only allows one set of repositories.Glit has built in support for online shared configurationYou can configure a set of repositories either in a local file or
point to a file in a git repository.
If choosing the latter storage type Glit will take care of keeping
the file in sync.Example config~/.glit/sets[work-common]folder:~/repos/work/storage:gitrepository:ssh://git.mycompany.com/project/configurationfile:~/work/common.repositories[work-team]folder:~/repos/work/storage:gitrepository:ssh://git.mycompany.com/myteam/configurationfile:~/work/myteam.repositories[private]folder:~/repos/private/storage:localfile:~/somecool.repositories~/somecool.repositories. git@github.com:vandmo/glit.git
cool/libs https://github.com/pallets/click
cool/libs https://github.com/awslabs/cfn-python-lint.gitCommandsglitclone-allglitclone-setprivateglit clone--to-setprivate--prefixvandmo/python git@github.com:vandmo/glit.gitglit configadd-locally-stored-set--folder'~/repos/private/''~/somecool.repositories'glit configadd-git-stored-set--folder'~/repos/private/'git@github.com:vandmo/glit.git 'somereallycool.repositories'
Package: glitch-python-hcl2
NoticeThis is a fork of Amplify'sPython HCL2repository. This repository went through minor changes to work better withGLITCHand as the changes concern only the structure representation to be compatible withGLITCHwe decided not to request updates for the upstream project. The two projects diverge in the representation of some structures and pushing new changes upstream doesn't make sense.Python HCL2A parser forHCL2written in Python usingLark. This parser only supports HCL2 and isn't backwards compatible
with HCL v1. It can be used to parse any HCL2 config file such as Terraform.About AmplifyAmplify builds innovative and compelling digital educational products that empower teachers and students across the
country. We have a long history as the leading innovator in K-12 education - and have been described as the best tech
company in education and the best education company in tech. While others try to shrink the learning experience into
the technology, we use technology to expand what is possible in real classrooms with real students and teachers.Learn more athttps://www.amplify.comGetting StartedPrerequisitespython-hcl2 requires Python 3.7 or higher to run.InstallingThis package can be installed usingpippip3installpython-hcl2Usageimporthcl2withopen('foo.tf','r')asfile:dict=hcl2.load(file)Building From SourceFor development,tox>=4.0.9is recommended.Running Testspython-hcl2 usestox. You will need to install tox withpip install tox.
Runningtoxwill automatically execute linters as well as the unit tests.You can also run them individually with the-eargument.For example,tox -e py37-unitwill run the unit tests for python 3.7To see all the available options, runtox -l.ReleasingTo create a new releaes go to Releases page, press 'Draft a new release', create a tag
with a version you want to be released, fill the release notes and press 'Publish release'.
Github actions will take care of publishing it to PyPi.Responsible DisclosureIf you have any security issue to report, contact project maintainers privately.
You can reach us atmailto:github@amplify.comContributingWe welcome pull requests! For your pull request to be accepted smoothly, we suggest that you:For any sizable change, first open a GitHub issue to discuss your idea.Create a pull request.  Explain why you want to make the change and what it’s for.We’ll try to answer any PR’s promptly.
