Saravanan666/pypi-org huggingface dataset:
Package: gimmick
gimmickIntroductionWhats Gimmick ? Its a library which allows us to generate images out of nothing, basically you tell gimmick what kind of images you would like to generate and what model to use and it does the rest.
Its easy to use as primary focus is on user friendlyness without exposing to much details, but you can be specific if you wanna be.What do we do internally ? we trained neuralnets, we add different type of networks making sure that interface always remain same, just plugin different algo and see how it perform.Installtionpip install gimmickjust like that, its python everything should be easyDependencyGimmick autmatically install all the dependency for you, but occationaly you run into one of those bugs, so in a nutshell we use tensorflow, sklearn.
i know torch is much faster, we will get theirExampleswe have already provided samples with the github repo, you can browse it, read it and run it.Lets demonstrateusecase 1 - I just wanna run as simple as i can.import gimmick
model = gimmick.learn(images, algo='autoencoder_dense')  # Just make sure that images is a numpy array which contains N number 3D or 2D images
images_gen = model.generate(16)  # I need 16 imagesusecase 2 - I wanna control how my model is trainedimport gimmick
model = gimmick.learn(images, algo='autoencoder_dense', epochs=500, batch_size=8,
                      optimizer='adam', learning_rate=0.01, loss_function='mae')
images_gen = model.generate(16, batch_size=8)usecase 3 - I wanna know everythingGo through the detailed documentation.
Package: gimmik
GiMMiK is a Python based kernel generator for
matrix multiplication kernels for various accelerator platforms.  For
small operator matrices the generated kernels are capable of
outperfoming the state-of-the-art general matrix multiplication
routines such as cuBLAS GEMM or clBLAS GEMM.  GiMMiK was originally
developed as part of Bartosz Wozniak’s master’s thesis in the
Department of Computing at Imperial College London and is currently
maintained by Freddie Witherden.
Package: gimodules
gimodules-pythonInformation on how to manually distribute this package can be found herehttps://packaging.python.org/en/latest/tutorials/packaging-projects/Distribute with CI / CDEdit setup.py version number and create a release.
-> Creating a release will trigger the workflow to push the package to PyPitestsrun tests locally:$pipenv run test -vor$pytestrequirementsTo create project all current requirements automatically:$pip3 freeze > requirements.txtNOTE:Remove the old gimodules version from requirements.txt before pushing (dependency conflict).
Package: gimpact
GImpact-PythonAn unofficial python extension for theGImpact collision library. This extension integrates directly with GImpact's C++ API using Cython.FeaturesCreate trimesh object from numpy arrayMesh decimation using Sven Forstmann's C++ mesh simplificationcodeAxis Aligned Bounding Box (AABB)AABB set for box prunningCollision of triangle mesh with the followingtriangle meshspherecapsuleplaneraySupports "first contact" or "all contacts" modesBuild Wheelpipinstall-qbuild
python-mbuildInstallationBuild requires numpy and cython (tested on Linux and Windows).pipinstallgimpactExample UsageAABBimportgimpactaabb1=gimpact.AABB(-1,1,-1,1,-1,1)aabb2=gimpact.AABB(-1,1,-1,1,1.5,2)print(aabb1.intersects(aabb2))print(aabb1.intersection(aabb2))aabb1.merge(aabb2)print(aabb1)Box Prunningimportgimpactaabb_set=gimpact.AABBSet(10)print(len(aabb_set))print(aabb_set.global_bounds)foraabbinaabb_set:aabb.bounds=(0.,0.,0.,0.,0.,0.)foraabbinaabb_set:print(aabb)print(aabb_set.global_bounds)pairs=aabb_set.find_intersections(aabb_set)print(pairs)delaabb_setprint(aabb.bounds)Collisionimportgimpactimportnumpyasnpcontacts=gimpact.trimesh_trimesh_collision(trimesh1,trimesh2)contacts=gimpact.trimesh_sphere_collision(trimesh1,[0.,0.,0.],1,True)contacts=gimpact.trimesh_capsule_collision(trimesh1,np.array([0.,0.,0.]),np.array([1.,0.,0.]),1,True)contacts=gimpact.trimesh_plane_collision(trimesh1,[0.,0.,1.,0.],True)