Package: glitchtip-cli
GlitchtipcliGlitch-tip Error tracking software command-line tool in python click.PrerequisitesPlease review glitchtipAPI DocumentationPython 3.8ClickDockerFeatures Remaining - (TODO)Push to PyPiContainerized cli tool.Make upstream changes to glitchtip backend to enable api token removal feature flag.Create a reporting feature flagCreate a Jira integrationGlitchtip commandline tool local setup.Step 1.Run the following commands to set up a virtual environment.python3 -m venv env  # 'venv' is a module, 'env' is the target directory
source env/bin/activateStep 2.Install the glitchtip-cli package using pip, this will add thegtccommand to your virtual environment.pip install glitchtip-cliStep 3.Generate a Gltich-tip API Token from your Glitchtip instance. Login into your hosted or self hosted Glitchtip instance in the appropriate organization.Step 3.1Goto-->Profile-->Auth TokensStep 3.2ClickCreate New Tokenbutton and give yourAuth Tokena name and apply the appropriate permissions.Step 4.You will be prompted for the API token and your Glitch-tip instance url the first time you run the command.Example output(output may differ due to future updates to the CLI)$ gtc 
   _________ __       __    __  _     
  / ____/ (_) /______/ /_  / /_(_)___ 
 / / __/ / / __/ ___/ __ \/ __/ / __ \
/ /_/ / / / /_/ /__/ / / / /_/ / /_/ /
\____/_/_/\__/\___/_/ /_/\__/_/ .___/ 
                             /_/      

GT, Open Source Error Tracking Software! ☕ By Mark Freer

Looks like you don't have a `.env` file set up yet. Let's do it!
GlitchTip project API key: my-api-key  
GlitchTip instance url (https://glitchtip.example.net): my-gt-instance-url
Config successfully written to `.env` file.
Do you want to add `.env` to your `.gitignore` file? (y/n): y
`.gitignore` file successfully updated.

Usage: glitchtipcli.py [OPTIONS] COMMAND [ARGS]...

  A Glitch-tip Command line tool to query the Glitch-tip Error tracking
  software API.

Options:
  --help  Show this message and exit.

Commands:
  create   Create a new glitchtip organization
  delete   Delete a glitchtip organization
  list     Print the list of glitchtip organizations
  members  Print the list of a glitchtip organization's membersASCIINEMA setup demoDev workflowTBDautopep8 --in-place --aggressive --aggressive glitchtipcli.pyPlease review the glitchtip contribution guideline forGetting-started.md
Package: glitter-sdk-test
Info:Seethe glitter sitefor more information. Seeglitter-sdk-pyfor the latest source.Documentation:Available atglitter-sdk-py.readthedocs.ioAuthor:tedTable of ContentsInstallationInstalling with pipDependenciesInstalling from sourceChangelog0.0.1 (2022-04-04)Installationglitter sdk is in thePython Package Index.
This tutorial walks you through how to set upglitter-sdk-pyfor local development.The glitter Python Driver depends on:Python 3.5+A recent Python 3 version of pipA recent Python 3 version of setuptoolsInstalling with pipWe recommend usingpipto install glitter_sdk on all platforms:$python3-mpipinstallglitter_sdkTo get a specific version of glitter:$python3-mpipinstallglitter_sdk==0.1.0To upgrade using pip:$python3-mpipinstall--upgradeglitter_sdkDependenciesYou can install all dependencies automatically with the following command:$python3-mpipinstallglitter_sdk[requests]Installing from sourceIf you’d rather install directly from the source (i.e. to stay on the bleeding edge), install the C extension dependencies then check out the latest source from GitHub and install the driver from the resulting tree:$gitclonehttps://github.com/glitternetwork/glitter-sdk-pyglitter-sdk-py$cdglitter-sdk-py/$python3setup.pyinstallChangelog0.0.1 (2022-04-04)Planning release on PyPI.