Package: gimp-console
First ideas on modify the GIMP Python interpreter to be more interactive.As for now, code from two sources has been grabbed:GIMP's Python Consoleunder GPLv3SvenFestersen's GtkPyInterpreterunder GPLv3Update (28/03/21):Huge refactor in original source codes.A namespace python package structure has been implemented, adapted to GIMPs needs.Logic from pure GTK3 is being split from GIMPs logic, then "apps" should have an isolated layer which runs outside GIMP.Running 4 different Python Console approaches only changing a flag, to explore them:console.ConsoleDialog.run: The current GIMP python console with the GIMP logic.pyconsole.run: Only the widget of the python console of the current GIMP pyhthon console.gtkpyinterpreter.run: SvenFestersen's Python Console. Has a problem with a constantNone: Noneoutput.gtkmatplotlibshell.run: SvenFestersen's Python Console with matplotlib integration!The main problem integrating IPython is that the current approach (by importing it and callIPython.embed()) yields
to a problem with the parsing of the inputs. For example:In [1]: import os
  File "<ipython-input-1-5c84a8230b9f>", line 1
    invalid syntax (<string>, line 1)
            ^
SyntaxError: invalid syntax


In [2]: a = 10
  File "<ipython-input-2-5c84a8230b9f>", line 1
    invalid syntax (<string>, line 1)
            ^
SyntaxError: invalid syntax


In [3]: hola
  File "<ipython-input-3-74399d33e912>", line 1
    name 'hola' is not defined
         ^
SyntaxError: invalid syntax
Package: gimp-labeling-converter
GIMP Labeling AssistanceConverting images labeled by GIMP to different formatsGNU Image Manipulation ProgramGIMP (GNU Image Manipulation Program) is a free and open-source raster graphics editor used for image manipulation (retouching) and image editing, free-form drawing, transcoding between different image file formats, and more specialized tasks. It is not designed to be used for drawing.Labeling ProcedureGIMP is a powerful photo editing software which has great potential to be used as a labeling software forsemantic segmentation. To do so, we provide a procedure for labeling images using GIMP software,Load an image into the software.Review the image to find candidate classes.Create a layer for each class (make sure that the background is transparent)Use pen tool and select a color (make sure the "hardness" and "opacity" properties are set to 100.Draw the areas belong to the class associated to the selected layer.Change the layer and repeat the labeling steps.Save the image as "xcf" file format.ProgramThis repository provides a CLI tool that converts XCF file to a new format suitable for labeling. The tool also make the users able to add their own handlers for addling support to other types of outputs. Currently, the tool supports two handlers,mask: convert the xcf files to a multiple folders named after extracted classes. Each folder contains the mask presenting the areas labeled as the associated class.coco: convert the xcf files to a single MS COCO file (JSON).class_map: convert the xcf files to images that each pixel labeled as its class index.The cli tool can be initialized in two ways: (a) using config files, and (b) using commandline arguments.OptionsThe CLI tool provides the following options,OptionsOther formsDescription--dir_in--dir -dDirectory containing the images.--file_out--out -oOutput Directory--handler--typeHandler Type [initially "mask", "class_map" and "coco"]--num_worker-wNumber of Workers--configConfiguration file path--name-nSet dataset name--infoSet description--urlSet URL--versionSet version--yearSet Year--contribSet contributor--category-cClass CategoriesInstallationpip install cython
pip install git+https://github.com/waspinator/coco.git@2.1.0
pip install git+https://github.com/waspinator/pycococreator.git@0.2.0

pip install gimp-labeling-converterUsageUsing optionsgimp_labeling_converter --dir "/home/phm/my-dataset/labeled" \
                --out "/home/phm/Documents/dataset" \