Package: glmdenoise
pyGLMdenoisepython implementation of GLMdenoisefull documentationInstallationpipinstallglmdenoiseUsagePythonfromglmdenoiseimportGLMdenoisegd=GLMdenoise()gd.fit(design,data,tr=2.0)gd.plot_figures()Consoleglmdenoise/my/data/dirPublic datasetglmdenoise///workshops/nih-2017/ds000114Unit testsTo run the unit tests:pythonsetup.pytests-q
Package: glm-express
GLM-ExpressThis is a package for modeling functional neuroimaging tasks. As the name implies, it's optimized to be simple and straightforward! Thetask_information.jsonfile stores all of the regressors and modeling specifications for each task; modifying this file allows you to test a range of analytical outcomes.IncludedThis package comes equipped with the following modeling objects:Subjectis a first-level modeler for subject-specific functional neuroimaging dataGroupLevelis a second-level modeler that is optimized to aggregate contrast maps derived by theSubjectobjectAggregatorapplies first-level models to all subjects in your BIDS project (not efficient for larger datasets)RestingStatefor analyses of subject-level resting state functional connectivityAssumptionsWe assume the following about your data:Your data is in validBIDSformatYour data has been preprocessed viafmriprepYour preprocessed data are stored in aderivativesfolder nested in yourBIDSprojectYou have adequate events TSV files for all of your functional tasksAny parametric modulators are stored within each event fileOtherwise, you can build custom design matrices and feed them into the modeling functionAbouttask_information.jsonGlossary of keys in thetask_informationfile; manipulating these allow you to quickly and effectively customize your modeling parameters without editing any source codetr: Repetition time (defined here, but can be overriden for any one subject)excludes: Subjects in your project you need to exclude for a given taskcondition_identifier: Column in your events file that denotes trial type;NOTEthis will be changed totrial_typein the scriptconfound_regressors: Regressors to include fromfmriprepoutputmodulators: Parametric modulators to weight trial type (these should be in your events file)block_identifier: Column in your events file that denotes block type; defaults tonulldesign_contrasts: Your defined contrasts! Include as few or as many as you see fit
Package: glm-mda-diffusion
glm_mda_diffusionorGlobule-Linker-Model, Minimum-Dissipation-Approximation diffusion coefficient calculatorMinimum dissipation approximation is a fast algorithm for predicting the diffusive properties of intrinsically disordered proteins.Installationpython3-mpipinstallglm_mda_diffusionUsage as moduleBasic usage:python3-mglm_mda_diffusion--sequenceMGSS[HHHHHH]SSGLVPRSample output:Computed GLM-MDA hydrodynamic radius [Ang]:
12.279165209438174Usage as packageBasic usageimportglm_mda_diffusionglm_mda_diffusion.hydrodynamic_radius(sequence="MGSS[HHHHHH]SSGLVPR")Advanced usage (all options displayed with default values).Optionssteric_radiusandhydrodynamic_radiuscontroll linker properties, whileeffective_densityandhydrdation_thicknesscontroll globular region properties.importglm_mda_diffusionglm_mda_diffusion.protein_hydrodynamic_radius(sequence="MGSS[HHHHHH]SSGLVPR",steric_radius=1.9025,# Anghydrodynamic_radius=4.2,# Angeffective_density=0.52,# Da / Ang^3hydration_thickness=3.0,# Angensemble_size=30,bootstrap_rounds=10,aminoacid_masses={"A":71.08,"C":103.14,"D":115.09,"E":129.12,"F":147.18,"G":57.06,"H":137.15,"I":113.17,"K":128.18,"L":113.17,"M":131.21,"N":114.11,"P":97.12,"Q":128.41,"R":156.2,"S":87.08,"T":101.11,"V":99.14,"W":186.21,"Y":163.18,"Z":0,"O":0,"U":0,"J":0,"X":0,"B":0,},# Da,)LicenseThis software is licensed under GPLv3 LicenseCopyright (c) Radost Waszkiewicz (2023).How to citeMinimum dissipation approximation: A fast algorithm for the prediction of diffusive properties of intrinsically disordered proteins.Radost Waszkiewicz, Agnieszka Michaś, Michał K. Białobrzewski, Barbara P. Klepka, Maja K. Cieplak-Rotowska, Zuzanna Staszałek, Bogdan Cichocki, Maciej Lisicki, Piotr Szymczak, and Anna Niedźwiecka; J. Phys. Chem. Lett. (submitted 2023)BibliographyDiffusion coefficients of elastic macromolecules.B. Cichocki, M. Rubin,  A. Niedzwiecka, and P. Szymczak; J. Fluid Mech. (2019)GRPY: An Accurate Bead Method for Calculation of Hydrodynamic Properties of Rigid Biomacromolecules.P. Zuk, B. Cichocki, and P. Szymczak; Biophysical Journal (2018)Pychastic: Precise Brownian dynamics using Taylor-Ito integrators in Python.R. Waszkiewicz, M. Bartczak, K. Kolasa, and M. Lisicki;  SciPost Phys. Codebases (2023)
Package: glmnet
This is a Python wrapper for the fortran library used in the R packageglmnet.
While the library includes linear, logistic, Cox, Poisson, and
multiple-response Gaussian, only linear and logistic are implemented in
this package.The API follows the conventions ofScikit-Learn, so it is expected to
work with tools from that ecosystem.Installationrequirementspython-glmnetrequires Python version >= 3.6,scikit-learn,numpy,
andscipy. Installation from source or viapiprequires a Fortran compiler.condacondainstall-cconda-forgeglmnetpippipinstallglmnetsourceglmnetdepends on numpy, scikit-learn and scipy.
A working Fortran compiler is also required to build the package.
For Mac users,brew install gccwill take care of this requirement.gitclonegit@github.com:civisanalytics/python-glmnet.gitcdpython-glmnetpythonsetup.pyinstallUsageGeneralBy default,LogitNetandElasticNetfit a series of models using
the lasso penalty (α = 1) and up to 100 values for λ (determined by the
algorithm). In addition, after computing the path of λ values,
performance metrics for each value of λ are computed using 3-fold cross
validation. The value of λ corresponding to the best performing model is
saved as thelambda_max_attribute and the largest value of λ such
that the model performance is withincut_point * standard_errorof
the best scoring model is saved as thelambda_best_attribute.Thepredictandpredict_probamethods accept an optional
parameterlambwhich is used to select which model(s) will be used
to make predictions. Iflambis omitted,lambda_best_is used.Both models will accept dense or sparse arrays.Regularized Logistic RegressionfromglmnetimportLogitNetm=LogitNet()m=m.fit(x,y)Prediction is similar to Scikit-Learn:# predict labelsp=m.predict(x)# or probability estimatesp=m.predict_proba(x)Regularized Linear RegressionfromglmnetimportElasticNetm=ElasticNet()m=m.fit(x,y)Predict:p=m.predict(x)
Package: glmnet2
This is a Python wrapper for the fortran library used in the R packageglmnet.
While the library includes linear, logistic, Cox, Poisson, and
multiple-response Gaussian, only linear and logistic are implemented in
this package.The API follows the conventions ofScikit-Learn, so it is expected to
work with tools from that ecosystem.Installationrequirementspython-glmnetrequires Python version >= 3.6,scikit-learn,numpy,
andscipy. Installation from source or viapiprequires a Fortran compiler.condacondainstall-cconda-forgeglmnetpippipinstallglmnetsourceglmnetdepends on numpy, scikit-learn and scipy.
A working Fortran compiler is also required to build the package.