                --type "mask" -w 5 \
                -c "defect" -c "surface_defect" \
                -n "Parham Test"orgimp_labeling_converter --dir "/home/phm/my-dataset/labeled" \
                --out "/home/phm/Documents/dataset/test.json" \
                --type "coco" -w 5 \
                -c "defect" -c "surface_defect" \
                -n "Parham Test" --info "Testing the coco handler" \
                --url "linkedlist.com" \
                --version "1.0.0" --year 2022 --contrib "Parham Nooralishahi"Using config fileAssuming the config file named as "./coco.json",gimp_labeling_converter --config "./coco.json"ContactParham Nooralishahi -parham.nooralishahi@gmail.com|@phm
Package: gimpml
This branch is under development. Dedicated for GIMP 3 and Python 3. :star: :star: :star: :star: are welcome.Waiting for GIMP 3 to release officially.Objectives[1] Model Ensembling.[2] Deep learning inference package for different computer vision tasks.[3] Bridge gap between CV research work and real world data.[4] Add AI to routine image editing workflows.ContributionWelcome people interested in contribution !!
Join us on Slack -->Contribution guidelines available -->Link.Use as a Python Packageimportcv2importgimpmlimage=cv2.imread('sampleinput/img.png')out=gimpml.kmeans(image)cv2.imwrite('output/tmp-kmeans.jpg',out)out=gimpml.deblur(image)cv2.imwrite('output/tmp-deblur.jpg',out)Use with GIMPInstallation Steps[1] InstallGIMP2.99.6  (Only windows and linux)[2] Clone this repository: git clonehttps://github.com/kritiksoman/GIMP-ML.git[3] Change branch :git checkout --track origin/GIMP3-ML[3] On linux, run for GPU/CPU:bash GIMP-ML/install.batOn windows, run for CPU:GIMP-ML\install.batOn windows, run for GPU:GIMP-ML\install.bat gpu[4] Follow steps that are printed in terminal or cmd.FYI: weights link -->LinkWindowsLinuxModel ZooNameLicenseDatasetdeblurBSD 3-clauseGoProfaceparseMITCelebAMask-HQcoloringMITImageNetmonodepthMITMultiplesuper-resolutionMITImageNetmattingNon-commercial purposesAdobe Deep Image Mattingsemantic-segmentationMITADE20KkmeansBSD-dehazingMITCustomdenoisingGPL3BSD68enlightenBSDCustominterpolate-framesMITHDinpaintingCC BY-NC 4.0CelebA, CelebHQ, Places2, Paris StreetViewDetect ObjectsApache-2.0COCOFilter FolderApache-2.0COCOCanny EdgeApache-2.0-CitationPlease cite using the following bibtex entry:@article{soman2020GIMPML,
  title={GIMP-ML: Python Plugins for using Computer Vision Models in GIMP},
  author={Soman, Kritik},
  journal={arXiv preprint arXiv:2004.13060},
  year={2020}
}
Package: gimpysolver
Readme!This is a simple and specific solver to gimpy captcha types like SEACE
PORTAL. The model has been trained with 4000 images and 92 porcent of
accuracy.stepspip install gimpysolverImport the package like: from gimpysolver import captchaSolverRPACreate your first object captchasolver. myCaptcha =
captchaSolverRPA.captcha_solver(path_img).Where path_img es where
you image exist.CautionOnly read *.png images and size image
would be 380,85If you don’t meet requirements about image dimension you can resize
with: myCaptcha.resize()To solve captcha you can call predict method: myCaptcha.predict()Gimpy captcha SEACE
example\ *!:https://i.ibb.co/RNHtgm5/captcha-capture-1.png
Package: gimulator-py
Gimulator Client for PythonA client forGimulator projectwritten in Python.InstallationStable releaseYou can install the stable release of the package via:pip install gimulator-pyLatest releaseInstalling and using the latest release isnot recommendedsince the project is still under heavy development. But if you are really keen to try the latest releases, run the following command:pip install git+https://github.com/Gimulator/client-python.gitUsage
Package: gin-config
GinGin provides a lightweight configuration framework for Python, based on
dependency injection. Functions or classes can be decorated with@gin.configurable, allowing default parameter values to be supplied from a
config file (or passed via the command line) using a simple but powerful syntax.