For Mac users,brew install gccwill take care of this requirement.gitclonegit@github.com:civisanalytics/python-glmnet.gitcdpython-glmnetpythonsetup.pyinstallUsageGeneralBy default,LogitNetandElasticNetfit a series of models using
the lasso penalty (Î± = 1) and up to 100 values for Î» (determined by the
algorithm). In addition, after computing the path of Î» values,
performance metrics for each value of Î» are computed using 3-fold cross
validation. The value of Î» corresponding to the best performing model is
saved as thelambda_max_attribute and the largest value of Î» such
that the model performance is withincut_point * standard_errorof
the best scoring model is saved as thelambda_best_attribute.Thepredictandpredict_probamethods accept an optional
parameterlambwhich is used to select which model(s) will be used
to make predictions. Iflambis omitted,lambda_best_is used.Both models will accept dense or sparse arrays.Regularized Logistic RegressionfromglmnetimportLogitNetm=LogitNet()m=m.fit(x,y)Prediction is similar to Scikit-Learn:# predict labelsp=m.predict(x)# or probability estimatesp=m.predict_proba(x)Regularized Linear RegressionfromglmnetimportElasticNetm=ElasticNet()m=m.fit(x,y)Predict:p=m.predict(x)
Package: glmnet-classifier
GlmnetClassifierA binomial classifier based on glmnet.ContactRolf Carlsonhrolfrc@gmail.comInstallUse pip to install glmnet-classifier.pip installglmnet-classifierIntroductionThe glmnet-classifier project provides GlmnetClassifier for the classification and prediction for two classes, the binomial case.  GlmnetClassifier is based on glmnet. A fortran compiler is required.GlmnetClassifier is designed for use withscikit-learnpipelines and composite estimators.Examplefrom glmnet_classifier import GlmnetClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_splitMake a classification problemseed = 42
X, y = make_classification(
    n_samples=30,
    n_features=5,
    n_informative=2,
    n_redundant=2,
    n_classes=2,
    random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)Train the classifiercls = GlmnetClassifier().fit(X_train, y_train)Get the score on unseen datacls.score(X_test, y_test)1.0AuthorsThe authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon. The Python package,glmnet_py, is maintained by B. J. Balakumar.The glmnet-classifier package was written by Rolf Carlson, as an adaptation ofglmnet_py.ReferencesReferences
Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010.Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2011). Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent Journal of Statistical Software, Vol. 39(5) 1-13.Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, Ryan J. Tibshirani. (2010). Strong Rules for Discarding Predictors in Lasso-type Problems Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(2), 245-266.Noah Simon, Jerome Friedman and Trevor Hastie (2013). A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
Package: glm-py
glm-pyPython tools for running General Lake Model (GLM) simulations.GLMGLM is a 1-dimensional lake water balance and stratification model. It is coupled with a powerful ecological modelling library to also support simulations of lake water quality and ecosystems processes.GLM is suitable for a wide range of natural and engineered lakes, including shallow (well-mixed) and deep (stratified) systems. The model has been successfully applied to systems from the scale of individual ponds and wetlands to the scale of Great Lakes.For more information about running GLM please see the model website'sscientific basis descriptionand theGLM workbook.TheGLM modelis available as an executable for Linux (Ubuntu), MacOS, and Windows. It is actively developed by the
Aquatic EcoDynamics research group at The University of Western Australia.Why GLM-py?GLM-py provides a series of classes, functions, and data structures that to support running GLM simulations, preparing model input data and configurations, and processing model outputs.Its goal is to make running and deploying GLM in a range of environments easy such as building web APIs around GLM within web applications or cloud services, running batches of GLM simulations on HPCs, or running GLM simulations locally within Python environments such as JupyterLab or QGIS.NMLClasses to store properties describing GLM simulation input data and configuration and methods that generate.nmlconfig files required for running GLM.DimensionsTake simple user descriptions of lake geometry or dimensions and generate detailed representations of lake morphometry required for GLM simulations.JSONTools to convert JSON data to.nmlformat data. Useful for handling client requests if GLM is deployed within a web API / REST API.SimulationsClasses to handle running GLM simulations and processing output data into CSV, JSON, or NetCDF files or generating a JSON stream to pass onto clients.
Package: glm-utils
GLM utilitiesTools for fitting Generalized Linear Models (GLMs) with time dependence (a.k.a. filters).Features:Time delay embedding (basics demo).Basis functions (bases demo)Followsscikit-learn'sAPI conventions. Can be used withscikit-learn pipelines(seedemo).For a more complete example seemultiple_inputs.InstallationPackage is onpypi:pip install glm-utilsUseful extensionsGroup lasso. Seedemo.Balancing withimbalanced-learn.Generalized Additive Models:pyGAM. Seedemo.