This removes the need to define and maintain configuration objects (e.g.
protos), or write boilerplate parameter plumbing and factory code, while often
dramatically expanding a project's flexibility and configurability.Gin is particularly well suited for machine learning experiments (e.g. using
TensorFlow), which tend to have many parameters, often nested in complex ways.Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan SilbermanContributors: Oscar Ramirez, Marek Fiser
Package: gine
gineInteractive netlist visualisation tools compatible with GDSFactoryFree software: MIT licenseDocumentation:https://gine.readthedocs.ioInstallationTo install use pip:$ pip install gineFor a development installation (requiresNode.jsandYarn version 1),$ git clone https://github.com/daquintero/gine.git
$ cd gine
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix gine
$ jupyter nbextension enable --py --sys-prefix gineWhen actively developing your extension for JupyterLab, run the command:$ jupyter labextension develop --overwrite gineThen you need to rebuild the JS when you make a code change:$ cd js
$ yarn run buildYou then need to refresh the JupyterLab page when your javascript changes.
Package: ginear
Ginear 🍸Ginear is the perfect blend of Git and Linear, creating a seamless integration that allows you to attach or create Linear issues with automatic branch switching.PrerequisitesBefore using Ginear, make sure you have the following prerequisites installed:fzf: Ginear relies on this utility for fuzzy searching.brewinstallfzfInstallationYou can install Ginear using pip:pipinstallginearGetting StartedTo start using Ginear, simply run the following command:ginOn your initial run, Ginear will onboard you to set the correct environment variables for your project. These variables are stored in a.ginearconfig file in your$HOME, which Ginear will use to provide a tailored integration experience. This configuration ensures that Ginear seamlessly connects Git and Linear for your specific project.Command OverviewGinear offers the following commands to enhance your workflow:ginThegincommand allows you to attach to an existing Linear issue or create a new one. When you rungin, Ginear will prompt you to select an existing issue from the list or create a new one.gin commitUsegin committo create a new Linear issue with automatic branch switching and accompanying git commit.gin createUsegin createto create a new Linear issue directly from the command line. Ginear will guide you through the issue creation process and automatically handle branch switching if necessary.gin projectWithgin project, you can switch to a different Linear project within your organization. This command allows you to change your project context, and Ginear will adapt to the selected project's settings.gin teamThegin teamcommand allows you to switch between different teams within your organization. When you rungin team, Ginear will prompt you to select the team you want to work with from a list of available options.
Package: ginga
Ginga is a toolkit designed for building viewers for scientific imagedata in Python, visualizing 2D pixel data in numpy arrays.The Ginga toolkit centers around an image display class which supportszooming and panning, color and intensity mapping, a choice of severalautomatic cut levels algorithms and canvases for plotting scalablegeometric forms.  In addition to this widget, a general purpose'reference' FITS viewer is provided, based on a plugin framework.Ginga is distributed under an open-source BSD licence. Please see thefile LICENSE.txt in the top-level directory for details.
Package: ginger-anchors
Ginger AnchorsImplementation and Extensions ofAnchors: High-Precision Model-Agnostic Explanations.
The re-implementation was done in the iML lecture (WS21/22).ContributionsImplementation of Anchors via the Bottom-up construction approach.Implementation of Beam Search on top of the Bottom-up construction.Simple interfaces with well-splitted main functions.Analysis on how 𝐵, 𝛿 and 𝜖 influence the results.SMAC3 as alternative anchor finder.ReferencesPaper Reference:https://homes.cs.washington.edu/~marcotcr/aaai18.pdfCode Reference:https://github.com/marcotcr/anchorInstallationNote: swig is needed to install SMAC3. Seeinstallation instructions.Create a conda environment$condacreate-nGingerAnchorspython=3.9
$condaactivateGingerAnchors
$pipinstallginger-anchorsUsageYou can get an explanation by setting up an Explainer and calling one of three search functions.exp=Explainer(X_df)anchor=exp.explain_bottom_up(instance,model,tau=0.95)print(anchor.get_explanation())For a more detailed example, seesrc/main.py.AnalysisThe plots were too large to put them into this repository. Please download them fromseafile.
To reproduce the raw data, run:ginger-anchors>pythonsrc/analysis.pyA preview can be found in our writeup:analysis.mdAuthorsJim Rhotert&Julian Bilsky
Package: ginger-emulator
GINGERGinger Is Not a GAL EmulatoRSo what is this then?This is a tool that reads GAL.pldfiles and uses.vecfiles to solve equation.
Some may call this an emulator, but it is not: it's just an equation solver that feels like it's "emulating" a real GAL.Quick startpipinstallginger-emulator
ginger./sample/addr.pld./sample/addr.vecPlease seesample GAL codeand correspondingvector file.Rules on writing.vecfiles:Lines that start with@are test case names.E.g.@ Test various stuffLines that start with<list signals that you want to input (each following line needs to set those signals high or low).E.g.< A15 A14 A13 A12 /RD /WRLines that start with>list signals that you want to display after every step.E.g.> /ROM /RAM /IOLines that start with?are test case assertions, they should contain key-value pairs for your tests. Ginger will exit with non-zero code if any test fails.E.g.? /ROM=1 /RAM=0 /IO=1Comments start with#. If a comment is added after a vector, it will be printed during run.Empty lines are ignored.All other non-empty lines are vectors. Whitespaces are ignored and can be added only for visual clarity.E.g.0101 1 1(equivalent to0 1 0 1 11or010111)RequirementsPython 3.xLark parserFeaturesSupported:Combinatorial logicRegistered logicTri-statesAutomated test-case assertionsArbitrary inversion of signals in inputs/outputs/assertions (e. g.? /RAM=1is equivalent to? RAM=0)Timing diagram generationNot supported (yet?):Output enable (ignored)Validation of inputs/outputsPerks:Vim syntax highlight file can be foundhere.Disclaimer & some technical warningsThis tool does not guarantee that the assembled.pldcode will behave in the same way on a real GAL.
Additionally, it does not guarantee that the.pldfile is a valid assembly (e. g. it does not validate
if user is trying to use input pins as outputs, term limit, etc, and and will allow the user to do all sorts of silly stuff).Ginger is using Lark for parsing.pldgrammar and only validates thesyntax, not thesemantics.So please use, say,galasmto assembled the.pldfile first in order to see if it can be used to program a GAL in the first place,
and then feel free to use Ginger!
Package: gingerfy
GingerfyRemove unginger colours from strings and replace them with the one true colour, GINGER!!importgingerfyresult=gingerfy.fix("Blonde haired people are the best!")print(result.get_fix())Output:Ginger haired people are the best!NotesThis project is obviously a joke, I come from a long line of Scottish and Irish gingers, so it's very much meant to be self-deprecating.It was created to help me learn the basics of Python and how to pipeline Python code.AuthorRobDWaller
Package: gingerino
GingerinoThis is a proof of conceptInstallationpipinstallgingerinoUsagefromdataclassesimportdataclassfromtypingimportLiteralfromgingerinoimportparserino@dataclassclassUserInfo:name:strage:intunit:Literal["years","months"]template="{{ name }} is {{ age }} {{ unit }} old"user=parserino(UserInfo,template,"Marco is 24 years old")print(user.name,user.age)# Marco 24
Package: ginny
GinnyA simple, convenient task manager that is similar to luigi framework but less blown up.