Package: glnet
Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy EnvironmentsAccepted by ICCVW 2021Remote Sensing scene image classification under clear and cloudy environments.Overview architecture of the proposed GLNet for the RS scene classification under clear and cloudy environments.Required librariespython 3.6pytorch 1.0+numpyPIltorchvisionUsageclone this repogit clone https://github.com/wuchangsheng951/GLNET.gitdownload the dataset fromgoogle drivetrain the baseline modelpython baseline.pyload the model dir you trained in model.pyrun the training by commandpython train.pyCitation{Huiming Sun, Yuewei Lin, Qin Zou, Shaoyue Song, Jianwu Fang, Hongkai Yu. Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments. IEEE International Conference on Computer Vision Workshop (ICCVW), 2021.}
Package: glnext
glnextHigh-Performance offscreen rendering for python.pipinstallglnextDocumentationglnext on Githubglnext on PyPIExamplesimportglnextfromglnext_compilerimportglslfromPILimportImageinstance=glnext.instance()task=instance.task()framebuffer=task.framebuffer((512,512))pipeline=framebuffer.render(vertex_shader=glsl('''#version 450#pragma shader_stage(vertex)layout (location = 0) in vec2 in_vert;layout (location = 1) in vec3 in_color;layout (location = 0) out vec3 out_color;void main() {gl_Position = vec4(in_vert, 0.0, 1.0);out_color = in_color;}'''),fragment_shader=glsl('''#version 450#pragma shader_stage(fragment)layout (location = 0) in vec3 in_color;layout (location = 0) out vec4 out_color;void main() {out_color = vec4(in_color, 1.0);}'''),vertex_format='2f 3f',vertex_count=3,)pipeline.update(vertex_buffer=glnext.pack([-0.5,-0.5,0.0,0.0,1.0,0.5,-0.5,0.0,1.0,0.0,0.0,0.5,1.0,0.0,0.0,]))task.run()data=framebuffer.output[0].read()img=Image.frombuffer('RGBA',(512,512),data)img.save('hello_world.png')WindowsWith up2date drivers the vulkan runtime binaries should already be on your system.
Install thevulkan-sdkif needed.LinuxInstall thevulkan-sdk.apt-get install libx11-devWithout GPUThis project is compatible withswiftshader.
TheCIalso runs on pure CPU. (Dockerfile)
Package: glnext-compiler
glnext-compilerpipinstallglnext-compilerDocumentationglnext-compiler on Githubglnext-compiler on PyPIThis library is a standalone GLSL compiler for vulkan projects.The binaries do not require the vulkan-sdk to be installed.This library usesshaderc.Examplefromglnext_compilerimportglslspv=glsl('''#version 450#pragma shader_stage(vertex)layout (location = 0) in vec2 in_vert;void main() {gl_Position = vec4(in_vert, 0.0, 1.0);}''')
Package: global-apbd-logging
--- Activity Logging ---Digunakan untuk log aplikasi nabire (apbd) 2019.Cara menggunakanTambahkanfrom global_apbd_logging import nabire_loggingCara Memanggilnabire_logging.logging(request, session_username, nama_db)UpdateVersi 1.0.1Kirim Notifikasi ke telegram apabila gagal logging
Package: globalarrays
globalarraysA Python package that contains theGlobal Arrays Toolkitshared libraries. The library will be compiled during the installation
process. Itcannotbe imported directly in Python.Python access to the library will be ensured by theminiga4pypackage if installed.globalarrayshas been developed to be used inLARGE.Install the packagepip3 install globalarraysOr download the whole repository:git clone https://bitbucket.org/bvidmar/globalarraysand thencd globalarraysand install as preferred:python3 setup.py installpip3 installWARNINGThis isbetacode, use at your own risk.CHANGESVer. 5.7.1b0 Changed repository for secure_snprintf.patch
Ver. 5.7.0b1 Changed Cython requirements
Ver. 5.7.0b0 first releaseThe AuthorRoberto VidmarThe mantainersRoberto VidmarNicola Creati