It allows easy exceution and scheduling of tasks locally and remotelty.Run locallyfromginnyimportDownloadTask,runresult=run(DownloadTask(url="https://static.wikia.nocookie.net/harrypotter/images/e/e9/Ginny-HQ-ginevra-ginny-weasley.jpg/revision/latest/scale-to-width-down/250?cb=20150228082608&path-prefix=de",destination='image.jpg'))Schedule tasks via command lineginny--taskginny.DownloadTaskurl"https://static.wikia.nocookie.net/harrypotter/images/e/e9/Ginny-HQ-ginevra-ginny-weasley.jpg/revision/latest/scale-to-width-down/250?cb=20150228082608&path-prefix=de"destination"image.jpg"# run every 5 minutesginny--taskginny.DownloadTask--every'minute'--count5url"https://static.wikia.nocookie.net/harrypotter/images/e/e9/Ginny-HQ-ginevra-ginny-weasley.jpg/revision/latest/scale-to-width-down/250?cb=20150228082608&path-prefix=de"destination"image.jpg"# EVERY DAY at 0:00ginny--taskginny.DownloadTask--every'day'--at"00:00"url"https://static.wikia.nocookie.net/harrypotter/images/e/e9/Ginny-HQ-ginevra-ginny-weasley.jpg/revision/latest/scale-to-width-down/250?cb=20150228082608&path-prefix=de"destination"image.jpg"Build your own tasksfromginnyimportrun,TaskclassMyTask(Task):def__init__(self,url:str):self.url=urldefdepends(self):# return tasks or targets that this task depends on# return LocalTarget("/tmp/data.json")# return [LocalTarget("/tmp/data.json"), LocalTarget("/tmp/data2.json")]return[LocalTarget("/tmp/data.json"),DownloadTask(self.url,"/tmp/data2.json")]defrun(self):target,download_task=self.depends()data1=target.read_json()data2=download_task.target().read_json()data1.update(data2)withself.target().open("w")aswriter:writer.write("done")deftarget(self):# define a target if the task should not be executed every time / has output datareturnLocalTarget("/tmp/target.json")# run the task (results of all tasks that will be executed are returned in results)task=MyTask(url=...)# delelte results of taskstask.delete(recursive=False)# set recursive=True, to also delete results of subtasksresults=run(task)Buld-in tasksfromginnyimportBashTask,S3DownloadTask,DownloadTask,S3UploadTask,Task,SSHCommandTask,DepTask,TempDownloadTask,runr=run(BashTask(['ls','-lha']))Run task remotely (WIP)# execute single task remotelyresults=BashTask(['ls','-lha']).remote('host','ubuntu',pem=None,executable='/home/ubuntu/venv/bin/python')Developmentpythonsetup.pyclean
pipinstall.TODOrun complete pipelines remotelyadd gpu support for running remotelylimit resoures to run tasksuse logging
Package: gino
GINO - GINO Is Not ORM - is a lightweight asynchronous ORM built on top ofSQLAlchemycore for Pythonasyncio. GINO 1.0 supports onlyPostgreSQLwithasyncpg.Free software: BSD licenseRequires: Python 3.5GINO is developed proudly with.Homepython-gino.orgDocumentationEnglishChineseInstallation$pipinstallginoFeaturesRobust SQLAlchemy-asyncpg bi-translator with no hard hackAsynchronous SQLAlchemy-alike engine and connectionAsynchronous dialect APIAsynchronous-friendly CRUD objective modelsWell-considered contextual connection and transaction managementReusing native SQLAlchemy core to build queries with grammar sugarsSupport SQLAlchemy ecosystem, e.g.Alembicfor migrationCommunity supportforStarlette/FastAPI,aiohttp,Sanic,TornadoandQuartRich PostgreSQL JSONB support